Dict[str, Any]:\n return {\n \"first_name\": \"Donald\",\n \"last_name\": \"Duck\",\n \"company\": \"Duck Co\",\n \"email\": \"donald.duck@duckburg.inv\",\n \"telephone\": \"991234567\",\n \"mobile\": \"+358991234567\",\n \"street\": \"1313 Webfoot Walk\",\n \"postal_code\": \"00000\",\n \"city\": \"Duckburg\",\n \"country\": \"US\",\n \"information\": \"Quack, damn you!\",\n \"items\": [],\n }","def get_transactions_as_json(self):\n transactions = self.transaction_map.values() \n transactions_json = list(map( lambda t: t.to_json(), transactions )) \n return transactions_json","def transactions(self, transactions: list):\n num_txs = len(transactions)\n transactions_size = num_txs * self._message_size['tx']\n return {\n 'id': 'transactions',\n 'transactions': transactions,\n 'size': kB_to_MB(transactions_size)\n }","def transactions(self):\n url = f'{self._ynab.api_url}/budgets/{self.id}/transactions'\n response = self._ynab.session.get(url)\n if not response.ok:\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\n response.text,\n response.status_code)\n return []\n return [Transaction(self._ynab, transaction)\n for transaction in response.json().get('data', {}).get('transactions', [])]","def fetch_bank_transactions(self):\n return self.fetch('/bank_transactions')","def transactions(self):\n url = f'{self._ynab.api_url}/budgets/{self.budget.id}/accounts/{self.id}/transactions'\n response = self._ynab.session.get(url)\n if not response.ok:\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\n response.text,\n response.status_code)\n return []\n return [Transaction(self._ynab, transaction)\n for transaction in response.json().get('data', {}).get('transactions', [])]","def get_transaction_data():\n data = parse_json()\n income_instances = create_transactions(data['incomes'])\n expense_instances = create_transactions(data['expenses'])\n for expense in expense_instances:\n expense.amount = -(expense.amount)\n transactions = income_instances + expense_instances\n return transactions","def test_wallets_get_transaction_list(self):\n pass","def get_transactions(self):\n # open a cursor object\n cur = self.get_cursor()\n\n # get transactions from database\n cur.execute(\"SELECT * FROM transactions\")\n transactions_data = cur.fetchall()\n\n # convert into a dict of values.\n transactions_list = []\n [transactions_list.append({'transaction_id': transaction[0],\n 'date': transaction[1],\n 'payee_id': transaction[2],\n 'description': transaction[3],\n 'amount': transaction[4]})\n for transaction in transactions_data]\n\n # close the cursor\n self.close_cursor()\n\n return transactions_list","def unbalanced(self):\n # TODO: Find a way to make a sql query to return all unbalanced transactions\n return []","def _buildTotalsDict(self, fromdt, todt):\r\n pairs = [(t, t.effectForPeriod(fromdt, todt)) for t in self.transactions]\r\n return dict(pairs)","def transactions(self):\r\n return tx.AccountTransactions(self)","def load_data():\n dataFile = open('transactions.json', 'r')\n data = json.load(dataFile)\n transactions = data['transactions']#TID\n items = data['items']#item sets\n return items, transactions","def report(self):\n result = {}\n result_buy = 0\n result_sell = 0\n result_outcome = 0\n for pair in self.pairs:\n orders = self.get_orders_for(pair)\n buy, sell = self.get_buy_and_sell_costs(orders)\n outcome = sell - buy\n result_pair = {\"buy\": buy, \"sell\": sell, \"outcome\": outcome}\n result.update({pair: result_pair})\n result_buy += buy\n result_sell += sell\n result_outcome += outcome\n\n # Add global results\n all = {\"buy\": result_buy, \"sell\": result_sell, \"outcome\": result_outcome}\n result.update({\"all\": all})\n return result","def transactions(self):\r\n return tx.Transactions(self)","def transactions(self):\n return copy.deepcopy(self._transactions)","def get_transactions():\n\n wallet = \"TTfoWGU2M939cgZm8CksPtz1ytJRM9GiN7\"\n\n url = \"https://api.trongrid.io/v1/accounts/{}/transactions\".format(wallet)\n\n response = requests.request(\"GET\", url)\n\n print(response.text)","def new_get_buys_transaction_history(self, cb_account):\n date: datetime = now()\n if cb_account == \"wallet_id_btc\":\n return MockAPIObject(\n data=[{\n \"created_at\": str(date + timedelta(days=-1)),\n \"resource\": \"buy\",\n \"status\": \"completed\",\n \"amount\": {\n \"amount\": 10,\n \"currency\": \"BTC\"\n },\n \"total\": {\n \"amount\": 10,\n \"currency\": \"BTC\"\n },\n \"fees\": [{\n \"amount\": {\n \"amount\": 1,\n \"currency\": \"EUR\"\n }\n }]\n }, {\n \"created_at\": str(date + timedelta(days=1)),\n \"resource\": \"buy\",\n \"status\": \"completed\",\n \"amount\": {\n \"amount\": 5,\n \"currency\": \"BTC\"\n },\n \"total\": {\n \"amount\": 5,\n \"currency\": \"BTC\"\n },\n \"fees\": [{\n \"amount\": {\n \"amount\": 0.5,\n \"currency\": \"EUR\"\n }\n }]\n }])\n else:\n return MockAPIObject()","def dict(self):\n return {\"data\": self.data.dict(), \"inventory\": self.inventory.dict()}","def get_pending_transactions():\n\n return History.get_pending().get()","def _amount_all(self):\n res = {}\n ut_obj = self.env['l10n.ut']\n for iwdl_brw in self.browse(self.ids):\n # Using a clousure to make this call shorter\n f_xc = ut_obj.sxc(\n iwdl_brw.invoice_id.company_id.currency_id.id,\n iwdl_brw.invoice_id.currency_id.id,\n iwdl_brw.islr_wh_doc_id.date_uid)\n\n res[iwdl_brw.id] = {\n 'amount': (iwdl_brw.base_amount * (iwdl_brw.retencion_islr / 100.0)) or 0.0,\n 'currency_amount': 0.0,\n 'currency_base_amount': 0.0,\n }\n for xml_brw in iwdl_brw.xml_ids:\n res[iwdl_brw.id]['amount'] = xml_brw.wh\n res[iwdl_brw.id]['currency_amount'] = f_xc(\n res[iwdl_brw.id]['amount'])\n res[iwdl_brw.id]['currency_base_amount'] = f_xc(\n iwdl_brw.base_amount)","def buildMarketOrdersData(self):\n d = {}\n for orderID, myMarketOrderDict in self.frame.mode.game.marketOrders.iteritems():\n if myMarketOrderDict['system'] == self.mySystemDict['id']:\n amount = myMarketOrderDict['amount']\n min = myMarketOrderDict['min']\n max = myMarketOrderDict['max']\n value = myMarketOrderDict['value']\n if myMarketOrderDict['type'] == 'sell':\n s = 'SELL (%d %s) for MIN=%d' % (amount, value, min)\n elif myMarketOrderDict['type'] == 'buy-all':\n s = 'BUY ALL (%d %s) for MAX=%d' % (amount, value, max)\n elif myMarketOrderDict['type'] == 'buy-any':\n s = 'BUY ANY (%d %s) for MAX=%d' % (amount, value, max)\n d[orderID] = s\n return d","def decoderawtransaction(self, hexstring):\n return dict(self.proxy.decoderawtransaction(hexstring))","def __preprocess_transactions(self):\n\n p_bar = tqdm(range(14), desc=\"Preprocessing transactions\", leave=False)\n\n try:\n # 0. If optional fields not in the transactions add missing\n optional_fields = [\n \"Sector\",\n \"Industry\",\n \"Country\",\n \"Region\",\n \"Fees\",\n \"Premium\",\n \"ISIN\",\n ]\n if not set(optional_fields).issubset(set(self.__transactions.columns)):\n for field in optional_fields:\n if field not in self.__transactions.columns:\n self.__transactions[field] = np.nan\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 1. Convert Date to datetime\n self.__transactions[\"Date\"] = pd.to_datetime(self.__transactions[\"Date\"])\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 2. Sort transactions by date\n self.__transactions = self.__transactions.sort_values(by=\"Date\")\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 3. Capitalize Ticker and Type [of instrument...]\n self.__transactions[\"Ticker\"] = self.__transactions[\"Ticker\"].map(\n lambda x: x.upper() if isinstance(x, str) else x\n )\n self.__transactions[\"Type\"] = self.__transactions[\"Type\"].map(\n lambda x: x.upper() if isinstance(x, str) else x\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 4. Translate side: [\"deposit\", \"buy\"] -> 1 and [\"withdrawal\", \"sell\"] -> -1\n self.__transactions[\"Signal\"] = self.__transactions[\"Side\"].map(\n lambda x: 1\n if x.lower() in [\"deposit\", \"buy\"]\n else (-1 if x.lower() in [\"withdrawal\", \"sell\"] else 0)\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 5. Convert quantity to signed integer\n self.__transactions[\"Quantity\"] = (\n abs(self.__transactions[\"Quantity\"]) * self.__transactions[\"Signal\"]\n )\n\n # Adjust quantity and price for splits\n for ticker in self.__transactions[\"Ticker\"].unique():\n try:\n splits_df = get_splits(ticker)\n if not splits_df.empty:\n splits_df = splits_df.tz_localize(tz=None)\n for split_date in splits_df.index:\n self.__transactions[\"Quantity\"] = np.where(\n (self.__transactions[\"Ticker\"] == ticker)\n & (self.__transactions[\"Date\"] < split_date),\n self.__transactions[\"Quantity\"]\n * splits_df.loc[split_date].values,\n self.__transactions[\"Quantity\"],\n )\n self.__transactions[\"Price\"] = np.where(\n (self.__transactions[\"Ticker\"] == ticker)\n & (self.__transactions[\"Date\"] < split_date),\n self.__transactions[\"Price\"]\n / splits_df.loc[split_date].values,\n self.__transactions[\"Price\"],\n )\n\n except Exception:\n console.print(\"\\nCould not get splits adjusted\")\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 6. Determining the investment/divestment value\n self.__transactions[\"Investment\"] = (\n self.__transactions[\"Quantity\"] * self.__transactions[\"Price\"]\n + self.__transactions[\"Fees\"]\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)\n crypto_trades = self.__transactions[self.__transactions.Type == \"CRYPTO\"]\n self.__transactions.loc[\n (self.__transactions.Type == \"CRYPTO\"), \"Ticker\"\n ] = [\n f\"{crypto}-{currency}\"\n for crypto, currency in zip(\n crypto_trades.Ticker, crypto_trades.Currency\n )\n ]\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.\n\n # If isin not valid ticker is empty\n self.__transactions[\"yf_Ticker\"] = self.__transactions[\"ISIN\"].apply(\n lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan\n )\n\n empty_tickers = list(\n self.__transactions[\n (self.__transactions[\"yf_Ticker\"] == \"\")\n | (self.__transactions[\"yf_Ticker\"].isna())\n ][\"Ticker\"].unique()\n )\n\n # If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported\n removed_tickers = []\n for item in empty_tickers:\n with suppress_stdout():\n # Suppress yfinance failed download message if occurs\n valid_ticker = not (\n yf.download(\n item,\n start=datetime.datetime.now() + datetime.timedelta(days=-5),\n progress=False,\n ).empty\n )\n if valid_ticker:\n # Invalid ISIN but valid ticker\n self.__transactions.loc[\n self.__transactions[\"Ticker\"] == item, \"yf_Ticker\"\n ] = np.nan\n else:\n self.__transactions.loc[\n self.__transactions[\"Ticker\"] == item, \"yf_Ticker\"\n ] = \"\"\n removed_tickers.append(item)\n\n # Merge reformatted tickers into Ticker\n self.__transactions[\"Ticker\"] = self.__transactions[\"yf_Ticker\"].fillna(\n self.__transactions[\"Ticker\"]\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 9. Remove unsupported ISINs that came out empty\n self.__transactions.drop(\n self.__transactions[self.__transactions[\"Ticker\"] == \"\"].index,\n inplace=True,\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 10. Create tickers dictionary with structure {'Type': [Ticker]}\n unsupported_type = self.__transactions[\n (~self.__transactions[\"Type\"].isin([\"STOCK\", \"ETF\", \"CRYPTO\"]))\n ].index\n if unsupported_type.any():\n self.__transactions.drop(unsupported_type, inplace=True)\n console.print(\n \"[red]Unsupported transaction type detected and removed. Supported types: stock, etf or crypto.[/red]\"\n )\n\n for ticker_type in set(self.__transactions[\"Type\"]):\n self.tickers[ticker_type] = list(\n set(\n self.__transactions[\n self.__transactions[\"Type\"].isin([ticker_type])\n ][\"Ticker\"]\n )\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 11. Create list with tickers except cash\n self.tickers_list = list(set(self.__transactions[\"Ticker\"]))\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 12. Save transactions inception date\n self.inception_date = self.__transactions[\"Date\"].iloc[0]\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 13. Populate fields Sector, Industry and Country\n if (\n self.__transactions.loc[\n self.__transactions[\"Type\"] == \"STOCK\",\n optional_fields,\n ]\n .isnull()\n .values.any()\n ):\n # If any fields is empty for stocks (overwrites any info there)\n self.__load_company_data()\n\n p_bar.n += 1\n p_bar.refresh()\n\n # Warn user of removed ISINs\n if removed_tickers:\n p_bar.disable = True\n console.print(\n f\"\\n[red]The following tickers are not supported and were removed: {removed_tickers}.\"\n f\"\\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN.\"\n f\"\\nSuffix info on 'Yahoo Finance market coverage':\"\n \" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html\"\n f\"\\nE.g. IWDA -> IWDA.AS[/red]\\n\"\n )\n except Exception:\n console.print(\"\\nCould not preprocess transactions.\")\n raise","def get_aggregated_values(self):\n if not self._initialized:\n raise Exception(\"To readout you must first initialize, then\"\n \"process batches!\")\n else:\n ret_vals = [q.readout() for q in self.quantities]\n return dict(zip(self.quantity_names, ret_vals))","def tx_prices(self) -> Dict[str, List[float]]:\n agent_pbk_to_name = self.game.configuration.agent_pbk_to_name\n results = {\n agent_name: [] for agent_name in agent_pbk_to_name.values()\n } # type: Dict[str, List[float]]\n\n temp_game = Game(self.game.configuration, self.game.initialization)\n\n # compute the partial scores for every agent after every transaction\n # (remember that indexes of the transaction start from one, because index 0 is reserved for the initial scores)\n for idx, tx in enumerate(self.game.transactions):\n temp_game.settle_transaction(tx)\n results[agent_pbk_to_name[tx.seller_pbk]].append(tx.amount)\n\n return results","def wem_market_value_all():\n __query = \"\"\"\n select\n date_trunc('month', wfs.trading_interval) AS trading_day,\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\n wf.fueltech_id\n from wem_facility_scada wfs\n left join wem_facility wf on wfs.facility_id = wf.code\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\n where\n wf.fueltech_id is not null\n group by 1, wf.fueltech_id\n order by 1 desc, 2 asc\n \"\"\"\n\n query = __query.format()\n\n json_envelope = {}\n\n with engine.connect() as c:\n rows = c.execute(query)\n\n current_tech = None\n\n for row in rows:\n\n current_tech = row[2]\n\n if current_tech not in json_envelope.keys():\n json_envelope[current_tech] = {\n \"id\": f\"wem.fuel_tech.{current_tech}.market_value\",\n \"fuel_tech\": current_tech,\n \"region\": \"wa\",\n \"type\": \"market_value\",\n \"units\": \"AUD\",\n \"history\": {\n \"interval\": \"1M\",\n \"start\": None,\n \"last\": None,\n \"data\": [],\n },\n }\n\n if (\n json_envelope[current_tech][\"history\"][\"start\"] == None\n or row[0] < json_envelope[current_tech][\"history\"][\"start\"]\n ):\n json_envelope[current_tech][\"history\"][\"start\"] = row[0]\n\n if (\n json_envelope[current_tech][\"history\"][\"last\"] == None\n or row[0] > json_envelope[current_tech][\"history\"][\"last\"]\n ):\n json_envelope[current_tech][\"history\"][\"last\"] = row[0]\n\n json_envelope[current_tech][\"history\"][\"data\"].append(row[1])\n\n return [json_envelope[i] for i in json_envelope.keys()]","def get_goal(self):\n request_name = \"list_inventory_orders\"\n\n orders = self.make_request(request_name)\n order_dict = dict()\n for order in orders:\n order_name = order[\"@id\"].encode('utf-8')\n item_dict = dict()\n for item in order[\"items\"]:\n item_id = item[\"inventory-item-id\"].encode('utf-8')\n item_quantity = item[\"quantity\"]\n item_dict[item_id] = item_quantity\n order_dict[order_name] = item_dict\n return order_dict","def _get_dicts(self, unit_set):\n name_dict = {}\n unit_dict = {}\n for unit in unit_set:\n name_dict[unit.name] = unit.coef\n unit_dict[unit.unit] = unit\n\n return name_dict, unit_dict","def get_transactions(filters, as_dict=1):\n\tfilter_by_voucher = 'AND gl.voucher_type = %(voucher_type)s' if filters.get('voucher_type') else ''\n\tgl_entries = frappe.db.sql(\"\"\"\n\t\tSELECT\n\n\t\t\t/* either debit or credit amount; always positive */\n\t\t\tcase gl.debit when 0 then gl.credit else gl.debit end as 'Umsatz (ohne Soll/Haben-Kz)',\n\n\t\t\t/* 'H' when credit, 'S' when debit */\n\t\t\tcase gl.debit when 0 then 'H' else 'S' end as 'Soll/Haben-Kennzeichen',\n\n\t\t\t/* account number or, if empty, party account number */\n\t\t\tacc.account_number as 'Konto',\n\n\t\t\t/* against number or, if empty, party against number */\n\t\t\t%(temporary_against_account_number)s as 'Gegenkonto (ohne BU-Schlüssel)',\n\n\t\t\tgl.posting_date as 'Belegdatum',\n\t\t\tgl.voucher_no as 'Belegfeld 1',\n\t\t\tLEFT(gl.remarks, 60) as 'Buchungstext',\n\t\t\tgl.voucher_type as 'Beleginfo - Art 1',\n\t\t\tgl.voucher_no as 'Beleginfo - Inhalt 1',\n\t\t\tgl.against_voucher_type as 'Beleginfo - Art 2',\n\t\t\tgl.against_voucher as 'Beleginfo - Inhalt 2',\n\t\t\tgl.party_type as 'Beleginfo - Art 3',\n\t\t\tgl.party as 'Beleginfo - Inhalt 3',\n\t\t\tcase gl.party_type when 'Customer' then 'Debitorennummer' when 'Supplier' then 'Kreditorennummer' else NULL end as 'Beleginfo - Art 4',\n\t\t\tpar.debtor_creditor_number as 'Beleginfo - Inhalt 4'\n\n\t\tFROM `tabGL Entry` gl\n\n\t\t\t/* Kontonummer */\n\t\t\tleft join `tabAccount` acc \n\t\t\ton gl.account = acc.name\n\n\t\t\tleft join `tabCustomer` cus\n\t\t\ton gl.party_type = 'Customer'\n\t\t\tand gl.party = cus.name\n\n\t\t\tleft join `tabSupplier` sup\n\t\t\ton gl.party_type = 'Supplier'\n\t\t\tand gl.party = sup.name\n\n\t\t\tleft join `tabParty Account` par\n\t\t\ton par.parent = gl.party\n\t\t\tand par.parenttype = gl.party_type\n\t\t\tand par.company = %(company)s\n\n\t\tWHERE gl.company = %(company)s \n\t\tAND DATE(gl.posting_date) >= %(from_date)s\n\t\tAND DATE(gl.posting_date) <= %(to_date)s\n\t\t{}\n\t\tORDER BY 'Belegdatum', gl.voucher_no\"\"\".format(filter_by_voucher), filters, as_dict=as_dict)\n\n\treturn gl_entries","def transactions(self):\n return self._call_account_method(\n 'transactions'\n )","def tx_counts(self) -> Dict[str, Dict[str, int]]:\n agent_pbk_to_name = self.game.configuration.agent_pbk_to_name\n result = {agent_name: 0 for agent_name in agent_pbk_to_name.values()}\n results = {\"seller\": result.copy(), \"buyer\": result.copy()}\n\n temp_game = Game(self.game.configuration, self.game.initialization)\n\n # compute the partial scores for every agent after every transaction\n # (remember that indexes of the transaction start from one, because index 0 is reserved for the initial scores)\n for idx, tx in enumerate(self.game.transactions):\n temp_game.settle_transaction(tx)\n results[\"seller\"][agent_pbk_to_name[tx.seller_pbk]] += 1\n results[\"buyer\"][agent_pbk_to_name[tx.buyer_pbk]] += 1\n\n return results","def query_symbol_bars(self, symbol: str):\n return self._call_txtrader_api('query_symbol_bars', {'symbol': symbol})","def compute_helper_mempool_dictionaries():\n txn_density_dict = {}\n txn_parents_dict = {}\n txn_size_dict = {}\n mempool_data = parse_mempool_csv()\n for elem in mempool_data:\n size = elem.weight/MAXIMUM_BLOCK_WEIGHT # weight mapped to (0,1)\n txn_size_dict[elem.txid] = size \n txn_density_dict[elem.txid] = elem.fee/size\n if elem.parents != '':\n txn_parents_dict[elem.txid] = elem.parents.strip().split(';')\n return txn_density_dict,txn_parents_dict,txn_size_dict","def get(self, transaction_ids):\n try:\n transaction_ids = list(set(list(transaction_ids)))\n request = {\"transaction_ids\": transaction_ids}\n response = {}\n # Validate User Input\n validations_result = validate_transaction_ids(transaction_ids)\n if validations_result is not None and len(validations_result) > 0:\n response = {\"ResponseCode\": ResponseCodes.InvalidRequestParameter.value,\n \"ResponseDesc\": ResponseCodes.InvalidRequestParameter.name,\n \"ValidationErrors\": validations_result}\n else:\n transaction_outputs_dict = {}\n for transaction_id in sorted(transaction_ids):\n transaction_outputs = db_session.query(TransactionOutput).filter(\n TransactionOutput.transaction_id == transaction_id).order_by(\n TransactionOutput.id.asc()).all()\n\n trans_output_as_list = []\n total_num_of_transaction_outputs = 0\n for transaction_output in transaction_outputs:\n output_address_response = json.loads(\n requests.get('http://localhost:5000/bitcoin/transactions/outputs/addresses',\n {'transaction_id': transaction_id,\n 'transaction_output_id': transaction_output.id}).text)\n if output_address_response[\"ResponseCode\"] == ResponseCodes.Success.value:\n trans_output_as_list.append(serialize_transaction_output(transaction_output,\n output_address_response[\n \"NumberOfOutputAddresses\"],\n output_address_response[\n \"OutputAddresses\"]))\n total_num_of_transaction_outputs = total_num_of_transaction_outputs + 1\n else:\n response = {\"ResponseCode\": output_address_response[\"ResponseCode\"],\n \"ResponseDesc\": output_address_response[\"ResponseDesc\"],\n \"ErrorMessage\": \"Internal Error in Transaction Output Address Service : \"\n + output_address_response[\"ErrorMessage\"]\n }\n break\n transaction_outputs_dict[transaction_id] = {\"NumberOfOutputs\": total_num_of_transaction_outputs,\n \"TransactionOutputs\": trans_output_as_list}\n\n if total_num_of_transaction_outputs > 0:\n response = {\"ResponseCode\": ResponseCodes.Success.value,\n \"ResponseDesc\": ResponseCodes.Success.name,\n \"TransactionOutputData\": transaction_outputs_dict\n }\n else:\n response = {\"ResponseCode\": ResponseCodes.NoDataFound.value,\n \"ResponseDesc\": ResponseCodes.NoDataFound.name,\n \"ErrorMessage\": ResponseDescriptions.NoDataFound.value}\n except Exception as ex:\n response = {\"ResponseCode\": ResponseCodes.InternalError.value,\n \"ResponseDesc\": ResponseCodes.InternalError.name,\n \"ErrorMessage\": str(ex)}\n finally:\n return response","def produce(self, request, meta, raven_variables, dispatch, t, level=None):\n #balance = defaultdict(float)\n interaction = self.get_interaction()\n balance, meta = interaction.produce(request, meta, raven_variables, dispatch, t, level)\n #for resource, quantity in int_balance.items():\n # balance[resource] += quantity\n return balance, meta","def raw_get_transaction(cls, txid):\n r = requests.get(cls.MAIN_TX_API.format(txid), timeout=DEFAULT_TIMEOUT)\n r.raise_for_status() # pragma: no cover\n return r.json()","def _balances(self) -> Dict[str, int]:\n\n return self.client.get(self._resources(\"balance\"))","def tx_transaction_mirs(self, txs_hash: str, pandas: bool=False) -> dict:\n \n tx_transaction_mirs = self.network + bf_tx_url + txs_hash + bf_tx_transaction_mirs_url\n\n response = query_blockfrost(tx_transaction_mirs, self.api_key, self.proxies)\n \n return pd.DataFrame.from_dict(response) if pandas else response","def get_transactions(self):\n\n df = self.__transactions[\n [\n \"Date\",\n \"Type\",\n \"Ticker\",\n \"Side\",\n \"Price\",\n \"Quantity\",\n \"Fees\",\n \"Investment\",\n \"Currency\",\n \"Sector\",\n \"Industry\",\n \"Country\",\n \"Region\",\n ]\n ]\n df = df.replace(np.nan, \"-\")\n df[\"Date\"] = df[\"Date\"].dt.strftime(\"%Y-%m-%d\")\n df.sort_values(by=\"Date\", ascending=False, inplace=True)\n return df","def jsonrpc_gettx(self, txid):\n txnw = self.node.storage.db.get(hex2b(txid))\n if txnw is None:\n return {}\n txnw = TxnWrapper.unserialize(SerializationBuffer(txnw))\n info = hex_bytes_in_dict(\n txnw.txn.to_dict())\n\n # Add blockheight\n info['blockheight'] = txnw.timestamp / TIME_MULTIPLIER\n return info","def stats(self):\n return {attr: getattr(self, attr) for attr in ['cash', 'rawg_quantity', 'rawg_demand', 'rawg_price', 'rig_quantity', 'rig_supply', 'rig_price']}","def get_df_transactions():\n\n _, res = DBX.files_download(c.io.FILE_TRANSACTIONS)\n return pd.read_excel(io.BytesIO(res.content), index_col=0)","def __init__(self, miser, fromdt, todt, numBars = 100):\r\n def keysToString(indict):\r\n \"\"\"Return a new dict that has converted `indict`'s keys from\r\n Transaction to string.\"\"\"\r\n newD = {}\r\n for k, v in indict.iteritems():\r\n newD[k.name] = v\r\n return newD\r\n\r\n self.income = dictToSortedList(keysToString(miser.income(fromdt, todt)))\r\n self.expenses = dictToSortedList(keysToString(miser.expenses(fromdt, todt)))\r\n self.numBars = numBars\r\n \r\n sumStr = \"\\nProfile of expenses:\"\r\n sumStr += self.expensesBar\r\n\r\n print sumStr","def get_balance(self):\n\n return {\n 'saturacion': (self.config['saturacion'] + 100) * 100.0 / 200.0,\n 'contraste': (self.config['contraste'] + 100) * 100.0 / 200.0,\n 'brillo': (self.config['brillo'] + 100) * 100.0 / 200.0,\n 'hue': (self.config['hue'] + 100) * 100.0 / 200.0,\n 'gamma': (self.config['gamma'] + 100) * 100.0 / 200.0\n }","def _amount_all(self, cr, uid, ids,field_name, arg, context={}):\n res={}\n for record in self.browse(cr, uid, ids, context=context):\n res[record.id] = { 'amount_untaxed': 0.0, 'amount_tax': 0.0, 'amount_total': 0.0}\n amount_untaxed = 0.0\n amount_tax = 0.0\n amount_total = 0.0\n\t if not record.allowances_lines_after and record.allowances_lines_before:\n \tfor line in record.allowances_lines_before:\n \tamount_untaxed += line.amount_untaxed\n \tamount_tax += line.amount_tax\n \tamount_total += line.amount_total\n \tres[record.id]['amount_untaxed'] = amount_untaxed \n \tres[record.id]['amount_tax'] = amount_tax \n \tres[record.id]['amount_total'] = amount_total \n\n\t elif record.allowances_lines_after and record.allowances_lines_before :\n \tfor line in record.allowances_lines_after:\n \tamount_untaxed += line.amount_untaxed\n \tamount_tax += line.amount_tax\n \tamount_total += line.amount_total\n \tres[record.id]['amount_untaxed'] = amount_untaxed \n \tres[record.id]['amount_tax'] = amount_tax \n \tres[record.id]['amount_total'] = amount_total \n return res","def get_biases(self):\n return list(self.b.values())","def get_biases(self):\n return list(self.b.values())","def get_biases(self):\n return list(self.b.values())","def get_biases(self):\n return list(self.b.values())","def determineUnitHistory():\n\tunitTracker = Unitiser()\n\t\n\timport transactions\n\ttrades = transactions.allTransactions()\n\t\n\thistory = dict()\n\t\n\tfor date in timeline():\n\t\t#print(\"\\ntimelime:\", date.strftime('%Y-%m-%d %H:%M:%S'))\n\t\timport valuator\n\t\tvalue = valuator.getPortfolioValueAt(date)\n\t\tif date in trades:\n\t\t\tprior = getPortfolioBefore(date)\n\t\t\tprior_value = valuator.getPortfolioValueAt(date, portfolio = prior)\n\n\t\t\tinvested = Decimal('0.0')\n\t\t\tfor equity in trades[date]:\n\t\t\t\ttrade = trades[date][equity]\n\t\t\t\t#print(equity, trade)\n\t\t\t\tif trade['action'] == 'buy':\n\t\t\t\t\tinvested = invested + Decimal(trade['value'])\n\t\t\t\telif trade['action'] == 'sell':\n\t\t\t\t\tinvested = invested - Decimal(trade['value'])\n\n\t\t\tsince = getPortfolioAt(date)\n\t\t\tsince_value = valuator.getPortfolioValueAt(date, portfolio = since)\n\n\t\t\t#print(\"change amount is\", invested)\n\t\t\tif invested > 0:\n\t\t\t\tunitTracker.invest(invested, prior_value)\n\t\t\telif invested < 0:\n\t\t\t\tunitTracker.divest(abs(invested), prior_value)\n\n\t\thistory[date] = {\n\t\t\t 'date' : date,\n\t\t\t 'value' : value.quantize(TWOPLACES),\n\t\t\t 'units' : unitTracker.numberOfUnits().quantize(TWOPLACES),\n\t\t\t 'price' : unitTracker.pricePerUnit(value).quantize(TWOPLACES),\n\t\t\t 'invested' : unitTracker.invested\n\t\t\t }\n\t\n\treturn history","def get_all_stat(self):\n all_stat=dict()\n for stat_type in self.log_book.keys():\n stat = self.get_stat(stat_type)\n all_stat[stat_type] = stat\n return all_stat","def T1s(self) -> Dict[int, Optional[float]]:\n return {qs.id: qs.T1 for qs in self.qubits_specs}","def get_string_of_transactions(self):\n s = \"\"\n for transaction in self.transactions:\n s += transaction.to_string()\n return s","def get_values(self, currency):\n curr_dict = {\n \"brazilian_real\": None,\n \"american_dollar\": None,\n \"european_euro\": None,\n \"british_pound\": None,\n \"japanese_yen\": None,\n \"swiss_frank\": None,\n \"canadian_dollar\": None,\n \"australian_dollar\": None\n }\n index = 0\n for key in curr_dict:\n if key != currency:\n # list comprehension to get values from data\n curr_dict[key] = [\n element for record in select_records(currency, 1) for element in record\n if element == record[index] and isinstance(element, float)\n ]\n index += 1\n else:\n continue\n return curr_dict","def submit(\n self,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any],\n ) -> List[Dict]:\n\n search_type = tracker.get_slot(\"search_type\")\n transaction_history = tracker.get_slot(\"transaction_history\")\n transactions_subset = transaction_history.get(search_type, {})\n vendor = tracker.get_slot(\"vendor_name\")\n if vendor:\n transactions = transactions_subset.get(vendor.lower())\n vendor = f\" with {vendor}\"\n else:\n transactions = [\n v for k in list(transactions_subset.values()) for v in k\n ]\n vendor = \"\"\n\n start_time = parser.isoparse(tracker.get_slot(\"start_time\"))\n end_time = parser.isoparse(tracker.get_slot(\"end_time\"))\n\n for i in range(len(transactions) - 1, -1, -1):\n transaction = transactions[i]\n transaction_date = parser.isoparse(transaction.get(\"date\"))\n\n if transaction_date < start_time or transaction_date > end_time:\n transactions.pop(i)\n\n numtransacts = len(transactions)\n total = sum([t.get(\"amount\") for t in transactions])\n slotvars = {\n \"total\": f\"{total:.2f}\",\n \"numtransacts\": numtransacts,\n \"start_time_formatted\": tracker.get_slot(\"start_time_formatted\"),\n \"end_time_formatted\": tracker.get_slot(\"end_time_formatted\"),\n \"vendor_name\": vendor,\n }\n\n dispatcher.utter_message(\n template=f\"utter_searching_{search_type}_transactions\", **slotvars\n )\n dispatcher.utter_message(\n template=f\"utter_found_{search_type}_transactions\", **slotvars\n )\n\n return [\n SlotSet(\"time\", None),\n SlotSet(\"time_formatted\", None),\n SlotSet(\"start_time\", None),\n SlotSet(\"end_time\", None),\n SlotSet(\"start_time_formatted\", None),\n SlotSet(\"end_time_formatted\", None),\n SlotSet(\"grain\", None),\n SlotSet(\"search_type\", None),\n SlotSet(\"vendor_name\", None),\n ]","def get_dict(self):\n return","def _table_tree(self, real_account):\n return [{\n 'account': ra.account,\n 'balances_children':\n serialize_inventory(realization.compute_balance(ra),\n at_cost=True),\n 'balances': serialize_inventory(ra.balance, at_cost=True),\n 'is_leaf': len(ra) == 0 or bool(ra.txn_postings),\n 'postings_count': len(ra.txn_postings)\n } for ra in realization.iter_children(real_account)]","def histogramintegrals(self):\n return {}","def showTransactions(self):\n self.scanTransactions()\n txns = []\n\n # Summarize the stats\n for x in range(len(self._trans)):\n stats = self._trans[x]\n trans_time = 0\n remote_calls = 0\n for name, stat in stats:\n trans_time += stat.total_tt\n remote_calls += 1\n txns.append((x, trans_time, remote_calls))\n\n results = [\"TX#\\tTime\\tCalls\",\n \"=\" * 22]\n\n for item in txns:\n results.append(\"%3d\\t%4f\\t%5d\" % item)\n \n return \"\\n\".join(results)","def get_all_orders():","def summary(self,attr='raw'):\n g = {}\n g['gid'] = map(lambda x : x.gid, self.taxonomies)\n g['sp'] = map(lambda x : x.presences.species , self.taxonomies)\n \n g['gns'] = map(lambda x : x.presences.genera , self.taxonomies) \n g['fam'] = map(lambda x : x.presences.families , self.taxonomies)\n g['ord'] = map(lambda x : x.presences.orders , self.taxonomies)\n g['cls'] = map(lambda x : x.presences.classes , self.taxonomies)\n g['phy'] = map(lambda x : x.presences.phyla , self.taxonomies)\n g['kng'] = map(lambda x : x.presences.kingdoms , self.taxonomies)\n #g['all'] = map(lambda x : (x.gid,int(x.presences.species),int(x.genera),int(x.families),int(x.orders),int(x.classes),int(x.phyla),int(x.kingdoms)),self.taxonomies)\n keys = settings.TAXONOMIC_TREE_KEYS\n if attr == 'int':\n for key in keys:\n g[key] = map(lambda p : int(p) ,g[key])\n elif attr == 'str':\n for key in keys:\n g[key] = map(lambda p : str(p) ,g[key]) \n elif attr == 'list':\n for key in keys:\n g[key] = map(lambda p : p.list ,g[key]) \n elif attr == 'mapping':\n for key in keys:\n g[key] = map(lambda p : p.map ,g[key]) \n elif attr == 'raw':\n return g\n else:\n logger.error(\"Wrong attribute selection\")\n return None\n \n return g","def test_get_transaction_types(self):\n pass","def dump(self):\n result = super(BattleshipTransaction, self).dump()\n\n result['Name'] = self._name\n result['Action'] = self._action\n result['Ships'] = self._ships\n if self._action == 'JOIN':\n result['Board'] = self._board\n if self._action == 'FIRE':\n result['Row'] = self._row\n result['Column'] = self._column\n if self._reveal_space is not None:\n result['RevealSpace'] = self._reveal_space\n if self._reveal_nonce is not None:\n result['RevealNonce'] = self._reveal_nonce\n\n return result","def _get_amount_total_base(self):\n res = {}\n for txt in self:\n res[txt.id] = 0.0\n for txt_line in txt.txt_ids:\n if txt_line.invoice_id.type in ['out_refund', 'in_refund']:\n res[txt.id] -= txt_line.untaxed\n else:\n res[txt.id] += txt_line.untaxed\n return res","async def b_chain() -> dict:\n authority_chain = await chain.consensus()\n return {\"chain\": authority_chain[\"chain\"]}","def chart_of_accounts(qbo_session, attrs = \"strict\"):\n\n #query all the accounts\n accounts = qbo_session.get_objects(\"Account\")\n\n #by strict, I mean the order the docs say to use when udpating:\n #https://developer.intuit.com/docs/0025_quickbooksapi/\n #0050_data_services/030_entity_services_reference/account\n\n if attrs == \"strict\":\n attrs = [\n \"Id\", \"SyncToken\", \"MetaData\", \"Name\", \"SubAccount\",\n \"ParentRef\", \"Description\", \"FullyQualifiedName\", \"Active\",\n \"Classification\", \"AccountType\", \"AccountSubType\", \"AcctNum\",\n \"OpeningBalance\", \"OpeningBalanceDate\", \"CurrentBalance\",\n \"CurentBalanceWithSubAccounts\", \"CurrencyRef\"\n ]\n\n else:\n #TODO: validate the attrs against the 'strict' list above\n pass\n\n #As a first cut, we'll sort them by AccountType in trial balance order\n\n tb_type_order = [\n \"Bank\", \"Accounts Receivable\", \"Other Current Asset\",\n \"Fixed Asset\", \"Other Asset\",\n \"Accounts Payable\", \"Credit Card\",\n \"Other Current Liability\", \"Other Liability\",\n \"Equity\",\n \"Income\", \"Other Income\",\n \"Expense\", \"Other Expense\", \"Cost of Goods Sold\"\n ]\n\n accounts_by_type = {} #{Accounts_Payable:[row_list]\n\n for a_id in accounts:\n a = accounts[a_id]\n at = a[\"AccountType\"]\n if at not in tb_type_order:\n raise Exception(\"Unexpected AccountType: %s\" % at)\n\n if at not in accounts_by_type:\n accounts_by_type[at]=[]\n\n this_row = []\n for field in attrs:\n if field not in a:\n this_row.append(\"\")\n else:\n value = a[field]\n if isinstance(value,(list,tuple,dict)):\n this_row.append(\"<complex>\")\n else:\n this_row.append(a[field])\n\n accounts_by_type[at].append(this_row)\n\n rows = [attrs] #headers are the first row\n for at in tb_type_order:\n if at in accounts_by_type:\n for row in accounts_by_type[at]:\n rows.append(row)\n\n return rows","def get_tarefa_mais_barata(tarefas):\n\tdict_custo_total = {}\n\ttarefa_barata = {}\n\tfor tarefa in tarefas:\n \t\ttarefa_id = tarefa['identificador']\n\t\tif not (dict_custo_total.has_key(tarefa_id)):\n\t\t\tdict_custo_total[tarefa_id] = {'tarefa': tarefa, 'custo': 0}\n\t\t\tfor outra_tarefa in tarefas:\n\t\t\t\tif (outra_tarefa['identificador'] != tarefa_id):\n\t\t\t\t\tdict_custo_total[tarefa_id]['custo'] += calcula_custo(tarefa['tempo_de_execucao'], outra_tarefa['custo_por_hora'])\n\n\t\tif (tarefa_barata == {} or (tarefa_barata['custo'] > dict_custo_total[tarefa_id]['custo'])):\n\t\t\ttarefa_barata = dict_custo_total[tarefa_id]\n\n\treturn tarefa_barata['tarefa']","def fetch_all_tx(self):\n transactions = []\n for block in self.chain:\n transactions.append(block.data)\n return transactions","async def test_all_transactions(self):\n response = await self.collect(get_request_json_return_value=self.GATLING_JSON)\n self.assert_measurement(response, value=\"2\", entities=self.expected_entities)","def to_json(self) -> Dict[str, Any]:\n\n return {\n **self.index.to_json(),\n \"timelock\": self.timelock,\n \"amount\": self.amount,\n \"spend_key\": self.spend_key.hex(),\n \"state\": self.state.value,\n }","def gettransaction(self, txid):\n return TransactionInfo(**self.proxy.gettransaction(txid))","def get_transaction_prices(self):\n cleaned_data = self.cleaned_data()\n supplier_cleaned_data = cleaned_data.get('cleaned_supplier_data')\n transaction_cleaned_data = cleaned_data.get('cleaned_transaction_data')\n merged_data = self.merge_supplier_transaction(supplier_cleaned_data, transaction_cleaned_data)\n calculated_data = self.calculate_prices(merged_data)\n self.export_calculated_prices(calculated_data)\n return calculated_data","def get_tx_info(tx):\n\n input_addresses = []\n output_addresses = []\n payments = []\n\n try:\n response = json.loads(make_request('http://tbtc.blockr.io/api/v1/tx/info/' + tx))\n except Exception as e:\n status = json.loads(e.message).get('status')\n if status in ['error', 'fail']:\n return {'from': None, 'to': None, 'amount': None, 'confirmations': 0}\n\n vins = response.get('data').get('vins')\n vouts = response.get('data').get('vouts')\n confirmations = response.get('data').get('confirmations')\n\n for i in range(len(vins)):\n if vins[i].get('address') not in input_addresses:\n input_addresses.append(vins[i].get('address'))\n for i in range(len(vouts)):\n output_addresses.append(vouts[i].get('address'))\n payments.append(vouts[i].get('amount'))\n\n return {'from': input_addresses, 'to': output_addresses, 'amount': payments, 'confirmations': confirmations}","def test_execute_dump_all_transaction(self):\n\n instruction = Instruction(\"dump()\")\n\n with std_out() as (out, err):\n self.transaction_manager.execute(instruction)\n\n output = out.getvalue().strip()\n\n self.assertEqual(output, \"{1: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 2: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x11': { x11: 110 }, 'x12': { x12: 120 }, 'x1': { x1: 10 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 3: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 4: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x3': { x3: 30 }, 'x12': { x12: 120 }, 'x13': { x13: 130 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 5: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 6: {'x14': { x14: 140 }, 'x20': { x20: 200 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x15': { x15: 150 }, 'x4': { x4: 40 }, 'x5': { x5: 50 }}, 7: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 8: {'x14': { x14: 140 }, 'x20': { x20: 200 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x7': { x7: 70 }, 'x4': { x4: 40 }, 'x17': { x17: 170 }}, 9: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 10: {'x19': { x19: 190 }, 'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x9': { x9: 90 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}}\")","def get_dict(self):\n return {\n \"type\": self.item_type,\n \"size\": self.size,\n \"toppings\": self.toppings,\n \"price\": self.get_price()\n }","def to_dict(self):\n d = {'Name': self._name,\n 'Account Type': self.account_type}\n if self._assets:\n d['Assets'] = [to_dict(asset) for asset in self._assets.values()]\n if self._cash != 0:\n d['Available Cash'] = self._cash\n return d","def _get_new_bar(self, symbol):\n for b in self.symbol_data[symbol]:\n yield b","def _construct_all_holdings(self):\n d = dict((s, 0.0) for s in self.symbol_list)\n d['datetime'] = self.backtest_date\n d['cash'] = self.initial_capital\n d['commission'] = 0.0\n d['total'] = self.initial_capital\n d['buy_times'] = 0\n d['sell_times'] = 0\n d['total_times'] = 0\n d['hold'] = 0\n return [d]","async def get_transactions(self, guild_id, user):\n doc = await self.db[str(guild_id)].find_one({'id': user.id})\n if doc is None or len(doc['transactions']) == 0:\n return -1\n else:\n return doc['transactions']","def getData(self):\n return dict(self._dump_data)","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def _retrieve_transaction_table_input(self, execution_arn: str) -> Dict:\n response = self.client.get_execution_history(executionArn=execution_arn,maxResults=1000)\n events = response[\"events\"]\n record_purchase_entered_events = [\n event\n for event in events\n if event[\"type\"] == \"TaskStateEntered\" and event[\"stateEnteredEventDetails\"][\"name\"] == \"InsertPurchase\"\n ]\n\n record_refund_entered_events = [\n event\n for event in events\n if event[\"type\"] == \"TaskStateEntered\" and event[\"stateEnteredEventDetails\"][\"name\"] == \"InsertRefund\"\n ]\n\n record_error_entered_events = [\n event\n for event in events\n if event[\"type\"] == \"TaskStateEntered\" and event[\"stateEnteredEventDetails\"][\"name\"] == \"InsertError\"\n ]\n \n self.assertTrue(\n record_purchase_entered_events,\n \"Cannot find InsertPurchase TaskStateEntered event\",\n )\n self.assertTrue(\n record_refund_entered_events,\n \"Cannot find InsertPurchase TaskStateEntered event\",\n )\n self.assertTrue(\n record_error_entered_events,\n \"Cannot find InsertPurchase TaskStateEntered event\",\n )\n purchase_table_input=[] #PurchaseTable inputs\n refund_table_input=[] # RefundTable inputs\n error_table_input=[] # ErrorTable inputs\n for transaction in record_purchase_entered_events:\n transaction_input = json.loads(transaction[\"stateEnteredEventDetails\"][\"input\"])\n\n purchase_table_input.append(transaction_input)\n self.inserted_purchase_record_id.append(transaction_input[\"TransactionId\"]) # save this ID for cleaning up PurchaseTable\n\n for transaction in record_refund_entered_events:\n transaction_input = json.loads(transaction[\"stateEnteredEventDetails\"][\"input\"])\n\n refund_table_input.append(transaction_input)\n self.inserted_refund_record_id.append(transaction_input[\"TransactionId\"]) # save this ID for cleaning up RefundTable\n\n for transaction in record_error_entered_events:\n transaction_input = json.loads(transaction[\"stateEnteredEventDetails\"][\"input\"])\n\n error_table_input.append(transaction_input)\n self.inserted_error_record_id.append(transaction_input[\"TransactionId\"]) # save this ID for cleaning up ErrorTable\n\n return purchase_table_input, refund_table_input, error_table_input","def get_transaction_totals(self, params=None):\n return self.get(f\"{self.gateway_path}/totals\", params)","def transactions_df():\n return pd.DataFrame(\n {\n \"user_id\": [1, 1, 1, 2, 2, 2, 3, 3, 3],\n \"item_id\": [11, 22, 22, 11, 22, 33, 33, 33, 44],\n \"amount\": [10, 20, 30, 40, 50, 60, 70, 80, 90],\n }\n )","def amenity_all():\n state_holder = []\n for state in models.storage.all(\"Amenity\").values():\n state_holder.append(state.to_dict())\n return_holder = jsonify(state_holder)\n return return_holder","def dictOfBetas(self, free=True, fixed=False):\n s = {}\n for e in self.children:\n d = e.dictOfBetas(free, fixed)\n s = dict(s, **d)\n return s","def _buildTransDict(self, fromdt, todt, ttype):\r\n totalsDict = self._buildTotalsDict(fromdt, todt)\r\n sortedTotsList = dictToSortedList(totalsDict)\r\n\r\n return dict([(k,v) for k,v in totalsDict.iteritems() if type(k) == ttype])","def return_as_dictionary(self):\n output_dict = Inventory.return_as_dictionary(self)\n output_dict['material'] = self.material\n output_dict['size'] = self.size\n\n return output_dict","def all_transactions(self, request):\n user_id = request.data[\"user\"]\n user = User.objects.get(id=user_id)\n user_transactions = user.transactions.all()\n serializer = TransactionSerializer(user_transactions, many=True)\n\n return Response(serializer.data)","def get_all_books() -> List[Dict]:\n pass","def dict_values(self):\n return self.__dict__","def getCurrentSystemFunds(self):\n e = self.myParent.myEmpireDict\n d = self.myParent.mySystemDict\n return [e['CR'], d['AL'],d['EC'],d['IA']]","def get_transactions_sum_data(month_objects, amount_type):\n if (amount_type == 'expenses'):\n chart_data = [\n result['amount__sum'].quantize(D('0.01')).copy_abs()\n if result['amount__sum'] != None\n else 0\n for result in \n [month.transactions.filter(amount__lte=0).aggregate(Sum('amount')) for month in month_objects]\n ]\n elif (amount_type == 'incomes'):\n chart_data = [\n result['amount__sum'].quantize(D('0.01')).copy_abs() \n if result['amount__sum'] != None\n else 0\n for result in \n [month.transactions.filter(amount__gt=0).aggregate(Sum('amount')) for month in month_objects]\n ]\n \n return chart_data","def get_chain_data(self, symbol: str): \n return self.trader.fetch_chain_data(symbol)","def _load_transactions(self):\r\n\t\tlogger.debug(\"Enter\")\r\n\t\ttry:\r\n\t\t\twith open(self._state_file, 'rb') as tmp:\r\n\t\t\t\tlogger.debug(\"There is a file.\")\r\n\t\t\t\ttmp_dict = pickle.load(tmp)\r\n\t\t\t\tlogger.debug(\"Dictionary loaded from file: %s\" % tmp_dict)\r\n\t\texcept IOError as e: # File doesn't exists\r\n\t\t\tlogger.debug(\"Exit - No file. Error message: %s\" % e)\r\n\t\t\ttmp_dict = {}\r\n\t\t\t\r\n\t\treturn tmp_dict","def return_as_dictionary(self):\n output_dict = Inventory.return_as_dictionary(self)\n output_dict['brand'] = self.brand\n output_dict['voltage'] = self.voltage\n\n return output_dict","def dictOfBetas(self, free=True, fixed=False):\n if fixed and self.status != 0:\n return {self.name: self}\n\n if free and self.status == 0:\n return {self.name: self}\n\n return dict()","def transaction(self, transaction):\n # Allow for a list of blocks..\n transaction = utils.request_type(transaction)\n\n res = r.get(self.url + self.tx_info + str(transaction))\n return self.execute(res)","def __init__(self):\n self.transaction_index = {}\n self.transaction_list = []"],"string":"[\n \"def transaction_data(self):\\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))\",\n \"def transaction_base() -> Dict[str, Any]:\\n return {\\n \\\"first_name\\\": \\\"Donald\\\",\\n \\\"last_name\\\": \\\"Duck\\\",\\n \\\"company\\\": \\\"Duck Co\\\",\\n \\\"email\\\": \\\"donald.duck@duckburg.inv\\\",\\n \\\"telephone\\\": \\\"991234567\\\",\\n \\\"mobile\\\": \\\"+358991234567\\\",\\n \\\"street\\\": \\\"1313 Webfoot Walk\\\",\\n \\\"postal_code\\\": \\\"00000\\\",\\n \\\"city\\\": \\\"Duckburg\\\",\\n \\\"country\\\": \\\"US\\\",\\n \\\"information\\\": \\\"Quack, damn you!\\\",\\n \\\"items\\\": [],\\n }\",\n \"def get_transactions_as_json(self):\\n transactions = self.transaction_map.values() \\n transactions_json = list(map( lambda t: t.to_json(), transactions )) \\n return transactions_json\",\n \"def transactions(self, transactions: list):\\n num_txs = len(transactions)\\n transactions_size = num_txs * self._message_size['tx']\\n return {\\n 'id': 'transactions',\\n 'transactions': transactions,\\n 'size': kB_to_MB(transactions_size)\\n }\",\n \"def transactions(self):\\n url = f'{self._ynab.api_url}/budgets/{self.id}/transactions'\\n response = self._ynab.session.get(url)\\n if not response.ok:\\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\\n response.text,\\n response.status_code)\\n return []\\n return [Transaction(self._ynab, transaction)\\n for transaction in response.json().get('data', {}).get('transactions', [])]\",\n \"def fetch_bank_transactions(self):\\n return self.fetch('/bank_transactions')\",\n \"def transactions(self):\\n url = f'{self._ynab.api_url}/budgets/{self.budget.id}/accounts/{self.id}/transactions'\\n response = self._ynab.session.get(url)\\n if not response.ok:\\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\\n response.text,\\n response.status_code)\\n return []\\n return [Transaction(self._ynab, transaction)\\n for transaction in response.json().get('data', {}).get('transactions', [])]\",\n \"def get_transaction_data():\\n data = parse_json()\\n income_instances = create_transactions(data['incomes'])\\n expense_instances = create_transactions(data['expenses'])\\n for expense in expense_instances:\\n expense.amount = -(expense.amount)\\n transactions = income_instances + expense_instances\\n return transactions\",\n \"def test_wallets_get_transaction_list(self):\\n pass\",\n \"def get_transactions(self):\\n # open a cursor object\\n cur = self.get_cursor()\\n\\n # get transactions from database\\n cur.execute(\\\"SELECT * FROM transactions\\\")\\n transactions_data = cur.fetchall()\\n\\n # convert into a dict of values.\\n transactions_list = []\\n [transactions_list.append({'transaction_id': transaction[0],\\n 'date': transaction[1],\\n 'payee_id': transaction[2],\\n 'description': transaction[3],\\n 'amount': transaction[4]})\\n for transaction in transactions_data]\\n\\n # close the cursor\\n self.close_cursor()\\n\\n return transactions_list\",\n \"def unbalanced(self):\\n # TODO: Find a way to make a sql query to return all unbalanced transactions\\n return []\",\n \"def _buildTotalsDict(self, fromdt, todt):\\r\\n pairs = [(t, t.effectForPeriod(fromdt, todt)) for t in self.transactions]\\r\\n return dict(pairs)\",\n \"def transactions(self):\\r\\n return tx.AccountTransactions(self)\",\n \"def load_data():\\n dataFile = open('transactions.json', 'r')\\n data = json.load(dataFile)\\n transactions = data['transactions']#TID\\n items = data['items']#item sets\\n return items, transactions\",\n \"def report(self):\\n result = {}\\n result_buy = 0\\n result_sell = 0\\n result_outcome = 0\\n for pair in self.pairs:\\n orders = self.get_orders_for(pair)\\n buy, sell = self.get_buy_and_sell_costs(orders)\\n outcome = sell - buy\\n result_pair = {\\\"buy\\\": buy, \\\"sell\\\": sell, \\\"outcome\\\": outcome}\\n result.update({pair: result_pair})\\n result_buy += buy\\n result_sell += sell\\n result_outcome += outcome\\n\\n # Add global results\\n all = {\\\"buy\\\": result_buy, \\\"sell\\\": result_sell, \\\"outcome\\\": result_outcome}\\n result.update({\\\"all\\\": all})\\n return result\",\n \"def transactions(self):\\r\\n return tx.Transactions(self)\",\n \"def transactions(self):\\n return copy.deepcopy(self._transactions)\",\n \"def get_transactions():\\n\\n wallet = \\\"TTfoWGU2M939cgZm8CksPtz1ytJRM9GiN7\\\"\\n\\n url = \\\"https://api.trongrid.io/v1/accounts/{}/transactions\\\".format(wallet)\\n\\n response = requests.request(\\\"GET\\\", url)\\n\\n print(response.text)\",\n \"def new_get_buys_transaction_history(self, cb_account):\\n date: datetime = now()\\n if cb_account == \\\"wallet_id_btc\\\":\\n return MockAPIObject(\\n data=[{\\n \\\"created_at\\\": str(date + timedelta(days=-1)),\\n \\\"resource\\\": \\\"buy\\\",\\n \\\"status\\\": \\\"completed\\\",\\n \\\"amount\\\": {\\n \\\"amount\\\": 10,\\n \\\"currency\\\": \\\"BTC\\\"\\n },\\n \\\"total\\\": {\\n \\\"amount\\\": 10,\\n \\\"currency\\\": \\\"BTC\\\"\\n },\\n \\\"fees\\\": [{\\n \\\"amount\\\": {\\n \\\"amount\\\": 1,\\n \\\"currency\\\": \\\"EUR\\\"\\n }\\n }]\\n }, {\\n \\\"created_at\\\": str(date + timedelta(days=1)),\\n \\\"resource\\\": \\\"buy\\\",\\n \\\"status\\\": \\\"completed\\\",\\n \\\"amount\\\": {\\n \\\"amount\\\": 5,\\n \\\"currency\\\": \\\"BTC\\\"\\n },\\n \\\"total\\\": {\\n \\\"amount\\\": 5,\\n \\\"currency\\\": \\\"BTC\\\"\\n },\\n \\\"fees\\\": [{\\n \\\"amount\\\": {\\n \\\"amount\\\": 0.5,\\n \\\"currency\\\": \\\"EUR\\\"\\n }\\n }]\\n }])\\n else:\\n return MockAPIObject()\",\n \"def dict(self):\\n return {\\\"data\\\": self.data.dict(), \\\"inventory\\\": self.inventory.dict()}\",\n \"def get_pending_transactions():\\n\\n return History.get_pending().get()\",\n \"def _amount_all(self):\\n res = {}\\n ut_obj = self.env['l10n.ut']\\n for iwdl_brw in self.browse(self.ids):\\n # Using a clousure to make this call shorter\\n f_xc = ut_obj.sxc(\\n iwdl_brw.invoice_id.company_id.currency_id.id,\\n iwdl_brw.invoice_id.currency_id.id,\\n iwdl_brw.islr_wh_doc_id.date_uid)\\n\\n res[iwdl_brw.id] = {\\n 'amount': (iwdl_brw.base_amount * (iwdl_brw.retencion_islr / 100.0)) or 0.0,\\n 'currency_amount': 0.0,\\n 'currency_base_amount': 0.0,\\n }\\n for xml_brw in iwdl_brw.xml_ids:\\n res[iwdl_brw.id]['amount'] = xml_brw.wh\\n res[iwdl_brw.id]['currency_amount'] = f_xc(\\n res[iwdl_brw.id]['amount'])\\n res[iwdl_brw.id]['currency_base_amount'] = f_xc(\\n iwdl_brw.base_amount)\",\n \"def buildMarketOrdersData(self):\\n d = {}\\n for orderID, myMarketOrderDict in self.frame.mode.game.marketOrders.iteritems():\\n if myMarketOrderDict['system'] == self.mySystemDict['id']:\\n amount = myMarketOrderDict['amount']\\n min = myMarketOrderDict['min']\\n max = myMarketOrderDict['max']\\n value = myMarketOrderDict['value']\\n if myMarketOrderDict['type'] == 'sell':\\n s = 'SELL (%d %s) for MIN=%d' % (amount, value, min)\\n elif myMarketOrderDict['type'] == 'buy-all':\\n s = 'BUY ALL (%d %s) for MAX=%d' % (amount, value, max)\\n elif myMarketOrderDict['type'] == 'buy-any':\\n s = 'BUY ANY (%d %s) for MAX=%d' % (amount, value, max)\\n d[orderID] = s\\n return d\",\n \"def decoderawtransaction(self, hexstring):\\n return dict(self.proxy.decoderawtransaction(hexstring))\",\n \"def __preprocess_transactions(self):\\n\\n p_bar = tqdm(range(14), desc=\\\"Preprocessing transactions\\\", leave=False)\\n\\n try:\\n # 0. If optional fields not in the transactions add missing\\n optional_fields = [\\n \\\"Sector\\\",\\n \\\"Industry\\\",\\n \\\"Country\\\",\\n \\\"Region\\\",\\n \\\"Fees\\\",\\n \\\"Premium\\\",\\n \\\"ISIN\\\",\\n ]\\n if not set(optional_fields).issubset(set(self.__transactions.columns)):\\n for field in optional_fields:\\n if field not in self.__transactions.columns:\\n self.__transactions[field] = np.nan\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 1. Convert Date to datetime\\n self.__transactions[\\\"Date\\\"] = pd.to_datetime(self.__transactions[\\\"Date\\\"])\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 2. Sort transactions by date\\n self.__transactions = self.__transactions.sort_values(by=\\\"Date\\\")\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 3. Capitalize Ticker and Type [of instrument...]\\n self.__transactions[\\\"Ticker\\\"] = self.__transactions[\\\"Ticker\\\"].map(\\n lambda x: x.upper() if isinstance(x, str) else x\\n )\\n self.__transactions[\\\"Type\\\"] = self.__transactions[\\\"Type\\\"].map(\\n lambda x: x.upper() if isinstance(x, str) else x\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 4. Translate side: [\\\"deposit\\\", \\\"buy\\\"] -> 1 and [\\\"withdrawal\\\", \\\"sell\\\"] -> -1\\n self.__transactions[\\\"Signal\\\"] = self.__transactions[\\\"Side\\\"].map(\\n lambda x: 1\\n if x.lower() in [\\\"deposit\\\", \\\"buy\\\"]\\n else (-1 if x.lower() in [\\\"withdrawal\\\", \\\"sell\\\"] else 0)\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 5. Convert quantity to signed integer\\n self.__transactions[\\\"Quantity\\\"] = (\\n abs(self.__transactions[\\\"Quantity\\\"]) * self.__transactions[\\\"Signal\\\"]\\n )\\n\\n # Adjust quantity and price for splits\\n for ticker in self.__transactions[\\\"Ticker\\\"].unique():\\n try:\\n splits_df = get_splits(ticker)\\n if not splits_df.empty:\\n splits_df = splits_df.tz_localize(tz=None)\\n for split_date in splits_df.index:\\n self.__transactions[\\\"Quantity\\\"] = np.where(\\n (self.__transactions[\\\"Ticker\\\"] == ticker)\\n & (self.__transactions[\\\"Date\\\"] < split_date),\\n self.__transactions[\\\"Quantity\\\"]\\n * splits_df.loc[split_date].values,\\n self.__transactions[\\\"Quantity\\\"],\\n )\\n self.__transactions[\\\"Price\\\"] = np.where(\\n (self.__transactions[\\\"Ticker\\\"] == ticker)\\n & (self.__transactions[\\\"Date\\\"] < split_date),\\n self.__transactions[\\\"Price\\\"]\\n / splits_df.loc[split_date].values,\\n self.__transactions[\\\"Price\\\"],\\n )\\n\\n except Exception:\\n console.print(\\\"\\\\nCould not get splits adjusted\\\")\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 6. Determining the investment/divestment value\\n self.__transactions[\\\"Investment\\\"] = (\\n self.__transactions[\\\"Quantity\\\"] * self.__transactions[\\\"Price\\\"]\\n + self.__transactions[\\\"Fees\\\"]\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)\\n crypto_trades = self.__transactions[self.__transactions.Type == \\\"CRYPTO\\\"]\\n self.__transactions.loc[\\n (self.__transactions.Type == \\\"CRYPTO\\\"), \\\"Ticker\\\"\\n ] = [\\n f\\\"{crypto}-{currency}\\\"\\n for crypto, currency in zip(\\n crypto_trades.Ticker, crypto_trades.Currency\\n )\\n ]\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.\\n\\n # If isin not valid ticker is empty\\n self.__transactions[\\\"yf_Ticker\\\"] = self.__transactions[\\\"ISIN\\\"].apply(\\n lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan\\n )\\n\\n empty_tickers = list(\\n self.__transactions[\\n (self.__transactions[\\\"yf_Ticker\\\"] == \\\"\\\")\\n | (self.__transactions[\\\"yf_Ticker\\\"].isna())\\n ][\\\"Ticker\\\"].unique()\\n )\\n\\n # If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported\\n removed_tickers = []\\n for item in empty_tickers:\\n with suppress_stdout():\\n # Suppress yfinance failed download message if occurs\\n valid_ticker = not (\\n yf.download(\\n item,\\n start=datetime.datetime.now() + datetime.timedelta(days=-5),\\n progress=False,\\n ).empty\\n )\\n if valid_ticker:\\n # Invalid ISIN but valid ticker\\n self.__transactions.loc[\\n self.__transactions[\\\"Ticker\\\"] == item, \\\"yf_Ticker\\\"\\n ] = np.nan\\n else:\\n self.__transactions.loc[\\n self.__transactions[\\\"Ticker\\\"] == item, \\\"yf_Ticker\\\"\\n ] = \\\"\\\"\\n removed_tickers.append(item)\\n\\n # Merge reformatted tickers into Ticker\\n self.__transactions[\\\"Ticker\\\"] = self.__transactions[\\\"yf_Ticker\\\"].fillna(\\n self.__transactions[\\\"Ticker\\\"]\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 9. Remove unsupported ISINs that came out empty\\n self.__transactions.drop(\\n self.__transactions[self.__transactions[\\\"Ticker\\\"] == \\\"\\\"].index,\\n inplace=True,\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 10. Create tickers dictionary with structure {'Type': [Ticker]}\\n unsupported_type = self.__transactions[\\n (~self.__transactions[\\\"Type\\\"].isin([\\\"STOCK\\\", \\\"ETF\\\", \\\"CRYPTO\\\"]))\\n ].index\\n if unsupported_type.any():\\n self.__transactions.drop(unsupported_type, inplace=True)\\n console.print(\\n \\\"[red]Unsupported transaction type detected and removed. Supported types: stock, etf or crypto.[/red]\\\"\\n )\\n\\n for ticker_type in set(self.__transactions[\\\"Type\\\"]):\\n self.tickers[ticker_type] = list(\\n set(\\n self.__transactions[\\n self.__transactions[\\\"Type\\\"].isin([ticker_type])\\n ][\\\"Ticker\\\"]\\n )\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 11. Create list with tickers except cash\\n self.tickers_list = list(set(self.__transactions[\\\"Ticker\\\"]))\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 12. Save transactions inception date\\n self.inception_date = self.__transactions[\\\"Date\\\"].iloc[0]\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 13. Populate fields Sector, Industry and Country\\n if (\\n self.__transactions.loc[\\n self.__transactions[\\\"Type\\\"] == \\\"STOCK\\\",\\n optional_fields,\\n ]\\n .isnull()\\n .values.any()\\n ):\\n # If any fields is empty for stocks (overwrites any info there)\\n self.__load_company_data()\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # Warn user of removed ISINs\\n if removed_tickers:\\n p_bar.disable = True\\n console.print(\\n f\\\"\\\\n[red]The following tickers are not supported and were removed: {removed_tickers}.\\\"\\n f\\\"\\\\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN.\\\"\\n f\\\"\\\\nSuffix info on 'Yahoo Finance market coverage':\\\"\\n \\\" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html\\\"\\n f\\\"\\\\nE.g. IWDA -> IWDA.AS[/red]\\\\n\\\"\\n )\\n except Exception:\\n console.print(\\\"\\\\nCould not preprocess transactions.\\\")\\n raise\",\n \"def get_aggregated_values(self):\\n if not self._initialized:\\n raise Exception(\\\"To readout you must first initialize, then\\\"\\n \\\"process batches!\\\")\\n else:\\n ret_vals = [q.readout() for q in self.quantities]\\n return dict(zip(self.quantity_names, ret_vals))\",\n \"def tx_prices(self) -> Dict[str, List[float]]:\\n agent_pbk_to_name = self.game.configuration.agent_pbk_to_name\\n results = {\\n agent_name: [] for agent_name in agent_pbk_to_name.values()\\n } # type: Dict[str, List[float]]\\n\\n temp_game = Game(self.game.configuration, self.game.initialization)\\n\\n # compute the partial scores for every agent after every transaction\\n # (remember that indexes of the transaction start from one, because index 0 is reserved for the initial scores)\\n for idx, tx in enumerate(self.game.transactions):\\n temp_game.settle_transaction(tx)\\n results[agent_pbk_to_name[tx.seller_pbk]].append(tx.amount)\\n\\n return results\",\n \"def wem_market_value_all():\\n __query = \\\"\\\"\\\"\\n select\\n date_trunc('month', wfs.trading_interval) AS trading_day,\\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\\n wf.fueltech_id\\n from wem_facility_scada wfs\\n left join wem_facility wf on wfs.facility_id = wf.code\\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\\n where\\n wf.fueltech_id is not null\\n group by 1, wf.fueltech_id\\n order by 1 desc, 2 asc\\n \\\"\\\"\\\"\\n\\n query = __query.format()\\n\\n json_envelope = {}\\n\\n with engine.connect() as c:\\n rows = c.execute(query)\\n\\n current_tech = None\\n\\n for row in rows:\\n\\n current_tech = row[2]\\n\\n if current_tech not in json_envelope.keys():\\n json_envelope[current_tech] = {\\n \\\"id\\\": f\\\"wem.fuel_tech.{current_tech}.market_value\\\",\\n \\\"fuel_tech\\\": current_tech,\\n \\\"region\\\": \\\"wa\\\",\\n \\\"type\\\": \\\"market_value\\\",\\n \\\"units\\\": \\\"AUD\\\",\\n \\\"history\\\": {\\n \\\"interval\\\": \\\"1M\\\",\\n \\\"start\\\": None,\\n \\\"last\\\": None,\\n \\\"data\\\": [],\\n },\\n }\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] == None\\n or row[0] < json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] = row[0]\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] == None\\n or row[0] > json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] = row[0]\\n\\n json_envelope[current_tech][\\\"history\\\"][\\\"data\\\"].append(row[1])\\n\\n return [json_envelope[i] for i in json_envelope.keys()]\",\n \"def get_goal(self):\\n request_name = \\\"list_inventory_orders\\\"\\n\\n orders = self.make_request(request_name)\\n order_dict = dict()\\n for order in orders:\\n order_name = order[\\\"@id\\\"].encode('utf-8')\\n item_dict = dict()\\n for item in order[\\\"items\\\"]:\\n item_id = item[\\\"inventory-item-id\\\"].encode('utf-8')\\n item_quantity = item[\\\"quantity\\\"]\\n item_dict[item_id] = item_quantity\\n order_dict[order_name] = item_dict\\n return order_dict\",\n \"def _get_dicts(self, unit_set):\\n name_dict = {}\\n unit_dict = {}\\n for unit in unit_set:\\n name_dict[unit.name] = unit.coef\\n unit_dict[unit.unit] = unit\\n\\n return name_dict, unit_dict\",\n \"def get_transactions(filters, as_dict=1):\\n\\tfilter_by_voucher = 'AND gl.voucher_type = %(voucher_type)s' if filters.get('voucher_type') else ''\\n\\tgl_entries = frappe.db.sql(\\\"\\\"\\\"\\n\\t\\tSELECT\\n\\n\\t\\t\\t/* either debit or credit amount; always positive */\\n\\t\\t\\tcase gl.debit when 0 then gl.credit else gl.debit end as 'Umsatz (ohne Soll/Haben-Kz)',\\n\\n\\t\\t\\t/* 'H' when credit, 'S' when debit */\\n\\t\\t\\tcase gl.debit when 0 then 'H' else 'S' end as 'Soll/Haben-Kennzeichen',\\n\\n\\t\\t\\t/* account number or, if empty, party account number */\\n\\t\\t\\tacc.account_number as 'Konto',\\n\\n\\t\\t\\t/* against number or, if empty, party against number */\\n\\t\\t\\t%(temporary_against_account_number)s as 'Gegenkonto (ohne BU-Schlüssel)',\\n\\n\\t\\t\\tgl.posting_date as 'Belegdatum',\\n\\t\\t\\tgl.voucher_no as 'Belegfeld 1',\\n\\t\\t\\tLEFT(gl.remarks, 60) as 'Buchungstext',\\n\\t\\t\\tgl.voucher_type as 'Beleginfo - Art 1',\\n\\t\\t\\tgl.voucher_no as 'Beleginfo - Inhalt 1',\\n\\t\\t\\tgl.against_voucher_type as 'Beleginfo - Art 2',\\n\\t\\t\\tgl.against_voucher as 'Beleginfo - Inhalt 2',\\n\\t\\t\\tgl.party_type as 'Beleginfo - Art 3',\\n\\t\\t\\tgl.party as 'Beleginfo - Inhalt 3',\\n\\t\\t\\tcase gl.party_type when 'Customer' then 'Debitorennummer' when 'Supplier' then 'Kreditorennummer' else NULL end as 'Beleginfo - Art 4',\\n\\t\\t\\tpar.debtor_creditor_number as 'Beleginfo - Inhalt 4'\\n\\n\\t\\tFROM `tabGL Entry` gl\\n\\n\\t\\t\\t/* Kontonummer */\\n\\t\\t\\tleft join `tabAccount` acc \\n\\t\\t\\ton gl.account = acc.name\\n\\n\\t\\t\\tleft join `tabCustomer` cus\\n\\t\\t\\ton gl.party_type = 'Customer'\\n\\t\\t\\tand gl.party = cus.name\\n\\n\\t\\t\\tleft join `tabSupplier` sup\\n\\t\\t\\ton gl.party_type = 'Supplier'\\n\\t\\t\\tand gl.party = sup.name\\n\\n\\t\\t\\tleft join `tabParty Account` par\\n\\t\\t\\ton par.parent = gl.party\\n\\t\\t\\tand par.parenttype = gl.party_type\\n\\t\\t\\tand par.company = %(company)s\\n\\n\\t\\tWHERE gl.company = %(company)s \\n\\t\\tAND DATE(gl.posting_date) >= %(from_date)s\\n\\t\\tAND DATE(gl.posting_date) <= %(to_date)s\\n\\t\\t{}\\n\\t\\tORDER BY 'Belegdatum', gl.voucher_no\\\"\\\"\\\".format(filter_by_voucher), filters, as_dict=as_dict)\\n\\n\\treturn gl_entries\",\n \"def transactions(self):\\n return self._call_account_method(\\n 'transactions'\\n )\",\n \"def tx_counts(self) -> Dict[str, Dict[str, int]]:\\n agent_pbk_to_name = self.game.configuration.agent_pbk_to_name\\n result = {agent_name: 0 for agent_name in agent_pbk_to_name.values()}\\n results = {\\\"seller\\\": result.copy(), \\\"buyer\\\": result.copy()}\\n\\n temp_game = Game(self.game.configuration, self.game.initialization)\\n\\n # compute the partial scores for every agent after every transaction\\n # (remember that indexes of the transaction start from one, because index 0 is reserved for the initial scores)\\n for idx, tx in enumerate(self.game.transactions):\\n temp_game.settle_transaction(tx)\\n results[\\\"seller\\\"][agent_pbk_to_name[tx.seller_pbk]] += 1\\n results[\\\"buyer\\\"][agent_pbk_to_name[tx.buyer_pbk]] += 1\\n\\n return results\",\n \"def query_symbol_bars(self, symbol: str):\\n return self._call_txtrader_api('query_symbol_bars', {'symbol': symbol})\",\n \"def compute_helper_mempool_dictionaries():\\n txn_density_dict = {}\\n txn_parents_dict = {}\\n txn_size_dict = {}\\n mempool_data = parse_mempool_csv()\\n for elem in mempool_data:\\n size = elem.weight/MAXIMUM_BLOCK_WEIGHT # weight mapped to (0,1)\\n txn_size_dict[elem.txid] = size \\n txn_density_dict[elem.txid] = elem.fee/size\\n if elem.parents != '':\\n txn_parents_dict[elem.txid] = elem.parents.strip().split(';')\\n return txn_density_dict,txn_parents_dict,txn_size_dict\",\n \"def get(self, transaction_ids):\\n try:\\n transaction_ids = list(set(list(transaction_ids)))\\n request = {\\\"transaction_ids\\\": transaction_ids}\\n response = {}\\n # Validate User Input\\n validations_result = validate_transaction_ids(transaction_ids)\\n if validations_result is not None and len(validations_result) > 0:\\n response = {\\\"ResponseCode\\\": ResponseCodes.InvalidRequestParameter.value,\\n \\\"ResponseDesc\\\": ResponseCodes.InvalidRequestParameter.name,\\n \\\"ValidationErrors\\\": validations_result}\\n else:\\n transaction_outputs_dict = {}\\n for transaction_id in sorted(transaction_ids):\\n transaction_outputs = db_session.query(TransactionOutput).filter(\\n TransactionOutput.transaction_id == transaction_id).order_by(\\n TransactionOutput.id.asc()).all()\\n\\n trans_output_as_list = []\\n total_num_of_transaction_outputs = 0\\n for transaction_output in transaction_outputs:\\n output_address_response = json.loads(\\n requests.get('http://localhost:5000/bitcoin/transactions/outputs/addresses',\\n {'transaction_id': transaction_id,\\n 'transaction_output_id': transaction_output.id}).text)\\n if output_address_response[\\\"ResponseCode\\\"] == ResponseCodes.Success.value:\\n trans_output_as_list.append(serialize_transaction_output(transaction_output,\\n output_address_response[\\n \\\"NumberOfOutputAddresses\\\"],\\n output_address_response[\\n \\\"OutputAddresses\\\"]))\\n total_num_of_transaction_outputs = total_num_of_transaction_outputs + 1\\n else:\\n response = {\\\"ResponseCode\\\": output_address_response[\\\"ResponseCode\\\"],\\n \\\"ResponseDesc\\\": output_address_response[\\\"ResponseDesc\\\"],\\n \\\"ErrorMessage\\\": \\\"Internal Error in Transaction Output Address Service : \\\"\\n + output_address_response[\\\"ErrorMessage\\\"]\\n }\\n break\\n transaction_outputs_dict[transaction_id] = {\\\"NumberOfOutputs\\\": total_num_of_transaction_outputs,\\n \\\"TransactionOutputs\\\": trans_output_as_list}\\n\\n if total_num_of_transaction_outputs > 0:\\n response = {\\\"ResponseCode\\\": ResponseCodes.Success.value,\\n \\\"ResponseDesc\\\": ResponseCodes.Success.name,\\n \\\"TransactionOutputData\\\": transaction_outputs_dict\\n }\\n else:\\n response = {\\\"ResponseCode\\\": ResponseCodes.NoDataFound.value,\\n \\\"ResponseDesc\\\": ResponseCodes.NoDataFound.name,\\n \\\"ErrorMessage\\\": ResponseDescriptions.NoDataFound.value}\\n except Exception as ex:\\n response = {\\\"ResponseCode\\\": ResponseCodes.InternalError.value,\\n \\\"ResponseDesc\\\": ResponseCodes.InternalError.name,\\n \\\"ErrorMessage\\\": str(ex)}\\n finally:\\n return response\",\n \"def produce(self, request, meta, raven_variables, dispatch, t, level=None):\\n #balance = defaultdict(float)\\n interaction = self.get_interaction()\\n balance, meta = interaction.produce(request, meta, raven_variables, dispatch, t, level)\\n #for resource, quantity in int_balance.items():\\n # balance[resource] += quantity\\n return balance, meta\",\n \"def raw_get_transaction(cls, txid):\\n r = requests.get(cls.MAIN_TX_API.format(txid), timeout=DEFAULT_TIMEOUT)\\n r.raise_for_status() # pragma: no cover\\n return r.json()\",\n \"def _balances(self) -> Dict[str, int]:\\n\\n return self.client.get(self._resources(\\\"balance\\\"))\",\n \"def tx_transaction_mirs(self, txs_hash: str, pandas: bool=False) -> dict:\\n \\n tx_transaction_mirs = self.network + bf_tx_url + txs_hash + bf_tx_transaction_mirs_url\\n\\n response = query_blockfrost(tx_transaction_mirs, self.api_key, self.proxies)\\n \\n return pd.DataFrame.from_dict(response) if pandas else response\",\n \"def get_transactions(self):\\n\\n df = self.__transactions[\\n [\\n \\\"Date\\\",\\n \\\"Type\\\",\\n \\\"Ticker\\\",\\n \\\"Side\\\",\\n \\\"Price\\\",\\n \\\"Quantity\\\",\\n \\\"Fees\\\",\\n \\\"Investment\\\",\\n \\\"Currency\\\",\\n \\\"Sector\\\",\\n \\\"Industry\\\",\\n \\\"Country\\\",\\n \\\"Region\\\",\\n ]\\n ]\\n df = df.replace(np.nan, \\\"-\\\")\\n df[\\\"Date\\\"] = df[\\\"Date\\\"].dt.strftime(\\\"%Y-%m-%d\\\")\\n df.sort_values(by=\\\"Date\\\", ascending=False, inplace=True)\\n return df\",\n \"def jsonrpc_gettx(self, txid):\\n txnw = self.node.storage.db.get(hex2b(txid))\\n if txnw is None:\\n return {}\\n txnw = TxnWrapper.unserialize(SerializationBuffer(txnw))\\n info = hex_bytes_in_dict(\\n txnw.txn.to_dict())\\n\\n # Add blockheight\\n info['blockheight'] = txnw.timestamp / TIME_MULTIPLIER\\n return info\",\n \"def stats(self):\\n return {attr: getattr(self, attr) for attr in ['cash', 'rawg_quantity', 'rawg_demand', 'rawg_price', 'rig_quantity', 'rig_supply', 'rig_price']}\",\n \"def get_df_transactions():\\n\\n _, res = DBX.files_download(c.io.FILE_TRANSACTIONS)\\n return pd.read_excel(io.BytesIO(res.content), index_col=0)\",\n \"def __init__(self, miser, fromdt, todt, numBars = 100):\\r\\n def keysToString(indict):\\r\\n \\\"\\\"\\\"Return a new dict that has converted `indict`'s keys from\\r\\n Transaction to string.\\\"\\\"\\\"\\r\\n newD = {}\\r\\n for k, v in indict.iteritems():\\r\\n newD[k.name] = v\\r\\n return newD\\r\\n\\r\\n self.income = dictToSortedList(keysToString(miser.income(fromdt, todt)))\\r\\n self.expenses = dictToSortedList(keysToString(miser.expenses(fromdt, todt)))\\r\\n self.numBars = numBars\\r\\n \\r\\n sumStr = \\\"\\\\nProfile of expenses:\\\"\\r\\n sumStr += self.expensesBar\\r\\n\\r\\n print sumStr\",\n \"def get_balance(self):\\n\\n return {\\n 'saturacion': (self.config['saturacion'] + 100) * 100.0 / 200.0,\\n 'contraste': (self.config['contraste'] + 100) * 100.0 / 200.0,\\n 'brillo': (self.config['brillo'] + 100) * 100.0 / 200.0,\\n 'hue': (self.config['hue'] + 100) * 100.0 / 200.0,\\n 'gamma': (self.config['gamma'] + 100) * 100.0 / 200.0\\n }\",\n \"def _amount_all(self, cr, uid, ids,field_name, arg, context={}):\\n res={}\\n for record in self.browse(cr, uid, ids, context=context):\\n res[record.id] = { 'amount_untaxed': 0.0, 'amount_tax': 0.0, 'amount_total': 0.0}\\n amount_untaxed = 0.0\\n amount_tax = 0.0\\n amount_total = 0.0\\n\\t if not record.allowances_lines_after and record.allowances_lines_before:\\n \\tfor line in record.allowances_lines_before:\\n \\tamount_untaxed += line.amount_untaxed\\n \\tamount_tax += line.amount_tax\\n \\tamount_total += line.amount_total\\n \\tres[record.id]['amount_untaxed'] = amount_untaxed \\n \\tres[record.id]['amount_tax'] = amount_tax \\n \\tres[record.id]['amount_total'] = amount_total \\n\\n\\t elif record.allowances_lines_after and record.allowances_lines_before :\\n \\tfor line in record.allowances_lines_after:\\n \\tamount_untaxed += line.amount_untaxed\\n \\tamount_tax += line.amount_tax\\n \\tamount_total += line.amount_total\\n \\tres[record.id]['amount_untaxed'] = amount_untaxed \\n \\tres[record.id]['amount_tax'] = amount_tax \\n \\tres[record.id]['amount_total'] = amount_total \\n return res\",\n \"def get_biases(self):\\n return list(self.b.values())\",\n \"def get_biases(self):\\n return list(self.b.values())\",\n \"def get_biases(self):\\n return list(self.b.values())\",\n \"def get_biases(self):\\n return list(self.b.values())\",\n \"def determineUnitHistory():\\n\\tunitTracker = Unitiser()\\n\\t\\n\\timport transactions\\n\\ttrades = transactions.allTransactions()\\n\\t\\n\\thistory = dict()\\n\\t\\n\\tfor date in timeline():\\n\\t\\t#print(\\\"\\\\ntimelime:\\\", date.strftime('%Y-%m-%d %H:%M:%S'))\\n\\t\\timport valuator\\n\\t\\tvalue = valuator.getPortfolioValueAt(date)\\n\\t\\tif date in trades:\\n\\t\\t\\tprior = getPortfolioBefore(date)\\n\\t\\t\\tprior_value = valuator.getPortfolioValueAt(date, portfolio = prior)\\n\\n\\t\\t\\tinvested = Decimal('0.0')\\n\\t\\t\\tfor equity in trades[date]:\\n\\t\\t\\t\\ttrade = trades[date][equity]\\n\\t\\t\\t\\t#print(equity, trade)\\n\\t\\t\\t\\tif trade['action'] == 'buy':\\n\\t\\t\\t\\t\\tinvested = invested + Decimal(trade['value'])\\n\\t\\t\\t\\telif trade['action'] == 'sell':\\n\\t\\t\\t\\t\\tinvested = invested - Decimal(trade['value'])\\n\\n\\t\\t\\tsince = getPortfolioAt(date)\\n\\t\\t\\tsince_value = valuator.getPortfolioValueAt(date, portfolio = since)\\n\\n\\t\\t\\t#print(\\\"change amount is\\\", invested)\\n\\t\\t\\tif invested > 0:\\n\\t\\t\\t\\tunitTracker.invest(invested, prior_value)\\n\\t\\t\\telif invested < 0:\\n\\t\\t\\t\\tunitTracker.divest(abs(invested), prior_value)\\n\\n\\t\\thistory[date] = {\\n\\t\\t\\t 'date' : date,\\n\\t\\t\\t 'value' : value.quantize(TWOPLACES),\\n\\t\\t\\t 'units' : unitTracker.numberOfUnits().quantize(TWOPLACES),\\n\\t\\t\\t 'price' : unitTracker.pricePerUnit(value).quantize(TWOPLACES),\\n\\t\\t\\t 'invested' : unitTracker.invested\\n\\t\\t\\t }\\n\\t\\n\\treturn history\",\n \"def get_all_stat(self):\\n all_stat=dict()\\n for stat_type in self.log_book.keys():\\n stat = self.get_stat(stat_type)\\n all_stat[stat_type] = stat\\n return all_stat\",\n \"def T1s(self) -> Dict[int, Optional[float]]:\\n return {qs.id: qs.T1 for qs in self.qubits_specs}\",\n \"def get_string_of_transactions(self):\\n s = \\\"\\\"\\n for transaction in self.transactions:\\n s += transaction.to_string()\\n return s\",\n \"def get_values(self, currency):\\n curr_dict = {\\n \\\"brazilian_real\\\": None,\\n \\\"american_dollar\\\": None,\\n \\\"european_euro\\\": None,\\n \\\"british_pound\\\": None,\\n \\\"japanese_yen\\\": None,\\n \\\"swiss_frank\\\": None,\\n \\\"canadian_dollar\\\": None,\\n \\\"australian_dollar\\\": None\\n }\\n index = 0\\n for key in curr_dict:\\n if key != currency:\\n # list comprehension to get values from data\\n curr_dict[key] = [\\n element for record in select_records(currency, 1) for element in record\\n if element == record[index] and isinstance(element, float)\\n ]\\n index += 1\\n else:\\n continue\\n return curr_dict\",\n \"def submit(\\n self,\\n dispatcher: CollectingDispatcher,\\n tracker: Tracker,\\n domain: Dict[Text, Any],\\n ) -> List[Dict]:\\n\\n search_type = tracker.get_slot(\\\"search_type\\\")\\n transaction_history = tracker.get_slot(\\\"transaction_history\\\")\\n transactions_subset = transaction_history.get(search_type, {})\\n vendor = tracker.get_slot(\\\"vendor_name\\\")\\n if vendor:\\n transactions = transactions_subset.get(vendor.lower())\\n vendor = f\\\" with {vendor}\\\"\\n else:\\n transactions = [\\n v for k in list(transactions_subset.values()) for v in k\\n ]\\n vendor = \\\"\\\"\\n\\n start_time = parser.isoparse(tracker.get_slot(\\\"start_time\\\"))\\n end_time = parser.isoparse(tracker.get_slot(\\\"end_time\\\"))\\n\\n for i in range(len(transactions) - 1, -1, -1):\\n transaction = transactions[i]\\n transaction_date = parser.isoparse(transaction.get(\\\"date\\\"))\\n\\n if transaction_date < start_time or transaction_date > end_time:\\n transactions.pop(i)\\n\\n numtransacts = len(transactions)\\n total = sum([t.get(\\\"amount\\\") for t in transactions])\\n slotvars = {\\n \\\"total\\\": f\\\"{total:.2f}\\\",\\n \\\"numtransacts\\\": numtransacts,\\n \\\"start_time_formatted\\\": tracker.get_slot(\\\"start_time_formatted\\\"),\\n \\\"end_time_formatted\\\": tracker.get_slot(\\\"end_time_formatted\\\"),\\n \\\"vendor_name\\\": vendor,\\n }\\n\\n dispatcher.utter_message(\\n template=f\\\"utter_searching_{search_type}_transactions\\\", **slotvars\\n )\\n dispatcher.utter_message(\\n template=f\\\"utter_found_{search_type}_transactions\\\", **slotvars\\n )\\n\\n return [\\n SlotSet(\\\"time\\\", None),\\n SlotSet(\\\"time_formatted\\\", None),\\n SlotSet(\\\"start_time\\\", None),\\n SlotSet(\\\"end_time\\\", None),\\n SlotSet(\\\"start_time_formatted\\\", None),\\n SlotSet(\\\"end_time_formatted\\\", None),\\n SlotSet(\\\"grain\\\", None),\\n SlotSet(\\\"search_type\\\", None),\\n SlotSet(\\\"vendor_name\\\", None),\\n ]\",\n \"def get_dict(self):\\n return\",\n \"def _table_tree(self, real_account):\\n return [{\\n 'account': ra.account,\\n 'balances_children':\\n serialize_inventory(realization.compute_balance(ra),\\n at_cost=True),\\n 'balances': serialize_inventory(ra.balance, at_cost=True),\\n 'is_leaf': len(ra) == 0 or bool(ra.txn_postings),\\n 'postings_count': len(ra.txn_postings)\\n } for ra in realization.iter_children(real_account)]\",\n \"def histogramintegrals(self):\\n return {}\",\n \"def showTransactions(self):\\n self.scanTransactions()\\n txns = []\\n\\n # Summarize the stats\\n for x in range(len(self._trans)):\\n stats = self._trans[x]\\n trans_time = 0\\n remote_calls = 0\\n for name, stat in stats:\\n trans_time += stat.total_tt\\n remote_calls += 1\\n txns.append((x, trans_time, remote_calls))\\n\\n results = [\\\"TX#\\\\tTime\\\\tCalls\\\",\\n \\\"=\\\" * 22]\\n\\n for item in txns:\\n results.append(\\\"%3d\\\\t%4f\\\\t%5d\\\" % item)\\n \\n return \\\"\\\\n\\\".join(results)\",\n \"def get_all_orders():\",\n \"def summary(self,attr='raw'):\\n g = {}\\n g['gid'] = map(lambda x : x.gid, self.taxonomies)\\n g['sp'] = map(lambda x : x.presences.species , self.taxonomies)\\n \\n g['gns'] = map(lambda x : x.presences.genera , self.taxonomies) \\n g['fam'] = map(lambda x : x.presences.families , self.taxonomies)\\n g['ord'] = map(lambda x : x.presences.orders , self.taxonomies)\\n g['cls'] = map(lambda x : x.presences.classes , self.taxonomies)\\n g['phy'] = map(lambda x : x.presences.phyla , self.taxonomies)\\n g['kng'] = map(lambda x : x.presences.kingdoms , self.taxonomies)\\n #g['all'] = map(lambda x : (x.gid,int(x.presences.species),int(x.genera),int(x.families),int(x.orders),int(x.classes),int(x.phyla),int(x.kingdoms)),self.taxonomies)\\n keys = settings.TAXONOMIC_TREE_KEYS\\n if attr == 'int':\\n for key in keys:\\n g[key] = map(lambda p : int(p) ,g[key])\\n elif attr == 'str':\\n for key in keys:\\n g[key] = map(lambda p : str(p) ,g[key]) \\n elif attr == 'list':\\n for key in keys:\\n g[key] = map(lambda p : p.list ,g[key]) \\n elif attr == 'mapping':\\n for key in keys:\\n g[key] = map(lambda p : p.map ,g[key]) \\n elif attr == 'raw':\\n return g\\n else:\\n logger.error(\\\"Wrong attribute selection\\\")\\n return None\\n \\n return g\",\n \"def test_get_transaction_types(self):\\n pass\",\n \"def dump(self):\\n result = super(BattleshipTransaction, self).dump()\\n\\n result['Name'] = self._name\\n result['Action'] = self._action\\n result['Ships'] = self._ships\\n if self._action == 'JOIN':\\n result['Board'] = self._board\\n if self._action == 'FIRE':\\n result['Row'] = self._row\\n result['Column'] = self._column\\n if self._reveal_space is not None:\\n result['RevealSpace'] = self._reveal_space\\n if self._reveal_nonce is not None:\\n result['RevealNonce'] = self._reveal_nonce\\n\\n return result\",\n \"def _get_amount_total_base(self):\\n res = {}\\n for txt in self:\\n res[txt.id] = 0.0\\n for txt_line in txt.txt_ids:\\n if txt_line.invoice_id.type in ['out_refund', 'in_refund']:\\n res[txt.id] -= txt_line.untaxed\\n else:\\n res[txt.id] += txt_line.untaxed\\n return res\",\n \"async def b_chain() -> dict:\\n authority_chain = await chain.consensus()\\n return {\\\"chain\\\": authority_chain[\\\"chain\\\"]}\",\n \"def chart_of_accounts(qbo_session, attrs = \\\"strict\\\"):\\n\\n #query all the accounts\\n accounts = qbo_session.get_objects(\\\"Account\\\")\\n\\n #by strict, I mean the order the docs say to use when udpating:\\n #https://developer.intuit.com/docs/0025_quickbooksapi/\\n #0050_data_services/030_entity_services_reference/account\\n\\n if attrs == \\\"strict\\\":\\n attrs = [\\n \\\"Id\\\", \\\"SyncToken\\\", \\\"MetaData\\\", \\\"Name\\\", \\\"SubAccount\\\",\\n \\\"ParentRef\\\", \\\"Description\\\", \\\"FullyQualifiedName\\\", \\\"Active\\\",\\n \\\"Classification\\\", \\\"AccountType\\\", \\\"AccountSubType\\\", \\\"AcctNum\\\",\\n \\\"OpeningBalance\\\", \\\"OpeningBalanceDate\\\", \\\"CurrentBalance\\\",\\n \\\"CurentBalanceWithSubAccounts\\\", \\\"CurrencyRef\\\"\\n ]\\n\\n else:\\n #TODO: validate the attrs against the 'strict' list above\\n pass\\n\\n #As a first cut, we'll sort them by AccountType in trial balance order\\n\\n tb_type_order = [\\n \\\"Bank\\\", \\\"Accounts Receivable\\\", \\\"Other Current Asset\\\",\\n \\\"Fixed Asset\\\", \\\"Other Asset\\\",\\n \\\"Accounts Payable\\\", \\\"Credit Card\\\",\\n \\\"Other Current Liability\\\", \\\"Other Liability\\\",\\n \\\"Equity\\\",\\n \\\"Income\\\", \\\"Other Income\\\",\\n \\\"Expense\\\", \\\"Other Expense\\\", \\\"Cost of Goods Sold\\\"\\n ]\\n\\n accounts_by_type = {} #{Accounts_Payable:[row_list]\\n\\n for a_id in accounts:\\n a = accounts[a_id]\\n at = a[\\\"AccountType\\\"]\\n if at not in tb_type_order:\\n raise Exception(\\\"Unexpected AccountType: %s\\\" % at)\\n\\n if at not in accounts_by_type:\\n accounts_by_type[at]=[]\\n\\n this_row = []\\n for field in attrs:\\n if field not in a:\\n this_row.append(\\\"\\\")\\n else:\\n value = a[field]\\n if isinstance(value,(list,tuple,dict)):\\n this_row.append(\\\"<complex>\\\")\\n else:\\n this_row.append(a[field])\\n\\n accounts_by_type[at].append(this_row)\\n\\n rows = [attrs] #headers are the first row\\n for at in tb_type_order:\\n if at in accounts_by_type:\\n for row in accounts_by_type[at]:\\n rows.append(row)\\n\\n return rows\",\n \"def get_tarefa_mais_barata(tarefas):\\n\\tdict_custo_total = {}\\n\\ttarefa_barata = {}\\n\\tfor tarefa in tarefas:\\n \\t\\ttarefa_id = tarefa['identificador']\\n\\t\\tif not (dict_custo_total.has_key(tarefa_id)):\\n\\t\\t\\tdict_custo_total[tarefa_id] = {'tarefa': tarefa, 'custo': 0}\\n\\t\\t\\tfor outra_tarefa in tarefas:\\n\\t\\t\\t\\tif (outra_tarefa['identificador'] != tarefa_id):\\n\\t\\t\\t\\t\\tdict_custo_total[tarefa_id]['custo'] += calcula_custo(tarefa['tempo_de_execucao'], outra_tarefa['custo_por_hora'])\\n\\n\\t\\tif (tarefa_barata == {} or (tarefa_barata['custo'] > dict_custo_total[tarefa_id]['custo'])):\\n\\t\\t\\ttarefa_barata = dict_custo_total[tarefa_id]\\n\\n\\treturn tarefa_barata['tarefa']\",\n \"def fetch_all_tx(self):\\n transactions = []\\n for block in self.chain:\\n transactions.append(block.data)\\n return transactions\",\n \"async def test_all_transactions(self):\\n response = await self.collect(get_request_json_return_value=self.GATLING_JSON)\\n self.assert_measurement(response, value=\\\"2\\\", entities=self.expected_entities)\",\n \"def to_json(self) -> Dict[str, Any]:\\n\\n return {\\n **self.index.to_json(),\\n \\\"timelock\\\": self.timelock,\\n \\\"amount\\\": self.amount,\\n \\\"spend_key\\\": self.spend_key.hex(),\\n \\\"state\\\": self.state.value,\\n }\",\n \"def gettransaction(self, txid):\\n return TransactionInfo(**self.proxy.gettransaction(txid))\",\n \"def get_transaction_prices(self):\\n cleaned_data = self.cleaned_data()\\n supplier_cleaned_data = cleaned_data.get('cleaned_supplier_data')\\n transaction_cleaned_data = cleaned_data.get('cleaned_transaction_data')\\n merged_data = self.merge_supplier_transaction(supplier_cleaned_data, transaction_cleaned_data)\\n calculated_data = self.calculate_prices(merged_data)\\n self.export_calculated_prices(calculated_data)\\n return calculated_data\",\n \"def get_tx_info(tx):\\n\\n input_addresses = []\\n output_addresses = []\\n payments = []\\n\\n try:\\n response = json.loads(make_request('http://tbtc.blockr.io/api/v1/tx/info/' + tx))\\n except Exception as e:\\n status = json.loads(e.message).get('status')\\n if status in ['error', 'fail']:\\n return {'from': None, 'to': None, 'amount': None, 'confirmations': 0}\\n\\n vins = response.get('data').get('vins')\\n vouts = response.get('data').get('vouts')\\n confirmations = response.get('data').get('confirmations')\\n\\n for i in range(len(vins)):\\n if vins[i].get('address') not in input_addresses:\\n input_addresses.append(vins[i].get('address'))\\n for i in range(len(vouts)):\\n output_addresses.append(vouts[i].get('address'))\\n payments.append(vouts[i].get('amount'))\\n\\n return {'from': input_addresses, 'to': output_addresses, 'amount': payments, 'confirmations': confirmations}\",\n \"def test_execute_dump_all_transaction(self):\\n\\n instruction = Instruction(\\\"dump()\\\")\\n\\n with std_out() as (out, err):\\n self.transaction_manager.execute(instruction)\\n\\n output = out.getvalue().strip()\\n\\n self.assertEqual(output, \\\"{1: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 2: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x11': { x11: 110 }, 'x12': { x12: 120 }, 'x1': { x1: 10 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 3: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 4: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x3': { x3: 30 }, 'x12': { x12: 120 }, 'x13': { x13: 130 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 5: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 6: {'x14': { x14: 140 }, 'x20': { x20: 200 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x15': { x15: 150 }, 'x4': { x4: 40 }, 'x5': { x5: 50 }}, 7: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 8: {'x14': { x14: 140 }, 'x20': { x20: 200 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x7': { x7: 70 }, 'x4': { x4: 40 }, 'x17': { x17: 170 }}, 9: {'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}, 10: {'x19': { x19: 190 }, 'x14': { x14: 140 }, 'x18': { x18: 180 }, 'x10': { x10: 100 }, 'x8': { x8: 80 }, 'x9': { x9: 90 }, 'x16': { x16: 160 }, 'x2': { x2: 20 }, 'x12': { x12: 120 }, 'x6': { x6: 60 }, 'x20': { x20: 200 }, 'x4': { x4: 40 }}}\\\")\",\n \"def get_dict(self):\\n return {\\n \\\"type\\\": self.item_type,\\n \\\"size\\\": self.size,\\n \\\"toppings\\\": self.toppings,\\n \\\"price\\\": self.get_price()\\n }\",\n \"def to_dict(self):\\n d = {'Name': self._name,\\n 'Account Type': self.account_type}\\n if self._assets:\\n d['Assets'] = [to_dict(asset) for asset in self._assets.values()]\\n if self._cash != 0:\\n d['Available Cash'] = self._cash\\n return d\",\n \"def _get_new_bar(self, symbol):\\n for b in self.symbol_data[symbol]:\\n yield b\",\n \"def _construct_all_holdings(self):\\n d = dict((s, 0.0) for s in self.symbol_list)\\n d['datetime'] = self.backtest_date\\n d['cash'] = self.initial_capital\\n d['commission'] = 0.0\\n d['total'] = self.initial_capital\\n d['buy_times'] = 0\\n d['sell_times'] = 0\\n d['total_times'] = 0\\n d['hold'] = 0\\n return [d]\",\n \"async def get_transactions(self, guild_id, user):\\n doc = await self.db[str(guild_id)].find_one({'id': user.id})\\n if doc is None or len(doc['transactions']) == 0:\\n return -1\\n else:\\n return doc['transactions']\",\n \"def getData(self):\\n return dict(self._dump_data)\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def _retrieve_transaction_table_input(self, execution_arn: str) -> Dict:\\n response = self.client.get_execution_history(executionArn=execution_arn,maxResults=1000)\\n events = response[\\\"events\\\"]\\n record_purchase_entered_events = [\\n event\\n for event in events\\n if event[\\\"type\\\"] == \\\"TaskStateEntered\\\" and event[\\\"stateEnteredEventDetails\\\"][\\\"name\\\"] == \\\"InsertPurchase\\\"\\n ]\\n\\n record_refund_entered_events = [\\n event\\n for event in events\\n if event[\\\"type\\\"] == \\\"TaskStateEntered\\\" and event[\\\"stateEnteredEventDetails\\\"][\\\"name\\\"] == \\\"InsertRefund\\\"\\n ]\\n\\n record_error_entered_events = [\\n event\\n for event in events\\n if event[\\\"type\\\"] == \\\"TaskStateEntered\\\" and event[\\\"stateEnteredEventDetails\\\"][\\\"name\\\"] == \\\"InsertError\\\"\\n ]\\n \\n self.assertTrue(\\n record_purchase_entered_events,\\n \\\"Cannot find InsertPurchase TaskStateEntered event\\\",\\n )\\n self.assertTrue(\\n record_refund_entered_events,\\n \\\"Cannot find InsertPurchase TaskStateEntered event\\\",\\n )\\n self.assertTrue(\\n record_error_entered_events,\\n \\\"Cannot find InsertPurchase TaskStateEntered event\\\",\\n )\\n purchase_table_input=[] #PurchaseTable inputs\\n refund_table_input=[] # RefundTable inputs\\n error_table_input=[] # ErrorTable inputs\\n for transaction in record_purchase_entered_events:\\n transaction_input = json.loads(transaction[\\\"stateEnteredEventDetails\\\"][\\\"input\\\"])\\n\\n purchase_table_input.append(transaction_input)\\n self.inserted_purchase_record_id.append(transaction_input[\\\"TransactionId\\\"]) # save this ID for cleaning up PurchaseTable\\n\\n for transaction in record_refund_entered_events:\\n transaction_input = json.loads(transaction[\\\"stateEnteredEventDetails\\\"][\\\"input\\\"])\\n\\n refund_table_input.append(transaction_input)\\n self.inserted_refund_record_id.append(transaction_input[\\\"TransactionId\\\"]) # save this ID for cleaning up RefundTable\\n\\n for transaction in record_error_entered_events:\\n transaction_input = json.loads(transaction[\\\"stateEnteredEventDetails\\\"][\\\"input\\\"])\\n\\n error_table_input.append(transaction_input)\\n self.inserted_error_record_id.append(transaction_input[\\\"TransactionId\\\"]) # save this ID for cleaning up ErrorTable\\n\\n return purchase_table_input, refund_table_input, error_table_input\",\n \"def get_transaction_totals(self, params=None):\\n return self.get(f\\\"{self.gateway_path}/totals\\\", params)\",\n \"def transactions_df():\\n return pd.DataFrame(\\n {\\n \\\"user_id\\\": [1, 1, 1, 2, 2, 2, 3, 3, 3],\\n \\\"item_id\\\": [11, 22, 22, 11, 22, 33, 33, 33, 44],\\n \\\"amount\\\": [10, 20, 30, 40, 50, 60, 70, 80, 90],\\n }\\n )\",\n \"def amenity_all():\\n state_holder = []\\n for state in models.storage.all(\\\"Amenity\\\").values():\\n state_holder.append(state.to_dict())\\n return_holder = jsonify(state_holder)\\n return return_holder\",\n \"def dictOfBetas(self, free=True, fixed=False):\\n s = {}\\n for e in self.children:\\n d = e.dictOfBetas(free, fixed)\\n s = dict(s, **d)\\n return s\",\n \"def _buildTransDict(self, fromdt, todt, ttype):\\r\\n totalsDict = self._buildTotalsDict(fromdt, todt)\\r\\n sortedTotsList = dictToSortedList(totalsDict)\\r\\n\\r\\n return dict([(k,v) for k,v in totalsDict.iteritems() if type(k) == ttype])\",\n \"def return_as_dictionary(self):\\n output_dict = Inventory.return_as_dictionary(self)\\n output_dict['material'] = self.material\\n output_dict['size'] = self.size\\n\\n return output_dict\",\n \"def all_transactions(self, request):\\n user_id = request.data[\\\"user\\\"]\\n user = User.objects.get(id=user_id)\\n user_transactions = user.transactions.all()\\n serializer = TransactionSerializer(user_transactions, many=True)\\n\\n return Response(serializer.data)\",\n \"def get_all_books() -> List[Dict]:\\n pass\",\n \"def dict_values(self):\\n return self.__dict__\",\n \"def getCurrentSystemFunds(self):\\n e = self.myParent.myEmpireDict\\n d = self.myParent.mySystemDict\\n return [e['CR'], d['AL'],d['EC'],d['IA']]\",\n \"def get_transactions_sum_data(month_objects, amount_type):\\n if (amount_type == 'expenses'):\\n chart_data = [\\n result['amount__sum'].quantize(D('0.01')).copy_abs()\\n if result['amount__sum'] != None\\n else 0\\n for result in \\n [month.transactions.filter(amount__lte=0).aggregate(Sum('amount')) for month in month_objects]\\n ]\\n elif (amount_type == 'incomes'):\\n chart_data = [\\n result['amount__sum'].quantize(D('0.01')).copy_abs() \\n if result['amount__sum'] != None\\n else 0\\n for result in \\n [month.transactions.filter(amount__gt=0).aggregate(Sum('amount')) for month in month_objects]\\n ]\\n \\n return chart_data\",\n \"def get_chain_data(self, symbol: str): \\n return self.trader.fetch_chain_data(symbol)\",\n \"def _load_transactions(self):\\r\\n\\t\\tlogger.debug(\\\"Enter\\\")\\r\\n\\t\\ttry:\\r\\n\\t\\t\\twith open(self._state_file, 'rb') as tmp:\\r\\n\\t\\t\\t\\tlogger.debug(\\\"There is a file.\\\")\\r\\n\\t\\t\\t\\ttmp_dict = pickle.load(tmp)\\r\\n\\t\\t\\t\\tlogger.debug(\\\"Dictionary loaded from file: %s\\\" % tmp_dict)\\r\\n\\t\\texcept IOError as e: # File doesn't exists\\r\\n\\t\\t\\tlogger.debug(\\\"Exit - No file. Error message: %s\\\" % e)\\r\\n\\t\\t\\ttmp_dict = {}\\r\\n\\t\\t\\t\\r\\n\\t\\treturn tmp_dict\",\n \"def return_as_dictionary(self):\\n output_dict = Inventory.return_as_dictionary(self)\\n output_dict['brand'] = self.brand\\n output_dict['voltage'] = self.voltage\\n\\n return output_dict\",\n \"def dictOfBetas(self, free=True, fixed=False):\\n if fixed and self.status != 0:\\n return {self.name: self}\\n\\n if free and self.status == 0:\\n return {self.name: self}\\n\\n return dict()\",\n \"def transaction(self, transaction):\\n # Allow for a list of blocks..\\n transaction = utils.request_type(transaction)\\n\\n res = r.get(self.url + self.tx_info + str(transaction))\\n return self.execute(res)\",\n \"def __init__(self):\\n self.transaction_index = {}\\n self.transaction_list = []\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63048834","0.602332","0.5960751","0.56639814","0.56584823","0.5563182","0.5531879","0.55191296","0.55072176","0.5477383","0.54718333","0.5458796","0.5426807","0.54197216","0.541453","0.5338547","0.5327534","0.53225285","0.53161556","0.5279584","0.52619934","0.5237087","0.52224517","0.52208483","0.52202314","0.52195555","0.5216679","0.5214561","0.5192317","0.5190232","0.51868635","0.51672924","0.5163985","0.51561975","0.51450855","0.5128815","0.5127976","0.51187205","0.50943434","0.50937515","0.5092108","0.50841814","0.506848","0.5064129","0.5063724","0.50629747","0.50383264","0.503129","0.503129","0.503129","0.503129","0.5001976","0.50000143","0.49497277","0.49371666","0.4936105","0.4934925","0.4934214","0.49326798","0.4931223","0.49275663","0.49220523","0.49218124","0.49053317","0.4904125","0.48903102","0.4890083","0.48821458","0.48813823","0.4880626","0.48732132","0.4867718","0.48675153","0.48642778","0.48608243","0.48523393","0.4852034","0.4846197","0.48452893","0.48409748","0.48356566","0.4831061","0.4829988","0.48288727","0.48272905","0.482643","0.48256853","0.48212123","0.48201984","0.48171985","0.48094684","0.48060596","0.48027426","0.48025575","0.4800572","0.47963676","0.47922558","0.47921076","0.47916767","0.47877666","0.47861665"],"string":"[\n \"0.63048834\",\n \"0.602332\",\n \"0.5960751\",\n \"0.56639814\",\n \"0.56584823\",\n \"0.5563182\",\n \"0.5531879\",\n \"0.55191296\",\n \"0.55072176\",\n \"0.5477383\",\n \"0.54718333\",\n \"0.5458796\",\n \"0.5426807\",\n \"0.54197216\",\n \"0.541453\",\n \"0.5338547\",\n \"0.5327534\",\n \"0.53225285\",\n \"0.53161556\",\n \"0.5279584\",\n \"0.52619934\",\n \"0.5237087\",\n \"0.52224517\",\n \"0.52208483\",\n \"0.52202314\",\n \"0.52195555\",\n \"0.5216679\",\n \"0.5214561\",\n \"0.5192317\",\n \"0.5190232\",\n \"0.51868635\",\n \"0.51672924\",\n \"0.5163985\",\n \"0.51561975\",\n \"0.51450855\",\n \"0.5128815\",\n \"0.5127976\",\n \"0.51187205\",\n \"0.50943434\",\n \"0.50937515\",\n \"0.5092108\",\n \"0.50841814\",\n \"0.506848\",\n \"0.5064129\",\n \"0.5063724\",\n \"0.50629747\",\n \"0.50383264\",\n \"0.503129\",\n \"0.503129\",\n \"0.503129\",\n \"0.503129\",\n \"0.5001976\",\n \"0.50000143\",\n \"0.49497277\",\n \"0.49371666\",\n \"0.4936105\",\n \"0.4934925\",\n \"0.4934214\",\n \"0.49326798\",\n \"0.4931223\",\n \"0.49275663\",\n \"0.49220523\",\n \"0.49218124\",\n \"0.49053317\",\n \"0.4904125\",\n \"0.48903102\",\n \"0.4890083\",\n \"0.48821458\",\n \"0.48813823\",\n \"0.4880626\",\n \"0.48732132\",\n \"0.4867718\",\n \"0.48675153\",\n \"0.48642778\",\n \"0.48608243\",\n \"0.48523393\",\n \"0.4852034\",\n \"0.4846197\",\n \"0.48452893\",\n \"0.48409748\",\n \"0.48356566\",\n \"0.4831061\",\n \"0.4829988\",\n \"0.48288727\",\n \"0.48272905\",\n \"0.482643\",\n \"0.48256853\",\n \"0.48212123\",\n \"0.48201984\",\n \"0.48171985\",\n \"0.48094684\",\n \"0.48060596\",\n \"0.48027426\",\n \"0.48025575\",\n \"0.4800572\",\n \"0.47963676\",\n \"0.47922558\",\n \"0.47921076\",\n \"0.47916767\",\n \"0.47877666\",\n \"0.47861665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":2662,"cells":{"query":{"kind":"string","value":"Force a computation of the current portfolio state."},"document":{"kind":"string","value":"def update_portfolio(self):\n if not self._dirty_portfolio:\n return\n\n portfolio = self._portfolio\n pt = self.position_tracker\n\n portfolio.positions = pt.get_positions()\n position_stats = pt.stats\n\n portfolio.positions_value = position_value = (\n position_stats.net_value\n )\n portfolio.positions_exposure = position_stats.net_exposure\n self._cash_flow(self._get_payout_total(pt.positions))\n\n start_value = portfolio.portfolio_value\n\n # update the new starting value\n portfolio.portfolio_value = end_value = portfolio.cash + position_value\n\n pnl = end_value - start_value\n if start_value != 0:\n returns = pnl / start_value\n else:\n returns = 0.0\n\n portfolio.pnl += pnl\n portfolio.returns = (\n (1 + portfolio.returns) *\n (1 + returns) -\n 1\n )\n\n # the portfolio has been fully synced\n self._dirty_portfolio = False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_portfolio(self, portfolio: PortfolioController):\n now = portfolio.get_history(seconds_back=0)\n future = portfolio.get_history(seconds_back=-self.update_interval)\n\n for fund in portfolio.funds:\n best_currency = max(portfolio.currencies, key=lambda currency: future_value(fund, currency, now, future))\n if best_currency != fund.currency:\n portfolio.request_transfer(fund, best_currency)","def _initalize_portfolio_with_cash(self):\n self.cash = copy.copy(self.starting_cash)\n\n if self.starting_cash > 0.0:\n self.history.append(\n PortfolioEvent.create_subscription(\n self.current_dt, self.starting_cash, self.starting_cash\n )\n )\n\n self.logger.info(\n '(%s) Funds subscribed to portfolio \"%s\" '\n '- Credit: %0.2f, Balance: %0.2f' % (\n self.current_dt.strftime(settings.LOGGING[\"DATE_FORMAT\"]),\n self.portfolio_id,\n round(self.starting_cash, 2),\n round(self.starting_cash, 2)\n )\n )","def backtest_portfolio(self):\n raise NotImplementedError(\"Should implement backtest_portfolio()!\")","def updateState(self):\n self.state = self.microgridPolicy.computeState();","def portfolio(self):\n self.update_portfolio()\n return self._immutable_portfolio","def after_run(self):\n # Calculate the performance of the strategy and portfolio\n self.portfolio.calc_stats()\n self.calc_performance()\n\n return self","def reset(self): \n ptf_asset_vals, bmk_asset_vals = self._generate_initial_portfolios()\n process_params = self._generate_initial_process()\n self.state_vars = StateVariables( timestamp=0,\n ptf_asset_vals=ptf_asset_vals, \n bmk_asset_vals=bmk_asset_vals, \n process_params=process_params )\n return self._get_observation_from_state_vars()","def track_portfolio(self, p):\n\n global st_refresh_thread\n\n if self.terminate:\n return\n\n p.refresh()\n\n self.lock.acquire()\n self.active_portfolio = p\n self.display_portfolio(p)\n self.lock.release()\n\n if not self.refresh_thread:\n thr_args = list()\n thr_args.append(self)\n self.refresh_thread = threading.Thread(target=st_refresh_thread,\n args=thr_args)\n self.refresh_thread.start()","def _ensure_calculated(self):\n # return immediately if already done\n if self._state > State.UNCALCULATED:\n return\n\n # do the actual calculation, which must be implemented by the subclass\n # but first, set default state that we expect unless the implementing class overrides\n self._state = State.QUALIFIED \n self._calculate()","def on_iteration_start(self):\n\n self.Xfprv = self.Xf.copy()\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\n BacktrackRobust)):\n self.Yfprv = self.Yf.copy()\n\n if self.opt['Monotone']:\n if self.k == 0:\n self.objfn = self.eval_objfn()\n self.objfn_prev = self.objfn","def set_cash(self, cash):\n portfolio = self.get_portfolio_object()\n if portfolio is not None:\n portfolio.cash += cash\n portfolio.initial_cash += cash","def compute(self):\n if self._computed:\n return\n\n self._compute()\n self._computed = True","def __calculate_portfolio_returns(self):\n\n p_bar = tqdm(range(1), desc=\" Calculating returns\", leave=False)\n\n trade_data = self.historical_trade_data\n\n # Helper functions to calculate cash inflow and outflow\n def f_min(x):\n return x.apply(lambda x: min(x, 0))\n\n def f_max(x):\n return x.apply(lambda x: max(x, 0))\n\n # Calculate cash inflow and outflow\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period cash inflow\"], self.tickers_list + [\"Total\"]]\n )\n ] = -1 * trade_data[\"Investment delta\"][:].apply(lambda x: f_min(x), axis=0)\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period cash outflow\"], self.tickers_list + [\"Total\"]]\n )\n ] = trade_data[\"Investment delta\"][:].apply(lambda x: f_max(x), axis=1)\n\n # Calculate period return\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period absolute return\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"End Value\"] + trade_data[\"Period cash inflow\"]) - (\n trade_data[\"End Value\"].shift(1).fillna(0)\n + trade_data[\"Period cash outflow\"]\n )\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period percentage return\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"End Value\"] + trade_data[\"Period cash inflow\"]) / (\n trade_data[\"End Value\"].shift(1).fillna(0)\n + trade_data[\"Period cash outflow\"]\n ) - 1\n\n trade_data[\"Period percentage return\"].fillna(0, inplace=True)\n\n self.historical_trade_data = trade_data\n\n self.portfolio_returns = self.historical_trade_data[\"Period percentage return\"][\n \"Total\"\n ]\n\n p_bar.n += 1\n p_bar.refresh()","def update_portfolio_on_market(self, market: MarketEvent):\n self._portfolio.update_market_value(market)","def __update_portfolio_handler(self, msg):\n pass","def on_iteration_start(self):\n\n self.Xprv = self.X.copy()\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\n BacktrackRobust)):\n self.Yprv = self.Y.copy()\n\n if self.opt['Monotone']:\n if self.k == 0:\n self.objfn = self.eval_objfn()\n self.objfn_prev = self.objfn","def rebalance(context, data):\n logger.debug('rebalancing on: %s', algo.get_datetime())\n\n context.trend_filter = False\n\n # new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\n\n new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\n\n for equity, row in new_portfolio.iterrows():\n logger.debug('new portfolio (before filtering) - equity: %s', equity)\n\n # print(new_portfolio)\n\n # new_portfolio = new_portfolio[new_portfolio['overall_rank'].notna() & new_portfolio['momentum'] > 40][:20]\n \n # new_portfolio = new_portfolio[(new_portfolio['momentum_decile'] > 8)][:20]\n\n new_portfolio = new_portfolio.nlargest(20, ['overall_rank', 'momentum']) #<- $600K PL in 10 years\n\n # new_portfolio = new_portfolio.nlargest(20, ['momentum', 'overall_rank']) #<- 1M PL in 10 years\n\n if logger.level is logging.DEBUG:\n for equity, row in new_portfolio.iterrows():\n logger.debug('new portfolio - (after filtering) equity: %s', equity)\n \n\n # print(len(new_portfolio.index))\n\n # volatility driven weights\n # new_portfolio['inverse_volatility'] = new_portfolio['volatility'].apply(lambda x: 1 / x)\n # inv_vola_sum = new_portfolio['inverse_volatility'].sum()\n # new_portfolio['target_weight'] = new_portfolio['inverse_volatility'].apply(lambda x: x / inv_vola_sum)\n\n # portfolio size driven weights\n # num_equities = len(new_portfolio.index)\n # new_portfolio['target_weight'] = 1 / num_equities\\\n\n # logger.info('len existing portfolio: %s', len(context.portfolio.positions))\n\n if logger.level is logging.DEBUG:\n for equity, values in context.portfolio.positions.items():\n logger.debug('context.portfolio.positions - equity: %s, amount: %s, cost_basis: %s, sold_on: %s, sold_at_price: %s', equity, values.amount, values.cost_basis, values.last_sale_date, values.last_sale_price)\n\n \n order_target(algo.sid('FIBBG000NTFYM5'), 0)\n logger.debug('selling all bonds')\n\n for equity in context.portfolio.positions:\n if equity is algo.sid('FIBBG000NTFYM5'): \n continue\n if equity not in set(new_portfolio.index.tolist()):\n # logger.info('selling %s', equity)\n order_target_percent(equity, 0)\n\n stock_weights = 1.0 / max(len(context.portfolio.positions), len(new_portfolio.index))\n\n logger.debug('len existing portfolio (afer ejection): %s', len(context.portfolio.positions))\n logger.debug('len new portfolio: %s', len(new_portfolio.index))\n logger.debug('stock_weights: %s', stock_weights)\n\n # print(context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5')))\n\n # spy = context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5'))\n\n # if (spy is not None) and (spy.amount > 0):\n # order_target_percent(algo.sid('FIBBG000NTFYM5'), 0)\n\n for equity, row in new_portfolio.iterrows():\n if row.trend_filter is True:\n # logger.info('buying %s', equity)\n context.trend_filter = True\n order_target_percent(equity, stock_weights)\n else:\n context.trend_filter = False\n \n logger.debug('cash: %s', context.portfolio.cash)\n logger.debug('portfolio_value: %s', context.portfolio.portfolio_value)\n logger.debug('num_positions: %s', len(context.portfolio.positions))\n logger.debug('positions: %s', context.portfolio.positions)","def recalculate() -> None:\n NotImplemented","def _on_state_update(self) -> None:\n super()._on_state_update()\n self._set_futures(True)","def precalculate():\n pass","def precalculate():\n pass","def final(self, state):\n \"*** YOUR CODE HERE ***\"\n return\n util.raiseNotDefined()","def execute(self) -> None:\n self.state()","def _run_computation(self):\n with self.swap(stats_jobs_continuous.StatisticsAggregator,\n 'get_statistics', self._mock_get_statistics):\n ModifiedUserImpactAggregator.start_computation()\n self.process_and_flush_pending_tasks()","def before_trading_start(context, data):\n factors = pipeline_output('ff_example')\n\n # get the data we're going to use\n returns = factors['returns']\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\n be_me = factors.sort_values(['be_me'], ascending=True)\n\n # to compose the six portfolios, split our universe into portions\n half = int(len(mkt_cap)*0.5)\n small_caps = mkt_cap[:half]\n big_caps = mkt_cap[half:]\n \n thirty = int(len(be_me)*0.3)\n seventy = int(len(be_me)*0.7)\n growth = be_me[:thirty]\n neutral = be_me[thirty:seventy]\n value = be_me[seventy:]\n\n # now use the portions to construct the portfolios.\n # note: these portfolios are just lists (indices) of equities\n small_value = small_caps.index.intersection(value.index)\n small_neutral = small_caps.index.intersection(neutral.index)\n small_growth = small_caps.index.intersection(growth.index)\n \n big_value = big_caps.index.intersection(value.index)\n big_neutral = big_caps.index.intersection(neutral.index)\n big_growth = big_caps.index.intersection(growth.index)\n\n # take the mean to get the portfolio return, assuming uniform\n # allocation to its constituent equities.\n sv = returns[small_value].mean()\n sn = returns[small_neutral].mean()\n sg = returns[small_growth].mean()\n \n bv = returns[big_value].mean()\n bn = returns[big_neutral].mean()\n bg = returns[big_growth].mean()\n\n # computing SMB\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\n\n # computing HML\n context.hml = (sv + bv)/2 - (sg + bg)/2","def reset(self):\n for key in self.portfolio.keys():\n self.portfolio[key] = {'holdings': 0}\n self.buys[key] = 0\n self.portfolio['balance'] = 2500000.0","def apply(self, gameState):\n pass","def liquidate(self) -> None:\n if self.position.is_close:\n return\n\n if self.position.pnl > 0:\n self.take_profit = self.position.qty, self.price\n else:\n self.stop_loss = self.position.qty, self.price","def make_uncurrent(self):\n pass","def calculate_portfolio(self, request, pk=None, **kwargs):\n goal = self.get_object()\n\n check_state(Goal.State(goal.state), Goal.State.ACTIVE)\n\n setting_str = request.query_params.get('setting', None)\n if not setting_str:\n raise ValidationError(\"Query parameter 'setting' must be specified and a valid JSON string\")\n try:\n setting = ujson.loads(setting_str)\n except ValueError:\n raise ValidationError(\"Query parameter 'setting' must be a valid json string\")\n\n # Create the settings object from the dict\n serializer = serializers.GoalSettingStatelessSerializer(data=setting)\n serializer.is_valid(raise_exception=True)\n settings = serializer.create_stateless(serializer.validated_data, goal)\n\n try:\n data = self.build_portfolio_data(calculate_portfolio(settings=settings,\n data_provider=DataProviderDjango(),\n execution_provider=ExecutionProviderDjango()))\n return Response(data)\n except Unsatisfiable as e:\n rdata = {'reason': \"No portfolio could be found: {}\".format(e)}\n if e.req_funds is not None:\n rdata['req_funds'] = e.req_funds\n\n return Response({'error': rdata}, status=status.HTTP_400_BAD_REQUEST)","def swap_active_portfolio(self):\n\n self.lock.acquire()\n\n self.clear_main()\n\n w = self.windows['MAIN']\n l = 1\n\n for p in self.portfolios:\n # Only support 9 portfolios since that makes this easier to deal\n # with.\n if l >= 10:\n break\n\n w.addstr(l, 0, '%2d' % l, curses.A_BOLD | curses.color_pair(1))\n w.addstr(l, 3, p.name)\n l += 1\n\n self.refresh()\n\n # Wait for the user to give is a key.\n while True:\n c = self.stdscr.getch()\n\n if c < ord('1') and c > ord('9'):\n continue\n\n index = c - ord('1')\n\n if index < len(self.portfolios):\n break\n\n self.portfolios[index].refresh()\n\n self.active_portfolio = self.portfolios[index]\n self.display_portfolio(self.active_portfolio)\n self.lock.release()","def set_curr_value(self, val):\n # only goal that is in progress can have it's current value changed\n if self._status != EGoalStatus.IN_PROGRESS:\n raise NotImplementedError('Cannot set value to finished or not started goal')\n # try cast to int - mainly for QuantifiedGoal representation\n val = self.fw.types.try_float_cast(val)\n # update both in the stages object and in raw data\n self._values[EStage.CURRENT] = self._data_process(val)\n self._skeleton.curr_value = val\n # use progressor to update the database\n self._progressor.dump_to_database(self)","def atomistic_step(self):\n # first minimize in vacuum, in either case, \n # fixes problems with langevin bond deformation.\n self.system.minimize()\n \n if self.system.should_solvate:\n with self.system.solvate() as sol:\n with self.system.minimize(**sol) as mn:\n with self.system.equilibriate(**mn) as eq:\n self.system.md(**eq)\n else:\n self.system.equilibriate()\n self.system.md()","def apply(self):\r\n return","def apply(self):\r\n return","def apply(self):\r\n return","def apply(self):\r\n return","def apply(self):\r\n return","def _reset_solution(self, firm_size):\n x_lower, x_upper = self.model.workers.lower, self.model.workers.upper\n y_upper = self.model.firms.upper\n initial_V = np.array([y_upper, firm_size])\n\n if self.model.assortativity == 'positive':\n self.ivp.set_initial_value(initial_V, x_upper)\n wage = self.evaluate_wage(x_upper, initial_V)\n profit = self.evaluate_profit(x_upper, initial_V)\n self._solution = np.hstack((x_upper, initial_V, wage, profit))\n else:\n self.ivp.set_initial_value(initial_V, x_lower)\n wage = self.evaluate_wage(x_lower, initial_V)\n profit = self.evaluate_profit(x_lower, initial_V)\n self._solution = np.hstack((x_lower, initial_V, wage, profit))","def simulate_trading(self):\n self._generate_trading_instances()\n self._run_backtest()\n self.portfolio.output_equity()\n res=self.portfolio.get_statistics()\n self.plot.plot_equity()\n return res","def reactivate(self):\n self.write({'active': True, 'state': 'running'})\n STAGE = self.env['anytracker.stage']\n for ticket in self:\n starts = STAGE.search([('method_id', '=', ticket.method_id.id),\n ('progress', '=', 0)])\n if len(starts) != 1:\n raise except_orm(\n _('Configuration error !'),\n _('One and only one stage should have a 0% progress'))\n # write stage in a separate line to recompute progress & risk\n ticket.write({'stage_id': starts[0].id})\n self.recompute_parents()","def initialize_portfolio(self):\n\n raise NotImplementedError('''\n Must implement initialize_portfolio. Call help() for details.\n ''')","def _calculate(self):\n raise NotYetImplemented()","def optimizeForReturn(required_return, stock_db, use_genetic):\n print('Optimizing portfolio for %f' % required_return)\n pf = PortfolioFactory(stock_db, required_return, use_genetic=use_genetic)\n desired_portfolio = pf.desired_portfolio\n print('Required Return: %f' % required_return)\n print('Expected Return: %f' % math.pow(\n desired_portfolio.average_return, Config.DAYS_IN_YEAR))\n print('Downside Risk: %f' % desired_portfolio.downside_risk)\n print('Downside Correl: %f' % desired_portfolio.downside_correl)\n print('Score: %f' % desired_portfolio.score)\n\n # Write desired portfolio.\n DataIO.writeDesiredPortfolio(\n desired_portfolio, stock_db,\n 'output/DesiredPortfolio_%.0f_%.4f_%s.csv' % (\n Config.MINIMUM_AMOUNT_DATA, required_return, Config.TODAY.date()))\n\n print('Finished for %f' % required_return)\n\n return desired_portfolio","def calc(self):\n return None","def update_state(self, progress, policy_state=None):\n raise NotImplementedError","def solve():\n game_state.is_solving = ~game_state.is_solving\n\n if game_state.is_solving:\n solve_button.set_label(\"Pause\")\n else:\n solve_button.set_label(\"Solve\")\n\n game_state.is_dirty = True\n\n return solve","def restart(self):\n self.state ='active'\n if self.budget <= 0:\n return self.leave()\n self.cards =BJCards()\n self.bet_amount =0","def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:\n assert portfolio is not None\n assert stock_market_data is not None\n assert stock_market_data.get_companies() == [Company.A, Company.B]\n\n current_state = self.states_compution(stock_market_data, portfolio)\n current_portfolio_value = portfolio.get_value(stock_market_data)\n\n if self.train_while_trading is False:\n # for test set use trained ann\n action = np.argmax(self.model.predict(current_state)[0])\n\n else:\n action = self.select_action(current_state, current_portfolio_value)\n if self.last_state is not None:\n reward = self.calc_reward(current_portfolio_value)\n self.append_memory(self.last_state, self.last_action_a, reward, current_state)\n # train model as soon as sufficient memory is reached\n if len(self.memory) > self.min_size_of_memory_before_training:\n self.train_model()\n\n # Split action into individual actions for Company A and B\n current_action_a = 0\n current_action_b = 0\n assert action < 9 and action >= 0\n if action == 0:\n current_action_a = OrderType.SELL\n current_action_b = OrderType.SELL\n amount_to_sell_a = portfolio.get_stock(Company.A)\n amount_to_sell_b = portfolio.get_stock(Company.B)\n elif action == 1:\n current_action_a = OrderType.SELL\n amount_to_sell_a = portfolio.get_stock(Company.A)\n current_action_b = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_b = int(portfolio.cash/stock_price)\n elif action == 2:\n current_action_a = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_a = int(portfolio.cash/stock_price)\n current_action_b = OrderType.SELL\n amount_to_sell_b = portfolio.get_stock(Company.B)\n elif action == 3:\n current_action_a = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_a = int((portfolio.cash/stock_price)/2)\n current_action_b = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.B)\n amount_to_buy_b = int((portfolio.cash/stock_price)/2)\n elif action == 4:\n current_action_a = OrderType.SELL\n amount_to_sell_a = portfolio.get_stock(Company.A)\n # current_action_b = \"hold\"\n elif action == 5:\n current_action_a = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_a = int(portfolio.cash/stock_price)\n # current_action_b = \"hold\"\n elif action == 6:\n # current_action_a = \"hold\"\n current_action_b = OrderType.SELL\n amount_to_sell_b = portfolio.get_stock(Company.B)\n elif action == 7:\n # current_action_a = \"hold\"\n current_action_b = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.B)\n amount_to_buy_b = int(portfolio.cash/stock_price)\n\n order_list = []\n\n if current_action_a != 0:\n if current_action_a == OrderType.SELL and amount_to_sell_a > 0:\n order_list.append(Order(current_action_a, Company.A, amount_to_sell_a))\n elif current_action_a == OrderType.BUY and portfolio.cash > 0:\n order_list.append(Order(current_action_a, Company.A, amount_to_buy_a))\n\n if current_action_b != 0:\n if current_action_b == OrderType.SELL and amount_to_sell_b > 0:\n order_list.append(Order(current_action_b, Company.B, amount_to_sell_b))\n elif current_action_b == OrderType.BUY and portfolio.cash > 0:\n order_list.append(Order(current_action_b, Company.B, amount_to_buy_b))\n\n self.last_action_a = action\n self.last_state = current_state\n self.last_portfolio_value = current_portfolio_value\n return order_list","def hedge_portfolio(context, data):\r\n factors = get_alphas_and_betas(context, data)\r\n beta_exposure = 0.0\r\n count = 0\r\n for asset in context.portfolio.positions:\r\n if asset in factors and asset != context.index:\r\n if not np.isnan(factors[asset].beta):\r\n beta_exposure += factors[asset].beta\r\n count += 1\r\n beta_hedge = -1.0 * beta_exposure / count\r\n dollar_amount = context.portfolio.portfolio_value * beta_hedge\r\n record(beta_hedge=beta_hedge)\r\n if not np.isnan(dollar_amount):\r\n order_target_value(context.index, dollar_amount)","def set_initial(self, value):\n # TODO: Make an Initial Stock Adjust here\n pass","def index():\n\n #select user's portfolio\n rows = db.execute(\"SELECT * FROM portfolio WHERE userid=:id\", id=session[\"user_id\"])\n\n #set temporary holding place for cash to zero\n tcash = 0\n\n #update the stock information in user's portfolio\n for row in rows:\n stock = row[\"stock\"]\n number = row[\"number\"]\n quote = lookup(stock)\n total = float(number) * float(quote[\"price\"])\n tcash += total\n db.execute(\"UPDATE portfolio SET price=:price, total=:total WHERE userid=:id AND stock=:stock AND number=:number\", price=usd(quote[\"price\"]), total=total, id=session[\"user_id\"], stock=stock, number=number)\n\n #select user's cash and updated portfolio\n updated_cash = db.execute(\"SELECT cash FROM users WHERE id=:id\", id=session[\"user_id\"])\n tcash += updated_cash[0][\"cash\"]\n updated_stock = db.execute(\"SELECT stock, SUM(number) AS number, price, SUM(total) AS stock_total FROM portfolio WHERE userid=:id GROUP BY stock HAVING SUM(number) > 0\", id=session[\"user_id\"])\n\n return render_template(\"index.html\", stocks=updated_stock, cash=usd(updated_cash[0][\"cash\"]), all_total=usd(tcash))","def ComputerFinalStateOfCharge(self):\r\n pass","def ramp_up(self) -> None:\n self.cash_balance: float = self.initial_cash_balance()\n for stock in self.stocks:\n initial_date_idx = 0\n self.cash_balance = stock.buy(initial_date_idx, self.cash_balance, self.buy_budget)","def display_portfolio(self, p):\n\n if self.terminate:\n return\n\n w = self.windows['MAIN']\n\n self.clear_main()\n self.__display_portfolio(p, w)\n self.clear_header()\n self.set_header(p)\n\n self.refresh()","def call_back(e):\n gh_env.Component.ExpireSolution(False)","def solution_update(self, theta, force=False):\n \n self.x = self.eval(theta, force)\n \n return","def _localSetState(self,pdict):\n super()._localSetState(pdict)\n self.transition = pdict.pop('transition')\n self.steadyStatePb = pdict.pop('steadyStatePb')","def reset_for_new_run(\n self,\n state: State\n ):\n\n super().reset_for_new_run(state)\n\n self.epsilon = self.original_epsilon\n self.greedy_action = list(self.Q.keys())[0]","def act(self, current_state, setpoint):\n s = self.last_s # Refers only to theta\n a = self.last_a # Refers only the last action taken\n s_p = self.map_state(\n current_state['theta'], current_state['theta_dot'])\n\n # Update the q-table based on the Bellman Equation\n r = self.reward_func(current_state)\n q_predict = self.q_table[s + (a,)]\n\n if not self.reset_env:\n q_target = r + self.params['gamma'] * np.max(self.q_table[s_p])\n else:\n q_target = r\n print(str(r) + ' Reset')\n self.reset_env = False\n # self.q_table[40,1]=90\n\n self.q_table[s+(a,)] += self.params['learn_rate'] * \\\n (q_target - q_predict)\n # print(self.params['epsilon'])\n # Take the next action\n if (np.random.uniform() < self.params['epsilon']) or (np.max(np.abs(self.q_table[s_p])) < 1e-3):\n ac = np.random.randint(self.num_actions)\n self.dist[ac] += 1\n else:\n ac = np.argmax(self.q_table[s_p])\n\n #ac = 0\n\n self.last_a = ac\n self.last_s = s_p\n if self.cont_decay == 1000:\n self.params['epsilon'] *= self.params['decay_rate']\n self.cont_decay = 0\n else:\n self.cont_decay += 1\n u = self.map_force(ac)\n f_u = self.F_cum + u\n print(self.params)\n print(self.q_table[30:51])\n return f_u","def apply(self):\n pass","def apply(self):\n pass","def forces(self):\n pass","def portfolio():\n #Query transactions by user id\n trans = Transactions.query.filter_by(owner=session['user_id']).all()\n \n #Create list of comanies user owns stock in\n companies = []\n for t in trans:\n if t.symbol not in companies:\n companies.append(t.symbol)\n\n #Create list of current stock dictionaries and total their values\n total = 0\n stocks = []\n for company in companies:\n trans = Transactions.query.filter_by(owner=session['user_id'], symbol=company).all()\n stock = {}\n stock['shares'] = 0\n for t in trans:\n stock['shares'] += t.shares\n if stock['shares'] > 0:\n stock['symbol'] = company\n stock['name'] = lookup(company)['name']\n stock['price'] = lookup(company)['price']\n stock['total'] = stock['shares'] * stock['price']\n stock['price'] = usd(stock['price'])\n stock['total'] = usd(stock['total'])\n total += float(stock['total'][1:].replace(',', ''))\n stocks.append(stock)\n\n #Set user cash and total values\n value = {}\n value['cash'] = usd(Users.query.filter_by(id=session['user_id']).first().cash)\n value['total'] = usd(total + float(value['cash'][1:].replace(',', '')))\n\n #Add values to list\n stocks.append(value)\n\n #Return list of dictionaries\n return stocks","def reset_new_conditions (self):\n self.solver.pop()\n self.solver.push()","def updater(self, func_name, ml_results=None):\n\n closed_pos = []\n\n # Iterates all active positions\n for pos in self.active_short_positions + self.active_long_positions:\n\n # Check if position reached target /expired / active strategy, and updates the position accordingly\n if getattr(pos, func_name)(ml_results=ml_results):\n\n closed_pos.append(pos)\n\n if pos.order_type == Consts.LONG and self.longs:\n self.active_long_positions.remove(pos)\n self.closed_long_positions.append(pos)\n if pos.order_type == Consts.SHORT and self.shorts:\n self.active_short_positions.remove(pos)\n self.closed_short_positions.append(pos)\n\n # Updates Portfolio stats according to new closed positions\n self.closed_positions(closed_pos)\n\n # Updates capital stats after last operations\n self.update_capital_stats()","def portfolio_performance(returns,weights):\r\n print('Calculating Portfolio Performance')\r\n # returns=target_asset_port_data_attributes['component_returns']\r\n # weights =target_asset_port_data_attributes['effective_weights']\r\n\r\n component_returns= returns\r\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\r\n compnent_weights.loc[weights.index,:] = weights\r\n\r\n portfolio_dates = component_returns.index\r\n components = component_returns.columns\r\n\r\n # pre-allocate\r\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\r\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\r\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\r\n \r\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\r\n previous_index_value = np.int64(1)\r\n\r\n pre_date = portfolio_dates[0]\r\n # set BoP to start weights\r\n for date,row in component_returns.iterrows():\r\n # print(date)\r\n # 1st date\r\n if date == portfolio_dates[0]:\r\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n\r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n # after first date\r\n else:\r\n BoP_df.loc[date] = EoP_df.loc[pre_date]\r\n # weights override\r\n if date in compnent_weights.index:\r\n none_NaN_index = ~compnent_weights.loc[date].isnull()\r\n if not compnent_weights.loc[date][none_NaN_index].empty:\r\n tmp_sum = BoP_df.loc[date].sum()\r\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\r\n\r\n \r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n \r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n\r\n portfolio_returns = portfolio_index.pct_change(1) \r\n portfolio_returns.columns = ['Returns']\r\n\r\n portfolio_index\r\n perf = portfolio_index.calc_stats()\r\n \r\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\r\n return output","def step(self):\n self.solver.integrate(self.t)\n self.state = self.solver.y","def solve(self, current_state: dict) -> dict:","def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return","def rebalance(self, date):\n eod_values = self.df.shift(1).loc[date, 'values'].mul(1 + self.tc.instrument_returns.loc[date, 'daily'])\n eod_portfolio_value = sum(eod_values.values)\n\n previous_values = self.df.loc[date, 'values'].copy()\n position_value = self.target_weights.mul(eod_portfolio_value)\n trading_cost = abs(eod_values.div(eod_portfolio_value) - self.target_weights) * eod_portfolio_value * \\\n self.tc.commission\n current_values = position_value - trading_cost\n self.df.loc[date, 'values'] = current_values.values\n future_values = self.tc.instrument_returns.loc[date:, 'cumulative'].div(\n self.tc.instrument_returns.loc[date, 'cumulative']).mul(current_values, axis=1)\n self.df.loc[date:, 'values'] = future_values.values\n trade = pd.Series(current_values - previous_values)\n # Once we have calculated the end-of-day value of the portfolio, we set the allocation by looking at the\n # dollars invested in each ETF\n self.df.loc[date:, 'allocations'] = future_values.div(future_values.sum(axis=1), axis=0).values\n\n return trade","def __setstate__(self, state):\n self.__dict__ = state\n self.get_esoh_solver = lru_cache()(self._get_esoh_solver)","def compute_portvals(start_date, end_date, orders_file, start_val):\n \n #Read order file\n orders = pd.read_csv( orders_file, parse_dates = [0])\n \n #Get symbols making up the portfolio\n stock_symbols = list( set( orders[\"Symbol\"] ) )\n dates = pd.date_range(start_date, end_date)\n \n #Read stock prices\n stock_prices = get_data(stock_symbols, dates)\n \n #Create a portfolio keeping track of positions, \n #_CASH column indicates cash position, _VALUE total portfolio value\n #_LEVERAGE the leverage of portfolio when we allow for short selling\n symbols = stock_symbols[:] #Shallow copy of the list\n symbols.append(\"_CASH\")\n symbols.append(\"_VALUE\")\n symbols.append(\"_LEVERAGE\")\n \n #Index contains only business days, same dates as stock prices\n portfolio = pd.DataFrame(index=stock_prices.index, columns = symbols )\n portfolio.fillna(0) \n portfolio[\"_CASH\"][0] = start_val\n portfolio[\"_VALUE\"][0] = start_val\n \n #Snapshot of a portfolio at any time. To avoid using numerical indexes\n portfolio_snapshot = dict.fromkeys ( symbols, 0 )\n portfolio_snapshot[\"_CASH\"] = start_val\n portfolio[\"_VALUE\"] = start_val\n \n #Now calcualte portfolio day by day\n for date in portfolio.index:\n #Check transactions for the day\n day_orders = orders[ orders[\"Date\"] == date ] \n \n for ord in day_orders.iterrows():\n symbol = ord[1][ \"Symbol\"] \n stock_price = stock_prices[ symbol ][ date ]\n shares = ord[1][\"Shares\" ]\n side = ord[1][\"Order\"]\n \n if side == \"BUY\":\n portfolio_snapshot[ \"_CASH\" ] -= stock_price * shares\n portfolio_snapshot[ symbol ] += shares \n elif side == \"SELL\":\n portfolio_snapshot[ \"_CASH\" ] += stock_price * shares\n portfolio_snapshot[ symbol ] -= shares\n else:\n raise \"Order not recognized.\"\n \n #Compute portfolio value\n portfolio_snapshot[ \"_VALUE\" ] = portfolio_snapshot[ \"_CASH\" ]\n shorts = longs = 0\n for symbol in stock_symbols: \n stock_price = stock_prices[ symbol ][ date ]\n shares = portfolio_snapshot[ symbol ]\n notional = stock_price*shares\n if shares > 0:\n longs += notional\n else:\n shorts += notional\n \n portfolio_snapshot[ \"_VALUE\" ] += notional\n \n #Compute leverage\n leverage = (longs+shorts)/(longs-shorts + portfolio_snapshot[ \"_CASH\" ] )\n portfolio_snapshot[ \"_LEVERAGE\" ] = leverage\n \n #Assert we never achieve a leverage > 2.0\n if leverage > 2:\n raise \"Leverage > 2.0 achieved\"\n \n #Update portfolio from the daily snapshot\n #TODO: Is this causing performance issues?\n for symbol in portfolio.keys():\n portfolio[ symbol ][ date ] = portfolio_snapshot[ symbol ]\n \n return portfolio","def force(self, **kwargs):\n log.info(\"Forcing a build\")\n self._force = True","def __call__(self):\n\n self.initialise()\n\n # //\n # // ToDo: Add exception wrappers for plugin calls\n #//\n subJobs = TrackerDB.getJobsByState(\"submitted\", self.cooloff)\n self.updateSubmitted(*subJobs.keys())\n runningJobs = TrackerDB.getJobsByState(\"running\")\n self.updateRunning(*runningJobs.keys())\n completeJobs = TrackerDB.getJobsByState(\"complete\")\n self.updateComplete(*completeJobs.keys())\n failedJobs = TrackerDB.getJobsByState(\"failed\")\n self.updateFailed(*failedJobs.keys())\n self.cleanup()\n\n return","def trigger(self):\n # Update current state.\n self.current_inst = self.future_inst\n self.current_timer = self.future_timer\n # Initialize future state.\n self.future_inst = self.current_inst\n self.future_timer = max(0, self.current_timer - 1)","def reset_state(self, noised_results, global_state):\n new_tree_state = self._tree_aggregator.reset_state(global_state.tree_state)\n return attr.evolve(\n global_state,\n samples_cumulative_sum=noised_results,\n tree_state=new_tree_state)","def _update_state(self) -> None:\n raise NotImplementedError(\"\")","def my_rebalance(context,data):\n log.info(\"rebalancing...\")\n context.output = pipeline_output('my_pipeline')\n log.info(\"retrieved pipeline output...\")\n \n # These are the securities that we are interested in trading each day.\n context.security_list = context.output.index\n \n if context.prime == False:\n order_target_percent(symbol('SPY'),1) #hold SPY as a default \n context.prime = True\n \n weight= 1.0/len(context.security_list)\n \n for stock in context.security_list:\n log.info(\"Buying %s\" % (stock.symbol))\n order_target_percent(stock, weight)\n \n #: Exit any positions we might have\n for stock in context.portfolio.positions:\n if data.can_trade(stock) and stock not in context.security_list:\n log.info(\"Exiting our positions on %s\" % (stock.symbol))\n order_target_percent(stock, 0)","def update_price_model(self, good, order_type, is_successful, clearing_price=0):\n\n SIGNIFICANT = 0.25 # 25% more or less is \"significant\"\n SIG_IMBALANCE = 0.33\n LOW_INVENTORY = 0.1 # 10% of ideal inventory = \"LOW\"\n HIGH_INVENTORY = 2.0 # 200% of ideal inventory = \"HIGH\"\n MIN_PRICE = 0.01 # lowest allowed price of a Good\n\n if is_successful:\n # add this trade to the observed trading range\n self.observed_trading_range[good].append(clearing_price)\n\n public_mean_price = self.market.mean_price(good)\n belief = self.price_belief[good]\n mean = belief.mean()\n wobble = 0.05 # the degree which the Pop should bid outside the belief\n\n # how different the public mean price is from the price belief\n delta_to_mean = mean - public_mean_price\n\n if is_successful:\n if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT:\n # this Pop overpaid, shift belief towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT:\n # this Pop underpaid!, shift belief towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n # increase the belief's certainty\n belief.low += wobble * mean\n belief.high -= wobble * mean\n\n else:\n # shift towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n # check for inventory special cases\n stocks = self.inventory.get_amount(good)\n ideal = self.inventory.get_ideal(good)\n\n # if we're buying and inventory is too low\n # meaning we're desperate to buy\n if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal:\n wobble *= 2\n\n # if we're selling and inventory is too high\n # meaning we're desperate to sell\n elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal:\n wobble *= 2\n # all other cases\n else:\n sells = self.market.history.sell_orders.average(good, 1)\n buys = self.market.history.buy_orders.average(good, 1)\n\n # TODO: figure out why this is sometimes 0\n if sells + buys > 0:\n\n supply_vs_demand = (sells - buys) / (sells + buys)\n\n if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE:\n # too much supply? lower bid lower to sell faster\n # too much demand? raise price to buy faster\n\n new_mean = public_mean_price * (1 - supply_vs_demand)\n delta_to_mean = mean - new_mean\n\n # shift the price belief to the new price mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n\n # decrease belief's certainty since we've just changed it (we could be wrong)\n belief.low -= wobble * mean\n belief.high += wobble * mean\n\n # make sure the price belief doesn't decrease below the minimum\n if belief.low < MIN_PRICE:\n belief.low = MIN_PRICE\n elif belief.high < MIN_PRICE:\n belief.high = MIN_PRICE","def step(self):\n y = np.random.rand(self.p.lambda_, self.p.d).T\n x = self.p.m.reshape(-1, 1) * y\n f = np.array(list(map(sum, x)))\n self.p.used_budget += self.p.lambda_\n self.p.population = Population(x, y, f)\n self.p.m_old = self.p.m.copy()\n self.p.m *= np.linalg.norm(y, axis=1).reshape(-1, 1)\n self.p.adapt()\n self.p.old_population = self.p.population.copy()","def backtest_portfolio(self):\n self.rank=dict()\n self.accuracy=dict()\n portfolio = dict()\n \n for algo in self.algos:\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\n self.pos_diff=dict()\n self.pos_diff[algo] = self.positions[algo].diff()\n \n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\n #portfolio['price_diff'][0:5] = 0.0\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\n d=np.array(portfolio[algo]['profit']).copy()\n d[d>0]=1\n d[d<0]=0\n d[np.array(self.positions[algo])==0]=1\n for i in np.arange(1,len(d)+1):\n c=float(sum(d[0:i]))/(i)\n d[i-1]=c\n portfolio[algo]['accuracy']=d\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\n self.returns=portfolio\n c=np.array(self.returns[algo]['profit'])\n c[c>0]=1\n c[c<0]=0\n c[np.array(self.positions[algo])==0]=1\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\n self.accuracy[algo]=accuracy\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\n self.ready=True\n return (portfolio, self.rank, self.ranking)","def _stage1(self):\n self.start_progress()\n tasks = list(self._chain_dict(self._model.adjust_tasks))\n if len(tasks) == 0:\n self._stage2(self._no_adjustments_case())\n else:\n task = lambda : self._run_adjust_tasks(tasks)\n locator.get(\"pool\").submit(task, self._stage2)","def test_reset(self):\n self.p.C[0][0] = np.inf\n self.step()","def apply(self):\n prequire(False, \"Called abstract version of apply\")","def apply(self, simulation):\n t = simulation.time\n dt = simulation.timeStep\n if main_rank == 0:\n simulation.printState()\n # OpenCL update\n self.numMethod(self.gpu_field.gpu_data[self.component],\n self.color)\n self.window.widget.updateGL()\n if simulation.currentIteration > 1:\n self.window.label.setText(\n self.labelText + \"t={0:6.2f}, fps={1:6.2f}\".format(\n t + dt,\n 1. / (self.timer.f_timers.values()[0].t - self.ctime)))\n self.ctime = self.timer.f_timers.values()[0].t","def apply_immediately(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"apply_immediately\")","def momentum(portfolio_item, transaction_volume, cash_allocation):\n from yahooquery import Ticker\n from math import floor\n import talib\n from .TradeHistoryItem import log_trade\n from API.Help import is_increasing, initialize_alpaca\n\n alpaca = initialize_alpaca()\n\n yahoo_ticker = Ticker(str(portfolio_item))\n info = yahoo_ticker.history()\n ma_5 = talib.SMA(info['close'], timeperiod=5)\n ma_20 = talib.SMA(info['close'], timeperiod=20)\n volume = info['volume']\n\n if portfolio_item.shares == 0:\n # if the price goes from below the sma to above, buy\n if ma_5[-1] > (ma_20[-1] * 1.1) and is_increasing(volume, 3):\n print('buying {} shares of {}'.format(transaction_volume, str(portfolio_item)))\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'buy', 'market', 'day')\n portfolio_item.buy(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=0)\n # if the price goes from above the sma to below, short\n elif ma_5[-1] < (ma_20[-1] * .9) and not is_increasing(volume, 3) and portfolio_item.shares == 0:\n transaction_volume = floor(cash_allocation / (portfolio_item.ticker.price_now * 1.1))\n print('shorting {} shares of {}'.format(transaction_volume, str(portfolio_item)))\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'sell', 'market', 'day')\n portfolio_item.short(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=3)","def update_job_state(self, job):","def reset_continued(self): \n self._recent_goal_continued = False\n self._update_action = False\n self._update_action_without_pause = False","def compute(self, *args):\n\n pass","def set_force(state):\n global _FORCE\n _FORCE = bool(state)","def solvate(self):\n\n pass","def reset(self) -> None:\n self.cash_balance = self.initial_cash_balance()","def reset_state(self, noised_results, global_state):\n del noised_results\n new_tree_state = self._tree_aggregator.reset_state(global_state.tree_state)\n return attr.evolve(\n global_state,\n previous_tree_noise=self._zero_initial_noise(),\n tree_state=new_tree_state)","def calculate_profit(self):","def assumed_state(self) -> bool:\n return self._optimistic","def start(self):\n if self.is_cancelled:\n with self.changing('is_finished'):\n self.finished = True\n return\n \n \n with self.changing('is_executing'):\n self.executing = True \n \n with self.changing('is_finished', 'is_executing'):\n try:\n # if we're starting, we no longer have any dependencies\n self.dependencies.clear()\n \n if not self.is_cancelled:\n self.main()\n finally:\n self.executing = False\n self.finished = True","def update_state(self, context):\n pass","def ExecuteBeforeSolutionLoop(self):\n super().ExecuteBeforeSolutionLoop()\n num_of_vaviables = len(self.variables) + len(self.nonhistorical_variables)\n self.values = [[-1e6] * num_of_vaviables for _ in self.found_positions]"],"string":"[\n \"def update_portfolio(self, portfolio: PortfolioController):\\n now = portfolio.get_history(seconds_back=0)\\n future = portfolio.get_history(seconds_back=-self.update_interval)\\n\\n for fund in portfolio.funds:\\n best_currency = max(portfolio.currencies, key=lambda currency: future_value(fund, currency, now, future))\\n if best_currency != fund.currency:\\n portfolio.request_transfer(fund, best_currency)\",\n \"def _initalize_portfolio_with_cash(self):\\n self.cash = copy.copy(self.starting_cash)\\n\\n if self.starting_cash > 0.0:\\n self.history.append(\\n PortfolioEvent.create_subscription(\\n self.current_dt, self.starting_cash, self.starting_cash\\n )\\n )\\n\\n self.logger.info(\\n '(%s) Funds subscribed to portfolio \\\"%s\\\" '\\n '- Credit: %0.2f, Balance: %0.2f' % (\\n self.current_dt.strftime(settings.LOGGING[\\\"DATE_FORMAT\\\"]),\\n self.portfolio_id,\\n round(self.starting_cash, 2),\\n round(self.starting_cash, 2)\\n )\\n )\",\n \"def backtest_portfolio(self):\\n raise NotImplementedError(\\\"Should implement backtest_portfolio()!\\\")\",\n \"def updateState(self):\\n self.state = self.microgridPolicy.computeState();\",\n \"def portfolio(self):\\n self.update_portfolio()\\n return self._immutable_portfolio\",\n \"def after_run(self):\\n # Calculate the performance of the strategy and portfolio\\n self.portfolio.calc_stats()\\n self.calc_performance()\\n\\n return self\",\n \"def reset(self): \\n ptf_asset_vals, bmk_asset_vals = self._generate_initial_portfolios()\\n process_params = self._generate_initial_process()\\n self.state_vars = StateVariables( timestamp=0,\\n ptf_asset_vals=ptf_asset_vals, \\n bmk_asset_vals=bmk_asset_vals, \\n process_params=process_params )\\n return self._get_observation_from_state_vars()\",\n \"def track_portfolio(self, p):\\n\\n global st_refresh_thread\\n\\n if self.terminate:\\n return\\n\\n p.refresh()\\n\\n self.lock.acquire()\\n self.active_portfolio = p\\n self.display_portfolio(p)\\n self.lock.release()\\n\\n if not self.refresh_thread:\\n thr_args = list()\\n thr_args.append(self)\\n self.refresh_thread = threading.Thread(target=st_refresh_thread,\\n args=thr_args)\\n self.refresh_thread.start()\",\n \"def _ensure_calculated(self):\\n # return immediately if already done\\n if self._state > State.UNCALCULATED:\\n return\\n\\n # do the actual calculation, which must be implemented by the subclass\\n # but first, set default state that we expect unless the implementing class overrides\\n self._state = State.QUALIFIED \\n self._calculate()\",\n \"def on_iteration_start(self):\\n\\n self.Xfprv = self.Xf.copy()\\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\\n BacktrackRobust)):\\n self.Yfprv = self.Yf.copy()\\n\\n if self.opt['Monotone']:\\n if self.k == 0:\\n self.objfn = self.eval_objfn()\\n self.objfn_prev = self.objfn\",\n \"def set_cash(self, cash):\\n portfolio = self.get_portfolio_object()\\n if portfolio is not None:\\n portfolio.cash += cash\\n portfolio.initial_cash += cash\",\n \"def compute(self):\\n if self._computed:\\n return\\n\\n self._compute()\\n self._computed = True\",\n \"def __calculate_portfolio_returns(self):\\n\\n p_bar = tqdm(range(1), desc=\\\" Calculating returns\\\", leave=False)\\n\\n trade_data = self.historical_trade_data\\n\\n # Helper functions to calculate cash inflow and outflow\\n def f_min(x):\\n return x.apply(lambda x: min(x, 0))\\n\\n def f_max(x):\\n return x.apply(lambda x: max(x, 0))\\n\\n # Calculate cash inflow and outflow\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period cash inflow\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = -1 * trade_data[\\\"Investment delta\\\"][:].apply(lambda x: f_min(x), axis=0)\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period cash outflow\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = trade_data[\\\"Investment delta\\\"][:].apply(lambda x: f_max(x), axis=1)\\n\\n # Calculate period return\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period absolute return\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"End Value\\\"] + trade_data[\\\"Period cash inflow\\\"]) - (\\n trade_data[\\\"End Value\\\"].shift(1).fillna(0)\\n + trade_data[\\\"Period cash outflow\\\"]\\n )\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period percentage return\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"End Value\\\"] + trade_data[\\\"Period cash inflow\\\"]) / (\\n trade_data[\\\"End Value\\\"].shift(1).fillna(0)\\n + trade_data[\\\"Period cash outflow\\\"]\\n ) - 1\\n\\n trade_data[\\\"Period percentage return\\\"].fillna(0, inplace=True)\\n\\n self.historical_trade_data = trade_data\\n\\n self.portfolio_returns = self.historical_trade_data[\\\"Period percentage return\\\"][\\n \\\"Total\\\"\\n ]\\n\\n p_bar.n += 1\\n p_bar.refresh()\",\n \"def update_portfolio_on_market(self, market: MarketEvent):\\n self._portfolio.update_market_value(market)\",\n \"def __update_portfolio_handler(self, msg):\\n pass\",\n \"def on_iteration_start(self):\\n\\n self.Xprv = self.X.copy()\\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\\n BacktrackRobust)):\\n self.Yprv = self.Y.copy()\\n\\n if self.opt['Monotone']:\\n if self.k == 0:\\n self.objfn = self.eval_objfn()\\n self.objfn_prev = self.objfn\",\n \"def rebalance(context, data):\\n logger.debug('rebalancing on: %s', algo.get_datetime())\\n\\n context.trend_filter = False\\n\\n # new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\\n\\n new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\\n\\n for equity, row in new_portfolio.iterrows():\\n logger.debug('new portfolio (before filtering) - equity: %s', equity)\\n\\n # print(new_portfolio)\\n\\n # new_portfolio = new_portfolio[new_portfolio['overall_rank'].notna() & new_portfolio['momentum'] > 40][:20]\\n \\n # new_portfolio = new_portfolio[(new_portfolio['momentum_decile'] > 8)][:20]\\n\\n new_portfolio = new_portfolio.nlargest(20, ['overall_rank', 'momentum']) #<- $600K PL in 10 years\\n\\n # new_portfolio = new_portfolio.nlargest(20, ['momentum', 'overall_rank']) #<- 1M PL in 10 years\\n\\n if logger.level is logging.DEBUG:\\n for equity, row in new_portfolio.iterrows():\\n logger.debug('new portfolio - (after filtering) equity: %s', equity)\\n \\n\\n # print(len(new_portfolio.index))\\n\\n # volatility driven weights\\n # new_portfolio['inverse_volatility'] = new_portfolio['volatility'].apply(lambda x: 1 / x)\\n # inv_vola_sum = new_portfolio['inverse_volatility'].sum()\\n # new_portfolio['target_weight'] = new_portfolio['inverse_volatility'].apply(lambda x: x / inv_vola_sum)\\n\\n # portfolio size driven weights\\n # num_equities = len(new_portfolio.index)\\n # new_portfolio['target_weight'] = 1 / num_equities\\\\\\n\\n # logger.info('len existing portfolio: %s', len(context.portfolio.positions))\\n\\n if logger.level is logging.DEBUG:\\n for equity, values in context.portfolio.positions.items():\\n logger.debug('context.portfolio.positions - equity: %s, amount: %s, cost_basis: %s, sold_on: %s, sold_at_price: %s', equity, values.amount, values.cost_basis, values.last_sale_date, values.last_sale_price)\\n\\n \\n order_target(algo.sid('FIBBG000NTFYM5'), 0)\\n logger.debug('selling all bonds')\\n\\n for equity in context.portfolio.positions:\\n if equity is algo.sid('FIBBG000NTFYM5'): \\n continue\\n if equity not in set(new_portfolio.index.tolist()):\\n # logger.info('selling %s', equity)\\n order_target_percent(equity, 0)\\n\\n stock_weights = 1.0 / max(len(context.portfolio.positions), len(new_portfolio.index))\\n\\n logger.debug('len existing portfolio (afer ejection): %s', len(context.portfolio.positions))\\n logger.debug('len new portfolio: %s', len(new_portfolio.index))\\n logger.debug('stock_weights: %s', stock_weights)\\n\\n # print(context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5')))\\n\\n # spy = context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5'))\\n\\n # if (spy is not None) and (spy.amount > 0):\\n # order_target_percent(algo.sid('FIBBG000NTFYM5'), 0)\\n\\n for equity, row in new_portfolio.iterrows():\\n if row.trend_filter is True:\\n # logger.info('buying %s', equity)\\n context.trend_filter = True\\n order_target_percent(equity, stock_weights)\\n else:\\n context.trend_filter = False\\n \\n logger.debug('cash: %s', context.portfolio.cash)\\n logger.debug('portfolio_value: %s', context.portfolio.portfolio_value)\\n logger.debug('num_positions: %s', len(context.portfolio.positions))\\n logger.debug('positions: %s', context.portfolio.positions)\",\n \"def recalculate() -> None:\\n NotImplemented\",\n \"def _on_state_update(self) -> None:\\n super()._on_state_update()\\n self._set_futures(True)\",\n \"def precalculate():\\n pass\",\n \"def precalculate():\\n pass\",\n \"def final(self, state):\\n \\\"*** YOUR CODE HERE ***\\\"\\n return\\n util.raiseNotDefined()\",\n \"def execute(self) -> None:\\n self.state()\",\n \"def _run_computation(self):\\n with self.swap(stats_jobs_continuous.StatisticsAggregator,\\n 'get_statistics', self._mock_get_statistics):\\n ModifiedUserImpactAggregator.start_computation()\\n self.process_and_flush_pending_tasks()\",\n \"def before_trading_start(context, data):\\n factors = pipeline_output('ff_example')\\n\\n # get the data we're going to use\\n returns = factors['returns']\\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\\n be_me = factors.sort_values(['be_me'], ascending=True)\\n\\n # to compose the six portfolios, split our universe into portions\\n half = int(len(mkt_cap)*0.5)\\n small_caps = mkt_cap[:half]\\n big_caps = mkt_cap[half:]\\n \\n thirty = int(len(be_me)*0.3)\\n seventy = int(len(be_me)*0.7)\\n growth = be_me[:thirty]\\n neutral = be_me[thirty:seventy]\\n value = be_me[seventy:]\\n\\n # now use the portions to construct the portfolios.\\n # note: these portfolios are just lists (indices) of equities\\n small_value = small_caps.index.intersection(value.index)\\n small_neutral = small_caps.index.intersection(neutral.index)\\n small_growth = small_caps.index.intersection(growth.index)\\n \\n big_value = big_caps.index.intersection(value.index)\\n big_neutral = big_caps.index.intersection(neutral.index)\\n big_growth = big_caps.index.intersection(growth.index)\\n\\n # take the mean to get the portfolio return, assuming uniform\\n # allocation to its constituent equities.\\n sv = returns[small_value].mean()\\n sn = returns[small_neutral].mean()\\n sg = returns[small_growth].mean()\\n \\n bv = returns[big_value].mean()\\n bn = returns[big_neutral].mean()\\n bg = returns[big_growth].mean()\\n\\n # computing SMB\\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\\n\\n # computing HML\\n context.hml = (sv + bv)/2 - (sg + bg)/2\",\n \"def reset(self):\\n for key in self.portfolio.keys():\\n self.portfolio[key] = {'holdings': 0}\\n self.buys[key] = 0\\n self.portfolio['balance'] = 2500000.0\",\n \"def apply(self, gameState):\\n pass\",\n \"def liquidate(self) -> None:\\n if self.position.is_close:\\n return\\n\\n if self.position.pnl > 0:\\n self.take_profit = self.position.qty, self.price\\n else:\\n self.stop_loss = self.position.qty, self.price\",\n \"def make_uncurrent(self):\\n pass\",\n \"def calculate_portfolio(self, request, pk=None, **kwargs):\\n goal = self.get_object()\\n\\n check_state(Goal.State(goal.state), Goal.State.ACTIVE)\\n\\n setting_str = request.query_params.get('setting', None)\\n if not setting_str:\\n raise ValidationError(\\\"Query parameter 'setting' must be specified and a valid JSON string\\\")\\n try:\\n setting = ujson.loads(setting_str)\\n except ValueError:\\n raise ValidationError(\\\"Query parameter 'setting' must be a valid json string\\\")\\n\\n # Create the settings object from the dict\\n serializer = serializers.GoalSettingStatelessSerializer(data=setting)\\n serializer.is_valid(raise_exception=True)\\n settings = serializer.create_stateless(serializer.validated_data, goal)\\n\\n try:\\n data = self.build_portfolio_data(calculate_portfolio(settings=settings,\\n data_provider=DataProviderDjango(),\\n execution_provider=ExecutionProviderDjango()))\\n return Response(data)\\n except Unsatisfiable as e:\\n rdata = {'reason': \\\"No portfolio could be found: {}\\\".format(e)}\\n if e.req_funds is not None:\\n rdata['req_funds'] = e.req_funds\\n\\n return Response({'error': rdata}, status=status.HTTP_400_BAD_REQUEST)\",\n \"def swap_active_portfolio(self):\\n\\n self.lock.acquire()\\n\\n self.clear_main()\\n\\n w = self.windows['MAIN']\\n l = 1\\n\\n for p in self.portfolios:\\n # Only support 9 portfolios since that makes this easier to deal\\n # with.\\n if l >= 10:\\n break\\n\\n w.addstr(l, 0, '%2d' % l, curses.A_BOLD | curses.color_pair(1))\\n w.addstr(l, 3, p.name)\\n l += 1\\n\\n self.refresh()\\n\\n # Wait for the user to give is a key.\\n while True:\\n c = self.stdscr.getch()\\n\\n if c < ord('1') and c > ord('9'):\\n continue\\n\\n index = c - ord('1')\\n\\n if index < len(self.portfolios):\\n break\\n\\n self.portfolios[index].refresh()\\n\\n self.active_portfolio = self.portfolios[index]\\n self.display_portfolio(self.active_portfolio)\\n self.lock.release()\",\n \"def set_curr_value(self, val):\\n # only goal that is in progress can have it's current value changed\\n if self._status != EGoalStatus.IN_PROGRESS:\\n raise NotImplementedError('Cannot set value to finished or not started goal')\\n # try cast to int - mainly for QuantifiedGoal representation\\n val = self.fw.types.try_float_cast(val)\\n # update both in the stages object and in raw data\\n self._values[EStage.CURRENT] = self._data_process(val)\\n self._skeleton.curr_value = val\\n # use progressor to update the database\\n self._progressor.dump_to_database(self)\",\n \"def atomistic_step(self):\\n # first minimize in vacuum, in either case, \\n # fixes problems with langevin bond deformation.\\n self.system.minimize()\\n \\n if self.system.should_solvate:\\n with self.system.solvate() as sol:\\n with self.system.minimize(**sol) as mn:\\n with self.system.equilibriate(**mn) as eq:\\n self.system.md(**eq)\\n else:\\n self.system.equilibriate()\\n self.system.md()\",\n \"def apply(self):\\r\\n return\",\n \"def apply(self):\\r\\n return\",\n \"def apply(self):\\r\\n return\",\n \"def apply(self):\\r\\n return\",\n \"def apply(self):\\r\\n return\",\n \"def _reset_solution(self, firm_size):\\n x_lower, x_upper = self.model.workers.lower, self.model.workers.upper\\n y_upper = self.model.firms.upper\\n initial_V = np.array([y_upper, firm_size])\\n\\n if self.model.assortativity == 'positive':\\n self.ivp.set_initial_value(initial_V, x_upper)\\n wage = self.evaluate_wage(x_upper, initial_V)\\n profit = self.evaluate_profit(x_upper, initial_V)\\n self._solution = np.hstack((x_upper, initial_V, wage, profit))\\n else:\\n self.ivp.set_initial_value(initial_V, x_lower)\\n wage = self.evaluate_wage(x_lower, initial_V)\\n profit = self.evaluate_profit(x_lower, initial_V)\\n self._solution = np.hstack((x_lower, initial_V, wage, profit))\",\n \"def simulate_trading(self):\\n self._generate_trading_instances()\\n self._run_backtest()\\n self.portfolio.output_equity()\\n res=self.portfolio.get_statistics()\\n self.plot.plot_equity()\\n return res\",\n \"def reactivate(self):\\n self.write({'active': True, 'state': 'running'})\\n STAGE = self.env['anytracker.stage']\\n for ticket in self:\\n starts = STAGE.search([('method_id', '=', ticket.method_id.id),\\n ('progress', '=', 0)])\\n if len(starts) != 1:\\n raise except_orm(\\n _('Configuration error !'),\\n _('One and only one stage should have a 0% progress'))\\n # write stage in a separate line to recompute progress & risk\\n ticket.write({'stage_id': starts[0].id})\\n self.recompute_parents()\",\n \"def initialize_portfolio(self):\\n\\n raise NotImplementedError('''\\n Must implement initialize_portfolio. Call help() for details.\\n ''')\",\n \"def _calculate(self):\\n raise NotYetImplemented()\",\n \"def optimizeForReturn(required_return, stock_db, use_genetic):\\n print('Optimizing portfolio for %f' % required_return)\\n pf = PortfolioFactory(stock_db, required_return, use_genetic=use_genetic)\\n desired_portfolio = pf.desired_portfolio\\n print('Required Return: %f' % required_return)\\n print('Expected Return: %f' % math.pow(\\n desired_portfolio.average_return, Config.DAYS_IN_YEAR))\\n print('Downside Risk: %f' % desired_portfolio.downside_risk)\\n print('Downside Correl: %f' % desired_portfolio.downside_correl)\\n print('Score: %f' % desired_portfolio.score)\\n\\n # Write desired portfolio.\\n DataIO.writeDesiredPortfolio(\\n desired_portfolio, stock_db,\\n 'output/DesiredPortfolio_%.0f_%.4f_%s.csv' % (\\n Config.MINIMUM_AMOUNT_DATA, required_return, Config.TODAY.date()))\\n\\n print('Finished for %f' % required_return)\\n\\n return desired_portfolio\",\n \"def calc(self):\\n return None\",\n \"def update_state(self, progress, policy_state=None):\\n raise NotImplementedError\",\n \"def solve():\\n game_state.is_solving = ~game_state.is_solving\\n\\n if game_state.is_solving:\\n solve_button.set_label(\\\"Pause\\\")\\n else:\\n solve_button.set_label(\\\"Solve\\\")\\n\\n game_state.is_dirty = True\\n\\n return solve\",\n \"def restart(self):\\n self.state ='active'\\n if self.budget <= 0:\\n return self.leave()\\n self.cards =BJCards()\\n self.bet_amount =0\",\n \"def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:\\n assert portfolio is not None\\n assert stock_market_data is not None\\n assert stock_market_data.get_companies() == [Company.A, Company.B]\\n\\n current_state = self.states_compution(stock_market_data, portfolio)\\n current_portfolio_value = portfolio.get_value(stock_market_data)\\n\\n if self.train_while_trading is False:\\n # for test set use trained ann\\n action = np.argmax(self.model.predict(current_state)[0])\\n\\n else:\\n action = self.select_action(current_state, current_portfolio_value)\\n if self.last_state is not None:\\n reward = self.calc_reward(current_portfolio_value)\\n self.append_memory(self.last_state, self.last_action_a, reward, current_state)\\n # train model as soon as sufficient memory is reached\\n if len(self.memory) > self.min_size_of_memory_before_training:\\n self.train_model()\\n\\n # Split action into individual actions for Company A and B\\n current_action_a = 0\\n current_action_b = 0\\n assert action < 9 and action >= 0\\n if action == 0:\\n current_action_a = OrderType.SELL\\n current_action_b = OrderType.SELL\\n amount_to_sell_a = portfolio.get_stock(Company.A)\\n amount_to_sell_b = portfolio.get_stock(Company.B)\\n elif action == 1:\\n current_action_a = OrderType.SELL\\n amount_to_sell_a = portfolio.get_stock(Company.A)\\n current_action_b = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_b = int(portfolio.cash/stock_price)\\n elif action == 2:\\n current_action_a = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_a = int(portfolio.cash/stock_price)\\n current_action_b = OrderType.SELL\\n amount_to_sell_b = portfolio.get_stock(Company.B)\\n elif action == 3:\\n current_action_a = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_a = int((portfolio.cash/stock_price)/2)\\n current_action_b = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.B)\\n amount_to_buy_b = int((portfolio.cash/stock_price)/2)\\n elif action == 4:\\n current_action_a = OrderType.SELL\\n amount_to_sell_a = portfolio.get_stock(Company.A)\\n # current_action_b = \\\"hold\\\"\\n elif action == 5:\\n current_action_a = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_a = int(portfolio.cash/stock_price)\\n # current_action_b = \\\"hold\\\"\\n elif action == 6:\\n # current_action_a = \\\"hold\\\"\\n current_action_b = OrderType.SELL\\n amount_to_sell_b = portfolio.get_stock(Company.B)\\n elif action == 7:\\n # current_action_a = \\\"hold\\\"\\n current_action_b = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.B)\\n amount_to_buy_b = int(portfolio.cash/stock_price)\\n\\n order_list = []\\n\\n if current_action_a != 0:\\n if current_action_a == OrderType.SELL and amount_to_sell_a > 0:\\n order_list.append(Order(current_action_a, Company.A, amount_to_sell_a))\\n elif current_action_a == OrderType.BUY and portfolio.cash > 0:\\n order_list.append(Order(current_action_a, Company.A, amount_to_buy_a))\\n\\n if current_action_b != 0:\\n if current_action_b == OrderType.SELL and amount_to_sell_b > 0:\\n order_list.append(Order(current_action_b, Company.B, amount_to_sell_b))\\n elif current_action_b == OrderType.BUY and portfolio.cash > 0:\\n order_list.append(Order(current_action_b, Company.B, amount_to_buy_b))\\n\\n self.last_action_a = action\\n self.last_state = current_state\\n self.last_portfolio_value = current_portfolio_value\\n return order_list\",\n \"def hedge_portfolio(context, data):\\r\\n factors = get_alphas_and_betas(context, data)\\r\\n beta_exposure = 0.0\\r\\n count = 0\\r\\n for asset in context.portfolio.positions:\\r\\n if asset in factors and asset != context.index:\\r\\n if not np.isnan(factors[asset].beta):\\r\\n beta_exposure += factors[asset].beta\\r\\n count += 1\\r\\n beta_hedge = -1.0 * beta_exposure / count\\r\\n dollar_amount = context.portfolio.portfolio_value * beta_hedge\\r\\n record(beta_hedge=beta_hedge)\\r\\n if not np.isnan(dollar_amount):\\r\\n order_target_value(context.index, dollar_amount)\",\n \"def set_initial(self, value):\\n # TODO: Make an Initial Stock Adjust here\\n pass\",\n \"def index():\\n\\n #select user's portfolio\\n rows = db.execute(\\\"SELECT * FROM portfolio WHERE userid=:id\\\", id=session[\\\"user_id\\\"])\\n\\n #set temporary holding place for cash to zero\\n tcash = 0\\n\\n #update the stock information in user's portfolio\\n for row in rows:\\n stock = row[\\\"stock\\\"]\\n number = row[\\\"number\\\"]\\n quote = lookup(stock)\\n total = float(number) * float(quote[\\\"price\\\"])\\n tcash += total\\n db.execute(\\\"UPDATE portfolio SET price=:price, total=:total WHERE userid=:id AND stock=:stock AND number=:number\\\", price=usd(quote[\\\"price\\\"]), total=total, id=session[\\\"user_id\\\"], stock=stock, number=number)\\n\\n #select user's cash and updated portfolio\\n updated_cash = db.execute(\\\"SELECT cash FROM users WHERE id=:id\\\", id=session[\\\"user_id\\\"])\\n tcash += updated_cash[0][\\\"cash\\\"]\\n updated_stock = db.execute(\\\"SELECT stock, SUM(number) AS number, price, SUM(total) AS stock_total FROM portfolio WHERE userid=:id GROUP BY stock HAVING SUM(number) > 0\\\", id=session[\\\"user_id\\\"])\\n\\n return render_template(\\\"index.html\\\", stocks=updated_stock, cash=usd(updated_cash[0][\\\"cash\\\"]), all_total=usd(tcash))\",\n \"def ComputerFinalStateOfCharge(self):\\r\\n pass\",\n \"def ramp_up(self) -> None:\\n self.cash_balance: float = self.initial_cash_balance()\\n for stock in self.stocks:\\n initial_date_idx = 0\\n self.cash_balance = stock.buy(initial_date_idx, self.cash_balance, self.buy_budget)\",\n \"def display_portfolio(self, p):\\n\\n if self.terminate:\\n return\\n\\n w = self.windows['MAIN']\\n\\n self.clear_main()\\n self.__display_portfolio(p, w)\\n self.clear_header()\\n self.set_header(p)\\n\\n self.refresh()\",\n \"def call_back(e):\\n gh_env.Component.ExpireSolution(False)\",\n \"def solution_update(self, theta, force=False):\\n \\n self.x = self.eval(theta, force)\\n \\n return\",\n \"def _localSetState(self,pdict):\\n super()._localSetState(pdict)\\n self.transition = pdict.pop('transition')\\n self.steadyStatePb = pdict.pop('steadyStatePb')\",\n \"def reset_for_new_run(\\n self,\\n state: State\\n ):\\n\\n super().reset_for_new_run(state)\\n\\n self.epsilon = self.original_epsilon\\n self.greedy_action = list(self.Q.keys())[0]\",\n \"def act(self, current_state, setpoint):\\n s = self.last_s # Refers only to theta\\n a = self.last_a # Refers only the last action taken\\n s_p = self.map_state(\\n current_state['theta'], current_state['theta_dot'])\\n\\n # Update the q-table based on the Bellman Equation\\n r = self.reward_func(current_state)\\n q_predict = self.q_table[s + (a,)]\\n\\n if not self.reset_env:\\n q_target = r + self.params['gamma'] * np.max(self.q_table[s_p])\\n else:\\n q_target = r\\n print(str(r) + ' Reset')\\n self.reset_env = False\\n # self.q_table[40,1]=90\\n\\n self.q_table[s+(a,)] += self.params['learn_rate'] * \\\\\\n (q_target - q_predict)\\n # print(self.params['epsilon'])\\n # Take the next action\\n if (np.random.uniform() < self.params['epsilon']) or (np.max(np.abs(self.q_table[s_p])) < 1e-3):\\n ac = np.random.randint(self.num_actions)\\n self.dist[ac] += 1\\n else:\\n ac = np.argmax(self.q_table[s_p])\\n\\n #ac = 0\\n\\n self.last_a = ac\\n self.last_s = s_p\\n if self.cont_decay == 1000:\\n self.params['epsilon'] *= self.params['decay_rate']\\n self.cont_decay = 0\\n else:\\n self.cont_decay += 1\\n u = self.map_force(ac)\\n f_u = self.F_cum + u\\n print(self.params)\\n print(self.q_table[30:51])\\n return f_u\",\n \"def apply(self):\\n pass\",\n \"def apply(self):\\n pass\",\n \"def forces(self):\\n pass\",\n \"def portfolio():\\n #Query transactions by user id\\n trans = Transactions.query.filter_by(owner=session['user_id']).all()\\n \\n #Create list of comanies user owns stock in\\n companies = []\\n for t in trans:\\n if t.symbol not in companies:\\n companies.append(t.symbol)\\n\\n #Create list of current stock dictionaries and total their values\\n total = 0\\n stocks = []\\n for company in companies:\\n trans = Transactions.query.filter_by(owner=session['user_id'], symbol=company).all()\\n stock = {}\\n stock['shares'] = 0\\n for t in trans:\\n stock['shares'] += t.shares\\n if stock['shares'] > 0:\\n stock['symbol'] = company\\n stock['name'] = lookup(company)['name']\\n stock['price'] = lookup(company)['price']\\n stock['total'] = stock['shares'] * stock['price']\\n stock['price'] = usd(stock['price'])\\n stock['total'] = usd(stock['total'])\\n total += float(stock['total'][1:].replace(',', ''))\\n stocks.append(stock)\\n\\n #Set user cash and total values\\n value = {}\\n value['cash'] = usd(Users.query.filter_by(id=session['user_id']).first().cash)\\n value['total'] = usd(total + float(value['cash'][1:].replace(',', '')))\\n\\n #Add values to list\\n stocks.append(value)\\n\\n #Return list of dictionaries\\n return stocks\",\n \"def reset_new_conditions (self):\\n self.solver.pop()\\n self.solver.push()\",\n \"def updater(self, func_name, ml_results=None):\\n\\n closed_pos = []\\n\\n # Iterates all active positions\\n for pos in self.active_short_positions + self.active_long_positions:\\n\\n # Check if position reached target /expired / active strategy, and updates the position accordingly\\n if getattr(pos, func_name)(ml_results=ml_results):\\n\\n closed_pos.append(pos)\\n\\n if pos.order_type == Consts.LONG and self.longs:\\n self.active_long_positions.remove(pos)\\n self.closed_long_positions.append(pos)\\n if pos.order_type == Consts.SHORT and self.shorts:\\n self.active_short_positions.remove(pos)\\n self.closed_short_positions.append(pos)\\n\\n # Updates Portfolio stats according to new closed positions\\n self.closed_positions(closed_pos)\\n\\n # Updates capital stats after last operations\\n self.update_capital_stats()\",\n \"def portfolio_performance(returns,weights):\\r\\n print('Calculating Portfolio Performance')\\r\\n # returns=target_asset_port_data_attributes['component_returns']\\r\\n # weights =target_asset_port_data_attributes['effective_weights']\\r\\n\\r\\n component_returns= returns\\r\\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\\r\\n compnent_weights.loc[weights.index,:] = weights\\r\\n\\r\\n portfolio_dates = component_returns.index\\r\\n components = component_returns.columns\\r\\n\\r\\n # pre-allocate\\r\\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\\r\\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\\r\\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\\r\\n \\r\\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\\r\\n previous_index_value = np.int64(1)\\r\\n\\r\\n pre_date = portfolio_dates[0]\\r\\n # set BoP to start weights\\r\\n for date,row in component_returns.iterrows():\\r\\n # print(date)\\r\\n # 1st date\\r\\n if date == portfolio_dates[0]:\\r\\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n\\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n # after first date\\r\\n else:\\r\\n BoP_df.loc[date] = EoP_df.loc[pre_date]\\r\\n # weights override\\r\\n if date in compnent_weights.index:\\r\\n none_NaN_index = ~compnent_weights.loc[date].isnull()\\r\\n if not compnent_weights.loc[date][none_NaN_index].empty:\\r\\n tmp_sum = BoP_df.loc[date].sum()\\r\\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\\r\\n\\r\\n \\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n \\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n\\r\\n portfolio_returns = portfolio_index.pct_change(1) \\r\\n portfolio_returns.columns = ['Returns']\\r\\n\\r\\n portfolio_index\\r\\n perf = portfolio_index.calc_stats()\\r\\n \\r\\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\\r\\n return output\",\n \"def step(self):\\n self.solver.integrate(self.t)\\n self.state = self.solver.y\",\n \"def solve(self, current_state: dict) -> dict:\",\n \"def _update_objective(self):\\n # rewrap the cost if the solver has been run\\n self.Finalize()\\n return\",\n \"def rebalance(self, date):\\n eod_values = self.df.shift(1).loc[date, 'values'].mul(1 + self.tc.instrument_returns.loc[date, 'daily'])\\n eod_portfolio_value = sum(eod_values.values)\\n\\n previous_values = self.df.loc[date, 'values'].copy()\\n position_value = self.target_weights.mul(eod_portfolio_value)\\n trading_cost = abs(eod_values.div(eod_portfolio_value) - self.target_weights) * eod_portfolio_value * \\\\\\n self.tc.commission\\n current_values = position_value - trading_cost\\n self.df.loc[date, 'values'] = current_values.values\\n future_values = self.tc.instrument_returns.loc[date:, 'cumulative'].div(\\n self.tc.instrument_returns.loc[date, 'cumulative']).mul(current_values, axis=1)\\n self.df.loc[date:, 'values'] = future_values.values\\n trade = pd.Series(current_values - previous_values)\\n # Once we have calculated the end-of-day value of the portfolio, we set the allocation by looking at the\\n # dollars invested in each ETF\\n self.df.loc[date:, 'allocations'] = future_values.div(future_values.sum(axis=1), axis=0).values\\n\\n return trade\",\n \"def __setstate__(self, state):\\n self.__dict__ = state\\n self.get_esoh_solver = lru_cache()(self._get_esoh_solver)\",\n \"def compute_portvals(start_date, end_date, orders_file, start_val):\\n \\n #Read order file\\n orders = pd.read_csv( orders_file, parse_dates = [0])\\n \\n #Get symbols making up the portfolio\\n stock_symbols = list( set( orders[\\\"Symbol\\\"] ) )\\n dates = pd.date_range(start_date, end_date)\\n \\n #Read stock prices\\n stock_prices = get_data(stock_symbols, dates)\\n \\n #Create a portfolio keeping track of positions, \\n #_CASH column indicates cash position, _VALUE total portfolio value\\n #_LEVERAGE the leverage of portfolio when we allow for short selling\\n symbols = stock_symbols[:] #Shallow copy of the list\\n symbols.append(\\\"_CASH\\\")\\n symbols.append(\\\"_VALUE\\\")\\n symbols.append(\\\"_LEVERAGE\\\")\\n \\n #Index contains only business days, same dates as stock prices\\n portfolio = pd.DataFrame(index=stock_prices.index, columns = symbols )\\n portfolio.fillna(0) \\n portfolio[\\\"_CASH\\\"][0] = start_val\\n portfolio[\\\"_VALUE\\\"][0] = start_val\\n \\n #Snapshot of a portfolio at any time. To avoid using numerical indexes\\n portfolio_snapshot = dict.fromkeys ( symbols, 0 )\\n portfolio_snapshot[\\\"_CASH\\\"] = start_val\\n portfolio[\\\"_VALUE\\\"] = start_val\\n \\n #Now calcualte portfolio day by day\\n for date in portfolio.index:\\n #Check transactions for the day\\n day_orders = orders[ orders[\\\"Date\\\"] == date ] \\n \\n for ord in day_orders.iterrows():\\n symbol = ord[1][ \\\"Symbol\\\"] \\n stock_price = stock_prices[ symbol ][ date ]\\n shares = ord[1][\\\"Shares\\\" ]\\n side = ord[1][\\\"Order\\\"]\\n \\n if side == \\\"BUY\\\":\\n portfolio_snapshot[ \\\"_CASH\\\" ] -= stock_price * shares\\n portfolio_snapshot[ symbol ] += shares \\n elif side == \\\"SELL\\\":\\n portfolio_snapshot[ \\\"_CASH\\\" ] += stock_price * shares\\n portfolio_snapshot[ symbol ] -= shares\\n else:\\n raise \\\"Order not recognized.\\\"\\n \\n #Compute portfolio value\\n portfolio_snapshot[ \\\"_VALUE\\\" ] = portfolio_snapshot[ \\\"_CASH\\\" ]\\n shorts = longs = 0\\n for symbol in stock_symbols: \\n stock_price = stock_prices[ symbol ][ date ]\\n shares = portfolio_snapshot[ symbol ]\\n notional = stock_price*shares\\n if shares > 0:\\n longs += notional\\n else:\\n shorts += notional\\n \\n portfolio_snapshot[ \\\"_VALUE\\\" ] += notional\\n \\n #Compute leverage\\n leverage = (longs+shorts)/(longs-shorts + portfolio_snapshot[ \\\"_CASH\\\" ] )\\n portfolio_snapshot[ \\\"_LEVERAGE\\\" ] = leverage\\n \\n #Assert we never achieve a leverage > 2.0\\n if leverage > 2:\\n raise \\\"Leverage > 2.0 achieved\\\"\\n \\n #Update portfolio from the daily snapshot\\n #TODO: Is this causing performance issues?\\n for symbol in portfolio.keys():\\n portfolio[ symbol ][ date ] = portfolio_snapshot[ symbol ]\\n \\n return portfolio\",\n \"def force(self, **kwargs):\\n log.info(\\\"Forcing a build\\\")\\n self._force = True\",\n \"def __call__(self):\\n\\n self.initialise()\\n\\n # //\\n # // ToDo: Add exception wrappers for plugin calls\\n #//\\n subJobs = TrackerDB.getJobsByState(\\\"submitted\\\", self.cooloff)\\n self.updateSubmitted(*subJobs.keys())\\n runningJobs = TrackerDB.getJobsByState(\\\"running\\\")\\n self.updateRunning(*runningJobs.keys())\\n completeJobs = TrackerDB.getJobsByState(\\\"complete\\\")\\n self.updateComplete(*completeJobs.keys())\\n failedJobs = TrackerDB.getJobsByState(\\\"failed\\\")\\n self.updateFailed(*failedJobs.keys())\\n self.cleanup()\\n\\n return\",\n \"def trigger(self):\\n # Update current state.\\n self.current_inst = self.future_inst\\n self.current_timer = self.future_timer\\n # Initialize future state.\\n self.future_inst = self.current_inst\\n self.future_timer = max(0, self.current_timer - 1)\",\n \"def reset_state(self, noised_results, global_state):\\n new_tree_state = self._tree_aggregator.reset_state(global_state.tree_state)\\n return attr.evolve(\\n global_state,\\n samples_cumulative_sum=noised_results,\\n tree_state=new_tree_state)\",\n \"def _update_state(self) -> None:\\n raise NotImplementedError(\\\"\\\")\",\n \"def my_rebalance(context,data):\\n log.info(\\\"rebalancing...\\\")\\n context.output = pipeline_output('my_pipeline')\\n log.info(\\\"retrieved pipeline output...\\\")\\n \\n # These are the securities that we are interested in trading each day.\\n context.security_list = context.output.index\\n \\n if context.prime == False:\\n order_target_percent(symbol('SPY'),1) #hold SPY as a default \\n context.prime = True\\n \\n weight= 1.0/len(context.security_list)\\n \\n for stock in context.security_list:\\n log.info(\\\"Buying %s\\\" % (stock.symbol))\\n order_target_percent(stock, weight)\\n \\n #: Exit any positions we might have\\n for stock in context.portfolio.positions:\\n if data.can_trade(stock) and stock not in context.security_list:\\n log.info(\\\"Exiting our positions on %s\\\" % (stock.symbol))\\n order_target_percent(stock, 0)\",\n \"def update_price_model(self, good, order_type, is_successful, clearing_price=0):\\n\\n SIGNIFICANT = 0.25 # 25% more or less is \\\"significant\\\"\\n SIG_IMBALANCE = 0.33\\n LOW_INVENTORY = 0.1 # 10% of ideal inventory = \\\"LOW\\\"\\n HIGH_INVENTORY = 2.0 # 200% of ideal inventory = \\\"HIGH\\\"\\n MIN_PRICE = 0.01 # lowest allowed price of a Good\\n\\n if is_successful:\\n # add this trade to the observed trading range\\n self.observed_trading_range[good].append(clearing_price)\\n\\n public_mean_price = self.market.mean_price(good)\\n belief = self.price_belief[good]\\n mean = belief.mean()\\n wobble = 0.05 # the degree which the Pop should bid outside the belief\\n\\n # how different the public mean price is from the price belief\\n delta_to_mean = mean - public_mean_price\\n\\n if is_successful:\\n if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT:\\n # this Pop overpaid, shift belief towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT:\\n # this Pop underpaid!, shift belief towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n # increase the belief's certainty\\n belief.low += wobble * mean\\n belief.high -= wobble * mean\\n\\n else:\\n # shift towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n # check for inventory special cases\\n stocks = self.inventory.get_amount(good)\\n ideal = self.inventory.get_ideal(good)\\n\\n # if we're buying and inventory is too low\\n # meaning we're desperate to buy\\n if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal:\\n wobble *= 2\\n\\n # if we're selling and inventory is too high\\n # meaning we're desperate to sell\\n elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal:\\n wobble *= 2\\n # all other cases\\n else:\\n sells = self.market.history.sell_orders.average(good, 1)\\n buys = self.market.history.buy_orders.average(good, 1)\\n\\n # TODO: figure out why this is sometimes 0\\n if sells + buys > 0:\\n\\n supply_vs_demand = (sells - buys) / (sells + buys)\\n\\n if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE:\\n # too much supply? lower bid lower to sell faster\\n # too much demand? raise price to buy faster\\n\\n new_mean = public_mean_price * (1 - supply_vs_demand)\\n delta_to_mean = mean - new_mean\\n\\n # shift the price belief to the new price mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n\\n # decrease belief's certainty since we've just changed it (we could be wrong)\\n belief.low -= wobble * mean\\n belief.high += wobble * mean\\n\\n # make sure the price belief doesn't decrease below the minimum\\n if belief.low < MIN_PRICE:\\n belief.low = MIN_PRICE\\n elif belief.high < MIN_PRICE:\\n belief.high = MIN_PRICE\",\n \"def step(self):\\n y = np.random.rand(self.p.lambda_, self.p.d).T\\n x = self.p.m.reshape(-1, 1) * y\\n f = np.array(list(map(sum, x)))\\n self.p.used_budget += self.p.lambda_\\n self.p.population = Population(x, y, f)\\n self.p.m_old = self.p.m.copy()\\n self.p.m *= np.linalg.norm(y, axis=1).reshape(-1, 1)\\n self.p.adapt()\\n self.p.old_population = self.p.population.copy()\",\n \"def backtest_portfolio(self):\\n self.rank=dict()\\n self.accuracy=dict()\\n portfolio = dict()\\n \\n for algo in self.algos:\\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\\n self.pos_diff=dict()\\n self.pos_diff[algo] = self.positions[algo].diff()\\n \\n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\\n #portfolio['price_diff'][0:5] = 0.0\\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\\n d=np.array(portfolio[algo]['profit']).copy()\\n d[d>0]=1\\n d[d<0]=0\\n d[np.array(self.positions[algo])==0]=1\\n for i in np.arange(1,len(d)+1):\\n c=float(sum(d[0:i]))/(i)\\n d[i-1]=c\\n portfolio[algo]['accuracy']=d\\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\\n self.returns=portfolio\\n c=np.array(self.returns[algo]['profit'])\\n c[c>0]=1\\n c[c<0]=0\\n c[np.array(self.positions[algo])==0]=1\\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\\n self.accuracy[algo]=accuracy\\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\\n self.ready=True\\n return (portfolio, self.rank, self.ranking)\",\n \"def _stage1(self):\\n self.start_progress()\\n tasks = list(self._chain_dict(self._model.adjust_tasks))\\n if len(tasks) == 0:\\n self._stage2(self._no_adjustments_case())\\n else:\\n task = lambda : self._run_adjust_tasks(tasks)\\n locator.get(\\\"pool\\\").submit(task, self._stage2)\",\n \"def test_reset(self):\\n self.p.C[0][0] = np.inf\\n self.step()\",\n \"def apply(self):\\n prequire(False, \\\"Called abstract version of apply\\\")\",\n \"def apply(self, simulation):\\n t = simulation.time\\n dt = simulation.timeStep\\n if main_rank == 0:\\n simulation.printState()\\n # OpenCL update\\n self.numMethod(self.gpu_field.gpu_data[self.component],\\n self.color)\\n self.window.widget.updateGL()\\n if simulation.currentIteration > 1:\\n self.window.label.setText(\\n self.labelText + \\\"t={0:6.2f}, fps={1:6.2f}\\\".format(\\n t + dt,\\n 1. / (self.timer.f_timers.values()[0].t - self.ctime)))\\n self.ctime = self.timer.f_timers.values()[0].t\",\n \"def apply_immediately(self) -> pulumi.Output[Optional[bool]]:\\n return pulumi.get(self, \\\"apply_immediately\\\")\",\n \"def momentum(portfolio_item, transaction_volume, cash_allocation):\\n from yahooquery import Ticker\\n from math import floor\\n import talib\\n from .TradeHistoryItem import log_trade\\n from API.Help import is_increasing, initialize_alpaca\\n\\n alpaca = initialize_alpaca()\\n\\n yahoo_ticker = Ticker(str(portfolio_item))\\n info = yahoo_ticker.history()\\n ma_5 = talib.SMA(info['close'], timeperiod=5)\\n ma_20 = talib.SMA(info['close'], timeperiod=20)\\n volume = info['volume']\\n\\n if portfolio_item.shares == 0:\\n # if the price goes from below the sma to above, buy\\n if ma_5[-1] > (ma_20[-1] * 1.1) and is_increasing(volume, 3):\\n print('buying {} shares of {}'.format(transaction_volume, str(portfolio_item)))\\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'buy', 'market', 'day')\\n portfolio_item.buy(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=0)\\n # if the price goes from above the sma to below, short\\n elif ma_5[-1] < (ma_20[-1] * .9) and not is_increasing(volume, 3) and portfolio_item.shares == 0:\\n transaction_volume = floor(cash_allocation / (portfolio_item.ticker.price_now * 1.1))\\n print('shorting {} shares of {}'.format(transaction_volume, str(portfolio_item)))\\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'sell', 'market', 'day')\\n portfolio_item.short(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=3)\",\n \"def update_job_state(self, job):\",\n \"def reset_continued(self): \\n self._recent_goal_continued = False\\n self._update_action = False\\n self._update_action_without_pause = False\",\n \"def compute(self, *args):\\n\\n pass\",\n \"def set_force(state):\\n global _FORCE\\n _FORCE = bool(state)\",\n \"def solvate(self):\\n\\n pass\",\n \"def reset(self) -> None:\\n self.cash_balance = self.initial_cash_balance()\",\n \"def reset_state(self, noised_results, global_state):\\n del noised_results\\n new_tree_state = self._tree_aggregator.reset_state(global_state.tree_state)\\n return attr.evolve(\\n global_state,\\n previous_tree_noise=self._zero_initial_noise(),\\n tree_state=new_tree_state)\",\n \"def calculate_profit(self):\",\n \"def assumed_state(self) -> bool:\\n return self._optimistic\",\n \"def start(self):\\n if self.is_cancelled:\\n with self.changing('is_finished'):\\n self.finished = True\\n return\\n \\n \\n with self.changing('is_executing'):\\n self.executing = True \\n \\n with self.changing('is_finished', 'is_executing'):\\n try:\\n # if we're starting, we no longer have any dependencies\\n self.dependencies.clear()\\n \\n if not self.is_cancelled:\\n self.main()\\n finally:\\n self.executing = False\\n self.finished = True\",\n \"def update_state(self, context):\\n pass\",\n \"def ExecuteBeforeSolutionLoop(self):\\n super().ExecuteBeforeSolutionLoop()\\n num_of_vaviables = len(self.variables) + len(self.nonhistorical_variables)\\n self.values = [[-1e6] * num_of_vaviables for _ in self.found_positions]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60446954","0.5951977","0.5695806","0.5687665","0.56560236","0.5653465","0.56520754","0.5633276","0.559409","0.5562694","0.54696906","0.54538536","0.5440695","0.5416885","0.5380987","0.5366457","0.5364525","0.5331849","0.5323494","0.53195906","0.53195906","0.53085035","0.53000546","0.5282828","0.52782464","0.52712125","0.52664864","0.52351665","0.52097136","0.52049536","0.51982033","0.51883304","0.5182944","0.5154984","0.5154984","0.5154984","0.5154984","0.5154984","0.5149544","0.5130684","0.51288253","0.51150465","0.51070714","0.5084055","0.5065029","0.5063094","0.5061666","0.505786","0.50572574","0.505325","0.5047704","0.5046374","0.5031111","0.50302446","0.50280464","0.5014093","0.5010491","0.5001591","0.49955475","0.49925703","0.49922067","0.49922067","0.4970376","0.49590984","0.49583974","0.4955242","0.49544924","0.49499753","0.4943746","0.49430194","0.4942275","0.4941596","0.49398565","0.493672","0.49363688","0.493313","0.49307764","0.49250042","0.4922194","0.4918863","0.4908058","0.49073812","0.49048898","0.48806018","0.48805353","0.48775002","0.48772317","0.48731717","0.48729417","0.48727772","0.48674056","0.4860246","0.48544538","0.48526222","0.48511964","0.48468214","0.48451972","0.48441783","0.48390174","0.48363125"],"string":"[\n \"0.60446954\",\n \"0.5951977\",\n \"0.5695806\",\n \"0.5687665\",\n \"0.56560236\",\n \"0.5653465\",\n \"0.56520754\",\n \"0.5633276\",\n \"0.559409\",\n \"0.5562694\",\n \"0.54696906\",\n \"0.54538536\",\n \"0.5440695\",\n \"0.5416885\",\n \"0.5380987\",\n \"0.5366457\",\n \"0.5364525\",\n \"0.5331849\",\n \"0.5323494\",\n \"0.53195906\",\n \"0.53195906\",\n \"0.53085035\",\n \"0.53000546\",\n \"0.5282828\",\n \"0.52782464\",\n \"0.52712125\",\n \"0.52664864\",\n \"0.52351665\",\n \"0.52097136\",\n \"0.52049536\",\n \"0.51982033\",\n \"0.51883304\",\n \"0.5182944\",\n \"0.5154984\",\n \"0.5154984\",\n \"0.5154984\",\n \"0.5154984\",\n \"0.5154984\",\n \"0.5149544\",\n \"0.5130684\",\n \"0.51288253\",\n \"0.51150465\",\n \"0.51070714\",\n \"0.5084055\",\n \"0.5065029\",\n \"0.5063094\",\n \"0.5061666\",\n \"0.505786\",\n \"0.50572574\",\n \"0.505325\",\n \"0.5047704\",\n \"0.5046374\",\n \"0.5031111\",\n \"0.50302446\",\n \"0.50280464\",\n \"0.5014093\",\n \"0.5010491\",\n \"0.5001591\",\n \"0.49955475\",\n \"0.49925703\",\n \"0.49922067\",\n \"0.49922067\",\n \"0.4970376\",\n \"0.49590984\",\n \"0.49583974\",\n \"0.4955242\",\n \"0.49544924\",\n \"0.49499753\",\n \"0.4943746\",\n \"0.49430194\",\n \"0.4942275\",\n \"0.4941596\",\n \"0.49398565\",\n \"0.493672\",\n \"0.49363688\",\n \"0.493313\",\n \"0.49307764\",\n \"0.49250042\",\n \"0.4922194\",\n \"0.4918863\",\n \"0.4908058\",\n \"0.49073812\",\n \"0.49048898\",\n \"0.48806018\",\n \"0.48805353\",\n \"0.48775002\",\n \"0.48772317\",\n \"0.48731717\",\n \"0.48729417\",\n \"0.48727772\",\n \"0.48674056\",\n \"0.4860246\",\n \"0.48544538\",\n \"0.48526222\",\n \"0.48511964\",\n \"0.48468214\",\n \"0.48451972\",\n \"0.48441783\",\n \"0.48390174\",\n \"0.48363125\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66357535"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2663,"cells":{"query":{"kind":"string","value":"Compute the current portfolio. Notes This is cached, repeated access will not recompute the portfolio until the portfolio may have changed."},"document":{"kind":"string","value":"def portfolio(self):\n self.update_portfolio()\n return self._immutable_portfolio"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_portfolio(self):\n if not self._dirty_portfolio:\n return\n\n portfolio = self._portfolio\n pt = self.position_tracker\n\n portfolio.positions = pt.get_positions()\n position_stats = pt.stats\n\n portfolio.positions_value = position_value = (\n position_stats.net_value\n )\n portfolio.positions_exposure = position_stats.net_exposure\n self._cash_flow(self._get_payout_total(pt.positions))\n\n start_value = portfolio.portfolio_value\n\n # update the new starting value\n portfolio.portfolio_value = end_value = portfolio.cash + position_value\n\n pnl = end_value - start_value\n if start_value != 0:\n returns = pnl / start_value\n else:\n returns = 0.0\n\n portfolio.pnl += pnl\n portfolio.returns = (\n (1 + portfolio.returns) *\n (1 + returns) -\n 1\n )\n\n # the portfolio has been fully synced\n self._dirty_portfolio = False","def get_portfolio_object(self):\n return self.__get_portfolio_object(self.portfolio_name, self.portfolio_user)","def update_portfolio(self, portfolio: PortfolioController):\n now = portfolio.get_history(seconds_back=0)\n future = portfolio.get_history(seconds_back=-self.update_interval)\n\n for fund in portfolio.funds:\n best_currency = max(portfolio.currencies, key=lambda currency: future_value(fund, currency, now, future))\n if best_currency != fund.currency:\n portfolio.request_transfer(fund, best_currency)","def get_portfolio_pnl(self):\n\n return self._portfolio","def portfolio():\n #Query transactions by user id\n trans = Transactions.query.filter_by(owner=session['user_id']).all()\n \n #Create list of comanies user owns stock in\n companies = []\n for t in trans:\n if t.symbol not in companies:\n companies.append(t.symbol)\n\n #Create list of current stock dictionaries and total their values\n total = 0\n stocks = []\n for company in companies:\n trans = Transactions.query.filter_by(owner=session['user_id'], symbol=company).all()\n stock = {}\n stock['shares'] = 0\n for t in trans:\n stock['shares'] += t.shares\n if stock['shares'] > 0:\n stock['symbol'] = company\n stock['name'] = lookup(company)['name']\n stock['price'] = lookup(company)['price']\n stock['total'] = stock['shares'] * stock['price']\n stock['price'] = usd(stock['price'])\n stock['total'] = usd(stock['total'])\n total += float(stock['total'][1:].replace(',', ''))\n stocks.append(stock)\n\n #Set user cash and total values\n value = {}\n value['cash'] = usd(Users.query.filter_by(id=session['user_id']).first().cash)\n value['total'] = usd(total + float(value['cash'][1:].replace(',', '')))\n\n #Add values to list\n stocks.append(value)\n\n #Return list of dictionaries\n return stocks","def current_portfolio_weights(self) -> 'pd.Series[float]':\n position_values = pd.Series({\n asset: (\n position.last_sale_price *\n position.amount *\n asset.price_multiplier\n )\n for asset, position in self.positions.items()\n }, dtype=\"float64\")\n return position_values / self.portfolio_value","def generate_portfolio_data(self):\n self.__load_portfolio_historical_prices()\n self.__populate_historical_trade_data()\n self.__calculate_portfolio_returns()\n self.__calculate_portfolio_performance()","def __calculate_portfolio_returns(self):\n\n p_bar = tqdm(range(1), desc=\" Calculating returns\", leave=False)\n\n trade_data = self.historical_trade_data\n\n # Helper functions to calculate cash inflow and outflow\n def f_min(x):\n return x.apply(lambda x: min(x, 0))\n\n def f_max(x):\n return x.apply(lambda x: max(x, 0))\n\n # Calculate cash inflow and outflow\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period cash inflow\"], self.tickers_list + [\"Total\"]]\n )\n ] = -1 * trade_data[\"Investment delta\"][:].apply(lambda x: f_min(x), axis=0)\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period cash outflow\"], self.tickers_list + [\"Total\"]]\n )\n ] = trade_data[\"Investment delta\"][:].apply(lambda x: f_max(x), axis=1)\n\n # Calculate period return\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period absolute return\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"End Value\"] + trade_data[\"Period cash inflow\"]) - (\n trade_data[\"End Value\"].shift(1).fillna(0)\n + trade_data[\"Period cash outflow\"]\n )\n\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Period percentage return\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"End Value\"] + trade_data[\"Period cash inflow\"]) / (\n trade_data[\"End Value\"].shift(1).fillna(0)\n + trade_data[\"Period cash outflow\"]\n ) - 1\n\n trade_data[\"Period percentage return\"].fillna(0, inplace=True)\n\n self.historical_trade_data = trade_data\n\n self.portfolio_returns = self.historical_trade_data[\"Period percentage return\"][\n \"Total\"\n ]\n\n p_bar.n += 1\n p_bar.refresh()","def __get_portfolio_object(self, name, user):\n portfolio = self.__get_object_portfolio_bulk(name, user)\n if portfolio is None:\n portfolio = self.__get_object_portfolio_bulk(name, user, \"portfolio_update\")\n if portfolio is None:\n portfolio = self.db_tool.session.query(Portfolio) \\\n .outerjoin(Orders)\\\n .join(Stock)\\\n .filter(name == Portfolio.name) \\\n .filter(user == Portfolio.user).first()\n self.bulk_data[\"portfolio_update\"].append(portfolio)\n return portfolio","def backtest_portfolio(self):\n self.rank=dict()\n self.accuracy=dict()\n portfolio = dict()\n \n for algo in self.algos:\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\n self.pos_diff=dict()\n self.pos_diff[algo] = self.positions[algo].diff()\n \n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\n #portfolio['price_diff'][0:5] = 0.0\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\n d=np.array(portfolio[algo]['profit']).copy()\n d[d>0]=1\n d[d<0]=0\n d[np.array(self.positions[algo])==0]=1\n for i in np.arange(1,len(d)+1):\n c=float(sum(d[0:i]))/(i)\n d[i-1]=c\n portfolio[algo]['accuracy']=d\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\n self.returns=portfolio\n c=np.array(self.returns[algo]['profit'])\n c[c>0]=1\n c[c<0]=0\n c[np.array(self.positions[algo])==0]=1\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\n self.accuracy[algo]=accuracy\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\n self.ready=True\n return (portfolio, self.rank, self.ranking)","def backtest_portfolio(self):\n raise NotImplementedError(\"Should implement backtest_portfolio()!\")","def compute_portvals(start_date, end_date, orders_file, start_val):\n \n #Read order file\n orders = pd.read_csv( orders_file, parse_dates = [0])\n \n #Get symbols making up the portfolio\n stock_symbols = list( set( orders[\"Symbol\"] ) )\n dates = pd.date_range(start_date, end_date)\n \n #Read stock prices\n stock_prices = get_data(stock_symbols, dates)\n \n #Create a portfolio keeping track of positions, \n #_CASH column indicates cash position, _VALUE total portfolio value\n #_LEVERAGE the leverage of portfolio when we allow for short selling\n symbols = stock_symbols[:] #Shallow copy of the list\n symbols.append(\"_CASH\")\n symbols.append(\"_VALUE\")\n symbols.append(\"_LEVERAGE\")\n \n #Index contains only business days, same dates as stock prices\n portfolio = pd.DataFrame(index=stock_prices.index, columns = symbols )\n portfolio.fillna(0) \n portfolio[\"_CASH\"][0] = start_val\n portfolio[\"_VALUE\"][0] = start_val\n \n #Snapshot of a portfolio at any time. To avoid using numerical indexes\n portfolio_snapshot = dict.fromkeys ( symbols, 0 )\n portfolio_snapshot[\"_CASH\"] = start_val\n portfolio[\"_VALUE\"] = start_val\n \n #Now calcualte portfolio day by day\n for date in portfolio.index:\n #Check transactions for the day\n day_orders = orders[ orders[\"Date\"] == date ] \n \n for ord in day_orders.iterrows():\n symbol = ord[1][ \"Symbol\"] \n stock_price = stock_prices[ symbol ][ date ]\n shares = ord[1][\"Shares\" ]\n side = ord[1][\"Order\"]\n \n if side == \"BUY\":\n portfolio_snapshot[ \"_CASH\" ] -= stock_price * shares\n portfolio_snapshot[ symbol ] += shares \n elif side == \"SELL\":\n portfolio_snapshot[ \"_CASH\" ] += stock_price * shares\n portfolio_snapshot[ symbol ] -= shares\n else:\n raise \"Order not recognized.\"\n \n #Compute portfolio value\n portfolio_snapshot[ \"_VALUE\" ] = portfolio_snapshot[ \"_CASH\" ]\n shorts = longs = 0\n for symbol in stock_symbols: \n stock_price = stock_prices[ symbol ][ date ]\n shares = portfolio_snapshot[ symbol ]\n notional = stock_price*shares\n if shares > 0:\n longs += notional\n else:\n shorts += notional\n \n portfolio_snapshot[ \"_VALUE\" ] += notional\n \n #Compute leverage\n leverage = (longs+shorts)/(longs-shorts + portfolio_snapshot[ \"_CASH\" ] )\n portfolio_snapshot[ \"_LEVERAGE\" ] = leverage\n \n #Assert we never achieve a leverage > 2.0\n if leverage > 2:\n raise \"Leverage > 2.0 achieved\"\n \n #Update portfolio from the daily snapshot\n #TODO: Is this causing performance issues?\n for symbol in portfolio.keys():\n portfolio[ symbol ][ date ] = portfolio_snapshot[ symbol ]\n \n return portfolio","def getPortfolioValue(self, start_t, t):\n sum_tmp=0\n for item in self.portfolio.keys():\n if \"DJI_\" in item:\n t_tmp=datetime.strftime(pd.date_range(end=t,periods=1,freq='B')[0],'%Y-%m-%d')\n price=universe.get_price_in_currency(item,t_tmp,'CAD')\n elif 'rf_rate' in item:\n price=universe.get_security(item).get_cc_return(start_t,t) \n else:\n price=universe.get_price_in_currency(item,t,'CAD')\n #price=universe.get_security(item).price[t]\n amount=self.portfolio[item]\n sum_tmp=sum_tmp+price*amount\n \n return sum_tmp","def get_portfolio_pnl_tsd(self):\n\n return self._tsd_portfolio","def portfolio_performance(returns,weights):\r\n print('Calculating Portfolio Performance')\r\n # returns=target_asset_port_data_attributes['component_returns']\r\n # weights =target_asset_port_data_attributes['effective_weights']\r\n\r\n component_returns= returns\r\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\r\n compnent_weights.loc[weights.index,:] = weights\r\n\r\n portfolio_dates = component_returns.index\r\n components = component_returns.columns\r\n\r\n # pre-allocate\r\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\r\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\r\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\r\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\r\n \r\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\r\n previous_index_value = np.int64(1)\r\n\r\n pre_date = portfolio_dates[0]\r\n # set BoP to start weights\r\n for date,row in component_returns.iterrows():\r\n # print(date)\r\n # 1st date\r\n if date == portfolio_dates[0]:\r\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n\r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n # after first date\r\n else:\r\n BoP_df.loc[date] = EoP_df.loc[pre_date]\r\n # weights override\r\n if date in compnent_weights.index:\r\n none_NaN_index = ~compnent_weights.loc[date].isnull()\r\n if not compnent_weights.loc[date][none_NaN_index].empty:\r\n tmp_sum = BoP_df.loc[date].sum()\r\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\r\n\r\n \r\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\r\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\r\n\r\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\r\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\r\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\r\n \r\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\r\n previous_index_value = portfolio_index.loc[date]\r\n pre_date = date\r\n\r\n\r\n portfolio_returns = portfolio_index.pct_change(1) \r\n portfolio_returns.columns = ['Returns']\r\n\r\n portfolio_index\r\n perf = portfolio_index.calc_stats()\r\n \r\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\r\n return output","def calculate_portfolio(self, request, pk=None, **kwargs):\n goal = self.get_object()\n\n check_state(Goal.State(goal.state), Goal.State.ACTIVE)\n\n setting_str = request.query_params.get('setting', None)\n if not setting_str:\n raise ValidationError(\"Query parameter 'setting' must be specified and a valid JSON string\")\n try:\n setting = ujson.loads(setting_str)\n except ValueError:\n raise ValidationError(\"Query parameter 'setting' must be a valid json string\")\n\n # Create the settings object from the dict\n serializer = serializers.GoalSettingStatelessSerializer(data=setting)\n serializer.is_valid(raise_exception=True)\n settings = serializer.create_stateless(serializer.validated_data, goal)\n\n try:\n data = self.build_portfolio_data(calculate_portfolio(settings=settings,\n data_provider=DataProviderDjango(),\n execution_provider=ExecutionProviderDjango()))\n return Response(data)\n except Unsatisfiable as e:\n rdata = {'reason': \"No portfolio could be found: {}\".format(e)}\n if e.req_funds is not None:\n rdata['req_funds'] = e.req_funds\n\n return Response({'error': rdata}, status=status.HTTP_400_BAD_REQUEST)","def initialize_portfolio(self):\n\n raise NotImplementedError('''\n Must implement initialize_portfolio. Call help() for details.\n ''')","def rebalance(context, data):\n logger.debug('rebalancing on: %s', algo.get_datetime())\n\n context.trend_filter = False\n\n # new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\n\n new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\n\n for equity, row in new_portfolio.iterrows():\n logger.debug('new portfolio (before filtering) - equity: %s', equity)\n\n # print(new_portfolio)\n\n # new_portfolio = new_portfolio[new_portfolio['overall_rank'].notna() & new_portfolio['momentum'] > 40][:20]\n \n # new_portfolio = new_portfolio[(new_portfolio['momentum_decile'] > 8)][:20]\n\n new_portfolio = new_portfolio.nlargest(20, ['overall_rank', 'momentum']) #<- $600K PL in 10 years\n\n # new_portfolio = new_portfolio.nlargest(20, ['momentum', 'overall_rank']) #<- 1M PL in 10 years\n\n if logger.level is logging.DEBUG:\n for equity, row in new_portfolio.iterrows():\n logger.debug('new portfolio - (after filtering) equity: %s', equity)\n \n\n # print(len(new_portfolio.index))\n\n # volatility driven weights\n # new_portfolio['inverse_volatility'] = new_portfolio['volatility'].apply(lambda x: 1 / x)\n # inv_vola_sum = new_portfolio['inverse_volatility'].sum()\n # new_portfolio['target_weight'] = new_portfolio['inverse_volatility'].apply(lambda x: x / inv_vola_sum)\n\n # portfolio size driven weights\n # num_equities = len(new_portfolio.index)\n # new_portfolio['target_weight'] = 1 / num_equities\\\n\n # logger.info('len existing portfolio: %s', len(context.portfolio.positions))\n\n if logger.level is logging.DEBUG:\n for equity, values in context.portfolio.positions.items():\n logger.debug('context.portfolio.positions - equity: %s, amount: %s, cost_basis: %s, sold_on: %s, sold_at_price: %s', equity, values.amount, values.cost_basis, values.last_sale_date, values.last_sale_price)\n\n \n order_target(algo.sid('FIBBG000NTFYM5'), 0)\n logger.debug('selling all bonds')\n\n for equity in context.portfolio.positions:\n if equity is algo.sid('FIBBG000NTFYM5'): \n continue\n if equity not in set(new_portfolio.index.tolist()):\n # logger.info('selling %s', equity)\n order_target_percent(equity, 0)\n\n stock_weights = 1.0 / max(len(context.portfolio.positions), len(new_portfolio.index))\n\n logger.debug('len existing portfolio (afer ejection): %s', len(context.portfolio.positions))\n logger.debug('len new portfolio: %s', len(new_portfolio.index))\n logger.debug('stock_weights: %s', stock_weights)\n\n # print(context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5')))\n\n # spy = context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5'))\n\n # if (spy is not None) and (spy.amount > 0):\n # order_target_percent(algo.sid('FIBBG000NTFYM5'), 0)\n\n for equity, row in new_portfolio.iterrows():\n if row.trend_filter is True:\n # logger.info('buying %s', equity)\n context.trend_filter = True\n order_target_percent(equity, stock_weights)\n else:\n context.trend_filter = False\n \n logger.debug('cash: %s', context.portfolio.cash)\n logger.debug('portfolio_value: %s', context.portfolio.portfolio_value)\n logger.debug('num_positions: %s', len(context.portfolio.positions))\n logger.debug('positions: %s', context.portfolio.positions)","def index():\n def getListOfCompanies(username, symbolOrPriceOrNumber):\n if symbolOrPriceOrNumber == \"symbol\" or symbolOrPriceOrNumber == \"price\" or symbolOrPriceOrNumber == \"number\":\n rows = db.execute(\"SELECT {0} FROM portfolio WHERE username=:username\".format(symbolOrPriceOrNumber), username=username)\n if symbolOrPriceOrNumber == \"symbol\" and len(rows) >= 1:\n namesList = []\n for row in rows:\n namesList.append(lookup(row[symbolOrPriceOrNumber])[\"name\"])\n return namesList\n elif symbolOrPriceOrNumber == \"price\" and len(rows) >= 1:\n pricseList = []\n for row in rows:\n pricseList.append(row[symbolOrPriceOrNumber])\n return pricseList\n elif symbolOrPriceOrNumber == \"number\" and len(rows) >= 1:\n numbersList = []\n for row in rows:\n numbersList.append(row[symbolOrPriceOrNumber])\n return numbersList\n else:\n return None\n else:\n return None\n\n def getTotalValueHolding(username):\n priceRow = db.execute(\"SELECT price FROM portfolio WHERE username=:username\", username=username)\n numberRow = db.execute(\"SELECT number FROM portfolio WHERE username=:username\", username=username)\n\n if len(priceRow) >= 1 and len(numberRow) >= 1 and len(priceRow) == len(numberRow):\n totalList = []\n for i in range(len(priceRow)):\n totalList.append(float(priceRow[i][\"price\"]) * float(numberRow[i][\"number\"]))\n\n return totalList\n\n username = db.execute(\"SELECT username FROM users WHERE id=:userId\", userId=session[\"user_id\"])[0][\"username\"]\n companiesNames = getListOfCompanies(username, \"symbol\")\n numberOfShares = getListOfCompanies(username, \"number\")\n prices = getListOfCompanies(username, \"price\")\n totalValueHolding = getTotalValueHolding(username)\n\n currentCashBalance = db.execute(\"SELECT cash FROM users WHERE id=:userId\", userId=session[\"user_id\"])[0][\"cash\"]\n total = 0\n if totalValueHolding:\n for totalValue in totalValueHolding:\n total = total + totalValue\n\n cashAndStocksTotalValue = float(currentCashBalance) + total\n\n return render_template(\"index.html\", username=username, companiesNames=companiesNames, numberOfShares=numberOfShares,\n prices=prices, totalValueHolding=totalValueHolding, currentCashBalance=currentCashBalance, cashAndStocksTotalValue=cashAndStocksTotalValue)","def portfolio_table(self):\n idx = set(name.split('-')[0].split('.')[0] for name, etf in self.etfs.items() if not etf.sold())\n table = pd.DataFrame({'Invested': 0, 'Shares':0, 'Share Price':0, 'Present Value':0, 'P/L':0, 'P/L%':0},index=idx)\n for name, etf in self.etfs.items():\n if not etf.sold():\n table.loc[name.split('-')[0].split('.')[0], 'Invested'] += etf.initial_investment()\n table.loc[name.split('-')[0].split('.')[0], 'Shares'] += etf.n_shares\n table.loc[name.split('-')[0].split('.')[0], 'Share Price'] = etf.stock_price()\n table.loc[name.split('-')[0].split('.')[0], 'Present Value'] += etf.present_value()\n table.loc[name.split('-')[0].split('.')[0], 'P/L'] += etf.profit_loss()\n table.insert(1, 'PMA', round(table['Invested'] / table['Shares'], 2))\n table.insert(3, 'Initial Weight', round(table['Invested'] / table['Invested'].sum() * 100, 2))\n table.insert(4, 'Present Weight', round(table['Present Value'] / table['Present Value'].sum() * 100, 2))\n table['P/L%'] = round(table['P/L'] / table['Invested'] * 100, 2)\n table['P/L'] = round(table['P/L'], 2)\n table['Present Value'] = round(table['Present Value'], 2)\n return table.sort_values('Invested', 0, ascending=False)","def index():\n\n #select user's portfolio\n rows = db.execute(\"SELECT * FROM portfolio WHERE userid=:id\", id=session[\"user_id\"])\n\n #set temporary holding place for cash to zero\n tcash = 0\n\n #update the stock information in user's portfolio\n for row in rows:\n stock = row[\"stock\"]\n number = row[\"number\"]\n quote = lookup(stock)\n total = float(number) * float(quote[\"price\"])\n tcash += total\n db.execute(\"UPDATE portfolio SET price=:price, total=:total WHERE userid=:id AND stock=:stock AND number=:number\", price=usd(quote[\"price\"]), total=total, id=session[\"user_id\"], stock=stock, number=number)\n\n #select user's cash and updated portfolio\n updated_cash = db.execute(\"SELECT cash FROM users WHERE id=:id\", id=session[\"user_id\"])\n tcash += updated_cash[0][\"cash\"]\n updated_stock = db.execute(\"SELECT stock, SUM(number) AS number, price, SUM(total) AS stock_total FROM portfolio WHERE userid=:id GROUP BY stock HAVING SUM(number) > 0\", id=session[\"user_id\"])\n\n return render_template(\"index.html\", stocks=updated_stock, cash=usd(updated_cash[0][\"cash\"]), all_total=usd(tcash))","def update_portfolio_on_market(self, market: MarketEvent):\n self._portfolio.update_market_value(market)","def portfolio_analytics(port_returns, market_returns):\n\n # add the intercept to the model\n x2 = sm.add_constant(market_returns)\n\n # train the model\n estimator = sm.OLS(port_returns, x2)\n model = estimator.fit()\n\n # get portfolio analytics\n alpha, beta = model.params\n r_squared = model.rsquared\n regression = model.predict()\n\n return alpha, beta, r_squared, regression","def portfolio_analytics(port_returns, market_returns):\n\n # add the intercept to the model\n x2 = sm.add_constant(market_returns)\n\n # train the model\n estimator = sm.OLS(port_returns, x2)\n model = estimator.fit()\n\n # get portfolio analytics\n alpha, beta = model.params\n r_squared = model.rsquared\n regression = model.predict()\n\n return alpha, beta, r_squared, regression","def __init__(\n self,\n portfolio,\n market=None,\n commission_min=5.00,\n commission_pct=0.0,\n buy_percent=1.0,\n sell_percent=1.0,\n pm_threshold=0.0,\n pm_order=1.0,\n risk_free_return=1.0,\n name=None\n ):\n\n # Assumptions\n self.name = name if name else portfolio.name\n self.commission_min = commission_min\n self.commission_pct = commission_pct\n self.buy_percent = buy_percent\n self.sell_percent = sell_percent\n self.pm_threshold = pm_threshold\n self.pm_order = pm_order\n self.risk_free_return = risk_free_return\n self.performance = {}\n\n # Inputs\n self.portfolio = portfolio\n self.market = copy.deepcopy(market) if market else Asset(np.ones(len(self.portfolio.dates)))\n\n # Trading states\n self.long_open = {symbol:False for symbol in portfolio.assets.keys()}\n self.short_open = {symbol:False for symbol in portfolio.assets.keys()}\n\n # Keep track of intermidiate results for performance\n self.trade_data = []\n recordings = [\n 'buy price', 'buy shares', 'buy fees', 'buy date',\n 'sell price', 'sell shares', 'sell fees', 'sell date',\n 'gain', 'profit', 'loss', 'return', 'win/loose',\n 'min balance', 'min date', 'max balance', 'max date',\n 'drawdown', 'drawdown days',\n 'volatility', 'expected_return', 'beta', 'lpm', 'hpm',\n 'max', 'mean', 'min'\n ]\n self.record = {symbol:pd.DataFrame(columns=recordings) for symbol in portfolio.assets.keys()}\n self.max = {symbol:[portfolio.assets[symbol].c.iloc[0], None] for symbol in portfolio.assets.keys()}\n self.min = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}\n self.drawdown = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}","def get_stock(self, investor):\n\n # Find out the stock details \n sym, qty, price = investor.portfolios[0].portfolios[0]\n # p = investor.portfolios[0]\n \n # Check if broker has a portfolio\n if self.portfolios[0]:\n self.portfolios[0].add_stock(sym, qty, price)\n else:\n # Broker doesn't have a portfolio\n p = Portfolio()\n #logging.info(\"p is: %s\" % p)\n p.add_stock(sym, qty, price)\n self.add_portfolio(p)\n logging.info(\"Broker's portfolios AFTER addition: %s\" % self)\n # logging.info(\"WHAT ARE YOU\")\n logging.info(\"Investor portfolio BEFORE removal: %s\" % investor.portfolios[0].portfolios)\n investor.portfolios[0].remove_stock(sym, qty)\n logging.info(\"Investor portfolio AFTER removal: %s\" % investor.portfolios[0])\n # investor.portfolios[0].portfolios.remove( (sym, qty, price) )\n \n # investor.portfolios[0].remove(sym, qty, price)\n total_price = qty * price\n investor.portfolios[0].value -= total_price\n investor.cash += qty * float(price)","def _initalize_portfolio_with_cash(self):\n self.cash = copy.copy(self.starting_cash)\n\n if self.starting_cash > 0.0:\n self.history.append(\n PortfolioEvent.create_subscription(\n self.current_dt, self.starting_cash, self.starting_cash\n )\n )\n\n self.logger.info(\n '(%s) Funds subscribed to portfolio \"%s\" '\n '- Credit: %0.2f, Balance: %0.2f' % (\n self.current_dt.strftime(settings.LOGGING[\"DATE_FORMAT\"]),\n self.portfolio_id,\n round(self.starting_cash, 2),\n round(self.starting_cash, 2)\n )\n )","def before_trading_start(context, data):\n factors = pipeline_output('ff_example')\n\n # get the data we're going to use\n returns = factors['returns']\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\n be_me = factors.sort_values(['be_me'], ascending=True)\n\n # to compose the six portfolios, split our universe into portions\n half = int(len(mkt_cap)*0.5)\n small_caps = mkt_cap[:half]\n big_caps = mkt_cap[half:]\n \n thirty = int(len(be_me)*0.3)\n seventy = int(len(be_me)*0.7)\n growth = be_me[:thirty]\n neutral = be_me[thirty:seventy]\n value = be_me[seventy:]\n\n # now use the portions to construct the portfolios.\n # note: these portfolios are just lists (indices) of equities\n small_value = small_caps.index.intersection(value.index)\n small_neutral = small_caps.index.intersection(neutral.index)\n small_growth = small_caps.index.intersection(growth.index)\n \n big_value = big_caps.index.intersection(value.index)\n big_neutral = big_caps.index.intersection(neutral.index)\n big_growth = big_caps.index.intersection(growth.index)\n\n # take the mean to get the portfolio return, assuming uniform\n # allocation to its constituent equities.\n sv = returns[small_value].mean()\n sn = returns[small_neutral].mean()\n sg = returns[small_growth].mean()\n \n bv = returns[big_value].mean()\n bn = returns[big_neutral].mean()\n bg = returns[big_growth].mean()\n\n # computing SMB\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\n\n # computing HML\n context.hml = (sv + bv)/2 - (sg + bg)/2","def calc_portfolio_risk(\n context,\n data,\n risk_func,\n hist_days=180,\n **kwargs):\n\n \n positions = context.portfolio.positions\n positions_index = pd.Index(positions)\n share_counts = pd.Series( \n index=positions_index, \n data=[positions[asset].amount for asset in positions] \n )\n\n current_prices = data.current(positions_index, 'price') \n current_weights = (\n share_counts * current_prices / context.portfolio.portfolio_value\n )\n \n prices = data.history(\n current_weights.index.tolist(),\n 'price',\n hist_days,\n '1d'\n )\n\n daily_rets = prices.pct_change()\n daily_rets = daily_rets - daily_rets.mean(skipna=True)\n daily_rets = daily_rets.fillna(0.0)\n\n risk = risk_func(current_weights.values, daily_rets, **kwargs)\n return risk","def __display_portfolio(self, p, w):\n\n global st_sort_key\n global st_reverse_sort\n\n line = 1\n total_assets = 0\n total_change = 0\n\n p.assets.sort(key=st_sort_key, reverse=st_reverse_sort)\n\n for s in p.assets:\n # Make sure we have space to write the portfolio totals.\n if line >= (curses.LINES - 3):\n break\n\n total_assets += (p.asset_counts[s.symb()] * s.price())\n total_change += (p.asset_counts[s.symb()] * s.change())\n\n # Color red/green for stocks going up/down.\n change_color = curses.color_pair(0)\n if s.change() > 0:\n change_color = curses.color_pair(1)\n elif s.change() < 0:\n change_color = curses.color_pair(2)\n\n direction = ''\n if s.change() > 0:\n direction = u'\\u25b2'\n elif s.change() < 0:\n direction = u'\\u25bc'\n\n w.addstr(line, 0, '%-15s' % s.name()[0:14])\n w.addstr(line, 16, '%-5s' % s.symb(), curses.A_BOLD)\n w.addstr(line, 22, '%9.2f' % s.price())\n w.addstr(line, 32, direction.encode('utf-8'), change_color)\n w.addstr(line, 33, '%6.2f %5.2f%%' % (abs(s.change()),\n abs(s.change_percent()) *\n 100),\n change_color)\n w.addstr(line, 47, '|')\n w.addstr(line, 49, '%-6d' % p.asset_counts[s.symb()])\n w.addstr(line, 56, '%11.2f' % (p.asset_counts[s.symb()] *\n s.price()))\n w.addstr(line, 68, '%10.2f' % (p.asset_counts[s.symb()] *\n s.change()),\n change_color)\n\n line += 1\n\n line += 1\n\n # Get overall change (of assets) for the portfolio.\n overall_change = total_assets - p.cost_basis()\n overall_color = curses.color_pair(0)\n if overall_change > 0:\n overall_color = curses.color_pair(1)\n elif overall_change < 0:\n overall_color = curses.color_pair(2)\n\n # Color red/green for assets changing.\n change_color = curses.color_pair(0)\n if total_change > 0:\n change_color = curses.color_pair(1)\n elif total_change < 0:\n change_color = curses.color_pair(2)\n\n # Print accumulated stats for the portfolio.\n w.addstr(line, 0, 'Daily:')\n w.addstr(line, 8, '$%.2f' % total_change,\n curses.A_BOLD | change_color)\n w.addstr(line, 23, 'Total:')\n w.addstr(line, 30, '$%.2f' % overall_change,\n curses.A_BOLD | overall_color)\n w.addstr(line + 1, 0, 'Assets:')\n w.addstr(line + 1, 8, '$%.2f' % total_assets)\n w.addstr(line + 1, 23, 'Cash: $%.2f' % p.cash)\n w.addstr(line + 1, 44, 'Total value:')\n w.addstr(line + 1, 58, '$%.2f' % (p.cash + total_assets),\n curses.A_BOLD)","def optimizeForReturn(required_return, stock_db, use_genetic):\n print('Optimizing portfolio for %f' % required_return)\n pf = PortfolioFactory(stock_db, required_return, use_genetic=use_genetic)\n desired_portfolio = pf.desired_portfolio\n print('Required Return: %f' % required_return)\n print('Expected Return: %f' % math.pow(\n desired_portfolio.average_return, Config.DAYS_IN_YEAR))\n print('Downside Risk: %f' % desired_portfolio.downside_risk)\n print('Downside Correl: %f' % desired_portfolio.downside_correl)\n print('Score: %f' % desired_portfolio.score)\n\n # Write desired portfolio.\n DataIO.writeDesiredPortfolio(\n desired_portfolio, stock_db,\n 'output/DesiredPortfolio_%.0f_%.4f_%s.csv' % (\n Config.MINIMUM_AMOUNT_DATA, required_return, Config.TODAY.date()))\n\n print('Finished for %f' % required_return)\n\n return desired_portfolio","def load_portfolio(self):\n\n if self.terminate:\n return\n\n # First get a file.\n self.lock.acquire()\n self.windows['ACTION'].erase()\n\n curses.curs_set(1)\n\n tp = curses.textpad.Textbox(self.windows['ACTION'])\n file_str = tp.edit()\n curses.curs_set(0)\n\n self.clear_action()\n self.lock.release()\n\n self.refresh()\n\n # Now that we have a portfolio file, let's load it up and\n # fire off a thread to update it.\n p = Portfolio(file_str.strip())\n self.portfolios.append(p)\n self.track_portfolio(p)","def display_portfolio(self, p):\n\n if self.terminate:\n return\n\n w = self.windows['MAIN']\n\n self.clear_main()\n self.__display_portfolio(p, w)\n self.clear_header()\n self.set_header(p)\n\n self.refresh()","def income_model_constant_portfolio_return(num_of_years=30, trials=100, method='normal'):\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # # dataframe for unsorted returns (normal)\n # median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n # median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\n #\n # # dataframe for smallest to largest returns\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\n #\n # # dataframe for unsorted returns (normal)\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n \n # -------------For Constant Growth Rates------------------------\n const_fia_index_ret = float(read_income_inputs.loc['const_fia_index_ret', 'inputs'])\n const_risky_port_ret = float(read_income_inputs.loc['const_risky_port_ret', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n # income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n # ----------CONSTANT FIA INDEX GROWTH RATE-------------------\n income_df.loc[:, 'index_returns'] = const_fia_index_ret\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # -------------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n # for c in range(len(r_cols)):\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_small.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\n\n elif method == 'largest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_large.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\n\n else:\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\n\n # store the simulated assets returns in one dictionary\n # returns_dict.update({str(runs): random_returns})\n\n # collect the asset based returns from all simulation and calculate the median returns.\n # def get_median_returns(sym):\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\n # return asset_df.median(axis=1)\n #\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\n #\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\n\n base_df = random_returns.copy()\n pre_income_base_df = random_returns.copy()\n\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\n\n fia_portfolio_df = random_returns.copy()\n pre_income_port_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # ---------Initial Investments for pre-income account values---------------------\n pre_income_base_inv = base_investment\n pre_income_port_inv = port_investment\n # ----------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n pre_income_base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n # ---------------For year 0, the year of investment------------\n\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_base_inv for c in range(len(boy_value))]\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n # base_investment = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_pre_income'] = base_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n # ----For years between the start of the investment and start if the income---------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total'] = base_investment * (1 + 0.06)\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\n\n else:\n\n # -------------For Years after the income started----------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n # -------------------Portfolio with PreIncome Values----------------------------\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\n # --------------------------------PreIncome Block Ends----------------------------\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n pre_income_port_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_port_inv for c in range(len(boy_value))]\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n # port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # ------------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n \n # -----------------------CONSTANT GROWTH RATE-----------------\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n \n # -------CONSTANT GROWTH RATE-----------------\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\n\n # -------------------Portfolio with PreIncome Values----------------------------\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\n \n # --------------------------------PreIncome Block Ends----------------------------\n\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\n total_income_by_age.fillna(0, inplace=True)\n income_dataframe = pd.DataFrame(total_income_by_age)\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\n lambda x: total_income_by_acturial_age - x)\n\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\n\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\n\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\n\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ---------------Portfolio for 45 years of simulation---------------------------------------\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ----------------Portfolio Simulation until the acturial age------------------------------\n acturial_years = life_expectancy - clients_age\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\n\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\n if base_success_portfolio_act_age.isnull().sum() == trials:\n base_max_portfolio = 0.0\n else:\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\n\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\n if port_success_portfolio_act_age.isnull().sum() == trials:\n port_max_portfolio = 0.0\n else:\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\n\n # ------------------------------Average age with full income------------------------------\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n # ----------------------------Median Age with full Income------------------------------------------\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n\n # # --------------Mean Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n #\n # # --------------Median Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n\n # -------Max Portfolio value at the end of acturial age----------------------------------------\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\n\n # -------Min Portfolio value at the end of acturial age----------------------------------------\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\n\n # ---------------------Lifetime Average Income----------------------------------\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\n port_total_income = income_from_fia + sim_port_income\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\n\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\n 'Max Portfolio Value', 'Min Portfolio Value',\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\n\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\n 'Median years of portfolios that meet the next years income needs for the lifetime',\n 'Average Clients Age',\n 'Median Clients Age',\n 'Average of terminal values for the portfolios at the end of the acturial life',\n 'Median of terminal values for the portfolios at the end of the acturial life',\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\n 'Average of total income generated by all portfolios at the end of the acturial life']\n\n simulation_stats.loc[:, 'Notes'] = comments\n\n # --------------------------------------------------------------------------------\n\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\n # base_df.to_csv(src + 'base_port_detail.csv')\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\n # income_breakdown_base.loc[:, 'social_security_income'] = social\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n #\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n # axis=1)\n #\n # # --------------------------------------Block Ends-----------------------------------------------------------\n #\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n #\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n # income_breakdown_port.loc[:, 'social_security_income'] = social\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n #\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n # axis=1)\n #\n # # ----------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\n\n # ------------------------------------------drop year 0-----------------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ---------------------------------plot for histogram for porfolios--------------------------------------\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\n # fact = 1 / len(base_term_value)\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\n # counts, bins, bars = plt.hist(base_ann_ret)\n\n # ------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n sim_base_total.clip(lower=0, inplace=True)\n\n # -------------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n\n writer = pd.ExcelWriter(dest_simulation + method + '_leveled_growth_simulation.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n # base_qcut.loc[:, 'clients_age'] = age_index\n # base_qcut.loc[:, 'comment'] = ''\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n # -----------------------To start with year 0---------------------------------\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\n base_inv, clients_age, np.nan]\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\n base_qcut = base_qcut.shift(1)\n base_qcut.iloc[0] = insert_col\n base_qcut.reset_index(drop=True, inplace=True)\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\n\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\n # port_qcut.loc[:, 'clients_age'] = age_index\n # port_qcut.loc[:, 'comment'] = ''\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\n port_qcut = port_qcut.shift(1)\n port_qcut.iloc[0] = insert_col\n port_qcut.reset_index(drop=True, inplace=True)\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'clients_age'] = age_index\n # prob_success_df.loc[:, 'comment'] = ''\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \"\")\n\n prob_success_df.loc[:, 'age'] = age_index\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\n\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\n\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\n\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\n\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\n\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\n\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\n\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\n # -------Add premium to year 0 value to get total portfolio value---------\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\n\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\n #\n # # -------------For Simulation slide - Proposed Portfolio --------------------\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\n #\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\n\n prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\n - social - cpn_income_port\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\n - income_from_fia - social - cpn_income_port\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # -------------------Write simulation Statistics-------------------------------------\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\n\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\n\n # if method == 'normal':\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n # median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n #\n # elif method == 'smallest':\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n # median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\n #\n # else:\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n # median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\n\n # ---------------------Histogram for S&P Forecast---------------------------------------\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\n bins, data = np.histogram(sp_random_ret, bins=20)\n df_ret = pd.DataFrame(data, columns=['Return_range'])\n df_bins = pd.DataFrame(bins, columns=['Count'])\n df_hist = df_ret.join(df_bins)\n\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\n writer.save()\n\n print(\"simulation completed....\")","def index():\n user_stocks_list = db.execute(\"SELECT stock FROM transactions WHERE id = :current_id\", current_id=session[\"user_id\"])\n user_stocks = []\n for stock in user_stocks_list:\n if stock['stock'] not in user_stocks:\n user_stocks.append(stock['stock'])\n\n stock_portfolio = []\n\n for possible_stock in user_stocks:\n bought_shares_list = db.execute(\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\",\n current_id=session[\"user_id\"], stock=possible_stock, t='B')\n bought_shares = 0\n bought_shares = bought_shares_list[0][\"SUM(units)\"]\n sold_shares_list = db.execute(\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\",\n current_id=session[\"user_id\"], stock=possible_stock, t='S')\n sold_shares = 0\n sold_shares = sold_shares_list[0][\"SUM(units)\"]\n if sold_shares == None:\n sold_shares = 0\n\n available_shares = 0\n if bought_shares != None and (bought_shares - sold_shares) > 0:\n available_shares = bought_shares - sold_shares\n current_price = int(lookup(possible_stock)[\"price\"])\n market_value = current_price * available_shares\n dict_stock = {}\n dict_stock['name_stock'] = possible_stock\n dict_stock['shares_quantity'] = available_shares\n dict_stock['current_price'] = current_price\n dict_stock['market_value'] = market_value\n stock_portfolio.append(dict_stock)\n else:\n pass\n\n available_money_list = db.execute(\"SELECT cash FROM users WHERE id = :current_id\", current_id=session[\"user_id\"])\n available_money = usd(available_money_list[0]['cash'])\n\n username_list = db.execute(\"SELECT username FROM users WHERE id = :current_id\", current_id=session[\"user_id\"])\n username = username_list[0][\"username\"]\n\n sum_market_values = 0\n for collection in stock_portfolio:\n sum_market_values += int(collection['market_value'])\n\n total_value = usd(available_money_list[0]['cash'] + sum_market_values)\n\n return render_template(\"index.html\", stock_portfolio=stock_portfolio, user_stocks=user_stocks, money=available_money, name=username, total_value=total_value)","def add_portfolio(self, portfolio):\n self.portfolios.append(portfolio)","def add_portfolio(self, portfolio):\n self.portfolios.append(portfolio)","def get_portfolio_value(prices, allocs, start_val):\n normed = prices/prices.iloc[0]\n alloced = np.multiply(allocs, normed)\n pos_vals = alloced * start_val\n port_val = pos_vals.sum(axis=1)\n return port_val","def hedge_portfolio(context, data):\r\n factors = get_alphas_and_betas(context, data)\r\n beta_exposure = 0.0\r\n count = 0\r\n for asset in context.portfolio.positions:\r\n if asset in factors and asset != context.index:\r\n if not np.isnan(factors[asset].beta):\r\n beta_exposure += factors[asset].beta\r\n count += 1\r\n beta_hedge = -1.0 * beta_exposure / count\r\n dollar_amount = context.portfolio.portfolio_value * beta_hedge\r\n record(beta_hedge=beta_hedge)\r\n if not np.isnan(dollar_amount):\r\n order_target_value(context.index, dollar_amount)","def after_run(self):\n # Calculate the performance of the strategy and portfolio\n self.portfolio.calc_stats()\n self.calc_performance()\n\n return self","def index():\n # Establish userID.\n userID = session[\"user_id\"]\n # Isolate all results from portfolio table for the current user.\n portfolio = db.execute(\"SELECT * FROM portfolio WHERE id=:userID\", userID=session[\"user_id\"])\n # Cash for current user (first row, cash column)\n cash = db.execute(\"SELECT cash FROM users WHERE id=:userID\", userID=userID)[0][\"cash\"]\n # Empty list to store stock data as iterating through rows.\n stockData = []\n # Set total for combined stoc value to 0.\n totalAllStocks = 0\n\n # Iterate over rows from portfolio and allocate a row for each stock that has more than 0 owned.\n for row in portfolio:\n if row[\"numOwned\"] != 0:\n stockData.append(row)\n\n # Iterate over rows in stock data and provide value for each column. Other values for use in html are already in list from previous loop.\n # Had to play around with usd, once in usd is a str rather than float so usd always has to be post calculations.\n for row in stockData:\n stock = lookup(row[\"symbol\"])\n row[\"name\"] = stock[\"name\"]\n row[\"currentPrice\"] = usd(stock[\"price\"])\n row[\"total\"] = usd(row[\"numOwned\"] * stock[\"price\"])\n totalAllStocks += row[\"numOwned\"] * stock[\"price\"]\n # Grand Total is combined stock values and cash value.\n grandTotal = totalAllStocks + cash\n # Return index.html input sources.\n return render_template(\"index.html\", stockData=stockData, cash=usd(cash), totalAllStocks = usd(totalAllStocks), grandTotal=usd(grandTotal))","def portfolio(user_id: int, db: Connection) -> Dict[str, Union[str, int, float]]:\n\n # accumulator\n user_portfolio = User_state_info([], 0, 0)\n\n # get user cash from `users` table\n if (cash := get_cash(user_id, db)) is None:\n return [-1, f\"`get_cash: {DB_ERROR} or {USER_NOT_FOUND}\"]\n\n # fill result structure with cash\n user_portfolio.cash = round(cash,2)\n\n # get user balance from `shares` table\n balance = get_shares_info(user_id, db)\n\n # fill result structure with `symbol` and `qty`\n user_portfolio.shares = fill_qty(balance)\n\n # fill result structure with 'company_name\" and `price`\n user_portfolio.shares = get_company_and_price(user_portfolio.shares)\n\n # calc and fill `total` field\n user_portfolio.total = sum([x.total for x in user_portfolio.shares]) + user_portfolio.cash\n \n return user_portfolio.__dict__","def track_portfolio(self, p):\n\n global st_refresh_thread\n\n if self.terminate:\n return\n\n p.refresh()\n\n self.lock.acquire()\n self.active_portfolio = p\n self.display_portfolio(p)\n self.lock.release()\n\n if not self.refresh_thread:\n thr_args = list()\n thr_args.append(self)\n self.refresh_thread = threading.Thread(target=st_refresh_thread,\n args=thr_args)\n self.refresh_thread.start()","def get_porfolio_signal(self):\n\n return self._portfolio_signal","async def list(self, ctx, user=None, date=None):\n if not user:\n user = ctx.message.author\n else:\n user = util.GetUserFromNameStr(ctx.message.server.members, user)\n change = GetPortfolioChange(user.id)\n portfolio = GetPortfolio(user.id, util.GetTimestamp(date))\n await self.bot.say(\n '```%s\\'s portfolio:\\n'\n 'Total Value: $%s (%.2f%s) \\n'\n '%s```' % (user, portfolio.Value(), change, \"%\", portfolio.AsTable()))","def index():\n # Use a place holder ':curr_id' to call the session id which is the user's id\n rows = db.execute(\"SELECT stocks.symbol, stocks.name, portfolio.shares FROM portfolio JOIN users ON users.id = portfolio.user_id JOIN stocks ON portfolio.stock_id = stocks.id WHERE users.id==:curr_id\", curr_id=session[\"user_id\"])\n # Make a select query only on cash to be able to display it in portfolio's table\n row_cash = db.execute(\"SELECT cash FROM users WHERE id==:curr_id\", curr_id=session[\"user_id\"])\n\n # gets the current price of each stock queried\n if rows:\n for r in rows:\n r_shares = r[\"shares\"]\n r_symbol = r[\"symbol\"]\n # run lookup function to get current price\n dict_2 = lookup(r_symbol)\n # Adds the key \"price\" and its value to the dictionary \"rows\"\n r[\"price\"] = dict_2[\"price\"]\n # Calculates the grand total (stocks’ total value plus cash)\n total = sum([r[\"price\"]*r[\"shares\"] for r in rows]) + row_cash[0][\"cash\"]\n return render_template(\"portfolio.html\", rows=rows, row_cash=row_cash, total=total)","def index():\n\n rows = db.execute(\"SELECT * FROM portfolio WHERE id = :id\", id=session[\"user_id\"])\n users = db.execute(\"SELECT * FROM users WHERE id = :id\", id=session[\"user_id\"])\n cash = users[0][\"cash\"]\n total = 0\n\n for row in rows:\n symbol = row[\"symbol\"]\n shares = row[\"shares\"]\n stock = lookup(symbol)\n price_t = float(stock[\"price\"]) * shares\n db.execute(\"UPDATE portfolio SET price=:price WHERE id=:id AND symbol=:symbol\",\n price=float(stock[\"price\"]), id=session[\"user_id\"], symbol=row[\"symbol\"])\n total += price_t\n\n TOTAL = total + cash\n return render_template(\"index.html\", rows=rows, cash=usd(cash), TOTAL=usd(TOTAL))","def evaluate_portfolio(username):\n user_obj = User.query.filter(User.username == username).first()\n date = request.args.get('date')\n\n if user_obj is None:\n return util.build_json_response('User does not exist')\n\n if not util.is_valid_date_string(date):\n return util.build_json_response(\"Not a valid date of the form YYYY-MM-DD\")\n\n following_date = util.add_days_to_date(date, 1)\n equities = db.session.query(Portfolio.ticker, func.sum(Portfolio.quantity))\\\n .filter(Portfolio.user_id == user_obj.id) \\\n .filter(Portfolio.transaction_date <= following_date) \\\n .group_by(Portfolio.ticker).all()\n\n e_total = 0\n for equity in equities:\n price = equity[1] * market_data.get_stock_price(equity[0], date, 'low')\n e_total += price\n\n total = round(e_total + user_obj.balance, 2)\n cash = round(user_obj.balance, 2)\n e_total = round(e_total, 2)\n\n return util.build_json_response(\"Portfolio totals retrieved\", equity_total=e_total, cash_balance=cash, account_total=total)","def optimize_portfolio(sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,1,1), \\\n syms=['GOOG','AAPL','GLD','XOM'], gen_plot=False):\n\n # Read in adjusted closing prices for given symbols, date range\n dates = pd.date_range(sd, ed)\n prices_all = get_data(syms, dates) # automatically adds SPY\n prices = prices_all[syms] # only portfolio symbols\n prices_SPY = prices_all['SPY'] # only SPY, for comparison later\n\n\t# find the allocations for the optimal portfolio\n #1 provide an initial guess for x\n allocs = np.ones(len(syms))/len(syms)\n #2 Provide constraints to the optimizer\n bounds = [(0,1) for i in syms]\n constraints = ({ 'type': 'eq', 'fun': lambda inputs: 1.0 - np.sum(inputs) })\n #3 call the optimizer\n res = spo.minimize(get_sharpe_ratio, allocs, \n \t\t\t\t\targs=prices, \n \t\t\t\t\tbounds = bounds,\n \t\t\t\t\tconstraints=constraints)\n allocs = res.x\n \n # Get daily portfolio value\n port_val = get_portfolio_value(prices, allocs, 1.0)\n \n # Get portfolio statistics\n cr, adr, sddr, sr = get_portfolio_stats(port_val, \n \t\t\t\t\t\t\t\t\t\tdaily_rf=0.0, \n \t\t\t\t\t\t\t\t\t\tsamples_per_year=252)\n \n # Compare daily portfolio value with SPY using a normalized plot\n if gen_plot:\n # add code to plot here\n df_temp = pd.concat([port_val, prices_SPY], keys=['Portfolio', 'SPY'], axis=1)\n plot_normalized_data(df_temp)\n\n return allocs, cr, adr, sddr, sr","def __update_portfolio_handler(self, msg):\n pass","def income_model_asset_based_portfolio_custom(num_of_years=30, trials=100, method='normal', income=True):\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n clean_names = list(read_returns_est.index)\n clean_names = [s.split(' ')[0] for s in clean_names]\n read_returns_est.loc[:, 'names'] = clean_names\n read_returns_est.set_index('names', drop=True, inplace=True)\n read_returns_est = read_returns_est[:-1]\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\n\n # ---------------Returns DataFrame based on the use input------------------------------------\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\n read_normal.loc[:, 'FIA'] = user_est_fia_return\n\n read_returns_est.loc['FIA', 'Annualized Returns'] = user_est_fia_return\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n # read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n # read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\n # read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n #\n # # dataframe for smallest to largest returns\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\n #\n # # dataframe for unsorted returns (normal)\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n while runs < trials:\n print(runs)\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # -------------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n if income:\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n else:\n req_annual_income = 0.0\n income_needed = 0.0\n income_net_fia_income = 0.0\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n # for c in range(len(r_cols)):\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n # this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, base_assets]\n\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\n\n # store the simulated assets returns in one dictionary\n # returns_dict.update({str(runs): random_returns})\n\n # collect the asset based returns from all simulation and calculate the median returns.\n # def get_median_returns(sym):\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\n # return asset_df.median(axis=1)\n #\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\n #\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\n\n base_df = random_returns.copy()\n\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\n\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # ----------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n if income:\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n else:\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] + \\\n income_from_fia\n\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\n\n runs += 1\n\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\n total_income_by_age.fillna(0, inplace=True)\n income_dataframe = pd.DataFrame(total_income_by_age)\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\n lambda x: total_income_by_acturial_age - x)\n\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\n\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\n\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\n\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ---------------Portfolio for 45 years of simulation---------------------------------------\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ----------------Portfolio Simulation until the acturial age------------------------------\n acturial_years = life_expectancy - clients_age\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\n\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\n if base_success_portfolio_act_age.isnull().sum() == trials:\n base_max_portfolio = 0.0\n else:\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\n\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\n if port_success_portfolio_act_age.isnull().sum() == trials:\n port_max_portfolio = 0.0\n else:\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\n # ------------------------------Average age with full income------------------------------\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n # ----------------------------Median Age with full Income------------------------------------------\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n\n # # --------------Mean Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n #\n # # --------------Median Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n\n # -------Max Portfolio value at the end of acturial age----------------------------------------\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\n\n # -------Min Portfolio value at the end of acturial age----------------------------------------\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\n\n # ---------------------Lifetime Average Income----------------------------------\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\n port_total_income = income_from_fia + sim_port_income\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\n\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\n 'Max Portfolio Value', 'Min Portfolio Value',\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\n\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\n 'Median years of portfolios that meet the next years income needs for the lifetime',\n 'Average Clients Age',\n 'Median Clients Age',\n 'Average of terminal values for the portfolios at the end of the acturial life',\n 'Median of terminal values for the portfolios at the end of the acturial life',\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\n 'Average of total income generated by all portfolios at the end of the acturial life']\n\n simulation_stats.loc[:, 'Notes'] = comments\n\n # --------------------------------------------------------------------------------\n\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\n # base_df.to_csv(src + 'base_port_detail.csv')\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\n # income_breakdown_base.loc[:, 'social_security_income'] = social\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n #\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n # axis=1)\n #\n # # --------------------------------------Block Ends-----------------------------------------------------------\n #\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n #\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n # income_breakdown_port.loc[:, 'social_security_income'] = social\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n #\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n # axis=1)\n #\n # # ----------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ------------------------------------------drop year 0-----------------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n sim_base_total.clip(lower=0, inplace=True)\n\n # -------------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n # base_qcut.loc[:, 'clients_age'] = age_index\n # base_qcut.loc[:, 'comment'] = ''\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n # --------To start with year 0---------------------------------\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\n base_inv, clients_age, np.nan]\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\n base_qcut = base_qcut.shift(1)\n base_qcut.iloc[0] = insert_col\n base_qcut.reset_index(drop=True, inplace=True)\n base_qcut.loc[:, 'Annual Return'] = base_qcut.loc[:, '50th'].pct_change().fillna(0)\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\n\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\n # port_qcut.loc[:, 'clients_age'] = age_index\n # port_qcut.loc[:, 'comment'] = ''\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\n port_qcut = port_qcut.shift(1)\n port_qcut.iloc[0] = insert_col\n port_qcut.reset_index(drop=True, inplace=True)\n port_qcut.loc[:, 'Annual Return'] = port_qcut.loc[:, '50th'].pct_change().fillna(0)\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'clients_age'] = age_index\n # prob_success_df.loc[:, 'comment'] = ''\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \"\")\n\n prob_success_df.loc[:, 'age'] = age_index\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\n\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\n\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\n\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\n\n prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\n - social - cpn_income_port\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\n - income_from_fia - social - cpn_income_port\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # -------------------Write simulation Statistics-------------------------------------\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\n\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\n\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed....\")","def portfolio_to_dict(self):\n holdings = {}\n for asset, pos in self.pos_handler.positions.items():\n holdings[asset] = {\n \"quantity\": pos.net_quantity,\n \"market_value\": pos.market_value,\n \"unrealised_pnl\": pos.unrealised_pnl,\n \"realised_pnl\": pos.realised_pnl,\n \"total_pnl\": pos.total_pnl\n }\n return holdings","async def value(self, ctx, user=None):\n if not user:\n user = ctx.message.author\n else:\n user = util.GetUserFromNameStr(ctx.message.server.members, user)\n portfolio = GetPortfolio(user.id)\n await self.bot.say('%s\\'s portfolio is now worth $%.2f.' % \n (user, portfolio.Value()))","def get_portfolio_prices(stocks: list, funds: list, etfs: list, start_date: str, end_date=today) -> pd.DataFrame:\r\n data_frames_stocks = get_assets_data_frames(\r\n stocks, inv.get_stock_historical_data, 'brazil', start_date=start_date, end_date=end_date)\r\n data_frames_funds = get_assets_data_frames(\r\n funds, inv.get_fund_historical_data, 'brazil', start_date=start_date, end_date=end_date)\r\n data_frames_etfs = get_assets_data_frames(\r\n etfs, inv.get_etf_historical_data, 'brazil', start_date=start_date, end_date=end_date)\r\n\r\n data_frames = [*data_frames_stocks, *data_frames_funds, *data_frames_etfs]\r\n\r\n assets = [*stocks, *funds, *etfs]\r\n\r\n portfolio_prices = build_multi_index_data_frame(\r\n data_frames, assets, ['Close', 'Open', 'High', 'Low'])\r\n\r\n return portfolio_prices","def __populate_historical_trade_data(self):\n\n trade_data = self.__transactions.pivot_table(\n index=\"Date\",\n columns=[\"Ticker\"],\n values=[\n \"Quantity\",\n \"Investment\",\n ],\n aggfunc={\"Quantity\": np.sum, \"Investment\": np.sum},\n )\n\n # Make historical prices columns a multi-index. This helps the merging.\n self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(\n [[\"Close\"], self.portfolio_historical_prices.columns]\n )\n\n trade_data = pd.merge(\n trade_data,\n self.portfolio_historical_prices,\n how=\"outer\",\n left_index=True,\n right_index=True,\n )\n\n trade_data[\"Close\"] = trade_data[\"Close\"].fillna(method=\"ffill\")\n trade_data.fillna(0, inplace=True)\n\n trade_data[\"Quantity\"] = trade_data[\"Quantity\"].cumsum()\n trade_data[\"Investment\"] = trade_data[\"Investment\"].cumsum()\n trade_data[\"Investment\", \"Total\"] = trade_data[\"Investment\"].sum(axis=1)\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Investment delta\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"Investment\"].diff(periods=1).fillna(trade_data[\"Investment\"]))\n\n # End Value = Quantity * Close\n trade_data[pd.MultiIndex.from_product([[\"End Value\"], self.tickers_list])] = (\n trade_data[\"Quantity\"][self.tickers_list]\n * trade_data[\"Close\"][self.tickers_list]\n )\n\n trade_data.loc[:, (\"End Value\", \"Total\")] = trade_data[\"End Value\"][\n self.tickers_list\n ].sum(axis=1)\n\n # Initial Value = Previous End Value + Investment changes\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Initial Value\"], self.tickers_list + [\"Total\"]]\n )\n ] = 0\n\n trade_data[\"Initial Value\"] = trade_data[\"End Value\"].shift(1) + trade_data[\n \"Investment\"\n ].diff(periods=1)\n\n # Set first day Initial Value as the Investment (NaNs break first period)\n for t in self.tickers_list + [\"Total\"]:\n trade_data.at[trade_data.index[0], (\"Initial Value\", t)] = trade_data.iloc[\n 0\n ][\"Investment\"][t]\n\n trade_data = trade_data.reindex(\n columns=[\n \"Quantity\",\n \"Investment\",\n \"Investment delta\",\n \"Close\",\n \"Initial Value\",\n \"End Value\",\n ],\n level=0,\n )\n self.historical_trade_data = trade_data","def read_portfolio_history(self, price_provider: Callable[[Security, dt.datetime], float]) -> Dict[dt.datetime, float]:\n if self._transactions_dataframe.empty:\n return dict()\n\n dated_transactions = self._transactions_dataframe.set_index(TRANSACTION_DATE).sort_index()\n all_share_ids = dated_transactions[SECURITY_ID].unique()\n\n date_index = dated_transactions.index.unique()\n share_history = pd.DataFrame(0, index=date_index, columns=all_share_ids)\n for share_id in all_share_ids:\n relevant_rows = dated_transactions[SECURITY_ID] == share_id\n transactions = dated_transactions.loc[relevant_rows, TRANSACTION_SHARE_AMOUNT]\n share_history.loc[dated_transactions.index[relevant_rows], share_id] = transactions\n share_history[share_id] = share_history[share_id].cumsum()\n\n share_prices = pd.DataFrame(0, index=date_index, columns=all_share_ids)\n for share_id in all_share_ids:\n date_to_price = lambda date: price_provider(Security(share_id), date)\n share_prices[share_id] = np.vectorize(date_to_price)(date_index.to_pydatetime())\n\n portfolio = (share_prices * share_history).apply(np.sum, axis=1)\n\n return {date.to_pydatetime(): price for date, price in portfolio.to_dict().items()}","def __repr__(self):\n return \"<Portfolio: %s | valued at %s>\" % (self.portfolios, self.value)","def income_model_asset_based_portfolio_quantile(num_of_years=30, trials=100, method='normal'):\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\n\n # read_income_inputs = pd.read_csv(src + \"income_model_inputs.csv\", index_col='Items')\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n # read_returns_est = pd.read_csv(src + \"income_assets_returns_estimates.csv\", index_col='Symbol')\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\n # read_portfolio_inputs = pd.read_csv(src + \"income_portfolio_inputs.csv\", index_col='Items')\n\n # read_asset_weights = pd.read_csv(src + \"asset_weights.csv\", index_col='Asset')\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\n\n # dataframe for smallest to largest returns\n median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\n\n # dataframe for unsorted returns (normal)\n median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n while runs < trials:\n print(runs)\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n\n if method == 'normal':\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n elif method == 'smallest':\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n else:\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # -------------------------------------BASE MODEL---------------------------------------------\n\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = ['r_{}'.format(name) for name in base_assets]\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n if method == 'smallest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_small.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\n\n elif method == 'largest':\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_large.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\n\n else:\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\n random_returns = read_normal.loc[:, this_run_cols]\n\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\n\n # store the simulated assets returns in one dictionary\n # returns_dict.update({str(runs): random_returns})\n\n # collect the asset based returns from all simulation and calculate the median returns.\n # def get_median_returns(sym):\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\n # return asset_df.median(axis=1)\n #\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\n #\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\n\n base_df = random_returns.copy()\n pre_income_base_df = random_returns.copy()\n\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\n\n fia_portfolio_df = random_returns.copy()\n pre_income_port_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # ---------Initial Investments for pre-income account values---------------------\n pre_income_base_inv = base_investment\n pre_income_port_inv = port_investment\n # ----------------------------------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n pre_income_base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n # ---------------For year 0, the year of investment------------\n\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_base_inv for c in range(len(boy_value))]\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_pre_income'] = base_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n # ----For years between the start of the investment and start if the income---------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\n\n else:\n\n # -------------For Years after the income started----------------------\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_base_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\n pre_income_base_df.loc[counter, 'adv_fees']\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n # ------------------------Portfolio with PreIncome Values----------------------------\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\n # --------------------------------PreIncome Block Ends----------------------------\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n pre_income_port_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\n pre_income_port_inv for c in range(len(boy_value))]\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # ------------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n # -------------Record the Pre Income Base Portfolio-----------------------------\n pre_income_port_df.loc[counter, boy_value] = [\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\n for c in range(len(boy_value))]\n\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\n pre_income_port_df.loc[counter, 'adv_fees']\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n # ------------------Pre Income Block Ends------------------------\n\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\n\n # -------------------Portfolio with PreIncome Values----------------------------\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\n # --------------------------------PreIncome Block Ends----------------------------\n\n runs += 1\n\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\n total_income_by_age.fillna(0, inplace=True)\n income_dataframe = pd.DataFrame(total_income_by_age)\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\n lambda x: total_income_by_acturial_age - x)\n\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\n\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\n\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\n\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ---------------Portfolio for 45 years of simulation---------------------------------------\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\n\n # ----------------Portfolio Simulation until the acturial age------------------------------\n acturial_years = life_expectancy - clients_age\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\n\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\n if base_success_portfolio_act_age.isnull().sum() == trials:\n base_max_portfolio = 0.0\n else:\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\n\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\n if port_success_portfolio_act_age.isnull().sum() == trials:\n port_max_portfolio = 0.0\n else:\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\n\n # ------------------------------Average age with full income------------------------------\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).mean()\n\n # ----------------------------Median Age with full Income------------------------------------------\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\n .isnull().sum()).median()\n\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\n\n # ---------------Median Value for all the portfolios at end of the acturial age--------------------\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\n\n # # --------------Mean Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\n #\n # # --------------Median Value for all the portfolios in the simulation--------------------\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\n\n # -------Max Portfolio value at the end of acturial age----------------------------------------\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\n\n # ----------------------Min Portfolio value at the end of acturial age----------------------------------------\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\n\n # -----------------------------------------Lifetime Average Income-----------------------------------------\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\n port_total_income = income_from_fia + sim_port_income\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\n\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\n 'Max Portfolio Value', 'Min Portfolio Value',\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\n\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\n 'Median years of portfolios that meet the next years income needs for the lifetime',\n 'Average Clients Age',\n 'Median Clients Age',\n 'Average of terminal values for the portfolios at the end of the acturial life',\n 'Median of terminal values for the portfolios at the end of the acturial life',\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\n 'Average of total income generated by all portfolios at the end of the acturial life']\n\n simulation_stats.loc[:, 'Notes'] = comments\n\n # --------------------------------------------------------------------------------\n\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\n # base_df.to_csv(src + 'base_port_detail.csv')\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\n # income_breakdown_base.loc[:, 'social_security_income'] = social\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n #\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n # axis=1)\n #\n # # --------------------------------------Block Ends-----------------------------------------------------------\n #\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n #\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n # income_breakdown_port.loc[:, 'social_security_income'] = social\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n #\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n # axis=1)\n #\n # # ----------------------------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n #\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\n\n # ------------------------------------------drop year 0-----------------------------------------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ---------------------------------plot for histogram for porfolios--------------------------------------\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\n # fact = 1 / len(base_term_value)\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\n # counts, bins, bars = plt.hist(base_ann_ret)\n\n # ------------------------quantile analysis for base terminal value-----------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n sim_base_total.clip(lower=0, inplace=True)\n\n # -------------------------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\n\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ----------------------------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\n\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\n\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\n\n # ------------------------------------WRITING FILES TO EXCEL ---------------------------\n\n writer = pd.ExcelWriter(dest_simulation + method + '_montecarlo_income_summary.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n # base_qcut.loc[:, 'clients_age'] = age_index\n # base_qcut.loc[:, 'comment'] = ''\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n\n # -----------------------To start with year 0---------------------------------\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\n base_inv, clients_age, np.nan]\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\n base_qcut = base_qcut.shift(1)\n base_qcut.iloc[0] = insert_col\n base_qcut.reset_index(drop=True, inplace=True)\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\n\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\n # port_qcut.loc[:, 'clients_age'] = age_index\n # port_qcut.loc[:, 'comment'] = ''\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\n port_qcut = port_qcut.shift(1)\n port_qcut.iloc[0] = insert_col\n port_qcut.reset_index(drop=True, inplace=True)\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \"\")\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\n\n # prob_success_df.loc[:, 'clients_age'] = age_index\n # prob_success_df.loc[:, 'comment'] = ''\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \"\")\n\n prob_success_df.loc[:, 'age'] = age_index\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\n\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\n\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\n\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\n\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\n\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\n\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\n\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\n\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\n # -------------Adding Premium to calculate the total initial investment--------------\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\n\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\n #\n # # -------------For Simulation slide - Proposed Portfolio --------------------\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\n # for c in range(len(cols)):\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\n #\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\n\n prob_success_df.to_excel(writer, sheet_name='success_probability')\n\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\n - social - cpn_income_port\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\n - income_from_fia - social - cpn_income_port\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # -------------------Write simulation Statistics-------------------------------------\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\n\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\n\n if method == 'normal':\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n elif method == 'smallest':\n median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\n\n else:\n median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\n\n # ---------------------Histogram for S&P Forecast---------------------------------------\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\n bins, data = np.histogram(sp_random_ret, bins=20)\n df_ret = pd.DataFrame(data, columns=['Return_range'])\n df_bins = pd.DataFrame(bins, columns=['Count'])\n df_hist = df_ret.join(df_bins)\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\n\n # ---------------------Histogram for FIA Portfolios TV>0 at the acturial age---------------------------------------\n tval_at_horizon = sim_port_total.loc[acturial_years, :]\n fact = 1 / acturial_years\n arr_returns = np.array((tval_at_horizon / 1000000) ** fact - 1)\n clean_ann_ret = arr_returns[~np.isnan(arr_returns)]\n p_bins, p_data = np.histogram(clean_ann_ret, bins=20)\n df_ret = pd.DataFrame(p_data, columns=['Return_range'])\n df_bins = pd.DataFrame(p_bins, columns=['Count'])\n df_hist = df_ret.join(df_bins)\n df_hist.to_excel(writer, sheet_name='fia_portfolio_histogram')\n\n tval_df = pd.DataFrame(sim_port_total.loc[acturial_years, :])\n tval_df.rename(columns={tval_df.columns[0]:'Terminal Values'}, inplace=True)\n tval_df.to_excel(writer, sheet_name='fia_ending_values_hist')\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed....\")","def __init__(\n self,\n start_dt,\n starting_cash=0.0,\n currency = \"USD\",\n portfolio_id=None,\n name=None\n ):\n self.start_dt = start_dt\n self.current_dt = start_dt\n self.starting_cash = starting_cash\n self.currency = currency\n self.portfolio_id = portfolio_id\n self.name = name\n\n self.pos_handler = PositionHandler()\n self.history = []\n\n self.logger = logging.getLogger('Portfolio')\n self.logger.setLevel(logging.DEBUG)\n self.logger.info(\n '(%s) Portfolio \"%s\" instance initalized' % (\n self.current_dt.strftime(settings.LOGGING[\"DATE_FORMAT\"]),\n self.portfolio_id\n )\n )\n\n self._initalize_portfolio_with_cash()","def generate_portfolio(S_0, params):\n\tpublic_client = gdax.PublicClient()\n\tallvar = []\n\tsumvar = 0\n\tfor coin in params:\n\t\ttheta = coin[0]\n\t\tv = theta[0]\n\t\tT = coin[1]\n\t\tprod_id = coin[2]\n\t\t# Get the current value of the coin, i.e. how much you bought\n\t\tname = prod_id + '-USD'\n\t\tstats = public_client.get_product_24hr_stats(name)\n\t\tvalue = (float(stats['high']) + float(stats['low']))/2\n\t\tallvar.append([prod_id, value, v])\n\t\tsumvar += v\n\tpriority = sorted(allvar, key=lambda i: i[2])\n\tportfolio = []\n\tfor i in priority:\n\t\tinvestment = S_0*i[2]/sumvar\n\t\tcurrency = investment/i[1]\n\t\tportfolio.append((i[0], currency, investment)) # id, investment, currency\n\tprint(\"\\nYour suggested investments are: \\n\")\n\tfor coin in portfolio:\n\t\tprint(\"%s: %s for %s USD\" % (coin[0], coin[1], coin[2]))\n\t# Prompt to save the portfolio\n\tdone = False\n\twhile done != True:\n\t\tinp = input(\"\\nWould you like to save this portfolio? (y/n)\t\")\n\t\ttry:\n\t\t\tif inp.lower() == 'y':\n\t\t\t\tpublic_client = gdax.PublicClient()\n\t\t\t\tcurrent_date = np.datetime64(public_client.get_time().get(\"iso\").split('T')[0])\n\t\t\t\t# Save the file\n\t\t\t\twith open(\"portfolios/%s.txt\" % (current_date), \"w\") as f:\n\t\t\t\t\tfor coin in portfolio:\n\t\t\t\t\t\tf.write(str(coin[0]) + ', ' + str(coin[1]) + ', ' + str(coin[2]) + '\\n')\n\t\t\t\tprint(\"Portfolio saved. Exiting.\\n\")\n\t\t\t\tdone = True\n\t\t\tif inp.lower() == 'n':\n\t\t\t\tprint(\"Program complete. Exiting.\\n\")\n\t\t\t\tdone = True\n\t\texcept ValueError:\n\t\t\tprint(\"Your input could not be interpreted.\")","def portfolio_returns(returns, weights):\n\n # the portfolio returns are given by the dot product of the weights matrix and the portfolio matrix\n port_returns = np.dot(weights, returns)\n\n return port_returns","def portfolio_returns(returns, weights):\n\n # the portfolio returns are given by the dot product of the weights matrix and the portfolio matrix\n port_returns = np.dot(weights, returns)\n\n return port_returns","def __get_object_portfolio_bulk(self, name, user, key=\"portfolio\"):\n portfolio = None\n for item in self.bulk_data[key]:\n if item and item.name == name and item.user == user:\n portfolio = item\n break\n return portfolio","def evaluate_cur_stocks(self):\n today = datetime.today()\n close_val = PRICE_DF.iloc[PRICE_DF.index.get_loc(today, method=\"ffill\")]\n close_val = close_val[self.cur_stocks.index]\n close_val = pd.DataFrame({\"PRICE_CURRENT\" : close_val.values}, index=self.cur_stocks.index)\n evaluated_stocks = pd.merge(self.cur_stocks, close_val, left_index=True, right_index=True)\n evaluated_stocks[\"VOLUME_CURRENT\"] = evaluated_stocks[\"AMOUNT\"] * evaluated_stocks[\"PRICE_CURRENT\"]\n evaluated_stocks[\"RETURN\"] = (evaluated_stocks[\"VOLUME_CURRENT\"] / evaluated_stocks[\"VOLUME_PURCHASE\"]) - 1\n return evaluated_stocks","def cumulative_returns(shares_allocation, capital, test_data):\n\n # list of DataFrames of cumulative returns for each stock\n daily_returns = []\n\n # iterates over every stock in the portfolio\n for stock in shares_allocation.index:\n\n # multiples shares by share prices in the validation dataset\n daily_returns.append(shares_allocation.loc[stock].values * test_data[stock])\n\n # concatenates every DataFrame in the above list to a single DataFrame\n daily_returns_df = pd.concat(daily_returns, axis=1).reset_index()\n\n # sets the index as the date\n daily_returns_df.set_index(\"Day\", inplace=True)\n\n # adds the cumulative returns for every stock\n cumulative_daily_returns = daily_returns_df.sum(axis=1)\n\n # returns the cumulative daily returns of the portfolio\n return cumulative_daily_returns","def index():\n\n # Get user's cash\n user = db.execute(\"SELECT cash FROM users WHERE id = ?\", session[\"user_id\"])\n\n # Get portfolio\n portfolios = db.execute(\"SELECT * FROM portfolios WHERE user_id = ?\", session[\"user_id\"])\n\n # Get symbol for each stock\n length = len(portfolios)\n for i in range(length):\n symbol = portfolios[i]['stocks']\n\n # Lookup stock price and add to portfolio information\n portfolios[i]['price'] = lookup(symbol)['price']\n portfolios[i]['total'] = float(portfolios[i]['price']) * portfolios[i]['shares']\n\n # Calculate total value of stocks\n value = 0\n for j in range(length):\n value += portfolios[j]['price']\n\n # Calculate grand total of stocks plus cash\n g_total = user[0][\"cash\"] + value\n\n return render_template(\"index.html\", portfolios=portfolios, cash=user[0][\"cash\"], g_total=g_total)","def __init__(self, instrumentsNAmounts, totalInvest):\n # add all necessary attributes here which link to portfolio and\n # will be tracked.\n self.portfolio = instrumentsNAmounts\n self.totalInvest = totalInvest","def compute_portfolio_stats(allocs,prices,rfr=0, sf=252):\n\n # portfolio value\n port_val = compute_port_val(allocs, prices)\n\n daily_rets = port_val/port_val.shift(1) - 1\n daily_rets = daily_rets[1:]\n\n # cumulative return\n cr = port_val.iloc[-1]/port_val.iloc[0] -1\n\n # avg daily return\n adr = daily_rets.mean()\n\n # std dev of daily return\n sddr = daily_rets.std()\n \n #sharpe_ratio\n k = math.sqrt(252)\n \n sr = k * ((daily_rets - 0).mean() / daily_rets.std())\n \n return cr, adr, sddr, sr","def prepare_portfolio_performance(self, portfolio_scope: str, portfolio_code: str):\n return Performance(\n entity_scope=portfolio_scope,\n entity_code=portfolio_code,\n src=self.portfolio_performance_source,\n block_store=self.block_store,\n perf_start=None,\n )","def set_portfolio(self, name, user, cash):\n if name and not self.exist_portfolio(name, user) \\\n and self.db_tool.is_connected():\n portfolio = Portfolio(\n name=name,\n user=user,\n cash=cash,\n initial_cash=cash\n )\n self.bulk_data[\"portfolio\"].append(portfolio)\n self.portfolio_name = name\n self.portfolio_user = user","def get_portfolio_pnl_desc(self):\n\n return self._tsd_portfolio.summary()","def performance_vs_index(self, index='SPY', dateIni='Ini', dateFin='Fin'):\n if dateFin == 'Fin':\n dateFin = self.data.index[-1]\n if dateIni == 'Ini':\n dateIni = self.data.index[0]\n portfolioGains = round(self.data.loc[self.data.index[-1], 'Profit/Loss%'], 2)\n else:\n pData = self.data.loc[dateIni:dateFin]\n pData.loc[:,'Profit/Loss'] = pData['Gains'].cumsum()\n pData.loc[:,'Profit/Loss%'] = pData['Profit/Loss'] / pData['Invested'] * 100\n portfolioGains = round(pData.loc[pData.index[-1], 'Profit/Loss%'], 2)\n indexData = yf.Ticker(index).history(start=dateIni, end=dateFin)\n indexData['Var%'] = (indexData.Close - indexData.Close[0]) / indexData.Close[0] * 100\n indexGains = round(indexData.loc[indexData.index[-1], 'Var%'], 2)\n return portfolioGains, indexGains, portfolioGains - indexGains","def index():\n stocks = []\n username = session.get(\"username\")\n symbol_list = db.execute(\"SELECT stock_symbol FROM history WHERE username=:username GROUP BY stock_symbol\", username=username)\n cash_balance = db.execute(\"SELECT cash FROM users WHERE username=:username\", username=username)[0][\"cash\"]\n total_value = cash_balance\n\n for sym in symbol_list:\n symbol = sym[\"stock_symbol\"]\n new_stock = Stock(username, symbol)\n stocks.append(new_stock)\n total_value += new_stock.quantity * new_stock.price\n\n\n return render_template(\"portfolio.html\", stocks = stocks, cash_balance=usd(cash_balance), total_value=usd(total_value))","def _computeIsosurface(self):\n data = self.getData(copy=False)\n\n if data is None:\n if self.isAutoLevel():\n self._level = float('nan')\n\n else:\n if self.isAutoLevel():\n st = time.time()\n try:\n level = float(self.getAutoLevelFunction()(data))\n\n except Exception:\n module_ = self.getAutoLevelFunction().__module__\n name = self.getAutoLevelFunction().__name__\n _logger.error(\n \"Error while executing iso level function %s.%s\",\n module_,\n name,\n exc_info=True)\n level = float('nan')\n\n else:\n _logger.info(\n 'Computed iso-level in %f s.', time.time() - st)\n\n if level != self._level:\n self._level = level\n self._updated(Item3DChangedType.ISO_LEVEL)\n\n if numpy.isfinite(self._level):\n st = time.time()\n vertices, normals, indices = MarchingCubes(\n data,\n isolevel=self._level)\n _logger.info('Computed iso-surface in %f s.', time.time() - st)\n\n if len(vertices) != 0:\n return vertices, normals, indices\n\n return None, None, None","def compute_port_val(allocs, prices):\n # normalized price\n # normalized prices\n normed = prices/prices.iloc[0]\n prices.head()\n alloced = normed * allocs\n\n # position values\n start_val = 1 # included to simplify adding ability to calc as $\n pos_vals = alloced * start_val\n\n # portfolio value\n port_val = pos_vals.sum(axis=1)\n\n return port_val","def market_value(self, ref_prices, suspensions=None):\n # TODO some securities could not be able to be traded\n if suspensions is None:\n suspensions = []\n \n market_value_float = 0.0\n market_value_frozen = 0.0 # suspended or high/low limit\n for sec in self.holding_securities:\n size = self.get_position(sec).current_size\n # TODO PortfolioManager object should not access price\n price = ref_prices[sec]\n mv_sec = price * size\n if sec in suspensions:\n market_value_frozen += mv_sec\n else:\n market_value_float += mv_sec\n \n return market_value_float, market_value_frozen","def swap_active_portfolio(self):\n\n self.lock.acquire()\n\n self.clear_main()\n\n w = self.windows['MAIN']\n l = 1\n\n for p in self.portfolios:\n # Only support 9 portfolios since that makes this easier to deal\n # with.\n if l >= 10:\n break\n\n w.addstr(l, 0, '%2d' % l, curses.A_BOLD | curses.color_pair(1))\n w.addstr(l, 3, p.name)\n l += 1\n\n self.refresh()\n\n # Wait for the user to give is a key.\n while True:\n c = self.stdscr.getch()\n\n if c < ord('1') and c > ord('9'):\n continue\n\n index = c - ord('1')\n\n if index < len(self.portfolios):\n break\n\n self.portfolios[index].refresh()\n\n self.active_portfolio = self.portfolios[index]\n self.display_portfolio(self.active_portfolio)\n self.lock.release()","def new_portfolio_flow(portfolio_list):\n while True:\n portfolio_name = prompt.shortcuts.input_dialog(\n title=\"Portfolio Name\", text=\"Please type the portfolio name:\"\n ).run()\n if portfolio_name is not None:\n portfolio_id: int = len(portfolio_list)\n stock_list = []\n stock_list = add_stock_flow(stock_list)\n portfolio_list.append(Portfolio(portfolio_name, portfolio_id, stock_list))\n return portfolio_list\n if portfolio_name is None:\n return None","def momentum(portfolio_item, transaction_volume, cash_allocation):\n from yahooquery import Ticker\n from math import floor\n import talib\n from .TradeHistoryItem import log_trade\n from API.Help import is_increasing, initialize_alpaca\n\n alpaca = initialize_alpaca()\n\n yahoo_ticker = Ticker(str(portfolio_item))\n info = yahoo_ticker.history()\n ma_5 = talib.SMA(info['close'], timeperiod=5)\n ma_20 = talib.SMA(info['close'], timeperiod=20)\n volume = info['volume']\n\n if portfolio_item.shares == 0:\n # if the price goes from below the sma to above, buy\n if ma_5[-1] > (ma_20[-1] * 1.1) and is_increasing(volume, 3):\n print('buying {} shares of {}'.format(transaction_volume, str(portfolio_item)))\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'buy', 'market', 'day')\n portfolio_item.buy(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=0)\n # if the price goes from above the sma to below, short\n elif ma_5[-1] < (ma_20[-1] * .9) and not is_increasing(volume, 3) and portfolio_item.shares == 0:\n transaction_volume = floor(cash_allocation / (portfolio_item.ticker.price_now * 1.1))\n print('shorting {} shares of {}'.format(transaction_volume, str(portfolio_item)))\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'sell', 'market', 'day')\n portfolio_item.short(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=3)","def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:\n assert portfolio is not None\n assert stock_market_data is not None\n assert stock_market_data.get_companies() == [Company.A, Company.B]\n\n current_state = self.states_compution(stock_market_data, portfolio)\n current_portfolio_value = portfolio.get_value(stock_market_data)\n\n if self.train_while_trading is False:\n # for test set use trained ann\n action = np.argmax(self.model.predict(current_state)[0])\n\n else:\n action = self.select_action(current_state, current_portfolio_value)\n if self.last_state is not None:\n reward = self.calc_reward(current_portfolio_value)\n self.append_memory(self.last_state, self.last_action_a, reward, current_state)\n # train model as soon as sufficient memory is reached\n if len(self.memory) > self.min_size_of_memory_before_training:\n self.train_model()\n\n # Split action into individual actions for Company A and B\n current_action_a = 0\n current_action_b = 0\n assert action < 9 and action >= 0\n if action == 0:\n current_action_a = OrderType.SELL\n current_action_b = OrderType.SELL\n amount_to_sell_a = portfolio.get_stock(Company.A)\n amount_to_sell_b = portfolio.get_stock(Company.B)\n elif action == 1:\n current_action_a = OrderType.SELL\n amount_to_sell_a = portfolio.get_stock(Company.A)\n current_action_b = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_b = int(portfolio.cash/stock_price)\n elif action == 2:\n current_action_a = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_a = int(portfolio.cash/stock_price)\n current_action_b = OrderType.SELL\n amount_to_sell_b = portfolio.get_stock(Company.B)\n elif action == 3:\n current_action_a = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_a = int((portfolio.cash/stock_price)/2)\n current_action_b = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.B)\n amount_to_buy_b = int((portfolio.cash/stock_price)/2)\n elif action == 4:\n current_action_a = OrderType.SELL\n amount_to_sell_a = portfolio.get_stock(Company.A)\n # current_action_b = \"hold\"\n elif action == 5:\n current_action_a = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.A)\n amount_to_buy_a = int(portfolio.cash/stock_price)\n # current_action_b = \"hold\"\n elif action == 6:\n # current_action_a = \"hold\"\n current_action_b = OrderType.SELL\n amount_to_sell_b = portfolio.get_stock(Company.B)\n elif action == 7:\n # current_action_a = \"hold\"\n current_action_b = OrderType.BUY\n stock_price = stock_market_data.get_most_recent_price(Company.B)\n amount_to_buy_b = int(portfolio.cash/stock_price)\n\n order_list = []\n\n if current_action_a != 0:\n if current_action_a == OrderType.SELL and amount_to_sell_a > 0:\n order_list.append(Order(current_action_a, Company.A, amount_to_sell_a))\n elif current_action_a == OrderType.BUY and portfolio.cash > 0:\n order_list.append(Order(current_action_a, Company.A, amount_to_buy_a))\n\n if current_action_b != 0:\n if current_action_b == OrderType.SELL and amount_to_sell_b > 0:\n order_list.append(Order(current_action_b, Company.B, amount_to_sell_b))\n elif current_action_b == OrderType.BUY and portfolio.cash > 0:\n order_list.append(Order(current_action_b, Company.B, amount_to_buy_b))\n\n self.last_action_a = action\n self.last_state = current_state\n self.last_portfolio_value = current_portfolio_value\n return order_list","def solve(self):\r\n # Make arrays of end-of-period assets and end-of-period marginal value\r\n \r\n if self.jac == True:\r\n \r\n if len(self.cList) == self.T_sim - self.s :\r\n self.Rfree = self.Rfree + self.dx\r\n else:\r\n self.Rfree = self.Rfree\r\n \r\n aNrm = self.prepare_to_calc_EndOfPrdvP()\r\n EndOfPrdvP = self.calc_EndOfPrdvP()\r\n\r\n # Construct a basic solution for this period\r\n if self.CubicBool:\r\n solution = self.make_basic_solution(\r\n EndOfPrdvP, aNrm, interpolator=self.make_cubic_cFunc\r\n )\r\n else:\r\n solution = self.make_basic_solution(\r\n EndOfPrdvP, aNrm, interpolator=self.make_linear_cFunc\r\n )\r\n\r\n solution = self.add_MPC_and_human_wealth(solution) # add a few things\r\n solution = self.add_stable_points(solution)\r\n \r\n \r\n \r\n self.cList.append(solution.cFunc)\r\n \r\n # Add the value function if requested, as well as the marginal marginal\r\n # value function if cubic splines were used (to prepare for next period)\r\n if self.vFuncBool:\r\n solution = self.add_vFunc(solution, EndOfPrdvP)\r\n if self.CubicBool:\r\n solution = self.add_vPPfunc(solution)\r\n return solution","def index():\n user_id = session[\"user_id\"]\n portfolio_table = port(user_id, db)\n \n if not isinstance(portfolio_table, dict): \n return apology(\"Error in portfolio\")\n \n return render_template(\"portfolio.html\",\n shares_list = portfolio_table[\"shares\"],\n cash = portfolio_table[\"cash\"],\n total = portfolio_table[\"total\"])","def calculate_all_portfolios(self, request, pk=None, **kwargs):\n goal = self.get_object()\n\n check_state(Goal.State(goal.state), Goal.State.ACTIVE)\n\n setting_str = request.query_params.get('setting', None)\n if not setting_str:\n logger.debug('setting parameter missing from calculate_all_portfolios query')\n raise ValidationError(\"Query parameter 'setting' must be specified and a valid JSON string\")\n try:\n setting = ujson.loads(setting_str)\n except ValueError:\n logger.debug('setting parameter for calculate_all_portfolios query not valid json')\n raise ValidationError(\"Query parameter 'setting' must be a valid json string\")\n\n # Create the settings from the dict\n serializer = serializers.GoalSettingStatelessSerializer(data=setting)\n serializer.is_valid(raise_exception=True)\n settings = serializer.create_stateless(serializer.validated_data, goal)\n\n # Calculate the portfolio\n try:\n data_provider = DataProviderDjango()\n execution_provider = ExecutionProviderDjango()\n data = [self.build_portfolio_data(item[1], item[0])\n for item in calculate_portfolios(setting=settings,\n data_provider=data_provider,\n execution_provider=execution_provider)]\n return Response(data)\n except Unsatisfiable as e:\n rdata = {'reason': \"No portfolio could be found: {}\".format(e)}\n\n if e.req_funds is not None:\n rdata['req_funds'] = e.req_funds\n\n return Response({'error': rdata}, status=status.HTTP_400_BAD_REQUEST)","def test_interest_vs_stockprice(self):\n stock_prices = np.array([[5, 10, 20, 40]], dtype=float)\n interest_rate = 2.0 # 200%\n test_case = StockMarket(5, stock_prices, interest_rate)\n test_case.dynamic_programming_bottom_up()\n for portfolio in set(test_case.backtracing_portfolio()):\n self.assertEqual(0, portfolio)","def portfolio(request):\n projects = Project.objects.all()\n categories = None\n\n if request.GET:\n if 'category' in request.GET:\n categories = request.GET['category'].split(',')\n projects = projects.filter(category__name__in=categories)\n categories = ProjectCategory.objects.filter(name__in=categories)\n\n context = {\n 'projects': projects,\n 'current_categories': categories,\n }\n\n return render(request, 'portfolio/portfolio.html', context)","def getTrades(current_portfolio, desired_portfolio):\n # Create trade factory between current and desired portfolio.\n # tf = TradeFactory.TradeFactory(current_portfolio, desired_portfolio)\n\n return {}","def get_cumportfolio(self):\n\n return self._cumportfolio","def portfolio():\n projects = get_projects()\n for project in projects:\n unicode_body = project[\"description\"].decode(\"utf-8\")\n html_body = markdown.markdown(unicode_body)\n safe_html_body = Markup(html_body)\n project[\"description\"] = safe_html_body\n context = {\n \"projects\": projects\n }\n return render_template(\"portfolio.html\", **context)","def summary(self):\n print '%s Portfolio\\'s %s Strategy' % (self.portfolio.name, self.name)\n print '-' * COL_DASH_WIDTH\n\n self.display_trades()\n\n for symbol in self.portfolio.assets.keys():\n perf = self.performance[symbol]\n\n print '\\nSummary for %s from %s (first trade) to %s (last trade)' % (symbol, perf['start'], perf['end'])\n print '.' * COL_DASH_WIDTH\n print 'Summary:'\n data = [[fmtn(perf['trades']), fmtn(perf['wins']), fmtn(perf['losses']), fmtn(perf['washes'])]]\n print tabulate.tabulate(data, headers=['Total Trades', '# Wins', '# Losses', '# Washes'])\n\n print '\\nPerformance:'\n data = [[\n fmtn(perf['profit']), fmtn(perf['loss']), fmtn(perf['net_profit']),\n fmtp(perf['profit_factor']), fmtp(perf['percent_profitable']), fmtn(perf['average_trade_net_profit'])\n ]]\n print tabulate.tabulate(data, headers=['Profit', 'Loss', 'Net Profit', 'Profit Factor', 'Percent Profitable', 'Average Net Profit per Trade'])\n\n print '\\nDrawdown:'\n data = [[fmtn(perf['max_drawdown']), fmtn(perf['average_drawdown']), fmtn(perf['max_drawdown_days']), fmtn(perf['average_drawdown_days'])]]\n print tabulate.tabulate(data, headers=['Max', 'Average', 'Max Days', 'Average Days'])\n\n print '\\nRisk:'\n data = [[fmtn(perf['volatility_risk']), fmtn(perf['beta']), fmtn(perf['lower_partial_moment_risk']), fmtn(perf['t_r']), fmtn(perf['s_r'])]]\n print tabulate.tabulate(data, headers=['Volatility', 'Beta', 'Lower Partial Moment', 'Treynor Ratio', 'Sharpe Ratio'])","def cumulative_returns(self):\n return (1 + self.pct_change).cumprod()","def project_cache(self):\n return self._project_cache","def simulate_trading(self):\n self._generate_trading_instances()\n self._run_backtest()\n self.portfolio.output_equity()\n res=self.portfolio.get_statistics()\n self.plot.plot_equity()\n return res","def index():\n\n # obtain cash info from users database\n cash = db.execute(\"SELECT cash FROM users WHERE id = :id\", id = session[\"user_id\"])\n grandtotal = cash[0][\"cash\"]\n \n # obtain stock info from portfolio database\n stocks = db.execute(\"SELECT symbol, shares FROM portfolio WHERE id = :id\", id=session[\"user_id\"])\n \n # for every stock in the user's portfolio, assign dict key/values for use in html/jinja\n for stock in stocks:\n symbol = str(stock[\"symbol\"])\n shares = int(stock[\"shares\"])\n name = \"\"\n price = \"\"\n total = \"\"\n quote = lookup(symbol)\n stock[\"name\"] = quote[\"name\"]\n stock[\"price\"] = \"{:.2f}\".format(quote[\"price\"])\n stock[\"total\"] = \"{:.2f}\".format(quote[\"price\"] * shares)\n stock[\"grandtotal\"] = quote[\"price\"] * shares\n grandtotal += stock[\"grandtotal\"]\n\n # format grandtotal to force 2 decimal places\n grandtotal = \"{:.2f}\".format(grandtotal)\n \n # render index page with some given values\n return render_template(\"index.html\", stocks = stocks, cash = cash, grandtotal = grandtotal)","async def init(self, ctx, *amount_and_symbol : str):\n user = ctx.message.author\n portfolio = GetPortfolio(user.id)\n for i in range(0, len(amount_and_symbol),2):\n portfolio.SetOwnedCurrency(amount_and_symbol[i], amount_and_symbol[i+1])\n await self.bot.say('%s\\'s portfolio is now worth $%.2f.' %\n (ctx.message.author, portfolio.Value()))\n portfolio.Save()","def solve_working(\n assets_this_period,\n assets_next_period,\n hc_this_period,\n hc_next_period,\n interest_rate,\n wage_rate,\n income_tax_rate,\n beta,\n gamma,\n sigma,\n neg,\n continuation_value,\n delta_hc,\n zeta,\n psi,\n n_gridpoints_capital,\n n_gridpoints_hc,\n efficiency,\n survival_rate,\n):\n # Initialize objects\n policy_labor_working_tmp = np.zeros(\n (n_gridpoints_capital, n_gridpoints_hc), dtype=np.float64\n )\n\n # Implied hc effort\n hc_effort = get_hc_effort(\n hc_this_period=hc_this_period,\n hc_next_period=hc_next_period,\n zeta=zeta,\n psi=psi,\n delta_hc=delta_hc,\n )\n\n hc_effort = np.where(np.isnan(hc_effort), 0.0, hc_effort)\n\n # Implied labor supply\n labor_input = 1 - hc_effort\n\n # Consumption\n consumption = get_consumption(\n assets_this_period=assets_this_period,\n assets_next_period=assets_next_period,\n pension_benefit=np.float64(0.0),\n labor_input=labor_input,\n interest_rate=interest_rate,\n wage_rate=wage_rate,\n income_tax_rate=income_tax_rate,\n productivity=hc_this_period,\n efficiency=efficiency,\n )\n\n # Flow utility\n flow_utility = util(\n consumption=consumption,\n labor_input=labor_input,\n hc_effort=hc_effort,\n gamma=gamma,\n sigma=sigma,\n )\n\n flow_utility = np.where(consumption < 0.0, neg, flow_utility)\n flow_utility = np.where(hc_effort > 1.0, neg, flow_utility)\n\n # Value function on the complete mesh (i.e. for all possible choices)\n value_full = flow_utility + beta * survival_rate * continuation_value\n\n # Derive optimal policies and store value function given optimal choices\n policy_capital_working_tmp = np.argmax(np.max(value_full, axis=3), axis=1)\n policy_hc_working_tmp = np.argmax(np.max(value_full, axis=1), axis=2)\n value_working_tmp = np.max(np.max(value_full, axis=3), axis=1)\n for assets_this_period_idx in range(n_gridpoints_capital):\n for hc_this_period_idx in range(n_gridpoints_hc):\n assets_next_period_idx = policy_capital_working_tmp[\n assets_this_period_idx, hc_this_period_idx\n ]\n hc_next_period_idx = policy_hc_working_tmp[\n assets_this_period_idx, hc_this_period_idx\n ]\n policy_labor_working_tmp[\n assets_this_period_idx, hc_this_period_idx\n ] = labor_input[\n assets_this_period_idx,\n assets_next_period_idx,\n hc_this_period_idx,\n hc_next_period_idx,\n ]\n\n return (\n policy_capital_working_tmp,\n policy_hc_working_tmp,\n policy_labor_working_tmp,\n value_working_tmp,\n )","def get_subportfolio(self, symbol_names_list = None, timeframes_list = None):\n if symbol_names_list is None:\n symbol_names_list = self.symbol_names_list\n \n sub_portfolio = copy.copy(self) #It only copies pointers\n sub_portfolio.del_all_symbols()\n \n for symbol_name in symbol_names_list:\n subsymbol = self[symbol_name].get_subsymbol(timeframes_list)\n sub_portfolio.add_symbol(symbol_name, symbol = subsymbol)\n \n return sub_portfolio","def test_get_portfolio(self, db_mock):\n repo = Repository()\n\n db_instance = db_mock.return_value\n db_instance.get_portfolio.return_value = None\n self.assertIsNone(repo.get_portfolio(\"123\"))\n\n db_instance.get_portfolio.return_value = (1, [])\n self.assertIsInstance(repo.get_portfolio(\"123\"), Portfolio)","def exist_portfolio(self, name, user):\n portfolio = self.__get_portfolio_object(name, user)\n return portfolio is not None","def calculate(self):\n\n final_quotas = self.calculate_quotas(sum(self.populations) / self.num_seats, [0] * self.states)\n final_fair_shares, final_quotas, modified_divisor, estimator = self.calculate_fair_shares([0] * self.states,\n final_quotas, sum(\n self.populations) / self.num_seats, sum(self.populations) / self.num_seats)\n\n lower_boundary = self.calculate_lower_boundary(modified_divisor)\n upper_boundary = self.calculate_upper_boundary(modified_divisor)\n\n if estimator is None:\n return None, None, None, None, None, None, None, None, None, None\n else:\n return self.original_divisor, modified_divisor, self.original_quotas, final_quotas, \\\n self.initial_fair_shares, final_fair_shares, sum(self.initial_fair_shares), lower_boundary, \\\n upper_boundary, self.estimator_history","def cvxpy_solver(self, verbose=False):\n self.gamma = self._gamma_from_drawdown_control()\n\n objective = 0\n constr = []\n\n # weights variable depends on whether there is a risk-free asset in data\n if self.rf_included is True:\n # variable with shape h+1 predictions so first row\n # can be the known (non-variable) portfolio weight at time t\n weights = cp.Variable(shape=(self.n_preds + 1, self.n_assets))\n else:\n # Set rf to zero in all preds and cov\n self.rets = np.insert(self.rets, self.n_assets, 0, axis=1)\n self.cov = np.insert(self.cov, self.n_assets, 0, axis=-2)\n self.cov = np.insert(self.cov, self.n_assets, 0, axis=-1) # Has to be done in two steps for cov due to dims\n self.start_weights = np.append(self.start_weights, 0)\n\n weights = cp.Variable(shape=(self.n_preds + 1, self.n_assets+1))\n rf_zero_weights = np.zeros(shape=self.n_preds)\n constr += [weights[1:, -1] == 0] # Keep rf pos at zero since it non-present in this case\n\n\n # Loop through each row in the weights variable and construct the optimization problem\n # Note this loop is very cpu-light since no actual computations takes place inside it\n for t in range(1, weights.shape[0]):\n # sum problem objectives. Weights are shifted 1 period forward compared to self.rets\n # Concatenates objective and constraints in lists\n objective += self.single_period_objective_func(weights[t], weights[t-1], self.rets[t-1], self.cov[t-1])\n constr += self.single_period_constraints(weights[t]) # Concatenate constraints\n\n constr += [weights[0] == self.start_weights] # first weights are fixed at known current portfolio\n\n prob = cp.Problem(cp.Maximize(objective), constr) # Construct maximization problem\n prob.solve(solver=cp.ECOS, verbose=verbose)\n\n if self.rf_included is True:\n opt_var = weights.value\n else:\n opt_var = weights.value[:, :-1]\n\n if verbose is True:\n print(\"Shape of var: \", opt_var.shape)\n temp_df = pd.DataFrame(opt_var).round(3)\n temp_df['sum_weights'] = np.sum(opt_var, axis=1)\n print(temp_df)\n\n return opt_var[1:] # Discard first row which is not a variable."],"string":"[\n \"def update_portfolio(self):\\n if not self._dirty_portfolio:\\n return\\n\\n portfolio = self._portfolio\\n pt = self.position_tracker\\n\\n portfolio.positions = pt.get_positions()\\n position_stats = pt.stats\\n\\n portfolio.positions_value = position_value = (\\n position_stats.net_value\\n )\\n portfolio.positions_exposure = position_stats.net_exposure\\n self._cash_flow(self._get_payout_total(pt.positions))\\n\\n start_value = portfolio.portfolio_value\\n\\n # update the new starting value\\n portfolio.portfolio_value = end_value = portfolio.cash + position_value\\n\\n pnl = end_value - start_value\\n if start_value != 0:\\n returns = pnl / start_value\\n else:\\n returns = 0.0\\n\\n portfolio.pnl += pnl\\n portfolio.returns = (\\n (1 + portfolio.returns) *\\n (1 + returns) -\\n 1\\n )\\n\\n # the portfolio has been fully synced\\n self._dirty_portfolio = False\",\n \"def get_portfolio_object(self):\\n return self.__get_portfolio_object(self.portfolio_name, self.portfolio_user)\",\n \"def update_portfolio(self, portfolio: PortfolioController):\\n now = portfolio.get_history(seconds_back=0)\\n future = portfolio.get_history(seconds_back=-self.update_interval)\\n\\n for fund in portfolio.funds:\\n best_currency = max(portfolio.currencies, key=lambda currency: future_value(fund, currency, now, future))\\n if best_currency != fund.currency:\\n portfolio.request_transfer(fund, best_currency)\",\n \"def get_portfolio_pnl(self):\\n\\n return self._portfolio\",\n \"def portfolio():\\n #Query transactions by user id\\n trans = Transactions.query.filter_by(owner=session['user_id']).all()\\n \\n #Create list of comanies user owns stock in\\n companies = []\\n for t in trans:\\n if t.symbol not in companies:\\n companies.append(t.symbol)\\n\\n #Create list of current stock dictionaries and total their values\\n total = 0\\n stocks = []\\n for company in companies:\\n trans = Transactions.query.filter_by(owner=session['user_id'], symbol=company).all()\\n stock = {}\\n stock['shares'] = 0\\n for t in trans:\\n stock['shares'] += t.shares\\n if stock['shares'] > 0:\\n stock['symbol'] = company\\n stock['name'] = lookup(company)['name']\\n stock['price'] = lookup(company)['price']\\n stock['total'] = stock['shares'] * stock['price']\\n stock['price'] = usd(stock['price'])\\n stock['total'] = usd(stock['total'])\\n total += float(stock['total'][1:].replace(',', ''))\\n stocks.append(stock)\\n\\n #Set user cash and total values\\n value = {}\\n value['cash'] = usd(Users.query.filter_by(id=session['user_id']).first().cash)\\n value['total'] = usd(total + float(value['cash'][1:].replace(',', '')))\\n\\n #Add values to list\\n stocks.append(value)\\n\\n #Return list of dictionaries\\n return stocks\",\n \"def current_portfolio_weights(self) -> 'pd.Series[float]':\\n position_values = pd.Series({\\n asset: (\\n position.last_sale_price *\\n position.amount *\\n asset.price_multiplier\\n )\\n for asset, position in self.positions.items()\\n }, dtype=\\\"float64\\\")\\n return position_values / self.portfolio_value\",\n \"def generate_portfolio_data(self):\\n self.__load_portfolio_historical_prices()\\n self.__populate_historical_trade_data()\\n self.__calculate_portfolio_returns()\\n self.__calculate_portfolio_performance()\",\n \"def __calculate_portfolio_returns(self):\\n\\n p_bar = tqdm(range(1), desc=\\\" Calculating returns\\\", leave=False)\\n\\n trade_data = self.historical_trade_data\\n\\n # Helper functions to calculate cash inflow and outflow\\n def f_min(x):\\n return x.apply(lambda x: min(x, 0))\\n\\n def f_max(x):\\n return x.apply(lambda x: max(x, 0))\\n\\n # Calculate cash inflow and outflow\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period cash inflow\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = -1 * trade_data[\\\"Investment delta\\\"][:].apply(lambda x: f_min(x), axis=0)\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period cash outflow\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = trade_data[\\\"Investment delta\\\"][:].apply(lambda x: f_max(x), axis=1)\\n\\n # Calculate period return\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period absolute return\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"End Value\\\"] + trade_data[\\\"Period cash inflow\\\"]) - (\\n trade_data[\\\"End Value\\\"].shift(1).fillna(0)\\n + trade_data[\\\"Period cash outflow\\\"]\\n )\\n\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Period percentage return\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"End Value\\\"] + trade_data[\\\"Period cash inflow\\\"]) / (\\n trade_data[\\\"End Value\\\"].shift(1).fillna(0)\\n + trade_data[\\\"Period cash outflow\\\"]\\n ) - 1\\n\\n trade_data[\\\"Period percentage return\\\"].fillna(0, inplace=True)\\n\\n self.historical_trade_data = trade_data\\n\\n self.portfolio_returns = self.historical_trade_data[\\\"Period percentage return\\\"][\\n \\\"Total\\\"\\n ]\\n\\n p_bar.n += 1\\n p_bar.refresh()\",\n \"def __get_portfolio_object(self, name, user):\\n portfolio = self.__get_object_portfolio_bulk(name, user)\\n if portfolio is None:\\n portfolio = self.__get_object_portfolio_bulk(name, user, \\\"portfolio_update\\\")\\n if portfolio is None:\\n portfolio = self.db_tool.session.query(Portfolio) \\\\\\n .outerjoin(Orders)\\\\\\n .join(Stock)\\\\\\n .filter(name == Portfolio.name) \\\\\\n .filter(user == Portfolio.user).first()\\n self.bulk_data[\\\"portfolio_update\\\"].append(portfolio)\\n return portfolio\",\n \"def backtest_portfolio(self):\\n self.rank=dict()\\n self.accuracy=dict()\\n portfolio = dict()\\n \\n for algo in self.algos:\\n portfolio[algo]=pd.DataFrame(index=self.positions.index)\\n self.pos_diff=dict()\\n self.pos_diff[algo] = self.positions[algo].diff()\\n \\n portfolio[algo]['price_diff'] = self.bars['Close']-self.bars['Open']\\n #portfolio['price_diff'][0:5] = 0.0\\n portfolio[algo]['profit'] = self.positions[algo] * portfolio[algo]['price_diff']\\n portfolio[algo]['total'] = self.initial_capital + portfolio[algo]['profit'].cumsum()\\n portfolio[algo]['returns'] = portfolio[algo]['total'].pct_change()\\n d=np.array(portfolio[algo]['profit']).copy()\\n d[d>0]=1\\n d[d<0]=0\\n d[np.array(self.positions[algo])==0]=1\\n for i in np.arange(1,len(d)+1):\\n c=float(sum(d[0:i]))/(i)\\n d[i-1]=c\\n portfolio[algo]['accuracy']=d\\n self.rank[algo]=float(portfolio[algo]['total'][-1] - portfolio[algo]['total'][0])\\n self.returns=portfolio\\n c=np.array(self.returns[algo]['profit'])\\n c[c>0]=1\\n c[c<0]=0\\n c[np.array(self.positions[algo])==0]=1\\n accuracy=round(float(c.sum())/len(c),2)*self.rank[algo]\\n self.accuracy[algo]=accuracy\\n #self.ranking= sorted(self.rank.items(), key=operator.itemgetter(1), reverse=True)\\n self.ranking= sorted(self.accuracy.items(), key=operator.itemgetter(1))\\n self.ready=True\\n return (portfolio, self.rank, self.ranking)\",\n \"def backtest_portfolio(self):\\n raise NotImplementedError(\\\"Should implement backtest_portfolio()!\\\")\",\n \"def compute_portvals(start_date, end_date, orders_file, start_val):\\n \\n #Read order file\\n orders = pd.read_csv( orders_file, parse_dates = [0])\\n \\n #Get symbols making up the portfolio\\n stock_symbols = list( set( orders[\\\"Symbol\\\"] ) )\\n dates = pd.date_range(start_date, end_date)\\n \\n #Read stock prices\\n stock_prices = get_data(stock_symbols, dates)\\n \\n #Create a portfolio keeping track of positions, \\n #_CASH column indicates cash position, _VALUE total portfolio value\\n #_LEVERAGE the leverage of portfolio when we allow for short selling\\n symbols = stock_symbols[:] #Shallow copy of the list\\n symbols.append(\\\"_CASH\\\")\\n symbols.append(\\\"_VALUE\\\")\\n symbols.append(\\\"_LEVERAGE\\\")\\n \\n #Index contains only business days, same dates as stock prices\\n portfolio = pd.DataFrame(index=stock_prices.index, columns = symbols )\\n portfolio.fillna(0) \\n portfolio[\\\"_CASH\\\"][0] = start_val\\n portfolio[\\\"_VALUE\\\"][0] = start_val\\n \\n #Snapshot of a portfolio at any time. To avoid using numerical indexes\\n portfolio_snapshot = dict.fromkeys ( symbols, 0 )\\n portfolio_snapshot[\\\"_CASH\\\"] = start_val\\n portfolio[\\\"_VALUE\\\"] = start_val\\n \\n #Now calcualte portfolio day by day\\n for date in portfolio.index:\\n #Check transactions for the day\\n day_orders = orders[ orders[\\\"Date\\\"] == date ] \\n \\n for ord in day_orders.iterrows():\\n symbol = ord[1][ \\\"Symbol\\\"] \\n stock_price = stock_prices[ symbol ][ date ]\\n shares = ord[1][\\\"Shares\\\" ]\\n side = ord[1][\\\"Order\\\"]\\n \\n if side == \\\"BUY\\\":\\n portfolio_snapshot[ \\\"_CASH\\\" ] -= stock_price * shares\\n portfolio_snapshot[ symbol ] += shares \\n elif side == \\\"SELL\\\":\\n portfolio_snapshot[ \\\"_CASH\\\" ] += stock_price * shares\\n portfolio_snapshot[ symbol ] -= shares\\n else:\\n raise \\\"Order not recognized.\\\"\\n \\n #Compute portfolio value\\n portfolio_snapshot[ \\\"_VALUE\\\" ] = portfolio_snapshot[ \\\"_CASH\\\" ]\\n shorts = longs = 0\\n for symbol in stock_symbols: \\n stock_price = stock_prices[ symbol ][ date ]\\n shares = portfolio_snapshot[ symbol ]\\n notional = stock_price*shares\\n if shares > 0:\\n longs += notional\\n else:\\n shorts += notional\\n \\n portfolio_snapshot[ \\\"_VALUE\\\" ] += notional\\n \\n #Compute leverage\\n leverage = (longs+shorts)/(longs-shorts + portfolio_snapshot[ \\\"_CASH\\\" ] )\\n portfolio_snapshot[ \\\"_LEVERAGE\\\" ] = leverage\\n \\n #Assert we never achieve a leverage > 2.0\\n if leverage > 2:\\n raise \\\"Leverage > 2.0 achieved\\\"\\n \\n #Update portfolio from the daily snapshot\\n #TODO: Is this causing performance issues?\\n for symbol in portfolio.keys():\\n portfolio[ symbol ][ date ] = portfolio_snapshot[ symbol ]\\n \\n return portfolio\",\n \"def getPortfolioValue(self, start_t, t):\\n sum_tmp=0\\n for item in self.portfolio.keys():\\n if \\\"DJI_\\\" in item:\\n t_tmp=datetime.strftime(pd.date_range(end=t,periods=1,freq='B')[0],'%Y-%m-%d')\\n price=universe.get_price_in_currency(item,t_tmp,'CAD')\\n elif 'rf_rate' in item:\\n price=universe.get_security(item).get_cc_return(start_t,t) \\n else:\\n price=universe.get_price_in_currency(item,t,'CAD')\\n #price=universe.get_security(item).price[t]\\n amount=self.portfolio[item]\\n sum_tmp=sum_tmp+price*amount\\n \\n return sum_tmp\",\n \"def get_portfolio_pnl_tsd(self):\\n\\n return self._tsd_portfolio\",\n \"def portfolio_performance(returns,weights):\\r\\n print('Calculating Portfolio Performance')\\r\\n # returns=target_asset_port_data_attributes['component_returns']\\r\\n # weights =target_asset_port_data_attributes['effective_weights']\\r\\n\\r\\n component_returns= returns\\r\\n compnent_weights = pd.DataFrame(data=np.nan,index= component_returns.index,columns=component_returns.columns)\\r\\n compnent_weights.loc[weights.index,:] = weights\\r\\n\\r\\n portfolio_dates = component_returns.index\\r\\n components = component_returns.columns\\r\\n\\r\\n # pre-allocate\\r\\n BoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n EoP_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n PnL_df = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=components)\\r\\n portfolio_BoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio BoP'])\\r\\n portfolio_EoP = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio EoP'])\\r\\n portfolio_PnL = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Portfolio PnL'])\\r\\n \\r\\n portfolio_index = pd.DataFrame(data=np.nan,index=portfolio_dates,columns=['Index'])\\r\\n previous_index_value = np.int64(1)\\r\\n\\r\\n pre_date = portfolio_dates[0]\\r\\n # set BoP to start weights\\r\\n for date,row in component_returns.iterrows():\\r\\n # print(date)\\r\\n # 1st date\\r\\n if date == portfolio_dates[0]:\\r\\n BoP_df.loc[date] = compnent_weights.iloc[0,:]\\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n\\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n # after first date\\r\\n else:\\r\\n BoP_df.loc[date] = EoP_df.loc[pre_date]\\r\\n # weights override\\r\\n if date in compnent_weights.index:\\r\\n none_NaN_index = ~compnent_weights.loc[date].isnull()\\r\\n if not compnent_weights.loc[date][none_NaN_index].empty:\\r\\n tmp_sum = BoP_df.loc[date].sum()\\r\\n BoP_df.loc[date][none_NaN_index.values] = (compnent_weights.loc[date][none_NaN_index.values].values)*tmp_sum\\r\\n\\r\\n \\r\\n EoP_df.loc[date] = BoP_df.loc[date] * (1+component_returns.loc[date])\\r\\n PnL_df.loc[date] = EoP_df.loc[date].subtract(BoP_df.loc[date])\\r\\n\\r\\n portfolio_BoP.loc[date] = BoP_df.loc[date].sum()\\r\\n portfolio_EoP.loc[date] = EoP_df.loc[date].sum()\\r\\n portfolio_PnL.loc[date] = PnL_df.loc[date].sum()\\r\\n \\r\\n portfolio_index.loc[date] = np.nansum([previous_index_value,portfolio_PnL.loc[date].values])\\r\\n previous_index_value = portfolio_index.loc[date]\\r\\n pre_date = date\\r\\n\\r\\n\\r\\n portfolio_returns = portfolio_index.pct_change(1) \\r\\n portfolio_returns.columns = ['Returns']\\r\\n\\r\\n portfolio_index\\r\\n perf = portfolio_index.calc_stats()\\r\\n \\r\\n output = pd.Series(data = [perf,PnL_df,portfolio_index,portfolio_BoP,portfolio_EoP,BoP_df], index=['Portfolio Perf','Component PnL','portfolio_index','portfolio_BoP','portfolio_EoP','BoP_df'])\\r\\n return output\",\n \"def calculate_portfolio(self, request, pk=None, **kwargs):\\n goal = self.get_object()\\n\\n check_state(Goal.State(goal.state), Goal.State.ACTIVE)\\n\\n setting_str = request.query_params.get('setting', None)\\n if not setting_str:\\n raise ValidationError(\\\"Query parameter 'setting' must be specified and a valid JSON string\\\")\\n try:\\n setting = ujson.loads(setting_str)\\n except ValueError:\\n raise ValidationError(\\\"Query parameter 'setting' must be a valid json string\\\")\\n\\n # Create the settings object from the dict\\n serializer = serializers.GoalSettingStatelessSerializer(data=setting)\\n serializer.is_valid(raise_exception=True)\\n settings = serializer.create_stateless(serializer.validated_data, goal)\\n\\n try:\\n data = self.build_portfolio_data(calculate_portfolio(settings=settings,\\n data_provider=DataProviderDjango(),\\n execution_provider=ExecutionProviderDjango()))\\n return Response(data)\\n except Unsatisfiable as e:\\n rdata = {'reason': \\\"No portfolio could be found: {}\\\".format(e)}\\n if e.req_funds is not None:\\n rdata['req_funds'] = e.req_funds\\n\\n return Response({'error': rdata}, status=status.HTTP_400_BAD_REQUEST)\",\n \"def initialize_portfolio(self):\\n\\n raise NotImplementedError('''\\n Must implement initialize_portfolio. Call help() for details.\\n ''')\",\n \"def rebalance(context, data):\\n logger.debug('rebalancing on: %s', algo.get_datetime())\\n\\n context.trend_filter = False\\n\\n # new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\\n\\n new_portfolio = algo.pipeline_output('pipeline').dropna(subset=['overall_rank']).sort_values('momentum', ascending=False)\\n\\n for equity, row in new_portfolio.iterrows():\\n logger.debug('new portfolio (before filtering) - equity: %s', equity)\\n\\n # print(new_portfolio)\\n\\n # new_portfolio = new_portfolio[new_portfolio['overall_rank'].notna() & new_portfolio['momentum'] > 40][:20]\\n \\n # new_portfolio = new_portfolio[(new_portfolio['momentum_decile'] > 8)][:20]\\n\\n new_portfolio = new_portfolio.nlargest(20, ['overall_rank', 'momentum']) #<- $600K PL in 10 years\\n\\n # new_portfolio = new_portfolio.nlargest(20, ['momentum', 'overall_rank']) #<- 1M PL in 10 years\\n\\n if logger.level is logging.DEBUG:\\n for equity, row in new_portfolio.iterrows():\\n logger.debug('new portfolio - (after filtering) equity: %s', equity)\\n \\n\\n # print(len(new_portfolio.index))\\n\\n # volatility driven weights\\n # new_portfolio['inverse_volatility'] = new_portfolio['volatility'].apply(lambda x: 1 / x)\\n # inv_vola_sum = new_portfolio['inverse_volatility'].sum()\\n # new_portfolio['target_weight'] = new_portfolio['inverse_volatility'].apply(lambda x: x / inv_vola_sum)\\n\\n # portfolio size driven weights\\n # num_equities = len(new_portfolio.index)\\n # new_portfolio['target_weight'] = 1 / num_equities\\\\\\n\\n # logger.info('len existing portfolio: %s', len(context.portfolio.positions))\\n\\n if logger.level is logging.DEBUG:\\n for equity, values in context.portfolio.positions.items():\\n logger.debug('context.portfolio.positions - equity: %s, amount: %s, cost_basis: %s, sold_on: %s, sold_at_price: %s', equity, values.amount, values.cost_basis, values.last_sale_date, values.last_sale_price)\\n\\n \\n order_target(algo.sid('FIBBG000NTFYM5'), 0)\\n logger.debug('selling all bonds')\\n\\n for equity in context.portfolio.positions:\\n if equity is algo.sid('FIBBG000NTFYM5'): \\n continue\\n if equity not in set(new_portfolio.index.tolist()):\\n # logger.info('selling %s', equity)\\n order_target_percent(equity, 0)\\n\\n stock_weights = 1.0 / max(len(context.portfolio.positions), len(new_portfolio.index))\\n\\n logger.debug('len existing portfolio (afer ejection): %s', len(context.portfolio.positions))\\n logger.debug('len new portfolio: %s', len(new_portfolio.index))\\n logger.debug('stock_weights: %s', stock_weights)\\n\\n # print(context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5')))\\n\\n # spy = context.portfolio.positions.get(algo.sid('FIBBG000NTFYM5'))\\n\\n # if (spy is not None) and (spy.amount > 0):\\n # order_target_percent(algo.sid('FIBBG000NTFYM5'), 0)\\n\\n for equity, row in new_portfolio.iterrows():\\n if row.trend_filter is True:\\n # logger.info('buying %s', equity)\\n context.trend_filter = True\\n order_target_percent(equity, stock_weights)\\n else:\\n context.trend_filter = False\\n \\n logger.debug('cash: %s', context.portfolio.cash)\\n logger.debug('portfolio_value: %s', context.portfolio.portfolio_value)\\n logger.debug('num_positions: %s', len(context.portfolio.positions))\\n logger.debug('positions: %s', context.portfolio.positions)\",\n \"def index():\\n def getListOfCompanies(username, symbolOrPriceOrNumber):\\n if symbolOrPriceOrNumber == \\\"symbol\\\" or symbolOrPriceOrNumber == \\\"price\\\" or symbolOrPriceOrNumber == \\\"number\\\":\\n rows = db.execute(\\\"SELECT {0} FROM portfolio WHERE username=:username\\\".format(symbolOrPriceOrNumber), username=username)\\n if symbolOrPriceOrNumber == \\\"symbol\\\" and len(rows) >= 1:\\n namesList = []\\n for row in rows:\\n namesList.append(lookup(row[symbolOrPriceOrNumber])[\\\"name\\\"])\\n return namesList\\n elif symbolOrPriceOrNumber == \\\"price\\\" and len(rows) >= 1:\\n pricseList = []\\n for row in rows:\\n pricseList.append(row[symbolOrPriceOrNumber])\\n return pricseList\\n elif symbolOrPriceOrNumber == \\\"number\\\" and len(rows) >= 1:\\n numbersList = []\\n for row in rows:\\n numbersList.append(row[symbolOrPriceOrNumber])\\n return numbersList\\n else:\\n return None\\n else:\\n return None\\n\\n def getTotalValueHolding(username):\\n priceRow = db.execute(\\\"SELECT price FROM portfolio WHERE username=:username\\\", username=username)\\n numberRow = db.execute(\\\"SELECT number FROM portfolio WHERE username=:username\\\", username=username)\\n\\n if len(priceRow) >= 1 and len(numberRow) >= 1 and len(priceRow) == len(numberRow):\\n totalList = []\\n for i in range(len(priceRow)):\\n totalList.append(float(priceRow[i][\\\"price\\\"]) * float(numberRow[i][\\\"number\\\"]))\\n\\n return totalList\\n\\n username = db.execute(\\\"SELECT username FROM users WHERE id=:userId\\\", userId=session[\\\"user_id\\\"])[0][\\\"username\\\"]\\n companiesNames = getListOfCompanies(username, \\\"symbol\\\")\\n numberOfShares = getListOfCompanies(username, \\\"number\\\")\\n prices = getListOfCompanies(username, \\\"price\\\")\\n totalValueHolding = getTotalValueHolding(username)\\n\\n currentCashBalance = db.execute(\\\"SELECT cash FROM users WHERE id=:userId\\\", userId=session[\\\"user_id\\\"])[0][\\\"cash\\\"]\\n total = 0\\n if totalValueHolding:\\n for totalValue in totalValueHolding:\\n total = total + totalValue\\n\\n cashAndStocksTotalValue = float(currentCashBalance) + total\\n\\n return render_template(\\\"index.html\\\", username=username, companiesNames=companiesNames, numberOfShares=numberOfShares,\\n prices=prices, totalValueHolding=totalValueHolding, currentCashBalance=currentCashBalance, cashAndStocksTotalValue=cashAndStocksTotalValue)\",\n \"def portfolio_table(self):\\n idx = set(name.split('-')[0].split('.')[0] for name, etf in self.etfs.items() if not etf.sold())\\n table = pd.DataFrame({'Invested': 0, 'Shares':0, 'Share Price':0, 'Present Value':0, 'P/L':0, 'P/L%':0},index=idx)\\n for name, etf in self.etfs.items():\\n if not etf.sold():\\n table.loc[name.split('-')[0].split('.')[0], 'Invested'] += etf.initial_investment()\\n table.loc[name.split('-')[0].split('.')[0], 'Shares'] += etf.n_shares\\n table.loc[name.split('-')[0].split('.')[0], 'Share Price'] = etf.stock_price()\\n table.loc[name.split('-')[0].split('.')[0], 'Present Value'] += etf.present_value()\\n table.loc[name.split('-')[0].split('.')[0], 'P/L'] += etf.profit_loss()\\n table.insert(1, 'PMA', round(table['Invested'] / table['Shares'], 2))\\n table.insert(3, 'Initial Weight', round(table['Invested'] / table['Invested'].sum() * 100, 2))\\n table.insert(4, 'Present Weight', round(table['Present Value'] / table['Present Value'].sum() * 100, 2))\\n table['P/L%'] = round(table['P/L'] / table['Invested'] * 100, 2)\\n table['P/L'] = round(table['P/L'], 2)\\n table['Present Value'] = round(table['Present Value'], 2)\\n return table.sort_values('Invested', 0, ascending=False)\",\n \"def index():\\n\\n #select user's portfolio\\n rows = db.execute(\\\"SELECT * FROM portfolio WHERE userid=:id\\\", id=session[\\\"user_id\\\"])\\n\\n #set temporary holding place for cash to zero\\n tcash = 0\\n\\n #update the stock information in user's portfolio\\n for row in rows:\\n stock = row[\\\"stock\\\"]\\n number = row[\\\"number\\\"]\\n quote = lookup(stock)\\n total = float(number) * float(quote[\\\"price\\\"])\\n tcash += total\\n db.execute(\\\"UPDATE portfolio SET price=:price, total=:total WHERE userid=:id AND stock=:stock AND number=:number\\\", price=usd(quote[\\\"price\\\"]), total=total, id=session[\\\"user_id\\\"], stock=stock, number=number)\\n\\n #select user's cash and updated portfolio\\n updated_cash = db.execute(\\\"SELECT cash FROM users WHERE id=:id\\\", id=session[\\\"user_id\\\"])\\n tcash += updated_cash[0][\\\"cash\\\"]\\n updated_stock = db.execute(\\\"SELECT stock, SUM(number) AS number, price, SUM(total) AS stock_total FROM portfolio WHERE userid=:id GROUP BY stock HAVING SUM(number) > 0\\\", id=session[\\\"user_id\\\"])\\n\\n return render_template(\\\"index.html\\\", stocks=updated_stock, cash=usd(updated_cash[0][\\\"cash\\\"]), all_total=usd(tcash))\",\n \"def update_portfolio_on_market(self, market: MarketEvent):\\n self._portfolio.update_market_value(market)\",\n \"def portfolio_analytics(port_returns, market_returns):\\n\\n # add the intercept to the model\\n x2 = sm.add_constant(market_returns)\\n\\n # train the model\\n estimator = sm.OLS(port_returns, x2)\\n model = estimator.fit()\\n\\n # get portfolio analytics\\n alpha, beta = model.params\\n r_squared = model.rsquared\\n regression = model.predict()\\n\\n return alpha, beta, r_squared, regression\",\n \"def portfolio_analytics(port_returns, market_returns):\\n\\n # add the intercept to the model\\n x2 = sm.add_constant(market_returns)\\n\\n # train the model\\n estimator = sm.OLS(port_returns, x2)\\n model = estimator.fit()\\n\\n # get portfolio analytics\\n alpha, beta = model.params\\n r_squared = model.rsquared\\n regression = model.predict()\\n\\n return alpha, beta, r_squared, regression\",\n \"def __init__(\\n self,\\n portfolio,\\n market=None,\\n commission_min=5.00,\\n commission_pct=0.0,\\n buy_percent=1.0,\\n sell_percent=1.0,\\n pm_threshold=0.0,\\n pm_order=1.0,\\n risk_free_return=1.0,\\n name=None\\n ):\\n\\n # Assumptions\\n self.name = name if name else portfolio.name\\n self.commission_min = commission_min\\n self.commission_pct = commission_pct\\n self.buy_percent = buy_percent\\n self.sell_percent = sell_percent\\n self.pm_threshold = pm_threshold\\n self.pm_order = pm_order\\n self.risk_free_return = risk_free_return\\n self.performance = {}\\n\\n # Inputs\\n self.portfolio = portfolio\\n self.market = copy.deepcopy(market) if market else Asset(np.ones(len(self.portfolio.dates)))\\n\\n # Trading states\\n self.long_open = {symbol:False for symbol in portfolio.assets.keys()}\\n self.short_open = {symbol:False for symbol in portfolio.assets.keys()}\\n\\n # Keep track of intermidiate results for performance\\n self.trade_data = []\\n recordings = [\\n 'buy price', 'buy shares', 'buy fees', 'buy date',\\n 'sell price', 'sell shares', 'sell fees', 'sell date',\\n 'gain', 'profit', 'loss', 'return', 'win/loose',\\n 'min balance', 'min date', 'max balance', 'max date',\\n 'drawdown', 'drawdown days',\\n 'volatility', 'expected_return', 'beta', 'lpm', 'hpm',\\n 'max', 'mean', 'min'\\n ]\\n self.record = {symbol:pd.DataFrame(columns=recordings) for symbol in portfolio.assets.keys()}\\n self.max = {symbol:[portfolio.assets[symbol].c.iloc[0], None] for symbol in portfolio.assets.keys()}\\n self.min = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}\\n self.drawdown = {symbol:[999999999999999, None] for symbol in portfolio.assets.keys()}\",\n \"def get_stock(self, investor):\\n\\n # Find out the stock details \\n sym, qty, price = investor.portfolios[0].portfolios[0]\\n # p = investor.portfolios[0]\\n \\n # Check if broker has a portfolio\\n if self.portfolios[0]:\\n self.portfolios[0].add_stock(sym, qty, price)\\n else:\\n # Broker doesn't have a portfolio\\n p = Portfolio()\\n #logging.info(\\\"p is: %s\\\" % p)\\n p.add_stock(sym, qty, price)\\n self.add_portfolio(p)\\n logging.info(\\\"Broker's portfolios AFTER addition: %s\\\" % self)\\n # logging.info(\\\"WHAT ARE YOU\\\")\\n logging.info(\\\"Investor portfolio BEFORE removal: %s\\\" % investor.portfolios[0].portfolios)\\n investor.portfolios[0].remove_stock(sym, qty)\\n logging.info(\\\"Investor portfolio AFTER removal: %s\\\" % investor.portfolios[0])\\n # investor.portfolios[0].portfolios.remove( (sym, qty, price) )\\n \\n # investor.portfolios[0].remove(sym, qty, price)\\n total_price = qty * price\\n investor.portfolios[0].value -= total_price\\n investor.cash += qty * float(price)\",\n \"def _initalize_portfolio_with_cash(self):\\n self.cash = copy.copy(self.starting_cash)\\n\\n if self.starting_cash > 0.0:\\n self.history.append(\\n PortfolioEvent.create_subscription(\\n self.current_dt, self.starting_cash, self.starting_cash\\n )\\n )\\n\\n self.logger.info(\\n '(%s) Funds subscribed to portfolio \\\"%s\\\" '\\n '- Credit: %0.2f, Balance: %0.2f' % (\\n self.current_dt.strftime(settings.LOGGING[\\\"DATE_FORMAT\\\"]),\\n self.portfolio_id,\\n round(self.starting_cash, 2),\\n round(self.starting_cash, 2)\\n )\\n )\",\n \"def before_trading_start(context, data):\\n factors = pipeline_output('ff_example')\\n\\n # get the data we're going to use\\n returns = factors['returns']\\n mkt_cap = factors.sort_values(['market_cap'], ascending=True)\\n be_me = factors.sort_values(['be_me'], ascending=True)\\n\\n # to compose the six portfolios, split our universe into portions\\n half = int(len(mkt_cap)*0.5)\\n small_caps = mkt_cap[:half]\\n big_caps = mkt_cap[half:]\\n \\n thirty = int(len(be_me)*0.3)\\n seventy = int(len(be_me)*0.7)\\n growth = be_me[:thirty]\\n neutral = be_me[thirty:seventy]\\n value = be_me[seventy:]\\n\\n # now use the portions to construct the portfolios.\\n # note: these portfolios are just lists (indices) of equities\\n small_value = small_caps.index.intersection(value.index)\\n small_neutral = small_caps.index.intersection(neutral.index)\\n small_growth = small_caps.index.intersection(growth.index)\\n \\n big_value = big_caps.index.intersection(value.index)\\n big_neutral = big_caps.index.intersection(neutral.index)\\n big_growth = big_caps.index.intersection(growth.index)\\n\\n # take the mean to get the portfolio return, assuming uniform\\n # allocation to its constituent equities.\\n sv = returns[small_value].mean()\\n sn = returns[small_neutral].mean()\\n sg = returns[small_growth].mean()\\n \\n bv = returns[big_value].mean()\\n bn = returns[big_neutral].mean()\\n bg = returns[big_growth].mean()\\n\\n # computing SMB\\n context.smb = (sv + sn + sg)/3 - (bv + bn + bg)/3\\n\\n # computing HML\\n context.hml = (sv + bv)/2 - (sg + bg)/2\",\n \"def calc_portfolio_risk(\\n context,\\n data,\\n risk_func,\\n hist_days=180,\\n **kwargs):\\n\\n \\n positions = context.portfolio.positions\\n positions_index = pd.Index(positions)\\n share_counts = pd.Series( \\n index=positions_index, \\n data=[positions[asset].amount for asset in positions] \\n )\\n\\n current_prices = data.current(positions_index, 'price') \\n current_weights = (\\n share_counts * current_prices / context.portfolio.portfolio_value\\n )\\n \\n prices = data.history(\\n current_weights.index.tolist(),\\n 'price',\\n hist_days,\\n '1d'\\n )\\n\\n daily_rets = prices.pct_change()\\n daily_rets = daily_rets - daily_rets.mean(skipna=True)\\n daily_rets = daily_rets.fillna(0.0)\\n\\n risk = risk_func(current_weights.values, daily_rets, **kwargs)\\n return risk\",\n \"def __display_portfolio(self, p, w):\\n\\n global st_sort_key\\n global st_reverse_sort\\n\\n line = 1\\n total_assets = 0\\n total_change = 0\\n\\n p.assets.sort(key=st_sort_key, reverse=st_reverse_sort)\\n\\n for s in p.assets:\\n # Make sure we have space to write the portfolio totals.\\n if line >= (curses.LINES - 3):\\n break\\n\\n total_assets += (p.asset_counts[s.symb()] * s.price())\\n total_change += (p.asset_counts[s.symb()] * s.change())\\n\\n # Color red/green for stocks going up/down.\\n change_color = curses.color_pair(0)\\n if s.change() > 0:\\n change_color = curses.color_pair(1)\\n elif s.change() < 0:\\n change_color = curses.color_pair(2)\\n\\n direction = ''\\n if s.change() > 0:\\n direction = u'\\\\u25b2'\\n elif s.change() < 0:\\n direction = u'\\\\u25bc'\\n\\n w.addstr(line, 0, '%-15s' % s.name()[0:14])\\n w.addstr(line, 16, '%-5s' % s.symb(), curses.A_BOLD)\\n w.addstr(line, 22, '%9.2f' % s.price())\\n w.addstr(line, 32, direction.encode('utf-8'), change_color)\\n w.addstr(line, 33, '%6.2f %5.2f%%' % (abs(s.change()),\\n abs(s.change_percent()) *\\n 100),\\n change_color)\\n w.addstr(line, 47, '|')\\n w.addstr(line, 49, '%-6d' % p.asset_counts[s.symb()])\\n w.addstr(line, 56, '%11.2f' % (p.asset_counts[s.symb()] *\\n s.price()))\\n w.addstr(line, 68, '%10.2f' % (p.asset_counts[s.symb()] *\\n s.change()),\\n change_color)\\n\\n line += 1\\n\\n line += 1\\n\\n # Get overall change (of assets) for the portfolio.\\n overall_change = total_assets - p.cost_basis()\\n overall_color = curses.color_pair(0)\\n if overall_change > 0:\\n overall_color = curses.color_pair(1)\\n elif overall_change < 0:\\n overall_color = curses.color_pair(2)\\n\\n # Color red/green for assets changing.\\n change_color = curses.color_pair(0)\\n if total_change > 0:\\n change_color = curses.color_pair(1)\\n elif total_change < 0:\\n change_color = curses.color_pair(2)\\n\\n # Print accumulated stats for the portfolio.\\n w.addstr(line, 0, 'Daily:')\\n w.addstr(line, 8, '$%.2f' % total_change,\\n curses.A_BOLD | change_color)\\n w.addstr(line, 23, 'Total:')\\n w.addstr(line, 30, '$%.2f' % overall_change,\\n curses.A_BOLD | overall_color)\\n w.addstr(line + 1, 0, 'Assets:')\\n w.addstr(line + 1, 8, '$%.2f' % total_assets)\\n w.addstr(line + 1, 23, 'Cash: $%.2f' % p.cash)\\n w.addstr(line + 1, 44, 'Total value:')\\n w.addstr(line + 1, 58, '$%.2f' % (p.cash + total_assets),\\n curses.A_BOLD)\",\n \"def optimizeForReturn(required_return, stock_db, use_genetic):\\n print('Optimizing portfolio for %f' % required_return)\\n pf = PortfolioFactory(stock_db, required_return, use_genetic=use_genetic)\\n desired_portfolio = pf.desired_portfolio\\n print('Required Return: %f' % required_return)\\n print('Expected Return: %f' % math.pow(\\n desired_portfolio.average_return, Config.DAYS_IN_YEAR))\\n print('Downside Risk: %f' % desired_portfolio.downside_risk)\\n print('Downside Correl: %f' % desired_portfolio.downside_correl)\\n print('Score: %f' % desired_portfolio.score)\\n\\n # Write desired portfolio.\\n DataIO.writeDesiredPortfolio(\\n desired_portfolio, stock_db,\\n 'output/DesiredPortfolio_%.0f_%.4f_%s.csv' % (\\n Config.MINIMUM_AMOUNT_DATA, required_return, Config.TODAY.date()))\\n\\n print('Finished for %f' % required_return)\\n\\n return desired_portfolio\",\n \"def load_portfolio(self):\\n\\n if self.terminate:\\n return\\n\\n # First get a file.\\n self.lock.acquire()\\n self.windows['ACTION'].erase()\\n\\n curses.curs_set(1)\\n\\n tp = curses.textpad.Textbox(self.windows['ACTION'])\\n file_str = tp.edit()\\n curses.curs_set(0)\\n\\n self.clear_action()\\n self.lock.release()\\n\\n self.refresh()\\n\\n # Now that we have a portfolio file, let's load it up and\\n # fire off a thread to update it.\\n p = Portfolio(file_str.strip())\\n self.portfolios.append(p)\\n self.track_portfolio(p)\",\n \"def display_portfolio(self, p):\\n\\n if self.terminate:\\n return\\n\\n w = self.windows['MAIN']\\n\\n self.clear_main()\\n self.__display_portfolio(p, w)\\n self.clear_header()\\n self.set_header(p)\\n\\n self.refresh()\",\n \"def income_model_constant_portfolio_return(num_of_years=30, trials=100, method='normal'):\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # # dataframe for unsorted returns (normal)\\n # median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n # median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n # median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\\n #\\n # # dataframe for smallest to largest returns\\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\\n #\\n # # dataframe for unsorted returns (normal)\\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n \\n # -------------For Constant Growth Rates------------------------\\n const_fia_index_ret = float(read_income_inputs.loc['const_fia_index_ret', 'inputs'])\\n const_risky_port_ret = float(read_income_inputs.loc['const_risky_port_ret', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n # income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n # ----------CONSTANT FIA INDEX GROWTH RATE-------------------\\n income_df.loc[:, 'index_returns'] = const_fia_index_ret\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # -------------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n # for c in range(len(r_cols)):\\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_small.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\\n\\n elif method == 'largest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_large.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\\n\\n else:\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\\n\\n # store the simulated assets returns in one dictionary\\n # returns_dict.update({str(runs): random_returns})\\n\\n # collect the asset based returns from all simulation and calculate the median returns.\\n # def get_median_returns(sym):\\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\\n # return asset_df.median(axis=1)\\n #\\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\\n #\\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\\n\\n base_df = random_returns.copy()\\n pre_income_base_df = random_returns.copy()\\n\\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\\n\\n fia_portfolio_df = random_returns.copy()\\n pre_income_port_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # ---------Initial Investments for pre-income account values---------------------\\n pre_income_base_inv = base_investment\\n pre_income_port_inv = port_investment\\n # ----------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n pre_income_base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n # ---------------For year 0, the year of investment------------\\n\\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_base_inv for c in range(len(boy_value))]\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n # base_investment = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_pre_income'] = base_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n # ----For years between the start of the investment and start if the income---------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total'] = base_investment * (1 + 0.06)\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\\n\\n else:\\n\\n # -------------For Years after the income started----------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total'] = base_investment * (1 + const_risky_port_ret)\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n # -------------------Portfolio with PreIncome Values----------------------------\\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n pre_income_port_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_port_inv for c in range(len(boy_value))]\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n # port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # ------------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n \\n # -----------------------CONSTANT GROWTH RATE-----------------\\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n \\n # -------CONSTANT GROWTH RATE-----------------\\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n # fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total'] = port_investment * (1 + const_risky_port_ret)\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\\n\\n # -------------------Portfolio with PreIncome Values----------------------------\\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\\n \\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\\n total_income_by_age.fillna(0, inplace=True)\\n income_dataframe = pd.DataFrame(total_income_by_age)\\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\\n lambda x: total_income_by_acturial_age - x)\\n\\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\\n\\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n\\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ---------------Portfolio for 45 years of simulation---------------------------------------\\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ----------------Portfolio Simulation until the acturial age------------------------------\\n acturial_years = life_expectancy - clients_age\\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\\n\\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\\n if base_success_portfolio_act_age.isnull().sum() == trials:\\n base_max_portfolio = 0.0\\n else:\\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\\n\\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\\n if port_success_portfolio_act_age.isnull().sum() == trials:\\n port_max_portfolio = 0.0\\n else:\\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\\n\\n # ------------------------------Average age with full income------------------------------\\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n # ----------------------------Median Age with full Income------------------------------------------\\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n\\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n\\n # # --------------Mean Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n #\\n # # --------------Median Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n\\n # -------Max Portfolio value at the end of acturial age----------------------------------------\\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n\\n # -------Min Portfolio value at the end of acturial age----------------------------------------\\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n\\n # ---------------------Lifetime Average Income----------------------------------\\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\\n port_total_income = income_from_fia + sim_port_income\\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\\n\\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\\n 'Max Portfolio Value', 'Min Portfolio Value',\\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\\n\\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\\n 'Median years of portfolios that meet the next years income needs for the lifetime',\\n 'Average Clients Age',\\n 'Median Clients Age',\\n 'Average of terminal values for the portfolios at the end of the acturial life',\\n 'Median of terminal values for the portfolios at the end of the acturial life',\\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\\n 'Average of total income generated by all portfolios at the end of the acturial life']\\n\\n simulation_stats.loc[:, 'Notes'] = comments\\n\\n # --------------------------------------------------------------------------------\\n\\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\\n # base_df.to_csv(src + 'base_port_detail.csv')\\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n # income_breakdown_base.loc[:, 'social_security_income'] = social\\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n #\\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n # axis=1)\\n #\\n # # --------------------------------------Block Ends-----------------------------------------------------------\\n #\\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n #\\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n # income_breakdown_port.loc[:, 'social_security_income'] = social\\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n #\\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n # axis=1)\\n #\\n # # ----------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ------------------------------------------drop year 0-----------------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ---------------------------------plot for histogram for porfolios--------------------------------------\\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\\n # fact = 1 / len(base_term_value)\\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\\n # counts, bins, bars = plt.hist(base_ann_ret)\\n\\n # ------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n sim_base_total.clip(lower=0, inplace=True)\\n\\n # -------------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n\\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n\\n writer = pd.ExcelWriter(dest_simulation + method + '_leveled_growth_simulation.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n # base_qcut.loc[:, 'clients_age'] = age_index\\n # base_qcut.loc[:, 'comment'] = ''\\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n # -----------------------To start with year 0---------------------------------\\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\\n base_inv, clients_age, np.nan]\\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\\n base_qcut = base_qcut.shift(1)\\n base_qcut.iloc[0] = insert_col\\n base_qcut.reset_index(drop=True, inplace=True)\\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\\n # port_qcut.loc[:, 'clients_age'] = age_index\\n # port_qcut.loc[:, 'comment'] = ''\\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\\n port_qcut = port_qcut.shift(1)\\n port_qcut.iloc[0] = insert_col\\n port_qcut.reset_index(drop=True, inplace=True)\\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'clients_age'] = age_index\\n # prob_success_df.loc[:, 'comment'] = ''\\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n prob_success_df.loc[:, 'age'] = age_index\\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\\n\\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\\n\\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\\n\\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\\n\\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\\n\\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\\n\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\\n\\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\\n # -------Add premium to year 0 value to get total portfolio value---------\\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\\n\\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # # -------------For Simulation slide - Proposed Portfolio --------------------\\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\\n\\n prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\\\\n - social - cpn_income_port\\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\\\\n - income_from_fia - social - cpn_income_port\\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # -------------------Write simulation Statistics-------------------------------------\\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\\n\\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\\n\\n # if method == 'normal':\\n # median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n # median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n #\\n # elif method == 'smallest':\\n # median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n # median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\\n #\\n # else:\\n # median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n # median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\\n\\n # ---------------------Histogram for S&P Forecast---------------------------------------\\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\\n bins, data = np.histogram(sp_random_ret, bins=20)\\n df_ret = pd.DataFrame(data, columns=['Return_range'])\\n df_bins = pd.DataFrame(bins, columns=['Count'])\\n df_hist = df_ret.join(df_bins)\\n\\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\\n writer.save()\\n\\n print(\\\"simulation completed....\\\")\",\n \"def index():\\n user_stocks_list = db.execute(\\\"SELECT stock FROM transactions WHERE id = :current_id\\\", current_id=session[\\\"user_id\\\"])\\n user_stocks = []\\n for stock in user_stocks_list:\\n if stock['stock'] not in user_stocks:\\n user_stocks.append(stock['stock'])\\n\\n stock_portfolio = []\\n\\n for possible_stock in user_stocks:\\n bought_shares_list = db.execute(\\\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\\\",\\n current_id=session[\\\"user_id\\\"], stock=possible_stock, t='B')\\n bought_shares = 0\\n bought_shares = bought_shares_list[0][\\\"SUM(units)\\\"]\\n sold_shares_list = db.execute(\\\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\\\",\\n current_id=session[\\\"user_id\\\"], stock=possible_stock, t='S')\\n sold_shares = 0\\n sold_shares = sold_shares_list[0][\\\"SUM(units)\\\"]\\n if sold_shares == None:\\n sold_shares = 0\\n\\n available_shares = 0\\n if bought_shares != None and (bought_shares - sold_shares) > 0:\\n available_shares = bought_shares - sold_shares\\n current_price = int(lookup(possible_stock)[\\\"price\\\"])\\n market_value = current_price * available_shares\\n dict_stock = {}\\n dict_stock['name_stock'] = possible_stock\\n dict_stock['shares_quantity'] = available_shares\\n dict_stock['current_price'] = current_price\\n dict_stock['market_value'] = market_value\\n stock_portfolio.append(dict_stock)\\n else:\\n pass\\n\\n available_money_list = db.execute(\\\"SELECT cash FROM users WHERE id = :current_id\\\", current_id=session[\\\"user_id\\\"])\\n available_money = usd(available_money_list[0]['cash'])\\n\\n username_list = db.execute(\\\"SELECT username FROM users WHERE id = :current_id\\\", current_id=session[\\\"user_id\\\"])\\n username = username_list[0][\\\"username\\\"]\\n\\n sum_market_values = 0\\n for collection in stock_portfolio:\\n sum_market_values += int(collection['market_value'])\\n\\n total_value = usd(available_money_list[0]['cash'] + sum_market_values)\\n\\n return render_template(\\\"index.html\\\", stock_portfolio=stock_portfolio, user_stocks=user_stocks, money=available_money, name=username, total_value=total_value)\",\n \"def add_portfolio(self, portfolio):\\n self.portfolios.append(portfolio)\",\n \"def add_portfolio(self, portfolio):\\n self.portfolios.append(portfolio)\",\n \"def get_portfolio_value(prices, allocs, start_val):\\n normed = prices/prices.iloc[0]\\n alloced = np.multiply(allocs, normed)\\n pos_vals = alloced * start_val\\n port_val = pos_vals.sum(axis=1)\\n return port_val\",\n \"def hedge_portfolio(context, data):\\r\\n factors = get_alphas_and_betas(context, data)\\r\\n beta_exposure = 0.0\\r\\n count = 0\\r\\n for asset in context.portfolio.positions:\\r\\n if asset in factors and asset != context.index:\\r\\n if not np.isnan(factors[asset].beta):\\r\\n beta_exposure += factors[asset].beta\\r\\n count += 1\\r\\n beta_hedge = -1.0 * beta_exposure / count\\r\\n dollar_amount = context.portfolio.portfolio_value * beta_hedge\\r\\n record(beta_hedge=beta_hedge)\\r\\n if not np.isnan(dollar_amount):\\r\\n order_target_value(context.index, dollar_amount)\",\n \"def after_run(self):\\n # Calculate the performance of the strategy and portfolio\\n self.portfolio.calc_stats()\\n self.calc_performance()\\n\\n return self\",\n \"def index():\\n # Establish userID.\\n userID = session[\\\"user_id\\\"]\\n # Isolate all results from portfolio table for the current user.\\n portfolio = db.execute(\\\"SELECT * FROM portfolio WHERE id=:userID\\\", userID=session[\\\"user_id\\\"])\\n # Cash for current user (first row, cash column)\\n cash = db.execute(\\\"SELECT cash FROM users WHERE id=:userID\\\", userID=userID)[0][\\\"cash\\\"]\\n # Empty list to store stock data as iterating through rows.\\n stockData = []\\n # Set total for combined stoc value to 0.\\n totalAllStocks = 0\\n\\n # Iterate over rows from portfolio and allocate a row for each stock that has more than 0 owned.\\n for row in portfolio:\\n if row[\\\"numOwned\\\"] != 0:\\n stockData.append(row)\\n\\n # Iterate over rows in stock data and provide value for each column. Other values for use in html are already in list from previous loop.\\n # Had to play around with usd, once in usd is a str rather than float so usd always has to be post calculations.\\n for row in stockData:\\n stock = lookup(row[\\\"symbol\\\"])\\n row[\\\"name\\\"] = stock[\\\"name\\\"]\\n row[\\\"currentPrice\\\"] = usd(stock[\\\"price\\\"])\\n row[\\\"total\\\"] = usd(row[\\\"numOwned\\\"] * stock[\\\"price\\\"])\\n totalAllStocks += row[\\\"numOwned\\\"] * stock[\\\"price\\\"]\\n # Grand Total is combined stock values and cash value.\\n grandTotal = totalAllStocks + cash\\n # Return index.html input sources.\\n return render_template(\\\"index.html\\\", stockData=stockData, cash=usd(cash), totalAllStocks = usd(totalAllStocks), grandTotal=usd(grandTotal))\",\n \"def portfolio(user_id: int, db: Connection) -> Dict[str, Union[str, int, float]]:\\n\\n # accumulator\\n user_portfolio = User_state_info([], 0, 0)\\n\\n # get user cash from `users` table\\n if (cash := get_cash(user_id, db)) is None:\\n return [-1, f\\\"`get_cash: {DB_ERROR} or {USER_NOT_FOUND}\\\"]\\n\\n # fill result structure with cash\\n user_portfolio.cash = round(cash,2)\\n\\n # get user balance from `shares` table\\n balance = get_shares_info(user_id, db)\\n\\n # fill result structure with `symbol` and `qty`\\n user_portfolio.shares = fill_qty(balance)\\n\\n # fill result structure with 'company_name\\\" and `price`\\n user_portfolio.shares = get_company_and_price(user_portfolio.shares)\\n\\n # calc and fill `total` field\\n user_portfolio.total = sum([x.total for x in user_portfolio.shares]) + user_portfolio.cash\\n \\n return user_portfolio.__dict__\",\n \"def track_portfolio(self, p):\\n\\n global st_refresh_thread\\n\\n if self.terminate:\\n return\\n\\n p.refresh()\\n\\n self.lock.acquire()\\n self.active_portfolio = p\\n self.display_portfolio(p)\\n self.lock.release()\\n\\n if not self.refresh_thread:\\n thr_args = list()\\n thr_args.append(self)\\n self.refresh_thread = threading.Thread(target=st_refresh_thread,\\n args=thr_args)\\n self.refresh_thread.start()\",\n \"def get_porfolio_signal(self):\\n\\n return self._portfolio_signal\",\n \"async def list(self, ctx, user=None, date=None):\\n if not user:\\n user = ctx.message.author\\n else:\\n user = util.GetUserFromNameStr(ctx.message.server.members, user)\\n change = GetPortfolioChange(user.id)\\n portfolio = GetPortfolio(user.id, util.GetTimestamp(date))\\n await self.bot.say(\\n '```%s\\\\'s portfolio:\\\\n'\\n 'Total Value: $%s (%.2f%s) \\\\n'\\n '%s```' % (user, portfolio.Value(), change, \\\"%\\\", portfolio.AsTable()))\",\n \"def index():\\n # Use a place holder ':curr_id' to call the session id which is the user's id\\n rows = db.execute(\\\"SELECT stocks.symbol, stocks.name, portfolio.shares FROM portfolio JOIN users ON users.id = portfolio.user_id JOIN stocks ON portfolio.stock_id = stocks.id WHERE users.id==:curr_id\\\", curr_id=session[\\\"user_id\\\"])\\n # Make a select query only on cash to be able to display it in portfolio's table\\n row_cash = db.execute(\\\"SELECT cash FROM users WHERE id==:curr_id\\\", curr_id=session[\\\"user_id\\\"])\\n\\n # gets the current price of each stock queried\\n if rows:\\n for r in rows:\\n r_shares = r[\\\"shares\\\"]\\n r_symbol = r[\\\"symbol\\\"]\\n # run lookup function to get current price\\n dict_2 = lookup(r_symbol)\\n # Adds the key \\\"price\\\" and its value to the dictionary \\\"rows\\\"\\n r[\\\"price\\\"] = dict_2[\\\"price\\\"]\\n # Calculates the grand total (stocks’ total value plus cash)\\n total = sum([r[\\\"price\\\"]*r[\\\"shares\\\"] for r in rows]) + row_cash[0][\\\"cash\\\"]\\n return render_template(\\\"portfolio.html\\\", rows=rows, row_cash=row_cash, total=total)\",\n \"def index():\\n\\n rows = db.execute(\\\"SELECT * FROM portfolio WHERE id = :id\\\", id=session[\\\"user_id\\\"])\\n users = db.execute(\\\"SELECT * FROM users WHERE id = :id\\\", id=session[\\\"user_id\\\"])\\n cash = users[0][\\\"cash\\\"]\\n total = 0\\n\\n for row in rows:\\n symbol = row[\\\"symbol\\\"]\\n shares = row[\\\"shares\\\"]\\n stock = lookup(symbol)\\n price_t = float(stock[\\\"price\\\"]) * shares\\n db.execute(\\\"UPDATE portfolio SET price=:price WHERE id=:id AND symbol=:symbol\\\",\\n price=float(stock[\\\"price\\\"]), id=session[\\\"user_id\\\"], symbol=row[\\\"symbol\\\"])\\n total += price_t\\n\\n TOTAL = total + cash\\n return render_template(\\\"index.html\\\", rows=rows, cash=usd(cash), TOTAL=usd(TOTAL))\",\n \"def evaluate_portfolio(username):\\n user_obj = User.query.filter(User.username == username).first()\\n date = request.args.get('date')\\n\\n if user_obj is None:\\n return util.build_json_response('User does not exist')\\n\\n if not util.is_valid_date_string(date):\\n return util.build_json_response(\\\"Not a valid date of the form YYYY-MM-DD\\\")\\n\\n following_date = util.add_days_to_date(date, 1)\\n equities = db.session.query(Portfolio.ticker, func.sum(Portfolio.quantity))\\\\\\n .filter(Portfolio.user_id == user_obj.id) \\\\\\n .filter(Portfolio.transaction_date <= following_date) \\\\\\n .group_by(Portfolio.ticker).all()\\n\\n e_total = 0\\n for equity in equities:\\n price = equity[1] * market_data.get_stock_price(equity[0], date, 'low')\\n e_total += price\\n\\n total = round(e_total + user_obj.balance, 2)\\n cash = round(user_obj.balance, 2)\\n e_total = round(e_total, 2)\\n\\n return util.build_json_response(\\\"Portfolio totals retrieved\\\", equity_total=e_total, cash_balance=cash, account_total=total)\",\n \"def optimize_portfolio(sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,1,1), \\\\\\n syms=['GOOG','AAPL','GLD','XOM'], gen_plot=False):\\n\\n # Read in adjusted closing prices for given symbols, date range\\n dates = pd.date_range(sd, ed)\\n prices_all = get_data(syms, dates) # automatically adds SPY\\n prices = prices_all[syms] # only portfolio symbols\\n prices_SPY = prices_all['SPY'] # only SPY, for comparison later\\n\\n\\t# find the allocations for the optimal portfolio\\n #1 provide an initial guess for x\\n allocs = np.ones(len(syms))/len(syms)\\n #2 Provide constraints to the optimizer\\n bounds = [(0,1) for i in syms]\\n constraints = ({ 'type': 'eq', 'fun': lambda inputs: 1.0 - np.sum(inputs) })\\n #3 call the optimizer\\n res = spo.minimize(get_sharpe_ratio, allocs, \\n \\t\\t\\t\\t\\targs=prices, \\n \\t\\t\\t\\t\\tbounds = bounds,\\n \\t\\t\\t\\t\\tconstraints=constraints)\\n allocs = res.x\\n \\n # Get daily portfolio value\\n port_val = get_portfolio_value(prices, allocs, 1.0)\\n \\n # Get portfolio statistics\\n cr, adr, sddr, sr = get_portfolio_stats(port_val, \\n \\t\\t\\t\\t\\t\\t\\t\\t\\t\\tdaily_rf=0.0, \\n \\t\\t\\t\\t\\t\\t\\t\\t\\t\\tsamples_per_year=252)\\n \\n # Compare daily portfolio value with SPY using a normalized plot\\n if gen_plot:\\n # add code to plot here\\n df_temp = pd.concat([port_val, prices_SPY], keys=['Portfolio', 'SPY'], axis=1)\\n plot_normalized_data(df_temp)\\n\\n return allocs, cr, adr, sddr, sr\",\n \"def __update_portfolio_handler(self, msg):\\n pass\",\n \"def income_model_asset_based_portfolio_custom(num_of_years=30, trials=100, method='normal', income=True):\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n clean_names = list(read_returns_est.index)\\n clean_names = [s.split(' ')[0] for s in clean_names]\\n read_returns_est.loc[:, 'names'] = clean_names\\n read_returns_est.set_index('names', drop=True, inplace=True)\\n read_returns_est = read_returns_est[:-1]\\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\\n\\n # ---------------Returns DataFrame based on the use input------------------------------------\\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\\n read_normal.loc[:, 'FIA'] = user_est_fia_return\\n\\n read_returns_est.loc['FIA', 'Annualized Returns'] = user_est_fia_return\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n # read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n # read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\\n # read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n #\\n # # dataframe for smallest to largest returns\\n # median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\\n # median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n # median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\\n #\\n # # dataframe for unsorted returns (normal)\\n # median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\\n # median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n # median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n while runs < trials:\\n print(runs)\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # -------------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n if income:\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n else:\\n req_annual_income = 0.0\\n income_needed = 0.0\\n income_net_fia_income = 0.0\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n # for c in range(len(r_cols)):\\n # ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n # this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, base_assets]\\n\\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\\n\\n # store the simulated assets returns in one dictionary\\n # returns_dict.update({str(runs): random_returns})\\n\\n # collect the asset based returns from all simulation and calculate the median returns.\\n # def get_median_returns(sym):\\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\\n # return asset_df.median(axis=1)\\n #\\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\\n #\\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\\n\\n base_df = random_returns.copy()\\n\\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\\n\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # ----------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n if income:\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n else:\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] + \\\\\\n income_from_fia\\n\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\\n\\n runs += 1\\n\\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\\n total_income_by_age.fillna(0, inplace=True)\\n income_dataframe = pd.DataFrame(total_income_by_age)\\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\\n lambda x: total_income_by_acturial_age - x)\\n\\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\\n\\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n\\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ---------------Portfolio for 45 years of simulation---------------------------------------\\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ----------------Portfolio Simulation until the acturial age------------------------------\\n acturial_years = life_expectancy - clients_age\\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\\n\\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\\n if base_success_portfolio_act_age.isnull().sum() == trials:\\n base_max_portfolio = 0.0\\n else:\\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\\n\\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\\n if port_success_portfolio_act_age.isnull().sum() == trials:\\n port_max_portfolio = 0.0\\n else:\\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\\n # ------------------------------Average age with full income------------------------------\\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n # ----------------------------Median Age with full Income------------------------------------------\\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n\\n # --------------Median Value for all the portfolios at end of the acturial age--------------------\\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n\\n # # --------------Mean Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n #\\n # # --------------Median Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n\\n # -------Max Portfolio value at the end of acturial age----------------------------------------\\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n\\n # -------Min Portfolio value at the end of acturial age----------------------------------------\\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n\\n # ---------------------Lifetime Average Income----------------------------------\\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\\n port_total_income = income_from_fia + sim_port_income\\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\\n\\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\\n 'Max Portfolio Value', 'Min Portfolio Value',\\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\\n\\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\\n 'Median years of portfolios that meet the next years income needs for the lifetime',\\n 'Average Clients Age',\\n 'Median Clients Age',\\n 'Average of terminal values for the portfolios at the end of the acturial life',\\n 'Median of terminal values for the portfolios at the end of the acturial life',\\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\\n 'Average of total income generated by all portfolios at the end of the acturial life']\\n\\n simulation_stats.loc[:, 'Notes'] = comments\\n\\n # --------------------------------------------------------------------------------\\n\\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\\n # base_df.to_csv(src + 'base_port_detail.csv')\\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n # income_breakdown_base.loc[:, 'social_security_income'] = social\\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n #\\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n # axis=1)\\n #\\n # # --------------------------------------Block Ends-----------------------------------------------------------\\n #\\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n #\\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n # income_breakdown_port.loc[:, 'social_security_income'] = social\\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n #\\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n # axis=1)\\n #\\n # # ----------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ------------------------------------------drop year 0-----------------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n sim_base_total.clip(lower=0, inplace=True)\\n\\n # -------------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n\\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n\\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n # base_qcut.loc[:, 'clients_age'] = age_index\\n # base_qcut.loc[:, 'comment'] = ''\\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n # --------To start with year 0---------------------------------\\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\\n base_inv, clients_age, np.nan]\\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\\n base_qcut = base_qcut.shift(1)\\n base_qcut.iloc[0] = insert_col\\n base_qcut.reset_index(drop=True, inplace=True)\\n base_qcut.loc[:, 'Annual Return'] = base_qcut.loc[:, '50th'].pct_change().fillna(0)\\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\\n # port_qcut.loc[:, 'clients_age'] = age_index\\n # port_qcut.loc[:, 'comment'] = ''\\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\\n port_qcut = port_qcut.shift(1)\\n port_qcut.iloc[0] = insert_col\\n port_qcut.reset_index(drop=True, inplace=True)\\n port_qcut.loc[:, 'Annual Return'] = port_qcut.loc[:, '50th'].pct_change().fillna(0)\\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'clients_age'] = age_index\\n # prob_success_df.loc[:, 'comment'] = ''\\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n prob_success_df.loc[:, 'age'] = age_index\\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\\n\\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\\n\\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\\n\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\\n\\n prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\\\\n - social - cpn_income_port\\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\\\\n - income_from_fia - social - cpn_income_port\\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # -------------------Write simulation Statistics-------------------------------------\\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\\n\\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\\n\\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed....\\\")\",\n \"def portfolio_to_dict(self):\\n holdings = {}\\n for asset, pos in self.pos_handler.positions.items():\\n holdings[asset] = {\\n \\\"quantity\\\": pos.net_quantity,\\n \\\"market_value\\\": pos.market_value,\\n \\\"unrealised_pnl\\\": pos.unrealised_pnl,\\n \\\"realised_pnl\\\": pos.realised_pnl,\\n \\\"total_pnl\\\": pos.total_pnl\\n }\\n return holdings\",\n \"async def value(self, ctx, user=None):\\n if not user:\\n user = ctx.message.author\\n else:\\n user = util.GetUserFromNameStr(ctx.message.server.members, user)\\n portfolio = GetPortfolio(user.id)\\n await self.bot.say('%s\\\\'s portfolio is now worth $%.2f.' % \\n (user, portfolio.Value()))\",\n \"def get_portfolio_prices(stocks: list, funds: list, etfs: list, start_date: str, end_date=today) -> pd.DataFrame:\\r\\n data_frames_stocks = get_assets_data_frames(\\r\\n stocks, inv.get_stock_historical_data, 'brazil', start_date=start_date, end_date=end_date)\\r\\n data_frames_funds = get_assets_data_frames(\\r\\n funds, inv.get_fund_historical_data, 'brazil', start_date=start_date, end_date=end_date)\\r\\n data_frames_etfs = get_assets_data_frames(\\r\\n etfs, inv.get_etf_historical_data, 'brazil', start_date=start_date, end_date=end_date)\\r\\n\\r\\n data_frames = [*data_frames_stocks, *data_frames_funds, *data_frames_etfs]\\r\\n\\r\\n assets = [*stocks, *funds, *etfs]\\r\\n\\r\\n portfolio_prices = build_multi_index_data_frame(\\r\\n data_frames, assets, ['Close', 'Open', 'High', 'Low'])\\r\\n\\r\\n return portfolio_prices\",\n \"def __populate_historical_trade_data(self):\\n\\n trade_data = self.__transactions.pivot_table(\\n index=\\\"Date\\\",\\n columns=[\\\"Ticker\\\"],\\n values=[\\n \\\"Quantity\\\",\\n \\\"Investment\\\",\\n ],\\n aggfunc={\\\"Quantity\\\": np.sum, \\\"Investment\\\": np.sum},\\n )\\n\\n # Make historical prices columns a multi-index. This helps the merging.\\n self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(\\n [[\\\"Close\\\"], self.portfolio_historical_prices.columns]\\n )\\n\\n trade_data = pd.merge(\\n trade_data,\\n self.portfolio_historical_prices,\\n how=\\\"outer\\\",\\n left_index=True,\\n right_index=True,\\n )\\n\\n trade_data[\\\"Close\\\"] = trade_data[\\\"Close\\\"].fillna(method=\\\"ffill\\\")\\n trade_data.fillna(0, inplace=True)\\n\\n trade_data[\\\"Quantity\\\"] = trade_data[\\\"Quantity\\\"].cumsum()\\n trade_data[\\\"Investment\\\"] = trade_data[\\\"Investment\\\"].cumsum()\\n trade_data[\\\"Investment\\\", \\\"Total\\\"] = trade_data[\\\"Investment\\\"].sum(axis=1)\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Investment delta\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"Investment\\\"].diff(periods=1).fillna(trade_data[\\\"Investment\\\"]))\\n\\n # End Value = Quantity * Close\\n trade_data[pd.MultiIndex.from_product([[\\\"End Value\\\"], self.tickers_list])] = (\\n trade_data[\\\"Quantity\\\"][self.tickers_list]\\n * trade_data[\\\"Close\\\"][self.tickers_list]\\n )\\n\\n trade_data.loc[:, (\\\"End Value\\\", \\\"Total\\\")] = trade_data[\\\"End Value\\\"][\\n self.tickers_list\\n ].sum(axis=1)\\n\\n # Initial Value = Previous End Value + Investment changes\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Initial Value\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = 0\\n\\n trade_data[\\\"Initial Value\\\"] = trade_data[\\\"End Value\\\"].shift(1) + trade_data[\\n \\\"Investment\\\"\\n ].diff(periods=1)\\n\\n # Set first day Initial Value as the Investment (NaNs break first period)\\n for t in self.tickers_list + [\\\"Total\\\"]:\\n trade_data.at[trade_data.index[0], (\\\"Initial Value\\\", t)] = trade_data.iloc[\\n 0\\n ][\\\"Investment\\\"][t]\\n\\n trade_data = trade_data.reindex(\\n columns=[\\n \\\"Quantity\\\",\\n \\\"Investment\\\",\\n \\\"Investment delta\\\",\\n \\\"Close\\\",\\n \\\"Initial Value\\\",\\n \\\"End Value\\\",\\n ],\\n level=0,\\n )\\n self.historical_trade_data = trade_data\",\n \"def read_portfolio_history(self, price_provider: Callable[[Security, dt.datetime], float]) -> Dict[dt.datetime, float]:\\n if self._transactions_dataframe.empty:\\n return dict()\\n\\n dated_transactions = self._transactions_dataframe.set_index(TRANSACTION_DATE).sort_index()\\n all_share_ids = dated_transactions[SECURITY_ID].unique()\\n\\n date_index = dated_transactions.index.unique()\\n share_history = pd.DataFrame(0, index=date_index, columns=all_share_ids)\\n for share_id in all_share_ids:\\n relevant_rows = dated_transactions[SECURITY_ID] == share_id\\n transactions = dated_transactions.loc[relevant_rows, TRANSACTION_SHARE_AMOUNT]\\n share_history.loc[dated_transactions.index[relevant_rows], share_id] = transactions\\n share_history[share_id] = share_history[share_id].cumsum()\\n\\n share_prices = pd.DataFrame(0, index=date_index, columns=all_share_ids)\\n for share_id in all_share_ids:\\n date_to_price = lambda date: price_provider(Security(share_id), date)\\n share_prices[share_id] = np.vectorize(date_to_price)(date_index.to_pydatetime())\\n\\n portfolio = (share_prices * share_history).apply(np.sum, axis=1)\\n\\n return {date.to_pydatetime(): price for date, price in portfolio.to_dict().items()}\",\n \"def __repr__(self):\\n return \\\"<Portfolio: %s | valued at %s>\\\" % (self.portfolios, self.value)\",\n \"def income_model_asset_based_portfolio_quantile(num_of_years=30, trials=100, method='normal'):\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_pre_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_total_preincome = pd.DataFrame(index=range(num_of_years + 1))\\n\\n # read_income_inputs = pd.read_csv(src + \\\"income_model_inputs.csv\\\", index_col='Items')\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n # read_returns_est = pd.read_csv(src + \\\"income_assets_returns_estimates.csv\\\", index_col='Symbol')\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n\\n # read_returns_est.drop(['BM', read_returns_est.index[-1]], axis=0, inplace=True)\\n # read_portfolio_inputs = pd.read_csv(src + \\\"income_portfolio_inputs.csv\\\", index_col='Items')\\n\\n # read_asset_weights = pd.read_csv(src + \\\"asset_weights.csv\\\", index_col='Asset')\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'sort_normal.csv', index_col=[0], parse_dates=True)\\n read_small = pd.read_csv(src + 'sort_small_to_large.csv', index_col=[0], parse_dates=True)\\n read_large = pd.read_csv(src + 'sort_large_to_small.csv', index_col=[0], parse_dates=True)\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = pd.DataFrame({t: asset_median_returns(read_normal, t) for t in tickers})\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'r_FIA')})\\n\\n # dataframe for smallest to largest returns\\n median_returns_smallest = pd.DataFrame({t: asset_median_returns(read_small, t) for t in tickers})\\n median_returns_smallest.loc[:, 'portfolio_return'] = median_returns_smallest.dot(wts)\\n median_smallest_fia = pd.DataFrame({'FIA': asset_median_returns(read_small, 'r_FIA')})\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_largest = pd.DataFrame({t: asset_median_returns(read_large, t) for t in tickers})\\n median_returns_largest.loc[:, 'portfolio_return'] = median_returns_largest.dot(wts)\\n median_largest_fia = pd.DataFrame({'FIA': asset_median_returns(read_large, 'r_FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n while runs < trials:\\n print(runs)\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n\\n if method == 'normal':\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n elif method == 'smallest':\\n income_df.loc[:, 'index_returns'] = read_small.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n else:\\n income_df.loc[:, 'index_returns'] = read_large.loc[:, '{}_{}'.format('r_FIA', str(runs))]\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # -------------------------------------BASE MODEL---------------------------------------------\\n\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = ['r_{}'.format(name) for name in base_assets]\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n if method == 'smallest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_small.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.sort(ret.flatten())\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.sort(ret.flatten())})\\n\\n elif method == 'largest':\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_large.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = np.flip(np.sort(ret.flatten()))\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): np.flip(np.sort(ret.flatten()))})\\n\\n else:\\n this_run_cols = ['{}_{}'.format(cname, str(runs)) for cname in r_cols]\\n random_returns = read_normal.loc[:, this_run_cols]\\n\\n # random_returns.loc[:, r_cols[c]] = ret.flatten()\\n # asset_dict.update({'{}_{}'.format(r_cols[c], str(runs)): ret.flatten()})\\n\\n # store the simulated assets returns in one dictionary\\n # returns_dict.update({str(runs): random_returns})\\n\\n # collect the asset based returns from all simulation and calculate the median returns.\\n # def get_median_returns(sym):\\n # cols = [sym + '_' + str(c) for c in np.arange(trials)]\\n # asset_df = pd.DataFrame({c: asset_dict.get(c) for c in cols})\\n # return asset_df.median(axis=1)\\n #\\n # asset_median_returns = pd.DataFrame({symbol: get_median_returns(symbol) for symbol in r_cols})\\n #\\n # asset_median_returns.loc[:, 'simulated_portfolio_median_returns'] = asset_median_returns.dot(base_weights)\\n\\n base_df = random_returns.copy()\\n pre_income_base_df = random_returns.copy()\\n\\n # base_investment = float(read_portfolio_inputs.loc['risky_assets', 'Base'])\\n\\n fia_portfolio_df = random_returns.copy()\\n pre_income_port_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # ---------Initial Investments for pre-income account values---------------------\\n pre_income_base_inv = base_investment\\n pre_income_port_inv = port_investment\\n # ----------------------------------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n pre_income_base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n # ---------------For year 0, the year of investment------------\\n\\n # ------------Calculate the annual portfolio returns - Gross Returns--------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_base_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_base_inv for c in range(len(boy_value))]\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_inv = pre_income_base_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_pre_income'] = base_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n # ----For years between the start of the investment and start if the income---------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total_net_fees']\\n\\n else:\\n\\n # -------------For Years after the income started----------------------\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_base_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_base_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_base_df.loc[counter, 'total'] = pre_income_base_df.loc[counter, boy_value].sum()\\n pre_income_base_df.loc[counter, 'adv_fees'] = pre_income_base_df.loc[counter, 'total'] * adv_fees\\n pre_income_base_df.loc[counter, 'total_net_fees'] = pre_income_base_df.loc[counter, 'total'] - \\\\\\n pre_income_base_df.loc[counter, 'adv_fees']\\n pre_income_base_inv = pre_income_base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_df.loc[counter, 'total_pre_income'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n # ------------------------Portfolio with PreIncome Values----------------------------\\n sim_base_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_base_df.loc[:, 'total_net_fees']\\n sim_base_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'Base']), inplace=True)\\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n sim_base_total_pre_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_pre_income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n pre_income_port_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n\\n pre_income_port_df.loc[counter, boy_value] = [base_weights[c] *\\n pre_income_port_inv for c in range(len(boy_value))]\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_inv = pre_income_port_df.loc[counter, boy_value].sum()\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = port_investment\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # ------------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n # -------------Record the Pre Income Base Portfolio-----------------------------\\n pre_income_port_df.loc[counter, boy_value] = [\\n base_weights[c] * pre_income_port_inv * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n\\n pre_income_port_df.loc[counter, 'total'] = pre_income_port_df.loc[counter, boy_value].sum()\\n pre_income_port_df.loc[counter, 'adv_fees'] = pre_income_port_df.loc[counter, 'total'] * adv_fees\\n pre_income_port_df.loc[counter, 'total_net_fees'] = pre_income_port_df.loc[counter, 'total'] - \\\\\\n pre_income_port_df.loc[counter, 'adv_fees']\\n pre_income_port_inv = pre_income_port_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n # ------------------Pre Income Block Ends------------------------\\n\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n fia_portfolio_df.loc[counter, 'total_pre_income'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n sim_port_total_pre_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_pre_income']\\n\\n # -------------------Portfolio with PreIncome Values----------------------------\\n sim_port_total_preincome.loc[:, 's_{}'.format(str(runs))] = pre_income_port_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_total_preincome.fillna(float(read_income_inputs.loc['risky_assets', 'FIA']), inplace=True)\\n # --------------------------------PreIncome Block Ends----------------------------\\n\\n runs += 1\\n\\n # ------------------Calculate % of portfolios ending value greater than required LIFETIME cumm. income---------\\n total_income_by_age = sim_base_income.loc[:, sim_base_income.columns[0]].cumsum()\\n total_income_by_acturial_age = total_income_by_age.loc[life_expectancy - clients_age]\\n total_income_by_age.fillna(0, inplace=True)\\n income_dataframe = pd.DataFrame(total_income_by_age)\\n income_dataframe.loc[:, 'remaining_income_by_acturial_age'] = total_income_by_age.apply(\\n lambda x: total_income_by_acturial_age - x)\\n\\n s = income_dataframe.loc[:, 'remaining_income_by_acturial_age']\\n base_prob_of_success = sim_base_total.gt(s, axis=0).sum(axis=1)\\n port_prob_of_success = sim_port_total.gt(s, axis=0).sum(axis=1)\\n\\n # ----------------------------Portfolio sufficient for NEXT YEARS income needs-------------------\\n next_year_income = sim_base_income.loc[:, sim_base_income.columns[0]].shift(-1).fillna(0) # Yearly Income Reqd.\\n base_success_next_year = sim_base_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n base_for_next_year_need = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n\\n port_success_next_year = sim_port_total.gt(next_year_income, axis=0).sum(axis=1)\\n\\n port_for_next_year_need = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ---------------Portfolio for 45 years of simulation---------------------------------------\\n base_success_portfolio = sim_base_total[sim_base_total.gt(next_year_income, axis=0)]\\n port_success_portfolio = sim_port_total[sim_port_total.gt(next_year_income, axis=0)]\\n\\n # ----------------Portfolio Simulation until the acturial age------------------------------\\n acturial_years = life_expectancy - clients_age\\n base_success_portfolio_act_age = base_success_portfolio.loc[acturial_years, :]\\n port_success_portfolio_act_age = port_success_portfolio.loc[acturial_years, :]\\n\\n # -------------------------Base Portfolio TS with max Terminal Value ----------------------------\\n if base_success_portfolio_act_age.isnull().sum() == trials:\\n base_max_portfolio = 0.0\\n else:\\n base_max_portfolio = base_success_portfolio.loc[:, base_success_portfolio_act_age.idxmax()]\\n\\n # -------------------------FIA Portfolio TS with max Terminal Value ----------------------------\\n if port_success_portfolio_act_age.isnull().sum() == trials:\\n port_max_portfolio = 0.0\\n else:\\n port_max_portfolio = port_success_portfolio.loc[:, port_success_portfolio_act_age.idxmax()]\\n\\n # ------------------------------Average age with full income------------------------------\\n base_mean_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n port_mean_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).mean()\\n\\n # ----------------------------Median Age with full Income------------------------------------------\\n base_median_age = ((life_expectancy - clients_age) - base_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n port_median_age = ((life_expectancy - clients_age) - port_success_portfolio.loc[1:life_expectancy - clients_age, :]\\n .isnull().sum()).median()\\n\\n # --------------Mean Value for all the portfolios at end of the acturial age--------------------\\n base_act_avg_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n port_act_avg_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).mean()\\n\\n # ---------------Median Value for all the portfolios at end of the acturial age--------------------\\n base_act_median_porfolio = base_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n port_act_median_porfolio = port_success_portfolio.loc[life_expectancy - clients_age, :].fillna(0).median()\\n\\n # # --------------Mean Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().mean()\\n #\\n # # --------------Median Value for all the portfolios in the simulation--------------------\\n # base_sim_mean = base_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n # port_sim_mean = port_success_portfolio.loc[1:life_expectancy - clients_age, :].mean().median()\\n\\n # -------Max Portfolio value at the end of acturial age----------------------------------------\\n base_act_max = base_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n port_act_max = port_success_portfolio.loc[life_expectancy - clients_age, :].max()\\n\\n # ----------------------Min Portfolio value at the end of acturial age----------------------------------------\\n base_act_min = base_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n port_act_min = port_success_portfolio.loc[life_expectancy - clients_age, :].min()\\n\\n # -----------------------------------------Lifetime Average Income-----------------------------------------\\n base_total_income = sim_base_income.cumsum().loc[acturial_years, :].mean()\\n port_total_income = income_from_fia + sim_port_income\\n port_total_income = port_total_income.cumsum().loc[acturial_years, :].mean()\\n\\n simulation_stats = pd.DataFrame(index=['Average Years', 'Median Years', 'Average Age', 'Median Age',\\n 'Average Portfolio (act.age)', 'Median Portfolio (act.age)',\\n 'Max Portfolio Value', 'Min Portfolio Value',\\n 'Average Lifetime Income'], columns=['Base', 'FIA'])\\n\\n simulation_stats.loc['Average Years', :] = [base_mean_age, base_mean_age]\\n simulation_stats.loc['Median Years', :] = [base_median_age, base_median_age]\\n simulation_stats.loc['Average Age', :] = [base_mean_age + clients_age, base_mean_age + clients_age]\\n simulation_stats.loc['Median Age', :] = [base_median_age + clients_age, base_median_age + clients_age]\\n simulation_stats.loc['Average Portfolio (act.age)', :] = [base_act_avg_porfolio, port_act_avg_porfolio]\\n simulation_stats.loc['Median Portfolio (act.age)', :] = [base_act_median_porfolio, port_act_median_porfolio]\\n simulation_stats.loc['Max Portfolio Value', :] = [base_act_max, port_act_max]\\n simulation_stats.loc['Min Portfolio Value', :] = [base_act_min, port_act_min]\\n simulation_stats.loc['Average Lifetime Income', :] = [base_total_income, port_total_income]\\n comments = ['Average years of portfolios that meet the next years income needs for the lifetime',\\n 'Median years of portfolios that meet the next years income needs for the lifetime',\\n 'Average Clients Age',\\n 'Median Clients Age',\\n 'Average of terminal values for the portfolios at the end of the acturial life',\\n 'Median of terminal values for the portfolios at the end of the acturial life',\\n 'Maximum of terminal values for the portfolios at the end of the acturial life',\\n 'Minimum of terminal values for the portfolios at the end of the acturial life',\\n 'Average of total income generated by all portfolios at the end of the acturial life']\\n\\n simulation_stats.loc[:, 'Notes'] = comments\\n\\n # --------------------------------------------------------------------------------\\n\\n # # -----------------------------------income breakdown for Base portfolio----------------------------------\\n # base_df.to_csv(src + 'base_port_detail.csv')\\n # sim_base_total.to_csv(src + 'base_ending_values.csv')\\n # income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n # income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n # income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n # income_breakdown_base.loc[:, 'social_security_income'] = social\\n # income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n #\\n # income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_base.loc[:, 'income_from_portfolio'][\\n # income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n # axis=1)\\n #\\n # # --------------------------------------Block Ends-----------------------------------------------------------\\n #\\n # # ---------------------------------------income breakdown for FIA portfolio----------------------------------\\n # fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n # sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n #\\n # income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n # income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n # income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n # income_breakdown_port.loc[:, 'social_security_income'] = social\\n # income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n #\\n # income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n # income_breakdown_port.loc[:, 'income_from_portfolio'][\\n # income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n # income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n # axis=1)\\n #\\n # # ----------------------------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.1, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n #\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '10th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ------------------------------------------drop year 0-----------------------------------------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ---------------------------------plot for histogram for porfolios--------------------------------------\\n # base_term_value = sim_base_total.loc[sim_base_total.index[:life_expectancy - clients_age], :]\\n # fact = 1 / len(base_term_value)\\n # base_ann_ret = (base_term_value.iloc[-1] / base_term_value.iloc[0]) ** fact - 1\\n # counts, bins, bars = plt.hist(base_ann_ret)\\n\\n # ------------------------quantile analysis for base terminal value-----------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n sim_base_total.clip(lower=0, inplace=True)\\n\\n # -------------------------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # ---------------------------------quantile analysis for portfolio terminal value ---------------\\n\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n # base_legacy_risk = (sim_base_total.loc[sim_base_total.index[-1]] < 0).sum() / (trials)\\n\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / trials\\n\\n # port_legacy_risk = (sim_port_total.loc[sim_port_total.index[-1]] <= 0).sum() / (trials)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / trials\\n\\n # ------------------------------------WRITING FILES TO EXCEL ---------------------------\\n\\n writer = pd.ExcelWriter(dest_simulation + method + '_montecarlo_income_summary.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n # read_portfolio_inputs.to_excel(writer, sheet_name='portfolio_inputs')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n # base_qcut.loc[:, 'clients_age'] = age_index\\n # base_qcut.loc[:, 'comment'] = ''\\n # base_qcut.loc[:, 'comment'] = np.where(base_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n base_inv = float(read_income_inputs.loc['risky_assets', 'Base'])\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n\\n # -----------------------To start with year 0---------------------------------\\n insert_col = [base_inv, base_inv, base_inv, base_inv, base_inv, base_inv,\\n base_inv, clients_age, np.nan]\\n base_qcut.loc[len(base_qcut) + 1, :] = 0.0\\n base_qcut = base_qcut.shift(1)\\n base_qcut.iloc[0] = insert_col\\n base_qcut.reset_index(drop=True, inplace=True)\\n base_qcut.to_excel(writer, sheet_name='base_ending_value_quantiles')\\n # base_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n # base_income_qcut = base_income_qcut[1:] base_income_qcut.loc[:, 'clients_age'] = age_index\\n # base_income_qcut.loc[:, 'comment'] = '' base_income_qcut.loc[:, 'comment'] = np.where(\\n # base_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n # age_index = list(range(clients_age+1, clients_age + len(port_qcut)+1))\\n # port_qcut.loc[:, 'clients_age'] = age_index\\n # port_qcut.loc[:, 'comment'] = ''\\n # port_qcut.loc[:, 'comment'] = np.where(port_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[len(port_qcut) + 1, :] = 0.0\\n port_qcut = port_qcut.shift(1)\\n port_qcut.iloc[0] = insert_col\\n port_qcut.reset_index(drop=True, inplace=True)\\n port_qcut.to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n # port_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n # port_income_qcut = port_income_qcut[1:] port_income_qcut.loc[:, 'clients_age'] = age_index\\n # port_income_qcut.loc[:, 'comment'] = '' port_income_qcut.loc[:, 'comment'] = np.where(\\n # port_income_qcut.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, :].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n prob_success_df = pd.concat([base_psuccess, port_psuccess], axis=1)\\n prob_success_df.rename(columns={prob_success_df.columns[0]: 'prob(ending_value>0)_base',\\n prob_success_df.columns[1]: 'prob(ending_value>0)_port'}, inplace=True)\\n\\n # prob_success_df.loc[:, 'clients_age'] = age_index\\n # prob_success_df.loc[:, 'comment'] = ''\\n # prob_success_df.loc[:, 'comment'] = np.where(prob_success_df.clients_age == life_expectancy, 'expected_life', \\\"\\\")\\n\\n prob_success_df.loc[:, 'age'] = age_index\\n prob_success_df.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_base'] = base_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>lifetime_req income)_port'] = port_prob_of_success / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_base'] = base_success_next_year / trials\\n prob_success_df.loc[:, 'prob(ending_value>next_year_req_income)_port'] = port_success_next_year / trials\\n prob_success_df.loc[:, 'base_max_portfolio_at_acturial_age'] = base_max_portfolio\\n prob_success_df.loc[:, 'port_max_portfolio_at_acturial_age'] = port_max_portfolio\\n\\n # --------------------Percentile Portfolio's based on Acturial Life------------------------\\n base_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_base']\\n port_success = prob_success_df.loc[life_expectancy - clients_age, 'prob(ending_value>next_year_req_income)_port']\\n\\n # acturial_age_base_tv = sim_base_total.loc[:life_expectancy - clients_age, ]\\n # percentile_base_tv = sim_base_total.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_base = base_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = base_for_next_year_need.copy().fillna(0)\\n percentile_base = base_for_next_year_need.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n base_pre_income_success = sim_base_total_preincome.apply(lambda x: np.nanpercentile(x, base_success), axis=1)\\n base_ann_ret_pre_income = base_pre_income_success.pct_change().fillna(0)\\n\\n # acturial_age_port_tv = sim_port_total.loc[:life_expectancy - clients_age, ]\\n # percentile_port_tv = sim_port_total.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----------------Year wise percentile portfolio to meet next year income. Based on the success at acturial age.\\n # Yearly portfolio values that can provide the next year income below the success rate at end of life (Percentile)-\\n\\n # acturial_age_port = port_for_next_year_need.loc[:life_expectancy - clients_age, ]\\n # acturial_age_base = port_for_next_year_need.copy().fillna(0)\\n percentile_port = port_for_next_year_need.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n\\n # ----Pre Income Portfolio based on the Probab. of Success to meet next year's income at the end on the Act. Age\\n port_pre_income_success = sim_port_total_preincome.apply(lambda x: np.nanpercentile(x, port_success), axis=1)\\n port_ann_ret_pre_income = port_pre_income_success.pct_change().fillna(0)\\n\\n prob_success_df.loc[:, 'acturial_success_percentile_base_portfolio'] = percentile_base\\n prob_success_df.loc[:, 'acturial_success_percentile_port_portfolio'] = percentile_port\\n\\n prob_success_df.loc[:, 'base_pre_income_ann_ret'] = base_ann_ret_pre_income\\n prob_success_df.loc[:, 'port_pre_income_ann_ret'] = port_ann_ret_pre_income\\n\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_base_portfolio'] = percentile_base_tv\\n # prob_success_df.loc[:, 'terminalVal_success_percentile_port_portfolio'] = percentile_port_tv\\n\\n sim_base_total_preincome.to_excel(writer, sheet_name='base_preincome_portfolios')\\n # -------------Adding Premium to calculate the total initial investment--------------\\n sim_port_total_preincome.iloc[0] = sim_port_total_preincome.iloc[0] + premium\\n sim_port_total_preincome.to_excel(writer, sheet_name='port_preincome_portfolios')\\n\\n # -------------For Simulation slide - BASE Portfolio - Can Delete --------------------\\n # base_qcut_preinc = pd.DataFrame(index=sim_base_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # base_qcut_preinc.loc[:, cols[c]] = sim_base_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # # -------------For Simulation slide - Proposed Portfolio --------------------\\n # port_qcut_preinc = pd.DataFrame(index=sim_port_total_preincome.index, columns=cols)\\n # for c in range(len(cols)):\\n # port_qcut_preinc.loc[:, cols[c]] = sim_port_total_preincome.quantile(q_cut[c], axis=1)\\n #\\n # base_qcut_preinc.to_excel(writer, sheet_name='base_preincome_quantiles')\\n # port_qcut_preinc.to_excel(writer, sheet_name='port_preincome_quantiles')\\n\\n prob_success_df.to_excel(writer, sheet_name='success_probability')\\n\\n # --------------BASE - Accumulation and Income Breakdown based on the success percentile portfolio---------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(base_success, axis=1))\\n income_breakdown_base.loc[:, 'income_from_risky_assets'] = sim_base_income.quantile(base_success, axis=1) \\\\\\n - social - cpn_income_port\\n income_breakdown_base.loc[:, 'guaranteed_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_risky_assets'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ----------FIA PORTFOLIO - Accumulation and Income Breakdown based on the success percentile portfolio-----------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(port_success, axis=1))\\n income_breakdown_port.loc[:, 'income_from_risky_assets'] = sim_port_income.quantile(port_success, axis=1) \\\\\\n - income_from_fia - social - cpn_income_port\\n income_breakdown_port.loc[:, 'guaranteed_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_risky_assets'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # -------------------Write simulation Statistics-------------------------------------\\n simulation_stats.to_excel(writer, sheet_name='simulation_statistics')\\n\\n # port_psuccess.to_excel(writer, sheet_name='fia_port_success_probability')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n legacy_risk.to_excel(writer, sheet_name='ruin_probability')\\n\\n if method == 'normal':\\n median_returns_normal.loc[:, 'fia_median_returns'] = median_normal_fia\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n elif method == 'smallest':\\n median_returns_smallest.loc[:, 'fia_median_returns'] = median_smallest_fia\\n median_returns_smallest.to_excel(writer, sheet_name='gr_port_median_asc')\\n\\n else:\\n median_returns_largest.loc[:, 'fia_median_returns'] = median_largest_fia\\n median_returns_largest.to_excel(writer, sheet_name='gr_port_median_desc')\\n\\n # ---------------------Histogram for S&P Forecast---------------------------------------\\n sp_returns = read_returns_est.loc['SPXT Index', 'Annualized Returns']\\n sp_risk = read_returns_est.loc['SPXT Index', 'Annualized Risk']\\n sp_random_ret = np.random.normal(loc=sp_returns, scale=sp_risk, size=10000)\\n bins, data = np.histogram(sp_random_ret, bins=20)\\n df_ret = pd.DataFrame(data, columns=['Return_range'])\\n df_bins = pd.DataFrame(bins, columns=['Count'])\\n df_hist = df_ret.join(df_bins)\\n df_hist.to_excel(writer, sheet_name='sp500_histogram')\\n\\n # ---------------------Histogram for FIA Portfolios TV>0 at the acturial age---------------------------------------\\n tval_at_horizon = sim_port_total.loc[acturial_years, :]\\n fact = 1 / acturial_years\\n arr_returns = np.array((tval_at_horizon / 1000000) ** fact - 1)\\n clean_ann_ret = arr_returns[~np.isnan(arr_returns)]\\n p_bins, p_data = np.histogram(clean_ann_ret, bins=20)\\n df_ret = pd.DataFrame(p_data, columns=['Return_range'])\\n df_bins = pd.DataFrame(p_bins, columns=['Count'])\\n df_hist = df_ret.join(df_bins)\\n df_hist.to_excel(writer, sheet_name='fia_portfolio_histogram')\\n\\n tval_df = pd.DataFrame(sim_port_total.loc[acturial_years, :])\\n tval_df.rename(columns={tval_df.columns[0]:'Terminal Values'}, inplace=True)\\n tval_df.to_excel(writer, sheet_name='fia_ending_values_hist')\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed....\\\")\",\n \"def __init__(\\n self,\\n start_dt,\\n starting_cash=0.0,\\n currency = \\\"USD\\\",\\n portfolio_id=None,\\n name=None\\n ):\\n self.start_dt = start_dt\\n self.current_dt = start_dt\\n self.starting_cash = starting_cash\\n self.currency = currency\\n self.portfolio_id = portfolio_id\\n self.name = name\\n\\n self.pos_handler = PositionHandler()\\n self.history = []\\n\\n self.logger = logging.getLogger('Portfolio')\\n self.logger.setLevel(logging.DEBUG)\\n self.logger.info(\\n '(%s) Portfolio \\\"%s\\\" instance initalized' % (\\n self.current_dt.strftime(settings.LOGGING[\\\"DATE_FORMAT\\\"]),\\n self.portfolio_id\\n )\\n )\\n\\n self._initalize_portfolio_with_cash()\",\n \"def generate_portfolio(S_0, params):\\n\\tpublic_client = gdax.PublicClient()\\n\\tallvar = []\\n\\tsumvar = 0\\n\\tfor coin in params:\\n\\t\\ttheta = coin[0]\\n\\t\\tv = theta[0]\\n\\t\\tT = coin[1]\\n\\t\\tprod_id = coin[2]\\n\\t\\t# Get the current value of the coin, i.e. how much you bought\\n\\t\\tname = prod_id + '-USD'\\n\\t\\tstats = public_client.get_product_24hr_stats(name)\\n\\t\\tvalue = (float(stats['high']) + float(stats['low']))/2\\n\\t\\tallvar.append([prod_id, value, v])\\n\\t\\tsumvar += v\\n\\tpriority = sorted(allvar, key=lambda i: i[2])\\n\\tportfolio = []\\n\\tfor i in priority:\\n\\t\\tinvestment = S_0*i[2]/sumvar\\n\\t\\tcurrency = investment/i[1]\\n\\t\\tportfolio.append((i[0], currency, investment)) # id, investment, currency\\n\\tprint(\\\"\\\\nYour suggested investments are: \\\\n\\\")\\n\\tfor coin in portfolio:\\n\\t\\tprint(\\\"%s: %s for %s USD\\\" % (coin[0], coin[1], coin[2]))\\n\\t# Prompt to save the portfolio\\n\\tdone = False\\n\\twhile done != True:\\n\\t\\tinp = input(\\\"\\\\nWould you like to save this portfolio? (y/n)\\t\\\")\\n\\t\\ttry:\\n\\t\\t\\tif inp.lower() == 'y':\\n\\t\\t\\t\\tpublic_client = gdax.PublicClient()\\n\\t\\t\\t\\tcurrent_date = np.datetime64(public_client.get_time().get(\\\"iso\\\").split('T')[0])\\n\\t\\t\\t\\t# Save the file\\n\\t\\t\\t\\twith open(\\\"portfolios/%s.txt\\\" % (current_date), \\\"w\\\") as f:\\n\\t\\t\\t\\t\\tfor coin in portfolio:\\n\\t\\t\\t\\t\\t\\tf.write(str(coin[0]) + ', ' + str(coin[1]) + ', ' + str(coin[2]) + '\\\\n')\\n\\t\\t\\t\\tprint(\\\"Portfolio saved. Exiting.\\\\n\\\")\\n\\t\\t\\t\\tdone = True\\n\\t\\t\\tif inp.lower() == 'n':\\n\\t\\t\\t\\tprint(\\\"Program complete. Exiting.\\\\n\\\")\\n\\t\\t\\t\\tdone = True\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint(\\\"Your input could not be interpreted.\\\")\",\n \"def portfolio_returns(returns, weights):\\n\\n # the portfolio returns are given by the dot product of the weights matrix and the portfolio matrix\\n port_returns = np.dot(weights, returns)\\n\\n return port_returns\",\n \"def portfolio_returns(returns, weights):\\n\\n # the portfolio returns are given by the dot product of the weights matrix and the portfolio matrix\\n port_returns = np.dot(weights, returns)\\n\\n return port_returns\",\n \"def __get_object_portfolio_bulk(self, name, user, key=\\\"portfolio\\\"):\\n portfolio = None\\n for item in self.bulk_data[key]:\\n if item and item.name == name and item.user == user:\\n portfolio = item\\n break\\n return portfolio\",\n \"def evaluate_cur_stocks(self):\\n today = datetime.today()\\n close_val = PRICE_DF.iloc[PRICE_DF.index.get_loc(today, method=\\\"ffill\\\")]\\n close_val = close_val[self.cur_stocks.index]\\n close_val = pd.DataFrame({\\\"PRICE_CURRENT\\\" : close_val.values}, index=self.cur_stocks.index)\\n evaluated_stocks = pd.merge(self.cur_stocks, close_val, left_index=True, right_index=True)\\n evaluated_stocks[\\\"VOLUME_CURRENT\\\"] = evaluated_stocks[\\\"AMOUNT\\\"] * evaluated_stocks[\\\"PRICE_CURRENT\\\"]\\n evaluated_stocks[\\\"RETURN\\\"] = (evaluated_stocks[\\\"VOLUME_CURRENT\\\"] / evaluated_stocks[\\\"VOLUME_PURCHASE\\\"]) - 1\\n return evaluated_stocks\",\n \"def cumulative_returns(shares_allocation, capital, test_data):\\n\\n # list of DataFrames of cumulative returns for each stock\\n daily_returns = []\\n\\n # iterates over every stock in the portfolio\\n for stock in shares_allocation.index:\\n\\n # multiples shares by share prices in the validation dataset\\n daily_returns.append(shares_allocation.loc[stock].values * test_data[stock])\\n\\n # concatenates every DataFrame in the above list to a single DataFrame\\n daily_returns_df = pd.concat(daily_returns, axis=1).reset_index()\\n\\n # sets the index as the date\\n daily_returns_df.set_index(\\\"Day\\\", inplace=True)\\n\\n # adds the cumulative returns for every stock\\n cumulative_daily_returns = daily_returns_df.sum(axis=1)\\n\\n # returns the cumulative daily returns of the portfolio\\n return cumulative_daily_returns\",\n \"def index():\\n\\n # Get user's cash\\n user = db.execute(\\\"SELECT cash FROM users WHERE id = ?\\\", session[\\\"user_id\\\"])\\n\\n # Get portfolio\\n portfolios = db.execute(\\\"SELECT * FROM portfolios WHERE user_id = ?\\\", session[\\\"user_id\\\"])\\n\\n # Get symbol for each stock\\n length = len(portfolios)\\n for i in range(length):\\n symbol = portfolios[i]['stocks']\\n\\n # Lookup stock price and add to portfolio information\\n portfolios[i]['price'] = lookup(symbol)['price']\\n portfolios[i]['total'] = float(portfolios[i]['price']) * portfolios[i]['shares']\\n\\n # Calculate total value of stocks\\n value = 0\\n for j in range(length):\\n value += portfolios[j]['price']\\n\\n # Calculate grand total of stocks plus cash\\n g_total = user[0][\\\"cash\\\"] + value\\n\\n return render_template(\\\"index.html\\\", portfolios=portfolios, cash=user[0][\\\"cash\\\"], g_total=g_total)\",\n \"def __init__(self, instrumentsNAmounts, totalInvest):\\n # add all necessary attributes here which link to portfolio and\\n # will be tracked.\\n self.portfolio = instrumentsNAmounts\\n self.totalInvest = totalInvest\",\n \"def compute_portfolio_stats(allocs,prices,rfr=0, sf=252):\\n\\n # portfolio value\\n port_val = compute_port_val(allocs, prices)\\n\\n daily_rets = port_val/port_val.shift(1) - 1\\n daily_rets = daily_rets[1:]\\n\\n # cumulative return\\n cr = port_val.iloc[-1]/port_val.iloc[0] -1\\n\\n # avg daily return\\n adr = daily_rets.mean()\\n\\n # std dev of daily return\\n sddr = daily_rets.std()\\n \\n #sharpe_ratio\\n k = math.sqrt(252)\\n \\n sr = k * ((daily_rets - 0).mean() / daily_rets.std())\\n \\n return cr, adr, sddr, sr\",\n \"def prepare_portfolio_performance(self, portfolio_scope: str, portfolio_code: str):\\n return Performance(\\n entity_scope=portfolio_scope,\\n entity_code=portfolio_code,\\n src=self.portfolio_performance_source,\\n block_store=self.block_store,\\n perf_start=None,\\n )\",\n \"def set_portfolio(self, name, user, cash):\\n if name and not self.exist_portfolio(name, user) \\\\\\n and self.db_tool.is_connected():\\n portfolio = Portfolio(\\n name=name,\\n user=user,\\n cash=cash,\\n initial_cash=cash\\n )\\n self.bulk_data[\\\"portfolio\\\"].append(portfolio)\\n self.portfolio_name = name\\n self.portfolio_user = user\",\n \"def get_portfolio_pnl_desc(self):\\n\\n return self._tsd_portfolio.summary()\",\n \"def performance_vs_index(self, index='SPY', dateIni='Ini', dateFin='Fin'):\\n if dateFin == 'Fin':\\n dateFin = self.data.index[-1]\\n if dateIni == 'Ini':\\n dateIni = self.data.index[0]\\n portfolioGains = round(self.data.loc[self.data.index[-1], 'Profit/Loss%'], 2)\\n else:\\n pData = self.data.loc[dateIni:dateFin]\\n pData.loc[:,'Profit/Loss'] = pData['Gains'].cumsum()\\n pData.loc[:,'Profit/Loss%'] = pData['Profit/Loss'] / pData['Invested'] * 100\\n portfolioGains = round(pData.loc[pData.index[-1], 'Profit/Loss%'], 2)\\n indexData = yf.Ticker(index).history(start=dateIni, end=dateFin)\\n indexData['Var%'] = (indexData.Close - indexData.Close[0]) / indexData.Close[0] * 100\\n indexGains = round(indexData.loc[indexData.index[-1], 'Var%'], 2)\\n return portfolioGains, indexGains, portfolioGains - indexGains\",\n \"def index():\\n stocks = []\\n username = session.get(\\\"username\\\")\\n symbol_list = db.execute(\\\"SELECT stock_symbol FROM history WHERE username=:username GROUP BY stock_symbol\\\", username=username)\\n cash_balance = db.execute(\\\"SELECT cash FROM users WHERE username=:username\\\", username=username)[0][\\\"cash\\\"]\\n total_value = cash_balance\\n\\n for sym in symbol_list:\\n symbol = sym[\\\"stock_symbol\\\"]\\n new_stock = Stock(username, symbol)\\n stocks.append(new_stock)\\n total_value += new_stock.quantity * new_stock.price\\n\\n\\n return render_template(\\\"portfolio.html\\\", stocks = stocks, cash_balance=usd(cash_balance), total_value=usd(total_value))\",\n \"def _computeIsosurface(self):\\n data = self.getData(copy=False)\\n\\n if data is None:\\n if self.isAutoLevel():\\n self._level = float('nan')\\n\\n else:\\n if self.isAutoLevel():\\n st = time.time()\\n try:\\n level = float(self.getAutoLevelFunction()(data))\\n\\n except Exception:\\n module_ = self.getAutoLevelFunction().__module__\\n name = self.getAutoLevelFunction().__name__\\n _logger.error(\\n \\\"Error while executing iso level function %s.%s\\\",\\n module_,\\n name,\\n exc_info=True)\\n level = float('nan')\\n\\n else:\\n _logger.info(\\n 'Computed iso-level in %f s.', time.time() - st)\\n\\n if level != self._level:\\n self._level = level\\n self._updated(Item3DChangedType.ISO_LEVEL)\\n\\n if numpy.isfinite(self._level):\\n st = time.time()\\n vertices, normals, indices = MarchingCubes(\\n data,\\n isolevel=self._level)\\n _logger.info('Computed iso-surface in %f s.', time.time() - st)\\n\\n if len(vertices) != 0:\\n return vertices, normals, indices\\n\\n return None, None, None\",\n \"def compute_port_val(allocs, prices):\\n # normalized price\\n # normalized prices\\n normed = prices/prices.iloc[0]\\n prices.head()\\n alloced = normed * allocs\\n\\n # position values\\n start_val = 1 # included to simplify adding ability to calc as $\\n pos_vals = alloced * start_val\\n\\n # portfolio value\\n port_val = pos_vals.sum(axis=1)\\n\\n return port_val\",\n \"def market_value(self, ref_prices, suspensions=None):\\n # TODO some securities could not be able to be traded\\n if suspensions is None:\\n suspensions = []\\n \\n market_value_float = 0.0\\n market_value_frozen = 0.0 # suspended or high/low limit\\n for sec in self.holding_securities:\\n size = self.get_position(sec).current_size\\n # TODO PortfolioManager object should not access price\\n price = ref_prices[sec]\\n mv_sec = price * size\\n if sec in suspensions:\\n market_value_frozen += mv_sec\\n else:\\n market_value_float += mv_sec\\n \\n return market_value_float, market_value_frozen\",\n \"def swap_active_portfolio(self):\\n\\n self.lock.acquire()\\n\\n self.clear_main()\\n\\n w = self.windows['MAIN']\\n l = 1\\n\\n for p in self.portfolios:\\n # Only support 9 portfolios since that makes this easier to deal\\n # with.\\n if l >= 10:\\n break\\n\\n w.addstr(l, 0, '%2d' % l, curses.A_BOLD | curses.color_pair(1))\\n w.addstr(l, 3, p.name)\\n l += 1\\n\\n self.refresh()\\n\\n # Wait for the user to give is a key.\\n while True:\\n c = self.stdscr.getch()\\n\\n if c < ord('1') and c > ord('9'):\\n continue\\n\\n index = c - ord('1')\\n\\n if index < len(self.portfolios):\\n break\\n\\n self.portfolios[index].refresh()\\n\\n self.active_portfolio = self.portfolios[index]\\n self.display_portfolio(self.active_portfolio)\\n self.lock.release()\",\n \"def new_portfolio_flow(portfolio_list):\\n while True:\\n portfolio_name = prompt.shortcuts.input_dialog(\\n title=\\\"Portfolio Name\\\", text=\\\"Please type the portfolio name:\\\"\\n ).run()\\n if portfolio_name is not None:\\n portfolio_id: int = len(portfolio_list)\\n stock_list = []\\n stock_list = add_stock_flow(stock_list)\\n portfolio_list.append(Portfolio(portfolio_name, portfolio_id, stock_list))\\n return portfolio_list\\n if portfolio_name is None:\\n return None\",\n \"def momentum(portfolio_item, transaction_volume, cash_allocation):\\n from yahooquery import Ticker\\n from math import floor\\n import talib\\n from .TradeHistoryItem import log_trade\\n from API.Help import is_increasing, initialize_alpaca\\n\\n alpaca = initialize_alpaca()\\n\\n yahoo_ticker = Ticker(str(portfolio_item))\\n info = yahoo_ticker.history()\\n ma_5 = talib.SMA(info['close'], timeperiod=5)\\n ma_20 = talib.SMA(info['close'], timeperiod=20)\\n volume = info['volume']\\n\\n if portfolio_item.shares == 0:\\n # if the price goes from below the sma to above, buy\\n if ma_5[-1] > (ma_20[-1] * 1.1) and is_increasing(volume, 3):\\n print('buying {} shares of {}'.format(transaction_volume, str(portfolio_item)))\\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'buy', 'market', 'day')\\n portfolio_item.buy(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=0)\\n # if the price goes from above the sma to below, short\\n elif ma_5[-1] < (ma_20[-1] * .9) and not is_increasing(volume, 3) and portfolio_item.shares == 0:\\n transaction_volume = floor(cash_allocation / (portfolio_item.ticker.price_now * 1.1))\\n print('shorting {} shares of {}'.format(transaction_volume, str(portfolio_item)))\\n alpaca.submit_order(str(portfolio_item), transaction_volume, 'sell', 'market', 'day')\\n portfolio_item.short(transaction_volume=transaction_volume, cash_allocated=cash_allocation)\\n log_trade(portfolio_item=portfolio_item, transaction_volume=transaction_volume, transaction_type=3)\",\n \"def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:\\n assert portfolio is not None\\n assert stock_market_data is not None\\n assert stock_market_data.get_companies() == [Company.A, Company.B]\\n\\n current_state = self.states_compution(stock_market_data, portfolio)\\n current_portfolio_value = portfolio.get_value(stock_market_data)\\n\\n if self.train_while_trading is False:\\n # for test set use trained ann\\n action = np.argmax(self.model.predict(current_state)[0])\\n\\n else:\\n action = self.select_action(current_state, current_portfolio_value)\\n if self.last_state is not None:\\n reward = self.calc_reward(current_portfolio_value)\\n self.append_memory(self.last_state, self.last_action_a, reward, current_state)\\n # train model as soon as sufficient memory is reached\\n if len(self.memory) > self.min_size_of_memory_before_training:\\n self.train_model()\\n\\n # Split action into individual actions for Company A and B\\n current_action_a = 0\\n current_action_b = 0\\n assert action < 9 and action >= 0\\n if action == 0:\\n current_action_a = OrderType.SELL\\n current_action_b = OrderType.SELL\\n amount_to_sell_a = portfolio.get_stock(Company.A)\\n amount_to_sell_b = portfolio.get_stock(Company.B)\\n elif action == 1:\\n current_action_a = OrderType.SELL\\n amount_to_sell_a = portfolio.get_stock(Company.A)\\n current_action_b = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_b = int(portfolio.cash/stock_price)\\n elif action == 2:\\n current_action_a = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_a = int(portfolio.cash/stock_price)\\n current_action_b = OrderType.SELL\\n amount_to_sell_b = portfolio.get_stock(Company.B)\\n elif action == 3:\\n current_action_a = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_a = int((portfolio.cash/stock_price)/2)\\n current_action_b = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.B)\\n amount_to_buy_b = int((portfolio.cash/stock_price)/2)\\n elif action == 4:\\n current_action_a = OrderType.SELL\\n amount_to_sell_a = portfolio.get_stock(Company.A)\\n # current_action_b = \\\"hold\\\"\\n elif action == 5:\\n current_action_a = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.A)\\n amount_to_buy_a = int(portfolio.cash/stock_price)\\n # current_action_b = \\\"hold\\\"\\n elif action == 6:\\n # current_action_a = \\\"hold\\\"\\n current_action_b = OrderType.SELL\\n amount_to_sell_b = portfolio.get_stock(Company.B)\\n elif action == 7:\\n # current_action_a = \\\"hold\\\"\\n current_action_b = OrderType.BUY\\n stock_price = stock_market_data.get_most_recent_price(Company.B)\\n amount_to_buy_b = int(portfolio.cash/stock_price)\\n\\n order_list = []\\n\\n if current_action_a != 0:\\n if current_action_a == OrderType.SELL and amount_to_sell_a > 0:\\n order_list.append(Order(current_action_a, Company.A, amount_to_sell_a))\\n elif current_action_a == OrderType.BUY and portfolio.cash > 0:\\n order_list.append(Order(current_action_a, Company.A, amount_to_buy_a))\\n\\n if current_action_b != 0:\\n if current_action_b == OrderType.SELL and amount_to_sell_b > 0:\\n order_list.append(Order(current_action_b, Company.B, amount_to_sell_b))\\n elif current_action_b == OrderType.BUY and portfolio.cash > 0:\\n order_list.append(Order(current_action_b, Company.B, amount_to_buy_b))\\n\\n self.last_action_a = action\\n self.last_state = current_state\\n self.last_portfolio_value = current_portfolio_value\\n return order_list\",\n \"def solve(self):\\r\\n # Make arrays of end-of-period assets and end-of-period marginal value\\r\\n \\r\\n if self.jac == True:\\r\\n \\r\\n if len(self.cList) == self.T_sim - self.s :\\r\\n self.Rfree = self.Rfree + self.dx\\r\\n else:\\r\\n self.Rfree = self.Rfree\\r\\n \\r\\n aNrm = self.prepare_to_calc_EndOfPrdvP()\\r\\n EndOfPrdvP = self.calc_EndOfPrdvP()\\r\\n\\r\\n # Construct a basic solution for this period\\r\\n if self.CubicBool:\\r\\n solution = self.make_basic_solution(\\r\\n EndOfPrdvP, aNrm, interpolator=self.make_cubic_cFunc\\r\\n )\\r\\n else:\\r\\n solution = self.make_basic_solution(\\r\\n EndOfPrdvP, aNrm, interpolator=self.make_linear_cFunc\\r\\n )\\r\\n\\r\\n solution = self.add_MPC_and_human_wealth(solution) # add a few things\\r\\n solution = self.add_stable_points(solution)\\r\\n \\r\\n \\r\\n \\r\\n self.cList.append(solution.cFunc)\\r\\n \\r\\n # Add the value function if requested, as well as the marginal marginal\\r\\n # value function if cubic splines were used (to prepare for next period)\\r\\n if self.vFuncBool:\\r\\n solution = self.add_vFunc(solution, EndOfPrdvP)\\r\\n if self.CubicBool:\\r\\n solution = self.add_vPPfunc(solution)\\r\\n return solution\",\n \"def index():\\n user_id = session[\\\"user_id\\\"]\\n portfolio_table = port(user_id, db)\\n \\n if not isinstance(portfolio_table, dict): \\n return apology(\\\"Error in portfolio\\\")\\n \\n return render_template(\\\"portfolio.html\\\",\\n shares_list = portfolio_table[\\\"shares\\\"],\\n cash = portfolio_table[\\\"cash\\\"],\\n total = portfolio_table[\\\"total\\\"])\",\n \"def calculate_all_portfolios(self, request, pk=None, **kwargs):\\n goal = self.get_object()\\n\\n check_state(Goal.State(goal.state), Goal.State.ACTIVE)\\n\\n setting_str = request.query_params.get('setting', None)\\n if not setting_str:\\n logger.debug('setting parameter missing from calculate_all_portfolios query')\\n raise ValidationError(\\\"Query parameter 'setting' must be specified and a valid JSON string\\\")\\n try:\\n setting = ujson.loads(setting_str)\\n except ValueError:\\n logger.debug('setting parameter for calculate_all_portfolios query not valid json')\\n raise ValidationError(\\\"Query parameter 'setting' must be a valid json string\\\")\\n\\n # Create the settings from the dict\\n serializer = serializers.GoalSettingStatelessSerializer(data=setting)\\n serializer.is_valid(raise_exception=True)\\n settings = serializer.create_stateless(serializer.validated_data, goal)\\n\\n # Calculate the portfolio\\n try:\\n data_provider = DataProviderDjango()\\n execution_provider = ExecutionProviderDjango()\\n data = [self.build_portfolio_data(item[1], item[0])\\n for item in calculate_portfolios(setting=settings,\\n data_provider=data_provider,\\n execution_provider=execution_provider)]\\n return Response(data)\\n except Unsatisfiable as e:\\n rdata = {'reason': \\\"No portfolio could be found: {}\\\".format(e)}\\n\\n if e.req_funds is not None:\\n rdata['req_funds'] = e.req_funds\\n\\n return Response({'error': rdata}, status=status.HTTP_400_BAD_REQUEST)\",\n \"def test_interest_vs_stockprice(self):\\n stock_prices = np.array([[5, 10, 20, 40]], dtype=float)\\n interest_rate = 2.0 # 200%\\n test_case = StockMarket(5, stock_prices, interest_rate)\\n test_case.dynamic_programming_bottom_up()\\n for portfolio in set(test_case.backtracing_portfolio()):\\n self.assertEqual(0, portfolio)\",\n \"def portfolio(request):\\n projects = Project.objects.all()\\n categories = None\\n\\n if request.GET:\\n if 'category' in request.GET:\\n categories = request.GET['category'].split(',')\\n projects = projects.filter(category__name__in=categories)\\n categories = ProjectCategory.objects.filter(name__in=categories)\\n\\n context = {\\n 'projects': projects,\\n 'current_categories': categories,\\n }\\n\\n return render(request, 'portfolio/portfolio.html', context)\",\n \"def getTrades(current_portfolio, desired_portfolio):\\n # Create trade factory between current and desired portfolio.\\n # tf = TradeFactory.TradeFactory(current_portfolio, desired_portfolio)\\n\\n return {}\",\n \"def get_cumportfolio(self):\\n\\n return self._cumportfolio\",\n \"def portfolio():\\n projects = get_projects()\\n for project in projects:\\n unicode_body = project[\\\"description\\\"].decode(\\\"utf-8\\\")\\n html_body = markdown.markdown(unicode_body)\\n safe_html_body = Markup(html_body)\\n project[\\\"description\\\"] = safe_html_body\\n context = {\\n \\\"projects\\\": projects\\n }\\n return render_template(\\\"portfolio.html\\\", **context)\",\n \"def summary(self):\\n print '%s Portfolio\\\\'s %s Strategy' % (self.portfolio.name, self.name)\\n print '-' * COL_DASH_WIDTH\\n\\n self.display_trades()\\n\\n for symbol in self.portfolio.assets.keys():\\n perf = self.performance[symbol]\\n\\n print '\\\\nSummary for %s from %s (first trade) to %s (last trade)' % (symbol, perf['start'], perf['end'])\\n print '.' * COL_DASH_WIDTH\\n print 'Summary:'\\n data = [[fmtn(perf['trades']), fmtn(perf['wins']), fmtn(perf['losses']), fmtn(perf['washes'])]]\\n print tabulate.tabulate(data, headers=['Total Trades', '# Wins', '# Losses', '# Washes'])\\n\\n print '\\\\nPerformance:'\\n data = [[\\n fmtn(perf['profit']), fmtn(perf['loss']), fmtn(perf['net_profit']),\\n fmtp(perf['profit_factor']), fmtp(perf['percent_profitable']), fmtn(perf['average_trade_net_profit'])\\n ]]\\n print tabulate.tabulate(data, headers=['Profit', 'Loss', 'Net Profit', 'Profit Factor', 'Percent Profitable', 'Average Net Profit per Trade'])\\n\\n print '\\\\nDrawdown:'\\n data = [[fmtn(perf['max_drawdown']), fmtn(perf['average_drawdown']), fmtn(perf['max_drawdown_days']), fmtn(perf['average_drawdown_days'])]]\\n print tabulate.tabulate(data, headers=['Max', 'Average', 'Max Days', 'Average Days'])\\n\\n print '\\\\nRisk:'\\n data = [[fmtn(perf['volatility_risk']), fmtn(perf['beta']), fmtn(perf['lower_partial_moment_risk']), fmtn(perf['t_r']), fmtn(perf['s_r'])]]\\n print tabulate.tabulate(data, headers=['Volatility', 'Beta', 'Lower Partial Moment', 'Treynor Ratio', 'Sharpe Ratio'])\",\n \"def cumulative_returns(self):\\n return (1 + self.pct_change).cumprod()\",\n \"def project_cache(self):\\n return self._project_cache\",\n \"def simulate_trading(self):\\n self._generate_trading_instances()\\n self._run_backtest()\\n self.portfolio.output_equity()\\n res=self.portfolio.get_statistics()\\n self.plot.plot_equity()\\n return res\",\n \"def index():\\n\\n # obtain cash info from users database\\n cash = db.execute(\\\"SELECT cash FROM users WHERE id = :id\\\", id = session[\\\"user_id\\\"])\\n grandtotal = cash[0][\\\"cash\\\"]\\n \\n # obtain stock info from portfolio database\\n stocks = db.execute(\\\"SELECT symbol, shares FROM portfolio WHERE id = :id\\\", id=session[\\\"user_id\\\"])\\n \\n # for every stock in the user's portfolio, assign dict key/values for use in html/jinja\\n for stock in stocks:\\n symbol = str(stock[\\\"symbol\\\"])\\n shares = int(stock[\\\"shares\\\"])\\n name = \\\"\\\"\\n price = \\\"\\\"\\n total = \\\"\\\"\\n quote = lookup(symbol)\\n stock[\\\"name\\\"] = quote[\\\"name\\\"]\\n stock[\\\"price\\\"] = \\\"{:.2f}\\\".format(quote[\\\"price\\\"])\\n stock[\\\"total\\\"] = \\\"{:.2f}\\\".format(quote[\\\"price\\\"] * shares)\\n stock[\\\"grandtotal\\\"] = quote[\\\"price\\\"] * shares\\n grandtotal += stock[\\\"grandtotal\\\"]\\n\\n # format grandtotal to force 2 decimal places\\n grandtotal = \\\"{:.2f}\\\".format(grandtotal)\\n \\n # render index page with some given values\\n return render_template(\\\"index.html\\\", stocks = stocks, cash = cash, grandtotal = grandtotal)\",\n \"async def init(self, ctx, *amount_and_symbol : str):\\n user = ctx.message.author\\n portfolio = GetPortfolio(user.id)\\n for i in range(0, len(amount_and_symbol),2):\\n portfolio.SetOwnedCurrency(amount_and_symbol[i], amount_and_symbol[i+1])\\n await self.bot.say('%s\\\\'s portfolio is now worth $%.2f.' %\\n (ctx.message.author, portfolio.Value()))\\n portfolio.Save()\",\n \"def solve_working(\\n assets_this_period,\\n assets_next_period,\\n hc_this_period,\\n hc_next_period,\\n interest_rate,\\n wage_rate,\\n income_tax_rate,\\n beta,\\n gamma,\\n sigma,\\n neg,\\n continuation_value,\\n delta_hc,\\n zeta,\\n psi,\\n n_gridpoints_capital,\\n n_gridpoints_hc,\\n efficiency,\\n survival_rate,\\n):\\n # Initialize objects\\n policy_labor_working_tmp = np.zeros(\\n (n_gridpoints_capital, n_gridpoints_hc), dtype=np.float64\\n )\\n\\n # Implied hc effort\\n hc_effort = get_hc_effort(\\n hc_this_period=hc_this_period,\\n hc_next_period=hc_next_period,\\n zeta=zeta,\\n psi=psi,\\n delta_hc=delta_hc,\\n )\\n\\n hc_effort = np.where(np.isnan(hc_effort), 0.0, hc_effort)\\n\\n # Implied labor supply\\n labor_input = 1 - hc_effort\\n\\n # Consumption\\n consumption = get_consumption(\\n assets_this_period=assets_this_period,\\n assets_next_period=assets_next_period,\\n pension_benefit=np.float64(0.0),\\n labor_input=labor_input,\\n interest_rate=interest_rate,\\n wage_rate=wage_rate,\\n income_tax_rate=income_tax_rate,\\n productivity=hc_this_period,\\n efficiency=efficiency,\\n )\\n\\n # Flow utility\\n flow_utility = util(\\n consumption=consumption,\\n labor_input=labor_input,\\n hc_effort=hc_effort,\\n gamma=gamma,\\n sigma=sigma,\\n )\\n\\n flow_utility = np.where(consumption < 0.0, neg, flow_utility)\\n flow_utility = np.where(hc_effort > 1.0, neg, flow_utility)\\n\\n # Value function on the complete mesh (i.e. for all possible choices)\\n value_full = flow_utility + beta * survival_rate * continuation_value\\n\\n # Derive optimal policies and store value function given optimal choices\\n policy_capital_working_tmp = np.argmax(np.max(value_full, axis=3), axis=1)\\n policy_hc_working_tmp = np.argmax(np.max(value_full, axis=1), axis=2)\\n value_working_tmp = np.max(np.max(value_full, axis=3), axis=1)\\n for assets_this_period_idx in range(n_gridpoints_capital):\\n for hc_this_period_idx in range(n_gridpoints_hc):\\n assets_next_period_idx = policy_capital_working_tmp[\\n assets_this_period_idx, hc_this_period_idx\\n ]\\n hc_next_period_idx = policy_hc_working_tmp[\\n assets_this_period_idx, hc_this_period_idx\\n ]\\n policy_labor_working_tmp[\\n assets_this_period_idx, hc_this_period_idx\\n ] = labor_input[\\n assets_this_period_idx,\\n assets_next_period_idx,\\n hc_this_period_idx,\\n hc_next_period_idx,\\n ]\\n\\n return (\\n policy_capital_working_tmp,\\n policy_hc_working_tmp,\\n policy_labor_working_tmp,\\n value_working_tmp,\\n )\",\n \"def get_subportfolio(self, symbol_names_list = None, timeframes_list = None):\\n if symbol_names_list is None:\\n symbol_names_list = self.symbol_names_list\\n \\n sub_portfolio = copy.copy(self) #It only copies pointers\\n sub_portfolio.del_all_symbols()\\n \\n for symbol_name in symbol_names_list:\\n subsymbol = self[symbol_name].get_subsymbol(timeframes_list)\\n sub_portfolio.add_symbol(symbol_name, symbol = subsymbol)\\n \\n return sub_portfolio\",\n \"def test_get_portfolio(self, db_mock):\\n repo = Repository()\\n\\n db_instance = db_mock.return_value\\n db_instance.get_portfolio.return_value = None\\n self.assertIsNone(repo.get_portfolio(\\\"123\\\"))\\n\\n db_instance.get_portfolio.return_value = (1, [])\\n self.assertIsInstance(repo.get_portfolio(\\\"123\\\"), Portfolio)\",\n \"def exist_portfolio(self, name, user):\\n portfolio = self.__get_portfolio_object(name, user)\\n return portfolio is not None\",\n \"def calculate(self):\\n\\n final_quotas = self.calculate_quotas(sum(self.populations) / self.num_seats, [0] * self.states)\\n final_fair_shares, final_quotas, modified_divisor, estimator = self.calculate_fair_shares([0] * self.states,\\n final_quotas, sum(\\n self.populations) / self.num_seats, sum(self.populations) / self.num_seats)\\n\\n lower_boundary = self.calculate_lower_boundary(modified_divisor)\\n upper_boundary = self.calculate_upper_boundary(modified_divisor)\\n\\n if estimator is None:\\n return None, None, None, None, None, None, None, None, None, None\\n else:\\n return self.original_divisor, modified_divisor, self.original_quotas, final_quotas, \\\\\\n self.initial_fair_shares, final_fair_shares, sum(self.initial_fair_shares), lower_boundary, \\\\\\n upper_boundary, self.estimator_history\",\n \"def cvxpy_solver(self, verbose=False):\\n self.gamma = self._gamma_from_drawdown_control()\\n\\n objective = 0\\n constr = []\\n\\n # weights variable depends on whether there is a risk-free asset in data\\n if self.rf_included is True:\\n # variable with shape h+1 predictions so first row\\n # can be the known (non-variable) portfolio weight at time t\\n weights = cp.Variable(shape=(self.n_preds + 1, self.n_assets))\\n else:\\n # Set rf to zero in all preds and cov\\n self.rets = np.insert(self.rets, self.n_assets, 0, axis=1)\\n self.cov = np.insert(self.cov, self.n_assets, 0, axis=-2)\\n self.cov = np.insert(self.cov, self.n_assets, 0, axis=-1) # Has to be done in two steps for cov due to dims\\n self.start_weights = np.append(self.start_weights, 0)\\n\\n weights = cp.Variable(shape=(self.n_preds + 1, self.n_assets+1))\\n rf_zero_weights = np.zeros(shape=self.n_preds)\\n constr += [weights[1:, -1] == 0] # Keep rf pos at zero since it non-present in this case\\n\\n\\n # Loop through each row in the weights variable and construct the optimization problem\\n # Note this loop is very cpu-light since no actual computations takes place inside it\\n for t in range(1, weights.shape[0]):\\n # sum problem objectives. Weights are shifted 1 period forward compared to self.rets\\n # Concatenates objective and constraints in lists\\n objective += self.single_period_objective_func(weights[t], weights[t-1], self.rets[t-1], self.cov[t-1])\\n constr += self.single_period_constraints(weights[t]) # Concatenate constraints\\n\\n constr += [weights[0] == self.start_weights] # first weights are fixed at known current portfolio\\n\\n prob = cp.Problem(cp.Maximize(objective), constr) # Construct maximization problem\\n prob.solve(solver=cp.ECOS, verbose=verbose)\\n\\n if self.rf_included is True:\\n opt_var = weights.value\\n else:\\n opt_var = weights.value[:, :-1]\\n\\n if verbose is True:\\n print(\\\"Shape of var: \\\", opt_var.shape)\\n temp_df = pd.DataFrame(opt_var).round(3)\\n temp_df['sum_weights'] = np.sum(opt_var, axis=1)\\n print(temp_df)\\n\\n return opt_var[1:] # Discard first row which is not a variable.\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7362575","0.6996456","0.6729907","0.6721263","0.6675463","0.66379255","0.6499","0.63661253","0.63091534","0.62968546","0.6240645","0.6196709","0.6144556","0.60243994","0.5963545","0.5962163","0.59054154","0.5858664","0.5812279","0.5781019","0.5715677","0.5695099","0.56832755","0.56832755","0.56502557","0.56142795","0.5601788","0.5591255","0.5557412","0.5552806","0.5548991","0.5546728","0.553356","0.5515437","0.5509156","0.5503026","0.5503026","0.5495613","0.5492177","0.5483658","0.5474354","0.5473066","0.54698133","0.5468401","0.5466222","0.5457872","0.5448533","0.544473","0.5442066","0.5431377","0.54243016","0.5423828","0.54235184","0.5408874","0.5393072","0.53806245","0.5379401","0.5367956","0.5364299","0.53607404","0.5357601","0.5357601","0.5350372","0.5319766","0.52956545","0.5282608","0.5277925","0.52768487","0.5256564","0.5251165","0.522619","0.52203435","0.52191967","0.5211993","0.52033794","0.51983654","0.5197244","0.51966023","0.5181622","0.5177389","0.5162396","0.5158838","0.5156645","0.515346","0.5146656","0.5139753","0.5139441","0.5121129","0.5118124","0.50915724","0.5089043","0.5082551","0.50619406","0.5057358","0.50527126","0.5051194","0.5046313","0.50354725","0.5031226","0.50246155"],"string":"[\n \"0.7362575\",\n \"0.6996456\",\n \"0.6729907\",\n \"0.6721263\",\n \"0.6675463\",\n \"0.66379255\",\n \"0.6499\",\n \"0.63661253\",\n \"0.63091534\",\n \"0.62968546\",\n \"0.6240645\",\n \"0.6196709\",\n \"0.6144556\",\n \"0.60243994\",\n \"0.5963545\",\n \"0.5962163\",\n \"0.59054154\",\n \"0.5858664\",\n \"0.5812279\",\n \"0.5781019\",\n \"0.5715677\",\n \"0.5695099\",\n \"0.56832755\",\n \"0.56832755\",\n \"0.56502557\",\n \"0.56142795\",\n \"0.5601788\",\n \"0.5591255\",\n \"0.5557412\",\n \"0.5552806\",\n \"0.5548991\",\n \"0.5546728\",\n \"0.553356\",\n \"0.5515437\",\n \"0.5509156\",\n \"0.5503026\",\n \"0.5503026\",\n \"0.5495613\",\n \"0.5492177\",\n \"0.5483658\",\n \"0.5474354\",\n \"0.5473066\",\n \"0.54698133\",\n \"0.5468401\",\n \"0.5466222\",\n \"0.5457872\",\n \"0.5448533\",\n \"0.544473\",\n \"0.5442066\",\n \"0.5431377\",\n \"0.54243016\",\n \"0.5423828\",\n \"0.54235184\",\n \"0.5408874\",\n \"0.5393072\",\n \"0.53806245\",\n \"0.5379401\",\n \"0.5367956\",\n \"0.5364299\",\n \"0.53607404\",\n \"0.5357601\",\n \"0.5357601\",\n \"0.5350372\",\n \"0.5319766\",\n \"0.52956545\",\n \"0.5282608\",\n \"0.5277925\",\n \"0.52768487\",\n \"0.5256564\",\n \"0.5251165\",\n \"0.522619\",\n \"0.52203435\",\n \"0.52191967\",\n \"0.5211993\",\n \"0.52033794\",\n \"0.51983654\",\n \"0.5197244\",\n \"0.51966023\",\n \"0.5181622\",\n \"0.5177389\",\n \"0.5162396\",\n \"0.5158838\",\n \"0.5156645\",\n \"0.515346\",\n \"0.5146656\",\n \"0.5139753\",\n \"0.5139441\",\n \"0.5121129\",\n \"0.5118124\",\n \"0.50915724\",\n \"0.5089043\",\n \"0.5082551\",\n \"0.50619406\",\n \"0.5057358\",\n \"0.50527126\",\n \"0.5051194\",\n \"0.5046313\",\n \"0.50354725\",\n \"0.5031226\",\n \"0.50246155\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81566226"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2664,"cells":{"query":{"kind":"string","value":"Override fields on ``self.account``."},"document":{"kind":"string","value":"def override_account_fields(self,\n settled_cash=not_overridden,\n accrued_interest=not_overridden,\n buying_power=not_overridden,\n equity_with_loan=not_overridden,\n total_positions_value=not_overridden,\n total_positions_exposure=not_overridden,\n regt_equity=not_overridden,\n regt_margin=not_overridden,\n initial_margin_requirement=not_overridden,\n maintenance_margin_requirement=not_overridden,\n available_funds=not_overridden,\n excess_liquidity=not_overridden,\n cushion=not_overridden,\n day_trades_remaining=not_overridden,\n leverage=not_overridden,\n net_leverage=not_overridden,\n net_liquidation=not_overridden):\n # mark that the portfolio is dirty to override the fields again\n self._dirty_account = True\n self._account_overrides = kwargs = {\n k: v for k, v in locals().items() if v is not not_overridden\n }\n del kwargs['self']"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def account(self, account):\n\n self._account = account","def account(self, account):\n\n self._account = account","def account(self, account):\n\n self._account = account","def account(self, account):\n\n self._account = account","def patch(self, account=None, user=None, account_id=None):\n return super().patch()","def account(self, account: str):\n self._account = account","def get_account(self, account):\n \n pass","def set_account(self):\n return self.__Account","def onAccountUpdate(self, data):\n pass","def account(self):\r\n return Account(self)","def put(self, account=None, user=None, account_id=None):\n return super().put()","def set_specific_fields(self):\n raise NotImplementedError(\"Must be defined by subclass!\")","def change_account(self, account):\r\n check_account = Account(account, steem_instance=self.steem)\r\n self.account = check_account[\"name\"]\r\n self.refresh()","def get_account_details(self):\n pass","def put_account(self, account):\n \n pass","def update_account_data(self):\n self.ensure_one()\n getattr(self, '%s_update_account_data' % self.provider, lambda: None)()","def account(self):\n return Account(self)","def account_information(self) -> MetatraderAccountInformation:\n return self._accountInformation","def __init__(self, client, account_id):\n\n super(AccountsMixin, self).__init__(client)\n self._account_id = account_id","def account_id(self, account_id):\n self._account_id = account_id","def account(self, account_code):\r\n return acc.Account(self, account_code)","def extended_account_data(self,accountinfo):\n\n data = {\n 'username' : accountinfo['username'],\n 'password' : accountinfo['password'],\n 'passwordconfirm' : accountinfo['password'],\n 'firstname' : accountinfo['firstname'],\n 'middlename' : accountinfo['middlename'],\n 'lastname' : accountinfo['lastname'],\n 'email' : accountinfo['email'],\n 'emailconfirm' : accountinfo['email'],\n 'gender' : 'Refused',\n 'disability' : 'Refused',\n 'hispanic' : 'Refused',\n 'race_refused' : 'Yes',\n 'captcha' : False,\n 'usageagreement' : True,\n }\n return data","def to_representation(self, instance):\n data = super(AccountSerializer, self).to_representation(instance)\n data[\"display_name\"] = instance.name\n return data","def _account(self) -> Account:\n if isinstance(self._node_cached_account, Account):\n return self._node_cached_account\n account = Account.retrieve(\n session=self.entity.session,\n entity=self.entity,\n account_id=self.account_id\n )\n self._node_cached_account = account\n return account","def account_amount(self, account_amount):\n\n self._account_amount = account_amount","def get_account(self):\n return self._account","def get_account(self):\n return self._account","def account_id(self, account_id):\n\n self._account_id = account_id","def account_id(self, account_id):\n\n self._account_id = account_id","def account_id(self, account_id):\n\n self._account_id = account_id","def account_id(self, account_id):\n\n self._account_id = account_id","def account_id(self, account_id):\n\n self._account_id = account_id","def account_id(self, account_id):\n\n self._account_id = account_id","def copy_fields(self, model):\n fields = super(HistoricalRecords, self).copy_fields(model)\n for name, field in self.additional_fields.items():\n assert name not in fields\n assert hasattr(self, 'get_%s_value' % name)\n fields[name] = field\n return fields","def account_name(self, account_name):\n\n self._account_name = account_name","def account_name(self, account_name):\n\n self._account_name = account_name","def accounts(self): # pragma: no coverage\r\n raise NotImplementedError()","def account(self) -> str:\n return self._account","def account(self) -> str:\n return self._account","def setAccount(self, account_id):\n self.data_struct['_setAccount'] = account_id","def describe_account_attributes():\n pass","def update(self, account):\n model = models.load('Account', account)\n return self.client.update_account(model=model)","def account(self, account_id):\r\n return Account(self, account_id)","def _postprocess_wim_account(wim_account, hide=_CONFIDENTIAL_FIELDS):\n # Fix fields from join\n for field in ('type', 'description', 'wim_url'):\n if field in wim_account:\n wim_account['wim.'+field] = wim_account.pop(field)\n\n for field in ('id', 'nfvo_tenant_id', 'wim_account_id'):\n if field in wim_account:\n wim_account['association.'+field] = wim_account.pop(field)\n\n wim_account = _postprocess_record(wim_account, hide)\n\n created = wim_account.get('created')\n wim_account['created'] = (created is True or created == 'true')\n\n return wim_account","def __init__(self, account, user_username, user_password):\n self. account = account\n self. user_username = user_username\n self.user_password = user_password","def save_account(self):\n Credential.account_list.append(self)","def to_representation(self, obj):\n data = super(AccountInfoSerializer, self).to_representation(obj)\n if obj.pk is None:\n source = mkt.LOGIN_SOURCE_LOOKUP[mkt.LOGIN_SOURCE_UNKNOWN]\n elif obj.source in self.ALLOWED_SOURCES:\n source = mkt.LOGIN_SOURCE_LOOKUP[obj.source]\n else:\n source = mkt.LOGIN_SOURCE_LOOKUP[mkt.LOGIN_SOURCE_BROWSERID]\n\n data[\"source\"] = source\n return data","def __init__(self):\n self.account_balance = 0\n self.amount = 0","def set_account(self, account: str):\n ret = self._call_txtrader_api('set_account', {'account': account})\n if ret:\n self.account = account\n return ret","async def on_account_information_updated(self, account_information: MetatraderAccountInformation):\n self._accountInformation = account_information","def custom_profile_fields(self, custom_profile_fields):\n\n self._custom_profile_fields = custom_profile_fields","def account_type(self, account_type):\n\n self._account_type = account_type","def account_type(self, account_type):\n\n self._account_type = account_type","def account_type(self, account_type):\n\n self._account_type = account_type","def account_type(self, account_type):\n\n self._account_type = account_type","def account_number(self, account_number):\n\n self._account_number = account_number","def __init__(self,Account,username,password):\n self.Account = Account\n self.username = username\n self.password = password","def get_account_information(self):\n self.account_information = retry(lambda: self.client\n .futures_account_v2())\n return self.account_information","def save(self, *args, **kwargs):\n self.username = self.username or self.email\n super().save(*args, **kwargs)","def add_account(self, account):\n self.accounts[account.account_number] = account.json()\n # We should save in database the new account using self.di, but not now in order to get our tests passed","def account_info(account):\n return {\n 'status': account.status,\n 'availability': account.availability,\n 'blurb': account.message,\n 'email': account.email,\n 'name': account.name,\n 'success': True\n }","def get(self, account=None, user=None, account_id=None):\n self.get_object = lambda: account\n return super().get()","def get_private(self):\n return self.__Account","def account_context():\n return dict(\n user=current_user,\n party=current_user.party,\n )","def accounts(self, accounts):\n\n self._accounts = accounts","def __init__(self,account,user_name,password,email):\n self.account = account\n self.user_name = user_name\n self.password = password\n self.email = email","def account_number(self):\n return self.__account_number","def on_account(self, account: AccountData):\n # self.on_event(EVENT_ACCOUNT, account)\n # self.on_event(EVENT_ACCOUNT + account.vt_accountid, account)\n pass","def account_summary(self):\n pass","def __init__(self, account_id, balance):\n self.account_id = account_id\n self.balance = balance","def to_solders(self) -> instruction.AccountMeta:\n return instruction.AccountMeta(\n pubkey=self.pubkey.to_solders(), is_signer=self.is_signer, is_writable=self.is_writable\n )","def __init__(self,\r\n account_id=None,\r\n name=None,\r\n org_no=None,\r\n uni_customer_no=None,\r\n created=None,\r\n last_modified=None,\r\n dealer_id=None,\r\n dealer_name=None,\r\n dealer_reference=None,\r\n enabled=None,\r\n additional_properties = {}):\r\n\r\n # Initialize members of the class\r\n self.account_id = account_id\r\n self.name = name\r\n self.org_no = org_no\r\n self.uni_customer_no = uni_customer_no\r\n self.created = APIHelper.RFC3339DateTime(created) if created else None\r\n self.last_modified = APIHelper.RFC3339DateTime(last_modified) if last_modified else None\r\n self.dealer_id = dealer_id\r\n self.dealer_name = dealer_name\r\n self.dealer_reference = dealer_reference\r\n self.enabled = enabled\r\n\r\n # Add additional model properties to the instance\r\n self.additional_properties = additional_properties","def __init__(self,account,username, password):\n self.account = account\n self.username = username\n self.password = password","def account_balance(self, account_balance):\n\n self._account_balance = account_balance","def account_balance(self, account_balance):\n\n self._account_balance = account_balance","def test_client_bank_account_partial_update(self):\n pass","def __init__(self, accountHolder=None):\n if accountHolder:\n self._getAccount = lambda: accountHolder.theAccount\n else:\n self._getAccount = lambda: self.theAccount","def __init__(self, *args, **kwargs):\n user = kwargs.pop('user', None)\n super().__init__(*args, **kwargs)\n if self.instance.id:\n for origin in Origin.ORIGIN_TYPES:\n # in case they had an origin that doesn't exist any more.\n try:\n self.fields[origin[0]].initial = self.instance.origins.filter(type=origin[0]).first()\n except Origin.DoesNotExist:\n pass\n # adjust fields for different users\n if user.has_perm('players.view_any_player'):\n allowed_fields = self.fields.keys()\n else:\n allowed_fields = PLAYER_ALLOWED_FIELDS\n if not self.instance.id:\n allowed_fields = allowed_fields + NEW_PLAYER_FIELDS\n self.fields = dict([(key, val) for key, val in self.fields.items() if key in allowed_fields])","def __init__(__self__, *,\n account_key: pulumi.Input[str],\n account_name: pulumi.Input[str],\n storage_type: pulumi.Input[str]):\n pulumi.set(__self__, \"account_key\", account_key)\n pulumi.set(__self__, \"account_name\", account_name)\n pulumi.set(__self__, \"storage_type\", 'StorageAccount')","def load_account_data(self):\n if self.acc is None:\n return\n\n self.name_entry.delete(0, tk.END)\n self.name_entry.insert(0, self.acc.name)\n self.name_entry.config(state='disabled')\n\n self.email_entry.delete(0, tk.END)\n self.email_entry.insert(0, self.acc.email)\n\n self.user_entry.delete(0, tk.END)\n self.user_entry.insert(0, self.acc.username)\n\n key = secrets.decrypt_field(self.us.crypt_key)\n self.pass_entry.delete(0, tk.END)\n self.pass_entry.insert(0, secrets.decrypt_data(key, self.acc.password))","def __init__(self, *args, **kw):\n super(SignupFormExtra, self).__init__(*args, **kw)\n # Put the first and last name at the top\n new_order = self.fields.keyOrder[:-2]\n new_order.insert(0, 'first_name')\n new_order.insert(1, 'last_name')\n self.fields.keyOrder = new_order","def fields(self, fields):\n\n self._fields = fields","def additional_fields(self, additional_fields):\n\n self._additional_fields = additional_fields","def account_holder(self, account_holder):\n\n self._account_holder = account_holder","def account_status(self, account_status):\n\n self._account_status = account_status","def account_look_up_info(self, account_look_up_info):\n\n self._account_look_up_info = account_look_up_info","def configure_account(self, alias: Alias = sentinel, margin_rate: DecimalNumber = sentinel):\n pass","def __init__(self, account_name, user_name, password):\n self.account_name = account_name\n self.user_name = user_name\n self.password = password","def __init__(self,account,username, password):\n self.user_name = username\n self.password = password\n self.account = account","def __init__(self, *args, **kwargs):\n super (EditUserLoginData, self).__init__ (*args, **kwargs)\n instance = getattr (self, 'instance', None)\n if instance and instance.id:\n self.fields['username'].widget.attrs['readonly'] = True\n self.fields['username'].widget.attrs['disabled'] = True","def _onchange_field(self):\n if not self.secretary_contact_id:\n return\n if self.partner_type in ['dr', 'patient', 'secretary']:\n self.update({\n 'secretary_contact_id': False\n })","def get_account_settings():\n pass","def __init__(self,account_name, username, password):\n self.account_name = account_name\n self.username = username\n self.password = password","def __init__(__self__, *,\n account_alias: pulumi.Input[str]):\n pulumi.set(__self__, \"account_alias\", account_alias)","def default_get(self, field_list):\n # NOTE: use field_list argument instead of fields for fix the pylint\n # error W0621 Redefining name 'fields' from outer scope\n context = self._context or {}\n res = super(AccountWhSrc, self).default_get(\n field_list)\n res.update({'uid_wh_agent': self._get_uid_wh_agent(\n )})\n res.update({'partner_list': self._get_partner_agent(\n )})\n\n return res","def get_account(self, account_id, **kwargs):\r\n\r\n if 'mask' not in kwargs:\r\n kwargs['mask'] = 'status'\r\n\r\n return self.account.getObject(id=account_id, **kwargs)","def _replace_fields(self):\n for name, value in self._cleaned_data.items():\n setattr(self, name, value)","def _set_user_info(self):\n sha = sha1(self.email).hexdigest()\n user_info = redis.hgetall(\"sl:account:{}\".format(sha))\n\n if (type(user_info) != dict or\n user_info.get(\"password\") != self.password):\n user_info = {}\n\n try:\n self.plan = Plan.from_id(user_info.get(\"plan\"))\n except SleekException:\n self.plan = None\n self.customer_token = str_to_none(\n user_info.get(\"customer_token\")\n )\n self.subscription_token = str_to_none(\n user_info.get(\"subscription_token\")\n )\n self.subscription_end = str_to_none(\n user_info.get(\"subscription_end\")\n )","def get_protected_fields(self):\n\t\tfields = super(PaymentObject, self).get_protected_fields()\n\t\treturn tuple(set(fields) | {'user_id'})","def default_account(\n PK=1,\n UtilityProvider=\"test_provider\",\n AccountNumber=\"test_account_number\",\n RawAccountNumber=\"test_raw_account_number\",\n SourceLink=\"test_source_link\",\n StatementType=\"test_statement_type\",\n StatementDate=date(2000, 2, 5),\n IntervalStart=date(2000, 1, 1),\n IntervalEnd=date(2000, 2, 1),\n TotalBillAmount=Decimal(100.0),\n AmountDue=Decimal(90.0),\n NewCharges=Decimal(80.0),\n OutstandingBalance=Decimal(70.0),\n PreviousBalance=Decimal(60.0),\n meters=None,\n floating_charges=None,\n):\n return Account(\n PK=PK,\n UtilityProvider=UtilityProvider,\n AccountNumber=AccountNumber,\n RawAccountNumber=RawAccountNumber,\n SourceLink=SourceLink,\n StatementType=StatementType,\n StatementDate=StatementDate,\n IntervalStart=IntervalStart,\n IntervalEnd=IntervalEnd,\n TotalBillAmount=TotalBillAmount,\n AmountDue=AmountDue,\n NewCharges=NewCharges,\n OutstandingBalance=OutstandingBalance,\n PreviousBalance=PreviousBalance,\n meters=[] if not meters else meters,\n floating_charges=[] if not floating_charges else floating_charges,\n )"],"string":"[\n \"def account(self, account):\\n\\n self._account = account\",\n \"def account(self, account):\\n\\n self._account = account\",\n \"def account(self, account):\\n\\n self._account = account\",\n \"def account(self, account):\\n\\n self._account = account\",\n \"def patch(self, account=None, user=None, account_id=None):\\n return super().patch()\",\n \"def account(self, account: str):\\n self._account = account\",\n \"def get_account(self, account):\\n \\n pass\",\n \"def set_account(self):\\n return self.__Account\",\n \"def onAccountUpdate(self, data):\\n pass\",\n \"def account(self):\\r\\n return Account(self)\",\n \"def put(self, account=None, user=None, account_id=None):\\n return super().put()\",\n \"def set_specific_fields(self):\\n raise NotImplementedError(\\\"Must be defined by subclass!\\\")\",\n \"def change_account(self, account):\\r\\n check_account = Account(account, steem_instance=self.steem)\\r\\n self.account = check_account[\\\"name\\\"]\\r\\n self.refresh()\",\n \"def get_account_details(self):\\n pass\",\n \"def put_account(self, account):\\n \\n pass\",\n \"def update_account_data(self):\\n self.ensure_one()\\n getattr(self, '%s_update_account_data' % self.provider, lambda: None)()\",\n \"def account(self):\\n return Account(self)\",\n \"def account_information(self) -> MetatraderAccountInformation:\\n return self._accountInformation\",\n \"def __init__(self, client, account_id):\\n\\n super(AccountsMixin, self).__init__(client)\\n self._account_id = account_id\",\n \"def account_id(self, account_id):\\n self._account_id = account_id\",\n \"def account(self, account_code):\\r\\n return acc.Account(self, account_code)\",\n \"def extended_account_data(self,accountinfo):\\n\\n data = {\\n 'username' : accountinfo['username'],\\n 'password' : accountinfo['password'],\\n 'passwordconfirm' : accountinfo['password'],\\n 'firstname' : accountinfo['firstname'],\\n 'middlename' : accountinfo['middlename'],\\n 'lastname' : accountinfo['lastname'],\\n 'email' : accountinfo['email'],\\n 'emailconfirm' : accountinfo['email'],\\n 'gender' : 'Refused',\\n 'disability' : 'Refused',\\n 'hispanic' : 'Refused',\\n 'race_refused' : 'Yes',\\n 'captcha' : False,\\n 'usageagreement' : True,\\n }\\n return data\",\n \"def to_representation(self, instance):\\n data = super(AccountSerializer, self).to_representation(instance)\\n data[\\\"display_name\\\"] = instance.name\\n return data\",\n \"def _account(self) -> Account:\\n if isinstance(self._node_cached_account, Account):\\n return self._node_cached_account\\n account = Account.retrieve(\\n session=self.entity.session,\\n entity=self.entity,\\n account_id=self.account_id\\n )\\n self._node_cached_account = account\\n return account\",\n \"def account_amount(self, account_amount):\\n\\n self._account_amount = account_amount\",\n \"def get_account(self):\\n return self._account\",\n \"def get_account(self):\\n return self._account\",\n \"def account_id(self, account_id):\\n\\n self._account_id = account_id\",\n \"def account_id(self, account_id):\\n\\n self._account_id = account_id\",\n \"def account_id(self, account_id):\\n\\n self._account_id = account_id\",\n \"def account_id(self, account_id):\\n\\n self._account_id = account_id\",\n \"def account_id(self, account_id):\\n\\n self._account_id = account_id\",\n \"def account_id(self, account_id):\\n\\n self._account_id = account_id\",\n \"def copy_fields(self, model):\\n fields = super(HistoricalRecords, self).copy_fields(model)\\n for name, field in self.additional_fields.items():\\n assert name not in fields\\n assert hasattr(self, 'get_%s_value' % name)\\n fields[name] = field\\n return fields\",\n \"def account_name(self, account_name):\\n\\n self._account_name = account_name\",\n \"def account_name(self, account_name):\\n\\n self._account_name = account_name\",\n \"def accounts(self): # pragma: no coverage\\r\\n raise NotImplementedError()\",\n \"def account(self) -> str:\\n return self._account\",\n \"def account(self) -> str:\\n return self._account\",\n \"def setAccount(self, account_id):\\n self.data_struct['_setAccount'] = account_id\",\n \"def describe_account_attributes():\\n pass\",\n \"def update(self, account):\\n model = models.load('Account', account)\\n return self.client.update_account(model=model)\",\n \"def account(self, account_id):\\r\\n return Account(self, account_id)\",\n \"def _postprocess_wim_account(wim_account, hide=_CONFIDENTIAL_FIELDS):\\n # Fix fields from join\\n for field in ('type', 'description', 'wim_url'):\\n if field in wim_account:\\n wim_account['wim.'+field] = wim_account.pop(field)\\n\\n for field in ('id', 'nfvo_tenant_id', 'wim_account_id'):\\n if field in wim_account:\\n wim_account['association.'+field] = wim_account.pop(field)\\n\\n wim_account = _postprocess_record(wim_account, hide)\\n\\n created = wim_account.get('created')\\n wim_account['created'] = (created is True or created == 'true')\\n\\n return wim_account\",\n \"def __init__(self, account, user_username, user_password):\\n self. account = account\\n self. user_username = user_username\\n self.user_password = user_password\",\n \"def save_account(self):\\n Credential.account_list.append(self)\",\n \"def to_representation(self, obj):\\n data = super(AccountInfoSerializer, self).to_representation(obj)\\n if obj.pk is None:\\n source = mkt.LOGIN_SOURCE_LOOKUP[mkt.LOGIN_SOURCE_UNKNOWN]\\n elif obj.source in self.ALLOWED_SOURCES:\\n source = mkt.LOGIN_SOURCE_LOOKUP[obj.source]\\n else:\\n source = mkt.LOGIN_SOURCE_LOOKUP[mkt.LOGIN_SOURCE_BROWSERID]\\n\\n data[\\\"source\\\"] = source\\n return data\",\n \"def __init__(self):\\n self.account_balance = 0\\n self.amount = 0\",\n \"def set_account(self, account: str):\\n ret = self._call_txtrader_api('set_account', {'account': account})\\n if ret:\\n self.account = account\\n return ret\",\n \"async def on_account_information_updated(self, account_information: MetatraderAccountInformation):\\n self._accountInformation = account_information\",\n \"def custom_profile_fields(self, custom_profile_fields):\\n\\n self._custom_profile_fields = custom_profile_fields\",\n \"def account_type(self, account_type):\\n\\n self._account_type = account_type\",\n \"def account_type(self, account_type):\\n\\n self._account_type = account_type\",\n \"def account_type(self, account_type):\\n\\n self._account_type = account_type\",\n \"def account_type(self, account_type):\\n\\n self._account_type = account_type\",\n \"def account_number(self, account_number):\\n\\n self._account_number = account_number\",\n \"def __init__(self,Account,username,password):\\n self.Account = Account\\n self.username = username\\n self.password = password\",\n \"def get_account_information(self):\\n self.account_information = retry(lambda: self.client\\n .futures_account_v2())\\n return self.account_information\",\n \"def save(self, *args, **kwargs):\\n self.username = self.username or self.email\\n super().save(*args, **kwargs)\",\n \"def add_account(self, account):\\n self.accounts[account.account_number] = account.json()\\n # We should save in database the new account using self.di, but not now in order to get our tests passed\",\n \"def account_info(account):\\n return {\\n 'status': account.status,\\n 'availability': account.availability,\\n 'blurb': account.message,\\n 'email': account.email,\\n 'name': account.name,\\n 'success': True\\n }\",\n \"def get(self, account=None, user=None, account_id=None):\\n self.get_object = lambda: account\\n return super().get()\",\n \"def get_private(self):\\n return self.__Account\",\n \"def account_context():\\n return dict(\\n user=current_user,\\n party=current_user.party,\\n )\",\n \"def accounts(self, accounts):\\n\\n self._accounts = accounts\",\n \"def __init__(self,account,user_name,password,email):\\n self.account = account\\n self.user_name = user_name\\n self.password = password\\n self.email = email\",\n \"def account_number(self):\\n return self.__account_number\",\n \"def on_account(self, account: AccountData):\\n # self.on_event(EVENT_ACCOUNT, account)\\n # self.on_event(EVENT_ACCOUNT + account.vt_accountid, account)\\n pass\",\n \"def account_summary(self):\\n pass\",\n \"def __init__(self, account_id, balance):\\n self.account_id = account_id\\n self.balance = balance\",\n \"def to_solders(self) -> instruction.AccountMeta:\\n return instruction.AccountMeta(\\n pubkey=self.pubkey.to_solders(), is_signer=self.is_signer, is_writable=self.is_writable\\n )\",\n \"def __init__(self,\\r\\n account_id=None,\\r\\n name=None,\\r\\n org_no=None,\\r\\n uni_customer_no=None,\\r\\n created=None,\\r\\n last_modified=None,\\r\\n dealer_id=None,\\r\\n dealer_name=None,\\r\\n dealer_reference=None,\\r\\n enabled=None,\\r\\n additional_properties = {}):\\r\\n\\r\\n # Initialize members of the class\\r\\n self.account_id = account_id\\r\\n self.name = name\\r\\n self.org_no = org_no\\r\\n self.uni_customer_no = uni_customer_no\\r\\n self.created = APIHelper.RFC3339DateTime(created) if created else None\\r\\n self.last_modified = APIHelper.RFC3339DateTime(last_modified) if last_modified else None\\r\\n self.dealer_id = dealer_id\\r\\n self.dealer_name = dealer_name\\r\\n self.dealer_reference = dealer_reference\\r\\n self.enabled = enabled\\r\\n\\r\\n # Add additional model properties to the instance\\r\\n self.additional_properties = additional_properties\",\n \"def __init__(self,account,username, password):\\n self.account = account\\n self.username = username\\n self.password = password\",\n \"def account_balance(self, account_balance):\\n\\n self._account_balance = account_balance\",\n \"def account_balance(self, account_balance):\\n\\n self._account_balance = account_balance\",\n \"def test_client_bank_account_partial_update(self):\\n pass\",\n \"def __init__(self, accountHolder=None):\\n if accountHolder:\\n self._getAccount = lambda: accountHolder.theAccount\\n else:\\n self._getAccount = lambda: self.theAccount\",\n \"def __init__(self, *args, **kwargs):\\n user = kwargs.pop('user', None)\\n super().__init__(*args, **kwargs)\\n if self.instance.id:\\n for origin in Origin.ORIGIN_TYPES:\\n # in case they had an origin that doesn't exist any more.\\n try:\\n self.fields[origin[0]].initial = self.instance.origins.filter(type=origin[0]).first()\\n except Origin.DoesNotExist:\\n pass\\n # adjust fields for different users\\n if user.has_perm('players.view_any_player'):\\n allowed_fields = self.fields.keys()\\n else:\\n allowed_fields = PLAYER_ALLOWED_FIELDS\\n if not self.instance.id:\\n allowed_fields = allowed_fields + NEW_PLAYER_FIELDS\\n self.fields = dict([(key, val) for key, val in self.fields.items() if key in allowed_fields])\",\n \"def __init__(__self__, *,\\n account_key: pulumi.Input[str],\\n account_name: pulumi.Input[str],\\n storage_type: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"account_key\\\", account_key)\\n pulumi.set(__self__, \\\"account_name\\\", account_name)\\n pulumi.set(__self__, \\\"storage_type\\\", 'StorageAccount')\",\n \"def load_account_data(self):\\n if self.acc is None:\\n return\\n\\n self.name_entry.delete(0, tk.END)\\n self.name_entry.insert(0, self.acc.name)\\n self.name_entry.config(state='disabled')\\n\\n self.email_entry.delete(0, tk.END)\\n self.email_entry.insert(0, self.acc.email)\\n\\n self.user_entry.delete(0, tk.END)\\n self.user_entry.insert(0, self.acc.username)\\n\\n key = secrets.decrypt_field(self.us.crypt_key)\\n self.pass_entry.delete(0, tk.END)\\n self.pass_entry.insert(0, secrets.decrypt_data(key, self.acc.password))\",\n \"def __init__(self, *args, **kw):\\n super(SignupFormExtra, self).__init__(*args, **kw)\\n # Put the first and last name at the top\\n new_order = self.fields.keyOrder[:-2]\\n new_order.insert(0, 'first_name')\\n new_order.insert(1, 'last_name')\\n self.fields.keyOrder = new_order\",\n \"def fields(self, fields):\\n\\n self._fields = fields\",\n \"def additional_fields(self, additional_fields):\\n\\n self._additional_fields = additional_fields\",\n \"def account_holder(self, account_holder):\\n\\n self._account_holder = account_holder\",\n \"def account_status(self, account_status):\\n\\n self._account_status = account_status\",\n \"def account_look_up_info(self, account_look_up_info):\\n\\n self._account_look_up_info = account_look_up_info\",\n \"def configure_account(self, alias: Alias = sentinel, margin_rate: DecimalNumber = sentinel):\\n pass\",\n \"def __init__(self, account_name, user_name, password):\\n self.account_name = account_name\\n self.user_name = user_name\\n self.password = password\",\n \"def __init__(self,account,username, password):\\n self.user_name = username\\n self.password = password\\n self.account = account\",\n \"def __init__(self, *args, **kwargs):\\n super (EditUserLoginData, self).__init__ (*args, **kwargs)\\n instance = getattr (self, 'instance', None)\\n if instance and instance.id:\\n self.fields['username'].widget.attrs['readonly'] = True\\n self.fields['username'].widget.attrs['disabled'] = True\",\n \"def _onchange_field(self):\\n if not self.secretary_contact_id:\\n return\\n if self.partner_type in ['dr', 'patient', 'secretary']:\\n self.update({\\n 'secretary_contact_id': False\\n })\",\n \"def get_account_settings():\\n pass\",\n \"def __init__(self,account_name, username, password):\\n self.account_name = account_name\\n self.username = username\\n self.password = password\",\n \"def __init__(__self__, *,\\n account_alias: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"account_alias\\\", account_alias)\",\n \"def default_get(self, field_list):\\n # NOTE: use field_list argument instead of fields for fix the pylint\\n # error W0621 Redefining name 'fields' from outer scope\\n context = self._context or {}\\n res = super(AccountWhSrc, self).default_get(\\n field_list)\\n res.update({'uid_wh_agent': self._get_uid_wh_agent(\\n )})\\n res.update({'partner_list': self._get_partner_agent(\\n )})\\n\\n return res\",\n \"def get_account(self, account_id, **kwargs):\\r\\n\\r\\n if 'mask' not in kwargs:\\r\\n kwargs['mask'] = 'status'\\r\\n\\r\\n return self.account.getObject(id=account_id, **kwargs)\",\n \"def _replace_fields(self):\\n for name, value in self._cleaned_data.items():\\n setattr(self, name, value)\",\n \"def _set_user_info(self):\\n sha = sha1(self.email).hexdigest()\\n user_info = redis.hgetall(\\\"sl:account:{}\\\".format(sha))\\n\\n if (type(user_info) != dict or\\n user_info.get(\\\"password\\\") != self.password):\\n user_info = {}\\n\\n try:\\n self.plan = Plan.from_id(user_info.get(\\\"plan\\\"))\\n except SleekException:\\n self.plan = None\\n self.customer_token = str_to_none(\\n user_info.get(\\\"customer_token\\\")\\n )\\n self.subscription_token = str_to_none(\\n user_info.get(\\\"subscription_token\\\")\\n )\\n self.subscription_end = str_to_none(\\n user_info.get(\\\"subscription_end\\\")\\n )\",\n \"def get_protected_fields(self):\\n\\t\\tfields = super(PaymentObject, self).get_protected_fields()\\n\\t\\treturn tuple(set(fields) | {'user_id'})\",\n \"def default_account(\\n PK=1,\\n UtilityProvider=\\\"test_provider\\\",\\n AccountNumber=\\\"test_account_number\\\",\\n RawAccountNumber=\\\"test_raw_account_number\\\",\\n SourceLink=\\\"test_source_link\\\",\\n StatementType=\\\"test_statement_type\\\",\\n StatementDate=date(2000, 2, 5),\\n IntervalStart=date(2000, 1, 1),\\n IntervalEnd=date(2000, 2, 1),\\n TotalBillAmount=Decimal(100.0),\\n AmountDue=Decimal(90.0),\\n NewCharges=Decimal(80.0),\\n OutstandingBalance=Decimal(70.0),\\n PreviousBalance=Decimal(60.0),\\n meters=None,\\n floating_charges=None,\\n):\\n return Account(\\n PK=PK,\\n UtilityProvider=UtilityProvider,\\n AccountNumber=AccountNumber,\\n RawAccountNumber=RawAccountNumber,\\n SourceLink=SourceLink,\\n StatementType=StatementType,\\n StatementDate=StatementDate,\\n IntervalStart=IntervalStart,\\n IntervalEnd=IntervalEnd,\\n TotalBillAmount=TotalBillAmount,\\n AmountDue=AmountDue,\\n NewCharges=NewCharges,\\n OutstandingBalance=OutstandingBalance,\\n PreviousBalance=PreviousBalance,\\n meters=[] if not meters else meters,\\n floating_charges=[] if not floating_charges else floating_charges,\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6413226","0.6413226","0.6413226","0.6413226","0.6343206","0.6329128","0.6322581","0.63135093","0.61441493","0.60770935","0.60361296","0.6022793","0.60135746","0.60068023","0.59981","0.59467715","0.58997744","0.5875439","0.5858766","0.5787134","0.5765871","0.5693723","0.5661822","0.56605464","0.5647849","0.5623891","0.5623891","0.56091964","0.56091964","0.56091964","0.56091964","0.56091964","0.56091964","0.55932355","0.5577527","0.5577527","0.5525663","0.5497245","0.5497245","0.54854614","0.5484916","0.54774356","0.5476911","0.546609","0.54583955","0.54424846","0.5410426","0.5377124","0.53760546","0.53738874","0.53401583","0.5329828","0.5329828","0.5329828","0.5329828","0.53200155","0.5298636","0.5294403","0.52843815","0.52803403","0.52714235","0.527081","0.52647203","0.52584964","0.5255239","0.52511203","0.52499014","0.5246141","0.5233991","0.52194643","0.5218058","0.52145964","0.5213871","0.52087873","0.52087873","0.5207795","0.52052665","0.5198356","0.5193696","0.5190282","0.51890326","0.5186539","0.5186149","0.51714903","0.5168949","0.5168342","0.5165137","0.5161807","0.51558155","0.51505286","0.514965","0.5140693","0.5138576","0.5135262","0.5132134","0.5115724","0.51141286","0.51140845","0.5109768","0.5108096"],"string":"[\n \"0.6413226\",\n \"0.6413226\",\n \"0.6413226\",\n \"0.6413226\",\n \"0.6343206\",\n \"0.6329128\",\n \"0.6322581\",\n \"0.63135093\",\n \"0.61441493\",\n \"0.60770935\",\n \"0.60361296\",\n \"0.6022793\",\n \"0.60135746\",\n \"0.60068023\",\n \"0.59981\",\n \"0.59467715\",\n \"0.58997744\",\n \"0.5875439\",\n \"0.5858766\",\n \"0.5787134\",\n \"0.5765871\",\n \"0.5693723\",\n \"0.5661822\",\n \"0.56605464\",\n \"0.5647849\",\n \"0.5623891\",\n \"0.5623891\",\n \"0.56091964\",\n \"0.56091964\",\n \"0.56091964\",\n \"0.56091964\",\n \"0.56091964\",\n \"0.56091964\",\n \"0.55932355\",\n \"0.5577527\",\n \"0.5577527\",\n \"0.5525663\",\n \"0.5497245\",\n \"0.5497245\",\n \"0.54854614\",\n \"0.5484916\",\n \"0.54774356\",\n \"0.5476911\",\n \"0.546609\",\n \"0.54583955\",\n \"0.54424846\",\n \"0.5410426\",\n \"0.5377124\",\n \"0.53760546\",\n \"0.53738874\",\n \"0.53401583\",\n \"0.5329828\",\n \"0.5329828\",\n \"0.5329828\",\n \"0.5329828\",\n \"0.53200155\",\n \"0.5298636\",\n \"0.5294403\",\n \"0.52843815\",\n \"0.52803403\",\n \"0.52714235\",\n \"0.527081\",\n \"0.52647203\",\n \"0.52584964\",\n \"0.5255239\",\n \"0.52511203\",\n \"0.52499014\",\n \"0.5246141\",\n \"0.5233991\",\n \"0.52194643\",\n \"0.5218058\",\n \"0.52145964\",\n \"0.5213871\",\n \"0.52087873\",\n \"0.52087873\",\n \"0.5207795\",\n \"0.52052665\",\n \"0.5198356\",\n \"0.5193696\",\n \"0.5190282\",\n \"0.51890326\",\n \"0.5186539\",\n \"0.5186149\",\n \"0.51714903\",\n \"0.5168949\",\n \"0.5168342\",\n \"0.5165137\",\n \"0.5161807\",\n \"0.51558155\",\n \"0.51505286\",\n \"0.514965\",\n \"0.5140693\",\n \"0.5138576\",\n \"0.5135262\",\n \"0.5132134\",\n \"0.5115724\",\n \"0.51141286\",\n \"0.51140845\",\n \"0.5109768\",\n \"0.5108096\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67399603"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2665,"cells":{"query":{"kind":"string","value":"Initializing method. Always starts with player 'X' going first. Also creates a blank board to begin playing on."},"document":{"kind":"string","value":"def __init__(self):\n\t\tself.current = Piece.EX\n\t\tself.board = [Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n # Current player\n self.player = X\n\n # Board\n self.board = [\n [None, None, None],\n [None, None, None],\n [None, None, None]\n ]\n\n # Winner\n self.winner = None\n\n # Game over\n self._gameover = False","def __init__(self):\n self.board = [\n BS, BS, BS, BS,\n BS, BS, BS,\n BS, BS, BS, BS,\n EM, EM, EM,\n WS, WS, WS, WS,\n WS, WS, WS,\n WS, WS, WS, WS\n ]\n self.curr_player = WHITE_PLAYER","def initBoard(self):\n pass","def new_game(self):\n self.board = [None] * 9\n self.player = \"X\"\n self.winner = None","def __init__(self):\n self.game_board = [' '] * 9\n self.size = len(self.game_board)\n self.move = 'X'\n self.player1 = None\n self.player2 = None\n self.current_player = None\n self.board_coords = {\n (1, 3): 0, (2, 3): 1, (3, 3): 2,\n (1, 2): 3, (2, 2): 4, (3, 2): 5,\n (1, 1): 6, (2, 1): 7, (3, 1): 8\n }\n\n self.winning_cases = [\n (0, 1, 2), (3, 4, 5), (6, 7, 8),\n (0, 3, 6), (1, 4, 7), (2, 5, 8),\n (0, 4, 8), (2, 4, 6)\n ]","def __init__(self):\n self.__grid = create_grid(\n Settings.SIZE_X, Settings.SIZE_Y, MarkerType.NONE)\n\n self.__turn = 0\n self.__state = GameState.PLAYING\n self.__winner = MarkerType.NONE\n self.__loser = MarkerType.NONE\n\n # Separate counter for turns, because __turn depends on starting player\n self.__turns_played = 0","def __init__(self):\r\n\t\tself.game_board = [['0','0','0'],['0','0','0'],['0','0','0']]\r\n\t\tself.count = 0\r\n\t\tself.x_turn = True\r\n\t\r\n\r\n\t\tpass","def __init__(self):\n # The starting counts are set to 0 and modified when the board is initiated.\n self.num_black_pieces = 0\n self.num_black_kings = 0\n self.num_white_pieces = 0\n self.num_white_kings = 0\n # Creates a new board and fills it with the appropriate pieces.\n self.board = self._initiate_board()\n self.moves = []","def __init__(self):\n self._game_state = \"UNFINISHED\"\n self._current_player = \"BLACK\"\n self._game_board = Board()","def setup(self):\n piece_order = ['ROOK','KNIGHT','BISHOP','QUEEN','KING','BISHOP','KNIGHT','ROOK']\n for row,colour in zip([0,7],['BLACK','WHITE']):\n for col,piece in enumerate(piece_order):\n self.board[row][col] = colour + '_' + piece\n \n for row,colour in zip([1,6],['BLACK','WHITE']):\n for i in range(8):\n self.board[row][i] = colour + '_' + 'PAWN'\n \n self.toplay = 'WHITE'","def setUp(self):\n self.gameBoard = Grid((100, 100), Cell)","def __init__(self, player_x_type, player_o_type):\n self.board = Board()\n self.player_x = player_x_type(self.FIRST_PLAYER_MARK)\n self.player_o = player_o_type(self.SECOND_PLAYER_MARK)","def initialize():\n\n global PLAYER # this means we use the global var PLAYER and cannot have a local var named PLAYER\n global LEVEL_COUNTER\n\n LEVEL_COUNTER = 1\n \n coordinates = generate_coords()\n\n PLAYER = Stark()\n tree = Tree()\n ww = WhiteWalker()\n crown = Crown()\n gray_gem = GrayGem()\n clear_board()\n GAME_BOARD.create(\"Snow\",\"Snow\")\n GAME_BOARD.draw_msg(\"Level \" + str(LEVEL_COUNTER) + \". Winter is coming.\")\n generate_level(coordinates, [PLAYER, ww, gray_gem, crown, tree, tree, gray_gem, tree, tree, gray_gem, tree])\n\n # for i in range(0,NUM_ELTS):\n # place_on_board(elts[i], coordinates[i][0], coordinates[i][1])","def __init__(self):\n self.players = {1: [\"Player_a\", \"\\u25CF\"], 2: [\"Player_b\", \"\\u25CB\"]}\n self.current_player = 1\n self.playing_player = self.players[1]\n self.grid = [[\" \"] * 6 for x in range(7)]","def start_game(self):\n self.board = Board(num_tableaus=self.tableau_qty, num_decks=self.decks, deal_3=self.deal_3)\n self.board.init_move_dict()\n self.board.deal(self.deck)\n\n if self.api_use:\n self.init_game_api()\n elif self.commandline:\n self.init_cl_game()\n else:\n self.init_pygame()","def initGameState(self):\n print(\"Setting game state: \")\n self.playGUI = GUI()\n self.playGUI.drawBoard(self.player)","def initialize_board(self):\n self.board = np.zeros(shape=(BOARD_SIZE, BOARD_SIZE), dtype=np.int) # another way of defining board: [[for x in range(cm.BOARD_SIZE)] for x in range(cm.BOARD_SIZE)]\n center = int(BOARD_SIZE / 2)\n self.board[center-1][center-1] = self.board[center][center] = WHITE # place the board according to position\n self.board[center][center-1] = self.board[center-1][center] = BLACK\n self.black_piece = 2\n self.white_piece = 2","def __init__(self):\n self.game_screen = pygame.display.set_mode((GameData.screen_dim, GameData.screen_dim))\n self.game_screen.fill(GameData.background_color)\n self.player = 1\n self.game_over = False\n self.board = np.zeros((GameData.rows, GameData.columns))","def initialize_board():\n # Wipe current board\n for x in range(len(THE_BOARD.positions)):\n for y in range(len(THE_BOARD.positions)):\n THE_BOARD.positions[x][y] = ' '\n\n all_pieces = []\n\n # Pawns\n white_pawns = [Pawn('white', (6, i)) for i in range(len(THE_BOARD.positions[6]))]\n black_pawns = [Pawn('black', (1, i)) for i in range(len(THE_BOARD.positions[1]))]\n all_pieces.extend(white_pawns)\n all_pieces.extend(black_pawns)\n\n # Rooks\n rook1 = Rook('black', (0, 0))\n all_pieces.append(rook1)\n rook2 = Rook('black', (0, 7))\n all_pieces.append(rook2)\n rook3 = Rook('white', (7, 0))\n all_pieces.append(rook3)\n rook4 = Rook('white', (7, 7))\n all_pieces.append(rook4)\n\n # Knights\n knight1 = Knight('black', (0, 1))\n all_pieces.append(knight1)\n knight2 = Knight('black', (0, 6))\n all_pieces.append(knight2)\n knight3 = Knight('white', (7, 1))\n all_pieces.append(knight3)\n knight4 = Knight('white', (7, 6))\n all_pieces.append(knight4)\n\n # Bishops\n bishop1 = Bishop('black', (0, 2))\n all_pieces.append(bishop1)\n bishop2 = Bishop('black', (0, 5))\n all_pieces.append(bishop2)\n bishop3 = Bishop('white', (7, 2))\n all_pieces.append(bishop3)\n bishop4 = Bishop('white', (7, 5))\n all_pieces.append(bishop4)\n\n # King and Queen\n queen1 = Queen('black', (0, 4))\n all_pieces.append(queen1)\n queen2 = Queen('white', (7, 4))\n all_pieces.append(queen2)\n king1 = King('black', (0, 3))\n all_pieces.append(king1)\n king2 = King('white', (7, 3))\n all_pieces.append(king2)\n\n # Add every single piece to the board. Only then can they update their spaces threatened\n for piece in all_pieces:\n THE_BOARD.update(piece)\n THE_BOARD.update_all_spaces_threatened()","def setUp(self):\n\n self.board = Board(3, 3)","def __init__(self):\n\n self.__turn_info = { 'turn': ChessGame.WHITE }\n self.init_board()","def init_game(self):\n nrows = len(self.array)\n self.game_over = False\n self.squares_left = nrows * nrows\n self.bombs_left = 0\n # clear the board\n for i in xrange(nrows):\n for j in xrange(nrows):\n self.array[i][j].reset()\n # put N random bombs\n for i in xrange(nrows):\n rand_num = random.randrange(nrows*nrows)\n if self.array[rand_num / nrows][rand_num % nrows].type \\\n != SquareType.BOMB:\n self.insert_bomb(rand_num / nrows, rand_num % nrows)\n self.squares_left -= self.bombs_left\n self.print_board()","def __init__(self, players):\n\n # Define the players\n self.players = players\n\n # Define who starts the game\n self.nplayer = 1 \n\n # Define the board\n self.board = [0] * 9","def __init__(self):\n self.board = Board()\n self.__root = BinaryNode(Board(), None, Board.PLAYER_1)\n self.player = Board.PLAYER_0\n self.win = False","def __init__(self, board=None):\n self.winner = None\n self.board = board or [self.__class__.EMPTY_POSITION_COUNTER] * 9","def setup_new_game(self):\r\n self._player = Player()\r\n self._stats = GameStats(self._bb_settings)\r\n self._scoreboard = Scoreboard(self._bb_settings, self._screen)","def __init__(self):\n self.board_dict = dict()\n for i in range(self.BOARD_WIDTH):\n for j in range(self.BOARD_WIDTH):\n self.board_dict[i, j] = 0, None\n\n self.players_locations = dict()\n self.last_moved = None","def start_at_beginning(self):\n b_pieces = [ChessPiece.B_ROOK,\n ChessPiece.B_KNIGHT,\n ChessPiece.B_BISHOP,\n ChessPiece.B_QUEEN,\n ChessPiece.B_KING,\n ChessPiece.B_BISHOP,\n ChessPiece.B_KNIGHT,\n ChessPiece.B_ROOK]\n w_pieces = [ChessPiece.W_ROOK,\n ChessPiece.W_KNIGHT,\n ChessPiece.W_BISHOP,\n ChessPiece.W_QUEEN,\n ChessPiece.W_KING,\n ChessPiece.W_BISHOP,\n ChessPiece.W_KNIGHT,\n ChessPiece.W_ROOK]\n\n for i in range(8):\n self.board.put_piece(b_pieces[i], 0, i)\n self.board.put_piece(ChessPiece.B_PAWN, 1, i)\n self.board.put_piece(w_pieces[i], 7, i)\n self.board.put_piece(ChessPiece.W_PAWN, 6, i)","def __init__(self):\n self.board = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n self.last_move = None","def __init__(self):\n super(CamTacToe, self).__init__()\n self.state = [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\n self.history = [self.state[:]]\n self.player_char = 'x'\n self.opponent_char = 'o'\n self.difficulty = 0\n self.player_start = 0","def clear_board(cls):\n # Set the board dimensions\n cls.board = [[cls.empty for x in range(cls.size)] for y in range(cls.size)]\n \n # Set allowed positions the user may provide\n cls.positions = [str(x) for x in range(1, cls.size**2 + 1)]\n\n cls.current_player = 'X'\n\n cls.display_board()\n\n cls.prompt_player()","def initializeGame(self):\n # Fill deck with cards and shuffle it\n self.deck.fill(104)\n self.deck.shuffle()\n #print \"Deck initialized\"\n\n # Initialize the field\n self.field.initialize(self.deck.draw(4))\n self.field.sortField()\n #self.field.printField()\n\n # Set players to initial state again\n # Distribute cards and set bulls to 0\n for p in self.players:\n p.bulls = 0\n p.setHand(self.deck.draw(10))","def init_board(self) -> None:\n\t\tself.canvas.create_rectangle(0, 0, self.canvas_width, self.canvas_height, fill=self.color_background)\n\t\tfor x in range(0, self.canvas_width, self.canvas_width//self.board_size):\n\t\t\tself.canvas.create_line(x, 0, x, self.canvas_width, fill=self.color_tile_border)\n\n\t\tfor y in range(0, self.canvas_width+1, self.canvas_height//self.board_size):\n\t\t\tself.canvas.create_line(0, y, self.canvas_height, y, fill=self.color_tile_border)\n\n\t\tself.text_area.delete('0.1', '2.1')","def initialize_board(self):\n self.board_values = {x:x for x in(range(1,10))}","def __init__ (self, cols = 6, rows = 7, requiredToWin = 4):\r\n\t\tself.cols = cols\r\n\t\tself.rows = rows\r\n\t\tself.win = requiredToWin\r\n\t\tself.board = [[NONE] * rows for _ in range(cols)]","def __init__(self):\r\n self._board = None\r\n self._bb_settings = Settings()\r\n self._screen = pygame.display.set_mode((self._bb_settings.screen_width,\r\n self._bb_settings.screen_height))\r\n self._player = Player()\r\n self._stats = GameStats(self._bb_settings)\r\n self._scoreboard = Scoreboard(self._bb_settings, self._screen)\r\n self._image = pygame.image.load('board.bmp')\r\n self._rect = self._image.get_rect()\r\n self._play_mode_button_list = self.make_play_mode_buttons()\r\n self._replay_button_list = self.make_replay_buttons()","def __init__(self):\n self._board = [\n\n ['', '', '', \"x\", '', '', '', ''],\n ['', '', '', '', '', '', '', ''],\n ['', '', '', '', '', '', '', ''],\n ['', '', '', '', '', '', '', ''],\n ['', '', '', '', '', '', '', ''],\n ['', '', '', '', '', '', '', ''],\n ['', '', '', '', '', '', '', ''],\n [\"o\", '', \"o\", '', \"o\", '', \"o\", ''],\n ]\n self._game_state = \"UNFINISHED\" # default game state\n self._current_row = 0 #helper used to enforce moving one row at a time\n self._current_x_row = 0 # tracks x's row coordinate\n self._current_x_column = 3 # tracks x's column coordinate\n\n #four coordinates tracking the available diagonal spaces of x\n self._lower_right = (self._current_x_row + 1, self._current_x_column + 1)\n self._lower_left = (self._current_x_row + 1, self._current_x_column - 1)\n self._upper_right = (self._current_x_row - 1, self._current_x_column + 1)\n self._upper_left = (self._current_x_row - 1, self._current_x_column - 1)\n\n #helper used to check if x is in the first column\n self._row1 = (\n self._board[0][0],\n self._board[1][0],\n self._board[2][0],\n self._board[3][0],\n self._board[4][0],\n self._board[5][0],\n self._board[6][0],\n self._board[7][0])\n #helper used to check if x is in the last column\n self._row7 = (\n self._board[0][7],\n self._board[1][7],\n self._board[2][7],\n self._board[3][7],\n self._board[4][7],\n self._board[5][7],\n self._board[6][7],\n self._board[7][7])","def __init__(self, player):\n \n self.colour = player\n self.game_in_head = Board()","def init_new_board(self) -> None:\r\n\r\n TkState.enable(self.settings_menu.winfo_children())\r\n TkState.enable(self.edit_menu.winfo_children())\r\n TkState.enable([self.play_button, self.step_button])\r\n TkState.disable([self.reset_button])\r\n\r\n self.gen_number.config(text = 0)\r\n self.speed_scale.set(self.INITIAL_TIME_PER_GEN)\r\n self.zoom_scale.set(self.INITIAL_ZOOM)\r\n\r\n self.animator.board = self.anim_board\r\n self.painter.board = self.anim_board\r\n self.painter.adjust_to_canvas()","def setUp(self):\n self.game = TTTBoard(3)","def init_board(self):\n\n self.__board = dict()\n order = ['rook', 'knight', 'bishop', 'queen', 'king', 'bishop',\n 'knight', 'rook']\n for j, name in enumerate(order):\n\n self.__board[(0, j)] = ChessGame.Piece( name, ChessGame.WHITE)\n self.__board[(7, j)] = ChessGame.Piece( name, ChessGame.BLACK)\n self.__board[(1, j)] = ChessGame.Piece('pawn', ChessGame.WHITE)\n self.__board[(6, j)] = ChessGame.Piece('pawn', ChessGame.BLACK)\n\n self.__players = { ChessGame.WHITE: set(), ChessGame.BLACK: set() }\n for color in (ChessGame.BLACK, ChessGame.WHITE):\n self.__players[color] = {(x, y) for (x, y), piece in\n self.__board.iteritems() if piece.color == color }\n\n return","def __init__(self, players):\n self.players = players\n self.board = Board()","def __init__(self, rows=6, columns=7, win_length=4):\n\n self._board = [[0 for i in xrange(columns)] for i in xrange(rows)]\n self._rows = rows\n self._columns = columns\n self._win_length = win_length\n self.current_player = None\n self.winner = None\n print \"The game is afoot!\"","def __init__(self):\n self._current_state = \"UNFINISHED\"\n self._start_color = \"RED\"\n self._board = Board()","def _initiate_board(self):\n grid = []\n for i in range(constant.BOARD_DIMENSION):\n # Starts each row\n current_row = []\n for j in range(constant.BOARD_DIMENSION):\n # Adds the pieces depending on the position\n if i < constant.ROWS_OF_PIECES:\n # Black pieces\n if (j + i) % 2 != 0:\n current_row.append(Piece(i, j, Player.black))\n self.num_black_pieces = self.num_black_pieces + 1\n else:\n current_row.append(None)\n\n elif i >= constant.BOARD_DIMENSION - constant.ROWS_OF_PIECES:\n # White pieces\n if (j + i) % 2 != 0:\n current_row.append(Piece(i, j, Player.white))\n self.num_white_pieces = self.num_white_pieces + 1\n else:\n current_row.append(None)\n\n else:\n current_row.append(None)\n\n grid.append(current_row)\n\n return grid","def setup_board(self):\n\n for row in range(10):\n\n row_list = list()\n\n for column in range(9):\n\n row_list.append(None)\n\n self._board.append(row_list)","def create_game(self):\n\n\t\tself.player_model.grid = []\n\t\tself.player_model.available_cells = []\n\n\t\tfor i in range(9):\n\t\t\tc = Cell(i, None)\n\t\t\tself.player_model.grid.append(c)\n\t\t\tself.player_model.available_cells.append(c)\n\n\t\tself.player_frame.setup_game(self.player_model.current_player.name)","def __init__(self):\n\n self._board = Board()","def setup(self):\n # Create your sprites and sprite lists here\n self.game: Game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, TILE_SIZE, 1, grid_layers = 4)\n self.game.game_message = \"Lead the Rabbit home\"\n\n # show the menu so that we see the instructions\n self.game.menu.button_list[0].text = \"Start\"\n self.game.menu.is_visible = True","def start_gameloop(self):\n print(\"Game Loop starting...\")\n while True:\n current_turn = self.who_goes_first()\n print('The ' + current_turn + ' will go first.')\n while self.is_active:\n if current_turn == \"player\":\n self.board.draw()\n move = get_player_move(\n self.board.positions, self.board.is_position_availible)\n self.board.make_move(move, self.player_letter)\n current_turn = \"computer\"\n else:\n move = self.npc.get_move(self.board)\n self.board.make_move(move, self.npc.letter)\n current_turn = \"player\"\n if self.board.is_winner(self.player_letter):\n self.board.draw()\n print(\"You won!\")\n self.is_active = False\n if self.board.is_winner(self.npc.letter):\n self.board.draw()\n print(\"You lost!\")\n self.is_active = False\n if self.board.is_board_full():\n self.board.draw()\n print(\"Tie\")\n self.is_active = False\n if request_play_again() is False:\n break\n self.is_active = True\n self.board = Board(request_board_size())","def start_game() -> None:\n rows = get_int()\n cols = get_int()\n state = game.GameState(rows, cols)\n\n line = next_line()\n if line == 'CONTENTS':\n rowList = []\n for i in range(rows):\n row = []\n line = raw_next_line()\n for index in range(cols):\n row.append(line[index])\n rowList.append(row)\n state.set_board_contents(rowList)\n\n while True:\n _display_board(state)\n line = next_line()\n if line == 'Q':\n return\n if line == '':\n if state.tick():\n _display_board(state)\n break\n else:\n _process_command(line, state)\n print('GAME OVER')","def initial_state() -> Board:\n board = (\"rnbqkbnr\", \"pppppppp\", \"........\", \"........\", \"........\",\n \"........\", \"PPPPPPPP\", \"RNBQKBNR\")\n\n return board","def __init__(self):\n self._board = []\n for i in range(10):\n self._board.append([None for i in range(9)])\n self.place_pieces()","def initGame(self, parent):\n\n\t\tself.resetBoard()\n\n\t\tself.parent = parent\n\t\twidth = self.boardSize * self.squareSize\n\t\theight = self.boardSize * self.squareSize\n\t\ttk.Frame.__init__(self, self.parent)\n\n\t\tself.canvas = tk.Canvas(self, borderwidth=0, highlightthickness=0, width=width, height=height)\n\t\tself.canvas.pack()\n\t\tself.canvas.bind('<Button-1>', self.click)\n\t\tself.parent.title(\"TicTacToe\")\n\n\t\tfor row in range(self.boardSize):\n\t\t\tfor col in range(self.boardSize):\n\t\t\t\tx1 = (col * self.squareSize)\n\t\t\t\ty1 = (row * self.squareSize)\n\t\t\t\tx2 = x1 + self.squareSize\n\t\t\t\ty2 = y1 + self.squareSize\n\t\t\t\tself.canvas.create_rectangle(x1, y1, x2, y2, outline=\"black\", tags='square')","def setup_new_board(self):\n\n logger.info(u'setup_new_board()')\n\n self.squares = [[None for j in xrange(8)] for i in xrange(8)]\n \n self.black_checkers = [ch.Checker(u'black', self) for i in xrange(12)]\n self.white_checkers = [ch.Checker(u'white', self) for i in xrange(12)]\n\n u\"\"\" Place checkers in starting squares \"\"\"\n i = 0\n for row in xrange(3):\n for column in xrange(8):\n if self.dark_square((row, column)):\n self.place_checker((row, column), self.white_checkers[i])\n i += 1\n\n i = 0\n for row in xrange(5, 8):\n for column in xrange(8):\n if self.dark_square((row, column)):\n self.place_checker((row, column), self.black_checkers[i])\n i += 1","def __init__(self, board):\n self.running = True\n self.state = \"waiting\"\n pygame.init()\n pygame.display.set_caption(\"Sudoku Solver\")\n\n self.define_grid()\n self.define_number_positions()\n self.define_button()\n self.board = board\n self.font = pygame.font.Font('ubuntu.ttf', NUMBERS_SIZE)\n self.sleep_time = 1 / CHANGES_PER_SECOND\n\n self.original_board = board.copy()","def __init__(self):\n self.board = [\n [None, None, None],\n [None, None, None],\n [None, None, None]\n ]","def __init__(self, initial_board):\n self.initial_board = initial_board","def __init__(self):\n self.computer_first = 0 # randint(0, 1)\n self.app = Tk()\n self.app.attributes(\"-toolwindow\", 1)\n self.app.title('Tic Tac Toe')\n self.app.resizable(width=False, height=False)\n self.board = Board()\n self.font = Font(family=\"Helvetica\", size=32)\n self.buttons = {}\n for x, y in self.board.fields:\n handler = lambda x=x, y=y: self.move(x, y)\n button = Button(self.app, command=handler, font=self.font,\n width=2, height=1)\n button.grid(row=y, column=x)\n self.buttons[x, y] = button\n handler = lambda: self.reset()\n button = Button(self.app, text='reset', command=handler)\n button.grid(row=self.board.size + 1, column=0,\n columnspan=self.board.size, stick='WE')\n self.update()\n if self.computer_first:\n self.move(randint(0, self.board.size - 1),\n randint(0, self.board.size - 1))","def __init__(self, dim, reverse = False, board = None):\n self.empty_squares = []\n if board == None:\n self.board = [[\"\", \"\", \"\"],\n [\"\", \"\", \"\"],\n [\"\", \"\", \"\"]]\n self.dim = dim\n self.board = board\n self.reverse = reverse\n self.win = None\n self.DRAW = 4\n self.EMPTY = 1\n self.PLAYERO = 2\n self.PLAYERX = 3","def initialize_board(self):\n seed = self.seed and self.seed.any()\n if not (self.shape or seed):\n raise Exception(\"Either a shape or a seed is required.\")\n\n elif self.shape and seed:\n # Center the seed on a game board\n board = self._center_seed(self.shape, self.seed)\n\n elif self.shape:\n # The probability a cell starts off dead\n prob_dead = [1 - self.game.weight]\n # Class probabilities for live cells\n probs_alive = [self.game.weight * (1/self.classes)] * self.classes\n\n board = np.random.choice(\n self.classes + 1,\n np.prod(self.shape),\n p = prob_dead + probs_alive\n ).reshape(self.shape)\n \n else: # Only a seed is given\n self.shape = self.seed.shape\n board = self.seed\n\n self.array = board\n self.start_array = board\n self.prev_array = None","def game_setup(self):\n self.deck = Shoe(6)\n self.initial_draw()\n self.pot = ask_for_bet(self.player.money)\n show_table(self.player, self.dealer, self.pot)\n self.surrender_and_insurance()","def __init__(self, master=None):\n super().__init__(master)\n self.masterframe = Frame(self.master)\n self.masterframe.pack()\n self.grid = Grid(3)\n self.canvasSize = 100\n self.mX = 0\n self.mY = 0\n self.working = True\n # True is Player 1 and False is Player 2\n self.turn = True\n self.createCanvas()","def __init__(self, board_width, board_height):\n # Create board using generate_grid_dict method with given width and height.\n board = self.generate_grid_dict(board_width, board_height)\n self.draw = False\n pygame.init()\n\n # Set the caption for the board and the font for the win prompt.\n pygame.display.set_caption('Connect4 - Player 1')\n self.game_font = pygame.freetype.Font(\"SF Distant Galaxy.ttf\", 40)\n\n # size of each square of the grid:\n self.square_size = 80\n # generate board width (amount of squares and square width):\n self.width = board_width * self.square_size\n # generate board height (amount of squares and square height):\n self.height = board_height * self.square_size\n # generate the radius of the chips depending on the square size:\n self.radius = int(self.square_size / 4)\n # find the middle of the square for chip placement:\n self.square_mid = int(self.square_size / 2)\n # set the screen size with the board width and height:\n self.screen = pygame.display.set_mode((self.width, self.height))\n\n # Fill the screen with a white background.\n background = pygame.Surface(self.screen.get_size())\n background.fill((255, 255, 255))\n self.background = background.convert()\n\n # Build the grid.\n for i in range(0, self.width, self.square_size):\n pygame.draw.rect(self.background, (0, 0, 0), (i, 0, 0, self.height))\n for i in range(0, self.height, self.square_size):\n pygame.draw.rect(self.background, (0, 0, 0), (0, i, self.width, 0))\n self.screen.blit(self.background, (0, 0))\n\n # Setup, so player one starts.\n self.playerOne = True\n self.red = 250\n self.blue = 0\n\n # Help dict for logic, when drawing a chip. Maps 0 -> size, 1 -> size - 1...\n self.draw_dict_mapping = {}\n for i in range(self.height//80 + 1):\n self.draw_dict_mapping[i] = self.height//80 - i\n\n # Start the game with the run game method.\n self.run_game(board)","def setup(self):\n self.board[(3, 3)] = -1\n self.board[(3, 4)] = -1\n self.board[(4, 3)] = 1\n self.board[(4, 4)] = 1\n\n self.stones_set = 4","def init(self):\n self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\n self.running = True\n self.state = \"waiting\"\n\n self.button_text = \"Solve!\"\n self.button_color = BUTTON_COLOR_SOLVE\n self.puzzle_state = \"unsolved\"\n\n if not self.checkBoardValid():\n self.state = \"failed\"\n self.puzzle_state = \"failed\"\n self.button_text = \"Impossible puzzle!\"","def init_board():\n\t# Generates a table 10*10 of 0s with -1 around and the initial state\n\t# of the board with 2 whites and 2 blacks in the middle\n\ttable = [[0 if i != 0 and i != 9 else -1 for i in range(10)] if j != 0 and j != 9 else [-1 for i in range(10)] for j in range(10)] #leaves a -1 line around the whole table of 0s\n\t#initial state is drawn and recorded\n\ttable[4][4] = 2\n\ttable[5][5] = 2\n\ttable[4][5] = 1\n\ttable[5][4] = 1\n\tdrawPiece((4,4),2)\n\tdrawPiece((5,5),2)\n\tdrawPiece((4,5),1)\n\tdrawPiece((5,4),1)\n\treturn table","def __init__(self, player, board):\n self.player = player\n self.board = board","def start(self):\n self.__init__()\n self.set_n_players()\n self.init_players()\n self.init_territory_selection_phase()\n self.init_troop_deployment_phase()\n # self.game_phase()","def setup_game(self, player, opponent):\n\n self.display.clear_screen()\n\n ship_index = 0\n\n while not player.ready(len(self.SHIP_INFO)):\n # prints the currrent board\n board = self.display.construct_player_board(player, opponent, True)\n self.display.print_board(board)\n\n ship_name, ship_length = self.SHIP_INFO[ship_index]\n ship_to_add = Ship(ship_name, ship_length)\n\n try:\n player.add_ship(ship_to_add)\n except Exception as e:\n ship_to_add = player.ships[ship_index]\n\n origin, orientation = self.display.prompt_for_ship_placement(\n ship_to_add)\n\n try:\n player.place_ship(ship_to_add, origin, orientation,\n self.BOARD_SIZE)\n except ValueError as ve:\n self.display.clear_screen()\n print(ve)\n print()\n continue\n\n self.display.clear_screen()\n ship_index += 1\n self.display.prompt_switch(opponent.name)","def __init__(self):\n self.board = Board()\n #self.player1 = player1\n #self.player2 = player2\n self.winner = None","def __init__(self):\n # create game object\n self.game = Game()\n self.players = (\"X's\", \"O's\")\n\n # define string constants for UI\n self.BG_COLOR = \"#DBF6E9\"\n self.FONT = \"Verdana\"\n self.PROMPT = \"{0}, it's your turn.\"\n self.SCORE_LABEL = \"{0}: {1}\"\n self.TIE_LABEL = \"Ties: {0}\"\n\n # create window and instructions at the top\n self.window = tk.Tk()\n self.window.title(\"Tic-tac-toe\")\n self.window.configure(padx=30, pady=30, bg=self.BG_COLOR)\n self.window.geometry(\"450x450\")\n self.instructions = self.create_label(self.window, self.PROMPT.format(self.players[self.game.whose_turn]))\n self.instructions.grid(row=0, column=0)\n # create score frame to hold results of previous games in this session\n self.score_frame = tk.Frame(self.window, bg=self.BG_COLOR)\n self.score_frame.grid(row=1, column=1, padx=20, pady=20, sticky='n')\n self.score_label = self.create_label(self.score_frame, 'Score')\n self.score_label.grid(row=0, column=0, sticky='w')\n self.player_0_score_label = self.create_label(self.score_frame,\n self.SCORE_LABEL.format(self.players[0], self.game.player_0_score))\n self.player_0_score_label.grid(row=1, column=0)\n self.player_1_score_label = self.create_label(self.score_frame,\n self.SCORE_LABEL.format(self.players[1], self.game.player_1_score))\n self.player_1_score_label.grid(row=2, column=0)\n self.num_ties_label = self.create_label(self.score_frame, self.TIE_LABEL.format(self.game.num_ties))\n self.num_ties_label.grid(row=3, column=0, sticky='w')\n # create game frame; each of the nine squares on the grid is represented as a button\n self.game_frame = tk.Frame(self.window)\n self.game_frame.grid(row=1, column=0, pady=20)\n self.button_list = self.create_buttons()\n self.place_buttons()\n\n self.window.mainloop()","def __init__(self, player1, player2):\n self.players = [player1, player2]\n self.tokens = {\n ' ': ' ',\n player1: 'X',\n player2: 'O',\n }\n self.score = {\n player1: 0,\n player2: 0,\n }\n self.moves = None\n self.winner = None\n self.turn = ''\n self.reset()","def play_game(self, x_player, o_player):\n #from connectfour import Player #either from connect4_player or connectfour\n \n current_side = \"X\"\n players = {\"X\": x_player, \"O\": o_player}\n while ((not self.win_for(\"X\")) and\n (not self.win_for(\"O\")) and\n (not self.is_full())):\n print()\n print(self)\n print()\n move = Board.INVALID_MOVE\n while not self.allows_move(move):\n if players[current_side] == \"human\":\n move = int(input(current_side + \"'s move: \"))\n else:\n move = players[current_side].next_move(self)\n print(\"Computer playing for \" + current_side +\n \" plays at \" + str(move))\n\n self.add_move(move, current_side)\n if current_side == \"X\":\n current_side = \"O\"\n else:\n current_side = \"X\"\n\n if self.win_for(\"X\"):\n print(\"X wins --- congratulations!\\n\")\n elif self.win_for(\"O\"):\n print(\"O wins --- congratulations!\\n\")\n else:\n print(\"Tied game!\\n\")\n\n print()\n print(self)","def __init__(self, parent, board, Player1, Player2, *args, **kwargs):\n\n\t\tself.button = [[\"\" for x in range(8)] for y in range(8)]\n\t\t# self.board = Board.Board()\n\t\tself.initialButtonPush = ()\n\t\tself.buttonPushed = \"\"\n\t\tself.nextButton = ()\n\n\t\t# setup tkinter frame\n\t\ttk.Frame.__init__(self, parent, *args, **kwargs)\n\t\tparent = parent\n\t\t# call setup functions\n\t\tself.setupButtonImages()\n\t\tself.setupBoard()\n\t\tself.NewBoard = board\n\t\tself.Player1 = Player1\n\t\tself.Player2 = Player2\n\t\t\n\t\tself.updateBoard()","def __init__(self, screen, win_size_x, win_size_y, player_num):\n self._player_list = list(Player(f\"Player {x}\") for x in range(player_num))\n self._player_num = player_num\n print(\"The number of players is: \", player_num)\n self._screen = screen\n self._player_turn = 0\n self._ui_player_turn = UI((1820, 10), (100, 50), f\"Player {self._player_turn}\")\n self._screen.blit(self._ui_player_turn.update(\n f\"Player {self._player_turn}\"), self._ui_player_turn._location)\n self._core_deck = Deck(\"Deck/test_deck.txt\")\n self._war_deck = []\n self._map = Map(self._player_num, win_size_x, win_size_y)\n self._clock = pygame.time.Clock()\n self._run = True\n self._fps = 30\n self.each_player_draws_hand(self._core_deck)","def __init__(self):\n\n self.frameCount = 0\n self._initScreen()\n self._initObjects()\n self._initControls()\n self._initLevel()\n self._start()\n print \"DEBUG: Initializing Game\"\n pass","def reset_board(self):\n self.board = [0, 0, 0, 0, 0, 0, 0, 0, 0]\n self.turn = 0\n\n self.change_button_img_to_null()\n\n #self.Score_Label.grid(row=0,column=1, ipadx=32)\n\n self.player_highlight()\n self.change_button_state('normal')\n self.update_score()","def __init__(self, num_players):\n self.num_players = num_players\n self.firework = [[], [], [], [], []]\n self.nb_blue_stone = MAX_BLUE_STONE\n self.nb_red_stone = MAX_RED_STONE\n self.draw = None\n self.hands = None\n self.fill_draw()\n random.shuffle(self.draw)\n self.discard = []\n self.draw_initial_hands()","def init_game(self):\n self.view.carregar_jogadores_possiveis(self._possible_players_list())\n self.view.put_view_in_main_loop()","def __init__(self, width, height, player, opponent):\r\n self.height = height\r\n self.width = width\r\n self.board = []\r\n for x in range(height):\r\n self.board.append([])\r\n for y in range(width):\r\n self.board[x].append(\"\\t\")\r\n self.player = player\r\n self.opponent = opponent\r\n self.lastMove = (0, 0)","def start(self):\n # store a sign controlling addition or subtraction so pieces move in the right direction\n self.board = fen_to_position(self.game.fen)\n self.transposition_table = dict()","def reset(self):\n self.board = Board()\n self.winner = None","def reset(self):\r\n self.board = [[0 for i in range(self.width)]\r\n for i in range(self.height)]\r\n self.new_tile()\r\n self.new_tile()","def __init__(self):\n self.start()\n while self.player.money > 0:\n self.game_loop()\n if self.player.money > 0:\n if not play_again():\n break\n elif self.player.money == 0:\n no_more_money()\n self.reset_table()","def initial_board():\n board = [OUTER] * 100\n for i in Othello.squares():\n board[i] = EMPTY\n # The middle four squares should hold the initial piece positions.\n board[44], board[45] = BLACK, WHITE\n board[54], board[55] = WHITE, BLACK\n return board","def __init__(self, players=None):\n self.game = Game()\n if players:\n self.player1 = players[0]\n self.player2 = players[1]\n else:\n self.player1 = Player('X')\n self.player2 = Player('O')\n self.record = Record()\n self.winning_moves = []","def __init__(self):\n\n super().__init__()\n self.setup_janggi_game()\n self._game_state = 'UNFINISHED'\n self._player_turn = 'BLUE'","def start(self):\n # asserts preconditions are met\n #assert self.validGameSettings()\n\n #draws initial welcome screen\n #self._text = GLabel(text=\"Press 'S' to Play\")\n #self._text.draw(self.view)\n\n # initializing instance variables\n self.setState(STATE_INACTIVE)\n self.setWave(None)\n self.setText(None)\n self.lastkeys = 0 #ADD MORE ATTRIBUTES\n\n # draws iniital welcome screen\n self.welcomeScreen()","def __init__(self,player1: Player = ManualPlayer(\"P1\"),\\\r\n player2: Player = ManualPlayer(\"P2\")):\r\n\r\n self.board = np.zeros((BOARD_SIZE,BOARD_SIZE)\\\r\n ,dtype=np.int8)\r\n self.board[3,3] = '2'\r\n self.board[4,4] = '2'\r\n self.board[3,4] = '1'\r\n self.board[4,3] = '1' \r\n\r\n self.players = []\r\n self.players.append(player1)\r\n self.players.append(player2)\r\n self.turn = 1\r\n self.count = 0","def play(self):\n board = Board()\n print(\"Let's play tic-tac-toe against computer!\")\n print(\"Here is your board!\")\n count = 1\n print(board)\n while True:\n board.person_move()\n status = board.get_status()\n if status == 'x' or status == '0':\n return(f\"Winner is {status}\")\n elif status == 'draw':\n return(\"Friendship won!\")\n board.make_computer_move()\n status = board.get_status()\n if status == 'x' or status == '0':\n return(f\"Winner is {status}\")\n elif status == 'draw':\n return(\"Friendship won!\")\n print(f\"Board after {count} action.\")\n count += 1\n print(board)","def __init__(self, board, turn):\n self.player = turn\n self.roll = self.roll_dice()\n #array of applied board states\n self.moves = []\n self.board = board\n self.generate_valid_moves()","def initial_state():\n board = [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]\n return board","def __init__(self):\n self.boards = [[False, False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False, False]]","def __init__(self):\n self.boards = [[False, False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False, False]]","def start(self):\n self.player = Player()\n self.dealer = Dealer()\n self.pot = 0\n self.side_bet = 0\n start_game()","def __init__(self):\n self.played_pos = []\n self.grid = [['-', '-', '-'],\n ['-', '-', '-'],\n ['-', '-', '-']]\n self.player_played_pos = {'p1': set(), 'p2': set()}","def start():\n display_board()\n print(\"\\n\")\n y_n_prompt()","def reset(self):\n self.board = Board()\n self.update()\n self.computer_first = randint(0, 1)\n if self.computer_first:\n self.move(randint(0, self.board.size - 1),\n randint(0, self.board.size - 1))","def start(self):\n self.playing = True\n self.table = table.Table(self.players)"],"string":"[\n \"def __init__(self):\\n # Current player\\n self.player = X\\n\\n # Board\\n self.board = [\\n [None, None, None],\\n [None, None, None],\\n [None, None, None]\\n ]\\n\\n # Winner\\n self.winner = None\\n\\n # Game over\\n self._gameover = False\",\n \"def __init__(self):\\n self.board = [\\n BS, BS, BS, BS,\\n BS, BS, BS,\\n BS, BS, BS, BS,\\n EM, EM, EM,\\n WS, WS, WS, WS,\\n WS, WS, WS,\\n WS, WS, WS, WS\\n ]\\n self.curr_player = WHITE_PLAYER\",\n \"def initBoard(self):\\n pass\",\n \"def new_game(self):\\n self.board = [None] * 9\\n self.player = \\\"X\\\"\\n self.winner = None\",\n \"def __init__(self):\\n self.game_board = [' '] * 9\\n self.size = len(self.game_board)\\n self.move = 'X'\\n self.player1 = None\\n self.player2 = None\\n self.current_player = None\\n self.board_coords = {\\n (1, 3): 0, (2, 3): 1, (3, 3): 2,\\n (1, 2): 3, (2, 2): 4, (3, 2): 5,\\n (1, 1): 6, (2, 1): 7, (3, 1): 8\\n }\\n\\n self.winning_cases = [\\n (0, 1, 2), (3, 4, 5), (6, 7, 8),\\n (0, 3, 6), (1, 4, 7), (2, 5, 8),\\n (0, 4, 8), (2, 4, 6)\\n ]\",\n \"def __init__(self):\\n self.__grid = create_grid(\\n Settings.SIZE_X, Settings.SIZE_Y, MarkerType.NONE)\\n\\n self.__turn = 0\\n self.__state = GameState.PLAYING\\n self.__winner = MarkerType.NONE\\n self.__loser = MarkerType.NONE\\n\\n # Separate counter for turns, because __turn depends on starting player\\n self.__turns_played = 0\",\n \"def __init__(self):\\r\\n\\t\\tself.game_board = [['0','0','0'],['0','0','0'],['0','0','0']]\\r\\n\\t\\tself.count = 0\\r\\n\\t\\tself.x_turn = True\\r\\n\\t\\r\\n\\r\\n\\t\\tpass\",\n \"def __init__(self):\\n # The starting counts are set to 0 and modified when the board is initiated.\\n self.num_black_pieces = 0\\n self.num_black_kings = 0\\n self.num_white_pieces = 0\\n self.num_white_kings = 0\\n # Creates a new board and fills it with the appropriate pieces.\\n self.board = self._initiate_board()\\n self.moves = []\",\n \"def __init__(self):\\n self._game_state = \\\"UNFINISHED\\\"\\n self._current_player = \\\"BLACK\\\"\\n self._game_board = Board()\",\n \"def setup(self):\\n piece_order = ['ROOK','KNIGHT','BISHOP','QUEEN','KING','BISHOP','KNIGHT','ROOK']\\n for row,colour in zip([0,7],['BLACK','WHITE']):\\n for col,piece in enumerate(piece_order):\\n self.board[row][col] = colour + '_' + piece\\n \\n for row,colour in zip([1,6],['BLACK','WHITE']):\\n for i in range(8):\\n self.board[row][i] = colour + '_' + 'PAWN'\\n \\n self.toplay = 'WHITE'\",\n \"def setUp(self):\\n self.gameBoard = Grid((100, 100), Cell)\",\n \"def __init__(self, player_x_type, player_o_type):\\n self.board = Board()\\n self.player_x = player_x_type(self.FIRST_PLAYER_MARK)\\n self.player_o = player_o_type(self.SECOND_PLAYER_MARK)\",\n \"def initialize():\\n\\n global PLAYER # this means we use the global var PLAYER and cannot have a local var named PLAYER\\n global LEVEL_COUNTER\\n\\n LEVEL_COUNTER = 1\\n \\n coordinates = generate_coords()\\n\\n PLAYER = Stark()\\n tree = Tree()\\n ww = WhiteWalker()\\n crown = Crown()\\n gray_gem = GrayGem()\\n clear_board()\\n GAME_BOARD.create(\\\"Snow\\\",\\\"Snow\\\")\\n GAME_BOARD.draw_msg(\\\"Level \\\" + str(LEVEL_COUNTER) + \\\". Winter is coming.\\\")\\n generate_level(coordinates, [PLAYER, ww, gray_gem, crown, tree, tree, gray_gem, tree, tree, gray_gem, tree])\\n\\n # for i in range(0,NUM_ELTS):\\n # place_on_board(elts[i], coordinates[i][0], coordinates[i][1])\",\n \"def __init__(self):\\n self.players = {1: [\\\"Player_a\\\", \\\"\\\\u25CF\\\"], 2: [\\\"Player_b\\\", \\\"\\\\u25CB\\\"]}\\n self.current_player = 1\\n self.playing_player = self.players[1]\\n self.grid = [[\\\" \\\"] * 6 for x in range(7)]\",\n \"def start_game(self):\\n self.board = Board(num_tableaus=self.tableau_qty, num_decks=self.decks, deal_3=self.deal_3)\\n self.board.init_move_dict()\\n self.board.deal(self.deck)\\n\\n if self.api_use:\\n self.init_game_api()\\n elif self.commandline:\\n self.init_cl_game()\\n else:\\n self.init_pygame()\",\n \"def initGameState(self):\\n print(\\\"Setting game state: \\\")\\n self.playGUI = GUI()\\n self.playGUI.drawBoard(self.player)\",\n \"def initialize_board(self):\\n self.board = np.zeros(shape=(BOARD_SIZE, BOARD_SIZE), dtype=np.int) # another way of defining board: [[for x in range(cm.BOARD_SIZE)] for x in range(cm.BOARD_SIZE)]\\n center = int(BOARD_SIZE / 2)\\n self.board[center-1][center-1] = self.board[center][center] = WHITE # place the board according to position\\n self.board[center][center-1] = self.board[center-1][center] = BLACK\\n self.black_piece = 2\\n self.white_piece = 2\",\n \"def __init__(self):\\n self.game_screen = pygame.display.set_mode((GameData.screen_dim, GameData.screen_dim))\\n self.game_screen.fill(GameData.background_color)\\n self.player = 1\\n self.game_over = False\\n self.board = np.zeros((GameData.rows, GameData.columns))\",\n \"def initialize_board():\\n # Wipe current board\\n for x in range(len(THE_BOARD.positions)):\\n for y in range(len(THE_BOARD.positions)):\\n THE_BOARD.positions[x][y] = ' '\\n\\n all_pieces = []\\n\\n # Pawns\\n white_pawns = [Pawn('white', (6, i)) for i in range(len(THE_BOARD.positions[6]))]\\n black_pawns = [Pawn('black', (1, i)) for i in range(len(THE_BOARD.positions[1]))]\\n all_pieces.extend(white_pawns)\\n all_pieces.extend(black_pawns)\\n\\n # Rooks\\n rook1 = Rook('black', (0, 0))\\n all_pieces.append(rook1)\\n rook2 = Rook('black', (0, 7))\\n all_pieces.append(rook2)\\n rook3 = Rook('white', (7, 0))\\n all_pieces.append(rook3)\\n rook4 = Rook('white', (7, 7))\\n all_pieces.append(rook4)\\n\\n # Knights\\n knight1 = Knight('black', (0, 1))\\n all_pieces.append(knight1)\\n knight2 = Knight('black', (0, 6))\\n all_pieces.append(knight2)\\n knight3 = Knight('white', (7, 1))\\n all_pieces.append(knight3)\\n knight4 = Knight('white', (7, 6))\\n all_pieces.append(knight4)\\n\\n # Bishops\\n bishop1 = Bishop('black', (0, 2))\\n all_pieces.append(bishop1)\\n bishop2 = Bishop('black', (0, 5))\\n all_pieces.append(bishop2)\\n bishop3 = Bishop('white', (7, 2))\\n all_pieces.append(bishop3)\\n bishop4 = Bishop('white', (7, 5))\\n all_pieces.append(bishop4)\\n\\n # King and Queen\\n queen1 = Queen('black', (0, 4))\\n all_pieces.append(queen1)\\n queen2 = Queen('white', (7, 4))\\n all_pieces.append(queen2)\\n king1 = King('black', (0, 3))\\n all_pieces.append(king1)\\n king2 = King('white', (7, 3))\\n all_pieces.append(king2)\\n\\n # Add every single piece to the board. Only then can they update their spaces threatened\\n for piece in all_pieces:\\n THE_BOARD.update(piece)\\n THE_BOARD.update_all_spaces_threatened()\",\n \"def setUp(self):\\n\\n self.board = Board(3, 3)\",\n \"def __init__(self):\\n\\n self.__turn_info = { 'turn': ChessGame.WHITE }\\n self.init_board()\",\n \"def init_game(self):\\n nrows = len(self.array)\\n self.game_over = False\\n self.squares_left = nrows * nrows\\n self.bombs_left = 0\\n # clear the board\\n for i in xrange(nrows):\\n for j in xrange(nrows):\\n self.array[i][j].reset()\\n # put N random bombs\\n for i in xrange(nrows):\\n rand_num = random.randrange(nrows*nrows)\\n if self.array[rand_num / nrows][rand_num % nrows].type \\\\\\n != SquareType.BOMB:\\n self.insert_bomb(rand_num / nrows, rand_num % nrows)\\n self.squares_left -= self.bombs_left\\n self.print_board()\",\n \"def __init__(self, players):\\n\\n # Define the players\\n self.players = players\\n\\n # Define who starts the game\\n self.nplayer = 1 \\n\\n # Define the board\\n self.board = [0] * 9\",\n \"def __init__(self):\\n self.board = Board()\\n self.__root = BinaryNode(Board(), None, Board.PLAYER_1)\\n self.player = Board.PLAYER_0\\n self.win = False\",\n \"def __init__(self, board=None):\\n self.winner = None\\n self.board = board or [self.__class__.EMPTY_POSITION_COUNTER] * 9\",\n \"def setup_new_game(self):\\r\\n self._player = Player()\\r\\n self._stats = GameStats(self._bb_settings)\\r\\n self._scoreboard = Scoreboard(self._bb_settings, self._screen)\",\n \"def __init__(self):\\n self.board_dict = dict()\\n for i in range(self.BOARD_WIDTH):\\n for j in range(self.BOARD_WIDTH):\\n self.board_dict[i, j] = 0, None\\n\\n self.players_locations = dict()\\n self.last_moved = None\",\n \"def start_at_beginning(self):\\n b_pieces = [ChessPiece.B_ROOK,\\n ChessPiece.B_KNIGHT,\\n ChessPiece.B_BISHOP,\\n ChessPiece.B_QUEEN,\\n ChessPiece.B_KING,\\n ChessPiece.B_BISHOP,\\n ChessPiece.B_KNIGHT,\\n ChessPiece.B_ROOK]\\n w_pieces = [ChessPiece.W_ROOK,\\n ChessPiece.W_KNIGHT,\\n ChessPiece.W_BISHOP,\\n ChessPiece.W_QUEEN,\\n ChessPiece.W_KING,\\n ChessPiece.W_BISHOP,\\n ChessPiece.W_KNIGHT,\\n ChessPiece.W_ROOK]\\n\\n for i in range(8):\\n self.board.put_piece(b_pieces[i], 0, i)\\n self.board.put_piece(ChessPiece.B_PAWN, 1, i)\\n self.board.put_piece(w_pieces[i], 7, i)\\n self.board.put_piece(ChessPiece.W_PAWN, 6, i)\",\n \"def __init__(self):\\n self.board = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]\\n self.last_move = None\",\n \"def __init__(self):\\n super(CamTacToe, self).__init__()\\n self.state = [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\\n self.history = [self.state[:]]\\n self.player_char = 'x'\\n self.opponent_char = 'o'\\n self.difficulty = 0\\n self.player_start = 0\",\n \"def clear_board(cls):\\n # Set the board dimensions\\n cls.board = [[cls.empty for x in range(cls.size)] for y in range(cls.size)]\\n \\n # Set allowed positions the user may provide\\n cls.positions = [str(x) for x in range(1, cls.size**2 + 1)]\\n\\n cls.current_player = 'X'\\n\\n cls.display_board()\\n\\n cls.prompt_player()\",\n \"def initializeGame(self):\\n # Fill deck with cards and shuffle it\\n self.deck.fill(104)\\n self.deck.shuffle()\\n #print \\\"Deck initialized\\\"\\n\\n # Initialize the field\\n self.field.initialize(self.deck.draw(4))\\n self.field.sortField()\\n #self.field.printField()\\n\\n # Set players to initial state again\\n # Distribute cards and set bulls to 0\\n for p in self.players:\\n p.bulls = 0\\n p.setHand(self.deck.draw(10))\",\n \"def init_board(self) -> None:\\n\\t\\tself.canvas.create_rectangle(0, 0, self.canvas_width, self.canvas_height, fill=self.color_background)\\n\\t\\tfor x in range(0, self.canvas_width, self.canvas_width//self.board_size):\\n\\t\\t\\tself.canvas.create_line(x, 0, x, self.canvas_width, fill=self.color_tile_border)\\n\\n\\t\\tfor y in range(0, self.canvas_width+1, self.canvas_height//self.board_size):\\n\\t\\t\\tself.canvas.create_line(0, y, self.canvas_height, y, fill=self.color_tile_border)\\n\\n\\t\\tself.text_area.delete('0.1', '2.1')\",\n \"def initialize_board(self):\\n self.board_values = {x:x for x in(range(1,10))}\",\n \"def __init__ (self, cols = 6, rows = 7, requiredToWin = 4):\\r\\n\\t\\tself.cols = cols\\r\\n\\t\\tself.rows = rows\\r\\n\\t\\tself.win = requiredToWin\\r\\n\\t\\tself.board = [[NONE] * rows for _ in range(cols)]\",\n \"def __init__(self):\\r\\n self._board = None\\r\\n self._bb_settings = Settings()\\r\\n self._screen = pygame.display.set_mode((self._bb_settings.screen_width,\\r\\n self._bb_settings.screen_height))\\r\\n self._player = Player()\\r\\n self._stats = GameStats(self._bb_settings)\\r\\n self._scoreboard = Scoreboard(self._bb_settings, self._screen)\\r\\n self._image = pygame.image.load('board.bmp')\\r\\n self._rect = self._image.get_rect()\\r\\n self._play_mode_button_list = self.make_play_mode_buttons()\\r\\n self._replay_button_list = self.make_replay_buttons()\",\n \"def __init__(self):\\n self._board = [\\n\\n ['', '', '', \\\"x\\\", '', '', '', ''],\\n ['', '', '', '', '', '', '', ''],\\n ['', '', '', '', '', '', '', ''],\\n ['', '', '', '', '', '', '', ''],\\n ['', '', '', '', '', '', '', ''],\\n ['', '', '', '', '', '', '', ''],\\n ['', '', '', '', '', '', '', ''],\\n [\\\"o\\\", '', \\\"o\\\", '', \\\"o\\\", '', \\\"o\\\", ''],\\n ]\\n self._game_state = \\\"UNFINISHED\\\" # default game state\\n self._current_row = 0 #helper used to enforce moving one row at a time\\n self._current_x_row = 0 # tracks x's row coordinate\\n self._current_x_column = 3 # tracks x's column coordinate\\n\\n #four coordinates tracking the available diagonal spaces of x\\n self._lower_right = (self._current_x_row + 1, self._current_x_column + 1)\\n self._lower_left = (self._current_x_row + 1, self._current_x_column - 1)\\n self._upper_right = (self._current_x_row - 1, self._current_x_column + 1)\\n self._upper_left = (self._current_x_row - 1, self._current_x_column - 1)\\n\\n #helper used to check if x is in the first column\\n self._row1 = (\\n self._board[0][0],\\n self._board[1][0],\\n self._board[2][0],\\n self._board[3][0],\\n self._board[4][0],\\n self._board[5][0],\\n self._board[6][0],\\n self._board[7][0])\\n #helper used to check if x is in the last column\\n self._row7 = (\\n self._board[0][7],\\n self._board[1][7],\\n self._board[2][7],\\n self._board[3][7],\\n self._board[4][7],\\n self._board[5][7],\\n self._board[6][7],\\n self._board[7][7])\",\n \"def __init__(self, player):\\n \\n self.colour = player\\n self.game_in_head = Board()\",\n \"def init_new_board(self) -> None:\\r\\n\\r\\n TkState.enable(self.settings_menu.winfo_children())\\r\\n TkState.enable(self.edit_menu.winfo_children())\\r\\n TkState.enable([self.play_button, self.step_button])\\r\\n TkState.disable([self.reset_button])\\r\\n\\r\\n self.gen_number.config(text = 0)\\r\\n self.speed_scale.set(self.INITIAL_TIME_PER_GEN)\\r\\n self.zoom_scale.set(self.INITIAL_ZOOM)\\r\\n\\r\\n self.animator.board = self.anim_board\\r\\n self.painter.board = self.anim_board\\r\\n self.painter.adjust_to_canvas()\",\n \"def setUp(self):\\n self.game = TTTBoard(3)\",\n \"def init_board(self):\\n\\n self.__board = dict()\\n order = ['rook', 'knight', 'bishop', 'queen', 'king', 'bishop',\\n 'knight', 'rook']\\n for j, name in enumerate(order):\\n\\n self.__board[(0, j)] = ChessGame.Piece( name, ChessGame.WHITE)\\n self.__board[(7, j)] = ChessGame.Piece( name, ChessGame.BLACK)\\n self.__board[(1, j)] = ChessGame.Piece('pawn', ChessGame.WHITE)\\n self.__board[(6, j)] = ChessGame.Piece('pawn', ChessGame.BLACK)\\n\\n self.__players = { ChessGame.WHITE: set(), ChessGame.BLACK: set() }\\n for color in (ChessGame.BLACK, ChessGame.WHITE):\\n self.__players[color] = {(x, y) for (x, y), piece in\\n self.__board.iteritems() if piece.color == color }\\n\\n return\",\n \"def __init__(self, players):\\n self.players = players\\n self.board = Board()\",\n \"def __init__(self, rows=6, columns=7, win_length=4):\\n\\n self._board = [[0 for i in xrange(columns)] for i in xrange(rows)]\\n self._rows = rows\\n self._columns = columns\\n self._win_length = win_length\\n self.current_player = None\\n self.winner = None\\n print \\\"The game is afoot!\\\"\",\n \"def __init__(self):\\n self._current_state = \\\"UNFINISHED\\\"\\n self._start_color = \\\"RED\\\"\\n self._board = Board()\",\n \"def _initiate_board(self):\\n grid = []\\n for i in range(constant.BOARD_DIMENSION):\\n # Starts each row\\n current_row = []\\n for j in range(constant.BOARD_DIMENSION):\\n # Adds the pieces depending on the position\\n if i < constant.ROWS_OF_PIECES:\\n # Black pieces\\n if (j + i) % 2 != 0:\\n current_row.append(Piece(i, j, Player.black))\\n self.num_black_pieces = self.num_black_pieces + 1\\n else:\\n current_row.append(None)\\n\\n elif i >= constant.BOARD_DIMENSION - constant.ROWS_OF_PIECES:\\n # White pieces\\n if (j + i) % 2 != 0:\\n current_row.append(Piece(i, j, Player.white))\\n self.num_white_pieces = self.num_white_pieces + 1\\n else:\\n current_row.append(None)\\n\\n else:\\n current_row.append(None)\\n\\n grid.append(current_row)\\n\\n return grid\",\n \"def setup_board(self):\\n\\n for row in range(10):\\n\\n row_list = list()\\n\\n for column in range(9):\\n\\n row_list.append(None)\\n\\n self._board.append(row_list)\",\n \"def create_game(self):\\n\\n\\t\\tself.player_model.grid = []\\n\\t\\tself.player_model.available_cells = []\\n\\n\\t\\tfor i in range(9):\\n\\t\\t\\tc = Cell(i, None)\\n\\t\\t\\tself.player_model.grid.append(c)\\n\\t\\t\\tself.player_model.available_cells.append(c)\\n\\n\\t\\tself.player_frame.setup_game(self.player_model.current_player.name)\",\n \"def __init__(self):\\n\\n self._board = Board()\",\n \"def setup(self):\\n # Create your sprites and sprite lists here\\n self.game: Game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, TILE_SIZE, 1, grid_layers = 4)\\n self.game.game_message = \\\"Lead the Rabbit home\\\"\\n\\n # show the menu so that we see the instructions\\n self.game.menu.button_list[0].text = \\\"Start\\\"\\n self.game.menu.is_visible = True\",\n \"def start_gameloop(self):\\n print(\\\"Game Loop starting...\\\")\\n while True:\\n current_turn = self.who_goes_first()\\n print('The ' + current_turn + ' will go first.')\\n while self.is_active:\\n if current_turn == \\\"player\\\":\\n self.board.draw()\\n move = get_player_move(\\n self.board.positions, self.board.is_position_availible)\\n self.board.make_move(move, self.player_letter)\\n current_turn = \\\"computer\\\"\\n else:\\n move = self.npc.get_move(self.board)\\n self.board.make_move(move, self.npc.letter)\\n current_turn = \\\"player\\\"\\n if self.board.is_winner(self.player_letter):\\n self.board.draw()\\n print(\\\"You won!\\\")\\n self.is_active = False\\n if self.board.is_winner(self.npc.letter):\\n self.board.draw()\\n print(\\\"You lost!\\\")\\n self.is_active = False\\n if self.board.is_board_full():\\n self.board.draw()\\n print(\\\"Tie\\\")\\n self.is_active = False\\n if request_play_again() is False:\\n break\\n self.is_active = True\\n self.board = Board(request_board_size())\",\n \"def start_game() -> None:\\n rows = get_int()\\n cols = get_int()\\n state = game.GameState(rows, cols)\\n\\n line = next_line()\\n if line == 'CONTENTS':\\n rowList = []\\n for i in range(rows):\\n row = []\\n line = raw_next_line()\\n for index in range(cols):\\n row.append(line[index])\\n rowList.append(row)\\n state.set_board_contents(rowList)\\n\\n while True:\\n _display_board(state)\\n line = next_line()\\n if line == 'Q':\\n return\\n if line == '':\\n if state.tick():\\n _display_board(state)\\n break\\n else:\\n _process_command(line, state)\\n print('GAME OVER')\",\n \"def initial_state() -> Board:\\n board = (\\\"rnbqkbnr\\\", \\\"pppppppp\\\", \\\"........\\\", \\\"........\\\", \\\"........\\\",\\n \\\"........\\\", \\\"PPPPPPPP\\\", \\\"RNBQKBNR\\\")\\n\\n return board\",\n \"def __init__(self):\\n self._board = []\\n for i in range(10):\\n self._board.append([None for i in range(9)])\\n self.place_pieces()\",\n \"def initGame(self, parent):\\n\\n\\t\\tself.resetBoard()\\n\\n\\t\\tself.parent = parent\\n\\t\\twidth = self.boardSize * self.squareSize\\n\\t\\theight = self.boardSize * self.squareSize\\n\\t\\ttk.Frame.__init__(self, self.parent)\\n\\n\\t\\tself.canvas = tk.Canvas(self, borderwidth=0, highlightthickness=0, width=width, height=height)\\n\\t\\tself.canvas.pack()\\n\\t\\tself.canvas.bind('<Button-1>', self.click)\\n\\t\\tself.parent.title(\\\"TicTacToe\\\")\\n\\n\\t\\tfor row in range(self.boardSize):\\n\\t\\t\\tfor col in range(self.boardSize):\\n\\t\\t\\t\\tx1 = (col * self.squareSize)\\n\\t\\t\\t\\ty1 = (row * self.squareSize)\\n\\t\\t\\t\\tx2 = x1 + self.squareSize\\n\\t\\t\\t\\ty2 = y1 + self.squareSize\\n\\t\\t\\t\\tself.canvas.create_rectangle(x1, y1, x2, y2, outline=\\\"black\\\", tags='square')\",\n \"def setup_new_board(self):\\n\\n logger.info(u'setup_new_board()')\\n\\n self.squares = [[None for j in xrange(8)] for i in xrange(8)]\\n \\n self.black_checkers = [ch.Checker(u'black', self) for i in xrange(12)]\\n self.white_checkers = [ch.Checker(u'white', self) for i in xrange(12)]\\n\\n u\\\"\\\"\\\" Place checkers in starting squares \\\"\\\"\\\"\\n i = 0\\n for row in xrange(3):\\n for column in xrange(8):\\n if self.dark_square((row, column)):\\n self.place_checker((row, column), self.white_checkers[i])\\n i += 1\\n\\n i = 0\\n for row in xrange(5, 8):\\n for column in xrange(8):\\n if self.dark_square((row, column)):\\n self.place_checker((row, column), self.black_checkers[i])\\n i += 1\",\n \"def __init__(self, board):\\n self.running = True\\n self.state = \\\"waiting\\\"\\n pygame.init()\\n pygame.display.set_caption(\\\"Sudoku Solver\\\")\\n\\n self.define_grid()\\n self.define_number_positions()\\n self.define_button()\\n self.board = board\\n self.font = pygame.font.Font('ubuntu.ttf', NUMBERS_SIZE)\\n self.sleep_time = 1 / CHANGES_PER_SECOND\\n\\n self.original_board = board.copy()\",\n \"def __init__(self):\\n self.board = [\\n [None, None, None],\\n [None, None, None],\\n [None, None, None]\\n ]\",\n \"def __init__(self, initial_board):\\n self.initial_board = initial_board\",\n \"def __init__(self):\\n self.computer_first = 0 # randint(0, 1)\\n self.app = Tk()\\n self.app.attributes(\\\"-toolwindow\\\", 1)\\n self.app.title('Tic Tac Toe')\\n self.app.resizable(width=False, height=False)\\n self.board = Board()\\n self.font = Font(family=\\\"Helvetica\\\", size=32)\\n self.buttons = {}\\n for x, y in self.board.fields:\\n handler = lambda x=x, y=y: self.move(x, y)\\n button = Button(self.app, command=handler, font=self.font,\\n width=2, height=1)\\n button.grid(row=y, column=x)\\n self.buttons[x, y] = button\\n handler = lambda: self.reset()\\n button = Button(self.app, text='reset', command=handler)\\n button.grid(row=self.board.size + 1, column=0,\\n columnspan=self.board.size, stick='WE')\\n self.update()\\n if self.computer_first:\\n self.move(randint(0, self.board.size - 1),\\n randint(0, self.board.size - 1))\",\n \"def __init__(self, dim, reverse = False, board = None):\\n self.empty_squares = []\\n if board == None:\\n self.board = [[\\\"\\\", \\\"\\\", \\\"\\\"],\\n [\\\"\\\", \\\"\\\", \\\"\\\"],\\n [\\\"\\\", \\\"\\\", \\\"\\\"]]\\n self.dim = dim\\n self.board = board\\n self.reverse = reverse\\n self.win = None\\n self.DRAW = 4\\n self.EMPTY = 1\\n self.PLAYERO = 2\\n self.PLAYERX = 3\",\n \"def initialize_board(self):\\n seed = self.seed and self.seed.any()\\n if not (self.shape or seed):\\n raise Exception(\\\"Either a shape or a seed is required.\\\")\\n\\n elif self.shape and seed:\\n # Center the seed on a game board\\n board = self._center_seed(self.shape, self.seed)\\n\\n elif self.shape:\\n # The probability a cell starts off dead\\n prob_dead = [1 - self.game.weight]\\n # Class probabilities for live cells\\n probs_alive = [self.game.weight * (1/self.classes)] * self.classes\\n\\n board = np.random.choice(\\n self.classes + 1,\\n np.prod(self.shape),\\n p = prob_dead + probs_alive\\n ).reshape(self.shape)\\n \\n else: # Only a seed is given\\n self.shape = self.seed.shape\\n board = self.seed\\n\\n self.array = board\\n self.start_array = board\\n self.prev_array = None\",\n \"def game_setup(self):\\n self.deck = Shoe(6)\\n self.initial_draw()\\n self.pot = ask_for_bet(self.player.money)\\n show_table(self.player, self.dealer, self.pot)\\n self.surrender_and_insurance()\",\n \"def __init__(self, master=None):\\n super().__init__(master)\\n self.masterframe = Frame(self.master)\\n self.masterframe.pack()\\n self.grid = Grid(3)\\n self.canvasSize = 100\\n self.mX = 0\\n self.mY = 0\\n self.working = True\\n # True is Player 1 and False is Player 2\\n self.turn = True\\n self.createCanvas()\",\n \"def __init__(self, board_width, board_height):\\n # Create board using generate_grid_dict method with given width and height.\\n board = self.generate_grid_dict(board_width, board_height)\\n self.draw = False\\n pygame.init()\\n\\n # Set the caption for the board and the font for the win prompt.\\n pygame.display.set_caption('Connect4 - Player 1')\\n self.game_font = pygame.freetype.Font(\\\"SF Distant Galaxy.ttf\\\", 40)\\n\\n # size of each square of the grid:\\n self.square_size = 80\\n # generate board width (amount of squares and square width):\\n self.width = board_width * self.square_size\\n # generate board height (amount of squares and square height):\\n self.height = board_height * self.square_size\\n # generate the radius of the chips depending on the square size:\\n self.radius = int(self.square_size / 4)\\n # find the middle of the square for chip placement:\\n self.square_mid = int(self.square_size / 2)\\n # set the screen size with the board width and height:\\n self.screen = pygame.display.set_mode((self.width, self.height))\\n\\n # Fill the screen with a white background.\\n background = pygame.Surface(self.screen.get_size())\\n background.fill((255, 255, 255))\\n self.background = background.convert()\\n\\n # Build the grid.\\n for i in range(0, self.width, self.square_size):\\n pygame.draw.rect(self.background, (0, 0, 0), (i, 0, 0, self.height))\\n for i in range(0, self.height, self.square_size):\\n pygame.draw.rect(self.background, (0, 0, 0), (0, i, self.width, 0))\\n self.screen.blit(self.background, (0, 0))\\n\\n # Setup, so player one starts.\\n self.playerOne = True\\n self.red = 250\\n self.blue = 0\\n\\n # Help dict for logic, when drawing a chip. Maps 0 -> size, 1 -> size - 1...\\n self.draw_dict_mapping = {}\\n for i in range(self.height//80 + 1):\\n self.draw_dict_mapping[i] = self.height//80 - i\\n\\n # Start the game with the run game method.\\n self.run_game(board)\",\n \"def setup(self):\\n self.board[(3, 3)] = -1\\n self.board[(3, 4)] = -1\\n self.board[(4, 3)] = 1\\n self.board[(4, 4)] = 1\\n\\n self.stones_set = 4\",\n \"def init(self):\\n self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\\n self.running = True\\n self.state = \\\"waiting\\\"\\n\\n self.button_text = \\\"Solve!\\\"\\n self.button_color = BUTTON_COLOR_SOLVE\\n self.puzzle_state = \\\"unsolved\\\"\\n\\n if not self.checkBoardValid():\\n self.state = \\\"failed\\\"\\n self.puzzle_state = \\\"failed\\\"\\n self.button_text = \\\"Impossible puzzle!\\\"\",\n \"def init_board():\\n\\t# Generates a table 10*10 of 0s with -1 around and the initial state\\n\\t# of the board with 2 whites and 2 blacks in the middle\\n\\ttable = [[0 if i != 0 and i != 9 else -1 for i in range(10)] if j != 0 and j != 9 else [-1 for i in range(10)] for j in range(10)] #leaves a -1 line around the whole table of 0s\\n\\t#initial state is drawn and recorded\\n\\ttable[4][4] = 2\\n\\ttable[5][5] = 2\\n\\ttable[4][5] = 1\\n\\ttable[5][4] = 1\\n\\tdrawPiece((4,4),2)\\n\\tdrawPiece((5,5),2)\\n\\tdrawPiece((4,5),1)\\n\\tdrawPiece((5,4),1)\\n\\treturn table\",\n \"def __init__(self, player, board):\\n self.player = player\\n self.board = board\",\n \"def start(self):\\n self.__init__()\\n self.set_n_players()\\n self.init_players()\\n self.init_territory_selection_phase()\\n self.init_troop_deployment_phase()\\n # self.game_phase()\",\n \"def setup_game(self, player, opponent):\\n\\n self.display.clear_screen()\\n\\n ship_index = 0\\n\\n while not player.ready(len(self.SHIP_INFO)):\\n # prints the currrent board\\n board = self.display.construct_player_board(player, opponent, True)\\n self.display.print_board(board)\\n\\n ship_name, ship_length = self.SHIP_INFO[ship_index]\\n ship_to_add = Ship(ship_name, ship_length)\\n\\n try:\\n player.add_ship(ship_to_add)\\n except Exception as e:\\n ship_to_add = player.ships[ship_index]\\n\\n origin, orientation = self.display.prompt_for_ship_placement(\\n ship_to_add)\\n\\n try:\\n player.place_ship(ship_to_add, origin, orientation,\\n self.BOARD_SIZE)\\n except ValueError as ve:\\n self.display.clear_screen()\\n print(ve)\\n print()\\n continue\\n\\n self.display.clear_screen()\\n ship_index += 1\\n self.display.prompt_switch(opponent.name)\",\n \"def __init__(self):\\n self.board = Board()\\n #self.player1 = player1\\n #self.player2 = player2\\n self.winner = None\",\n \"def __init__(self):\\n # create game object\\n self.game = Game()\\n self.players = (\\\"X's\\\", \\\"O's\\\")\\n\\n # define string constants for UI\\n self.BG_COLOR = \\\"#DBF6E9\\\"\\n self.FONT = \\\"Verdana\\\"\\n self.PROMPT = \\\"{0}, it's your turn.\\\"\\n self.SCORE_LABEL = \\\"{0}: {1}\\\"\\n self.TIE_LABEL = \\\"Ties: {0}\\\"\\n\\n # create window and instructions at the top\\n self.window = tk.Tk()\\n self.window.title(\\\"Tic-tac-toe\\\")\\n self.window.configure(padx=30, pady=30, bg=self.BG_COLOR)\\n self.window.geometry(\\\"450x450\\\")\\n self.instructions = self.create_label(self.window, self.PROMPT.format(self.players[self.game.whose_turn]))\\n self.instructions.grid(row=0, column=0)\\n # create score frame to hold results of previous games in this session\\n self.score_frame = tk.Frame(self.window, bg=self.BG_COLOR)\\n self.score_frame.grid(row=1, column=1, padx=20, pady=20, sticky='n')\\n self.score_label = self.create_label(self.score_frame, 'Score')\\n self.score_label.grid(row=0, column=0, sticky='w')\\n self.player_0_score_label = self.create_label(self.score_frame,\\n self.SCORE_LABEL.format(self.players[0], self.game.player_0_score))\\n self.player_0_score_label.grid(row=1, column=0)\\n self.player_1_score_label = self.create_label(self.score_frame,\\n self.SCORE_LABEL.format(self.players[1], self.game.player_1_score))\\n self.player_1_score_label.grid(row=2, column=0)\\n self.num_ties_label = self.create_label(self.score_frame, self.TIE_LABEL.format(self.game.num_ties))\\n self.num_ties_label.grid(row=3, column=0, sticky='w')\\n # create game frame; each of the nine squares on the grid is represented as a button\\n self.game_frame = tk.Frame(self.window)\\n self.game_frame.grid(row=1, column=0, pady=20)\\n self.button_list = self.create_buttons()\\n self.place_buttons()\\n\\n self.window.mainloop()\",\n \"def __init__(self, player1, player2):\\n self.players = [player1, player2]\\n self.tokens = {\\n ' ': ' ',\\n player1: 'X',\\n player2: 'O',\\n }\\n self.score = {\\n player1: 0,\\n player2: 0,\\n }\\n self.moves = None\\n self.winner = None\\n self.turn = ''\\n self.reset()\",\n \"def play_game(self, x_player, o_player):\\n #from connectfour import Player #either from connect4_player or connectfour\\n \\n current_side = \\\"X\\\"\\n players = {\\\"X\\\": x_player, \\\"O\\\": o_player}\\n while ((not self.win_for(\\\"X\\\")) and\\n (not self.win_for(\\\"O\\\")) and\\n (not self.is_full())):\\n print()\\n print(self)\\n print()\\n move = Board.INVALID_MOVE\\n while not self.allows_move(move):\\n if players[current_side] == \\\"human\\\":\\n move = int(input(current_side + \\\"'s move: \\\"))\\n else:\\n move = players[current_side].next_move(self)\\n print(\\\"Computer playing for \\\" + current_side +\\n \\\" plays at \\\" + str(move))\\n\\n self.add_move(move, current_side)\\n if current_side == \\\"X\\\":\\n current_side = \\\"O\\\"\\n else:\\n current_side = \\\"X\\\"\\n\\n if self.win_for(\\\"X\\\"):\\n print(\\\"X wins --- congratulations!\\\\n\\\")\\n elif self.win_for(\\\"O\\\"):\\n print(\\\"O wins --- congratulations!\\\\n\\\")\\n else:\\n print(\\\"Tied game!\\\\n\\\")\\n\\n print()\\n print(self)\",\n \"def __init__(self, parent, board, Player1, Player2, *args, **kwargs):\\n\\n\\t\\tself.button = [[\\\"\\\" for x in range(8)] for y in range(8)]\\n\\t\\t# self.board = Board.Board()\\n\\t\\tself.initialButtonPush = ()\\n\\t\\tself.buttonPushed = \\\"\\\"\\n\\t\\tself.nextButton = ()\\n\\n\\t\\t# setup tkinter frame\\n\\t\\ttk.Frame.__init__(self, parent, *args, **kwargs)\\n\\t\\tparent = parent\\n\\t\\t# call setup functions\\n\\t\\tself.setupButtonImages()\\n\\t\\tself.setupBoard()\\n\\t\\tself.NewBoard = board\\n\\t\\tself.Player1 = Player1\\n\\t\\tself.Player2 = Player2\\n\\t\\t\\n\\t\\tself.updateBoard()\",\n \"def __init__(self, screen, win_size_x, win_size_y, player_num):\\n self._player_list = list(Player(f\\\"Player {x}\\\") for x in range(player_num))\\n self._player_num = player_num\\n print(\\\"The number of players is: \\\", player_num)\\n self._screen = screen\\n self._player_turn = 0\\n self._ui_player_turn = UI((1820, 10), (100, 50), f\\\"Player {self._player_turn}\\\")\\n self._screen.blit(self._ui_player_turn.update(\\n f\\\"Player {self._player_turn}\\\"), self._ui_player_turn._location)\\n self._core_deck = Deck(\\\"Deck/test_deck.txt\\\")\\n self._war_deck = []\\n self._map = Map(self._player_num, win_size_x, win_size_y)\\n self._clock = pygame.time.Clock()\\n self._run = True\\n self._fps = 30\\n self.each_player_draws_hand(self._core_deck)\",\n \"def __init__(self):\\n\\n self.frameCount = 0\\n self._initScreen()\\n self._initObjects()\\n self._initControls()\\n self._initLevel()\\n self._start()\\n print \\\"DEBUG: Initializing Game\\\"\\n pass\",\n \"def reset_board(self):\\n self.board = [0, 0, 0, 0, 0, 0, 0, 0, 0]\\n self.turn = 0\\n\\n self.change_button_img_to_null()\\n\\n #self.Score_Label.grid(row=0,column=1, ipadx=32)\\n\\n self.player_highlight()\\n self.change_button_state('normal')\\n self.update_score()\",\n \"def __init__(self, num_players):\\n self.num_players = num_players\\n self.firework = [[], [], [], [], []]\\n self.nb_blue_stone = MAX_BLUE_STONE\\n self.nb_red_stone = MAX_RED_STONE\\n self.draw = None\\n self.hands = None\\n self.fill_draw()\\n random.shuffle(self.draw)\\n self.discard = []\\n self.draw_initial_hands()\",\n \"def init_game(self):\\n self.view.carregar_jogadores_possiveis(self._possible_players_list())\\n self.view.put_view_in_main_loop()\",\n \"def __init__(self, width, height, player, opponent):\\r\\n self.height = height\\r\\n self.width = width\\r\\n self.board = []\\r\\n for x in range(height):\\r\\n self.board.append([])\\r\\n for y in range(width):\\r\\n self.board[x].append(\\\"\\\\t\\\")\\r\\n self.player = player\\r\\n self.opponent = opponent\\r\\n self.lastMove = (0, 0)\",\n \"def start(self):\\n # store a sign controlling addition or subtraction so pieces move in the right direction\\n self.board = fen_to_position(self.game.fen)\\n self.transposition_table = dict()\",\n \"def reset(self):\\n self.board = Board()\\n self.winner = None\",\n \"def reset(self):\\r\\n self.board = [[0 for i in range(self.width)]\\r\\n for i in range(self.height)]\\r\\n self.new_tile()\\r\\n self.new_tile()\",\n \"def __init__(self):\\n self.start()\\n while self.player.money > 0:\\n self.game_loop()\\n if self.player.money > 0:\\n if not play_again():\\n break\\n elif self.player.money == 0:\\n no_more_money()\\n self.reset_table()\",\n \"def initial_board():\\n board = [OUTER] * 100\\n for i in Othello.squares():\\n board[i] = EMPTY\\n # The middle four squares should hold the initial piece positions.\\n board[44], board[45] = BLACK, WHITE\\n board[54], board[55] = WHITE, BLACK\\n return board\",\n \"def __init__(self, players=None):\\n self.game = Game()\\n if players:\\n self.player1 = players[0]\\n self.player2 = players[1]\\n else:\\n self.player1 = Player('X')\\n self.player2 = Player('O')\\n self.record = Record()\\n self.winning_moves = []\",\n \"def __init__(self):\\n\\n super().__init__()\\n self.setup_janggi_game()\\n self._game_state = 'UNFINISHED'\\n self._player_turn = 'BLUE'\",\n \"def start(self):\\n # asserts preconditions are met\\n #assert self.validGameSettings()\\n\\n #draws initial welcome screen\\n #self._text = GLabel(text=\\\"Press 'S' to Play\\\")\\n #self._text.draw(self.view)\\n\\n # initializing instance variables\\n self.setState(STATE_INACTIVE)\\n self.setWave(None)\\n self.setText(None)\\n self.lastkeys = 0 #ADD MORE ATTRIBUTES\\n\\n # draws iniital welcome screen\\n self.welcomeScreen()\",\n \"def __init__(self,player1: Player = ManualPlayer(\\\"P1\\\"),\\\\\\r\\n player2: Player = ManualPlayer(\\\"P2\\\")):\\r\\n\\r\\n self.board = np.zeros((BOARD_SIZE,BOARD_SIZE)\\\\\\r\\n ,dtype=np.int8)\\r\\n self.board[3,3] = '2'\\r\\n self.board[4,4] = '2'\\r\\n self.board[3,4] = '1'\\r\\n self.board[4,3] = '1' \\r\\n\\r\\n self.players = []\\r\\n self.players.append(player1)\\r\\n self.players.append(player2)\\r\\n self.turn = 1\\r\\n self.count = 0\",\n \"def play(self):\\n board = Board()\\n print(\\\"Let's play tic-tac-toe against computer!\\\")\\n print(\\\"Here is your board!\\\")\\n count = 1\\n print(board)\\n while True:\\n board.person_move()\\n status = board.get_status()\\n if status == 'x' or status == '0':\\n return(f\\\"Winner is {status}\\\")\\n elif status == 'draw':\\n return(\\\"Friendship won!\\\")\\n board.make_computer_move()\\n status = board.get_status()\\n if status == 'x' or status == '0':\\n return(f\\\"Winner is {status}\\\")\\n elif status == 'draw':\\n return(\\\"Friendship won!\\\")\\n print(f\\\"Board after {count} action.\\\")\\n count += 1\\n print(board)\",\n \"def __init__(self, board, turn):\\n self.player = turn\\n self.roll = self.roll_dice()\\n #array of applied board states\\n self.moves = []\\n self.board = board\\n self.generate_valid_moves()\",\n \"def initial_state():\\n board = [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\\n return board\",\n \"def __init__(self):\\n self.boards = [[False, False, False, False, False, False, False, False, False],\\n [False, False, False, False, False, False, False, False, False],\\n [False, False, False, False, False, False, False, False, False]]\",\n \"def __init__(self):\\n self.boards = [[False, False, False, False, False, False, False, False, False],\\n [False, False, False, False, False, False, False, False, False],\\n [False, False, False, False, False, False, False, False, False]]\",\n \"def start(self):\\n self.player = Player()\\n self.dealer = Dealer()\\n self.pot = 0\\n self.side_bet = 0\\n start_game()\",\n \"def __init__(self):\\n self.played_pos = []\\n self.grid = [['-', '-', '-'],\\n ['-', '-', '-'],\\n ['-', '-', '-']]\\n self.player_played_pos = {'p1': set(), 'p2': set()}\",\n \"def start():\\n display_board()\\n print(\\\"\\\\n\\\")\\n y_n_prompt()\",\n \"def reset(self):\\n self.board = Board()\\n self.update()\\n self.computer_first = randint(0, 1)\\n if self.computer_first:\\n self.move(randint(0, self.board.size - 1),\\n randint(0, self.board.size - 1))\",\n \"def start(self):\\n self.playing = True\\n self.table = table.Table(self.players)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76133895","0.74191046","0.7272769","0.7215476","0.70663446","0.7060645","0.7049015","0.7029267","0.69543284","0.69497305","0.692294","0.6910041","0.6903089","0.6872871","0.6863269","0.68311656","0.68238926","0.68191206","0.6769023","0.67465365","0.67353773","0.6735311","0.6722465","0.6718771","0.6690097","0.66767114","0.66105705","0.65862644","0.65823096","0.6577102","0.65610796","0.65481275","0.65418315","0.6535146","0.6523749","0.65145266","0.65011114","0.6476084","0.64714104","0.6470568","0.6452707","0.6451656","0.64408195","0.6436849","0.643481","0.6429563","0.64281636","0.64098865","0.64046603","0.63997084","0.63976705","0.6365669","0.6364156","0.6357598","0.635603","0.63416636","0.6334285","0.63112533","0.6305934","0.6304511","0.6300643","0.63001174","0.6299671","0.629563","0.62948847","0.628704","0.6286675","0.6286281","0.6280643","0.6272809","0.62654567","0.62516403","0.6242899","0.6237107","0.62330437","0.6206097","0.6199669","0.6189039","0.61612475","0.6160348","0.6159054","0.61552453","0.61494493","0.6147866","0.6128221","0.6123399","0.6121669","0.61187667","0.6113047","0.6112451","0.6104364","0.6100864","0.6092064","0.6090432","0.6090432","0.6089558","0.60893905","0.6086315","0.60845226","0.6084323"],"string":"[\n \"0.76133895\",\n \"0.74191046\",\n \"0.7272769\",\n \"0.7215476\",\n \"0.70663446\",\n \"0.7060645\",\n \"0.7049015\",\n \"0.7029267\",\n \"0.69543284\",\n \"0.69497305\",\n \"0.692294\",\n \"0.6910041\",\n \"0.6903089\",\n \"0.6872871\",\n \"0.6863269\",\n \"0.68311656\",\n \"0.68238926\",\n \"0.68191206\",\n \"0.6769023\",\n \"0.67465365\",\n \"0.67353773\",\n \"0.6735311\",\n \"0.6722465\",\n \"0.6718771\",\n \"0.6690097\",\n \"0.66767114\",\n \"0.66105705\",\n \"0.65862644\",\n \"0.65823096\",\n \"0.6577102\",\n \"0.65610796\",\n \"0.65481275\",\n \"0.65418315\",\n \"0.6535146\",\n \"0.6523749\",\n \"0.65145266\",\n \"0.65011114\",\n \"0.6476084\",\n \"0.64714104\",\n \"0.6470568\",\n \"0.6452707\",\n \"0.6451656\",\n \"0.64408195\",\n \"0.6436849\",\n \"0.643481\",\n \"0.6429563\",\n \"0.64281636\",\n \"0.64098865\",\n \"0.64046603\",\n \"0.63997084\",\n \"0.63976705\",\n \"0.6365669\",\n \"0.6364156\",\n \"0.6357598\",\n \"0.635603\",\n \"0.63416636\",\n \"0.6334285\",\n \"0.63112533\",\n \"0.6305934\",\n \"0.6304511\",\n \"0.6300643\",\n \"0.63001174\",\n \"0.6299671\",\n \"0.629563\",\n \"0.62948847\",\n \"0.628704\",\n \"0.6286675\",\n \"0.6286281\",\n \"0.6280643\",\n \"0.6272809\",\n \"0.62654567\",\n \"0.62516403\",\n \"0.6242899\",\n \"0.6237107\",\n \"0.62330437\",\n \"0.6206097\",\n \"0.6199669\",\n \"0.6189039\",\n \"0.61612475\",\n \"0.6160348\",\n \"0.6159054\",\n \"0.61552453\",\n \"0.61494493\",\n \"0.6147866\",\n \"0.6128221\",\n \"0.6123399\",\n \"0.6121669\",\n \"0.61187667\",\n \"0.6113047\",\n \"0.6112451\",\n \"0.6104364\",\n \"0.6100864\",\n \"0.6092064\",\n \"0.6090432\",\n \"0.6090432\",\n \"0.6089558\",\n \"0.60893905\",\n \"0.6086315\",\n \"0.60845226\",\n \"0.6084323\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6897718"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":2666,"cells":{"query":{"kind":"string","value":"Switches whose turn it is."},"document":{"kind":"string","value":"def switchPlayer(self):\n\t\tif (self.current is Piece.EX):\n\t\t\tself.current = Piece.OH \n\t\telse:\n\t\t\tself.current = Piece.EX"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _switch_turn(self, cur_player):\n if cur_player == \"W\":\n self._turn = \"B\"\n else:\n self._turn = \"W\"","def turn(self):\n pass","def get_switches(self) -> tuple:\n return self.switches","def changeTurn(self):\n\t\tif self.turn == 1:\n\t\t\tself.turn = 2\n\t\telse:\n\t\t\tself.turn = 1","def getSwitch(self, projNumber):","def set_switches_from_rule_nbr(self):\n for rule_switch, enabled in zip(CA_World.bin_0_to_7, self.int_to_8_bit_binary(self.rule_nbr)):\n SimEngine.gui_set(rule_switch, value=(True if enabled=='1' else False))","def toDo(switch):\n func = \"InitSwitches.\" \"switch\" + str(switch)\n eval(func + \"()\")","def set_switch(self, value):\n act = SwitchAction(self, value)\n return act.invoke()","def do_attribute_switches(self):\n event = None\n if self.rhs:\n event = self.get_event_from_args(self.rhs, check_host=True)\n else:\n proj = self.project\n if not proj:\n raise self.CalCmdError(\n \"You must use /create first or specify an event.\"\n )\n if \"largesse\" in self.switches:\n return self.set_largesse(event)\n if \"date\" in self.switches or \"reschedule\" in self.switches:\n return self.set_date(event)\n if \"location\" in self.switches:\n return self.set_location(event)\n if \"desc\" in self.switches:\n return self.set_event_desc(event)\n if \"roomdesc\" in self.switches:\n return self.set_room_desc(event)\n if \"plotroom\" in self.switches:\n return self.set_plotroom(event)\n if \"private\" in self.switches:\n return self.set_private(event)\n if \"host\" in self.switches:\n return self.add_or_remove_host(event)\n if \"gm\" in self.switches:\n return self.add_or_remove_gm(event)\n if \"invite\" in self.switches:\n return self.invite_org_or_player(event)\n if \"uninvite\" in self.switches:\n return self.uninvite_org_or_player(event)\n if \"action\" in self.switches:\n return self.set_crisis_action(event)\n if \"risk\" in self.switches:\n return self.set_risk(event)","def turn(self):\n return self._turn","def update_turn(self):\n pass","def _flip_turn(self):\n self._turn = self._next_turn\n return self._turn","def switch(self) -> bool:\n return bool(self.pressed & 0x1)","async def get_switches(self):\n return await self.get_states_by_tag_prefix(\"led\")","def pswitchon(chan) :\n s.phaseSwitching(True, chan)","def get_on_turn(self):\n return deepcopy(self.__on_turn)","def read_switch(self):\n return GPIO.input(SWITCH_PIN)","def switches(self):\n return {k:v for k, v in self._data.items() \n if v[\"type\"] == \"SWITCH\"}","def switch_turn(self):\n if self.completed is not None:\n self.current_turn = self.creator if self.current_turn != self.creator else self.opponent\n self.save()","def _get_turn(self):\n raise NotImplementedError","def change_mode(self):\n return (self.mode + 1) % 2","def keyed_rotor(self):\n turnover = self.on_turnover()\n self.inc_rotor_setting()\n return turnover","def internal_switch(self) -> s11.InternalSwitch:\n return self.calobs.internal_switch","def op_set(op):\r\n\tglobal fo\r\n\tif op == cat:\r\n\t\tfo = cat.who()\r\n\t\treturn cat_turn\r\n\telif op == ninja:\r\n\t\tfo = ninja.who()\r\n\t\treturn ninja_turn\r\n\telse:\r\n\t\tprint \"This is no one's turn!!!\"","def TURN_OPTIONS() -> tuple:\n return \"Hit me! (Draw another Card)\", \"Stand (End round, stop drawing)\"","def list_switches(self):\n return [x for x,y in self.devices.items() if y.device_type == \"Switch\"]","def is_switch(G):\n return False","def get_all_switches(name):\n return [False,False,False,False] #TODO Implement","def switch_pseudocode(self, *args):\n return _ida_hexrays.Hexrays_Hooks_switch_pseudocode(self, *args)","def multipleSwitch(randomhouse):\n\n randomBatteries = randomhouse.possible_connections\n\n randomBatteries.sort(key=lambda x: x[1])\n currentbattery = randomhouse.connection\n for randomBattery in randomBatteries:\n rb = randomBattery[0]\n if rb != currentbattery:\n if rb.capacity >= randomhouse.output:\n switch(randomhouse, rb)\n return","def get_switch(self, conf, dpid):\n\t\tpass","def change_state_controller(self, switches):\n index = self.__index\n for dummy in range(len(switches)):\t\t\n index = index ^ (2**(switches[dummy] - 1))\n\n for state in self.__next_state:\n if any(control[0] == 'control' for control in state.__control_list) \\\n and all(~(state.__index ^ index) & 2**(switches[dummy] - 1) for dummy in range(len(switches))):\n state.__active_control = None\n self.__active_control = None\n return state\n return None","def swap(self, *args, **kwargs):\n return self.switch(*args, **kwargs)","def _next_turn(self):\n return self.TURNS[self._turn is self.BLACK]","def changeTurn(self):\r\n # Undoes clicked pieces and removes potential moves from the bar area\r\n for point in self.points:\r\n if point.isClicked():\r\n point.undoClick()\r\n if not self.bar[self.currentPlayer].isEmpty():\r\n self.undoPossibleBarMoves()\r\n \r\n # Changes by turn changing the currentPlayer index and updating all the \r\n # necessary board objects\r\n self.currentPlayer = (self.currentPlayer + 1) % 2\r\n self.dice.makeActive()\r\n for point in self.points:\r\n point.setActiveTurn()\r\n for bar in self.bar:\r\n bar.update()\r\n bar.setActiveTurn()\r\n self.message.resetText('It\\'s ' + self.getCurrentString() + '\\'s turn!', self.surface)\r\n self.turnchanger.setFillColor(self.getTurn())\r\n self.turnchanger.draw(self.surface)\r\n pygame.display.flip()","def incTurn(self):\n self.turnOn = (self.turnOn+1)%self.turns","def init_turn(self):\n self.before_turn_switches()\n if self.battlefield.win is not None:\n return\n self.before_turn()","def switch_pivot():\n for piv_switcher in get_one_switcher():\n piv_switcher.switch()","def __process_xx_switch_arg(self, argument):\n _method_name = '__process_xx_switch_arg'\n\n match = self.__xx_args_switch_regex.match(argument)\n xarg = match.group(2)\n on_or_off = match.group(1)\n if on_or_off == '+':\n on_or_off_text = 'on'\n else:\n on_or_off_text = 'off'\n\n if 'switch' not in self.__xx_args:\n self.__xx_args['switch'] = OrderedDict()\n self._logger.finer('WLSDPLY-08304', argument, xarg, on_or_off_text,\n class_name=self._class_name, method_name=_method_name)\n self.__xx_args['switch'][xarg] = on_or_off","def turn():\n \n robottype = get_type()\n if robottype == RobotType.PAWN:\n pawn_turn()\n else:\n overlord_turn()\n bytecode = get_bytecode()","def turn():\n \n robottype = get_type()\n if robottype == RobotType.PAWN:\n pawn_turn()\n else:\n overlord_turn()\n bytecode = get_bytecode()","def swint(self) -> None:","def check_switching_action(self):\n current_switching = {}\n for devices in self.switching_systems:\n command = self.build_command(devices, \"get_closed_channels\")\n switching = str(self.vcw.query(devices, command)).strip()\n switching = self.pick_switch_response(devices, switching)\n current_switching.update({devices[\"Device_name\"]: switching})\n self.settings[\"settings\"][\"current_switching\"][\n devices[\"Device_name\"]\n ] = current_switching\n return current_switching","def get_turn(self):\n return self._turn","def switch_player(player):\n if player == PLAYERX:\n return PLAYERO\n else:\n return PLAYERX","def switch_player(player):\n if player == PLAYERX:\n return PLAYERO\n else:\n return PLAYERX","def _advance_turn(self):\n\n self.__turn_info['turn'] = ChessGame.BLACK if\n self.__turn_info['turn'] == ChessGame.WHITE else ChessGame.WHITE","def switchPlayer(self):\n\n \n tmp = self.current\n self.current = self.other\n self.other = tmp\n\n self.topSelector.toggleActive()\n self.bottomSelector.toggleActive()","def begin_turn(self):\n pass","def turn(self, dir):\n if dir.upper() == 'R':\n if self.direction == 3:\n self.direction = 0\n else:\n self.direction += 1\n if dir.upper() == 'L':\n if self.direction == 0:\n self.direction = 3\n else:\n self.direction -= 1","def lightning_turnon(self):\n self.turnOn()","def Steering(x1):\n #RETURNS A LIST WITH THE RIGHTSPEED and LEFTSPEED for the appropriate turn\n \n LeftSp=127\n RightSp=127\n\n if x1 < 320:\n print \"I'm turning Left\"\n LeftSp = 50\n RightSp = 240\n\n if x1 > 320:\n print \"I'm turning Right\"\n LeftSp = 240\n RightSp = 50\n\n return [RightSp, LeftSp]","def test_switch_returns(self):\n\n #Player 1 and Player 2 are represented by 1 and -1\n #Multiplying current_player by -1 will flip them\n current_player = self.controller.game_state.player * -1\n\n #after running flip_current_player function in the controller,\n # test current player\n self.assertEqual(self.controller.game_state.flip_current_player(),\n current_player)","def computer_turn(self):\r\n\r\n print(\r\n '\\nTURN: Computer -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=')\r\n\r\n # Scan through memory to see if the computer already knows a matching pair\r\n loc1, loc2 = self.computer_scan_memory()\r\n\r\n if loc1 and loc2: # when there is a pair inside the computer's memory\r\n x1, y1 = loc1\r\n x2, y2 = loc2\r\n\r\n # Check point\r\n assert x1 != None and y1 != None, 'x1 or y1 is None type'\r\n assert x2 != None and y2 != None, 'x2 or y2 is None type'\r\n\r\n choice1_key = self.state[x1, y1]\r\n choice2_key = self.state[x2, y2]\r\n else: # when there is no pair inside the computer's memory\r\n # Randomly select one card then scan memory\r\n x1, y1 = self.computer_random_select()\r\n choice1_key = self.state[x1, y1]\r\n\r\n # Scan through memory\r\n loc = self.computer_scan_memory(pick=choice1_key)\r\n\r\n if loc and (x1 != loc[0] or y1 != loc[1]): # there is a common value card in the computer's memory\r\n x2, y2 = loc # and that memory is not the same as the first choice\r\n\r\n # Check point\r\n assert x2 != None and y2 != None, 'x2 or y2 is None type'\r\n\r\n choice2_key = self.state[x2, y2]\r\n else: # There is no common value in the computer's memory\r\n while True: # select a card different from the first choice\r\n x2, y2 = self.computer_random_select()\r\n if x2 != x1 or y2 != y1:\r\n break\r\n choice2_key = self.state[x2, y2]\r\n\r\n print('First choice: {0} ({1}, {2})'.format(self.deck[choice1_key], x1, y1))\r\n print('Second choice: {0} ({1}, {2})'.format(self.deck[choice2_key], x2, y2))\r\n\r\n # Check if it is a match or not\r\n if self.check_card(self.deck[choice1_key], self.deck[choice2_key]):\r\n print('MATCH')\r\n # Replace the corresponding cards in the remaining inventory and current state with -1\r\n self.remaining[choice1_key] = -1\r\n self.remaining[choice2_key] = -1\r\n self.state[x1, y1] = -1\r\n self.state[x2, y2] = -1\r\n self.computer_cards += 2 # the computer gets 2 cards\r\n self.bin.append([x1, y1]) # move the location of the card to the already-taken bin\r\n self.bin.append([x2, y2])\r\n self.forget_memory(choice1_key) # remove from computer's memory\r\n self.forget_memory(choice2_key)\r\n self.match = 0 # The computer will continue to choose cards\r\n else:\r\n print('NOT a match')\r\n # Add these cards to the computer's memory\r\n self.computer_memory[choice1_key] = [x1, y1]\r\n self.computer_memory[choice2_key] = [x2, y2]\r\n self.match = 1 # The player's turn\r","def __checkSwitch ( self, letter, value ):\n\n #-- 1 --\n # [ if letter is a key in self.switchMap -> I\n # else ->\n # sys.stderr +:= (usage message) + (error message)\n # stop execution ]\n if not self.switchMap.has_key ( letter ):\n usage ( self.switchSpecs, self.posSpecs,\n \"No such switch: -%s\" % letter )\n\n #-- 2 --\n if len(value) == 0:\n self.switchMap[letter] = 1\n else:\n self.switchMap[letter] = value","def change_player_turn(self):\r\n self._player_turn *= -1","def test_switch(self):\n\n # Switch to the 'orig' data pipe.\n pipes.switch('orig')\n\n # Test the current data pipe.\n self.assertEqual(pipes.cdp_name(), 'orig')\n\n # Switch to the 'empty' data pipe.\n pipes.switch('empty')\n\n # Test the current data pipe.\n self.assertEqual(pipes.cdp_name(), 'empty')","def turn(self):\n return repr(self._turn)","def turn(self, **kwargs: Any) -> None:\n pass","def get_limit_switch(name,switch):\n return False #TODO Implement","def on_turnover(self):\n return True if self.rotor_setting in self.turnover_characters else False","def _do_switch(self, runner, recipe, extra_info=''):\n if 'batch_augments' in recipe:\n self._switch_batch_augments(runner, recipe['batch_augments'])\n runner.logger.info(f'Switch batch augments{extra_info}.')\n\n if 'train_pipeline' in recipe:\n self._switch_train_pipeline(runner, recipe['train_pipeline'])\n runner.logger.info(f'Switch train pipeline{extra_info}.')\n\n if 'loss' in recipe:\n self._switch_loss(runner, recipe['loss'])\n runner.logger.info(f'Switch loss{extra_info}.')","def get_all_switch(self, conf):\n\t\tpass","def __init__(self):\r\n self.turn = 0","def get_hw_switch_states(self):\n hw_states = dict()\n #k = self._kp.keypad()\n k = \"\"\n for number, sw in self.switches.items():\n if number == k:\n hw_states[number] = 1\n else:\n hw_states[number] = 0\n return hw_states","def set_turn(self):\n if self.status == self.PLAYER_TURN:\n return\n self.status = self.PLAYER_TURN\n self.client.send_player_turn(10)","def RunTurn( lobound=1, hibound=20 ):\n\tpass","def _action_toggle(self, flag):\n if flag:\n return {\"toggle\": \"ON\"}\n else:\n return {\"toggle\": \"OFF\"}","def switch_player(current, player1, player2):\r\n if current == player1:\r\n return player2\r\n else:\r\n return player1","def turn_clockwise(a):\r\n if a==\"N\":\r\n return \"E\"\r\n elif a==\"E\":\r\n return \"S\"\r\n elif a==\"S\":\r\n return \"W\"\r\n elif a==\"W\":\r\n return \"N\"","def state(self) -> bool:\n return self.get_state(self.entity_ids[\"switch\"])","def switch(self, u_stuff, i_stuff):\n\n if self.sim_options['user_based']:\n return u_stuff, i_stuff\n else:\n return i_stuff, u_stuff","def __toggle(self,x):\n\t\tif x == 1:\n\t\t\treturn 0\n\t\telse:\n\t\t\treturn 1","def next_turn(self):\n if self.turn == BLUE and self.ai:\n self.ai_turn = True\n self.turn = RED\n elif self.turn == BLUE:\n self.turn = RED\n else:\n self.turn = BLUE\n\n self.selected_piece = None\n self.selected_legal_moves = []\n self.check_game_over()","def switch(ind, status):\n print(\"Switching :\", ind, \">>\", status == 'on')\n GPIO.output(ind, status == 'on')","def test_switch_channels(self):\n\t\t# not available yet, experimental\n\t\tpass","def switch(cond, ift, iff):","def __switch_restriction_set(self, name: str):\n switch = {\n \"user\": self.userRestrictionCategories,\n \"role\": self.roleRestrictionCategories,\n \"channel\": self.channelRestrictionCategories\n }\n return switch[name]","def state(self):\n # None will return False\n return bool(self.switch.value)","def update_player_turn(self):\n\n if self.get_player_turn() != 'BLUE':\n\n self._player_turn = 'BLUE'\n\n else:\n\n self._player_turn = 'RED'","def doTurn(self, gamestate):\n raise NotImplementedError(\"Please Implement this method\")","def play_turn(self, cur_board):\n pass","def turn_left(self):\n pass","def _get_switch(self, switch):\n switch = self.switch_by_label(switch)\n id = self.switches[switch.label].id\n # make sure that the serial port is open\n self.assure_serial()\n # create command for the arduino and send it\n input_string = 'r' + str(id[0]) + str(id[1])\n self.serial.write(input_string.encode('ascii'))\n time.sleep(self.READ_DELAY)\n # retrieve result\n result = self.serial.readline().decode().rstrip()\n time.sleep(self.READ_DELAY)\n # store the indicators to the switch\n switch.indicators = (int(result[0]), int(result[1]))\n # raise error if the indicators show an error\n if switch.state is None:\n raise SwitchError(\"Reading the state was unsuccessful: Indicators \"\n f\"of the switch show {switch.indicators}.\")\n return switch.state","def turn_right(self):\n pass","def take_turn(self):\r\n self._choose_best_option()\r\n self._do_draw()","def turn(self, turnDir):\n if turnDir == 0: # left\n if self.dir == \"N\":\n self.dir = \"W\"\n elif self.dir == \"W\":\n self.dir = \"S\"\n elif self.dir == \"S\":\n self.dir = \"E\"\n elif self.dir == \"E\":\n self.dir = \"N\"\n else:\n raise ValueError(\"invalid dir %s\" % self.dir)\n elif turnDir == 1: # right\n if self.dir == \"N\":\n self.dir = \"E\"\n elif self.dir == \"E\":\n self.dir = \"S\"\n elif self.dir == \"S\":\n self.dir = \"W\"\n elif self.dir == \"W\":\n self.dir = \"N\"\n else:\n raise ValueError(\"invalid dir %s\" % self.dir)\n else:\n raise ValueError(\"invalid turnDir %d\" % turnDir)","def switch_to_state(self, state):\n self.switch_state = state","def switchPlayer():\n\n #escrever o condicional do modo de jogo.\n if (modoDeJogo == \"1\" or modoDeJogo == 1):\n quemJoga = player[1]\n\n if (player[0] == quemJoga):\n quemJoga = \"pc\"\n else: \n quemJoga = player[0]\n\n return quemJoga #quemComeca\n else:\n quemJoga = player[2]\n\n if (player[0] == quemJoga):\n quemJoga = player[1]\n else: \n quemJoga = player[0]\n \n return quemJoga #quemComeca","def get_current_turn(self):\n return self._turn","def switcher_secret(self):\n return self._switcher_secret","def get_turn(self):\n return self.__turn_info['turn']","def __le__(self, *args):\n return _ida_hexrays.cswitch_t___le__(self, *args)","def _sense_and_act(self):\n pass","def get_switch_stringlist(self):\n return text_switch","def switch_to_main(self):\n return main.switch()","def tempo_mode_switch(event):\n value = gremlin.actions.Value(event.is_pressed)\n tempo_mode_switch_container(event, value)","def the(target: Target) -> \"SwitchTo\":\n return SwitchTo(target)","def getTurn(self):\r\n return self.players[self.getCurrentPlayer()].getColor()","def switched_with(self):\n return self"],"string":"[\n \"def _switch_turn(self, cur_player):\\n if cur_player == \\\"W\\\":\\n self._turn = \\\"B\\\"\\n else:\\n self._turn = \\\"W\\\"\",\n \"def turn(self):\\n pass\",\n \"def get_switches(self) -> tuple:\\n return self.switches\",\n \"def changeTurn(self):\\n\\t\\tif self.turn == 1:\\n\\t\\t\\tself.turn = 2\\n\\t\\telse:\\n\\t\\t\\tself.turn = 1\",\n \"def getSwitch(self, projNumber):\",\n \"def set_switches_from_rule_nbr(self):\\n for rule_switch, enabled in zip(CA_World.bin_0_to_7, self.int_to_8_bit_binary(self.rule_nbr)):\\n SimEngine.gui_set(rule_switch, value=(True if enabled=='1' else False))\",\n \"def toDo(switch):\\n func = \\\"InitSwitches.\\\" \\\"switch\\\" + str(switch)\\n eval(func + \\\"()\\\")\",\n \"def set_switch(self, value):\\n act = SwitchAction(self, value)\\n return act.invoke()\",\n \"def do_attribute_switches(self):\\n event = None\\n if self.rhs:\\n event = self.get_event_from_args(self.rhs, check_host=True)\\n else:\\n proj = self.project\\n if not proj:\\n raise self.CalCmdError(\\n \\\"You must use /create first or specify an event.\\\"\\n )\\n if \\\"largesse\\\" in self.switches:\\n return self.set_largesse(event)\\n if \\\"date\\\" in self.switches or \\\"reschedule\\\" in self.switches:\\n return self.set_date(event)\\n if \\\"location\\\" in self.switches:\\n return self.set_location(event)\\n if \\\"desc\\\" in self.switches:\\n return self.set_event_desc(event)\\n if \\\"roomdesc\\\" in self.switches:\\n return self.set_room_desc(event)\\n if \\\"plotroom\\\" in self.switches:\\n return self.set_plotroom(event)\\n if \\\"private\\\" in self.switches:\\n return self.set_private(event)\\n if \\\"host\\\" in self.switches:\\n return self.add_or_remove_host(event)\\n if \\\"gm\\\" in self.switches:\\n return self.add_or_remove_gm(event)\\n if \\\"invite\\\" in self.switches:\\n return self.invite_org_or_player(event)\\n if \\\"uninvite\\\" in self.switches:\\n return self.uninvite_org_or_player(event)\\n if \\\"action\\\" in self.switches:\\n return self.set_crisis_action(event)\\n if \\\"risk\\\" in self.switches:\\n return self.set_risk(event)\",\n \"def turn(self):\\n return self._turn\",\n \"def update_turn(self):\\n pass\",\n \"def _flip_turn(self):\\n self._turn = self._next_turn\\n return self._turn\",\n \"def switch(self) -> bool:\\n return bool(self.pressed & 0x1)\",\n \"async def get_switches(self):\\n return await self.get_states_by_tag_prefix(\\\"led\\\")\",\n \"def pswitchon(chan) :\\n s.phaseSwitching(True, chan)\",\n \"def get_on_turn(self):\\n return deepcopy(self.__on_turn)\",\n \"def read_switch(self):\\n return GPIO.input(SWITCH_PIN)\",\n \"def switches(self):\\n return {k:v for k, v in self._data.items() \\n if v[\\\"type\\\"] == \\\"SWITCH\\\"}\",\n \"def switch_turn(self):\\n if self.completed is not None:\\n self.current_turn = self.creator if self.current_turn != self.creator else self.opponent\\n self.save()\",\n \"def _get_turn(self):\\n raise NotImplementedError\",\n \"def change_mode(self):\\n return (self.mode + 1) % 2\",\n \"def keyed_rotor(self):\\n turnover = self.on_turnover()\\n self.inc_rotor_setting()\\n return turnover\",\n \"def internal_switch(self) -> s11.InternalSwitch:\\n return self.calobs.internal_switch\",\n \"def op_set(op):\\r\\n\\tglobal fo\\r\\n\\tif op == cat:\\r\\n\\t\\tfo = cat.who()\\r\\n\\t\\treturn cat_turn\\r\\n\\telif op == ninja:\\r\\n\\t\\tfo = ninja.who()\\r\\n\\t\\treturn ninja_turn\\r\\n\\telse:\\r\\n\\t\\tprint \\\"This is no one's turn!!!\\\"\",\n \"def TURN_OPTIONS() -> tuple:\\n return \\\"Hit me! (Draw another Card)\\\", \\\"Stand (End round, stop drawing)\\\"\",\n \"def list_switches(self):\\n return [x for x,y in self.devices.items() if y.device_type == \\\"Switch\\\"]\",\n \"def is_switch(G):\\n return False\",\n \"def get_all_switches(name):\\n return [False,False,False,False] #TODO Implement\",\n \"def switch_pseudocode(self, *args):\\n return _ida_hexrays.Hexrays_Hooks_switch_pseudocode(self, *args)\",\n \"def multipleSwitch(randomhouse):\\n\\n randomBatteries = randomhouse.possible_connections\\n\\n randomBatteries.sort(key=lambda x: x[1])\\n currentbattery = randomhouse.connection\\n for randomBattery in randomBatteries:\\n rb = randomBattery[0]\\n if rb != currentbattery:\\n if rb.capacity >= randomhouse.output:\\n switch(randomhouse, rb)\\n return\",\n \"def get_switch(self, conf, dpid):\\n\\t\\tpass\",\n \"def change_state_controller(self, switches):\\n index = self.__index\\n for dummy in range(len(switches)):\\t\\t\\n index = index ^ (2**(switches[dummy] - 1))\\n\\n for state in self.__next_state:\\n if any(control[0] == 'control' for control in state.__control_list) \\\\\\n and all(~(state.__index ^ index) & 2**(switches[dummy] - 1) for dummy in range(len(switches))):\\n state.__active_control = None\\n self.__active_control = None\\n return state\\n return None\",\n \"def swap(self, *args, **kwargs):\\n return self.switch(*args, **kwargs)\",\n \"def _next_turn(self):\\n return self.TURNS[self._turn is self.BLACK]\",\n \"def changeTurn(self):\\r\\n # Undoes clicked pieces and removes potential moves from the bar area\\r\\n for point in self.points:\\r\\n if point.isClicked():\\r\\n point.undoClick()\\r\\n if not self.bar[self.currentPlayer].isEmpty():\\r\\n self.undoPossibleBarMoves()\\r\\n \\r\\n # Changes by turn changing the currentPlayer index and updating all the \\r\\n # necessary board objects\\r\\n self.currentPlayer = (self.currentPlayer + 1) % 2\\r\\n self.dice.makeActive()\\r\\n for point in self.points:\\r\\n point.setActiveTurn()\\r\\n for bar in self.bar:\\r\\n bar.update()\\r\\n bar.setActiveTurn()\\r\\n self.message.resetText('It\\\\'s ' + self.getCurrentString() + '\\\\'s turn!', self.surface)\\r\\n self.turnchanger.setFillColor(self.getTurn())\\r\\n self.turnchanger.draw(self.surface)\\r\\n pygame.display.flip()\",\n \"def incTurn(self):\\n self.turnOn = (self.turnOn+1)%self.turns\",\n \"def init_turn(self):\\n self.before_turn_switches()\\n if self.battlefield.win is not None:\\n return\\n self.before_turn()\",\n \"def switch_pivot():\\n for piv_switcher in get_one_switcher():\\n piv_switcher.switch()\",\n \"def __process_xx_switch_arg(self, argument):\\n _method_name = '__process_xx_switch_arg'\\n\\n match = self.__xx_args_switch_regex.match(argument)\\n xarg = match.group(2)\\n on_or_off = match.group(1)\\n if on_or_off == '+':\\n on_or_off_text = 'on'\\n else:\\n on_or_off_text = 'off'\\n\\n if 'switch' not in self.__xx_args:\\n self.__xx_args['switch'] = OrderedDict()\\n self._logger.finer('WLSDPLY-08304', argument, xarg, on_or_off_text,\\n class_name=self._class_name, method_name=_method_name)\\n self.__xx_args['switch'][xarg] = on_or_off\",\n \"def turn():\\n \\n robottype = get_type()\\n if robottype == RobotType.PAWN:\\n pawn_turn()\\n else:\\n overlord_turn()\\n bytecode = get_bytecode()\",\n \"def turn():\\n \\n robottype = get_type()\\n if robottype == RobotType.PAWN:\\n pawn_turn()\\n else:\\n overlord_turn()\\n bytecode = get_bytecode()\",\n \"def swint(self) -> None:\",\n \"def check_switching_action(self):\\n current_switching = {}\\n for devices in self.switching_systems:\\n command = self.build_command(devices, \\\"get_closed_channels\\\")\\n switching = str(self.vcw.query(devices, command)).strip()\\n switching = self.pick_switch_response(devices, switching)\\n current_switching.update({devices[\\\"Device_name\\\"]: switching})\\n self.settings[\\\"settings\\\"][\\\"current_switching\\\"][\\n devices[\\\"Device_name\\\"]\\n ] = current_switching\\n return current_switching\",\n \"def get_turn(self):\\n return self._turn\",\n \"def switch_player(player):\\n if player == PLAYERX:\\n return PLAYERO\\n else:\\n return PLAYERX\",\n \"def switch_player(player):\\n if player == PLAYERX:\\n return PLAYERO\\n else:\\n return PLAYERX\",\n \"def _advance_turn(self):\\n\\n self.__turn_info['turn'] = ChessGame.BLACK if\\n self.__turn_info['turn'] == ChessGame.WHITE else ChessGame.WHITE\",\n \"def switchPlayer(self):\\n\\n \\n tmp = self.current\\n self.current = self.other\\n self.other = tmp\\n\\n self.topSelector.toggleActive()\\n self.bottomSelector.toggleActive()\",\n \"def begin_turn(self):\\n pass\",\n \"def turn(self, dir):\\n if dir.upper() == 'R':\\n if self.direction == 3:\\n self.direction = 0\\n else:\\n self.direction += 1\\n if dir.upper() == 'L':\\n if self.direction == 0:\\n self.direction = 3\\n else:\\n self.direction -= 1\",\n \"def lightning_turnon(self):\\n self.turnOn()\",\n \"def Steering(x1):\\n #RETURNS A LIST WITH THE RIGHTSPEED and LEFTSPEED for the appropriate turn\\n \\n LeftSp=127\\n RightSp=127\\n\\n if x1 < 320:\\n print \\\"I'm turning Left\\\"\\n LeftSp = 50\\n RightSp = 240\\n\\n if x1 > 320:\\n print \\\"I'm turning Right\\\"\\n LeftSp = 240\\n RightSp = 50\\n\\n return [RightSp, LeftSp]\",\n \"def test_switch_returns(self):\\n\\n #Player 1 and Player 2 are represented by 1 and -1\\n #Multiplying current_player by -1 will flip them\\n current_player = self.controller.game_state.player * -1\\n\\n #after running flip_current_player function in the controller,\\n # test current player\\n self.assertEqual(self.controller.game_state.flip_current_player(),\\n current_player)\",\n \"def computer_turn(self):\\r\\n\\r\\n print(\\r\\n '\\\\nTURN: Computer -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=')\\r\\n\\r\\n # Scan through memory to see if the computer already knows a matching pair\\r\\n loc1, loc2 = self.computer_scan_memory()\\r\\n\\r\\n if loc1 and loc2: # when there is a pair inside the computer's memory\\r\\n x1, y1 = loc1\\r\\n x2, y2 = loc2\\r\\n\\r\\n # Check point\\r\\n assert x1 != None and y1 != None, 'x1 or y1 is None type'\\r\\n assert x2 != None and y2 != None, 'x2 or y2 is None type'\\r\\n\\r\\n choice1_key = self.state[x1, y1]\\r\\n choice2_key = self.state[x2, y2]\\r\\n else: # when there is no pair inside the computer's memory\\r\\n # Randomly select one card then scan memory\\r\\n x1, y1 = self.computer_random_select()\\r\\n choice1_key = self.state[x1, y1]\\r\\n\\r\\n # Scan through memory\\r\\n loc = self.computer_scan_memory(pick=choice1_key)\\r\\n\\r\\n if loc and (x1 != loc[0] or y1 != loc[1]): # there is a common value card in the computer's memory\\r\\n x2, y2 = loc # and that memory is not the same as the first choice\\r\\n\\r\\n # Check point\\r\\n assert x2 != None and y2 != None, 'x2 or y2 is None type'\\r\\n\\r\\n choice2_key = self.state[x2, y2]\\r\\n else: # There is no common value in the computer's memory\\r\\n while True: # select a card different from the first choice\\r\\n x2, y2 = self.computer_random_select()\\r\\n if x2 != x1 or y2 != y1:\\r\\n break\\r\\n choice2_key = self.state[x2, y2]\\r\\n\\r\\n print('First choice: {0} ({1}, {2})'.format(self.deck[choice1_key], x1, y1))\\r\\n print('Second choice: {0} ({1}, {2})'.format(self.deck[choice2_key], x2, y2))\\r\\n\\r\\n # Check if it is a match or not\\r\\n if self.check_card(self.deck[choice1_key], self.deck[choice2_key]):\\r\\n print('MATCH')\\r\\n # Replace the corresponding cards in the remaining inventory and current state with -1\\r\\n self.remaining[choice1_key] = -1\\r\\n self.remaining[choice2_key] = -1\\r\\n self.state[x1, y1] = -1\\r\\n self.state[x2, y2] = -1\\r\\n self.computer_cards += 2 # the computer gets 2 cards\\r\\n self.bin.append([x1, y1]) # move the location of the card to the already-taken bin\\r\\n self.bin.append([x2, y2])\\r\\n self.forget_memory(choice1_key) # remove from computer's memory\\r\\n self.forget_memory(choice2_key)\\r\\n self.match = 0 # The computer will continue to choose cards\\r\\n else:\\r\\n print('NOT a match')\\r\\n # Add these cards to the computer's memory\\r\\n self.computer_memory[choice1_key] = [x1, y1]\\r\\n self.computer_memory[choice2_key] = [x2, y2]\\r\\n self.match = 1 # The player's turn\\r\",\n \"def __checkSwitch ( self, letter, value ):\\n\\n #-- 1 --\\n # [ if letter is a key in self.switchMap -> I\\n # else ->\\n # sys.stderr +:= (usage message) + (error message)\\n # stop execution ]\\n if not self.switchMap.has_key ( letter ):\\n usage ( self.switchSpecs, self.posSpecs,\\n \\\"No such switch: -%s\\\" % letter )\\n\\n #-- 2 --\\n if len(value) == 0:\\n self.switchMap[letter] = 1\\n else:\\n self.switchMap[letter] = value\",\n \"def change_player_turn(self):\\r\\n self._player_turn *= -1\",\n \"def test_switch(self):\\n\\n # Switch to the 'orig' data pipe.\\n pipes.switch('orig')\\n\\n # Test the current data pipe.\\n self.assertEqual(pipes.cdp_name(), 'orig')\\n\\n # Switch to the 'empty' data pipe.\\n pipes.switch('empty')\\n\\n # Test the current data pipe.\\n self.assertEqual(pipes.cdp_name(), 'empty')\",\n \"def turn(self):\\n return repr(self._turn)\",\n \"def turn(self, **kwargs: Any) -> None:\\n pass\",\n \"def get_limit_switch(name,switch):\\n return False #TODO Implement\",\n \"def on_turnover(self):\\n return True if self.rotor_setting in self.turnover_characters else False\",\n \"def _do_switch(self, runner, recipe, extra_info=''):\\n if 'batch_augments' in recipe:\\n self._switch_batch_augments(runner, recipe['batch_augments'])\\n runner.logger.info(f'Switch batch augments{extra_info}.')\\n\\n if 'train_pipeline' in recipe:\\n self._switch_train_pipeline(runner, recipe['train_pipeline'])\\n runner.logger.info(f'Switch train pipeline{extra_info}.')\\n\\n if 'loss' in recipe:\\n self._switch_loss(runner, recipe['loss'])\\n runner.logger.info(f'Switch loss{extra_info}.')\",\n \"def get_all_switch(self, conf):\\n\\t\\tpass\",\n \"def __init__(self):\\r\\n self.turn = 0\",\n \"def get_hw_switch_states(self):\\n hw_states = dict()\\n #k = self._kp.keypad()\\n k = \\\"\\\"\\n for number, sw in self.switches.items():\\n if number == k:\\n hw_states[number] = 1\\n else:\\n hw_states[number] = 0\\n return hw_states\",\n \"def set_turn(self):\\n if self.status == self.PLAYER_TURN:\\n return\\n self.status = self.PLAYER_TURN\\n self.client.send_player_turn(10)\",\n \"def RunTurn( lobound=1, hibound=20 ):\\n\\tpass\",\n \"def _action_toggle(self, flag):\\n if flag:\\n return {\\\"toggle\\\": \\\"ON\\\"}\\n else:\\n return {\\\"toggle\\\": \\\"OFF\\\"}\",\n \"def switch_player(current, player1, player2):\\r\\n if current == player1:\\r\\n return player2\\r\\n else:\\r\\n return player1\",\n \"def turn_clockwise(a):\\r\\n if a==\\\"N\\\":\\r\\n return \\\"E\\\"\\r\\n elif a==\\\"E\\\":\\r\\n return \\\"S\\\"\\r\\n elif a==\\\"S\\\":\\r\\n return \\\"W\\\"\\r\\n elif a==\\\"W\\\":\\r\\n return \\\"N\\\"\",\n \"def state(self) -> bool:\\n return self.get_state(self.entity_ids[\\\"switch\\\"])\",\n \"def switch(self, u_stuff, i_stuff):\\n\\n if self.sim_options['user_based']:\\n return u_stuff, i_stuff\\n else:\\n return i_stuff, u_stuff\",\n \"def __toggle(self,x):\\n\\t\\tif x == 1:\\n\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\treturn 1\",\n \"def next_turn(self):\\n if self.turn == BLUE and self.ai:\\n self.ai_turn = True\\n self.turn = RED\\n elif self.turn == BLUE:\\n self.turn = RED\\n else:\\n self.turn = BLUE\\n\\n self.selected_piece = None\\n self.selected_legal_moves = []\\n self.check_game_over()\",\n \"def switch(ind, status):\\n print(\\\"Switching :\\\", ind, \\\">>\\\", status == 'on')\\n GPIO.output(ind, status == 'on')\",\n \"def test_switch_channels(self):\\n\\t\\t# not available yet, experimental\\n\\t\\tpass\",\n \"def switch(cond, ift, iff):\",\n \"def __switch_restriction_set(self, name: str):\\n switch = {\\n \\\"user\\\": self.userRestrictionCategories,\\n \\\"role\\\": self.roleRestrictionCategories,\\n \\\"channel\\\": self.channelRestrictionCategories\\n }\\n return switch[name]\",\n \"def state(self):\\n # None will return False\\n return bool(self.switch.value)\",\n \"def update_player_turn(self):\\n\\n if self.get_player_turn() != 'BLUE':\\n\\n self._player_turn = 'BLUE'\\n\\n else:\\n\\n self._player_turn = 'RED'\",\n \"def doTurn(self, gamestate):\\n raise NotImplementedError(\\\"Please Implement this method\\\")\",\n \"def play_turn(self, cur_board):\\n pass\",\n \"def turn_left(self):\\n pass\",\n \"def _get_switch(self, switch):\\n switch = self.switch_by_label(switch)\\n id = self.switches[switch.label].id\\n # make sure that the serial port is open\\n self.assure_serial()\\n # create command for the arduino and send it\\n input_string = 'r' + str(id[0]) + str(id[1])\\n self.serial.write(input_string.encode('ascii'))\\n time.sleep(self.READ_DELAY)\\n # retrieve result\\n result = self.serial.readline().decode().rstrip()\\n time.sleep(self.READ_DELAY)\\n # store the indicators to the switch\\n switch.indicators = (int(result[0]), int(result[1]))\\n # raise error if the indicators show an error\\n if switch.state is None:\\n raise SwitchError(\\\"Reading the state was unsuccessful: Indicators \\\"\\n f\\\"of the switch show {switch.indicators}.\\\")\\n return switch.state\",\n \"def turn_right(self):\\n pass\",\n \"def take_turn(self):\\r\\n self._choose_best_option()\\r\\n self._do_draw()\",\n \"def turn(self, turnDir):\\n if turnDir == 0: # left\\n if self.dir == \\\"N\\\":\\n self.dir = \\\"W\\\"\\n elif self.dir == \\\"W\\\":\\n self.dir = \\\"S\\\"\\n elif self.dir == \\\"S\\\":\\n self.dir = \\\"E\\\"\\n elif self.dir == \\\"E\\\":\\n self.dir = \\\"N\\\"\\n else:\\n raise ValueError(\\\"invalid dir %s\\\" % self.dir)\\n elif turnDir == 1: # right\\n if self.dir == \\\"N\\\":\\n self.dir = \\\"E\\\"\\n elif self.dir == \\\"E\\\":\\n self.dir = \\\"S\\\"\\n elif self.dir == \\\"S\\\":\\n self.dir = \\\"W\\\"\\n elif self.dir == \\\"W\\\":\\n self.dir = \\\"N\\\"\\n else:\\n raise ValueError(\\\"invalid dir %s\\\" % self.dir)\\n else:\\n raise ValueError(\\\"invalid turnDir %d\\\" % turnDir)\",\n \"def switch_to_state(self, state):\\n self.switch_state = state\",\n \"def switchPlayer():\\n\\n #escrever o condicional do modo de jogo.\\n if (modoDeJogo == \\\"1\\\" or modoDeJogo == 1):\\n quemJoga = player[1]\\n\\n if (player[0] == quemJoga):\\n quemJoga = \\\"pc\\\"\\n else: \\n quemJoga = player[0]\\n\\n return quemJoga #quemComeca\\n else:\\n quemJoga = player[2]\\n\\n if (player[0] == quemJoga):\\n quemJoga = player[1]\\n else: \\n quemJoga = player[0]\\n \\n return quemJoga #quemComeca\",\n \"def get_current_turn(self):\\n return self._turn\",\n \"def switcher_secret(self):\\n return self._switcher_secret\",\n \"def get_turn(self):\\n return self.__turn_info['turn']\",\n \"def __le__(self, *args):\\n return _ida_hexrays.cswitch_t___le__(self, *args)\",\n \"def _sense_and_act(self):\\n pass\",\n \"def get_switch_stringlist(self):\\n return text_switch\",\n \"def switch_to_main(self):\\n return main.switch()\",\n \"def tempo_mode_switch(event):\\n value = gremlin.actions.Value(event.is_pressed)\\n tempo_mode_switch_container(event, value)\",\n \"def the(target: Target) -> \\\"SwitchTo\\\":\\n return SwitchTo(target)\",\n \"def getTurn(self):\\r\\n return self.players[self.getCurrentPlayer()].getColor()\",\n \"def switched_with(self):\\n return self\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68772453","0.6781878","0.6612293","0.65573156","0.6287292","0.62218165","0.6109247","0.6078702","0.6077556","0.5993894","0.5986418","0.59811705","0.5940087","0.59389865","0.59333205","0.59090734","0.5887211","0.58654636","0.5862874","0.58553296","0.5841399","0.5838009","0.5806558","0.5773462","0.57688683","0.57571375","0.57520115","0.5721215","0.57196224","0.5717366","0.57122654","0.5701516","0.5687098","0.56653416","0.56640774","0.56614393","0.5652782","0.5649384","0.5645647","0.56424916","0.56424916","0.5641763","0.562853","0.56163853","0.5602817","0.5602817","0.5593657","0.5585743","0.5580623","0.5570371","0.55684465","0.55560786","0.5554986","0.55540043","0.5540065","0.5536568","0.55251193","0.5517301","0.5509242","0.5506791","0.55039185","0.54923385","0.54886043","0.5488171","0.5486333","0.5484282","0.5483648","0.5464573","0.5445681","0.5441241","0.54407334","0.543451","0.54331815","0.5423839","0.54155165","0.54118997","0.5401977","0.53860795","0.5378013","0.53747207","0.5373839","0.5367682","0.5359754","0.53596544","0.5353335","0.53507483","0.5350191","0.5343651","0.53433496","0.53419024","0.53406274","0.53352916","0.5333471","0.53216046","0.53169477","0.53163195","0.53147125","0.5307582","0.5299107","0.5297001"],"string":"[\n \"0.68772453\",\n \"0.6781878\",\n \"0.6612293\",\n \"0.65573156\",\n \"0.6287292\",\n \"0.62218165\",\n \"0.6109247\",\n \"0.6078702\",\n \"0.6077556\",\n \"0.5993894\",\n \"0.5986418\",\n \"0.59811705\",\n \"0.5940087\",\n \"0.59389865\",\n \"0.59333205\",\n \"0.59090734\",\n \"0.5887211\",\n \"0.58654636\",\n \"0.5862874\",\n \"0.58553296\",\n \"0.5841399\",\n \"0.5838009\",\n \"0.5806558\",\n \"0.5773462\",\n \"0.57688683\",\n \"0.57571375\",\n \"0.57520115\",\n \"0.5721215\",\n \"0.57196224\",\n \"0.5717366\",\n \"0.57122654\",\n \"0.5701516\",\n \"0.5687098\",\n \"0.56653416\",\n \"0.56640774\",\n \"0.56614393\",\n \"0.5652782\",\n \"0.5649384\",\n \"0.5645647\",\n \"0.56424916\",\n \"0.56424916\",\n \"0.5641763\",\n \"0.562853\",\n \"0.56163853\",\n \"0.5602817\",\n \"0.5602817\",\n \"0.5593657\",\n \"0.5585743\",\n \"0.5580623\",\n \"0.5570371\",\n \"0.55684465\",\n \"0.55560786\",\n \"0.5554986\",\n \"0.55540043\",\n \"0.5540065\",\n \"0.5536568\",\n \"0.55251193\",\n \"0.5517301\",\n \"0.5509242\",\n \"0.5506791\",\n \"0.55039185\",\n \"0.54923385\",\n \"0.54886043\",\n \"0.5488171\",\n \"0.5486333\",\n \"0.5484282\",\n \"0.5483648\",\n \"0.5464573\",\n \"0.5445681\",\n \"0.5441241\",\n \"0.54407334\",\n \"0.543451\",\n \"0.54331815\",\n \"0.5423839\",\n \"0.54155165\",\n \"0.54118997\",\n \"0.5401977\",\n \"0.53860795\",\n \"0.5378013\",\n \"0.53747207\",\n \"0.5373839\",\n \"0.5367682\",\n \"0.5359754\",\n \"0.53596544\",\n \"0.5353335\",\n \"0.53507483\",\n \"0.5350191\",\n \"0.5343651\",\n \"0.53433496\",\n \"0.53419024\",\n \"0.53406274\",\n \"0.53352916\",\n \"0.5333471\",\n \"0.53216046\",\n \"0.53169477\",\n \"0.53163195\",\n \"0.53147125\",\n \"0.5307582\",\n \"0.5299107\",\n \"0.5297001\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5775022"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":2667,"cells":{"query":{"kind":"string","value":"Trys to make a move. If the move is successful returns 1. If the move string is not able to interpreted correctly or if that place is already full the move fails and the function returns 0"},"document":{"kind":"string","value":"def makeMove(self, move):\n\t\ttry:\n\t\t\tif (self.board[int(move) - 1] is Piece.BLANK):\n\t\t\t\tself.board[int(move) - 1] = self.current\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0\n\t\texcept:\n\t\t\treturn 0"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def move_valid(move):\n return True","def handle_move(self, move_string):\n def map_move(move):\n col = int(ascii_lowercase.find(move[0])) + 1 # dummy col\n row = int(move[1:])\n # if not 0 < col <= game[\"board_width\"]:\n # raise ValueError('bad coord; invalid col in ' + coord)\n # if not 0 < row <= game[\"board_height\"]:\n # raise ValueError('bad coord; invalid row in ' + coord)\n return row*(self.rules[\"row_len\"]) + col\n move = list(map(map_move,move_string.split(' ')))\n self.turn[\"board\"][move[0]].make_move(*move[1:])\n self.turn[\"half_move_clock\"] += 1\n if self.turn[\"active_player\"] == 1:\n self.turn[\"full_move_clock\"] += 1\n self.turn[\"active_player\"] = (self.turn[\"active_player\"] + 1) % 2\n # self.turn[\"board\"][move_start].make_move(move_end)","def test_check_move_with_valid(self):\n board = [\n [\" \"] * 6,\n [\" \"] * 6,\n [\" \"] * 6,\n [\" \"] * 6,\n [\"\\u25cb\"] + [\" \"] * 5,\n [\" \"] * 6,\n [\" \"] * 6\n ]\n valid = self.game.check_move(board, 3)\n self.assertTrue(valid)","def attempt_move(self, move_input):\n # handle undo move\n if move_input == ['UN', 0, 'UN']:\n self.undo_move()\n return True\n\n # handle stock draw Special Action first\n if move_input == ['S0', 0, 'S0']:\n self.save_board_state()\n self.stock.deal_to_wp(self.wp)\n self.moves += 1\n return True\n\n # handle basic cases\n if len(move_input) != 3:\n return False\n if move_input[0] not in self.move_dict or move_input[2] not in self.move_dict:\n return False\n if type(move_input[1]) is not int:\n return False\n if move_input[2] == \"W0\":\n return False\n\n orig_pile = self.move_dict[move_input[0]]\n orig_ind = move_input[1]\n dest_pile = self.move_dict[move_input[2]]\n if orig_ind >= orig_pile.get_length():\n return False\n\n # handle flip tableau card Special Action\n if move_input[0][0] == 'T' and orig_pile == dest_pile and orig_ind == 0:\n orig_pile.reveal_top_card()\n\n # basic conditions have been met\n adj_ind = orig_pile.get_length() - orig_ind - 1\n if orig_pile.is_valid_retrieval(orig_ind):\n self.save_board_state()\n move_pile = orig_pile.remove_cards(orig_ind + 1)\n if dest_pile.is_valid_placement(move_pile):\n dest_pile.merge_pile(move_pile)\n if move_input[0][0] == 'T' and self.auto_flip_tab:\n orig_pile.reveal_top_card()\n self.moves += 1\n return True\n else:\n orig_pile.merge_pile(move_pile)\n self.board_states.pop()\n return False\n return False","def make_move(move):\n global manatee_pos\n global hyacinths\n global hyacinth_pos\n\n # Ends the program if movement is out of bounds\n if move == (0, 0):\n return None\n new_pos = (manatee_pos[0] + move[0], manatee_pos[1] + move[1])\n if new_pos[0] < 0 or new_pos[0] >= len(map):\n return None\n if new_pos[1] < 0 or new_pos[1] >= len(map[new_pos[0]]):\n return None\n\n entity = map[new_pos[0]][new_pos[1]]\n if entity == \"#\" or entity == \"G\":\n # Runs if movement is impossible\n return None\n if entity == \" \" or entity == \".\":\n # Runs if normal movement is possible\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n return None\n if entity == \"O\":\n # Runs if manatee wins game\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n return \"win\"\n if entity == \"\\\\\":\n # Runs if manatee eats hyacinth\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n hyacinths += 1\n if len(hyacinth_pos) == hyacinths:\n map[grate_pos[0]][grate_pos[1]] = \"O\"\n return None\n if entity == \"*\":\n # Checks if manatee can push boat\n if move[0] == 0:\n new_boat_pos = (new_pos[0] + move[0], new_pos[1] + move[1])\n if new_boat_pos[0] < 0 or new_boat_pos[0] >= len(map):\n return None\n if new_boat_pos[1] < 0 \\\n or new_boat_pos[1] >= len(map[new_boat_pos[0]]):\n return None\n if map[new_boat_pos[0]][new_boat_pos[1]] == \" \":\n map[new_boat_pos[0]][new_boat_pos[1]] = \"*\"\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n return None\n return None","def check_move(self, move):\n\n if str(move) in self.moves_made:\n return False\n return True","def CheckMove(self,move):\n\t\tif(move=='w'):\n\t\t\tif(self.x==0):\n\t\t\t\treturn 0\n\t\t\treturn 1\n\t\telif(move=='s'):\n\t\t\tif(self.x==15):\n\t\t\t\treturn 0\n\t\t\treturn 1\n\t\telif(move=='d'):\n\t\t\tif(self.y==35):\n\t\t\t\treturn 0\n\t\t\treturn 1\n\t\telif(move=='a'):\n\t\t\tif(self.y==0):\n\t\t\t\treturn 0\n\t\t\treturn 1","def check4move(st, selected_unit, direction):\n return 1","def test_check_move_with_barely_valid(self):\n board = [\n [\" \"] * 6,\n [\" \"] * 6,\n [\" \"] * 6,\n [\" \"] * 6,\n [\"\\u25cb\"] * 5 + [\" \"],\n [\" \"] * 6,\n [\" \"] * 6\n ]\n valid = self.game.check_move(board, 4)\n self.assertTrue(valid)","def make_legal_move(move, board, start, end, move_number, en_passant_square, king_w, king_b, rook_w_l, rook_w_r,\n rook_b_l,\n rook_b_r, promotion_piece):\n # this function should have essentially what i had in main at first without making the move\n test_board = board.copy()\n valid_move = False\n piece = find_piece(board, start)\n end_piece = find_piece(board, end)\n\n if switch_player(move_number): # for whites move\n if (65 <= ord(piece) <= 90) and (validate_move(board, end, move_number)):\n if piece == \"P\":\n if pawn(board, start, end, move_number):\n if end[0] == 8:\n promotion(test_board, start, end, promotion_piece)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n promotion(board, start, end, promotion_piece)\n return True\n else:\n valid_move = False\n else:\n valid_move = True\n else:\n if can_en_passant(board, en_passant_square, end, start, move_number):\n execute_enpassant(test_board, start, end, move_number)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n execute_enpassant(board, start, end, move_number)\n return True\n else:\n return False\n elif piece == \"K\":\n if king(start, end):\n if controlled_squares(board, move_number, end):\n valid_move = True\n elif piece == \"N\":\n if knight(start, end):\n valid_move = True\n elif piece == \"B\":\n if bishop(board, start, end):\n valid_move = True\n elif piece == \"Q\":\n if queen(board, start, end):\n valid_move = True\n elif piece == \"R\":\n if rook(board, start, end):\n valid_move = True\n else:\n valid_move = False\n if valid_move:\n update_board(test_board, start, end)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n update_board(board, start, end)\n return True\n else:\n retract_move(test_board, start, end, end_piece)\n print(\"Illegal Move\")\n return False\n if not switch_player(move_number):\n if (97 <= ord(piece) <= 122) and validate_move(board, end, move_number):\n if piece == \"p\":\n if pawn(board, start, end, move_number):\n if end[0] == 1:\n promotion(test_board, start, end, promotion_piece)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n promotion(board, start, end, promotion_piece)\n return True\n else:\n valid_move = False\n else:\n valid_move = True\n else:\n if can_en_passant(board, en_passant_square, end, start, move_number):\n execute_enpassant(test_board, start, end, move_number)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n execute_enpassant(board, start, end, move_number)\n return True\n else:\n valid_move = False\n elif piece == \"k\":\n if king(start, end):\n if controlled_squares(board, move_number, end):\n valid_move = True\n elif piece == \"n\":\n if knight(start, end):\n valid_move = True\n elif piece == \"b\":\n if bishop(board, start, end):\n valid_move = True\n elif piece == \"q\":\n if queen(board, start, end):\n valid_move = True\n elif piece == \"r\":\n if rook(board, start, end):\n valid_move = True\n else:\n valid_move = False\n if valid_move:\n update_board(test_board, start, end)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n update_board(board, start, end)\n return True\n else:\n retract_move(test_board, start, end, end_piece)\n print(\"Illegal Move\")\n return False","def validMove(move):\r\n\r\n\tglobal tile1, tile2, tile3, tile4, tile5, tile6, tile7, tile8, tile9\r\n\r\n\ta=eval(\"tile\"+str(move)+\"==0\")\r\n\treturn a","def check_one_move(self):\n count_moves = 0\n one_move = None\n for direction in self.directions:\n if self.valid_move(self.loc, direction):\n count_moves += 1\n one_move = direction\n if count_moves != 1:\n return None\n return one_move","def make_move(self, board: Board) -> int:\n\n move = input()\n move = int(move)\n\n while move not in board.get_valid_moves():\n print(\"That is not a valid move\")\n move = input()\n move = int(move)\n\n return move","def test_check_move_with_invalid(self):\n board = [\n [\" \"] * 6,\n [\" \"] * 6,\n [\" \"] * 6,\n [\" \"] * 6,\n [\"\\u25cb\"] * 6,\n [\" \"] * 6,\n [\" \"] * 6\n ]\n valid = self.game.check_move(board, 4)\n self.assertFalse(valid)","def takeNaiveMove():\r\n\tnotFound=True\r\n\twhile notFound:\r\n\t\tmove=random.randint(1,9)\r\n\t\tif validMove(move):\r\n\t\t\tnotFound=False\r\n\treturn move","def valid_move(self, player, move):\n return (True)","def is_valid_move(state, move):\n row, col = move\n if row not in [1, 2, 3] or col not in [1, 2, 3]:\n print(\"Invalid move! Specify correct game square!\")\n return False\n if state[row-1][col-1] != '_':\n print('Invalid move! Place your marker on a free square!')\n return False\n return True","def getMove(player,first_move=False):\n while True: \n move = raw_input(\"MAKE YOUR MOVE: \").upper()\n\n # handle special commands\n if move == \"QUIT\" or move == \"Q\":\n wannaQuit()\n continue\n elif move == \"QUIT!\" or move == \"Q!\":\n if SAY_PROC:\n SAY_PROC.terminate()\n sys.exit()\n continue\n elif move == \"HELP\" or move == \"H\":\n help()\n continue\n elif move == \"HELP!\" or move == \"H!\":\n say(helpString())\n continue\n elif move == \"SHUTUP!\" or move == \"S!\":\n shutUp(fuck=True)\n continue\n elif move == \"SHUTUP\" or move == \"S\":\n shutUp()\n continue\n elif move == \"BOARD\" or move == \"B\":\n printBoard()\n continue\n elif move == \"CLEAR\" or move == \"C\": \n clearTerminal()\n printBoard()\n continue\n elif move == \"CLEAR!\" or move == \"C!\": # TODO board -> clear, you end up with a half drawn new board. clear again fixes this\n clearTerminalAndBuffer()\n printBoard()\n continue\n elif move == \"PASS\" or move == \"P\": \n if wannaPass():\n break\n else:\n continue\n elif move == \"PASS!\" or move == \"P!\": \n break\n \n # mostly used to catch blank lines or me typing ASFADS like an asshole\n if len(move) < 7:\n print \"That's too short to be a command.\"\n continue\n\n parts=move.split(\":\")\n if len(parts) != 3:\n print \"Can't find all the parts of the move command. Maybe you're missing/have too many \\\":\\\"?\"\n continue\n\n for item in parts:\n if len(item) == 0:\n print \"Found a blank command. Maybe you left in an extra \\\":\\\"?\"\n continue\n\n coords = parts[0].replace(\" \",\"\") # incase of space inbetween file and rank\n direction = parts[1].strip()\n word = parts[2].strip()\n\n if not coords[0].isalpha():\n print \"I don't know where to put your word (Bad file coord).\"\n continue\n\n if not coords[1:].isdigit():\n print \"I don't know where to put your word (Bad rank coord).\"\n continue\n\n x = gridCharToInt(coords[0])\n y = int(coords[1:]) - 1\n if 14 < x < 0 or 14 < y < 0:\n print \"Those aren't coords on the board. Valid Files are from A-O, valid Ranks are 1-15.\"\n continue\n\n if first_move:\n if x != 7 or y != 7:\n print \"The first move must start from the center (H8).\"\n continue\n\n #compact that command\n if direction == \"ACROSS\":\n direction = \"A\"\n elif direction == \"DOWN\":\n direction = \"D\"\n if direction != \"A\" and direction !=\"D\":\n print \"I don't know where to put your word (Across or Down?).\"\n continue\n \n score,placed_tiles = checkWords(x,y,direction,word,first_move)\n if not score: #error reporting is handling in check words\n continue\n else:\n for tile in placed_tiles:\n if not tile in player.rack:\n print \"You don't have the tiles to play that!\"\n continue\n player.rack.remove(tile)\n print player.name+\" scored \"+str(score)+\" on that last play!\"\n player.score+=score\n for tile in placed_tiles:\n player.rack.remove(tile)\n break #YAY","def move_check(self):\r\n \r\n if not self.run:\r\n return False\r\n \r\n if self.get_num_legal_moves() == 0:\r\n SlTrace.lg(\"NO more legal moves!\", \"nolegalmoves\")\r\n ###return False \r\n \r\n if self.new_move:\r\n self.announce_player(\"start_move\")\r\n if SlTrace.trace(\"selected\"):\r\n self.list_selected(\"After start_move\")\r\n self.new_move = False\r\n player = self.get_player()\r\n if player is None:\r\n return False\r\n \r\n return True","def checkMove(guess, xPos, yPos):\n\n\t# Return 0 if x position or y position are not valid\n\tif(xPos not in range(0, 5) or yPos not in range(0, 5)):\n\t\treturn 0\n\n\t# Return 0 f the guessed position is not water\n\tif(guess[yPos][xPos] != \"~\"):\n\t\treturn 0\n\n\treturn 1","def is_valid(self, move):\r\n return move > 10 and move < 89","def validate_move(move):\n if move[0] in cc.VALID_RANKS and move[1] in cc.VALID_RANKS:\n valid = True\n else:\n valid = False\n return valid","def make_move(self, move: Tuple[int, int]) -> MoveError:\n\n # Make sure our move is going to be valid\n if self.is_winner():\n return MoveError.GAME_WON\n\n elif move[0] >= self._board_size or move[0] < 0 or move[1] >= self._board_size or move[1] < 0:\n return MoveError.OUT_OF_RANGE\n\n elif self._board[move[1]][move[0]] != self.NEUTRAL_PLAYER:\n return MoveError.TAKEN\n\n # If we make it to here, then it is valid to make the move\n self._board[move[1]][move[0]] = self._players[self._current_player]\n self._number_of_moves = self._number_of_moves + 1\n self._last_move = move\n\n self._check_for_winner()\n\n # Only change who the player is if we didn't get a winner,\n # otherwise the final board's color will be wrong\n if not self.is_winner():\n self._current_player = (self._current_player + 1) % len(self._players)\n\n return MoveError.OKAY","def is_valid_move(self, move):\n if type(move) == str:\n move = int(move)\n\n return move in self.get_possible_moves()","def move_invalid():\n check50.run(run_command).stdin(\"EAST\").stdout(\"Invalid command.\")","def make_move(state: str, section_num: int, move: str) -> str:\n if move == wf.CHECK:\n check_result = wf.check_section(state, section_num)\n if check_result:\n print('The section is correct')\n else:\n print('The section is incorrect')\n else:\n state = wf.change_state(state, section_num, move) \n return state","def test_verify_move(self):\n self._verify([self.applied_commands['move']])","def check_move(self, x, y):\n try:\n return self.map[self.y+y][self.x+x] == \" \" or [self.x+x, self.y+y] == self.end_pos\n except IndexError:\n return False","def _get_move_result(self, unlocked_before_move : bool, err = None):\n if err:\n return Moveresult.INVALID\n elif self.current_turn.entity in self.game_state.get_completed_characters():\n return Moveresult.EXIT\n elif self.game_state.is_character_expelled(self.current_turn.entity):\n return Moveresult.EJECT\n elif self.game_state.is_current_level_unlocked() and not unlocked_before_move:\n return Moveresult.KEY\n else:\n return Moveresult.OK","def human_move(board,player):\r\n \r\n s = input(\"Please input a legal move in a format of \\\"current_position-landing_position\\\", if the move is cantering or plain. In case of a capturing move, follow \\\"current_position-landing_position-enemy piece\\\": \")\r\n move = s.split('-')\r\n legal = legal_moves(board,player)\r\n execution(move,legal,board,player)","def make_move(self, start, end):\r\n start_pos = self.parse_pos(start) # Start and end position are lists that contain column and row\r\n end_pos = self.parse_pos(end)\r\n\r\n start_row = start_pos[0] # Position of row and columns are assigned to variables\r\n start_col = start_pos[1]\r\n end_row = end_pos[0]\r\n end_col = end_pos[1]\r\n\r\n board = self._board.get_board()\r\n start_piece = board[start_row][start_col].get_piece()\r\n end_piece = board[end_row][end_col].get_piece()\r\n\r\n\r\n # If there is no piece to be moved or game is over or piece is to be moved to its original location\r\n if start_piece is None or self._game_state != \"UNFINISHED\"\\\r\n or (start_row == end_row and start_col == end_col):\r\n return False\r\n\r\n start_piece_id = start_piece.get_player_id() # Contains the player id associated with the piece\r\n end_piece_player_id = None\r\n if end_piece is not None: # Executes if end piece contains a piece object\r\n end_piece_player_id = end_piece.get_player_id()\r\n\r\n # If Red's turn\r\n if self._player_turn == 1:\r\n if start_piece_id != 'r': # If red moves a black piece\r\n return False\r\n if start_piece.is_legal_move(start, end, start_piece, end_piece_player_id, board) : # Checks the legal move conditions\r\n if self.move_piece(start, end): # Returns False if move is invalid\r\n # Checks if move violates flying general and puts self in check\r\n if self.is_not_flying_general() is True and self.is_in_check(\"red\") is False:\r\n self.change_player_turn()\r\n self.is_in_checkmate()\r\n return True\r\n else: # Reverses the move if violates flying general rule\r\n self.reverse_move(start, end, board,end_piece_player_id, end_piece)\r\n return False\r\n\r\n else:\r\n return False\r\n else:\r\n return False\r\n\r\n # If Black's turn\r\n elif self._player_turn == -1:\r\n if start_piece_id != 'b': # If black moves a red piece\r\n return False\r\n if start_piece.is_legal_move(start, end, start_piece, end_piece_player_id, board): # Checks the legal move conditions\r\n if self.move_piece(start, end): # Returns False if move is invalid\r\n if self.is_not_flying_general() is True and self.is_in_check(\"black\") is False:\r\n self.change_player_turn()\r\n self.is_in_checkmate()\r\n return True\r\n else: # Reverses the move if violates flying general rule\r\n self.reverse_move(start, end, board, end_piece_player_id, end_piece)\r\n return False\r\n else:\r\n return False\r\n else:\r\n return False","def make_move(self, move, check_valid=True):\r\n self.board[move.sr][move.sc] = \"--\"\r\n self.board[move.er][move.ec] = move.pieceMoved\r\n self.moveLog.append(move)\r\n self.turn_white = not self.turn_white\r\n if move.pieceMoved == 'wk':\r\n self.wKingPos = (move.er, move.ec)\r\n elif move.pieceMoved == 'bk':\r\n self.bKingPos = (move.er, move.ec)\r\n\r\n if move.isEnpassantMove:\r\n self.board[move.sr][move.ec] = \"--\"\r\n\r\n if move.pieceMoved[1] == 'p' and abs(move.sr - move.er) == 2:\r\n self.enpas_pos = ((move.er + move.sr) // 2, move.ec)\r\n else:\r\n self.enpas_pos = ()\r\n\r\n if move.isPawnPromotion and not check_valid:\r\n promoted_piece = \"a\"\r\n while promoted_piece not in ('q', 'r', 'b', 'n'):\r\n promoted_piece = input(\"Promote to q, r, b, or n: \")\r\n self.board[move.er][move.ec] = move.pieceMoved[0] + promoted_piece\r\n\r\n # castle\r\n if move.castle:\r\n if move.ec - move.sc == 2:\r\n self.board[move.er][move.ec - 1] = self.board[move.er][move.ec + 1]\r\n self.board[move.er][move.ec + 1] = '--'\r\n else:\r\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 2]\r\n self.board[move.er][move.ec - 2] = '--'\r\n\r\n # castle rights on rook, king move\r\n self.update_castle_rights(move)\r\n self.castleRightsLog.append(CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\r\n self.cr_castle_r.wqs, self.cr_castle_r.bqs))","def valid_move(self, row, col):\n if not self._game_over:\n i_row, i_col = row-1, col-1\n #i_row and i_col wil be used to index the board (hence the i)\n (valid, flip_lst) = self._valid_placement(i_row, i_col)\n #print(\"FOR TESTING. Tiles Flipped: \", flip_lst)\n \n if valid:\n #Big Change: You decided to make determining validity\n # and flipping separate operations\n self._flip(i_row, i_col, flip_lst)\n else:\n print(\"\\nPlease enter a valid move!\")\n return False\n\n if self._board_is_full():\n self._game_over = True\n self._set_winner() \n \n self._switch_turn(self._turn)\n if not self._valid_move_exists(): #Check if the other player has any valid moves\n print(\"\\nNo valid moves exist for {0}. {0}'s turn has been skipped\".format(self._turn))\n self._switch_turn(self._turn) #Switch turn back to player before skip was determined\n if not self._valid_move_exists(): #Check if the other player has any valid moves\n print(\"No valid moves exist for {0}. {0}'s turn has been skipped\".format(self._turn))\n print(\"No moves exist for either player. GAME OVER\")\n self._game_over = True\n self._set_winner()\n return False\n\n return True\n elif self._game_over:\n print(\"The game is over. No more moves can be made!\")\n #TODO: Replace this^ with an exception later?\n return False","def make_move(self, move):\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \"\" or int(move) % 2 == 0:\n raise ValueError(\"{} is not a valid move for {}\".format(move, self.board))\n DotsAndBoxesState.score1 += self.check_score(move)\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \"\\u001b[0m\"\n self.turn = get_opponent(self.turn) #change into another player's trun","def test_no_moves(self):\n game = self.ending(['bw..wwww'], 8, 1)\n game.man_move(0, 2)\n self.assertEqual(game.finish_state,\n (400, game.first_player, 'No moves'))","def move(self, initial_pos, final_pos):\n\n ops = [ self.op_move_north, self.op_move_nwest, self.op_move_neast,\n self.op_jump_north, self.op_jump_nwest, self.op_jump_neast,\n self.op_capture_north, self.op_capture_east, self.op_capture_west,\n self.op_capture_nwest, self.op_capture_neast, self.op_add_piece\n ]\n\n # Check if the final position is empty\n if final_pos and self.board.get_element(*final_pos) != self.EMPTY:\n return False\n\n # Check if the intial position is a valid start piece\n if initial_pos not in self.next_pieces:\n return False\n\n # Test for add piece operation\n if self.next_move == self.ADDPIECE_1 or self.next_move == self.ADDPIECE_2:\n res = self.op_add_piece(initial_pos)\n if type(res) != type(False):\n return res\n\n # Test for other operations\n for op in ops:\n res = op(initial_pos)\n\n if type(res) != type(False) and (res.last_piece == final_pos):\n return res\n\n return False","def move(self, piece):\n\n if list(piece) in self.find_moves():\n self.block[tuple( self.find_free() )] = self.block[tuple(piece)]\n self.block[tuple(piece)] = 0\n return \"success\"\n else:\n return \"error\"","def get_move(self, board):\n\n valid_moves = [move for move in board.legal_moves]\n is_valid_move = False\n while not is_valid_move:\n move = input(\"Enter a valid move in uci format: \").lower()\n if len(move) == 4 or len(move) == 5:\n try:\n player_move = chess.Move.from_uci(move)\n\n if board.is_legal(player_move):\n try:\n board.push(player_move)\n return player_move\n except:\n print(\"invalid move...\")\n else:\n print(\"invalid move...\")\n except:\n print(\"invalid move...\")\n else:\n print(\"invalid move...\")","def _move(self, start: (int, int), dest: (int, int), extra_info=''):\n moving_piece = self.board[start[0]][start[1]]\n end_piece = self.board[dest[0]][dest[1]]\n\n # Check if the move is valid\n possible_moves = self.get_all_moves()\n if State.convert_to_EAN(start, dest, extra_info) not in possible_moves:\n return False\n\n # Invalidate castling\n self._invalidate_castles()\n\n # Update half turn counters since capture (updates 50 move draw)\n # reset on capture, which is when the destination piece is a different color\n self.half_moves += 1\n if not State._is_same_color(moving_piece, end_piece):\n self.half_moves = 0\n\n # Update full moves after black's turn\n if not self.white_to_move:\n self.full_moves += 1\n\n # Update the board to reflect the move\n self._update_board(start, dest, extra_info)\n\n # Update move history TODO\n # Detect three move repetition TODO\n\n # Update whose turn it is\n self.white_to_move = not self.white_to_move","def make_move(self, position_from, position_to):\n\n # Returns False if position_from or position_to are not an actual position\n # ranging from 'a1' to 'i10' for columns 'a' through 'i' and rows '1' \n # through '10'.\n if not isinstance(position_to, str) or\\\n not isinstance(position_from,str) or\\\n len(position_to) > 3 or len(position_to) < 2 or\\\n len(position_from) > 3 or len(position_from) < 2:\n\n return False \n\n # Returns False if game has been won.\n elif self.get_game_state() != 'UNFINISHED':\n\n return False\n\n position_to = position_to.lower()\n position_from = position_from.lower()\n current_player = self.get_player_turn()\n\n # Returns False if move is not legal based on Janggi game rules.\n if self.legal_move(position_to, position_from) is not True:\n\n return False\n\n # Returns True if current player wishes to pass on their move.\n # Any position on the board will suffice, including a General's position.\n # Tested in legal_move to exit legal_move safely. The second test for pass\n # updates player turn and returns True, so long as the player is not\n # currently in check.\n if position_to == position_from:\n\n if self.is_in_check(current_player) is True:\n \n return False\n\n self.update_player_turn()\n return True\n\n # Move is valid. Adjusted board, update potential moves for all GamePieces,\n # test checkmate (change game_state if True), update player turn, and\n # return True.\n else:\n\n game_piece_object = self.get_game_piece_object_at_position(position_from)\n self.adjust_board(game_piece_object, position_to, position_from)\n self.update_potential_moves()\n \n # Sets the General's Board position attribute if the position has\n # changed.\n if isinstance(game_piece_object, General):\n\n if current_player == 'BLUE':\n\n self.set_general_position_blue(position_to)\n\n else:\n\n self.set_general_position_red(position_to)\n\n # Sets game_state to the player who won if checkmate is detected.\n if self.is_checkmate() is True:\n\n if current_player == 'BLUE':\n\n self.set_game_state('BLUE_WON')\n \n else:\n\n self.set_game_state('RED_WON')\n\n self.update_player_turn()\n return True","def _is_valid_move(self, vector, current_piece, other_piece):\n return True","def is_valid_move(self, position, dest_square):\n if self.symbol.isupper() and position.turn != 'w':\n return False\n elif self.symbol.islower() and position.turn != 'b':\n return False\n elif dest_square not in self.calculate_scope(position):\n return False\n else:\n return True","def test_valid_move(self, move):\n if self.game_state[move[0]][move[1]] is not None:\n return False\n return True","def move(self, row, column, piece):\n\n if row < 0 or row >= self._dimension or column < 0 or column >= self._dimension or self._board[row][column] != ' ':\n print('Move cannot be made')\n return False\n else:\n self._board[row][column] = piece\n self._number_of_moves += 1","def makeMove(self, _pos, _check_legality=True):\n\n # Check legality is an argument mainly for testing purposes; not for live gameplay.\n if _check_legality:\n legality = self.board.getMoveLegality(self, _pos)\n is_legal_move, is_capturing = legality['is_legal_move'], legality['is_capturing']\n if not is_legal_move:\n # Will need to update this line with interface update.\n # exit(\"ILLEGAL MOVE of {} at {}\".format(self.color, _pos))\n return 'illegal'\n else:\n # _is_capturing is necessary because of the not too often situation of a move with\n # no liberties, but is legal because it's capturing.\n if is_capturing: self.board.playerMakesMove(self, _pos, _is_capturing=True)\n else: self.board.playerMakesMove(self, _pos)\n else:\n self.board.playerMakesMove(self, _pos)","def takeStrategicMove():\r\n\tglobal move1, move2\r\n\r\n\tif move1==0 or move2==0:\r\n\t\tif validMove(1):\r\n\t\t\treturn 1\r\n\t\telif validMove(5):\r\n\t\t\treturn 5\r\n\telif winningMove():\r\n\t\treturn winningMove()\t\t\r\n\telif blockingMove():\r\n\t\treturn blockingMove()\r\n\telse:\r\n\t\treturn takeNaiveMove()","def check_move(board, move):\n\n player, spike_index, fields_to_move = Judge._validate_move(move)\n\n # 1. moving out of the bar\n # 2. check if the source is of the valid player\n # 3. check if the destination is valid\n\n board.set_player_perspective(player)\n\n # 1.\n if spike_index == OUT_OF_BAR_SPECIAL_MOVE:\n if board.bar[player] < 1:\n return False\n\n if not board.valid_dest(fields_to_move - 1):\n return False\n\n return True\n\n # 2.\n if not board.valid_source(spike_index):\n return False\n # 3.\n dest_spike_index = spike_index + fields_to_move\n\n if dest_spike_index >= len(INITIAL_SPIKES_STATE):\n return board.all_at_home()\n \n return board.valid_dest(dest_spike_index)","def make_move(self, starting_pos, ending_pos):\n\n print(\"make_move, input =\", starting_pos, ending_pos)\n\n # convert inputs to work with table\n starting_loc = self.parse_input(starting_pos)\n ending_loc = self.parse_input(ending_pos)\n\n # conditionals to check\n valid = starting_loc[0] is not False and ending_loc[0] is not False\n invalid = starting_loc[0] is False or ending_loc[0] is False\n\n # check if piece is at starting_loc\n self.validate_move_check_for_piece(starting_loc)\n\n valid = starting_loc[0] is not False and ending_loc[0] is not False\n if valid:\n self.validate_move_game_conditions(starting_loc, ending_loc)\n\n # if still valid, check conditions involving the generals\n valid = starting_loc[0] is not False and ending_loc[0] is not False\n if valid:\n # Move will put king in own king check\n self.validate_move_general_conditions(starting_loc, ending_loc)\n\n # ensure move is valid to move on\n invalid = starting_loc[0] is False or ending_loc[0] is False\n if invalid:\n return False\n\n # move piece\n self.move_pieces(starting_loc, ending_loc)\n piece = self._game_board[ending_loc[0]][ending_loc[1]]\n piece.update_location(ending_loc)\n self.set_player_turn()\n\n # check if game has been won\n if self.gen_is_in_check(self._player_turn, True):\n self.set_game_state(self._player_turn)\n\n self.printBoardWithStyling()\n return True","def execute_move(board, move):\n\n player, spike_index, fields_to_move = Judge._validate_move(move)\n\n board.set_player_perspective(player)\n \n if spike_index == OUT_OF_BAR_SPECIAL_MOVE:\n dest_spike_index = fields_to_move - 1\n board.remove_checker_from_bar()\n else:\n dest_spike_index = spike_index + fields_to_move\n board.pop_player_checker(spike_index)\n\n if dest_spike_index >= len(INITIAL_SPIKES_STATE):\n return board\n\n board.push_player_checker(dest_spike_index)\n\n return board","def is_move_valid(self, direction, reference_board=None):\n # Verify a left move does not take you off the board.\n if (direction == \"l\"):\n if (self._current_loc.get_column() == 0):\n return False\n # Verify an up move does not take you off the board.\n elif (direction == \"u\"):\n # Verify the move does not take you off the board.\n if (self._current_loc.get_row() == 0):\n return False\n # Verify a right move does not take you off the board.\n elif (direction == \"r\"):\n current_row = self._current_loc.get_row()\n max_column_number = len(self._untraversed_board[current_row])\n if self._current_loc.get_column() + 1 == max_column_number:\n return False\n # Verify a down move does not take you off the board.\n elif (direction == \"d\"):\n if self._current_loc.get_row() + 1 == len(self._untraversed_board):\n return False\n else:\n assert False, \"Invalid move direction.\"\n\n # Get the new location for a move in the specified direction.\n new_location = self._calculate_move_location(direction)\n new_row = new_location.get_row()\n new_col = new_location.get_column()\n # Verify the space is available\n if(reference_board is None):\n return BoardPath._untraversed_board[new_row][new_col] != \"#\"\n else:\n return reference_board[new_row][new_col] != \"#\"","def make_move(board, move, ch):\n board[move['row']][move['col']] = ch\n \n winner = board_winner(board)\n \n if winner is not None:\n return True, winner\n \n if not board_has_move(board):\n return True, None\n \n return False, None","def make_move(self):\r\n if self.running and self.run:\r\n if self.board is None:\r\n SlTrace.lg(\"sp.board is None\")\r\n return False\r\n \r\n SlTrace.lg(\"running_loop self.running and self.run\", \"running_loop\")\r\n SlTrace.lg(\"running_loop self.start_move\", \"running_loop\")\r\n if self.start_move():\r\n SlTrace.lg(\"running_loop successful start_move\", \"running_loop\")\r\n self.next_move_no()\r\n SlTrace.lg(\"running_loop after start_move\", \"running_loop\")\r\n if self.to_pause:\r\n self.pause_cmd()\r\n self.to_pause = False\r\n return True","def make_move(self, move, player):\n if not self.test_valid_move( move):\n return False\n self.game_state[move[0]][move[1]] = player","def move(x, y, direction, board):\n\n piece_at_xy = starter.get_piece(x, y, board); # Getting necessary pieces\n\n assert piece_at_xy != '*', \"Error in swipe logic\"; # Logical debug case\n valid_direction = (direction == \"left\" or\n direction == \"right\" or\n direction == \"up\" or\n direction == \"down\");\n assert valid_direction, \"Invalid direction passed in\"; # Logical debug case\n\n # The new x and y for the current piece (adjacent's current position) are stored alongside adjacent (fewer ifs + redundant code)\n if direction == \"left\":\n adjacent = (starter.get_piece(x - 1, y, board), x - 1, y);\n elif direction == \"right\":\n adjacent = (starter.get_piece(x + 1, y, board), x + 1, y);\n elif direction == \"up\":\n adjacent = (starter.get_piece(x, y - 1, board), x, y - 1);\n elif direction == \"down\":\n adjacent = (starter.get_piece(x, y + 1, board), x, y + 1);\n\n if adjacent[0] == None: # Edge of the board case (no action taken)\n return False;\n\n elif piece_at_xy != adjacent[0] and adjacent[0] != '*': # Can't combine two numbers case (no action taken)\n return False;\n\n elif adjacent[0] == '*': # Empty spot adjacent case (recursive movement in direction)\n starter.place_piece('*', x, y, board);\n starter.place_piece(piece_at_xy, adjacent[1], adjacent[2], board);\n move(adjacent[1], adjacent[2], direction, board);\n return True;\n\n elif piece_at_xy == adjacent[0]: # Adjacent same numbers case (combine them)\n starter.place_piece('*', x, y, board);\n starter.place_piece(str(int(adjacent[0]) * 2), adjacent[1], adjacent[2], board);\n move(adjacent[1], adjacent[2], direction, board);\n return True;\n\n else:\n # Logical debug case\n assert False, \"No way you should be in here. Error in move logic\";\n\n return False;","def apply_move(self, move):\n if self.check_move(move=move):\n self.board_list[move] = self.current_player.marker # changes value in the board to player which is either X or O\n self.moves_made += str(move) # keeps track of all moves\n return True\n else:\n return False","def move(self):\r\n their_move = self.last_moves[\"their_move\"]\r\n return (their_move == \"\" and random.choice(moves) or their_move)","def _isvalidmove(self, from_, to_):\n if self.board[from_].occupant is None:\n print(\"Moving from empty square\")\n return False\n piece = self.board[from_].occupant\n\n if piece.color != self.to_move:\n print(\"Wrong color\")\n return False\n\n if self.is_checked:\n if piece.notation != 'K':\n print(\"King is checked!\")\n return False\n\n diff = (\n to_cartesian(to_)[0] - to_cartesian(from_)[0],\n to_cartesian(to_)[1] - to_cartesian(from_)[1]\n )\n if not piece.hopping:\n if self.board.isblocked(from_, to_):\n print(\"Move blocked by other pieces\")\n return False\n\n if self.board[to_].occupant is not None:\n if piece.color == self.board[to_].occupant.color:\n print(\"Cannot capture friendly\")\n return False\n\n if diff not in piece.get_captures():\n print(\"Invalid piece capture\")\n return False\n\n if diff not in piece.get_moves():\n print(\"Invalid piece move\")\n return False\n\n return True","def validate_movement(self, piece, from_col, from_row, to_col, to_row):\n col_diff = abs(ord(from_col) - ord(to_col))\n row_diff = abs(from_row - to_row)\n\n # For any piece, it must actually move...\n if col_diff == 0 and row_diff == 0:\n return False\n # ...and there must be empty spaces in between the from/to squares (when on a column, row, or diagonal)\n if not self.validate_empty_between(from_col, from_row, to_col, to_row):\n return False\n\n # White pawn\n if piece == 'P':\n if col_diff == 1 and (to_row - from_row == 1):\n # Can move diagonally up one square, if taking another piece in that square or by en-passant\n return self.piece_colour(to_col, to_row) == 'B' \\\n or self.is_en_passant(from_col, from_row, to_col, to_row)\n elif col_diff != 0:\n # Otherwise, it can't change columns\n return False\n elif from_row == 2:\n # From initial position, can go up one or two rows (but can't take a piece)\n return (to_row == 3 or to_row == 4) and self.get_square(to_col, to_row) == ' '\n else:\n # Otherwise, can only move up one row (but can't take a piece)\n return to_row - from_row == 1 and self.get_square(to_col, to_row) == ' '\n # Black pawn\n elif piece == 'p':\n if col_diff == 1 and (from_row - to_row == 1):\n # Can move diagonally down one square, if taking another piece in that square or by en-passant\n return self.piece_colour(to_col, to_row) == 'W' \\\n or self.is_en_passant(from_col, from_row, to_col, to_row)\n elif col_diff != 0:\n # Otherwise, it can't change columns\n return False\n elif from_row == 7:\n # From initial position, can go down one or two rows (but can't take a piece)\n return (to_row == 6 or to_row == 5) and self.get_square(to_col, to_row) == ' '\n else:\n # Otherwise, can only move down one row (but can't take a piece)\n return from_row - to_row == 1 and self.get_square(to_col, to_row) == ' '\n # Rook\n elif piece.lower() == 'r':\n # Must remain in same column or same row\n return col_diff == 0 or row_diff == 0\n # Knight\n elif piece.lower() == 'n':\n # Jumps in a 2+1 pattern\n return (col_diff == 2 and row_diff == 1) or (col_diff == 1 and row_diff == 2)\n # Bishop\n elif piece.lower() == 'b':\n # Moves along diagonals\n return col_diff == row_diff\n # Queen\n elif piece.lower() == 'q':\n # Can move along columns, rows, or diagonals\n return col_diff == 0 or row_diff == 0 or col_diff == row_diff\n # King\n elif piece.lower() == 'k':\n # Can move a single square in any direction\n if not(0 <= col_diff <= 1) or not(0 <= row_diff <= 1):\n return False\n\n # But not next to the other king\n other_king = 'k' if piece.isupper() else 'K'\n # Get valid border squares\n border_squares = list(filter(\n lambda b_square: 'a' <= b_square[0] <= 'f' and 1 <= b_square[1] <= 8,\n [\n (chr(ord(to_col) - 1), to_row - 1), (to_col, to_row - 1), (chr(ord(to_col) + 1), to_row - 1),\n (chr(ord(to_col) - 1), to_row), (to_col, to_row), (chr(ord(to_col) + 1), to_row),\n (chr(ord(to_col) - 1), to_row + 1), (to_col, to_row + 1), (chr(ord(to_col) + 1), to_row + 1)\n ]\n ))\n # Check for the other king\n for square in border_squares:\n if self.get_square(square[0], square[1]) == other_king:\n return False\n\n return True","def step(self, move):\n if self.is_done:\n raise Exception('You have to reset the game first.')\n action = self.get_action(move)\n\n # adjust in case too big, could also count as invalid if action >= field_width\n # this is for the human player to make always valid moves\n action = action % self.n_actions\n\n step_column = self.get(action, self.dim[-1] - 1)\n is_invalid = step_column != 0\n\n # we should consider to end the game after an invalid move since further play might not be meaningful\n if is_invalid: # board filled => invalid move.\n info = {'winner': 0, 'game_over': False, 'invalid_move': True}\n # current player is not changed.\n self.invalid_moves += 1\n self._is_done = True\n return self.board, self.invalid_move_reward, self.is_done, info,\n \n else: # not filled => valid move\n self.steps += 1\n height = self.get_height(action) # getting the _height at which the token must fall to\n self.set(action, height, value=self.current_player)\n\n winner = self.check_winner(self.board, action, height)\n if winner != 0:\n info = {'winner': winner, 'game_over': True, 'invalid_move': False}\n reward = self.winning_reward\n self._is_done = True\n else:\n if self.is_full():\n # Tie\n info = {'winner': winner, 'game_over': True, 'invalid_move': False}\n reward = self.tie_reward\n self._is_done = True\n else:\n # Proceeding to next turn\n if self.check_opponent_winning(): # checks whether opponent can win\n reward = self.losing_reward\n else:\n reward = 0\n info = {'winner': winner, 'game_over': False, 'invalid_move': False}\n self.current_player *= -1 \n \n return self.board, reward, self.is_done, info","def move(self, board):\n\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\n\n while not board.check_move(move[0], move[1]):\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\n\n return move","def apply_move(b,player,move):\n move = move.strip().lower()\n if len(move)!=2:\n raise Exception(\"Valid move is two characters (e.g. A2 or B3)\")\n if move[0] not in COLS:\n move = move[::-1]\n if move[0] not in COLS:\n raise Exception(\"No column spec found\")\n j = COLS.index(move[0])\n i = int(move[1])-1\n if b[i][j] != \" \":\n raise Exception(\"Another move already filled that position\")\n b[i][j] = player","def test_move(self):\n # Run a handful of GCMC moves\n n_moves = 10\n std_gcmc_system_sampler.move(std_gcmc_system_simulation.context, n_moves)\n\n # Check that all of the appropriate variables seem to have been updated\n # Hard to test individual moves as they are rarely accepted - just need to check the overall behaviour\n assert std_gcmc_system_sampler.n_moves == n_moves\n assert 0 <= std_gcmc_system_sampler.n_accepted <= n_moves\n assert len(std_gcmc_system_sampler.Ns) == n_moves\n assert len(std_gcmc_system_sampler.acceptance_probabilities) == n_moves\n assert isinstance(std_gcmc_system_sampler.energy, Quantity)\n assert std_gcmc_system_sampler.energy.unit.is_compatible(kilocalories_per_mole)\n\n return None","def test_move(self):\n # Run a handful of GCMC moves\n n_moves = 10\n std_gcmc_sphere_sampler.move(std_gcmc_sphere_simulation.context, n_moves)\n\n # Check that all of the appropriate variables seem to have been updated\n # Hard to test individual moves as they are rarely accepted - just need to check the overall behaviour\n assert std_gcmc_sphere_sampler.n_moves == n_moves\n assert 0 <= std_gcmc_sphere_sampler.n_accepted <= n_moves\n assert len(std_gcmc_sphere_sampler.Ns) == n_moves\n assert len(std_gcmc_sphere_sampler.acceptance_probabilities) == n_moves\n assert isinstance(std_gcmc_sphere_sampler.energy, Quantity)\n assert std_gcmc_sphere_sampler.energy.unit.is_compatible(kilocalories_per_mole)\n\n return None","def move(self, action):\n \n self.counter += 1\n\n if action not in self.ACTIONS:\n raise Exception(\"Invalid action\")\n\n \n\n d_x, d_y = self.MOVEMENTS[action]\n x, y = self.position\n new_x, new_y = x + d_x, y + d_y\n new_X,new_Y=self.position_to_xy(new_x, new_y)\n \n\n if (new_x, new_y) not in self.cases:\n return self._get_state(), -3, False, self.ACTIONS\n \n \n \n elif (self.openGoal(new_x,new_y))&(new_X>-400):\n self.position = new_x, new_y\n self.positionxy = self.position_to_xy(new_x, new_y)\n \n return self._get_state(), 20, True, self.ACTIONS\n \n # elif not self.openGoal(new_x,new_y):\n # self.position = new_x, new_y\n # self.positionxy = self.position_to_xy(new_x, new_y)\n # return self._get_state(), -1, False, self.ACTIONS\n \n elif self.counter > 100:\n self.position = new_x, new_y\n self.positionxy = self.position_to_xy(new_x, new_y)\n return self._get_state(), -1, True, self.ACTIONS\n \n else:\n self.position = new_x, new_y\n self.positionxy = self.position_to_xy(new_x, new_y)\n return self._get_state(), -1, False, self.ACTIONS","def test_valid_move():\n\n board = Board()\n\n # a col outside the width of the board should be false\n assert board.valid_move(board.get_grid_size()[1] + 1) is False\n\n # only positive cols should be considered for a move\n assert board.valid_move(-2) is False\n\n # since board is empty all cols should have moves\n for i in range(board.get_grid_size()[1]):\n assert board.valid_move(i) is True\n\n # if a col is full no move can be made\n for i in range(board.get_grid_size()[1]):\n if i % 2 == 0:\n board.move(board.P1, 0)\n else:\n board.move(board.P2, 0)\n\n \"\"\"\n board now looks like this...\n \n 0 1 2 3 4 5 6 \n +-+-+-+-+-+-+-+\n 0|O|-|-|-|-|-|-|0\n +-+-+-+-+-+-+-+\n 1|X|-|-|-|-|-|-|1\n +-+-+-+-+-+-+-+\n 2|O|-|-|-|-|-|-|2\n +-+-+-+-+-+-+-+\n 3|X|-|-|-|-|-|-|3\n +-+-+-+-+-+-+-+\n 4|O|-|-|-|-|-|-|4\n +-+-+-+-+-+-+-+\n 5|X|-|-|-|-|-|-|5\n +-+-+-+-+-+-+-+\n 0 1 2 3 4 5 6 \n\n \"\"\"\n assert board.valid_move(0) is False","def get_move(self, find_move_name):\n frame_data = self._get_frame_data()\n sprites = self._get_sprites()\n\n # Need to check both names separately\n for move in frame_data.keys():\n if '\"' in find_move_name:\n temp_move_name = find_move_name.replace('\"', '')\n if temp_move_name == move:\n frame_data_name = move\n break\n else:\n continue\n elif find_move_name.lower() == move.lower():\n frame_data_name = move\n break\n\n else:\n for move in frame_data.keys():\n if find_move_name.lower() in move.lower():\n frame_data_name = move\n break\n else:\n raise MoveNotFound\n\n sprite_name = None\n\n # temporary fix for the 214/236B/22x/5AD meme\n if '214b' in frame_data_name.lower() and not '214bc' in frame_data_name.lower():\n for move in sprites.keys():\n if '214A/B' in move:\n sprite_name = move\n break\n elif '236b' in frame_data_name.lower() and not '236bc' in frame_data_name.lower():\n for move in sprites.keys():\n if '236A/B' in move:\n sprite_name = move\n break\n\n elif '22' in frame_data_name.lower():\n for move in sprites.keys():\n if '22A/B' in move and '22c' not in frame_data_name.lower():\n sprite_name = move\n break\n elif '22A/B/C' in move and '22c' in frame_data_name.lower():\n sprite_name = move\n break\n\n elif 'reversal' in frame_data_name.lower():\n for move in sprites.keys():\n if '5AD' in move:\n sprite_name = move\n break\n\n for move in sprites.keys():\n if sprite_name is not None:\n break\n if 'j.' in frame_data_name.lower() and ' ' in frame_data_name:\n for split_name in frame_data_name.split(' '):\n if move.lower() == split_name.lower():\n sprite_name = move\n break\n elif move.lower() == frame_data_name.lower():\n sprite_name = move\n break\n else:\n for move in sprites.keys():\n if sprite_name is not None:\n break\n if 'j.' in frame_data_name.lower() and ' ' in frame_data_name:\n for split_name in frame_data_name.split(' '):\n if move.lower() in split_name.lower():\n sprite_name = move\n break\n elif move.lower() in frame_data_name.lower() and '22' not in find_move_name:\n print('ok')\n sprite_name = move\n break\n elif find_move_name.lower() in move.lower():\n sprite_name = move\n break\n else:\n sprite_name = None\n\n if sprite_name is None:\n sprite = ''\n else:\n sprite = self._get_high_quality_sprite(sprites[sprite_name])\n\n return {\n frame_data_name: {\n 'fd': frame_data[frame_data_name],\n 'sprite': sprite\n }\n }","def nextMoveGNU(self, move=\"go\", board=None):\n # get move\n if self.pawning:\n while not rospy.is_shutdown():\n for row in [2,3,4,5]:\n for col in ['a','b','c','d','e','f','g','h']:\n p1 = board.getPiece(col,row)\n if p1 != None and abs(p1.type) == ChessPiece.WHITE_PAWN:\n p2 = board.getPiece(col,row+1)\n if p2 == None:\n # this is a candidate \n m = col + str(row) + col + str(row+1)\n self.history.append(m)\n return m\n else:\n self.engine.sendline(move) \n if self.engine.expect(['My move is','Illegal move']) == 1:\n return None \n self.engine.expect('([a-h][1-8][a-h][1-8][RrNnBbQq(\\r\\n)])')\n m = self.engine.after.rstrip()\n self.history.append(m)\n return m","def move(self, row: int, col: int, player: int):\n def addup(dict_name, invalid_set, another_invalid, locx, locy):\n if locx == locy:\n diag_name = (1,1)\n if diag_name not in invalid_set:\n dict_name[diag_name] += 1\n if dict_name[diag_name] == self.tar:\n return player\n another_invalid.add(diag_name)\n if locx == self.tar-1-locy:\n diag_name = (-1, -1)\n if diag_name not in invalid_set:\n dict_name[diag_name] += 1\n if dict_name[diag_name] == self.tar:\n return player\n another_invalid.add(diag_name)\n curcol = (locy, None)\n currow = (None, locx)\n if curcol not in invalid_set:\n dict_name[curcol] += 1\n if dict_name[curcol] == self.tar:\n return player\n another_invalid.add(curcol)\n if currow not in invalid_set:\n dict_name[currow] += 1\n if dict_name[currow] == self.tar:\n return player\n another_invalid.add(currow)\n return 0\n res = 0\n if (row, col) not in self.walked:\n if player == 1:\n res = addup(self.p1, self.invalid_1, self.invalid_2, row, col)\n if player == 2:\n res = addup(self.p2, self.invalid_2, self.invalid_1, row, col)\n self.walked.add((row, col))\n return res","def next_move(board, player):\n \n move_row = \"move\"\n move_column = \"move\"\n\n while not move_row.isnumeric():\n move_row = input(\"{}, pick row to place your {}. > \".format(player.name, player.char))\n while not move_column.isnumeric(): \n move_column = input(\"Pick column in row {} to place your {}. > \".format(move_row, player.char))\n\n move_row = int(move_row)\n move_column = int(move_column)\n\n move = Move(player, (move_row, move_column))\n \n # Check if move is out of bounds\n if (move_row >= len(board.current_board) or\n move_column >= len(board.current_board)):\n print(\"Move out of bounds. Choose a valid move.\")\n return board\n\n # Check if space is already used\n if board.current_board[move_row][move_column] != \"-\":\n print(\"Spot already played. Pick an unused space.\")\n return board\n\n board.last_move = player.name\n board.add_move(move)\n\n return board","async def move(self, board, valid_actions):\n self._move = None\n output_move_row = Value('d', -1)\n output_move_column = Value('d', 0)\n try:\n # await self.search(board, valid_actions) \n p = Process(\n target=self.search, \n args=(\n self._color, board, valid_actions, \n output_move_row, output_move_column))\n p.start()\n while p.is_alive():\n await asyncio.sleep(0.1)\n self._move = np.array([output_move_row.value,output_move_column.value],dtype=np.int32)\n except asyncio.CancelledError as e:\n print('The previous player is interrupted by a user or a timer.')\n except Exception as e:\n print(type(e).__name__)\n print('move() Traceback (most recent call last): ')\n traceback.print_tb(e.__traceback__)\n finally:\n p.kill()\n self._move = np.array(\n [output_move_row.value, output_move_column.value],\n dtype=np.int32)\n return self.best_move","def validate_move(board: list, character: list, direction: str) -> bool:\n max_x_y_coordinates = board[-1]\n valid_options = []\n if character[1] < max_x_y_coordinates[0]:\n valid_options.append(\"d\")\n if character[1] > 0:\n valid_options.append(\"a\")\n if character[0] < max_x_y_coordinates[1]:\n valid_options.append(\"s\")\n if character[0] > 0:\n valid_options.append(\"w\")\n if direction in valid_options:\n return True\n else:\n return False","def check_score(self, move):\n\n i = int(move) // 7\n j = int(move) % 7 #find the corresponding index of the input move\n\n if i == 0: #top\n if self.board[i+1][j-1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i+2][j] != \"\":\n return 1\n return 0\n if i == 6: #bottom\n if self.board[i-1][j-1] != \"\" and self.board[i-1][j+1] != \"\" and self.board[i-2][j] != \"\":\n return 1\n return 0\n if j == 0: #left\n if self.board[i-1][j+1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i][j+2] != \"\":\n return 1\n return 0\n if j == 6: #right\n if self.board[i-1][j-1] != \"\" and self.board[i+1][j-1] != \"\" and self.board[i][j-2] != \"\":\n return 1\n return 0\n if i == 2 or i == 4: # horizontal\n score = 0\n if self.board[i-1][j-1] != \"\" and self.board[i-1][j+1] != \"\" and self.board[i-2][j] != \"\":\n score += 1\n if self.board[i+1][j-1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i+2][j] != \"\":\n score += 1\n return score\n\n if j == 2 or j == 4: # vertical\n score = 0\n if self.board[i-1][j-1] != \"\" and self.board[i+1][j-1] != \"\" and self.board[i][j-2] != \"\":\n score += 1\n if self.board[i-1][j+1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i][j+2] != \"\":\n score += 1\n return score","def move_possible(x, y, board):\n\n piece_at_xy = starter.get_piece(x, y, board);\n if piece_at_xy == None:\n return False;\n elif piece_at_xy == '*': # An empty space means a move is always possible\n return True;\n\n return (\n piece_at_xy == starter.get_piece(x + 1, y, board) or\n piece_at_xy == starter.get_piece(x - 1, y, board) or\n piece_at_xy == starter.get_piece(x, y + 1, board) or\n piece_at_xy == starter.get_piece(x, y - 1, board)\n );","def fix_move(self, invalid_move: QMove):\n\n # TODO: reduce time_per_game second by second\n ERROR_MSG = f\"INVALID_MOVE {invalid_move.to_string()}\"\n\n if self.is_ai and self.proc is not None:\n self.proc.stdin.write(str.encode(ERROR_MSG + '\\n'))\n self.proc.stdin.flush()\n new_move = QMove(os.read(self.proc.stdout.fileno(), 100))\n else:\n new_move = QMove(\n input(\"Move was invalid, enter a valid move:\\n\\t>> \"))\n\n return new_move","def move(self, motor, position, timeout):\n \n try:\n caput(motor, position, wait=True, timeout=timeout)\n except:\n e = sys.exc_info()\n print str(e)\n print \"ERROR: caput failed.\"\n print (motor + \" pos:\" + str(position) + \" timeout:\" + str(timeout))\n return self.__g.FAIL\n \n rdbd = motor + \".RDBD\"\n rbv = motor + \".RBV\"\n\n final_pos = caget(rbv)\n deadband = caget(rdbd)\n\n success = True\n\n if ((final_pos < position-deadband) or (final_pos > position+deadband)):\n print \"ERROR: final_pos out of deadband.\"\n print (motor + \" \" + str(position) + \" \" + str(timeout) + \" \" \n + str(final_pos) + \" \" + str(deadband))\n success = False\n\n if (success):\n return self.postMoveCheck(motor)\n else:\n self.postMoveCheck(motor)\n return self.__g.FAIL","def move(puzzle: str, direction: str):\r\n position_index = puzzle.index(EMPTY)\r\n position = position_index + 1\r\n grid_width = get_grid_width(puzzle)\r\n\r\n # What direction to moved the tile if it's a valid move\r\n if direction == UP:\r\n if (position) > grid_width:\r\n return swap_position(puzzle, position_index, position_index - grid_width)\r\n\r\n elif direction == DOWN:\r\n if (len(puzzle) - position) >= grid_width:\r\n return swap_position(puzzle, position_index, position_index + grid_width)\r\n\r\n elif direction == LEFT:\r\n if (position - 1) % grid_width != 0:\r\n return swap_position(puzzle, position_index, position_index - 1)\r\n\r\n elif direction == RIGHT:\r\n if position % grid_width != 0:\r\n return swap_position(puzzle, position_index, position_index + 1)\r\n\r\n return None","def is_valid_move(x:int, y:int,board_length) -> bool:\n if x < 0 or y < 0 or x == board_length or y == board_length:\n return False\n return True","def move(self):\r\n my_move = self.last_moves[\"my_move\"]\r\n return (my_move != \"\" and moves[(moves.index(my_move)+1) % 3] or\r\n random.choice(moves))","def find_best_move(state: GameState) -> None:","def nextMoveGNU(self, move=\"go\", board=None):\n # get move\n if self.pawning:\n while not rospy.is_shutdown():\n rows = [2,3,4,5]\n piece = ChessPiece.WHITE_PAWN\n if board.side == board.BLACK:\n rows = [7,6,5,4]\n piece = ChessPiece.BLACK_PAWN\n for row in rows:\n for col in ['a','b','c','d','e','f','g','h']:\n p1 = board.getPiece(col,row)\n if p1 != None and abs(p1.type) == piece:\n p2 = board.getPiece(col,row+1)\n if p2 == None:\n # this is a candidate\n m = col + str(row) + col + str(row+1)\n self.history.append(m)\n return m\n else:\n self.engine.sendline(move)\n if self.engine.expect(['My move is','Illegal move']) == 1:\n return None\n self.engine.expect('([a-h][1-8][a-h][1-8][RrNnBbQq(\\r\\n)])')\n m = self.engine.after.rstrip()\n self.history.append(m)\n return m","def makeMove(self, moveStr):\r\n\t\tmoveStr = str(moveStr)\r\n\r\n\t\tmoveUci = self._userParseSanToUci(moveStr)\r\n\t\t# print(moveUci)\r\n\r\n\t\tif moveUci is None:\r\n\t\t\treturn\r\n\r\n\t\tresponse = requests.post(f'https://lichess.org/api/board/game/{self.gameId}/move/{moveUci}', headers=self.authHeader)\r\n\r\n\t\tif response.status_code == 200:\r\n\t\t\tlog.debug('Move Successfully Sent')\r\n\r\n\t\telse:\r\n\t\t\tlog.warning(f'Move Unsuccessfully Sent. Status Code: {response.status_code}')","def make_move(self, board):\n user_input = self.get_user_input(\n 'coordinates of next move (x,y): '\n )\n move = self.transform_user_input(user_input)\n\n valid = board.move_is_valid(move)\n while not valid:\n user_input = self.get_user_input(\n 'Invalid move, coordinate of next move: '\n )\n move = self.transform_user_input(user_input)\n valid = board.move_is_valid(move)\n board.set_piece(move, color=self.color)","def process_move(player, board):\r\n c = player.__repr__()\r\n print(c, \"'s turn\")\r\n move = player.next_move(board)\r\n board.add_checker(player.checker, move)\r\n print()\r\n print(board)\r\n if board.is_win_for(player.checker):\r\n i = player.num_moves\r\n print(player.__repr__(), \"wins in \", i, \"moves\")\r\n print(\"Congratulations!\")\r\n return True\r\n elif board.is_full() and not board.is_win_for(player.checker):\r\n print(\"It's a tie!\")\r\n return True\r\n else:\r\n return False","def move_piece(self, move: Move, ignore_check=False):\n # no need to check the values of Position as they are guaranteed to be sane.\n\n # sanity: cant move from a position onto itself\n if move.from_pos.to_str == move.to_pos.to_str: # TODO compare by value, not str\n raise InvalidMoveException('cant move a piece onto itself (%s)' % move.from_pos.to_str())\n\n piece = self.rubric(move.from_pos)\n if piece is None:\n raise Exception(\"assert failed: found empty rubric at: %s\" % move.from_pos.to_str())\n\n # sanity: there must be a piece in the start position:\n if piece.piece_type == PieceType.PLACEHOLDER:\n raise InvalidMoveException('cant move from empty rubric (%s)' % (move.from_pos.to_str()))\n\n # sanity: ensure the move is valid for this turn's color\n if piece.color != self._current_side_color:\n raise InvalidMoveException(\"cant move a piece of this color at this turn\")\n\n # sanity: if capturing, pieces must have different colors\n captured_piece = self.rubric(move.to_pos)\n if captured_piece.piece_type != PieceType.PLACEHOLDER:\n if captured_piece.color == piece.color:\n raise InvalidMoveException('cant capture a piece of the same color (start: %s, end: %s)' %\n (move.from_pos.to_str(), move.to_pos.to_str()))\n\n # handle movement in Check\n king_attackers = self.get_king_attackers()\n if not ignore_check and len(king_attackers) > 0:\n # if the king is under attack (== Check), the only valid moves are those that\n # resolve the situation:\n # a) moving the king to an unattacked position\n # b) capturing the attacker by the king, provided its position is NOT attacked by another piece\n # c) capturing the attacker by another piece\n # d) blocking the check by a rook, queen or bishop: placing them between\n # the king and the attacker - NOTE I will not implement this\n\n # if there are no valid moves, the game is over (Checkmate) - this is handled elsewhere\n\n # determine if the move will resolve the Check:\n # create a copy of the board, run the proposed move (ignore the check)\n # and then determine if we're still in check afterwards\n board_copy = self.get_board_copy()\n board_copy.move_piece(move, ignore_check=True)\n if len(board_copy.get_king_attackers()) > 0:\n # the move did not resolve the check, so it wasnt valid:\n raise InvalidMoveException(\"move failed to resolve Check\")\n\n if piece.is_valid_move(move, self._rubrics):\n # handle capture scenario\n if captured_piece.piece_type != PieceType.PLACEHOLDER:\n # remove the captured piece\n self.remove_piece(captured_piece)\n\n # move the piece to its destination\n self.set_rubric(piece, move.to_pos)\n\n # set empty placeholder in the origin rubric\n self.set_rubric(PlaceHolder(Position), move.from_pos)\n\n return True","def _maybe_move(self, source_chunk, target_chunk, path_index, move_func):\n if len(source_chunk.paths) <= 1:\n return False\n\n move_time = source_chunk.paths[path_index].time\n\n new_source_badness = self._badness(source_chunk.time - move_time)\n new_target_badness = self._badness(target_chunk.time + move_time)\n\n delta_badness = ((new_source_badness + new_target_badness) -\n (source_chunk.badness + target_chunk.badness))\n if delta_badness < 0:\n move_func()\n return True\n\n return False","def choose_move(self):\n return 0","def is_legal_move(self, start_pos, end_pos, start_piece, end_piece_player_id, board):\r\n parsed_positions = self.parse_positions(start_pos, end_pos)\r\n\r\n start_row = parsed_positions[0]\r\n start_col = parsed_positions[1]\r\n end_row = parsed_positions[2]\r\n end_col = parsed_positions[3]\r\n count = 0 # Count will track how many pieces are between start and end_pos\r\n\r\n if start_row != end_row and start_col != end_col: # Moving diagonally\r\n return False\r\n\r\n # If cannon moves to an empty position\r\n # if end_piece_player_id is None:\r\n\r\n if start_row == end_row: # Moving horizontally\r\n col_difference = end_col - start_col\r\n\r\n if col_difference > 0: # Moving to the right of the board\r\n for col in range(start_col + 1, end_col): # Checks if there is a piece between start_col and end_col\r\n if board[start_row][col].get_piece() is not None:\r\n count += 1\r\n\r\n if col_difference < 0: # Moving to the left of the board\r\n for col in range(start_col - 1, end_col, -1): # Checks to the left of the board\r\n # If there is a piece to block movement to the end_pos, return False\r\n if board[start_row][col].get_piece() is not None:\r\n count += 1\r\n\r\n if start_col == end_col: # Moving vertically\r\n row_difference = end_row - start_row\r\n\r\n if row_difference > 0: # Moving down the board\r\n for row in range(start_row + 1, end_row):\r\n if board[row][start_col].get_piece() is not None: # If no piece is impeding path to end_pos\r\n count += 1\r\n\r\n\r\n if row_difference < 0: # Moving up the board\r\n for row in range(start_row -1, end_row, -1):\r\n if board[row][start_col].get_piece() is not None: # If no piece is impeding path to end_pos\r\n count += 1\r\n\r\n # 1 piece between start_pos and end_pos and end_pos contains a chess piece\r\n if count == 1 and end_piece_player_id is not None:\r\n return True\r\n # end_pos has no piece and there are no pieces to impede path\r\n elif end_piece_player_id is None and count == 0:\r\n return True\r\n # Returns False for all other scenarios\r\n else:\r\n return False","def validate_move(board: list, character: dict, direction: str) -> bool:\n if direction.strip().upper() == \"N\":\n return (character[\"Position\"][0] - 1, character[\"Position\"][1]) in board\n elif direction.strip().upper() == \"S\":\n return (character[\"Position\"][0] + 1, character[\"Position\"][1]) in board\n elif direction.strip().upper() == \"W\":\n return (character[\"Position\"][0], character[\"Position\"][1] - 1) in board\n elif direction.strip().upper() == \"E\":\n return (character[\"Position\"][0], character[\"Position\"][1] + 1) in board\n else:\n print(\"Please enter only directions shown above\")\n return False","def make_move(self, board: Board) -> int:\n return random.choice(board.get_valid_moves())","def is_valid_move(self, position: Point) -> bool:\n\t\tif self.tiles[position.x][position.y] == 0:\n\t\t\treturn True\n\t\treturn False","def move(self,x_pos,y_pos,x_dir,y_dir,user):\r\n\r\n if self.finished == True:\r\n return Move.finish\r\n\r\n size = len(self)\r\n if not self.__on_the_board(x_pos+x_dir, y_pos+y_dir):\r\n return Move.out_of_bounds\r\n\r\n moving_piece = self.board[y_pos][x_pos]\r\n\r\n if moving_piece == 0 or user * moving_piece < 0:\r\n return Move.no_piece_found\r\n\r\n if not self.__valid_direction(x_dir,y_dir):\r\n return Move.invalid_destination\r\n\r\n if self.board[y_pos+y_dir][x_pos+x_dir] != 0:\r\n return Move.path_blocked\r\n \r\n # List of moves the user can make if captures are possible.\r\n user_capture_options = [forced_move for forced_move in self.capture_options if forced_move.user == moving_piece]\r\n\r\n if user_capture_options != []:\r\n if not self.__is_capture_move(moving_piece,x_pos,y_pos,x_dir,y_dir):\r\n return Move.capture_ignored\r\n \r\n if abs(x_dir) == 1:\r\n return self.__move_single_tile(moving_piece,x_pos,y_pos,x_dir,y_dir,user)\r\n\r\n if abs(x_dir) == 2:\r\n return self.__move_double_tile(moving_piece,x_pos,y_pos,x_dir,y_dir,user)\r\n\r\n # This code should not be reached, as abs(x_dir) is forced to be either 1 or 2 before looking at the if-clauses.\r\n # However, may this filter ever need to be altered, here's a handy error to let you know that you did something wrong.\r\n raise NotImplementedError(\"This code should not be reached!\")","def test_perform_move(self):\n p = hw.create_tile_puzzle(3, 3)\n self.assertFalse(p.perform_move(\"taco\"))\n self.assertTrue(p.perform_move('up'))\n self.assertEqual(p.get_board(), [[1,2,3],[4,5,0],[7,8,6]])\n self.assertFalse(p.perform_move('right'))\n p = hw.create_tile_puzzle(2, 4)\n self.assertTrue(p.perform_move('left'))\n self.assertTrue(p.perform_move('up'))\n self.assertFalse(p.perform_move('up'))\n self.assertEqual(p.get_board(), [[1,2,0,4],[5,6,3,7]])\n p = hw.create_tile_puzzle(1, 4)\n self.assertTrue(p.perform_move('left'))\n self.assertTrue(p.perform_move('left'))\n self.assertTrue(p.perform_move('left'))\n self.assertFalse(p.perform_move('down'))\n self.assertFalse(p.perform_move('left'))\n self.assertEqual(p.get_board(), [[0,1,2,3]])","def is_pawn_move_valid(self, from_row, from_col, to_row, to_col):\n # Setup variables used\n piece = self.board.squares[from_row][from_col]\n piece_color = self.piece_color(piece)\n to_piece = self.board.squares[to_row][to_col]\n row_diff = abs(from_row - to_row)\n col_diff = abs(from_col - to_col)\n dc = 0\n\n # Set flag for first move of pawn\n first_move = True if from_row == 6 or from_row == 1 else False\n\n # If direction is not correct for white, exit\n if to_row - from_row > 0:\n dr = 1\n if self.piece_color(piece) == \"white\":\n return False\n\n # If direction is not correct for black, exit\n if to_row - from_row < 0:\n dr = -1\n if self.piece_color(piece) == \"black\":\n return False\n\n # If moving straight\n if from_col == to_col:\n # if not legal straight move, exit\n if not (row_diff == 1 or (first_move and row_diff == 2)):\n return False\n\n # make sure to move has no pieces on straight path\n dm = row_diff + 1\n\n # return value\n retVal = self._any_piece_in_way(from_row, from_col, dr, dc, dm)\n\n# if retVal and not self.testing:\n# # self.pawn_promotion(to_row, to_col, piece_color)\n# self.board.overwrite_board_square(to_row, to_col)\n# if piece_color == \"black\":\n# self.board.put_piece(self.B_QUEEN, to_row, to_col)\n# else:\n# self.board.put_piece(self.W_QUEEN, to_row, to_col)\n\n return retVal\n\n # WHITE en passant\n # move from moveHistory => (\"piece\", fromRow, fromCol, toRow, toCol)\n if (self.moveHistory[-1][2] == self.moveHistory[-1][4] == (to_col)) and \\\n self.moveHistory[-1][0] == \"♟\" and self.moveHistory[-1][1] == 1 and\\\n self.moveHistory[-1][3] == 3 and piece_color == \"white\":\n if col_diff == 1 and row_diff == 1 and to_piece == None:\n if not self.testing:\n self.board.overwrite_board_square(self.moveHistory[-1][3], self.moveHistory[-1][4])\n self.board.squares[self.moveHistory[-1][3]][self.moveHistory[-1][4]] = None\n return True\n\n # BLACK en passant\n if (self.moveHistory[-1][2] == self.moveHistory[-1][4] == (to_col)) and \\\n self.moveHistory[-1][0] == \"♙\" and self.moveHistory[-1][1] == 6 and\\\n self.moveHistory[-1][3] == 4 and piece_color == \"black\":\n if col_diff == 1 and row_diff == 1 and to_piece == None:\n if not self.testing:\n self.board.overwrite_board_square(self.moveHistory[-1][3], self.moveHistory[-1][4])\n self.board.squares[self.moveHistory[-1][3]][self.moveHistory[-1][4]] = None\n return True\n\n # else move must be taking piece directly move\n # if legal taking piece move and (opponent-already check for own piece) piece at to-square\n if col_diff == 1 and row_diff == 1 and to_piece != None:\n\n# if not self.testing:\n# # self.pawn_promotion(to_row, to_col, piece_color)\n# self.board.overwrite_board_square(to_row, to_col)\n# if piece_color == \"black\":\n# self.board.put_piece(self.B_QUEEN, to_row, to_col)\n# else:\n# self.board.put_piece(self.W_QUEEN, to_row, to_col)\n return True\n\n return False","def get_next_move(self):\n return int(input('Enter your move: '))","def move(self, board):\n\n if board.get_number_of_moves() == 0:\n random_row = randint(0, 2)\n random_column = randint(0, 2)\n\n if random_row == 1 or random_column == 1:\n random_row = 1\n random_column = 1\n elif random_row == 2:\n random_row = board.get_dimension()-1\n\n if random_column == 2:\n random_column = board.get_dimension()-1\n\n move = (random_row, random_column)\n elif board.get_number_of_moves() == 1 or board.get_number_of_moves() == 2:\n if board.get_piece(1,1) == ' ':\n move = (1, 1)\n else:\n board_dimension = board.get_dimension()-1\n corners = [(0, 0), (0, board_dimension), (board_dimension, 0), (board_dimension, board_dimension)]\n corners = self.remove_filled_positions(corners, board)\n\n move = corners[randint(0, len(corners)-1)]\n else:\n move = self.check_for_winner(board)\n\n if move == (-1, -1):\n board_dimension = board.get_dimension()-1\n corner1_moves = self.remove_filled_positions([(0, 0), (2, 2)], board)\n corner2_moves = self.remove_filled_positions([(0, 2), (2, 0)], board)\n\n non_corner_moves = self.remove_filled_positions([(1, 0), (2, 1), (1, 2), (0, 1)], board)\n\n center_piece = board.get_piece(1, 1)\n corner_pieces = [board.get_piece(0, 0), board.get_piece(board_dimension, 0), board.get_piece(0, board_dimension), board.get_piece(board_dimension, board_dimension)]\n\n if corner_pieces[0] != self._piece and corner_pieces[0] != ' ' and corner_pieces[0] == corner_pieces[3]:\n move = non_corner_moves[randint(0, 3)]\n elif corner_pieces[1] != self._piece and corner_pieces[1] != ' ' and corner_pieces[1] == corner_pieces[2]:\n move = non_corner_moves[randint(0, 3)]\n elif len(corner2_moves) > 0 and corner_pieces[0] != self._piece and corner_pieces[0] == center_piece and corner_pieces[3] == self._piece:\n move = corner2_moves[0]\n elif len(corner1_moves) > 0 and corner_pieces[1] != self._piece and corner_pieces[1] == center_piece and corner_pieces[2] == self._piece:\n move = corner1_moves[0]\n elif len(corner1_moves) > 0 and corner_pieces[2] != self._piece and corner_pieces[2] == center_piece and corner_pieces[1] == self._piece:\n move = corner1_moves[0]\n elif len(corner2_moves) > 0 and corner_pieces[3] != self._piece and corner_pieces[3] == center_piece and corner_pieces[0] == self._piece:\n move = corner2_moves[0]\n else:\n move = self.can_complete_two_in_row(board)\n\n if move == (-1, -1):\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\n\n while not board.check_move(move[0], move[1]):\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\n\n return move","def askMove(self,posibleMoves):\n print(\"Where will you move?\")\n while True:\n pos = raw_input(\"Type Colum and Row 'CR' Ex:a1 for first column/row: \")\n if len(pos) == 2:\n c = ord(pos[0])-97\n r = int(pos[1])-1\n move = c+r*8\n if move in posibleMoves:\n return move\n print(\"Invalid move, try again\")\n return","def parse_move(move):\n if not (len(move) == 2):\n return None, None\n try:\n row = ord(move[0].upper()) - 65\n col = int(move[1])\n except:\n return None, None\n return row, col","def move(self, row, column, symbol):\n game_state = self.determine_game_state()\n if game_state not in (GameState.GAME_NOT_STARTED, GameState.GAME_IN_PROGRESS):\n return MoveResults.MOVE_INVALID\n\n # check for initial move\n if self.board == BLANK_BOARD and symbol == O_SYMBOL:\n return MoveResults.MOVE_INVALID\n\n # check for invalid row and column\n if row < 0 or row > 2 or column < 0 or column > 2:\n return MoveResults.MOVE_INVALID\n\n # make sure the game piece is valid\n if symbol != X_SYMBOL and symbol != O_SYMBOL:\n return MoveResults.MOVE_INVALID\n\n # make sure the game piece isn't moving out of turn\n x_moves = self.board.count(X_SYMBOL)\n o_moves = self.board.count(O_SYMBOL)\n if symbol == X_SYMBOL and x_moves > o_moves:\n return MoveResults.MOVE_INVALID\n elif symbol == O_SYMBOL and o_moves >= x_moves:\n # note that x always goes first.\n return MoveResults.MOVE_INVALID \n\n # figure out position.\n position = (3 * row) + column\n\n # make sure there's not already a piece there.\n if self.board[position] != EMPTY_SYMBOL:\n return MoveResults.MOVE_INVALID\n\n self.board = self.board[:position] + symbol + self.board[position+1:] \n return MoveResults.MOVE_VALID","def test_human_move_char(self):\n self.ri.return_value = 'c'\n assert False == self.T.human_move()","def is_valid_move(self, move: Any) -> bool:\n return move in self.get_possible_moves()"],"string":"[\n \"def move_valid(move):\\n return True\",\n \"def handle_move(self, move_string):\\n def map_move(move):\\n col = int(ascii_lowercase.find(move[0])) + 1 # dummy col\\n row = int(move[1:])\\n # if not 0 < col <= game[\\\"board_width\\\"]:\\n # raise ValueError('bad coord; invalid col in ' + coord)\\n # if not 0 < row <= game[\\\"board_height\\\"]:\\n # raise ValueError('bad coord; invalid row in ' + coord)\\n return row*(self.rules[\\\"row_len\\\"]) + col\\n move = list(map(map_move,move_string.split(' ')))\\n self.turn[\\\"board\\\"][move[0]].make_move(*move[1:])\\n self.turn[\\\"half_move_clock\\\"] += 1\\n if self.turn[\\\"active_player\\\"] == 1:\\n self.turn[\\\"full_move_clock\\\"] += 1\\n self.turn[\\\"active_player\\\"] = (self.turn[\\\"active_player\\\"] + 1) % 2\\n # self.turn[\\\"board\\\"][move_start].make_move(move_end)\",\n \"def test_check_move_with_valid(self):\\n board = [\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\"\\\\u25cb\\\"] + [\\\" \\\"] * 5,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6\\n ]\\n valid = self.game.check_move(board, 3)\\n self.assertTrue(valid)\",\n \"def attempt_move(self, move_input):\\n # handle undo move\\n if move_input == ['UN', 0, 'UN']:\\n self.undo_move()\\n return True\\n\\n # handle stock draw Special Action first\\n if move_input == ['S0', 0, 'S0']:\\n self.save_board_state()\\n self.stock.deal_to_wp(self.wp)\\n self.moves += 1\\n return True\\n\\n # handle basic cases\\n if len(move_input) != 3:\\n return False\\n if move_input[0] not in self.move_dict or move_input[2] not in self.move_dict:\\n return False\\n if type(move_input[1]) is not int:\\n return False\\n if move_input[2] == \\\"W0\\\":\\n return False\\n\\n orig_pile = self.move_dict[move_input[0]]\\n orig_ind = move_input[1]\\n dest_pile = self.move_dict[move_input[2]]\\n if orig_ind >= orig_pile.get_length():\\n return False\\n\\n # handle flip tableau card Special Action\\n if move_input[0][0] == 'T' and orig_pile == dest_pile and orig_ind == 0:\\n orig_pile.reveal_top_card()\\n\\n # basic conditions have been met\\n adj_ind = orig_pile.get_length() - orig_ind - 1\\n if orig_pile.is_valid_retrieval(orig_ind):\\n self.save_board_state()\\n move_pile = orig_pile.remove_cards(orig_ind + 1)\\n if dest_pile.is_valid_placement(move_pile):\\n dest_pile.merge_pile(move_pile)\\n if move_input[0][0] == 'T' and self.auto_flip_tab:\\n orig_pile.reveal_top_card()\\n self.moves += 1\\n return True\\n else:\\n orig_pile.merge_pile(move_pile)\\n self.board_states.pop()\\n return False\\n return False\",\n \"def make_move(move):\\n global manatee_pos\\n global hyacinths\\n global hyacinth_pos\\n\\n # Ends the program if movement is out of bounds\\n if move == (0, 0):\\n return None\\n new_pos = (manatee_pos[0] + move[0], manatee_pos[1] + move[1])\\n if new_pos[0] < 0 or new_pos[0] >= len(map):\\n return None\\n if new_pos[1] < 0 or new_pos[1] >= len(map[new_pos[0]]):\\n return None\\n\\n entity = map[new_pos[0]][new_pos[1]]\\n if entity == \\\"#\\\" or entity == \\\"G\\\":\\n # Runs if movement is impossible\\n return None\\n if entity == \\\" \\\" or entity == \\\".\\\":\\n # Runs if normal movement is possible\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return None\\n if entity == \\\"O\\\":\\n # Runs if manatee wins game\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return \\\"win\\\"\\n if entity == \\\"\\\\\\\\\\\":\\n # Runs if manatee eats hyacinth\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n hyacinths += 1\\n if len(hyacinth_pos) == hyacinths:\\n map[grate_pos[0]][grate_pos[1]] = \\\"O\\\"\\n return None\\n if entity == \\\"*\\\":\\n # Checks if manatee can push boat\\n if move[0] == 0:\\n new_boat_pos = (new_pos[0] + move[0], new_pos[1] + move[1])\\n if new_boat_pos[0] < 0 or new_boat_pos[0] >= len(map):\\n return None\\n if new_boat_pos[1] < 0 \\\\\\n or new_boat_pos[1] >= len(map[new_boat_pos[0]]):\\n return None\\n if map[new_boat_pos[0]][new_boat_pos[1]] == \\\" \\\":\\n map[new_boat_pos[0]][new_boat_pos[1]] = \\\"*\\\"\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return None\\n return None\",\n \"def check_move(self, move):\\n\\n if str(move) in self.moves_made:\\n return False\\n return True\",\n \"def CheckMove(self,move):\\n\\t\\tif(move=='w'):\\n\\t\\t\\tif(self.x==0):\\n\\t\\t\\t\\treturn 0\\n\\t\\t\\treturn 1\\n\\t\\telif(move=='s'):\\n\\t\\t\\tif(self.x==15):\\n\\t\\t\\t\\treturn 0\\n\\t\\t\\treturn 1\\n\\t\\telif(move=='d'):\\n\\t\\t\\tif(self.y==35):\\n\\t\\t\\t\\treturn 0\\n\\t\\t\\treturn 1\\n\\t\\telif(move=='a'):\\n\\t\\t\\tif(self.y==0):\\n\\t\\t\\t\\treturn 0\\n\\t\\t\\treturn 1\",\n \"def check4move(st, selected_unit, direction):\\n return 1\",\n \"def test_check_move_with_barely_valid(self):\\n board = [\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\"\\\\u25cb\\\"] * 5 + [\\\" \\\"],\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6\\n ]\\n valid = self.game.check_move(board, 4)\\n self.assertTrue(valid)\",\n \"def make_legal_move(move, board, start, end, move_number, en_passant_square, king_w, king_b, rook_w_l, rook_w_r,\\n rook_b_l,\\n rook_b_r, promotion_piece):\\n # this function should have essentially what i had in main at first without making the move\\n test_board = board.copy()\\n valid_move = False\\n piece = find_piece(board, start)\\n end_piece = find_piece(board, end)\\n\\n if switch_player(move_number): # for whites move\\n if (65 <= ord(piece) <= 90) and (validate_move(board, end, move_number)):\\n if piece == \\\"P\\\":\\n if pawn(board, start, end, move_number):\\n if end[0] == 8:\\n promotion(test_board, start, end, promotion_piece)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n promotion(board, start, end, promotion_piece)\\n return True\\n else:\\n valid_move = False\\n else:\\n valid_move = True\\n else:\\n if can_en_passant(board, en_passant_square, end, start, move_number):\\n execute_enpassant(test_board, start, end, move_number)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n execute_enpassant(board, start, end, move_number)\\n return True\\n else:\\n return False\\n elif piece == \\\"K\\\":\\n if king(start, end):\\n if controlled_squares(board, move_number, end):\\n valid_move = True\\n elif piece == \\\"N\\\":\\n if knight(start, end):\\n valid_move = True\\n elif piece == \\\"B\\\":\\n if bishop(board, start, end):\\n valid_move = True\\n elif piece == \\\"Q\\\":\\n if queen(board, start, end):\\n valid_move = True\\n elif piece == \\\"R\\\":\\n if rook(board, start, end):\\n valid_move = True\\n else:\\n valid_move = False\\n if valid_move:\\n update_board(test_board, start, end)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n update_board(board, start, end)\\n return True\\n else:\\n retract_move(test_board, start, end, end_piece)\\n print(\\\"Illegal Move\\\")\\n return False\\n if not switch_player(move_number):\\n if (97 <= ord(piece) <= 122) and validate_move(board, end, move_number):\\n if piece == \\\"p\\\":\\n if pawn(board, start, end, move_number):\\n if end[0] == 1:\\n promotion(test_board, start, end, promotion_piece)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n promotion(board, start, end, promotion_piece)\\n return True\\n else:\\n valid_move = False\\n else:\\n valid_move = True\\n else:\\n if can_en_passant(board, en_passant_square, end, start, move_number):\\n execute_enpassant(test_board, start, end, move_number)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n execute_enpassant(board, start, end, move_number)\\n return True\\n else:\\n valid_move = False\\n elif piece == \\\"k\\\":\\n if king(start, end):\\n if controlled_squares(board, move_number, end):\\n valid_move = True\\n elif piece == \\\"n\\\":\\n if knight(start, end):\\n valid_move = True\\n elif piece == \\\"b\\\":\\n if bishop(board, start, end):\\n valid_move = True\\n elif piece == \\\"q\\\":\\n if queen(board, start, end):\\n valid_move = True\\n elif piece == \\\"r\\\":\\n if rook(board, start, end):\\n valid_move = True\\n else:\\n valid_move = False\\n if valid_move:\\n update_board(test_board, start, end)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n update_board(board, start, end)\\n return True\\n else:\\n retract_move(test_board, start, end, end_piece)\\n print(\\\"Illegal Move\\\")\\n return False\",\n \"def validMove(move):\\r\\n\\r\\n\\tglobal tile1, tile2, tile3, tile4, tile5, tile6, tile7, tile8, tile9\\r\\n\\r\\n\\ta=eval(\\\"tile\\\"+str(move)+\\\"==0\\\")\\r\\n\\treturn a\",\n \"def check_one_move(self):\\n count_moves = 0\\n one_move = None\\n for direction in self.directions:\\n if self.valid_move(self.loc, direction):\\n count_moves += 1\\n one_move = direction\\n if count_moves != 1:\\n return None\\n return one_move\",\n \"def make_move(self, board: Board) -> int:\\n\\n move = input()\\n move = int(move)\\n\\n while move not in board.get_valid_moves():\\n print(\\\"That is not a valid move\\\")\\n move = input()\\n move = int(move)\\n\\n return move\",\n \"def test_check_move_with_invalid(self):\\n board = [\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\"\\\\u25cb\\\"] * 6,\\n [\\\" \\\"] * 6,\\n [\\\" \\\"] * 6\\n ]\\n valid = self.game.check_move(board, 4)\\n self.assertFalse(valid)\",\n \"def takeNaiveMove():\\r\\n\\tnotFound=True\\r\\n\\twhile notFound:\\r\\n\\t\\tmove=random.randint(1,9)\\r\\n\\t\\tif validMove(move):\\r\\n\\t\\t\\tnotFound=False\\r\\n\\treturn move\",\n \"def valid_move(self, player, move):\\n return (True)\",\n \"def is_valid_move(state, move):\\n row, col = move\\n if row not in [1, 2, 3] or col not in [1, 2, 3]:\\n print(\\\"Invalid move! Specify correct game square!\\\")\\n return False\\n if state[row-1][col-1] != '_':\\n print('Invalid move! Place your marker on a free square!')\\n return False\\n return True\",\n \"def getMove(player,first_move=False):\\n while True: \\n move = raw_input(\\\"MAKE YOUR MOVE: \\\").upper()\\n\\n # handle special commands\\n if move == \\\"QUIT\\\" or move == \\\"Q\\\":\\n wannaQuit()\\n continue\\n elif move == \\\"QUIT!\\\" or move == \\\"Q!\\\":\\n if SAY_PROC:\\n SAY_PROC.terminate()\\n sys.exit()\\n continue\\n elif move == \\\"HELP\\\" or move == \\\"H\\\":\\n help()\\n continue\\n elif move == \\\"HELP!\\\" or move == \\\"H!\\\":\\n say(helpString())\\n continue\\n elif move == \\\"SHUTUP!\\\" or move == \\\"S!\\\":\\n shutUp(fuck=True)\\n continue\\n elif move == \\\"SHUTUP\\\" or move == \\\"S\\\":\\n shutUp()\\n continue\\n elif move == \\\"BOARD\\\" or move == \\\"B\\\":\\n printBoard()\\n continue\\n elif move == \\\"CLEAR\\\" or move == \\\"C\\\": \\n clearTerminal()\\n printBoard()\\n continue\\n elif move == \\\"CLEAR!\\\" or move == \\\"C!\\\": # TODO board -> clear, you end up with a half drawn new board. clear again fixes this\\n clearTerminalAndBuffer()\\n printBoard()\\n continue\\n elif move == \\\"PASS\\\" or move == \\\"P\\\": \\n if wannaPass():\\n break\\n else:\\n continue\\n elif move == \\\"PASS!\\\" or move == \\\"P!\\\": \\n break\\n \\n # mostly used to catch blank lines or me typing ASFADS like an asshole\\n if len(move) < 7:\\n print \\\"That's too short to be a command.\\\"\\n continue\\n\\n parts=move.split(\\\":\\\")\\n if len(parts) != 3:\\n print \\\"Can't find all the parts of the move command. Maybe you're missing/have too many \\\\\\\":\\\\\\\"?\\\"\\n continue\\n\\n for item in parts:\\n if len(item) == 0:\\n print \\\"Found a blank command. Maybe you left in an extra \\\\\\\":\\\\\\\"?\\\"\\n continue\\n\\n coords = parts[0].replace(\\\" \\\",\\\"\\\") # incase of space inbetween file and rank\\n direction = parts[1].strip()\\n word = parts[2].strip()\\n\\n if not coords[0].isalpha():\\n print \\\"I don't know where to put your word (Bad file coord).\\\"\\n continue\\n\\n if not coords[1:].isdigit():\\n print \\\"I don't know where to put your word (Bad rank coord).\\\"\\n continue\\n\\n x = gridCharToInt(coords[0])\\n y = int(coords[1:]) - 1\\n if 14 < x < 0 or 14 < y < 0:\\n print \\\"Those aren't coords on the board. Valid Files are from A-O, valid Ranks are 1-15.\\\"\\n continue\\n\\n if first_move:\\n if x != 7 or y != 7:\\n print \\\"The first move must start from the center (H8).\\\"\\n continue\\n\\n #compact that command\\n if direction == \\\"ACROSS\\\":\\n direction = \\\"A\\\"\\n elif direction == \\\"DOWN\\\":\\n direction = \\\"D\\\"\\n if direction != \\\"A\\\" and direction !=\\\"D\\\":\\n print \\\"I don't know where to put your word (Across or Down?).\\\"\\n continue\\n \\n score,placed_tiles = checkWords(x,y,direction,word,first_move)\\n if not score: #error reporting is handling in check words\\n continue\\n else:\\n for tile in placed_tiles:\\n if not tile in player.rack:\\n print \\\"You don't have the tiles to play that!\\\"\\n continue\\n player.rack.remove(tile)\\n print player.name+\\\" scored \\\"+str(score)+\\\" on that last play!\\\"\\n player.score+=score\\n for tile in placed_tiles:\\n player.rack.remove(tile)\\n break #YAY\",\n \"def move_check(self):\\r\\n \\r\\n if not self.run:\\r\\n return False\\r\\n \\r\\n if self.get_num_legal_moves() == 0:\\r\\n SlTrace.lg(\\\"NO more legal moves!\\\", \\\"nolegalmoves\\\")\\r\\n ###return False \\r\\n \\r\\n if self.new_move:\\r\\n self.announce_player(\\\"start_move\\\")\\r\\n if SlTrace.trace(\\\"selected\\\"):\\r\\n self.list_selected(\\\"After start_move\\\")\\r\\n self.new_move = False\\r\\n player = self.get_player()\\r\\n if player is None:\\r\\n return False\\r\\n \\r\\n return True\",\n \"def checkMove(guess, xPos, yPos):\\n\\n\\t# Return 0 if x position or y position are not valid\\n\\tif(xPos not in range(0, 5) or yPos not in range(0, 5)):\\n\\t\\treturn 0\\n\\n\\t# Return 0 f the guessed position is not water\\n\\tif(guess[yPos][xPos] != \\\"~\\\"):\\n\\t\\treturn 0\\n\\n\\treturn 1\",\n \"def is_valid(self, move):\\r\\n return move > 10 and move < 89\",\n \"def validate_move(move):\\n if move[0] in cc.VALID_RANKS and move[1] in cc.VALID_RANKS:\\n valid = True\\n else:\\n valid = False\\n return valid\",\n \"def make_move(self, move: Tuple[int, int]) -> MoveError:\\n\\n # Make sure our move is going to be valid\\n if self.is_winner():\\n return MoveError.GAME_WON\\n\\n elif move[0] >= self._board_size or move[0] < 0 or move[1] >= self._board_size or move[1] < 0:\\n return MoveError.OUT_OF_RANGE\\n\\n elif self._board[move[1]][move[0]] != self.NEUTRAL_PLAYER:\\n return MoveError.TAKEN\\n\\n # If we make it to here, then it is valid to make the move\\n self._board[move[1]][move[0]] = self._players[self._current_player]\\n self._number_of_moves = self._number_of_moves + 1\\n self._last_move = move\\n\\n self._check_for_winner()\\n\\n # Only change who the player is if we didn't get a winner,\\n # otherwise the final board's color will be wrong\\n if not self.is_winner():\\n self._current_player = (self._current_player + 1) % len(self._players)\\n\\n return MoveError.OKAY\",\n \"def is_valid_move(self, move):\\n if type(move) == str:\\n move = int(move)\\n\\n return move in self.get_possible_moves()\",\n \"def move_invalid():\\n check50.run(run_command).stdin(\\\"EAST\\\").stdout(\\\"Invalid command.\\\")\",\n \"def make_move(state: str, section_num: int, move: str) -> str:\\n if move == wf.CHECK:\\n check_result = wf.check_section(state, section_num)\\n if check_result:\\n print('The section is correct')\\n else:\\n print('The section is incorrect')\\n else:\\n state = wf.change_state(state, section_num, move) \\n return state\",\n \"def test_verify_move(self):\\n self._verify([self.applied_commands['move']])\",\n \"def check_move(self, x, y):\\n try:\\n return self.map[self.y+y][self.x+x] == \\\" \\\" or [self.x+x, self.y+y] == self.end_pos\\n except IndexError:\\n return False\",\n \"def _get_move_result(self, unlocked_before_move : bool, err = None):\\n if err:\\n return Moveresult.INVALID\\n elif self.current_turn.entity in self.game_state.get_completed_characters():\\n return Moveresult.EXIT\\n elif self.game_state.is_character_expelled(self.current_turn.entity):\\n return Moveresult.EJECT\\n elif self.game_state.is_current_level_unlocked() and not unlocked_before_move:\\n return Moveresult.KEY\\n else:\\n return Moveresult.OK\",\n \"def human_move(board,player):\\r\\n \\r\\n s = input(\\\"Please input a legal move in a format of \\\\\\\"current_position-landing_position\\\\\\\", if the move is cantering or plain. In case of a capturing move, follow \\\\\\\"current_position-landing_position-enemy piece\\\\\\\": \\\")\\r\\n move = s.split('-')\\r\\n legal = legal_moves(board,player)\\r\\n execution(move,legal,board,player)\",\n \"def make_move(self, start, end):\\r\\n start_pos = self.parse_pos(start) # Start and end position are lists that contain column and row\\r\\n end_pos = self.parse_pos(end)\\r\\n\\r\\n start_row = start_pos[0] # Position of row and columns are assigned to variables\\r\\n start_col = start_pos[1]\\r\\n end_row = end_pos[0]\\r\\n end_col = end_pos[1]\\r\\n\\r\\n board = self._board.get_board()\\r\\n start_piece = board[start_row][start_col].get_piece()\\r\\n end_piece = board[end_row][end_col].get_piece()\\r\\n\\r\\n\\r\\n # If there is no piece to be moved or game is over or piece is to be moved to its original location\\r\\n if start_piece is None or self._game_state != \\\"UNFINISHED\\\"\\\\\\r\\n or (start_row == end_row and start_col == end_col):\\r\\n return False\\r\\n\\r\\n start_piece_id = start_piece.get_player_id() # Contains the player id associated with the piece\\r\\n end_piece_player_id = None\\r\\n if end_piece is not None: # Executes if end piece contains a piece object\\r\\n end_piece_player_id = end_piece.get_player_id()\\r\\n\\r\\n # If Red's turn\\r\\n if self._player_turn == 1:\\r\\n if start_piece_id != 'r': # If red moves a black piece\\r\\n return False\\r\\n if start_piece.is_legal_move(start, end, start_piece, end_piece_player_id, board) : # Checks the legal move conditions\\r\\n if self.move_piece(start, end): # Returns False if move is invalid\\r\\n # Checks if move violates flying general and puts self in check\\r\\n if self.is_not_flying_general() is True and self.is_in_check(\\\"red\\\") is False:\\r\\n self.change_player_turn()\\r\\n self.is_in_checkmate()\\r\\n return True\\r\\n else: # Reverses the move if violates flying general rule\\r\\n self.reverse_move(start, end, board,end_piece_player_id, end_piece)\\r\\n return False\\r\\n\\r\\n else:\\r\\n return False\\r\\n else:\\r\\n return False\\r\\n\\r\\n # If Black's turn\\r\\n elif self._player_turn == -1:\\r\\n if start_piece_id != 'b': # If black moves a red piece\\r\\n return False\\r\\n if start_piece.is_legal_move(start, end, start_piece, end_piece_player_id, board): # Checks the legal move conditions\\r\\n if self.move_piece(start, end): # Returns False if move is invalid\\r\\n if self.is_not_flying_general() is True and self.is_in_check(\\\"black\\\") is False:\\r\\n self.change_player_turn()\\r\\n self.is_in_checkmate()\\r\\n return True\\r\\n else: # Reverses the move if violates flying general rule\\r\\n self.reverse_move(start, end, board, end_piece_player_id, end_piece)\\r\\n return False\\r\\n else:\\r\\n return False\\r\\n else:\\r\\n return False\",\n \"def make_move(self, move, check_valid=True):\\r\\n self.board[move.sr][move.sc] = \\\"--\\\"\\r\\n self.board[move.er][move.ec] = move.pieceMoved\\r\\n self.moveLog.append(move)\\r\\n self.turn_white = not self.turn_white\\r\\n if move.pieceMoved == 'wk':\\r\\n self.wKingPos = (move.er, move.ec)\\r\\n elif move.pieceMoved == 'bk':\\r\\n self.bKingPos = (move.er, move.ec)\\r\\n\\r\\n if move.isEnpassantMove:\\r\\n self.board[move.sr][move.ec] = \\\"--\\\"\\r\\n\\r\\n if move.pieceMoved[1] == 'p' and abs(move.sr - move.er) == 2:\\r\\n self.enpas_pos = ((move.er + move.sr) // 2, move.ec)\\r\\n else:\\r\\n self.enpas_pos = ()\\r\\n\\r\\n if move.isPawnPromotion and not check_valid:\\r\\n promoted_piece = \\\"a\\\"\\r\\n while promoted_piece not in ('q', 'r', 'b', 'n'):\\r\\n promoted_piece = input(\\\"Promote to q, r, b, or n: \\\")\\r\\n self.board[move.er][move.ec] = move.pieceMoved[0] + promoted_piece\\r\\n\\r\\n # castle\\r\\n if move.castle:\\r\\n if move.ec - move.sc == 2:\\r\\n self.board[move.er][move.ec - 1] = self.board[move.er][move.ec + 1]\\r\\n self.board[move.er][move.ec + 1] = '--'\\r\\n else:\\r\\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 2]\\r\\n self.board[move.er][move.ec - 2] = '--'\\r\\n\\r\\n # castle rights on rook, king move\\r\\n self.update_castle_rights(move)\\r\\n self.castleRightsLog.append(CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\\r\\n self.cr_castle_r.wqs, self.cr_castle_r.bqs))\",\n \"def valid_move(self, row, col):\\n if not self._game_over:\\n i_row, i_col = row-1, col-1\\n #i_row and i_col wil be used to index the board (hence the i)\\n (valid, flip_lst) = self._valid_placement(i_row, i_col)\\n #print(\\\"FOR TESTING. Tiles Flipped: \\\", flip_lst)\\n \\n if valid:\\n #Big Change: You decided to make determining validity\\n # and flipping separate operations\\n self._flip(i_row, i_col, flip_lst)\\n else:\\n print(\\\"\\\\nPlease enter a valid move!\\\")\\n return False\\n\\n if self._board_is_full():\\n self._game_over = True\\n self._set_winner() \\n \\n self._switch_turn(self._turn)\\n if not self._valid_move_exists(): #Check if the other player has any valid moves\\n print(\\\"\\\\nNo valid moves exist for {0}. {0}'s turn has been skipped\\\".format(self._turn))\\n self._switch_turn(self._turn) #Switch turn back to player before skip was determined\\n if not self._valid_move_exists(): #Check if the other player has any valid moves\\n print(\\\"No valid moves exist for {0}. {0}'s turn has been skipped\\\".format(self._turn))\\n print(\\\"No moves exist for either player. GAME OVER\\\")\\n self._game_over = True\\n self._set_winner()\\n return False\\n\\n return True\\n elif self._game_over:\\n print(\\\"The game is over. No more moves can be made!\\\")\\n #TODO: Replace this^ with an exception later?\\n return False\",\n \"def make_move(self, move):\\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \\\"\\\" or int(move) % 2 == 0:\\n raise ValueError(\\\"{} is not a valid move for {}\\\".format(move, self.board))\\n DotsAndBoxesState.score1 += self.check_score(move)\\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \\\"\\\\u001b[0m\\\"\\n self.turn = get_opponent(self.turn) #change into another player's trun\",\n \"def test_no_moves(self):\\n game = self.ending(['bw..wwww'], 8, 1)\\n game.man_move(0, 2)\\n self.assertEqual(game.finish_state,\\n (400, game.first_player, 'No moves'))\",\n \"def move(self, initial_pos, final_pos):\\n\\n ops = [ self.op_move_north, self.op_move_nwest, self.op_move_neast,\\n self.op_jump_north, self.op_jump_nwest, self.op_jump_neast,\\n self.op_capture_north, self.op_capture_east, self.op_capture_west,\\n self.op_capture_nwest, self.op_capture_neast, self.op_add_piece\\n ]\\n\\n # Check if the final position is empty\\n if final_pos and self.board.get_element(*final_pos) != self.EMPTY:\\n return False\\n\\n # Check if the intial position is a valid start piece\\n if initial_pos not in self.next_pieces:\\n return False\\n\\n # Test for add piece operation\\n if self.next_move == self.ADDPIECE_1 or self.next_move == self.ADDPIECE_2:\\n res = self.op_add_piece(initial_pos)\\n if type(res) != type(False):\\n return res\\n\\n # Test for other operations\\n for op in ops:\\n res = op(initial_pos)\\n\\n if type(res) != type(False) and (res.last_piece == final_pos):\\n return res\\n\\n return False\",\n \"def move(self, piece):\\n\\n if list(piece) in self.find_moves():\\n self.block[tuple( self.find_free() )] = self.block[tuple(piece)]\\n self.block[tuple(piece)] = 0\\n return \\\"success\\\"\\n else:\\n return \\\"error\\\"\",\n \"def get_move(self, board):\\n\\n valid_moves = [move for move in board.legal_moves]\\n is_valid_move = False\\n while not is_valid_move:\\n move = input(\\\"Enter a valid move in uci format: \\\").lower()\\n if len(move) == 4 or len(move) == 5:\\n try:\\n player_move = chess.Move.from_uci(move)\\n\\n if board.is_legal(player_move):\\n try:\\n board.push(player_move)\\n return player_move\\n except:\\n print(\\\"invalid move...\\\")\\n else:\\n print(\\\"invalid move...\\\")\\n except:\\n print(\\\"invalid move...\\\")\\n else:\\n print(\\\"invalid move...\\\")\",\n \"def _move(self, start: (int, int), dest: (int, int), extra_info=''):\\n moving_piece = self.board[start[0]][start[1]]\\n end_piece = self.board[dest[0]][dest[1]]\\n\\n # Check if the move is valid\\n possible_moves = self.get_all_moves()\\n if State.convert_to_EAN(start, dest, extra_info) not in possible_moves:\\n return False\\n\\n # Invalidate castling\\n self._invalidate_castles()\\n\\n # Update half turn counters since capture (updates 50 move draw)\\n # reset on capture, which is when the destination piece is a different color\\n self.half_moves += 1\\n if not State._is_same_color(moving_piece, end_piece):\\n self.half_moves = 0\\n\\n # Update full moves after black's turn\\n if not self.white_to_move:\\n self.full_moves += 1\\n\\n # Update the board to reflect the move\\n self._update_board(start, dest, extra_info)\\n\\n # Update move history TODO\\n # Detect three move repetition TODO\\n\\n # Update whose turn it is\\n self.white_to_move = not self.white_to_move\",\n \"def make_move(self, position_from, position_to):\\n\\n # Returns False if position_from or position_to are not an actual position\\n # ranging from 'a1' to 'i10' for columns 'a' through 'i' and rows '1' \\n # through '10'.\\n if not isinstance(position_to, str) or\\\\\\n not isinstance(position_from,str) or\\\\\\n len(position_to) > 3 or len(position_to) < 2 or\\\\\\n len(position_from) > 3 or len(position_from) < 2:\\n\\n return False \\n\\n # Returns False if game has been won.\\n elif self.get_game_state() != 'UNFINISHED':\\n\\n return False\\n\\n position_to = position_to.lower()\\n position_from = position_from.lower()\\n current_player = self.get_player_turn()\\n\\n # Returns False if move is not legal based on Janggi game rules.\\n if self.legal_move(position_to, position_from) is not True:\\n\\n return False\\n\\n # Returns True if current player wishes to pass on their move.\\n # Any position on the board will suffice, including a General's position.\\n # Tested in legal_move to exit legal_move safely. The second test for pass\\n # updates player turn and returns True, so long as the player is not\\n # currently in check.\\n if position_to == position_from:\\n\\n if self.is_in_check(current_player) is True:\\n \\n return False\\n\\n self.update_player_turn()\\n return True\\n\\n # Move is valid. Adjusted board, update potential moves for all GamePieces,\\n # test checkmate (change game_state if True), update player turn, and\\n # return True.\\n else:\\n\\n game_piece_object = self.get_game_piece_object_at_position(position_from)\\n self.adjust_board(game_piece_object, position_to, position_from)\\n self.update_potential_moves()\\n \\n # Sets the General's Board position attribute if the position has\\n # changed.\\n if isinstance(game_piece_object, General):\\n\\n if current_player == 'BLUE':\\n\\n self.set_general_position_blue(position_to)\\n\\n else:\\n\\n self.set_general_position_red(position_to)\\n\\n # Sets game_state to the player who won if checkmate is detected.\\n if self.is_checkmate() is True:\\n\\n if current_player == 'BLUE':\\n\\n self.set_game_state('BLUE_WON')\\n \\n else:\\n\\n self.set_game_state('RED_WON')\\n\\n self.update_player_turn()\\n return True\",\n \"def _is_valid_move(self, vector, current_piece, other_piece):\\n return True\",\n \"def is_valid_move(self, position, dest_square):\\n if self.symbol.isupper() and position.turn != 'w':\\n return False\\n elif self.symbol.islower() and position.turn != 'b':\\n return False\\n elif dest_square not in self.calculate_scope(position):\\n return False\\n else:\\n return True\",\n \"def test_valid_move(self, move):\\n if self.game_state[move[0]][move[1]] is not None:\\n return False\\n return True\",\n \"def move(self, row, column, piece):\\n\\n if row < 0 or row >= self._dimension or column < 0 or column >= self._dimension or self._board[row][column] != ' ':\\n print('Move cannot be made')\\n return False\\n else:\\n self._board[row][column] = piece\\n self._number_of_moves += 1\",\n \"def makeMove(self, _pos, _check_legality=True):\\n\\n # Check legality is an argument mainly for testing purposes; not for live gameplay.\\n if _check_legality:\\n legality = self.board.getMoveLegality(self, _pos)\\n is_legal_move, is_capturing = legality['is_legal_move'], legality['is_capturing']\\n if not is_legal_move:\\n # Will need to update this line with interface update.\\n # exit(\\\"ILLEGAL MOVE of {} at {}\\\".format(self.color, _pos))\\n return 'illegal'\\n else:\\n # _is_capturing is necessary because of the not too often situation of a move with\\n # no liberties, but is legal because it's capturing.\\n if is_capturing: self.board.playerMakesMove(self, _pos, _is_capturing=True)\\n else: self.board.playerMakesMove(self, _pos)\\n else:\\n self.board.playerMakesMove(self, _pos)\",\n \"def takeStrategicMove():\\r\\n\\tglobal move1, move2\\r\\n\\r\\n\\tif move1==0 or move2==0:\\r\\n\\t\\tif validMove(1):\\r\\n\\t\\t\\treturn 1\\r\\n\\t\\telif validMove(5):\\r\\n\\t\\t\\treturn 5\\r\\n\\telif winningMove():\\r\\n\\t\\treturn winningMove()\\t\\t\\r\\n\\telif blockingMove():\\r\\n\\t\\treturn blockingMove()\\r\\n\\telse:\\r\\n\\t\\treturn takeNaiveMove()\",\n \"def check_move(board, move):\\n\\n player, spike_index, fields_to_move = Judge._validate_move(move)\\n\\n # 1. moving out of the bar\\n # 2. check if the source is of the valid player\\n # 3. check if the destination is valid\\n\\n board.set_player_perspective(player)\\n\\n # 1.\\n if spike_index == OUT_OF_BAR_SPECIAL_MOVE:\\n if board.bar[player] < 1:\\n return False\\n\\n if not board.valid_dest(fields_to_move - 1):\\n return False\\n\\n return True\\n\\n # 2.\\n if not board.valid_source(spike_index):\\n return False\\n # 3.\\n dest_spike_index = spike_index + fields_to_move\\n\\n if dest_spike_index >= len(INITIAL_SPIKES_STATE):\\n return board.all_at_home()\\n \\n return board.valid_dest(dest_spike_index)\",\n \"def make_move(self, starting_pos, ending_pos):\\n\\n print(\\\"make_move, input =\\\", starting_pos, ending_pos)\\n\\n # convert inputs to work with table\\n starting_loc = self.parse_input(starting_pos)\\n ending_loc = self.parse_input(ending_pos)\\n\\n # conditionals to check\\n valid = starting_loc[0] is not False and ending_loc[0] is not False\\n invalid = starting_loc[0] is False or ending_loc[0] is False\\n\\n # check if piece is at starting_loc\\n self.validate_move_check_for_piece(starting_loc)\\n\\n valid = starting_loc[0] is not False and ending_loc[0] is not False\\n if valid:\\n self.validate_move_game_conditions(starting_loc, ending_loc)\\n\\n # if still valid, check conditions involving the generals\\n valid = starting_loc[0] is not False and ending_loc[0] is not False\\n if valid:\\n # Move will put king in own king check\\n self.validate_move_general_conditions(starting_loc, ending_loc)\\n\\n # ensure move is valid to move on\\n invalid = starting_loc[0] is False or ending_loc[0] is False\\n if invalid:\\n return False\\n\\n # move piece\\n self.move_pieces(starting_loc, ending_loc)\\n piece = self._game_board[ending_loc[0]][ending_loc[1]]\\n piece.update_location(ending_loc)\\n self.set_player_turn()\\n\\n # check if game has been won\\n if self.gen_is_in_check(self._player_turn, True):\\n self.set_game_state(self._player_turn)\\n\\n self.printBoardWithStyling()\\n return True\",\n \"def execute_move(board, move):\\n\\n player, spike_index, fields_to_move = Judge._validate_move(move)\\n\\n board.set_player_perspective(player)\\n \\n if spike_index == OUT_OF_BAR_SPECIAL_MOVE:\\n dest_spike_index = fields_to_move - 1\\n board.remove_checker_from_bar()\\n else:\\n dest_spike_index = spike_index + fields_to_move\\n board.pop_player_checker(spike_index)\\n\\n if dest_spike_index >= len(INITIAL_SPIKES_STATE):\\n return board\\n\\n board.push_player_checker(dest_spike_index)\\n\\n return board\",\n \"def is_move_valid(self, direction, reference_board=None):\\n # Verify a left move does not take you off the board.\\n if (direction == \\\"l\\\"):\\n if (self._current_loc.get_column() == 0):\\n return False\\n # Verify an up move does not take you off the board.\\n elif (direction == \\\"u\\\"):\\n # Verify the move does not take you off the board.\\n if (self._current_loc.get_row() == 0):\\n return False\\n # Verify a right move does not take you off the board.\\n elif (direction == \\\"r\\\"):\\n current_row = self._current_loc.get_row()\\n max_column_number = len(self._untraversed_board[current_row])\\n if self._current_loc.get_column() + 1 == max_column_number:\\n return False\\n # Verify a down move does not take you off the board.\\n elif (direction == \\\"d\\\"):\\n if self._current_loc.get_row() + 1 == len(self._untraversed_board):\\n return False\\n else:\\n assert False, \\\"Invalid move direction.\\\"\\n\\n # Get the new location for a move in the specified direction.\\n new_location = self._calculate_move_location(direction)\\n new_row = new_location.get_row()\\n new_col = new_location.get_column()\\n # Verify the space is available\\n if(reference_board is None):\\n return BoardPath._untraversed_board[new_row][new_col] != \\\"#\\\"\\n else:\\n return reference_board[new_row][new_col] != \\\"#\\\"\",\n \"def make_move(board, move, ch):\\n board[move['row']][move['col']] = ch\\n \\n winner = board_winner(board)\\n \\n if winner is not None:\\n return True, winner\\n \\n if not board_has_move(board):\\n return True, None\\n \\n return False, None\",\n \"def make_move(self):\\r\\n if self.running and self.run:\\r\\n if self.board is None:\\r\\n SlTrace.lg(\\\"sp.board is None\\\")\\r\\n return False\\r\\n \\r\\n SlTrace.lg(\\\"running_loop self.running and self.run\\\", \\\"running_loop\\\")\\r\\n SlTrace.lg(\\\"running_loop self.start_move\\\", \\\"running_loop\\\")\\r\\n if self.start_move():\\r\\n SlTrace.lg(\\\"running_loop successful start_move\\\", \\\"running_loop\\\")\\r\\n self.next_move_no()\\r\\n SlTrace.lg(\\\"running_loop after start_move\\\", \\\"running_loop\\\")\\r\\n if self.to_pause:\\r\\n self.pause_cmd()\\r\\n self.to_pause = False\\r\\n return True\",\n \"def make_move(self, move, player):\\n if not self.test_valid_move( move):\\n return False\\n self.game_state[move[0]][move[1]] = player\",\n \"def move(x, y, direction, board):\\n\\n piece_at_xy = starter.get_piece(x, y, board); # Getting necessary pieces\\n\\n assert piece_at_xy != '*', \\\"Error in swipe logic\\\"; # Logical debug case\\n valid_direction = (direction == \\\"left\\\" or\\n direction == \\\"right\\\" or\\n direction == \\\"up\\\" or\\n direction == \\\"down\\\");\\n assert valid_direction, \\\"Invalid direction passed in\\\"; # Logical debug case\\n\\n # The new x and y for the current piece (adjacent's current position) are stored alongside adjacent (fewer ifs + redundant code)\\n if direction == \\\"left\\\":\\n adjacent = (starter.get_piece(x - 1, y, board), x - 1, y);\\n elif direction == \\\"right\\\":\\n adjacent = (starter.get_piece(x + 1, y, board), x + 1, y);\\n elif direction == \\\"up\\\":\\n adjacent = (starter.get_piece(x, y - 1, board), x, y - 1);\\n elif direction == \\\"down\\\":\\n adjacent = (starter.get_piece(x, y + 1, board), x, y + 1);\\n\\n if adjacent[0] == None: # Edge of the board case (no action taken)\\n return False;\\n\\n elif piece_at_xy != adjacent[0] and adjacent[0] != '*': # Can't combine two numbers case (no action taken)\\n return False;\\n\\n elif adjacent[0] == '*': # Empty spot adjacent case (recursive movement in direction)\\n starter.place_piece('*', x, y, board);\\n starter.place_piece(piece_at_xy, adjacent[1], adjacent[2], board);\\n move(adjacent[1], adjacent[2], direction, board);\\n return True;\\n\\n elif piece_at_xy == adjacent[0]: # Adjacent same numbers case (combine them)\\n starter.place_piece('*', x, y, board);\\n starter.place_piece(str(int(adjacent[0]) * 2), adjacent[1], adjacent[2], board);\\n move(adjacent[1], adjacent[2], direction, board);\\n return True;\\n\\n else:\\n # Logical debug case\\n assert False, \\\"No way you should be in here. Error in move logic\\\";\\n\\n return False;\",\n \"def apply_move(self, move):\\n if self.check_move(move=move):\\n self.board_list[move] = self.current_player.marker # changes value in the board to player which is either X or O\\n self.moves_made += str(move) # keeps track of all moves\\n return True\\n else:\\n return False\",\n \"def move(self):\\r\\n their_move = self.last_moves[\\\"their_move\\\"]\\r\\n return (their_move == \\\"\\\" and random.choice(moves) or their_move)\",\n \"def _isvalidmove(self, from_, to_):\\n if self.board[from_].occupant is None:\\n print(\\\"Moving from empty square\\\")\\n return False\\n piece = self.board[from_].occupant\\n\\n if piece.color != self.to_move:\\n print(\\\"Wrong color\\\")\\n return False\\n\\n if self.is_checked:\\n if piece.notation != 'K':\\n print(\\\"King is checked!\\\")\\n return False\\n\\n diff = (\\n to_cartesian(to_)[0] - to_cartesian(from_)[0],\\n to_cartesian(to_)[1] - to_cartesian(from_)[1]\\n )\\n if not piece.hopping:\\n if self.board.isblocked(from_, to_):\\n print(\\\"Move blocked by other pieces\\\")\\n return False\\n\\n if self.board[to_].occupant is not None:\\n if piece.color == self.board[to_].occupant.color:\\n print(\\\"Cannot capture friendly\\\")\\n return False\\n\\n if diff not in piece.get_captures():\\n print(\\\"Invalid piece capture\\\")\\n return False\\n\\n if diff not in piece.get_moves():\\n print(\\\"Invalid piece move\\\")\\n return False\\n\\n return True\",\n \"def validate_movement(self, piece, from_col, from_row, to_col, to_row):\\n col_diff = abs(ord(from_col) - ord(to_col))\\n row_diff = abs(from_row - to_row)\\n\\n # For any piece, it must actually move...\\n if col_diff == 0 and row_diff == 0:\\n return False\\n # ...and there must be empty spaces in between the from/to squares (when on a column, row, or diagonal)\\n if not self.validate_empty_between(from_col, from_row, to_col, to_row):\\n return False\\n\\n # White pawn\\n if piece == 'P':\\n if col_diff == 1 and (to_row - from_row == 1):\\n # Can move diagonally up one square, if taking another piece in that square or by en-passant\\n return self.piece_colour(to_col, to_row) == 'B' \\\\\\n or self.is_en_passant(from_col, from_row, to_col, to_row)\\n elif col_diff != 0:\\n # Otherwise, it can't change columns\\n return False\\n elif from_row == 2:\\n # From initial position, can go up one or two rows (but can't take a piece)\\n return (to_row == 3 or to_row == 4) and self.get_square(to_col, to_row) == ' '\\n else:\\n # Otherwise, can only move up one row (but can't take a piece)\\n return to_row - from_row == 1 and self.get_square(to_col, to_row) == ' '\\n # Black pawn\\n elif piece == 'p':\\n if col_diff == 1 and (from_row - to_row == 1):\\n # Can move diagonally down one square, if taking another piece in that square or by en-passant\\n return self.piece_colour(to_col, to_row) == 'W' \\\\\\n or self.is_en_passant(from_col, from_row, to_col, to_row)\\n elif col_diff != 0:\\n # Otherwise, it can't change columns\\n return False\\n elif from_row == 7:\\n # From initial position, can go down one or two rows (but can't take a piece)\\n return (to_row == 6 or to_row == 5) and self.get_square(to_col, to_row) == ' '\\n else:\\n # Otherwise, can only move down one row (but can't take a piece)\\n return from_row - to_row == 1 and self.get_square(to_col, to_row) == ' '\\n # Rook\\n elif piece.lower() == 'r':\\n # Must remain in same column or same row\\n return col_diff == 0 or row_diff == 0\\n # Knight\\n elif piece.lower() == 'n':\\n # Jumps in a 2+1 pattern\\n return (col_diff == 2 and row_diff == 1) or (col_diff == 1 and row_diff == 2)\\n # Bishop\\n elif piece.lower() == 'b':\\n # Moves along diagonals\\n return col_diff == row_diff\\n # Queen\\n elif piece.lower() == 'q':\\n # Can move along columns, rows, or diagonals\\n return col_diff == 0 or row_diff == 0 or col_diff == row_diff\\n # King\\n elif piece.lower() == 'k':\\n # Can move a single square in any direction\\n if not(0 <= col_diff <= 1) or not(0 <= row_diff <= 1):\\n return False\\n\\n # But not next to the other king\\n other_king = 'k' if piece.isupper() else 'K'\\n # Get valid border squares\\n border_squares = list(filter(\\n lambda b_square: 'a' <= b_square[0] <= 'f' and 1 <= b_square[1] <= 8,\\n [\\n (chr(ord(to_col) - 1), to_row - 1), (to_col, to_row - 1), (chr(ord(to_col) + 1), to_row - 1),\\n (chr(ord(to_col) - 1), to_row), (to_col, to_row), (chr(ord(to_col) + 1), to_row),\\n (chr(ord(to_col) - 1), to_row + 1), (to_col, to_row + 1), (chr(ord(to_col) + 1), to_row + 1)\\n ]\\n ))\\n # Check for the other king\\n for square in border_squares:\\n if self.get_square(square[0], square[1]) == other_king:\\n return False\\n\\n return True\",\n \"def step(self, move):\\n if self.is_done:\\n raise Exception('You have to reset the game first.')\\n action = self.get_action(move)\\n\\n # adjust in case too big, could also count as invalid if action >= field_width\\n # this is for the human player to make always valid moves\\n action = action % self.n_actions\\n\\n step_column = self.get(action, self.dim[-1] - 1)\\n is_invalid = step_column != 0\\n\\n # we should consider to end the game after an invalid move since further play might not be meaningful\\n if is_invalid: # board filled => invalid move.\\n info = {'winner': 0, 'game_over': False, 'invalid_move': True}\\n # current player is not changed.\\n self.invalid_moves += 1\\n self._is_done = True\\n return self.board, self.invalid_move_reward, self.is_done, info,\\n \\n else: # not filled => valid move\\n self.steps += 1\\n height = self.get_height(action) # getting the _height at which the token must fall to\\n self.set(action, height, value=self.current_player)\\n\\n winner = self.check_winner(self.board, action, height)\\n if winner != 0:\\n info = {'winner': winner, 'game_over': True, 'invalid_move': False}\\n reward = self.winning_reward\\n self._is_done = True\\n else:\\n if self.is_full():\\n # Tie\\n info = {'winner': winner, 'game_over': True, 'invalid_move': False}\\n reward = self.tie_reward\\n self._is_done = True\\n else:\\n # Proceeding to next turn\\n if self.check_opponent_winning(): # checks whether opponent can win\\n reward = self.losing_reward\\n else:\\n reward = 0\\n info = {'winner': winner, 'game_over': False, 'invalid_move': False}\\n self.current_player *= -1 \\n \\n return self.board, reward, self.is_done, info\",\n \"def move(self, board):\\n\\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\\n\\n while not board.check_move(move[0], move[1]):\\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\\n\\n return move\",\n \"def apply_move(b,player,move):\\n move = move.strip().lower()\\n if len(move)!=2:\\n raise Exception(\\\"Valid move is two characters (e.g. A2 or B3)\\\")\\n if move[0] not in COLS:\\n move = move[::-1]\\n if move[0] not in COLS:\\n raise Exception(\\\"No column spec found\\\")\\n j = COLS.index(move[0])\\n i = int(move[1])-1\\n if b[i][j] != \\\" \\\":\\n raise Exception(\\\"Another move already filled that position\\\")\\n b[i][j] = player\",\n \"def test_move(self):\\n # Run a handful of GCMC moves\\n n_moves = 10\\n std_gcmc_system_sampler.move(std_gcmc_system_simulation.context, n_moves)\\n\\n # Check that all of the appropriate variables seem to have been updated\\n # Hard to test individual moves as they are rarely accepted - just need to check the overall behaviour\\n assert std_gcmc_system_sampler.n_moves == n_moves\\n assert 0 <= std_gcmc_system_sampler.n_accepted <= n_moves\\n assert len(std_gcmc_system_sampler.Ns) == n_moves\\n assert len(std_gcmc_system_sampler.acceptance_probabilities) == n_moves\\n assert isinstance(std_gcmc_system_sampler.energy, Quantity)\\n assert std_gcmc_system_sampler.energy.unit.is_compatible(kilocalories_per_mole)\\n\\n return None\",\n \"def test_move(self):\\n # Run a handful of GCMC moves\\n n_moves = 10\\n std_gcmc_sphere_sampler.move(std_gcmc_sphere_simulation.context, n_moves)\\n\\n # Check that all of the appropriate variables seem to have been updated\\n # Hard to test individual moves as they are rarely accepted - just need to check the overall behaviour\\n assert std_gcmc_sphere_sampler.n_moves == n_moves\\n assert 0 <= std_gcmc_sphere_sampler.n_accepted <= n_moves\\n assert len(std_gcmc_sphere_sampler.Ns) == n_moves\\n assert len(std_gcmc_sphere_sampler.acceptance_probabilities) == n_moves\\n assert isinstance(std_gcmc_sphere_sampler.energy, Quantity)\\n assert std_gcmc_sphere_sampler.energy.unit.is_compatible(kilocalories_per_mole)\\n\\n return None\",\n \"def move(self, action):\\n \\n self.counter += 1\\n\\n if action not in self.ACTIONS:\\n raise Exception(\\\"Invalid action\\\")\\n\\n \\n\\n d_x, d_y = self.MOVEMENTS[action]\\n x, y = self.position\\n new_x, new_y = x + d_x, y + d_y\\n new_X,new_Y=self.position_to_xy(new_x, new_y)\\n \\n\\n if (new_x, new_y) not in self.cases:\\n return self._get_state(), -3, False, self.ACTIONS\\n \\n \\n \\n elif (self.openGoal(new_x,new_y))&(new_X>-400):\\n self.position = new_x, new_y\\n self.positionxy = self.position_to_xy(new_x, new_y)\\n \\n return self._get_state(), 20, True, self.ACTIONS\\n \\n # elif not self.openGoal(new_x,new_y):\\n # self.position = new_x, new_y\\n # self.positionxy = self.position_to_xy(new_x, new_y)\\n # return self._get_state(), -1, False, self.ACTIONS\\n \\n elif self.counter > 100:\\n self.position = new_x, new_y\\n self.positionxy = self.position_to_xy(new_x, new_y)\\n return self._get_state(), -1, True, self.ACTIONS\\n \\n else:\\n self.position = new_x, new_y\\n self.positionxy = self.position_to_xy(new_x, new_y)\\n return self._get_state(), -1, False, self.ACTIONS\",\n \"def test_valid_move():\\n\\n board = Board()\\n\\n # a col outside the width of the board should be false\\n assert board.valid_move(board.get_grid_size()[1] + 1) is False\\n\\n # only positive cols should be considered for a move\\n assert board.valid_move(-2) is False\\n\\n # since board is empty all cols should have moves\\n for i in range(board.get_grid_size()[1]):\\n assert board.valid_move(i) is True\\n\\n # if a col is full no move can be made\\n for i in range(board.get_grid_size()[1]):\\n if i % 2 == 0:\\n board.move(board.P1, 0)\\n else:\\n board.move(board.P2, 0)\\n\\n \\\"\\\"\\\"\\n board now looks like this...\\n \\n 0 1 2 3 4 5 6 \\n +-+-+-+-+-+-+-+\\n 0|O|-|-|-|-|-|-|0\\n +-+-+-+-+-+-+-+\\n 1|X|-|-|-|-|-|-|1\\n +-+-+-+-+-+-+-+\\n 2|O|-|-|-|-|-|-|2\\n +-+-+-+-+-+-+-+\\n 3|X|-|-|-|-|-|-|3\\n +-+-+-+-+-+-+-+\\n 4|O|-|-|-|-|-|-|4\\n +-+-+-+-+-+-+-+\\n 5|X|-|-|-|-|-|-|5\\n +-+-+-+-+-+-+-+\\n 0 1 2 3 4 5 6 \\n\\n \\\"\\\"\\\"\\n assert board.valid_move(0) is False\",\n \"def get_move(self, find_move_name):\\n frame_data = self._get_frame_data()\\n sprites = self._get_sprites()\\n\\n # Need to check both names separately\\n for move in frame_data.keys():\\n if '\\\"' in find_move_name:\\n temp_move_name = find_move_name.replace('\\\"', '')\\n if temp_move_name == move:\\n frame_data_name = move\\n break\\n else:\\n continue\\n elif find_move_name.lower() == move.lower():\\n frame_data_name = move\\n break\\n\\n else:\\n for move in frame_data.keys():\\n if find_move_name.lower() in move.lower():\\n frame_data_name = move\\n break\\n else:\\n raise MoveNotFound\\n\\n sprite_name = None\\n\\n # temporary fix for the 214/236B/22x/5AD meme\\n if '214b' in frame_data_name.lower() and not '214bc' in frame_data_name.lower():\\n for move in sprites.keys():\\n if '214A/B' in move:\\n sprite_name = move\\n break\\n elif '236b' in frame_data_name.lower() and not '236bc' in frame_data_name.lower():\\n for move in sprites.keys():\\n if '236A/B' in move:\\n sprite_name = move\\n break\\n\\n elif '22' in frame_data_name.lower():\\n for move in sprites.keys():\\n if '22A/B' in move and '22c' not in frame_data_name.lower():\\n sprite_name = move\\n break\\n elif '22A/B/C' in move and '22c' in frame_data_name.lower():\\n sprite_name = move\\n break\\n\\n elif 'reversal' in frame_data_name.lower():\\n for move in sprites.keys():\\n if '5AD' in move:\\n sprite_name = move\\n break\\n\\n for move in sprites.keys():\\n if sprite_name is not None:\\n break\\n if 'j.' in frame_data_name.lower() and ' ' in frame_data_name:\\n for split_name in frame_data_name.split(' '):\\n if move.lower() == split_name.lower():\\n sprite_name = move\\n break\\n elif move.lower() == frame_data_name.lower():\\n sprite_name = move\\n break\\n else:\\n for move in sprites.keys():\\n if sprite_name is not None:\\n break\\n if 'j.' in frame_data_name.lower() and ' ' in frame_data_name:\\n for split_name in frame_data_name.split(' '):\\n if move.lower() in split_name.lower():\\n sprite_name = move\\n break\\n elif move.lower() in frame_data_name.lower() and '22' not in find_move_name:\\n print('ok')\\n sprite_name = move\\n break\\n elif find_move_name.lower() in move.lower():\\n sprite_name = move\\n break\\n else:\\n sprite_name = None\\n\\n if sprite_name is None:\\n sprite = ''\\n else:\\n sprite = self._get_high_quality_sprite(sprites[sprite_name])\\n\\n return {\\n frame_data_name: {\\n 'fd': frame_data[frame_data_name],\\n 'sprite': sprite\\n }\\n }\",\n \"def nextMoveGNU(self, move=\\\"go\\\", board=None):\\n # get move\\n if self.pawning:\\n while not rospy.is_shutdown():\\n for row in [2,3,4,5]:\\n for col in ['a','b','c','d','e','f','g','h']:\\n p1 = board.getPiece(col,row)\\n if p1 != None and abs(p1.type) == ChessPiece.WHITE_PAWN:\\n p2 = board.getPiece(col,row+1)\\n if p2 == None:\\n # this is a candidate \\n m = col + str(row) + col + str(row+1)\\n self.history.append(m)\\n return m\\n else:\\n self.engine.sendline(move) \\n if self.engine.expect(['My move is','Illegal move']) == 1:\\n return None \\n self.engine.expect('([a-h][1-8][a-h][1-8][RrNnBbQq(\\\\r\\\\n)])')\\n m = self.engine.after.rstrip()\\n self.history.append(m)\\n return m\",\n \"def move(self, row: int, col: int, player: int):\\n def addup(dict_name, invalid_set, another_invalid, locx, locy):\\n if locx == locy:\\n diag_name = (1,1)\\n if diag_name not in invalid_set:\\n dict_name[diag_name] += 1\\n if dict_name[diag_name] == self.tar:\\n return player\\n another_invalid.add(diag_name)\\n if locx == self.tar-1-locy:\\n diag_name = (-1, -1)\\n if diag_name not in invalid_set:\\n dict_name[diag_name] += 1\\n if dict_name[diag_name] == self.tar:\\n return player\\n another_invalid.add(diag_name)\\n curcol = (locy, None)\\n currow = (None, locx)\\n if curcol not in invalid_set:\\n dict_name[curcol] += 1\\n if dict_name[curcol] == self.tar:\\n return player\\n another_invalid.add(curcol)\\n if currow not in invalid_set:\\n dict_name[currow] += 1\\n if dict_name[currow] == self.tar:\\n return player\\n another_invalid.add(currow)\\n return 0\\n res = 0\\n if (row, col) not in self.walked:\\n if player == 1:\\n res = addup(self.p1, self.invalid_1, self.invalid_2, row, col)\\n if player == 2:\\n res = addup(self.p2, self.invalid_2, self.invalid_1, row, col)\\n self.walked.add((row, col))\\n return res\",\n \"def next_move(board, player):\\n \\n move_row = \\\"move\\\"\\n move_column = \\\"move\\\"\\n\\n while not move_row.isnumeric():\\n move_row = input(\\\"{}, pick row to place your {}. > \\\".format(player.name, player.char))\\n while not move_column.isnumeric(): \\n move_column = input(\\\"Pick column in row {} to place your {}. > \\\".format(move_row, player.char))\\n\\n move_row = int(move_row)\\n move_column = int(move_column)\\n\\n move = Move(player, (move_row, move_column))\\n \\n # Check if move is out of bounds\\n if (move_row >= len(board.current_board) or\\n move_column >= len(board.current_board)):\\n print(\\\"Move out of bounds. Choose a valid move.\\\")\\n return board\\n\\n # Check if space is already used\\n if board.current_board[move_row][move_column] != \\\"-\\\":\\n print(\\\"Spot already played. Pick an unused space.\\\")\\n return board\\n\\n board.last_move = player.name\\n board.add_move(move)\\n\\n return board\",\n \"async def move(self, board, valid_actions):\\n self._move = None\\n output_move_row = Value('d', -1)\\n output_move_column = Value('d', 0)\\n try:\\n # await self.search(board, valid_actions) \\n p = Process(\\n target=self.search, \\n args=(\\n self._color, board, valid_actions, \\n output_move_row, output_move_column))\\n p.start()\\n while p.is_alive():\\n await asyncio.sleep(0.1)\\n self._move = np.array([output_move_row.value,output_move_column.value],dtype=np.int32)\\n except asyncio.CancelledError as e:\\n print('The previous player is interrupted by a user or a timer.')\\n except Exception as e:\\n print(type(e).__name__)\\n print('move() Traceback (most recent call last): ')\\n traceback.print_tb(e.__traceback__)\\n finally:\\n p.kill()\\n self._move = np.array(\\n [output_move_row.value, output_move_column.value],\\n dtype=np.int32)\\n return self.best_move\",\n \"def validate_move(board: list, character: list, direction: str) -> bool:\\n max_x_y_coordinates = board[-1]\\n valid_options = []\\n if character[1] < max_x_y_coordinates[0]:\\n valid_options.append(\\\"d\\\")\\n if character[1] > 0:\\n valid_options.append(\\\"a\\\")\\n if character[0] < max_x_y_coordinates[1]:\\n valid_options.append(\\\"s\\\")\\n if character[0] > 0:\\n valid_options.append(\\\"w\\\")\\n if direction in valid_options:\\n return True\\n else:\\n return False\",\n \"def check_score(self, move):\\n\\n i = int(move) // 7\\n j = int(move) % 7 #find the corresponding index of the input move\\n\\n if i == 0: #top\\n if self.board[i+1][j-1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i+2][j] != \\\"\\\":\\n return 1\\n return 0\\n if i == 6: #bottom\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i-1][j+1] != \\\"\\\" and self.board[i-2][j] != \\\"\\\":\\n return 1\\n return 0\\n if j == 0: #left\\n if self.board[i-1][j+1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i][j+2] != \\\"\\\":\\n return 1\\n return 0\\n if j == 6: #right\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i+1][j-1] != \\\"\\\" and self.board[i][j-2] != \\\"\\\":\\n return 1\\n return 0\\n if i == 2 or i == 4: # horizontal\\n score = 0\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i-1][j+1] != \\\"\\\" and self.board[i-2][j] != \\\"\\\":\\n score += 1\\n if self.board[i+1][j-1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i+2][j] != \\\"\\\":\\n score += 1\\n return score\\n\\n if j == 2 or j == 4: # vertical\\n score = 0\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i+1][j-1] != \\\"\\\" and self.board[i][j-2] != \\\"\\\":\\n score += 1\\n if self.board[i-1][j+1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i][j+2] != \\\"\\\":\\n score += 1\\n return score\",\n \"def move_possible(x, y, board):\\n\\n piece_at_xy = starter.get_piece(x, y, board);\\n if piece_at_xy == None:\\n return False;\\n elif piece_at_xy == '*': # An empty space means a move is always possible\\n return True;\\n\\n return (\\n piece_at_xy == starter.get_piece(x + 1, y, board) or\\n piece_at_xy == starter.get_piece(x - 1, y, board) or\\n piece_at_xy == starter.get_piece(x, y + 1, board) or\\n piece_at_xy == starter.get_piece(x, y - 1, board)\\n );\",\n \"def fix_move(self, invalid_move: QMove):\\n\\n # TODO: reduce time_per_game second by second\\n ERROR_MSG = f\\\"INVALID_MOVE {invalid_move.to_string()}\\\"\\n\\n if self.is_ai and self.proc is not None:\\n self.proc.stdin.write(str.encode(ERROR_MSG + '\\\\n'))\\n self.proc.stdin.flush()\\n new_move = QMove(os.read(self.proc.stdout.fileno(), 100))\\n else:\\n new_move = QMove(\\n input(\\\"Move was invalid, enter a valid move:\\\\n\\\\t>> \\\"))\\n\\n return new_move\",\n \"def move(self, motor, position, timeout):\\n \\n try:\\n caput(motor, position, wait=True, timeout=timeout)\\n except:\\n e = sys.exc_info()\\n print str(e)\\n print \\\"ERROR: caput failed.\\\"\\n print (motor + \\\" pos:\\\" + str(position) + \\\" timeout:\\\" + str(timeout))\\n return self.__g.FAIL\\n \\n rdbd = motor + \\\".RDBD\\\"\\n rbv = motor + \\\".RBV\\\"\\n\\n final_pos = caget(rbv)\\n deadband = caget(rdbd)\\n\\n success = True\\n\\n if ((final_pos < position-deadband) or (final_pos > position+deadband)):\\n print \\\"ERROR: final_pos out of deadband.\\\"\\n print (motor + \\\" \\\" + str(position) + \\\" \\\" + str(timeout) + \\\" \\\" \\n + str(final_pos) + \\\" \\\" + str(deadband))\\n success = False\\n\\n if (success):\\n return self.postMoveCheck(motor)\\n else:\\n self.postMoveCheck(motor)\\n return self.__g.FAIL\",\n \"def move(puzzle: str, direction: str):\\r\\n position_index = puzzle.index(EMPTY)\\r\\n position = position_index + 1\\r\\n grid_width = get_grid_width(puzzle)\\r\\n\\r\\n # What direction to moved the tile if it's a valid move\\r\\n if direction == UP:\\r\\n if (position) > grid_width:\\r\\n return swap_position(puzzle, position_index, position_index - grid_width)\\r\\n\\r\\n elif direction == DOWN:\\r\\n if (len(puzzle) - position) >= grid_width:\\r\\n return swap_position(puzzle, position_index, position_index + grid_width)\\r\\n\\r\\n elif direction == LEFT:\\r\\n if (position - 1) % grid_width != 0:\\r\\n return swap_position(puzzle, position_index, position_index - 1)\\r\\n\\r\\n elif direction == RIGHT:\\r\\n if position % grid_width != 0:\\r\\n return swap_position(puzzle, position_index, position_index + 1)\\r\\n\\r\\n return None\",\n \"def is_valid_move(x:int, y:int,board_length) -> bool:\\n if x < 0 or y < 0 or x == board_length or y == board_length:\\n return False\\n return True\",\n \"def move(self):\\r\\n my_move = self.last_moves[\\\"my_move\\\"]\\r\\n return (my_move != \\\"\\\" and moves[(moves.index(my_move)+1) % 3] or\\r\\n random.choice(moves))\",\n \"def find_best_move(state: GameState) -> None:\",\n \"def nextMoveGNU(self, move=\\\"go\\\", board=None):\\n # get move\\n if self.pawning:\\n while not rospy.is_shutdown():\\n rows = [2,3,4,5]\\n piece = ChessPiece.WHITE_PAWN\\n if board.side == board.BLACK:\\n rows = [7,6,5,4]\\n piece = ChessPiece.BLACK_PAWN\\n for row in rows:\\n for col in ['a','b','c','d','e','f','g','h']:\\n p1 = board.getPiece(col,row)\\n if p1 != None and abs(p1.type) == piece:\\n p2 = board.getPiece(col,row+1)\\n if p2 == None:\\n # this is a candidate\\n m = col + str(row) + col + str(row+1)\\n self.history.append(m)\\n return m\\n else:\\n self.engine.sendline(move)\\n if self.engine.expect(['My move is','Illegal move']) == 1:\\n return None\\n self.engine.expect('([a-h][1-8][a-h][1-8][RrNnBbQq(\\\\r\\\\n)])')\\n m = self.engine.after.rstrip()\\n self.history.append(m)\\n return m\",\n \"def makeMove(self, moveStr):\\r\\n\\t\\tmoveStr = str(moveStr)\\r\\n\\r\\n\\t\\tmoveUci = self._userParseSanToUci(moveStr)\\r\\n\\t\\t# print(moveUci)\\r\\n\\r\\n\\t\\tif moveUci is None:\\r\\n\\t\\t\\treturn\\r\\n\\r\\n\\t\\tresponse = requests.post(f'https://lichess.org/api/board/game/{self.gameId}/move/{moveUci}', headers=self.authHeader)\\r\\n\\r\\n\\t\\tif response.status_code == 200:\\r\\n\\t\\t\\tlog.debug('Move Successfully Sent')\\r\\n\\r\\n\\t\\telse:\\r\\n\\t\\t\\tlog.warning(f'Move Unsuccessfully Sent. Status Code: {response.status_code}')\",\n \"def make_move(self, board):\\n user_input = self.get_user_input(\\n 'coordinates of next move (x,y): '\\n )\\n move = self.transform_user_input(user_input)\\n\\n valid = board.move_is_valid(move)\\n while not valid:\\n user_input = self.get_user_input(\\n 'Invalid move, coordinate of next move: '\\n )\\n move = self.transform_user_input(user_input)\\n valid = board.move_is_valid(move)\\n board.set_piece(move, color=self.color)\",\n \"def process_move(player, board):\\r\\n c = player.__repr__()\\r\\n print(c, \\\"'s turn\\\")\\r\\n move = player.next_move(board)\\r\\n board.add_checker(player.checker, move)\\r\\n print()\\r\\n print(board)\\r\\n if board.is_win_for(player.checker):\\r\\n i = player.num_moves\\r\\n print(player.__repr__(), \\\"wins in \\\", i, \\\"moves\\\")\\r\\n print(\\\"Congratulations!\\\")\\r\\n return True\\r\\n elif board.is_full() and not board.is_win_for(player.checker):\\r\\n print(\\\"It's a tie!\\\")\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def move_piece(self, move: Move, ignore_check=False):\\n # no need to check the values of Position as they are guaranteed to be sane.\\n\\n # sanity: cant move from a position onto itself\\n if move.from_pos.to_str == move.to_pos.to_str: # TODO compare by value, not str\\n raise InvalidMoveException('cant move a piece onto itself (%s)' % move.from_pos.to_str())\\n\\n piece = self.rubric(move.from_pos)\\n if piece is None:\\n raise Exception(\\\"assert failed: found empty rubric at: %s\\\" % move.from_pos.to_str())\\n\\n # sanity: there must be a piece in the start position:\\n if piece.piece_type == PieceType.PLACEHOLDER:\\n raise InvalidMoveException('cant move from empty rubric (%s)' % (move.from_pos.to_str()))\\n\\n # sanity: ensure the move is valid for this turn's color\\n if piece.color != self._current_side_color:\\n raise InvalidMoveException(\\\"cant move a piece of this color at this turn\\\")\\n\\n # sanity: if capturing, pieces must have different colors\\n captured_piece = self.rubric(move.to_pos)\\n if captured_piece.piece_type != PieceType.PLACEHOLDER:\\n if captured_piece.color == piece.color:\\n raise InvalidMoveException('cant capture a piece of the same color (start: %s, end: %s)' %\\n (move.from_pos.to_str(), move.to_pos.to_str()))\\n\\n # handle movement in Check\\n king_attackers = self.get_king_attackers()\\n if not ignore_check and len(king_attackers) > 0:\\n # if the king is under attack (== Check), the only valid moves are those that\\n # resolve the situation:\\n # a) moving the king to an unattacked position\\n # b) capturing the attacker by the king, provided its position is NOT attacked by another piece\\n # c) capturing the attacker by another piece\\n # d) blocking the check by a rook, queen or bishop: placing them between\\n # the king and the attacker - NOTE I will not implement this\\n\\n # if there are no valid moves, the game is over (Checkmate) - this is handled elsewhere\\n\\n # determine if the move will resolve the Check:\\n # create a copy of the board, run the proposed move (ignore the check)\\n # and then determine if we're still in check afterwards\\n board_copy = self.get_board_copy()\\n board_copy.move_piece(move, ignore_check=True)\\n if len(board_copy.get_king_attackers()) > 0:\\n # the move did not resolve the check, so it wasnt valid:\\n raise InvalidMoveException(\\\"move failed to resolve Check\\\")\\n\\n if piece.is_valid_move(move, self._rubrics):\\n # handle capture scenario\\n if captured_piece.piece_type != PieceType.PLACEHOLDER:\\n # remove the captured piece\\n self.remove_piece(captured_piece)\\n\\n # move the piece to its destination\\n self.set_rubric(piece, move.to_pos)\\n\\n # set empty placeholder in the origin rubric\\n self.set_rubric(PlaceHolder(Position), move.from_pos)\\n\\n return True\",\n \"def _maybe_move(self, source_chunk, target_chunk, path_index, move_func):\\n if len(source_chunk.paths) <= 1:\\n return False\\n\\n move_time = source_chunk.paths[path_index].time\\n\\n new_source_badness = self._badness(source_chunk.time - move_time)\\n new_target_badness = self._badness(target_chunk.time + move_time)\\n\\n delta_badness = ((new_source_badness + new_target_badness) -\\n (source_chunk.badness + target_chunk.badness))\\n if delta_badness < 0:\\n move_func()\\n return True\\n\\n return False\",\n \"def choose_move(self):\\n return 0\",\n \"def is_legal_move(self, start_pos, end_pos, start_piece, end_piece_player_id, board):\\r\\n parsed_positions = self.parse_positions(start_pos, end_pos)\\r\\n\\r\\n start_row = parsed_positions[0]\\r\\n start_col = parsed_positions[1]\\r\\n end_row = parsed_positions[2]\\r\\n end_col = parsed_positions[3]\\r\\n count = 0 # Count will track how many pieces are between start and end_pos\\r\\n\\r\\n if start_row != end_row and start_col != end_col: # Moving diagonally\\r\\n return False\\r\\n\\r\\n # If cannon moves to an empty position\\r\\n # if end_piece_player_id is None:\\r\\n\\r\\n if start_row == end_row: # Moving horizontally\\r\\n col_difference = end_col - start_col\\r\\n\\r\\n if col_difference > 0: # Moving to the right of the board\\r\\n for col in range(start_col + 1, end_col): # Checks if there is a piece between start_col and end_col\\r\\n if board[start_row][col].get_piece() is not None:\\r\\n count += 1\\r\\n\\r\\n if col_difference < 0: # Moving to the left of the board\\r\\n for col in range(start_col - 1, end_col, -1): # Checks to the left of the board\\r\\n # If there is a piece to block movement to the end_pos, return False\\r\\n if board[start_row][col].get_piece() is not None:\\r\\n count += 1\\r\\n\\r\\n if start_col == end_col: # Moving vertically\\r\\n row_difference = end_row - start_row\\r\\n\\r\\n if row_difference > 0: # Moving down the board\\r\\n for row in range(start_row + 1, end_row):\\r\\n if board[row][start_col].get_piece() is not None: # If no piece is impeding path to end_pos\\r\\n count += 1\\r\\n\\r\\n\\r\\n if row_difference < 0: # Moving up the board\\r\\n for row in range(start_row -1, end_row, -1):\\r\\n if board[row][start_col].get_piece() is not None: # If no piece is impeding path to end_pos\\r\\n count += 1\\r\\n\\r\\n # 1 piece between start_pos and end_pos and end_pos contains a chess piece\\r\\n if count == 1 and end_piece_player_id is not None:\\r\\n return True\\r\\n # end_pos has no piece and there are no pieces to impede path\\r\\n elif end_piece_player_id is None and count == 0:\\r\\n return True\\r\\n # Returns False for all other scenarios\\r\\n else:\\r\\n return False\",\n \"def validate_move(board: list, character: dict, direction: str) -> bool:\\n if direction.strip().upper() == \\\"N\\\":\\n return (character[\\\"Position\\\"][0] - 1, character[\\\"Position\\\"][1]) in board\\n elif direction.strip().upper() == \\\"S\\\":\\n return (character[\\\"Position\\\"][0] + 1, character[\\\"Position\\\"][1]) in board\\n elif direction.strip().upper() == \\\"W\\\":\\n return (character[\\\"Position\\\"][0], character[\\\"Position\\\"][1] - 1) in board\\n elif direction.strip().upper() == \\\"E\\\":\\n return (character[\\\"Position\\\"][0], character[\\\"Position\\\"][1] + 1) in board\\n else:\\n print(\\\"Please enter only directions shown above\\\")\\n return False\",\n \"def make_move(self, board: Board) -> int:\\n return random.choice(board.get_valid_moves())\",\n \"def is_valid_move(self, position: Point) -> bool:\\n\\t\\tif self.tiles[position.x][position.y] == 0:\\n\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def move(self,x_pos,y_pos,x_dir,y_dir,user):\\r\\n\\r\\n if self.finished == True:\\r\\n return Move.finish\\r\\n\\r\\n size = len(self)\\r\\n if not self.__on_the_board(x_pos+x_dir, y_pos+y_dir):\\r\\n return Move.out_of_bounds\\r\\n\\r\\n moving_piece = self.board[y_pos][x_pos]\\r\\n\\r\\n if moving_piece == 0 or user * moving_piece < 0:\\r\\n return Move.no_piece_found\\r\\n\\r\\n if not self.__valid_direction(x_dir,y_dir):\\r\\n return Move.invalid_destination\\r\\n\\r\\n if self.board[y_pos+y_dir][x_pos+x_dir] != 0:\\r\\n return Move.path_blocked\\r\\n \\r\\n # List of moves the user can make if captures are possible.\\r\\n user_capture_options = [forced_move for forced_move in self.capture_options if forced_move.user == moving_piece]\\r\\n\\r\\n if user_capture_options != []:\\r\\n if not self.__is_capture_move(moving_piece,x_pos,y_pos,x_dir,y_dir):\\r\\n return Move.capture_ignored\\r\\n \\r\\n if abs(x_dir) == 1:\\r\\n return self.__move_single_tile(moving_piece,x_pos,y_pos,x_dir,y_dir,user)\\r\\n\\r\\n if abs(x_dir) == 2:\\r\\n return self.__move_double_tile(moving_piece,x_pos,y_pos,x_dir,y_dir,user)\\r\\n\\r\\n # This code should not be reached, as abs(x_dir) is forced to be either 1 or 2 before looking at the if-clauses.\\r\\n # However, may this filter ever need to be altered, here's a handy error to let you know that you did something wrong.\\r\\n raise NotImplementedError(\\\"This code should not be reached!\\\")\",\n \"def test_perform_move(self):\\n p = hw.create_tile_puzzle(3, 3)\\n self.assertFalse(p.perform_move(\\\"taco\\\"))\\n self.assertTrue(p.perform_move('up'))\\n self.assertEqual(p.get_board(), [[1,2,3],[4,5,0],[7,8,6]])\\n self.assertFalse(p.perform_move('right'))\\n p = hw.create_tile_puzzle(2, 4)\\n self.assertTrue(p.perform_move('left'))\\n self.assertTrue(p.perform_move('up'))\\n self.assertFalse(p.perform_move('up'))\\n self.assertEqual(p.get_board(), [[1,2,0,4],[5,6,3,7]])\\n p = hw.create_tile_puzzle(1, 4)\\n self.assertTrue(p.perform_move('left'))\\n self.assertTrue(p.perform_move('left'))\\n self.assertTrue(p.perform_move('left'))\\n self.assertFalse(p.perform_move('down'))\\n self.assertFalse(p.perform_move('left'))\\n self.assertEqual(p.get_board(), [[0,1,2,3]])\",\n \"def is_pawn_move_valid(self, from_row, from_col, to_row, to_col):\\n # Setup variables used\\n piece = self.board.squares[from_row][from_col]\\n piece_color = self.piece_color(piece)\\n to_piece = self.board.squares[to_row][to_col]\\n row_diff = abs(from_row - to_row)\\n col_diff = abs(from_col - to_col)\\n dc = 0\\n\\n # Set flag for first move of pawn\\n first_move = True if from_row == 6 or from_row == 1 else False\\n\\n # If direction is not correct for white, exit\\n if to_row - from_row > 0:\\n dr = 1\\n if self.piece_color(piece) == \\\"white\\\":\\n return False\\n\\n # If direction is not correct for black, exit\\n if to_row - from_row < 0:\\n dr = -1\\n if self.piece_color(piece) == \\\"black\\\":\\n return False\\n\\n # If moving straight\\n if from_col == to_col:\\n # if not legal straight move, exit\\n if not (row_diff == 1 or (first_move and row_diff == 2)):\\n return False\\n\\n # make sure to move has no pieces on straight path\\n dm = row_diff + 1\\n\\n # return value\\n retVal = self._any_piece_in_way(from_row, from_col, dr, dc, dm)\\n\\n# if retVal and not self.testing:\\n# # self.pawn_promotion(to_row, to_col, piece_color)\\n# self.board.overwrite_board_square(to_row, to_col)\\n# if piece_color == \\\"black\\\":\\n# self.board.put_piece(self.B_QUEEN, to_row, to_col)\\n# else:\\n# self.board.put_piece(self.W_QUEEN, to_row, to_col)\\n\\n return retVal\\n\\n # WHITE en passant\\n # move from moveHistory => (\\\"piece\\\", fromRow, fromCol, toRow, toCol)\\n if (self.moveHistory[-1][2] == self.moveHistory[-1][4] == (to_col)) and \\\\\\n self.moveHistory[-1][0] == \\\"♟\\\" and self.moveHistory[-1][1] == 1 and\\\\\\n self.moveHistory[-1][3] == 3 and piece_color == \\\"white\\\":\\n if col_diff == 1 and row_diff == 1 and to_piece == None:\\n if not self.testing:\\n self.board.overwrite_board_square(self.moveHistory[-1][3], self.moveHistory[-1][4])\\n self.board.squares[self.moveHistory[-1][3]][self.moveHistory[-1][4]] = None\\n return True\\n\\n # BLACK en passant\\n if (self.moveHistory[-1][2] == self.moveHistory[-1][4] == (to_col)) and \\\\\\n self.moveHistory[-1][0] == \\\"♙\\\" and self.moveHistory[-1][1] == 6 and\\\\\\n self.moveHistory[-1][3] == 4 and piece_color == \\\"black\\\":\\n if col_diff == 1 and row_diff == 1 and to_piece == None:\\n if not self.testing:\\n self.board.overwrite_board_square(self.moveHistory[-1][3], self.moveHistory[-1][4])\\n self.board.squares[self.moveHistory[-1][3]][self.moveHistory[-1][4]] = None\\n return True\\n\\n # else move must be taking piece directly move\\n # if legal taking piece move and (opponent-already check for own piece) piece at to-square\\n if col_diff == 1 and row_diff == 1 and to_piece != None:\\n\\n# if not self.testing:\\n# # self.pawn_promotion(to_row, to_col, piece_color)\\n# self.board.overwrite_board_square(to_row, to_col)\\n# if piece_color == \\\"black\\\":\\n# self.board.put_piece(self.B_QUEEN, to_row, to_col)\\n# else:\\n# self.board.put_piece(self.W_QUEEN, to_row, to_col)\\n return True\\n\\n return False\",\n \"def get_next_move(self):\\n return int(input('Enter your move: '))\",\n \"def move(self, board):\\n\\n if board.get_number_of_moves() == 0:\\n random_row = randint(0, 2)\\n random_column = randint(0, 2)\\n\\n if random_row == 1 or random_column == 1:\\n random_row = 1\\n random_column = 1\\n elif random_row == 2:\\n random_row = board.get_dimension()-1\\n\\n if random_column == 2:\\n random_column = board.get_dimension()-1\\n\\n move = (random_row, random_column)\\n elif board.get_number_of_moves() == 1 or board.get_number_of_moves() == 2:\\n if board.get_piece(1,1) == ' ':\\n move = (1, 1)\\n else:\\n board_dimension = board.get_dimension()-1\\n corners = [(0, 0), (0, board_dimension), (board_dimension, 0), (board_dimension, board_dimension)]\\n corners = self.remove_filled_positions(corners, board)\\n\\n move = corners[randint(0, len(corners)-1)]\\n else:\\n move = self.check_for_winner(board)\\n\\n if move == (-1, -1):\\n board_dimension = board.get_dimension()-1\\n corner1_moves = self.remove_filled_positions([(0, 0), (2, 2)], board)\\n corner2_moves = self.remove_filled_positions([(0, 2), (2, 0)], board)\\n\\n non_corner_moves = self.remove_filled_positions([(1, 0), (2, 1), (1, 2), (0, 1)], board)\\n\\n center_piece = board.get_piece(1, 1)\\n corner_pieces = [board.get_piece(0, 0), board.get_piece(board_dimension, 0), board.get_piece(0, board_dimension), board.get_piece(board_dimension, board_dimension)]\\n\\n if corner_pieces[0] != self._piece and corner_pieces[0] != ' ' and corner_pieces[0] == corner_pieces[3]:\\n move = non_corner_moves[randint(0, 3)]\\n elif corner_pieces[1] != self._piece and corner_pieces[1] != ' ' and corner_pieces[1] == corner_pieces[2]:\\n move = non_corner_moves[randint(0, 3)]\\n elif len(corner2_moves) > 0 and corner_pieces[0] != self._piece and corner_pieces[0] == center_piece and corner_pieces[3] == self._piece:\\n move = corner2_moves[0]\\n elif len(corner1_moves) > 0 and corner_pieces[1] != self._piece and corner_pieces[1] == center_piece and corner_pieces[2] == self._piece:\\n move = corner1_moves[0]\\n elif len(corner1_moves) > 0 and corner_pieces[2] != self._piece and corner_pieces[2] == center_piece and corner_pieces[1] == self._piece:\\n move = corner1_moves[0]\\n elif len(corner2_moves) > 0 and corner_pieces[3] != self._piece and corner_pieces[3] == center_piece and corner_pieces[0] == self._piece:\\n move = corner2_moves[0]\\n else:\\n move = self.can_complete_two_in_row(board)\\n\\n if move == (-1, -1):\\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\\n\\n while not board.check_move(move[0], move[1]):\\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\\n\\n return move\",\n \"def askMove(self,posibleMoves):\\n print(\\\"Where will you move?\\\")\\n while True:\\n pos = raw_input(\\\"Type Colum and Row 'CR' Ex:a1 for first column/row: \\\")\\n if len(pos) == 2:\\n c = ord(pos[0])-97\\n r = int(pos[1])-1\\n move = c+r*8\\n if move in posibleMoves:\\n return move\\n print(\\\"Invalid move, try again\\\")\\n return\",\n \"def parse_move(move):\\n if not (len(move) == 2):\\n return None, None\\n try:\\n row = ord(move[0].upper()) - 65\\n col = int(move[1])\\n except:\\n return None, None\\n return row, col\",\n \"def move(self, row, column, symbol):\\n game_state = self.determine_game_state()\\n if game_state not in (GameState.GAME_NOT_STARTED, GameState.GAME_IN_PROGRESS):\\n return MoveResults.MOVE_INVALID\\n\\n # check for initial move\\n if self.board == BLANK_BOARD and symbol == O_SYMBOL:\\n return MoveResults.MOVE_INVALID\\n\\n # check for invalid row and column\\n if row < 0 or row > 2 or column < 0 or column > 2:\\n return MoveResults.MOVE_INVALID\\n\\n # make sure the game piece is valid\\n if symbol != X_SYMBOL and symbol != O_SYMBOL:\\n return MoveResults.MOVE_INVALID\\n\\n # make sure the game piece isn't moving out of turn\\n x_moves = self.board.count(X_SYMBOL)\\n o_moves = self.board.count(O_SYMBOL)\\n if symbol == X_SYMBOL and x_moves > o_moves:\\n return MoveResults.MOVE_INVALID\\n elif symbol == O_SYMBOL and o_moves >= x_moves:\\n # note that x always goes first.\\n return MoveResults.MOVE_INVALID \\n\\n # figure out position.\\n position = (3 * row) + column\\n\\n # make sure there's not already a piece there.\\n if self.board[position] != EMPTY_SYMBOL:\\n return MoveResults.MOVE_INVALID\\n\\n self.board = self.board[:position] + symbol + self.board[position+1:] \\n return MoveResults.MOVE_VALID\",\n \"def test_human_move_char(self):\\n self.ri.return_value = 'c'\\n assert False == self.T.human_move()\",\n \"def is_valid_move(self, move: Any) -> bool:\\n return move in self.get_possible_moves()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70150155","0.676564","0.6584836","0.6568262","0.6568117","0.6563307","0.6558569","0.64923847","0.64853036","0.6477489","0.645601","0.6451311","0.6451188","0.64166987","0.6414095","0.63909614","0.63762623","0.636292","0.6332474","0.63271296","0.63165474","0.6294423","0.6260845","0.625517","0.6251586","0.62417924","0.6229471","0.6223107","0.6220301","0.6190982","0.617644","0.61361843","0.61217","0.6101234","0.60865843","0.6076552","0.6073566","0.6059842","0.6054433","0.605089","0.6050613","0.6046398","0.6046322","0.6045086","0.6041365","0.60356534","0.60348153","0.60320616","0.60260147","0.60234237","0.60175395","0.6001694","0.5991489","0.5986339","0.59817743","0.5975904","0.5975037","0.59716666","0.5969712","0.5967952","0.59671646","0.5965469","0.59599257","0.59503084","0.5941529","0.59398395","0.5938152","0.5937943","0.5937562","0.5934122","0.5929441","0.59280646","0.59250766","0.592403","0.59234107","0.59057945","0.590375","0.58987814","0.5897164","0.589089","0.5887902","0.5864931","0.5863642","0.58553916","0.58510023","0.5849331","0.5841226","0.58408874","0.58407456","0.5830376","0.5822612","0.5821459","0.58180714","0.5808107","0.58081067","0.58067983","0.58012354","0.57960457","0.5795296","0.5791194"],"string":"[\n \"0.70150155\",\n \"0.676564\",\n \"0.6584836\",\n \"0.6568262\",\n \"0.6568117\",\n \"0.6563307\",\n \"0.6558569\",\n \"0.64923847\",\n \"0.64853036\",\n \"0.6477489\",\n \"0.645601\",\n \"0.6451311\",\n \"0.6451188\",\n \"0.64166987\",\n \"0.6414095\",\n \"0.63909614\",\n \"0.63762623\",\n \"0.636292\",\n \"0.6332474\",\n \"0.63271296\",\n \"0.63165474\",\n \"0.6294423\",\n \"0.6260845\",\n \"0.625517\",\n \"0.6251586\",\n \"0.62417924\",\n \"0.6229471\",\n \"0.6223107\",\n \"0.6220301\",\n \"0.6190982\",\n \"0.617644\",\n \"0.61361843\",\n \"0.61217\",\n \"0.6101234\",\n \"0.60865843\",\n \"0.6076552\",\n \"0.6073566\",\n \"0.6059842\",\n \"0.6054433\",\n \"0.605089\",\n \"0.6050613\",\n \"0.6046398\",\n \"0.6046322\",\n \"0.6045086\",\n \"0.6041365\",\n \"0.60356534\",\n \"0.60348153\",\n \"0.60320616\",\n \"0.60260147\",\n \"0.60234237\",\n \"0.60175395\",\n \"0.6001694\",\n \"0.5991489\",\n \"0.5986339\",\n \"0.59817743\",\n \"0.5975904\",\n \"0.5975037\",\n \"0.59716666\",\n \"0.5969712\",\n \"0.5967952\",\n \"0.59671646\",\n \"0.5965469\",\n \"0.59599257\",\n \"0.59503084\",\n \"0.5941529\",\n \"0.59398395\",\n \"0.5938152\",\n \"0.5937943\",\n \"0.5937562\",\n \"0.5934122\",\n \"0.5929441\",\n \"0.59280646\",\n \"0.59250766\",\n \"0.592403\",\n \"0.59234107\",\n \"0.59057945\",\n \"0.590375\",\n \"0.58987814\",\n \"0.5897164\",\n \"0.589089\",\n \"0.5887902\",\n \"0.5864931\",\n \"0.5863642\",\n \"0.58553916\",\n \"0.58510023\",\n \"0.5849331\",\n \"0.5841226\",\n \"0.58408874\",\n \"0.58407456\",\n \"0.5830376\",\n \"0.5822612\",\n \"0.5821459\",\n \"0.58180714\",\n \"0.5808107\",\n \"0.58081067\",\n \"0.58067983\",\n \"0.58012354\",\n \"0.57960457\",\n \"0.5795296\",\n \"0.5791194\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6508602"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":2668,"cells":{"query":{"kind":"string","value":"Returns the winning peice if the game is over. If the game is a draw it returns the empty peice, and if the game is not over returns false."},"document":{"kind":"string","value":"def isOver(self):\n\t\tisFull = Piece.BLANK\n\t\tfor a,b,c in [[0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 3, 6], [1, 4, 7], [2, 5, 8], [0, 4, 8], [2, 4, 6]]:\n\t\t\tif (self.board[a] is self.board[b] is self.board[c] and self.board[a] is not Piece.BLANK):\n\t\t\t\treturn self.board[a]\n\t\t\tif (self.board[a] is Piece.BLANK or self.board[b] is Piece.BLANK or self.board[c] is Piece.BLANK):\n\t\t\t\tisFull = False\n\t\treturn isFull"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False","def is_game_over(self):\r\n\r\n if self.winner != 0:\r\n return True\r\n\r\n return False","def is_game_over(self):\n\n # This checks whether or not the board is full...\n if len(self.board.values()) == 100 and \\\n 0 not in self.board.values():\n p1 = self._longest_chain(1)\n p2 = self._longest_chain(2)\n if len(p1) > len(p2):\n return 1\n elif len(p2) > len(p1):\n return 2\n else:\n return 0\n\n # If it's not full. We check for boxes\n else:\n for x in range(self.width-1):\n for y in range(self.height-1):\n slice = self._slice((x,y), (2,2))\n if 0 not in slice[0] and 0 not in slice[1]:\n # is this slice a box?\n if slice[0][0] == slice[0][1] and \\\n slice[0][1] == slice[1][0] and \\\n slice[1][0] == slice[1][1]:\n return slice[0][0] # winner\n\n return -1 # game is not over","def is_game_over(self):\n bk = False\n wk = False\n\n # Find the kings\n for row in range(8):\n for col in range(8):\n if self.board.squares[row][col] == ChessPiece.B_KING: # Black king symbol\n bk = True\n break\n if self.board.squares[row][col] == ChessPiece.W_KING: # Black king symbol\n wk = True\n break\n\n # If a king is missing, end the game. This fixes a bug we were having\n if bk == False:\n return 1\n if wk == False:\n return 2\n\n if self.white_wins():\n return 1\n elif self.black_wins():\n return 2\n elif self.tie():\n return 3\n else:\n return 0","def is_over(self):\n alive_players = [1 if p.status == \"alive\" else 0 for p in self.players]\n # If only one player is alive, the game is over.\n if sum(alive_players) == 1:\n return True\n\n # If all rounds are finshed\n if self.round_counter >= 2:\n return True\n return False","def is_over(self):\n winner = TictactoeMatch.get_winner(self.inputs_)\n if winner:\n self.result_ = winner\n if Config.USER['debug']['enabled']:\n print \"It is over! Player \"+str(self.result_)+\" (\"+str(self.player_label_[self.result_])+\") wins!\"\n return True\n for value in self.inputs_:\n if value == TictactoeMatch.EMPTY:\n if Config.USER['debug']['enabled']:\n print \"Go!\"\n return False\n self.result_ = TictactoeMatch.DRAW\n if Config.USER['debug']['enabled']:\n print \"It is over! Draw!\"\n return True","def check_game_over(self):\n red, blue = self.board.count_piece()\n if blue == 0:\n self.ui.show_result(\"RED WIN!\")\n self.turn = RED\n elif red == 0:\n self.ui.show_result(\"BLUE WIN!\")\n self.turn = BLUE\n elif red == blue == 1:\n self.ui.show_result(\"DRAW!\")","def game_over(self):\n return bool(self.last_round and self.last_player == self.current_player)","def check_over(self):\n if self.board.has_winner() == 1:\n return 1\n elif self.board.has_winner() == 2:\n return 2\n elif self.board.check_cats_game():\n return 0\n else:\n return -1","def verify_winner(self):\r\n return self.count_pegs() == 1","def _check_game_over(self):\n return self.game_board.check_game_over()","def game_over(self):\n # TODO: Define the game over condition for Adventure.\n # use self.over to determine if the game if over\n return self.over","def game_over(self):\n red_minion = 0\n blue_minion = 0\n red_master = 0\n blue_master = 0\n only_masters = True\n for row in self.board:\n for piece in row:\n if piece != 0:\n if not piece.master:\n if piece.player:\n blue_minion += 1\n else:\n red_minion += 1\n only_masters = False\n else:\n if piece.player:\n blue_master += 1\n else:\n red_master += 1\n if blue_minion + blue_master == 0:\n self.winner = \"Red\"\n self.red_victories += 1\n self.number_of_games +=1\n self.game_over_screen()\n return True\n elif red_minion + red_master == 0:\n self.winner = \"Blue\"\n self.blue_victories += 1\n self.number_of_games +=1\n self.game_over_screen()\n return True\n elif only_masters:\n if red_master > blue_master:\n self.winner = \"Red\"\n self.red_victories += 1\n elif blue_master > red_master:\n self.winner = \"Blue\"\n self.blue_victories += 1\n else:\n self.winner = \"Nobody\"\n self.number_of_games +=1\n self.game_over_screen()\n return True\n \n return False","def game_over(players):\n active_players = players_with_decks(players)\n if not active_players or len(active_players) == 1:\n return True\n return False","def is_game_over(self):\n board = list(self.board)\n for wins in self.WINNING:\n # Create a tuple\n w = (board[wins[0]], board[wins[1]], board[wins[2]])\n if w == ('X', 'X', 'X'):\n return 'X'\n if w == ('O', 'O', 'O'):\n return 'O'\n # Check for stalemate\n if ' ' in board:\n return None\n return ' '","def check_win(self):\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)","def check_win(self):\r\n wins = [self.check_rows(), self.check_cols(), self.check_diag()]\r\n for case, pos in wins:\r\n if case != -1:\r\n print('Game over!')\r\n if self.grid[case][-1] == self.computer:\r\n print('The computer won!')\r\n return (True, pos)\r\n print('The player won!')\r\n return (True, pos)\r\n\r\n return (self.check_draw(), None)","def check_if_game_over():\n check_for_winner()\n check_for_tie()","def game_over(state):\n return wins(state, HUMAN) or wins(state, COMP)","def game_over(state):\r\n return wins(state, HUMAN) or wins(state, COMP)","def check_if_game_over():\n # Calling check for winners.\n check_for_winner()\n # Calling check it's tie or not.\n check_if_tie()","def gameOver(self):\n\t\treturn self.lives == 0","def __game_is_over(self, x, y):\n\t\tif np.count_nonzero(self.board) >= 42:\n\t\t\treturn True\n\n\t\tlines = self.__extract_lines(x, y)\n\n\t\tfor line in lines:\n\t\t\tif self.__winner_in_line(line) != 0:\n\t\t\t\treturn True\n\n\t\treturn False","def _checkRoundOver(self):\n\n if not any(player.isAlive() for player in self.teams[0].players):\n self.endGame()","def check_game(self):\n gameOver = None\n if self.turn > 4:\n gameOver = self.check_x_won()\n if gameOver is True:\n self.game_x_won()\n return\n\n gameOver = None\n if self.turn > 5:\n gameOver = self.check_o_won()\n if gameOver is True:\n self.game_o_won()\n return\n\n if self.turn >= 9:\n self.game_tie()\n return","def is_over(self):\n return self.game.is_over()","def game_over(self) -> bool:\n return self.rstate.game_over()","def is_game_over(self):\n\n if len(self.next_pieces) == 0:\n return True","def is_over(self):\n for el1, el2, el3 in self.WINNING_POSITIONS:\n if self.board[el1] == self.board[el2] == self.board[el3]:\n if self.board[el1] == 0:\n continue\n\n self.winner = self.board[el1]\n return True\n\n if self.__class__.EMPTY_POSITION_COUNTER not in self.board:\n return True\n\n return False","def is_game_over(board):\n winner = check_winner(board)\n draw = check_draw(winner, board)\n return True if winner or draw else False","def is_round_over(whose_turn,players):\n if ((len(players[whose_turn].hand.cards) == 0) and (players[whose_turn].has_discarded == True)):\n round_over = True\n else:\n round_over = False\n return round_over","def game_over(self):\n return self.lives() < 0","def check_for_game_won(self):\n all_moscuvites_captured = True\n king_captured = True\n king_escaped = True\n for piece in self.game_pieces:\n if piece.player == 2:\n all_moscuvites_captured = False\n elif piece.player == 3:\n king_captured = False\n king_coords = (piece.x,piece.y)\n escape_coords = [(0, 0), (0, 8),\n (8, 0), (8, 8)]\n if king_coords not in escape_coords:\n king_escaped = False\n if king_captured:\n return 2\n elif king_escaped or all_moscuvites_captured:\n return 1\n else:\n return 0","def is_over(self):\n winner = self.get_winner()\n status = bool(winner or not self.available_moves)\n return status, winner","def __game_is_over(self):\n return not (self.__playing and self.__bricks_total > 0 and self.__num_lives > 0)","def is_game_over(cls):\n cls.record_winner()\n cls.record_tie()","def is_game_over(self):\n return self.state.all_avatars_placed() and self.state.is_game_over()","def is_game_over(self):\n if self.just_cheated_a or self.just_cheated_b:\n return False\n if self.game_stage == 3:\n return (self.die_a.current_value == \"5\" and self.die_b.current_value == \"6\" or\n self.die_a.current_value == \"6\" and self.die_b.current_value == \"5\")\n else:\n return False","def is_game_over(self):\n if max([max(row) for row in self.grid]) == 2 ** (self.grid_size ** 2):\n raise GameException('Congrats, You won !')\n\n # If there is a zero then the game is not over\n for row in self.grid:\n if 0 in row:\n return False\n\n # Check if two consecutive number (vertically or horizontally) are\n # equal. In this case the game is not over.\n for i in range(self.grid_size):\n for j in range(self.grid_size):\n # horizontal check\n if (i < self.grid_size - 1 and\n self.grid[i][j] == self.grid[i + 1][j]):\n return False\n # vertical check\n if (j < self.grid_size - 1 and\n self.grid[i][j] == self.grid[i][j + 1]):\n return False\n\n return True","def isGameOver(self):\n for i in range(self.rows):\n for j in range(self.columns):\n if self.grid[i][j].face == 'down':\n return False\n #if here then all cards must be face up\n return True","def is_game_win(self):\n return not self.deck and not self.hand","def _checkRoundOver(self):\n\n # if we already ended it doesn't matter\n if self.hasEnded():\n return\n\n if not any(player.isAlive() for player in self.teams[0].players):\n # allow continuing after wave 1\n if self._wave > 1:\n self.continueOrEndGame()\n else:\n self.endGame()","def game_won(self):\n return all((foundation.is_full() for foundation in self.foundations.values()))","def check_if_over(self):\n if self.remainingBalls == 0:\n self.check_if_won()\n self.game_over = True","def is_game_over(self):\n if (self.check_win(HexBoard.RED) or self.check_win(HexBoard.BLUE) or \n len(self.get_move_list())==0):\n self.game_over = True\n return self.game_over","def gameWon(self):\n \n wins = [ threeInARow( self.squares[0], self.squares[1], self.squares[2] ),\n threeInARow( self.squares[3], self.squares[4], self.squares[5] ),\n threeInARow( self.squares[6], self.squares[7], self.squares[8] ),\n threeInARow( self.squares[0], self.squares[3], self.squares[6] ),\n threeInARow( self.squares[1], self.squares[4], self.squares[7] ),\n threeInARow( self.squares[2], self.squares[5], self.squares[8] ),\n threeInARow( self.squares[0], self.squares[4], self.squares[8] ),\n threeInARow( self.squares[2], self.squares[4], self.squares[6] ) ]\n \n return any(wins)","def check_player_reached():\n global round_start_timer, round_over\n\n if player1.alive and player1.rect.top < (platform_width // 2):\n add_time_points()\n reset_players()\n player1.wins += 1\n return True\n\n elif player2.alive and (player2.rect.top + player2.image.get_height()) > \\\n (SCREEN_HEIGHT - platform_width):\n player2.wins += 1\n round_over = True\n add_time_points()\n reset_players()\n return True","def isGameOver(self):\n pass","def game_tie(self):\n\n shape = self.board.shape\n if np.count_nonzero(self.board) == (shape[0] * shape[1]):\n # The board is full\n player = 0\n return True\n else:\n return False","def winning_game_player(players):\n\n # in order for there to be a winner, the game must\n # be over\n if not game_over(players):\n return None\n\n # if the game is over, it could be that there is no\n # winner\n active_players = players_with_decks(players)\n if not active_players:\n return False\n\n # if the game is over than find the winner\n return players_with_decks(players)[0]","def is_won(self):\n combinations = [*[(i, i + 3, i + 6) for i in range(3)],\n *[(i*3, i*3 + 1, i*3 + 2) for i in range(3)],\n (0, 4, 8), (2, 4, 6)]\n\n win = [*filter(lambda x: self[x[0]] == self[x[1]] == self[x[2]] and\n self[x[0]] != self.CELL_EMPTY, combinations)]\n return self[win[0][0]] if len(win) > 0 else self.CELL_EMPTY","def is_game_over(self) -> bool:\n return self._is_game_over","def won(self):\n for y in range(self.size):\n winning = []\n for x in range(self.size):\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n for x in range(self.size):\n winning = []\n for y in range(self.size):\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n winning = []\n for y in range(self.size):\n x = y\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n winning = []\n for y in range(self.size):\n x = self.size-1-y\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n return None","def is_game_won(self):\n if self.game_is_tied():\n return False\n my_available_steps = self.steps_available(self.loc)\n opp_available_steps = self.steps_available(self.opponent_loc)\n if my_available_steps == 0 or opp_available_steps == 0:\n return True\n else:\n return False","def game_over(self):\n\n if self._number_of_moves == 9:\n return True\n\n return self._number_of_moves == 9 or self.winner_found()","def checkWin(self):\n winstates = [(0, 1, 2),\n (3, 4, 5),\n (6, 7, 8),\n (0, 3, 6),\n (1, 4, 7),\n (2, 5, 8),\n (0, 4, 8),\n (2, 4, 6)]\n win = False\n for state in winstates:\n if (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == 3:\n self.handleWin(1)\n win = True\n elif (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == -3:\n self.handleWin(-1)\n win = True\n\n if len([i for i in range(9) if self.gameState[i] == 0]) == 0 and not win:\n print(\"Draw yo\")\n self.handleDraw()\n return None","def isGameOver(self):\n for row in range(0, self.rows):\n for col in range(0, self.cols):\n if self.isMine(row, col) and self.isClicked(row, col):\n return True\n return False","def gameover(self):\n if self._gameover:\n return True\n \n if self.terminal():\n self._gameover = True\n return True\n \n return False","def is_game_won(self):\n return True","def __check_winner(self):\n for i in range(0, 3):\n col = self.__get_col(i)\n if col.get(self.player_char) == 3:\n print('\\nYou win!')\n self.game_ended = True\n return\n if col.get(self.opponent_char) == 3:\n print('\\nYou lose.')\n self.game_ended = True\n return\n row = self.__get_row(i)\n if row.get(self.player_char) == 3:\n print('\\nYou win!')\n self.game_ended = True\n return\n if row.get(self.opponent_char) == 3:\n print('\\nYou lose.')\n self.game_ended = True\n return\n for i in range(0, 2):\n diag = self.__get_diag(i)\n if diag.get(self.player_char) == 3:\n print('\\nYou win!')\n self.game_ended = True\n return\n if diag.get(self.opponent_char) == 3:\n print('\\nYou lose.')\n self.game_ended = True\n return\n if self.state.count(' ') == 0:\n print('\\nDraw!')\n self.game_ended = True","def game_is_over(self) -> models.Conclusion:\n raise NotImplementedError","async def check_game_over(self, game_id):\n game = await self.get_game(game_id)\n player1_stand = await self.check_player_standing(game[1])\n player2_stand = await self.check_player_standing(game[2])\n if player1_stand and player2_stand:\n return True\n else:\n return False","def game_over(self) -> bool:\n for row in range(9):\n for col in range(9):\n if self._grid_sol[row][col] != self.get_cell(row, col):\n return False\n return True","def won_game(self):\n for player in self.players:\n if len(player.cards) == 0:\n\n return True\n return False","def check_game_status(self):\n for player in (\"1\", \"2\"):\n row_win = np.apply_along_axis(\n lambda x: set(x) == {player}, 1, self.board\n ).any()\n col_win = np.apply_along_axis(\n lambda x: set(x) == {player}, 0, self.board\n ).any()\n d1_win = set(self.data[[0, 4, 8]]) == {player}\n d2_win = set(self.data[[2, 4, 6]]) == {player}\n if any([row_win, col_win, d1_win, d2_win]):\n return (\"win\", player)\n\n if self.counter[\"_\"] == 0:\n return (\"tie\", None)\n else:\n return (\"turn\", \"1\" if self.counter[\"1\"] == self.counter[\"2\"] else \"2\")","def is_game_won(self) -> int:\n\n b = self.board\n for c1, c2, c3, c4 in _WINDOWS:\n if b[c1] and (b[c1] == b[c2] == b[c3] == b[c4]):\n print(\"win\", c1, c2, c3, c4)\n return b[c1]","def winner(self):\n\n if self.game_ended():\n return self.winning()\n else:\n return 0","def hasWin(self) :\n comparison = self.compareNumberUser()\n if (comparison == 'equal') :\n return True\n else :\n return False","def terminal_test(self, state):\r\n # Anyone won already?\r\n ended, winner = self.anyone_won(state)\r\n\r\n # There is no draw stage in this game, WTF.\r\n # It's always on !!\r\n if ended:\r\n return ended, winner\r\n else:\r\n # Checking if still game play is left\r\n return False, None","def game_on(self):\n doc = self.documentation\n return (self.draw.accepted or doc[len(doc)-1].accepted) and (self.board.stones_set < self.board.max_nr_stones) and (self.board.score[opponent(self.draw.player)] > 0)","def win(self, player):\n if player == 1:\n a = self.player_one.moves\n else:\n a = self.player_two.moves\n winning_moves = []\n for i in range(1, 9, 3):\n winning_moves.append(range(i, i + 3))\n for i in range(1, 4):\n winning_moves.append(range(i, i + 7, 3))\n winning_moves.append([1, 5, 9])\n winning_moves.append([3, 5, 7])\n for move in winning_moves:\n flg = True\n for index in move:\n if index not in a:\n flg = False\n break\n if flg:\n return True, player\n if len(self.player_one.moves) + len(self.player_two.moves) == 9:\n self.print_space()\n self.display_board()\n self.print_space()\n print \" Games is drawn\"\n self.logging.debug(\"Game is draw, nobody won\")\n self.logging.debug(\"Enjoy the game again :)\")\n sys.exit(100)\n return False, player","def is_winning(game: List[int]) -> bool:\n # performs the Vertical XOR by reducing as list of bool (lst) with xor lambda\n reduce_xor = (lambda lst: reduce(__xor__, lst, False))\n\n # converts game into binary and the converts/permutes the row and col\n game_bin_row_col = row_to_col(game_to_bin(game))\n\n # performs Vertical XOR on every column\n res_vert_xor = list(map(reduce_xor, game_bin_row_col))\n\n return reduce(__or__, res_vert_xor, False)","def is_game_complete(game):\n game_round = min(len(game.creator_scores), len(game.invitee_scores))\n creator_score = sum(game.creator_scores[:game_round])\n invitee_score = sum(game.invitee_scores[:game_round])\n return creator_score >= GAME_SCORE_TO_WIN or invitee_score >= GAME_SCORE_TO_WIN","def check_winner(self):\n\t\tif self.check_diagonals() or self.check_rows() or self.check_columns():\n\t\t\treturn True\n\t\telif self.board_is_full():\n\t\t\tprint(\"There was a draw, everyone lost\")\n\t\t\treturn None\n\t\treturn False","def has_won(board, player):\r\n return False","def is_end_game(self):\n win = self.is_game_won()\n tie = self.game_is_tied()\n return win or tie","def check_win(self):\n win = None\n for pos in self.winning_pos:\n win = self.is_match(set(self.get_cells(pos)))\n if win:\n return win\n if not self.open_tiles():\n return \"Draw\"\n return win","def has_winner(self):\r\n\r\n\t\t\"Check for horizonal win\"\r\n\r\n\t\tfor x in range(0, 3):\r\n\r\n\t\t\tif self.game_board[x][0] == self.game_board[x][1] and self.game_board[x][1] == self.game_board[x][2]:\r\n\r\n\t\t\t\treturn self.game_board[x][0]\r\n\r\n\t\t\"Check for vertical win\"\r\n\r\n\t\tfor y in range(0, 3):\r\n\r\n\t\t\tif self.game_board[0][y] == self.game_board[1][y] and self.game_board[1][y] == self.game_board[2][y]:\r\n\r\n\t\t\t\treturn self.game_board[0][y]\r\n\r\n\t\t\"Check for diagonal from left to right\"\r\n\t\r\n\t\tif self.game_board[0][0] == self.game_board[1][1] and self.game_board[1][1] == self.game_board[2][2]:\r\n\t\t\treturn self.game_board[1][1]\t\r\n\r\n\t\tif self.game_board[0][2] == self.game_board[1][1] and self.game_board[1][1] == self.game_board[2][0]:\r\n\t\t\treturn self.game_board[1][1]\t\r\n\r\n\t\tif self.count == 8:\r\n\r\n\t\t\treturn \"Tie\"\r\n\r\n\t\telse:\r\n\r\n\t\t\treturn \"0\"\r\n\r\n\r\n\t\tpass","def check_win_lose(self):\n if self.b.get_player_i() == 7: # player got to the bank\n return 1 # win\n if self.b.get_chaser_i() == self.b.get_player_i(): # chaser catch the player\n return 2 # lose\n return 0 # nothing","def has_won(board, player):\n return False","def check_game_over(self, row, col):\n player_symbol = self.board[row][col]\n\n # Top Right: Row -1 Col 1\n # Bottom Left: Row 1 Col -1\n self.check_four_in_a_row(player_symbol, row, col, -1, 1, 1, -1)\n\n # Top Left: Row -1 Col -1\n # Bottom Right Row 1 Col 1\n self.check_four_in_a_row(player_symbol, row, col, -1, -1, 1, 1)\n\n # Horizontal: Row 0 Col 1, Row 0 Col -1\n self.check_four_in_a_row(player_symbol, row, col, 0, 1, 0, -1)\n\n # Vertical: Row 1 Col 0, Row -1 Col 0\n self.check_four_in_a_row(player_symbol, row, col, 1, 0, -1, 0)\n\n if self.turns >= self.num_playable_rows * self.num_playable_columns:\n self.game_over = True\n self.board_full = True","def has_winner(self):\n\n if self.num_black_pieces == 0 or len(self.get_all_valid_moves(Player.black)) == 0:\n return Player.white\n elif self.num_white_pieces == 0 or len(self.get_all_valid_moves(Player.white)) == 0:\n return Player.black\n elif self.repetition_happened() or self.passive_game():\n return \"Tie\"\n else:\n return None","def check_win(self):\n\n # Sets rings initially to False\n black_ring = False\n white_ring = False\n\n # Gets board\n board = self._game_board.get_board_area()\n\n # Iterate through stone-holding points\n for i in range(1, 19):\n for j in range(1, 19):\n # Get the surroundings of each point\n surround = {board[i+1][j], board[i-1][j], board[i+1][j+1], board[i+1][j-1], board[i-1][j],\n board[i-1][j-1], board[i-1][j+1], board[i][j-1]}\n\n # If the point is empty and surrounded by \"B\", there's a black ring\n if board[i][j] == \" \" and surround == {\"B\"}:\n black_ring = True\n\n # If the point is empty and surrounded by \"W\", there's a white ring\n if board[i][j] == \" \" and surround == {\"W\"}:\n white_ring = True\n\n if not black_ring:\n return \"WHITE_WON\"\n\n if not white_ring:\n return \"BLACK_WON\"\n\n return \"UNFINISHED\"","def check_for_winner(self):\r\n\r\n # Iterate through the rows\r\n for row in range(self.height):\r\n if self.board[row][0] == self.board[row][1] == self.board[row][2] and self.board[row][0] != None:\r\n return Board.WIN if self.board[row][0] else Board.LOSS\r\n\r\n # Iterate through the columns\r\n for col in range(self.width):\r\n if self.board[0][col] == self.board[1][col] == self.board[2][col] and self.board[0][col] != None:\r\n return Board.WIN if self.board[0][col] else Board.LOSS\r\n\r\n # Diagonals\r\n if self.board[0][0] == self.board[1][1] == self.board[2][2] and self.board[0][0] != None:\r\n return Board.WIN if self.board[0][0] else Board.LOSS\r\n if self.board[0][2] == self.board[1][1] == self.board[2][0] and self.board[0][2] != None:\r\n return Board.WIN if self.board[0][2] else Board.LOSS\r\n\r\n # No winner yet\r\n return 0","def checkWinAll(self, model, previousWin):\r\n previous = self.__render\r\n self.__render = Render.NOTHING # avoid rendering anything during execution of the check games\r\n\r\n win = 0\r\n lose = 0\r\n \r\n cellsRanking = {}\r\n sumForProb = 0\r\n for cell in self.allowableCells:\r\n if self.play(model, cell)[0] == Status.WIN:\r\n win += 1\r\n else:\r\n lose += 1\r\n\r\n self.__render = previous # restore previous rendering setting\r\n\r\n logging.info(\"won: {} | lost: {} | win rate: {:.5f}\".format(win, lose, win / (win + lose)))\r\n\r\n result = True if lose == 0 else False\r\n \r\n if lose == 0:\r\n previousWin += 1\r\n else:\r\n previousWin = 0 \r\n\r\n return result, win / (win + lose), previousWin","def play(self):\n if self.stats['round'] == 0:\n if self.data['personalities'] and self.data['events']:\n self.choose_opponent()\n self.resolve_conflict()\n else:\n self.stats['round'] += 1\n elif self.stats['round'] == 1:\n if self.data['locations']:\n self.choose_location()\n self.resolve_conflict()\n else:\n self.stats['round'] += 1\n else:\n print(\"You've won\")\n self.game_over = True\n return self.stats","def is_not_game_ended(data_map):\n\n continue_game = True\n loser = None\n winner = None\n\n # If a player has not any units, the other player win.\n for i in range(2):\n if not len(data_map['player' + str(i + 1)]) and continue_game:\n loser = data_map['player' + str(i + 1)]\n winner = data_map['player' + str(3 - (i + 1))]\n continue_game = False\n\n # If there's 20 turn without any attack, player1 loose and player2 win.\n if float(data_map['attack_turn']) / 2 > 19:\n loser = data_map['player1']\n winner = data_map['player2']\n continue_game = False\n\n return continue_game, loser, winner","def determineEndGame(self):\n\n print(\"noWinners: \" + str(self.noWinners) + \", noTotKids: \" + str(self.noTotKids))\n\n # TODO scegliere come determinare la fine del gioco\n # if self.noWinners == self.noTotKids - 1: # end-game test\n if self.noWinners == self.noTotKids:\n print(\"ho determinato la fine del gioco\")\n return True\n else:\n print(\"colore toccato ma la partita non e' finita\")\n return False","def is_unhappy(self):\n #checked!#\n ###your code here###\n same=0\n for i in self.home.neighbors:\n if i.occupant!=None:\n if i.occupant.group==self.group:\n same+=1\n happniess=float(same)/len(self.home.neighbors)\n if happniess<self.happiness_threshold:\n return True\n else:\n return False","def is_over(self):\n return (self.possible_moves() == []) or self.loss_condition()","def is_over(self, board):\n if _winner(board) != 0:\n return True\n return False","def check_game_over(board: Board, whites_turn: bool) -> bool:\n if is_in_check(board, whites_turn) and can_move(board, whites_turn):\n turn = 'White' if whites_turn else 'Black'\n print()\n print(f'{turn} is in check')\n return False\n elif is_in_check(board, whites_turn) and can_move(board, whites_turn) == False:\n print()\n print('Checkmate')\n return True\n elif is_stalemate(board, whites_turn):\n print()\n print('Stalemate')\n return True\n else:\n return False","def play_game(self):\n while self.over is False:\n self.board.print_board()\n winner = self.check_over()\n if winner != -1:\n return winner\n self.p1.get_move(self.board)\n self.board.print_board()\n winner = self.check_over()\n if winner != -1:\n return winner\n self.p2.get_move(self.board)","def check_win(players: List[Player]) -> Tuple[bool, Optional[Player]]:\n total_players = len(players)\n for player in players:\n if player.influence == 0:\n total_players -= 1\n if total_players == 1:\n for player in players:\n if player.influence >0:\n return True, player\n return False, None","def game_over(_user_id):\n _board = boards[_user_id]\n return _board.is_game_over()","def gameOver(self):\n i = 0 # accumulator for the number of None objects\n for x in range(ALIEN_ROWS):\n for y in range(ALIENS_IN_ROW):\n if self._aliens[x][y] ==None:\n i +=1\n if i == ALIEN_ROWS * ALIENS_IN_ROW:\n self._gameOver = True\n\n for x in range(ALIEN_ROWS):\n for y in range(ALIENS_IN_ROW):\n if self._aliens[x][y] !=None:\n positiony = self._aliens[x][y].getAY() - ALIEN_HEIGHT/2\n if posy<= self._dline:\n self._gameOver = False","def victory_check(self):\n\n # get essential values\n board = self.get_game_space()\n affinity = self.get_affinity()\n \n # pick the right check for the game we are playing\n if isinstance(board, Gomoku):\n \n # get the possible ways to win\n possible_wins = board.get_wins(affinity)\n \n # if we can win, pick a good win \n if len(possible_wins) == 1: return possible_wins[0]\n elif len(possible_wins) > 1:\n best_win = None\n wins_by_x = {}\n wins_by_y = {}\n for win in possible_wins:\n if win[0] not in wins_by_x.keys():\n wins_by_x[win[0]] = []\n if win[1] not in wins_by_y.keys():\n wins_by_y[win[1]] = []\n wins_by_x[win[0]].append(win)\n wins_by_y[win[1]].append(win)\n for y in wins_by_y:\n if len(wins_by_y[y]) > 1: \n for win in wins_by_y[y]:\n if best_win is None or win[0] < best_win[0]:\n best_win = win \n return best_win\n\n else: return None","def is_game_lost(self):\n values = [self.hand[i]._lvalue + self.hand[i]._rvalue for i in range(len(self.hand))]\n return not sum_in_list_dyn(values, self.number_point)","def isGameOver(board, *args, **kwargs):\n black_win = consecutive_score(\"black\", board, board.size)\n white_win = consecutive_score(\"white\", board, board.size)\n if black_win >= 100 and white_win >= 100:\n return \"tie\"\n elif black_win >= 100 and white_win <= 100:\n return \"black\"\n elif black_win <= 100 and white_win >= 100:\n return \"white\"\n else:\n return -1","def winning_event(self, player):\n # vertical check\n for col in range(GameData.columns):\n if self.board[0][col] == player and self.board[1][col] == player and self.board[2][col] == player:\n self.draw_vertical_winning_line(col, player)\n print(\"Player {} has won the game!\".format(player))\n self.game_over = True\n return True\n\n # horizontal check\n for row in range(GameData.rows):\n if self.board[row][0] == player and self.board[row][1] == player and self.board[row][2] == player:\n self.draw_horizontal_winning_line(row, player)\n print(\"Player {} has won the game!\".format(player))\n self.game_over = True\n return True\n\n # ascending diagonal heck\n if self.board[2][0] == player and self.board[1][1] == player and self.board[0][2] == player:\n self.draw_asc_diagonal(player)\n print(\"Player {} has won the game!\".format(player))\n self.game_over = True\n return True\n\n # descending diagonal win chek\n if self.board[0][0] == player and self.board[1][1] == player and self.board[2][2] == player:\n self.draw_desc_diagonal(player)\n print(\"Player {} has won the game!\".format(player))\n self.game_over = True\n return True\n\n return False"],"string":"[\n \"def gameOver():\\n if len(p1)==0 and len(p1winnings)==0:\\n return True\\n elif len(p2)==0 and len(p2winnings)==0:\\n return True\\n return False\",\n \"def is_game_over(self):\\r\\n\\r\\n if self.winner != 0:\\r\\n return True\\r\\n\\r\\n return False\",\n \"def is_game_over(self):\\n\\n # This checks whether or not the board is full...\\n if len(self.board.values()) == 100 and \\\\\\n 0 not in self.board.values():\\n p1 = self._longest_chain(1)\\n p2 = self._longest_chain(2)\\n if len(p1) > len(p2):\\n return 1\\n elif len(p2) > len(p1):\\n return 2\\n else:\\n return 0\\n\\n # If it's not full. We check for boxes\\n else:\\n for x in range(self.width-1):\\n for y in range(self.height-1):\\n slice = self._slice((x,y), (2,2))\\n if 0 not in slice[0] and 0 not in slice[1]:\\n # is this slice a box?\\n if slice[0][0] == slice[0][1] and \\\\\\n slice[0][1] == slice[1][0] and \\\\\\n slice[1][0] == slice[1][1]:\\n return slice[0][0] # winner\\n\\n return -1 # game is not over\",\n \"def is_game_over(self):\\n bk = False\\n wk = False\\n\\n # Find the kings\\n for row in range(8):\\n for col in range(8):\\n if self.board.squares[row][col] == ChessPiece.B_KING: # Black king symbol\\n bk = True\\n break\\n if self.board.squares[row][col] == ChessPiece.W_KING: # Black king symbol\\n wk = True\\n break\\n\\n # If a king is missing, end the game. This fixes a bug we were having\\n if bk == False:\\n return 1\\n if wk == False:\\n return 2\\n\\n if self.white_wins():\\n return 1\\n elif self.black_wins():\\n return 2\\n elif self.tie():\\n return 3\\n else:\\n return 0\",\n \"def is_over(self):\\n alive_players = [1 if p.status == \\\"alive\\\" else 0 for p in self.players]\\n # If only one player is alive, the game is over.\\n if sum(alive_players) == 1:\\n return True\\n\\n # If all rounds are finshed\\n if self.round_counter >= 2:\\n return True\\n return False\",\n \"def is_over(self):\\n winner = TictactoeMatch.get_winner(self.inputs_)\\n if winner:\\n self.result_ = winner\\n if Config.USER['debug']['enabled']:\\n print \\\"It is over! Player \\\"+str(self.result_)+\\\" (\\\"+str(self.player_label_[self.result_])+\\\") wins!\\\"\\n return True\\n for value in self.inputs_:\\n if value == TictactoeMatch.EMPTY:\\n if Config.USER['debug']['enabled']:\\n print \\\"Go!\\\"\\n return False\\n self.result_ = TictactoeMatch.DRAW\\n if Config.USER['debug']['enabled']:\\n print \\\"It is over! Draw!\\\"\\n return True\",\n \"def check_game_over(self):\\n red, blue = self.board.count_piece()\\n if blue == 0:\\n self.ui.show_result(\\\"RED WIN!\\\")\\n self.turn = RED\\n elif red == 0:\\n self.ui.show_result(\\\"BLUE WIN!\\\")\\n self.turn = BLUE\\n elif red == blue == 1:\\n self.ui.show_result(\\\"DRAW!\\\")\",\n \"def game_over(self):\\n return bool(self.last_round and self.last_player == self.current_player)\",\n \"def check_over(self):\\n if self.board.has_winner() == 1:\\n return 1\\n elif self.board.has_winner() == 2:\\n return 2\\n elif self.board.check_cats_game():\\n return 0\\n else:\\n return -1\",\n \"def verify_winner(self):\\r\\n return self.count_pegs() == 1\",\n \"def _check_game_over(self):\\n return self.game_board.check_game_over()\",\n \"def game_over(self):\\n # TODO: Define the game over condition for Adventure.\\n # use self.over to determine if the game if over\\n return self.over\",\n \"def game_over(self):\\n red_minion = 0\\n blue_minion = 0\\n red_master = 0\\n blue_master = 0\\n only_masters = True\\n for row in self.board:\\n for piece in row:\\n if piece != 0:\\n if not piece.master:\\n if piece.player:\\n blue_minion += 1\\n else:\\n red_minion += 1\\n only_masters = False\\n else:\\n if piece.player:\\n blue_master += 1\\n else:\\n red_master += 1\\n if blue_minion + blue_master == 0:\\n self.winner = \\\"Red\\\"\\n self.red_victories += 1\\n self.number_of_games +=1\\n self.game_over_screen()\\n return True\\n elif red_minion + red_master == 0:\\n self.winner = \\\"Blue\\\"\\n self.blue_victories += 1\\n self.number_of_games +=1\\n self.game_over_screen()\\n return True\\n elif only_masters:\\n if red_master > blue_master:\\n self.winner = \\\"Red\\\"\\n self.red_victories += 1\\n elif blue_master > red_master:\\n self.winner = \\\"Blue\\\"\\n self.blue_victories += 1\\n else:\\n self.winner = \\\"Nobody\\\"\\n self.number_of_games +=1\\n self.game_over_screen()\\n return True\\n \\n return False\",\n \"def game_over(players):\\n active_players = players_with_decks(players)\\n if not active_players or len(active_players) == 1:\\n return True\\n return False\",\n \"def is_game_over(self):\\n board = list(self.board)\\n for wins in self.WINNING:\\n # Create a tuple\\n w = (board[wins[0]], board[wins[1]], board[wins[2]])\\n if w == ('X', 'X', 'X'):\\n return 'X'\\n if w == ('O', 'O', 'O'):\\n return 'O'\\n # Check for stalemate\\n if ' ' in board:\\n return None\\n return ' '\",\n \"def check_win(self):\\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)\",\n \"def check_win(self):\\r\\n wins = [self.check_rows(), self.check_cols(), self.check_diag()]\\r\\n for case, pos in wins:\\r\\n if case != -1:\\r\\n print('Game over!')\\r\\n if self.grid[case][-1] == self.computer:\\r\\n print('The computer won!')\\r\\n return (True, pos)\\r\\n print('The player won!')\\r\\n return (True, pos)\\r\\n\\r\\n return (self.check_draw(), None)\",\n \"def check_if_game_over():\\n check_for_winner()\\n check_for_tie()\",\n \"def game_over(state):\\n return wins(state, HUMAN) or wins(state, COMP)\",\n \"def game_over(state):\\r\\n return wins(state, HUMAN) or wins(state, COMP)\",\n \"def check_if_game_over():\\n # Calling check for winners.\\n check_for_winner()\\n # Calling check it's tie or not.\\n check_if_tie()\",\n \"def gameOver(self):\\n\\t\\treturn self.lives == 0\",\n \"def __game_is_over(self, x, y):\\n\\t\\tif np.count_nonzero(self.board) >= 42:\\n\\t\\t\\treturn True\\n\\n\\t\\tlines = self.__extract_lines(x, y)\\n\\n\\t\\tfor line in lines:\\n\\t\\t\\tif self.__winner_in_line(line) != 0:\\n\\t\\t\\t\\treturn True\\n\\n\\t\\treturn False\",\n \"def _checkRoundOver(self):\\n\\n if not any(player.isAlive() for player in self.teams[0].players):\\n self.endGame()\",\n \"def check_game(self):\\n gameOver = None\\n if self.turn > 4:\\n gameOver = self.check_x_won()\\n if gameOver is True:\\n self.game_x_won()\\n return\\n\\n gameOver = None\\n if self.turn > 5:\\n gameOver = self.check_o_won()\\n if gameOver is True:\\n self.game_o_won()\\n return\\n\\n if self.turn >= 9:\\n self.game_tie()\\n return\",\n \"def is_over(self):\\n return self.game.is_over()\",\n \"def game_over(self) -> bool:\\n return self.rstate.game_over()\",\n \"def is_game_over(self):\\n\\n if len(self.next_pieces) == 0:\\n return True\",\n \"def is_over(self):\\n for el1, el2, el3 in self.WINNING_POSITIONS:\\n if self.board[el1] == self.board[el2] == self.board[el3]:\\n if self.board[el1] == 0:\\n continue\\n\\n self.winner = self.board[el1]\\n return True\\n\\n if self.__class__.EMPTY_POSITION_COUNTER not in self.board:\\n return True\\n\\n return False\",\n \"def is_game_over(board):\\n winner = check_winner(board)\\n draw = check_draw(winner, board)\\n return True if winner or draw else False\",\n \"def is_round_over(whose_turn,players):\\n if ((len(players[whose_turn].hand.cards) == 0) and (players[whose_turn].has_discarded == True)):\\n round_over = True\\n else:\\n round_over = False\\n return round_over\",\n \"def game_over(self):\\n return self.lives() < 0\",\n \"def check_for_game_won(self):\\n all_moscuvites_captured = True\\n king_captured = True\\n king_escaped = True\\n for piece in self.game_pieces:\\n if piece.player == 2:\\n all_moscuvites_captured = False\\n elif piece.player == 3:\\n king_captured = False\\n king_coords = (piece.x,piece.y)\\n escape_coords = [(0, 0), (0, 8),\\n (8, 0), (8, 8)]\\n if king_coords not in escape_coords:\\n king_escaped = False\\n if king_captured:\\n return 2\\n elif king_escaped or all_moscuvites_captured:\\n return 1\\n else:\\n return 0\",\n \"def is_over(self):\\n winner = self.get_winner()\\n status = bool(winner or not self.available_moves)\\n return status, winner\",\n \"def __game_is_over(self):\\n return not (self.__playing and self.__bricks_total > 0 and self.__num_lives > 0)\",\n \"def is_game_over(cls):\\n cls.record_winner()\\n cls.record_tie()\",\n \"def is_game_over(self):\\n return self.state.all_avatars_placed() and self.state.is_game_over()\",\n \"def is_game_over(self):\\n if self.just_cheated_a or self.just_cheated_b:\\n return False\\n if self.game_stage == 3:\\n return (self.die_a.current_value == \\\"5\\\" and self.die_b.current_value == \\\"6\\\" or\\n self.die_a.current_value == \\\"6\\\" and self.die_b.current_value == \\\"5\\\")\\n else:\\n return False\",\n \"def is_game_over(self):\\n if max([max(row) for row in self.grid]) == 2 ** (self.grid_size ** 2):\\n raise GameException('Congrats, You won !')\\n\\n # If there is a zero then the game is not over\\n for row in self.grid:\\n if 0 in row:\\n return False\\n\\n # Check if two consecutive number (vertically or horizontally) are\\n # equal. In this case the game is not over.\\n for i in range(self.grid_size):\\n for j in range(self.grid_size):\\n # horizontal check\\n if (i < self.grid_size - 1 and\\n self.grid[i][j] == self.grid[i + 1][j]):\\n return False\\n # vertical check\\n if (j < self.grid_size - 1 and\\n self.grid[i][j] == self.grid[i][j + 1]):\\n return False\\n\\n return True\",\n \"def isGameOver(self):\\n for i in range(self.rows):\\n for j in range(self.columns):\\n if self.grid[i][j].face == 'down':\\n return False\\n #if here then all cards must be face up\\n return True\",\n \"def is_game_win(self):\\n return not self.deck and not self.hand\",\n \"def _checkRoundOver(self):\\n\\n # if we already ended it doesn't matter\\n if self.hasEnded():\\n return\\n\\n if not any(player.isAlive() for player in self.teams[0].players):\\n # allow continuing after wave 1\\n if self._wave > 1:\\n self.continueOrEndGame()\\n else:\\n self.endGame()\",\n \"def game_won(self):\\n return all((foundation.is_full() for foundation in self.foundations.values()))\",\n \"def check_if_over(self):\\n if self.remainingBalls == 0:\\n self.check_if_won()\\n self.game_over = True\",\n \"def is_game_over(self):\\n if (self.check_win(HexBoard.RED) or self.check_win(HexBoard.BLUE) or \\n len(self.get_move_list())==0):\\n self.game_over = True\\n return self.game_over\",\n \"def gameWon(self):\\n \\n wins = [ threeInARow( self.squares[0], self.squares[1], self.squares[2] ),\\n threeInARow( self.squares[3], self.squares[4], self.squares[5] ),\\n threeInARow( self.squares[6], self.squares[7], self.squares[8] ),\\n threeInARow( self.squares[0], self.squares[3], self.squares[6] ),\\n threeInARow( self.squares[1], self.squares[4], self.squares[7] ),\\n threeInARow( self.squares[2], self.squares[5], self.squares[8] ),\\n threeInARow( self.squares[0], self.squares[4], self.squares[8] ),\\n threeInARow( self.squares[2], self.squares[4], self.squares[6] ) ]\\n \\n return any(wins)\",\n \"def check_player_reached():\\n global round_start_timer, round_over\\n\\n if player1.alive and player1.rect.top < (platform_width // 2):\\n add_time_points()\\n reset_players()\\n player1.wins += 1\\n return True\\n\\n elif player2.alive and (player2.rect.top + player2.image.get_height()) > \\\\\\n (SCREEN_HEIGHT - platform_width):\\n player2.wins += 1\\n round_over = True\\n add_time_points()\\n reset_players()\\n return True\",\n \"def isGameOver(self):\\n pass\",\n \"def game_tie(self):\\n\\n shape = self.board.shape\\n if np.count_nonzero(self.board) == (shape[0] * shape[1]):\\n # The board is full\\n player = 0\\n return True\\n else:\\n return False\",\n \"def winning_game_player(players):\\n\\n # in order for there to be a winner, the game must\\n # be over\\n if not game_over(players):\\n return None\\n\\n # if the game is over, it could be that there is no\\n # winner\\n active_players = players_with_decks(players)\\n if not active_players:\\n return False\\n\\n # if the game is over than find the winner\\n return players_with_decks(players)[0]\",\n \"def is_won(self):\\n combinations = [*[(i, i + 3, i + 6) for i in range(3)],\\n *[(i*3, i*3 + 1, i*3 + 2) for i in range(3)],\\n (0, 4, 8), (2, 4, 6)]\\n\\n win = [*filter(lambda x: self[x[0]] == self[x[1]] == self[x[2]] and\\n self[x[0]] != self.CELL_EMPTY, combinations)]\\n return self[win[0][0]] if len(win) > 0 else self.CELL_EMPTY\",\n \"def is_game_over(self) -> bool:\\n return self._is_game_over\",\n \"def won(self):\\n for y in range(self.size):\\n winning = []\\n for x in range(self.size):\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n for x in range(self.size):\\n winning = []\\n for y in range(self.size):\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n winning = []\\n for y in range(self.size):\\n x = y\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n winning = []\\n for y in range(self.size):\\n x = self.size-1-y\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n return None\",\n \"def is_game_won(self):\\n if self.game_is_tied():\\n return False\\n my_available_steps = self.steps_available(self.loc)\\n opp_available_steps = self.steps_available(self.opponent_loc)\\n if my_available_steps == 0 or opp_available_steps == 0:\\n return True\\n else:\\n return False\",\n \"def game_over(self):\\n\\n if self._number_of_moves == 9:\\n return True\\n\\n return self._number_of_moves == 9 or self.winner_found()\",\n \"def checkWin(self):\\n winstates = [(0, 1, 2),\\n (3, 4, 5),\\n (6, 7, 8),\\n (0, 3, 6),\\n (1, 4, 7),\\n (2, 5, 8),\\n (0, 4, 8),\\n (2, 4, 6)]\\n win = False\\n for state in winstates:\\n if (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == 3:\\n self.handleWin(1)\\n win = True\\n elif (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == -3:\\n self.handleWin(-1)\\n win = True\\n\\n if len([i for i in range(9) if self.gameState[i] == 0]) == 0 and not win:\\n print(\\\"Draw yo\\\")\\n self.handleDraw()\\n return None\",\n \"def isGameOver(self):\\n for row in range(0, self.rows):\\n for col in range(0, self.cols):\\n if self.isMine(row, col) and self.isClicked(row, col):\\n return True\\n return False\",\n \"def gameover(self):\\n if self._gameover:\\n return True\\n \\n if self.terminal():\\n self._gameover = True\\n return True\\n \\n return False\",\n \"def is_game_won(self):\\n return True\",\n \"def __check_winner(self):\\n for i in range(0, 3):\\n col = self.__get_col(i)\\n if col.get(self.player_char) == 3:\\n print('\\\\nYou win!')\\n self.game_ended = True\\n return\\n if col.get(self.opponent_char) == 3:\\n print('\\\\nYou lose.')\\n self.game_ended = True\\n return\\n row = self.__get_row(i)\\n if row.get(self.player_char) == 3:\\n print('\\\\nYou win!')\\n self.game_ended = True\\n return\\n if row.get(self.opponent_char) == 3:\\n print('\\\\nYou lose.')\\n self.game_ended = True\\n return\\n for i in range(0, 2):\\n diag = self.__get_diag(i)\\n if diag.get(self.player_char) == 3:\\n print('\\\\nYou win!')\\n self.game_ended = True\\n return\\n if diag.get(self.opponent_char) == 3:\\n print('\\\\nYou lose.')\\n self.game_ended = True\\n return\\n if self.state.count(' ') == 0:\\n print('\\\\nDraw!')\\n self.game_ended = True\",\n \"def game_is_over(self) -> models.Conclusion:\\n raise NotImplementedError\",\n \"async def check_game_over(self, game_id):\\n game = await self.get_game(game_id)\\n player1_stand = await self.check_player_standing(game[1])\\n player2_stand = await self.check_player_standing(game[2])\\n if player1_stand and player2_stand:\\n return True\\n else:\\n return False\",\n \"def game_over(self) -> bool:\\n for row in range(9):\\n for col in range(9):\\n if self._grid_sol[row][col] != self.get_cell(row, col):\\n return False\\n return True\",\n \"def won_game(self):\\n for player in self.players:\\n if len(player.cards) == 0:\\n\\n return True\\n return False\",\n \"def check_game_status(self):\\n for player in (\\\"1\\\", \\\"2\\\"):\\n row_win = np.apply_along_axis(\\n lambda x: set(x) == {player}, 1, self.board\\n ).any()\\n col_win = np.apply_along_axis(\\n lambda x: set(x) == {player}, 0, self.board\\n ).any()\\n d1_win = set(self.data[[0, 4, 8]]) == {player}\\n d2_win = set(self.data[[2, 4, 6]]) == {player}\\n if any([row_win, col_win, d1_win, d2_win]):\\n return (\\\"win\\\", player)\\n\\n if self.counter[\\\"_\\\"] == 0:\\n return (\\\"tie\\\", None)\\n else:\\n return (\\\"turn\\\", \\\"1\\\" if self.counter[\\\"1\\\"] == self.counter[\\\"2\\\"] else \\\"2\\\")\",\n \"def is_game_won(self) -> int:\\n\\n b = self.board\\n for c1, c2, c3, c4 in _WINDOWS:\\n if b[c1] and (b[c1] == b[c2] == b[c3] == b[c4]):\\n print(\\\"win\\\", c1, c2, c3, c4)\\n return b[c1]\",\n \"def winner(self):\\n\\n if self.game_ended():\\n return self.winning()\\n else:\\n return 0\",\n \"def hasWin(self) :\\n comparison = self.compareNumberUser()\\n if (comparison == 'equal') :\\n return True\\n else :\\n return False\",\n \"def terminal_test(self, state):\\r\\n # Anyone won already?\\r\\n ended, winner = self.anyone_won(state)\\r\\n\\r\\n # There is no draw stage in this game, WTF.\\r\\n # It's always on !!\\r\\n if ended:\\r\\n return ended, winner\\r\\n else:\\r\\n # Checking if still game play is left\\r\\n return False, None\",\n \"def game_on(self):\\n doc = self.documentation\\n return (self.draw.accepted or doc[len(doc)-1].accepted) and (self.board.stones_set < self.board.max_nr_stones) and (self.board.score[opponent(self.draw.player)] > 0)\",\n \"def win(self, player):\\n if player == 1:\\n a = self.player_one.moves\\n else:\\n a = self.player_two.moves\\n winning_moves = []\\n for i in range(1, 9, 3):\\n winning_moves.append(range(i, i + 3))\\n for i in range(1, 4):\\n winning_moves.append(range(i, i + 7, 3))\\n winning_moves.append([1, 5, 9])\\n winning_moves.append([3, 5, 7])\\n for move in winning_moves:\\n flg = True\\n for index in move:\\n if index not in a:\\n flg = False\\n break\\n if flg:\\n return True, player\\n if len(self.player_one.moves) + len(self.player_two.moves) == 9:\\n self.print_space()\\n self.display_board()\\n self.print_space()\\n print \\\" Games is drawn\\\"\\n self.logging.debug(\\\"Game is draw, nobody won\\\")\\n self.logging.debug(\\\"Enjoy the game again :)\\\")\\n sys.exit(100)\\n return False, player\",\n \"def is_winning(game: List[int]) -> bool:\\n # performs the Vertical XOR by reducing as list of bool (lst) with xor lambda\\n reduce_xor = (lambda lst: reduce(__xor__, lst, False))\\n\\n # converts game into binary and the converts/permutes the row and col\\n game_bin_row_col = row_to_col(game_to_bin(game))\\n\\n # performs Vertical XOR on every column\\n res_vert_xor = list(map(reduce_xor, game_bin_row_col))\\n\\n return reduce(__or__, res_vert_xor, False)\",\n \"def is_game_complete(game):\\n game_round = min(len(game.creator_scores), len(game.invitee_scores))\\n creator_score = sum(game.creator_scores[:game_round])\\n invitee_score = sum(game.invitee_scores[:game_round])\\n return creator_score >= GAME_SCORE_TO_WIN or invitee_score >= GAME_SCORE_TO_WIN\",\n \"def check_winner(self):\\n\\t\\tif self.check_diagonals() or self.check_rows() or self.check_columns():\\n\\t\\t\\treturn True\\n\\t\\telif self.board_is_full():\\n\\t\\t\\tprint(\\\"There was a draw, everyone lost\\\")\\n\\t\\t\\treturn None\\n\\t\\treturn False\",\n \"def has_won(board, player):\\r\\n return False\",\n \"def is_end_game(self):\\n win = self.is_game_won()\\n tie = self.game_is_tied()\\n return win or tie\",\n \"def check_win(self):\\n win = None\\n for pos in self.winning_pos:\\n win = self.is_match(set(self.get_cells(pos)))\\n if win:\\n return win\\n if not self.open_tiles():\\n return \\\"Draw\\\"\\n return win\",\n \"def has_winner(self):\\r\\n\\r\\n\\t\\t\\\"Check for horizonal win\\\"\\r\\n\\r\\n\\t\\tfor x in range(0, 3):\\r\\n\\r\\n\\t\\t\\tif self.game_board[x][0] == self.game_board[x][1] and self.game_board[x][1] == self.game_board[x][2]:\\r\\n\\r\\n\\t\\t\\t\\treturn self.game_board[x][0]\\r\\n\\r\\n\\t\\t\\\"Check for vertical win\\\"\\r\\n\\r\\n\\t\\tfor y in range(0, 3):\\r\\n\\r\\n\\t\\t\\tif self.game_board[0][y] == self.game_board[1][y] and self.game_board[1][y] == self.game_board[2][y]:\\r\\n\\r\\n\\t\\t\\t\\treturn self.game_board[0][y]\\r\\n\\r\\n\\t\\t\\\"Check for diagonal from left to right\\\"\\r\\n\\t\\r\\n\\t\\tif self.game_board[0][0] == self.game_board[1][1] and self.game_board[1][1] == self.game_board[2][2]:\\r\\n\\t\\t\\treturn self.game_board[1][1]\\t\\r\\n\\r\\n\\t\\tif self.game_board[0][2] == self.game_board[1][1] and self.game_board[1][1] == self.game_board[2][0]:\\r\\n\\t\\t\\treturn self.game_board[1][1]\\t\\r\\n\\r\\n\\t\\tif self.count == 8:\\r\\n\\r\\n\\t\\t\\treturn \\\"Tie\\\"\\r\\n\\r\\n\\t\\telse:\\r\\n\\r\\n\\t\\t\\treturn \\\"0\\\"\\r\\n\\r\\n\\r\\n\\t\\tpass\",\n \"def check_win_lose(self):\\n if self.b.get_player_i() == 7: # player got to the bank\\n return 1 # win\\n if self.b.get_chaser_i() == self.b.get_player_i(): # chaser catch the player\\n return 2 # lose\\n return 0 # nothing\",\n \"def has_won(board, player):\\n return False\",\n \"def check_game_over(self, row, col):\\n player_symbol = self.board[row][col]\\n\\n # Top Right: Row -1 Col 1\\n # Bottom Left: Row 1 Col -1\\n self.check_four_in_a_row(player_symbol, row, col, -1, 1, 1, -1)\\n\\n # Top Left: Row -1 Col -1\\n # Bottom Right Row 1 Col 1\\n self.check_four_in_a_row(player_symbol, row, col, -1, -1, 1, 1)\\n\\n # Horizontal: Row 0 Col 1, Row 0 Col -1\\n self.check_four_in_a_row(player_symbol, row, col, 0, 1, 0, -1)\\n\\n # Vertical: Row 1 Col 0, Row -1 Col 0\\n self.check_four_in_a_row(player_symbol, row, col, 1, 0, -1, 0)\\n\\n if self.turns >= self.num_playable_rows * self.num_playable_columns:\\n self.game_over = True\\n self.board_full = True\",\n \"def has_winner(self):\\n\\n if self.num_black_pieces == 0 or len(self.get_all_valid_moves(Player.black)) == 0:\\n return Player.white\\n elif self.num_white_pieces == 0 or len(self.get_all_valid_moves(Player.white)) == 0:\\n return Player.black\\n elif self.repetition_happened() or self.passive_game():\\n return \\\"Tie\\\"\\n else:\\n return None\",\n \"def check_win(self):\\n\\n # Sets rings initially to False\\n black_ring = False\\n white_ring = False\\n\\n # Gets board\\n board = self._game_board.get_board_area()\\n\\n # Iterate through stone-holding points\\n for i in range(1, 19):\\n for j in range(1, 19):\\n # Get the surroundings of each point\\n surround = {board[i+1][j], board[i-1][j], board[i+1][j+1], board[i+1][j-1], board[i-1][j],\\n board[i-1][j-1], board[i-1][j+1], board[i][j-1]}\\n\\n # If the point is empty and surrounded by \\\"B\\\", there's a black ring\\n if board[i][j] == \\\" \\\" and surround == {\\\"B\\\"}:\\n black_ring = True\\n\\n # If the point is empty and surrounded by \\\"W\\\", there's a white ring\\n if board[i][j] == \\\" \\\" and surround == {\\\"W\\\"}:\\n white_ring = True\\n\\n if not black_ring:\\n return \\\"WHITE_WON\\\"\\n\\n if not white_ring:\\n return \\\"BLACK_WON\\\"\\n\\n return \\\"UNFINISHED\\\"\",\n \"def check_for_winner(self):\\r\\n\\r\\n # Iterate through the rows\\r\\n for row in range(self.height):\\r\\n if self.board[row][0] == self.board[row][1] == self.board[row][2] and self.board[row][0] != None:\\r\\n return Board.WIN if self.board[row][0] else Board.LOSS\\r\\n\\r\\n # Iterate through the columns\\r\\n for col in range(self.width):\\r\\n if self.board[0][col] == self.board[1][col] == self.board[2][col] and self.board[0][col] != None:\\r\\n return Board.WIN if self.board[0][col] else Board.LOSS\\r\\n\\r\\n # Diagonals\\r\\n if self.board[0][0] == self.board[1][1] == self.board[2][2] and self.board[0][0] != None:\\r\\n return Board.WIN if self.board[0][0] else Board.LOSS\\r\\n if self.board[0][2] == self.board[1][1] == self.board[2][0] and self.board[0][2] != None:\\r\\n return Board.WIN if self.board[0][2] else Board.LOSS\\r\\n\\r\\n # No winner yet\\r\\n return 0\",\n \"def checkWinAll(self, model, previousWin):\\r\\n previous = self.__render\\r\\n self.__render = Render.NOTHING # avoid rendering anything during execution of the check games\\r\\n\\r\\n win = 0\\r\\n lose = 0\\r\\n \\r\\n cellsRanking = {}\\r\\n sumForProb = 0\\r\\n for cell in self.allowableCells:\\r\\n if self.play(model, cell)[0] == Status.WIN:\\r\\n win += 1\\r\\n else:\\r\\n lose += 1\\r\\n\\r\\n self.__render = previous # restore previous rendering setting\\r\\n\\r\\n logging.info(\\\"won: {} | lost: {} | win rate: {:.5f}\\\".format(win, lose, win / (win + lose)))\\r\\n\\r\\n result = True if lose == 0 else False\\r\\n \\r\\n if lose == 0:\\r\\n previousWin += 1\\r\\n else:\\r\\n previousWin = 0 \\r\\n\\r\\n return result, win / (win + lose), previousWin\",\n \"def play(self):\\n if self.stats['round'] == 0:\\n if self.data['personalities'] and self.data['events']:\\n self.choose_opponent()\\n self.resolve_conflict()\\n else:\\n self.stats['round'] += 1\\n elif self.stats['round'] == 1:\\n if self.data['locations']:\\n self.choose_location()\\n self.resolve_conflict()\\n else:\\n self.stats['round'] += 1\\n else:\\n print(\\\"You've won\\\")\\n self.game_over = True\\n return self.stats\",\n \"def is_not_game_ended(data_map):\\n\\n continue_game = True\\n loser = None\\n winner = None\\n\\n # If a player has not any units, the other player win.\\n for i in range(2):\\n if not len(data_map['player' + str(i + 1)]) and continue_game:\\n loser = data_map['player' + str(i + 1)]\\n winner = data_map['player' + str(3 - (i + 1))]\\n continue_game = False\\n\\n # If there's 20 turn without any attack, player1 loose and player2 win.\\n if float(data_map['attack_turn']) / 2 > 19:\\n loser = data_map['player1']\\n winner = data_map['player2']\\n continue_game = False\\n\\n return continue_game, loser, winner\",\n \"def determineEndGame(self):\\n\\n print(\\\"noWinners: \\\" + str(self.noWinners) + \\\", noTotKids: \\\" + str(self.noTotKids))\\n\\n # TODO scegliere come determinare la fine del gioco\\n # if self.noWinners == self.noTotKids - 1: # end-game test\\n if self.noWinners == self.noTotKids:\\n print(\\\"ho determinato la fine del gioco\\\")\\n return True\\n else:\\n print(\\\"colore toccato ma la partita non e' finita\\\")\\n return False\",\n \"def is_unhappy(self):\\n #checked!#\\n ###your code here###\\n same=0\\n for i in self.home.neighbors:\\n if i.occupant!=None:\\n if i.occupant.group==self.group:\\n same+=1\\n happniess=float(same)/len(self.home.neighbors)\\n if happniess<self.happiness_threshold:\\n return True\\n else:\\n return False\",\n \"def is_over(self):\\n return (self.possible_moves() == []) or self.loss_condition()\",\n \"def is_over(self, board):\\n if _winner(board) != 0:\\n return True\\n return False\",\n \"def check_game_over(board: Board, whites_turn: bool) -> bool:\\n if is_in_check(board, whites_turn) and can_move(board, whites_turn):\\n turn = 'White' if whites_turn else 'Black'\\n print()\\n print(f'{turn} is in check')\\n return False\\n elif is_in_check(board, whites_turn) and can_move(board, whites_turn) == False:\\n print()\\n print('Checkmate')\\n return True\\n elif is_stalemate(board, whites_turn):\\n print()\\n print('Stalemate')\\n return True\\n else:\\n return False\",\n \"def play_game(self):\\n while self.over is False:\\n self.board.print_board()\\n winner = self.check_over()\\n if winner != -1:\\n return winner\\n self.p1.get_move(self.board)\\n self.board.print_board()\\n winner = self.check_over()\\n if winner != -1:\\n return winner\\n self.p2.get_move(self.board)\",\n \"def check_win(players: List[Player]) -> Tuple[bool, Optional[Player]]:\\n total_players = len(players)\\n for player in players:\\n if player.influence == 0:\\n total_players -= 1\\n if total_players == 1:\\n for player in players:\\n if player.influence >0:\\n return True, player\\n return False, None\",\n \"def game_over(_user_id):\\n _board = boards[_user_id]\\n return _board.is_game_over()\",\n \"def gameOver(self):\\n i = 0 # accumulator for the number of None objects\\n for x in range(ALIEN_ROWS):\\n for y in range(ALIENS_IN_ROW):\\n if self._aliens[x][y] ==None:\\n i +=1\\n if i == ALIEN_ROWS * ALIENS_IN_ROW:\\n self._gameOver = True\\n\\n for x in range(ALIEN_ROWS):\\n for y in range(ALIENS_IN_ROW):\\n if self._aliens[x][y] !=None:\\n positiony = self._aliens[x][y].getAY() - ALIEN_HEIGHT/2\\n if posy<= self._dline:\\n self._gameOver = False\",\n \"def victory_check(self):\\n\\n # get essential values\\n board = self.get_game_space()\\n affinity = self.get_affinity()\\n \\n # pick the right check for the game we are playing\\n if isinstance(board, Gomoku):\\n \\n # get the possible ways to win\\n possible_wins = board.get_wins(affinity)\\n \\n # if we can win, pick a good win \\n if len(possible_wins) == 1: return possible_wins[0]\\n elif len(possible_wins) > 1:\\n best_win = None\\n wins_by_x = {}\\n wins_by_y = {}\\n for win in possible_wins:\\n if win[0] not in wins_by_x.keys():\\n wins_by_x[win[0]] = []\\n if win[1] not in wins_by_y.keys():\\n wins_by_y[win[1]] = []\\n wins_by_x[win[0]].append(win)\\n wins_by_y[win[1]].append(win)\\n for y in wins_by_y:\\n if len(wins_by_y[y]) > 1: \\n for win in wins_by_y[y]:\\n if best_win is None or win[0] < best_win[0]:\\n best_win = win \\n return best_win\\n\\n else: return None\",\n \"def is_game_lost(self):\\n values = [self.hand[i]._lvalue + self.hand[i]._rvalue for i in range(len(self.hand))]\\n return not sum_in_list_dyn(values, self.number_point)\",\n \"def isGameOver(board, *args, **kwargs):\\n black_win = consecutive_score(\\\"black\\\", board, board.size)\\n white_win = consecutive_score(\\\"white\\\", board, board.size)\\n if black_win >= 100 and white_win >= 100:\\n return \\\"tie\\\"\\n elif black_win >= 100 and white_win <= 100:\\n return \\\"black\\\"\\n elif black_win <= 100 and white_win >= 100:\\n return \\\"white\\\"\\n else:\\n return -1\",\n \"def winning_event(self, player):\\n # vertical check\\n for col in range(GameData.columns):\\n if self.board[0][col] == player and self.board[1][col] == player and self.board[2][col] == player:\\n self.draw_vertical_winning_line(col, player)\\n print(\\\"Player {} has won the game!\\\".format(player))\\n self.game_over = True\\n return True\\n\\n # horizontal check\\n for row in range(GameData.rows):\\n if self.board[row][0] == player and self.board[row][1] == player and self.board[row][2] == player:\\n self.draw_horizontal_winning_line(row, player)\\n print(\\\"Player {} has won the game!\\\".format(player))\\n self.game_over = True\\n return True\\n\\n # ascending diagonal heck\\n if self.board[2][0] == player and self.board[1][1] == player and self.board[0][2] == player:\\n self.draw_asc_diagonal(player)\\n print(\\\"Player {} has won the game!\\\".format(player))\\n self.game_over = True\\n return True\\n\\n # descending diagonal win chek\\n if self.board[0][0] == player and self.board[1][1] == player and self.board[2][2] == player:\\n self.draw_desc_diagonal(player)\\n print(\\\"Player {} has won the game!\\\".format(player))\\n self.game_over = True\\n return True\\n\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75529164","0.74890757","0.73833823","0.73335296","0.7333373","0.72050595","0.70841414","0.705282","0.7051845","0.70401967","0.6998241","0.69875365","0.6977598","0.6947065","0.69441944","0.6942049","0.69409615","0.69100434","0.6907182","0.6895013","0.689254","0.6864129","0.6858663","0.6856255","0.6853972","0.6834625","0.67975646","0.67975074","0.6787823","0.67834437","0.6777791","0.6768507","0.67675555","0.67673045","0.67617464","0.6742236","0.6735091","0.67267764","0.6723736","0.67235","0.6714662","0.66961753","0.6663297","0.6650618","0.66456383","0.66229564","0.66226405","0.6611483","0.6606639","0.6596068","0.6590693","0.6589285","0.65796876","0.6564864","0.65572286","0.6554924","0.65484995","0.65458715","0.6490743","0.6484579","0.64818376","0.64560556","0.6442077","0.6441256","0.6434982","0.64334285","0.6427821","0.64125234","0.640933","0.64040637","0.6393729","0.6390652","0.6376509","0.6368323","0.63620615","0.6357347","0.6357237","0.6354516","0.6341487","0.63261455","0.6320126","0.63093585","0.63076043","0.6301174","0.6297625","0.6296961","0.62967575","0.6280389","0.6269796","0.62618935","0.62520576","0.62463385","0.6243144","0.62399","0.6239806","0.62264806","0.6218997","0.62182856","0.62125236","0.6212372"],"string":"[\n \"0.75529164\",\n \"0.74890757\",\n \"0.73833823\",\n \"0.73335296\",\n \"0.7333373\",\n \"0.72050595\",\n \"0.70841414\",\n \"0.705282\",\n \"0.7051845\",\n \"0.70401967\",\n \"0.6998241\",\n \"0.69875365\",\n \"0.6977598\",\n \"0.6947065\",\n \"0.69441944\",\n \"0.6942049\",\n \"0.69409615\",\n \"0.69100434\",\n \"0.6907182\",\n \"0.6895013\",\n \"0.689254\",\n \"0.6864129\",\n \"0.6858663\",\n \"0.6856255\",\n \"0.6853972\",\n \"0.6834625\",\n \"0.67975646\",\n \"0.67975074\",\n \"0.6787823\",\n \"0.67834437\",\n \"0.6777791\",\n \"0.6768507\",\n \"0.67675555\",\n \"0.67673045\",\n \"0.67617464\",\n \"0.6742236\",\n \"0.6735091\",\n \"0.67267764\",\n \"0.6723736\",\n \"0.67235\",\n \"0.6714662\",\n \"0.66961753\",\n \"0.6663297\",\n \"0.6650618\",\n \"0.66456383\",\n \"0.66229564\",\n \"0.66226405\",\n \"0.6611483\",\n \"0.6606639\",\n \"0.6596068\",\n \"0.6590693\",\n \"0.6589285\",\n \"0.65796876\",\n \"0.6564864\",\n \"0.65572286\",\n \"0.6554924\",\n \"0.65484995\",\n \"0.65458715\",\n \"0.6490743\",\n \"0.6484579\",\n \"0.64818376\",\n \"0.64560556\",\n \"0.6442077\",\n \"0.6441256\",\n \"0.6434982\",\n \"0.64334285\",\n \"0.6427821\",\n \"0.64125234\",\n \"0.640933\",\n \"0.64040637\",\n \"0.6393729\",\n \"0.6390652\",\n \"0.6376509\",\n \"0.6368323\",\n \"0.63620615\",\n \"0.6357347\",\n \"0.6357237\",\n \"0.6354516\",\n \"0.6341487\",\n \"0.63261455\",\n \"0.6320126\",\n \"0.63093585\",\n \"0.63076043\",\n \"0.6301174\",\n \"0.6297625\",\n \"0.6296961\",\n \"0.62967575\",\n \"0.6280389\",\n \"0.6269796\",\n \"0.62618935\",\n \"0.62520576\",\n \"0.62463385\",\n \"0.6243144\",\n \"0.62399\",\n \"0.6239806\",\n \"0.62264806\",\n \"0.6218997\",\n \"0.62182856\",\n \"0.62125236\",\n \"0.6212372\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.691553"},"document_rank":{"kind":"string","value":"17"}}},{"rowIdx":2669,"cells":{"query":{"kind":"string","value":"Returns a string interpretation of the board."},"document":{"kind":"string","value":"def __str__(self):\n\t\tboardString = \"\\n{0}|{1}|{2}\\n-----\\n{3}|{4}|{5}\\n-----\\n{6}|{7}|{8}\\n\"\n\t\treturn boardString.format(self.board[0], self.board[1], self.board[2], self.board[3],\n\t\t\tself.board[4], self.board[5], self.board[6], self.board[7], self.board[8])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def board_string(self):\n s = \"\"\n for i, v in enumerate(self.board):\n # if i % 81 == 0:\n # s += \"\\n\"\n if v is None:\n s += \"0\"\n else:\n if v.color == StoneColor.black:\n s += \"1\"\n else:\n s += \"2\"\n return s","def __str__(self):\n board = ''\n for row in range(self.height):\n if row > 0:\n board += '\\n'\n for col in range(self.width):\n if self.board[row][col] == '':\n board += '| '\n else:\n board += ('|' + self.board[row][col])\n board += '|'\n board += ('\\n' + '-' * 2 * self.width + '\\n')\n for i in range(self.width):\n board += (' ' + str(i))\n return board","def __repr__(self):\n W = self.width\n H = self.height\n\n s = '' # the string to return\n for row in range(0, H):\n s += '|'\n for col in range(0, W):\n s += self.data[row][col] + '|'\n s += '\\n'\n\n s += (2 * W + 1) * '-' # bottom of the board\n s += '\\n'\n\n x = -1\n for i in range(W):\n if x == 9:\n x = 0\n s += \" \" + str(x)\n else:\n x += 1\n s += \" \" + str(x)\n\n return s # the board is complete, return it","def __repr__(self):\n t = ''\n for x in range(len(self.board)):\n for y in range(len(self.board[0])):\n t += str(self.board[x][y]) + ' '\n t += '\\n'\n return t","def uninterpret_board(self):\n\t\tboard_string = ''\n\t\tfor i in range(len(self.board)):\n\t\t\tfor j in range(len(self.board[i])):\n\t\t\t\tboard_string+=self.board[i][j]\n\n\t\treturn board_string","def __str__(self):\n board = \"\"\" 0 1 2 3 4 5\\n\"\"\"\n\n for y in range(Board.board_size):\n board += str(y) + \" \"\n for x in range(Board.board_size):\n piece = self.board[x][y] if self.board[x][y] is not None else \".\"\n\n piece = str(piece).lower() if piece in self.player_1_pieces else str(piece)\n\n board += piece\n board += \" \"\n board += \"\\n\"\n return board","def StringFromBoard(board):\n\trows = []\n\tfor row in board:\n\t\trows.append('|'.join([' '+square+' ' for square in row]))\n\treturn '\\n-----------\\n'.join(rows)","def __str__(self):\n rep = \"\"\n for row in range(self._dim):\n for col in range(self._dim):\n rep += STRMAP[self._board[row][col]]\n if col == self._dim - 1:\n rep += \"\\n\"\n else:\n rep += \" | \"\n if row != self._dim - 1:\n rep += \"-\" * (4 * self._dim - 3)\n rep += \"\\n\"\n return rep","def __repr__(self) -> str:\n\t\t\n\t\trepr = \"\"\n\t\tfor row in self.board:\n\t\t\tfor element in row:\n\t\t\t\tif element:\n\t\t\t\t\trepr = repr + \"o \"\n\t\t\t\telse:\n\t\t\t\t\trepr = repr + \"@ \"\n\t\t\trepr = repr + \"\\n\"\n\t\treturn repr","def boardToString(self, margins={}):\n b = self.board\n rg = range(b.size())\n left = ' '*margins.get('left', 0)\n s = '\\n'.join(\n [left + ' '.join([self.getCellStr(x, y) for x in rg]) for y in rg])\n return s","def board(self) -> str:\n divider = \"+\" + \"-\" * 23 + \"+\"\n b = [divider]\n for i in range(9):\n r = []\n for j in range(3):\n s = tool.index_counter(i, j * 3)\n r.append(' '.join(str(i) if i > 0 else ' '\n for i in self.grid[s:s+3]))\n b.append(f\"| {r[0]} | {r[1]} | {r[2]} |\")\n if (i + 1) % 3 == 0:\n b.append(divider)\n return \"\\n\".join(b)","def __str__(self):\n\t\tstring = \"\"\n\t\tfor i in self.board:\n\t\t\tfor j in i:\n\t\t\t\tstring += str(j)\n\t\t\tstring += \"\\n\"\n\t\treturn string","def __str__(self):\r\n return str(self.board)","def __str__(self):\r\n \r\n #return \"The 2048 board is \" + str(self._cells)\r\n string = \"\"\r\n for row in range(self._grid_height):\r\n for column in range(self._grid_width):\r\n if column == self._grid_width -1:\r\n string += str(self._cells[row][column]) + \"\\n\"\r\n else:\r\n string += str(self._cells[row][column]) +\", \"\r\n return \"The 2048 board is \"+ str(self._grid_height) + \"x\" + str(self._grid_width) + \" and contains: \" + \"\\n\" + string","def __str__(self):\n result = \"\"\n for i in range(3):\n for j in range(3):\n if self.board[i][j] == 5:\n result += \" x\"\n elif self.board[i][j] == 7:\n result += \" о\"\n else:\n result += \" #\"\n result += \"\\n\"\n return result","def __str__(self):\n s = \" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\\n\"\n board = initial_board()\n count = 1\n for i in self.occupied:\n board[i[0]][i[1]] = self.occupied[i]\n space = ''\n for i in range(0, 16):\n space += ' '\n start = '---'.join(space)\n s += start+'\\n|'\n for row in range(1,16):\n for col in range(1,16):\n if use_color and (row, col) == self.action:\n s += '\\033[91m'\n if board[row][col] == 0:\n s += ' |'\n elif board[row][col] == 1:\n s += ' O |'\n else:\n s += ' X |'\n if use_color and (row, col) == self.action:\n s += '\\033[0m'\n s += '\\033[0m'\n s+=str(count)+'\\n'+start+'\\n|'\n count += 1\n\n s = s[:len(s)-1]\n s += \"\\n*****************************************************************************\"\n return s[:len(s)-1]","def __str__(self):\n \n # top row\n result = ' '\n result = '\\n ' + '-' * (self.DIM*2+5) + '\\n'\n \n # board rows\n for row in range(self.DIM):\n if row is 3 or row is 6:\n result += '|' + '-' * (self.DIM*2+5) + '|' + '\\n'\n # result += '|-------+-------+-------|\\n'\n result += '| '\n for col in range(self.DIM):\n if col is 3 or col is 6:\n result += '| '\n if self.board[row][col] == SudokuConfig.EMPTY:\n result += '.'\n else:\n result += str(str(self.board[row][col]))\n if col != self.DIM-1:\n result += ' '\n result += ' |' + '\\n'\n \n # bottom row\n result += ' ' + '-' * (self.DIM*2+5) + '\\n'\n result += ' '\n result += '\\n'\n \n return result","def __str__(self):\n if self._active_player:\n def piece_to_index(piece):\n return (piece & 0xF)\n else:\n def piece_to_index(piece):\n return (piece & 0xE) | (0 if piece & 1 else 1)\n\n return '\\n'.join(map(\n lambda posY, row: ''.join(map(\n lambda posX, piece: self.EMOJI[\n piece_to_index(piece)\n if piece else\n 14 + ((posY + posX) % 2)],\n count(), row)),\n count(),\n self.board if self._active_player else reversed(\n [reversed(row) for row in self.board])))","def __str__(self):\n board_lists = [['_']*self.__width for rows in range(self.__height)]\n for car in self.__cars:\n car_coords = car.car_coordinates()\n for item in car_coords:\n if item == (3,7):\n pass\n else:\n board_lists[item[0]][item[1]] = car.get_name()\n board_str = '\\n'.join(' '.join(sub) for sub in board_lists)\n return board_str","def __str__(self):\r\n result = \"\"\r\n for line in self.board:\r\n for i in line:\r\n if i is None:\r\n result += \" \"\r\n else:\r\n result += i + \" \"\r\n result += \"\\n\"\r\n\r\n return result","def _to_string(board: Tuple[Tuple[Optional[int]]], width: int) -> str:\n display = \"\\n\"\n for i in range(width):\n for j in range(width):\n line = board[j][i * width:i * width + width]\n start = j * width ** 2 + i * width\n for k, space in enumerate(line):\n if space == 0:\n space = start + k\n else:\n space = (\"X\" if space == 1\n else \"O\" if space == -1\n else \"-\")\n display += \"{0:>4}\".format(space)\n display += \" \" * width\n display += \"\\n\"\n return display","def __repr__(self):\n r = \"\"\n for row in self.board:\n for cell in row:\n if cell == \"\":\n cell = color_magenta(\"_\")\n r += cell + \" \" # for all the empty strings, we will replace it with an '_'.\n r += \"\\n\"\n return r","def __str__(self):\n return '\\n'.join(str(self._board[j]) for j in range(self._n))","def __str__(self):\n # replace with your code\n board = \"\"\n for index in range(self.grid_height):\n board += \"[\"\n for inner_index in range(self.grid_width):\n board += str(self.board[index][inner_index]) + \" \"\n else:\n board += \"]\\n\"\n return board","def __str__(self):\n b = ''\n for i in range(7): # 7 steps in the board\n if i == self.chaser_i: # chaser position\n b += '|' + str(i) + '| chaser |\\n'\n elif i == self.player_i: # player position\n b += '|' + str(i) + '| player |\\n'\n else:\n b += '|' + str(i) + '| |\\n'\n b += '|7| bank |\\n' # bank position\n return b","def convertBoard(self):\n \n board = \"\"\n \n for m in self.squares:\n board += str(convertMarker(m)) + \" \"\n \n return board","def print_board(self):\n board = \"\"\n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += self.board[i][j]\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def __repr__(self):\n s = dashes = \"\".join([ ' -' for i in range(self.BoardSize) ])\n for row in range( self.BoardSize ):\n sRow = '|'\n for col in range( self.BoardSize ):\n sRow += str(self.CurrentGameboard[row][col]) + '|'\n s += '\\n' + sRow + '\\n' + dashes\n return s","def __str__(self):\n #formatting board correctly\n formatted_board = \"\"\n for i in range(self.size):\n formatted_board += str(self.board[i]) + \"\\n\"\n return \"Board size: \" + str(self.size) + \"\\n\" + \"Number of Queens placed: \" + str(self.num_queens_placed) + \"\\n\" + str(formatted_board)","def get_board_as_string(game):\n str_board = \"\\n\" # every board starts with a blank line\n row = 0 # used to print the board\n\n # creates a board of 5 lines. 3 rows, 2 dashed.\n for line in range(1, 6):\n\n # every odd line\n if line % 2 != 0:\n\n # add a row to the string str_board\n str_board += \"{} | {} | {}\".format(game['board'][row][0], game['board'][row][1], game['board'][row][2])\n\n # increment the row\n row += 1\n\n # every even line\n else:\n str_board += \"--------------\"\n\n str_board += \"\\n\" # add line break at the end of every line\n\n return str_board","def macro_str(self):\n str = '-' * (2 * self.SIZE + 1) + '\\n'\n for row in self.boards:\n str += ' '\n for board in row:\n str += board.state.value + ' '\n str += '\\n' + '-' * (2 * self.SIZE + 1) + '\\n'\n return str","def get_board_state_pretty(self):\n\n board_state = ''\n for i in range(0, 3):\n board_state += ' | '.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\n board_state += '\\n'\n return board_state","def __str__(self) -> str:\n return self.board","def __repr__(self):\r\n numLetters = self.numLetters\r\n S = ''\r\n S += 3*'\\n'\r\n S += ' '\r\n for i in range(numLetters):\r\n S += self.currentBoard[i] + ' '\r\n\r\n return S","def __repr__(self):\n return f'Board({ self.board !r})'","def displayBoard(self):\n res = ''\n for i in range(0, self.size):\n res += '|'\n for j in range(0, self.size):\n res += ' ' + str(self.board[i][j])\n res += '\\n'\n res += '+'\n for i in range(0, self.size * 2):\n res += '-'\n res += '\\n '\n for i in range(1, (self.size + 1)):\n res += (' ' + str(i))\n return res","def __str__(self):\n def align_column(grid):\n board = \"\"\n for i in range(self.n):\n board += str(grid[i]) + \"\\n\"\n return board.strip()\n return (\"===Current Stage===\\n\"\n \"{}\\n\"\n \"====Goal Board=====\\n\"\n \"{}\".format(align_column(self.from_grid),\n align_column(self.to_grid)))","def board_to_string(board):\n ordered_vals = []\n for r in ROW:\n for c in COL:\n ordered_vals.append(str(board[r + c]))\n return ''.join(ordered_vals)","def board_to_string(board):\n ordered_vals = []\n for r in ROW:\n for c in COL:\n ordered_vals.append(str(board[r + c]))\n return ''.join(ordered_vals)","def __str__(self):\n str = '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\n'\n for row in self.boards:\n for i in range(self.SIZE):\n str += '|'\n for board in row:\n for square in board.export_grid()[i]:\n str += square.value\n str += '|'\n str += '\\n'\n str += '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\n'\n return str","def board2str(board, end='\\n'):\n s = ''\n for x in range(board.shape[0]):\n for y in range(board.shape[1]):\n s += str(board[x][y]) + '\\t'\n s += end\n return s[:-len(end)]","def __str__(self):\n board = ''\n board_2 = ''\n\n for row in self.from_grid:\n for space in row:\n board += ' ' + space\n board += '\\n'\n\n for row in self.to_grid:\n for space in row:\n board_2 += ' ' + space\n board_2 += '\\n'\n\n return 'Current State:\\n' + board + 'Target State:\\n' + board_2","def __repr__(self):\n\n # Creates a deep copy of _board and changes all None values to underscores.\n temp_board = copy.deepcopy(self.get_board())\n\n for row_index, row in enumerate(temp_board):\n\n for column_index, column in enumerate(row):\n\n if column is None:\n\n row[column_index] = '____' + self.reverse_position(column_index, row_index) + '_____'\n\n # str() with position is mapped onto each list within _board list,\n # converted to string, and then joined by a nextline character.\n return '\\n\\n'.join(map(str, temp_board))","def __str__(self):\n line = ''\n line += self.board_state.__str__()\n line += self.move.__str__()\n line += '\\n'\n return line","def __getAsciiString(self):\n lines = []\n horizontalLine = ('-' * (26))\n lines.append(horizontalLine)\n for row in self.board:\n rowLine = '|'\n for col in row:\n if col == -1:\n col = 'O'\n if col == 0:\n col = '-'\n if col == 1:\n col = 'X'\n rowLine = rowLine + ' ' + col.__str__() + ' |'\n lines.append(rowLine)\n lines.append(horizontalLine)\n return '\\n'.join(lines)","def __str__(self) -> str:\r\n output: str = \"\"\r\n\r\n for row_i in range(Board._NUM_ROWS):\r\n for col_i in range(Board._NUM_COLS):\r\n pos: Pos2D = Pos2D(col_i, row_i)\r\n output += (\"{} \".format(self.squares[pos].get_representation()))\r\n # Finished row, add new line.\r\n output += \"\\n\"\r\n\r\n return output","def __str__(self):\r\n # replace with your code\r\n return str(self._board[0]) + \"\\n\" + str(self._board[1]) + \"\\n\" + str(self._board[2]) + \"\\n\" + str(self._board[3]) + \"\\n\\n\"","def __str__(self) -> str:\n not_actual = self.current_board\n representation = self.current_board\n\n for index in range(len(not_actual)):\n if not_actual[index: index + 2] in ['31', '32', '33', '34', '36',\n '37', '38']:\n representation = representation.replace(\n not_actual[index: index + 2], '@')\n if not_actual[index: index + 2] in ['41', '42', '43', '44', '45',\n '46', '47', '48']:\n representation = representation.replace(\n not_actual[index: index + 2], '@')\n if not_actual[index: index + 2] in ['51', '52', '53', '54', '55',\n '56', '57', '58']:\n representation = representation.replace(\n not_actual[index: index + 2], '@')\n return representation","def get_board_state(self):\n\n board_state = ''\n for i in range(0, 3):\n board_state += ''.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\n return board_state","def __str__(self):\n string = \"\"\n for row in self.layout:\n for tile in row:\n string+= str(tile) + \" \"\n string+= \"\\n\"\n return string","def print_board(board):\n rep = ''\n for row in xrange(8, 0, -1):\n begin, end = 10*row + 1, 10*row + 9\n rep += '%d %s\\n' % (row-1, ' '.join(board[begin:end]))\n rep += ' %s\\n' % ' '.join(map(str, range(8)))\n return rep","def __str__(self):\n\t\t\n\t\tdef mapping(x):\n\t\t\tif x == 1:\n\t\t\t\t# WHITE\n\t\t\t\treturn 'O'\n\t\t\telif x == -1:\n\t\t\t\t# BLACK\n\t\t\t\treturn 'X'\n\t\t\telse:\n\t\t\t\t# Empty\n\t\t\t\treturn '-'\n\t\t\n\t\ts = 'BLACK - X\\n'\n\t\ts += 'WHITE - O\\n\\n'\n\t\tfor j in self.rows:\n\t\t\ts += j\n\t\t\ts += ' '\n\t\t\ts += ''.join(mapping(self[i+j]) for i in self.columns)\n\t\t\ts += '\\n'\n\t\treturn s + '\\n ' + self.columns + '\\n'","def showBoard(self):\n \n brd = \"\\n | | \\n\" + \\\n \" \" + self.squares[0] + \" | \" + self.squares[1] + \" | \" + self.squares[2] + \" \\n\" + \\\n \"___|___|___\\n\" + \\\n \" | | \\n\" + \\\n \" \" + self.squares[3] + \" | \" + self.squares[4] + \" | \" + self.squares[5] + \" \\n\" + \\\n \"___|___|___\\n\" + \\\n \" | | \\n\" + \\\n \" \" + self.squares[6] + \" | \" + self.squares[7] + \" | \" + self.squares[8] + \" \\n\" + \\\n \" | | \\n\"\n\n return brd","def __str__(self):\n ans = \"\"\n for row in range(self._grid_height):\n ans += str(self._cells[row])\n ans += \"\\n\"\n return ans","def __str__(self):\r\n\t\toutStr = \"\"\r\n\t\toutStr += \"Heuristic Level: \" + str(self.heuristic)\r\n\t\toutStr += \"\\n-\" + \"-----\"*self.n\r\n\t\tfor row in self.board:\r\n\t\t\ttempStr = (\"\\n|\" + \" %2d |\" * self.n)\r\n\t\t\toutStr += tempStr % tuple(row)\r\n\t\t\toutStr += \"\\n-\" + \"-----\"*self.n\r\n\r\n\t\treturn outStr","def __repr__(self):\r\n rep_tpl = (self.__board_size, self.__bombs_dict, self.__ships)\r\n return str(rep_tpl)","def __str__(self):\n puzzle_string = '—' * 13 + '\\n'\n for i in range(self.PUZZLE_NUM_ROWS):\n for j in range(self.PUZZLE_NUM_COLUMNS):\n puzzle_string += '│{0: >2}'.format(str(self.position[i][j]))\n if j == self.PUZZLE_NUM_COLUMNS - 1:\n puzzle_string += '│\\n'\n\n puzzle_string += '—' * 13 + '\\n'\n return puzzle_string","def __str__(self):\n string = ''\n for row in self.board:\n for item in row:\n if item == None:\n string += \"_ \"\n else:\n string += f\"{item.name:<2}\"\n string += '\\n'\n \n return string","def __repr__(self):\r\n # Initialize the return string\r\n s = ''\r\n\r\n for row in range(self.height):\r\n # Print the index of the row\r\n s = s + str(row % 10) + ' |'\r\n\r\n for col in range(self.width):\r\n s += self.grid[row][col]\r\n s += '|'\r\n\r\n s += '\\n'\r\n s += '--' * (self.width + 1)\r\n s += '-'\r\n s += '\\n'\r\n \r\n s += ' '\r\n for i in range(self.width):\r\n s += ' ' + str(i % 10) \r\n \r\n return s","def __repr__(self):\r\n answer = '\\n'\r\n for i in range(len(self)):\r\n for j in range(len(self)):\r\n if (i+j)%2 == 0:\r\n answer += '{}'.format(self.quantum_board[i][j])\r\n else:\r\n answer += '-'\r\n answer += '\\t'\r\n answer += '\\n'\r\n return answer","def __str__(self):\n value = str(self.puzzle) + str(\" \") + str(self.g) + str(\" \") + str(self.h)\n return value","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __repr__(self):\r\n answer = '\\n'\r\n for i in range(len(self)):\r\n for j in range(len(self)):\r\n if (i+j)%2 == 0:\r\n answer += '{}'.format(self.board[i][j])\r\n else:\r\n answer += '-'\r\n answer += '\\t'\r\n answer += '\\n'\r\n return answer","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\n string = ''\n\n for row in range(0, self.height):\n for col in range(0, self.width):\n string += self.cells[(row, col)]\n string += '\\n'\n\n return string","def board_string(self, players):\n if len(self.user_guess) == 1:\n board = \"\\n-------------------\\n\"\n board += f\"Player {players[0]}: {self.user_guess[0]}, {self.applied_guess[0]}\\n\"\n board += f\"Player {players[1]}: {self.user_guess[0]}, {self.applied_guess[0]}\\n\"\n board += \"-------------------\\n\"\n\n board = \"\\n-------------------\\n\"\n board += f\"Player {players[0].get_name()}: {self.user_guess[0]}, {self.applied_guess[0]}\\n\"\n board += f\"Player {players[1].get_name()}: {self.user_guess[1]}, {self.applied_guess[1]}\\n\"\n board += \"-------------------\\n\"\n\n return board","def print_board(self):\n print('Board:')\n print('\\n'.join([''.join(['{:4}'.format(item) for item in row]) for row in self.board]))","def __repr__(self):\n representantion = ''\n\n for i in range(3):\n for j in range(3):\n representantion += str(self.state[3 * i + j])\n\n if j == 2 and i != 2:\n representantion += '\\n'\n else:\n representantion += ' '\n\n return representantion","def printBoard(self):\n if self.side == self.WHITE or self.side == None:\n for r in [8,7,6,5,4,3,2,1]:\n for c in 'abcdefgh':\n p = self.getPiece(c,r) # print a8 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"\n else:\n for r in [1,2,3,4,5,6,7,8]:\n for c in 'hgfedcba':\n p = self.getPiece(c,r) # print h1 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"\n\n for r in [8,7,6,5,4,3,2,1]:\n for c in 'abcdefgh':\n p = self.getPiece(c,r)\n #if p != None and p.header.frame_id == \"chess_board\":\n # print \"Warning, frame is chess_board:\", c+str(r)","def state_to_string(board_state):\n return str(board_state)","def transferBoardToString(self,board):\n #int(False) will be 0\n outputString = str()\n for i in board:\n outputString = outputString + str(int(i))\n return outputString","def getCellStr(self, x, y): # TODO: refactor regarding issue #11\n c = self.board.getCell(x, y)\n\n if c == 0:\n return '.' if self.__azmode else ' .'\n\n elif self.__azmode:\n az = {}\n for i in range(1, int(math.log(self.board.goal(), 2))):\n az[2 ** i] = chr(i + 96)\n\n if c not in az:\n return '?'\n s = az[c]\n elif c == 1024:\n s = ' 1k'\n elif c == 2048:\n s = ' 2k'\n else:\n s = '%3d' % c\n\n return self.__colors.get(c, Fore.RESET) + s + Style.RESET_ALL","def __str__(self):\n return \"Board: \" + str(self._board) + \" Workers: \" + str(self._workers.items())","def draw_board(self):\n board = \"-------------------\\n\"\n board += \"| %s | %s | %s |\\n\" % (self.board_values[1], self.board_values[2], self.board_values[3])\n board += \"-------------------\\n\"\n board += \"| %s | %s | %s |\\n\" % (self.board_values[4], self.board_values[5], self.board_values[6])\n board += \"-------------------\\n\"\n board += \"| %s | %s | %s |\\n\" % (self.board_values[7], self.board_values[8], self.board_values[9])\n board += \"-------------------\\n\"\n return board","def __str__(self):\n s = ''\n for i in range(5):\n for j in range(5):\n ani = False\n if self[i][j] == 0:\n s += ' 0 '\n elif self[i][j].species == 'Elephant':\n s += ' E'\n ani = True\n elif self[i][j].species == 'Rhinoceros':\n s += ' R'\n ani = True\n else:\n s += ' B '\n if ani:\n if self[i][j].direction[0] == 0 and self[i][j].direction[1] == 1:\n d = '> '\n elif self[i][j].direction[0] == -1 and self[i][j].direction[1] == 0:\n d = '∧ '\n elif self[i][j].direction[0] == 0 and self[i][j].direction[1] == -1:\n d = '< '\n else:\n d = '∨ '\n s += d\n s += '\\n \\n'\n return s","def printBoard(self):\n if self.side == self.WHITE or self.side == None:\n for r in [8,7,6,5,4,3,2,1]:\n for c in 'abcdefgh':\n p = self.getPiece(c,r) # print a8 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"\n else:\n for r in [1,2,3,4,5,6,7,8]:\n for c in 'hgfedcba':\n p = self.getPiece(c,r) # print h1 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"","def __str__(self):\n result = \"\"\n for row in self._cells:\n result += \" \".join(map(str, row))\n result += \"\\n\"\n return result","def __repr__(self):\n s = \"\"\n for y in range(0,HEIGHT):\n temp=\"\"\n for x in range(0,WIDTH):\n temp = temp+ str(self.gameState[x,y])\n s += temp+\"\\n\"\n return s","def __str__(self) -> str:\n output = self.columns * \" __\" + \"\\n\"\n for i in range(self.rows):\n for j in range(self.columns):\n filling = \"__\"\n if len(self.cells[i][j].agents) == 1:\n filling = \"_§\"\n elif len(self.cells[i][j].agents) == 2:\n filling = \"§§\"\n elif len(self.cells[i][j].agents) > 2:\n filling = \"++\"\n output += \"|\" + filling\n if j == self.columns - 1:\n output += \"|\"\n output += \"\\n\"\n return output","def __str__(self):\n result = ''\n for row in range(self.getHeight()):\n for col in range(self.getWidth()):\n result += str(self.data[row][col]) + ' '\n result += '\\n'\n return result","def __str__(self):\n grid_str = \"\"\n for i in range(len(self.grid)):\n for j in range(len(self.grid[i])):\n grid_str = grid_str + self.grid[i][j] + '\\t'\n grid_str = grid_str.strip('\\t')\n grid_str = grid_str + '\\n'\n return grid_str","def __str__(self):\n s=\"\"\n for y in range(0,HEIGHT):\n for x in range(0,WIDTH):\n s+=str(self.gameState[x,y])\n return s","def __repr__(self) -> str:\n return \"P1's Turn: {} - Board: {}\".format(self.p1_turn,\n self.board)","def __repr__(self) -> str:\n return \"P1's Turn: {} - Board: {}\".format(self.p1_turn,\n self.current_board)","def __str__(self):\r\n # replace with your code\r\n result = ''\r\n for row in range(0, self._grid_height):\r\n result += str(self._grid_tile[row]) + '\\n'\r\n return result","def __str__(self):\r\n grid_text = \"\\n-------------------\\n|\"\r\n for i in range(len(self.grid)):\r\n grid_text = grid_text + ' %s '%(self.grid[i][-1])\r\n\r\n if i%3 == 2:\r\n grid_text = grid_text + '|\\n-------------------\\n|'\r\n else:\r\n grid_text = grid_text + '|'\r\n return grid_text[0:len(grid_text)-1]","def __str__(self):\r\n # The full representative string\r\n str_matrix = \"\"\r\n\r\n if self.matrix is not None:\r\n # Save the lenght into a variable\r\n # to send this number to the tiles method\r\n # and calculate the number of spaces\r\n spaces = len(self.matrix)\r\n for i in range(0, spaces):\r\n nums = list(filter(lambda x: x != \"_\", self.matrix[i]))\r\n str_matrix += self.tiles(nums, (i+1), (spaces - i))\r\n\r\n return str_matrix","def __str__(self):\n grid_string = ''\n for row in range(self.grid_height):\n grid_string += str(self.grid[row]) + '\\n'\n return grid_string","def get_cards_as_string(self):\n return '' \\\n ' {}\\n' \\\n ' {}\\n' \\\n ' {}\\n' \\\n ' {}\\n' \\\n ' {}\\n'.format(*self.get_cards_high_to_low())","def print_board(self):\n \n # How to show empty/p1/p2\n VALS = \".XO\"\n\n print(\"\\n a b c d e f g\")\n print(\" /--+-+-+-+-+-+--\\\\\")\n for r in range(_HEIGHT - 1, -1, -1):\n s = \"%s |\" % r\n for c in range(_WIDTH):\n # Print mark next to most recent move\n mark = \">\" if self.last_play_rc == (r, c) else \" \"\n s += mark + VALS[self.board[r * 7 + c]]\n print(s + \" |\")\n print(\" \\\\--+-+-+-+-+-+--/\")\n print(\" a b c d e f g\\n\")","def __str__(self) -> str:\n return self._color + str(self._value) + SudokuTile.C_END","def __str__(self):\n grid_str = \"\"\n for row in range(self.grid_height):\n grid_str += str(self.grid[row])+'\\n'\n return grid_str","def print_board(self):\n to_join = [\"-\" * self.DIMENSIONS[0]]\n for row in self.grid:\n to_join.append(\"\".join([ch.letter if ch is not None else \" \" for ch in row]))\n\n print(\"\\n\".join(to_join))","def __str__(self):\n ans = \"\"\n for i in range(self.row):\n for j in range(self.col):\n ans+=str(self.array[i][j])+\" \"\n ans+=\"\\n\"\n return ans","def printBoard(self):\n\t\tkey = [' ', 'X', 'O']\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[0][0]] + ' | ' + key[self.state[0][1]] + ' | ' + key[self.state[0][2]])\n\t\tprint(' | |')\n\t\tprint('-----------')\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[1][0]] + ' | ' + key[self.state[1][1]] + ' | ' + key[self.state[1][2]])\n\t\tprint(' | |')\n\t\tprint('-----------')\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[2][0]] + ' | ' + key[self.state[2][1]] + ' | ' + key[self.state[2][2]])\n\t\tprint(' | |')"],"string":"[\n \"def board_string(self):\\n s = \\\"\\\"\\n for i, v in enumerate(self.board):\\n # if i % 81 == 0:\\n # s += \\\"\\\\n\\\"\\n if v is None:\\n s += \\\"0\\\"\\n else:\\n if v.color == StoneColor.black:\\n s += \\\"1\\\"\\n else:\\n s += \\\"2\\\"\\n return s\",\n \"def __str__(self):\\n board = ''\\n for row in range(self.height):\\n if row > 0:\\n board += '\\\\n'\\n for col in range(self.width):\\n if self.board[row][col] == '':\\n board += '| '\\n else:\\n board += ('|' + self.board[row][col])\\n board += '|'\\n board += ('\\\\n' + '-' * 2 * self.width + '\\\\n')\\n for i in range(self.width):\\n board += (' ' + str(i))\\n return board\",\n \"def __repr__(self):\\n W = self.width\\n H = self.height\\n\\n s = '' # the string to return\\n for row in range(0, H):\\n s += '|'\\n for col in range(0, W):\\n s += self.data[row][col] + '|'\\n s += '\\\\n'\\n\\n s += (2 * W + 1) * '-' # bottom of the board\\n s += '\\\\n'\\n\\n x = -1\\n for i in range(W):\\n if x == 9:\\n x = 0\\n s += \\\" \\\" + str(x)\\n else:\\n x += 1\\n s += \\\" \\\" + str(x)\\n\\n return s # the board is complete, return it\",\n \"def __repr__(self):\\n t = ''\\n for x in range(len(self.board)):\\n for y in range(len(self.board[0])):\\n t += str(self.board[x][y]) + ' '\\n t += '\\\\n'\\n return t\",\n \"def uninterpret_board(self):\\n\\t\\tboard_string = ''\\n\\t\\tfor i in range(len(self.board)):\\n\\t\\t\\tfor j in range(len(self.board[i])):\\n\\t\\t\\t\\tboard_string+=self.board[i][j]\\n\\n\\t\\treturn board_string\",\n \"def __str__(self):\\n board = \\\"\\\"\\\" 0 1 2 3 4 5\\\\n\\\"\\\"\\\"\\n\\n for y in range(Board.board_size):\\n board += str(y) + \\\" \\\"\\n for x in range(Board.board_size):\\n piece = self.board[x][y] if self.board[x][y] is not None else \\\".\\\"\\n\\n piece = str(piece).lower() if piece in self.player_1_pieces else str(piece)\\n\\n board += piece\\n board += \\\" \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def StringFromBoard(board):\\n\\trows = []\\n\\tfor row in board:\\n\\t\\trows.append('|'.join([' '+square+' ' for square in row]))\\n\\treturn '\\\\n-----------\\\\n'.join(rows)\",\n \"def __str__(self):\\n rep = \\\"\\\"\\n for row in range(self._dim):\\n for col in range(self._dim):\\n rep += STRMAP[self._board[row][col]]\\n if col == self._dim - 1:\\n rep += \\\"\\\\n\\\"\\n else:\\n rep += \\\" | \\\"\\n if row != self._dim - 1:\\n rep += \\\"-\\\" * (4 * self._dim - 3)\\n rep += \\\"\\\\n\\\"\\n return rep\",\n \"def __repr__(self) -> str:\\n\\t\\t\\n\\t\\trepr = \\\"\\\"\\n\\t\\tfor row in self.board:\\n\\t\\t\\tfor element in row:\\n\\t\\t\\t\\tif element:\\n\\t\\t\\t\\t\\trepr = repr + \\\"o \\\"\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\trepr = repr + \\\"@ \\\"\\n\\t\\t\\trepr = repr + \\\"\\\\n\\\"\\n\\t\\treturn repr\",\n \"def boardToString(self, margins={}):\\n b = self.board\\n rg = range(b.size())\\n left = ' '*margins.get('left', 0)\\n s = '\\\\n'.join(\\n [left + ' '.join([self.getCellStr(x, y) for x in rg]) for y in rg])\\n return s\",\n \"def board(self) -> str:\\n divider = \\\"+\\\" + \\\"-\\\" * 23 + \\\"+\\\"\\n b = [divider]\\n for i in range(9):\\n r = []\\n for j in range(3):\\n s = tool.index_counter(i, j * 3)\\n r.append(' '.join(str(i) if i > 0 else ' '\\n for i in self.grid[s:s+3]))\\n b.append(f\\\"| {r[0]} | {r[1]} | {r[2]} |\\\")\\n if (i + 1) % 3 == 0:\\n b.append(divider)\\n return \\\"\\\\n\\\".join(b)\",\n \"def __str__(self):\\n\\t\\tstring = \\\"\\\"\\n\\t\\tfor i in self.board:\\n\\t\\t\\tfor j in i:\\n\\t\\t\\t\\tstring += str(j)\\n\\t\\t\\tstring += \\\"\\\\n\\\"\\n\\t\\treturn string\",\n \"def __str__(self):\\r\\n return str(self.board)\",\n \"def __str__(self):\\r\\n \\r\\n #return \\\"The 2048 board is \\\" + str(self._cells)\\r\\n string = \\\"\\\"\\r\\n for row in range(self._grid_height):\\r\\n for column in range(self._grid_width):\\r\\n if column == self._grid_width -1:\\r\\n string += str(self._cells[row][column]) + \\\"\\\\n\\\"\\r\\n else:\\r\\n string += str(self._cells[row][column]) +\\\", \\\"\\r\\n return \\\"The 2048 board is \\\"+ str(self._grid_height) + \\\"x\\\" + str(self._grid_width) + \\\" and contains: \\\" + \\\"\\\\n\\\" + string\",\n \"def __str__(self):\\n result = \\\"\\\"\\n for i in range(3):\\n for j in range(3):\\n if self.board[i][j] == 5:\\n result += \\\" x\\\"\\n elif self.board[i][j] == 7:\\n result += \\\" о\\\"\\n else:\\n result += \\\" #\\\"\\n result += \\\"\\\\n\\\"\\n return result\",\n \"def __str__(self):\\n s = \\\" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\\\\n\\\"\\n board = initial_board()\\n count = 1\\n for i in self.occupied:\\n board[i[0]][i[1]] = self.occupied[i]\\n space = ''\\n for i in range(0, 16):\\n space += ' '\\n start = '---'.join(space)\\n s += start+'\\\\n|'\\n for row in range(1,16):\\n for col in range(1,16):\\n if use_color and (row, col) == self.action:\\n s += '\\\\033[91m'\\n if board[row][col] == 0:\\n s += ' |'\\n elif board[row][col] == 1:\\n s += ' O |'\\n else:\\n s += ' X |'\\n if use_color and (row, col) == self.action:\\n s += '\\\\033[0m'\\n s += '\\\\033[0m'\\n s+=str(count)+'\\\\n'+start+'\\\\n|'\\n count += 1\\n\\n s = s[:len(s)-1]\\n s += \\\"\\\\n*****************************************************************************\\\"\\n return s[:len(s)-1]\",\n \"def __str__(self):\\n \\n # top row\\n result = ' '\\n result = '\\\\n ' + '-' * (self.DIM*2+5) + '\\\\n'\\n \\n # board rows\\n for row in range(self.DIM):\\n if row is 3 or row is 6:\\n result += '|' + '-' * (self.DIM*2+5) + '|' + '\\\\n'\\n # result += '|-------+-------+-------|\\\\n'\\n result += '| '\\n for col in range(self.DIM):\\n if col is 3 or col is 6:\\n result += '| '\\n if self.board[row][col] == SudokuConfig.EMPTY:\\n result += '.'\\n else:\\n result += str(str(self.board[row][col]))\\n if col != self.DIM-1:\\n result += ' '\\n result += ' |' + '\\\\n'\\n \\n # bottom row\\n result += ' ' + '-' * (self.DIM*2+5) + '\\\\n'\\n result += ' '\\n result += '\\\\n'\\n \\n return result\",\n \"def __str__(self):\\n if self._active_player:\\n def piece_to_index(piece):\\n return (piece & 0xF)\\n else:\\n def piece_to_index(piece):\\n return (piece & 0xE) | (0 if piece & 1 else 1)\\n\\n return '\\\\n'.join(map(\\n lambda posY, row: ''.join(map(\\n lambda posX, piece: self.EMOJI[\\n piece_to_index(piece)\\n if piece else\\n 14 + ((posY + posX) % 2)],\\n count(), row)),\\n count(),\\n self.board if self._active_player else reversed(\\n [reversed(row) for row in self.board])))\",\n \"def __str__(self):\\n board_lists = [['_']*self.__width for rows in range(self.__height)]\\n for car in self.__cars:\\n car_coords = car.car_coordinates()\\n for item in car_coords:\\n if item == (3,7):\\n pass\\n else:\\n board_lists[item[0]][item[1]] = car.get_name()\\n board_str = '\\\\n'.join(' '.join(sub) for sub in board_lists)\\n return board_str\",\n \"def __str__(self):\\r\\n result = \\\"\\\"\\r\\n for line in self.board:\\r\\n for i in line:\\r\\n if i is None:\\r\\n result += \\\" \\\"\\r\\n else:\\r\\n result += i + \\\" \\\"\\r\\n result += \\\"\\\\n\\\"\\r\\n\\r\\n return result\",\n \"def _to_string(board: Tuple[Tuple[Optional[int]]], width: int) -> str:\\n display = \\\"\\\\n\\\"\\n for i in range(width):\\n for j in range(width):\\n line = board[j][i * width:i * width + width]\\n start = j * width ** 2 + i * width\\n for k, space in enumerate(line):\\n if space == 0:\\n space = start + k\\n else:\\n space = (\\\"X\\\" if space == 1\\n else \\\"O\\\" if space == -1\\n else \\\"-\\\")\\n display += \\\"{0:>4}\\\".format(space)\\n display += \\\" \\\" * width\\n display += \\\"\\\\n\\\"\\n return display\",\n \"def __repr__(self):\\n r = \\\"\\\"\\n for row in self.board:\\n for cell in row:\\n if cell == \\\"\\\":\\n cell = color_magenta(\\\"_\\\")\\n r += cell + \\\" \\\" # for all the empty strings, we will replace it with an '_'.\\n r += \\\"\\\\n\\\"\\n return r\",\n \"def __str__(self):\\n return '\\\\n'.join(str(self._board[j]) for j in range(self._n))\",\n \"def __str__(self):\\n # replace with your code\\n board = \\\"\\\"\\n for index in range(self.grid_height):\\n board += \\\"[\\\"\\n for inner_index in range(self.grid_width):\\n board += str(self.board[index][inner_index]) + \\\" \\\"\\n else:\\n board += \\\"]\\\\n\\\"\\n return board\",\n \"def __str__(self):\\n b = ''\\n for i in range(7): # 7 steps in the board\\n if i == self.chaser_i: # chaser position\\n b += '|' + str(i) + '| chaser |\\\\n'\\n elif i == self.player_i: # player position\\n b += '|' + str(i) + '| player |\\\\n'\\n else:\\n b += '|' + str(i) + '| |\\\\n'\\n b += '|7| bank |\\\\n' # bank position\\n return b\",\n \"def convertBoard(self):\\n \\n board = \\\"\\\"\\n \\n for m in self.squares:\\n board += str(convertMarker(m)) + \\\" \\\"\\n \\n return board\",\n \"def print_board(self):\\n board = \\\"\\\"\\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += self.board[i][j]\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def __repr__(self):\\n s = dashes = \\\"\\\".join([ ' -' for i in range(self.BoardSize) ])\\n for row in range( self.BoardSize ):\\n sRow = '|'\\n for col in range( self.BoardSize ):\\n sRow += str(self.CurrentGameboard[row][col]) + '|'\\n s += '\\\\n' + sRow + '\\\\n' + dashes\\n return s\",\n \"def __str__(self):\\n #formatting board correctly\\n formatted_board = \\\"\\\"\\n for i in range(self.size):\\n formatted_board += str(self.board[i]) + \\\"\\\\n\\\"\\n return \\\"Board size: \\\" + str(self.size) + \\\"\\\\n\\\" + \\\"Number of Queens placed: \\\" + str(self.num_queens_placed) + \\\"\\\\n\\\" + str(formatted_board)\",\n \"def get_board_as_string(game):\\n str_board = \\\"\\\\n\\\" # every board starts with a blank line\\n row = 0 # used to print the board\\n\\n # creates a board of 5 lines. 3 rows, 2 dashed.\\n for line in range(1, 6):\\n\\n # every odd line\\n if line % 2 != 0:\\n\\n # add a row to the string str_board\\n str_board += \\\"{} | {} | {}\\\".format(game['board'][row][0], game['board'][row][1], game['board'][row][2])\\n\\n # increment the row\\n row += 1\\n\\n # every even line\\n else:\\n str_board += \\\"--------------\\\"\\n\\n str_board += \\\"\\\\n\\\" # add line break at the end of every line\\n\\n return str_board\",\n \"def macro_str(self):\\n str = '-' * (2 * self.SIZE + 1) + '\\\\n'\\n for row in self.boards:\\n str += ' '\\n for board in row:\\n str += board.state.value + ' '\\n str += '\\\\n' + '-' * (2 * self.SIZE + 1) + '\\\\n'\\n return str\",\n \"def get_board_state_pretty(self):\\n\\n board_state = ''\\n for i in range(0, 3):\\n board_state += ' | '.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\\n board_state += '\\\\n'\\n return board_state\",\n \"def __str__(self) -> str:\\n return self.board\",\n \"def __repr__(self):\\r\\n numLetters = self.numLetters\\r\\n S = ''\\r\\n S += 3*'\\\\n'\\r\\n S += ' '\\r\\n for i in range(numLetters):\\r\\n S += self.currentBoard[i] + ' '\\r\\n\\r\\n return S\",\n \"def __repr__(self):\\n return f'Board({ self.board !r})'\",\n \"def displayBoard(self):\\n res = ''\\n for i in range(0, self.size):\\n res += '|'\\n for j in range(0, self.size):\\n res += ' ' + str(self.board[i][j])\\n res += '\\\\n'\\n res += '+'\\n for i in range(0, self.size * 2):\\n res += '-'\\n res += '\\\\n '\\n for i in range(1, (self.size + 1)):\\n res += (' ' + str(i))\\n return res\",\n \"def __str__(self):\\n def align_column(grid):\\n board = \\\"\\\"\\n for i in range(self.n):\\n board += str(grid[i]) + \\\"\\\\n\\\"\\n return board.strip()\\n return (\\\"===Current Stage===\\\\n\\\"\\n \\\"{}\\\\n\\\"\\n \\\"====Goal Board=====\\\\n\\\"\\n \\\"{}\\\".format(align_column(self.from_grid),\\n align_column(self.to_grid)))\",\n \"def board_to_string(board):\\n ordered_vals = []\\n for r in ROW:\\n for c in COL:\\n ordered_vals.append(str(board[r + c]))\\n return ''.join(ordered_vals)\",\n \"def board_to_string(board):\\n ordered_vals = []\\n for r in ROW:\\n for c in COL:\\n ordered_vals.append(str(board[r + c]))\\n return ''.join(ordered_vals)\",\n \"def __str__(self):\\n str = '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\\\n'\\n for row in self.boards:\\n for i in range(self.SIZE):\\n str += '|'\\n for board in row:\\n for square in board.export_grid()[i]:\\n str += square.value\\n str += '|'\\n str += '\\\\n'\\n str += '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\\\n'\\n return str\",\n \"def board2str(board, end='\\\\n'):\\n s = ''\\n for x in range(board.shape[0]):\\n for y in range(board.shape[1]):\\n s += str(board[x][y]) + '\\\\t'\\n s += end\\n return s[:-len(end)]\",\n \"def __str__(self):\\n board = ''\\n board_2 = ''\\n\\n for row in self.from_grid:\\n for space in row:\\n board += ' ' + space\\n board += '\\\\n'\\n\\n for row in self.to_grid:\\n for space in row:\\n board_2 += ' ' + space\\n board_2 += '\\\\n'\\n\\n return 'Current State:\\\\n' + board + 'Target State:\\\\n' + board_2\",\n \"def __repr__(self):\\n\\n # Creates a deep copy of _board and changes all None values to underscores.\\n temp_board = copy.deepcopy(self.get_board())\\n\\n for row_index, row in enumerate(temp_board):\\n\\n for column_index, column in enumerate(row):\\n\\n if column is None:\\n\\n row[column_index] = '____' + self.reverse_position(column_index, row_index) + '_____'\\n\\n # str() with position is mapped onto each list within _board list,\\n # converted to string, and then joined by a nextline character.\\n return '\\\\n\\\\n'.join(map(str, temp_board))\",\n \"def __str__(self):\\n line = ''\\n line += self.board_state.__str__()\\n line += self.move.__str__()\\n line += '\\\\n'\\n return line\",\n \"def __getAsciiString(self):\\n lines = []\\n horizontalLine = ('-' * (26))\\n lines.append(horizontalLine)\\n for row in self.board:\\n rowLine = '|'\\n for col in row:\\n if col == -1:\\n col = 'O'\\n if col == 0:\\n col = '-'\\n if col == 1:\\n col = 'X'\\n rowLine = rowLine + ' ' + col.__str__() + ' |'\\n lines.append(rowLine)\\n lines.append(horizontalLine)\\n return '\\\\n'.join(lines)\",\n \"def __str__(self) -> str:\\r\\n output: str = \\\"\\\"\\r\\n\\r\\n for row_i in range(Board._NUM_ROWS):\\r\\n for col_i in range(Board._NUM_COLS):\\r\\n pos: Pos2D = Pos2D(col_i, row_i)\\r\\n output += (\\\"{} \\\".format(self.squares[pos].get_representation()))\\r\\n # Finished row, add new line.\\r\\n output += \\\"\\\\n\\\"\\r\\n\\r\\n return output\",\n \"def __str__(self):\\r\\n # replace with your code\\r\\n return str(self._board[0]) + \\\"\\\\n\\\" + str(self._board[1]) + \\\"\\\\n\\\" + str(self._board[2]) + \\\"\\\\n\\\" + str(self._board[3]) + \\\"\\\\n\\\\n\\\"\",\n \"def __str__(self) -> str:\\n not_actual = self.current_board\\n representation = self.current_board\\n\\n for index in range(len(not_actual)):\\n if not_actual[index: index + 2] in ['31', '32', '33', '34', '36',\\n '37', '38']:\\n representation = representation.replace(\\n not_actual[index: index + 2], '@')\\n if not_actual[index: index + 2] in ['41', '42', '43', '44', '45',\\n '46', '47', '48']:\\n representation = representation.replace(\\n not_actual[index: index + 2], '@')\\n if not_actual[index: index + 2] in ['51', '52', '53', '54', '55',\\n '56', '57', '58']:\\n representation = representation.replace(\\n not_actual[index: index + 2], '@')\\n return representation\",\n \"def get_board_state(self):\\n\\n board_state = ''\\n for i in range(0, 3):\\n board_state += ''.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\\n return board_state\",\n \"def __str__(self):\\n string = \\\"\\\"\\n for row in self.layout:\\n for tile in row:\\n string+= str(tile) + \\\" \\\"\\n string+= \\\"\\\\n\\\"\\n return string\",\n \"def print_board(board):\\n rep = ''\\n for row in xrange(8, 0, -1):\\n begin, end = 10*row + 1, 10*row + 9\\n rep += '%d %s\\\\n' % (row-1, ' '.join(board[begin:end]))\\n rep += ' %s\\\\n' % ' '.join(map(str, range(8)))\\n return rep\",\n \"def __str__(self):\\n\\t\\t\\n\\t\\tdef mapping(x):\\n\\t\\t\\tif x == 1:\\n\\t\\t\\t\\t# WHITE\\n\\t\\t\\t\\treturn 'O'\\n\\t\\t\\telif x == -1:\\n\\t\\t\\t\\t# BLACK\\n\\t\\t\\t\\treturn 'X'\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# Empty\\n\\t\\t\\t\\treturn '-'\\n\\t\\t\\n\\t\\ts = 'BLACK - X\\\\n'\\n\\t\\ts += 'WHITE - O\\\\n\\\\n'\\n\\t\\tfor j in self.rows:\\n\\t\\t\\ts += j\\n\\t\\t\\ts += ' '\\n\\t\\t\\ts += ''.join(mapping(self[i+j]) for i in self.columns)\\n\\t\\t\\ts += '\\\\n'\\n\\t\\treturn s + '\\\\n ' + self.columns + '\\\\n'\",\n \"def showBoard(self):\\n \\n brd = \\\"\\\\n | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[0] + \\\" | \\\" + self.squares[1] + \\\" | \\\" + self.squares[2] + \\\" \\\\n\\\" + \\\\\\n \\\"___|___|___\\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[3] + \\\" | \\\" + self.squares[4] + \\\" | \\\" + self.squares[5] + \\\" \\\\n\\\" + \\\\\\n \\\"___|___|___\\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[6] + \\\" | \\\" + self.squares[7] + \\\" | \\\" + self.squares[8] + \\\" \\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\"\\n\\n return brd\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._grid_height):\\n ans += str(self._cells[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\r\\n\\t\\toutStr = \\\"\\\"\\r\\n\\t\\toutStr += \\\"Heuristic Level: \\\" + str(self.heuristic)\\r\\n\\t\\toutStr += \\\"\\\\n-\\\" + \\\"-----\\\"*self.n\\r\\n\\t\\tfor row in self.board:\\r\\n\\t\\t\\ttempStr = (\\\"\\\\n|\\\" + \\\" %2d |\\\" * self.n)\\r\\n\\t\\t\\toutStr += tempStr % tuple(row)\\r\\n\\t\\t\\toutStr += \\\"\\\\n-\\\" + \\\"-----\\\"*self.n\\r\\n\\r\\n\\t\\treturn outStr\",\n \"def __repr__(self):\\r\\n rep_tpl = (self.__board_size, self.__bombs_dict, self.__ships)\\r\\n return str(rep_tpl)\",\n \"def __str__(self):\\n puzzle_string = '—' * 13 + '\\\\n'\\n for i in range(self.PUZZLE_NUM_ROWS):\\n for j in range(self.PUZZLE_NUM_COLUMNS):\\n puzzle_string += '│{0: >2}'.format(str(self.position[i][j]))\\n if j == self.PUZZLE_NUM_COLUMNS - 1:\\n puzzle_string += '│\\\\n'\\n\\n puzzle_string += '—' * 13 + '\\\\n'\\n return puzzle_string\",\n \"def __str__(self):\\n string = ''\\n for row in self.board:\\n for item in row:\\n if item == None:\\n string += \\\"_ \\\"\\n else:\\n string += f\\\"{item.name:<2}\\\"\\n string += '\\\\n'\\n \\n return string\",\n \"def __repr__(self):\\r\\n # Initialize the return string\\r\\n s = ''\\r\\n\\r\\n for row in range(self.height):\\r\\n # Print the index of the row\\r\\n s = s + str(row % 10) + ' |'\\r\\n\\r\\n for col in range(self.width):\\r\\n s += self.grid[row][col]\\r\\n s += '|'\\r\\n\\r\\n s += '\\\\n'\\r\\n s += '--' * (self.width + 1)\\r\\n s += '-'\\r\\n s += '\\\\n'\\r\\n \\r\\n s += ' '\\r\\n for i in range(self.width):\\r\\n s += ' ' + str(i % 10) \\r\\n \\r\\n return s\",\n \"def __repr__(self):\\r\\n answer = '\\\\n'\\r\\n for i in range(len(self)):\\r\\n for j in range(len(self)):\\r\\n if (i+j)%2 == 0:\\r\\n answer += '{}'.format(self.quantum_board[i][j])\\r\\n else:\\r\\n answer += '-'\\r\\n answer += '\\\\t'\\r\\n answer += '\\\\n'\\r\\n return answer\",\n \"def __str__(self):\\n value = str(self.puzzle) + str(\\\" \\\") + str(self.g) + str(\\\" \\\") + str(self.h)\\n return value\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __repr__(self):\\r\\n answer = '\\\\n'\\r\\n for i in range(len(self)):\\r\\n for j in range(len(self)):\\r\\n if (i+j)%2 == 0:\\r\\n answer += '{}'.format(self.board[i][j])\\r\\n else:\\r\\n answer += '-'\\r\\n answer += '\\\\t'\\r\\n answer += '\\\\n'\\r\\n return answer\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\n string = ''\\n\\n for row in range(0, self.height):\\n for col in range(0, self.width):\\n string += self.cells[(row, col)]\\n string += '\\\\n'\\n\\n return string\",\n \"def board_string(self, players):\\n if len(self.user_guess) == 1:\\n board = \\\"\\\\n-------------------\\\\n\\\"\\n board += f\\\"Player {players[0]}: {self.user_guess[0]}, {self.applied_guess[0]}\\\\n\\\"\\n board += f\\\"Player {players[1]}: {self.user_guess[0]}, {self.applied_guess[0]}\\\\n\\\"\\n board += \\\"-------------------\\\\n\\\"\\n\\n board = \\\"\\\\n-------------------\\\\n\\\"\\n board += f\\\"Player {players[0].get_name()}: {self.user_guess[0]}, {self.applied_guess[0]}\\\\n\\\"\\n board += f\\\"Player {players[1].get_name()}: {self.user_guess[1]}, {self.applied_guess[1]}\\\\n\\\"\\n board += \\\"-------------------\\\\n\\\"\\n\\n return board\",\n \"def print_board(self):\\n print('Board:')\\n print('\\\\n'.join([''.join(['{:4}'.format(item) for item in row]) for row in self.board]))\",\n \"def __repr__(self):\\n representantion = ''\\n\\n for i in range(3):\\n for j in range(3):\\n representantion += str(self.state[3 * i + j])\\n\\n if j == 2 and i != 2:\\n representantion += '\\\\n'\\n else:\\n representantion += ' '\\n\\n return representantion\",\n \"def printBoard(self):\\n if self.side == self.WHITE or self.side == None:\\n for r in [8,7,6,5,4,3,2,1]:\\n for c in 'abcdefgh':\\n p = self.getPiece(c,r) # print a8 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\\n else:\\n for r in [1,2,3,4,5,6,7,8]:\\n for c in 'hgfedcba':\\n p = self.getPiece(c,r) # print h1 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\\n\\n for r in [8,7,6,5,4,3,2,1]:\\n for c in 'abcdefgh':\\n p = self.getPiece(c,r)\\n #if p != None and p.header.frame_id == \\\"chess_board\\\":\\n # print \\\"Warning, frame is chess_board:\\\", c+str(r)\",\n \"def state_to_string(board_state):\\n return str(board_state)\",\n \"def transferBoardToString(self,board):\\n #int(False) will be 0\\n outputString = str()\\n for i in board:\\n outputString = outputString + str(int(i))\\n return outputString\",\n \"def getCellStr(self, x, y): # TODO: refactor regarding issue #11\\n c = self.board.getCell(x, y)\\n\\n if c == 0:\\n return '.' if self.__azmode else ' .'\\n\\n elif self.__azmode:\\n az = {}\\n for i in range(1, int(math.log(self.board.goal(), 2))):\\n az[2 ** i] = chr(i + 96)\\n\\n if c not in az:\\n return '?'\\n s = az[c]\\n elif c == 1024:\\n s = ' 1k'\\n elif c == 2048:\\n s = ' 2k'\\n else:\\n s = '%3d' % c\\n\\n return self.__colors.get(c, Fore.RESET) + s + Style.RESET_ALL\",\n \"def __str__(self):\\n return \\\"Board: \\\" + str(self._board) + \\\" Workers: \\\" + str(self._workers.items())\",\n \"def draw_board(self):\\n board = \\\"-------------------\\\\n\\\"\\n board += \\\"| %s | %s | %s |\\\\n\\\" % (self.board_values[1], self.board_values[2], self.board_values[3])\\n board += \\\"-------------------\\\\n\\\"\\n board += \\\"| %s | %s | %s |\\\\n\\\" % (self.board_values[4], self.board_values[5], self.board_values[6])\\n board += \\\"-------------------\\\\n\\\"\\n board += \\\"| %s | %s | %s |\\\\n\\\" % (self.board_values[7], self.board_values[8], self.board_values[9])\\n board += \\\"-------------------\\\\n\\\"\\n return board\",\n \"def __str__(self):\\n s = ''\\n for i in range(5):\\n for j in range(5):\\n ani = False\\n if self[i][j] == 0:\\n s += ' 0 '\\n elif self[i][j].species == 'Elephant':\\n s += ' E'\\n ani = True\\n elif self[i][j].species == 'Rhinoceros':\\n s += ' R'\\n ani = True\\n else:\\n s += ' B '\\n if ani:\\n if self[i][j].direction[0] == 0 and self[i][j].direction[1] == 1:\\n d = '> '\\n elif self[i][j].direction[0] == -1 and self[i][j].direction[1] == 0:\\n d = '∧ '\\n elif self[i][j].direction[0] == 0 and self[i][j].direction[1] == -1:\\n d = '< '\\n else:\\n d = '∨ '\\n s += d\\n s += '\\\\n \\\\n'\\n return s\",\n \"def printBoard(self):\\n if self.side == self.WHITE or self.side == None:\\n for r in [8,7,6,5,4,3,2,1]:\\n for c in 'abcdefgh':\\n p = self.getPiece(c,r) # print a8 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\\n else:\\n for r in [1,2,3,4,5,6,7,8]:\\n for c in 'hgfedcba':\\n p = self.getPiece(c,r) # print h1 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\",\n \"def __str__(self):\\n result = \\\"\\\"\\n for row in self._cells:\\n result += \\\" \\\".join(map(str, row))\\n result += \\\"\\\\n\\\"\\n return result\",\n \"def __repr__(self):\\n s = \\\"\\\"\\n for y in range(0,HEIGHT):\\n temp=\\\"\\\"\\n for x in range(0,WIDTH):\\n temp = temp+ str(self.gameState[x,y])\\n s += temp+\\\"\\\\n\\\"\\n return s\",\n \"def __str__(self) -> str:\\n output = self.columns * \\\" __\\\" + \\\"\\\\n\\\"\\n for i in range(self.rows):\\n for j in range(self.columns):\\n filling = \\\"__\\\"\\n if len(self.cells[i][j].agents) == 1:\\n filling = \\\"_§\\\"\\n elif len(self.cells[i][j].agents) == 2:\\n filling = \\\"§§\\\"\\n elif len(self.cells[i][j].agents) > 2:\\n filling = \\\"++\\\"\\n output += \\\"|\\\" + filling\\n if j == self.columns - 1:\\n output += \\\"|\\\"\\n output += \\\"\\\\n\\\"\\n return output\",\n \"def __str__(self):\\n result = ''\\n for row in range(self.getHeight()):\\n for col in range(self.getWidth()):\\n result += str(self.data[row][col]) + ' '\\n result += '\\\\n'\\n return result\",\n \"def __str__(self):\\n grid_str = \\\"\\\"\\n for i in range(len(self.grid)):\\n for j in range(len(self.grid[i])):\\n grid_str = grid_str + self.grid[i][j] + '\\\\t'\\n grid_str = grid_str.strip('\\\\t')\\n grid_str = grid_str + '\\\\n'\\n return grid_str\",\n \"def __str__(self):\\n s=\\\"\\\"\\n for y in range(0,HEIGHT):\\n for x in range(0,WIDTH):\\n s+=str(self.gameState[x,y])\\n return s\",\n \"def __repr__(self) -> str:\\n return \\\"P1's Turn: {} - Board: {}\\\".format(self.p1_turn,\\n self.board)\",\n \"def __repr__(self) -> str:\\n return \\\"P1's Turn: {} - Board: {}\\\".format(self.p1_turn,\\n self.current_board)\",\n \"def __str__(self):\\r\\n # replace with your code\\r\\n result = ''\\r\\n for row in range(0, self._grid_height):\\r\\n result += str(self._grid_tile[row]) + '\\\\n'\\r\\n return result\",\n \"def __str__(self):\\r\\n grid_text = \\\"\\\\n-------------------\\\\n|\\\"\\r\\n for i in range(len(self.grid)):\\r\\n grid_text = grid_text + ' %s '%(self.grid[i][-1])\\r\\n\\r\\n if i%3 == 2:\\r\\n grid_text = grid_text + '|\\\\n-------------------\\\\n|'\\r\\n else:\\r\\n grid_text = grid_text + '|'\\r\\n return grid_text[0:len(grid_text)-1]\",\n \"def __str__(self):\\r\\n # The full representative string\\r\\n str_matrix = \\\"\\\"\\r\\n\\r\\n if self.matrix is not None:\\r\\n # Save the lenght into a variable\\r\\n # to send this number to the tiles method\\r\\n # and calculate the number of spaces\\r\\n spaces = len(self.matrix)\\r\\n for i in range(0, spaces):\\r\\n nums = list(filter(lambda x: x != \\\"_\\\", self.matrix[i]))\\r\\n str_matrix += self.tiles(nums, (i+1), (spaces - i))\\r\\n\\r\\n return str_matrix\",\n \"def __str__(self):\\n grid_string = ''\\n for row in range(self.grid_height):\\n grid_string += str(self.grid[row]) + '\\\\n'\\n return grid_string\",\n \"def get_cards_as_string(self):\\n return '' \\\\\\n ' {}\\\\n' \\\\\\n ' {}\\\\n' \\\\\\n ' {}\\\\n' \\\\\\n ' {}\\\\n' \\\\\\n ' {}\\\\n'.format(*self.get_cards_high_to_low())\",\n \"def print_board(self):\\n \\n # How to show empty/p1/p2\\n VALS = \\\".XO\\\"\\n\\n print(\\\"\\\\n a b c d e f g\\\")\\n print(\\\" /--+-+-+-+-+-+--\\\\\\\\\\\")\\n for r in range(_HEIGHT - 1, -1, -1):\\n s = \\\"%s |\\\" % r\\n for c in range(_WIDTH):\\n # Print mark next to most recent move\\n mark = \\\">\\\" if self.last_play_rc == (r, c) else \\\" \\\"\\n s += mark + VALS[self.board[r * 7 + c]]\\n print(s + \\\" |\\\")\\n print(\\\" \\\\\\\\--+-+-+-+-+-+--/\\\")\\n print(\\\" a b c d e f g\\\\n\\\")\",\n \"def __str__(self) -> str:\\n return self._color + str(self._value) + SudokuTile.C_END\",\n \"def __str__(self):\\n grid_str = \\\"\\\"\\n for row in range(self.grid_height):\\n grid_str += str(self.grid[row])+'\\\\n'\\n return grid_str\",\n \"def print_board(self):\\n to_join = [\\\"-\\\" * self.DIMENSIONS[0]]\\n for row in self.grid:\\n to_join.append(\\\"\\\".join([ch.letter if ch is not None else \\\" \\\" for ch in row]))\\n\\n print(\\\"\\\\n\\\".join(to_join))\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for i in range(self.row):\\n for j in range(self.col):\\n ans+=str(self.array[i][j])+\\\" \\\"\\n ans+=\\\"\\\\n\\\"\\n return ans\",\n \"def printBoard(self):\\n\\t\\tkey = [' ', 'X', 'O']\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[0][0]] + ' | ' + key[self.state[0][1]] + ' | ' + key[self.state[0][2]])\\n\\t\\tprint(' | |')\\n\\t\\tprint('-----------')\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[1][0]] + ' | ' + key[self.state[1][1]] + ' | ' + key[self.state[1][2]])\\n\\t\\tprint(' | |')\\n\\t\\tprint('-----------')\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[2][0]] + ' | ' + key[self.state[2][1]] + ' | ' + key[self.state[2][2]])\\n\\t\\tprint(' | |')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84243935","0.8055579","0.8017218","0.78294134","0.7797174","0.77931315","0.7776164","0.77571356","0.77145636","0.7699537","0.7682327","0.7632191","0.76316303","0.76192135","0.7602081","0.7599799","0.758553","0.7570357","0.75595516","0.7550093","0.7537935","0.7514287","0.7499289","0.7492063","0.7480225","0.74771684","0.7468804","0.744572","0.743624","0.7418388","0.7418208","0.7397805","0.73942703","0.73898166","0.7384276","0.7383382","0.73743814","0.7365193","0.7365193","0.7363094","0.7350477","0.7309778","0.72961354","0.7294264","0.72914183","0.7285849","0.7250098","0.7218796","0.7198089","0.71431416","0.7140117","0.71355754","0.71270055","0.71012276","0.7091507","0.70888555","0.7082249","0.7058949","0.70419264","0.7034083","0.7031354","0.702456","0.702456","0.702456","0.702456","0.701469","0.69672674","0.69672674","0.69672674","0.6948384","0.69478524","0.6892123","0.6885317","0.6873642","0.68593305","0.6854289","0.6853239","0.6849875","0.6848315","0.6842803","0.68405086","0.68397176","0.68177366","0.6811835","0.6811544","0.6802742","0.6785202","0.67664397","0.6761365","0.67566323","0.67458403","0.67448026","0.67379135","0.6737263","0.6735821","0.67301553","0.6727139","0.6724389","0.6718744","0.6715472"],"string":"[\n \"0.84243935\",\n \"0.8055579\",\n \"0.8017218\",\n \"0.78294134\",\n \"0.7797174\",\n \"0.77931315\",\n \"0.7776164\",\n \"0.77571356\",\n \"0.77145636\",\n \"0.7699537\",\n \"0.7682327\",\n \"0.7632191\",\n \"0.76316303\",\n \"0.76192135\",\n \"0.7602081\",\n \"0.7599799\",\n \"0.758553\",\n \"0.7570357\",\n \"0.75595516\",\n \"0.7550093\",\n \"0.7537935\",\n \"0.7514287\",\n \"0.7499289\",\n \"0.7492063\",\n \"0.7480225\",\n \"0.74771684\",\n \"0.7468804\",\n \"0.744572\",\n \"0.743624\",\n \"0.7418388\",\n \"0.7418208\",\n \"0.7397805\",\n \"0.73942703\",\n \"0.73898166\",\n \"0.7384276\",\n \"0.7383382\",\n \"0.73743814\",\n \"0.7365193\",\n \"0.7365193\",\n \"0.7363094\",\n \"0.7350477\",\n \"0.7309778\",\n \"0.72961354\",\n \"0.7294264\",\n \"0.72914183\",\n \"0.7285849\",\n \"0.7250098\",\n \"0.7218796\",\n \"0.7198089\",\n \"0.71431416\",\n \"0.7140117\",\n \"0.71355754\",\n \"0.71270055\",\n \"0.71012276\",\n \"0.7091507\",\n \"0.70888555\",\n \"0.7082249\",\n \"0.7058949\",\n \"0.70419264\",\n \"0.7034083\",\n \"0.7031354\",\n \"0.702456\",\n \"0.702456\",\n \"0.702456\",\n \"0.702456\",\n \"0.701469\",\n \"0.69672674\",\n \"0.69672674\",\n \"0.69672674\",\n \"0.6948384\",\n \"0.69478524\",\n \"0.6892123\",\n \"0.6885317\",\n \"0.6873642\",\n \"0.68593305\",\n \"0.6854289\",\n \"0.6853239\",\n \"0.6849875\",\n \"0.6848315\",\n \"0.6842803\",\n \"0.68405086\",\n \"0.68397176\",\n \"0.68177366\",\n \"0.6811835\",\n \"0.6811544\",\n \"0.6802742\",\n \"0.6785202\",\n \"0.67664397\",\n \"0.6761365\",\n \"0.67566323\",\n \"0.67458403\",\n \"0.67448026\",\n \"0.67379135\",\n \"0.6737263\",\n \"0.6735821\",\n \"0.67301553\",\n \"0.6727139\",\n \"0.6724389\",\n \"0.6718744\",\n \"0.6715472\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79764"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":2670,"cells":{"query":{"kind":"string","value":"The main method for running the game."},"document":{"kind":"string","value":"def main():\n\tprint(\"Welcome to TicTacToe\")\n\tboard = Board()\n\twhile (not board.isOver()):\n\t\tprint(\"It is {0}'s turn\".format(board.current) + board.__str__())\n\t\tmove = input('Where would you like to go? : ').strip()\n\t\tif (move == 'q'):\n\t\t\tbreak\n\t\telif (board.makeMove(move) == 1):\n\t\t\tboard.switchPlayer()\n\t\telse:\n\t\t\tprint(\"I didn't understand your input, these are the valid inputs:\\nentering 'q' will quit out of the game.\\n\")\n\t\t\tprint(\"entering a number will place the peice in that box, the numbers are as follows:\\n \\n1|2|3\\n-----\\n4|5|6\\n-----\\n7|8|9\\n\")\n\tprint(board.__str__() + \"\\nGame Over\")\n\tif (board.isOver() is Piece.EX or board.isOver() is Piece.OH):\n\t\tprint(\"Player {0} wins!\".format(board.isOver())) \n\telse:\n\t\tprint(\"It was a draw\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main():\n g = Game(800, 600)\n g.start()","def main():\r\n gameclass = data.game.GameClass()\r\n gameclass.main_loop()","def main():\n game = RiichiMahjongApp()\n game.run()","def main():\n g = DemoGame(800, 600)\n g.start()","def main():\n game = Game(TIMES, HARDNESS)\n game.start()\n game.print_score()","def main():\n\n name, game = select_game(vgc.KNOWN_GAMES)\n print('---- Launching: %s -----'%name)\n game.game.main()\n sys.exit(0)","def main():\n boba_blast_game.main()","def main():\n game = TinkerGame()\n game.setup()\n while game.calculate_points() > 0 and not game.game_over:\n game.play()\n game.end()","def main():\n pygame.init()\n os.environ['SDL_VIDEO_CENTERED'] = '1'\n pygame.display.set_caption('8-Puzzle game')\n screen = pygame.display.set_mode((800, 500))\n fpsclock = pygame.time.Clock()\n program = SlidePuzzle((3, 3), 160, 5, difficulty=10) # program is also the gym environment\n\n choice = program.selectPlayerMenu(fpsclock, screen)\n if choice == \"AI\":\n pygame.display.quit()\n trainAI(program)\n elif choice == \"human\":\n launchWithGUI(program, fpsclock, screen)\n del program","def main():\n if \"cli\" in sys.argv:\n run_cli_game()\n else:\n run_gui_game()","def main():\n display, clock = game.init_pygame()\n highscores = HighScores(display, clock)\n highscores.run()","def main():\n game_of_life(10, 20)","def main():\n field = Field(10, 10)\n snake = Snake((0, 0))\n game = Game(field, snake)\n game.start()","def main():\n\n # Create logging file, rotate if filesize exceeds 1MB\n logger.add(\"logs/{time}.log\", rotation=\"1 MB\")\n\n GameContainer()\n logger.info(\"Started the game launcher. Make sure to support pygame!\")","def main():\n args = get_parser().parse_args()\n players = prepare_game(\n decks_count=args.decks,\n auto_mode=args.auto_mode,\n player_one_name=args.name_player,\n players_count=args.players,\n )\n game(players=players)","def main() -> None:\r\n game = advanced_game(MAP_FILE)\r\n\r\n root = tk.Tk()\r\n root.title('EndOfDayz')\r\n if TASK == 1:\r\n gui = BasicGraphicalInterface\r\n elif TASK == 2:\r\n gui = ImageGraphicalInterface\r\n # else:\r\n # gui = MastersGraphicalInterface\r\n app = gui(root, game.get_grid().get_size())\r\n app.play(game)\r\n root.mainloop()","def main(argv):\n config_options = parse_config(CONFIG_FILE_NAME)\n arguments_options = parse_args(argv, **config_options)\n playgame.main(arguments_options)","def main():\n game = Blackjack()\n game.play()","def main():\r\n\r\n #set the display, caption, and timer\r\n pygame.init()\r\n mainClock = pygame.time.Clock()\r\n windowSurface = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT), 0, 32)\r\n pygame.display.set_caption(\"Cat's Big Adventure\")\r\n\r\n #Display a menu, choose a level and instantiate a game\r\n display_menu(windowSurface)\r\n\r\n #initialize the game\r\n stats = [6]\r\n game = Game(stats)\r\n \r\n # run the game loop until the user quits\r\n while True:\r\n # Process events (keystrokes, mouse clicks, etc)\r\n game.process_events(windowSurface)\r\n\r\n # Update object positions, check for collisions\r\n game.run_logic()\r\n \r\n # Draw the current frame\r\n game.display_frame(windowSurface)\r\n\r\n #draw background image\r\n background_image = pygame.image.load(\"sky.png\").convert()\r\n windowSurface.blit(background_image, [0, 0])\r\n \r\n mainClock.tick(FRAMERATE)","def main():\n play_game(progression)","def main():\n \n # load_and_initialize_func()\n\n loop_and_update_forever()\n\n pygame.quit()","def main():\r\n # create the EdenLudo sample\r\n EdenEvolves = EdenLudo()\r\n # ru-n the scene\r\n run()","def main(self):\n _age = info.getInfo(self)\n _flag = game.check_age(self, _age)\n if _flag == False:\n exit()\n game.wants_to_play(0)","def main():\n even_game()","def main():\n initialize()\n inputs = InputsTemp()\n\n ui_font = pygame.font.SysFont(\"Comic Sans MS\", 50)\n\n assets_library = AssetsLibrary((Path(__file__).parent / \"Assets\"))\n\n # todo: create display class to wrap display from pygame\n window = setup_display(inputs.width_height)\n\n background_img = assets_library.assets.bg_black\n\n run = True\n FPS = 60\n lives = 5\n level = 1\n clock = pygame.time.Clock()\n\n ui_margin = {\n \"left\": 10,\n \"right\": 10,\n \"top\": 10,\n \"bottom\": 10,\n }\n\n def redraw_window():\n window.blit(background_img.get_image(inputs.width_height), (0, 0))\n\n lives_label = ui_font.render(f\"lives: {lives}\", 1, (255, 255, 255))\n level_label = ui_font.render(f\"level: {level}\", 1, (255, 255, 255))\n\n window.blit(lives_label, (ui_margin[\"left\"], ui_margin[\"top\"]))\n window.blit(level_label, (inputs.width_height[0] - level_label.get_width() - ui_margin[\"right\"], ui_margin[\"top\"]))\n pygame.display.update()\n\n while run:\n clock.tick(FPS)\n\n redraw_window()\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n print(\"Game ended\")","def main():\n pygame.init()\n\n try:\n filename = sys.argv[1]\n except IndexError:\n usage()\n\n game = Game.from_file(filename)\n grid = game.get_grid()\n width, height = grid.get_width(), grid.get_height()\n win = pygame.display.set_mode((width*CELL_SIZE, height*CELL_SIZE))\n selected = 0 # default selected player\n select_player(selected)\n render(win, grid)\n\n while not (game.winning() or game.losing()):\n for event in pygame.event.get():\n if event.type == pygame.MOUSEBUTTONUP:\n selected = handle_click_event(grid, selected)\n\n elif event.type == pygame.KEYUP and event.key in KEY_DIRECTIONS:\n game.next_step(selected, KEY_DIRECTIONS[event.key])\n render(win, grid)\n\n # QUIT\n elif event.type == pygame.QUIT:\n pygame.quit()\n\n if game.winning():\n text = \"You win!\"\n game_status = \"win\"\n elif game.losing():\n text = \"You lose!\"\n game_status = \"lose\"\n\n display_end_screen(win, text, game_status)","def main():\n run_it = scene.Control()\n state_dict = {\"TITLE\" : title.Title(),\n \"INTRO\" : cutscene.Cutscene0(),\n \"GAMEPLAY\" : gameplay.gamePlay(),\n \"ENDING\" : cutscene.Cutscene1()\n }\n run_it.setup_states(state_dict, \"TITLE\")\n run_it.main()","def main():\n app = RunSnakeRunApp(0)\n app.MainLoop()","def main():\n print(\"is Running!\")","def main(cls):\n parser = optparse.OptionParser()\n parser.add_option('-c', '--columns', type=\"int\", default=16)\n parser.add_option('-r', '--rows', type=\"int\", default=16)\n parser.add_option('-m', '--mines-density', type=\"float\", default=0.2,\n help=\"percent of mines: 0.15 is trivial, 0.2 good [default], 0.25 hard\")\n (options, args) = parser.parse_args()\n if args:\n parser.error(\"unexpected arguments: \" + \" \".join(args))\n \n game = cls(options.columns, options.rows, options.mines_density)\n game.window.mainloop()","def main():\n # Initialize the event manager.\n event_manager = events.EventManager()\n AppState.get_state().set_event_manager(event_manager)\n\n # Initialize and register the application heartbeat.\n heart_beat = HeartBeat()\n event_manager.register_listener(heart_beat)\n\n # Initialize and register the world.\n basic_experiment = experiment.basic.BasicExperiment()\n world = basic_experiment.get_world()\n event_manager.register_listener(world)\n AppState.get_state().set_world(world)\n\n # Initialize pygame.\n surface = init()\n\n # Initialize and register the view.\n main_view = view.View(surface)\n event_manager.register_listener(main_view)\n\n # Initialize and register the controller.\n main_controller = controller.Controller()\n event_manager.register_listener(main_controller)\n\n # Start the heartbeat.\n heart_beat.run()","def main():\n Main()","def run():\n\tif len(sys.argv) > 1 and sys.argv[1] in {'-V', '--version'}:\n\t\tprint(\"pokesim - Pokémon Battle Simulator - Version %s\" % __version__)\n\t\texit()\n\n\trandom.seed()\n\ttry:\n\t\tmain()\n\texcept (KeyboardInterrupt, EOFError):\n\t\texit(0)","def main():\n arcade.open_window(WINDOW_WIDTH, WINDOW_HEIGHT, \"Snake.exe\")\n # Set the window background colour\n arcade.set_background_color(light_green)\n\n # Calls the on_draw method every 1/3(20 seconds) of a second\n arcade.schedule(on_draw, 1/3)\n # Keeps the window open until closed by the user\n arcade.run()","def run():\n main()","def main():\n\n # Fix crackling audio\n util.set_environment('PULSE_LATENCY_MSEC', '60')\n\n # Replace launcher with game exe in proton arguments\n util.replace_command('FF9_Launcher.exe', 'x64/FF9.exe')","def start_game(self):\n\n\t\tpass","def main(self) -> None:\n pass","def run(self, GameState):\n pass","def main():\n pass","def main():\n run_program()","def main() -> int:\n setup(\"main\", WIDTH, HEIGHT)\n init()\n for _ in range(randint(10, 10)):\n add_rand_circ()\n for _ in range(randint(10, 10)):\n add_rand_rect()\n mainloop()\n update_leaderboard()\n return 0","def main():\n return","def run_gui_game():\n # Set up game\n view = GuiView()\n game = GameEngine(view)","def main():\r\n\r\n pygame.init()\r\n pygame.display.init()\r\n\r\n # Set the pygame clock\r\n clock = pygame.time.Clock()\r\n\r\n pygame.display.set_caption(\"Blackbox game\")\r\n current_game = BlackBoxGame()\r\n clock = pygame.time.Clock()\r\n\r\n while True:\r\n current_game.check_events()\r\n clock.tick(60)\r\n current_game.update_screen()\r\n\r\n pygame.quit()","def run_game():\r\n pygame.init()\r\n ai_settings = Settings()\r\n screen = pygame.display.set_mode(\r\n (ai_settings.screen_width, ai_settings.screen_height))\r\n pygame.display.set_caption(\"Stars\")\r\n\r\n # Make a group of stars.\r\n stars = Group()\r\n\r\n # Create a star system\r\n gf.create_star_system(ai_settings, screen, stars)\r\n \r\n # Main game loop.\r\n while True:\r\n \r\n # Let's player quit the game.\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n\r\n gf.update_screen(ai_settings, screen, stars)","def main(**kwargs):\n print('Start')\n agent = initAgent(**kwargs)\n kwargs['agent'] = agent\n result = []\n\n def mainsub(*args):\n game = Game(**kwargs)\n game.display(kwargs['noshow'])\n while True:\n # get_input = getch(\"Enter direction (w/a/s/d): \")\n get_input = game.action()\n if get_input in keypad:\n game.move(keypad.index(get_input))\n game.update()\n # elif get_input == \"q\":\n # break\n # else:\n # print(\"\\nInvalid choice.\")\n # continue\n if game.end:\n game.savegame()\n game.display(kwargs['noshow'])\n print(\"Result:\", game.nturn, game.score)\n break\n game.display(kwargs['noshow'])\n result.append((game.score, game.nturn))\n game.agent.replay()\n if kwargs['train']:\n game.agent.save()\n game.reset()\n if kwargs['train']:\n np.save('result.%s' % game.agent.algo, np.array(result))\n\n map(mainsub, range(kwargs['n']))\n print(\"Thanks for playing.\")","def start_game(self):\n print(\"hi there, game started!\")\n self.draw()","def main():\n game = Hangman()\n game.play_hangman()","def main():\n\n # This is for text mode.\n\n if len(sys.argv) == 2 and sys.argv[1] == '-t':\n model.main()\n sys.exit(0)\n\n # Do initialization.\n\n pygame.init()\n screen = pygame.display.set_mode(DISPLAY_MODE)\n pygame.display.set_caption(TITLE)\n clock = pygame.time.Clock()\n background = pygame.Surface(screen.get_size()).convert()\n background.fill(BACKGROUND)\n pygame.display.flip()\n\n game_model = model.Game()\n board_view = view.Board(game_model)\n score_board = view.ScoreBoard(game_model)\n rendering_groups = [board_view, score_board]\n\n while True:\n\n clock.tick(FRAMES_PER_SEC)\n scheduler.tick()\n\n # Handle user input.\n\n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key in (K_ESCAPE, K_q) or event.type == QUIT:\n sys.exit(0)\n elif event.key == K_h:\n url = \"file://\" + os.path.abspath(data.find(\"help.html\"))\n webbrowser.open(url, new=True)\n elif event.key == K_r:\n game_model.reset()\n elif event.type == MOUSEBUTTONDOWN:\n for square_view in board_view:\n if square_view.rect.collidepoint(*pygame.mouse.get_pos()):\n xyz = square_view.square_model.xyz\n try:\n game_model.move(xyz)\n except ValueError:\n pass\n break\n\n # Provide the simulation and render it.\n\n for i in rendering_groups:\n i.update()\n i.clear(screen, background)\n pygame.display.update(i.draw(screen))","def main():\n print(\"Call your main application code here\")","def main():\n print(\"Call your main application code here\")","def main():\n print(\"Call your main application code here\")","def main(args):\n global numBirds, numToads\n if len(args) > 2:\n numBirds = int(args[1])\n numToads = int(args[2])\n gameLoop(GameManager(MapSize, numBirds, numToads))","def main():\r\n app = application.Application()\r\n app.initializeLayer(menu.Menu())\r\n app.run()","def play_game():\n pass","def run(self):\n print(\"WELCOME TO MINESWEEPER!\")\n\n\n while True:\n\n self.get_input()\n start_game(self.rows, self.cols, self.mines)","def main():\n secret_word = get_word()\n play_game(secret_word)","def main(self,Surf):\n while True:\n if self.state == \"GAME\":\n self.event_loop()\n self.update(Surf)\n elif self.state == \"QUIT\":\n break\n pg.display.update()\n self.Clock.tick(65)","def Gameloop():","def main():\n print(\"It works!!! ;-)\")\n ###TODO### do something with the various methods/functions of this file","def start() -> None:\n\n # PREPARE\n clone_game_files()\n\n # SIMULATE\n turns = run.simulation()\n\n # LOG\n logs = read.combine_logs(turns)\n\n # CALCULATE\n results = calculate.results(logs)\n\n # DISPLAY\n visualize.charts(results)\n\n # CLEAN\n remove_cloned_files()","def main():\r\n # Initialize words from specific file\r\n words_list = hangman_helper.load_words()\r\n # Run single game with given word list to choose from\r\n run_single_game(words_list)\r\n # Ask the user if he would like to play again\r\n request = hangman_helper.get_input()\r\n if request[INPUT_TYPE] == hangman_helper.PLAY_AGAIN:\r\n if request[INPUT_VALUE]:\r\n run_single_game(words_list)","def main(self):\r\n pass","def main():\n greetings()\n run_jarvis()","def main():\n dealCards().mainloop()","def main():\n #Initialize pygame\n pygame.init()\n\n #Set up the display and draw it to screen\n display = DisplayUpdater()\n display.generate_display()\n\n #Set up the audio player\n sound = AudioPlayer()\n\n sound.play_menu_music()\n #Set up the controls\n controls = PlayerInput()\n\n #Start off in the main menu, can go to credits, leaderboard, or game\n display.show_main_menu()\n user_input = controls.get_menu_input(sound)\n #While the user hasn't quit from the main menu\n while user_input != INPUT.ESCAPE:\n #If the player hits ENTER, launch the game\n if user_input == INPUT.ENTER:\n play_demon_music = game(display, sound, controls)\n sound.play_menu_music(play_demon_music)\n controls.clear_menu_input()\n user_input = INPUT.SPACE\n\n #If the player hits SPACE, go to the leaderboard\n if user_input == INPUT.SPACE:\n display.show_leaderboard()\n user_input = controls.get_menu_input(sound)\n #If the player hits C, go to the credits\n if user_input == INPUT.C:\n display.show_credits()\n user_input = controls.get_menu_input(sound)\n\n #If the player hits ESC, return to the main menu.\n #must be in own if statement so we don't quit\n if user_input in (INPUT.ESCAPE, INPUT.SPACE, INPUT.C):\n display.show_main_menu()\n user_input = controls.get_menu_input(sound)","def run_game_logic(self):\n pass","def main():\n global TURRET\n os.environ['SDL_VIDEO_CENTERED'] = '1'\n pg.init()\n pg.display.set_caption(CAPTION)\n pg.display.set_mode(SCREEN_SIZE)\n TURRET = pg.image.load(\"turret.png\").convert()\n TURRET.set_colorkey(COLOR_KEY)\n Control().main_loop()\n pg.quit()\n sys.exit()","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.001, display=True) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():\n global levels\n difficulty = select_difficulty()\n start_game(difficulty)","def run_game(self) -> None:\n decision = 0\n if self._initial:\n self._initial = False\n while decision != 1:\n try:\n display_no_combat_init(self.hero)\n decision = get_user_input([1, 2, -1])\n if decision == -1:\n self._quit()\n elif decision == 2:\n self._show_bag()\n else:\n break\n except KeyboardInterrupt:\n print(\"[!] If you want to quit, use the provided user interface\")\n\n while not self.hero.is_dead:\n try:\n self._load_map()\n except KeyboardInterrupt:\n print(\"[!] If you want to quit, use the provided user interface\")","def main():\r\n if check_argv():\r\n if len(sys.argv) == 3:\r\n gui = GUI(sys.argv[1], int(sys.argv[2]), True, ip=None)\r\n gui.create_board()\r\n gui.root.title(\"Server\")\r\n if not gui.is_human():\r\n gui.ai.find_legal_move(gui.game, gui.update_game)\r\n gui.run_game()\r\n elif len(sys.argv) == 4:\r\n ip = socket.gethostbyname(socket.gethostname())\r\n gui = GUI(sys.argv[1], int(sys.argv[2]), False, ip)\r\n gui.create_board()\r\n gui.root.title(\"Client\")\r\n if not gui.is_human():\r\n gui.ai.find_legal_move(gui.game, gui.update_game)\r\n gui.run_game()","def main():\n\n\tif len(sys.argv) > 1 and sys.argv[1]:\n\t\t_, _, hash = read_file(sys.argv[1])\n\t\toffset_x = 0\n\t\toffset_y = 0\n\telse:\n\t\toffset_x, offset_y, hash = screenshot()\n\n\tprint(hash)\n\tgame = eliza_logic.Game(0)\n\tgame.exact_setup(hash)\n\tprint(game)\n\tresult = game.global_solve(-1)\n\tprint(result)\n\n\t# If it was a screen grab, we can actually do this -- just type n/q/c to quit or anything else to continue\n\tif result is not None and offset_x and offset_y:\n\t\tx = six.moves.input(\"Ready for automated solution? \")\n\t\tif x.lower() in [\"n\", \"q\", \"c\"]:\n\t\t\treturn\n\n\t\texecute_solution(offset_x, offset_y, result)","def main():\n doctest.testmod()\n game()","def run(self):\n pygame.init()\n pygame.display.set_caption(\"Genetic Game\")\n self.screen = pygame.display.set_mode((self.SCREEN_W, self.SCREEN_H), 0, 32)\n\n self.ominus_sprites = [OminusSprite(self.screen, o, PLAYERS_COLORS[o.id]) for o in self.model.get_players()]\n for o in self.ominus_sprites:\n self.agent_group.add(o)\n\n self.wall_sprites = [WallSprite(self.screen, w) for w in self.model.get_walls()]\n for w in self.wall_sprites:\n self.terrain_group.add(w)","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline= True ) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","def main() -> None:\n return","def main():\n global lp\n global games, selected_game\n \n # Create a Launchpad instance and start it up\n lp = Launchpad()\n lp.open()\n lp.reset()\n \n populate_games()\n draw_main_menu()\n \n playing = True\n\n # Timekeeping\n last_time = 0\n anim_timer = 0\n\n while playing:\n # Get delta_time this loop\n cur_time = perf_counter()\n delta_time = cur_time - last_time\n last_time = cur_time\n\n # Get next input\n button = lp.button_state_xy()\n \n # Animate border\n anim_timer += delta_time\n if anim_timer > BORDER_ANIM_DELAY:\n anim_timer = 0\n draw_border()\n \n # Handle input (if any)\n if button:\n x = button[0]\n y = button[1]\n \n # If button pressed\n if button[2]:\n # Write red to pressed button if circle button\n if x == 8 or y == 0:\n lp.led_ctrl_xy(x, y, 3, 0)\n \n if (x, y) == BUT_LEFT:\n selected_game = (selected_game + 1) % len(games)\n draw_cover()\n \n if (x, y) == BUT_RIGHT:\n selected_game = (selected_game - 1) % len(games)\n draw_cover()\n \n # If button released\n if not button[2]:\n # Write menu color to released button if circle button\n if y == 0:\n lp.led_ctrl_xy(x, y, *menu_leds[x]) # Top row\n if x == 8:\n lp.led_ctrl_xy(x, y, *menu_leds[7 + y]) # Right column\n \n if (x, y) == BUT_START:\n lp.clear_input()\n lp.reset()\n games[selected_game].play()\n draw_main_menu()\n \n if (x, y) == BUT_QUIT:\n playing = False\n \n lp.draw()\n \n lp.reset()\n lp.close()"],"string":"[\n \"def main():\\n g = Game(800, 600)\\n g.start()\",\n \"def main():\\r\\n gameclass = data.game.GameClass()\\r\\n gameclass.main_loop()\",\n \"def main():\\n game = RiichiMahjongApp()\\n game.run()\",\n \"def main():\\n g = DemoGame(800, 600)\\n g.start()\",\n \"def main():\\n game = Game(TIMES, HARDNESS)\\n game.start()\\n game.print_score()\",\n \"def main():\\n\\n name, game = select_game(vgc.KNOWN_GAMES)\\n print('---- Launching: %s -----'%name)\\n game.game.main()\\n sys.exit(0)\",\n \"def main():\\n boba_blast_game.main()\",\n \"def main():\\n game = TinkerGame()\\n game.setup()\\n while game.calculate_points() > 0 and not game.game_over:\\n game.play()\\n game.end()\",\n \"def main():\\n pygame.init()\\n os.environ['SDL_VIDEO_CENTERED'] = '1'\\n pygame.display.set_caption('8-Puzzle game')\\n screen = pygame.display.set_mode((800, 500))\\n fpsclock = pygame.time.Clock()\\n program = SlidePuzzle((3, 3), 160, 5, difficulty=10) # program is also the gym environment\\n\\n choice = program.selectPlayerMenu(fpsclock, screen)\\n if choice == \\\"AI\\\":\\n pygame.display.quit()\\n trainAI(program)\\n elif choice == \\\"human\\\":\\n launchWithGUI(program, fpsclock, screen)\\n del program\",\n \"def main():\\n if \\\"cli\\\" in sys.argv:\\n run_cli_game()\\n else:\\n run_gui_game()\",\n \"def main():\\n display, clock = game.init_pygame()\\n highscores = HighScores(display, clock)\\n highscores.run()\",\n \"def main():\\n game_of_life(10, 20)\",\n \"def main():\\n field = Field(10, 10)\\n snake = Snake((0, 0))\\n game = Game(field, snake)\\n game.start()\",\n \"def main():\\n\\n # Create logging file, rotate if filesize exceeds 1MB\\n logger.add(\\\"logs/{time}.log\\\", rotation=\\\"1 MB\\\")\\n\\n GameContainer()\\n logger.info(\\\"Started the game launcher. Make sure to support pygame!\\\")\",\n \"def main():\\n args = get_parser().parse_args()\\n players = prepare_game(\\n decks_count=args.decks,\\n auto_mode=args.auto_mode,\\n player_one_name=args.name_player,\\n players_count=args.players,\\n )\\n game(players=players)\",\n \"def main() -> None:\\r\\n game = advanced_game(MAP_FILE)\\r\\n\\r\\n root = tk.Tk()\\r\\n root.title('EndOfDayz')\\r\\n if TASK == 1:\\r\\n gui = BasicGraphicalInterface\\r\\n elif TASK == 2:\\r\\n gui = ImageGraphicalInterface\\r\\n # else:\\r\\n # gui = MastersGraphicalInterface\\r\\n app = gui(root, game.get_grid().get_size())\\r\\n app.play(game)\\r\\n root.mainloop()\",\n \"def main(argv):\\n config_options = parse_config(CONFIG_FILE_NAME)\\n arguments_options = parse_args(argv, **config_options)\\n playgame.main(arguments_options)\",\n \"def main():\\n game = Blackjack()\\n game.play()\",\n \"def main():\\r\\n\\r\\n #set the display, caption, and timer\\r\\n pygame.init()\\r\\n mainClock = pygame.time.Clock()\\r\\n windowSurface = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT), 0, 32)\\r\\n pygame.display.set_caption(\\\"Cat's Big Adventure\\\")\\r\\n\\r\\n #Display a menu, choose a level and instantiate a game\\r\\n display_menu(windowSurface)\\r\\n\\r\\n #initialize the game\\r\\n stats = [6]\\r\\n game = Game(stats)\\r\\n \\r\\n # run the game loop until the user quits\\r\\n while True:\\r\\n # Process events (keystrokes, mouse clicks, etc)\\r\\n game.process_events(windowSurface)\\r\\n\\r\\n # Update object positions, check for collisions\\r\\n game.run_logic()\\r\\n \\r\\n # Draw the current frame\\r\\n game.display_frame(windowSurface)\\r\\n\\r\\n #draw background image\\r\\n background_image = pygame.image.load(\\\"sky.png\\\").convert()\\r\\n windowSurface.blit(background_image, [0, 0])\\r\\n \\r\\n mainClock.tick(FRAMERATE)\",\n \"def main():\\n play_game(progression)\",\n \"def main():\\n \\n # load_and_initialize_func()\\n\\n loop_and_update_forever()\\n\\n pygame.quit()\",\n \"def main():\\r\\n # create the EdenLudo sample\\r\\n EdenEvolves = EdenLudo()\\r\\n # ru-n the scene\\r\\n run()\",\n \"def main(self):\\n _age = info.getInfo(self)\\n _flag = game.check_age(self, _age)\\n if _flag == False:\\n exit()\\n game.wants_to_play(0)\",\n \"def main():\\n even_game()\",\n \"def main():\\n initialize()\\n inputs = InputsTemp()\\n\\n ui_font = pygame.font.SysFont(\\\"Comic Sans MS\\\", 50)\\n\\n assets_library = AssetsLibrary((Path(__file__).parent / \\\"Assets\\\"))\\n\\n # todo: create display class to wrap display from pygame\\n window = setup_display(inputs.width_height)\\n\\n background_img = assets_library.assets.bg_black\\n\\n run = True\\n FPS = 60\\n lives = 5\\n level = 1\\n clock = pygame.time.Clock()\\n\\n ui_margin = {\\n \\\"left\\\": 10,\\n \\\"right\\\": 10,\\n \\\"top\\\": 10,\\n \\\"bottom\\\": 10,\\n }\\n\\n def redraw_window():\\n window.blit(background_img.get_image(inputs.width_height), (0, 0))\\n\\n lives_label = ui_font.render(f\\\"lives: {lives}\\\", 1, (255, 255, 255))\\n level_label = ui_font.render(f\\\"level: {level}\\\", 1, (255, 255, 255))\\n\\n window.blit(lives_label, (ui_margin[\\\"left\\\"], ui_margin[\\\"top\\\"]))\\n window.blit(level_label, (inputs.width_height[0] - level_label.get_width() - ui_margin[\\\"right\\\"], ui_margin[\\\"top\\\"]))\\n pygame.display.update()\\n\\n while run:\\n clock.tick(FPS)\\n\\n redraw_window()\\n\\n for event in pygame.event.get():\\n if event.type == pygame.QUIT:\\n run = False\\n\\n print(\\\"Game ended\\\")\",\n \"def main():\\n pygame.init()\\n\\n try:\\n filename = sys.argv[1]\\n except IndexError:\\n usage()\\n\\n game = Game.from_file(filename)\\n grid = game.get_grid()\\n width, height = grid.get_width(), grid.get_height()\\n win = pygame.display.set_mode((width*CELL_SIZE, height*CELL_SIZE))\\n selected = 0 # default selected player\\n select_player(selected)\\n render(win, grid)\\n\\n while not (game.winning() or game.losing()):\\n for event in pygame.event.get():\\n if event.type == pygame.MOUSEBUTTONUP:\\n selected = handle_click_event(grid, selected)\\n\\n elif event.type == pygame.KEYUP and event.key in KEY_DIRECTIONS:\\n game.next_step(selected, KEY_DIRECTIONS[event.key])\\n render(win, grid)\\n\\n # QUIT\\n elif event.type == pygame.QUIT:\\n pygame.quit()\\n\\n if game.winning():\\n text = \\\"You win!\\\"\\n game_status = \\\"win\\\"\\n elif game.losing():\\n text = \\\"You lose!\\\"\\n game_status = \\\"lose\\\"\\n\\n display_end_screen(win, text, game_status)\",\n \"def main():\\n run_it = scene.Control()\\n state_dict = {\\\"TITLE\\\" : title.Title(),\\n \\\"INTRO\\\" : cutscene.Cutscene0(),\\n \\\"GAMEPLAY\\\" : gameplay.gamePlay(),\\n \\\"ENDING\\\" : cutscene.Cutscene1()\\n }\\n run_it.setup_states(state_dict, \\\"TITLE\\\")\\n run_it.main()\",\n \"def main():\\n app = RunSnakeRunApp(0)\\n app.MainLoop()\",\n \"def main():\\n print(\\\"is Running!\\\")\",\n \"def main(cls):\\n parser = optparse.OptionParser()\\n parser.add_option('-c', '--columns', type=\\\"int\\\", default=16)\\n parser.add_option('-r', '--rows', type=\\\"int\\\", default=16)\\n parser.add_option('-m', '--mines-density', type=\\\"float\\\", default=0.2,\\n help=\\\"percent of mines: 0.15 is trivial, 0.2 good [default], 0.25 hard\\\")\\n (options, args) = parser.parse_args()\\n if args:\\n parser.error(\\\"unexpected arguments: \\\" + \\\" \\\".join(args))\\n \\n game = cls(options.columns, options.rows, options.mines_density)\\n game.window.mainloop()\",\n \"def main():\\n # Initialize the event manager.\\n event_manager = events.EventManager()\\n AppState.get_state().set_event_manager(event_manager)\\n\\n # Initialize and register the application heartbeat.\\n heart_beat = HeartBeat()\\n event_manager.register_listener(heart_beat)\\n\\n # Initialize and register the world.\\n basic_experiment = experiment.basic.BasicExperiment()\\n world = basic_experiment.get_world()\\n event_manager.register_listener(world)\\n AppState.get_state().set_world(world)\\n\\n # Initialize pygame.\\n surface = init()\\n\\n # Initialize and register the view.\\n main_view = view.View(surface)\\n event_manager.register_listener(main_view)\\n\\n # Initialize and register the controller.\\n main_controller = controller.Controller()\\n event_manager.register_listener(main_controller)\\n\\n # Start the heartbeat.\\n heart_beat.run()\",\n \"def main():\\n Main()\",\n \"def run():\\n\\tif len(sys.argv) > 1 and sys.argv[1] in {'-V', '--version'}:\\n\\t\\tprint(\\\"pokesim - Pokémon Battle Simulator - Version %s\\\" % __version__)\\n\\t\\texit()\\n\\n\\trandom.seed()\\n\\ttry:\\n\\t\\tmain()\\n\\texcept (KeyboardInterrupt, EOFError):\\n\\t\\texit(0)\",\n \"def main():\\n arcade.open_window(WINDOW_WIDTH, WINDOW_HEIGHT, \\\"Snake.exe\\\")\\n # Set the window background colour\\n arcade.set_background_color(light_green)\\n\\n # Calls the on_draw method every 1/3(20 seconds) of a second\\n arcade.schedule(on_draw, 1/3)\\n # Keeps the window open until closed by the user\\n arcade.run()\",\n \"def run():\\n main()\",\n \"def main():\\n\\n # Fix crackling audio\\n util.set_environment('PULSE_LATENCY_MSEC', '60')\\n\\n # Replace launcher with game exe in proton arguments\\n util.replace_command('FF9_Launcher.exe', 'x64/FF9.exe')\",\n \"def start_game(self):\\n\\n\\t\\tpass\",\n \"def main(self) -> None:\\n pass\",\n \"def run(self, GameState):\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n run_program()\",\n \"def main() -> int:\\n setup(\\\"main\\\", WIDTH, HEIGHT)\\n init()\\n for _ in range(randint(10, 10)):\\n add_rand_circ()\\n for _ in range(randint(10, 10)):\\n add_rand_rect()\\n mainloop()\\n update_leaderboard()\\n return 0\",\n \"def main():\\n return\",\n \"def run_gui_game():\\n # Set up game\\n view = GuiView()\\n game = GameEngine(view)\",\n \"def main():\\r\\n\\r\\n pygame.init()\\r\\n pygame.display.init()\\r\\n\\r\\n # Set the pygame clock\\r\\n clock = pygame.time.Clock()\\r\\n\\r\\n pygame.display.set_caption(\\\"Blackbox game\\\")\\r\\n current_game = BlackBoxGame()\\r\\n clock = pygame.time.Clock()\\r\\n\\r\\n while True:\\r\\n current_game.check_events()\\r\\n clock.tick(60)\\r\\n current_game.update_screen()\\r\\n\\r\\n pygame.quit()\",\n \"def run_game():\\r\\n pygame.init()\\r\\n ai_settings = Settings()\\r\\n screen = pygame.display.set_mode(\\r\\n (ai_settings.screen_width, ai_settings.screen_height))\\r\\n pygame.display.set_caption(\\\"Stars\\\")\\r\\n\\r\\n # Make a group of stars.\\r\\n stars = Group()\\r\\n\\r\\n # Create a star system\\r\\n gf.create_star_system(ai_settings, screen, stars)\\r\\n \\r\\n # Main game loop.\\r\\n while True:\\r\\n \\r\\n # Let's player quit the game.\\r\\n for event in pygame.event.get():\\r\\n if event.type == pygame.QUIT:\\r\\n pygame.quit()\\r\\n sys.exit()\\r\\n\\r\\n gf.update_screen(ai_settings, screen, stars)\",\n \"def main(**kwargs):\\n print('Start')\\n agent = initAgent(**kwargs)\\n kwargs['agent'] = agent\\n result = []\\n\\n def mainsub(*args):\\n game = Game(**kwargs)\\n game.display(kwargs['noshow'])\\n while True:\\n # get_input = getch(\\\"Enter direction (w/a/s/d): \\\")\\n get_input = game.action()\\n if get_input in keypad:\\n game.move(keypad.index(get_input))\\n game.update()\\n # elif get_input == \\\"q\\\":\\n # break\\n # else:\\n # print(\\\"\\\\nInvalid choice.\\\")\\n # continue\\n if game.end:\\n game.savegame()\\n game.display(kwargs['noshow'])\\n print(\\\"Result:\\\", game.nturn, game.score)\\n break\\n game.display(kwargs['noshow'])\\n result.append((game.score, game.nturn))\\n game.agent.replay()\\n if kwargs['train']:\\n game.agent.save()\\n game.reset()\\n if kwargs['train']:\\n np.save('result.%s' % game.agent.algo, np.array(result))\\n\\n map(mainsub, range(kwargs['n']))\\n print(\\\"Thanks for playing.\\\")\",\n \"def start_game(self):\\n print(\\\"hi there, game started!\\\")\\n self.draw()\",\n \"def main():\\n game = Hangman()\\n game.play_hangman()\",\n \"def main():\\n\\n # This is for text mode.\\n\\n if len(sys.argv) == 2 and sys.argv[1] == '-t':\\n model.main()\\n sys.exit(0)\\n\\n # Do initialization.\\n\\n pygame.init()\\n screen = pygame.display.set_mode(DISPLAY_MODE)\\n pygame.display.set_caption(TITLE)\\n clock = pygame.time.Clock()\\n background = pygame.Surface(screen.get_size()).convert()\\n background.fill(BACKGROUND)\\n pygame.display.flip()\\n\\n game_model = model.Game()\\n board_view = view.Board(game_model)\\n score_board = view.ScoreBoard(game_model)\\n rendering_groups = [board_view, score_board]\\n\\n while True:\\n\\n clock.tick(FRAMES_PER_SEC)\\n scheduler.tick()\\n\\n # Handle user input.\\n\\n for event in pygame.event.get():\\n if event.type == KEYDOWN:\\n if event.key in (K_ESCAPE, K_q) or event.type == QUIT:\\n sys.exit(0)\\n elif event.key == K_h:\\n url = \\\"file://\\\" + os.path.abspath(data.find(\\\"help.html\\\"))\\n webbrowser.open(url, new=True)\\n elif event.key == K_r:\\n game_model.reset()\\n elif event.type == MOUSEBUTTONDOWN:\\n for square_view in board_view:\\n if square_view.rect.collidepoint(*pygame.mouse.get_pos()):\\n xyz = square_view.square_model.xyz\\n try:\\n game_model.move(xyz)\\n except ValueError:\\n pass\\n break\\n\\n # Provide the simulation and render it.\\n\\n for i in rendering_groups:\\n i.update()\\n i.clear(screen, background)\\n pygame.display.update(i.draw(screen))\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main(args):\\n global numBirds, numToads\\n if len(args) > 2:\\n numBirds = int(args[1])\\n numToads = int(args[2])\\n gameLoop(GameManager(MapSize, numBirds, numToads))\",\n \"def main():\\r\\n app = application.Application()\\r\\n app.initializeLayer(menu.Menu())\\r\\n app.run()\",\n \"def play_game():\\n pass\",\n \"def run(self):\\n print(\\\"WELCOME TO MINESWEEPER!\\\")\\n\\n\\n while True:\\n\\n self.get_input()\\n start_game(self.rows, self.cols, self.mines)\",\n \"def main():\\n secret_word = get_word()\\n play_game(secret_word)\",\n \"def main(self,Surf):\\n while True:\\n if self.state == \\\"GAME\\\":\\n self.event_loop()\\n self.update(Surf)\\n elif self.state == \\\"QUIT\\\":\\n break\\n pg.display.update()\\n self.Clock.tick(65)\",\n \"def Gameloop():\",\n \"def main():\\n print(\\\"It works!!! ;-)\\\")\\n ###TODO### do something with the various methods/functions of this file\",\n \"def start() -> None:\\n\\n # PREPARE\\n clone_game_files()\\n\\n # SIMULATE\\n turns = run.simulation()\\n\\n # LOG\\n logs = read.combine_logs(turns)\\n\\n # CALCULATE\\n results = calculate.results(logs)\\n\\n # DISPLAY\\n visualize.charts(results)\\n\\n # CLEAN\\n remove_cloned_files()\",\n \"def main():\\r\\n # Initialize words from specific file\\r\\n words_list = hangman_helper.load_words()\\r\\n # Run single game with given word list to choose from\\r\\n run_single_game(words_list)\\r\\n # Ask the user if he would like to play again\\r\\n request = hangman_helper.get_input()\\r\\n if request[INPUT_TYPE] == hangman_helper.PLAY_AGAIN:\\r\\n if request[INPUT_VALUE]:\\r\\n run_single_game(words_list)\",\n \"def main(self):\\r\\n pass\",\n \"def main():\\n greetings()\\n run_jarvis()\",\n \"def main():\\n dealCards().mainloop()\",\n \"def main():\\n #Initialize pygame\\n pygame.init()\\n\\n #Set up the display and draw it to screen\\n display = DisplayUpdater()\\n display.generate_display()\\n\\n #Set up the audio player\\n sound = AudioPlayer()\\n\\n sound.play_menu_music()\\n #Set up the controls\\n controls = PlayerInput()\\n\\n #Start off in the main menu, can go to credits, leaderboard, or game\\n display.show_main_menu()\\n user_input = controls.get_menu_input(sound)\\n #While the user hasn't quit from the main menu\\n while user_input != INPUT.ESCAPE:\\n #If the player hits ENTER, launch the game\\n if user_input == INPUT.ENTER:\\n play_demon_music = game(display, sound, controls)\\n sound.play_menu_music(play_demon_music)\\n controls.clear_menu_input()\\n user_input = INPUT.SPACE\\n\\n #If the player hits SPACE, go to the leaderboard\\n if user_input == INPUT.SPACE:\\n display.show_leaderboard()\\n user_input = controls.get_menu_input(sound)\\n #If the player hits C, go to the credits\\n if user_input == INPUT.C:\\n display.show_credits()\\n user_input = controls.get_menu_input(sound)\\n\\n #If the player hits ESC, return to the main menu.\\n #must be in own if statement so we don't quit\\n if user_input in (INPUT.ESCAPE, INPUT.SPACE, INPUT.C):\\n display.show_main_menu()\\n user_input = controls.get_menu_input(sound)\",\n \"def run_game_logic(self):\\n pass\",\n \"def main():\\n global TURRET\\n os.environ['SDL_VIDEO_CENTERED'] = '1'\\n pg.init()\\n pg.display.set_caption(CAPTION)\\n pg.display.set_mode(SCREEN_SIZE)\\n TURRET = pg.image.load(\\\"turret.png\\\").convert()\\n TURRET.set_colorkey(COLOR_KEY)\\n Control().main_loop()\\n pg.quit()\\n sys.exit()\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.001, display=True) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\\n global levels\\n difficulty = select_difficulty()\\n start_game(difficulty)\",\n \"def run_game(self) -> None:\\n decision = 0\\n if self._initial:\\n self._initial = False\\n while decision != 1:\\n try:\\n display_no_combat_init(self.hero)\\n decision = get_user_input([1, 2, -1])\\n if decision == -1:\\n self._quit()\\n elif decision == 2:\\n self._show_bag()\\n else:\\n break\\n except KeyboardInterrupt:\\n print(\\\"[!] If you want to quit, use the provided user interface\\\")\\n\\n while not self.hero.is_dead:\\n try:\\n self._load_map()\\n except KeyboardInterrupt:\\n print(\\\"[!] If you want to quit, use the provided user interface\\\")\",\n \"def main():\\r\\n if check_argv():\\r\\n if len(sys.argv) == 3:\\r\\n gui = GUI(sys.argv[1], int(sys.argv[2]), True, ip=None)\\r\\n gui.create_board()\\r\\n gui.root.title(\\\"Server\\\")\\r\\n if not gui.is_human():\\r\\n gui.ai.find_legal_move(gui.game, gui.update_game)\\r\\n gui.run_game()\\r\\n elif len(sys.argv) == 4:\\r\\n ip = socket.gethostbyname(socket.gethostname())\\r\\n gui = GUI(sys.argv[1], int(sys.argv[2]), False, ip)\\r\\n gui.create_board()\\r\\n gui.root.title(\\\"Client\\\")\\r\\n if not gui.is_human():\\r\\n gui.ai.find_legal_move(gui.game, gui.update_game)\\r\\n gui.run_game()\",\n \"def main():\\n\\n\\tif len(sys.argv) > 1 and sys.argv[1]:\\n\\t\\t_, _, hash = read_file(sys.argv[1])\\n\\t\\toffset_x = 0\\n\\t\\toffset_y = 0\\n\\telse:\\n\\t\\toffset_x, offset_y, hash = screenshot()\\n\\n\\tprint(hash)\\n\\tgame = eliza_logic.Game(0)\\n\\tgame.exact_setup(hash)\\n\\tprint(game)\\n\\tresult = game.global_solve(-1)\\n\\tprint(result)\\n\\n\\t# If it was a screen grab, we can actually do this -- just type n/q/c to quit or anything else to continue\\n\\tif result is not None and offset_x and offset_y:\\n\\t\\tx = six.moves.input(\\\"Ready for automated solution? \\\")\\n\\t\\tif x.lower() in [\\\"n\\\", \\\"q\\\", \\\"c\\\"]:\\n\\t\\t\\treturn\\n\\n\\t\\texecute_solution(offset_x, offset_y, result)\",\n \"def main():\\n doctest.testmod()\\n game()\",\n \"def run(self):\\n pygame.init()\\n pygame.display.set_caption(\\\"Genetic Game\\\")\\n self.screen = pygame.display.set_mode((self.SCREEN_W, self.SCREEN_H), 0, 32)\\n\\n self.ominus_sprites = [OminusSprite(self.screen, o, PLAYERS_COLORS[o.id]) for o in self.model.get_players()]\\n for o in self.ominus_sprites:\\n self.agent_group.add(o)\\n\\n self.wall_sprites = [WallSprite(self.screen, w) for w in self.model.get_walls()]\\n for w in self.wall_sprites:\\n self.terrain_group.add(w)\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline= True ) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"def main() -> None:\\n return\",\n \"def main():\\n global lp\\n global games, selected_game\\n \\n # Create a Launchpad instance and start it up\\n lp = Launchpad()\\n lp.open()\\n lp.reset()\\n \\n populate_games()\\n draw_main_menu()\\n \\n playing = True\\n\\n # Timekeeping\\n last_time = 0\\n anim_timer = 0\\n\\n while playing:\\n # Get delta_time this loop\\n cur_time = perf_counter()\\n delta_time = cur_time - last_time\\n last_time = cur_time\\n\\n # Get next input\\n button = lp.button_state_xy()\\n \\n # Animate border\\n anim_timer += delta_time\\n if anim_timer > BORDER_ANIM_DELAY:\\n anim_timer = 0\\n draw_border()\\n \\n # Handle input (if any)\\n if button:\\n x = button[0]\\n y = button[1]\\n \\n # If button pressed\\n if button[2]:\\n # Write red to pressed button if circle button\\n if x == 8 or y == 0:\\n lp.led_ctrl_xy(x, y, 3, 0)\\n \\n if (x, y) == BUT_LEFT:\\n selected_game = (selected_game + 1) % len(games)\\n draw_cover()\\n \\n if (x, y) == BUT_RIGHT:\\n selected_game = (selected_game - 1) % len(games)\\n draw_cover()\\n \\n # If button released\\n if not button[2]:\\n # Write menu color to released button if circle button\\n if y == 0:\\n lp.led_ctrl_xy(x, y, *menu_leds[x]) # Top row\\n if x == 8:\\n lp.led_ctrl_xy(x, y, *menu_leds[7 + y]) # Right column\\n \\n if (x, y) == BUT_START:\\n lp.clear_input()\\n lp.reset()\\n games[selected_game].play()\\n draw_main_menu()\\n \\n if (x, y) == BUT_QUIT:\\n playing = False\\n \\n lp.draw()\\n \\n lp.reset()\\n lp.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85276115","0.8359565","0.8280766","0.8256087","0.8189385","0.79771304","0.78113306","0.7789521","0.7754325","0.77015805","0.7665701","0.7611436","0.76085716","0.7576111","0.7546943","0.7546619","0.7520529","0.7456335","0.74490446","0.74308777","0.7388939","0.7355476","0.73381764","0.73325497","0.7302963","0.7289043","0.7269477","0.723349","0.7210045","0.7197681","0.7184768","0.7162537","0.7155853","0.7155467","0.71550477","0.7152539","0.7121594","0.71179414","0.7111066","0.7096553","0.7062233","0.704228","0.70236814","0.70173675","0.7007432","0.7003049","0.70021015","0.69968724","0.6994511","0.6961736","0.69601816","0.69601816","0.69601816","0.6957019","0.6950015","0.6931413","0.6925387","0.6921405","0.6908866","0.6895347","0.6893785","0.6888733","0.6878322","0.68602824","0.6843059","0.6841527","0.68347126","0.68150145","0.68089354","0.68072563","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.68067425","0.6797973","0.67977375","0.67907566","0.6789781","0.67890084","0.67847353","0.6783034","0.6774448","0.6771813"],"string":"[\n \"0.85276115\",\n \"0.8359565\",\n \"0.8280766\",\n \"0.8256087\",\n \"0.8189385\",\n \"0.79771304\",\n \"0.78113306\",\n \"0.7789521\",\n \"0.7754325\",\n \"0.77015805\",\n \"0.7665701\",\n \"0.7611436\",\n \"0.76085716\",\n \"0.7576111\",\n \"0.7546943\",\n \"0.7546619\",\n \"0.7520529\",\n \"0.7456335\",\n \"0.74490446\",\n \"0.74308777\",\n \"0.7388939\",\n \"0.7355476\",\n \"0.73381764\",\n \"0.73325497\",\n \"0.7302963\",\n \"0.7289043\",\n \"0.7269477\",\n \"0.723349\",\n \"0.7210045\",\n \"0.7197681\",\n \"0.7184768\",\n \"0.7162537\",\n \"0.7155853\",\n \"0.7155467\",\n \"0.71550477\",\n \"0.7152539\",\n \"0.7121594\",\n \"0.71179414\",\n \"0.7111066\",\n \"0.7096553\",\n \"0.7062233\",\n \"0.704228\",\n \"0.70236814\",\n \"0.70173675\",\n \"0.7007432\",\n \"0.7003049\",\n \"0.70021015\",\n \"0.69968724\",\n \"0.6994511\",\n \"0.6961736\",\n \"0.69601816\",\n \"0.69601816\",\n \"0.69601816\",\n \"0.6957019\",\n \"0.6950015\",\n \"0.6931413\",\n \"0.6925387\",\n \"0.6921405\",\n \"0.6908866\",\n \"0.6895347\",\n \"0.6893785\",\n \"0.6888733\",\n \"0.6878322\",\n \"0.68602824\",\n \"0.6843059\",\n \"0.6841527\",\n \"0.68347126\",\n \"0.68150145\",\n \"0.68089354\",\n \"0.68072563\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.68067425\",\n \"0.6797973\",\n \"0.67977375\",\n \"0.67907566\",\n \"0.6789781\",\n \"0.67890084\",\n \"0.67847353\",\n \"0.6783034\",\n \"0.6774448\",\n \"0.6771813\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":2671,"cells":{"query":{"kind":"string","value":"Remove temporary partition files from disk. The removed files' names are deleted from the _temporary_files set. The intended use is to delete individual files as part of the garbage collection process and to delete all files when python exits. This is quite brutal and may break partitions if used unwisely. It is not recommended to be used as a general tidyup function."},"document":{"kind":"string","value":"def _remove_temporary_files(filename=None):\n if filename is not None:\n if filename in _temporary_files:\n # If this condition is not met then probably\n # _remove_temporary_files() has already been run at\n # exit\n dirname, _lock_file, _other_lock_files = _temporary_files[filename]\n try:\n remove(_lock_file)\n except OSError:\n pass\n\n # Only remove the temporary file if it is not being\n # used by any other ranks\n if not _lock_files_present(_other_lock_files):\n # Remove the given temporary file\n try:\n remove(filename)\n rmdir(dirname)\n except OSError:\n pass\n del _temporary_files[filename]\n # --- End: if\n\n return\n\n # Still here? Then remove all temporary files and lock files\n for filename in _temporary_files:\n try:\n remove(filename)\n except OSError:\n pass\n dirname, _lock_file, _other_lock_files = _temporary_files[filename]\n try:\n remove(_lock_file)\n except OSError:\n pass\n for lock_file in _other_lock_files:\n try:\n remove(lock_file)\n except OSError:\n pass\n # --- End: for\n\n try:\n rmdir(dirname)\n except OSError:\n pass\n # --- End: for\n\n _temporary_files.clear()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def remove_temporary_files():\n try:\n xml_file_path, bin_file_path = get_ida_exported_files()\n if os.path.isfile(xml_file_path):\n os.remove(xml_file_path)\n\n if os.path.isfile(bin_file_path):\n os.remove(bin_file_path)\n\n except Exception:\n print(\"GhIDA:: [!] Unexpected error while removing temporary files.\")","def _remove_tmpfiles():\n for f in tmpfiles:\n try:\n os.remove(f)\n except OSError:\n pass","def del_tmp() -> None:\n for elem in os.listdir('./tmp'):\n path = f\"./tmp/{elem}\"\n if os.path.isfile(path):\n os.remove(path)\n else:\n shutil.rmtree(path)","def clean_up_temp_dir():\n files = glob.glob(f'{CONFIG_DIR}/tmp/*')\n for f in files:\n try:\n os.remove(f)\n except Exception:\n pass","def clear_tempfiles(self, remove=True):\n while self._tempfiles:\n self.pop(remove)\n self.push()","def remover_files():\n directory = os.getcwd()\n for file_name in glob.glob((\"{}/tmp/*\").format(directory)):\n remove(file_name)","def clear_temp(remove_all=True):\n tf_list = []\n\n if remove_all:\n temp_dir = _get_temp_dir(False)\n temp_dir += (\n os.path.sep if os.path.sep not in temp_dir[len(temp_dir) - 1] else \"\"\n )\n tf_list = glob.glob(\"{0}TESS_*\".format(temp_dir))\n else:\n global _tempfiles\n\n tf_list = list(_tempfiles)\n _tempfiles.clear()\n\n for tf in tf_list:\n if os.path.isfile(tf):\n _remove_file(tf)","def delete_temporary_files(request, tmp_path_factory):\r\n _tmp_path_factory = tmp_path_factory\r\n\r\n def cleanup():\r\n tmp_path = _tmp_path_factory.getbasetemp()\r\n if pathlib.Path(tmp_path).exists() and pathlib.Path(tmp_path).is_dir():\r\n shutil.rmtree(tmp_path)\r\n\r\n request.addfinalizer(cleanup)","def cleanup_tmpdir(tmpdir):\n try:\n for filename in glob.glob(os.path.join(tmpdir, \"*\")):\n os.remove(filename)\n os.rmdir(tmpdir)\n except OSError, inst:\n sys.stderr.write('WARNING: could not remove temp files'\n ' in ' + tmpdir + '\\n' + str(inst) + '\\n')","def _delete_temp():\n global _TEMP_NAME\n\n try:\n database.delete_temp(_TEMP_NAME)\n outputtools.delete_temp(_TEMP_NAME)\n except:\n raise","def remove_tmp_sources(source_filename):\n logging.info('Removing temporary files ...')\n source_dir = os.path.dirname(source_filename)\n if os.path.exists(source_filename):\n os.remove(source_filename)\n for f in os.listdir(source_dir):\n if f.startswith('tmp_'):\n os.remove(os.path.join(source_dir, f))","def _clean_up_temporary_files(dataset_dir):\n filename = _DATA_URL.split('/')[-1]\n filepath = os.path.join(dataset_dir, filename)\n tf.gfile.Remove(filepath)\n\n tmp_dir = os.path.join(dataset_dir, 'cifar-100-python')\n tf.gfile.DeleteRecursively(tmp_dir)","def clean_temp_storage_dir(self, filenames):\n for fn in filenames:\n try:\n pathlib.Path(pathlib.PurePath(self.temp_storage_dir, fn)).unlink()\n except FileNotFoundError:\n pass","def _clean_up_temporary_files(dataset_dir):\n return","def cleanUpTemporaryFiles(options):\n os.system(\"rm \"+options.output_directory_per_run+\"/*.abundance\")\n os.system(\"rm \"+options.output_directory_per_run+\"/*.phasing_score\")\n os.system(\"rm \"+options.output_directory_per_run+\"/*regionsOfInterest*\")\n os.system(\"mv \"+options.output_directory_per_run+\"/* \"+options.output_directory_per_run+\"/../\")\n os.system(\"rm -rf \"+options.output_directory_per_run)","def clear_tmp_folder(self):\r\n for file in os.listdir(self.temp_dir):\r\n if file.endswith('.png') or file.endswith('.jpg'):\r\n path = os.path.join(self.temp_dir, file)\r\n print ('Cleaned up {}'.format(path))\r\n os.remove(path)","def tearDown(self):\n for f in os.listdir('/tmp'):\n if not f.startswith(self.FILE_PREFIX):\n continue\n\n os.remove(os.path.join('/tmp', f))","def remove_temp_folders(self) -> None:\n if self.args.deletefolders:\n time.sleep(2)\n for f in self.created_folders:\n shutil.rmtree(path.join(self.args.output, f))\n print(f\"{self.args.output}/{f} was deleted\")","def remove_intermediate_files(dir_):\n file_list = glob.glob(f'{dir_}/*temp*')\n [os.remove(f) for f in file_list]","def clean():\n for f in [f for f in os.listdir() if f.endswith(\".part\")]:\n os.remove(f)","def clean_up_temp_files():\n global __tmp_model_dir\n\n if __tmp_model_dir is not None:\n FileUtils.deleteDirectory(__tmp_model_dir)\n __tmp_model_dir = None","def _clean_up_optimization():\n for (root, dirs, files) in walk(TEMP_MODULES_DIR_PATH, topdown=False):\n for file in files:\n if file.startswith(\"__temp_\"):\n remove(f\"{root}/{file}\")\n try:\n rmdir(root)\n except OSError:\n G.warn_(f\"Unidentified file found in temporary directory: {root}\")","def tearDown(self):\n for root, dirs, files in os.walk(TEMPDIR, topdown=False):\n for name in files:\n os.remove(os.path.join(root, name))\n for name in dirs:\n os.rmdir(os.path.join(root, name))\n os.rmdir(root)","def cleanTempDirs(olderThanDays=None):\n from armi import runLog\n from armi.utils.pathTools import cleanPath\n\n disconnectAllHdfDBs()\n printMsg = runLog.getVerbosity() <= DEBUG\n if _FAST_PATH_IS_TEMPORARY and os.path.exists(_FAST_PATH):\n if printMsg:\n print(\n \"Cleaning up temporary files in: {}\".format(_FAST_PATH),\n file=sys.stdout,\n )\n try:\n cleanPath(_FAST_PATH, mpiRank=MPI_RANK)\n except Exception as error:\n for outputStream in (sys.stderr, sys.stdout):\n if printMsg:\n print(\n \"Failed to delete temporary files in: {}\\n\"\n \" error: {}\".format(_FAST_PATH, error),\n file=outputStream,\n )\n\n if olderThanDays is not None:\n cleanAllArmiTempDirs(olderThanDays)","def _remove_temp_path():\n if os.path.exists(_temp_path):\n if os.path.isdir(_temp_path):\n def onerror(function, path, excinfo):\n persist.printf(\"{}: Unable to delete '{}' while cleaning up temporary directory\"\n .format(p_name, path))\n import traceback\n traceback.print_exc(*excinfo)\n import shutil\n shutil.rmtree(_temp_path, onerror=onerror)\n else:\n persist.printf(\"{}: For some reason, '{}' is a file. Removing...\"\n .format(p_name, _temp_path))\n os.remove(_temp_path)","def task_clean_tmp_files():\n client = google.cloud.storage.Client()\n blobs = client.list_blobs(settings.PODCAST_STORAGE_BUCKET,\n prefix=settings.PODCAST_TMP_STORAGE_DIRECTORY)\n for blob in blobs:\n if blob.time_created.replace(tzinfo=None) + datetime.timedelta(1) <= datetime.datetime.now():\n blob.delete()\n\n return OK_RESPONSE","def clean_tmp (tmp_path, keep_tmp):\n\n # check if folder exists\n if isdir(tmp_path):\n\n # delete [tmp_path] folder if [set_bb.keep_tmp] not True\n if not keep_tmp:\n shutil.rmtree(tmp_path)\n log.info ('removing temporary folder: {}'.format(tmp_path))\n\n else:\n # otherwise fpack its fits images\n #list_2pack = glob.glob('{}/*.fits'.format(tmp_path))\n list_2pack = list_files(tmp_path, end_str='.fits')\n\n for filename in list_2pack:\n fpack (filename)\n\n else:\n log.warning ('tmp folder {} does not exist'.format(tmp_path))\n\n\n return","def tearDown(self):\r\n remove_files(self.files_to_remove, False)\r\n if self.tmpdir:\r\n rmtree(self.tmpdir)\r\n\r\n # clean up the file from init_flowgram_file\r\n if (hasattr(self, \"tmp_filename\") and exists(self.tmp_filename)):\r\n remove(self.tmp_filename)","def clean_filesystem(files=[]):\n remove_files(files + find_cache_files())","def clearTemp():\n Installer.tempDir.rmtree(safety='Temp')","def remove_frames(tmpdir, files):\n for fname in files: os.remove(os.path.join(tmpdir, fname))\n if not(tmpdir == None): os.rmdir(tmpdir)","def clean(self):\n os.remove(\"temp.py\") # Delete the file \"temp.py\", to free up disk space","def purge(self, maxtemp=300, maxlock=600):\n # get list of intermediate directories\n dirs = []\n self.__get_list_of_interm_dirs(dirs)\n # remove all but old temporary or locked elements\n oldtemp = maxtemp != 0 and time.time() - maxtemp or 0\n oldlock = maxlock != 0 and time.time() - maxlock or 0\n if oldtemp or oldlock:\n for _dir in dirs:\n path = '%s/%s' % (self.path, _dir)\n tmp_lock_elems = [x for x in os.listdir(path)\n if re.search('(%s|%s)$' %\n (TEMPORARY_SUFFIX,\n LOCKED_SUFFIX), x)]\n for old in tmp_lock_elems:\n try:\n stat = os.stat('%s/%s' % (path, old))\n except OSError:\n error = sys.exc_info()[1]\n if error.errno == errno.ENOENT:\n continue\n raise error\n if (old.endswith(TEMPORARY_SUFFIX) and\n stat.st_mtime >= oldtemp):\n continue\n if old.endswith(LOCKED_SUFFIX) and \\\n stat.st_mtime >= oldlock:\n continue\n _warn(\"WARNING: removing too old volatile file: %s/%s\" %\n (self.path, old))\n try:\n os.unlink('%s/%s' % (path, old))\n except OSError:\n error = sys.exc_info()[1]\n if error.errno != errno.ENOENT:\n raise error\n # try to purge all but the last intermediate directory\n if len(dirs) > 1:\n dirs.sort()\n dirs.pop()\n for _dir in dirs:\n path = '%s/%s' % (self.path, _dir)\n if len(os.listdir(path)) == 0:\n _special_rmdir(path)","def clean(recursive=0):\n temp_files = set([\n \".blg\", \".bbl\", \".aux\", \".log\", \".brf\", \".nlo\", \".out\", \".dvi\", \".ps\",\n \".lof\", \".toc\", \".fls\", \".fdb_latexmk\", \".pdfsync\", \".synctex.gz\",\n \".ind\", \".ilg\", \".idx\"\n ])\n\n try:\n from send2trash import send2trash\n rm = send2trash\n except ImportError:\n print \"Send2Trash is not installed, use os.remove instead\"\n rm = os.remove\n\n _clean(os.getcwd(), 0, recursive, temp_files, rm)","def delete_b_files(intermediate_files: List[File]) -> None:\n for f in intermediate_files:\n f.remove()","def RemoveTempDirContents():\n temp_dir = os.path.abspath(tempfile.gettempdir())\n print 'Removing contents of %s' % temp_dir\n\n print ' Inspecting args for files to skip'\n whitelist = set()\n for i in sys.argv:\n try:\n if '=' in i:\n i = i.split('=')[1]\n low = os.path.abspath(i.lower())\n if low.startswith(temp_dir.lower()):\n whitelist.add(low)\n except TypeError:\n # If the argument is too long, windows will freak out and pop a TypeError.\n pass\n if whitelist:\n print ' Whitelisting:'\n for w in whitelist:\n print ' %r' % w\n\n start_time = time.time()\n for root, dirs, files in os.walk(temp_dir):\n for f in files:\n p = os.path.join(root, f)\n if p.lower() not in whitelist:\n try:\n os.remove(p)\n except OSError:\n pass\n else:\n print ' Keeping file %r (whitelisted)' % p\n for d in dirs[:]:\n p = os.path.join(root, d)\n if p.lower() not in whitelist:\n try:\n # TODO(iannucci): Make this deal with whitelisted items which are\n # inside of |d|\n\n # chromium_utils.RemoveDirectory gives access denied error when called\n # in this loop.\n shutil.rmtree(p, ignore_errors=True)\n # Remove it so that os.walk() doesn't try to recurse into\n # a non-existing directory.\n dirs.remove(d)\n except OSError:\n pass\n else:\n print ' Keeping dir %r (whitelisted)' % p\n print ' Removing temp contents took %.1f s' % (time.time() - start_time)","def remove_local():\n\n try:\n # if str(Settings.SKIP_DELETE) == \"True\":\n # Settings.maybe_print(\"skipping local remove\")\n # return\n # Settings.print('Deleting Local File(s)')\n # delete /tmp\n tmp = File.get_tmp()\n if os.path.exists(tmp):\n shutil.rmtree(tmp)\n Settings.print('Local File(s) Removed')\n else:\n Settings.print('Local Files Not Found')\n except Exception as e:\n Settings.dev_print(e)","def cleanup(self):\r\n if self.tempDirectory != None:\r\n shutil.rmtree(self.tempDirectory, True)\r\n self.tempDirectory = None","def delete_temp_folder():\n\n tempFolder = os.path.join(os.getenv(\"APPDATA\"), \"GARI\\Temp\")\n\n if os.path.exists(tempFolder):\n for file in os.listdir(tempFolder):\n arcpy.Delete_management(os.path.join(tempFolder, file))","def _cleanup(self):\n try:\n tmpdir = self.tmpdir\n except AttributeError:\n # Don't need to do anything if the temp dir isn't set\n return\n shutil.rmtree(tmpdir)","def clearTempDir(self):\n shutil.rmtree(tempDir.as_posix(), ignore_errors=True)","def clean():\n folders = ['utils_dfn/temp', 'utils_dfn/img', 'utils_dfn/mask', 'utils_dfn/output']\n for folder in folders:\n for item in os.listdir(folder):\n item_path = os.path.join(folder, item)\n if os.path.isdir(item_path):\n shutil.rmtree(item_path)\n elif os.path.isfile(item_path):\n os.remove(item_path)","def RemoveChromeTemporaryFiles():\n # NOTE: print out what is cleaned up so the bots don't timeout if\n # there is a lot to cleanup and also se we see the leaks in the\n # build logs.\n # At some point a leading dot got added, support with and without it.\n kLogRegex = r'^\\.?(com\\.google\\.Chrome|org\\.chromium)\\.'\n if chromium_utils.IsWindows():\n RemoveTempDirContents()\n RemoveChromeDesktopFiles()\n RemoveJumpListFiles()\n elif chromium_utils.IsLinux():\n LogAndRemoveFiles(tempfile.gettempdir(), kLogRegex)\n LogAndRemoveFiles('/dev/shm', kLogRegex)\n elif chromium_utils.IsMac():\n nstempdir_path = '/usr/local/libexec/nstempdir'\n if os.path.exists(nstempdir_path):\n ns_temp_dir = subprocess.check_output([nstempdir_path]).strip()\n if ns_temp_dir:\n LogAndRemoveFiles(ns_temp_dir, kLogRegex)\n for i in ('Chromium', 'Google Chrome'):\n # Remove dumps.\n crash_path = '%s/Library/Application Support/%s/Crash Reports' % (\n os.environ['HOME'], i)\n LogAndRemoveFiles(crash_path, r'^.+\\.dmp$')\n else:\n raise NotImplementedError(\n 'Platform \"%s\" is not currently supported.' % sys.platform)","def cleanup_intermediate_files():\n\n dirs = (DIR_PAGE, DIR_SRGB, DIR_VTI, DIR_TIFF, DIR_BACK, DIR_TEXT)\n map(lambda dir: shutil.rmtree(os.path.join(cwd, dir)) , dirs)","def do_clean(VERBOSE=0):\r\n thisdir = os.getcwd()\r\n tempdir = \"TEMP\"\r\n\r\n if os.path.isdir(os.path.join(thisdir, \"TEMP\")):\r\n tempdir = os.path.join(thisdir, \"TEMP\")\r\n elif os.path.isdir(os.path.join(thisdir, \"..\", \"TEMP\")):\r\n tempdir = os.path.join(thisdir, \"..\", \"TEMP\")\r\n\r\n if os.path.isdir(tempdir):\r\n os.chdir(tempdir)\r\n if VERBOSE > 0:\r\n print \" cleaning temporary directory '%s'\" % tempdir\r\n filestogo = glob.glob(\"*.*\")\r\n gonecount = 0\r\n for fg in filestogo:\r\n try:\r\n os.remove(fg)\r\n gonecount = gonecount +1\r\n except:\r\n if VERBOSE > 0:\r\n print \" !!! COULDN@T DELETE FILE '%s'\" % fg\r\n else:\r\n pass\r\n if VERBOSE > 0:\r\n print \" Deleted %s files\" % gonecount\r\n print\r\n os.chdir(thisdir)","def deleteIntermediateFiles(self):\n uniq_files = set(self.files_to_delete)\n print (\"Deleting %d intermediate files\" % len(uniq_files))\n for fn in uniq_files:\n # don't delete log files\n if not fn.endswith(\".log\"):\n os.remove(fn)","def teardown():\n for filename in files_to_delete:\n delete_file(filename)","def _remove_files(self):\n if hasattr(self, 'files'):\n for file in self.files:\n if os.path.exists(file):\n os.remove(file)\n\n self._remove_changes()\n self._remove_temporary_files()","def delete_tempdirs(self):\n\n if self._noWebDir:\n shutil.rmtree(self.webTopDir)","def _remove_all_ww_tmp_dirs():\n\t \n\t\t\n\t\tfor tmp_dir in glob.glob(f\"{TEST_TMP_DIR}/ww_*\"):\n\t\t\tprint(f\"removing tmp_dir = {tmp_dir}\")\n\t\t\tTest_Base._remove_ww_tmp_dir(tmp_dir)\n\t\t\t\n\t\treturn","def cleanUp(self):\n\n tapeList = sorted(glob.glob('TAPE?'))\n tapeList = ['TAPE%d' % num for num in [1, 2, 5, 6, 7, 10]]\n for tape in tapeList:\n if os.path.isfile(tape): os.remove(tape)\n # end TAPE loop","def deleteOutFiles(self, onlytmp=True):\n self.linkNodes()\n for node in self.sort():\n file = node.outputpath\n if (not onlytmp or file[0:4]=='tmp.'):\n logger.info(\"Deleting output file '%s'\" % file)\n dfs.delete(file)","def cleanup_intermediate_files(self):\n self.cmd(\"rm -f {local_temp_dir}/*rg_dict* \\\n {local_temp_dir}/*aln* \\\n {local_temp_dir}/snappy*\".\n format(\n local_temp_dir=self.local_temp_dir\n ),\n shell=True)","def cleanup_files(self):\n\n self.backup_files()\n self.delete_files()","def tearDown(self):\n for d in os.listdir(tmp_dir_path):\n d_path = os.path.join(tmp_dir_path,d)\n try:\n os.remove(d_path)\n except:\n for f in os.listdir(d_path):\n f_path = os.path.join(d_path,f)\n os.remove(f_path)\n os.rmdir(d_path)\n assert os.listdir(tmp_dir_path) == []","def remove_temp_folder(context):\n\n app = context.unrestrictedTraverse(\"/\")\n broken_id = \"temp_folder\"\n if broken_id in app.objectIds():\n temp_folder = app.unrestrictedTraverse(broken_id, None)\n if not isinstance(temp_folder, Broken):\n logger.info(\"%s is not broken, so we keep it.\", broken_id)\n return\n app._delObject(broken_id)\n logger.info(\"Removed broken %s from Zope root.\", broken_id)\n\n # The root Zope object has a dictionary '_mount_points.\n # >>> app._mount_points\n # {'temp_folder': MountedObject(id='temp_folder')}\n if not hasattr(app, \"_mount_points\"):\n return\n if broken_id in app._mount_points:\n del app._mount_points[broken_id]\n app._p_changed = True\n logger.info(\"Removed %s from Zope root _mount_points.\", broken_id)","def removeTmpDirs():\n p = Path(\".\")\n eggDirs = [x for x in p.glob(\"*.egg-info\") if x.is_dir()]\n eggDirs.append(Path(\"build\"))\n\n for d in eggDirs:\n if d.is_dir():\n shutil.rmtree(d)","def tmp_dir():\n tmpdir = tempfile.mkdtemp()\n yield tmpdir\n shutil.rmtree(tmpdir)","def tearDown(self):\n for fn in self.tempImages:\n os.remove(os.path.join(self.root, fn))\n os.rmdir(self.root)","def _cleanup(self):\n os.system(\"rm -r %s/*\" %(self._snippet_index_dir))\n os.system(\"rm %s/*\" %(self._para_dir))\n os.system(\"rm %s/*\" %(self._temp_dir))\n os.system(\"rm %s/*\" %(self._snippet_result_dir))","def a_temp_file():\n filename = None\n try:\n tmpfile = tempfile.NamedTemporaryFile(delete=False)\n filename = tmpfile.name\n yield tmpfile\n finally:\n if filename and os.path.exists(filename):\n os.remove(filename)","def cleanup(e):\n for f in e.files:\n try:\n if os.path.isfile(f):\n os.remove(f)\n except OSError:\n continue\n\n return","def CleanUp(self, path):\n try:\n if os.path.exists(path):\n os.remove(path)\n except (OSError, IOError) as e:\n logging.info(\"Failed to remove temporary file %s. Err: %s\", path, e)","def clean_up(self):\n directory = os.path.join(os.getcwd(), self.TMP_FOLDER)\n if os.path.exists(directory) and os.path.isdir(directory):\n shutil.rmtree(directory)","def clear_base_files(self):\r\n compilelock.get_lock()\r\n try:\r\n for base_dir in ('cuda_ndarray', 'cutils_ext', 'lazylinker_ext',\r\n 'scan_perform'):\r\n to_delete = os.path.join(self.dirname, base_dir + '.delete.me')\r\n if os.path.isdir(to_delete):\r\n try:\r\n shutil.rmtree(to_delete)\r\n _logger.debug('Deleted: %s', to_delete)\r\n except Exception:\r\n _logger.warning('Could not delete %s', to_delete)\r\n continue\r\n to_rename = os.path.join(self.dirname, base_dir)\r\n if os.path.isdir(to_rename):\r\n try:\r\n shutil.move(to_rename, to_delete)\r\n except Exception:\r\n _logger.warning('Could not move %s to %s',\r\n to_rename, to_delete)\r\n finally:\r\n compilelock.release_lock()","def tearDown(self) -> None:\n filtered = [f for f in glob.glob('steps/tests/test_output/*') if not re.match(r'\\.keep', f)]\n for file in filtered:\n try:\n if Path(file).is_dir():\n shutil.rmtree(file)\n else:\n os.remove(file)\n except PermissionError as pe:\n # We don't necessarily care that much\n continue","def clean_data():\n for clean_file in clean_files:\n file_list = [f for f in os.listdir(\".\") if f.endswith(clean_file)]\n for f in file_list:\n os.remove(f)","def create_temp_files(containers):\n for name in containers:\n run_cmd(f\"rm -rf /tmp/{name}.img\", True)\n for name in containers:\n run_cmd(f\"truncate -s 1G /tmp/{name}.img\", True)","def purge():\n all_hashes = read_all()\n used_hashes = read_used()\n\n for kind, hashes in used_hashes.items():\n to_remove = all_hashes[kind].difference(hashes)\n if kind == 'evs':\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\n elif kind == 'cache':\n delete_from_directory_by_hashes(CACHE_DIRECTORY, to_remove)\n elif kind == 'seeds':\n delete_from_directory_by_hashes(SEED_DIRECTORY, to_remove)\n\n reset_used()","def mktemp(self):\n try:\n fd, fn = tempfile.mkstemp(dir=self.tempdir)\n yield fn\n finally:\n try:\n os.close(fd)\n os.unlink(fn)\n except (OSError, IOError) as e:\n print(\"could not remove temporary file: %s\" % e,\n file=sys.stderr)","def cleanup(tempdir):\n try:\n shutil.rmtree(tempdir)\n except OSError:\n pass","def __del__(self) -> None:\n try:\n shutil.rmtree(self.temp_path)\n except FileNotFoundError:\n pass","def temporary_folder():\r\n tempdir = mkdtemp()\r\n try:\r\n yield tempdir\r\n finally:\r\n rmtree(tempdir)","def clean(files):\n\tfor file in files:\n\t\ttry:\n\t\t\tos.remove(file)\n\t\texcept Exception as e:\n\t\t\tprint(e)","def remove_unused_files(self):\n\n response_list = self.client.api_call(\n f'files.list?'\n f'count=1000&'\n )\n assert response_list['ok']\n\n for file in [\n f for f in response_list['files']\n if not f['channels'] and not f['groups'] and not f['ims']\n ]:\n response_delete = self.client.api_call(\n f'files.delete?'\n f'file={file[\"id\"]}'\n )\n assert response_delete['ok']","def delete_previous_files():\n def delete(root: Path):\n shutil.rmtree(root / 'output', ignore_errors=True)\n for p in root.iterdir():\n if str(p).endswith(('.log', 'jobs.csv', 'csv.lock', '.yaml')):\n p.unlink()\n\n delete(wt_registration_dir)\n delete(mut_registration_dir)","def clean():\n clean_files()","def clean(self):\n if os.path.exists(self.initial):\n if os.path.exists(self.path) and os.stat(self.path).st_size == os.stat(\n self.initial).st_size:\n os.remove(self.initial)\n else:\n # if it doesn't match, something probably crashed; rename the temporary file and\n # it'll get uploaded at some point\n self.auto_filename()\n self.rename()\n self.connect()\n os.remove(self.initial)\n if os.path.exists(self.path):\n os.remove(self.path)\n self.filename_set = False","def __del__(self):\n\t\tif self.temp_dir:\n\t\t\tself.temp_dir.cleanup()","def temporary_directory(request):\n path = tempfile.mkdtemp()\n\n def cleanup():\n \"\"\"Remove temporary directory.\"\"\"\n shutil.rmtree(path)\n\n request.addfinalizer(cleanup)\n\n return path","def tearDown(self):\r\n remove_files(self.files_to_remove)\r\n\r\n # Remove directories last, so we don't get errors trying to remove\r\n # files which may be in the directories.\r\n for d in self.dirs_to_remove:\r\n if exists(d):\r\n rmtree(d)","def cleanup_temp_dir(context):\n\n try:\n os.chdir(context.cwd)\n except:\n print(\"Current working file record does not exist\")\n\n try:\n context.tempdir.cleanup()\n except:\n print(\"Temporary directory cannot be cleaned up - does it exist?\")","def _cleanup_files(self):\n\n for root, dirs, files in os.walk(self.build_directory):\n dirs_to_delete = [\n Path(root).joinpath(x) for x in dirs if x == '__pycache__'\n ]\n files_to_delete = [\n Path(root).joinpath(x) for x in files if Path(x).suffix == '.pyc'\n ]\n for d in dirs_to_delete:\n logger.info('Deleting: %s', d)\n shutil.rmtree(d)\n for f in files_to_delete:\n logger.info('Deleting: %s', f)\n f.unlink()","def delete_temp_dir(app_name):\n sudo('rm -rf /tmp/.fab-deploy-{}'.format(app_name))","def tearDown(self):\n print(\n \"\\nDeleting temporary files...\\n\")\n try:\n shutil.rmtree(TEST_DIR)\n except OSError:\n pass","def delete_temp_file(filename):\n try:\n os.remove(filename)\n except OSError as e:\n if e.errno != errno.ENOENT:\n raise e","def clean(session):\n clean_dirs = (\n get_path(\".cache\"),\n get_path(\".coverage\"),\n get_path(\".pytest_cache\"),\n get_path(\"__pycache__\"),\n get_path(\"build\"),\n get_path(\"dist\"),\n get_path(\"docs\", \"__pycache__\"),\n get_path(\"docs\", \"build\"),\n get_path(\"scripts\", \"macos\", \"__pycache__\"),\n get_path(\"src\", \"python\", \"bezier.egg-info\"),\n get_path(\"src\", \"python\", \"bezier\", \"__pycache__\"),\n get_path(\"tests\", \"__pycache__\"),\n get_path(\"tests\", \"functional\", \"__pycache__\"),\n get_path(\"tests\", \"unit\", \"__pycache__\"),\n get_path(\"tests\", \"unit\", \"hazmat\", \"__pycache__\"),\n get_path(\"wheelhouse\"),\n )\n clean_globs = (\n get_path(\".coverage\"),\n get_path(\"*.mod\"),\n get_path(\"*.pyc\"),\n get_path(\"docs\", \"abi\", \"example\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.pyc\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.pyd\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.so\"),\n get_path(\"src\", \"fortran\", \"*.o\"),\n get_path(\"tests\", \"*.pyc\"),\n get_path(\"tests\", \"functional\", \"*.pyc\"),\n get_path(\"tests\", \"unit\", \"*.pyc\"),\n )\n for dir_path in clean_dirs:\n session.run(shutil.rmtree, dir_path, ignore_errors=True)\n for glob_path in clean_globs:\n for filename in glob.glob(glob_path):\n session.run(os.remove, filename)","def clean_files_for(file):\n for f in [file, f\"{file}.json\", f\"{file}.lock\"]:\n if os.path.isfile(f):\n os.remove(f)","def destroyer(): # ;-)\n\n def find_files_to_remove(pyfile):\n for filename in (\"%sc\" % pyfile, \"%so\" % pyfile):\n if exists(filename):\n yield filename\n\n counter = 0\n try:\n while True:\n pyfile = (yield)\n for filename in find_files_to_remove(pyfile):\n try:\n log.debug('removing %s', filename)\n remove(filename)\n counter += 1\n except (IOError, OSError), e:\n log.error('cannot remove %s', filename)\n log.debug(e)\n except GeneratorExit:\n log.info(\"removed files: %s\", counter)","def remove_persisted_files():\r\n persistIncarnations = get_persist_incarnation_dirs()\r\n for p in persistIncarnations:\r\n clear_dir(p)\r\n os.remove(p)\r\n clear_dir(get_persist_src_backup_dir())\r\n clear_dir(get_persist_src_dir())\r\n clear_dir(get_persist_root_dir()) \r\n\r\n #make sure the persist kb data structures aren't keeping any info \r\n global PERSISTED_LOAD_IDS\r\n AGENT_KB_MAP.clear()\r\n KB_WORKING_SET.clear()\r\n copy = PERSISTED_LOAD_IDS[:]\r\n for x in copy:\r\n PERSISTED_LOAD_IDS.remove(x)","def delete_tempfolder(path):\n try:\n rmtree(path)\n except:\n pass","def mp_tmpdir():\n # shutil.rmtree(TEMP_DIR, ignore_errors=True)\n os.makedirs(TEMP_DIR)\n yield TEMP_DIR\n shutil.rmtree(TEMP_DIR, ignore_errors=True)","def TemporaryDirectory():\n name = tempfile.mkdtemp()\n try:\n yield name\n finally:\n shutil.rmtree(name)","def cleanup(self):\n\n if self.do_nothing_bl is False:\n if os.path.exists(self.local_fileP_str) is True:\n if self.temp_dirP_obj is not None:\n self.temp_dirP_obj.cleanup()\n\n elif os.path.isdir(self.local_fileP_str) is True:\n log_obj.debug('Removing directory \"{:s}\"'.format(self.local_fileP_str))\n shutil.rmtree(self.local_fileP_str)\n\n else:\n log_obj.debug('Removing file \"{:s}\"'.format(self.local_fileP_str))\n os.remove(self.local_fileP_str)","def clean_chunk_files(dirpath):\n workdir = os.getcwd()\n os.chdir(dirpath)\n for filename in glob.glob(\"[0-9]*_[0-9]*_[0-9]*.hdf5\"):\n os.remove(filename)\n os.chdir(workdir)","def cleanup(self):\n try:\n self.wc = os.path.dirname(self.wc)\n rmtree2(self.wc)\n except IOError, err:\n self.log.exception(\"Can't remove working copy %s: %s\" % (self.wc, err))","def _clean_workdir(self):\n\t\ttoremove = [self._get_config_filepath(), self._get_params_filepath(), self._get_conv_filepath(), self._get_psf_filepath()]\n\t\tfor filepath in toremove:\n\t\t\tif os.path.exists(filepath):\t\n\t\t\t\tlogger.debug(\"Removing existing file %s...\" % (filepath))\n\t\t\t\tos.remove(filepath)","def teardown_upload(self, upload, filesystem_only=True):\n # This is like \"rm -rf path\"\n shutil.rmtree(upload.path, ignore_errors=True)\n if filesystem_only:\n return\n for input in upload.input_set.all():\n input.delete()\n upload.delete()","def cleanup(self):\n files = self.nlst()\n latest = self.latest_filename\n for filename in files:\n if filename != latest:\n result = self.delete(filename)\n logger.info(f\"Deleted old export from FTP: {result}\")","def clean(args):\n log = 'removing tmp dir %s ' % (args.tmpdir)\n if args.tmpdir.endswith('STAR'):\n cmd = ['rm -rf %s' % (args.tmpdir)]\n run_subprocess(cmd,args,log)\n log = \"remove tmp files from output dir\"\n cmd = ['mv %s/crick_joinedLog.final.out %s/Crick_joinedLog.final.out' % (args.output_dir, args.output_dir)]\n run_subprocess(cmd, args, log)\n cmd = ['mv %s/watson_joinedLog.final.out %s/Watson_joinedLog.final.out' % (args.output_dir, args.output_dir)]\n run_subprocess(cmd, args, log)\n cmd = ['mv %s/crick_mergedLog.final.out %s/Crick_mergedLog.final.out' % (args.output_dir, args.output_dir)]\n run_subprocess(cmd, args, log)\n cmd = ['mv %s/watson_mergedLog.final.out %s/Watson_mergedLog.final.out' % (args.output_dir, args.output_dir)]\n run_subprocess(cmd, args, log)\n cmd = ['rm -rf %s/crick_*' % args.output_dir]\n run_subprocess(cmd, args, log)\n cmd = ['rm -rf %s/watson_*' % args.output_dir]\n run_subprocess(cmd, args, log)\n cmd = ['rm -rf %s/joined* header.sam' % args.output_dir]\n run_subprocess(cmd, args, log)"],"string":"[\n \"def remove_temporary_files():\\n try:\\n xml_file_path, bin_file_path = get_ida_exported_files()\\n if os.path.isfile(xml_file_path):\\n os.remove(xml_file_path)\\n\\n if os.path.isfile(bin_file_path):\\n os.remove(bin_file_path)\\n\\n except Exception:\\n print(\\\"GhIDA:: [!] Unexpected error while removing temporary files.\\\")\",\n \"def _remove_tmpfiles():\\n for f in tmpfiles:\\n try:\\n os.remove(f)\\n except OSError:\\n pass\",\n \"def del_tmp() -> None:\\n for elem in os.listdir('./tmp'):\\n path = f\\\"./tmp/{elem}\\\"\\n if os.path.isfile(path):\\n os.remove(path)\\n else:\\n shutil.rmtree(path)\",\n \"def clean_up_temp_dir():\\n files = glob.glob(f'{CONFIG_DIR}/tmp/*')\\n for f in files:\\n try:\\n os.remove(f)\\n except Exception:\\n pass\",\n \"def clear_tempfiles(self, remove=True):\\n while self._tempfiles:\\n self.pop(remove)\\n self.push()\",\n \"def remover_files():\\n directory = os.getcwd()\\n for file_name in glob.glob((\\\"{}/tmp/*\\\").format(directory)):\\n remove(file_name)\",\n \"def clear_temp(remove_all=True):\\n tf_list = []\\n\\n if remove_all:\\n temp_dir = _get_temp_dir(False)\\n temp_dir += (\\n os.path.sep if os.path.sep not in temp_dir[len(temp_dir) - 1] else \\\"\\\"\\n )\\n tf_list = glob.glob(\\\"{0}TESS_*\\\".format(temp_dir))\\n else:\\n global _tempfiles\\n\\n tf_list = list(_tempfiles)\\n _tempfiles.clear()\\n\\n for tf in tf_list:\\n if os.path.isfile(tf):\\n _remove_file(tf)\",\n \"def delete_temporary_files(request, tmp_path_factory):\\r\\n _tmp_path_factory = tmp_path_factory\\r\\n\\r\\n def cleanup():\\r\\n tmp_path = _tmp_path_factory.getbasetemp()\\r\\n if pathlib.Path(tmp_path).exists() and pathlib.Path(tmp_path).is_dir():\\r\\n shutil.rmtree(tmp_path)\\r\\n\\r\\n request.addfinalizer(cleanup)\",\n \"def cleanup_tmpdir(tmpdir):\\n try:\\n for filename in glob.glob(os.path.join(tmpdir, \\\"*\\\")):\\n os.remove(filename)\\n os.rmdir(tmpdir)\\n except OSError, inst:\\n sys.stderr.write('WARNING: could not remove temp files'\\n ' in ' + tmpdir + '\\\\n' + str(inst) + '\\\\n')\",\n \"def _delete_temp():\\n global _TEMP_NAME\\n\\n try:\\n database.delete_temp(_TEMP_NAME)\\n outputtools.delete_temp(_TEMP_NAME)\\n except:\\n raise\",\n \"def remove_tmp_sources(source_filename):\\n logging.info('Removing temporary files ...')\\n source_dir = os.path.dirname(source_filename)\\n if os.path.exists(source_filename):\\n os.remove(source_filename)\\n for f in os.listdir(source_dir):\\n if f.startswith('tmp_'):\\n os.remove(os.path.join(source_dir, f))\",\n \"def _clean_up_temporary_files(dataset_dir):\\n filename = _DATA_URL.split('/')[-1]\\n filepath = os.path.join(dataset_dir, filename)\\n tf.gfile.Remove(filepath)\\n\\n tmp_dir = os.path.join(dataset_dir, 'cifar-100-python')\\n tf.gfile.DeleteRecursively(tmp_dir)\",\n \"def clean_temp_storage_dir(self, filenames):\\n for fn in filenames:\\n try:\\n pathlib.Path(pathlib.PurePath(self.temp_storage_dir, fn)).unlink()\\n except FileNotFoundError:\\n pass\",\n \"def _clean_up_temporary_files(dataset_dir):\\n return\",\n \"def cleanUpTemporaryFiles(options):\\n os.system(\\\"rm \\\"+options.output_directory_per_run+\\\"/*.abundance\\\")\\n os.system(\\\"rm \\\"+options.output_directory_per_run+\\\"/*.phasing_score\\\")\\n os.system(\\\"rm \\\"+options.output_directory_per_run+\\\"/*regionsOfInterest*\\\")\\n os.system(\\\"mv \\\"+options.output_directory_per_run+\\\"/* \\\"+options.output_directory_per_run+\\\"/../\\\")\\n os.system(\\\"rm -rf \\\"+options.output_directory_per_run)\",\n \"def clear_tmp_folder(self):\\r\\n for file in os.listdir(self.temp_dir):\\r\\n if file.endswith('.png') or file.endswith('.jpg'):\\r\\n path = os.path.join(self.temp_dir, file)\\r\\n print ('Cleaned up {}'.format(path))\\r\\n os.remove(path)\",\n \"def tearDown(self):\\n for f in os.listdir('/tmp'):\\n if not f.startswith(self.FILE_PREFIX):\\n continue\\n\\n os.remove(os.path.join('/tmp', f))\",\n \"def remove_temp_folders(self) -> None:\\n if self.args.deletefolders:\\n time.sleep(2)\\n for f in self.created_folders:\\n shutil.rmtree(path.join(self.args.output, f))\\n print(f\\\"{self.args.output}/{f} was deleted\\\")\",\n \"def remove_intermediate_files(dir_):\\n file_list = glob.glob(f'{dir_}/*temp*')\\n [os.remove(f) for f in file_list]\",\n \"def clean():\\n for f in [f for f in os.listdir() if f.endswith(\\\".part\\\")]:\\n os.remove(f)\",\n \"def clean_up_temp_files():\\n global __tmp_model_dir\\n\\n if __tmp_model_dir is not None:\\n FileUtils.deleteDirectory(__tmp_model_dir)\\n __tmp_model_dir = None\",\n \"def _clean_up_optimization():\\n for (root, dirs, files) in walk(TEMP_MODULES_DIR_PATH, topdown=False):\\n for file in files:\\n if file.startswith(\\\"__temp_\\\"):\\n remove(f\\\"{root}/{file}\\\")\\n try:\\n rmdir(root)\\n except OSError:\\n G.warn_(f\\\"Unidentified file found in temporary directory: {root}\\\")\",\n \"def tearDown(self):\\n for root, dirs, files in os.walk(TEMPDIR, topdown=False):\\n for name in files:\\n os.remove(os.path.join(root, name))\\n for name in dirs:\\n os.rmdir(os.path.join(root, name))\\n os.rmdir(root)\",\n \"def cleanTempDirs(olderThanDays=None):\\n from armi import runLog\\n from armi.utils.pathTools import cleanPath\\n\\n disconnectAllHdfDBs()\\n printMsg = runLog.getVerbosity() <= DEBUG\\n if _FAST_PATH_IS_TEMPORARY and os.path.exists(_FAST_PATH):\\n if printMsg:\\n print(\\n \\\"Cleaning up temporary files in: {}\\\".format(_FAST_PATH),\\n file=sys.stdout,\\n )\\n try:\\n cleanPath(_FAST_PATH, mpiRank=MPI_RANK)\\n except Exception as error:\\n for outputStream in (sys.stderr, sys.stdout):\\n if printMsg:\\n print(\\n \\\"Failed to delete temporary files in: {}\\\\n\\\"\\n \\\" error: {}\\\".format(_FAST_PATH, error),\\n file=outputStream,\\n )\\n\\n if olderThanDays is not None:\\n cleanAllArmiTempDirs(olderThanDays)\",\n \"def _remove_temp_path():\\n if os.path.exists(_temp_path):\\n if os.path.isdir(_temp_path):\\n def onerror(function, path, excinfo):\\n persist.printf(\\\"{}: Unable to delete '{}' while cleaning up temporary directory\\\"\\n .format(p_name, path))\\n import traceback\\n traceback.print_exc(*excinfo)\\n import shutil\\n shutil.rmtree(_temp_path, onerror=onerror)\\n else:\\n persist.printf(\\\"{}: For some reason, '{}' is a file. Removing...\\\"\\n .format(p_name, _temp_path))\\n os.remove(_temp_path)\",\n \"def task_clean_tmp_files():\\n client = google.cloud.storage.Client()\\n blobs = client.list_blobs(settings.PODCAST_STORAGE_BUCKET,\\n prefix=settings.PODCAST_TMP_STORAGE_DIRECTORY)\\n for blob in blobs:\\n if blob.time_created.replace(tzinfo=None) + datetime.timedelta(1) <= datetime.datetime.now():\\n blob.delete()\\n\\n return OK_RESPONSE\",\n \"def clean_tmp (tmp_path, keep_tmp):\\n\\n # check if folder exists\\n if isdir(tmp_path):\\n\\n # delete [tmp_path] folder if [set_bb.keep_tmp] not True\\n if not keep_tmp:\\n shutil.rmtree(tmp_path)\\n log.info ('removing temporary folder: {}'.format(tmp_path))\\n\\n else:\\n # otherwise fpack its fits images\\n #list_2pack = glob.glob('{}/*.fits'.format(tmp_path))\\n list_2pack = list_files(tmp_path, end_str='.fits')\\n\\n for filename in list_2pack:\\n fpack (filename)\\n\\n else:\\n log.warning ('tmp folder {} does not exist'.format(tmp_path))\\n\\n\\n return\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove, False)\\r\\n if self.tmpdir:\\r\\n rmtree(self.tmpdir)\\r\\n\\r\\n # clean up the file from init_flowgram_file\\r\\n if (hasattr(self, \\\"tmp_filename\\\") and exists(self.tmp_filename)):\\r\\n remove(self.tmp_filename)\",\n \"def clean_filesystem(files=[]):\\n remove_files(files + find_cache_files())\",\n \"def clearTemp():\\n Installer.tempDir.rmtree(safety='Temp')\",\n \"def remove_frames(tmpdir, files):\\n for fname in files: os.remove(os.path.join(tmpdir, fname))\\n if not(tmpdir == None): os.rmdir(tmpdir)\",\n \"def clean(self):\\n os.remove(\\\"temp.py\\\") # Delete the file \\\"temp.py\\\", to free up disk space\",\n \"def purge(self, maxtemp=300, maxlock=600):\\n # get list of intermediate directories\\n dirs = []\\n self.__get_list_of_interm_dirs(dirs)\\n # remove all but old temporary or locked elements\\n oldtemp = maxtemp != 0 and time.time() - maxtemp or 0\\n oldlock = maxlock != 0 and time.time() - maxlock or 0\\n if oldtemp or oldlock:\\n for _dir in dirs:\\n path = '%s/%s' % (self.path, _dir)\\n tmp_lock_elems = [x for x in os.listdir(path)\\n if re.search('(%s|%s)$' %\\n (TEMPORARY_SUFFIX,\\n LOCKED_SUFFIX), x)]\\n for old in tmp_lock_elems:\\n try:\\n stat = os.stat('%s/%s' % (path, old))\\n except OSError:\\n error = sys.exc_info()[1]\\n if error.errno == errno.ENOENT:\\n continue\\n raise error\\n if (old.endswith(TEMPORARY_SUFFIX) and\\n stat.st_mtime >= oldtemp):\\n continue\\n if old.endswith(LOCKED_SUFFIX) and \\\\\\n stat.st_mtime >= oldlock:\\n continue\\n _warn(\\\"WARNING: removing too old volatile file: %s/%s\\\" %\\n (self.path, old))\\n try:\\n os.unlink('%s/%s' % (path, old))\\n except OSError:\\n error = sys.exc_info()[1]\\n if error.errno != errno.ENOENT:\\n raise error\\n # try to purge all but the last intermediate directory\\n if len(dirs) > 1:\\n dirs.sort()\\n dirs.pop()\\n for _dir in dirs:\\n path = '%s/%s' % (self.path, _dir)\\n if len(os.listdir(path)) == 0:\\n _special_rmdir(path)\",\n \"def clean(recursive=0):\\n temp_files = set([\\n \\\".blg\\\", \\\".bbl\\\", \\\".aux\\\", \\\".log\\\", \\\".brf\\\", \\\".nlo\\\", \\\".out\\\", \\\".dvi\\\", \\\".ps\\\",\\n \\\".lof\\\", \\\".toc\\\", \\\".fls\\\", \\\".fdb_latexmk\\\", \\\".pdfsync\\\", \\\".synctex.gz\\\",\\n \\\".ind\\\", \\\".ilg\\\", \\\".idx\\\"\\n ])\\n\\n try:\\n from send2trash import send2trash\\n rm = send2trash\\n except ImportError:\\n print \\\"Send2Trash is not installed, use os.remove instead\\\"\\n rm = os.remove\\n\\n _clean(os.getcwd(), 0, recursive, temp_files, rm)\",\n \"def delete_b_files(intermediate_files: List[File]) -> None:\\n for f in intermediate_files:\\n f.remove()\",\n \"def RemoveTempDirContents():\\n temp_dir = os.path.abspath(tempfile.gettempdir())\\n print 'Removing contents of %s' % temp_dir\\n\\n print ' Inspecting args for files to skip'\\n whitelist = set()\\n for i in sys.argv:\\n try:\\n if '=' in i:\\n i = i.split('=')[1]\\n low = os.path.abspath(i.lower())\\n if low.startswith(temp_dir.lower()):\\n whitelist.add(low)\\n except TypeError:\\n # If the argument is too long, windows will freak out and pop a TypeError.\\n pass\\n if whitelist:\\n print ' Whitelisting:'\\n for w in whitelist:\\n print ' %r' % w\\n\\n start_time = time.time()\\n for root, dirs, files in os.walk(temp_dir):\\n for f in files:\\n p = os.path.join(root, f)\\n if p.lower() not in whitelist:\\n try:\\n os.remove(p)\\n except OSError:\\n pass\\n else:\\n print ' Keeping file %r (whitelisted)' % p\\n for d in dirs[:]:\\n p = os.path.join(root, d)\\n if p.lower() not in whitelist:\\n try:\\n # TODO(iannucci): Make this deal with whitelisted items which are\\n # inside of |d|\\n\\n # chromium_utils.RemoveDirectory gives access denied error when called\\n # in this loop.\\n shutil.rmtree(p, ignore_errors=True)\\n # Remove it so that os.walk() doesn't try to recurse into\\n # a non-existing directory.\\n dirs.remove(d)\\n except OSError:\\n pass\\n else:\\n print ' Keeping dir %r (whitelisted)' % p\\n print ' Removing temp contents took %.1f s' % (time.time() - start_time)\",\n \"def remove_local():\\n\\n try:\\n # if str(Settings.SKIP_DELETE) == \\\"True\\\":\\n # Settings.maybe_print(\\\"skipping local remove\\\")\\n # return\\n # Settings.print('Deleting Local File(s)')\\n # delete /tmp\\n tmp = File.get_tmp()\\n if os.path.exists(tmp):\\n shutil.rmtree(tmp)\\n Settings.print('Local File(s) Removed')\\n else:\\n Settings.print('Local Files Not Found')\\n except Exception as e:\\n Settings.dev_print(e)\",\n \"def cleanup(self):\\r\\n if self.tempDirectory != None:\\r\\n shutil.rmtree(self.tempDirectory, True)\\r\\n self.tempDirectory = None\",\n \"def delete_temp_folder():\\n\\n tempFolder = os.path.join(os.getenv(\\\"APPDATA\\\"), \\\"GARI\\\\Temp\\\")\\n\\n if os.path.exists(tempFolder):\\n for file in os.listdir(tempFolder):\\n arcpy.Delete_management(os.path.join(tempFolder, file))\",\n \"def _cleanup(self):\\n try:\\n tmpdir = self.tmpdir\\n except AttributeError:\\n # Don't need to do anything if the temp dir isn't set\\n return\\n shutil.rmtree(tmpdir)\",\n \"def clearTempDir(self):\\n shutil.rmtree(tempDir.as_posix(), ignore_errors=True)\",\n \"def clean():\\n folders = ['utils_dfn/temp', 'utils_dfn/img', 'utils_dfn/mask', 'utils_dfn/output']\\n for folder in folders:\\n for item in os.listdir(folder):\\n item_path = os.path.join(folder, item)\\n if os.path.isdir(item_path):\\n shutil.rmtree(item_path)\\n elif os.path.isfile(item_path):\\n os.remove(item_path)\",\n \"def RemoveChromeTemporaryFiles():\\n # NOTE: print out what is cleaned up so the bots don't timeout if\\n # there is a lot to cleanup and also se we see the leaks in the\\n # build logs.\\n # At some point a leading dot got added, support with and without it.\\n kLogRegex = r'^\\\\.?(com\\\\.google\\\\.Chrome|org\\\\.chromium)\\\\.'\\n if chromium_utils.IsWindows():\\n RemoveTempDirContents()\\n RemoveChromeDesktopFiles()\\n RemoveJumpListFiles()\\n elif chromium_utils.IsLinux():\\n LogAndRemoveFiles(tempfile.gettempdir(), kLogRegex)\\n LogAndRemoveFiles('/dev/shm', kLogRegex)\\n elif chromium_utils.IsMac():\\n nstempdir_path = '/usr/local/libexec/nstempdir'\\n if os.path.exists(nstempdir_path):\\n ns_temp_dir = subprocess.check_output([nstempdir_path]).strip()\\n if ns_temp_dir:\\n LogAndRemoveFiles(ns_temp_dir, kLogRegex)\\n for i in ('Chromium', 'Google Chrome'):\\n # Remove dumps.\\n crash_path = '%s/Library/Application Support/%s/Crash Reports' % (\\n os.environ['HOME'], i)\\n LogAndRemoveFiles(crash_path, r'^.+\\\\.dmp$')\\n else:\\n raise NotImplementedError(\\n 'Platform \\\"%s\\\" is not currently supported.' % sys.platform)\",\n \"def cleanup_intermediate_files():\\n\\n dirs = (DIR_PAGE, DIR_SRGB, DIR_VTI, DIR_TIFF, DIR_BACK, DIR_TEXT)\\n map(lambda dir: shutil.rmtree(os.path.join(cwd, dir)) , dirs)\",\n \"def do_clean(VERBOSE=0):\\r\\n thisdir = os.getcwd()\\r\\n tempdir = \\\"TEMP\\\"\\r\\n\\r\\n if os.path.isdir(os.path.join(thisdir, \\\"TEMP\\\")):\\r\\n tempdir = os.path.join(thisdir, \\\"TEMP\\\")\\r\\n elif os.path.isdir(os.path.join(thisdir, \\\"..\\\", \\\"TEMP\\\")):\\r\\n tempdir = os.path.join(thisdir, \\\"..\\\", \\\"TEMP\\\")\\r\\n\\r\\n if os.path.isdir(tempdir):\\r\\n os.chdir(tempdir)\\r\\n if VERBOSE > 0:\\r\\n print \\\" cleaning temporary directory '%s'\\\" % tempdir\\r\\n filestogo = glob.glob(\\\"*.*\\\")\\r\\n gonecount = 0\\r\\n for fg in filestogo:\\r\\n try:\\r\\n os.remove(fg)\\r\\n gonecount = gonecount +1\\r\\n except:\\r\\n if VERBOSE > 0:\\r\\n print \\\" !!! COULDN@T DELETE FILE '%s'\\\" % fg\\r\\n else:\\r\\n pass\\r\\n if VERBOSE > 0:\\r\\n print \\\" Deleted %s files\\\" % gonecount\\r\\n print\\r\\n os.chdir(thisdir)\",\n \"def deleteIntermediateFiles(self):\\n uniq_files = set(self.files_to_delete)\\n print (\\\"Deleting %d intermediate files\\\" % len(uniq_files))\\n for fn in uniq_files:\\n # don't delete log files\\n if not fn.endswith(\\\".log\\\"):\\n os.remove(fn)\",\n \"def teardown():\\n for filename in files_to_delete:\\n delete_file(filename)\",\n \"def _remove_files(self):\\n if hasattr(self, 'files'):\\n for file in self.files:\\n if os.path.exists(file):\\n os.remove(file)\\n\\n self._remove_changes()\\n self._remove_temporary_files()\",\n \"def delete_tempdirs(self):\\n\\n if self._noWebDir:\\n shutil.rmtree(self.webTopDir)\",\n \"def _remove_all_ww_tmp_dirs():\\n\\t \\n\\t\\t\\n\\t\\tfor tmp_dir in glob.glob(f\\\"{TEST_TMP_DIR}/ww_*\\\"):\\n\\t\\t\\tprint(f\\\"removing tmp_dir = {tmp_dir}\\\")\\n\\t\\t\\tTest_Base._remove_ww_tmp_dir(tmp_dir)\\n\\t\\t\\t\\n\\t\\treturn\",\n \"def cleanUp(self):\\n\\n tapeList = sorted(glob.glob('TAPE?'))\\n tapeList = ['TAPE%d' % num for num in [1, 2, 5, 6, 7, 10]]\\n for tape in tapeList:\\n if os.path.isfile(tape): os.remove(tape)\\n # end TAPE loop\",\n \"def deleteOutFiles(self, onlytmp=True):\\n self.linkNodes()\\n for node in self.sort():\\n file = node.outputpath\\n if (not onlytmp or file[0:4]=='tmp.'):\\n logger.info(\\\"Deleting output file '%s'\\\" % file)\\n dfs.delete(file)\",\n \"def cleanup_intermediate_files(self):\\n self.cmd(\\\"rm -f {local_temp_dir}/*rg_dict* \\\\\\n {local_temp_dir}/*aln* \\\\\\n {local_temp_dir}/snappy*\\\".\\n format(\\n local_temp_dir=self.local_temp_dir\\n ),\\n shell=True)\",\n \"def cleanup_files(self):\\n\\n self.backup_files()\\n self.delete_files()\",\n \"def tearDown(self):\\n for d in os.listdir(tmp_dir_path):\\n d_path = os.path.join(tmp_dir_path,d)\\n try:\\n os.remove(d_path)\\n except:\\n for f in os.listdir(d_path):\\n f_path = os.path.join(d_path,f)\\n os.remove(f_path)\\n os.rmdir(d_path)\\n assert os.listdir(tmp_dir_path) == []\",\n \"def remove_temp_folder(context):\\n\\n app = context.unrestrictedTraverse(\\\"/\\\")\\n broken_id = \\\"temp_folder\\\"\\n if broken_id in app.objectIds():\\n temp_folder = app.unrestrictedTraverse(broken_id, None)\\n if not isinstance(temp_folder, Broken):\\n logger.info(\\\"%s is not broken, so we keep it.\\\", broken_id)\\n return\\n app._delObject(broken_id)\\n logger.info(\\\"Removed broken %s from Zope root.\\\", broken_id)\\n\\n # The root Zope object has a dictionary '_mount_points.\\n # >>> app._mount_points\\n # {'temp_folder': MountedObject(id='temp_folder')}\\n if not hasattr(app, \\\"_mount_points\\\"):\\n return\\n if broken_id in app._mount_points:\\n del app._mount_points[broken_id]\\n app._p_changed = True\\n logger.info(\\\"Removed %s from Zope root _mount_points.\\\", broken_id)\",\n \"def removeTmpDirs():\\n p = Path(\\\".\\\")\\n eggDirs = [x for x in p.glob(\\\"*.egg-info\\\") if x.is_dir()]\\n eggDirs.append(Path(\\\"build\\\"))\\n\\n for d in eggDirs:\\n if d.is_dir():\\n shutil.rmtree(d)\",\n \"def tmp_dir():\\n tmpdir = tempfile.mkdtemp()\\n yield tmpdir\\n shutil.rmtree(tmpdir)\",\n \"def tearDown(self):\\n for fn in self.tempImages:\\n os.remove(os.path.join(self.root, fn))\\n os.rmdir(self.root)\",\n \"def _cleanup(self):\\n os.system(\\\"rm -r %s/*\\\" %(self._snippet_index_dir))\\n os.system(\\\"rm %s/*\\\" %(self._para_dir))\\n os.system(\\\"rm %s/*\\\" %(self._temp_dir))\\n os.system(\\\"rm %s/*\\\" %(self._snippet_result_dir))\",\n \"def a_temp_file():\\n filename = None\\n try:\\n tmpfile = tempfile.NamedTemporaryFile(delete=False)\\n filename = tmpfile.name\\n yield tmpfile\\n finally:\\n if filename and os.path.exists(filename):\\n os.remove(filename)\",\n \"def cleanup(e):\\n for f in e.files:\\n try:\\n if os.path.isfile(f):\\n os.remove(f)\\n except OSError:\\n continue\\n\\n return\",\n \"def CleanUp(self, path):\\n try:\\n if os.path.exists(path):\\n os.remove(path)\\n except (OSError, IOError) as e:\\n logging.info(\\\"Failed to remove temporary file %s. Err: %s\\\", path, e)\",\n \"def clean_up(self):\\n directory = os.path.join(os.getcwd(), self.TMP_FOLDER)\\n if os.path.exists(directory) and os.path.isdir(directory):\\n shutil.rmtree(directory)\",\n \"def clear_base_files(self):\\r\\n compilelock.get_lock()\\r\\n try:\\r\\n for base_dir in ('cuda_ndarray', 'cutils_ext', 'lazylinker_ext',\\r\\n 'scan_perform'):\\r\\n to_delete = os.path.join(self.dirname, base_dir + '.delete.me')\\r\\n if os.path.isdir(to_delete):\\r\\n try:\\r\\n shutil.rmtree(to_delete)\\r\\n _logger.debug('Deleted: %s', to_delete)\\r\\n except Exception:\\r\\n _logger.warning('Could not delete %s', to_delete)\\r\\n continue\\r\\n to_rename = os.path.join(self.dirname, base_dir)\\r\\n if os.path.isdir(to_rename):\\r\\n try:\\r\\n shutil.move(to_rename, to_delete)\\r\\n except Exception:\\r\\n _logger.warning('Could not move %s to %s',\\r\\n to_rename, to_delete)\\r\\n finally:\\r\\n compilelock.release_lock()\",\n \"def tearDown(self) -> None:\\n filtered = [f for f in glob.glob('steps/tests/test_output/*') if not re.match(r'\\\\.keep', f)]\\n for file in filtered:\\n try:\\n if Path(file).is_dir():\\n shutil.rmtree(file)\\n else:\\n os.remove(file)\\n except PermissionError as pe:\\n # We don't necessarily care that much\\n continue\",\n \"def clean_data():\\n for clean_file in clean_files:\\n file_list = [f for f in os.listdir(\\\".\\\") if f.endswith(clean_file)]\\n for f in file_list:\\n os.remove(f)\",\n \"def create_temp_files(containers):\\n for name in containers:\\n run_cmd(f\\\"rm -rf /tmp/{name}.img\\\", True)\\n for name in containers:\\n run_cmd(f\\\"truncate -s 1G /tmp/{name}.img\\\", True)\",\n \"def purge():\\n all_hashes = read_all()\\n used_hashes = read_used()\\n\\n for kind, hashes in used_hashes.items():\\n to_remove = all_hashes[kind].difference(hashes)\\n if kind == 'evs':\\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\\n elif kind == 'cache':\\n delete_from_directory_by_hashes(CACHE_DIRECTORY, to_remove)\\n elif kind == 'seeds':\\n delete_from_directory_by_hashes(SEED_DIRECTORY, to_remove)\\n\\n reset_used()\",\n \"def mktemp(self):\\n try:\\n fd, fn = tempfile.mkstemp(dir=self.tempdir)\\n yield fn\\n finally:\\n try:\\n os.close(fd)\\n os.unlink(fn)\\n except (OSError, IOError) as e:\\n print(\\\"could not remove temporary file: %s\\\" % e,\\n file=sys.stderr)\",\n \"def cleanup(tempdir):\\n try:\\n shutil.rmtree(tempdir)\\n except OSError:\\n pass\",\n \"def __del__(self) -> None:\\n try:\\n shutil.rmtree(self.temp_path)\\n except FileNotFoundError:\\n pass\",\n \"def temporary_folder():\\r\\n tempdir = mkdtemp()\\r\\n try:\\r\\n yield tempdir\\r\\n finally:\\r\\n rmtree(tempdir)\",\n \"def clean(files):\\n\\tfor file in files:\\n\\t\\ttry:\\n\\t\\t\\tos.remove(file)\\n\\t\\texcept Exception as e:\\n\\t\\t\\tprint(e)\",\n \"def remove_unused_files(self):\\n\\n response_list = self.client.api_call(\\n f'files.list?'\\n f'count=1000&'\\n )\\n assert response_list['ok']\\n\\n for file in [\\n f for f in response_list['files']\\n if not f['channels'] and not f['groups'] and not f['ims']\\n ]:\\n response_delete = self.client.api_call(\\n f'files.delete?'\\n f'file={file[\\\"id\\\"]}'\\n )\\n assert response_delete['ok']\",\n \"def delete_previous_files():\\n def delete(root: Path):\\n shutil.rmtree(root / 'output', ignore_errors=True)\\n for p in root.iterdir():\\n if str(p).endswith(('.log', 'jobs.csv', 'csv.lock', '.yaml')):\\n p.unlink()\\n\\n delete(wt_registration_dir)\\n delete(mut_registration_dir)\",\n \"def clean():\\n clean_files()\",\n \"def clean(self):\\n if os.path.exists(self.initial):\\n if os.path.exists(self.path) and os.stat(self.path).st_size == os.stat(\\n self.initial).st_size:\\n os.remove(self.initial)\\n else:\\n # if it doesn't match, something probably crashed; rename the temporary file and\\n # it'll get uploaded at some point\\n self.auto_filename()\\n self.rename()\\n self.connect()\\n os.remove(self.initial)\\n if os.path.exists(self.path):\\n os.remove(self.path)\\n self.filename_set = False\",\n \"def __del__(self):\\n\\t\\tif self.temp_dir:\\n\\t\\t\\tself.temp_dir.cleanup()\",\n \"def temporary_directory(request):\\n path = tempfile.mkdtemp()\\n\\n def cleanup():\\n \\\"\\\"\\\"Remove temporary directory.\\\"\\\"\\\"\\n shutil.rmtree(path)\\n\\n request.addfinalizer(cleanup)\\n\\n return path\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove)\\r\\n\\r\\n # Remove directories last, so we don't get errors trying to remove\\r\\n # files which may be in the directories.\\r\\n for d in self.dirs_to_remove:\\r\\n if exists(d):\\r\\n rmtree(d)\",\n \"def cleanup_temp_dir(context):\\n\\n try:\\n os.chdir(context.cwd)\\n except:\\n print(\\\"Current working file record does not exist\\\")\\n\\n try:\\n context.tempdir.cleanup()\\n except:\\n print(\\\"Temporary directory cannot be cleaned up - does it exist?\\\")\",\n \"def _cleanup_files(self):\\n\\n for root, dirs, files in os.walk(self.build_directory):\\n dirs_to_delete = [\\n Path(root).joinpath(x) for x in dirs if x == '__pycache__'\\n ]\\n files_to_delete = [\\n Path(root).joinpath(x) for x in files if Path(x).suffix == '.pyc'\\n ]\\n for d in dirs_to_delete:\\n logger.info('Deleting: %s', d)\\n shutil.rmtree(d)\\n for f in files_to_delete:\\n logger.info('Deleting: %s', f)\\n f.unlink()\",\n \"def delete_temp_dir(app_name):\\n sudo('rm -rf /tmp/.fab-deploy-{}'.format(app_name))\",\n \"def tearDown(self):\\n print(\\n \\\"\\\\nDeleting temporary files...\\\\n\\\")\\n try:\\n shutil.rmtree(TEST_DIR)\\n except OSError:\\n pass\",\n \"def delete_temp_file(filename):\\n try:\\n os.remove(filename)\\n except OSError as e:\\n if e.errno != errno.ENOENT:\\n raise e\",\n \"def clean(session):\\n clean_dirs = (\\n get_path(\\\".cache\\\"),\\n get_path(\\\".coverage\\\"),\\n get_path(\\\".pytest_cache\\\"),\\n get_path(\\\"__pycache__\\\"),\\n get_path(\\\"build\\\"),\\n get_path(\\\"dist\\\"),\\n get_path(\\\"docs\\\", \\\"__pycache__\\\"),\\n get_path(\\\"docs\\\", \\\"build\\\"),\\n get_path(\\\"scripts\\\", \\\"macos\\\", \\\"__pycache__\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier.egg-info\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"functional\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"hazmat\\\", \\\"__pycache__\\\"),\\n get_path(\\\"wheelhouse\\\"),\\n )\\n clean_globs = (\\n get_path(\\\".coverage\\\"),\\n get_path(\\\"*.mod\\\"),\\n get_path(\\\"*.pyc\\\"),\\n get_path(\\\"docs\\\", \\\"abi\\\", \\\"example\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.pyc\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.pyd\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.so\\\"),\\n get_path(\\\"src\\\", \\\"fortran\\\", \\\"*.o\\\"),\\n get_path(\\\"tests\\\", \\\"*.pyc\\\"),\\n get_path(\\\"tests\\\", \\\"functional\\\", \\\"*.pyc\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"*.pyc\\\"),\\n )\\n for dir_path in clean_dirs:\\n session.run(shutil.rmtree, dir_path, ignore_errors=True)\\n for glob_path in clean_globs:\\n for filename in glob.glob(glob_path):\\n session.run(os.remove, filename)\",\n \"def clean_files_for(file):\\n for f in [file, f\\\"{file}.json\\\", f\\\"{file}.lock\\\"]:\\n if os.path.isfile(f):\\n os.remove(f)\",\n \"def destroyer(): # ;-)\\n\\n def find_files_to_remove(pyfile):\\n for filename in (\\\"%sc\\\" % pyfile, \\\"%so\\\" % pyfile):\\n if exists(filename):\\n yield filename\\n\\n counter = 0\\n try:\\n while True:\\n pyfile = (yield)\\n for filename in find_files_to_remove(pyfile):\\n try:\\n log.debug('removing %s', filename)\\n remove(filename)\\n counter += 1\\n except (IOError, OSError), e:\\n log.error('cannot remove %s', filename)\\n log.debug(e)\\n except GeneratorExit:\\n log.info(\\\"removed files: %s\\\", counter)\",\n \"def remove_persisted_files():\\r\\n persistIncarnations = get_persist_incarnation_dirs()\\r\\n for p in persistIncarnations:\\r\\n clear_dir(p)\\r\\n os.remove(p)\\r\\n clear_dir(get_persist_src_backup_dir())\\r\\n clear_dir(get_persist_src_dir())\\r\\n clear_dir(get_persist_root_dir()) \\r\\n\\r\\n #make sure the persist kb data structures aren't keeping any info \\r\\n global PERSISTED_LOAD_IDS\\r\\n AGENT_KB_MAP.clear()\\r\\n KB_WORKING_SET.clear()\\r\\n copy = PERSISTED_LOAD_IDS[:]\\r\\n for x in copy:\\r\\n PERSISTED_LOAD_IDS.remove(x)\",\n \"def delete_tempfolder(path):\\n try:\\n rmtree(path)\\n except:\\n pass\",\n \"def mp_tmpdir():\\n # shutil.rmtree(TEMP_DIR, ignore_errors=True)\\n os.makedirs(TEMP_DIR)\\n yield TEMP_DIR\\n shutil.rmtree(TEMP_DIR, ignore_errors=True)\",\n \"def TemporaryDirectory():\\n name = tempfile.mkdtemp()\\n try:\\n yield name\\n finally:\\n shutil.rmtree(name)\",\n \"def cleanup(self):\\n\\n if self.do_nothing_bl is False:\\n if os.path.exists(self.local_fileP_str) is True:\\n if self.temp_dirP_obj is not None:\\n self.temp_dirP_obj.cleanup()\\n\\n elif os.path.isdir(self.local_fileP_str) is True:\\n log_obj.debug('Removing directory \\\"{:s}\\\"'.format(self.local_fileP_str))\\n shutil.rmtree(self.local_fileP_str)\\n\\n else:\\n log_obj.debug('Removing file \\\"{:s}\\\"'.format(self.local_fileP_str))\\n os.remove(self.local_fileP_str)\",\n \"def clean_chunk_files(dirpath):\\n workdir = os.getcwd()\\n os.chdir(dirpath)\\n for filename in glob.glob(\\\"[0-9]*_[0-9]*_[0-9]*.hdf5\\\"):\\n os.remove(filename)\\n os.chdir(workdir)\",\n \"def cleanup(self):\\n try:\\n self.wc = os.path.dirname(self.wc)\\n rmtree2(self.wc)\\n except IOError, err:\\n self.log.exception(\\\"Can't remove working copy %s: %s\\\" % (self.wc, err))\",\n \"def _clean_workdir(self):\\n\\t\\ttoremove = [self._get_config_filepath(), self._get_params_filepath(), self._get_conv_filepath(), self._get_psf_filepath()]\\n\\t\\tfor filepath in toremove:\\n\\t\\t\\tif os.path.exists(filepath):\\t\\n\\t\\t\\t\\tlogger.debug(\\\"Removing existing file %s...\\\" % (filepath))\\n\\t\\t\\t\\tos.remove(filepath)\",\n \"def teardown_upload(self, upload, filesystem_only=True):\\n # This is like \\\"rm -rf path\\\"\\n shutil.rmtree(upload.path, ignore_errors=True)\\n if filesystem_only:\\n return\\n for input in upload.input_set.all():\\n input.delete()\\n upload.delete()\",\n \"def cleanup(self):\\n files = self.nlst()\\n latest = self.latest_filename\\n for filename in files:\\n if filename != latest:\\n result = self.delete(filename)\\n logger.info(f\\\"Deleted old export from FTP: {result}\\\")\",\n \"def clean(args):\\n log = 'removing tmp dir %s ' % (args.tmpdir)\\n if args.tmpdir.endswith('STAR'):\\n cmd = ['rm -rf %s' % (args.tmpdir)]\\n run_subprocess(cmd,args,log)\\n log = \\\"remove tmp files from output dir\\\"\\n cmd = ['mv %s/crick_joinedLog.final.out %s/Crick_joinedLog.final.out' % (args.output_dir, args.output_dir)]\\n run_subprocess(cmd, args, log)\\n cmd = ['mv %s/watson_joinedLog.final.out %s/Watson_joinedLog.final.out' % (args.output_dir, args.output_dir)]\\n run_subprocess(cmd, args, log)\\n cmd = ['mv %s/crick_mergedLog.final.out %s/Crick_mergedLog.final.out' % (args.output_dir, args.output_dir)]\\n run_subprocess(cmd, args, log)\\n cmd = ['mv %s/watson_mergedLog.final.out %s/Watson_mergedLog.final.out' % (args.output_dir, args.output_dir)]\\n run_subprocess(cmd, args, log)\\n cmd = ['rm -rf %s/crick_*' % args.output_dir]\\n run_subprocess(cmd, args, log)\\n cmd = ['rm -rf %s/watson_*' % args.output_dir]\\n run_subprocess(cmd, args, log)\\n cmd = ['rm -rf %s/joined* header.sam' % args.output_dir]\\n run_subprocess(cmd, args, log)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7633441","0.74177027","0.72147197","0.7178067","0.6853436","0.6829257","0.67934287","0.6738931","0.67164594","0.66771114","0.66554505","0.66493994","0.6645599","0.66091245","0.65514976","0.6517317","0.6492228","0.64918196","0.6469804","0.6463625","0.6384307","0.6337415","0.63244194","0.630464","0.6293487","0.6289055","0.6287042","0.6251614","0.6228589","0.6225558","0.6214598","0.62001336","0.61762774","0.6172705","0.6162901","0.6132595","0.6106373","0.6087703","0.6058779","0.6049737","0.60460365","0.6030746","0.6006572","0.60036427","0.598809","0.597805","0.59555465","0.5937812","0.59225976","0.5917463","0.59167314","0.590231","0.5875408","0.5851276","0.5846214","0.58213806","0.58002186","0.57968944","0.5793422","0.578492","0.5784089","0.5783603","0.57478046","0.5740641","0.57396","0.57326055","0.5731126","0.5731013","0.5730098","0.5728579","0.5713782","0.568548","0.5678711","0.56634337","0.56577975","0.5654923","0.5641932","0.56248987","0.56214225","0.56198853","0.5611203","0.5601234","0.5590344","0.5584606","0.5580987","0.5569908","0.5568132","0.55641216","0.5557665","0.5545068","0.55425787","0.5535302","0.55308425","0.55295897","0.5525493","0.5525173","0.5517086","0.55144644","0.5514001","0.5510968"],"string":"[\n \"0.7633441\",\n \"0.74177027\",\n \"0.72147197\",\n \"0.7178067\",\n \"0.6853436\",\n \"0.6829257\",\n \"0.67934287\",\n \"0.6738931\",\n \"0.67164594\",\n \"0.66771114\",\n \"0.66554505\",\n \"0.66493994\",\n \"0.6645599\",\n \"0.66091245\",\n \"0.65514976\",\n \"0.6517317\",\n \"0.6492228\",\n \"0.64918196\",\n \"0.6469804\",\n \"0.6463625\",\n \"0.6384307\",\n \"0.6337415\",\n \"0.63244194\",\n \"0.630464\",\n \"0.6293487\",\n \"0.6289055\",\n \"0.6287042\",\n \"0.6251614\",\n \"0.6228589\",\n \"0.6225558\",\n \"0.6214598\",\n \"0.62001336\",\n \"0.61762774\",\n \"0.6172705\",\n \"0.6162901\",\n \"0.6132595\",\n \"0.6106373\",\n \"0.6087703\",\n \"0.6058779\",\n \"0.6049737\",\n \"0.60460365\",\n \"0.6030746\",\n \"0.6006572\",\n \"0.60036427\",\n \"0.598809\",\n \"0.597805\",\n \"0.59555465\",\n \"0.5937812\",\n \"0.59225976\",\n \"0.5917463\",\n \"0.59167314\",\n \"0.590231\",\n \"0.5875408\",\n \"0.5851276\",\n \"0.5846214\",\n \"0.58213806\",\n \"0.58002186\",\n \"0.57968944\",\n \"0.5793422\",\n \"0.578492\",\n \"0.5784089\",\n \"0.5783603\",\n \"0.57478046\",\n \"0.5740641\",\n \"0.57396\",\n \"0.57326055\",\n \"0.5731126\",\n \"0.5731013\",\n \"0.5730098\",\n \"0.5728579\",\n \"0.5713782\",\n \"0.568548\",\n \"0.5678711\",\n \"0.56634337\",\n \"0.56577975\",\n \"0.5654923\",\n \"0.5641932\",\n \"0.56248987\",\n \"0.56214225\",\n \"0.56198853\",\n \"0.5611203\",\n \"0.5601234\",\n \"0.5590344\",\n \"0.5584606\",\n \"0.5580987\",\n \"0.5569908\",\n \"0.5568132\",\n \"0.55641216\",\n \"0.5557665\",\n \"0.5545068\",\n \"0.55425787\",\n \"0.5535302\",\n \"0.55308425\",\n \"0.55295897\",\n \"0.5525493\",\n \"0.5525173\",\n \"0.5517086\",\n \"0.55144644\",\n \"0.5514001\",\n \"0.5510968\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7262391"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":2672,"cells":{"query":{"kind":"string","value":"Used if copy.deepcopy is called on the variable."},"document":{"kind":"string","value":"def __deepcopy__(self, memo):\n return self.copy()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def varcopy(self, vars):","def copy(self):\n pass","def copy(self):\n pass","def copy(self):\n pass","def copy(self):","def shallow_copy(self):\n # TODO: Rename this to __copy__()?\n raise NotImplementedError(\"shallow_copy is not implemented\")","def __copy__(self):\n return type(self)(self.value)","def v_full_copy(self, val):\n val = bool(val)\n self._full_copy = val","def _prepare_cache(self, value):\n\n return deepcopy(value)","def __copy__(self):\n return self.copy()","def _copy_(self):\n return copy.copy(self)","def __copy__(self):\n raise NotImplementedError","def copy(self):\n return type(self)(self._val, lsd=self._lsd)","def copy(self): # real signature unknown; restored from __doc__\n pass","def copy(self): # real signature unknown; restored from __doc__\n pass","def copy(self): # real signature unknown; restored from __doc__\n pass","def deepcopy(self):\n return self.copy()","def __getstate__(self):\n copy = self.__dict__.copy()\n copy['_workaround'] = None\n return copy","def copy(self):\n return super().copy()","def copy(self):\r\n raise Exception, \"not implemented\"","def test_context_immutable_deepcopy():\n context: Context = Context()\n assert context is deepcopy(context)","def __deepcopy__(self, memo):\r\n new_inst = super().__deepcopy__(memo)\r\n new_inst.road_width = self.road_width\r\n new_inst.road_length = self.road_length\r\n new_inst.surface = self.surface\r\n \r\n return new_inst","def test_deepcopy(self):\n t = Precision()\n t.transform([2])\n copy.deepcopy(t)","def __originate__(self):\n self.pos_to_num = deepcopy(self.o_pos_to_num)\n self.num_to_pos = deepcopy(self.o_num_to_pos)","def __deepcopy__(self, memodict=None):\n return self.copy()","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def copy(self):\n cpy = deepcopy(self)\n # usually we use copy to perform transformations on the board\n # so it's good to reset memoized values\n cpy._memoized_compact = None \n return cpy","def test_deepcopy(self):\n t = Reverse(Quantize())\n t.transform([2])\n copy.deepcopy(t)","def test_deepcopy_removes_cached_values(self):\n foreign_object = Membership._meta.get_field(\"person\")\n # Trigger storage of cached_property into ForeignObject's __dict__.\n foreign_object.path_infos\n foreign_object.reverse_path_infos\n # The ForeignObjectRel doesn't have reverse_path_infos.\n foreign_object.remote_field.path_infos\n self.assertIn(\"path_infos\", foreign_object.__dict__)\n self.assertIn(\"reverse_path_infos\", foreign_object.__dict__)\n self.assertIn(\"path_infos\", foreign_object.remote_field.__dict__)\n # Cached value is removed via __getstate__() on ForeignObjectRel\n # because no __deepcopy__() method exists, so __reduce_ex__() is used.\n remote_field_copy = copy.deepcopy(foreign_object.remote_field)\n self.assertNotIn(\"path_infos\", remote_field_copy.__dict__)\n # Field.__deepcopy__() internally uses __copy__() on both the\n # ForeignObject and ForeignObjectRel, so all cached values are removed.\n foreign_object_copy = copy.deepcopy(foreign_object)\n self.assertNotIn(\"path_infos\", foreign_object_copy.__dict__)\n self.assertNotIn(\"reverse_path_infos\", foreign_object_copy.__dict__)\n self.assertNotIn(\"path_infos\", foreign_object_copy.remote_field.__dict__)","def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)","def copy(self, deep=False):\n return _(copy.deepcopy(self._) if deep else copy.copy(self._))","def _datacopied(arr, original):\n if arr is original:\n return False\n if not isinstance(original, numpy.ndarray) and hasattr(original, '__array__'):\n return False\n return arr.base is None","def test_deepcopy(self):\n t = Enumerate([2, \"asfa\", \"ipsi\"])\n # Copy won't fail if vectorized function is not called at least once.\n t.transform([2])\n copy.deepcopy(t)","def copy(self):\n\t\treturn pythoncopy.deepcopy(self)","def clone(self):\n return None","def copy(self):\r\n return self.replace()","def _get_reuse_value(self):\n return self.__reuse_value","def copy(self):\r\n return copy.copy(self)","def __deepcopy__(self, memo):\n copy = self.__class__()\n copy.wvalues = self.wvalues\n return copy","def __deepcopy__(self, memo):\n from copy import deepcopy\n return self.__class__(deepcopy(self.items(), memo), self.strict)","def __deepcopy__(self, memo):\n return Quantity(copy.deepcopy(self._value, memo), self.unit)","def deepcopy(self):\n return copy.deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def _datacopied(arr, original):\n if arr is original:\n return False\n if not isinstance(original, np.ndarray) and hasattr(original, '__array__'):\n return False\n return arr.base is None","def v_full_copy(self):\n return self._full_copy","def deepcopy(self):\n return copymod.deepcopy(self)","def copy(self):\n from copy import deepcopy\n return deepcopy(self)","def copy(self):\n return self.__class__(self.value, self.is_cloud)","def debug_copies(self):\n self.b_debug = self.B.clone().detach()\n self.Bc_debug = self.Bc.clone().detach()\n self.W_debug = self.W.clone().detach()\n self.Wc_debug = self.Wc.clone().detach()","def __copy__(self):\n return type(self)(self.number)","def copy(self):\n return self.mutate().simple_copy()","def test_copy(self):\n data = [[0, 1], [1, 0]]\n b1 = Board(data)\n b2 = b1.copy()\n # test if proper copy\n self.assertListEqual(b1.data, b2.data)\n # teset if not just a shallow copy\n b1.data[0][0] = 1\n self.assertNotEqual(b1.data[0][0], b2.data[0][0])","def __getstate__(self):\n result = super(Parameter, self).__getstate__()\n\n # If we don't need a full copy of the Parameter (because a single process needs\n # only access to a single point in the parameter space) we can delete the rest\n if not self._full_copy:\n result[\"_explored_range\"] = []\n\n return result","def test_deepcopy(self):\n t = Identity()\n t.transform([2])\n copy.deepcopy(t)","def copy(self) -> \"Param\":\n copied = super().copy()\n copied._stack = OrderedDiot(\n [(key, param.copy()) for key, param in self._stack.items()]\n )\n return copied","def cdup (self):\r\n pass","def __deepcopy__(self, memo):\n memo[id(self)] = self\n return self","def __deepcopy__(self, memo):\n memo[id(self)] = self\n return self","def _save_state_as_orig(self):\n self._orig = None\n self._orig = deepcopy(self)","def deep_copy(value: TValue) -> TValue:\n def pattern_dispatcher(v, memo=None):\n return v # we don't need to copy a regex pattern object, it's read-only\n\n old_dispatcher = copy._deepcopy_dispatch.get(PatternType, None)\n copy._deepcopy_dispatch[PatternType] = pattern_dispatcher\n try:\n return copy.deepcopy(value)\n finally:\n if old_dispatcher is not None: # pragma: no cover\n copy._deepcopy_dispatch[PatternType] = old_dispatcher\n else:\n del copy._deepcopy_dispatch[PatternType]","def test_deepcopy_RiakDisabledForTest(self):\n rdft = RiakDisabledForTest()\n self.assertEqual(rdft, deepcopy(rdft))","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def test_deepcopy(self):\n t = Quantize()\n t.transform([2])\n copy.deepcopy(t)","def clone(self):","def copy(self):\n return copy(self)","def copy(self):\n return copy(self)","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k in ('_iter', '_result_cache'):\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def copy(self):\n \n return deepcopy(self)","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)","def test_copy_removes_direct_cached_values(self):\n foreign_object = Membership._meta.get_field(\"person\")\n # Trigger storage of cached_property into ForeignObject's __dict__.\n foreign_object.path_infos\n foreign_object.reverse_path_infos\n # The ForeignObjectRel doesn't have reverse_path_infos.\n foreign_object.remote_field.path_infos\n self.assertIn(\"path_infos\", foreign_object.__dict__)\n self.assertIn(\"reverse_path_infos\", foreign_object.__dict__)\n self.assertIn(\"path_infos\", foreign_object.remote_field.__dict__)\n # Cached value is removed via __getstate__() on ForeignObjectRel\n # because no __copy__() method exists, so __reduce_ex__() is used.\n remote_field_copy = copy.copy(foreign_object.remote_field)\n self.assertNotIn(\"path_infos\", remote_field_copy.__dict__)\n # Cached values are removed via __copy__() on ForeignObject for\n # consistency of behavior.\n foreign_object_copy = copy.copy(foreign_object)\n self.assertNotIn(\"path_infos\", foreign_object_copy.__dict__)\n self.assertNotIn(\"reverse_path_infos\", foreign_object_copy.__dict__)\n # ForeignObjectRel's remains because it's part of a shallow copy.\n self.assertIn(\"path_infos\", foreign_object_copy.remote_field.__dict__)","def copy(self):\n copied = super().copy()\n copied.anonymize()\n return copied","def copy (self):\n import copy\n return copy.copy(self)","def copy(self):\r\n return copy.deepcopy(self)","def __deepcopy__(self, memo):\n id_self = id(self)\n _copy = memo.get(id_self)\n if _copy is None:\n _copy = type(self)(\n deepcopy(self.value, memo))\n memo[id_self] = _copy\n return _copy","def dup():\n stack = currentframe().f_back.f_locals.setdefault(SN, [])\n value = stack[-1]\n stack.append(value)\n return value","def is_assign(self):\n return self.var.initializer is not None","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k == '_result_cache':\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def copy(self):\n # YOUR CODE HERE\n raise NotImplementedError()"],"string":"[\n \"def varcopy(self, vars):\",\n \"def copy(self):\\n pass\",\n \"def copy(self):\\n pass\",\n \"def copy(self):\\n pass\",\n \"def copy(self):\",\n \"def shallow_copy(self):\\n # TODO: Rename this to __copy__()?\\n raise NotImplementedError(\\\"shallow_copy is not implemented\\\")\",\n \"def __copy__(self):\\n return type(self)(self.value)\",\n \"def v_full_copy(self, val):\\n val = bool(val)\\n self._full_copy = val\",\n \"def _prepare_cache(self, value):\\n\\n return deepcopy(value)\",\n \"def __copy__(self):\\n return self.copy()\",\n \"def _copy_(self):\\n return copy.copy(self)\",\n \"def __copy__(self):\\n raise NotImplementedError\",\n \"def copy(self):\\n return type(self)(self._val, lsd=self._lsd)\",\n \"def copy(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def copy(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def copy(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def deepcopy(self):\\n return self.copy()\",\n \"def __getstate__(self):\\n copy = self.__dict__.copy()\\n copy['_workaround'] = None\\n return copy\",\n \"def copy(self):\\n return super().copy()\",\n \"def copy(self):\\r\\n raise Exception, \\\"not implemented\\\"\",\n \"def test_context_immutable_deepcopy():\\n context: Context = Context()\\n assert context is deepcopy(context)\",\n \"def __deepcopy__(self, memo):\\r\\n new_inst = super().__deepcopy__(memo)\\r\\n new_inst.road_width = self.road_width\\r\\n new_inst.road_length = self.road_length\\r\\n new_inst.surface = self.surface\\r\\n \\r\\n return new_inst\",\n \"def test_deepcopy(self):\\n t = Precision()\\n t.transform([2])\\n copy.deepcopy(t)\",\n \"def __originate__(self):\\n self.pos_to_num = deepcopy(self.o_pos_to_num)\\n self.num_to_pos = deepcopy(self.o_num_to_pos)\",\n \"def __deepcopy__(self, memodict=None):\\n return self.copy()\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def copy(self):\\n cpy = deepcopy(self)\\n # usually we use copy to perform transformations on the board\\n # so it's good to reset memoized values\\n cpy._memoized_compact = None \\n return cpy\",\n \"def test_deepcopy(self):\\n t = Reverse(Quantize())\\n t.transform([2])\\n copy.deepcopy(t)\",\n \"def test_deepcopy_removes_cached_values(self):\\n foreign_object = Membership._meta.get_field(\\\"person\\\")\\n # Trigger storage of cached_property into ForeignObject's __dict__.\\n foreign_object.path_infos\\n foreign_object.reverse_path_infos\\n # The ForeignObjectRel doesn't have reverse_path_infos.\\n foreign_object.remote_field.path_infos\\n self.assertIn(\\\"path_infos\\\", foreign_object.__dict__)\\n self.assertIn(\\\"reverse_path_infos\\\", foreign_object.__dict__)\\n self.assertIn(\\\"path_infos\\\", foreign_object.remote_field.__dict__)\\n # Cached value is removed via __getstate__() on ForeignObjectRel\\n # because no __deepcopy__() method exists, so __reduce_ex__() is used.\\n remote_field_copy = copy.deepcopy(foreign_object.remote_field)\\n self.assertNotIn(\\\"path_infos\\\", remote_field_copy.__dict__)\\n # Field.__deepcopy__() internally uses __copy__() on both the\\n # ForeignObject and ForeignObjectRel, so all cached values are removed.\\n foreign_object_copy = copy.deepcopy(foreign_object)\\n self.assertNotIn(\\\"path_infos\\\", foreign_object_copy.__dict__)\\n self.assertNotIn(\\\"reverse_path_infos\\\", foreign_object_copy.__dict__)\\n self.assertNotIn(\\\"path_infos\\\", foreign_object_copy.remote_field.__dict__)\",\n \"def copy(self):\\n import copy as pcopy\\n return pcopy.deepcopy(self)\",\n \"def copy(self, deep=False):\\n return _(copy.deepcopy(self._) if deep else copy.copy(self._))\",\n \"def _datacopied(arr, original):\\n if arr is original:\\n return False\\n if not isinstance(original, numpy.ndarray) and hasattr(original, '__array__'):\\n return False\\n return arr.base is None\",\n \"def test_deepcopy(self):\\n t = Enumerate([2, \\\"asfa\\\", \\\"ipsi\\\"])\\n # Copy won't fail if vectorized function is not called at least once.\\n t.transform([2])\\n copy.deepcopy(t)\",\n \"def copy(self):\\n\\t\\treturn pythoncopy.deepcopy(self)\",\n \"def clone(self):\\n return None\",\n \"def copy(self):\\r\\n return self.replace()\",\n \"def _get_reuse_value(self):\\n return self.__reuse_value\",\n \"def copy(self):\\r\\n return copy.copy(self)\",\n \"def __deepcopy__(self, memo):\\n copy = self.__class__()\\n copy.wvalues = self.wvalues\\n return copy\",\n \"def __deepcopy__(self, memo):\\n from copy import deepcopy\\n return self.__class__(deepcopy(self.items(), memo), self.strict)\",\n \"def __deepcopy__(self, memo):\\n return Quantity(copy.deepcopy(self._value, memo), self.unit)\",\n \"def deepcopy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def _datacopied(arr, original):\\n if arr is original:\\n return False\\n if not isinstance(original, np.ndarray) and hasattr(original, '__array__'):\\n return False\\n return arr.base is None\",\n \"def v_full_copy(self):\\n return self._full_copy\",\n \"def deepcopy(self):\\n return copymod.deepcopy(self)\",\n \"def copy(self):\\n from copy import deepcopy\\n return deepcopy(self)\",\n \"def copy(self):\\n return self.__class__(self.value, self.is_cloud)\",\n \"def debug_copies(self):\\n self.b_debug = self.B.clone().detach()\\n self.Bc_debug = self.Bc.clone().detach()\\n self.W_debug = self.W.clone().detach()\\n self.Wc_debug = self.Wc.clone().detach()\",\n \"def __copy__(self):\\n return type(self)(self.number)\",\n \"def copy(self):\\n return self.mutate().simple_copy()\",\n \"def test_copy(self):\\n data = [[0, 1], [1, 0]]\\n b1 = Board(data)\\n b2 = b1.copy()\\n # test if proper copy\\n self.assertListEqual(b1.data, b2.data)\\n # teset if not just a shallow copy\\n b1.data[0][0] = 1\\n self.assertNotEqual(b1.data[0][0], b2.data[0][0])\",\n \"def __getstate__(self):\\n result = super(Parameter, self).__getstate__()\\n\\n # If we don't need a full copy of the Parameter (because a single process needs\\n # only access to a single point in the parameter space) we can delete the rest\\n if not self._full_copy:\\n result[\\\"_explored_range\\\"] = []\\n\\n return result\",\n \"def test_deepcopy(self):\\n t = Identity()\\n t.transform([2])\\n copy.deepcopy(t)\",\n \"def copy(self) -> \\\"Param\\\":\\n copied = super().copy()\\n copied._stack = OrderedDiot(\\n [(key, param.copy()) for key, param in self._stack.items()]\\n )\\n return copied\",\n \"def cdup (self):\\r\\n pass\",\n \"def __deepcopy__(self, memo):\\n memo[id(self)] = self\\n return self\",\n \"def __deepcopy__(self, memo):\\n memo[id(self)] = self\\n return self\",\n \"def _save_state_as_orig(self):\\n self._orig = None\\n self._orig = deepcopy(self)\",\n \"def deep_copy(value: TValue) -> TValue:\\n def pattern_dispatcher(v, memo=None):\\n return v # we don't need to copy a regex pattern object, it's read-only\\n\\n old_dispatcher = copy._deepcopy_dispatch.get(PatternType, None)\\n copy._deepcopy_dispatch[PatternType] = pattern_dispatcher\\n try:\\n return copy.deepcopy(value)\\n finally:\\n if old_dispatcher is not None: # pragma: no cover\\n copy._deepcopy_dispatch[PatternType] = old_dispatcher\\n else:\\n del copy._deepcopy_dispatch[PatternType]\",\n \"def test_deepcopy_RiakDisabledForTest(self):\\n rdft = RiakDisabledForTest()\\n self.assertEqual(rdft, deepcopy(rdft))\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def test_deepcopy(self):\\n t = Quantize()\\n t.transform([2])\\n copy.deepcopy(t)\",\n \"def clone(self):\",\n \"def copy(self):\\n return copy(self)\",\n \"def copy(self):\\n return copy(self)\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k in ('_iter', '_result_cache'):\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def copy(self):\\n \\n return deepcopy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def test_copy_removes_direct_cached_values(self):\\n foreign_object = Membership._meta.get_field(\\\"person\\\")\\n # Trigger storage of cached_property into ForeignObject's __dict__.\\n foreign_object.path_infos\\n foreign_object.reverse_path_infos\\n # The ForeignObjectRel doesn't have reverse_path_infos.\\n foreign_object.remote_field.path_infos\\n self.assertIn(\\\"path_infos\\\", foreign_object.__dict__)\\n self.assertIn(\\\"reverse_path_infos\\\", foreign_object.__dict__)\\n self.assertIn(\\\"path_infos\\\", foreign_object.remote_field.__dict__)\\n # Cached value is removed via __getstate__() on ForeignObjectRel\\n # because no __copy__() method exists, so __reduce_ex__() is used.\\n remote_field_copy = copy.copy(foreign_object.remote_field)\\n self.assertNotIn(\\\"path_infos\\\", remote_field_copy.__dict__)\\n # Cached values are removed via __copy__() on ForeignObject for\\n # consistency of behavior.\\n foreign_object_copy = copy.copy(foreign_object)\\n self.assertNotIn(\\\"path_infos\\\", foreign_object_copy.__dict__)\\n self.assertNotIn(\\\"reverse_path_infos\\\", foreign_object_copy.__dict__)\\n # ForeignObjectRel's remains because it's part of a shallow copy.\\n self.assertIn(\\\"path_infos\\\", foreign_object_copy.remote_field.__dict__)\",\n \"def copy(self):\\n copied = super().copy()\\n copied.anonymize()\\n return copied\",\n \"def copy (self):\\n import copy\\n return copy.copy(self)\",\n \"def copy(self):\\r\\n return copy.deepcopy(self)\",\n \"def __deepcopy__(self, memo):\\n id_self = id(self)\\n _copy = memo.get(id_self)\\n if _copy is None:\\n _copy = type(self)(\\n deepcopy(self.value, memo))\\n memo[id_self] = _copy\\n return _copy\",\n \"def dup():\\n stack = currentframe().f_back.f_locals.setdefault(SN, [])\\n value = stack[-1]\\n stack.append(value)\\n return value\",\n \"def is_assign(self):\\n return self.var.initializer is not None\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k == '_result_cache':\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def copy(self):\\n # YOUR CODE HERE\\n raise NotImplementedError()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6630489","0.63350475","0.63350475","0.63350475","0.6280567","0.6222454","0.6153182","0.6143717","0.61114347","0.6110205","0.6047252","0.5978211","0.5953629","0.59274524","0.59274524","0.59274524","0.58838916","0.5878963","0.5824744","0.5819304","0.5793884","0.5777081","0.5758991","0.5758069","0.572324","0.5719103","0.5719103","0.5719103","0.5719103","0.56930023","0.56777954","0.5674818","0.5668968","0.5664992","0.56465626","0.56462395","0.5645335","0.56293344","0.5628501","0.5623695","0.56125873","0.56009096","0.5600017","0.55993164","0.55986196","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.55928713","0.5592555","0.5588263","0.55844194","0.5568928","0.55683833","0.5560667","0.55541945","0.555172","0.55359006","0.55347115","0.55344105","0.5534381","0.55002373","0.5499811","0.5499811","0.54897","0.54830164","0.5482144","0.547824","0.547824","0.547824","0.547824","0.5478107","0.5470999","0.54693365","0.54693365","0.5465802","0.5452104","0.544655","0.544655","0.544655","0.54386574","0.54320234","0.54219204","0.5413256","0.5394411","0.5393753","0.539016","0.5378328","0.5375993"],"string":"[\n \"0.6630489\",\n \"0.63350475\",\n \"0.63350475\",\n \"0.63350475\",\n \"0.6280567\",\n \"0.6222454\",\n \"0.6153182\",\n \"0.6143717\",\n \"0.61114347\",\n \"0.6110205\",\n \"0.6047252\",\n \"0.5978211\",\n \"0.5953629\",\n \"0.59274524\",\n \"0.59274524\",\n \"0.59274524\",\n \"0.58838916\",\n \"0.5878963\",\n \"0.5824744\",\n \"0.5819304\",\n \"0.5793884\",\n \"0.5777081\",\n \"0.5758991\",\n \"0.5758069\",\n \"0.572324\",\n \"0.5719103\",\n \"0.5719103\",\n \"0.5719103\",\n \"0.5719103\",\n \"0.56930023\",\n \"0.56777954\",\n \"0.5674818\",\n \"0.5668968\",\n \"0.5664992\",\n \"0.56465626\",\n \"0.56462395\",\n \"0.5645335\",\n \"0.56293344\",\n \"0.5628501\",\n \"0.5623695\",\n \"0.56125873\",\n \"0.56009096\",\n \"0.5600017\",\n \"0.55993164\",\n \"0.55986196\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.55928713\",\n \"0.5592555\",\n \"0.5588263\",\n \"0.55844194\",\n \"0.5568928\",\n \"0.55683833\",\n \"0.5560667\",\n \"0.55541945\",\n \"0.555172\",\n \"0.55359006\",\n \"0.55347115\",\n \"0.55344105\",\n \"0.5534381\",\n \"0.55002373\",\n \"0.5499811\",\n \"0.5499811\",\n \"0.54897\",\n \"0.54830164\",\n \"0.5482144\",\n \"0.547824\",\n \"0.547824\",\n \"0.547824\",\n \"0.547824\",\n \"0.5478107\",\n \"0.5470999\",\n \"0.54693365\",\n \"0.54693365\",\n \"0.5465802\",\n \"0.5452104\",\n \"0.544655\",\n \"0.544655\",\n \"0.544655\",\n \"0.54386574\",\n \"0.54320234\",\n \"0.54219204\",\n \"0.5413256\",\n \"0.5394411\",\n \"0.5393753\",\n \"0.539016\",\n \"0.5378328\",\n \"0.5375993\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60275984"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":2673,"cells":{"query":{"kind":"string","value":"Called when the partition's reference count reaches zero. If the partition contains a temporary file which is not referenced by any other partition then the temporary file is removed from disk. If the partition contains a nontemporary file which is not referenced by any other partition then the file is closed."},"document":{"kind":"string","value":"def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n # than 3 references to it then it is not unique.\n if getrefcount is not None:\n self._decrement_file_counter()\n if subarray is None or getrefcount(subarray) > 3:\n return\n else:\n # getrefcount has itself been deleted or is in the process\n # of being torn down\n return\n\n _partition_file = getattr(subarray, \"_partition_file\", None)\n if _partition_file is not None:\n # This partition contains a temporary file which is not\n # referenced by any other partition on this process, so if\n # there are no lock files present remove the file from\n # disk.\n _remove_temporary_files(_partition_file)\n\n else:\n try:\n if FileArray is not None and isinstance(subarray, FileArray):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if self.file_counter.get(filename, 999) <= 0:\n # This partition contains a non-temporary file\n # which is not referenced by any other\n # partitions, so close the file.\n subarray.close()\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass\n # --- End: if"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_file_deleted(self):\n try:\n with get_temp_file() as (fd, name):\n os.unlink(name)\n except Exception as err:\n self.fail('Failed with exception \"{}\"'.format(err))","def _Close(self):\n self._fsfat_volume = None\n self._file_object = None","def __del__(self):\n if (\n self._fpointer is not None and not self._fpointer.closed\n ): # pragma: no mutate\n self._fpointer.close()","def clean(self):\n if os.path.exists(self.initial):\n if os.path.exists(self.path) and os.stat(self.path).st_size == os.stat(\n self.initial).st_size:\n os.remove(self.initial)\n else:\n # if it doesn't match, something probably crashed; rename the temporary file and\n # it'll get uploaded at some point\n self.auto_filename()\n self.rename()\n self.connect()\n os.remove(self.initial)\n if os.path.exists(self.path):\n os.remove(self.path)\n self.filename_set = False","def _cleanup(self, fnum):\n while os.path.exists('%s.%s' % (self.name, fnum)):\n try:\n fname = '%s.%s' % (self.name, fnum)\n os.unlink(fname)\n # self.log.debug(\"Cleaned up file: %s\", fname)\n except:\n pass\n fnum -= 1","def test_file_unused(self):\n try:\n with get_temp_file() as (fd, name):\n pass\n except Exception as err:\n self.fail('Failed with exception \"{}\"'.format(err))\n else:\n file_exists = os.access(name, os.F_OK)\n self.assertFalse(file_exists)","def __purge_old_files(self):\n\n chkpts = self.checkpointer.sorted_checkpoints()\n p_chkpts = []\n e_chkpts = []\n for c in chkpts:\n if c.startswith(self.checkpointer.prefix + CheckpointingCallback.PERIODIC_PREFIX):\n p_chkpts.append(c)\n\n if c.startswith(self.checkpointer.prefix + CheckpointingCallback.EPOCH_PREFIX):\n e_chkpts.append(c)\n\n # Delete periodic checkpoints\n if self.max_files is not None and len(p_chkpts) > self.max_files:\n for c in p_chkpts[self.max_files:]:\n log.debug(\"CheckpointingCallback deleting {}\".format(c))\n self.checkpointer.delete(c)\n\n # Delete older epochs\n if self.max_epochs is not None and len(e_chkpts) > self.max_epochs:\n for c in e_chkpts[self.max_epochs:]:\n log.debug(\"CheckpointingCallback deleting (epoch) {}\".format(c))\n self.checkpointer.delete(c)","def __del__(self):\n if self.file is None:\n return\n try:\n self.file.close()\n del self.file\n self.file = None\n except:\n getLogger(__name__).warning('Error on file close', exc_info=True)","def __del__(self):\n self.close_files()","def __del__(self):\r\n self.filename.close()","def flow_file_chunk_delete(sender, instance, **kwargs):\n instance.file.delete(False)","def _recover_disk_space(self):\n while self.used_disk_space > self.cache_size:\n space_to_recover = self.used_disk_space - self.cache_size\n logger.info('Recovering disk space %s', space_to_recover)\n lru_file = self.touch_list.pop(0)\n file_path = self._path_to_file(lru_file)\n logger.info('Deleting %s', file_path)\n os.remove(file_path)\n del self.index[lru_file]","def __del__(self):\n\t\tif self.temp_dir:\n\t\t\tself.temp_dir.cleanup()","def tearDown(self):\n try:\n os.remove(self.junk_file)\n except OSError as doh:\n if doh.errno == 2:\n # No such File, ignore\n pass\n else:\n raise","def __del__(self) -> None:\n try:\n shutil.rmtree(self.temp_path)\n except FileNotFoundError:\n pass","def Close(self):\n super(CPIOArchiveFile, self).Close()\n self._file_entries = None","def dispose(self):\n rmtree(self._temp_path)","def unlink(self,):\n self._wait()\n self.fd.close()\n self.fd = None\n os.unlink(self.fname)","def __del__(self):\n self.file.close()","def __del__(self):\r\n self.chunk = None","def endWrite(self, withErrors):\r\n #if withErrors or self._file_obj.get_seek()>0:\r\n self.provider.cache_fs.remove(self.path)","def release(self, path, fh, *args, **pargs):\n with(self.rwlock):\n # If we're closing a FLACCue file...\n if(path in self._open_subtracks):\n # Delete the file handle from the stored list.\n del self._open_subtracks[path]['Positions'][fh]\n # Close the OS reference to the file.\n return os.close(fh)","def WriteAbort(self):\n if self._file_object:\n self._file_object.close()\n self._file_object = None\n\n if os.path.exists(self.name):\n os.remove(self.name)","def __del__(self):\n for handle in self._filehandles:\n handle.close()","def __del__(self):\n self.file_out.close()","def __del__(self):\n self.f.close()","def unique_files(self):\n self._tempfiles[-1].ctr = -1","def test_ClearOldFile(self):\n q = Queue(self.path, chunksize=10)\n for i in range(15):\n q.put('var1')\n\n for i in range(11):\n q.get()\n\n q = Queue(self.path, chunksize=10)\n self.assertEqual(q.qsize(), 15)\n\n for i in range(11):\n q.get()\n q.task_done()\n self.assertEqual(q.qsize(), 4)","def release(self):\n #关闭文件，删除文件\n if self.fd is not None:\n os.close(self.fd)\n os.unlink(self.lockfile)\n self.is_locked = False\n self.fd = None","def releaseFile(self, fid):\n if fid in self.files:\n del self.files[fid]","def __del__(self):\n for f in self._files:\n f.close()","def test_removed(self):\n path = None\n with TemporaryDirectory() as tmp:\n path = tmp\n self.assertTrue(os.path.isdir(tmp))\n tmpfile = os.path.join(tmp, \"a_temp_file\")\n open(tmpfile, \"w\").write(\"data\")\n self.assertTrue(os.path.isfile(tmpfile))\n self.assertFalse(os.path.isdir(path))\n self.assertFalse(os.path.exists(path))","def test_disconnect_file_watchers_removes_refs(self):\n session = _create_test_session()\n\n # Various listeners should have references to session file/pages/secrets changed\n # handlers.\n self.assertGreater(len(gc.get_referrers(session)), 0)\n\n session.disconnect_file_watchers()\n # Ensure that we don't count refs to session from an object that would have been\n # garbage collected along with it.\n gc.collect(2)\n\n self.assertEqual(len(gc.get_referrers(session)), 0)","def Close(self):\n if not self._is_open:\n raise IOError('Storage file already closed.')\n\n if not self._read_only:\n self.Flush()\n\n if self._serializers_profiler:\n self._serializers_profiler.Write()\n\n # Make sure to flush the caches so that zipfile can be closed and freed.\n # Otherwise on Windows the ZIP file remains locked and cannot be renamed.\n\n self._offset_tables = {}\n self._offset_tables_lfu = []\n\n self._open_streams = {}\n self._streams_lfu = []\n\n self._event_timestamp_tables = {}\n self._event_timestamp_tables_lfu = []\n\n self._zipfile.close()\n self._zipfile = None\n self._is_open = False\n\n file_renamed = False\n if self._path != self._zipfile_path and os.path.exists(self._zipfile_path):\n # On Windows the file can sometimes be still in use and we have to wait.\n for attempt in range(1, self._MAXIMUM_NUMBER_OF_LOCKED_FILE_ATTEMPTS):\n try:\n os.rename(self._zipfile_path, self._path)\n file_renamed = True\n break\n\n except OSError:\n if attempt == self._MAXIMUM_NUMBER_OF_LOCKED_FILE_ATTEMPTS:\n raise\n time.sleep(self._LOCKED_FILE_SLEEP_TIME)\n\n self._path = None\n self._zipfile_path = None\n\n if self._path != self._zipfile_path and not file_renamed:\n raise IOError('Unable to close storage file.')","def test_file_closed(self):\n try:\n with get_temp_file() as (fd, name):\n os.close(fd)\n except Exception as err:\n self.fail('Failed with exception \"{}\"'.format(err))\n else:\n file_exists = os.access(name, os.F_OK)\n self.assertFalse(file_exists)","def teardown(self):\n super(TestCisPickleInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def __del__(self):\n for component_name, file in self._file_list.items():\n file.close()","def finalize(self):\n if self._file:\n toLog(\"Closing file `{0}`\".format(self._fname), True)\n self._file.close()\n self._file = None","def purge(self):\n if self.is_open and self.direction == 'recv':\n try:\n self.fd.seek(0, os.SEEK_END)\n except (AttributeError, ValueError): # pragma: debug\n if self.is_open:\n raise","def cleanup_files(self):\n\n self.backup_files()\n self.delete_files()","def clear_tempfiles(self, remove=True):\n while self._tempfiles:\n self.pop(remove)\n self.push()","def attempt_file_reset(f):\r\n if hasattr(f, 'seek'):\r\n f.seek(0)","def teardown(self):\n super(TestCisPickleOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def _try_cleanup_uploads(self):\n try:\n with GlobalLock(\"BLOB_CLEANUP\", lock_ttl=LOCK_TTL):\n self._cleanup_uploads()\n except LockNotAcquiredException:\n logger.debug(\"Could not acquire global lock for blob upload cleanup worker\")\n return","def purge(self):\n self.remaining = 0","def delete_io( hash ):\n res = 0\n record_used('cache', hash)\n for packet in get_filenames_for_hash(CACHE_DIRECTORY, hash):\n try:\n os.remove(packet)\n res = res + 1\n except:\n if not os.environ.get('CALIENDO_TEST_SUITE', None):\n logger.warning( \"Failed to remove file: \" + packet )\n return res","def close(self):\n if self.current_file_number is not None:\n self.fh_raw.close()\n self.current_file_number = None","def erase_files(self):\n self.ofile_handle()\n self.efile_handle()\n\n os.remove(self.ofile_name())\n os.remove(self.efile_name())\n return None","def classCleanup(cls):\n cls.RemoveTempFile(\"child_send1.txt\")\n cls.RemoveTempFile(\"child_read1.txt\")\n cls.RemoveTempFile(\"child_send2.txt\")\n cls.RemoveTempFile(\"child_read2.txt\")","def cleanup(self):\n files = self.nlst()\n latest = self.latest_filename\n for filename in files:\n if filename != latest:\n result = self.delete(filename)\n logger.info(f\"Deleted old export from FTP: {result}\")","def task_clean_tmp_files():\n client = google.cloud.storage.Client()\n blobs = client.list_blobs(settings.PODCAST_STORAGE_BUCKET,\n prefix=settings.PODCAST_TMP_STORAGE_DIRECTORY)\n for blob in blobs:\n if blob.time_created.replace(tzinfo=None) + datetime.timedelta(1) <= datetime.datetime.now():\n blob.delete()\n\n return OK_RESPONSE","def __del__(self):\n if hasattr(self, \"_uniquefile_created\"):\n self._unlock()\n else:\n # When instance attributes don't exist, we probably had an error\n # in the construction process (like an invalid argument to\n # __init__()). In that case, there's no chance we have a unique\n # file or a lock to clean up.\n pass","def file_pointer(self):\n\n try:\n self.__file.seek(self.__file.tell() - 1)\n except Exception as e:\n raise e","def file_close(self):\n if self.on_disk:\n self._subarray.close()","def cleanup(self):\n\n if self.do_nothing_bl is False:\n if os.path.exists(self.local_fileP_str) is True:\n if self.temp_dirP_obj is not None:\n self.temp_dirP_obj.cleanup()\n\n elif os.path.isdir(self.local_fileP_str) is True:\n log_obj.debug('Removing directory \"{:s}\"'.format(self.local_fileP_str))\n shutil.rmtree(self.local_fileP_str)\n\n else:\n log_obj.debug('Removing file \"{:s}\"'.format(self.local_fileP_str))\n os.remove(self.local_fileP_str)","def _decrement_file_counter(self):\n self._add_to_file_counter(-1)","def __del__(self):\n for filename in self.files:\n unlink(filename)","def finalize(self):\n # 027 Not needed in the simple FilesAdaptor. \n pass","def __del__(self):\n if not self.sigfile.closed:\n self.sigfile.close()","def delete_partition(self, partition):\n raise NotImplementedError('delete_file')","def teardown(self):\n self.file_comm.remove_file()\n super(TestCisAsciiFileOutput, self).teardown()","def release(self):\n if self._ctx is None:\n return\n self.atomicfile.delete()\n try:\n self._ctx.__exit__(None, None, None)\n finally:\n self._ctx = None","def cleanup(self):\r\n for f in [i for d in self.data.values() for i in d[\"filenames\"]]:\r\n try:\r\n os.unlink(f)\r\n except Exception: pass\r\n self.Destroy()","def wipe(self):","def wipe(self):","def _wipe(self):\n log_method_call(self, self.name, status=self.status)\n\n start = self.partedPartition.geometry.start\n part_len = self.partedPartition.geometry.end - start\n bs = self.partedPartition.geometry.device.sectorSize\n device = self.partedPartition.geometry.device.path\n\n # Erase 1MiB or to end of partition\n count = int(Size(\"1 MiB\") / bs)\n count = min(count, part_len)\n\n cmd = [\"dd\", \"if=/dev/zero\", \"of=%s\" % device, \"bs=%s\" % bs,\n \"seek=%s\" % start, \"count=%s\" % count]\n try:\n util.run_program(cmd)\n except OSError as e:\n log.error(str(e))\n finally:\n # If a udev device is created with the watch option, then\n # a change uevent is synthesized and we need to wait for\n # things to settle.\n udev.settle()","def file(self):\n del self._file","def cleanup(self):\n if self.log_fo:\n self.log_fo.close()","def release(self):\n self.filelock.set()\n self.locked = False\n self.exclusive = False","def release(self):\n self.filelock.set()\n self.locked = False\n self.exclusive = False","def current_remove(self):\n storage.close()","def test_deleting_local_file_using_file_io() -> None:\n with tempfile.TemporaryDirectory() as tmpdirname:\n # Write to the temporary file\n output_file_location = os.path.join(tmpdirname, \"foo.txt\")\n with open(output_file_location, \"wb\") as f:\n f.write(b\"foo\")\n\n # Instantiate the file-io\n file_io = PyArrowFileIO()\n\n # Confirm that the file initially exists\n assert os.path.exists(output_file_location)\n\n # Delete the file using the file-io implementations delete method\n file_io.delete(output_file_location)\n\n # Confirm that the file no longer exists\n assert not os.path.exists(output_file_location)","def clear_data():\n for i in range(_MAX_NUM_TESTS):\n rand, ref = filename(i)\n if os.path.exists(rand):\n os.remove(rand)\n if os.path.exists(ref):\n os.remove(ref)","def remove(self):\n for ref_node in self.node.find_references():\n ref_node.destroy()\n File.remove(self)","def _cleanup(self):\n if self.pidfile:\n os.unlink(self.pidfile)","def clean(self):\n\t\tself.archiver.closeFile()","def prune_empty(self): # FileObj.prune_empty\n return False # can't prune a file","def close_ref(self, filename):\n ctx = self.open_ref_contexts.pop(filename)\n ctx.close()\n ref = ctx.get_completed_ref()\n self.task_record.publish_ref(ref)\n return ref","def cleanup(self):\n try:\n self.wc = os.path.dirname(self.wc)\n rmtree2(self.wc)\n except IOError, err:\n self.log.exception(\"Can't remove working copy %s: %s\" % (self.wc, err))","def _remove_unique_file(self):\n if self._uniquefile_created:\n self._unlink(self.uniquefile)\n self._uniquefile_created = False\n self._p(\"Unique file deleted: %s\" % self.uniquefile)","def __del__(self):\n\n if self._is_open:\n self.close()","def preview_file_cleanup(sender, **kwargs):\n\n instance = kwargs.get('instance')\n filename = instance.path.url[1:]\n if os.path.exists(filename):\n os.remove(filename)","def _read_xid_from_fp_xid_temp_and_delete_file(self, fn_temp):\n\t\tif os.path.isfile(fn_temp): \n\t\t\tif os.stat(fn_temp).st_size > 0:\n\t\t\t\txid = at.Table.read(fn_temp, format='ascii.csv', comment='#')\n\t\t\t\tos.remove(fn_temp)\n\t\t\t\txid = self._xid_pick_only_closest(xid)\n\t\t\telse: \n\t\t\t\tprint(\"[hscObj] no object found - xid file empty\")\n\t\t\t\tos.remove(fn_temp)\n\t\t\t\txid = None\n\t\telse: \n\t\t\tprint(\"[hscObj] query failed\")\n\t\t\txid = None\n\t\treturn xid","def _clean_up_temporary_files(dataset_dir):\n return","def teardown(self):\n super(TestCisAsciiFileInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def track_ref_for_deletion(self, ref):\n if ref not in self.__refs_for_deletion:\n self.__refs_for_deletion.append(ref)","def tearDown(self):\r\n remove_files(self.files_to_remove, False)\r\n if self.tmpdir:\r\n rmtree(self.tmpdir)\r\n\r\n # clean up the file from init_flowgram_file\r\n if (hasattr(self, \"tmp_filename\") and exists(self.tmp_filename)):\r\n remove(self.tmp_filename)","def teardown(self):\n super(TestCisObjInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def wipeFile(file_name):\r\n WipeFileThread(file_name)","def tearDown(self):\n for f in os.listdir('/tmp'):\n if not f.startswith(self.FILE_PREFIX):\n continue\n\n os.remove(os.path.join('/tmp', f))","def _delete_temp():\n global _TEMP_NAME\n\n try:\n database.delete_temp(_TEMP_NAME)\n outputtools.delete_temp(_TEMP_NAME)\n except:\n raise","def tearDown(self):\n os.remove(self._file)","def the_ending(self):\n storage.close()","def flow_file_delete(sender, instance, **kwargs):\n if FLOWJS_REMOVE_FILES_ON_DELETE:\n try:\n default_storage.delete(instance.path)\n except NotImplementedError:\n pass","def cleanup(self):\n if os.path.exists(self.tgzfile):\n os.remove(self.tgzfile)\n\n if os.path.exists(self.dirname):\n shutil.rmtree(self.dirname)","def __exit__(self, exc_type, exc_val, exc_tb):\n self.remove_files()\n return False","def doRollover(self):\r\n if self.maxBytes <= 0:\r\n return\r\n\r\n if not (self.stream.tell() >= self.maxBytes):\r\n return\r\n\r\n self.stream.close()\r\n if self.backupCount > 0:\r\n for i in range(self.backupCount - 1, 0, -1):\r\n sfn = \"%s.%d\" % (self.baseFilename, i)\r\n dfn = \"%s.%d\" % (self.baseFilename, i + 1)\r\n if os.path.exists(sfn):\r\n self.removeAndRename(sfn, dfn)\r\n dfn = self.baseFilename + \".1\"\r\n self.removeAndRename(self.baseFilename, dfn)\r\n self.stream = open(self.baseFilename, 'w')","def clean_temp_storage_dir(self, filenames):\n for fn in filenames:\n try:\n pathlib.Path(pathlib.PurePath(self.temp_storage_dir, fn)).unlink()\n except FileNotFoundError:\n pass","def stopWatchingFileSystem(self) :\n\n self.continueWatchingFS = False","def test_monitor_correctly_deletes_temporary_directory_in_the_case_of_any_error(\n self,\n ):\n # Arrange\n feed_pages = [fixtures.PROQUEST_FEED_PAGE_1, fixtures.PROQUEST_FEED_PAGE_2]\n\n client = create_autospec(spec=ProQuestAPIClient)\n client.download_all_feed_pages = MagicMock(\n return_value=list(map(fixtures.serialize, feed_pages))\n )\n\n client_factory = create_autospec(spec=ProQuestAPIClientFactory)\n client_factory.create = MagicMock(return_value=client)\n\n monitor = ProQuestOPDS2ImportMonitor(\n client_factory, self._db, self._proquest_collection, ProQuestOPDS2Importer\n )\n monitor.import_one_feed = MagicMock(return_value=([], []))\n\n results = {\"temp_directory\": None, \"temp_files\": []}\n original_mkdtemp = tempfile.mkdtemp\n original_temp_file_constructor = tempfile.NamedTemporaryFile\n original_rmtree = shutil.rmtree\n\n def create_temp_directory():\n results[\"temp_directory\"] = original_mkdtemp()\n\n return results[\"temp_directory\"]\n\n def create_temp_file(**kwargs):\n temp_file = original_temp_file_constructor(**kwargs)\n results[\"temp_files\"].append(temp_file.name)\n\n return temp_file\n\n # Act\n with patch(\"tempfile.mkdtemp\") as mkdtemp_mock, patch(\n \"tempfile.NamedTemporaryFile\"\n ) as named_temporary_file_constructor_mock, patch(\n \"shutil.rmtree\"\n ) as rmtree_mock, patch(\n \"api.proquest.importer.parse_feed\"\n ) as parse_feed_mock:\n mkdtemp_mock.side_effect = create_temp_directory\n named_temporary_file_constructor_mock.side_effect = create_temp_file\n rmtree_mock.side_effect = original_rmtree\n parse_feed_mock.side_effect = core.opds2_import.parse_feed\n\n # An exception will be raised while trying to parse the feed page.\n parse_feed_mock.side_effect = Exception(\"\")\n\n monitor.run_once(False)\n\n # Assert\n # Ensure that the temp directory was successfully created.\n tempfile.mkdtemp.assert_called_once()\n\n # Ensure that only one temp file was created, after this an exception was raised and the process stopped.\n tempfile.NamedTemporaryFile.assert_has_calls(\n [call(mode=\"r+\", dir=results[\"temp_directory\"], delete=False)]\n )\n\n # Ensure that parse_feed method was called only once.\n parse_feed_mock.assert_has_calls([call(ANY, silent=False)])\n\n # Ensure that the temp directory was successfully removed.\n shutil.rmtree.assert_called_once_with(results[\"temp_directory\"])\n assert False == os.path.exists(results[\"temp_directory\"])"],"string":"[\n \"def test_file_deleted(self):\\n try:\\n with get_temp_file() as (fd, name):\\n os.unlink(name)\\n except Exception as err:\\n self.fail('Failed with exception \\\"{}\\\"'.format(err))\",\n \"def _Close(self):\\n self._fsfat_volume = None\\n self._file_object = None\",\n \"def __del__(self):\\n if (\\n self._fpointer is not None and not self._fpointer.closed\\n ): # pragma: no mutate\\n self._fpointer.close()\",\n \"def clean(self):\\n if os.path.exists(self.initial):\\n if os.path.exists(self.path) and os.stat(self.path).st_size == os.stat(\\n self.initial).st_size:\\n os.remove(self.initial)\\n else:\\n # if it doesn't match, something probably crashed; rename the temporary file and\\n # it'll get uploaded at some point\\n self.auto_filename()\\n self.rename()\\n self.connect()\\n os.remove(self.initial)\\n if os.path.exists(self.path):\\n os.remove(self.path)\\n self.filename_set = False\",\n \"def _cleanup(self, fnum):\\n while os.path.exists('%s.%s' % (self.name, fnum)):\\n try:\\n fname = '%s.%s' % (self.name, fnum)\\n os.unlink(fname)\\n # self.log.debug(\\\"Cleaned up file: %s\\\", fname)\\n except:\\n pass\\n fnum -= 1\",\n \"def test_file_unused(self):\\n try:\\n with get_temp_file() as (fd, name):\\n pass\\n except Exception as err:\\n self.fail('Failed with exception \\\"{}\\\"'.format(err))\\n else:\\n file_exists = os.access(name, os.F_OK)\\n self.assertFalse(file_exists)\",\n \"def __purge_old_files(self):\\n\\n chkpts = self.checkpointer.sorted_checkpoints()\\n p_chkpts = []\\n e_chkpts = []\\n for c in chkpts:\\n if c.startswith(self.checkpointer.prefix + CheckpointingCallback.PERIODIC_PREFIX):\\n p_chkpts.append(c)\\n\\n if c.startswith(self.checkpointer.prefix + CheckpointingCallback.EPOCH_PREFIX):\\n e_chkpts.append(c)\\n\\n # Delete periodic checkpoints\\n if self.max_files is not None and len(p_chkpts) > self.max_files:\\n for c in p_chkpts[self.max_files:]:\\n log.debug(\\\"CheckpointingCallback deleting {}\\\".format(c))\\n self.checkpointer.delete(c)\\n\\n # Delete older epochs\\n if self.max_epochs is not None and len(e_chkpts) > self.max_epochs:\\n for c in e_chkpts[self.max_epochs:]:\\n log.debug(\\\"CheckpointingCallback deleting (epoch) {}\\\".format(c))\\n self.checkpointer.delete(c)\",\n \"def __del__(self):\\n if self.file is None:\\n return\\n try:\\n self.file.close()\\n del self.file\\n self.file = None\\n except:\\n getLogger(__name__).warning('Error on file close', exc_info=True)\",\n \"def __del__(self):\\n self.close_files()\",\n \"def __del__(self):\\r\\n self.filename.close()\",\n \"def flow_file_chunk_delete(sender, instance, **kwargs):\\n instance.file.delete(False)\",\n \"def _recover_disk_space(self):\\n while self.used_disk_space > self.cache_size:\\n space_to_recover = self.used_disk_space - self.cache_size\\n logger.info('Recovering disk space %s', space_to_recover)\\n lru_file = self.touch_list.pop(0)\\n file_path = self._path_to_file(lru_file)\\n logger.info('Deleting %s', file_path)\\n os.remove(file_path)\\n del self.index[lru_file]\",\n \"def __del__(self):\\n\\t\\tif self.temp_dir:\\n\\t\\t\\tself.temp_dir.cleanup()\",\n \"def tearDown(self):\\n try:\\n os.remove(self.junk_file)\\n except OSError as doh:\\n if doh.errno == 2:\\n # No such File, ignore\\n pass\\n else:\\n raise\",\n \"def __del__(self) -> None:\\n try:\\n shutil.rmtree(self.temp_path)\\n except FileNotFoundError:\\n pass\",\n \"def Close(self):\\n super(CPIOArchiveFile, self).Close()\\n self._file_entries = None\",\n \"def dispose(self):\\n rmtree(self._temp_path)\",\n \"def unlink(self,):\\n self._wait()\\n self.fd.close()\\n self.fd = None\\n os.unlink(self.fname)\",\n \"def __del__(self):\\n self.file.close()\",\n \"def __del__(self):\\r\\n self.chunk = None\",\n \"def endWrite(self, withErrors):\\r\\n #if withErrors or self._file_obj.get_seek()>0:\\r\\n self.provider.cache_fs.remove(self.path)\",\n \"def release(self, path, fh, *args, **pargs):\\n with(self.rwlock):\\n # If we're closing a FLACCue file...\\n if(path in self._open_subtracks):\\n # Delete the file handle from the stored list.\\n del self._open_subtracks[path]['Positions'][fh]\\n # Close the OS reference to the file.\\n return os.close(fh)\",\n \"def WriteAbort(self):\\n if self._file_object:\\n self._file_object.close()\\n self._file_object = None\\n\\n if os.path.exists(self.name):\\n os.remove(self.name)\",\n \"def __del__(self):\\n for handle in self._filehandles:\\n handle.close()\",\n \"def __del__(self):\\n self.file_out.close()\",\n \"def __del__(self):\\n self.f.close()\",\n \"def unique_files(self):\\n self._tempfiles[-1].ctr = -1\",\n \"def test_ClearOldFile(self):\\n q = Queue(self.path, chunksize=10)\\n for i in range(15):\\n q.put('var1')\\n\\n for i in range(11):\\n q.get()\\n\\n q = Queue(self.path, chunksize=10)\\n self.assertEqual(q.qsize(), 15)\\n\\n for i in range(11):\\n q.get()\\n q.task_done()\\n self.assertEqual(q.qsize(), 4)\",\n \"def release(self):\\n #关闭文件，删除文件\\n if self.fd is not None:\\n os.close(self.fd)\\n os.unlink(self.lockfile)\\n self.is_locked = False\\n self.fd = None\",\n \"def releaseFile(self, fid):\\n if fid in self.files:\\n del self.files[fid]\",\n \"def __del__(self):\\n for f in self._files:\\n f.close()\",\n \"def test_removed(self):\\n path = None\\n with TemporaryDirectory() as tmp:\\n path = tmp\\n self.assertTrue(os.path.isdir(tmp))\\n tmpfile = os.path.join(tmp, \\\"a_temp_file\\\")\\n open(tmpfile, \\\"w\\\").write(\\\"data\\\")\\n self.assertTrue(os.path.isfile(tmpfile))\\n self.assertFalse(os.path.isdir(path))\\n self.assertFalse(os.path.exists(path))\",\n \"def test_disconnect_file_watchers_removes_refs(self):\\n session = _create_test_session()\\n\\n # Various listeners should have references to session file/pages/secrets changed\\n # handlers.\\n self.assertGreater(len(gc.get_referrers(session)), 0)\\n\\n session.disconnect_file_watchers()\\n # Ensure that we don't count refs to session from an object that would have been\\n # garbage collected along with it.\\n gc.collect(2)\\n\\n self.assertEqual(len(gc.get_referrers(session)), 0)\",\n \"def Close(self):\\n if not self._is_open:\\n raise IOError('Storage file already closed.')\\n\\n if not self._read_only:\\n self.Flush()\\n\\n if self._serializers_profiler:\\n self._serializers_profiler.Write()\\n\\n # Make sure to flush the caches so that zipfile can be closed and freed.\\n # Otherwise on Windows the ZIP file remains locked and cannot be renamed.\\n\\n self._offset_tables = {}\\n self._offset_tables_lfu = []\\n\\n self._open_streams = {}\\n self._streams_lfu = []\\n\\n self._event_timestamp_tables = {}\\n self._event_timestamp_tables_lfu = []\\n\\n self._zipfile.close()\\n self._zipfile = None\\n self._is_open = False\\n\\n file_renamed = False\\n if self._path != self._zipfile_path and os.path.exists(self._zipfile_path):\\n # On Windows the file can sometimes be still in use and we have to wait.\\n for attempt in range(1, self._MAXIMUM_NUMBER_OF_LOCKED_FILE_ATTEMPTS):\\n try:\\n os.rename(self._zipfile_path, self._path)\\n file_renamed = True\\n break\\n\\n except OSError:\\n if attempt == self._MAXIMUM_NUMBER_OF_LOCKED_FILE_ATTEMPTS:\\n raise\\n time.sleep(self._LOCKED_FILE_SLEEP_TIME)\\n\\n self._path = None\\n self._zipfile_path = None\\n\\n if self._path != self._zipfile_path and not file_renamed:\\n raise IOError('Unable to close storage file.')\",\n \"def test_file_closed(self):\\n try:\\n with get_temp_file() as (fd, name):\\n os.close(fd)\\n except Exception as err:\\n self.fail('Failed with exception \\\"{}\\\"'.format(err))\\n else:\\n file_exists = os.access(name, os.F_OK)\\n self.assertFalse(file_exists)\",\n \"def teardown(self):\\n super(TestCisPickleInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def __del__(self):\\n for component_name, file in self._file_list.items():\\n file.close()\",\n \"def finalize(self):\\n if self._file:\\n toLog(\\\"Closing file `{0}`\\\".format(self._fname), True)\\n self._file.close()\\n self._file = None\",\n \"def purge(self):\\n if self.is_open and self.direction == 'recv':\\n try:\\n self.fd.seek(0, os.SEEK_END)\\n except (AttributeError, ValueError): # pragma: debug\\n if self.is_open:\\n raise\",\n \"def cleanup_files(self):\\n\\n self.backup_files()\\n self.delete_files()\",\n \"def clear_tempfiles(self, remove=True):\\n while self._tempfiles:\\n self.pop(remove)\\n self.push()\",\n \"def attempt_file_reset(f):\\r\\n if hasattr(f, 'seek'):\\r\\n f.seek(0)\",\n \"def teardown(self):\\n super(TestCisPickleOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def _try_cleanup_uploads(self):\\n try:\\n with GlobalLock(\\\"BLOB_CLEANUP\\\", lock_ttl=LOCK_TTL):\\n self._cleanup_uploads()\\n except LockNotAcquiredException:\\n logger.debug(\\\"Could not acquire global lock for blob upload cleanup worker\\\")\\n return\",\n \"def purge(self):\\n self.remaining = 0\",\n \"def delete_io( hash ):\\n res = 0\\n record_used('cache', hash)\\n for packet in get_filenames_for_hash(CACHE_DIRECTORY, hash):\\n try:\\n os.remove(packet)\\n res = res + 1\\n except:\\n if not os.environ.get('CALIENDO_TEST_SUITE', None):\\n logger.warning( \\\"Failed to remove file: \\\" + packet )\\n return res\",\n \"def close(self):\\n if self.current_file_number is not None:\\n self.fh_raw.close()\\n self.current_file_number = None\",\n \"def erase_files(self):\\n self.ofile_handle()\\n self.efile_handle()\\n\\n os.remove(self.ofile_name())\\n os.remove(self.efile_name())\\n return None\",\n \"def classCleanup(cls):\\n cls.RemoveTempFile(\\\"child_send1.txt\\\")\\n cls.RemoveTempFile(\\\"child_read1.txt\\\")\\n cls.RemoveTempFile(\\\"child_send2.txt\\\")\\n cls.RemoveTempFile(\\\"child_read2.txt\\\")\",\n \"def cleanup(self):\\n files = self.nlst()\\n latest = self.latest_filename\\n for filename in files:\\n if filename != latest:\\n result = self.delete(filename)\\n logger.info(f\\\"Deleted old export from FTP: {result}\\\")\",\n \"def task_clean_tmp_files():\\n client = google.cloud.storage.Client()\\n blobs = client.list_blobs(settings.PODCAST_STORAGE_BUCKET,\\n prefix=settings.PODCAST_TMP_STORAGE_DIRECTORY)\\n for blob in blobs:\\n if blob.time_created.replace(tzinfo=None) + datetime.timedelta(1) <= datetime.datetime.now():\\n blob.delete()\\n\\n return OK_RESPONSE\",\n \"def __del__(self):\\n if hasattr(self, \\\"_uniquefile_created\\\"):\\n self._unlock()\\n else:\\n # When instance attributes don't exist, we probably had an error\\n # in the construction process (like an invalid argument to\\n # __init__()). In that case, there's no chance we have a unique\\n # file or a lock to clean up.\\n pass\",\n \"def file_pointer(self):\\n\\n try:\\n self.__file.seek(self.__file.tell() - 1)\\n except Exception as e:\\n raise e\",\n \"def file_close(self):\\n if self.on_disk:\\n self._subarray.close()\",\n \"def cleanup(self):\\n\\n if self.do_nothing_bl is False:\\n if os.path.exists(self.local_fileP_str) is True:\\n if self.temp_dirP_obj is not None:\\n self.temp_dirP_obj.cleanup()\\n\\n elif os.path.isdir(self.local_fileP_str) is True:\\n log_obj.debug('Removing directory \\\"{:s}\\\"'.format(self.local_fileP_str))\\n shutil.rmtree(self.local_fileP_str)\\n\\n else:\\n log_obj.debug('Removing file \\\"{:s}\\\"'.format(self.local_fileP_str))\\n os.remove(self.local_fileP_str)\",\n \"def _decrement_file_counter(self):\\n self._add_to_file_counter(-1)\",\n \"def __del__(self):\\n for filename in self.files:\\n unlink(filename)\",\n \"def finalize(self):\\n # 027 Not needed in the simple FilesAdaptor. \\n pass\",\n \"def __del__(self):\\n if not self.sigfile.closed:\\n self.sigfile.close()\",\n \"def delete_partition(self, partition):\\n raise NotImplementedError('delete_file')\",\n \"def teardown(self):\\n self.file_comm.remove_file()\\n super(TestCisAsciiFileOutput, self).teardown()\",\n \"def release(self):\\n if self._ctx is None:\\n return\\n self.atomicfile.delete()\\n try:\\n self._ctx.__exit__(None, None, None)\\n finally:\\n self._ctx = None\",\n \"def cleanup(self):\\r\\n for f in [i for d in self.data.values() for i in d[\\\"filenames\\\"]]:\\r\\n try:\\r\\n os.unlink(f)\\r\\n except Exception: pass\\r\\n self.Destroy()\",\n \"def wipe(self):\",\n \"def wipe(self):\",\n \"def _wipe(self):\\n log_method_call(self, self.name, status=self.status)\\n\\n start = self.partedPartition.geometry.start\\n part_len = self.partedPartition.geometry.end - start\\n bs = self.partedPartition.geometry.device.sectorSize\\n device = self.partedPartition.geometry.device.path\\n\\n # Erase 1MiB or to end of partition\\n count = int(Size(\\\"1 MiB\\\") / bs)\\n count = min(count, part_len)\\n\\n cmd = [\\\"dd\\\", \\\"if=/dev/zero\\\", \\\"of=%s\\\" % device, \\\"bs=%s\\\" % bs,\\n \\\"seek=%s\\\" % start, \\\"count=%s\\\" % count]\\n try:\\n util.run_program(cmd)\\n except OSError as e:\\n log.error(str(e))\\n finally:\\n # If a udev device is created with the watch option, then\\n # a change uevent is synthesized and we need to wait for\\n # things to settle.\\n udev.settle()\",\n \"def file(self):\\n del self._file\",\n \"def cleanup(self):\\n if self.log_fo:\\n self.log_fo.close()\",\n \"def release(self):\\n self.filelock.set()\\n self.locked = False\\n self.exclusive = False\",\n \"def release(self):\\n self.filelock.set()\\n self.locked = False\\n self.exclusive = False\",\n \"def current_remove(self):\\n storage.close()\",\n \"def test_deleting_local_file_using_file_io() -> None:\\n with tempfile.TemporaryDirectory() as tmpdirname:\\n # Write to the temporary file\\n output_file_location = os.path.join(tmpdirname, \\\"foo.txt\\\")\\n with open(output_file_location, \\\"wb\\\") as f:\\n f.write(b\\\"foo\\\")\\n\\n # Instantiate the file-io\\n file_io = PyArrowFileIO()\\n\\n # Confirm that the file initially exists\\n assert os.path.exists(output_file_location)\\n\\n # Delete the file using the file-io implementations delete method\\n file_io.delete(output_file_location)\\n\\n # Confirm that the file no longer exists\\n assert not os.path.exists(output_file_location)\",\n \"def clear_data():\\n for i in range(_MAX_NUM_TESTS):\\n rand, ref = filename(i)\\n if os.path.exists(rand):\\n os.remove(rand)\\n if os.path.exists(ref):\\n os.remove(ref)\",\n \"def remove(self):\\n for ref_node in self.node.find_references():\\n ref_node.destroy()\\n File.remove(self)\",\n \"def _cleanup(self):\\n if self.pidfile:\\n os.unlink(self.pidfile)\",\n \"def clean(self):\\n\\t\\tself.archiver.closeFile()\",\n \"def prune_empty(self): # FileObj.prune_empty\\n return False # can't prune a file\",\n \"def close_ref(self, filename):\\n ctx = self.open_ref_contexts.pop(filename)\\n ctx.close()\\n ref = ctx.get_completed_ref()\\n self.task_record.publish_ref(ref)\\n return ref\",\n \"def cleanup(self):\\n try:\\n self.wc = os.path.dirname(self.wc)\\n rmtree2(self.wc)\\n except IOError, err:\\n self.log.exception(\\\"Can't remove working copy %s: %s\\\" % (self.wc, err))\",\n \"def _remove_unique_file(self):\\n if self._uniquefile_created:\\n self._unlink(self.uniquefile)\\n self._uniquefile_created = False\\n self._p(\\\"Unique file deleted: %s\\\" % self.uniquefile)\",\n \"def __del__(self):\\n\\n if self._is_open:\\n self.close()\",\n \"def preview_file_cleanup(sender, **kwargs):\\n\\n instance = kwargs.get('instance')\\n filename = instance.path.url[1:]\\n if os.path.exists(filename):\\n os.remove(filename)\",\n \"def _read_xid_from_fp_xid_temp_and_delete_file(self, fn_temp):\\n\\t\\tif os.path.isfile(fn_temp): \\n\\t\\t\\tif os.stat(fn_temp).st_size > 0:\\n\\t\\t\\t\\txid = at.Table.read(fn_temp, format='ascii.csv', comment='#')\\n\\t\\t\\t\\tos.remove(fn_temp)\\n\\t\\t\\t\\txid = self._xid_pick_only_closest(xid)\\n\\t\\t\\telse: \\n\\t\\t\\t\\tprint(\\\"[hscObj] no object found - xid file empty\\\")\\n\\t\\t\\t\\tos.remove(fn_temp)\\n\\t\\t\\t\\txid = None\\n\\t\\telse: \\n\\t\\t\\tprint(\\\"[hscObj] query failed\\\")\\n\\t\\t\\txid = None\\n\\t\\treturn xid\",\n \"def _clean_up_temporary_files(dataset_dir):\\n return\",\n \"def teardown(self):\\n super(TestCisAsciiFileInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def track_ref_for_deletion(self, ref):\\n if ref not in self.__refs_for_deletion:\\n self.__refs_for_deletion.append(ref)\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove, False)\\r\\n if self.tmpdir:\\r\\n rmtree(self.tmpdir)\\r\\n\\r\\n # clean up the file from init_flowgram_file\\r\\n if (hasattr(self, \\\"tmp_filename\\\") and exists(self.tmp_filename)):\\r\\n remove(self.tmp_filename)\",\n \"def teardown(self):\\n super(TestCisObjInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def wipeFile(file_name):\\r\\n WipeFileThread(file_name)\",\n \"def tearDown(self):\\n for f in os.listdir('/tmp'):\\n if not f.startswith(self.FILE_PREFIX):\\n continue\\n\\n os.remove(os.path.join('/tmp', f))\",\n \"def _delete_temp():\\n global _TEMP_NAME\\n\\n try:\\n database.delete_temp(_TEMP_NAME)\\n outputtools.delete_temp(_TEMP_NAME)\\n except:\\n raise\",\n \"def tearDown(self):\\n os.remove(self._file)\",\n \"def the_ending(self):\\n storage.close()\",\n \"def flow_file_delete(sender, instance, **kwargs):\\n if FLOWJS_REMOVE_FILES_ON_DELETE:\\n try:\\n default_storage.delete(instance.path)\\n except NotImplementedError:\\n pass\",\n \"def cleanup(self):\\n if os.path.exists(self.tgzfile):\\n os.remove(self.tgzfile)\\n\\n if os.path.exists(self.dirname):\\n shutil.rmtree(self.dirname)\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n self.remove_files()\\n return False\",\n \"def doRollover(self):\\r\\n if self.maxBytes <= 0:\\r\\n return\\r\\n\\r\\n if not (self.stream.tell() >= self.maxBytes):\\r\\n return\\r\\n\\r\\n self.stream.close()\\r\\n if self.backupCount > 0:\\r\\n for i in range(self.backupCount - 1, 0, -1):\\r\\n sfn = \\\"%s.%d\\\" % (self.baseFilename, i)\\r\\n dfn = \\\"%s.%d\\\" % (self.baseFilename, i + 1)\\r\\n if os.path.exists(sfn):\\r\\n self.removeAndRename(sfn, dfn)\\r\\n dfn = self.baseFilename + \\\".1\\\"\\r\\n self.removeAndRename(self.baseFilename, dfn)\\r\\n self.stream = open(self.baseFilename, 'w')\",\n \"def clean_temp_storage_dir(self, filenames):\\n for fn in filenames:\\n try:\\n pathlib.Path(pathlib.PurePath(self.temp_storage_dir, fn)).unlink()\\n except FileNotFoundError:\\n pass\",\n \"def stopWatchingFileSystem(self) :\\n\\n self.continueWatchingFS = False\",\n \"def test_monitor_correctly_deletes_temporary_directory_in_the_case_of_any_error(\\n self,\\n ):\\n # Arrange\\n feed_pages = [fixtures.PROQUEST_FEED_PAGE_1, fixtures.PROQUEST_FEED_PAGE_2]\\n\\n client = create_autospec(spec=ProQuestAPIClient)\\n client.download_all_feed_pages = MagicMock(\\n return_value=list(map(fixtures.serialize, feed_pages))\\n )\\n\\n client_factory = create_autospec(spec=ProQuestAPIClientFactory)\\n client_factory.create = MagicMock(return_value=client)\\n\\n monitor = ProQuestOPDS2ImportMonitor(\\n client_factory, self._db, self._proquest_collection, ProQuestOPDS2Importer\\n )\\n monitor.import_one_feed = MagicMock(return_value=([], []))\\n\\n results = {\\\"temp_directory\\\": None, \\\"temp_files\\\": []}\\n original_mkdtemp = tempfile.mkdtemp\\n original_temp_file_constructor = tempfile.NamedTemporaryFile\\n original_rmtree = shutil.rmtree\\n\\n def create_temp_directory():\\n results[\\\"temp_directory\\\"] = original_mkdtemp()\\n\\n return results[\\\"temp_directory\\\"]\\n\\n def create_temp_file(**kwargs):\\n temp_file = original_temp_file_constructor(**kwargs)\\n results[\\\"temp_files\\\"].append(temp_file.name)\\n\\n return temp_file\\n\\n # Act\\n with patch(\\\"tempfile.mkdtemp\\\") as mkdtemp_mock, patch(\\n \\\"tempfile.NamedTemporaryFile\\\"\\n ) as named_temporary_file_constructor_mock, patch(\\n \\\"shutil.rmtree\\\"\\n ) as rmtree_mock, patch(\\n \\\"api.proquest.importer.parse_feed\\\"\\n ) as parse_feed_mock:\\n mkdtemp_mock.side_effect = create_temp_directory\\n named_temporary_file_constructor_mock.side_effect = create_temp_file\\n rmtree_mock.side_effect = original_rmtree\\n parse_feed_mock.side_effect = core.opds2_import.parse_feed\\n\\n # An exception will be raised while trying to parse the feed page.\\n parse_feed_mock.side_effect = Exception(\\\"\\\")\\n\\n monitor.run_once(False)\\n\\n # Assert\\n # Ensure that the temp directory was successfully created.\\n tempfile.mkdtemp.assert_called_once()\\n\\n # Ensure that only one temp file was created, after this an exception was raised and the process stopped.\\n tempfile.NamedTemporaryFile.assert_has_calls(\\n [call(mode=\\\"r+\\\", dir=results[\\\"temp_directory\\\"], delete=False)]\\n )\\n\\n # Ensure that parse_feed method was called only once.\\n parse_feed_mock.assert_has_calls([call(ANY, silent=False)])\\n\\n # Ensure that the temp directory was successfully removed.\\n shutil.rmtree.assert_called_once_with(results[\\\"temp_directory\\\"])\\n assert False == os.path.exists(results[\\\"temp_directory\\\"])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.637165","0.6141179","0.6079966","0.60165036","0.5893113","0.5771018","0.5769767","0.5752214","0.5731687","0.5730452","0.57243013","0.5715975","0.5715163","0.5711628","0.5696097","0.56944114","0.56878215","0.56815344","0.5681272","0.5633728","0.5630855","0.56236386","0.5620949","0.5599183","0.5533983","0.5530115","0.552735","0.5516672","0.55129987","0.5509583","0.5503348","0.5479684","0.54776025","0.54776025","0.5476024","0.5467237","0.5454183","0.5448012","0.5440473","0.54392135","0.54392","0.54382205","0.5435678","0.542985","0.5427196","0.54195726","0.54117715","0.53861576","0.53838927","0.5373599","0.537312","0.53651375","0.53607184","0.5358157","0.5340452","0.53342813","0.5334151","0.5323054","0.53058237","0.5295279","0.5289411","0.5282252","0.5279168","0.5266135","0.5266135","0.52655274","0.5265467","0.52640206","0.52572554","0.52572554","0.52481645","0.5247483","0.524704","0.52389145","0.52380466","0.5229377","0.52276593","0.5226616","0.5217799","0.5217318","0.5216734","0.5212733","0.5212685","0.52046865","0.5198442","0.51889175","0.5185455","0.5184727","0.5181946","0.51801217","0.51741946","0.5173718","0.5170676","0.5169946","0.51690054","0.51684195","0.51642966","0.5161535","0.5157501","0.51567405"],"string":"[\n \"0.637165\",\n \"0.6141179\",\n \"0.6079966\",\n \"0.60165036\",\n \"0.5893113\",\n \"0.5771018\",\n \"0.5769767\",\n \"0.5752214\",\n \"0.5731687\",\n \"0.5730452\",\n \"0.57243013\",\n \"0.5715975\",\n \"0.5715163\",\n \"0.5711628\",\n \"0.5696097\",\n \"0.56944114\",\n \"0.56878215\",\n \"0.56815344\",\n \"0.5681272\",\n \"0.5633728\",\n \"0.5630855\",\n \"0.56236386\",\n \"0.5620949\",\n \"0.5599183\",\n \"0.5533983\",\n \"0.5530115\",\n \"0.552735\",\n \"0.5516672\",\n \"0.55129987\",\n \"0.5509583\",\n \"0.5503348\",\n \"0.5479684\",\n \"0.54776025\",\n \"0.54776025\",\n \"0.5476024\",\n \"0.5467237\",\n \"0.5454183\",\n \"0.5448012\",\n \"0.5440473\",\n \"0.54392135\",\n \"0.54392\",\n \"0.54382205\",\n \"0.5435678\",\n \"0.542985\",\n \"0.5427196\",\n \"0.54195726\",\n \"0.54117715\",\n \"0.53861576\",\n \"0.53838927\",\n \"0.5373599\",\n \"0.537312\",\n \"0.53651375\",\n \"0.53607184\",\n \"0.5358157\",\n \"0.5340452\",\n \"0.53342813\",\n \"0.5334151\",\n \"0.5323054\",\n \"0.53058237\",\n \"0.5295279\",\n \"0.5289411\",\n \"0.5282252\",\n \"0.5279168\",\n \"0.5266135\",\n \"0.5266135\",\n \"0.52655274\",\n \"0.5265467\",\n \"0.52640206\",\n \"0.52572554\",\n \"0.52572554\",\n \"0.52481645\",\n \"0.5247483\",\n \"0.524704\",\n \"0.52389145\",\n \"0.52380466\",\n \"0.5229377\",\n \"0.52276593\",\n \"0.5226616\",\n \"0.5217799\",\n \"0.5217318\",\n \"0.5216734\",\n \"0.5212733\",\n \"0.5212685\",\n \"0.52046865\",\n \"0.5198442\",\n \"0.51889175\",\n \"0.5185455\",\n \"0.5184727\",\n \"0.5181946\",\n \"0.51801217\",\n \"0.51741946\",\n \"0.5173718\",\n \"0.5170676\",\n \"0.5169946\",\n \"0.51690054\",\n \"0.51684195\",\n \"0.51642966\",\n \"0.5161535\",\n \"0.5157501\",\n \"0.51567405\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61570215"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2674,"cells":{"query":{"kind":"string","value":"Add i to the count of subarrays referencing the file of this partition's subarray. Only do this if self._subarray is an instance of FileArray, but not a temporary FileArray."},"document":{"kind":"string","value":"def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if filename is None:\n return\n\n file_counter = self.file_counter\n # count = file_counter.get(filename, 0)\n # file_counter[filename] = count + i\n # if file_counter[filename] <= 0:\n count = file_counter.get(filename, 0) + i\n if count <= 0:\n # Remove the file from the dictionary if its count has\n # dropped to zero\n file_counter.pop(filename, None)\n else:\n file_counter[filename] = count\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def n_subfile(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=False):\n n += 1\n return n","def update(self, i, v):\n # index in BTree is 1 more than index in arr[]\n i += 1\n\n # Traverse to ancestors of BITree[i]\n while i <= self.size:\n self.BITree[i] += v\n\n # Update index to next set bit in binary representation\n i += i & (-i)","def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n # than 3 references to it then it is not unique.\n if getrefcount is not None:\n self._decrement_file_counter()\n if subarray is None or getrefcount(subarray) > 3:\n return\n else:\n # getrefcount has itself been deleted or is in the process\n # of being torn down\n return\n\n _partition_file = getattr(subarray, \"_partition_file\", None)\n if _partition_file is not None:\n # This partition contains a temporary file which is not\n # referenced by any other partition on this process, so if\n # there are no lock files present remove the file from\n # disk.\n _remove_temporary_files(_partition_file)\n\n else:\n try:\n if FileArray is not None and isinstance(subarray, FileArray):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if self.file_counter.get(filename, 999) <= 0:\n # This partition contains a non-temporary file\n # which is not referenced by any other\n # partitions, so close the file.\n subarray.close()\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass\n # --- End: if","def write_sub_index(self):\n for sie in self.subIndex:\n self.db_file.write(sie.get_representation())","def add(self, i: int, v: int) -> None:\n while i < self.size:\n self.tree[i] += v\n i += self._lsb(i)","def __setitem__(self, filenr, data_arr):\n cvcfile = self.filenames[filenr]\n cvcpath = os.path.join(self.filefolder, cvcfile)\n data_arr.tofile(cvcpath)","def _increment_file_counter(self):\n self._add_to_file_counter(1)","def add_index(self, idx, subproblem_shape):\n self.indices.append(int(idx))\n self.subproblem_shapes.append(subproblem_shape)","def add_photo(self, new_photo, i):\r\n self.__photos[i] = new_photo","def append(self, i):\n \n self.ret.append(i)","def array(self):\n config = self.config\n\n unique_array = config[\"unique_subarray\"]\n\n p_axes = self.axes\n p_flip = self.flip\n p_part = self.part\n p_units = self.Units\n p_shape = self.shape\n p_location = self.location\n subarray = self._subarray\n\n len_p_axes = len(p_axes)\n\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is not in memory.\n #\n # It could be in a file on disk or implied by a FileArray\n # object, etc.\n # --------------------------------------------------------\n self._original = self.copy()\n\n unique_array = True\n update = True\n copy = False\n\n if not p_part:\n indices = Ellipsis\n else:\n indices = tuple(p_part)\n\n # Read from a file into a numpy array\n p_data = subarray[indices]\n\n # We've just copied p_data from disk, so in place changes\n # are not possible\n in_place_changes = False\n else:\n # --------------------------------------------------------\n # The subarray is in memory\n # --------------------------------------------------------\n update = config[\"update\"]\n\n if p_part:\n p_data = get_subspace(subarray, p_part)\n elif not unique_array:\n p_data = subarray.view()\n else:\n p_data = subarray\n\n copy = config[\"extra_memory\"]\n\n # In place changes to p_data might be possible if we're not\n # copying the data\n in_place_changes = not copy\n\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\n # --------------------------------------------------------\n # p_data is a numpy number (like numpy.int64) which does\n # not support assignment, so convert it to a numpy array.\n # --------------------------------------------------------\n p_data = numpy_array(p_data)\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n\n masked = numpy_ma_isMA(p_data)\n if masked:\n # The p_data is a masked array\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\n p_data\n ):\n # There are no missing data points so recast as an\n # unmasked numpy array\n p_data = p_data.data\n masked = False\n # --- End: if\n\n if masked:\n # Set the hardness of the mask\n if config[\"hardmask\"]:\n p_data.harden_mask()\n else:\n p_data.soften_mask()\n # --- End: if\n\n self.masked = masked\n\n # ------------------------------------------------------------\n # Make sure that the data array has the correct units. This\n # process will deep copy the data array if required (e.g. if\n # another partition is referencing this numpy array), even if\n # the units are already correct.\n # ------------------------------------------------------------\n func = config.get(\"func\")\n units = config[\"units\"]\n if func is None:\n if not p_units.equals(units) and bool(p_units) is bool(units):\n func = Units.conform\n\n if func is not None:\n inplace = not copy\n p_data = func(p_data, p_units, units, inplace)\n p_units = units\n\n if not inplace:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n flip = config.get(\"flip\", None)\n if flip or p_flip:\n flip_axes = set(p_flip).symmetric_difference(flip)\n else:\n flip_axes = None\n\n axes = config[\"axes\"]\n\n if p_data.size > 1:\n # --------------------------------------------------------\n # Flip axes\n # --------------------------------------------------------\n if flip_axes:\n indices = [\n (\n slice(None, None, -1)\n if axis in flip_axes\n else slice(None)\n )\n for axis in p_axes\n ]\n p_data = p_data[tuple(indices)]\n\n # --------------------------------------------------------\n # Transpose axes\n # --------------------------------------------------------\n if p_axes != axes:\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\n\n if len_p_axes > len(iaxes):\n for i in range(len_p_axes):\n if i not in iaxes:\n # iaxes.append(i)\n iaxes.insert(i, i)\n # --- End: if\n\n p_data = numpy_transpose(p_data, iaxes)\n # --- End: if\n\n # ------------------------------------------------------------\n # Remove excessive/insert missing size 1 axes\n # ------------------------------------------------------------\n if p_shape != p_data.shape:\n # if len_p_axes != len(p_shape):\n p_data = p_data.reshape(p_shape)\n\n # ------------------------------------------------------------\n # Apply the auxiliary mask\n # ------------------------------------------------------------\n auxiliary_mask = config[\"auxiliary_mask\"]\n if auxiliary_mask:\n for mask in auxiliary_mask:\n if mask.any():\n if not masked:\n p_data = p_data.view(numpy_ma_MaskedArray)\n masked = True\n\n p_data.mask = (mask | p_data.mask).array\n # --- End: for\n\n self.masked = True\n\n # ------------------------------------------------------------\n # Convert the array's data type\n # ------------------------------------------------------------\n p_dtype = p_data.dtype\n dtype = config.get(\"dtype\", None)\n if dtype is not None and dtype != p_dtype:\n try:\n p_data = p_data.astype(dtype) # Note: returns a copy\n except ValueError:\n raise ValueError(\n \"Can't recast partition array from {} to {}\".format(\n p_dtype.name, dtype.name\n )\n )\n else:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n # ------------------------------------------------------------\n # Copy the array\n # -----------------------------------------------------------\n if copy:\n if p_dtype.char != \"O\":\n if not masked or p_data.ndim > 0:\n p_data = p_data.copy()\n else:\n # This is because numpy.ma.copy doesn't work for\n # scalar arrays (at the moment, at least)\n p_data = numpy_ma_masked_all((), p_data.dtype)\n\n # We've just copied p_data, so in place changes are\n # not possible\n in_place_changes = False\n else:\n # whilst netCDF4.netcdftime.datetime is mucking bout,\n # don't copy!!!!\n # p_data = _copy(p_data)\n pass\n # --- End: if\n\n # ------------------------------------------------------------\n # Update the partition\n # ------------------------------------------------------------\n if update:\n self.subarray = p_data # ?? DCH CHECK\n self.Units = p_units\n self.part = []\n self.axes = axes\n self.flip = flip\n self.flatten = []\n self.shape = p_shape\n self.location = p_location\n\n self._in_place_changes = in_place_changes\n\n # ------------------------------------------------------------\n # Return the numpy array\n # ------------------------------------------------------------\n return p_data","def __len__(self):\n return len(self.files[self.split])","def _extend_contiguous_traj_field(self, run_idx, traj_idx, field_path, field_data):\n\n traj_grp = self.h5['{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx)]\n field = traj_grp[field_path]\n\n # make sure this is a feature vector\n assert len(field_data.shape) > 1, \\\n \"field_data must be a feature vector with the same number of dimensions as the number\"\n\n # of datase new frames\n n_new_frames = field_data.shape[0]\n\n # check the field to make sure it is not empty\n if all([i == 0 for i in field.shape]):\n\n # check the feature shape against the maxshape which gives\n # the feature dimensions for an empty dataset\n assert field_data.shape[1:] == field.maxshape[1:], \\\n \"field feature dimensions must be the same, i.e. all but the first dimension\"\n\n # if it is empty resize it to make an array the size of\n # the new field_data with the maxshape for the feature\n # dimensions\n feature_dims = field.maxshape[1:]\n field.resize( (n_new_frames, *feature_dims) )\n\n # set the new data to this\n field[0:, ...] = field_data\n\n else:\n # make sure the new data has the right dimensions against\n # the shape it already has\n assert field_data.shape[1:] == field.shape[1:], \\\n \"field feature dimensions must be the same, i.e. all but the first dimension\"\n\n\n # append to the dataset on the first dimension, keeping the\n # others the same, these must be feature vectors and therefore\n # must exist\n field.resize( (field.shape[0] + n_new_frames, *field.shape[1:]) )\n # add the new data\n field[-n_new_frames:, ...] = field_data","def append_filepath(self, filepath):\n idx = len(self.t_sect['filepaths'])\n self.t_sect['filepaths'].append(filepath)\n return idx","def write_sub_4(self):\n self.subIndex[constants.sub_4_genre_albums].offset = (\n self.db_file.tell())\n self.subIndex[constants.sub_4_genre_albums].size = 8\n self.subIndex[constants.sub_4_genre_albums].count = (\n len(self.genreIndex) - 1)\n\n entry_offset = 0\n for giEntry in self.genreIndex[1:]:\n self.db_file.write(\n struct.pack(\n \"<HHHH\",\n giEntry.number,\n entry_offset,\n giEntry.number_of_albums,\n 0x0000))\n entry_offset += giEntry.number_of_albums","def load(self, i: int) -> np.ndarray:\n raise NotImplementedError(\"Do not call load from BaseLoader\")","def write_all_sub_indices(self):\n\n # remember where we are.\n temp_offset_1 = self.db_file.tell()\n\n # Write a filler for the relative offset to the first table\n self.db_file.write(struct.pack(\"<I\", 0x00000000))\n\n # Write the sub index entries (blank at this stage)\n self.write_sub_index()\n\n # self.subIndex[constants.sub_0_genre_performers].offset = \\\n # self.db_file.tell()\n self.write_sub_0()\n\n # self.subIndex[constants.sub_1_genre_performer_albums].offset = \\\n # self.db_file.tell()\n self.write_sub_1()\n\n # self.subIndex[constants.sub_2_genre_performer_album_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_2()\n\n # self.subIndex[constants.sub_3_genre_ordered_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_3()\n\n # self.subIndex[constants.sub_4_genre_albums].offset = \\\n # self.db_file.tell()\n self.write_sub_4()\n\n # self.subIndex[constants.sub_5_genre_album_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_5()\n\n # self.subIndex[constants.sub_6_genre_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_6()\n\n # self.subIndex[constants.sub_7_performer_albums].offset = \\\n # self.db_file.tell()\n self.write_sub_7()\n\n # self.subIndex[constants.sub_8_performer_album_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_8()\n\n # self.subIndex[constants.sub_9_performer_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_9()\n\n # self.subIndex[constants.sub_10_genre_performers].offset = \\\n # self.db_file.tell()\n self.write_sub_10()\n\n # self.subIndex[constants.sub_11_genre_performer_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_11()\n\n # self.subIndex[constants.sub_12_genre_ordered_titles].offset = \\\n # self.db_file.tell()\n self.write_sub_12()\n\n # Remeber where we are\n temp_offset_2 = self.db_file.tell()\n\n # Go back to the start\n self.db_file.seek(temp_offset_1)\n\n # Write the offset to the first table\n self.db_file.write(\n struct.pack(\n \"<I\",\n self.subIndex[constants.sub_0_genre_performers].offset -\n temp_offset_1))\n\n # Write the real data now\n self.write_sub_index()\n\n # Go to the end\n self.db_file.seek(temp_offset_2)","def extend(self, i):\n for x in i:\n self.add(x)","def update_subvarga(self, subvarga):\n\t\tself.subvarga = subvarga\n\t\tself.subvargaNum += 1","def _write_array_on_file(self, pa_array):\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\n self._num_bytes += pa_array.nbytes\n self.pa_writer.write_batch(pa_batch)","def add(perm, i):\n for j in self.increasing_children(i):\n add(perm, j)\n perm.append(i)","def enqueue(self, i):\n if len(self) == self.capacity:\n self._resize(self.capacity*2)\n\n if self.tail == self.capacity:\n self.tail = 0\n\n self.lst[self.tail] = i\n self.tail += 1\n self.n += 1","def _update_subfiles(self) -> None:\n\t\t# Clear list of subfiles\n\t\tself.subfiles.clear()\n\t\t# Iterate over Nodes\n\t\tfor node in self.nodes:\n\t\t\tfor file in node.get_subfiles():\n\t\t\t\tself.subfiles.add(\"{}/{}\".format(self.xml_dir, file))\n\t\t# Iterate over SubNodes\n\t\tfor subnode in self.subnodes:\n\t\t\tfor file in subnode.filenames:\n\t\t\t\tself.subfiles.add(\"{}/{}\".format(self.xml_dir, file))","def sequential_files(self, ctr=0):\n self._tempfiles[-1].ctr = ctr","def add(perm, i):\n for j in self.decreasing_children(i):\n add(perm, j)\n perm.append(i)","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def _dfs(self, i):\n self.tracks[i] = self.cnt\n for j in self.edges[i]:\n if self.tracks[j] == -1:\n self._dfs(j)","def get_sum(self, i):\n s = 0\n\n # index in BITree is 1 more than index in arr[]\n i += 1\n\n # Traverse to leaves of BITree[i]:\n while i > 0:\n s += self.BITree[i]\n\n # Move index to parent node (next set bit in binary representation)\n i -= i & (-i)\n\n return s","def nbytes_at(self, device_id:int):\n if self._slices:\n if isinstance(self._coherence._local_states[device_id], dict): # there are subarrays no this device\n if self._slices_hash in self._coherence._local_states[device_id].keys(): # this subarray is already there\n return self._array.nbytes_at(device_id)\n else: # the subarray will be moved to there\n return self._array.nbytes_at(device_id) + self.subarray_nbytes # add the incoming subarray size\n else: # there is a complete copy on this device, no need to prepare subarray\n return self.nbytes\n else:\n return self.nbytes","def inc_size(self):\r\n self.__length += 1","def i(self, i):\n\n self._i = i","def __add__(self, i):\n self.n += i\n plt.subplot(self.nx, self.ny, self.n)\n return True","def numberFiles(self):\n return self.n","def numberFiles(self):\n with open(self.inputfile) as fin:\n for n, _ in enumerate(fin, start=1): pass\n self.n = n\n return self.n","def append(self, file, idx):\n\n # print \"append %s %d\" % (file, idx)\n src = \"%s/%s\" % (self._dir, file)\n dst = \"%s/.%d.new\" % (self._tempdir, idx)\n copyfile(src, dst)\n result = self._run(\"%s --%d --block-size %d --bits %d --quiet --threads %d %s --mode %s --rehash %s %s\" %\n (self._ishakesumd, self._mode, self._block_size, self._output_bits, self._threads,\n self._profile, self._alg, self._hash, self._tempdir))\n os.remove(dst)\n return result","def add(self, index):\n index_bytes = int(index).to_bytes(self._index_size,\n byteorder=\"little\", signed=False)\n self._fout.write(index_bytes)","def file_close(self):\n if self.on_disk:\n self._subarray.close()","def write_sub_6(self):\n self.subIndex[constants.sub_6_genre_titles].offset = (\n self.offsets[constants.genre_title_offset])\n self.subIndex[constants.sub_6_genre_titles].size = (2)\n self.subIndex[constants.sub_6_genre_titles].count = (\n self.genre_title_table_length) # TODO: Could be len(mainIndex)","def putarow(self,i_,subi_,vali_):\n nzi_ = None\n if nzi_ is None:\n nzi_ = len(subi_)\n elif nzi_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nzi_ is None:\n nzi_ = len(vali_)\n elif nzi_ != len(vali_):\n raise IndexError(\"Inconsistent length of array vali\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if vali_ is None:\n raise ValueError(\"Argument vali cannot be None\")\n if vali_ is None:\n raise ValueError(\"Argument vali may not be None\")\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\n _vali_copyarray = False\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif vali_ is not None:\n _vali_copyarray = True\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\n _vali_np_tmp[:] = vali_\n assert _vali_np_tmp.flags.contiguous\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _vali_copyarray = False\n _vali_tmp = None\n \n res = __library__.MSK_XX_putarow(self.__nativep,i_,nzi_,_subi_tmp,_vali_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def assignMoreVectors(self, i):\n return","def __getitem__(self, i):\n new_data = super().__getitem__(i)\n if isinstance(i, slice):\n new_data = self.__class__(new_data)\n new_data.global_settings = copy.copy(self.global_settings)\n return new_data","def parse_subfiles(self) -> None:\n\t\t# Exit if type has no subfiles\n\n\t\tif not self.has_subfile:\n\t\t\treturn\n\t\t# Variables\n\t\tfile: str\n\t\tsubfiles: Set[str] = set()\n\n\t\t# Set has_subfile to false, allows parsing subfiles only as needed\n\t\tself.has_subfile = False\n\n\t\t# Iterate over RootNode children\n\t\tfor rootnode in self.root_nodes:\n\t\t\tfor file in rootnode.get_subfiles():\n\t\t\t\t# Check if rootnode already exists, skip if so.\n\t\t\t\tif self._valid_rootnode_file(file):\n\t\t\t\t\t# Add to subfiles list if not already present\n\t\t\t\t\tif file not in subfiles:\n\t\t\t\t\t\tsubfiles.add(file)\n\n\t\t# Add subfiles as RootNodes\n\t\tfor subfile in subfiles:\n\t\t\tself.add_rootnode(subfile)\n\t\t# Update\n\t\tself.update()\n\n\t\t# Rerun if new subfiles were added\n\t\tif self.has_subfile:\n\t\t\tself.parse_subfiles()","def add_slice(self, value):\n if isinstance(value, str):\n self._add_slice_length(len(value))\n self._data += value.encode(\"utf-8\")\n elif isinstance(value, (bytes, bytearray)):\n self._add_slice_length(len(value))\n self._data += value\n else:\n length = 0\n for _ in value:\n length += 1\n self._add_slice_length(length)\n self.add_array(value)","def refine(self, ijk):\n if self.cbc is None or not self.sub_block_count:\n raise ValueError(\n \"Cannot refine sub block model without specifying number \"\n \"of parent and sub blocks\"\n )\n try:\n inds = self.ijk_array_to_indices(ijk)\n except ValueError:\n inds = self.ijk_to_index(ijk)\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137","def write_sub_3(self):\n self.subIndex[constants.sub_3_genre_ordered_titles].offset = (\n self.offsets[constants.genre_title_order_offset])\n self.subIndex[constants.sub_3_genre_ordered_titles].size = 2\n # TODO: Could be len(mainIndex)\n self.subIndex[constants.sub_3_genre_ordered_titles].count = (\n self.genre_title_order_table_length)","def __len__(self) -> int:\n return len(self.files)","def appendsize(self, numents):\n pass","def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def load_chunk(self, idx):\n for f in self.filenames[idx:]:\n ...","def add_item(self, i, k):\n if k == self.K:\n self.K += 1\n self.m_N_numerators[k, :] = self.prior.k_0*self.prior.m_0\n self.S_N_partials[k, :] = self.prior.S_0 + self.prior.k_0*self._cached_prior_square_m_0\n self.m_N_numerators[k, :] += self.X[i]\n self.S_N_partials[k, :] += self._cached_square[i]\n self.counts[k] += 1\n self._update_log_prod_vars_and_inv_vars(k)\n self.assignments[i] = k","def _setup_n_ints_in_file(self):\n self.n_ints_in_file = sigproc.calc_n_ints_in_file(self.filename)","def add(self, data):\n if data.shape != self.shape:\n self.shape = data.shape\n if isinstance(self.child, vmedian):\n self.child.add(data)\n if (self.child.index == 0):\n self.buffer[self.index, :] = self.child.get(reshape=False)\n self.index = self.index + 1\n else:\n self.buffer[self.index, :] = np.ravel(data)\n self.index = self.index + 1\n\n if self.index == 3:\n self.index = 0\n self.initialized = True","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def reduce(self, array, index):\n\n return 0","def __setitem__(self, i, value):\n self._ar[i] = value","def n_total_files(self):\n return len(self.fileinfo)","def number_idx(self, filename):\n with open(filename) as fh:\n firstline = fh.readline()\n parts = firstline.split('\\t')\n # only add if there are 4 parts\n if len(parts) != 4:\n return\n\n count = 1\n def writeline(fho, line, count):\n fho.write(line.rstrip() + '\\t' + str(count) + '\\n')\n\n with open(filename + '.tmp', 'w+b') as fho:\n writeline(fho, firstline, count)\n count += 1\n for line in fh:\n writeline(fho, line, count)\n count += 1\n\n shutil.move(filename + '.tmp', filename)","def append(self, item):\n\n # resize array to 2*capacity if max capacity reached\n if self.count == self.capacity:\n self._resize(2 * self.capacity)\n\n # Append the item at the end of array\n self.the_array[self.count] = item\n self.count += 1","def add_overlap(self, other):\n assert isinstance(other, Chunk)\n overlap_slices = self._get_overlap_slices(other.slices)\n self.array[overlap_slices] += other.array[overlap_slices]","def add_error(self, i: int, error: Exception):\n\t\twith self.get_locks(self.error_case_indices,\n\t\t self.errors,\n\t\t self.error_count):\n\t\t\tself.error_case_indices[self.error_count.value] = i\n\t\t\tself.errors[self.error_count.value] = ERROR_TO_ID[error.__class__]\n\t\t\tself.error_count.value += 1","def set_chopped_reduced_files(self, run_number, slicer_key, gsas_file_list, append):\n # get tracker\n tracker = self.get_tracker(run_number, slicer_key)\n assert isinstance(tracker, DataReductionTracker), 'Must be a DataReductionTracker'\n\n # add files\n tracker.set_reduced_files(gsas_file_list, append)\n\n return","def add_subROI(self, ROI_in):\n self.subROIs.append(ROI_in)","def push_substructure_fraction(self):\n Total_sub_fraction = np.zeros((0,), dtype=np.float)\n ParType0_sub_fraction = np.zeros((0,), dtype=np.float)\n ParType1_sub_fraction = np.zeros((0,), dtype=np.float)\n ParType4_sub_fraction = np.zeros((0,), dtype=np.float)\n ParType5_sub_fraction = np.zeros((0,), dtype=np.float)\n\n for r in self.cluster.generate_apertures():\n part_sub_fraction_aperture = self.cluster.group_substructure_fraction(aperture_radius=r, \n out_allPartTypes=True)\n ParType0_sub_fraction = np.concatenate((ParType0_sub_fraction, [part_sub_fraction_aperture[0]]), axis=0)\n ParType1_sub_fraction = np.concatenate((ParType1_sub_fraction, [part_sub_fraction_aperture[1]]), axis=0)\n ParType4_sub_fraction = np.concatenate((ParType4_sub_fraction, [part_sub_fraction_aperture[2]]), axis=0)\n ParType5_sub_fraction = np.concatenate((ParType5_sub_fraction, [part_sub_fraction_aperture[3]]), axis=0)\n Total_sub_fraction_aperture = self.cluster.group_substructure_fraction(aperture_radius=r, \n out_allPartTypes=False)\n Total_sub_fraction = np.concatenate((Total_sub_fraction, [Total_sub_fraction_aperture]), axis=0)\n\n data = {'/Total_substructure_fraction' : np.array(Total_sub_fraction),\n '/ParType0_substructure_fraction': np.array(ParType0_sub_fraction),\n '/ParType1_substructure_fraction': np.array(ParType1_sub_fraction),\n '/ParType4_substructure_fraction': np.array(ParType4_sub_fraction),\n '/ParType5_substructure_fraction': np.array(ParType5_sub_fraction)}\n\n attributes = {'Description': \"\"\"Datasets with the fraction of mass bound to subhalos of the cluster, calculated \n from particles within a specific aperture radius from the Centre of Potential. Individual datasets contain \n substructure fraction information about each particle type separately, as well as one with combined \n total contribution.\n The substructure fraction is computed according to the equation:\n substructure_fraction = (total mass - fuzz mass) / total mass.\n\n Note: The substructure fraction can also be used as an indicator for a merging index.\n \"\"\",\n 'Units': '[None]'}\n\n out = FOFOutput(self.cluster, filename='substructure_fraction.hdf5', data=data, attrs=attributes)\n out.makefile()","def undo_scan(self, sub_array_id: int):","def write_sub_5(self):\n self.subIndex[constants.sub_5_genre_album_titles].offset = (\n self.db_file.tell())\n self.subIndex[constants.sub_5_genre_album_titles].size = 8\n\n entry_offset = len(self.genreIndex[0].titles)\n count = 0\n for giEntry in self.genreIndex[1:]:\n for album in giEntry.album_numbers:\n # print(\"Sub5 Album: {}\".format(album))\n number_of_titles = giEntry.number_of_titles_for_album(album)\n if number_of_titles > 0:\n self.db_file.write(\n struct.pack(\n \"<HHHH\",\n album,\n entry_offset,\n number_of_titles,\n 0x0000))\n entry_offset += number_of_titles\n count += 1\n\n self.subIndex[constants.sub_5_genre_album_titles].count = (count)","def files(self) -> _SeqNumSlicer:\n if self._seq_num_slicer is None:\n self._seq_num_slicer = _SeqNumSlicer(self)\n return self._seq_num_slicer","def __len__(self):\n return len(self.files)","def append(self, element):\n if self.n == self.capacity:\n self._resize(2*self.capacity) # resizing by 2x if size is not enough\n\n self.original_array[self.n] = element\n self.n += 1","def save(self, patch):\n internalSlices = self._get_internal_slices(patch.slices)\n self.array[internalSlices] = patch.array","def compute_accumulation(self, i):\n\n #These are just pointer reassignments, not a deep-copy.\n dx = self.dx_arr\n phi = self.porosity\n area = self.res_area\n cf = self.compressibility\n Bw = self.form_volume_factor\n\n return area * dx[i] * phi[i] * cf / Bw","def __len__(self):\n\n return len(self._file_list)","def __setitem__(self, i: int, item: Any) -> None:\n if i < 0:\n i = self._length + i\n\n curr = self._first\n index_so_far = 0\n\n while curr is not None:\n if index_so_far == i:\n curr.item = item\n break\n index_so_far += 1\n curr = curr.next\n if curr is None:\n raise IndexError","def propagateDirty(self, slot, subindex, roi):\n totalIndex = (self._subSlots.index(slot),) + subindex\n self.operator.propagateDirty(self, totalIndex, roi)","def totalfiles(self):\n return len([sz for sz in self.iterate()])","def addAtTail(self, val: int) -> None:\n self.addAtIndex(self.size, val)","def _average_data(self):\n # TODO: This function looks inefficient - can it be improved?\n output = np.zeros([self.nints_file, self.ngroups_file,\n self.rows, self.columns], dtype=self.data.dtype)\n count = np.zeros([self.nints_file, self.ngroups_file, 1, 1],\n dtype=self.data.dtype)\n\n for intg in range(0, self.nints):\n intg_file = intg // self.intavg\n for grp in range(0, self.ngroups):\n grp_file = grp // self.grpavg\n output[intg_file, grp_file, :, :] += self.data[intg, grp, :, :]\n count[intg_file, grp_file, 0, 0] += 1.0\n\n # Avoid divide by zero.\n iszero = np.where(count < 1.0)\n if iszero:\n count[iszero] = 1.0\n output = output / count\n\n del count \n return output","def insert(self, item: Crop) -> None:\n self._content.append(item)\n self._file_counts[item.annot_type] = self._file_counts.get(item.annot_type, 0) + 1","def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setitem__(self, i, val):\n\t\tif i < self.n:\n\t\t\tself.v[i] = val","def subarray(self):\n return self._subarray","def fileCount(self):\n pass","def __getitem__(self, index):\n row = self.metadata.iloc[index]\n vid_id = row.filename.split('.')[0]\n array_dict = self.dp.get_arrays(vid_id, self.num_subbursts, self.padding, self.random_start_points)\n array_dict['subbursts'] = self.apply_transform(array_dict['subbursts'])\n return array_dict","def getarow(self,i_,subi_,vali_):\n nzi_ = ctypes.c_int32()\n _subi_minlength = self.getarownumnz((i_))\n if self.getarownumnz((i_)) > 0 and subi_ is not None and len(subi_) != self.getarownumnz((i_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),self.getarownumnz((i_))))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _vali_minlength = self.getarownumnz((i_))\n if self.getarownumnz((i_)) > 0 and vali_ is not None and len(vali_) != self.getarownumnz((i_)):\n raise ValueError(\"Array argument vali is not long enough: Is %d, expected %d\" % (len(vali_),self.getarownumnz((i_))))\n if isinstance(vali_,numpy.ndarray) and not vali_.flags.writeable:\n raise ValueError(\"Argument vali must be writable\")\n if vali_ is None:\n raise ValueError(\"Argument vali may not be None\")\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\n _vali_copyarray = False\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif vali_ is not None:\n _vali_copyarray = True\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\n _vali_np_tmp[:] = vali_\n assert _vali_np_tmp.flags.contiguous\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _vali_copyarray = False\n _vali_tmp = None\n \n res = __library__.MSK_XX_getarow(self.__nativep,i_,ctypes.byref(nzi_),_subi_tmp,_vali_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _vali_copyarray:\n vali_[:] = _vali_np_tmp\n return (_nzi_return_value)","def getFileCount(self) -> int:\n ...","def add_filechild(self, node):\n self._filechildren.append(node)","def unique_files(self):\n self._tempfiles[-1].ctr = -1","def append(self, obj):\n if self._size == self._capacity: # not enough room\n self._resize(2 * self._capacity) # so double capacity\n self._Array[self._size] = obj\n self._size += 1","def appendsize(self, numents):\n self._numents += numents","def _add_field(self, field_path, data, sparse_idxs=None,\n force=False):\n\n for i, run_idx in enumerate(self.run_idxs):\n if sparse_idxs is not None:\n self._add_run_field(run_idx, field_path, data[i], sparse_idxs=sparse_idxs[i],\n force=force)\n else:\n self._add_run_field(run_idx, field_path, data[i],\n force=force)","def add(self, filename, n_pages):\n with open(filename, 'rb') as f:\n content = f.read()\n size = len(content)\n\n # Before storing the filename I strip first part of output path which is the parent\n # directory of all of these files. We don't want that info in here because it will become\n # wrong if these files are ever moved, and we want them to be relocatable without\n # breaking anything.\n filename = os.path.relpath(filename, self.output_path)\n\n self.fileinfo[filename] = {'n_pages': n_pages,\n 'size': size,\n 'hash': hashlib.sha256(content).hexdigest(),\n }\n\n self.n_total_pages += n_pages\n self.n_total_bytes += size","def addFile(self, filePath): \n \n self.filePathDict[filePath] = [0,[]]","def _append_x(self, i):\n if not 0 <= i < self.num_qubits:\n raise QiskitError(\"X qubit out of bounds.\")\n self.shift[i] = (self.shift[i] + 1) % 2","def set_iload(self):\n k = self.istore[0]\n v = self.stencil.get_all_velocities()\n indices = self.istore[1:].copy()\n indices[1] += v[k].T[1]\n self.iload.append(np.concatenate([k[np.newaxis, :], indices]))","def record(self, variable, t_start=None): # @UnusedVariable\n for comp_array in self.component_arrays:\n comp_array.record(variable)","def __init__(self, arr, n):\n self.BITree = [0] * (n+1)\n self.size = n\n\n for i in range(n):\n self.update(i, arr[i])","def putarow(self,i_,subi,vali): # 3\n nzi_ = None\n if nzi_ is None:\n nzi_ = len(subi)\n elif nzi_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nzi_ is None:\n nzi_ = len(vali)\n elif nzi_ != len(vali):\n raise IndexError(\"Inconsistent length of array vali\")\n if nzi_ is None: nzi_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if vali is None: raise TypeError(\"Invalid type for argument vali\")\n if vali is None:\n vali_ = None\n else:\n try:\n vali_ = memoryview(vali)\n except TypeError:\n try:\n _tmparr_vali = array.array(\"d\",vali)\n except TypeError:\n raise TypeError(\"Argument vali has wrong type\")\n else:\n vali_ = memoryview(_tmparr_vali)\n \n else:\n if vali_.format != \"d\":\n vali_ = memoryview(array.array(\"d\",vali))\n \n res = self.__obj.putarow(i_,nzi_,subi_,vali_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __len__(self):\n return len(self.file_paths)","def append(self, narray, name):\n if narray is NoneArray:\n # if NoneArray, nothing to do.\n return\n\n if self.Association == ArrayAssociation.POINT:\n arrLength = self.DataSet.GetNumberOfPoints()\n elif self.Association == ArrayAssociation.CELL:\n arrLength = self.DataSet.GetNumberOfCells()\n elif self.Association == ArrayAssociation.ROW \\\n and self.DataSet.GetNumberOfColumns() > 0:\n arrLength = self.DataSet.GetNumberOfRows()\n else:\n if not isinstance(narray, numpy.ndarray):\n arrLength = 1\n else:\n arrLength = narray.shape[0]\n\n # if input is not a valid array (i.e. unexpected shape[0]),\n # create a new array and copy input for each element\n if not isinstance(narray, numpy.ndarray) or numpy.ndim(narray) == 0: # Scalar input\n dtype = narray.dtype if isinstance(narray, numpy.ndarray) else type(narray)\n tmparray = numpy.empty(arrLength, dtype=dtype)\n tmparray.fill(narray)\n narray = tmparray\n elif narray.shape[0] != arrLength: # Vector input\n components = 1\n for l in narray.shape:\n components *= l\n try:\n tmparray = numpy.empty((arrLength, components), dtype=narray.dtype)\n except numpy.core._exceptions._ArrayMemoryError as npErr:\n sys.stderr.write(\"Fail to copy input array for each dataset element: array is too big to be duplicated.\\n\"\n \"Input should either be small enough to be duplicated for each element, or shape[0] should \"\n \"match number of element.\\n\"\n \"Example of correct usage: to add a point PointData array, it is common to have\\n\"\n \"array.shape[0] == 3 or array.shape[0] == dataset.GetNumberOfPoints()\\n\"\n )\n sys.stderr.write(str(type(npErr)) + \"\\n\")\n sys.stderr.write(str(npErr))\n return\n\n tmparray[:] = narray.flatten()\n narray = tmparray\n\n shape = narray.shape\n\n if len(shape) == 3:\n # Array of matrices. We need to make sure the order in memory is right.\n # If column order (c order), transpose. VTK wants row order (fortran\n # order). The deep copy later will make sure that the array is contiguous.\n # If row order but not contiguous, transpose so that the deep copy below\n # does not happen.\n size = narray.dtype.itemsize\n if (narray.strides[1]/size == 3 and narray.strides[2]/size == 1) or \\\n (narray.strides[1]/size == 1 and narray.strides[2]/size == 3 and \\\n not narray.flags.contiguous):\n narray = narray.transpose(0, 2, 1)\n\n # If array is not contiguous, make a deep copy that is contiguous\n if not narray.flags.contiguous:\n narray = numpy.ascontiguousarray(narray)\n\n # Flatten array of matrices to array of vectors\n if len(shape) == 3:\n narray = narray.reshape(shape[0], shape[1]*shape[2])\n\n # this handle the case when an input array is directly appended on the\n # output. We want to make sure that the array added to the output is not\n # referring to the input dataset.\n copy = VTKArray(narray)\n try:\n copy.VTKObject = narray.VTKObject\n except AttributeError: pass\n arr = numpyTovtkDataArray(copy, name)\n self.VTKObject.AddArray(arr)","def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass","def n_file(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=True):\n n += 1\n return n"],"string":"[\n \"def n_subfile(self):\\n self.assert_is_dir_and_exists()\\n n = 0\\n for _ in self.select_file(recursive=False):\\n n += 1\\n return n\",\n \"def update(self, i, v):\\n # index in BTree is 1 more than index in arr[]\\n i += 1\\n\\n # Traverse to ancestors of BITree[i]\\n while i <= self.size:\\n self.BITree[i] += v\\n\\n # Update index to next set bit in binary representation\\n i += i & (-i)\",\n \"def __del__(self):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n # If the subarray is unique it will have 2 references to\\n # it plus 1 within this method, making 3. If it has more\\n # than 3 references to it then it is not unique.\\n if getrefcount is not None:\\n self._decrement_file_counter()\\n if subarray is None or getrefcount(subarray) > 3:\\n return\\n else:\\n # getrefcount has itself been deleted or is in the process\\n # of being torn down\\n return\\n\\n _partition_file = getattr(subarray, \\\"_partition_file\\\", None)\\n if _partition_file is not None:\\n # This partition contains a temporary file which is not\\n # referenced by any other partition on this process, so if\\n # there are no lock files present remove the file from\\n # disk.\\n _remove_temporary_files(_partition_file)\\n\\n else:\\n try:\\n if FileArray is not None and isinstance(subarray, FileArray):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if self.file_counter.get(filename, 999) <= 0:\\n # This partition contains a non-temporary file\\n # which is not referenced by any other\\n # partitions, so close the file.\\n subarray.close()\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\\n # --- End: if\",\n \"def write_sub_index(self):\\n for sie in self.subIndex:\\n self.db_file.write(sie.get_representation())\",\n \"def add(self, i: int, v: int) -> None:\\n while i < self.size:\\n self.tree[i] += v\\n i += self._lsb(i)\",\n \"def __setitem__(self, filenr, data_arr):\\n cvcfile = self.filenames[filenr]\\n cvcpath = os.path.join(self.filefolder, cvcfile)\\n data_arr.tofile(cvcpath)\",\n \"def _increment_file_counter(self):\\n self._add_to_file_counter(1)\",\n \"def add_index(self, idx, subproblem_shape):\\n self.indices.append(int(idx))\\n self.subproblem_shapes.append(subproblem_shape)\",\n \"def add_photo(self, new_photo, i):\\r\\n self.__photos[i] = new_photo\",\n \"def append(self, i):\\n \\n self.ret.append(i)\",\n \"def array(self):\\n config = self.config\\n\\n unique_array = config[\\\"unique_subarray\\\"]\\n\\n p_axes = self.axes\\n p_flip = self.flip\\n p_part = self.part\\n p_units = self.Units\\n p_shape = self.shape\\n p_location = self.location\\n subarray = self._subarray\\n\\n len_p_axes = len(p_axes)\\n\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is not in memory.\\n #\\n # It could be in a file on disk or implied by a FileArray\\n # object, etc.\\n # --------------------------------------------------------\\n self._original = self.copy()\\n\\n unique_array = True\\n update = True\\n copy = False\\n\\n if not p_part:\\n indices = Ellipsis\\n else:\\n indices = tuple(p_part)\\n\\n # Read from a file into a numpy array\\n p_data = subarray[indices]\\n\\n # We've just copied p_data from disk, so in place changes\\n # are not possible\\n in_place_changes = False\\n else:\\n # --------------------------------------------------------\\n # The subarray is in memory\\n # --------------------------------------------------------\\n update = config[\\\"update\\\"]\\n\\n if p_part:\\n p_data = get_subspace(subarray, p_part)\\n elif not unique_array:\\n p_data = subarray.view()\\n else:\\n p_data = subarray\\n\\n copy = config[\\\"extra_memory\\\"]\\n\\n # In place changes to p_data might be possible if we're not\\n # copying the data\\n in_place_changes = not copy\\n\\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\\n # --------------------------------------------------------\\n # p_data is a numpy number (like numpy.int64) which does\\n # not support assignment, so convert it to a numpy array.\\n # --------------------------------------------------------\\n p_data = numpy_array(p_data)\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n\\n masked = numpy_ma_isMA(p_data)\\n if masked:\\n # The p_data is a masked array\\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\\n p_data\\n ):\\n # There are no missing data points so recast as an\\n # unmasked numpy array\\n p_data = p_data.data\\n masked = False\\n # --- End: if\\n\\n if masked:\\n # Set the hardness of the mask\\n if config[\\\"hardmask\\\"]:\\n p_data.harden_mask()\\n else:\\n p_data.soften_mask()\\n # --- End: if\\n\\n self.masked = masked\\n\\n # ------------------------------------------------------------\\n # Make sure that the data array has the correct units. This\\n # process will deep copy the data array if required (e.g. if\\n # another partition is referencing this numpy array), even if\\n # the units are already correct.\\n # ------------------------------------------------------------\\n func = config.get(\\\"func\\\")\\n units = config[\\\"units\\\"]\\n if func is None:\\n if not p_units.equals(units) and bool(p_units) is bool(units):\\n func = Units.conform\\n\\n if func is not None:\\n inplace = not copy\\n p_data = func(p_data, p_units, units, inplace)\\n p_units = units\\n\\n if not inplace:\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n # --- End: if\\n\\n flip = config.get(\\\"flip\\\", None)\\n if flip or p_flip:\\n flip_axes = set(p_flip).symmetric_difference(flip)\\n else:\\n flip_axes = None\\n\\n axes = config[\\\"axes\\\"]\\n\\n if p_data.size > 1:\\n # --------------------------------------------------------\\n # Flip axes\\n # --------------------------------------------------------\\n if flip_axes:\\n indices = [\\n (\\n slice(None, None, -1)\\n if axis in flip_axes\\n else slice(None)\\n )\\n for axis in p_axes\\n ]\\n p_data = p_data[tuple(indices)]\\n\\n # --------------------------------------------------------\\n # Transpose axes\\n # --------------------------------------------------------\\n if p_axes != axes:\\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\\n\\n if len_p_axes > len(iaxes):\\n for i in range(len_p_axes):\\n if i not in iaxes:\\n # iaxes.append(i)\\n iaxes.insert(i, i)\\n # --- End: if\\n\\n p_data = numpy_transpose(p_data, iaxes)\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Remove excessive/insert missing size 1 axes\\n # ------------------------------------------------------------\\n if p_shape != p_data.shape:\\n # if len_p_axes != len(p_shape):\\n p_data = p_data.reshape(p_shape)\\n\\n # ------------------------------------------------------------\\n # Apply the auxiliary mask\\n # ------------------------------------------------------------\\n auxiliary_mask = config[\\\"auxiliary_mask\\\"]\\n if auxiliary_mask:\\n for mask in auxiliary_mask:\\n if mask.any():\\n if not masked:\\n p_data = p_data.view(numpy_ma_MaskedArray)\\n masked = True\\n\\n p_data.mask = (mask | p_data.mask).array\\n # --- End: for\\n\\n self.masked = True\\n\\n # ------------------------------------------------------------\\n # Convert the array's data type\\n # ------------------------------------------------------------\\n p_dtype = p_data.dtype\\n dtype = config.get(\\\"dtype\\\", None)\\n if dtype is not None and dtype != p_dtype:\\n try:\\n p_data = p_data.astype(dtype) # Note: returns a copy\\n except ValueError:\\n raise ValueError(\\n \\\"Can't recast partition array from {} to {}\\\".format(\\n p_dtype.name, dtype.name\\n )\\n )\\n else:\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Copy the array\\n # -----------------------------------------------------------\\n if copy:\\n if p_dtype.char != \\\"O\\\":\\n if not masked or p_data.ndim > 0:\\n p_data = p_data.copy()\\n else:\\n # This is because numpy.ma.copy doesn't work for\\n # scalar arrays (at the moment, at least)\\n p_data = numpy_ma_masked_all((), p_data.dtype)\\n\\n # We've just copied p_data, so in place changes are\\n # not possible\\n in_place_changes = False\\n else:\\n # whilst netCDF4.netcdftime.datetime is mucking bout,\\n # don't copy!!!!\\n # p_data = _copy(p_data)\\n pass\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Update the partition\\n # ------------------------------------------------------------\\n if update:\\n self.subarray = p_data # ?? DCH CHECK\\n self.Units = p_units\\n self.part = []\\n self.axes = axes\\n self.flip = flip\\n self.flatten = []\\n self.shape = p_shape\\n self.location = p_location\\n\\n self._in_place_changes = in_place_changes\\n\\n # ------------------------------------------------------------\\n # Return the numpy array\\n # ------------------------------------------------------------\\n return p_data\",\n \"def __len__(self):\\n return len(self.files[self.split])\",\n \"def _extend_contiguous_traj_field(self, run_idx, traj_idx, field_path, field_data):\\n\\n traj_grp = self.h5['{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx)]\\n field = traj_grp[field_path]\\n\\n # make sure this is a feature vector\\n assert len(field_data.shape) > 1, \\\\\\n \\\"field_data must be a feature vector with the same number of dimensions as the number\\\"\\n\\n # of datase new frames\\n n_new_frames = field_data.shape[0]\\n\\n # check the field to make sure it is not empty\\n if all([i == 0 for i in field.shape]):\\n\\n # check the feature shape against the maxshape which gives\\n # the feature dimensions for an empty dataset\\n assert field_data.shape[1:] == field.maxshape[1:], \\\\\\n \\\"field feature dimensions must be the same, i.e. all but the first dimension\\\"\\n\\n # if it is empty resize it to make an array the size of\\n # the new field_data with the maxshape for the feature\\n # dimensions\\n feature_dims = field.maxshape[1:]\\n field.resize( (n_new_frames, *feature_dims) )\\n\\n # set the new data to this\\n field[0:, ...] = field_data\\n\\n else:\\n # make sure the new data has the right dimensions against\\n # the shape it already has\\n assert field_data.shape[1:] == field.shape[1:], \\\\\\n \\\"field feature dimensions must be the same, i.e. all but the first dimension\\\"\\n\\n\\n # append to the dataset on the first dimension, keeping the\\n # others the same, these must be feature vectors and therefore\\n # must exist\\n field.resize( (field.shape[0] + n_new_frames, *field.shape[1:]) )\\n # add the new data\\n field[-n_new_frames:, ...] = field_data\",\n \"def append_filepath(self, filepath):\\n idx = len(self.t_sect['filepaths'])\\n self.t_sect['filepaths'].append(filepath)\\n return idx\",\n \"def write_sub_4(self):\\n self.subIndex[constants.sub_4_genre_albums].offset = (\\n self.db_file.tell())\\n self.subIndex[constants.sub_4_genre_albums].size = 8\\n self.subIndex[constants.sub_4_genre_albums].count = (\\n len(self.genreIndex) - 1)\\n\\n entry_offset = 0\\n for giEntry in self.genreIndex[1:]:\\n self.db_file.write(\\n struct.pack(\\n \\\"<HHHH\\\",\\n giEntry.number,\\n entry_offset,\\n giEntry.number_of_albums,\\n 0x0000))\\n entry_offset += giEntry.number_of_albums\",\n \"def load(self, i: int) -> np.ndarray:\\n raise NotImplementedError(\\\"Do not call load from BaseLoader\\\")\",\n \"def write_all_sub_indices(self):\\n\\n # remember where we are.\\n temp_offset_1 = self.db_file.tell()\\n\\n # Write a filler for the relative offset to the first table\\n self.db_file.write(struct.pack(\\\"<I\\\", 0x00000000))\\n\\n # Write the sub index entries (blank at this stage)\\n self.write_sub_index()\\n\\n # self.subIndex[constants.sub_0_genre_performers].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_0()\\n\\n # self.subIndex[constants.sub_1_genre_performer_albums].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_1()\\n\\n # self.subIndex[constants.sub_2_genre_performer_album_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_2()\\n\\n # self.subIndex[constants.sub_3_genre_ordered_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_3()\\n\\n # self.subIndex[constants.sub_4_genre_albums].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_4()\\n\\n # self.subIndex[constants.sub_5_genre_album_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_5()\\n\\n # self.subIndex[constants.sub_6_genre_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_6()\\n\\n # self.subIndex[constants.sub_7_performer_albums].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_7()\\n\\n # self.subIndex[constants.sub_8_performer_album_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_8()\\n\\n # self.subIndex[constants.sub_9_performer_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_9()\\n\\n # self.subIndex[constants.sub_10_genre_performers].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_10()\\n\\n # self.subIndex[constants.sub_11_genre_performer_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_11()\\n\\n # self.subIndex[constants.sub_12_genre_ordered_titles].offset = \\\\\\n # self.db_file.tell()\\n self.write_sub_12()\\n\\n # Remeber where we are\\n temp_offset_2 = self.db_file.tell()\\n\\n # Go back to the start\\n self.db_file.seek(temp_offset_1)\\n\\n # Write the offset to the first table\\n self.db_file.write(\\n struct.pack(\\n \\\"<I\\\",\\n self.subIndex[constants.sub_0_genre_performers].offset -\\n temp_offset_1))\\n\\n # Write the real data now\\n self.write_sub_index()\\n\\n # Go to the end\\n self.db_file.seek(temp_offset_2)\",\n \"def extend(self, i):\\n for x in i:\\n self.add(x)\",\n \"def update_subvarga(self, subvarga):\\n\\t\\tself.subvarga = subvarga\\n\\t\\tself.subvargaNum += 1\",\n \"def _write_array_on_file(self, pa_array):\\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\\n self._num_bytes += pa_array.nbytes\\n self.pa_writer.write_batch(pa_batch)\",\n \"def add(perm, i):\\n for j in self.increasing_children(i):\\n add(perm, j)\\n perm.append(i)\",\n \"def enqueue(self, i):\\n if len(self) == self.capacity:\\n self._resize(self.capacity*2)\\n\\n if self.tail == self.capacity:\\n self.tail = 0\\n\\n self.lst[self.tail] = i\\n self.tail += 1\\n self.n += 1\",\n \"def _update_subfiles(self) -> None:\\n\\t\\t# Clear list of subfiles\\n\\t\\tself.subfiles.clear()\\n\\t\\t# Iterate over Nodes\\n\\t\\tfor node in self.nodes:\\n\\t\\t\\tfor file in node.get_subfiles():\\n\\t\\t\\t\\tself.subfiles.add(\\\"{}/{}\\\".format(self.xml_dir, file))\\n\\t\\t# Iterate over SubNodes\\n\\t\\tfor subnode in self.subnodes:\\n\\t\\t\\tfor file in subnode.filenames:\\n\\t\\t\\t\\tself.subfiles.add(\\\"{}/{}\\\".format(self.xml_dir, file))\",\n \"def sequential_files(self, ctr=0):\\n self._tempfiles[-1].ctr = ctr\",\n \"def add(perm, i):\\n for j in self.decreasing_children(i):\\n add(perm, j)\\n perm.append(i)\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def _dfs(self, i):\\n self.tracks[i] = self.cnt\\n for j in self.edges[i]:\\n if self.tracks[j] == -1:\\n self._dfs(j)\",\n \"def get_sum(self, i):\\n s = 0\\n\\n # index in BITree is 1 more than index in arr[]\\n i += 1\\n\\n # Traverse to leaves of BITree[i]:\\n while i > 0:\\n s += self.BITree[i]\\n\\n # Move index to parent node (next set bit in binary representation)\\n i -= i & (-i)\\n\\n return s\",\n \"def nbytes_at(self, device_id:int):\\n if self._slices:\\n if isinstance(self._coherence._local_states[device_id], dict): # there are subarrays no this device\\n if self._slices_hash in self._coherence._local_states[device_id].keys(): # this subarray is already there\\n return self._array.nbytes_at(device_id)\\n else: # the subarray will be moved to there\\n return self._array.nbytes_at(device_id) + self.subarray_nbytes # add the incoming subarray size\\n else: # there is a complete copy on this device, no need to prepare subarray\\n return self.nbytes\\n else:\\n return self.nbytes\",\n \"def inc_size(self):\\r\\n self.__length += 1\",\n \"def i(self, i):\\n\\n self._i = i\",\n \"def __add__(self, i):\\n self.n += i\\n plt.subplot(self.nx, self.ny, self.n)\\n return True\",\n \"def numberFiles(self):\\n return self.n\",\n \"def numberFiles(self):\\n with open(self.inputfile) as fin:\\n for n, _ in enumerate(fin, start=1): pass\\n self.n = n\\n return self.n\",\n \"def append(self, file, idx):\\n\\n # print \\\"append %s %d\\\" % (file, idx)\\n src = \\\"%s/%s\\\" % (self._dir, file)\\n dst = \\\"%s/.%d.new\\\" % (self._tempdir, idx)\\n copyfile(src, dst)\\n result = self._run(\\\"%s --%d --block-size %d --bits %d --quiet --threads %d %s --mode %s --rehash %s %s\\\" %\\n (self._ishakesumd, self._mode, self._block_size, self._output_bits, self._threads,\\n self._profile, self._alg, self._hash, self._tempdir))\\n os.remove(dst)\\n return result\",\n \"def add(self, index):\\n index_bytes = int(index).to_bytes(self._index_size,\\n byteorder=\\\"little\\\", signed=False)\\n self._fout.write(index_bytes)\",\n \"def file_close(self):\\n if self.on_disk:\\n self._subarray.close()\",\n \"def write_sub_6(self):\\n self.subIndex[constants.sub_6_genre_titles].offset = (\\n self.offsets[constants.genre_title_offset])\\n self.subIndex[constants.sub_6_genre_titles].size = (2)\\n self.subIndex[constants.sub_6_genre_titles].count = (\\n self.genre_title_table_length) # TODO: Could be len(mainIndex)\",\n \"def putarow(self,i_,subi_,vali_):\\n nzi_ = None\\n if nzi_ is None:\\n nzi_ = len(subi_)\\n elif nzi_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nzi_ is None:\\n nzi_ = len(vali_)\\n elif nzi_ != len(vali_):\\n raise IndexError(\\\"Inconsistent length of array vali\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if vali_ is None:\\n raise ValueError(\\\"Argument vali cannot be None\\\")\\n if vali_ is None:\\n raise ValueError(\\\"Argument vali may not be None\\\")\\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\\n _vali_copyarray = False\\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif vali_ is not None:\\n _vali_copyarray = True\\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\\n _vali_np_tmp[:] = vali_\\n assert _vali_np_tmp.flags.contiguous\\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _vali_copyarray = False\\n _vali_tmp = None\\n \\n res = __library__.MSK_XX_putarow(self.__nativep,i_,nzi_,_subi_tmp,_vali_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def assignMoreVectors(self, i):\\n return\",\n \"def __getitem__(self, i):\\n new_data = super().__getitem__(i)\\n if isinstance(i, slice):\\n new_data = self.__class__(new_data)\\n new_data.global_settings = copy.copy(self.global_settings)\\n return new_data\",\n \"def parse_subfiles(self) -> None:\\n\\t\\t# Exit if type has no subfiles\\n\\n\\t\\tif not self.has_subfile:\\n\\t\\t\\treturn\\n\\t\\t# Variables\\n\\t\\tfile: str\\n\\t\\tsubfiles: Set[str] = set()\\n\\n\\t\\t# Set has_subfile to false, allows parsing subfiles only as needed\\n\\t\\tself.has_subfile = False\\n\\n\\t\\t# Iterate over RootNode children\\n\\t\\tfor rootnode in self.root_nodes:\\n\\t\\t\\tfor file in rootnode.get_subfiles():\\n\\t\\t\\t\\t# Check if rootnode already exists, skip if so.\\n\\t\\t\\t\\tif self._valid_rootnode_file(file):\\n\\t\\t\\t\\t\\t# Add to subfiles list if not already present\\n\\t\\t\\t\\t\\tif file not in subfiles:\\n\\t\\t\\t\\t\\t\\tsubfiles.add(file)\\n\\n\\t\\t# Add subfiles as RootNodes\\n\\t\\tfor subfile in subfiles:\\n\\t\\t\\tself.add_rootnode(subfile)\\n\\t\\t# Update\\n\\t\\tself.update()\\n\\n\\t\\t# Rerun if new subfiles were added\\n\\t\\tif self.has_subfile:\\n\\t\\t\\tself.parse_subfiles()\",\n \"def add_slice(self, value):\\n if isinstance(value, str):\\n self._add_slice_length(len(value))\\n self._data += value.encode(\\\"utf-8\\\")\\n elif isinstance(value, (bytes, bytearray)):\\n self._add_slice_length(len(value))\\n self._data += value\\n else:\\n length = 0\\n for _ in value:\\n length += 1\\n self._add_slice_length(length)\\n self.add_array(value)\",\n \"def refine(self, ijk):\\n if self.cbc is None or not self.sub_block_count:\\n raise ValueError(\\n \\\"Cannot refine sub block model without specifying number \\\"\\n \\\"of parent and sub blocks\\\"\\n )\\n try:\\n inds = self.ijk_array_to_indices(ijk)\\n except ValueError:\\n inds = self.ijk_to_index(ijk)\\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137\",\n \"def write_sub_3(self):\\n self.subIndex[constants.sub_3_genre_ordered_titles].offset = (\\n self.offsets[constants.genre_title_order_offset])\\n self.subIndex[constants.sub_3_genre_ordered_titles].size = 2\\n # TODO: Could be len(mainIndex)\\n self.subIndex[constants.sub_3_genre_ordered_titles].count = (\\n self.genre_title_order_table_length)\",\n \"def __len__(self) -> int:\\n return len(self.files)\",\n \"def appendsize(self, numents):\\n pass\",\n \"def putaijlist(self,subi_,subj_,valij_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij_)\\n elif num_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def load_chunk(self, idx):\\n for f in self.filenames[idx:]:\\n ...\",\n \"def add_item(self, i, k):\\n if k == self.K:\\n self.K += 1\\n self.m_N_numerators[k, :] = self.prior.k_0*self.prior.m_0\\n self.S_N_partials[k, :] = self.prior.S_0 + self.prior.k_0*self._cached_prior_square_m_0\\n self.m_N_numerators[k, :] += self.X[i]\\n self.S_N_partials[k, :] += self._cached_square[i]\\n self.counts[k] += 1\\n self._update_log_prod_vars_and_inv_vars(k)\\n self.assignments[i] = k\",\n \"def _setup_n_ints_in_file(self):\\n self.n_ints_in_file = sigproc.calc_n_ints_in_file(self.filename)\",\n \"def add(self, data):\\n if data.shape != self.shape:\\n self.shape = data.shape\\n if isinstance(self.child, vmedian):\\n self.child.add(data)\\n if (self.child.index == 0):\\n self.buffer[self.index, :] = self.child.get(reshape=False)\\n self.index = self.index + 1\\n else:\\n self.buffer[self.index, :] = np.ravel(data)\\n self.index = self.index + 1\\n\\n if self.index == 3:\\n self.index = 0\\n self.initialized = True\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def reduce(self, array, index):\\n\\n return 0\",\n \"def __setitem__(self, i, value):\\n self._ar[i] = value\",\n \"def n_total_files(self):\\n return len(self.fileinfo)\",\n \"def number_idx(self, filename):\\n with open(filename) as fh:\\n firstline = fh.readline()\\n parts = firstline.split('\\\\t')\\n # only add if there are 4 parts\\n if len(parts) != 4:\\n return\\n\\n count = 1\\n def writeline(fho, line, count):\\n fho.write(line.rstrip() + '\\\\t' + str(count) + '\\\\n')\\n\\n with open(filename + '.tmp', 'w+b') as fho:\\n writeline(fho, firstline, count)\\n count += 1\\n for line in fh:\\n writeline(fho, line, count)\\n count += 1\\n\\n shutil.move(filename + '.tmp', filename)\",\n \"def append(self, item):\\n\\n # resize array to 2*capacity if max capacity reached\\n if self.count == self.capacity:\\n self._resize(2 * self.capacity)\\n\\n # Append the item at the end of array\\n self.the_array[self.count] = item\\n self.count += 1\",\n \"def add_overlap(self, other):\\n assert isinstance(other, Chunk)\\n overlap_slices = self._get_overlap_slices(other.slices)\\n self.array[overlap_slices] += other.array[overlap_slices]\",\n \"def add_error(self, i: int, error: Exception):\\n\\t\\twith self.get_locks(self.error_case_indices,\\n\\t\\t self.errors,\\n\\t\\t self.error_count):\\n\\t\\t\\tself.error_case_indices[self.error_count.value] = i\\n\\t\\t\\tself.errors[self.error_count.value] = ERROR_TO_ID[error.__class__]\\n\\t\\t\\tself.error_count.value += 1\",\n \"def set_chopped_reduced_files(self, run_number, slicer_key, gsas_file_list, append):\\n # get tracker\\n tracker = self.get_tracker(run_number, slicer_key)\\n assert isinstance(tracker, DataReductionTracker), 'Must be a DataReductionTracker'\\n\\n # add files\\n tracker.set_reduced_files(gsas_file_list, append)\\n\\n return\",\n \"def add_subROI(self, ROI_in):\\n self.subROIs.append(ROI_in)\",\n \"def push_substructure_fraction(self):\\n Total_sub_fraction = np.zeros((0,), dtype=np.float)\\n ParType0_sub_fraction = np.zeros((0,), dtype=np.float)\\n ParType1_sub_fraction = np.zeros((0,), dtype=np.float)\\n ParType4_sub_fraction = np.zeros((0,), dtype=np.float)\\n ParType5_sub_fraction = np.zeros((0,), dtype=np.float)\\n\\n for r in self.cluster.generate_apertures():\\n part_sub_fraction_aperture = self.cluster.group_substructure_fraction(aperture_radius=r, \\n out_allPartTypes=True)\\n ParType0_sub_fraction = np.concatenate((ParType0_sub_fraction, [part_sub_fraction_aperture[0]]), axis=0)\\n ParType1_sub_fraction = np.concatenate((ParType1_sub_fraction, [part_sub_fraction_aperture[1]]), axis=0)\\n ParType4_sub_fraction = np.concatenate((ParType4_sub_fraction, [part_sub_fraction_aperture[2]]), axis=0)\\n ParType5_sub_fraction = np.concatenate((ParType5_sub_fraction, [part_sub_fraction_aperture[3]]), axis=0)\\n Total_sub_fraction_aperture = self.cluster.group_substructure_fraction(aperture_radius=r, \\n out_allPartTypes=False)\\n Total_sub_fraction = np.concatenate((Total_sub_fraction, [Total_sub_fraction_aperture]), axis=0)\\n\\n data = {'/Total_substructure_fraction' : np.array(Total_sub_fraction),\\n '/ParType0_substructure_fraction': np.array(ParType0_sub_fraction),\\n '/ParType1_substructure_fraction': np.array(ParType1_sub_fraction),\\n '/ParType4_substructure_fraction': np.array(ParType4_sub_fraction),\\n '/ParType5_substructure_fraction': np.array(ParType5_sub_fraction)}\\n\\n attributes = {'Description': \\\"\\\"\\\"Datasets with the fraction of mass bound to subhalos of the cluster, calculated \\n from particles within a specific aperture radius from the Centre of Potential. Individual datasets contain \\n substructure fraction information about each particle type separately, as well as one with combined \\n total contribution.\\n The substructure fraction is computed according to the equation:\\n substructure_fraction = (total mass - fuzz mass) / total mass.\\n\\n Note: The substructure fraction can also be used as an indicator for a merging index.\\n \\\"\\\"\\\",\\n 'Units': '[None]'}\\n\\n out = FOFOutput(self.cluster, filename='substructure_fraction.hdf5', data=data, attrs=attributes)\\n out.makefile()\",\n \"def undo_scan(self, sub_array_id: int):\",\n \"def write_sub_5(self):\\n self.subIndex[constants.sub_5_genre_album_titles].offset = (\\n self.db_file.tell())\\n self.subIndex[constants.sub_5_genre_album_titles].size = 8\\n\\n entry_offset = len(self.genreIndex[0].titles)\\n count = 0\\n for giEntry in self.genreIndex[1:]:\\n for album in giEntry.album_numbers:\\n # print(\\\"Sub5 Album: {}\\\".format(album))\\n number_of_titles = giEntry.number_of_titles_for_album(album)\\n if number_of_titles > 0:\\n self.db_file.write(\\n struct.pack(\\n \\\"<HHHH\\\",\\n album,\\n entry_offset,\\n number_of_titles,\\n 0x0000))\\n entry_offset += number_of_titles\\n count += 1\\n\\n self.subIndex[constants.sub_5_genre_album_titles].count = (count)\",\n \"def files(self) -> _SeqNumSlicer:\\n if self._seq_num_slicer is None:\\n self._seq_num_slicer = _SeqNumSlicer(self)\\n return self._seq_num_slicer\",\n \"def __len__(self):\\n return len(self.files)\",\n \"def append(self, element):\\n if self.n == self.capacity:\\n self._resize(2*self.capacity) # resizing by 2x if size is not enough\\n\\n self.original_array[self.n] = element\\n self.n += 1\",\n \"def save(self, patch):\\n internalSlices = self._get_internal_slices(patch.slices)\\n self.array[internalSlices] = patch.array\",\n \"def compute_accumulation(self, i):\\n\\n #These are just pointer reassignments, not a deep-copy.\\n dx = self.dx_arr\\n phi = self.porosity\\n area = self.res_area\\n cf = self.compressibility\\n Bw = self.form_volume_factor\\n\\n return area * dx[i] * phi[i] * cf / Bw\",\n \"def __len__(self):\\n\\n return len(self._file_list)\",\n \"def __setitem__(self, i: int, item: Any) -> None:\\n if i < 0:\\n i = self._length + i\\n\\n curr = self._first\\n index_so_far = 0\\n\\n while curr is not None:\\n if index_so_far == i:\\n curr.item = item\\n break\\n index_so_far += 1\\n curr = curr.next\\n if curr is None:\\n raise IndexError\",\n \"def propagateDirty(self, slot, subindex, roi):\\n totalIndex = (self._subSlots.index(slot),) + subindex\\n self.operator.propagateDirty(self, totalIndex, roi)\",\n \"def totalfiles(self):\\n return len([sz for sz in self.iterate()])\",\n \"def addAtTail(self, val: int) -> None:\\n self.addAtIndex(self.size, val)\",\n \"def _average_data(self):\\n # TODO: This function looks inefficient - can it be improved?\\n output = np.zeros([self.nints_file, self.ngroups_file,\\n self.rows, self.columns], dtype=self.data.dtype)\\n count = np.zeros([self.nints_file, self.ngroups_file, 1, 1],\\n dtype=self.data.dtype)\\n\\n for intg in range(0, self.nints):\\n intg_file = intg // self.intavg\\n for grp in range(0, self.ngroups):\\n grp_file = grp // self.grpavg\\n output[intg_file, grp_file, :, :] += self.data[intg, grp, :, :]\\n count[intg_file, grp_file, 0, 0] += 1.0\\n\\n # Avoid divide by zero.\\n iszero = np.where(count < 1.0)\\n if iszero:\\n count[iszero] = 1.0\\n output = output / count\\n\\n del count \\n return output\",\n \"def insert(self, item: Crop) -> None:\\n self._content.append(item)\\n self._file_counts[item.annot_type] = self._file_counts.get(item.annot_type, 0) + 1\",\n \"def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(alphaptrb_)\\n elif num_ != len(alphaptrb_):\\n raise IndexError(\\\"Inconsistent length of array alphaptrb\\\")\\n if num_ is None:\\n num_ = len(alphaptre_)\\n elif num_ != len(alphaptre_):\\n raise IndexError(\\\"Inconsistent length of array alphaptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb cannot be None\\\")\\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb may not be None\\\")\\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptrb_ is not None:\\n _alphaptrb_copyarray = True\\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\\n _alphaptrb_np_tmp[:] = alphaptrb_\\n assert _alphaptrb_np_tmp.flags.contiguous\\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = None\\n \\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre cannot be None\\\")\\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre may not be None\\\")\\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptre_ is not None:\\n _alphaptre_copyarray = True\\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\\n _alphaptre_np_tmp[:] = alphaptre_\\n assert _alphaptre_np_tmp.flags.contiguous\\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = None\\n \\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setitem__(self, i, val):\\n\\t\\tif i < self.n:\\n\\t\\t\\tself.v[i] = val\",\n \"def subarray(self):\\n return self._subarray\",\n \"def fileCount(self):\\n pass\",\n \"def __getitem__(self, index):\\n row = self.metadata.iloc[index]\\n vid_id = row.filename.split('.')[0]\\n array_dict = self.dp.get_arrays(vid_id, self.num_subbursts, self.padding, self.random_start_points)\\n array_dict['subbursts'] = self.apply_transform(array_dict['subbursts'])\\n return array_dict\",\n \"def getarow(self,i_,subi_,vali_):\\n nzi_ = ctypes.c_int32()\\n _subi_minlength = self.getarownumnz((i_))\\n if self.getarownumnz((i_)) > 0 and subi_ is not None and len(subi_) != self.getarownumnz((i_)):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),self.getarownumnz((i_))))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _vali_minlength = self.getarownumnz((i_))\\n if self.getarownumnz((i_)) > 0 and vali_ is not None and len(vali_) != self.getarownumnz((i_)):\\n raise ValueError(\\\"Array argument vali is not long enough: Is %d, expected %d\\\" % (len(vali_),self.getarownumnz((i_))))\\n if isinstance(vali_,numpy.ndarray) and not vali_.flags.writeable:\\n raise ValueError(\\\"Argument vali must be writable\\\")\\n if vali_ is None:\\n raise ValueError(\\\"Argument vali may not be None\\\")\\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\\n _vali_copyarray = False\\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif vali_ is not None:\\n _vali_copyarray = True\\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\\n _vali_np_tmp[:] = vali_\\n assert _vali_np_tmp.flags.contiguous\\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _vali_copyarray = False\\n _vali_tmp = None\\n \\n res = __library__.MSK_XX_getarow(self.__nativep,i_,ctypes.byref(nzi_),_subi_tmp,_vali_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzi_ = nzi_.value\\n _nzi_return_value = nzi_\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\n if _vali_copyarray:\\n vali_[:] = _vali_np_tmp\\n return (_nzi_return_value)\",\n \"def getFileCount(self) -> int:\\n ...\",\n \"def add_filechild(self, node):\\n self._filechildren.append(node)\",\n \"def unique_files(self):\\n self._tempfiles[-1].ctr = -1\",\n \"def append(self, obj):\\n if self._size == self._capacity: # not enough room\\n self._resize(2 * self._capacity) # so double capacity\\n self._Array[self._size] = obj\\n self._size += 1\",\n \"def appendsize(self, numents):\\n self._numents += numents\",\n \"def _add_field(self, field_path, data, sparse_idxs=None,\\n force=False):\\n\\n for i, run_idx in enumerate(self.run_idxs):\\n if sparse_idxs is not None:\\n self._add_run_field(run_idx, field_path, data[i], sparse_idxs=sparse_idxs[i],\\n force=force)\\n else:\\n self._add_run_field(run_idx, field_path, data[i],\\n force=force)\",\n \"def add(self, filename, n_pages):\\n with open(filename, 'rb') as f:\\n content = f.read()\\n size = len(content)\\n\\n # Before storing the filename I strip first part of output path which is the parent\\n # directory of all of these files. We don't want that info in here because it will become\\n # wrong if these files are ever moved, and we want them to be relocatable without\\n # breaking anything.\\n filename = os.path.relpath(filename, self.output_path)\\n\\n self.fileinfo[filename] = {'n_pages': n_pages,\\n 'size': size,\\n 'hash': hashlib.sha256(content).hexdigest(),\\n }\\n\\n self.n_total_pages += n_pages\\n self.n_total_bytes += size\",\n \"def addFile(self, filePath): \\n \\n self.filePathDict[filePath] = [0,[]]\",\n \"def _append_x(self, i):\\n if not 0 <= i < self.num_qubits:\\n raise QiskitError(\\\"X qubit out of bounds.\\\")\\n self.shift[i] = (self.shift[i] + 1) % 2\",\n \"def set_iload(self):\\n k = self.istore[0]\\n v = self.stencil.get_all_velocities()\\n indices = self.istore[1:].copy()\\n indices[1] += v[k].T[1]\\n self.iload.append(np.concatenate([k[np.newaxis, :], indices]))\",\n \"def record(self, variable, t_start=None): # @UnusedVariable\\n for comp_array in self.component_arrays:\\n comp_array.record(variable)\",\n \"def __init__(self, arr, n):\\n self.BITree = [0] * (n+1)\\n self.size = n\\n\\n for i in range(n):\\n self.update(i, arr[i])\",\n \"def putarow(self,i_,subi,vali): # 3\\n nzi_ = None\\n if nzi_ is None:\\n nzi_ = len(subi)\\n elif nzi_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nzi_ is None:\\n nzi_ = len(vali)\\n elif nzi_ != len(vali):\\n raise IndexError(\\\"Inconsistent length of array vali\\\")\\n if nzi_ is None: nzi_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if vali is None: raise TypeError(\\\"Invalid type for argument vali\\\")\\n if vali is None:\\n vali_ = None\\n else:\\n try:\\n vali_ = memoryview(vali)\\n except TypeError:\\n try:\\n _tmparr_vali = array.array(\\\"d\\\",vali)\\n except TypeError:\\n raise TypeError(\\\"Argument vali has wrong type\\\")\\n else:\\n vali_ = memoryview(_tmparr_vali)\\n \\n else:\\n if vali_.format != \\\"d\\\":\\n vali_ = memoryview(array.array(\\\"d\\\",vali))\\n \\n res = self.__obj.putarow(i_,nzi_,subi_,vali_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __len__(self):\\n return len(self.file_paths)\",\n \"def append(self, narray, name):\\n if narray is NoneArray:\\n # if NoneArray, nothing to do.\\n return\\n\\n if self.Association == ArrayAssociation.POINT:\\n arrLength = self.DataSet.GetNumberOfPoints()\\n elif self.Association == ArrayAssociation.CELL:\\n arrLength = self.DataSet.GetNumberOfCells()\\n elif self.Association == ArrayAssociation.ROW \\\\\\n and self.DataSet.GetNumberOfColumns() > 0:\\n arrLength = self.DataSet.GetNumberOfRows()\\n else:\\n if not isinstance(narray, numpy.ndarray):\\n arrLength = 1\\n else:\\n arrLength = narray.shape[0]\\n\\n # if input is not a valid array (i.e. unexpected shape[0]),\\n # create a new array and copy input for each element\\n if not isinstance(narray, numpy.ndarray) or numpy.ndim(narray) == 0: # Scalar input\\n dtype = narray.dtype if isinstance(narray, numpy.ndarray) else type(narray)\\n tmparray = numpy.empty(arrLength, dtype=dtype)\\n tmparray.fill(narray)\\n narray = tmparray\\n elif narray.shape[0] != arrLength: # Vector input\\n components = 1\\n for l in narray.shape:\\n components *= l\\n try:\\n tmparray = numpy.empty((arrLength, components), dtype=narray.dtype)\\n except numpy.core._exceptions._ArrayMemoryError as npErr:\\n sys.stderr.write(\\\"Fail to copy input array for each dataset element: array is too big to be duplicated.\\\\n\\\"\\n \\\"Input should either be small enough to be duplicated for each element, or shape[0] should \\\"\\n \\\"match number of element.\\\\n\\\"\\n \\\"Example of correct usage: to add a point PointData array, it is common to have\\\\n\\\"\\n \\\"array.shape[0] == 3 or array.shape[0] == dataset.GetNumberOfPoints()\\\\n\\\"\\n )\\n sys.stderr.write(str(type(npErr)) + \\\"\\\\n\\\")\\n sys.stderr.write(str(npErr))\\n return\\n\\n tmparray[:] = narray.flatten()\\n narray = tmparray\\n\\n shape = narray.shape\\n\\n if len(shape) == 3:\\n # Array of matrices. We need to make sure the order in memory is right.\\n # If column order (c order), transpose. VTK wants row order (fortran\\n # order). The deep copy later will make sure that the array is contiguous.\\n # If row order but not contiguous, transpose so that the deep copy below\\n # does not happen.\\n size = narray.dtype.itemsize\\n if (narray.strides[1]/size == 3 and narray.strides[2]/size == 1) or \\\\\\n (narray.strides[1]/size == 1 and narray.strides[2]/size == 3 and \\\\\\n not narray.flags.contiguous):\\n narray = narray.transpose(0, 2, 1)\\n\\n # If array is not contiguous, make a deep copy that is contiguous\\n if not narray.flags.contiguous:\\n narray = numpy.ascontiguousarray(narray)\\n\\n # Flatten array of matrices to array of vectors\\n if len(shape) == 3:\\n narray = narray.reshape(shape[0], shape[1]*shape[2])\\n\\n # this handle the case when an input array is directly appended on the\\n # output. We want to make sure that the array added to the output is not\\n # referring to the input dataset.\\n copy = VTKArray(narray)\\n try:\\n copy.VTKObject = narray.VTKObject\\n except AttributeError: pass\\n arr = numpyTovtkDataArray(copy, name)\\n self.VTKObject.AddArray(arr)\",\n \"def close(self, **kwargs):\\n config = getattr(self, \\\"config\\\", None)\\n\\n if config is None:\\n return\\n\\n if kwargs:\\n config.update(kwargs)\\n\\n original = getattr(self, \\\"_original\\\", None)\\n logger.partitioning(\\\"Partition.close: original = {}\\\".format(original))\\n\\n if not original:\\n originally_on_disk = False\\n original_subarray = None\\n else:\\n originally_on_disk = not original.in_memory\\n original_subarray = original._subarray\\n\\n config = self.config\\n logger.partitioning(\\\" config = {}\\\".format(config))\\n\\n if config[\\\"serial\\\"]:\\n # --------------------------------------------------------\\n # SERIAL\\n # --------------------------------------------------------\\n logger.partitioning(\\\" serial\\\")\\n\\n if config[\\\"readonly\\\"]:\\n logger.partitioning(\\\" readonly=True\\\")\\n\\n if originally_on_disk:\\n logger.partitioning(\\\" subarray originally on disk\\\")\\n\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.1.1 The original subarray was on disk,\\n # we don't want to keep the current\\n # subarray in memory, and we are happy\\n # to discard any changes that may have\\n # been made to the subarray.\\n logger.partitioning(\\\" 1.1.1.1 revert\\\")\\n self.revert()\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.1.2 The original subarray was on disk,\\n # we are happy to keep the current\\n # subarray in memory, but there is not\\n # enough free memory to do so.\\n logger.partitioning(\\n \\\" 1.1.1.2 revert ({} <= {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n self.revert()\\n else:\\n # 1.1.1.3 The original subarray was on disk\\n # and there is enough memory to keep\\n # the current subarray in memory\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # The original subarray was a temporary\\n # file which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n del self.masked\\n logger.partitioning(\\n \\\" 1.1.1.3 del masked ({} > {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n\\n else:\\n logger.partitioning(\\\" subarray originally in memory\\\")\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.2.1 Original subarray was in memory and\\n # we don't want to keep the current\\n # subarray in memory\\n logger.partitioning(\\\" 1.1.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.2.2 Original subarray was in memory and\\n # unique but there is not enough\\n # memory to keep the current subarray\\n logger.partitioning(\\\" 1.1.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.1.2.3 Original subarray was in memory and\\n # unique and there is enough memory to\\n # keep the current subarray in memory\\n logger.partitioning(\\\" 1.1.2.3 pass\\\")\\n pass\\n else:\\n # config['readonly'] is False\\n if originally_on_disk:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.1.1 Original subarray was on disk and\\n # there and we don't want to keep the\\n # array\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.1.2 Original subarray was on disk but\\n # there is not enough memory to keep\\n # it\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.1.3 Original subarray was on disk and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.1.3 pass\\\")\\n del self.masked\\n else:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.2.1 Original subarray was in memory but\\n # we don't want to keep it\\n logger.partitioning(\\\" 1.2.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.2.2 Original subarray was an in memory\\n # but there is not enough memory to\\n # keep it\\n logger.partitioning(\\\" 1.2.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.2.3 Original subarray was in memory and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.2.3 del masked\\\")\\n del self.masked\\n else:\\n logger.partitioning(\\\"Partition.close: parallel\\\")\\n # --------------------------------------------------------\\n # PARALLEL\\n # --------------------------------------------------------\\n pass\\n\\n # if hasattr(self, '_original'):\\n # del self._original\\n\\n # print(hasattr(self, 'config')),\\n try:\\n del self.config\\n except AttributeError:\\n pass\",\n \"def n_file(self):\\n self.assert_is_dir_and_exists()\\n n = 0\\n for _ in self.select_file(recursive=True):\\n n += 1\\n return n\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5323221","0.5309046","0.5237938","0.52028364","0.51664454","0.5117871","0.51150346","0.5107957","0.50491905","0.50423014","0.5001557","0.499577","0.49840355","0.4932267","0.4909338","0.48659304","0.48641643","0.48607743","0.48591715","0.4854085","0.48242405","0.4823937","0.481012","0.48052314","0.479596","0.47512335","0.47405446","0.47338173","0.4703466","0.4691803","0.4688549","0.46817592","0.4680104","0.4654203","0.46459475","0.46297154","0.46291628","0.46258196","0.46218735","0.46185297","0.46144783","0.46128556","0.4603453","0.46022874","0.45898673","0.45790026","0.45789003","0.4577212","0.4553954","0.4548344","0.45458877","0.45420754","0.45385808","0.45285302","0.45233715","0.45175835","0.4508444","0.44914016","0.4490856","0.44868115","0.44860977","0.44850603","0.44798177","0.447787","0.44748962","0.44737417","0.4465449","0.44598728","0.4451822","0.4449522","0.44396365","0.44305938","0.4429977","0.44233325","0.4397269","0.43967542","0.43899348","0.43896294","0.43873632","0.4383333","0.43830273","0.4368216","0.43651482","0.4364754","0.4362301","0.43620268","0.43598688","0.4359031","0.43568614","0.43551305","0.4352283","0.43510228","0.43477228","0.43430525","0.434094","0.43387714","0.4334496","0.4331667","0.43255523","0.43240017"],"string":"[\n \"0.5323221\",\n \"0.5309046\",\n \"0.5237938\",\n \"0.52028364\",\n \"0.51664454\",\n \"0.5117871\",\n \"0.51150346\",\n \"0.5107957\",\n \"0.50491905\",\n \"0.50423014\",\n \"0.5001557\",\n \"0.499577\",\n \"0.49840355\",\n \"0.4932267\",\n \"0.4909338\",\n \"0.48659304\",\n \"0.48641643\",\n \"0.48607743\",\n \"0.48591715\",\n \"0.4854085\",\n \"0.48242405\",\n \"0.4823937\",\n \"0.481012\",\n \"0.48052314\",\n \"0.479596\",\n \"0.47512335\",\n \"0.47405446\",\n \"0.47338173\",\n \"0.4703466\",\n \"0.4691803\",\n \"0.4688549\",\n \"0.46817592\",\n \"0.4680104\",\n \"0.4654203\",\n \"0.46459475\",\n \"0.46297154\",\n \"0.46291628\",\n \"0.46258196\",\n \"0.46218735\",\n \"0.46185297\",\n \"0.46144783\",\n \"0.46128556\",\n \"0.4603453\",\n \"0.46022874\",\n \"0.45898673\",\n \"0.45790026\",\n \"0.45789003\",\n \"0.4577212\",\n \"0.4553954\",\n \"0.4548344\",\n \"0.45458877\",\n \"0.45420754\",\n \"0.45385808\",\n \"0.45285302\",\n \"0.45233715\",\n \"0.45175835\",\n \"0.4508444\",\n \"0.44914016\",\n \"0.4490856\",\n \"0.44868115\",\n \"0.44860977\",\n \"0.44850603\",\n \"0.44798177\",\n \"0.447787\",\n \"0.44748962\",\n \"0.44737417\",\n \"0.4465449\",\n \"0.44598728\",\n \"0.4451822\",\n \"0.4449522\",\n \"0.44396365\",\n \"0.44305938\",\n \"0.4429977\",\n \"0.44233325\",\n \"0.4397269\",\n \"0.43967542\",\n \"0.43899348\",\n \"0.43896294\",\n \"0.43873632\",\n \"0.4383333\",\n \"0.43830273\",\n \"0.4368216\",\n \"0.43651482\",\n \"0.4364754\",\n \"0.4362301\",\n \"0.43620268\",\n \"0.43598688\",\n \"0.4359031\",\n \"0.43568614\",\n \"0.43551305\",\n \"0.4352283\",\n \"0.43510228\",\n \"0.43477228\",\n \"0.43430525\",\n \"0.434094\",\n \"0.43387714\",\n \"0.4334496\",\n \"0.4331667\",\n \"0.43255523\",\n \"0.43240017\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8270168"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2675,"cells":{"query":{"kind":"string","value":"Add 1 to the Partition.file_counter if self._subarray is an instance of FileArray and not a temporary FileArray."},"document":{"kind":"string","value":"def _increment_file_counter(self):\n self._add_to_file_counter(1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if filename is None:\n return\n\n file_counter = self.file_counter\n # count = file_counter.get(filename, 0)\n # file_counter[filename] = count + i\n # if file_counter[filename] <= 0:\n count = file_counter.get(filename, 0) + i\n if count <= 0:\n # Remove the file from the dictionary if its count has\n # dropped to zero\n file_counter.pop(filename, None)\n else:\n file_counter[filename] = count\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass","def __len__(self):\n return len(self.files[self.split])","def _decrement_file_counter(self):\n self._add_to_file_counter(-1)","def fileCount(self):\n pass","def getFileCount(self) -> int:\n ...","def numberFiles(self):\n with open(self.inputfile) as fin:\n for n, _ in enumerate(fin, start=1): pass\n self.n = n\n return self.n","def numberFiles(self):\n return self.n","def count_files(self):\n self.file_count = 0\n self.count_files_loop(self.dirpath)\n return","def unique_files(self):\n self._tempfiles[-1].ctr = -1","def __len__(self) -> int:\n return len(self.files)","def stat_beg_file(self, filename):\n\n self.batchvals['numfiles'] += 1\n self.filevals['filename'] = filename\n self.filevals['start_time'] = time.time()\n\n return -1","def __len__(self):\n return len(self.files)","def file_num_increment(full_fpath):\r\n while os.path.isfile(full_fpath) == True:\r\n \r\n fpath, fext = os.path.splitext(full_fpath) #['C:\\Users\\Desktop\\file(1)', '.ext']\r\n\r\n if re.findall(\"[(]\\d+[)]\", fpath) != []: #Check if there is (x) in the path.\r\n for counter in range(1000): #Loop 1000 times\r\n if fpath.endswith(f\"({counter})\"): \r\n fpath = replace_last(fpath, f\"({counter})\", f\"({counter+1})\") #Replace the last occurence of (counter) in the string.\r\n full_fpath = fpath + fext\r\n break\r\n else: #here we pass for cases where (counter) is in the file/folder name itself. We skip them.\r\n continue\r\n else: #If there is no (counter), we create (1)\r\n counter = 1\r\n full_fpath = fpath + '(' + str(counter) + ')' + fext\r\n\r\n return full_fpath","def __len__(self):\n\n return len(self._file_list)","def sequential_files(self, ctr=0):\n self._tempfiles[-1].ctr = ctr","def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n # than 3 references to it then it is not unique.\n if getrefcount is not None:\n self._decrement_file_counter()\n if subarray is None or getrefcount(subarray) > 3:\n return\n else:\n # getrefcount has itself been deleted or is in the process\n # of being torn down\n return\n\n _partition_file = getattr(subarray, \"_partition_file\", None)\n if _partition_file is not None:\n # This partition contains a temporary file which is not\n # referenced by any other partition on this process, so if\n # there are no lock files present remove the file from\n # disk.\n _remove_temporary_files(_partition_file)\n\n else:\n try:\n if FileArray is not None and isinstance(subarray, FileArray):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if self.file_counter.get(filename, 999) <= 0:\n # This partition contains a non-temporary file\n # which is not referenced by any other\n # partitions, so close the file.\n subarray.close()\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass\n # --- End: if","def _write_array_on_file(self, pa_array):\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\n self._num_bytes += pa_array.nbytes\n self.pa_writer.write_batch(pa_batch)","def on_disk(self):\n return isinstance(self._subarray, FileArray)","def n_subfile(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=False):\n n += 1\n return n","def __number_of_files(self):\n self.__get_files()\n return len(self.files)","def fileCounter(directory):","def n_total_files(self):\n return len(self.fileinfo)","def setNextFile(self):\n\n if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile):\n self.nReadFiles=self.nReadFiles+1\n if self.nReadFiles > self.nTotalReadFiles:\n self.flagNoMoreFiles=1\n raise schainpy.admin.SchainWarning('No more files to read')\n\n print('------------------- [Opening file] ------------------------------',self.nReadFiles)\n self.nReadBlocks = 0\n #if self.nReadBlocks==0:\n # self.readFirstHeader()","def number_idx(self, filename):\n with open(filename) as fh:\n firstline = fh.readline()\n parts = firstline.split('\\t')\n # only add if there are 4 parts\n if len(parts) != 4:\n return\n\n count = 1\n def writeline(fho, line, count):\n fho.write(line.rstrip() + '\\t' + str(count) + '\\n')\n\n with open(filename + '.tmp', 'w+b') as fho:\n writeline(fho, firstline, count)\n count += 1\n for line in fh:\n writeline(fho, line, count)\n count += 1\n\n shutil.move(filename + '.tmp', filename)","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def __len__(self):\n return len(self.file_paths)","def increment_count(self):\n self.image_count +=1\n if self.image_count > self.max_count:\n self.image_count = self.count_start # overflow","def totalfiles(self):\n return len([sz for sz in self.iterate()])","def append_filepath(self, filepath):\n idx = len(self.t_sect['filepaths'])\n self.t_sect['filepaths'].append(filepath)\n return idx","def incr_counter(self, path):\n res = self.read_counter(path)\n # print 'incr_counter:', path, res, '->', res + 1\n res += 1\n self.cursor.execute('REPLACE INTO counter(fullpath, count) VALUES(?, ?)', (path, res))\n self.conn.commit()\n pass","def inc_size(self):\r\n self.__length += 1","def n_file(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=True):\n n += 1\n return n","def count_lines(file_obj):\n for idx, line in enumerate(file_obj):\n pass\n file_obj.seek(0)\n return idx + 1","def __setitem__(self, filenr, data_arr):\n cvcfile = self.filenames[filenr]\n cvcpath = os.path.join(self.filefolder, cvcfile)\n data_arr.tofile(cvcpath)","def files_processed(self) -> int:\n with self.lock:\n return self._files_processed","def getnrfiles(self):\n return len(self.filenames)","def __len__(self):\n return len(self.frame1_files)","def file_close(self):\n if self.on_disk:\n self._subarray.close()","def get_num_files(self, file_type):\n return self.file_type_counter.get(file_type, 0)","def fileno(self):\n return 1","def increment(self):\n self.pos += 1\n if self.pos == len(self.progress) - 1:\n self.pos = 0","def getFileCount(self, startingWithPath=\"\"):\n return self.__controller._getRecordsCount(startingWithPath)","def file_count(self) -> int:\n if self.dataset is None:\n raise ValueError('No known dataset found!')\n return self._max_file_count","def inc(self):\n \n self.count += 1","def __len__(self):\n return len(self.files_self_A_rgbd)","def getFileCount(self):\n\n if self.filecount == -1:\n self.filecount = self.db.filecount()\n\n return self.filecount","def increment(self):\n self.increments += 1\n if self.increments == self.length:\n self.finished = True","def updateCounts(self):\n found = False\n fileName = \"counts\"\n if not os.access(fileName, os.F_OK):\n try:\n TFH = open(fileName, \"w\")\n TFH.close()\n except IOError as inst: # @UnusedVariable\n self.logIt(__name__ + \".updateCounts(): Unable to open \" + fileName + \" for write.\" + \" => \" + str(\n inst.errno) + \":\" + str(inst.strerror) + \"\\n\")\n raise\n\n self.logIt(__name__ + \".updateCounts(): fileName=\" + fileName + \"\\n\")\n try:\n FH = open(fileName, \"rb+\")\n # FH = posixfile.open(fileName, \"rb+\") # posixfile has been deprecated.\n # FH.lock('w|')\n data = None\n while 1:\n data = str(FH.readline())\n if data is None or data == \"\": break\n data = re.sub(\"\\n\", \"\", data)\n self.debug(__name__ + \".updateCounts(): data is \" + str(data) + \"\\n\")\n ms = str(self.msgNum) + \"=\"\n self.debug(__name__ + \".updateCounts(): ms is\" + str(ms) + \"\\n\")\n if re.search(ms, data):\n found = True\n self.debug(__name__ + \".updateCounts(): DEBUG0.5\\n\")\n break\n self.debug(__name__ + \".updateCounts(): DEBUG1\\n\")\n if data and found:\n self.debug(__name__ + \".updateCounts(): DEBUG2\\n\")\n eloc = FH.tell()\n self.debug(__name__ + \".updateCounts(): eloc=\" + str(eloc) + \"\\n\")\n sloc = eloc - len(data) - 1\n self.debug(__name__ + \".updateCounts(): sloc=\" + str(sloc) + \"\\n\")\n FH.seek(sloc, os.SEEK_SET)\n cloc = FH.tell()\n self.debug(__name__ + \".updateCounts(): cloc=\" + str(cloc) + \"\\n\")\n myList = list()\n myList = data.split('=')\n icount = int(myList[1]) + 1\n FH.write(str(self.msgNum) + \"=\" + str(icount) + \"\\n\")\n else:\n self.debug(__name__ + \".updateCounts(): DEBUG3\\n\")\n FH.write(str(self.msgNum) + \"=1\" + \"\\n\")\n FH.lock('u')\n FH.close()\n except IOError as inst: # @UnusedVariable\n pass\n # self.logIt( __name__ + \".updateCounts(): Unable to open \" + fileName + \" for write.\" + \" => \" + str( inst.errno ) + \":\" + str( inst.strerror ) + \"\\n\" )\n # Endtry","def _get_file_length(self, file):\n self[file] = file.stat().st_size","def fget(self):\n if not hasattr(self, \"_n\"):\n self._n = 0\n self._n += 1\n return self._n","def incrementWriteCount(self):\n self.writeCount += 1","def __len__(self):\n return self._num_samples_per_file * len(self._files) // self._world_size","def inc( self ):\n self.count += 1","def _setcounter():\n fname = os.path.basename(camera.status.lastfile)\n tname = fname.split('.')[0]\n i = len(tname)-1\n if i > -1:\n while tname[i].isdigit() and i>-1:\n i = i - 1\n nname = fname[:-4]\n bname = tname[:i+1]\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9].cntr'):\n os.remove(file)\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9][0-9].cntr'):\n os.remove(file)\n f = open('/data/counters/'+nname+'cntr','w')\n f.close()","def advance_image_count(self,file_id=None,image_num=None):\n # self.next_image = (self.next_image+1) % self.num_images\n if file_id is not None:\n self.file_id = file_id\n if image_num is None:\n self.next_image += 1\n else:\n self.next_image = image_num + 1\n if self.next_image >= self.num_images:\n self.next_image = 0\n self.file_id += 1\n self.signal_next_image_num.emit(self.next_image)\n self.signal_file_id.emit(self.file_id)","def _register_temporary_file(self):\n _partition_file = self._subarray._partition_file\n _partition_dir = self._subarray._partition_dir\n if _partition_file not in _temporary_files:\n fd, _lock_file = mkstemp(\n prefix=_partition_file + \"_\", dir=_partition_dir\n )\n close(fd)\n _temporary_files[_partition_file] = (\n _partition_dir,\n _lock_file,\n set(),\n )\n else:\n _, _lock_file, _ = _temporary_files[_partition_file]\n\n return _lock_file","def getNumTimeDataFiles(self):\n return self.nTimeDataFiles","def file_len(f):\n\n for n, l in enumerate(f, 1):\n pass\n f.seek(0) # rewind\n return n","def fs_files_total(self):\n return self._fs_files_total","def increase_counter(self):\n self.values = self.values + 1","def increment(self, features, fname, v=1):\n if fname not in features:\n features[fname] = 0\n features[fname] += v","def get_nrof_aux(self):\n aux = 0\n for l in self.aux_array:\n if l:\n aux += 1\n return aux","def fix_index(self):\n if self.record_size <= self.size:\n self.record_size += 1\n if self.index % self.size == 0:\n self.isFull = True if len(self._storage) == self.size else False\n if self.replace_flag:\n self.index = 1\n return self.index\n else:\n sys.stderr.write('Experience replay buff is full and replace is set to FALSE!\\n')\n return -1\n else:\n self.index += 1\n return self.index","def fileno(self):\r\n raise NotImplementedError()","def _setup_n_ints_in_file(self):\n self.n_ints_in_file = sigproc.calc_n_ints_in_file(self.filename)","def increment_counter(self) -> None:","def file_progress_sig_handler(self, bytes_read: int):\n # Increment the bytes read\n self.file_bytes_read += bytes_read\n\n # Update the progress bar\n self.fileAnalyzeProgressBar.setValue(self.file_bytes_read)\n\n logging.debug(\"Analyzing File Progress: \" + str(self.file_bytes_read))","def getNumStatDataFiles(self):\n return self.nStatDataFiles","def has_subfile(self) -> bool:\n\t\tself._update_subfiles()\n\t\treturn bool(len(self.subfiles))","def size(self) -> int:\n return sum(p.size for p in self.iterfiles())","def add_count(self):\n self.count += 1","def file_num(self):\n command = \"SELECT COUNT(id) FROM files;\"\n return self.c.execute(command)","def __len__(self):\n return int(np.floor(len(self.wav_paths)))","def touched_files(self, parent):","def file_count(self) -> str:\n return pulumi.get(self, \"file_count\")","def len(self):\n # print(self.processed_file_names)\n return self.len_","def updateCounter(self):\n self.counter = self.counter + 1\n self.syncDataStructure[\"+\"][str(self.instanceID)] = self.counter","def handle_put_progress(self, filegen):\n # print \"bytes so-far: \", filegen.bytes_read\n\n if self.maybe_touch():\n self.log(\"UPLOAD_PROGRESS\", level=INFO)\n self.touch()\n Backend.touch(\n self.current_upload,\n bytes_downloaded=filegen.bytes_read,\n location=self.location)","def segment_counter(self, _):\n raise NotImplementedError(\n \"We do not support externally altering the segment counter\")","def get_data_id(self):\n self.data_id = len(glob.glob(osp.join(self.save_dir, 'depth', '*npy')))\n return self.data_id","def get_lenght(self):\n return len(self.filelist)","def file_number(self, file_number):\n\n self._file_number = file_number","def file_number(self, file_number):\n\n self._file_number = file_number","def initiallize_buffer(self):\n assert os.path.isdir(self.directory)\n #sorting files topologically, files' format is -> data_num.h5 \n files_list = sorted(os.listdir(self.directory + '/' + self.name + '/'), key = lambda x: int(x.split(\"_\")[1].split(\".\")[0]))\n self.files_counter = 0\n if files_list != []: \n for file_name in files_list:\n self.memorize(name = file_name, error = 1)\n self.files_counter += 1\n self.files_tracker = file_name\n else:\n self.files_tracker = 'data_-1.h5'","def add_file(self, letter, block_size):\n cluster = 1\n i = 0\n j = 0\n\n continuous = True\n while(i<self.size and j<block_size):\n if(self.disk_mem[i]==\".\"):\n self.disk_mem[i] = letter\n if not continuous:\n continuous = True\n cluster += 1\n j+=1\n else:\n continuous = False\n i+=1\n return cluster","def upload_files(self):\n return 1 << 0","def addFile(self, filePath): \n \n self.filePathDict[filePath] = [0,[]]","def _inc_counter(self) -> None:\n self._state_storage.increment_counter()","def fs_files_used(self):\n return self._fs_files_used","def get_num_files(self):\n\t\tif self.num_files_in_set is None and self.set_type == FAST5SET_TARBALL:\n\t\t\tself.num_files_in_set = len(self.files)\n\t\treturn self.num_files_in_set","def addPointCountToEdge(self, edge):\n attributes = edge.getAttributes()\n if self.edge_id__count.has_key(attributes.get(self.shapeFileUniqueId)):\n self.edge_id__count[attributes.get(self.shapeFileUniqueId)] = self.edge_id__count[attributes.get(self.shapeFileUniqueId)] + 1\n else:\n self.edge_id__count[attributes.get(self.shapeFileUniqueId)] = 1\n edge.setAttributes(attributes)","def process(self):\n self.reader += 1","def count(self):\n\t\treturn sum(read.copy for read in self.__iter__())","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def min_file_histogram(self):\n return self._min_file_histogram","def _update_count(self):\n self._count = len(self._items)","def __len__(self):\n\n return math.ceil(len(self.img_files) * self.gen_count / self.batch_size)","def reduce(self, array, index):\n\n return 0","def __len__(self):\n return len(self.image_file_names)","def fileno(self):\n return None"],"string":"[\n \"def _add_to_file_counter(self, i):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n if subarray is None:\\n return\\n\\n try:\\n if isinstance(subarray, FileArray) and not isinstance(\\n subarray, CachedArray\\n ):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if filename is None:\\n return\\n\\n file_counter = self.file_counter\\n # count = file_counter.get(filename, 0)\\n # file_counter[filename] = count + i\\n # if file_counter[filename] <= 0:\\n count = file_counter.get(filename, 0) + i\\n if count <= 0:\\n # Remove the file from the dictionary if its count has\\n # dropped to zero\\n file_counter.pop(filename, None)\\n else:\\n file_counter[filename] = count\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\",\n \"def __len__(self):\\n return len(self.files[self.split])\",\n \"def _decrement_file_counter(self):\\n self._add_to_file_counter(-1)\",\n \"def fileCount(self):\\n pass\",\n \"def getFileCount(self) -> int:\\n ...\",\n \"def numberFiles(self):\\n with open(self.inputfile) as fin:\\n for n, _ in enumerate(fin, start=1): pass\\n self.n = n\\n return self.n\",\n \"def numberFiles(self):\\n return self.n\",\n \"def count_files(self):\\n self.file_count = 0\\n self.count_files_loop(self.dirpath)\\n return\",\n \"def unique_files(self):\\n self._tempfiles[-1].ctr = -1\",\n \"def __len__(self) -> int:\\n return len(self.files)\",\n \"def stat_beg_file(self, filename):\\n\\n self.batchvals['numfiles'] += 1\\n self.filevals['filename'] = filename\\n self.filevals['start_time'] = time.time()\\n\\n return -1\",\n \"def __len__(self):\\n return len(self.files)\",\n \"def file_num_increment(full_fpath):\\r\\n while os.path.isfile(full_fpath) == True:\\r\\n \\r\\n fpath, fext = os.path.splitext(full_fpath) #['C:\\\\Users\\\\Desktop\\\\file(1)', '.ext']\\r\\n\\r\\n if re.findall(\\\"[(]\\\\d+[)]\\\", fpath) != []: #Check if there is (x) in the path.\\r\\n for counter in range(1000): #Loop 1000 times\\r\\n if fpath.endswith(f\\\"({counter})\\\"): \\r\\n fpath = replace_last(fpath, f\\\"({counter})\\\", f\\\"({counter+1})\\\") #Replace the last occurence of (counter) in the string.\\r\\n full_fpath = fpath + fext\\r\\n break\\r\\n else: #here we pass for cases where (counter) is in the file/folder name itself. We skip them.\\r\\n continue\\r\\n else: #If there is no (counter), we create (1)\\r\\n counter = 1\\r\\n full_fpath = fpath + '(' + str(counter) + ')' + fext\\r\\n\\r\\n return full_fpath\",\n \"def __len__(self):\\n\\n return len(self._file_list)\",\n \"def sequential_files(self, ctr=0):\\n self._tempfiles[-1].ctr = ctr\",\n \"def __del__(self):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n # If the subarray is unique it will have 2 references to\\n # it plus 1 within this method, making 3. If it has more\\n # than 3 references to it then it is not unique.\\n if getrefcount is not None:\\n self._decrement_file_counter()\\n if subarray is None or getrefcount(subarray) > 3:\\n return\\n else:\\n # getrefcount has itself been deleted or is in the process\\n # of being torn down\\n return\\n\\n _partition_file = getattr(subarray, \\\"_partition_file\\\", None)\\n if _partition_file is not None:\\n # This partition contains a temporary file which is not\\n # referenced by any other partition on this process, so if\\n # there are no lock files present remove the file from\\n # disk.\\n _remove_temporary_files(_partition_file)\\n\\n else:\\n try:\\n if FileArray is not None and isinstance(subarray, FileArray):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if self.file_counter.get(filename, 999) <= 0:\\n # This partition contains a non-temporary file\\n # which is not referenced by any other\\n # partitions, so close the file.\\n subarray.close()\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\\n # --- End: if\",\n \"def _write_array_on_file(self, pa_array):\\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\\n self._num_bytes += pa_array.nbytes\\n self.pa_writer.write_batch(pa_batch)\",\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def n_subfile(self):\\n self.assert_is_dir_and_exists()\\n n = 0\\n for _ in self.select_file(recursive=False):\\n n += 1\\n return n\",\n \"def __number_of_files(self):\\n self.__get_files()\\n return len(self.files)\",\n \"def fileCounter(directory):\",\n \"def n_total_files(self):\\n return len(self.fileinfo)\",\n \"def setNextFile(self):\\n\\n if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile):\\n self.nReadFiles=self.nReadFiles+1\\n if self.nReadFiles > self.nTotalReadFiles:\\n self.flagNoMoreFiles=1\\n raise schainpy.admin.SchainWarning('No more files to read')\\n\\n print('------------------- [Opening file] ------------------------------',self.nReadFiles)\\n self.nReadBlocks = 0\\n #if self.nReadBlocks==0:\\n # self.readFirstHeader()\",\n \"def number_idx(self, filename):\\n with open(filename) as fh:\\n firstline = fh.readline()\\n parts = firstline.split('\\\\t')\\n # only add if there are 4 parts\\n if len(parts) != 4:\\n return\\n\\n count = 1\\n def writeline(fho, line, count):\\n fho.write(line.rstrip() + '\\\\t' + str(count) + '\\\\n')\\n\\n with open(filename + '.tmp', 'w+b') as fho:\\n writeline(fho, firstline, count)\\n count += 1\\n for line in fh:\\n writeline(fho, line, count)\\n count += 1\\n\\n shutil.move(filename + '.tmp', filename)\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def __len__(self):\\n return len(self.file_paths)\",\n \"def increment_count(self):\\n self.image_count +=1\\n if self.image_count > self.max_count:\\n self.image_count = self.count_start # overflow\",\n \"def totalfiles(self):\\n return len([sz for sz in self.iterate()])\",\n \"def append_filepath(self, filepath):\\n idx = len(self.t_sect['filepaths'])\\n self.t_sect['filepaths'].append(filepath)\\n return idx\",\n \"def incr_counter(self, path):\\n res = self.read_counter(path)\\n # print 'incr_counter:', path, res, '->', res + 1\\n res += 1\\n self.cursor.execute('REPLACE INTO counter(fullpath, count) VALUES(?, ?)', (path, res))\\n self.conn.commit()\\n pass\",\n \"def inc_size(self):\\r\\n self.__length += 1\",\n \"def n_file(self):\\n self.assert_is_dir_and_exists()\\n n = 0\\n for _ in self.select_file(recursive=True):\\n n += 1\\n return n\",\n \"def count_lines(file_obj):\\n for idx, line in enumerate(file_obj):\\n pass\\n file_obj.seek(0)\\n return idx + 1\",\n \"def __setitem__(self, filenr, data_arr):\\n cvcfile = self.filenames[filenr]\\n cvcpath = os.path.join(self.filefolder, cvcfile)\\n data_arr.tofile(cvcpath)\",\n \"def files_processed(self) -> int:\\n with self.lock:\\n return self._files_processed\",\n \"def getnrfiles(self):\\n return len(self.filenames)\",\n \"def __len__(self):\\n return len(self.frame1_files)\",\n \"def file_close(self):\\n if self.on_disk:\\n self._subarray.close()\",\n \"def get_num_files(self, file_type):\\n return self.file_type_counter.get(file_type, 0)\",\n \"def fileno(self):\\n return 1\",\n \"def increment(self):\\n self.pos += 1\\n if self.pos == len(self.progress) - 1:\\n self.pos = 0\",\n \"def getFileCount(self, startingWithPath=\\\"\\\"):\\n return self.__controller._getRecordsCount(startingWithPath)\",\n \"def file_count(self) -> int:\\n if self.dataset is None:\\n raise ValueError('No known dataset found!')\\n return self._max_file_count\",\n \"def inc(self):\\n \\n self.count += 1\",\n \"def __len__(self):\\n return len(self.files_self_A_rgbd)\",\n \"def getFileCount(self):\\n\\n if self.filecount == -1:\\n self.filecount = self.db.filecount()\\n\\n return self.filecount\",\n \"def increment(self):\\n self.increments += 1\\n if self.increments == self.length:\\n self.finished = True\",\n \"def updateCounts(self):\\n found = False\\n fileName = \\\"counts\\\"\\n if not os.access(fileName, os.F_OK):\\n try:\\n TFH = open(fileName, \\\"w\\\")\\n TFH.close()\\n except IOError as inst: # @UnusedVariable\\n self.logIt(__name__ + \\\".updateCounts(): Unable to open \\\" + fileName + \\\" for write.\\\" + \\\" => \\\" + str(\\n inst.errno) + \\\":\\\" + str(inst.strerror) + \\\"\\\\n\\\")\\n raise\\n\\n self.logIt(__name__ + \\\".updateCounts(): fileName=\\\" + fileName + \\\"\\\\n\\\")\\n try:\\n FH = open(fileName, \\\"rb+\\\")\\n # FH = posixfile.open(fileName, \\\"rb+\\\") # posixfile has been deprecated.\\n # FH.lock('w|')\\n data = None\\n while 1:\\n data = str(FH.readline())\\n if data is None or data == \\\"\\\": break\\n data = re.sub(\\\"\\\\n\\\", \\\"\\\", data)\\n self.debug(__name__ + \\\".updateCounts(): data is \\\" + str(data) + \\\"\\\\n\\\")\\n ms = str(self.msgNum) + \\\"=\\\"\\n self.debug(__name__ + \\\".updateCounts(): ms is\\\" + str(ms) + \\\"\\\\n\\\")\\n if re.search(ms, data):\\n found = True\\n self.debug(__name__ + \\\".updateCounts(): DEBUG0.5\\\\n\\\")\\n break\\n self.debug(__name__ + \\\".updateCounts(): DEBUG1\\\\n\\\")\\n if data and found:\\n self.debug(__name__ + \\\".updateCounts(): DEBUG2\\\\n\\\")\\n eloc = FH.tell()\\n self.debug(__name__ + \\\".updateCounts(): eloc=\\\" + str(eloc) + \\\"\\\\n\\\")\\n sloc = eloc - len(data) - 1\\n self.debug(__name__ + \\\".updateCounts(): sloc=\\\" + str(sloc) + \\\"\\\\n\\\")\\n FH.seek(sloc, os.SEEK_SET)\\n cloc = FH.tell()\\n self.debug(__name__ + \\\".updateCounts(): cloc=\\\" + str(cloc) + \\\"\\\\n\\\")\\n myList = list()\\n myList = data.split('=')\\n icount = int(myList[1]) + 1\\n FH.write(str(self.msgNum) + \\\"=\\\" + str(icount) + \\\"\\\\n\\\")\\n else:\\n self.debug(__name__ + \\\".updateCounts(): DEBUG3\\\\n\\\")\\n FH.write(str(self.msgNum) + \\\"=1\\\" + \\\"\\\\n\\\")\\n FH.lock('u')\\n FH.close()\\n except IOError as inst: # @UnusedVariable\\n pass\\n # self.logIt( __name__ + \\\".updateCounts(): Unable to open \\\" + fileName + \\\" for write.\\\" + \\\" => \\\" + str( inst.errno ) + \\\":\\\" + str( inst.strerror ) + \\\"\\\\n\\\" )\\n # Endtry\",\n \"def _get_file_length(self, file):\\n self[file] = file.stat().st_size\",\n \"def fget(self):\\n if not hasattr(self, \\\"_n\\\"):\\n self._n = 0\\n self._n += 1\\n return self._n\",\n \"def incrementWriteCount(self):\\n self.writeCount += 1\",\n \"def __len__(self):\\n return self._num_samples_per_file * len(self._files) // self._world_size\",\n \"def inc( self ):\\n self.count += 1\",\n \"def _setcounter():\\n fname = os.path.basename(camera.status.lastfile)\\n tname = fname.split('.')[0]\\n i = len(tname)-1\\n if i > -1:\\n while tname[i].isdigit() and i>-1:\\n i = i - 1\\n nname = fname[:-4]\\n bname = tname[:i+1]\\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9].cntr'):\\n os.remove(file)\\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9][0-9].cntr'):\\n os.remove(file)\\n f = open('/data/counters/'+nname+'cntr','w')\\n f.close()\",\n \"def advance_image_count(self,file_id=None,image_num=None):\\n # self.next_image = (self.next_image+1) % self.num_images\\n if file_id is not None:\\n self.file_id = file_id\\n if image_num is None:\\n self.next_image += 1\\n else:\\n self.next_image = image_num + 1\\n if self.next_image >= self.num_images:\\n self.next_image = 0\\n self.file_id += 1\\n self.signal_next_image_num.emit(self.next_image)\\n self.signal_file_id.emit(self.file_id)\",\n \"def _register_temporary_file(self):\\n _partition_file = self._subarray._partition_file\\n _partition_dir = self._subarray._partition_dir\\n if _partition_file not in _temporary_files:\\n fd, _lock_file = mkstemp(\\n prefix=_partition_file + \\\"_\\\", dir=_partition_dir\\n )\\n close(fd)\\n _temporary_files[_partition_file] = (\\n _partition_dir,\\n _lock_file,\\n set(),\\n )\\n else:\\n _, _lock_file, _ = _temporary_files[_partition_file]\\n\\n return _lock_file\",\n \"def getNumTimeDataFiles(self):\\n return self.nTimeDataFiles\",\n \"def file_len(f):\\n\\n for n, l in enumerate(f, 1):\\n pass\\n f.seek(0) # rewind\\n return n\",\n \"def fs_files_total(self):\\n return self._fs_files_total\",\n \"def increase_counter(self):\\n self.values = self.values + 1\",\n \"def increment(self, features, fname, v=1):\\n if fname not in features:\\n features[fname] = 0\\n features[fname] += v\",\n \"def get_nrof_aux(self):\\n aux = 0\\n for l in self.aux_array:\\n if l:\\n aux += 1\\n return aux\",\n \"def fix_index(self):\\n if self.record_size <= self.size:\\n self.record_size += 1\\n if self.index % self.size == 0:\\n self.isFull = True if len(self._storage) == self.size else False\\n if self.replace_flag:\\n self.index = 1\\n return self.index\\n else:\\n sys.stderr.write('Experience replay buff is full and replace is set to FALSE!\\\\n')\\n return -1\\n else:\\n self.index += 1\\n return self.index\",\n \"def fileno(self):\\r\\n raise NotImplementedError()\",\n \"def _setup_n_ints_in_file(self):\\n self.n_ints_in_file = sigproc.calc_n_ints_in_file(self.filename)\",\n \"def increment_counter(self) -> None:\",\n \"def file_progress_sig_handler(self, bytes_read: int):\\n # Increment the bytes read\\n self.file_bytes_read += bytes_read\\n\\n # Update the progress bar\\n self.fileAnalyzeProgressBar.setValue(self.file_bytes_read)\\n\\n logging.debug(\\\"Analyzing File Progress: \\\" + str(self.file_bytes_read))\",\n \"def getNumStatDataFiles(self):\\n return self.nStatDataFiles\",\n \"def has_subfile(self) -> bool:\\n\\t\\tself._update_subfiles()\\n\\t\\treturn bool(len(self.subfiles))\",\n \"def size(self) -> int:\\n return sum(p.size for p in self.iterfiles())\",\n \"def add_count(self):\\n self.count += 1\",\n \"def file_num(self):\\n command = \\\"SELECT COUNT(id) FROM files;\\\"\\n return self.c.execute(command)\",\n \"def __len__(self):\\n return int(np.floor(len(self.wav_paths)))\",\n \"def touched_files(self, parent):\",\n \"def file_count(self) -> str:\\n return pulumi.get(self, \\\"file_count\\\")\",\n \"def len(self):\\n # print(self.processed_file_names)\\n return self.len_\",\n \"def updateCounter(self):\\n self.counter = self.counter + 1\\n self.syncDataStructure[\\\"+\\\"][str(self.instanceID)] = self.counter\",\n \"def handle_put_progress(self, filegen):\\n # print \\\"bytes so-far: \\\", filegen.bytes_read\\n\\n if self.maybe_touch():\\n self.log(\\\"UPLOAD_PROGRESS\\\", level=INFO)\\n self.touch()\\n Backend.touch(\\n self.current_upload,\\n bytes_downloaded=filegen.bytes_read,\\n location=self.location)\",\n \"def segment_counter(self, _):\\n raise NotImplementedError(\\n \\\"We do not support externally altering the segment counter\\\")\",\n \"def get_data_id(self):\\n self.data_id = len(glob.glob(osp.join(self.save_dir, 'depth', '*npy')))\\n return self.data_id\",\n \"def get_lenght(self):\\n return len(self.filelist)\",\n \"def file_number(self, file_number):\\n\\n self._file_number = file_number\",\n \"def file_number(self, file_number):\\n\\n self._file_number = file_number\",\n \"def initiallize_buffer(self):\\n assert os.path.isdir(self.directory)\\n #sorting files topologically, files' format is -> data_num.h5 \\n files_list = sorted(os.listdir(self.directory + '/' + self.name + '/'), key = lambda x: int(x.split(\\\"_\\\")[1].split(\\\".\\\")[0]))\\n self.files_counter = 0\\n if files_list != []: \\n for file_name in files_list:\\n self.memorize(name = file_name, error = 1)\\n self.files_counter += 1\\n self.files_tracker = file_name\\n else:\\n self.files_tracker = 'data_-1.h5'\",\n \"def add_file(self, letter, block_size):\\n cluster = 1\\n i = 0\\n j = 0\\n\\n continuous = True\\n while(i<self.size and j<block_size):\\n if(self.disk_mem[i]==\\\".\\\"):\\n self.disk_mem[i] = letter\\n if not continuous:\\n continuous = True\\n cluster += 1\\n j+=1\\n else:\\n continuous = False\\n i+=1\\n return cluster\",\n \"def upload_files(self):\\n return 1 << 0\",\n \"def addFile(self, filePath): \\n \\n self.filePathDict[filePath] = [0,[]]\",\n \"def _inc_counter(self) -> None:\\n self._state_storage.increment_counter()\",\n \"def fs_files_used(self):\\n return self._fs_files_used\",\n \"def get_num_files(self):\\n\\t\\tif self.num_files_in_set is None and self.set_type == FAST5SET_TARBALL:\\n\\t\\t\\tself.num_files_in_set = len(self.files)\\n\\t\\treturn self.num_files_in_set\",\n \"def addPointCountToEdge(self, edge):\\n attributes = edge.getAttributes()\\n if self.edge_id__count.has_key(attributes.get(self.shapeFileUniqueId)):\\n self.edge_id__count[attributes.get(self.shapeFileUniqueId)] = self.edge_id__count[attributes.get(self.shapeFileUniqueId)] + 1\\n else:\\n self.edge_id__count[attributes.get(self.shapeFileUniqueId)] = 1\\n edge.setAttributes(attributes)\",\n \"def process(self):\\n self.reader += 1\",\n \"def count(self):\\n\\t\\treturn sum(read.copy for read in self.__iter__())\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def min_file_histogram(self):\\n return self._min_file_histogram\",\n \"def _update_count(self):\\n self._count = len(self._items)\",\n \"def __len__(self):\\n\\n return math.ceil(len(self.img_files) * self.gen_count / self.batch_size)\",\n \"def reduce(self, array, index):\\n\\n return 0\",\n \"def __len__(self):\\n return len(self.image_file_names)\",\n \"def fileno(self):\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83802605","0.6079216","0.60073787","0.5972287","0.5871502","0.58649766","0.58356875","0.5813897","0.5656929","0.5650024","0.5628978","0.5588129","0.5575472","0.55536973","0.55466735","0.55383646","0.5529106","0.5502226","0.54975855","0.5481056","0.54359984","0.5412819","0.5331949","0.52709615","0.5260593","0.52554363","0.5216349","0.5193865","0.51864076","0.51768965","0.5171308","0.5161445","0.5160157","0.5151611","0.512528","0.51065946","0.51014495","0.5088919","0.50852406","0.5068842","0.5065401","0.50386566","0.50366586","0.5024731","0.5022408","0.50217134","0.501115","0.50025594","0.49950272","0.4993197","0.49873495","0.49836847","0.4965577","0.4941178","0.49391267","0.4934542","0.4930227","0.49134943","0.49080187","0.49061012","0.49033248","0.4892175","0.4879754","0.48753807","0.48634028","0.48473728","0.4834655","0.48266846","0.48257676","0.48153964","0.48047906","0.48020706","0.47917888","0.47833127","0.4780719","0.47712538","0.4770419","0.47691157","0.47683647","0.47659448","0.47630748","0.47630462","0.47630462","0.47607407","0.47483426","0.47433466","0.47384593","0.47356316","0.47345176","0.47258684","0.472575","0.4718935","0.4715791","0.47103822","0.46854302","0.46809283","0.46724072","0.4670188","0.46681052","0.466746"],"string":"[\n \"0.83802605\",\n \"0.6079216\",\n \"0.60073787\",\n \"0.5972287\",\n \"0.5871502\",\n \"0.58649766\",\n \"0.58356875\",\n \"0.5813897\",\n \"0.5656929\",\n \"0.5650024\",\n \"0.5628978\",\n \"0.5588129\",\n \"0.5575472\",\n \"0.55536973\",\n \"0.55466735\",\n \"0.55383646\",\n \"0.5529106\",\n \"0.5502226\",\n \"0.54975855\",\n \"0.5481056\",\n \"0.54359984\",\n \"0.5412819\",\n \"0.5331949\",\n \"0.52709615\",\n \"0.5260593\",\n \"0.52554363\",\n \"0.5216349\",\n \"0.5193865\",\n \"0.51864076\",\n \"0.51768965\",\n \"0.5171308\",\n \"0.5161445\",\n \"0.5160157\",\n \"0.5151611\",\n \"0.512528\",\n \"0.51065946\",\n \"0.51014495\",\n \"0.5088919\",\n \"0.50852406\",\n \"0.5068842\",\n \"0.5065401\",\n \"0.50386566\",\n \"0.50366586\",\n \"0.5024731\",\n \"0.5022408\",\n \"0.50217134\",\n \"0.501115\",\n \"0.50025594\",\n \"0.49950272\",\n \"0.4993197\",\n \"0.49873495\",\n \"0.49836847\",\n \"0.4965577\",\n \"0.4941178\",\n \"0.49391267\",\n \"0.4934542\",\n \"0.4930227\",\n \"0.49134943\",\n \"0.49080187\",\n \"0.49061012\",\n \"0.49033248\",\n \"0.4892175\",\n \"0.4879754\",\n \"0.48753807\",\n \"0.48634028\",\n \"0.48473728\",\n \"0.4834655\",\n \"0.48266846\",\n \"0.48257676\",\n \"0.48153964\",\n \"0.48047906\",\n \"0.48020706\",\n \"0.47917888\",\n \"0.47833127\",\n \"0.4780719\",\n \"0.47712538\",\n \"0.4770419\",\n \"0.47691157\",\n \"0.47683647\",\n \"0.47659448\",\n \"0.47630748\",\n \"0.47630462\",\n \"0.47630462\",\n \"0.47607407\",\n \"0.47483426\",\n \"0.47433466\",\n \"0.47384593\",\n \"0.47356316\",\n \"0.47345176\",\n \"0.47258684\",\n \"0.472575\",\n \"0.4718935\",\n \"0.4715791\",\n \"0.47103822\",\n \"0.46854302\",\n \"0.46809283\",\n \"0.46724072\",\n \"0.4670188\",\n \"0.46681052\",\n \"0.466746\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71174276"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2676,"cells":{"query":{"kind":"string","value":"Subtract 1 from the Partition.file_counter if self._subarray is an instance of FileArray and not a temporary FileArray."},"document":{"kind":"string","value":"def _decrement_file_counter(self):\n self._add_to_file_counter(-1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if filename is None:\n return\n\n file_counter = self.file_counter\n # count = file_counter.get(filename, 0)\n # file_counter[filename] = count + i\n # if file_counter[filename] <= 0:\n count = file_counter.get(filename, 0) + i\n if count <= 0:\n # Remove the file from the dictionary if its count has\n # dropped to zero\n file_counter.pop(filename, None)\n else:\n file_counter[filename] = count\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass","def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n # than 3 references to it then it is not unique.\n if getrefcount is not None:\n self._decrement_file_counter()\n if subarray is None or getrefcount(subarray) > 3:\n return\n else:\n # getrefcount has itself been deleted or is in the process\n # of being torn down\n return\n\n _partition_file = getattr(subarray, \"_partition_file\", None)\n if _partition_file is not None:\n # This partition contains a temporary file which is not\n # referenced by any other partition on this process, so if\n # there are no lock files present remove the file from\n # disk.\n _remove_temporary_files(_partition_file)\n\n else:\n try:\n if FileArray is not None and isinstance(subarray, FileArray):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if self.file_counter.get(filename, 999) <= 0:\n # This partition contains a non-temporary file\n # which is not referenced by any other\n # partitions, so close the file.\n subarray.close()\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass\n # --- End: if","def _increment_file_counter(self):\n self._add_to_file_counter(1)","def __len__(self):\n return len(self.files[self.split])","def unique_files(self):\n self._tempfiles[-1].ctr = -1","def numberFiles(self):\n return self.n","def getFileCount(self) -> int:\n ...","def fileCount(self):\n pass","def __len__(self) -> int:\n return len(self.files)","def numberFiles(self):\n with open(self.inputfile) as fin:\n for n, _ in enumerate(fin, start=1): pass\n self.n = n\n return self.n","def __len__(self):\n return len(self.files)","def stat_beg_file(self, filename):\n\n self.batchvals['numfiles'] += 1\n self.filevals['filename'] = filename\n self.filevals['start_time'] = time.time()\n\n return -1","def file_close(self):\n if self.on_disk:\n self._subarray.close()","def count_files(self):\n self.file_count = 0\n self.count_files_loop(self.dirpath)\n return","def __len__(self):\n\n return len(self._file_list)","def on_disk(self):\n return isinstance(self._subarray, FileArray)","def n_subfile(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=False):\n n += 1\n return n","def sequential_files(self, ctr=0):\n self._tempfiles[-1].ctr = ctr","def getFileCount(self):\n\n if self.filecount == -1:\n self.filecount = self.db.filecount()\n\n return self.filecount","def __number_of_files(self):\n self.__get_files()\n return len(self.files)","def __len__(self):\n return len(self.frame1_files)","def file_count(self) -> int:\n if self.dataset is None:\n raise ValueError('No known dataset found!')\n return self._max_file_count","def get_nrof_aux(self):\n aux = 0\n for l in self.aux_array:\n if l:\n aux += 1\n return aux","def fix_index(self):\n if self.record_size <= self.size:\n self.record_size += 1\n if self.index % self.size == 0:\n self.isFull = True if len(self._storage) == self.size else False\n if self.replace_flag:\n self.index = 1\n return self.index\n else:\n sys.stderr.write('Experience replay buff is full and replace is set to FALSE!\\n')\n return -1\n else:\n self.index += 1\n return self.index","def count_deleted_bytes(self): # FileObj.count_deleted_bytes\n if self.deleted:\n return self.bytes \n else:\n return 0","def getnrfiles(self):\n return len(self.filenames)","def reduce(self, array, index):\n\n return 0","def n_total_files(self):\n return len(self.fileinfo)","def __len__(self):\n return len(self.file_paths)","def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass","def files_processed(self) -> int:\n with self.lock:\n return self._files_processed","def file_pointer(self):\n\n try:\n self.__file.seek(self.__file.tell() - 1)\n except Exception as e:\n raise e","def undo_scan(self, sub_array_id: int):","def _get_file_length(self, file):\n self[file] = file.stat().st_size","def get_num_files(self, file_type):\n return self.file_type_counter.get(file_type, 0)","def files(self) -> _SeqNumSlicer:\n if self._seq_num_slicer is None:\n self._seq_num_slicer = _SeqNumSlicer(self)\n return self._seq_num_slicer","def fileno(self):\n return 1","def __len__(self):\n return int(np.floor(len(self.wav_paths)))","def count_deleted(self): # FileObj.count_deleted\n if self.deleted:\n return 1\n else:\n return 0","def __len__(self):\n return len(self.files_self_A_rgbd)","def totalfiles(self):\n return len([sz for sz in self.iterate()])","def touched_files(self, parent):","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def fileno(self):\r\n raise NotImplementedError()","def fileCounter(directory):","def getNumTimeDataFiles(self):\n return self.nTimeDataFiles","def __len__(self):\n return self._num_samples_per_file * len(self._files) // self._world_size","def count_lines(file_obj):\n for idx, line in enumerate(file_obj):\n pass\n file_obj.seek(0)\n return idx + 1","def file_len(f):\n\n for n, l in enumerate(f, 1):\n pass\n f.seek(0) # rewind\n return n","def fget(self):\n if not hasattr(self, \"_n\"):\n self._n = 0\n self._n += 1\n return self._n","def min_file_histogram(self):\n return self._min_file_histogram","def __len__(self):\n return self._fa.faidx.index[self.name].rlen","def fileno(self):\n return None","def fileno(self):\n return None","def __len__(self):\n return len(self._current_block) - 1","def _write_array_on_file(self, pa_array):\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\n self._num_bytes += pa_array.nbytes\n self.pa_writer.write_batch(pa_batch)","def get_lenght(self):\n return len(self.filelist)","def n_file(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=True):\n n += 1\n return n","def file_num_increment(full_fpath):\r\n while os.path.isfile(full_fpath) == True:\r\n \r\n fpath, fext = os.path.splitext(full_fpath) #['C:\\Users\\Desktop\\file(1)', '.ext']\r\n\r\n if re.findall(\"[(]\\d+[)]\", fpath) != []: #Check if there is (x) in the path.\r\n for counter in range(1000): #Loop 1000 times\r\n if fpath.endswith(f\"({counter})\"): \r\n fpath = replace_last(fpath, f\"({counter})\", f\"({counter+1})\") #Replace the last occurence of (counter) in the string.\r\n full_fpath = fpath + fext\r\n break\r\n else: #here we pass for cases where (counter) is in the file/folder name itself. We skip them.\r\n continue\r\n else: #If there is no (counter), we create (1)\r\n counter = 1\r\n full_fpath = fpath + '(' + str(counter) + ')' + fext\r\n\r\n return full_fpath","def getFileCount(self, startingWithPath=\"\"):\n return self.__controller._getRecordsCount(startingWithPath)","def FreeFile():\n existing = VBFiles.getOpenChannels()\n if existing:\n return max(existing) + 1\n else:\n return 1","def __len__(self):\n return int(np.ceil(len(self.image_filenames) / (self.batch_size)))","def reset(self):\n self.all_files_idx = np.arange(self._div*self._nb_dir)\n\n if self.shuffle>1:\n np.random.shuffle(self.all_files_idx)\n\n self.idx_folder = self.all_files_idx//self._div\n self.idx_file = self.all_files_idx % self._div\n self.current_folder = self.idx_folder[0]\n self.current_file = self.idx_file[0]","def fs_files_total(self):\n return self._fs_files_total","def process(self):\n\n if len(self.files) == self._file_ptr:\n raise pipeline.PipelineStopIteration\n\n # Collect garbage to remove any prior CorrData objects\n gc.collect()\n\n # Fetch and remove the first item in the list\n file_ = self.files[self._file_ptr]\n self._file_ptr += 1\n\n # Set up product selection\n # NOTE: this probably doesn't work with stacked data\n prod_sel = None\n if self.only_autos:\n rd = andata.CorrReader(file_)\n prod_sel = np.array(\n [ii for (ii, pp) in enumerate(rd.prod) if pp[0] == pp[1]]\n )\n\n # Load file\n if (\n isinstance(self.freq_sel, slice)\n and (prod_sel is None)\n and (self.datasets is None)\n ):\n self.log.info(\n \"Reading file %i of %i. (%s) [fast io]\",\n self._file_ptr,\n len(self.files),\n file_,\n )\n ts = andata.CorrData.from_acq_h5_fast(\n file_, freq_sel=self.freq_sel, comm=self.comm\n )\n else:\n self.log.info(\n \"Reading file %i of %i. (%s) [slow io]\",\n self._file_ptr,\n len(self.files),\n file_,\n )\n ts = andata.CorrData.from_acq_h5(\n file_,\n datasets=self.datasets,\n distributed=True,\n comm=self.comm,\n freq_sel=self.freq_sel,\n prod_sel=prod_sel,\n )\n\n # Store file name\n ts.attrs[\"filename\"] = file_\n\n # Use a simple incrementing string as the tag\n if \"tag\" not in ts.attrs:\n tag = \"file%03i\" % self._file_ptr\n ts.attrs[\"tag\"] = tag\n\n # Add a weight dataset if needed\n if \"vis_weight\" not in ts.flags:\n weight_dset = ts.create_flag(\n \"vis_weight\",\n shape=ts.vis.shape,\n dtype=np.uint8,\n distributed=True,\n distributed_axis=0,\n )\n weight_dset.attrs[\"axis\"] = ts.vis.attrs[\"axis\"]\n\n # Set weight to maximum value (255), unless the vis value is\n # zero which presumably came from missing data. NOTE: this may have\n # a small bias\n weight_dset[:] = np.where(ts.vis[:] == 0.0, 0, 255)\n\n # Return timestream\n if self.use_draco_container:\n ts = containers.CHIMETimeStream.from_corrdata(ts)\n\n return ts","def fill_values(self, array: List[float]) -> int:\n\n if self._max_collected_data_time <= 0:\n return 0\n\n current_time = self.start_time\n index = 0\n while current_time < self._max_collected_data_time and index < len(array):\n array[index] = self.feedback(current_time)\n index += 1\n current_time = index * self.time_step\n\n # subtract one since index - 1 is the actual last index that was\n # written to\n return index - 1","def get_size(self, fileobject):\n # move the cursor to the end of the file\n fileobject.seek(0, 2)\n size = fileobject.tell()\n # move the cursor to the begin of the file\n fileobject.seek(0)\n return size","def file_num(self):\n command = \"SELECT COUNT(id) FROM files;\"\n return self.c.execute(command)","def number_idx(self, filename):\n with open(filename) as fh:\n firstline = fh.readline()\n parts = firstline.split('\\t')\n # only add if there are 4 parts\n if len(parts) != 4:\n return\n\n count = 1\n def writeline(fho, line, count):\n fho.write(line.rstrip() + '\\t' + str(count) + '\\n')\n\n with open(filename + '.tmp', 'w+b') as fho:\n writeline(fho, firstline, count)\n count += 1\n for line in fh:\n writeline(fho, line, count)\n count += 1\n\n shutil.move(filename + '.tmp', filename)","def get_data_id(self):\n self.data_id = len(glob.glob(osp.join(self.save_dir, 'depth', '*npy')))\n return self.data_id","def len(self):\n # print(self.processed_file_names)\n return self.len_","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def sacar_ficha(self, letra):\n \n self.__estado[letra][\"cantidad\"] = self.__estado[letra][\"cantidad\"] - 1\n self.__cant_fichas = self.__cant_fichas - 1\n self.__fichas_disponibles.remove(letra)\n self.__actualizar_letra(letra)","def file_count(self) -> str:\n return pulumi.get(self, \"file_count\")","def array(self):\n config = self.config\n\n unique_array = config[\"unique_subarray\"]\n\n p_axes = self.axes\n p_flip = self.flip\n p_part = self.part\n p_units = self.Units\n p_shape = self.shape\n p_location = self.location\n subarray = self._subarray\n\n len_p_axes = len(p_axes)\n\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is not in memory.\n #\n # It could be in a file on disk or implied by a FileArray\n # object, etc.\n # --------------------------------------------------------\n self._original = self.copy()\n\n unique_array = True\n update = True\n copy = False\n\n if not p_part:\n indices = Ellipsis\n else:\n indices = tuple(p_part)\n\n # Read from a file into a numpy array\n p_data = subarray[indices]\n\n # We've just copied p_data from disk, so in place changes\n # are not possible\n in_place_changes = False\n else:\n # --------------------------------------------------------\n # The subarray is in memory\n # --------------------------------------------------------\n update = config[\"update\"]\n\n if p_part:\n p_data = get_subspace(subarray, p_part)\n elif not unique_array:\n p_data = subarray.view()\n else:\n p_data = subarray\n\n copy = config[\"extra_memory\"]\n\n # In place changes to p_data might be possible if we're not\n # copying the data\n in_place_changes = not copy\n\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\n # --------------------------------------------------------\n # p_data is a numpy number (like numpy.int64) which does\n # not support assignment, so convert it to a numpy array.\n # --------------------------------------------------------\n p_data = numpy_array(p_data)\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n\n masked = numpy_ma_isMA(p_data)\n if masked:\n # The p_data is a masked array\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\n p_data\n ):\n # There are no missing data points so recast as an\n # unmasked numpy array\n p_data = p_data.data\n masked = False\n # --- End: if\n\n if masked:\n # Set the hardness of the mask\n if config[\"hardmask\"]:\n p_data.harden_mask()\n else:\n p_data.soften_mask()\n # --- End: if\n\n self.masked = masked\n\n # ------------------------------------------------------------\n # Make sure that the data array has the correct units. This\n # process will deep copy the data array if required (e.g. if\n # another partition is referencing this numpy array), even if\n # the units are already correct.\n # ------------------------------------------------------------\n func = config.get(\"func\")\n units = config[\"units\"]\n if func is None:\n if not p_units.equals(units) and bool(p_units) is bool(units):\n func = Units.conform\n\n if func is not None:\n inplace = not copy\n p_data = func(p_data, p_units, units, inplace)\n p_units = units\n\n if not inplace:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n flip = config.get(\"flip\", None)\n if flip or p_flip:\n flip_axes = set(p_flip).symmetric_difference(flip)\n else:\n flip_axes = None\n\n axes = config[\"axes\"]\n\n if p_data.size > 1:\n # --------------------------------------------------------\n # Flip axes\n # --------------------------------------------------------\n if flip_axes:\n indices = [\n (\n slice(None, None, -1)\n if axis in flip_axes\n else slice(None)\n )\n for axis in p_axes\n ]\n p_data = p_data[tuple(indices)]\n\n # --------------------------------------------------------\n # Transpose axes\n # --------------------------------------------------------\n if p_axes != axes:\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\n\n if len_p_axes > len(iaxes):\n for i in range(len_p_axes):\n if i not in iaxes:\n # iaxes.append(i)\n iaxes.insert(i, i)\n # --- End: if\n\n p_data = numpy_transpose(p_data, iaxes)\n # --- End: if\n\n # ------------------------------------------------------------\n # Remove excessive/insert missing size 1 axes\n # ------------------------------------------------------------\n if p_shape != p_data.shape:\n # if len_p_axes != len(p_shape):\n p_data = p_data.reshape(p_shape)\n\n # ------------------------------------------------------------\n # Apply the auxiliary mask\n # ------------------------------------------------------------\n auxiliary_mask = config[\"auxiliary_mask\"]\n if auxiliary_mask:\n for mask in auxiliary_mask:\n if mask.any():\n if not masked:\n p_data = p_data.view(numpy_ma_MaskedArray)\n masked = True\n\n p_data.mask = (mask | p_data.mask).array\n # --- End: for\n\n self.masked = True\n\n # ------------------------------------------------------------\n # Convert the array's data type\n # ------------------------------------------------------------\n p_dtype = p_data.dtype\n dtype = config.get(\"dtype\", None)\n if dtype is not None and dtype != p_dtype:\n try:\n p_data = p_data.astype(dtype) # Note: returns a copy\n except ValueError:\n raise ValueError(\n \"Can't recast partition array from {} to {}\".format(\n p_dtype.name, dtype.name\n )\n )\n else:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n # ------------------------------------------------------------\n # Copy the array\n # -----------------------------------------------------------\n if copy:\n if p_dtype.char != \"O\":\n if not masked or p_data.ndim > 0:\n p_data = p_data.copy()\n else:\n # This is because numpy.ma.copy doesn't work for\n # scalar arrays (at the moment, at least)\n p_data = numpy_ma_masked_all((), p_data.dtype)\n\n # We've just copied p_data, so in place changes are\n # not possible\n in_place_changes = False\n else:\n # whilst netCDF4.netcdftime.datetime is mucking bout,\n # don't copy!!!!\n # p_data = _copy(p_data)\n pass\n # --- End: if\n\n # ------------------------------------------------------------\n # Update the partition\n # ------------------------------------------------------------\n if update:\n self.subarray = p_data # ?? DCH CHECK\n self.Units = p_units\n self.part = []\n self.axes = axes\n self.flip = flip\n self.flatten = []\n self.shape = p_shape\n self.location = p_location\n\n self._in_place_changes = in_place_changes\n\n # ------------------------------------------------------------\n # Return the numpy array\n # ------------------------------------------------------------\n return p_data","def len(self):\n return self._fsize - self._fp.tell()","def files_processed(self) -> float:\n return pulumi.get(self, \"files_processed\")","def DeleteFiles(self, min_size=0):\n\n ndeleted = 0\n for filename, counts in list(self.mCounts.items()):\n if counts < min_size:\n os.remove(filename)\n ndeleted += 1\n\n return ndeleted","def setNextFile(self):\n\n if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile):\n self.nReadFiles=self.nReadFiles+1\n if self.nReadFiles > self.nTotalReadFiles:\n self.flagNoMoreFiles=1\n raise schainpy.admin.SchainWarning('No more files to read')\n\n print('------------------- [Opening file] ------------------------------',self.nReadFiles)\n self.nReadBlocks = 0\n #if self.nReadBlocks==0:\n # self.readFirstHeader()","def fileno(self):\n return self.file.fileno()","def len(self):\n return self._fsize - self._tell","def _setcounter():\n fname = os.path.basename(camera.status.lastfile)\n tname = fname.split('.')[0]\n i = len(tname)-1\n if i > -1:\n while tname[i].isdigit() and i>-1:\n i = i - 1\n nname = fname[:-4]\n bname = tname[:i+1]\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9].cntr'):\n os.remove(file)\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9][0-9].cntr'):\n os.remove(file)\n f = open('/data/counters/'+nname+'cntr','w')\n f.close()","def fileno(self):\n raise io.UnsupportedOperation","def getFirstObjectIndex(self):\n if not self.fileInfo.isEsp(): raise StateError(_('FileRefs.renumberObjects is for esps only.'))\n for cell in self.cells:\n objects = cell.getObjects()\n for object in objects.list():\n if object[0] == 0:\n return object[1]\n return 0","def test_op_sub_array_int(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=int)\n o = a + 1\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a - o\n\n offl_a = stream.bind(a)\n offl_r = offl_a - o\n offl_a.update_host()\n r = offl_r.update_host().array\n stream.sync()\n\n self.assertEqual(r.shape, a.shape)\n self.assertEqual(r.dtype, a.dtype)\n self.assertTrue((a == old_a).all(),\n \"Input array operand 1 must not be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def __calculate_number_of_frames(self):\n # Save current position\n current_pos = self.__file_object.tell()\n\n # Go to start of first frame\n self.__file_object.seek(self.__first_frame_raw_data_position)\n self.number_of_frames = 0\n\n while True:\n if not self.__file_object.read(self.__frame_raw_data_size):\n break\n\n self.__file_object.readline()\n self.number_of_frames += 1\n\n # Restore file pointer\n self.__file_object.seek(current_pos)\n print('Number of frames:', self.number_of_frames)","def trace_file_len(fname):\n try:\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n return i - 1\n except FileNotFoundError:\n return 0","def get_file(self) -> int:\r\n return self.file","def prune_empty(self): # EntryList.prune_empty\n prevCount = self.count_deleted()\n for name, e in allFiles.contents.iteritems():\n e.prune_empty()\n return allFiles.count_deleted() - prevCount","def __len__(self):\n\n return math.ceil(len(self.img_files) * self.gen_count / self.batch_size)","def refreshSize(self):\n if self.isLoaded:\n return 0\n else:\n return self.fileInfo.size","def Rear(self):\n return -1 if self.isEmpty() else self.__buffer[(self.__start+self.__size-1) % len(self.__buffer)]","def seek(self, *args) -> \"int\":\n return _ida_fpro.qfile_t_seek(self, *args)","def in_cached_file(self):\n return isinstance(self._subarray, CachedArray)","def getModificationNumber(self) -> long:\n ...","def size(self) -> int:\n return sum(p.size for p in self.iterfiles())","def tell(self):\n return self._upload_position","def has_subfile(self) -> bool:\n\t\tself._update_subfiles()\n\t\treturn bool(len(self.subfiles))","def __len__(self):\n return len(self.array)","def __len__(self):\n return len(self.array)"],"string":"[\n \"def _add_to_file_counter(self, i):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n if subarray is None:\\n return\\n\\n try:\\n if isinstance(subarray, FileArray) and not isinstance(\\n subarray, CachedArray\\n ):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if filename is None:\\n return\\n\\n file_counter = self.file_counter\\n # count = file_counter.get(filename, 0)\\n # file_counter[filename] = count + i\\n # if file_counter[filename] <= 0:\\n count = file_counter.get(filename, 0) + i\\n if count <= 0:\\n # Remove the file from the dictionary if its count has\\n # dropped to zero\\n file_counter.pop(filename, None)\\n else:\\n file_counter[filename] = count\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\",\n \"def __del__(self):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n # If the subarray is unique it will have 2 references to\\n # it plus 1 within this method, making 3. If it has more\\n # than 3 references to it then it is not unique.\\n if getrefcount is not None:\\n self._decrement_file_counter()\\n if subarray is None or getrefcount(subarray) > 3:\\n return\\n else:\\n # getrefcount has itself been deleted or is in the process\\n # of being torn down\\n return\\n\\n _partition_file = getattr(subarray, \\\"_partition_file\\\", None)\\n if _partition_file is not None:\\n # This partition contains a temporary file which is not\\n # referenced by any other partition on this process, so if\\n # there are no lock files present remove the file from\\n # disk.\\n _remove_temporary_files(_partition_file)\\n\\n else:\\n try:\\n if FileArray is not None and isinstance(subarray, FileArray):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if self.file_counter.get(filename, 999) <= 0:\\n # This partition contains a non-temporary file\\n # which is not referenced by any other\\n # partitions, so close the file.\\n subarray.close()\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\\n # --- End: if\",\n \"def _increment_file_counter(self):\\n self._add_to_file_counter(1)\",\n \"def __len__(self):\\n return len(self.files[self.split])\",\n \"def unique_files(self):\\n self._tempfiles[-1].ctr = -1\",\n \"def numberFiles(self):\\n return self.n\",\n \"def getFileCount(self) -> int:\\n ...\",\n \"def fileCount(self):\\n pass\",\n \"def __len__(self) -> int:\\n return len(self.files)\",\n \"def numberFiles(self):\\n with open(self.inputfile) as fin:\\n for n, _ in enumerate(fin, start=1): pass\\n self.n = n\\n return self.n\",\n \"def __len__(self):\\n return len(self.files)\",\n \"def stat_beg_file(self, filename):\\n\\n self.batchvals['numfiles'] += 1\\n self.filevals['filename'] = filename\\n self.filevals['start_time'] = time.time()\\n\\n return -1\",\n \"def file_close(self):\\n if self.on_disk:\\n self._subarray.close()\",\n \"def count_files(self):\\n self.file_count = 0\\n self.count_files_loop(self.dirpath)\\n return\",\n \"def __len__(self):\\n\\n return len(self._file_list)\",\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def n_subfile(self):\\n self.assert_is_dir_and_exists()\\n n = 0\\n for _ in self.select_file(recursive=False):\\n n += 1\\n return n\",\n \"def sequential_files(self, ctr=0):\\n self._tempfiles[-1].ctr = ctr\",\n \"def getFileCount(self):\\n\\n if self.filecount == -1:\\n self.filecount = self.db.filecount()\\n\\n return self.filecount\",\n \"def __number_of_files(self):\\n self.__get_files()\\n return len(self.files)\",\n \"def __len__(self):\\n return len(self.frame1_files)\",\n \"def file_count(self) -> int:\\n if self.dataset is None:\\n raise ValueError('No known dataset found!')\\n return self._max_file_count\",\n \"def get_nrof_aux(self):\\n aux = 0\\n for l in self.aux_array:\\n if l:\\n aux += 1\\n return aux\",\n \"def fix_index(self):\\n if self.record_size <= self.size:\\n self.record_size += 1\\n if self.index % self.size == 0:\\n self.isFull = True if len(self._storage) == self.size else False\\n if self.replace_flag:\\n self.index = 1\\n return self.index\\n else:\\n sys.stderr.write('Experience replay buff is full and replace is set to FALSE!\\\\n')\\n return -1\\n else:\\n self.index += 1\\n return self.index\",\n \"def count_deleted_bytes(self): # FileObj.count_deleted_bytes\\n if self.deleted:\\n return self.bytes \\n else:\\n return 0\",\n \"def getnrfiles(self):\\n return len(self.filenames)\",\n \"def reduce(self, array, index):\\n\\n return 0\",\n \"def n_total_files(self):\\n return len(self.fileinfo)\",\n \"def __len__(self):\\n return len(self.file_paths)\",\n \"def close(self, **kwargs):\\n config = getattr(self, \\\"config\\\", None)\\n\\n if config is None:\\n return\\n\\n if kwargs:\\n config.update(kwargs)\\n\\n original = getattr(self, \\\"_original\\\", None)\\n logger.partitioning(\\\"Partition.close: original = {}\\\".format(original))\\n\\n if not original:\\n originally_on_disk = False\\n original_subarray = None\\n else:\\n originally_on_disk = not original.in_memory\\n original_subarray = original._subarray\\n\\n config = self.config\\n logger.partitioning(\\\" config = {}\\\".format(config))\\n\\n if config[\\\"serial\\\"]:\\n # --------------------------------------------------------\\n # SERIAL\\n # --------------------------------------------------------\\n logger.partitioning(\\\" serial\\\")\\n\\n if config[\\\"readonly\\\"]:\\n logger.partitioning(\\\" readonly=True\\\")\\n\\n if originally_on_disk:\\n logger.partitioning(\\\" subarray originally on disk\\\")\\n\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.1.1 The original subarray was on disk,\\n # we don't want to keep the current\\n # subarray in memory, and we are happy\\n # to discard any changes that may have\\n # been made to the subarray.\\n logger.partitioning(\\\" 1.1.1.1 revert\\\")\\n self.revert()\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.1.2 The original subarray was on disk,\\n # we are happy to keep the current\\n # subarray in memory, but there is not\\n # enough free memory to do so.\\n logger.partitioning(\\n \\\" 1.1.1.2 revert ({} <= {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n self.revert()\\n else:\\n # 1.1.1.3 The original subarray was on disk\\n # and there is enough memory to keep\\n # the current subarray in memory\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # The original subarray was a temporary\\n # file which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n del self.masked\\n logger.partitioning(\\n \\\" 1.1.1.3 del masked ({} > {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n\\n else:\\n logger.partitioning(\\\" subarray originally in memory\\\")\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.2.1 Original subarray was in memory and\\n # we don't want to keep the current\\n # subarray in memory\\n logger.partitioning(\\\" 1.1.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.2.2 Original subarray was in memory and\\n # unique but there is not enough\\n # memory to keep the current subarray\\n logger.partitioning(\\\" 1.1.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.1.2.3 Original subarray was in memory and\\n # unique and there is enough memory to\\n # keep the current subarray in memory\\n logger.partitioning(\\\" 1.1.2.3 pass\\\")\\n pass\\n else:\\n # config['readonly'] is False\\n if originally_on_disk:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.1.1 Original subarray was on disk and\\n # there and we don't want to keep the\\n # array\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.1.2 Original subarray was on disk but\\n # there is not enough memory to keep\\n # it\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.1.3 Original subarray was on disk and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.1.3 pass\\\")\\n del self.masked\\n else:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.2.1 Original subarray was in memory but\\n # we don't want to keep it\\n logger.partitioning(\\\" 1.2.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.2.2 Original subarray was an in memory\\n # but there is not enough memory to\\n # keep it\\n logger.partitioning(\\\" 1.2.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.2.3 Original subarray was in memory and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.2.3 del masked\\\")\\n del self.masked\\n else:\\n logger.partitioning(\\\"Partition.close: parallel\\\")\\n # --------------------------------------------------------\\n # PARALLEL\\n # --------------------------------------------------------\\n pass\\n\\n # if hasattr(self, '_original'):\\n # del self._original\\n\\n # print(hasattr(self, 'config')),\\n try:\\n del self.config\\n except AttributeError:\\n pass\",\n \"def files_processed(self) -> int:\\n with self.lock:\\n return self._files_processed\",\n \"def file_pointer(self):\\n\\n try:\\n self.__file.seek(self.__file.tell() - 1)\\n except Exception as e:\\n raise e\",\n \"def undo_scan(self, sub_array_id: int):\",\n \"def _get_file_length(self, file):\\n self[file] = file.stat().st_size\",\n \"def get_num_files(self, file_type):\\n return self.file_type_counter.get(file_type, 0)\",\n \"def files(self) -> _SeqNumSlicer:\\n if self._seq_num_slicer is None:\\n self._seq_num_slicer = _SeqNumSlicer(self)\\n return self._seq_num_slicer\",\n \"def fileno(self):\\n return 1\",\n \"def __len__(self):\\n return int(np.floor(len(self.wav_paths)))\",\n \"def count_deleted(self): # FileObj.count_deleted\\n if self.deleted:\\n return 1\\n else:\\n return 0\",\n \"def __len__(self):\\n return len(self.files_self_A_rgbd)\",\n \"def totalfiles(self):\\n return len([sz for sz in self.iterate()])\",\n \"def touched_files(self, parent):\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def fileno(self):\\r\\n raise NotImplementedError()\",\n \"def fileCounter(directory):\",\n \"def getNumTimeDataFiles(self):\\n return self.nTimeDataFiles\",\n \"def __len__(self):\\n return self._num_samples_per_file * len(self._files) // self._world_size\",\n \"def count_lines(file_obj):\\n for idx, line in enumerate(file_obj):\\n pass\\n file_obj.seek(0)\\n return idx + 1\",\n \"def file_len(f):\\n\\n for n, l in enumerate(f, 1):\\n pass\\n f.seek(0) # rewind\\n return n\",\n \"def fget(self):\\n if not hasattr(self, \\\"_n\\\"):\\n self._n = 0\\n self._n += 1\\n return self._n\",\n \"def min_file_histogram(self):\\n return self._min_file_histogram\",\n \"def __len__(self):\\n return self._fa.faidx.index[self.name].rlen\",\n \"def fileno(self):\\n return None\",\n \"def fileno(self):\\n return None\",\n \"def __len__(self):\\n return len(self._current_block) - 1\",\n \"def _write_array_on_file(self, pa_array):\\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\\n self._num_bytes += pa_array.nbytes\\n self.pa_writer.write_batch(pa_batch)\",\n \"def get_lenght(self):\\n return len(self.filelist)\",\n \"def n_file(self):\\n self.assert_is_dir_and_exists()\\n n = 0\\n for _ in self.select_file(recursive=True):\\n n += 1\\n return n\",\n \"def file_num_increment(full_fpath):\\r\\n while os.path.isfile(full_fpath) == True:\\r\\n \\r\\n fpath, fext = os.path.splitext(full_fpath) #['C:\\\\Users\\\\Desktop\\\\file(1)', '.ext']\\r\\n\\r\\n if re.findall(\\\"[(]\\\\d+[)]\\\", fpath) != []: #Check if there is (x) in the path.\\r\\n for counter in range(1000): #Loop 1000 times\\r\\n if fpath.endswith(f\\\"({counter})\\\"): \\r\\n fpath = replace_last(fpath, f\\\"({counter})\\\", f\\\"({counter+1})\\\") #Replace the last occurence of (counter) in the string.\\r\\n full_fpath = fpath + fext\\r\\n break\\r\\n else: #here we pass for cases where (counter) is in the file/folder name itself. We skip them.\\r\\n continue\\r\\n else: #If there is no (counter), we create (1)\\r\\n counter = 1\\r\\n full_fpath = fpath + '(' + str(counter) + ')' + fext\\r\\n\\r\\n return full_fpath\",\n \"def getFileCount(self, startingWithPath=\\\"\\\"):\\n return self.__controller._getRecordsCount(startingWithPath)\",\n \"def FreeFile():\\n existing = VBFiles.getOpenChannels()\\n if existing:\\n return max(existing) + 1\\n else:\\n return 1\",\n \"def __len__(self):\\n return int(np.ceil(len(self.image_filenames) / (self.batch_size)))\",\n \"def reset(self):\\n self.all_files_idx = np.arange(self._div*self._nb_dir)\\n\\n if self.shuffle>1:\\n np.random.shuffle(self.all_files_idx)\\n\\n self.idx_folder = self.all_files_idx//self._div\\n self.idx_file = self.all_files_idx % self._div\\n self.current_folder = self.idx_folder[0]\\n self.current_file = self.idx_file[0]\",\n \"def fs_files_total(self):\\n return self._fs_files_total\",\n \"def process(self):\\n\\n if len(self.files) == self._file_ptr:\\n raise pipeline.PipelineStopIteration\\n\\n # Collect garbage to remove any prior CorrData objects\\n gc.collect()\\n\\n # Fetch and remove the first item in the list\\n file_ = self.files[self._file_ptr]\\n self._file_ptr += 1\\n\\n # Set up product selection\\n # NOTE: this probably doesn't work with stacked data\\n prod_sel = None\\n if self.only_autos:\\n rd = andata.CorrReader(file_)\\n prod_sel = np.array(\\n [ii for (ii, pp) in enumerate(rd.prod) if pp[0] == pp[1]]\\n )\\n\\n # Load file\\n if (\\n isinstance(self.freq_sel, slice)\\n and (prod_sel is None)\\n and (self.datasets is None)\\n ):\\n self.log.info(\\n \\\"Reading file %i of %i. (%s) [fast io]\\\",\\n self._file_ptr,\\n len(self.files),\\n file_,\\n )\\n ts = andata.CorrData.from_acq_h5_fast(\\n file_, freq_sel=self.freq_sel, comm=self.comm\\n )\\n else:\\n self.log.info(\\n \\\"Reading file %i of %i. (%s) [slow io]\\\",\\n self._file_ptr,\\n len(self.files),\\n file_,\\n )\\n ts = andata.CorrData.from_acq_h5(\\n file_,\\n datasets=self.datasets,\\n distributed=True,\\n comm=self.comm,\\n freq_sel=self.freq_sel,\\n prod_sel=prod_sel,\\n )\\n\\n # Store file name\\n ts.attrs[\\\"filename\\\"] = file_\\n\\n # Use a simple incrementing string as the tag\\n if \\\"tag\\\" not in ts.attrs:\\n tag = \\\"file%03i\\\" % self._file_ptr\\n ts.attrs[\\\"tag\\\"] = tag\\n\\n # Add a weight dataset if needed\\n if \\\"vis_weight\\\" not in ts.flags:\\n weight_dset = ts.create_flag(\\n \\\"vis_weight\\\",\\n shape=ts.vis.shape,\\n dtype=np.uint8,\\n distributed=True,\\n distributed_axis=0,\\n )\\n weight_dset.attrs[\\\"axis\\\"] = ts.vis.attrs[\\\"axis\\\"]\\n\\n # Set weight to maximum value (255), unless the vis value is\\n # zero which presumably came from missing data. NOTE: this may have\\n # a small bias\\n weight_dset[:] = np.where(ts.vis[:] == 0.0, 0, 255)\\n\\n # Return timestream\\n if self.use_draco_container:\\n ts = containers.CHIMETimeStream.from_corrdata(ts)\\n\\n return ts\",\n \"def fill_values(self, array: List[float]) -> int:\\n\\n if self._max_collected_data_time <= 0:\\n return 0\\n\\n current_time = self.start_time\\n index = 0\\n while current_time < self._max_collected_data_time and index < len(array):\\n array[index] = self.feedback(current_time)\\n index += 1\\n current_time = index * self.time_step\\n\\n # subtract one since index - 1 is the actual last index that was\\n # written to\\n return index - 1\",\n \"def get_size(self, fileobject):\\n # move the cursor to the end of the file\\n fileobject.seek(0, 2)\\n size = fileobject.tell()\\n # move the cursor to the begin of the file\\n fileobject.seek(0)\\n return size\",\n \"def file_num(self):\\n command = \\\"SELECT COUNT(id) FROM files;\\\"\\n return self.c.execute(command)\",\n \"def number_idx(self, filename):\\n with open(filename) as fh:\\n firstline = fh.readline()\\n parts = firstline.split('\\\\t')\\n # only add if there are 4 parts\\n if len(parts) != 4:\\n return\\n\\n count = 1\\n def writeline(fho, line, count):\\n fho.write(line.rstrip() + '\\\\t' + str(count) + '\\\\n')\\n\\n with open(filename + '.tmp', 'w+b') as fho:\\n writeline(fho, firstline, count)\\n count += 1\\n for line in fh:\\n writeline(fho, line, count)\\n count += 1\\n\\n shutil.move(filename + '.tmp', filename)\",\n \"def get_data_id(self):\\n self.data_id = len(glob.glob(osp.join(self.save_dir, 'depth', '*npy')))\\n return self.data_id\",\n \"def len(self):\\n # print(self.processed_file_names)\\n return self.len_\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def sacar_ficha(self, letra):\\n \\n self.__estado[letra][\\\"cantidad\\\"] = self.__estado[letra][\\\"cantidad\\\"] - 1\\n self.__cant_fichas = self.__cant_fichas - 1\\n self.__fichas_disponibles.remove(letra)\\n self.__actualizar_letra(letra)\",\n \"def file_count(self) -> str:\\n return pulumi.get(self, \\\"file_count\\\")\",\n \"def array(self):\\n config = self.config\\n\\n unique_array = config[\\\"unique_subarray\\\"]\\n\\n p_axes = self.axes\\n p_flip = self.flip\\n p_part = self.part\\n p_units = self.Units\\n p_shape = self.shape\\n p_location = self.location\\n subarray = self._subarray\\n\\n len_p_axes = len(p_axes)\\n\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is not in memory.\\n #\\n # It could be in a file on disk or implied by a FileArray\\n # object, etc.\\n # --------------------------------------------------------\\n self._original = self.copy()\\n\\n unique_array = True\\n update = True\\n copy = False\\n\\n if not p_part:\\n indices = Ellipsis\\n else:\\n indices = tuple(p_part)\\n\\n # Read from a file into a numpy array\\n p_data = subarray[indices]\\n\\n # We've just copied p_data from disk, so in place changes\\n # are not possible\\n in_place_changes = False\\n else:\\n # --------------------------------------------------------\\n # The subarray is in memory\\n # --------------------------------------------------------\\n update = config[\\\"update\\\"]\\n\\n if p_part:\\n p_data = get_subspace(subarray, p_part)\\n elif not unique_array:\\n p_data = subarray.view()\\n else:\\n p_data = subarray\\n\\n copy = config[\\\"extra_memory\\\"]\\n\\n # In place changes to p_data might be possible if we're not\\n # copying the data\\n in_place_changes = not copy\\n\\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\\n # --------------------------------------------------------\\n # p_data is a numpy number (like numpy.int64) which does\\n # not support assignment, so convert it to a numpy array.\\n # --------------------------------------------------------\\n p_data = numpy_array(p_data)\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n\\n masked = numpy_ma_isMA(p_data)\\n if masked:\\n # The p_data is a masked array\\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\\n p_data\\n ):\\n # There are no missing data points so recast as an\\n # unmasked numpy array\\n p_data = p_data.data\\n masked = False\\n # --- End: if\\n\\n if masked:\\n # Set the hardness of the mask\\n if config[\\\"hardmask\\\"]:\\n p_data.harden_mask()\\n else:\\n p_data.soften_mask()\\n # --- End: if\\n\\n self.masked = masked\\n\\n # ------------------------------------------------------------\\n # Make sure that the data array has the correct units. This\\n # process will deep copy the data array if required (e.g. if\\n # another partition is referencing this numpy array), even if\\n # the units are already correct.\\n # ------------------------------------------------------------\\n func = config.get(\\\"func\\\")\\n units = config[\\\"units\\\"]\\n if func is None:\\n if not p_units.equals(units) and bool(p_units) is bool(units):\\n func = Units.conform\\n\\n if func is not None:\\n inplace = not copy\\n p_data = func(p_data, p_units, units, inplace)\\n p_units = units\\n\\n if not inplace:\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n # --- End: if\\n\\n flip = config.get(\\\"flip\\\", None)\\n if flip or p_flip:\\n flip_axes = set(p_flip).symmetric_difference(flip)\\n else:\\n flip_axes = None\\n\\n axes = config[\\\"axes\\\"]\\n\\n if p_data.size > 1:\\n # --------------------------------------------------------\\n # Flip axes\\n # --------------------------------------------------------\\n if flip_axes:\\n indices = [\\n (\\n slice(None, None, -1)\\n if axis in flip_axes\\n else slice(None)\\n )\\n for axis in p_axes\\n ]\\n p_data = p_data[tuple(indices)]\\n\\n # --------------------------------------------------------\\n # Transpose axes\\n # --------------------------------------------------------\\n if p_axes != axes:\\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\\n\\n if len_p_axes > len(iaxes):\\n for i in range(len_p_axes):\\n if i not in iaxes:\\n # iaxes.append(i)\\n iaxes.insert(i, i)\\n # --- End: if\\n\\n p_data = numpy_transpose(p_data, iaxes)\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Remove excessive/insert missing size 1 axes\\n # ------------------------------------------------------------\\n if p_shape != p_data.shape:\\n # if len_p_axes != len(p_shape):\\n p_data = p_data.reshape(p_shape)\\n\\n # ------------------------------------------------------------\\n # Apply the auxiliary mask\\n # ------------------------------------------------------------\\n auxiliary_mask = config[\\\"auxiliary_mask\\\"]\\n if auxiliary_mask:\\n for mask in auxiliary_mask:\\n if mask.any():\\n if not masked:\\n p_data = p_data.view(numpy_ma_MaskedArray)\\n masked = True\\n\\n p_data.mask = (mask | p_data.mask).array\\n # --- End: for\\n\\n self.masked = True\\n\\n # ------------------------------------------------------------\\n # Convert the array's data type\\n # ------------------------------------------------------------\\n p_dtype = p_data.dtype\\n dtype = config.get(\\\"dtype\\\", None)\\n if dtype is not None and dtype != p_dtype:\\n try:\\n p_data = p_data.astype(dtype) # Note: returns a copy\\n except ValueError:\\n raise ValueError(\\n \\\"Can't recast partition array from {} to {}\\\".format(\\n p_dtype.name, dtype.name\\n )\\n )\\n else:\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Copy the array\\n # -----------------------------------------------------------\\n if copy:\\n if p_dtype.char != \\\"O\\\":\\n if not masked or p_data.ndim > 0:\\n p_data = p_data.copy()\\n else:\\n # This is because numpy.ma.copy doesn't work for\\n # scalar arrays (at the moment, at least)\\n p_data = numpy_ma_masked_all((), p_data.dtype)\\n\\n # We've just copied p_data, so in place changes are\\n # not possible\\n in_place_changes = False\\n else:\\n # whilst netCDF4.netcdftime.datetime is mucking bout,\\n # don't copy!!!!\\n # p_data = _copy(p_data)\\n pass\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Update the partition\\n # ------------------------------------------------------------\\n if update:\\n self.subarray = p_data # ?? DCH CHECK\\n self.Units = p_units\\n self.part = []\\n self.axes = axes\\n self.flip = flip\\n self.flatten = []\\n self.shape = p_shape\\n self.location = p_location\\n\\n self._in_place_changes = in_place_changes\\n\\n # ------------------------------------------------------------\\n # Return the numpy array\\n # ------------------------------------------------------------\\n return p_data\",\n \"def len(self):\\n return self._fsize - self._fp.tell()\",\n \"def files_processed(self) -> float:\\n return pulumi.get(self, \\\"files_processed\\\")\",\n \"def DeleteFiles(self, min_size=0):\\n\\n ndeleted = 0\\n for filename, counts in list(self.mCounts.items()):\\n if counts < min_size:\\n os.remove(filename)\\n ndeleted += 1\\n\\n return ndeleted\",\n \"def setNextFile(self):\\n\\n if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile):\\n self.nReadFiles=self.nReadFiles+1\\n if self.nReadFiles > self.nTotalReadFiles:\\n self.flagNoMoreFiles=1\\n raise schainpy.admin.SchainWarning('No more files to read')\\n\\n print('------------------- [Opening file] ------------------------------',self.nReadFiles)\\n self.nReadBlocks = 0\\n #if self.nReadBlocks==0:\\n # self.readFirstHeader()\",\n \"def fileno(self):\\n return self.file.fileno()\",\n \"def len(self):\\n return self._fsize - self._tell\",\n \"def _setcounter():\\n fname = os.path.basename(camera.status.lastfile)\\n tname = fname.split('.')[0]\\n i = len(tname)-1\\n if i > -1:\\n while tname[i].isdigit() and i>-1:\\n i = i - 1\\n nname = fname[:-4]\\n bname = tname[:i+1]\\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9].cntr'):\\n os.remove(file)\\n for file in glob.glob('/data/counters/'+bname+'[0-9][0-9][0-9][0-9].cntr'):\\n os.remove(file)\\n f = open('/data/counters/'+nname+'cntr','w')\\n f.close()\",\n \"def fileno(self):\\n raise io.UnsupportedOperation\",\n \"def getFirstObjectIndex(self):\\n if not self.fileInfo.isEsp(): raise StateError(_('FileRefs.renumberObjects is for esps only.'))\\n for cell in self.cells:\\n objects = cell.getObjects()\\n for object in objects.list():\\n if object[0] == 0:\\n return object[1]\\n return 0\",\n \"def test_op_sub_array_int(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=int)\\n o = a + 1\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a - o\\n\\n offl_a = stream.bind(a)\\n offl_r = offl_a - o\\n offl_a.update_host()\\n r = offl_r.update_host().array\\n stream.sync()\\n\\n self.assertEqual(r.shape, a.shape)\\n self.assertEqual(r.dtype, a.dtype)\\n self.assertTrue((a == old_a).all(),\\n \\\"Input array operand 1 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def __calculate_number_of_frames(self):\\n # Save current position\\n current_pos = self.__file_object.tell()\\n\\n # Go to start of first frame\\n self.__file_object.seek(self.__first_frame_raw_data_position)\\n self.number_of_frames = 0\\n\\n while True:\\n if not self.__file_object.read(self.__frame_raw_data_size):\\n break\\n\\n self.__file_object.readline()\\n self.number_of_frames += 1\\n\\n # Restore file pointer\\n self.__file_object.seek(current_pos)\\n print('Number of frames:', self.number_of_frames)\",\n \"def trace_file_len(fname):\\n try:\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n return i - 1\\n except FileNotFoundError:\\n return 0\",\n \"def get_file(self) -> int:\\r\\n return self.file\",\n \"def prune_empty(self): # EntryList.prune_empty\\n prevCount = self.count_deleted()\\n for name, e in allFiles.contents.iteritems():\\n e.prune_empty()\\n return allFiles.count_deleted() - prevCount\",\n \"def __len__(self):\\n\\n return math.ceil(len(self.img_files) * self.gen_count / self.batch_size)\",\n \"def refreshSize(self):\\n if self.isLoaded:\\n return 0\\n else:\\n return self.fileInfo.size\",\n \"def Rear(self):\\n return -1 if self.isEmpty() else self.__buffer[(self.__start+self.__size-1) % len(self.__buffer)]\",\n \"def seek(self, *args) -> \\\"int\\\":\\n return _ida_fpro.qfile_t_seek(self, *args)\",\n \"def in_cached_file(self):\\n return isinstance(self._subarray, CachedArray)\",\n \"def getModificationNumber(self) -> long:\\n ...\",\n \"def size(self) -> int:\\n return sum(p.size for p in self.iterfiles())\",\n \"def tell(self):\\n return self._upload_position\",\n \"def has_subfile(self) -> bool:\\n\\t\\tself._update_subfiles()\\n\\t\\treturn bool(len(self.subfiles))\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def __len__(self):\\n return len(self.array)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7747842","0.63648546","0.6072333","0.60351396","0.5918244","0.57059807","0.56674904","0.5664642","0.56031275","0.5573989","0.5520654","0.5453089","0.5448838","0.54281026","0.5422772","0.5378375","0.5307265","0.5244931","0.52217174","0.5220032","0.5216291","0.51331407","0.5115629","0.5106185","0.50907373","0.5089161","0.5085614","0.507725","0.5066728","0.50590724","0.50359875","0.5020529","0.50147223","0.49775988","0.49749458","0.49738547","0.49649253","0.49604303","0.49502134","0.49456447","0.4944183","0.49393135","0.49268013","0.49217892","0.48981854","0.48959568","0.48843467","0.48842198","0.48836866","0.4881064","0.4877542","0.48724556","0.4844187","0.4844187","0.48379225","0.48361284","0.4816804","0.48031083","0.4801541","0.47996238","0.4778122","0.47719982","0.47565272","0.47562754","0.4756017","0.47559637","0.47401142","0.4727482","0.47270516","0.4725971","0.47178772","0.47169724","0.47143152","0.4712884","0.47011933","0.47006902","0.4695959","0.46883664","0.46880865","0.46875745","0.46849707","0.46837255","0.46647176","0.4656943","0.46559966","0.465177","0.46486926","0.4644861","0.46445367","0.4642497","0.46409014","0.46387452","0.4635584","0.46266317","0.4622208","0.46218398","0.46215257","0.46171033","0.4611914","0.4611914"],"string":"[\n \"0.7747842\",\n \"0.63648546\",\n \"0.6072333\",\n \"0.60351396\",\n \"0.5918244\",\n \"0.57059807\",\n \"0.56674904\",\n \"0.5664642\",\n \"0.56031275\",\n \"0.5573989\",\n \"0.5520654\",\n \"0.5453089\",\n \"0.5448838\",\n \"0.54281026\",\n \"0.5422772\",\n \"0.5378375\",\n \"0.5307265\",\n \"0.5244931\",\n \"0.52217174\",\n \"0.5220032\",\n \"0.5216291\",\n \"0.51331407\",\n \"0.5115629\",\n \"0.5106185\",\n \"0.50907373\",\n \"0.5089161\",\n \"0.5085614\",\n \"0.507725\",\n \"0.5066728\",\n \"0.50590724\",\n \"0.50359875\",\n \"0.5020529\",\n \"0.50147223\",\n \"0.49775988\",\n \"0.49749458\",\n \"0.49738547\",\n \"0.49649253\",\n \"0.49604303\",\n \"0.49502134\",\n \"0.49456447\",\n \"0.4944183\",\n \"0.49393135\",\n \"0.49268013\",\n \"0.49217892\",\n \"0.48981854\",\n \"0.48959568\",\n \"0.48843467\",\n \"0.48842198\",\n \"0.48836866\",\n \"0.4881064\",\n \"0.4877542\",\n \"0.48724556\",\n \"0.4844187\",\n \"0.4844187\",\n \"0.48379225\",\n \"0.48361284\",\n \"0.4816804\",\n \"0.48031083\",\n \"0.4801541\",\n \"0.47996238\",\n \"0.4778122\",\n \"0.47719982\",\n \"0.47565272\",\n \"0.47562754\",\n \"0.4756017\",\n \"0.47559637\",\n \"0.47401142\",\n \"0.4727482\",\n \"0.47270516\",\n \"0.4725971\",\n \"0.47178772\",\n \"0.47169724\",\n \"0.47143152\",\n \"0.4712884\",\n \"0.47011933\",\n \"0.47006902\",\n \"0.4695959\",\n \"0.46883664\",\n \"0.46880865\",\n \"0.46875745\",\n \"0.46849707\",\n \"0.46837255\",\n \"0.46647176\",\n \"0.4656943\",\n \"0.46559966\",\n \"0.465177\",\n \"0.46486926\",\n \"0.4644861\",\n \"0.46445367\",\n \"0.4642497\",\n \"0.46409014\",\n \"0.46387452\",\n \"0.4635584\",\n \"0.46266317\",\n \"0.4622208\",\n \"0.46218398\",\n \"0.46215257\",\n \"0.46171033\",\n \"0.4611914\",\n \"0.4611914\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6648334"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2677,"cells":{"query":{"kind":"string","value":"Add the auxiliary mask to the config dictionary. Assumes that ``self.config`` already exists."},"document":{"kind":"string","value":"def _configure_auxiliary_mask(self, auxiliary_mask):\n indices = self.indices\n\n new = [\n mask[\n tuple(\n [\n (slice(None) if n == 1 else index)\n for n, index in zip(mask.shape, indices)\n ]\n )\n ]\n for mask in auxiliary_mask\n ]\n\n # # If the partition is to be parallelised then get rid of mask\n # # components which are all False so the mask component does\n # # not get copied to the child process\n # if not config['serial']:\n # new = [mask for mask in new if not mask.any()]\n\n self.config[\"auxiliary_mask\"] = new"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def configure_masking(self, masks):\n self.masks = masks","def add_config(self):\n\n config = {\n 'invert_byte': InvertByte,\n 'invert_word': InvertWord,\n 'invert_double_word': InvertDoubleWord,\n 'and_byte': AndByte,\n 'and_word': AndWord,\n 'and_double_word': AndDoubleWord,\n 'or_byte': OrByte,\n 'or_word': OrWord,\n 'or_double_word': OrDoubleWord,\n 'exclusive_or_byte': ExclusiveOrByte,\n 'exclusive_or_word': ExclusiveOrWord,\n 'exclusive_or_double_word': ExclusiveOrDoubleWord\n }\n\n return config","def _updateMaskedValueSet():\n global masked_value_set\n for confName in controller.CONF:\n # Add all needed values to masked_value_set\n if (controller.getParamKeyValue(confName, \"MASK_INPUT\") == True):\n masked_value_set.add(controller.CONF[confName])","def update_mask(self, mask):\n\n # Get general mask\n general_mask = self.general_mask\n\n # Complete with the input mask\n new_mask = (general_mask | mask)\n\n # Update attribute\n self.mask = new_mask\n\n # Correct i_bounds if it was not specified\n # self.update_i_bnds()\n\n # Re-compute weights\n self.weights, self.weights_k_idx = self.compute_weights()\n\n return","def add(self, files, mask):\n pass","def update_mask(self):\r\n \r\n # Binary mask from ML detection\r\n if len(self.selected_ML_Index) > 0:\r\n # Delete items in dictionary that are not roi items\r\n roi_dict = self.selected_cells_infor_dict.copy()\r\n del_key_list=[]\r\n for key in roi_dict:\r\n print(key)\r\n if 'ROIitem' not in key:\r\n del_key_list.append(key)\r\n for key in del_key_list:\r\n del roi_dict[key]\r\n \r\n self.MLmask = ProcessImage.ROIitem2Mask(roi_dict, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\r\n # Binary mask of added rois\r\n self.addedROIitemMask = ProcessImage.ROIitem2Mask(self.roi_list_freehandl_added, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\r\n \r\n self.intergrate_into_final_mask()","def add_mask_layer(self):\n return Masking(mask_value=self.mask_value, input_shape=(self.max_sequence_size, 1))","def open(self, config):\n unique_subarray = getrefcount(self._subarray) <= 2\n\n config = config.copy()\n config[\"unique_subarray\"] = unique_subarray\n\n self.config = config\n\n if config.get(\"auxiliary_mask\"):\n self._configure_auxiliary_mask(config[\"auxiliary_mask\"])\n\n self.config[\"extra_memory\"] = self.extra_memory()\n\n self._in_place_changes = True\n self.masked = True\n\n if hasattr(self, \"output\"):\n del self.output\n\n return config","def __set_special_config_values(cfg: __Config, config: dict) -> \"__Config\":\n cfg.file_name_plane_masks = lambda i: str(i) + config['file_name_plane_mask_suf']\n cfg.file_name_planercnn_image = lambda i: str(i) + config['file_name_planercnn_image_suf']\n cfg.dir_results = f\"{cfg.edge_detection_type}\" # will be the output folder, create in data dir\n cfg.image_size = tuple(int(x) for x in config['image_size'].split(\" \"))\n return cfg","def _buildMaskImage(self,maskname, mask_array):\n # If an old version of the maskfile was present,\n # remove it and rebuild it.\n if fileutil.findFile(maskname):\n fileutil.removeFile(maskname)\n\n _file = pyfits.open(maskname,mode='append')\n _phdu = pyfits.PrimaryHDU(data=mask_array)\n\n _file.append(_phdu)\n _file.close()\n del _file, _phdu","def setup_mask(self, d25scale): \n\n logger = logging.getLogger(name=\"ShotSensitivity\")\n \n # see if this is a bad shot\n #print(\"Bad shot from \", self.conf.badshot)\n badshot = loadtxt(self.conf.badshot, dtype=int)\n badtpshots = loadtxt(self.conf.lowtpshots, dtype=int)\n if (self.shotid in badshot) or (self.shotid in badtpshots):\n logger.warn(\"Shot is in bad. Making mask zero everywhere\")\n self.badshot = True\n else:\n self.badshot = False\n \n # set up bad amps\n logger.info(\"Bad amps from {:s}\".format(self.conf.badamp))\n self.bad_amps = Table.read(self.conf.badamp)\n sel_shot = (self.bad_amps[\"shotid\"] == self.shotid)\n self.bad_amps = self.bad_amps[sel_shot]\n \n # set up galaxy mask\n logger.info(\"Galaxy mask from {:s}\".format(self.conf.rc3cat))\n galaxy_cat = Table.read(self.conf.rc3cat, format='ascii')\n gal_coords = SkyCoord(galaxy_cat['Coords'], frame='icrs')\n shot_coords = SkyCoord(ra=self.shot_ra, dec=self.shot_dec,\n unit=\"deg\")\n sel_reg = where(shot_coords.separation(gal_coords) < 1.*u.deg)[0]\n\n self.gal_regions = []\n if len(sel_reg) > 0:\n for idx in sel_reg:\n self.gal_regions.append(create_gal_ellipse(galaxy_cat, \n row_index=idx, \n d25scale=d25scale))\n \n # set up meteor mask\n # check if there are any meteors in the shot:\n logger.info(\"Meteors from {:s}\".format(self.conf.meteor))\n self.met_tab = Table.read(self.conf.meteor, format=\"ascii\")\n self.met_tab = self.met_tab[self.shotid == self.met_tab[\"shotid\"]]","def add_mask(self):\n return xr.open_dataset(f'/{test.dlfile_directory}/{test.climate}_mask_{test.mask_str}_dldata_traintest.nc')","def apply_mask(self, mask_band=None, mask_val=None):\n pass","def prepareMask(self, mask):\n\n # Make sure that the mask has the same\n # number of voxels as the atlas image.\n # Use nearest neighbour interpolation\n # for resampling, as it is most likely\n # that the mask is binary.\n try:\n mask, xform = resample.resample(\n mask, self.shape[:3], dtype=np.float32, order=0)\n\n except ValueError:\n raise MaskError('Mask has wrong number of dimensions')\n\n # TODO allow non-aligned mask - as long as it overlaps\n # in world coordinates, it should be allowed\n if not fslimage.Image(mask, xform=xform).sameSpace(self):\n raise MaskError('Mask is not in the same space as atlas')\n\n return mask","def customize_experiment_config(self, config):\n # TODO: use ConfigList from Coach launcher, and share customization code.\n hyperparams_dict = json.loads(os.environ.get(\"SM_HPS\", \"{}\"))\n\n # Set output dir to intermediate\n # TODO: move this to before customer-specified so they can override\n hyperparams_dict[\"rl.training.local_dir\"] = \"/opt/ml/output/intermediate\"\n\n self.hyperparameters = ConfigurationList() # TODO: move to shared\n for name, value in hyperparams_dict.items():\n # self.map_hyperparameter(name, val) #TODO\n if name.startswith(\"rl.\"):\n # self.apply_hyperparameter(name, value) #TODO\n self.hyperparameters.store(name, value)\n # else:\n # raise ValueError(\"Unknown hyperparameter %s\" % name)\n\n self.hyperparameters.apply_subset(config, \"rl.\")\n return config","def mask_custom(self, custom_mask):\n\t\t## combine the list and remove the duplicates\n\t\tself.mask = list(set().union(self.mask, custom_mask))\n\n\t\tself.wave = np.delete(self.oriWave, list(self.mask))\n\t\tself.flux = np.delete(self.oriFlux, list(self.mask))\n\t\tself.noise = np.delete(self.oriNoise, list(self.mask))\n\n\t\treturn self","def add_extra(self, entry, value):\n\n config_spec = vim.vm.ConfigSpec()\n self.logger.info(\"Adding/Updating extra config: {0} = {1}\".format(entry, value))\n opt = vim.option.OptionValue()\n opt.key = entry\n opt.value = value\n config_spec.extraConfig = [opt]\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)","def attention_mask(model, x):\n config = model.config\n input_mask = model.inputs[\"input_mask\"]\n final_mask = model.builder.customOp(opName=\"AttentionMask\",\n opVersion=1,\n domain=\"ai.graphcore\",\n inputs=[input_mask, x],\n attributes={\"dataType\": model.config.popart_dtype})[0]\n final_mask = model.detach(final_mask)\n return final_mask","def merge_config(self_config, indict):\n\n self_config.merge(indict)\n patch_config(self_config, indict)","def set_measurement_mask(self, program_name, mask_name, begins, lengths) -> Tuple[numpy.ndarray, numpy.ndarray]:","def add_config(self, conf_map):\n if self.active.isChecked():\n self.add_feat_conf(conf_map)","def add_config(self, config):\n clean=lambda n: n.strip().strip('\"').lower()\n for line in config.split('\\n'):\n items=line.strip().split()\n if items and len(items) >= 3:\n cmd, evt, hnd=items[:3]\n \"\"\" NOTE\n - just 'bind' command expected right now\n - '+' prepended ti the handler means REPEAT (make sense just for keyboard keys actually)\n \"\"\"\n cmd=clean(cmd)\n if cmd in ['bind']:\n evt,hnd=(clean(evt), clean(hnd))\n if not cmd in self.config: self.config[cmd]={}\n repeat=hnd.startswith('+')\n if repeat: hnd=hnd[1:]\n self.config[cmd].update([[evt, [hnd, repeat]]])","def _draw_mask_on_image(self, mask):\n mask = self.STANDARD_COLORS_ARRAY[mask]\n cv2.addWeighted(mask,self.config.ALPHA,self.image,1.0,0,self.image)","def _update_mask_type(configs, mask_type):\n configs[\"train_input_config\"].mask_type = mask_type\n _update_all_eval_input_configs(configs, \"mask_type\", mask_type)","def createMaskDictionary(self):\n try:\n self.maskMap = dict(list(zip(self.inds,list(range(len(self.inds))))))\n self.maskSet = set(self.inds)\n except Exception as error:\n print(\"failed in createMaskDictionary\", error)","def add_mask(self, bg, mask):\n # if mask is to tall for the background image, decrease the size by 50%\n if bg.shape[0] < mask.shape[0]:\n mask = cv2.resize(mask, (int(0.5*mask.shape[0]), int(0.5*mask.shape[1])), interpolation=cv2.INTER_AREA)\n h_mask, w_mask = mask.shape[:2]\n h, w = bg.shape[:2]\n \n # select random location for mask\n h_rand = np.random.rand() * 0.9\n h_rand = np.clip(h_rand, 0, 1.0 - h_mask/h)\n h_update = int(h_rand * h)\n w_rand = np.random.rand() * 0.9\n w_rand = np.clip(w_rand, 0, 1.0 - w_mask/w)\n w_update = int(w_rand * w)\n \n # define filter for a mask\n filt = (mask == 0)\n \n # place the mask in the bg img\n mod = bg.copy()\n mod[h_update:h_update+h_mask, w_update:w_update+w_mask, :] *= filt\n mod[h_update:h_update+h_mask, w_update:w_update+w_mask, :] += mask\n \n # yolo dim for mask\n locy = (h_update+h_update+h_mask)/2/h\n locx = (w_update+w_update+w_mask)/2/w\n sizey = (h_mask/h)\n sizex = (w_mask/w)\n \n dim = [locx, locy, sizex, sizey]\n \n return mod, dim","def _GetChangesForMask(config_sed_input):\n config_sed = config_sed_input\n config_sed += [(r'WALLTIME_MINUTES=100',\n (r'WALLTIME_MINUTES=100\\n'\n r'export CONT=mlperf-nvidia:object_detection\\n'\n r'export DATADIR=\\/data\\n'\n r'export PKLDIR=\\/data\\/coco2017\\/pkl_coco\\n'\n r'export NEXP=1'))]\n if MASKRCNN_BATCH_SIZE.value:\n config_sed.append(\n (r'BATCHSIZE=.*', fr'BATCHSIZE={MASKRCNN_BATCH_SIZE.value}'))\n return config_sed","def apply_mask(self, mask, parameters=None):\n if parameters is None:\n self.dates = self.dates[mask]\n for key in self.data.keys():\n self.data[key] = self.data[key][mask]\n\n self.manufacturer = self.manufacturer[mask]\n self.data_file = self.data_file[mask]\n self.serial_number = self.serial_number[mask]\n else:\n for parameter in parameters:\n self.data[parameter][~mask] = np.nan","def add_dimension(self, name, bit_index=None, default=False):\n if not self.flag_masks.get(name) is None:\n raise ValueError(\"the name %s is already in this flag space\" % name)\n bit_nums = list(self.flag_bits.values())\n if bit_index is None:\n bit_index = 0\n #assign the lowest currently unused bit number\n while bit_index in bit_nums:\n bit_index += 1\n if bit_index in bit_nums:\n raise ValueError(\"bit_index %d is already taken\" % bit_index)\n self.flag_bits[name] = bit_index\n self.flag_masks[name] = 2**bit_index\n self.default_dict[name] = default","def _configure(self) -> None:\n reg_data = self.configuration\n conf_data = reg_data & ~0xC0 | 0x80\n # check if already in the right configuration, do not re-configure on and on again\n if reg_data != conf_data:\n self.configuration = conf_data","def get_config(self):\n layer_config = {\n \"anchors\": self._anchors, \n \"classes\": self._classes,\n \"ignore_thresh\": self._ignore_thresh, \n \"truth_thresh\": self._truth_thresh, \n \"iou_thresh\": self._iou_thresh, \n \"loss_type\": self._loss_type, \n \"iou_normalizer\": self._iou_normalizer,\n \"cls_normalizer\": self._cls_normalizer, \n \"scale_x_y\": self._scale_x_y, \n }\n layer_config.update(super().get_config())\n return layer_config","def AddWithColourMask(*args, **kwargs):\n return _gdi_.ImageList_AddWithColourMask(*args, **kwargs)","def mask(self, mask):\n\n self._mask = mask","def mask(self, mask):\n\n self._mask = mask","def setup(self, config, base, xsize, ysize, ignore, logger):\n # .. Do any custom setup you need to do.\n # Probably want to call the base class setup function to do the normal determination\n # of the size and position values.\n\n # Extra processing of 'bandpass' argument\n # Most needed type-checking is done in galsim.bandpass\n self._req_bp_fields = ['throughput', 'wave_type']\n self._opt_bp_fields = ['red_limit', 'blue_limit', 'zeropoint']\n try:\n bp = config['bandpass']\n for req in self._req_bp_fields:\n if req not in bp.keys():\n raise ValueError('Must pass field {} for a bandpass object!'.format(req))\n # for opt in self._opt_bp_fields:\n # if opt not in bp.keys():\n # config['bandpass'][opt] = None\n for key in bp.keys():\n if key not in (self._req_bp_fields+self._opt_bp_fields):\n raise ValueError('Field {} is not a valid entry for a bandpass!'.format(key))\n except KeyError:\n raise KeyError('`bandpass` is a required field for a COSMOSChromatic stamp!')\n\n extra_ignore = ignore + ['bandpass']\n return super(self.__class__, self).setup(config, base, xsize, ysize, extra_ignore, logger)","def set_mask(self, mask):\n self.mask = mask","def add_config(self, config):\n\n if config.identifier in self.configs:\n raise DuplicateConfigException(\n \"Builder already has config with identifier : {}\".format(\n config.identifier\n )\n )\n\n self.configs[config.identifier] = config","def set_derived_configs(self):\n if 'dim' in self.config and self.config['dim'] <= 0:\n self.config['dim'] = self.descriptors['input']['dim']","def add_log_config(self, monitor_name, log_config):\n pass","def ApplyMask(data,mask):\n \n # loop through portions\n for portion in data.keys():\n # match data keys and apply mask \n for key in data[portion].keys():\n if key in 'xyerr':\n if mask != 'UnMasked':\n data[portion][key].mask = data[portion]['UnMasked']\n data[portion][key].mask = data[portion][mask]\n\t\n return data","def addMasking(self):\n self.abundance_df['masked'] = [False]*len(self.abundance_df.index)\n self.abundance_df['colour'] = ['undefined']*len(self.abundance_df.index)","def apply_additional_mask(\n old_mask_file=None,\n new_mask_file=None,\n new_thresh=0.0,\n operation='AND'\n):\n if root_mask == None:\n logger.info(\"Specify a cube root file name.\")\n return\n\n myia = au.createCasaTool(casaStuff.iatool)\n myia.open(new_mask_file)\n new_mask = myia.getchunk()\n myia.close()\n\n myia.open(old_mask_file)\n mask = myia.getchunk()\n if operation == \"AND\":\n mask *= (new_mask > new_thresh)\n else:\n mask = (mask + (new_mask > new_thresh)) >= 1.0\n myia.putchunk(mask)\n myia.close()\n\n return","def set_mask_key(self, func):\r\n self.get_mask_key = func","def update_configuration(self, config):\n\n config[\"data_transformation\"][\"n_classification_bins\"] = config[\"n_classification_bins\"]\n config[\"data_transformation\"][\"nassets\"] = config[\"nassets\"]\n config[\"data_transformation\"][\"classify_per_series\"] = config[\"classify_per_series\"]\n config[\"data_transformation\"][\"normalise_per_series\"] = config[\"normalise_per_series\"]\n\n return config","def _addDefaultsToMaskedValueSet():\n global masked_value_set\n for group in controller.getAllGroups():\n for param in group.getAllParams():\n # Keep default password values masked, but ignore default empty values\n if ((param.getKey(\"MASK_INPUT\") == True) and param.getKey(\"DEFAULT_VALUE\") != \"\"):\n masked_value_set.add(param.getKey(\"DEFAULT_VALUE\"))\n\n # Add deault consts we want to mask\n # TODO: add future consts to mask here\n masked_value_set.add(basedefs.CONST_CA_PASS)","def _add_mask(batch, num_batch_dims):\n mask = tf.ones(tf.shape(list(batch.values())[0])[:num_batch_dims])\n if \"mask\" in batch:\n mask *= batch[\"mask\"]\n batch[\"mask\"] = mask\n return batch","def paintMask(self):\n if self.avatarConfiguration[\"mask\"]:\n if not os.path.isfile(MASK_UPLOAD):\n image = self.parent.getPlayer().getImageLabel()\n filePath = GG.genteguada.GenteGuada.getInstance().getDataPath(image)\n guiobjects.generateImageSize(filePath, [244, 244], IMG_UPLOAD)\n self.generateMask(\"imgUpload.png\")\n imgPath = MASK_UPLOAD\n else:\n imgPath = GG.genteguada.GenteGuada.getInstance().getDataPath(os.path.join(PATH_EDITOR_IMG, self.avatarConfiguration[\"gender\"], self.avatarConfiguration[\"headSize\"], \"mask.png\"))\n self.newAvatarImage(imgPath, \"mask\")","def set_mask(self, mask):\n self.mask = self._image_to_vector(mask)\n\n # PCA needs to be rerun\n self._mark_cache_invalid()","def apply_mask(self):\n for mask, param in self.masked_parameters:\n param.mul_(mask)","def add_mask(self, image_id: int, category_id: int, segmentation: np.ndarray, score: float):\n rle = pycocotools.mask.encode(np.asfortranarray(segmentation.astype(np.uint8)))\n rle['counts'] = rle['counts'].decode('ascii') # json.dump doesn't like bytes strings\n\n self.mask_data.append({\n 'image_id': int(image_id),\n 'category_id': get_coco_cat(int(category_id)),\n 'segmentation': rle,\n 'score': float(score)\n })","def setup_masking(self, inclusion_masks, exclusion_masks):\n if len(inclusion_masks) != MAX_INCLUSION_MASKS_COUNT:\n raise ValueError(\"The inclusion mask list has {0} masks. That exceeds the expected maximum mask count of \"\n \"{1}.\".format(len(inclusion_masks), MAX_INCLUSION_MASKS_COUNT))\n\n if len(exclusion_masks) != MAX_EXCLUSION_MASKS_COUNT:\n raise ValueError(\"The exclusion mask list has {0} masks. That exceeds the expected maximum mask count of \"\n \"{1}.\".format(len(exclusion_masks), MAX_EXCLUSION_MASKS_COUNT))\n\n masking_prefix = self._base_pv_name + \":\" + self._slot_number\n for i in range(MAX_INCLUSION_MASKS_COUNT):\n self._run_cmd(CAPUT + \" \" + masking_prefix + \":INCLUSION\" + str(i + 1) + \" \" +\n str(inclusion_masks[i]))\n\n for i in range(MAX_EXCLUSION_MASKS_COUNT):\n self._run_cmd(CAPUT + \" \" + masking_prefix + \":EXCLUSION\" + str(i + 1) + \" \" + str(exclusion_masks[i]))","def _initial_set_mask(self, mask):\n if mask is None:\n mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool),\n self._wcs, shape=self.value.shape)\n elif isinstance(mask, np.ndarray):\n if mask.shape != self.value.shape:\n raise ValueError(\"Mask shape must match the {0} shape.\"\n .format(self.__class__.__name__)\n )\n mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape)\n elif isinstance(mask, MaskBase):\n pass\n else:\n raise TypeError(\"mask of type {} is not a supported mask \"\n \"type.\".format(type(mask)))\n\n # Validate the mask before setting\n mask._validate_wcs(new_data=self.value, new_wcs=self._wcs,\n wcs_tolerance=self._wcs_tolerance)\n\n self._mask = mask","def update_network_section(self):\n rconfig = configparser.RawConfigParser()\n rconfig.read(self.conf_file)\n if self.ext_net:\n if not rconfig.has_section('network'):\n rconfig.add_section('network')\n rconfig.set('network', 'public_network_id', self.ext_net.id)\n rconfig.set('network', 'floating_network_name', self.ext_net.name)\n rconfig.set('network-feature-enabled', 'floating_ips', True)\n else:\n if not rconfig.has_section('network-feature-enabled'):\n rconfig.add_section('network-feature-enabled')\n rconfig.set('network-feature-enabled', 'floating_ips', False)\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\n rconfig.write(config_file)","def add_config(self):\n\n config = {\n 'byte_to_integer': ByteToInteger,\n 'integer_to_byte': IntegerToByte,\n 'integer_to_double_integer': IntegerToDoubleInteger,\n 'integer_to_string': IntegerToString,\n 'double_integer_to_integer': DoubleIntegerToInteger,\n 'double_integer_to_real': DoubleIntegerToReal,\n 'double_integer_to_string': DoubleIntegerToString,\n 'binary_coded_decimal_to_integer': BinaryCodedDecimalToInteger,\n 'integer_to_binary_coded_decimal': IntegerToBinaryCodedDecimal,\n 'round': Round,\n 'truncate': Truncate,\n 'real_to_string': RealToString,\n 'integer_to_ascii': IntegerToASCII,\n 'double_integer_to_ascii': DoubleIntegerToASCII,\n 'real_to_ascii': RealToASCII,\n 'ascii_to_hexadecimal': ASCIIToHexadecimal,\n 'hexadecimal_to_ascii': HexadecimalToASCII,\n 'string_to_integer': StringToInteger,\n 'string_to_double_integer': StringToDoubleInteger,\n 'string_to_real': StringToReal,\n 'decode': Decode,\n 'encode': Encode,\n 'segment': Segment\n }\n\n return config","def addHostmask(self, hostmask):\n assert ircutils.isUserHostmask(hostmask), 'got %s' % hostmask\n if len(unWildcardHostmask(hostmask)) < 8:\n raise ValueError, \\\n 'Hostmask must contain at least 8 non-wildcard characters.'\n self.hostmasks.add(hostmask)","def _postprocess_config(self, config: Dict[str, Any]) -> Dict[str, Any]:\n new_config = self.config_space.copy()\n new_config.update(cast_config_values(config, config_space=self.config_space))\n return new_config","def editMaskVariable(self, product, mask_variable):\r\n return mask_variable","def merge_jupyter_config_data(self, config, in_config):\n self.log.debug(f\"\"\"[lite][config][merge] ..... {config}\"\"\")\n self.log.debug(f\"\"\"[lite][config][merge] ..... {in_config}\"\"\")\n\n config = config or {}\n in_config = in_config or {}\n\n for k, v in in_config.items():\n if k in [DISABLED_EXTENSIONS, FEDERATED_EXTENSIONS]:\n config[k] = [*config.get(k, []), *v]\n elif k in [SETTINGS_OVERRIDES]:\n config[k] = config.get(k, {})\n for pkg, pkg_config in v.items():\n config[k][pkg] = config[k].get(pkg, {})\n config[k][pkg].update(pkg_config)\n else:\n config[k] = v\n self.log.debug(f\"\"\"[lite][config][merge] ..... {config}\"\"\")\n return config","def merge_jupyter_config_data(self, config, in_config):\n self.log.debug(f\"\"\"[lite][config][merge] ..... {config}\"\"\")\n self.log.debug(f\"\"\"[lite][config][merge] ..... {in_config}\"\"\")\n\n config = config or {}\n in_config = in_config or {}\n\n for k, v in in_config.items():\n if k in [DISABLED_EXTENSIONS, FEDERATED_EXTENSIONS]:\n config[k] = [*config.get(k, []), *v]\n elif k in [SETTINGS_OVERRIDES]:\n config[k] = config.get(k, {})\n for pkg, pkg_config in v.items():\n config[k][pkg] = config[k].get(pkg, {})\n config[k][pkg].update(pkg_config)\n else:\n config[k] = v\n self.log.debug(f\"\"\"[lite][config][merge] ..... {config}\"\"\")\n return config","def _update_retain_original_image_additional_channels(\n eval_config,\n retain_original_image_additional_channels):\n eval_config.retain_original_image_additional_channels = (\n retain_original_image_additional_channels)","def build_attention_mask_3d_padding(source_mask, target_mask):\n mask = make_attention_mask_3d(source_mask, target_mask)\n # invert mask for Megatron\n return mask < 0.5","def set_datamask(self, mask, update=True):\n if mask is not None and np.shape(mask) != self.shape:\n raise TypeError(\"the shape of the given mask does not match the data shape\")\n \n self._side_properties[\"datamask\"] = mask","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(MatrixReLU, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def RPC_DigitizationBasicCfg(flags, **kwargs):\n acc = MuonGeoModelCfg(flags)\n if \"PileUpTools\" not in kwargs:\n PileUpTools = acc.popToolsAndMerge(RPC_DigitizationToolCfg(flags))\n kwargs[\"PileUpTools\"] = PileUpTools\n acc.merge(PileUpToolsCfg(flags, **kwargs))\n return acc","def add_feat_conf(self, conf_map):\n pass","def addAuth(self, hostmask):\n if self.checkHostmask(hostmask, useAuth=False) or not self.secure:\n self.auth.append((time.time(), hostmask))\n else:\n raise ValueError, 'secure flag set, unmatched hostmask'","def Expand_Mask(mask, feature_dict):\n new_mask = np.zeros(mask.shape + (len(feature_dict),))\n for i in feature_dict.keys():\n ni = int(i)\n new_mask[mask == ni,ni] = 1 \n return new_mask","def update_config(self, config):\n # add follower public folder to the CKAN's list of public folders\n here = os.path.dirname(__file__)\n public_dir = os.path.join(here, 'public')\n if config.get('extra_public_paths'):\n config['extra_public_paths'] += ',' + public_dir\n else:\n config['extra_public_paths'] = public_dir\n # add follower template folder to the CKAN's list of template folders\n template_dir = os.path.join(here, 'templates')\n if config.get('extra_template_paths'):\n config['extra_template_paths'] += ',' + template_dir\n else:\n config['extra_template_paths'] = template_dir","def generate_MLmask(self):\r\n self.MLmask = np.zeros((self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\r\n \r\n if len(self.selected_ML_Index) > 0:\r\n for selected_index in self.selected_ML_Index:\r\n self.MLmask = np.add(self.MLmask, self.Mask[:,:,selected_index])\r\n \r\n self.intergrate_into_final_mask()\r\n \r\n self.add_rois_of_selected()\r\n \r\n else:\r\n self.intergrate_into_final_mask()","def Adjust_Data(img,mask,feature_dict, normalize):\n ## Normalize image\n if normalize:\n img = Normalize_Image(img)\n\n ## Assume mask shape has 4 dimensions - mask is (batch, x, y, color-channel)\n ## color-channels are redundant, so just choose the first. \n mask = mask[:,:,:,0]\n \n ## Image_datagen performs interpolation when rotating, resulting in non-integer\n ## mask values. Round these back to integers before expanding the mask. \n mask = mask.round() \n mask = Expand_Mask(mask, feature_dict)\n #print(mask.shape, np.unique(mask, axis = 0))\n return (img,mask)","def _update(self, mask):\n if self.reporting:\n for pin in self.pins:\n if pin.mode is INPUT:\n pin_nr = pin.pin_number - self.port_number * 8\n pin.value = (mask & (1 << pin_nr)) > 0","def get_config(self):\n config = {\n }\n base_config = super(MatrixConcat, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def update_add_mask_sym(\n A: dshape(\"M... * M * complex\"),\n B: dshape(\"M... * M * complex\"),\n ind: dshape(\"M... * complex\"),\n mask: dshape(\"M... * complex\"),\n # symmetric: bool = True,\n) -> dshape(\"M... * M * complex\"):\n for (i, ind_i), (j, ind_j) in itertools.product(enumerate(ind), repeat=2):\n if mask[i]:\n A[ind_i, ind_j] += B[i, j]\n\n return A","def concat_config(config, new_config):\n for new_path in new_config:\n if new_path not in config:\n config[new_path] = new_config[new_path]\n else:\n config[new_path][0] = config[new_path][0] or new_config[new_path][0]\n for filename in config[new_path]:\n if filename != 0:\n if filename in new_config[new_path]:\n for opt in config[new_path][filename]:\n if opt in new_config[new_path][filename]:\n new_config[new_path][filename][opt]\\\n .update(config[new_path][filename][opt])\n else:\n new_config[new_path][filename][opt] = \\\n config[new_path][filename][opt]\n else:\n new_config[new_path][filename] = config[new_path][filename]\n return config","def __set_mask_regions(self):\n self.bottom_clip = np.int32(np.int32([[[60,0], [1179,0], [1179,650], [60,650]]]))\n self.roi_clip = np.int32(np.int32([[[640, 425], [1179,550], [979,719],\n [299,719], [100, 550], [640, 425]]]))","def load_lab_config(self, lab, host):\n _lab_conf = create_lab_config(lab, host)\n ip = self._hosts.get(host)\n\n # Some labs require additional customization\n # We don't know yet if customization is required\n _custom_conf = None\n\n # lab 8 (Multicast) customization\n if lab == str(8) and host == 'vrdevice':\n\n # customizing lab config\n _lab_conf = customize_lab8_config(_lab_conf)\n\n # loading lab config\n load_cfg_pyez(ip, _lab_conf, self._user, self._pass, mode='merge')\n\n # creating additional (custom) config\n configs = ['Rec1.conf', 'Rec3.conf', 'Rec4.conf', 'Rec2.conf']\n _custom_conf = prepare_custom_config(lab, host, configs)\n\n # loading custom config\n load_cfg_pyez(ip, _custom_conf, self._user, self._pass, mode='merge')\n\n # load multiping.slax script\n install_script(lab, host, ip, self._user, self._pass, 'multiping.slax', '/var/db/scripts/op/')\n\n # No customization required\n else:\n load_cfg_pyez(ip, _lab_conf, self._user, self._pass, mode='merge')\n\n # we want only interfaces used in the lab to be used, the rest disabled\n try:\n interfaces_to_use = get_interfaces(_lab_conf)\n if _custom_conf:\n interfaces_to_use = interfaces_to_use + get_interfaces(_custom_conf)\n disable_unused_interfaces(ip, self._user, self._pass, interfaces_to_use)\n except Exception as e:\n print('Cannot disable unused interfaces due to an error, ')\n print('however this is not critical - you can proceed further')\n # TO IMPLEMENT LATER - LOG THE EXCEPTION\n print(e)","def extend(clself, other):\n clself._cfg_def.extend(other._cfg_def)\n for key, optdef in clself._cfg_def.options.iteritems():\n setattr(clself, key, optdef)","def extend_mask_nonlocal(mask,kernel=np.ones((3,3))):\n\tassert (mask.dtype is np.dtype(np.bool)), \"input mask must be of bool type\"\n\n\text_mask = mask.copy()\n\tinp_ind = masked_indices(mask)\n\tim_h, im_w = mask.shape\n\n\tker_y, ker_x = kernel.shape\n\tassert(ker_x%2>0), \"kernel must have odd dimensions\"\n\tassert(ker_y%2>0), \"kernel must have odd dimensions\"\n\n\t# indices of the nonzero kernel elements\n\tker_ind_y, ker_ind_x = np.nonzero(kernel)\n\tker_ind_x -= ker_x//2\n\tker_ind_y -= ker_y//2\n\n\tfor ind_x, ind_y in zip(inp_ind%im_w, inp_ind//im_w):\n\t\tfor i, j in zip(ker_ind_x, ker_ind_y):\n\t\t\text_mask[min(max(0,ind_y+j),im_h-1),min(max(0,ind_x+i),im_w-1)] = True\n\n\treturn ext_mask","def _ApplyFlags(cls, config_values, flag_values):\n super(ContainerSpec, cls)._ApplyFlags(config_values, flag_values)\n if flag_values['image'].present:\n config_values['image'] = flag_values.image\n if flag_values['static_container_image'].present:\n config_values['static_image'] = flag_values.static_container_image","def configFeAsic(self,gain,shape,base,slk=None,slkh=None,monitorBandgap=None,monitorTemp=None):\n pass","def set_config(self, cfg):\n\n cfg.add_section(self.name)\n for attr, value in self.__dict__.items():\n if value not in [\"false\", \"none\", \"0\"] and attr != \"name\":\n attr = attr.replace(\"_\", \"-\")\n cfg.set(self.name, attr, value)","def with_mask(self, mask):\n return self[mask]","def update(self, config):\n # find keys are in config but not in self.config\n extra_keys = set(config.keys()) - set(self.config.keys())\n if len(extra_keys) > 0:\n raise ValueError(\"keys {} in config are not in Config.config\".format(extra_keys))\n # update self.config by config\n else:\n self.config.update(config)","def setMask(self, mask):\n self.mask = mask","def read_config(self, config):\n try:\n newconfig = ConfigObj(config, interpolation=False,\n configspec=self._configspec)\n except ConfigObjError as e:\n raise ConfigError(e)\n newconfig = self._validate(newconfig)\n self._config.merge(newconfig)\n logger.info(\"Loaded additional config: {0}\".format(config))","def setup_b_instance(self,norm,add_ps_mask=True):\n inst_tag = self.tag + '_'+str(self.flux_array_ebin)\n b = bsm.bayesian_scan_NPTF(tag=inst_tag,nside=self.nside,work_dir='/tmp/'+self.tag+'/',psf_dir=psf_dir,nlive=700)\n # Input the data, using the external data if provided\n if self.use_external_data:\n b.load_external_data(self.f1.CTB_en_bins,[self.external_data[self.flux_array_ebin]],self.f1.CTB_exposure_maps)\n else:\n b.load_external_data(self.f1.CTB_en_bins,self.f1.CTB_count_maps,self.f1.CTB_exposure_maps)\n\n if add_ps_mask:\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False,ps_mask_array = self.f1.ps_mask_array)\n else:\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False)\n\n b.add_new_template(self.f1.template_dict)\n b.rebin_external_data(1)\n\n b.add_poiss_model('ps_model','$A_{ps}$',[0.0,3.0],False)\n b.add_poiss_model('p7','$A_{p7}$',[0.0,2.0],False)\n b.add_poiss_model('bubs','$A_{bubs}$',[0.0,2.0],False)\n b.add_poiss_model('iso','$A_{iso}$',[0.0,3.0],False)\n # Add in a fixed J_map template\n b.add_fixed_templates({'J_map':[norm*self.J_map_arr[self.flux_array_ebin]/np.mean(self.J_map_arr[self.flux_array_ebin])]})\n\n b.initiate_poissonian_edep()\n return b","def _update_classification_mask(self, obj_class, mask_coords):\n # Remove background where object resides\n self.classification_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 0\n # Set classification mask where object resides\n self.classification_mask[obj_class, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 1","def update_compute_section(self):\n rconfig = configparser.RawConfigParser()\n rconfig.read(self.conf_file)\n if not rconfig.has_section('compute'):\n rconfig.add_section('compute')\n rconfig.set(\n 'compute', 'fixed_network_name',\n self.network.name if self.network else env.get(\"EXTERNAL_NETWORK\"))\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\n rconfig.write(config_file)","def add_config_field(self, content_type, name, *args, **kwargs):\n if name == 'representation_args':\n raise ValueError('{} is a reserved Config field name'.format(name))\n self._add_config_arg(ConfigField, content_type, name, *args, **kwargs)","def config_extra_settings(self, data_dir):\n # load data directory configuration\n self.label_path = data_dir\n self.label_config_dir = os.path.join(self.label_path, 'labelconfig')\n self.label_config_suffix = 'lbl'\n\n # set icon configuration\n self._icon_dir=get_icon_dir()\n\n # set window title\n self.setWindowTitle('FreeROI')\n #self.resize(1280, 1000)\n self.center()\n # set window icon\n self.setWindowIcon(QIcon(os.path.join(self._icon_dir,'icon.png')))\n\n self._init_configuration()\n self._init_label_config_center()\n\n # create actions\n self._create_actions()\n\n # create menus\n self._create_menus()","def add_noise(self, mdct_norm, masking_threshold):\n return self.psychoacoustic.add_noise(mdct_norm, masking_threshold)","def build_mask(cell, wgt, final_layer=None, final_datatype=None):\n fl = wgt.clad_layer+1 if final_layer==None else final_layer\n fd = 0 if final_datatype==None else final_datatype\n\n polygons = cell.get_polygons(by_spec=True)\n try:\n pWG = polygons[(wgt.wg_layer, wgt.wg_datatype)]\n pCLAD = polygons[(wgt.clad_layer, wgt.clad_datatype)]\n except KeyError:\n print(\"Warning! No objects written to layer/datatype specified by WaveguideTemplate\")\n if wgt.resist=='+':\n cell.add(gdspy.fast_boolean(pWG, pCLAD, 'xor', precision=0.001, max_points=199, layer=fl, datatype=fd))\n elif wgt.resist=='-':\n cell.add(gdspy.fast_boolean(pWG, pCLAD, 'and', precision=0.001, max_points=199, layer=fl, datatype=fd))","def set_config(self, cfg):\n\n cfg.add_section(self.name)\n for attr, value in self.__dict__.items():\n if value not in [\"false\", \"none\"] and attr != \"name\":\n attr = attr.replace(\"_\", \"-\")\n cfg.set(self.name, attr, value)","def set_config(self, cfg):\n\n cfg.add_section(self.name)\n for attr, value in self.__dict__.items():\n if value not in [\"false\", \"none\"] and attr != \"name\":\n attr = attr.replace(\"_\", \"-\")\n cfg.set(self.name, attr, value)","def set_config(self, cfg):\n\n cfg.add_section(self.name)\n for attr, value in self.__dict__.items():\n if value not in [\"false\", \"none\"] and attr != \"name\":\n attr = attr.replace(\"_\", \"-\")\n cfg.set(self.name, attr, value)","def add(self, host, **kwargs):\n self.configs_[0][1].add(host, **kwargs)","def set_feature_mask(self, feature_mask):\n self.feature_mask = feature_mask","def add_config(self):\n\n config = {\n 'count_up': CountUp,\n 'count_down': CountDown,\n 'count_up_or_down': CountUpOrDown,\n 'high_speed_counter_definition': HighSpeedCounterDefinition,\n 'high_speed_counter': HighSpeedCounter,\n 'pulse_output': PulseOutput\n }\n\n return config","def populate_config(self, config):\n self.use_wine_mappings.set_active(config['use_wine_mappings'])\n self.force_recheck.set_active(config['force_recheck'])\n self._previous_force_recheck = config['force_recheck']\n self.resume.set_active(config['resume'])\n try:\n self.glade.get_widget('time_added_checkbox').set_active(\n 'time_added' in config['transfer_meta'])\n except KeyError:\n pass\n self.resume_dat_entry.set_text(config['previous_resume_dat_path'])","def get_config(self):\n\n # these are all that is needed to rebuild this class\n config = dict(hidden_size=self.hidden_size,\n word_embedding=self.word_embedding,\n detection_embedding=self.detection_embedding,\n mode=self.mode,\n decoder_pos_emb=self.decoder_pos_emb,\n ** self.kwargs)\n\n base_config = super(RegionFeature, self).get_config()\n return dict(list(base_config.items()) +\n list(config.items()))"],"string":"[\n \"def configure_masking(self, masks):\\n self.masks = masks\",\n \"def add_config(self):\\n\\n config = {\\n 'invert_byte': InvertByte,\\n 'invert_word': InvertWord,\\n 'invert_double_word': InvertDoubleWord,\\n 'and_byte': AndByte,\\n 'and_word': AndWord,\\n 'and_double_word': AndDoubleWord,\\n 'or_byte': OrByte,\\n 'or_word': OrWord,\\n 'or_double_word': OrDoubleWord,\\n 'exclusive_or_byte': ExclusiveOrByte,\\n 'exclusive_or_word': ExclusiveOrWord,\\n 'exclusive_or_double_word': ExclusiveOrDoubleWord\\n }\\n\\n return config\",\n \"def _updateMaskedValueSet():\\n global masked_value_set\\n for confName in controller.CONF:\\n # Add all needed values to masked_value_set\\n if (controller.getParamKeyValue(confName, \\\"MASK_INPUT\\\") == True):\\n masked_value_set.add(controller.CONF[confName])\",\n \"def update_mask(self, mask):\\n\\n # Get general mask\\n general_mask = self.general_mask\\n\\n # Complete with the input mask\\n new_mask = (general_mask | mask)\\n\\n # Update attribute\\n self.mask = new_mask\\n\\n # Correct i_bounds if it was not specified\\n # self.update_i_bnds()\\n\\n # Re-compute weights\\n self.weights, self.weights_k_idx = self.compute_weights()\\n\\n return\",\n \"def add(self, files, mask):\\n pass\",\n \"def update_mask(self):\\r\\n \\r\\n # Binary mask from ML detection\\r\\n if len(self.selected_ML_Index) > 0:\\r\\n # Delete items in dictionary that are not roi items\\r\\n roi_dict = self.selected_cells_infor_dict.copy()\\r\\n del_key_list=[]\\r\\n for key in roi_dict:\\r\\n print(key)\\r\\n if 'ROIitem' not in key:\\r\\n del_key_list.append(key)\\r\\n for key in del_key_list:\\r\\n del roi_dict[key]\\r\\n \\r\\n self.MLmask = ProcessImage.ROIitem2Mask(roi_dict, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n # Binary mask of added rois\\r\\n self.addedROIitemMask = ProcessImage.ROIitem2Mask(self.roi_list_freehandl_added, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n \\r\\n self.intergrate_into_final_mask()\",\n \"def add_mask_layer(self):\\n return Masking(mask_value=self.mask_value, input_shape=(self.max_sequence_size, 1))\",\n \"def open(self, config):\\n unique_subarray = getrefcount(self._subarray) <= 2\\n\\n config = config.copy()\\n config[\\\"unique_subarray\\\"] = unique_subarray\\n\\n self.config = config\\n\\n if config.get(\\\"auxiliary_mask\\\"):\\n self._configure_auxiliary_mask(config[\\\"auxiliary_mask\\\"])\\n\\n self.config[\\\"extra_memory\\\"] = self.extra_memory()\\n\\n self._in_place_changes = True\\n self.masked = True\\n\\n if hasattr(self, \\\"output\\\"):\\n del self.output\\n\\n return config\",\n \"def __set_special_config_values(cfg: __Config, config: dict) -> \\\"__Config\\\":\\n cfg.file_name_plane_masks = lambda i: str(i) + config['file_name_plane_mask_suf']\\n cfg.file_name_planercnn_image = lambda i: str(i) + config['file_name_planercnn_image_suf']\\n cfg.dir_results = f\\\"{cfg.edge_detection_type}\\\" # will be the output folder, create in data dir\\n cfg.image_size = tuple(int(x) for x in config['image_size'].split(\\\" \\\"))\\n return cfg\",\n \"def _buildMaskImage(self,maskname, mask_array):\\n # If an old version of the maskfile was present,\\n # remove it and rebuild it.\\n if fileutil.findFile(maskname):\\n fileutil.removeFile(maskname)\\n\\n _file = pyfits.open(maskname,mode='append')\\n _phdu = pyfits.PrimaryHDU(data=mask_array)\\n\\n _file.append(_phdu)\\n _file.close()\\n del _file, _phdu\",\n \"def setup_mask(self, d25scale): \\n\\n logger = logging.getLogger(name=\\\"ShotSensitivity\\\")\\n \\n # see if this is a bad shot\\n #print(\\\"Bad shot from \\\", self.conf.badshot)\\n badshot = loadtxt(self.conf.badshot, dtype=int)\\n badtpshots = loadtxt(self.conf.lowtpshots, dtype=int)\\n if (self.shotid in badshot) or (self.shotid in badtpshots):\\n logger.warn(\\\"Shot is in bad. Making mask zero everywhere\\\")\\n self.badshot = True\\n else:\\n self.badshot = False\\n \\n # set up bad amps\\n logger.info(\\\"Bad amps from {:s}\\\".format(self.conf.badamp))\\n self.bad_amps = Table.read(self.conf.badamp)\\n sel_shot = (self.bad_amps[\\\"shotid\\\"] == self.shotid)\\n self.bad_amps = self.bad_amps[sel_shot]\\n \\n # set up galaxy mask\\n logger.info(\\\"Galaxy mask from {:s}\\\".format(self.conf.rc3cat))\\n galaxy_cat = Table.read(self.conf.rc3cat, format='ascii')\\n gal_coords = SkyCoord(galaxy_cat['Coords'], frame='icrs')\\n shot_coords = SkyCoord(ra=self.shot_ra, dec=self.shot_dec,\\n unit=\\\"deg\\\")\\n sel_reg = where(shot_coords.separation(gal_coords) < 1.*u.deg)[0]\\n\\n self.gal_regions = []\\n if len(sel_reg) > 0:\\n for idx in sel_reg:\\n self.gal_regions.append(create_gal_ellipse(galaxy_cat, \\n row_index=idx, \\n d25scale=d25scale))\\n \\n # set up meteor mask\\n # check if there are any meteors in the shot:\\n logger.info(\\\"Meteors from {:s}\\\".format(self.conf.meteor))\\n self.met_tab = Table.read(self.conf.meteor, format=\\\"ascii\\\")\\n self.met_tab = self.met_tab[self.shotid == self.met_tab[\\\"shotid\\\"]]\",\n \"def add_mask(self):\\n return xr.open_dataset(f'/{test.dlfile_directory}/{test.climate}_mask_{test.mask_str}_dldata_traintest.nc')\",\n \"def apply_mask(self, mask_band=None, mask_val=None):\\n pass\",\n \"def prepareMask(self, mask):\\n\\n # Make sure that the mask has the same\\n # number of voxels as the atlas image.\\n # Use nearest neighbour interpolation\\n # for resampling, as it is most likely\\n # that the mask is binary.\\n try:\\n mask, xform = resample.resample(\\n mask, self.shape[:3], dtype=np.float32, order=0)\\n\\n except ValueError:\\n raise MaskError('Mask has wrong number of dimensions')\\n\\n # TODO allow non-aligned mask - as long as it overlaps\\n # in world coordinates, it should be allowed\\n if not fslimage.Image(mask, xform=xform).sameSpace(self):\\n raise MaskError('Mask is not in the same space as atlas')\\n\\n return mask\",\n \"def customize_experiment_config(self, config):\\n # TODO: use ConfigList from Coach launcher, and share customization code.\\n hyperparams_dict = json.loads(os.environ.get(\\\"SM_HPS\\\", \\\"{}\\\"))\\n\\n # Set output dir to intermediate\\n # TODO: move this to before customer-specified so they can override\\n hyperparams_dict[\\\"rl.training.local_dir\\\"] = \\\"/opt/ml/output/intermediate\\\"\\n\\n self.hyperparameters = ConfigurationList() # TODO: move to shared\\n for name, value in hyperparams_dict.items():\\n # self.map_hyperparameter(name, val) #TODO\\n if name.startswith(\\\"rl.\\\"):\\n # self.apply_hyperparameter(name, value) #TODO\\n self.hyperparameters.store(name, value)\\n # else:\\n # raise ValueError(\\\"Unknown hyperparameter %s\\\" % name)\\n\\n self.hyperparameters.apply_subset(config, \\\"rl.\\\")\\n return config\",\n \"def mask_custom(self, custom_mask):\\n\\t\\t## combine the list and remove the duplicates\\n\\t\\tself.mask = list(set().union(self.mask, custom_mask))\\n\\n\\t\\tself.wave = np.delete(self.oriWave, list(self.mask))\\n\\t\\tself.flux = np.delete(self.oriFlux, list(self.mask))\\n\\t\\tself.noise = np.delete(self.oriNoise, list(self.mask))\\n\\n\\t\\treturn self\",\n \"def add_extra(self, entry, value):\\n\\n config_spec = vim.vm.ConfigSpec()\\n self.logger.info(\\\"Adding/Updating extra config: {0} = {1}\\\".format(entry, value))\\n opt = vim.option.OptionValue()\\n opt.key = entry\\n opt.value = value\\n config_spec.extraConfig = [opt]\\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)\",\n \"def attention_mask(model, x):\\n config = model.config\\n input_mask = model.inputs[\\\"input_mask\\\"]\\n final_mask = model.builder.customOp(opName=\\\"AttentionMask\\\",\\n opVersion=1,\\n domain=\\\"ai.graphcore\\\",\\n inputs=[input_mask, x],\\n attributes={\\\"dataType\\\": model.config.popart_dtype})[0]\\n final_mask = model.detach(final_mask)\\n return final_mask\",\n \"def merge_config(self_config, indict):\\n\\n self_config.merge(indict)\\n patch_config(self_config, indict)\",\n \"def set_measurement_mask(self, program_name, mask_name, begins, lengths) -> Tuple[numpy.ndarray, numpy.ndarray]:\",\n \"def add_config(self, conf_map):\\n if self.active.isChecked():\\n self.add_feat_conf(conf_map)\",\n \"def add_config(self, config):\\n clean=lambda n: n.strip().strip('\\\"').lower()\\n for line in config.split('\\\\n'):\\n items=line.strip().split()\\n if items and len(items) >= 3:\\n cmd, evt, hnd=items[:3]\\n \\\"\\\"\\\" NOTE\\n - just 'bind' command expected right now\\n - '+' prepended ti the handler means REPEAT (make sense just for keyboard keys actually)\\n \\\"\\\"\\\"\\n cmd=clean(cmd)\\n if cmd in ['bind']:\\n evt,hnd=(clean(evt), clean(hnd))\\n if not cmd in self.config: self.config[cmd]={}\\n repeat=hnd.startswith('+')\\n if repeat: hnd=hnd[1:]\\n self.config[cmd].update([[evt, [hnd, repeat]]])\",\n \"def _draw_mask_on_image(self, mask):\\n mask = self.STANDARD_COLORS_ARRAY[mask]\\n cv2.addWeighted(mask,self.config.ALPHA,self.image,1.0,0,self.image)\",\n \"def _update_mask_type(configs, mask_type):\\n configs[\\\"train_input_config\\\"].mask_type = mask_type\\n _update_all_eval_input_configs(configs, \\\"mask_type\\\", mask_type)\",\n \"def createMaskDictionary(self):\\n try:\\n self.maskMap = dict(list(zip(self.inds,list(range(len(self.inds))))))\\n self.maskSet = set(self.inds)\\n except Exception as error:\\n print(\\\"failed in createMaskDictionary\\\", error)\",\n \"def add_mask(self, bg, mask):\\n # if mask is to tall for the background image, decrease the size by 50%\\n if bg.shape[0] < mask.shape[0]:\\n mask = cv2.resize(mask, (int(0.5*mask.shape[0]), int(0.5*mask.shape[1])), interpolation=cv2.INTER_AREA)\\n h_mask, w_mask = mask.shape[:2]\\n h, w = bg.shape[:2]\\n \\n # select random location for mask\\n h_rand = np.random.rand() * 0.9\\n h_rand = np.clip(h_rand, 0, 1.0 - h_mask/h)\\n h_update = int(h_rand * h)\\n w_rand = np.random.rand() * 0.9\\n w_rand = np.clip(w_rand, 0, 1.0 - w_mask/w)\\n w_update = int(w_rand * w)\\n \\n # define filter for a mask\\n filt = (mask == 0)\\n \\n # place the mask in the bg img\\n mod = bg.copy()\\n mod[h_update:h_update+h_mask, w_update:w_update+w_mask, :] *= filt\\n mod[h_update:h_update+h_mask, w_update:w_update+w_mask, :] += mask\\n \\n # yolo dim for mask\\n locy = (h_update+h_update+h_mask)/2/h\\n locx = (w_update+w_update+w_mask)/2/w\\n sizey = (h_mask/h)\\n sizex = (w_mask/w)\\n \\n dim = [locx, locy, sizex, sizey]\\n \\n return mod, dim\",\n \"def _GetChangesForMask(config_sed_input):\\n config_sed = config_sed_input\\n config_sed += [(r'WALLTIME_MINUTES=100',\\n (r'WALLTIME_MINUTES=100\\\\n'\\n r'export CONT=mlperf-nvidia:object_detection\\\\n'\\n r'export DATADIR=\\\\/data\\\\n'\\n r'export PKLDIR=\\\\/data\\\\/coco2017\\\\/pkl_coco\\\\n'\\n r'export NEXP=1'))]\\n if MASKRCNN_BATCH_SIZE.value:\\n config_sed.append(\\n (r'BATCHSIZE=.*', fr'BATCHSIZE={MASKRCNN_BATCH_SIZE.value}'))\\n return config_sed\",\n \"def apply_mask(self, mask, parameters=None):\\n if parameters is None:\\n self.dates = self.dates[mask]\\n for key in self.data.keys():\\n self.data[key] = self.data[key][mask]\\n\\n self.manufacturer = self.manufacturer[mask]\\n self.data_file = self.data_file[mask]\\n self.serial_number = self.serial_number[mask]\\n else:\\n for parameter in parameters:\\n self.data[parameter][~mask] = np.nan\",\n \"def add_dimension(self, name, bit_index=None, default=False):\\n if not self.flag_masks.get(name) is None:\\n raise ValueError(\\\"the name %s is already in this flag space\\\" % name)\\n bit_nums = list(self.flag_bits.values())\\n if bit_index is None:\\n bit_index = 0\\n #assign the lowest currently unused bit number\\n while bit_index in bit_nums:\\n bit_index += 1\\n if bit_index in bit_nums:\\n raise ValueError(\\\"bit_index %d is already taken\\\" % bit_index)\\n self.flag_bits[name] = bit_index\\n self.flag_masks[name] = 2**bit_index\\n self.default_dict[name] = default\",\n \"def _configure(self) -> None:\\n reg_data = self.configuration\\n conf_data = reg_data & ~0xC0 | 0x80\\n # check if already in the right configuration, do not re-configure on and on again\\n if reg_data != conf_data:\\n self.configuration = conf_data\",\n \"def get_config(self):\\n layer_config = {\\n \\\"anchors\\\": self._anchors, \\n \\\"classes\\\": self._classes,\\n \\\"ignore_thresh\\\": self._ignore_thresh, \\n \\\"truth_thresh\\\": self._truth_thresh, \\n \\\"iou_thresh\\\": self._iou_thresh, \\n \\\"loss_type\\\": self._loss_type, \\n \\\"iou_normalizer\\\": self._iou_normalizer,\\n \\\"cls_normalizer\\\": self._cls_normalizer, \\n \\\"scale_x_y\\\": self._scale_x_y, \\n }\\n layer_config.update(super().get_config())\\n return layer_config\",\n \"def AddWithColourMask(*args, **kwargs):\\n return _gdi_.ImageList_AddWithColourMask(*args, **kwargs)\",\n \"def mask(self, mask):\\n\\n self._mask = mask\",\n \"def mask(self, mask):\\n\\n self._mask = mask\",\n \"def setup(self, config, base, xsize, ysize, ignore, logger):\\n # .. Do any custom setup you need to do.\\n # Probably want to call the base class setup function to do the normal determination\\n # of the size and position values.\\n\\n # Extra processing of 'bandpass' argument\\n # Most needed type-checking is done in galsim.bandpass\\n self._req_bp_fields = ['throughput', 'wave_type']\\n self._opt_bp_fields = ['red_limit', 'blue_limit', 'zeropoint']\\n try:\\n bp = config['bandpass']\\n for req in self._req_bp_fields:\\n if req not in bp.keys():\\n raise ValueError('Must pass field {} for a bandpass object!'.format(req))\\n # for opt in self._opt_bp_fields:\\n # if opt not in bp.keys():\\n # config['bandpass'][opt] = None\\n for key in bp.keys():\\n if key not in (self._req_bp_fields+self._opt_bp_fields):\\n raise ValueError('Field {} is not a valid entry for a bandpass!'.format(key))\\n except KeyError:\\n raise KeyError('`bandpass` is a required field for a COSMOSChromatic stamp!')\\n\\n extra_ignore = ignore + ['bandpass']\\n return super(self.__class__, self).setup(config, base, xsize, ysize, extra_ignore, logger)\",\n \"def set_mask(self, mask):\\n self.mask = mask\",\n \"def add_config(self, config):\\n\\n if config.identifier in self.configs:\\n raise DuplicateConfigException(\\n \\\"Builder already has config with identifier : {}\\\".format(\\n config.identifier\\n )\\n )\\n\\n self.configs[config.identifier] = config\",\n \"def set_derived_configs(self):\\n if 'dim' in self.config and self.config['dim'] <= 0:\\n self.config['dim'] = self.descriptors['input']['dim']\",\n \"def add_log_config(self, monitor_name, log_config):\\n pass\",\n \"def ApplyMask(data,mask):\\n \\n # loop through portions\\n for portion in data.keys():\\n # match data keys and apply mask \\n for key in data[portion].keys():\\n if key in 'xyerr':\\n if mask != 'UnMasked':\\n data[portion][key].mask = data[portion]['UnMasked']\\n data[portion][key].mask = data[portion][mask]\\n\\t\\n return data\",\n \"def addMasking(self):\\n self.abundance_df['masked'] = [False]*len(self.abundance_df.index)\\n self.abundance_df['colour'] = ['undefined']*len(self.abundance_df.index)\",\n \"def apply_additional_mask(\\n old_mask_file=None,\\n new_mask_file=None,\\n new_thresh=0.0,\\n operation='AND'\\n):\\n if root_mask == None:\\n logger.info(\\\"Specify a cube root file name.\\\")\\n return\\n\\n myia = au.createCasaTool(casaStuff.iatool)\\n myia.open(new_mask_file)\\n new_mask = myia.getchunk()\\n myia.close()\\n\\n myia.open(old_mask_file)\\n mask = myia.getchunk()\\n if operation == \\\"AND\\\":\\n mask *= (new_mask > new_thresh)\\n else:\\n mask = (mask + (new_mask > new_thresh)) >= 1.0\\n myia.putchunk(mask)\\n myia.close()\\n\\n return\",\n \"def set_mask_key(self, func):\\r\\n self.get_mask_key = func\",\n \"def update_configuration(self, config):\\n\\n config[\\\"data_transformation\\\"][\\\"n_classification_bins\\\"] = config[\\\"n_classification_bins\\\"]\\n config[\\\"data_transformation\\\"][\\\"nassets\\\"] = config[\\\"nassets\\\"]\\n config[\\\"data_transformation\\\"][\\\"classify_per_series\\\"] = config[\\\"classify_per_series\\\"]\\n config[\\\"data_transformation\\\"][\\\"normalise_per_series\\\"] = config[\\\"normalise_per_series\\\"]\\n\\n return config\",\n \"def _addDefaultsToMaskedValueSet():\\n global masked_value_set\\n for group in controller.getAllGroups():\\n for param in group.getAllParams():\\n # Keep default password values masked, but ignore default empty values\\n if ((param.getKey(\\\"MASK_INPUT\\\") == True) and param.getKey(\\\"DEFAULT_VALUE\\\") != \\\"\\\"):\\n masked_value_set.add(param.getKey(\\\"DEFAULT_VALUE\\\"))\\n\\n # Add deault consts we want to mask\\n # TODO: add future consts to mask here\\n masked_value_set.add(basedefs.CONST_CA_PASS)\",\n \"def _add_mask(batch, num_batch_dims):\\n mask = tf.ones(tf.shape(list(batch.values())[0])[:num_batch_dims])\\n if \\\"mask\\\" in batch:\\n mask *= batch[\\\"mask\\\"]\\n batch[\\\"mask\\\"] = mask\\n return batch\",\n \"def paintMask(self):\\n if self.avatarConfiguration[\\\"mask\\\"]:\\n if not os.path.isfile(MASK_UPLOAD):\\n image = self.parent.getPlayer().getImageLabel()\\n filePath = GG.genteguada.GenteGuada.getInstance().getDataPath(image)\\n guiobjects.generateImageSize(filePath, [244, 244], IMG_UPLOAD)\\n self.generateMask(\\\"imgUpload.png\\\")\\n imgPath = MASK_UPLOAD\\n else:\\n imgPath = GG.genteguada.GenteGuada.getInstance().getDataPath(os.path.join(PATH_EDITOR_IMG, self.avatarConfiguration[\\\"gender\\\"], self.avatarConfiguration[\\\"headSize\\\"], \\\"mask.png\\\"))\\n self.newAvatarImage(imgPath, \\\"mask\\\")\",\n \"def set_mask(self, mask):\\n self.mask = self._image_to_vector(mask)\\n\\n # PCA needs to be rerun\\n self._mark_cache_invalid()\",\n \"def apply_mask(self):\\n for mask, param in self.masked_parameters:\\n param.mul_(mask)\",\n \"def add_mask(self, image_id: int, category_id: int, segmentation: np.ndarray, score: float):\\n rle = pycocotools.mask.encode(np.asfortranarray(segmentation.astype(np.uint8)))\\n rle['counts'] = rle['counts'].decode('ascii') # json.dump doesn't like bytes strings\\n\\n self.mask_data.append({\\n 'image_id': int(image_id),\\n 'category_id': get_coco_cat(int(category_id)),\\n 'segmentation': rle,\\n 'score': float(score)\\n })\",\n \"def setup_masking(self, inclusion_masks, exclusion_masks):\\n if len(inclusion_masks) != MAX_INCLUSION_MASKS_COUNT:\\n raise ValueError(\\\"The inclusion mask list has {0} masks. That exceeds the expected maximum mask count of \\\"\\n \\\"{1}.\\\".format(len(inclusion_masks), MAX_INCLUSION_MASKS_COUNT))\\n\\n if len(exclusion_masks) != MAX_EXCLUSION_MASKS_COUNT:\\n raise ValueError(\\\"The exclusion mask list has {0} masks. That exceeds the expected maximum mask count of \\\"\\n \\\"{1}.\\\".format(len(exclusion_masks), MAX_EXCLUSION_MASKS_COUNT))\\n\\n masking_prefix = self._base_pv_name + \\\":\\\" + self._slot_number\\n for i in range(MAX_INCLUSION_MASKS_COUNT):\\n self._run_cmd(CAPUT + \\\" \\\" + masking_prefix + \\\":INCLUSION\\\" + str(i + 1) + \\\" \\\" +\\n str(inclusion_masks[i]))\\n\\n for i in range(MAX_EXCLUSION_MASKS_COUNT):\\n self._run_cmd(CAPUT + \\\" \\\" + masking_prefix + \\\":EXCLUSION\\\" + str(i + 1) + \\\" \\\" + str(exclusion_masks[i]))\",\n \"def _initial_set_mask(self, mask):\\n if mask is None:\\n mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool),\\n self._wcs, shape=self.value.shape)\\n elif isinstance(mask, np.ndarray):\\n if mask.shape != self.value.shape:\\n raise ValueError(\\\"Mask shape must match the {0} shape.\\\"\\n .format(self.__class__.__name__)\\n )\\n mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape)\\n elif isinstance(mask, MaskBase):\\n pass\\n else:\\n raise TypeError(\\\"mask of type {} is not a supported mask \\\"\\n \\\"type.\\\".format(type(mask)))\\n\\n # Validate the mask before setting\\n mask._validate_wcs(new_data=self.value, new_wcs=self._wcs,\\n wcs_tolerance=self._wcs_tolerance)\\n\\n self._mask = mask\",\n \"def update_network_section(self):\\n rconfig = configparser.RawConfigParser()\\n rconfig.read(self.conf_file)\\n if self.ext_net:\\n if not rconfig.has_section('network'):\\n rconfig.add_section('network')\\n rconfig.set('network', 'public_network_id', self.ext_net.id)\\n rconfig.set('network', 'floating_network_name', self.ext_net.name)\\n rconfig.set('network-feature-enabled', 'floating_ips', True)\\n else:\\n if not rconfig.has_section('network-feature-enabled'):\\n rconfig.add_section('network-feature-enabled')\\n rconfig.set('network-feature-enabled', 'floating_ips', False)\\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\\n rconfig.write(config_file)\",\n \"def add_config(self):\\n\\n config = {\\n 'byte_to_integer': ByteToInteger,\\n 'integer_to_byte': IntegerToByte,\\n 'integer_to_double_integer': IntegerToDoubleInteger,\\n 'integer_to_string': IntegerToString,\\n 'double_integer_to_integer': DoubleIntegerToInteger,\\n 'double_integer_to_real': DoubleIntegerToReal,\\n 'double_integer_to_string': DoubleIntegerToString,\\n 'binary_coded_decimal_to_integer': BinaryCodedDecimalToInteger,\\n 'integer_to_binary_coded_decimal': IntegerToBinaryCodedDecimal,\\n 'round': Round,\\n 'truncate': Truncate,\\n 'real_to_string': RealToString,\\n 'integer_to_ascii': IntegerToASCII,\\n 'double_integer_to_ascii': DoubleIntegerToASCII,\\n 'real_to_ascii': RealToASCII,\\n 'ascii_to_hexadecimal': ASCIIToHexadecimal,\\n 'hexadecimal_to_ascii': HexadecimalToASCII,\\n 'string_to_integer': StringToInteger,\\n 'string_to_double_integer': StringToDoubleInteger,\\n 'string_to_real': StringToReal,\\n 'decode': Decode,\\n 'encode': Encode,\\n 'segment': Segment\\n }\\n\\n return config\",\n \"def addHostmask(self, hostmask):\\n assert ircutils.isUserHostmask(hostmask), 'got %s' % hostmask\\n if len(unWildcardHostmask(hostmask)) < 8:\\n raise ValueError, \\\\\\n 'Hostmask must contain at least 8 non-wildcard characters.'\\n self.hostmasks.add(hostmask)\",\n \"def _postprocess_config(self, config: Dict[str, Any]) -> Dict[str, Any]:\\n new_config = self.config_space.copy()\\n new_config.update(cast_config_values(config, config_space=self.config_space))\\n return new_config\",\n \"def editMaskVariable(self, product, mask_variable):\\r\\n return mask_variable\",\n \"def merge_jupyter_config_data(self, config, in_config):\\n self.log.debug(f\\\"\\\"\\\"[lite][config][merge] ..... {config}\\\"\\\"\\\")\\n self.log.debug(f\\\"\\\"\\\"[lite][config][merge] ..... {in_config}\\\"\\\"\\\")\\n\\n config = config or {}\\n in_config = in_config or {}\\n\\n for k, v in in_config.items():\\n if k in [DISABLED_EXTENSIONS, FEDERATED_EXTENSIONS]:\\n config[k] = [*config.get(k, []), *v]\\n elif k in [SETTINGS_OVERRIDES]:\\n config[k] = config.get(k, {})\\n for pkg, pkg_config in v.items():\\n config[k][pkg] = config[k].get(pkg, {})\\n config[k][pkg].update(pkg_config)\\n else:\\n config[k] = v\\n self.log.debug(f\\\"\\\"\\\"[lite][config][merge] ..... {config}\\\"\\\"\\\")\\n return config\",\n \"def merge_jupyter_config_data(self, config, in_config):\\n self.log.debug(f\\\"\\\"\\\"[lite][config][merge] ..... {config}\\\"\\\"\\\")\\n self.log.debug(f\\\"\\\"\\\"[lite][config][merge] ..... {in_config}\\\"\\\"\\\")\\n\\n config = config or {}\\n in_config = in_config or {}\\n\\n for k, v in in_config.items():\\n if k in [DISABLED_EXTENSIONS, FEDERATED_EXTENSIONS]:\\n config[k] = [*config.get(k, []), *v]\\n elif k in [SETTINGS_OVERRIDES]:\\n config[k] = config.get(k, {})\\n for pkg, pkg_config in v.items():\\n config[k][pkg] = config[k].get(pkg, {})\\n config[k][pkg].update(pkg_config)\\n else:\\n config[k] = v\\n self.log.debug(f\\\"\\\"\\\"[lite][config][merge] ..... {config}\\\"\\\"\\\")\\n return config\",\n \"def _update_retain_original_image_additional_channels(\\n eval_config,\\n retain_original_image_additional_channels):\\n eval_config.retain_original_image_additional_channels = (\\n retain_original_image_additional_channels)\",\n \"def build_attention_mask_3d_padding(source_mask, target_mask):\\n mask = make_attention_mask_3d(source_mask, target_mask)\\n # invert mask for Megatron\\n return mask < 0.5\",\n \"def set_datamask(self, mask, update=True):\\n if mask is not None and np.shape(mask) != self.shape:\\n raise TypeError(\\\"the shape of the given mask does not match the data shape\\\")\\n \\n self._side_properties[\\\"datamask\\\"] = mask\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(MatrixReLU, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def RPC_DigitizationBasicCfg(flags, **kwargs):\\n acc = MuonGeoModelCfg(flags)\\n if \\\"PileUpTools\\\" not in kwargs:\\n PileUpTools = acc.popToolsAndMerge(RPC_DigitizationToolCfg(flags))\\n kwargs[\\\"PileUpTools\\\"] = PileUpTools\\n acc.merge(PileUpToolsCfg(flags, **kwargs))\\n return acc\",\n \"def add_feat_conf(self, conf_map):\\n pass\",\n \"def addAuth(self, hostmask):\\n if self.checkHostmask(hostmask, useAuth=False) or not self.secure:\\n self.auth.append((time.time(), hostmask))\\n else:\\n raise ValueError, 'secure flag set, unmatched hostmask'\",\n \"def Expand_Mask(mask, feature_dict):\\n new_mask = np.zeros(mask.shape + (len(feature_dict),))\\n for i in feature_dict.keys():\\n ni = int(i)\\n new_mask[mask == ni,ni] = 1 \\n return new_mask\",\n \"def update_config(self, config):\\n # add follower public folder to the CKAN's list of public folders\\n here = os.path.dirname(__file__)\\n public_dir = os.path.join(here, 'public')\\n if config.get('extra_public_paths'):\\n config['extra_public_paths'] += ',' + public_dir\\n else:\\n config['extra_public_paths'] = public_dir\\n # add follower template folder to the CKAN's list of template folders\\n template_dir = os.path.join(here, 'templates')\\n if config.get('extra_template_paths'):\\n config['extra_template_paths'] += ',' + template_dir\\n else:\\n config['extra_template_paths'] = template_dir\",\n \"def generate_MLmask(self):\\r\\n self.MLmask = np.zeros((self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n \\r\\n if len(self.selected_ML_Index) > 0:\\r\\n for selected_index in self.selected_ML_Index:\\r\\n self.MLmask = np.add(self.MLmask, self.Mask[:,:,selected_index])\\r\\n \\r\\n self.intergrate_into_final_mask()\\r\\n \\r\\n self.add_rois_of_selected()\\r\\n \\r\\n else:\\r\\n self.intergrate_into_final_mask()\",\n \"def Adjust_Data(img,mask,feature_dict, normalize):\\n ## Normalize image\\n if normalize:\\n img = Normalize_Image(img)\\n\\n ## Assume mask shape has 4 dimensions - mask is (batch, x, y, color-channel)\\n ## color-channels are redundant, so just choose the first. \\n mask = mask[:,:,:,0]\\n \\n ## Image_datagen performs interpolation when rotating, resulting in non-integer\\n ## mask values. Round these back to integers before expanding the mask. \\n mask = mask.round() \\n mask = Expand_Mask(mask, feature_dict)\\n #print(mask.shape, np.unique(mask, axis = 0))\\n return (img,mask)\",\n \"def _update(self, mask):\\n if self.reporting:\\n for pin in self.pins:\\n if pin.mode is INPUT:\\n pin_nr = pin.pin_number - self.port_number * 8\\n pin.value = (mask & (1 << pin_nr)) > 0\",\n \"def get_config(self):\\n config = {\\n }\\n base_config = super(MatrixConcat, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def update_add_mask_sym(\\n A: dshape(\\\"M... * M * complex\\\"),\\n B: dshape(\\\"M... * M * complex\\\"),\\n ind: dshape(\\\"M... * complex\\\"),\\n mask: dshape(\\\"M... * complex\\\"),\\n # symmetric: bool = True,\\n) -> dshape(\\\"M... * M * complex\\\"):\\n for (i, ind_i), (j, ind_j) in itertools.product(enumerate(ind), repeat=2):\\n if mask[i]:\\n A[ind_i, ind_j] += B[i, j]\\n\\n return A\",\n \"def concat_config(config, new_config):\\n for new_path in new_config:\\n if new_path not in config:\\n config[new_path] = new_config[new_path]\\n else:\\n config[new_path][0] = config[new_path][0] or new_config[new_path][0]\\n for filename in config[new_path]:\\n if filename != 0:\\n if filename in new_config[new_path]:\\n for opt in config[new_path][filename]:\\n if opt in new_config[new_path][filename]:\\n new_config[new_path][filename][opt]\\\\\\n .update(config[new_path][filename][opt])\\n else:\\n new_config[new_path][filename][opt] = \\\\\\n config[new_path][filename][opt]\\n else:\\n new_config[new_path][filename] = config[new_path][filename]\\n return config\",\n \"def __set_mask_regions(self):\\n self.bottom_clip = np.int32(np.int32([[[60,0], [1179,0], [1179,650], [60,650]]]))\\n self.roi_clip = np.int32(np.int32([[[640, 425], [1179,550], [979,719],\\n [299,719], [100, 550], [640, 425]]]))\",\n \"def load_lab_config(self, lab, host):\\n _lab_conf = create_lab_config(lab, host)\\n ip = self._hosts.get(host)\\n\\n # Some labs require additional customization\\n # We don't know yet if customization is required\\n _custom_conf = None\\n\\n # lab 8 (Multicast) customization\\n if lab == str(8) and host == 'vrdevice':\\n\\n # customizing lab config\\n _lab_conf = customize_lab8_config(_lab_conf)\\n\\n # loading lab config\\n load_cfg_pyez(ip, _lab_conf, self._user, self._pass, mode='merge')\\n\\n # creating additional (custom) config\\n configs = ['Rec1.conf', 'Rec3.conf', 'Rec4.conf', 'Rec2.conf']\\n _custom_conf = prepare_custom_config(lab, host, configs)\\n\\n # loading custom config\\n load_cfg_pyez(ip, _custom_conf, self._user, self._pass, mode='merge')\\n\\n # load multiping.slax script\\n install_script(lab, host, ip, self._user, self._pass, 'multiping.slax', '/var/db/scripts/op/')\\n\\n # No customization required\\n else:\\n load_cfg_pyez(ip, _lab_conf, self._user, self._pass, mode='merge')\\n\\n # we want only interfaces used in the lab to be used, the rest disabled\\n try:\\n interfaces_to_use = get_interfaces(_lab_conf)\\n if _custom_conf:\\n interfaces_to_use = interfaces_to_use + get_interfaces(_custom_conf)\\n disable_unused_interfaces(ip, self._user, self._pass, interfaces_to_use)\\n except Exception as e:\\n print('Cannot disable unused interfaces due to an error, ')\\n print('however this is not critical - you can proceed further')\\n # TO IMPLEMENT LATER - LOG THE EXCEPTION\\n print(e)\",\n \"def extend(clself, other):\\n clself._cfg_def.extend(other._cfg_def)\\n for key, optdef in clself._cfg_def.options.iteritems():\\n setattr(clself, key, optdef)\",\n \"def extend_mask_nonlocal(mask,kernel=np.ones((3,3))):\\n\\tassert (mask.dtype is np.dtype(np.bool)), \\\"input mask must be of bool type\\\"\\n\\n\\text_mask = mask.copy()\\n\\tinp_ind = masked_indices(mask)\\n\\tim_h, im_w = mask.shape\\n\\n\\tker_y, ker_x = kernel.shape\\n\\tassert(ker_x%2>0), \\\"kernel must have odd dimensions\\\"\\n\\tassert(ker_y%2>0), \\\"kernel must have odd dimensions\\\"\\n\\n\\t# indices of the nonzero kernel elements\\n\\tker_ind_y, ker_ind_x = np.nonzero(kernel)\\n\\tker_ind_x -= ker_x//2\\n\\tker_ind_y -= ker_y//2\\n\\n\\tfor ind_x, ind_y in zip(inp_ind%im_w, inp_ind//im_w):\\n\\t\\tfor i, j in zip(ker_ind_x, ker_ind_y):\\n\\t\\t\\text_mask[min(max(0,ind_y+j),im_h-1),min(max(0,ind_x+i),im_w-1)] = True\\n\\n\\treturn ext_mask\",\n \"def _ApplyFlags(cls, config_values, flag_values):\\n super(ContainerSpec, cls)._ApplyFlags(config_values, flag_values)\\n if flag_values['image'].present:\\n config_values['image'] = flag_values.image\\n if flag_values['static_container_image'].present:\\n config_values['static_image'] = flag_values.static_container_image\",\n \"def configFeAsic(self,gain,shape,base,slk=None,slkh=None,monitorBandgap=None,monitorTemp=None):\\n pass\",\n \"def set_config(self, cfg):\\n\\n cfg.add_section(self.name)\\n for attr, value in self.__dict__.items():\\n if value not in [\\\"false\\\", \\\"none\\\", \\\"0\\\"] and attr != \\\"name\\\":\\n attr = attr.replace(\\\"_\\\", \\\"-\\\")\\n cfg.set(self.name, attr, value)\",\n \"def with_mask(self, mask):\\n return self[mask]\",\n \"def update(self, config):\\n # find keys are in config but not in self.config\\n extra_keys = set(config.keys()) - set(self.config.keys())\\n if len(extra_keys) > 0:\\n raise ValueError(\\\"keys {} in config are not in Config.config\\\".format(extra_keys))\\n # update self.config by config\\n else:\\n self.config.update(config)\",\n \"def setMask(self, mask):\\n self.mask = mask\",\n \"def read_config(self, config):\\n try:\\n newconfig = ConfigObj(config, interpolation=False,\\n configspec=self._configspec)\\n except ConfigObjError as e:\\n raise ConfigError(e)\\n newconfig = self._validate(newconfig)\\n self._config.merge(newconfig)\\n logger.info(\\\"Loaded additional config: {0}\\\".format(config))\",\n \"def setup_b_instance(self,norm,add_ps_mask=True):\\n inst_tag = self.tag + '_'+str(self.flux_array_ebin)\\n b = bsm.bayesian_scan_NPTF(tag=inst_tag,nside=self.nside,work_dir='/tmp/'+self.tag+'/',psf_dir=psf_dir,nlive=700)\\n # Input the data, using the external data if provided\\n if self.use_external_data:\\n b.load_external_data(self.f1.CTB_en_bins,[self.external_data[self.flux_array_ebin]],self.f1.CTB_exposure_maps)\\n else:\\n b.load_external_data(self.f1.CTB_en_bins,self.f1.CTB_count_maps,self.f1.CTB_exposure_maps)\\n\\n if add_ps_mask:\\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False,ps_mask_array = self.f1.ps_mask_array)\\n else:\\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False)\\n\\n b.add_new_template(self.f1.template_dict)\\n b.rebin_external_data(1)\\n\\n b.add_poiss_model('ps_model','$A_{ps}$',[0.0,3.0],False)\\n b.add_poiss_model('p7','$A_{p7}$',[0.0,2.0],False)\\n b.add_poiss_model('bubs','$A_{bubs}$',[0.0,2.0],False)\\n b.add_poiss_model('iso','$A_{iso}$',[0.0,3.0],False)\\n # Add in a fixed J_map template\\n b.add_fixed_templates({'J_map':[norm*self.J_map_arr[self.flux_array_ebin]/np.mean(self.J_map_arr[self.flux_array_ebin])]})\\n\\n b.initiate_poissonian_edep()\\n return b\",\n \"def _update_classification_mask(self, obj_class, mask_coords):\\n # Remove background where object resides\\n self.classification_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 0\\n # Set classification mask where object resides\\n self.classification_mask[obj_class, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 1\",\n \"def update_compute_section(self):\\n rconfig = configparser.RawConfigParser()\\n rconfig.read(self.conf_file)\\n if not rconfig.has_section('compute'):\\n rconfig.add_section('compute')\\n rconfig.set(\\n 'compute', 'fixed_network_name',\\n self.network.name if self.network else env.get(\\\"EXTERNAL_NETWORK\\\"))\\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\\n rconfig.write(config_file)\",\n \"def add_config_field(self, content_type, name, *args, **kwargs):\\n if name == 'representation_args':\\n raise ValueError('{} is a reserved Config field name'.format(name))\\n self._add_config_arg(ConfigField, content_type, name, *args, **kwargs)\",\n \"def config_extra_settings(self, data_dir):\\n # load data directory configuration\\n self.label_path = data_dir\\n self.label_config_dir = os.path.join(self.label_path, 'labelconfig')\\n self.label_config_suffix = 'lbl'\\n\\n # set icon configuration\\n self._icon_dir=get_icon_dir()\\n\\n # set window title\\n self.setWindowTitle('FreeROI')\\n #self.resize(1280, 1000)\\n self.center()\\n # set window icon\\n self.setWindowIcon(QIcon(os.path.join(self._icon_dir,'icon.png')))\\n\\n self._init_configuration()\\n self._init_label_config_center()\\n\\n # create actions\\n self._create_actions()\\n\\n # create menus\\n self._create_menus()\",\n \"def add_noise(self, mdct_norm, masking_threshold):\\n return self.psychoacoustic.add_noise(mdct_norm, masking_threshold)\",\n \"def build_mask(cell, wgt, final_layer=None, final_datatype=None):\\n fl = wgt.clad_layer+1 if final_layer==None else final_layer\\n fd = 0 if final_datatype==None else final_datatype\\n\\n polygons = cell.get_polygons(by_spec=True)\\n try:\\n pWG = polygons[(wgt.wg_layer, wgt.wg_datatype)]\\n pCLAD = polygons[(wgt.clad_layer, wgt.clad_datatype)]\\n except KeyError:\\n print(\\\"Warning! No objects written to layer/datatype specified by WaveguideTemplate\\\")\\n if wgt.resist=='+':\\n cell.add(gdspy.fast_boolean(pWG, pCLAD, 'xor', precision=0.001, max_points=199, layer=fl, datatype=fd))\\n elif wgt.resist=='-':\\n cell.add(gdspy.fast_boolean(pWG, pCLAD, 'and', precision=0.001, max_points=199, layer=fl, datatype=fd))\",\n \"def set_config(self, cfg):\\n\\n cfg.add_section(self.name)\\n for attr, value in self.__dict__.items():\\n if value not in [\\\"false\\\", \\\"none\\\"] and attr != \\\"name\\\":\\n attr = attr.replace(\\\"_\\\", \\\"-\\\")\\n cfg.set(self.name, attr, value)\",\n \"def set_config(self, cfg):\\n\\n cfg.add_section(self.name)\\n for attr, value in self.__dict__.items():\\n if value not in [\\\"false\\\", \\\"none\\\"] and attr != \\\"name\\\":\\n attr = attr.replace(\\\"_\\\", \\\"-\\\")\\n cfg.set(self.name, attr, value)\",\n \"def set_config(self, cfg):\\n\\n cfg.add_section(self.name)\\n for attr, value in self.__dict__.items():\\n if value not in [\\\"false\\\", \\\"none\\\"] and attr != \\\"name\\\":\\n attr = attr.replace(\\\"_\\\", \\\"-\\\")\\n cfg.set(self.name, attr, value)\",\n \"def add(self, host, **kwargs):\\n self.configs_[0][1].add(host, **kwargs)\",\n \"def set_feature_mask(self, feature_mask):\\n self.feature_mask = feature_mask\",\n \"def add_config(self):\\n\\n config = {\\n 'count_up': CountUp,\\n 'count_down': CountDown,\\n 'count_up_or_down': CountUpOrDown,\\n 'high_speed_counter_definition': HighSpeedCounterDefinition,\\n 'high_speed_counter': HighSpeedCounter,\\n 'pulse_output': PulseOutput\\n }\\n\\n return config\",\n \"def populate_config(self, config):\\n self.use_wine_mappings.set_active(config['use_wine_mappings'])\\n self.force_recheck.set_active(config['force_recheck'])\\n self._previous_force_recheck = config['force_recheck']\\n self.resume.set_active(config['resume'])\\n try:\\n self.glade.get_widget('time_added_checkbox').set_active(\\n 'time_added' in config['transfer_meta'])\\n except KeyError:\\n pass\\n self.resume_dat_entry.set_text(config['previous_resume_dat_path'])\",\n \"def get_config(self):\\n\\n # these are all that is needed to rebuild this class\\n config = dict(hidden_size=self.hidden_size,\\n word_embedding=self.word_embedding,\\n detection_embedding=self.detection_embedding,\\n mode=self.mode,\\n decoder_pos_emb=self.decoder_pos_emb,\\n ** self.kwargs)\\n\\n base_config = super(RegionFeature, self).get_config()\\n return dict(list(base_config.items()) +\\n list(config.items()))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5756575","0.5562516","0.54862016","0.5367435","0.5347834","0.53455114","0.5306387","0.52775955","0.522134","0.5194576","0.51778084","0.5152401","0.51056355","0.51011837","0.50790006","0.5067728","0.5065551","0.4962702","0.4946801","0.4946075","0.49183005","0.49159494","0.49100894","0.49084634","0.49076694","0.48849526","0.48177648","0.48108754","0.48106894","0.47964016","0.47839794","0.4772327","0.47660932","0.47660932","0.47464186","0.47456405","0.47291517","0.47210765","0.47192547","0.47168988","0.47162175","0.47138405","0.470761","0.47030455","0.46968415","0.46941915","0.46916538","0.46812856","0.46789327","0.46658984","0.46528703","0.4652148","0.46498463","0.4648871","0.46485573","0.46315843","0.46312842","0.4624812","0.4624812","0.46230158","0.46130076","0.46051002","0.46008947","0.4593308","0.4580749","0.45794028","0.45785326","0.45618272","0.4560683","0.45586264","0.45581818","0.4551106","0.45469415","0.45461303","0.45377606","0.4531351","0.4521042","0.45142546","0.45125037","0.4503131","0.4499034","0.4498298","0.4493728","0.4490731","0.44901147","0.44876668","0.4478467","0.44779912","0.44751477","0.44688734","0.446438","0.44607773","0.44565517","0.44565517","0.44565517","0.44534683","0.444959","0.4445261","0.44449583","0.44417897"],"string":"[\n \"0.5756575\",\n \"0.5562516\",\n \"0.54862016\",\n \"0.5367435\",\n \"0.5347834\",\n \"0.53455114\",\n \"0.5306387\",\n \"0.52775955\",\n \"0.522134\",\n \"0.5194576\",\n \"0.51778084\",\n \"0.5152401\",\n \"0.51056355\",\n \"0.51011837\",\n \"0.50790006\",\n \"0.5067728\",\n \"0.5065551\",\n \"0.4962702\",\n \"0.4946801\",\n \"0.4946075\",\n \"0.49183005\",\n \"0.49159494\",\n \"0.49100894\",\n \"0.49084634\",\n \"0.49076694\",\n \"0.48849526\",\n \"0.48177648\",\n \"0.48108754\",\n \"0.48106894\",\n \"0.47964016\",\n \"0.47839794\",\n \"0.4772327\",\n \"0.47660932\",\n \"0.47660932\",\n \"0.47464186\",\n \"0.47456405\",\n \"0.47291517\",\n \"0.47210765\",\n \"0.47192547\",\n \"0.47168988\",\n \"0.47162175\",\n \"0.47138405\",\n \"0.470761\",\n \"0.47030455\",\n \"0.46968415\",\n \"0.46941915\",\n \"0.46916538\",\n \"0.46812856\",\n \"0.46789327\",\n \"0.46658984\",\n \"0.46528703\",\n \"0.4652148\",\n \"0.46498463\",\n \"0.4648871\",\n \"0.46485573\",\n \"0.46315843\",\n \"0.46312842\",\n \"0.4624812\",\n \"0.4624812\",\n \"0.46230158\",\n \"0.46130076\",\n \"0.46051002\",\n \"0.46008947\",\n \"0.4593308\",\n \"0.4580749\",\n \"0.45794028\",\n \"0.45785326\",\n \"0.45618272\",\n \"0.4560683\",\n \"0.45586264\",\n \"0.45581818\",\n \"0.4551106\",\n \"0.45469415\",\n \"0.45461303\",\n \"0.45377606\",\n \"0.4531351\",\n \"0.4521042\",\n \"0.45142546\",\n \"0.45125037\",\n \"0.4503131\",\n \"0.4499034\",\n \"0.4498298\",\n \"0.4493728\",\n \"0.4490731\",\n \"0.44901147\",\n \"0.44876668\",\n \"0.4478467\",\n \"0.44779912\",\n \"0.44751477\",\n \"0.44688734\",\n \"0.446438\",\n \"0.44607773\",\n \"0.44565517\",\n \"0.44565517\",\n \"0.44565517\",\n \"0.44534683\",\n \"0.444959\",\n \"0.4445261\",\n \"0.44449583\",\n \"0.44417897\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71955097"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2678,"cells":{"query":{"kind":"string","value":"The indices of the master array which correspond to this partition's data array."},"document":{"kind":"string","value":"def indices(self):\n return tuple([slice(*r) for r in self.location])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def indices(self) -> np.ndarray:\n return self.impl.indices","def master_ndindex(self): # itermaster_indices(self):\n return itertools_product(\n *[range(*r) for r in self.location]\n ) # TODO check","def atom_idxs(self):\n\n return np.array([atom.atom_idxs for atom in self])","def get_index_array(self):\n return self.region_pairs","def get_indices(self):\r\n return self._indices","def indices(self):\n return self.index.indices","def get_data_idx(self)->list:\n return self.__data_idx","def mainIndices(self):\n return self.i1, self.i2","def indices(self):\n return self._kbounded_partitions","def _get_split_indices(self):\n\n cumsum = np.cumsum(\n np.concatenate((np.array([0], dtype=np.int8), self.split_sizes)))\n \n fold_inds = np.array(\n [(cumsum[n], cumsum[n + 1]) for n in range(self.n_splits)])\n\n return fold_inds","def get_indexes(self, dataset: BaseDataset) -> int:\n\n index = [np.random.randint(0, len(dataset)) for _ in range(1)]\n\n return index","def indices(self):\n _indices = []\n for h in self.miller.indices():\n _indices.append(self.indices_hkl(*h)[0])\n return _indices","def getIndices(self):\r\n return self._indices","def get_main_branch_indices(self):\n\n assert self.halt is not None\n prog_main_index = self.halt_index\n prog_main_indices = self.halt.prop(\n 'progenitor.main.indices', self.halt_index)\n self.main_branch_indices = prog_main_indices\n return prog_main_indices","def batch_indices(self):\n b = self.batch_size\n return [np.arange(i*b, i*b+b) for i in range(self.num_batches)]","def get_all_master_idx_paths(self):\n paths = utilities.get_all_master_index_paths(rootdir=constants.flow_data_dir)\n return paths","def indices(self):\n return range(len(self))","def getLandmarkindices(self):\n return self.subsetnodes_indices","def getLandmarkindices(self):\n return self.subsetindices","def indices_of_split(self, split_name='train'):\n return self.indices_of('split', split_name)","def get_all_master_ids(self):\r\n return self._handler.get_all_master_ids()","def indices(self):\n i, j, _edge = self.indicesAndEdge()\n return i, j","def get_34index_list(self):\n msk = self.load_mask()\n return [i for (i,v) in enumerate(msk) if v==1]","def index(self):\n return self.data.index.values","def get_indexes(self):\n indexes = []\n for c in self.components:\n indexes.extend(c.get_indexes())\n return indexes","def get_indexes(self, dataset: BaseDataset) -> int:\n\n indexes = [random.randint(0, len(dataset)) for _ in range(3)]\n return indexes","def _load_split_indices(self):\n split_file = self.SPLITS.get(self.split)\n indices_file = self._filepath(split_file)\n\n with open(indices_file) as txt_file:\n idx_data = [int(i) for i in txt_file.readline().split()]\n\n return idx_data","def get_indexes(self, dataset):\n\n indexs = [random.randint(0, len(dataset)) for _ in range(3)]\n return indexs","def SectionIndicesConnectedToSoma(self):\n indices = []\n index = 0\n for each_section in self._section_list:\n if each_section.ParentId() == -1:\n indices.append(index)\n index += 1\n return indices","def get_active_register_indices(self):\n assert self.sketch.ndim == 1, 'Currently only support 1-dimensional sketch.'\n return np.flatnonzero(self.sketch)","def get_subset_inds(self, adata_parent):\r\n subset_inds = np.ones(len(adata_parent), dtype=bool)\r\n for condition, values in self.subset_cond.items():\r\n subset_inds *= adata_parent.obs[condition].isin(values)\r\n return subset_inds","def data_index_from_selection(self, sel_indexes):\n data_indexes = []\n # Translate the landmark indexes to the original data indexes\n for i in sel_indexes:\n data_indexes.append(self.analysis.landmark_orig_indexes[i])\n return data_indexes","def get_local_indices(self, part, ctx):\n return self.map_to_global(\n F.arange(0, self.local_size(part), ctx=ctx), part\n )","def _getNonPrototypeIndices(self, clusters: ndarray) -> ndarray:\n return np.delete(np.arange(self.dataSize), clusters.flatten())","def vir_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==0:\n indices.append(index)\n return indices","def inidices(self):\n return [Index(name, index) for name, index in self._data['indices'].iteritems()]","def childWellIndices(self):\n return self._wellIndices","def get_idxvals(self):\n input_rows = list()\n input_cols = list()\n for key in self.index:\n input_rows.append(key[0])\n input_cols.append(key[1])\n\n return list(OrderedSet(input_rows)), list(OrderedSet(input_cols))","def ordered_indices(self):\n return self.base_dataset.ordered_indices()","def _family_index(self):\n\n if hasattr(self, \"_family_index_cached\"):\n return self._family_index_cached\n\n ind = np.empty((len(self),), dtype='int8')\n for i, f in enumerate(self.ancestor.families()):\n ind[self._get_family_slice(f)] = i\n\n self._family_index_cached = ind\n\n return ind","def data(self) -> List[int]:\n return self.__ids","def data(self) -> List[int]:\n return self.__ids","def data(self) -> List[int]:\n return self.__ids","def indices(self):\n if self._indices is None:\n i = []\n\n # TODO: this is not right for multi-column keys\n # TODO: new style indexes\n\n global_name = '^DD(%s,0,\"IX\",\"0\")' % self.fileid\n prefix = '^DD(%s,0,\"IX\",' % self.fileid\n while 1:\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\"\"))[0]\n if not global_name or not global_name.startswith(prefix):\n break\n suffix = global_name[len(prefix):-1]\n parts = suffix.split(\",\")\n idx_name = parts[0][1:-1]\n idx_table = parts[1]\n idx_columns = parts[2:]\n index = Index(idx_name, idx_table, idx_columns)\n i.append(index)\n\n # A second list, gives indices for a field\n columns = {}\n for idx in i:\n for c in idx.columns:\n columns[c] = 1\n\n # Now trawl the listed columns in the data dictionary, and load their\n # cross references.\n cr_names = {}\n for c in columns.keys():\n idx_root = M.Globals[\"^DD\"][self.fileid][c][1]\n if not idx_root[0].exists():\n continue\n for cr_id, val in idx_root.keys_with_decendants():\n if float(cr_id) > 0:\n cr_header = idx_root[cr_id][0].value\n parts = cr_header.split(\"^\")\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\n f = cr_names.get(parts[1], list())\n f.append(c)\n cr_names[parts[1]] = f\n\n # Now, just delete items from the index list if they are not in cr_names\n self._indices = []\n for index in i:\n cr = cr_names.get(index.name)\n if cr:\n # verify columns - lots of errors in real systems\n if len(cr) == len(index.columns):\n invalid = False\n for c in cr:\n if c not in index.columns:\n invalid = True\n continue\n if not invalid:\n self._indices.append(index)\n\n return self._indices","def get_indices(self):\n selection_model = self.selectionModel()\n return selection_model.selectedRows()","def get_instance_index(self):\n return np.unique([tp[0] for tp in self._innercontainer])","def get_raster_ids(self):\n return numpy.array(range(self._lo_atom, self._lo_atom + self._n_atoms))","def ordered_indices(self):\n return self.d1.ordered_indices()\n # RETURN BASED ON D1's sizes","def occ_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==1:\n indices.append(index)\n return indices","def labeled_indices(self):\n return self._labeled_indices","def index(self):\n return self.dataset.index","def _get_indexes(self, participants):\n tr_idx = int(np.floor(self.tr_size*len(participants)))\n j = self.val_size + self.tr_size\n val_idx = int(np.floor(j*len(participants)))\n return tr_idx, val_idx","def getIndexes(self, val):\n # Find where this value is listed. \n valNdx = (self.values == val).nonzero()[0]\n \n # If this value is not actually in those listed, then we \n # must return empty indexes\n if len(valNdx) == 0:\n start = 0\n end = 0\n else:\n # The index into counts, etc. for this value. \n valNdx = valNdx[0]\n start = self.start[valNdx]\n end = self.end[valNdx]\n \n # Create a tuple of index arrays, one for each index of the original array. \n ndx = ()\n for i in range(self.nDims):\n ndx += (self.indexes[start:end, i], )\n return ndx","def indices(self):\n\n # We used lookup tables here. Read more about other methods here:\n # https://chessprogramming.wikispaces.com/Bitboard+Serialization\n\n if self.num == 0:\n return []\n\n bits = []\n\n for i in [0, 1, 2, 3, 4, 5, 6, 7]:\n row = (self.num >> UINT64_PADDING[i]) & EIGHT_ONES\n indices = row_to_indices[row]\n for index in indices:\n bits.append(index + i*8)\n\n return bits","def all_sampled_nodes_indexes(self) -> torch.LongTensor:\n all_sampled_nodes_indexes: _typing.Any = self.__all_sampled_nodes_indexes\n return all_sampled_nodes_indexes","def indices(self, position=None):\n \n raise NotImplementedError()","def sparse_arrays(self):\n return self._sparse_arrays","def get_multi_index(self):\n return self.basis.elements","def index(self):\n return self.data.index","def _dofidxs(self):\n return [const['dofidxs'] for i, const in self._constraints_df.iterrows()]","def expert_to_batch_indices(self):\n return tf.split(\n self._batch_index, self._part_sizes_tensor, 0, num=self._num_experts)","def idx_adjacency_lists(self) -> List[List[int]]:\n result = []\n\n for intersection in self._intersection_list:\n nbs = []\n\n for nb in self.adj_dict[intersection]:\n nbs.append(self._intersection_to_idx[nb])\n\n result.append(nbs)\n\n return result","def _get_indices_1(image_set, num_labels=2, num_protected=2):\r\n indices = [[[] for _ in range(num_protected)] for _ in range(num_labels)]\r\n for _, label, cluster, index in image_set:\r\n indices[label][cluster].append(index)\r\n\r\n return indices","def getBitArrayIndices(self, key):\n\t\treturnList = []\n\t\tfor i in range(1, self.k + 1):\n\t\t\treturnList.append((hash(key) + i * mmh3.hash(key)) % self.m)\n\t\t#print \"Indices list for key: \", key, \" is: \", str(returnList)\n\t\treturn returnList","def ordered_indices(self):\r\n return np.arange(len(self), dtype=np.int64)","def _raveled_index(self):\n return np.r_[:self.size]","def _raveled_index(self):\n return np.r_[:self.size]","def cluster_ids(self):\n return self.model.cluster_ids","def geneIds(self):\n\t\treturn self._dataframe.index.tolist()","def getGlobalIdxVals( self, i : int ):\n return range(self._layout.starts[i],self._layout.ends[i])","def main_rep_idxs(self):\n\n if '{}/{}'.format(SETTINGS, MAIN_REP_IDXS) in self.h5:\n return self.h5['{}/{}'.format(SETTINGS, MAIN_REP_IDXS)][:]\n else:\n return None","def target_nodes_indexes(self) -> _TargetNodes:\n return self.__target_nodes_indexes","def get_index(self):\n return self.disk.partitions.index(self)","def _compute_indices(self):\n self.indices = np.arange(len(self.im_filenames))\n np.random.shuffle(self.indices)","def _get_chunk_indexer(self, array):\n if self.data.num_chunks == 1:\n return np.broadcast_to(0, len(array))\n return np.digitize(array, self.offsets[1:])","def _idxs_postformat_array(self):\n self.idxs = np.array(self.idxs)","def get_final_pruned_indices(self):\n return self.final_pruned_indices","def getind(self,start,end,blk):\n\n if blk is None:\n # Return all blocks\n blk = np.arange(self.ind[start].size)\n\n ind=np.array([])\n for k,val in enumerate(blk):\n ind=np.append(ind,np.arange(self.ind[start][val],self.ind[end][val]))\n return ind.astype(int)","def get_indexes(self):\n return set(k.index for k in self if k.has_index)","def activeChildWellIndices(self):\n return self._activeWellIndices","def _calculate_chunk_offsets(self):\n offset = 0\n offsets = []\n for chunk in self.data.iterchunks():\n offsets.append(offset)\n offset += len(chunk)\n return np.array(offsets)","def index(self):\n return list(self._innercontainer)","def _selected_indices(self, subset):\n # We want the DataFrame to be indexed the same way its values array is\n ftr = self.frametracks.reset_index(drop=True)\n if subset is not None:\n ftr['tmpindex'] = ftr.index.values\n ftr = ftr.set_index('particle').reindex(subset).set_index('tmpindex')\n if self.autoclip:\n # Boundaries are computed for the whole system\n xmin = self.frametracks.x.min() + self.nncutoff\n xmax = self.frametracks.x.max() - self.nncutoff\n ymin = self.frametracks.y.min() + self.nncutoff\n ymax = self.frametracks.y.max() - self.nncutoff\n r = ftr.index[ (ftr.x > xmin) & (ftr.x < xmax) & \\\n (ftr.y > ymin) & (ftr.y < ymax) ].values.astype(int)\n else:\n r = ftr.index.values.astype(int)\n if self.fast:\n return np.random.permutation(r)[:int(len(r) / 10)]\n else:\n return r","def indexes(self) -> list:\n return self._indexes","def get_cluster_indices(self,dataset, cluster_number):\n\t\tself.__init__(dataset, self.k)\n\t\tself.e_step() #got responsibilities\n\t\tmax_cluster = np.argmax(self.w, axis = 1)\n\t\tindices = []\n\t\tfor i in range(dataset.shape[0]):\n\t\t\tif max_cluster[i] == cluster_number:\n\t\t\t\tindices.append(i)\n\t\treturn indices","def _prog_field_indices(self):\n\n if self._pfi is not None:\n return self._pfi\n\n self.arbor._grow_tree(self)\n self._pfi = np.array([node.tree_id for node in self._prog_nodes])\n return self._pfi","def edges_indices(self) -> numpy.array:\n r = numpy.array(self._receivers, dtype=numpy.int16)\n s = numpy.array(self._senders, dtype=numpy.int16)\n return numpy.asarray([r, s], dtype=numpy.int16)","def get_all_master_idx_dfs(self):\n dfs = utilities.get_all_master_index_dfs(master_index_paths=self.all_master_idx_paths)\n return dfs","def get_index(self, x, y):\n i = (y - self.y0) // self.dy\n j = (x - self.x0) // self.dx\n i = min(max(i, 0), self.n-1)\n j = min(max(j, 0), self.m-1)\n return [i, j]","def index_dict(self):\n msk = self.load_mask()\n mski = enumerate(msk)\n ifiltered = (i for (i, m) in mski if m == 1)\n return {i: j for (j, i) in enumerate(ifiltered)}","def index_dict(self):\n msk = self.load_mask()\n mski = enumerate(msk)\n ifiltered = (i for (i, m) in mski if m == 1)\n return {i: j for (j, i) in enumerate(ifiltered)}","def agent_locs_idx(self):\n return tuple(self.agent_locs.T)","def ordered_indices(self):\r\n '''we need random order'''\r\n if self.shuffle:\r\n indices = np.random.permutation(len(self))\r\n else:\r\n indices = np.arange(len(self))\r\n '''\r\n if self.tgt_sizes is not None:\r\n indices = indices[np.argsort(self.tgt_sizes[indices], kind='mergesort')]\r\n return indices[np.argsort(self.src_sizes[indices], kind='mergesort')]\r\n '''\r\n return indices","def get_data(self):\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\n return self._data[idxs].copy()","def get_indexes(self, dataset):\n\n for i in range(self.max_iters):\n index = random.randint(0, len(dataset))\n gt_bboxes_i = dataset.get_ann_info(index)['bboxes']\n if len(gt_bboxes_i) != 0:\n break\n\n return index","def _notstaticneighs_get_corestored_by_inds_slice(self, inds):\n inds = [inds] if type(inds) == int else inds\n idxs = self.idxs\n if self.sp_relative_pos is not None:\n sp_relative_pos = []\n for k in range(len(self.sp_relative_pos)):\n sp_relative_pos += [[self.sp_relative_pos[k][i] for i in inds]]\n else:\n sp_relative_pos = None\n return idxs, sp_relative_pos","def _tree_field_indices(self):\n\n if self._tfi is not None:\n return self._tfi\n\n self.arbor._grow_tree(self)\n self._tfi = np.array([node.tree_id for node in self._tree_nodes])\n return self._tfi","def _get_k_indices(self, ks):\n if self.staticneighs:\n idx_ks = ks\n else:\n idx_ks = [self.ks.index(e) for e in ks]\n return idx_ks","def GetPartitioningArray(self):\n return _hypre.HypreParVector_GetPartitioningArray(self)","def run_idxs(self):\n return list(range(len(self._h5[RUNS])))"],"string":"[\n \"def indices(self) -> np.ndarray:\\n return self.impl.indices\",\n \"def master_ndindex(self): # itermaster_indices(self):\\n return itertools_product(\\n *[range(*r) for r in self.location]\\n ) # TODO check\",\n \"def atom_idxs(self):\\n\\n return np.array([atom.atom_idxs for atom in self])\",\n \"def get_index_array(self):\\n return self.region_pairs\",\n \"def get_indices(self):\\r\\n return self._indices\",\n \"def indices(self):\\n return self.index.indices\",\n \"def get_data_idx(self)->list:\\n return self.__data_idx\",\n \"def mainIndices(self):\\n return self.i1, self.i2\",\n \"def indices(self):\\n return self._kbounded_partitions\",\n \"def _get_split_indices(self):\\n\\n cumsum = np.cumsum(\\n np.concatenate((np.array([0], dtype=np.int8), self.split_sizes)))\\n \\n fold_inds = np.array(\\n [(cumsum[n], cumsum[n + 1]) for n in range(self.n_splits)])\\n\\n return fold_inds\",\n \"def get_indexes(self, dataset: BaseDataset) -> int:\\n\\n index = [np.random.randint(0, len(dataset)) for _ in range(1)]\\n\\n return index\",\n \"def indices(self):\\n _indices = []\\n for h in self.miller.indices():\\n _indices.append(self.indices_hkl(*h)[0])\\n return _indices\",\n \"def getIndices(self):\\r\\n return self._indices\",\n \"def get_main_branch_indices(self):\\n\\n assert self.halt is not None\\n prog_main_index = self.halt_index\\n prog_main_indices = self.halt.prop(\\n 'progenitor.main.indices', self.halt_index)\\n self.main_branch_indices = prog_main_indices\\n return prog_main_indices\",\n \"def batch_indices(self):\\n b = self.batch_size\\n return [np.arange(i*b, i*b+b) for i in range(self.num_batches)]\",\n \"def get_all_master_idx_paths(self):\\n paths = utilities.get_all_master_index_paths(rootdir=constants.flow_data_dir)\\n return paths\",\n \"def indices(self):\\n return range(len(self))\",\n \"def getLandmarkindices(self):\\n return self.subsetnodes_indices\",\n \"def getLandmarkindices(self):\\n return self.subsetindices\",\n \"def indices_of_split(self, split_name='train'):\\n return self.indices_of('split', split_name)\",\n \"def get_all_master_ids(self):\\r\\n return self._handler.get_all_master_ids()\",\n \"def indices(self):\\n i, j, _edge = self.indicesAndEdge()\\n return i, j\",\n \"def get_34index_list(self):\\n msk = self.load_mask()\\n return [i for (i,v) in enumerate(msk) if v==1]\",\n \"def index(self):\\n return self.data.index.values\",\n \"def get_indexes(self):\\n indexes = []\\n for c in self.components:\\n indexes.extend(c.get_indexes())\\n return indexes\",\n \"def get_indexes(self, dataset: BaseDataset) -> int:\\n\\n indexes = [random.randint(0, len(dataset)) for _ in range(3)]\\n return indexes\",\n \"def _load_split_indices(self):\\n split_file = self.SPLITS.get(self.split)\\n indices_file = self._filepath(split_file)\\n\\n with open(indices_file) as txt_file:\\n idx_data = [int(i) for i in txt_file.readline().split()]\\n\\n return idx_data\",\n \"def get_indexes(self, dataset):\\n\\n indexs = [random.randint(0, len(dataset)) for _ in range(3)]\\n return indexs\",\n \"def SectionIndicesConnectedToSoma(self):\\n indices = []\\n index = 0\\n for each_section in self._section_list:\\n if each_section.ParentId() == -1:\\n indices.append(index)\\n index += 1\\n return indices\",\n \"def get_active_register_indices(self):\\n assert self.sketch.ndim == 1, 'Currently only support 1-dimensional sketch.'\\n return np.flatnonzero(self.sketch)\",\n \"def get_subset_inds(self, adata_parent):\\r\\n subset_inds = np.ones(len(adata_parent), dtype=bool)\\r\\n for condition, values in self.subset_cond.items():\\r\\n subset_inds *= adata_parent.obs[condition].isin(values)\\r\\n return subset_inds\",\n \"def data_index_from_selection(self, sel_indexes):\\n data_indexes = []\\n # Translate the landmark indexes to the original data indexes\\n for i in sel_indexes:\\n data_indexes.append(self.analysis.landmark_orig_indexes[i])\\n return data_indexes\",\n \"def get_local_indices(self, part, ctx):\\n return self.map_to_global(\\n F.arange(0, self.local_size(part), ctx=ctx), part\\n )\",\n \"def _getNonPrototypeIndices(self, clusters: ndarray) -> ndarray:\\n return np.delete(np.arange(self.dataSize), clusters.flatten())\",\n \"def vir_indices(self):\\n indices = []\\n for index,item in enumerate(self):\\n if item==0:\\n indices.append(index)\\n return indices\",\n \"def inidices(self):\\n return [Index(name, index) for name, index in self._data['indices'].iteritems()]\",\n \"def childWellIndices(self):\\n return self._wellIndices\",\n \"def get_idxvals(self):\\n input_rows = list()\\n input_cols = list()\\n for key in self.index:\\n input_rows.append(key[0])\\n input_cols.append(key[1])\\n\\n return list(OrderedSet(input_rows)), list(OrderedSet(input_cols))\",\n \"def ordered_indices(self):\\n return self.base_dataset.ordered_indices()\",\n \"def _family_index(self):\\n\\n if hasattr(self, \\\"_family_index_cached\\\"):\\n return self._family_index_cached\\n\\n ind = np.empty((len(self),), dtype='int8')\\n for i, f in enumerate(self.ancestor.families()):\\n ind[self._get_family_slice(f)] = i\\n\\n self._family_index_cached = ind\\n\\n return ind\",\n \"def data(self) -> List[int]:\\n return self.__ids\",\n \"def data(self) -> List[int]:\\n return self.__ids\",\n \"def data(self) -> List[int]:\\n return self.__ids\",\n \"def indices(self):\\n if self._indices is None:\\n i = []\\n\\n # TODO: this is not right for multi-column keys\\n # TODO: new style indexes\\n\\n global_name = '^DD(%s,0,\\\"IX\\\",\\\"0\\\")' % self.fileid\\n prefix = '^DD(%s,0,\\\"IX\\\",' % self.fileid\\n while 1:\\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\\\"\\\"))[0]\\n if not global_name or not global_name.startswith(prefix):\\n break\\n suffix = global_name[len(prefix):-1]\\n parts = suffix.split(\\\",\\\")\\n idx_name = parts[0][1:-1]\\n idx_table = parts[1]\\n idx_columns = parts[2:]\\n index = Index(idx_name, idx_table, idx_columns)\\n i.append(index)\\n\\n # A second list, gives indices for a field\\n columns = {}\\n for idx in i:\\n for c in idx.columns:\\n columns[c] = 1\\n\\n # Now trawl the listed columns in the data dictionary, and load their\\n # cross references.\\n cr_names = {}\\n for c in columns.keys():\\n idx_root = M.Globals[\\\"^DD\\\"][self.fileid][c][1]\\n if not idx_root[0].exists():\\n continue\\n for cr_id, val in idx_root.keys_with_decendants():\\n if float(cr_id) > 0:\\n cr_header = idx_root[cr_id][0].value\\n parts = cr_header.split(\\\"^\\\")\\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\\n f = cr_names.get(parts[1], list())\\n f.append(c)\\n cr_names[parts[1]] = f\\n\\n # Now, just delete items from the index list if they are not in cr_names\\n self._indices = []\\n for index in i:\\n cr = cr_names.get(index.name)\\n if cr:\\n # verify columns - lots of errors in real systems\\n if len(cr) == len(index.columns):\\n invalid = False\\n for c in cr:\\n if c not in index.columns:\\n invalid = True\\n continue\\n if not invalid:\\n self._indices.append(index)\\n\\n return self._indices\",\n \"def get_indices(self):\\n selection_model = self.selectionModel()\\n return selection_model.selectedRows()\",\n \"def get_instance_index(self):\\n return np.unique([tp[0] for tp in self._innercontainer])\",\n \"def get_raster_ids(self):\\n return numpy.array(range(self._lo_atom, self._lo_atom + self._n_atoms))\",\n \"def ordered_indices(self):\\n return self.d1.ordered_indices()\\n # RETURN BASED ON D1's sizes\",\n \"def occ_indices(self):\\n indices = []\\n for index,item in enumerate(self):\\n if item==1:\\n indices.append(index)\\n return indices\",\n \"def labeled_indices(self):\\n return self._labeled_indices\",\n \"def index(self):\\n return self.dataset.index\",\n \"def _get_indexes(self, participants):\\n tr_idx = int(np.floor(self.tr_size*len(participants)))\\n j = self.val_size + self.tr_size\\n val_idx = int(np.floor(j*len(participants)))\\n return tr_idx, val_idx\",\n \"def getIndexes(self, val):\\n # Find where this value is listed. \\n valNdx = (self.values == val).nonzero()[0]\\n \\n # If this value is not actually in those listed, then we \\n # must return empty indexes\\n if len(valNdx) == 0:\\n start = 0\\n end = 0\\n else:\\n # The index into counts, etc. for this value. \\n valNdx = valNdx[0]\\n start = self.start[valNdx]\\n end = self.end[valNdx]\\n \\n # Create a tuple of index arrays, one for each index of the original array. \\n ndx = ()\\n for i in range(self.nDims):\\n ndx += (self.indexes[start:end, i], )\\n return ndx\",\n \"def indices(self):\\n\\n # We used lookup tables here. Read more about other methods here:\\n # https://chessprogramming.wikispaces.com/Bitboard+Serialization\\n\\n if self.num == 0:\\n return []\\n\\n bits = []\\n\\n for i in [0, 1, 2, 3, 4, 5, 6, 7]:\\n row = (self.num >> UINT64_PADDING[i]) & EIGHT_ONES\\n indices = row_to_indices[row]\\n for index in indices:\\n bits.append(index + i*8)\\n\\n return bits\",\n \"def all_sampled_nodes_indexes(self) -> torch.LongTensor:\\n all_sampled_nodes_indexes: _typing.Any = self.__all_sampled_nodes_indexes\\n return all_sampled_nodes_indexes\",\n \"def indices(self, position=None):\\n \\n raise NotImplementedError()\",\n \"def sparse_arrays(self):\\n return self._sparse_arrays\",\n \"def get_multi_index(self):\\n return self.basis.elements\",\n \"def index(self):\\n return self.data.index\",\n \"def _dofidxs(self):\\n return [const['dofidxs'] for i, const in self._constraints_df.iterrows()]\",\n \"def expert_to_batch_indices(self):\\n return tf.split(\\n self._batch_index, self._part_sizes_tensor, 0, num=self._num_experts)\",\n \"def idx_adjacency_lists(self) -> List[List[int]]:\\n result = []\\n\\n for intersection in self._intersection_list:\\n nbs = []\\n\\n for nb in self.adj_dict[intersection]:\\n nbs.append(self._intersection_to_idx[nb])\\n\\n result.append(nbs)\\n\\n return result\",\n \"def _get_indices_1(image_set, num_labels=2, num_protected=2):\\r\\n indices = [[[] for _ in range(num_protected)] for _ in range(num_labels)]\\r\\n for _, label, cluster, index in image_set:\\r\\n indices[label][cluster].append(index)\\r\\n\\r\\n return indices\",\n \"def getBitArrayIndices(self, key):\\n\\t\\treturnList = []\\n\\t\\tfor i in range(1, self.k + 1):\\n\\t\\t\\treturnList.append((hash(key) + i * mmh3.hash(key)) % self.m)\\n\\t\\t#print \\\"Indices list for key: \\\", key, \\\" is: \\\", str(returnList)\\n\\t\\treturn returnList\",\n \"def ordered_indices(self):\\r\\n return np.arange(len(self), dtype=np.int64)\",\n \"def _raveled_index(self):\\n return np.r_[:self.size]\",\n \"def _raveled_index(self):\\n return np.r_[:self.size]\",\n \"def cluster_ids(self):\\n return self.model.cluster_ids\",\n \"def geneIds(self):\\n\\t\\treturn self._dataframe.index.tolist()\",\n \"def getGlobalIdxVals( self, i : int ):\\n return range(self._layout.starts[i],self._layout.ends[i])\",\n \"def main_rep_idxs(self):\\n\\n if '{}/{}'.format(SETTINGS, MAIN_REP_IDXS) in self.h5:\\n return self.h5['{}/{}'.format(SETTINGS, MAIN_REP_IDXS)][:]\\n else:\\n return None\",\n \"def target_nodes_indexes(self) -> _TargetNodes:\\n return self.__target_nodes_indexes\",\n \"def get_index(self):\\n return self.disk.partitions.index(self)\",\n \"def _compute_indices(self):\\n self.indices = np.arange(len(self.im_filenames))\\n np.random.shuffle(self.indices)\",\n \"def _get_chunk_indexer(self, array):\\n if self.data.num_chunks == 1:\\n return np.broadcast_to(0, len(array))\\n return np.digitize(array, self.offsets[1:])\",\n \"def _idxs_postformat_array(self):\\n self.idxs = np.array(self.idxs)\",\n \"def get_final_pruned_indices(self):\\n return self.final_pruned_indices\",\n \"def getind(self,start,end,blk):\\n\\n if blk is None:\\n # Return all blocks\\n blk = np.arange(self.ind[start].size)\\n\\n ind=np.array([])\\n for k,val in enumerate(blk):\\n ind=np.append(ind,np.arange(self.ind[start][val],self.ind[end][val]))\\n return ind.astype(int)\",\n \"def get_indexes(self):\\n return set(k.index for k in self if k.has_index)\",\n \"def activeChildWellIndices(self):\\n return self._activeWellIndices\",\n \"def _calculate_chunk_offsets(self):\\n offset = 0\\n offsets = []\\n for chunk in self.data.iterchunks():\\n offsets.append(offset)\\n offset += len(chunk)\\n return np.array(offsets)\",\n \"def index(self):\\n return list(self._innercontainer)\",\n \"def _selected_indices(self, subset):\\n # We want the DataFrame to be indexed the same way its values array is\\n ftr = self.frametracks.reset_index(drop=True)\\n if subset is not None:\\n ftr['tmpindex'] = ftr.index.values\\n ftr = ftr.set_index('particle').reindex(subset).set_index('tmpindex')\\n if self.autoclip:\\n # Boundaries are computed for the whole system\\n xmin = self.frametracks.x.min() + self.nncutoff\\n xmax = self.frametracks.x.max() - self.nncutoff\\n ymin = self.frametracks.y.min() + self.nncutoff\\n ymax = self.frametracks.y.max() - self.nncutoff\\n r = ftr.index[ (ftr.x > xmin) & (ftr.x < xmax) & \\\\\\n (ftr.y > ymin) & (ftr.y < ymax) ].values.astype(int)\\n else:\\n r = ftr.index.values.astype(int)\\n if self.fast:\\n return np.random.permutation(r)[:int(len(r) / 10)]\\n else:\\n return r\",\n \"def indexes(self) -> list:\\n return self._indexes\",\n \"def get_cluster_indices(self,dataset, cluster_number):\\n\\t\\tself.__init__(dataset, self.k)\\n\\t\\tself.e_step() #got responsibilities\\n\\t\\tmax_cluster = np.argmax(self.w, axis = 1)\\n\\t\\tindices = []\\n\\t\\tfor i in range(dataset.shape[0]):\\n\\t\\t\\tif max_cluster[i] == cluster_number:\\n\\t\\t\\t\\tindices.append(i)\\n\\t\\treturn indices\",\n \"def _prog_field_indices(self):\\n\\n if self._pfi is not None:\\n return self._pfi\\n\\n self.arbor._grow_tree(self)\\n self._pfi = np.array([node.tree_id for node in self._prog_nodes])\\n return self._pfi\",\n \"def edges_indices(self) -> numpy.array:\\n r = numpy.array(self._receivers, dtype=numpy.int16)\\n s = numpy.array(self._senders, dtype=numpy.int16)\\n return numpy.asarray([r, s], dtype=numpy.int16)\",\n \"def get_all_master_idx_dfs(self):\\n dfs = utilities.get_all_master_index_dfs(master_index_paths=self.all_master_idx_paths)\\n return dfs\",\n \"def get_index(self, x, y):\\n i = (y - self.y0) // self.dy\\n j = (x - self.x0) // self.dx\\n i = min(max(i, 0), self.n-1)\\n j = min(max(j, 0), self.m-1)\\n return [i, j]\",\n \"def index_dict(self):\\n msk = self.load_mask()\\n mski = enumerate(msk)\\n ifiltered = (i for (i, m) in mski if m == 1)\\n return {i: j for (j, i) in enumerate(ifiltered)}\",\n \"def index_dict(self):\\n msk = self.load_mask()\\n mski = enumerate(msk)\\n ifiltered = (i for (i, m) in mski if m == 1)\\n return {i: j for (j, i) in enumerate(ifiltered)}\",\n \"def agent_locs_idx(self):\\n return tuple(self.agent_locs.T)\",\n \"def ordered_indices(self):\\r\\n '''we need random order'''\\r\\n if self.shuffle:\\r\\n indices = np.random.permutation(len(self))\\r\\n else:\\r\\n indices = np.arange(len(self))\\r\\n '''\\r\\n if self.tgt_sizes is not None:\\r\\n indices = indices[np.argsort(self.tgt_sizes[indices], kind='mergesort')]\\r\\n return indices[np.argsort(self.src_sizes[indices], kind='mergesort')]\\r\\n '''\\r\\n return indices\",\n \"def get_data(self):\\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\\n return self._data[idxs].copy()\",\n \"def get_indexes(self, dataset):\\n\\n for i in range(self.max_iters):\\n index = random.randint(0, len(dataset))\\n gt_bboxes_i = dataset.get_ann_info(index)['bboxes']\\n if len(gt_bboxes_i) != 0:\\n break\\n\\n return index\",\n \"def _notstaticneighs_get_corestored_by_inds_slice(self, inds):\\n inds = [inds] if type(inds) == int else inds\\n idxs = self.idxs\\n if self.sp_relative_pos is not None:\\n sp_relative_pos = []\\n for k in range(len(self.sp_relative_pos)):\\n sp_relative_pos += [[self.sp_relative_pos[k][i] for i in inds]]\\n else:\\n sp_relative_pos = None\\n return idxs, sp_relative_pos\",\n \"def _tree_field_indices(self):\\n\\n if self._tfi is not None:\\n return self._tfi\\n\\n self.arbor._grow_tree(self)\\n self._tfi = np.array([node.tree_id for node in self._tree_nodes])\\n return self._tfi\",\n \"def _get_k_indices(self, ks):\\n if self.staticneighs:\\n idx_ks = ks\\n else:\\n idx_ks = [self.ks.index(e) for e in ks]\\n return idx_ks\",\n \"def GetPartitioningArray(self):\\n return _hypre.HypreParVector_GetPartitioningArray(self)\",\n \"def run_idxs(self):\\n return list(range(len(self._h5[RUNS])))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70275134","0.6850407","0.683511","0.6796057","0.6708753","0.6668921","0.6662458","0.66586286","0.6644368","0.6611067","0.66031367","0.65675586","0.65319914","0.64623576","0.6393266","0.6372617","0.6343201","0.63220435","0.6305004","0.62970823","0.628111","0.62674505","0.624798","0.6227296","0.6190301","0.6181901","0.61509717","0.61253864","0.612371","0.6105406","0.610102","0.6082863","0.6060004","0.6039434","0.602746","0.6016745","0.6013818","0.5987249","0.5986039","0.5977027","0.59651345","0.59651345","0.59651345","0.5962879","0.5957046","0.5941614","0.59289116","0.5906297","0.59025055","0.5901664","0.5886664","0.5878906","0.5851613","0.5848616","0.5815533","0.58128786","0.58127564","0.5805883","0.5803346","0.58005875","0.5794765","0.5793853","0.5788719","0.57824725","0.5780825","0.5760377","0.5760377","0.5759219","0.57566726","0.5742169","0.5740475","0.57397515","0.57309896","0.5730029","0.5720656","0.5713963","0.56969166","0.56939006","0.5684604","0.5675086","0.56661737","0.5665716","0.5657009","0.5651558","0.5648632","0.5644165","0.5638548","0.56361437","0.5634593","0.5634037","0.5634037","0.5624275","0.5617037","0.5616045","0.5596753","0.5595638","0.55885994","0.5587997","0.5573761","0.5570025"],"string":"[\n \"0.70275134\",\n \"0.6850407\",\n \"0.683511\",\n \"0.6796057\",\n \"0.6708753\",\n \"0.6668921\",\n \"0.6662458\",\n \"0.66586286\",\n \"0.6644368\",\n \"0.6611067\",\n \"0.66031367\",\n \"0.65675586\",\n \"0.65319914\",\n \"0.64623576\",\n \"0.6393266\",\n \"0.6372617\",\n \"0.6343201\",\n \"0.63220435\",\n \"0.6305004\",\n \"0.62970823\",\n \"0.628111\",\n \"0.62674505\",\n \"0.624798\",\n \"0.6227296\",\n \"0.6190301\",\n \"0.6181901\",\n \"0.61509717\",\n \"0.61253864\",\n \"0.612371\",\n \"0.6105406\",\n \"0.610102\",\n \"0.6082863\",\n \"0.6060004\",\n \"0.6039434\",\n \"0.602746\",\n \"0.6016745\",\n \"0.6013818\",\n \"0.5987249\",\n \"0.5986039\",\n \"0.5977027\",\n \"0.59651345\",\n \"0.59651345\",\n \"0.59651345\",\n \"0.5962879\",\n \"0.5957046\",\n \"0.5941614\",\n \"0.59289116\",\n \"0.5906297\",\n \"0.59025055\",\n \"0.5901664\",\n \"0.5886664\",\n \"0.5878906\",\n \"0.5851613\",\n \"0.5848616\",\n \"0.5815533\",\n \"0.58128786\",\n \"0.58127564\",\n \"0.5805883\",\n \"0.5803346\",\n \"0.58005875\",\n \"0.5794765\",\n \"0.5793853\",\n \"0.5788719\",\n \"0.57824725\",\n \"0.5780825\",\n \"0.5760377\",\n \"0.5760377\",\n \"0.5759219\",\n \"0.57566726\",\n \"0.5742169\",\n \"0.5740475\",\n \"0.57397515\",\n \"0.57309896\",\n \"0.5730029\",\n \"0.5720656\",\n \"0.5713963\",\n \"0.56969166\",\n \"0.56939006\",\n \"0.5684604\",\n \"0.5675086\",\n \"0.56661737\",\n \"0.5665716\",\n \"0.5657009\",\n \"0.5651558\",\n \"0.5648632\",\n \"0.5644165\",\n \"0.5638548\",\n \"0.56361437\",\n \"0.5634593\",\n \"0.5634037\",\n \"0.5634037\",\n \"0.5624275\",\n \"0.5617037\",\n \"0.5616045\",\n \"0.5596753\",\n \"0.5595638\",\n \"0.55885994\",\n \"0.5587997\",\n \"0.5573761\",\n \"0.5570025\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62476814"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":2679,"cells":{"query":{"kind":"string","value":"True if and only if the partition's subarray is in memory as opposed to on disk."},"document":{"kind":"string","value":"def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def on_disk(self):\n return isinstance(self._subarray, FileArray)","def in_cached_file(self):\n return isinstance(self._subarray, CachedArray)","def has_shareable_memory(a):\r\n return _get_backing_memmap(a) is not None","def is_full(self):\n if len(self._page_map) >= self.memory_size:\n return True\n return False","def IsAllocated(self):\n return self._fsntfs_file_entry.is_allocated()","def is_full(self):\n elements_in_sects = sum(\n map(opr.attrgetter(\"size\"), self.sects.values())\n )\n elements_in_total = fct.reduce(\n opr.mul, type(self).flatten_shape(self.shape), 1\n )\n res = elements_in_sects >= elements_in_total\n return res","def is_array(self):\n return len(self.descriptor) > 1","def is_full(self) -> bool:\n return self._array[0].all()","def pageable(self):\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN","def is_space_available(partition, size):\n available_space = psutil.disk_usage(partition).free\n return False if available_space < size else True","def is_full(self) -> bool:\r\n return self.size == self.capacity","def is_full(self):\r\n if self.size == self.capacity:\r\n return True\r\n return False","def full(self):\n return self.size >= self.maxsize","def isFull(self) -> bool:\n return self._elems == self._k","def full(self):\n return self._current_size == self._size","def is_full(self):\n return self.heap_size >= self.capacity","def isSetSize(self):\n return _libsbml.Compartment_isSetSize(self)","def has_next(self):\n while self._row < self._n and not self._arr[self._row]: # current sub-array is empty\n self._row += 1 # move to next sub-array\n self._col = 0\n if self._row >= self._n: # end of master-array already\n return False\n return True","def isFull(self):\n\t\treturn self.size == self.capacity","def full(self) -> bool:\n return self.maxsize and self.qsize() >= self.maxsize","def is_full(self):\n\n return self.count == len(self.array)","def has_full_batch(self) -> bool:","def is_in_heap(self, address):\n return self.is_address_of_type(address, MemoryType.MajorHeap, MemoryType.MinorHeap)","def _is_full(self):\n if self.allocated_spaces == self.capacity:\n return True\n elif self.allocated_spaces < self.capacity:\n return False","def is_free(self):\n return self._size > 0","def is_partition(disk): #TODO: Could change to use \"Whole\" attrib. Good idea?\n\n return \"s\" in disk.split(\"disk\")[1]","def full(self):\r\n if self._maxsize <= 0:\r\n return False\r\n else:\r\n return self.qsize() >= self._maxsize","def check_free(self, arr):\n cell_location = self.cartesian_to_cell(arr)\n cell = self.occ_matrix[cell_location[0], cell_location[1]]\n return cell == 0","def is_full(self):\n return len(self.__occupied_slots__) >= self.__size__","def _loaded_data(self):\n try:\n dsize = [int(d) for d\n in self.run('fits size', via='get').split()]\n except (ValueError, TypeError, AttributeError) as err:\n log.debug(f' FITS size error: {err}')\n return False\n else:\n if 0 in dsize:\n return False\n else:\n return True","def __check_flat_array__(self):\n if self.flat_array is not None:\n return True\n else:\n return False","def has_definite_size(iterable):\n return hasattr(iterable, '__len__')","def is_array(self):\n return False","def isFull(self):\n return self.rear == self.size","def has_subfile(self) -> bool:\n\t\tself._update_subfiles()\n\t\treturn bool(len(self.subfiles))","def is_contiguous(arr):\n mn, mx = min(arr), max(arr)\n s = sum(arr)\n sn = (mn*(mn-1))/2 if mn!=0 else 0\n sx = (mx*(mx+1))/2\n if s == sx-sn:\n return True\n else:\n return False","def is_in(self, starting_address, size):\r\n if starting_address > self.starting_address:\r\n return (self.starting_address+self.size) > starting_address\r\n elif starting_address < self.starting_address:\r\n return (starting_address + size) > self.starting_address\r\n return True","def is_enough_space(self) -> bool:\n return self._free_space() > self.minimum_disk","def _is_size_bound(self, path):\n return path.suffix == \".bin\"","def is_full(self):\n return self.idx == self.len","def is_array(a):\n try:\n shape = a.shape\n return len(shape) >= 1\n except AttributeError:\n return False","def _is_partitioned(self):\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\n partitions = self.table_config[\"partitions\"]\n if partitions is None or len(partitions) == 0:\n return False\n\n if isinstance(partitions, list):\n # check if any None inside list.\n # False if it is the case Ex: [None, 'partition']\n # True otherwise Ex: ['partition1', 'partition2']\n return all(item is not None for item in partitions)\n\n raise ValueError(\"Partitions must be a list or None\")","def is_fragmentable(fragment_size, offset, chunk_size):\n return ((chunk_size - fragment_size) / offset) % 1 == 0","def _array_name_implies_ND_slice(self, array_name):\n for v in self._split_arrays.values():\n if array_name in v:\n return True\n\n generic_match = re.findall(\"^(.+)_[xyz]$\", array_name)\n loadable_keys = self.loadable_keys()\n keys = list(self.keys())\n if len(generic_match) == 1 and generic_match[0] not in self._split_arrays:\n return generic_match[0] in loadable_keys or generic_match[0] in keys\n return False","def has_max_size(self, max_size):\n return self.polyp_size and sorted(self.polyp_size, reverse=True)[0].get_max_dim() < max_size","def _test_obssize(t):\n return t.shape[0] != len(t.ids(axis='observation'))","def contains_offset(self, offset):\n return (offset >= self.offset) and (offset < self.offset + self.filesize)","def is_subset(subset: np.array, superset: np.array) -> bool:\n superset_lookup = set(superset)\n for val in subset:\n if val not in superset_lookup:\n return False\n\n return True","def is_empty(self):\n return self.size == []","def full(self):\n return self.q_size.current_value == self._maxsize","def isscalar(array):\n arr = ma.array(array)\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\n return True\n return False","def __contains__(self, item):\n if len(item) != len(self.sizes):\n raise ValueError('Point dimension does not match grid dimension')\n for i in range(len(self.sizes)):\n if not 1 <= item[i] < self.sizes[i] - 1:\n return False\n return True","def isFull(self) -> bool:\n return self.size == self.maxlen","def full(self) -> bool:\n\n return (self._size == self._capacity)","def is_slice(self) -> bool:\n return self._is_slice","def isFull(T):\r\n return len(T.data) >= T.max_data","def isFull(self):\n if len(self.batch) == self.__batch_size:\n return True\n return False","def is_full(self):\n return len(self._data) == 1","def is_complete(self) -> bool:\n return (\n (\n self.materialized_subset | self.failed_and_downstream_subset\n ).num_partitions_and_non_partitioned_assets\n == self.target_subset.num_partitions_and_non_partitioned_assets\n )","def hasmem(state, mem):\n if mem <= state[HEAD][MEM]:\n return True\n else:\n state[HEAD][STATUS] = OOM\n return False","def is_empty(self):\n return self.heap_size <= 0","def is_array(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_array)","def dictsub_sparse(small,big):\n\treturn all([(r,v) in catalog(big) for r,v in catalog(small)])","def is_virtualized (self):\n return len([i for i in self.infras if\n i.infra_type not in (self.TYPE_INFRA_SDN_SW, self.TYPE_INFRA_EE,\n self.TYPE_INFRA_STATIC_EE)]) > 0","def full(self) -> bool:\n return self.current_offset == self.max_offset","def check_subarray(array1, array2):\r\n \r\n # check assumption\r\n if (len(array2.shape) != 1) or (array2.shape[0] != array1.shape[-1]):\r\n raise ValueError('Attempting to check for subarray equality when shape assumption does not hold.')\r\n \r\n return np.all(array1==array2, axis=-1)","def is_array(self, arr):\n return isinstance(arr, np.ndarray)","def device_out_of_memory(self) -> bool:\n return pulumi.get(self, \"device_out_of_memory\")","def NeedsArray(self, type_):\n return self._NameComponents(type_) in self._array_types","def _is_empty(self):\n return self.size == 0","def stop_loading(self):\n return psutil.virtual_memory()[2] >= self.max_memory","def stop_loading(self):\n return psutil.virtual_memory()[2] >= self.max_memory","def isFull(self):\n if len(self._data) == self._length:\n return True\n else:\n return False","def isFull(self) -> bool:\n return self.count == self.capacity","def isFull(self) -> bool:\n return self.count == self.capacity","def PartiallyEmpty(self):\n return None==self.piecesToRecover","def __bool__(self):\n return self.end < len(self.data)","def is_empty(self):\n return self.__size == 0","def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\n file_path = join(output_path, subset + '.npy')\n return os.path.exists(file_path)","def is_out_of_memory(self):\n\n return self._state == \"OUT_OF_MEMORY\"","def is_empty(self):\n return self._size == 0","def is_subdivision_available(self, position: np.ndarray) -> bool:\n\t\tsubdivision_x_index = int(position[0]) // self.square_subdivision_length\n\t\tsubdivision_y_index = int(position[1]) // self.square_subdivision_length\n\t\treturn self.plane_subdivisions_availability[subdivision_x_index, subdivision_y_index] == 1","def iseod(self):\n\n return self.byte_ptr >= len(self.data)","def _is_empty(self):\n if self.allocated_spaces == 0:\n return True\n else:\n return False","def canPartition(self, nums):\n cache = {}\n\n def helper(nums, i, k):\n if (i, k) in cache:\n return False\n if i >= len(nums):\n return False\n if k == 0:\n return True\n include_curr = helper(nums, i + 1, k - nums[i])\n exclude_curr = helper(nums, i + 1, k)\n if include_curr:\n cache[(i, k)] = False\n return include_curr or exclude_curr\n if not nums:\n return True\n s = sum(nums)\n if s % 2 != 0:\n return False\n return helper(nums, 0, s/2)","def is_empty(self) -> bool:\n return self.heap.length() == 0","def is_empty(self) -> bool:\n return self.heap.length() == 0","def __contains__(self, point):\n for component, dim in zip(point, self.dimensions):\n if component not in dim:\n return False\n return True","def isPeakAssigned(peak, fully=True):\n\n n = 0\n for peakDim in peak.peakDims:\n if len(peakDim.peakDimContribs) > 0:\n n +=1\n \n if n == len(peak.peakDims):\n return True\n \n elif n > 0:\n if fully:\n return False\n else:\n return True\n \n else:\n return False","def is_empty(self):\n return len(self.__heap) == 0","def isFull(self):\n return self.rear - self.front == self.size","def allocate(self) -> bool:\n if hasattr(self.at_options, 'allocate'):\n return self.at_options.allocate == 1\n return False","def is_full(self):\n return len(self.keys) > self.m","def is_full(self):\n return len(self.cache_data) >= self.MAX_ITEMS","def healthy_test(obj: np.ndarray) -> bool:\n nb_rows, nb_cols = obj.shape\n return nb_rows == nb_cols > 1 and np.array_equal(obj, colony(nb_rows))","def is_full(self) -> bool:","def isleaf(self):\n no_kids = super(PartitionDevice, self).isleaf\n # it is possible that the disk that originally contained this partition\n # no longer contains a disklabel, in which case we can assume that this\n # device is a leaf\n if self.disk and self.partedPartition and \\\n self.disk.format.type == \"disklabel\" and \\\n self.partedPartition in self.disk.format.partitions:\n disklabel = self.disk.format\n else:\n disklabel = None\n\n extended_has_logical = (self.isExtended and\n (disklabel and disklabel.logicalPartitions))\n return (no_kids and not extended_has_logical)","def is_distributed(self) -> bool:\n return self.size > 1"],"string":"[\n \"def in_memory(self) -> bool:\\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def in_cached_file(self):\\n return isinstance(self._subarray, CachedArray)\",\n \"def has_shareable_memory(a):\\r\\n return _get_backing_memmap(a) is not None\",\n \"def is_full(self):\\n if len(self._page_map) >= self.memory_size:\\n return True\\n return False\",\n \"def IsAllocated(self):\\n return self._fsntfs_file_entry.is_allocated()\",\n \"def is_full(self):\\n elements_in_sects = sum(\\n map(opr.attrgetter(\\\"size\\\"), self.sects.values())\\n )\\n elements_in_total = fct.reduce(\\n opr.mul, type(self).flatten_shape(self.shape), 1\\n )\\n res = elements_in_sects >= elements_in_total\\n return res\",\n \"def is_array(self):\\n return len(self.descriptor) > 1\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def pageable(self):\\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN\",\n \"def is_space_available(partition, size):\\n available_space = psutil.disk_usage(partition).free\\n return False if available_space < size else True\",\n \"def is_full(self) -> bool:\\r\\n return self.size == self.capacity\",\n \"def is_full(self):\\r\\n if self.size == self.capacity:\\r\\n return True\\r\\n return False\",\n \"def full(self):\\n return self.size >= self.maxsize\",\n \"def isFull(self) -> bool:\\n return self._elems == self._k\",\n \"def full(self):\\n return self._current_size == self._size\",\n \"def is_full(self):\\n return self.heap_size >= self.capacity\",\n \"def isSetSize(self):\\n return _libsbml.Compartment_isSetSize(self)\",\n \"def has_next(self):\\n while self._row < self._n and not self._arr[self._row]: # current sub-array is empty\\n self._row += 1 # move to next sub-array\\n self._col = 0\\n if self._row >= self._n: # end of master-array already\\n return False\\n return True\",\n \"def isFull(self):\\n\\t\\treturn self.size == self.capacity\",\n \"def full(self) -> bool:\\n return self.maxsize and self.qsize() >= self.maxsize\",\n \"def is_full(self):\\n\\n return self.count == len(self.array)\",\n \"def has_full_batch(self) -> bool:\",\n \"def is_in_heap(self, address):\\n return self.is_address_of_type(address, MemoryType.MajorHeap, MemoryType.MinorHeap)\",\n \"def _is_full(self):\\n if self.allocated_spaces == self.capacity:\\n return True\\n elif self.allocated_spaces < self.capacity:\\n return False\",\n \"def is_free(self):\\n return self._size > 0\",\n \"def is_partition(disk): #TODO: Could change to use \\\"Whole\\\" attrib. Good idea?\\n\\n return \\\"s\\\" in disk.split(\\\"disk\\\")[1]\",\n \"def full(self):\\r\\n if self._maxsize <= 0:\\r\\n return False\\r\\n else:\\r\\n return self.qsize() >= self._maxsize\",\n \"def check_free(self, arr):\\n cell_location = self.cartesian_to_cell(arr)\\n cell = self.occ_matrix[cell_location[0], cell_location[1]]\\n return cell == 0\",\n \"def is_full(self):\\n return len(self.__occupied_slots__) >= self.__size__\",\n \"def _loaded_data(self):\\n try:\\n dsize = [int(d) for d\\n in self.run('fits size', via='get').split()]\\n except (ValueError, TypeError, AttributeError) as err:\\n log.debug(f' FITS size error: {err}')\\n return False\\n else:\\n if 0 in dsize:\\n return False\\n else:\\n return True\",\n \"def __check_flat_array__(self):\\n if self.flat_array is not None:\\n return True\\n else:\\n return False\",\n \"def has_definite_size(iterable):\\n return hasattr(iterable, '__len__')\",\n \"def is_array(self):\\n return False\",\n \"def isFull(self):\\n return self.rear == self.size\",\n \"def has_subfile(self) -> bool:\\n\\t\\tself._update_subfiles()\\n\\t\\treturn bool(len(self.subfiles))\",\n \"def is_contiguous(arr):\\n mn, mx = min(arr), max(arr)\\n s = sum(arr)\\n sn = (mn*(mn-1))/2 if mn!=0 else 0\\n sx = (mx*(mx+1))/2\\n if s == sx-sn:\\n return True\\n else:\\n return False\",\n \"def is_in(self, starting_address, size):\\r\\n if starting_address > self.starting_address:\\r\\n return (self.starting_address+self.size) > starting_address\\r\\n elif starting_address < self.starting_address:\\r\\n return (starting_address + size) > self.starting_address\\r\\n return True\",\n \"def is_enough_space(self) -> bool:\\n return self._free_space() > self.minimum_disk\",\n \"def _is_size_bound(self, path):\\n return path.suffix == \\\".bin\\\"\",\n \"def is_full(self):\\n return self.idx == self.len\",\n \"def is_array(a):\\n try:\\n shape = a.shape\\n return len(shape) >= 1\\n except AttributeError:\\n return False\",\n \"def _is_partitioned(self):\\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\\n partitions = self.table_config[\\\"partitions\\\"]\\n if partitions is None or len(partitions) == 0:\\n return False\\n\\n if isinstance(partitions, list):\\n # check if any None inside list.\\n # False if it is the case Ex: [None, 'partition']\\n # True otherwise Ex: ['partition1', 'partition2']\\n return all(item is not None for item in partitions)\\n\\n raise ValueError(\\\"Partitions must be a list or None\\\")\",\n \"def is_fragmentable(fragment_size, offset, chunk_size):\\n return ((chunk_size - fragment_size) / offset) % 1 == 0\",\n \"def _array_name_implies_ND_slice(self, array_name):\\n for v in self._split_arrays.values():\\n if array_name in v:\\n return True\\n\\n generic_match = re.findall(\\\"^(.+)_[xyz]$\\\", array_name)\\n loadable_keys = self.loadable_keys()\\n keys = list(self.keys())\\n if len(generic_match) == 1 and generic_match[0] not in self._split_arrays:\\n return generic_match[0] in loadable_keys or generic_match[0] in keys\\n return False\",\n \"def has_max_size(self, max_size):\\n return self.polyp_size and sorted(self.polyp_size, reverse=True)[0].get_max_dim() < max_size\",\n \"def _test_obssize(t):\\n return t.shape[0] != len(t.ids(axis='observation'))\",\n \"def contains_offset(self, offset):\\n return (offset >= self.offset) and (offset < self.offset + self.filesize)\",\n \"def is_subset(subset: np.array, superset: np.array) -> bool:\\n superset_lookup = set(superset)\\n for val in subset:\\n if val not in superset_lookup:\\n return False\\n\\n return True\",\n \"def is_empty(self):\\n return self.size == []\",\n \"def full(self):\\n return self.q_size.current_value == self._maxsize\",\n \"def isscalar(array):\\n arr = ma.array(array)\\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\\n return True\\n return False\",\n \"def __contains__(self, item):\\n if len(item) != len(self.sizes):\\n raise ValueError('Point dimension does not match grid dimension')\\n for i in range(len(self.sizes)):\\n if not 1 <= item[i] < self.sizes[i] - 1:\\n return False\\n return True\",\n \"def isFull(self) -> bool:\\n return self.size == self.maxlen\",\n \"def full(self) -> bool:\\n\\n return (self._size == self._capacity)\",\n \"def is_slice(self) -> bool:\\n return self._is_slice\",\n \"def isFull(T):\\r\\n return len(T.data) >= T.max_data\",\n \"def isFull(self):\\n if len(self.batch) == self.__batch_size:\\n return True\\n return False\",\n \"def is_full(self):\\n return len(self._data) == 1\",\n \"def is_complete(self) -> bool:\\n return (\\n (\\n self.materialized_subset | self.failed_and_downstream_subset\\n ).num_partitions_and_non_partitioned_assets\\n == self.target_subset.num_partitions_and_non_partitioned_assets\\n )\",\n \"def hasmem(state, mem):\\n if mem <= state[HEAD][MEM]:\\n return True\\n else:\\n state[HEAD][STATUS] = OOM\\n return False\",\n \"def is_empty(self):\\n return self.heap_size <= 0\",\n \"def is_array(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_array)\",\n \"def dictsub_sparse(small,big):\\n\\treturn all([(r,v) in catalog(big) for r,v in catalog(small)])\",\n \"def is_virtualized (self):\\n return len([i for i in self.infras if\\n i.infra_type not in (self.TYPE_INFRA_SDN_SW, self.TYPE_INFRA_EE,\\n self.TYPE_INFRA_STATIC_EE)]) > 0\",\n \"def full(self) -> bool:\\n return self.current_offset == self.max_offset\",\n \"def check_subarray(array1, array2):\\r\\n \\r\\n # check assumption\\r\\n if (len(array2.shape) != 1) or (array2.shape[0] != array1.shape[-1]):\\r\\n raise ValueError('Attempting to check for subarray equality when shape assumption does not hold.')\\r\\n \\r\\n return np.all(array1==array2, axis=-1)\",\n \"def is_array(self, arr):\\n return isinstance(arr, np.ndarray)\",\n \"def device_out_of_memory(self) -> bool:\\n return pulumi.get(self, \\\"device_out_of_memory\\\")\",\n \"def NeedsArray(self, type_):\\n return self._NameComponents(type_) in self._array_types\",\n \"def _is_empty(self):\\n return self.size == 0\",\n \"def stop_loading(self):\\n return psutil.virtual_memory()[2] >= self.max_memory\",\n \"def stop_loading(self):\\n return psutil.virtual_memory()[2] >= self.max_memory\",\n \"def isFull(self):\\n if len(self._data) == self._length:\\n return True\\n else:\\n return False\",\n \"def isFull(self) -> bool:\\n return self.count == self.capacity\",\n \"def isFull(self) -> bool:\\n return self.count == self.capacity\",\n \"def PartiallyEmpty(self):\\n return None==self.piecesToRecover\",\n \"def __bool__(self):\\n return self.end < len(self.data)\",\n \"def is_empty(self):\\n return self.__size == 0\",\n \"def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\\n file_path = join(output_path, subset + '.npy')\\n return os.path.exists(file_path)\",\n \"def is_out_of_memory(self):\\n\\n return self._state == \\\"OUT_OF_MEMORY\\\"\",\n \"def is_empty(self):\\n return self._size == 0\",\n \"def is_subdivision_available(self, position: np.ndarray) -> bool:\\n\\t\\tsubdivision_x_index = int(position[0]) // self.square_subdivision_length\\n\\t\\tsubdivision_y_index = int(position[1]) // self.square_subdivision_length\\n\\t\\treturn self.plane_subdivisions_availability[subdivision_x_index, subdivision_y_index] == 1\",\n \"def iseod(self):\\n\\n return self.byte_ptr >= len(self.data)\",\n \"def _is_empty(self):\\n if self.allocated_spaces == 0:\\n return True\\n else:\\n return False\",\n \"def canPartition(self, nums):\\n cache = {}\\n\\n def helper(nums, i, k):\\n if (i, k) in cache:\\n return False\\n if i >= len(nums):\\n return False\\n if k == 0:\\n return True\\n include_curr = helper(nums, i + 1, k - nums[i])\\n exclude_curr = helper(nums, i + 1, k)\\n if include_curr:\\n cache[(i, k)] = False\\n return include_curr or exclude_curr\\n if not nums:\\n return True\\n s = sum(nums)\\n if s % 2 != 0:\\n return False\\n return helper(nums, 0, s/2)\",\n \"def is_empty(self) -> bool:\\n return self.heap.length() == 0\",\n \"def is_empty(self) -> bool:\\n return self.heap.length() == 0\",\n \"def __contains__(self, point):\\n for component, dim in zip(point, self.dimensions):\\n if component not in dim:\\n return False\\n return True\",\n \"def isPeakAssigned(peak, fully=True):\\n\\n n = 0\\n for peakDim in peak.peakDims:\\n if len(peakDim.peakDimContribs) > 0:\\n n +=1\\n \\n if n == len(peak.peakDims):\\n return True\\n \\n elif n > 0:\\n if fully:\\n return False\\n else:\\n return True\\n \\n else:\\n return False\",\n \"def is_empty(self):\\n return len(self.__heap) == 0\",\n \"def isFull(self):\\n return self.rear - self.front == self.size\",\n \"def allocate(self) -> bool:\\n if hasattr(self.at_options, 'allocate'):\\n return self.at_options.allocate == 1\\n return False\",\n \"def is_full(self):\\n return len(self.keys) > self.m\",\n \"def is_full(self):\\n return len(self.cache_data) >= self.MAX_ITEMS\",\n \"def healthy_test(obj: np.ndarray) -> bool:\\n nb_rows, nb_cols = obj.shape\\n return nb_rows == nb_cols > 1 and np.array_equal(obj, colony(nb_rows))\",\n \"def is_full(self) -> bool:\",\n \"def isleaf(self):\\n no_kids = super(PartitionDevice, self).isleaf\\n # it is possible that the disk that originally contained this partition\\n # no longer contains a disklabel, in which case we can assume that this\\n # device is a leaf\\n if self.disk and self.partedPartition and \\\\\\n self.disk.format.type == \\\"disklabel\\\" and \\\\\\n self.partedPartition in self.disk.format.partitions:\\n disklabel = self.disk.format\\n else:\\n disklabel = None\\n\\n extended_has_logical = (self.isExtended and\\n (disklabel and disklabel.logicalPartitions))\\n return (no_kids and not extended_has_logical)\",\n \"def is_distributed(self) -> bool:\\n return self.size > 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7677807","0.75874203","0.73676527","0.6567502","0.6373806","0.62520576","0.6168975","0.6139325","0.61339194","0.6126576","0.611511","0.608777","0.6025861","0.6025105","0.60162103","0.5953649","0.59421575","0.59196216","0.5899218","0.5882104","0.5880964","0.5842848","0.584197","0.583599","0.5819761","0.5816238","0.5800492","0.57884943","0.57869345","0.5768234","0.57646","0.57633954","0.57630956","0.5754871","0.5750774","0.57436514","0.5730692","0.57179475","0.5693052","0.5687919","0.568315","0.56760603","0.5665462","0.5658374","0.56544167","0.5648596","0.563141","0.5621424","0.5620027","0.56016713","0.55919033","0.55896264","0.5574594","0.557013","0.5567939","0.5558107","0.5557331","0.5543551","0.55386424","0.55360925","0.5528201","0.55249494","0.55246234","0.55162436","0.5515016","0.55120623","0.5498643","0.54982555","0.5493889","0.54934096","0.54904944","0.54704463","0.54613495","0.54613495","0.54604244","0.544921","0.544921","0.54372215","0.54320014","0.543098","0.54293364","0.54267144","0.54163945","0.5414924","0.5410628","0.54078674","0.5404977","0.54027945","0.54027945","0.5401461","0.53973633","0.53939974","0.5391803","0.53884137","0.53869176","0.5377037","0.5375299","0.53736126","0.53706396","0.5369923"],"string":"[\n \"0.7677807\",\n \"0.75874203\",\n \"0.73676527\",\n \"0.6567502\",\n \"0.6373806\",\n \"0.62520576\",\n \"0.6168975\",\n \"0.6139325\",\n \"0.61339194\",\n \"0.6126576\",\n \"0.611511\",\n \"0.608777\",\n \"0.6025861\",\n \"0.6025105\",\n \"0.60162103\",\n \"0.5953649\",\n \"0.59421575\",\n \"0.59196216\",\n \"0.5899218\",\n \"0.5882104\",\n \"0.5880964\",\n \"0.5842848\",\n \"0.584197\",\n \"0.583599\",\n \"0.5819761\",\n \"0.5816238\",\n \"0.5800492\",\n \"0.57884943\",\n \"0.57869345\",\n \"0.5768234\",\n \"0.57646\",\n \"0.57633954\",\n \"0.57630956\",\n \"0.5754871\",\n \"0.5750774\",\n \"0.57436514\",\n \"0.5730692\",\n \"0.57179475\",\n \"0.5693052\",\n \"0.5687919\",\n \"0.568315\",\n \"0.56760603\",\n \"0.5665462\",\n \"0.5658374\",\n \"0.56544167\",\n \"0.5648596\",\n \"0.563141\",\n \"0.5621424\",\n \"0.5620027\",\n \"0.56016713\",\n \"0.55919033\",\n \"0.55896264\",\n \"0.5574594\",\n \"0.557013\",\n \"0.5567939\",\n \"0.5558107\",\n \"0.5557331\",\n \"0.5543551\",\n \"0.55386424\",\n \"0.55360925\",\n \"0.5528201\",\n \"0.55249494\",\n \"0.55246234\",\n \"0.55162436\",\n \"0.5515016\",\n \"0.55120623\",\n \"0.5498643\",\n \"0.54982555\",\n \"0.5493889\",\n \"0.54934096\",\n \"0.54904944\",\n \"0.54704463\",\n \"0.54613495\",\n \"0.54613495\",\n \"0.54604244\",\n \"0.544921\",\n \"0.544921\",\n \"0.54372215\",\n \"0.54320014\",\n \"0.543098\",\n \"0.54293364\",\n \"0.54267144\",\n \"0.54163945\",\n \"0.5414924\",\n \"0.5410628\",\n \"0.54078674\",\n \"0.5404977\",\n \"0.54027945\",\n \"0.54027945\",\n \"0.5401461\",\n \"0.53973633\",\n \"0.53939974\",\n \"0.5391803\",\n \"0.53884137\",\n \"0.53869176\",\n \"0.5377037\",\n \"0.5375299\",\n \"0.53736126\",\n \"0.53706396\",\n \"0.5369923\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7635218"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2680,"cells":{"query":{"kind":"string","value":"True if and only if the partition's subarray is on disk in a temporary file."},"document":{"kind":"string","value":"def in_cached_file(self):\n return isinstance(self._subarray, CachedArray)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def on_disk(self):\n return isinstance(self._subarray, FileArray)","def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\n file_path = join(output_path, subset + '.npy')\n return os.path.exists(file_path)","def has_subfile(self) -> bool:\n\t\tself._update_subfiles()\n\t\treturn bool(len(self.subfiles))","def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())","def in_file(self):\n return self.on_disk and not self.in_cached_file","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")","def output_files_exist(self):\n return all([split.exists() for split in self.split_files])","def has_fileout(self):\n return self.fileout is not None","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def prune_empty(self): # FileObj.prune_empty\n return False # can't prune a file","def in_folder(self):\n return len(os.path.split(self.file_path)) > 1","def is_file_ingested(self, original_name, tablename):\n prep_stmt = self.session.prepare(\n 'SELECT * FROM {0} WHERE {1}=?'.format(tablename, COLUMNS_META[2])\n )\n bound = prep_stmt.bind([original_name])\n results = self.session.execute(bound)\n return True if len(results.current_rows) > 0 else False","def has_full_batch(self) -> bool:","def file_populated(filepath):\n\n return file_exists(filepath) and os.stat(filepath).st_size > 0","def exists(self):\n\t\tif self.hasUdim:\n\t\t\treturn len( self.udimPaths ) != 0\n\t\treturn super( textureFile, self ).exists","def has_local_tails_file(self) -> bool:\n tails_file_path = Path(self.get_receiving_tails_local_path())\n return tails_file_path.is_file()","def check_for_file(self):\n if self.task.file_name in os.listdir(self.task.file_storage):\n return True\n return False","def is_local(self):\n try:\n return os.path.isfile(self.get_absolute_path())\n except ValueError:\n logger.error(\"'%s' is not a file\", self.get_absolute_path())\n except TypeError: # no datafile available or file does not exist\n pass\n return False","def is_multi_file(self):\n return 'files' in self.torrent['info']","def is_dataset_exported(filename):\n try:\n with open(filename):\n return True\n except IOError:\n return False","def is_complete(self) -> bool:\n return (\n (\n self.materialized_subset | self.failed_and_downstream_subset\n ).num_partitions_and_non_partitioned_assets\n == self.target_subset.num_partitions_and_non_partitioned_assets\n )","def check_corrupted_files(self):\r\n for store in STORES:\r\n path = f\"{self.system.config_path}/.storage/{STORES[store]}\"\r\n if os.path.exists(path):\r\n if os.stat(path).st_size == 0:\r\n # File is empty (corrupted)\r\n return True\r\n return False","def isTemp(self,object):\n return (object in self.tempObjects)","def isfile(self):\n return os.path.isfile(self.path)","def IsAllocated(self):\n return self._fsntfs_file_entry.is_allocated()","def _check_truncation(self):\n\n temp_pos = self._handle.tell()\n self._handle.seek(-28, 2)\n eof = self._handle.read()\n self._handle.seek(temp_pos)\n if eof == _bgzf_eof:\n return False\n else:\n warnings.BytesWarning('No EOF character found. File may be truncated')\n return True","def exists(self):\r\n return os.path.exists(self.full_path)","def verify_results(outdir_path, original_array_path, R, O, file_format, addition, split_merge=False):\n\n if file_format == \"HDF5\":\n file_manager = HDF5_manager()\n else:\n print(\"File format not supported yet. Aborting...\")\n sys.exit(1)\n\n partition = get_blocks_shape(R, O)\n orig_arr_data = file_manager.read_all(original_array_path)\n all_true = True\n\n if split_merge:\n result_arrpath = os.path.join(outdir_path, \"0_0_0.hdf5\")\n return file_manager.check_split_merge(original_array_path, result_arrpath)\n\n for i in range(partition[0]):\n for j in range(partition[1]):\n for k in range(partition[2]):\n outfilepath = os.path.join(outdir_path, str(i) + \"_\" + str(j) + \"_\" + str(k) + \".hdf5\")\n data_stored = file_manager.read_all(outfilepath)\n ground_truth = orig_arr_data[i*O[0]:(i+1)*O[0],j*O[1]:(j+1)*O[1],k*O[2]:(k+1)*O[2]]\n \n if addition:\n ground_truth = ground_truth +1\n\n try:\n assert np.allclose(data_stored, ground_truth, rtol=1e-02)\n # print(f\"Good output file {outfilepath}\")\n except:\n print(f\"Error: bad rechunking {outfilepath}\")\n print(f\"Slices from ground truth {i*O[0]}:{(i+1)*O[0]}, {j*O[1]}:{(j+1)*O[1]}, {k*O[2]}:{(k+1)*O[2]}\")\n print(\"data_stored\", data_stored)\n print(\"ground_truth\", ground_truth)\n all_true = False # do not return here to see all failures\n\n file_manager.close_infiles() # close all files\n return all_true","def ofile_exists(self):\n return os.path.isfile(self.ofile)","def eof_check(self) -> bool:\n eof = False\n curr_pos = self.fileobject.tell()\n # print(curr_pos, self.st_size)\n chunk = self.fileobject.read(25)\n if chunk == '':\n # Is there something on the back burner??\n if len(self._backburner) > 0:\n self.fileobject = self._backburner.pop()\n # TODO: what if it is the end of the back burner file? Is that handled?\n else:\n eof = True\n else:\n self.fileobject.seek(curr_pos)\n return eof","def is_partition(disk): #TODO: Could change to use \"Whole\" attrib. Good idea?\n\n return \"s\" in disk.split(\"disk\")[1]","def check_coords_file(self):\n if path.exists(self.coords_file):\n return True\n return False","def has_file(self) -> bool:\n return self._file is not None","def _register_temporary_file(self):\n _partition_file = self._subarray._partition_file\n _partition_dir = self._subarray._partition_dir\n if _partition_file not in _temporary_files:\n fd, _lock_file = mkstemp(\n prefix=_partition_file + \"_\", dir=_partition_dir\n )\n close(fd)\n _temporary_files[_partition_file] = (\n _partition_dir,\n _lock_file,\n set(),\n )\n else:\n _, _lock_file, _ = _temporary_files[_partition_file]\n\n return _lock_file","def check_already_extracted(video_parts):\n filename_no_ext, _ = video_parts\n return bool(os.path.exists(os.path.join(output_dir,\n filename_no_ext + '-0030.jpg')))","def found_empty_file(self):\n self.is_empty = True","def objectsReady(self, n):\n return len(self.files) >= n","def contains_files(self):\n if self.file_list is None:\n self._set_file_list()\n for individual_file in self.file_list:\n if not os.path.exists(os.path.join(self.base_dir, individual_file)):\n return False\n return True","def is_writable_file(obj):\n try:\n obj.write(\"\")\n except(AttributeError, OSError, IOError):\n return False\n else:\n return True","def has_measurement(self, filepath):\n entries = self.get_measurement(file_path=filepath, select_list=[COL_NAME_FILES_MEASID])\n return len(entries) == 1","def has_contents(self):\n return len(self.byteruns())>0","def exists(self):\n return self.path.is_file()","def meshfn_is_used(meshfn):\n if glob.glob(meshfn):\n return True\n else:\n return False","def path_contains_data(bucket, root_path, min_file_size=0, file_extension=None):\n for key in bucket.list(root_path):\n if file_extension and not key.name.endswith(file_extension):\n continue\n if key.size > min_file_size:\n return True\n\n return False","def test_get_subgrid_snapshots():\n subgrid_snapshots = get_subgrid_snapshots(nTime=200)\n\n subgrid_snapshots_corr = np.load('./preprocessing/tests/test_data/\\\nsubgrid_snapshots.npy')\n\n assert (subgrid_snapshots == subgrid_snapshots_corr).all()","def exist(self):\n return self.file_path.exists()","def has_file(self, doc):\n return len(doc.package.files) != 0","def is_empty(self):\n if self.file_exists:\n with open_hdf5(self.file_name) as h:\n return len(h.keys()) == 0\n else:\n return True","def is_separate_file(self):\n return self.uri is not None and not self.has_data_uri","def check_helpers(self):\n paths = self.get_helper_out_paths()\n\n for p in paths:\n full_path = p + \".data-00000-of-00001\"\n file = Path(full_path)\n if not file.exists():\n return False\n\n return True","def checkFileInObject(self, obj, fp):\n\n containted = False\n ## Haso of all files\n filehash = self.getObjectFilesHash(obj)\n\n ## Local hash\n BLOCKSIZE = 65536\n hasher = hashlib.sha1()\n\n with fp.open('rb') as afile:\n buf = afile.read(BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = afile.read(BLOCKSIZE)\n localhash = hasher.hexdigest()\n\n if localhash.upper() in filehash:\n containted = True\n\n return containted","def check_needs_upload(self, path):\n if self.upload_always:\n return True\n fn = '/'.join([self.hdfs_home, '.knitDeps', os.path.basename(path)])\n if self.hdfs and self.hdfs.exists(fn):\n st = os.stat(path)\n size = st.st_size\n t = st.st_mtime\n info = self.hdfs.info(fn)\n if info['size'] == size and t < info['last_mod']:\n return False\n else:\n return True\n else:\n return True","def check_enough_space(dataset_local_dir, remote_fname, local_fname,\n max_disk_usage=0.9):\n storage_need = os.path.getsize(remote_fname)\n storage_total, storage_used = disk_usage(dataset_local_dir)\n\n # Instead of only looking if there's enough space, we ensure we do not\n # go over max disk usage level to avoid filling the disk/partition\n return ((storage_used + storage_need) <\n (storage_total * max_disk_usage))","def contains_offset(self, offset):\n return (offset >= self.offset) and (offset < self.offset + self.filesize)","def assert_data_fragments_correct(self) -> bool:\n read_path = Path(os.environ[\"DATA_PATH\"]) / \"fragments\"\n if not read_path.exists():\n return False\n bin_images = [img for img in read_path.iterdir() if \"binarized\" in img.name]\n if len(bin_images) == 0:\n return False\n return True","def is_subtag(tag_name, subtag_name, user_path, current_user) -> bool:\n user = current_user[0]\n subtag_list = os.listdir((user_path + '\\\\' + user + '\\\\' + tag_name).encode('unicode_escape'))\n temp = list(map(bytes.decode, subtag_list))\n\n if subtag_name + '.txt' in temp:\n return True\n else:\n return False","def is_wave(self,path):\n return len(path[0])>1","def efile_exists(self):\n return os.path.isfile(self.efile)","def exist_partition(self, partition_spec):\n return partition_spec in self.partitions","def _has_processed_data(self):\n return \\\n os.path.exists(\n os.path.join(self._data_root_path, self._processed_train_data_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._processed_dev_data_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._word_vocab_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._char_vocab_file_name))","def check(self, evidence, path_on_disk):\n return True","def _supports_binary_writing(path):\n return not path.startswith(\"/bigstore\")","def _split_exists( file_list, target_locus ):\n for filename in file_list:\n basename = filename.split('.')[0]\n parts = basename.split('_')\n if parts[-1] in ['5p', '3p'] and parts[-2] == target_locus:\n return True\n return False","def is_empty_file(fpath):\n return \\\n fpath is not None and \\\n os.path.isfile(fpath) and \\\n os.path.getsize(fpath) == 0","def is_input_file(self):\r\n return self.depth == 0","def _loaded_data(self):\n try:\n dsize = [int(d) for d\n in self.run('fits size', via='get').split()]\n except (ValueError, TypeError, AttributeError) as err:\n log.debug(f' FITS size error: {err}')\n return False\n else:\n if 0 in dsize:\n return False\n else:\n return True","def test_as_file_false(self):\n with TemporaryDirectory() as tmp:\n # define path to file\n fp = os.path.join(tmp, \"asdf.txt\")\n\n # invoke atomic_write with param as_file set to False\n # this should return a temporary file path string\n with atomic_write(fp, as_file=False) as f:\n self.assertIsInstance(f, str)","def has_file(self, name):\n return bool(self.input(name).__class__.__name__ == 'cgi_FieldStorage')","def check_already_extracted(video_parts):\n train_or_test, classname, filename_no_ext, _ = video_parts\n return bool(os.path.exists(os.path.join(\"/data/niteshku001/Ravdess\", train_or_test, classname,\n filename_no_ext + '-0001.jpg')))","def f_supports_fast_access(self):\n return not self.f_is_empty()","def test_my_subset_files_exist():\n assert isinstance(dist_a_subset, pd.DataFrame)\n assert isinstance(dist_b_subset, pd.DataFrame)\n assert isinstance(clone_df_subset, pd.DataFrame)","def object_exists(self, fname):\n return False","def seekable(self):\n # Not seekable, but we do support tell...\n return False","def is_full(self) -> bool:\n return self._array[0].all()","def cleanup_incomplete_uploads_from_blob_store() -> bool:\n\n DAYS_TO_RETAIN = 1\n\n # Get current time in UTC timezone\n now = datetime.datetime.now(pytz.timezone(\"UTC\"))\n\n client = get_s3_client(settings=node.settings)\n incomplete_upload_objs = client.list_multipart_uploads(Bucket=node.id.no_dash).get(\n \"Uploads\", []\n )\n\n for obj in incomplete_upload_objs:\n # Get the upload id and object name\n upload_id: str = obj[\"UploadId\"]\n obj_name: str = obj[\"Key\"]\n\n # Get the list of all parts of the object uploaded\n # This step is required to get the upload time of the object\n object_parts: list = client.list_parts(\n Bucket=node.id.no_dash, UploadId=upload_id, Key=obj_name\n ).get(\"Parts\", [])\n\n obj_part_expired = False\n for part in object_parts:\n # Normalize upload time to UTC timezone\n part_upload_time = pytz.timezone(\"UTC\").normalize(part[\"LastModified\"])\n\n # If upload time of any part of the object\n # crosses DAYS_TO_RETAIN, then expire the whole object\n if (now - part_upload_time).days > DAYS_TO_RETAIN:\n obj_part_expired = True\n break\n\n if obj_part_expired:\n # Abort multipart upload\n client.abort_multipart_upload(\n UploadId=upload_id,\n Key=obj_name,\n Bucket=node.id.no_dash,\n )\n\n return True","def seekable(self):\n return True","def PartiallyEmpty(self):\n return None==self.piecesToRecover","def object_exists(self, fname):\n return True","def isSegmentFile(self, segment):\n return os.path.isfile(\"{wd}/{jn}-run/{seg}.rst7\".format( wd=self.workdir, jn=self.jobname, seg=segment.getNameString()))","def is_primary_file(self):\n return self.file_type() == FileType.FILE_TYPE_PRIMARY","def is_partition_the_last(dbapi, partition):\n idisk_uuid = partition.get('idisk_uuid')\n onidisk_parts = dbapi.partition_get_by_idisk(idisk_uuid)\n part_number = get_part_number(partition.get('device_path'))\n\n if int(part_number) != len(onidisk_parts):\n return False\n\n return True","def hdf_exist_p(store, data_name):\n if not data_name.startswith(\"/\"):\n data_name = \"/\" + data_name\n if os.path.isfile(store):\n with HDFStore(store) as hdf:\n return data_name in hdf.keys()\n else:\n return False","def seekable(self):\n self._check_not_closed()\n return False","def is_in_swestore(f):\n with open(os.devnull, 'w') as null:\n try:\n check_call(['ils', f], stdout=null, stderr=null)\n except CalledProcessError:\n # ils will fail if the file does not exist in swestore\n return False\n else:\n return True","def isfile (self, path):\r\n pass","def isfile(self):\n return not self.isdir()","def _index_file_exists(idx_fn):\n if os.path.exists(idx_fn + \".npy\") and os.path.exists(idx_fn + \".info\"):\n return True\n else:\n return False","def check_valid_device(self, path, run_as_root=True):\n sheepdog_handle = path\n\n if sheepdog_handle is None:\n return False\n\n original_offset = sheepdog_handle.tell()\n\n try:\n sheepdog_handle.read(4096)\n except Exception as e:\n LOG.error(\"Failed to access sheepdog device \"\n \"handle: %(error)s\",\n {\"error\": e})\n return False\n finally:\n sheepdog_handle.seek(original_offset, 0)\n\n return True","def checkTrueArrayIsDeleted(node):\n if (node.raw) is not None or (node.raw_housing) is not None:\n raise Exception(\"The true data array has not been deleted\")","def _is_partitioned(self):\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\n partitions = self.table_config[\"partitions\"]\n if partitions is None or len(partitions) == 0:\n return False\n\n if isinstance(partitions, list):\n # check if any None inside list.\n # False if it is the case Ex: [None, 'partition']\n # True otherwise Ex: ['partition1', 'partition2']\n return all(item is not None for item in partitions)\n\n raise ValueError(\"Partitions must be a list or None\")","def coeff_write_ok(self):\n return os.access(self.coeffroot, os.W_OK)","def is_file_exist(self):\n return os.path.isfile(os.path.join(self.output_path, 'amr_corpus_ext.pickle'))","def exists_in_path(self):\n return os.path.isfile(self.IN_PATH)","def test_record_is_dataset_file(self) -> None:\n path = \"/home/user/dataset.record\"\n result = is_dataset_file(path)\n self.assertTrue(result)","def exists(path: str) -> bool:\n tdb_uri = paths.tiledb_uri_from_path(path)\n try:\n tiledb.cloud.array.info(tdb_uri)\n return True\n except tiledb.cloud.TileDBCloudError:\n pass\n return False","def _test_obssize(t):\n return t.shape[0] != len(t.ids(axis='observation'))","def is_empty(self): # DirObj.is_empty\n\n for fileName, fileEntry in self.files.iteritems():\n if not fileEntry.deleted and not fileEntry.ignore:\n #print '# ' + self.pathname + ' is not empty due to a file ' + fileEntry.name\n return False\n\n for dirName, subdir in self.subdirs.iteritems():\n if not subdir.deleted and not subdir.is_empty() and not subdir.ignore:\n #print '# ' + self.pathname + ' is not empty due to a dir ' + subdir.name\n return False\n\n #print '# ' + self.pathname + ' is empty!'\n return True","def is_file(file_to_test):\r\n return all(hasattr(file_to_test, method) for method in ['read', 'name'])","def is_signed(self):\n file_size = os.stat(self._file_name).st_size\n self._document.seek(file_size - self._append_size)\n last = self._document.read()\n self._document.seek(0)\n\n if not (chr(last[0]) == self._seperator and chr(last[-1]) == self._seperator):\n return False\n else:\n return True"],"string":"[\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\\n file_path = join(output_path, subset + '.npy')\\n return os.path.exists(file_path)\",\n \"def has_subfile(self) -> bool:\\n\\t\\tself._update_subfiles()\\n\\t\\treturn bool(len(self.subfiles))\",\n \"def in_memory(self) -> bool:\\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())\",\n \"def in_file(self):\\n return self.on_disk and not self.in_cached_file\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def in_memory(self):\\n return hasattr(self._subarray, \\\"__array_interface__\\\")\",\n \"def output_files_exist(self):\\n return all([split.exists() for split in self.split_files])\",\n \"def has_fileout(self):\\n return self.fileout is not None\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def prune_empty(self): # FileObj.prune_empty\\n return False # can't prune a file\",\n \"def in_folder(self):\\n return len(os.path.split(self.file_path)) > 1\",\n \"def is_file_ingested(self, original_name, tablename):\\n prep_stmt = self.session.prepare(\\n 'SELECT * FROM {0} WHERE {1}=?'.format(tablename, COLUMNS_META[2])\\n )\\n bound = prep_stmt.bind([original_name])\\n results = self.session.execute(bound)\\n return True if len(results.current_rows) > 0 else False\",\n \"def has_full_batch(self) -> bool:\",\n \"def file_populated(filepath):\\n\\n return file_exists(filepath) and os.stat(filepath).st_size > 0\",\n \"def exists(self):\\n\\t\\tif self.hasUdim:\\n\\t\\t\\treturn len( self.udimPaths ) != 0\\n\\t\\treturn super( textureFile, self ).exists\",\n \"def has_local_tails_file(self) -> bool:\\n tails_file_path = Path(self.get_receiving_tails_local_path())\\n return tails_file_path.is_file()\",\n \"def check_for_file(self):\\n if self.task.file_name in os.listdir(self.task.file_storage):\\n return True\\n return False\",\n \"def is_local(self):\\n try:\\n return os.path.isfile(self.get_absolute_path())\\n except ValueError:\\n logger.error(\\\"'%s' is not a file\\\", self.get_absolute_path())\\n except TypeError: # no datafile available or file does not exist\\n pass\\n return False\",\n \"def is_multi_file(self):\\n return 'files' in self.torrent['info']\",\n \"def is_dataset_exported(filename):\\n try:\\n with open(filename):\\n return True\\n except IOError:\\n return False\",\n \"def is_complete(self) -> bool:\\n return (\\n (\\n self.materialized_subset | self.failed_and_downstream_subset\\n ).num_partitions_and_non_partitioned_assets\\n == self.target_subset.num_partitions_and_non_partitioned_assets\\n )\",\n \"def check_corrupted_files(self):\\r\\n for store in STORES:\\r\\n path = f\\\"{self.system.config_path}/.storage/{STORES[store]}\\\"\\r\\n if os.path.exists(path):\\r\\n if os.stat(path).st_size == 0:\\r\\n # File is empty (corrupted)\\r\\n return True\\r\\n return False\",\n \"def isTemp(self,object):\\n return (object in self.tempObjects)\",\n \"def isfile(self):\\n return os.path.isfile(self.path)\",\n \"def IsAllocated(self):\\n return self._fsntfs_file_entry.is_allocated()\",\n \"def _check_truncation(self):\\n\\n temp_pos = self._handle.tell()\\n self._handle.seek(-28, 2)\\n eof = self._handle.read()\\n self._handle.seek(temp_pos)\\n if eof == _bgzf_eof:\\n return False\\n else:\\n warnings.BytesWarning('No EOF character found. File may be truncated')\\n return True\",\n \"def exists(self):\\r\\n return os.path.exists(self.full_path)\",\n \"def verify_results(outdir_path, original_array_path, R, O, file_format, addition, split_merge=False):\\n\\n if file_format == \\\"HDF5\\\":\\n file_manager = HDF5_manager()\\n else:\\n print(\\\"File format not supported yet. Aborting...\\\")\\n sys.exit(1)\\n\\n partition = get_blocks_shape(R, O)\\n orig_arr_data = file_manager.read_all(original_array_path)\\n all_true = True\\n\\n if split_merge:\\n result_arrpath = os.path.join(outdir_path, \\\"0_0_0.hdf5\\\")\\n return file_manager.check_split_merge(original_array_path, result_arrpath)\\n\\n for i in range(partition[0]):\\n for j in range(partition[1]):\\n for k in range(partition[2]):\\n outfilepath = os.path.join(outdir_path, str(i) + \\\"_\\\" + str(j) + \\\"_\\\" + str(k) + \\\".hdf5\\\")\\n data_stored = file_manager.read_all(outfilepath)\\n ground_truth = orig_arr_data[i*O[0]:(i+1)*O[0],j*O[1]:(j+1)*O[1],k*O[2]:(k+1)*O[2]]\\n \\n if addition:\\n ground_truth = ground_truth +1\\n\\n try:\\n assert np.allclose(data_stored, ground_truth, rtol=1e-02)\\n # print(f\\\"Good output file {outfilepath}\\\")\\n except:\\n print(f\\\"Error: bad rechunking {outfilepath}\\\")\\n print(f\\\"Slices from ground truth {i*O[0]}:{(i+1)*O[0]}, {j*O[1]}:{(j+1)*O[1]}, {k*O[2]}:{(k+1)*O[2]}\\\")\\n print(\\\"data_stored\\\", data_stored)\\n print(\\\"ground_truth\\\", ground_truth)\\n all_true = False # do not return here to see all failures\\n\\n file_manager.close_infiles() # close all files\\n return all_true\",\n \"def ofile_exists(self):\\n return os.path.isfile(self.ofile)\",\n \"def eof_check(self) -> bool:\\n eof = False\\n curr_pos = self.fileobject.tell()\\n # print(curr_pos, self.st_size)\\n chunk = self.fileobject.read(25)\\n if chunk == '':\\n # Is there something on the back burner??\\n if len(self._backburner) > 0:\\n self.fileobject = self._backburner.pop()\\n # TODO: what if it is the end of the back burner file? Is that handled?\\n else:\\n eof = True\\n else:\\n self.fileobject.seek(curr_pos)\\n return eof\",\n \"def is_partition(disk): #TODO: Could change to use \\\"Whole\\\" attrib. Good idea?\\n\\n return \\\"s\\\" in disk.split(\\\"disk\\\")[1]\",\n \"def check_coords_file(self):\\n if path.exists(self.coords_file):\\n return True\\n return False\",\n \"def has_file(self) -> bool:\\n return self._file is not None\",\n \"def _register_temporary_file(self):\\n _partition_file = self._subarray._partition_file\\n _partition_dir = self._subarray._partition_dir\\n if _partition_file not in _temporary_files:\\n fd, _lock_file = mkstemp(\\n prefix=_partition_file + \\\"_\\\", dir=_partition_dir\\n )\\n close(fd)\\n _temporary_files[_partition_file] = (\\n _partition_dir,\\n _lock_file,\\n set(),\\n )\\n else:\\n _, _lock_file, _ = _temporary_files[_partition_file]\\n\\n return _lock_file\",\n \"def check_already_extracted(video_parts):\\n filename_no_ext, _ = video_parts\\n return bool(os.path.exists(os.path.join(output_dir,\\n filename_no_ext + '-0030.jpg')))\",\n \"def found_empty_file(self):\\n self.is_empty = True\",\n \"def objectsReady(self, n):\\n return len(self.files) >= n\",\n \"def contains_files(self):\\n if self.file_list is None:\\n self._set_file_list()\\n for individual_file in self.file_list:\\n if not os.path.exists(os.path.join(self.base_dir, individual_file)):\\n return False\\n return True\",\n \"def is_writable_file(obj):\\n try:\\n obj.write(\\\"\\\")\\n except(AttributeError, OSError, IOError):\\n return False\\n else:\\n return True\",\n \"def has_measurement(self, filepath):\\n entries = self.get_measurement(file_path=filepath, select_list=[COL_NAME_FILES_MEASID])\\n return len(entries) == 1\",\n \"def has_contents(self):\\n return len(self.byteruns())>0\",\n \"def exists(self):\\n return self.path.is_file()\",\n \"def meshfn_is_used(meshfn):\\n if glob.glob(meshfn):\\n return True\\n else:\\n return False\",\n \"def path_contains_data(bucket, root_path, min_file_size=0, file_extension=None):\\n for key in bucket.list(root_path):\\n if file_extension and not key.name.endswith(file_extension):\\n continue\\n if key.size > min_file_size:\\n return True\\n\\n return False\",\n \"def test_get_subgrid_snapshots():\\n subgrid_snapshots = get_subgrid_snapshots(nTime=200)\\n\\n subgrid_snapshots_corr = np.load('./preprocessing/tests/test_data/\\\\\\nsubgrid_snapshots.npy')\\n\\n assert (subgrid_snapshots == subgrid_snapshots_corr).all()\",\n \"def exist(self):\\n return self.file_path.exists()\",\n \"def has_file(self, doc):\\n return len(doc.package.files) != 0\",\n \"def is_empty(self):\\n if self.file_exists:\\n with open_hdf5(self.file_name) as h:\\n return len(h.keys()) == 0\\n else:\\n return True\",\n \"def is_separate_file(self):\\n return self.uri is not None and not self.has_data_uri\",\n \"def check_helpers(self):\\n paths = self.get_helper_out_paths()\\n\\n for p in paths:\\n full_path = p + \\\".data-00000-of-00001\\\"\\n file = Path(full_path)\\n if not file.exists():\\n return False\\n\\n return True\",\n \"def checkFileInObject(self, obj, fp):\\n\\n containted = False\\n ## Haso of all files\\n filehash = self.getObjectFilesHash(obj)\\n\\n ## Local hash\\n BLOCKSIZE = 65536\\n hasher = hashlib.sha1()\\n\\n with fp.open('rb') as afile:\\n buf = afile.read(BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = afile.read(BLOCKSIZE)\\n localhash = hasher.hexdigest()\\n\\n if localhash.upper() in filehash:\\n containted = True\\n\\n return containted\",\n \"def check_needs_upload(self, path):\\n if self.upload_always:\\n return True\\n fn = '/'.join([self.hdfs_home, '.knitDeps', os.path.basename(path)])\\n if self.hdfs and self.hdfs.exists(fn):\\n st = os.stat(path)\\n size = st.st_size\\n t = st.st_mtime\\n info = self.hdfs.info(fn)\\n if info['size'] == size and t < info['last_mod']:\\n return False\\n else:\\n return True\\n else:\\n return True\",\n \"def check_enough_space(dataset_local_dir, remote_fname, local_fname,\\n max_disk_usage=0.9):\\n storage_need = os.path.getsize(remote_fname)\\n storage_total, storage_used = disk_usage(dataset_local_dir)\\n\\n # Instead of only looking if there's enough space, we ensure we do not\\n # go over max disk usage level to avoid filling the disk/partition\\n return ((storage_used + storage_need) <\\n (storage_total * max_disk_usage))\",\n \"def contains_offset(self, offset):\\n return (offset >= self.offset) and (offset < self.offset + self.filesize)\",\n \"def assert_data_fragments_correct(self) -> bool:\\n read_path = Path(os.environ[\\\"DATA_PATH\\\"]) / \\\"fragments\\\"\\n if not read_path.exists():\\n return False\\n bin_images = [img for img in read_path.iterdir() if \\\"binarized\\\" in img.name]\\n if len(bin_images) == 0:\\n return False\\n return True\",\n \"def is_subtag(tag_name, subtag_name, user_path, current_user) -> bool:\\n user = current_user[0]\\n subtag_list = os.listdir((user_path + '\\\\\\\\' + user + '\\\\\\\\' + tag_name).encode('unicode_escape'))\\n temp = list(map(bytes.decode, subtag_list))\\n\\n if subtag_name + '.txt' in temp:\\n return True\\n else:\\n return False\",\n \"def is_wave(self,path):\\n return len(path[0])>1\",\n \"def efile_exists(self):\\n return os.path.isfile(self.efile)\",\n \"def exist_partition(self, partition_spec):\\n return partition_spec in self.partitions\",\n \"def _has_processed_data(self):\\n return \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._processed_train_data_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._processed_dev_data_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._word_vocab_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._char_vocab_file_name))\",\n \"def check(self, evidence, path_on_disk):\\n return True\",\n \"def _supports_binary_writing(path):\\n return not path.startswith(\\\"/bigstore\\\")\",\n \"def _split_exists( file_list, target_locus ):\\n for filename in file_list:\\n basename = filename.split('.')[0]\\n parts = basename.split('_')\\n if parts[-1] in ['5p', '3p'] and parts[-2] == target_locus:\\n return True\\n return False\",\n \"def is_empty_file(fpath):\\n return \\\\\\n fpath is not None and \\\\\\n os.path.isfile(fpath) and \\\\\\n os.path.getsize(fpath) == 0\",\n \"def is_input_file(self):\\r\\n return self.depth == 0\",\n \"def _loaded_data(self):\\n try:\\n dsize = [int(d) for d\\n in self.run('fits size', via='get').split()]\\n except (ValueError, TypeError, AttributeError) as err:\\n log.debug(f' FITS size error: {err}')\\n return False\\n else:\\n if 0 in dsize:\\n return False\\n else:\\n return True\",\n \"def test_as_file_false(self):\\n with TemporaryDirectory() as tmp:\\n # define path to file\\n fp = os.path.join(tmp, \\\"asdf.txt\\\")\\n\\n # invoke atomic_write with param as_file set to False\\n # this should return a temporary file path string\\n with atomic_write(fp, as_file=False) as f:\\n self.assertIsInstance(f, str)\",\n \"def has_file(self, name):\\n return bool(self.input(name).__class__.__name__ == 'cgi_FieldStorage')\",\n \"def check_already_extracted(video_parts):\\n train_or_test, classname, filename_no_ext, _ = video_parts\\n return bool(os.path.exists(os.path.join(\\\"/data/niteshku001/Ravdess\\\", train_or_test, classname,\\n filename_no_ext + '-0001.jpg')))\",\n \"def f_supports_fast_access(self):\\n return not self.f_is_empty()\",\n \"def test_my_subset_files_exist():\\n assert isinstance(dist_a_subset, pd.DataFrame)\\n assert isinstance(dist_b_subset, pd.DataFrame)\\n assert isinstance(clone_df_subset, pd.DataFrame)\",\n \"def object_exists(self, fname):\\n return False\",\n \"def seekable(self):\\n # Not seekable, but we do support tell...\\n return False\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def cleanup_incomplete_uploads_from_blob_store() -> bool:\\n\\n DAYS_TO_RETAIN = 1\\n\\n # Get current time in UTC timezone\\n now = datetime.datetime.now(pytz.timezone(\\\"UTC\\\"))\\n\\n client = get_s3_client(settings=node.settings)\\n incomplete_upload_objs = client.list_multipart_uploads(Bucket=node.id.no_dash).get(\\n \\\"Uploads\\\", []\\n )\\n\\n for obj in incomplete_upload_objs:\\n # Get the upload id and object name\\n upload_id: str = obj[\\\"UploadId\\\"]\\n obj_name: str = obj[\\\"Key\\\"]\\n\\n # Get the list of all parts of the object uploaded\\n # This step is required to get the upload time of the object\\n object_parts: list = client.list_parts(\\n Bucket=node.id.no_dash, UploadId=upload_id, Key=obj_name\\n ).get(\\\"Parts\\\", [])\\n\\n obj_part_expired = False\\n for part in object_parts:\\n # Normalize upload time to UTC timezone\\n part_upload_time = pytz.timezone(\\\"UTC\\\").normalize(part[\\\"LastModified\\\"])\\n\\n # If upload time of any part of the object\\n # crosses DAYS_TO_RETAIN, then expire the whole object\\n if (now - part_upload_time).days > DAYS_TO_RETAIN:\\n obj_part_expired = True\\n break\\n\\n if obj_part_expired:\\n # Abort multipart upload\\n client.abort_multipart_upload(\\n UploadId=upload_id,\\n Key=obj_name,\\n Bucket=node.id.no_dash,\\n )\\n\\n return True\",\n \"def seekable(self):\\n return True\",\n \"def PartiallyEmpty(self):\\n return None==self.piecesToRecover\",\n \"def object_exists(self, fname):\\n return True\",\n \"def isSegmentFile(self, segment):\\n return os.path.isfile(\\\"{wd}/{jn}-run/{seg}.rst7\\\".format( wd=self.workdir, jn=self.jobname, seg=segment.getNameString()))\",\n \"def is_primary_file(self):\\n return self.file_type() == FileType.FILE_TYPE_PRIMARY\",\n \"def is_partition_the_last(dbapi, partition):\\n idisk_uuid = partition.get('idisk_uuid')\\n onidisk_parts = dbapi.partition_get_by_idisk(idisk_uuid)\\n part_number = get_part_number(partition.get('device_path'))\\n\\n if int(part_number) != len(onidisk_parts):\\n return False\\n\\n return True\",\n \"def hdf_exist_p(store, data_name):\\n if not data_name.startswith(\\\"/\\\"):\\n data_name = \\\"/\\\" + data_name\\n if os.path.isfile(store):\\n with HDFStore(store) as hdf:\\n return data_name in hdf.keys()\\n else:\\n return False\",\n \"def seekable(self):\\n self._check_not_closed()\\n return False\",\n \"def is_in_swestore(f):\\n with open(os.devnull, 'w') as null:\\n try:\\n check_call(['ils', f], stdout=null, stderr=null)\\n except CalledProcessError:\\n # ils will fail if the file does not exist in swestore\\n return False\\n else:\\n return True\",\n \"def isfile (self, path):\\r\\n pass\",\n \"def isfile(self):\\n return not self.isdir()\",\n \"def _index_file_exists(idx_fn):\\n if os.path.exists(idx_fn + \\\".npy\\\") and os.path.exists(idx_fn + \\\".info\\\"):\\n return True\\n else:\\n return False\",\n \"def check_valid_device(self, path, run_as_root=True):\\n sheepdog_handle = path\\n\\n if sheepdog_handle is None:\\n return False\\n\\n original_offset = sheepdog_handle.tell()\\n\\n try:\\n sheepdog_handle.read(4096)\\n except Exception as e:\\n LOG.error(\\\"Failed to access sheepdog device \\\"\\n \\\"handle: %(error)s\\\",\\n {\\\"error\\\": e})\\n return False\\n finally:\\n sheepdog_handle.seek(original_offset, 0)\\n\\n return True\",\n \"def checkTrueArrayIsDeleted(node):\\n if (node.raw) is not None or (node.raw_housing) is not None:\\n raise Exception(\\\"The true data array has not been deleted\\\")\",\n \"def _is_partitioned(self):\\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\\n partitions = self.table_config[\\\"partitions\\\"]\\n if partitions is None or len(partitions) == 0:\\n return False\\n\\n if isinstance(partitions, list):\\n # check if any None inside list.\\n # False if it is the case Ex: [None, 'partition']\\n # True otherwise Ex: ['partition1', 'partition2']\\n return all(item is not None for item in partitions)\\n\\n raise ValueError(\\\"Partitions must be a list or None\\\")\",\n \"def coeff_write_ok(self):\\n return os.access(self.coeffroot, os.W_OK)\",\n \"def is_file_exist(self):\\n return os.path.isfile(os.path.join(self.output_path, 'amr_corpus_ext.pickle'))\",\n \"def exists_in_path(self):\\n return os.path.isfile(self.IN_PATH)\",\n \"def test_record_is_dataset_file(self) -> None:\\n path = \\\"/home/user/dataset.record\\\"\\n result = is_dataset_file(path)\\n self.assertTrue(result)\",\n \"def exists(path: str) -> bool:\\n tdb_uri = paths.tiledb_uri_from_path(path)\\n try:\\n tiledb.cloud.array.info(tdb_uri)\\n return True\\n except tiledb.cloud.TileDBCloudError:\\n pass\\n return False\",\n \"def _test_obssize(t):\\n return t.shape[0] != len(t.ids(axis='observation'))\",\n \"def is_empty(self): # DirObj.is_empty\\n\\n for fileName, fileEntry in self.files.iteritems():\\n if not fileEntry.deleted and not fileEntry.ignore:\\n #print '# ' + self.pathname + ' is not empty due to a file ' + fileEntry.name\\n return False\\n\\n for dirName, subdir in self.subdirs.iteritems():\\n if not subdir.deleted and not subdir.is_empty() and not subdir.ignore:\\n #print '# ' + self.pathname + ' is not empty due to a dir ' + subdir.name\\n return False\\n\\n #print '# ' + self.pathname + ' is empty!'\\n return True\",\n \"def is_file(file_to_test):\\r\\n return all(hasattr(file_to_test, method) for method in ['read', 'name'])\",\n \"def is_signed(self):\\n file_size = os.stat(self._file_name).st_size\\n self._document.seek(file_size - self._append_size)\\n last = self._document.read()\\n self._document.seek(0)\\n\\n if not (chr(last[0]) == self._seperator and chr(last[-1]) == self._seperator):\\n return False\\n else:\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72301924","0.65045905","0.64364123","0.5942543","0.5936233","0.591641","0.5817689","0.5676702","0.5673538","0.5658594","0.5589181","0.5555981","0.5479751","0.5440639","0.5393117","0.53697217","0.5364029","0.5358785","0.5355252","0.53492653","0.53334475","0.5329815","0.53274125","0.5301783","0.5297761","0.52869755","0.5279954","0.5278576","0.5257029","0.524318","0.52423215","0.5241412","0.52393115","0.5205895","0.5195221","0.51760715","0.5174296","0.51727843","0.5172245","0.51699734","0.5169771","0.51676923","0.51647234","0.5161527","0.51583415","0.51561344","0.5154286","0.5152785","0.5142926","0.5141158","0.51403356","0.51370573","0.512613","0.5125592","0.5118623","0.5115187","0.5111936","0.5109239","0.5108766","0.5105998","0.5101779","0.5098058","0.5095226","0.50934654","0.5084821","0.50838816","0.50832033","0.5078422","0.50760955","0.50751746","0.5072461","0.5069113","0.50690866","0.506589","0.5064742","0.50637734","0.50543684","0.5052863","0.5049823","0.50463456","0.50451845","0.503784","0.50215477","0.50103503","0.5008163","0.49985757","0.49982268","0.49915206","0.49892285","0.49871543","0.49817294","0.49776906","0.49693653","0.49679446","0.49560532","0.49558458","0.4954451","0.49513745","0.4950765","0.49480093"],"string":"[\n \"0.72301924\",\n \"0.65045905\",\n \"0.64364123\",\n \"0.5942543\",\n \"0.5936233\",\n \"0.591641\",\n \"0.5817689\",\n \"0.5676702\",\n \"0.5673538\",\n \"0.5658594\",\n \"0.5589181\",\n \"0.5555981\",\n \"0.5479751\",\n \"0.5440639\",\n \"0.5393117\",\n \"0.53697217\",\n \"0.5364029\",\n \"0.5358785\",\n \"0.5355252\",\n \"0.53492653\",\n \"0.53334475\",\n \"0.5329815\",\n \"0.53274125\",\n \"0.5301783\",\n \"0.5297761\",\n \"0.52869755\",\n \"0.5279954\",\n \"0.5278576\",\n \"0.5257029\",\n \"0.524318\",\n \"0.52423215\",\n \"0.5241412\",\n \"0.52393115\",\n \"0.5205895\",\n \"0.5195221\",\n \"0.51760715\",\n \"0.5174296\",\n \"0.51727843\",\n \"0.5172245\",\n \"0.51699734\",\n \"0.5169771\",\n \"0.51676923\",\n \"0.51647234\",\n \"0.5161527\",\n \"0.51583415\",\n \"0.51561344\",\n \"0.5154286\",\n \"0.5152785\",\n \"0.5142926\",\n \"0.5141158\",\n \"0.51403356\",\n \"0.51370573\",\n \"0.512613\",\n \"0.5125592\",\n \"0.5118623\",\n \"0.5115187\",\n \"0.5111936\",\n \"0.5109239\",\n \"0.5108766\",\n \"0.5105998\",\n \"0.5101779\",\n \"0.5098058\",\n \"0.5095226\",\n \"0.50934654\",\n \"0.5084821\",\n \"0.50838816\",\n \"0.50832033\",\n \"0.5078422\",\n \"0.50760955\",\n \"0.50751746\",\n \"0.5072461\",\n \"0.5069113\",\n \"0.50690866\",\n \"0.506589\",\n \"0.5064742\",\n \"0.50637734\",\n \"0.50543684\",\n \"0.5052863\",\n \"0.5049823\",\n \"0.50463456\",\n \"0.50451845\",\n \"0.503784\",\n \"0.50215477\",\n \"0.50103503\",\n \"0.5008163\",\n \"0.49985757\",\n \"0.49982268\",\n \"0.49915206\",\n \"0.49892285\",\n \"0.49871543\",\n \"0.49817294\",\n \"0.49776906\",\n \"0.49693653\",\n \"0.49679446\",\n \"0.49560532\",\n \"0.49558458\",\n \"0.4954451\",\n \"0.49513745\",\n \"0.4950765\",\n \"0.49480093\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58127344"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":2681,"cells":{"query":{"kind":"string","value":"True if and only if the partition's subarray is on disk as opposed to in memory."},"document":{"kind":"string","value":"def on_disk(self):\n return isinstance(self._subarray, FileArray)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")","def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def is_partition(disk): #TODO: Could change to use \"Whole\" attrib. Good idea?\n\n return \"s\" in disk.split(\"disk\")[1]","def in_cached_file(self):\n return isinstance(self._subarray, CachedArray)","def has_subfile(self) -> bool:\n\t\tself._update_subfiles()\n\t\treturn bool(len(self.subfiles))","def _is_partitioned(self):\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\n partitions = self.table_config[\"partitions\"]\n if partitions is None or len(partitions) == 0:\n return False\n\n if isinstance(partitions, list):\n # check if any None inside list.\n # False if it is the case Ex: [None, 'partition']\n # True otherwise Ex: ['partition1', 'partition2']\n return all(item is not None for item in partitions)\n\n raise ValueError(\"Partitions must be a list or None\")","def IsAllocated(self):\n return self._fsntfs_file_entry.is_allocated()","def isleaf(self):\n no_kids = super(PartitionDevice, self).isleaf\n # it is possible that the disk that originally contained this partition\n # no longer contains a disklabel, in which case we can assume that this\n # device is a leaf\n if self.disk and self.partedPartition and \\\n self.disk.format.type == \"disklabel\" and \\\n self.partedPartition in self.disk.format.partitions:\n disklabel = self.disk.format\n else:\n disklabel = None\n\n extended_has_logical = (self.isExtended and\n (disklabel and disklabel.logicalPartitions))\n return (no_kids and not extended_has_logical)","def is_array(self):\n return len(self.descriptor) > 1","def is_space_available(partition, size):\n available_space = psutil.disk_usage(partition).free\n return False if available_space < size else True","def is_full(self) -> bool:\n return self._array[0].all()","def in_file(self):\n return self.on_disk and not self.in_cached_file","def is_partition_the_last(dbapi, partition):\n idisk_uuid = partition.get('idisk_uuid')\n onidisk_parts = dbapi.partition_get_by_idisk(idisk_uuid)\n part_number = get_part_number(partition.get('device_path'))\n\n if int(part_number) != len(onidisk_parts):\n return False\n\n return True","def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\n file_path = join(output_path, subset + '.npy')\n return os.path.exists(file_path)","def _is_size_bound(self, path):\n return path.suffix == \".bin\"","def _array_name_implies_ND_slice(self, array_name):\n for v in self._split_arrays.values():\n if array_name in v:\n return True\n\n generic_match = re.findall(\"^(.+)_[xyz]$\", array_name)\n loadable_keys = self.loadable_keys()\n keys = list(self.keys())\n if len(generic_match) == 1 and generic_match[0] not in self._split_arrays:\n return generic_match[0] in loadable_keys or generic_match[0] in keys\n return False","def has_shareable_memory(a):\r\n return _get_backing_memmap(a) is not None","def is_partition(dev):\n dev = os.path.realpath(dev)\n if not stat.S_ISBLK(os.lstat(dev).st_mode):\n raise Error('not a block device', dev)\n\n name = get_dev_name(dev)\n if os.path.exists(os.path.join('/sys/block', name)):\n return False\n\n # make sure it is a partition of something else\n for basename in os.listdir('/sys/block'):\n if os.path.exists(os.path.join('/sys/block', basename, name)):\n return True\n\n raise Error('not a disk or partition', dev)","def has_full_batch(self) -> bool:","def is_part_of_disk(part_device_path, disk_device_path):\n is_part_of_disk = False\n\n if disk_device_path in part_device_path:\n is_part_of_disk = True\n elif constants.DEVICE_NAME_MPATH in disk_device_path:\n path_split = disk_device_path.split(constants.DEVICE_NAME_MPATH)\n if (path_split[0] in part_device_path and\n path_split[1] in part_device_path):\n is_part_of_disk = True\n\n return is_part_of_disk","def has_next(self):\n while self._row < self._n and not self._arr[self._row]: # current sub-array is empty\n self._row += 1 # move to next sub-array\n self._col = 0\n if self._row >= self._n: # end of master-array already\n return False\n return True","def exist_partition(self, partition_spec):\n return partition_spec in self.partitions","def isFull(self) -> bool:\n return self._elems == self._k","def _loaded_data(self):\n try:\n dsize = [int(d) for d\n in self.run('fits size', via='get').split()]\n except (ValueError, TypeError, AttributeError) as err:\n log.debug(f' FITS size error: {err}')\n return False\n else:\n if 0 in dsize:\n return False\n else:\n return True","def __check_flat_array__(self):\n if self.flat_array is not None:\n return True\n else:\n return False","def is_slice(self) -> bool:\n return self._is_slice","def contains_offset(self, offset):\n return (offset >= self.offset) and (offset < self.offset + self.filesize)","def is_mounted(device):\n\n partitions = psutil.disk_partitions()\n device_path = \"/dev/\" + device\n for i in partitions:\n if i.device == device_path:\n return True\n return False","def full(self):\n return self.size >= self.maxsize","def in_folder(self):\n return len(os.path.split(self.file_path)) > 1","def full(self):\n return self._current_size == self._size","def is_full(self) -> bool:\r\n return self.size == self.capacity","def is_full(self):\r\n if self.size == self.capacity:\r\n return True\r\n return False","def is_complete(self) -> bool:\n return (\n (\n self.materialized_subset | self.failed_and_downstream_subset\n ).num_partitions_and_non_partitioned_assets\n == self.target_subset.num_partitions_and_non_partitioned_assets\n )","def is_full(self):\n elements_in_sects = sum(\n map(opr.attrgetter(\"size\"), self.sects.values())\n )\n elements_in_total = fct.reduce(\n opr.mul, type(self).flatten_shape(self.shape), 1\n )\n res = elements_in_sects >= elements_in_total\n return res","def isFull(self):\n\t\treturn self.size == self.capacity","def is_full(self):\n\n return self.count == len(self.array)","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def is_array(self):\n return False","def is_full(self):\n return self.idx == self.len","def f_supports_fast_access(self):\n return len(self._data) == 1 and self.v_name in self._data","def is_distributed(self) -> bool:\n return self.size > 1","def isFull(self):\n return self.rear == self.size","def is_subdivision_available(self, position: np.ndarray) -> bool:\n\t\tsubdivision_x_index = int(position[0]) // self.square_subdivision_length\n\t\tsubdivision_y_index = int(position[1]) // self.square_subdivision_length\n\t\treturn self.plane_subdivisions_availability[subdivision_x_index, subdivision_y_index] == 1","def is_subset(subset: np.array, superset: np.array) -> bool:\n superset_lookup = set(superset)\n for val in subset:\n if val not in superset_lookup:\n return False\n\n return True","def is_array(a):\n try:\n shape = a.shape\n return len(shape) >= 1\n except AttributeError:\n return False","def is_array(self, arr):\n return isinstance(arr, np.ndarray)","def checkTrueArrayIsDeleted(node):\n if (node.raw) is not None or (node.raw_housing) is not None:\n raise Exception(\"The true data array has not been deleted\")","def is_full(self):\n return len(self._data) == 1","def isFull(self):\n if len(self.batch) == self.__batch_size:\n return True\n return False","def is_enough_space(self) -> bool:\n return self._free_space() > self.minimum_disk","def pageable(self):\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN","def is_full(self) -> bool:","def f_supports_fast_access(self):\n return not self.f_is_empty()","def is_virtualized (self):\n return len([i for i in self.infras if\n i.infra_type not in (self.TYPE_INFRA_SDN_SW, self.TYPE_INFRA_EE,\n self.TYPE_INFRA_STATIC_EE)]) > 0","def is_full(self):\n if len(self._page_map) >= self.memory_size:\n return True\n return False","def is_leaf(self, pos):\n if pos >= (self.size//2) and pos <= self.size: \n return True\n return False","def regular(self):\n if all(self._volumes - self._volumes[0] == 0):\n return True\n else:\n return False","def storage_can_read(self):\n return True","def full(self) -> bool:\n return self.maxsize and self.qsize() >= self.maxsize","def is_bad_partition(par):\n return 'Letter' not in par or REGEX_BAD_PARTITION.search(par['FileSystem'])","def isFull(self):\n if len(self._data) == self._length:\n return True\n else:\n return False","def has_definite_size(iterable):\n return hasattr(iterable, '__len__')","def _supports_binary_writing(path):\n return not path.startswith(\"/bigstore\")","def _has_processed_data(self):\n return \\\n os.path.exists(\n os.path.join(self._data_root_path, self._processed_train_data_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._processed_dev_data_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._word_vocab_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._char_vocab_file_name))","def check_subarray(array1, array2):\r\n \r\n # check assumption\r\n if (len(array2.shape) != 1) or (array2.shape[0] != array1.shape[-1]):\r\n raise ValueError('Attempting to check for subarray equality when shape assumption does not hold.')\r\n \r\n return np.all(array1==array2, axis=-1)","def prune_empty(self): # FileObj.prune_empty\n return False # can't prune a file","def is_fragmentable(fragment_size, offset, chunk_size):\n return ((chunk_size - fragment_size) / offset) % 1 == 0","def NeedsArray(self, type_):\n return self._NameComponents(type_) in self._array_types","def disk_is_valid(dhandle):\n if is_64bits:\n return dhandle.value != c_uint64(0).value\n else:\n return dhandle.value != c_uint32(0).value","def full(self) -> bool:\n return self.current_offset == self.max_offset","def isscalar(array):\n arr = ma.array(array)\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\n return True\n return False","def is_full(self):\n return self.heap_size >= self.capacity","def is_dataset(self):\n return self._dataset is not None","def data_available(dataset_name=None):\r\n for file_list in data_resources[dataset_name]['files']:\r\n for file in file_list:\r\n if not os.path.exists(os.path.join(data_path, dataset_name, file)):\r\n return False\r\n return True","def is_internal(self):\n if self.is_leaf() or self.is_semileaf():\n return False\n return True","def __bool__(self):\n for root, products in self.rel_paths():\n if products:\n return True\n return False","def PartiallyEmpty(self):\n return None==self.piecesToRecover","def path_contains_data(bucket, root_path, min_file_size=0, file_extension=None):\n for key in bucket.list(root_path):\n if file_extension and not key.name.endswith(file_extension):\n continue\n if key.size > min_file_size:\n return True\n\n return False","def isFull(self) -> bool:\n return self.size == self.maxlen","def has_contents(self):\n return len(self.byteruns())>0","def is_empty(self):\n return self.size == []","def fs_ok(fs_info):\n if fs_info.mountpoint == '/':\n return True\n\n if (fs_info.device == fs_info.fstype or fs_info.fstype == 'nullfs' or\n '/docker' in fs_info.mountpoint or\n fs_info.mountpoint.startswith('/etc') or\n fs_info.mountpoint.startswith('/lib/modules')):\n return False\n\n if fs_info.device.startswith('/dev/'):\n return True\n\n return False","def _test_obssize(t):\n return t.shape[0] != len(t.ids(axis='observation'))","def is_total_slice(item, shape: Tuple[int]) -> bool:\n\n # N.B., assume shape is normalized\n\n if item == Ellipsis:\n return True\n if item == slice(None):\n return True\n if isinstance(item, slice):\n item = (item,)\n if isinstance(item, tuple):\n return all(\n (\n isinstance(it, slice)\n and ((it == slice(None)) or ((it.stop - it.start == sh) and (it.step in [1, None])))\n )\n for it, sh in zip(item, shape)\n )\n else:\n raise TypeError(\"expected slice or tuple of slices, found %r\" % item)","def isFull(T):\r\n return len(T.data) >= T.max_data","def is_array(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_array)","def has_leaf(self) -> bool:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"has_leaf\"))\r\n return self._hvac_mode == \"eco\"","def full(self):\r\n if self._maxsize <= 0:\r\n return False\r\n else:\r\n return self.qsize() >= self._maxsize","def storage_available(self):\n logger.debug('Function storage_available start')\n \n # 2.9 GB\n max_size = 2.9*10**9\n \n if self.total_image_data_size >= max_size:\n logger.info(\"Storage not available\")\n return False\n else:\n logger.info(\"Storage available\")\n return True\n\n logger.debug('Function storage_available end')","def __contains__(self, x):\n indexes = self.get_indexes(x)\n return self.sketch[indexes] > 0","def has_catalog_data (ff_hdus_list, which_hdu=1):\n if (which_hdu == 0): # not allowed? FITS 4.0: 3.3.2\n return False\n else: # it's an extension and so marked\n return ( (len(ff_hdus_list) > which_hdu) and\n (ff_hdus_list[which_hdu].header.get('XTENSION') in ['BINTABLE', 'TABLE']) )","def is_leaf(self):\n return self.pixel_count > 0","def utilize_ephemeral_storage(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"utilize_ephemeral_storage\")","def _is_leaf(self, index):\r\n return 2*index+1 > self._size - 1","def full(self) -> bool:\n\n return (self._size == self._capacity)","def _is_full(self):\n if self.allocated_spaces == self.capacity:\n return True\n elif self.allocated_spaces < self.capacity:\n return False","def _is_empty(self):\n return self.size == 0","def iseod(self):\n\n return self.byte_ptr >= len(self.data)"],"string":"[\n \"def in_memory(self):\\n return hasattr(self._subarray, \\\"__array_interface__\\\")\",\n \"def in_memory(self) -> bool:\\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def is_partition(disk): #TODO: Could change to use \\\"Whole\\\" attrib. Good idea?\\n\\n return \\\"s\\\" in disk.split(\\\"disk\\\")[1]\",\n \"def in_cached_file(self):\\n return isinstance(self._subarray, CachedArray)\",\n \"def has_subfile(self) -> bool:\\n\\t\\tself._update_subfiles()\\n\\t\\treturn bool(len(self.subfiles))\",\n \"def _is_partitioned(self):\\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\\n partitions = self.table_config[\\\"partitions\\\"]\\n if partitions is None or len(partitions) == 0:\\n return False\\n\\n if isinstance(partitions, list):\\n # check if any None inside list.\\n # False if it is the case Ex: [None, 'partition']\\n # True otherwise Ex: ['partition1', 'partition2']\\n return all(item is not None for item in partitions)\\n\\n raise ValueError(\\\"Partitions must be a list or None\\\")\",\n \"def IsAllocated(self):\\n return self._fsntfs_file_entry.is_allocated()\",\n \"def isleaf(self):\\n no_kids = super(PartitionDevice, self).isleaf\\n # it is possible that the disk that originally contained this partition\\n # no longer contains a disklabel, in which case we can assume that this\\n # device is a leaf\\n if self.disk and self.partedPartition and \\\\\\n self.disk.format.type == \\\"disklabel\\\" and \\\\\\n self.partedPartition in self.disk.format.partitions:\\n disklabel = self.disk.format\\n else:\\n disklabel = None\\n\\n extended_has_logical = (self.isExtended and\\n (disklabel and disklabel.logicalPartitions))\\n return (no_kids and not extended_has_logical)\",\n \"def is_array(self):\\n return len(self.descriptor) > 1\",\n \"def is_space_available(partition, size):\\n available_space = psutil.disk_usage(partition).free\\n return False if available_space < size else True\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def in_file(self):\\n return self.on_disk and not self.in_cached_file\",\n \"def is_partition_the_last(dbapi, partition):\\n idisk_uuid = partition.get('idisk_uuid')\\n onidisk_parts = dbapi.partition_get_by_idisk(idisk_uuid)\\n part_number = get_part_number(partition.get('device_path'))\\n\\n if int(part_number) != len(onidisk_parts):\\n return False\\n\\n return True\",\n \"def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\\n file_path = join(output_path, subset + '.npy')\\n return os.path.exists(file_path)\",\n \"def _is_size_bound(self, path):\\n return path.suffix == \\\".bin\\\"\",\n \"def _array_name_implies_ND_slice(self, array_name):\\n for v in self._split_arrays.values():\\n if array_name in v:\\n return True\\n\\n generic_match = re.findall(\\\"^(.+)_[xyz]$\\\", array_name)\\n loadable_keys = self.loadable_keys()\\n keys = list(self.keys())\\n if len(generic_match) == 1 and generic_match[0] not in self._split_arrays:\\n return generic_match[0] in loadable_keys or generic_match[0] in keys\\n return False\",\n \"def has_shareable_memory(a):\\r\\n return _get_backing_memmap(a) is not None\",\n \"def is_partition(dev):\\n dev = os.path.realpath(dev)\\n if not stat.S_ISBLK(os.lstat(dev).st_mode):\\n raise Error('not a block device', dev)\\n\\n name = get_dev_name(dev)\\n if os.path.exists(os.path.join('/sys/block', name)):\\n return False\\n\\n # make sure it is a partition of something else\\n for basename in os.listdir('/sys/block'):\\n if os.path.exists(os.path.join('/sys/block', basename, name)):\\n return True\\n\\n raise Error('not a disk or partition', dev)\",\n \"def has_full_batch(self) -> bool:\",\n \"def is_part_of_disk(part_device_path, disk_device_path):\\n is_part_of_disk = False\\n\\n if disk_device_path in part_device_path:\\n is_part_of_disk = True\\n elif constants.DEVICE_NAME_MPATH in disk_device_path:\\n path_split = disk_device_path.split(constants.DEVICE_NAME_MPATH)\\n if (path_split[0] in part_device_path and\\n path_split[1] in part_device_path):\\n is_part_of_disk = True\\n\\n return is_part_of_disk\",\n \"def has_next(self):\\n while self._row < self._n and not self._arr[self._row]: # current sub-array is empty\\n self._row += 1 # move to next sub-array\\n self._col = 0\\n if self._row >= self._n: # end of master-array already\\n return False\\n return True\",\n \"def exist_partition(self, partition_spec):\\n return partition_spec in self.partitions\",\n \"def isFull(self) -> bool:\\n return self._elems == self._k\",\n \"def _loaded_data(self):\\n try:\\n dsize = [int(d) for d\\n in self.run('fits size', via='get').split()]\\n except (ValueError, TypeError, AttributeError) as err:\\n log.debug(f' FITS size error: {err}')\\n return False\\n else:\\n if 0 in dsize:\\n return False\\n else:\\n return True\",\n \"def __check_flat_array__(self):\\n if self.flat_array is not None:\\n return True\\n else:\\n return False\",\n \"def is_slice(self) -> bool:\\n return self._is_slice\",\n \"def contains_offset(self, offset):\\n return (offset >= self.offset) and (offset < self.offset + self.filesize)\",\n \"def is_mounted(device):\\n\\n partitions = psutil.disk_partitions()\\n device_path = \\\"/dev/\\\" + device\\n for i in partitions:\\n if i.device == device_path:\\n return True\\n return False\",\n \"def full(self):\\n return self.size >= self.maxsize\",\n \"def in_folder(self):\\n return len(os.path.split(self.file_path)) > 1\",\n \"def full(self):\\n return self._current_size == self._size\",\n \"def is_full(self) -> bool:\\r\\n return self.size == self.capacity\",\n \"def is_full(self):\\r\\n if self.size == self.capacity:\\r\\n return True\\r\\n return False\",\n \"def is_complete(self) -> bool:\\n return (\\n (\\n self.materialized_subset | self.failed_and_downstream_subset\\n ).num_partitions_and_non_partitioned_assets\\n == self.target_subset.num_partitions_and_non_partitioned_assets\\n )\",\n \"def is_full(self):\\n elements_in_sects = sum(\\n map(opr.attrgetter(\\\"size\\\"), self.sects.values())\\n )\\n elements_in_total = fct.reduce(\\n opr.mul, type(self).flatten_shape(self.shape), 1\\n )\\n res = elements_in_sects >= elements_in_total\\n return res\",\n \"def isFull(self):\\n\\t\\treturn self.size == self.capacity\",\n \"def is_full(self):\\n\\n return self.count == len(self.array)\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def is_array(self):\\n return False\",\n \"def is_full(self):\\n return self.idx == self.len\",\n \"def f_supports_fast_access(self):\\n return len(self._data) == 1 and self.v_name in self._data\",\n \"def is_distributed(self) -> bool:\\n return self.size > 1\",\n \"def isFull(self):\\n return self.rear == self.size\",\n \"def is_subdivision_available(self, position: np.ndarray) -> bool:\\n\\t\\tsubdivision_x_index = int(position[0]) // self.square_subdivision_length\\n\\t\\tsubdivision_y_index = int(position[1]) // self.square_subdivision_length\\n\\t\\treturn self.plane_subdivisions_availability[subdivision_x_index, subdivision_y_index] == 1\",\n \"def is_subset(subset: np.array, superset: np.array) -> bool:\\n superset_lookup = set(superset)\\n for val in subset:\\n if val not in superset_lookup:\\n return False\\n\\n return True\",\n \"def is_array(a):\\n try:\\n shape = a.shape\\n return len(shape) >= 1\\n except AttributeError:\\n return False\",\n \"def is_array(self, arr):\\n return isinstance(arr, np.ndarray)\",\n \"def checkTrueArrayIsDeleted(node):\\n if (node.raw) is not None or (node.raw_housing) is not None:\\n raise Exception(\\\"The true data array has not been deleted\\\")\",\n \"def is_full(self):\\n return len(self._data) == 1\",\n \"def isFull(self):\\n if len(self.batch) == self.__batch_size:\\n return True\\n return False\",\n \"def is_enough_space(self) -> bool:\\n return self._free_space() > self.minimum_disk\",\n \"def pageable(self):\\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN\",\n \"def is_full(self) -> bool:\",\n \"def f_supports_fast_access(self):\\n return not self.f_is_empty()\",\n \"def is_virtualized (self):\\n return len([i for i in self.infras if\\n i.infra_type not in (self.TYPE_INFRA_SDN_SW, self.TYPE_INFRA_EE,\\n self.TYPE_INFRA_STATIC_EE)]) > 0\",\n \"def is_full(self):\\n if len(self._page_map) >= self.memory_size:\\n return True\\n return False\",\n \"def is_leaf(self, pos):\\n if pos >= (self.size//2) and pos <= self.size: \\n return True\\n return False\",\n \"def regular(self):\\n if all(self._volumes - self._volumes[0] == 0):\\n return True\\n else:\\n return False\",\n \"def storage_can_read(self):\\n return True\",\n \"def full(self) -> bool:\\n return self.maxsize and self.qsize() >= self.maxsize\",\n \"def is_bad_partition(par):\\n return 'Letter' not in par or REGEX_BAD_PARTITION.search(par['FileSystem'])\",\n \"def isFull(self):\\n if len(self._data) == self._length:\\n return True\\n else:\\n return False\",\n \"def has_definite_size(iterable):\\n return hasattr(iterable, '__len__')\",\n \"def _supports_binary_writing(path):\\n return not path.startswith(\\\"/bigstore\\\")\",\n \"def _has_processed_data(self):\\n return \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._processed_train_data_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._processed_dev_data_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._word_vocab_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._char_vocab_file_name))\",\n \"def check_subarray(array1, array2):\\r\\n \\r\\n # check assumption\\r\\n if (len(array2.shape) != 1) or (array2.shape[0] != array1.shape[-1]):\\r\\n raise ValueError('Attempting to check for subarray equality when shape assumption does not hold.')\\r\\n \\r\\n return np.all(array1==array2, axis=-1)\",\n \"def prune_empty(self): # FileObj.prune_empty\\n return False # can't prune a file\",\n \"def is_fragmentable(fragment_size, offset, chunk_size):\\n return ((chunk_size - fragment_size) / offset) % 1 == 0\",\n \"def NeedsArray(self, type_):\\n return self._NameComponents(type_) in self._array_types\",\n \"def disk_is_valid(dhandle):\\n if is_64bits:\\n return dhandle.value != c_uint64(0).value\\n else:\\n return dhandle.value != c_uint32(0).value\",\n \"def full(self) -> bool:\\n return self.current_offset == self.max_offset\",\n \"def isscalar(array):\\n arr = ma.array(array)\\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\\n return True\\n return False\",\n \"def is_full(self):\\n return self.heap_size >= self.capacity\",\n \"def is_dataset(self):\\n return self._dataset is not None\",\n \"def data_available(dataset_name=None):\\r\\n for file_list in data_resources[dataset_name]['files']:\\r\\n for file in file_list:\\r\\n if not os.path.exists(os.path.join(data_path, dataset_name, file)):\\r\\n return False\\r\\n return True\",\n \"def is_internal(self):\\n if self.is_leaf() or self.is_semileaf():\\n return False\\n return True\",\n \"def __bool__(self):\\n for root, products in self.rel_paths():\\n if products:\\n return True\\n return False\",\n \"def PartiallyEmpty(self):\\n return None==self.piecesToRecover\",\n \"def path_contains_data(bucket, root_path, min_file_size=0, file_extension=None):\\n for key in bucket.list(root_path):\\n if file_extension and not key.name.endswith(file_extension):\\n continue\\n if key.size > min_file_size:\\n return True\\n\\n return False\",\n \"def isFull(self) -> bool:\\n return self.size == self.maxlen\",\n \"def has_contents(self):\\n return len(self.byteruns())>0\",\n \"def is_empty(self):\\n return self.size == []\",\n \"def fs_ok(fs_info):\\n if fs_info.mountpoint == '/':\\n return True\\n\\n if (fs_info.device == fs_info.fstype or fs_info.fstype == 'nullfs' or\\n '/docker' in fs_info.mountpoint or\\n fs_info.mountpoint.startswith('/etc') or\\n fs_info.mountpoint.startswith('/lib/modules')):\\n return False\\n\\n if fs_info.device.startswith('/dev/'):\\n return True\\n\\n return False\",\n \"def _test_obssize(t):\\n return t.shape[0] != len(t.ids(axis='observation'))\",\n \"def is_total_slice(item, shape: Tuple[int]) -> bool:\\n\\n # N.B., assume shape is normalized\\n\\n if item == Ellipsis:\\n return True\\n if item == slice(None):\\n return True\\n if isinstance(item, slice):\\n item = (item,)\\n if isinstance(item, tuple):\\n return all(\\n (\\n isinstance(it, slice)\\n and ((it == slice(None)) or ((it.stop - it.start == sh) and (it.step in [1, None])))\\n )\\n for it, sh in zip(item, shape)\\n )\\n else:\\n raise TypeError(\\\"expected slice or tuple of slices, found %r\\\" % item)\",\n \"def isFull(T):\\r\\n return len(T.data) >= T.max_data\",\n \"def is_array(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_array)\",\n \"def has_leaf(self) -> bool:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"has_leaf\\\"))\\r\\n return self._hvac_mode == \\\"eco\\\"\",\n \"def full(self):\\r\\n if self._maxsize <= 0:\\r\\n return False\\r\\n else:\\r\\n return self.qsize() >= self._maxsize\",\n \"def storage_available(self):\\n logger.debug('Function storage_available start')\\n \\n # 2.9 GB\\n max_size = 2.9*10**9\\n \\n if self.total_image_data_size >= max_size:\\n logger.info(\\\"Storage not available\\\")\\n return False\\n else:\\n logger.info(\\\"Storage available\\\")\\n return True\\n\\n logger.debug('Function storage_available end')\",\n \"def __contains__(self, x):\\n indexes = self.get_indexes(x)\\n return self.sketch[indexes] > 0\",\n \"def has_catalog_data (ff_hdus_list, which_hdu=1):\\n if (which_hdu == 0): # not allowed? FITS 4.0: 3.3.2\\n return False\\n else: # it's an extension and so marked\\n return ( (len(ff_hdus_list) > which_hdu) and\\n (ff_hdus_list[which_hdu].header.get('XTENSION') in ['BINTABLE', 'TABLE']) )\",\n \"def is_leaf(self):\\n return self.pixel_count > 0\",\n \"def utilize_ephemeral_storage(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"utilize_ephemeral_storage\\\")\",\n \"def _is_leaf(self, index):\\r\\n return 2*index+1 > self._size - 1\",\n \"def full(self) -> bool:\\n\\n return (self._size == self._capacity)\",\n \"def _is_full(self):\\n if self.allocated_spaces == self.capacity:\\n return True\\n elif self.allocated_spaces < self.capacity:\\n return False\",\n \"def _is_empty(self):\\n return self.size == 0\",\n \"def iseod(self):\\n\\n return self.byte_ptr >= len(self.data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69458973","0.67629313","0.6696292","0.656141","0.63517463","0.6290223","0.61257756","0.59961635","0.59486187","0.59276515","0.59175736","0.5881189","0.5803704","0.577553","0.5774722","0.5755425","0.5737828","0.57311875","0.5723483","0.5720749","0.5682879","0.5678204","0.56532705","0.5638273","0.56376","0.5626054","0.5618182","0.5615703","0.56024516","0.5587035","0.5584256","0.557709","0.5576315","0.55673695","0.5561574","0.5530081","0.5504011","0.5486583","0.54836535","0.5483039","0.54767233","0.5468246","0.5459933","0.545245","0.54522634","0.54515356","0.54295284","0.54217386","0.5419535","0.54073864","0.5398171","0.53974444","0.53786916","0.53773993","0.53724277","0.5371112","0.5369092","0.53521395","0.5340982","0.5307776","0.5302411","0.5292718","0.528551","0.5282625","0.52814937","0.52775794","0.52765363","0.5272008","0.5271376","0.52550143","0.5254123","0.52478075","0.52476114","0.5246837","0.5246544","0.5245442","0.52408814","0.5235635","0.5234526","0.52259785","0.52233267","0.5223169","0.52195394","0.521708","0.5217009","0.52162397","0.52134025","0.5211867","0.5211654","0.5211134","0.52094615","0.52087545","0.52043337","0.5201568","0.51984894","0.51961607","0.5194446","0.5194344","0.51878417","0.51835746"],"string":"[\n \"0.69458973\",\n \"0.67629313\",\n \"0.6696292\",\n \"0.656141\",\n \"0.63517463\",\n \"0.6290223\",\n \"0.61257756\",\n \"0.59961635\",\n \"0.59486187\",\n \"0.59276515\",\n \"0.59175736\",\n \"0.5881189\",\n \"0.5803704\",\n \"0.577553\",\n \"0.5774722\",\n \"0.5755425\",\n \"0.5737828\",\n \"0.57311875\",\n \"0.5723483\",\n \"0.5720749\",\n \"0.5682879\",\n \"0.5678204\",\n \"0.56532705\",\n \"0.5638273\",\n \"0.56376\",\n \"0.5626054\",\n \"0.5618182\",\n \"0.5615703\",\n \"0.56024516\",\n \"0.5587035\",\n \"0.5584256\",\n \"0.557709\",\n \"0.5576315\",\n \"0.55673695\",\n \"0.5561574\",\n \"0.5530081\",\n \"0.5504011\",\n \"0.5486583\",\n \"0.54836535\",\n \"0.5483039\",\n \"0.54767233\",\n \"0.5468246\",\n \"0.5459933\",\n \"0.545245\",\n \"0.54522634\",\n \"0.54515356\",\n \"0.54295284\",\n \"0.54217386\",\n \"0.5419535\",\n \"0.54073864\",\n \"0.5398171\",\n \"0.53974444\",\n \"0.53786916\",\n \"0.53773993\",\n \"0.53724277\",\n \"0.5371112\",\n \"0.5369092\",\n \"0.53521395\",\n \"0.5340982\",\n \"0.5307776\",\n \"0.5302411\",\n \"0.5292718\",\n \"0.528551\",\n \"0.5282625\",\n \"0.52814937\",\n \"0.52775794\",\n \"0.52765363\",\n \"0.5272008\",\n \"0.5271376\",\n \"0.52550143\",\n \"0.5254123\",\n \"0.52478075\",\n \"0.52476114\",\n \"0.5246837\",\n \"0.5246544\",\n \"0.5245442\",\n \"0.52408814\",\n \"0.5235635\",\n \"0.5234526\",\n \"0.52259785\",\n \"0.52233267\",\n \"0.5223169\",\n \"0.52195394\",\n \"0.521708\",\n \"0.5217009\",\n \"0.52162397\",\n \"0.52134025\",\n \"0.5211867\",\n \"0.5211654\",\n \"0.5211134\",\n \"0.52094615\",\n \"0.52087545\",\n \"0.52043337\",\n \"0.5201568\",\n \"0.51984894\",\n \"0.51961607\",\n \"0.5194446\",\n \"0.5194344\",\n \"0.51878417\",\n \"0.51835746\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80375713"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2682,"cells":{"query":{"kind":"string","value":"True if and only if the partition's subarray is on disk as opposed to in memory."},"document":{"kind":"string","value":"def in_file(self):\n return self.on_disk and not self.in_cached_file"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def on_disk(self):\n return isinstance(self._subarray, FileArray)","def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")","def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def is_partition(disk): #TODO: Could change to use \"Whole\" attrib. Good idea?\n\n return \"s\" in disk.split(\"disk\")[1]","def in_cached_file(self):\n return isinstance(self._subarray, CachedArray)","def has_subfile(self) -> bool:\n\t\tself._update_subfiles()\n\t\treturn bool(len(self.subfiles))","def _is_partitioned(self):\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\n partitions = self.table_config[\"partitions\"]\n if partitions is None or len(partitions) == 0:\n return False\n\n if isinstance(partitions, list):\n # check if any None inside list.\n # False if it is the case Ex: [None, 'partition']\n # True otherwise Ex: ['partition1', 'partition2']\n return all(item is not None for item in partitions)\n\n raise ValueError(\"Partitions must be a list or None\")","def IsAllocated(self):\n return self._fsntfs_file_entry.is_allocated()","def isleaf(self):\n no_kids = super(PartitionDevice, self).isleaf\n # it is possible that the disk that originally contained this partition\n # no longer contains a disklabel, in which case we can assume that this\n # device is a leaf\n if self.disk and self.partedPartition and \\\n self.disk.format.type == \"disklabel\" and \\\n self.partedPartition in self.disk.format.partitions:\n disklabel = self.disk.format\n else:\n disklabel = None\n\n extended_has_logical = (self.isExtended and\n (disklabel and disklabel.logicalPartitions))\n return (no_kids and not extended_has_logical)","def is_array(self):\n return len(self.descriptor) > 1","def is_space_available(partition, size):\n available_space = psutil.disk_usage(partition).free\n return False if available_space < size else True","def is_full(self) -> bool:\n return self._array[0].all()","def is_partition_the_last(dbapi, partition):\n idisk_uuid = partition.get('idisk_uuid')\n onidisk_parts = dbapi.partition_get_by_idisk(idisk_uuid)\n part_number = get_part_number(partition.get('device_path'))\n\n if int(part_number) != len(onidisk_parts):\n return False\n\n return True","def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\n file_path = join(output_path, subset + '.npy')\n return os.path.exists(file_path)","def _is_size_bound(self, path):\n return path.suffix == \".bin\"","def _array_name_implies_ND_slice(self, array_name):\n for v in self._split_arrays.values():\n if array_name in v:\n return True\n\n generic_match = re.findall(\"^(.+)_[xyz]$\", array_name)\n loadable_keys = self.loadable_keys()\n keys = list(self.keys())\n if len(generic_match) == 1 and generic_match[0] not in self._split_arrays:\n return generic_match[0] in loadable_keys or generic_match[0] in keys\n return False","def has_shareable_memory(a):\r\n return _get_backing_memmap(a) is not None","def is_partition(dev):\n dev = os.path.realpath(dev)\n if not stat.S_ISBLK(os.lstat(dev).st_mode):\n raise Error('not a block device', dev)\n\n name = get_dev_name(dev)\n if os.path.exists(os.path.join('/sys/block', name)):\n return False\n\n # make sure it is a partition of something else\n for basename in os.listdir('/sys/block'):\n if os.path.exists(os.path.join('/sys/block', basename, name)):\n return True\n\n raise Error('not a disk or partition', dev)","def has_full_batch(self) -> bool:","def is_part_of_disk(part_device_path, disk_device_path):\n is_part_of_disk = False\n\n if disk_device_path in part_device_path:\n is_part_of_disk = True\n elif constants.DEVICE_NAME_MPATH in disk_device_path:\n path_split = disk_device_path.split(constants.DEVICE_NAME_MPATH)\n if (path_split[0] in part_device_path and\n path_split[1] in part_device_path):\n is_part_of_disk = True\n\n return is_part_of_disk","def has_next(self):\n while self._row < self._n and not self._arr[self._row]: # current sub-array is empty\n self._row += 1 # move to next sub-array\n self._col = 0\n if self._row >= self._n: # end of master-array already\n return False\n return True","def exist_partition(self, partition_spec):\n return partition_spec in self.partitions","def isFull(self) -> bool:\n return self._elems == self._k","def _loaded_data(self):\n try:\n dsize = [int(d) for d\n in self.run('fits size', via='get').split()]\n except (ValueError, TypeError, AttributeError) as err:\n log.debug(f' FITS size error: {err}')\n return False\n else:\n if 0 in dsize:\n return False\n else:\n return True","def __check_flat_array__(self):\n if self.flat_array is not None:\n return True\n else:\n return False","def is_slice(self) -> bool:\n return self._is_slice","def contains_offset(self, offset):\n return (offset >= self.offset) and (offset < self.offset + self.filesize)","def is_mounted(device):\n\n partitions = psutil.disk_partitions()\n device_path = \"/dev/\" + device\n for i in partitions:\n if i.device == device_path:\n return True\n return False","def full(self):\n return self.size >= self.maxsize","def in_folder(self):\n return len(os.path.split(self.file_path)) > 1","def full(self):\n return self._current_size == self._size","def is_full(self) -> bool:\r\n return self.size == self.capacity","def is_full(self):\r\n if self.size == self.capacity:\r\n return True\r\n return False","def is_complete(self) -> bool:\n return (\n (\n self.materialized_subset | self.failed_and_downstream_subset\n ).num_partitions_and_non_partitioned_assets\n == self.target_subset.num_partitions_and_non_partitioned_assets\n )","def is_full(self):\n elements_in_sects = sum(\n map(opr.attrgetter(\"size\"), self.sects.values())\n )\n elements_in_total = fct.reduce(\n opr.mul, type(self).flatten_shape(self.shape), 1\n )\n res = elements_in_sects >= elements_in_total\n return res","def isFull(self):\n\t\treturn self.size == self.capacity","def is_full(self):\n\n return self.count == len(self.array)","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def is_array(self):\n return False","def is_full(self):\n return self.idx == self.len","def f_supports_fast_access(self):\n return len(self._data) == 1 and self.v_name in self._data","def is_distributed(self) -> bool:\n return self.size > 1","def isFull(self):\n return self.rear == self.size","def is_subdivision_available(self, position: np.ndarray) -> bool:\n\t\tsubdivision_x_index = int(position[0]) // self.square_subdivision_length\n\t\tsubdivision_y_index = int(position[1]) // self.square_subdivision_length\n\t\treturn self.plane_subdivisions_availability[subdivision_x_index, subdivision_y_index] == 1","def is_subset(subset: np.array, superset: np.array) -> bool:\n superset_lookup = set(superset)\n for val in subset:\n if val not in superset_lookup:\n return False\n\n return True","def is_array(a):\n try:\n shape = a.shape\n return len(shape) >= 1\n except AttributeError:\n return False","def is_array(self, arr):\n return isinstance(arr, np.ndarray)","def checkTrueArrayIsDeleted(node):\n if (node.raw) is not None or (node.raw_housing) is not None:\n raise Exception(\"The true data array has not been deleted\")","def is_full(self):\n return len(self._data) == 1","def isFull(self):\n if len(self.batch) == self.__batch_size:\n return True\n return False","def is_enough_space(self) -> bool:\n return self._free_space() > self.minimum_disk","def pageable(self):\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN","def is_full(self) -> bool:","def f_supports_fast_access(self):\n return not self.f_is_empty()","def is_virtualized (self):\n return len([i for i in self.infras if\n i.infra_type not in (self.TYPE_INFRA_SDN_SW, self.TYPE_INFRA_EE,\n self.TYPE_INFRA_STATIC_EE)]) > 0","def is_full(self):\n if len(self._page_map) >= self.memory_size:\n return True\n return False","def is_leaf(self, pos):\n if pos >= (self.size//2) and pos <= self.size: \n return True\n return False","def regular(self):\n if all(self._volumes - self._volumes[0] == 0):\n return True\n else:\n return False","def storage_can_read(self):\n return True","def full(self) -> bool:\n return self.maxsize and self.qsize() >= self.maxsize","def is_bad_partition(par):\n return 'Letter' not in par or REGEX_BAD_PARTITION.search(par['FileSystem'])","def isFull(self):\n if len(self._data) == self._length:\n return True\n else:\n return False","def has_definite_size(iterable):\n return hasattr(iterable, '__len__')","def _supports_binary_writing(path):\n return not path.startswith(\"/bigstore\")","def _has_processed_data(self):\n return \\\n os.path.exists(\n os.path.join(self._data_root_path, self._processed_train_data_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._processed_dev_data_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._word_vocab_file_name)) and \\\n os.path.exists(\n os.path.join(self._data_root_path, self._char_vocab_file_name))","def check_subarray(array1, array2):\r\n \r\n # check assumption\r\n if (len(array2.shape) != 1) or (array2.shape[0] != array1.shape[-1]):\r\n raise ValueError('Attempting to check for subarray equality when shape assumption does not hold.')\r\n \r\n return np.all(array1==array2, axis=-1)","def prune_empty(self): # FileObj.prune_empty\n return False # can't prune a file","def is_fragmentable(fragment_size, offset, chunk_size):\n return ((chunk_size - fragment_size) / offset) % 1 == 0","def NeedsArray(self, type_):\n return self._NameComponents(type_) in self._array_types","def disk_is_valid(dhandle):\n if is_64bits:\n return dhandle.value != c_uint64(0).value\n else:\n return dhandle.value != c_uint32(0).value","def full(self) -> bool:\n return self.current_offset == self.max_offset","def isscalar(array):\n arr = ma.array(array)\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\n return True\n return False","def is_full(self):\n return self.heap_size >= self.capacity","def is_dataset(self):\n return self._dataset is not None","def data_available(dataset_name=None):\r\n for file_list in data_resources[dataset_name]['files']:\r\n for file in file_list:\r\n if not os.path.exists(os.path.join(data_path, dataset_name, file)):\r\n return False\r\n return True","def is_internal(self):\n if self.is_leaf() or self.is_semileaf():\n return False\n return True","def __bool__(self):\n for root, products in self.rel_paths():\n if products:\n return True\n return False","def PartiallyEmpty(self):\n return None==self.piecesToRecover","def path_contains_data(bucket, root_path, min_file_size=0, file_extension=None):\n for key in bucket.list(root_path):\n if file_extension and not key.name.endswith(file_extension):\n continue\n if key.size > min_file_size:\n return True\n\n return False","def isFull(self) -> bool:\n return self.size == self.maxlen","def has_contents(self):\n return len(self.byteruns())>0","def is_empty(self):\n return self.size == []","def fs_ok(fs_info):\n if fs_info.mountpoint == '/':\n return True\n\n if (fs_info.device == fs_info.fstype or fs_info.fstype == 'nullfs' or\n '/docker' in fs_info.mountpoint or\n fs_info.mountpoint.startswith('/etc') or\n fs_info.mountpoint.startswith('/lib/modules')):\n return False\n\n if fs_info.device.startswith('/dev/'):\n return True\n\n return False","def _test_obssize(t):\n return t.shape[0] != len(t.ids(axis='observation'))","def is_total_slice(item, shape: Tuple[int]) -> bool:\n\n # N.B., assume shape is normalized\n\n if item == Ellipsis:\n return True\n if item == slice(None):\n return True\n if isinstance(item, slice):\n item = (item,)\n if isinstance(item, tuple):\n return all(\n (\n isinstance(it, slice)\n and ((it == slice(None)) or ((it.stop - it.start == sh) and (it.step in [1, None])))\n )\n for it, sh in zip(item, shape)\n )\n else:\n raise TypeError(\"expected slice or tuple of slices, found %r\" % item)","def isFull(T):\r\n return len(T.data) >= T.max_data","def is_array(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_array)","def has_leaf(self) -> bool:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"has_leaf\"))\r\n return self._hvac_mode == \"eco\"","def full(self):\r\n if self._maxsize <= 0:\r\n return False\r\n else:\r\n return self.qsize() >= self._maxsize","def storage_available(self):\n logger.debug('Function storage_available start')\n \n # 2.9 GB\n max_size = 2.9*10**9\n \n if self.total_image_data_size >= max_size:\n logger.info(\"Storage not available\")\n return False\n else:\n logger.info(\"Storage available\")\n return True\n\n logger.debug('Function storage_available end')","def __contains__(self, x):\n indexes = self.get_indexes(x)\n return self.sketch[indexes] > 0","def has_catalog_data (ff_hdus_list, which_hdu=1):\n if (which_hdu == 0): # not allowed? FITS 4.0: 3.3.2\n return False\n else: # it's an extension and so marked\n return ( (len(ff_hdus_list) > which_hdu) and\n (ff_hdus_list[which_hdu].header.get('XTENSION') in ['BINTABLE', 'TABLE']) )","def is_leaf(self):\n return self.pixel_count > 0","def utilize_ephemeral_storage(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"utilize_ephemeral_storage\")","def _is_leaf(self, index):\r\n return 2*index+1 > self._size - 1","def full(self) -> bool:\n\n return (self._size == self._capacity)","def _is_full(self):\n if self.allocated_spaces == self.capacity:\n return True\n elif self.allocated_spaces < self.capacity:\n return False","def _is_empty(self):\n return self.size == 0","def iseod(self):\n\n return self.byte_ptr >= len(self.data)"],"string":"[\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def in_memory(self):\\n return hasattr(self._subarray, \\\"__array_interface__\\\")\",\n \"def in_memory(self) -> bool:\\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def is_partition(disk): #TODO: Could change to use \\\"Whole\\\" attrib. Good idea?\\n\\n return \\\"s\\\" in disk.split(\\\"disk\\\")[1]\",\n \"def in_cached_file(self):\\n return isinstance(self._subarray, CachedArray)\",\n \"def has_subfile(self) -> bool:\\n\\t\\tself._update_subfiles()\\n\\t\\treturn bool(len(self.subfiles))\",\n \"def _is_partitioned(self):\\n ## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic\\n partitions = self.table_config[\\\"partitions\\\"]\\n if partitions is None or len(partitions) == 0:\\n return False\\n\\n if isinstance(partitions, list):\\n # check if any None inside list.\\n # False if it is the case Ex: [None, 'partition']\\n # True otherwise Ex: ['partition1', 'partition2']\\n return all(item is not None for item in partitions)\\n\\n raise ValueError(\\\"Partitions must be a list or None\\\")\",\n \"def IsAllocated(self):\\n return self._fsntfs_file_entry.is_allocated()\",\n \"def isleaf(self):\\n no_kids = super(PartitionDevice, self).isleaf\\n # it is possible that the disk that originally contained this partition\\n # no longer contains a disklabel, in which case we can assume that this\\n # device is a leaf\\n if self.disk and self.partedPartition and \\\\\\n self.disk.format.type == \\\"disklabel\\\" and \\\\\\n self.partedPartition in self.disk.format.partitions:\\n disklabel = self.disk.format\\n else:\\n disklabel = None\\n\\n extended_has_logical = (self.isExtended and\\n (disklabel and disklabel.logicalPartitions))\\n return (no_kids and not extended_has_logical)\",\n \"def is_array(self):\\n return len(self.descriptor) > 1\",\n \"def is_space_available(partition, size):\\n available_space = psutil.disk_usage(partition).free\\n return False if available_space < size else True\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def is_partition_the_last(dbapi, partition):\\n idisk_uuid = partition.get('idisk_uuid')\\n onidisk_parts = dbapi.partition_get_by_idisk(idisk_uuid)\\n part_number = get_part_number(partition.get('device_path'))\\n\\n if int(part_number) != len(onidisk_parts):\\n return False\\n\\n return True\",\n \"def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\\n file_path = join(output_path, subset + '.npy')\\n return os.path.exists(file_path)\",\n \"def _is_size_bound(self, path):\\n return path.suffix == \\\".bin\\\"\",\n \"def _array_name_implies_ND_slice(self, array_name):\\n for v in self._split_arrays.values():\\n if array_name in v:\\n return True\\n\\n generic_match = re.findall(\\\"^(.+)_[xyz]$\\\", array_name)\\n loadable_keys = self.loadable_keys()\\n keys = list(self.keys())\\n if len(generic_match) == 1 and generic_match[0] not in self._split_arrays:\\n return generic_match[0] in loadable_keys or generic_match[0] in keys\\n return False\",\n \"def has_shareable_memory(a):\\r\\n return _get_backing_memmap(a) is not None\",\n \"def is_partition(dev):\\n dev = os.path.realpath(dev)\\n if not stat.S_ISBLK(os.lstat(dev).st_mode):\\n raise Error('not a block device', dev)\\n\\n name = get_dev_name(dev)\\n if os.path.exists(os.path.join('/sys/block', name)):\\n return False\\n\\n # make sure it is a partition of something else\\n for basename in os.listdir('/sys/block'):\\n if os.path.exists(os.path.join('/sys/block', basename, name)):\\n return True\\n\\n raise Error('not a disk or partition', dev)\",\n \"def has_full_batch(self) -> bool:\",\n \"def is_part_of_disk(part_device_path, disk_device_path):\\n is_part_of_disk = False\\n\\n if disk_device_path in part_device_path:\\n is_part_of_disk = True\\n elif constants.DEVICE_NAME_MPATH in disk_device_path:\\n path_split = disk_device_path.split(constants.DEVICE_NAME_MPATH)\\n if (path_split[0] in part_device_path and\\n path_split[1] in part_device_path):\\n is_part_of_disk = True\\n\\n return is_part_of_disk\",\n \"def has_next(self):\\n while self._row < self._n and not self._arr[self._row]: # current sub-array is empty\\n self._row += 1 # move to next sub-array\\n self._col = 0\\n if self._row >= self._n: # end of master-array already\\n return False\\n return True\",\n \"def exist_partition(self, partition_spec):\\n return partition_spec in self.partitions\",\n \"def isFull(self) -> bool:\\n return self._elems == self._k\",\n \"def _loaded_data(self):\\n try:\\n dsize = [int(d) for d\\n in self.run('fits size', via='get').split()]\\n except (ValueError, TypeError, AttributeError) as err:\\n log.debug(f' FITS size error: {err}')\\n return False\\n else:\\n if 0 in dsize:\\n return False\\n else:\\n return True\",\n \"def __check_flat_array__(self):\\n if self.flat_array is not None:\\n return True\\n else:\\n return False\",\n \"def is_slice(self) -> bool:\\n return self._is_slice\",\n \"def contains_offset(self, offset):\\n return (offset >= self.offset) and (offset < self.offset + self.filesize)\",\n \"def is_mounted(device):\\n\\n partitions = psutil.disk_partitions()\\n device_path = \\\"/dev/\\\" + device\\n for i in partitions:\\n if i.device == device_path:\\n return True\\n return False\",\n \"def full(self):\\n return self.size >= self.maxsize\",\n \"def in_folder(self):\\n return len(os.path.split(self.file_path)) > 1\",\n \"def full(self):\\n return self._current_size == self._size\",\n \"def is_full(self) -> bool:\\r\\n return self.size == self.capacity\",\n \"def is_full(self):\\r\\n if self.size == self.capacity:\\r\\n return True\\r\\n return False\",\n \"def is_complete(self) -> bool:\\n return (\\n (\\n self.materialized_subset | self.failed_and_downstream_subset\\n ).num_partitions_and_non_partitioned_assets\\n == self.target_subset.num_partitions_and_non_partitioned_assets\\n )\",\n \"def is_full(self):\\n elements_in_sects = sum(\\n map(opr.attrgetter(\\\"size\\\"), self.sects.values())\\n )\\n elements_in_total = fct.reduce(\\n opr.mul, type(self).flatten_shape(self.shape), 1\\n )\\n res = elements_in_sects >= elements_in_total\\n return res\",\n \"def isFull(self):\\n\\t\\treturn self.size == self.capacity\",\n \"def is_full(self):\\n\\n return self.count == len(self.array)\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def is_array(self):\\n return False\",\n \"def is_full(self):\\n return self.idx == self.len\",\n \"def f_supports_fast_access(self):\\n return len(self._data) == 1 and self.v_name in self._data\",\n \"def is_distributed(self) -> bool:\\n return self.size > 1\",\n \"def isFull(self):\\n return self.rear == self.size\",\n \"def is_subdivision_available(self, position: np.ndarray) -> bool:\\n\\t\\tsubdivision_x_index = int(position[0]) // self.square_subdivision_length\\n\\t\\tsubdivision_y_index = int(position[1]) // self.square_subdivision_length\\n\\t\\treturn self.plane_subdivisions_availability[subdivision_x_index, subdivision_y_index] == 1\",\n \"def is_subset(subset: np.array, superset: np.array) -> bool:\\n superset_lookup = set(superset)\\n for val in subset:\\n if val not in superset_lookup:\\n return False\\n\\n return True\",\n \"def is_array(a):\\n try:\\n shape = a.shape\\n return len(shape) >= 1\\n except AttributeError:\\n return False\",\n \"def is_array(self, arr):\\n return isinstance(arr, np.ndarray)\",\n \"def checkTrueArrayIsDeleted(node):\\n if (node.raw) is not None or (node.raw_housing) is not None:\\n raise Exception(\\\"The true data array has not been deleted\\\")\",\n \"def is_full(self):\\n return len(self._data) == 1\",\n \"def isFull(self):\\n if len(self.batch) == self.__batch_size:\\n return True\\n return False\",\n \"def is_enough_space(self) -> bool:\\n return self._free_space() > self.minimum_disk\",\n \"def pageable(self):\\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN\",\n \"def is_full(self) -> bool:\",\n \"def f_supports_fast_access(self):\\n return not self.f_is_empty()\",\n \"def is_virtualized (self):\\n return len([i for i in self.infras if\\n i.infra_type not in (self.TYPE_INFRA_SDN_SW, self.TYPE_INFRA_EE,\\n self.TYPE_INFRA_STATIC_EE)]) > 0\",\n \"def is_full(self):\\n if len(self._page_map) >= self.memory_size:\\n return True\\n return False\",\n \"def is_leaf(self, pos):\\n if pos >= (self.size//2) and pos <= self.size: \\n return True\\n return False\",\n \"def regular(self):\\n if all(self._volumes - self._volumes[0] == 0):\\n return True\\n else:\\n return False\",\n \"def storage_can_read(self):\\n return True\",\n \"def full(self) -> bool:\\n return self.maxsize and self.qsize() >= self.maxsize\",\n \"def is_bad_partition(par):\\n return 'Letter' not in par or REGEX_BAD_PARTITION.search(par['FileSystem'])\",\n \"def isFull(self):\\n if len(self._data) == self._length:\\n return True\\n else:\\n return False\",\n \"def has_definite_size(iterable):\\n return hasattr(iterable, '__len__')\",\n \"def _supports_binary_writing(path):\\n return not path.startswith(\\\"/bigstore\\\")\",\n \"def _has_processed_data(self):\\n return \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._processed_train_data_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._processed_dev_data_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._word_vocab_file_name)) and \\\\\\n os.path.exists(\\n os.path.join(self._data_root_path, self._char_vocab_file_name))\",\n \"def check_subarray(array1, array2):\\r\\n \\r\\n # check assumption\\r\\n if (len(array2.shape) != 1) or (array2.shape[0] != array1.shape[-1]):\\r\\n raise ValueError('Attempting to check for subarray equality when shape assumption does not hold.')\\r\\n \\r\\n return np.all(array1==array2, axis=-1)\",\n \"def prune_empty(self): # FileObj.prune_empty\\n return False # can't prune a file\",\n \"def is_fragmentable(fragment_size, offset, chunk_size):\\n return ((chunk_size - fragment_size) / offset) % 1 == 0\",\n \"def NeedsArray(self, type_):\\n return self._NameComponents(type_) in self._array_types\",\n \"def disk_is_valid(dhandle):\\n if is_64bits:\\n return dhandle.value != c_uint64(0).value\\n else:\\n return dhandle.value != c_uint32(0).value\",\n \"def full(self) -> bool:\\n return self.current_offset == self.max_offset\",\n \"def isscalar(array):\\n arr = ma.array(array)\\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\\n return True\\n return False\",\n \"def is_full(self):\\n return self.heap_size >= self.capacity\",\n \"def is_dataset(self):\\n return self._dataset is not None\",\n \"def data_available(dataset_name=None):\\r\\n for file_list in data_resources[dataset_name]['files']:\\r\\n for file in file_list:\\r\\n if not os.path.exists(os.path.join(data_path, dataset_name, file)):\\r\\n return False\\r\\n return True\",\n \"def is_internal(self):\\n if self.is_leaf() or self.is_semileaf():\\n return False\\n return True\",\n \"def __bool__(self):\\n for root, products in self.rel_paths():\\n if products:\\n return True\\n return False\",\n \"def PartiallyEmpty(self):\\n return None==self.piecesToRecover\",\n \"def path_contains_data(bucket, root_path, min_file_size=0, file_extension=None):\\n for key in bucket.list(root_path):\\n if file_extension and not key.name.endswith(file_extension):\\n continue\\n if key.size > min_file_size:\\n return True\\n\\n return False\",\n \"def isFull(self) -> bool:\\n return self.size == self.maxlen\",\n \"def has_contents(self):\\n return len(self.byteruns())>0\",\n \"def is_empty(self):\\n return self.size == []\",\n \"def fs_ok(fs_info):\\n if fs_info.mountpoint == '/':\\n return True\\n\\n if (fs_info.device == fs_info.fstype or fs_info.fstype == 'nullfs' or\\n '/docker' in fs_info.mountpoint or\\n fs_info.mountpoint.startswith('/etc') or\\n fs_info.mountpoint.startswith('/lib/modules')):\\n return False\\n\\n if fs_info.device.startswith('/dev/'):\\n return True\\n\\n return False\",\n \"def _test_obssize(t):\\n return t.shape[0] != len(t.ids(axis='observation'))\",\n \"def is_total_slice(item, shape: Tuple[int]) -> bool:\\n\\n # N.B., assume shape is normalized\\n\\n if item == Ellipsis:\\n return True\\n if item == slice(None):\\n return True\\n if isinstance(item, slice):\\n item = (item,)\\n if isinstance(item, tuple):\\n return all(\\n (\\n isinstance(it, slice)\\n and ((it == slice(None)) or ((it.stop - it.start == sh) and (it.step in [1, None])))\\n )\\n for it, sh in zip(item, shape)\\n )\\n else:\\n raise TypeError(\\\"expected slice or tuple of slices, found %r\\\" % item)\",\n \"def isFull(T):\\r\\n return len(T.data) >= T.max_data\",\n \"def is_array(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_array)\",\n \"def has_leaf(self) -> bool:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"has_leaf\\\"))\\r\\n return self._hvac_mode == \\\"eco\\\"\",\n \"def full(self):\\r\\n if self._maxsize <= 0:\\r\\n return False\\r\\n else:\\r\\n return self.qsize() >= self._maxsize\",\n \"def storage_available(self):\\n logger.debug('Function storage_available start')\\n \\n # 2.9 GB\\n max_size = 2.9*10**9\\n \\n if self.total_image_data_size >= max_size:\\n logger.info(\\\"Storage not available\\\")\\n return False\\n else:\\n logger.info(\\\"Storage available\\\")\\n return True\\n\\n logger.debug('Function storage_available end')\",\n \"def __contains__(self, x):\\n indexes = self.get_indexes(x)\\n return self.sketch[indexes] > 0\",\n \"def has_catalog_data (ff_hdus_list, which_hdu=1):\\n if (which_hdu == 0): # not allowed? FITS 4.0: 3.3.2\\n return False\\n else: # it's an extension and so marked\\n return ( (len(ff_hdus_list) > which_hdu) and\\n (ff_hdus_list[which_hdu].header.get('XTENSION') in ['BINTABLE', 'TABLE']) )\",\n \"def is_leaf(self):\\n return self.pixel_count > 0\",\n \"def utilize_ephemeral_storage(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"utilize_ephemeral_storage\\\")\",\n \"def _is_leaf(self, index):\\r\\n return 2*index+1 > self._size - 1\",\n \"def full(self) -> bool:\\n\\n return (self._size == self._capacity)\",\n \"def _is_full(self):\\n if self.allocated_spaces == self.capacity:\\n return True\\n elif self.allocated_spaces < self.capacity:\\n return False\",\n \"def _is_empty(self):\\n return self.size == 0\",\n \"def iseod(self):\\n\\n return self.byte_ptr >= len(self.data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80375713","0.69458973","0.67629313","0.6696292","0.656141","0.63517463","0.6290223","0.61257756","0.59961635","0.59486187","0.59276515","0.59175736","0.5881189","0.577553","0.5774722","0.5755425","0.5737828","0.57311875","0.5723483","0.5720749","0.5682879","0.5678204","0.56532705","0.5638273","0.56376","0.5626054","0.5618182","0.5615703","0.56024516","0.5587035","0.5584256","0.557709","0.5576315","0.55673695","0.5561574","0.5530081","0.5504011","0.5486583","0.54836535","0.5483039","0.54767233","0.5468246","0.5459933","0.545245","0.54522634","0.54515356","0.54295284","0.54217386","0.5419535","0.54073864","0.5398171","0.53974444","0.53786916","0.53773993","0.53724277","0.5371112","0.5369092","0.53521395","0.5340982","0.5307776","0.5302411","0.5292718","0.528551","0.5282625","0.52814937","0.52775794","0.52765363","0.5272008","0.5271376","0.52550143","0.5254123","0.52478075","0.52476114","0.5246837","0.5246544","0.5245442","0.52408814","0.5235635","0.5234526","0.52259785","0.52233267","0.5223169","0.52195394","0.521708","0.5217009","0.52162397","0.52134025","0.5211867","0.5211654","0.5211134","0.52094615","0.52087545","0.52043337","0.5201568","0.51984894","0.51961607","0.5194446","0.5194344","0.51878417","0.51835746"],"string":"[\n \"0.80375713\",\n \"0.69458973\",\n \"0.67629313\",\n \"0.6696292\",\n \"0.656141\",\n \"0.63517463\",\n \"0.6290223\",\n \"0.61257756\",\n \"0.59961635\",\n \"0.59486187\",\n \"0.59276515\",\n \"0.59175736\",\n \"0.5881189\",\n \"0.577553\",\n \"0.5774722\",\n \"0.5755425\",\n \"0.5737828\",\n \"0.57311875\",\n \"0.5723483\",\n \"0.5720749\",\n \"0.5682879\",\n \"0.5678204\",\n \"0.56532705\",\n \"0.5638273\",\n \"0.56376\",\n \"0.5626054\",\n \"0.5618182\",\n \"0.5615703\",\n \"0.56024516\",\n \"0.5587035\",\n \"0.5584256\",\n \"0.557709\",\n \"0.5576315\",\n \"0.55673695\",\n \"0.5561574\",\n \"0.5530081\",\n \"0.5504011\",\n \"0.5486583\",\n \"0.54836535\",\n \"0.5483039\",\n \"0.54767233\",\n \"0.5468246\",\n \"0.5459933\",\n \"0.545245\",\n \"0.54522634\",\n \"0.54515356\",\n \"0.54295284\",\n \"0.54217386\",\n \"0.5419535\",\n \"0.54073864\",\n \"0.5398171\",\n \"0.53974444\",\n \"0.53786916\",\n \"0.53773993\",\n \"0.53724277\",\n \"0.5371112\",\n \"0.5369092\",\n \"0.53521395\",\n \"0.5340982\",\n \"0.5307776\",\n \"0.5302411\",\n \"0.5292718\",\n \"0.528551\",\n \"0.5282625\",\n \"0.52814937\",\n \"0.52775794\",\n \"0.52765363\",\n \"0.5272008\",\n \"0.5271376\",\n \"0.52550143\",\n \"0.5254123\",\n \"0.52478075\",\n \"0.52476114\",\n \"0.5246837\",\n \"0.5246544\",\n \"0.5245442\",\n \"0.52408814\",\n \"0.5235635\",\n \"0.5234526\",\n \"0.52259785\",\n \"0.52233267\",\n \"0.5223169\",\n \"0.52195394\",\n \"0.521708\",\n \"0.5217009\",\n \"0.52162397\",\n \"0.52134025\",\n \"0.5211867\",\n \"0.5211654\",\n \"0.5211134\",\n \"0.52094615\",\n \"0.52087545\",\n \"0.52043337\",\n \"0.5201568\",\n \"0.51984894\",\n \"0.51961607\",\n \"0.5194446\",\n \"0.5194344\",\n \"0.51878417\",\n \"0.51835746\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5803704"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":2683,"cells":{"query":{"kind":"string","value":"The data type of the master array."},"document":{"kind":"string","value":"def dtype(self):\n return self.config[\"dtype\"]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def datatype_name(self):\n return 'array'","def data_type(self):\r\n return self._data_type","def data_type(self):\n return self._data_type","def data_type(self):\n return self._data_type","def data_type(self):\n return self._data_type","def dtype(self):\n return self.array.dtype","def dtype(self):\n return self.data.dtype","def dtype(self):\n return self._data.dtype","def dtype(self):\n return self._data.dtype","def type(self):\n return self.data.type","def recarrtype(self):\n return str(self.dtype.shape) + self.dtype.base.str[1:]","def dataType(self, data):\n if isinstance(data,str):\n return STRING\n elif isinstance(data,dict):\n return ASSOC\n elif isinstance(data,int) or isinstance(data,float):\n return STRING\n elif is_python2() and isinstance(data,long):\n return STRING\n elif isinstance(data, SpecArray.SpecArrayData):\n self.rows, self.cols = data.shape\n return data.type","def dtype(self):\n return self.dataset.dtype","def form_datatype(self):\n et = get_lh5_element_type(self)\n return 'array<1>{array<1>{' + et + '}}'","def get_data_type(self, idx):\n return(self.data[idx].dtype)","def data_type(self) -> int:\n return self.data[\"args\"][\"dataType\"]","def dtype(self):\n return self._vars[0].dtype","def dtype(self):\n return self.initial_value.dtype","def required_data_type(self):\n return Data","def datatype(self):\n # datatype is type of first dataarg\n return self[self.dataargs()[0]].typename","def type_array():\n return []","def GetDataType(self):\n return _gmat_py.ElementWrapper_GetDataType(self)","def dtype(self):\n return self.__dtype","def dtype(self):\n return self._dtype","def dtype(self):\n return self._dtype","def dtype(self):\n return self._dtype","def type(self) -> np.dtype:\n return self._tensorInfo.dtype","def dtype(self):\n return self._dtype","def dtype(self):\n return self._channel.datatype","def dtype(self) -> np.dtype:\n return self._channel_arrays[0].dtype","def type(self) -> DataType:\n return self._type","def get_array_element_type(self):\r\n return conf.lib.clang_getArrayElementType(self)","def type(self) -> 'Data_Type':\n return Data_Type(self._info.htype, self._info.ptype)","def dtype(self) -> np.dtype:\n ...","def dtype(self):\n return self._fl.raw.dtype","def array_type(self):\n return exclusions.closed()","def getDataType(self):\n\n return self._dataType","def NumPyDataType(self):\n datatype = self.DataType\n if datatype == gdalconst.GDT_Byte:\n pixeltype = self.GetMetadataItem('PIXELTYPE', 'IMAGE_STRUCTURE')\n if pixeltype == 'SIGNEDBYTE':\n return numpy.int8\n return numpy.uint8\n elif datatype == gdalconst.GDT_UInt16:\n return numpy.uint16\n elif datatype == gdalconst.GDT_UInt32:\n return numpy.uint32\n elif datatype == gdalconst.GDT_Int16:\n return numpy.int16\n elif datatype == gdalconst.GDT_Int32:\n return numpy.int32\n elif datatype == gdalconst.GDT_Float32:\n return numpy.float32\n elif datatype == gdalconst.GDT_Float64:\n return numpy.float64\n else:\n raise ValueError(\n \"Cannot handle DataType: {0}\".format(\n gdal.GetDataTypeName(datatype)\n )\n )","def dtype(self) -> np.dtype:\n return self._dtype","def datatype(self):\n return self._datatype","def data_types(self):","def data_types(self):\n return self['data_types']","def dtype(self) -> np.dtype[np.void]:\n return np.dtype(list(self.items()))","async def get_dtype(self):\r\n pass","def dtype(self) -> str:\n return self._dtype","def type(self):\n # type: () -> type\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]","def dtype(self) -> DataType:\n return self._dtype","def dtype(a):\n return a.dtype","def to_numpy(self) -> np.dtype:\n return self._numpy_type","def kind(self):\n # type () -> str\n return np.dtype(self.type).kind","def dtype(self) -> DtypeLike:\n\n return self.data.dtype","def type(self):\n return struct.unpack('<B', self.raw_data[0])[0]","def data_type():\n return DataTypeUtil.getDTypeForName(DataTypeUtil.getDtypeFromContext())","def data_type(self):\n try:\n return self.attributes.workspace.attributes['library:datatype']['items']\n except Exception as e:\n self._logger.debug(f\"data_category {e}\")\n return None","def datatype(self):\n hcell = self._get_hcell2()\n celltype = hcell[\"celltype\"]\n assert celltype == \"structured\"\n return hcell[\"datatype\"]","def memtype(self):\n # easy enough\n return self._memtype","def data_type(self):\n return self.unpack_dword(0xC) & DEVPROP_MASK_TYPE","def dtype(self):\n if self.num_polygons < 2 ** 8:\n dtype = numpy.uint8\n elif self.num_polygons < 2 ** 16:\n dtype = numpy.uint16\n else:\n dtype = numpy.uint32\n return dtype","def _variable_array_types(self):\n return [\n 'BinaryArray',\n 'KeyValueArray',\n 'StringArray',\n 'TCEntityArray',\n 'TCEnhancedEntityArray',\n ]","def dtype(self):\n # type: () -> ExtensionDtype\n return self._dtype","def array_axis_physical_types(self) -> Iterable[Tuple[str, ...]]:","def typecode (self) :\r\n return self.numeric_typecode","def astype(self, dtype):\n return NoneArray","def getDataSetType(self):\n return self.__data_set_type__","def type(self):\n return _python_type_map[self.arrow_dtype.id]","def outputDataType(self):\n raise NotImplementedError()","def data_type(self) -> pulumi.Input['AssetModelDataType']:\n return pulumi.get(self, \"data_type\")","def infer_dtype(self):\n raise NotImplementedError","def base_dtype(self):\r\n _base_dtype = [\r\n (ptidCol, '<i8'),\r\n (surveyidCol, 'S25'),\r\n (siteCol, 'S10'),\r\n (tranCol, '<i4'),\r\n (datetimeCol, '<M8[us]'),\r\n (dateCol, 'S12'),\r\n (timeCol, 'S11'),\r\n (latCol, '<f8'),\r\n (lonCol, '<f8'),\r\n (depObsCol, '<f8'),\r\n (depInterpCol, '<f8'),\r\n (videoCol, '<i4'),\r\n ]\r\n return _base_dtype","def get_type(self):\n\n return self.scalertype","def value_type(self) -> global___Type:","def data_particle_type(self):\n if self._data_particle_type is None:\n raise NotImplementedException(\"_data_particle_type not initialized\")\n\n return self._data_particle_type","def _dtype(self):\n dref = {\n \"data_mode\": object,\n \"latitude\": np.float64,\n \"longitude\": np.float64,\n \"position_qc\": np.int64,\n \"time\": object,\n \"time_qc\": np.int64,\n \"direction\": object,\n \"platform_number\": np.int64,\n \"config_mission_number\": np.int64,\n \"vertical_sampling_scheme\": object,\n \"cycle_number\": np.int64,\n \"pres\": np.float64,\n \"temp\": np.float64,\n \"psal\": np.float64,\n \"doxy\": np.float64,\n \"pres_qc\": np.int64,\n \"temp_qc\": object,\n \"psal_qc\": object,\n \"doxy_qc\": object,\n \"pres_adjusted\": np.float64,\n \"temp_adjusted\": np.float64,\n \"psal_adjusted\": np.float64,\n \"doxy_adjusted\": np.float64,\n \"pres_adjusted_qc\": object,\n \"temp_adjusted_qc\": object,\n \"psal_adjusted_qc\": object,\n \"doxy_adjusted_qc\": object,\n \"pres_adjusted_error\": np.float64,\n \"temp_adjusted_error\": np.float64,\n \"psal_adjusted_error\": np.float64,\n \"doxy_adjusted_error\": np.float64,\n \"ptmp\": np.float64,\n }\n plist = self._minimal_vlist\n response = {}\n for p in plist:\n response[p] = dref[p]\n return response","def dtype():\n return RaggedDtype()","def dtype(self):\n return self.MJD.dtype","def identity(self):\n return self.dtype()","def type(self):\n # easy enough\n return self._dataset._pyre_id.type","def _variable_types(self):\n return self._variable_single_types + self._variable_array_types","def basetype(series):\n if not series.dtype == types.ObjectType:\n # Type is specific already; return it without further inspection.\n return series.dtype\n # Now type is Object (string) See if any more specific type can be deduced:","def properties_dtype(self):\n return self.properties.dtype()","def data_type_str(self):\n return data_ref_type_str(self.data_type)","def test_datatype(self):\n with Pandas() as pd:\n if pd is None:\n return\n with Numpy() as np: # noqa\n if numpy is None:\n return\n sys.stderr.write(\"\\n\")\n\n df, hist1, hist2, hist3 = get_test_histograms1()\n\n assert hist1.datatype == str\n np.testing.assert_array_equal(hist2.datatype, [numpy.number, str])\n np.testing.assert_array_equal(hist3.datatype, [numpy.datetime64, numpy.number, str])","def data_type_id(self) -> str:\n return self._data_type_id","def get_data_type(self, col):\n if ((self.data_df[col].dtype == np.int64) or (self.data_df[col].dtype == np.int32)):\n return 'int'\n elif ((self.data_df[col].dtype == np.float64) or (self.data_df[col].dtype == np.float32)):\n return 'float'\n else:\n raise ValueError(\"Unknown data type of feature %s: must be int or float\" % col)","def _getDTypeList(self):\n return self._dtype","def get_data_type(self) -> Type[ATTRIBUTE_TYPES]:\n if isinstance(self.value, (list, tuple, set)):\n value = next(iter(self.value))\n else:\n value = self.value\n value = cast(ATTRIBUTE_TYPES, value)\n return type(value)","def make_series_dtype(self):\n return numpy.dtype([\n # Timestep\n # ('t', numpy.int32),\n\n # Some data we store\n # If you add it here, you'll need to update the getters below too\n ('count', numpy.float64),\n # ('explored_by_type', numpy.float64, self.num_types)\n ])","def data_types():\n\n return ...","def dtype(self) -> tf.dtypes.DType:","def _dtype(self):\n if self._dtype_ is not None:\n return self._dtype_\n dtype = None\n for raw_extra, filename in zip(self._raw_extras, self._filenames):\n for ent in raw_extra[\"ent\"]:\n if ent is not None:\n with _fiff_get_fid(filename) as fid:\n fid.seek(ent.pos, 0)\n tag = read_tag_info(fid)\n if tag is not None:\n if tag.type in (\n FIFF.FIFFT_COMPLEX_FLOAT,\n FIFF.FIFFT_COMPLEX_DOUBLE,\n ):\n dtype = np.complex128\n else:\n dtype = np.float64\n if dtype is not None:\n break\n if dtype is not None:\n break\n if dtype is None:\n raise RuntimeError(\"bug in reading\")\n self._dtype_ = dtype\n return dtype","def data_type(x):\n return (\n DATA_TYPES.get(file_format(x)) or\n DATA_TYPES.get(genomic_file_ext(x))\n )","def test_out_dtype(self):\n byt = bytscl(self.array1)\n dtype = byt.dtype\n self.assertEqual(dtype, 'uint8')","def data_format(self):\n return self._data_format","def type(self):\r\n return self.__type","def create_data_types(self):\n for col in self.all_columns:\n try:\n if float(self.train[col].iloc[-3]):\n self.train[col] = self.train[col].astype(np.float32)\n except:\n pass\n self.d_types = self.train.dtypes","def explore_type(name, datatype, is_child):\n target_type = datatype.target()\n print (\"%s is an array of '%s'.\" % (name, str(target_type)))\n\n Explorer.explore_type(\"the array element of %s\" % name, target_type,\n is_child)\n return False","def datatype_conversion(self):\n\n category_cols = self.FEATURE_TYPES[\"category_cols\"]\n integer_cols = self.FEATURE_TYPES[\"integer_cols\"]\n float_cols = self.FEATURE_TYPES[\"float_cols\"]\n datetime_cols = self.FEATURE_TYPES[\"datetime_cols\"]\n string_cols = self.FEATURE_TYPES[\"string_cols\"]\n bool_cols = self.FEATURE_TYPES[\"bool_cols\"]\n data = self.data\n \n data[category_cols] = data[category_cols].astype('category',copy=False) \n data[integer_cols] = data[integer_cols].astype('int64',copy=False)\n data[float_cols] = data[float_cols].astype('float64',copy=False)\n data[datetime_cols] = data[datetime_cols].astype('datetime64[ns]',copy=False)\n data[string_cols] = data[string_cols].astype('str',copy=False)\n data[bool_cols] = data[bool_cols].astype('bool', copy=False)\n\n return data","def get_data_structure_type(self) -> str:\n return \"ColorGrid\"","def test_array_dtype(self):\n dt1 = np.dtype('f4', (2,))\n dt2 = np.dtype('f4', [2])\n dt3 = np.dtype('f4', 2)\n dt4 = np.dtype('f4', 2.1)\n ht1 = h5t.py_create(dt1)\n ht2 = h5t.py_create(dt2)\n ht3 = h5t.py_create(dt3)\n ht4 = h5t.py_create(dt4)\n self.assertEqual(ht1.dtype, dt1)\n self.assertEqual(ht2.dtype, dt1)\n self.assertEqual(ht3.dtype, dt1)\n self.assertEqual(ht4.dtype, dt1)","def GetType(vDataSet):\r\n return imaris_types[str(vDataSet.GetType())]"],"string":"[\n \"def datatype_name(self):\\n return 'array'\",\n \"def data_type(self):\\r\\n return self._data_type\",\n \"def data_type(self):\\n return self._data_type\",\n \"def data_type(self):\\n return self._data_type\",\n \"def data_type(self):\\n return self._data_type\",\n \"def dtype(self):\\n return self.array.dtype\",\n \"def dtype(self):\\n return self.data.dtype\",\n \"def dtype(self):\\n return self._data.dtype\",\n \"def dtype(self):\\n return self._data.dtype\",\n \"def type(self):\\n return self.data.type\",\n \"def recarrtype(self):\\n return str(self.dtype.shape) + self.dtype.base.str[1:]\",\n \"def dataType(self, data):\\n if isinstance(data,str):\\n return STRING\\n elif isinstance(data,dict):\\n return ASSOC\\n elif isinstance(data,int) or isinstance(data,float):\\n return STRING\\n elif is_python2() and isinstance(data,long):\\n return STRING\\n elif isinstance(data, SpecArray.SpecArrayData):\\n self.rows, self.cols = data.shape\\n return data.type\",\n \"def dtype(self):\\n return self.dataset.dtype\",\n \"def form_datatype(self):\\n et = get_lh5_element_type(self)\\n return 'array<1>{array<1>{' + et + '}}'\",\n \"def get_data_type(self, idx):\\n return(self.data[idx].dtype)\",\n \"def data_type(self) -> int:\\n return self.data[\\\"args\\\"][\\\"dataType\\\"]\",\n \"def dtype(self):\\n return self._vars[0].dtype\",\n \"def dtype(self):\\n return self.initial_value.dtype\",\n \"def required_data_type(self):\\n return Data\",\n \"def datatype(self):\\n # datatype is type of first dataarg\\n return self[self.dataargs()[0]].typename\",\n \"def type_array():\\n return []\",\n \"def GetDataType(self):\\n return _gmat_py.ElementWrapper_GetDataType(self)\",\n \"def dtype(self):\\n return self.__dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def type(self) -> np.dtype:\\n return self._tensorInfo.dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._channel.datatype\",\n \"def dtype(self) -> np.dtype:\\n return self._channel_arrays[0].dtype\",\n \"def type(self) -> DataType:\\n return self._type\",\n \"def get_array_element_type(self):\\r\\n return conf.lib.clang_getArrayElementType(self)\",\n \"def type(self) -> 'Data_Type':\\n return Data_Type(self._info.htype, self._info.ptype)\",\n \"def dtype(self) -> np.dtype:\\n ...\",\n \"def dtype(self):\\n return self._fl.raw.dtype\",\n \"def array_type(self):\\n return exclusions.closed()\",\n \"def getDataType(self):\\n\\n return self._dataType\",\n \"def NumPyDataType(self):\\n datatype = self.DataType\\n if datatype == gdalconst.GDT_Byte:\\n pixeltype = self.GetMetadataItem('PIXELTYPE', 'IMAGE_STRUCTURE')\\n if pixeltype == 'SIGNEDBYTE':\\n return numpy.int8\\n return numpy.uint8\\n elif datatype == gdalconst.GDT_UInt16:\\n return numpy.uint16\\n elif datatype == gdalconst.GDT_UInt32:\\n return numpy.uint32\\n elif datatype == gdalconst.GDT_Int16:\\n return numpy.int16\\n elif datatype == gdalconst.GDT_Int32:\\n return numpy.int32\\n elif datatype == gdalconst.GDT_Float32:\\n return numpy.float32\\n elif datatype == gdalconst.GDT_Float64:\\n return numpy.float64\\n else:\\n raise ValueError(\\n \\\"Cannot handle DataType: {0}\\\".format(\\n gdal.GetDataTypeName(datatype)\\n )\\n )\",\n \"def dtype(self) -> np.dtype:\\n return self._dtype\",\n \"def datatype(self):\\n return self._datatype\",\n \"def data_types(self):\",\n \"def data_types(self):\\n return self['data_types']\",\n \"def dtype(self) -> np.dtype[np.void]:\\n return np.dtype(list(self.items()))\",\n \"async def get_dtype(self):\\r\\n pass\",\n \"def dtype(self) -> str:\\n return self._dtype\",\n \"def type(self):\\n # type: () -> type\\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]\",\n \"def dtype(self) -> DataType:\\n return self._dtype\",\n \"def dtype(a):\\n return a.dtype\",\n \"def to_numpy(self) -> np.dtype:\\n return self._numpy_type\",\n \"def kind(self):\\n # type () -> str\\n return np.dtype(self.type).kind\",\n \"def dtype(self) -> DtypeLike:\\n\\n return self.data.dtype\",\n \"def type(self):\\n return struct.unpack('<B', self.raw_data[0])[0]\",\n \"def data_type():\\n return DataTypeUtil.getDTypeForName(DataTypeUtil.getDtypeFromContext())\",\n \"def data_type(self):\\n try:\\n return self.attributes.workspace.attributes['library:datatype']['items']\\n except Exception as e:\\n self._logger.debug(f\\\"data_category {e}\\\")\\n return None\",\n \"def datatype(self):\\n hcell = self._get_hcell2()\\n celltype = hcell[\\\"celltype\\\"]\\n assert celltype == \\\"structured\\\"\\n return hcell[\\\"datatype\\\"]\",\n \"def memtype(self):\\n # easy enough\\n return self._memtype\",\n \"def data_type(self):\\n return self.unpack_dword(0xC) & DEVPROP_MASK_TYPE\",\n \"def dtype(self):\\n if self.num_polygons < 2 ** 8:\\n dtype = numpy.uint8\\n elif self.num_polygons < 2 ** 16:\\n dtype = numpy.uint16\\n else:\\n dtype = numpy.uint32\\n return dtype\",\n \"def _variable_array_types(self):\\n return [\\n 'BinaryArray',\\n 'KeyValueArray',\\n 'StringArray',\\n 'TCEntityArray',\\n 'TCEnhancedEntityArray',\\n ]\",\n \"def dtype(self):\\n # type: () -> ExtensionDtype\\n return self._dtype\",\n \"def array_axis_physical_types(self) -> Iterable[Tuple[str, ...]]:\",\n \"def typecode (self) :\\r\\n return self.numeric_typecode\",\n \"def astype(self, dtype):\\n return NoneArray\",\n \"def getDataSetType(self):\\n return self.__data_set_type__\",\n \"def type(self):\\n return _python_type_map[self.arrow_dtype.id]\",\n \"def outputDataType(self):\\n raise NotImplementedError()\",\n \"def data_type(self) -> pulumi.Input['AssetModelDataType']:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"def infer_dtype(self):\\n raise NotImplementedError\",\n \"def base_dtype(self):\\r\\n _base_dtype = [\\r\\n (ptidCol, '<i8'),\\r\\n (surveyidCol, 'S25'),\\r\\n (siteCol, 'S10'),\\r\\n (tranCol, '<i4'),\\r\\n (datetimeCol, '<M8[us]'),\\r\\n (dateCol, 'S12'),\\r\\n (timeCol, 'S11'),\\r\\n (latCol, '<f8'),\\r\\n (lonCol, '<f8'),\\r\\n (depObsCol, '<f8'),\\r\\n (depInterpCol, '<f8'),\\r\\n (videoCol, '<i4'),\\r\\n ]\\r\\n return _base_dtype\",\n \"def get_type(self):\\n\\n return self.scalertype\",\n \"def value_type(self) -> global___Type:\",\n \"def data_particle_type(self):\\n if self._data_particle_type is None:\\n raise NotImplementedException(\\\"_data_particle_type not initialized\\\")\\n\\n return self._data_particle_type\",\n \"def _dtype(self):\\n dref = {\\n \\\"data_mode\\\": object,\\n \\\"latitude\\\": np.float64,\\n \\\"longitude\\\": np.float64,\\n \\\"position_qc\\\": np.int64,\\n \\\"time\\\": object,\\n \\\"time_qc\\\": np.int64,\\n \\\"direction\\\": object,\\n \\\"platform_number\\\": np.int64,\\n \\\"config_mission_number\\\": np.int64,\\n \\\"vertical_sampling_scheme\\\": object,\\n \\\"cycle_number\\\": np.int64,\\n \\\"pres\\\": np.float64,\\n \\\"temp\\\": np.float64,\\n \\\"psal\\\": np.float64,\\n \\\"doxy\\\": np.float64,\\n \\\"pres_qc\\\": np.int64,\\n \\\"temp_qc\\\": object,\\n \\\"psal_qc\\\": object,\\n \\\"doxy_qc\\\": object,\\n \\\"pres_adjusted\\\": np.float64,\\n \\\"temp_adjusted\\\": np.float64,\\n \\\"psal_adjusted\\\": np.float64,\\n \\\"doxy_adjusted\\\": np.float64,\\n \\\"pres_adjusted_qc\\\": object,\\n \\\"temp_adjusted_qc\\\": object,\\n \\\"psal_adjusted_qc\\\": object,\\n \\\"doxy_adjusted_qc\\\": object,\\n \\\"pres_adjusted_error\\\": np.float64,\\n \\\"temp_adjusted_error\\\": np.float64,\\n \\\"psal_adjusted_error\\\": np.float64,\\n \\\"doxy_adjusted_error\\\": np.float64,\\n \\\"ptmp\\\": np.float64,\\n }\\n plist = self._minimal_vlist\\n response = {}\\n for p in plist:\\n response[p] = dref[p]\\n return response\",\n \"def dtype():\\n return RaggedDtype()\",\n \"def dtype(self):\\n return self.MJD.dtype\",\n \"def identity(self):\\n return self.dtype()\",\n \"def type(self):\\n # easy enough\\n return self._dataset._pyre_id.type\",\n \"def _variable_types(self):\\n return self._variable_single_types + self._variable_array_types\",\n \"def basetype(series):\\n if not series.dtype == types.ObjectType:\\n # Type is specific already; return it without further inspection.\\n return series.dtype\\n # Now type is Object (string) See if any more specific type can be deduced:\",\n \"def properties_dtype(self):\\n return self.properties.dtype()\",\n \"def data_type_str(self):\\n return data_ref_type_str(self.data_type)\",\n \"def test_datatype(self):\\n with Pandas() as pd:\\n if pd is None:\\n return\\n with Numpy() as np: # noqa\\n if numpy is None:\\n return\\n sys.stderr.write(\\\"\\\\n\\\")\\n\\n df, hist1, hist2, hist3 = get_test_histograms1()\\n\\n assert hist1.datatype == str\\n np.testing.assert_array_equal(hist2.datatype, [numpy.number, str])\\n np.testing.assert_array_equal(hist3.datatype, [numpy.datetime64, numpy.number, str])\",\n \"def data_type_id(self) -> str:\\n return self._data_type_id\",\n \"def get_data_type(self, col):\\n if ((self.data_df[col].dtype == np.int64) or (self.data_df[col].dtype == np.int32)):\\n return 'int'\\n elif ((self.data_df[col].dtype == np.float64) or (self.data_df[col].dtype == np.float32)):\\n return 'float'\\n else:\\n raise ValueError(\\\"Unknown data type of feature %s: must be int or float\\\" % col)\",\n \"def _getDTypeList(self):\\n return self._dtype\",\n \"def get_data_type(self) -> Type[ATTRIBUTE_TYPES]:\\n if isinstance(self.value, (list, tuple, set)):\\n value = next(iter(self.value))\\n else:\\n value = self.value\\n value = cast(ATTRIBUTE_TYPES, value)\\n return type(value)\",\n \"def make_series_dtype(self):\\n return numpy.dtype([\\n # Timestep\\n # ('t', numpy.int32),\\n\\n # Some data we store\\n # If you add it here, you'll need to update the getters below too\\n ('count', numpy.float64),\\n # ('explored_by_type', numpy.float64, self.num_types)\\n ])\",\n \"def data_types():\\n\\n return ...\",\n \"def dtype(self) -> tf.dtypes.DType:\",\n \"def _dtype(self):\\n if self._dtype_ is not None:\\n return self._dtype_\\n dtype = None\\n for raw_extra, filename in zip(self._raw_extras, self._filenames):\\n for ent in raw_extra[\\\"ent\\\"]:\\n if ent is not None:\\n with _fiff_get_fid(filename) as fid:\\n fid.seek(ent.pos, 0)\\n tag = read_tag_info(fid)\\n if tag is not None:\\n if tag.type in (\\n FIFF.FIFFT_COMPLEX_FLOAT,\\n FIFF.FIFFT_COMPLEX_DOUBLE,\\n ):\\n dtype = np.complex128\\n else:\\n dtype = np.float64\\n if dtype is not None:\\n break\\n if dtype is not None:\\n break\\n if dtype is None:\\n raise RuntimeError(\\\"bug in reading\\\")\\n self._dtype_ = dtype\\n return dtype\",\n \"def data_type(x):\\n return (\\n DATA_TYPES.get(file_format(x)) or\\n DATA_TYPES.get(genomic_file_ext(x))\\n )\",\n \"def test_out_dtype(self):\\n byt = bytscl(self.array1)\\n dtype = byt.dtype\\n self.assertEqual(dtype, 'uint8')\",\n \"def data_format(self):\\n return self._data_format\",\n \"def type(self):\\r\\n return self.__type\",\n \"def create_data_types(self):\\n for col in self.all_columns:\\n try:\\n if float(self.train[col].iloc[-3]):\\n self.train[col] = self.train[col].astype(np.float32)\\n except:\\n pass\\n self.d_types = self.train.dtypes\",\n \"def explore_type(name, datatype, is_child):\\n target_type = datatype.target()\\n print (\\\"%s is an array of '%s'.\\\" % (name, str(target_type)))\\n\\n Explorer.explore_type(\\\"the array element of %s\\\" % name, target_type,\\n is_child)\\n return False\",\n \"def datatype_conversion(self):\\n\\n category_cols = self.FEATURE_TYPES[\\\"category_cols\\\"]\\n integer_cols = self.FEATURE_TYPES[\\\"integer_cols\\\"]\\n float_cols = self.FEATURE_TYPES[\\\"float_cols\\\"]\\n datetime_cols = self.FEATURE_TYPES[\\\"datetime_cols\\\"]\\n string_cols = self.FEATURE_TYPES[\\\"string_cols\\\"]\\n bool_cols = self.FEATURE_TYPES[\\\"bool_cols\\\"]\\n data = self.data\\n \\n data[category_cols] = data[category_cols].astype('category',copy=False) \\n data[integer_cols] = data[integer_cols].astype('int64',copy=False)\\n data[float_cols] = data[float_cols].astype('float64',copy=False)\\n data[datetime_cols] = data[datetime_cols].astype('datetime64[ns]',copy=False)\\n data[string_cols] = data[string_cols].astype('str',copy=False)\\n data[bool_cols] = data[bool_cols].astype('bool', copy=False)\\n\\n return data\",\n \"def get_data_structure_type(self) -> str:\\n return \\\"ColorGrid\\\"\",\n \"def test_array_dtype(self):\\n dt1 = np.dtype('f4', (2,))\\n dt2 = np.dtype('f4', [2])\\n dt3 = np.dtype('f4', 2)\\n dt4 = np.dtype('f4', 2.1)\\n ht1 = h5t.py_create(dt1)\\n ht2 = h5t.py_create(dt2)\\n ht3 = h5t.py_create(dt3)\\n ht4 = h5t.py_create(dt4)\\n self.assertEqual(ht1.dtype, dt1)\\n self.assertEqual(ht2.dtype, dt1)\\n self.assertEqual(ht3.dtype, dt1)\\n self.assertEqual(ht4.dtype, dt1)\",\n \"def GetType(vDataSet):\\r\\n return imaris_types[str(vDataSet.GetType())]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7694177","0.76688254","0.75949335","0.75949335","0.75949335","0.7573461","0.74812984","0.7376351","0.7376351","0.72733635","0.7250659","0.71817577","0.7178896","0.71689445","0.7107126","0.7105419","0.70681125","0.7060085","0.7039309","0.7015803","0.7010048","0.69812846","0.698086","0.6973812","0.6973812","0.6973812","0.6966532","0.6961613","0.69350046","0.6929445","0.68953294","0.6893467","0.6888927","0.68875015","0.68765545","0.68448097","0.6796803","0.6780559","0.6774283","0.6772126","0.67576337","0.6742372","0.6720827","0.66934305","0.6689053","0.66881853","0.6669848","0.6665709","0.66450894","0.664375","0.66313803","0.66223556","0.65659994","0.6565347","0.6561051","0.6527298","0.65221816","0.65153736","0.6506231","0.64676327","0.646236","0.64607257","0.6450405","0.64491147","0.6432219","0.6426516","0.64049697","0.6378265","0.63739735","0.6357614","0.6315173","0.6305308","0.6292668","0.6290685","0.62734383","0.6234966","0.62208104","0.6169394","0.61483353","0.61366826","0.6118393","0.60987866","0.6091577","0.6091375","0.60773116","0.6072602","0.60595286","0.60502255","0.60499704","0.60252285","0.59982294","0.5995335","0.5992722","0.5991097","0.5979544","0.5970867","0.59702563","0.5968463","0.5956033","0.5953662"],"string":"[\n \"0.7694177\",\n \"0.76688254\",\n \"0.75949335\",\n \"0.75949335\",\n \"0.75949335\",\n \"0.7573461\",\n \"0.74812984\",\n \"0.7376351\",\n \"0.7376351\",\n \"0.72733635\",\n \"0.7250659\",\n \"0.71817577\",\n \"0.7178896\",\n \"0.71689445\",\n \"0.7107126\",\n \"0.7105419\",\n \"0.70681125\",\n \"0.7060085\",\n \"0.7039309\",\n \"0.7015803\",\n \"0.7010048\",\n \"0.69812846\",\n \"0.698086\",\n \"0.6973812\",\n \"0.6973812\",\n \"0.6973812\",\n \"0.6966532\",\n \"0.6961613\",\n \"0.69350046\",\n \"0.6929445\",\n \"0.68953294\",\n \"0.6893467\",\n \"0.6888927\",\n \"0.68875015\",\n \"0.68765545\",\n \"0.68448097\",\n \"0.6796803\",\n \"0.6780559\",\n \"0.6774283\",\n \"0.6772126\",\n \"0.67576337\",\n \"0.6742372\",\n \"0.6720827\",\n \"0.66934305\",\n \"0.6689053\",\n \"0.66881853\",\n \"0.6669848\",\n \"0.6665709\",\n \"0.66450894\",\n \"0.664375\",\n \"0.66313803\",\n \"0.66223556\",\n \"0.65659994\",\n \"0.6565347\",\n \"0.6561051\",\n \"0.6527298\",\n \"0.65221816\",\n \"0.65153736\",\n \"0.6506231\",\n \"0.64676327\",\n \"0.646236\",\n \"0.64607257\",\n \"0.6450405\",\n \"0.64491147\",\n \"0.6432219\",\n \"0.6426516\",\n \"0.64049697\",\n \"0.6378265\",\n \"0.63739735\",\n \"0.6357614\",\n \"0.6315173\",\n \"0.6305308\",\n \"0.6292668\",\n \"0.6290685\",\n \"0.62734383\",\n \"0.6234966\",\n \"0.62208104\",\n \"0.6169394\",\n \"0.61483353\",\n \"0.61366826\",\n \"0.6118393\",\n \"0.60987866\",\n \"0.6091577\",\n \"0.6091375\",\n \"0.60773116\",\n \"0.6072602\",\n \"0.60595286\",\n \"0.60502255\",\n \"0.60499704\",\n \"0.60252285\",\n \"0.59982294\",\n \"0.5995335\",\n \"0.5992722\",\n \"0.5991097\",\n \"0.5979544\",\n \"0.5970867\",\n \"0.59702563\",\n \"0.5968463\",\n \"0.5956033\",\n \"0.5953662\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69170535"},"document_rank":{"kind":"string","value":"30"}}},{"rowIdx":2684,"cells":{"query":{"kind":"string","value":"True if and only if the partition's data array is a scalar array."},"document":{"kind":"string","value":"def isscalar(self):\n return not self.axes"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_scalar(obj: _std_typing.Any) -> bool:\n return obj.ndim == 0","def is_scalar(x: Any) -> bool:\r\n return np.isscalar(x) or (isinstance(x, np.ndarray) and x.ndim == 0)","def is_array(self):\n return False","def is_scalar(self):\n return len(self.coeffs.shape[self.sdim:]) == 0","def is_array(self):\n return len(self.descriptor) > 1","def is_np_scalar(x):\n return isinstance(x, np.generic)","def is_scalar(x):\n return x.ndim == 0","def is_array(self, arr):\n return isinstance(arr, np.ndarray)","def is_scalar(self):","def _is_scalar(shape):\n return F.shape_mul(shape) == 1","def is_scalar(val,\n include_np: bool = True,\n include_torch: bool = True) -> bool:\n if isinstance(val, numbers.Number):\n return True\n elif include_np and isinstance(val, np.ndarray) and val.ndim == 0:\n return True\n elif include_torch and isinstance(val, torch.Tensor) and len(val) == 1:\n return True\n else:\n return False","def _is_scalar_from_shape(shape):\n return _logical_equal(_ndims_from_shape(shape), 0)","def isScalar(self) -> bool:\n\n indices = list(range(self.layout.gaDims))\n indices.remove(self.layout.gradeList.index(0))\n\n for i in indices:\n if abs(self.value[i]) < _eps:\n continue\n else:\n return False\n\n return True","def is_array(a):\n try:\n shape = a.shape\n return len(shape) >= 1\n except AttributeError:\n return False","def isscalar(array):\n arr = ma.array(array)\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\n return True\n return False","def isarray(a):\r\n try:\r\n validity = isinstance(a, ndarray)\r\n except:\r\n validity = False\r\n\r\n return validity","def isarray(a):\n try:\n validity=isinstance(a,ndarray)\n except:\n validity=False\n\n return validity","def is_array(t):\n return isinstance(t, ast.Array)","def _is_key_value_array(self, data):\n for d in data:\n if not self._is_key_value(d):\n return False\n return True","def is_scalar(self, typ: Union[Type, None] = _Any) -> bool:\n if isinstance(self.yaml_node, yaml.ScalarNode):\n if typ != _Any and typ in scalar_type_to_tag:\n if typ is None:\n typ = type(None)\n return cast(str, self.yaml_node.tag) == scalar_type_to_tag[typ]\n\n if typ is _Any:\n return True\n raise ValueError('Invalid scalar type passed to is_scalar()')\n return False","def is_array(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_array)","def is_scalar_type(type):\n return type.code in Explorer._SCALAR_TYPE_LIST","def _is_1d_varray(arr):\r\n return len(arr.shape) < 2 or arr.shape[1] == 1","def _is_DataArrays(data):\n if isinstance(data, (Dataset, DataArray)):\n return True\n if isinstance(data, Mapping):\n for da in data.values():\n if not isinstance(da, DataArray):\n raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n if isinstance(data, Iterable):\n for da in data:\n if not isinstance(da, DataArray):\n return False\n # raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n return False","def is_array(val):\n return (\n isinstance(val, tuple) or \\\n isinstance(val, dict) or \\\n isinstance(val, list)\n )","def f_supports(self, data):\n dtype = type(data)\n if dtype is tuple or dtype is list and len(data) == 0:\n return True # ArrayParameter does support empty tuples\n elif dtype is np.ndarray and data.size == 0 and data.ndim == 1:\n return True # ArrayParameter supports empty numpy arrays\n else:\n return super(ArrayParameter, self).f_supports(data)","def NeedsArray(self, type_):\n return self._NameComponents(type_) in self._array_types","def __check_is_xarray(self, data):\n if type(data) is xr.core.dataarray.DataArray or \\\n type(data) is xr.core.dataarray.Dataset:\n\n return True\n else:\n msg = \"Variable {data} is not an xarray DataArray/Dataset\"\n raise Exception(msg)","def is_scalar(space, w_obj):\n return space.wrap(w_obj.tp in (space.tp_int, space.tp_float,\n space.tp_str, space.tp_bool))","def on_disk(self):\n return isinstance(self._subarray, FileArray)","def __check_flat_array__(self):\n if self.flat_array is not None:\n return True\n else:\n return False","def isScalar(self):\n return _libsbml.Rule_isScalar(self)","def IsArray(obj):\n return isinstance(obj, (list, tuple))","def is_scalar(self):\n return self.expr.lhs.is_Symbol","def is_array(schema_obj):\n\n if isinstance(schema_obj, schema.Field):\n return schema_obj.is_array\n return False","def is_vector(self):\n return True if self.width > 1 else False","def _is_tc_entity_array(self, data):\n for d in data:\n if not self._is_tc_entity(d):\n return False\n return True","def is_arraylike(obj):\n if isinstance(obj, list):\n return True\n elif isinstance(obj, np.ndarray):\n return True\n elif isinstance(obj, pd.Series):\n return True\n elif isinstance(obj, pd.DataFrame):\n return True\n return False","def is_array(type):\n nake_type = remove_alias(type)\n nake_type = remove_reference(nake_type)\n nake_type = remove_cv(nake_type)\n return isinstance(nake_type, cpptypes.array_t)","def isfloatarray(cell):\n try:\n cell.astype(float)\n return True\n except ValueError:\n return False","def isscalar(x):\n arrayed_x = asarray(x)\n return asarray(x).ndim == 0 and arrayed_x.dtype != 'object'","def is_intscalar(x: Any) -> bool:\r\n return isinstance(x, (\r\n int,\r\n np.int8,\r\n np.int16,\r\n np.int32,\r\n np.int64,\r\n np.uint8,\r\n np.uint16,\r\n np.uint32,\r\n np.uint64,\r\n ))","def testscfvaluetype(self):\r\n assert isinstance(self.data.scfvalues, list)\r\n assert isinstance(self.data.scfvalues[0], numpy.ndarray)","def _isvalid(self, data):\n if data is None:\n return False\n elif isinstance(data, (list,tuple)):\n if len(data) <= 0:\n return False\n else:\n return True\n elif isinstance(data, (np.ndarray)):\n if data.size <= 0:\n return False\n else:\n return True\n elif not data:\n return False\n else:\n return True","def _is_1d_harray(arr):\r\n return len(arr.shape) < 2 or arr.shape[0] == 1","def isscalar(cls, dataset, dim, per_geom=False):\n dim = dataset.get_dimension(dim)\n if (dim in cls.geom_dims(dataset)):\n return False\n elif per_geom:\n return all(isscalar(v) or len(list(unique_array(v))) == 1\n for v in dataset.data[dim.name])\n dim = dataset.get_dimension(dim)\n return len(dataset.data[dim.name].unique()) == 1","def is_vector(self):\n return len(self.coeffs.shape[self.sdim:]) == 1","def is_scalar(value):\n return not isinstance(value, (list, tuple, dict))","def is_vector(x):\r\n return len(x.shape) == 1","def can_insert(data):\n if not isinstance(data, np.ndarray):\n return False\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\n return False\n return np.issubdtype(data.dtype, np.number)","def is_tensor(self):\n return not self.is_scalar","def test_isarray_vrt(self):\n self.assertIsInstance(_test_array(landsat_vrt), np.ndarray)","def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")","def payload_valid(self, payload):\n return (\n isinstance(payload, DPTArray)\n and len(payload.value) == self.dpt_class.payload_length\n )","def is_string_array(self):\n return self.type == Property.PropertyType.stringArray","def is_scalar(self, indexable, axis):\n index = self._obj.index\n complete_key = False\n partial_key = False\n duplicated_key = False\n if axis == 0 and self._has_fancy_index():\n try:\n if type(indexable) is tuple:\n complete_key = (len(indexable) == len(index.levshape) and\n indexable in index)\n partial_key = not complete_key and indexable in index\n except TypeError: # Unhashable type, no biggie\n pass\n if index.has_duplicates:\n duplicated_key = indexable in index.get_duplicates()\n return (not duplicated_key and\n ((np.isscalar(indexable) and not partial_key) or complete_key))","def is_array_type(self, objtype):\n return isinstance(objtype, self.__arrayt) # _ctypes.PyCArrayType","def isHandleArray(self):\n return (self.decl.name in mpi_array_calls\n and self.pos in mpi_array_calls[self.decl.name])","def is_arrayexpress_array(val):\n return arrayexpress_array_regexp.match(val)","def is_scalar_assign(self):\n return self.is_scalar and not self.is_Increment","def _check_input_for_asarray(array_like):\n if isinstance(array_like, (Tensor, list, tuple, int, float, bool, onp.ndarray)):\n return True\n raise TypeError(\"input data must be `int`, `float`, `bool`, `Tensor`, `list`, `tuple`\" + \\\n f\"or numpy.ndarray, but got {type(array_like)}\")","def is_multiobjects(x: Any) -> bool:\r\n return (is_listlike(x) or (isinstance(x, np.ndarray)\r\n and x.dtype == \"O\")) and len(x) > 0 and not is_scalar(x[0])","def is_floatscalar(x: Any) -> bool:\r\n return isinstance(x, (\r\n float,\r\n np.float16,\r\n np.float32,\r\n np.float64,\r\n ))","def has_vector_type(obj: _std_typing.Any) -> bool:\n return obj.dtype == sc.DType.vector3","def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)","def is_numpy(obj):\n return 'numpy' in str(type(obj))","def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())","def is_dataset(self):\n return self._dataset is not None","def isdense(qob):\n return isinstance(qob, np.ndarray)","def really1d(arr):\n if np.ndim(arr) != 1:\n return False\n # Empty list or array\n if len(arr) == 0:\n return True\n if np.any(np.vectorize(np.ndim)(arr)):\n return False\n return True","def is_a_numpy_array(obj):\n return type(obj).__module__ == np.__name__","def is_splitable_var(var: Any) -> bool:\n if isinstance(var, DataSample):\n return True\n if isinstance(var, torch.Tensor):\n return True\n if isinstance(var, np.ndarray):\n return True\n if isinstance(var, abc.Sequence) and not isinstance(var, str):\n return True\n return False","def can_insert(data):\n types = (float, complex, int, np.long)\n if isinstance(data, types) and not isinstance(data, bool):\n return True\n elif isinstance(data, np.number):\n return data.dtype.char not in UNSUPPORTED_NUMERIC_TYPE_CODES","def test_scalar_index(self):\n dset = self.f.create_dataset('x', shape=(), dtype='f')\n out = dset[...]\n self.assertIsInstance(out, np.ndarray)\n self.assertEqual(out.shape, ())","def HasArrayOuts(self, function):\n if function.callback:\n for param in function.callback.params:\n if self._IsOrContainsArray(param.type_):\n return True\n return function.returns and self._IsOrContainsArray(function.returns)","def is_line_vec(x):\n return x.ndim == 1","def is_real(self):\n return all([isinstance(dim, Real) for dim in self.dimensions])","def is_vec(x):\n return x.ndim == 1 or (x.ndim == 2 and \n (x.shape[0] == 1 or x.shape[1] == 1))","def isAny(self,test):\n for x in np.nditer(self.t, op_flags=['readonly']):\n if op(x):\n return True\n return False","def test_data(self):\n\n self.assertIsInstance(self.image.data, np.ndarray)","def is_pyvista_dataset(obj):\n return isinstance(obj, (pyvista.DataSet, pyvista.MultiBlock))","def _is_unicode(arr):\n if (isinstance(arr, str) or\n issubclass(numpy.asarray(arr).dtype.type, str)):\n return True\n return False","def _is_number(data):\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)","def _datacopied(arr, original):\n if arr is original:\n return False\n if not isinstance(original, np.ndarray) and hasattr(original, '__array__'):\n return False\n return arr.base is None","def isNumeric(data):\n\tif type(data) == list or type(data) == np.ndarray:\n\t\tcol = pd.Series(data)\n\telse:\n\t\tcol = data\n\treturn col.dtype == np.int32 or col.dtype == np.int64 or col.dtype == np.float32 or col.dtype == np.float64","def is_full(self) -> bool:\n return self._array[0].all()","def _values_of_same_type(self, val1, val2):\n if (type(val1) in (np.ndarray, tuple, np.matrix)) and (\n type(val2) is type(val1)\n ):\n return True\n else:\n return super(ArrayParameter, self)._values_of_same_type(val1, val2)","def is_array_of_basic_type(self, objtype):\n return self.is_array_type(objtype) and hasattr(objtype, '_type_') and self.is_basic_type(objtype._type_)","def _check_output_is_scalar(cls, module: Module) -> None:\n if module.output.numel() != 1:\n raise ValueError(\n \"Output must be scalar. Got {}\".format(module.output.shape)\n )","def isscalar(self):\n return not bool(self.shape)","def _datacopied(arr, original):\n if arr is original:\n return False\n if not isinstance(original, numpy.ndarray) and hasattr(original, '__array__'):\n return False\n return arr.base is None","def _isscalar(x):\n return np.isscalar(x) or hasattr(x, \"shape\") and x.shape == ()","def isvec(qob):\n shp = qob.shape\n return len(shp) == 1 or (len(shp) == 2 and (shp[0] == 1 or shp[1] == 1))","def is_co(self, astype):\n if isinstance(astype, (tuple, list)):\n return self.package(\"DataStructure\").CO in astype\n\n return astype is self.package(\"DataStructure\").CO","def check_array(self, v, t):\n raise NotImplementedError('check_array')","def _type_check(data):\n if data.__class__.__name__ != \"Matrix3\":\n return False\n return True","def isFloat(data):\n\tif type(data) == list or type(data) == np.ndarray:\n\t\tcol = pd.Series(data)\n\telse:\n\t\tcol = data\n\treturn col.dtype == np.float32 or col.dtype == np.float64","def is_terminal(self, u1):\n\t\treturn (u1 in self.T) # returns True if in array, else False","def is1d(a):\n return np.sum(asarray(asarray(a).shape) > 1) <= 1","def valid(self):\n if (self._npix == []\n or self._gpix == []\n or self._epix == []\n or self._ppix == []) :\n return False\n return True","def NeedsOptionalArray(self, type_):\n return self._NameComponents(type_) in self._optional_array_types"],"string":"[\n \"def is_scalar(obj: _std_typing.Any) -> bool:\\n return obj.ndim == 0\",\n \"def is_scalar(x: Any) -> bool:\\r\\n return np.isscalar(x) or (isinstance(x, np.ndarray) and x.ndim == 0)\",\n \"def is_array(self):\\n return False\",\n \"def is_scalar(self):\\n return len(self.coeffs.shape[self.sdim:]) == 0\",\n \"def is_array(self):\\n return len(self.descriptor) > 1\",\n \"def is_np_scalar(x):\\n return isinstance(x, np.generic)\",\n \"def is_scalar(x):\\n return x.ndim == 0\",\n \"def is_array(self, arr):\\n return isinstance(arr, np.ndarray)\",\n \"def is_scalar(self):\",\n \"def _is_scalar(shape):\\n return F.shape_mul(shape) == 1\",\n \"def is_scalar(val,\\n include_np: bool = True,\\n include_torch: bool = True) -> bool:\\n if isinstance(val, numbers.Number):\\n return True\\n elif include_np and isinstance(val, np.ndarray) and val.ndim == 0:\\n return True\\n elif include_torch and isinstance(val, torch.Tensor) and len(val) == 1:\\n return True\\n else:\\n return False\",\n \"def _is_scalar_from_shape(shape):\\n return _logical_equal(_ndims_from_shape(shape), 0)\",\n \"def isScalar(self) -> bool:\\n\\n indices = list(range(self.layout.gaDims))\\n indices.remove(self.layout.gradeList.index(0))\\n\\n for i in indices:\\n if abs(self.value[i]) < _eps:\\n continue\\n else:\\n return False\\n\\n return True\",\n \"def is_array(a):\\n try:\\n shape = a.shape\\n return len(shape) >= 1\\n except AttributeError:\\n return False\",\n \"def isscalar(array):\\n arr = ma.array(array)\\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\\n return True\\n return False\",\n \"def isarray(a):\\r\\n try:\\r\\n validity = isinstance(a, ndarray)\\r\\n except:\\r\\n validity = False\\r\\n\\r\\n return validity\",\n \"def isarray(a):\\n try:\\n validity=isinstance(a,ndarray)\\n except:\\n validity=False\\n\\n return validity\",\n \"def is_array(t):\\n return isinstance(t, ast.Array)\",\n \"def _is_key_value_array(self, data):\\n for d in data:\\n if not self._is_key_value(d):\\n return False\\n return True\",\n \"def is_scalar(self, typ: Union[Type, None] = _Any) -> bool:\\n if isinstance(self.yaml_node, yaml.ScalarNode):\\n if typ != _Any and typ in scalar_type_to_tag:\\n if typ is None:\\n typ = type(None)\\n return cast(str, self.yaml_node.tag) == scalar_type_to_tag[typ]\\n\\n if typ is _Any:\\n return True\\n raise ValueError('Invalid scalar type passed to is_scalar()')\\n return False\",\n \"def is_array(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_array)\",\n \"def is_scalar_type(type):\\n return type.code in Explorer._SCALAR_TYPE_LIST\",\n \"def _is_1d_varray(arr):\\r\\n return len(arr.shape) < 2 or arr.shape[1] == 1\",\n \"def _is_DataArrays(data):\\n if isinstance(data, (Dataset, DataArray)):\\n return True\\n if isinstance(data, Mapping):\\n for da in data.values():\\n if not isinstance(da, DataArray):\\n raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n if isinstance(data, Iterable):\\n for da in data:\\n if not isinstance(da, DataArray):\\n return False\\n # raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n return False\",\n \"def is_array(val):\\n return (\\n isinstance(val, tuple) or \\\\\\n isinstance(val, dict) or \\\\\\n isinstance(val, list)\\n )\",\n \"def f_supports(self, data):\\n dtype = type(data)\\n if dtype is tuple or dtype is list and len(data) == 0:\\n return True # ArrayParameter does support empty tuples\\n elif dtype is np.ndarray and data.size == 0 and data.ndim == 1:\\n return True # ArrayParameter supports empty numpy arrays\\n else:\\n return super(ArrayParameter, self).f_supports(data)\",\n \"def NeedsArray(self, type_):\\n return self._NameComponents(type_) in self._array_types\",\n \"def __check_is_xarray(self, data):\\n if type(data) is xr.core.dataarray.DataArray or \\\\\\n type(data) is xr.core.dataarray.Dataset:\\n\\n return True\\n else:\\n msg = \\\"Variable {data} is not an xarray DataArray/Dataset\\\"\\n raise Exception(msg)\",\n \"def is_scalar(space, w_obj):\\n return space.wrap(w_obj.tp in (space.tp_int, space.tp_float,\\n space.tp_str, space.tp_bool))\",\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def __check_flat_array__(self):\\n if self.flat_array is not None:\\n return True\\n else:\\n return False\",\n \"def isScalar(self):\\n return _libsbml.Rule_isScalar(self)\",\n \"def IsArray(obj):\\n return isinstance(obj, (list, tuple))\",\n \"def is_scalar(self):\\n return self.expr.lhs.is_Symbol\",\n \"def is_array(schema_obj):\\n\\n if isinstance(schema_obj, schema.Field):\\n return schema_obj.is_array\\n return False\",\n \"def is_vector(self):\\n return True if self.width > 1 else False\",\n \"def _is_tc_entity_array(self, data):\\n for d in data:\\n if not self._is_tc_entity(d):\\n return False\\n return True\",\n \"def is_arraylike(obj):\\n if isinstance(obj, list):\\n return True\\n elif isinstance(obj, np.ndarray):\\n return True\\n elif isinstance(obj, pd.Series):\\n return True\\n elif isinstance(obj, pd.DataFrame):\\n return True\\n return False\",\n \"def is_array(type):\\n nake_type = remove_alias(type)\\n nake_type = remove_reference(nake_type)\\n nake_type = remove_cv(nake_type)\\n return isinstance(nake_type, cpptypes.array_t)\",\n \"def isfloatarray(cell):\\n try:\\n cell.astype(float)\\n return True\\n except ValueError:\\n return False\",\n \"def isscalar(x):\\n arrayed_x = asarray(x)\\n return asarray(x).ndim == 0 and arrayed_x.dtype != 'object'\",\n \"def is_intscalar(x: Any) -> bool:\\r\\n return isinstance(x, (\\r\\n int,\\r\\n np.int8,\\r\\n np.int16,\\r\\n np.int32,\\r\\n np.int64,\\r\\n np.uint8,\\r\\n np.uint16,\\r\\n np.uint32,\\r\\n np.uint64,\\r\\n ))\",\n \"def testscfvaluetype(self):\\r\\n assert isinstance(self.data.scfvalues, list)\\r\\n assert isinstance(self.data.scfvalues[0], numpy.ndarray)\",\n \"def _isvalid(self, data):\\n if data is None:\\n return False\\n elif isinstance(data, (list,tuple)):\\n if len(data) <= 0:\\n return False\\n else:\\n return True\\n elif isinstance(data, (np.ndarray)):\\n if data.size <= 0:\\n return False\\n else:\\n return True\\n elif not data:\\n return False\\n else:\\n return True\",\n \"def _is_1d_harray(arr):\\r\\n return len(arr.shape) < 2 or arr.shape[0] == 1\",\n \"def isscalar(cls, dataset, dim, per_geom=False):\\n dim = dataset.get_dimension(dim)\\n if (dim in cls.geom_dims(dataset)):\\n return False\\n elif per_geom:\\n return all(isscalar(v) or len(list(unique_array(v))) == 1\\n for v in dataset.data[dim.name])\\n dim = dataset.get_dimension(dim)\\n return len(dataset.data[dim.name].unique()) == 1\",\n \"def is_vector(self):\\n return len(self.coeffs.shape[self.sdim:]) == 1\",\n \"def is_scalar(value):\\n return not isinstance(value, (list, tuple, dict))\",\n \"def is_vector(x):\\r\\n return len(x.shape) == 1\",\n \"def can_insert(data):\\n if not isinstance(data, np.ndarray):\\n return False\\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\\n return False\\n return np.issubdtype(data.dtype, np.number)\",\n \"def is_tensor(self):\\n return not self.is_scalar\",\n \"def test_isarray_vrt(self):\\n self.assertIsInstance(_test_array(landsat_vrt), np.ndarray)\",\n \"def in_memory(self):\\n return hasattr(self._subarray, \\\"__array_interface__\\\")\",\n \"def payload_valid(self, payload):\\n return (\\n isinstance(payload, DPTArray)\\n and len(payload.value) == self.dpt_class.payload_length\\n )\",\n \"def is_string_array(self):\\n return self.type == Property.PropertyType.stringArray\",\n \"def is_scalar(self, indexable, axis):\\n index = self._obj.index\\n complete_key = False\\n partial_key = False\\n duplicated_key = False\\n if axis == 0 and self._has_fancy_index():\\n try:\\n if type(indexable) is tuple:\\n complete_key = (len(indexable) == len(index.levshape) and\\n indexable in index)\\n partial_key = not complete_key and indexable in index\\n except TypeError: # Unhashable type, no biggie\\n pass\\n if index.has_duplicates:\\n duplicated_key = indexable in index.get_duplicates()\\n return (not duplicated_key and\\n ((np.isscalar(indexable) and not partial_key) or complete_key))\",\n \"def is_array_type(self, objtype):\\n return isinstance(objtype, self.__arrayt) # _ctypes.PyCArrayType\",\n \"def isHandleArray(self):\\n return (self.decl.name in mpi_array_calls\\n and self.pos in mpi_array_calls[self.decl.name])\",\n \"def is_arrayexpress_array(val):\\n return arrayexpress_array_regexp.match(val)\",\n \"def is_scalar_assign(self):\\n return self.is_scalar and not self.is_Increment\",\n \"def _check_input_for_asarray(array_like):\\n if isinstance(array_like, (Tensor, list, tuple, int, float, bool, onp.ndarray)):\\n return True\\n raise TypeError(\\\"input data must be `int`, `float`, `bool`, `Tensor`, `list`, `tuple`\\\" + \\\\\\n f\\\"or numpy.ndarray, but got {type(array_like)}\\\")\",\n \"def is_multiobjects(x: Any) -> bool:\\r\\n return (is_listlike(x) or (isinstance(x, np.ndarray)\\r\\n and x.dtype == \\\"O\\\")) and len(x) > 0 and not is_scalar(x[0])\",\n \"def is_floatscalar(x: Any) -> bool:\\r\\n return isinstance(x, (\\r\\n float,\\r\\n np.float16,\\r\\n np.float32,\\r\\n np.float64,\\r\\n ))\",\n \"def has_vector_type(obj: _std_typing.Any) -> bool:\\n return obj.dtype == sc.DType.vector3\",\n \"def _is_string(data):\\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)\",\n \"def is_numpy(obj):\\n return 'numpy' in str(type(obj))\",\n \"def in_memory(self) -> bool:\\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())\",\n \"def is_dataset(self):\\n return self._dataset is not None\",\n \"def isdense(qob):\\n return isinstance(qob, np.ndarray)\",\n \"def really1d(arr):\\n if np.ndim(arr) != 1:\\n return False\\n # Empty list or array\\n if len(arr) == 0:\\n return True\\n if np.any(np.vectorize(np.ndim)(arr)):\\n return False\\n return True\",\n \"def is_a_numpy_array(obj):\\n return type(obj).__module__ == np.__name__\",\n \"def is_splitable_var(var: Any) -> bool:\\n if isinstance(var, DataSample):\\n return True\\n if isinstance(var, torch.Tensor):\\n return True\\n if isinstance(var, np.ndarray):\\n return True\\n if isinstance(var, abc.Sequence) and not isinstance(var, str):\\n return True\\n return False\",\n \"def can_insert(data):\\n types = (float, complex, int, np.long)\\n if isinstance(data, types) and not isinstance(data, bool):\\n return True\\n elif isinstance(data, np.number):\\n return data.dtype.char not in UNSUPPORTED_NUMERIC_TYPE_CODES\",\n \"def test_scalar_index(self):\\n dset = self.f.create_dataset('x', shape=(), dtype='f')\\n out = dset[...]\\n self.assertIsInstance(out, np.ndarray)\\n self.assertEqual(out.shape, ())\",\n \"def HasArrayOuts(self, function):\\n if function.callback:\\n for param in function.callback.params:\\n if self._IsOrContainsArray(param.type_):\\n return True\\n return function.returns and self._IsOrContainsArray(function.returns)\",\n \"def is_line_vec(x):\\n return x.ndim == 1\",\n \"def is_real(self):\\n return all([isinstance(dim, Real) for dim in self.dimensions])\",\n \"def is_vec(x):\\n return x.ndim == 1 or (x.ndim == 2 and \\n (x.shape[0] == 1 or x.shape[1] == 1))\",\n \"def isAny(self,test):\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n if op(x):\\n return True\\n return False\",\n \"def test_data(self):\\n\\n self.assertIsInstance(self.image.data, np.ndarray)\",\n \"def is_pyvista_dataset(obj):\\n return isinstance(obj, (pyvista.DataSet, pyvista.MultiBlock))\",\n \"def _is_unicode(arr):\\n if (isinstance(arr, str) or\\n issubclass(numpy.asarray(arr).dtype.type, str)):\\n return True\\n return False\",\n \"def _is_number(data):\\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)\",\n \"def _datacopied(arr, original):\\n if arr is original:\\n return False\\n if not isinstance(original, np.ndarray) and hasattr(original, '__array__'):\\n return False\\n return arr.base is None\",\n \"def isNumeric(data):\\n\\tif type(data) == list or type(data) == np.ndarray:\\n\\t\\tcol = pd.Series(data)\\n\\telse:\\n\\t\\tcol = data\\n\\treturn col.dtype == np.int32 or col.dtype == np.int64 or col.dtype == np.float32 or col.dtype == np.float64\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def _values_of_same_type(self, val1, val2):\\n if (type(val1) in (np.ndarray, tuple, np.matrix)) and (\\n type(val2) is type(val1)\\n ):\\n return True\\n else:\\n return super(ArrayParameter, self)._values_of_same_type(val1, val2)\",\n \"def is_array_of_basic_type(self, objtype):\\n return self.is_array_type(objtype) and hasattr(objtype, '_type_') and self.is_basic_type(objtype._type_)\",\n \"def _check_output_is_scalar(cls, module: Module) -> None:\\n if module.output.numel() != 1:\\n raise ValueError(\\n \\\"Output must be scalar. Got {}\\\".format(module.output.shape)\\n )\",\n \"def isscalar(self):\\n return not bool(self.shape)\",\n \"def _datacopied(arr, original):\\n if arr is original:\\n return False\\n if not isinstance(original, numpy.ndarray) and hasattr(original, '__array__'):\\n return False\\n return arr.base is None\",\n \"def _isscalar(x):\\n return np.isscalar(x) or hasattr(x, \\\"shape\\\") and x.shape == ()\",\n \"def isvec(qob):\\n shp = qob.shape\\n return len(shp) == 1 or (len(shp) == 2 and (shp[0] == 1 or shp[1] == 1))\",\n \"def is_co(self, astype):\\n if isinstance(astype, (tuple, list)):\\n return self.package(\\\"DataStructure\\\").CO in astype\\n\\n return astype is self.package(\\\"DataStructure\\\").CO\",\n \"def check_array(self, v, t):\\n raise NotImplementedError('check_array')\",\n \"def _type_check(data):\\n if data.__class__.__name__ != \\\"Matrix3\\\":\\n return False\\n return True\",\n \"def isFloat(data):\\n\\tif type(data) == list or type(data) == np.ndarray:\\n\\t\\tcol = pd.Series(data)\\n\\telse:\\n\\t\\tcol = data\\n\\treturn col.dtype == np.float32 or col.dtype == np.float64\",\n \"def is_terminal(self, u1):\\n\\t\\treturn (u1 in self.T) # returns True if in array, else False\",\n \"def is1d(a):\\n return np.sum(asarray(asarray(a).shape) > 1) <= 1\",\n \"def valid(self):\\n if (self._npix == []\\n or self._gpix == []\\n or self._epix == []\\n or self._ppix == []) :\\n return False\\n return True\",\n \"def NeedsOptionalArray(self, type_):\\n return self._NameComponents(type_) in self._optional_array_types\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74611837","0.74560386","0.7210494","0.71899956","0.7152722","0.7101354","0.70952576","0.7088053","0.7086914","0.69344157","0.6923261","0.69007254","0.68844897","0.68752134","0.67174804","0.66352606","0.66348445","0.65968424","0.658544","0.65799415","0.6557284","0.6505468","0.6459045","0.64290345","0.64111173","0.6410244","0.63991654","0.6393071","0.6372672","0.6341929","0.6273665","0.62705076","0.6268065","0.62492824","0.62067956","0.61981165","0.6171595","0.6159577","0.61583346","0.614487","0.61434245","0.6134229","0.6120269","0.61083704","0.6101795","0.61015964","0.6101361","0.60985875","0.6081736","0.60817266","0.6054252","0.6035537","0.6031037","0.60202837","0.6018561","0.6013677","0.6006127","0.60021734","0.5986654","0.597252","0.5967119","0.59473664","0.59448504","0.5941868","0.59343165","0.5914852","0.59124994","0.59072024","0.5895152","0.58922434","0.5892022","0.5872773","0.5872195","0.5865648","0.5818467","0.5796972","0.57894266","0.57872","0.57867545","0.5771822","0.5769761","0.5764762","0.5757891","0.5754791","0.57460696","0.5742229","0.57361573","0.5730914","0.5722491","0.5722441","0.5717465","0.5705535","0.57010245","0.5694234","0.568672","0.568411","0.56804854","0.5678261","0.5663252","0.5645139","0.5644997"],"string":"[\n \"0.74611837\",\n \"0.74560386\",\n \"0.7210494\",\n \"0.71899956\",\n \"0.7152722\",\n \"0.7101354\",\n \"0.70952576\",\n \"0.7088053\",\n \"0.7086914\",\n \"0.69344157\",\n \"0.6923261\",\n \"0.69007254\",\n \"0.68844897\",\n \"0.68752134\",\n \"0.67174804\",\n \"0.66352606\",\n \"0.66348445\",\n \"0.65968424\",\n \"0.658544\",\n \"0.65799415\",\n \"0.6557284\",\n \"0.6505468\",\n \"0.6459045\",\n \"0.64290345\",\n \"0.64111173\",\n \"0.6410244\",\n \"0.63991654\",\n \"0.6393071\",\n \"0.6372672\",\n \"0.6341929\",\n \"0.6273665\",\n \"0.62705076\",\n \"0.6268065\",\n \"0.62492824\",\n \"0.62067956\",\n \"0.61981165\",\n \"0.6171595\",\n \"0.6159577\",\n \"0.61583346\",\n \"0.614487\",\n \"0.61434245\",\n \"0.6134229\",\n \"0.6120269\",\n \"0.61083704\",\n \"0.6101795\",\n \"0.61015964\",\n \"0.6101361\",\n \"0.60985875\",\n \"0.6081736\",\n \"0.60817266\",\n \"0.6054252\",\n \"0.6035537\",\n \"0.6031037\",\n \"0.60202837\",\n \"0.6018561\",\n \"0.6013677\",\n \"0.6006127\",\n \"0.60021734\",\n \"0.5986654\",\n \"0.597252\",\n \"0.5967119\",\n \"0.59473664\",\n \"0.59448504\",\n \"0.5941868\",\n \"0.59343165\",\n \"0.5914852\",\n \"0.59124994\",\n \"0.59072024\",\n \"0.5895152\",\n \"0.58922434\",\n \"0.5892022\",\n \"0.5872773\",\n \"0.5872195\",\n \"0.5865648\",\n \"0.5818467\",\n \"0.5796972\",\n \"0.57894266\",\n \"0.57872\",\n \"0.57867545\",\n \"0.5771822\",\n \"0.5769761\",\n \"0.5764762\",\n \"0.5757891\",\n \"0.5754791\",\n \"0.57460696\",\n \"0.5742229\",\n \"0.57361573\",\n \"0.5730914\",\n \"0.5722491\",\n \"0.5722441\",\n \"0.5717465\",\n \"0.5705535\",\n \"0.57010245\",\n \"0.5694234\",\n \"0.568672\",\n \"0.568411\",\n \"0.56804854\",\n \"0.5678261\",\n \"0.5663252\",\n \"0.5645139\",\n \"0.5644997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":2685,"cells":{"query":{"kind":"string","value":"The size in bytes of the subarray. The size takes into account the datatype and assumes that there is a boolean mask, unless it can be ascertained that there isn't one."},"document":{"kind":"string","value":"def nbytes(self):\n dtype = self.config[\"dtype\"]\n if dtype is None:\n return None\n\n size = reduce(mul, self.shape, 1)\n nbytes = size * dtype.itemsize\n\n if getattr(self, \"masked\", True):\n nbytes += size\n\n return nbytes"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def arraySize( cls, value, typeCode = None ):\n return value.size","def array_size(self):\n return self._array_size","def container_size(self):\n import cPickle\n import sys\n t = cPickle.dumps(self.filter_bitarray)\n return sys.getsizeof(t)","def size(self):\n ret = 0\n for ii in self.__data:\n ret += int(ii.get_size())\n return ret","def size(self):\n return self.dtype.itemsize","def ndarray_size(self) -> int:\n pass","def getArrayLength(self):\r\n return self.arraySize","def get_size(self, shape_info):\r\n if shape_info:\r\n return numpy.prod(shape_info) * numpy.dtype(self.dtype).itemsize\r\n else: # a scalar\r\n return numpy.dtype(self.dtype).itemsize","def mask_size(self):\n m = self.size * self.mask()\n return m.astype(np.int8)","def _size(self):\n return self._logicalSize","def __len__(self):\n return int(self.size._value)","def size(self):\n return len(self.arr)","def size(self):\r\n return self._logicalSize","def size(self):\n size = 1\n for current_slice in self.slices:\n size *= current_slice.stop - current_slice.start\n return size","def size(self):\n return len(self.array_form)","def get_array_size(self):\r\n return conf.lib.clang_getArraySize(self)","def size(self):\n return self.N","def size(self):\n return self._N","def nbytes(self):\n\n return self.data.type.datasize","def __len__(self):\r\n if self.is_superset:\r\n length = 0\r\n for ds in self.data:\r\n length += len(ds)\r\n return length\r\n else:\r\n return len(self.data)","def get_size(self):\n return (\n sys.getsizeof(self.children) +\n sys.getsizeof(self.parent) +\n sys.getsizeof(self.dataset_id) +\n sys.getsizeof(self.k) +\n self.filter.get_size()\n )","def size(self) -> int:\n size = self.da.length()\n return size","def size(self):\n return self.data.size","def ndarray_size(data, dtype=\"int32\"):\n return _make.ndarray_size(data, dtype)","def __len__(self) -> int:\n\n return self.shape.shape[1]","def arrayByteCount( cls, value, typeCode = None ):\n try:\n return value.nbytes\n except AttributeError:\n if cls.ERROR_ON_COPY:\n raise error.CopyError(\n \"\"\"Non-numpy array passed to numpy arrayByteCount: %s\"\"\",\n type(value),\n )\n value = cls.asArray( value, typeCode )\n return value.nbytes","def size(self):\n size = 0\n size += self.data.size * sys.getsizeof(self.data)\n return size / 1024.0 / 1024.0 / 1024.0","def nbytes(self):\n return self.nnz * self.dtype.itemsize","def sizeof(shape, dtype=\"uint8\"):\n itemsize = numpy.dtype(dtype).itemsize\n cnt = 1\n if \"__len__\" in dir(shape):\n for dim in shape:\n cnt *= dim\n else:\n cnt = int(shape)\n return cnt * itemsize","def size (self):\n\t\timport struct\n\t\treturn struct.calcsize (self.struct)","def __len__(self):\n return self._arr.shape[1]","def size(self) -> int:","def size(self):\n return int(misc.intprod(self.shape))","def size(self) -> int:\r\n return self.da.length()","def size(self):\n\t\treturn self.dims","def size(self) -> int:\n return self.da.length()","def size(self) -> int:\n return self.da.length()","def size(self) -> int:\n return self.da.length()","def size(self) -> int:\n return self.da.length()","def size(self):\r\n return self.__length","def get_size(self):\n return self._data_size","def itemsize(self):\n return self.dtype.base.itemsize","def itemsize(self):\n return self.dtype.base.itemsize","def itemsize(self):\n return self.dtype.base.itemsize","def itemsize(self):\n return self.dtype.base.itemsize","def size(self):\r\n return self.size.data","def size(self):\n return self.__length","def size(self) -> Tuple[groupable, pdarray]:\n return self.count()","def __len__(self):\n a = 1\n for size in self.sizes:\n a *= size\n return a","def numel(self):\n return self.t.size","def size(self):\n return self.__size","def get_size(self):\n tmpsize = 0\n for variable in self.variables:\n tmpsize += variable.get_size()\n for subchunk in self.subchunks:\n tmpsize += subchunk.get_size()\n return tmpsize","def size(self):\r\n # Anthony stage 2\r\n return number_size(self.n) - 1","def get_size(self):\n # return the size along the index dimension\n size = 0\n if self._data is not None:\n size = shape(self._data)[self.index_dimension]\n\n return size","def count(self):\r\n return self.data_array.size","def __len__(self):\n # type: () -> int\n return self.shape[0]","def size(self):\r\n return self._size","def bitSizeOf() -> int:\n\n return 1","def __len__(self):\n return len(np.where(np.logical_not(self.data.mask))[0])","def size(self):\n return self._length","def __len__(self):\n return len(self.array)","def __len__(self):\n return len(self.array)","def size(self) -> int:\n return int(np.multiply(*self.shape))","def size(self):\n return len(self.data)","def size(self):\n return len(self.data)","def size(self):\n return len(self.data)","def size(self):\n return len(self.data)","def size(self):\n return len(self.data)","def size(self, index):\n return self.base_dataset.size(index)","def size(self):\n return self.__size","def size(self):\n return self.__size","def size(self):\n return self.__size","def size(self):\n return self.__size","def size(self):\n return self.__size","def size(self):\n return self.unpack_dword(0x4)","def size(self):\n return self.unpack_dword(0x4)","def __len__(self):\n return np.size(self.A,0)","def nbytes_at(self, device_id:int):\n if self._slices:\n if isinstance(self._coherence._local_states[device_id], dict): # there are subarrays no this device\n if self._slices_hash in self._coherence._local_states[device_id].keys(): # this subarray is already there\n return self._array.nbytes_at(device_id)\n else: # the subarray will be moved to there\n return self._array.nbytes_at(device_id) + self.subarray_nbytes # add the incoming subarray size\n else: # there is a complete copy on this device, no need to prepare subarray\n return self.nbytes\n else:\n return self.nbytes","def __len__(self):\n\t\treturn self._size","def size(self):\n\t\treturn self._size","def size(self):\n return self.__size","def unitSize( cls, value, typeCode=None ):\n return value.shape[-1]","def __len__(self) -> int:\n\n return self.__size","def __len__(self):\r\n return self._size","def __len__(self):\r\n return self._size","def size(self):\n if type(self._shape).__name__ == 'tuple':\n return self._shape[-1]\n else:\n return self._shape","def __len__(self):\n if not self.opt.union:\n return min(len(self.dataset), self.opt.max_dataset_size)\n else:\n return len(self.batch_sampler)","def __len__(self) -> int:\n return self.size","def __len__(self) -> int:\n return self.size","def __len__(self) -> int:\n return self.size","def size(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size","def __len__(self):\n return self._size"],"string":"[\n \"def arraySize( cls, value, typeCode = None ):\\n return value.size\",\n \"def array_size(self):\\n return self._array_size\",\n \"def container_size(self):\\n import cPickle\\n import sys\\n t = cPickle.dumps(self.filter_bitarray)\\n return sys.getsizeof(t)\",\n \"def size(self):\\n ret = 0\\n for ii in self.__data:\\n ret += int(ii.get_size())\\n return ret\",\n \"def size(self):\\n return self.dtype.itemsize\",\n \"def ndarray_size(self) -> int:\\n pass\",\n \"def getArrayLength(self):\\r\\n return self.arraySize\",\n \"def get_size(self, shape_info):\\r\\n if shape_info:\\r\\n return numpy.prod(shape_info) * numpy.dtype(self.dtype).itemsize\\r\\n else: # a scalar\\r\\n return numpy.dtype(self.dtype).itemsize\",\n \"def mask_size(self):\\n m = self.size * self.mask()\\n return m.astype(np.int8)\",\n \"def _size(self):\\n return self._logicalSize\",\n \"def __len__(self):\\n return int(self.size._value)\",\n \"def size(self):\\n return len(self.arr)\",\n \"def size(self):\\r\\n return self._logicalSize\",\n \"def size(self):\\n size = 1\\n for current_slice in self.slices:\\n size *= current_slice.stop - current_slice.start\\n return size\",\n \"def size(self):\\n return len(self.array_form)\",\n \"def get_array_size(self):\\r\\n return conf.lib.clang_getArraySize(self)\",\n \"def size(self):\\n return self.N\",\n \"def size(self):\\n return self._N\",\n \"def nbytes(self):\\n\\n return self.data.type.datasize\",\n \"def __len__(self):\\r\\n if self.is_superset:\\r\\n length = 0\\r\\n for ds in self.data:\\r\\n length += len(ds)\\r\\n return length\\r\\n else:\\r\\n return len(self.data)\",\n \"def get_size(self):\\n return (\\n sys.getsizeof(self.children) +\\n sys.getsizeof(self.parent) +\\n sys.getsizeof(self.dataset_id) +\\n sys.getsizeof(self.k) +\\n self.filter.get_size()\\n )\",\n \"def size(self) -> int:\\n size = self.da.length()\\n return size\",\n \"def size(self):\\n return self.data.size\",\n \"def ndarray_size(data, dtype=\\\"int32\\\"):\\n return _make.ndarray_size(data, dtype)\",\n \"def __len__(self) -> int:\\n\\n return self.shape.shape[1]\",\n \"def arrayByteCount( cls, value, typeCode = None ):\\n try:\\n return value.nbytes\\n except AttributeError:\\n if cls.ERROR_ON_COPY:\\n raise error.CopyError(\\n \\\"\\\"\\\"Non-numpy array passed to numpy arrayByteCount: %s\\\"\\\"\\\",\\n type(value),\\n )\\n value = cls.asArray( value, typeCode )\\n return value.nbytes\",\n \"def size(self):\\n size = 0\\n size += self.data.size * sys.getsizeof(self.data)\\n return size / 1024.0 / 1024.0 / 1024.0\",\n \"def nbytes(self):\\n return self.nnz * self.dtype.itemsize\",\n \"def sizeof(shape, dtype=\\\"uint8\\\"):\\n itemsize = numpy.dtype(dtype).itemsize\\n cnt = 1\\n if \\\"__len__\\\" in dir(shape):\\n for dim in shape:\\n cnt *= dim\\n else:\\n cnt = int(shape)\\n return cnt * itemsize\",\n \"def size (self):\\n\\t\\timport struct\\n\\t\\treturn struct.calcsize (self.struct)\",\n \"def __len__(self):\\n return self._arr.shape[1]\",\n \"def size(self) -> int:\",\n \"def size(self):\\n return int(misc.intprod(self.shape))\",\n \"def size(self) -> int:\\r\\n return self.da.length()\",\n \"def size(self):\\n\\t\\treturn self.dims\",\n \"def size(self) -> int:\\n return self.da.length()\",\n \"def size(self) -> int:\\n return self.da.length()\",\n \"def size(self) -> int:\\n return self.da.length()\",\n \"def size(self) -> int:\\n return self.da.length()\",\n \"def size(self):\\r\\n return self.__length\",\n \"def get_size(self):\\n return self._data_size\",\n \"def itemsize(self):\\n return self.dtype.base.itemsize\",\n \"def itemsize(self):\\n return self.dtype.base.itemsize\",\n \"def itemsize(self):\\n return self.dtype.base.itemsize\",\n \"def itemsize(self):\\n return self.dtype.base.itemsize\",\n \"def size(self):\\r\\n return self.size.data\",\n \"def size(self):\\n return self.__length\",\n \"def size(self) -> Tuple[groupable, pdarray]:\\n return self.count()\",\n \"def __len__(self):\\n a = 1\\n for size in self.sizes:\\n a *= size\\n return a\",\n \"def numel(self):\\n return self.t.size\",\n \"def size(self):\\n return self.__size\",\n \"def get_size(self):\\n tmpsize = 0\\n for variable in self.variables:\\n tmpsize += variable.get_size()\\n for subchunk in self.subchunks:\\n tmpsize += subchunk.get_size()\\n return tmpsize\",\n \"def size(self):\\r\\n # Anthony stage 2\\r\\n return number_size(self.n) - 1\",\n \"def get_size(self):\\n # return the size along the index dimension\\n size = 0\\n if self._data is not None:\\n size = shape(self._data)[self.index_dimension]\\n\\n return size\",\n \"def count(self):\\r\\n return self.data_array.size\",\n \"def __len__(self):\\n # type: () -> int\\n return self.shape[0]\",\n \"def size(self):\\r\\n return self._size\",\n \"def bitSizeOf() -> int:\\n\\n return 1\",\n \"def __len__(self):\\n return len(np.where(np.logical_not(self.data.mask))[0])\",\n \"def size(self):\\n return self._length\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def size(self) -> int:\\n return int(np.multiply(*self.shape))\",\n \"def size(self):\\n return len(self.data)\",\n \"def size(self):\\n return len(self.data)\",\n \"def size(self):\\n return len(self.data)\",\n \"def size(self):\\n return len(self.data)\",\n \"def size(self):\\n return len(self.data)\",\n \"def size(self, index):\\n return self.base_dataset.size(index)\",\n \"def size(self):\\n return self.__size\",\n \"def size(self):\\n return self.__size\",\n \"def size(self):\\n return self.__size\",\n \"def size(self):\\n return self.__size\",\n \"def size(self):\\n return self.__size\",\n \"def size(self):\\n return self.unpack_dword(0x4)\",\n \"def size(self):\\n return self.unpack_dword(0x4)\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def nbytes_at(self, device_id:int):\\n if self._slices:\\n if isinstance(self._coherence._local_states[device_id], dict): # there are subarrays no this device\\n if self._slices_hash in self._coherence._local_states[device_id].keys(): # this subarray is already there\\n return self._array.nbytes_at(device_id)\\n else: # the subarray will be moved to there\\n return self._array.nbytes_at(device_id) + self.subarray_nbytes # add the incoming subarray size\\n else: # there is a complete copy on this device, no need to prepare subarray\\n return self.nbytes\\n else:\\n return self.nbytes\",\n \"def __len__(self):\\n\\t\\treturn self._size\",\n \"def size(self):\\n\\t\\treturn self._size\",\n \"def size(self):\\n return self.__size\",\n \"def unitSize( cls, value, typeCode=None ):\\n return value.shape[-1]\",\n \"def __len__(self) -> int:\\n\\n return self.__size\",\n \"def __len__(self):\\r\\n return self._size\",\n \"def __len__(self):\\r\\n return self._size\",\n \"def size(self):\\n if type(self._shape).__name__ == 'tuple':\\n return self._shape[-1]\\n else:\\n return self._shape\",\n \"def __len__(self):\\n if not self.opt.union:\\n return min(len(self.dataset), self.opt.max_dataset_size)\\n else:\\n return len(self.batch_sampler)\",\n \"def __len__(self) -> int:\\n return self.size\",\n \"def __len__(self) -> int:\\n return self.size\",\n \"def __len__(self) -> int:\\n return self.size\",\n \"def size(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.718968\",\n \"0.71867967\",\n \"0.7167967\",\n \"0.70721114\",\n \"0.69927114\",\n \"0.69399047\",\n \"0.6921515\",\n \"0.68736595\",\n \"0.6852514\",\n \"0.6835874\",\n \"0.6802148\",\n \"0.67929095\",\n \"0.67913187\",\n \"0.67521507\",\n \"0.6731404\",\n \"0.6717999\",\n \"0.67143357\",\n \"0.6703314\",\n \"0.67020184\",\n \"0.6700556\",\n \"0.6697106\",\n \"0.6682291\",\n \"0.6651554\",\n \"0.66490614\",\n \"0.6648868\",\n \"0.66486084\",\n \"0.6644012\",\n \"0.6624058\",\n \"0.6616036\",\n \"0.6614012\",\n \"0.6613987\",\n \"0.65858674\",\n \"0.6571506\",\n \"0.6548967\",\n \"0.6544703\",\n \"0.6543546\",\n \"0.6543546\",\n \"0.6543546\",\n \"0.6543546\",\n \"0.6538286\",\n \"0.6535692\",\n \"0.65194714\",\n \"0.65194714\",\n \"0.65194714\",\n \"0.65194714\",\n \"0.6514875\",\n \"0.6498841\",\n \"0.6498648\",\n \"0.6492229\",\n \"0.64905584\",\n \"0.6470526\",\n \"0.64686674\",\n \"0.6458288\",\n \"0.64574635\",\n \"0.645642\",\n \"0.64554864\",\n \"0.64527833\",\n \"0.6442754\",\n \"0.6437928\",\n \"0.6437905\",\n \"0.6433788\",\n \"0.6433788\",\n \"0.64305586\",\n \"0.6421952\",\n \"0.6421952\",\n \"0.6421952\",\n \"0.6421952\",\n \"0.6421952\",\n \"0.64153194\",\n \"0.641009\",\n \"0.641009\",\n \"0.641009\",\n \"0.641009\",\n \"0.641009\",\n \"0.64043105\",\n \"0.64043105\",\n \"0.6402194\",\n \"0.6402177\",\n \"0.6399923\",\n \"0.63992476\",\n \"0.6390697\",\n \"0.63888264\",\n \"0.63844407\",\n \"0.63751054\",\n \"0.63751054\",\n \"0.6372049\",\n \"0.63711333\",\n \"0.6369956\",\n \"0.6369956\",\n \"0.6369956\",\n \"0.63686407\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\",\n \"0.636768\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6896727"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":2686,"cells":{"query":{"kind":"string","value":"Number of array dimensions."},"document":{"kind":"string","value":"def ndim(self):\n return len(self.shape)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def n_dims(self):\n return len(self.dimensions)","def num_dim(self):\n return len(self._dimensions)","def num_dim(self):\n return len(self._dimensions)","def get_num_dimensions(self):\n dimensions = self.data.shape\n return dimensions[1]","def count_dims(da):\n return len(da.dims)","def ndims(x):\n return len(x.get_shape())","def dims(x):\n return len(x.shape)","def size(self)->int:\n\n return np.prod([axes if is_integer(axes) else len(axes) for axes in self._dim_axes])","def n_dims(self):\n return self.pdm.n_dims","def num_dims(self):\n return self.h5['{}/{}'.format(SETTINGS, N_DIMS_STR)][()]","def getNumDimensions(self):\n return len(self.di.keys())","def dim(self) -> int:\n pass","def ndarray_size(self) -> int:\n pass","def size(self):\n\t\treturn self.dims","def dim(self):\n return len(self._n)","def dim(self) -> int:\n return self._n_dim","def dim(self):\n return len(self.shape)","def dim(self):\n return len(self.shape)","def n_dim(self):\n return self._n_dim","def dimensionality(self):\n return int(self.nDims)","def ndim(self):\n # type: () -> int\n return len(self.shape)","def dimension(self):\n return self.__N","def ndim(self):\n return len(self.nvars)","def dim(self) -> int:\n return self.atoms.shape[:-1]","def dim(self) -> int:","def num_dimensions(self):\n if self.__num_dimensions__ == 0:\n # Try to get the number of dimensions from the first point or bounding box\n if len(self.points) > 0:\n self.__num_dimensions__ = len(self.points[0].coordinate)\n elif len(self.bounding_boxes) > 0:\n self.__num_dimensions__ = len(self.bounding_boxes[0].start)\n return self.__num_dimensions__","def ndim(self):\n return len(self._shape)","def ndim(self) -> int:\r\n return len(self.plates)","def dimensions():","def get_dimension_number(self) -> int:\n return np.squeeze(self._channel_arrays[0]).ndim","def dimension(self):\r\n a = 0\r\n for x in self.faces():\r\n if (len(x) > a):\r\n a = len(x) \r\n return a-1","def dimension_count(self):\n return self._dimensionCount","def get_dimension_length(self):\n pass","def __len__(self) -> int:\n\n return self.layout.gaDims","def dims(self):\n raise NotImplementedError('Please use Vector2Array or Vector3Array')","def ndim(self):\n return self.data.ndim","def get_dims(self):\n row_lbl, col_lbl = self.get_idxvals()\n return len(row_lbl), len(col_lbl)","def ndim(self):\n return len(self.point)","def n_dims(self):\n return self.model.template_instance.n_dims","def _get_observation_dimension(self):\n return len(self._get_observation_np())","def dim(self,mat):\n result = np.shape(mat)\n self.dimensions = result\n return self.dimensions","def dimensions(self):\n return len(self.parameter_names)","def N(self):\n return self._dimensions","def ndim(self):\n return self._ndim","def xdim(self):\n return len(self._x)","def ndim(self) -> int:\n return self[0].ndim","def ndim(self) -> int:\n\n return 1 + len(self.shape)","def ndim(self):\n return self.__value.ndim","def size(self):\n return int(misc.intprod(self.shape))","def dimension_size(self):\n return self._dim","def ndim(self):\n return len(self.edges)","def dims(self):\n return self[0].dims","def ndims(self, varname):\n if self.handle == None: return None\n try:\n var = self.handle.variables[varname]\n except KeyError:\n return None\n return len(var.dimensions)","def dim(self):\n return self.m, self.n","def array_dimensions(array):\n height = len(array)\n width = len(array[0])\n\n return width, height","def get_dimensions(self):\n return self.lon_arr.shape","def ndim(self):\n return self.X.ndim","def dimensionality(self):\n if self.vector.shape is ():\n return 0\n if len(self.vector.shape) is 1:\n return 1\n _, dim = self.vector.shape\n return dim","def array_size(self):\n return self._array_size","def dimension(self):\n return len(self.qubit_values)","def __len__(self) -> int:\n\n return self.shape.shape[1]","def ndim(self):\n\n self._check_assigned()\n\n if (\n self.lazy\n and self.transformer is not None\n and hasattr(self.transformer, \"get_transformed_shape\")\n ):\n return len(self.transformer.get_transformed_shape(self.values))\n else:\n return self.__array__().ndim","def dim(self):\n raise NotImplementedError","def dimension(self):\n return 3*self.genus - 3 + self.n","def get_nb_element_per_dimension(recipe):\n return len(recipe[\"r\"]), len(recipe[\"c\"]), len(recipe[\"z\"])","def ndim(self):\n if self._ndim is None:\n self._ndim = self.get_mesh_dimension()\n\n return self._ndim","def ndim(x):\n dims = x.get_shape()._dims\n if dims is not None:\n return len(dims)\n return None","def _get_ndim(self):\n return len(self.level_shapes[0])","def width(self) -> int:\n return self._obj[self.x_dim].size","def ndim(a):\n if isinstance(a, np.ndarray):\n return a.ndim\n else:\n return K.ndim(a)","def __len__(self):\n return self.flatten_dim(self.shape[0])","def size(self):\n return numpy.prod(self.shape)","def __len__(self):\n return self._arr.shape[1]","def dim(self):\n return self.__dim__","def _N(self):\n return len(self._array)","def dimensions(self) -> int:\n return pulumi.get(self, \"dimensions\")","def ndim(self):\n return self.initial_value.ndim","def getDimension(self):\n return len(self.components)","def ndim(self):\n return np.ndim(self.MJD)","def outdim(self):\n return len(self.getSensors())","def dataDimensions(data):\n logging.info('Number of rows of data: %s' % len(data))\n logging.info('Number of columns of data: %s' % len(data[1]))","def __len__(self):\n return self.N.shape[0]","def ndarray_size(data, dtype=\"int32\"):\n return _make.ndarray_size(data, dtype)","def ndim(self):\n futures = self.client.map(_call_ndim, self.vecDask, pure=False)\n ndims = self.client.gather(futures)\n return ndims","def ndim(self):\n return 2","def dim(self):\n return (self.n, )","def __len__(self):\n return np.size(self.A,0)","def getDimensions():","def dim(self):\n raise NotImplementedError()","def size(self):\n return self.N","def dimension(self):\n return np.prod(np.asarray(self.subsystem_dims))","def size(self):\n return len(self.array_form)","def dimension(self):","def _get_num_tensor_dimensions(input_tensor):\n\n return len(input_tensor.get_shape().as_list())","def size(self):\n if type(self._shape).__name__ == 'tuple':\n return self._shape[-1]\n else:\n return self._shape"],"string":"[\n \"def n_dims(self):\\n return len(self.dimensions)\",\n \"def num_dim(self):\\n return len(self._dimensions)\",\n \"def num_dim(self):\\n return len(self._dimensions)\",\n \"def get_num_dimensions(self):\\n dimensions = self.data.shape\\n return dimensions[1]\",\n \"def count_dims(da):\\n return len(da.dims)\",\n \"def ndims(x):\\n return len(x.get_shape())\",\n \"def dims(x):\\n return len(x.shape)\",\n \"def size(self)->int:\\n\\n return np.prod([axes if is_integer(axes) else len(axes) for axes in self._dim_axes])\",\n \"def n_dims(self):\\n return self.pdm.n_dims\",\n \"def num_dims(self):\\n return self.h5['{}/{}'.format(SETTINGS, N_DIMS_STR)][()]\",\n \"def getNumDimensions(self):\\n return len(self.di.keys())\",\n \"def dim(self) -> int:\\n pass\",\n \"def ndarray_size(self) -> int:\\n pass\",\n \"def size(self):\\n\\t\\treturn self.dims\",\n \"def dim(self):\\n return len(self._n)\",\n \"def dim(self) -> int:\\n return self._n_dim\",\n \"def dim(self):\\n return len(self.shape)\",\n \"def dim(self):\\n return len(self.shape)\",\n \"def n_dim(self):\\n return self._n_dim\",\n \"def dimensionality(self):\\n return int(self.nDims)\",\n \"def ndim(self):\\n # type: () -> int\\n return len(self.shape)\",\n \"def dimension(self):\\n return self.__N\",\n \"def ndim(self):\\n return len(self.nvars)\",\n \"def dim(self) -> int:\\n return self.atoms.shape[:-1]\",\n \"def dim(self) -> int:\",\n \"def num_dimensions(self):\\n if self.__num_dimensions__ == 0:\\n # Try to get the number of dimensions from the first point or bounding box\\n if len(self.points) > 0:\\n self.__num_dimensions__ = len(self.points[0].coordinate)\\n elif len(self.bounding_boxes) > 0:\\n self.__num_dimensions__ = len(self.bounding_boxes[0].start)\\n return self.__num_dimensions__\",\n \"def ndim(self):\\n return len(self._shape)\",\n \"def ndim(self) -> int:\\r\\n return len(self.plates)\",\n \"def dimensions():\",\n \"def get_dimension_number(self) -> int:\\n return np.squeeze(self._channel_arrays[0]).ndim\",\n \"def dimension(self):\\r\\n a = 0\\r\\n for x in self.faces():\\r\\n if (len(x) > a):\\r\\n a = len(x) \\r\\n return a-1\",\n \"def dimension_count(self):\\n return self._dimensionCount\",\n \"def get_dimension_length(self):\\n pass\",\n \"def __len__(self) -> int:\\n\\n return self.layout.gaDims\",\n \"def dims(self):\\n raise NotImplementedError('Please use Vector2Array or Vector3Array')\",\n \"def ndim(self):\\n return self.data.ndim\",\n \"def get_dims(self):\\n row_lbl, col_lbl = self.get_idxvals()\\n return len(row_lbl), len(col_lbl)\",\n \"def ndim(self):\\n return len(self.point)\",\n \"def n_dims(self):\\n return self.model.template_instance.n_dims\",\n \"def _get_observation_dimension(self):\\n return len(self._get_observation_np())\",\n \"def dim(self,mat):\\n result = np.shape(mat)\\n self.dimensions = result\\n return self.dimensions\",\n \"def dimensions(self):\\n return len(self.parameter_names)\",\n \"def N(self):\\n return self._dimensions\",\n \"def ndim(self):\\n return self._ndim\",\n \"def xdim(self):\\n return len(self._x)\",\n \"def ndim(self) -> int:\\n return self[0].ndim\",\n \"def ndim(self) -> int:\\n\\n return 1 + len(self.shape)\",\n \"def ndim(self):\\n return self.__value.ndim\",\n \"def size(self):\\n return int(misc.intprod(self.shape))\",\n \"def dimension_size(self):\\n return self._dim\",\n \"def ndim(self):\\n return len(self.edges)\",\n \"def dims(self):\\n return self[0].dims\",\n \"def ndims(self, varname):\\n if self.handle == None: return None\\n try:\\n var = self.handle.variables[varname]\\n except KeyError:\\n return None\\n return len(var.dimensions)\",\n \"def dim(self):\\n return self.m, self.n\",\n \"def array_dimensions(array):\\n height = len(array)\\n width = len(array[0])\\n\\n return width, height\",\n \"def get_dimensions(self):\\n return self.lon_arr.shape\",\n \"def ndim(self):\\n return self.X.ndim\",\n \"def dimensionality(self):\\n if self.vector.shape is ():\\n return 0\\n if len(self.vector.shape) is 1:\\n return 1\\n _, dim = self.vector.shape\\n return dim\",\n \"def array_size(self):\\n return self._array_size\",\n \"def dimension(self):\\n return len(self.qubit_values)\",\n \"def __len__(self) -> int:\\n\\n return self.shape.shape[1]\",\n \"def ndim(self):\\n\\n self._check_assigned()\\n\\n if (\\n self.lazy\\n and self.transformer is not None\\n and hasattr(self.transformer, \\\"get_transformed_shape\\\")\\n ):\\n return len(self.transformer.get_transformed_shape(self.values))\\n else:\\n return self.__array__().ndim\",\n \"def dim(self):\\n raise NotImplementedError\",\n \"def dimension(self):\\n return 3*self.genus - 3 + self.n\",\n \"def get_nb_element_per_dimension(recipe):\\n return len(recipe[\\\"r\\\"]), len(recipe[\\\"c\\\"]), len(recipe[\\\"z\\\"])\",\n \"def ndim(self):\\n if self._ndim is None:\\n self._ndim = self.get_mesh_dimension()\\n\\n return self._ndim\",\n \"def ndim(x):\\n dims = x.get_shape()._dims\\n if dims is not None:\\n return len(dims)\\n return None\",\n \"def _get_ndim(self):\\n return len(self.level_shapes[0])\",\n \"def width(self) -> int:\\n return self._obj[self.x_dim].size\",\n \"def ndim(a):\\n if isinstance(a, np.ndarray):\\n return a.ndim\\n else:\\n return K.ndim(a)\",\n \"def __len__(self):\\n return self.flatten_dim(self.shape[0])\",\n \"def size(self):\\n return numpy.prod(self.shape)\",\n \"def __len__(self):\\n return self._arr.shape[1]\",\n \"def dim(self):\\n return self.__dim__\",\n \"def _N(self):\\n return len(self._array)\",\n \"def dimensions(self) -> int:\\n return pulumi.get(self, \\\"dimensions\\\")\",\n \"def ndim(self):\\n return self.initial_value.ndim\",\n \"def getDimension(self):\\n return len(self.components)\",\n \"def ndim(self):\\n return np.ndim(self.MJD)\",\n \"def outdim(self):\\n return len(self.getSensors())\",\n \"def dataDimensions(data):\\n logging.info('Number of rows of data: %s' % len(data))\\n logging.info('Number of columns of data: %s' % len(data[1]))\",\n \"def __len__(self):\\n return self.N.shape[0]\",\n \"def ndarray_size(data, dtype=\\\"int32\\\"):\\n return _make.ndarray_size(data, dtype)\",\n \"def ndim(self):\\n futures = self.client.map(_call_ndim, self.vecDask, pure=False)\\n ndims = self.client.gather(futures)\\n return ndims\",\n \"def ndim(self):\\n return 2\",\n \"def dim(self):\\n return (self.n, )\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def getDimensions():\",\n \"def dim(self):\\n raise NotImplementedError()\",\n \"def size(self):\\n return self.N\",\n \"def dimension(self):\\n return np.prod(np.asarray(self.subsystem_dims))\",\n \"def size(self):\\n return len(self.array_form)\",\n \"def dimension(self):\",\n \"def _get_num_tensor_dimensions(input_tensor):\\n\\n return len(input_tensor.get_shape().as_list())\",\n \"def size(self):\\n if type(self._shape).__name__ == 'tuple':\\n return self._shape[-1]\\n else:\\n return self._shape\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.87556416","0.85560286","0.85560286","0.84606373","0.8365305","0.83450353","0.8314268","0.8273276","0.82264763","0.8216497","0.81538165","0.81395245","0.8122816","0.81203264","0.809687","0.8096407","0.80840564","0.80840564","0.80781686","0.80665874","0.8047602","0.7954937","0.7948822","0.79326594","0.7924109","0.7922604","0.78415036","0.7841272","0.78214836","0.78011036","0.778965","0.7747739","0.7715197","0.76978576","0.76526695","0.764585","0.7645568","0.7638701","0.7619169","0.76085335","0.76058596","0.75977","0.75922275","0.7586435","0.7580205","0.7560079","0.7544165","0.75441396","0.7507646","0.74952996","0.7486465","0.7479848","0.7472327","0.7465935","0.74540037","0.74502015","0.7419761","0.74158365","0.7409149","0.7398527","0.73965997","0.7387083","0.73813313","0.7363035","0.7343015","0.7328039","0.73250973","0.7313946","0.73021036","0.7288522","0.72838926","0.72679627","0.7259089","0.72545916","0.72456527","0.7242537","0.7236153","0.7227688","0.7225149","0.72219056","0.72211355","0.720171","0.7199945","0.71981937","0.7174733","0.7168553","0.71669364","0.7163261","0.71495384","0.71281266","0.71204454","0.7120444","0.7117927","0.7106828","0.7098302"],"string":"[\n \"0.87556416\",\n \"0.85560286\",\n \"0.85560286\",\n \"0.84606373\",\n \"0.8365305\",\n \"0.83450353\",\n \"0.8314268\",\n \"0.8273276\",\n \"0.82264763\",\n \"0.8216497\",\n \"0.81538165\",\n \"0.81395245\",\n \"0.8122816\",\n \"0.81203264\",\n \"0.809687\",\n \"0.8096407\",\n \"0.80840564\",\n \"0.80840564\",\n \"0.80781686\",\n \"0.80665874\",\n \"0.8047602\",\n \"0.7954937\",\n \"0.7948822\",\n \"0.79326594\",\n \"0.7924109\",\n \"0.7922604\",\n \"0.78415036\",\n \"0.7841272\",\n \"0.78214836\",\n \"0.78011036\",\n \"0.778965\",\n \"0.7747739\",\n \"0.7715197\",\n \"0.76978576\",\n \"0.76526695\",\n \"0.764585\",\n \"0.7645568\",\n \"0.7638701\",\n \"0.7619169\",\n \"0.76085335\",\n \"0.76058596\",\n \"0.75977\",\n \"0.75922275\",\n \"0.7586435\",\n \"0.7580205\",\n \"0.7560079\",\n \"0.7544165\",\n \"0.75441396\",\n \"0.7507646\",\n \"0.74952996\",\n \"0.7486465\",\n \"0.7479848\",\n \"0.7472327\",\n \"0.7465935\",\n \"0.74540037\",\n \"0.74502015\",\n \"0.7419761\",\n \"0.74158365\",\n \"0.7409149\",\n \"0.7398527\",\n \"0.73965997\",\n \"0.7387083\",\n \"0.73813313\",\n \"0.7363035\",\n \"0.7343015\",\n \"0.7328039\",\n \"0.73250973\",\n \"0.7313946\",\n \"0.73021036\",\n \"0.7288522\",\n \"0.72838926\",\n \"0.72679627\",\n \"0.7259089\",\n \"0.72545916\",\n \"0.72456527\",\n \"0.7242537\",\n \"0.7236153\",\n \"0.7227688\",\n \"0.7225149\",\n \"0.72219056\",\n \"0.72211355\",\n \"0.720171\",\n \"0.7199945\",\n \"0.71981937\",\n \"0.7174733\",\n \"0.7168553\",\n \"0.71669364\",\n \"0.7163261\",\n \"0.71495384\",\n \"0.71281266\",\n \"0.71204454\",\n \"0.7120444\",\n \"0.7117927\",\n \"0.7106828\",\n \"0.7098302\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79104465"},"document_rank":{"kind":"string","value":"30"}}},{"rowIdx":2687,"cells":{"query":{"kind":"string","value":"Number of elements in the partition's data array (not its subarray)."},"document":{"kind":"string","value":"def size(self):\n return reduce(mul, self.shape, 1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def count(self):\r\n return self.data_array.size","def count_elements_in_dataset(dataset):\n return dataset.count()","def size(self):\n ret = 0\n for ii in self.__data:\n ret += int(ii.get_size())\n return ret","def n_elements(self) -> int:\n n_elem = np.prod(self.shape)\n if self.n_timesteps > 1:\n n_elem = int(n_elem / self.n_timesteps)\n return n_elem","def num_elements(self):\n return self.subset.num_elements()","def _N(self):\n return len(self._array)","def n_points(self):\n\n if self.data_reduced:\n return len(self.data_reduced[0])\n else:\n return 0","def __len__(self):\r\n if self.is_superset:\r\n length = 0\r\n for ds in self.data:\r\n length += len(ds)\r\n return length\r\n else:\r\n return len(self.data)","def get_total_item_size(dataset):\n total_items = 0\n for element in dataset:\n total_items += 1\n return total_items","def numel(self):\n return self.t.size","def num(an_array):\n return an_array.size","def size(self):\n size = 1\n for current_slice in self.slices:\n size *= current_slice.stop - current_slice.start\n return size","def data_count(self):\n return(len(self.data))","def __len__(self):\n return np.size(self.A,0)","def __len__(self) -> int:\n import h5py\n\n with h5py.File(\n os.path.join(self.root, self.data_dir, self.img_file_name), \"r\"\n ) as f:\n num_datapoints: int = f[self.split][\"pv_log\"].shape[0]\n\n return num_datapoints","def nbytes_at(self, device_id:int):\n if self._slices:\n if isinstance(self._coherence._local_states[device_id], dict): # there are subarrays no this device\n if self._slices_hash in self._coherence._local_states[device_id].keys(): # this subarray is already there\n return self._array.nbytes_at(device_id)\n else: # the subarray will be moved to there\n return self._array.nbytes_at(device_id) + self.subarray_nbytes # add the incoming subarray size\n else: # there is a complete copy on this device, no need to prepare subarray\n return self.nbytes\n else:\n return self.nbytes","def size(self):\n return len(self.arr)","def get_length(self):\n if self.opt.num_buckets > 1:\n return sum([len(bucket) for bucket in self.data])\n else:\n return len(self.data)","def noOfElem(classObj, index):\r\n return len(classObj.dataSet[:, index])","def nbytes(self):\n return self.nnz * self.dtype.itemsize","def count_data(self):\n try:\n ndata = len(self.x)\n logger.info(\"Number of data points: {0}\".format(ndata))\n except AttributeError:\n logger.error(\"Data object has not been defined\")\n ndata = 0\n return ndata","def getNumData(self):\n return len(self.data)","def GetNumberOfElements(self, assoc):\n result = 0\n for dataset in self:\n result += dataset.GetNumberOfElements(assoc)\n return int(result)","def get_length(array):\n return len(list(array))","def getDataSetCount(self):\n\t\treturn int(self.numberOfImages / self.slicesPerTimepoint)","def size(self) -> Tuple[groupable, pdarray]:\n return self.count()","def numel(array):\n _import_modules()\n module_name = type(array).__module__.split(\".\")[0]\n if module_name in [\"numpy\", \"numpy.ma.core\"]:\n return array.size\n elif module_name == \"torch\":\n return array.numel()\n elif module_name.split(\".\")[0] == \"jax\":\n return array.size\n elif module_name.split(\".\")[0] == \"tensorflow\":\n return tf.size(array)\n raise UnknownArrayTypeException(\n f\"The provided input of type {type(array)} is\"\n \"not a supported array type.\"\n )","def __len__(self):\n nsamp = self.data.shape[-1]\n kernel = int(self.kernel * self.fs)\n stride = int(self.stride * self.fs)\n n_stride = int(np.ceil((nsamp - kernel) / stride) + 1)\n return max(0, n_stride)","def get_data_size(self):\n if self._policy_str == EDGE_PART_POLICY:\n return len(self._partition_book.partid2eids(self._part_id))\n elif self._policy_str == NODE_PART_POLICY:\n return len(self._partition_book.partid2nids(self._part_id))\n else:\n raise RuntimeError('Cannot support policy: %s ' % self._policy_str)","def get_num_points(self):\n dimensions = self.data.shape\n return dimensions[0]","def __len__(self):\n return self._dataset.size(dirs=self._dirs)","def dim(self):\n return len(self._n)","def dataDimensions(data):\n logging.info('Number of rows of data: %s' % len(data))\n logging.info('Number of columns of data: %s' % len(data[1]))","def size(self):\n return self.N","def n(self):\n return nrow(self._array)","def elements_count(self):\n return self.__elements_count","def size(self):\n return self._N","def analysis_function_total_elements(self,clustering):\n return clustering.total_number_of_elements","def size(self)->int:\n\n return np.prod([axes if is_integer(axes) else len(axes) for axes in self._dim_axes])","def __len__(self):\n return sum(len(p) for p in self.parts)","def data_count(self):\r\n\r\n shp = self.df.shape\r\n row_count = shp[0]\r\n return row_count","def getNumElements(self):\n return 0","def _num_samples(x: npt.ArrayLike) -> int:\n if not hasattr(x, \"__len__\") and not hasattr(x, \"shape\"):\n if hasattr(x, \"__array__\"):\n x = np.asarray(x)\n else:\n raise TypeError(\"Expected sequence or array-like, got %s\" % type(x))\n if hasattr(x, \"shape\"):\n if len(x.shape) == 0:\n raise TypeError(\"Singleton array %r cannot be considered\" \" a valid collection.\" % x)\n # Check that shape is returning an integer or default to len\n # Dask dataframes may not return numeric shape[0] value\n if isinstance(x.shape[0], numbers.Integral):\n return x.shape[0]\n else:\n return len(x)\n else:\n return len(x)","def numIncrementals(self) -> int:\n return len(self._dataArrays)","def numpoints(self):\n return len(self.pars) + 1 # so dof is 1","def size(self):\r\n # Anthony stage 2\r\n return number_size(self.n) - 1","def __len__(self):\n ret = self.data.shape[0]\n return ret","def __len__(self):\n return self._arr.shape[1]","def getNrEntries(self):\n return len(self.data)","def __len__(self):\n return len(self.array)","def __len__(self):\n return len(self.array)","def dim(self) -> int:","def getNumElements(self):\n return 1","def __len__(self):\n n = 1\n for valTuple in self._valListOfLists:\n n *= len(valTuple)\n return n","def dimension(self):\n return len(self.qubit_values)","def __len__(self):\n return int(np.floor(len(self.dataset_df) / self.batch_size))","def partition_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"partition_count\")","def nbytes(self) -> int:\n\n return self.data.nbytes + self.shape.nbytes","def number_of_data_nodes(self):\n return int(self._data['number_of_data_nodes'])","def len():\n if not CpuMap.arr:\n CpuMap.arr = CpuMap._cpus()\n return len(CpuMap.arr)","def array_size(self):\n return self._array_size","def get_num_dimensions(self):\n dimensions = self.data.shape\n return dimensions[1]","def getNbRows(self):\n return self.data.shape[1]","def __len__(self) -> float:\n return len(self.elements)","def length(self, data: Sequence[Sequence[torch.Tensor]]) -> int:\n return self.n_batch","def getNumElements(self):\n return 1","def length(self):\n length = 0\n a = self.array_form\n for i in xrange(len(a)):\n if a[i] != i:\n length += 1\n return length","def ndarray_size(data, dtype=\"int32\"):\n return _make.ndarray_size(data, dtype)","def nbytes(self):\n # type: () -> int\n size = 0\n for chunk in self.data.chunks:\n for buf in chunk.buffers():\n size += buf.size\n return size","def nbytes(self):\n\n return self.data.type.datasize","def __len__(self): \r\n length = len(self.data) - 2* self.skip_window\r\n #print ('length', length)\r\n return length\r\n #raise NotImplementedError('Implement the __len__ method of the dataset')\r","def __len__(self):\n return self.data.num_samples","def __len__(self):\n return len(self.dataset) * self.samples_per_pair","def voxel_count(self):\n return self.cols * self.rows * self.sections","def size(self, index):\n return self.base_dataset.size(index)","def get_nb_element_per_dimension(recipe):\n return len(recipe[\"r\"]), len(recipe[\"c\"]), len(recipe[\"z\"])","def getNumElements(self):\n raise Exception(\"Didn't expect this to get called.\")","def ndarray_size(self) -> int:\n pass","def numel(self) -> int:\n return sum(p.numel() for p in self.parameters)","def nbytes(self):\n dtype = self.config[\"dtype\"]\n if dtype is None:\n return None\n\n size = reduce(mul, self.shape, 1)\n nbytes = size * dtype.itemsize\n\n if getattr(self, \"masked\", True):\n nbytes += size\n\n return nbytes","def __len__(self):\n return self.dataset.shape[0]","def num_partitions(self): # -> int:\n ...","def count(self):\n return len(self._elements)","def n_thres(self):\n return np.size(self.thres)","def get_array_size():\n tg_file = 'NA_CAS_gauges.txt'\n lines = open(tg_file).readlines()\n tg_nbr = len(lines)\n return tg_nbr","def __len__(self):\n return int(np.floor(len(self.indexes) / self.batch_size))","def __len__(self):\n return int(np.floor(len(self.indexes) / self.batch_size))","def __len__(self):\n return int(np.floor(len(self.indexes) / self.batch_size))","def count(self):\n return len(self.read_ints())","def nbytes(self):\n # type: () -> int\n size = 0\n for chunk in self.data.chunks:\n for buf in chunk.buffers():\n if buf is not None:\n size += buf.size\n return size","def Num_Elem_Pila(self):\n return len(self.pila)","def __len__(self) -> int:\n if self.preload:\n return len(self.data_ram)\n else:\n return len(self.data)","def count(self):\n return self.data_container.count","def __len__(self) -> int:\n\n return self.shape.shape[1]","def dimension(self):\r\n a = 0\r\n for x in self.faces():\r\n if (len(x) > a):\r\n a = len(x) \r\n return a-1","def element_count(self):\n return self._internal.get_element_count()","def __len__(self) -> int:\n return len(self.__elements)","def __len__(self) -> int:\n return int(np.floor(len(self.list_IDs) / self.batch_size))","def ndim(self):\n return self._hist.rank()","def dim(self) -> int:\n return self._n_dim","def size(self):\n return len(self.array_form)"],"string":"[\n \"def count(self):\\r\\n return self.data_array.size\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def size(self):\\n ret = 0\\n for ii in self.__data:\\n ret += int(ii.get_size())\\n return ret\",\n \"def n_elements(self) -> int:\\n n_elem = np.prod(self.shape)\\n if self.n_timesteps > 1:\\n n_elem = int(n_elem / self.n_timesteps)\\n return n_elem\",\n \"def num_elements(self):\\n return self.subset.num_elements()\",\n \"def _N(self):\\n return len(self._array)\",\n \"def n_points(self):\\n\\n if self.data_reduced:\\n return len(self.data_reduced[0])\\n else:\\n return 0\",\n \"def __len__(self):\\r\\n if self.is_superset:\\r\\n length = 0\\r\\n for ds in self.data:\\r\\n length += len(ds)\\r\\n return length\\r\\n else:\\r\\n return len(self.data)\",\n \"def get_total_item_size(dataset):\\n total_items = 0\\n for element in dataset:\\n total_items += 1\\n return total_items\",\n \"def numel(self):\\n return self.t.size\",\n \"def num(an_array):\\n return an_array.size\",\n \"def size(self):\\n size = 1\\n for current_slice in self.slices:\\n size *= current_slice.stop - current_slice.start\\n return size\",\n \"def data_count(self):\\n return(len(self.data))\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def __len__(self) -> int:\\n import h5py\\n\\n with h5py.File(\\n os.path.join(self.root, self.data_dir, self.img_file_name), \\\"r\\\"\\n ) as f:\\n num_datapoints: int = f[self.split][\\\"pv_log\\\"].shape[0]\\n\\n return num_datapoints\",\n \"def nbytes_at(self, device_id:int):\\n if self._slices:\\n if isinstance(self._coherence._local_states[device_id], dict): # there are subarrays no this device\\n if self._slices_hash in self._coherence._local_states[device_id].keys(): # this subarray is already there\\n return self._array.nbytes_at(device_id)\\n else: # the subarray will be moved to there\\n return self._array.nbytes_at(device_id) + self.subarray_nbytes # add the incoming subarray size\\n else: # there is a complete copy on this device, no need to prepare subarray\\n return self.nbytes\\n else:\\n return self.nbytes\",\n \"def size(self):\\n return len(self.arr)\",\n \"def get_length(self):\\n if self.opt.num_buckets > 1:\\n return sum([len(bucket) for bucket in self.data])\\n else:\\n return len(self.data)\",\n \"def noOfElem(classObj, index):\\r\\n return len(classObj.dataSet[:, index])\",\n \"def nbytes(self):\\n return self.nnz * self.dtype.itemsize\",\n \"def count_data(self):\\n try:\\n ndata = len(self.x)\\n logger.info(\\\"Number of data points: {0}\\\".format(ndata))\\n except AttributeError:\\n logger.error(\\\"Data object has not been defined\\\")\\n ndata = 0\\n return ndata\",\n \"def getNumData(self):\\n return len(self.data)\",\n \"def GetNumberOfElements(self, assoc):\\n result = 0\\n for dataset in self:\\n result += dataset.GetNumberOfElements(assoc)\\n return int(result)\",\n \"def get_length(array):\\n return len(list(array))\",\n \"def getDataSetCount(self):\\n\\t\\treturn int(self.numberOfImages / self.slicesPerTimepoint)\",\n \"def size(self) -> Tuple[groupable, pdarray]:\\n return self.count()\",\n \"def numel(array):\\n _import_modules()\\n module_name = type(array).__module__.split(\\\".\\\")[0]\\n if module_name in [\\\"numpy\\\", \\\"numpy.ma.core\\\"]:\\n return array.size\\n elif module_name == \\\"torch\\\":\\n return array.numel()\\n elif module_name.split(\\\".\\\")[0] == \\\"jax\\\":\\n return array.size\\n elif module_name.split(\\\".\\\")[0] == \\\"tensorflow\\\":\\n return tf.size(array)\\n raise UnknownArrayTypeException(\\n f\\\"The provided input of type {type(array)} is\\\"\\n \\\"not a supported array type.\\\"\\n )\",\n \"def __len__(self):\\n nsamp = self.data.shape[-1]\\n kernel = int(self.kernel * self.fs)\\n stride = int(self.stride * self.fs)\\n n_stride = int(np.ceil((nsamp - kernel) / stride) + 1)\\n return max(0, n_stride)\",\n \"def get_data_size(self):\\n if self._policy_str == EDGE_PART_POLICY:\\n return len(self._partition_book.partid2eids(self._part_id))\\n elif self._policy_str == NODE_PART_POLICY:\\n return len(self._partition_book.partid2nids(self._part_id))\\n else:\\n raise RuntimeError('Cannot support policy: %s ' % self._policy_str)\",\n \"def get_num_points(self):\\n dimensions = self.data.shape\\n return dimensions[0]\",\n \"def __len__(self):\\n return self._dataset.size(dirs=self._dirs)\",\n \"def dim(self):\\n return len(self._n)\",\n \"def dataDimensions(data):\\n logging.info('Number of rows of data: %s' % len(data))\\n logging.info('Number of columns of data: %s' % len(data[1]))\",\n \"def size(self):\\n return self.N\",\n \"def n(self):\\n return nrow(self._array)\",\n \"def elements_count(self):\\n return self.__elements_count\",\n \"def size(self):\\n return self._N\",\n \"def analysis_function_total_elements(self,clustering):\\n return clustering.total_number_of_elements\",\n \"def size(self)->int:\\n\\n return np.prod([axes if is_integer(axes) else len(axes) for axes in self._dim_axes])\",\n \"def __len__(self):\\n return sum(len(p) for p in self.parts)\",\n \"def data_count(self):\\r\\n\\r\\n shp = self.df.shape\\r\\n row_count = shp[0]\\r\\n return row_count\",\n \"def getNumElements(self):\\n return 0\",\n \"def _num_samples(x: npt.ArrayLike) -> int:\\n if not hasattr(x, \\\"__len__\\\") and not hasattr(x, \\\"shape\\\"):\\n if hasattr(x, \\\"__array__\\\"):\\n x = np.asarray(x)\\n else:\\n raise TypeError(\\\"Expected sequence or array-like, got %s\\\" % type(x))\\n if hasattr(x, \\\"shape\\\"):\\n if len(x.shape) == 0:\\n raise TypeError(\\\"Singleton array %r cannot be considered\\\" \\\" a valid collection.\\\" % x)\\n # Check that shape is returning an integer or default to len\\n # Dask dataframes may not return numeric shape[0] value\\n if isinstance(x.shape[0], numbers.Integral):\\n return x.shape[0]\\n else:\\n return len(x)\\n else:\\n return len(x)\",\n \"def numIncrementals(self) -> int:\\n return len(self._dataArrays)\",\n \"def numpoints(self):\\n return len(self.pars) + 1 # so dof is 1\",\n \"def size(self):\\r\\n # Anthony stage 2\\r\\n return number_size(self.n) - 1\",\n \"def __len__(self):\\n ret = self.data.shape[0]\\n return ret\",\n \"def __len__(self):\\n return self._arr.shape[1]\",\n \"def getNrEntries(self):\\n return len(self.data)\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def dim(self) -> int:\",\n \"def getNumElements(self):\\n return 1\",\n \"def __len__(self):\\n n = 1\\n for valTuple in self._valListOfLists:\\n n *= len(valTuple)\\n return n\",\n \"def dimension(self):\\n return len(self.qubit_values)\",\n \"def __len__(self):\\n return int(np.floor(len(self.dataset_df) / self.batch_size))\",\n \"def partition_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"partition_count\\\")\",\n \"def nbytes(self) -> int:\\n\\n return self.data.nbytes + self.shape.nbytes\",\n \"def number_of_data_nodes(self):\\n return int(self._data['number_of_data_nodes'])\",\n \"def len():\\n if not CpuMap.arr:\\n CpuMap.arr = CpuMap._cpus()\\n return len(CpuMap.arr)\",\n \"def array_size(self):\\n return self._array_size\",\n \"def get_num_dimensions(self):\\n dimensions = self.data.shape\\n return dimensions[1]\",\n \"def getNbRows(self):\\n return self.data.shape[1]\",\n \"def __len__(self) -> float:\\n return len(self.elements)\",\n \"def length(self, data: Sequence[Sequence[torch.Tensor]]) -> int:\\n return self.n_batch\",\n \"def getNumElements(self):\\n return 1\",\n \"def length(self):\\n length = 0\\n a = self.array_form\\n for i in xrange(len(a)):\\n if a[i] != i:\\n length += 1\\n return length\",\n \"def ndarray_size(data, dtype=\\\"int32\\\"):\\n return _make.ndarray_size(data, dtype)\",\n \"def nbytes(self):\\n # type: () -> int\\n size = 0\\n for chunk in self.data.chunks:\\n for buf in chunk.buffers():\\n size += buf.size\\n return size\",\n \"def nbytes(self):\\n\\n return self.data.type.datasize\",\n \"def __len__(self): \\r\\n length = len(self.data) - 2* self.skip_window\\r\\n #print ('length', length)\\r\\n return length\\r\\n #raise NotImplementedError('Implement the __len__ method of the dataset')\\r\",\n \"def __len__(self):\\n return self.data.num_samples\",\n \"def __len__(self):\\n return len(self.dataset) * self.samples_per_pair\",\n \"def voxel_count(self):\\n return self.cols * self.rows * self.sections\",\n \"def size(self, index):\\n return self.base_dataset.size(index)\",\n \"def get_nb_element_per_dimension(recipe):\\n return len(recipe[\\\"r\\\"]), len(recipe[\\\"c\\\"]), len(recipe[\\\"z\\\"])\",\n \"def getNumElements(self):\\n raise Exception(\\\"Didn't expect this to get called.\\\")\",\n \"def ndarray_size(self) -> int:\\n pass\",\n \"def numel(self) -> int:\\n return sum(p.numel() for p in self.parameters)\",\n \"def nbytes(self):\\n dtype = self.config[\\\"dtype\\\"]\\n if dtype is None:\\n return None\\n\\n size = reduce(mul, self.shape, 1)\\n nbytes = size * dtype.itemsize\\n\\n if getattr(self, \\\"masked\\\", True):\\n nbytes += size\\n\\n return nbytes\",\n \"def __len__(self):\\n return self.dataset.shape[0]\",\n \"def num_partitions(self): # -> int:\\n ...\",\n \"def count(self):\\n return len(self._elements)\",\n \"def n_thres(self):\\n return np.size(self.thres)\",\n \"def get_array_size():\\n tg_file = 'NA_CAS_gauges.txt'\\n lines = open(tg_file).readlines()\\n tg_nbr = len(lines)\\n return tg_nbr\",\n \"def __len__(self):\\n return int(np.floor(len(self.indexes) / self.batch_size))\",\n \"def __len__(self):\\n return int(np.floor(len(self.indexes) / self.batch_size))\",\n \"def __len__(self):\\n return int(np.floor(len(self.indexes) / self.batch_size))\",\n \"def count(self):\\n return len(self.read_ints())\",\n \"def nbytes(self):\\n # type: () -> int\\n size = 0\\n for chunk in self.data.chunks:\\n for buf in chunk.buffers():\\n if buf is not None:\\n size += buf.size\\n return size\",\n \"def Num_Elem_Pila(self):\\n return len(self.pila)\",\n \"def __len__(self) -> int:\\n if self.preload:\\n return len(self.data_ram)\\n else:\\n return len(self.data)\",\n \"def count(self):\\n return self.data_container.count\",\n \"def __len__(self) -> int:\\n\\n return self.shape.shape[1]\",\n \"def dimension(self):\\r\\n a = 0\\r\\n for x in self.faces():\\r\\n if (len(x) > a):\\r\\n a = len(x) \\r\\n return a-1\",\n \"def element_count(self):\\n return self._internal.get_element_count()\",\n \"def __len__(self) -> int:\\n return len(self.__elements)\",\n \"def __len__(self) -> int:\\n return int(np.floor(len(self.list_IDs) / self.batch_size))\",\n \"def ndim(self):\\n return self._hist.rank()\",\n \"def dim(self) -> int:\\n return self._n_dim\",\n \"def size(self):\\n return len(self.array_form)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.76638305\",\n \"0.73945427\",\n \"0.7326349\",\n \"0.7308581\",\n \"0.7271908\",\n \"0.72121114\",\n \"0.71300155\",\n \"0.7121346\",\n \"0.71050775\",\n \"0.7088031\",\n \"0.70531565\",\n \"0.6979757\",\n \"0.69550794\",\n \"0.694825\",\n \"0.69397396\",\n \"0.69286805\",\n \"0.6899676\",\n \"0.6895468\",\n \"0.68825126\",\n \"0.687968\",\n \"0.687458\",\n \"0.6859505\",\n \"0.6857238\",\n \"0.6852797\",\n \"0.6845907\",\n \"0.6819525\",\n \"0.68078864\",\n \"0.67656296\",\n \"0.67622924\",\n \"0.67458427\",\n \"0.6743274\",\n \"0.67397213\",\n \"0.67181766\",\n \"0.6713872\",\n \"0.67106515\",\n \"0.670989\",\n \"0.6701366\",\n \"0.6698873\",\n \"0.6694914\",\n \"0.66894263\",\n \"0.6686565\",\n \"0.6674225\",\n \"0.66702485\",\n \"0.6666631\",\n \"0.6664025\",\n \"0.6643706\",\n \"0.6619851\",\n \"0.6615853\",\n \"0.66132253\",\n \"0.6612713\",\n \"0.6612713\",\n \"0.66110504\",\n \"0.65963167\",\n \"0.65948194\",\n \"0.6593723\",\n \"0.65934736\",\n \"0.6585986\",\n \"0.65820646\",\n \"0.65718216\",\n \"0.65695786\",\n \"0.6566921\",\n \"0.6565889\",\n \"0.6555226\",\n \"0.652751\",\n \"0.65264636\",\n \"0.6525014\",\n \"0.6524313\",\n \"0.65238404\",\n \"0.6515606\",\n \"0.65119374\",\n \"0.6499892\",\n \"0.64963764\",\n \"0.64856577\",\n \"0.6481219\",\n \"0.6476438\",\n \"0.64578205\",\n \"0.64506274\",\n \"0.6448778\",\n \"0.64480317\",\n \"0.6445686\",\n \"0.64429843\",\n \"0.6436247\",\n \"0.6429239\",\n \"0.64247173\",\n \"0.6422584\",\n \"0.6419411\",\n \"0.6419411\",\n \"0.6419411\",\n \"0.64175814\",\n \"0.64171\",\n \"0.6415757\",\n \"0.6412586\",\n \"0.6411072\",\n \"0.64089394\",\n \"0.6407548\",\n \"0.6404272\",\n \"0.6400729\",\n \"0.63993484\",\n \"0.6394548\",\n \"0.6389457\",\n \"0.6377886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":2688,"cells":{"query":{"kind":"string","value":"The partition's subarray of data."},"document":{"kind":"string","value":"def subarray(self):\n return self._subarray"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def subarray(self) -> Subarray:\n return Subarray.from_pybind11(self._ctx, self._subarray)","def partition(self, sep):\n return asarray(partition(self, sep))","def array(self) -> ndarray:\n if self._slices: # so this is a sub-parray object\n # index into origin array by saved slices\n ret = self._array.get_by_global_slices(self._current_device_index, self._slices[0])\n for s in self._slices[1:]:\n ret = ret[s]\n return ret\n else: # this is a complete copy\n ret = self._array.get(self._current_device_index)\n\n if isinstance(ret, list): # get a subarray instead\n raise IndexError(\"Current device doesn't have a complete copy of this array\")\n return ret","def array(self):\n config = self.config\n\n unique_array = config[\"unique_subarray\"]\n\n p_axes = self.axes\n p_flip = self.flip\n p_part = self.part\n p_units = self.Units\n p_shape = self.shape\n p_location = self.location\n subarray = self._subarray\n\n len_p_axes = len(p_axes)\n\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is not in memory.\n #\n # It could be in a file on disk or implied by a FileArray\n # object, etc.\n # --------------------------------------------------------\n self._original = self.copy()\n\n unique_array = True\n update = True\n copy = False\n\n if not p_part:\n indices = Ellipsis\n else:\n indices = tuple(p_part)\n\n # Read from a file into a numpy array\n p_data = subarray[indices]\n\n # We've just copied p_data from disk, so in place changes\n # are not possible\n in_place_changes = False\n else:\n # --------------------------------------------------------\n # The subarray is in memory\n # --------------------------------------------------------\n update = config[\"update\"]\n\n if p_part:\n p_data = get_subspace(subarray, p_part)\n elif not unique_array:\n p_data = subarray.view()\n else:\n p_data = subarray\n\n copy = config[\"extra_memory\"]\n\n # In place changes to p_data might be possible if we're not\n # copying the data\n in_place_changes = not copy\n\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\n # --------------------------------------------------------\n # p_data is a numpy number (like numpy.int64) which does\n # not support assignment, so convert it to a numpy array.\n # --------------------------------------------------------\n p_data = numpy_array(p_data)\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n\n masked = numpy_ma_isMA(p_data)\n if masked:\n # The p_data is a masked array\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\n p_data\n ):\n # There are no missing data points so recast as an\n # unmasked numpy array\n p_data = p_data.data\n masked = False\n # --- End: if\n\n if masked:\n # Set the hardness of the mask\n if config[\"hardmask\"]:\n p_data.harden_mask()\n else:\n p_data.soften_mask()\n # --- End: if\n\n self.masked = masked\n\n # ------------------------------------------------------------\n # Make sure that the data array has the correct units. This\n # process will deep copy the data array if required (e.g. if\n # another partition is referencing this numpy array), even if\n # the units are already correct.\n # ------------------------------------------------------------\n func = config.get(\"func\")\n units = config[\"units\"]\n if func is None:\n if not p_units.equals(units) and bool(p_units) is bool(units):\n func = Units.conform\n\n if func is not None:\n inplace = not copy\n p_data = func(p_data, p_units, units, inplace)\n p_units = units\n\n if not inplace:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n flip = config.get(\"flip\", None)\n if flip or p_flip:\n flip_axes = set(p_flip).symmetric_difference(flip)\n else:\n flip_axes = None\n\n axes = config[\"axes\"]\n\n if p_data.size > 1:\n # --------------------------------------------------------\n # Flip axes\n # --------------------------------------------------------\n if flip_axes:\n indices = [\n (\n slice(None, None, -1)\n if axis in flip_axes\n else slice(None)\n )\n for axis in p_axes\n ]\n p_data = p_data[tuple(indices)]\n\n # --------------------------------------------------------\n # Transpose axes\n # --------------------------------------------------------\n if p_axes != axes:\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\n\n if len_p_axes > len(iaxes):\n for i in range(len_p_axes):\n if i not in iaxes:\n # iaxes.append(i)\n iaxes.insert(i, i)\n # --- End: if\n\n p_data = numpy_transpose(p_data, iaxes)\n # --- End: if\n\n # ------------------------------------------------------------\n # Remove excessive/insert missing size 1 axes\n # ------------------------------------------------------------\n if p_shape != p_data.shape:\n # if len_p_axes != len(p_shape):\n p_data = p_data.reshape(p_shape)\n\n # ------------------------------------------------------------\n # Apply the auxiliary mask\n # ------------------------------------------------------------\n auxiliary_mask = config[\"auxiliary_mask\"]\n if auxiliary_mask:\n for mask in auxiliary_mask:\n if mask.any():\n if not masked:\n p_data = p_data.view(numpy_ma_MaskedArray)\n masked = True\n\n p_data.mask = (mask | p_data.mask).array\n # --- End: for\n\n self.masked = True\n\n # ------------------------------------------------------------\n # Convert the array's data type\n # ------------------------------------------------------------\n p_dtype = p_data.dtype\n dtype = config.get(\"dtype\", None)\n if dtype is not None and dtype != p_dtype:\n try:\n p_data = p_data.astype(dtype) # Note: returns a copy\n except ValueError:\n raise ValueError(\n \"Can't recast partition array from {} to {}\".format(\n p_dtype.name, dtype.name\n )\n )\n else:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n # ------------------------------------------------------------\n # Copy the array\n # -----------------------------------------------------------\n if copy:\n if p_dtype.char != \"O\":\n if not masked or p_data.ndim > 0:\n p_data = p_data.copy()\n else:\n # This is because numpy.ma.copy doesn't work for\n # scalar arrays (at the moment, at least)\n p_data = numpy_ma_masked_all((), p_data.dtype)\n\n # We've just copied p_data, so in place changes are\n # not possible\n in_place_changes = False\n else:\n # whilst netCDF4.netcdftime.datetime is mucking bout,\n # don't copy!!!!\n # p_data = _copy(p_data)\n pass\n # --- End: if\n\n # ------------------------------------------------------------\n # Update the partition\n # ------------------------------------------------------------\n if update:\n self.subarray = p_data # ?? DCH CHECK\n self.Units = p_units\n self.part = []\n self.axes = axes\n self.flip = flip\n self.flatten = []\n self.shape = p_shape\n self.location = p_location\n\n self._in_place_changes = in_place_changes\n\n # ------------------------------------------------------------\n # Return the numpy array\n # ------------------------------------------------------------\n return p_data","def partitionData(data, labels, partition):\n\treturn [s[partition] for s in data], labels[partition]","def GetPartitioningArray(self):\n return _hypre.HypreParVector_GetPartitioningArray(self)","def as_slice(self):\n # slice for accessing arrays of values\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)","def partition(data, indecies):\n\tsplitdata = [data[:indecies[0]]]\n\tsplitdata += [data[indecies[i-1]:indecies[i]] for i in range(1,len(indecies))]\n\tsplitdata.append(data[indecies[-1]:])\n\treturn splitdata","def get_partions(self) -> Union[ndarray, Tuple[ndarray, ndarray]]:\n if self.fragmented:\n return (self[self._begin:], self[:self._end])\n else:\n return self[self._begin:self._end]","def subset(arr, start, end):\n return [[row_data for row_data in row[start[1]:end[1]]] for row in arr[start[0]:end[0]]]","def rpartition(self, sep):\n return asarray(rpartition(self, sep))","def subset(self, data, subset_size):\n subset_size_q = int((subset_size - 1) / 2)\n subset_image = []\n\n for i in range(-subset_size_q, subset_size_q + 1):\n for j in range(-subset_size_q, subset_size_q + 1):\n subset_roll = np.roll(data, i, axis=0)\n subset_roll = np.roll(subset_roll, j, axis=1)\n subset_image.append(subset_roll)\n\n return np.sum(np.asarray(subset_image), axis=0)","def __getslice__(self, i, j):\n return self.dtrs[i:j]","def __getslice__(self,i,j):\n return self.x[i:j]","def _fprop_slice_np(h, stride, H, roi_offset):\n hstart = int(np.floor(float(h) * stride))\n hend = int(np.ceil(float(h + 1) * stride))\n\n hstart = min(max(hstart + roi_offset, 0), H)\n hend = min(max(hend + roi_offset, 0), H)\n\n return slice(hstart, hend), hend - hstart","def subdataset(self):\n return self._clip_metadata.get(\"subdataset\")","def getData(self, slice=None):\n\t\traise NotImplementedError","def slice(data, size):\n\treturn dice(data, size).T","def __getslice__( self, *args):\n return array.array.__getslice__(self, *args).tostring()","def get_subset(self, tile, band=0):\r\n # access window bounds\r\n bounds = rasterio.windows.bounds(tile, self.dataset.transform)\r\n return (\r\n self.__arr[(band,) + tile.toslices()],\r\n bounds,\r\n ) # Shape of array is announced with (bands, height, width)\r","def row_slice(self, xt, nproc):\n if nproc is None: nproc = self.nproc\n cs = xt.shape[0]//nproc #chuncksize\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\n if nproc*cs != xt.shape[0]:\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\n return tmp","def slices(self):\n return self._slices","def _slice(self, start, stop, step=None):\n\n slices = [slice(None)] * self.data.ndim\n slices[self.axis] = slice(start, stop, step)\n return tuple(slices)","def __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","def get_part_array(array, part, additional_area, offset={'x': 0, 'y': 0}):\n\tresult = []\n\n\toffset_before = {\n\t\t'x': offset['x'] - additional_area['x'],\n\t\t'y': offset['y'] - additional_area['y']\n\t}\n\tif offset_before['x'] < 0:\n\t\tprint('set x')\n\t\toffset_before['x'] = 0\n\tif offset_before['y'] < 0:\n\t\tprint('set y')\n\t\toffset_before['y'] = 0\n\n\tfor i in array[offset_before['y'] : offset['y']+part['y']+additional_area['y']]:\n\t\tresult.append(i[offset_before['x'] : offset['x']+part['x']+additional_area['x']])\n\treturn np.array(result)","def subimage(image_as_array, step):\r\n\tsubimage_2d_array = image_as_array[200-int(step):200+int(step)]\r\n\treturn subimage_2d_array","def _extract_data_sub_stack(self, startRow, endRow):\n # Grab the shape of the image stack\n nz, ny, nx = self.shape\n\n # Compute the number of rows in this sub stack\n numberOfRows = endRow - startRow\n\n # Build an array for storing output\n outData = np.zeros((nz, numberOfRows, nx))\n\n # Loop through each image and extract its data\n for zInd, img in enumerate(self.imageList):\n outData[zInd, :, :] = img.data[startRow:endRow, :]\n\n return np.ma.array(outData)","def get_slice(self):\n return self.locs[tuple(self.indices), :]","def __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def getPartition(self):\n\t\treturn self.partition","def get(self):\n return self._partition","def subsample(self, dataset):\n sample_idx = np.random.choice(\n dataset.shape[0], self.sample_size, replace=True)\n sample = dataset[sample_idx,...]\n return sample","def vectorized_slice(self, data):\n\n # TODO: finish this.\n sum_of_rows = np.sum(data[:100,:], axis=1)\n max_indx = np.argmax(sum_of_rows)\n return (sum_of_rows[max_indx], max_indx)","def get_subarray(input_array, reference):\n\n if (reference.meta.subarray.xstart is None or\n reference.meta.subarray.xsize is None or\n reference.meta.subarray.ystart is None or\n reference.meta.subarray.ysize is None):\n raise ValueError('subarray metadata values not found')\n\n xstart = reference.meta.subarray.xstart - 1\n xstop = xstart + reference.meta.subarray.xsize\n ystart = reference.meta.subarray.ystart - 1\n ystop = ystart + reference.meta.subarray.ysize\n log.debug(\"xstart=%d, xstop=%d, ystart=%d, ystop=%d\" \\\n % (xstart, xstop, ystart, ystop))\n\n return input_array[..., ystart:ystop, xstart:xstop]","def GetRowPartArray(self):\n return _hypre.HypreParMatrix_GetRowPartArray(self)","def cfdGetBoundaryElementsSubArrayForBoundaryPatch(self):\r\n\r\n for iBPatch, theBCInfo in self.cfdBoundaryPatchesArray.items():\r\n \r\n startBElement=self.numberOfElements+self.cfdBoundaryPatchesArray[iBPatch]['startFaceIndex']-self.numberOfInteriorFaces\r\n endBElement=startBElement+self.cfdBoundaryPatchesArray[iBPatch]['numberOfBFaces']\r\n \r\n self.cfdBoundaryPatchesArray[iBPatch]['iBElements']=list(range(int(startBElement),int(endBElement)))","def slice_batch(x, n_gpus, part):\n sh = K.shape(x)\n L = sh[0] // n_gpus\n if part == n_gpus - 1:\n return x[part*L:]\n return x[part*L:(part+1)*L]","def __getslice__(self, i, j):\n return self.__getitem__(slice(i, j))","def slice(self) -> Tuple[slice, ...]:\n\n total_slice = tuple(slice(None) for _ in self.collection_shape)\n for obj in self.objects.flat:\n for i, current_slice in enumerate(obj.slices):\n if total_slice[i].start is None:\n total_slice = total_slice[:i] + (current_slice,) + total_slice[i + 1:]\n else:\n if current_slice.start < total_slice[i].start:\n total_slice = total_slice[:i] + (\n slice(current_slice.start, total_slice[i].stop, total_slice[i].step),) + total_slice[i + 1:]\n if current_slice.stop > total_slice[i].stop:\n total_slice = total_slice[:i] + (\n slice(total_slice[i].start, current_slice.stop, total_slice[i].step),) + total_slice[i + 1:]\n return total_slice","def chunk(self):\n # easy enough\n return self.dcpl.getChunk(rank=len(self.shape))","def partition(data: list, parts: list, *args: float) -> list:\n random.seed(42)\n partition_names = parts\n random.shuffle(data)\n n = len(data)\n rem, a, b = n, 0, 0\n parts = []\n\n for p in args:\n b = a + int(n*p)\n parts.append(data[a:b])\n rem -= (b - a)\n a = b\n # end\n\n parts.append(data[-rem:])\n return parts","def _get_partition_list(self):\n raise NotImplementedError('Must be implemented in subclasses.')","def get_partition(self):\n return self._partition","def data_array(self):\n return self._data_array","def get_data(self):\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\n return self._data[idxs].copy()","def __getitem__(self, idx):\r\n if self.is_superset:\r\n for ds in self.data:\r\n if idx >= len(ds):\r\n continue\r\n return ds[idx]\r\n else:\r\n return self.data[idx]","def partition(self, data, labels):\n\t\treturn self.kfold.split(labels)","def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0","def sub_rows(arr, sub_size):\n rows, cols = arr.shape\n for i in range(rows):\n for j in range(cols - sub_size + 1):\n yield arr[i, range(j, j + sub_size)]","def subdimension(self, key):\n \n index = self.to_index(key)\n if isinstance(index, int):\n return None\n\n # here index is a slice\n if index.stop - index.start <= 1:\n # Here key represent a single element\n return None\n units = self.to_unit(index) # recompute units for clean borders\n return AffineDimension([units.start, units.stop], index.stop - index.start)","def partition(self, data, labels):\n\t\t#TODO remove\n\t\tprint(\"label shape {}\".format(labels.shape))\n\t\treturn self.kfold.split(data[0], labels)","def tondarray(self):\r\n return self.data;","def slice_timeseries(n_slices,dataset):\n\n n,l=np.shape(dataset)\n\n X = np.reshape(dataset,(n*n_slices,l//n_slices))\n\n print('sliced data shape (nr. of slices, slice length):',np.shape(X))\n print('#####################################')\n \n return X","def get(self):\r\n return self.data_array","def __getitem__(self, index: slice) -> List:\n\n return self.data[index]","def batch_dataset(x, batch_size):\r\n\tsize_modulo = len(x) % batch_size # hack to ensure data is batches successfully\r\n\tif size_modulo != 0:\r\n\t\tx = x[:-size_modulo]\r\n\tpartitioned = np.split(x, batch_size)\r\n\treturn partitioned","def __getitem__(self,k):\n if type(k) is IntType: return self.data[k, 0]\n \n vec = [type(x) is SliceType for x in k]\n \n if True in vec: #suppose only one slice\n ii=vec.index(True)\n indices=[]\n k = list(k)\n import numpy\n rep = numpy.zeros((self.dims[ii],), 'd')\n for i in range(self.dims[ii]):\n k[ii] = i\n rep[i] = self.data[self.comp(k), 0]\n return rep\n else:\n return self.data[self.comp(k), 0]","def split_data(data, squeeze=False):\n vdata = np.atleast_2d(data)\n nr_freqs = int(vdata.shape[1] / 2)\n part1 = vdata[:, 0:nr_freqs]\n part2 = vdata[:, nr_freqs:]\n if(squeeze):\n part1 = part1.squeeze()\n part2 = part2.squeeze()\n return part1, part2","def to_slice(self):\n return np.index_exp[self.start[2]:self.end[2], #\n self.start[1]:self.end[1], #\n self.start[0]:self.end[0]]","def get(self):\n return np.hstack((self.data[:, self.cur:], self.data[:, :self.cur])) #Concatena los datos en horizontal","def split_data_set(data_set, index, value, part=0):\n # save the subdataset\n res_data_set = []\n \n for entry in data_set:\n # find the data set to the left of the partition point\n if part == 0 and float(entry[index])<= value: #求划分点左侧的数据集\n reduced_entry = entry[:index]\n # after partitioning, the value of the index column in the data is removed\n reduced_entry.extend(entry[index + 1:]) \n res_data_set.append(reduced_entry)\n # find the data set to the right of the partition point\n if part ==1 and float(entry[index])> value: \n reduced_entry = entry[:index]\n reduced_entry.extend(entry[index + 1:])\n res_data_set.append(reduced_entry)\n return res_data_set","def as_list(self) -> List[int]:\n return self.my_partition","def segmentArray(array, pos = 0):\n\t\n#\tfrom jot.basic.countDistinctValues import countDistinctValues\n\t\n\tfinalarray = []\n\tprovarray = list(countDistinctValues(array, pos).keys())\n\t\n\tfor i in provarray:\n\t\n\t\ttemparray = []\n\t\t\n\t\tfor j in array:\n\t\t\n\t\t\tif i == j[0]:\n\t\t\t\ttemparray.append(j[1:])\n\t\t\t\t\n\t\tfinalarray.append(temparray)\n\t\t\n\treturn finalarray","def GetDataSlice(vDataSet,z,c,t):\r\n dtype = GetType(vDataSet)\r\n if dtype == np.uint8 or dtype == np.uint16:\r\n arr = np.array(vDataSet.GetDataSliceShorts(z,c,t),dtype)\r\n else:\r\n arr = np.array(vDataSet.GetDataSliceFloats(z,c,t),dtype)\r\n return arr.swapaxes(0,1)","def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass","def core_slices(self, chunk):\n intersect_slices = []\n for s, b, olap, idx in zip(chunk.slices, self.bounds, self.overlap, range(0, len(chunk.slices))):\n if s.start == b.start:\n intersect_slices.append(slice(s.start + olap, s.stop))\n elif s.stop == b.stop:\n intersect_slices.append(slice(s.start, s.stop - olap))\n else:\n intersect_slices.append(s)\n\n return tuple(self.remove_chunk_overlap(chunk, intersect_slices))","def paginator_slice(self, pageno):\n return self._data[pageno * self.height : pageno * self.height + self.height]","def slice_data(data, start, end):\n rtemp = data[0][start:end]\n if len(data) == 4:\n r, s, A, As = data\n stemp = s[start:end]\n Atemp = A[start:end]\n Astemp = As[start:end]\n datatemp = [rtemp, stemp, Atemp, Astemp]\n else:\n r, h, dMdh = data\n htemp = h[start:end]\n dMdhtemp = dMdh[start:end]\n datatemp = [rtemp, htemp, dMdhtemp]\n return datatemp","def test_slice_other_dimension(self):\n for i, shape in enumerate([(3, 0), (1, 2, 0), (2, 0, 1)]):\n dset = self.f.create_dataset('x%d'%i, shape, dtype=int, maxshape=(None,)*len(shape))\n self.assertEqual(dset.shape, shape)\n out = dset[:1]\n self.assertIsInstance(out, np.ndarray)\n self.assertEqual(out.shape, (1,)+shape[1:])","def write_subarray(data, z_arr, start):\n stop = tuple(s+c for s, c in zip(start, data.shape))\n assert start[0] < stop[0]\n assert start[1] < stop[1]\n assert start[2] < stop[2]\n z_arr[start[0]:stop[0], start[1]:stop[1], start[2]:stop[2]] = data","def array_partition_views(array, partition):\n views = dictlist.copy(partition)\n pathsshapes = sorted(list(dictlist.leafs(partition)))\n\n n_used = 0\n for path, shape in pathsshapes:\n item = dictlist.get(partition, path)\n shape = (item,) if isinstance(item, (int, long)) else item\n size = int(np.prod(shape))\n dictlist.set_(views, path, array[n_used:n_used + size].reshape(shape))\n n_used += size\n\n return views","def findSubArray(list):\n # converts number list to a list of 0,1 indicated by its primality \n binaryArray = [int(isPrime(k)) for k in list]\n \n # explore subarrays from biggest (length of list) to smallest (length=2)\n # breaks when one subarray matches the requirements \n lengthSubArray = len(binaryArray)\n found = False\n while( lengthSubArray > 1 and not found ):\n \n # move startIndex to create subarrays from same length\n numberSubArrays = len(binaryArray) - lengthSubArray + 1\n for startIndex in range(0, numberSubArrays):\n endIndex = startIndex + lengthSubArray\n binarySubArray = binaryArray[startIndex:endIndex]\n \n # verify condition requested in subarray is met\n found = sum(binarySubArray) > (lengthSubArray / 2)\n if found: break\n \n lengthSubArray -= 1 \n \n # returns the subarray using the indexes from the binarySubArray found\n return list[startIndex:endIndex]","def batch_split(self) -> np.array:\n pass","def subsampleData(self, count):\n size = 0\n for block in self.blocks: size += len(block[1])\n subset = numpy.random.permutation(size)[:count]\n subset.sort()\n\n pos = 0\n index = 0\n ret = Dataset()\n for block in self.blocks:\n while subset[index]<(pos+len(block[1])):\n loc = subset[index] - pos\n ret.add(block[0][loc,:], block[1][loc])\n index += 1\n if index==subset.shape[0]: return ret\n pos += len(block[1])\n \n return ret","def slice_data(xdata, ydata, x_range):\n\tdata = zip(xdata, ydata)\n\tsliced_data = [d for d in data if d[0] >= x_range[0] and d[0] <= x_range[1]]\n\treturn array(zip(*sliced_data))","def _chunk_data(X, slices):\n\n # from object array to list\n slices = [sl for sl in slices if len(sl)]\n selected_times = np.hstack([np.ravel(sl) for sl in slices])\n start = np.min(selected_times)\n stop = np.max(selected_times) + 1\n slices_chunk = [sl - start for sl in slices]\n X_chunk = X[:, :, start:stop]\n return X_chunk, slices_chunk","def __getslice__(self, i, j):\n if self.__pepth__ != 0:\n if '__warned__' not in plist.__getslice__.__dict__:\n qj('Slicing of inner plist elements with negative indices in python 2.7 does not work, and the error cannot be detected or corrected!\\n'\n 'Instead of slicing with one or two arguments: `plist._[-2:]`, use the three argument slice: `plist._[-2::1]`.\\n'\n 'This avoids the broken code path in the python compiler.', 'WARNING!')\n plist.__getslice__.__dict__['__warned__'] = True\n return plist.__getattr__(self, '__getslice__')(i, j)\n try:\n if self is self.__root__:\n return plist(list.__getslice__(self, i, j))\n return plist(list.__getslice__(self, i, j), root=plist(list.__getslice__(self.__root__, i, j)))\n except Exception:\n return plist.__getitem__(self, slice(i, j))","def __getitem__(self, keep):\n ndim = len(self.dataset.shape)\n # Ensure that keep is a tuple (then turn it into a list to simplify further processing)\n keep = list(keep) if isinstance(keep, tuple) else [keep]\n # The original keep tuple will be passed to data transform chain\n original_keep = tuple(keep)\n # Ensure that keep is same length as data dimension (truncate or pad with blanket slices as necessary)\n keep = keep[:ndim] + [slice(None)] * (ndim - len(keep))\n # Map current selection to original data indices based on any existing initial selection, per data dimension\n keep = [(dkeep if dlookup is None else dlookup[dkeep]) for dkeep, dlookup in zip(keep, self._lookup)]\n # Iterate over dimensions of dataset, storing information on selection on each dimension:\n # `selection` is a list with one element per dimension; each element is a list of contiguous segments along\n # the dimension, and each segment is represented by a tuple of 3 elements:\n # (dataset selection, post-selection, output array selection)\n # Similarly, `segment_sizes` is a list of lists of segment lengths (empty lists for scalar-selected dimensions)\n selection, segment_sizes = [], []\n for dim_keep, dim_len in zip(keep, self.dataset.shape):\n if np.isscalar(dim_keep):\n # If selection is a scalar, pass directly to dataset selector and remove dimension from output\n selection.append([(dim_keep, None, None)])\n segment_sizes.append([])\n elif isinstance(dim_keep, slice):\n # If selection is a slice, pass directly to dataset selector without post-selection\n start, stop, stride = dim_keep.indices(dim_len)\n segm_size = len(range(start, stop, stride))\n selection.append([(slice(start, stop, stride), slice(None), slice(0, segm_size, 1))])\n segment_sizes.append([segm_size])\n elif len(dim_keep) == 0:\n # If selection is empty, pass to post-selector, as HDF5 datasets do not support zero-length selection\n selection.append([(slice(0, 1, 1), slice(0, 0, 1), slice(0, 0, 1))])\n segment_sizes.append([0])\n else:\n # Anything else is advanced indexing via bool or integer sequences\n dim_keep = np.atleast_1d(dim_keep)\n # Turn boolean mask into integer indices (True means keep that index)\n if dim_keep.dtype == bool and len(dim_keep) == dim_len:\n dim_keep = np.nonzero(dim_keep)[0]\n elif not np.all(dim_keep == np.unique(dim_keep)):\n raise TypeError('LazyIndexer cannot handle duplicate or unsorted advanced integer indices')\n # Split indices into multiple contiguous segments (specified by first and one-past-last data indices)\n jumps = np.nonzero(np.diff(dim_keep) > 1)[0]\n first = [dim_keep[0]] + dim_keep[jumps + 1].tolist()\n last = dim_keep[jumps].tolist() + [dim_keep[-1]]\n segments = np.c_[first, np.array(last) + 1]\n if len(dim_keep) > 0.2 * dim_len and len(segments) > 1:\n # If more than 20% of data are selected in 2 or more separate segments (the Ratcliffian benchmark),\n # select data at dataset level with a single slice spanning segments and then postselect the ndarray\n selection.append([(slice(segments[0, 0], segments[-1, 1], 1),\n dim_keep - dim_keep[0], slice(0, len(dim_keep), 1))])\n segment_sizes.append([len(dim_keep)])\n else:\n # Turn each segment into a separate slice at dataset level without post-selection,\n # and construct contiguous output slices of the same segment sizes\n segm_sizes = [end - start for start, end in segments]\n segm_starts = np.cumsum([0] + segm_sizes)\n selection.append([(slice(start, end, 1), slice(None), slice(segm_starts[n], segm_starts[n + 1], 1))\n for n, (start, end) in enumerate(segments)])\n segment_sizes.append(segm_sizes)\n # Short-circuit the selection if all dimensions are selected with scalars (resulting in a scalar output)\n if segment_sizes == [[]] * ndim:\n out_data = self.dataset[tuple([select[0][0] for select in selection])]\n else:\n # Use dense N-dimensional meshgrid to slice data set into chunks, based on segments along each dimension\n chunk_indices = np.mgrid[[slice(0, len(select), 1) for select in selection]]\n # Pre-allocate output ndarray to have the correct shape and dtype (will be at least 1-dimensional)\n out_data = np.empty([np.sum(segments) for segments in segment_sizes if segments], dtype=self.dataset.dtype)\n # Iterate over chunks, extracting them from dataset and inserting them into the right spot in output array\n for chunk_index in chunk_indices.reshape(ndim, -1).T:\n # Extract chunk from dataset (don't use any advanced indexing here, only scalars and slices)\n dataset_select = tuple([select[segment][0] for select, segment in zip(selection, chunk_index)])\n chunk = self.dataset[dataset_select]\n # Perform post-selection on chunk (can be fancier / advanced indexing because chunk is now an ndarray)\n post_select = [select[segment][1] for select, segment in zip(selection, chunk_index)]\n # If any dimensions were dropped due to scalar indexing, drop them from post_select/out_select tuples\n post_select = tuple([select for select in post_select if select is not None])\n # Do post-selection one dimension at a time, as ndarray does not allow simultaneous advanced indexing\n # on more than one dimension. This caters for the scenario where more than one dimension satisfies\n # the Ratcliffian benchmark (the only way to get advanced post-selection).\n for dim in range(len(chunk.shape)):\n # Only do post-selection on this dimension if non-trivial (otherwise an unnecessary copy happens)\n if not (isinstance(post_select[dim], slice) and post_select[dim] == slice(None)):\n # Prepend the appropriate number of colons to the selection to place it at correct dimension\n chunk = chunk[[slice(None)] * dim + [post_select[dim]]]\n # Determine appropriate output selection and insert chunk into output array\n out_select = [select[segment][2] for select, segment in zip(selection, chunk_index)]\n out_select = tuple([select for select in out_select if select is not None])\n out_data[out_select] = chunk\n # Apply transform chain to output data, if any\n return reduce(lambda data, transform: transform(data, original_keep), self.transforms, out_data)","def rpartition(self, x):\n pass","def partition(self, data, labels):\n\t\traise Exception(\"Not implmented\")","def partition(self):\n return self.tag(\"partition\")","def getacolslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.var,(first_),(last_))\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_val = False\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n _copyback_val = True\n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n _copyback_val = True\n if val_ is not None and len(val_) != (maxnumnz_):\n raise ValueError(\"Array argument val has wrong length\")\n res = self.__obj.getacolslicetrip(first_,last_,maxnumnz_,len(subi),subi_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_val:\n val[:] = _tmparr_val\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def get_partitioning(self):\n raise Exception(\"Unimplemented\")","def get_data(self):\n return self.data[self._size:self._size + self._len]","def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _val_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and val_ is not None and len(val_) != (maxnumnz_):\n raise ValueError(\"Array argument val is not long enough: Is %d, expected %d\" % (len(val_),(maxnumnz_)))\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\n raise ValueError(\"Argument val must be writable\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n surp_ = ctypes.c_int64(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _val_copyarray:\n val_[:] = _val_np_tmp","def subFarms(self,partition,full_name=None):\n if self.inUse.value() is None: self.load()\n got = []\n for i in xrange(len(self.inUse.data)):\n if self.inUse.data[i]==partition:\n if full_name:\n got.append(self.name+'_'+self.subfarms.data[i])\n else:\n got.append(self.subfarms.data[i])\n return got","def sub_columns(arr, sub_size):\n return sub_rows(arr.T, sub_size)","def subsection_at(self, index):\n return self.child_at(index)","def partitions(self):\n self._get_latest_content()\n return self._data.get('partitions', [])","def slice_during(self, e):\r\n\r\n if not isinstance(e, Epochs):\r\n raise ValueError('e has to be of Epochs type')\r\n\r\n if e.data.ndim > 0:\r\n raise NotImplementedError('e has to be a scalar Epoch')\r\n\r\n if self.ndim != 1:\r\n e_s = 'slicing only implemented for 1-d TimeArrays'\r\n return NotImplementedError(e_s)\r\n i_start = self.index_at(e.start)\r\n i_stop = self.index_at(e.stop)\r\n if e.start > self[i_start]: # make sure self[i_start] is in epoch e\r\n i_start += 1\r\n if e.stop > self[i_stop]: # make sure to include self[i_stop]\r\n i_stop += 1\r\n\r\n return slice(i_start, i_stop)","def segment(data):","def _get_slice(segments, shape):\n\n if not (1 <= len(shape) <= 2):\n raise ValueError('Cannot segment array of shape: %s' % str(shape))\n else:\n size = shape[0]\n slice_length = np.ceil(float(size) / segments)\n start_idx = 0\n end_idx = slice_length\n while start_idx < size:\n if len(shape) == 1:\n yield slice(start_idx, end_idx)\n else:\n yield (slice(start_idx, end_idx), slice(None))\n start_idx = end_idx\n end_idx = min(start_idx + slice_length, size)","def to_ndarray(self):\n # Create an ndarray of the right shape, filled with self.defval.\n ndshape = type(self).flatten_shape(self.shape)\n res = np.full(ndshape, self.defval, dtype=self.dtype)\n if 0 in ndshape:\n return res\n shp, qhp = type(self)._sorted_shape_qhape(tensor=self)\n # ranges is like shape, but every number d is replaced by a tuple\n # (a, a+d) where a is the sum of all the previous entries in the same\n # dim.\n ranges = []\n for dim in shp:\n prv = dim[0]\n r = [(0, prv)]\n for d in dim[1:]:\n nxt = prv + d\n r.append((prv, nxt))\n prv = nxt\n ranges.append(r)\n # Copy the elements of each sector to the right place in the result.\n for k, v in self.sects.items():\n slc = ()\n for i, qnum in enumerate(k):\n r = ranges[i][qhp[i].index(qnum)]\n slc += (slice(r[0], r[1]),)\n res[slc] = v\n return res","def slice_dims(data_array: sc.DataArray, slices: Dict[str, slice]) -> sc.DataArray:\n out = data_array\n for dim, sl in slices.items():\n out = out[dim, sl]\n return out","def Get(self,k:int): \n ### get partitions depending on the partition schemes C that depends on k!\n return subsets_k(list(range(self._n)),k)","def __getitem__(self, data):\n i,j = data\n return self._data[i][j]","def _get_item(self, index):\n data, label = self.data[index], self.label[index]\n coordmax = np.max(data, axis=0)\n coordmin = np.min(data, axis=0)\n nsubvolume_x = np.ceil((coordmax[0]-coordmin[0])/1.5).astype(np.int32)\n nsubvolume_y = np.ceil((coordmax[1]-coordmin[1])/1.5).astype(np.int32)\n batch_data, batch_label = [], []\n for i in range(nsubvolume_x):\n for j in range(nsubvolume_y):\n curmin = coordmin + [i*1.5, j*1.5, 0]\n curmax = coordmin+ [(i+1)*1.5, (j+1)*1.5, coordmax[2]-coordmin[2]]\n crop_ids = np.sum((data>=(curmin-0.2)) * (data<=(curmax+0.2)), axis=1) == 3\n if sum(crop_ids) == 0: continue\n crop_data = data[crop_ids]\n crop_label = label[crop_ids]\n mask = np.sum((crop_data>=(curmin-0.001)) * (crop_data<=(curmax+0.001)), axis=1) == 3\n ids = np.random.choice(crop_label.size, self.npoints, replace=True)\n this_data = crop_data[ids]\n this_label = crop_label[ids]\n this_mask = mask[ids]\n if sum(this_mask) * 1. / this_mask.size < 0.01: continue\n this_label *= this_mask\n if self.normalize:\n this_data = utils.normalize_point_cloud(this_data)\n batch_data.append(this_data[None,:,:])\n batch_label.append(this_label[None,:])\n batch_data = np.concatenate(tuple(batch_data), axis=0)\n batch_label = np.concatenate(tuple(batch_label), axis=0)\n return batch_data, batch_label","def segments(self):\n return (self._subset((i,i+1)) for i in range(len(self)-1))","def partition(array, first, last):\n # partition up until final value\n pivot = array[last]\n i = first - 1\n\n for count in range(first, last):\n # split array\n if array[count] < pivot:\n i += 1\n # assign array positions\n array[i],array[count] = array[count],array[i]\n # reassign\n array[i+1],array[last] = array[last],array[i+1]\n return (i+1)","def get_split_data(self):\n X, y, _, _ = self.get_subsets()\n return train_test_split(X, y, test_size=0.3, random_state=42)"],"string":"[\n \"def subarray(self) -> Subarray:\\n return Subarray.from_pybind11(self._ctx, self._subarray)\",\n \"def partition(self, sep):\\n return asarray(partition(self, sep))\",\n \"def array(self) -> ndarray:\\n if self._slices: # so this is a sub-parray object\\n # index into origin array by saved slices\\n ret = self._array.get_by_global_slices(self._current_device_index, self._slices[0])\\n for s in self._slices[1:]:\\n ret = ret[s]\\n return ret\\n else: # this is a complete copy\\n ret = self._array.get(self._current_device_index)\\n\\n if isinstance(ret, list): # get a subarray instead\\n raise IndexError(\\\"Current device doesn't have a complete copy of this array\\\")\\n return ret\",\n \"def array(self):\\n config = self.config\\n\\n unique_array = config[\\\"unique_subarray\\\"]\\n\\n p_axes = self.axes\\n p_flip = self.flip\\n p_part = self.part\\n p_units = self.Units\\n p_shape = self.shape\\n p_location = self.location\\n subarray = self._subarray\\n\\n len_p_axes = len(p_axes)\\n\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is not in memory.\\n #\\n # It could be in a file on disk or implied by a FileArray\\n # object, etc.\\n # --------------------------------------------------------\\n self._original = self.copy()\\n\\n unique_array = True\\n update = True\\n copy = False\\n\\n if not p_part:\\n indices = Ellipsis\\n else:\\n indices = tuple(p_part)\\n\\n # Read from a file into a numpy array\\n p_data = subarray[indices]\\n\\n # We've just copied p_data from disk, so in place changes\\n # are not possible\\n in_place_changes = False\\n else:\\n # --------------------------------------------------------\\n # The subarray is in memory\\n # --------------------------------------------------------\\n update = config[\\\"update\\\"]\\n\\n if p_part:\\n p_data = get_subspace(subarray, p_part)\\n elif not unique_array:\\n p_data = subarray.view()\\n else:\\n p_data = subarray\\n\\n copy = config[\\\"extra_memory\\\"]\\n\\n # In place changes to p_data might be possible if we're not\\n # copying the data\\n in_place_changes = not copy\\n\\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\\n # --------------------------------------------------------\\n # p_data is a numpy number (like numpy.int64) which does\\n # not support assignment, so convert it to a numpy array.\\n # --------------------------------------------------------\\n p_data = numpy_array(p_data)\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n\\n masked = numpy_ma_isMA(p_data)\\n if masked:\\n # The p_data is a masked array\\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\\n p_data\\n ):\\n # There are no missing data points so recast as an\\n # unmasked numpy array\\n p_data = p_data.data\\n masked = False\\n # --- End: if\\n\\n if masked:\\n # Set the hardness of the mask\\n if config[\\\"hardmask\\\"]:\\n p_data.harden_mask()\\n else:\\n p_data.soften_mask()\\n # --- End: if\\n\\n self.masked = masked\\n\\n # ------------------------------------------------------------\\n # Make sure that the data array has the correct units. This\\n # process will deep copy the data array if required (e.g. if\\n # another partition is referencing this numpy array), even if\\n # the units are already correct.\\n # ------------------------------------------------------------\\n func = config.get(\\\"func\\\")\\n units = config[\\\"units\\\"]\\n if func is None:\\n if not p_units.equals(units) and bool(p_units) is bool(units):\\n func = Units.conform\\n\\n if func is not None:\\n inplace = not copy\\n p_data = func(p_data, p_units, units, inplace)\\n p_units = units\\n\\n if not inplace:\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n # --- End: if\\n\\n flip = config.get(\\\"flip\\\", None)\\n if flip or p_flip:\\n flip_axes = set(p_flip).symmetric_difference(flip)\\n else:\\n flip_axes = None\\n\\n axes = config[\\\"axes\\\"]\\n\\n if p_data.size > 1:\\n # --------------------------------------------------------\\n # Flip axes\\n # --------------------------------------------------------\\n if flip_axes:\\n indices = [\\n (\\n slice(None, None, -1)\\n if axis in flip_axes\\n else slice(None)\\n )\\n for axis in p_axes\\n ]\\n p_data = p_data[tuple(indices)]\\n\\n # --------------------------------------------------------\\n # Transpose axes\\n # --------------------------------------------------------\\n if p_axes != axes:\\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\\n\\n if len_p_axes > len(iaxes):\\n for i in range(len_p_axes):\\n if i not in iaxes:\\n # iaxes.append(i)\\n iaxes.insert(i, i)\\n # --- End: if\\n\\n p_data = numpy_transpose(p_data, iaxes)\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Remove excessive/insert missing size 1 axes\\n # ------------------------------------------------------------\\n if p_shape != p_data.shape:\\n # if len_p_axes != len(p_shape):\\n p_data = p_data.reshape(p_shape)\\n\\n # ------------------------------------------------------------\\n # Apply the auxiliary mask\\n # ------------------------------------------------------------\\n auxiliary_mask = config[\\\"auxiliary_mask\\\"]\\n if auxiliary_mask:\\n for mask in auxiliary_mask:\\n if mask.any():\\n if not masked:\\n p_data = p_data.view(numpy_ma_MaskedArray)\\n masked = True\\n\\n p_data.mask = (mask | p_data.mask).array\\n # --- End: for\\n\\n self.masked = True\\n\\n # ------------------------------------------------------------\\n # Convert the array's data type\\n # ------------------------------------------------------------\\n p_dtype = p_data.dtype\\n dtype = config.get(\\\"dtype\\\", None)\\n if dtype is not None and dtype != p_dtype:\\n try:\\n p_data = p_data.astype(dtype) # Note: returns a copy\\n except ValueError:\\n raise ValueError(\\n \\\"Can't recast partition array from {} to {}\\\".format(\\n p_dtype.name, dtype.name\\n )\\n )\\n else:\\n # We've just copied p_data, so in place changes are\\n # not possible\\n copy = False\\n in_place_changes = False\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Copy the array\\n # -----------------------------------------------------------\\n if copy:\\n if p_dtype.char != \\\"O\\\":\\n if not masked or p_data.ndim > 0:\\n p_data = p_data.copy()\\n else:\\n # This is because numpy.ma.copy doesn't work for\\n # scalar arrays (at the moment, at least)\\n p_data = numpy_ma_masked_all((), p_data.dtype)\\n\\n # We've just copied p_data, so in place changes are\\n # not possible\\n in_place_changes = False\\n else:\\n # whilst netCDF4.netcdftime.datetime is mucking bout,\\n # don't copy!!!!\\n # p_data = _copy(p_data)\\n pass\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Update the partition\\n # ------------------------------------------------------------\\n if update:\\n self.subarray = p_data # ?? DCH CHECK\\n self.Units = p_units\\n self.part = []\\n self.axes = axes\\n self.flip = flip\\n self.flatten = []\\n self.shape = p_shape\\n self.location = p_location\\n\\n self._in_place_changes = in_place_changes\\n\\n # ------------------------------------------------------------\\n # Return the numpy array\\n # ------------------------------------------------------------\\n return p_data\",\n \"def partitionData(data, labels, partition):\\n\\treturn [s[partition] for s in data], labels[partition]\",\n \"def GetPartitioningArray(self):\\n return _hypre.HypreParVector_GetPartitioningArray(self)\",\n \"def as_slice(self):\\n # slice for accessing arrays of values\\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)\",\n \"def partition(data, indecies):\\n\\tsplitdata = [data[:indecies[0]]]\\n\\tsplitdata += [data[indecies[i-1]:indecies[i]] for i in range(1,len(indecies))]\\n\\tsplitdata.append(data[indecies[-1]:])\\n\\treturn splitdata\",\n \"def get_partions(self) -> Union[ndarray, Tuple[ndarray, ndarray]]:\\n if self.fragmented:\\n return (self[self._begin:], self[:self._end])\\n else:\\n return self[self._begin:self._end]\",\n \"def subset(arr, start, end):\\n return [[row_data for row_data in row[start[1]:end[1]]] for row in arr[start[0]:end[0]]]\",\n \"def rpartition(self, sep):\\n return asarray(rpartition(self, sep))\",\n \"def subset(self, data, subset_size):\\n subset_size_q = int((subset_size - 1) / 2)\\n subset_image = []\\n\\n for i in range(-subset_size_q, subset_size_q + 1):\\n for j in range(-subset_size_q, subset_size_q + 1):\\n subset_roll = np.roll(data, i, axis=0)\\n subset_roll = np.roll(subset_roll, j, axis=1)\\n subset_image.append(subset_roll)\\n\\n return np.sum(np.asarray(subset_image), axis=0)\",\n \"def __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\n \"def __getslice__(self,i,j):\\n return self.x[i:j]\",\n \"def _fprop_slice_np(h, stride, H, roi_offset):\\n hstart = int(np.floor(float(h) * stride))\\n hend = int(np.ceil(float(h + 1) * stride))\\n\\n hstart = min(max(hstart + roi_offset, 0), H)\\n hend = min(max(hend + roi_offset, 0), H)\\n\\n return slice(hstart, hend), hend - hstart\",\n \"def subdataset(self):\\n return self._clip_metadata.get(\\\"subdataset\\\")\",\n \"def getData(self, slice=None):\\n\\t\\traise NotImplementedError\",\n \"def slice(data, size):\\n\\treturn dice(data, size).T\",\n \"def __getslice__( self, *args):\\n return array.array.__getslice__(self, *args).tostring()\",\n \"def get_subset(self, tile, band=0):\\r\\n # access window bounds\\r\\n bounds = rasterio.windows.bounds(tile, self.dataset.transform)\\r\\n return (\\r\\n self.__arr[(band,) + tile.toslices()],\\r\\n bounds,\\r\\n ) # Shape of array is announced with (bands, height, width)\\r\",\n \"def row_slice(self, xt, nproc):\\n if nproc is None: nproc = self.nproc\\n cs = xt.shape[0]//nproc #chuncksize\\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\\n if nproc*cs != xt.shape[0]:\\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\\n return tmp\",\n \"def slices(self):\\n return self._slices\",\n \"def _slice(self, start, stop, step=None):\\n\\n slices = [slice(None)] * self.data.ndim\\n slices[self.axis] = slice(start, stop, step)\\n return tuple(slices)\",\n \"def __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\n \"def get_part_array(array, part, additional_area, offset={'x': 0, 'y': 0}):\\n\\tresult = []\\n\\n\\toffset_before = {\\n\\t\\t'x': offset['x'] - additional_area['x'],\\n\\t\\t'y': offset['y'] - additional_area['y']\\n\\t}\\n\\tif offset_before['x'] < 0:\\n\\t\\tprint('set x')\\n\\t\\toffset_before['x'] = 0\\n\\tif offset_before['y'] < 0:\\n\\t\\tprint('set y')\\n\\t\\toffset_before['y'] = 0\\n\\n\\tfor i in array[offset_before['y'] : offset['y']+part['y']+additional_area['y']]:\\n\\t\\tresult.append(i[offset_before['x'] : offset['x']+part['x']+additional_area['x']])\\n\\treturn np.array(result)\",\n \"def subimage(image_as_array, step):\\r\\n\\tsubimage_2d_array = image_as_array[200-int(step):200+int(step)]\\r\\n\\treturn subimage_2d_array\",\n \"def _extract_data_sub_stack(self, startRow, endRow):\\n # Grab the shape of the image stack\\n nz, ny, nx = self.shape\\n\\n # Compute the number of rows in this sub stack\\n numberOfRows = endRow - startRow\\n\\n # Build an array for storing output\\n outData = np.zeros((nz, numberOfRows, nx))\\n\\n # Loop through each image and extract its data\\n for zInd, img in enumerate(self.imageList):\\n outData[zInd, :, :] = img.data[startRow:endRow, :]\\n\\n return np.ma.array(outData)\",\n \"def get_slice(self):\\n return self.locs[tuple(self.indices), :]\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def getPartition(self):\\n\\t\\treturn self.partition\",\n \"def get(self):\\n return self._partition\",\n \"def subsample(self, dataset):\\n sample_idx = np.random.choice(\\n dataset.shape[0], self.sample_size, replace=True)\\n sample = dataset[sample_idx,...]\\n return sample\",\n \"def vectorized_slice(self, data):\\n\\n # TODO: finish this.\\n sum_of_rows = np.sum(data[:100,:], axis=1)\\n max_indx = np.argmax(sum_of_rows)\\n return (sum_of_rows[max_indx], max_indx)\",\n \"def get_subarray(input_array, reference):\\n\\n if (reference.meta.subarray.xstart is None or\\n reference.meta.subarray.xsize is None or\\n reference.meta.subarray.ystart is None or\\n reference.meta.subarray.ysize is None):\\n raise ValueError('subarray metadata values not found')\\n\\n xstart = reference.meta.subarray.xstart - 1\\n xstop = xstart + reference.meta.subarray.xsize\\n ystart = reference.meta.subarray.ystart - 1\\n ystop = ystart + reference.meta.subarray.ysize\\n log.debug(\\\"xstart=%d, xstop=%d, ystart=%d, ystop=%d\\\" \\\\\\n % (xstart, xstop, ystart, ystop))\\n\\n return input_array[..., ystart:ystop, xstart:xstop]\",\n \"def GetRowPartArray(self):\\n return _hypre.HypreParMatrix_GetRowPartArray(self)\",\n \"def cfdGetBoundaryElementsSubArrayForBoundaryPatch(self):\\r\\n\\r\\n for iBPatch, theBCInfo in self.cfdBoundaryPatchesArray.items():\\r\\n \\r\\n startBElement=self.numberOfElements+self.cfdBoundaryPatchesArray[iBPatch]['startFaceIndex']-self.numberOfInteriorFaces\\r\\n endBElement=startBElement+self.cfdBoundaryPatchesArray[iBPatch]['numberOfBFaces']\\r\\n \\r\\n self.cfdBoundaryPatchesArray[iBPatch]['iBElements']=list(range(int(startBElement),int(endBElement)))\",\n \"def slice_batch(x, n_gpus, part):\\n sh = K.shape(x)\\n L = sh[0] // n_gpus\\n if part == n_gpus - 1:\\n return x[part*L:]\\n return x[part*L:(part+1)*L]\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i, j))\",\n \"def slice(self) -> Tuple[slice, ...]:\\n\\n total_slice = tuple(slice(None) for _ in self.collection_shape)\\n for obj in self.objects.flat:\\n for i, current_slice in enumerate(obj.slices):\\n if total_slice[i].start is None:\\n total_slice = total_slice[:i] + (current_slice,) + total_slice[i + 1:]\\n else:\\n if current_slice.start < total_slice[i].start:\\n total_slice = total_slice[:i] + (\\n slice(current_slice.start, total_slice[i].stop, total_slice[i].step),) + total_slice[i + 1:]\\n if current_slice.stop > total_slice[i].stop:\\n total_slice = total_slice[:i] + (\\n slice(total_slice[i].start, current_slice.stop, total_slice[i].step),) + total_slice[i + 1:]\\n return total_slice\",\n \"def chunk(self):\\n # easy enough\\n return self.dcpl.getChunk(rank=len(self.shape))\",\n \"def partition(data: list, parts: list, *args: float) -> list:\\n random.seed(42)\\n partition_names = parts\\n random.shuffle(data)\\n n = len(data)\\n rem, a, b = n, 0, 0\\n parts = []\\n\\n for p in args:\\n b = a + int(n*p)\\n parts.append(data[a:b])\\n rem -= (b - a)\\n a = b\\n # end\\n\\n parts.append(data[-rem:])\\n return parts\",\n \"def _get_partition_list(self):\\n raise NotImplementedError('Must be implemented in subclasses.')\",\n \"def get_partition(self):\\n return self._partition\",\n \"def data_array(self):\\n return self._data_array\",\n \"def get_data(self):\\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\\n return self._data[idxs].copy()\",\n \"def __getitem__(self, idx):\\r\\n if self.is_superset:\\r\\n for ds in self.data:\\r\\n if idx >= len(ds):\\r\\n continue\\r\\n return ds[idx]\\r\\n else:\\r\\n return self.data[idx]\",\n \"def partition(self, data, labels):\\n\\t\\treturn self.kfold.split(labels)\",\n \"def extra_memory(self):\\n if not self.in_memory:\\n # --------------------------------------------------------\\n # The subarray is on disk so getting the partition's data\\n # array will require extra memory\\n # --------------------------------------------------------\\n extra_memory = True\\n else:\\n # --------------------------------------------------------\\n # The subarray is already in memory\\n # --------------------------------------------------------\\n config = self.config\\n\\n p_part = self.part\\n if p_part:\\n extra_memory = True\\n elif not config[\\\"unique_subarray\\\"]:\\n extra_memory = True\\n else:\\n p_data = self._subarray\\n\\n if not numpy_ma_isMA(p_data):\\n # The p_data is not a masked array\\n extra_memory = isinstance(p_data.base, numpy_ndarray)\\n else:\\n # The p_data is a masked array\\n memory_overlap = isinstance(\\n p_data.data.base, numpy_ndarray\\n )\\n if not (\\n p_data.mask is numpy_ma_nomask\\n or not numpy_ma_is_masked(p_data)\\n ):\\n # There is at least one missing data point\\n memory_overlap |= isinstance(\\n p_data.mask.base, numpy_ndarray\\n )\\n\\n extra_memory = memory_overlap\\n # --- End: if\\n\\n p_dtype = p_data.dtype\\n\\n if not extra_memory:\\n if config[\\\"func\\\"] is not None:\\n extra_memory = True\\n else:\\n p_units = self.Units\\n units = config[\\\"units\\\"]\\n if (\\n not p_units.equals(units)\\n and bool(p_units) is bool(units)\\n and not (\\n p_data.flags[\\\"C_CONTIGUOUS\\\"]\\n and p_dtype.kind == \\\"f\\\"\\n )\\n ):\\n extra_memory = True\\n\\n # ------------------------------------------------------------\\n # Extra memory is required if the dtype needs changing\\n # ------------------------------------------------------------\\n if not extra_memory:\\n dtype = config[\\\"dtype\\\"]\\n if dtype is not None and dtype != p_data.dtype:\\n extra_memory = True\\n # --- End: if\\n\\n # ------------------------------------------------------------\\n # Amount of extra memory (in bytes) required to access the\\n # array\\n # ------------------------------------------------------------\\n return self.nbytes if extra_memory else 0\",\n \"def sub_rows(arr, sub_size):\\n rows, cols = arr.shape\\n for i in range(rows):\\n for j in range(cols - sub_size + 1):\\n yield arr[i, range(j, j + sub_size)]\",\n \"def subdimension(self, key):\\n \\n index = self.to_index(key)\\n if isinstance(index, int):\\n return None\\n\\n # here index is a slice\\n if index.stop - index.start <= 1:\\n # Here key represent a single element\\n return None\\n units = self.to_unit(index) # recompute units for clean borders\\n return AffineDimension([units.start, units.stop], index.stop - index.start)\",\n \"def partition(self, data, labels):\\n\\t\\t#TODO remove\\n\\t\\tprint(\\\"label shape {}\\\".format(labels.shape))\\n\\t\\treturn self.kfold.split(data[0], labels)\",\n \"def tondarray(self):\\r\\n return self.data;\",\n \"def slice_timeseries(n_slices,dataset):\\n\\n n,l=np.shape(dataset)\\n\\n X = np.reshape(dataset,(n*n_slices,l//n_slices))\\n\\n print('sliced data shape (nr. of slices, slice length):',np.shape(X))\\n print('#####################################')\\n \\n return X\",\n \"def get(self):\\r\\n return self.data_array\",\n \"def __getitem__(self, index: slice) -> List:\\n\\n return self.data[index]\",\n \"def batch_dataset(x, batch_size):\\r\\n\\tsize_modulo = len(x) % batch_size # hack to ensure data is batches successfully\\r\\n\\tif size_modulo != 0:\\r\\n\\t\\tx = x[:-size_modulo]\\r\\n\\tpartitioned = np.split(x, batch_size)\\r\\n\\treturn partitioned\",\n \"def __getitem__(self,k):\\n if type(k) is IntType: return self.data[k, 0]\\n \\n vec = [type(x) is SliceType for x in k]\\n \\n if True in vec: #suppose only one slice\\n ii=vec.index(True)\\n indices=[]\\n k = list(k)\\n import numpy\\n rep = numpy.zeros((self.dims[ii],), 'd')\\n for i in range(self.dims[ii]):\\n k[ii] = i\\n rep[i] = self.data[self.comp(k), 0]\\n return rep\\n else:\\n return self.data[self.comp(k), 0]\",\n \"def split_data(data, squeeze=False):\\n vdata = np.atleast_2d(data)\\n nr_freqs = int(vdata.shape[1] / 2)\\n part1 = vdata[:, 0:nr_freqs]\\n part2 = vdata[:, nr_freqs:]\\n if(squeeze):\\n part1 = part1.squeeze()\\n part2 = part2.squeeze()\\n return part1, part2\",\n \"def to_slice(self):\\n return np.index_exp[self.start[2]:self.end[2], #\\n self.start[1]:self.end[1], #\\n self.start[0]:self.end[0]]\",\n \"def get(self):\\n return np.hstack((self.data[:, self.cur:], self.data[:, :self.cur])) #Concatena los datos en horizontal\",\n \"def split_data_set(data_set, index, value, part=0):\\n # save the subdataset\\n res_data_set = []\\n \\n for entry in data_set:\\n # find the data set to the left of the partition point\\n if part == 0 and float(entry[index])<= value: #求划分点左侧的数据集\\n reduced_entry = entry[:index]\\n # after partitioning, the value of the index column in the data is removed\\n reduced_entry.extend(entry[index + 1:]) \\n res_data_set.append(reduced_entry)\\n # find the data set to the right of the partition point\\n if part ==1 and float(entry[index])> value: \\n reduced_entry = entry[:index]\\n reduced_entry.extend(entry[index + 1:])\\n res_data_set.append(reduced_entry)\\n return res_data_set\",\n \"def as_list(self) -> List[int]:\\n return self.my_partition\",\n \"def segmentArray(array, pos = 0):\\n\\t\\n#\\tfrom jot.basic.countDistinctValues import countDistinctValues\\n\\t\\n\\tfinalarray = []\\n\\tprovarray = list(countDistinctValues(array, pos).keys())\\n\\t\\n\\tfor i in provarray:\\n\\t\\n\\t\\ttemparray = []\\n\\t\\t\\n\\t\\tfor j in array:\\n\\t\\t\\n\\t\\t\\tif i == j[0]:\\n\\t\\t\\t\\ttemparray.append(j[1:])\\n\\t\\t\\t\\t\\n\\t\\tfinalarray.append(temparray)\\n\\t\\t\\n\\treturn finalarray\",\n \"def GetDataSlice(vDataSet,z,c,t):\\r\\n dtype = GetType(vDataSet)\\r\\n if dtype == np.uint8 or dtype == np.uint16:\\r\\n arr = np.array(vDataSet.GetDataSliceShorts(z,c,t),dtype)\\r\\n else:\\r\\n arr = np.array(vDataSet.GetDataSliceFloats(z,c,t),dtype)\\r\\n return arr.swapaxes(0,1)\",\n \"def close(self, **kwargs):\\n config = getattr(self, \\\"config\\\", None)\\n\\n if config is None:\\n return\\n\\n if kwargs:\\n config.update(kwargs)\\n\\n original = getattr(self, \\\"_original\\\", None)\\n logger.partitioning(\\\"Partition.close: original = {}\\\".format(original))\\n\\n if not original:\\n originally_on_disk = False\\n original_subarray = None\\n else:\\n originally_on_disk = not original.in_memory\\n original_subarray = original._subarray\\n\\n config = self.config\\n logger.partitioning(\\\" config = {}\\\".format(config))\\n\\n if config[\\\"serial\\\"]:\\n # --------------------------------------------------------\\n # SERIAL\\n # --------------------------------------------------------\\n logger.partitioning(\\\" serial\\\")\\n\\n if config[\\\"readonly\\\"]:\\n logger.partitioning(\\\" readonly=True\\\")\\n\\n if originally_on_disk:\\n logger.partitioning(\\\" subarray originally on disk\\\")\\n\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.1.1 The original subarray was on disk,\\n # we don't want to keep the current\\n # subarray in memory, and we are happy\\n # to discard any changes that may have\\n # been made to the subarray.\\n logger.partitioning(\\\" 1.1.1.1 revert\\\")\\n self.revert()\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.1.2 The original subarray was on disk,\\n # we are happy to keep the current\\n # subarray in memory, but there is not\\n # enough free memory to do so.\\n logger.partitioning(\\n \\\" 1.1.1.2 revert ({} <= {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n self.revert()\\n else:\\n # 1.1.1.3 The original subarray was on disk\\n # and there is enough memory to keep\\n # the current subarray in memory\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # The original subarray was a temporary\\n # file which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n del self.masked\\n logger.partitioning(\\n \\\" 1.1.1.3 del masked ({} > {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n\\n else:\\n logger.partitioning(\\\" subarray originally in memory\\\")\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.2.1 Original subarray was in memory and\\n # we don't want to keep the current\\n # subarray in memory\\n logger.partitioning(\\\" 1.1.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.2.2 Original subarray was in memory and\\n # unique but there is not enough\\n # memory to keep the current subarray\\n logger.partitioning(\\\" 1.1.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.1.2.3 Original subarray was in memory and\\n # unique and there is enough memory to\\n # keep the current subarray in memory\\n logger.partitioning(\\\" 1.1.2.3 pass\\\")\\n pass\\n else:\\n # config['readonly'] is False\\n if originally_on_disk:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.1.1 Original subarray was on disk and\\n # there and we don't want to keep the\\n # array\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.1.2 Original subarray was on disk but\\n # there is not enough memory to keep\\n # it\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.1.3 Original subarray was on disk and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.1.3 pass\\\")\\n del self.masked\\n else:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.2.1 Original subarray was in memory but\\n # we don't want to keep it\\n logger.partitioning(\\\" 1.2.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.2.2 Original subarray was an in memory\\n # but there is not enough memory to\\n # keep it\\n logger.partitioning(\\\" 1.2.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.2.3 Original subarray was in memory and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.2.3 del masked\\\")\\n del self.masked\\n else:\\n logger.partitioning(\\\"Partition.close: parallel\\\")\\n # --------------------------------------------------------\\n # PARALLEL\\n # --------------------------------------------------------\\n pass\\n\\n # if hasattr(self, '_original'):\\n # del self._original\\n\\n # print(hasattr(self, 'config')),\\n try:\\n del self.config\\n except AttributeError:\\n pass\",\n \"def core_slices(self, chunk):\\n intersect_slices = []\\n for s, b, olap, idx in zip(chunk.slices, self.bounds, self.overlap, range(0, len(chunk.slices))):\\n if s.start == b.start:\\n intersect_slices.append(slice(s.start + olap, s.stop))\\n elif s.stop == b.stop:\\n intersect_slices.append(slice(s.start, s.stop - olap))\\n else:\\n intersect_slices.append(s)\\n\\n return tuple(self.remove_chunk_overlap(chunk, intersect_slices))\",\n \"def paginator_slice(self, pageno):\\n return self._data[pageno * self.height : pageno * self.height + self.height]\",\n \"def slice_data(data, start, end):\\n rtemp = data[0][start:end]\\n if len(data) == 4:\\n r, s, A, As = data\\n stemp = s[start:end]\\n Atemp = A[start:end]\\n Astemp = As[start:end]\\n datatemp = [rtemp, stemp, Atemp, Astemp]\\n else:\\n r, h, dMdh = data\\n htemp = h[start:end]\\n dMdhtemp = dMdh[start:end]\\n datatemp = [rtemp, htemp, dMdhtemp]\\n return datatemp\",\n \"def test_slice_other_dimension(self):\\n for i, shape in enumerate([(3, 0), (1, 2, 0), (2, 0, 1)]):\\n dset = self.f.create_dataset('x%d'%i, shape, dtype=int, maxshape=(None,)*len(shape))\\n self.assertEqual(dset.shape, shape)\\n out = dset[:1]\\n self.assertIsInstance(out, np.ndarray)\\n self.assertEqual(out.shape, (1,)+shape[1:])\",\n \"def write_subarray(data, z_arr, start):\\n stop = tuple(s+c for s, c in zip(start, data.shape))\\n assert start[0] < stop[0]\\n assert start[1] < stop[1]\\n assert start[2] < stop[2]\\n z_arr[start[0]:stop[0], start[1]:stop[1], start[2]:stop[2]] = data\",\n \"def array_partition_views(array, partition):\\n views = dictlist.copy(partition)\\n pathsshapes = sorted(list(dictlist.leafs(partition)))\\n\\n n_used = 0\\n for path, shape in pathsshapes:\\n item = dictlist.get(partition, path)\\n shape = (item,) if isinstance(item, (int, long)) else item\\n size = int(np.prod(shape))\\n dictlist.set_(views, path, array[n_used:n_used + size].reshape(shape))\\n n_used += size\\n\\n return views\",\n \"def findSubArray(list):\\n # converts number list to a list of 0,1 indicated by its primality \\n binaryArray = [int(isPrime(k)) for k in list]\\n \\n # explore subarrays from biggest (length of list) to smallest (length=2)\\n # breaks when one subarray matches the requirements \\n lengthSubArray = len(binaryArray)\\n found = False\\n while( lengthSubArray > 1 and not found ):\\n \\n # move startIndex to create subarrays from same length\\n numberSubArrays = len(binaryArray) - lengthSubArray + 1\\n for startIndex in range(0, numberSubArrays):\\n endIndex = startIndex + lengthSubArray\\n binarySubArray = binaryArray[startIndex:endIndex]\\n \\n # verify condition requested in subarray is met\\n found = sum(binarySubArray) > (lengthSubArray / 2)\\n if found: break\\n \\n lengthSubArray -= 1 \\n \\n # returns the subarray using the indexes from the binarySubArray found\\n return list[startIndex:endIndex]\",\n \"def batch_split(self) -> np.array:\\n pass\",\n \"def subsampleData(self, count):\\n size = 0\\n for block in self.blocks: size += len(block[1])\\n subset = numpy.random.permutation(size)[:count]\\n subset.sort()\\n\\n pos = 0\\n index = 0\\n ret = Dataset()\\n for block in self.blocks:\\n while subset[index]<(pos+len(block[1])):\\n loc = subset[index] - pos\\n ret.add(block[0][loc,:], block[1][loc])\\n index += 1\\n if index==subset.shape[0]: return ret\\n pos += len(block[1])\\n \\n return ret\",\n \"def slice_data(xdata, ydata, x_range):\\n\\tdata = zip(xdata, ydata)\\n\\tsliced_data = [d for d in data if d[0] >= x_range[0] and d[0] <= x_range[1]]\\n\\treturn array(zip(*sliced_data))\",\n \"def _chunk_data(X, slices):\\n\\n # from object array to list\\n slices = [sl for sl in slices if len(sl)]\\n selected_times = np.hstack([np.ravel(sl) for sl in slices])\\n start = np.min(selected_times)\\n stop = np.max(selected_times) + 1\\n slices_chunk = [sl - start for sl in slices]\\n X_chunk = X[:, :, start:stop]\\n return X_chunk, slices_chunk\",\n \"def __getslice__(self, i, j):\\n if self.__pepth__ != 0:\\n if '__warned__' not in plist.__getslice__.__dict__:\\n qj('Slicing of inner plist elements with negative indices in python 2.7 does not work, and the error cannot be detected or corrected!\\\\n'\\n 'Instead of slicing with one or two arguments: `plist._[-2:]`, use the three argument slice: `plist._[-2::1]`.\\\\n'\\n 'This avoids the broken code path in the python compiler.', 'WARNING!')\\n plist.__getslice__.__dict__['__warned__'] = True\\n return plist.__getattr__(self, '__getslice__')(i, j)\\n try:\\n if self is self.__root__:\\n return plist(list.__getslice__(self, i, j))\\n return plist(list.__getslice__(self, i, j), root=plist(list.__getslice__(self.__root__, i, j)))\\n except Exception:\\n return plist.__getitem__(self, slice(i, j))\",\n \"def __getitem__(self, keep):\\n ndim = len(self.dataset.shape)\\n # Ensure that keep is a tuple (then turn it into a list to simplify further processing)\\n keep = list(keep) if isinstance(keep, tuple) else [keep]\\n # The original keep tuple will be passed to data transform chain\\n original_keep = tuple(keep)\\n # Ensure that keep is same length as data dimension (truncate or pad with blanket slices as necessary)\\n keep = keep[:ndim] + [slice(None)] * (ndim - len(keep))\\n # Map current selection to original data indices based on any existing initial selection, per data dimension\\n keep = [(dkeep if dlookup is None else dlookup[dkeep]) for dkeep, dlookup in zip(keep, self._lookup)]\\n # Iterate over dimensions of dataset, storing information on selection on each dimension:\\n # `selection` is a list with one element per dimension; each element is a list of contiguous segments along\\n # the dimension, and each segment is represented by a tuple of 3 elements:\\n # (dataset selection, post-selection, output array selection)\\n # Similarly, `segment_sizes` is a list of lists of segment lengths (empty lists for scalar-selected dimensions)\\n selection, segment_sizes = [], []\\n for dim_keep, dim_len in zip(keep, self.dataset.shape):\\n if np.isscalar(dim_keep):\\n # If selection is a scalar, pass directly to dataset selector and remove dimension from output\\n selection.append([(dim_keep, None, None)])\\n segment_sizes.append([])\\n elif isinstance(dim_keep, slice):\\n # If selection is a slice, pass directly to dataset selector without post-selection\\n start, stop, stride = dim_keep.indices(dim_len)\\n segm_size = len(range(start, stop, stride))\\n selection.append([(slice(start, stop, stride), slice(None), slice(0, segm_size, 1))])\\n segment_sizes.append([segm_size])\\n elif len(dim_keep) == 0:\\n # If selection is empty, pass to post-selector, as HDF5 datasets do not support zero-length selection\\n selection.append([(slice(0, 1, 1), slice(0, 0, 1), slice(0, 0, 1))])\\n segment_sizes.append([0])\\n else:\\n # Anything else is advanced indexing via bool or integer sequences\\n dim_keep = np.atleast_1d(dim_keep)\\n # Turn boolean mask into integer indices (True means keep that index)\\n if dim_keep.dtype == bool and len(dim_keep) == dim_len:\\n dim_keep = np.nonzero(dim_keep)[0]\\n elif not np.all(dim_keep == np.unique(dim_keep)):\\n raise TypeError('LazyIndexer cannot handle duplicate or unsorted advanced integer indices')\\n # Split indices into multiple contiguous segments (specified by first and one-past-last data indices)\\n jumps = np.nonzero(np.diff(dim_keep) > 1)[0]\\n first = [dim_keep[0]] + dim_keep[jumps + 1].tolist()\\n last = dim_keep[jumps].tolist() + [dim_keep[-1]]\\n segments = np.c_[first, np.array(last) + 1]\\n if len(dim_keep) > 0.2 * dim_len and len(segments) > 1:\\n # If more than 20% of data are selected in 2 or more separate segments (the Ratcliffian benchmark),\\n # select data at dataset level with a single slice spanning segments and then postselect the ndarray\\n selection.append([(slice(segments[0, 0], segments[-1, 1], 1),\\n dim_keep - dim_keep[0], slice(0, len(dim_keep), 1))])\\n segment_sizes.append([len(dim_keep)])\\n else:\\n # Turn each segment into a separate slice at dataset level without post-selection,\\n # and construct contiguous output slices of the same segment sizes\\n segm_sizes = [end - start for start, end in segments]\\n segm_starts = np.cumsum([0] + segm_sizes)\\n selection.append([(slice(start, end, 1), slice(None), slice(segm_starts[n], segm_starts[n + 1], 1))\\n for n, (start, end) in enumerate(segments)])\\n segment_sizes.append(segm_sizes)\\n # Short-circuit the selection if all dimensions are selected with scalars (resulting in a scalar output)\\n if segment_sizes == [[]] * ndim:\\n out_data = self.dataset[tuple([select[0][0] for select in selection])]\\n else:\\n # Use dense N-dimensional meshgrid to slice data set into chunks, based on segments along each dimension\\n chunk_indices = np.mgrid[[slice(0, len(select), 1) for select in selection]]\\n # Pre-allocate output ndarray to have the correct shape and dtype (will be at least 1-dimensional)\\n out_data = np.empty([np.sum(segments) for segments in segment_sizes if segments], dtype=self.dataset.dtype)\\n # Iterate over chunks, extracting them from dataset and inserting them into the right spot in output array\\n for chunk_index in chunk_indices.reshape(ndim, -1).T:\\n # Extract chunk from dataset (don't use any advanced indexing here, only scalars and slices)\\n dataset_select = tuple([select[segment][0] for select, segment in zip(selection, chunk_index)])\\n chunk = self.dataset[dataset_select]\\n # Perform post-selection on chunk (can be fancier / advanced indexing because chunk is now an ndarray)\\n post_select = [select[segment][1] for select, segment in zip(selection, chunk_index)]\\n # If any dimensions were dropped due to scalar indexing, drop them from post_select/out_select tuples\\n post_select = tuple([select for select in post_select if select is not None])\\n # Do post-selection one dimension at a time, as ndarray does not allow simultaneous advanced indexing\\n # on more than one dimension. This caters for the scenario where more than one dimension satisfies\\n # the Ratcliffian benchmark (the only way to get advanced post-selection).\\n for dim in range(len(chunk.shape)):\\n # Only do post-selection on this dimension if non-trivial (otherwise an unnecessary copy happens)\\n if not (isinstance(post_select[dim], slice) and post_select[dim] == slice(None)):\\n # Prepend the appropriate number of colons to the selection to place it at correct dimension\\n chunk = chunk[[slice(None)] * dim + [post_select[dim]]]\\n # Determine appropriate output selection and insert chunk into output array\\n out_select = [select[segment][2] for select, segment in zip(selection, chunk_index)]\\n out_select = tuple([select for select in out_select if select is not None])\\n out_data[out_select] = chunk\\n # Apply transform chain to output data, if any\\n return reduce(lambda data, transform: transform(data, original_keep), self.transforms, out_data)\",\n \"def rpartition(self, x):\\n pass\",\n \"def partition(self, data, labels):\\n\\t\\traise Exception(\\\"Not implmented\\\")\",\n \"def partition(self):\\n return self.tag(\\\"partition\\\")\",\n \"def getacolslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.var,(first_),(last_))\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_val = False\\n if val is None:\\n val_ = None\\n else:\\n try:\\n val_ = memoryview(val)\\n except TypeError:\\n try:\\n _tmparr_val = array.array(\\\"d\\\",val)\\n except TypeError:\\n raise TypeError(\\\"Argument val has wrong type\\\")\\n else:\\n val_ = memoryview(_tmparr_val)\\n _copyback_val = True\\n else:\\n if val_.format != \\\"d\\\":\\n val_ = memoryview(array.array(\\\"d\\\",val))\\n _copyback_val = True\\n if val_ is not None and len(val_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument val has wrong length\\\")\\n res = self.__obj.getacolslicetrip(first_,last_,maxnumnz_,len(subi),subi_,subj_,val_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_val:\\n val[:] = _tmparr_val\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def get_partitioning(self):\\n raise Exception(\\\"Unimplemented\\\")\",\n \"def get_data(self):\\n return self.data[self._size:self._size + self._len]\",\n \"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\\n raise ValueError(\\\"Argument subj must be writable\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _val_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and val_ is not None and len(val_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument val is not long enough: Is %d, expected %d\\\" % (len(val_),(maxnumnz_)))\\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\\n raise ValueError(\\\"Argument val must be writable\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n surp_ = ctypes.c_int64(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def subFarms(self,partition,full_name=None):\\n if self.inUse.value() is None: self.load()\\n got = []\\n for i in xrange(len(self.inUse.data)):\\n if self.inUse.data[i]==partition:\\n if full_name:\\n got.append(self.name+'_'+self.subfarms.data[i])\\n else:\\n got.append(self.subfarms.data[i])\\n return got\",\n \"def sub_columns(arr, sub_size):\\n return sub_rows(arr.T, sub_size)\",\n \"def subsection_at(self, index):\\n return self.child_at(index)\",\n \"def partitions(self):\\n self._get_latest_content()\\n return self._data.get('partitions', [])\",\n \"def slice_during(self, e):\\r\\n\\r\\n if not isinstance(e, Epochs):\\r\\n raise ValueError('e has to be of Epochs type')\\r\\n\\r\\n if e.data.ndim > 0:\\r\\n raise NotImplementedError('e has to be a scalar Epoch')\\r\\n\\r\\n if self.ndim != 1:\\r\\n e_s = 'slicing only implemented for 1-d TimeArrays'\\r\\n return NotImplementedError(e_s)\\r\\n i_start = self.index_at(e.start)\\r\\n i_stop = self.index_at(e.stop)\\r\\n if e.start > self[i_start]: # make sure self[i_start] is in epoch e\\r\\n i_start += 1\\r\\n if e.stop > self[i_stop]: # make sure to include self[i_stop]\\r\\n i_stop += 1\\r\\n\\r\\n return slice(i_start, i_stop)\",\n \"def segment(data):\",\n \"def _get_slice(segments, shape):\\n\\n if not (1 <= len(shape) <= 2):\\n raise ValueError('Cannot segment array of shape: %s' % str(shape))\\n else:\\n size = shape[0]\\n slice_length = np.ceil(float(size) / segments)\\n start_idx = 0\\n end_idx = slice_length\\n while start_idx < size:\\n if len(shape) == 1:\\n yield slice(start_idx, end_idx)\\n else:\\n yield (slice(start_idx, end_idx), slice(None))\\n start_idx = end_idx\\n end_idx = min(start_idx + slice_length, size)\",\n \"def to_ndarray(self):\\n # Create an ndarray of the right shape, filled with self.defval.\\n ndshape = type(self).flatten_shape(self.shape)\\n res = np.full(ndshape, self.defval, dtype=self.dtype)\\n if 0 in ndshape:\\n return res\\n shp, qhp = type(self)._sorted_shape_qhape(tensor=self)\\n # ranges is like shape, but every number d is replaced by a tuple\\n # (a, a+d) where a is the sum of all the previous entries in the same\\n # dim.\\n ranges = []\\n for dim in shp:\\n prv = dim[0]\\n r = [(0, prv)]\\n for d in dim[1:]:\\n nxt = prv + d\\n r.append((prv, nxt))\\n prv = nxt\\n ranges.append(r)\\n # Copy the elements of each sector to the right place in the result.\\n for k, v in self.sects.items():\\n slc = ()\\n for i, qnum in enumerate(k):\\n r = ranges[i][qhp[i].index(qnum)]\\n slc += (slice(r[0], r[1]),)\\n res[slc] = v\\n return res\",\n \"def slice_dims(data_array: sc.DataArray, slices: Dict[str, slice]) -> sc.DataArray:\\n out = data_array\\n for dim, sl in slices.items():\\n out = out[dim, sl]\\n return out\",\n \"def Get(self,k:int): \\n ### get partitions depending on the partition schemes C that depends on k!\\n return subsets_k(list(range(self._n)),k)\",\n \"def __getitem__(self, data):\\n i,j = data\\n return self._data[i][j]\",\n \"def _get_item(self, index):\\n data, label = self.data[index], self.label[index]\\n coordmax = np.max(data, axis=0)\\n coordmin = np.min(data, axis=0)\\n nsubvolume_x = np.ceil((coordmax[0]-coordmin[0])/1.5).astype(np.int32)\\n nsubvolume_y = np.ceil((coordmax[1]-coordmin[1])/1.5).astype(np.int32)\\n batch_data, batch_label = [], []\\n for i in range(nsubvolume_x):\\n for j in range(nsubvolume_y):\\n curmin = coordmin + [i*1.5, j*1.5, 0]\\n curmax = coordmin+ [(i+1)*1.5, (j+1)*1.5, coordmax[2]-coordmin[2]]\\n crop_ids = np.sum((data>=(curmin-0.2)) * (data<=(curmax+0.2)), axis=1) == 3\\n if sum(crop_ids) == 0: continue\\n crop_data = data[crop_ids]\\n crop_label = label[crop_ids]\\n mask = np.sum((crop_data>=(curmin-0.001)) * (crop_data<=(curmax+0.001)), axis=1) == 3\\n ids = np.random.choice(crop_label.size, self.npoints, replace=True)\\n this_data = crop_data[ids]\\n this_label = crop_label[ids]\\n this_mask = mask[ids]\\n if sum(this_mask) * 1. / this_mask.size < 0.01: continue\\n this_label *= this_mask\\n if self.normalize:\\n this_data = utils.normalize_point_cloud(this_data)\\n batch_data.append(this_data[None,:,:])\\n batch_label.append(this_label[None,:])\\n batch_data = np.concatenate(tuple(batch_data), axis=0)\\n batch_label = np.concatenate(tuple(batch_label), axis=0)\\n return batch_data, batch_label\",\n \"def segments(self):\\n return (self._subset((i,i+1)) for i in range(len(self)-1))\",\n \"def partition(array, first, last):\\n # partition up until final value\\n pivot = array[last]\\n i = first - 1\\n\\n for count in range(first, last):\\n # split array\\n if array[count] < pivot:\\n i += 1\\n # assign array positions\\n array[i],array[count] = array[count],array[i]\\n # reassign\\n array[i+1],array[last] = array[last],array[i+1]\\n return (i+1)\",\n \"def get_split_data(self):\\n X, y, _, _ = self.get_subsets()\\n return train_test_split(X, y, test_size=0.3, random_state=42)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68206084","0.6535615","0.6457086","0.6347438","0.62131214","0.61877143","0.61062545","0.6101903","0.60762966","0.6068932","0.6029079","0.6012183","0.59961045","0.59653914","0.5928066","0.58841175","0.58761495","0.58514863","0.5848823","0.5791028","0.5779958","0.57720095","0.5763268","0.5713823","0.5713595","0.56957734","0.5685574","0.5683506","0.56661355","0.5665764","0.56596345","0.5659613","0.56538606","0.56397116","0.56286144","0.56241447","0.5622422","0.5611998","0.5586044","0.5581166","0.55736196","0.5570869","0.5562308","0.55604047","0.5557058","0.5535347","0.5523683","0.5520575","0.5519391","0.5514903","0.5513096","0.54939085","0.5488341","0.54794407","0.5473635","0.54733855","0.54686856","0.5453024","0.5446765","0.54447645","0.544219","0.5428138","0.54233164","0.53955066","0.5367499","0.5364493","0.536259","0.53601974","0.5352783","0.53390896","0.53365284","0.5336055","0.53340626","0.5333585","0.5325604","0.53202784","0.5316346","0.53149754","0.5312976","0.5308708","0.5307463","0.53028697","0.529681","0.5295519","0.52954924","0.5295376","0.52942693","0.52923304","0.5271146","0.52641356","0.5260086","0.52566856","0.5254645","0.5253552","0.52519685","0.5250993","0.52420545","0.52414733","0.5240278","0.52392286"],"string":"[\n \"0.68206084\",\n \"0.6535615\",\n \"0.6457086\",\n \"0.6347438\",\n \"0.62131214\",\n \"0.61877143\",\n \"0.61062545\",\n \"0.6101903\",\n \"0.60762966\",\n \"0.6068932\",\n \"0.6029079\",\n \"0.6012183\",\n \"0.59961045\",\n \"0.59653914\",\n \"0.5928066\",\n \"0.58841175\",\n \"0.58761495\",\n \"0.58514863\",\n \"0.5848823\",\n \"0.5791028\",\n \"0.5779958\",\n \"0.57720095\",\n \"0.5763268\",\n \"0.5713823\",\n \"0.5713595\",\n \"0.56957734\",\n \"0.5685574\",\n \"0.5683506\",\n \"0.56661355\",\n \"0.5665764\",\n \"0.56596345\",\n \"0.5659613\",\n \"0.56538606\",\n \"0.56397116\",\n \"0.56286144\",\n \"0.56241447\",\n \"0.5622422\",\n \"0.5611998\",\n \"0.5586044\",\n \"0.5581166\",\n \"0.55736196\",\n \"0.5570869\",\n \"0.5562308\",\n \"0.55604047\",\n \"0.5557058\",\n \"0.5535347\",\n \"0.5523683\",\n \"0.5520575\",\n \"0.5519391\",\n \"0.5514903\",\n \"0.5513096\",\n \"0.54939085\",\n \"0.5488341\",\n \"0.54794407\",\n \"0.5473635\",\n \"0.54733855\",\n \"0.54686856\",\n \"0.5453024\",\n \"0.5446765\",\n \"0.54447645\",\n \"0.544219\",\n \"0.5428138\",\n \"0.54233164\",\n \"0.53955066\",\n \"0.5367499\",\n \"0.5364493\",\n \"0.536259\",\n \"0.53601974\",\n \"0.5352783\",\n \"0.53390896\",\n \"0.53365284\",\n \"0.5336055\",\n \"0.53340626\",\n \"0.5333585\",\n \"0.5325604\",\n \"0.53202784\",\n \"0.5316346\",\n \"0.53149754\",\n \"0.5312976\",\n \"0.5308708\",\n \"0.5307463\",\n \"0.53028697\",\n \"0.529681\",\n \"0.5295519\",\n \"0.52954924\",\n \"0.5295376\",\n \"0.52942693\",\n \"0.52923304\",\n \"0.5271146\",\n \"0.52641356\",\n \"0.5260086\",\n \"0.52566856\",\n \"0.5254645\",\n \"0.5253552\",\n \"0.52519685\",\n \"0.5250993\",\n \"0.52420545\",\n \"0.52414733\",\n \"0.5240278\",\n \"0.52392286\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7297865"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2689,"cells":{"query":{"kind":"string","value":"Change the axis names. The axis names are arbitrary, so mapping them to another arbitrary collection does not change the data array values, units, nor axis order."},"document":{"kind":"string","value":"def change_axis_names(self, axis_map):\n axes = self.axes\n\n # Partition axes\n self.axes = [axis_map[axis] for axis in axes]\n\n # Flipped axes\n flip = self.flip\n if flip:\n self.flip = [axis_map[axis] for axis in flip]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setAxisName(name, axes='XYZ'):\n dislin.name(name, axes)","def setAxesNames(self):\n \n labels = ['T', 'Z', 'Y', 'X'] + [chr(ord('S')-i) for i in xrange(18)]\n if (len(self.axisList) >= 4):\n i = 0\n else:\n i = 4 - len(self.axisList)\n \n for axis in self.axisList:\n self.axesNames.append(labels[i] + ' - ' + axis.id)\n i += 1","def set_index_names(self, names, axis=0):\n self.get_axis(axis).names = names","def axesNames(self, data, info):\n return []","def setaxesnames(self):\n if not self._axesnames or self.prop['skipsai']:\n return\n debug('ControllerStartup.setaxesnames()')\n oldaxes = self.pidevice.qSAI_ALL()\n for i, newaxis in enumerate(self.axesnames):\n if newaxis != oldaxes[i] or self.prop['forcesai']:\n setstage = False\n if self.pidevice.HasqCST():\n if self.pidevice.qCST()[oldaxes[i]] == 'NOSTAGE':\n try:\n debug('try rename NOSTAGE to TEMP (0x3C)')\n self.pidevice.SPA(oldaxes[i], 0x3c, 'TEMP')\n setstage = True\n except GCSError:\n pass\n self.pidevice.SAI(oldaxes[i], newaxis)\n if setstage:\n self.pidevice.SPA(newaxis, 0x3c, 'NOSTAGE')\n debug('restore NOSTAGE (0x3C)')","def _default_axis_names(n_dims):\n _DEFAULT_NAMES = (\"z\", \"y\", \"x\")\n return _DEFAULT_NAMES[-n_dims:]","def customAxisNames(self):\n return []","def setAllAxisUnits(self,units): \n self.__axis_units__ = units","def setAxisUnits(self, dim, units): \n try:\n self.__axis_units__[dim] = units\n except IndexError:\n self.__axis_units__.append(units)","def axesnames(self, axesnames):\n if axesnames is None:\n self._axesnames = None\n else:\n assert isinstance(axesnames, list), 'axesnames must be list'\n self._axesnames = axesnames\n debug('ControllerStartup.axesnames = %s', itemstostr(self._axesnames))","def set_index_name(self, name, axis=0):\n self.get_axis(axis).name = name","def setAllAxisLabels(self, labels):\n self.__axis_labels__ = labels","def _update_axes(self):\n data_shape = self.data.shape\n if len(self.axes) < self.data.ndim + 1:\n self._axes.append(Axis())\n for index in range(self.data.ndim):\n if len(self.axes[index].values) != data_shape[index]:\n self.axes[index].values = np.arange(data_shape[index],\n dtype=np.float64)","def process_custom_axes(axis_names):\n return axis_names.strip().strip(\"'\").strip('\"').split(',')","def axis_name(self):\n return self._axis_name","def setAxisNameColor(idx=-1, axes='XYZ'):\n dislin.axclrs(idx, 'Name', axes)","def setAxisNameJustification(jus, axes='XYZ'):\n dislin.namjus(justdict[jus],axes)","def axesnames(self):\n return self._axesnames","def _handle_setup_axis(self, axis_args):\n axis_name = axis_args['name']\n axes_dict = self.server.axes\n\n if axis_name not in [name for name, _ in axes_dict.items()]:\n print \"Adding a new axis:\", axis_name\n axis_count = len(axes_dict)\n newaxis = self.server.figure.add_subplot(axis_count+1, 1, axis_count+1)\n axes_dict[axis_name] = newaxis\n axes_dict[axis_name].grid(True)\n axes_dict[axis_name].set_xlabel(axis_args['x_label'])\n axes_dict[axis_name].set_ylabel(axis_args['y_label'])\n # TODO: support *.set_title(\"Title\")\n if FLAGS.logy:\n axes_dict[axis_name].set_yscale('log', nonposy='clip')\n\n if axis_count != 0:\n # Resize other axes if the above wasn't the first.\n axis_count = len(axes_dict)\n for row,(name, _) in enumerate(axes_dict.items(), 1):\n print name, axis_count, row\n axes_dict[name].change_geometry(axis_count, 1, row)","def setAxisLabel(self, dim, label): \n try:\n self.__axis_labels__[dim] = label\n except IndexError:\n self.__axis_labels__.append(label)","def convert_axis( mv, axisold, axisindnew ):\n (axisnew, indexina3) = axisindnew\n axes = allAxes(mv)\n kold = None\n for k in range(len(axes)):\n if axes[k]==axisold: kold=k\n if kold==None:\n print \"ERROR. convert_axis cannot find axis\",axisold,\" in variable\",mv\n if len(axisold)==len(axisnew):\n mv.setAxis( kold, axisnew )\n return\n # Here's what we would do in 1-D:\n # newdata = ma.ones(len(axisnew))*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\n # for i in range(len(axisold)):\n # newdata[ indexina3[i] ] = ma[i]\n # newmv = cdms2.createVariable( newdata, id=mv.id )\n # >1-D is the same idea, but more dimensions are coming along for the ride,\n # making it more complicated...\n shape0 = mv.shape\n shape0[kold] = len(axisnew)\n newdata = ma.ones(shape0)*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\n # We want to copy ma to newdata - except that we need indirect indexing for the kold-th axis.\n # There seems to be nothing in numpy for treating one axis differently from the rest\n # (except for ellipsis, but it makes sense to use only one ellipsis and we would need two here).\n # The following will do the job. It would be very slow for an array with many big dimensions,\n # but the arrays here have already been reduced for graphics; the index sets will be small or\n # empty...\n ranges = map( range, shape0[0:kold] )\n for i in range(len(axisold)):\n for idx in apply(itertools.product,ranges):\n idx = idx + [indexina3(i)] + [Ellipsis]\n idxo = idx + [i] + [Ellipsis]\n newdata[ tuple(idx) ] = mv[idxo]\n newmv = cdms2.createVariable( newdata, id=mv.id )","def swapaxes(self, a1, a2):\n an = self.axes_names[:]\n ia1, ia2 = self.get_axis_id(a1), self.get_axis_id(a2)\n an[ia2], an[ia1] = an[ia1], an[ia2]\n return xndarray(np.swapaxes(self.data, ia1, ia2), an, self.axes_domains,\n self.value_label, self.meta_data)","def set_label_names(self, x: Union[np.ndarray, Dict[int, str]]) -> None:\n if isinstance(x, np.ndarray):\n label_names = x\n elif isinstance(x, dict):\n label_names = np.full(max(x.keys()) + 1, \"\", dtype=\"object\")\n label_names[list(x.keys())] = list(x.values())\n else:\n raise ValueError(f\"Unsupported {type(x)=}\")\n self._label_names_array = label_names","def _set_axis(axis):\n\n def axis_setter(self, labels):\n new_qc = DataFrameDefault.register(pandas.DataFrame.set_axis)(\n self, axis=axis, labels=labels\n )\n self.__dict__.update(new_qc.__dict__)\n\n return axis_setter","def _update_axislabels(self, x='x', **kwargs):\n if x not in 'xy':\n return\n # Update label on this axes\n axis = getattr(self, x + 'axis')\n axis.label.update(kwargs)\n kwargs.pop('color', None)\n\n # Defer to parent (main) axes if possible, then get the axes\n # shared by that parent\n ax = self._panel_parent or self\n ax = getattr(ax, '_share' + x) or ax\n\n # Apply to spanning axes and their panels\n axs = [ax]\n if getattr(ax.figure, '_span' + x):\n s = axis.get_label_position()[0]\n if s in 'lb':\n axs = ax._get_side_axes(s)\n for ax in axs:\n getattr(ax, x + 'axis').label.update(kwargs) # apply to main axes\n pax = getattr(ax, '_share' + x)\n if pax is not None: # apply to panel?\n getattr(pax, x + 'axis').label.update(kwargs)","def setAxisNameDistance(dist,axes='XYZ'):\n dislin.namdis(dist, axes)","def setIndexNames(self):\n self.xi = self.i1\n self.yi = self.i2","def set_axis_label(self, label, axis):\n if axis == 'x':\n self.axplot.set_xlabel(label)\n elif axis == 'y':\n self.axplot.set_ylabel(label)\n else:\n errmsg = 'Valid axis names are x and y.'\n raise ValueError(errmsg)","def _set_plot_axes_labels(self, data, viewer_id):\n viewer = self._viewer_by_id(viewer_id)\n\n # Get the units of the data to be loaded.\n spectral_axis_unit_type = data.spectral_axis.unit.physical_type.title()\n flux_unit_type = data.flux.unit.physical_type.title()\n\n if data.spectral_axis.unit.is_equivalent(u.m):\n spectral_axis_unit_type = \"Wavelength\"\n elif data.spectral_axis.unit.is_equivalent(u.pixel):\n spectral_axis_unit_type = \"pixel\"\n\n viewer.figure.axes[0].label = f\"{spectral_axis_unit_type} [{data.spectral_axis.unit.to_string()}]\"\n viewer.figure.axes[1].label = f\"{flux_unit_type} [{data.flux.unit.to_string()}]\"\n\n # Make it so y axis label is not covering tick numbers.\n viewer.figure.axes[1].label_offset = \"-50\"","def setnames(self, *args, **kwargs):\n return _coordsys.coordsys_setnames(self, *args, **kwargs)","def set_axis(self, axis_list):\n if self.table_ready:\n final_axis_list = []\n for i, axis in enumerate(axis_list):\n if axis:\n final_axis_list.append(\"1 \" + str(i + 1))\n else:\n final_axis_list.append(\"0 \" + str(i + 1))\n\n command = self.build_command(\n self.device, (\"set_axis\", final_axis_list), single_commands=True\n )\n self.vcw.write(self.device, command)","def canonicalize_axis_name(axis_name):\n if not axis_name:\n return []\n if (isinstance(axis_name, str) or\n not isinstance(axis_name, collections.Iterable)):\n return [axis_name]\n return list(axis_name)","def _declare_auto_axes_idx(self):\n if not self.axes_idx:\n self.axes_idx = BiMapping(to_first=range(len(self.name_elements)), to_second=range(len(self.name_elements)))","def SwapAxis(self, axis0, axis1):\n\n axis0 = int(axis0)\n axis1 = int(axis1)\n\n self.points[:,[axis0,axis1]] = self.points[:,[axis1,axis0]]","def set_axis_label(\n self,\n axis: Union[int, Sequence[int]],\n label: Union[str, Sequence[str]],\n ):\n if isinstance(axis, Integral):\n axis = assert_axis_in_bounds(axis, self.ndim)\n if self.axis_labels[axis] != str(label):\n full_axis_labels = list(self.axis_labels)\n full_axis_labels[axis] = str(label)\n self.axis_labels = full_axis_labels\n self.last_used = axis\n else:\n full_axis_labels = list(self.axis_labels)\n # cast label to list for list comparison below\n label = list(label) # type: ignore\n axis = tuple(axis) # type: ignore\n if len(axis) != len(label):\n raise ValueError(\n trans._(\"axis and label sequences must have equal length\")\n )\n if label != full_axis_labels:\n for ax, val in zip(axis, label):\n ax = assert_axis_in_bounds(int(ax), self.ndim)\n full_axis_labels[ax] = val\n self.axis_labels = full_axis_labels","def axisinfo(unit, axis):\n if isinstance(unit, tuple):\n unit = unit[0]\n unit_obj = unit if isinstance(unit, Unit) else Unit(unit)\n name = unyt_arrayConverter._axisnames.get(axis, \"\")\n if unit_obj.is_dimensionless:\n label = name\n else:\n name += \" \"\n unit_str = unit_obj.latex_representation()\n if unyt_arrayConverter._labelstyle == \"[]\":\n label = name + \"$\\\\left[\" + unit_str + \"\\\\right]$\"\n elif unyt_arrayConverter._labelstyle == \"/\":\n axsym = \"$q_{\\\\rm\" + axis.axis_name + \"}$\"\n name = axsym if name == \" \" else name\n if \"/\" in unit_str:\n label = name + \"$\\\\;/\\\\;\\\\left(\" + unit_str + \"\\\\right)$\"\n else:\n label = name + \"$\\\\;/\\\\;\" + unit_str + \"$\"\n else:\n label = name + \"$\\\\left(\" + unit_str + \"\\\\right)$\"\n return AxisInfo(label=label.strip())","def set_orientation(self, axes):\n if debug:\n logger.debug('set_orientation ...')\n logger.debug('%s -> %s', str(self.axes_names), str(axes))\n\n if set(axes) != set(self.axes_names):\n raise Exception('Required orientation %s does not contain '\n 'all axes %s' % (str(axes), str(self.axes_names)))\n\n if axes == self.axes_names: # already in the asked orientation\n return\n\n for i, axis in enumerate(axes):\n logger.debug('Rolling axis %s, cur pos=%d -> dest pos=%d',\n axis, self.axes_names.index(axis), i)\n logger.debug('Shape: %s', str(self.data.shape))\n cur_i = self.axes_names.index(axis)\n self.data = np.rollaxis(self.data, cur_i, i)\n self.axes_names.pop(cur_i)\n self.axes_names.insert(i, axis)\n logger.debug('After rolling. Shape: %s, new axes: %s',\n str(self.data.shape), str(self.axes_names))\n logger.debug('')\n\n self.axes_ids = dict([(a, i) for i, a in enumerate(self.axes_names)])","def replace_axis(self, dim:NamedIndex,\n mapping_or_old:'Union[Mapping[NamedIndex, NamedIndex], NamedIndex]',\n new:'Optional[NamedIndex]'=None):\n\n axes = self[dim] # disable idx_dim access\n # axes = self.get(dim) or self._dim_axes[dim] # dim:'Union[int, NamedIndex]'\n is_tuple_axes = is_namedtuple(axes)\n assert isinstance(axes, dict) or is_tuple_axes, (\n f'unnamed dim({dim!r}) cannot be renamed')\n\n axes_keys = axes._fields if is_tuple_axes else axes.keys()\n axes_iter = iter(zip(axes._fields, axes)) if is_tuple_axes else axes.items()\n axes_ = OrderedDict()\n\n if new is None:\n assert isinstance(mapping_or_old, dict), (\n f\"'mapping_or_old'({type(mapping_or_old)}) is expected to be a dict \"\n \"when 'new' is None\")\n\n mapping = mapping_or_old\n for axis, index in axes_iter:\n axis = mapping.get(axis, axis)\n assert axis not in axes_, f'axis {axis!r} in mapping is conflicted'\n\n axes_[axis] = index\n else:\n assert new not in axes_keys, f'new axis({new!r}) is confilicted'\n\n old = mapping_or_old\n for axis, index in axes_iter:\n axes_[new if axis == old else axis] = index\n\n axes_ = namedtuple(dim, axes_.keys())(**axes_) if is_tuple_axes else type(axes)(axes_)\n ret = OrderedDict()\n for dim_, axes in self.items():\n ret[dim_] = axes_ if dim_ == dim else axes\n\n return type(self)(ret)","def set_nAxis(self, newval):\n rest_val = str(newval)\n return self._setAttr(\"nAxis\", rest_val)","def set_axes(self, a):\r\n self.axes = a","def axes(*x: Iterable[int]):\n return [_ti_core.Axis(i) for i in x]","def label_axis(self, name, label):\n\n axis = self._find_axis(name)\n axis.axis_label = label","def __init__(self, axes=()):\n self._axes = []\n self._dimension = 0\n for axis in axes:\n self.add_axis(axis)","def _appendAxisDefinition(self, axis):\n length = len(axis)\n\n self.na_dict[\"NX\"].append(length)\n self.na_dict[\"XNAME\"].append(xarray_utils.getBestName(axis))\n\n # If only one item in axis values\n if length < 2:\n self.na_dict[\"DX\"].append(0)\n self.na_dict[\"NXDEF\"].append(length)\n self.na_dict[\"X\"].append(axis.data.tolist()) \n return\n\n incr = xarray_utils.get_interval(axis, 0, 1)\n\n for i in range(1, length):\n if (axis[i] - axis[i - 1]) != incr:\n self.na_dict[\"DX\"].append(0)\n self.na_dict[\"NXDEF\"].append(length)\n self.na_dict[\"X\"].append(axis.data.tolist())\n break\n\n else: # If did not break out of the loop\n max_length = length\n if length > 3: \n max_length = 3\n\n self.na_dict[\"DX\"].append(incr)\n self.na_dict[\"NXDEF\"].append(max_length)\n self.na_dict[\"X\"].append(axis[:max_length])","def reorderAxesEvent(self):\n axisB = self.sender().text()\n self.myParent.swapAxes(self.axisName, axisB)\n self.myParent.setVistrailsVariableAxes()","def replace_dim(self, old:NamedIndex, new:NamedIndex):\n\n assert new not in self, f'new dim({new!r}) is confilicted'\n\n ret = OrderedDict()\n for dim, axes in self.items():\n if dim == old:\n if is_namedtuple(axes):\n axes = namedtuple(new, axes._fields)(*axes)\n ret[new] = axes\n else:\n ret[old] = axes\n\n return type(self)(ret)","def xaxis(self,label,units):\n if units != \"\": label = label + \" (\" + units + \")\"\n self.subplot.set_xlabel(label)\n pass","def addAxes(self):\n numDims = len(self.relation.fieldNames) - 1\n angle = 360 / numDims\n axisDomains = self.relation.axisDomains\n for i in range(numDims):\n axis = PlotAxis(self)\n self.scene().addItem(axis)\n if self.axisAngles and i < len(self.axisAngles):\n axis.setRotation(self.axisAngles[i])\n else:\n axis.setRotation(angle * i)\n self.axes.append(axis)\n\n domain = axisDomains[i]\n text = PlotAxisLabel(\"{}\\n[{:.2f},{:.2f}]\".format(self.relation.fieldNames[i], domain[0], domain[1]))\n text.setFont(self.labelFont)\n self.axisLabels.append(text)\n text.setParentItem(axis)","def default_units(x, axis):\n # In the case where the first matplotlib command is setting limits,\n # x may be a tuple of length two (with the same units).\n if isinstance(x, tuple):\n name = getattr(x[0], \"name\", \"\")\n units = x[0].units\n else:\n name = getattr(x, \"name\", \"\")\n units = x.units\n\n # maintain a mapping between Axis and name since Axis does not point to\n # its underlying data and we want to propagate the name to the axis\n # label in the subsequent call to axisinfo\n unyt_arrayConverter._axisnames[axis] = name if name is not None else \"\"\n return units","def set_labels(ax, label_value, label_axis):\n if label_axis == 'x':\n ax.set_xticks(np.arange(len(label_value)))\n axis = ax.get_xticklabels()\n else:\n ax.set_yticks(np.arange(len(label_value)) + 1)\n axis = ax.get_yticklabels()\n\n # fetch labels\n labels = [items.get_text() for items in axis]\n\n # init a count variable\n if label_axis == 'x':\n count = 0\n else:\n count = len(label_value) - 1\n\n # iterate through all the labels and change the label name\n for i in range(len(labels)):\n labels[i] = label_value[count]\n\n if label_axis == 'x':\n count += 1\n else:\n count -= 1\n\n return labels","def reset(self):\n # Don't reset axis labels\n self.range = ((0, 2, 1),) * self.ndim\n self.current_step = (0,) * self.ndim\n self.order = tuple(range(self.ndim))","def new_axes(self, name):\n\n return self.figure.add_axes([0.05, 0.05, 0.9, 0.9], label=name)","def rename_levels(self, name_dict, axis=1, inplace=False):\n\n def apply_func(obj_index):\n return index_fns.rename_levels(obj_index, name_dict)\n\n return self.apply_on_index(apply_func, axis=axis, inplace=inplace)","def reorder_axes(cls, array: np.ndarray, axes: str) -> np.ndarray:\n return cls._reorder_axes(array, axes, cls.array_axis_order)","def series_axis(self, series_axis):\n\n self.container['series_axis'] = series_axis","def setAxisParts(lowx='all', lefty='all', upx='ticks', righty='ticks'):\n partdict = {'none':'NONE','lines':'LINE','ticks':'TICKS',\n 'labels':'LABELS', 'all':'NAME'} \n dislin.setgrf(partdict[lowx], partdict[lefty],\\\n partdict[upx], partdict[righty])","def _default_axis_units(n_dims):\n return (\"-\",) * n_dims","def _update_column_name(self, column, idx, old_name, name):\n dtype = self.dtype\n # Updating the names on the dtype should suffice\n dtype.names = dtype.names[:idx] + (name,) + dtype.names[idx + 1 :]","def SetAxisOrientation(*args, **kwargs):\n return _gdi_.DC_SetAxisOrientation(*args, **kwargs)","def setupVariableAxes(self):\n if self.var is None:\n return\n \n if (self.axisList is None):\n self.axisList = self.var.getAxisList()\n self.axisOrder = range(len(self.axisList))\n\n self.clear() \n self.setAxesNames()\n \n # Iterate through the variables axes & init each axis widget\n axisIndex = 0\n for axis, axisName in zip(self.axisList, self.axesNames):\n # Create the axis widget\n axisWidget = QAxis(axis, axisName, axisIndex, self)\n axisWidget.setAxisButtonText(axisName)\n self.axisWidgets.append(axisWidget)\n\n # Setup the layout for each axis\n row = self.gridLayout.rowCount()\n self.gridLayout.addWidget(axisWidget.getAxisButton(), row, 0)\n self.gridLayout.addWidget(axisWidget, row, 1) \n self.gridLayout.addWidget(axisWidget.getAxisOperationsButton(), row, 2)\n\n # Create separator line between each axis widget\n vline = QtGui.QFrame()\n vline.setFrameStyle(QtGui.QFrame.HLine | QtGui.QFrame.Sunken)\n self.gridLayout.addWidget(vline, row+1, 0, 1,\n self.gridLayout.columnCount())\n\n axisIndex += 1\n\n self.gridLayout.setRowStretch(self.gridLayout.rowCount(), 1)","def _relabel(array, renames):\n\n att_renames = []\n dim_renames = []\n\n for k, v in renames.items():\n if k in array.att_names:\n att_renames.extend([k, v])\n elif k in array.dim_names:\n dim_renames.extend([k, v])\n else:\n raise ValueError(\"Invalid array attribute: %s\" % k)\n\n return array.attribute_rename(*att_renames).dimension_rename(*dim_renames)","def change_figname(self, selection):\r\n # get desired aliases list\r\n if selection == \"Title\":\r\n options = self.titles\r\n elif selection == \"Axes\":\r\n options = self.axes\r\n else:\r\n options = self.fignums\r\n # replace current list with desired aliases\r\n for index, option in enumerate(options):\r\n item = self.listWidget.item(index)\r\n item.setText(option)","def get_axis_labels(\n self,\n x_label: float | str | Mobject = \"x\",\n y_label: float | str | Mobject = \"y\",\n ) -> VGroup:\n\n self.axis_labels = VGroup(\n self.get_x_axis_label(x_label),\n self.get_y_axis_label(y_label),\n )\n return self.axis_labels","def __init__(self, narray, axes_names=None, axes_domains=None,\n value_label=\"value\", meta_data=None):\n logger.debug('xndarray.__init__ ...')\n\n narray = np.asarray(narray)\n self.data = narray\n self.value_label = value_label\n self.meta_data = meta_data\n self.has_deprecated_xml_header = True\n\n nbDims = self.data.ndim\n\n if axes_names is None:\n self.axes_names = ['dim' + str(i) for i in xrange(nbDims)]\n else:\n assert type(axes_names) == list\n if len(axes_names) != nbDims:\n raise Exception(\"length of axes_names (%d) is different \"\n \"from nb of dimensions (%d).\\n\"\n \"Got axes names: %s\"\n % (len(axes_names), nbDims, str(axes_names)))\n\n self.axes_names = axes_names[:]\n\n self.axes_ids = dict([(self.axes_names[i], i) for i in xrange(nbDims)])\n\n # By default: domain of axis = array of slice indexes\n sh = self.data.shape\n self.axes_domains = dict([(axis, np.arange(sh[i]))\n for i, axis in enumerate(self.axes_names)])\n\n if axes_domains is not None:\n assert isinstance(axes_domains, dict)\n\n for an, dom in axes_domains.iteritems():\n if an not in self.axes_names:\n raise Exception('Axis \"%s\" defined in domains not '\n 'found in axes (%s)'\n % (an, ','.join(self.axes_names)))\n\n ia = self.axes_names.index(an)\n l = self.data.shape[ia]\n if len(dom) != l:\n raise Exception('Length of domain for axis \"%s\" (%d) '\n 'does not match length of data '\n 'axis %d (%d) ' % (an, len(dom), ia, l))\n\n if len(set(dom)) != len(dom):\n raise Exception('Domain of axis \"%s\" does not contain '\n 'unique values' % an)\n\n axes_domains[an] = np.asarray(dom)\n\n self.axes_domains.update(axes_domains)\n\n logger.debug('Axes names: %s', str(self.axes_names))\n logger.debug('Axes domains: %s', str(self.axes_domains))","def get_axis_name(self, axis_id):\n if isinstance(axis_id, str):\n if axis_id in self.axes_names:\n return axis_id\n else:\n return None\n assert np.isreal(axis_id) and np.round(axis_id) == axis_id\n if axis_id >= 0 and axis_id < self.get_ndims():\n return self.axes_names[axis_id]\n else:\n return None","def names(self):\n labels = [\n \"$X_{%i}$\" % i if d.name is None else d.name\n for i, d in enumerate(self.dimensions)\n ]\n return labels","def findaxisbyname(self, *args, **kwargs):\n return _coordsys.coordsys_findaxisbyname(self, *args, **kwargs)","def render_axis_labels(self, axes=None):\n axes = plt if axes is None else axes\n if self.x_label is not None:\n axes.set_xlabel(self.x_label)\n if self.y_label is not None:\n axes.set_ylabel(self.y_label)","def _short_tick_names(ax, label_length=20, ticklabel_length=10):\n ax.set_yticks(ax.get_yticks().tolist())\n ax.set_xticks(ax.get_xticks().tolist())\n ax.set_xticklabels(\n [_shortname(t.get_text(), maxlen=ticklabel_length)\n for t in ax.get_xticklabels()]\n )\n ax.set_yticklabels(\n [_shortname(t.get_text(), maxlen=ticklabel_length)\n for t in ax.get_yticklabels()]\n )\n ax.set_xlabel(_shortname(ax.get_xlabel(), maxlen=label_length))\n ax.set_ylabel(_shortname(ax.get_ylabel(), maxlen=label_length))","def set_axis_x(self, new_axis_point):\r\n self.__x_axis = new_axis_point","def render_axis_labels(self, axes=None):\n raise NotImplementedError()","def getAllAxisLabels(self):\n import copy\n return copy.copy(self.__axis_labels__)","def _moveaxis(self, arr, source, dest):\n try:\n source = list(source)\n except TypeError:\n source = [source]\n try:\n dest = list(dest)\n except TypeError:\n dest = [dest]\n\n source = [a + arr.ndim if a < 0 else a for a in source]\n dest = [a + arr.ndim if a < 0 else a for a in dest]\n\n order = [n for n in range(arr.ndim) if n not in source]\n\n for dest, src in sorted(zip(dest, source)):\n order.insert(dest, src)\n\n return arr.transpose(order)","def test_default_axis_nxdata(self, nexus_base):\n assert isinstance(nexus_base.default_axis, np.ndarray)","def populate_plot_axis(self,plot,ax='x'):\n\n fig=plt.gcf()\n\n extra_ax=[]\n\n if ax=='x':\n\n ticks=plot.get_xticks()\n\n lim=plot.get_xlim()\n\n for i in range(len(self.names)):\n\n if i==0:\n\n axn=plot\n\n axn.spines['bottom'].set_position(('outward',10))\n\n axn.spines['bottom'].set_visible(True)\n\n else:\n\n dy_fig=0.08\n\n prev_ax_position=axn.get_position()\n\n extra_ax.append(fig.add_axes(\\\n (prev_ax_position.x0,\\\n prev_ax_position.y0-2*dy_fig,\\\n prev_ax_position.width,\\\n 0),'autoscalex_on',True))\n\n axn=extra_ax[i-1]\n\n axn.yaxis.set_visible(False)\n\n for side in axn.spines.keys():\n\n axn.spines[side].set_linewidth(1)\n\n axn.set_xticks(ticks)\n\n ticksnames=[float(str(x)) for x in self.values[i]]\n\n axn.set_xticklabels(\\\n [\"{:.2f}\".format(x).rstrip('0').rstrip('.') for x in ticksnames],\\\n rotation = 45)\n\n xlab=axn.set_xlabel(self.names[i])\n\n xlab.set_fontsize(10)\n\n axn.tick_params(axis='x',labelsize=10)\n\n axn.set_xlim(lim)\n\n\n\n elif ax=='y':\n\n ticks=plot.get_yticks()\n\n lim=plot.get_ylim()\n\n for i in range(len(self.names)):\n\n if i==0:\n\n axn=plot\n\n axn.spines['left'].set_position(('outward',10))\n\n axn.spines['left'].set_visible(True)\n\n else:\n\n dx_fig=0.08\n\n plot_position=plot.get_position()\n\n prev_ax_position=axn.get_position()\n\n extra_ax.append(fig.add_axes(\\\n (prev_ax_position.x0-2*dx_fig,\\\n prev_ax_position.y0,\\\n 0,\\\n prev_ax_position.height),'autoscalex_on',True))\n\n axn=extra_ax[i-1]\n\n axn.xaxis.set_visible(False) # hide the yaxis\n\n for side in axn.spines.keys(): # 'top', 'bottom', 'left', 'right'\n\n axn.spines[side].set_linewidth(1)\n\n axn.set_yticks(ticks)\n\n ticksnames=[float(str(x)) for x in self.values[i]]\n\n axn.set_yticklabels(\\\n [\"{:.2f}\".format(x).rstrip('0').rstrip('.') for x in ticksnames],\\\n rotation = 45)\n\n ylab=axn.set_ylabel(self.names[i])\n\n ylab.set_fontsize(10)\n\n axn.tick_params(axis='y',labelsize=10)\n\n axn.set_ylim(lim)\n\n else:\n\n raise ValueError(\"Axis can be 'x' or 'y'\")","def _swap_axis(input_tensor, dim_index, last_index, name=None):\n return array_ops.transpose(\n input_tensor,\n array_ops.concat([\n math_ops.range(dim_index), [last_index],\n math_ops.range(dim_index + 1, last_index), [dim_index]\n ], 0),\n name=name)","def move_axis(data, label, new_position):\n # find current position of axis\n try:\n pos = data.dims.index(label)\n except ValueError as e:\n raise ValueError(\n f'Axis name {label} does not exist in input data') from e\n\n # create list of labels with new ordering\n axis_labels = list(data.dims)\n # the new position will be _before_ the given index, so will fail with a negative index\n # convert to a positive index in that case\n if new_position < 0:\n new_position += len(axis_labels)\n axis_labels.insert(new_position, axis_labels.pop(pos))\n # do the move\n return data.transpose(*axis_labels)","def cleanAxis(axisName, ticksOnly=False, complete=False):\r\n axisName.xaxis.set_ticks_position('bottom')\r\n axisName.yaxis.set_ticks_position('left')\r\n axisName.xaxis.labelpad = 2\r\n if not ticksOnly:\r\n axisName.spines['top'].set_visible(False)\r\n axisName.spines['right'].set_visible(False)\r\n if complete:\r\n axisName.spines['top'].set_visible(False)\r\n axisName.spines['right'].set_visible(False)\r\n axisName.spines['bottom'].set_visible(False)\r\n axisName.spines['left'].set_visible(False)\r\n return axisName","def set_axis(axis_number):\n robots = get_robot_roots()\n if not robots:\n pm.warning('Nothing Selected; Select a valid robot')\n return\n\n # These are specific to how the robots are rigged in relation to Maya's\n # coordinate system\n rotation_axes = ['Y', 'X', 'X', 'Z', 'X', 'Z']\n\n try: # if the text field is empty, or not a float value, skip it\n rotation_axis = rotation_axes[axis_number - 1]\n val = float(pm.textField('t_a{}'.format(axis_number),\n query=True,\n text=True))\n\n for robot in robots:\n ns = robot.namespace()\n pm.setAttr('{0}|{1}robot_GRP|{1}FK_CTRLS|{1}a{2}FK_CTRL.rotate{3}'.format(robot, ns, axis_number, rotation_axis), val)\n except:\n pass","def category_axis(self, category_axis):\n\n self.container['category_axis'] = category_axis","def fix_facetgrid_axis_labels(facet_grid, shared_in_center=False,\n x=True, y=True) -> None:\n # regarding the choice of shared_in_center: WWMDD?\n if shared_in_center:\n # TODO maybe add a axes over / under the FacetGrid axes, with the same\n # shape, and label that one (i think i did this in my gui or one of the\n # plotting fns. maybe plot_traces?)\n raise NotImplementedError\n else:\n for ax in facet_grid.axes.flat:\n if not (ax.is_first_col() and ax.is_last_row()):\n if x:\n ax.set_xlabel('')\n if y:\n ax.set_ylabel('')","def undimensionize(self, names=None, mapper_to_meta=False):\n mapper = self.undimensionizing_mapper(names)\n self.rename_from_mapper(mapper)\n if mapper_to_meta: self._meta['sets']['rename_mapper'] = mapper\n if not names: self.set_dim_comp(False)","def _update_data_transforms(self, axisOrder='col-major'):\n self._dataTransform = QtGui.QTransform()\n self._inverseDataTransform = QtGui.QTransform()\n if self.axisOrder == 'row-major': # transpose both\n self._dataTransform.scale(1, -1)\n self._dataTransform.rotate(-90)\n self._inverseDataTransform.scale(1, -1)\n self._inverseDataTransform.rotate(-90)","def reset_name_labels(infr):\n infr.print('reset_name_labels', 1)\n orig_names = infr.get_node_attrs('orig_name_label')\n infr.set_node_attrs('name_label', orig_names)","def readAxes(self):\n for axisElement in self.root.findall(\".axes/axis\"):\n axis = {}\n axis['name'] = name = axisElement.attrib.get(\"name\")\n axis['tag'] = axisElement.attrib.get(\"tag\")\n axis['minimum'] = float(axisElement.attrib.get(\"minimum\"))\n axis['maximum'] = float(axisElement.attrib.get(\"maximum\"))\n axis['default'] = float(axisElement.attrib.get(\"default\"))\n # we're not using the map for anything.\n axis['map'] = []\n for warpPoint in axisElement.findall(\".map\"):\n inputValue = float(warpPoint.attrib.get(\"input\"))\n outputValue = float(warpPoint.attrib.get(\"output\"))\n axis['map'].append((inputValue, outputValue))\n # there are labelnames in the element\n # but we don't need them for building the fonts.\n self.axes[name] = axis\n self.axesOrder.append(axis['name'])","def alias(requestContext, seriesList, newName):\n try:\n seriesList.name = newName\n except AttributeError:\n for series in seriesList:\n series.name = newName\n return seriesList","def swapaxes(a, axis1, axis2):\n # TODO(okuta): check type\n return a.swapaxes(axis1, axis2)","def _axis_labels(self, data_name: str) -> Tuple[str, str]:\n\n # Single activity attributes (column name must be present in summary dataframe)\n if data_name == 'distance':\n if self.config.distance_unit == 'km':\n return 'distance_2d_km', 'Distance (km)'\n elif self.config.distance_unit == 'mile':\n return 'distance_2d_mile', 'Distance (miles)'\n elif data_name == 'duration':\n return 'duration', 'Duration (minutes)'\n\n # These can be used for either single activities (summary dataframe) or aggregates (time series dataframe)\n elif data_name == 'mean_speed':\n if self.config.distance_unit == 'km':\n return 'mean_kmph', 'Average speed (km/hour)'\n elif self.config.distance_unit == 'mile':\n return 'mean_mph', 'Average speed (miles/hour)'\n elif data_name == 'mean_hr':\n return 'mean_hr', 'Average heart rate (beats/minute)'\n\n # Aggregate attributes (column name must be present in time series dataframe)\n elif data_name == 'total_distance':\n if self.config.distance_unit == 'km':\n return 'total_distance_2d_km', 'Total distance (km)'\n elif self.config.distance_unit == 'mile':\n return 'total_distance_2d_mile', 'Total distance (miles)'\n elif data_name == 'total_duration':\n return 'total_duration', 'Total duration (minutes)'\n elif data_name == 'activity_count':\n return 'activity_count', 'Number of activities'\n\n else:\n raise ValueError(f'Bad value for `data_name`: \"{data_name}\".')","def duplicate_axes(isl_obj, duplicate_inames, new_inames):\n if isinstance(isl_obj, list):\n return [\n duplicate_axes(i, duplicate_inames, new_inames)\n for i in isl_obj]\n\n if not duplicate_inames:\n return isl_obj\n\n def _align_and_intersect(d1, d2):\n d1, d2 = isl.align_two(d1, d2)\n return d1 & d2\n\n old_name_to_new_name = dict(zip(duplicate_inames, new_inames))\n\n dup_isl_obj = isl_obj\n\n for old_name, (dt, pos) in isl_obj.get_var_dict().items():\n dup_isl_obj = dup_isl_obj.set_dim_name(dt, pos,\n old_name_to_new_name.get(old_name,\n old_name))\n\n return _align_and_intersect(dup_isl_obj, isl_obj)","def fix(self):\n for namespace in pm.listNamespaces():\n for elem in namespace.ls():\n elem.rename(elem.split(\":\")[-1])\n namespace.remove()\n\n self.run()","def setIndexNames(self):\n self.theta = self.i1\n self.radial = self.i2","def addExtraAxis(slab,newaxis=None,axis=0,verbose=False):\n\n import cdms2 as cdms\n import MV2 as MV\n\n if newaxis is None:\n newaxis=cdms.createAxis([1,])\n newaxis.units=''\n\n # add new axis to axis list of input <slab>\n axislist=slab.getAxisList()\n axislist.insert(axis,newaxis)\n\n #----------------Reshape----------------\n shape=list(slab.shape)\n shape.insert(axis,len(newaxis))\n slab2=MV.reshape(slab,shape)\n\n #------------Create variable------------\n att_dict=attribute_obj2dict(slab)\n slab2=cdms.createVariable(slab2,axes=axislist,attributes=att_dict,\\\n typecode='f')\n slab2.id=slab.id\n\n if verbose:\n print('\\n# <addExtraAxis>: Originial variable shape:',slab.shape)\n print('# <addExtraAxis>: New variable shape:',slab2.shape)\n\n return slab2","def names(self, names):\n\n self._names = names","def getAllAxisUnits(self):\n import copy\n return copy.copy(self.__axis_units__)","def addAxis(self, tag, name, minimum, maximum, default, warpMap=None):\n axisElement = ET.Element(\"axis\")\n axisElement.attrib['name'] = name\n axisElement.attrib['tag'] = tag\n axisElement.attrib['minimum'] = str(minimum)\n axisElement.attrib['maximum'] = str(maximum)\n axisElement.attrib['default'] = str(default)\n if warpMap is not None:\n for a, b in warpMap:\n warpPt = ET.Element(\"map\")\n warpPt.attrib['input'] = str(a)\n warpPt.attrib['output'] = str(b)\n axisElement.append(warpPt)\n self.root.findall('.axes')[0].append(axisElement)","def set_axis_tick_width(params):\n # type: (Dict) -> Dict\n for panel_id, p in params['local'].items():\n obj_axis = params['internal']['canvas']['axes'][panel_id]\n for k in ['x', 'y']:\n attr = \"get_{}axis\".format(k)\n ax = getattr(obj_axis, attr)()\n for m in ['major', 'minor']:\n ax.set_tick_params(\n which=m,\n width=p['tick'][m]['width'][k])\n\n return params","def make_axes(info:PartialAxesInfo,\n cls_name:'Optional[str]'=None)->NamedAxes:\n\n if is_integer(info):\n assert info >= 0, f'naxis({info}) should be non-negative'\n assert cls_name is None, f'unnamed axes({info}) cannot have class name'\n\n return info\n\n if isinstance(info, Iterable):\n info = list(info)\n try:\n # try parsing as Mapping[NamedIndex, int] tuples\n axes = dict(info) # throw TypeError, ValueError\n assert all(map(is_integer, axes.values())), 'mapped indices should be integers'\n\n slots = np.zeros(len(axes), dtype=bool)\n slots[list(axes.values())] = True # throw IndexError\n assert slots.sum() == len(slots), 'bad axis indices' # HINT: all(0 <= axes < naxis)?\n except IndexError:\n raise ValueError('bad axis indices')\n except (TypeError, ValueError):\n # fallback to enumerate indices for axes\n assert all(map(lambda axis: isinstance(axis, NamedIndex), info))\n\n axes = dict(zip(info, range(len(info))))\n else:\n # sort name by indices\n axes_ = axes\n axes = {k: v if v >= 0 else len(axes) + v for k, v in axes.items()}\n keys = sorted(axes.keys(), key=axes.get)\n axes = OrderedDict()\n for key in keys:\n axes[key] = axes_[key]\n\n if cls_name is not None:\n axes = namedtuple(cls_name, axes.keys())(**axes)\n\n return axes\n\n raise TypeError(f\"unsupported 'info' type({type(info)})\")","def xaxis(self,label,units):\r\n if units != \"\": label = label + \" (\" + units + \")\"\r\n self.xbox.set_text(r\"$%s$\" % (label))\r\n pass","def _create_diagram_axis_info(self, axis, pinfo):\n\n nkey = f\"{axis}colname\"\n colname = pinfo[nkey]\n defs = self._refdefs.info.get(colname, {})\n title = defs.get(\"title\", colname)\n\n fontfmt = {\"family\" : \"Arial, sans-serif\",\n \"size\" : 18,\n \"color\" : \"black\"}\n\n axis = {\"showline\" : True,\n \"showgrid\" : True,\n \"title\" : title,\n \"titlefont\" : fontfmt,\n \"ticks\" : \"outside\",\n \"tickwidth\" : 1,\n \"showticklabels\" : True,\n \"linewidth\" : 1,\n \"linecolor\" : \"black\",\n \"zeroline\" : True,\n \"zerolinewidth\" : 1,\n \"zerolinecolor\" : \"black\"}\n\n if defs.get(\"unit\") == \"nanosecond\":\n axis[\"tickformat\"] = \".3s\"\n axis[\"ticksuffix\"] = \"s\"\n axis[\"hoverformat\"] = \".4s\"\n elif colname == \"Percentile\":\n axis[\"ticksuffix\"] = \"%\"\n\n return axis","def upAxis(*args, axis: Union[AnyStr, bool]=\"\", rotateView: bool=True, q=True, query=True,\n **kwargs)->Union[None, Any]:\n pass"],"string":"[\n \"def setAxisName(name, axes='XYZ'):\\n dislin.name(name, axes)\",\n \"def setAxesNames(self):\\n \\n labels = ['T', 'Z', 'Y', 'X'] + [chr(ord('S')-i) for i in xrange(18)]\\n if (len(self.axisList) >= 4):\\n i = 0\\n else:\\n i = 4 - len(self.axisList)\\n \\n for axis in self.axisList:\\n self.axesNames.append(labels[i] + ' - ' + axis.id)\\n i += 1\",\n \"def set_index_names(self, names, axis=0):\\n self.get_axis(axis).names = names\",\n \"def axesNames(self, data, info):\\n return []\",\n \"def setaxesnames(self):\\n if not self._axesnames or self.prop['skipsai']:\\n return\\n debug('ControllerStartup.setaxesnames()')\\n oldaxes = self.pidevice.qSAI_ALL()\\n for i, newaxis in enumerate(self.axesnames):\\n if newaxis != oldaxes[i] or self.prop['forcesai']:\\n setstage = False\\n if self.pidevice.HasqCST():\\n if self.pidevice.qCST()[oldaxes[i]] == 'NOSTAGE':\\n try:\\n debug('try rename NOSTAGE to TEMP (0x3C)')\\n self.pidevice.SPA(oldaxes[i], 0x3c, 'TEMP')\\n setstage = True\\n except GCSError:\\n pass\\n self.pidevice.SAI(oldaxes[i], newaxis)\\n if setstage:\\n self.pidevice.SPA(newaxis, 0x3c, 'NOSTAGE')\\n debug('restore NOSTAGE (0x3C)')\",\n \"def _default_axis_names(n_dims):\\n _DEFAULT_NAMES = (\\\"z\\\", \\\"y\\\", \\\"x\\\")\\n return _DEFAULT_NAMES[-n_dims:]\",\n \"def customAxisNames(self):\\n return []\",\n \"def setAllAxisUnits(self,units): \\n self.__axis_units__ = units\",\n \"def setAxisUnits(self, dim, units): \\n try:\\n self.__axis_units__[dim] = units\\n except IndexError:\\n self.__axis_units__.append(units)\",\n \"def axesnames(self, axesnames):\\n if axesnames is None:\\n self._axesnames = None\\n else:\\n assert isinstance(axesnames, list), 'axesnames must be list'\\n self._axesnames = axesnames\\n debug('ControllerStartup.axesnames = %s', itemstostr(self._axesnames))\",\n \"def set_index_name(self, name, axis=0):\\n self.get_axis(axis).name = name\",\n \"def setAllAxisLabels(self, labels):\\n self.__axis_labels__ = labels\",\n \"def _update_axes(self):\\n data_shape = self.data.shape\\n if len(self.axes) < self.data.ndim + 1:\\n self._axes.append(Axis())\\n for index in range(self.data.ndim):\\n if len(self.axes[index].values) != data_shape[index]:\\n self.axes[index].values = np.arange(data_shape[index],\\n dtype=np.float64)\",\n \"def process_custom_axes(axis_names):\\n return axis_names.strip().strip(\\\"'\\\").strip('\\\"').split(',')\",\n \"def axis_name(self):\\n return self._axis_name\",\n \"def setAxisNameColor(idx=-1, axes='XYZ'):\\n dislin.axclrs(idx, 'Name', axes)\",\n \"def setAxisNameJustification(jus, axes='XYZ'):\\n dislin.namjus(justdict[jus],axes)\",\n \"def axesnames(self):\\n return self._axesnames\",\n \"def _handle_setup_axis(self, axis_args):\\n axis_name = axis_args['name']\\n axes_dict = self.server.axes\\n\\n if axis_name not in [name for name, _ in axes_dict.items()]:\\n print \\\"Adding a new axis:\\\", axis_name\\n axis_count = len(axes_dict)\\n newaxis = self.server.figure.add_subplot(axis_count+1, 1, axis_count+1)\\n axes_dict[axis_name] = newaxis\\n axes_dict[axis_name].grid(True)\\n axes_dict[axis_name].set_xlabel(axis_args['x_label'])\\n axes_dict[axis_name].set_ylabel(axis_args['y_label'])\\n # TODO: support *.set_title(\\\"Title\\\")\\n if FLAGS.logy:\\n axes_dict[axis_name].set_yscale('log', nonposy='clip')\\n\\n if axis_count != 0:\\n # Resize other axes if the above wasn't the first.\\n axis_count = len(axes_dict)\\n for row,(name, _) in enumerate(axes_dict.items(), 1):\\n print name, axis_count, row\\n axes_dict[name].change_geometry(axis_count, 1, row)\",\n \"def setAxisLabel(self, dim, label): \\n try:\\n self.__axis_labels__[dim] = label\\n except IndexError:\\n self.__axis_labels__.append(label)\",\n \"def convert_axis( mv, axisold, axisindnew ):\\n (axisnew, indexina3) = axisindnew\\n axes = allAxes(mv)\\n kold = None\\n for k in range(len(axes)):\\n if axes[k]==axisold: kold=k\\n if kold==None:\\n print \\\"ERROR. convert_axis cannot find axis\\\",axisold,\\\" in variable\\\",mv\\n if len(axisold)==len(axisnew):\\n mv.setAxis( kold, axisnew )\\n return\\n # Here's what we would do in 1-D:\\n # newdata = ma.ones(len(axisnew))*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\\n # for i in range(len(axisold)):\\n # newdata[ indexina3[i] ] = ma[i]\\n # newmv = cdms2.createVariable( newdata, id=mv.id )\\n # >1-D is the same idea, but more dimensions are coming along for the ride,\\n # making it more complicated...\\n shape0 = mv.shape\\n shape0[kold] = len(axisnew)\\n newdata = ma.ones(shape0)*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\\n # We want to copy ma to newdata - except that we need indirect indexing for the kold-th axis.\\n # There seems to be nothing in numpy for treating one axis differently from the rest\\n # (except for ellipsis, but it makes sense to use only one ellipsis and we would need two here).\\n # The following will do the job. It would be very slow for an array with many big dimensions,\\n # but the arrays here have already been reduced for graphics; the index sets will be small or\\n # empty...\\n ranges = map( range, shape0[0:kold] )\\n for i in range(len(axisold)):\\n for idx in apply(itertools.product,ranges):\\n idx = idx + [indexina3(i)] + [Ellipsis]\\n idxo = idx + [i] + [Ellipsis]\\n newdata[ tuple(idx) ] = mv[idxo]\\n newmv = cdms2.createVariable( newdata, id=mv.id )\",\n \"def swapaxes(self, a1, a2):\\n an = self.axes_names[:]\\n ia1, ia2 = self.get_axis_id(a1), self.get_axis_id(a2)\\n an[ia2], an[ia1] = an[ia1], an[ia2]\\n return xndarray(np.swapaxes(self.data, ia1, ia2), an, self.axes_domains,\\n self.value_label, self.meta_data)\",\n \"def set_label_names(self, x: Union[np.ndarray, Dict[int, str]]) -> None:\\n if isinstance(x, np.ndarray):\\n label_names = x\\n elif isinstance(x, dict):\\n label_names = np.full(max(x.keys()) + 1, \\\"\\\", dtype=\\\"object\\\")\\n label_names[list(x.keys())] = list(x.values())\\n else:\\n raise ValueError(f\\\"Unsupported {type(x)=}\\\")\\n self._label_names_array = label_names\",\n \"def _set_axis(axis):\\n\\n def axis_setter(self, labels):\\n new_qc = DataFrameDefault.register(pandas.DataFrame.set_axis)(\\n self, axis=axis, labels=labels\\n )\\n self.__dict__.update(new_qc.__dict__)\\n\\n return axis_setter\",\n \"def _update_axislabels(self, x='x', **kwargs):\\n if x not in 'xy':\\n return\\n # Update label on this axes\\n axis = getattr(self, x + 'axis')\\n axis.label.update(kwargs)\\n kwargs.pop('color', None)\\n\\n # Defer to parent (main) axes if possible, then get the axes\\n # shared by that parent\\n ax = self._panel_parent or self\\n ax = getattr(ax, '_share' + x) or ax\\n\\n # Apply to spanning axes and their panels\\n axs = [ax]\\n if getattr(ax.figure, '_span' + x):\\n s = axis.get_label_position()[0]\\n if s in 'lb':\\n axs = ax._get_side_axes(s)\\n for ax in axs:\\n getattr(ax, x + 'axis').label.update(kwargs) # apply to main axes\\n pax = getattr(ax, '_share' + x)\\n if pax is not None: # apply to panel?\\n getattr(pax, x + 'axis').label.update(kwargs)\",\n \"def setAxisNameDistance(dist,axes='XYZ'):\\n dislin.namdis(dist, axes)\",\n \"def setIndexNames(self):\\n self.xi = self.i1\\n self.yi = self.i2\",\n \"def set_axis_label(self, label, axis):\\n if axis == 'x':\\n self.axplot.set_xlabel(label)\\n elif axis == 'y':\\n self.axplot.set_ylabel(label)\\n else:\\n errmsg = 'Valid axis names are x and y.'\\n raise ValueError(errmsg)\",\n \"def _set_plot_axes_labels(self, data, viewer_id):\\n viewer = self._viewer_by_id(viewer_id)\\n\\n # Get the units of the data to be loaded.\\n spectral_axis_unit_type = data.spectral_axis.unit.physical_type.title()\\n flux_unit_type = data.flux.unit.physical_type.title()\\n\\n if data.spectral_axis.unit.is_equivalent(u.m):\\n spectral_axis_unit_type = \\\"Wavelength\\\"\\n elif data.spectral_axis.unit.is_equivalent(u.pixel):\\n spectral_axis_unit_type = \\\"pixel\\\"\\n\\n viewer.figure.axes[0].label = f\\\"{spectral_axis_unit_type} [{data.spectral_axis.unit.to_string()}]\\\"\\n viewer.figure.axes[1].label = f\\\"{flux_unit_type} [{data.flux.unit.to_string()}]\\\"\\n\\n # Make it so y axis label is not covering tick numbers.\\n viewer.figure.axes[1].label_offset = \\\"-50\\\"\",\n \"def setnames(self, *args, **kwargs):\\n return _coordsys.coordsys_setnames(self, *args, **kwargs)\",\n \"def set_axis(self, axis_list):\\n if self.table_ready:\\n final_axis_list = []\\n for i, axis in enumerate(axis_list):\\n if axis:\\n final_axis_list.append(\\\"1 \\\" + str(i + 1))\\n else:\\n final_axis_list.append(\\\"0 \\\" + str(i + 1))\\n\\n command = self.build_command(\\n self.device, (\\\"set_axis\\\", final_axis_list), single_commands=True\\n )\\n self.vcw.write(self.device, command)\",\n \"def canonicalize_axis_name(axis_name):\\n if not axis_name:\\n return []\\n if (isinstance(axis_name, str) or\\n not isinstance(axis_name, collections.Iterable)):\\n return [axis_name]\\n return list(axis_name)\",\n \"def _declare_auto_axes_idx(self):\\n if not self.axes_idx:\\n self.axes_idx = BiMapping(to_first=range(len(self.name_elements)), to_second=range(len(self.name_elements)))\",\n \"def SwapAxis(self, axis0, axis1):\\n\\n axis0 = int(axis0)\\n axis1 = int(axis1)\\n\\n self.points[:,[axis0,axis1]] = self.points[:,[axis1,axis0]]\",\n \"def set_axis_label(\\n self,\\n axis: Union[int, Sequence[int]],\\n label: Union[str, Sequence[str]],\\n ):\\n if isinstance(axis, Integral):\\n axis = assert_axis_in_bounds(axis, self.ndim)\\n if self.axis_labels[axis] != str(label):\\n full_axis_labels = list(self.axis_labels)\\n full_axis_labels[axis] = str(label)\\n self.axis_labels = full_axis_labels\\n self.last_used = axis\\n else:\\n full_axis_labels = list(self.axis_labels)\\n # cast label to list for list comparison below\\n label = list(label) # type: ignore\\n axis = tuple(axis) # type: ignore\\n if len(axis) != len(label):\\n raise ValueError(\\n trans._(\\\"axis and label sequences must have equal length\\\")\\n )\\n if label != full_axis_labels:\\n for ax, val in zip(axis, label):\\n ax = assert_axis_in_bounds(int(ax), self.ndim)\\n full_axis_labels[ax] = val\\n self.axis_labels = full_axis_labels\",\n \"def axisinfo(unit, axis):\\n if isinstance(unit, tuple):\\n unit = unit[0]\\n unit_obj = unit if isinstance(unit, Unit) else Unit(unit)\\n name = unyt_arrayConverter._axisnames.get(axis, \\\"\\\")\\n if unit_obj.is_dimensionless:\\n label = name\\n else:\\n name += \\\" \\\"\\n unit_str = unit_obj.latex_representation()\\n if unyt_arrayConverter._labelstyle == \\\"[]\\\":\\n label = name + \\\"$\\\\\\\\left[\\\" + unit_str + \\\"\\\\\\\\right]$\\\"\\n elif unyt_arrayConverter._labelstyle == \\\"/\\\":\\n axsym = \\\"$q_{\\\\\\\\rm\\\" + axis.axis_name + \\\"}$\\\"\\n name = axsym if name == \\\" \\\" else name\\n if \\\"/\\\" in unit_str:\\n label = name + \\\"$\\\\\\\\;/\\\\\\\\;\\\\\\\\left(\\\" + unit_str + \\\"\\\\\\\\right)$\\\"\\n else:\\n label = name + \\\"$\\\\\\\\;/\\\\\\\\;\\\" + unit_str + \\\"$\\\"\\n else:\\n label = name + \\\"$\\\\\\\\left(\\\" + unit_str + \\\"\\\\\\\\right)$\\\"\\n return AxisInfo(label=label.strip())\",\n \"def set_orientation(self, axes):\\n if debug:\\n logger.debug('set_orientation ...')\\n logger.debug('%s -> %s', str(self.axes_names), str(axes))\\n\\n if set(axes) != set(self.axes_names):\\n raise Exception('Required orientation %s does not contain '\\n 'all axes %s' % (str(axes), str(self.axes_names)))\\n\\n if axes == self.axes_names: # already in the asked orientation\\n return\\n\\n for i, axis in enumerate(axes):\\n logger.debug('Rolling axis %s, cur pos=%d -> dest pos=%d',\\n axis, self.axes_names.index(axis), i)\\n logger.debug('Shape: %s', str(self.data.shape))\\n cur_i = self.axes_names.index(axis)\\n self.data = np.rollaxis(self.data, cur_i, i)\\n self.axes_names.pop(cur_i)\\n self.axes_names.insert(i, axis)\\n logger.debug('After rolling. Shape: %s, new axes: %s',\\n str(self.data.shape), str(self.axes_names))\\n logger.debug('')\\n\\n self.axes_ids = dict([(a, i) for i, a in enumerate(self.axes_names)])\",\n \"def replace_axis(self, dim:NamedIndex,\\n mapping_or_old:'Union[Mapping[NamedIndex, NamedIndex], NamedIndex]',\\n new:'Optional[NamedIndex]'=None):\\n\\n axes = self[dim] # disable idx_dim access\\n # axes = self.get(dim) or self._dim_axes[dim] # dim:'Union[int, NamedIndex]'\\n is_tuple_axes = is_namedtuple(axes)\\n assert isinstance(axes, dict) or is_tuple_axes, (\\n f'unnamed dim({dim!r}) cannot be renamed')\\n\\n axes_keys = axes._fields if is_tuple_axes else axes.keys()\\n axes_iter = iter(zip(axes._fields, axes)) if is_tuple_axes else axes.items()\\n axes_ = OrderedDict()\\n\\n if new is None:\\n assert isinstance(mapping_or_old, dict), (\\n f\\\"'mapping_or_old'({type(mapping_or_old)}) is expected to be a dict \\\"\\n \\\"when 'new' is None\\\")\\n\\n mapping = mapping_or_old\\n for axis, index in axes_iter:\\n axis = mapping.get(axis, axis)\\n assert axis not in axes_, f'axis {axis!r} in mapping is conflicted'\\n\\n axes_[axis] = index\\n else:\\n assert new not in axes_keys, f'new axis({new!r}) is confilicted'\\n\\n old = mapping_or_old\\n for axis, index in axes_iter:\\n axes_[new if axis == old else axis] = index\\n\\n axes_ = namedtuple(dim, axes_.keys())(**axes_) if is_tuple_axes else type(axes)(axes_)\\n ret = OrderedDict()\\n for dim_, axes in self.items():\\n ret[dim_] = axes_ if dim_ == dim else axes\\n\\n return type(self)(ret)\",\n \"def set_nAxis(self, newval):\\n rest_val = str(newval)\\n return self._setAttr(\\\"nAxis\\\", rest_val)\",\n \"def set_axes(self, a):\\r\\n self.axes = a\",\n \"def axes(*x: Iterable[int]):\\n return [_ti_core.Axis(i) for i in x]\",\n \"def label_axis(self, name, label):\\n\\n axis = self._find_axis(name)\\n axis.axis_label = label\",\n \"def __init__(self, axes=()):\\n self._axes = []\\n self._dimension = 0\\n for axis in axes:\\n self.add_axis(axis)\",\n \"def _appendAxisDefinition(self, axis):\\n length = len(axis)\\n\\n self.na_dict[\\\"NX\\\"].append(length)\\n self.na_dict[\\\"XNAME\\\"].append(xarray_utils.getBestName(axis))\\n\\n # If only one item in axis values\\n if length < 2:\\n self.na_dict[\\\"DX\\\"].append(0)\\n self.na_dict[\\\"NXDEF\\\"].append(length)\\n self.na_dict[\\\"X\\\"].append(axis.data.tolist()) \\n return\\n\\n incr = xarray_utils.get_interval(axis, 0, 1)\\n\\n for i in range(1, length):\\n if (axis[i] - axis[i - 1]) != incr:\\n self.na_dict[\\\"DX\\\"].append(0)\\n self.na_dict[\\\"NXDEF\\\"].append(length)\\n self.na_dict[\\\"X\\\"].append(axis.data.tolist())\\n break\\n\\n else: # If did not break out of the loop\\n max_length = length\\n if length > 3: \\n max_length = 3\\n\\n self.na_dict[\\\"DX\\\"].append(incr)\\n self.na_dict[\\\"NXDEF\\\"].append(max_length)\\n self.na_dict[\\\"X\\\"].append(axis[:max_length])\",\n \"def reorderAxesEvent(self):\\n axisB = self.sender().text()\\n self.myParent.swapAxes(self.axisName, axisB)\\n self.myParent.setVistrailsVariableAxes()\",\n \"def replace_dim(self, old:NamedIndex, new:NamedIndex):\\n\\n assert new not in self, f'new dim({new!r}) is confilicted'\\n\\n ret = OrderedDict()\\n for dim, axes in self.items():\\n if dim == old:\\n if is_namedtuple(axes):\\n axes = namedtuple(new, axes._fields)(*axes)\\n ret[new] = axes\\n else:\\n ret[old] = axes\\n\\n return type(self)(ret)\",\n \"def xaxis(self,label,units):\\n if units != \\\"\\\": label = label + \\\" (\\\" + units + \\\")\\\"\\n self.subplot.set_xlabel(label)\\n pass\",\n \"def addAxes(self):\\n numDims = len(self.relation.fieldNames) - 1\\n angle = 360 / numDims\\n axisDomains = self.relation.axisDomains\\n for i in range(numDims):\\n axis = PlotAxis(self)\\n self.scene().addItem(axis)\\n if self.axisAngles and i < len(self.axisAngles):\\n axis.setRotation(self.axisAngles[i])\\n else:\\n axis.setRotation(angle * i)\\n self.axes.append(axis)\\n\\n domain = axisDomains[i]\\n text = PlotAxisLabel(\\\"{}\\\\n[{:.2f},{:.2f}]\\\".format(self.relation.fieldNames[i], domain[0], domain[1]))\\n text.setFont(self.labelFont)\\n self.axisLabels.append(text)\\n text.setParentItem(axis)\",\n \"def default_units(x, axis):\\n # In the case where the first matplotlib command is setting limits,\\n # x may be a tuple of length two (with the same units).\\n if isinstance(x, tuple):\\n name = getattr(x[0], \\\"name\\\", \\\"\\\")\\n units = x[0].units\\n else:\\n name = getattr(x, \\\"name\\\", \\\"\\\")\\n units = x.units\\n\\n # maintain a mapping between Axis and name since Axis does not point to\\n # its underlying data and we want to propagate the name to the axis\\n # label in the subsequent call to axisinfo\\n unyt_arrayConverter._axisnames[axis] = name if name is not None else \\\"\\\"\\n return units\",\n \"def set_labels(ax, label_value, label_axis):\\n if label_axis == 'x':\\n ax.set_xticks(np.arange(len(label_value)))\\n axis = ax.get_xticklabels()\\n else:\\n ax.set_yticks(np.arange(len(label_value)) + 1)\\n axis = ax.get_yticklabels()\\n\\n # fetch labels\\n labels = [items.get_text() for items in axis]\\n\\n # init a count variable\\n if label_axis == 'x':\\n count = 0\\n else:\\n count = len(label_value) - 1\\n\\n # iterate through all the labels and change the label name\\n for i in range(len(labels)):\\n labels[i] = label_value[count]\\n\\n if label_axis == 'x':\\n count += 1\\n else:\\n count -= 1\\n\\n return labels\",\n \"def reset(self):\\n # Don't reset axis labels\\n self.range = ((0, 2, 1),) * self.ndim\\n self.current_step = (0,) * self.ndim\\n self.order = tuple(range(self.ndim))\",\n \"def new_axes(self, name):\\n\\n return self.figure.add_axes([0.05, 0.05, 0.9, 0.9], label=name)\",\n \"def rename_levels(self, name_dict, axis=1, inplace=False):\\n\\n def apply_func(obj_index):\\n return index_fns.rename_levels(obj_index, name_dict)\\n\\n return self.apply_on_index(apply_func, axis=axis, inplace=inplace)\",\n \"def reorder_axes(cls, array: np.ndarray, axes: str) -> np.ndarray:\\n return cls._reorder_axes(array, axes, cls.array_axis_order)\",\n \"def series_axis(self, series_axis):\\n\\n self.container['series_axis'] = series_axis\",\n \"def setAxisParts(lowx='all', lefty='all', upx='ticks', righty='ticks'):\\n partdict = {'none':'NONE','lines':'LINE','ticks':'TICKS',\\n 'labels':'LABELS', 'all':'NAME'} \\n dislin.setgrf(partdict[lowx], partdict[lefty],\\\\\\n partdict[upx], partdict[righty])\",\n \"def _default_axis_units(n_dims):\\n return (\\\"-\\\",) * n_dims\",\n \"def _update_column_name(self, column, idx, old_name, name):\\n dtype = self.dtype\\n # Updating the names on the dtype should suffice\\n dtype.names = dtype.names[:idx] + (name,) + dtype.names[idx + 1 :]\",\n \"def SetAxisOrientation(*args, **kwargs):\\n return _gdi_.DC_SetAxisOrientation(*args, **kwargs)\",\n \"def setupVariableAxes(self):\\n if self.var is None:\\n return\\n \\n if (self.axisList is None):\\n self.axisList = self.var.getAxisList()\\n self.axisOrder = range(len(self.axisList))\\n\\n self.clear() \\n self.setAxesNames()\\n \\n # Iterate through the variables axes & init each axis widget\\n axisIndex = 0\\n for axis, axisName in zip(self.axisList, self.axesNames):\\n # Create the axis widget\\n axisWidget = QAxis(axis, axisName, axisIndex, self)\\n axisWidget.setAxisButtonText(axisName)\\n self.axisWidgets.append(axisWidget)\\n\\n # Setup the layout for each axis\\n row = self.gridLayout.rowCount()\\n self.gridLayout.addWidget(axisWidget.getAxisButton(), row, 0)\\n self.gridLayout.addWidget(axisWidget, row, 1) \\n self.gridLayout.addWidget(axisWidget.getAxisOperationsButton(), row, 2)\\n\\n # Create separator line between each axis widget\\n vline = QtGui.QFrame()\\n vline.setFrameStyle(QtGui.QFrame.HLine | QtGui.QFrame.Sunken)\\n self.gridLayout.addWidget(vline, row+1, 0, 1,\\n self.gridLayout.columnCount())\\n\\n axisIndex += 1\\n\\n self.gridLayout.setRowStretch(self.gridLayout.rowCount(), 1)\",\n \"def _relabel(array, renames):\\n\\n att_renames = []\\n dim_renames = []\\n\\n for k, v in renames.items():\\n if k in array.att_names:\\n att_renames.extend([k, v])\\n elif k in array.dim_names:\\n dim_renames.extend([k, v])\\n else:\\n raise ValueError(\\\"Invalid array attribute: %s\\\" % k)\\n\\n return array.attribute_rename(*att_renames).dimension_rename(*dim_renames)\",\n \"def change_figname(self, selection):\\r\\n # get desired aliases list\\r\\n if selection == \\\"Title\\\":\\r\\n options = self.titles\\r\\n elif selection == \\\"Axes\\\":\\r\\n options = self.axes\\r\\n else:\\r\\n options = self.fignums\\r\\n # replace current list with desired aliases\\r\\n for index, option in enumerate(options):\\r\\n item = self.listWidget.item(index)\\r\\n item.setText(option)\",\n \"def get_axis_labels(\\n self,\\n x_label: float | str | Mobject = \\\"x\\\",\\n y_label: float | str | Mobject = \\\"y\\\",\\n ) -> VGroup:\\n\\n self.axis_labels = VGroup(\\n self.get_x_axis_label(x_label),\\n self.get_y_axis_label(y_label),\\n )\\n return self.axis_labels\",\n \"def __init__(self, narray, axes_names=None, axes_domains=None,\\n value_label=\\\"value\\\", meta_data=None):\\n logger.debug('xndarray.__init__ ...')\\n\\n narray = np.asarray(narray)\\n self.data = narray\\n self.value_label = value_label\\n self.meta_data = meta_data\\n self.has_deprecated_xml_header = True\\n\\n nbDims = self.data.ndim\\n\\n if axes_names is None:\\n self.axes_names = ['dim' + str(i) for i in xrange(nbDims)]\\n else:\\n assert type(axes_names) == list\\n if len(axes_names) != nbDims:\\n raise Exception(\\\"length of axes_names (%d) is different \\\"\\n \\\"from nb of dimensions (%d).\\\\n\\\"\\n \\\"Got axes names: %s\\\"\\n % (len(axes_names), nbDims, str(axes_names)))\\n\\n self.axes_names = axes_names[:]\\n\\n self.axes_ids = dict([(self.axes_names[i], i) for i in xrange(nbDims)])\\n\\n # By default: domain of axis = array of slice indexes\\n sh = self.data.shape\\n self.axes_domains = dict([(axis, np.arange(sh[i]))\\n for i, axis in enumerate(self.axes_names)])\\n\\n if axes_domains is not None:\\n assert isinstance(axes_domains, dict)\\n\\n for an, dom in axes_domains.iteritems():\\n if an not in self.axes_names:\\n raise Exception('Axis \\\"%s\\\" defined in domains not '\\n 'found in axes (%s)'\\n % (an, ','.join(self.axes_names)))\\n\\n ia = self.axes_names.index(an)\\n l = self.data.shape[ia]\\n if len(dom) != l:\\n raise Exception('Length of domain for axis \\\"%s\\\" (%d) '\\n 'does not match length of data '\\n 'axis %d (%d) ' % (an, len(dom), ia, l))\\n\\n if len(set(dom)) != len(dom):\\n raise Exception('Domain of axis \\\"%s\\\" does not contain '\\n 'unique values' % an)\\n\\n axes_domains[an] = np.asarray(dom)\\n\\n self.axes_domains.update(axes_domains)\\n\\n logger.debug('Axes names: %s', str(self.axes_names))\\n logger.debug('Axes domains: %s', str(self.axes_domains))\",\n \"def get_axis_name(self, axis_id):\\n if isinstance(axis_id, str):\\n if axis_id in self.axes_names:\\n return axis_id\\n else:\\n return None\\n assert np.isreal(axis_id) and np.round(axis_id) == axis_id\\n if axis_id >= 0 and axis_id < self.get_ndims():\\n return self.axes_names[axis_id]\\n else:\\n return None\",\n \"def names(self):\\n labels = [\\n \\\"$X_{%i}$\\\" % i if d.name is None else d.name\\n for i, d in enumerate(self.dimensions)\\n ]\\n return labels\",\n \"def findaxisbyname(self, *args, **kwargs):\\n return _coordsys.coordsys_findaxisbyname(self, *args, **kwargs)\",\n \"def render_axis_labels(self, axes=None):\\n axes = plt if axes is None else axes\\n if self.x_label is not None:\\n axes.set_xlabel(self.x_label)\\n if self.y_label is not None:\\n axes.set_ylabel(self.y_label)\",\n \"def _short_tick_names(ax, label_length=20, ticklabel_length=10):\\n ax.set_yticks(ax.get_yticks().tolist())\\n ax.set_xticks(ax.get_xticks().tolist())\\n ax.set_xticklabels(\\n [_shortname(t.get_text(), maxlen=ticklabel_length)\\n for t in ax.get_xticklabels()]\\n )\\n ax.set_yticklabels(\\n [_shortname(t.get_text(), maxlen=ticklabel_length)\\n for t in ax.get_yticklabels()]\\n )\\n ax.set_xlabel(_shortname(ax.get_xlabel(), maxlen=label_length))\\n ax.set_ylabel(_shortname(ax.get_ylabel(), maxlen=label_length))\",\n \"def set_axis_x(self, new_axis_point):\\r\\n self.__x_axis = new_axis_point\",\n \"def render_axis_labels(self, axes=None):\\n raise NotImplementedError()\",\n \"def getAllAxisLabels(self):\\n import copy\\n return copy.copy(self.__axis_labels__)\",\n \"def _moveaxis(self, arr, source, dest):\\n try:\\n source = list(source)\\n except TypeError:\\n source = [source]\\n try:\\n dest = list(dest)\\n except TypeError:\\n dest = [dest]\\n\\n source = [a + arr.ndim if a < 0 else a for a in source]\\n dest = [a + arr.ndim if a < 0 else a for a in dest]\\n\\n order = [n for n in range(arr.ndim) if n not in source]\\n\\n for dest, src in sorted(zip(dest, source)):\\n order.insert(dest, src)\\n\\n return arr.transpose(order)\",\n \"def test_default_axis_nxdata(self, nexus_base):\\n assert isinstance(nexus_base.default_axis, np.ndarray)\",\n \"def populate_plot_axis(self,plot,ax='x'):\\n\\n fig=plt.gcf()\\n\\n extra_ax=[]\\n\\n if ax=='x':\\n\\n ticks=plot.get_xticks()\\n\\n lim=plot.get_xlim()\\n\\n for i in range(len(self.names)):\\n\\n if i==0:\\n\\n axn=plot\\n\\n axn.spines['bottom'].set_position(('outward',10))\\n\\n axn.spines['bottom'].set_visible(True)\\n\\n else:\\n\\n dy_fig=0.08\\n\\n prev_ax_position=axn.get_position()\\n\\n extra_ax.append(fig.add_axes(\\\\\\n (prev_ax_position.x0,\\\\\\n prev_ax_position.y0-2*dy_fig,\\\\\\n prev_ax_position.width,\\\\\\n 0),'autoscalex_on',True))\\n\\n axn=extra_ax[i-1]\\n\\n axn.yaxis.set_visible(False)\\n\\n for side in axn.spines.keys():\\n\\n axn.spines[side].set_linewidth(1)\\n\\n axn.set_xticks(ticks)\\n\\n ticksnames=[float(str(x)) for x in self.values[i]]\\n\\n axn.set_xticklabels(\\\\\\n [\\\"{:.2f}\\\".format(x).rstrip('0').rstrip('.') for x in ticksnames],\\\\\\n rotation = 45)\\n\\n xlab=axn.set_xlabel(self.names[i])\\n\\n xlab.set_fontsize(10)\\n\\n axn.tick_params(axis='x',labelsize=10)\\n\\n axn.set_xlim(lim)\\n\\n\\n\\n elif ax=='y':\\n\\n ticks=plot.get_yticks()\\n\\n lim=plot.get_ylim()\\n\\n for i in range(len(self.names)):\\n\\n if i==0:\\n\\n axn=plot\\n\\n axn.spines['left'].set_position(('outward',10))\\n\\n axn.spines['left'].set_visible(True)\\n\\n else:\\n\\n dx_fig=0.08\\n\\n plot_position=plot.get_position()\\n\\n prev_ax_position=axn.get_position()\\n\\n extra_ax.append(fig.add_axes(\\\\\\n (prev_ax_position.x0-2*dx_fig,\\\\\\n prev_ax_position.y0,\\\\\\n 0,\\\\\\n prev_ax_position.height),'autoscalex_on',True))\\n\\n axn=extra_ax[i-1]\\n\\n axn.xaxis.set_visible(False) # hide the yaxis\\n\\n for side in axn.spines.keys(): # 'top', 'bottom', 'left', 'right'\\n\\n axn.spines[side].set_linewidth(1)\\n\\n axn.set_yticks(ticks)\\n\\n ticksnames=[float(str(x)) for x in self.values[i]]\\n\\n axn.set_yticklabels(\\\\\\n [\\\"{:.2f}\\\".format(x).rstrip('0').rstrip('.') for x in ticksnames],\\\\\\n rotation = 45)\\n\\n ylab=axn.set_ylabel(self.names[i])\\n\\n ylab.set_fontsize(10)\\n\\n axn.tick_params(axis='y',labelsize=10)\\n\\n axn.set_ylim(lim)\\n\\n else:\\n\\n raise ValueError(\\\"Axis can be 'x' or 'y'\\\")\",\n \"def _swap_axis(input_tensor, dim_index, last_index, name=None):\\n return array_ops.transpose(\\n input_tensor,\\n array_ops.concat([\\n math_ops.range(dim_index), [last_index],\\n math_ops.range(dim_index + 1, last_index), [dim_index]\\n ], 0),\\n name=name)\",\n \"def move_axis(data, label, new_position):\\n # find current position of axis\\n try:\\n pos = data.dims.index(label)\\n except ValueError as e:\\n raise ValueError(\\n f'Axis name {label} does not exist in input data') from e\\n\\n # create list of labels with new ordering\\n axis_labels = list(data.dims)\\n # the new position will be _before_ the given index, so will fail with a negative index\\n # convert to a positive index in that case\\n if new_position < 0:\\n new_position += len(axis_labels)\\n axis_labels.insert(new_position, axis_labels.pop(pos))\\n # do the move\\n return data.transpose(*axis_labels)\",\n \"def cleanAxis(axisName, ticksOnly=False, complete=False):\\r\\n axisName.xaxis.set_ticks_position('bottom')\\r\\n axisName.yaxis.set_ticks_position('left')\\r\\n axisName.xaxis.labelpad = 2\\r\\n if not ticksOnly:\\r\\n axisName.spines['top'].set_visible(False)\\r\\n axisName.spines['right'].set_visible(False)\\r\\n if complete:\\r\\n axisName.spines['top'].set_visible(False)\\r\\n axisName.spines['right'].set_visible(False)\\r\\n axisName.spines['bottom'].set_visible(False)\\r\\n axisName.spines['left'].set_visible(False)\\r\\n return axisName\",\n \"def set_axis(axis_number):\\n robots = get_robot_roots()\\n if not robots:\\n pm.warning('Nothing Selected; Select a valid robot')\\n return\\n\\n # These are specific to how the robots are rigged in relation to Maya's\\n # coordinate system\\n rotation_axes = ['Y', 'X', 'X', 'Z', 'X', 'Z']\\n\\n try: # if the text field is empty, or not a float value, skip it\\n rotation_axis = rotation_axes[axis_number - 1]\\n val = float(pm.textField('t_a{}'.format(axis_number),\\n query=True,\\n text=True))\\n\\n for robot in robots:\\n ns = robot.namespace()\\n pm.setAttr('{0}|{1}robot_GRP|{1}FK_CTRLS|{1}a{2}FK_CTRL.rotate{3}'.format(robot, ns, axis_number, rotation_axis), val)\\n except:\\n pass\",\n \"def category_axis(self, category_axis):\\n\\n self.container['category_axis'] = category_axis\",\n \"def fix_facetgrid_axis_labels(facet_grid, shared_in_center=False,\\n x=True, y=True) -> None:\\n # regarding the choice of shared_in_center: WWMDD?\\n if shared_in_center:\\n # TODO maybe add a axes over / under the FacetGrid axes, with the same\\n # shape, and label that one (i think i did this in my gui or one of the\\n # plotting fns. maybe plot_traces?)\\n raise NotImplementedError\\n else:\\n for ax in facet_grid.axes.flat:\\n if not (ax.is_first_col() and ax.is_last_row()):\\n if x:\\n ax.set_xlabel('')\\n if y:\\n ax.set_ylabel('')\",\n \"def undimensionize(self, names=None, mapper_to_meta=False):\\n mapper = self.undimensionizing_mapper(names)\\n self.rename_from_mapper(mapper)\\n if mapper_to_meta: self._meta['sets']['rename_mapper'] = mapper\\n if not names: self.set_dim_comp(False)\",\n \"def _update_data_transforms(self, axisOrder='col-major'):\\n self._dataTransform = QtGui.QTransform()\\n self._inverseDataTransform = QtGui.QTransform()\\n if self.axisOrder == 'row-major': # transpose both\\n self._dataTransform.scale(1, -1)\\n self._dataTransform.rotate(-90)\\n self._inverseDataTransform.scale(1, -1)\\n self._inverseDataTransform.rotate(-90)\",\n \"def reset_name_labels(infr):\\n infr.print('reset_name_labels', 1)\\n orig_names = infr.get_node_attrs('orig_name_label')\\n infr.set_node_attrs('name_label', orig_names)\",\n \"def readAxes(self):\\n for axisElement in self.root.findall(\\\".axes/axis\\\"):\\n axis = {}\\n axis['name'] = name = axisElement.attrib.get(\\\"name\\\")\\n axis['tag'] = axisElement.attrib.get(\\\"tag\\\")\\n axis['minimum'] = float(axisElement.attrib.get(\\\"minimum\\\"))\\n axis['maximum'] = float(axisElement.attrib.get(\\\"maximum\\\"))\\n axis['default'] = float(axisElement.attrib.get(\\\"default\\\"))\\n # we're not using the map for anything.\\n axis['map'] = []\\n for warpPoint in axisElement.findall(\\\".map\\\"):\\n inputValue = float(warpPoint.attrib.get(\\\"input\\\"))\\n outputValue = float(warpPoint.attrib.get(\\\"output\\\"))\\n axis['map'].append((inputValue, outputValue))\\n # there are labelnames in the element\\n # but we don't need them for building the fonts.\\n self.axes[name] = axis\\n self.axesOrder.append(axis['name'])\",\n \"def alias(requestContext, seriesList, newName):\\n try:\\n seriesList.name = newName\\n except AttributeError:\\n for series in seriesList:\\n series.name = newName\\n return seriesList\",\n \"def swapaxes(a, axis1, axis2):\\n # TODO(okuta): check type\\n return a.swapaxes(axis1, axis2)\",\n \"def _axis_labels(self, data_name: str) -> Tuple[str, str]:\\n\\n # Single activity attributes (column name must be present in summary dataframe)\\n if data_name == 'distance':\\n if self.config.distance_unit == 'km':\\n return 'distance_2d_km', 'Distance (km)'\\n elif self.config.distance_unit == 'mile':\\n return 'distance_2d_mile', 'Distance (miles)'\\n elif data_name == 'duration':\\n return 'duration', 'Duration (minutes)'\\n\\n # These can be used for either single activities (summary dataframe) or aggregates (time series dataframe)\\n elif data_name == 'mean_speed':\\n if self.config.distance_unit == 'km':\\n return 'mean_kmph', 'Average speed (km/hour)'\\n elif self.config.distance_unit == 'mile':\\n return 'mean_mph', 'Average speed (miles/hour)'\\n elif data_name == 'mean_hr':\\n return 'mean_hr', 'Average heart rate (beats/minute)'\\n\\n # Aggregate attributes (column name must be present in time series dataframe)\\n elif data_name == 'total_distance':\\n if self.config.distance_unit == 'km':\\n return 'total_distance_2d_km', 'Total distance (km)'\\n elif self.config.distance_unit == 'mile':\\n return 'total_distance_2d_mile', 'Total distance (miles)'\\n elif data_name == 'total_duration':\\n return 'total_duration', 'Total duration (minutes)'\\n elif data_name == 'activity_count':\\n return 'activity_count', 'Number of activities'\\n\\n else:\\n raise ValueError(f'Bad value for `data_name`: \\\"{data_name}\\\".')\",\n \"def duplicate_axes(isl_obj, duplicate_inames, new_inames):\\n if isinstance(isl_obj, list):\\n return [\\n duplicate_axes(i, duplicate_inames, new_inames)\\n for i in isl_obj]\\n\\n if not duplicate_inames:\\n return isl_obj\\n\\n def _align_and_intersect(d1, d2):\\n d1, d2 = isl.align_two(d1, d2)\\n return d1 & d2\\n\\n old_name_to_new_name = dict(zip(duplicate_inames, new_inames))\\n\\n dup_isl_obj = isl_obj\\n\\n for old_name, (dt, pos) in isl_obj.get_var_dict().items():\\n dup_isl_obj = dup_isl_obj.set_dim_name(dt, pos,\\n old_name_to_new_name.get(old_name,\\n old_name))\\n\\n return _align_and_intersect(dup_isl_obj, isl_obj)\",\n \"def fix(self):\\n for namespace in pm.listNamespaces():\\n for elem in namespace.ls():\\n elem.rename(elem.split(\\\":\\\")[-1])\\n namespace.remove()\\n\\n self.run()\",\n \"def setIndexNames(self):\\n self.theta = self.i1\\n self.radial = self.i2\",\n \"def addExtraAxis(slab,newaxis=None,axis=0,verbose=False):\\n\\n import cdms2 as cdms\\n import MV2 as MV\\n\\n if newaxis is None:\\n newaxis=cdms.createAxis([1,])\\n newaxis.units=''\\n\\n # add new axis to axis list of input <slab>\\n axislist=slab.getAxisList()\\n axislist.insert(axis,newaxis)\\n\\n #----------------Reshape----------------\\n shape=list(slab.shape)\\n shape.insert(axis,len(newaxis))\\n slab2=MV.reshape(slab,shape)\\n\\n #------------Create variable------------\\n att_dict=attribute_obj2dict(slab)\\n slab2=cdms.createVariable(slab2,axes=axislist,attributes=att_dict,\\\\\\n typecode='f')\\n slab2.id=slab.id\\n\\n if verbose:\\n print('\\\\n# <addExtraAxis>: Originial variable shape:',slab.shape)\\n print('# <addExtraAxis>: New variable shape:',slab2.shape)\\n\\n return slab2\",\n \"def names(self, names):\\n\\n self._names = names\",\n \"def getAllAxisUnits(self):\\n import copy\\n return copy.copy(self.__axis_units__)\",\n \"def addAxis(self, tag, name, minimum, maximum, default, warpMap=None):\\n axisElement = ET.Element(\\\"axis\\\")\\n axisElement.attrib['name'] = name\\n axisElement.attrib['tag'] = tag\\n axisElement.attrib['minimum'] = str(minimum)\\n axisElement.attrib['maximum'] = str(maximum)\\n axisElement.attrib['default'] = str(default)\\n if warpMap is not None:\\n for a, b in warpMap:\\n warpPt = ET.Element(\\\"map\\\")\\n warpPt.attrib['input'] = str(a)\\n warpPt.attrib['output'] = str(b)\\n axisElement.append(warpPt)\\n self.root.findall('.axes')[0].append(axisElement)\",\n \"def set_axis_tick_width(params):\\n # type: (Dict) -> Dict\\n for panel_id, p in params['local'].items():\\n obj_axis = params['internal']['canvas']['axes'][panel_id]\\n for k in ['x', 'y']:\\n attr = \\\"get_{}axis\\\".format(k)\\n ax = getattr(obj_axis, attr)()\\n for m in ['major', 'minor']:\\n ax.set_tick_params(\\n which=m,\\n width=p['tick'][m]['width'][k])\\n\\n return params\",\n \"def make_axes(info:PartialAxesInfo,\\n cls_name:'Optional[str]'=None)->NamedAxes:\\n\\n if is_integer(info):\\n assert info >= 0, f'naxis({info}) should be non-negative'\\n assert cls_name is None, f'unnamed axes({info}) cannot have class name'\\n\\n return info\\n\\n if isinstance(info, Iterable):\\n info = list(info)\\n try:\\n # try parsing as Mapping[NamedIndex, int] tuples\\n axes = dict(info) # throw TypeError, ValueError\\n assert all(map(is_integer, axes.values())), 'mapped indices should be integers'\\n\\n slots = np.zeros(len(axes), dtype=bool)\\n slots[list(axes.values())] = True # throw IndexError\\n assert slots.sum() == len(slots), 'bad axis indices' # HINT: all(0 <= axes < naxis)?\\n except IndexError:\\n raise ValueError('bad axis indices')\\n except (TypeError, ValueError):\\n # fallback to enumerate indices for axes\\n assert all(map(lambda axis: isinstance(axis, NamedIndex), info))\\n\\n axes = dict(zip(info, range(len(info))))\\n else:\\n # sort name by indices\\n axes_ = axes\\n axes = {k: v if v >= 0 else len(axes) + v for k, v in axes.items()}\\n keys = sorted(axes.keys(), key=axes.get)\\n axes = OrderedDict()\\n for key in keys:\\n axes[key] = axes_[key]\\n\\n if cls_name is not None:\\n axes = namedtuple(cls_name, axes.keys())(**axes)\\n\\n return axes\\n\\n raise TypeError(f\\\"unsupported 'info' type({type(info)})\\\")\",\n \"def xaxis(self,label,units):\\r\\n if units != \\\"\\\": label = label + \\\" (\\\" + units + \\\")\\\"\\r\\n self.xbox.set_text(r\\\"$%s$\\\" % (label))\\r\\n pass\",\n \"def _create_diagram_axis_info(self, axis, pinfo):\\n\\n nkey = f\\\"{axis}colname\\\"\\n colname = pinfo[nkey]\\n defs = self._refdefs.info.get(colname, {})\\n title = defs.get(\\\"title\\\", colname)\\n\\n fontfmt = {\\\"family\\\" : \\\"Arial, sans-serif\\\",\\n \\\"size\\\" : 18,\\n \\\"color\\\" : \\\"black\\\"}\\n\\n axis = {\\\"showline\\\" : True,\\n \\\"showgrid\\\" : True,\\n \\\"title\\\" : title,\\n \\\"titlefont\\\" : fontfmt,\\n \\\"ticks\\\" : \\\"outside\\\",\\n \\\"tickwidth\\\" : 1,\\n \\\"showticklabels\\\" : True,\\n \\\"linewidth\\\" : 1,\\n \\\"linecolor\\\" : \\\"black\\\",\\n \\\"zeroline\\\" : True,\\n \\\"zerolinewidth\\\" : 1,\\n \\\"zerolinecolor\\\" : \\\"black\\\"}\\n\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\":\\n axis[\\\"tickformat\\\"] = \\\".3s\\\"\\n axis[\\\"ticksuffix\\\"] = \\\"s\\\"\\n axis[\\\"hoverformat\\\"] = \\\".4s\\\"\\n elif colname == \\\"Percentile\\\":\\n axis[\\\"ticksuffix\\\"] = \\\"%\\\"\\n\\n return axis\",\n \"def upAxis(*args, axis: Union[AnyStr, bool]=\\\"\\\", rotateView: bool=True, q=True, query=True,\\n **kwargs)->Union[None, Any]:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7356323","0.7326029","0.6893155","0.6677015","0.64332557","0.6372005","0.63517636","0.62905","0.628417","0.62775946","0.6271645","0.6077482","0.6047013","0.6024936","0.5993588","0.59712166","0.5967535","0.596201","0.5952742","0.58923167","0.589169","0.5884654","0.578875","0.57817596","0.5780179","0.5779107","0.57761216","0.57437253","0.5734455","0.5707204","0.5703792","0.56781435","0.56337297","0.56219816","0.5616132","0.5611061","0.56026804","0.558611","0.5579622","0.556776","0.556412","0.55292183","0.5517566","0.5511644","0.54836136","0.5480594","0.5459653","0.54482764","0.5446676","0.54211926","0.54127645","0.538706","0.5370458","0.5343439","0.53327024","0.53163123","0.5315587","0.53030705","0.5300485","0.5289558","0.52566266","0.52388656","0.52194154","0.52121794","0.5208994","0.5203703","0.5189778","0.51700723","0.51655865","0.5151354","0.5145567","0.5135191","0.5116793","0.51126283","0.51120114","0.5109633","0.5105329","0.5104444","0.5100494","0.50953746","0.50880253","0.5063333","0.5059572","0.5056057","0.505014","0.50489455","0.50468504","0.50337374","0.50332844","0.5029569","0.5022714","0.5019628","0.50166655","0.5010822","0.5007343","0.5006219","0.4989394","0.49820545","0.49815145","0.4972247"],"string":"[\n \"0.7356323\",\n \"0.7326029\",\n \"0.6893155\",\n \"0.6677015\",\n \"0.64332557\",\n \"0.6372005\",\n \"0.63517636\",\n \"0.62905\",\n \"0.628417\",\n \"0.62775946\",\n \"0.6271645\",\n \"0.6077482\",\n \"0.6047013\",\n \"0.6024936\",\n \"0.5993588\",\n \"0.59712166\",\n \"0.5967535\",\n \"0.596201\",\n \"0.5952742\",\n \"0.58923167\",\n \"0.589169\",\n \"0.5884654\",\n \"0.578875\",\n \"0.57817596\",\n \"0.5780179\",\n \"0.5779107\",\n \"0.57761216\",\n \"0.57437253\",\n \"0.5734455\",\n \"0.5707204\",\n \"0.5703792\",\n \"0.56781435\",\n \"0.56337297\",\n \"0.56219816\",\n \"0.5616132\",\n \"0.5611061\",\n \"0.56026804\",\n \"0.558611\",\n \"0.5579622\",\n \"0.556776\",\n \"0.556412\",\n \"0.55292183\",\n \"0.5517566\",\n \"0.5511644\",\n \"0.54836136\",\n \"0.5480594\",\n \"0.5459653\",\n \"0.54482764\",\n \"0.5446676\",\n \"0.54211926\",\n \"0.54127645\",\n \"0.538706\",\n \"0.5370458\",\n \"0.5343439\",\n \"0.53327024\",\n \"0.53163123\",\n \"0.5315587\",\n \"0.53030705\",\n \"0.5300485\",\n \"0.5289558\",\n \"0.52566266\",\n \"0.52388656\",\n \"0.52194154\",\n \"0.52121794\",\n \"0.5208994\",\n \"0.5203703\",\n \"0.5189778\",\n \"0.51700723\",\n \"0.51655865\",\n \"0.5151354\",\n \"0.5145567\",\n \"0.5135191\",\n \"0.5116793\",\n \"0.51126283\",\n \"0.51120114\",\n \"0.5109633\",\n \"0.5105329\",\n \"0.5104444\",\n \"0.5100494\",\n \"0.50953746\",\n \"0.50880253\",\n \"0.5063333\",\n \"0.5059572\",\n \"0.5056057\",\n \"0.505014\",\n \"0.50489455\",\n \"0.50468504\",\n \"0.50337374\",\n \"0.50332844\",\n \"0.5029569\",\n \"0.5022714\",\n \"0.5019628\",\n \"0.50166655\",\n \"0.5010822\",\n \"0.5007343\",\n \"0.5006219\",\n \"0.4989394\",\n \"0.49820545\",\n \"0.49815145\",\n \"0.4972247\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75589085"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2690,"cells":{"query":{"kind":"string","value":"Close the partition after it has been conformed. The partition should usually be closed after its `array` method has been called to prevent memory leaks. Closing the partition does one of the following, depending on the values of the partition's `!_original` attribute and on the"},"document":{"kind":"string","value":"def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def close(self):\n return self.close_array","def file_close(self):\n if self.on_disk:\n self._subarray.close()","def close(self):\n self.ix.close()","def close (self):\n pass\n #TODO: implement more realistic closing semantics","def close(self):\n self.data.close()","def close(self):\n self.drill = None","def _close( self ):\n for sji in self._sji_data:\n sji.close()","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close():","def close(self) -> None:\n ...","def close(self) -> None:\n ...","def _close(self):\n self.write_data(self.write_queue)\n self.write_compound(self.write_compound_queue)","def close(self):\n\t\tself.filep.close()","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def Close(self):","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n raise NotImplementedError","def close(self):\n ...","def close(self):\n ...","def close (self):\n raise NotImplementedError( 'Needs implementation' )","def close(self):\n self.__exit__(None, None, None)","def close(self):\n self.__exit__(None, None, None)","def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n # than 3 references to it then it is not unique.\n if getrefcount is not None:\n self._decrement_file_counter()\n if subarray is None or getrefcount(subarray) > 3:\n return\n else:\n # getrefcount has itself been deleted or is in the process\n # of being torn down\n return\n\n _partition_file = getattr(subarray, \"_partition_file\", None)\n if _partition_file is not None:\n # This partition contains a temporary file which is not\n # referenced by any other partition on this process, so if\n # there are no lock files present remove the file from\n # disk.\n _remove_temporary_files(_partition_file)\n\n else:\n try:\n if FileArray is not None and isinstance(subarray, FileArray):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if self.file_counter.get(filename, 999) <= 0:\n # This partition contains a non-temporary file\n # which is not referenced by any other\n # partitions, so close the file.\n subarray.close()\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass\n # --- End: if","def Close(self):\n raise NotImplementedError('Implement this')","def _close(self):\n log.Debug('dpbx.close():')","def close(self) -> None:\n pass","def close(self):\n \n self.__exit__(None, None, None)\n return","def close(self) -> None:\n self.pages = []\n for fo, _reader in self.inputs:\n fo.close()\n\n self.inputs = []\n self.output = None","def close(self):\n raise NotImplemented","def close(self):\r\n pass","def Close(self):\n self._RaiseIfNotWritable()\n\n self._storage_file.Close()\n self._storage_file = None","def close(self):\n self.closed = True","def close(self) -> None:\r\n pass","def close(self):\n raise NotImplementedError()","def close(self):\n raise NotImplementedError()","def close (self):\n pass","def close(self):\n raise NotImplementedError(\"Implement this method in child class\")","def close(self):\n for m in self._mappers:\n m.close()\n self._mappers = []\n self._offsets = []\n self._sizes = []\n self._handler = None","def close(self):\r\n pass","def close(self):\r\n pass","def close(self):\r\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","async def end_array(self):","def close(self) -> None:\n\n raise NotImplementedError","def close(self):\r\n raise NotImplementedError(\"`close` method is not implemented!\")","def close(self):\n pass","def close(self):\n pass","def close(self):\n # This is a NOOP by default"],"string":"[\n \"def close(self):\\n return self.close_array\",\n \"def file_close(self):\\n if self.on_disk:\\n self._subarray.close()\",\n \"def close(self):\\n self.ix.close()\",\n \"def close (self):\\n pass\\n #TODO: implement more realistic closing semantics\",\n \"def close(self):\\n self.data.close()\",\n \"def close(self):\\n self.drill = None\",\n \"def _close( self ):\\n for sji in self._sji_data:\\n sji.close()\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close():\",\n \"def close(self) -> None:\\n ...\",\n \"def close(self) -> None:\\n ...\",\n \"def _close(self):\\n self.write_data(self.write_queue)\\n self.write_compound(self.write_compound_queue)\",\n \"def close(self):\\n\\t\\tself.filep.close()\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def Close(self):\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n raise NotImplementedError\",\n \"def close(self):\\n ...\",\n \"def close(self):\\n ...\",\n \"def close (self):\\n raise NotImplementedError( 'Needs implementation' )\",\n \"def close(self):\\n self.__exit__(None, None, None)\",\n \"def close(self):\\n self.__exit__(None, None, None)\",\n \"def __del__(self):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n # If the subarray is unique it will have 2 references to\\n # it plus 1 within this method, making 3. If it has more\\n # than 3 references to it then it is not unique.\\n if getrefcount is not None:\\n self._decrement_file_counter()\\n if subarray is None or getrefcount(subarray) > 3:\\n return\\n else:\\n # getrefcount has itself been deleted or is in the process\\n # of being torn down\\n return\\n\\n _partition_file = getattr(subarray, \\\"_partition_file\\\", None)\\n if _partition_file is not None:\\n # This partition contains a temporary file which is not\\n # referenced by any other partition on this process, so if\\n # there are no lock files present remove the file from\\n # disk.\\n _remove_temporary_files(_partition_file)\\n\\n else:\\n try:\\n if FileArray is not None and isinstance(subarray, FileArray):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if self.file_counter.get(filename, 999) <= 0:\\n # This partition contains a non-temporary file\\n # which is not referenced by any other\\n # partitions, so close the file.\\n subarray.close()\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\\n # --- End: if\",\n \"def Close(self):\\n raise NotImplementedError('Implement this')\",\n \"def _close(self):\\n log.Debug('dpbx.close():')\",\n \"def close(self) -> None:\\n pass\",\n \"def close(self):\\n \\n self.__exit__(None, None, None)\\n return\",\n \"def close(self) -> None:\\n self.pages = []\\n for fo, _reader in self.inputs:\\n fo.close()\\n\\n self.inputs = []\\n self.output = None\",\n \"def close(self):\\n raise NotImplemented\",\n \"def close(self):\\r\\n pass\",\n \"def Close(self):\\n self._RaiseIfNotWritable()\\n\\n self._storage_file.Close()\\n self._storage_file = None\",\n \"def close(self):\\n self.closed = True\",\n \"def close(self) -> None:\\r\\n pass\",\n \"def close(self):\\n raise NotImplementedError()\",\n \"def close(self):\\n raise NotImplementedError()\",\n \"def close (self):\\n pass\",\n \"def close(self):\\n raise NotImplementedError(\\\"Implement this method in child class\\\")\",\n \"def close(self):\\n for m in self._mappers:\\n m.close()\\n self._mappers = []\\n self._offsets = []\\n self._sizes = []\\n self._handler = None\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"async def end_array(self):\",\n \"def close(self) -> None:\\n\\n raise NotImplementedError\",\n \"def close(self):\\r\\n raise NotImplementedError(\\\"`close` method is not implemented!\\\")\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n # This is a NOOP by default\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6764165","0.6518303","0.60208786","0.5811839","0.5795013","0.579095","0.5784808","0.57648385","0.57648385","0.57648385","0.57648385","0.57648385","0.57648385","0.57648385","0.57648385","0.5713722","0.5699953","0.5699953","0.5665161","0.5664806","0.56545746","0.56545746","0.56545746","0.56545746","0.56545746","0.56545746","0.56545746","0.56545746","0.56545746","0.56545746","0.5634785","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.5615191","0.56032425","0.56032425","0.5597058","0.5588722","0.5588722","0.5582289","0.55412054","0.5529768","0.552122","0.5511022","0.549194","0.54891145","0.5474664","0.5472895","0.5469488","0.54694533","0.5460811","0.5460811","0.54573655","0.5454711","0.5450347","0.5449983","0.5449983","0.5449983","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544837","0.544616","0.5440888","0.5440648","0.5439066","0.5439066","0.54387116"],"string":"[\n \"0.6764165\",\n \"0.6518303\",\n \"0.60208786\",\n \"0.5811839\",\n \"0.5795013\",\n \"0.579095\",\n \"0.5784808\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.57648385\",\n \"0.5713722\",\n \"0.5699953\",\n \"0.5699953\",\n \"0.5665161\",\n \"0.5664806\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.56545746\",\n \"0.5634785\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.5615191\",\n \"0.56032425\",\n \"0.56032425\",\n \"0.5597058\",\n \"0.5588722\",\n \"0.5588722\",\n \"0.5582289\",\n \"0.55412054\",\n \"0.5529768\",\n \"0.552122\",\n \"0.5511022\",\n \"0.549194\",\n \"0.54891145\",\n \"0.5474664\",\n \"0.5472895\",\n \"0.5469488\",\n \"0.54694533\",\n \"0.5460811\",\n \"0.5460811\",\n \"0.54573655\",\n \"0.5454711\",\n \"0.5450347\",\n \"0.5449983\",\n \"0.5449983\",\n \"0.5449983\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544837\",\n \"0.544616\",\n \"0.5440888\",\n \"0.5440648\",\n \"0.5439066\",\n \"0.5439066\",\n \"0.54387116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70555735"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2691,"cells":{"query":{"kind":"string","value":"Return a deep copy. ``p.copy()`` is equivalent to ``copy.deepcopy(p)``."},"document":{"kind":"string","value":"def copy(self):\n new = Partition.__new__(Partition)\n new.__dict__ = self.__dict__.copy()\n\n self._increment_file_counter()\n\n return new"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def deepcopy(self):\n return copymod.deepcopy(self)","def copy(self):\n\t\treturn pythoncopy.deepcopy(self)","def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)","def deepcopy(self):\n return self.copy()","def copy(self):\r\n return copy.deepcopy(self)","def deepcopy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self, deep=False):\n return _(copy.deepcopy(self._) if deep else copy.copy(self._))","def __deepcopy__(self, memodict=None):\n return self.copy()","def copy(self):\n from copy import deepcopy\n return deepcopy(self)","def clone(self):\n return copy.deepcopy(self)","def clone(self):\n return deepcopy(self)","def clone(self):\n return deepcopy(self)","def clone(self):\n return deepcopy(self)","def clone(self):\n return deepcopy(self)","def shallow_copy(self):\n # TODO: Rename this to __copy__()?\n raise NotImplementedError(\"shallow_copy is not implemented\")","def clone(self):\n from copy import deepcopy\n return deepcopy(self)","def copy(self):\n try:\n return self.__class__(self, copy=True)\n except TypeError:\n new = self.__class__(copy.deepcopy(self))\n return new","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def _copy_(self):\n return copy.copy(self)","def copy (self):\n import copy\n return copy.copy(self)","def clone(self) -> Any:\n return cp.copy(self)","def copy(self):\n\t\ttemp = self.__class__()\n\t\ttemp.copy_from(self)\n\t\treturn temp","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\r\n return copy.copy(self)","def copy(self):\n \n return deepcopy(self)","def clone(self):\n return self.copy()","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)","def __copy__(self):\n return self.copy()","def copy(self):\n return pdict(dict.copy(self))","def copy(self):\n cpy = deepcopy(self)\n # usually we use copy to perform transformations on the board\n # so it's good to reset memoized values\n cpy._memoized_compact = None \n return cpy","def deep_copy(self):\n return self.__class__(self.inputs, self.outputs, self.middle)","def clone(self):\n return shallow_clone(self)","def copy (self, **kwargs):\n out = copy.deepcopy (self)\n out.update (**kwargs)\n return out","def clone(self):\n memo = dict()\n c = self._clone(memo)\n c._clone_rip(memo)\n return c","def __copy__(self, *args, **kwargs):\n return self.copy()","def copy(self, **kwargs):\n\n # Future versions may add new options here\n with KWArgs(kwargs) as k:\n deep = k.optional(\"deep\", True)\n\n if deep:\n return copy.deepcopy(self)\n else:\n return copy.copy(self)","def __deepcopy__(self, memodict=None):\n return self.__class__(self.m, self.n, deepcopy(self.data))","def copy(self):\n return self._new_rep(self._func(self.rep))","def copy(self):\n return copy(self)","def copy(self):\n return copy(self)","def copy(self):\n\n return deepcopy(self)","def copy(self):\n\n return deepcopy(self)","def copy(self):\n\n return deepcopy(self)","def copy(self):\n new = self\n return new","def copy(self):\n return self.mutate().simple_copy()","def deepcopy(self):\n\t\tlpcopy = LpProblem(name = self.name, sense = self.sense)\n\t\tif lpcopy.objective != None:\n\t\t\tlpcopy.objective = self.objective.copy()\n\t\tlpcopy.constraints = {}\n\t\tfor k,v in self.constraints.iteritems():\n\t\t\tlpcopy.constraints[k] = v.copy()\n\t\tlpcopy.sos1 = self.sos1.copy()\n\t\tlpcopy.sos2 = self.sos2.copy()\n\t\treturn lpcopy","def __deepcopy__(self, memo):\n return self.copy()","def _copy(self, p):\n p._.d = self._.d\n p._.n = self._.n\n if self._has(\"p\"):\n p._.p = copy(self._.p)\n if self._has(\"q\"):\n p._.q = copy(self._.q)\n if self._has(\"P\"):\n p._.P = copy(self._.P)\n if self._has(\"Q\"):\n p._.Q = copy(self._.Q)\n if self._has(\"k\"):\n p._.k = self._.k\n if self._has(\"m\"):\n p._.m = self._.m\n if self._has(\"fsd\"):\n p._.fsd = self._.fsd\n if self._has(\"pPolynomial_ordering\"):\n p._.pPolynomial_ordering = self._.pPolynomial_ordering\n if self._has(\"qPolynomial_ordering\"):\n p._.qPolynomial_ordering = self._.qPolynomial_ordering\n if self._has(\"complement\"):\n p._.complement = self._.complement\n p._.fusion_schemes.update(self._.fusion_schemes)\n p._.subschemes.update(self._.subschemes)\n p._.subconstituents = list(self._.subconstituents)\n p._.triple.update(self._.triple)\n p._.triple_solution.update(self._.triple_solution)\n p._.triple_solution_generator.update(self._.triple_solution_generator)\n p._.quadruple.update(self._.quadruple)","def copy(self, deep=False):\n if deep:\n raise NotImplementedError(\"Deep Copy is not supported\")\n\n return type(self)(self.data)","def copy(self):\n return self.__class__(**vars(self))","def copy (self):\n return self.__class__(self.name, self[:])","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def deepcopy(self, memo=None):\n from copy import deepcopy\n return deepcopy(self, memo)","def __copy__(self):\n return self.__class__(self.m, self.n, self.data)","def __copy__(self):\n cls = self.__class__\n result = cls.__new__(cls)\n to_copy = {\"_cache\", \"_buffers\", \"_parameters\", \"_modules\"}\n result.__dict__.update(\n {k: v.copy() if k in to_copy else v for k, v in self.__dict__.items()}\n )\n return result","def copy(self):\n copy = type(self).__new__(type(self))\n copy._tree_sequence = self._tree_sequence\n copy._ll_tree = self._ll_tree.copy()\n copy._make_arrays()\n return copy","def copy(self):\n cls = self.__class__\n result = cls.__new__(cls)\n result.__dict__.update(self.__dict__)\n return result","def copy(self):\n cls = type(self)\n new = cls()\n new.default = deepcopy(self.default)\n new.current = deepcopy(self.current)\n new.stepnames = deepcopy(self.stepnames)\n return new","def copy(self):\n new = self.__class__(integration=None, data=None)\n for attribute, value in self.__dict__.items():\n if attribute in self.referenced_attributes:\n setattr(new, attribute, value)\n elif hasattr(value, 'copy'):\n setattr(new, attribute, value.copy())\n else:\n setattr(new, attribute, deepcopy(value))\n return new","def copy(self):\n return self.__class__(self)","def copy(self):\n return self.__class__(self)","def copy(self):\n copy = self.__class__()\n copy.a = self.a\n copy.b = self.b\n copy.peak = self.peak\n copy.orientation = self.orientation\n copy.i = self.i\n copy.coords = self.coords.copy()\n return copy","def copy(self) -> \"Param\":\n copied = super().copy()\n copied._stack = OrderedDiot(\n [(key, param.copy()) for key, param in self._stack.items()]\n )\n return copied","def copy(self):\n return self.__class__(dict(self))"],"string":"[\n \"def deepcopy(self):\\n return copymod.deepcopy(self)\",\n \"def copy(self):\\n\\t\\treturn pythoncopy.deepcopy(self)\",\n \"def copy(self):\\n import copy as pcopy\\n return pcopy.deepcopy(self)\",\n \"def deepcopy(self):\\n return self.copy()\",\n \"def copy(self):\\r\\n return copy.deepcopy(self)\",\n \"def deepcopy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self, deep=False):\\n return _(copy.deepcopy(self._) if deep else copy.copy(self._))\",\n \"def __deepcopy__(self, memodict=None):\\n return self.copy()\",\n \"def copy(self):\\n from copy import deepcopy\\n return deepcopy(self)\",\n \"def clone(self):\\n return copy.deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def shallow_copy(self):\\n # TODO: Rename this to __copy__()?\\n raise NotImplementedError(\\\"shallow_copy is not implemented\\\")\",\n \"def clone(self):\\n from copy import deepcopy\\n return deepcopy(self)\",\n \"def copy(self):\\n try:\\n return self.__class__(self, copy=True)\\n except TypeError:\\n new = self.__class__(copy.deepcopy(self))\\n return new\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def _copy_(self):\\n return copy.copy(self)\",\n \"def copy (self):\\n import copy\\n return copy.copy(self)\",\n \"def clone(self) -> Any:\\n return cp.copy(self)\",\n \"def copy(self):\\n\\t\\ttemp = self.__class__()\\n\\t\\ttemp.copy_from(self)\\n\\t\\treturn temp\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\r\\n return copy.copy(self)\",\n \"def copy(self):\\n \\n return deepcopy(self)\",\n \"def clone(self):\\n return self.copy()\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def __copy__(self):\\n return self.copy()\",\n \"def copy(self):\\n return pdict(dict.copy(self))\",\n \"def copy(self):\\n cpy = deepcopy(self)\\n # usually we use copy to perform transformations on the board\\n # so it's good to reset memoized values\\n cpy._memoized_compact = None \\n return cpy\",\n \"def deep_copy(self):\\n return self.__class__(self.inputs, self.outputs, self.middle)\",\n \"def clone(self):\\n return shallow_clone(self)\",\n \"def copy (self, **kwargs):\\n out = copy.deepcopy (self)\\n out.update (**kwargs)\\n return out\",\n \"def clone(self):\\n memo = dict()\\n c = self._clone(memo)\\n c._clone_rip(memo)\\n return c\",\n \"def __copy__(self, *args, **kwargs):\\n return self.copy()\",\n \"def copy(self, **kwargs):\\n\\n # Future versions may add new options here\\n with KWArgs(kwargs) as k:\\n deep = k.optional(\\\"deep\\\", True)\\n\\n if deep:\\n return copy.deepcopy(self)\\n else:\\n return copy.copy(self)\",\n \"def __deepcopy__(self, memodict=None):\\n return self.__class__(self.m, self.n, deepcopy(self.data))\",\n \"def copy(self):\\n return self._new_rep(self._func(self.rep))\",\n \"def copy(self):\\n return copy(self)\",\n \"def copy(self):\\n return copy(self)\",\n \"def copy(self):\\n\\n return deepcopy(self)\",\n \"def copy(self):\\n\\n return deepcopy(self)\",\n \"def copy(self):\\n\\n return deepcopy(self)\",\n \"def copy(self):\\n new = self\\n return new\",\n \"def copy(self):\\n return self.mutate().simple_copy()\",\n \"def deepcopy(self):\\n\\t\\tlpcopy = LpProblem(name = self.name, sense = self.sense)\\n\\t\\tif lpcopy.objective != None:\\n\\t\\t\\tlpcopy.objective = self.objective.copy()\\n\\t\\tlpcopy.constraints = {}\\n\\t\\tfor k,v in self.constraints.iteritems():\\n\\t\\t\\tlpcopy.constraints[k] = v.copy()\\n\\t\\tlpcopy.sos1 = self.sos1.copy()\\n\\t\\tlpcopy.sos2 = self.sos2.copy()\\n\\t\\treturn lpcopy\",\n \"def __deepcopy__(self, memo):\\n return self.copy()\",\n \"def _copy(self, p):\\n p._.d = self._.d\\n p._.n = self._.n\\n if self._has(\\\"p\\\"):\\n p._.p = copy(self._.p)\\n if self._has(\\\"q\\\"):\\n p._.q = copy(self._.q)\\n if self._has(\\\"P\\\"):\\n p._.P = copy(self._.P)\\n if self._has(\\\"Q\\\"):\\n p._.Q = copy(self._.Q)\\n if self._has(\\\"k\\\"):\\n p._.k = self._.k\\n if self._has(\\\"m\\\"):\\n p._.m = self._.m\\n if self._has(\\\"fsd\\\"):\\n p._.fsd = self._.fsd\\n if self._has(\\\"pPolynomial_ordering\\\"):\\n p._.pPolynomial_ordering = self._.pPolynomial_ordering\\n if self._has(\\\"qPolynomial_ordering\\\"):\\n p._.qPolynomial_ordering = self._.qPolynomial_ordering\\n if self._has(\\\"complement\\\"):\\n p._.complement = self._.complement\\n p._.fusion_schemes.update(self._.fusion_schemes)\\n p._.subschemes.update(self._.subschemes)\\n p._.subconstituents = list(self._.subconstituents)\\n p._.triple.update(self._.triple)\\n p._.triple_solution.update(self._.triple_solution)\\n p._.triple_solution_generator.update(self._.triple_solution_generator)\\n p._.quadruple.update(self._.quadruple)\",\n \"def copy(self, deep=False):\\n if deep:\\n raise NotImplementedError(\\\"Deep Copy is not supported\\\")\\n\\n return type(self)(self.data)\",\n \"def copy(self):\\n return self.__class__(**vars(self))\",\n \"def copy (self):\\n return self.__class__(self.name, self[:])\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def deepcopy(self, memo=None):\\n from copy import deepcopy\\n return deepcopy(self, memo)\",\n \"def __copy__(self):\\n return self.__class__(self.m, self.n, self.data)\",\n \"def __copy__(self):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n to_copy = {\\\"_cache\\\", \\\"_buffers\\\", \\\"_parameters\\\", \\\"_modules\\\"}\\n result.__dict__.update(\\n {k: v.copy() if k in to_copy else v for k, v in self.__dict__.items()}\\n )\\n return result\",\n \"def copy(self):\\n copy = type(self).__new__(type(self))\\n copy._tree_sequence = self._tree_sequence\\n copy._ll_tree = self._ll_tree.copy()\\n copy._make_arrays()\\n return copy\",\n \"def copy(self):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n result.__dict__.update(self.__dict__)\\n return result\",\n \"def copy(self):\\n cls = type(self)\\n new = cls()\\n new.default = deepcopy(self.default)\\n new.current = deepcopy(self.current)\\n new.stepnames = deepcopy(self.stepnames)\\n return new\",\n \"def copy(self):\\n new = self.__class__(integration=None, data=None)\\n for attribute, value in self.__dict__.items():\\n if attribute in self.referenced_attributes:\\n setattr(new, attribute, value)\\n elif hasattr(value, 'copy'):\\n setattr(new, attribute, value.copy())\\n else:\\n setattr(new, attribute, deepcopy(value))\\n return new\",\n \"def copy(self):\\n return self.__class__(self)\",\n \"def copy(self):\\n return self.__class__(self)\",\n \"def copy(self):\\n copy = self.__class__()\\n copy.a = self.a\\n copy.b = self.b\\n copy.peak = self.peak\\n copy.orientation = self.orientation\\n copy.i = self.i\\n copy.coords = self.coords.copy()\\n return copy\",\n \"def copy(self) -> \\\"Param\\\":\\n copied = super().copy()\\n copied._stack = OrderedDiot(\\n [(key, param.copy()) for key, param in self._stack.items()]\\n )\\n return copied\",\n \"def copy(self):\\n return self.__class__(dict(self))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7977393","0.7835692","0.7835209","0.7829921","0.7808746","0.7806165","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.77548367","0.7629336","0.7570929","0.7535955","0.75320345","0.75203437","0.75203437","0.75203437","0.75203437","0.7498102","0.74954945","0.74743545","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.74322134","0.7424588","0.7396001","0.73653597","0.7358803","0.73552966","0.73552966","0.73552966","0.73552966","0.73510087","0.7347772","0.73406434","0.73107153","0.73107153","0.73107153","0.7301712","0.7294297","0.7289994","0.7266033","0.7254224","0.72527796","0.7248667","0.7238254","0.7210287","0.7209984","0.72056806","0.7187993","0.7187993","0.71772134","0.71772134","0.71772134","0.71601623","0.71387434","0.71079034","0.7101826","0.70932615","0.708079","0.7047189","0.7042109","0.703998","0.703998","0.703998","0.703998","0.7035339","0.70211947","0.70145977","0.69918305","0.6985467","0.6981408","0.6974594","0.6974594","0.69734454","0.69436586","0.69393694"],"string":"[\n \"0.7977393\",\n \"0.7835692\",\n \"0.7835209\",\n \"0.7829921\",\n \"0.7808746\",\n \"0.7806165\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.77548367\",\n \"0.7629336\",\n \"0.7570929\",\n \"0.7535955\",\n \"0.75320345\",\n \"0.75203437\",\n \"0.75203437\",\n \"0.75203437\",\n \"0.75203437\",\n \"0.7498102\",\n \"0.74954945\",\n \"0.74743545\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.74322134\",\n \"0.7424588\",\n \"0.7396001\",\n \"0.73653597\",\n \"0.7358803\",\n \"0.73552966\",\n \"0.73552966\",\n \"0.73552966\",\n \"0.73552966\",\n \"0.73510087\",\n \"0.7347772\",\n \"0.73406434\",\n \"0.73107153\",\n \"0.73107153\",\n \"0.73107153\",\n \"0.7301712\",\n \"0.7294297\",\n \"0.7289994\",\n \"0.7266033\",\n \"0.7254224\",\n \"0.72527796\",\n \"0.7248667\",\n \"0.7238254\",\n \"0.7210287\",\n \"0.7209984\",\n \"0.72056806\",\n \"0.7187993\",\n \"0.7187993\",\n \"0.71772134\",\n \"0.71772134\",\n \"0.71772134\",\n \"0.71601623\",\n \"0.71387434\",\n \"0.71079034\",\n \"0.7101826\",\n \"0.70932615\",\n \"0.708079\",\n \"0.7047189\",\n \"0.7042109\",\n \"0.703998\",\n \"0.703998\",\n \"0.703998\",\n \"0.703998\",\n \"0.7035339\",\n \"0.70211947\",\n \"0.70145977\",\n \"0.69918305\",\n \"0.6985467\",\n \"0.6981408\",\n \"0.6974594\",\n \"0.6974594\",\n \"0.69734454\",\n \"0.69436586\",\n \"0.69393694\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":2692,"cells":{"query":{"kind":"string","value":"Returns the partition's data array. After a partition has been conformed, the partition must be closed (with the `close` method) before another partition is conformed,"},"document":{"kind":"string","value":"def array(self):\n config = self.config\n\n unique_array = config[\"unique_subarray\"]\n\n p_axes = self.axes\n p_flip = self.flip\n p_part = self.part\n p_units = self.Units\n p_shape = self.shape\n p_location = self.location\n subarray = self._subarray\n\n len_p_axes = len(p_axes)\n\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is not in memory.\n #\n # It could be in a file on disk or implied by a FileArray\n # object, etc.\n # --------------------------------------------------------\n self._original = self.copy()\n\n unique_array = True\n update = True\n copy = False\n\n if not p_part:\n indices = Ellipsis\n else:\n indices = tuple(p_part)\n\n # Read from a file into a numpy array\n p_data = subarray[indices]\n\n # We've just copied p_data from disk, so in place changes\n # are not possible\n in_place_changes = False\n else:\n # --------------------------------------------------------\n # The subarray is in memory\n # --------------------------------------------------------\n update = config[\"update\"]\n\n if p_part:\n p_data = get_subspace(subarray, p_part)\n elif not unique_array:\n p_data = subarray.view()\n else:\n p_data = subarray\n\n copy = config[\"extra_memory\"]\n\n # In place changes to p_data might be possible if we're not\n # copying the data\n in_place_changes = not copy\n\n if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):\n # --------------------------------------------------------\n # p_data is a numpy number (like numpy.int64) which does\n # not support assignment, so convert it to a numpy array.\n # --------------------------------------------------------\n p_data = numpy_array(p_data)\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n\n masked = numpy_ma_isMA(p_data)\n if masked:\n # The p_data is a masked array\n if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(\n p_data\n ):\n # There are no missing data points so recast as an\n # unmasked numpy array\n p_data = p_data.data\n masked = False\n # --- End: if\n\n if masked:\n # Set the hardness of the mask\n if config[\"hardmask\"]:\n p_data.harden_mask()\n else:\n p_data.soften_mask()\n # --- End: if\n\n self.masked = masked\n\n # ------------------------------------------------------------\n # Make sure that the data array has the correct units. This\n # process will deep copy the data array if required (e.g. if\n # another partition is referencing this numpy array), even if\n # the units are already correct.\n # ------------------------------------------------------------\n func = config.get(\"func\")\n units = config[\"units\"]\n if func is None:\n if not p_units.equals(units) and bool(p_units) is bool(units):\n func = Units.conform\n\n if func is not None:\n inplace = not copy\n p_data = func(p_data, p_units, units, inplace)\n p_units = units\n\n if not inplace:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n flip = config.get(\"flip\", None)\n if flip or p_flip:\n flip_axes = set(p_flip).symmetric_difference(flip)\n else:\n flip_axes = None\n\n axes = config[\"axes\"]\n\n if p_data.size > 1:\n # --------------------------------------------------------\n # Flip axes\n # --------------------------------------------------------\n if flip_axes:\n indices = [\n (\n slice(None, None, -1)\n if axis in flip_axes\n else slice(None)\n )\n for axis in p_axes\n ]\n p_data = p_data[tuple(indices)]\n\n # --------------------------------------------------------\n # Transpose axes\n # --------------------------------------------------------\n if p_axes != axes:\n iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]\n\n if len_p_axes > len(iaxes):\n for i in range(len_p_axes):\n if i not in iaxes:\n # iaxes.append(i)\n iaxes.insert(i, i)\n # --- End: if\n\n p_data = numpy_transpose(p_data, iaxes)\n # --- End: if\n\n # ------------------------------------------------------------\n # Remove excessive/insert missing size 1 axes\n # ------------------------------------------------------------\n if p_shape != p_data.shape:\n # if len_p_axes != len(p_shape):\n p_data = p_data.reshape(p_shape)\n\n # ------------------------------------------------------------\n # Apply the auxiliary mask\n # ------------------------------------------------------------\n auxiliary_mask = config[\"auxiliary_mask\"]\n if auxiliary_mask:\n for mask in auxiliary_mask:\n if mask.any():\n if not masked:\n p_data = p_data.view(numpy_ma_MaskedArray)\n masked = True\n\n p_data.mask = (mask | p_data.mask).array\n # --- End: for\n\n self.masked = True\n\n # ------------------------------------------------------------\n # Convert the array's data type\n # ------------------------------------------------------------\n p_dtype = p_data.dtype\n dtype = config.get(\"dtype\", None)\n if dtype is not None and dtype != p_dtype:\n try:\n p_data = p_data.astype(dtype) # Note: returns a copy\n except ValueError:\n raise ValueError(\n \"Can't recast partition array from {} to {}\".format(\n p_dtype.name, dtype.name\n )\n )\n else:\n # We've just copied p_data, so in place changes are\n # not possible\n copy = False\n in_place_changes = False\n # --- End: if\n\n # ------------------------------------------------------------\n # Copy the array\n # -----------------------------------------------------------\n if copy:\n if p_dtype.char != \"O\":\n if not masked or p_data.ndim > 0:\n p_data = p_data.copy()\n else:\n # This is because numpy.ma.copy doesn't work for\n # scalar arrays (at the moment, at least)\n p_data = numpy_ma_masked_all((), p_data.dtype)\n\n # We've just copied p_data, so in place changes are\n # not possible\n in_place_changes = False\n else:\n # whilst netCDF4.netcdftime.datetime is mucking bout,\n # don't copy!!!!\n # p_data = _copy(p_data)\n pass\n # --- End: if\n\n # ------------------------------------------------------------\n # Update the partition\n # ------------------------------------------------------------\n if update:\n self.subarray = p_data # ?? DCH CHECK\n self.Units = p_units\n self.part = []\n self.axes = axes\n self.flip = flip\n self.flatten = []\n self.shape = p_shape\n self.location = p_location\n\n self._in_place_changes = in_place_changes\n\n # ------------------------------------------------------------\n # Return the numpy array\n # ------------------------------------------------------------\n return p_data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_data ( self ):\n return self._data_pntr.ReadAsArray()","def get_data ( self ):\n return self._data_pntr.ReadAsArray()","def data_array(self):\n return self._data_array","def getData(self):\n return self._array","def get(self):\r\n return self.data_array","def data(self):\n\t\tself.dworker()\n\t\treturn self.d","def get_data(self):\n return self.data[self._size:self._size + self._len]","def data(self) -> np.ndarray:\n return self._data","def read(self) -> np.ndarray:\n return self[self._head]","def get_data(self):\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\n return self._data[idxs].copy()","def get_data(self):\n return [\n self.offset, self.diag, self.orig_freq_diag, self.orig_freq_dims\n ]","def __array__(self, copy=None):\n return self.data.to_pandas().values","def tondarray(self):\r\n return self.data;","def GetPartitioningArray(self):\n return _hypre.HypreParVector_GetPartitioningArray(self)","def data(self):\n self._data: np.ndarray\n return self._data","def _read_data(self):\n return [np.array([]), np.array([])]","def all_data(self) -> Optional[np.ndarray]:\n if self._data_store is None:\n return None\n return self._data_store[:self._count, :]","def getData(self, slice=None):\n\t\traise NotImplementedError","def data(self) -> List[ndarray]:\n return self._data","def _read(self):\n return np.copy(self.memory[self.head_pos])","def get_data(self):\n if self.ser.in_waiting:\n data_string = self.ser.readline().decode().strip()\n if not data_string: return self.data\n self.data = [\n float(element) for element in data_string.split()\n ]\n self.ser.reset_input_buffer()\n return self.data","def data(self):\n return getXarray(self.__mdsnode__,strict=self.__strict__)","def __array__(self):\n return np.asarray(self.data)","def GetData(self):\r\n \r\n return self._data","def array(self):\n return self.get_array()","def __array__(self):\n return pa.column(\"dummy\", self.data).to_pandas().values","def _get_heap_data(self):\n if self._heapsize:\n raw_data = self._get_raw_data().view(np.ubyte)\n heap_end = self._heapoffset + self._heapsize\n return raw_data[self._heapoffset : heap_end]\n else:\n return np.array([], dtype=np.ubyte)","def array(self) -> ndarray:\n if self._slices: # so this is a sub-parray object\n # index into origin array by saved slices\n ret = self._array.get_by_global_slices(self._current_device_index, self._slices[0])\n for s in self._slices[1:]:\n ret = ret[s]\n return ret\n else: # this is a complete copy\n ret = self._array.get(self._current_device_index)\n\n if isinstance(ret, list): # get a subarray instead\n raise IndexError(\"Current device doesn't have a complete copy of this array\")\n return ret","def get(self):\n return self._partition","def getCurentData(self):\n if not self.labExperiment:\n super().getCurentData()\n else:\n return np.array(self.connection.query('get_actuator_data'))","def partitions(self):\n self._get_latest_content()\n return self._data.get('partitions', [])","def to_fits_array(self):\n return self.data","def _get_data_chunk(self):\n if self._start_pos < self.max_pos:\n self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,\n self.sho_spec_inds_per_forc * (self._current_forc + 1))\n self._end_pos = int(min(self.h5_main.shape[0], self._start_pos + self.max_pos))\n self.data = self.h5_main[self._start_pos:self._end_pos, self._current_sho_spec_slice]\n elif self._current_forc < self._num_forcs - 1:\n # Resest for next FORC\n self._current_forc += 1\n\n self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,\n self.sho_spec_inds_per_forc * (self._current_forc + 1))\n self._current_met_spec_slice = slice(self.metrics_spec_inds_per_forc * self._current_forc,\n self.metrics_spec_inds_per_forc * (self._current_forc + 1))\n self._get_dc_offset()\n\n self._start_pos = 0\n self._end_pos = int(min(self.h5_main.shape[0], self._start_pos + self.max_pos))\n self.data = self.h5_main[self._start_pos:self._end_pos, self._current_sho_spec_slice]\n\n else:\n self.data = None\n\n return","def get_data(self):\n oshape = (ctypes.c_uint * 4)()\n ostride = ctypes.c_uint()\n ret = cxnlib.CXNIOGetData(self.handle,\n oshape, ctypes.byref(ostride))\n return ctypes2numpyT(ret, [x for x in oshape], 'float32', ostride.value)","def getData(self):\r\n return self._data","def _get_partition_list(self):\n raise NotImplementedError('Must be implemented in subclasses.')","def data(self):\n return self._fastqc_data","def arr(self):\n return self._arr","def read_data(self, filename, chunksize = 2048):\n pdt_tipsy = np.dtype([('mass', 'f4'),('pos', 'f4', 3),('vel', 'f4', 3), ('eps', 'f4'), ('phi', 'f4')])\n\n # helper functions\n def convert_to_fof_particle_partition(index, iterator): \n for s in iterator: \n p_arr = np.frombuffer(s, pdt_tipsy)\n new_arr = np.zeros(len(p_arr), dtype=pdt)\n new_arr['pos'] = p_arr['pos'] \n if count: \n npart_acc.add({index: len(new_arr)})\n yield new_arr\n\n def set_particle_IDs_partition(index, iterator): \n p_counts = partition_counts.value\n local_index = 0\n start_index = sum([p_counts[i] for i in range(index)])\n for arr in iterator:\n arr['iOrder'] = range(start_index + local_index, start_index + local_index + len(arr))\n local_index += len(arr)\n yield arr\n \n sc = self.sc\n\n rec_rdd = sc.binaryRecords(filename, pdt_tipsy.itemsize*chunksize)\n nPartitions = rec_rdd.getNumPartitions()\n # set the partition count accumulator\n npart_acc = sc.accumulator({i:0 for i in range(nPartitions)}, dictAdd())\n count=True\n # read the data and count the particles per partition\n rec_rdd = rec_rdd.mapPartitionsWithIndex(convert_to_fof_particle_partition)\n rec_rdd.count()\n count=False\n\n partition_counts = sc.broadcast(npart_acc.value)\n\n rec_rdd = rec_rdd.mapPartitionsWithIndex(set_particle_IDs_partition)\n rec_rdd = (self._partition_rdd(rec_rdd, partition_array).partitionBy(self.nPartitions) \n .map(lambda (_,v): v, preservesPartitioning=True)) \n return rec_rdd","def get_data(self):\n return self.wordsArray","def get_data(self):\n return self.read_sample_win()","def data(self):\n try:\n return self.get_nowait()\n except Empty:\n return None","def _readData(self):\n # Debug. This fn should be called only after checking canRead()\n if not self._canRead():\n raise Exception(\"Trying to read more data than there is.\")\n\n data = self.buffer[:self._expectedByteCount]\n self.buffer = self.buffer[self._expectedByteCount:]\n\n return data","def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass","def getData(self):\n return self._data","def getData(self):\n return self._data","def update(self) -> np.ndarray:\r\n # Read chunk from array\r\n signal_slice: np.ndarray = np.array(\r\n self._signal_data[self._pointer : self._pointer + self._chunk]\r\n )\r\n # Move index over\r\n self._pointer += self._chunk\r\n if self._pointer > len(self._signal_data):\r\n # Go back to beginning\r\n self._pointer = 0\r\n self._restart_flag = True\r\n print(\"Restarting stream...\")\r\n\r\n return signal_slice","def update(self) -> np.ndarray:\r\n # Read chunk from array\r\n signal_slice: np.ndarray = np.array(\r\n self._signal_data[self._pointer : self._pointer + self._chunk]\r\n )\r\n # Move index over\r\n self._pointer += self._chunk\r\n if self._pointer > len(self._signal_data):\r\n # Go back to beginning\r\n self._pointer = 0\r\n self._restart_flag = True\r\n print(\"Restarting stream...\")\r\n\r\n return signal_slice","def read_data(self):\n raise NotImplementedError","def _data(self):\n cell = self._get_cell()\n return deepcopy(cell.data)","def get_data(self):\r\n return self.data.copy()","def extract_volume(self):\n\n # RDD or array of [(partition, vol)]\n vols = None\n if self.usespark:\n vols = self._retrieve_vol(self.current_spot, None)\n else:\n vols = self._retrieve_vol(self.current_spot, len(self.partitions))\n self.current_spot += len(self.partitions)\n \n return vols","def getData(self):\n return self.__data","def data(self):\r\n return self._data","def data(self):\r\n return self._data","def data(self):\r\n return self._data","def data(self):\n if self._data is None:\n self._get_data()\n\n return self._data","def data(self):\n return self._buf[self._offset : self._offset + self._size]","def getPartition(self):\n\t\treturn self.partition","def partition(self, sep):\n return asarray(partition(self, sep))","def in_memory_data(self):\n return self._in_memory_data","def read(self) -> np.array:\n return self._stream.read(self._frame_size)","def getData(self):\n return self.data","def getData(self):\n return self.data","def get_data(self):\n try:\n data = self._queue.get(block=False)\n except Empty:\n data = None\n return data","def data(self):\n return self._data","def getArray( self, par, path, unit = None ):\n\n return self.db.getArrayPar( par, path, unit = unit )","def data(self) -> List[T]:\n return self._data","def exposed_get_data(self, chunk_id):\n local_filename = self.chunk_filename(chunk_id)\n with open(local_filename, \"r\") as file:\n data = file.read()\n return data","def as_list(self) -> List[int]:\n return self.my_partition","def getPoolData(self):\r\n # type: () -> (list[Data.Data])\r\n output = []\r\n # start from the beginning of the pool area\r\n ea = self.func_ea + self.getSize(withPool=False)\r\n while ea < self.getSize(withPool=True):\r\n # create and append the data item\r\n data = Data.Data(ea)\r\n output.append(data)\r\n # advance ea to the next item\r\n ea += data.getSize()\r\n return output","def getData(self):\n\n if not self.data:\n raise ValueError('No data file given.')\n\n return self.data.getData()","def data(self):\n if not hasattr(self, \"_data\"):\n self._data = self._get_data()\n return self._data","def getNdArray(self):\n futures = self.client.map(_call_getNdArray, self.vecDask, pure=False)\n arrays = self.client.gather(futures)\n return arrays","def get_partitions(self):\n return self.partitions","def getData(self, taskId:int):\n return self.pool.getData(taskId)","def get_data(self):\n raise NotImplementedError(\"Not implemented!\")","def numpy(self):\n return self.data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self):\n return self._data","def data(self) -> Generator:\n # Not using a consumer group and setting partitions manually so it's a smaller\n # jump to make this deterministic/repeatable with multiple workers later on.\n\n self.connect()\n\n self.approx_position = 0\n for partition_id, start_offset, end_offset in self._partition_ranges():\n # TODO - confirm this can never jump to another partition\n tp = TopicPartition(topic=self.topic, partition=partition_id)\n self.client.assign([tp])\n\n self.items_to_fetch = end_offset - start_offset\n self.client.seek(tp, start_offset)\n\n if self.items_to_fetch <= 0:\n msg = f\"Invalid offsets {start_offset}:{end_offset} for partition {partition_id}\"\n raise ValueError(msg)\n\n for m in self.client:\n self.approx_position += 1\n yield Pinnate(data=m.value)\n\n if end_offset is not None and m.offset >= end_offset:\n break","def partitions(self):\n return self._partitions"],"string":"[\n \"def get_data ( self ):\\n return self._data_pntr.ReadAsArray()\",\n \"def get_data ( self ):\\n return self._data_pntr.ReadAsArray()\",\n \"def data_array(self):\\n return self._data_array\",\n \"def getData(self):\\n return self._array\",\n \"def get(self):\\r\\n return self.data_array\",\n \"def data(self):\\n\\t\\tself.dworker()\\n\\t\\treturn self.d\",\n \"def get_data(self):\\n return self.data[self._size:self._size + self._len]\",\n \"def data(self) -> np.ndarray:\\n return self._data\",\n \"def read(self) -> np.ndarray:\\n return self[self._head]\",\n \"def get_data(self):\\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\\n return self._data[idxs].copy()\",\n \"def get_data(self):\\n return [\\n self.offset, self.diag, self.orig_freq_diag, self.orig_freq_dims\\n ]\",\n \"def __array__(self, copy=None):\\n return self.data.to_pandas().values\",\n \"def tondarray(self):\\r\\n return self.data;\",\n \"def GetPartitioningArray(self):\\n return _hypre.HypreParVector_GetPartitioningArray(self)\",\n \"def data(self):\\n self._data: np.ndarray\\n return self._data\",\n \"def _read_data(self):\\n return [np.array([]), np.array([])]\",\n \"def all_data(self) -> Optional[np.ndarray]:\\n if self._data_store is None:\\n return None\\n return self._data_store[:self._count, :]\",\n \"def getData(self, slice=None):\\n\\t\\traise NotImplementedError\",\n \"def data(self) -> List[ndarray]:\\n return self._data\",\n \"def _read(self):\\n return np.copy(self.memory[self.head_pos])\",\n \"def get_data(self):\\n if self.ser.in_waiting:\\n data_string = self.ser.readline().decode().strip()\\n if not data_string: return self.data\\n self.data = [\\n float(element) for element in data_string.split()\\n ]\\n self.ser.reset_input_buffer()\\n return self.data\",\n \"def data(self):\\n return getXarray(self.__mdsnode__,strict=self.__strict__)\",\n \"def __array__(self):\\n return np.asarray(self.data)\",\n \"def GetData(self):\\r\\n \\r\\n return self._data\",\n \"def array(self):\\n return self.get_array()\",\n \"def __array__(self):\\n return pa.column(\\\"dummy\\\", self.data).to_pandas().values\",\n \"def _get_heap_data(self):\\n if self._heapsize:\\n raw_data = self._get_raw_data().view(np.ubyte)\\n heap_end = self._heapoffset + self._heapsize\\n return raw_data[self._heapoffset : heap_end]\\n else:\\n return np.array([], dtype=np.ubyte)\",\n \"def array(self) -> ndarray:\\n if self._slices: # so this is a sub-parray object\\n # index into origin array by saved slices\\n ret = self._array.get_by_global_slices(self._current_device_index, self._slices[0])\\n for s in self._slices[1:]:\\n ret = ret[s]\\n return ret\\n else: # this is a complete copy\\n ret = self._array.get(self._current_device_index)\\n\\n if isinstance(ret, list): # get a subarray instead\\n raise IndexError(\\\"Current device doesn't have a complete copy of this array\\\")\\n return ret\",\n \"def get(self):\\n return self._partition\",\n \"def getCurentData(self):\\n if not self.labExperiment:\\n super().getCurentData()\\n else:\\n return np.array(self.connection.query('get_actuator_data'))\",\n \"def partitions(self):\\n self._get_latest_content()\\n return self._data.get('partitions', [])\",\n \"def to_fits_array(self):\\n return self.data\",\n \"def _get_data_chunk(self):\\n if self._start_pos < self.max_pos:\\n self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,\\n self.sho_spec_inds_per_forc * (self._current_forc + 1))\\n self._end_pos = int(min(self.h5_main.shape[0], self._start_pos + self.max_pos))\\n self.data = self.h5_main[self._start_pos:self._end_pos, self._current_sho_spec_slice]\\n elif self._current_forc < self._num_forcs - 1:\\n # Resest for next FORC\\n self._current_forc += 1\\n\\n self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,\\n self.sho_spec_inds_per_forc * (self._current_forc + 1))\\n self._current_met_spec_slice = slice(self.metrics_spec_inds_per_forc * self._current_forc,\\n self.metrics_spec_inds_per_forc * (self._current_forc + 1))\\n self._get_dc_offset()\\n\\n self._start_pos = 0\\n self._end_pos = int(min(self.h5_main.shape[0], self._start_pos + self.max_pos))\\n self.data = self.h5_main[self._start_pos:self._end_pos, self._current_sho_spec_slice]\\n\\n else:\\n self.data = None\\n\\n return\",\n \"def get_data(self):\\n oshape = (ctypes.c_uint * 4)()\\n ostride = ctypes.c_uint()\\n ret = cxnlib.CXNIOGetData(self.handle,\\n oshape, ctypes.byref(ostride))\\n return ctypes2numpyT(ret, [x for x in oshape], 'float32', ostride.value)\",\n \"def getData(self):\\r\\n return self._data\",\n \"def _get_partition_list(self):\\n raise NotImplementedError('Must be implemented in subclasses.')\",\n \"def data(self):\\n return self._fastqc_data\",\n \"def arr(self):\\n return self._arr\",\n \"def read_data(self, filename, chunksize = 2048):\\n pdt_tipsy = np.dtype([('mass', 'f4'),('pos', 'f4', 3),('vel', 'f4', 3), ('eps', 'f4'), ('phi', 'f4')])\\n\\n # helper functions\\n def convert_to_fof_particle_partition(index, iterator): \\n for s in iterator: \\n p_arr = np.frombuffer(s, pdt_tipsy)\\n new_arr = np.zeros(len(p_arr), dtype=pdt)\\n new_arr['pos'] = p_arr['pos'] \\n if count: \\n npart_acc.add({index: len(new_arr)})\\n yield new_arr\\n\\n def set_particle_IDs_partition(index, iterator): \\n p_counts = partition_counts.value\\n local_index = 0\\n start_index = sum([p_counts[i] for i in range(index)])\\n for arr in iterator:\\n arr['iOrder'] = range(start_index + local_index, start_index + local_index + len(arr))\\n local_index += len(arr)\\n yield arr\\n \\n sc = self.sc\\n\\n rec_rdd = sc.binaryRecords(filename, pdt_tipsy.itemsize*chunksize)\\n nPartitions = rec_rdd.getNumPartitions()\\n # set the partition count accumulator\\n npart_acc = sc.accumulator({i:0 for i in range(nPartitions)}, dictAdd())\\n count=True\\n # read the data and count the particles per partition\\n rec_rdd = rec_rdd.mapPartitionsWithIndex(convert_to_fof_particle_partition)\\n rec_rdd.count()\\n count=False\\n\\n partition_counts = sc.broadcast(npart_acc.value)\\n\\n rec_rdd = rec_rdd.mapPartitionsWithIndex(set_particle_IDs_partition)\\n rec_rdd = (self._partition_rdd(rec_rdd, partition_array).partitionBy(self.nPartitions) \\n .map(lambda (_,v): v, preservesPartitioning=True)) \\n return rec_rdd\",\n \"def get_data(self):\\n return self.wordsArray\",\n \"def get_data(self):\\n return self.read_sample_win()\",\n \"def data(self):\\n try:\\n return self.get_nowait()\\n except Empty:\\n return None\",\n \"def _readData(self):\\n # Debug. This fn should be called only after checking canRead()\\n if not self._canRead():\\n raise Exception(\\\"Trying to read more data than there is.\\\")\\n\\n data = self.buffer[:self._expectedByteCount]\\n self.buffer = self.buffer[self._expectedByteCount:]\\n\\n return data\",\n \"def close(self, **kwargs):\\n config = getattr(self, \\\"config\\\", None)\\n\\n if config is None:\\n return\\n\\n if kwargs:\\n config.update(kwargs)\\n\\n original = getattr(self, \\\"_original\\\", None)\\n logger.partitioning(\\\"Partition.close: original = {}\\\".format(original))\\n\\n if not original:\\n originally_on_disk = False\\n original_subarray = None\\n else:\\n originally_on_disk = not original.in_memory\\n original_subarray = original._subarray\\n\\n config = self.config\\n logger.partitioning(\\\" config = {}\\\".format(config))\\n\\n if config[\\\"serial\\\"]:\\n # --------------------------------------------------------\\n # SERIAL\\n # --------------------------------------------------------\\n logger.partitioning(\\\" serial\\\")\\n\\n if config[\\\"readonly\\\"]:\\n logger.partitioning(\\\" readonly=True\\\")\\n\\n if originally_on_disk:\\n logger.partitioning(\\\" subarray originally on disk\\\")\\n\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.1.1 The original subarray was on disk,\\n # we don't want to keep the current\\n # subarray in memory, and we are happy\\n # to discard any changes that may have\\n # been made to the subarray.\\n logger.partitioning(\\\" 1.1.1.1 revert\\\")\\n self.revert()\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.1.2 The original subarray was on disk,\\n # we are happy to keep the current\\n # subarray in memory, but there is not\\n # enough free memory to do so.\\n logger.partitioning(\\n \\\" 1.1.1.2 revert ({} <= {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n self.revert()\\n else:\\n # 1.1.1.3 The original subarray was on disk\\n # and there is enough memory to keep\\n # the current subarray in memory\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # The original subarray was a temporary\\n # file which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n del self.masked\\n logger.partitioning(\\n \\\" 1.1.1.3 del masked ({} > {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n\\n else:\\n logger.partitioning(\\\" subarray originally in memory\\\")\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.2.1 Original subarray was in memory and\\n # we don't want to keep the current\\n # subarray in memory\\n logger.partitioning(\\\" 1.1.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.2.2 Original subarray was in memory and\\n # unique but there is not enough\\n # memory to keep the current subarray\\n logger.partitioning(\\\" 1.1.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.1.2.3 Original subarray was in memory and\\n # unique and there is enough memory to\\n # keep the current subarray in memory\\n logger.partitioning(\\\" 1.1.2.3 pass\\\")\\n pass\\n else:\\n # config['readonly'] is False\\n if originally_on_disk:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.1.1 Original subarray was on disk and\\n # there and we don't want to keep the\\n # array\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.1.2 Original subarray was on disk but\\n # there is not enough memory to keep\\n # it\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.1.3 Original subarray was on disk and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.1.3 pass\\\")\\n del self.masked\\n else:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.2.1 Original subarray was in memory but\\n # we don't want to keep it\\n logger.partitioning(\\\" 1.2.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.2.2 Original subarray was an in memory\\n # but there is not enough memory to\\n # keep it\\n logger.partitioning(\\\" 1.2.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.2.3 Original subarray was in memory and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.2.3 del masked\\\")\\n del self.masked\\n else:\\n logger.partitioning(\\\"Partition.close: parallel\\\")\\n # --------------------------------------------------------\\n # PARALLEL\\n # --------------------------------------------------------\\n pass\\n\\n # if hasattr(self, '_original'):\\n # del self._original\\n\\n # print(hasattr(self, 'config')),\\n try:\\n del self.config\\n except AttributeError:\\n pass\",\n \"def getData(self):\\n return self._data\",\n \"def getData(self):\\n return self._data\",\n \"def update(self) -> np.ndarray:\\r\\n # Read chunk from array\\r\\n signal_slice: np.ndarray = np.array(\\r\\n self._signal_data[self._pointer : self._pointer + self._chunk]\\r\\n )\\r\\n # Move index over\\r\\n self._pointer += self._chunk\\r\\n if self._pointer > len(self._signal_data):\\r\\n # Go back to beginning\\r\\n self._pointer = 0\\r\\n self._restart_flag = True\\r\\n print(\\\"Restarting stream...\\\")\\r\\n\\r\\n return signal_slice\",\n \"def update(self) -> np.ndarray:\\r\\n # Read chunk from array\\r\\n signal_slice: np.ndarray = np.array(\\r\\n self._signal_data[self._pointer : self._pointer + self._chunk]\\r\\n )\\r\\n # Move index over\\r\\n self._pointer += self._chunk\\r\\n if self._pointer > len(self._signal_data):\\r\\n # Go back to beginning\\r\\n self._pointer = 0\\r\\n self._restart_flag = True\\r\\n print(\\\"Restarting stream...\\\")\\r\\n\\r\\n return signal_slice\",\n \"def read_data(self):\\n raise NotImplementedError\",\n \"def _data(self):\\n cell = self._get_cell()\\n return deepcopy(cell.data)\",\n \"def get_data(self):\\r\\n return self.data.copy()\",\n \"def extract_volume(self):\\n\\n # RDD or array of [(partition, vol)]\\n vols = None\\n if self.usespark:\\n vols = self._retrieve_vol(self.current_spot, None)\\n else:\\n vols = self._retrieve_vol(self.current_spot, len(self.partitions))\\n self.current_spot += len(self.partitions)\\n \\n return vols\",\n \"def getData(self):\\n return self.__data\",\n \"def data(self):\\r\\n return self._data\",\n \"def data(self):\\r\\n return self._data\",\n \"def data(self):\\r\\n return self._data\",\n \"def data(self):\\n if self._data is None:\\n self._get_data()\\n\\n return self._data\",\n \"def data(self):\\n return self._buf[self._offset : self._offset + self._size]\",\n \"def getPartition(self):\\n\\t\\treturn self.partition\",\n \"def partition(self, sep):\\n return asarray(partition(self, sep))\",\n \"def in_memory_data(self):\\n return self._in_memory_data\",\n \"def read(self) -> np.array:\\n return self._stream.read(self._frame_size)\",\n \"def getData(self):\\n return self.data\",\n \"def getData(self):\\n return self.data\",\n \"def get_data(self):\\n try:\\n data = self._queue.get(block=False)\\n except Empty:\\n data = None\\n return data\",\n \"def data(self):\\n return self._data\",\n \"def getArray( self, par, path, unit = None ):\\n\\n return self.db.getArrayPar( par, path, unit = unit )\",\n \"def data(self) -> List[T]:\\n return self._data\",\n \"def exposed_get_data(self, chunk_id):\\n local_filename = self.chunk_filename(chunk_id)\\n with open(local_filename, \\\"r\\\") as file:\\n data = file.read()\\n return data\",\n \"def as_list(self) -> List[int]:\\n return self.my_partition\",\n \"def getPoolData(self):\\r\\n # type: () -> (list[Data.Data])\\r\\n output = []\\r\\n # start from the beginning of the pool area\\r\\n ea = self.func_ea + self.getSize(withPool=False)\\r\\n while ea < self.getSize(withPool=True):\\r\\n # create and append the data item\\r\\n data = Data.Data(ea)\\r\\n output.append(data)\\r\\n # advance ea to the next item\\r\\n ea += data.getSize()\\r\\n return output\",\n \"def getData(self):\\n\\n if not self.data:\\n raise ValueError('No data file given.')\\n\\n return self.data.getData()\",\n \"def data(self):\\n if not hasattr(self, \\\"_data\\\"):\\n self._data = self._get_data()\\n return self._data\",\n \"def getNdArray(self):\\n futures = self.client.map(_call_getNdArray, self.vecDask, pure=False)\\n arrays = self.client.gather(futures)\\n return arrays\",\n \"def get_partitions(self):\\n return self.partitions\",\n \"def getData(self, taskId:int):\\n return self.pool.getData(taskId)\",\n \"def get_data(self):\\n raise NotImplementedError(\\\"Not implemented!\\\")\",\n \"def numpy(self):\\n return self.data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self):\\n return self._data\",\n \"def data(self) -> Generator:\\n # Not using a consumer group and setting partitions manually so it's a smaller\\n # jump to make this deterministic/repeatable with multiple workers later on.\\n\\n self.connect()\\n\\n self.approx_position = 0\\n for partition_id, start_offset, end_offset in self._partition_ranges():\\n # TODO - confirm this can never jump to another partition\\n tp = TopicPartition(topic=self.topic, partition=partition_id)\\n self.client.assign([tp])\\n\\n self.items_to_fetch = end_offset - start_offset\\n self.client.seek(tp, start_offset)\\n\\n if self.items_to_fetch <= 0:\\n msg = f\\\"Invalid offsets {start_offset}:{end_offset} for partition {partition_id}\\\"\\n raise ValueError(msg)\\n\\n for m in self.client:\\n self.approx_position += 1\\n yield Pinnate(data=m.value)\\n\\n if end_offset is not None and m.offset >= end_offset:\\n break\",\n \"def partitions(self):\\n return self._partitions\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66712004","0.66712004","0.6514655","0.62883234","0.62340677","0.6196425","0.6086166","0.6076796","0.5936824","0.59225637","0.5890117","0.5851978","0.5830991","0.5823379","0.5809332","0.57856905","0.5781631","0.57773644","0.57653815","0.5727279","0.5719797","0.5713546","0.56780607","0.5648781","0.5639347","0.56386054","0.5624693","0.5615178","0.5605869","0.5559736","0.5544451","0.55281126","0.54828036","0.5477017","0.5470059","0.5456417","0.54526037","0.54519075","0.5449001","0.544266","0.544167","0.5435135","0.54187924","0.54090464","0.54024386","0.54024386","0.5398539","0.5398539","0.5395827","0.5395746","0.53955626","0.53931326","0.5384674","0.5380315","0.5380315","0.5380315","0.53746235","0.5372373","0.536975","0.5366125","0.53609025","0.53437394","0.5339596","0.5339596","0.5334339","0.53260964","0.53231436","0.5322855","0.53211737","0.53188425","0.53174394","0.53168225","0.5307165","0.53046256","0.53039026","0.5295094","0.52826726","0.5278804","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.5278098","0.52712214","0.5270703"],"string":"[\n \"0.66712004\",\n \"0.66712004\",\n \"0.6514655\",\n \"0.62883234\",\n \"0.62340677\",\n \"0.6196425\",\n \"0.6086166\",\n \"0.6076796\",\n \"0.5936824\",\n \"0.59225637\",\n \"0.5890117\",\n \"0.5851978\",\n \"0.5830991\",\n \"0.5823379\",\n \"0.5809332\",\n \"0.57856905\",\n \"0.5781631\",\n \"0.57773644\",\n \"0.57653815\",\n \"0.5727279\",\n \"0.5719797\",\n \"0.5713546\",\n \"0.56780607\",\n \"0.5648781\",\n \"0.5639347\",\n \"0.56386054\",\n \"0.5624693\",\n \"0.5615178\",\n \"0.5605869\",\n \"0.5559736\",\n \"0.5544451\",\n \"0.55281126\",\n \"0.54828036\",\n \"0.5477017\",\n \"0.5470059\",\n \"0.5456417\",\n \"0.54526037\",\n \"0.54519075\",\n \"0.5449001\",\n \"0.544266\",\n \"0.544167\",\n \"0.5435135\",\n \"0.54187924\",\n \"0.54090464\",\n \"0.54024386\",\n \"0.54024386\",\n \"0.5398539\",\n \"0.5398539\",\n \"0.5395827\",\n \"0.5395746\",\n \"0.53955626\",\n \"0.53931326\",\n \"0.5384674\",\n \"0.5380315\",\n \"0.5380315\",\n \"0.5380315\",\n \"0.53746235\",\n \"0.5372373\",\n \"0.536975\",\n \"0.5366125\",\n \"0.53609025\",\n \"0.53437394\",\n \"0.5339596\",\n \"0.5339596\",\n \"0.5334339\",\n \"0.53260964\",\n \"0.53231436\",\n \"0.5322855\",\n \"0.53211737\",\n \"0.53188425\",\n \"0.53174394\",\n \"0.53168225\",\n \"0.5307165\",\n \"0.53046256\",\n \"0.53039026\",\n \"0.5295094\",\n \"0.52826726\",\n \"0.5278804\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.5278098\",\n \"0.52712214\",\n \"0.5270703\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5538118"},"document_rank":{"kind":"string","value":"31"}}},{"rowIdx":2693,"cells":{"query":{"kind":"string","value":"True if the subarray contains datetime objects."},"document":{"kind":"string","value":"def isdt(self):\n return self.Units.isreftime and self._subarray.dtype == _dtype_object"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def has_datetime_type(obj: _std_typing.Any) -> bool:\n return obj.dtype == sc.DType.datetime64","def _uses_datetimeblock(dtype: Union[np.dtype, ExtensionDtype]) -> bool:\n vtype = dtype.type\n return issubclass(vtype, np.datetime64)","def is_datetime(self) -> bool:\n return False","def are_all_datetimes(values: List[Union[str, int, float]]):\n for value in values:\n if not is_datetime(value):\n return False\n return True","def has_time(self):\n return isinstance(self._start, datetime.datetime)","def is_datetime(self):\n answer = self._call('is_datetime')\n return answer.yes","def is_datetime_type(val):\n return (\n pd.api.types.is_datetime64_any_dtype(val)\n or isinstance(val, pd.Timestamp)\n or isinstance(val, datetime)\n )","def is_all_dates(self) -> bool:\n return False","def is_normalized(self) -> bool:\n return is_date_array_normalized(self.asi8, self.tz, reso=self._creso)","def has_start_stop_acqtamps(self):\n try:\n if not all([isinstance(x, datetime) for x in self.start_acq]):\n raise Exception(\"Invalid value encountered in start_acq\")\n if not all([isinstance(x, datetime) for x in self.stop_acq]):\n raise Exception(\"Invalid value encountered in stop_acq\")\n if not all([len(self) == len(x) for x in [self.start_acq,\\\n self.stop_acq]]):\n raise Exception(\"Lengths of arrays do not match...\")\n return True\n except Exception as e:\n print((repr(e)))\n return False","def has_t(self):\n return any(map(lambda s: s.is_temporal, self))","def time_series(self) -> bool:\n return self._time_series","def check_dataset_dates(self):\n # TODO: graph traverse and date checking\n pass","def __contains__(self, ts):\n if not isinstance(ts, datetime.datetime):\n return False\n base_key = self.floor_time(key)\n return self.first_timestamp <= base_key <= self.last_timestamp","def is_date_dtype(df, col_name):\n dtype = df.dtypes[col_name]\n return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)","def check_dates(dates):\n for date in dates:\n if type(date) != datetime.datetime:\n raise TypeError('Input date, %s, not datetime object' % date)","def test_validate_datetime(self):\n self.datatrue = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/report_2_counts.csv'))\n\n self.datafalse = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/random_date1.csv'))\n\n self.test1 = utils.validate_datetime(self.datatrue)\n\n self.test2 = utils.validate_datetime(self.datafalse)\n\n self.assertTrue(isinstance(self.test1, pd.DataFrame))\n\n self.assertTrue(np.dtype('datetime64[ns]') in self.test1.dtypes.tolist())\n\n self.assertFalse(np.dtype('datetime64[ns]') in self.test2.dtypes.tolist())","def has_timestamp(self):\n return (self.data_type() & 0x100 == 0x100) and (self.raw_data_length() >= 8)","def _checkData(data: Sequence[HistoryElement]):\r\n if not all(x.timeStamp for x in data):\r\n raise ValueError(\"At least one element in data doesn't have a TimeStamp\")","def could_be_datetime(val, fmt):\n\n if val == None or fmt == None:\n return False\n\n if isinstance(val, datetime):\n return True\n\n if isinstance(val, (str, unicode)):\n if Record.is_empty_str(val) or Record.is_empty_str(fmt):\n return False\n\n try:\n d = datetime.strptime(val, fmt)\n if not isinstance(d, datetime):\n raise ValueError\n else:\n return True\n except Exception as e:\n logging.error(e)\n return False\n\n #otherwise\n return False","def test_14_digit_datetime_detection(self):\n obj = awstats_reader.awstats_datetime('20091130165230')\n self.assertTrue(isinstance(obj, awstats_reader.AwstatsDateTime))","def _is_key_value_array(self, data):\n for d in data:\n if not self._is_key_value(d):\n return False\n return True","def __eq__(self, t):\n if not isinstance(t, DateTime):\n return False\n return (self._micros, self._tz) == (t._micros, t._tz)","def is_date(dt):\n return isinstance(dt, datetime.date) and not isinstance(dt, datetime.datetime)","def has_data(self, fit_id, species_id, start=None, stop=None):\n if not (fit_id in self.raw_results and species_id in\\\n self.raw_results[fit_id]):\n return False\n if all([isinstance(x, datetime) for x in [start, stop]]):\n ts = self.raw_results[fit_id][\"start\"]\n if not any([start < x < stop for x in ts]):\n return False\n return True","def _is_tc_entity_array(self, data):\n for d in data:\n if not self._is_tc_entity(d):\n return False\n return True","def _check_dates_tareas(self, cr, uid, ids, context=None):\n for leave in self.read(cr, uid, ids, ['date_start_tarea', 'date_end_tarea'], context=context):\n if leave['date_start_tarea'] and leave['date_end_tarea']:\n if leave['date_start_tarea'] > leave['date_end_tarea']:\n return False\n return True","def check_consistency(object) -> bool:\n time = np.array(list(object.keys()))\n time_diff = time[1:] - time[0:-1]\n return np.all(time_diff == 1)","def __len__(self):\n return len(self.dates)","def table_has_any_timestamp_fields(table_object) -> bool:\n mapper = sqlalchemy.inspect(table_object)\n for column in mapper.all_orm_descriptors:\n try:\n if isinstance(column.type, PSQL_TIMESTAMP) or isinstance(column.type, SQLITE_TIMESTAMP):\n return True\n except Exception:\n pass\n return False","def check_dt_consistency(date_dt):\n\n # https://en.wikipedia.org/wiki/Tz_database\n # https://www.iana.org/time-zones\n \n if date_dt.tzinfo is None:\n return True\n else:\n \n # This check is quite heavy but there is apparently no other way to do it.\n if date_dt.utcoffset() != dt_from_s(s_from_dt(date_dt), tz=date_dt.tzinfo).utcoffset():\n return False\n else:\n return True","def is_time(self) -> bool:\n return self.times > 1","def is_noncomplex(obj):\n if type(obj) is time.struct_time:\n return True\n return False","def on_disk(self):\n return isinstance(self._subarray, FileArray)","def NeedsArray(self, type_):\n return self._NameComponents(type_) in self._array_types","def is_datetime(s: Union[str, int, float]):\n if is_number(s):\n return False\n\n try:\n parse_datetime(s)\n return True\n except Exception:\n return False","def _checkDT(self):\r\n dt = np.diff(self.tsec)\r\n \r\n dt_unique = np.unique(dt)\r\n \r\n if np.size(dt_unique) == 1:\r\n self.isequal = True\r\n else:\r\n self.isequal = False\r\n \r\n try:\r\n self.dt = dt[1]\r\n except:\r\n self.dt = 0.0","def _dateisrange(value):\n if len(re.findall('([0-9])/([0-9])', value)) > 1:\n NotImplemented\n elif len(re.findall('([0-9])/([0-9])', value)) == 1:\n return True\n else:\n return False","def is_temporal(self, e):\n if e in self.temporal:\n return True","def is_interval(self):\n return len(self.interval_list) > 0","def _is_DataArrays(data):\n if isinstance(data, (Dataset, DataArray)):\n return True\n if isinstance(data, Mapping):\n for da in data.values():\n if not isinstance(da, DataArray):\n raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n if isinstance(data, Iterable):\n for da in data:\n if not isinstance(da, DataArray):\n return False\n # raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n return False","def test_date_obj_within_t_delta(self):\n max_hour_count = (None, None, None)\n self.deque.append((self.datetime_obj, self.timestamp))\n result = feature_5(self.deque,\n self.heap,\n self.expected_dict,\n self.top_n,\n max_hour_count,\n self.time_rollover_queue)\n self.assertEqual(len(self.deque), 2)\n self.assertEqual(self.deque[-1], (self.datetime_obj, self.timestamp))\n self.assertEqual(result, max_hour_count)","def is_array(self):\n return False","def _test_df_datetime(self, df):\n date_raw = df['DateListed'].iloc[0] # e.g. '2016-01-07 00:00:00'\n first_date_time = datetime.strptime(date_raw, \"%Y-%m-%d %H:%M:%S\")\n assert first_date_time >= datetime(2016, 1, 1, 0, 0) and \\\n first_date_time < datetime(2017, 1, 1, 0, 0)","def isscalar(array):\n arr = ma.array(array)\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\n return True\n return False","def _has_unicode_fields(array):\n dtypes = (d[0] for d in array.dtype.fields.values())\n return any(d.kind == \"U\" for d in dtypes)","def valid_datetime(dt):\n if isinstance(dt.tzinfo, tzinfo) and not datetime_ambiguous(dt):\n return True\n return False","def check_daterange(sdate, edate, st_dict):\n \n reflist = pd.date_range(start=sdate, end=edate, freq='MS').to_list()\n checklist = [] \n for y in st_dict['years']:\n for m in st_dict[y]['months']:\n checklist.append(pd.to_datetime(y + '-' + m))\n \n if list(set(checklist) & set(reflist)):\n return True","def is_array(self):\n return len(self.descriptor) > 1","def is_dateline(vertices):\n vertices = np.asarray(vertices, dtype=\"d\")\n longitudes = vertices[:, 0]\n return np.abs(longitudes.min(axis=0) - longitudes.max(axis=0)) > 180","def __is_initialized(self):\n if self.start_datetime is None or self.end_datetime is None:\n raise Exception(\"TimeSeriesDataFrame Object is not initialized!\")\n\n return True","def test_list_of_datetimes(self):\n plot_index = pd.date_range(start=\"2000-1-1\", freq=\"D\", periods=10000)\n freq = 'M'\n\n dates = pd.Series(1, index=plot_index).resample(freq).index\n tl = formatter.TimestampLocator(plot_index, xticks=dates)\n test = tl._process(3, 900)\n\n tl = formatter.TimestampLocator(plot_index, freq=freq)\n correct = tl._process(3, 900)\n tm.assert_almost_equal(test, correct)\n\n freq = 'MS'\n dates = pd.Series(1, index=plot_index).resample(freq).index\n tl = formatter.TimestampLocator(plot_index, xticks=dates)\n test = tl._process(3, 900)\n\n tl = formatter.TimestampLocator(plot_index, freq=freq)\n correct = tl._process(3, 900)\n tm.assert_almost_equal(test, correct)\n\n # straight list of dates\n freq = 'MS'\n dates = pd.Series(1, index=plot_index).resample(freq).index\n dates = list(dates)\n tl = formatter.TimestampLocator(plot_index, xticks=dates)\n test = tl._process(3, 900)\n\n tl = formatter.TimestampLocator(plot_index, freq=freq)\n correct = tl._process(3, 900)\n tm.assert_almost_equal(test, correct)","def is_multiobjects(x: Any) -> bool:\r\n return (is_listlike(x) or (isinstance(x, np.ndarray)\r\n and x.dtype == \"O\")) and len(x) > 0 and not is_scalar(x[0])","def has_value(cls, value):\n return bool(isinstance(value, numbers.Number) or isinstance(value, time) or \\\n isinstance(value, datetime) or value)","def test_dates(self):\n result = export.processExport(houseId=1,\n startDate = datetime.datetime(2013, 01, 06) #5 Days\n )\n\n self.assertEqual(result.shape, (1440, 2))\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 06))\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 10, 23, 55))\n\n\n #Stop at 00:00 on the 5th\n result = export.processExport(houseId=1,\n endDate = datetime.datetime(2013, 01, 05, 23, 55) #5 Days\n )\n\n self.assertEqual(result.shape, (1440, 2))\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 01))\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 05, 23, 55))","def validateTiming(obj):\n if obj.start_time:\n if obj.end_time:\n return obj.start_time <= timezone.now() and obj.end_time > timezone.now()\n else:\n return obj.start_time <= timezone.now()\n else:\n if obj.end_time:\n return obj.end_time > timezone.now()\n else:\n return True","def is_interpretable(self):\n return bool(self.as_date() or self.as_time())","def validDateTime( dateTime ):\n try:\n datetime.strptime( dateTime, \"%Y-%m-%dT%H:%M:%S.%fZ\" )\n return True\n except ValueError:\n return False","def test_paid_at(self):\n\n self.assertIsInstance(self.obj.paid_at, datetime)","def validate(self, test_data):\n if not isinstance(test_data, datetime.datetime):\n raise ValidationError('Invalid type/value.', 'datetime.datetime',\n type(test_data))","def datetime_has_tz(dt):\n\n if type(dt) != datetime.datetime:\n raise TypeError(f\"dt must be type datetime.datetime, not {type(dt)}\")\n\n return dt.tzinfo is not None and dt.tzinfo.utcoffset(dt) is not None","def is_arraylike(obj):\n if isinstance(obj, list):\n return True\n elif isinstance(obj, np.ndarray):\n return True\n elif isinstance(obj, pd.Series):\n return True\n elif isinstance(obj, pd.DataFrame):\n return True\n return False","def test_8_digit_date_detection(self):\n obj = awstats_reader.awstats_datetime('20091130')\n self.assertTrue(isinstance(obj, awstats_reader.AwstatsDate))","def is_pandas_collection(l):\n if not (hasattr(l, '__iter__') and len(l) > 0):\n # make sure it's iterable\n # don't include empty iterables because\n # that would include some sklearn estimator objects\n return False\n\n if isinstance(l, dict):\n l = l.values()\n\n for i in l:\n if not (isinstance(i, pd.DataFrame) or isinstance(i, pd.Series)):\n return False\n\n return True","def test_hk_getdata_timeline_array_type(self):\n _, timelines = load_data(self._file)\n assert isinstance(timelines['group0']['t'], np.ndarray)","def is_rejoinee(self):\n return len(self._start_date) > 1","def in_cached_file(self):\n return isinstance(self._subarray, CachedArray)","def test_empty_dates(self):\n\n for func in self.main_functions:\n self.assertFalse(func(20190120, 20130201).size)","def is_array(a):\n try:\n shape = a.shape\n return len(shape) >= 1\n except AttributeError:\n return False","def test_created_at(self):\n self.assertIsInstance(self.obj.created_at, datetime)","def test_created_at(self):\n self.assertIsInstance(self.obj.created_at, datetime)","def test_created_at(self):\n self.assertIsInstance(self.obj.created_at, datetime)","def __len__(self):\n return len(self._timeseriesData)","def test_update_at(self):\n self.assertIsInstance(self.obj.update_at, datetime)","def test_update_at(self):\n self.assertIsInstance(self.obj.update_at, datetime)","def test_update_at(self):\n self.assertIsInstance(self.obj.update_at, datetime)","def isscalar(x):\n arrayed_x = asarray(x)\n return asarray(x).ndim == 0 and arrayed_x.dtype != 'object'","def is_dataset_in_t_range(dataset, t_min, t_max):\n for obj in dataset.object_names:\n obj_data = dataset.data[obj]\n t_min_obj = np.min(obj_data['mjd'])\n t_max_obj = np.max(obj_data['mjd'])\n if (t_min_obj < t_min) or (t_max_obj > t_max):\n return False\n return True","def valid_time_range(self, range_indexes):\n ts_id = self.get_time_series().id\n if self.df_class.loc[range_indexes, ts_id].eq(True).all():\n logging.warning(msg_injection_all_anomalies())\n return False\n else:\n return True","def __isub__(self, *args, **kwargs):\n return _uhd_swig.time_spec_t___isub__(self, *args, **kwargs)","def test_dict_keys_datetime_passthrough(self):\n assert (\n orjson.dumps(\n {datetime.datetime(2000, 1, 1, 2, 3, 4, 123): True},\n option=orjson.OPT_NON_STR_KEYS | orjson.OPT_PASSTHROUGH_DATETIME,\n )\n == b'{\"2000-01-01T02:03:04.000123\":true}'\n )","def is_timestamp(schema_obj):\n\n if isinstance(schema_obj, schema.Field):\n return is_timestamp(schema_obj.metadata)\n elif isinstance(schema_obj, schema.Struct):\n return schema_obj.full_name == 'google.protobuf.Timestamp'\n return False","def is_array(self, arr):\n return isinstance(arr, np.ndarray)","def test_created_at(self):\n self.assertIsInstance(self.certificate_history.datetime, datetime.datetime)","def is_array_type(self, objtype):\n return isinstance(objtype, self.__arrayt) # _ctypes.PyCArrayType","def valid_visa(visa):\n if dates_difference(visa[\"date\"]) < 730:\n return True\n return False","def is_interval(self):\n return True","def test_TimeArray_repr():","def toc(self,timestamp):\n return self._timestamp > timestamp","def _check_dates_tarea_start(self, cr, uid, ids, context=None):\n for leave in self.read(cr, uid, ids, ['date_start_tarea', 'date_start_proyecto'], context=context):\n if leave['date_start_tarea'] and leave['date_start_proyecto']:\n if leave['date_start_tarea'] < leave['date_start_proyecto']:\n return False\n return True","def is_categoric(series: List) -> bool:\n if series.dtype == str or series.dtype == np.object:\n try:\n if (\n int(re.split(r\"[^\\w\\s]\", series[0])[0]) >= 1900\n and len(re.split(r\"[^\\w\\s]\", series[0])) >= 3\n ):\n return False\n else:\n return True\n except:\n if (\n len(series.unique()) / len(series) == 1\n or \"id\" in series.name.lower()\n ):\n return False\n elif (\n True in series.unique().tolist()\n and False in series.unique().tolist()\n ):\n return False\n elif (\n \"True\" in series.unique().tolist()\n and \"False\" in series.unique().tolist()\n ):\n return False\n else:\n return True\n else:\n return False","def __isZeroEverywhere(self, array):\n epsilon = numpy.finfo( type(array[0]) ).eps\n boolList = numpy.less_equal(numpy.abs(array), epsilon)\n\n for b in boolList:\n if not b:\n return False\n return True","def _validate_dict_entry(self, dict_entry):\r\n try:\r\n # Type-check all of the type-critical items.\r\n if (\r\n type(dict_entry[\"id\"]) == int and\r\n type(dict_entry[\"date\"]) == datetime.date and\r\n type(dict_entry[\"time\"]) == datetime.time and\r\n type(dict_entry[\"datetime\"]) == datetime.datetime and\r\n type(dict_entry[\"duration\"]) == datetime.timedelta):\r\n return True\r\n else:\r\n return False\r\n # end if\r\n except Exception as err:\r\n _z_exc(\"logentry.py/_validate_dict_entry\", err)\r\n # end try\r","def one_day(self):\n return self.end.date() == self.date","def valid_format(self):\n\n # If candidate is None, return true\n if not self.dt:\n print \"dt empty\"\n return True\n\n # Verify if time format is ok and stores in into a time-tuple format\n try:\n stime = datetime.strptime(self.dt, \"%Y-%m-%d %H:%M:%S\")\n except ValueError:\n return False\n else:\n return True","def is_full(self) -> bool:\n return self._array[0].all()","def is_data(i):\n keys = ['_id', '_time']\n return all(i != k for k in keys)","def graph_has_temporal(g):\n return any(any(edge.get(p) == 'time' for p in {'argmax', 'argmin', 'type'}) or 'num' in edge for edge in g.get('edgeSet', []))","def check_date(created_at, start, end):\n x = get_date(created_at)\n return x <= end and x >= start","def is_array(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_array)"],"string":"[\n \"def has_datetime_type(obj: _std_typing.Any) -> bool:\\n return obj.dtype == sc.DType.datetime64\",\n \"def _uses_datetimeblock(dtype: Union[np.dtype, ExtensionDtype]) -> bool:\\n vtype = dtype.type\\n return issubclass(vtype, np.datetime64)\",\n \"def is_datetime(self) -> bool:\\n return False\",\n \"def are_all_datetimes(values: List[Union[str, int, float]]):\\n for value in values:\\n if not is_datetime(value):\\n return False\\n return True\",\n \"def has_time(self):\\n return isinstance(self._start, datetime.datetime)\",\n \"def is_datetime(self):\\n answer = self._call('is_datetime')\\n return answer.yes\",\n \"def is_datetime_type(val):\\n return (\\n pd.api.types.is_datetime64_any_dtype(val)\\n or isinstance(val, pd.Timestamp)\\n or isinstance(val, datetime)\\n )\",\n \"def is_all_dates(self) -> bool:\\n return False\",\n \"def is_normalized(self) -> bool:\\n return is_date_array_normalized(self.asi8, self.tz, reso=self._creso)\",\n \"def has_start_stop_acqtamps(self):\\n try:\\n if not all([isinstance(x, datetime) for x in self.start_acq]):\\n raise Exception(\\\"Invalid value encountered in start_acq\\\")\\n if not all([isinstance(x, datetime) for x in self.stop_acq]):\\n raise Exception(\\\"Invalid value encountered in stop_acq\\\")\\n if not all([len(self) == len(x) for x in [self.start_acq,\\\\\\n self.stop_acq]]):\\n raise Exception(\\\"Lengths of arrays do not match...\\\")\\n return True\\n except Exception as e:\\n print((repr(e)))\\n return False\",\n \"def has_t(self):\\n return any(map(lambda s: s.is_temporal, self))\",\n \"def time_series(self) -> bool:\\n return self._time_series\",\n \"def check_dataset_dates(self):\\n # TODO: graph traverse and date checking\\n pass\",\n \"def __contains__(self, ts):\\n if not isinstance(ts, datetime.datetime):\\n return False\\n base_key = self.floor_time(key)\\n return self.first_timestamp <= base_key <= self.last_timestamp\",\n \"def is_date_dtype(df, col_name):\\n dtype = df.dtypes[col_name]\\n return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)\",\n \"def check_dates(dates):\\n for date in dates:\\n if type(date) != datetime.datetime:\\n raise TypeError('Input date, %s, not datetime object' % date)\",\n \"def test_validate_datetime(self):\\n self.datatrue = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/report_2_counts.csv'))\\n\\n self.datafalse = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/random_date1.csv'))\\n\\n self.test1 = utils.validate_datetime(self.datatrue)\\n\\n self.test2 = utils.validate_datetime(self.datafalse)\\n\\n self.assertTrue(isinstance(self.test1, pd.DataFrame))\\n\\n self.assertTrue(np.dtype('datetime64[ns]') in self.test1.dtypes.tolist())\\n\\n self.assertFalse(np.dtype('datetime64[ns]') in self.test2.dtypes.tolist())\",\n \"def has_timestamp(self):\\n return (self.data_type() & 0x100 == 0x100) and (self.raw_data_length() >= 8)\",\n \"def _checkData(data: Sequence[HistoryElement]):\\r\\n if not all(x.timeStamp for x in data):\\r\\n raise ValueError(\\\"At least one element in data doesn't have a TimeStamp\\\")\",\n \"def could_be_datetime(val, fmt):\\n\\n if val == None or fmt == None:\\n return False\\n\\n if isinstance(val, datetime):\\n return True\\n\\n if isinstance(val, (str, unicode)):\\n if Record.is_empty_str(val) or Record.is_empty_str(fmt):\\n return False\\n\\n try:\\n d = datetime.strptime(val, fmt)\\n if not isinstance(d, datetime):\\n raise ValueError\\n else:\\n return True\\n except Exception as e:\\n logging.error(e)\\n return False\\n\\n #otherwise\\n return False\",\n \"def test_14_digit_datetime_detection(self):\\n obj = awstats_reader.awstats_datetime('20091130165230')\\n self.assertTrue(isinstance(obj, awstats_reader.AwstatsDateTime))\",\n \"def _is_key_value_array(self, data):\\n for d in data:\\n if not self._is_key_value(d):\\n return False\\n return True\",\n \"def __eq__(self, t):\\n if not isinstance(t, DateTime):\\n return False\\n return (self._micros, self._tz) == (t._micros, t._tz)\",\n \"def is_date(dt):\\n return isinstance(dt, datetime.date) and not isinstance(dt, datetime.datetime)\",\n \"def has_data(self, fit_id, species_id, start=None, stop=None):\\n if not (fit_id in self.raw_results and species_id in\\\\\\n self.raw_results[fit_id]):\\n return False\\n if all([isinstance(x, datetime) for x in [start, stop]]):\\n ts = self.raw_results[fit_id][\\\"start\\\"]\\n if not any([start < x < stop for x in ts]):\\n return False\\n return True\",\n \"def _is_tc_entity_array(self, data):\\n for d in data:\\n if not self._is_tc_entity(d):\\n return False\\n return True\",\n \"def _check_dates_tareas(self, cr, uid, ids, context=None):\\n for leave in self.read(cr, uid, ids, ['date_start_tarea', 'date_end_tarea'], context=context):\\n if leave['date_start_tarea'] and leave['date_end_tarea']:\\n if leave['date_start_tarea'] > leave['date_end_tarea']:\\n return False\\n return True\",\n \"def check_consistency(object) -> bool:\\n time = np.array(list(object.keys()))\\n time_diff = time[1:] - time[0:-1]\\n return np.all(time_diff == 1)\",\n \"def __len__(self):\\n return len(self.dates)\",\n \"def table_has_any_timestamp_fields(table_object) -> bool:\\n mapper = sqlalchemy.inspect(table_object)\\n for column in mapper.all_orm_descriptors:\\n try:\\n if isinstance(column.type, PSQL_TIMESTAMP) or isinstance(column.type, SQLITE_TIMESTAMP):\\n return True\\n except Exception:\\n pass\\n return False\",\n \"def check_dt_consistency(date_dt):\\n\\n # https://en.wikipedia.org/wiki/Tz_database\\n # https://www.iana.org/time-zones\\n \\n if date_dt.tzinfo is None:\\n return True\\n else:\\n \\n # This check is quite heavy but there is apparently no other way to do it.\\n if date_dt.utcoffset() != dt_from_s(s_from_dt(date_dt), tz=date_dt.tzinfo).utcoffset():\\n return False\\n else:\\n return True\",\n \"def is_time(self) -> bool:\\n return self.times > 1\",\n \"def is_noncomplex(obj):\\n if type(obj) is time.struct_time:\\n return True\\n return False\",\n \"def on_disk(self):\\n return isinstance(self._subarray, FileArray)\",\n \"def NeedsArray(self, type_):\\n return self._NameComponents(type_) in self._array_types\",\n \"def is_datetime(s: Union[str, int, float]):\\n if is_number(s):\\n return False\\n\\n try:\\n parse_datetime(s)\\n return True\\n except Exception:\\n return False\",\n \"def _checkDT(self):\\r\\n dt = np.diff(self.tsec)\\r\\n \\r\\n dt_unique = np.unique(dt)\\r\\n \\r\\n if np.size(dt_unique) == 1:\\r\\n self.isequal = True\\r\\n else:\\r\\n self.isequal = False\\r\\n \\r\\n try:\\r\\n self.dt = dt[1]\\r\\n except:\\r\\n self.dt = 0.0\",\n \"def _dateisrange(value):\\n if len(re.findall('([0-9])/([0-9])', value)) > 1:\\n NotImplemented\\n elif len(re.findall('([0-9])/([0-9])', value)) == 1:\\n return True\\n else:\\n return False\",\n \"def is_temporal(self, e):\\n if e in self.temporal:\\n return True\",\n \"def is_interval(self):\\n return len(self.interval_list) > 0\",\n \"def _is_DataArrays(data):\\n if isinstance(data, (Dataset, DataArray)):\\n return True\\n if isinstance(data, Mapping):\\n for da in data.values():\\n if not isinstance(da, DataArray):\\n raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n if isinstance(data, Iterable):\\n for da in data:\\n if not isinstance(da, DataArray):\\n return False\\n # raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n return False\",\n \"def test_date_obj_within_t_delta(self):\\n max_hour_count = (None, None, None)\\n self.deque.append((self.datetime_obj, self.timestamp))\\n result = feature_5(self.deque,\\n self.heap,\\n self.expected_dict,\\n self.top_n,\\n max_hour_count,\\n self.time_rollover_queue)\\n self.assertEqual(len(self.deque), 2)\\n self.assertEqual(self.deque[-1], (self.datetime_obj, self.timestamp))\\n self.assertEqual(result, max_hour_count)\",\n \"def is_array(self):\\n return False\",\n \"def _test_df_datetime(self, df):\\n date_raw = df['DateListed'].iloc[0] # e.g. '2016-01-07 00:00:00'\\n first_date_time = datetime.strptime(date_raw, \\\"%Y-%m-%d %H:%M:%S\\\")\\n assert first_date_time >= datetime(2016, 1, 1, 0, 0) and \\\\\\n first_date_time < datetime(2017, 1, 1, 0, 0)\",\n \"def isscalar(array):\\n arr = ma.array(array)\\n if not hasattr(arr, '__len__') or arr.shape == () or len(arr) == 1:\\n return True\\n return False\",\n \"def _has_unicode_fields(array):\\n dtypes = (d[0] for d in array.dtype.fields.values())\\n return any(d.kind == \\\"U\\\" for d in dtypes)\",\n \"def valid_datetime(dt):\\n if isinstance(dt.tzinfo, tzinfo) and not datetime_ambiguous(dt):\\n return True\\n return False\",\n \"def check_daterange(sdate, edate, st_dict):\\n \\n reflist = pd.date_range(start=sdate, end=edate, freq='MS').to_list()\\n checklist = [] \\n for y in st_dict['years']:\\n for m in st_dict[y]['months']:\\n checklist.append(pd.to_datetime(y + '-' + m))\\n \\n if list(set(checklist) & set(reflist)):\\n return True\",\n \"def is_array(self):\\n return len(self.descriptor) > 1\",\n \"def is_dateline(vertices):\\n vertices = np.asarray(vertices, dtype=\\\"d\\\")\\n longitudes = vertices[:, 0]\\n return np.abs(longitudes.min(axis=0) - longitudes.max(axis=0)) > 180\",\n \"def __is_initialized(self):\\n if self.start_datetime is None or self.end_datetime is None:\\n raise Exception(\\\"TimeSeriesDataFrame Object is not initialized!\\\")\\n\\n return True\",\n \"def test_list_of_datetimes(self):\\n plot_index = pd.date_range(start=\\\"2000-1-1\\\", freq=\\\"D\\\", periods=10000)\\n freq = 'M'\\n\\n dates = pd.Series(1, index=plot_index).resample(freq).index\\n tl = formatter.TimestampLocator(plot_index, xticks=dates)\\n test = tl._process(3, 900)\\n\\n tl = formatter.TimestampLocator(plot_index, freq=freq)\\n correct = tl._process(3, 900)\\n tm.assert_almost_equal(test, correct)\\n\\n freq = 'MS'\\n dates = pd.Series(1, index=plot_index).resample(freq).index\\n tl = formatter.TimestampLocator(plot_index, xticks=dates)\\n test = tl._process(3, 900)\\n\\n tl = formatter.TimestampLocator(plot_index, freq=freq)\\n correct = tl._process(3, 900)\\n tm.assert_almost_equal(test, correct)\\n\\n # straight list of dates\\n freq = 'MS'\\n dates = pd.Series(1, index=plot_index).resample(freq).index\\n dates = list(dates)\\n tl = formatter.TimestampLocator(plot_index, xticks=dates)\\n test = tl._process(3, 900)\\n\\n tl = formatter.TimestampLocator(plot_index, freq=freq)\\n correct = tl._process(3, 900)\\n tm.assert_almost_equal(test, correct)\",\n \"def is_multiobjects(x: Any) -> bool:\\r\\n return (is_listlike(x) or (isinstance(x, np.ndarray)\\r\\n and x.dtype == \\\"O\\\")) and len(x) > 0 and not is_scalar(x[0])\",\n \"def has_value(cls, value):\\n return bool(isinstance(value, numbers.Number) or isinstance(value, time) or \\\\\\n isinstance(value, datetime) or value)\",\n \"def test_dates(self):\\n result = export.processExport(houseId=1,\\n startDate = datetime.datetime(2013, 01, 06) #5 Days\\n )\\n\\n self.assertEqual(result.shape, (1440, 2))\\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 06))\\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 10, 23, 55))\\n\\n\\n #Stop at 00:00 on the 5th\\n result = export.processExport(houseId=1,\\n endDate = datetime.datetime(2013, 01, 05, 23, 55) #5 Days\\n )\\n\\n self.assertEqual(result.shape, (1440, 2))\\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 01))\\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 05, 23, 55))\",\n \"def validateTiming(obj):\\n if obj.start_time:\\n if obj.end_time:\\n return obj.start_time <= timezone.now() and obj.end_time > timezone.now()\\n else:\\n return obj.start_time <= timezone.now()\\n else:\\n if obj.end_time:\\n return obj.end_time > timezone.now()\\n else:\\n return True\",\n \"def is_interpretable(self):\\n return bool(self.as_date() or self.as_time())\",\n \"def validDateTime( dateTime ):\\n try:\\n datetime.strptime( dateTime, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\" )\\n return True\\n except ValueError:\\n return False\",\n \"def test_paid_at(self):\\n\\n self.assertIsInstance(self.obj.paid_at, datetime)\",\n \"def validate(self, test_data):\\n if not isinstance(test_data, datetime.datetime):\\n raise ValidationError('Invalid type/value.', 'datetime.datetime',\\n type(test_data))\",\n \"def datetime_has_tz(dt):\\n\\n if type(dt) != datetime.datetime:\\n raise TypeError(f\\\"dt must be type datetime.datetime, not {type(dt)}\\\")\\n\\n return dt.tzinfo is not None and dt.tzinfo.utcoffset(dt) is not None\",\n \"def is_arraylike(obj):\\n if isinstance(obj, list):\\n return True\\n elif isinstance(obj, np.ndarray):\\n return True\\n elif isinstance(obj, pd.Series):\\n return True\\n elif isinstance(obj, pd.DataFrame):\\n return True\\n return False\",\n \"def test_8_digit_date_detection(self):\\n obj = awstats_reader.awstats_datetime('20091130')\\n self.assertTrue(isinstance(obj, awstats_reader.AwstatsDate))\",\n \"def is_pandas_collection(l):\\n if not (hasattr(l, '__iter__') and len(l) > 0):\\n # make sure it's iterable\\n # don't include empty iterables because\\n # that would include some sklearn estimator objects\\n return False\\n\\n if isinstance(l, dict):\\n l = l.values()\\n\\n for i in l:\\n if not (isinstance(i, pd.DataFrame) or isinstance(i, pd.Series)):\\n return False\\n\\n return True\",\n \"def test_hk_getdata_timeline_array_type(self):\\n _, timelines = load_data(self._file)\\n assert isinstance(timelines['group0']['t'], np.ndarray)\",\n \"def is_rejoinee(self):\\n return len(self._start_date) > 1\",\n \"def in_cached_file(self):\\n return isinstance(self._subarray, CachedArray)\",\n \"def test_empty_dates(self):\\n\\n for func in self.main_functions:\\n self.assertFalse(func(20190120, 20130201).size)\",\n \"def is_array(a):\\n try:\\n shape = a.shape\\n return len(shape) >= 1\\n except AttributeError:\\n return False\",\n \"def test_created_at(self):\\n self.assertIsInstance(self.obj.created_at, datetime)\",\n \"def test_created_at(self):\\n self.assertIsInstance(self.obj.created_at, datetime)\",\n \"def test_created_at(self):\\n self.assertIsInstance(self.obj.created_at, datetime)\",\n \"def __len__(self):\\n return len(self._timeseriesData)\",\n \"def test_update_at(self):\\n self.assertIsInstance(self.obj.update_at, datetime)\",\n \"def test_update_at(self):\\n self.assertIsInstance(self.obj.update_at, datetime)\",\n \"def test_update_at(self):\\n self.assertIsInstance(self.obj.update_at, datetime)\",\n \"def isscalar(x):\\n arrayed_x = asarray(x)\\n return asarray(x).ndim == 0 and arrayed_x.dtype != 'object'\",\n \"def is_dataset_in_t_range(dataset, t_min, t_max):\\n for obj in dataset.object_names:\\n obj_data = dataset.data[obj]\\n t_min_obj = np.min(obj_data['mjd'])\\n t_max_obj = np.max(obj_data['mjd'])\\n if (t_min_obj < t_min) or (t_max_obj > t_max):\\n return False\\n return True\",\n \"def valid_time_range(self, range_indexes):\\n ts_id = self.get_time_series().id\\n if self.df_class.loc[range_indexes, ts_id].eq(True).all():\\n logging.warning(msg_injection_all_anomalies())\\n return False\\n else:\\n return True\",\n \"def __isub__(self, *args, **kwargs):\\n return _uhd_swig.time_spec_t___isub__(self, *args, **kwargs)\",\n \"def test_dict_keys_datetime_passthrough(self):\\n assert (\\n orjson.dumps(\\n {datetime.datetime(2000, 1, 1, 2, 3, 4, 123): True},\\n option=orjson.OPT_NON_STR_KEYS | orjson.OPT_PASSTHROUGH_DATETIME,\\n )\\n == b'{\\\"2000-01-01T02:03:04.000123\\\":true}'\\n )\",\n \"def is_timestamp(schema_obj):\\n\\n if isinstance(schema_obj, schema.Field):\\n return is_timestamp(schema_obj.metadata)\\n elif isinstance(schema_obj, schema.Struct):\\n return schema_obj.full_name == 'google.protobuf.Timestamp'\\n return False\",\n \"def is_array(self, arr):\\n return isinstance(arr, np.ndarray)\",\n \"def test_created_at(self):\\n self.assertIsInstance(self.certificate_history.datetime, datetime.datetime)\",\n \"def is_array_type(self, objtype):\\n return isinstance(objtype, self.__arrayt) # _ctypes.PyCArrayType\",\n \"def valid_visa(visa):\\n if dates_difference(visa[\\\"date\\\"]) < 730:\\n return True\\n return False\",\n \"def is_interval(self):\\n return True\",\n \"def test_TimeArray_repr():\",\n \"def toc(self,timestamp):\\n return self._timestamp > timestamp\",\n \"def _check_dates_tarea_start(self, cr, uid, ids, context=None):\\n for leave in self.read(cr, uid, ids, ['date_start_tarea', 'date_start_proyecto'], context=context):\\n if leave['date_start_tarea'] and leave['date_start_proyecto']:\\n if leave['date_start_tarea'] < leave['date_start_proyecto']:\\n return False\\n return True\",\n \"def is_categoric(series: List) -> bool:\\n if series.dtype == str or series.dtype == np.object:\\n try:\\n if (\\n int(re.split(r\\\"[^\\\\w\\\\s]\\\", series[0])[0]) >= 1900\\n and len(re.split(r\\\"[^\\\\w\\\\s]\\\", series[0])) >= 3\\n ):\\n return False\\n else:\\n return True\\n except:\\n if (\\n len(series.unique()) / len(series) == 1\\n or \\\"id\\\" in series.name.lower()\\n ):\\n return False\\n elif (\\n True in series.unique().tolist()\\n and False in series.unique().tolist()\\n ):\\n return False\\n elif (\\n \\\"True\\\" in series.unique().tolist()\\n and \\\"False\\\" in series.unique().tolist()\\n ):\\n return False\\n else:\\n return True\\n else:\\n return False\",\n \"def __isZeroEverywhere(self, array):\\n epsilon = numpy.finfo( type(array[0]) ).eps\\n boolList = numpy.less_equal(numpy.abs(array), epsilon)\\n\\n for b in boolList:\\n if not b:\\n return False\\n return True\",\n \"def _validate_dict_entry(self, dict_entry):\\r\\n try:\\r\\n # Type-check all of the type-critical items.\\r\\n if (\\r\\n type(dict_entry[\\\"id\\\"]) == int and\\r\\n type(dict_entry[\\\"date\\\"]) == datetime.date and\\r\\n type(dict_entry[\\\"time\\\"]) == datetime.time and\\r\\n type(dict_entry[\\\"datetime\\\"]) == datetime.datetime and\\r\\n type(dict_entry[\\\"duration\\\"]) == datetime.timedelta):\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n # end if\\r\\n except Exception as err:\\r\\n _z_exc(\\\"logentry.py/_validate_dict_entry\\\", err)\\r\\n # end try\\r\",\n \"def one_day(self):\\n return self.end.date() == self.date\",\n \"def valid_format(self):\\n\\n # If candidate is None, return true\\n if not self.dt:\\n print \\\"dt empty\\\"\\n return True\\n\\n # Verify if time format is ok and stores in into a time-tuple format\\n try:\\n stime = datetime.strptime(self.dt, \\\"%Y-%m-%d %H:%M:%S\\\")\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def is_data(i):\\n keys = ['_id', '_time']\\n return all(i != k for k in keys)\",\n \"def graph_has_temporal(g):\\n return any(any(edge.get(p) == 'time' for p in {'argmax', 'argmin', 'type'}) or 'num' in edge for edge in g.get('edgeSet', []))\",\n \"def check_date(created_at, start, end):\\n x = get_date(created_at)\\n return x <= end and x >= start\",\n \"def is_array(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_array)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72152364","0.68011504","0.67946845","0.6540459","0.6300369","0.62526584","0.6159265","0.612599","0.612436","0.60484034","0.60277694","0.6001856","0.5956571","0.5938945","0.58849597","0.58664906","0.58624506","0.5751764","0.56482595","0.56456316","0.5640433","0.5638849","0.5635289","0.55743665","0.55646634","0.5558836","0.55414283","0.5526302","0.5519423","0.55170965","0.54992944","0.5493077","0.5477467","0.54586625","0.5452428","0.5445292","0.54435813","0.54297835","0.5429741","0.5426795","0.5426606","0.5404101","0.53979665","0.5396521","0.53841734","0.53830475","0.53734213","0.5367579","0.5356255","0.5337177","0.53363216","0.53201467","0.53149575","0.5311156","0.53082126","0.530581","0.5278697","0.52702135","0.5264012","0.524948","0.5249242","0.5245368","0.5243252","0.52327543","0.52320415","0.5206431","0.52006215","0.5182226","0.5177959","0.5167405","0.5167405","0.5167405","0.5166753","0.5162158","0.5162158","0.5162158","0.5156749","0.5152084","0.51493293","0.51463586","0.51457274","0.5144246","0.5137541","0.5130838","0.512798","0.51277894","0.5126735","0.51212716","0.51109326","0.51072645","0.51022786","0.5096927","0.5096412","0.5095657","0.50938547","0.5085799","0.50855654","0.5081769","0.50763565","0.5069899"],"string":"[\n \"0.72152364\",\n \"0.68011504\",\n \"0.67946845\",\n \"0.6540459\",\n \"0.6300369\",\n \"0.62526584\",\n \"0.6159265\",\n \"0.612599\",\n \"0.612436\",\n \"0.60484034\",\n \"0.60277694\",\n \"0.6001856\",\n \"0.5956571\",\n \"0.5938945\",\n \"0.58849597\",\n \"0.58664906\",\n \"0.58624506\",\n \"0.5751764\",\n \"0.56482595\",\n \"0.56456316\",\n \"0.5640433\",\n \"0.5638849\",\n \"0.5635289\",\n \"0.55743665\",\n \"0.55646634\",\n \"0.5558836\",\n \"0.55414283\",\n \"0.5526302\",\n \"0.5519423\",\n \"0.55170965\",\n \"0.54992944\",\n \"0.5493077\",\n \"0.5477467\",\n \"0.54586625\",\n \"0.5452428\",\n \"0.5445292\",\n \"0.54435813\",\n \"0.54297835\",\n \"0.5429741\",\n \"0.5426795\",\n \"0.5426606\",\n \"0.5404101\",\n \"0.53979665\",\n \"0.5396521\",\n \"0.53841734\",\n \"0.53830475\",\n \"0.53734213\",\n \"0.5367579\",\n \"0.5356255\",\n \"0.5337177\",\n \"0.53363216\",\n \"0.53201467\",\n \"0.53149575\",\n \"0.5311156\",\n \"0.53082126\",\n \"0.530581\",\n \"0.5278697\",\n \"0.52702135\",\n \"0.5264012\",\n \"0.524948\",\n \"0.5249242\",\n \"0.5245368\",\n \"0.5243252\",\n \"0.52327543\",\n \"0.52320415\",\n \"0.5206431\",\n \"0.52006215\",\n \"0.5182226\",\n \"0.5177959\",\n \"0.5167405\",\n \"0.5167405\",\n \"0.5167405\",\n \"0.5166753\",\n \"0.5162158\",\n \"0.5162158\",\n \"0.5162158\",\n \"0.5156749\",\n \"0.5152084\",\n \"0.51493293\",\n \"0.51463586\",\n \"0.51457274\",\n \"0.5144246\",\n \"0.5137541\",\n \"0.5130838\",\n \"0.512798\",\n \"0.51277894\",\n \"0.5126735\",\n \"0.51212716\",\n \"0.51109326\",\n \"0.51072645\",\n \"0.51022786\",\n \"0.5096927\",\n \"0.5096412\",\n \"0.5095657\",\n \"0.50938547\",\n \"0.5085799\",\n \"0.50855654\",\n \"0.5081769\",\n \"0.50763565\",\n \"0.5069899\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71551335"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2694,"cells":{"query":{"kind":"string","value":"Close the file containing the subarray, if there is one."},"document":{"kind":"string","value":"def file_close(self):\n if self.on_disk:\n self._subarray.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def close_file(self):\n self.root_group.close()","def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n # than 3 references to it then it is not unique.\n if getrefcount is not None:\n self._decrement_file_counter()\n if subarray is None or getrefcount(subarray) > 3:\n return\n else:\n # getrefcount has itself been deleted or is in the process\n # of being torn down\n return\n\n _partition_file = getattr(subarray, \"_partition_file\", None)\n if _partition_file is not None:\n # This partition contains a temporary file which is not\n # referenced by any other partition on this process, so if\n # there are no lock files present remove the file from\n # disk.\n _remove_temporary_files(_partition_file)\n\n else:\n try:\n if FileArray is not None and isinstance(subarray, FileArray):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if self.file_counter.get(filename, 999) <= 0:\n # This partition contains a non-temporary file\n # which is not referenced by any other\n # partitions, so close the file.\n subarray.close()\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass\n # --- End: if","def close(self):\n return self.close_array","def close_raster_file(self):\n try:\n if self.dataset:\n del self.dataset\n self.dataset = None\n except AttributeError:\n pass","def close_datafile(fs):\r\n fs.close() # fs is the output from init_datafile\r","def close_file(self):\r\n self.file.close()","def close(self):\n self.fileobj.close()","def close(self):\n self.fileobj.close()","def close(self):\n\t\tself.filep.close()","def close(self):\n if self.current_file_number is not None:\n self.fh_raw.close()\n self.current_file_number = None","def release(self, path, fh, *args, **pargs):\n with(self.rwlock):\n # If we're closing a FLACCue file...\n if(path in self._open_subtracks):\n # Delete the file handle from the stored list.\n del self._open_subtracks[path]['Positions'][fh]\n # Close the OS reference to the file.\n return os.close(fh)","def close(self):\n self.fout.close()","def close_file(self, data_set):\n if hasattr(data_set, '_h5_base_group'):\n data_set._h5_base_group.close()\n # Removes reference to closed file\n del data_set._h5_base_group\n else:\n logging.warning(\n 'Cannot close file, data_set has no open hdf5 file')","def _close( self ):\n for raster in self._raster_data:\n if raster != []:\n raster.close()","def close_file(self):\n self.file.close()","def close_file(self):\n self.file.close()","def Close(self):\n super(CPIOArchiveFile, self).Close()\n self._file_entries = None","def test_fileobj_not_closed(self):\n\n f = open(self.data(\"test0.fits\"), \"rb\")\n _ = fits.getdata(f)\n assert not f.closed\n\n f.seek(0)\n _ = fits.getheader(f)\n assert not f.closed\n\n f.close() # Close it now","def _close(self):\n self.fh.close()","def close_csv_file(self):\n if self.file is not None:\n self.file.close()","def close(self):\n self.file.close()","def close(self):\n self.file.close()","def close(self):\n if self.mode == \"w\":\n # Write the content index\n self.cnt.write(self.file)\n\n self.file.close()","def close_file(file):\n file.close()","def close(self):\n self.__file.close()","def close_file_serializer(self):\n if self.file_writer:\n self.file_writer.close()\n self._output_file = None","def close(self):\n self.file_out.close()","def closeJson(f):\n f.write(']')\n f.close()","def close(self):\n if not self.file.closed:\n self.file.close()","def close(self):\n self.__file_object.close()","def _close( self ):\n for sji in self._sji_data:\n sji.close()","def close(cls, file):\n file.close()","def close(self):\n self.f.close()","def close(self):\n self.f.close()","def close_subblock(self, lines: Tuple[int, int]) -> None:","def close(self):\r\n self._fp.close()","def close(self):\n self._file.close()","def close(self):\n self._file.close()","def close(self):\n if self.file is not None:\n self.file.close()\n self.file = None","def close_file_deserializer(self):\n if self._input_file:\n self._input_file.close()\n self._input_file = None","def close(self):\n\t\tif self.is_open:\n\t\t\tself.hdf5file.close()\n\t\t\tself.is_open = False","def close(self):\n self.out_file.close()","def close_hdf_file(self):\n\t\tself.h5f.close()","def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass","def close(self):\n if self._open:\n self.grp.file.close()\n self._open = False","def closeButton(self):\n #Close button\n # if there is a shuffle, reorder the samples for the files\n if self.shuffle:\n self.matrix = [self.matrix[i] for i in self.list_numbers]\n \n for i in range(0,len(self.matrix)):\n self.f.write(str(self.matrix[i][0])+\" \"+str(self.matrix[i][1])+\" \"+str(self.matrix[i][2])+\"\\n\")\n self.f.close()\n self.v.quit()","def close(self):\n if self.record_mode:\n self.fout.close()\n self.record_mode = False","def close(self):\n if self.record_mode:\n self.fout.close()\n self.record_mode = False","def test_fileobj_not_closed(self):\n\n f = open(self.data('test0.fits'), 'rb')\n data = fits.getdata(f)\n assert not f.closed\n\n f.seek(0)\n header = fits.getheader(f)\n assert not f.closed","def test_close(self):\r\n\r\n b = BufferedWriter(self.test_fp, buf_size=2)\r\n b.write(\"1\")\r\n b.close()\r\n content = open(self.test_fp, \"r\").readlines()\r\n self.assertEquals(content, [\"1\"])","def close(self) -> None:\n self.f.close()","def close_file_handle(self):\n if self.file_handle and self.output_file:\n self.file_handle.close()","def close(self):\n self.file.close()\n self.file = None","def __del__(self):\n self.file.close()","def close(self):\n self._fp.close()","def close(self):\r\n if self._filename and self._fh:\r\n self._fh.close()\r\n self._fh = None","def close(self):\n self.data.close()","def write_file(self):\n print 'Writing '+self.name+' binary...'\n if self.vals is not None:\n if len(self.vals) == self.size:\n stream = self.pack_mem()\n with open(self.name+'.bin','wb') as f:\n f.write(stream)\n print 'File written: '+self.name+'.bin'\n else:\n print 'Error: input array for '+self.name+'is not the right '+\\\n 'size (should be '+str(self.size)+'). Skipping.'\n else:\n print 'No array provided, skipping.'","def close(self, verify_file=True):\n if self.fp:\n do_close(self.fp)\n self.fp = None","def Close(self): \n posix.close(self.File)","def close(self):\n file = self.file\n self.file = None\n self.filename = None\n self.current_line = None\n file.close()","def close(self):\n self.ix.close()","def close(self):\n if callable(getattr(self._file, 'close', None)):\n self._iterator.close()\n self._iterator = None\n self._unconsumed = None\n self.closed = True","def close(self):\n if hasattr(self, '_FITS'):\n if self._FITS is not None:\n self._FITS.close()\n self._FITS = None\n self._filename = None\n self.mode = None\n self.charmode = None\n self.intmode = None\n self.hdu_list = None\n self.hdu_map = None","def endfile(self) :\n\n\t\tself.fd.close()\n\t\treturn 1","def close():","def close_file(self, mds_fh):\n seqid=0 #FIXME: seqid must be !=0\n fh, stateid = self.filehandles[mds_fh]\n ops = [op.putfh(fh)] + [op.close(seqid, stateid)]\n res = self.execute(ops)\n # ignoring return\n del self.filehandles[mds_fh]","def close(self):\n if self.__mode == 'w':\n self.__buff.insert(0, self.__header())\n self.__buff.append(';\\nEND;\\n')\n self.__fobj.write(''.join(self.__buff))\n return self.__fobj.close()","def close(self):\n self._outfile.write(struct.pack('>2h', 4, 0x0400))\n if self._close:\n self._outfile.close()","def close(self):\n self._outfile.write(struct.pack(\">2H\", 4, 0x0400))\n if self._close:\n self._outfile.close()","def __del__(self):\n self.f.close()","def close(self):\n if not self._f:\n return\n\n logger.info(\"Closed {} ({})\".format(self.name, self.num))\n\n self._f.close()\n self._f = None","def close(self):\n if self.write_buffer is not None:\n self.write_buffer.close()","def close( self ):\r\n self.oodocument.close( 1 )","def close(self):\r\n self._report_file.close()\r\n # Make sure everything's closed.\r\n for files in self._output_files.values():\r\n for f in files.values():\r\n f.close()","def close(self):\n self.flush()\n self.file.close()\n self.file = None","def endFileOutput(self):\n self._outputFile.close()","def __del__(self):\n self.file_out.close()","def close(self):\n self.hdfile.close()","def close(self):\n self.hdfile.close()","def finish(self):\n self._fout.add_size()\n self._fout.add_padding()\n self._fout.close()","def close( self ):\n if not self.sji is None:\n self.sji._close()\n if not self.raster is None:\n self.raster._close()","def _CloseOutputFiles(self):\n self.gfile.close()\n self.efile.close()","def closeJson(self, data):\n with open(self.file, 'w') as f:\n json.dump(data, f)","def close(self):\n try:\n self._failed = True\n if self._file is not None:\n self._file.close()\n self._failed = False\n finally:\n self._file = None","def close( self ):\n \n for file in self._files:\n ir.file_hub.close( file )","def close(self, _file):\n if isinstance(file, _file):\n _file.close()\n else:\n raise TypeError(\n \"Excepting a FileType not %s \" % (type(_file))\n )","def close(self, close_file=True):\n if self.__ref is not None:\n self.__lib.flush()\n if close_file == True:\n self.__ref.close()\n self.__ref = None\n self.__lib = None","def save(file, arr, allow_pickle=True, fix_imports=True):\n\n return numpy.save(file, array_create.array(arr, bohrium=False), allow_pickle, fix_imports)","def close_comprar(self):\n self.comprar.destroy()","def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ):\n try:\n filename = subarray.get_filename()\n except Exception:\n filename = None\n\n if filename is None:\n return\n\n file_counter = self.file_counter\n # count = file_counter.get(filename, 0)\n # file_counter[filename] = count + i\n # if file_counter[filename] <= 0:\n count = file_counter.get(filename, 0) + i\n if count <= 0:\n # Remove the file from the dictionary if its count has\n # dropped to zero\n file_counter.pop(filename, None)\n else:\n file_counter[filename] = count\n except Exception:\n # If we're here then it is likely that FileArray has been\n # torn down, so just do nothing.\n pass","def finalize(self):\n if self._file:\n toLog(\"Closing file `{0}`\".format(self._fname), True)\n self._file.close()\n self._file = None","def quick_save_array(data, file_name, delimiter=',', ):\n data.tofile(file_name, sep=delimiter)","def _write_array_on_file(self, pa_array):\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\n self._num_bytes += pa_array.nbytes\n self.pa_writer.write_batch(pa_batch)","def close(self):\r\n if self._session:\r\n self._session.close()\r\n self._session = None\r\n try:\r\n self._writer.remove_file()\r\n self._reader.remove_file()\r\n except Oct2PyError:\r\n pass","def save_data(self, f): \n if not self.sampling:\n self.convert_to_array()\n np.save(f, self.reads)","def Close(self):\n self._RaiseIfNotWritable()\n\n self._storage_file.Close()\n self._storage_file = None","def __del__(self):\r\n self.filename.close()","def __del__(self):\n if self.file is None:\n return\n try:\n self.file.close()\n del self.file\n self.file = None\n except:\n getLogger(__name__).warning('Error on file close', exc_info=True)","def close(self, **kw) -> None:\n super().close(**kw)\n self._file.close()"],"string":"[\n \"def close_file(self):\\n self.root_group.close()\",\n \"def __del__(self):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n # If the subarray is unique it will have 2 references to\\n # it plus 1 within this method, making 3. If it has more\\n # than 3 references to it then it is not unique.\\n if getrefcount is not None:\\n self._decrement_file_counter()\\n if subarray is None or getrefcount(subarray) > 3:\\n return\\n else:\\n # getrefcount has itself been deleted or is in the process\\n # of being torn down\\n return\\n\\n _partition_file = getattr(subarray, \\\"_partition_file\\\", None)\\n if _partition_file is not None:\\n # This partition contains a temporary file which is not\\n # referenced by any other partition on this process, so if\\n # there are no lock files present remove the file from\\n # disk.\\n _remove_temporary_files(_partition_file)\\n\\n else:\\n try:\\n if FileArray is not None and isinstance(subarray, FileArray):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if self.file_counter.get(filename, 999) <= 0:\\n # This partition contains a non-temporary file\\n # which is not referenced by any other\\n # partitions, so close the file.\\n subarray.close()\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\\n # --- End: if\",\n \"def close(self):\\n return self.close_array\",\n \"def close_raster_file(self):\\n try:\\n if self.dataset:\\n del self.dataset\\n self.dataset = None\\n except AttributeError:\\n pass\",\n \"def close_datafile(fs):\\r\\n fs.close() # fs is the output from init_datafile\\r\",\n \"def close_file(self):\\r\\n self.file.close()\",\n \"def close(self):\\n self.fileobj.close()\",\n \"def close(self):\\n self.fileobj.close()\",\n \"def close(self):\\n\\t\\tself.filep.close()\",\n \"def close(self):\\n if self.current_file_number is not None:\\n self.fh_raw.close()\\n self.current_file_number = None\",\n \"def release(self, path, fh, *args, **pargs):\\n with(self.rwlock):\\n # If we're closing a FLACCue file...\\n if(path in self._open_subtracks):\\n # Delete the file handle from the stored list.\\n del self._open_subtracks[path]['Positions'][fh]\\n # Close the OS reference to the file.\\n return os.close(fh)\",\n \"def close(self):\\n self.fout.close()\",\n \"def close_file(self, data_set):\\n if hasattr(data_set, '_h5_base_group'):\\n data_set._h5_base_group.close()\\n # Removes reference to closed file\\n del data_set._h5_base_group\\n else:\\n logging.warning(\\n 'Cannot close file, data_set has no open hdf5 file')\",\n \"def _close( self ):\\n for raster in self._raster_data:\\n if raster != []:\\n raster.close()\",\n \"def close_file(self):\\n self.file.close()\",\n \"def close_file(self):\\n self.file.close()\",\n \"def Close(self):\\n super(CPIOArchiveFile, self).Close()\\n self._file_entries = None\",\n \"def test_fileobj_not_closed(self):\\n\\n f = open(self.data(\\\"test0.fits\\\"), \\\"rb\\\")\\n _ = fits.getdata(f)\\n assert not f.closed\\n\\n f.seek(0)\\n _ = fits.getheader(f)\\n assert not f.closed\\n\\n f.close() # Close it now\",\n \"def _close(self):\\n self.fh.close()\",\n \"def close_csv_file(self):\\n if self.file is not None:\\n self.file.close()\",\n \"def close(self):\\n self.file.close()\",\n \"def close(self):\\n self.file.close()\",\n \"def close(self):\\n if self.mode == \\\"w\\\":\\n # Write the content index\\n self.cnt.write(self.file)\\n\\n self.file.close()\",\n \"def close_file(file):\\n file.close()\",\n \"def close(self):\\n self.__file.close()\",\n \"def close_file_serializer(self):\\n if self.file_writer:\\n self.file_writer.close()\\n self._output_file = None\",\n \"def close(self):\\n self.file_out.close()\",\n \"def closeJson(f):\\n f.write(']')\\n f.close()\",\n \"def close(self):\\n if not self.file.closed:\\n self.file.close()\",\n \"def close(self):\\n self.__file_object.close()\",\n \"def _close( self ):\\n for sji in self._sji_data:\\n sji.close()\",\n \"def close(cls, file):\\n file.close()\",\n \"def close(self):\\n self.f.close()\",\n \"def close(self):\\n self.f.close()\",\n \"def close_subblock(self, lines: Tuple[int, int]) -> None:\",\n \"def close(self):\\r\\n self._fp.close()\",\n \"def close(self):\\n self._file.close()\",\n \"def close(self):\\n self._file.close()\",\n \"def close(self):\\n if self.file is not None:\\n self.file.close()\\n self.file = None\",\n \"def close_file_deserializer(self):\\n if self._input_file:\\n self._input_file.close()\\n self._input_file = None\",\n \"def close(self):\\n\\t\\tif self.is_open:\\n\\t\\t\\tself.hdf5file.close()\\n\\t\\t\\tself.is_open = False\",\n \"def close(self):\\n self.out_file.close()\",\n \"def close_hdf_file(self):\\n\\t\\tself.h5f.close()\",\n \"def close(self, **kwargs):\\n config = getattr(self, \\\"config\\\", None)\\n\\n if config is None:\\n return\\n\\n if kwargs:\\n config.update(kwargs)\\n\\n original = getattr(self, \\\"_original\\\", None)\\n logger.partitioning(\\\"Partition.close: original = {}\\\".format(original))\\n\\n if not original:\\n originally_on_disk = False\\n original_subarray = None\\n else:\\n originally_on_disk = not original.in_memory\\n original_subarray = original._subarray\\n\\n config = self.config\\n logger.partitioning(\\\" config = {}\\\".format(config))\\n\\n if config[\\\"serial\\\"]:\\n # --------------------------------------------------------\\n # SERIAL\\n # --------------------------------------------------------\\n logger.partitioning(\\\" serial\\\")\\n\\n if config[\\\"readonly\\\"]:\\n logger.partitioning(\\\" readonly=True\\\")\\n\\n if originally_on_disk:\\n logger.partitioning(\\\" subarray originally on disk\\\")\\n\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.1.1 The original subarray was on disk,\\n # we don't want to keep the current\\n # subarray in memory, and we are happy\\n # to discard any changes that may have\\n # been made to the subarray.\\n logger.partitioning(\\\" 1.1.1.1 revert\\\")\\n self.revert()\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.1.2 The original subarray was on disk,\\n # we are happy to keep the current\\n # subarray in memory, but there is not\\n # enough free memory to do so.\\n logger.partitioning(\\n \\\" 1.1.1.2 revert ({} <= {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n self.revert()\\n else:\\n # 1.1.1.3 The original subarray was on disk\\n # and there is enough memory to keep\\n # the current subarray in memory\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # The original subarray was a temporary\\n # file which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n del self.masked\\n logger.partitioning(\\n \\\" 1.1.1.3 del masked ({} > {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n\\n else:\\n logger.partitioning(\\\" subarray originally in memory\\\")\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.2.1 Original subarray was in memory and\\n # we don't want to keep the current\\n # subarray in memory\\n logger.partitioning(\\\" 1.1.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.2.2 Original subarray was in memory and\\n # unique but there is not enough\\n # memory to keep the current subarray\\n logger.partitioning(\\\" 1.1.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.1.2.3 Original subarray was in memory and\\n # unique and there is enough memory to\\n # keep the current subarray in memory\\n logger.partitioning(\\\" 1.1.2.3 pass\\\")\\n pass\\n else:\\n # config['readonly'] is False\\n if originally_on_disk:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.1.1 Original subarray was on disk and\\n # there and we don't want to keep the\\n # array\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.1.2 Original subarray was on disk but\\n # there is not enough memory to keep\\n # it\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.1.3 Original subarray was on disk and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.1.3 pass\\\")\\n del self.masked\\n else:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.2.1 Original subarray was in memory but\\n # we don't want to keep it\\n logger.partitioning(\\\" 1.2.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.2.2 Original subarray was an in memory\\n # but there is not enough memory to\\n # keep it\\n logger.partitioning(\\\" 1.2.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.2.3 Original subarray was in memory and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.2.3 del masked\\\")\\n del self.masked\\n else:\\n logger.partitioning(\\\"Partition.close: parallel\\\")\\n # --------------------------------------------------------\\n # PARALLEL\\n # --------------------------------------------------------\\n pass\\n\\n # if hasattr(self, '_original'):\\n # del self._original\\n\\n # print(hasattr(self, 'config')),\\n try:\\n del self.config\\n except AttributeError:\\n pass\",\n \"def close(self):\\n if self._open:\\n self.grp.file.close()\\n self._open = False\",\n \"def closeButton(self):\\n #Close button\\n # if there is a shuffle, reorder the samples for the files\\n if self.shuffle:\\n self.matrix = [self.matrix[i] for i in self.list_numbers]\\n \\n for i in range(0,len(self.matrix)):\\n self.f.write(str(self.matrix[i][0])+\\\" \\\"+str(self.matrix[i][1])+\\\" \\\"+str(self.matrix[i][2])+\\\"\\\\n\\\")\\n self.f.close()\\n self.v.quit()\",\n \"def close(self):\\n if self.record_mode:\\n self.fout.close()\\n self.record_mode = False\",\n \"def close(self):\\n if self.record_mode:\\n self.fout.close()\\n self.record_mode = False\",\n \"def test_fileobj_not_closed(self):\\n\\n f = open(self.data('test0.fits'), 'rb')\\n data = fits.getdata(f)\\n assert not f.closed\\n\\n f.seek(0)\\n header = fits.getheader(f)\\n assert not f.closed\",\n \"def test_close(self):\\r\\n\\r\\n b = BufferedWriter(self.test_fp, buf_size=2)\\r\\n b.write(\\\"1\\\")\\r\\n b.close()\\r\\n content = open(self.test_fp, \\\"r\\\").readlines()\\r\\n self.assertEquals(content, [\\\"1\\\"])\",\n \"def close(self) -> None:\\n self.f.close()\",\n \"def close_file_handle(self):\\n if self.file_handle and self.output_file:\\n self.file_handle.close()\",\n \"def close(self):\\n self.file.close()\\n self.file = None\",\n \"def __del__(self):\\n self.file.close()\",\n \"def close(self):\\n self._fp.close()\",\n \"def close(self):\\r\\n if self._filename and self._fh:\\r\\n self._fh.close()\\r\\n self._fh = None\",\n \"def close(self):\\n self.data.close()\",\n \"def write_file(self):\\n print 'Writing '+self.name+' binary...'\\n if self.vals is not None:\\n if len(self.vals) == self.size:\\n stream = self.pack_mem()\\n with open(self.name+'.bin','wb') as f:\\n f.write(stream)\\n print 'File written: '+self.name+'.bin'\\n else:\\n print 'Error: input array for '+self.name+'is not the right '+\\\\\\n 'size (should be '+str(self.size)+'). Skipping.'\\n else:\\n print 'No array provided, skipping.'\",\n \"def close(self, verify_file=True):\\n if self.fp:\\n do_close(self.fp)\\n self.fp = None\",\n \"def Close(self): \\n posix.close(self.File)\",\n \"def close(self):\\n file = self.file\\n self.file = None\\n self.filename = None\\n self.current_line = None\\n file.close()\",\n \"def close(self):\\n self.ix.close()\",\n \"def close(self):\\n if callable(getattr(self._file, 'close', None)):\\n self._iterator.close()\\n self._iterator = None\\n self._unconsumed = None\\n self.closed = True\",\n \"def close(self):\\n if hasattr(self, '_FITS'):\\n if self._FITS is not None:\\n self._FITS.close()\\n self._FITS = None\\n self._filename = None\\n self.mode = None\\n self.charmode = None\\n self.intmode = None\\n self.hdu_list = None\\n self.hdu_map = None\",\n \"def endfile(self) :\\n\\n\\t\\tself.fd.close()\\n\\t\\treturn 1\",\n \"def close():\",\n \"def close_file(self, mds_fh):\\n seqid=0 #FIXME: seqid must be !=0\\n fh, stateid = self.filehandles[mds_fh]\\n ops = [op.putfh(fh)] + [op.close(seqid, stateid)]\\n res = self.execute(ops)\\n # ignoring return\\n del self.filehandles[mds_fh]\",\n \"def close(self):\\n if self.__mode == 'w':\\n self.__buff.insert(0, self.__header())\\n self.__buff.append(';\\\\nEND;\\\\n')\\n self.__fobj.write(''.join(self.__buff))\\n return self.__fobj.close()\",\n \"def close(self):\\n self._outfile.write(struct.pack('>2h', 4, 0x0400))\\n if self._close:\\n self._outfile.close()\",\n \"def close(self):\\n self._outfile.write(struct.pack(\\\">2H\\\", 4, 0x0400))\\n if self._close:\\n self._outfile.close()\",\n \"def __del__(self):\\n self.f.close()\",\n \"def close(self):\\n if not self._f:\\n return\\n\\n logger.info(\\\"Closed {} ({})\\\".format(self.name, self.num))\\n\\n self._f.close()\\n self._f = None\",\n \"def close(self):\\n if self.write_buffer is not None:\\n self.write_buffer.close()\",\n \"def close( self ):\\r\\n self.oodocument.close( 1 )\",\n \"def close(self):\\r\\n self._report_file.close()\\r\\n # Make sure everything's closed.\\r\\n for files in self._output_files.values():\\r\\n for f in files.values():\\r\\n f.close()\",\n \"def close(self):\\n self.flush()\\n self.file.close()\\n self.file = None\",\n \"def endFileOutput(self):\\n self._outputFile.close()\",\n \"def __del__(self):\\n self.file_out.close()\",\n \"def close(self):\\n self.hdfile.close()\",\n \"def close(self):\\n self.hdfile.close()\",\n \"def finish(self):\\n self._fout.add_size()\\n self._fout.add_padding()\\n self._fout.close()\",\n \"def close( self ):\\n if not self.sji is None:\\n self.sji._close()\\n if not self.raster is None:\\n self.raster._close()\",\n \"def _CloseOutputFiles(self):\\n self.gfile.close()\\n self.efile.close()\",\n \"def closeJson(self, data):\\n with open(self.file, 'w') as f:\\n json.dump(data, f)\",\n \"def close(self):\\n try:\\n self._failed = True\\n if self._file is not None:\\n self._file.close()\\n self._failed = False\\n finally:\\n self._file = None\",\n \"def close( self ):\\n \\n for file in self._files:\\n ir.file_hub.close( file )\",\n \"def close(self, _file):\\n if isinstance(file, _file):\\n _file.close()\\n else:\\n raise TypeError(\\n \\\"Excepting a FileType not %s \\\" % (type(_file))\\n )\",\n \"def close(self, close_file=True):\\n if self.__ref is not None:\\n self.__lib.flush()\\n if close_file == True:\\n self.__ref.close()\\n self.__ref = None\\n self.__lib = None\",\n \"def save(file, arr, allow_pickle=True, fix_imports=True):\\n\\n return numpy.save(file, array_create.array(arr, bohrium=False), allow_pickle, fix_imports)\",\n \"def close_comprar(self):\\n self.comprar.destroy()\",\n \"def _add_to_file_counter(self, i):\\n # subarray = getattr(self, '_subarray', None)\\n subarray = self._subarray\\n\\n if subarray is None:\\n return\\n\\n try:\\n if isinstance(subarray, FileArray) and not isinstance(\\n subarray, CachedArray\\n ):\\n try:\\n filename = subarray.get_filename()\\n except Exception:\\n filename = None\\n\\n if filename is None:\\n return\\n\\n file_counter = self.file_counter\\n # count = file_counter.get(filename, 0)\\n # file_counter[filename] = count + i\\n # if file_counter[filename] <= 0:\\n count = file_counter.get(filename, 0) + i\\n if count <= 0:\\n # Remove the file from the dictionary if its count has\\n # dropped to zero\\n file_counter.pop(filename, None)\\n else:\\n file_counter[filename] = count\\n except Exception:\\n # If we're here then it is likely that FileArray has been\\n # torn down, so just do nothing.\\n pass\",\n \"def finalize(self):\\n if self._file:\\n toLog(\\\"Closing file `{0}`\\\".format(self._fname), True)\\n self._file.close()\\n self._file = None\",\n \"def quick_save_array(data, file_name, delimiter=',', ):\\n data.tofile(file_name, sep=delimiter)\",\n \"def _write_array_on_file(self, pa_array):\\n pa_batch = pa.RecordBatch.from_struct_array(pa_array)\\n self._num_bytes += pa_array.nbytes\\n self.pa_writer.write_batch(pa_batch)\",\n \"def close(self):\\r\\n if self._session:\\r\\n self._session.close()\\r\\n self._session = None\\r\\n try:\\r\\n self._writer.remove_file()\\r\\n self._reader.remove_file()\\r\\n except Oct2PyError:\\r\\n pass\",\n \"def save_data(self, f): \\n if not self.sampling:\\n self.convert_to_array()\\n np.save(f, self.reads)\",\n \"def Close(self):\\n self._RaiseIfNotWritable()\\n\\n self._storage_file.Close()\\n self._storage_file = None\",\n \"def __del__(self):\\r\\n self.filename.close()\",\n \"def __del__(self):\\n if self.file is None:\\n return\\n try:\\n self.file.close()\\n del self.file\\n self.file = None\\n except:\\n getLogger(__name__).warning('Error on file close', exc_info=True)\",\n \"def close(self, **kw) -> None:\\n super().close(**kw)\\n self._file.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61085176","0.6024418","0.59545076","0.5824396","0.58042437","0.5736725","0.57022864","0.57022864","0.56994104","0.56970567","0.56860274","0.564539","0.56289333","0.56183493","0.5591367","0.5591367","0.55517685","0.5509945","0.5487693","0.54802656","0.54625976","0.54625976","0.5456624","0.54523706","0.5448531","0.5446381","0.5442482","0.54284096","0.54231614","0.54186165","0.5413946","0.54104435","0.5409187","0.5409187","0.54071975","0.5406899","0.53935415","0.53935415","0.5379382","0.53783435","0.53725004","0.534277","0.53378725","0.53273296","0.5323528","0.5314895","0.52969813","0.52969813","0.5270878","0.5255893","0.52350175","0.52156496","0.5192522","0.5183164","0.5158139","0.5153369","0.5146133","0.51337415","0.5131872","0.5121358","0.51112723","0.51011467","0.5095343","0.5090327","0.5072971","0.5066568","0.5065016","0.50645465","0.50528437","0.5050413","0.5037242","0.502396","0.5004375","0.50040233","0.49919924","0.49884698","0.49803802","0.49771494","0.49765706","0.49765706","0.49632463","0.4956913","0.49437773","0.49353203","0.4934011","0.49181095","0.4915223","0.49125916","0.49105388","0.49062186","0.48947278","0.4894391","0.4892698","0.48923147","0.48912776","0.48895654","0.48867685","0.48850223","0.48696014","0.4866721"],"string":"[\n \"0.61085176\",\n \"0.6024418\",\n \"0.59545076\",\n \"0.5824396\",\n \"0.58042437\",\n \"0.5736725\",\n \"0.57022864\",\n \"0.57022864\",\n \"0.56994104\",\n \"0.56970567\",\n \"0.56860274\",\n \"0.564539\",\n \"0.56289333\",\n \"0.56183493\",\n \"0.5591367\",\n \"0.5591367\",\n \"0.55517685\",\n \"0.5509945\",\n \"0.5487693\",\n \"0.54802656\",\n \"0.54625976\",\n \"0.54625976\",\n \"0.5456624\",\n \"0.54523706\",\n \"0.5448531\",\n \"0.5446381\",\n \"0.5442482\",\n \"0.54284096\",\n \"0.54231614\",\n \"0.54186165\",\n \"0.5413946\",\n \"0.54104435\",\n \"0.5409187\",\n \"0.5409187\",\n \"0.54071975\",\n \"0.5406899\",\n \"0.53935415\",\n \"0.53935415\",\n \"0.5379382\",\n \"0.53783435\",\n \"0.53725004\",\n \"0.534277\",\n \"0.53378725\",\n \"0.53273296\",\n \"0.5323528\",\n \"0.5314895\",\n \"0.52969813\",\n \"0.52969813\",\n \"0.5270878\",\n \"0.5255893\",\n \"0.52350175\",\n \"0.52156496\",\n \"0.5192522\",\n \"0.5183164\",\n \"0.5158139\",\n \"0.5153369\",\n \"0.5146133\",\n \"0.51337415\",\n \"0.5131872\",\n \"0.5121358\",\n \"0.51112723\",\n \"0.51011467\",\n \"0.5095343\",\n \"0.5090327\",\n \"0.5072971\",\n \"0.5066568\",\n \"0.5065016\",\n \"0.50645465\",\n \"0.50528437\",\n \"0.5050413\",\n \"0.5037242\",\n \"0.502396\",\n \"0.5004375\",\n \"0.50040233\",\n \"0.49919924\",\n \"0.49884698\",\n \"0.49803802\",\n \"0.49771494\",\n \"0.49765706\",\n \"0.49765706\",\n \"0.49632463\",\n \"0.4956913\",\n \"0.49437773\",\n \"0.49353203\",\n \"0.4934011\",\n \"0.49181095\",\n \"0.4915223\",\n \"0.49125916\",\n \"0.49105388\",\n \"0.49062186\",\n \"0.48947278\",\n \"0.4894391\",\n \"0.4892698\",\n \"0.48923147\",\n \"0.48912776\",\n \"0.48895654\",\n \"0.48867685\",\n \"0.48850223\",\n \"0.48696014\",\n \"0.4866721\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7828047"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2695,"cells":{"query":{"kind":"string","value":"Inspect the object for debugging."},"document":{"kind":"string","value":"def inspect(self):\n print(cf_inspect(self))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inspect(obj:Any) -> None:\n\t\tLogging._log(Logging.logLevel, obj)","def debug(self):\n raise NotImplementedError","def output_debug_info(self):","def inspect_obj(self, line=None):\n if not line:\n return\n\n # evaluate the line to get a python object\n python_object = self.shell.ev(line)\n\n # Pretty Print/Format the object\n # Print the output, but don't return anything (otherwise, we'd\n # potentially get a wall of color-coded text.\n formatted_dict = pformat(python_object.__dict__)\n print(highlight(formatted_dict, lexer, formatter).strip())\n pprint(python_object)","def debug():","def source_code(obj):\n print(inspect.getsource(obj))","def NeedsDebugInfo(self):\n return True","def print_debug(obj):\n\n if not DEBUG:\n return False\n\n if hasattr(obj, 'lower'):\n # string/unicode... just print it.\n print('Debug: {}'.format(obj))\n elif isinstance(obj, (list, tuple)):\n # list/tuple, format it...\n header = 'Debug: '\n spaces = (' ' * len(header))\n if obj:\n print('{}{}'.format(header, obj[0]))\n if len(obj) > 1:\n otherlines = '\\n{}'.format(spaces).join(obj[1:])\n print('\\n{}{}'.format(spaces, otherlines))\n else:\n # different type of object\n print('Debug: {!r}'.format(obj))\n\n return True","def debug(self):\n return self._debug","def debug(self):\n try:\n super(FaucetTopoTestBase, self).debug()\n except Exception:\n pprint.pprint(self.host_information)\n raise","def print_object_details(obj: object) -> None:\n print_section(obj, 'Type', print_type)\n print_section(obj, 'Documentation', print_documentation)\n print_section(obj, 'Attributes', print_attributes)\n print_section(obj, 'Methods', print_methods)\n print_section_delimiter()","def __repr__(self):\n return repr(self.__dict__['_obj'])","def _inspect(self,meth,oname,**kw):\n \n oname = oname.strip()\n found,obj,ns,ds,ismagic = self._ofind(oname)\n if found:\n pmethod = getattr(self.shell.inspector,meth)\n if meth == 'pdoc':\n formatter = ismagic and self.format_screen or None\n pmethod(obj,oname,formatter)\n elif meth == 'pinfo':\n formatter = ismagic and self.format_screen or None\n pmethod(obj,oname,ns,formatter,ismagic=ismagic,**kw)\n else:\n pmethod(obj,oname)\n else:\n print 'Object `%s` not found.' % oname\n return 'not found' # so callers can take other action","def debug(self, *args, **kwargs):","def NeedsDebugInfo(self):\n return False","def pprint(self):\n # just here for defining the interface; work is done in subclasses\n pass","def DebugInfo( self, request_data ):\n pass","def debug(self):\n print(self.memory)\n print('r0 = %s, ip = %s' % (self.r0, self.ip))","def inspect_object(obj):\n raise TryNext","def dump_objects():\n pass","def debug(self):\n return Debug(self)","def do_inspect(self, arg):\n self._do_inspect(arg, with_source=False)","def debug(self):\n neighbors = len(self.__neighbors)\n string = self.__repr__() + f' neighbors: {self.living_neighbors()}/{neighbors}'\n for neighbor in self.__neighbors:\n string += '\\n ' + neighbor.__repr__()\n print(string)","def print(self):\r\n print(\"[DEBUG] STACK: \", self.__memory.__repr__())","def compute_debug(self):","def debug(self):\n \n #path\n print('Path information:')\n for k, v in self.__path.items():\n print(k, v)\n \n #sample count\n print('Sample statistic of each phase')\n for k, v in self.__phase_sample_count.items():\n print(k, v)\n \n print('Sample statistic of each class')\n for k, v in self.__area_sample_count.items():\n print(k, v)\n \n print('Sample statistic of each train')\n for k, v in self.__train_sample_count.items():\n print(k, v)","def pprint(obj):\n for argname in sorted([x for x in dir(obj) if not x.startswith('__')]):\n # Skip callables\n if hasattr(getattr(obj, argname), '__call__'):\n continue\n print(\"{} : {}\".format(argname, getattr(obj, argname)))","def debug() -> bool:","def debug(self):\n #breakpoint() # infinite loop\n print(self.ttl)","def debug(self) -> bool:\n return self._data[ATTR_DEBUG]","def debug_print(self):\n print self.title\n print self.storyline\n print self.poster_image_url\n print self.trailer_youtube_url\n print \"------\"","def test_debug(self) -> Debug:\n return self._test_debug","def Print(self):\n print(self.__dict__)","def info(self):\n\t\timport inspect\n\t\n\t\tmessage = \"All variables available for star ID %i\" % self.ID\t\t\n\t\tprint message\n\t\tprint '-'*len(message)\n\t\tattributes = inspect.getmembers(self, lambda a:not(inspect.isroutine(a)))\n\t\tfor a in attributes:\n\t\t\tif (a[0].startswith('__') and a[0].endswith('__')): continue\n\t\t\tprint a[0], \"=\", a[1]","def print_verbose(self) -> None:\n print(self)\n if self.meta is not None:\n print(self.meta.__repr__())","def _inspect_mode(self):\n return self._project._inspect_mode","def _print_attribute(self):\n print(vars(self))","def debug_string(self):\n\n raise NotImplementedError","def debug(node):\n print \"%r\" % node","def print_dict(self):\n print(self.__dict__)","def debug(self, *args: Any, **kwargs) -> None:\n ...","def __debug_print__(self):\n print(self.question_data)","def __repr__(self):\n return self.pretty_print(self.__dict__)","def dump(self):\n self.logger.debug(self)","def dump_objects(self):\n #print 'Object Count: ', self.object_store.len()\n \n for item in self.object_store:\n print 'Object Name: ', item.__dict__['Name'], ' LocalID: ', item.__dict__['LocalID']","def debug():\n return exported_res_dict","def __str__(self):\n return stringify(\n Inspect(\n self,\n help=True,\n methods=True,\n private=True,\n dunder=False,\n sort=True,\n all=False,\n ),\n maxlen=-1,\n )","def debug(self) -> str:\n\n return Widget.debug(self)","def show_debug_msg(self) -> None:\n if self.debug_mode:\n for point in self.points:\n print(point.debug_info())","def debug_allowed(request_object):\n \n return utilities.debug_allowed(request_object)","def print_debug(self):\n print()\n print(\"Variable names ({} total):\".format(len(self.variable_names)))\n print()\n for variable in self.variable_names:\n print(variable)\n print()\n\n print(\"Clauses:\")\n print()\n for clause in self.abstract_clauses:\n print(clause)","def dump():\n\t\treturn self.__dict__;","def _debugGenerator(self, generatorName, obj):\n\n debug.println(debug.LEVEL_FINER,\n \"GENERATOR: %s\" % generatorName)\n debug.println(debug.LEVEL_FINER,\n \" obj = %s\" % obj.name)\n debug.println(debug.LEVEL_FINER,\n \" role = %s\" % obj.getRoleName())","def info(self):","def info(self):","def info(self):\n print self.id, self.type, self.xyz.get_xyz","def pprint(self):\n print(self.pprint_str())","def _debug_pyomo_print(self, m):\n print('/' + '='*80)\n print('DEBUGG model pieces:')\n print(' -> objective:')\n print(' ', m.obj.pprint())\n print(' -> variables:')\n for var in m.component_objects(pyo.Var):\n print(' ', var.pprint())\n print(' -> constraints:')\n for constr in m.component_objects(pyo.Constraint):\n print(' ', constr.pprint())\n print('\\\\' + '='*80)\n print('')","def debug(self, message):\r\n pass","def details(self):\n pass","def dump(self):\n return","def debug(self, input):\n # Pass the debug information that you may think is important for your\n # evaluators\n debug_info = 'debug info'\n return debug_info","def debug(self, input):\n # Pass the debug information that you may think is important for your\n # evaluators\n debug_info = 'debug info'\n return debug_info","def debug():\n # written before I knew about the pdb module\n caller = currentframe().f_back\n method_name = caller.f_code.co_name\n line_no = getframeinfo(caller).lineno\n print(method_name + \": line \" + str(line_no))\n code.interact(local=dict(globals(), **caller.f_locals))","def print_attributes1(obj):\n\tfor attr in vars(obj):\n\t\tprint(attr, getattr(obj, attr))","def printer(obj, ident=''):\n import inspect\n print ident + obj.__class__.__name__.upper()\n ident += ' '\n lists = []\n for name in dir(obj):\n elem = getattr(obj, name)\n if isinstance(elem, list) and name != u'decoded_content':\n lists.append(elem)\n elif not inspect.ismethod(elem):\n if not name.startswith('__'):\n if name == u'data' and elem:\n print ident + u'data = '\n printer(elem, ident + ' ')\n else:\n print ident + u'%s\\t= %s' % (name, getattr(obj, name))\n for l in lists:\n for i in l:\n printer(i, ident + ' ')","def printer(obj, ident=''):\n import inspect\n print ident + obj.__class__.__name__.upper()\n ident += ' '\n lists = []\n for name in dir(obj):\n elem = getattr(obj, name)\n if isinstance(elem, list) and name != u'decoded_content':\n lists.append(elem)\n elif not inspect.ismethod(elem):\n if not name.startswith('__'):\n if name == u'data' and elem:\n print ident + u'data = '\n printer(elem, ident + ' ')\n else:\n print ident + u'%s\\t= %s' % (name, getattr(obj, name))\n for l in lists:\n for i in l:\n printer(i, ident + ' ')","def _print_properties(self):\n return NotImplemented","def __debug(msg):\n\n pass","def debug_info_struct(fdt):\n # Traverse node tree in depth first\n depth = 0\n path = b''\n root = fdt.get_root_node()\n debug_node(fdt, root, depth, path)","def __debug(self):\n\t\tprint \"Dumping Object Chat\"\n\t\tprint self.userA.username +' + '+ self.userB.username","def _debug_stack(self):\n debug(\"current stack: %s\" % self.calc.stack)","def print_attributes2(obj):\n\tfor attr, val in vars(obj).items():\n\t\tprint(attr, val)","def debug(self):\n return Config.DEBUG","def dump(self) -> None:\n ...","def __repr__(self):\n\n return repr(self.__dict__)","def debug_print(self):\n os.system('cls' if os.name == 'nt' else 'clear')\n\n print('\\nPosition')\n print(self.tetromino.position())\n print('\\nBlock coordinates')\n print(self.tetromino.block_coordinates())\n print('\\nBoard')\n print(self.board)\n print('\\nBoard heights')\n print(self.board.get_height())\n\n if self.pause:\n print('\\nPaused')","def PrettyPrint(self):\r\n print(self.data)\r\n return","def info(obj=None):\n if obj is None:\n print (\"Python keywords:\")\n import keyword\n for kwname in keyword.kwlist:\n print (\" \", kwname)\n print(\"Built in objects:\")\n for bi_object_name in sorted(__builtins__.keys()):\n bi_object = __builtins__[bi_object_name]\n if callable(bi_object):\n if type(bi_object) is types.ClassType:\n print(\" {} (class)\".format(bi_object.__name__))\n elif type(bi_object) is types.FunctionType:\n print(\" {} (function)\".format(bi_object.__name__))\n elif hasattr(obj, \"__doc__\") and obj.__doc__ is not None:\n print (\"Documentation for %s :\\n\" % (obj.__name__))\n print (obj.__doc__)\n elif type(obj) is types.ModuleType:\n pprint(dir(obj))\n elif type(obj) is types.ClassType:\n pprint(dir(obj))\n elif type(obj) is types.InstanceType:\n pprint(dir(obj))\n pprint(dir(obj.__class__))\n return \"\"","def magic_pinfo(self, parameter_s=''):\n \n # look for the given object in all namespaces\n qmark1,oname,qmark2 = re.match('(\\?*)([^?]*)(\\??)',parameter_s).groups()\n # detail_level: 0 -> obj? , 1 -> obj??\n detail_level = 0\n if qmark1 or qmark2:\n detail_level = 1\n self._inspect('pinfo',oname,detail_level=detail_level)","def __str__(self):\r\n\r\n for att in self.__dict__:\r\n print(\"%s: %r\" % (att, getattr(self, att)))\r\n\r\n return \"Planet Population class object attributes\"","def do_show(self, arg):\n obj = self.verify(arg, 1)\n if obj:\n print(obj)","def debug(self):\r\n for test in self:\r\n test.debug()","def dump_methods_and_datatypes(self):\n\n print(self._client)","def __str__(self):\n \n for att in self.__dict__:\n print('%s: %r' % (att, getattr(self, att)))\n \n return 'Background Sources class object attributes'","def debug(self):\n return self.settings['debug']","def print_objects(self):\n print(\"Spaces: {}\".format([s.name for s in self.spaces]))\n print(\"Characters: {}\".format([c.name for c in self.characters]))\n print(\"Items: {}\".format([i.name for i in self.items]))","def debugprint(debugobject, debugstring):\n if CMDLINEARGS.debug:\n print \"===== \" + debugstring + \" =====\"\n pprint.pprint(debugobject)\n print \"===== \" + debugstring + \" =====\"\n print \"\"","def debugprint(debugobject, debugstring):\n if CMDLINEARGS.debug:\n print \"===== \" + debugstring + \" =====\"\n pprint.pprint(debugobject)\n print \"===== \" + debugstring + \" =====\"\n print \"\"","def _showProperty(self):\n pass","def repr_(object_):\n return repr(object_)","def print_performance_info(self):\n pass","def visualizar(self):\n print(self.stack)","def inspect(self):\n\n input_array = []\n for mech in list(self.originMechanisms.mechanisms):\n input_array.append(mech.value)\n input_array = np.array(input_array)\n\n recurrent_init_array = []\n for mech in list(self.recurrentInitMechanisms.mechanisms):\n recurrent_init_array.append(mech.value)\n recurrent_init_array = np.array(recurrent_init_array)\n\n output_state_names = []\n output_value_array = []\n for mech in list(self.terminalMechanisms.mechanisms):\n output_value_array.append(mech.outputValue)\n for name in mech.outputStates:\n output_state_names.append(name)\n output_value_array = np.array(output_value_array)\n\n from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection\n from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection\n learning_projections = []\n controlled_parameters = []\n for mech in list(self.mechanisms):\n for parameter_state in mech.parameterStates:\n try:\n for projection in parameter_state.receivesFromProjections:\n if isinstance(projection, ControlProjection):\n controlled_parameters.append(parameter_state)\n except AttributeError:\n pass\n for output_state in mech.outputStates:\n try:\n for projection in output_state.sendsToProjections:\n for parameter_state in projection.paramaterStates:\n for sender in parameter_state.receivesFromProjections:\n if isinstance(sender, LearningProjection):\n learning_projections.append(projection)\n except AttributeError:\n pass\n\n inspect_dict = {\n PROCESSES: self.processes,\n MECHANISMS: self.mechanisms,\n ORIGIN_MECHANISMS: self.originMechanisms.mechanisms,\n INPUT_ARRAY: input_array,\n RECURRENT_MECHANISMS: self.recurrentInitMechanisms,\n RECURRENT_INIT_ARRAY: recurrent_init_array,\n TERMINAL_MECHANISMS: self.terminalMechanisms.mechanisms,\n OUTPUT_STATE_NAMES: output_state_names,\n OUTPUT_VALUE_ARRAY: output_value_array,\n NUM_PHASES_PER_TRIAL: self.numPhases,\n MONITORING_MECHANISMS: self.monitoringMechanisms,\n TARGET_MECHANISMS: self.targetMechanisms,\n LEARNING_PROJECTION_RECEIVERS: learning_projections,\n CONTROL_MECHANISMS: self.controlMechanism,\n CONTROL_PROJECTION_RECEIVERS: controlled_parameters,\n }\n\n return inspect_dict","def do_display_object(self, address):\n address = self.ParseAddressExpr(address)\n if self.reader.IsAlignedAddress(address):\n address = address + 1\n elif not self.heap.IsTaggedObjectAddress(address):\n print(\"Address doesn't look like a valid pointer!\")\n return\n heap_object = self.padawan.SenseObject(address)\n if heap_object:\n heap_object.Print(Printer())\n else:\n print(\"Address cannot be interpreted as object!\")","def debug():\n # print(\" ++ debugging 'pyre.filesystem'\")\n # attach {Extent} as the metaclass of Node and Filesystem so we can verify that all\n # instances of these classes are properly garbage collected\n from ..patterns.Extent import Extent\n\n global _metaclass_Node\n _metaclass_Node = Extent\n # all done\n return","def show_class_details(name, f):\n print '%s:' % name\n print '\\tobject:', f\n print '\\t__name__:', \n try:\n print f.__name__\n except AttributeError:\n print '(no __name__)'\n print '\\t__doc__', repr(f.__doc__)\n return","def dump(self):\n outputs = [\"Code object : %s\" % self.name]\n outputs.append(\" Type : %s\" % self.object_type)\n for source_line in self.source:\n # Each line is a (line_number, code) pair\n outputs.append('%d: %s' % source_line)\n return \"\".join(outputs)","def __str__(self):\n return str(self.__dict__['_obj'])","def debug(self):\r\n print(\"_,.-^ DEBUG ^-.,_\")\r\n print(\"state = %s %s\"%(self.state, self.error))\r\n print(\"\".join(self.memory))\r\n print ((self.pointer*\" \")+\"^\")\r\n print(\"PROGRAM\")\r\n print(\" {:16}{:7}{:7}{:7}{:16}\".format(\"State\", \"symbol\", \"write\", \"move\", \"new_state\"))\r\n for row in self.program:\r\n if row.state == self.state and row.symbol == self.memory[self.pointer]:\r\n print(\">\", end=\"\")\r\n else:\r\n print(\" \", end=\"\")\r\n print(row)"],"string":"[\n \"def inspect(obj:Any) -> None:\\n\\t\\tLogging._log(Logging.logLevel, obj)\",\n \"def debug(self):\\n raise NotImplementedError\",\n \"def output_debug_info(self):\",\n \"def inspect_obj(self, line=None):\\n if not line:\\n return\\n\\n # evaluate the line to get a python object\\n python_object = self.shell.ev(line)\\n\\n # Pretty Print/Format the object\\n # Print the output, but don't return anything (otherwise, we'd\\n # potentially get a wall of color-coded text.\\n formatted_dict = pformat(python_object.__dict__)\\n print(highlight(formatted_dict, lexer, formatter).strip())\\n pprint(python_object)\",\n \"def debug():\",\n \"def source_code(obj):\\n print(inspect.getsource(obj))\",\n \"def NeedsDebugInfo(self):\\n return True\",\n \"def print_debug(obj):\\n\\n if not DEBUG:\\n return False\\n\\n if hasattr(obj, 'lower'):\\n # string/unicode... just print it.\\n print('Debug: {}'.format(obj))\\n elif isinstance(obj, (list, tuple)):\\n # list/tuple, format it...\\n header = 'Debug: '\\n spaces = (' ' * len(header))\\n if obj:\\n print('{}{}'.format(header, obj[0]))\\n if len(obj) > 1:\\n otherlines = '\\\\n{}'.format(spaces).join(obj[1:])\\n print('\\\\n{}{}'.format(spaces, otherlines))\\n else:\\n # different type of object\\n print('Debug: {!r}'.format(obj))\\n\\n return True\",\n \"def debug(self):\\n return self._debug\",\n \"def debug(self):\\n try:\\n super(FaucetTopoTestBase, self).debug()\\n except Exception:\\n pprint.pprint(self.host_information)\\n raise\",\n \"def print_object_details(obj: object) -> None:\\n print_section(obj, 'Type', print_type)\\n print_section(obj, 'Documentation', print_documentation)\\n print_section(obj, 'Attributes', print_attributes)\\n print_section(obj, 'Methods', print_methods)\\n print_section_delimiter()\",\n \"def __repr__(self):\\n return repr(self.__dict__['_obj'])\",\n \"def _inspect(self,meth,oname,**kw):\\n \\n oname = oname.strip()\\n found,obj,ns,ds,ismagic = self._ofind(oname)\\n if found:\\n pmethod = getattr(self.shell.inspector,meth)\\n if meth == 'pdoc':\\n formatter = ismagic and self.format_screen or None\\n pmethod(obj,oname,formatter)\\n elif meth == 'pinfo':\\n formatter = ismagic and self.format_screen or None\\n pmethod(obj,oname,ns,formatter,ismagic=ismagic,**kw)\\n else:\\n pmethod(obj,oname)\\n else:\\n print 'Object `%s` not found.' % oname\\n return 'not found' # so callers can take other action\",\n \"def debug(self, *args, **kwargs):\",\n \"def NeedsDebugInfo(self):\\n return False\",\n \"def pprint(self):\\n # just here for defining the interface; work is done in subclasses\\n pass\",\n \"def DebugInfo( self, request_data ):\\n pass\",\n \"def debug(self):\\n print(self.memory)\\n print('r0 = %s, ip = %s' % (self.r0, self.ip))\",\n \"def inspect_object(obj):\\n raise TryNext\",\n \"def dump_objects():\\n pass\",\n \"def debug(self):\\n return Debug(self)\",\n \"def do_inspect(self, arg):\\n self._do_inspect(arg, with_source=False)\",\n \"def debug(self):\\n neighbors = len(self.__neighbors)\\n string = self.__repr__() + f' neighbors: {self.living_neighbors()}/{neighbors}'\\n for neighbor in self.__neighbors:\\n string += '\\\\n ' + neighbor.__repr__()\\n print(string)\",\n \"def print(self):\\r\\n print(\\\"[DEBUG] STACK: \\\", self.__memory.__repr__())\",\n \"def compute_debug(self):\",\n \"def debug(self):\\n \\n #path\\n print('Path information:')\\n for k, v in self.__path.items():\\n print(k, v)\\n \\n #sample count\\n print('Sample statistic of each phase')\\n for k, v in self.__phase_sample_count.items():\\n print(k, v)\\n \\n print('Sample statistic of each class')\\n for k, v in self.__area_sample_count.items():\\n print(k, v)\\n \\n print('Sample statistic of each train')\\n for k, v in self.__train_sample_count.items():\\n print(k, v)\",\n \"def pprint(obj):\\n for argname in sorted([x for x in dir(obj) if not x.startswith('__')]):\\n # Skip callables\\n if hasattr(getattr(obj, argname), '__call__'):\\n continue\\n print(\\\"{} : {}\\\".format(argname, getattr(obj, argname)))\",\n \"def debug() -> bool:\",\n \"def debug(self):\\n #breakpoint() # infinite loop\\n print(self.ttl)\",\n \"def debug(self) -> bool:\\n return self._data[ATTR_DEBUG]\",\n \"def debug_print(self):\\n print self.title\\n print self.storyline\\n print self.poster_image_url\\n print self.trailer_youtube_url\\n print \\\"------\\\"\",\n \"def test_debug(self) -> Debug:\\n return self._test_debug\",\n \"def Print(self):\\n print(self.__dict__)\",\n \"def info(self):\\n\\t\\timport inspect\\n\\t\\n\\t\\tmessage = \\\"All variables available for star ID %i\\\" % self.ID\\t\\t\\n\\t\\tprint message\\n\\t\\tprint '-'*len(message)\\n\\t\\tattributes = inspect.getmembers(self, lambda a:not(inspect.isroutine(a)))\\n\\t\\tfor a in attributes:\\n\\t\\t\\tif (a[0].startswith('__') and a[0].endswith('__')): continue\\n\\t\\t\\tprint a[0], \\\"=\\\", a[1]\",\n \"def print_verbose(self) -> None:\\n print(self)\\n if self.meta is not None:\\n print(self.meta.__repr__())\",\n \"def _inspect_mode(self):\\n return self._project._inspect_mode\",\n \"def _print_attribute(self):\\n print(vars(self))\",\n \"def debug_string(self):\\n\\n raise NotImplementedError\",\n \"def debug(node):\\n print \\\"%r\\\" % node\",\n \"def print_dict(self):\\n print(self.__dict__)\",\n \"def debug(self, *args: Any, **kwargs) -> None:\\n ...\",\n \"def __debug_print__(self):\\n print(self.question_data)\",\n \"def __repr__(self):\\n return self.pretty_print(self.__dict__)\",\n \"def dump(self):\\n self.logger.debug(self)\",\n \"def dump_objects(self):\\n #print 'Object Count: ', self.object_store.len()\\n \\n for item in self.object_store:\\n print 'Object Name: ', item.__dict__['Name'], ' LocalID: ', item.__dict__['LocalID']\",\n \"def debug():\\n return exported_res_dict\",\n \"def __str__(self):\\n return stringify(\\n Inspect(\\n self,\\n help=True,\\n methods=True,\\n private=True,\\n dunder=False,\\n sort=True,\\n all=False,\\n ),\\n maxlen=-1,\\n )\",\n \"def debug(self) -> str:\\n\\n return Widget.debug(self)\",\n \"def show_debug_msg(self) -> None:\\n if self.debug_mode:\\n for point in self.points:\\n print(point.debug_info())\",\n \"def debug_allowed(request_object):\\n \\n return utilities.debug_allowed(request_object)\",\n \"def print_debug(self):\\n print()\\n print(\\\"Variable names ({} total):\\\".format(len(self.variable_names)))\\n print()\\n for variable in self.variable_names:\\n print(variable)\\n print()\\n\\n print(\\\"Clauses:\\\")\\n print()\\n for clause in self.abstract_clauses:\\n print(clause)\",\n \"def dump():\\n\\t\\treturn self.__dict__;\",\n \"def _debugGenerator(self, generatorName, obj):\\n\\n debug.println(debug.LEVEL_FINER,\\n \\\"GENERATOR: %s\\\" % generatorName)\\n debug.println(debug.LEVEL_FINER,\\n \\\" obj = %s\\\" % obj.name)\\n debug.println(debug.LEVEL_FINER,\\n \\\" role = %s\\\" % obj.getRoleName())\",\n \"def info(self):\",\n \"def info(self):\",\n \"def info(self):\\n print self.id, self.type, self.xyz.get_xyz\",\n \"def pprint(self):\\n print(self.pprint_str())\",\n \"def _debug_pyomo_print(self, m):\\n print('/' + '='*80)\\n print('DEBUGG model pieces:')\\n print(' -> objective:')\\n print(' ', m.obj.pprint())\\n print(' -> variables:')\\n for var in m.component_objects(pyo.Var):\\n print(' ', var.pprint())\\n print(' -> constraints:')\\n for constr in m.component_objects(pyo.Constraint):\\n print(' ', constr.pprint())\\n print('\\\\\\\\' + '='*80)\\n print('')\",\n \"def debug(self, message):\\r\\n pass\",\n \"def details(self):\\n pass\",\n \"def dump(self):\\n return\",\n \"def debug(self, input):\\n # Pass the debug information that you may think is important for your\\n # evaluators\\n debug_info = 'debug info'\\n return debug_info\",\n \"def debug(self, input):\\n # Pass the debug information that you may think is important for your\\n # evaluators\\n debug_info = 'debug info'\\n return debug_info\",\n \"def debug():\\n # written before I knew about the pdb module\\n caller = currentframe().f_back\\n method_name = caller.f_code.co_name\\n line_no = getframeinfo(caller).lineno\\n print(method_name + \\\": line \\\" + str(line_no))\\n code.interact(local=dict(globals(), **caller.f_locals))\",\n \"def print_attributes1(obj):\\n\\tfor attr in vars(obj):\\n\\t\\tprint(attr, getattr(obj, attr))\",\n \"def printer(obj, ident=''):\\n import inspect\\n print ident + obj.__class__.__name__.upper()\\n ident += ' '\\n lists = []\\n for name in dir(obj):\\n elem = getattr(obj, name)\\n if isinstance(elem, list) and name != u'decoded_content':\\n lists.append(elem)\\n elif not inspect.ismethod(elem):\\n if not name.startswith('__'):\\n if name == u'data' and elem:\\n print ident + u'data = '\\n printer(elem, ident + ' ')\\n else:\\n print ident + u'%s\\\\t= %s' % (name, getattr(obj, name))\\n for l in lists:\\n for i in l:\\n printer(i, ident + ' ')\",\n \"def printer(obj, ident=''):\\n import inspect\\n print ident + obj.__class__.__name__.upper()\\n ident += ' '\\n lists = []\\n for name in dir(obj):\\n elem = getattr(obj, name)\\n if isinstance(elem, list) and name != u'decoded_content':\\n lists.append(elem)\\n elif not inspect.ismethod(elem):\\n if not name.startswith('__'):\\n if name == u'data' and elem:\\n print ident + u'data = '\\n printer(elem, ident + ' ')\\n else:\\n print ident + u'%s\\\\t= %s' % (name, getattr(obj, name))\\n for l in lists:\\n for i in l:\\n printer(i, ident + ' ')\",\n \"def _print_properties(self):\\n return NotImplemented\",\n \"def __debug(msg):\\n\\n pass\",\n \"def debug_info_struct(fdt):\\n # Traverse node tree in depth first\\n depth = 0\\n path = b''\\n root = fdt.get_root_node()\\n debug_node(fdt, root, depth, path)\",\n \"def __debug(self):\\n\\t\\tprint \\\"Dumping Object Chat\\\"\\n\\t\\tprint self.userA.username +' + '+ self.userB.username\",\n \"def _debug_stack(self):\\n debug(\\\"current stack: %s\\\" % self.calc.stack)\",\n \"def print_attributes2(obj):\\n\\tfor attr, val in vars(obj).items():\\n\\t\\tprint(attr, val)\",\n \"def debug(self):\\n return Config.DEBUG\",\n \"def dump(self) -> None:\\n ...\",\n \"def __repr__(self):\\n\\n return repr(self.__dict__)\",\n \"def debug_print(self):\\n os.system('cls' if os.name == 'nt' else 'clear')\\n\\n print('\\\\nPosition')\\n print(self.tetromino.position())\\n print('\\\\nBlock coordinates')\\n print(self.tetromino.block_coordinates())\\n print('\\\\nBoard')\\n print(self.board)\\n print('\\\\nBoard heights')\\n print(self.board.get_height())\\n\\n if self.pause:\\n print('\\\\nPaused')\",\n \"def PrettyPrint(self):\\r\\n print(self.data)\\r\\n return\",\n \"def info(obj=None):\\n if obj is None:\\n print (\\\"Python keywords:\\\")\\n import keyword\\n for kwname in keyword.kwlist:\\n print (\\\" \\\", kwname)\\n print(\\\"Built in objects:\\\")\\n for bi_object_name in sorted(__builtins__.keys()):\\n bi_object = __builtins__[bi_object_name]\\n if callable(bi_object):\\n if type(bi_object) is types.ClassType:\\n print(\\\" {} (class)\\\".format(bi_object.__name__))\\n elif type(bi_object) is types.FunctionType:\\n print(\\\" {} (function)\\\".format(bi_object.__name__))\\n elif hasattr(obj, \\\"__doc__\\\") and obj.__doc__ is not None:\\n print (\\\"Documentation for %s :\\\\n\\\" % (obj.__name__))\\n print (obj.__doc__)\\n elif type(obj) is types.ModuleType:\\n pprint(dir(obj))\\n elif type(obj) is types.ClassType:\\n pprint(dir(obj))\\n elif type(obj) is types.InstanceType:\\n pprint(dir(obj))\\n pprint(dir(obj.__class__))\\n return \\\"\\\"\",\n \"def magic_pinfo(self, parameter_s=''):\\n \\n # look for the given object in all namespaces\\n qmark1,oname,qmark2 = re.match('(\\\\?*)([^?]*)(\\\\??)',parameter_s).groups()\\n # detail_level: 0 -> obj? , 1 -> obj??\\n detail_level = 0\\n if qmark1 or qmark2:\\n detail_level = 1\\n self._inspect('pinfo',oname,detail_level=detail_level)\",\n \"def __str__(self):\\r\\n\\r\\n for att in self.__dict__:\\r\\n print(\\\"%s: %r\\\" % (att, getattr(self, att)))\\r\\n\\r\\n return \\\"Planet Population class object attributes\\\"\",\n \"def do_show(self, arg):\\n obj = self.verify(arg, 1)\\n if obj:\\n print(obj)\",\n \"def debug(self):\\r\\n for test in self:\\r\\n test.debug()\",\n \"def dump_methods_and_datatypes(self):\\n\\n print(self._client)\",\n \"def __str__(self):\\n \\n for att in self.__dict__:\\n print('%s: %r' % (att, getattr(self, att)))\\n \\n return 'Background Sources class object attributes'\",\n \"def debug(self):\\n return self.settings['debug']\",\n \"def print_objects(self):\\n print(\\\"Spaces: {}\\\".format([s.name for s in self.spaces]))\\n print(\\\"Characters: {}\\\".format([c.name for c in self.characters]))\\n print(\\\"Items: {}\\\".format([i.name for i in self.items]))\",\n \"def debugprint(debugobject, debugstring):\\n if CMDLINEARGS.debug:\\n print \\\"===== \\\" + debugstring + \\\" =====\\\"\\n pprint.pprint(debugobject)\\n print \\\"===== \\\" + debugstring + \\\" =====\\\"\\n print \\\"\\\"\",\n \"def debugprint(debugobject, debugstring):\\n if CMDLINEARGS.debug:\\n print \\\"===== \\\" + debugstring + \\\" =====\\\"\\n pprint.pprint(debugobject)\\n print \\\"===== \\\" + debugstring + \\\" =====\\\"\\n print \\\"\\\"\",\n \"def _showProperty(self):\\n pass\",\n \"def repr_(object_):\\n return repr(object_)\",\n \"def print_performance_info(self):\\n pass\",\n \"def visualizar(self):\\n print(self.stack)\",\n \"def inspect(self):\\n\\n input_array = []\\n for mech in list(self.originMechanisms.mechanisms):\\n input_array.append(mech.value)\\n input_array = np.array(input_array)\\n\\n recurrent_init_array = []\\n for mech in list(self.recurrentInitMechanisms.mechanisms):\\n recurrent_init_array.append(mech.value)\\n recurrent_init_array = np.array(recurrent_init_array)\\n\\n output_state_names = []\\n output_value_array = []\\n for mech in list(self.terminalMechanisms.mechanisms):\\n output_value_array.append(mech.outputValue)\\n for name in mech.outputStates:\\n output_state_names.append(name)\\n output_value_array = np.array(output_value_array)\\n\\n from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection\\n from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection\\n learning_projections = []\\n controlled_parameters = []\\n for mech in list(self.mechanisms):\\n for parameter_state in mech.parameterStates:\\n try:\\n for projection in parameter_state.receivesFromProjections:\\n if isinstance(projection, ControlProjection):\\n controlled_parameters.append(parameter_state)\\n except AttributeError:\\n pass\\n for output_state in mech.outputStates:\\n try:\\n for projection in output_state.sendsToProjections:\\n for parameter_state in projection.paramaterStates:\\n for sender in parameter_state.receivesFromProjections:\\n if isinstance(sender, LearningProjection):\\n learning_projections.append(projection)\\n except AttributeError:\\n pass\\n\\n inspect_dict = {\\n PROCESSES: self.processes,\\n MECHANISMS: self.mechanisms,\\n ORIGIN_MECHANISMS: self.originMechanisms.mechanisms,\\n INPUT_ARRAY: input_array,\\n RECURRENT_MECHANISMS: self.recurrentInitMechanisms,\\n RECURRENT_INIT_ARRAY: recurrent_init_array,\\n TERMINAL_MECHANISMS: self.terminalMechanisms.mechanisms,\\n OUTPUT_STATE_NAMES: output_state_names,\\n OUTPUT_VALUE_ARRAY: output_value_array,\\n NUM_PHASES_PER_TRIAL: self.numPhases,\\n MONITORING_MECHANISMS: self.monitoringMechanisms,\\n TARGET_MECHANISMS: self.targetMechanisms,\\n LEARNING_PROJECTION_RECEIVERS: learning_projections,\\n CONTROL_MECHANISMS: self.controlMechanism,\\n CONTROL_PROJECTION_RECEIVERS: controlled_parameters,\\n }\\n\\n return inspect_dict\",\n \"def do_display_object(self, address):\\n address = self.ParseAddressExpr(address)\\n if self.reader.IsAlignedAddress(address):\\n address = address + 1\\n elif not self.heap.IsTaggedObjectAddress(address):\\n print(\\\"Address doesn't look like a valid pointer!\\\")\\n return\\n heap_object = self.padawan.SenseObject(address)\\n if heap_object:\\n heap_object.Print(Printer())\\n else:\\n print(\\\"Address cannot be interpreted as object!\\\")\",\n \"def debug():\\n # print(\\\" ++ debugging 'pyre.filesystem'\\\")\\n # attach {Extent} as the metaclass of Node and Filesystem so we can verify that all\\n # instances of these classes are properly garbage collected\\n from ..patterns.Extent import Extent\\n\\n global _metaclass_Node\\n _metaclass_Node = Extent\\n # all done\\n return\",\n \"def show_class_details(name, f):\\n print '%s:' % name\\n print '\\\\tobject:', f\\n print '\\\\t__name__:', \\n try:\\n print f.__name__\\n except AttributeError:\\n print '(no __name__)'\\n print '\\\\t__doc__', repr(f.__doc__)\\n return\",\n \"def dump(self):\\n outputs = [\\\"Code object : %s\\\" % self.name]\\n outputs.append(\\\" Type : %s\\\" % self.object_type)\\n for source_line in self.source:\\n # Each line is a (line_number, code) pair\\n outputs.append('%d: %s' % source_line)\\n return \\\"\\\".join(outputs)\",\n \"def __str__(self):\\n return str(self.__dict__['_obj'])\",\n \"def debug(self):\\r\\n print(\\\"_,.-^ DEBUG ^-.,_\\\")\\r\\n print(\\\"state = %s %s\\\"%(self.state, self.error))\\r\\n print(\\\"\\\".join(self.memory))\\r\\n print ((self.pointer*\\\" \\\")+\\\"^\\\")\\r\\n print(\\\"PROGRAM\\\")\\r\\n print(\\\" {:16}{:7}{:7}{:7}{:16}\\\".format(\\\"State\\\", \\\"symbol\\\", \\\"write\\\", \\\"move\\\", \\\"new_state\\\"))\\r\\n for row in self.program:\\r\\n if row.state == self.state and row.symbol == self.memory[self.pointer]:\\r\\n print(\\\">\\\", end=\\\"\\\")\\r\\n else:\\r\\n print(\\\" \\\", end=\\\"\\\")\\r\\n print(row)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7372906","0.72778416","0.6883075","0.6746992","0.6737449","0.6555419","0.6541405","0.65274495","0.6431801","0.64068395","0.6402232","0.640157","0.6398631","0.63798773","0.6376369","0.6375551","0.63642555","0.63500595","0.6315535","0.6302645","0.62807316","0.6275674","0.62132704","0.618943","0.6182986","0.6180341","0.61715746","0.6112123","0.6110644","0.6097784","0.60940063","0.6092642","0.60903","0.6054216","0.60380286","0.6020348","0.5998989","0.59823835","0.59593666","0.5959058","0.594984","0.5948973","0.59344953","0.5915146","0.5907818","0.5907362","0.5894905","0.5893206","0.5857184","0.58565533","0.5852448","0.5839369","0.5834062","0.583272","0.583272","0.5808855","0.57997626","0.5795582","0.57859826","0.5776516","0.57371455","0.5736569","0.5736569","0.57317114","0.5720329","0.5709732","0.5709732","0.5707328","0.57054234","0.5702372","0.57020366","0.5690056","0.56893","0.5686834","0.5685593","0.5681018","0.5676573","0.5669005","0.56560576","0.56527","0.5635127","0.5633602","0.56335855","0.56276417","0.56270635","0.56117773","0.56024456","0.5600739","0.5600739","0.5596689","0.5595998","0.55835223","0.55768037","0.5572136","0.55677575","0.55615526","0.55614966","0.5560985","0.5558589","0.5535195"],"string":"[\n \"0.7372906\",\n \"0.72778416\",\n \"0.6883075\",\n \"0.6746992\",\n \"0.6737449\",\n \"0.6555419\",\n \"0.6541405\",\n \"0.65274495\",\n \"0.6431801\",\n \"0.64068395\",\n \"0.6402232\",\n \"0.640157\",\n \"0.6398631\",\n \"0.63798773\",\n \"0.6376369\",\n \"0.6375551\",\n \"0.63642555\",\n \"0.63500595\",\n \"0.6315535\",\n \"0.6302645\",\n \"0.62807316\",\n \"0.6275674\",\n \"0.62132704\",\n \"0.618943\",\n \"0.6182986\",\n \"0.6180341\",\n \"0.61715746\",\n \"0.6112123\",\n \"0.6110644\",\n \"0.6097784\",\n \"0.60940063\",\n \"0.6092642\",\n \"0.60903\",\n \"0.6054216\",\n \"0.60380286\",\n \"0.6020348\",\n \"0.5998989\",\n \"0.59823835\",\n \"0.59593666\",\n \"0.5959058\",\n \"0.594984\",\n \"0.5948973\",\n \"0.59344953\",\n \"0.5915146\",\n \"0.5907818\",\n \"0.5907362\",\n \"0.5894905\",\n \"0.5893206\",\n \"0.5857184\",\n \"0.58565533\",\n \"0.5852448\",\n \"0.5839369\",\n \"0.5834062\",\n \"0.583272\",\n \"0.583272\",\n \"0.5808855\",\n \"0.57997626\",\n \"0.5795582\",\n \"0.57859826\",\n \"0.5776516\",\n \"0.57371455\",\n \"0.5736569\",\n \"0.5736569\",\n \"0.57317114\",\n \"0.5720329\",\n \"0.5709732\",\n \"0.5709732\",\n \"0.5707328\",\n \"0.57054234\",\n \"0.5702372\",\n \"0.57020366\",\n \"0.5690056\",\n \"0.56893\",\n \"0.5686834\",\n \"0.5685593\",\n \"0.5681018\",\n \"0.5676573\",\n \"0.5669005\",\n \"0.56560576\",\n \"0.56527\",\n \"0.5635127\",\n \"0.5633602\",\n \"0.56335855\",\n \"0.56276417\",\n \"0.56270635\",\n \"0.56117773\",\n \"0.56024456\",\n \"0.5600739\",\n \"0.5600739\",\n \"0.5596689\",\n \"0.5595998\",\n \"0.55835223\",\n \"0.55768037\",\n \"0.5572136\",\n \"0.55677575\",\n \"0.55615526\",\n \"0.55614966\",\n \"0.5560985\",\n \"0.5558589\",\n \"0.5535195\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6848801"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":2696,"cells":{"query":{"kind":"string","value":"Return an iterator over indices of the master array which are spanned by the data array."},"document":{"kind":"string","value":"def master_ndindex(self): # itermaster_indices(self):\n return itertools_product(\n *[range(*r) for r in self.location]\n ) # TODO check"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_subset_inds(self, adata_parent):\r\n subset_inds = np.ones(len(adata_parent), dtype=bool)\r\n for condition, values in self.subset_cond.items():\r\n subset_inds *= adata_parent.obs[condition].isin(values)\r\n return subset_inds","def enumerate(self):\n # go through the container and tile in sync\n for index, value in zip(self.tile, self.data):\n # hand the {index} and the corresponding value to the caller\n yield index, value\n # all done\n return","def indices(self):\n return range(len(self))","def mainIndices(self):\n return self.i1, self.i2","def __iter__(self):\n indices = []\n for i, size in enumerate(self.group_sizes):\n if size == 0:\n continue\n indice = np.where(self.flag == i)[0]\n if not len(indice) == size:\n raise ValueError('the length of the indice should be equal to the size')\n np.random.shuffle(indice)\n num_extra = int(np.ceil(size / self.samples_per_gpu)\n ) * self.samples_per_gpu - len(indice)\n indice = np.concatenate([indice, indice[:num_extra]])\n indices.append(indice)\n indices = np.concatenate(indices)\n indices = [\n indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]\n for i in np.random.permutation(\n range(len(indices) // self.samples_per_gpu))\n ]\n indices = np.concatenate(indices)\n indices = torch.from_numpy(indices).long()\n if not len(indices) == self.num_samples:\n raise ValueError(\"the length of the indices should be equal to num_samples\")\n return iter(indices)","def indices(self):\n return tuple([slice(*r) for r in self.location])","def indices(self) -> np.ndarray:\n return self.impl.indices","def get_index_array(self):\n return self.region_pairs","def __iter__(self):\n\n starts = range(0, self.data.shape[self.axis], self.chunksize)\n\n for t in zip_longest(starts, starts[1:], fillvalue=None):\n yield self.data[self._slice(*t)]","def __iter__(self):\n while True:\n if self.batches is None:\n for indexed_sentence in self.indexed_sentences:\n yield indexed_sentence\n else:\n for batch in self.batches:\n yield batch[:-1, :], batch[1:, :] # Return batch and target indices\n\n if not self.repeat:\n return","def get_data_idx(self)->list:\n return self.__data_idx","def get_indices(self):\r\n return self._indices","def getind(self,start,end,blk):\n\n if blk is None:\n # Return all blocks\n blk = np.arange(self.ind[start].size)\n\n ind=np.array([])\n for k,val in enumerate(blk):\n ind=np.append(ind,np.arange(self.ind[start][val],self.ind[end][val]))\n return ind.astype(int)","def inidices(self):\n return [Index(name, index) for name, index in self._data['indices'].iteritems()]","def index_iterator((x_min, x_max, y_min, y_max)):\n for row in xrange(y_min, y_max):\n for col in xrange(x_min, x_max):\n yield (row, col)","def _get_split_indices(self):\n\n cumsum = np.cumsum(\n np.concatenate((np.array([0], dtype=np.int8), self.split_sizes)))\n \n fold_inds = np.array(\n [(cumsum[n], cumsum[n + 1]) for n in range(self.n_splits)])\n\n return fold_inds","def get_indexes(self, dataset: BaseDataset) -> int:\n\n index = [np.random.randint(0, len(dataset)) for _ in range(1)]\n\n return index","def __iter__(self):\n # deterministically shuffle based on epoch\n if self.shuffle:\n g = torch.Generator()\n g.manual_seed(self.epoch)\n indices = torch.randperm(len(self.dataset), generator=g).tolist()\n else:\n indices = torch.arange(len(self.dataset)).tolist()\n\n # add extra samples to make it evenly divisible\n indices += indices[:(self.total_size - len(indices))]\n if not len(indices) == self.total_size:\n raise ValueError('the length of the indices should be equal to total_size')\n\n # subsample\n indices = indices[self.rank:self.total_size:self.num_replicas]\n if not len(indices) == self.num_samples:\n raise ValueError(\"the length of the indices should be equal to num_samples in subsample\")\n\n return iter(indices)","def _get_chunk_indexer(self, array):\n if self.data.num_chunks == 1:\n return np.broadcast_to(0, len(array))\n return np.digitize(array, self.offsets[1:])","def get_intra_sample_indices(self):\n intra_sample_indices = []\n for group_index in range(self.num_groups):\n num_images_in_group = self.num_images_per_group[group_index]\n if self.intra_group_option == \"forward\":\n for i in range(num_images_in_group):\n for j in range(i):\n # j < i\n intra_sample_indices.append(\n ((group_index, j), (group_index, i))\n )\n elif self.intra_group_option == \"backward\":\n for i in range(num_images_in_group):\n for j in range(i):\n # i > j\n intra_sample_indices.append(\n ((group_index, i), (group_index, j))\n )\n elif self.intra_group_option == \"unconstrained\":\n for i in range(num_images_in_group):\n for j in range(i):\n # j < i, i > j\n intra_sample_indices.append(\n ((group_index, j), (group_index, i))\n )\n intra_sample_indices.append(\n ((group_index, i), (group_index, j))\n )\n else:\n raise ValueError(\n \"Unknown intra_group_option, must be forward/backward/unconstrained\"\n )\n return intra_sample_indices","def create_repeated_indexes(data):\n from numpy import arange\n\n index_range = arange(0, len(data))\n return (index_range for i in index_range)","def get_indices_entire_sequence(data: pd.Dataframe, window_size: int, step_size: int) -> list:\n stop_position = len(data)-1 # 1- because of 0 indexing\n\n # Start the first sub-sequence at index position 0\n subseq_first_idx = 0\n\n subseq_last_idx = subseq_first_idx + window_size\n\n indices = []\n\n while subseq_last_idx <= stop_position:\n indices.append((subseq_first_idx, subseq_last_idx))\n subseq_first_idx += step_size\n subseq_last_idx += step_size\n return indices","def batch_indices(self):\n b = self.batch_size\n return [np.arange(i*b, i*b+b) for i in range(self.num_batches)]","def atom_idxs(self):\n\n return np.array([atom.atom_idxs for atom in self])","def indices(self):\n _indices = []\n for h in self.miller.indices():\n _indices.append(self.indices_hkl(*h)[0])\n return _indices","def getLandmarkindices(self):\n return self.subsetindices","def __iter__(self):\n batch = []\n for i_batch in range(self.episode_num):\n classes = torch.randperm(len(self.idx_list))[: self.way_num]\n for c in classes:\n idxes = self.idx_list[c.item()]\n pos = torch.randperm(idxes.size(0))[: self.image_num]\n batch.append(idxes[pos])\n if len(batch) == self.episode_size * self.way_num:\n batch = torch.stack(batch).reshape(-1)\n yield batch\n batch = []","def _iterCoordsets(self):\n\n for i in range(self._n_csets):\n yield self._coords[i]","def iterCoordsets(self):\n\n for i in range(self._n_csets):\n yield self._coords[i].copy()","def __iter__(self):\n worker_info = torch.utils.data.get_worker_info()\n num_batches = len(self.coords_batcher)\n if worker_info is None:\n # In single-processing mode\n start, end = 0, num_batches\n else:\n worker_id = worker_info.id\n num_workers = worker_info.num_workers\n shard_size = int(np.ceil(num_batches / num_workers))\n start = shard_size * worker_id\n end = min(start + shard_size, num_batches)\n return (self.get_batch(i) for i in range(start, end))","def index(self):\n return list(self._innercontainer)","def get_indexes(self, dataset):\n\n indexs = [random.randint(0, len(dataset)) for _ in range(3)]\n return indexs","def indices_of_split(self, split_name='train'):\n return self.indices_of('split', split_name)","def iter_sites(self):\n for i in range(self.num_sites):\n indices = list()\n for dim in self.site_idxs:\n indices.append(i % dim)\n i = i // dim\n yield tuple(indices)","def get_34index_list(self):\n msk = self.load_mask()\n return [i for (i,v) in enumerate(msk) if v==1]","def _get_index_iterator(indexes, length):\n return combinations(indexes, length)","def _iter_indices(self, frame, y):\n pass","def getIndices(self):\r\n return self._indices","def indices(self):\n return self.index.indices","def SectionIndicesConnectedToSoma(self):\n indices = []\n index = 0\n for each_section in self._section_list:\n if each_section.ParentId() == -1:\n indices.append(index)\n index += 1\n return indices","def get_idxvals(self):\n input_rows = list()\n input_cols = list()\n for key in self.index:\n input_rows.append(key[0])\n input_cols.append(key[1])\n\n return list(OrderedSet(input_rows)), list(OrderedSet(input_cols))","def _compute_data_offsets(store: Store,\n datas: Tuple[DataSegment, ...],\n module_instance: ModuleInstance) -> Iterable[numpy.uint32]:\n for data_segment in datas:\n frame = Frame(\n module=module_instance,\n locals=[],\n instructions=InstructionSequence(data_segment.offset),\n arity=1,\n )\n config = Configuration(store=store)\n config.push_frame(frame)\n result = config.execute()\n if len(result) != 1:\n raise Exception(\"Invariant: data segment offset returned empty result\")\n offset = numpy.uint32(cast(int, result[0]))\n\n memory_address = module_instance.memory_addrs[data_segment.memory_idx]\n memory_instance = store.mems[memory_address]\n\n if offset + len(data_segment.init) > len(memory_instance.data):\n raise Unlinkable(\n f\"Computed data segment offset exceeds memory size: {offset} \"\n f\"+ {len(data_segment.init)} > {len(memory_instance.data)}\"\n )\n yield offset","def getAllIndex(ldata, fldata):\n\treturn list(map(lambda e : fldata.index(e), ldata))","def __iter__(self):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch)\n\n indices = []\n for i, size in enumerate(self.group_sizes):\n if size > 0:\n indice = np.where(self.flag == i)[0]\n if not len(indice) == size:\n raise ValueError(\"the length of the indice should be equal to size\")\n indice = indice[list(torch.randperm(int(size),\n generator=g))].tolist()\n extra = int(\n math.ceil(\n size * 1.0 / self.samples_per_gpu / self.num_replicas)\n ) * self.samples_per_gpu * self.num_replicas - len(indice)\n indice += indice[:extra]\n indices += indice\n\n if not len(indices) == self.total_size:\n raise ValueError(\"the length of the indices should be equal to total_size\")\n\n indices = [\n indices[j] for i in list(\n torch.randperm(\n len(indices) // self.samples_per_gpu, generator=g))\n for j in range(i * self.samples_per_gpu, (i + 1) * self.samples_per_gpu)\n ]\n\n # subsample\n offset = self.num_samples * self.rank\n indices = indices[offset:offset + self.num_samples]\n if not len(indices) == self.num_samples:\n raise ValueError(\"the length of the indices should be equal to num_samplers in subsample\")\n\n return iter(indices)","def next(self):\n # Keeps under lock only the mechanism which advances\n # the indexing of each batch.\n with self.lock:\n index_array = next(self.index_generator)\n # The transformation of images is not under thread lock\n # so it can be done in parallel\n return self._get_batches_of_transformed_samples(index_array)","def indices(self):\n if self._indices is None:\n i = []\n\n # TODO: this is not right for multi-column keys\n # TODO: new style indexes\n\n global_name = '^DD(%s,0,\"IX\",\"0\")' % self.fileid\n prefix = '^DD(%s,0,\"IX\",' % self.fileid\n while 1:\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\"\"))[0]\n if not global_name or not global_name.startswith(prefix):\n break\n suffix = global_name[len(prefix):-1]\n parts = suffix.split(\",\")\n idx_name = parts[0][1:-1]\n idx_table = parts[1]\n idx_columns = parts[2:]\n index = Index(idx_name, idx_table, idx_columns)\n i.append(index)\n\n # A second list, gives indices for a field\n columns = {}\n for idx in i:\n for c in idx.columns:\n columns[c] = 1\n\n # Now trawl the listed columns in the data dictionary, and load their\n # cross references.\n cr_names = {}\n for c in columns.keys():\n idx_root = M.Globals[\"^DD\"][self.fileid][c][1]\n if not idx_root[0].exists():\n continue\n for cr_id, val in idx_root.keys_with_decendants():\n if float(cr_id) > 0:\n cr_header = idx_root[cr_id][0].value\n parts = cr_header.split(\"^\")\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\n f = cr_names.get(parts[1], list())\n f.append(c)\n cr_names[parts[1]] = f\n\n # Now, just delete items from the index list if they are not in cr_names\n self._indices = []\n for index in i:\n cr = cr_names.get(index.name)\n if cr:\n # verify columns - lots of errors in real systems\n if len(cr) == len(index.columns):\n invalid = False\n for c in cr:\n if c not in index.columns:\n invalid = True\n continue\n if not invalid:\n self._indices.append(index)\n\n return self._indices","def get_indexes(self, dataset):\n\n for i in range(self.max_iters):\n index = random.randint(0, len(dataset))\n gt_bboxes_i = dataset.get_ann_info(index)['bboxes']\n if len(gt_bboxes_i) != 0:\n break\n\n return index","def get_indexes(self, items: Iterable[_T]) -> List[int]:\n return [self.get_index(item) for item in items]","def get_indexes(self, variable, *args):\n\n return [get_subset_idxs(data, min, max)\n for data, (min, max) in args]","def getLandmarkindices(self):\n return self.subsetnodes_indices","def getIndexes(self, val):\n # Find where this value is listed. \n valNdx = (self.values == val).nonzero()[0]\n \n # If this value is not actually in those listed, then we \n # must return empty indexes\n if len(valNdx) == 0:\n start = 0\n end = 0\n else:\n # The index into counts, etc. for this value. \n valNdx = valNdx[0]\n start = self.start[valNdx]\n end = self.end[valNdx]\n \n # Create a tuple of index arrays, one for each index of the original array. \n ndx = ()\n for i in range(self.nDims):\n ndx += (self.indexes[start:end, i], )\n return ndx","def get_raster_ids(self):\n return numpy.array(range(self._lo_atom, self._lo_atom + self._n_atoms))","def get_indexes(self):\n indexes = []\n for c in self.components:\n indexes.extend(c.get_indexes())\n return indexes","def get_indexes(self, dataset: BaseDataset) -> int:\n\n indexes = [random.randint(0, len(dataset)) for _ in range(3)]\n return indexes","def next(self):\n with self.lock:\n index_array = next(self.index_generator)\n # The transformation of images is not under thread lock\n # so it can be done in parallel\n return self._get_batches_of_transformed_samples(index_array)","def iter_leaf_idx(self):\n def leaf_idx(tree, total):\n total[0] += 1\n for elem in tree[1:]:\n if isinstance(elem, Tree):\n for elem2 in leaf_idx(elem, total):\n yield total[0]\n else:\n yield total[0]\n total[0] += 1\n return leaf_idx(self, [0])","def indices(self):\n i, j, _edge = self.indicesAndEdge()\n return i, j","def vir_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==0:\n indices.append(index)\n return indices","def test_get_indices_several_existing_items(self):\r\n control_ids = ['PC.354', 'PC.355', 'PC.356', 'PC.481', 'PC.593']\r\n exp_control_indices = [0, 1, 2, 3, 4]\r\n\r\n fast_ids = ['PC.607', 'PC.634', 'PC.635', 'PC.636']\r\n exp_fast_indices = [5, 6, 7, 8]\r\n\r\n obs_control = _get_indices(self.dist_matrix_header, control_ids)\r\n self.assertEqual(obs_control, exp_control_indices)\r\n\r\n obs_fast = _get_indices(self.dist_matrix_header, fast_ids)\r\n self.assertEqual(obs_fast, exp_fast_indices)","def create_jackknife_indexes(data):\n from numpy import arange, delete\n\n index_range = arange(0, len(data))\n return (delete(index_range, i) for i in index_range)","def __iter__(self):\n for i in range(len(self.ks)):\n yield self.get_neighs([i]), self.get_sp_rel_pos([i]),\\\n [self.ks[i]], self.iss","def get_inter_sample_indices(self):\n inter_sample_indices = []\n for group_index1 in range(self.num_groups):\n for group_index2 in range(self.num_groups):\n num_images_in_group1 = self.num_images_per_group[group_index1]\n num_images_in_group2 = self.num_images_per_group[group_index2]\n for image_index1 in range(num_images_in_group1):\n for image_index2 in range(num_images_in_group2):\n inter_sample_indices.append(\n ((group_index1, image_index1), (group_index2, image_index2))\n )\n return inter_sample_indices","def getContents(self):\r\n cont=[]\r\n for i in range (len(self._indices)):\r\n cont.append(self._dataset.getPoint(self._indices[i]))\r\n return cont","def _notstaticneighs_get_corestored_by_inds_slice(self, inds):\n inds = [inds] if type(inds) == int else inds\n idxs = self.idxs\n if self.sp_relative_pos is not None:\n sp_relative_pos = []\n for k in range(len(self.sp_relative_pos)):\n sp_relative_pos += [[self.sp_relative_pos[k][i] for i in inds]]\n else:\n sp_relative_pos = None\n return idxs, sp_relative_pos","def __iter__(self):\n batch = []\n for idx in self._sampler:\n batch.append(idx)\n if len(batch) == self._batch_size:\n batch = sum(batch, [])\n yield batch\n batch = []\n if len(batch) > 0:\n batch = sum(batch, [])\n yield batch","def getNeighbours(seg,meta,inversedIndex):\n return np.unique(np.fromiter( (inversedIndex[x] for x in np.concatenate([meta.loc[seg]['ins'],meta.loc[seg]['outs']])),dtype=np.int))","def _calculate_chunk_offsets(self):\n offset = 0\n offsets = []\n for chunk in self.data.iterchunks():\n offsets.append(offset)\n offset += len(chunk)\n return np.array(offsets)","def read_ix(self, indexes: np.ndarray) -> Iterator[XData]:\n index_list = np.array_split(\n indexes, range(self.batchsize, indexes.shape[0], self.batchsize)\n )\n return self._run(index_list)","def iteritems(self):\n for name, data_containers in self._name_index.items():\n for data_container in data_containers:\n yield data_container","def __iter__(self):\n for i in range(self.m):\n for j in range(self.n):\n yield self[i, j]","def _pair_indices(self):\n indices_src = []\n indices_dst = []\n for i in range(self.walk_len):\n for j in range(max(i - self.l, 0), i):\n indices_src.append(i)\n indices_dst.append(j)\n for j in range(i + 1, min(i + self.r + 1, self.walk_len)):\n indices_src.append(i)\n indices_dst.append(j)\n return indices_src, indices_dst","def _get_indices_from_payload(self):\n for _, value in self.s_namespaces.items():\n for index in value['indexes'].items():\n yield index","def find_offsets(haystack, needle):\n\toffs = -1\n\twhile True:\n\t\toffs = haystack.find(needle, offs+1)\n\t\tif offs == -1:\n\t\t\tbreak\n\t\telse:\n\t\t\tyield offs","def index(self):\n return copy.deepcopy(self._innercontainer)","def byte_offsets(self) -> Iterator[int]:\n\n start_index = self.pixels_offset\n end_index = self.pixels_offset + self.pixel_size_bytes\n scanline_bytes = self.pixel_size_bytes // self.height\n\n for scanline in range(start_index, end_index, scanline_bytes):\n yield from range(scanline, scanline + self.width * 3)","def batch_iter(data, batch_size, shuffle=False):\n batch_num = math.ceil(len(data) / batch_size)\n index_array = list(range(len(data)))\n\n if shuffle:\n np.random.shuffle(index_array)\n\n for i in range(batch_num):\n indices = index_array[i * batch_size: (i + 1) * batch_size]\n examples = [data[idx] for idx in indices]\n\n examples = sorted(examples, key=lambda e: len(e[0]), reverse=True)\n src_sents = [e[0] for e in examples]\n tgt_sents = [e[1] for e in examples]\n\n yield src_sents, tgt_sents","def batch_iter(data, batch_size, shuffle=False):\n batch_num = math.ceil(len(data) / batch_size)\n index_array = list(range(len(data)))\n\n if shuffle:\n np.random.shuffle(index_array)\n\n for i in range(batch_num):\n indices = index_array[i * batch_size: (i + 1) * batch_size]\n examples = [data[idx] for idx in indices]\n\n examples = sorted(examples, key=lambda e: len(e[0]), reverse=True)\n src_sents = [e[0] for e in examples]\n tgt_sents = [e[1] for e in examples]\n\n yield src_sents, tgt_sents","def __iter__(self):\n for i in self.loopindices:\n pid = self.frametracks.particle.values[i]\n yield pid, self.neighbors(pid)","def __getitem__(self, index):\n #print(\"%d / %d\" %(index, np.floor(len(self.list_IDs) / self.batch_size)))\n indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n\n # Find list of IDs\n list_IDs_temp = [self.list_IDs[k] for k in indexes]\n\n # Generate data\n X, y = self.__data_generation(list_IDs_temp)\n\n return X, y","def indices(self):\n return self._kbounded_partitions","def _staticneighs_get_corestored_by_inds_slice(self, inds):\n inds = [inds] if type(inds) == int else inds\n idxs = self.idxs\n if self.sp_relative_pos is not None:\n sp_relative_pos = [self.sp_relative_pos[i] for i in inds]\n else:\n sp_relative_pos = None\n return idxs, sp_relative_pos","def _getscanind(self):\n \n zamin = self.za.min()\n first = np.where(self.za==zamin)[0]\n self.scan = np.zeros(self.spec.shape[0])\n if zamin < 0:\n cs = first[np.where((first - np.roll(first, 1)) != 1)[0]]\n ss = first[np.where((np.roll(first,-1) - first) != 1)[0]] + 1\n ce = ss \n se = np.roll((cs - 1) % self.za.size, -1) + 1\n for k, val in enumerate(cs):\n self.scan[val:se[k] + 1] = k\n else:\n moves = np.diff(self.za)\n max_ind = np.where(moves==moves.max())[0]\n turnover = self.za.size\n diffs = np.diff(max_ind)\n if np.unique(diffs).size > 1:\n raise ValueError, 'Can\\'t deal with non-uniform cal data yet.'\n if max_ind.size > 1:\n turnover = diffs[0]\n cs = ce = np.array([])\n ss = np.arange(self.za.size)[::turnover]\n se = np.roll((ss - 1) % self.za.size, -1)\n for k, val in enumerate(ss):\n self.scan[val:se[k] + 1] = k\n \n self.ind = {'cs': cs, 'ce': ce, 'ss': ss, 'se': se}\n self.nscan = np.unique(self.scan).size","def get_indices_input_target(num_obs, input_len, step_size, forecast_horizon, target_len):\n input_len = round(input_len) # just a precaution\n start_position = 0\n stop_position = num_obs - 1\n\n inpseq_first_idx = start_position\n inpseq_last_idx = inpseq_first_idx + input_len\n target_first_idx = inpseq_last_idx + forecast_horizon\n target_last_idx = target_first_idx + target_len\n print(\"target_last_idx = {}\".format(target_last_idx))\n print(\"stop_position = {}\".format(stop_position))\n indices = []\n while target_last_idx <= stop_position:\n indices.append((inpseq_first_idx, inpseq_last_idx, target_first_idx, target_last_idx))\n inpseq_first_idx += step_size\n inpseq_last_idx += step_size\n target_first_idx += inpseq_last_idx + forecast_horizon\n target_last_idx += target_first_idx + target_len\n return indices","def test_should_only_get_specific_indices(self):\n print(\"Testing that get_region returns correct amount of data\")\n\n test_many = get_region_data(self.wmo_boxes, self.float_name, self.config,\n self.index, self.pres)\n\n self.assertTrue(test_many[0].shape[1] == self.index.__len__())\n\n test_one = get_region_data(self.wmo_boxes, self.float_name, self.config,\n [50], self.pres)\n\n self.assertTrue(test_one[0].shape[1] == 1)","def iter_positions(self):\n for loc in self.iter_locations():\n yield loc.position","def _get_child_indices(self, current_index: int) -> List[int]:\n multiplier = current_index * 2\n left_index = multiplier + 1\n right_index = multiplier + 2\n\n return [left_index, right_index]","def _get_next_minibatch_inds(self):\n img_next = self._cursor[0].next()\n msk_next = self._cursor[1].next()\n if img_next and msk_next:\n pass\n else:\n print 'BlobFetcher to begin because of cursor point to end.'\n self._cursor = [self._txn[0].cursor(), self._txn[1].cursor()]\n self._cursor[0].next()\n self._cursor[1].next()","def _get_indices_from_iss(self, iss):\n iss = [iss] if type(iss) not in [np.ndarray, list] else iss\n if self.iss is not None:\n inds = []\n for i in iss:\n inds.append(list(self.iss).index(i))\n# else:\n# inds = iss\n return inds","def get_idx_dataset(self, split):\r\n def generator():\r\n while True:\r\n batch_idx = self.get_batch_idx(split)\r\n yield tf.constant(batch_idx, dtype=tf.int32)\r\n return tf.data.Dataset.from_generator(\r\n generator,\r\n output_types=tf.int32,\r\n output_shapes=[None])","def getscanind(self,blk=None,zarange=[0,90]):\n ind=self.getind('ss','se',arr(blk))\n ind=ind[(self.za[ind]>=zarange[0]) & (self.za[ind]<=zarange[1])]\n return ind","def getidx(self, ind: Union[str, int]) -> List[Any]:\n output = []\n for data in self.data:\n output.append(data[ind])\n return output","def get_batch(self, idxs):\r\n return self.data[(self.start + idxs) % self.maxlen]","def get_data(self):\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\n return self._data[idxs].copy()","def _next(self):\n i = 0\n while i < self.size:\n yield self.data[i]\n i += 1","def get_cached_indices(self, start=None, end=None):\n params = {}\n indices = [\n y[\"sample_identifier\"]\n for y in self.mongo_database.cache.find(\n params, {\"_id\": 0, \"sample_identifier\": 1}\n )[start:end]\n ]\n return np.unique(indices).tolist()","def __iter__(self):\n\n # Open the data reader\n self.data.open()\n\n starts = np.arange(self.start, self.stop, self.chunksize)\n for a, b in zip_longest(starts, starts[1:], fillvalue=self.stop):\n yield self.data.read(a, b, **self.kwargs)","def __iter__(self):\n for i in range(len(self.data)):\n yield self.data[i]","def index(self):\n return self.data.index.values","def _iter_sims(self):\n for idx, lineset in enumerate(self.linesets[:-1]):\n for lineset2 in self.linesets[idx + 1 :]:\n yield from self._find_common(lineset, lineset2)","def _neuron_location(self, m, n):\n for i in range(m):\n for j in range(n):\n yield np.array([i, j])"],"string":"[\n \"def get_subset_inds(self, adata_parent):\\r\\n subset_inds = np.ones(len(adata_parent), dtype=bool)\\r\\n for condition, values in self.subset_cond.items():\\r\\n subset_inds *= adata_parent.obs[condition].isin(values)\\r\\n return subset_inds\",\n \"def enumerate(self):\\n # go through the container and tile in sync\\n for index, value in zip(self.tile, self.data):\\n # hand the {index} and the corresponding value to the caller\\n yield index, value\\n # all done\\n return\",\n \"def indices(self):\\n return range(len(self))\",\n \"def mainIndices(self):\\n return self.i1, self.i2\",\n \"def __iter__(self):\\n indices = []\\n for i, size in enumerate(self.group_sizes):\\n if size == 0:\\n continue\\n indice = np.where(self.flag == i)[0]\\n if not len(indice) == size:\\n raise ValueError('the length of the indice should be equal to the size')\\n np.random.shuffle(indice)\\n num_extra = int(np.ceil(size / self.samples_per_gpu)\\n ) * self.samples_per_gpu - len(indice)\\n indice = np.concatenate([indice, indice[:num_extra]])\\n indices.append(indice)\\n indices = np.concatenate(indices)\\n indices = [\\n indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]\\n for i in np.random.permutation(\\n range(len(indices) // self.samples_per_gpu))\\n ]\\n indices = np.concatenate(indices)\\n indices = torch.from_numpy(indices).long()\\n if not len(indices) == self.num_samples:\\n raise ValueError(\\\"the length of the indices should be equal to num_samples\\\")\\n return iter(indices)\",\n \"def indices(self):\\n return tuple([slice(*r) for r in self.location])\",\n \"def indices(self) -> np.ndarray:\\n return self.impl.indices\",\n \"def get_index_array(self):\\n return self.region_pairs\",\n \"def __iter__(self):\\n\\n starts = range(0, self.data.shape[self.axis], self.chunksize)\\n\\n for t in zip_longest(starts, starts[1:], fillvalue=None):\\n yield self.data[self._slice(*t)]\",\n \"def __iter__(self):\\n while True:\\n if self.batches is None:\\n for indexed_sentence in self.indexed_sentences:\\n yield indexed_sentence\\n else:\\n for batch in self.batches:\\n yield batch[:-1, :], batch[1:, :] # Return batch and target indices\\n\\n if not self.repeat:\\n return\",\n \"def get_data_idx(self)->list:\\n return self.__data_idx\",\n \"def get_indices(self):\\r\\n return self._indices\",\n \"def getind(self,start,end,blk):\\n\\n if blk is None:\\n # Return all blocks\\n blk = np.arange(self.ind[start].size)\\n\\n ind=np.array([])\\n for k,val in enumerate(blk):\\n ind=np.append(ind,np.arange(self.ind[start][val],self.ind[end][val]))\\n return ind.astype(int)\",\n \"def inidices(self):\\n return [Index(name, index) for name, index in self._data['indices'].iteritems()]\",\n \"def index_iterator((x_min, x_max, y_min, y_max)):\\n for row in xrange(y_min, y_max):\\n for col in xrange(x_min, x_max):\\n yield (row, col)\",\n \"def _get_split_indices(self):\\n\\n cumsum = np.cumsum(\\n np.concatenate((np.array([0], dtype=np.int8), self.split_sizes)))\\n \\n fold_inds = np.array(\\n [(cumsum[n], cumsum[n + 1]) for n in range(self.n_splits)])\\n\\n return fold_inds\",\n \"def get_indexes(self, dataset: BaseDataset) -> int:\\n\\n index = [np.random.randint(0, len(dataset)) for _ in range(1)]\\n\\n return index\",\n \"def __iter__(self):\\n # deterministically shuffle based on epoch\\n if self.shuffle:\\n g = torch.Generator()\\n g.manual_seed(self.epoch)\\n indices = torch.randperm(len(self.dataset), generator=g).tolist()\\n else:\\n indices = torch.arange(len(self.dataset)).tolist()\\n\\n # add extra samples to make it evenly divisible\\n indices += indices[:(self.total_size - len(indices))]\\n if not len(indices) == self.total_size:\\n raise ValueError('the length of the indices should be equal to total_size')\\n\\n # subsample\\n indices = indices[self.rank:self.total_size:self.num_replicas]\\n if not len(indices) == self.num_samples:\\n raise ValueError(\\\"the length of the indices should be equal to num_samples in subsample\\\")\\n\\n return iter(indices)\",\n \"def _get_chunk_indexer(self, array):\\n if self.data.num_chunks == 1:\\n return np.broadcast_to(0, len(array))\\n return np.digitize(array, self.offsets[1:])\",\n \"def get_intra_sample_indices(self):\\n intra_sample_indices = []\\n for group_index in range(self.num_groups):\\n num_images_in_group = self.num_images_per_group[group_index]\\n if self.intra_group_option == \\\"forward\\\":\\n for i in range(num_images_in_group):\\n for j in range(i):\\n # j < i\\n intra_sample_indices.append(\\n ((group_index, j), (group_index, i))\\n )\\n elif self.intra_group_option == \\\"backward\\\":\\n for i in range(num_images_in_group):\\n for j in range(i):\\n # i > j\\n intra_sample_indices.append(\\n ((group_index, i), (group_index, j))\\n )\\n elif self.intra_group_option == \\\"unconstrained\\\":\\n for i in range(num_images_in_group):\\n for j in range(i):\\n # j < i, i > j\\n intra_sample_indices.append(\\n ((group_index, j), (group_index, i))\\n )\\n intra_sample_indices.append(\\n ((group_index, i), (group_index, j))\\n )\\n else:\\n raise ValueError(\\n \\\"Unknown intra_group_option, must be forward/backward/unconstrained\\\"\\n )\\n return intra_sample_indices\",\n \"def create_repeated_indexes(data):\\n from numpy import arange\\n\\n index_range = arange(0, len(data))\\n return (index_range for i in index_range)\",\n \"def get_indices_entire_sequence(data: pd.Dataframe, window_size: int, step_size: int) -> list:\\n stop_position = len(data)-1 # 1- because of 0 indexing\\n\\n # Start the first sub-sequence at index position 0\\n subseq_first_idx = 0\\n\\n subseq_last_idx = subseq_first_idx + window_size\\n\\n indices = []\\n\\n while subseq_last_idx <= stop_position:\\n indices.append((subseq_first_idx, subseq_last_idx))\\n subseq_first_idx += step_size\\n subseq_last_idx += step_size\\n return indices\",\n \"def batch_indices(self):\\n b = self.batch_size\\n return [np.arange(i*b, i*b+b) for i in range(self.num_batches)]\",\n \"def atom_idxs(self):\\n\\n return np.array([atom.atom_idxs for atom in self])\",\n \"def indices(self):\\n _indices = []\\n for h in self.miller.indices():\\n _indices.append(self.indices_hkl(*h)[0])\\n return _indices\",\n \"def getLandmarkindices(self):\\n return self.subsetindices\",\n \"def __iter__(self):\\n batch = []\\n for i_batch in range(self.episode_num):\\n classes = torch.randperm(len(self.idx_list))[: self.way_num]\\n for c in classes:\\n idxes = self.idx_list[c.item()]\\n pos = torch.randperm(idxes.size(0))[: self.image_num]\\n batch.append(idxes[pos])\\n if len(batch) == self.episode_size * self.way_num:\\n batch = torch.stack(batch).reshape(-1)\\n yield batch\\n batch = []\",\n \"def _iterCoordsets(self):\\n\\n for i in range(self._n_csets):\\n yield self._coords[i]\",\n \"def iterCoordsets(self):\\n\\n for i in range(self._n_csets):\\n yield self._coords[i].copy()\",\n \"def __iter__(self):\\n worker_info = torch.utils.data.get_worker_info()\\n num_batches = len(self.coords_batcher)\\n if worker_info is None:\\n # In single-processing mode\\n start, end = 0, num_batches\\n else:\\n worker_id = worker_info.id\\n num_workers = worker_info.num_workers\\n shard_size = int(np.ceil(num_batches / num_workers))\\n start = shard_size * worker_id\\n end = min(start + shard_size, num_batches)\\n return (self.get_batch(i) for i in range(start, end))\",\n \"def index(self):\\n return list(self._innercontainer)\",\n \"def get_indexes(self, dataset):\\n\\n indexs = [random.randint(0, len(dataset)) for _ in range(3)]\\n return indexs\",\n \"def indices_of_split(self, split_name='train'):\\n return self.indices_of('split', split_name)\",\n \"def iter_sites(self):\\n for i in range(self.num_sites):\\n indices = list()\\n for dim in self.site_idxs:\\n indices.append(i % dim)\\n i = i // dim\\n yield tuple(indices)\",\n \"def get_34index_list(self):\\n msk = self.load_mask()\\n return [i for (i,v) in enumerate(msk) if v==1]\",\n \"def _get_index_iterator(indexes, length):\\n return combinations(indexes, length)\",\n \"def _iter_indices(self, frame, y):\\n pass\",\n \"def getIndices(self):\\r\\n return self._indices\",\n \"def indices(self):\\n return self.index.indices\",\n \"def SectionIndicesConnectedToSoma(self):\\n indices = []\\n index = 0\\n for each_section in self._section_list:\\n if each_section.ParentId() == -1:\\n indices.append(index)\\n index += 1\\n return indices\",\n \"def get_idxvals(self):\\n input_rows = list()\\n input_cols = list()\\n for key in self.index:\\n input_rows.append(key[0])\\n input_cols.append(key[1])\\n\\n return list(OrderedSet(input_rows)), list(OrderedSet(input_cols))\",\n \"def _compute_data_offsets(store: Store,\\n datas: Tuple[DataSegment, ...],\\n module_instance: ModuleInstance) -> Iterable[numpy.uint32]:\\n for data_segment in datas:\\n frame = Frame(\\n module=module_instance,\\n locals=[],\\n instructions=InstructionSequence(data_segment.offset),\\n arity=1,\\n )\\n config = Configuration(store=store)\\n config.push_frame(frame)\\n result = config.execute()\\n if len(result) != 1:\\n raise Exception(\\\"Invariant: data segment offset returned empty result\\\")\\n offset = numpy.uint32(cast(int, result[0]))\\n\\n memory_address = module_instance.memory_addrs[data_segment.memory_idx]\\n memory_instance = store.mems[memory_address]\\n\\n if offset + len(data_segment.init) > len(memory_instance.data):\\n raise Unlinkable(\\n f\\\"Computed data segment offset exceeds memory size: {offset} \\\"\\n f\\\"+ {len(data_segment.init)} > {len(memory_instance.data)}\\\"\\n )\\n yield offset\",\n \"def getAllIndex(ldata, fldata):\\n\\treturn list(map(lambda e : fldata.index(e), ldata))\",\n \"def __iter__(self):\\n # deterministically shuffle based on epoch\\n g = torch.Generator()\\n g.manual_seed(self.epoch)\\n\\n indices = []\\n for i, size in enumerate(self.group_sizes):\\n if size > 0:\\n indice = np.where(self.flag == i)[0]\\n if not len(indice) == size:\\n raise ValueError(\\\"the length of the indice should be equal to size\\\")\\n indice = indice[list(torch.randperm(int(size),\\n generator=g))].tolist()\\n extra = int(\\n math.ceil(\\n size * 1.0 / self.samples_per_gpu / self.num_replicas)\\n ) * self.samples_per_gpu * self.num_replicas - len(indice)\\n indice += indice[:extra]\\n indices += indice\\n\\n if not len(indices) == self.total_size:\\n raise ValueError(\\\"the length of the indices should be equal to total_size\\\")\\n\\n indices = [\\n indices[j] for i in list(\\n torch.randperm(\\n len(indices) // self.samples_per_gpu, generator=g))\\n for j in range(i * self.samples_per_gpu, (i + 1) * self.samples_per_gpu)\\n ]\\n\\n # subsample\\n offset = self.num_samples * self.rank\\n indices = indices[offset:offset + self.num_samples]\\n if not len(indices) == self.num_samples:\\n raise ValueError(\\\"the length of the indices should be equal to num_samplers in subsample\\\")\\n\\n return iter(indices)\",\n \"def next(self):\\n # Keeps under lock only the mechanism which advances\\n # the indexing of each batch.\\n with self.lock:\\n index_array = next(self.index_generator)\\n # The transformation of images is not under thread lock\\n # so it can be done in parallel\\n return self._get_batches_of_transformed_samples(index_array)\",\n \"def indices(self):\\n if self._indices is None:\\n i = []\\n\\n # TODO: this is not right for multi-column keys\\n # TODO: new style indexes\\n\\n global_name = '^DD(%s,0,\\\"IX\\\",\\\"0\\\")' % self.fileid\\n prefix = '^DD(%s,0,\\\"IX\\\",' % self.fileid\\n while 1:\\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\\\"\\\"))[0]\\n if not global_name or not global_name.startswith(prefix):\\n break\\n suffix = global_name[len(prefix):-1]\\n parts = suffix.split(\\\",\\\")\\n idx_name = parts[0][1:-1]\\n idx_table = parts[1]\\n idx_columns = parts[2:]\\n index = Index(idx_name, idx_table, idx_columns)\\n i.append(index)\\n\\n # A second list, gives indices for a field\\n columns = {}\\n for idx in i:\\n for c in idx.columns:\\n columns[c] = 1\\n\\n # Now trawl the listed columns in the data dictionary, and load their\\n # cross references.\\n cr_names = {}\\n for c in columns.keys():\\n idx_root = M.Globals[\\\"^DD\\\"][self.fileid][c][1]\\n if not idx_root[0].exists():\\n continue\\n for cr_id, val in idx_root.keys_with_decendants():\\n if float(cr_id) > 0:\\n cr_header = idx_root[cr_id][0].value\\n parts = cr_header.split(\\\"^\\\")\\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\\n f = cr_names.get(parts[1], list())\\n f.append(c)\\n cr_names[parts[1]] = f\\n\\n # Now, just delete items from the index list if they are not in cr_names\\n self._indices = []\\n for index in i:\\n cr = cr_names.get(index.name)\\n if cr:\\n # verify columns - lots of errors in real systems\\n if len(cr) == len(index.columns):\\n invalid = False\\n for c in cr:\\n if c not in index.columns:\\n invalid = True\\n continue\\n if not invalid:\\n self._indices.append(index)\\n\\n return self._indices\",\n \"def get_indexes(self, dataset):\\n\\n for i in range(self.max_iters):\\n index = random.randint(0, len(dataset))\\n gt_bboxes_i = dataset.get_ann_info(index)['bboxes']\\n if len(gt_bboxes_i) != 0:\\n break\\n\\n return index\",\n \"def get_indexes(self, items: Iterable[_T]) -> List[int]:\\n return [self.get_index(item) for item in items]\",\n \"def get_indexes(self, variable, *args):\\n\\n return [get_subset_idxs(data, min, max)\\n for data, (min, max) in args]\",\n \"def getLandmarkindices(self):\\n return self.subsetnodes_indices\",\n \"def getIndexes(self, val):\\n # Find where this value is listed. \\n valNdx = (self.values == val).nonzero()[0]\\n \\n # If this value is not actually in those listed, then we \\n # must return empty indexes\\n if len(valNdx) == 0:\\n start = 0\\n end = 0\\n else:\\n # The index into counts, etc. for this value. \\n valNdx = valNdx[0]\\n start = self.start[valNdx]\\n end = self.end[valNdx]\\n \\n # Create a tuple of index arrays, one for each index of the original array. \\n ndx = ()\\n for i in range(self.nDims):\\n ndx += (self.indexes[start:end, i], )\\n return ndx\",\n \"def get_raster_ids(self):\\n return numpy.array(range(self._lo_atom, self._lo_atom + self._n_atoms))\",\n \"def get_indexes(self):\\n indexes = []\\n for c in self.components:\\n indexes.extend(c.get_indexes())\\n return indexes\",\n \"def get_indexes(self, dataset: BaseDataset) -> int:\\n\\n indexes = [random.randint(0, len(dataset)) for _ in range(3)]\\n return indexes\",\n \"def next(self):\\n with self.lock:\\n index_array = next(self.index_generator)\\n # The transformation of images is not under thread lock\\n # so it can be done in parallel\\n return self._get_batches_of_transformed_samples(index_array)\",\n \"def iter_leaf_idx(self):\\n def leaf_idx(tree, total):\\n total[0] += 1\\n for elem in tree[1:]:\\n if isinstance(elem, Tree):\\n for elem2 in leaf_idx(elem, total):\\n yield total[0]\\n else:\\n yield total[0]\\n total[0] += 1\\n return leaf_idx(self, [0])\",\n \"def indices(self):\\n i, j, _edge = self.indicesAndEdge()\\n return i, j\",\n \"def vir_indices(self):\\n indices = []\\n for index,item in enumerate(self):\\n if item==0:\\n indices.append(index)\\n return indices\",\n \"def test_get_indices_several_existing_items(self):\\r\\n control_ids = ['PC.354', 'PC.355', 'PC.356', 'PC.481', 'PC.593']\\r\\n exp_control_indices = [0, 1, 2, 3, 4]\\r\\n\\r\\n fast_ids = ['PC.607', 'PC.634', 'PC.635', 'PC.636']\\r\\n exp_fast_indices = [5, 6, 7, 8]\\r\\n\\r\\n obs_control = _get_indices(self.dist_matrix_header, control_ids)\\r\\n self.assertEqual(obs_control, exp_control_indices)\\r\\n\\r\\n obs_fast = _get_indices(self.dist_matrix_header, fast_ids)\\r\\n self.assertEqual(obs_fast, exp_fast_indices)\",\n \"def create_jackknife_indexes(data):\\n from numpy import arange, delete\\n\\n index_range = arange(0, len(data))\\n return (delete(index_range, i) for i in index_range)\",\n \"def __iter__(self):\\n for i in range(len(self.ks)):\\n yield self.get_neighs([i]), self.get_sp_rel_pos([i]),\\\\\\n [self.ks[i]], self.iss\",\n \"def get_inter_sample_indices(self):\\n inter_sample_indices = []\\n for group_index1 in range(self.num_groups):\\n for group_index2 in range(self.num_groups):\\n num_images_in_group1 = self.num_images_per_group[group_index1]\\n num_images_in_group2 = self.num_images_per_group[group_index2]\\n for image_index1 in range(num_images_in_group1):\\n for image_index2 in range(num_images_in_group2):\\n inter_sample_indices.append(\\n ((group_index1, image_index1), (group_index2, image_index2))\\n )\\n return inter_sample_indices\",\n \"def getContents(self):\\r\\n cont=[]\\r\\n for i in range (len(self._indices)):\\r\\n cont.append(self._dataset.getPoint(self._indices[i]))\\r\\n return cont\",\n \"def _notstaticneighs_get_corestored_by_inds_slice(self, inds):\\n inds = [inds] if type(inds) == int else inds\\n idxs = self.idxs\\n if self.sp_relative_pos is not None:\\n sp_relative_pos = []\\n for k in range(len(self.sp_relative_pos)):\\n sp_relative_pos += [[self.sp_relative_pos[k][i] for i in inds]]\\n else:\\n sp_relative_pos = None\\n return idxs, sp_relative_pos\",\n \"def __iter__(self):\\n batch = []\\n for idx in self._sampler:\\n batch.append(idx)\\n if len(batch) == self._batch_size:\\n batch = sum(batch, [])\\n yield batch\\n batch = []\\n if len(batch) > 0:\\n batch = sum(batch, [])\\n yield batch\",\n \"def getNeighbours(seg,meta,inversedIndex):\\n return np.unique(np.fromiter( (inversedIndex[x] for x in np.concatenate([meta.loc[seg]['ins'],meta.loc[seg]['outs']])),dtype=np.int))\",\n \"def _calculate_chunk_offsets(self):\\n offset = 0\\n offsets = []\\n for chunk in self.data.iterchunks():\\n offsets.append(offset)\\n offset += len(chunk)\\n return np.array(offsets)\",\n \"def read_ix(self, indexes: np.ndarray) -> Iterator[XData]:\\n index_list = np.array_split(\\n indexes, range(self.batchsize, indexes.shape[0], self.batchsize)\\n )\\n return self._run(index_list)\",\n \"def iteritems(self):\\n for name, data_containers in self._name_index.items():\\n for data_container in data_containers:\\n yield data_container\",\n \"def __iter__(self):\\n for i in range(self.m):\\n for j in range(self.n):\\n yield self[i, j]\",\n \"def _pair_indices(self):\\n indices_src = []\\n indices_dst = []\\n for i in range(self.walk_len):\\n for j in range(max(i - self.l, 0), i):\\n indices_src.append(i)\\n indices_dst.append(j)\\n for j in range(i + 1, min(i + self.r + 1, self.walk_len)):\\n indices_src.append(i)\\n indices_dst.append(j)\\n return indices_src, indices_dst\",\n \"def _get_indices_from_payload(self):\\n for _, value in self.s_namespaces.items():\\n for index in value['indexes'].items():\\n yield index\",\n \"def find_offsets(haystack, needle):\\n\\toffs = -1\\n\\twhile True:\\n\\t\\toffs = haystack.find(needle, offs+1)\\n\\t\\tif offs == -1:\\n\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\tyield offs\",\n \"def index(self):\\n return copy.deepcopy(self._innercontainer)\",\n \"def byte_offsets(self) -> Iterator[int]:\\n\\n start_index = self.pixels_offset\\n end_index = self.pixels_offset + self.pixel_size_bytes\\n scanline_bytes = self.pixel_size_bytes // self.height\\n\\n for scanline in range(start_index, end_index, scanline_bytes):\\n yield from range(scanline, scanline + self.width * 3)\",\n \"def batch_iter(data, batch_size, shuffle=False):\\n batch_num = math.ceil(len(data) / batch_size)\\n index_array = list(range(len(data)))\\n\\n if shuffle:\\n np.random.shuffle(index_array)\\n\\n for i in range(batch_num):\\n indices = index_array[i * batch_size: (i + 1) * batch_size]\\n examples = [data[idx] for idx in indices]\\n\\n examples = sorted(examples, key=lambda e: len(e[0]), reverse=True)\\n src_sents = [e[0] for e in examples]\\n tgt_sents = [e[1] for e in examples]\\n\\n yield src_sents, tgt_sents\",\n \"def batch_iter(data, batch_size, shuffle=False):\\n batch_num = math.ceil(len(data) / batch_size)\\n index_array = list(range(len(data)))\\n\\n if shuffle:\\n np.random.shuffle(index_array)\\n\\n for i in range(batch_num):\\n indices = index_array[i * batch_size: (i + 1) * batch_size]\\n examples = [data[idx] for idx in indices]\\n\\n examples = sorted(examples, key=lambda e: len(e[0]), reverse=True)\\n src_sents = [e[0] for e in examples]\\n tgt_sents = [e[1] for e in examples]\\n\\n yield src_sents, tgt_sents\",\n \"def __iter__(self):\\n for i in self.loopindices:\\n pid = self.frametracks.particle.values[i]\\n yield pid, self.neighbors(pid)\",\n \"def __getitem__(self, index):\\n #print(\\\"%d / %d\\\" %(index, np.floor(len(self.list_IDs) / self.batch_size)))\\n indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\\n\\n # Find list of IDs\\n list_IDs_temp = [self.list_IDs[k] for k in indexes]\\n\\n # Generate data\\n X, y = self.__data_generation(list_IDs_temp)\\n\\n return X, y\",\n \"def indices(self):\\n return self._kbounded_partitions\",\n \"def _staticneighs_get_corestored_by_inds_slice(self, inds):\\n inds = [inds] if type(inds) == int else inds\\n idxs = self.idxs\\n if self.sp_relative_pos is not None:\\n sp_relative_pos = [self.sp_relative_pos[i] for i in inds]\\n else:\\n sp_relative_pos = None\\n return idxs, sp_relative_pos\",\n \"def _getscanind(self):\\n \\n zamin = self.za.min()\\n first = np.where(self.za==zamin)[0]\\n self.scan = np.zeros(self.spec.shape[0])\\n if zamin < 0:\\n cs = first[np.where((first - np.roll(first, 1)) != 1)[0]]\\n ss = first[np.where((np.roll(first,-1) - first) != 1)[0]] + 1\\n ce = ss \\n se = np.roll((cs - 1) % self.za.size, -1) + 1\\n for k, val in enumerate(cs):\\n self.scan[val:se[k] + 1] = k\\n else:\\n moves = np.diff(self.za)\\n max_ind = np.where(moves==moves.max())[0]\\n turnover = self.za.size\\n diffs = np.diff(max_ind)\\n if np.unique(diffs).size > 1:\\n raise ValueError, 'Can\\\\'t deal with non-uniform cal data yet.'\\n if max_ind.size > 1:\\n turnover = diffs[0]\\n cs = ce = np.array([])\\n ss = np.arange(self.za.size)[::turnover]\\n se = np.roll((ss - 1) % self.za.size, -1)\\n for k, val in enumerate(ss):\\n self.scan[val:se[k] + 1] = k\\n \\n self.ind = {'cs': cs, 'ce': ce, 'ss': ss, 'se': se}\\n self.nscan = np.unique(self.scan).size\",\n \"def get_indices_input_target(num_obs, input_len, step_size, forecast_horizon, target_len):\\n input_len = round(input_len) # just a precaution\\n start_position = 0\\n stop_position = num_obs - 1\\n\\n inpseq_first_idx = start_position\\n inpseq_last_idx = inpseq_first_idx + input_len\\n target_first_idx = inpseq_last_idx + forecast_horizon\\n target_last_idx = target_first_idx + target_len\\n print(\\\"target_last_idx = {}\\\".format(target_last_idx))\\n print(\\\"stop_position = {}\\\".format(stop_position))\\n indices = []\\n while target_last_idx <= stop_position:\\n indices.append((inpseq_first_idx, inpseq_last_idx, target_first_idx, target_last_idx))\\n inpseq_first_idx += step_size\\n inpseq_last_idx += step_size\\n target_first_idx += inpseq_last_idx + forecast_horizon\\n target_last_idx += target_first_idx + target_len\\n return indices\",\n \"def test_should_only_get_specific_indices(self):\\n print(\\\"Testing that get_region returns correct amount of data\\\")\\n\\n test_many = get_region_data(self.wmo_boxes, self.float_name, self.config,\\n self.index, self.pres)\\n\\n self.assertTrue(test_many[0].shape[1] == self.index.__len__())\\n\\n test_one = get_region_data(self.wmo_boxes, self.float_name, self.config,\\n [50], self.pres)\\n\\n self.assertTrue(test_one[0].shape[1] == 1)\",\n \"def iter_positions(self):\\n for loc in self.iter_locations():\\n yield loc.position\",\n \"def _get_child_indices(self, current_index: int) -> List[int]:\\n multiplier = current_index * 2\\n left_index = multiplier + 1\\n right_index = multiplier + 2\\n\\n return [left_index, right_index]\",\n \"def _get_next_minibatch_inds(self):\\n img_next = self._cursor[0].next()\\n msk_next = self._cursor[1].next()\\n if img_next and msk_next:\\n pass\\n else:\\n print 'BlobFetcher to begin because of cursor point to end.'\\n self._cursor = [self._txn[0].cursor(), self._txn[1].cursor()]\\n self._cursor[0].next()\\n self._cursor[1].next()\",\n \"def _get_indices_from_iss(self, iss):\\n iss = [iss] if type(iss) not in [np.ndarray, list] else iss\\n if self.iss is not None:\\n inds = []\\n for i in iss:\\n inds.append(list(self.iss).index(i))\\n# else:\\n# inds = iss\\n return inds\",\n \"def get_idx_dataset(self, split):\\r\\n def generator():\\r\\n while True:\\r\\n batch_idx = self.get_batch_idx(split)\\r\\n yield tf.constant(batch_idx, dtype=tf.int32)\\r\\n return tf.data.Dataset.from_generator(\\r\\n generator,\\r\\n output_types=tf.int32,\\r\\n output_shapes=[None])\",\n \"def getscanind(self,blk=None,zarange=[0,90]):\\n ind=self.getind('ss','se',arr(blk))\\n ind=ind[(self.za[ind]>=zarange[0]) & (self.za[ind]<=zarange[1])]\\n return ind\",\n \"def getidx(self, ind: Union[str, int]) -> List[Any]:\\n output = []\\n for data in self.data:\\n output.append(data[ind])\\n return output\",\n \"def get_batch(self, idxs):\\r\\n return self.data[(self.start + idxs) % self.maxlen]\",\n \"def get_data(self):\\n idxs = self.get_indexes(self._start, self._length, self.maxsize)\\n return self._data[idxs].copy()\",\n \"def _next(self):\\n i = 0\\n while i < self.size:\\n yield self.data[i]\\n i += 1\",\n \"def get_cached_indices(self, start=None, end=None):\\n params = {}\\n indices = [\\n y[\\\"sample_identifier\\\"]\\n for y in self.mongo_database.cache.find(\\n params, {\\\"_id\\\": 0, \\\"sample_identifier\\\": 1}\\n )[start:end]\\n ]\\n return np.unique(indices).tolist()\",\n \"def __iter__(self):\\n\\n # Open the data reader\\n self.data.open()\\n\\n starts = np.arange(self.start, self.stop, self.chunksize)\\n for a, b in zip_longest(starts, starts[1:], fillvalue=self.stop):\\n yield self.data.read(a, b, **self.kwargs)\",\n \"def __iter__(self):\\n for i in range(len(self.data)):\\n yield self.data[i]\",\n \"def index(self):\\n return self.data.index.values\",\n \"def _iter_sims(self):\\n for idx, lineset in enumerate(self.linesets[:-1]):\\n for lineset2 in self.linesets[idx + 1 :]:\\n yield from self._find_common(lineset, lineset2)\",\n \"def _neuron_location(self, m, n):\\n for i in range(m):\\n for j in range(n):\\n yield np.array([i, j])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61020994","0.6072502","0.60690475","0.60687184","0.60543483","0.60275173","0.5924552","0.5918545","0.59088135","0.58821535","0.58567846","0.58429295","0.5828838","0.58083194","0.5794418","0.5793355","0.57828283","0.57748425","0.5756645","0.57550335","0.5752208","0.5744148","0.57440686","0.5740753","0.5721396","0.5708537","0.5681665","0.5668879","0.566354","0.5635181","0.5628518","0.5624042","0.5622284","0.56018215","0.5578469","0.5578117","0.5565186","0.55578977","0.55574375","0.55430126","0.5518523","0.5515029","0.55139816","0.55010974","0.5495244","0.5489729","0.5484076","0.5473604","0.54710835","0.5469012","0.5457938","0.5456938","0.5447555","0.54415137","0.54287857","0.5424696","0.54225737","0.5422566","0.5420563","0.5418336","0.5418161","0.54104763","0.54052794","0.54046303","0.53832614","0.5380916","0.5380549","0.53744274","0.5372589","0.5363425","0.53574026","0.53566873","0.53384525","0.53252476","0.53204423","0.5319284","0.5319284","0.53137565","0.53118867","0.53116673","0.53102565","0.5307818","0.5307543","0.530584","0.53044134","0.5303639","0.53010774","0.5301048","0.52996683","0.52966356","0.52903336","0.5284297","0.5282697","0.52732414","0.5264803","0.5264675","0.52633446","0.5261429","0.52567405","0.5251312"],"string":"[\n \"0.61020994\",\n \"0.6072502\",\n \"0.60690475\",\n \"0.60687184\",\n \"0.60543483\",\n \"0.60275173\",\n \"0.5924552\",\n \"0.5918545\",\n \"0.59088135\",\n \"0.58821535\",\n \"0.58567846\",\n \"0.58429295\",\n \"0.5828838\",\n \"0.58083194\",\n \"0.5794418\",\n \"0.5793355\",\n \"0.57828283\",\n \"0.57748425\",\n \"0.5756645\",\n \"0.57550335\",\n \"0.5752208\",\n \"0.5744148\",\n \"0.57440686\",\n \"0.5740753\",\n \"0.5721396\",\n \"0.5708537\",\n \"0.5681665\",\n \"0.5668879\",\n \"0.566354\",\n \"0.5635181\",\n \"0.5628518\",\n \"0.5624042\",\n \"0.5622284\",\n \"0.56018215\",\n \"0.5578469\",\n \"0.5578117\",\n \"0.5565186\",\n \"0.55578977\",\n \"0.55574375\",\n \"0.55430126\",\n \"0.5518523\",\n \"0.5515029\",\n \"0.55139816\",\n \"0.55010974\",\n \"0.5495244\",\n \"0.5489729\",\n \"0.5484076\",\n \"0.5473604\",\n \"0.54710835\",\n \"0.5469012\",\n \"0.5457938\",\n \"0.5456938\",\n \"0.5447555\",\n \"0.54415137\",\n \"0.54287857\",\n \"0.5424696\",\n \"0.54225737\",\n \"0.5422566\",\n \"0.5420563\",\n \"0.5418336\",\n \"0.5418161\",\n \"0.54104763\",\n \"0.54052794\",\n \"0.54046303\",\n \"0.53832614\",\n \"0.5380916\",\n \"0.5380549\",\n \"0.53744274\",\n \"0.5372589\",\n \"0.5363425\",\n \"0.53574026\",\n \"0.53566873\",\n \"0.53384525\",\n \"0.53252476\",\n \"0.53204423\",\n \"0.5319284\",\n \"0.5319284\",\n \"0.53137565\",\n \"0.53118867\",\n \"0.53116673\",\n \"0.53102565\",\n \"0.5307818\",\n \"0.5307543\",\n \"0.530584\",\n \"0.53044134\",\n \"0.5303639\",\n \"0.53010774\",\n \"0.5301048\",\n \"0.52996683\",\n \"0.52966356\",\n \"0.52903336\",\n \"0.5284297\",\n \"0.5282697\",\n \"0.52732414\",\n \"0.5264803\",\n \"0.5264675\",\n \"0.52633446\",\n \"0.5261429\",\n \"0.52567405\",\n \"0.5251312\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6835397"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2697,"cells":{"query":{"kind":"string","value":"Update the `!part` attribute inplace for new indices of the master array."},"document":{"kind":"string","value":"def new_part(self, indices, master_axis_to_position, master_flip):\n shape = self.shape\n\n if indices == [slice(0, stop, 1) for stop in shape]:\n return\n\n # ------------------------------------------------------------\n # If a dimension runs in the wrong direction then change its\n # index to account for this.\n #\n # For example, if a dimension with the wrong direction has\n # size 10 and its index is slice(3,8,2) then after the\n # direction is set correctly, the index needs to changed to\n # slice(6,0,-2):\n #\n # >>> a = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n # >>> a[slice(3, 8, 2)]\n # [6, 4, 2]\n # >>> a.reverse()\n # >>> print(a)\n # >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n # >>> a[slice(6, 0, -2)]\n # [6, 4, 2]\n # ------------------------------------------------------------\n\n if self._subarray.size > 1:\n indices = indices[:]\n\n p_flip = self.flip\n\n for axis, i in master_axis_to_position.items():\n\n if (axis not in p_flip and axis not in master_flip) or (\n axis in p_flip and axis in master_flip\n ):\n # This axis runs in the correct direction\n continue\n\n # Still here? Then this axis runs in the wrong\n # direction.\n\n # Reset the direction\n p_flip = p_flip[:]\n if axis in self.flip:\n p_flip.remove(axis)\n else:\n p_flip.append(axis)\n\n # Modify the index to account for the changed\n # direction\n size = shape[i]\n\n if isinstance(indices[i], slice):\n start, stop, step = indices[i].indices(size)\n # Note that step is assumed to be always +ve here\n div, mod = divmod(stop - start - 1, step)\n start = size - 1 - start\n stop = start - div * step - 1\n if stop < 0:\n stop = None\n indices[i] = slice(start, stop, -step)\n else:\n size -= 1\n indices[i] = [size - j for j in indices[i]]\n # --- End: for\n\n self.flip = p_flip\n # --- End: if\n\n slice_None = slice(None)\n\n # Reorder the new indices\n indices = [\n (\n indices[master_axis_to_position[axis]]\n if axis in master_axis_to_position\n else slice_None\n )\n for axis in self.axes\n ]\n\n part = self.part\n\n if not part:\n self.part = indices\n return\n\n # Still here? update an existing part\n p_part = []\n for part_index, index, size in zip(\n part, indices, self._subarray.shape\n ):\n\n if index == slice_None:\n p_part.append(part_index)\n continue\n\n if isinstance(part_index, slice):\n if isinstance(index, slice):\n\n start, stop, step = part_index.indices(size)\n\n size1, mod = divmod(stop - start - 1, step)\n\n start1, stop1, step1 = index.indices(size1 + 1)\n\n size2, mod = divmod(stop1 - start1, step1)\n\n if mod != 0:\n size2 += 1\n\n start += start1 * step\n step *= step1\n stop = start + (size2 - 1) * step\n\n if step > 0:\n stop += 1\n else:\n stop -= 1\n if stop < 0:\n stop = None\n p_part.append(slice(start, stop, step))\n\n continue\n else:\n new_part = list(range(*part_index.indices(size)))\n new_part = [new_part[i] for i in index]\n else:\n if isinstance(index, slice):\n new_part = part_index[index]\n else:\n new_part = [part_index[i] for i in index]\n # --- End: if\n\n # Still here? Then the new element of p_part is a list of\n # integers, so let's see if we can convert it to a slice\n # before appending it.\n new_part0 = new_part[0]\n if len(new_part) == 1:\n # Convert a single element list to a slice object\n new_part = slice(new_part0, new_part0 + 1, 1)\n else:\n step = new_part[1] - new_part0\n if step:\n if step > 0:\n start, stop = new_part0, new_part[-1] + 1\n else:\n start, stop = new_part0, new_part[-1] - 1\n if new_part == list(range(start, stop, step)):\n if stop < 0:\n stop = None\n new_part = slice(start, stop, step)\n # --- End: if\n\n p_part.append(new_part)\n # --- End: for\n\n self.part = p_part"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save(self, patch):\n internalSlices = self._get_internal_slices(patch.slices)\n self.array[internalSlices] = patch.array","def _idx_changed(self, idx):\n self.refresh_memory()","def _loadpart(self, part):\n new_partidx = util.Partname(part.partname).idx\n for idx, seq_part in enumerate(self._values):\n partidx = util.Partname(seq_part.partname).idx\n if partidx > new_partidx:\n self._values.insert(idx, part)\n return\n self._values.append(part)","def _idxs_postformat_array(self):\n self.idxs = np.array(self.idxs)","def __update(self, idx):\n parent = (idx - 1) // 2\n while parent >= 0:\n left, right = 2 * parent + 1, 2 * parent + 2\n self.__tree[parent] = self.__tree[left] + self.__tree[right]\n parent = (parent - 1) // 2","def part_ids(self, part_ids):\n\n self._part_ids = part_ids","def update_chunk(self):\n for key, value in self.piece_coordinates.items():\n # Why is the key a numpy.int type ???\n self.chunk[value] = key","def update_idx(self):\n self.idx = (self.F * self.FMUL +\n self.E * self.EMUL +\n self.Z * self.ZMUL +\n self.A * self.AMUL +\n self.B * self.BMUL )","def setInternalIndex(self,ind):\n\t\tself.trMtrxNode_ind = ind","def reindex_subcomponent_taxa(self):\n ti_mutable = self.taxon_set._is_mutable\n self.taxon_set._is_mutable = True\n new_map = CharacterDataMap()\n for taxon, seq in self.taxon_seq_map.items():\n taxon = self.taxon_set.require_taxon(label=taxon.label)\n new_map[taxon] = seq\n self.taxon_set._is_mutable = ti_mutable\n self.taxon_seq_map = new_map","def set(self, idx_in, vals, fill=False):\n o = np.broadcast_arrays(vals, *idx_in)\n vals = np.ravel(o[0])\n\n # TODO: Determine whether new vs. existing indices are being\n # addressed, in the latter case we only need to update data\n # array\n\n vals = np.array(vals, ndmin=1)\n idx_flat_in, msk_in = self._to_flat_index(idx_in)\n vals = np.asanyarray(vals, dtype=self.data.dtype)\n idx, data = merge_sparse_arrays(\n idx_flat_in, vals, self.idx, self.data, fill=fill\n )\n\n # Remove elements equal to fill value\n msk = data != self._fill_value\n idx = idx[msk]\n data = data[msk]\n self._idx = idx\n self._data = data\n # idx, msk = find_in_array(idx_flat_in, self.idx)\n # self._data[idx[msk]] = vals[msk]","def _setActiveChildWellIndices(self,activeWellIndices):\n if activeWellIndices is None:\n activeWellIndices=list(range(0,len(self.childWellIndices())))\n\n if len(activeWellIndices) > 0:\n includedIndices=set()\n for localdataidx in activeWellIndices:\n if localdataidx < 0 or localdataidx >= len(self.childWellIndices()):\n raise RuntimeError(\"local index \"+str(localdataidx)+\" out of range\")\n if localdataidx in includedIndices:\n raise RuntimeError(\"local index \"+str(localdataidx)+\" given multiple times\")\n includedIndices.add(localdataidx)\n\n self._activeWellIndices=activeWellIndices","def __setitem__(self, key, value):\n mixed_positions, vindex_positions = _advanced_indexer_subspaces(key)\n self._array[key] = np.moveaxis(value, vindex_positions, mixed_positions)","def reindex(self):\n self._index = {w: i for i, w in enumerate(self._words)}\n self.n, self.d = self._vecs.shape\n assert self.n == len(self._words) == len(self._index)\n self._neighbors = None","def update(self, idx, add):\n idx += 1\n while idx < len(self.array):\n self.array[idx] += add\n idx += idx & -idx #Adding the last bit","def propagateDirty(self, slot, subindex, roi):\n totalIndex = (self._subSlots.index(slot),) + subindex\n self.operator.propagateDirty(self, totalIndex, roi)","def set_index_ub(self, param, length):\n if tik.Dprofile().get_product_name() in (MINI, CLOUD, HISI_ES):\n sum_mask_ub = self.instance.Tensor(self.dtype, (16,),\n name=\"sum_mask_ub\",\n scope=tik.scope_ubuf)\n work_tensor_ub = self.instance.Tensor(self.dtype, (16,),\n name=\"work_tensor_ub\",\n scope=tik.scope_ubuf)\n self.instance.vec_reduce_add(self.mask, sum_mask_ub, param['reduce_mask_ub'], work_tensor_ub, 1, 8)\n\n mask_scalar = self.instance.Scalar(\"uint16\", name=\"mask_scalar\")\n mask_scalar.set_as(sum_mask_ub[0])\n with self.instance.if_scope(mask_scalar != 0):\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\n with self.instance.for_range(0, length) as mask_index:\n param['index_offset'].set_as(param['index_offset'] + 1)\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\n mask_scalar.set_as(param['reduce_mask_ub'][mask_index])\n\n # 1 fp16 == 15360 uint16\n with self.instance.if_scope(mask_scalar == 15360):\n param['index_ub'][param['count']].set_as(\n param['index_offset'])\n param['count'].set_as(param['count'] + 1)\n with self.instance.else_scope():\n param['index_offset'].set_as(param['index_offset'] + length)","def update_rec(self):\n import copy\n \n self.leftrec, self.rightrec = copy.copy(self.rec), copy.copy(self.rec)\n self.leftrec[2*self.dim + 1], self.rightrec[2*self.dim] = self.node.dimension[self.dim], self.node.dimension[self.dim]","def update(self, idx, value):\n idx = self.__capacity - 1 + idx\n self.__tree[idx] = value\n self.__update(idx)","def reindex(self):","def reindex(self):","def __setitem__(self, index, value):\n if isinstance(index, types.SliceType):\n keys = self._main._sequence[index]\n if len(keys) != len(value):\n raise ValueError('attempt to assign sequence of size %s '\n 'to slice of size %s' % (len(name), len(keys)))\n # FIXME: efficiency? Would be better to calculate the indexes\n # directly from the slice object\n # NOTE: the new keys can collide with existing keys (or even\n # contain duplicates) - these will overwrite\n for key, val in zip(keys, value):\n self._main[key] = val\n else:\n self._main[self._main._sequence[index]] = value","def preCommitFixup(self):\n log_method_call(self, self.name)\n if not self.exists or not self.disklabelSupported:\n return\n\n # find the correct partition on the original parted.Disk since the\n # name/number we're now using may no longer match\n _disklabel = self.disk.originalFormat\n\n if self.isExtended:\n # getPartitionBySector doesn't work on extended partitions\n _partition = _disklabel.extendedPartition\n log.debug(\"extended lookup found partition %s\",\n devicePathToName(getattr(_partition, \"path\", None) or \"(none)\"))\n else:\n # lookup the partition by sector to avoid the renumbering\n # nonsense entirely\n _sector = self.partedPartition.geometry.start\n _partition = _disklabel.partedDisk.getPartitionBySector(_sector)\n log.debug(\"sector-based lookup found partition %s\",\n devicePathToName(getattr(_partition, \"path\", None) or \"(none)\"))\n\n self.partedPartition = _partition","def untie_everything(self):\r\n self.tied_indices = []","def get_local_indices(self, part, ctx):\n return self.map_to_global(\n F.arange(0, self.local_size(part), ctx=ctx), part\n )","def setActiveChildWellIndices(self,activeWellIndices):\n if not self.isReplicateGroup():\n raise RuntimeError(self.fullId()+\": cannot change active child well indices, this is not a replicate group.\")\n self._setActiveChildWellIndices(activeWellIndices)\n self.clsParent.modified=True","def setReference(self, updatedIndices):\n # self.colors[:] = [self.colors[i] for i in updatedIndices]\n self.cellData[:] = [self.cellData[i] for i in updatedIndices]","def set_position(self, idx, pos):\n if self.EMULATOR_MODE:\n return\n if idx >= self.nleaflets or idx < 0:\n raise IndexError('index specified is out of bounds')\n self._fserial.write(self.MAGIC_BYTES + bytes([idx]) + pos.to_bytes(2, byteorder='big', signed=False) )\n self._fserial.reset_input_buffer()","def _setPartedPartition(self, partition):\n log_method_call(self, self.name)\n\n if partition is not None and not isinstance(partition, parted.Partition):\n raise ValueError(\"partition must be None or a parted.Partition instance\")\n\n log.debug(\"device %s new partedPartition %s\", self.name, partition)\n self._partedPartition = partition\n self.updateName()","def undo_scan(self, sub_array_id: int):","def reindex(self):\n if self.channels is None:\n return\n\n self.data = None\n\n keep_indices = self.channels.new_indices_in_old()\n self.channels.reindex()\n\n if self.parms is not None:\n self.parms = self.integration.get_dependents(\n self.get_config_name())\n\n channel_attributes = self.channel_dependent_attributes\n\n for attribute, value in self.__dict__.items():\n if attribute not in channel_attributes:\n continue\n if not isinstance(value, np.ndarray):\n continue\n setattr(self, attribute, value[keep_indices])","def __setitem__(self, index, item):\n if isinstance(index, types.SliceType):\n # NOTE: item must be an iterable (list of tuples)\n self._main[index] = OrderedDict(item)\n else:\n # FIXME: Does this raise a sensible error?\n orig = self._main.keys[index]\n key, value = item\n if self._main.strict and key in self and (key != orig):\n raise ValueError('slice assignment must be from '\n 'unique keys')\n # delete the current one\n del self._main[self._main._sequence[index]]\n self._main.insert(index, key, value)","def __setitem__(self, index, name):\n if isinstance(index, types.SliceType):\n # FIXME: efficiency?\n # check length is the same\n indexes = range(len(self._main._sequence))[index]\n if len(indexes) != len(name):\n raise ValueError('attempt to assign sequence of size %s '\n 'to slice of size %s' % (len(name), len(indexes)))\n # check they are the same keys\n # FIXME: Use set\n old_keys = self._main._sequence[index]\n new_keys = list(name)\n old_keys.sort()\n new_keys.sort()\n if old_keys != new_keys:\n raise KeyError('Keylist is not the same as current keylist.')\n orig_vals = [self._main[k] for k in name]\n del self._main[index]\n vals = zip(indexes, name, orig_vals)\n vals.sort()\n for i, k, v in vals:\n if self._main.strict and k in self._main:\n raise ValueError('slice assignment must be from '\n 'unique keys')\n self._main.insert(i, k, v)\n else:\n raise ValueError('Cannot assign to keys')","def update_dimensions(self):\n self.chunk = numpy.full((self.current_height, self.current_width), fill_value=Constants.VALUE_INITIALIZER,\n dtype=\"int16\")","def _cte_postformat(self):\n# if type(self.idxs) == list:\n# self.idxs = np.array(self.idxs)\n if self.sp_relative_pos is not None:\n if type(self.sp_relative_pos) == list:\n self.sp_relative_pos = np.array(self.sp_relative_pos)","def _set_post_op_offset(self, spec: DTensorSpec, old_offset: int) -> None:\n dtensor_shape = spec.shape\n\n from torch.distributed._tensor.ops.utils import prod\n\n numel = prod(dtensor_shape)\n # pytorch: offset must be multiple of 4\n # source: aten/src/ATen/cuda/CUDAGeneratorImpl.cpp\n numel = (numel + 3) // 4 * 4\n self.set_offset(\"parallel-rng\", old_offset + numel)","def _UpdateCoords(self, new_coords):\n for i in range(self.mol.n_atoms):\n for j in range(const.NUMDIM):\n self.mol.atoms[i].coords[j] = new_coords[i][j]","def insert_parts(self, parts):\r\n self.board.insert_parts(parts)\r\n self.set_changed(parts)","def _reset_stored(self):\n ## Main information\n self.idxs = None\n self.sp_relative_pos = None\n self._setted = False\n self.ks = None\n self.iss = [0]","def _adjust(self, offset, size, *keep_refs):\n for basic_block in self._cfg.values():\n for instr in basic_block:\n instr.adjust(offset, size, instr in keep_refs)","def _configure_auxiliary_mask(self, auxiliary_mask):\n indices = self.indices\n\n new = [\n mask[\n tuple(\n [\n (slice(None) if n == 1 else index)\n for n, index in zip(mask.shape, indices)\n ]\n )\n ]\n for mask in auxiliary_mask\n ]\n\n # # If the partition is to be parallelised then get rid of mask\n # # components which are all False so the mask component does\n # # not get copied to the child process\n # if not config['serial']:\n # new = [mask for mask in new if not mask.any()]\n\n self.config[\"auxiliary_mask\"] = new","def update_parts():\n syt.log_info(\"$$$ Get Rebrickable Part info\")\n part_list = [x[0] for x in reapi.pull_all_pieces()] # ['piece_id', 'descr', 'category')\n part_list.pop(0) # Remove the header\n secondary_parts.add_parts_to_database(part_list, type=\"re\")\n # Todo: need to create a scraper for rebrickable piece num information\n syt.log_info(\"%%% Rebrickable Part info added to parts table\")","def remove_parts(self, parts):\r\n self.board.remove_parts(parts)\r\n self.set_changed(parts)","def _array_only_set_rel_pos(self, rel_pos):\n ## Preformatting\n rel_pos = np.array(rel_pos)\n if len(rel_pos.shape) == 1:\n rel_pos = rel_pos.reshape((len(rel_pos), 1))\n n_iss = len(self.iss)\n sp_relative_pos = np.array([rel_pos for i in range(n_iss)])\n ## Not staticneighs\n if not self.staticneighs:\n n_k = len(self.idxs)\n sp_relative_pos = np.array([sp_relative_pos for i in range(n_k)])\n self.sp_relative_pos = sp_relative_pos","def edit(self):\n wmap = 512\n if not self.mapd:\n data = self.data[24:]\n mapd = self.mapd = []\n for index in xrange(0,3*wmap*wmap,3):\n mapd.append(data[index:index+3])\n self.setChanged()","def local_inplace_setsubtensor(node):\r\n if isinstance(node.op, IncSubtensor) and not node.op.inplace:\r\n new_op = node.op.__class__(\r\n node.op.idx_list, inplace=True,\r\n set_instead_of_inc=node.op.set_instead_of_inc,\r\n destroyhandler_tolerate_aliased=node.op.destroyhandler_tolerate_aliased)\r\n new_node = new_op(*node.inputs)\r\n return [new_node]\r\n return False","def update_array(old_array, indices, val):\n\n # Sanity check.\n if (not isinstance(old_array, list)):\n old_array = []\n\n # 1-d array?\n if (len(indices) == 1):\n \n # Do we need to extend the length of the list to include the indices?\n index = int(indices[0])\n if (index >= len(old_array)):\n old_array.extend([0] * (index - len(old_array) + 1))\n old_array[index] = val\n\n # 2-d array?\n elif (len(indices) == 2):\n\n # Do we need to extend the length of the list to include the indices?\n index = int(indices[0])\n index1 = int(indices[1])\n if (index >= len(old_array)):\n # NOTE: Don't do 'old_array.extend([[]] * (index - len(old_array) + 1))' here.\n # The [] added with extend refers to the same list so any modification\n # to 1 sublist shows up in all of them.\n for _ in range(0, (index - len(old_array) + 1)):\n old_array.append([])\n if (index1 >= len(old_array[index])):\n old_array[index].extend([0] * (index1 - len(old_array[index]) + 1))\n old_array[index][index1] = val\n \n # Done.\n return old_array","def set_offset(self, offset):\r\n for b in self.buf:\r\n b.set_offset(offset)","def _update_block(self, block_idx):\n my_coord_indices = self.block_idxs[block_idx]\n for i,j in enumerate(my_coord_indices):\n soln = self._partial_min_solution(j)\n with self.block_locks[block_idx]:\n self.blocks[block_idx][i] = soln","def reintegrate_state(self, state_chunks):\n num_chunks = len(state_chunks.keys())\n\n # Get the bounds\n bounds = self.chunk_bounds(num_chunks)\n\n # Now reset the master state vector\n for cnum, bnds in bounds.items():\n self.state[dict(location=slice(bnds[0],bnds[1]))] = state_chunks[cnum].state","def __setitem__(self, name, item):\n if self.is_derived_array(name) and not self.auto_propagate_off:\n raise RuntimeError(\"Derived array is not writable\")\n\n if isinstance(name, tuple) or isinstance(name, list):\n index = name[1]\n name = name[0]\n else:\n index = None\n\n self._assert_not_family_array(name)\n\n if isinstance(item, array.SimArray):\n ax = item\n else:\n ax = np.asanyarray(item).view(array.SimArray)\n\n if name not in list(self.keys()):\n # Array needs to be created. We do this through the\n # private _create_array method, so that if we are operating\n # within a particle-specific subview we automatically create\n # a particle-specific array\n try:\n ndim = len(ax[0])\n except TypeError:\n ndim = 1\n except IndexError:\n ndim = ax.shape[-1] if len(ax.shape) > 1 else 1\n\n # The dtype will be the same as an existing family array if\n # one exists, or the dtype of the source array we are copying\n dtype = self._get_preferred_dtype(name)\n if dtype is None:\n dtype = getattr(item, 'dtype', None)\n\n self._create_array(name, ndim, dtype=dtype)\n\n # Copy in contents if the contents isn't actually pointing to\n # the same data (which will be the case following operations like\n # += etc, since these call __setitem__).\n self._set_array(name, ax, index)","def originalData(self): \n self.__exampleIndices = array(list(range(0, self.__numExamples)))","def update_part(session=None, data=None):\n data_dict = format_and_check_update_part_request(data)\n if data_dict is None:\n return False\n\n with mc.MCSessionWrapper(session=session) as session:\n for dkey, dval in data_dict.items():\n hpn_to_change = dval[0][0]\n rev_to_change = dval[0][1]\n part_rec = session.query(Parts).filter(\n (func.upper(Parts.hpn) == hpn_to_change.upper())\n & (func.upper(Parts.hpn_rev) == rev_to_change.upper())\n )\n num_part = part_rec.count()\n if num_part == 0:\n part = Parts()\n elif num_part == 1:\n part = part_rec.first()\n set_an_attrib = False\n for d in dval:\n try:\n getattr(part, d[2])\n setattr(part, d[2], d[3])\n set_an_attrib = True\n except AttributeError:\n print(d[2], \"does not exist as a field\")\n continue\n if set_an_attrib:\n session.add(part)\n session.commit()\n cm_utils.log(\"cm_partconnect part update\", data_dict=data_dict)\n\n return True","def __setitem__(self, key, val):\n if isinstance(key, types.SliceType):\n if not isinstance(val, OrderedDict):\n # FIXME: allow a list of tuples?\n raise TypeError('slice assignment requires an OrderedDict')\n keys = self._sequence[key]\n # NOTE: Could use ``range(*key.indices(len(self._sequence)))``\n indexes = range(len(self._sequence))[key]\n if key.step is None:\n # NOTE: new slice may not be the same size as the one being\n # overwritten !\n # NOTE: What is the algorithm for an impossible slice?\n # e.g. d[5:3]\n pos = key.start or 0\n del self[key]\n newkeys = val.keys()\n for k in newkeys:\n if k in self:\n if self.strict:\n raise ValueError('slice assignment must be from '\n 'unique keys')\n else:\n # NOTE: This removes duplicate keys *first*\n # so start position might have changed?\n del self[k]\n self._sequence = (self._sequence[:pos] + newkeys +\n self._sequence[pos:])\n dict.update(self, val)\n else:\n # extended slice - length of new slice must be the same\n # as the one being replaced\n if len(keys) != len(val):\n raise ValueError('attempt to assign sequence of size %s '\n 'to extended slice of size %s' % (len(val), len(keys)))\n # FIXME: efficiency?\n del self[key]\n item_list = zip(indexes, val.items())\n # smallest indexes first - higher indexes not guaranteed to\n # exist\n item_list.sort()\n for pos, (newkey, newval) in item_list:\n if self.strict and newkey in self:\n raise ValueError('slice assignment must be from unique'\n ' keys')\n self.insert(pos, newkey, newval)\n else:\n if key not in self:\n self._sequence.append(key)\n dict.__setitem__(self, key, val)","def delete(self, idx):\n self.arr[idx] = self.arr[self.current-1]\n self.current -= 1","def updatePartnerIdices(self):\n ## In order to access the partners by their indices the current\n # indices must be updated in each time step.\n plant_indices = np.array(range(0, self.no_plants))\n for i in self._partner_names:\n if not i:\n self._partner_indices.append([])\n else:\n h = []\n for j in i:\n a = plant_indices[np.where(self._plant_names == j)][0]\n h.append(a)\n self._partner_indices.append(h)","def in_place_offset(self, offset):\n self.p += offset * self.cross_z.normalized()","def reindex(self):\n super().reindex()\n self._depths, self._heights = None, None\n for p in self.positions():\n self._compute_depth(p)\n self._compute_height(p)","def _update_farness_map(self,ind):","def _child_reindex(self, old_id, model):\n assert old_id in self._models # detect logic errors\n new_id = model.get_id()\n assert new_id != None\n assert new_id not in self._models\n del self._models[old_id]\n self._models[new_id] = model","def _set_view_slice(self, indices):\n\n vertices = self._mesh_vertices\n faces = self._mesh_triangles\n\n if len(faces) == 0:\n self._node.set_data(vertices=None, faces=None)\n else:\n self._node.set_data(vertices=vertices[:, ::-1], faces=faces,\n color=self.color)\n\n self._need_visual_update = True\n self._update()","def update_E(self):\n self.grid.E[0, :, :, :] = self.grid.E[-1, :, :, :]","def update_E(self):\n self.grid.E[:, 0, :, :] = self.grid.E[:, -1, :, :]","def update_E(self):\n self.grid.E[:, :, 0, :] = self.grid.E[:, :, -1, :]","def __setitem__(self, inds, value):\n i, j = inds\n self.array[i][j] = value","def set_member_indexing(self, var_idx: int) -> None:\n if len(self._carry_register_) < var_idx or len(self._carry_register_) == 0:\n return # TODO: Throw an error\n if self._carry_variables_[self._carry_register_[var_idx]] not in [List, list, Dict, dict, str]:\n return # TODO: Throw an error\n\n # Done checking\n self.indexing = self._get_available_var_()\n self.__prefix__ += self.indexing + \" in \" + self._carry_register_[var_idx]\n self.__prefix__ += \":\"","def test_partial_updates(self):\r\n m1 = TestSetModel.create(int_set={1, 2, 3, 4})\r\n\r\n m1.int_set.add(5)\r\n m1.int_set.remove(1)\r\n assert m1.int_set == {2, 3, 4, 5}\r\n\r\n m1.save()\r\n\r\n m2 = TestSetModel.get(partition=m1.partition)\r\n assert m2.int_set == {2, 3, 4, 5}","def __getitem__(self, i):\n new_data = super().__getitem__(i)\n if isinstance(i, slice):\n new_data = self.__class__(new_data)\n new_data.global_settings = copy.copy(self.global_settings)\n return new_data","def beam_update(self, idx, positions, beam_size):\n for e in self._all:\n a, br, d = e.size()\n sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]\n sentStates.data.copy_(\n sentStates.data.index_select(1, positions))","def offset_index(self, offset):\n if self.has_index:\n self.index += offset","def test00(self):\n a = np.arange(1, 111)\n b = bcolz.carray(a, chunklen=10)\n sl = [3, 1]\n b[sl] = (10, 20)\n a[sl] = (10, 20)\n # print \"b[%s] -> %r\" % (sl, b)\n assert_array_equal(b[:], a, \"fancy indexing does not work correctly\")","def _dirty(self, name):\n\n name = self._array_name_1D_to_ND(name) or name\n if name=='pos':\n for v in self.ancestor._persistent_objects.values():\n if 'kdtree' in v:\n del v['kdtree']\n\n if not self.auto_propagate_off:\n for d_ar in self._dependency_tracker.get_dependents(name):\n if d_ar in self or self.has_family_key(d_ar):\n if self.is_derived_array(d_ar):\n del self[d_ar]\n self._dirty(d_ar)","def set_custom_data(self, index_from, data):\r\n self.unif[index_from:(index_from + len(data))] = data","def map_to_global(self, idxs, part_id):\n return F.zerocopy_from_dgl_ndarray(\n _CAPI_DGLNDArrayPartitionMapToGlobal(\n self._partition, F.zerocopy_to_dgl_ndarray(idxs), part_id\n )\n )","def _array_array_set_rel_pos(self, rel_pos):\n# self.staticneighs = True\n if self.staticneighs:\n self.sp_relative_pos = np.array(rel_pos)\n else:\n len_ks = 1 if self.ks is None else len(self.ks)\n self.sp_relative_pos = np.array([rel_pos for k in range(len_ks)])","def set_changed(self, parts):\r\n self.command_manager.set_changed(parts)","def __setitem__(self, index, value):\n if isinstance(index, slice):\n del self[index]\n offset = 0\n if len(self) == 0:\n for x in value:\n self.append(x)\n else:\n for x in xrange(*index.indices(len(self))):\n self.__insert(x + offset, value)\n offset += value.length\n if not index.step:\n break\n return\n\n self.__verify_index(index)\n\n if index < 0:\n index += self.length\n\n index, prev_node, cur_node = self.__find_node_index(index)\n cur_node.data_list[index] = value","def move(self, coord, mark):\n self.arr[coord] = mark","def update_state(self):\n self.reset_state()\n for piece in self.pieces:\n coordinates = piece.get_block_positions()\n for coor in coordinates:\n x, y = coor\n self.state[y][x] = piece","def expand(self):\n self.vertices[-1, :] = self.expanded","def partition_indexed(part_index):\n return module_globals[\n partition_tmpl % (name, part_index)\n ]","def _array_array_array_set_rel_pos(self, rel_pos):\n if self.staticneighs:\n self.sp_relative_pos = rel_pos[0]\n else:\n self.sp_relative_pos = rel_pos","def __setitem__(self, index, item):\n # type: (int, Any) -> None\n items = self._refs(item) if isinstance(index, slice) else self.ref(item)\n return list.__setitem__(self, index, items)","def _set_neighs_array_lvl1(self, key):\n #sh = key.shape\n ## If only array of neighs\n if self.staticneighs:\n self.idxs = np.array([key for i in range(len(self.iss))])\n else:\n self.ks = range(1) if self.ks is None else self.ks\n len_ks = len(self.ks)\n self.idxs = np.array([[key for i in range(len(self.iss))]\n for i in range(len_ks)])\n self._setted = True","def _reinit_indexes(self):\n print('Reinitializing indexes...')\n for identity in self.groundtruth_metadata.keys():\n self.groundtruth_metadata[identity]['index'] = 0\n print('Indexes reinitialized!')","def _remove_element(cls, d, idx):\n d[idx, 2] = 0\n return d","def update_partition(self, event) -> None:\n self.min_width = 150 * len(self.partition)\n self.request_update()","def __setstate__(self, state):\n if \"_version\" not in state.keys(): # Compatibility mode\n self._array = state['_array']\n self._version = 0.1 # promote to the latest version\n x = state['_x']\n y = state['_y']\n z = state['_z']\n dx = state['_dx']\n dy = state['_dy']\n dz = state['_dz']\n if x is not None and y is not None and z is not None:\n self[x:x+dx, y:y+dy, z:z+dz]\n elif x is not None and y is not None:\n self[x:x+dx, y:y+dy]\n elif x is not None:\n self[x:x+dx]\n else:\n self._view = self._array\n self._view_item = None\n else:\n self.__dict__ = state\n if self._view_item is not None:\n self.__getitem__(self._view_item)\n else:\n self._view = self._array","def update(self, instance, validated_data):\n for attr, value in list(validated_data.items()):\n if attr == 'part_details':\n for a, v in list(value.items()):\n setattr(instance.part_details, attr, value)\n else:\n setattr(instance, attr, value)\n instance.save()\n return instance","def _set_pre_op_offset(self, spec: DTensorSpec) -> None:\n dtensor_shape = spec.shape\n mesh = spec.mesh\n dim_map = spec.dim_map\n\n # Compute shard coordinate:\n # The coordinate on each tensor dim is a tuple (idx, range)\n # If a DTensor is partitioned on its dim i into n shards, and the current rank\n # holds the j-th, then its shard coordinate will be (idx=j, range=n) on dim i\n coordinate = mesh.get_coordinate()\n assert coordinate is not None\n shard_coord = [\n coordinate[mesh_dim] if mesh_dim >= 0 else 0 for mesh_dim in dim_map\n ]\n shard_size = [\n mesh.size(mesh_dim) if mesh_dim >= 0 else 1 for mesh_dim in dim_map\n ]\n\n # compute shard linear index\n shard_linear_idx = self._calc_shard_linear_idx(shard_coord, shard_size)\n\n # compute starting offset using the first shard's size\n local_size_on_rank_0 = list(dtensor_shape)\n for idx, placement in enumerate(spec.placements):\n if isinstance(placement, Shard):\n mesh_dim_size = mesh.size(idx)\n shard_dim = placement.dim\n local_size_on_rank_0[shard_dim] = placement._local_shard_size_on_dim(\n dtensor_shape[shard_dim],\n mesh_dim_size,\n 0,\n return_offset=False,\n )[0]\n\n from torch.distributed._tensor.ops.utils import prod\n\n local_size = prod(local_size_on_rank_0)\n\n # get current RNG offset\n current_offset = self.get_offset(\"parallel-rng\")\n\n # pytorch: offset must be multiple of 4\n # source: aten/src/ATen/cuda/CUDAGeneratorImpl.cpp\n offset_incr = (shard_linear_idx * local_size + 3) // 4 * 4\n self.set_offset(\"parallel-rng\", current_offset + offset_incr)","def set_offset(self, trace, offset):\n trace = self.traces_to_idx(trace)\n self._offset[trace] = offset\n # Inform descendants of change\n self.set_changed()","def reindex(self, using=None):\n self.clear(using=using)\n self.update(using=using)","def __setitem__(self, idx, value):\n assert(isinstance(idx, int))\n nidx = self._normalize_idx(idx)\n if nidx >= len(self.data):\n raise IndexError\n self.data[nidx] = value","def set_part(self, connection_part):\n self.part = connection_part","def __setitem__(self, item, value):\n index = self.reindex(item)\n self.parent.__setitem__(index, value)","def reset_s(self):\n self.s = np.copy(self.f_uniq) # (current) solution, selected column","def reindex(self):\n raise NotImplementedError()","def _restore_global_position(x, root_pos, root_idx=None):\n x = x + root_pos\n if root_idx is not None:\n x = np.insert(x, root_idx, root_pos.squeeze(1), axis=1)\n return x","def update_from_indexes(self, data, **kw):\n for i in data:\n self.update_from_index(i, **kw)","def __setitem__(self, idx, val):\n self.rows[idx[0]][idx[1]] = val"],"string":"[\n \"def save(self, patch):\\n internalSlices = self._get_internal_slices(patch.slices)\\n self.array[internalSlices] = patch.array\",\n \"def _idx_changed(self, idx):\\n self.refresh_memory()\",\n \"def _loadpart(self, part):\\n new_partidx = util.Partname(part.partname).idx\\n for idx, seq_part in enumerate(self._values):\\n partidx = util.Partname(seq_part.partname).idx\\n if partidx > new_partidx:\\n self._values.insert(idx, part)\\n return\\n self._values.append(part)\",\n \"def _idxs_postformat_array(self):\\n self.idxs = np.array(self.idxs)\",\n \"def __update(self, idx):\\n parent = (idx - 1) // 2\\n while parent >= 0:\\n left, right = 2 * parent + 1, 2 * parent + 2\\n self.__tree[parent] = self.__tree[left] + self.__tree[right]\\n parent = (parent - 1) // 2\",\n \"def part_ids(self, part_ids):\\n\\n self._part_ids = part_ids\",\n \"def update_chunk(self):\\n for key, value in self.piece_coordinates.items():\\n # Why is the key a numpy.int type ???\\n self.chunk[value] = key\",\n \"def update_idx(self):\\n self.idx = (self.F * self.FMUL +\\n self.E * self.EMUL +\\n self.Z * self.ZMUL +\\n self.A * self.AMUL +\\n self.B * self.BMUL )\",\n \"def setInternalIndex(self,ind):\\n\\t\\tself.trMtrxNode_ind = ind\",\n \"def reindex_subcomponent_taxa(self):\\n ti_mutable = self.taxon_set._is_mutable\\n self.taxon_set._is_mutable = True\\n new_map = CharacterDataMap()\\n for taxon, seq in self.taxon_seq_map.items():\\n taxon = self.taxon_set.require_taxon(label=taxon.label)\\n new_map[taxon] = seq\\n self.taxon_set._is_mutable = ti_mutable\\n self.taxon_seq_map = new_map\",\n \"def set(self, idx_in, vals, fill=False):\\n o = np.broadcast_arrays(vals, *idx_in)\\n vals = np.ravel(o[0])\\n\\n # TODO: Determine whether new vs. existing indices are being\\n # addressed, in the latter case we only need to update data\\n # array\\n\\n vals = np.array(vals, ndmin=1)\\n idx_flat_in, msk_in = self._to_flat_index(idx_in)\\n vals = np.asanyarray(vals, dtype=self.data.dtype)\\n idx, data = merge_sparse_arrays(\\n idx_flat_in, vals, self.idx, self.data, fill=fill\\n )\\n\\n # Remove elements equal to fill value\\n msk = data != self._fill_value\\n idx = idx[msk]\\n data = data[msk]\\n self._idx = idx\\n self._data = data\\n # idx, msk = find_in_array(idx_flat_in, self.idx)\\n # self._data[idx[msk]] = vals[msk]\",\n \"def _setActiveChildWellIndices(self,activeWellIndices):\\n if activeWellIndices is None:\\n activeWellIndices=list(range(0,len(self.childWellIndices())))\\n\\n if len(activeWellIndices) > 0:\\n includedIndices=set()\\n for localdataidx in activeWellIndices:\\n if localdataidx < 0 or localdataidx >= len(self.childWellIndices()):\\n raise RuntimeError(\\\"local index \\\"+str(localdataidx)+\\\" out of range\\\")\\n if localdataidx in includedIndices:\\n raise RuntimeError(\\\"local index \\\"+str(localdataidx)+\\\" given multiple times\\\")\\n includedIndices.add(localdataidx)\\n\\n self._activeWellIndices=activeWellIndices\",\n \"def __setitem__(self, key, value):\\n mixed_positions, vindex_positions = _advanced_indexer_subspaces(key)\\n self._array[key] = np.moveaxis(value, vindex_positions, mixed_positions)\",\n \"def reindex(self):\\n self._index = {w: i for i, w in enumerate(self._words)}\\n self.n, self.d = self._vecs.shape\\n assert self.n == len(self._words) == len(self._index)\\n self._neighbors = None\",\n \"def update(self, idx, add):\\n idx += 1\\n while idx < len(self.array):\\n self.array[idx] += add\\n idx += idx & -idx #Adding the last bit\",\n \"def propagateDirty(self, slot, subindex, roi):\\n totalIndex = (self._subSlots.index(slot),) + subindex\\n self.operator.propagateDirty(self, totalIndex, roi)\",\n \"def set_index_ub(self, param, length):\\n if tik.Dprofile().get_product_name() in (MINI, CLOUD, HISI_ES):\\n sum_mask_ub = self.instance.Tensor(self.dtype, (16,),\\n name=\\\"sum_mask_ub\\\",\\n scope=tik.scope_ubuf)\\n work_tensor_ub = self.instance.Tensor(self.dtype, (16,),\\n name=\\\"work_tensor_ub\\\",\\n scope=tik.scope_ubuf)\\n self.instance.vec_reduce_add(self.mask, sum_mask_ub, param['reduce_mask_ub'], work_tensor_ub, 1, 8)\\n\\n mask_scalar = self.instance.Scalar(\\\"uint16\\\", name=\\\"mask_scalar\\\")\\n mask_scalar.set_as(sum_mask_ub[0])\\n with self.instance.if_scope(mask_scalar != 0):\\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\\n with self.instance.for_range(0, length) as mask_index:\\n param['index_offset'].set_as(param['index_offset'] + 1)\\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\\n mask_scalar.set_as(param['reduce_mask_ub'][mask_index])\\n\\n # 1 fp16 == 15360 uint16\\n with self.instance.if_scope(mask_scalar == 15360):\\n param['index_ub'][param['count']].set_as(\\n param['index_offset'])\\n param['count'].set_as(param['count'] + 1)\\n with self.instance.else_scope():\\n param['index_offset'].set_as(param['index_offset'] + length)\",\n \"def update_rec(self):\\n import copy\\n \\n self.leftrec, self.rightrec = copy.copy(self.rec), copy.copy(self.rec)\\n self.leftrec[2*self.dim + 1], self.rightrec[2*self.dim] = self.node.dimension[self.dim], self.node.dimension[self.dim]\",\n \"def update(self, idx, value):\\n idx = self.__capacity - 1 + idx\\n self.__tree[idx] = value\\n self.__update(idx)\",\n \"def reindex(self):\",\n \"def reindex(self):\",\n \"def __setitem__(self, index, value):\\n if isinstance(index, types.SliceType):\\n keys = self._main._sequence[index]\\n if len(keys) != len(value):\\n raise ValueError('attempt to assign sequence of size %s '\\n 'to slice of size %s' % (len(name), len(keys)))\\n # FIXME: efficiency? Would be better to calculate the indexes\\n # directly from the slice object\\n # NOTE: the new keys can collide with existing keys (or even\\n # contain duplicates) - these will overwrite\\n for key, val in zip(keys, value):\\n self._main[key] = val\\n else:\\n self._main[self._main._sequence[index]] = value\",\n \"def preCommitFixup(self):\\n log_method_call(self, self.name)\\n if not self.exists or not self.disklabelSupported:\\n return\\n\\n # find the correct partition on the original parted.Disk since the\\n # name/number we're now using may no longer match\\n _disklabel = self.disk.originalFormat\\n\\n if self.isExtended:\\n # getPartitionBySector doesn't work on extended partitions\\n _partition = _disklabel.extendedPartition\\n log.debug(\\\"extended lookup found partition %s\\\",\\n devicePathToName(getattr(_partition, \\\"path\\\", None) or \\\"(none)\\\"))\\n else:\\n # lookup the partition by sector to avoid the renumbering\\n # nonsense entirely\\n _sector = self.partedPartition.geometry.start\\n _partition = _disklabel.partedDisk.getPartitionBySector(_sector)\\n log.debug(\\\"sector-based lookup found partition %s\\\",\\n devicePathToName(getattr(_partition, \\\"path\\\", None) or \\\"(none)\\\"))\\n\\n self.partedPartition = _partition\",\n \"def untie_everything(self):\\r\\n self.tied_indices = []\",\n \"def get_local_indices(self, part, ctx):\\n return self.map_to_global(\\n F.arange(0, self.local_size(part), ctx=ctx), part\\n )\",\n \"def setActiveChildWellIndices(self,activeWellIndices):\\n if not self.isReplicateGroup():\\n raise RuntimeError(self.fullId()+\\\": cannot change active child well indices, this is not a replicate group.\\\")\\n self._setActiveChildWellIndices(activeWellIndices)\\n self.clsParent.modified=True\",\n \"def setReference(self, updatedIndices):\\n # self.colors[:] = [self.colors[i] for i in updatedIndices]\\n self.cellData[:] = [self.cellData[i] for i in updatedIndices]\",\n \"def set_position(self, idx, pos):\\n if self.EMULATOR_MODE:\\n return\\n if idx >= self.nleaflets or idx < 0:\\n raise IndexError('index specified is out of bounds')\\n self._fserial.write(self.MAGIC_BYTES + bytes([idx]) + pos.to_bytes(2, byteorder='big', signed=False) )\\n self._fserial.reset_input_buffer()\",\n \"def _setPartedPartition(self, partition):\\n log_method_call(self, self.name)\\n\\n if partition is not None and not isinstance(partition, parted.Partition):\\n raise ValueError(\\\"partition must be None or a parted.Partition instance\\\")\\n\\n log.debug(\\\"device %s new partedPartition %s\\\", self.name, partition)\\n self._partedPartition = partition\\n self.updateName()\",\n \"def undo_scan(self, sub_array_id: int):\",\n \"def reindex(self):\\n if self.channels is None:\\n return\\n\\n self.data = None\\n\\n keep_indices = self.channels.new_indices_in_old()\\n self.channels.reindex()\\n\\n if self.parms is not None:\\n self.parms = self.integration.get_dependents(\\n self.get_config_name())\\n\\n channel_attributes = self.channel_dependent_attributes\\n\\n for attribute, value in self.__dict__.items():\\n if attribute not in channel_attributes:\\n continue\\n if not isinstance(value, np.ndarray):\\n continue\\n setattr(self, attribute, value[keep_indices])\",\n \"def __setitem__(self, index, item):\\n if isinstance(index, types.SliceType):\\n # NOTE: item must be an iterable (list of tuples)\\n self._main[index] = OrderedDict(item)\\n else:\\n # FIXME: Does this raise a sensible error?\\n orig = self._main.keys[index]\\n key, value = item\\n if self._main.strict and key in self and (key != orig):\\n raise ValueError('slice assignment must be from '\\n 'unique keys')\\n # delete the current one\\n del self._main[self._main._sequence[index]]\\n self._main.insert(index, key, value)\",\n \"def __setitem__(self, index, name):\\n if isinstance(index, types.SliceType):\\n # FIXME: efficiency?\\n # check length is the same\\n indexes = range(len(self._main._sequence))[index]\\n if len(indexes) != len(name):\\n raise ValueError('attempt to assign sequence of size %s '\\n 'to slice of size %s' % (len(name), len(indexes)))\\n # check they are the same keys\\n # FIXME: Use set\\n old_keys = self._main._sequence[index]\\n new_keys = list(name)\\n old_keys.sort()\\n new_keys.sort()\\n if old_keys != new_keys:\\n raise KeyError('Keylist is not the same as current keylist.')\\n orig_vals = [self._main[k] for k in name]\\n del self._main[index]\\n vals = zip(indexes, name, orig_vals)\\n vals.sort()\\n for i, k, v in vals:\\n if self._main.strict and k in self._main:\\n raise ValueError('slice assignment must be from '\\n 'unique keys')\\n self._main.insert(i, k, v)\\n else:\\n raise ValueError('Cannot assign to keys')\",\n \"def update_dimensions(self):\\n self.chunk = numpy.full((self.current_height, self.current_width), fill_value=Constants.VALUE_INITIALIZER,\\n dtype=\\\"int16\\\")\",\n \"def _cte_postformat(self):\\n# if type(self.idxs) == list:\\n# self.idxs = np.array(self.idxs)\\n if self.sp_relative_pos is not None:\\n if type(self.sp_relative_pos) == list:\\n self.sp_relative_pos = np.array(self.sp_relative_pos)\",\n \"def _set_post_op_offset(self, spec: DTensorSpec, old_offset: int) -> None:\\n dtensor_shape = spec.shape\\n\\n from torch.distributed._tensor.ops.utils import prod\\n\\n numel = prod(dtensor_shape)\\n # pytorch: offset must be multiple of 4\\n # source: aten/src/ATen/cuda/CUDAGeneratorImpl.cpp\\n numel = (numel + 3) // 4 * 4\\n self.set_offset(\\\"parallel-rng\\\", old_offset + numel)\",\n \"def _UpdateCoords(self, new_coords):\\n for i in range(self.mol.n_atoms):\\n for j in range(const.NUMDIM):\\n self.mol.atoms[i].coords[j] = new_coords[i][j]\",\n \"def insert_parts(self, parts):\\r\\n self.board.insert_parts(parts)\\r\\n self.set_changed(parts)\",\n \"def _reset_stored(self):\\n ## Main information\\n self.idxs = None\\n self.sp_relative_pos = None\\n self._setted = False\\n self.ks = None\\n self.iss = [0]\",\n \"def _adjust(self, offset, size, *keep_refs):\\n for basic_block in self._cfg.values():\\n for instr in basic_block:\\n instr.adjust(offset, size, instr in keep_refs)\",\n \"def _configure_auxiliary_mask(self, auxiliary_mask):\\n indices = self.indices\\n\\n new = [\\n mask[\\n tuple(\\n [\\n (slice(None) if n == 1 else index)\\n for n, index in zip(mask.shape, indices)\\n ]\\n )\\n ]\\n for mask in auxiliary_mask\\n ]\\n\\n # # If the partition is to be parallelised then get rid of mask\\n # # components which are all False so the mask component does\\n # # not get copied to the child process\\n # if not config['serial']:\\n # new = [mask for mask in new if not mask.any()]\\n\\n self.config[\\\"auxiliary_mask\\\"] = new\",\n \"def update_parts():\\n syt.log_info(\\\"$$$ Get Rebrickable Part info\\\")\\n part_list = [x[0] for x in reapi.pull_all_pieces()] # ['piece_id', 'descr', 'category')\\n part_list.pop(0) # Remove the header\\n secondary_parts.add_parts_to_database(part_list, type=\\\"re\\\")\\n # Todo: need to create a scraper for rebrickable piece num information\\n syt.log_info(\\\"%%% Rebrickable Part info added to parts table\\\")\",\n \"def remove_parts(self, parts):\\r\\n self.board.remove_parts(parts)\\r\\n self.set_changed(parts)\",\n \"def _array_only_set_rel_pos(self, rel_pos):\\n ## Preformatting\\n rel_pos = np.array(rel_pos)\\n if len(rel_pos.shape) == 1:\\n rel_pos = rel_pos.reshape((len(rel_pos), 1))\\n n_iss = len(self.iss)\\n sp_relative_pos = np.array([rel_pos for i in range(n_iss)])\\n ## Not staticneighs\\n if not self.staticneighs:\\n n_k = len(self.idxs)\\n sp_relative_pos = np.array([sp_relative_pos for i in range(n_k)])\\n self.sp_relative_pos = sp_relative_pos\",\n \"def edit(self):\\n wmap = 512\\n if not self.mapd:\\n data = self.data[24:]\\n mapd = self.mapd = []\\n for index in xrange(0,3*wmap*wmap,3):\\n mapd.append(data[index:index+3])\\n self.setChanged()\",\n \"def local_inplace_setsubtensor(node):\\r\\n if isinstance(node.op, IncSubtensor) and not node.op.inplace:\\r\\n new_op = node.op.__class__(\\r\\n node.op.idx_list, inplace=True,\\r\\n set_instead_of_inc=node.op.set_instead_of_inc,\\r\\n destroyhandler_tolerate_aliased=node.op.destroyhandler_tolerate_aliased)\\r\\n new_node = new_op(*node.inputs)\\r\\n return [new_node]\\r\\n return False\",\n \"def update_array(old_array, indices, val):\\n\\n # Sanity check.\\n if (not isinstance(old_array, list)):\\n old_array = []\\n\\n # 1-d array?\\n if (len(indices) == 1):\\n \\n # Do we need to extend the length of the list to include the indices?\\n index = int(indices[0])\\n if (index >= len(old_array)):\\n old_array.extend([0] * (index - len(old_array) + 1))\\n old_array[index] = val\\n\\n # 2-d array?\\n elif (len(indices) == 2):\\n\\n # Do we need to extend the length of the list to include the indices?\\n index = int(indices[0])\\n index1 = int(indices[1])\\n if (index >= len(old_array)):\\n # NOTE: Don't do 'old_array.extend([[]] * (index - len(old_array) + 1))' here.\\n # The [] added with extend refers to the same list so any modification\\n # to 1 sublist shows up in all of them.\\n for _ in range(0, (index - len(old_array) + 1)):\\n old_array.append([])\\n if (index1 >= len(old_array[index])):\\n old_array[index].extend([0] * (index1 - len(old_array[index]) + 1))\\n old_array[index][index1] = val\\n \\n # Done.\\n return old_array\",\n \"def set_offset(self, offset):\\r\\n for b in self.buf:\\r\\n b.set_offset(offset)\",\n \"def _update_block(self, block_idx):\\n my_coord_indices = self.block_idxs[block_idx]\\n for i,j in enumerate(my_coord_indices):\\n soln = self._partial_min_solution(j)\\n with self.block_locks[block_idx]:\\n self.blocks[block_idx][i] = soln\",\n \"def reintegrate_state(self, state_chunks):\\n num_chunks = len(state_chunks.keys())\\n\\n # Get the bounds\\n bounds = self.chunk_bounds(num_chunks)\\n\\n # Now reset the master state vector\\n for cnum, bnds in bounds.items():\\n self.state[dict(location=slice(bnds[0],bnds[1]))] = state_chunks[cnum].state\",\n \"def __setitem__(self, name, item):\\n if self.is_derived_array(name) and not self.auto_propagate_off:\\n raise RuntimeError(\\\"Derived array is not writable\\\")\\n\\n if isinstance(name, tuple) or isinstance(name, list):\\n index = name[1]\\n name = name[0]\\n else:\\n index = None\\n\\n self._assert_not_family_array(name)\\n\\n if isinstance(item, array.SimArray):\\n ax = item\\n else:\\n ax = np.asanyarray(item).view(array.SimArray)\\n\\n if name not in list(self.keys()):\\n # Array needs to be created. We do this through the\\n # private _create_array method, so that if we are operating\\n # within a particle-specific subview we automatically create\\n # a particle-specific array\\n try:\\n ndim = len(ax[0])\\n except TypeError:\\n ndim = 1\\n except IndexError:\\n ndim = ax.shape[-1] if len(ax.shape) > 1 else 1\\n\\n # The dtype will be the same as an existing family array if\\n # one exists, or the dtype of the source array we are copying\\n dtype = self._get_preferred_dtype(name)\\n if dtype is None:\\n dtype = getattr(item, 'dtype', None)\\n\\n self._create_array(name, ndim, dtype=dtype)\\n\\n # Copy in contents if the contents isn't actually pointing to\\n # the same data (which will be the case following operations like\\n # += etc, since these call __setitem__).\\n self._set_array(name, ax, index)\",\n \"def originalData(self): \\n self.__exampleIndices = array(list(range(0, self.__numExamples)))\",\n \"def update_part(session=None, data=None):\\n data_dict = format_and_check_update_part_request(data)\\n if data_dict is None:\\n return False\\n\\n with mc.MCSessionWrapper(session=session) as session:\\n for dkey, dval in data_dict.items():\\n hpn_to_change = dval[0][0]\\n rev_to_change = dval[0][1]\\n part_rec = session.query(Parts).filter(\\n (func.upper(Parts.hpn) == hpn_to_change.upper())\\n & (func.upper(Parts.hpn_rev) == rev_to_change.upper())\\n )\\n num_part = part_rec.count()\\n if num_part == 0:\\n part = Parts()\\n elif num_part == 1:\\n part = part_rec.first()\\n set_an_attrib = False\\n for d in dval:\\n try:\\n getattr(part, d[2])\\n setattr(part, d[2], d[3])\\n set_an_attrib = True\\n except AttributeError:\\n print(d[2], \\\"does not exist as a field\\\")\\n continue\\n if set_an_attrib:\\n session.add(part)\\n session.commit()\\n cm_utils.log(\\\"cm_partconnect part update\\\", data_dict=data_dict)\\n\\n return True\",\n \"def __setitem__(self, key, val):\\n if isinstance(key, types.SliceType):\\n if not isinstance(val, OrderedDict):\\n # FIXME: allow a list of tuples?\\n raise TypeError('slice assignment requires an OrderedDict')\\n keys = self._sequence[key]\\n # NOTE: Could use ``range(*key.indices(len(self._sequence)))``\\n indexes = range(len(self._sequence))[key]\\n if key.step is None:\\n # NOTE: new slice may not be the same size as the one being\\n # overwritten !\\n # NOTE: What is the algorithm for an impossible slice?\\n # e.g. d[5:3]\\n pos = key.start or 0\\n del self[key]\\n newkeys = val.keys()\\n for k in newkeys:\\n if k in self:\\n if self.strict:\\n raise ValueError('slice assignment must be from '\\n 'unique keys')\\n else:\\n # NOTE: This removes duplicate keys *first*\\n # so start position might have changed?\\n del self[k]\\n self._sequence = (self._sequence[:pos] + newkeys +\\n self._sequence[pos:])\\n dict.update(self, val)\\n else:\\n # extended slice - length of new slice must be the same\\n # as the one being replaced\\n if len(keys) != len(val):\\n raise ValueError('attempt to assign sequence of size %s '\\n 'to extended slice of size %s' % (len(val), len(keys)))\\n # FIXME: efficiency?\\n del self[key]\\n item_list = zip(indexes, val.items())\\n # smallest indexes first - higher indexes not guaranteed to\\n # exist\\n item_list.sort()\\n for pos, (newkey, newval) in item_list:\\n if self.strict and newkey in self:\\n raise ValueError('slice assignment must be from unique'\\n ' keys')\\n self.insert(pos, newkey, newval)\\n else:\\n if key not in self:\\n self._sequence.append(key)\\n dict.__setitem__(self, key, val)\",\n \"def delete(self, idx):\\n self.arr[idx] = self.arr[self.current-1]\\n self.current -= 1\",\n \"def updatePartnerIdices(self):\\n ## In order to access the partners by their indices the current\\n # indices must be updated in each time step.\\n plant_indices = np.array(range(0, self.no_plants))\\n for i in self._partner_names:\\n if not i:\\n self._partner_indices.append([])\\n else:\\n h = []\\n for j in i:\\n a = plant_indices[np.where(self._plant_names == j)][0]\\n h.append(a)\\n self._partner_indices.append(h)\",\n \"def in_place_offset(self, offset):\\n self.p += offset * self.cross_z.normalized()\",\n \"def reindex(self):\\n super().reindex()\\n self._depths, self._heights = None, None\\n for p in self.positions():\\n self._compute_depth(p)\\n self._compute_height(p)\",\n \"def _update_farness_map(self,ind):\",\n \"def _child_reindex(self, old_id, model):\\n assert old_id in self._models # detect logic errors\\n new_id = model.get_id()\\n assert new_id != None\\n assert new_id not in self._models\\n del self._models[old_id]\\n self._models[new_id] = model\",\n \"def _set_view_slice(self, indices):\\n\\n vertices = self._mesh_vertices\\n faces = self._mesh_triangles\\n\\n if len(faces) == 0:\\n self._node.set_data(vertices=None, faces=None)\\n else:\\n self._node.set_data(vertices=vertices[:, ::-1], faces=faces,\\n color=self.color)\\n\\n self._need_visual_update = True\\n self._update()\",\n \"def update_E(self):\\n self.grid.E[0, :, :, :] = self.grid.E[-1, :, :, :]\",\n \"def update_E(self):\\n self.grid.E[:, 0, :, :] = self.grid.E[:, -1, :, :]\",\n \"def update_E(self):\\n self.grid.E[:, :, 0, :] = self.grid.E[:, :, -1, :]\",\n \"def __setitem__(self, inds, value):\\n i, j = inds\\n self.array[i][j] = value\",\n \"def set_member_indexing(self, var_idx: int) -> None:\\n if len(self._carry_register_) < var_idx or len(self._carry_register_) == 0:\\n return # TODO: Throw an error\\n if self._carry_variables_[self._carry_register_[var_idx]] not in [List, list, Dict, dict, str]:\\n return # TODO: Throw an error\\n\\n # Done checking\\n self.indexing = self._get_available_var_()\\n self.__prefix__ += self.indexing + \\\" in \\\" + self._carry_register_[var_idx]\\n self.__prefix__ += \\\":\\\"\",\n \"def test_partial_updates(self):\\r\\n m1 = TestSetModel.create(int_set={1, 2, 3, 4})\\r\\n\\r\\n m1.int_set.add(5)\\r\\n m1.int_set.remove(1)\\r\\n assert m1.int_set == {2, 3, 4, 5}\\r\\n\\r\\n m1.save()\\r\\n\\r\\n m2 = TestSetModel.get(partition=m1.partition)\\r\\n assert m2.int_set == {2, 3, 4, 5}\",\n \"def __getitem__(self, i):\\n new_data = super().__getitem__(i)\\n if isinstance(i, slice):\\n new_data = self.__class__(new_data)\\n new_data.global_settings = copy.copy(self.global_settings)\\n return new_data\",\n \"def beam_update(self, idx, positions, beam_size):\\n for e in self._all:\\n a, br, d = e.size()\\n sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]\\n sentStates.data.copy_(\\n sentStates.data.index_select(1, positions))\",\n \"def offset_index(self, offset):\\n if self.has_index:\\n self.index += offset\",\n \"def test00(self):\\n a = np.arange(1, 111)\\n b = bcolz.carray(a, chunklen=10)\\n sl = [3, 1]\\n b[sl] = (10, 20)\\n a[sl] = (10, 20)\\n # print \\\"b[%s] -> %r\\\" % (sl, b)\\n assert_array_equal(b[:], a, \\\"fancy indexing does not work correctly\\\")\",\n \"def _dirty(self, name):\\n\\n name = self._array_name_1D_to_ND(name) or name\\n if name=='pos':\\n for v in self.ancestor._persistent_objects.values():\\n if 'kdtree' in v:\\n del v['kdtree']\\n\\n if not self.auto_propagate_off:\\n for d_ar in self._dependency_tracker.get_dependents(name):\\n if d_ar in self or self.has_family_key(d_ar):\\n if self.is_derived_array(d_ar):\\n del self[d_ar]\\n self._dirty(d_ar)\",\n \"def set_custom_data(self, index_from, data):\\r\\n self.unif[index_from:(index_from + len(data))] = data\",\n \"def map_to_global(self, idxs, part_id):\\n return F.zerocopy_from_dgl_ndarray(\\n _CAPI_DGLNDArrayPartitionMapToGlobal(\\n self._partition, F.zerocopy_to_dgl_ndarray(idxs), part_id\\n )\\n )\",\n \"def _array_array_set_rel_pos(self, rel_pos):\\n# self.staticneighs = True\\n if self.staticneighs:\\n self.sp_relative_pos = np.array(rel_pos)\\n else:\\n len_ks = 1 if self.ks is None else len(self.ks)\\n self.sp_relative_pos = np.array([rel_pos for k in range(len_ks)])\",\n \"def set_changed(self, parts):\\r\\n self.command_manager.set_changed(parts)\",\n \"def __setitem__(self, index, value):\\n if isinstance(index, slice):\\n del self[index]\\n offset = 0\\n if len(self) == 0:\\n for x in value:\\n self.append(x)\\n else:\\n for x in xrange(*index.indices(len(self))):\\n self.__insert(x + offset, value)\\n offset += value.length\\n if not index.step:\\n break\\n return\\n\\n self.__verify_index(index)\\n\\n if index < 0:\\n index += self.length\\n\\n index, prev_node, cur_node = self.__find_node_index(index)\\n cur_node.data_list[index] = value\",\n \"def move(self, coord, mark):\\n self.arr[coord] = mark\",\n \"def update_state(self):\\n self.reset_state()\\n for piece in self.pieces:\\n coordinates = piece.get_block_positions()\\n for coor in coordinates:\\n x, y = coor\\n self.state[y][x] = piece\",\n \"def expand(self):\\n self.vertices[-1, :] = self.expanded\",\n \"def partition_indexed(part_index):\\n return module_globals[\\n partition_tmpl % (name, part_index)\\n ]\",\n \"def _array_array_array_set_rel_pos(self, rel_pos):\\n if self.staticneighs:\\n self.sp_relative_pos = rel_pos[0]\\n else:\\n self.sp_relative_pos = rel_pos\",\n \"def __setitem__(self, index, item):\\n # type: (int, Any) -> None\\n items = self._refs(item) if isinstance(index, slice) else self.ref(item)\\n return list.__setitem__(self, index, items)\",\n \"def _set_neighs_array_lvl1(self, key):\\n #sh = key.shape\\n ## If only array of neighs\\n if self.staticneighs:\\n self.idxs = np.array([key for i in range(len(self.iss))])\\n else:\\n self.ks = range(1) if self.ks is None else self.ks\\n len_ks = len(self.ks)\\n self.idxs = np.array([[key for i in range(len(self.iss))]\\n for i in range(len_ks)])\\n self._setted = True\",\n \"def _reinit_indexes(self):\\n print('Reinitializing indexes...')\\n for identity in self.groundtruth_metadata.keys():\\n self.groundtruth_metadata[identity]['index'] = 0\\n print('Indexes reinitialized!')\",\n \"def _remove_element(cls, d, idx):\\n d[idx, 2] = 0\\n return d\",\n \"def update_partition(self, event) -> None:\\n self.min_width = 150 * len(self.partition)\\n self.request_update()\",\n \"def __setstate__(self, state):\\n if \\\"_version\\\" not in state.keys(): # Compatibility mode\\n self._array = state['_array']\\n self._version = 0.1 # promote to the latest version\\n x = state['_x']\\n y = state['_y']\\n z = state['_z']\\n dx = state['_dx']\\n dy = state['_dy']\\n dz = state['_dz']\\n if x is not None and y is not None and z is not None:\\n self[x:x+dx, y:y+dy, z:z+dz]\\n elif x is not None and y is not None:\\n self[x:x+dx, y:y+dy]\\n elif x is not None:\\n self[x:x+dx]\\n else:\\n self._view = self._array\\n self._view_item = None\\n else:\\n self.__dict__ = state\\n if self._view_item is not None:\\n self.__getitem__(self._view_item)\\n else:\\n self._view = self._array\",\n \"def update(self, instance, validated_data):\\n for attr, value in list(validated_data.items()):\\n if attr == 'part_details':\\n for a, v in list(value.items()):\\n setattr(instance.part_details, attr, value)\\n else:\\n setattr(instance, attr, value)\\n instance.save()\\n return instance\",\n \"def _set_pre_op_offset(self, spec: DTensorSpec) -> None:\\n dtensor_shape = spec.shape\\n mesh = spec.mesh\\n dim_map = spec.dim_map\\n\\n # Compute shard coordinate:\\n # The coordinate on each tensor dim is a tuple (idx, range)\\n # If a DTensor is partitioned on its dim i into n shards, and the current rank\\n # holds the j-th, then its shard coordinate will be (idx=j, range=n) on dim i\\n coordinate = mesh.get_coordinate()\\n assert coordinate is not None\\n shard_coord = [\\n coordinate[mesh_dim] if mesh_dim >= 0 else 0 for mesh_dim in dim_map\\n ]\\n shard_size = [\\n mesh.size(mesh_dim) if mesh_dim >= 0 else 1 for mesh_dim in dim_map\\n ]\\n\\n # compute shard linear index\\n shard_linear_idx = self._calc_shard_linear_idx(shard_coord, shard_size)\\n\\n # compute starting offset using the first shard's size\\n local_size_on_rank_0 = list(dtensor_shape)\\n for idx, placement in enumerate(spec.placements):\\n if isinstance(placement, Shard):\\n mesh_dim_size = mesh.size(idx)\\n shard_dim = placement.dim\\n local_size_on_rank_0[shard_dim] = placement._local_shard_size_on_dim(\\n dtensor_shape[shard_dim],\\n mesh_dim_size,\\n 0,\\n return_offset=False,\\n )[0]\\n\\n from torch.distributed._tensor.ops.utils import prod\\n\\n local_size = prod(local_size_on_rank_0)\\n\\n # get current RNG offset\\n current_offset = self.get_offset(\\\"parallel-rng\\\")\\n\\n # pytorch: offset must be multiple of 4\\n # source: aten/src/ATen/cuda/CUDAGeneratorImpl.cpp\\n offset_incr = (shard_linear_idx * local_size + 3) // 4 * 4\\n self.set_offset(\\\"parallel-rng\\\", current_offset + offset_incr)\",\n \"def set_offset(self, trace, offset):\\n trace = self.traces_to_idx(trace)\\n self._offset[trace] = offset\\n # Inform descendants of change\\n self.set_changed()\",\n \"def reindex(self, using=None):\\n self.clear(using=using)\\n self.update(using=using)\",\n \"def __setitem__(self, idx, value):\\n assert(isinstance(idx, int))\\n nidx = self._normalize_idx(idx)\\n if nidx >= len(self.data):\\n raise IndexError\\n self.data[nidx] = value\",\n \"def set_part(self, connection_part):\\n self.part = connection_part\",\n \"def __setitem__(self, item, value):\\n index = self.reindex(item)\\n self.parent.__setitem__(index, value)\",\n \"def reset_s(self):\\n self.s = np.copy(self.f_uniq) # (current) solution, selected column\",\n \"def reindex(self):\\n raise NotImplementedError()\",\n \"def _restore_global_position(x, root_pos, root_idx=None):\\n x = x + root_pos\\n if root_idx is not None:\\n x = np.insert(x, root_idx, root_pos.squeeze(1), axis=1)\\n return x\",\n \"def update_from_indexes(self, data, **kw):\\n for i in data:\\n self.update_from_index(i, **kw)\",\n \"def __setitem__(self, idx, val):\\n self.rows[idx[0]][idx[1]] = val\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5455975","0.5450583","0.5443926","0.53721666","0.5359862","0.5275372","0.52543634","0.52315736","0.5228334","0.51149035","0.50902224","0.50782204","0.5062653","0.50265366","0.50074023","0.49909484","0.49905938","0.49756426","0.4970326","0.496184","0.496184","0.49535567","0.4943507","0.49215022","0.49166226","0.49110836","0.49097493","0.4902477","0.4890401","0.48781407","0.48481715","0.48267615","0.48146898","0.4808053","0.4803822","0.47912985","0.4790657","0.4761948","0.47523195","0.4748287","0.47477698","0.4742629","0.47417226","0.47413313","0.47361755","0.47345686","0.47320303","0.47253257","0.47216284","0.47195795","0.47145712","0.4712462","0.471057","0.4703475","0.46985236","0.46983114","0.46902126","0.46876916","0.46836734","0.4682486","0.4681186","0.46764952","0.46704277","0.46687114","0.4668263","0.46472108","0.4644216","0.46413085","0.46374607","0.46330908","0.46295178","0.46290186","0.46264744","0.462511","0.46247083","0.460113","0.45928112","0.45908034","0.45895377","0.4584424","0.45834067","0.45831272","0.457653","0.45688072","0.45678675","0.4567391","0.45571727","0.45562705","0.4555775","0.45550337","0.45533147","0.45525464","0.4552538","0.45442653","0.4540668","0.45402852","0.4537582","0.45278376","0.45230293","0.45187727"],"string":"[\n \"0.5455975\",\n \"0.5450583\",\n \"0.5443926\",\n \"0.53721666\",\n \"0.5359862\",\n \"0.5275372\",\n \"0.52543634\",\n \"0.52315736\",\n \"0.5228334\",\n \"0.51149035\",\n \"0.50902224\",\n \"0.50782204\",\n \"0.5062653\",\n \"0.50265366\",\n \"0.50074023\",\n \"0.49909484\",\n \"0.49905938\",\n \"0.49756426\",\n \"0.4970326\",\n \"0.496184\",\n \"0.496184\",\n \"0.49535567\",\n \"0.4943507\",\n \"0.49215022\",\n \"0.49166226\",\n \"0.49110836\",\n \"0.49097493\",\n \"0.4902477\",\n \"0.4890401\",\n \"0.48781407\",\n \"0.48481715\",\n \"0.48267615\",\n \"0.48146898\",\n \"0.4808053\",\n \"0.4803822\",\n \"0.47912985\",\n \"0.4790657\",\n \"0.4761948\",\n \"0.47523195\",\n \"0.4748287\",\n \"0.47477698\",\n \"0.4742629\",\n \"0.47417226\",\n \"0.47413313\",\n \"0.47361755\",\n \"0.47345686\",\n \"0.47320303\",\n \"0.47253257\",\n \"0.47216284\",\n \"0.47195795\",\n \"0.47145712\",\n \"0.4712462\",\n \"0.471057\",\n \"0.4703475\",\n \"0.46985236\",\n \"0.46983114\",\n \"0.46902126\",\n \"0.46876916\",\n \"0.46836734\",\n \"0.4682486\",\n \"0.4681186\",\n \"0.46764952\",\n \"0.46704277\",\n \"0.46687114\",\n \"0.4668263\",\n \"0.46472108\",\n \"0.4644216\",\n \"0.46413085\",\n \"0.46374607\",\n \"0.46330908\",\n \"0.46295178\",\n \"0.46290186\",\n \"0.46264744\",\n \"0.462511\",\n \"0.46247083\",\n \"0.460113\",\n \"0.45928112\",\n \"0.45908034\",\n \"0.45895377\",\n \"0.4584424\",\n \"0.45834067\",\n \"0.45831272\",\n \"0.457653\",\n \"0.45688072\",\n \"0.45678675\",\n \"0.4567391\",\n \"0.45571727\",\n \"0.45562705\",\n \"0.4555775\",\n \"0.45550337\",\n \"0.45533147\",\n \"0.45525464\",\n \"0.4552538\",\n \"0.45442653\",\n \"0.4540668\",\n \"0.45402852\",\n \"0.4537582\",\n \"0.45278376\",\n \"0.45230293\",\n \"0.45187727\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5784396"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2698,"cells":{"query":{"kind":"string","value":"The extra memory required to access the array."},"document":{"kind":"string","value":"def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n # array will require extra memory\n # --------------------------------------------------------\n extra_memory = True\n else:\n # --------------------------------------------------------\n # The subarray is already in memory\n # --------------------------------------------------------\n config = self.config\n\n p_part = self.part\n if p_part:\n extra_memory = True\n elif not config[\"unique_subarray\"]:\n extra_memory = True\n else:\n p_data = self._subarray\n\n if not numpy_ma_isMA(p_data):\n # The p_data is not a masked array\n extra_memory = isinstance(p_data.base, numpy_ndarray)\n else:\n # The p_data is a masked array\n memory_overlap = isinstance(\n p_data.data.base, numpy_ndarray\n )\n if not (\n p_data.mask is numpy_ma_nomask\n or not numpy_ma_is_masked(p_data)\n ):\n # There is at least one missing data point\n memory_overlap |= isinstance(\n p_data.mask.base, numpy_ndarray\n )\n\n extra_memory = memory_overlap\n # --- End: if\n\n p_dtype = p_data.dtype\n\n if not extra_memory:\n if config[\"func\"] is not None:\n extra_memory = True\n else:\n p_units = self.Units\n units = config[\"units\"]\n if (\n not p_units.equals(units)\n and bool(p_units) is bool(units)\n and not (\n p_data.flags[\"C_CONTIGUOUS\"]\n and p_dtype.kind == \"f\"\n )\n ):\n extra_memory = True\n\n # ------------------------------------------------------------\n # Extra memory is required if the dtype needs changing\n # ------------------------------------------------------------\n if not extra_memory:\n dtype = config[\"dtype\"]\n if dtype is not None and dtype != p_data.dtype:\n extra_memory = True\n # --- End: if\n\n # ------------------------------------------------------------\n # Amount of extra memory (in bytes) required to access the\n # array\n # ------------------------------------------------------------\n return self.nbytes if extra_memory else 0"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def allocated_memory(self):\n return self._allocated_memory","def memory(self):\r\n return self._memory","def __len__(self):\n\t\treturn len(self.memory)","def __len__(self):\r\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def __len__(self):\n return len(self.memory)","def allocatememory(self):\n pass","def get_array_size(self):\r\n return conf.lib.clang_getArraySize(self)","def MAXMEM(self):","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory():\n return tracemalloc.take_snapshot()","def getMemory(self):\n return self.memory","def array_size(self):\n return self._array_size","def MEM (self,n):","def get_memory(self):\n return self.loss_memory","def memory(self) -> Optional[Any]:\n return pulumi.get(self, \"memory\")","def memory(self) -> Optional[Any]:\n return pulumi.get(self, \"memory\")","def deviceMemory(self):\n return 1","def memory_get_usage():\n raise NotImplementedError()","def read_memory(self):\n if self.mem_empty == True:\n if self.mem_idx == 0:\n m_x = np.zeros(self.n)\n m_d = 0\n else:\n m_x = np.mean(self.mem_x[:self.mem_idx+1], axis=0)\n m_d = np.mean(self.mem_d[:self.mem_idx])\n else:\n m_x = np.mean(self.mem_x, axis=0)\n m_d = np.mean(np.delete(self.mem_d, self.mem_idx))\n self.mem_idx += 1\n if self.mem_idx > len(self.mem_x)-1:\n self.mem_idx = 0\n self.mem_empty = False\n return m_d, m_x","def getArrayLength(self):\r\n return self.arraySize","def _read(self):\n return np.copy(self.memory[self.head_pos])","def get_used_mem(self):\n return self.used_mem","def memory_used(self) -> int:\r\n return self._memory_used","def memory_real_in_bytes(self):\n cdef INT64_t total_memory = 0\n\n # col\n total_memory += self.__nnz * sizeof(INT64_t)\n # ind\n total_memory += (self.__nrow + 1) * sizeof(INT64_t)\n # val\n total_memory += self.__nnz * sizeof(FLOAT32_t)\n\n return total_memory","def getSize(self):\n return self.bf.memory()","def __len__(self):\n return len(self.array)","def __len__(self):\n return len(self.array)","def size(self):\n return self._N","def BytesOfStorage(self):\n return (self.NumBits() + 7) / 8","def getSize(self) -> int:\n return len(self.mem)","def estimated_lookup_memory(self):\n return 60 * len(self.docvecs.offset2doctag) + 140 * len(self.docvecs.doctags)","def memory_utilization(self) -> float:\r\n return self._memory_utilization","def total_memory(self):\n return self._total_memory","def __len__(self):\n\n return len(self.data) * 8","def __len__(self) -> int:\n return self.disp_size ** 2","def _get_raw_data(self):\n raw_data_bytes = self.nbytes + self._heapsize\n base = self\n while hasattr(base, \"base\") and base.base is not None:\n base = base.base\n # Variable-length-arrays: should take into account the case of\n # empty arrays\n if hasattr(base, \"_heapoffset\"):\n if hasattr(base, \"nbytes\") and base.nbytes > raw_data_bytes:\n return base\n # non variable-length-arrays\n else:\n if hasattr(base, \"nbytes\") and base.nbytes >= raw_data_bytes:\n return base","def update_size(self):\n return 3 + self.memory_unit_size","def memory():\n sin = psutil.virtual_memory()\n return round((sin.total / sin.used) / 100, 3)","def allocated_storage(self):\n return self._allocated_storage","def memUsedGpu(self):\n return None # amount not known","def total_actual_memory(self):\n return self._total_actual_memory","def get_total_memory_size(self):\n return self.drt_manager.get_total_memory_size()","def in_memory_data(self):\n return self._in_memory_data","def get_size(self):","def _N(self):\n return len(self._array)","def __len__(self):\n return self.current_size","def __len__(self):\n\t\treturn self._size","def get_total_memory_size(self):\n memory = 0\n for i in range(4):\n for j in range(4):\n memory += self.system.operator[i, j].memory\n return memory","def get_free_mem(self):\n return self.free_mem","def get_mem(self) -> list:\n return self.__mem","def size(self):\r\n return self.size.data","def size(self):\n return len(self.arr)","def __len__(self):\r\n return 100000","def size(self):\n return self.ptr","def getallocatedblocks(): # real signature unknown; restored from __doc__\n return 0","def __len__(self):\r\n return self._size","def __len__(self):\r\n return self._size","def display_memory(self) -> None:\n return self.__memory","def size(self):\n return self.__size","def get_total_mem(self):\n return self.total_mem","def mem_per_proc(self):\n return self._mem_per_proc","def size(self):\n size = 0\n size += self.data.size * sys.getsizeof(self.data)\n return size / 1024.0 / 1024.0 / 1024.0","def get_space_used():\n fs.get_space_used()","def array(self):","def size(self):\r\n # Anthony stage 2\r\n return number_size(self.n) - 1","def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")","def size(self):\r\n return self._size","def _nbytes(self, deep: bool = False) -> int:\n # for implementations with no useful getsizeof (PyPy)\n objsize = 24\n\n level_nbytes = sum(i.memory_usage(deep=deep) for i in self.levels)\n label_nbytes = sum(i.nbytes for i in self.codes)\n names_nbytes = sum(getsizeof(i, objsize) for i in self.names)\n result = level_nbytes + label_nbytes + names_nbytes\n\n # include our engine hashtable\n result += self._engine.sizeof(deep=deep)\n return result","def get_memory(self):\n return (self.K.get_value(), self.V.get_value(), self.A.get_value())","def size(self) -> int:","def __len__(self):\n return self._arr.shape[1]","def allocate(self):\n raise NotImplementedError","def size(self):\n return self._size","def master_mem_overhead(self):\n return self._master_mem_overhead","def get_size(self):\n ...","def __len__(self):\n return self._size","def __len__(self):\n return self._size"],"string":"[\n \"def allocated_memory(self):\\n return self._allocated_memory\",\n \"def memory(self):\\r\\n return self._memory\",\n \"def __len__(self):\\n\\t\\treturn len(self.memory)\",\n \"def __len__(self):\\r\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def __len__(self):\\n return len(self.memory)\",\n \"def allocatememory(self):\\n pass\",\n \"def get_array_size(self):\\r\\n return conf.lib.clang_getArraySize(self)\",\n \"def MAXMEM(self):\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory():\\n return tracemalloc.take_snapshot()\",\n \"def getMemory(self):\\n return self.memory\",\n \"def array_size(self):\\n return self._array_size\",\n \"def MEM (self,n):\",\n \"def get_memory(self):\\n return self.loss_memory\",\n \"def memory(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"memory\\\")\",\n \"def memory(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"memory\\\")\",\n \"def deviceMemory(self):\\n return 1\",\n \"def memory_get_usage():\\n raise NotImplementedError()\",\n \"def read_memory(self):\\n if self.mem_empty == True:\\n if self.mem_idx == 0:\\n m_x = np.zeros(self.n)\\n m_d = 0\\n else:\\n m_x = np.mean(self.mem_x[:self.mem_idx+1], axis=0)\\n m_d = np.mean(self.mem_d[:self.mem_idx])\\n else:\\n m_x = np.mean(self.mem_x, axis=0)\\n m_d = np.mean(np.delete(self.mem_d, self.mem_idx))\\n self.mem_idx += 1\\n if self.mem_idx > len(self.mem_x)-1:\\n self.mem_idx = 0\\n self.mem_empty = False\\n return m_d, m_x\",\n \"def getArrayLength(self):\\r\\n return self.arraySize\",\n \"def _read(self):\\n return np.copy(self.memory[self.head_pos])\",\n \"def get_used_mem(self):\\n return self.used_mem\",\n \"def memory_used(self) -> int:\\r\\n return self._memory_used\",\n \"def memory_real_in_bytes(self):\\n cdef INT64_t total_memory = 0\\n\\n # col\\n total_memory += self.__nnz * sizeof(INT64_t)\\n # ind\\n total_memory += (self.__nrow + 1) * sizeof(INT64_t)\\n # val\\n total_memory += self.__nnz * sizeof(FLOAT32_t)\\n\\n return total_memory\",\n \"def getSize(self):\\n return self.bf.memory()\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def __len__(self):\\n return len(self.array)\",\n \"def size(self):\\n return self._N\",\n \"def BytesOfStorage(self):\\n return (self.NumBits() + 7) / 8\",\n \"def getSize(self) -> int:\\n return len(self.mem)\",\n \"def estimated_lookup_memory(self):\\n return 60 * len(self.docvecs.offset2doctag) + 140 * len(self.docvecs.doctags)\",\n \"def memory_utilization(self) -> float:\\r\\n return self._memory_utilization\",\n \"def total_memory(self):\\n return self._total_memory\",\n \"def __len__(self):\\n\\n return len(self.data) * 8\",\n \"def __len__(self) -> int:\\n return self.disp_size ** 2\",\n \"def _get_raw_data(self):\\n raw_data_bytes = self.nbytes + self._heapsize\\n base = self\\n while hasattr(base, \\\"base\\\") and base.base is not None:\\n base = base.base\\n # Variable-length-arrays: should take into account the case of\\n # empty arrays\\n if hasattr(base, \\\"_heapoffset\\\"):\\n if hasattr(base, \\\"nbytes\\\") and base.nbytes > raw_data_bytes:\\n return base\\n # non variable-length-arrays\\n else:\\n if hasattr(base, \\\"nbytes\\\") and base.nbytes >= raw_data_bytes:\\n return base\",\n \"def update_size(self):\\n return 3 + self.memory_unit_size\",\n \"def memory():\\n sin = psutil.virtual_memory()\\n return round((sin.total / sin.used) / 100, 3)\",\n \"def allocated_storage(self):\\n return self._allocated_storage\",\n \"def memUsedGpu(self):\\n return None # amount not known\",\n \"def total_actual_memory(self):\\n return self._total_actual_memory\",\n \"def get_total_memory_size(self):\\n return self.drt_manager.get_total_memory_size()\",\n \"def in_memory_data(self):\\n return self._in_memory_data\",\n \"def get_size(self):\",\n \"def _N(self):\\n return len(self._array)\",\n \"def __len__(self):\\n return self.current_size\",\n \"def __len__(self):\\n\\t\\treturn self._size\",\n \"def get_total_memory_size(self):\\n memory = 0\\n for i in range(4):\\n for j in range(4):\\n memory += self.system.operator[i, j].memory\\n return memory\",\n \"def get_free_mem(self):\\n return self.free_mem\",\n \"def get_mem(self) -> list:\\n return self.__mem\",\n \"def size(self):\\r\\n return self.size.data\",\n \"def size(self):\\n return len(self.arr)\",\n \"def __len__(self):\\r\\n return 100000\",\n \"def size(self):\\n return self.ptr\",\n \"def getallocatedblocks(): # real signature unknown; restored from __doc__\\n return 0\",\n \"def __len__(self):\\r\\n return self._size\",\n \"def __len__(self):\\r\\n return self._size\",\n \"def display_memory(self) -> None:\\n return self.__memory\",\n \"def size(self):\\n return self.__size\",\n \"def get_total_mem(self):\\n return self.total_mem\",\n \"def mem_per_proc(self):\\n return self._mem_per_proc\",\n \"def size(self):\\n size = 0\\n size += self.data.size * sys.getsizeof(self.data)\\n return size / 1024.0 / 1024.0 / 1024.0\",\n \"def get_space_used():\\n fs.get_space_used()\",\n \"def array(self):\",\n \"def size(self):\\r\\n # Anthony stage 2\\r\\n return number_size(self.n) - 1\",\n \"def in_memory(self):\\n return hasattr(self._subarray, \\\"__array_interface__\\\")\",\n \"def size(self):\\r\\n return self._size\",\n \"def _nbytes(self, deep: bool = False) -> int:\\n # for implementations with no useful getsizeof (PyPy)\\n objsize = 24\\n\\n level_nbytes = sum(i.memory_usage(deep=deep) for i in self.levels)\\n label_nbytes = sum(i.nbytes for i in self.codes)\\n names_nbytes = sum(getsizeof(i, objsize) for i in self.names)\\n result = level_nbytes + label_nbytes + names_nbytes\\n\\n # include our engine hashtable\\n result += self._engine.sizeof(deep=deep)\\n return result\",\n \"def get_memory(self):\\n return (self.K.get_value(), self.V.get_value(), self.A.get_value())\",\n \"def size(self) -> int:\",\n \"def __len__(self):\\n return self._arr.shape[1]\",\n \"def allocate(self):\\n raise NotImplementedError\",\n \"def size(self):\\n return self._size\",\n \"def master_mem_overhead(self):\\n return self._master_mem_overhead\",\n \"def get_size(self):\\n ...\",\n \"def __len__(self):\\n return self._size\",\n \"def __len__(self):\\n return self._size\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6821264","0.6811845","0.66922146","0.66735834","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.6612898","0.65476745","0.6454627","0.6434698","0.64256907","0.64256907","0.64256907","0.64256907","0.64256907","0.64256907","0.64256907","0.6421089","0.6355993","0.6355911","0.63380694","0.6234925","0.6234925","0.6210194","0.61880624","0.61694604","0.6157968","0.6144876","0.61090446","0.610004","0.60976934","0.60961306","0.60599285","0.60599285","0.6057573","0.60571706","0.6045886","0.6040636","0.60312796","0.6009837","0.6006144","0.59940344","0.5988107","0.59719926","0.59624505","0.59426904","0.59420973","0.59121144","0.5909753","0.5905127","0.5891123","0.5885706","0.5857103","0.58505625","0.5841439","0.5815311","0.58117014","0.58079964","0.5805348","0.5803843","0.5794774","0.5793059","0.5782983","0.5782983","0.57791406","0.57715523","0.5770453","0.5761753","0.57322794","0.57259846","0.5725839","0.57202536","0.5716401","0.5711541","0.5709581","0.57084954","0.57082736","0.5704399","0.5701274","0.56990856","0.56973356","0.5694242","0.5694148","0.5694148"],"string":"[\n \"0.6821264\",\n \"0.6811845\",\n \"0.66922146\",\n \"0.66735834\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.6612898\",\n \"0.65476745\",\n \"0.6454627\",\n \"0.6434698\",\n \"0.64256907\",\n \"0.64256907\",\n \"0.64256907\",\n \"0.64256907\",\n \"0.64256907\",\n \"0.64256907\",\n \"0.64256907\",\n \"0.6421089\",\n \"0.6355993\",\n \"0.6355911\",\n \"0.63380694\",\n \"0.6234925\",\n \"0.6234925\",\n \"0.6210194\",\n \"0.61880624\",\n \"0.61694604\",\n \"0.6157968\",\n \"0.6144876\",\n \"0.61090446\",\n \"0.610004\",\n \"0.60976934\",\n \"0.60961306\",\n \"0.60599285\",\n \"0.60599285\",\n \"0.6057573\",\n \"0.60571706\",\n \"0.6045886\",\n \"0.6040636\",\n \"0.60312796\",\n \"0.6009837\",\n \"0.6006144\",\n \"0.59940344\",\n \"0.5988107\",\n \"0.59719926\",\n \"0.59624505\",\n \"0.59426904\",\n \"0.59420973\",\n \"0.59121144\",\n \"0.5909753\",\n \"0.5905127\",\n \"0.5891123\",\n \"0.5885706\",\n \"0.5857103\",\n \"0.58505625\",\n \"0.5841439\",\n \"0.5815311\",\n \"0.58117014\",\n \"0.58079964\",\n \"0.5805348\",\n \"0.5803843\",\n \"0.5794774\",\n \"0.5793059\",\n \"0.5782983\",\n \"0.5782983\",\n \"0.57791406\",\n \"0.57715523\",\n \"0.5770453\",\n \"0.5761753\",\n \"0.57322794\",\n \"0.57259846\",\n \"0.5725839\",\n \"0.57202536\",\n \"0.5716401\",\n \"0.5711541\",\n \"0.5709581\",\n \"0.57084954\",\n \"0.57082736\",\n \"0.5704399\",\n \"0.5701274\",\n \"0.56990856\",\n \"0.56973356\",\n \"0.5694242\",\n \"0.5694148\",\n \"0.5694148\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73394907"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2699,"cells":{"query":{"kind":"string","value":"Open the partition prior to getting its array."},"document":{"kind":"string","value":"def open(self, config):\n unique_subarray = getrefcount(self._subarray) <= 2\n\n config = config.copy()\n config[\"unique_subarray\"] = unique_subarray\n\n self.config = config\n\n if config.get(\"auxiliary_mask\"):\n self._configure_auxiliary_mask(config[\"auxiliary_mask\"])\n\n self.config[\"extra_memory\"] = self.extra_memory()\n\n self._in_place_changes = True\n self.masked = True\n\n if hasattr(self, \"output\"):\n del self.output\n\n return config"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def open(self):\n return self.open_array","def partition_book(self):\n ...","def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n prefix=tfa._partition_file + \"_\", dir=tfa._partition_dir\n )\n close(fd)\n\n self.subarray = tfa\n _temporary_files[tfa._partition_file] = (\n tfa._partition_dir,\n _lock_file,\n set(),\n )\n\n if reopen:\n # Re-open the partition\n self.open(self.config)\n\n return True","def close(self, **kwargs):\n config = getattr(self, \"config\", None)\n\n if config is None:\n return\n\n if kwargs:\n config.update(kwargs)\n\n original = getattr(self, \"_original\", None)\n logger.partitioning(\"Partition.close: original = {}\".format(original))\n\n if not original:\n originally_on_disk = False\n original_subarray = None\n else:\n originally_on_disk = not original.in_memory\n original_subarray = original._subarray\n\n config = self.config\n logger.partitioning(\" config = {}\".format(config))\n\n if config[\"serial\"]:\n # --------------------------------------------------------\n # SERIAL\n # --------------------------------------------------------\n logger.partitioning(\" serial\")\n\n if config[\"readonly\"]:\n logger.partitioning(\" readonly=True\")\n\n if originally_on_disk:\n logger.partitioning(\" subarray originally on disk\")\n\n if config.get(\"to_disk\", False):\n # 1.1.1.1 The original subarray was on disk,\n # we don't want to keep the current\n # subarray in memory, and we are happy\n # to discard any changes that may have\n # been made to the subarray.\n logger.partitioning(\" 1.1.1.1 revert\")\n self.revert()\n elif free_memory() <= cf_fm_threshold():\n # 1.1.1.2 The original subarray was on disk,\n # we are happy to keep the current\n # subarray in memory, but there is not\n # enough free memory to do so.\n logger.partitioning(\n \" 1.1.1.2 revert ({} <= {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n self.revert()\n else:\n # 1.1.1.3 The original subarray was on disk\n # and there is enough memory to keep\n # the current subarray in memory\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # The original subarray was a temporary\n # file which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n del self.masked\n logger.partitioning(\n \" 1.1.1.3 del masked ({} > {})\".format(\n free_memory(), cf_fm_threshold()\n )\n )\n\n else:\n logger.partitioning(\" subarray originally in memory\")\n if config.get(\"to_disk\", False):\n # 1.1.2.1 Original subarray was in memory and\n # we don't want to keep the current\n # subarray in memory\n logger.partitioning(\" 1.1.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.1.2.2 Original subarray was in memory and\n # unique but there is not enough\n # memory to keep the current subarray\n logger.partitioning(\" 1.1.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.1.2.3 Original subarray was in memory and\n # unique and there is enough memory to\n # keep the current subarray in memory\n logger.partitioning(\" 1.1.2.3 pass\")\n pass\n else:\n # config['readonly'] is False\n if originally_on_disk:\n if config.get(\"to_disk\", False):\n # 1.2.1.1 Original subarray was on disk and\n # there and we don't want to keep the\n # array\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.1.2 Original subarray was on disk but\n # there is not enough memory to keep\n # it\n if config[\"unique_subarray\"] and isinstance(\n original_subarray, CachedArray\n ):\n # Original subarray was a temporary file\n # on disk which is not referenced by any\n # other partitions\n _remove_temporary_files(\n original_subarray._partition_file\n )\n\n logger.partitioning(\" 1.2.1.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.1.3 Original subarray was on disk and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.1.3 pass\")\n del self.masked\n else:\n if config.get(\"to_disk\", False):\n # 1.2.2.1 Original subarray was in memory but\n # we don't want to keep it\n logger.partitioning(\" 1.2.2.1 to_disk\")\n self.to_disk(reopen=False)\n elif free_memory() <= cf_fm_threshold():\n # 1.2.2.2 Original subarray was an in memory\n # but there is not enough memory to\n # keep it\n logger.partitioning(\" 1.2.2.2 to_disk\")\n self.to_disk(reopen=False)\n else:\n # 1.2.2.3 Original subarray was in memory and\n # there is enough memory to keep it\n logger.partitioning(\" 1.2.2.3 del masked\")\n del self.masked\n else:\n logger.partitioning(\"Partition.close: parallel\")\n # --------------------------------------------------------\n # PARALLEL\n # --------------------------------------------------------\n pass\n\n # if hasattr(self, '_original'):\n # del self._original\n\n # print(hasattr(self, 'config')),\n try:\n del self.config\n except AttributeError:\n pass","async def begin_array(self):","def _load_disk(self):","def _load_disk(self):","def open_position(self, position: int):\n # get row, column, and path to the well\n row_name = self.positions[position]['row']\n col_name = self.positions[position]['col']\n well_path = os.path.join(os.path.join(self.root_path, row_name), col_name)\n\n # check to see if this well exists (row/column)\n if os.path.exists(well_path):\n pos_name = self.positions[position]['name']\n pos_path = os.path.join(well_path, pos_name)\n\n # check to see if the position exists\n if os.path.exists(pos_path):\n\n if self.verbose: print(f'Opening subgroup {row_name}/{col_name}/{pos_name}')\n\n # update trackers to note the current status of the writer\n self.current_pos_group = self.store[row_name][col_name][pos_name]\n self.current_well_group = self.store[row_name][col_name]\n self.current_position = position\n\n else:\n raise FileNotFoundError(f'Could not find zarr position subgroup at {row_name}/{col_name}/{pos_name}\\\n Check spelling or create position subgroup with create_position')\n else:\n raise FileNotFoundError(f'Could not find zarr position subgroup at {row_name}/{col_name}/\\\n Check spelling or create column/position subgroup with create_position')","def lazy_read_file(self):\n store = zarr.DirectoryStore(self.fpath)\n z_array = zarr.open(store=store, mode='r')\n self.da_input = da.from_array(z_array)\n self.data = self.da_input\n self.data_dim = self.data.shape\n self.chunk_size = z_array.chunks","def _load_disk(self):\r\n pass","def _get_partition_list(self):\n raise NotImplementedError('Must be implemented in subclasses.')","def a_subarray_in_the_idle_state():","def provide_partition_info(self):\n self.partition_info = True","def getPartition(self):\n\t\treturn self.partition","def initialize(self):\r\n self.bucket_array.initialize()","def set_xp_partition(self,xp_partition):\n # setup partition: set x grid\n self.xp_partition = xp_partition\n self.xp_partition.setup_x_grid(xx=[0,self.L])\n # local copies of x and p grids\n self.x=self.xp_partition.x\n self.p=self.xp_partition.p\n # allocate fmci_XP array\n self.fmci_XP=np.zeros((self.xp_partition.nx,self.xp_partition.np))","def partition(self, arr, low, high, pivot):\n i_less = low\n for i in range(low, high):\n if arr[i] <= pivot: # note the equal\n arr[i], arr[i_less] = arr[i_less], arr[i]\n i_less += 1 # do not forget this\n arr[i_less], arr[high] = arr[high], arr[i_less] # put the pivot into the mid position\n return i_less","def open(self) -> None:\n if not self.__opened:\n if self.path is None:\n self.path = HID.enumerate_devices(self.vendor_id)[0]\n self.device.open_path(self.path)\n self.device.set_nonblocking(True)\n self.__opened = True","def next(self):\n partition_start = self._partitions[self._current_index]\n partition_end = partition_start + self._partition_length\n self._current_index += 1\n return self._dataframe[partition_start:partition_end]","def get_partitioning(self):\n raise Exception(\"Unimplemented\")","def allocate(self,partition, num_farms, activity, runinfo_dp):\n if self.load():\n nf = num_farms\n got = 0\n used = Online.PVSS.StringVector()\n farms = Online.PVSS.StringVector()\n dpv = Online.PVSS.DataPointVector()\n for i in xrange(len(self.inUse.data)):\n f = self.inUse.data[i]\n n = self.subfarms.data[i]\n if len(f)==0 and got<nf:\n dpv.push_back(self.dp2(self.name+'_'+n,'UsedBy'))\n dpv.back().data = partition\n dpv.push_back(self.dp2(self.name+'_'+n,'RunInfo'))\n dpv.back().data = runinfo_dp\n dpv.push_back(self.dp2(self.name+'_'+n,'Activity'))\n dpv.back().data = activity\n used.push_back(partition)\n farms.push_back(n)\n got = got + 1\n else:\n used.push_back(f)\n if got==nf:\n if len(runinfo_dp)>0:\n #dpv.push_back(self.dp2(runinfo_dp,'general.partName'))\n #dpv.back().data = partition\n #dpv.push_back(self.dp2(runinfo_dp,'HLTFarm.nSubFarms'))\n #dpv.back().data = nf\n dpv.push_back(self.dp2(runinfo_dp,'HLTFarm.subFarms'))\n dpv.back().data = farms\n self.inUse.data = used\n self.writer.add(dpv)\n self.writer.add(self.inUse)\n if self.writer.execute():\n return 'SUCCESS'\n self.error('Failed to update allocation information for partition '+\\\n partition+' in farm system:'+self.name)\n return None\n return self.error('Not enough free subfarms availible for partition '+partition+\\\n ' in farm system:'+self.name)\n return self.error('Failed to load information for partition '+partition+\\\n ' in farm system:'+self.name)","def one_basis(self):\n return self._kbounded_partitions([])","def test_partitioner_iter(self):\n partitioner = self.tx_client.SetPartitioner(\"xyzzy\", \"iddqd\")\n self.assertEqual(list(partitioner), [1])","def _lock_partition(self, partition, shared=False):\n pass","def allocate(self):\n raise NotImplementedError","def testRead(self):\n self._TestRead(self._tsk_partition_path_spec)","def testRead(self):\n self._TestRead(self._tsk_partition_path_spec)","def partition(seq):\n\n return 0","def __init__(self):\n self.arr = []\n self.size = 0","def load_chunk(self, start): # TODO parallelize this whole process\n self.X = queue.Queue()\n n = 0 # number of loaded batches\n print('stop loading : %s' % self.stop_loading())\n print('start + n : %s' % str(start + n))\n while (not self.stop_loading()) and (start + n) < self.size:\n print('load')\n self.X.put(np.load(self.data_filenames[start+n]))\n n += 1\n print('return chunk')\n return n","def set_partition(self, begin=0, end=0):\r\n self.partition = (begin, end)","def get_free_partition_index(dev):\n try:\n lines = _check_output(\n args=[\n 'parted',\n '--machine',\n '--',\n dev,\n 'print',\n ],\n )\n except subprocess.CalledProcessError as e:\n print 'cannot read partition index; assume it isn\\'t present\\n (Error: %s)' % e\n return 1\n\n if not lines:\n raise Error('parted failed to output anything')\n if ('CHS;' not in lines and\n 'CYL;' not in lines and\n 'BYT;' not in lines):\n raise Error('parted output expected to contain one of ' +\n 'CHH; CYL; or BYT; : ' + lines)\n if dev not in lines:\n raise Error('parted output expected to contain ' + dev + ': ' + lines)\n _, partitions = lines.split(dev)\n partition_numbers = extract_parted_partition_numbers(partitions)\n if partition_numbers:\n return max(partition_numbers) + 1\n else:\n return 1","def _read(self):\n return np.copy(self.memory[self.head_pos])","def __init__(self, partition, test=False, local_test_data_dir=_LOCAL_TEST_DATA_DIR):\n assert sum(partition) == 100, 'The sum of the partition list must be 100: {}'.format(partition)\n self._partition = partition\n self._test = test\n # Split the files up according to the self._partition list.\n self._partitioned_filenames = []\n filenames = data_filenames(shuffle=False, test=self._test,\n local_test_data_dir=local_test_data_dir)\n part_start = 0\n for i, part_size in enumerate(self._partition):\n part_end = part_start + int(len(filenames) * 0.01 * part_size)\n assert part_end - part_start > 0, 'The number of files in partition {} is zero.'.format(i)\n self._partitioned_filenames.append(filenames[part_start:part_end])","def get_partition(self, nIndex):\n\t\treturn handle_to_object(call_sdk_function('PrlVmDevHd_GetPartition', self.handle, nIndex))","def add_partition(self):\n\t\treturn handle_to_object(call_sdk_function('PrlVmDevHd_AddPartition', self.handle))","def _preload_range(self):\n queue = self._read_queue\n size = self._buffer_size\n start = self._seek\n end = int(start + size * self._max_buffers)\n workers_submit = self._workers.submit\n indexes = tuple(range(start, end, size))\n\n for seek in tuple(queue):\n if seek not in indexes:\n del queue[seek]\n\n read_range = self._read_range\n for seek in indexes:\n if seek not in queue:\n queue[seek] = workers_submit(read_range, seek, seek + size)","def resume_reading(self):\n raise NotImplementedError","def get_partition():\n if selection is None:\n warning(\"You need to pick something first.\")\n return\n if not selection.obj_type in ['partition']:\n warning(\"You need to partition the selection first.\")\n return\n res = askItems([['property',[1]]],\n caption='Partition property')\n if res:\n prop = res['property']\n getPartition(selection,prop)\n highlightPartitions(selection)","def rpartition(self, x):\n pass","def get_index(self):\n return self.disk.partitions.index(self)","def num_partitions(self): # -> None:\n ...","def push_front(self, param):\n if self.size == self.capacity:\n self.resize(2 * self.size)\n for _ in range(self.arr):\n pass","def _partition(array, order, pivot, lower, upper):\n # The pivot element is made the last not to interfere with other swaping \n # operations\n array[pivot], array[upper] = array[upper], array[pivot]\n\n # Last index of smaller/greater (asc/desc) elements\n idx = lower\n\n for j in range(lower, upper):\n if (order == Order.ASC and array[j] < array[upper] or # ASCENDING\n order == Order.DESC and array[j] > array[upper]): # DESCENDING\n # Moves the current element at the end of the sub-array with\n # the smaller/grater (asc/desc) elements\n array[idx], array[j] = array[j], array[idx]\n idx += 1\n\n # Moves pivot as separator between greater and smaller numbers\n array[idx], array[upper] = array[upper], array[idx]\n\n return idx","def _open_dataset(self):\n\n try:\n\n datasets = [\n _open_dataset(\n record[self.path_column_name],\n record[self.variable_column_name],\n xarray_open_kwargs=self.xarray_open_kwargs,\n preprocess=self.preprocess,\n expand_dims={\n agg.attribute_name: [record[agg.attribute_name]]\n for agg in self.aggregations\n if agg.type.value == 'join_new'\n },\n requested_variables=self.requested_variables,\n additional_attrs=record.to_dict(),\n )\n for _, record in self.df.iterrows()\n ]\n\n datasets = dask.compute(*datasets)\n if len(datasets) == 1:\n self._ds = datasets[0]\n else:\n datasets = sorted(\n datasets,\n key=lambda ds: tuple(\n f'{INTAKE_ESM_ATTRS_PREFIX}/{agg.attribute_name}'\n for agg in self.aggregations\n ),\n )\n with dask.config.set(\n {'scheduler': 'single-threaded', 'array.slicing.split_large_chunks': True}\n ): # Use single-threaded scheduler\n datasets = [\n ds.set_coords(set(ds.variables) - set(ds.attrs[INTAKE_ESM_VARS_KEY]))\n for ds in datasets\n ]\n self._ds = xr.combine_by_coords(\n datasets, **self.xarray_combine_by_coords_kwargs\n )\n\n self._ds.attrs[INTAKE_ESM_DATASET_KEY] = self.key\n\n except Exception as exc:\n raise ESMDataSourceError(\n f\"\"\"Failed to load dataset with key='{self.key}'\n You can use `cat['{self.key}'].df` to inspect the assets/files for this key.\n \"\"\"\n ) from exc","def test_partition(self):\n mat = self.mat\n self.assertSequenceEqual(\n [mat.m, mat.n, mat.shape[2], mat.shape[3], mat.dtype],\n [self.m, self.n, self.p, self.q, self.dtype]\n )\n if not mat.is_active:\n self.assertSequenceEqual(\n [mat.mloc, mat.mstart, mat.mend, mat.nloc, mat.nstart, mat.nend],\n [0, 0, 0, 0, 0, 0]\n )\n else:\n pass","def get_partition(self):\n return self._partition","def grow(self):\n while self.splittable_nodes:\n self.split_next()","def get(self):\n return self._partition","def __init__(self, my_partition: List[int]):\n self.my_partition = my_partition\n self.my_partition.sort(reverse=True)\n if self.my_partition[-1]==0:\n first_zero = self.my_partition.index(0)\n self.my_partitition = self.my_partition[0:first_zero]\n self.my_n = sum(self.my_partition)","def test_open_fill(self):","def __init__(self, arr):\n self._arr = arr\n self._n = len(arr) # no. sub-arrays \n self._row = 0 # initialize cursor at position (0,0)\n self._col = 0","def newpart(self, device, primary, ncyls, swap=False):\n # This is a simple partitioning tool, which only supports\n # adding partitions sequentially, with all primary partitions\n # being before the extended partition, so once a logical\n # partition has been added, it is not possible to add further\n # primary ones.\n di = DiskInfo(device)\n pmax = 0 # Record highest partition number\n lim = -1 # Used for seeking last used cylinder\n exp = 0 # Number of extended partition\n ex0, ex1 = 0, -1 # Extended partition start and end\n log0, log1 = 0, -1 # Start and end of area used by logical partitions\n for p in di.parts:\n pn = int(p[0][len(device):])\n scyl, ecyl = p[1:3]\n if pn <= 4:\n if exp:\n run_error(_(\"Not supported: primary partition (%s%d)\\n\"\n \"has higher partition number than extended \"\n \"partition\") % (device, pn))\n return \"\"\n if scyl <= lim:\n run_error(_(\"Partitions must be ordered on the device.\\n\"\n \"%s%d is out of order.\") % (device, pn))\n return \"\"\n if p[3] in (\"5\", \"f\"):\n # extended\n exp = pn\n ex0, ex1 = scyl, ecyl\n continue\n pmax = pn\n lim = ecyl\n\n startcyl = lim + 1\n endcyl = lim + ncyls\n if endcyl >= di.drvcyls:\n run_error(_(\"Too little space at end of drive for new partition\"))\n return \"\"\n if exp and (pmax <= 4):\n # Remove the extended partition, which is empty anyway\n if not self.rmpart(device, exp):\n return \"\"\n pmax = exp - 1\n if primary:\n if pmax >= 4:\n run_error(_(\"Cannot add primary partition to %s\") % device)\n return \"\"\n t = \"primary\"\n else:\n t = \"logical\"\n if pmax > 4:\n # resize extended partition\n if not self.xcheck(\"resizepart\", device, str(exp),\n str(ex0), str(endcyl),\n onfail=_(\"Couldn't resize extended partition %s%d\")\n % (device, exp)):\n return False\n else:\n # create extended partition\n if not self.xcheck(\"newpart\", device,\n str(startcyl), str(endcyl), \"extended\",\n onfail=_(\"Couldn't create extended partition on %s\")\n % device):\n return False\n if pmax < 4:\n pmax = 4\n\n if self.xcheck(\"newpart\", device, str(startcyl), str(endcyl),\n t, \"linux-swap\" if swap else \"ext2\"):\n return \"%s%d\" % (device, pmax + 1)\n else:\n run_error(_(\"Couldn't add new partition to %s\") % device)\n return \"\"","def get_open_disk_space(self):\n count = 0\n for i in range(self.size):\n if self.disk_mem[i]==\".\":\n count += 1\n return count","def update(self) -> np.ndarray:\r\n # Read chunk from array\r\n signal_slice: np.ndarray = np.array(\r\n self._signal_data[self._pointer : self._pointer + self._chunk]\r\n )\r\n # Move index over\r\n self._pointer += self._chunk\r\n if self._pointer > len(self._signal_data):\r\n # Go back to beginning\r\n self._pointer = 0\r\n self._restart_flag = True\r\n print(\"Restarting stream...\")\r\n\r\n return signal_slice","def update(self) -> np.ndarray:\r\n # Read chunk from array\r\n signal_slice: np.ndarray = np.array(\r\n self._signal_data[self._pointer : self._pointer + self._chunk]\r\n )\r\n # Move index over\r\n self._pointer += self._chunk\r\n if self._pointer > len(self._signal_data):\r\n # Go back to beginning\r\n self._pointer = 0\r\n self._restart_flag = True\r\n print(\"Restarting stream...\")\r\n\r\n return signal_slice","def partition_session(self):\n if self.user['drive']['name'] is not None:\n\n # Set root size\n if self.user['root_freespace'] is True:\n self.user['root_size'] = 'freespace'\n\n # Set partition parameters\n self.user['partitions'] = {'name': ['boot', 'root'],\n 'size': [self.user['boot_size'],\n self.user['root_size']],\n 'filesystem': ['fat32', 'ext4'],\n 'mountpoint': ['/mnt/boot', '/mnt'],\n 'mountorder': [1, 0]}\n\n # Set swap size and filesystem\n if 'Swap' in self.user['optional_partitions']:\n self.user['partitions']['size'].insert(1, self.user['swap_size'])\n self.user['partitions']['filesystem'].insert(1, 'swap')\n\n # Set home size and filesystem\n if 'Home' in self.user['optional_partitions']:\n if self.user['home_freespace'] is True:\n self.user['home_size'] = 'freespace'\n self.user['partitions']['size'].append(self.user['home_size'])\n self.user['partitions']['filesystem'].append('ext4')\n\n # Custom partitions\n else:\n\n # Set partition parameters\n self.user['partitions'] = {\n 'name': ['boot', 'root'],\n 'drive_id': [self.user['boot_id'].split()[0],\n self.user['root_id'].split()[0]],\n 'mountpoint': ['/mnt/boot', '/mnt'],\n 'mountorder': [1, 0]}\n\n # Set swap drive ID\n if self.user['swap_id'] is not None:\n self.user['partitions']['drive_id'].insert(\n 1, self.user['swap_id'].split()[0])\n\n # Set home drive ID\n if self.user['home_id'] is not None:\n self.user['partitions']['drive_id'].append(\n self.user['home_id'].split()[0])\n\n # Set swap parameters\n if ('Swap' in self.user['optional_partitions']) or \\\n (self.user['swap_id'] is not None):\n self.user['partitions']['name'].insert(1, 'swap')\n self.user['partitions']['mountpoint'].insert(1, 'swap')\n self.user['partitions']['mountorder'].insert(1, 2)\n\n # Set home parameters\n if 'Home' in self.user['optional_partitions'] or \\\n (self.user['home_id'] is not None):\n self.user['partitions']['name'].append('home')\n self.user['partitions']['mountpoint'].append('/mnt/home')\n self.user['partitions']['mountorder'].append(3)","def num_partitions(self): # -> int:\n ...","def partition1(self, partition1):\n\n self._partition1 = partition1","def smallest_partition():\n try:\n usb_partitions = sort_partitions()\n smallest = usb_partitions[0]\n except IndexError:\n print(\"Not enough USB devices available\")\n exit(1)\n else:\n return str(smallest[0])","def GetPartitioningArray(self):\n return _hypre.HypreParVector_GetPartitioningArray(self)","def partition(cls, key):\n return cls.partition_indexed(\n cls.hash_ring.select_bucket(key),\n )","def init_disk(self):\n self.put(self.SIZE/2-1, self.SIZE/2-1,\n Disk(self.SIZE/2-1, self.SIZE/2-1, 255, self, self.DISKSIZE))\n self.put(self.SIZE/2-1, self.SIZE/2,\n Disk(self.SIZE/2-1, self.SIZE/2, 0, self, self.DISKSIZE))\n self.put(self.SIZE/2, self.SIZE/2-1,\n Disk(self.SIZE/2, self.SIZE/2-1, 0, self, self.DISKSIZE))\n self.put(self.SIZE/2, self.SIZE/2,\n Disk(self.SIZE/2, self.SIZE/2, 255, self, self.DISKSIZE))","def test_partition(self):\n # one swap at the end\n list = [5, 6, 7, 8, 9, 2]\n partition(list, 0, 5)\n # assert list == [2, 6, 7, 8, 9, 5] # should be improved in future","def grow(self):\n # expansion - get all fanins of this gate, except for ones already in\n next_frontier = set()\n added = 0\n remove = set()\n for g in self.frontier:\n new_fin = len((self.ckt[g].fins - self.members)) - 1\n if (new_fin + self.w) < self.max_w:\n print \"Adding\", g, \"to partition\"\n # add this to the partition\n self.members.add(g)\n next_frontier |= self.ckt[g].fins - self.members\n self.w += new_fin + 1\n else:\n remove.add(g)\n self.frontier = next_frontier\n if len(self.frontier) == 0:\n return None\n else:\n return True","def choose_partition():\n # Ask the user wether the partitions should be taken from the original partitions, or from the home-made partitions\n file_name = selector([\"The original partition given by the instructor\", \"The homemade partition file\"], [\"ORIGINAL\", \"HOMEMADE\"])\n\n # Open the corresponding file\n if file_name == \"1\" or file_name == \"ORIGINAL\":\n file = open(\"./assets/partitions.txt\", \"r\")\n elif file_name == \"2\" or file_name == \"HOMEMADE\":\n file = open(\"./assets/homemade_partitions.txt\", \"r\")\n\n skip_lines(-1)\n\n # Print all song's names in the partitions\n lines = file.readlines()\n file.close()\n for i in range(0, len(lines), 2):\n print(lines[i][:-1])\n\n # Ask the user to choose for a song\n song_index = choose_number(len(lines) / 2)\n\n # Get the corresponding song's partition and convert notes to Note instances\n partition = lines[song_index * 2 - 1][:-1].replace(' ', '')\n raw_notes = get_notes_from_line(partition)\n parsed_notes = [Note(note) for note in raw_notes]\n return parsed_notes","def _download_input_chunk(self):\n #FIXME choose the mip level based on the chunk key\n volume = Precomputed(self._storage)\n self._data = volume[self._xmin:self._xmax,\n self._ymin:self._ymax,\n self._zmin:self._zmax]","def _get_partitions():\n partitions = []\n\n try:\n with files.FileReader('/proc/partitions') as f:\n lines = f.readlines()[2:]\n for line in lines:\n _, _, _, name = line.split()\n if name[-1].isdigit():\n partitions.append(name)\n # This will catch access denied and file not found errors, which is expected\n # on non-Linux/limited access systems. All other errors will raise as normal.\n except files.Error:\n pass\n\n return partitions","def on_add_clicked(self,button):\n\t\tself.list_partitions.add_partition()","def open(self, dataset: DatasetDB):\n self._es.open_index(dataset_records_index(dataset.id))","def partition(self, sep):\n return asarray(partition(self, sep))","def open(self):\n return xr.open_dataset(self)","def open(self, mode):\n if not self._open:\n self.grp = zarr.open_group(self.store_path, mode)[self.group_path]\n self._open = True","def get_partition(self, partition_spec):\n return self.partitions[partition_spec]","def __init__(self, width, partition_points):\n self.width = width\n self.a = Signal(width, reset_less=True)\n self.b = Signal(width, reset_less=True)\n self.partition_points = PartitionPoints(partition_points)\n self.mwidth = len(self.partition_points)+1\n self.output = Signal(self.mwidth, reset_less=True)\n if not self.partition_points.fits_in_width(width):\n raise ValueError(\"partition_points doesn't fit in width\")","def __init__(self):\n super().__init__()\n self.__size = 0\n self.__capacity = self.INITIAL_CAPACITY\n self.__array = self.__get_new_array(self.__capacity)\n\n self.__getitem_status = self.GETITEM_NIL\n self.__insert_status = self.GETITEM_NIL\n self.__delete_status = self.DELETE_NIL","def open(self, chunks: dict = None):\n return open_nwmdataset(self,chunks=chunks)","def __missing__(self, key):\n # deferred import so arrayjob can be used without load_memmap_offset\n from cgp.utils.load_memmap_offset import open_memmap\n return open_memmap(\"%s/%s.npy\" % (self.pardir, key), **self.kwargs)","def indices(self):\n return self._kbounded_partitions","def partition(array, first, last):\n # partition up until final value\n pivot = array[last]\n i = first - 1\n\n for count in range(first, last):\n # split array\n if array[count] < pivot:\n i += 1\n # assign array positions\n array[i],array[count] = array[count],array[i]\n # reassign\n array[i+1],array[last] = array[last],array[i+1]\n return (i+1)","def take_snapshot(self):\r\n self.snapshot = self.name, self.size, copy.copy(self.cells)\r\n self.bucket_array.take_snapshot()","def num_partitions(self): # -> Unknown:\n ...","def set_partition(self, partition=0):\n if not isinstance(partition, int):\n raise TypeError('partition must be an integer')\n if partition <= 0:\n raise ValueError('partition must be positive')\n if self.connected:\n self.producer.send(\"PART:\"+str(partition))","def do_stage_partition(cls, part, source_params, creator, cr_workdir,\n oe_builddir, bootimg_dir, kernel_dir,\n native_sysroot):\n logger.debug(\"SourcePlugin: do_stage_partition: part: %s\", part)","def addPartition(self,partitionData):\n self.PCAs[partitionData.id] = partitionData\n self.pcaStatemachineLock[partitionData.id] = threading.Lock()\n self.StateMachineForPca[partitionData.id] = Statemachine(self.StateMachineFile,\"Unconfigured\")\n self.isPCAinTransition[partitionData.id] = False\n self.pcaSequenceNumber[partitionData.id] = 0","def partition(self):\n return self.tag(\"partition\")","def _load_array(self, array_name, fam=None):\n raise OSError(\"No lazy-loading implemented\")","def prevSplit(self):\n pass","def on_splitter_size_allocate(self, splitter, allocation):\n\t\tself.emit('splitter-position-changed')","def __init__(self, growth_factor=2):\n self._length = 0 # Number of elements in array\n self._capacity = 1 # Capacity of array before expanding\n self._arr = self._create_array(self._capacity) # Compact array of pointers\n self._growth_factor = max(2, growth_factor) # Factor to grow array when capacity reached","def get_partition(self, partid):\n #TODO(zhengda) add implementation later.","def get_partition(self, partid):\n #TODO(zhengda) add implementation later.","def get_partitions(self):\n return self.partitions","def bin_open(img: np.ndarray, disk_size: int) -> np.ndarray:\n selem = disk(disk_size)\n res = binary_opening(img, selem)\n return res","def _setPartedPartition(self, partition):\n log_method_call(self, self.name)\n\n if partition is not None and not isinstance(partition, parted.Partition):\n raise ValueError(\"partition must be None or a parted.Partition instance\")\n\n log.debug(\"device %s new partedPartition %s\", self.name, partition)\n self._partedPartition = partition\n self.updateName()","def __init__(self, period):\n\n self.open = StreamArray(period)\n self.high = StreamArray(period)\n self.low = StreamArray(period)\n self.close = StreamArray(period)","def _loadpart(self, part):\n new_partidx = util.Partname(part.partname).idx\n for idx, seq_part in enumerate(self._values):\n partidx = util.Partname(seq_part.partname).idx\n if partidx > new_partidx:\n self._values.insert(idx, part)\n return\n self._values.append(part)","def __init__(self, k: int):\n self.front = 0\n self.rear = 0\n self.capacity = k + 1\n self.arr = [0 for _ in range(self.capacity)]","def initialize(self):\n\t\ttable = NetworkTables.getTable('SmartDashboard')\n\t\tinitial_pos = table.getData('initial_pos')\n\t\t#XXX Doing this will start the path following immediately. May want this\n\t\t# in the execute or something\n\t\tpath = self.dt.which_path(initial_pos)","def __init__(self, nrows):\n\n # create the data array\n dummy_list = []\n\n # set the row number\n self._nrows = nrows\n\n # append the reuqested number of None\n # for index in range(nrows):\n # self._data.append(None)\n\n # append the reuqested number of None\n # perhaps a bit faster than the above lines\n self._data = list(map(dummy_list.append, range(nrows)))\n\n # set the row number\n self._nrows = nrows","def partition(data, num_partitions=None, by=None, **kwargs):\n return Component(\n \"Partition\",\n arguments={\n 'data': Component.of(data),\n 'num_partitions': Component.of(num_partitions),\n 'by': Component.of(by)\n },\n options={\n \n },\n constraints=kwargs)"],"string":"[\n \"def open(self):\\n return self.open_array\",\n \"def partition_book(self):\\n ...\",\n \"def to_disk(self, reopen=True):\\n # try:\\n tfa = CachedArray(self.array)\\n # except Exception:\\n # return False\\n\\n fd, _lock_file = mkstemp(\\n prefix=tfa._partition_file + \\\"_\\\", dir=tfa._partition_dir\\n )\\n close(fd)\\n\\n self.subarray = tfa\\n _temporary_files[tfa._partition_file] = (\\n tfa._partition_dir,\\n _lock_file,\\n set(),\\n )\\n\\n if reopen:\\n # Re-open the partition\\n self.open(self.config)\\n\\n return True\",\n \"def close(self, **kwargs):\\n config = getattr(self, \\\"config\\\", None)\\n\\n if config is None:\\n return\\n\\n if kwargs:\\n config.update(kwargs)\\n\\n original = getattr(self, \\\"_original\\\", None)\\n logger.partitioning(\\\"Partition.close: original = {}\\\".format(original))\\n\\n if not original:\\n originally_on_disk = False\\n original_subarray = None\\n else:\\n originally_on_disk = not original.in_memory\\n original_subarray = original._subarray\\n\\n config = self.config\\n logger.partitioning(\\\" config = {}\\\".format(config))\\n\\n if config[\\\"serial\\\"]:\\n # --------------------------------------------------------\\n # SERIAL\\n # --------------------------------------------------------\\n logger.partitioning(\\\" serial\\\")\\n\\n if config[\\\"readonly\\\"]:\\n logger.partitioning(\\\" readonly=True\\\")\\n\\n if originally_on_disk:\\n logger.partitioning(\\\" subarray originally on disk\\\")\\n\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.1.1 The original subarray was on disk,\\n # we don't want to keep the current\\n # subarray in memory, and we are happy\\n # to discard any changes that may have\\n # been made to the subarray.\\n logger.partitioning(\\\" 1.1.1.1 revert\\\")\\n self.revert()\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.1.2 The original subarray was on disk,\\n # we are happy to keep the current\\n # subarray in memory, but there is not\\n # enough free memory to do so.\\n logger.partitioning(\\n \\\" 1.1.1.2 revert ({} <= {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n self.revert()\\n else:\\n # 1.1.1.3 The original subarray was on disk\\n # and there is enough memory to keep\\n # the current subarray in memory\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # The original subarray was a temporary\\n # file which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n del self.masked\\n logger.partitioning(\\n \\\" 1.1.1.3 del masked ({} > {})\\\".format(\\n free_memory(), cf_fm_threshold()\\n )\\n )\\n\\n else:\\n logger.partitioning(\\\" subarray originally in memory\\\")\\n if config.get(\\\"to_disk\\\", False):\\n # 1.1.2.1 Original subarray was in memory and\\n # we don't want to keep the current\\n # subarray in memory\\n logger.partitioning(\\\" 1.1.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.1.2.2 Original subarray was in memory and\\n # unique but there is not enough\\n # memory to keep the current subarray\\n logger.partitioning(\\\" 1.1.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.1.2.3 Original subarray was in memory and\\n # unique and there is enough memory to\\n # keep the current subarray in memory\\n logger.partitioning(\\\" 1.1.2.3 pass\\\")\\n pass\\n else:\\n # config['readonly'] is False\\n if originally_on_disk:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.1.1 Original subarray was on disk and\\n # there and we don't want to keep the\\n # array\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.1.2 Original subarray was on disk but\\n # there is not enough memory to keep\\n # it\\n if config[\\\"unique_subarray\\\"] and isinstance(\\n original_subarray, CachedArray\\n ):\\n # Original subarray was a temporary file\\n # on disk which is not referenced by any\\n # other partitions\\n _remove_temporary_files(\\n original_subarray._partition_file\\n )\\n\\n logger.partitioning(\\\" 1.2.1.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.1.3 Original subarray was on disk and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.1.3 pass\\\")\\n del self.masked\\n else:\\n if config.get(\\\"to_disk\\\", False):\\n # 1.2.2.1 Original subarray was in memory but\\n # we don't want to keep it\\n logger.partitioning(\\\" 1.2.2.1 to_disk\\\")\\n self.to_disk(reopen=False)\\n elif free_memory() <= cf_fm_threshold():\\n # 1.2.2.2 Original subarray was an in memory\\n # but there is not enough memory to\\n # keep it\\n logger.partitioning(\\\" 1.2.2.2 to_disk\\\")\\n self.to_disk(reopen=False)\\n else:\\n # 1.2.2.3 Original subarray was in memory and\\n # there is enough memory to keep it\\n logger.partitioning(\\\" 1.2.2.3 del masked\\\")\\n del self.masked\\n else:\\n logger.partitioning(\\\"Partition.close: parallel\\\")\\n # --------------------------------------------------------\\n # PARALLEL\\n # --------------------------------------------------------\\n pass\\n\\n # if hasattr(self, '_original'):\\n # del self._original\\n\\n # print(hasattr(self, 'config')),\\n try:\\n del self.config\\n except AttributeError:\\n pass\",\n \"async def begin_array(self):\",\n \"def _load_disk(self):\",\n \"def _load_disk(self):\",\n \"def open_position(self, position: int):\\n # get row, column, and path to the well\\n row_name = self.positions[position]['row']\\n col_name = self.positions[position]['col']\\n well_path = os.path.join(os.path.join(self.root_path, row_name), col_name)\\n\\n # check to see if this well exists (row/column)\\n if os.path.exists(well_path):\\n pos_name = self.positions[position]['name']\\n pos_path = os.path.join(well_path, pos_name)\\n\\n # check to see if the position exists\\n if os.path.exists(pos_path):\\n\\n if self.verbose: print(f'Opening subgroup {row_name}/{col_name}/{pos_name}')\\n\\n # update trackers to note the current status of the writer\\n self.current_pos_group = self.store[row_name][col_name][pos_name]\\n self.current_well_group = self.store[row_name][col_name]\\n self.current_position = position\\n\\n else:\\n raise FileNotFoundError(f'Could not find zarr position subgroup at {row_name}/{col_name}/{pos_name}\\\\\\n Check spelling or create position subgroup with create_position')\\n else:\\n raise FileNotFoundError(f'Could not find zarr position subgroup at {row_name}/{col_name}/\\\\\\n Check spelling or create column/position subgroup with create_position')\",\n \"def lazy_read_file(self):\\n store = zarr.DirectoryStore(self.fpath)\\n z_array = zarr.open(store=store, mode='r')\\n self.da_input = da.from_array(z_array)\\n self.data = self.da_input\\n self.data_dim = self.data.shape\\n self.chunk_size = z_array.chunks\",\n \"def _load_disk(self):\\r\\n pass\",\n \"def _get_partition_list(self):\\n raise NotImplementedError('Must be implemented in subclasses.')\",\n \"def a_subarray_in_the_idle_state():\",\n \"def provide_partition_info(self):\\n self.partition_info = True\",\n \"def getPartition(self):\\n\\t\\treturn self.partition\",\n \"def initialize(self):\\r\\n self.bucket_array.initialize()\",\n \"def set_xp_partition(self,xp_partition):\\n # setup partition: set x grid\\n self.xp_partition = xp_partition\\n self.xp_partition.setup_x_grid(xx=[0,self.L])\\n # local copies of x and p grids\\n self.x=self.xp_partition.x\\n self.p=self.xp_partition.p\\n # allocate fmci_XP array\\n self.fmci_XP=np.zeros((self.xp_partition.nx,self.xp_partition.np))\",\n \"def partition(self, arr, low, high, pivot):\\n i_less = low\\n for i in range(low, high):\\n if arr[i] <= pivot: # note the equal\\n arr[i], arr[i_less] = arr[i_less], arr[i]\\n i_less += 1 # do not forget this\\n arr[i_less], arr[high] = arr[high], arr[i_less] # put the pivot into the mid position\\n return i_less\",\n \"def open(self) -> None:\\n if not self.__opened:\\n if self.path is None:\\n self.path = HID.enumerate_devices(self.vendor_id)[0]\\n self.device.open_path(self.path)\\n self.device.set_nonblocking(True)\\n self.__opened = True\",\n \"def next(self):\\n partition_start = self._partitions[self._current_index]\\n partition_end = partition_start + self._partition_length\\n self._current_index += 1\\n return self._dataframe[partition_start:partition_end]\",\n \"def get_partitioning(self):\\n raise Exception(\\\"Unimplemented\\\")\",\n \"def allocate(self,partition, num_farms, activity, runinfo_dp):\\n if self.load():\\n nf = num_farms\\n got = 0\\n used = Online.PVSS.StringVector()\\n farms = Online.PVSS.StringVector()\\n dpv = Online.PVSS.DataPointVector()\\n for i in xrange(len(self.inUse.data)):\\n f = self.inUse.data[i]\\n n = self.subfarms.data[i]\\n if len(f)==0 and got<nf:\\n dpv.push_back(self.dp2(self.name+'_'+n,'UsedBy'))\\n dpv.back().data = partition\\n dpv.push_back(self.dp2(self.name+'_'+n,'RunInfo'))\\n dpv.back().data = runinfo_dp\\n dpv.push_back(self.dp2(self.name+'_'+n,'Activity'))\\n dpv.back().data = activity\\n used.push_back(partition)\\n farms.push_back(n)\\n got = got + 1\\n else:\\n used.push_back(f)\\n if got==nf:\\n if len(runinfo_dp)>0:\\n #dpv.push_back(self.dp2(runinfo_dp,'general.partName'))\\n #dpv.back().data = partition\\n #dpv.push_back(self.dp2(runinfo_dp,'HLTFarm.nSubFarms'))\\n #dpv.back().data = nf\\n dpv.push_back(self.dp2(runinfo_dp,'HLTFarm.subFarms'))\\n dpv.back().data = farms\\n self.inUse.data = used\\n self.writer.add(dpv)\\n self.writer.add(self.inUse)\\n if self.writer.execute():\\n return 'SUCCESS'\\n self.error('Failed to update allocation information for partition '+\\\\\\n partition+' in farm system:'+self.name)\\n return None\\n return self.error('Not enough free subfarms availible for partition '+partition+\\\\\\n ' in farm system:'+self.name)\\n return self.error('Failed to load information for partition '+partition+\\\\\\n ' in farm system:'+self.name)\",\n \"def one_basis(self):\\n return self._kbounded_partitions([])\",\n \"def test_partitioner_iter(self):\\n partitioner = self.tx_client.SetPartitioner(\\\"xyzzy\\\", \\\"iddqd\\\")\\n self.assertEqual(list(partitioner), [1])\",\n \"def _lock_partition(self, partition, shared=False):\\n pass\",\n \"def allocate(self):\\n raise NotImplementedError\",\n \"def testRead(self):\\n self._TestRead(self._tsk_partition_path_spec)\",\n \"def testRead(self):\\n self._TestRead(self._tsk_partition_path_spec)\",\n \"def partition(seq):\\n\\n return 0\",\n \"def __init__(self):\\n self.arr = []\\n self.size = 0\",\n \"def load_chunk(self, start): # TODO parallelize this whole process\\n self.X = queue.Queue()\\n n = 0 # number of loaded batches\\n print('stop loading : %s' % self.stop_loading())\\n print('start + n : %s' % str(start + n))\\n while (not self.stop_loading()) and (start + n) < self.size:\\n print('load')\\n self.X.put(np.load(self.data_filenames[start+n]))\\n n += 1\\n print('return chunk')\\n return n\",\n \"def set_partition(self, begin=0, end=0):\\r\\n self.partition = (begin, end)\",\n \"def get_free_partition_index(dev):\\n try:\\n lines = _check_output(\\n args=[\\n 'parted',\\n '--machine',\\n '--',\\n dev,\\n 'print',\\n ],\\n )\\n except subprocess.CalledProcessError as e:\\n print 'cannot read partition index; assume it isn\\\\'t present\\\\n (Error: %s)' % e\\n return 1\\n\\n if not lines:\\n raise Error('parted failed to output anything')\\n if ('CHS;' not in lines and\\n 'CYL;' not in lines and\\n 'BYT;' not in lines):\\n raise Error('parted output expected to contain one of ' +\\n 'CHH; CYL; or BYT; : ' + lines)\\n if dev not in lines:\\n raise Error('parted output expected to contain ' + dev + ': ' + lines)\\n _, partitions = lines.split(dev)\\n partition_numbers = extract_parted_partition_numbers(partitions)\\n if partition_numbers:\\n return max(partition_numbers) + 1\\n else:\\n return 1\",\n \"def _read(self):\\n return np.copy(self.memory[self.head_pos])\",\n \"def __init__(self, partition, test=False, local_test_data_dir=_LOCAL_TEST_DATA_DIR):\\n assert sum(partition) == 100, 'The sum of the partition list must be 100: {}'.format(partition)\\n self._partition = partition\\n self._test = test\\n # Split the files up according to the self._partition list.\\n self._partitioned_filenames = []\\n filenames = data_filenames(shuffle=False, test=self._test,\\n local_test_data_dir=local_test_data_dir)\\n part_start = 0\\n for i, part_size in enumerate(self._partition):\\n part_end = part_start + int(len(filenames) * 0.01 * part_size)\\n assert part_end - part_start > 0, 'The number of files in partition {} is zero.'.format(i)\\n self._partitioned_filenames.append(filenames[part_start:part_end])\",\n \"def get_partition(self, nIndex):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlVmDevHd_GetPartition', self.handle, nIndex))\",\n \"def add_partition(self):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlVmDevHd_AddPartition', self.handle))\",\n \"def _preload_range(self):\\n queue = self._read_queue\\n size = self._buffer_size\\n start = self._seek\\n end = int(start + size * self._max_buffers)\\n workers_submit = self._workers.submit\\n indexes = tuple(range(start, end, size))\\n\\n for seek in tuple(queue):\\n if seek not in indexes:\\n del queue[seek]\\n\\n read_range = self._read_range\\n for seek in indexes:\\n if seek not in queue:\\n queue[seek] = workers_submit(read_range, seek, seek + size)\",\n \"def resume_reading(self):\\n raise NotImplementedError\",\n \"def get_partition():\\n if selection is None:\\n warning(\\\"You need to pick something first.\\\")\\n return\\n if not selection.obj_type in ['partition']:\\n warning(\\\"You need to partition the selection first.\\\")\\n return\\n res = askItems([['property',[1]]],\\n caption='Partition property')\\n if res:\\n prop = res['property']\\n getPartition(selection,prop)\\n highlightPartitions(selection)\",\n \"def rpartition(self, x):\\n pass\",\n \"def get_index(self):\\n return self.disk.partitions.index(self)\",\n \"def num_partitions(self): # -> None:\\n ...\",\n \"def push_front(self, param):\\n if self.size == self.capacity:\\n self.resize(2 * self.size)\\n for _ in range(self.arr):\\n pass\",\n \"def _partition(array, order, pivot, lower, upper):\\n # The pivot element is made the last not to interfere with other swaping \\n # operations\\n array[pivot], array[upper] = array[upper], array[pivot]\\n\\n # Last index of smaller/greater (asc/desc) elements\\n idx = lower\\n\\n for j in range(lower, upper):\\n if (order == Order.ASC and array[j] < array[upper] or # ASCENDING\\n order == Order.DESC and array[j] > array[upper]): # DESCENDING\\n # Moves the current element at the end of the sub-array with\\n # the smaller/grater (asc/desc) elements\\n array[idx], array[j] = array[j], array[idx]\\n idx += 1\\n\\n # Moves pivot as separator between greater and smaller numbers\\n array[idx], array[upper] = array[upper], array[idx]\\n\\n return idx\",\n \"def _open_dataset(self):\\n\\n try:\\n\\n datasets = [\\n _open_dataset(\\n record[self.path_column_name],\\n record[self.variable_column_name],\\n xarray_open_kwargs=self.xarray_open_kwargs,\\n preprocess=self.preprocess,\\n expand_dims={\\n agg.attribute_name: [record[agg.attribute_name]]\\n for agg in self.aggregations\\n if agg.type.value == 'join_new'\\n },\\n requested_variables=self.requested_variables,\\n additional_attrs=record.to_dict(),\\n )\\n for _, record in self.df.iterrows()\\n ]\\n\\n datasets = dask.compute(*datasets)\\n if len(datasets) == 1:\\n self._ds = datasets[0]\\n else:\\n datasets = sorted(\\n datasets,\\n key=lambda ds: tuple(\\n f'{INTAKE_ESM_ATTRS_PREFIX}/{agg.attribute_name}'\\n for agg in self.aggregations\\n ),\\n )\\n with dask.config.set(\\n {'scheduler': 'single-threaded', 'array.slicing.split_large_chunks': True}\\n ): # Use single-threaded scheduler\\n datasets = [\\n ds.set_coords(set(ds.variables) - set(ds.attrs[INTAKE_ESM_VARS_KEY]))\\n for ds in datasets\\n ]\\n self._ds = xr.combine_by_coords(\\n datasets, **self.xarray_combine_by_coords_kwargs\\n )\\n\\n self._ds.attrs[INTAKE_ESM_DATASET_KEY] = self.key\\n\\n except Exception as exc:\\n raise ESMDataSourceError(\\n f\\\"\\\"\\\"Failed to load dataset with key='{self.key}'\\n You can use `cat['{self.key}'].df` to inspect the assets/files for this key.\\n \\\"\\\"\\\"\\n ) from exc\",\n \"def test_partition(self):\\n mat = self.mat\\n self.assertSequenceEqual(\\n [mat.m, mat.n, mat.shape[2], mat.shape[3], mat.dtype],\\n [self.m, self.n, self.p, self.q, self.dtype]\\n )\\n if not mat.is_active:\\n self.assertSequenceEqual(\\n [mat.mloc, mat.mstart, mat.mend, mat.nloc, mat.nstart, mat.nend],\\n [0, 0, 0, 0, 0, 0]\\n )\\n else:\\n pass\",\n \"def get_partition(self):\\n return self._partition\",\n \"def grow(self):\\n while self.splittable_nodes:\\n self.split_next()\",\n \"def get(self):\\n return self._partition\",\n \"def __init__(self, my_partition: List[int]):\\n self.my_partition = my_partition\\n self.my_partition.sort(reverse=True)\\n if self.my_partition[-1]==0:\\n first_zero = self.my_partition.index(0)\\n self.my_partitition = self.my_partition[0:first_zero]\\n self.my_n = sum(self.my_partition)\",\n \"def test_open_fill(self):\",\n \"def __init__(self, arr):\\n self._arr = arr\\n self._n = len(arr) # no. sub-arrays \\n self._row = 0 # initialize cursor at position (0,0)\\n self._col = 0\",\n \"def newpart(self, device, primary, ncyls, swap=False):\\n # This is a simple partitioning tool, which only supports\\n # adding partitions sequentially, with all primary partitions\\n # being before the extended partition, so once a logical\\n # partition has been added, it is not possible to add further\\n # primary ones.\\n di = DiskInfo(device)\\n pmax = 0 # Record highest partition number\\n lim = -1 # Used for seeking last used cylinder\\n exp = 0 # Number of extended partition\\n ex0, ex1 = 0, -1 # Extended partition start and end\\n log0, log1 = 0, -1 # Start and end of area used by logical partitions\\n for p in di.parts:\\n pn = int(p[0][len(device):])\\n scyl, ecyl = p[1:3]\\n if pn <= 4:\\n if exp:\\n run_error(_(\\\"Not supported: primary partition (%s%d)\\\\n\\\"\\n \\\"has higher partition number than extended \\\"\\n \\\"partition\\\") % (device, pn))\\n return \\\"\\\"\\n if scyl <= lim:\\n run_error(_(\\\"Partitions must be ordered on the device.\\\\n\\\"\\n \\\"%s%d is out of order.\\\") % (device, pn))\\n return \\\"\\\"\\n if p[3] in (\\\"5\\\", \\\"f\\\"):\\n # extended\\n exp = pn\\n ex0, ex1 = scyl, ecyl\\n continue\\n pmax = pn\\n lim = ecyl\\n\\n startcyl = lim + 1\\n endcyl = lim + ncyls\\n if endcyl >= di.drvcyls:\\n run_error(_(\\\"Too little space at end of drive for new partition\\\"))\\n return \\\"\\\"\\n if exp and (pmax <= 4):\\n # Remove the extended partition, which is empty anyway\\n if not self.rmpart(device, exp):\\n return \\\"\\\"\\n pmax = exp - 1\\n if primary:\\n if pmax >= 4:\\n run_error(_(\\\"Cannot add primary partition to %s\\\") % device)\\n return \\\"\\\"\\n t = \\\"primary\\\"\\n else:\\n t = \\\"logical\\\"\\n if pmax > 4:\\n # resize extended partition\\n if not self.xcheck(\\\"resizepart\\\", device, str(exp),\\n str(ex0), str(endcyl),\\n onfail=_(\\\"Couldn't resize extended partition %s%d\\\")\\n % (device, exp)):\\n return False\\n else:\\n # create extended partition\\n if not self.xcheck(\\\"newpart\\\", device,\\n str(startcyl), str(endcyl), \\\"extended\\\",\\n onfail=_(\\\"Couldn't create extended partition on %s\\\")\\n % device):\\n return False\\n if pmax < 4:\\n pmax = 4\\n\\n if self.xcheck(\\\"newpart\\\", device, str(startcyl), str(endcyl),\\n t, \\\"linux-swap\\\" if swap else \\\"ext2\\\"):\\n return \\\"%s%d\\\" % (device, pmax + 1)\\n else:\\n run_error(_(\\\"Couldn't add new partition to %s\\\") % device)\\n return \\\"\\\"\",\n \"def get_open_disk_space(self):\\n count = 0\\n for i in range(self.size):\\n if self.disk_mem[i]==\\\".\\\":\\n count += 1\\n return count\",\n \"def update(self) -> np.ndarray:\\r\\n # Read chunk from array\\r\\n signal_slice: np.ndarray = np.array(\\r\\n self._signal_data[self._pointer : self._pointer + self._chunk]\\r\\n )\\r\\n # Move index over\\r\\n self._pointer += self._chunk\\r\\n if self._pointer > len(self._signal_data):\\r\\n # Go back to beginning\\r\\n self._pointer = 0\\r\\n self._restart_flag = True\\r\\n print(\\\"Restarting stream...\\\")\\r\\n\\r\\n return signal_slice\",\n \"def update(self) -> np.ndarray:\\r\\n # Read chunk from array\\r\\n signal_slice: np.ndarray = np.array(\\r\\n self._signal_data[self._pointer : self._pointer + self._chunk]\\r\\n )\\r\\n # Move index over\\r\\n self._pointer += self._chunk\\r\\n if self._pointer > len(self._signal_data):\\r\\n # Go back to beginning\\r\\n self._pointer = 0\\r\\n self._restart_flag = True\\r\\n print(\\\"Restarting stream...\\\")\\r\\n\\r\\n return signal_slice\",\n \"def partition_session(self):\\n if self.user['drive']['name'] is not None:\\n\\n # Set root size\\n if self.user['root_freespace'] is True:\\n self.user['root_size'] = 'freespace'\\n\\n # Set partition parameters\\n self.user['partitions'] = {'name': ['boot', 'root'],\\n 'size': [self.user['boot_size'],\\n self.user['root_size']],\\n 'filesystem': ['fat32', 'ext4'],\\n 'mountpoint': ['/mnt/boot', '/mnt'],\\n 'mountorder': [1, 0]}\\n\\n # Set swap size and filesystem\\n if 'Swap' in self.user['optional_partitions']:\\n self.user['partitions']['size'].insert(1, self.user['swap_size'])\\n self.user['partitions']['filesystem'].insert(1, 'swap')\\n\\n # Set home size and filesystem\\n if 'Home' in self.user['optional_partitions']:\\n if self.user['home_freespace'] is True:\\n self.user['home_size'] = 'freespace'\\n self.user['partitions']['size'].append(self.user['home_size'])\\n self.user['partitions']['filesystem'].append('ext4')\\n\\n # Custom partitions\\n else:\\n\\n # Set partition parameters\\n self.user['partitions'] = {\\n 'name': ['boot', 'root'],\\n 'drive_id': [self.user['boot_id'].split()[0],\\n self.user['root_id'].split()[0]],\\n 'mountpoint': ['/mnt/boot', '/mnt'],\\n 'mountorder': [1, 0]}\\n\\n # Set swap drive ID\\n if self.user['swap_id'] is not None:\\n self.user['partitions']['drive_id'].insert(\\n 1, self.user['swap_id'].split()[0])\\n\\n # Set home drive ID\\n if self.user['home_id'] is not None:\\n self.user['partitions']['drive_id'].append(\\n self.user['home_id'].split()[0])\\n\\n # Set swap parameters\\n if ('Swap' in self.user['optional_partitions']) or \\\\\\n (self.user['swap_id'] is not None):\\n self.user['partitions']['name'].insert(1, 'swap')\\n self.user['partitions']['mountpoint'].insert(1, 'swap')\\n self.user['partitions']['mountorder'].insert(1, 2)\\n\\n # Set home parameters\\n if 'Home' in self.user['optional_partitions'] or \\\\\\n (self.user['home_id'] is not None):\\n self.user['partitions']['name'].append('home')\\n self.user['partitions']['mountpoint'].append('/mnt/home')\\n self.user['partitions']['mountorder'].append(3)\",\n \"def num_partitions(self): # -> int:\\n ...\",\n \"def partition1(self, partition1):\\n\\n self._partition1 = partition1\",\n \"def smallest_partition():\\n try:\\n usb_partitions = sort_partitions()\\n smallest = usb_partitions[0]\\n except IndexError:\\n print(\\\"Not enough USB devices available\\\")\\n exit(1)\\n else:\\n return str(smallest[0])\",\n \"def GetPartitioningArray(self):\\n return _hypre.HypreParVector_GetPartitioningArray(self)\",\n \"def partition(cls, key):\\n return cls.partition_indexed(\\n cls.hash_ring.select_bucket(key),\\n )\",\n \"def init_disk(self):\\n self.put(self.SIZE/2-1, self.SIZE/2-1,\\n Disk(self.SIZE/2-1, self.SIZE/2-1, 255, self, self.DISKSIZE))\\n self.put(self.SIZE/2-1, self.SIZE/2,\\n Disk(self.SIZE/2-1, self.SIZE/2, 0, self, self.DISKSIZE))\\n self.put(self.SIZE/2, self.SIZE/2-1,\\n Disk(self.SIZE/2, self.SIZE/2-1, 0, self, self.DISKSIZE))\\n self.put(self.SIZE/2, self.SIZE/2,\\n Disk(self.SIZE/2, self.SIZE/2, 255, self, self.DISKSIZE))\",\n \"def test_partition(self):\\n # one swap at the end\\n list = [5, 6, 7, 8, 9, 2]\\n partition(list, 0, 5)\\n # assert list == [2, 6, 7, 8, 9, 5] # should be improved in future\",\n \"def grow(self):\\n # expansion - get all fanins of this gate, except for ones already in\\n next_frontier = set()\\n added = 0\\n remove = set()\\n for g in self.frontier:\\n new_fin = len((self.ckt[g].fins - self.members)) - 1\\n if (new_fin + self.w) < self.max_w:\\n print \\\"Adding\\\", g, \\\"to partition\\\"\\n # add this to the partition\\n self.members.add(g)\\n next_frontier |= self.ckt[g].fins - self.members\\n self.w += new_fin + 1\\n else:\\n remove.add(g)\\n self.frontier = next_frontier\\n if len(self.frontier) == 0:\\n return None\\n else:\\n return True\",\n \"def choose_partition():\\n # Ask the user wether the partitions should be taken from the original partitions, or from the home-made partitions\\n file_name = selector([\\\"The original partition given by the instructor\\\", \\\"The homemade partition file\\\"], [\\\"ORIGINAL\\\", \\\"HOMEMADE\\\"])\\n\\n # Open the corresponding file\\n if file_name == \\\"1\\\" or file_name == \\\"ORIGINAL\\\":\\n file = open(\\\"./assets/partitions.txt\\\", \\\"r\\\")\\n elif file_name == \\\"2\\\" or file_name == \\\"HOMEMADE\\\":\\n file = open(\\\"./assets/homemade_partitions.txt\\\", \\\"r\\\")\\n\\n skip_lines(-1)\\n\\n # Print all song's names in the partitions\\n lines = file.readlines()\\n file.close()\\n for i in range(0, len(lines), 2):\\n print(lines[i][:-1])\\n\\n # Ask the user to choose for a song\\n song_index = choose_number(len(lines) / 2)\\n\\n # Get the corresponding song's partition and convert notes to Note instances\\n partition = lines[song_index * 2 - 1][:-1].replace(' ', '')\\n raw_notes = get_notes_from_line(partition)\\n parsed_notes = [Note(note) for note in raw_notes]\\n return parsed_notes\",\n \"def _download_input_chunk(self):\\n #FIXME choose the mip level based on the chunk key\\n volume = Precomputed(self._storage)\\n self._data = volume[self._xmin:self._xmax,\\n self._ymin:self._ymax,\\n self._zmin:self._zmax]\",\n \"def _get_partitions():\\n partitions = []\\n\\n try:\\n with files.FileReader('/proc/partitions') as f:\\n lines = f.readlines()[2:]\\n for line in lines:\\n _, _, _, name = line.split()\\n if name[-1].isdigit():\\n partitions.append(name)\\n # This will catch access denied and file not found errors, which is expected\\n # on non-Linux/limited access systems. All other errors will raise as normal.\\n except files.Error:\\n pass\\n\\n return partitions\",\n \"def on_add_clicked(self,button):\\n\\t\\tself.list_partitions.add_partition()\",\n \"def open(self, dataset: DatasetDB):\\n self._es.open_index(dataset_records_index(dataset.id))\",\n \"def partition(self, sep):\\n return asarray(partition(self, sep))\",\n \"def open(self):\\n return xr.open_dataset(self)\",\n \"def open(self, mode):\\n if not self._open:\\n self.grp = zarr.open_group(self.store_path, mode)[self.group_path]\\n self._open = True\",\n \"def get_partition(self, partition_spec):\\n return self.partitions[partition_spec]\",\n \"def __init__(self, width, partition_points):\\n self.width = width\\n self.a = Signal(width, reset_less=True)\\n self.b = Signal(width, reset_less=True)\\n self.partition_points = PartitionPoints(partition_points)\\n self.mwidth = len(self.partition_points)+1\\n self.output = Signal(self.mwidth, reset_less=True)\\n if not self.partition_points.fits_in_width(width):\\n raise ValueError(\\\"partition_points doesn't fit in width\\\")\",\n \"def __init__(self):\\n super().__init__()\\n self.__size = 0\\n self.__capacity = self.INITIAL_CAPACITY\\n self.__array = self.__get_new_array(self.__capacity)\\n\\n self.__getitem_status = self.GETITEM_NIL\\n self.__insert_status = self.GETITEM_NIL\\n self.__delete_status = self.DELETE_NIL\",\n \"def open(self, chunks: dict = None):\\n return open_nwmdataset(self,chunks=chunks)\",\n \"def __missing__(self, key):\\n # deferred import so arrayjob can be used without load_memmap_offset\\n from cgp.utils.load_memmap_offset import open_memmap\\n return open_memmap(\\\"%s/%s.npy\\\" % (self.pardir, key), **self.kwargs)\",\n \"def indices(self):\\n return self._kbounded_partitions\",\n \"def partition(array, first, last):\\n # partition up until final value\\n pivot = array[last]\\n i = first - 1\\n\\n for count in range(first, last):\\n # split array\\n if array[count] < pivot:\\n i += 1\\n # assign array positions\\n array[i],array[count] = array[count],array[i]\\n # reassign\\n array[i+1],array[last] = array[last],array[i+1]\\n return (i+1)\",\n \"def take_snapshot(self):\\r\\n self.snapshot = self.name, self.size, copy.copy(self.cells)\\r\\n self.bucket_array.take_snapshot()\",\n \"def num_partitions(self): # -> Unknown:\\n ...\",\n \"def set_partition(self, partition=0):\\n if not isinstance(partition, int):\\n raise TypeError('partition must be an integer')\\n if partition <= 0:\\n raise ValueError('partition must be positive')\\n if self.connected:\\n self.producer.send(\\\"PART:\\\"+str(partition))\",\n \"def do_stage_partition(cls, part, source_params, creator, cr_workdir,\\n oe_builddir, bootimg_dir, kernel_dir,\\n native_sysroot):\\n logger.debug(\\\"SourcePlugin: do_stage_partition: part: %s\\\", part)\",\n \"def addPartition(self,partitionData):\\n self.PCAs[partitionData.id] = partitionData\\n self.pcaStatemachineLock[partitionData.id] = threading.Lock()\\n self.StateMachineForPca[partitionData.id] = Statemachine(self.StateMachineFile,\\\"Unconfigured\\\")\\n self.isPCAinTransition[partitionData.id] = False\\n self.pcaSequenceNumber[partitionData.id] = 0\",\n \"def partition(self):\\n return self.tag(\\\"partition\\\")\",\n \"def _load_array(self, array_name, fam=None):\\n raise OSError(\\\"No lazy-loading implemented\\\")\",\n \"def prevSplit(self):\\n pass\",\n \"def on_splitter_size_allocate(self, splitter, allocation):\\n\\t\\tself.emit('splitter-position-changed')\",\n \"def __init__(self, growth_factor=2):\\n self._length = 0 # Number of elements in array\\n self._capacity = 1 # Capacity of array before expanding\\n self._arr = self._create_array(self._capacity) # Compact array of pointers\\n self._growth_factor = max(2, growth_factor) # Factor to grow array when capacity reached\",\n \"def get_partition(self, partid):\\n #TODO(zhengda) add implementation later.\",\n \"def get_partition(self, partid):\\n #TODO(zhengda) add implementation later.\",\n \"def get_partitions(self):\\n return self.partitions\",\n \"def bin_open(img: np.ndarray, disk_size: int) -> np.ndarray:\\n selem = disk(disk_size)\\n res = binary_opening(img, selem)\\n return res\",\n \"def _setPartedPartition(self, partition):\\n log_method_call(self, self.name)\\n\\n if partition is not None and not isinstance(partition, parted.Partition):\\n raise ValueError(\\\"partition must be None or a parted.Partition instance\\\")\\n\\n log.debug(\\\"device %s new partedPartition %s\\\", self.name, partition)\\n self._partedPartition = partition\\n self.updateName()\",\n \"def __init__(self, period):\\n\\n self.open = StreamArray(period)\\n self.high = StreamArray(period)\\n self.low = StreamArray(period)\\n self.close = StreamArray(period)\",\n \"def _loadpart(self, part):\\n new_partidx = util.Partname(part.partname).idx\\n for idx, seq_part in enumerate(self._values):\\n partidx = util.Partname(seq_part.partname).idx\\n if partidx > new_partidx:\\n self._values.insert(idx, part)\\n return\\n self._values.append(part)\",\n \"def __init__(self, k: int):\\n self.front = 0\\n self.rear = 0\\n self.capacity = k + 1\\n self.arr = [0 for _ in range(self.capacity)]\",\n \"def initialize(self):\\n\\t\\ttable = NetworkTables.getTable('SmartDashboard')\\n\\t\\tinitial_pos = table.getData('initial_pos')\\n\\t\\t#XXX Doing this will start the path following immediately. May want this\\n\\t\\t# in the execute or something\\n\\t\\tpath = self.dt.which_path(initial_pos)\",\n \"def __init__(self, nrows):\\n\\n # create the data array\\n dummy_list = []\\n\\n # set the row number\\n self._nrows = nrows\\n\\n # append the reuqested number of None\\n # for index in range(nrows):\\n # self._data.append(None)\\n\\n # append the reuqested number of None\\n # perhaps a bit faster than the above lines\\n self._data = list(map(dummy_list.append, range(nrows)))\\n\\n # set the row number\\n self._nrows = nrows\",\n \"def partition(data, num_partitions=None, by=None, **kwargs):\\n return Component(\\n \\\"Partition\\\",\\n arguments={\\n 'data': Component.of(data),\\n 'num_partitions': Component.of(num_partitions),\\n 'by': Component.of(by)\\n },\\n options={\\n \\n },\\n constraints=kwargs)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5840582","0.5428586","0.5294434","0.52601844","0.51751643","0.50962955","0.50962955","0.509415","0.5024122","0.5011268","0.49543542","0.4883474","0.48718578","0.48603144","0.48522878","0.48517695","0.4839195","0.48225853","0.48027864","0.48000965","0.4790247","0.47798887","0.4720501","0.47029617","0.46842965","0.4682525","0.4682525","0.46814677","0.46763003","0.46511462","0.4649878","0.46495765","0.46389672","0.4622136","0.46061173","0.45910683","0.4584389","0.45805055","0.45802802","0.45794904","0.45771983","0.45748523","0.45747092","0.457036","0.45691523","0.45593444","0.45571756","0.4557058","0.45556217","0.45504922","0.45442882","0.4525282","0.45250976","0.45197967","0.45153186","0.45153186","0.45109043","0.45079452","0.4506449","0.45022196","0.44995156","0.44910493","0.44774902","0.44759083","0.44756046","0.44744152","0.44724086","0.4470217","0.44698712","0.44659707","0.4462444","0.44618163","0.44603375","0.44594052","0.44553822","0.44536054","0.44517532","0.44502246","0.44423303","0.4438988","0.44290558","0.44272983","0.44218904","0.4417824","0.4414225","0.44139224","0.44068182","0.4406671","0.44063008","0.4403438","0.44031662","0.44031662","0.4402446","0.44017848","0.43955085","0.43933836","0.439055","0.43902653","0.4387042","0.43869588","0.43843147"],"string":"[\n \"0.5840582\",\n \"0.5428586\",\n \"0.5294434\",\n \"0.52601844\",\n \"0.51751643\",\n \"0.50962955\",\n \"0.50962955\",\n \"0.509415\",\n \"0.5024122\",\n \"0.5011268\",\n \"0.49543542\",\n \"0.4883474\",\n \"0.48718578\",\n \"0.48603144\",\n \"0.48522878\",\n \"0.48517695\",\n \"0.4839195\",\n \"0.48225853\",\n \"0.48027864\",\n \"0.48000965\",\n \"0.4790247\",\n \"0.47798887\",\n \"0.4720501\",\n \"0.47029617\",\n \"0.46842965\",\n \"0.4682525\",\n \"0.4682525\",\n \"0.46814677\",\n \"0.46763003\",\n \"0.46511462\",\n \"0.4649878\",\n \"0.46495765\",\n \"0.46389672\",\n \"0.4622136\",\n \"0.46061173\",\n \"0.45910683\",\n \"0.4584389\",\n \"0.45805055\",\n \"0.45802802\",\n \"0.45794904\",\n \"0.45771983\",\n \"0.45748523\",\n \"0.45747092\",\n \"0.457036\",\n \"0.45691523\",\n \"0.45593444\",\n \"0.45571756\",\n \"0.4557058\",\n \"0.45556217\",\n \"0.45504922\",\n \"0.45442882\",\n \"0.4525282\",\n \"0.45250976\",\n \"0.45197967\",\n \"0.45153186\",\n \"0.45153186\",\n \"0.45109043\",\n \"0.45079452\",\n \"0.4506449\",\n \"0.45022196\",\n \"0.44995156\",\n \"0.44910493\",\n \"0.44774902\",\n \"0.44759083\",\n \"0.44756046\",\n \"0.44744152\",\n \"0.44724086\",\n \"0.4470217\",\n \"0.44698712\",\n \"0.44659707\",\n \"0.4462444\",\n \"0.44618163\",\n \"0.44603375\",\n \"0.44594052\",\n \"0.44553822\",\n \"0.44536054\",\n \"0.44517532\",\n \"0.44502246\",\n \"0.44423303\",\n \"0.4438988\",\n \"0.44290558\",\n \"0.44272983\",\n \"0.44218904\",\n \"0.4417824\",\n \"0.4414225\",\n \"0.44139224\",\n \"0.44068182\",\n \"0.4406671\",\n \"0.44063008\",\n \"0.4403438\",\n \"0.44031662\",\n \"0.44031662\",\n \"0.4402446\",\n \"0.44017848\",\n \"0.43955085\",\n \"0.43933836\",\n \"0.439055\",\n \"0.43902653\",\n \"0.4387042\",\n \"0.43869588\",\n \"0.43843147\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":26,"numItemsPerPage":100,"numTotalItems":95772,"offset":2600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzA3NjkzMCwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzMwODA1MzAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.rNwTHjeYTqi_eDPlYgxP-UUEDmfCbW7jyuCUoSR9ogNgjyd6YFiFAB59z_n7E_xCfcOyIEOtJzCXdLlhZfHRDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Access the run at a given index. This is required by QtQuick | def data(self, index, role=Qt.DisplayRole):
if not index.isValid():
return QVariant()
run = self._runs[index.row()]
if role == Qt.DisplayRole:
return run
return QVariant() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, i):\n return self._runs[i]",
"def run(self, run_idx):\n return self._h5['{}/{}'.format(RUNS, int(run_idx))]",
"def run(self, run_number):\n return self[self.run_cache[run_number]]",
"def __getitem__(self, index):\n # NOTE: this automatically supports slicing :-)\n ... | [
"0.72931045",
"0.6952865",
"0.6678343",
"0.6282501",
"0.61454105",
"0.60703266",
"0.60688543",
"0.60501313",
"0.60471225",
"0.59921044",
"0.5978085",
"0.59680146",
"0.59228444",
"0.58857644",
"0.5878358",
"0.5874778",
"0.5870897",
"0.5781742",
"0.5779231",
"0.5774915",
"0.575... | 0.6371707 | 3 |
Update the data at a given index. This is required by QtQuick | def setData(self, index, value, role=Qt.EditRole):
if not index.isValid():
return False
if role == Qt.Edit:
self._runs[index.row()] = value
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateRow(self, index: int) -> None:\n ...",
"def set(self, index, data):\n self.data[index] = data",
"def set_at_index(self, index: int, value: object) -> None:\n self.data[index] = value",
"def _update_value_at(self, index, value):\n node = self._get_node_at(index)\n if n... | [
"0.7554738",
"0.7046055",
"0.70130014",
"0.6715155",
"0.6603428",
"0.6596355",
"0.6492101",
"0.6436119",
"0.63908285",
"0.6352602",
"0.63500535",
"0.6314473",
"0.6250634",
"0.62338316",
"0.62338316",
"0.62338316",
"0.62338316",
"0.622466",
"0.62043625",
"0.6203889",
"0.619686... | 0.0 | -1 |
A description of the model properties required by QtQuick | def flags(self, index):
if not index.isValid():
return Qt.ItemIsEditable
return Qt.ItemIsEnabled | Qt.ItemIsEditable | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def properties(self):\n pass",
"def properties(self):",
"def properties(self):",
"def properties(self):",
"def properties(self):\n raise NotImplementedError",
"def properties(self):\n return None",
"def properties(self):\n return None",
"def get_properties(self):\n retu... | [
"0.69124717",
"0.6807334",
"0.6807334",
"0.6807334",
"0.67613196",
"0.66567373",
"0.66567373",
"0.66386944",
"0.6318433",
"0.6283687",
"0.6269547",
"0.625697",
"0.62384415",
"0.6229266",
"0.620358",
"0.61727464",
"0.61452335",
"0.6096329",
"0.6087369",
"0.6087369",
"0.6087369... | 0.0 | -1 |
Create a new run starting at the given coordinates | def append(self, startx, starty):
if self._runs:
angles = self._runs[-1]._angles
pos = self._runs[-1]._position
else:
angles = [0]
pos = None
run = SingleRun(self, startx, starty, angles=angles, position=pos)
self.beginInsertRows(QModelIndex(),
len(self._runs),
len(self._runs))
self._runs.append(run)
self.endInsertRows()
self.scriptChanged.emit()
self.validChanged.emit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(self, coordinates = None):\n\n # start- and endpoints of lines are nodes, but they do not need to have a point object associated to them\n # in this case, point coordinates should be set\n if (self.geo):\n coordinates = rs.PointCoordinates(self.geo)\n\n self.x = rou... | [
"0.5933312",
"0.58285725",
"0.5704058",
"0.5686776",
"0.5686776",
"0.56424886",
"0.56335765",
"0.5611578",
"0.5542422",
"0.5480548",
"0.54717386",
"0.54646283",
"0.5367157",
"0.533819",
"0.5320714",
"0.52867836",
"0.52701634",
"0.52323407",
"0.5213995",
"0.5198065",
"0.518935... | 0.5322073 | 14 |
Change the ending coordinates of the most recent run | def update(self, x, y):
delta_x = x-self._runs[-1]._x # pylint: disable=W0212
delta_y = y-self._runs[-1]._y # pylint: disable=W0212
if abs(delta_x) > abs(delta_y):
self._runs[-1]._vertical = False # pylint: disable=W0212
self._runs[-1]._length = delta_x # pylint: disable=W0212
else:
self._runs[-1]._vertical = True # pylint: disable=W0212
self._runs[-1]._length = delta_y # pylint: disable=W0212
i = len(self._runs) - 1
self.dataChanged.emit(self.index(i, 0), self.index(i, 0))
self.scriptChanged.emit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_end(self, coordinates):\n self._end = coordinates",
"def end(self):\n self.set_initial_offset(1e6)",
"def set_finishing_pos(self, finish):\n if finish and self.is_unoccupied(*finish):\n self.finish_pos = finish[:]\n else:\n self.set_random_pos('finishi... | [
"0.6409029",
"0.6067654",
"0.6002653",
"0.59474105",
"0.5929103",
"0.5818964",
"0.5757306",
"0.5729312",
"0.5727549",
"0.5715503",
"0.56266606",
"0.56220305",
"0.5599335",
"0.55922794",
"0.5553596",
"0.55360717",
"0.55360717",
"0.5516263",
"0.54933006",
"0.5479724",
"0.546685... | 0.0 | -1 |
Access a single run. | def get(self, i):
return self._runs[i] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self, run_number):\n return self[self.run_cache[run_number]]",
"def get_run(self, run_id: str) -> sqlite3.Row:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELEC... | [
"0.7563216",
"0.75471956",
"0.74779475",
"0.7438003",
"0.6921127",
"0.690785",
"0.6889391",
"0.6586346",
"0.6453123",
"0.64372474",
"0.6409679",
"0.632492",
"0.6297225",
"0.62775546",
"0.6272261",
"0.6258751",
"0.62365556",
"0.6131638",
"0.6095218",
"0.60870063",
"0.6036361",... | 0.6402159 | 11 |
The instrument script the performs the requested runs | def script(self):
temp = "\n\n".join([r.script_line(
self._angle_command,
self._horizontal_command,
self._vertical_command,
self._origin,
self._frame_width,
self._frame_height)
for r in self._runs])
return temp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_experiment():\n pass",
"def run(self):\n self.run_measurement()\n self.run_analysis()\n if self.get_param_value('update'):\n self.run_update()",
"def run_script(self):\n pass",
"def run(self,measurements,actions):\n raise NotImplementedError",
"def r... | [
"0.68662244",
"0.68151057",
"0.6767565",
"0.65983254",
"0.6565703",
"0.65549105",
"0.6449239",
"0.6410411",
"0.6353265",
"0.6352231",
"0.63458776",
"0.6331616",
"0.6285805",
"0.6264376",
"0.62491834",
"0.6246712",
"0.62430114",
"0.62197703",
"0.621477",
"0.620736",
"0.6206820... | 0.0 | -1 |
Can the current model be exported into a usable script | def valid(self):
if not self._runs:
return False
return all([r.valid for r in self._runs]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump_model(self):",
"def export_model(self, save_path: str, save_format: Optional[str] = None) -> None:",
"def save_model(self, filename):\r\n pass",
"def export_model(model, name):\n\tpath = \"data/{}/\".format(name)\n\tfilename = \"{}.model\".format(name)\n\tif os.path.isdir(path):\n\t\tprint(\"mode... | [
"0.6738641",
"0.66990805",
"0.6495466",
"0.6315162",
"0.62201536",
"0.61410815",
"0.6110016",
"0.6058837",
"0.60287017",
"0.5983388",
"0.5955291",
"0.59451455",
"0.5926416",
"0.5924503",
"0.5913353",
"0.58991826",
"0.5894664",
"0.5845883",
"0.5842756",
"0.58416146",
"0.583656... | 0.0 | -1 |
A helper function to perform a 3 year moving window filter for a single land cover value (such as Forest as 1) for one three year window representing year(i1), year(i), year(i+1) annual land cover classifications. This function applies on one window, and should only be called using the function applyWindow3years. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The temporal filter inspects the central position of three consecutive years, and if the extremities of the consecutive years are identical but the centre position is not, then the central pixels are reclassified to match its temporal neighbour class. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def mask3(imagem, value, bandNames):
mask = imagem.select(bandNames[0]).eq(value) \
.bitwiseAnd(imagem.select(bandNames[1]).neq(value)) \
.bitwiseAnd(imagem.select(bandNames[2]).eq(value))
change_img = imagem.select(bandNames[1]).mask(mask.eq(1)).where(mask.eq(1), value)
img_out = imagem.select(bandNames[1]).blend(change_img)
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow3years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-1):\n img_out = img_out.addBands(mask3(imagem, value,bandNames[(i-1):(i+2)]))\n img_out = img_out.addBands(imagem.select(bandNames[-1]))\n return img_out",
"def apply... | [
"0.77075994",
"0.6490901",
"0.642566",
"0.5768101",
"0.5706588",
"0.548362",
"0.5444088",
"0.52517235",
"0.50446564",
"0.50012696",
"0.49663457",
"0.4957738",
"0.49230793",
"0.47313127",
"0.4699657",
"0.46902734",
"0.4674825",
"0.46389544",
"0.45470658",
"0.45407534",
"0.4527... | 0.46771812 | 16 |
A helper function to perform a 4 year moving window filter for a single land cover value (such as Forest as 1) for one four year window representing year(i1), year(i), year(i+1), and year(i+2) annual land cover classifications. This function applies on one window, and should only be called using the function applyWindow4years. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The temporal filter inspects the central position of four consecutive years, and if the extremities of the consecutive years are identical but the centre position is not, then the central pixels are reclassified to match its temporal neighbour class. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def mask4(imagem, value, bandNames):
mask = imagem.select(bandNames[0]).eq(value) \
.bitwiseAnd(imagem.select(bandNames[1]).neq(value)) \
.bitwiseAnd(imagem.select(bandNames[2]).neq(value)) \
.bitwiseAnd(imagem.select(bandNames[3]).eq(value))
change_img = imagem.select(bandNames[1]).mask(mask.eq(1)).where(mask.eq(1), value)
change_img1 = imagem.select(bandNames[2]).mask(mask.eq(1)).where(mask.eq(1), value)
img_out = imagem.select(bandNames[1]).blend(change_img).blend(change_img1)
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow4years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-2):\n img_out = img_out.addBands(mask4(imagem, value,bandNames[(i-1):(i+3)]))\n img_out = img_out.addBands(imagem.select(bandNames[-2]))\n img_out = img_out.addBands(im... | [
"0.77455264",
"0.69279945",
"0.6802912",
"0.58571285",
"0.5832874",
"0.5571592",
"0.5528454",
"0.51787966",
"0.50988805",
"0.49669826",
"0.49113834",
"0.4877425",
"0.48612544",
"0.48353228",
"0.4781184",
"0.47058412",
"0.46759126",
"0.4645408",
"0.45981827",
"0.45730013",
"0.... | 0.49336118 | 10 |
A helper function to perform 5 year moving window filter for a single land cover value (such as Forest as 1) for one five year window representing year(i1), year(i), year(i+1), year(i+2), and year(i+3) annual land cover classifications. This function applies on one window, and should only be called using the function applyWindow5years. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The temporal filter inspects the central position of five consecutive years, and if the extremities of the consecutive years are identical but the centre position is not, then the central pixels are reclassified to match its temporal neighbour class. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def mask5(imagem, value, bandNames):
mask = imagem.select(bandNames[0]).eq(value) \
.bitwiseAnd(imagem.select(bandNames[1]).neq(value)) \
.bitwiseAnd(imagem.select(bandNames[2]).neq(value)) \
.bitwiseAnd(imagem.select(bandNames[3]).neq(value)) \
.bitwiseAnd(imagem.select(bandNames[4]).eq(value))
change_img = imagem.select(bandNames[1]).mask(mask.eq(1)).where(mask.eq(1), value)
change_img1 = imagem.select(bandNames[2]).mask(mask.eq(1)).where(mask.eq(1), value)
change_img2 = imagem.select(bandNames[3]).mask(mask.eq(1)).where(mask.eq(1), value)
img_out = imagem.select(bandNames[1]).blend(change_img).blend(change_img1).blend(change_img2)
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow5years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-3):\n img_out = img_out.addBands(mask5(imagem, value,bandNames[(i-1):(i+4)]))\n img_out = img_out.addBands(imagem.select(bandNames[-3]))\n img_out = img_out.addBands(im... | [
"0.80519295",
"0.7068984",
"0.69225854",
"0.57098013",
"0.5579009",
"0.55375785",
"0.5408724",
"0.5229599",
"0.50659686",
"0.5013549",
"0.494298",
"0.49226463",
"0.48234645",
"0.4740639",
"0.46834993",
"0.46194592",
"0.46066454",
"0.45539072",
"0.45349857",
"0.45121947",
"0.4... | 0.5577494 | 5 |
Function to perform a 5 year moving window filter for a single land cover value (such as Forest as 1) for all years in an image. Calls the function mask5. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The temporal filter inspects the central position of five consecutive years, and if the extremities of the consecutive years are identical but the centre position is not, then the central pixels are reclassified to match its temporal neighbour class. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def applyWindow5years(imagem, value, bandNames):
img_out = imagem.select(bandNames[0])
for i in np.arange(1, len(bandNames)-3):
img_out = img_out.addBands(mask5(imagem, value,bandNames[(i-1):(i+4)]))
img_out = img_out.addBands(imagem.select(bandNames[-3]))
img_out = img_out.addBands(imagem.select(bandNames[-2]))
img_out = img_out.addBands(imagem.select(bandNames[-1]))
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mask5(imagem, value, bandNames):\n mask = imagem.select(bandNames[0]).eq(value) \\\n .bitwiseAnd(imagem.select(bandNames[1]).neq(value)) \\\n .bitwiseAnd(imagem.select(bandNames[2]).neq(value)) \\\n .bitwiseAnd(imagem.select(bandNames[3]).neq(value)) \\\n .bitwiseAnd(imagem.selec... | [
"0.66861373",
"0.65514684",
"0.6478541",
"0.5613955",
"0.54316807",
"0.5428405",
"0.5313811",
"0.520505",
"0.51899797",
"0.50904",
"0.50412196",
"0.5036899",
"0.49864736",
"0.47982645",
"0.4767842",
"0.47645608",
"0.4758746",
"0.47383195",
"0.46717232",
"0.46011153",
"0.45707... | 0.80881137 | 0 |
Function to perform a 4 year moving window filter for a single land cover value (such as Forest as 1) for all years in an image. Calls the function mask4. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The temporal filter inspects the central position of four consecutive years, and if the extremities of the consecutive years are identical but the centre position is not, then the central pixels are reclassified to match its temporal neighbour class. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def applyWindow4years(imagem, value, bandNames):
img_out = imagem.select(bandNames[0])
for i in np.arange(1, len(bandNames)-2):
img_out = img_out.addBands(mask4(imagem, value,bandNames[(i-1):(i+3)]))
img_out = img_out.addBands(imagem.select(bandNames[-2]))
img_out = img_out.addBands(imagem.select(bandNames[-1]))
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow5years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-3):\n img_out = img_out.addBands(mask5(imagem, value,bandNames[(i-1):(i+4)]))\n img_out = img_out.addBands(imagem.select(bandNames[-3]))\n img_out = img_out.addBands(im... | [
"0.6658748",
"0.64081764",
"0.6048875",
"0.5828636",
"0.53740543",
"0.5318547",
"0.5314894",
"0.5258221",
"0.5224046",
"0.49241713",
"0.48995483",
"0.48989847",
"0.48840585",
"0.47406876",
"0.47206843",
"0.4713083",
"0.46992207",
"0.46542522",
"0.4640126",
"0.4630526",
"0.462... | 0.76945615 | 0 |
Function to perform a 3 year moving window filter for a single land cover value (such as Forest as 1) for all years in an image. Calls the function mask3. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The temporal filter inspects the central position of three consecutive years, and if the extremities of the consecutive years are identical but the centre position is not, then the central pixels are reclassified to match its temporal neighbour class. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def applyWindow3years(imagem, value, bandNames):
img_out = imagem.select(bandNames[0])
for i in np.arange(1, len(bandNames)-1):
img_out = img_out.addBands(mask3(imagem, value,bandNames[(i-1):(i+2)]))
img_out = img_out.addBands(imagem.select(bandNames[-1]))
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow4years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-2):\n img_out = img_out.addBands(mask4(imagem, value,bandNames[(i-1):(i+3)]))\n img_out = img_out.addBands(imagem.select(bandNames[-2]))\n img_out = img_out.addBands(im... | [
"0.6262926",
"0.61455876",
"0.6122352",
"0.59712267",
"0.5810346",
"0.56390077",
"0.5292091",
"0.5260056",
"0.52369714",
"0.5159779",
"0.5131087",
"0.50615054",
"0.49572933",
"0.48659185",
"0.4854936",
"0.4852849",
"0.48406282",
"0.48388356",
"0.47845525",
"0.47620434",
"0.47... | 0.7711574 | 0 |
Function to perform a 3 year window filter for a single land cover value (such as Forest as 1) for the first year in an image. The image bands do not need to be in order, but the bandNames argument must be in chronological order. For the first year of land cover classifications, a three consecutive years window is used and if the classifications of the first and last years are different from its neighbours, this values are replaced by the classification of its matching neighbours. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def applyMask3first(imagem, value, bandNames):
mask = imagem.select(bandNames[0]).neq(value) \
.bitwiseAnd(imagem.select(bandNames[1]).eq(value)) \
.bitwiseAnd(imagem.select(bandNames[2]).eq(value))
change_img = imagem.select(bandNames[0]).mask(mask.eq(1)).where(mask.eq(1), value)
img_out = imagem.select(bandNames[0]).blend(change_img)
img_out = img_out.addBands(imagem.select(bandNames[1:]))
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow3years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-1):\n img_out = img_out.addBands(mask3(imagem, value,bandNames[(i-1):(i+2)]))\n img_out = img_out.addBands(imagem.select(bandNames[-1]))\n return img_out",
"def apply... | [
"0.713149",
"0.6458987",
"0.6343248",
"0.63266295",
"0.613911",
"0.57802016",
"0.5521701",
"0.5485822",
"0.5108832",
"0.49993712",
"0.49877465",
"0.4840823",
"0.47786245",
"0.47172713",
"0.46206245",
"0.46117097",
"0.4591191",
"0.45893195",
"0.45841068",
"0.45762342",
"0.4554... | 0.52166533 | 8 |
Function to perform a 3 year window filter for a single land cover value (such as Forest as 1) for the final year in an image. The image bands do not need to be in order, but the bandNames argument must be in chronological order. For the first year of land cover classifications, a three consecutive years window is used and if the classifications of the first and last years are different from its neighbours, this values are replaced by the classification of its matching neighbours. This function can be applied to whichever land cover values the user decides, such as all of the land cover values or a select few. | def applyMask3last(imagem, value, bandNames):
mask = imagem.select(bandNames[-3]).eq(value) \
.bitwiseAnd(imagem.select(bandNames[-2]).eq(value)) \
.bitwiseAnd(imagem.select(bandNames[-1]).neq(value))
change_img = imagem.select(bandNames[-1]).mask(mask.eq(1)).where(mask.eq(1), value)
img_out = imagem.select(bandNames[0:-1])
img_out = img_out.addBands(imagem.select(bandNames[-1]).blend(change_img))
return img_out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyWindow3years(imagem, value, bandNames):\n img_out = imagem.select(bandNames[0])\n for i in np.arange(1, len(bandNames)-1):\n img_out = img_out.addBands(mask3(imagem, value,bandNames[(i-1):(i+2)]))\n img_out = img_out.addBands(imagem.select(bandNames[-1]))\n return img_out",
"def apply... | [
"0.73957485",
"0.67384607",
"0.65364456",
"0.6378453",
"0.61525947",
"0.61262476",
"0.5607345",
"0.55978113",
"0.5576969",
"0.4884514",
"0.48811036",
"0.48680866",
"0.4859982",
"0.47968277",
"0.47706997",
"0.476356",
"0.47634727",
"0.47294396",
"0.47226104",
"0.4682663",
"0.4... | 0.53521746 | 9 |
Function to perform a forward moving gap fill for all years in an image. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The forward gap fill is applied iteratively from the first year of bandNames through the final year, where if the current image has missing data, it is filled with the following year's values. | def applyForwardNoDataFilter(image, bandNames):
#Get a list of band names from year(1) through the last year
bandNamesEE = ee.List(bandNames[1:])
#Define forwards filter
#In first iteration, bandName=bandNames[1] and previousImage is image.select(bandNames[0]), or the classifications for the first year
#currentImage = image.select(bandNames[1]), the image for the second year
#previousImage = image.select(bandNames[0]), the first year
#Find where the second year has missing data, replace those values with the values of the first year
#Append previousImage to currentImage, so now currentImage is a two band image, with the first band being the second year with the gap fill
#and the second band is the first years classification
#The iteration continues, now with followingImage.select[0] being the second year with the gap fill applied, and bandName is the third year
def forwardNoDataFilter(bandName, previousImage):
currentImage = image.select(ee.String(bandName))
previousImage = ee.Image(previousImage)
currentImage = currentImage.unmask(previousImage.select([0]))
return currentImage.addBands(previousImage)
#Iterate through all the years, starting with the first year's classification
filtered = bandNamesEE.iterate(forwardNoDataFilter,ee.Image(image.select(bandNames[0])))
filtered = ee.Image(filtered)
return filtered.select(bandNames) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyBackwardNoDataFilter(image, bandNames):\n #Get a list of band names to iterate over, from year(-2) through year(0)\n bandNamesEE = ee.List(bandNames[:-1]).reverse()\n \n #Define backwards filter\n #In first iteration, bandName=bandNames[-2] and followingImage is image.select(bandNames[-1]),... | [
"0.65311575",
"0.5876845",
"0.5747447",
"0.5284138",
"0.52226",
"0.5096014",
"0.5058866",
"0.5047209",
"0.49857956",
"0.49474898",
"0.48613372",
"0.48247787",
"0.47784477",
"0.46002764",
"0.45790556",
"0.45642906",
"0.4548984",
"0.4521293",
"0.45085937",
"0.44796604",
"0.4468... | 0.70489925 | 0 |
Function to perform a backward moving gap fill for all years in an image. The image bands do not need to be in order, but the bandNames argument must be in chronological order. The backward gap fill is applied iteratively from the last year of bandNames through the first year, where if the current image has missing data, it is filled with the previous year's values. | def applyBackwardNoDataFilter(image, bandNames):
#Get a list of band names to iterate over, from year(-2) through year(0)
bandNamesEE = ee.List(bandNames[:-1]).reverse()
#Define backwards filter
#In first iteration, bandName=bandNames[-2] and followingImage is image.select(bandNames[-1]), or the classifications for the final year
#currentImage = image.select(bandNames[-2]), the second to last year
#followingImage = image.select(bandNames[-1]), the final year
#Find where the second to last year has missing data, replace those values with the values of the following year
#Append followingImage to currentImage, so now currentImage is a two band image, with the first band being the second to last year with the gap fill
#and the second band is the final years classification
#The iteration continues, now with followingImage.select[0] being the second to last year with the gap fill applied, and bandName is the third to last year
def backwardNoDataFilter(bandName, followingImage):
currentImage = image.select(ee.String(bandName))
followingImage = ee.Image(followingImage)
currentImage = currentImage.unmask(followingImage.select([0]))
return currentImage.addBands(followingImage)
#Apply backwards filter, starting with the final year and iterating through to year(0)
filtered = bandNamesEE.iterate(backwardNoDataFilter,ee.Image(image.select(bandNames[-1])))
#Re-order bands to be in chronological order
filtered = ee.Image(filtered)
return filtered.select(bandNames) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyForwardNoDataFilter(image, bandNames):\n #Get a list of band names from year(1) through the last year\n bandNamesEE = ee.List(bandNames[1:])\n \n #Define forwards filter\n #In first iteration, bandName=bandNames[1] and previousImage is image.select(bandNames[0]), or the classifications for ... | [
"0.7032449",
"0.5949247",
"0.5812707",
"0.5253641",
"0.51127684",
"0.49725264",
"0.47500893",
"0.46962532",
"0.46487218",
"0.45113215",
"0.44795865",
"0.44701588",
"0.4448179",
"0.4420199",
"0.4388033",
"0.43850613",
"0.43616962",
"0.4360365",
"0.43425742",
"0.43269235",
"0.4... | 0.7573101 | 0 |
Function to apply forward gap filling and backward gap filling to an image. The image bands do not need to be in order, but the bandNames argument must be in chronological order. This funciton calls applyForwardNoDataFilter then applyBackwardNoDataFilter | def applyGapFilter(image, bandNames):
filtered = applyForwardNoDataFilter(image, bandNames)
filtered = applyBackwardNoDataFilter(filtered, bandNames)
return filtered | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyForwardNoDataFilter(image, bandNames):\n #Get a list of band names from year(1) through the last year\n bandNamesEE = ee.List(bandNames[1:])\n \n #Define forwards filter\n #In first iteration, bandName=bandNames[1] and previousImage is image.select(bandNames[0]), or the classifications for ... | [
"0.7110317",
"0.704121",
"0.5153824",
"0.51472473",
"0.50976783",
"0.5092685",
"0.50910985",
"0.5038838",
"0.4983372",
"0.4950319",
"0.48741138",
"0.48643586",
"0.4801015",
"0.47917843",
"0.47886074",
"0.47822264",
"0.4780743",
"0.47651857",
"0.47144574",
"0.4703689",
"0.4698... | 0.745405 | 0 |
Function to calculate the total number of times a pixel changed classes across the time series | def calculateNumberOfChanges(image, bandNames):
#Get a collection of images where each image has 2 bands: classifications for year(i) and classifications for year(i+1)
lc_one_change_col = npv.getYearStackIC(image,bandNames, band_indices=[0,1])
#Get a collection of images where each image represents whether there was change from year(i) to year(i+1) and convert to an image
lc_one_change_col = lc_one_change_col.map(npv.LC_OneChange)
lc_one_change_image = lc_one_change_col.toBands()
#Calculate the number of changes by applying the sum reducer
lc_sum_changes = lc_one_change_image.reduce(ee.Reducer.sum().unweighted())
return lc_sum_changes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_class_count(df):\r\n \r\n return df[\"class\"].value_counts()",
"def resultCounter(detections):\n counter = 0\n for attribute, value in classIterator(detections):\n if 'crease' in attribute:\n counter += len(value)\n return counter",
"def num_classes(self) -> int:\n ... | [
"0.63271403",
"0.6253417",
"0.6221684",
"0.6215353",
"0.61849016",
"0.6137284",
"0.6085611",
"0.60084146",
"0.59348655",
"0.5917543",
"0.588262",
"0.588262",
"0.588262",
"0.58785236",
"0.58546215",
"0.58513844",
"0.58418506",
"0.58400005",
"0.5837566",
"0.5830539",
"0.5821067... | 0.6240185 | 2 |
Function to apply an incidence filter. The incidence filter finds all pixels that changed more than numChangesCutoff times and is connected to less than connectedPixelCutoff pixels, then replaces those pixels with the MODE value of that given pixel position in the stack of years. | def applyIncidenceFilter(image, bandNames, classDictionary, numChangesCutoff = 8, connectedPixelCutoff=6):
#Calculate the number of times a pixel changes throughout the time series and determine if it is over the numChangesCutoff
num_changes = calculateNumberOfChanges(image, bandNames)
too_many_changes = num_changes.gt(numChangesCutoff)
#Get binary images of the land cover classifications for the current year
binary_class_images = npv.convertClassificationsToBinaryImages(image, classDictionary)
#Calculate the number of connected pixels for each land cover class and year, reduce to a single band image representing the number
#of connected pixels of the same land cover class as the central pixel, and determine if it is over the connectedPixelCutoff
connected_pixel_count = ee.ImageCollection(binary_class_images.map(lambda x: x.mask(x).connectedPixelCount(100,False).reduce(ee.Reducer.sum()).lt(connectedPixelCutoff)))
#Get a bitwiseAnd determination if the number of connected pixels <= connectedPixelCutoff and the number of changes > numChangesCutoff
incidence_filter = ee.ImageCollection(connected_pixel_count.map(lambda x: x.bitwiseAnd(too_many_changes))).toBands().rename(bandNames)
#Get an image that represents the mode of the land cover classes in each pixel
mode_image = image.reduce(ee.Reducer.mode())
#Replace pixels of image where incidence_filter is True with mode_image
incidence_filtered = image.where(incidence_filter, mode_image)
return incidence_filtered | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyFrequencyFilter(image, yearBandNames, classDictionary, filterParams): \n #Grab land cover classes as a list of strings\n lc_classes = classDictionary.keys().getInfo()\n \n #Get binary images of the land cover classifications for the current year\n binary_class_images = npv.convertClassif... | [
"0.52584434",
"0.501605",
"0.50095785",
"0.5009003",
"0.5002469",
"0.49740124",
"0.49180493",
"0.49099478",
"0.48357704",
"0.47827873",
"0.47746482",
"0.47566548",
"0.4741615",
"0.4737234",
"0.46963915",
"0.46842596",
"0.4639385",
"0.46338367",
"0.46156648",
"0.46046755",
"0.... | 0.7578108 | 0 |
Function to apply an frequency filter. This filter takes into consideration the occurrence frequency throughout the entire time series. Thus, all class occurrence with less than given percentage of temporal persistence (eg. 3 years or fewer out of 33) are replaced with the MODE value of that given pixel position in the stack of years. | def applyFrequencyFilter(image, yearBandNames, classDictionary, filterParams):
#Grab land cover classes as a list of strings
lc_classes = classDictionary.keys().getInfo()
#Get binary images of the land cover classifications for the current year
binary_class_images = npv.convertClassificationsToBinaryImages(image, classDictionary)
#Get the frequency of each class through the years by reducing the image collection to an image using the sum reducer
class_frequency = binary_class_images.reduce(ee.Reducer.sum().unweighted()).rename(lc_classes)
#Get an image that represents the mode of the land cover classes in each pixel
mode_image = image.reduce(ee.Reducer.mode())
#Define an image to add bands with frequency filter applied
out_img = ee.Image()
#Loop through years
for yearBand in yearBandNames:
#Select the target year from the image
yearImage = image.select(yearBand)
#Loop through land cover classes in filterParams
for lc_class in lc_classes:
#Get the minimum occurance allowed in that land cover class
min_occurance = filterParams.get(lc_class)
#Find if the land cover class had less than the number of min_occurances in each pixel
change_class = class_frequency.select(lc_class).lt(ee.Image.constant(min_occurance))
#If change_class==1, then replace that pixel with the mode of all the years in that pixel
#This filter is only applied to pixels of this land cover class
#First mask yearImage to pixels of this land cover class, then get the union of pixels where change_class==1,
#if both conditions are true, then the pixel is replaced with the mode
yearImage = yearImage.where(yearImage.eq(ee.Number(classDictionary.get(lc_class))).bitwiseAnd(change_class),mode_image)
#Rename yearImage to bandName
yearImage = yearImage.rename(yearBand)
#Append to output image
out_img = out_img.addBands(yearImage)
return out_img | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def frequency_filter(fc, L, srf, KIND=2):\n\n if hasattr(KIND, \"__len__\"):\n PASS = KIND\n KIND = 2\n else:\n PASS = [2,3]\n KIND = [KIND]\n\n # fourier transform of lateral inhibitory function \n\n # tonotopic axis\n if issubclass(type(fc), str):\n fc = float(fc... | [
"0.60981953",
"0.56942517",
"0.5577544",
"0.5543279",
"0.55281085",
"0.5500619",
"0.5476223",
"0.5473143",
"0.54327935",
"0.5337489",
"0.5303522",
"0.5286453",
"0.52189946",
"0.521232",
"0.5156549",
"0.51182044",
"0.51102227",
"0.50913894",
"0.5088179",
"0.5085512",
"0.501948... | 0.69544667 | 0 |
Function to apply a probability filter to land cover probabilities in each image of imageCollection. The user defines which classes will be filtered and how to filter them in the params list. The params list is a list of dictionaries, one for each class the user wants to filter. | def applyProbabilityCutoffs(imageCollection, params):
#Define function to map across imageCollection
def probabilityFilter(image):
#Get the classifications from the class with the highest probability
classifications = npv.probabilityToClassification(image)
#Loop through parameters
for param in params:
#Load parameter values
class_name = param.get('class_name')
class_value = param.get('class_value')
filter_name = param.get('filter')
threshold = param.get('threshold')
if filter_name=='gt':
#Find where the class_name is greater than threshold
prob_mask = image.select(class_name).gt(ee.Image.constant(threshold))
#Replace those pixels with the class value
classifications = classifications.where(prob_mask,class_value)
elif filter_name=='gte':
#Find where the class_name is greater than or equal to threshold
prob_mask = image.select(class_name).gte(ee.Image.constant(threshold))
#Replace those pixels with the class value
classifications = classifications.where(prob_mask,class_value)
elif filter_name == 'lte':
#Find where the class_name is less than or equal to threshold
prob_mask = image.select(class_name).lte(ee.Image.constant(threshold))
#Find where classifications are equal to class value
class_mask = classifications.eq(class_value)
#We only want to replace pixels where the class probability<=threshold AND classification==class_value
reclass_mask = prob_mask.bitwiseAnd(class_mask)
#Define square kernel of surrounding pixels
kernel = ee.Kernel.square(1)
#Convert to a multiband image, one band for each neighbor
neighs = classifications.neighborhoodToBands(kernel)
#Reduce to find the majority class in neighborhood
majority = neighs.reduce(ee.Reducer.mode())
#Replace pixels where the class probability<=threshold AND classification==class_value with the neighborhood majority class
classifications = classifications.where(reclass_mask,majority)
else:
#Find where the class_name is less than or equal to threshold
prob_mask = image.select(class_name).lt(ee.Image.constant(threshold))
#Find where classifications are equal to class value
class_mask = classifications.eq(class_value)
#We only want to replace pixels where the class probability<=threshold AND classification==class_value
reclass_mask = prob_mask.bitwiseAnd(class_mask)
#Define square kernel of surrounding pixels
kernel = ee.Kernel.square(1)
#Convert to a multiband image, one band for each neighbor
neighs = classifications.neighborhoodToBands(kernel)
#Reduce to find the majority class in neighborhood
majority = neighs.reduce(ee.Reducer.mode())
#Replace pixels where the class probability<=threshold AND classification==class_value with the neighborhood majority class
classifications = classifications.where(reclass_mask,majority)
return ee.Image(classifications)
return ee.ImageCollection(imageCollection.map(probabilityFilter)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyFrequencyFilter(image, yearBandNames, classDictionary, filterParams): \n #Grab land cover classes as a list of strings\n lc_classes = classDictionary.keys().getInfo()\n \n #Get binary images of the land cover classifications for the current year\n binary_class_images = npv.convertClassif... | [
"0.6045485",
"0.5797399",
"0.5739651",
"0.57369685",
"0.5731208",
"0.56458217",
"0.55604017",
"0.55141634",
"0.5474806",
"0.5470038",
"0.5451431",
"0.5425758",
"0.53987706",
"0.53475237",
"0.5286839",
"0.52788055",
"0.52607375",
"0.5203152",
"0.5157591",
"0.5144034",
"0.51254... | 0.79373574 | 0 |
Return a logger with a default ColoredFormatter. | def setup_logger():
formatter = ColoredFormatter(
(
'%(log_color)s%(levelname)-5s%(reset)s '
'%(yellow)s[%(asctime)s]%(reset)s'
'%(green)s %(name)s %(purple)s %(filename)s %(purple)s %(funcName)s %(purple)s:%(lineno)d%(reset)s '
'%(bold_blue)s%(message)s%(reset)s'
),
datefmt='%y-%m-%d %H;%M:%S',
log_colors={
'DEBUG': 'blue',
'INFO': 'yellow',
'WARNING': 'red',
'ERROR': 'blue,bg_bold_red',
'CRITICAL': 'red,bg_white',
}
)
logger = logging.getLogger('shen-yue-is-beautiful')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
return logger | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_logger():\n log = logging.getLogger() # root logger\n log.setLevel(logging.DEBUG)\n format_str = '%(asctime)s - %(levelname)-8s - %(message)s'\n date_format = '%Y-%m-%d %H:%M:%S'\n if os.isatty(2):\n cformat = '%(log_color)s' + format_str\n colors = {'DEBUG': 'reset',\n ... | [
"0.6996591",
"0.69907147",
"0.6911573",
"0.682854",
"0.65775317",
"0.65120155",
"0.6396354",
"0.6385912",
"0.6341703",
"0.6268824",
"0.61986893",
"0.6196991",
"0.618999",
"0.61819875",
"0.6148457",
"0.6129842",
"0.6128831",
"0.6113148",
"0.61027133",
"0.60929334",
"0.60799354... | 0.67095196 | 4 |
Create and use a logger. | def main():
logger = setup_logger()
logger.debug('a debug message')
logger.info('an info message')
logger.warning('a warning message')
logger.error('an error message')
logger.critical('a critical message') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_logger() -> logging.Logger:\n pass # TODO: Replace with implementation!",
"def create_logger():\r\n global logger\r\n logger = logging.getLogger(logger_name)\r\n\r\n formatter = logging.Formatter(fmt='%(asctime)s %(levelname)s %(message)s')\r\n \r\n handler = logging.StreamHandler()... | [
"0.81048304",
"0.80497605",
"0.78264886",
"0.77451503",
"0.7742907",
"0.762832",
"0.7588399",
"0.7549163",
"0.75487506",
"0.7536273",
"0.74903685",
"0.74829865",
"0.74677",
"0.7460846",
"0.7454843",
"0.74143606",
"0.74058366",
"0.73974717",
"0.73834455",
"0.73752475",
"0.7360... | 0.0 | -1 |
Converts text data to feature data with the instance's vectorizer. Returns None. | def process_data(self):
self.processed_data = dict()
for split,text_data_ in self.text_data.items():
y = text_data_[self.target_col].values
print("Vectorizing for split: "+split)
x = np.array([self.vectorizer(x_) for x_ in text_data_['Text']])
self.processed_data[split] = {'x':x,'y':y}
self.set_split(self.split_) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def TransformData(text):\n global COUNT_VECTORIZER\n if COUNT_VECTORIZER is None:\n COUNT_VECTORIZER = CountVectorizer(analyzer = 'word', lowercase = True)\n COUNT_VECTORIZER.fit(text)\n features = COUNT_VECTORIZER.transform(text)\n features_nd = features.toarray() # for easy usage\n ... | [
"0.7076867",
"0.70379716",
"0.67895293",
"0.67181784",
"0.6577113",
"0.64362943",
"0.63970965",
"0.6308338",
"0.6303764",
"0.6243157",
"0.6215836",
"0.62142926",
"0.61975324",
"0.6172654",
"0.61442214",
"0.61173296",
"0.6084646",
"0.6082078",
"0.6061634",
"0.6042774",
"0.6021... | 0.61769956 | 13 |
Sets the current active partition. | def set_split(self,split='train'):
self._target_data = self.processed_data[split]
self.split_ = split | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setActive(self, active):\n\n self._active = active",
"def set_active(self, active):\n self._active = active",
"def set_active(self, active):\n self.active = active",
"def active(self, active):\n\n self._active = active",
"def active(self, active):\n\n self._active = a... | [
"0.6547811",
"0.65143806",
"0.6484135",
"0.6361823",
"0.6361823",
"0.6361823",
"0.6361823",
"0.6254472",
"0.6218527",
"0.6194217",
"0.61550754",
"0.61421317",
"0.61362016",
"0.5958744",
"0.5954031",
"0.5954031",
"0.5954031",
"0.5954031",
"0.5954031",
"0.58707124",
"0.5751636"... | 0.0 | -1 |
If data has not been processed, calls process_data. Returns None. | def check_Data(self):
if self._target_data is None:
self.processData() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(proc_data):\n\n # No further processing\n return proc_data",
"def process_data(self, data):\n return data",
"def process_data_impl(\n self,\n data_dir: Path,\n output_processed_data_dir: Path,\n ) -> NoReturn:\n pass",
"def run(self, data):\... | [
"0.78714263",
"0.7426427",
"0.7234299",
"0.72270745",
"0.71373206",
"0.69578606",
"0.67860335",
"0.67860335",
"0.67753035",
"0.67753035",
"0.67753035",
"0.67753035",
"0.67753035",
"0.67753035",
"0.66798294",
"0.6571338",
"0.6558113",
"0.6555273",
"0.6539222",
"0.64652324",
"0... | 0.70434296 | 5 |
Determines the number of batches. | def get_num_batches(self,batch_size):
return len(self) // batch_size | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_of_batches(self):\n return int(np.floor(len(self.file_paths_list) / self.batch_size))",
"def batch_size(self) -> int:\n ...",
"def num_batches(self):\n\t\t\n\t\treturn len(self.batch_stats)",
"def _update_num_batches(self):\n # maximum possible number of batches is equal to nu... | [
"0.8359936",
"0.8323407",
"0.80367476",
"0.7839606",
"0.778999",
"0.77374494",
"0.76503813",
"0.7589813",
"0.752585",
"0.7504559",
"0.7504559",
"0.7504559",
"0.7504559",
"0.7446192",
"0.7433917",
"0.7410418",
"0.73296297",
"0.7293308",
"0.7237539",
"0.7229498",
"0.7215725",
... | 0.82500005 | 2 |
Returns the number of features in the processed data. Returns int Feature size. | def get_num_features(self):
return len(self[0]['x']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def features_size(self) -> int:\n return len(self.data[0].features) if len(self.data) > 0 and self.data[0].features is not None else None",
"def getNrFeatures(self):\n return self.featureNames.size",
"def num_features(self):\n if self.x is None:\n return 0\n return 1 if s... | [
"0.8408814",
"0.83368707",
"0.8241575",
"0.8142289",
"0.80863315",
"0.80641556",
"0.8048011",
"0.79128486",
"0.7869567",
"0.7836357",
"0.78265285",
"0.78225327",
"0.77721834",
"0.7672442",
"0.75243306",
"0.74740946",
"0.7469065",
"0.7446943",
"0.74352556",
"0.7432964",
"0.742... | 0.8641283 | 0 |
Returns a list of the class labels. Returns list List of class labels.. | def get_class_labels(self):
y = self.get_data()['y']
if type(y) == torch.Tensor:
return y.unique().numpy()
else:
return sorted(list(set(y))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def class_labels(self):\n return self._class_labels",
"def classes(self) -> List[Any]:\n return list(self.label_counts.keys())",
"def get_labels(self) -> List[str]:\n return self.labels",
"def get_labels(self) -> List[str]:\n raise NotImplementedError()",
"def label_names(self) ... | [
"0.84051836",
"0.7856161",
"0.7742177",
"0.7669178",
"0.7610997",
"0.757372",
"0.754162",
"0.75304675",
"0.7472855",
"0.72751015",
"0.72687674",
"0.71715975",
"0.71615297",
"0.71615297",
"0.71615297",
"0.71615297",
"0.71615297",
"0.71615297",
"0.71392363",
"0.71387213",
"0.71... | 0.73184854 | 9 |
Returns the index corresponding to the given class label. | def lookup_class_idx(self,label):
return self.class_labels[label] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_class_index(self, label):\n assert label in CLASSES\n return CLASSES.index(label)",
"def get_class_index(label):\n if isinstance(label,str) is False:\n basic.outputlogMessage('input label must be a string')\n assert(False)\n length = len(class_label)\n for i in range(... | [
"0.8939161",
"0.8781351",
"0.8290658",
"0.79299194",
"0.75193024",
"0.7453748",
"0.7260702",
"0.7260702",
"0.7200521",
"0.7144214",
"0.6997018",
"0.6986183",
"0.6928649",
"0.68628794",
"0.67985356",
"0.6562468",
"0.64826816",
"0.6404319",
"0.6269193",
"0.6264292",
"0.62591416... | 0.9071037 | 0 |
Returns ndarrays or Tensors of all data in the current split. | def get_data(self,split=None,numpy=True):
if split is not None:
split_ = self.split_
self.set_split(split)
dataloader = DataLoader(self,batch_size=len(self),shuffle=False,
drop_last=False)
for i,data_item in enumerate(dataloader):
assert(i==0)
x = data_item['x']
y = data_item['y']
if numpy:
if type(x) == torch.Tensor:
x = x.detach().numpy()
else:
x = np.array(x)
if type(y) == torch.Tensor:
y = y.detach().numpy()
else:
y = np.array(y)
if split is not None:
self.split_ = split_
return {'x':x,'y':y} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_data(self) -> Optional[np.ndarray]:\n if self._data_store is None:\n return None\n return self._data_store[:self._count, :]",
"def full_batch(self):\n return self.X_data, self.Y_data",
"def getNdArray(self):\n futures = self.client.map(_call_getNdArray, self.vecDask, pure=False... | [
"0.62569696",
"0.618584",
"0.6073904",
"0.6042331",
"0.5926986",
"0.5832924",
"0.5821185",
"0.5761303",
"0.5756968",
"0.57559574",
"0.57297504",
"0.57242715",
"0.57101953",
"0.570451",
"0.5688236",
"0.56806684",
"0.56590205",
"0.564544",
"0.56339717",
"0.55949557",
"0.5587822... | 0.5634289 | 18 |
Partitions the full data into a list of ndarrays/Tensors. | def get_n_folds(self,split=None,N=5,numpy=True,perm=None):
data = self.get_data(split,numpy)
X = data['x']
y = data['y']
size = len(y)
if perm is None:
perm = np.random.permutation(size)
elif len(perm) != size:
raise Exception("Permutation provided is wrong length: "+\
str(len(perm))+" vs "+str(size))
X = X[perm,:]
y = y[perm,:]
x_folds = np.split(X,N,axis=0)
y_folds = np.split(y,N,axis=0)
return [{'x':x_folds[i],'y':y_folds[i]} for i in range(N)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _batchify(data: nd.NDArray, batch_size):\n # Work out how cleanly we can divide the dataset into bsz parts.\n nbatch = len(data) // batch_size\n # Trim off any extra elements that wouldn't cleanly fit (remainders).\n data = data[0: nbatch * batch_size]\n # Evenly divide the d... | [
"0.6259908",
"0.62205553",
"0.60901254",
"0.6081688",
"0.6013226",
"0.6012804",
"0.600478",
"0.5999807",
"0.59407836",
"0.5916167",
"0.59141505",
"0.5829531",
"0.5814003",
"0.5805772",
"0.58050174",
"0.5766101",
"0.5750351",
"0.5743768",
"0.5734869",
"0.57283336",
"0.57187784... | 0.0 | -1 |
Applies a function mapping to each element in the feature data. | def apply_fn(self,fn):
self.check_Data()
for split,data_ in self.processed_data.items():
x = data_['x']
x = np.array([fn(xi) for xi in x])
data_['x'] = x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map(self, function=lambda value: value):\n for j, value in enumerate(self):\n self[j] = function(value)",
"def map(self, function=lambda item: item):\n for i, row in enumerate(self):\n for j, item in enumerate(row):\n row[j] = function(item)",
"def map(sel... | [
"0.73664135",
"0.7220546",
"0.70397",
"0.70174754",
"0.685345",
"0.684726",
"0.68348914",
"0.6801942",
"0.6742367",
"0.6730765",
"0.6695242",
"0.6670184",
"0.6574957",
"0.6559686",
"0.6507356",
"0.6469651",
"0.6460323",
"0.6443097",
"0.64101386",
"0.64018846",
"0.63930345",
... | 0.7359208 | 1 |
Converts a string of text into a numerical vector of features based on the word embedding LTM. | def vectorize(self,text):
lv_active = set()
words = word_tokenize(text)
for word in words:
if word in self.tree:
ancestors = self.tree.word_ancestors(word)
lv_active.update(ancestors)
return self.nl.isin(lv_active).values | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_vector(text, model, idf, is_tokenized=False):\n if not is_tokenized: text= text.split() # splits the text by space and returns a list of words\n vec = np.zeros(300) # creates an empty vector of 300 dimensions\n for word in text: # iterates over the sentence\n if (word in model) & (word in id... | [
"0.70021975",
"0.6557391",
"0.65419817",
"0.65404963",
"0.6515521",
"0.64831823",
"0.6474055",
"0.64408255",
"0.64127994",
"0.6378021",
"0.63599265",
"0.63383466",
"0.6331677",
"0.6309186",
"0.63056415",
"0.6245646",
"0.6237467",
"0.62303",
"0.62136185",
"0.61973566",
"0.6189... | 0.0 | -1 |
Calls super mehtod and adds a MLTM vector to the data dict. | def vectorize(self, source_text, target_text, use_dataset_max_lengths=True):
data = super().vectorize(source_text, target_text, use_dataset_max_lengths)
mltm_x_vector = self.mltm_vectorizer.vectorize(source_text.lower())
mltm_x_vector = mltm_x_vector.astype(np.float32)
data["x_source_mltm_vector"] = mltm_x_vector
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_data(self, v, m, x, pos=1):\n if x is not None:\n if v in self.variables:\n if m in self.models:\n self.data.update({self.__gen_key(m, v, pos): x})\n self.pos.update({self.__gen_key(m, v, pos): pos})\n else:\n ... | [
"0.62161565",
"0.60280395",
"0.5786776",
"0.5678434",
"0.5514574",
"0.54189706",
"0.538351",
"0.53775567",
"0.5377541",
"0.53738487",
"0.53701663",
"0.5347109",
"0.53244966",
"0.52985543",
"0.52572984",
"0.5225952",
"0.5215003",
"0.52027744",
"0.5197203",
"0.51853836",
"0.518... | 0.0 | -1 |
the primary entry point method for PyTorch datasets | def __getitem__(self, index):
row = self._target_df.iloc[index]
vector_dict = self._vectorizer.vectorize(row.source_language, row.target_language)
return {"x_source": vector_dict["source_vector"],
"x_target": vector_dict["target_x_vector"],
"y_target": vector_dict["target_y_vector"],
"x_source_length": vector_dict["source_length"],
"x_source_mltm_vector": vector_dict["x_source_mltm_vector"]} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadData(self):\n batch_size = 256\n \n #if self.conv_sg == True:\n # batch_size = 1 \n \n download = True\n root = self.root + self.dataset\n if self.dataset == \"MNIST\": \n transform = transforms.Compose([transforms.ToTensor(), tra... | [
"0.71006465",
"0.67667514",
"0.67667514",
"0.6723365",
"0.6689075",
"0.66602546",
"0.66412747",
"0.6627261",
"0.65898776",
"0.658745",
"0.6563074",
"0.6560465",
"0.65538913",
"0.6544277",
"0.6510383",
"0.6499532",
"0.6479281",
"0.6478729",
"0.64679545",
"0.6463354",
"0.645740... | 0.0 | -1 |
Generates a new MLP using the nn.Sequential class. Returns | def generate(self):
components = []
components.append(nn.Linear(self.n_features,self.hidden_sizes[0]))
self._activation(components,self.activation)
self._dropout(components,self.dropout)
for i in range(1,len(self.hidden_sizes)):
components.append(nn.Linear(self.hidden_sizes[i-1],self.hidden_sizes[i]))
self._activation(components,self.activation)
self._dropout(components,self.dropout)
components.append(nn.Linear(self.hidden_sizes[-1],self.n_classes))
mlp = nn.Sequential(*components)
num_params = sum(p.numel() for p in mlp.parameters() if p.requires_grad)
print("Created MLP with "+str(num_params)+" learnable params")
return mlp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_mlp_model():\n return snt.Sequential([\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\n snt.LayerNorm()\n ])",
"def mlp_model(self):\n\n model = Sequential()\n model.add(Dense(self.dense1, input_shape=(784,)))\n model.add(Activation(self... | [
"0.7659937",
"0.75626165",
"0.74614346",
"0.7456196",
"0.7027464",
"0.70230585",
"0.6835712",
"0.67514044",
"0.67060864",
"0.6647528",
"0.6643573",
"0.659611",
"0.6572788",
"0.6491478",
"0.6484547",
"0.6470002",
"0.64629227",
"0.64441985",
"0.64162695",
"0.6321424",
"0.628487... | 0.8253452 | 0 |
Creates a new activation function and adds it to the list of components. | def _activation(self,components,activation):
if activation == "ReLU":
components.append(nn.ReLU())
elif activation == "Sigmoid":
components.append(nn.Sigmoid())
else:
raise Exception("Invalid activation fn: "+activation) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def construct_activation_function(self):\n # Add the activation function\n if not self.activation_function is None:\n # Check if it is a string\n if isinstance(self.activation_function, str):\n activation_function = get_activation_function_by_name(\n ... | [
"0.7639841",
"0.6542784",
"0.61630833",
"0.6001237",
"0.6001181",
"0.59439",
"0.591768",
"0.5908562",
"0.58893776",
"0.58730316",
"0.5850897",
"0.5848719",
"0.58140385",
"0.58140385",
"0.5795392",
"0.5792487",
"0.5785407",
"0.57467926",
"0.57267064",
"0.5675934",
"0.5668909",... | 0.7331862 | 1 |
Adds a dropout object to the list of components | def _dropout(self,components,dropout=None):
if dropout is not None:
components.append(nn.Dropout(dropout)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(self, component) -> None:\n pass",
"def add_component(self, componentInstance):\n\n #print \"Componet being added to %s entity.\"%(self._sName)\n #print componentInstance\n \n self._dComponents[componentInstance.get_name()] = componentInstance\n\n #These if state... | [
"0.63641316",
"0.56994003",
"0.5660805",
"0.5521632",
"0.5519748",
"0.5501247",
"0.54634714",
"0.5435919",
"0.53915066",
"0.5373497",
"0.53394043",
"0.53063345",
"0.5302059",
"0.52375495",
"0.52294815",
"0.5180793",
"0.51650107",
"0.51634353",
"0.515684",
"0.51533777",
"0.514... | 0.673964 | 0 |
Sets model, data, and training algo parameters. | def __init__(self,model,dataset,args):
self.args = args
self.dataset = dataset
self.model = model.to(args.device)
self.optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=self.optimizer,
mode='min', factor=0.5,
patience=1)
vectorizer = dataset.get_vectorizer()
self.mask_index = vectorizer.target_vocab.mask_index
self.train_state = make_train_state(args)
self.epoch_bar = tqdm_notebook(desc='training routine',
total=args.num_epochs,
position=0)
self.dataset.set_split('train')
self.train_bar = tqdm_notebook(desc='split=train',
total=dataset.get_num_batches(args.batch_size),
position=1,
leave=True)
self.dataset.set_split('val')
self.val_bar = tqdm_notebook(desc='split=val',
total=dataset.get_num_batches(args.batch_size),
position=1,
leave=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_params(self, params: Dict):\n\n if params['training_instances'] is not None:\n self.training_instances = params['training_instances']\n if params['n'] is not None:\n self.n = params['n']\n if params['lda'] is not None:\n self.lda = params['lda']\n ... | [
"0.6915247",
"0.6885677",
"0.6774289",
"0.67406434",
"0.6733088",
"0.6642049",
"0.6622964",
"0.65708476",
"0.6508057",
"0.64983726",
"0.6465158",
"0.6464744",
"0.6418009",
"0.6410264",
"0.6372147",
"0.63709724",
"0.636245",
"0.63596547",
"0.63532597",
"0.6346492",
"0.63230634... | 0.0 | -1 |
Runs the training algorithm. Returns None. | def train(self):
args = self.args
model = self.model
dataset = self.dataset
train_state = self.train_state
optimizer = self.optimizer
scheduler = self.scheduler
train_bar = self.train_bar
val_bar = self.val_bar
epoch_bar = self.epoch_bar
for epoch_index in range(args.num_epochs):
train_state['epoch_index'] = epoch_index
# Iterate over training dataset
running_loss,running_acc = self.train_loop(epoch_index, args,
model, dataset,
optimizer, train_bar)
train_state['train_loss'].append(running_loss)
train_state['train_acc'].append(running_acc)
running_loss,running_acc = self.val_loop(epoch_index, args, model,
dataset, optimizer, val_bar)
train_state['val_loss'].append(running_loss)
train_state['val_acc'].append(running_acc)
print("Epoch "+str(epoch_index+1)+": Running loss="+ \
str(running_loss)+", Running Acc="+str(running_acc))
train_state = update_train_state(args=args, model=model,
train_state=train_state)
scheduler.step(train_state['val_loss'][-1])
if train_state['stop_early']:
break
train_bar.n = 0
val_bar.n = 0
epoch_bar.set_postfix(best_val=train_state['early_stopping_best_val'] )
epoch_bar.update()
state_dict = torch.load(train_state['model_filename'])
model.load_state_dict(state_dict)
return model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train():\n # YOUR TRAINING CODE GOES HERE",
"def train():\n pass",
"def train(self):\n # Change directory to the code directory\n current_working_directory = os.getcwd()\n\n os.chdir(self.model_parameters[\"NN_code_directory\"])\n\n self.call_training_routine()\n\n ... | [
"0.77021474",
"0.7665013",
"0.75775045",
"0.754038",
"0.74701804",
"0.7316239",
"0.72936034",
"0.7281923",
"0.7249765",
"0.7234686",
"0.72101897",
"0.7197399",
"0.7190534",
"0.71843356",
"0.7157707",
"0.7157707",
"0.7157707",
"0.7157707",
"0.7157707",
"0.71547514",
"0.7094425... | 0.0 | -1 |
Runs the training for a single epoch. | def train_loop(self,epoch_index,args,model,dataset,optimizer,train_bar):
dataset.set_split('train')
batch_generator = generate_nmt_batches(dataset,
batch_size=args.batch_size,
device=args.device)
running_loss = 0.0
running_acc = 0.0
model.train()
for batch_index, batch_dict in enumerate(batch_generator):
# the training routine is these 5 steps:
# --------------------------------------
# step 1. zero the gradients
optimizer.zero_grad()
# step 2. compute the output
if isinstance(model,NMTModelWithMLTM):
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_mltm_vector'],
batch_dict['x_source_length'],
batch_dict['x_target'])
else:
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_length'],
batch_dict['x_target'])
# step 3. compute the loss
loss = sequence_loss(y_pred, batch_dict['y_target'], self.mask_index)
# step 4. use loss to produce gradients
loss.backward()
# step 5. use optimizer to take gradient step
optimizer.step()
# -----------------------------------------
# compute the running loss and running accuracy
running_loss += (loss.item() - running_loss) / (batch_index + 1)
acc_t = compute_accuracy(y_pred, batch_dict['y_target'], self.mask_index)
running_acc += (acc_t - running_acc) / (batch_index + 1)
# update bar
train_bar.set_postfix(loss=running_loss, acc=running_acc,
epoch=epoch_index)
train_bar.update()
return running_loss,running_acc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_one_epoch(self):\n raise NotImplementedError",
"def train_one_epoch(self):\n\t\tself.model.train()\n\t\ttrain_loss = 0\n\n\t\tfor batch_idx, data in enumerate(self.data_loader.train_loader):\n\t\t\tInput = data[0].float().to(self.device)\n\t\t\tOutput = data[1].float().to(self.device)\n\n\t\t\ts... | [
"0.8152491",
"0.7976923",
"0.78916997",
"0.78650874",
"0.78335357",
"0.78335357",
"0.78335357",
"0.78335357",
"0.7772364",
"0.7753938",
"0.77308905",
"0.7689781",
"0.76347524",
"0.7558736",
"0.75391996",
"0.75388575",
"0.75285155",
"0.7487567",
"0.7441571",
"0.7418234",
"0.73... | 0.0 | -1 |
Evaluates the model on the validation set. | def val_loop(self,epoch_index,args,model,dataset,optimizer,val_bar):
dataset.set_split('val')
batch_generator = generate_nmt_batches(dataset,
batch_size=args.batch_size,
device=args.device)
running_loss = 0.0
running_acc = 0.0
model.eval()
for batch_index, batch_dict in enumerate(batch_generator):
# step 1. compute the output
if isinstance(model,NMTModelWithMLTM):
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_mltm_vector'],
batch_dict['x_source_length'],
batch_dict['x_target'])
else:
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_length'],
batch_dict['x_target'])
# step 2. compute the loss
loss = sequence_loss(y_pred, batch_dict['y_target'], self.mask_index)
# -----------------------------------------
# compute the running loss and running accuracy
running_loss += (loss.item() - running_loss) / (batch_index + 1)
acc_t = compute_accuracy(y_pred, batch_dict['y_target'], self.mask_index)
running_acc += (acc_t - running_acc) / (batch_index + 1)
# update bar
val_bar.set_postfix(loss=running_loss, acc=running_acc,
epoch=epoch_index)
val_bar.update()
return running_loss,running_acc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(self):\n self.set_model_mode('eval')\n self.evaluator.reset()\n losses = MetricMeter()\n\n print('Do evaluation on {} set'.format('valid set'))\n data_loader = self.val_loader\n assert data_loader is not None\n for batch_idx, batch in enumerate(data_loa... | [
"0.7588236",
"0.712658",
"0.7041165",
"0.70291466",
"0.68915606",
"0.6771378",
"0.67110956",
"0.6702142",
"0.66912436",
"0.66912436",
"0.66912436",
"0.6676459",
"0.6667484",
"0.6665729",
"0.6603032",
"0.6589405",
"0.6569131",
"0.6567912",
"0.655075",
"0.6545247",
"0.65360427"... | 0.0 | -1 |
Tests the model on the test set, measuring accuracy. Returns float Total accuracy of the model on the test set. | def test(self):
args = self.args
model = self.model
dataset = self.dataset
dataset.set_split('test')
batch_generator = generate_nmt_batches(dataset,
batch_size=len(dataset),
device=args.device)
acc_sum = 0.0
model.eval()
for batch_index, batch_dict in enumerate(batch_generator):
# step 1. compute the output
if isinstance(model,NMTModelWithMLTM):
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_mltm_vector'],
batch_dict['x_source_length'],
batch_dict['x_target'])
else:
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_length'],
batch_dict['x_target'])
acc_t = compute_accuracy(y_pred, batch_dict['y_target'], self.mask_index)
acc_sum += acc_t
return acc_sum / (batch_index+1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_test_accuracy(self):\n print('\\n# Evaluate on test data')\n results = self.model.evaluate(self.data.test_dataset)\n print('\\ntest loss, test acc:', results)",
"def get_accuracy(self) -> float:\n self.network.load_data()\n self.network.train()\n\n n = len(self.network.y_t... | [
"0.78987217",
"0.7703377",
"0.76911086",
"0.76736206",
"0.7662402",
"0.7599939",
"0.75526947",
"0.7543788",
"0.7528232",
"0.7517148",
"0.74728966",
"0.74700576",
"0.74215174",
"0.73989594",
"0.73596543",
"0.7353871",
"0.7339963",
"0.73214674",
"0.73190075",
"0.7318403",
"0.72... | 0.71598864 | 25 |
Runs a training procedure on a PyTorch module using the dataset and loss function. | def train_model(self,model):
train_state = {'stop_early': False,
'early_stopping_step': 0,
'early_stopping_best_val': 1e8,
'learning_rate': self.lr,
'epoch_index': 0,
'train_loss': [],
'val_loss': [],
'best_model':model}
dataset = self.dataset
loss_fn = self.loss_fn
dataset.set_split('train')
print("Training module with "+str(len(dataset))+" examples")
data_loader = DataLoader(dataset,batch_size=self.batch_size,shuffle=True,
drop_last=True)
optimizer = optim.Adam(model.parameters(), lr=self.lr)
for epoch in range(self.epochs):
train_state['epoch_index'] = epoch
#First step in each epoch is to train over all batches
model.train()
dataset.set_split('train')
train_loss = 0
for b_i,batch_data in enumerate(data_loader):
#Step 1: zero gradients
optimizer.zero_grad()
#Step 2: run forward
X = batch_data['x']
output = model(X)
#Step 3: compute loss
target = batch_data['y']
loss = loss_fn(output,target)
#Step 4: run backward
loss.backward()
#Step 5: update
optimizer.step()
#Record accumulated loss
new_loss = loss.item()
train_loss += new_loss
train_loss /= b_i
train_state['train_loss'].append(train_loss)
#After training, compute loss on validation set and check for early stop
model.eval()
dataset.set_split('val')
val_loss = 0
for b_i,batch_data in enumerate(data_loader):
#Step 1: run forward
X = batch_data['x']
output = model(X)
#Step 2: compute loss
target = batch_data['y']
loss = loss_fn(output,target)
#Record accumulated loss
new_loss = loss.item()
val_loss += new_loss
val_loss /= b_i
train_state['val_loss'].append(val_loss)
print("Finished epoch "+str(epoch+1)+". Train loss="+\
str(train_loss)+", Val loss="+str(val_loss))
if val_loss < train_state['early_stopping_best_val']:
#new best model, reset stopping counter, store model
train_state['early_stopping_step'] = 0
train_state['early_stopping_best_val'] = val_loss
best_model = copy.deepcopy(model)
best_model.load_state_dict(model.state_dict())
train_state['best_model'] = best_model
else:
#val loss not improved; increase early stopping counter
train_state['early_stopping_step'] += 1
if train_state['early_stopping_step'] >= self.early_stopping_criteria:
train_state['stop_early'] = True
print("Val loss failed to improve. Stopping early.")
break
return train_state['best_model'],train_state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(train_dataset: torch.utils.data.Dataset, test_dataset: torch.utils.data.Dataset,\n training_config: dict = train_config, global_config: dict = global_config):\n\n for path in global_config.values():\n create_dirs(path)\n\n # wrap datasets with Dataloader classes\n train_loader = ... | [
"0.7443008",
"0.7346822",
"0.7322582",
"0.7247038",
"0.6962793",
"0.6957798",
"0.6951179",
"0.68994415",
"0.6894855",
"0.68578106",
"0.684405",
"0.6805708",
"0.6804601",
"0.67843026",
"0.6783091",
"0.67713857",
"0.6762238",
"0.67368186",
"0.6734932",
"0.6691646",
"0.6680826",... | 0.0 | -1 |
Generates predictions and attentions for a batch. | def apply_to_batch(self, batch_dict):
self._last_batch = batch_dict
if isinstance(self.model,NMTModelWithMLTM):
y_pred = self.model(x_source=batch_dict['x_source'],
x_mltm=batch_dict['x_source_mltm_vector'],
x_source_lengths=batch_dict['x_source_length'],
target_sequence=batch_dict['x_target'])
else:
y_pred = self.model(x_source=batch_dict['x_source'],
x_source_lengths=batch_dict['x_source_length'],
target_sequence=batch_dict['x_target'])
self._last_batch['y_pred'] = y_pred
attention_batched = np.stack(self.model.decoder._cached_p_attn).transpose(1, 0, 2)
self._last_batch['attention'] = attention_batched | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):\n raise NotImplementedError",
"def predict_on_batch(engine, batch):\n\t\tengine.model.eval()\n\t\tengine.model.rpn.nms_thresh = 0.3\n\t\twith torch.no_grad():\n\t\t\timgs, target = prepare_batch(batch, device=get_device(... | [
"0.7235579",
"0.7028992",
"0.70033675",
"0.69987303",
"0.6998135",
"0.6970026",
"0.69600385",
"0.6889559",
"0.67980856",
"0.6786844",
"0.6781948",
"0.6712437",
"0.6697618",
"0.66913795",
"0.66129225",
"0.65865463",
"0.6565857",
"0.6542319",
"0.6535848",
"0.65276676",
"0.65126... | 0.6662756 | 14 |
Splits a DataFrame into 3 distinct DataFrames based on the given percentages and returns a dict of the data. | def split_data(text_df,splits=None,rand_perm=True):
if splits is None:
splits = {'train':0.6,'val':0.1,'test':0.3}
if np.round(np.sum(list(splits.values())),4) != 1:
raise Exception("Split percentages do not sum to 1")
size = len(text_df)
if rand_perm:
perm_idx = np.random.permutation(size)
else:
perm_idx = np.arange(size)
text_df = text_df.iloc[perm_idx,:]
all_data = dict()
keys = list(splits.keys())
pct = list(splits.values())
count = np.round(np.array(pct) * size).astype(np.int32)
split_idx = np.cumsum(count)[:-1]
data_list = np.split(text_df,split_idx,axis=0)
all_data = {keys[i]:data for i,data in enumerate(data_list)}
return all_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_data(df_data, clusters):\n\n if clusters is None:\n\n return {0: df_data}\n\n return {\n k: df_data.loc[clusters.index[clusters == k]]\n for k in clusters.unique()\n }",
"def split_train_dev_set(df, percent=0.2):\n train = []\n dev = []\n for k, g in df.groupby(\"... | [
"0.59946746",
"0.5814314",
"0.5597616",
"0.55113435",
"0.5504025",
"0.54890805",
"0.5455985",
"0.54370016",
"0.54187405",
"0.54187405",
"0.54108846",
"0.54088694",
"0.5390827",
"0.5368966",
"0.53635633",
"0.53455263",
"0.53044295",
"0.5286926",
"0.52688",
"0.5268422",
"0.5253... | 0.58939797 | 1 |
Performs a standard classification test with the given classifier. | def classify(dataset,classifier,feat_mask=None):
train = dataset.get_data('train',True)
X_train = train['x']
if feat_mask is not None:
X_train = X_train[:,feat_mask]
y_train = train['y']
classifier.fit(X_train,y_train)
test = dataset.get_data('test',True)
X_test = test['x']
if feat_mask is not None:
X_test = X_test[:,feat_mask]
y_test = test['y']
pred = classifier.predict(X_test)
acc = np.count_nonzero(pred==y_test) / len(y_test)
return acc,y_test,pred | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runClassifier(clf,title,xtrain,ytrain,xtest,ytest):\n # train the model using the classifier's fit function\n # use a dummy variable to avoid gibberish being printed\n clf.fit(xtrain, ytrain)\n\n # use the model to predict labels for the test set\n # note: this step is redundant if you just want... | [
"0.71080446",
"0.7000874",
"0.685494",
"0.66270775",
"0.66078466",
"0.6513761",
"0.64321357",
"0.6407826",
"0.63719994",
"0.636399",
"0.635611",
"0.62839556",
"0.6274965",
"0.6272943",
"0.62652",
"0.62624466",
"0.6240519",
"0.62150085",
"0.6208279",
"0.6197915",
"0.61721116",... | 0.6559476 | 5 |
Reads a English > French text file and filters the lines based on the given filter_fn. If filter_fn is None, the default filter will be | def filter_nmt_file(filename,filter_fn=None):
if filter_fn is None:
filter_fn = lambda en : en.lower().startswith('i am') or \
en.lower().startswith('he is') or \
en.lower().startswith('she is') or \
en.lower().startswith('they are') or \
en.lower().startswith('you are') or \
en.lower().startswith('we are')
filtered_lines = []
with open(filename) as file:
lines = file.readlines()
for line in lines:
text = line.split('\t')
en = text[0]
fra = text[1]
if filter_fn(en):
filtered_lines.append(en.lower() + '\t' + fra.lower())
return filtered_lines | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_filter(self, fname, interp=True, lamb=None, *args, **kwargs):\n try:\n fil = UnitFilter.from_ascii(fname, *args, **kwargs)\n except Exception:\n content = self.content\n r = [k for k in content if fname in k]\n\n if len(r) <= 0: # try all lower f... | [
"0.594911",
"0.5432073",
"0.5307598",
"0.5264026",
"0.5253835",
"0.52506196",
"0.5130263",
"0.49939936",
"0.49880716",
"0.49808767",
"0.4957064",
"0.494303",
"0.49124965",
"0.49102247",
"0.49089",
"0.48704535",
"0.48687115",
"0.48383683",
"0.48080763",
"0.47974768",
"0.479605... | 0.73677015 | 0 |
Given a list of lines of English/French text, creates a DataFrame with train/val/test split labels. | def create_nmt_data(text,train_pct=0.7,val_pct=0.15):
if train_pct + val_pct >= 1:
raise Exception("train_pct + val_pct must be < 1.0")
source = []
target = []
for line in text:
text = line.split('\t')
source.append(text[0])
target.append(text[1])
text_df = pd.DataFrame({'source_language':source,'target_language':target})
text_df['split'] = 'train'
text_df = text_df.sample(frac=1).reset_index(drop=True)
idx = int(len(text_df)*train_pct)
text_df.loc[:idx,'split'] = 'train'
idx2 = idx + int(len(text_df)*val_pct)
text_df.loc[idx:idx2,'split'] = 'val'
text_df.loc[idx2:,'split'] = 'test'
return text_df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_training_data_file(list_of_word_lines, language):\r\n # To store each feature vector\r\n feature_vector = []\r\n\r\n # To store the entire dataset\r\n data = []\r\n\r\n for sentence in list_of_word_lines:\r\n\r\n # Contains Q\r\n CONTAINS_Q = 'N'\r\n\r\n # Contains Q\... | [
"0.6267126",
"0.6177644",
"0.6156957",
"0.614642",
"0.6129366",
"0.612271",
"0.6122403",
"0.60591143",
"0.5948062",
"0.59287256",
"0.592589",
"0.5915757",
"0.58811563",
"0.5854081",
"0.58518916",
"0.5847722",
"0.58189434",
"0.58174044",
"0.58112806",
"0.58017504",
"0.5774996"... | 0.6742829 | 0 |
Reads a glove word embedding text file and generates a DataFrame with the embeddings. | def process_glove_data(filename):
word_list = []
embed_list = []
with open(filename,encoding="utf8") as file:
lines = file.readlines()
for line in lines:
toks = line.split(' ')
word_list.append(toks[0])
vec = [float(tok) for tok in toks[1:]]
embed_list.append(vec)
embed = np.array(embed_list,dtype=float)
embed_df = pd.DataFrame(embed,index=word_list)
embed_df.index = embed_df.index.str.lower()
return embed_df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_embeddings(filename):\n labels = []\n rows = []\n with open(filename, encoding='utf-8') as infile:\n for i, line in enumerate(infile):\n items = line.rstrip().split(' ')\n if len(items) == 2:\n # This is a header row giving the shape of the matrix\n ... | [
"0.7637871",
"0.72984564",
"0.72906333",
"0.7238396",
"0.7030938",
"0.6931128",
"0.69254005",
"0.6910939",
"0.6879712",
"0.6877971",
"0.68639934",
"0.6831232",
"0.6824664",
"0.68067014",
"0.679522",
"0.6794145",
"0.6751845",
"0.67482585",
"0.67427385",
"0.66889936",
"0.666392... | 0.7851951 | 0 |
Creates a Tensor for use as an Embedding initialization from the source vocabulary and predefined word embeddings. | def get_pretrained_embeddings(source_vocab,embed_df):
num_tokens = len(source_vocab)
embedding_dim = embed_df.shape[1]
weights = np.zeros((num_tokens,embedding_dim),dtype=np.float32)
for idx in range(num_tokens):
token = source_vocab.lookup_index(idx)
if token in embed_df.index:
weights[idx,:] = embed_df.loc[token]
else:
weights[idx,:] = np.random.randn(1,embedding_dim)
embed_tensor = torch.FloatTensor(weights)
return embed_tensor | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_embedding_layer(inputs_, vocab_size, embed_size):\n embedding = tf.Variable(tf.random_uniform((vocab_size, embed_size), -1, 1))\n embed = tf.nn.embedding_lookup(embedding, inputs_)\n \n return embed",
"def init_word_embed(config):\n embedding_mat_val = np.load(config.wordembed_params)\n ... | [
"0.74340177",
"0.7306449",
"0.72952855",
"0.70558393",
"0.7044115",
"0.68895066",
"0.67812735",
"0.6750932",
"0.67492104",
"0.67476356",
"0.67427427",
"0.6717155",
"0.6711093",
"0.6680487",
"0.6676144",
"0.6672202",
"0.6647878",
"0.6621589",
"0.66202843",
"0.65980107",
"0.658... | 0.66311824 | 17 |
Evaluates the trained model on the test set using the bleu_score method from NLTK. | def eval_nmt_bleu(model,dataset,vectorizer,args):
model = model.eval().to(args.device)
sampler = NMTSamplerWithMLTM(vectorizer, model)
dataset.set_split('test')
batch_generator = generate_nmt_batches(dataset,
batch_size=args.batch_size,
device=args.device)
test_results = []
for batch_dict in batch_generator:
sampler.apply_to_batch(batch_dict)
for i in range(args.batch_size):
test_results.append(sampler.get_ith_item(i, False))
bleu4 = np.array([r['bleu-4'] for r in test_results])*100
return np.mean(bleu4),bleu4 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate_bleu_score(cls, predictions, targets, ticks=False, corpus=True):\n if ticks:\n ref_sentences = cls._ticks_to_sentences(targets)\n cand_sentences = cls._ticks_to_sentences(predictions)\n else:\n ref_sentences = [[str(x) for x in seq] for seq in predictions... | [
"0.7001942",
"0.69130796",
"0.68518585",
"0.68095434",
"0.6744196",
"0.6740567",
"0.6694026",
"0.6630554",
"0.6620787",
"0.66182154",
"0.65482765",
"0.6532654",
"0.65137815",
"0.6487489",
"0.64665717",
"0.64459497",
"0.6421892",
"0.64201504",
"0.6412638",
"0.64110035",
"0.639... | 0.6284775 | 32 |
Selftest function will try to connect to the LDAP instance. Fail if any exceptions are raised. | def selftest_function(opts):
domains_list = get_domains_list(opts)
ldap = LDAPDomains(opts)
state = "success"
reason = "N/A"
domain = "N/A"
conn = ""
for domain_name in domains_list:
try:
"""
If labels are given to the servers in the app.config `domain_name` will start with 'fn_ldap_utilities:' else if
labels are not given then `domain_name` will equal 'fn_ldap_utilites'.
If `domain_name` contains ':' then a labels have been given to the servers and `domain` will be set to the label given to the server else
if `domain_name` does not contain ':' then servers have not been labled and `domain` will be set to `domain_name` which will equal 'fn_ldap_utilities'.
"""
domain = domain_name[domain_name.index(":")+1:] if ":" in domain_name else domain_name
# Instansiate helper (which gets appconfigs from file)
helper = LDAPUtilitiesHelper(ldap.ldap_domain_name_test(domain, domains_list))
options = opts.get(domain_name, {})
log.info(f"Verifying app.config values for {str(options.get('ldap_server'))} config section")
# Instansiate LDAP Server and Connection
conn = helper.get_ldap_connection()
# Bind to the connection
log.info("Verifying LDAP connection...")
conn.bind()
log.info("Test was successful\n")
except Exception as err:
state = "failure"
reason = err
break
finally:
# Unbind connection
if conn:
conn.unbind()
if state == "success":
return {"state": state}
return {
"state": state,
"reason": reason,
"domain": domain
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_simple_auth_error(self):\n client = LDAPClient(self.url)\n client.set_credentials(\"SIMPLE\", (\"cn=wrong\", \"wronger\"))\n self.assertRaises(bonsai.AuthenticationError, client.connect)",
"def connect(self):\n conf = self.conf\n\n if not conf.uris or not conf.base:\n ... | [
"0.7001593",
"0.6606929",
"0.65416896",
"0.6496196",
"0.64913446",
"0.6485445",
"0.6349957",
"0.61097586",
"0.6085969",
"0.60859203",
"0.6060914",
"0.60118145",
"0.6002777",
"0.5980626",
"0.59615344",
"0.59526885",
"0.59469163",
"0.5924804",
"0.5921622",
"0.5895566",
"0.58762... | 0.6377324 | 6 |
Processes a list of splits by modifying any positions as needed. | def handle_splits(self, splits):
total_leftover_cash = 0
for instrument, ratio in splits:
if instrument in self.positions:
self._dirty_stats = True
# Make the position object handle the split. It returns the
# leftover cash from a fractional share, if there is any.
position = self.positions[instrument]
leftover_cash = position.handle_split(instrument, ratio)
total_leftover_cash += leftover_cash
return total_leftover_cash | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split(self, splits, catchall=False):\r\n raise NotImplementedError()",
"def _setup_splits(self):\n #ntot = self.reredux_conf['nperfile']\n ntot = self.reredux_conf['Ngals']\n npersplit = self.runconf['nper']\n\n self.beglist, self.endlist = get_splits(ntot, npersplit)",
"def ... | [
"0.69268346",
"0.57384413",
"0.5705373",
"0.5634635",
"0.56108665",
"0.5545612",
"0.5541551",
"0.5517076",
"0.5466351",
"0.54659456",
"0.54127634",
"0.5412377",
"0.53936285",
"0.5370275",
"0.53439856",
"0.5326818",
"0.53085357",
"0.5286673",
"0.5274086",
"0.5254431",
"0.52331... | 0.6112161 | 1 |
Given a list of dividends whose ex_dates are all the next trading day, calculate and store the cash and/or stock payments to be paid on each dividend's pay date. | def earn_dividends(self, cash_dividends, stock_dividends):
for cash_dividend in cash_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
# Store the earned dividends so that they can be paid on the
# dividends' pay_dates.
div_owed = self.positions[cash_dividend.instrument].earn_dividend(
cash_dividend,
)
try:
self._unpaid_dividends[cash_dividend.pay_date].append(div_owed)
except KeyError:
self._unpaid_dividends[cash_dividend.pay_date] = [div_owed]
for stock_dividend in stock_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
div_owed = self.positions[
stock_dividend.instrument
].earn_stock_dividend(stock_dividend)
try:
self._unpaid_stock_dividends[stock_dividend.pay_date].append(
div_owed,
)
except KeyError:
self._unpaid_stock_dividends[stock_dividend.pay_date] = [
div_owed,
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pay_dividends(self, next_trading_day):\n net_cash_payment = 0.0\n\n try:\n payments = self._unpaid_dividends[next_trading_day]\n # Mark these dividends as paid by dropping them from our unpaid\n del self._unpaid_dividends[next_trading_day]\n except KeyError... | [
"0.6943807",
"0.61015797",
"0.59446824",
"0.5888466",
"0.575217",
"0.57305545",
"0.57155085",
"0.55656976",
"0.5565659",
"0.55222803",
"0.55169284",
"0.54701203",
"0.5462021",
"0.53821945",
"0.53751284",
"0.53381366",
"0.5314847",
"0.53086144",
"0.5304677",
"0.529244",
"0.526... | 0.62604964 | 1 |
Returns a cash payment based on the dividends that should be paid out according to the accumulated bookkeeping of earned, unpaid, and stock dividends. | def pay_dividends(self, next_trading_day):
net_cash_payment = 0.0
try:
payments = self._unpaid_dividends[next_trading_day]
# Mark these dividends as paid by dropping them from our unpaid
del self._unpaid_dividends[next_trading_day]
except KeyError:
payments = []
# representing the fact that we're required to reimburse the owner of
# the stock for any dividends paid while borrowing.
for payment in payments:
net_cash_payment += payment['amount']
# Add stock for any stock dividends paid. Again, the values here may
# be negative in the case of short positions.
try:
stock_payments = self._unpaid_stock_dividends[next_trading_day]
except KeyError:
stock_payments = []
for stock_payment in stock_payments:
payment_instrument = stock_payment['payment_instrument']
share_count = stock_payment['share_count']
# note we create a Position for stock dividend if we don't
# already own the instrument
if payment_instrument in self.positions:
position = self.positions[payment_instrument]
else:
position = self.positions[payment_instrument] = Position(
payment_instrument,
)
position.amount += share_count
return net_cash_payment | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cash_flow(self):\n _cash_flow = self.after_tax_profit() + self.depreciation()\n return _cash_flow",
"def cash(self, qtt_100s, qtt_50s, qtt_20s):\n return (qtt_100s * 100) + (qtt_50s * 50) + (qtt_20s * 20)",
"def test_discounted_payment_below_debit(self):\n debit_jobs([(self.job,... | [
"0.65616626",
"0.6471666",
"0.6241464",
"0.6134051",
"0.6083674",
"0.6017929",
"0.59529567",
"0.59450686",
"0.57963526",
"0.5787191",
"0.5777359",
"0.57426834",
"0.5719649",
"0.56672525",
"0.5616136",
"0.5607058",
"0.55491143",
"0.5521608",
"0.5502035",
"0.5492246",
"0.545119... | 0.7525171 | 0 |
The current status of the positions. Returns | def stats(self):
if self._dirty_stats:
calculate_position_tracker_stats(self.positions, self._stats)
self._dirty_stats = False
return self._stats | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getstatus(self):\n with self.lock:\n return (self.status, self.time_start)",
"def status(self):\n\t\treturn self._status",
"def status(self):\n pass",
"def status(self):\n pass",
"def status(self):\n return self.state",
"def status(self):",
"def status(self):\... | [
"0.7082658",
"0.7037973",
"0.7028998",
"0.7028998",
"0.70281035",
"0.70246303",
"0.6955653",
"0.6926556",
"0.68866",
"0.68866",
"0.68487513",
"0.68487513",
"0.68325317",
"0.6804291",
"0.6804291",
"0.6804291",
"0.6804291",
"0.6804291",
"0.6804291",
"0.6804291",
"0.6804291",
... | 0.0 | -1 |
Add a transaction to ledger, updating the current state as needed. | def process_transaction(self, transaction):
instrument = transaction.instrument
if isinstance(instrument, Future):
try:
old_price = self._payout_last_sale_prices[instrument]
except KeyError:
self._payout_last_sale_prices[instrument] = transaction.price
else:
position = self.position_tracker.positions[instrument]
amount = position.amount
price = transaction.price
self._cash_flow(
self._calculate_payout(
instrument.multiplier,
amount,
old_price,
price,
),
)
if amount + transaction.amount == 0:
del self._payout_last_sale_prices[instrument]
else:
self._payout_last_sale_prices[instrument] = price
else:
self._cash_flow(-(transaction.price * transaction.amount))
self.position_tracker.execute_transaction(transaction)
# we only ever want the dict form from now on
transaction_dict = transaction.to_dict()
try:
self._processed_transactions[transaction.dt].append(
transaction_dict,
)
except KeyError:
self._processed_transactions[transaction.dt] = [transaction_dict] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addTransaction(self, transaction):\n self.transactions.append(transaction)\n self.transactionIDs.add(transaction.id)",
"def add(self, transaction):\n if isinstance(transaction, Transaction):\n # If the transaction already exists\n if(transaction.hash in self.transac... | [
"0.73368466",
"0.7106181",
"0.6942782",
"0.66168725",
"0.65834856",
"0.6570095",
"0.6479877",
"0.64787537",
"0.6422333",
"0.64186",
"0.6378191",
"0.63757354",
"0.63565505",
"0.63033175",
"0.630115",
"0.6284071",
"0.6255765",
"0.62522966",
"0.6241244",
"0.6228167",
"0.6210412"... | 0.0 | -1 |
Retrieve the dictform of all of the transactions in a given bar or for the whole simulation. | def transactions(self, dt=None):
if dt is None:
# flatten the by-day transactions
return [
txn
for by_day in itervalues(self._processed_transactions)
for txn in by_day
]
return self._processed_transactions.get(dt, []) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transaction_data(self):\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))",
"def transaction_base() -> Dict[str, Any]:\n return {\n \"first_name\": \"Donald\",\n \"last_name\": \"Duck\",\n \"company\": \"Duck Co\",\n \"email\": \... | [
"0.63048834",
"0.602332",
"0.5960751",
"0.56639814",
"0.56584823",
"0.5563182",
"0.5531879",
"0.55191296",
"0.55072176",
"0.5477383",
"0.54718333",
"0.5458796",
"0.5426807",
"0.54197216",
"0.541453",
"0.5338547",
"0.5327534",
"0.53225285",
"0.53161556",
"0.5279584",
"0.526199... | 0.0 | -1 |
Force a computation of the current portfolio state. | def update_portfolio(self):
if not self._dirty_portfolio:
return
portfolio = self._portfolio
pt = self.position_tracker
portfolio.positions = pt.get_positions()
position_stats = pt.stats
portfolio.positions_value = position_value = (
position_stats.net_value
)
portfolio.positions_exposure = position_stats.net_exposure
self._cash_flow(self._get_payout_total(pt.positions))
start_value = portfolio.portfolio_value
# update the new starting value
portfolio.portfolio_value = end_value = portfolio.cash + position_value
pnl = end_value - start_value
if start_value != 0:
returns = pnl / start_value
else:
returns = 0.0
portfolio.pnl += pnl
portfolio.returns = (
(1 + portfolio.returns) *
(1 + returns) -
1
)
# the portfolio has been fully synced
self._dirty_portfolio = False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_portfolio(self, portfolio: PortfolioController):\n now = portfolio.get_history(seconds_back=0)\n future = portfolio.get_history(seconds_back=-self.update_interval)\n\n for fund in portfolio.funds:\n best_currency = max(portfolio.currencies, key=lambda currency: future_val... | [
"0.60446954",
"0.5951977",
"0.5695806",
"0.5687665",
"0.56560236",
"0.5653465",
"0.56520754",
"0.5633276",
"0.559409",
"0.5562694",
"0.54696906",
"0.54538536",
"0.5440695",
"0.5416885",
"0.5380987",
"0.5366457",
"0.5364525",
"0.5331849",
"0.5323494",
"0.53195906",
"0.53195906... | 0.66357535 | 0 |
Compute the current portfolio. Notes This is cached, repeated access will not recompute the portfolio until the portfolio may have changed. | def portfolio(self):
self.update_portfolio()
return self._immutable_portfolio | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_portfolio(self):\n if not self._dirty_portfolio:\n return\n\n portfolio = self._portfolio\n pt = self.position_tracker\n\n portfolio.positions = pt.get_positions()\n position_stats = pt.stats\n\n portfolio.positions_value = position_value = (\n ... | [
"0.7362575",
"0.6996456",
"0.6729907",
"0.6721263",
"0.6675463",
"0.66379255",
"0.6499",
"0.63661253",
"0.63091534",
"0.62968546",
"0.6240645",
"0.6196709",
"0.6144556",
"0.60243994",
"0.5963545",
"0.5962163",
"0.59054154",
"0.5858664",
"0.5812279",
"0.5781019",
"0.5715677",
... | 0.81566226 | 0 |
Override fields on ``self.account``. | def override_account_fields(self,
settled_cash=not_overridden,
accrued_interest=not_overridden,
buying_power=not_overridden,
equity_with_loan=not_overridden,
total_positions_value=not_overridden,
total_positions_exposure=not_overridden,
regt_equity=not_overridden,
regt_margin=not_overridden,
initial_margin_requirement=not_overridden,
maintenance_margin_requirement=not_overridden,
available_funds=not_overridden,
excess_liquidity=not_overridden,
cushion=not_overridden,
day_trades_remaining=not_overridden,
leverage=not_overridden,
net_leverage=not_overridden,
net_liquidation=not_overridden):
# mark that the portfolio is dirty to override the fields again
self._dirty_account = True
self._account_overrides = kwargs = {
k: v for k, v in locals().items() if v is not not_overridden
}
del kwargs['self'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def account(self, account):\n\n self._account = account",
"def account(self, account):\n\n self._account = account",
"def account(self, account):\n\n self._account = account",
"def account(self, account):\n\n self._account = account",
"def patch(self, account=None, user=None, ac... | [
"0.6413226",
"0.6413226",
"0.6413226",
"0.6413226",
"0.6343206",
"0.6329128",
"0.6322581",
"0.63135093",
"0.61441493",
"0.60770935",
"0.60361296",
"0.6022793",
"0.60135746",
"0.60068023",
"0.59981",
"0.59467715",
"0.58997744",
"0.5875439",
"0.5858766",
"0.5787134",
"0.5765871... | 0.67399603 | 0 |
Initializing method. Always starts with player 'X' going first. Also creates a blank board to begin playing on. | def __init__(self):
self.current = Piece.EX
self.board = [Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK, Piece.BLANK] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n # Current player\n self.player = X\n\n # Board\n self.board = [\n [None, None, None],\n [None, None, None],\n [None, None, None]\n ]\n\n # Winner\n self.winner = None\n\n # Game over\n self._gameov... | [
"0.76133895",
"0.74191046",
"0.7272769",
"0.7215476",
"0.70663446",
"0.7060645",
"0.7049015",
"0.7029267",
"0.69543284",
"0.69497305",
"0.692294",
"0.6910041",
"0.6903089",
"0.6872871",
"0.6863269",
"0.68311656",
"0.68238926",
"0.68191206",
"0.6769023",
"0.67465365",
"0.67353... | 0.6897718 | 13 |
Switches whose turn it is. | def switchPlayer(self):
if (self.current is Piece.EX):
self.current = Piece.OH
else:
self.current = Piece.EX | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _switch_turn(self, cur_player):\n if cur_player == \"W\":\n self._turn = \"B\"\n else:\n self._turn = \"W\"",
"def turn(self):\n pass",
"def get_switches(self) -> tuple:\n return self.switches",
"def changeTurn(self):\n\t\tif self.turn == 1:\n\t\t\tself.t... | [
"0.68772453",
"0.6781878",
"0.6612293",
"0.65573156",
"0.6287292",
"0.62218165",
"0.6109247",
"0.6078702",
"0.6077556",
"0.5993894",
"0.5986418",
"0.59811705",
"0.5940087",
"0.59389865",
"0.59333205",
"0.59090734",
"0.5887211",
"0.58654636",
"0.5862874",
"0.58553296",
"0.5841... | 0.5775022 | 23 |
Trys to make a move. If the move is successful returns 1. If the move string is not able to interpreted correctly or if that place is already full the move fails and the function returns 0 | def makeMove(self, move):
try:
if (self.board[int(move) - 1] is Piece.BLANK):
self.board[int(move) - 1] = self.current
return 1
else:
return 0
except:
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_valid(move):\n return True",
"def handle_move(self, move_string):\n def map_move(move):\n col = int(ascii_lowercase.find(move[0])) + 1 # dummy col\n row = int(move[1:])\n # if not 0 < col <= game[\"board_width\"]:\n # raise ValueError('bad coord;... | [
"0.70150155",
"0.676564",
"0.6584836",
"0.6568262",
"0.6568117",
"0.6563307",
"0.6558569",
"0.64923847",
"0.64853036",
"0.6477489",
"0.645601",
"0.6451311",
"0.6451188",
"0.64166987",
"0.6414095",
"0.63909614",
"0.63762623",
"0.636292",
"0.6332474",
"0.63271296",
"0.63165474"... | 0.6508602 | 7 |
Returns the winning peice if the game is over. If the game is a draw it returns the empty peice, and if the game is not over returns false. | def isOver(self):
isFull = Piece.BLANK
for a,b,c in [[0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 3, 6], [1, 4, 7], [2, 5, 8], [0, 4, 8], [2, 4, 6]]:
if (self.board[a] is self.board[b] is self.board[c] and self.board[a] is not Piece.BLANK):
return self.board[a]
if (self.board[a] is Piece.BLANK or self.board[b] is Piece.BLANK or self.board[c] is Piece.BLANK):
isFull = False
return isFull | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False",
"def is_game_over(self):\r\n\r\n if self.winner != 0:\r\n return True\r\n\r\n return False",
"def is_game_over(sel... | [
"0.75529164",
"0.74890757",
"0.73833823",
"0.73335296",
"0.7333373",
"0.72050595",
"0.70841414",
"0.705282",
"0.7051845",
"0.70401967",
"0.6998241",
"0.69875365",
"0.6977598",
"0.6947065",
"0.69441944",
"0.6942049",
"0.69409615",
"0.69100434",
"0.6907182",
"0.6895013",
"0.689... | 0.691553 | 17 |
Returns a string interpretation of the board. | def __str__(self):
boardString = "\n{0}|{1}|{2}\n-----\n{3}|{4}|{5}\n-----\n{6}|{7}|{8}\n"
return boardString.format(self.board[0], self.board[1], self.board[2], self.board[3],
self.board[4], self.board[5], self.board[6], self.board[7], self.board[8]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def board_string(self):\n s = \"\"\n for i, v in enumerate(self.board):\n # if i % 81 == 0:\n # s += \"\\n\"\n if v is None:\n s += \"0\"\n else:\n if v.color == StoneColor.black:\n s += \"1\"\n ... | [
"0.84243935",
"0.8055579",
"0.8017218",
"0.78294134",
"0.7797174",
"0.77931315",
"0.7776164",
"0.77571356",
"0.77145636",
"0.7699537",
"0.7682327",
"0.7632191",
"0.76316303",
"0.76192135",
"0.7602081",
"0.7599799",
"0.758553",
"0.7570357",
"0.75595516",
"0.7550093",
"0.753793... | 0.79764 | 3 |
The main method for running the game. | def main():
print("Welcome to TicTacToe")
board = Board()
while (not board.isOver()):
print("It is {0}'s turn".format(board.current) + board.__str__())
move = input('Where would you like to go? : ').strip()
if (move == 'q'):
break
elif (board.makeMove(move) == 1):
board.switchPlayer()
else:
print("I didn't understand your input, these are the valid inputs:\nentering 'q' will quit out of the game.\n")
print("entering a number will place the peice in that box, the numbers are as follows:\n \n1|2|3\n-----\n4|5|6\n-----\n7|8|9\n")
print(board.__str__() + "\nGame Over")
if (board.isOver() is Piece.EX or board.isOver() is Piece.OH):
print("Player {0} wins!".format(board.isOver()))
else:
print("It was a draw") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n g = Game(800, 600)\n g.start()",
"def main():\r\n gameclass = data.game.GameClass()\r\n gameclass.main_loop()",
"def main():\n game = RiichiMahjongApp()\n game.run()",
"def main():\n g = DemoGame(800, 600)\n g.start()",
"def main():\n game = Game(TIMES, HARDNESS)\n ... | [
"0.85276115",
"0.8359565",
"0.8280766",
"0.8256087",
"0.8189385",
"0.79771304",
"0.78113306",
"0.7789521",
"0.7754325",
"0.77015805",
"0.7665701",
"0.7611436",
"0.76085716",
"0.7576111",
"0.7546943",
"0.7546619",
"0.7520529",
"0.7456335",
"0.74490446",
"0.74308777",
"0.738893... | 0.0 | -1 |
Remove temporary partition files from disk. The removed files' names are deleted from the _temporary_files set. The intended use is to delete individual files as part of the garbage collection process and to delete all files when python exits. This is quite brutal and may break partitions if used unwisely. It is not recommended to be used as a general tidyup function. | def _remove_temporary_files(filename=None):
if filename is not None:
if filename in _temporary_files:
# If this condition is not met then probably
# _remove_temporary_files() has already been run at
# exit
dirname, _lock_file, _other_lock_files = _temporary_files[filename]
try:
remove(_lock_file)
except OSError:
pass
# Only remove the temporary file if it is not being
# used by any other ranks
if not _lock_files_present(_other_lock_files):
# Remove the given temporary file
try:
remove(filename)
rmdir(dirname)
except OSError:
pass
del _temporary_files[filename]
# --- End: if
return
# Still here? Then remove all temporary files and lock files
for filename in _temporary_files:
try:
remove(filename)
except OSError:
pass
dirname, _lock_file, _other_lock_files = _temporary_files[filename]
try:
remove(_lock_file)
except OSError:
pass
for lock_file in _other_lock_files:
try:
remove(lock_file)
except OSError:
pass
# --- End: for
try:
rmdir(dirname)
except OSError:
pass
# --- End: for
_temporary_files.clear() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_temporary_files():\n try:\n xml_file_path, bin_file_path = get_ida_exported_files()\n if os.path.isfile(xml_file_path):\n os.remove(xml_file_path)\n\n if os.path.isfile(bin_file_path):\n os.remove(bin_file_path)\n\n except Exception:\n print(\"GhID... | [
"0.7633441",
"0.74177027",
"0.72147197",
"0.7178067",
"0.6853436",
"0.6829257",
"0.67934287",
"0.6738931",
"0.67164594",
"0.66771114",
"0.66554505",
"0.66493994",
"0.6645599",
"0.66091245",
"0.65514976",
"0.6517317",
"0.6492228",
"0.64918196",
"0.6469804",
"0.6463625",
"0.638... | 0.7262391 | 2 |
Used if copy.deepcopy is called on the variable. | def __deepcopy__(self, memo):
return self.copy() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def varcopy(self, vars):",
"def copy(self):\n pass",
"def copy(self):\n pass",
"def copy(self):\n pass",
"def copy(self):",
"def shallow_copy(self):\n # TODO: Rename this to __copy__()?\n raise NotImplementedError(\"shallow_copy is not implemented\")",
"def __copy__(s... | [
"0.6630489",
"0.63350475",
"0.63350475",
"0.63350475",
"0.6280567",
"0.6222454",
"0.6153182",
"0.6143717",
"0.61114347",
"0.6110205",
"0.6047252",
"0.5978211",
"0.5953629",
"0.59274524",
"0.59274524",
"0.59274524",
"0.58838916",
"0.5878963",
"0.5824744",
"0.5819304",
"0.57938... | 0.60275984 | 11 |
Called when the partition's reference count reaches zero. If the partition contains a temporary file which is not referenced by any other partition then the temporary file is removed from disk. If the partition contains a nontemporary file which is not referenced by any other partition then the file is closed. | def __del__(self):
# subarray = getattr(self, '_subarray', None)
subarray = self._subarray
# If the subarray is unique it will have 2 references to
# it plus 1 within this method, making 3. If it has more
# than 3 references to it then it is not unique.
if getrefcount is not None:
self._decrement_file_counter()
if subarray is None or getrefcount(subarray) > 3:
return
else:
# getrefcount has itself been deleted or is in the process
# of being torn down
return
_partition_file = getattr(subarray, "_partition_file", None)
if _partition_file is not None:
# This partition contains a temporary file which is not
# referenced by any other partition on this process, so if
# there are no lock files present remove the file from
# disk.
_remove_temporary_files(_partition_file)
else:
try:
if FileArray is not None and isinstance(subarray, FileArray):
try:
filename = subarray.get_filename()
except Exception:
filename = None
if self.file_counter.get(filename, 999) <= 0:
# This partition contains a non-temporary file
# which is not referenced by any other
# partitions, so close the file.
subarray.close()
except Exception:
# If we're here then it is likely that FileArray has been
# torn down, so just do nothing.
pass
# --- End: if | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_file_deleted(self):\n try:\n with get_temp_file() as (fd, name):\n os.unlink(name)\n except Exception as err:\n self.fail('Failed with exception \"{}\"'.format(err))",
"def _Close(self):\n self._fsfat_volume = None\n self._file_object = None",
"... | [
"0.637165",
"0.6141179",
"0.6079966",
"0.60165036",
"0.5893113",
"0.5771018",
"0.5769767",
"0.5752214",
"0.5731687",
"0.5730452",
"0.57243013",
"0.5715975",
"0.5715163",
"0.5711628",
"0.5696097",
"0.56944114",
"0.56878215",
"0.56815344",
"0.5681272",
"0.5633728",
"0.5630855",... | 0.61570215 | 1 |
Add i to the count of subarrays referencing the file of this partition's subarray. Only do this if self._subarray is an instance of FileArray, but not a temporary FileArray. | def _add_to_file_counter(self, i):
# subarray = getattr(self, '_subarray', None)
subarray = self._subarray
if subarray is None:
return
try:
if isinstance(subarray, FileArray) and not isinstance(
subarray, CachedArray
):
try:
filename = subarray.get_filename()
except Exception:
filename = None
if filename is None:
return
file_counter = self.file_counter
# count = file_counter.get(filename, 0)
# file_counter[filename] = count + i
# if file_counter[filename] <= 0:
count = file_counter.get(filename, 0) + i
if count <= 0:
# Remove the file from the dictionary if its count has
# dropped to zero
file_counter.pop(filename, None)
else:
file_counter[filename] = count
except Exception:
# If we're here then it is likely that FileArray has been
# torn down, so just do nothing.
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def n_subfile(self):\n self.assert_is_dir_and_exists()\n n = 0\n for _ in self.select_file(recursive=False):\n n += 1\n return n",
"def update(self, i, v):\n # index in BTree is 1 more than index in arr[]\n i += 1\n\n # Traverse to ancestors of BITree[i... | [
"0.5323221",
"0.5309046",
"0.5237938",
"0.52028364",
"0.51664454",
"0.5117871",
"0.51150346",
"0.5107957",
"0.50491905",
"0.50423014",
"0.5001557",
"0.499577",
"0.49840355",
"0.4932267",
"0.4909338",
"0.48659304",
"0.48641643",
"0.48607743",
"0.48591715",
"0.4854085",
"0.4824... | 0.8270168 | 0 |
Add 1 to the Partition.file_counter if self._subarray is an instance of FileArray and not a temporary FileArray. | def _increment_file_counter(self):
self._add_to_file_counter(1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ... | [
"0.83802605",
"0.6079216",
"0.60073787",
"0.5972287",
"0.5871502",
"0.58649766",
"0.58356875",
"0.5813897",
"0.5656929",
"0.5650024",
"0.5628978",
"0.5588129",
"0.5575472",
"0.55536973",
"0.55466735",
"0.55383646",
"0.5529106",
"0.5502226",
"0.54975855",
"0.5481056",
"0.54359... | 0.71174276 | 1 |
Subtract 1 from the Partition.file_counter if self._subarray is an instance of FileArray and not a temporary FileArray. | def _decrement_file_counter(self):
self._add_to_file_counter(-1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ... | [
"0.7747842",
"0.63648546",
"0.6072333",
"0.60351396",
"0.5918244",
"0.57059807",
"0.56674904",
"0.5664642",
"0.56031275",
"0.5573989",
"0.5520654",
"0.5453089",
"0.5448838",
"0.54281026",
"0.5422772",
"0.5378375",
"0.5307265",
"0.5244931",
"0.52217174",
"0.5220032",
"0.521629... | 0.6648334 | 1 |
Add the auxiliary mask to the config dictionary. Assumes that ``self.config`` already exists. | def _configure_auxiliary_mask(self, auxiliary_mask):
indices = self.indices
new = [
mask[
tuple(
[
(slice(None) if n == 1 else index)
for n, index in zip(mask.shape, indices)
]
)
]
for mask in auxiliary_mask
]
# # If the partition is to be parallelised then get rid of mask
# # components which are all False so the mask component does
# # not get copied to the child process
# if not config['serial']:
# new = [mask for mask in new if not mask.any()]
self.config["auxiliary_mask"] = new | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_masking(self, masks):\n self.masks = masks",
"def add_config(self):\n\n config = {\n 'invert_byte': InvertByte,\n 'invert_word': InvertWord,\n 'invert_double_word': InvertDoubleWord,\n 'and_byte': AndByte,\n 'and_word': AndWord,\n... | [
"0.5756575",
"0.5562516",
"0.54862016",
"0.5367435",
"0.5347834",
"0.53455114",
"0.5306387",
"0.52775955",
"0.522134",
"0.5194576",
"0.51778084",
"0.5152401",
"0.51056355",
"0.51011837",
"0.50790006",
"0.5067728",
"0.5065551",
"0.4962702",
"0.4946801",
"0.4946075",
"0.4918300... | 0.71955097 | 0 |
The indices of the master array which correspond to this partition's data array. | def indices(self):
return tuple([slice(*r) for r in self.location]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def indices(self) -> np.ndarray:\n return self.impl.indices",
"def master_ndindex(self): # itermaster_indices(self):\n return itertools_product(\n *[range(*r) for r in self.location]\n ) # TODO check",
"def atom_idxs(self):\n\n return np.array([atom.atom_idxs for atom i... | [
"0.70275134",
"0.6850407",
"0.683511",
"0.6796057",
"0.6708753",
"0.6668921",
"0.6662458",
"0.66586286",
"0.6644368",
"0.6611067",
"0.66031367",
"0.65675586",
"0.65319914",
"0.64623576",
"0.6393266",
"0.6372617",
"0.6343201",
"0.63220435",
"0.6305004",
"0.62970823",
"0.628111... | 0.62476814 | 23 |
True if and only if the partition's subarray is in memory as opposed to on disk. | def in_memory(self):
return hasattr(self._subarray, "__array_interface__") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())",
"def extra_memory(self):\n if not self.in_memory:\n # --------------------------------------------------------\n # The subarray is on disk so getting the partition's data\n ... | [
"0.7677807",
"0.75874203",
"0.73676527",
"0.6567502",
"0.6373806",
"0.62520576",
"0.6168975",
"0.6139325",
"0.61339194",
"0.6126576",
"0.611511",
"0.608777",
"0.6025861",
"0.6025105",
"0.60162103",
"0.5953649",
"0.59421575",
"0.59196216",
"0.5899218",
"0.5882104",
"0.5880964"... | 0.7635218 | 1 |
True if and only if the partition's subarray is on disk in a temporary file. | def in_cached_file(self):
return isinstance(self._subarray, CachedArray) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_disk(self):\n return isinstance(self._subarray, FileArray)",
"def _are_features_already_extracted(self, output_path: str, subset: str) -> bool:\n file_path = join(output_path, subset + '.npy')\n return os.path.exists(file_path)",
"def has_subfile(self) -> bool:\n\t\tself._update_sub... | [
"0.72301924",
"0.65045905",
"0.64364123",
"0.5942543",
"0.5936233",
"0.591641",
"0.5817689",
"0.5676702",
"0.5673538",
"0.5658594",
"0.5589181",
"0.5555981",
"0.5479751",
"0.5440639",
"0.5393117",
"0.53697217",
"0.5364029",
"0.5358785",
"0.5355252",
"0.53492653",
"0.53334475"... | 0.58127344 | 7 |
True if and only if the partition's subarray is on disk as opposed to in memory. | def on_disk(self):
return isinstance(self._subarray, FileArray) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")",
"def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())",
"def extra_memory(self):\n if not self.in_memory:\n # ------------------------------------------... | [
"0.69458973",
"0.67629313",
"0.6696292",
"0.656141",
"0.63517463",
"0.6290223",
"0.61257756",
"0.59961635",
"0.59486187",
"0.59276515",
"0.59175736",
"0.5881189",
"0.5803704",
"0.577553",
"0.5774722",
"0.5755425",
"0.5737828",
"0.57311875",
"0.5723483",
"0.5720749",
"0.568287... | 0.80375713 | 0 |
True if and only if the partition's subarray is on disk as opposed to in memory. | def in_file(self):
return self.on_disk and not self.in_cached_file | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_disk(self):\n return isinstance(self._subarray, FileArray)",
"def in_memory(self):\n return hasattr(self._subarray, \"__array_interface__\")",
"def in_memory(self) -> bool:\n return all(isinstance(x, np.ndarray) for x in self.chunks.values())",
"def extra_memory(self):\n if... | [
"0.80375713",
"0.69458973",
"0.67629313",
"0.6696292",
"0.656141",
"0.63517463",
"0.6290223",
"0.61257756",
"0.59961635",
"0.59486187",
"0.59276515",
"0.59175736",
"0.5881189",
"0.577553",
"0.5774722",
"0.5755425",
"0.5737828",
"0.57311875",
"0.5723483",
"0.5720749",
"0.56828... | 0.5803704 | 13 |
The data type of the master array. | def dtype(self):
return self.config["dtype"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def datatype_name(self):\n return 'array'",
"def data_type(self):\r\n return self._data_type",
"def data_type(self):\n return self._data_type",
"def data_type(self):\n return self._data_type",
"def data_type(self):\n return self._data_type",
"def dtype(self):\n r... | [
"0.7694177",
"0.76688254",
"0.75949335",
"0.75949335",
"0.75949335",
"0.7573461",
"0.74812984",
"0.7376351",
"0.7376351",
"0.72733635",
"0.7250659",
"0.71817577",
"0.7178896",
"0.71689445",
"0.7107126",
"0.7105419",
"0.70681125",
"0.7060085",
"0.7039309",
"0.7015803",
"0.7010... | 0.69170535 | 30 |
True if and only if the partition's data array is a scalar array. | def isscalar(self):
return not self.axes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_scalar(obj: _std_typing.Any) -> bool:\n return obj.ndim == 0",
"def is_scalar(x: Any) -> bool:\r\n return np.isscalar(x) or (isinstance(x, np.ndarray) and x.ndim == 0)",
"def is_array(self):\n return False",
"def is_scalar(self):\n return len(self.coeffs.shape[self.sdim:]) == 0",
... | [
"0.74611837",
"0.74560386",
"0.7210494",
"0.71899956",
"0.7152722",
"0.7101354",
"0.70952576",
"0.7088053",
"0.7086914",
"0.69344157",
"0.6923261",
"0.69007254",
"0.68844897",
"0.68752134",
"0.67174804",
"0.66352606",
"0.66348445",
"0.65968424",
"0.658544",
"0.65799415",
"0.6... | 0.0 | -1 |
The size in bytes of the subarray. The size takes into account the datatype and assumes that there is a boolean mask, unless it can be ascertained that there isn't one. | def nbytes(self):
dtype = self.config["dtype"]
if dtype is None:
return None
size = reduce(mul, self.shape, 1)
nbytes = size * dtype.itemsize
if getattr(self, "masked", True):
nbytes += size
return nbytes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def arraySize( cls, value, typeCode = None ):\n return value.size",
"def array_size(self):\n return self._array_size",
"def container_size(self):\n import cPickle\n import sys\n t = cPickle.dumps(self.filter_bitarray)\n return sys.getsizeof(t)",
"def size(self):\... | [
"0.718968",
"0.71867967",
"0.7167967",
"0.70721114",
"0.69927114",
"0.69399047",
"0.6921515",
"0.68736595",
"0.6852514",
"0.6835874",
"0.6802148",
"0.67929095",
"0.67913187",
"0.67521507",
"0.6731404",
"0.6717999",
"0.67143357",
"0.6703314",
"0.67020184",
"0.6700556",
"0.6697... | 0.6896727 | 7 |
Number of array dimensions. | def ndim(self):
return len(self.shape) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def n_dims(self):\n return len(self.dimensions)",
"def num_dim(self):\n return len(self._dimensions)",
"def num_dim(self):\n return len(self._dimensions)",
"def get_num_dimensions(self):\n dimensions = self.data.shape\n return dimensions[1]",
"def count_dims(da):\n ret... | [
"0.87556416",
"0.85560286",
"0.85560286",
"0.84606373",
"0.8365305",
"0.83450353",
"0.8314268",
"0.8273276",
"0.82264763",
"0.8216497",
"0.81538165",
"0.81395245",
"0.8122816",
"0.81203264",
"0.809687",
"0.8096407",
"0.80840564",
"0.80840564",
"0.80781686",
"0.80665874",
"0.8... | 0.79104465 | 30 |
Number of elements in the partition's data array (not its subarray). | def size(self):
return reduce(mul, self.shape, 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count(self):\r\n return self.data_array.size",
"def count_elements_in_dataset(dataset):\n return dataset.count()",
"def size(self):\n ret = 0\n for ii in self.__data:\n ret += int(ii.get_size())\n return ret",
"def n_elements(self) -> int:\n n_elem = np.prod(self.shape)\n ... | [
"0.76638305",
"0.73945427",
"0.7326349",
"0.7308581",
"0.7271908",
"0.72121114",
"0.71300155",
"0.7121346",
"0.71050775",
"0.7088031",
"0.70531565",
"0.6979757",
"0.69550794",
"0.694825",
"0.69397396",
"0.69286805",
"0.6899676",
"0.6895468",
"0.68825126",
"0.687968",
"0.68745... | 0.0 | -1 |
The partition's subarray of data. | def subarray(self):
return self._subarray | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subarray(self) -> Subarray:\n return Subarray.from_pybind11(self._ctx, self._subarray)",
"def partition(self, sep):\n return asarray(partition(self, sep))",
"def array(self) -> ndarray:\n if self._slices: # so this is a sub-parray object\n # index into origin array by saved... | [
"0.68206084",
"0.6535615",
"0.6457086",
"0.6347438",
"0.62131214",
"0.61877143",
"0.61062545",
"0.6101903",
"0.60762966",
"0.6068932",
"0.6029079",
"0.6012183",
"0.59961045",
"0.59653914",
"0.5928066",
"0.58841175",
"0.58761495",
"0.58514863",
"0.5848823",
"0.5791028",
"0.577... | 0.7297865 | 0 |
Change the axis names. The axis names are arbitrary, so mapping them to another arbitrary collection does not change the data array values, units, nor axis order. | def change_axis_names(self, axis_map):
axes = self.axes
# Partition axes
self.axes = [axis_map[axis] for axis in axes]
# Flipped axes
flip = self.flip
if flip:
self.flip = [axis_map[axis] for axis in flip] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setAxisName(name, axes='XYZ'):\n dislin.name(name, axes)",
"def setAxesNames(self):\n \n labels = ['T', 'Z', 'Y', 'X'] + [chr(ord('S')-i) for i in xrange(18)]\n if (len(self.axisList) >= 4):\n i = 0\n else:\n i = 4 - len(self.axisList)\n \n ... | [
"0.7356323",
"0.7326029",
"0.6893155",
"0.6677015",
"0.64332557",
"0.6372005",
"0.63517636",
"0.62905",
"0.628417",
"0.62775946",
"0.6271645",
"0.6077482",
"0.6047013",
"0.6024936",
"0.5993588",
"0.59712166",
"0.5967535",
"0.596201",
"0.5952742",
"0.58923167",
"0.589169",
"... | 0.75589085 | 0 |
Close the partition after it has been conformed. The partition should usually be closed after its `array` method has been called to prevent memory leaks. Closing the partition does one of the following, depending on the values of the partition's `!_original` attribute and on the | def close(self, **kwargs):
config = getattr(self, "config", None)
if config is None:
return
if kwargs:
config.update(kwargs)
original = getattr(self, "_original", None)
logger.partitioning("Partition.close: original = {}".format(original))
if not original:
originally_on_disk = False
original_subarray = None
else:
originally_on_disk = not original.in_memory
original_subarray = original._subarray
config = self.config
logger.partitioning(" config = {}".format(config))
if config["serial"]:
# --------------------------------------------------------
# SERIAL
# --------------------------------------------------------
logger.partitioning(" serial")
if config["readonly"]:
logger.partitioning(" readonly=True")
if originally_on_disk:
logger.partitioning(" subarray originally on disk")
if config.get("to_disk", False):
# 1.1.1.1 The original subarray was on disk,
# we don't want to keep the current
# subarray in memory, and we are happy
# to discard any changes that may have
# been made to the subarray.
logger.partitioning(" 1.1.1.1 revert")
self.revert()
elif free_memory() <= cf_fm_threshold():
# 1.1.1.2 The original subarray was on disk,
# we are happy to keep the current
# subarray in memory, but there is not
# enough free memory to do so.
logger.partitioning(
" 1.1.1.2 revert ({} <= {})".format(
free_memory(), cf_fm_threshold()
)
)
self.revert()
else:
# 1.1.1.3 The original subarray was on disk
# and there is enough memory to keep
# the current subarray in memory
if config["unique_subarray"] and isinstance(
original_subarray, CachedArray
):
# The original subarray was a temporary
# file which is not referenced by any
# other partitions
_remove_temporary_files(
original_subarray._partition_file
)
del self.masked
logger.partitioning(
" 1.1.1.3 del masked ({} > {})".format(
free_memory(), cf_fm_threshold()
)
)
else:
logger.partitioning(" subarray originally in memory")
if config.get("to_disk", False):
# 1.1.2.1 Original subarray was in memory and
# we don't want to keep the current
# subarray in memory
logger.partitioning(" 1.1.2.1 to_disk")
self.to_disk(reopen=False)
elif free_memory() <= cf_fm_threshold():
# 1.1.2.2 Original subarray was in memory and
# unique but there is not enough
# memory to keep the current subarray
logger.partitioning(" 1.1.2.2 to_disk")
self.to_disk(reopen=False)
else:
# 1.1.2.3 Original subarray was in memory and
# unique and there is enough memory to
# keep the current subarray in memory
logger.partitioning(" 1.1.2.3 pass")
pass
else:
# config['readonly'] is False
if originally_on_disk:
if config.get("to_disk", False):
# 1.2.1.1 Original subarray was on disk and
# there and we don't want to keep the
# array
if config["unique_subarray"] and isinstance(
original_subarray, CachedArray
):
# Original subarray was a temporary file
# on disk which is not referenced by any
# other partitions
_remove_temporary_files(
original_subarray._partition_file
)
logger.partitioning(" 1.2.1.1 to_disk")
self.to_disk(reopen=False)
elif free_memory() <= cf_fm_threshold():
# 1.2.1.2 Original subarray was on disk but
# there is not enough memory to keep
# it
if config["unique_subarray"] and isinstance(
original_subarray, CachedArray
):
# Original subarray was a temporary file
# on disk which is not referenced by any
# other partitions
_remove_temporary_files(
original_subarray._partition_file
)
logger.partitioning(" 1.2.1.2 to_disk")
self.to_disk(reopen=False)
else:
# 1.2.1.3 Original subarray was on disk and
# there is enough memory to keep it
logger.partitioning(" 1.2.1.3 pass")
del self.masked
else:
if config.get("to_disk", False):
# 1.2.2.1 Original subarray was in memory but
# we don't want to keep it
logger.partitioning(" 1.2.2.1 to_disk")
self.to_disk(reopen=False)
elif free_memory() <= cf_fm_threshold():
# 1.2.2.2 Original subarray was an in memory
# but there is not enough memory to
# keep it
logger.partitioning(" 1.2.2.2 to_disk")
self.to_disk(reopen=False)
else:
# 1.2.2.3 Original subarray was in memory and
# there is enough memory to keep it
logger.partitioning(" 1.2.2.3 del masked")
del self.masked
else:
logger.partitioning("Partition.close: parallel")
# --------------------------------------------------------
# PARALLEL
# --------------------------------------------------------
pass
# if hasattr(self, '_original'):
# del self._original
# print(hasattr(self, 'config')),
try:
del self.config
except AttributeError:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def close(self):\n return self.close_array",
"def file_close(self):\n if self.on_disk:\n self._subarray.close()",
"def close(self):\n self.ix.close()",
"def close (self):\n pass\n #TODO: implement more realistic closing semantics",
"def close(self):\n self.data.... | [
"0.6764165",
"0.6518303",
"0.60208786",
"0.5811839",
"0.5795013",
"0.579095",
"0.5784808",
"0.57648385",
"0.57648385",
"0.57648385",
"0.57648385",
"0.57648385",
"0.57648385",
"0.57648385",
"0.57648385",
"0.5713722",
"0.5699953",
"0.5699953",
"0.5665161",
"0.5664806",
"0.56545... | 0.70555735 | 0 |
Return a deep copy. ``p.copy()`` is equivalent to ``copy.deepcopy(p)``. | def copy(self):
new = Partition.__new__(Partition)
new.__dict__ = self.__dict__.copy()
self._increment_file_counter()
return new | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deepcopy(self):\n return copymod.deepcopy(self)",
"def copy(self):\n\t\treturn pythoncopy.deepcopy(self)",
"def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)",
"def deepcopy(self):\n return self.copy()",
"def copy(self):\r\n return copy.deepcopy(self)",
"... | [
"0.7977393",
"0.7835692",
"0.7835209",
"0.7829921",
"0.7808746",
"0.7806165",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0.77548367",
"0... | 0.0 | -1 |
Returns the partition's data array. After a partition has been conformed, the partition must be closed (with the `close` method) before another partition is conformed, | def array(self):
config = self.config
unique_array = config["unique_subarray"]
p_axes = self.axes
p_flip = self.flip
p_part = self.part
p_units = self.Units
p_shape = self.shape
p_location = self.location
subarray = self._subarray
len_p_axes = len(p_axes)
if not self.in_memory:
# --------------------------------------------------------
# The subarray is not in memory.
#
# It could be in a file on disk or implied by a FileArray
# object, etc.
# --------------------------------------------------------
self._original = self.copy()
unique_array = True
update = True
copy = False
if not p_part:
indices = Ellipsis
else:
indices = tuple(p_part)
# Read from a file into a numpy array
p_data = subarray[indices]
# We've just copied p_data from disk, so in place changes
# are not possible
in_place_changes = False
else:
# --------------------------------------------------------
# The subarray is in memory
# --------------------------------------------------------
update = config["update"]
if p_part:
p_data = get_subspace(subarray, p_part)
elif not unique_array:
p_data = subarray.view()
else:
p_data = subarray
copy = config["extra_memory"]
# In place changes to p_data might be possible if we're not
# copying the data
in_place_changes = not copy
if not p_data.ndim and isinstance(p_data, (numpy_number, numpy_bool_)):
# --------------------------------------------------------
# p_data is a numpy number (like numpy.int64) which does
# not support assignment, so convert it to a numpy array.
# --------------------------------------------------------
p_data = numpy_array(p_data)
# We've just copied p_data, so in place changes are
# not possible
copy = False
in_place_changes = False
masked = numpy_ma_isMA(p_data)
if masked:
# The p_data is a masked array
if p_data.mask is numpy_ma_nomask or not numpy_ma_is_masked(
p_data
):
# There are no missing data points so recast as an
# unmasked numpy array
p_data = p_data.data
masked = False
# --- End: if
if masked:
# Set the hardness of the mask
if config["hardmask"]:
p_data.harden_mask()
else:
p_data.soften_mask()
# --- End: if
self.masked = masked
# ------------------------------------------------------------
# Make sure that the data array has the correct units. This
# process will deep copy the data array if required (e.g. if
# another partition is referencing this numpy array), even if
# the units are already correct.
# ------------------------------------------------------------
func = config.get("func")
units = config["units"]
if func is None:
if not p_units.equals(units) and bool(p_units) is bool(units):
func = Units.conform
if func is not None:
inplace = not copy
p_data = func(p_data, p_units, units, inplace)
p_units = units
if not inplace:
# We've just copied p_data, so in place changes are
# not possible
copy = False
in_place_changes = False
# --- End: if
flip = config.get("flip", None)
if flip or p_flip:
flip_axes = set(p_flip).symmetric_difference(flip)
else:
flip_axes = None
axes = config["axes"]
if p_data.size > 1:
# --------------------------------------------------------
# Flip axes
# --------------------------------------------------------
if flip_axes:
indices = [
(
slice(None, None, -1)
if axis in flip_axes
else slice(None)
)
for axis in p_axes
]
p_data = p_data[tuple(indices)]
# --------------------------------------------------------
# Transpose axes
# --------------------------------------------------------
if p_axes != axes:
iaxes = [p_axes.index(axis) for axis in axes if axis in p_axes]
if len_p_axes > len(iaxes):
for i in range(len_p_axes):
if i not in iaxes:
# iaxes.append(i)
iaxes.insert(i, i)
# --- End: if
p_data = numpy_transpose(p_data, iaxes)
# --- End: if
# ------------------------------------------------------------
# Remove excessive/insert missing size 1 axes
# ------------------------------------------------------------
if p_shape != p_data.shape:
# if len_p_axes != len(p_shape):
p_data = p_data.reshape(p_shape)
# ------------------------------------------------------------
# Apply the auxiliary mask
# ------------------------------------------------------------
auxiliary_mask = config["auxiliary_mask"]
if auxiliary_mask:
for mask in auxiliary_mask:
if mask.any():
if not masked:
p_data = p_data.view(numpy_ma_MaskedArray)
masked = True
p_data.mask = (mask | p_data.mask).array
# --- End: for
self.masked = True
# ------------------------------------------------------------
# Convert the array's data type
# ------------------------------------------------------------
p_dtype = p_data.dtype
dtype = config.get("dtype", None)
if dtype is not None and dtype != p_dtype:
try:
p_data = p_data.astype(dtype) # Note: returns a copy
except ValueError:
raise ValueError(
"Can't recast partition array from {} to {}".format(
p_dtype.name, dtype.name
)
)
else:
# We've just copied p_data, so in place changes are
# not possible
copy = False
in_place_changes = False
# --- End: if
# ------------------------------------------------------------
# Copy the array
# -----------------------------------------------------------
if copy:
if p_dtype.char != "O":
if not masked or p_data.ndim > 0:
p_data = p_data.copy()
else:
# This is because numpy.ma.copy doesn't work for
# scalar arrays (at the moment, at least)
p_data = numpy_ma_masked_all((), p_data.dtype)
# We've just copied p_data, so in place changes are
# not possible
in_place_changes = False
else:
# whilst netCDF4.netcdftime.datetime is mucking bout,
# don't copy!!!!
# p_data = _copy(p_data)
pass
# --- End: if
# ------------------------------------------------------------
# Update the partition
# ------------------------------------------------------------
if update:
self.subarray = p_data # ?? DCH CHECK
self.Units = p_units
self.part = []
self.axes = axes
self.flip = flip
self.flatten = []
self.shape = p_shape
self.location = p_location
self._in_place_changes = in_place_changes
# ------------------------------------------------------------
# Return the numpy array
# ------------------------------------------------------------
return p_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data ( self ):\n return self._data_pntr.ReadAsArray()",
"def get_data ( self ):\n return self._data_pntr.ReadAsArray()",
"def data_array(self):\n return self._data_array",
"def getData(self):\n return self._array",
"def get(self):\r\n return self.data_array",
"d... | [
"0.66712004",
"0.66712004",
"0.6514655",
"0.62883234",
"0.62340677",
"0.6196425",
"0.6086166",
"0.6076796",
"0.5936824",
"0.59225637",
"0.5890117",
"0.5851978",
"0.5830991",
"0.5823379",
"0.5809332",
"0.57856905",
"0.5781631",
"0.57773644",
"0.57653815",
"0.5727279",
"0.57197... | 0.5538118 | 31 |
True if the subarray contains datetime objects. | def isdt(self):
return self.Units.isreftime and self._subarray.dtype == _dtype_object | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_datetime_type(obj: _std_typing.Any) -> bool:\n return obj.dtype == sc.DType.datetime64",
"def _uses_datetimeblock(dtype: Union[np.dtype, ExtensionDtype]) -> bool:\n vtype = dtype.type\n return issubclass(vtype, np.datetime64)",
"def is_datetime(self) -> bool:\n return False",
"def are... | [
"0.72152364",
"0.68011504",
"0.67946845",
"0.6540459",
"0.6300369",
"0.62526584",
"0.6159265",
"0.612599",
"0.612436",
"0.60484034",
"0.60277694",
"0.6001856",
"0.5956571",
"0.5938945",
"0.58849597",
"0.58664906",
"0.58624506",
"0.5751764",
"0.56482595",
"0.56456316",
"0.5640... | 0.71551335 | 1 |
Close the file containing the subarray, if there is one. | def file_close(self):
if self.on_disk:
self._subarray.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def close_file(self):\n self.root_group.close()",
"def __del__(self):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n # If the subarray is unique it will have 2 references to\n # it plus 1 within this method, making 3. If it has more\n ... | [
"0.61085176",
"0.6024418",
"0.59545076",
"0.5824396",
"0.58042437",
"0.5736725",
"0.57022864",
"0.57022864",
"0.56994104",
"0.56970567",
"0.56860274",
"0.564539",
"0.56289333",
"0.56183493",
"0.5591367",
"0.5591367",
"0.55517685",
"0.5509945",
"0.5487693",
"0.54802656",
"0.54... | 0.7828047 | 0 |
Inspect the object for debugging. | def inspect(self):
print(cf_inspect(self)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inspect(obj:Any) -> None:\n\t\tLogging._log(Logging.logLevel, obj)",
"def debug(self):\n raise NotImplementedError",
"def output_debug_info(self):",
"def inspect_obj(self, line=None):\n if not line:\n return\n\n # evaluate the line to get a python object\n python_ob... | [
"0.7372906",
"0.72778416",
"0.6883075",
"0.6746992",
"0.6737449",
"0.6555419",
"0.6541405",
"0.65274495",
"0.6431801",
"0.64068395",
"0.6402232",
"0.640157",
"0.6398631",
"0.63798773",
"0.6376369",
"0.6375551",
"0.63642555",
"0.63500595",
"0.6315535",
"0.6302645",
"0.62807316... | 0.6848801 | 3 |
Return an iterator over indices of the master array which are spanned by the data array. | def master_ndindex(self): # itermaster_indices(self):
return itertools_product(
*[range(*r) for r in self.location]
) # TODO check | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_subset_inds(self, adata_parent):\r\n subset_inds = np.ones(len(adata_parent), dtype=bool)\r\n for condition, values in self.subset_cond.items():\r\n subset_inds *= adata_parent.obs[condition].isin(values)\r\n return subset_inds",
"def enumerate(self):\n # go through... | [
"0.61020994",
"0.6072502",
"0.60690475",
"0.60687184",
"0.60543483",
"0.60275173",
"0.5924552",
"0.5918545",
"0.59088135",
"0.58821535",
"0.58567846",
"0.58429295",
"0.5828838",
"0.58083194",
"0.5794418",
"0.5793355",
"0.57828283",
"0.57748425",
"0.5756645",
"0.57550335",
"0.... | 0.6835397 | 0 |
Update the `!part` attribute inplace for new indices of the master array. | def new_part(self, indices, master_axis_to_position, master_flip):
shape = self.shape
if indices == [slice(0, stop, 1) for stop in shape]:
return
# ------------------------------------------------------------
# If a dimension runs in the wrong direction then change its
# index to account for this.
#
# For example, if a dimension with the wrong direction has
# size 10 and its index is slice(3,8,2) then after the
# direction is set correctly, the index needs to changed to
# slice(6,0,-2):
#
# >>> a = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
# >>> a[slice(3, 8, 2)]
# [6, 4, 2]
# >>> a.reverse()
# >>> print(a)
# >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# >>> a[slice(6, 0, -2)]
# [6, 4, 2]
# ------------------------------------------------------------
if self._subarray.size > 1:
indices = indices[:]
p_flip = self.flip
for axis, i in master_axis_to_position.items():
if (axis not in p_flip and axis not in master_flip) or (
axis in p_flip and axis in master_flip
):
# This axis runs in the correct direction
continue
# Still here? Then this axis runs in the wrong
# direction.
# Reset the direction
p_flip = p_flip[:]
if axis in self.flip:
p_flip.remove(axis)
else:
p_flip.append(axis)
# Modify the index to account for the changed
# direction
size = shape[i]
if isinstance(indices[i], slice):
start, stop, step = indices[i].indices(size)
# Note that step is assumed to be always +ve here
div, mod = divmod(stop - start - 1, step)
start = size - 1 - start
stop = start - div * step - 1
if stop < 0:
stop = None
indices[i] = slice(start, stop, -step)
else:
size -= 1
indices[i] = [size - j for j in indices[i]]
# --- End: for
self.flip = p_flip
# --- End: if
slice_None = slice(None)
# Reorder the new indices
indices = [
(
indices[master_axis_to_position[axis]]
if axis in master_axis_to_position
else slice_None
)
for axis in self.axes
]
part = self.part
if not part:
self.part = indices
return
# Still here? update an existing part
p_part = []
for part_index, index, size in zip(
part, indices, self._subarray.shape
):
if index == slice_None:
p_part.append(part_index)
continue
if isinstance(part_index, slice):
if isinstance(index, slice):
start, stop, step = part_index.indices(size)
size1, mod = divmod(stop - start - 1, step)
start1, stop1, step1 = index.indices(size1 + 1)
size2, mod = divmod(stop1 - start1, step1)
if mod != 0:
size2 += 1
start += start1 * step
step *= step1
stop = start + (size2 - 1) * step
if step > 0:
stop += 1
else:
stop -= 1
if stop < 0:
stop = None
p_part.append(slice(start, stop, step))
continue
else:
new_part = list(range(*part_index.indices(size)))
new_part = [new_part[i] for i in index]
else:
if isinstance(index, slice):
new_part = part_index[index]
else:
new_part = [part_index[i] for i in index]
# --- End: if
# Still here? Then the new element of p_part is a list of
# integers, so let's see if we can convert it to a slice
# before appending it.
new_part0 = new_part[0]
if len(new_part) == 1:
# Convert a single element list to a slice object
new_part = slice(new_part0, new_part0 + 1, 1)
else:
step = new_part[1] - new_part0
if step:
if step > 0:
start, stop = new_part0, new_part[-1] + 1
else:
start, stop = new_part0, new_part[-1] - 1
if new_part == list(range(start, stop, step)):
if stop < 0:
stop = None
new_part = slice(start, stop, step)
# --- End: if
p_part.append(new_part)
# --- End: for
self.part = p_part | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, patch):\n internalSlices = self._get_internal_slices(patch.slices)\n self.array[internalSlices] = patch.array",
"def _idx_changed(self, idx):\n self.refresh_memory()",
"def _loadpart(self, part):\n new_partidx = util.Partname(part.partname).idx\n for idx, seq_p... | [
"0.5455975",
"0.5450583",
"0.5443926",
"0.53721666",
"0.5359862",
"0.5275372",
"0.52543634",
"0.52315736",
"0.5228334",
"0.51149035",
"0.50902224",
"0.50782204",
"0.5062653",
"0.50265366",
"0.50074023",
"0.49909484",
"0.49905938",
"0.49756426",
"0.4970326",
"0.496184",
"0.496... | 0.5784396 | 0 |
The extra memory required to access the array. | def extra_memory(self):
if not self.in_memory:
# --------------------------------------------------------
# The subarray is on disk so getting the partition's data
# array will require extra memory
# --------------------------------------------------------
extra_memory = True
else:
# --------------------------------------------------------
# The subarray is already in memory
# --------------------------------------------------------
config = self.config
p_part = self.part
if p_part:
extra_memory = True
elif not config["unique_subarray"]:
extra_memory = True
else:
p_data = self._subarray
if not numpy_ma_isMA(p_data):
# The p_data is not a masked array
extra_memory = isinstance(p_data.base, numpy_ndarray)
else:
# The p_data is a masked array
memory_overlap = isinstance(
p_data.data.base, numpy_ndarray
)
if not (
p_data.mask is numpy_ma_nomask
or not numpy_ma_is_masked(p_data)
):
# There is at least one missing data point
memory_overlap |= isinstance(
p_data.mask.base, numpy_ndarray
)
extra_memory = memory_overlap
# --- End: if
p_dtype = p_data.dtype
if not extra_memory:
if config["func"] is not None:
extra_memory = True
else:
p_units = self.Units
units = config["units"]
if (
not p_units.equals(units)
and bool(p_units) is bool(units)
and not (
p_data.flags["C_CONTIGUOUS"]
and p_dtype.kind == "f"
)
):
extra_memory = True
# ------------------------------------------------------------
# Extra memory is required if the dtype needs changing
# ------------------------------------------------------------
if not extra_memory:
dtype = config["dtype"]
if dtype is not None and dtype != p_data.dtype:
extra_memory = True
# --- End: if
# ------------------------------------------------------------
# Amount of extra memory (in bytes) required to access the
# array
# ------------------------------------------------------------
return self.nbytes if extra_memory else 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allocated_memory(self):\n return self._allocated_memory",
"def memory(self):\r\n return self._memory",
"def __len__(self):\n\t\treturn len(self.memory)",
"def __len__(self):\r\n return len(self.memory)",
"def __len__(self):\n return len(self.memory)",
"def __len__(self):\n... | [
"0.6821264",
"0.6811845",
"0.66922146",
"0.66735834",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
"0.6612898",
... | 0.73394907 | 0 |
Open the partition prior to getting its array. | def open(self, config):
unique_subarray = getrefcount(self._subarray) <= 2
config = config.copy()
config["unique_subarray"] = unique_subarray
self.config = config
if config.get("auxiliary_mask"):
self._configure_auxiliary_mask(config["auxiliary_mask"])
self.config["extra_memory"] = self.extra_memory()
self._in_place_changes = True
self.masked = True
if hasattr(self, "output"):
del self.output
return config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open(self):\n return self.open_array",
"def partition_book(self):\n ...",
"def to_disk(self, reopen=True):\n # try:\n tfa = CachedArray(self.array)\n # except Exception:\n # return False\n\n fd, _lock_file = mkstemp(\n pre... | [
"0.5840582",
"0.5428586",
"0.5294434",
"0.52601844",
"0.51751643",
"0.50962955",
"0.50962955",
"0.509415",
"0.5024122",
"0.5011268",
"0.49543542",
"0.4883474",
"0.48718578",
"0.48603144",
"0.48522878",
"0.48517695",
"0.4839195",
"0.48225853",
"0.48027864",
"0.48000965",
"0.47... | 0.0 | -1 |
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