id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
7,700 | adamrehn/slidingwindow | slidingwindow/WindowDistance.py | generateDistanceMatrix | def generateDistanceMatrix(width, height):
"""
Generates a matrix specifying the distance of each point in a window to its centre.
"""
# Determine the coordinates of the exact centre of the window
originX = width / 2
originY = height / 2
# Generate the distance matrix
distances = zerosFactory((height,width), dtype=np.float)
for index, val in np.ndenumerate(distances):
y,x = index
distances[(y,x)] = math.sqrt( math.pow(x - originX, 2) + math.pow(y - originY, 2) )
return distances | python | def generateDistanceMatrix(width, height):
"""
Generates a matrix specifying the distance of each point in a window to its centre.
"""
# Determine the coordinates of the exact centre of the window
originX = width / 2
originY = height / 2
# Generate the distance matrix
distances = zerosFactory((height,width), dtype=np.float)
for index, val in np.ndenumerate(distances):
y,x = index
distances[(y,x)] = math.sqrt( math.pow(x - originX, 2) + math.pow(y - originY, 2) )
return distances | [
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7,701 | adamrehn/slidingwindow | slidingwindow/ArrayUtils.py | _requiredSize | def _requiredSize(shape, dtype):
"""
Determines the number of bytes required to store a NumPy array with
the specified shape and datatype.
"""
return math.floor(np.prod(np.asarray(shape, dtype=np.uint64)) * np.dtype(dtype).itemsize) | python | def _requiredSize(shape, dtype):
"""
Determines the number of bytes required to store a NumPy array with
the specified shape and datatype.
"""
return math.floor(np.prod(np.asarray(shape, dtype=np.uint64)) * np.dtype(dtype).itemsize) | [
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7,702 | adamrehn/slidingwindow | slidingwindow/ArrayUtils.py | arrayFactory | def arrayFactory(shape, dtype=float):
"""
Creates a new ndarray of the specified shape and datatype, storing
it in memory if there is sufficient available space or else using
a memory-mapped temporary file to provide the underlying buffer.
"""
# Determine the number of bytes required to store the array
requiredBytes = _requiredSize(shape, dtype)
# Determine if there is sufficient available memory
vmem = psutil.virtual_memory()
if vmem.available > requiredBytes:
return np.ndarray(shape=shape, dtype=dtype)
else:
return TempfileBackedArray(shape=shape, dtype=dtype) | python | def arrayFactory(shape, dtype=float):
"""
Creates a new ndarray of the specified shape and datatype, storing
it in memory if there is sufficient available space or else using
a memory-mapped temporary file to provide the underlying buffer.
"""
# Determine the number of bytes required to store the array
requiredBytes = _requiredSize(shape, dtype)
# Determine if there is sufficient available memory
vmem = psutil.virtual_memory()
if vmem.available > requiredBytes:
return np.ndarray(shape=shape, dtype=dtype)
else:
return TempfileBackedArray(shape=shape, dtype=dtype) | [
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7,703 | adamrehn/slidingwindow | slidingwindow/ArrayUtils.py | arrayCast | def arrayCast(source, dtype):
"""
Casts a NumPy array to the specified datatype, storing the copy
in memory if there is sufficient available space or else using a
memory-mapped temporary file to provide the underlying buffer.
"""
# Determine the number of bytes required to store the array
requiredBytes = _requiredSize(source.shape, dtype)
# Determine if there is sufficient available memory
vmem = psutil.virtual_memory()
if vmem.available > requiredBytes:
return source.astype(dtype, subok=False)
else:
dest = arrayFactory(source.shape, dtype)
np.copyto(dest, source, casting='unsafe')
return dest | python | def arrayCast(source, dtype):
"""
Casts a NumPy array to the specified datatype, storing the copy
in memory if there is sufficient available space or else using a
memory-mapped temporary file to provide the underlying buffer.
"""
# Determine the number of bytes required to store the array
requiredBytes = _requiredSize(source.shape, dtype)
# Determine if there is sufficient available memory
vmem = psutil.virtual_memory()
if vmem.available > requiredBytes:
return source.astype(dtype, subok=False)
else:
dest = arrayFactory(source.shape, dtype)
np.copyto(dest, source, casting='unsafe')
return dest | [
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7,704 | adamrehn/slidingwindow | slidingwindow/ArrayUtils.py | determineMaxWindowSize | def determineMaxWindowSize(dtype, limit=None):
"""
Determines the largest square window size that can be used, based on
the specified datatype and amount of currently available system memory.
If `limit` is specified, then this value will be returned in the event
that it is smaller than the maximum computed size.
"""
vmem = psutil.virtual_memory()
maxSize = math.floor(math.sqrt(vmem.available / np.dtype(dtype).itemsize))
if limit is None or limit >= maxSize:
return maxSize
else:
return limit | python | def determineMaxWindowSize(dtype, limit=None):
"""
Determines the largest square window size that can be used, based on
the specified datatype and amount of currently available system memory.
If `limit` is specified, then this value will be returned in the event
that it is smaller than the maximum computed size.
"""
vmem = psutil.virtual_memory()
maxSize = math.floor(math.sqrt(vmem.available / np.dtype(dtype).itemsize))
if limit is None or limit >= maxSize:
return maxSize
else:
return limit | [
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7,705 | kwikteam/phy | phy/cluster/views/base.py | ManualClusteringView.set_state | def set_state(self, state):
"""Set the view state.
The passed object is the persisted `self.state` bunch.
May be overriden.
"""
for k, v in state.items():
setattr(self, k, v) | python | def set_state(self, state):
"""Set the view state.
The passed object is the persisted `self.state` bunch.
May be overriden.
"""
for k, v in state.items():
setattr(self, k, v) | [
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7,706 | kwikteam/phy | phy/utils/_misc.py | _fullname | def _fullname(o):
"""Return the fully-qualified name of a function."""
return o.__module__ + "." + o.__name__ if o.__module__ else o.__name__ | python | def _fullname(o):
"""Return the fully-qualified name of a function."""
return o.__module__ + "." + o.__name__ if o.__module__ else o.__name__ | [
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7,707 | kwikteam/phy | phy/cluster/views/feature.py | FeatureView._get_axis_label | def _get_axis_label(self, dim):
"""Return the channel id from a dimension, if applicable."""
if u(dim[:-1]).isdecimal():
n = len(self.channel_ids)
return str(self.channel_ids[int(dim[:-1]) % n]) + dim[-1]
else:
return dim | python | def _get_axis_label(self, dim):
"""Return the channel id from a dimension, if applicable."""
if u(dim[:-1]).isdecimal():
n = len(self.channel_ids)
return str(self.channel_ids[int(dim[:-1]) % n]) + dim[-1]
else:
return dim | [
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7,708 | kwikteam/phy | phy/cluster/views/feature.py | FeatureView._get_axis_data | def _get_axis_data(self, bunch, dim, cluster_id=None, load_all=None):
"""Extract the points from the data on a given dimension.
bunch is returned by the features() function.
dim is the string specifying the dimensions to extract for the data.
"""
if dim in self.attributes:
return self.attributes[dim](cluster_id, load_all=load_all)
masks = bunch.get('masks', None)
assert dim not in self.attributes # This is called only on PC data.
s = 'ABCDEFGHIJ'
# Channel relative index.
c_rel = int(dim[:-1])
# Get the channel_id from the currently-selected channels.
channel_id = self.channel_ids[c_rel % len(self.channel_ids)]
# Skup the plot if the channel id is not displayed.
if channel_id not in bunch.channel_ids: # pragma: no cover
return None
# Get the column index of the current channel in data.
c = list(bunch.channel_ids).index(channel_id)
# Principal component: A=0, B=1, etc.
d = s.index(dim[-1])
if masks is not None:
masks = masks[:, c]
return Bunch(data=bunch.data[:, c, d],
masks=masks,
) | python | def _get_axis_data(self, bunch, dim, cluster_id=None, load_all=None):
"""Extract the points from the data on a given dimension.
bunch is returned by the features() function.
dim is the string specifying the dimensions to extract for the data.
"""
if dim in self.attributes:
return self.attributes[dim](cluster_id, load_all=load_all)
masks = bunch.get('masks', None)
assert dim not in self.attributes # This is called only on PC data.
s = 'ABCDEFGHIJ'
# Channel relative index.
c_rel = int(dim[:-1])
# Get the channel_id from the currently-selected channels.
channel_id = self.channel_ids[c_rel % len(self.channel_ids)]
# Skup the plot if the channel id is not displayed.
if channel_id not in bunch.channel_ids: # pragma: no cover
return None
# Get the column index of the current channel in data.
c = list(bunch.channel_ids).index(channel_id)
# Principal component: A=0, B=1, etc.
d = s.index(dim[-1])
if masks is not None:
masks = masks[:, c]
return Bunch(data=bunch.data[:, c, d],
masks=masks,
) | [
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7,709 | kwikteam/phy | phy/cluster/views/feature.py | FeatureView._plot_labels | def _plot_labels(self):
"""Plot feature labels along left and bottom edge of subplots"""
# iterate simultaneously over kth row in left column and
# kth column in bottom row:
br = self.n_cols - 1 # bottom row
for k in range(0, self.n_cols):
dim_x, _ = self.grid_dim[0][k].split(',')
_, dim_y = self.grid_dim[k][br].split(',')
# Get the channel ids corresponding to the relative channel indices
# specified in the dimensions. Channel 0 corresponds to the first
# best channel for the selected cluster, and so on.
dim_x = self._get_axis_label(dim_x)
dim_y = self._get_axis_label(dim_y)
# Left edge of left column of subplots.
self[k, 0].text(pos=[-1., 0.],
text=dim_y,
anchor=[-1.03, 0.],
data_bounds=None,
)
# Bottom edge of bottom row of subplots.
self[br, k].text(pos=[0., -1.],
text=dim_x,
anchor=[0., -1.04],
data_bounds=None,
) | python | def _plot_labels(self):
"""Plot feature labels along left and bottom edge of subplots"""
# iterate simultaneously over kth row in left column and
# kth column in bottom row:
br = self.n_cols - 1 # bottom row
for k in range(0, self.n_cols):
dim_x, _ = self.grid_dim[0][k].split(',')
_, dim_y = self.grid_dim[k][br].split(',')
# Get the channel ids corresponding to the relative channel indices
# specified in the dimensions. Channel 0 corresponds to the first
# best channel for the selected cluster, and so on.
dim_x = self._get_axis_label(dim_x)
dim_y = self._get_axis_label(dim_y)
# Left edge of left column of subplots.
self[k, 0].text(pos=[-1., 0.],
text=dim_y,
anchor=[-1.03, 0.],
data_bounds=None,
)
# Bottom edge of bottom row of subplots.
self[br, k].text(pos=[0., -1.],
text=dim_x,
anchor=[0., -1.04],
data_bounds=None,
) | [
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7,710 | kwikteam/phy | phy/cluster/views/feature.py | FeatureView.on_channel_click | def on_channel_click(self, channel_id=None, key=None, button=None):
"""Respond to the click on a channel."""
channels = self.channel_ids
if channels is None:
return
if len(channels) == 1:
self.on_select()
return
assert len(channels) >= 2
# Get the axis from the pressed button (1, 2, etc.)
# axis = 'x' if button == 1 else 'y'
d = 0 if button == 1 else 1
# Change the first or second best channel.
old = channels[d]
# Avoid updating the view if the channel doesn't change.
if channel_id == old:
return
channels[d] = channel_id
# Ensure that the first two channels are different.
if channels[1 - d] == channel_id:
channels[1 - d] = old
assert channels[0] != channels[1]
# Remove duplicate channels.
self.channel_ids = _uniq(channels)
logger.debug("Choose channels %d and %d in feature view.",
*channels[:2])
# Fix the channels temporarily.
self.on_select(fixed_channels=True) | python | def on_channel_click(self, channel_id=None, key=None, button=None):
"""Respond to the click on a channel."""
channels = self.channel_ids
if channels is None:
return
if len(channels) == 1:
self.on_select()
return
assert len(channels) >= 2
# Get the axis from the pressed button (1, 2, etc.)
# axis = 'x' if button == 1 else 'y'
d = 0 if button == 1 else 1
# Change the first or second best channel.
old = channels[d]
# Avoid updating the view if the channel doesn't change.
if channel_id == old:
return
channels[d] = channel_id
# Ensure that the first two channels are different.
if channels[1 - d] == channel_id:
channels[1 - d] = old
assert channels[0] != channels[1]
# Remove duplicate channels.
self.channel_ids = _uniq(channels)
logger.debug("Choose channels %d and %d in feature view.",
*channels[:2])
# Fix the channels temporarily.
self.on_select(fixed_channels=True) | [
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7,711 | kwikteam/phy | phy/cluster/views/feature.py | FeatureView.on_request_split | def on_request_split(self):
"""Return the spikes enclosed by the lasso."""
if (self.lasso.count < 3 or
not len(self.cluster_ids)): # pragma: no cover
return np.array([], dtype=np.int64)
assert len(self.channel_ids)
# Get the dimensions of the lassoed subplot.
i, j = self.lasso.box
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
# Get all points from all clusters.
pos = []
spike_ids = []
for cluster_id in self.cluster_ids:
# Load all spikes.
bunch = self.features(cluster_id,
channel_ids=self.channel_ids,
load_all=True)
px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id,
load_all=True)
py = self._get_axis_data(bunch, dim_y, cluster_id=cluster_id,
load_all=True)
points = np.c_[px.data, py.data]
# Normalize the points.
xmin, xmax = self._get_axis_bounds(dim_x, px)
ymin, ymax = self._get_axis_bounds(dim_y, py)
r = Range((xmin, ymin, xmax, ymax))
points = r.apply(points)
pos.append(points)
spike_ids.append(bunch.spike_ids)
pos = np.vstack(pos)
spike_ids = np.concatenate(spike_ids)
# Find lassoed spikes.
ind = self.lasso.in_polygon(pos)
self.lasso.clear()
return np.unique(spike_ids[ind]) | python | def on_request_split(self):
"""Return the spikes enclosed by the lasso."""
if (self.lasso.count < 3 or
not len(self.cluster_ids)): # pragma: no cover
return np.array([], dtype=np.int64)
assert len(self.channel_ids)
# Get the dimensions of the lassoed subplot.
i, j = self.lasso.box
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
# Get all points from all clusters.
pos = []
spike_ids = []
for cluster_id in self.cluster_ids:
# Load all spikes.
bunch = self.features(cluster_id,
channel_ids=self.channel_ids,
load_all=True)
px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id,
load_all=True)
py = self._get_axis_data(bunch, dim_y, cluster_id=cluster_id,
load_all=True)
points = np.c_[px.data, py.data]
# Normalize the points.
xmin, xmax = self._get_axis_bounds(dim_x, px)
ymin, ymax = self._get_axis_bounds(dim_y, py)
r = Range((xmin, ymin, xmax, ymax))
points = r.apply(points)
pos.append(points)
spike_ids.append(bunch.spike_ids)
pos = np.vstack(pos)
spike_ids = np.concatenate(spike_ids)
# Find lassoed spikes.
ind = self.lasso.in_polygon(pos)
self.lasso.clear()
return np.unique(spike_ids[ind]) | [
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7,712 | kwikteam/phy | phy/plot/interact.py | Boxed.get_closest_box | def get_closest_box(self, pos):
"""Get the box closest to some position."""
pos = np.atleast_2d(pos)
d = np.sum((np.array(self.box_pos) - pos) ** 2, axis=1)
idx = np.argmin(d)
return idx | python | def get_closest_box(self, pos):
"""Get the box closest to some position."""
pos = np.atleast_2d(pos)
d = np.sum((np.array(self.box_pos) - pos) ** 2, axis=1)
idx = np.argmin(d)
return idx | [
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7,713 | kwikteam/phy | phy/plot/interact.py | Boxed.update_boxes | def update_boxes(self, box_pos, box_size):
"""Set the box bounds from specified box positions and sizes."""
assert box_pos.shape == (self.n_boxes, 2)
assert len(box_size) == 2
self.box_bounds = _get_boxes(box_pos,
size=box_size,
keep_aspect_ratio=self.keep_aspect_ratio,
) | python | def update_boxes(self, box_pos, box_size):
"""Set the box bounds from specified box positions and sizes."""
assert box_pos.shape == (self.n_boxes, 2)
assert len(box_size) == 2
self.box_bounds = _get_boxes(box_pos,
size=box_size,
keep_aspect_ratio=self.keep_aspect_ratio,
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7,714 | kwikteam/phy | phy/gui/qt.py | require_qt | def require_qt(func):
"""Specify that a function requires a Qt application.
Use this decorator to specify that a function needs a running
Qt application before it can run. An error is raised if that is not
the case.
"""
@wraps(func)
def wrapped(*args, **kwargs):
if not QApplication.instance(): # pragma: no cover
raise RuntimeError("A Qt application must be created.")
return func(*args, **kwargs)
return wrapped | python | def require_qt(func):
"""Specify that a function requires a Qt application.
Use this decorator to specify that a function needs a running
Qt application before it can run. An error is raised if that is not
the case.
"""
@wraps(func)
def wrapped(*args, **kwargs):
if not QApplication.instance(): # pragma: no cover
raise RuntimeError("A Qt application must be created.")
return func(*args, **kwargs)
return wrapped | [
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7,715 | kwikteam/phy | phy/gui/qt.py | create_app | def create_app():
"""Create a Qt application."""
global QT_APP
QT_APP = QApplication.instance()
if QT_APP is None: # pragma: no cover
QT_APP = QApplication(sys.argv)
return QT_APP | python | def create_app():
"""Create a Qt application."""
global QT_APP
QT_APP = QApplication.instance()
if QT_APP is None: # pragma: no cover
QT_APP = QApplication(sys.argv)
return QT_APP | [
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7,716 | kwikteam/phy | phy/gui/qt.py | AsyncCaller.set | def set(self, f):
"""Call a function after a delay, unless another function is set
in the meantime."""
self.stop()
self._create_timer(f)
self.start() | python | def set(self, f):
"""Call a function after a delay, unless another function is set
in the meantime."""
self.stop()
self._create_timer(f)
self.start() | [
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7,717 | kwikteam/phy | phy/gui/qt.py | AsyncCaller.stop | def stop(self):
"""Stop the current timer if there is one and cancel the async call."""
if self._timer:
self._timer.stop()
self._timer.deleteLater() | python | def stop(self):
"""Stop the current timer if there is one and cancel the async call."""
if self._timer:
self._timer.stop()
self._timer.deleteLater() | [
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7,718 | kwikteam/phy | phy/gui/actions.py | _wrap_callback_args | def _wrap_callback_args(f, docstring=None): # pragma: no cover
"""Display a Qt dialog when a function has arguments.
The user can write function arguments as if it was a snippet.
"""
def wrapped(checked, *args):
if args:
return f(*args)
if isinstance(f, partial):
argspec = inspect.getargspec(f.func)
else:
argspec = inspect.getargspec(f)
f_args = argspec.args
if 'self' in f_args:
f_args.remove('self')
# Remove arguments with defaults from the list.
if len(argspec.defaults or ()):
f_args = f_args[:-len(argspec.defaults)]
# Remove arguments supplied in a partial.
if isinstance(f, partial):
f_args = f_args[len(f.args):]
f_args = [arg for arg in f_args if arg not in f.keywords]
# If there are no remaining args, we can just run the fu nction.
if not f_args:
return f()
# There are args, need to display the dialog.
# Extract Example: `...` in the docstring to put a predefined text
# in the input dialog.
r = re.search('Example: `([^`]+)`', docstring)
docstring_ = docstring[:r.start()].strip() if r else docstring
text = r.group(1) if r else None
s, ok = _input_dialog(getattr(f, '__name__', 'action'),
docstring_, text)
if not ok or not s:
return
# Parse user-supplied arguments and call the function.
args = _parse_snippet(s)
return f(*args)
return wrapped | python | def _wrap_callback_args(f, docstring=None): # pragma: no cover
"""Display a Qt dialog when a function has arguments.
The user can write function arguments as if it was a snippet.
"""
def wrapped(checked, *args):
if args:
return f(*args)
if isinstance(f, partial):
argspec = inspect.getargspec(f.func)
else:
argspec = inspect.getargspec(f)
f_args = argspec.args
if 'self' in f_args:
f_args.remove('self')
# Remove arguments with defaults from the list.
if len(argspec.defaults or ()):
f_args = f_args[:-len(argspec.defaults)]
# Remove arguments supplied in a partial.
if isinstance(f, partial):
f_args = f_args[len(f.args):]
f_args = [arg for arg in f_args if arg not in f.keywords]
# If there are no remaining args, we can just run the fu nction.
if not f_args:
return f()
# There are args, need to display the dialog.
# Extract Example: `...` in the docstring to put a predefined text
# in the input dialog.
r = re.search('Example: `([^`]+)`', docstring)
docstring_ = docstring[:r.start()].strip() if r else docstring
text = r.group(1) if r else None
s, ok = _input_dialog(getattr(f, '__name__', 'action'),
docstring_, text)
if not ok or not s:
return
# Parse user-supplied arguments and call the function.
args = _parse_snippet(s)
return f(*args)
return wrapped | [
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7,719 | kwikteam/phy | phy/gui/actions.py | _get_shortcut_string | def _get_shortcut_string(shortcut):
"""Return a string representation of a shortcut."""
if shortcut is None:
return ''
if isinstance(shortcut, (tuple, list)):
return ', '.join([_get_shortcut_string(s) for s in shortcut])
if isinstance(shortcut, string_types):
if hasattr(QKeySequence, shortcut):
shortcut = QKeySequence(getattr(QKeySequence, shortcut))
else:
return shortcut.lower()
assert isinstance(shortcut, QKeySequence)
s = shortcut.toString() or ''
return str(s).lower() | python | def _get_shortcut_string(shortcut):
"""Return a string representation of a shortcut."""
if shortcut is None:
return ''
if isinstance(shortcut, (tuple, list)):
return ', '.join([_get_shortcut_string(s) for s in shortcut])
if isinstance(shortcut, string_types):
if hasattr(QKeySequence, shortcut):
shortcut = QKeySequence(getattr(QKeySequence, shortcut))
else:
return shortcut.lower()
assert isinstance(shortcut, QKeySequence)
s = shortcut.toString() or ''
return str(s).lower() | [
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7,720 | kwikteam/phy | phy/gui/actions.py | _get_qkeysequence | def _get_qkeysequence(shortcut):
"""Return a QKeySequence or list of QKeySequence from a shortcut string."""
if shortcut is None:
return []
if isinstance(shortcut, (tuple, list)):
return [_get_qkeysequence(s) for s in shortcut]
assert isinstance(shortcut, string_types)
if hasattr(QKeySequence, shortcut):
return QKeySequence(getattr(QKeySequence, shortcut))
sequence = QKeySequence.fromString(shortcut)
assert not sequence.isEmpty()
return sequence | python | def _get_qkeysequence(shortcut):
"""Return a QKeySequence or list of QKeySequence from a shortcut string."""
if shortcut is None:
return []
if isinstance(shortcut, (tuple, list)):
return [_get_qkeysequence(s) for s in shortcut]
assert isinstance(shortcut, string_types)
if hasattr(QKeySequence, shortcut):
return QKeySequence(getattr(QKeySequence, shortcut))
sequence = QKeySequence.fromString(shortcut)
assert not sequence.isEmpty()
return sequence | [
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7,721 | kwikteam/phy | phy/gui/actions.py | _show_shortcuts | def _show_shortcuts(shortcuts, name=None):
"""Display shortcuts."""
name = name or ''
print('')
if name:
name = ' for ' + name
print('Keyboard shortcuts' + name)
for name in sorted(shortcuts):
shortcut = _get_shortcut_string(shortcuts[name])
if not name.startswith('_'):
print('- {0:<40}: {1:s}'.format(name, shortcut)) | python | def _show_shortcuts(shortcuts, name=None):
"""Display shortcuts."""
name = name or ''
print('')
if name:
name = ' for ' + name
print('Keyboard shortcuts' + name)
for name in sorted(shortcuts):
shortcut = _get_shortcut_string(shortcuts[name])
if not name.startswith('_'):
print('- {0:<40}: {1:s}'.format(name, shortcut)) | [
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7,722 | kwikteam/phy | phy/gui/actions.py | Actions.add | def add(self, callback=None, name=None, shortcut=None, alias=None,
docstring=None, menu=None, verbose=True):
"""Add an action with a keyboard shortcut."""
if callback is None:
# Allow to use either add(func) or @add or @add(...).
return partial(self.add, name=name, shortcut=shortcut,
alias=alias, menu=menu)
assert callback
# Get the name from the callback function if needed.
name = name or callback.__name__
alias = alias or _alias(name)
name = name.replace('&', '')
shortcut = shortcut or self._default_shortcuts.get(name, None)
# Skip existing action.
if name in self._actions_dict:
return
# Set the status tip from the function's docstring.
docstring = docstring or callback.__doc__ or name
docstring = re.sub(r'[ \t\r\f\v]{2,}', ' ', docstring.strip())
# Create and register the action.
action = _create_qaction(self.gui, name, callback,
shortcut,
docstring=docstring,
alias=alias,
)
action_obj = Bunch(qaction=action, name=name, alias=alias,
shortcut=shortcut, callback=callback, menu=menu)
if verbose and not name.startswith('_'):
logger.log(5, "Add action `%s` (%s).", name,
_get_shortcut_string(action.shortcut()))
self.gui.addAction(action)
# Add the action to the menu.
menu = menu or self.menu
# Do not show private actions in the menu.
if menu and not name.startswith('_'):
self.gui.get_menu(menu).addAction(action)
self._actions_dict[name] = action_obj
# Register the alias -> name mapping.
self._aliases[alias] = name
# Set the callback method.
if callback:
setattr(self, name, callback) | python | def add(self, callback=None, name=None, shortcut=None, alias=None,
docstring=None, menu=None, verbose=True):
"""Add an action with a keyboard shortcut."""
if callback is None:
# Allow to use either add(func) or @add or @add(...).
return partial(self.add, name=name, shortcut=shortcut,
alias=alias, menu=menu)
assert callback
# Get the name from the callback function if needed.
name = name or callback.__name__
alias = alias or _alias(name)
name = name.replace('&', '')
shortcut = shortcut or self._default_shortcuts.get(name, None)
# Skip existing action.
if name in self._actions_dict:
return
# Set the status tip from the function's docstring.
docstring = docstring or callback.__doc__ or name
docstring = re.sub(r'[ \t\r\f\v]{2,}', ' ', docstring.strip())
# Create and register the action.
action = _create_qaction(self.gui, name, callback,
shortcut,
docstring=docstring,
alias=alias,
)
action_obj = Bunch(qaction=action, name=name, alias=alias,
shortcut=shortcut, callback=callback, menu=menu)
if verbose and not name.startswith('_'):
logger.log(5, "Add action `%s` (%s).", name,
_get_shortcut_string(action.shortcut()))
self.gui.addAction(action)
# Add the action to the menu.
menu = menu or self.menu
# Do not show private actions in the menu.
if menu and not name.startswith('_'):
self.gui.get_menu(menu).addAction(action)
self._actions_dict[name] = action_obj
# Register the alias -> name mapping.
self._aliases[alias] = name
# Set the callback method.
if callback:
setattr(self, name, callback) | [
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7,723 | kwikteam/phy | phy/gui/actions.py | Actions.separator | def separator(self, menu=None):
"""Add a separator"""
self.gui.get_menu(menu or self.menu).addSeparator() | python | def separator(self, menu=None):
"""Add a separator"""
self.gui.get_menu(menu or self.menu).addSeparator() | [
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7,724 | kwikteam/phy | phy/gui/actions.py | Actions.disable | def disable(self, name=None):
"""Disable one or all actions."""
if name is None:
for name in self._actions_dict:
self.disable(name)
return
self._actions_dict[name].qaction.setEnabled(False) | python | def disable(self, name=None):
"""Disable one or all actions."""
if name is None:
for name in self._actions_dict:
self.disable(name)
return
self._actions_dict[name].qaction.setEnabled(False) | [
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7,725 | kwikteam/phy | phy/gui/actions.py | Actions.enable | def enable(self, name=None):
"""Enable one or all actions."""
if name is None:
for name in self._actions_dict:
self.enable(name)
return
self._actions_dict[name].qaction.setEnabled(True) | python | def enable(self, name=None):
"""Enable one or all actions."""
if name is None:
for name in self._actions_dict:
self.enable(name)
return
self._actions_dict[name].qaction.setEnabled(True) | [
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7,726 | kwikteam/phy | phy/gui/actions.py | Actions.run | def run(self, name, *args):
"""Run an action as specified by its name."""
assert isinstance(name, string_types)
# Resolve the alias if it is an alias.
name = self._aliases.get(name, name)
# Get the action.
action = self._actions_dict.get(name, None)
if not action:
raise ValueError("Action `{}` doesn't exist.".format(name))
if not name.startswith('_'):
logger.debug("Execute action `%s`.", name)
return action.callback(*args) | python | def run(self, name, *args):
"""Run an action as specified by its name."""
assert isinstance(name, string_types)
# Resolve the alias if it is an alias.
name = self._aliases.get(name, name)
# Get the action.
action = self._actions_dict.get(name, None)
if not action:
raise ValueError("Action `{}` doesn't exist.".format(name))
if not name.startswith('_'):
logger.debug("Execute action `%s`.", name)
return action.callback(*args) | [
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7,727 | kwikteam/phy | phy/gui/actions.py | Actions.remove | def remove(self, name):
"""Remove an action."""
self.gui.removeAction(self._actions_dict[name].qaction)
del self._actions_dict[name]
delattr(self, name) | python | def remove(self, name):
"""Remove an action."""
self.gui.removeAction(self._actions_dict[name].qaction)
del self._actions_dict[name]
delattr(self, name) | [
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7,728 | kwikteam/phy | phy/gui/actions.py | Actions.remove_all | def remove_all(self):
"""Remove all actions."""
names = sorted(self._actions_dict.keys())
for name in names:
self.remove(name) | python | def remove_all(self):
"""Remove all actions."""
names = sorted(self._actions_dict.keys())
for name in names:
self.remove(name) | [
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7,729 | kwikteam/phy | phy/gui/actions.py | Actions.shortcuts | def shortcuts(self):
"""A dictionary of action shortcuts."""
return {name: action.shortcut
for name, action in self._actions_dict.items()} | python | def shortcuts(self):
"""A dictionary of action shortcuts."""
return {name: action.shortcut
for name, action in self._actions_dict.items()} | [
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7,730 | kwikteam/phy | phy/gui/actions.py | Actions.show_shortcuts | def show_shortcuts(self):
"""Print all shortcuts."""
gui_name = self.gui.name
actions_name = self.name
name = ('{} - {}'.format(gui_name, actions_name)
if actions_name else gui_name)
_show_shortcuts(self.shortcuts, name) | python | def show_shortcuts(self):
"""Print all shortcuts."""
gui_name = self.gui.name
actions_name = self.name
name = ('{} - {}'.format(gui_name, actions_name)
if actions_name else gui_name)
_show_shortcuts(self.shortcuts, name) | [
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7,731 | kwikteam/phy | phy/gui/actions.py | Snippets.command | def command(self):
"""This is used to write a snippet message in the status bar.
A cursor is appended at the end.
"""
msg = self.gui.status_message
n = len(msg)
n_cur = len(self.cursor)
return msg[:n - n_cur] | python | def command(self):
"""This is used to write a snippet message in the status bar.
A cursor is appended at the end.
"""
msg = self.gui.status_message
n = len(msg)
n_cur = len(self.cursor)
return msg[:n - n_cur] | [
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7,732 | kwikteam/phy | phy/gui/actions.py | Snippets._backspace | def _backspace(self):
"""Erase the last character in the snippet command."""
if self.command == ':':
return
logger.log(5, "Snippet keystroke `Backspace`.")
self.command = self.command[:-1] | python | def _backspace(self):
"""Erase the last character in the snippet command."""
if self.command == ':':
return
logger.log(5, "Snippet keystroke `Backspace`.")
self.command = self.command[:-1] | [
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7,733 | kwikteam/phy | phy/gui/actions.py | Snippets._enter | def _enter(self):
"""Disable the snippet mode and execute the command."""
command = self.command
logger.log(5, "Snippet keystroke `Enter`.")
# NOTE: we need to set back the actions (mode_off) before running
# the command.
self.mode_off()
self.run(command) | python | def _enter(self):
"""Disable the snippet mode and execute the command."""
command = self.command
logger.log(5, "Snippet keystroke `Enter`.")
# NOTE: we need to set back the actions (mode_off) before running
# the command.
self.mode_off()
self.run(command) | [
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7,734 | kwikteam/phy | phy/gui/actions.py | Snippets._create_snippet_actions | def _create_snippet_actions(self):
"""Add mock Qt actions for snippet keystrokes.
Used to enable snippet mode.
"""
# One action per allowed character.
for i, char in enumerate(self._snippet_chars):
def _make_func(char):
def callback():
logger.log(5, "Snippet keystroke `%s`.", char)
self.command += char
return callback
self.actions.add(name='_snippet_{}'.format(i),
shortcut=char,
callback=_make_func(char))
self.actions.add(name='_snippet_backspace',
shortcut='backspace',
callback=self._backspace)
self.actions.add(name='_snippet_activate',
shortcut=('enter', 'return'),
callback=self._enter)
self.actions.add(name='_snippet_disable',
shortcut='escape',
callback=self.mode_off) | python | def _create_snippet_actions(self):
"""Add mock Qt actions for snippet keystrokes.
Used to enable snippet mode.
"""
# One action per allowed character.
for i, char in enumerate(self._snippet_chars):
def _make_func(char):
def callback():
logger.log(5, "Snippet keystroke `%s`.", char)
self.command += char
return callback
self.actions.add(name='_snippet_{}'.format(i),
shortcut=char,
callback=_make_func(char))
self.actions.add(name='_snippet_backspace',
shortcut='backspace',
callback=self._backspace)
self.actions.add(name='_snippet_activate',
shortcut=('enter', 'return'),
callback=self._enter)
self.actions.add(name='_snippet_disable',
shortcut='escape',
callback=self.mode_off) | [
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7,735 | kwikteam/phy | phy/gui/actions.py | Snippets.run | def run(self, snippet):
"""Executes a snippet command.
May be overridden.
"""
assert snippet[0] == ':'
snippet = snippet[1:]
snippet_args = _parse_snippet(snippet)
name = snippet_args[0]
logger.info("Processing snippet `%s`.", snippet)
try:
# Try to run the snippet on all attached Actions instances.
for actions in self.gui.actions:
try:
actions.run(name, *snippet_args[1:])
return
except ValueError:
# This Actions instance doesn't contain the requested
# snippet, trying the next attached Actions instance.
pass
logger.warn("Couldn't find action `%s`.", name)
except Exception as e:
logger.warn("Error when executing snippet: \"%s\".", str(e))
logger.debug(''.join(traceback.format_exception(*sys.exc_info()))) | python | def run(self, snippet):
"""Executes a snippet command.
May be overridden.
"""
assert snippet[0] == ':'
snippet = snippet[1:]
snippet_args = _parse_snippet(snippet)
name = snippet_args[0]
logger.info("Processing snippet `%s`.", snippet)
try:
# Try to run the snippet on all attached Actions instances.
for actions in self.gui.actions:
try:
actions.run(name, *snippet_args[1:])
return
except ValueError:
# This Actions instance doesn't contain the requested
# snippet, trying the next attached Actions instance.
pass
logger.warn("Couldn't find action `%s`.", name)
except Exception as e:
logger.warn("Error when executing snippet: \"%s\".", str(e))
logger.debug(''.join(traceback.format_exception(*sys.exc_info()))) | [
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7,736 | kwikteam/phy | phy/traces/waveform.py | _before_after | def _before_after(n_samples):
"""Get the number of samples before and after."""
if not isinstance(n_samples, (tuple, list)):
before = n_samples // 2
after = n_samples - before
else:
assert len(n_samples) == 2
before, after = n_samples
n_samples = before + after
assert before >= 0
assert after >= 0
assert before + after == n_samples
return before, after | python | def _before_after(n_samples):
"""Get the number of samples before and after."""
if not isinstance(n_samples, (tuple, list)):
before = n_samples // 2
after = n_samples - before
else:
assert len(n_samples) == 2
before, after = n_samples
n_samples = before + after
assert before >= 0
assert after >= 0
assert before + after == n_samples
return before, after | [
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7,737 | kwikteam/phy | phy/traces/waveform.py | _slice | def _slice(index, n_samples, margin=None):
"""Return a waveform slice."""
if margin is None:
margin = (0, 0)
assert isinstance(n_samples, (tuple, list))
assert len(n_samples) == 2
before, after = n_samples
assert isinstance(margin, (tuple, list))
assert len(margin) == 2
margin_before, margin_after = margin
before += margin_before
after += margin_after
index = int(index)
before = int(before)
after = int(after)
return slice(max(0, index - before), index + after, None) | python | def _slice(index, n_samples, margin=None):
"""Return a waveform slice."""
if margin is None:
margin = (0, 0)
assert isinstance(n_samples, (tuple, list))
assert len(n_samples) == 2
before, after = n_samples
assert isinstance(margin, (tuple, list))
assert len(margin) == 2
margin_before, margin_after = margin
before += margin_before
after += margin_after
index = int(index)
before = int(before)
after = int(after)
return slice(max(0, index - before), index + after, None) | [
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7,738 | kwikteam/phy | phy/traces/waveform.py | WaveformLoader._load_at | def _load_at(self, time, channels=None):
"""Load a waveform at a given time."""
if channels is None:
channels = slice(None, None, None)
time = int(time)
time_o = time
ns = self.n_samples_trace
if not (0 <= time_o < ns):
raise ValueError("Invalid time {0:d}/{1:d}.".format(time_o, ns))
slice_extract = _slice(time_o,
self.n_samples_before_after,
self._filter_margin)
extract = self._traces[slice_extract][:, channels].astype(np.float32)
# Pad the extracted chunk if needed.
if slice_extract.start <= 0:
extract = _pad(extract, self._n_samples_extract, 'left')
elif slice_extract.stop >= ns - 1:
extract = _pad(extract, self._n_samples_extract, 'right')
assert extract.shape[0] == self._n_samples_extract
return extract | python | def _load_at(self, time, channels=None):
"""Load a waveform at a given time."""
if channels is None:
channels = slice(None, None, None)
time = int(time)
time_o = time
ns = self.n_samples_trace
if not (0 <= time_o < ns):
raise ValueError("Invalid time {0:d}/{1:d}.".format(time_o, ns))
slice_extract = _slice(time_o,
self.n_samples_before_after,
self._filter_margin)
extract = self._traces[slice_extract][:, channels].astype(np.float32)
# Pad the extracted chunk if needed.
if slice_extract.start <= 0:
extract = _pad(extract, self._n_samples_extract, 'left')
elif slice_extract.stop >= ns - 1:
extract = _pad(extract, self._n_samples_extract, 'right')
assert extract.shape[0] == self._n_samples_extract
return extract | [
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7,739 | kwikteam/phy | phy/traces/waveform.py | WaveformLoader.get | def get(self, spike_ids, channels=None):
"""Load the waveforms of the specified spikes."""
if isinstance(spike_ids, slice):
spike_ids = _range_from_slice(spike_ids,
start=0,
stop=self.n_spikes,
)
if not hasattr(spike_ids, '__len__'):
spike_ids = [spike_ids]
if channels is None:
channels = slice(None, None, None)
nc = self.n_channels
else:
channels = np.asarray(channels, dtype=np.int32)
assert np.all(channels < self.n_channels)
nc = len(channels)
# Ensure a list of time samples are being requested.
spike_ids = _as_array(spike_ids)
n_spikes = len(spike_ids)
# Initialize the array.
# NOTE: last dimension is time to simplify things.
shape = (n_spikes, nc, self._n_samples_extract)
waveforms = np.zeros(shape, dtype=np.float32)
# No traces: return null arrays.
if self.n_samples_trace == 0:
return np.transpose(waveforms, (0, 2, 1))
# Load all spikes.
for i, spike_id in enumerate(spike_ids):
assert 0 <= spike_id < self.n_spikes
time = self._spike_samples[spike_id]
# Extract the waveforms on the unmasked channels.
try:
w = self._load_at(time, channels)
except ValueError as e: # pragma: no cover
logger.warn("Error while loading waveform: %s", str(e))
continue
assert w.shape == (self._n_samples_extract, nc)
waveforms[i, :, :] = w.T
# Filter the waveforms.
waveforms_f = waveforms.reshape((-1, self._n_samples_extract))
# Only filter the non-zero waveforms.
unmasked = waveforms_f.max(axis=1) != 0
waveforms_f[unmasked] = self._filter(waveforms_f[unmasked], axis=1)
waveforms_f = waveforms_f.reshape((n_spikes, nc,
self._n_samples_extract))
# Remove the margin.
margin_before, margin_after = self._filter_margin
if margin_after > 0:
assert margin_before >= 0
waveforms_f = waveforms_f[:, :, margin_before:-margin_after]
assert waveforms_f.shape == (n_spikes,
nc,
self.n_samples_waveforms,
)
# NOTE: we transpose before returning the array.
return np.transpose(waveforms_f, (0, 2, 1)) | python | def get(self, spike_ids, channels=None):
"""Load the waveforms of the specified spikes."""
if isinstance(spike_ids, slice):
spike_ids = _range_from_slice(spike_ids,
start=0,
stop=self.n_spikes,
)
if not hasattr(spike_ids, '__len__'):
spike_ids = [spike_ids]
if channels is None:
channels = slice(None, None, None)
nc = self.n_channels
else:
channels = np.asarray(channels, dtype=np.int32)
assert np.all(channels < self.n_channels)
nc = len(channels)
# Ensure a list of time samples are being requested.
spike_ids = _as_array(spike_ids)
n_spikes = len(spike_ids)
# Initialize the array.
# NOTE: last dimension is time to simplify things.
shape = (n_spikes, nc, self._n_samples_extract)
waveforms = np.zeros(shape, dtype=np.float32)
# No traces: return null arrays.
if self.n_samples_trace == 0:
return np.transpose(waveforms, (0, 2, 1))
# Load all spikes.
for i, spike_id in enumerate(spike_ids):
assert 0 <= spike_id < self.n_spikes
time = self._spike_samples[spike_id]
# Extract the waveforms on the unmasked channels.
try:
w = self._load_at(time, channels)
except ValueError as e: # pragma: no cover
logger.warn("Error while loading waveform: %s", str(e))
continue
assert w.shape == (self._n_samples_extract, nc)
waveforms[i, :, :] = w.T
# Filter the waveforms.
waveforms_f = waveforms.reshape((-1, self._n_samples_extract))
# Only filter the non-zero waveforms.
unmasked = waveforms_f.max(axis=1) != 0
waveforms_f[unmasked] = self._filter(waveforms_f[unmasked], axis=1)
waveforms_f = waveforms_f.reshape((n_spikes, nc,
self._n_samples_extract))
# Remove the margin.
margin_before, margin_after = self._filter_margin
if margin_after > 0:
assert margin_before >= 0
waveforms_f = waveforms_f[:, :, margin_before:-margin_after]
assert waveforms_f.shape == (n_spikes,
nc,
self.n_samples_waveforms,
)
# NOTE: we transpose before returning the array.
return np.transpose(waveforms_f, (0, 2, 1)) | [
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7,740 | kwikteam/phy | phy/stats/clusters.py | get_waveform_amplitude | def get_waveform_amplitude(mean_masks, mean_waveforms):
"""Return the amplitude of the waveforms on all channels."""
assert mean_waveforms.ndim == 2
n_samples, n_channels = mean_waveforms.shape
assert mean_masks.ndim == 1
assert mean_masks.shape == (n_channels,)
mean_waveforms = mean_waveforms * mean_masks
assert mean_waveforms.shape == (n_samples, n_channels)
# Amplitudes.
m, M = mean_waveforms.min(axis=0), mean_waveforms.max(axis=0)
return M - m | python | def get_waveform_amplitude(mean_masks, mean_waveforms):
"""Return the amplitude of the waveforms on all channels."""
assert mean_waveforms.ndim == 2
n_samples, n_channels = mean_waveforms.shape
assert mean_masks.ndim == 1
assert mean_masks.shape == (n_channels,)
mean_waveforms = mean_waveforms * mean_masks
assert mean_waveforms.shape == (n_samples, n_channels)
# Amplitudes.
m, M = mean_waveforms.min(axis=0), mean_waveforms.max(axis=0)
return M - m | [
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7,741 | kwikteam/phy | phy/stats/clusters.py | get_mean_masked_features_distance | def get_mean_masked_features_distance(mean_features_0,
mean_features_1,
mean_masks_0,
mean_masks_1,
n_features_per_channel=None,
):
"""Compute the distance between the mean masked features."""
assert n_features_per_channel > 0
mu_0 = mean_features_0.ravel()
mu_1 = mean_features_1.ravel()
omeg_0 = mean_masks_0
omeg_1 = mean_masks_1
omeg_0 = np.repeat(omeg_0, n_features_per_channel)
omeg_1 = np.repeat(omeg_1, n_features_per_channel)
d_0 = mu_0 * omeg_0
d_1 = mu_1 * omeg_1
return np.linalg.norm(d_0 - d_1) | python | def get_mean_masked_features_distance(mean_features_0,
mean_features_1,
mean_masks_0,
mean_masks_1,
n_features_per_channel=None,
):
"""Compute the distance between the mean masked features."""
assert n_features_per_channel > 0
mu_0 = mean_features_0.ravel()
mu_1 = mean_features_1.ravel()
omeg_0 = mean_masks_0
omeg_1 = mean_masks_1
omeg_0 = np.repeat(omeg_0, n_features_per_channel)
omeg_1 = np.repeat(omeg_1, n_features_per_channel)
d_0 = mu_0 * omeg_0
d_1 = mu_1 * omeg_1
return np.linalg.norm(d_0 - d_1) | [
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7,742 | kwikteam/phy | phy/cluster/clustering.py | _extend_spikes | def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids,
assume_unique=True)
return extended_spike_ids | python | def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids,
assume_unique=True)
return extended_spike_ids | [
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7,743 | kwikteam/phy | phy/cluster/clustering.py | Clustering.reset | def reset(self):
"""Reset the clustering to the original clustering.
All changes are lost.
"""
self._undo_stack.clear()
self._spike_clusters = self._spike_clusters_base
self._new_cluster_id = self._new_cluster_id_0 | python | def reset(self):
"""Reset the clustering to the original clustering.
All changes are lost.
"""
self._undo_stack.clear()
self._spike_clusters = self._spike_clusters_base
self._new_cluster_id = self._new_cluster_id_0 | [
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7,744 | kwikteam/phy | phy/cluster/clustering.py | Clustering._do_assign | def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids,
old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up | python | def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids,
old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up | [
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7,745 | kwikteam/phy | phy/cluster/clustering.py | Clustering.merge | def merge(self, cluster_ids, to=None):
"""Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
"""
if not _is_array_like(cluster_ids):
raise ValueError("The first argument should be a list or "
"an array.")
cluster_ids = sorted(cluster_ids)
if not set(cluster_ids) <= set(self.cluster_ids):
raise ValueError("Some clusters do not exist.")
# Find the new cluster number.
if to is None:
to = self.new_cluster_id()
if to < self.new_cluster_id():
raise ValueError("The new cluster numbers should be higher than "
"{0}.".format(self.new_cluster_id()))
# NOTE: we could have called self.assign() here, but we don't.
# We circumvent self.assign() for performance reasons.
# assign() is a relatively costly operation, whereas merging is a much
# cheaper operation.
# Find all spikes in the specified clusters.
spike_ids = _spikes_in_clusters(self.spike_clusters, cluster_ids)
up = self._do_merge(spike_ids, cluster_ids, to)
undo_state = self.emit('request_undo_state', up)
# Add to stack.
self._undo_stack.add((spike_ids, [to], undo_state))
self.emit('cluster', up)
return up | python | def merge(self, cluster_ids, to=None):
"""Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
"""
if not _is_array_like(cluster_ids):
raise ValueError("The first argument should be a list or "
"an array.")
cluster_ids = sorted(cluster_ids)
if not set(cluster_ids) <= set(self.cluster_ids):
raise ValueError("Some clusters do not exist.")
# Find the new cluster number.
if to is None:
to = self.new_cluster_id()
if to < self.new_cluster_id():
raise ValueError("The new cluster numbers should be higher than "
"{0}.".format(self.new_cluster_id()))
# NOTE: we could have called self.assign() here, but we don't.
# We circumvent self.assign() for performance reasons.
# assign() is a relatively costly operation, whereas merging is a much
# cheaper operation.
# Find all spikes in the specified clusters.
spike_ids = _spikes_in_clusters(self.spike_clusters, cluster_ids)
up = self._do_merge(spike_ids, cluster_ids, to)
undo_state = self.emit('request_undo_state', up)
# Add to stack.
self._undo_stack.add((spike_ids, [to], undo_state))
self.emit('cluster', up)
return up | [
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The id of the new cluster. By default, this is `new_cluster_id()`.
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7,746 | kwikteam/phy | phy/cluster/clustering.py | Clustering.assign | def assign(self, spike_ids, spike_clusters_rel=0):
"""Make new spike cluster assignments.
Parameters
----------
spike_ids : array-like
List of spike ids.
spike_clusters_rel : array-like
Relative cluster ids of the spikes in `spike_ids`. This
must have the same size as `spike_ids`.
Returns
-------
up : UpdateInfo instance
Note
----
`spike_clusters_rel` contain *relative* cluster indices. Their values
don't matter: what matters is whether two give spikes
should end up in the same cluster or not. Adding a constant number
to all elements in `spike_clusters_rel` results in exactly the same
operation.
The final cluster ids are automatically generated by the `Clustering`
class. This is because we must ensure that all modified clusters
get brand new ids. The whole library is based on the assumption that
cluster ids are unique and "disposable". Changing a cluster always
results in a new cluster id being assigned.
If a spike is assigned to a new cluster, then all other spikes
belonging to the same cluster are assigned to a brand new cluster,
even if they were not changed explicitely by the `assign()` method.
In other words, the list of spikes affected by an `assign()` is almost
always a strict superset of the `spike_ids` parameter. The only case
where this is not true is when whole clusters change: this is called
a merge. It is implemented in a separate `merge()` method because it
is logically much simpler, and faster to execute.
"""
assert not isinstance(spike_ids, slice)
# Ensure `spike_clusters_rel` is an array-like.
if not hasattr(spike_clusters_rel, '__len__'):
spike_clusters_rel = spike_clusters_rel * np.ones(len(spike_ids),
dtype=np.int64)
spike_ids = _as_array(spike_ids)
if len(spike_ids) == 0:
return UpdateInfo()
assert len(spike_ids) == len(spike_clusters_rel)
assert spike_ids.min() >= 0
assert spike_ids.max() < self._n_spikes, "Some spikes don't exist."
# Normalize the spike-cluster assignment such that
# there are only new or dead clusters, not modified clusters.
# This implies that spikes not explicitly selected, but that
# belong to clusters affected by the operation, will be assigned
# to brand new clusters.
spike_ids, cluster_ids = _extend_assignment(spike_ids,
self._spike_clusters,
spike_clusters_rel,
self.new_cluster_id(),
)
up = self._do_assign(spike_ids, cluster_ids)
undo_state = self.emit('request_undo_state', up)
# Add the assignment to the undo stack.
self._undo_stack.add((spike_ids, cluster_ids, undo_state))
self.emit('cluster', up)
return up | python | def assign(self, spike_ids, spike_clusters_rel=0):
"""Make new spike cluster assignments.
Parameters
----------
spike_ids : array-like
List of spike ids.
spike_clusters_rel : array-like
Relative cluster ids of the spikes in `spike_ids`. This
must have the same size as `spike_ids`.
Returns
-------
up : UpdateInfo instance
Note
----
`spike_clusters_rel` contain *relative* cluster indices. Their values
don't matter: what matters is whether two give spikes
should end up in the same cluster or not. Adding a constant number
to all elements in `spike_clusters_rel` results in exactly the same
operation.
The final cluster ids are automatically generated by the `Clustering`
class. This is because we must ensure that all modified clusters
get brand new ids. The whole library is based on the assumption that
cluster ids are unique and "disposable". Changing a cluster always
results in a new cluster id being assigned.
If a spike is assigned to a new cluster, then all other spikes
belonging to the same cluster are assigned to a brand new cluster,
even if they were not changed explicitely by the `assign()` method.
In other words, the list of spikes affected by an `assign()` is almost
always a strict superset of the `spike_ids` parameter. The only case
where this is not true is when whole clusters change: this is called
a merge. It is implemented in a separate `merge()` method because it
is logically much simpler, and faster to execute.
"""
assert not isinstance(spike_ids, slice)
# Ensure `spike_clusters_rel` is an array-like.
if not hasattr(spike_clusters_rel, '__len__'):
spike_clusters_rel = spike_clusters_rel * np.ones(len(spike_ids),
dtype=np.int64)
spike_ids = _as_array(spike_ids)
if len(spike_ids) == 0:
return UpdateInfo()
assert len(spike_ids) == len(spike_clusters_rel)
assert spike_ids.min() >= 0
assert spike_ids.max() < self._n_spikes, "Some spikes don't exist."
# Normalize the spike-cluster assignment such that
# there are only new or dead clusters, not modified clusters.
# This implies that spikes not explicitly selected, but that
# belong to clusters affected by the operation, will be assigned
# to brand new clusters.
spike_ids, cluster_ids = _extend_assignment(spike_ids,
self._spike_clusters,
spike_clusters_rel,
self.new_cluster_id(),
)
up = self._do_assign(spike_ids, cluster_ids)
undo_state = self.emit('request_undo_state', up)
# Add the assignment to the undo stack.
self._undo_stack.add((spike_ids, cluster_ids, undo_state))
self.emit('cluster', up)
return up | [
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Parameters
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spike_ids : array-like
List of spike ids.
spike_clusters_rel : array-like
Relative cluster ids of the spikes in `spike_ids`. This
must have the same size as `spike_ids`.
Returns
-------
up : UpdateInfo instance
Note
----
`spike_clusters_rel` contain *relative* cluster indices. Their values
don't matter: what matters is whether two give spikes
should end up in the same cluster or not. Adding a constant number
to all elements in `spike_clusters_rel` results in exactly the same
operation.
The final cluster ids are automatically generated by the `Clustering`
class. This is because we must ensure that all modified clusters
get brand new ids. The whole library is based on the assumption that
cluster ids are unique and "disposable". Changing a cluster always
results in a new cluster id being assigned.
If a spike is assigned to a new cluster, then all other spikes
belonging to the same cluster are assigned to a brand new cluster,
even if they were not changed explicitely by the `assign()` method.
In other words, the list of spikes affected by an `assign()` is almost
always a strict superset of the `spike_ids` parameter. The only case
where this is not true is when whole clusters change: this is called
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7,747 | kwikteam/phy | phy/cluster/clustering.py | Clustering.undo | def undo(self):
"""Undo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
_, _, undo_state = self._undo_stack.back()
# Retrieve the initial spike_cluster structure.
spike_clusters_new = self._spike_clusters_base.copy()
# Loop over the history (except the last item because we undo).
for spike_ids, cluster_ids, _ in self._undo_stack:
# We update the spike clusters accordingly.
if spike_ids is not None:
spike_clusters_new[spike_ids] = cluster_ids
# What are the spikes affected by the last changes?
changed = np.nonzero(self._spike_clusters !=
spike_clusters_new)[0]
clusters_changed = spike_clusters_new[changed]
up = self._do_assign(changed, clusters_changed)
up.history = 'undo'
# Add the undo_state object from the undone object.
up.undo_state = undo_state
self.emit('cluster', up)
return up | python | def undo(self):
"""Undo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
_, _, undo_state = self._undo_stack.back()
# Retrieve the initial spike_cluster structure.
spike_clusters_new = self._spike_clusters_base.copy()
# Loop over the history (except the last item because we undo).
for spike_ids, cluster_ids, _ in self._undo_stack:
# We update the spike clusters accordingly.
if spike_ids is not None:
spike_clusters_new[spike_ids] = cluster_ids
# What are the spikes affected by the last changes?
changed = np.nonzero(self._spike_clusters !=
spike_clusters_new)[0]
clusters_changed = spike_clusters_new[changed]
up = self._do_assign(changed, clusters_changed)
up.history = 'undo'
# Add the undo_state object from the undone object.
up.undo_state = undo_state
self.emit('cluster', up)
return up | [
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7,748 | kwikteam/phy | phy/cluster/clustering.py | Clustering.redo | def redo(self):
"""Redo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
# Go forward in the stack, and retrieve the new assignment.
item = self._undo_stack.forward()
if item is None:
# No redo has been performed: abort.
return
# NOTE: the undo_state object is only returned when undoing.
# It represents data associated to the state
# *before* the action. What might be more useful would be the
# undo_state object of the next item in the list (if it exists).
spike_ids, cluster_ids, undo_state = item
assert spike_ids is not None
# We apply the new assignment.
up = self._do_assign(spike_ids, cluster_ids)
up.history = 'redo'
self.emit('cluster', up)
return up | python | def redo(self):
"""Redo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
# Go forward in the stack, and retrieve the new assignment.
item = self._undo_stack.forward()
if item is None:
# No redo has been performed: abort.
return
# NOTE: the undo_state object is only returned when undoing.
# It represents data associated to the state
# *before* the action. What might be more useful would be the
# undo_state object of the next item in the list (if it exists).
spike_ids, cluster_ids, undo_state = item
assert spike_ids is not None
# We apply the new assignment.
up = self._do_assign(spike_ids, cluster_ids)
up.history = 'redo'
self.emit('cluster', up)
return up | [
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7,749 | kwikteam/phy | phy/stats/ccg.py | _increment | def _increment(arr, indices):
"""Increment some indices in a 1D vector of non-negative integers.
Repeated indices are taken into account."""
arr = _as_array(arr)
indices = _as_array(indices)
bbins = np.bincount(indices)
arr[:len(bbins)] += bbins
return arr | python | def _increment(arr, indices):
"""Increment some indices in a 1D vector of non-negative integers.
Repeated indices are taken into account."""
arr = _as_array(arr)
indices = _as_array(indices)
bbins = np.bincount(indices)
arr[:len(bbins)] += bbins
return arr | [
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7,750 | kwikteam/phy | phy/stats/ccg.py | _symmetrize_correlograms | def _symmetrize_correlograms(correlograms):
"""Return the symmetrized version of the CCG arrays."""
n_clusters, _, n_bins = correlograms.shape
assert n_clusters == _
# We symmetrize c[i, j, 0].
# This is necessary because the algorithm in correlograms()
# is sensitive to the order of identical spikes.
correlograms[..., 0] = np.maximum(correlograms[..., 0],
correlograms[..., 0].T)
sym = correlograms[..., 1:][..., ::-1]
sym = np.transpose(sym, (1, 0, 2))
return np.dstack((sym, correlograms)) | python | def _symmetrize_correlograms(correlograms):
"""Return the symmetrized version of the CCG arrays."""
n_clusters, _, n_bins = correlograms.shape
assert n_clusters == _
# We symmetrize c[i, j, 0].
# This is necessary because the algorithm in correlograms()
# is sensitive to the order of identical spikes.
correlograms[..., 0] = np.maximum(correlograms[..., 0],
correlograms[..., 0].T)
sym = correlograms[..., 1:][..., ::-1]
sym = np.transpose(sym, (1, 0, 2))
return np.dstack((sym, correlograms)) | [
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7,751 | kwikteam/phy | phy/stats/ccg.py | correlograms | def correlograms(spike_times,
spike_clusters,
cluster_ids=None,
sample_rate=1.,
bin_size=None,
window_size=None,
symmetrize=True,
):
"""Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
"""
assert sample_rate > 0.
assert np.all(np.diff(spike_times) >= 0), ("The spike times must be "
"increasing.")
# Get the spike samples.
spike_times = np.asarray(spike_times, dtype=np.float64)
spike_samples = (spike_times * sample_rate).astype(np.int64)
spike_clusters = _as_array(spike_clusters)
assert spike_samples.ndim == 1
assert spike_samples.shape == spike_clusters.shape
# Find `binsize`.
bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds
binsize = int(sample_rate * bin_size) # in samples
assert binsize >= 1
# Find `winsize_bins`.
window_size = np.clip(window_size, 1e-5, 1e5) # in seconds
winsize_bins = 2 * int(.5 * window_size / bin_size) + 1
assert winsize_bins >= 1
assert winsize_bins % 2 == 1
# Take the cluster oder into account.
if cluster_ids is None:
clusters = _unique(spike_clusters)
else:
clusters = _as_array(cluster_ids)
n_clusters = len(clusters)
# Like spike_clusters, but with 0..n_clusters-1 indices.
spike_clusters_i = _index_of(spike_clusters, clusters)
# Shift between the two copies of the spike trains.
shift = 1
# At a given shift, the mask precises which spikes have matching spikes
# within the correlogram time window.
mask = np.ones_like(spike_samples, dtype=np.bool)
correlograms = _create_correlograms_array(n_clusters, winsize_bins)
# The loop continues as long as there is at least one spike with
# a matching spike.
while mask[:-shift].any():
# Number of time samples between spike i and spike i+shift.
spike_diff = _diff_shifted(spike_samples, shift)
# Binarize the delays between spike i and spike i+shift.
spike_diff_b = spike_diff // binsize
# Spikes with no matching spikes are masked.
mask[:-shift][spike_diff_b > (winsize_bins // 2)] = False
# Cache the masked spike delays.
m = mask[:-shift].copy()
d = spike_diff_b[m]
# # Update the masks given the clusters to update.
# m0 = np.in1d(spike_clusters[:-shift], clusters)
# m = m & m0
# d = spike_diff_b[m]
d = spike_diff_b[m]
# Find the indices in the raveled correlograms array that need
# to be incremented, taking into account the spike clusters.
indices = np.ravel_multi_index((spike_clusters_i[:-shift][m],
spike_clusters_i[+shift:][m],
d),
correlograms.shape)
# Increment the matching spikes in the correlograms array.
_increment(correlograms.ravel(), indices)
shift += 1
# Remove ACG peaks.
correlograms[np.arange(n_clusters),
np.arange(n_clusters),
0] = 0
if symmetrize:
return _symmetrize_correlograms(correlograms)
else:
return correlograms | python | def correlograms(spike_times,
spike_clusters,
cluster_ids=None,
sample_rate=1.,
bin_size=None,
window_size=None,
symmetrize=True,
):
"""Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
"""
assert sample_rate > 0.
assert np.all(np.diff(spike_times) >= 0), ("The spike times must be "
"increasing.")
# Get the spike samples.
spike_times = np.asarray(spike_times, dtype=np.float64)
spike_samples = (spike_times * sample_rate).astype(np.int64)
spike_clusters = _as_array(spike_clusters)
assert spike_samples.ndim == 1
assert spike_samples.shape == spike_clusters.shape
# Find `binsize`.
bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds
binsize = int(sample_rate * bin_size) # in samples
assert binsize >= 1
# Find `winsize_bins`.
window_size = np.clip(window_size, 1e-5, 1e5) # in seconds
winsize_bins = 2 * int(.5 * window_size / bin_size) + 1
assert winsize_bins >= 1
assert winsize_bins % 2 == 1
# Take the cluster oder into account.
if cluster_ids is None:
clusters = _unique(spike_clusters)
else:
clusters = _as_array(cluster_ids)
n_clusters = len(clusters)
# Like spike_clusters, but with 0..n_clusters-1 indices.
spike_clusters_i = _index_of(spike_clusters, clusters)
# Shift between the two copies of the spike trains.
shift = 1
# At a given shift, the mask precises which spikes have matching spikes
# within the correlogram time window.
mask = np.ones_like(spike_samples, dtype=np.bool)
correlograms = _create_correlograms_array(n_clusters, winsize_bins)
# The loop continues as long as there is at least one spike with
# a matching spike.
while mask[:-shift].any():
# Number of time samples between spike i and spike i+shift.
spike_diff = _diff_shifted(spike_samples, shift)
# Binarize the delays between spike i and spike i+shift.
spike_diff_b = spike_diff // binsize
# Spikes with no matching spikes are masked.
mask[:-shift][spike_diff_b > (winsize_bins // 2)] = False
# Cache the masked spike delays.
m = mask[:-shift].copy()
d = spike_diff_b[m]
# # Update the masks given the clusters to update.
# m0 = np.in1d(spike_clusters[:-shift], clusters)
# m = m & m0
# d = spike_diff_b[m]
d = spike_diff_b[m]
# Find the indices in the raveled correlograms array that need
# to be incremented, taking into account the spike clusters.
indices = np.ravel_multi_index((spike_clusters_i[:-shift][m],
spike_clusters_i[+shift:][m],
d),
correlograms.shape)
# Increment the matching spikes in the correlograms array.
_increment(correlograms.ravel(), indices)
shift += 1
# Remove ACG peaks.
correlograms[np.arange(n_clusters),
np.arange(n_clusters),
0] = 0
if symmetrize:
return _symmetrize_correlograms(correlograms)
else:
return correlograms | [
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Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
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7,752 | kwikteam/phy | phy/cluster/views/correlogram.py | CorrelogramView.set_bin_window | def set_bin_window(self, bin_size=None, window_size=None):
"""Set the bin and window sizes."""
bin_size = bin_size or self.bin_size
window_size = window_size or self.window_size
assert 1e-6 < bin_size < 1e3
assert 1e-6 < window_size < 1e3
assert bin_size < window_size
self.bin_size = bin_size
self.window_size = window_size
# Set the status message.
b, w = self.bin_size * 1000, self.window_size * 1000
self.set_status('Bin: {:.1f} ms. Window: {:.1f} ms.'.format(b, w)) | python | def set_bin_window(self, bin_size=None, window_size=None):
"""Set the bin and window sizes."""
bin_size = bin_size or self.bin_size
window_size = window_size or self.window_size
assert 1e-6 < bin_size < 1e3
assert 1e-6 < window_size < 1e3
assert bin_size < window_size
self.bin_size = bin_size
self.window_size = window_size
# Set the status message.
b, w = self.bin_size * 1000, self.window_size * 1000
self.set_status('Bin: {:.1f} ms. Window: {:.1f} ms.'.format(b, w)) | [
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7,753 | kwikteam/phy | phy/io/datasets.py | _md5 | def _md5(path, blocksize=2 ** 20):
"""Compute the checksum of a file."""
m = hashlib.md5()
with open(path, 'rb') as f:
while True:
buf = f.read(blocksize)
if not buf:
break
m.update(buf)
return m.hexdigest() | python | def _md5(path, blocksize=2 ** 20):
"""Compute the checksum of a file."""
m = hashlib.md5()
with open(path, 'rb') as f:
while True:
buf = f.read(blocksize)
if not buf:
break
m.update(buf)
return m.hexdigest() | [
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7,754 | kwikteam/phy | phy/io/datasets.py | download_file | def download_file(url, output_path):
"""Download a binary file from an URL.
The checksum will be downloaded from `URL + .md5`. If this download
succeeds, the file's MD5 will be compared to the expected checksum.
Parameters
----------
url : str
The file's URL.
output_path : str
The path where the file is to be saved.
"""
output_path = op.realpath(output_path)
assert output_path is not None
if op.exists(output_path):
checked = _check_md5_of_url(output_path, url)
if checked is False:
logger.debug("The file `%s` already exists "
"but is invalid: redownloading.", output_path)
elif checked is True:
logger.debug("The file `%s` already exists: skipping.",
output_path)
return output_path
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
logger.debug("The checksum doesn't match: retrying the download.")
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
raise RuntimeError("The checksum of the downloaded file "
"doesn't match the provided checksum.")
return | python | def download_file(url, output_path):
"""Download a binary file from an URL.
The checksum will be downloaded from `URL + .md5`. If this download
succeeds, the file's MD5 will be compared to the expected checksum.
Parameters
----------
url : str
The file's URL.
output_path : str
The path where the file is to be saved.
"""
output_path = op.realpath(output_path)
assert output_path is not None
if op.exists(output_path):
checked = _check_md5_of_url(output_path, url)
if checked is False:
logger.debug("The file `%s` already exists "
"but is invalid: redownloading.", output_path)
elif checked is True:
logger.debug("The file `%s` already exists: skipping.",
output_path)
return output_path
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
logger.debug("The checksum doesn't match: retrying the download.")
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
raise RuntimeError("The checksum of the downloaded file "
"doesn't match the provided checksum.")
return | [
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7,755 | kwikteam/phy | phy/plot/plot.py | _make_class | def _make_class(cls, **kwargs):
"""Return a custom Visual class with given parameters."""
kwargs = {k: (v if v is not None else getattr(cls, k, None))
for k, v in kwargs.items()}
# The class name contains a hash of the custom parameters.
name = cls.__name__ + '_' + _hash(kwargs)
if name not in _CLASSES:
logger.log(5, "Create class %s %s.", name, kwargs)
cls = type(name, (cls,), kwargs)
_CLASSES[name] = cls
return _CLASSES[name] | python | def _make_class(cls, **kwargs):
"""Return a custom Visual class with given parameters."""
kwargs = {k: (v if v is not None else getattr(cls, k, None))
for k, v in kwargs.items()}
# The class name contains a hash of the custom parameters.
name = cls.__name__ + '_' + _hash(kwargs)
if name not in _CLASSES:
logger.log(5, "Create class %s %s.", name, kwargs)
cls = type(name, (cls,), kwargs)
_CLASSES[name] = cls
return _CLASSES[name] | [
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7,756 | kwikteam/phy | phy/plot/plot.py | View._add_item | def _add_item(self, cls, *args, **kwargs):
"""Add a plot item."""
box_index = kwargs.pop('box_index', self._default_box_index)
data = cls.validate(*args, **kwargs)
n = cls.vertex_count(**data)
if not isinstance(box_index, np.ndarray):
k = len(self._default_box_index)
box_index = _get_array(box_index, (n, k))
data['box_index'] = box_index
if cls not in self._items:
self._items[cls] = []
self._items[cls].append(data)
return data | python | def _add_item(self, cls, *args, **kwargs):
"""Add a plot item."""
box_index = kwargs.pop('box_index', self._default_box_index)
data = cls.validate(*args, **kwargs)
n = cls.vertex_count(**data)
if not isinstance(box_index, np.ndarray):
k = len(self._default_box_index)
box_index = _get_array(box_index, (n, k))
data['box_index'] = box_index
if cls not in self._items:
self._items[cls] = []
self._items[cls].append(data)
return data | [
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7,757 | kwikteam/phy | phy/plot/plot.py | View.scatter | def scatter(self, *args, **kwargs):
"""Add a scatter plot."""
cls = _make_class(ScatterVisual,
_default_marker=kwargs.pop('marker', None),
)
return self._add_item(cls, *args, **kwargs) | python | def scatter(self, *args, **kwargs):
"""Add a scatter plot."""
cls = _make_class(ScatterVisual,
_default_marker=kwargs.pop('marker', None),
)
return self._add_item(cls, *args, **kwargs) | [
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7,758 | kwikteam/phy | phy/plot/plot.py | View.build | def build(self):
"""Build all added items.
Visuals are created, added, and built. The `set_data()` methods can
be called afterwards.
"""
for cls, data_list in self._items.items():
# Some variables are not concatenated. They are specified
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data = _accumulate(data_list, cls.allow_list)
box_index = data.pop('box_index')
visual = cls()
self.add_visual(visual)
visual.set_data(**data)
# NOTE: visual.program.__contains__ is implemented in vispy master
# so we can replace this with `if 'a_box_index' in visual.program`
# after the next VisPy release.
if 'a_box_index' in visual.program._code_variables:
visual.program['a_box_index'] = box_index.astype(np.float32)
# TODO: refactor this when there is the possibility to update existing
# visuals without recreating the whole scene.
if self.lasso:
self.lasso.create_visual()
self.update() | python | def build(self):
"""Build all added items.
Visuals are created, added, and built. The `set_data()` methods can
be called afterwards.
"""
for cls, data_list in self._items.items():
# Some variables are not concatenated. They are specified
# in `allow_list`.
data = _accumulate(data_list, cls.allow_list)
box_index = data.pop('box_index')
visual = cls()
self.add_visual(visual)
visual.set_data(**data)
# NOTE: visual.program.__contains__ is implemented in vispy master
# so we can replace this with `if 'a_box_index' in visual.program`
# after the next VisPy release.
if 'a_box_index' in visual.program._code_variables:
visual.program['a_box_index'] = box_index.astype(np.float32)
# TODO: refactor this when there is the possibility to update existing
# visuals without recreating the whole scene.
if self.lasso:
self.lasso.create_visual()
self.update() | [
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7,759 | kwikteam/phy | phy/io/array.py | _range_from_slice | def _range_from_slice(myslice, start=None, stop=None, step=None, length=None):
"""Convert a slice to an array of integers."""
assert isinstance(myslice, slice)
# Find 'step'.
step = myslice.step if myslice.step is not None else step
if step is None:
step = 1
# Find 'start'.
start = myslice.start if myslice.start is not None else start
if start is None:
start = 0
# Find 'stop' as a function of length if 'stop' is unspecified.
stop = myslice.stop if myslice.stop is not None else stop
if length is not None:
stop_inferred = floor(start + step * length)
if stop is not None and stop < stop_inferred:
raise ValueError("'stop' ({stop}) and ".format(stop=stop) +
"'length' ({length}) ".format(length=length) +
"are not compatible.")
stop = stop_inferred
if stop is None and length is None:
raise ValueError("'stop' and 'length' cannot be both unspecified.")
myrange = np.arange(start, stop, step)
# Check the length if it was specified.
if length is not None:
assert len(myrange) == length
return myrange | python | def _range_from_slice(myslice, start=None, stop=None, step=None, length=None):
"""Convert a slice to an array of integers."""
assert isinstance(myslice, slice)
# Find 'step'.
step = myslice.step if myslice.step is not None else step
if step is None:
step = 1
# Find 'start'.
start = myslice.start if myslice.start is not None else start
if start is None:
start = 0
# Find 'stop' as a function of length if 'stop' is unspecified.
stop = myslice.stop if myslice.stop is not None else stop
if length is not None:
stop_inferred = floor(start + step * length)
if stop is not None and stop < stop_inferred:
raise ValueError("'stop' ({stop}) and ".format(stop=stop) +
"'length' ({length}) ".format(length=length) +
"are not compatible.")
stop = stop_inferred
if stop is None and length is None:
raise ValueError("'stop' and 'length' cannot be both unspecified.")
myrange = np.arange(start, stop, step)
# Check the length if it was specified.
if length is not None:
assert len(myrange) == length
return myrange | [
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7,760 | kwikteam/phy | phy/io/array.py | _index_of | def _index_of(arr, lookup):
"""Replace scalars in an array by their indices in a lookup table.
Implicitely assume that:
* All elements of arr and lookup are non-negative integers.
* All elements or arr belong to lookup.
This is not checked for performance reasons.
"""
# Equivalent of np.digitize(arr, lookup) - 1, but much faster.
# TODO: assertions to disable in production for performance reasons.
# TODO: np.searchsorted(lookup, arr) is faster on small arrays with large
# values
lookup = np.asarray(lookup, dtype=np.int32)
m = (lookup.max() if len(lookup) else 0) + 1
tmp = np.zeros(m + 1, dtype=np.int)
# Ensure that -1 values are kept.
tmp[-1] = -1
if len(lookup):
tmp[lookup] = np.arange(len(lookup))
return tmp[arr] | python | def _index_of(arr, lookup):
"""Replace scalars in an array by their indices in a lookup table.
Implicitely assume that:
* All elements of arr and lookup are non-negative integers.
* All elements or arr belong to lookup.
This is not checked for performance reasons.
"""
# Equivalent of np.digitize(arr, lookup) - 1, but much faster.
# TODO: assertions to disable in production for performance reasons.
# TODO: np.searchsorted(lookup, arr) is faster on small arrays with large
# values
lookup = np.asarray(lookup, dtype=np.int32)
m = (lookup.max() if len(lookup) else 0) + 1
tmp = np.zeros(m + 1, dtype=np.int)
# Ensure that -1 values are kept.
tmp[-1] = -1
if len(lookup):
tmp[lookup] = np.arange(len(lookup))
return tmp[arr] | [
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7,761 | kwikteam/phy | phy/io/array.py | _pad | def _pad(arr, n, dir='right'):
"""Pad an array with zeros along the first axis.
Parameters
----------
n : int
Size of the returned array in the first axis.
dir : str
Direction of the padding. Must be one 'left' or 'right'.
"""
assert dir in ('left', 'right')
if n < 0:
raise ValueError("'n' must be positive: {0}.".format(n))
elif n == 0:
return np.zeros((0,) + arr.shape[1:], dtype=arr.dtype)
n_arr = arr.shape[0]
shape = (n,) + arr.shape[1:]
if n_arr == n:
assert arr.shape == shape
return arr
elif n_arr < n:
out = np.zeros(shape, dtype=arr.dtype)
if dir == 'left':
out[-n_arr:, ...] = arr
elif dir == 'right':
out[:n_arr, ...] = arr
assert out.shape == shape
return out
else:
if dir == 'left':
out = arr[-n:, ...]
elif dir == 'right':
out = arr[:n, ...]
assert out.shape == shape
return out | python | def _pad(arr, n, dir='right'):
"""Pad an array with zeros along the first axis.
Parameters
----------
n : int
Size of the returned array in the first axis.
dir : str
Direction of the padding. Must be one 'left' or 'right'.
"""
assert dir in ('left', 'right')
if n < 0:
raise ValueError("'n' must be positive: {0}.".format(n))
elif n == 0:
return np.zeros((0,) + arr.shape[1:], dtype=arr.dtype)
n_arr = arr.shape[0]
shape = (n,) + arr.shape[1:]
if n_arr == n:
assert arr.shape == shape
return arr
elif n_arr < n:
out = np.zeros(shape, dtype=arr.dtype)
if dir == 'left':
out[-n_arr:, ...] = arr
elif dir == 'right':
out[:n_arr, ...] = arr
assert out.shape == shape
return out
else:
if dir == 'left':
out = arr[-n:, ...]
elif dir == 'right':
out = arr[:n, ...]
assert out.shape == shape
return out | [
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Direction of the padding. Must be one 'left' or 'right'. | [
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7,762 | kwikteam/phy | phy/io/array.py | _in_polygon | def _in_polygon(points, polygon):
"""Return the points that are inside a polygon."""
from matplotlib.path import Path
points = _as_array(points)
polygon = _as_array(polygon)
assert points.ndim == 2
assert polygon.ndim == 2
if len(polygon):
polygon = np.vstack((polygon, polygon[0]))
path = Path(polygon, closed=True)
return path.contains_points(points) | python | def _in_polygon(points, polygon):
"""Return the points that are inside a polygon."""
from matplotlib.path import Path
points = _as_array(points)
polygon = _as_array(polygon)
assert points.ndim == 2
assert polygon.ndim == 2
if len(polygon):
polygon = np.vstack((polygon, polygon[0]))
path = Path(polygon, closed=True)
return path.contains_points(points) | [
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7,763 | kwikteam/phy | phy/io/array.py | read_array | def read_array(path, mmap_mode=None):
"""Read a .npy array."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.load(path, mmap_mode=mmap_mode)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.") | python | def read_array(path, mmap_mode=None):
"""Read a .npy array."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.load(path, mmap_mode=mmap_mode)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.") | [
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7,764 | kwikteam/phy | phy/io/array.py | write_array | def write_array(path, arr):
"""Write an array to a .npy file."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.save(path, arr)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.") | python | def write_array(path, arr):
"""Write an array to a .npy file."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.save(path, arr)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.") | [
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7,765 | kwikteam/phy | phy/io/array.py | _concatenate_virtual_arrays | def _concatenate_virtual_arrays(arrs, cols=None, scaling=None):
"""Return a virtual concatenate of several NumPy arrays."""
return None if not len(arrs) else ConcatenatedArrays(arrs, cols,
scaling=scaling) | python | def _concatenate_virtual_arrays(arrs, cols=None, scaling=None):
"""Return a virtual concatenate of several NumPy arrays."""
return None if not len(arrs) else ConcatenatedArrays(arrs, cols,
scaling=scaling) | [
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7,766 | kwikteam/phy | phy/io/array.py | _excerpt_step | def _excerpt_step(n_samples, n_excerpts=None, excerpt_size=None):
"""Compute the step of an excerpt set as a function of the number
of excerpts or their sizes."""
assert n_excerpts >= 2
step = max((n_samples - excerpt_size) // (n_excerpts - 1),
excerpt_size)
return step | python | def _excerpt_step(n_samples, n_excerpts=None, excerpt_size=None):
"""Compute the step of an excerpt set as a function of the number
of excerpts or their sizes."""
assert n_excerpts >= 2
step = max((n_samples - excerpt_size) // (n_excerpts - 1),
excerpt_size)
return step | [
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7,767 | kwikteam/phy | phy/io/array.py | chunk_bounds | def chunk_bounds(n_samples, chunk_size, overlap=0):
"""Return chunk bounds.
Chunks have the form:
[ overlap/2 | chunk_size-overlap | overlap/2 ]
s_start keep_start keep_end s_end
Except for the first and last chunks which do not have a left/right
overlap.
This generator yields (s_start, s_end, keep_start, keep_end).
"""
s_start = 0
s_end = chunk_size
keep_start = s_start
keep_end = s_end - overlap // 2
yield s_start, s_end, keep_start, keep_end
while s_end - overlap + chunk_size < n_samples:
s_start = s_end - overlap
s_end = s_start + chunk_size
keep_start = keep_end
keep_end = s_end - overlap // 2
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end
s_start = s_end - overlap
s_end = n_samples
keep_start = keep_end
keep_end = s_end
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end | python | def chunk_bounds(n_samples, chunk_size, overlap=0):
"""Return chunk bounds.
Chunks have the form:
[ overlap/2 | chunk_size-overlap | overlap/2 ]
s_start keep_start keep_end s_end
Except for the first and last chunks which do not have a left/right
overlap.
This generator yields (s_start, s_end, keep_start, keep_end).
"""
s_start = 0
s_end = chunk_size
keep_start = s_start
keep_end = s_end - overlap // 2
yield s_start, s_end, keep_start, keep_end
while s_end - overlap + chunk_size < n_samples:
s_start = s_end - overlap
s_end = s_start + chunk_size
keep_start = keep_end
keep_end = s_end - overlap // 2
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end
s_start = s_end - overlap
s_end = n_samples
keep_start = keep_end
keep_end = s_end
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end | [
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7,768 | kwikteam/phy | phy/io/array.py | data_chunk | def data_chunk(data, chunk, with_overlap=False):
"""Get a data chunk."""
assert isinstance(chunk, tuple)
if len(chunk) == 2:
i, j = chunk
elif len(chunk) == 4:
if with_overlap:
i, j = chunk[:2]
else:
i, j = chunk[2:]
else:
raise ValueError("'chunk' should have 2 or 4 elements, "
"not {0:d}".format(len(chunk)))
return data[i:j, ...] | python | def data_chunk(data, chunk, with_overlap=False):
"""Get a data chunk."""
assert isinstance(chunk, tuple)
if len(chunk) == 2:
i, j = chunk
elif len(chunk) == 4:
if with_overlap:
i, j = chunk[:2]
else:
i, j = chunk[2:]
else:
raise ValueError("'chunk' should have 2 or 4 elements, "
"not {0:d}".format(len(chunk)))
return data[i:j, ...] | [
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7,769 | kwikteam/phy | phy/io/array.py | _spikes_in_clusters | def _spikes_in_clusters(spike_clusters, clusters):
"""Return the ids of all spikes belonging to the specified clusters."""
if len(spike_clusters) == 0 or len(clusters) == 0:
return np.array([], dtype=np.int)
return np.nonzero(np.in1d(spike_clusters, clusters))[0] | python | def _spikes_in_clusters(spike_clusters, clusters):
"""Return the ids of all spikes belonging to the specified clusters."""
if len(spike_clusters) == 0 or len(clusters) == 0:
return np.array([], dtype=np.int)
return np.nonzero(np.in1d(spike_clusters, clusters))[0] | [
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7,770 | kwikteam/phy | phy/io/array.py | grouped_mean | def grouped_mean(arr, spike_clusters):
"""Compute the mean of a spike-dependent quantity for every cluster.
The two arguments should be 1D array with `n_spikes` elements.
The output is a 1D array with `n_clusters` elements. The clusters are
sorted in increasing order.
"""
arr = np.asarray(arr)
spike_clusters = np.asarray(spike_clusters)
assert arr.ndim == 1
assert arr.shape[0] == len(spike_clusters)
cluster_ids = _unique(spike_clusters)
spike_clusters_rel = _index_of(spike_clusters, cluster_ids)
spike_counts = np.bincount(spike_clusters_rel)
assert len(spike_counts) == len(cluster_ids)
t = np.zeros(len(cluster_ids))
# Compute the sum with possible repetitions.
np.add.at(t, spike_clusters_rel, arr)
return t / spike_counts | python | def grouped_mean(arr, spike_clusters):
"""Compute the mean of a spike-dependent quantity for every cluster.
The two arguments should be 1D array with `n_spikes` elements.
The output is a 1D array with `n_clusters` elements. The clusters are
sorted in increasing order.
"""
arr = np.asarray(arr)
spike_clusters = np.asarray(spike_clusters)
assert arr.ndim == 1
assert arr.shape[0] == len(spike_clusters)
cluster_ids = _unique(spike_clusters)
spike_clusters_rel = _index_of(spike_clusters, cluster_ids)
spike_counts = np.bincount(spike_clusters_rel)
assert len(spike_counts) == len(cluster_ids)
t = np.zeros(len(cluster_ids))
# Compute the sum with possible repetitions.
np.add.at(t, spike_clusters_rel, arr)
return t / spike_counts | [
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7,771 | kwikteam/phy | phy/io/array.py | regular_subset | def regular_subset(spikes, n_spikes_max=None, offset=0):
"""Prune the current selection to get at most n_spikes_max spikes."""
assert spikes is not None
# Nothing to do if the selection already satisfies n_spikes_max.
if n_spikes_max is None or len(spikes) <= n_spikes_max: # pragma: no cover
return spikes
step = math.ceil(np.clip(1. / n_spikes_max * len(spikes),
1, len(spikes)))
step = int(step)
# Note: randomly-changing selections are confusing...
my_spikes = spikes[offset::step][:n_spikes_max]
assert len(my_spikes) <= len(spikes)
assert len(my_spikes) <= n_spikes_max
return my_spikes | python | def regular_subset(spikes, n_spikes_max=None, offset=0):
"""Prune the current selection to get at most n_spikes_max spikes."""
assert spikes is not None
# Nothing to do if the selection already satisfies n_spikes_max.
if n_spikes_max is None or len(spikes) <= n_spikes_max: # pragma: no cover
return spikes
step = math.ceil(np.clip(1. / n_spikes_max * len(spikes),
1, len(spikes)))
step = int(step)
# Note: randomly-changing selections are confusing...
my_spikes = spikes[offset::step][:n_spikes_max]
assert len(my_spikes) <= len(spikes)
assert len(my_spikes) <= n_spikes_max
return my_spikes | [
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7,772 | kwikteam/phy | phy/io/array.py | select_spikes | def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None,
batch_size=None,
subset=None,
):
"""Return a selection of spikes belonging to the specified clusters."""
subset = subset or 'regular'
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spike_ids = spikes_per_cluster(cluster)
if subset == 'regular':
# Regular subselection.
if batch_size is None or len(spike_ids) <= max(batch_size, n):
spike_ids = regular_subset(spike_ids, n_spikes_max=n)
else:
# Batch selections of spikes.
spike_ids = get_excerpts(spike_ids,
n // batch_size,
batch_size)
elif subset == 'random' and len(spike_ids) > n:
# Random subselection.
spike_ids = np.random.choice(spike_ids, n, replace=False)
spike_ids = np.unique(spike_ids)
selection[cluster] = spike_ids
return _flatten_per_cluster(selection) | python | def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None,
batch_size=None,
subset=None,
):
"""Return a selection of spikes belonging to the specified clusters."""
subset = subset or 'regular'
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spike_ids = spikes_per_cluster(cluster)
if subset == 'regular':
# Regular subselection.
if batch_size is None or len(spike_ids) <= max(batch_size, n):
spike_ids = regular_subset(spike_ids, n_spikes_max=n)
else:
# Batch selections of spikes.
spike_ids = get_excerpts(spike_ids,
n // batch_size,
batch_size)
elif subset == 'random' and len(spike_ids) > n:
# Random subselection.
spike_ids = np.random.choice(spike_ids, n, replace=False)
spike_ids = np.unique(spike_ids)
selection[cluster] = spike_ids
return _flatten_per_cluster(selection) | [
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7,773 | kwikteam/phy | phy/io/array.py | ConcatenatedArrays._get_recording | def _get_recording(self, index):
"""Return the recording that contains a given index."""
assert index >= 0
recs = np.nonzero((index - self.offsets[:-1]) >= 0)[0]
if len(recs) == 0: # pragma: no cover
# If the index is greater than the total size,
# return the last recording.
return len(self.arrs) - 1
# Return the last recording such that the index is greater than
# its offset.
return recs[-1] | python | def _get_recording(self, index):
"""Return the recording that contains a given index."""
assert index >= 0
recs = np.nonzero((index - self.offsets[:-1]) >= 0)[0]
if len(recs) == 0: # pragma: no cover
# If the index is greater than the total size,
# return the last recording.
return len(self.arrs) - 1
# Return the last recording such that the index is greater than
# its offset.
return recs[-1] | [
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7,774 | kwikteam/phy | phy/traces/filter.py | bandpass_filter | def bandpass_filter(rate=None, low=None, high=None, order=None):
"""Butterworth bandpass filter."""
assert low < high
assert order >= 1
return signal.butter(order,
(low / (rate / 2.), high / (rate / 2.)),
'pass') | python | def bandpass_filter(rate=None, low=None, high=None, order=None):
"""Butterworth bandpass filter."""
assert low < high
assert order >= 1
return signal.butter(order,
(low / (rate / 2.), high / (rate / 2.)),
'pass') | [
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7,775 | kwikteam/phy | phy/traces/filter.py | apply_filter | def apply_filter(x, filter=None, axis=0):
"""Apply a filter to an array."""
x = _as_array(x)
if x.shape[axis] == 0:
return x
b, a = filter
return signal.filtfilt(b, a, x, axis=axis) | python | def apply_filter(x, filter=None, axis=0):
"""Apply a filter to an array."""
x = _as_array(x)
if x.shape[axis] == 0:
return x
b, a = filter
return signal.filtfilt(b, a, x, axis=axis) | [
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7,776 | kwikteam/phy | phy/traces/filter.py | Whitening.fit | def fit(self, x, fudge=1e-18):
"""Compute the whitening matrix.
Parameters
----------
x : array
An `(n_samples, n_channels)` array.
"""
assert x.ndim == 2
ns, nc = x.shape
x_cov = np.cov(x, rowvar=0)
assert x_cov.shape == (nc, nc)
d, v = np.linalg.eigh(x_cov)
d = np.diag(1. / np.sqrt(d + fudge))
# This is equivalent, but seems much slower...
# w = np.einsum('il,lk,jk->ij', v, d, v)
w = np.dot(np.dot(v, d), v.T)
self._matrix = w
return w | python | def fit(self, x, fudge=1e-18):
"""Compute the whitening matrix.
Parameters
----------
x : array
An `(n_samples, n_channels)` array.
"""
assert x.ndim == 2
ns, nc = x.shape
x_cov = np.cov(x, rowvar=0)
assert x_cov.shape == (nc, nc)
d, v = np.linalg.eigh(x_cov)
d = np.diag(1. / np.sqrt(d + fudge))
# This is equivalent, but seems much slower...
# w = np.einsum('il,lk,jk->ij', v, d, v)
w = np.dot(np.dot(v, d), v.T)
self._matrix = w
return w | [
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7,777 | kwikteam/phy | phy/cluster/_history.py | History.current_item | def current_item(self):
"""Return the current element."""
if self._history and self._index >= 0:
self._check_index()
return self._history[self._index] | python | def current_item(self):
"""Return the current element."""
if self._history and self._index >= 0:
self._check_index()
return self._history[self._index] | [
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7,778 | kwikteam/phy | phy/cluster/_history.py | History._check_index | def _check_index(self):
"""Check that the index is without the bounds of _history."""
assert 0 <= self._index <= len(self._history) - 1
# There should always be the base item at least.
assert len(self._history) >= 1 | python | def _check_index(self):
"""Check that the index is without the bounds of _history."""
assert 0 <= self._index <= len(self._history) - 1
# There should always be the base item at least.
assert len(self._history) >= 1 | [
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7,779 | kwikteam/phy | phy/cluster/_history.py | History.iter | def iter(self, start=0, end=None):
"""Iterate through successive history items.
Parameters
----------
end : int
Index of the last item to loop through + 1.
start : int
Initial index for the loop (0 by default).
"""
if end is None:
end = self._index + 1
elif end == 0:
raise StopIteration()
if start >= end:
raise StopIteration()
# Check arguments.
assert 0 <= end <= len(self._history)
assert 0 <= start <= end - 1
for i in range(start, end):
yield self._history[i] | python | def iter(self, start=0, end=None):
"""Iterate through successive history items.
Parameters
----------
end : int
Index of the last item to loop through + 1.
start : int
Initial index for the loop (0 by default).
"""
if end is None:
end = self._index + 1
elif end == 0:
raise StopIteration()
if start >= end:
raise StopIteration()
# Check arguments.
assert 0 <= end <= len(self._history)
assert 0 <= start <= end - 1
for i in range(start, end):
yield self._history[i] | [
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7,780 | kwikteam/phy | phy/cluster/_history.py | History.add | def add(self, item):
"""Add an item in the history."""
self._check_index()
# Possibly truncate the history up to the current point.
self._history = self._history[:self._index + 1]
# Append the item
self._history.append(item)
# Increment the index.
self._index += 1
self._check_index()
# Check that the current element is what was provided to the function.
assert id(self.current_item) == id(item) | python | def add(self, item):
"""Add an item in the history."""
self._check_index()
# Possibly truncate the history up to the current point.
self._history = self._history[:self._index + 1]
# Append the item
self._history.append(item)
# Increment the index.
self._index += 1
self._check_index()
# Check that the current element is what was provided to the function.
assert id(self.current_item) == id(item) | [
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7,781 | kwikteam/phy | phy/cluster/_history.py | History.back | def back(self):
"""Go back in history if possible.
Return the undone item.
"""
if self._index <= 0:
return None
undone = self.current_item
self._index -= 1
self._check_index()
return undone | python | def back(self):
"""Go back in history if possible.
Return the undone item.
"""
if self._index <= 0:
return None
undone = self.current_item
self._index -= 1
self._check_index()
return undone | [
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7,782 | kwikteam/phy | phy/cluster/_history.py | History.forward | def forward(self):
"""Go forward in history if possible.
Return the current item after going forward.
"""
if self._index >= len(self._history) - 1:
return None
self._index += 1
self._check_index()
return self.current_item | python | def forward(self):
"""Go forward in history if possible.
Return the current item after going forward.
"""
if self._index >= len(self._history) - 1:
return None
self._index += 1
self._check_index()
return self.current_item | [
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7,783 | kwikteam/phy | phy/cluster/_history.py | GlobalHistory.add_to_current_action | def add_to_current_action(self, controller):
"""Add a controller to the current action."""
item = self.current_item
self._history[self._index] = item + (controller,) | python | def add_to_current_action(self, controller):
"""Add a controller to the current action."""
item = self.current_item
self._history[self._index] = item + (controller,) | [
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7,784 | kwikteam/phy | phy/cluster/_history.py | GlobalHistory.redo | def redo(self):
"""Redo the last action.
This will call `redo()` on all controllers involved in this action.
"""
controllers = self.forward()
if controllers is None:
ups = ()
else:
ups = tuple([controller.redo() for
controller in controllers])
if self.process_ups is not None:
return self.process_ups(ups)
else:
return ups | python | def redo(self):
"""Redo the last action.
This will call `redo()` on all controllers involved in this action.
"""
controllers = self.forward()
if controllers is None:
ups = ()
else:
ups = tuple([controller.redo() for
controller in controllers])
if self.process_ups is not None:
return self.process_ups(ups)
else:
return ups | [
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7,785 | kwikteam/phy | phy/plot/base.py | _insert_glsl | def _insert_glsl(vertex, fragment, to_insert):
"""Insert snippets in a shader.
to_insert is a dict `{(shader_type, location): snippet}`.
Snippets can contain `{{ var }}` placeholders for the transformed variable
name.
"""
# Find the place where to insert the GLSL snippet.
# This is "gl_Position = transform(data_var_name);" where
# data_var_name is typically an attribute.
vs_regex = re.compile(r'gl_Position = transform\(([\S]+)\);')
r = vs_regex.search(vertex)
if not r:
logger.debug("The vertex shader doesn't contain the transform "
"placeholder: skipping the transform chain "
"GLSL insertion.")
return vertex, fragment
assert r
logger.log(5, "Found transform placeholder in vertex code: `%s`",
r.group(0))
# Find the GLSL variable with the data (should be a `vec2`).
var = r.group(1)
assert var and var in vertex
# Headers.
vertex = to_insert['vert', 'header'] + '\n\n' + vertex
fragment = to_insert['frag', 'header'] + '\n\n' + fragment
# Get the pre and post transforms.
vs_insert = to_insert['vert', 'before_transforms']
vs_insert += to_insert['vert', 'transforms']
vs_insert += to_insert['vert', 'after_transforms']
# Insert the GLSL snippet in the vertex shader.
vertex = vs_regex.sub(indent(vs_insert), vertex)
# Now, we make the replacements in the fragment shader.
fs_regex = re.compile(r'(void main\(\)\s*\{)')
# NOTE: we add the `void main(){` that was removed by the regex.
fs_insert = '\\1\n' + to_insert['frag', 'before_transforms']
fragment = fs_regex.sub(indent(fs_insert), fragment)
# Replace the transformed variable placeholder by its name.
vertex = vertex.replace('{{ var }}', var)
return vertex, fragment | python | def _insert_glsl(vertex, fragment, to_insert):
"""Insert snippets in a shader.
to_insert is a dict `{(shader_type, location): snippet}`.
Snippets can contain `{{ var }}` placeholders for the transformed variable
name.
"""
# Find the place where to insert the GLSL snippet.
# This is "gl_Position = transform(data_var_name);" where
# data_var_name is typically an attribute.
vs_regex = re.compile(r'gl_Position = transform\(([\S]+)\);')
r = vs_regex.search(vertex)
if not r:
logger.debug("The vertex shader doesn't contain the transform "
"placeholder: skipping the transform chain "
"GLSL insertion.")
return vertex, fragment
assert r
logger.log(5, "Found transform placeholder in vertex code: `%s`",
r.group(0))
# Find the GLSL variable with the data (should be a `vec2`).
var = r.group(1)
assert var and var in vertex
# Headers.
vertex = to_insert['vert', 'header'] + '\n\n' + vertex
fragment = to_insert['frag', 'header'] + '\n\n' + fragment
# Get the pre and post transforms.
vs_insert = to_insert['vert', 'before_transforms']
vs_insert += to_insert['vert', 'transforms']
vs_insert += to_insert['vert', 'after_transforms']
# Insert the GLSL snippet in the vertex shader.
vertex = vs_regex.sub(indent(vs_insert), vertex)
# Now, we make the replacements in the fragment shader.
fs_regex = re.compile(r'(void main\(\)\s*\{)')
# NOTE: we add the `void main(){` that was removed by the regex.
fs_insert = '\\1\n' + to_insert['frag', 'before_transforms']
fragment = fs_regex.sub(indent(fs_insert), fragment)
# Replace the transformed variable placeholder by its name.
vertex = vertex.replace('{{ var }}', var)
return vertex, fragment | [
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7,786 | kwikteam/phy | phy/plot/base.py | BaseVisual.on_draw | def on_draw(self):
"""Draw the visual."""
# Skip the drawing if the program hasn't been built yet.
# The program is built by the interact.
if self.program:
# Draw the program.
self.program.draw(self.gl_primitive_type)
else: # pragma: no cover
logger.debug("Skipping drawing visual `%s` because the program "
"has not been built yet.", self) | python | def on_draw(self):
"""Draw the visual."""
# Skip the drawing if the program hasn't been built yet.
# The program is built by the interact.
if self.program:
# Draw the program.
self.program.draw(self.gl_primitive_type)
else: # pragma: no cover
logger.debug("Skipping drawing visual `%s` because the program "
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7,787 | kwikteam/phy | phy/plot/base.py | GLSLInserter.add_transform_chain | def add_transform_chain(self, tc):
"""Insert the GLSL snippets of a transform chain."""
# Generate the transforms snippet.
for t in tc.gpu_transforms:
if isinstance(t, Clip):
# Set the varying value in the vertex shader.
self.insert_vert('v_temp_pos_tr = temp_pos_tr;')
continue
self.insert_vert(t.glsl('temp_pos_tr'))
# Clipping.
clip = tc.get('Clip')
if clip:
self.insert_frag(clip.glsl('v_temp_pos_tr'), 'before_transforms') | python | def add_transform_chain(self, tc):
"""Insert the GLSL snippets of a transform chain."""
# Generate the transforms snippet.
for t in tc.gpu_transforms:
if isinstance(t, Clip):
# Set the varying value in the vertex shader.
self.insert_vert('v_temp_pos_tr = temp_pos_tr;')
continue
self.insert_vert(t.glsl('temp_pos_tr'))
# Clipping.
clip = tc.get('Clip')
if clip:
self.insert_frag(clip.glsl('v_temp_pos_tr'), 'before_transforms') | [
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7,788 | kwikteam/phy | phy/plot/base.py | GLSLInserter.insert_into_shaders | def insert_into_shaders(self, vertex, fragment):
"""Apply the insertions to shader code."""
to_insert = defaultdict(str)
to_insert.update({key: '\n'.join(self._to_insert[key]) + '\n'
for key in self._to_insert})
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"""Apply the insertions to shader code."""
to_insert = defaultdict(str)
to_insert.update({key: '\n'.join(self._to_insert[key]) + '\n'
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return _insert_glsl(vertex, fragment, to_insert) | [
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7,789 | kwikteam/phy | phy/plot/base.py | BaseCanvas.add_visual | def add_visual(self, visual):
"""Add a visual to the canvas, and build its program by the same
occasion.
We can't build the visual's program before, because we need the canvas'
transforms first.
"""
# Retrieve the visual's GLSL inserter.
inserter = visual.inserter
# Add the visual's transforms.
inserter.add_transform_chain(visual.transforms)
# Then, add the canvas' transforms.
canvas_transforms = visual.canvas_transforms_filter(self.transforms)
inserter.add_transform_chain(canvas_transforms)
# Also, add the canvas' inserter.
inserter += self.inserter
# Now, we insert the transforms GLSL into the shaders.
vs, fs = visual.vertex_shader, visual.fragment_shader
vs, fs = inserter.insert_into_shaders(vs, fs)
# Finally, we create the visual's program.
visual.program = gloo.Program(vs, fs)
logger.log(5, "Vertex shader: %s", vs)
logger.log(5, "Fragment shader: %s", fs)
# Initialize the size.
visual.on_resize(self.size)
# Register the visual in the list of visuals in the canvas.
self.visuals.append(visual)
self.events.visual_added(visual=visual) | python | def add_visual(self, visual):
"""Add a visual to the canvas, and build its program by the same
occasion.
We can't build the visual's program before, because we need the canvas'
transforms first.
"""
# Retrieve the visual's GLSL inserter.
inserter = visual.inserter
# Add the visual's transforms.
inserter.add_transform_chain(visual.transforms)
# Then, add the canvas' transforms.
canvas_transforms = visual.canvas_transforms_filter(self.transforms)
inserter.add_transform_chain(canvas_transforms)
# Also, add the canvas' inserter.
inserter += self.inserter
# Now, we insert the transforms GLSL into the shaders.
vs, fs = visual.vertex_shader, visual.fragment_shader
vs, fs = inserter.insert_into_shaders(vs, fs)
# Finally, we create the visual's program.
visual.program = gloo.Program(vs, fs)
logger.log(5, "Vertex shader: %s", vs)
logger.log(5, "Fragment shader: %s", fs)
# Initialize the size.
visual.on_resize(self.size)
# Register the visual in the list of visuals in the canvas.
self.visuals.append(visual)
self.events.visual_added(visual=visual) | [
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7,790 | kwikteam/phy | phy/plot/base.py | BaseCanvas.on_resize | def on_resize(self, event):
"""Resize the OpenGL context."""
self.context.set_viewport(0, 0, event.size[0], event.size[1])
for visual in self.visuals:
visual.on_resize(event.size)
self.update() | python | def on_resize(self, event):
"""Resize the OpenGL context."""
self.context.set_viewport(0, 0, event.size[0], event.size[1])
for visual in self.visuals:
visual.on_resize(event.size)
self.update() | [
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7,791 | kwikteam/phy | phy/plot/base.py | BaseCanvas.on_draw | def on_draw(self, e):
"""Draw all visuals."""
gloo.clear()
for visual in self.visuals:
logger.log(5, "Draw visual `%s`.", visual)
visual.on_draw() | python | def on_draw(self, e):
"""Draw all visuals."""
gloo.clear()
for visual in self.visuals:
logger.log(5, "Draw visual `%s`.", visual)
visual.on_draw() | [
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7,792 | kwikteam/phy | phy/plot/base.py | BaseInteract.update | def update(self):
"""Update all visuals in the attached canvas."""
if not self.canvas:
return
for visual in self.canvas.visuals:
self.update_program(visual.program)
self.canvas.update() | python | def update(self):
"""Update all visuals in the attached canvas."""
if not self.canvas:
return
for visual in self.canvas.visuals:
self.update_program(visual.program)
self.canvas.update() | [
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7,793 | kwikteam/phy | phy/utils/cli.py | _add_log_file | def _add_log_file(filename):
"""Create a `phy.log` log file with DEBUG level in the
current directory."""
handler = logging.FileHandler(filename)
handler.setLevel(logging.DEBUG)
formatter = _Formatter(fmt=_logger_fmt,
datefmt='%Y-%m-%d %H:%M:%S')
handler.setFormatter(formatter)
logging.getLogger().addHandler(handler) | python | def _add_log_file(filename):
"""Create a `phy.log` log file with DEBUG level in the
current directory."""
handler = logging.FileHandler(filename)
handler.setLevel(logging.DEBUG)
formatter = _Formatter(fmt=_logger_fmt,
datefmt='%Y-%m-%d %H:%M:%S')
handler.setFormatter(formatter)
logging.getLogger().addHandler(handler) | [
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7,794 | kwikteam/phy | phy/utils/cli.py | _run_cmd | def _run_cmd(cmd, ctx, glob, loc): # pragma: no cover
"""Run a command with optionally a debugger, IPython, or profiling."""
if PDB:
_enable_pdb()
if IPYTHON:
from IPython import start_ipython
args_ipy = ['-i', '--gui=qt']
ns = glob.copy()
ns.update(loc)
return start_ipython(args_ipy, user_ns=ns)
# Profiling. The builtin `profile` is added in __init__.
prof = __builtins__.get('profile', None)
if prof:
prof = __builtins__['profile']
return _profile(prof, cmd, glob, loc)
return exec_(cmd, glob, loc) | python | def _run_cmd(cmd, ctx, glob, loc): # pragma: no cover
"""Run a command with optionally a debugger, IPython, or profiling."""
if PDB:
_enable_pdb()
if IPYTHON:
from IPython import start_ipython
args_ipy = ['-i', '--gui=qt']
ns = glob.copy()
ns.update(loc)
return start_ipython(args_ipy, user_ns=ns)
# Profiling. The builtin `profile` is added in __init__.
prof = __builtins__.get('profile', None)
if prof:
prof = __builtins__['profile']
return _profile(prof, cmd, glob, loc)
return exec_(cmd, glob, loc) | [
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7,795 | kwikteam/phy | phy/utils/cli.py | load_cli_plugins | def load_cli_plugins(cli, config_dir=None):
"""Load all plugins and attach them to a CLI object."""
from .config import load_master_config
config = load_master_config(config_dir=config_dir)
plugins = discover_plugins(config.Plugins.dirs)
for plugin in plugins:
if not hasattr(plugin, 'attach_to_cli'): # pragma: no cover
continue
logger.debug("Attach plugin `%s` to CLI.", _fullname(plugin))
# NOTE: plugin is a class, so we need to instantiate it.
try:
plugin().attach_to_cli(cli)
except Exception as e: # pragma: no cover
logger.error("Error when loading plugin `%s`: %s", plugin, e) | python | def load_cli_plugins(cli, config_dir=None):
"""Load all plugins and attach them to a CLI object."""
from .config import load_master_config
config = load_master_config(config_dir=config_dir)
plugins = discover_plugins(config.Plugins.dirs)
for plugin in plugins:
if not hasattr(plugin, 'attach_to_cli'): # pragma: no cover
continue
logger.debug("Attach plugin `%s` to CLI.", _fullname(plugin))
# NOTE: plugin is a class, so we need to instantiate it.
try:
plugin().attach_to_cli(cli)
except Exception as e: # pragma: no cover
logger.error("Error when loading plugin `%s`: %s", plugin, e) | [
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7,796 | kwikteam/phy | phy/plot/panzoom.py | PanZoom.get_mouse_pos | def get_mouse_pos(self, pos):
"""Return the mouse coordinates in NDC, taking panzoom into account."""
position = np.asarray(self._normalize(pos))
zoom = np.asarray(self._zoom_aspect())
pan = np.asarray(self.pan)
mouse_pos = ((position / zoom) - pan)
return mouse_pos | python | def get_mouse_pos(self, pos):
"""Return the mouse coordinates in NDC, taking panzoom into account."""
position = np.asarray(self._normalize(pos))
zoom = np.asarray(self._zoom_aspect())
pan = np.asarray(self.pan)
mouse_pos = ((position / zoom) - pan)
return mouse_pos | [
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7,797 | kwikteam/phy | phy/plot/panzoom.py | PanZoom.pan | def pan(self, value):
"""Pan translation."""
assert len(value) == 2
self._pan[:] = value
self._constrain_pan()
self.update() | python | def pan(self, value):
"""Pan translation."""
assert len(value) == 2
self._pan[:] = value
self._constrain_pan()
self.update() | [
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7,798 | kwikteam/phy | phy/plot/panzoom.py | PanZoom.zoom | def zoom(self, value):
"""Zoom level."""
if isinstance(value, (int, float)):
value = (value, value)
assert len(value) == 2
self._zoom = np.clip(value, self._zmin, self._zmax)
# Constrain bounding box.
self._constrain_pan()
self._constrain_zoom()
self.update() | python | def zoom(self, value):
"""Zoom level."""
if isinstance(value, (int, float)):
value = (value, value)
assert len(value) == 2
self._zoom = np.clip(value, self._zmin, self._zmax)
# Constrain bounding box.
self._constrain_pan()
self._constrain_zoom()
self.update() | [
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7,799 | kwikteam/phy | phy/plot/panzoom.py | PanZoom.pan_delta | def pan_delta(self, d):
"""Pan the view by a given amount."""
dx, dy = d
pan_x, pan_y = self.pan
zoom_x, zoom_y = self._zoom_aspect(self._zoom)
self.pan = (pan_x + dx / zoom_x, pan_y + dy / zoom_y)
self.update() | python | def pan_delta(self, d):
"""Pan the view by a given amount."""
dx, dy = d
pan_x, pan_y = self.pan
zoom_x, zoom_y = self._zoom_aspect(self._zoom)
self.pan = (pan_x + dx / zoom_x, pan_y + dy / zoom_y)
self.update() | [
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