0.0:\n # If the body is not exactly straight up/down, it has an azimuth.\n # Invert the angle to produce degrees eastward from north.\n az = math.degrees(-math.atan2(pw, pn))\n if az < 0:\n az += 360\n else:\n # The body is straight up/down, so it does not have an azimuth.\n # Report an arbitrary but reasonable value.\n az = 0.0\n\n # zd = the angle of the body away from the observer's zenith.\n zd = math.degrees(math.atan2(proj, pz))\n hor_ra = ra\n hor_dec = dec\n\n if refraction != Refraction.Airless:\n zd0 = zd\n refr = RefractionAngle(refraction, 90.0 - zd)\n zd -= refr\n if refr > 0.0 and zd > 3.0e-4:\n zdrad = math.radians(zd)\n sinzd = math.sin(zdrad)\n coszd = math.cos(zdrad)\n zd0rad = math.radians(zd0)\n sinzd0 = math.sin(zd0rad)\n coszd0 = math.cos(zd0rad)\n\n pr = [(((p[j] - coszd0 * uz[j]) / sinzd0)*sinzd + uz[j]*coszd) for j in range(3)]\n proj = math.sqrt(pr[0]*pr[0] + pr[1]*pr[1])\n if proj > 0:\n hor_ra = _RAD2HOUR * math.atan2(pr[1], pr[0])\n if hor_ra < 0:\n hor_ra += 24\n else:\n hor_ra = 0\n hor_dec = math.degrees(math.atan2(pr[2], proj))\n\n return HorizontalCoordinates(az, 90.0 - zd, hor_ra, hor_dec)","def test_check_sun_above_horizon():\n pass","def wind_shear(self):\n return self.flow_field.wind_shear","def dynamic_viscosity_of_air(self) -> float:\n\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\n self.ambient_temperature + 110.4\n )","def lower_arm(self):\r\n # ---------------------------------------------------------------------\r\n # Done: 8. Implement this method; it is a ONE-LINER!\r\n # ---------------------------------------------------------------------\r\n if self.is_calibrated == False:\r\n self.calibrate_arm()\r\n self.move_arm_to_position(0)#America\r","def checkSun(ontology_sun):\n elevation = ontology_sun.has_elevation[0] #gets the elevation value of the Sun in the ontology. \n azimuth = ontology_sun.has_azimuth[0] #gets the azimuth value of the Sun in the ontology. \n intensity = ontology_sun.has_intensity[0] #gets the intensity value of the Sun in the ontology.\n return xosc.Sun(intensity,azimuth,elevation)","def power(self):\n return irradiance_on_plane(self.vnorm, self.h,\n self.date, self.lat) * self.s * self.eff","def get_rain():\n global rain\n\n # Report rain only if the condition is 'rainy' (and not always).\n if weather_condition == CONDITION_RAINY and random.random() > 0.7:\n rain += round(random.random(), 2)\n return rain","def isstationary(self):\n if np.all(np.abs(self.arroots) > 1.0):\n return True\n else:\n return False","def get_radiation():\n sun_pos = get_sun_position()\n if sun_pos <= POSITION_MIN or sun_pos >= POSITION_MAX:\n return 0\n else:\n # Calculate a new delta.\n delta = random.randint(0, RADIATION_DELTA)\n if random.random() > 0.5:\n delta = -1 * delta\n # Calculate the radiation based on the sun position.\n new_radiation = round(-0.1279 * pow(sun_pos, 2) + 46.05 * sun_pos - 3100)\n # Apply the delta and return the value.\n return new_radiation + delta","def horizontal_arcs_iglu():\n arc(screen, BLACK, (50, 560, 300, 20), 3.14, 0)\n arc(screen, BLACK, (60, 510, 280, 20), 3.14, 0)\n arc(screen, BLACK, (80, 460, 240, 20), 3.14, 0)\n arc(screen, BLACK, (120, 420, 160, 20), 3.14, 0)","def signal_hammer(icu, icu_slope, hammer_icu, hammer_slope):\n\n return (icu > hammer_icu and icu_slope > 0) or (icu_slope > hammer_slope)","def sun(xs, ys, s, n):\n yellow = (255, 255, 0) # sun color\n\n circle(screen, yellow, (xs, ys), 30 * s) # sun body\n for k in range(n + 1): # sun rays on the upper side of the sun\n polygon(screen, yellow,\n [(xs + 45 * s * np.cos(np.pi / n * (k - 1 / 2)), ys - 45 * s * np.sin(np.pi / n * (k - 1 / 2))),\n (xs + 30 * s * np.cos(np.pi * (k - 1) / n), ys - 30 * s * np.sin(np.pi * (k - 1) / n)),\n (xs + 30 * s * np.cos(np.pi * k / n), ys - 30 * s * np.sin(np.pi * k / n))], 0)\n for k in range(n + 1): # sun rays on the lower side of the sun\n polygon(screen, yellow,\n [(xs + 45 * s * np.cos(np.pi / n * (k - 1 / 2)), ys + 45 * s * np.sin(np.pi / n * (k - 1 / 2))),\n (xs + 30 * s * np.cos(np.pi * (k - 1) / n), ys + 30 * s * np.sin(np.pi * (k - 1) / n)),\n (xs + 30 * s * np.cos(np.pi * k / n), ys + 30 * s * np.sin(np.pi * k / n))], 0)","def testRadial(self):\n self.doTest(afwGeom.makeRadialTransform([0, 1.01, 1e-7]))","def testRadial(self):\n self.doTest(afwGeom.makeRadialTransform([0, 1.01, 1e-7]))","def rhe(m):\n \n m = m*u.kg.to(u.M_sun)\n \n logr = np.full(m.shape,0)\n \n iless = np.where(m<=2.5)\n igreater = np.where(m>2.5)\n \n logr[iless] = 3.0965 - 2.013*np.log10(m[iless])\n logr[igreater] = 0.0557*(np.log10(m[igreater])-0.172)**-2.5\n return (10**logr)*u.Rsun.to(u.m)","def event2511():\n header(2511)\n\n wait(1) # Longer delay because Soothing Sunlight will keep triggering it.\n\n if_event_flag_on(1, EVENT.LustrousRuneActive)\n\n if_player_has_special_effect(-1, SPEFFECT.RunicHit0)\n if_player_has_special_effect(-1, SPEFFECT.RunicHit1)\n if_player_has_special_effect(-1, SPEFFECT.RunicHit2)\n if_player_has_special_effect(-1, SPEFFECT.RunicHit3)\n if_player_has_special_effect(-1, SPEFFECT.RunicHit4)\n if_player_has_special_effect(-1, SPEFFECT.RunicHit5)\n if_condition_true(2, -1)\n if_event_flag_off(3, EVENT.DarkAnorLondo)\n if_in_world_area(-2, 10, 1)\n if_in_world_area(-2, 10, 2)\n if_in_world_area(-2, 14, 0)\n if_in_world_area(-2, 15, 0)\n if_in_world_area(-2, 15, 1)\n if_in_world_area(-2, 17, 0)\n if_in_world_area(-2, 18, 1)\n if_condition_true(3, -2)\n if_condition_true(-3, 3)\n if_event_flag_off(4, EVENT.EarlyOolacile)\n if_in_world_area(4, 12, 1)\n if_condition_true(-3, 4)\n if_condition_true(2, -3)\n\n if_condition_true(-4, 2)\n\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive0)\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive1)\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive2)\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive3)\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive4)\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive5)\n if_condition_true(5, -5)\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight)\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 1)\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 2)\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 3)\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 4)\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight)\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 1)\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 2)\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 3)\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 4)\n if_condition_true(5, -6)\n\n if_condition_true(-4, 5)\n\n if_condition_true(1, -4)\n\n if_condition_true(0, 1)\n\n # Cancel any previous Lustrous buffs.\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune0)\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune1)\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune2)\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune3)\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune4)\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune5)\n\n # Apply appropriate level of Lustrous Rune effect, and register appropriate no-weapon condition.\n if_player_has_special_effect(1, SPEFFECT.RunicHit0)\n skip_if_condition_false(2, 1)\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune0)\n restart()\n\n if_player_has_special_effect(2, SPEFFECT.RunicHit1)\n skip_if_condition_false(2, 2)\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune1)\n restart()\n\n if_player_has_special_effect(3, SPEFFECT.RunicHit2)\n skip_if_condition_false(2, 3)\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune2)\n restart()\n\n if_player_has_special_effect(4, SPEFFECT.RunicHit3)\n skip_if_condition_false(2, 4)\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune3)\n restart()\n\n if_player_has_special_effect(5, SPEFFECT.RunicHit4)\n skip_if_condition_false(2, 5)\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune4)\n restart()\n\n if_player_has_special_effect(6, SPEFFECT.RunicHit5)\n skip_if_condition_false(1, 6)\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune5)\n\n restart()","def rainfall_event(self):\n\n # assign local variables\n datatype = 'strds'\n increment = str(self.rain_interval)+' minutes'\n raster = 'raster'\n iterations = int(self.rain_duration)/int(self.rain_interval)\n rain_excess = 'rain_excess'\n net_difference = 'net_difference'\n\n # create raster space time datasets\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.elevation_timeseries,\n title=self.elevation_title,\n description=self.elevation_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.depth_timeseries,\n title=self.depth_title,\n description=self.depth_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.erdep_timeseries,\n title=self.erdep_title,\n description=self.erdep_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.flux_timeseries,\n title=self.flux_title,\n description=self.flux_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.difference_timeseries,\n title=self.difference_title,\n description=self.difference_description,\n overwrite=True)\n\n # register the initial digital elevation model\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=self.elevation,\n start=self.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # create evolution object\n evol = Evolution(elevation=self.elevation,\n precipitation=self.precipitation,\n start=self.start,\n rain_intensity=self.rain_intensity,\n rain_interval=self.rain_interval,\n rain_duration=self.rain_duration,\n walkers=self.walkers,\n runoff=self.runoff,\n mannings=self.mannings,\n detachment=self.detachment,\n transport=self.transport,\n shearstress=self.shearstress,\n density=self.density,\n mass=self.mass,\n grav_diffusion=self.grav_diffusion,\n erdepmin=self.erdepmin,\n erdepmax=self.erdepmax,\n k_factor=self.k_factor,\n c_factor=self.c_factor,\n m=self.m,\n n=self.n,\n threads=self.threads,\n fill_depressions=self.fill_depressions)\n\n # determine mode and run model\n if self.mode == 'simwe_mode':\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.erosion_deposition()\n # remove relative timestamps from r.sim.water and r.sim.sediment\n gscript.run_command(\n 'r.timestamp',\n map=depth,\n date='none')\n gscript.run_command(\n 'r.timestamp',\n map=erosion_deposition,\n date='none')\n\n elif self.mode == \"usped_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.usped()\n\n elif self.mode == \"rusle_mode\":\n (evolved_elevation, time, depth, sediment_flux,\n difference) = evol.rusle()\n\n else:\n raise RuntimeError(\n '{mode} mode does not exist').format(mode=self.mode)\n\n # register the evolved maps\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=evolved_elevation,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.depth_timeseries,\n maps=depth,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.erdep_timeseries,\n maps=erosion_deposition,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n except (NameError, CalledModuleError):\n pass\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.flux_timeseries,\n maps=sediment_flux,\n start=evol.start,\n increment=increment,\n flags='i', overwrite=True)\n except (NameError, CalledModuleError):\n pass\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.difference_timeseries,\n maps=difference,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # run the landscape evolution model\n # as a series of rainfall intervals in a rainfall event\n i = 1\n while i < iterations:\n\n # update the elevation\n evol.elevation = evolved_elevation\n print evol.elevation\n\n # update time\n evol.start = time\n print evol.start\n\n # derive excess water (mm/hr) from rainfall rate (mm/hr)\n # plus the depth (m) per rainfall interval (min)\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_excess}\"\n \"={rain_intensity}\"\n \"+{depth}\"\n \"/1000.\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_excess=rain_excess,\n rain_intensity=self.rain_intensity,\n depth=depth,\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # update excess rainfall\n rain_intensity = 'rain_intensity'\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity} = {rain_excess}\".format(\n rain_intensity='rain_intensity',\n rain_excess=rain_excess),\n overwrite=True)\n evol.rain_intensity = rain_intensity\n\n # determine mode and run model\n if self.mode == \"simwe_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.erosion_deposition()\n # remove relative timestamps\n # from r.sim.water and r.sim.sediment\n gscript.run_command(\n 'r.timestamp',\n map=depth,\n date='none')\n gscript.run_command(\n 'r.timestamp',\n map=erosion_deposition,\n date='none')\n\n elif self.mode == \"usped_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.usped()\n\n elif self.mode == \"rusle_mode\":\n (evolved_elevation, time, depth, sediment_flux,\n difference) = evol.rusle()\n\n else:\n raise RuntimeError(\n '{mode} mode does not exist').format(mode=self.mode)\n\n # register the evolved maps\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=evolved_elevation,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.depth_timeseries,\n maps=depth,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.erdep_timeseries,\n maps=erosion_deposition,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n except (NameError, CalledModuleError):\n pass\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.flux_timeseries,\n maps=sediment_flux,\n start=evol.start,\n increment=increment,\n flags='i', overwrite=True)\n except (NameError, CalledModuleError):\n pass\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.difference_timeseries,\n maps=difference,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # remove temporary maps\n gscript.run_command(\n 'g.remove',\n type='raster',\n name=['rain_excess'],\n flags='f')\n\n i = i+1\n\n # compute net elevation change\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{net_difference}\"\n \"={evolved_elevation}-{elevation}\".format(\n net_difference=net_difference,\n elevation=self.elevation,\n evolved_elevation=evol.elevation),\n overwrite=True)\n gscript.write_command(\n 'r.colors',\n map=net_difference,\n rules='-',\n stdin=difference_colors)","def global_irradiance_overcast(latitude_deg, longitude_deg, utc_datetime, \n elevation = elevation_default, temperature_celsius = 25, \n pressure_millibars = 1013.25): \n ghioc = (572 * (solar.GetAltitude(latitude_deg, longitude_deg, utc_datetime, \n elevation , temperature_celsius , pressure_millibars )))\n \n return ghioc","def is_winter(et_cell, foo_day):\n if et_cell.latitude > 0 and (foo_day.month < 4 or foo_day.month > 10):\n # if et_cell.cell_lat > 0 and (foo_day.month < 4 or foo_day.month > 10):\n # Northern hemisphere\n return True\n else:\n # Southern hemisphere\n return False","def get_allsky(self):\n band = self.get_band()\n septon = self.is_septon()\n if band == '10_90' or band == '30_90' or septon:\n allsky = True\n else:\n allsky = False\n return allsky","def remove_rain_norain_discontinuity(R):\n R = R.copy()\n zerovalue = np.nanmin(R)\n threshold = np.nanmin(R[R > zerovalue])\n R[R > zerovalue] -= threshold - zerovalue\n R -= np.nanmin(R)\n\n return R","def bare_soil(sil):\n sil.get_color_params()\n if ((sil.l_mean < 160) and (sil.a_std > 3)):\n return False\n else:\n return True","def heaviside(rain_value):\n x = N.arange(0,70)\n P = N.where(rain_value <= x, 1., 0)\n return P","def IRound(d):\n # http://en.wikipedia.org/wiki/List_of_area_moments_of_inertia\n return np.pi / 4 * (d/2)**4","def horiz_angle(time, data):\n\n # TODO What should 0deg be? Set it to inline w/ target? facing target?\n\n # direction of the sun. measured in degrees counted clockwise from north.\n azimuth = data[time]['azimuth']\n\n h_angle = (azimuth / 2 - 90)\n\n # returns answer between -180 and 180 degrees\n return round(((h_angle + 180) % 360) - 180, 4)","def sunrise(cls, date):\n return (date + Clock.days_from_hours(6) + \n ((cls.UJJAIN.longitude - cls.LOCATION.longitude) / 360) -\n cls.equation_of_time(date) +\n ((1577917828/1582237828 / 360) *\n (cls.ascensional_difference(date, cls.LOCATION) +\n (1/4 * cls.solar_sidereal_difference(date)))))","def _is_solar_channel(self, chn):\n return self[chn].wavelength_range[2] < 3.9 or chn in[\n 'HRV', 'VIS006', 'VIS008', 'IR_016']","def power_output_existing_wind_rule(_m, g, y, s, t):\r\n\r\n if y != m.Y.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_4[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\r\n == 0)\r\n else:\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_4[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\r\n == 0)","def annular_eclipse(sat, earth, sun, time):\n\n theta, theta_e, theta_s = eclipse_parameters(sat, earth, sun, time)\n return np.logical_and(theta_s > theta_e,\n theta < (theta_s - theta_e))","def oceansim(sun_az,sun_zen,cam_head,cam_elev=0,m2=1.33,npart=1.08,mu=3.483, debug=True):\n\n #Water surface norm\n n = np.array([0,0,1])\n m1 = 1.0\n #vector from sun:\n ki = -np.asarray([np.sin(sun_az)*np.sin(sun_zen),\n np.cos(sun_az)*np.sin(sun_zen),\n np.cos(sun_zen)])\n xi = norm_cross(n,ki)\n #transmitted sunlight\n #tx, ty are the transmission amplitude coefficients in the xt, yt directions\n kt,tx,ty = Fresnel.transmission(ki,n,m1,m2)\n xt = xi\n #vector to camera\n kc = -np.asarray([np.sin(cam_head)*np.cos(cam_elev),\n np.cos(cam_head)*np.cos(cam_elev),\n np.sin(cam_elev)])*np.linalg.norm(kt)\n xc = norm_cross(n, kc) #right\n yc = norm_cross(kc, xc) #up\n #vectors for scattering\n ys = norm_cross(kt, kc) # y-axis of scattering event\n xst = norm_cross(ys, kt) # x-axis of scattering event relative to transmitted sunlight\n xsc = norm_cross(ys, kc) # x-axis of scattering event relative to camera\n #Mueller matrices\n # transmission through water surface:\n mm1 = Mueller.polarizer(tx,ty)\n # rotate to scattering plane\n mm2 = Mrotv(kt,xt,xst)\n # scatter\n th_s = vector_angle(kt,kc)\n #mm3 = Mocean(rad2deg(th_s)) #using Empirical ocean scattering\n mm3 = Mueller.rayleigh_norm(th_s) #normalized Rayleigh scattering matrix\n #b = Scattering.bsf_fournier(npart,mu)\n b = Scattering.vspf_fournier(th_s,npart,mu)\n # transform to camera's horizontal and up vectors\n mm4 = Mxform(xsc,ys, xc,yc)\n #Combined: mm4 . (b*mm3) . mm2 . mm1\n m = mm4.dot(b*mm3.dot(mm2.dot(mm1)))\n #stokes vector\n s = m.dot([1,0,0,0])\n if debug:\n return s,m,(ki,xi),(kt,xt,xst),(kc,xc,xsc),(mm1,mm2,mm3,b,mm4)\n else:\n return s,m","def vac2air(w):\n return w / (1.0 + 2.735182E-4 + 131.4182 / w ** 2 + 2.76249E8 / w ** 4)","def umbral_eclipse(sat, earth, sun, time):\n\n theta, theta_e, theta_s = eclipse_parameters(sat, earth, sun, time)\n return np.logical_and(theta_e > theta_s,\n theta < (theta_e - theta_s))","def draw_sun():\n lisandro.penup()\n lisandro.goto(40, 90)\n lisandro.begin_fill()\n lisandro.circle(150) # draws out a circle with a radius of 150 for the sun.\n lisandro.end_fill()\n lisandro.hideturtle()","def is_facing_north(): #py:is_facing_north\n return RUR._is_facing_north_()","def sun_is_down(check_time, observatory):\n sun = get_sun(check_time).transform_to(AltAz(obstime=check_time, location=observatory))\n return sun.alt.value < -14","def alpha_crit_fromEarth(a_p): #OK\n a_earth = 1. #in AU\n if a_p > a_earth:\n alpha_max = np.arcsin(a_earth/a_p) #occurs at quadrature\n else: #if a_p < a_earth:\n alpha_max = np.pi #0 deg when opposite side of sta180 deg on same side of star\n return alpha_max","def start_region(self, x, y):\n if x>0 and x<100 and y>0 and y<100:\n return 1\n elif x>700 and x<800 and y>0 and y<100:\n return 2\n elif x>0 and x<100 and y>400 and y<500:\n return 3\n elif x>700 and x<800 and y>400 and y<500:\n return 4\n return 0","def _calc_qair_ilwr(rpn_hr):\n # saturation water vapour at the dew point in the pure phase\n # which within 0.5% is that of moist air\n ew = 6.112 * numpy.exp(17.62 * rpn_hr.TD / (243.12 + rpn_hr.TD))\n xvw = ew / (0.01 * rpn_hr.PN) # converting P to hectopascals\n r = 0.62198 * xvw / (1 - xvw) # as at Td r = rw\n qair = xarray.DataArray(r / (1 + r))\n # saturation water vapour at the current temperature in the pure phase\n TT = rpn_hr.TT - 273.15 # change temperature back to Celcius\n eT = 6.112 * numpy.exp(17.62 * TT / (243.12 + TT))\n rh = 100 * (ew / eT)\n\n ew = ew / 10.0 # Change vapour pressure to from hecto pascal to kPa\n w = 465 * ew / rpn_hr.TT\n Lclr = (\n 59.38 + 113.7 * (rpn_hr.TT / 273.16) ** 6 + 96.96 * numpy.sqrt(w / 2.5)\n ) # Dilley\n # Unsworth\n sigma = 5.6697e-8\n eclr = Lclr / (sigma * rpn_hr.TT ** 4)\n ewc = (1 - 0.84 * rpn_hr.NT) * eclr + 0.84 * rpn_hr.NT\n ilwr = xarray.DataArray(ewc * sigma * rpn_hr.TT ** 4)\n\n return qair, ilwr, rh","def drawRadii(self):\n #horizontal line (which i will extend later)\n global radius_horiz_end_val\n radius_horiz_end_val = ValueTracker(self.x_max)\n global radius_horiz\n radius_horiz = always_redraw(\n lambda : Line(start=dot_center.get_center(), end=self.coords_to_point(radius_horiz_end_val.get_value(),0))\n )\n global radius_horiz_end_dot\n radius_horiz_end_dot = always_redraw(\n lambda : Dot(radius_horiz.get_end())\n )\n\n #angled radius - can alter based on ValueTracker\n global theta\n theta = ValueTracker(0)\n global radius_ang_end_dot\n radius_ang_end_dot = always_redraw(\n lambda : Dot(self.coords_to_point(self.x_max * np.cos(theta.get_value()*DEGREES),\n equation_upper(self.x_max * np.cos(theta.get_value()*DEGREES))))\n )\n\n global radius_ang\n radius_ang = always_redraw(\n lambda : Line(start=dot_center.get_center() ,end=radius_ang_end_dot.get_center())\n )\n\n self.play(Write(radius_horiz), Write(radius_horiz_end_dot),\n Write(radius_ang_end_dot), Write(radius_ang))\n self.play(theta.animate.set_value(60))","def triangulate_analytic_sun_at_center(self,r1,x2,y2,r2,x3,y3,r3):\n gamma=(r1**2+x2**2+y2**2-r2**2)/(2.0*x2)\n\ta=(y2**2)/(float(x2**2))\n\tb=-2.0*gamma*y2/x2\n\tc=gamma**2-r1**2\n\ty_plus=(-b+np.sqrt((b**2)-4*a*c))/(2.0*a)\n\ty_minus=(-b-np.sqrt((b**2)-4*a*c))/(2.0*a)\n x_plus=gamma-y_plus*y2/float(x2)\n x_minus=gamma-y_minus*y2/float(x2)\n difference_plus=(x_plus-x3)**2+(y_plus-y3)**2-r3**2\n difference_minus=(x_minus-x3)**2+(y_minus-y3)**2-r3**2\n if abs(difference_minus) < abs(difference_plus):\n print \"Difference minus\", difference_minus\n print x_minus, y_minus\n return x_minus, x_plus, difference_minus\n else:\n print \"Difference plus\", difference_plus\n print x_plus, y_plus\n return x_plus, y_plus, difference_plus","def addHorizon(horizon_altitude=np.radians(30.), lat_telescope=np.radians(33.35731944), raCen=0.):\n step = .02\n az = np.arange(0, np.pi * 2.0 + step, step)\n alt = np.ones(len(az), float) * horizon_altitude\n obs = ephem.Observer()\n obs.lat = lat_telescope\n # Set obs lon to zero, just to fix the location.\n # Note that this is not the true observatory longitude, but as long as\n # we calculate the RA at zenith for this longitude, we can still calculate\n # HA appropriately.\n obs.lon = 0\n obs.pressure = 0\n # Given obs lon at zero, find the equivalent ra overhead.\n zenithra, zenithlat = obs.radec_of(0, 90)\n lon = np.zeros(len(az), float)\n lat = np.zeros(len(az), float)\n for i, (alti, azi) in enumerate(zip(alt, az)):\n # Find the equivalent ra/dec values for an alt/az circle.\n r, lat[i] = obs.radec_of(azi, alti)\n # Correct the ra value by the zenith ra value, to get the HA.\n lon[i] = r - zenithra\n lon = -(lon - np.pi) % (np.pi * 2) - np.pi\n return lon, lat","def shooting_star(self):\n self.data['shooting_star'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*4)) & \\\n ((self.data['High'] - self.data['Close']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)) & \\\n ((self.data['High'] - self.data['Open']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)))","def mask_nighttime(lon, lat, date=date, mask_daytime=mask_daytime,\n ref_date=datetime.datetime(1899, 12, 31, 12),\n buffer_hours=buffer_hours, debug=False):\n # --- get lat and lon values from columns\n if debug:\n print((\"--- (s4-1) %s seconds ---\" % (time.time() - start_time)))\n # --- get sunrise and sunset for location\n o = ephem.Observer()\n # set lat (decimal?), lon (decimal?), and date (UTC)\n o.lat = str(lat)\n o.long = str(lon)\n o.date = date\n # planetary body\n s = ephem.Sun()\n if debug:\n print((\"--- (s4-2) %s seconds ---\" % (time.time() - start_time)))\n\n # Compute sun vs observer\n s.compute()\n if debug:\n print((\"--- (s4-3) %s seconds ---\" % (time.time() - start_time)))\n\n # Work out if day or night based on sunrises and sunsets\n mask_value = 0\n try:\n\n # get sunrise time and date\n next_rising = o.next_rising(s)\n next_setting = o.next_setting(s)\n\n # convert to datetime.datetime\n next_rising = add_days(ref_date, next_rising)\n next_setting = add_days(ref_date, next_setting)\n\n # did the sun last rise or set? (inc. any adjustments)\n sun_last_rose = False\n if next_setting < next_rising:\n sun_last_rose = True\n\n # Add buffer to rising/setting if provided with buffer_hours\n if buffer_hours != 0:\n\n # Calculate last rise\n previous_rising = o.previous_rising(s)\n # convert to datetime.datetime\n previous_rising = add_days(ref_date, previous_rising)\n # Calculate last setting\n previous_setting = o.previous_setting(s)\n # convert to datetime.datetime\n previous_setting = add_days(ref_date, previous_setting)\n\n # Calculate absolute difference\n time_from_rise = (date-previous_rising).total_seconds()\n time_till_set = (date-next_setting).total_seconds()\n time_from_set = (date-previous_setting).total_seconds()\n time_till_rise = (date-next_rising).total_seconds()\n\n # If absolutely difference less than buffer\n if abs(time_from_rise)/60./60. <= buffer_hours:\n mask_value = 1\n elif abs(time_till_set)/60./60. <= buffer_hours:\n mask_value = 1\n elif abs(time_from_set)/60./60. < buffer_hours:\n mask_value = 1\n elif abs(time_till_rise)/60./60. < buffer_hours:\n mask_value = 1\n\n # --- Check if daytime or nighttime and mask if condition met.\n if sun_last_rose:\n if mask_daytime:\n # ... and has not set yet, it must be daytime\n if (date < next_setting):\n mask_value = 1\n\n # if the sun last set... (mask nighttime is default)\n else:\n # if mask nighttime (aka not mask_daytime)\n if not mask_daytime:\n # ... and has not risen yet, it must be nighttime\n if (date < next_rising):\n mask_value = 1\n\n # Add gotcha for locations where sun is always up.\n except AlwaysUpError:\n if mask_daytime:\n mask_value = 1\n\n # Add gotcha for locations where sun is always down.\n except NeverUpError:\n if not mask_daytime:\n mask_value = 1\n\n except:\n print('FAIL')\n sys.exit()\n\n # Mask value in array\n return mask_value","def constrain_sun(self, position: str) -> bool:\n if (position == \"up\" and self.sun_up()) or (\n position == \"down\" and self.sun_down()\n ):\n return True\n return False","def hydro_solver(self):\n u_dx = self.central_x(self.u)\n w_dy = self.central_y(self.w)\n P_dx = self.central_x(self.P)\n P_dy = self.central_y(self.P)\n\n rho_dx_upwind = self.upwind_x(self.rho, self.u)\n rho_dy_upwwind = self.upwind_y(self.rho, self.w)\n rho_udx_upwind = self.upwind_x(self.rho * self.u, self.u)\n rho_udy_upwind = self.upwind_y(self.rho * self.u, self.w)\n rho_wdx_upwind = self.upwind_x(self.rho * self.w, self.u)\n rho_wdy_upwind = self.upwind_y(self.rho * self.w, self.w)\n u_dx_uu = self.upwind_x(self.u, self.u)\n u_dx_uw = self.upwind_x(self.u, self.w)\n w_dy_uu = self.upwind_y(self.w, self.u)\n w_dy_uw = self.upwind_y(self.w, self.w)\n e_dx = self.upwind_x(self.e, self.u)\n e_dy = self.upwind_y(self.e, self.w)\n\n self.rho_dt = (\n -self.rho * (u_dx + w_dy)\n - self.u * rho_dx_upwind\n - self.w * rho_dy_upwwind\n )\n self.e_dt = (\n -(self.e + self.P) * (u_dx + w_dy) - self.u * e_dx - self.w * e_dy\n )\n self.rho_udt = (\n -self.rho * self.u * (u_dx_uu + w_dy_uu)\n - self.u * rho_udx_upwind\n - self.w * rho_udy_upwind\n - P_dx\n )\n self.rho_wdt = (\n -self.rho * self.w * (u_dx_uw + w_dy_uw)\n - self.u * rho_wdx_upwind\n - self.w * rho_wdy_upwind\n - P_dy\n + self.rho * self.g\n )\n\n self.time_step()\n rho_previous = np.zeros_like(self.rho)\n rho_previous[:, :] = self.rho\n self.rho[:, :] = self.rho + self.rho_dt * self.dt\n self.e[:, :] = self.e + self.e_dt * self.dt\n self.u[:, :] = (\n rho_previous * self.u + self.rho_udt * self.dt\n ) / self.rho\n self.w[:, :] = (\n rho_previous * self.w + self.rho_wdt * self.dt\n ) / self.rho\n\n self.boundary_conditions()\n self.T[:, :] = (\n (self.Y - 1) * self.e * self.mu * self.m_u / (self.kb * self.rho)\n )\n self.P[:, :] = (self.Y - 1) * self.e\n uw = (self.u, self.w)\n v = np.linalg.norm(uw)\n dt = self.dt\n\n return dt","def r2_law(array, i):\n npix = np.shape(array)[1]\n M = np.array([int(npix/2), int(npix/2)])\n yy, xx = np.ogrid[-M[0]:npix-M[0], -M[1]:npix-M[1]]\n\n np.seterr(divide='ignore')\n light = npix / (xx ** 2 + (yy / (np.cos(np.deg2rad(i)))) ** 2)\n\n light[light == np.inf] = 0\n return light * array","def looks_azimuth(self) -> Optional[int]:\n return self._get_property(LOOKS_AZIMUTH_PROP, int)","def alt_sunrise(cls, date):\n rise = cls.UJJAIN.dawn(date, angle(0, 47, 0))\n return 1/24 * 1/60 * iround(rise * 24 * 60)","def runoff_land(water_raster, lambda_param, wp_raster, p_raster, s_pre_raster, fk_raster):\n r_land = Con(water_raster == 0,\n lambda_param * ((s_pre - wp_raster) ** 2) + overflow(p_raster, s_pre_raster, fk_raster), 0)\n\n print(time.strftime(\"%H:%M:%S: \") + \"Landabfluss ausgeführt.\")\n return r_land","def check_if_in_shadow(psi, a_sat_vector, sun_pos):\n dot_prod = np.dot(a * n_vector.T, unit_sun_r(sun_pos))\n\n check = np.zeros((len(a_sat_vector)))\n if np.cos(psi) < 0 and a_sat_vector < r_earth and dot_prod <= r_earth:\n check = True\n\n return check","def chance_of_rain(self):\r\n # Amount of yesterday's rain indicating chance of it occurring.\r\n NO_RAIN = 0.1\r\n LITTLE_RAIN = 3\r\n SOME_RAIN = 8\r\n # Chance of rain occurring.\r\n NONE = 0\r\n MILD = 40\r\n PROBABLE = 75\r\n LIKELY = 90\r\n\r\n if self._yesterdays_weather.get_rainfall() < NO_RAIN:\r\n chance_of_rain = NONE\r\n elif self._yesterdays_weather.get_rainfall() < LITTLE_RAIN:\r\n chance_of_rain = MILD\r\n elif self._yesterdays_weather.get_rainfall() < SOME_RAIN:\r\n chance_of_rain = PROBABLE\r\n else:\r\n chance_of_rain = LIKELY\r\n\r\n return chance_of_rain","def set_rowland_radius(self, Rm, isJohansson=False):\n self.oe1.F_EXT = 1\n self.oe1.FMIRR = 1\n if isJohansson:\n self.oe1.set_johansson(Rm*2.)\n else:\n self.oe1.F_JOHANSSON = 0\n self.oe1.set_radius(Rm*2.)","def _create_rain(self):\n r_calc = self._calculate_spacing()\n # Create the full screen of raindrops.\n for raindrop_y in range(r_calc[3]):\n self._create_raindrops_y(raindrop_y)","def fireWest(self):\n self.rotate('w')\n gun = Laser(self)\n gun.shoot('w')\n self.agent.actionCompleted()","def calc_incidence_angle():\n \n Delta_r, lat_r, Omega_r, Zenith_r, Azimuth_r, Elev_angle = solar_model()\n \n # Beta is equal to angle of tilted surface to horizontal (in radians)\n roof_slopes_west = section_coordinates()\n Beta_r = np.arctan(roof_slopes_west) \n incidence_angles_west = np.zeros(101)\n \n \n for i in range(0,len(roof_slopes_west)):\n incidence_angles_west[i] = np.arccos(np.sin(Delta_r)* np.sin(lat_r) * np.cos(Beta_r[i]) - np.sin(Delta_r) * np.cos(lat_r) * np.sin(Beta_r[i]) * np.cos(Azimuth_r) + np.cos(Delta_r) * np.cos(lat_r) * np.cos(Beta_r[i]) * np.cos(Omega_r) + np.cos(Delta_r) * np.sin(lat_r) * np.sin(Beta_r[i]) * np.cos(Azimuth_r) * np.cos(Omega_r) + np.cos(Delta_r) * np.sin(Beta_r[i]) * np.sin(Azimuth_r) * np.sin(Omega_r))","def get_horizontal_solar(region: int) -> np.ndarray:\n\n return read_conditions.get_horizontal_solar(region)","def penumbral_eclipse(sat, earth, sun, time):\n\n theta, theta_e, theta_s = eclipse_parameters(sat, earth, sun, time)\n return np.logical_and(np.abs(theta_e - theta_s) < theta,\n theta < (theta_e + theta_s))","def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature","def event1924():\n header(1924)\n if_not_in_world_area(1, 13, 0)\n if_not_in_world_area(1, 13, 1)\n if_condition_true(0, 1)\n flag.disable(11310201)","def test_1b_is_stormy(self):\n random.seed( 9 )\n self.assertFalse(self.weather.is_stormy())","def wears_jacket(temp, raining):\n\treturn raining or temp<60","def test_handle_weather_tomorrow_calls_darksky_correctly(self):\n pass","def aire(self, r):\r\n self.r_num(r)\r\n return self.r**2 * pi","def fireEast(self):\n self.rotate('e')\n gun = Laser(self)\n gun.shoot('e')\n self.agent.actionCompleted()","def func(self):\n account = self.account\n city_name = 'Phoenix' if not self.args else self.args\n a = Astral()\n a.solar_depression = 'civil'\n city = a[city_name]\n if not city:\n return\n timezone = city.timezone\n sun = city.sun(date=datetime.date.today(), local=True)\n\n account.msg('Information for %s/%s\\n' % (city_name, city.region))\n account.msg('Timezone: %s' % timezone)\n account.msg('Latitude: %.02f; Longitude: %.02f' % (city.latitude, city.longitude))\n account.msg('Dawn: %s' % str(sun['dawn']))\n account.msg('Sunrise: %s' % str(sun['sunrise']))\n account.msg('Noon: %s' % str(sun['noon']))\n account.msg('Sunset: %s' % str(sun['sunset']))\n account.msg('Dusk: %s' % str(sun['dusk']))","def is_slope(self):\n\t\tif self.high_elevation != self.low_elevation:\n\t\t\treturn True\n\t\treturn False","def apparent(self):\n jd = self.jd\n position_au = self.position.au.copy()\n observer = self.observer\n\n if observer.geocentric:\n include_earth_deflection = array((False,))\n else:\n limb_angle, nadir_angle = compute_limb_angle(\n position_au, observer.position.au)\n include_earth_deflection = limb_angle >= 0.8\n\n add_deflection(position_au, observer.position.au, observer.ephemeris,\n jd, include_earth_deflection)\n add_aberration(position_au, observer.velocity.au_per_d, self.light_time)\n\n a = Apparent(position_au, jd=jd)\n a.observer = self.observer\n return a","def throw(self):\n\n self.vx = (2 * random.random()) - 1\n self.vy = (4 * random.random()) + 4","def is_armed_night(self):\n return self in (\n ArmingState.ARMED_STAY_NIGHT,\n ArmingState.ARMED_STAY_NIGHT_BYPASS_PROA7,\n ArmingState.ARMED_STAY_NIGHT_INSTANT_PROA7,\n ArmingState.ARMED_STAY_NIGHT_INSTANT_BYPASS_PROA7,\n )","def test_interpolation():\n spiral_arm = survey.get_spiral_slice(track = \"perseus\", \n interpolate = True)\n spiral_arm2 = survey.get_spiral_slice(track = \"Per\", \n interpolate = False)\n\n assert np.allclose(spiral_arm[\"INTEN\"], spiral_arm2[\"INTEN\"], equal_nan = True)","def sun_isoperimetric_ratio(image, mode_viz=False): # On distorted image\r\n try :\r\n _, _, sun_mask = sun_center(image)\r\n except TypeError :\r\n return np.nan\r\n # We blurr the image and re-binarize it\r\n blurred_mask = mahotas.gaussian_filter(sun_mask, 0.7)\r\n blurred_mask = (blurred_mask > blurred_mask.mean())\r\n # Obtain a binary image with the sun border in white pixels\r\n sun_perim_mask = mahotas.labeled.bwperim(blurred_mask, 8)\r\n # Compute the perimeter in pixels\r\n sun_perim = int(perimeter(sun_perim_mask))\r\n # Compute the surface in pixels\r\n sun_surface = np.sum(blurred_mask)\r\n # ratio = 4*pi*S/(P^2). Is in [0,1], equals 1 for a circle\r\n ratio = 4*np.pi*sun_surface/(sun_perim**2)\r\n if mode_viz:\r\n # Plot\r\n # print(f\"perimeter = {sun_perim} | surface = {sun_surface}\")\r\n fig = plt.figure(figsize=(12, 6))\r\n ax1 = fig.add_subplot(121)\r\n ax1.imshow(blurred_mask, cmap=\"gray\")\r\n ax2 = fig.add_subplot(122)\r\n ax2.imshow(sun_perim_mask, cmap=\"gray\")\r\n plt.show()\r\n return np.round(ratio, 3)","def H_outside(self, r):\n\n mu_star = self.mu_star(r)\n\n res = 0.5 * self.S * (0.5 * (1. - mu_star**2) -\n quad(self.H_integ_outside, mu_star, 1,\n args=(r,))[0])\n return res","def isFim(self):\r\n return self.sair","def is_artificial(self):\n\t\treturn 0","def power_output_existing_solar_rule(_m, g, y, s, t):\r\n\r\n if y != m.Y.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_6[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\r\n == 0)\r\n\r\n else:\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_6[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\r\n == 0)","def background(self):\n sun = graphics.Circle(graphics.Point(200, 310), 50)\n sun.setFill('yellow')\n sun.draw(self.win)\n \n earth = graphics.Circle(graphics.Point(40, 250), 30)\n earth.setFill('blue')\n earth.draw(self.win)\n continent = graphics.Circle(graphics.Point(30, 265), 10)\n continent.setFill('green')\n continent.draw(self.win)\n cont_2 = graphics.Circle(graphics.Point(30, 235), 10)\n cont_2.setFill('green')\n cont_2.draw(self.win)\n cont_3 = graphics.Circle(graphics.Point(55, 245), 10)\n cont_3.setFill('green')\n cont_3.draw(self.win)\n \n stars = graphics.Circle(graphics.Point(250, 250), 5)\n stars.setFill('white')\n stars.draw(self.win)\n star1 = graphics.Circle(graphics.Point(100, 250), 5)\n star1.setFill('white')\n star1.draw(self.win)\n star2 = graphics.Circle(graphics.Point(150, 150), 5)\n star2.setFill('white')\n star2.draw(self.win)\n star3 = graphics.Circle(graphics.Point(50, 100), 5)\n star3.setFill('white')\n star3.draw(self.win)\n star3 = graphics.Circle(graphics.Point(100, 50), 5)\n star3.setFill('white')\n star3.draw(self.win)\n star4 = graphics.Circle(graphics.Point(250, 80), 5)\n star4.setFill('white')\n star4.draw(self.win)\n star4 = graphics.Circle(graphics.Point(200, 60), 5)\n star4.setFill('white')\n star4.draw(self.win)","def radialOuter(self):\n if self.radial in range(1, len(self.ThRZmesh.getPositions(label=\"R\"))):\n R = self.ThRZmesh.getUpper(label=\"R\", n=(self.radial))\n else:\n runLog.warning(\n \"Error: Radial Index ({0}) location not INSIDE mesh \".format(\n self.radial\n )\n )\n runLog.warning(self.ThRZmesh.getPositions(label=\"R\"))\n R = None\n return R","def draw_aim(self):\n polygon(screen, self.color, [(self.x, self.y), (self.x + self.r * 1.71 / 2, self.y - self.r / 2),\n (self.x + self.r * 1.71, self.y), (self.x + self.r * 1.71, self.y + self.r),\n (self.x + self.r * 1.71 / 2, self.y + 3 * self.r / 2), (self.x, self.y + self.r)])","def rusle(self):\n\n # assign variables\n ls_factor = 'ls_factor'\n slope = 'slope'\n grow_slope = 'grow_slope'\n flowacc = 'flowacc'\n sedflow = 'sedflow'\n sedflux = 'flux'\n\n # parse, advance, and stamp time\n (evolved_elevation, time, depth, sediment_flux, erosion_deposition,\n difference) = self.parse_time()\n\n # compute event-based erosivity (R) factor (MJ mm ha^-1 hr^-1 yr^-1)\n r_factor = self.event_based_r_factor()\n\n # compute slope\n gscript.run_command(\n 'r.slope.aspect',\n elevation=self.elevation,\n slope=slope,\n overwrite=True)\n\n # grow border to fix edge effects of moving window computations\n gscript.run_command(\n 'r.grow.distance',\n input=slope,\n value=grow_slope,\n overwrite=True)\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{slope}={grow_slope}\".format(\n slope=slope,\n grow_slope=grow_slope),\n overwrite=True)\n\n # compute flow accumulation\n gscript.run_command(\n 'r.watershed',\n elevation=self.elevation,\n accumulation=flowacc,\n flags=\"a\",\n overwrite=True)\n region = gscript.parse_command(\n 'g.region', flags='g')\n res = region['nsres']\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{depth}\"\n \"=({flowacc}*{res})\".format(\n depth=depth,\n flowacc=flowacc,\n res=res),\n overwrite=True)\n\n # compute dimensionless topographic factor\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{ls_factor}\"\n \"=({m}+1.0)\"\n \"*(({flowacc}/22.1)^{m})\"\n \"*((sin({slope})/5.14)^{n})\".format(\n ls_factor=ls_factor,\n m=self.m,\n flowacc=depth,\n slope=slope,\n n=self.n),\n overwrite=True)\n\n # compute sediment flow\n \"\"\"E = R * K * LS * C * P\n where\n E is average annual soil loss\n R is erosivity factor\n K is soil erodibility factor\n LS is a dimensionless topographic (length-slope) factor\n C is a dimensionless land cover factor\n P is a dimensionless prevention measures factor\n \"\"\"\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{sedflow}\"\n \"={r_factor}\"\n \"*{k_factor}\"\n \"*{ls_factor}\"\n \"*{c_factor}\".format(\n sedflow=sedflow,\n r_factor=r_factor,\n k_factor=self.k_factor,\n ls_factor=ls_factor,\n c_factor=self.c_factor),\n overwrite=True)\n\n # convert sediment flow from tons/ha/yr to kg/m^2s\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{converted_sedflow}\"\n \"={sedflow}\"\n \"*{ton_to_kg}\"\n \"/{ha_to_m2}\"\n \"/{yr_to_s}\".format(\n converted_sedflow=sedflux,\n sedflow=sedflow,\n ton_to_kg=1000.,\n ha_to_m2=10000.,\n yr_to_s=31557600.),\n overwrite=True)\n\n # filter outliers\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{sediment_flux}\"\n \"=if({sedflux}>{erdepmax},{erdepmax},{sedflux})\".format(\n sediment_flux=sediment_flux,\n sedflux=sedflux,\n erdepmax=self.erdepmax),\n overwrite=True)\n gscript.run_command(\n 'r.colors',\n map=sediment_flux,\n color='viridis',\n flags='g')\n\n # evolve landscape\n \"\"\"\n change in elevation (m)\n = change in time (s)\n * sediment flux (kg/ms)\n / mass of sediment per unit area (kg/m^2)\n \"\"\"\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{evolved_elevation}\"\n \"={elevation}\"\n \"-({rain_interval}*60\"\n \"*{sediment_flux}\"\n \"/{mass})\".format(\n evolved_elevation=evolved_elevation,\n elevation=self.elevation,\n rain_interval=self.rain_interval,\n sediment_flux=sediment_flux,\n mass=self.mass),\n overwrite=True)\n\n # gravitational diffusion\n evolved_elevation = self.gravitational_diffusion(evolved_elevation)\n\n # compute elevation change\n difference = self.compute_difference(evolved_elevation, difference)\n\n # remove temporary maps\n gscript.run_command(\n 'g.remove',\n type='raster',\n name=['slope',\n 'grow_slope',\n 'flowacc',\n 'sedflow',\n 'flux',\n 'settled_elevation',\n 'divergence',\n 'r_factor',\n 'ls_factor'],\n flags='f')\n\n return (evolved_elevation, time, depth, sediment_flux, difference)","def _check_und_conds(xss, axs, **kw):\n und = []\n for xs in xss:\n und += [(i.y <= 0) for i in xs.conds]\n if(any(und)):\n #draw ground surface line\n xl = axs[0].get_xlim()\n kw['H'].append(axs[0].plot(xl, [0]*2, ':', color=_ground_surface_color,\n linewidth=_ground_surface_linewidth)[0])\n kw['L'].append('Ground Surface')\n for ax in axs:\n #apply padding\n yl = ax.get_ylim()\n r = yl[1] - yl[0]\n ax.set_ylim(yl[0] - r*0.05, yl[1])\n else:\n for ax in axs:\n yl = ax.get_ylim()\n ax.set_ylim(0, yl[1])","def update_conditions(self) -> None:\n self.log.debug(\"Updating conditions.\")\n\n self.models[\"sky\"].update(self.models[\"observatory_state\"].time)\n\n if self.is_night is None:\n self.log.debug(\"Driver not initialized yet. Computing night parameters.\")\n # Driver was not initialized yet. Need to compute night\n # boundaries\n\n (self.current_sunset, self.current_sunrise) = self.models[\n \"sky\"\n ].get_night_boundaries(self.parameters.night_boundary)\n\n self.is_night = (\n self.current_sunset\n <= self.models[\"observatory_state\"].time\n < self.current_sunrise\n )\n\n self.log.debug(\n f\"Sunset/Sunrise: {self.current_sunset}/{self.current_sunrise}, \"\n f\"sun @ {self.parameters.night_boundary} degrees.\"\n )\n\n is_night = self.is_night\n\n self.is_night = (\n self.current_sunset\n <= self.models[\"observatory_state\"].time\n < self.current_sunrise\n )\n\n # Only compute night boundaries when we transition from nighttime to\n # daytime. Possibilities are:\n # 1 - self.is_night=True and is_night = True: During the night (no need\n # to compute anything).\n # 2 - self.is_night=False and is_night = True: Transitioned from\n # night/day (need to recompute night boundaries).\n # 3 - self.is_night=True and is_night = False: Transitioned from\n # day/night (no need to compute anything).\n # 4 - self.is_night=False and is_night = False: During the day, no need\n # to compute anything.\n if not self.is_night and is_night:\n self.log.debug(\n \"Night over. Computing next night boundaries. \"\n f\"Assuming sun elevation of {self.parameters.night_boundary}.\"\n )\n self.night += 1\n (self.current_sunset, self.current_sunrise) = self.models[\n \"sky\"\n ].get_night_boundaries(self.parameters.night_boundary)\n\n self.log.debug(\n f\"[{self.night}]: Sunset/Sunrise: {self.current_sunset}/{self.current_sunrise} \"\n )","def inverted_hammer(self):\n self.data['inverted_hammer'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*3)) & \\\n ((self.data['High'] - self.data['Close']) / ((.001 + self.data['High'] - self.data['Low']) > 0.6)) & \\\n ((self.data['High'] - self.data['Open']) / ((.001 + self.data['High'] - self.data['Low']) > 0.6)))","def get_weather_with_time(time):\n global DARK\n\n if TIME in range(6, 9):\n DARK = False\n return 1\n elif TIME in range(9, 13):\n return 2\n elif TIME in range(13, 16):\n return 3\n elif TIME in range(16, 19):\n if HAS_RAINCOAT:\n return 4\n else:\n if not NICE_WEATHER:\n add_strength(False, 10)\n return 5\n\n elif TIME in range(19, 22):\n if HAS_RAINCOAT:\n return 7\n else:\n if not NICE_WEATHER:\n add_strength(False, 10)\n return 6\n\n else: # 9 - 6am\n DARK = True\n if HAS_FLASHLIGHT:\n return 9\n else:\n return 8","def elevation(self, rover):\n\t\tcurrent_tile = rover.planet.tiles[rover.y][rover.x]\n\t\t#current_tile is slope\n\t\tif current_tile.is_slope():\n\t\t\t#self is slope current_tile is slope\n\t\t\tif self.is_slope():\n\t\t\t\tif current_tile.high_elevation == self.low_elevation:\n\t\t\t\t\treturn \"/\"\n\t\t\t\tif current_tile.low_elevation == self.high_elevation:\n\t\t\t\t\treturn \"\\\\\"\n\t\t\t\tif self.high_elevation < current_tile.low_elevation:\n\t\t\t\t\treturn \"-\"\n\t\t\t\tif self.low_elevation > current_tile.high_elevation:\n\t\t\t\t\treturn \"+\"\n\t\t\t\tif self.low_elevation == current_tile.low_elevation\\\n\t\t\t\t\tand self.high_elevation == current_tile.high_elevation:\n\t\t\t\t\treturn \" \"\n\t\t\t#self is flat current_tile is slope\n\t\t\telse:\n\t\t\t\tif self.low_elevation > current_tile.high_elevation:\n\t\t\t\t\treturn \"+\"\n\t\t\t\tif self.low_elevation < current_tile.low_elevation:\n\t\t\t\t\treturn \"-\"\n\t\t\t\treturn \" \"\n\n\n\t\telse: #current_tile is flat\n\t\t\t#self is slope current_tile is flat\n\t\t\tif self.is_slope():\n\t\t\t\tif self.low_elevation == current_tile.low_elevation:\n\t\t\t\t\treturn \"/\"\n\t\t\t\tif self.high_elevation == current_tile.low_elevation:\n\t\t\t\t\treturn \"\\\\\"\n\t\t\t\tif self.low_elevation > current_tile.low_elevation:\n\t\t\t\t\treturn \"+\"\n\t\t\t\tif self.high_elevation < current_tile.low_elevation:\n\t\t\t\t\treturn \"-\"\n\t\t\t#self is flat current_tile is flat\n\t\t\telse:\n\t\t\t\tif self.low_elevation > current_tile.low_elevation:\n\t\t\t\t\treturn \"+\"\n\t\t\t\tif self.high_elevation < current_tile.low_elevation:\n\t\t\t\t\treturn \"-\"\n\t\t\treturn \" \"","def calc_spectral_olr(self, atmosphere, surface):\n f, _, irradiance_field, _ = self.calc_spectral_irradiance_field(\n atmosphere=atmosphere, t_surface=surface[\"temperature\"][0]\n )\n return f, irradiance_field[:, -1, 0, 0, 1]","def __init__(self, thresh=0.1, ar_depth=1, rnd_key=random.PRNGKey(42)):\n self.thresh = thresh\n self.ar_depth = ar_depth\n\n params = random.normal(rnd_key, shape=(ar_depth+1, ))\n self.did_fit = False\n super().__init__('RainDay', params)","def slopecal(ton):\r\n global _ton_old\r\n global _slope\r\n\r\n print(\"ton: \", ton, \" _ton_old: \", _ton_old, \" substraction: \", round((ton - _ton_old),2))\r\n\r\n if round((ton - _ton_old), 2) >= 5:\r\n _slope = 1\r\n if round((ton - _ton_old), 2) <= -10:\r\n _slope = -1\r\n _ton_old = ton","def sunrise(self):\r\n try:\r\n return str(self.connect()['sys']['sunrise'])\r\n except:\r\n return '@weather_sunrise'","def morir(self):\n self.energia = 0\n self.vivo = False","def determine_analytic_solution(self):\n\n self._Janalytic = np.where(self.xr <= self.xint, self.S, 0.5 * self.S)\n self._Hanalytic = np.where(self.xr <= self.xint, 0, 0.25 * self.S)\n self._Kanalytic = np.where(self.xr <= self.xint, 1./3. * self.S,\n 1./6. * self.S)","def hanging_man(self):\n self.data['hanging_man'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*4)) & \\\n ((self.data['Close'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)) &\\\n ((self.data['Open'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= .075)))","def _update_rain(self):\n self.rain.update()\n self._make_new_drops()","def SearchRiseSet(body, observer, direction, startTime, limitDays):\n if body == Body.Earth:\n raise EarthNotAllowedError()\n elif body == Body.Sun:\n body_radius = _SUN_RADIUS_AU\n elif body == Body.Moon:\n body_radius = _MOON_EQUATORIAL_RADIUS_AU\n else:\n body_radius = 0.0\n\n if direction == Direction.Rise:\n ha_before = 12.0 # minimum altitude (bottom) happens BEFORE the body rises.\n ha_after = 0.0 # maximum altitude (culmination) happens AFTER the body rises.\n elif direction == Direction.Set:\n ha_before = 0.0 # culmination happens BEFORE the body sets.\n ha_after = 12.0 # bottom happens AFTER the body sets.\n else:\n raise Error('Invalid value for direction parameter')\n\n context = _peak_altitude_context(body, direction, observer, body_radius)\n\n # See if the body is currently above/below the horizon.\n # If we are looking for next rise time and the body is below the horizon,\n # we use the current time as the lower time bound and the next culmination\n # as the upper bound.\n # If the body is above the horizon, we search for the next bottom and use it\n # as the lower bound and the next culmination after that bottom as the upper bound.\n # The same logic applies for finding set times, only we swap the hour angles.\n time_start = startTime\n alt_before = _peak_altitude(context, time_start)\n if alt_before > 0.0:\n # We are past the sought event, so we have to wait for the next \"before\" event (culm/bottom).\n evt_before = SearchHourAngle(body, observer, ha_before, time_start)\n time_before = evt_before.time\n alt_before = _peak_altitude(context, time_before)\n else:\n # We are before or at the sought ebvent, so we find the next \"after\" event (bottom/culm),\n # and use the current time as the \"before\" event.\n time_before = time_start\n\n evt_after = SearchHourAngle(body, observer, ha_after, time_before)\n alt_after = _peak_altitude(context, evt_after.time)\n\n while True:\n if alt_before <= 0.0 and alt_after > 0.0:\n # Search between the \"before time\" and the \"after time\" for the desired event.\n event_time = Search(_peak_altitude, context, time_before, evt_after.time, 1.0)\n if event_time is not None:\n return event_time\n # We didn't find the desired event, so use the \"after\" time to find the next \"before\" event.\n evt_before = SearchHourAngle(body, observer, ha_before, evt_after.time)\n evt_after = SearchHourAngle(body, observer, ha_after, evt_before.time)\n if evt_before.time.ut >= time_start.ut + limitDays:\n return None\n time_before = evt_before.time\n alt_before = _peak_altitude(context, evt_before.time)\n alt_after = _peak_altitude(context, evt_after.time)","def check_angle_of_arcs(self):\n\n if self.thin_arc_start_angle >= 3600:\n self.thin_arc_start_angle %= 360\n self.thin_arc_start_angle += 360\n\n elif self.thin_arc_start_angle <= -3600:\n self.thin_arc_start_angle %= 360\n self.thin_arc_start_angle -= 360\n\n if self.thin_arc_end_angle >= 3600:\n self.thin_arc_end_angle %= 360\n self.thin_arc_end_angle += 360\n\n elif self.thin_arc_end_angle <= -3600:\n self.thin_arc_end_angle %= 360\n self.thin_arc_end_angle -= 360\n\n if self.thick_arc_start_angle >= 3600:\n self.thick_arc_start_angle %= 360\n self.thick_arc_start_angle += 360\n\n elif self.thick_arc_start_angle <= -3600:\n self.thick_arc_start_angle %= 360\n self.thick_arc_start_angle -= 360\n\n if self.thick_arc_end_angle >= 3600:\n self.thick_arc_end_angle %= 360\n self.thick_arc_end_angle += 360\n\n elif self.thick_arc_end_angle <= -3600:\n self.thick_arc_end_angle %= 360\n self.thick_arc_end_angle -= 360","def boundary_conditions(self):\n ce = 2 * self.dy * self.g * self.mu * self.m_u / self.kb\n self.e[0, :] = (4 * self.e[1, :] - self.e[2, :]) / (\n ce / self.T[0, :] + 3\n )\n self.rho[0, :] = (\n self.e[0, :]\n * (self.Y - 1)\n * self.mu\n * self.m_u\n / (self.kb * self.T[0, :])\n )\n self.u[0, :] = (4 * self.u[1, :] - self.u[2, :]) / 3\n self.w[0, :] = 0\n\n self.e[-1, :] = (4 * self.e[-2, :] - self.e[-3, :]) / (\n 3 - ce / self.T[-1, :]\n )\n self.rho[-1, :] = (\n self.e[-1, :]\n * (self.Y - 1)\n * self.mu\n * self.m_u\n / (self.kb * self.T[-1, :])\n )\n self.u[-1, :] = (4 * self.u[-2, :] - self.u[-3, :]) / 3\n self.w[-1, :] = 0","def daily_insolation_limits(irrad, clearsky, daily_min=0.4, daily_max=1.25):\n daily_irradiance = _daily_total(irrad)\n daily_clearsky = _daily_total(clearsky)\n good_days = quality.util.check_limits(\n daily_irradiance/daily_clearsky,\n upper_bound=daily_max,\n lower_bound=daily_min\n )\n return good_days.reindex(irrad.index, method='pad', fill_value=False)","def Illumination(body, time):\n if body == Body.Earth:\n raise EarthNotAllowedError()\n earth = _CalcEarth(time)\n if body == Body.Sun:\n gc = Vector(-earth.x, -earth.y, -earth.z, time)\n hc = Vector(0.0, 0.0, 0.0, time)\n phase = 0.0 # placeholder value; the Sun does not have a phase angle.\n else:\n if body == Body.Moon:\n # For extra numeric precision, use geocentric moon formula directly.\n gc = GeoMoon(time)\n hc = Vector(earth.x + gc.x, earth.y + gc.y, earth.z + gc.z, time)\n else:\n # For planets, heliocentric vector is most direct to calculate.\n hc = HelioVector(body, time)\n gc = Vector(hc.x - earth.x, hc.y - earth.y, hc.z - earth.z, time)\n phase = AngleBetween(gc, hc)\n\n geo_dist = gc.Length() # distance from body to center of Earth\n helio_dist = hc.Length() # distance from body to center of Sun\n ring_tilt = None # only reported for Saturn\n if body == Body.Sun:\n mag = -0.17 + 5.0*math.log10(geo_dist / _AU_PER_PARSEC)\n elif body == Body.Moon:\n mag = _MoonMagnitude(phase, helio_dist, geo_dist)\n elif body == Body.Saturn:\n mag, ring_tilt = _SaturnMagnitude(phase, helio_dist, geo_dist, gc, time)\n else:\n mag = _VisualMagnitude(body, phase, helio_dist, geo_dist)\n return IlluminationInfo(time, mag, phase, helio_dist, geo_dist, gc, hc, ring_tilt)"],"string":"[\n \"def rad_field_initial_condition(self):\\n\\n # revert in viewing direct\\n angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)\\n # Looking at the sky\\n if angle < 90:\\n I_init = (\\n self.sun_intensity\\n * f.delta_func(self.sun_elevation - self.receiver_elevation)\\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\\n )\\n\\n # Looking at the ground\\n elif angle > 90:\\n I_ground = RT_model_1D.calc_direct_beam_intensity(self, 0)\\n\\n I_lambert = (\\n I_ground\\n * self.ground_albedo\\n * np.cos(np.deg2rad((self.sun_elevation + 180) % 360))\\n )\\n\\n I_specular = (\\n I_ground\\n * self.ground_albedo\\n * f.delta_func(self.sun_elevation + self.receiver_elevation - 180)\\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\\n )\\n\\n I_init = (\\n 1 - self.reflection_type\\n ) * I_lambert + self.reflection_type * I_specular\\n\\n else:\\n I_init = np.empty(self.stokes_dim)\\n I_init.fill(np.nan)\\n\\n return I_init\",\n \"def civil_twilight(topos, earth, sun, time):\\n\\n location = earth + topos\\n astrocentric = location.at(time).observe(sun).apparent()\\n alt, _, _ = astrocentric.altaz('standard')\\n return alt.degrees <= -6.0 # definition of civil twilight\",\n \"def Horizon(time, observer, ra, dec, refraction):\\n if not (Refraction.Airless.value <= refraction.value <= Refraction.JplHorizons.value):\\n raise Error('Invalid refraction type')\\n\\n latrad = math.radians(observer.latitude)\\n lonrad = math.radians(observer.longitude)\\n decrad = math.radians(dec)\\n rarad = ra * _HOUR2RAD\\n\\n sinlat = math.sin(latrad)\\n coslat = math.cos(latrad)\\n sinlon = math.sin(lonrad)\\n coslon = math.cos(lonrad)\\n sindc = math.sin(decrad)\\n cosdc = math.cos(decrad)\\n sinra = math.sin(rarad)\\n cosra = math.cos(rarad)\\n\\n # Calculate three mutually perpendicular unit vectors\\n # in equatorial coordinates: uze, une, uwe.\\n #\\n # uze = The direction of the observer's local zenith (straight up).\\n # une = The direction toward due north on the observer's horizon.\\n # uwe = The direction toward due west on the observer's horizon.\\n #\\n # HOWEVER, these are uncorrected for the Earth's rotation due to the time of day.\\n #\\n # The components of these 3 vectors are as follows:\\n # [0] = x = direction from center of Earth toward 0 degrees longitude (the prime meridian) on equator.\\n # [1] = y = direction from center of Earth toward 90 degrees west longitude on equator.\\n # [2] = z = direction from center of Earth toward the north pole.\\n\\n uze = [coslat*coslon, coslat*sinlon, sinlat]\\n une = [-sinlat*coslon, -sinlat*sinlon, coslat]\\n uwe = [sinlon, -coslon, 0.0]\\n\\n # Correct the vectors uze, une, uwe for the Earth's rotation by calculating\\n # sideral time. Call spin() for each uncorrected vector to rotate about\\n # the Earth's axis to yield corrected unit vectors uz, un, uw.\\n # Multiply sidereal hours by -15 to convert to degrees and flip eastward\\n # rotation of the Earth to westward apparent movement of objects with time.\\n\\n angle = -15.0 * _sidereal_time(time)\\n uz = _spin(angle, uze)\\n un = _spin(angle, une)\\n uw = _spin(angle, uwe)\\n\\n # Convert angular equatorial coordinates (RA, DEC) to\\n # cartesian equatorial coordinates in 'p', using the\\n # same orientation system as uze, une, uwe.\\n\\n p = [cosdc*cosra, cosdc*sinra, sindc]\\n\\n # Use dot products of p with the zenith, north, and west\\n # vectors to obtain the cartesian coordinates of the body in\\n # the observer's horizontal orientation system.\\n #\\n # pz = zenith component [-1, +1]\\n # pn = north component [-1, +1]\\n # pw = west component [-1, +1]\\n\\n pz = p[0]*uz[0] + p[1]*uz[1] + p[2]*uz[2]\\n pn = p[0]*un[0] + p[1]*un[1] + p[2]*un[2]\\n pw = p[0]*uw[0] + p[1]*uw[1] + p[2]*uw[2]\\n\\n # proj is the \\\"shadow\\\" of the body vector along the observer's flat ground.\\n proj = math.sqrt(pn*pn + pw*pw)\\n\\n # Calculate az = azimuth (compass direction clockwise from East.)\\n if proj > 0.0:\\n # If the body is not exactly straight up/down, it has an azimuth.\\n # Invert the angle to produce degrees eastward from north.\\n az = math.degrees(-math.atan2(pw, pn))\\n if az < 0:\\n az += 360\\n else:\\n # The body is straight up/down, so it does not have an azimuth.\\n # Report an arbitrary but reasonable value.\\n az = 0.0\\n\\n # zd = the angle of the body away from the observer's zenith.\\n zd = math.degrees(math.atan2(proj, pz))\\n hor_ra = ra\\n hor_dec = dec\\n\\n if refraction != Refraction.Airless:\\n zd0 = zd\\n refr = RefractionAngle(refraction, 90.0 - zd)\\n zd -= refr\\n if refr > 0.0 and zd > 3.0e-4:\\n zdrad = math.radians(zd)\\n sinzd = math.sin(zdrad)\\n coszd = math.cos(zdrad)\\n zd0rad = math.radians(zd0)\\n sinzd0 = math.sin(zd0rad)\\n coszd0 = math.cos(zd0rad)\\n\\n pr = [(((p[j] - coszd0 * uz[j]) / sinzd0)*sinzd + uz[j]*coszd) for j in range(3)]\\n proj = math.sqrt(pr[0]*pr[0] + pr[1]*pr[1])\\n if proj > 0:\\n hor_ra = _RAD2HOUR * math.atan2(pr[1], pr[0])\\n if hor_ra < 0:\\n hor_ra += 24\\n else:\\n hor_ra = 0\\n hor_dec = math.degrees(math.atan2(pr[2], proj))\\n\\n return HorizontalCoordinates(az, 90.0 - zd, hor_ra, hor_dec)\",\n \"def test_check_sun_above_horizon():\\n pass\",\n \"def wind_shear(self):\\n return self.flow_field.wind_shear\",\n \"def dynamic_viscosity_of_air(self) -> float:\\n\\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\\n self.ambient_temperature + 110.4\\n )\",\n \"def lower_arm(self):\\r\\n # ---------------------------------------------------------------------\\r\\n # Done: 8. Implement this method; it is a ONE-LINER!\\r\\n # ---------------------------------------------------------------------\\r\\n if self.is_calibrated == False:\\r\\n self.calibrate_arm()\\r\\n self.move_arm_to_position(0)#America\\r\",\n \"def checkSun(ontology_sun):\\n elevation = ontology_sun.has_elevation[0] #gets the elevation value of the Sun in the ontology. \\n azimuth = ontology_sun.has_azimuth[0] #gets the azimuth value of the Sun in the ontology. \\n intensity = ontology_sun.has_intensity[0] #gets the intensity value of the Sun in the ontology.\\n return xosc.Sun(intensity,azimuth,elevation)\",\n \"def power(self):\\n return irradiance_on_plane(self.vnorm, self.h,\\n self.date, self.lat) * self.s * self.eff\",\n \"def get_rain():\\n global rain\\n\\n # Report rain only if the condition is 'rainy' (and not always).\\n if weather_condition == CONDITION_RAINY and random.random() > 0.7:\\n rain += round(random.random(), 2)\\n return rain\",\n \"def isstationary(self):\\n if np.all(np.abs(self.arroots) > 1.0):\\n return True\\n else:\\n return False\",\n \"def get_radiation():\\n sun_pos = get_sun_position()\\n if sun_pos <= POSITION_MIN or sun_pos >= POSITION_MAX:\\n return 0\\n else:\\n # Calculate a new delta.\\n delta = random.randint(0, RADIATION_DELTA)\\n if random.random() > 0.5:\\n delta = -1 * delta\\n # Calculate the radiation based on the sun position.\\n new_radiation = round(-0.1279 * pow(sun_pos, 2) + 46.05 * sun_pos - 3100)\\n # Apply the delta and return the value.\\n return new_radiation + delta\",\n \"def horizontal_arcs_iglu():\\n arc(screen, BLACK, (50, 560, 300, 20), 3.14, 0)\\n arc(screen, BLACK, (60, 510, 280, 20), 3.14, 0)\\n arc(screen, BLACK, (80, 460, 240, 20), 3.14, 0)\\n arc(screen, BLACK, (120, 420, 160, 20), 3.14, 0)\",\n \"def signal_hammer(icu, icu_slope, hammer_icu, hammer_slope):\\n\\n return (icu > hammer_icu and icu_slope > 0) or (icu_slope > hammer_slope)\",\n \"def sun(xs, ys, s, n):\\n yellow = (255, 255, 0) # sun color\\n\\n circle(screen, yellow, (xs, ys), 30 * s) # sun body\\n for k in range(n + 1): # sun rays on the upper side of the sun\\n polygon(screen, yellow,\\n [(xs + 45 * s * np.cos(np.pi / n * (k - 1 / 2)), ys - 45 * s * np.sin(np.pi / n * (k - 1 / 2))),\\n (xs + 30 * s * np.cos(np.pi * (k - 1) / n), ys - 30 * s * np.sin(np.pi * (k - 1) / n)),\\n (xs + 30 * s * np.cos(np.pi * k / n), ys - 30 * s * np.sin(np.pi * k / n))], 0)\\n for k in range(n + 1): # sun rays on the lower side of the sun\\n polygon(screen, yellow,\\n [(xs + 45 * s * np.cos(np.pi / n * (k - 1 / 2)), ys + 45 * s * np.sin(np.pi / n * (k - 1 / 2))),\\n (xs + 30 * s * np.cos(np.pi * (k - 1) / n), ys + 30 * s * np.sin(np.pi * (k - 1) / n)),\\n (xs + 30 * s * np.cos(np.pi * k / n), ys + 30 * s * np.sin(np.pi * k / n))], 0)\",\n \"def testRadial(self):\\n self.doTest(afwGeom.makeRadialTransform([0, 1.01, 1e-7]))\",\n \"def testRadial(self):\\n self.doTest(afwGeom.makeRadialTransform([0, 1.01, 1e-7]))\",\n \"def rhe(m):\\n \\n m = m*u.kg.to(u.M_sun)\\n \\n logr = np.full(m.shape,0)\\n \\n iless = np.where(m<=2.5)\\n igreater = np.where(m>2.5)\\n \\n logr[iless] = 3.0965 - 2.013*np.log10(m[iless])\\n logr[igreater] = 0.0557*(np.log10(m[igreater])-0.172)**-2.5\\n return (10**logr)*u.Rsun.to(u.m)\",\n \"def event2511():\\n header(2511)\\n\\n wait(1) # Longer delay because Soothing Sunlight will keep triggering it.\\n\\n if_event_flag_on(1, EVENT.LustrousRuneActive)\\n\\n if_player_has_special_effect(-1, SPEFFECT.RunicHit0)\\n if_player_has_special_effect(-1, SPEFFECT.RunicHit1)\\n if_player_has_special_effect(-1, SPEFFECT.RunicHit2)\\n if_player_has_special_effect(-1, SPEFFECT.RunicHit3)\\n if_player_has_special_effect(-1, SPEFFECT.RunicHit4)\\n if_player_has_special_effect(-1, SPEFFECT.RunicHit5)\\n if_condition_true(2, -1)\\n if_event_flag_off(3, EVENT.DarkAnorLondo)\\n if_in_world_area(-2, 10, 1)\\n if_in_world_area(-2, 10, 2)\\n if_in_world_area(-2, 14, 0)\\n if_in_world_area(-2, 15, 0)\\n if_in_world_area(-2, 15, 1)\\n if_in_world_area(-2, 17, 0)\\n if_in_world_area(-2, 18, 1)\\n if_condition_true(3, -2)\\n if_condition_true(-3, 3)\\n if_event_flag_off(4, EVENT.EarlyOolacile)\\n if_in_world_area(4, 12, 1)\\n if_condition_true(-3, 4)\\n if_condition_true(2, -3)\\n\\n if_condition_true(-4, 2)\\n\\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive0)\\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive1)\\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive2)\\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive3)\\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive4)\\n if_player_has_special_effect(-5, SPEFFECT.RunicPassive5)\\n if_condition_true(5, -5)\\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight)\\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 1)\\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 2)\\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 3)\\n if_player_has_special_effect(-6, SPEFFECT.SoothingSunlight.value + 4)\\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight)\\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 1)\\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 2)\\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 3)\\n if_player_has_special_effect(-6, SPEFFECT.BountifulSunlight.value + 4)\\n if_condition_true(5, -6)\\n\\n if_condition_true(-4, 5)\\n\\n if_condition_true(1, -4)\\n\\n if_condition_true(0, 1)\\n\\n # Cancel any previous Lustrous buffs.\\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune0)\\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune1)\\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune2)\\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune3)\\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune4)\\n chr.cancel_special_effect(CHR.Player, SPEFFECT.LustrousRune5)\\n\\n # Apply appropriate level of Lustrous Rune effect, and register appropriate no-weapon condition.\\n if_player_has_special_effect(1, SPEFFECT.RunicHit0)\\n skip_if_condition_false(2, 1)\\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune0)\\n restart()\\n\\n if_player_has_special_effect(2, SPEFFECT.RunicHit1)\\n skip_if_condition_false(2, 2)\\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune1)\\n restart()\\n\\n if_player_has_special_effect(3, SPEFFECT.RunicHit2)\\n skip_if_condition_false(2, 3)\\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune2)\\n restart()\\n\\n if_player_has_special_effect(4, SPEFFECT.RunicHit3)\\n skip_if_condition_false(2, 4)\\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune3)\\n restart()\\n\\n if_player_has_special_effect(5, SPEFFECT.RunicHit4)\\n skip_if_condition_false(2, 5)\\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune4)\\n restart()\\n\\n if_player_has_special_effect(6, SPEFFECT.RunicHit5)\\n skip_if_condition_false(1, 6)\\n chr.set_special_effect(CHR.Player, SPEFFECT.LustrousRune5)\\n\\n restart()\",\n \"def rainfall_event(self):\\n\\n # assign local variables\\n datatype = 'strds'\\n increment = str(self.rain_interval)+' minutes'\\n raster = 'raster'\\n iterations = int(self.rain_duration)/int(self.rain_interval)\\n rain_excess = 'rain_excess'\\n net_difference = 'net_difference'\\n\\n # create raster space time datasets\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.elevation_timeseries,\\n title=self.elevation_title,\\n description=self.elevation_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.depth_timeseries,\\n title=self.depth_title,\\n description=self.depth_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.erdep_timeseries,\\n title=self.erdep_title,\\n description=self.erdep_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.flux_timeseries,\\n title=self.flux_title,\\n description=self.flux_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.difference_timeseries,\\n title=self.difference_title,\\n description=self.difference_description,\\n overwrite=True)\\n\\n # register the initial digital elevation model\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=self.elevation,\\n start=self.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # create evolution object\\n evol = Evolution(elevation=self.elevation,\\n precipitation=self.precipitation,\\n start=self.start,\\n rain_intensity=self.rain_intensity,\\n rain_interval=self.rain_interval,\\n rain_duration=self.rain_duration,\\n walkers=self.walkers,\\n runoff=self.runoff,\\n mannings=self.mannings,\\n detachment=self.detachment,\\n transport=self.transport,\\n shearstress=self.shearstress,\\n density=self.density,\\n mass=self.mass,\\n grav_diffusion=self.grav_diffusion,\\n erdepmin=self.erdepmin,\\n erdepmax=self.erdepmax,\\n k_factor=self.k_factor,\\n c_factor=self.c_factor,\\n m=self.m,\\n n=self.n,\\n threads=self.threads,\\n fill_depressions=self.fill_depressions)\\n\\n # determine mode and run model\\n if self.mode == 'simwe_mode':\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.erosion_deposition()\\n # remove relative timestamps from r.sim.water and r.sim.sediment\\n gscript.run_command(\\n 'r.timestamp',\\n map=depth,\\n date='none')\\n gscript.run_command(\\n 'r.timestamp',\\n map=erosion_deposition,\\n date='none')\\n\\n elif self.mode == \\\"usped_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.usped()\\n\\n elif self.mode == \\\"rusle_mode\\\":\\n (evolved_elevation, time, depth, sediment_flux,\\n difference) = evol.rusle()\\n\\n else:\\n raise RuntimeError(\\n '{mode} mode does not exist').format(mode=self.mode)\\n\\n # register the evolved maps\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=evolved_elevation,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.depth_timeseries,\\n maps=depth,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.erdep_timeseries,\\n maps=erosion_deposition,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.flux_timeseries,\\n maps=sediment_flux,\\n start=evol.start,\\n increment=increment,\\n flags='i', overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.difference_timeseries,\\n maps=difference,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # run the landscape evolution model\\n # as a series of rainfall intervals in a rainfall event\\n i = 1\\n while i < iterations:\\n\\n # update the elevation\\n evol.elevation = evolved_elevation\\n print evol.elevation\\n\\n # update time\\n evol.start = time\\n print evol.start\\n\\n # derive excess water (mm/hr) from rainfall rate (mm/hr)\\n # plus the depth (m) per rainfall interval (min)\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_excess}\\\"\\n \\\"={rain_intensity}\\\"\\n \\\"+{depth}\\\"\\n \\\"/1000.\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_excess=rain_excess,\\n rain_intensity=self.rain_intensity,\\n depth=depth,\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # update excess rainfall\\n rain_intensity = 'rain_intensity'\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity} = {rain_excess}\\\".format(\\n rain_intensity='rain_intensity',\\n rain_excess=rain_excess),\\n overwrite=True)\\n evol.rain_intensity = rain_intensity\\n\\n # determine mode and run model\\n if self.mode == \\\"simwe_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.erosion_deposition()\\n # remove relative timestamps\\n # from r.sim.water and r.sim.sediment\\n gscript.run_command(\\n 'r.timestamp',\\n map=depth,\\n date='none')\\n gscript.run_command(\\n 'r.timestamp',\\n map=erosion_deposition,\\n date='none')\\n\\n elif self.mode == \\\"usped_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.usped()\\n\\n elif self.mode == \\\"rusle_mode\\\":\\n (evolved_elevation, time, depth, sediment_flux,\\n difference) = evol.rusle()\\n\\n else:\\n raise RuntimeError(\\n '{mode} mode does not exist').format(mode=self.mode)\\n\\n # register the evolved maps\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=evolved_elevation,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.depth_timeseries,\\n maps=depth,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.erdep_timeseries,\\n maps=erosion_deposition,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.flux_timeseries,\\n maps=sediment_flux,\\n start=evol.start,\\n increment=increment,\\n flags='i', overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.difference_timeseries,\\n maps=difference,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # remove temporary maps\\n gscript.run_command(\\n 'g.remove',\\n type='raster',\\n name=['rain_excess'],\\n flags='f')\\n\\n i = i+1\\n\\n # compute net elevation change\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{net_difference}\\\"\\n \\\"={evolved_elevation}-{elevation}\\\".format(\\n net_difference=net_difference,\\n elevation=self.elevation,\\n evolved_elevation=evol.elevation),\\n overwrite=True)\\n gscript.write_command(\\n 'r.colors',\\n map=net_difference,\\n rules='-',\\n stdin=difference_colors)\",\n \"def global_irradiance_overcast(latitude_deg, longitude_deg, utc_datetime, \\n elevation = elevation_default, temperature_celsius = 25, \\n pressure_millibars = 1013.25): \\n ghioc = (572 * (solar.GetAltitude(latitude_deg, longitude_deg, utc_datetime, \\n elevation , temperature_celsius , pressure_millibars )))\\n \\n return ghioc\",\n \"def is_winter(et_cell, foo_day):\\n if et_cell.latitude > 0 and (foo_day.month < 4 or foo_day.month > 10):\\n # if et_cell.cell_lat > 0 and (foo_day.month < 4 or foo_day.month > 10):\\n # Northern hemisphere\\n return True\\n else:\\n # Southern hemisphere\\n return False\",\n \"def get_allsky(self):\\n band = self.get_band()\\n septon = self.is_septon()\\n if band == '10_90' or band == '30_90' or septon:\\n allsky = True\\n else:\\n allsky = False\\n return allsky\",\n \"def remove_rain_norain_discontinuity(R):\\n R = R.copy()\\n zerovalue = np.nanmin(R)\\n threshold = np.nanmin(R[R > zerovalue])\\n R[R > zerovalue] -= threshold - zerovalue\\n R -= np.nanmin(R)\\n\\n return R\",\n \"def bare_soil(sil):\\n sil.get_color_params()\\n if ((sil.l_mean < 160) and (sil.a_std > 3)):\\n return False\\n else:\\n return True\",\n \"def heaviside(rain_value):\\n x = N.arange(0,70)\\n P = N.where(rain_value <= x, 1., 0)\\n return P\",\n \"def IRound(d):\\n # http://en.wikipedia.org/wiki/List_of_area_moments_of_inertia\\n return np.pi / 4 * (d/2)**4\",\n \"def horiz_angle(time, data):\\n\\n # TODO What should 0deg be? Set it to inline w/ target? facing target?\\n\\n # direction of the sun. measured in degrees counted clockwise from north.\\n azimuth = data[time]['azimuth']\\n\\n h_angle = (azimuth / 2 - 90)\\n\\n # returns answer between -180 and 180 degrees\\n return round(((h_angle + 180) % 360) - 180, 4)\",\n \"def sunrise(cls, date):\\n return (date + Clock.days_from_hours(6) + \\n ((cls.UJJAIN.longitude - cls.LOCATION.longitude) / 360) -\\n cls.equation_of_time(date) +\\n ((1577917828/1582237828 / 360) *\\n (cls.ascensional_difference(date, cls.LOCATION) +\\n (1/4 * cls.solar_sidereal_difference(date)))))\",\n \"def _is_solar_channel(self, chn):\\n return self[chn].wavelength_range[2] < 3.9 or chn in[\\n 'HRV', 'VIS006', 'VIS008', 'IR_016']\",\n \"def power_output_existing_wind_rule(_m, g, y, s, t):\\r\\n\\r\\n if y != m.Y.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_4[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\\r\\n == 0)\\r\\n else:\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_4[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\\r\\n == 0)\",\n \"def annular_eclipse(sat, earth, sun, time):\\n\\n theta, theta_e, theta_s = eclipse_parameters(sat, earth, sun, time)\\n return np.logical_and(theta_s > theta_e,\\n theta < (theta_s - theta_e))\",\n \"def oceansim(sun_az,sun_zen,cam_head,cam_elev=0,m2=1.33,npart=1.08,mu=3.483, debug=True):\\n\\n #Water surface norm\\n n = np.array([0,0,1])\\n m1 = 1.0\\n #vector from sun:\\n ki = -np.asarray([np.sin(sun_az)*np.sin(sun_zen),\\n np.cos(sun_az)*np.sin(sun_zen),\\n np.cos(sun_zen)])\\n xi = norm_cross(n,ki)\\n #transmitted sunlight\\n #tx, ty are the transmission amplitude coefficients in the xt, yt directions\\n kt,tx,ty = Fresnel.transmission(ki,n,m1,m2)\\n xt = xi\\n #vector to camera\\n kc = -np.asarray([np.sin(cam_head)*np.cos(cam_elev),\\n np.cos(cam_head)*np.cos(cam_elev),\\n np.sin(cam_elev)])*np.linalg.norm(kt)\\n xc = norm_cross(n, kc) #right\\n yc = norm_cross(kc, xc) #up\\n #vectors for scattering\\n ys = norm_cross(kt, kc) # y-axis of scattering event\\n xst = norm_cross(ys, kt) # x-axis of scattering event relative to transmitted sunlight\\n xsc = norm_cross(ys, kc) # x-axis of scattering event relative to camera\\n #Mueller matrices\\n # transmission through water surface:\\n mm1 = Mueller.polarizer(tx,ty)\\n # rotate to scattering plane\\n mm2 = Mrotv(kt,xt,xst)\\n # scatter\\n th_s = vector_angle(kt,kc)\\n #mm3 = Mocean(rad2deg(th_s)) #using Empirical ocean scattering\\n mm3 = Mueller.rayleigh_norm(th_s) #normalized Rayleigh scattering matrix\\n #b = Scattering.bsf_fournier(npart,mu)\\n b = Scattering.vspf_fournier(th_s,npart,mu)\\n # transform to camera's horizontal and up vectors\\n mm4 = Mxform(xsc,ys, xc,yc)\\n #Combined: mm4 . (b*mm3) . mm2 . mm1\\n m = mm4.dot(b*mm3.dot(mm2.dot(mm1)))\\n #stokes vector\\n s = m.dot([1,0,0,0])\\n if debug:\\n return s,m,(ki,xi),(kt,xt,xst),(kc,xc,xsc),(mm1,mm2,mm3,b,mm4)\\n else:\\n return s,m\",\n \"def vac2air(w):\\n return w / (1.0 + 2.735182E-4 + 131.4182 / w ** 2 + 2.76249E8 / w ** 4)\",\n \"def umbral_eclipse(sat, earth, sun, time):\\n\\n theta, theta_e, theta_s = eclipse_parameters(sat, earth, sun, time)\\n return np.logical_and(theta_e > theta_s,\\n theta < (theta_e - theta_s))\",\n \"def draw_sun():\\n lisandro.penup()\\n lisandro.goto(40, 90)\\n lisandro.begin_fill()\\n lisandro.circle(150) # draws out a circle with a radius of 150 for the sun.\\n lisandro.end_fill()\\n lisandro.hideturtle()\",\n \"def is_facing_north(): #py:is_facing_north\\n return RUR._is_facing_north_()\",\n \"def sun_is_down(check_time, observatory):\\n sun = get_sun(check_time).transform_to(AltAz(obstime=check_time, location=observatory))\\n return sun.alt.value < -14\",\n \"def alpha_crit_fromEarth(a_p): #OK\\n a_earth = 1. #in AU\\n if a_p > a_earth:\\n alpha_max = np.arcsin(a_earth/a_p) #occurs at quadrature\\n else: #if a_p < a_earth:\\n alpha_max = np.pi #0 deg when opposite side of sta180 deg on same side of star\\n return alpha_max\",\n \"def start_region(self, x, y):\\n if x>0 and x<100 and y>0 and y<100:\\n return 1\\n elif x>700 and x<800 and y>0 and y<100:\\n return 2\\n elif x>0 and x<100 and y>400 and y<500:\\n return 3\\n elif x>700 and x<800 and y>400 and y<500:\\n return 4\\n return 0\",\n \"def _calc_qair_ilwr(rpn_hr):\\n # saturation water vapour at the dew point in the pure phase\\n # which within 0.5% is that of moist air\\n ew = 6.112 * numpy.exp(17.62 * rpn_hr.TD / (243.12 + rpn_hr.TD))\\n xvw = ew / (0.01 * rpn_hr.PN) # converting P to hectopascals\\n r = 0.62198 * xvw / (1 - xvw) # as at Td r = rw\\n qair = xarray.DataArray(r / (1 + r))\\n # saturation water vapour at the current temperature in the pure phase\\n TT = rpn_hr.TT - 273.15 # change temperature back to Celcius\\n eT = 6.112 * numpy.exp(17.62 * TT / (243.12 + TT))\\n rh = 100 * (ew / eT)\\n\\n ew = ew / 10.0 # Change vapour pressure to from hecto pascal to kPa\\n w = 465 * ew / rpn_hr.TT\\n Lclr = (\\n 59.38 + 113.7 * (rpn_hr.TT / 273.16) ** 6 + 96.96 * numpy.sqrt(w / 2.5)\\n ) # Dilley\\n # Unsworth\\n sigma = 5.6697e-8\\n eclr = Lclr / (sigma * rpn_hr.TT ** 4)\\n ewc = (1 - 0.84 * rpn_hr.NT) * eclr + 0.84 * rpn_hr.NT\\n ilwr = xarray.DataArray(ewc * sigma * rpn_hr.TT ** 4)\\n\\n return qair, ilwr, rh\",\n \"def drawRadii(self):\\n #horizontal line (which i will extend later)\\n global radius_horiz_end_val\\n radius_horiz_end_val = ValueTracker(self.x_max)\\n global radius_horiz\\n radius_horiz = always_redraw(\\n lambda : Line(start=dot_center.get_center(), end=self.coords_to_point(radius_horiz_end_val.get_value(),0))\\n )\\n global radius_horiz_end_dot\\n radius_horiz_end_dot = always_redraw(\\n lambda : Dot(radius_horiz.get_end())\\n )\\n\\n #angled radius - can alter based on ValueTracker\\n global theta\\n theta = ValueTracker(0)\\n global radius_ang_end_dot\\n radius_ang_end_dot = always_redraw(\\n lambda : Dot(self.coords_to_point(self.x_max * np.cos(theta.get_value()*DEGREES),\\n equation_upper(self.x_max * np.cos(theta.get_value()*DEGREES))))\\n )\\n\\n global radius_ang\\n radius_ang = always_redraw(\\n lambda : Line(start=dot_center.get_center() ,end=radius_ang_end_dot.get_center())\\n )\\n\\n self.play(Write(radius_horiz), Write(radius_horiz_end_dot),\\n Write(radius_ang_end_dot), Write(radius_ang))\\n self.play(theta.animate.set_value(60))\",\n \"def triangulate_analytic_sun_at_center(self,r1,x2,y2,r2,x3,y3,r3):\\n gamma=(r1**2+x2**2+y2**2-r2**2)/(2.0*x2)\\n\\ta=(y2**2)/(float(x2**2))\\n\\tb=-2.0*gamma*y2/x2\\n\\tc=gamma**2-r1**2\\n\\ty_plus=(-b+np.sqrt((b**2)-4*a*c))/(2.0*a)\\n\\ty_minus=(-b-np.sqrt((b**2)-4*a*c))/(2.0*a)\\n x_plus=gamma-y_plus*y2/float(x2)\\n x_minus=gamma-y_minus*y2/float(x2)\\n difference_plus=(x_plus-x3)**2+(y_plus-y3)**2-r3**2\\n difference_minus=(x_minus-x3)**2+(y_minus-y3)**2-r3**2\\n if abs(difference_minus) < abs(difference_plus):\\n print \\\"Difference minus\\\", difference_minus\\n print x_minus, y_minus\\n return x_minus, x_plus, difference_minus\\n else:\\n print \\\"Difference plus\\\", difference_plus\\n print x_plus, y_plus\\n return x_plus, y_plus, difference_plus\",\n \"def addHorizon(horizon_altitude=np.radians(30.), lat_telescope=np.radians(33.35731944), raCen=0.):\\n step = .02\\n az = np.arange(0, np.pi * 2.0 + step, step)\\n alt = np.ones(len(az), float) * horizon_altitude\\n obs = ephem.Observer()\\n obs.lat = lat_telescope\\n # Set obs lon to zero, just to fix the location.\\n # Note that this is not the true observatory longitude, but as long as\\n # we calculate the RA at zenith for this longitude, we can still calculate\\n # HA appropriately.\\n obs.lon = 0\\n obs.pressure = 0\\n # Given obs lon at zero, find the equivalent ra overhead.\\n zenithra, zenithlat = obs.radec_of(0, 90)\\n lon = np.zeros(len(az), float)\\n lat = np.zeros(len(az), float)\\n for i, (alti, azi) in enumerate(zip(alt, az)):\\n # Find the equivalent ra/dec values for an alt/az circle.\\n r, lat[i] = obs.radec_of(azi, alti)\\n # Correct the ra value by the zenith ra value, to get the HA.\\n lon[i] = r - zenithra\\n lon = -(lon - np.pi) % (np.pi * 2) - np.pi\\n return lon, lat\",\n \"def shooting_star(self):\\n self.data['shooting_star'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*4)) & \\\\\\n ((self.data['High'] - self.data['Close']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)) & \\\\\\n ((self.data['High'] - self.data['Open']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)))\",\n \"def mask_nighttime(lon, lat, date=date, mask_daytime=mask_daytime,\\n ref_date=datetime.datetime(1899, 12, 31, 12),\\n buffer_hours=buffer_hours, debug=False):\\n # --- get lat and lon values from columns\\n if debug:\\n print((\\\"--- (s4-1) %s seconds ---\\\" % (time.time() - start_time)))\\n # --- get sunrise and sunset for location\\n o = ephem.Observer()\\n # set lat (decimal?), lon (decimal?), and date (UTC)\\n o.lat = str(lat)\\n o.long = str(lon)\\n o.date = date\\n # planetary body\\n s = ephem.Sun()\\n if debug:\\n print((\\\"--- (s4-2) %s seconds ---\\\" % (time.time() - start_time)))\\n\\n # Compute sun vs observer\\n s.compute()\\n if debug:\\n print((\\\"--- (s4-3) %s seconds ---\\\" % (time.time() - start_time)))\\n\\n # Work out if day or night based on sunrises and sunsets\\n mask_value = 0\\n try:\\n\\n # get sunrise time and date\\n next_rising = o.next_rising(s)\\n next_setting = o.next_setting(s)\\n\\n # convert to datetime.datetime\\n next_rising = add_days(ref_date, next_rising)\\n next_setting = add_days(ref_date, next_setting)\\n\\n # did the sun last rise or set? (inc. any adjustments)\\n sun_last_rose = False\\n if next_setting < next_rising:\\n sun_last_rose = True\\n\\n # Add buffer to rising/setting if provided with buffer_hours\\n if buffer_hours != 0:\\n\\n # Calculate last rise\\n previous_rising = o.previous_rising(s)\\n # convert to datetime.datetime\\n previous_rising = add_days(ref_date, previous_rising)\\n # Calculate last setting\\n previous_setting = o.previous_setting(s)\\n # convert to datetime.datetime\\n previous_setting = add_days(ref_date, previous_setting)\\n\\n # Calculate absolute difference\\n time_from_rise = (date-previous_rising).total_seconds()\\n time_till_set = (date-next_setting).total_seconds()\\n time_from_set = (date-previous_setting).total_seconds()\\n time_till_rise = (date-next_rising).total_seconds()\\n\\n # If absolutely difference less than buffer\\n if abs(time_from_rise)/60./60. <= buffer_hours:\\n mask_value = 1\\n elif abs(time_till_set)/60./60. <= buffer_hours:\\n mask_value = 1\\n elif abs(time_from_set)/60./60. < buffer_hours:\\n mask_value = 1\\n elif abs(time_till_rise)/60./60. < buffer_hours:\\n mask_value = 1\\n\\n # --- Check if daytime or nighttime and mask if condition met.\\n if sun_last_rose:\\n if mask_daytime:\\n # ... and has not set yet, it must be daytime\\n if (date < next_setting):\\n mask_value = 1\\n\\n # if the sun last set... (mask nighttime is default)\\n else:\\n # if mask nighttime (aka not mask_daytime)\\n if not mask_daytime:\\n # ... and has not risen yet, it must be nighttime\\n if (date < next_rising):\\n mask_value = 1\\n\\n # Add gotcha for locations where sun is always up.\\n except AlwaysUpError:\\n if mask_daytime:\\n mask_value = 1\\n\\n # Add gotcha for locations where sun is always down.\\n except NeverUpError:\\n if not mask_daytime:\\n mask_value = 1\\n\\n except:\\n print('FAIL')\\n sys.exit()\\n\\n # Mask value in array\\n return mask_value\",\n \"def constrain_sun(self, position: str) -> bool:\\n if (position == \\\"up\\\" and self.sun_up()) or (\\n position == \\\"down\\\" and self.sun_down()\\n ):\\n return True\\n return False\",\n \"def hydro_solver(self):\\n u_dx = self.central_x(self.u)\\n w_dy = self.central_y(self.w)\\n P_dx = self.central_x(self.P)\\n P_dy = self.central_y(self.P)\\n\\n rho_dx_upwind = self.upwind_x(self.rho, self.u)\\n rho_dy_upwwind = self.upwind_y(self.rho, self.w)\\n rho_udx_upwind = self.upwind_x(self.rho * self.u, self.u)\\n rho_udy_upwind = self.upwind_y(self.rho * self.u, self.w)\\n rho_wdx_upwind = self.upwind_x(self.rho * self.w, self.u)\\n rho_wdy_upwind = self.upwind_y(self.rho * self.w, self.w)\\n u_dx_uu = self.upwind_x(self.u, self.u)\\n u_dx_uw = self.upwind_x(self.u, self.w)\\n w_dy_uu = self.upwind_y(self.w, self.u)\\n w_dy_uw = self.upwind_y(self.w, self.w)\\n e_dx = self.upwind_x(self.e, self.u)\\n e_dy = self.upwind_y(self.e, self.w)\\n\\n self.rho_dt = (\\n -self.rho * (u_dx + w_dy)\\n - self.u * rho_dx_upwind\\n - self.w * rho_dy_upwwind\\n )\\n self.e_dt = (\\n -(self.e + self.P) * (u_dx + w_dy) - self.u * e_dx - self.w * e_dy\\n )\\n self.rho_udt = (\\n -self.rho * self.u * (u_dx_uu + w_dy_uu)\\n - self.u * rho_udx_upwind\\n - self.w * rho_udy_upwind\\n - P_dx\\n )\\n self.rho_wdt = (\\n -self.rho * self.w * (u_dx_uw + w_dy_uw)\\n - self.u * rho_wdx_upwind\\n - self.w * rho_wdy_upwind\\n - P_dy\\n + self.rho * self.g\\n )\\n\\n self.time_step()\\n rho_previous = np.zeros_like(self.rho)\\n rho_previous[:, :] = self.rho\\n self.rho[:, :] = self.rho + self.rho_dt * self.dt\\n self.e[:, :] = self.e + self.e_dt * self.dt\\n self.u[:, :] = (\\n rho_previous * self.u + self.rho_udt * self.dt\\n ) / self.rho\\n self.w[:, :] = (\\n rho_previous * self.w + self.rho_wdt * self.dt\\n ) / self.rho\\n\\n self.boundary_conditions()\\n self.T[:, :] = (\\n (self.Y - 1) * self.e * self.mu * self.m_u / (self.kb * self.rho)\\n )\\n self.P[:, :] = (self.Y - 1) * self.e\\n uw = (self.u, self.w)\\n v = np.linalg.norm(uw)\\n dt = self.dt\\n\\n return dt\",\n \"def r2_law(array, i):\\n npix = np.shape(array)[1]\\n M = np.array([int(npix/2), int(npix/2)])\\n yy, xx = np.ogrid[-M[0]:npix-M[0], -M[1]:npix-M[1]]\\n\\n np.seterr(divide='ignore')\\n light = npix / (xx ** 2 + (yy / (np.cos(np.deg2rad(i)))) ** 2)\\n\\n light[light == np.inf] = 0\\n return light * array\",\n \"def looks_azimuth(self) -> Optional[int]:\\n return self._get_property(LOOKS_AZIMUTH_PROP, int)\",\n \"def alt_sunrise(cls, date):\\n rise = cls.UJJAIN.dawn(date, angle(0, 47, 0))\\n return 1/24 * 1/60 * iround(rise * 24 * 60)\",\n \"def runoff_land(water_raster, lambda_param, wp_raster, p_raster, s_pre_raster, fk_raster):\\n r_land = Con(water_raster == 0,\\n lambda_param * ((s_pre - wp_raster) ** 2) + overflow(p_raster, s_pre_raster, fk_raster), 0)\\n\\n print(time.strftime(\\\"%H:%M:%S: \\\") + \\\"Landabfluss ausgeführt.\\\")\\n return r_land\",\n \"def check_if_in_shadow(psi, a_sat_vector, sun_pos):\\n dot_prod = np.dot(a * n_vector.T, unit_sun_r(sun_pos))\\n\\n check = np.zeros((len(a_sat_vector)))\\n if np.cos(psi) < 0 and a_sat_vector < r_earth and dot_prod <= r_earth:\\n check = True\\n\\n return check\",\n \"def chance_of_rain(self):\\r\\n # Amount of yesterday's rain indicating chance of it occurring.\\r\\n NO_RAIN = 0.1\\r\\n LITTLE_RAIN = 3\\r\\n SOME_RAIN = 8\\r\\n # Chance of rain occurring.\\r\\n NONE = 0\\r\\n MILD = 40\\r\\n PROBABLE = 75\\r\\n LIKELY = 90\\r\\n\\r\\n if self._yesterdays_weather.get_rainfall() < NO_RAIN:\\r\\n chance_of_rain = NONE\\r\\n elif self._yesterdays_weather.get_rainfall() < LITTLE_RAIN:\\r\\n chance_of_rain = MILD\\r\\n elif self._yesterdays_weather.get_rainfall() < SOME_RAIN:\\r\\n chance_of_rain = PROBABLE\\r\\n else:\\r\\n chance_of_rain = LIKELY\\r\\n\\r\\n return chance_of_rain\",\n \"def set_rowland_radius(self, Rm, isJohansson=False):\\n self.oe1.F_EXT = 1\\n self.oe1.FMIRR = 1\\n if isJohansson:\\n self.oe1.set_johansson(Rm*2.)\\n else:\\n self.oe1.F_JOHANSSON = 0\\n self.oe1.set_radius(Rm*2.)\",\n \"def _create_rain(self):\\n r_calc = self._calculate_spacing()\\n # Create the full screen of raindrops.\\n for raindrop_y in range(r_calc[3]):\\n self._create_raindrops_y(raindrop_y)\",\n \"def fireWest(self):\\n self.rotate('w')\\n gun = Laser(self)\\n gun.shoot('w')\\n self.agent.actionCompleted()\",\n \"def calc_incidence_angle():\\n \\n Delta_r, lat_r, Omega_r, Zenith_r, Azimuth_r, Elev_angle = solar_model()\\n \\n # Beta is equal to angle of tilted surface to horizontal (in radians)\\n roof_slopes_west = section_coordinates()\\n Beta_r = np.arctan(roof_slopes_west) \\n incidence_angles_west = np.zeros(101)\\n \\n \\n for i in range(0,len(roof_slopes_west)):\\n incidence_angles_west[i] = np.arccos(np.sin(Delta_r)* np.sin(lat_r) * np.cos(Beta_r[i]) - np.sin(Delta_r) * np.cos(lat_r) * np.sin(Beta_r[i]) * np.cos(Azimuth_r) + np.cos(Delta_r) * np.cos(lat_r) * np.cos(Beta_r[i]) * np.cos(Omega_r) + np.cos(Delta_r) * np.sin(lat_r) * np.sin(Beta_r[i]) * np.cos(Azimuth_r) * np.cos(Omega_r) + np.cos(Delta_r) * np.sin(Beta_r[i]) * np.sin(Azimuth_r) * np.sin(Omega_r))\",\n \"def get_horizontal_solar(region: int) -> np.ndarray:\\n\\n return read_conditions.get_horizontal_solar(region)\",\n \"def penumbral_eclipse(sat, earth, sun, time):\\n\\n theta, theta_e, theta_s = eclipse_parameters(sat, earth, sun, time)\\n return np.logical_and(np.abs(theta_e - theta_s) < theta,\\n theta < (theta_e + theta_s))\",\n \"def thermal_expansivity_of_air(self) -> float:\\n\\n return 1 / self.ambient_temperature\",\n \"def event1924():\\n header(1924)\\n if_not_in_world_area(1, 13, 0)\\n if_not_in_world_area(1, 13, 1)\\n if_condition_true(0, 1)\\n flag.disable(11310201)\",\n \"def test_1b_is_stormy(self):\\n random.seed( 9 )\\n self.assertFalse(self.weather.is_stormy())\",\n \"def wears_jacket(temp, raining):\\n\\treturn raining or temp<60\",\n \"def test_handle_weather_tomorrow_calls_darksky_correctly(self):\\n pass\",\n \"def aire(self, r):\\r\\n self.r_num(r)\\r\\n return self.r**2 * pi\",\n \"def fireEast(self):\\n self.rotate('e')\\n gun = Laser(self)\\n gun.shoot('e')\\n self.agent.actionCompleted()\",\n \"def func(self):\\n account = self.account\\n city_name = 'Phoenix' if not self.args else self.args\\n a = Astral()\\n a.solar_depression = 'civil'\\n city = a[city_name]\\n if not city:\\n return\\n timezone = city.timezone\\n sun = city.sun(date=datetime.date.today(), local=True)\\n\\n account.msg('Information for %s/%s\\\\n' % (city_name, city.region))\\n account.msg('Timezone: %s' % timezone)\\n account.msg('Latitude: %.02f; Longitude: %.02f' % (city.latitude, city.longitude))\\n account.msg('Dawn: %s' % str(sun['dawn']))\\n account.msg('Sunrise: %s' % str(sun['sunrise']))\\n account.msg('Noon: %s' % str(sun['noon']))\\n account.msg('Sunset: %s' % str(sun['sunset']))\\n account.msg('Dusk: %s' % str(sun['dusk']))\",\n \"def is_slope(self):\\n\\t\\tif self.high_elevation != self.low_elevation:\\n\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def apparent(self):\\n jd = self.jd\\n position_au = self.position.au.copy()\\n observer = self.observer\\n\\n if observer.geocentric:\\n include_earth_deflection = array((False,))\\n else:\\n limb_angle, nadir_angle = compute_limb_angle(\\n position_au, observer.position.au)\\n include_earth_deflection = limb_angle >= 0.8\\n\\n add_deflection(position_au, observer.position.au, observer.ephemeris,\\n jd, include_earth_deflection)\\n add_aberration(position_au, observer.velocity.au_per_d, self.light_time)\\n\\n a = Apparent(position_au, jd=jd)\\n a.observer = self.observer\\n return a\",\n \"def throw(self):\\n\\n self.vx = (2 * random.random()) - 1\\n self.vy = (4 * random.random()) + 4\",\n \"def is_armed_night(self):\\n return self in (\\n ArmingState.ARMED_STAY_NIGHT,\\n ArmingState.ARMED_STAY_NIGHT_BYPASS_PROA7,\\n ArmingState.ARMED_STAY_NIGHT_INSTANT_PROA7,\\n ArmingState.ARMED_STAY_NIGHT_INSTANT_BYPASS_PROA7,\\n )\",\n \"def test_interpolation():\\n spiral_arm = survey.get_spiral_slice(track = \\\"perseus\\\", \\n interpolate = True)\\n spiral_arm2 = survey.get_spiral_slice(track = \\\"Per\\\", \\n interpolate = False)\\n\\n assert np.allclose(spiral_arm[\\\"INTEN\\\"], spiral_arm2[\\\"INTEN\\\"], equal_nan = True)\",\n \"def sun_isoperimetric_ratio(image, mode_viz=False): # On distorted image\\r\\n try :\\r\\n _, _, sun_mask = sun_center(image)\\r\\n except TypeError :\\r\\n return np.nan\\r\\n # We blurr the image and re-binarize it\\r\\n blurred_mask = mahotas.gaussian_filter(sun_mask, 0.7)\\r\\n blurred_mask = (blurred_mask > blurred_mask.mean())\\r\\n # Obtain a binary image with the sun border in white pixels\\r\\n sun_perim_mask = mahotas.labeled.bwperim(blurred_mask, 8)\\r\\n # Compute the perimeter in pixels\\r\\n sun_perim = int(perimeter(sun_perim_mask))\\r\\n # Compute the surface in pixels\\r\\n sun_surface = np.sum(blurred_mask)\\r\\n # ratio = 4*pi*S/(P^2). Is in [0,1], equals 1 for a circle\\r\\n ratio = 4*np.pi*sun_surface/(sun_perim**2)\\r\\n if mode_viz:\\r\\n # Plot\\r\\n # print(f\\\"perimeter = {sun_perim} | surface = {sun_surface}\\\")\\r\\n fig = plt.figure(figsize=(12, 6))\\r\\n ax1 = fig.add_subplot(121)\\r\\n ax1.imshow(blurred_mask, cmap=\\\"gray\\\")\\r\\n ax2 = fig.add_subplot(122)\\r\\n ax2.imshow(sun_perim_mask, cmap=\\\"gray\\\")\\r\\n plt.show()\\r\\n return np.round(ratio, 3)\",\n \"def H_outside(self, r):\\n\\n mu_star = self.mu_star(r)\\n\\n res = 0.5 * self.S * (0.5 * (1. - mu_star**2) -\\n quad(self.H_integ_outside, mu_star, 1,\\n args=(r,))[0])\\n return res\",\n \"def isFim(self):\\r\\n return self.sair\",\n \"def is_artificial(self):\\n\\t\\treturn 0\",\n \"def power_output_existing_solar_rule(_m, g, y, s, t):\\r\\n\\r\\n if y != m.Y.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_6[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\\r\\n == 0)\\r\\n\\r\\n else:\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_6[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\\r\\n == 0)\",\n \"def background(self):\\n sun = graphics.Circle(graphics.Point(200, 310), 50)\\n sun.setFill('yellow')\\n sun.draw(self.win)\\n \\n earth = graphics.Circle(graphics.Point(40, 250), 30)\\n earth.setFill('blue')\\n earth.draw(self.win)\\n continent = graphics.Circle(graphics.Point(30, 265), 10)\\n continent.setFill('green')\\n continent.draw(self.win)\\n cont_2 = graphics.Circle(graphics.Point(30, 235), 10)\\n cont_2.setFill('green')\\n cont_2.draw(self.win)\\n cont_3 = graphics.Circle(graphics.Point(55, 245), 10)\\n cont_3.setFill('green')\\n cont_3.draw(self.win)\\n \\n stars = graphics.Circle(graphics.Point(250, 250), 5)\\n stars.setFill('white')\\n stars.draw(self.win)\\n star1 = graphics.Circle(graphics.Point(100, 250), 5)\\n star1.setFill('white')\\n star1.draw(self.win)\\n star2 = graphics.Circle(graphics.Point(150, 150), 5)\\n star2.setFill('white')\\n star2.draw(self.win)\\n star3 = graphics.Circle(graphics.Point(50, 100), 5)\\n star3.setFill('white')\\n star3.draw(self.win)\\n star3 = graphics.Circle(graphics.Point(100, 50), 5)\\n star3.setFill('white')\\n star3.draw(self.win)\\n star4 = graphics.Circle(graphics.Point(250, 80), 5)\\n star4.setFill('white')\\n star4.draw(self.win)\\n star4 = graphics.Circle(graphics.Point(200, 60), 5)\\n star4.setFill('white')\\n star4.draw(self.win)\",\n \"def radialOuter(self):\\n if self.radial in range(1, len(self.ThRZmesh.getPositions(label=\\\"R\\\"))):\\n R = self.ThRZmesh.getUpper(label=\\\"R\\\", n=(self.radial))\\n else:\\n runLog.warning(\\n \\\"Error: Radial Index ({0}) location not INSIDE mesh \\\".format(\\n self.radial\\n )\\n )\\n runLog.warning(self.ThRZmesh.getPositions(label=\\\"R\\\"))\\n R = None\\n return R\",\n \"def draw_aim(self):\\n polygon(screen, self.color, [(self.x, self.y), (self.x + self.r * 1.71 / 2, self.y - self.r / 2),\\n (self.x + self.r * 1.71, self.y), (self.x + self.r * 1.71, self.y + self.r),\\n (self.x + self.r * 1.71 / 2, self.y + 3 * self.r / 2), (self.x, self.y + self.r)])\",\n \"def rusle(self):\\n\\n # assign variables\\n ls_factor = 'ls_factor'\\n slope = 'slope'\\n grow_slope = 'grow_slope'\\n flowacc = 'flowacc'\\n sedflow = 'sedflow'\\n sedflux = 'flux'\\n\\n # parse, advance, and stamp time\\n (evolved_elevation, time, depth, sediment_flux, erosion_deposition,\\n difference) = self.parse_time()\\n\\n # compute event-based erosivity (R) factor (MJ mm ha^-1 hr^-1 yr^-1)\\n r_factor = self.event_based_r_factor()\\n\\n # compute slope\\n gscript.run_command(\\n 'r.slope.aspect',\\n elevation=self.elevation,\\n slope=slope,\\n overwrite=True)\\n\\n # grow border to fix edge effects of moving window computations\\n gscript.run_command(\\n 'r.grow.distance',\\n input=slope,\\n value=grow_slope,\\n overwrite=True)\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{slope}={grow_slope}\\\".format(\\n slope=slope,\\n grow_slope=grow_slope),\\n overwrite=True)\\n\\n # compute flow accumulation\\n gscript.run_command(\\n 'r.watershed',\\n elevation=self.elevation,\\n accumulation=flowacc,\\n flags=\\\"a\\\",\\n overwrite=True)\\n region = gscript.parse_command(\\n 'g.region', flags='g')\\n res = region['nsres']\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{depth}\\\"\\n \\\"=({flowacc}*{res})\\\".format(\\n depth=depth,\\n flowacc=flowacc,\\n res=res),\\n overwrite=True)\\n\\n # compute dimensionless topographic factor\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{ls_factor}\\\"\\n \\\"=({m}+1.0)\\\"\\n \\\"*(({flowacc}/22.1)^{m})\\\"\\n \\\"*((sin({slope})/5.14)^{n})\\\".format(\\n ls_factor=ls_factor,\\n m=self.m,\\n flowacc=depth,\\n slope=slope,\\n n=self.n),\\n overwrite=True)\\n\\n # compute sediment flow\\n \\\"\\\"\\\"E = R * K * LS * C * P\\n where\\n E is average annual soil loss\\n R is erosivity factor\\n K is soil erodibility factor\\n LS is a dimensionless topographic (length-slope) factor\\n C is a dimensionless land cover factor\\n P is a dimensionless prevention measures factor\\n \\\"\\\"\\\"\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{sedflow}\\\"\\n \\\"={r_factor}\\\"\\n \\\"*{k_factor}\\\"\\n \\\"*{ls_factor}\\\"\\n \\\"*{c_factor}\\\".format(\\n sedflow=sedflow,\\n r_factor=r_factor,\\n k_factor=self.k_factor,\\n ls_factor=ls_factor,\\n c_factor=self.c_factor),\\n overwrite=True)\\n\\n # convert sediment flow from tons/ha/yr to kg/m^2s\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{converted_sedflow}\\\"\\n \\\"={sedflow}\\\"\\n \\\"*{ton_to_kg}\\\"\\n \\\"/{ha_to_m2}\\\"\\n \\\"/{yr_to_s}\\\".format(\\n converted_sedflow=sedflux,\\n sedflow=sedflow,\\n ton_to_kg=1000.,\\n ha_to_m2=10000.,\\n yr_to_s=31557600.),\\n overwrite=True)\\n\\n # filter outliers\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{sediment_flux}\\\"\\n \\\"=if({sedflux}>{erdepmax},{erdepmax},{sedflux})\\\".format(\\n sediment_flux=sediment_flux,\\n sedflux=sedflux,\\n erdepmax=self.erdepmax),\\n overwrite=True)\\n gscript.run_command(\\n 'r.colors',\\n map=sediment_flux,\\n color='viridis',\\n flags='g')\\n\\n # evolve landscape\\n \\\"\\\"\\\"\\n change in elevation (m)\\n = change in time (s)\\n * sediment flux (kg/ms)\\n / mass of sediment per unit area (kg/m^2)\\n \\\"\\\"\\\"\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{evolved_elevation}\\\"\\n \\\"={elevation}\\\"\\n \\\"-({rain_interval}*60\\\"\\n \\\"*{sediment_flux}\\\"\\n \\\"/{mass})\\\".format(\\n evolved_elevation=evolved_elevation,\\n elevation=self.elevation,\\n rain_interval=self.rain_interval,\\n sediment_flux=sediment_flux,\\n mass=self.mass),\\n overwrite=True)\\n\\n # gravitational diffusion\\n evolved_elevation = self.gravitational_diffusion(evolved_elevation)\\n\\n # compute elevation change\\n difference = self.compute_difference(evolved_elevation, difference)\\n\\n # remove temporary maps\\n gscript.run_command(\\n 'g.remove',\\n type='raster',\\n name=['slope',\\n 'grow_slope',\\n 'flowacc',\\n 'sedflow',\\n 'flux',\\n 'settled_elevation',\\n 'divergence',\\n 'r_factor',\\n 'ls_factor'],\\n flags='f')\\n\\n return (evolved_elevation, time, depth, sediment_flux, difference)\",\n \"def _check_und_conds(xss, axs, **kw):\\n und = []\\n for xs in xss:\\n und += [(i.y <= 0) for i in xs.conds]\\n if(any(und)):\\n #draw ground surface line\\n xl = axs[0].get_xlim()\\n kw['H'].append(axs[0].plot(xl, [0]*2, ':', color=_ground_surface_color,\\n linewidth=_ground_surface_linewidth)[0])\\n kw['L'].append('Ground Surface')\\n for ax in axs:\\n #apply padding\\n yl = ax.get_ylim()\\n r = yl[1] - yl[0]\\n ax.set_ylim(yl[0] - r*0.05, yl[1])\\n else:\\n for ax in axs:\\n yl = ax.get_ylim()\\n ax.set_ylim(0, yl[1])\",\n \"def update_conditions(self) -> None:\\n self.log.debug(\\\"Updating conditions.\\\")\\n\\n self.models[\\\"sky\\\"].update(self.models[\\\"observatory_state\\\"].time)\\n\\n if self.is_night is None:\\n self.log.debug(\\\"Driver not initialized yet. Computing night parameters.\\\")\\n # Driver was not initialized yet. Need to compute night\\n # boundaries\\n\\n (self.current_sunset, self.current_sunrise) = self.models[\\n \\\"sky\\\"\\n ].get_night_boundaries(self.parameters.night_boundary)\\n\\n self.is_night = (\\n self.current_sunset\\n <= self.models[\\\"observatory_state\\\"].time\\n < self.current_sunrise\\n )\\n\\n self.log.debug(\\n f\\\"Sunset/Sunrise: {self.current_sunset}/{self.current_sunrise}, \\\"\\n f\\\"sun @ {self.parameters.night_boundary} degrees.\\\"\\n )\\n\\n is_night = self.is_night\\n\\n self.is_night = (\\n self.current_sunset\\n <= self.models[\\\"observatory_state\\\"].time\\n < self.current_sunrise\\n )\\n\\n # Only compute night boundaries when we transition from nighttime to\\n # daytime. Possibilities are:\\n # 1 - self.is_night=True and is_night = True: During the night (no need\\n # to compute anything).\\n # 2 - self.is_night=False and is_night = True: Transitioned from\\n # night/day (need to recompute night boundaries).\\n # 3 - self.is_night=True and is_night = False: Transitioned from\\n # day/night (no need to compute anything).\\n # 4 - self.is_night=False and is_night = False: During the day, no need\\n # to compute anything.\\n if not self.is_night and is_night:\\n self.log.debug(\\n \\\"Night over. Computing next night boundaries. \\\"\\n f\\\"Assuming sun elevation of {self.parameters.night_boundary}.\\\"\\n )\\n self.night += 1\\n (self.current_sunset, self.current_sunrise) = self.models[\\n \\\"sky\\\"\\n ].get_night_boundaries(self.parameters.night_boundary)\\n\\n self.log.debug(\\n f\\\"[{self.night}]: Sunset/Sunrise: {self.current_sunset}/{self.current_sunrise} \\\"\\n )\",\n \"def inverted_hammer(self):\\n self.data['inverted_hammer'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*3)) & \\\\\\n ((self.data['High'] - self.data['Close']) / ((.001 + self.data['High'] - self.data['Low']) > 0.6)) & \\\\\\n ((self.data['High'] - self.data['Open']) / ((.001 + self.data['High'] - self.data['Low']) > 0.6)))\",\n \"def get_weather_with_time(time):\\n global DARK\\n\\n if TIME in range(6, 9):\\n DARK = False\\n return 1\\n elif TIME in range(9, 13):\\n return 2\\n elif TIME in range(13, 16):\\n return 3\\n elif TIME in range(16, 19):\\n if HAS_RAINCOAT:\\n return 4\\n else:\\n if not NICE_WEATHER:\\n add_strength(False, 10)\\n return 5\\n\\n elif TIME in range(19, 22):\\n if HAS_RAINCOAT:\\n return 7\\n else:\\n if not NICE_WEATHER:\\n add_strength(False, 10)\\n return 6\\n\\n else: # 9 - 6am\\n DARK = True\\n if HAS_FLASHLIGHT:\\n return 9\\n else:\\n return 8\",\n \"def elevation(self, rover):\\n\\t\\tcurrent_tile = rover.planet.tiles[rover.y][rover.x]\\n\\t\\t#current_tile is slope\\n\\t\\tif current_tile.is_slope():\\n\\t\\t\\t#self is slope current_tile is slope\\n\\t\\t\\tif self.is_slope():\\n\\t\\t\\t\\tif current_tile.high_elevation == self.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"/\\\"\\n\\t\\t\\t\\tif current_tile.low_elevation == self.high_elevation:\\n\\t\\t\\t\\t\\treturn \\\"\\\\\\\\\\\"\\n\\t\\t\\t\\tif self.high_elevation < current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"-\\\"\\n\\t\\t\\t\\tif self.low_elevation > current_tile.high_elevation:\\n\\t\\t\\t\\t\\treturn \\\"+\\\"\\n\\t\\t\\t\\tif self.low_elevation == current_tile.low_elevation\\\\\\n\\t\\t\\t\\t\\tand self.high_elevation == current_tile.high_elevation:\\n\\t\\t\\t\\t\\treturn \\\" \\\"\\n\\t\\t\\t#self is flat current_tile is slope\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif self.low_elevation > current_tile.high_elevation:\\n\\t\\t\\t\\t\\treturn \\\"+\\\"\\n\\t\\t\\t\\tif self.low_elevation < current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"-\\\"\\n\\t\\t\\t\\treturn \\\" \\\"\\n\\n\\n\\t\\telse: #current_tile is flat\\n\\t\\t\\t#self is slope current_tile is flat\\n\\t\\t\\tif self.is_slope():\\n\\t\\t\\t\\tif self.low_elevation == current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"/\\\"\\n\\t\\t\\t\\tif self.high_elevation == current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"\\\\\\\\\\\"\\n\\t\\t\\t\\tif self.low_elevation > current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"+\\\"\\n\\t\\t\\t\\tif self.high_elevation < current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"-\\\"\\n\\t\\t\\t#self is flat current_tile is flat\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif self.low_elevation > current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"+\\\"\\n\\t\\t\\t\\tif self.high_elevation < current_tile.low_elevation:\\n\\t\\t\\t\\t\\treturn \\\"-\\\"\\n\\t\\t\\treturn \\\" \\\"\",\n \"def calc_spectral_olr(self, atmosphere, surface):\\n f, _, irradiance_field, _ = self.calc_spectral_irradiance_field(\\n atmosphere=atmosphere, t_surface=surface[\\\"temperature\\\"][0]\\n )\\n return f, irradiance_field[:, -1, 0, 0, 1]\",\n \"def __init__(self, thresh=0.1, ar_depth=1, rnd_key=random.PRNGKey(42)):\\n self.thresh = thresh\\n self.ar_depth = ar_depth\\n\\n params = random.normal(rnd_key, shape=(ar_depth+1, ))\\n self.did_fit = False\\n super().__init__('RainDay', params)\",\n \"def slopecal(ton):\\r\\n global _ton_old\\r\\n global _slope\\r\\n\\r\\n print(\\\"ton: \\\", ton, \\\" _ton_old: \\\", _ton_old, \\\" substraction: \\\", round((ton - _ton_old),2))\\r\\n\\r\\n if round((ton - _ton_old), 2) >= 5:\\r\\n _slope = 1\\r\\n if round((ton - _ton_old), 2) <= -10:\\r\\n _slope = -1\\r\\n _ton_old = ton\",\n \"def sunrise(self):\\r\\n try:\\r\\n return str(self.connect()['sys']['sunrise'])\\r\\n except:\\r\\n return '@weather_sunrise'\",\n \"def morir(self):\\n self.energia = 0\\n self.vivo = False\",\n \"def determine_analytic_solution(self):\\n\\n self._Janalytic = np.where(self.xr <= self.xint, self.S, 0.5 * self.S)\\n self._Hanalytic = np.where(self.xr <= self.xint, 0, 0.25 * self.S)\\n self._Kanalytic = np.where(self.xr <= self.xint, 1./3. * self.S,\\n 1./6. * self.S)\",\n \"def hanging_man(self):\\n self.data['hanging_man'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*4)) & \\\\\\n ((self.data['Close'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)) &\\\\\\n ((self.data['Open'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= .075)))\",\n \"def _update_rain(self):\\n self.rain.update()\\n self._make_new_drops()\",\n \"def SearchRiseSet(body, observer, direction, startTime, limitDays):\\n if body == Body.Earth:\\n raise EarthNotAllowedError()\\n elif body == Body.Sun:\\n body_radius = _SUN_RADIUS_AU\\n elif body == Body.Moon:\\n body_radius = _MOON_EQUATORIAL_RADIUS_AU\\n else:\\n body_radius = 0.0\\n\\n if direction == Direction.Rise:\\n ha_before = 12.0 # minimum altitude (bottom) happens BEFORE the body rises.\\n ha_after = 0.0 # maximum altitude (culmination) happens AFTER the body rises.\\n elif direction == Direction.Set:\\n ha_before = 0.0 # culmination happens BEFORE the body sets.\\n ha_after = 12.0 # bottom happens AFTER the body sets.\\n else:\\n raise Error('Invalid value for direction parameter')\\n\\n context = _peak_altitude_context(body, direction, observer, body_radius)\\n\\n # See if the body is currently above/below the horizon.\\n # If we are looking for next rise time and the body is below the horizon,\\n # we use the current time as the lower time bound and the next culmination\\n # as the upper bound.\\n # If the body is above the horizon, we search for the next bottom and use it\\n # as the lower bound and the next culmination after that bottom as the upper bound.\\n # The same logic applies for finding set times, only we swap the hour angles.\\n time_start = startTime\\n alt_before = _peak_altitude(context, time_start)\\n if alt_before > 0.0:\\n # We are past the sought event, so we have to wait for the next \\\"before\\\" event (culm/bottom).\\n evt_before = SearchHourAngle(body, observer, ha_before, time_start)\\n time_before = evt_before.time\\n alt_before = _peak_altitude(context, time_before)\\n else:\\n # We are before or at the sought ebvent, so we find the next \\\"after\\\" event (bottom/culm),\\n # and use the current time as the \\\"before\\\" event.\\n time_before = time_start\\n\\n evt_after = SearchHourAngle(body, observer, ha_after, time_before)\\n alt_after = _peak_altitude(context, evt_after.time)\\n\\n while True:\\n if alt_before <= 0.0 and alt_after > 0.0:\\n # Search between the \\\"before time\\\" and the \\\"after time\\\" for the desired event.\\n event_time = Search(_peak_altitude, context, time_before, evt_after.time, 1.0)\\n if event_time is not None:\\n return event_time\\n # We didn't find the desired event, so use the \\\"after\\\" time to find the next \\\"before\\\" event.\\n evt_before = SearchHourAngle(body, observer, ha_before, evt_after.time)\\n evt_after = SearchHourAngle(body, observer, ha_after, evt_before.time)\\n if evt_before.time.ut >= time_start.ut + limitDays:\\n return None\\n time_before = evt_before.time\\n alt_before = _peak_altitude(context, evt_before.time)\\n alt_after = _peak_altitude(context, evt_after.time)\",\n \"def check_angle_of_arcs(self):\\n\\n if self.thin_arc_start_angle >= 3600:\\n self.thin_arc_start_angle %= 360\\n self.thin_arc_start_angle += 360\\n\\n elif self.thin_arc_start_angle <= -3600:\\n self.thin_arc_start_angle %= 360\\n self.thin_arc_start_angle -= 360\\n\\n if self.thin_arc_end_angle >= 3600:\\n self.thin_arc_end_angle %= 360\\n self.thin_arc_end_angle += 360\\n\\n elif self.thin_arc_end_angle <= -3600:\\n self.thin_arc_end_angle %= 360\\n self.thin_arc_end_angle -= 360\\n\\n if self.thick_arc_start_angle >= 3600:\\n self.thick_arc_start_angle %= 360\\n self.thick_arc_start_angle += 360\\n\\n elif self.thick_arc_start_angle <= -3600:\\n self.thick_arc_start_angle %= 360\\n self.thick_arc_start_angle -= 360\\n\\n if self.thick_arc_end_angle >= 3600:\\n self.thick_arc_end_angle %= 360\\n self.thick_arc_end_angle += 360\\n\\n elif self.thick_arc_end_angle <= -3600:\\n self.thick_arc_end_angle %= 360\\n self.thick_arc_end_angle -= 360\",\n \"def boundary_conditions(self):\\n ce = 2 * self.dy * self.g * self.mu * self.m_u / self.kb\\n self.e[0, :] = (4 * self.e[1, :] - self.e[2, :]) / (\\n ce / self.T[0, :] + 3\\n )\\n self.rho[0, :] = (\\n self.e[0, :]\\n * (self.Y - 1)\\n * self.mu\\n * self.m_u\\n / (self.kb * self.T[0, :])\\n )\\n self.u[0, :] = (4 * self.u[1, :] - self.u[2, :]) / 3\\n self.w[0, :] = 0\\n\\n self.e[-1, :] = (4 * self.e[-2, :] - self.e[-3, :]) / (\\n 3 - ce / self.T[-1, :]\\n )\\n self.rho[-1, :] = (\\n self.e[-1, :]\\n * (self.Y - 1)\\n * self.mu\\n * self.m_u\\n / (self.kb * self.T[-1, :])\\n )\\n self.u[-1, :] = (4 * self.u[-2, :] - self.u[-3, :]) / 3\\n self.w[-1, :] = 0\",\n \"def daily_insolation_limits(irrad, clearsky, daily_min=0.4, daily_max=1.25):\\n daily_irradiance = _daily_total(irrad)\\n daily_clearsky = _daily_total(clearsky)\\n good_days = quality.util.check_limits(\\n daily_irradiance/daily_clearsky,\\n upper_bound=daily_max,\\n lower_bound=daily_min\\n )\\n return good_days.reindex(irrad.index, method='pad', fill_value=False)\",\n \"def Illumination(body, time):\\n if body == Body.Earth:\\n raise EarthNotAllowedError()\\n earth = _CalcEarth(time)\\n if body == Body.Sun:\\n gc = Vector(-earth.x, -earth.y, -earth.z, time)\\n hc = Vector(0.0, 0.0, 0.0, time)\\n phase = 0.0 # placeholder value; the Sun does not have a phase angle.\\n else:\\n if body == Body.Moon:\\n # For extra numeric precision, use geocentric moon formula directly.\\n gc = GeoMoon(time)\\n hc = Vector(earth.x + gc.x, earth.y + gc.y, earth.z + gc.z, time)\\n else:\\n # For planets, heliocentric vector is most direct to calculate.\\n hc = HelioVector(body, time)\\n gc = Vector(hc.x - earth.x, hc.y - earth.y, hc.z - earth.z, time)\\n phase = AngleBetween(gc, hc)\\n\\n geo_dist = gc.Length() # distance from body to center of Earth\\n helio_dist = hc.Length() # distance from body to center of Sun\\n ring_tilt = None # only reported for Saturn\\n if body == Body.Sun:\\n mag = -0.17 + 5.0*math.log10(geo_dist / _AU_PER_PARSEC)\\n elif body == Body.Moon:\\n mag = _MoonMagnitude(phase, helio_dist, geo_dist)\\n elif body == Body.Saturn:\\n mag, ring_tilt = _SaturnMagnitude(phase, helio_dist, geo_dist, gc, time)\\n else:\\n mag = _VisualMagnitude(body, phase, helio_dist, geo_dist)\\n return IlluminationInfo(time, mag, phase, helio_dist, geo_dist, gc, hc, ring_tilt)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.634991","0.565729","0.5482073","0.5471151","0.543911","0.5316347","0.52773964","0.5272393","0.52664614","0.52653426","0.52644473","0.519665","0.51890105","0.5169418","0.50999045","0.5082697","0.5082697","0.508089","0.5074152","0.50740224","0.5064365","0.5052793","0.5047427","0.5037336","0.50334084","0.50233996","0.50164187","0.5014618","0.5007739","0.4992038","0.49896023","0.4988123","0.49747714","0.4971595","0.49702844","0.4951463","0.49514607","0.49501485","0.4949333","0.4941237","0.49176484","0.49059463","0.48967886","0.48962113","0.48892975","0.4877545","0.487514","0.4873331","0.4873321","0.48716813","0.48707005","0.48685095","0.48572937","0.485399","0.484714","0.4833243","0.48318493","0.4831488","0.48306358","0.48290846","0.48256275","0.48211417","0.4817233","0.4815825","0.48155496","0.48070467","0.47940925","0.47915906","0.4788916","0.47854358","0.47853774","0.4778968","0.47783625","0.47763062","0.47753468","0.47741693","0.47698867","0.47681624","0.47667426","0.47654617","0.47627532","0.47613105","0.47591153","0.47581643","0.47565708","0.47548732","0.4754591","0.4745711","0.4745501","0.47453845","0.47419783","0.47368607","0.4724615","0.4719487","0.47148094","0.47128704","0.47048137","0.4703963","0.4699489","0.469699"],"string":"[\n \"0.634991\",\n \"0.565729\",\n \"0.5482073\",\n \"0.5471151\",\n \"0.543911\",\n \"0.5316347\",\n \"0.52773964\",\n \"0.5272393\",\n \"0.52664614\",\n \"0.52653426\",\n \"0.52644473\",\n \"0.519665\",\n \"0.51890105\",\n \"0.5169418\",\n \"0.50999045\",\n \"0.5082697\",\n \"0.5082697\",\n \"0.508089\",\n \"0.5074152\",\n \"0.50740224\",\n \"0.5064365\",\n \"0.5052793\",\n \"0.5047427\",\n \"0.5037336\",\n \"0.50334084\",\n \"0.50233996\",\n \"0.50164187\",\n \"0.5014618\",\n \"0.5007739\",\n \"0.4992038\",\n \"0.49896023\",\n \"0.4988123\",\n \"0.49747714\",\n \"0.4971595\",\n \"0.49702844\",\n \"0.4951463\",\n \"0.49514607\",\n \"0.49501485\",\n \"0.4949333\",\n \"0.4941237\",\n \"0.49176484\",\n \"0.49059463\",\n \"0.48967886\",\n \"0.48962113\",\n \"0.48892975\",\n \"0.4877545\",\n \"0.487514\",\n \"0.4873331\",\n \"0.4873321\",\n \"0.48716813\",\n \"0.48707005\",\n \"0.48685095\",\n \"0.48572937\",\n \"0.485399\",\n \"0.484714\",\n \"0.4833243\",\n \"0.48318493\",\n \"0.4831488\",\n \"0.48306358\",\n \"0.48290846\",\n \"0.48256275\",\n \"0.48211417\",\n \"0.4817233\",\n \"0.4815825\",\n \"0.48155496\",\n \"0.48070467\",\n \"0.47940925\",\n \"0.47915906\",\n \"0.4788916\",\n \"0.47854358\",\n \"0.47853774\",\n \"0.4778968\",\n \"0.47783625\",\n \"0.47763062\",\n \"0.47753468\",\n \"0.47741693\",\n \"0.47698867\",\n \"0.47681624\",\n \"0.47667426\",\n \"0.47654617\",\n \"0.47627532\",\n \"0.47613105\",\n \"0.47591153\",\n \"0.47581643\",\n \"0.47565708\",\n \"0.47548732\",\n \"0.4754591\",\n \"0.4745711\",\n \"0.4745501\",\n \"0.47453845\",\n \"0.47419783\",\n \"0.47368607\",\n \"0.4724615\",\n \"0.4719487\",\n \"0.47148094\",\n \"0.47128704\",\n \"0.47048137\",\n \"0.4703963\",\n \"0.4699489\",\n \"0.469699\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5284681"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":2391,"cells":{"query":{"kind":"string","value":"The kinematic viscosity of air varies as a function of temperature."},"document":{"kind":"string","value":"def kinematic_viscosity_of_air(self) -> float:\n\n return self.dynamic_viscosity_of_air / self.density_of_air"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dynamic_viscosity_of_air(self) -> float:\n\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\n self.ambient_temperature + 110.4\n )","def latent_heat_vapourisation(self, tair):\n return (2.501 - 0.00237 * tair) * 1E06","def dispersion(self, k):\n return np.sqrt(\n 1 + (\n (k*self.material.hbar_m)**2 / (2*self.material.m_star_m) -\n self.material.z\n )**2\n )","def viscosity(altitude):\n t_ref = temperature(0) # R\n t = temperature(altitude) # R\n s = 198.72 # R\n mu_ref = 3.737 * 10 ** (-7) # [slug/(ft*s)]\n mu = mu_ref*((t/t_ref)**(3/2))*(t_ref + s)/(t + s) # [slug/(ft*s)]\n return mu","def dispersion(self, k):\n return 2*self.material.gamma*np.sqrt(self.material.z)","def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference suggests this equation is accurate to 1%.\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646","def kts(self):\n return CAL_TO_J * 0.0077 * (self.rho/1000.0) * (self.rho/1000.0)","def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature","def feller(self):\n return 2 * self.kappa_y * self.mean_v - self.eta_y**2 > 0","def sky_temperature(self) -> float:\n\n return 0.0552 * (self.ambient_temperature**1.5)","def car_dynamics(self,x, t, u, p):\n # f = vehicle_dynamics_ks(x, u, p)\n f = vehicle_dynamics_st(x, u, p)\n # f = vehicle_dynamics_std(x, u, p)\n # f = vehicle_dynamics_mb(x, u, p)\n return f","def von_klitzing_constant(self):\n return self._von_klitzing_constant","def VTrue(h,Vc,p,Temp_m):\n M = Mach(h,Vc,p)\n return M*np.sqrt(gamma*R*Static_T(Temp_m,M))","def temperature() -> float:","def conductivity(self, T):\n m = self.mass\n mu = self.viscosity(T)\n K = (15/4) * kB * mu / m\n return K","def env_temperature(v3: \"float\", v4: \"float\") -> \"float\":","def air_density(self):\n return self.flow_field.air_density","def _k(self, T):\n RT = Rgas * T\n return (self.parameters.A1 / np.exp(self.parameters.E1 / RT),\n self.parameters.A2 / np.exp(self.parameters.E2 / RT))","def kineticEnergy(self):\n return self.params['kinetic']","def viscosity(self, T):\n m = self.mass\n w = self.omega\n Tr = self.t_ref\n dr = self.d_ref\n a = self.alpha\n \n mu_ref_numerator = (5 * (1 + a) * (2 + a) * sqrt(kB * m * Tr / pi))\n mu_ref_denominator = 4 * a * dr **2 * (7 - 2 * w) * (5 - 2 * w)\n mu_ref = mu_ref_numerator / mu_ref_denominator\n \n mu = mu_ref * (T / Tr) ** w\n return mu","def calculate_visibility(qv,qc,qr,qi,qs,T,p):\n Rd = 287.\n COEFLC = 144.7\n COEFLP = 2.24\n COEFFC = 327.8\n COEFFP = 10.36\n EXPLC = 0.88\n EXPLP = 0.75\n EXPFC = 1.\n EXPFP = 0.7776\n\n Tv = T * (1+0.61*qv) # Virtual temperature\n\n rhoa = p/(Rd*Tv) # Air density [kg m^-3]\n rhow = 1e3 # Water density [kg m^-3]\n rhoi = 0.917e3 # Ice density [kg m^-3]\n\n vovmd = (1+qv)/rhoa + (qc+qr)/rhow + (qi+qs)/rhoi\n\n conc_lc = 1e3*qc/vovmd\n conc_lp = 1e3*qr/vovmd\n conc_fc = 1e3*qi/vovmd\n conc_fp = 1e3*qs/vovmd\n\n # Make sure all concentrations are positive\n conc_lc[conc_lc < 0] = 0\n conc_lp[conc_lp < 0] = 0\n conc_fc[conc_fc < 0] = 0\n conc_fp[conc_fp < 0] = 0\n\n betav = COEFFC*conc_fc**EXPFC\\\n + COEFFP*conc_fp**EXPFP\\\n + COEFLC*conc_lc**EXPLC\\\n + COEFLP*conc_lp**EXPLP+1E-10\n\n vis = -np.log(0.02)/betav # Visibility [km]\n vis[vis > 24.135] = 24.135\n\n return vis","def kelvin_effect(pres, surft, temp, mw_ba, dcell):\n volm = mw_ba/1e3 # approximation: using density 1000 kg/m3\n return pres*exp(-4*surft*volm/(dcell*gas_constant*temp))","def kinetic_energy(self, sys):\n v = sys.velocities\n m = sys.mass\n return 0.5*np.dot(m, np.multiply(v, v))","def func_kc_318(n, series):\n if series == \"3D3\":\n try:\n return 2*np.pi/(wl_3D3[str(n)]*1e-9)\n except:\n return 0","def test_virtual_potential_temperature():\n p = 999. * units.mbar\n t = 288. * units.kelvin\n qv = .0016 * units.dimensionless # kg/kg\n theta_v = virtual_potential_temperature(p, t, qv)\n assert_almost_equal(theta_v, 288.3620 * units.kelvin, 3)","def width_h_invis(self):\n if m_higgs > 2.0 * self.mx:\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\n\n val = (\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\n / (8.0 * m_higgs**2 * np.pi)\n ).real\n\n assert val >= 0\n\n return val\n else:\n return 0.0","def kinetic_energy(self):\r\n return self.mass * np.dot(self.vel, self.vel) / 2","def molar_mass_dry_air():\n return 28.9647","def k_Ni00(wind_ms, temp_C):\n\n U = wind_ms\n\n Sc = schmidt_number(temp_C)\n k = (0.333 * U + 0.222 * U ** 2) * (600 / Sc) ** 0.5\n\n return k","def liquid_viscosity(id, temperature=298.15, pressure=constants.atm): # noqa: A002\n return rx._misc._get_chemical(id, temperature, pressure).mul # noqa: SLF001","def kinetic_energy(self):\r\n position, velocity, escaped_particles,impact, wall_collision,mom = self.box_collision_info()\r\n for j in xrange(1,self.n):\r\n abs_velocity = np.sqrt(velocity[:,0]**2+velocity[:,1]**2\r\n + velocity[:,2]**2)\r\n KE = 0.5*self.m*abs_velocity**2\r\n total_KE = np.sum(KE)\r\n invid_KE = total_KE/self.Npart\r\n\r\n return total_KE, invid_KE","def variable_vis(self):\n return self._variable_vis","def thermal_velocity(charge, temperature, mass):\n return np.sqrt(2*abs(charge)*temperature/mass)","def get_D_C3H8_air_eff(self, T):\n\n Kn = self.get_Kn(T)\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\n\n if np.isscalar(Kn):\n if Kn <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n else:\n if Kn.any() <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n self.D_C3H8_air_eff = D_C3H8_air_eff\n\n return D_C3H8_air_eff","def get_vsolar(self):\n return self.read_register(4098, 1, 3)","def _ani_ic_on_planck(electron_energy, soft_photon_temperature, gamma_energy, theta):\n Ktomec2 = 1.6863699549e-10\n soft_photon_temperature *= Ktomec2\n\n def G12(x, a):\n \"\"\"\n Eqs 20, 24, 25\n \"\"\"\n alpha, a, beta, b = a\n pi26 = np.pi ** 2 / 6.0\n G = (pi26 + x) * np.exp(-x)\n tmp = 1 + b * x ** beta\n g = 1. / (a * x ** alpha / tmp + 1.)\n return G * g\n\n gamma_energy = np.vstack(gamma_energy)\n # Parameters from Eqs 21, 22\n a1 = [0.857, 0.153, 1.840, 0.254]\n a2 = [0.691, 1.330, 1.668, 0.534]\n z = gamma_energy / electron_energy\n ttheta = 2. * electron_energy * soft_photon_temperature * (1. - np.cos(theta))\n x = z / (1 - z) / ttheta\n # Eq. 11\n cross_section = z ** 2 / (2 * (1 - z)) * G12(x, a1) + G12(x, a2)\n tmp = (soft_photon_temperature / electron_energy) ** 2\n # r0 = (e**2 / m_e / c**2).to('cm')\n # (2 * r0 ** 2 * m_e ** 3 * c ** 4 / (pi * hbar ** 3)).cgs\n tmp *= 2.6318735743809104e+16\n cross_section = tmp * cross_section\n cc = ((gamma_energy < electron_energy) * (electron_energy > 1))\n return np.where(cc, cross_section,\n np.zeros_like(cross_section))","def test_virtual_temperature():\n t = 288. * units.kelvin\n qv = .0016 * units.dimensionless # kg/kg\n tv = virtual_temperature(t, qv)\n assert_almost_equal(tv, 288.2796 * units.kelvin, 3)","def KineticEnergy(self):\n return Particle.TotalEnergy(self) - Particle.RestEnergy(self)","def k3(self) -> float:\n return self.distortion_coefficients[2]","def get_evaporation_latent_heat() -> float:\n theta = 28.0\n return 2500.8 - 2.3668 * theta","def kin_energy (self):\n\n for planet in self.planets:\n planet.kenergy = 0.5*planet.mass*((np.linalg.norm(planet.velocity))**2) # every 'kenergy' depends by the body's mass and velocity","def density(self):\n return (1e-3*self.molar_mass) * self.pressure / (gas_constant * self.temperature) # kg/m^3","def _CloudVar(self): \n # q is MA order of ARMA(1,q)\n q = int(round(self.lambda_avg/self.lambda_s))\n a = exp(-self.lambda_s / self.lambda_p) \n (var, var_ratio) = self._ARMAvar(q, a)\n # This variance is a multiple of the variance of the noise driving the\n # AR(1) model. This variance, in turn, is a multiple of the underlying\n # measurement variance, with the relationship given in Gillespie 96\n var = var * (1. - exp(-2*self.lambda_s / self.lambda_p))/2\n # print q, a\n return var","def density_of_air(self) -> float:\n\n return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature)","def temperature(k, kmax):\n return 1.0 / 500 * (1.0 / k - 1.0 / kmax)","def true_an(self):\n vel = self.getvel_sph()\n e_vec = self.get_ecc()\n pos = self.getpos_xyz()\n if np.linalg.norm(e_vec) == 0:\n h_mom = self.sp_ang_mom()\n e_vec = np.cross([0, 0, 1], h_mom)\n true_th = np.arccos(np.dot(e_vec, pos)/(np.linalg.norm(e_vec) *\n np.linalg.norm(pos)))\n if vel[0] < 0:\n true_th = 2*np.pi - true_th\n return true_th","def rad_field_initial_condition(self):\n\n # revert in viewing direct\n angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)\n # Looking at the sky\n if angle < 90:\n I_init = (\n self.sun_intensity\n * f.delta_func(self.sun_elevation - self.receiver_elevation)\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\n )\n\n # Looking at the ground\n elif angle > 90:\n I_ground = RT_model_1D.calc_direct_beam_intensity(self, 0)\n\n I_lambert = (\n I_ground\n * self.ground_albedo\n * np.cos(np.deg2rad((self.sun_elevation + 180) % 360))\n )\n\n I_specular = (\n I_ground\n * self.ground_albedo\n * f.delta_func(self.sun_elevation + self.receiver_elevation - 180)\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\n )\n\n I_init = (\n 1 - self.reflection_type\n ) * I_lambert + self.reflection_type * I_specular\n\n else:\n I_init = np.empty(self.stokes_dim)\n I_init.fill(np.nan)\n\n return I_init","def is_artificial(self):\n\t\treturn 0","def mTV(self):\n distance = abs(self.vertPosT - self.vertPosW) # distance between htp and vortex shred plane,\n # approximated with the wing root chordplane\n return distance / (self.spanW / 2)","def LotkaVolterra_Dynamics(self):\n LV_c = self.toConceptual(self.state) # (nF, nR)\n LV_c = LV_c.mul((1 - LV_c) + self.LV_inhM.mm(LV_c))\n LV_s = self.toNeural(LV_c)\n\n return LV_c, LV_s","def compute_energy_density(kT):\n h=u.planck\n c=u.speed_of_light\n pi=np.pi\n return (8*pi/(h*c)**3)*((pi*kT)**4/15)","async def air_density(self, temperature, station_pressure):\n if temperature is not None and station_pressure is not None:\n kelvin = temperature + 273.15\n pressure = station_pressure\n r_specific = 287.058\n decimals = 2\n\n air_dens = (pressure * 100) / (r_specific * kelvin)\n\n if self._unit_system == UNITS_IMPERIAL:\n air_dens = air_dens * 0.06243\n decimals = 4\n\n return round(air_dens, decimals)\n\n _LOGGER.error(\"FUNC: air_density ERROR: Temperature or Pressure value was reported as NoneType. Check the sensor\")","def thermal_i(mu,Ti):\n return 9.79*1.e5/np.sqrt(mu/Ti)/1.e2","def calc_k(self):\n\t\n\tself.k = -np.array([self.sth*self.cphi, self.sth*self.sphi, self.cth])\n\n\treturn","def constraint_k_pi_invis(self):\n width_contr = 0.0\n ms = self.ms\n\n # Make sure scalar mass doesn't fall outside of kinematic bounds\n if np.any([s[0] <= ms**2 <= s[1] for s in k_pi_invis_obs.s_bounds]):\n widths_s = self.partial_widths()\n width_s = widths_s[\"total\"]\n width_s_sm = width_s - widths_s[\"x x\"] # Gamma_{S->SM}\n\n # Magnitude of S' 3-momentum\n ps = np.sqrt(\n (mk - mpi - ms) * (mk + mpi - ms) * (mk - mpi + ms) * (mk + mpi + ms)\n ) / (2.0 * mk)\n # Probability that S decays outside the detector\n pr_invis = np.exp(-k_pi_invis_obs.r_max * cm_to_inv_MeV * width_s * ms / ps)\n\n # Compute the total contribution to the invisible decay width\n width_contr = (\n self.width_k_pi_s() * (widths_s[\"x x\"] + pr_invis * width_s_sm) / width_s\n )\n\n return k_pi_invis_obs.width_bound - width_contr","def test_velocity(self):\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\n # r must contain 2 elements, otherwise the density and pressure are nan\n r = np.array([0.7, 0.8])\n t = 6.25e-6\n solrt = sol(r, t)\n np.testing.assert_allclose(solrt.velocity[0], 144000.0)","def ambient_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_c\"))\r\n return kelvin_to_celsius(self._ambient_temperature)","def getEnthalpyOfVaporization(self,Temperature):\n\t\tB = self.Antoine_params[1]\n\t\tC = self.Antoine_params[2]\n\n\t\t# Eqn 7 from Epstein et al 2009\n\t\tHvap = 2.303*8.3145*Temperature*Temperature*B/((C + Temperature - 273.15)*(C + Temperature - 273.15))\n\t\treturn Hvap # units are J/molK","def get_force(self):\n # @todo: Probably need to check the state of the landing gear for this (e.g. are they on the track?).\n # Note: you can get the state of the landing gear by going through self.sim \n return 0.0","def calc_VPD(t_celsius, rel_humidity):\n # according to Licor LI-6400 manual pg 14-10\n # and Buck AL (1981). New equations for computing vapor pressure and\n # enhancement factor. J Appl Meteor 20:1527-1532\n vp_sat = 0.61365 * math.exp((17.502 * t_celsius) / (240.97 + t_celsius))\n\n vp_air = vp_sat * rel_humidity\n return vp_sat - vp_air # or vp_sat * (1 - rel_humidity)","def thermal_conductivity(temperature):\n a0 = -4.1236\n a1 = 13.788\n a2 = -26.068\n a3 = 26.272\n a4 = -14.663\n a5 = 4.4954\n a6 = -0.6905\n a7 = 0.0397\n log_t = math.log10(temperature)\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\n g10_thermal_conductivity = 10.0**f_exp\n return g10_thermal_conductivity","def vcrit(Te):\n vcrit = 3.0*np.sqrt(np.pi)/4.*(2.*eV2J/me)**(1.5)*(me/mi)*np.sqrt(Te**3.)\n return vcrit","def k_Li86(wind_ms, temp_C):\n from numpy import zeros_like\n\n U = wind_ms\n T = temp_C\n\n Sc = schmidt_number(T)\n k = zeros_like(temp_C)\n\n i1 = U <= 3.6\n i2 = (U > 3.6) & (U < 13.0)\n i3 = U >= 13.0\n\n k[i1] = (0.17 * U[i1]) * (Sc[i1] / 600) ** (-2.0 / 3.0)\n k[i2] = ((U[i2] - 3.4) * 2.8) * (600 / Sc[i2]) ** 0.5\n k[i3] = ((U[i3] - 8.4) * 5.9) * (600 / Sc[i3]) ** 0.5\n\n return k","def sky_observed(seed=425, th=150, old=False):\n \n # impact parameters\n M = 3e7*u.Msun\n #M = 6e7*u.Msun\n B = 19.95*u.kpc\n V = 190*u.km/u.s\n phi = coord.Angle(0*u.deg)\n theta = coord.Angle(th*u.deg)\n Tenc = 0.01*u.Gyr\n T = 0.5*u.Gyr\n dt = 0.05*u.Myr\n rs = 0*u.pc\n \n old_label = ''\n \n if old:\n old_label = '_old'\n \n # impact parameters\n M = 5e7*u.Msun\n B = 19.8*u.kpc\n V = 210*u.km/u.s\n phi = coord.Angle(0*u.deg)\n th = 150\n theta = coord.Angle(th*u.deg)\n Tenc = 0.05*u.Gyr\n T = 2*u.Gyr\n dt = 0.1*u.Myr\n #dt = 1*u.Myr\n rs = 0*u.pc\n \n # potential parameters\n potential = 3\n Vh = 220*u.km/u.s\n q = 1*u.Unit(1)\n rhalo = 20*u.pc\n par_pot = np.array([Vh.si.value, q.value, rhalo.si.value])\n \n # setup tube\n Nstar = 1400\n wx = 30*u.kpc\n wy = 0*u.pc\n wz = 0*u.pc\n sx = 0*u.km/u.s\n \n np.random.seed(seed)\n observer = {'z_sun': 27.*u.pc, 'galcen_distance': 8.3*u.kpc, 'roll': 60*u.deg, 'galcen_coord': coord.SkyCoord(ra=300*u.deg, dec=-90*u.deg, frame='icrs')}\n alt_observer = {'z_sun': 27.*u.pc, 'galcen_distance': 8.3*u.kpc, 'roll': -45*u.deg, 'galcen_coord': coord.SkyCoord(ra=300*u.deg, dec=-90*u.deg, frame='icrs')}\n vobs = {'vcirc': 220*u.km/u.s, 'vlsr': [0, 0, 0]*u.km/u.s}\n wangle = 180*u.deg\n \n xphi = np.linspace(-0.3*np.pi,0.3*np.pi, Nstar)\n xphi0 = np.linspace(-0.1*np.pi, 0.1*np.pi, 1000)\n xphi1 = np.linspace(-0.28*np.pi, -0.1*np.pi, 200)\n xphi2 = np.linspace(0.1*np.pi, 0.32*np.pi, 200)\n xphi = np.concatenate([xphi1, xphi0, xphi2])\n \n xr = 20*u.kpc + np.random.randn(Nstar)*0.0*u.kpc\n x = np.sin(xphi) * xr\n y = np.cos(xphi) * xr\n z = x * 0\n vx = -np.cos(xphi) * Vh\n vy = np.sin(xphi) * Vh\n vz = vx * 0\n # closest to impact\n ienc = np.argmin(np.abs(x))\n \n # generate stream model\n potential_perturb = 1\n par_perturb = np.array([M.si.value, 0., 0., 0.])\n x1, x2, x3, v1, v2, v3 = interact.interact(par_perturb, B.si.value, phi.rad, V.si.value, theta.rad, Tenc.si.value, T.si.value, dt.si.value, par_pot, potential, potential_perturb, x.si.value, y.si.value, z.si.value, vx.si.value, vy.si.value, vz.si.value)\n stream = {}\n stream['x'] = (np.array([x1, x2, x3])*u.m).to(u.pc)\n stream['v'] = (np.array([v1, v2, v3])*u.m/u.s).to(u.km/u.s)\n \n # sky coordinates\n xgal = coord.Galactocentric(stream['x'], **observer)\n xeq = xgal.transform_to(coord.ICRS)\n veq_ = gc.vgal_to_hel(xeq, stream['v'], **vobs)\n veq = [None] * 3\n veq[0] = veq_[0].to(u.mas/u.yr)\n veq[1] = veq_[1].to(u.mas/u.yr)\n veq[2] = veq_[2].to(u.km/u.s)\n \n # alternative sky coordinates\n xgal_alt = coord.Galactocentric(stream['x'], **alt_observer)\n xeq_alt = xgal_alt.transform_to(coord.ICRS)\n veq_alt_ = gc.vgal_to_hel(xeq_alt, stream['v'], **vobs)\n veq_alt = [None] * 3\n veq_alt[0] = veq_alt_[0].to(u.mas/u.yr)\n veq_alt[1] = veq_alt_[1].to(u.mas/u.yr)\n veq_alt[2] = veq_alt_[2].to(u.km/u.s)\n \n # unperturbed stream\n par_perturb = np.array([0*M.si.value, 0., 0., 0.])\n x1, x2, x3, v1, v2, v3 = interact.interact(par_perturb, B.si.value, phi.rad, V.si.value, theta.rad, Tenc.si.value, T.si.value, dt.si.value, par_pot, potential, potential_perturb, x.si.value, y.si.value, z.si.value, vx.si.value, vy.si.value, vz.si.value)\n stream0 = {}\n stream0['x'] = (np.array([x1, x2, x3])*u.m).to(u.pc)\n stream0['v'] = (np.array([v1, v2, v3])*u.m/u.s).to(u.km/u.s)\n \n # sky coordinates\n xgal0 = coord.Galactocentric(stream0['x'], **observer)\n xeq0 = xgal0.transform_to(coord.ICRS)\n veq0_ = gc.vgal_to_hel(xeq0, stream0['v'], **vobs)\n veq0 = [None] * 3\n veq0[0] = veq0_[0].to(u.mas/u.yr)\n veq0[1] = veq0_[1].to(u.mas/u.yr)\n veq0[2] = veq0_[2].to(u.km/u.s)\n \n # alternative sky coordinates\n xgal0_alt = coord.Galactocentric(stream0['x'], **alt_observer)\n xeq0_alt = xgal0_alt.transform_to(coord.ICRS)\n veq0_alt_ = gc.vgal_to_hel(xeq0_alt, stream0['v'], **vobs)\n veq0_alt = [None] * 3\n veq0_alt[0] = veq0_alt_[0].to(u.mas/u.yr)\n veq0_alt[1] = veq0_alt_[1].to(u.mas/u.yr)\n veq0_alt[2] = veq0_alt_[2].to(u.km/u.s)\n \n # rotate to native coordinate system\n R = find_greatcircle(xeq0.ra.deg[::10], xeq0.dec.deg[::10])\n xi0, eta0 = myutils.rotate_angles(xeq0.ra, xeq0.dec, R)\n xi0 = coord.Angle(xi0*u.deg)\n \n # place gap at xi~0\n xioff = xi0[ienc]\n xi0 -= xioff\n \n xi, eta = myutils.rotate_angles(xeq.ra, xeq.dec, R)\n xi = coord.Angle(xi*u.deg)\n xi -= xioff\n \n # alternative observer\n R_alt = find_greatcircle(xeq0_alt.ra.deg[::10], xeq0_alt.dec.deg[::10])\n xi0_alt, eta0_alt = myutils.rotate_angles(xeq0_alt.ra, xeq0_alt.dec, R_alt)\n xi0_alt = coord.Angle(xi0_alt*u.deg)\n \n # place gap at xi~0\n xioff_alt = xi0_alt[ienc]\n xi0_alt -= xioff_alt\n \n xi_alt, eta_alt = myutils.rotate_angles(xeq_alt.ra, xeq_alt.dec, R_alt)\n xi_alt = coord.Angle(xi_alt*u.deg)\n xi_alt -= xioff_alt\n \n\n # observed gd1\n g = Table(fits.getdata('/home/ana/projects/GD1-DR2/output/gd1_members.fits'))\n \n vlabel = ['$\\mu_{\\\\alpha_\\star}$ [mas yr$^{-1}$]','$\\mu_{\\delta}$ [mas yr$^{-1}$]', '$V_r$ [km s$^{-1}$]']\n ylims = [[-1.5, 1.5], [-1.5, 1.5], [-30,30]]\n color = '0.35'\n ms = 4\n alpha = 0.7\n \n # plotting\n plt.close()\n fig, ax = plt.subplots(2,4,figsize=(17,8), sharex=True, sharey='col')\n \n plt.sca(ax[0][0])\n plt.plot(xi.wrap_at(wangle), eta, '.', mec='none', color=color, ms=ms, label='Simulated GD-1')\n \n #plt.xlabel('$\\phi_1$ [deg]')\n plt.ylabel('$\\phi_2$ [deg]')\n plt.xlim(-20,20)\n plt.ylim(-5,5)\n \n plt.sca(ax[1][0])\n plt.plot(xi_alt.wrap_at(wangle), eta_alt, '.', mec='none', color=color, ms=ms, label='Simulated GD-1')\n \n plt.xlabel('$\\phi_1$ [deg]')\n plt.ylabel('$\\phi_2$ [deg]')\n plt.xlim(-20,20)\n plt.ylim(-5,5)\n \n xeqs = [xeq.ra, xeq.dec, xeq.distance.to(u.kpc)]\n dv = []\n dv_alt = []\n for i in range(3):\n plt.sca(ax[0][i+1])\n \n # interpolate expected kinematics from an unperturbed stream\n vexp = np.interp(xi.wrap_at(wangle), xi0.wrap_at(wangle), veq0[i].value) * veq0[i].unit\n dv += [veq[i]-vexp]\n plt.plot(xi.wrap_at(wangle), dv[i], '.', mec='none', color=color, ms=ms)\n \n plt.ylabel('$\\Delta$ {}'.format(vlabel[i]))\n plt.ylim(*ylims[i])\n \n plt.sca(ax[1][i+1])\n # interpolate expected kinematics from an unperturbed stream\n vexp_alt = np.interp(xi_alt.wrap_at(wangle), xi0_alt.wrap_at(wangle), veq0_alt[i].value) * veq0_alt[i].unit\n dv_alt += [veq_alt[i]-vexp_alt]\n plt.plot(xi_alt.wrap_at(wangle), dv_alt[i], '.', mec='none', color=color, ms=ms)\n \n plt.ylabel('$\\Delta$ {}'.format(vlabel[i]))\n plt.ylim(*ylims[i])\n plt.xlabel('$\\phi_1$ [deg]')\n \n # find closest model star to the gd-1 stars\n Ngd1 = len(g)\n p = np.array([g['phi1']+40, g['phi2']])\n q = np.array([xi.wrap_at(wangle).to(u.deg).value, eta])\n idmin = np.empty(Ngd1, dtype='int')\n \n for i in range(Ngd1):\n dist = np.sqrt((p[0,i]-q[0])**2 + (p[1,i]-q[1])**2)\n idmin[i] = np.argmin(dist)\n\n # mask stream, mask spur\n onstream_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>-0.2) & (g['phi2']<0.2))\n spur_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>1) & (g['phi2']<1.4))\n all_mask = np.ones(Ngd1, dtype='bool')\n \n # plot scaled data uncertainties on model pm drawn from a corresponding obs uncertainty\n np.random.seed(seed+1)\n fgaia = np.sqrt(2/5)\n print(2/5, fgaia)\n phi1 = xi[idmin].wrap_at(wangle).to(u.deg).value\n phi2 = eta[idmin]\n pmra = dv[0][idmin] + g['pmra_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\n pmdec = dv[1][idmin] + g['pmdec_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\n \n colors = ['tab:red', 'tab:blue', '0.4']\n labels = ['Stream', 'Spur']\n labels = ['Gaia DR4', '']\n \n for e, mask in enumerate([onstream_mask, spur_mask]):\n plt.sca(ax[0][0])\n plt.plot(phi1[mask], phi2[mask], 'o', color=colors[e], mec='none', alpha=alpha, label=labels[e])\n \n plt.sca(ax[0][1])\n plt.errorbar(phi1[mask], pmra[mask].value, yerr=g['pmra_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\n \n plt.sca(ax[0][2])\n plt.errorbar(phi1[mask], pmdec[mask].value, yerr=g['pmdec_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\n \n print(np.sqrt(np.sum(g['pmra_error'][mask]**2))/np.sum(mask))\n print(np.sqrt(np.sum(g['pmdec_error'][mask]**2))/np.sum(mask))\n\n Nfield = 2\n p2 = np.array([np.array([-32.77,-32.77])+40, [1.167,0]])\n q = np.array([xi.wrap_at(wangle).to(u.deg).value, eta])\n idmin2 = np.empty(Nfield, dtype='int')\n \n for i in range(Nfield):\n dist = np.sqrt((p2[0,i]-q[0])**2 + (p2[1,i]-q[1])**2)\n idmin2[i] = np.argmin(dist)\n \n pmerr = np.array([0.0848, 0.0685])\n \n np.random.seed(seed+2)\n phi1 = xi[idmin2].wrap_at(wangle).to(u.deg).value\n phi2 = eta[idmin2]\n pmra = dv[0][idmin2].value + pmerr*np.random.randn(Nfield)\n pmdec = dv[1][idmin2].value + pmerr*np.random.randn(Nfield)\n \n plt.sca(ax[0][0])\n plt.errorbar(phi1, phi2, color='k', fmt='o', label='HST')\n \n plt.sca(ax[0][1])\n plt.errorbar(phi1, pmra, yerr=pmerr, color='k', fmt='o')\n \n plt.sca(ax[0][2])\n plt.errorbar(phi1, pmdec, yerr=pmerr, color='k', fmt='o')\n \n \n ##############\n # alt observer\n \n # find closest model star to the gd-1 stars\n Ngd1 = len(g)\n p = np.array([g['phi1']+40, g['phi2']])\n q = np.array([xi_alt.wrap_at(wangle).to(u.deg).value, eta_alt])\n idmin = np.empty(Ngd1, dtype='int')\n \n for i in range(Ngd1):\n dist = np.sqrt((p[0,i]-q[0])**2 + (p[1,i]-q[1])**2)\n idmin[i] = np.argmin(dist)\n\n # mask stream, mask spur\n onstream_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>-0.2) & (g['phi2']<0.2))\n spur_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>1) & (g['phi2']<1.4))\n all_mask = np.ones(Ngd1, dtype='bool')\n \n # plot scaled data uncertainties on model pm drawn from a corresponding obs uncertainty\n #np.random.seed(seed+3)\n phi1 = xi_alt[idmin].wrap_at(wangle).to(u.deg).value\n phi2 = eta_alt[idmin]\n pmra = dv_alt[0][idmin] + g['pmra_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\n pmdec = dv_alt[1][idmin] + g['pmdec_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\n \n colors = ['tab:red', 'tab:blue', '0.4']\n labels = ['Gaia DR4', '']\n \n for e, mask in enumerate([onstream_mask, spur_mask]):\n plt.sca(ax[1][0])\n plt.plot(phi1[mask], phi2[mask], 'o', color=colors[e], mec='none', alpha=alpha, label=labels[e])\n \n plt.sca(ax[1][1])\n plt.errorbar(phi1[mask], pmra[mask].value, yerr=g['pmra_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\n \n plt.sca(ax[1][2])\n plt.errorbar(phi1[mask], pmdec[mask].value, yerr=g['pmdec_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\n \n Nfield = 2\n p2 = np.array([np.array([-32.77,-32.77])+40, [1.167,0]])\n q = np.array([xi_alt.wrap_at(wangle).to(u.deg).value, eta_alt])\n idmin2 = np.empty(Nfield, dtype='int')\n \n for i in range(Nfield):\n dist = np.sqrt((p2[0,i]-q[0])**2 + (p2[1,i]-q[1])**2)\n idmin2[i] = np.argmin(dist)\n \n pmerr = np.array([0.11, 0.08])\n \n np.random.seed(seed+6)\n phi1 = xi_alt[idmin2].wrap_at(wangle).to(u.deg).value\n phi2 = eta_alt[idmin2]\n pmra = dv_alt[0][idmin2].value + pmerr*np.random.randn(Nfield)\n pmdec = dv_alt[1][idmin2].value + pmerr*np.random.randn(Nfield)\n \n plt.sca(ax[1][0])\n plt.errorbar(phi1, phi2, color='k', fmt='o', label='HST')\n \n plt.sca(ax[1][1])\n plt.errorbar(phi1, pmra, yerr=pmerr, color='k', fmt='o')\n \n plt.sca(ax[1][2])\n plt.errorbar(phi1, pmdec, yerr=pmerr, color='k', fmt='o')\n \n \n plt.sca(ax[0][0])\n plt.text(0.1,0.85, '$\\\\theta_{roll}$ = 60$^\\circ$', fontsize='small', transform=plt.gca().transAxes)\n\n plt.sca(ax[1][0])\n plt.text(0.1,0.85, '$\\\\theta_{roll}$ = -45$^\\circ$', fontsize='small', transform=plt.gca().transAxes)\n plt.legend(fontsize='small', loc=3, handlelength=0.2)\n \n plt.suptitle('Expected astrometric performance', fontsize='medium')\n plt.tight_layout(rect=[0,0,1,0.94])\n plt.savefig('../plots/astrometric_performance.png')","def get_D_C3H8_air(self, T):\n\n self.set_TempPres_dependents(T)\n\n self.D_C3H8_air = (\n 2. / 3. * np.sqrt(const.k_B * T / np.pi * 0.5 * (1. /\n self.air.m + 1. / self.fuel.m)) / (np.pi * (0.5 *\n (self.air.d + self.fuel.d)) ** 2.) / self.air.n\n )\n\n return self.D_C3H8_air","def useKineticEnergy(self):\n return self.params['useKinetic']","def sensitivity(gas, surf, old_data, temp, dk, thermo=False):\n rxns = []\n sens1 = []\n sens2 = []\n sens3 = []\n sens4 = []\n sens5 = []\n sens6 = []\n sens7 = []\n sens8 = []\n sens9 = []\n sens10 = []\n sens11 = []\n sens12 = []\n\n gas_out_data, gas_names_data, dist_array_data, T_array_data = old_data\n\n reference = []\n for a in range(len(gas_names_data)):\n reference.append([gas_names_data[a], [gas_out_data[:, a]]])\n\n # getting the ratio\n for x in reference:\n if x[0] == 'CH4(2)':\n ch4_in = x[1][0][0]\n if x[0] == 'O2(3)':\n o2_in = x[1][0][0]\n if x[0] == 'Ar':\n ar_in = x[1][0][0]\n ratio = ch4_in / (2 * o2_in)\n moles_in = [ch4_in, o2_in, ar_in]\n\n for x in reference:\n if x[0] == 'CH4(2)':\n ch4_in = x[1][0][0]\n ch4_out = x[1][0][-1]\n if ch4_out < 0:\n ch4_out = 0.\n ch4_depletion = ch4_in - ch4_out\n if ch4_depletion <= 1.0e-8:\n ch4_depletion = 1.0e-8\n reference_ch4_conv = 1.0e-8\n else:\n reference_ch4_conv = ch4_depletion / ch4_in # Sensitivity definition 7: CH4 conversion\n if x[0] == 'Ar':\n ar = x[1][0][-1]\n if x[0] == 'O2(3)':\n o2_out = x[1][0][-1]\n if o2_out < 0:\n o2_out = 1.0e-15 # O2 can't be negative\n elif o2_out > o2_in:\n o2_out = o2_in # O2 can't be created, to make it equal to O2 in\n if x[0] == 'CO(7)':\n co_out = x[1][0][-1]\n if x[0] == 'H2(6)':\n h2_out = x[1][0][-1]\n if x[0] == 'H2O(5)':\n h2o_out = x[1][0][-1]\n if x[0] == 'CO2(4)':\n co2_out = x[1][0][-1]\n\n if reference_ch4_conv <= 1.0e-8:\n reference_h2_sel = 1.0e-8\n reference_co_sel = 1.0e-8\n reference_syngas_selectivity = 1.0e-8\n reference_syngas_yield = 1.0e-8\n reference_co_yield = 1.0e-8\n reference_h2_yield = 1.0e-8\n reference_full_oxidation_selectivity = 1.0e-8\n reference_full_oxidation_yield = 1.0e-8\n else:\n # negative sensitivity is higher selectivity\n reference_h2_sel = h2_out / (ch4_depletion * 2) # Sensitivity definition 5: H2 selectivity\n if reference_h2_sel <= 0:\n reference_h2_sel = 1.0e-15 # selectivity can't be 0\n\n reference_co_sel = co_out / ch4_depletion # Sensitivity definition 3: CO selectivity\n if reference_co_sel <= 0:\n reference_co_sel = 1.0e-15 # selectivity can't be 0\n\n reference_syngas_selectivity = reference_co_sel + reference_h2_sel # Sensitivity definition 1: SYNGAS selectivity\n\n reference_syngas_yield = reference_syngas_selectivity * reference_ch4_conv # Sensitivity definition 2: SYNGAS yield\n if reference_syngas_yield <= 0:\n reference_syngas_yield = 1.0e-15 # yield can't be 0\n\n reference_co_yield = co_out / ch4_in # Sensitivity definition 4: CO % yield\n # reference_co_yield = reference_co_sel * reference_ch4_conv\n\n reference_h2_yield = h2_out / (2 * ch4_in) # Sensitivity definition 6: H2 % yield\n # reference_h2_yield = reference_h2_sel * reference_ch4_conv\n\n # Sensitivity definition 8: H2O + CO2 selectivity\n reference_h2o_sel = h2o_out / (ch4_depletion * 2)\n reference_co2_sel = co2_out / ch4_depletion\n if reference_h2o_sel <= 0:\n reference_h2o_sel = 1.0e-15 # H2O selectivity can't be 0\n if reference_co2_sel <= 0:\n reference_co2_sel = 1.0e-15 # CO2 selectivity can't be 0\n reference_full_oxidation_selectivity = reference_h2o_sel + reference_co2_sel\n\n # Sensitivity definition 9: H2O + CO2 yield\n reference_full_oxidation_yield = reference_full_oxidation_selectivity * reference_ch4_conv\n\n # Sensitivity definition 10: exit temperature\n reference_exit_temp = T_array_data[-1]\n\n # Sensitivity definition 11: peak temperature\n reference_peak_temp = max(T_array_data)\n\n # Sensitivity definition 12: distance to peak temperautre\n reference_peak_temp_dist = dist_array_data[T_array_data.index(max(T_array_data))]\n\n # run the simulations\n if thermo is True:\n for m in range(surf.n_species):\n s = surf.species(m)\n original_coeffs = s.thermo.coeffs\n perturbed_coeffs = np.ones_like(original_coeffs)\n perturbed_coeffs[0] = original_coeffs[0]\n perturbed_coeffs[1:6] = original_coeffs[1:6]\n perturbed_coeffs[7:13] = original_coeffs[7:13]\n perturbed_coeffs[14] = original_coeffs[14]\n # perturbed_coeffs[6] = original_coeffs[6] + original_coeffs[6]*dk\n # perturbed_coeffs[13] = original_coeffs[13] + original_coeffs[13]*dk\n perturbed_coeffs[6] = original_coeffs[6] + dk\n perturbed_coeffs[13] = original_coeffs[13] + dk\n s.thermo = ct.NasaPoly2(100.000, 5000.000, ct.one_atm, perturbed_coeffs)\n surf.modify_species(m, s)\n c = monolithFull(gas, surf, temp, moles_in)\n\n gas_out, surf_out, gas_names, surf_names, dist_array, T_array = c\n\n new_amts = []\n for a in range(len(gas_names)):\n new_amts.append([gas_names[a], [gas_out[:, a]]])\n\n for x in new_amts:\n if x[0] == 'CH4(2)':\n new_ch4_in = x[1][0][0]\n new_ch4_out = x[1][0][-1]\n if new_ch4_out < 0:\n new_ch4_out = 0.\n new_ch4_depletion = new_ch4_in - new_ch4_out\n if new_ch4_depletion <= 1e-8:\n new_ch4_depletion = 1e-8\n new_ch4_conv = 1e-8\n else:\n new_ch4_conv = new_ch4_depletion / new_ch4_in # Sensitivity definition 7: CH4 conversion\n if x[0] == 'Ar':\n ar = x[1][0][-1]\n if x[0] == 'O2(3)':\n new_o2_in = x[1][0][0]\n new_o2_out = x[1][0][-1]\n if new_o2_out < 0:\n new_o2_out = 1.0e-15\n elif new_o2_out > new_o2_in:\n new_o2_out = new_o2_in\n if x[0] == 'CO(7)':\n new_co_out = x[1][0][-1]\n if x[0] == 'H2(6)':\n new_h2_out = x[1][0][-1]\n if x[0] == 'H2O(5)':\n new_h2o_out = x[1][0][-1]\n if x[0] == 'CO2(4)':\n new_co2_out = x[1][0][-1]\n\n if new_ch4_conv <= 1e-8:\n new_h2_sel = 1.0e-8\n new_co_sel = 1.0e-8\n new_syngas_selectivity = 1.0e-8\n new_syngas_yield = 1.0e-8\n new_co_yield = 1.0e-8\n new_h2_yield = 1.0e-8\n new_full_oxidation_selectivity = 1.0e-8\n new_full_oxidation_yield = 1.0e-8\n else:\n new_h2_sel = new_h2_out / (new_ch4_depletion * 2) # Sensitivity definition 5: H2 selectivity\n new_co_sel = new_co_out / new_ch4_depletion # Sensitivity definition 3: CO selectivity\n new_syngas_selectivity = new_co_sel + new_h2_sel # Sensitivity definition 1: SYNGAS selectivity\n new_syngas_yield = new_syngas_selectivity * new_ch4_conv # Sensitivity definition 2: SYNGAS yield\n new_co_yield = new_co_out / new_ch4_in # Sensitivity definition 4: CO % yield\n new_h2_yield = new_h2_out / (2 * new_ch4_in) # Sensitivity definition 6: H2 % yield\n new_h2o_sel = new_h2o_out / (new_ch4_depletion * 2) # Sensitivity definition 8: H2O + CO2 selectivity\n new_co2_sel = new_co2_out / new_ch4_depletion\n new_full_oxidation_selectivity = new_h2o_sel + new_co2_sel\n new_full_oxidation_yield = new_full_oxidation_selectivity * new_ch4_conv # Sensitivity definition 9: C2O + CO2 yield\n\n Sens5 = (new_h2_sel - reference_h2_sel) / (reference_h2_sel * dk)\n sens5.append(Sens5)\n\n Sens3 = (new_co_sel - reference_co_sel) / (reference_co_sel * dk)\n sens3.append(Sens3)\n\n Sens1 = (new_syngas_selectivity - reference_syngas_selectivity) / (reference_syngas_selectivity * dk)\n sens1.append(Sens1)\n\n Sens2 = (new_syngas_yield - reference_syngas_yield) / (reference_syngas_yield * dk)\n sens2.append(Sens2)\n\n Sens4 = (new_co_yield - reference_co_yield) / (reference_co_yield * dk)\n sens4.append(Sens4)\n\n Sens6 = (new_h2_yield - reference_h2_yield) / (reference_h2_yield * dk)\n sens6.append(Sens6)\n\n Sens7 = (new_ch4_conv - reference_ch4_conv) / (\n reference_ch4_conv * dk)\n sens7.append(Sens7)\n\n Sens8 = (new_full_oxidation_selectivity - reference_full_oxidation_selectivity) / (\n reference_full_oxidation_selectivity * dk)\n sens8.append(Sens8)\n\n Sens9 = (new_full_oxidation_yield - reference_full_oxidation_yield) / (reference_full_oxidation_yield * dk)\n sens9.append(Sens9)\n\n new_exit_temp = T_array[-1] # Sensitivity definition 10: exit temperature\n Sens10 = (new_exit_temp - reference_exit_temp) / (reference_exit_temp * dk)\n sens10.append(Sens10)\n\n new_peak_temp = max(T_array) # Sensitivity definition 11: peak temperature\n Sens11 = (new_peak_temp - reference_peak_temp) / (reference_peak_temp * dk)\n sens11.append(Sens11)\n\n new_peak_temp_dist = dist_array[\n T_array.index(max(T_array))] # Sensitivity definition 12: dist to peak temperature\n Sens12 = (new_peak_temp_dist - reference_peak_temp_dist) / (reference_peak_temp_dist * dk)\n sens12.append(Sens12)\n\n print \"%d %s %.3F %.3F\" % (m, surf.species_name(m), Sens1, Sens2)\n rxns.append(surf.species_name(m))\n\n # this step is essential, otherwise mechanism will have been altered\n s.thermo = ct.NasaPoly2(100.000, 5000.000, ct.one_atm, original_coeffs)\n surf.modify_species(m, s)\n else:\n for rxn in range(surf.n_reactions):\n c = monolithFull(gas, surf, temp, moles_in, sens=[dk, rxn])\n gas_out, surf_out, gas_names, surf_names, dist_array, T_array = c\n\n new_amts = []\n for a in range(len(gas_names)):\n new_amts.append([gas_names[a], [gas_out[:, a]]])\n\n for x in new_amts:\n if x[0] == 'CH4(2)':\n new_ch4_in = x[1][0][0]\n new_ch4_out = x[1][0][-1]\n if new_ch4_out < 0:\n new_ch4_out = 0.\n new_ch4_depletion = new_ch4_in - new_ch4_out\n if new_ch4_depletion <= 1e-8:\n new_ch4_depletion = 1e-8\n new_ch4_conv = 1e-8\n else:\n new_ch4_conv = new_ch4_depletion / new_ch4_in # Sensitivity definition 7: CH4 conversion\n if x[0] == 'Ar':\n ar = x[1][0][-1]\n if x[0] == 'O2(3)':\n new_o2_in = x[1][0][0]\n new_o2_out = x[1][0][-1]\n if new_o2_out < 0:\n new_o2_out = 1.0e-15\n elif new_o2_out > new_o2_in:\n new_o2_out = new_o2_in\n if x[0] == 'CO(7)':\n new_co_out = x[1][0][-1]\n if x[0] == 'H2(6)':\n new_h2_out = x[1][0][-1]\n if x[0] == 'H2O(5)':\n new_h2o_out = x[1][0][-1]\n if x[0] == 'CO2(4)':\n new_co2_out = x[1][0][-1]\n\n if new_ch4_conv <= 1e-8:\n new_h2_sel = 1.0e-8\n new_co_sel = 1.0e-8\n new_syngas_selectivity = 1.0e-8\n new_syngas_yield = 1.0e-8\n new_co_yield = 1.0e-8\n new_h2_yield = 1.0e-8\n new_full_oxidation_selectivity = 1.0e-8\n new_full_oxidation_yield = 1.0e-8\n else:\n new_h2_sel = new_h2_out / (new_ch4_depletion * 2) # Sensitivity definition 5: H2 selectivity\n new_co_sel = new_co_out / new_ch4_depletion # Sensitivity definition 3: CO selectivity\n new_syngas_selectivity = new_co_sel + new_h2_sel # Sensitivity definition 1: SYNGAS selectivity\n new_syngas_yield = new_syngas_selectivity * new_ch4_conv # Sensitivity definition 2: SYNGAS yield\n new_co_yield = new_co_out / new_ch4_in # Sensitivity definition 4: CO % yield\n new_h2_yield = new_h2_out / (2 * new_ch4_in) # Sensitivity definition 6: H2 % yield\n new_h2o_sel = new_h2o_out / (new_ch4_depletion * 2) # Sensitivity definition 8: H2O + CO2 selectivity\n new_co2_sel = new_co2_out / new_ch4_depletion\n new_full_oxidation_selectivity = new_h2o_sel + new_co2_sel\n new_full_oxidation_yield = new_full_oxidation_selectivity * new_ch4_conv # Sensitivity definition 9: C2O + CO2 yield\n\n Sens5 = (new_h2_sel - reference_h2_sel) / (reference_h2_sel * dk)\n sens5.append(Sens5)\n\n Sens3 = (new_co_sel - reference_co_sel) / (reference_co_sel * dk)\n sens3.append(Sens3)\n\n Sens1 = (new_syngas_selectivity - reference_syngas_selectivity) / (reference_syngas_selectivity * dk)\n sens1.append(Sens1)\n\n Sens2 = (new_syngas_yield - reference_syngas_yield) / (reference_syngas_yield * dk)\n sens2.append(Sens2)\n\n Sens4 = (new_co_yield - reference_co_yield) / (reference_co_yield * dk)\n sens4.append(Sens4)\n\n Sens6 = (new_h2_yield - reference_h2_yield) / (reference_h2_yield * dk)\n sens6.append(Sens6)\n\n Sens7 = (new_ch4_conv - reference_ch4_conv) / (\n reference_ch4_conv * dk)\n sens7.append(Sens7)\n\n Sens8 = (new_full_oxidation_selectivity - reference_full_oxidation_selectivity) / (\n reference_full_oxidation_selectivity * dk)\n sens8.append(Sens8)\n\n Sens9 = (new_full_oxidation_yield - reference_full_oxidation_yield) / (reference_full_oxidation_yield * dk)\n sens9.append(Sens9)\n\n new_exit_temp = T_array[-1] # Sensitivity definition 10: exit temperature\n Sens10 = (new_exit_temp - reference_exit_temp) / (reference_exit_temp * dk)\n sens10.append(Sens10)\n\n new_peak_temp = max(T_array) # Sensitivity definition 11: peak temperature\n Sens11 = (new_peak_temp - reference_peak_temp) / (reference_peak_temp * dk)\n sens11.append(Sens11)\n\n new_peak_temp_dist = dist_array[T_array.index(max(T_array))] # Sensitivity definition 12: dist to peak temperature\n Sens12 = (new_peak_temp_dist - reference_peak_temp_dist) / (reference_peak_temp_dist * dk)\n sens12.append(Sens12)\n\n print \"%d %s %.3F %.3F\" % (rxn, surf.reaction_equations()[rxn], Sens1, Sens2)\n rxns.append(surf.reaction_equations()[rxn])\n\n return rxns, sens1, sens2, sens3, sens4, sens5, sens6, sens7, sens8, sens9, sens10, sens11, sens12","def _iso_ic_on_planck(electron_energy, soft_photon_temperature, gamma_energy):\n Ktomec2 = 1.6863699549e-10\n soft_photon_temperature *= Ktomec2\n\n def G34(x, a):\n \"\"\"\n Eqs 20, 24, 25\n \"\"\"\n alpha, a, beta, b, c = a\n pi26 = np.pi ** 2 / 6.0\n tmp = (1 + c * x) / (1 + pi26 * c * x)\n G = pi26 * tmp * np.exp(-x)\n tmp = 1 + b * x ** beta\n g = 1. / (a * x ** alpha / tmp + 1.)\n return G * g\n\n gamma_energy = np.vstack(gamma_energy)\n # Parameters from Eqs 26, 27\n a3 = [0.606, 0.443, 1.481, 0.540, 0.319]\n a4 = [0.461, 0.726, 1.457, 0.382, 6.620]\n z = gamma_energy / electron_energy\n x = z / (1 - z) / (4. * electron_energy * soft_photon_temperature)\n # Eq. 14\n cross_section = z ** 2 / (2 * (1 - z)) * G34(x, a3) + G34(x, a4)\n tmp = (soft_photon_temperature / electron_energy) ** 2\n # r0 = (e**2 / m_e / c**2).to('cm')\n # (2 * r0 ** 2 * m_e ** 3 * c ** 4 / (pi * hbar ** 3)).cgs\n tmp *= 2.6318735743809104e+16\n cross_section = tmp * cross_section\n cc = ((gamma_energy < electron_energy) * (electron_energy > 1))\n return np.where(cc, cross_section,\n np.zeros_like(cross_section))","def get_vcond(lambdam, taum):\n return 2 * lambdam / taum","def calc_metric3(K_tilda):\n trace = np.trace(K_tilda)\n # determinant = np.linalg.det(K_tilda)\n _, log_determinant = np.linalg.slogdet(K_tilda)\n diff = trace - log_determinant\n print(trace, log_determinant, diff)\n return diff","def TM_fluid(layer, kx, om):\n\n h = layer.d\n rho = layer.medium.rho\n K = layer.medium.K\n k = om*np.sqrt(rho/K)\n ky = np.sqrt(k**2-kx**2)\n T = np.zeros((2, 2), dtype=complex)\n T[0, 0] = np.cos(ky*h)\n T[1, 0] = (om**2*rho/ky)*np.sin(ky*h)\n T[0, 1] = -(ky/(om**2*rho))*np.sin(ky*h)\n T[1, 1] = np.cos(ky*h)\n return T","def isKelvin(self):\n return _libsbml.Unit_isKelvin(self)","def neutrino_thermal_velocity(self, z):\n fac = 5./3.*5.*ss.zeta(5.)/ss.zeta(3.)\n vel = np.zeros(self.N_nu)\n vel = fac**.5*(const.kB*self.T_nu/self.M_nu)*(1.+z)*const.c\n return vel","def func_d23_318(n, series):\n if series == \"3D3\":\n try: \n return np.sqrt((3*os_3D3[str(n)]*wl_3D3[str(n)]*1e-9*hbar*e**2)/(4*np.pi*m_e*c))\n except:\n return 0","def kn(self):\n if self.enginePos == 'wing': # wing mounted engines\n return -4.0\n elif self.enginePos == 'fuselage': # tail mounted engines\n return -2.5","def _leg_kinematics(self, knee_pos):\n knee_pos = np.abs(knee_pos)\n sign = np.sign(knee_pos)\n t = np.sqrt(self.p ** 2 + self.q ** 2 + self.r ** 2 - 2 * np.sqrt(self.p ** 2 + self.q ** 2) * self.r * np.cos(knee_pos))\n phi = np.arccos((self.c ** 2 + t ** 2 - self.s **2) / (2 * self.c * t))\n delta = np.arcsin((self.c * np.sin(phi)) / self.s)\n beta = np.arcsin((self.r * np.sin(knee_pos)) / t)\n epsilon = np.pi - (delta + beta)\n Ax = self.r * np.cos(knee_pos) + self.u\n Ay = self.r * np.sin(knee_pos) + self.v\n Bx = self.s * np.cos(epsilon) + self.u + self.p\n By = self.s * np.cos(epsilon) + self.v + self.q\n Cx = Ax + ((Bx - Ax) * self.e + (Ay - By) * self.h) / self.c\n Cy = Ay + ((By - Ay) * self.e + (Ax - Bx) * self.h) / self.c\n alpha = np.arctan((Cy - Ay) / (Cx - Ax))\n return alpha * sign","def test_vertical_velocity_pressure_dry_air():\n w = 1 * units('cm/s')\n omega_truth = -1.25073619 * units('microbar/second')\n omega_test = vertical_velocity_pressure(w, 1000. * units.mbar, 273.15 * units.K)\n assert_almost_equal(omega_test, omega_truth, 6)","def variance(self):\n return self.k * self.theta ** 2","def _kvol(sunAz, sunZen, viewAz, viewZen):\n\t\t\t\n\t\t\trelative_azimuth = sunAz.subtract(viewAz).rename(['relAz'])\n\t\t\tpa1 = viewZen.cos() \\\n\t\t\t\t.multiply(sunZen.cos())\n\t\t\tpa2 = viewZen.sin() \\\n\t\t\t\t.multiply(sunZen.sin()) \\\n\t\t\t\t.multiply(relative_azimuth.cos())\n\t\t\tphase_angle1 = pa1.add(pa2)\n\t\t\tphase_angle = phase_angle1.acos()\n\t\t\tp1 = ee.Image(PI().divide(2)).subtract(phase_angle)\n\t\t\tp2 = p1.multiply(phase_angle1)\n\t\t\tp3 = p2.add(phase_angle.sin())\n\t\t\tp4 = sunZen.cos().add(viewZen.cos())\n\t\t\tp5 = ee.Image(PI().divide(4))\n\n\t\t\tkvol = p3.divide(p4).subtract(p5).rename(['kvol'])\n\n\t\t\tviewZen0 = ee.Image(0)\n\t\t\tpa10 = viewZen0.cos() \\\n\t\t\t\t.multiply(sunZen.cos())\n\t\t\tpa20 = viewZen0.sin() \\\n\t\t\t\t.multiply(sunZen.sin()) \\\n\t\t\t\t.multiply(relative_azimuth.cos())\n\t\t\tphase_angle10 = pa10.add(pa20)\n\t\t\tphase_angle0 = phase_angle10.acos()\n\t\t\tp10 = ee.Image(PI().divide(2)).subtract(phase_angle0)\n\t\t\tp20 = p10.multiply(phase_angle10)\n\t\t\tp30 = p20.add(phase_angle0.sin())\n\t\t\tp40 = sunZen.cos().add(viewZen0.cos())\n\t\t\tp50 = ee.Image(PI().divide(4))\n\n\t\t\tkvol0 = p30.divide(p40).subtract(p50).rename(['kvol0'])\n\n\t\t\treturn (kvol, kvol0)","def aliveness(self, physics):\n return 0.","def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\n k = get_kappa(K_medium, rho_medium, c_medium)\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial","def fuel_cond(T):\n\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\n \n return kc","def Local_Kinetic(Walker):\n\n # laplacian -0.5 \\nabla^2 \\Psi / \\Psi\n h = 0.001\n h2 = h*h\n K = 0.0\n Psi_R = wfs(Walker)\n for i in range(Walker.Ne):\n for j in range(Walker.sys_dim):\n Y=Walker.Re[i][j]\n Walker.Re[i][j]-=h\n wfs1 = wfs(Walker)\n Walker.Re[i][j]+=2.0*h\n wfs2 = wfs(Walker)\n K -= 0.5*(wfs1+wfs2-2.0*Psi_R)/h2\n Walker.Re[i][j]=Y\n return K/Psi_R","def wind_chill(T_a, v):\r\n return 13.12 + 0.6215*(T_a) - 11.37*(v)**0.16 + 0.3965*(T_a)*(v)**0.16","def calculateTemperature(self):\n \n # CIE XYZ space\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\n\n # CIE Chromaticities xy\n self.x = self.X/(self.X + self.Y + self.Z)\n self.y = self.Y/(self.X + self.Y + self.Z)\n \n # CIE Chromaticities uv\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\n \n # constant for McCamy's/Hernandez-Andrés formula\n n = (self.x - self.x_e)/(self.y - self.y_e)\n \n # Correlated color temperature according to Hernández-Andrés (1999)\n self.color_temp = ( self.A_0 + \n self.A_1*np.exp(-n/self.t_1) + \n self.A_2*np.exp(-n/self.t_2) + \n self.A_3*np.exp(-n/self.t_3) )\n \n # Delete too high values\n self.color_temp[self.color_temp > 30000] = 0\n \n # Affichage de la CCT\n self.mean_temp = int(round(self.color_temp.mean()))\n self.mean_temp_label.setText(\"Temperature moyenne = \"+str(self.mean_temp))\n self.mean_temp_label.adjustSize()\n \t\n # Affichage de l'illuminance (Y)\n self.mean_illu = int(round((self.Y.mean())))\n self.illuminance_label.setText(\"Illuminance moyenne = \"+str(self.mean_illu))\n self.illuminance_label.adjustSize()","def a_realization(self):\n if self.t==1:\n return self.kmonomial()\n else:\n return self.kHallLittlewoodP()","def get_variables(self, z0, u_inf):\n # Get the ambient data from the CTD profile\n Ta, Sa, P = self.profile.get_values(z0, ['temperature', 'salinity',\n 'pressure'])\n rho = seawater.density(Ta, Sa, P)\n \n # Compute the properties of each dispersed-phase particle\n us = np.zeros(len(self.particles))\n rho_p = np.zeros(len(self.particles))\n m_p = np.zeros(len(self.particles))\n B_p = np.zeros(len(self.particles))\n for i in range(len(self.particles)):\n m0 = self.particles[i].m0\n T0 = self.particles[i].T0\n m_p[i] = np.sum(m0) * self.particles[i].nb0\n if m_p[i] > 0.:\n # Particles exist, get properties. Make sure the algorithm \n # uses the dirty bubble properties since this is supposed\n # to be the rise velocity averaged over the whole plume.\n us[i], rho_p[i]= self.particles[i].properties(m0, T0, P, Sa, \n Ta, np.inf)[0:2]\n B_p[i] = (rho - rho_p[i]) / rho * 9.81 * (m_p[i] / rho_p[i])\n else:\n # Particles dissolved, set to ambient conditions\n us[i] = 0.\n rho_p[i] = rho\n B_p[i] = 0.\n \n # Select the correct slip velocity\n u_slip = us[0]\n for i in range(len(self.particles) - 1):\n if B_p[i+1] > B_p[i]:\n u_slip = us[i+1]\n \n # Compute the total buoyancy flux\n B = np.sum(B_p)\n \n # Get the ambient buoyancy frequency\n N = self.profile.buoyancy_frequency(z0)\n \n # Return the governing parameters\n return (B, N, u_slip, u_inf)","def get_sky_data_flag(self,data):\n sky_data_flag = ~Calibration.get_vane_flag(data['level1']) \n features = self.getFeatures(data)\n features = np.log10(features)/np.log10(2)\n sky_data_flag = sky_data_flag & np.isfinite(features) & (features != 16)\n\n return sky_data_flag","def dvdt(self, args: Dict) -> float:\n if self.channel_bool['leak']:\n i_leak: float = self.leak.i(args['v'])\n else:\n i_leak: float = 0.\n \n if self.channel_bool['nav']:\n i_nav: float = self.nav.i(args['v'], h=args['h_nav'])\n else:\n i_nav: float = 0.\n\n if self.channel_bool['kvhh']:\n i_kvhh: float = self.kvhh.i(args['v'], n=args['n_kvhh'])\n else:\n i_kvhh: float = 0.\n\n if self.channel_bool['kva']:\n i_kva: float = self.kva.i(args['v'], h=args['h_kva'])\n else:\n i_kva: float = 0.\n\n if self.channel_bool['kvsi']:\n i_kvsi: float = self.kvsi.i(args['v'], m=args['m_kvsi'])\n else:\n i_kvsi: float = 0.\n\n if self.channel_bool['cav']:\n i_cav: float = self.cav.i(args['v'])\n else:\n i_cav: float = 0.\n\n if self.channel_bool['kca']:\n i_kca: float = self.kca.i(args['v'], ca=args['ca'])\n else:\n i_kca: float = 0.\n \n if self.channel_bool['nap']:\n i_nap: float = self.nap.i(args['v'])\n else:\n i_nap: float = 0.\n\n if self.channel_bool['kir']:\n i_kir: float = self.kir.i(args['v'])\n else:\n i_kir: float = 0.\n\n if self.channel_bool['ampar']:\n i_ampar: float = self.ampar.i(args['v'], s=args['s_ampar'])\n else:\n i_ampar: float = 0.\n\n if self.channel_bool['nmdar']:\n i_nmdar: float = self.nmdar.i(args['v'], s=args['s_nmdar'])\n else:\n i_nmdar: float = 0.\n\n if self.channel_bool['gabar']:\n i_gabar: float = self.gabar.i(args['v'], s=args['s_gabar'])\n else:\n i_gabar: float = 0.\n\n return ((-10.0*self.params.area \n * (i_leak\n + i_nav \n + i_kvhh \n + i_kva \n + i_kvsi \n + i_cav \n + i_kca \n + i_nap \n + i_kir) \n - (i_ampar \n + i_nmdar \n + i_gabar))\n / (10.0*self.params.cm*self.params.area))","def is_solvated(molecular_system):\n\n from molsysmt.basic import get\n\n output = False\n\n n_waters, volume = get(molecular_system, element='system', n_waters=True, box_volume=True)\n\n if (n_waters>0) and (volume is not None):\n\n density_number = puw.get_value((n_waters/volume), to_unit='1/nm**3')\n\n if (density_number)>15:\n\n output = True\n\n return output","def kinetic_energy(vel):\r\n return 0.5 * (vel ** 2).sum(axis=1)","def get_force(self):\n displ = self.get_displ()\n equil = displ / np.linalg.norm(displ) * self.L0\n return self.k * (displ - equil)","def test_vertical_velocity_dry_air():\n omega = 1 * units('microbar/second')\n w_truth = -0.7995291 * units('cm/s')\n w_test = vertical_velocity(omega, 1000. * units.mbar, 273.15 * units.K)\n assert_almost_equal(w_test, w_truth, 6)","def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\n\n # Compute constants.\n kT = kB * temperature\n gamma = collision_rate\n\n # Create a new custom integrator.\n super(VVVRIntegrator, self).__init__(timestep)\n\n #\n # Integrator initialization.\n #\n self.addGlobalVariable(\"kT\", kT) # thermal energy\n self.addGlobalVariable(\"b\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\n self.addPerDofVariable(\"sigma\", 0)\n self.addPerDofVariable(\"x1\", 0) # position before application of constraints\n\n #\n # Allow context updating here.\n #\n self.addUpdateContextState()\n\n #\n # Pre-computation.\n # This only needs to be done once, but it needs to be done for each degree of freedom.\n # Could move this to initialization?\n #\n self.addComputePerDof(\"sigma\", \"sqrt(kT/m)\")\n\n #\n # Velocity perturbation.\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()\n\n #\n # Metropolized symplectic step.\n #\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m\")\n self.addComputePerDof(\"x\", \"x + v*dt\")\n self.addComputePerDof(\"x1\", \"x\")\n self.addConstrainPositions()\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m + (x-x1)/dt\")\n self.addConstrainVelocities()\n\n #\n # Velocity randomization\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()","def get_specific_heat() -> float:\n return 1006.0","def compute_ground_truth_volume(self, display_opt):\n\n self.meshActor.GetProperty().SetOpacity(0.2)\n self.meshActor.GetProperty().SetColor(1, 0, 0)\n\n clean = vtk.vtkCleanPolyData()\n clean.SetInputData(self.endo_poly)\n\n d3 = vtk.vtkDelaunay3D()\n d3.SetInputConnection(clean.GetOutputPort())\n d3.SetTolerance(0.01)\n d3.SetAlpha(0.0)\n d3.Update()\n\n surfaceFilter = vtk.vtkDataSetSurfaceFilter() # output is triangular mesh\n surfaceFilter.SetInputConnection(d3.GetOutputPort())\n surfaceFilter.Update()\n\n Mass = vtk.vtkMassProperties()\n Mass.SetInputConnection(surfaceFilter.GetOutputPort())\n Mass.Update()\n\n self.ground_truth_vol = Mass.GetVolume()/1000.0\n\n if display_opt:\n\n m = vtk.vtkDataSetMapper()\n m.SetInputConnection(d3.GetOutputPort())\n\n a = vtk.vtkActor()\n a.SetMapper(m)\n\n # set mapper for epi for visualization\n m2 = vtk.vtkDataSetMapper()\n m2.SetInputData(self.epi_poly)\n\n epi_actor = vtk.vtkActor()\n epi_actor.SetMapper(m2)\n epi_actor.GetProperty().SetOpacity(0.3)\n epi_actor.GetProperty().SetColor(1,0,0)\n\n ren = vtk.vtkRenderer()\n ren.SetBackground(0.0, 0.0, 0.0)\n ren.AddActor(epi_actor)\n ren.AddActor(a)\n\n vtk_show(ren)","def acc_visc(j,rA,vA,mA,rhoA,PA,hA,dW=kernel.dW_M4):\n assert rA.shape[0] == vA.shape[0] == mA.shape[0] == rhoA.shape[0] == hA.shape[0], \"arrays are not matched\"\n N = len(mA)\n c_j = c_gas(j,rhoA,PA)\n\n tot = 0\n for i in range(N):\n if i != j:\n\n r_ij = rA[j,:] - rA[i,:]\n r_ij1 = np.linalg.norm(r_ij)\n v_ij = vA[j,:] - vA[i,:]\n m_i = mA[i]\n c_i = c_gas(i,rhoA,PA)\n c_ij = 0.5 * (c_i + c_j)\n h_ij = 0.5 * (hA[i] + hA[j])\n rho_ij = 0.5 * (rhoA[i] + rhoA[j])\n\n c = np.dot(v_ij,r_ij)\n mu_ij = ( c * h_ij ) / ( r_ij1**2 + 0.01*h_ij**2 )\n\n a = ( -alpha * mu_ij * c_ij + beta * mu_ij**2 ) / rho_ij\n b = 0\n Pi_ij = a*dm.heavi(-c) + b*dm.heavi(c)\n\n # if Pi_ij == 0:\n # print(\"i,j:\",i,j)\n # print(\"c:\",c)\n # print(\"c_ij\",c_ij)\n # print(\"\")\n # assert Pi_ij != 0\n\n tot += m_i * h_ij**(-4) * Pi_ij * dW(r_ij1,h_ij) * (r_ij/r_ij1)\n\n return - tot","def var(self):\n\n return self.scale ** -2 \\\n * (m.gamma(1 + 2 * self.shape ** -1) - m.gamma(1 + self.shape ** -1) ** 2)","def simulated_reflectivity(pressure, temperature, vapor_mixing_ratio, liquid_mixing_ratio, snow_mixing_ratio=None,\n graupel_mixing_ratio=None, use_varint=False, use_liqskin=False):\n # Set values for constants with variable intercept\n R1 = 1e-15\n RON = 8e6\n RON2 = 1e10\n SON = 2e7\n GON = 5e7\n RON_MIN = 8e6\n RON_QR0 = 0.00010\n RON_DELQR0 = 0.25*RON_QR0\n RON_CONST1R = (RON2-RON_MIN)*0.5\n RON_CONST2R = (RON2+RON_MIN)*0.5\n\n # set constant intercepts\n rno_l = 8e6\n rno_s = 2e7\n rno_g = 4e6\n\n qvapor = da.clip(vapor_mixing_ratio, 0., None)\n qliquid = da.clip(liquid_mixing_ratio, 0., None)\n\n # If qgraupel but not qsnow, set qgraupel = qsnow\n if snow_mixing_ratio is None:\n if graupel_mixing_ratio is None:\n qsnow = da.zeros_like(qliquid)\n qgraupel = da.zeros_like(qliquid)\n else:\n qgraupel = da.clip(graupel_mixing_ratio, 0., None)\n qsnow = da.zeros_like(graupel_mixing_ratio)\n qsnow[temperature <= 273.15] = qgraupel[temperature <= 273.15]\n else:\n qsnow = da.clip(snow_mixing_ratio, 0., None)\n qgraupel = da.clip(graupel_mixing_ratio, 0., None)\n\n # density for liquid, snow, and graupel (kg m-3)\n rho_l = 1000. # liquid\n rho_i = 100. # snow\n rho_g = 400. # graupel\n\n # constant evaluation of gamma distribution\n gamma = 720.\n\n # Alpha constant\n alpha = 0.224\n\n # constant multiplication factors\n factor_l = gamma * 1e18 * (1./(np.pi*rho_l))**1.75\n s = gamma * 1e18 * (1./(np.pi*rho_i))**1.75 * (rho_i/rho_l)**2 * alpha\n g = gamma * 1e18 * (1./(np.pi*rho_g))**1.75 * (rho_g/rho_l)**2 * alpha\n\n # calculate virtual temperature\n virtual_t = virtual_temperature(temperature, qvapor)\n\n # dry gas constant\n Rd = 287.\n rho_air = pressure/(Rd*virtual_t)\n\n # adjust for brightband if use_liqskin=True\n if use_liqskin:\n raise NotImplementedError('Liquid skin correction not implemented')\n # factor_s = da.full_like(temperature, s)\n # factor_g = da.full_like(temperature, g)\n # try:\n # factor_s[temperature >= 273.15] = factor_s[temperature >= 273.15] / da.array([alpha])\n # factor_g[temperature >= 273.15] = factor_g[temperature >= 273.15] / da.array([alpha])\n # except ValueError:\n # factor_s = s\n # factor_g = g\n else:\n factor_s = s\n factor_g = g\n\n # calculate variable intercept if use_varint=True\n if use_varint:\n raise NotImplementedError('Variable intercepts not yet implemented')\n # temp_c = da.clip(temperature-273.15, temperature.min(), -0.001)\n # sonv = MIN(2.0D8, 2.0D6*EXP(-0.12D0*temp_c))\n #\n # gonv = gon\n # IF (qgr(i,j,k) .GT. R1) THEN\n # gonv = 2.38D0 * (PI*RHO_G/(rhoair*qgr(i,j,k)))**0.92D0\n # gonv = MAX(1.D4, MIN(gonv,GON))\n # END IF\n #\n # ronv = RON2\n # IF (qra(i,j,k) .GT. R1) THEN\n # ronv = RON_CONST1R*TANH((RON_QR0 - qra(i,j,k))/RON_DELQR0) + RON_CONST2R\n # END IF\n else:\n ronv = rno_l\n sonv = rno_s\n gonv = rno_g\n\n # Total equivalent reflectivity factor (z_e, in mm^6 m^-3) is\n # the sum of z_e for each hydrometeor species:\n z_e = (((factor_l*(rho_air*qliquid)**1.75)/(ronv**.75)) +\n ((factor_s*(rho_air*qsnow)**1.75)/(sonv**.75)) +\n ((factor_g*(rho_air*qgraupel)**1.75)/(gonv**.75)))\n\n # Adjust small values of Z_e so that dBZ is no lower than -30\n z_e = da.clip(z_e, .001, None)\n\n # Convert to dBZ\n dbz = 10.*da.log10(z_e)\n return dbz","def fGasAcousticVelocity(GasGravity, Temperature, Pressure):\n\tGasBulkModulus = fGasBulkModulus(GasGravity, Temperature, Pressure) # Pascals\n\tGasDensity = fGasDensity(GasGravity, Temperature, Pressure) * 1000 # Kg\n\treturn (GasBulkModulus / GasDensity)**0.5 # m/s"],"string":"[\n \"def dynamic_viscosity_of_air(self) -> float:\\n\\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\\n self.ambient_temperature + 110.4\\n )\",\n \"def latent_heat_vapourisation(self, tair):\\n return (2.501 - 0.00237 * tair) * 1E06\",\n \"def dispersion(self, k):\\n return np.sqrt(\\n 1 + (\\n (k*self.material.hbar_m)**2 / (2*self.material.m_star_m) -\\n self.material.z\\n )**2\\n )\",\n \"def viscosity(altitude):\\n t_ref = temperature(0) # R\\n t = temperature(altitude) # R\\n s = 198.72 # R\\n mu_ref = 3.737 * 10 ** (-7) # [slug/(ft*s)]\\n mu = mu_ref*((t/t_ref)**(3/2))*(t_ref + s)/(t + s) # [slug/(ft*s)]\\n return mu\",\n \"def dispersion(self, k):\\n return 2*self.material.gamma*np.sqrt(self.material.z)\",\n \"def thermal_conductivity_of_air(self) -> float:\\n\\n # This more accurate equation is not used by the paper.\\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\\n # )\\n\\n # The reference suggests this equation is accurate to 1%.\\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646\",\n \"def kts(self):\\n return CAL_TO_J * 0.0077 * (self.rho/1000.0) * (self.rho/1000.0)\",\n \"def thermal_expansivity_of_air(self) -> float:\\n\\n return 1 / self.ambient_temperature\",\n \"def feller(self):\\n return 2 * self.kappa_y * self.mean_v - self.eta_y**2 > 0\",\n \"def sky_temperature(self) -> float:\\n\\n return 0.0552 * (self.ambient_temperature**1.5)\",\n \"def car_dynamics(self,x, t, u, p):\\n # f = vehicle_dynamics_ks(x, u, p)\\n f = vehicle_dynamics_st(x, u, p)\\n # f = vehicle_dynamics_std(x, u, p)\\n # f = vehicle_dynamics_mb(x, u, p)\\n return f\",\n \"def von_klitzing_constant(self):\\n return self._von_klitzing_constant\",\n \"def VTrue(h,Vc,p,Temp_m):\\n M = Mach(h,Vc,p)\\n return M*np.sqrt(gamma*R*Static_T(Temp_m,M))\",\n \"def temperature() -> float:\",\n \"def conductivity(self, T):\\n m = self.mass\\n mu = self.viscosity(T)\\n K = (15/4) * kB * mu / m\\n return K\",\n \"def env_temperature(v3: \\\"float\\\", v4: \\\"float\\\") -> \\\"float\\\":\",\n \"def air_density(self):\\n return self.flow_field.air_density\",\n \"def _k(self, T):\\n RT = Rgas * T\\n return (self.parameters.A1 / np.exp(self.parameters.E1 / RT),\\n self.parameters.A2 / np.exp(self.parameters.E2 / RT))\",\n \"def kineticEnergy(self):\\n return self.params['kinetic']\",\n \"def viscosity(self, T):\\n m = self.mass\\n w = self.omega\\n Tr = self.t_ref\\n dr = self.d_ref\\n a = self.alpha\\n \\n mu_ref_numerator = (5 * (1 + a) * (2 + a) * sqrt(kB * m * Tr / pi))\\n mu_ref_denominator = 4 * a * dr **2 * (7 - 2 * w) * (5 - 2 * w)\\n mu_ref = mu_ref_numerator / mu_ref_denominator\\n \\n mu = mu_ref * (T / Tr) ** w\\n return mu\",\n \"def calculate_visibility(qv,qc,qr,qi,qs,T,p):\\n Rd = 287.\\n COEFLC = 144.7\\n COEFLP = 2.24\\n COEFFC = 327.8\\n COEFFP = 10.36\\n EXPLC = 0.88\\n EXPLP = 0.75\\n EXPFC = 1.\\n EXPFP = 0.7776\\n\\n Tv = T * (1+0.61*qv) # Virtual temperature\\n\\n rhoa = p/(Rd*Tv) # Air density [kg m^-3]\\n rhow = 1e3 # Water density [kg m^-3]\\n rhoi = 0.917e3 # Ice density [kg m^-3]\\n\\n vovmd = (1+qv)/rhoa + (qc+qr)/rhow + (qi+qs)/rhoi\\n\\n conc_lc = 1e3*qc/vovmd\\n conc_lp = 1e3*qr/vovmd\\n conc_fc = 1e3*qi/vovmd\\n conc_fp = 1e3*qs/vovmd\\n\\n # Make sure all concentrations are positive\\n conc_lc[conc_lc < 0] = 0\\n conc_lp[conc_lp < 0] = 0\\n conc_fc[conc_fc < 0] = 0\\n conc_fp[conc_fp < 0] = 0\\n\\n betav = COEFFC*conc_fc**EXPFC\\\\\\n + COEFFP*conc_fp**EXPFP\\\\\\n + COEFLC*conc_lc**EXPLC\\\\\\n + COEFLP*conc_lp**EXPLP+1E-10\\n\\n vis = -np.log(0.02)/betav # Visibility [km]\\n vis[vis > 24.135] = 24.135\\n\\n return vis\",\n \"def kelvin_effect(pres, surft, temp, mw_ba, dcell):\\n volm = mw_ba/1e3 # approximation: using density 1000 kg/m3\\n return pres*exp(-4*surft*volm/(dcell*gas_constant*temp))\",\n \"def kinetic_energy(self, sys):\\n v = sys.velocities\\n m = sys.mass\\n return 0.5*np.dot(m, np.multiply(v, v))\",\n \"def func_kc_318(n, series):\\n if series == \\\"3D3\\\":\\n try:\\n return 2*np.pi/(wl_3D3[str(n)]*1e-9)\\n except:\\n return 0\",\n \"def test_virtual_potential_temperature():\\n p = 999. * units.mbar\\n t = 288. * units.kelvin\\n qv = .0016 * units.dimensionless # kg/kg\\n theta_v = virtual_potential_temperature(p, t, qv)\\n assert_almost_equal(theta_v, 288.3620 * units.kelvin, 3)\",\n \"def width_h_invis(self):\\n if m_higgs > 2.0 * self.mx:\\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\\n\\n val = (\\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\\n / (8.0 * m_higgs**2 * np.pi)\\n ).real\\n\\n assert val >= 0\\n\\n return val\\n else:\\n return 0.0\",\n \"def kinetic_energy(self):\\r\\n return self.mass * np.dot(self.vel, self.vel) / 2\",\n \"def molar_mass_dry_air():\\n return 28.9647\",\n \"def k_Ni00(wind_ms, temp_C):\\n\\n U = wind_ms\\n\\n Sc = schmidt_number(temp_C)\\n k = (0.333 * U + 0.222 * U ** 2) * (600 / Sc) ** 0.5\\n\\n return k\",\n \"def liquid_viscosity(id, temperature=298.15, pressure=constants.atm): # noqa: A002\\n return rx._misc._get_chemical(id, temperature, pressure).mul # noqa: SLF001\",\n \"def kinetic_energy(self):\\r\\n position, velocity, escaped_particles,impact, wall_collision,mom = self.box_collision_info()\\r\\n for j in xrange(1,self.n):\\r\\n abs_velocity = np.sqrt(velocity[:,0]**2+velocity[:,1]**2\\r\\n + velocity[:,2]**2)\\r\\n KE = 0.5*self.m*abs_velocity**2\\r\\n total_KE = np.sum(KE)\\r\\n invid_KE = total_KE/self.Npart\\r\\n\\r\\n return total_KE, invid_KE\",\n \"def variable_vis(self):\\n return self._variable_vis\",\n \"def thermal_velocity(charge, temperature, mass):\\n return np.sqrt(2*abs(charge)*temperature/mass)\",\n \"def get_D_C3H8_air_eff(self, T):\\n\\n Kn = self.get_Kn(T)\\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\\n\\n if np.isscalar(Kn):\\n if Kn <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n else:\\n if Kn.any() <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n self.D_C3H8_air_eff = D_C3H8_air_eff\\n\\n return D_C3H8_air_eff\",\n \"def get_vsolar(self):\\n return self.read_register(4098, 1, 3)\",\n \"def _ani_ic_on_planck(electron_energy, soft_photon_temperature, gamma_energy, theta):\\n Ktomec2 = 1.6863699549e-10\\n soft_photon_temperature *= Ktomec2\\n\\n def G12(x, a):\\n \\\"\\\"\\\"\\n Eqs 20, 24, 25\\n \\\"\\\"\\\"\\n alpha, a, beta, b = a\\n pi26 = np.pi ** 2 / 6.0\\n G = (pi26 + x) * np.exp(-x)\\n tmp = 1 + b * x ** beta\\n g = 1. / (a * x ** alpha / tmp + 1.)\\n return G * g\\n\\n gamma_energy = np.vstack(gamma_energy)\\n # Parameters from Eqs 21, 22\\n a1 = [0.857, 0.153, 1.840, 0.254]\\n a2 = [0.691, 1.330, 1.668, 0.534]\\n z = gamma_energy / electron_energy\\n ttheta = 2. * electron_energy * soft_photon_temperature * (1. - np.cos(theta))\\n x = z / (1 - z) / ttheta\\n # Eq. 11\\n cross_section = z ** 2 / (2 * (1 - z)) * G12(x, a1) + G12(x, a2)\\n tmp = (soft_photon_temperature / electron_energy) ** 2\\n # r0 = (e**2 / m_e / c**2).to('cm')\\n # (2 * r0 ** 2 * m_e ** 3 * c ** 4 / (pi * hbar ** 3)).cgs\\n tmp *= 2.6318735743809104e+16\\n cross_section = tmp * cross_section\\n cc = ((gamma_energy < electron_energy) * (electron_energy > 1))\\n return np.where(cc, cross_section,\\n np.zeros_like(cross_section))\",\n \"def test_virtual_temperature():\\n t = 288. * units.kelvin\\n qv = .0016 * units.dimensionless # kg/kg\\n tv = virtual_temperature(t, qv)\\n assert_almost_equal(tv, 288.2796 * units.kelvin, 3)\",\n \"def KineticEnergy(self):\\n return Particle.TotalEnergy(self) - Particle.RestEnergy(self)\",\n \"def k3(self) -> float:\\n return self.distortion_coefficients[2]\",\n \"def get_evaporation_latent_heat() -> float:\\n theta = 28.0\\n return 2500.8 - 2.3668 * theta\",\n \"def kin_energy (self):\\n\\n for planet in self.planets:\\n planet.kenergy = 0.5*planet.mass*((np.linalg.norm(planet.velocity))**2) # every 'kenergy' depends by the body's mass and velocity\",\n \"def density(self):\\n return (1e-3*self.molar_mass) * self.pressure / (gas_constant * self.temperature) # kg/m^3\",\n \"def _CloudVar(self): \\n # q is MA order of ARMA(1,q)\\n q = int(round(self.lambda_avg/self.lambda_s))\\n a = exp(-self.lambda_s / self.lambda_p) \\n (var, var_ratio) = self._ARMAvar(q, a)\\n # This variance is a multiple of the variance of the noise driving the\\n # AR(1) model. This variance, in turn, is a multiple of the underlying\\n # measurement variance, with the relationship given in Gillespie 96\\n var = var * (1. - exp(-2*self.lambda_s / self.lambda_p))/2\\n # print q, a\\n return var\",\n \"def density_of_air(self) -> float:\\n\\n return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature)\",\n \"def temperature(k, kmax):\\n return 1.0 / 500 * (1.0 / k - 1.0 / kmax)\",\n \"def true_an(self):\\n vel = self.getvel_sph()\\n e_vec = self.get_ecc()\\n pos = self.getpos_xyz()\\n if np.linalg.norm(e_vec) == 0:\\n h_mom = self.sp_ang_mom()\\n e_vec = np.cross([0, 0, 1], h_mom)\\n true_th = np.arccos(np.dot(e_vec, pos)/(np.linalg.norm(e_vec) *\\n np.linalg.norm(pos)))\\n if vel[0] < 0:\\n true_th = 2*np.pi - true_th\\n return true_th\",\n \"def rad_field_initial_condition(self):\\n\\n # revert in viewing direct\\n angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)\\n # Looking at the sky\\n if angle < 90:\\n I_init = (\\n self.sun_intensity\\n * f.delta_func(self.sun_elevation - self.receiver_elevation)\\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\\n )\\n\\n # Looking at the ground\\n elif angle > 90:\\n I_ground = RT_model_1D.calc_direct_beam_intensity(self, 0)\\n\\n I_lambert = (\\n I_ground\\n * self.ground_albedo\\n * np.cos(np.deg2rad((self.sun_elevation + 180) % 360))\\n )\\n\\n I_specular = (\\n I_ground\\n * self.ground_albedo\\n * f.delta_func(self.sun_elevation + self.receiver_elevation - 180)\\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\\n )\\n\\n I_init = (\\n 1 - self.reflection_type\\n ) * I_lambert + self.reflection_type * I_specular\\n\\n else:\\n I_init = np.empty(self.stokes_dim)\\n I_init.fill(np.nan)\\n\\n return I_init\",\n \"def is_artificial(self):\\n\\t\\treturn 0\",\n \"def mTV(self):\\n distance = abs(self.vertPosT - self.vertPosW) # distance between htp and vortex shred plane,\\n # approximated with the wing root chordplane\\n return distance / (self.spanW / 2)\",\n \"def LotkaVolterra_Dynamics(self):\\n LV_c = self.toConceptual(self.state) # (nF, nR)\\n LV_c = LV_c.mul((1 - LV_c) + self.LV_inhM.mm(LV_c))\\n LV_s = self.toNeural(LV_c)\\n\\n return LV_c, LV_s\",\n \"def compute_energy_density(kT):\\n h=u.planck\\n c=u.speed_of_light\\n pi=np.pi\\n return (8*pi/(h*c)**3)*((pi*kT)**4/15)\",\n \"async def air_density(self, temperature, station_pressure):\\n if temperature is not None and station_pressure is not None:\\n kelvin = temperature + 273.15\\n pressure = station_pressure\\n r_specific = 287.058\\n decimals = 2\\n\\n air_dens = (pressure * 100) / (r_specific * kelvin)\\n\\n if self._unit_system == UNITS_IMPERIAL:\\n air_dens = air_dens * 0.06243\\n decimals = 4\\n\\n return round(air_dens, decimals)\\n\\n _LOGGER.error(\\\"FUNC: air_density ERROR: Temperature or Pressure value was reported as NoneType. Check the sensor\\\")\",\n \"def thermal_i(mu,Ti):\\n return 9.79*1.e5/np.sqrt(mu/Ti)/1.e2\",\n \"def calc_k(self):\\n\\t\\n\\tself.k = -np.array([self.sth*self.cphi, self.sth*self.sphi, self.cth])\\n\\n\\treturn\",\n \"def constraint_k_pi_invis(self):\\n width_contr = 0.0\\n ms = self.ms\\n\\n # Make sure scalar mass doesn't fall outside of kinematic bounds\\n if np.any([s[0] <= ms**2 <= s[1] for s in k_pi_invis_obs.s_bounds]):\\n widths_s = self.partial_widths()\\n width_s = widths_s[\\\"total\\\"]\\n width_s_sm = width_s - widths_s[\\\"x x\\\"] # Gamma_{S->SM}\\n\\n # Magnitude of S' 3-momentum\\n ps = np.sqrt(\\n (mk - mpi - ms) * (mk + mpi - ms) * (mk - mpi + ms) * (mk + mpi + ms)\\n ) / (2.0 * mk)\\n # Probability that S decays outside the detector\\n pr_invis = np.exp(-k_pi_invis_obs.r_max * cm_to_inv_MeV * width_s * ms / ps)\\n\\n # Compute the total contribution to the invisible decay width\\n width_contr = (\\n self.width_k_pi_s() * (widths_s[\\\"x x\\\"] + pr_invis * width_s_sm) / width_s\\n )\\n\\n return k_pi_invis_obs.width_bound - width_contr\",\n \"def test_velocity(self):\\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\\n # r must contain 2 elements, otherwise the density and pressure are nan\\n r = np.array([0.7, 0.8])\\n t = 6.25e-6\\n solrt = sol(r, t)\\n np.testing.assert_allclose(solrt.velocity[0], 144000.0)\",\n \"def ambient_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._ambient_temperature)\",\n \"def getEnthalpyOfVaporization(self,Temperature):\\n\\t\\tB = self.Antoine_params[1]\\n\\t\\tC = self.Antoine_params[2]\\n\\n\\t\\t# Eqn 7 from Epstein et al 2009\\n\\t\\tHvap = 2.303*8.3145*Temperature*Temperature*B/((C + Temperature - 273.15)*(C + Temperature - 273.15))\\n\\t\\treturn Hvap # units are J/molK\",\n \"def get_force(self):\\n # @todo: Probably need to check the state of the landing gear for this (e.g. are they on the track?).\\n # Note: you can get the state of the landing gear by going through self.sim \\n return 0.0\",\n \"def calc_VPD(t_celsius, rel_humidity):\\n # according to Licor LI-6400 manual pg 14-10\\n # and Buck AL (1981). New equations for computing vapor pressure and\\n # enhancement factor. J Appl Meteor 20:1527-1532\\n vp_sat = 0.61365 * math.exp((17.502 * t_celsius) / (240.97 + t_celsius))\\n\\n vp_air = vp_sat * rel_humidity\\n return vp_sat - vp_air # or vp_sat * (1 - rel_humidity)\",\n \"def thermal_conductivity(temperature):\\n a0 = -4.1236\\n a1 = 13.788\\n a2 = -26.068\\n a3 = 26.272\\n a4 = -14.663\\n a5 = 4.4954\\n a6 = -0.6905\\n a7 = 0.0397\\n log_t = math.log10(temperature)\\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\\\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\\n g10_thermal_conductivity = 10.0**f_exp\\n return g10_thermal_conductivity\",\n \"def vcrit(Te):\\n vcrit = 3.0*np.sqrt(np.pi)/4.*(2.*eV2J/me)**(1.5)*(me/mi)*np.sqrt(Te**3.)\\n return vcrit\",\n \"def k_Li86(wind_ms, temp_C):\\n from numpy import zeros_like\\n\\n U = wind_ms\\n T = temp_C\\n\\n Sc = schmidt_number(T)\\n k = zeros_like(temp_C)\\n\\n i1 = U <= 3.6\\n i2 = (U > 3.6) & (U < 13.0)\\n i3 = U >= 13.0\\n\\n k[i1] = (0.17 * U[i1]) * (Sc[i1] / 600) ** (-2.0 / 3.0)\\n k[i2] = ((U[i2] - 3.4) * 2.8) * (600 / Sc[i2]) ** 0.5\\n k[i3] = ((U[i3] - 8.4) * 5.9) * (600 / Sc[i3]) ** 0.5\\n\\n return k\",\n \"def sky_observed(seed=425, th=150, old=False):\\n \\n # impact parameters\\n M = 3e7*u.Msun\\n #M = 6e7*u.Msun\\n B = 19.95*u.kpc\\n V = 190*u.km/u.s\\n phi = coord.Angle(0*u.deg)\\n theta = coord.Angle(th*u.deg)\\n Tenc = 0.01*u.Gyr\\n T = 0.5*u.Gyr\\n dt = 0.05*u.Myr\\n rs = 0*u.pc\\n \\n old_label = ''\\n \\n if old:\\n old_label = '_old'\\n \\n # impact parameters\\n M = 5e7*u.Msun\\n B = 19.8*u.kpc\\n V = 210*u.km/u.s\\n phi = coord.Angle(0*u.deg)\\n th = 150\\n theta = coord.Angle(th*u.deg)\\n Tenc = 0.05*u.Gyr\\n T = 2*u.Gyr\\n dt = 0.1*u.Myr\\n #dt = 1*u.Myr\\n rs = 0*u.pc\\n \\n # potential parameters\\n potential = 3\\n Vh = 220*u.km/u.s\\n q = 1*u.Unit(1)\\n rhalo = 20*u.pc\\n par_pot = np.array([Vh.si.value, q.value, rhalo.si.value])\\n \\n # setup tube\\n Nstar = 1400\\n wx = 30*u.kpc\\n wy = 0*u.pc\\n wz = 0*u.pc\\n sx = 0*u.km/u.s\\n \\n np.random.seed(seed)\\n observer = {'z_sun': 27.*u.pc, 'galcen_distance': 8.3*u.kpc, 'roll': 60*u.deg, 'galcen_coord': coord.SkyCoord(ra=300*u.deg, dec=-90*u.deg, frame='icrs')}\\n alt_observer = {'z_sun': 27.*u.pc, 'galcen_distance': 8.3*u.kpc, 'roll': -45*u.deg, 'galcen_coord': coord.SkyCoord(ra=300*u.deg, dec=-90*u.deg, frame='icrs')}\\n vobs = {'vcirc': 220*u.km/u.s, 'vlsr': [0, 0, 0]*u.km/u.s}\\n wangle = 180*u.deg\\n \\n xphi = np.linspace(-0.3*np.pi,0.3*np.pi, Nstar)\\n xphi0 = np.linspace(-0.1*np.pi, 0.1*np.pi, 1000)\\n xphi1 = np.linspace(-0.28*np.pi, -0.1*np.pi, 200)\\n xphi2 = np.linspace(0.1*np.pi, 0.32*np.pi, 200)\\n xphi = np.concatenate([xphi1, xphi0, xphi2])\\n \\n xr = 20*u.kpc + np.random.randn(Nstar)*0.0*u.kpc\\n x = np.sin(xphi) * xr\\n y = np.cos(xphi) * xr\\n z = x * 0\\n vx = -np.cos(xphi) * Vh\\n vy = np.sin(xphi) * Vh\\n vz = vx * 0\\n # closest to impact\\n ienc = np.argmin(np.abs(x))\\n \\n # generate stream model\\n potential_perturb = 1\\n par_perturb = np.array([M.si.value, 0., 0., 0.])\\n x1, x2, x3, v1, v2, v3 = interact.interact(par_perturb, B.si.value, phi.rad, V.si.value, theta.rad, Tenc.si.value, T.si.value, dt.si.value, par_pot, potential, potential_perturb, x.si.value, y.si.value, z.si.value, vx.si.value, vy.si.value, vz.si.value)\\n stream = {}\\n stream['x'] = (np.array([x1, x2, x3])*u.m).to(u.pc)\\n stream['v'] = (np.array([v1, v2, v3])*u.m/u.s).to(u.km/u.s)\\n \\n # sky coordinates\\n xgal = coord.Galactocentric(stream['x'], **observer)\\n xeq = xgal.transform_to(coord.ICRS)\\n veq_ = gc.vgal_to_hel(xeq, stream['v'], **vobs)\\n veq = [None] * 3\\n veq[0] = veq_[0].to(u.mas/u.yr)\\n veq[1] = veq_[1].to(u.mas/u.yr)\\n veq[2] = veq_[2].to(u.km/u.s)\\n \\n # alternative sky coordinates\\n xgal_alt = coord.Galactocentric(stream['x'], **alt_observer)\\n xeq_alt = xgal_alt.transform_to(coord.ICRS)\\n veq_alt_ = gc.vgal_to_hel(xeq_alt, stream['v'], **vobs)\\n veq_alt = [None] * 3\\n veq_alt[0] = veq_alt_[0].to(u.mas/u.yr)\\n veq_alt[1] = veq_alt_[1].to(u.mas/u.yr)\\n veq_alt[2] = veq_alt_[2].to(u.km/u.s)\\n \\n # unperturbed stream\\n par_perturb = np.array([0*M.si.value, 0., 0., 0.])\\n x1, x2, x3, v1, v2, v3 = interact.interact(par_perturb, B.si.value, phi.rad, V.si.value, theta.rad, Tenc.si.value, T.si.value, dt.si.value, par_pot, potential, potential_perturb, x.si.value, y.si.value, z.si.value, vx.si.value, vy.si.value, vz.si.value)\\n stream0 = {}\\n stream0['x'] = (np.array([x1, x2, x3])*u.m).to(u.pc)\\n stream0['v'] = (np.array([v1, v2, v3])*u.m/u.s).to(u.km/u.s)\\n \\n # sky coordinates\\n xgal0 = coord.Galactocentric(stream0['x'], **observer)\\n xeq0 = xgal0.transform_to(coord.ICRS)\\n veq0_ = gc.vgal_to_hel(xeq0, stream0['v'], **vobs)\\n veq0 = [None] * 3\\n veq0[0] = veq0_[0].to(u.mas/u.yr)\\n veq0[1] = veq0_[1].to(u.mas/u.yr)\\n veq0[2] = veq0_[2].to(u.km/u.s)\\n \\n # alternative sky coordinates\\n xgal0_alt = coord.Galactocentric(stream0['x'], **alt_observer)\\n xeq0_alt = xgal0_alt.transform_to(coord.ICRS)\\n veq0_alt_ = gc.vgal_to_hel(xeq0_alt, stream0['v'], **vobs)\\n veq0_alt = [None] * 3\\n veq0_alt[0] = veq0_alt_[0].to(u.mas/u.yr)\\n veq0_alt[1] = veq0_alt_[1].to(u.mas/u.yr)\\n veq0_alt[2] = veq0_alt_[2].to(u.km/u.s)\\n \\n # rotate to native coordinate system\\n R = find_greatcircle(xeq0.ra.deg[::10], xeq0.dec.deg[::10])\\n xi0, eta0 = myutils.rotate_angles(xeq0.ra, xeq0.dec, R)\\n xi0 = coord.Angle(xi0*u.deg)\\n \\n # place gap at xi~0\\n xioff = xi0[ienc]\\n xi0 -= xioff\\n \\n xi, eta = myutils.rotate_angles(xeq.ra, xeq.dec, R)\\n xi = coord.Angle(xi*u.deg)\\n xi -= xioff\\n \\n # alternative observer\\n R_alt = find_greatcircle(xeq0_alt.ra.deg[::10], xeq0_alt.dec.deg[::10])\\n xi0_alt, eta0_alt = myutils.rotate_angles(xeq0_alt.ra, xeq0_alt.dec, R_alt)\\n xi0_alt = coord.Angle(xi0_alt*u.deg)\\n \\n # place gap at xi~0\\n xioff_alt = xi0_alt[ienc]\\n xi0_alt -= xioff_alt\\n \\n xi_alt, eta_alt = myutils.rotate_angles(xeq_alt.ra, xeq_alt.dec, R_alt)\\n xi_alt = coord.Angle(xi_alt*u.deg)\\n xi_alt -= xioff_alt\\n \\n\\n # observed gd1\\n g = Table(fits.getdata('/home/ana/projects/GD1-DR2/output/gd1_members.fits'))\\n \\n vlabel = ['$\\\\mu_{\\\\\\\\alpha_\\\\star}$ [mas yr$^{-1}$]','$\\\\mu_{\\\\delta}$ [mas yr$^{-1}$]', '$V_r$ [km s$^{-1}$]']\\n ylims = [[-1.5, 1.5], [-1.5, 1.5], [-30,30]]\\n color = '0.35'\\n ms = 4\\n alpha = 0.7\\n \\n # plotting\\n plt.close()\\n fig, ax = plt.subplots(2,4,figsize=(17,8), sharex=True, sharey='col')\\n \\n plt.sca(ax[0][0])\\n plt.plot(xi.wrap_at(wangle), eta, '.', mec='none', color=color, ms=ms, label='Simulated GD-1')\\n \\n #plt.xlabel('$\\\\phi_1$ [deg]')\\n plt.ylabel('$\\\\phi_2$ [deg]')\\n plt.xlim(-20,20)\\n plt.ylim(-5,5)\\n \\n plt.sca(ax[1][0])\\n plt.plot(xi_alt.wrap_at(wangle), eta_alt, '.', mec='none', color=color, ms=ms, label='Simulated GD-1')\\n \\n plt.xlabel('$\\\\phi_1$ [deg]')\\n plt.ylabel('$\\\\phi_2$ [deg]')\\n plt.xlim(-20,20)\\n plt.ylim(-5,5)\\n \\n xeqs = [xeq.ra, xeq.dec, xeq.distance.to(u.kpc)]\\n dv = []\\n dv_alt = []\\n for i in range(3):\\n plt.sca(ax[0][i+1])\\n \\n # interpolate expected kinematics from an unperturbed stream\\n vexp = np.interp(xi.wrap_at(wangle), xi0.wrap_at(wangle), veq0[i].value) * veq0[i].unit\\n dv += [veq[i]-vexp]\\n plt.plot(xi.wrap_at(wangle), dv[i], '.', mec='none', color=color, ms=ms)\\n \\n plt.ylabel('$\\\\Delta$ {}'.format(vlabel[i]))\\n plt.ylim(*ylims[i])\\n \\n plt.sca(ax[1][i+1])\\n # interpolate expected kinematics from an unperturbed stream\\n vexp_alt = np.interp(xi_alt.wrap_at(wangle), xi0_alt.wrap_at(wangle), veq0_alt[i].value) * veq0_alt[i].unit\\n dv_alt += [veq_alt[i]-vexp_alt]\\n plt.plot(xi_alt.wrap_at(wangle), dv_alt[i], '.', mec='none', color=color, ms=ms)\\n \\n plt.ylabel('$\\\\Delta$ {}'.format(vlabel[i]))\\n plt.ylim(*ylims[i])\\n plt.xlabel('$\\\\phi_1$ [deg]')\\n \\n # find closest model star to the gd-1 stars\\n Ngd1 = len(g)\\n p = np.array([g['phi1']+40, g['phi2']])\\n q = np.array([xi.wrap_at(wangle).to(u.deg).value, eta])\\n idmin = np.empty(Ngd1, dtype='int')\\n \\n for i in range(Ngd1):\\n dist = np.sqrt((p[0,i]-q[0])**2 + (p[1,i]-q[1])**2)\\n idmin[i] = np.argmin(dist)\\n\\n # mask stream, mask spur\\n onstream_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>-0.2) & (g['phi2']<0.2))\\n spur_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>1) & (g['phi2']<1.4))\\n all_mask = np.ones(Ngd1, dtype='bool')\\n \\n # plot scaled data uncertainties on model pm drawn from a corresponding obs uncertainty\\n np.random.seed(seed+1)\\n fgaia = np.sqrt(2/5)\\n print(2/5, fgaia)\\n phi1 = xi[idmin].wrap_at(wangle).to(u.deg).value\\n phi2 = eta[idmin]\\n pmra = dv[0][idmin] + g['pmra_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n pmdec = dv[1][idmin] + g['pmdec_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n \\n colors = ['tab:red', 'tab:blue', '0.4']\\n labels = ['Stream', 'Spur']\\n labels = ['Gaia DR4', '']\\n \\n for e, mask in enumerate([onstream_mask, spur_mask]):\\n plt.sca(ax[0][0])\\n plt.plot(phi1[mask], phi2[mask], 'o', color=colors[e], mec='none', alpha=alpha, label=labels[e])\\n \\n plt.sca(ax[0][1])\\n plt.errorbar(phi1[mask], pmra[mask].value, yerr=g['pmra_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n plt.sca(ax[0][2])\\n plt.errorbar(phi1[mask], pmdec[mask].value, yerr=g['pmdec_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n print(np.sqrt(np.sum(g['pmra_error'][mask]**2))/np.sum(mask))\\n print(np.sqrt(np.sum(g['pmdec_error'][mask]**2))/np.sum(mask))\\n\\n Nfield = 2\\n p2 = np.array([np.array([-32.77,-32.77])+40, [1.167,0]])\\n q = np.array([xi.wrap_at(wangle).to(u.deg).value, eta])\\n idmin2 = np.empty(Nfield, dtype='int')\\n \\n for i in range(Nfield):\\n dist = np.sqrt((p2[0,i]-q[0])**2 + (p2[1,i]-q[1])**2)\\n idmin2[i] = np.argmin(dist)\\n \\n pmerr = np.array([0.0848, 0.0685])\\n \\n np.random.seed(seed+2)\\n phi1 = xi[idmin2].wrap_at(wangle).to(u.deg).value\\n phi2 = eta[idmin2]\\n pmra = dv[0][idmin2].value + pmerr*np.random.randn(Nfield)\\n pmdec = dv[1][idmin2].value + pmerr*np.random.randn(Nfield)\\n \\n plt.sca(ax[0][0])\\n plt.errorbar(phi1, phi2, color='k', fmt='o', label='HST')\\n \\n plt.sca(ax[0][1])\\n plt.errorbar(phi1, pmra, yerr=pmerr, color='k', fmt='o')\\n \\n plt.sca(ax[0][2])\\n plt.errorbar(phi1, pmdec, yerr=pmerr, color='k', fmt='o')\\n \\n \\n ##############\\n # alt observer\\n \\n # find closest model star to the gd-1 stars\\n Ngd1 = len(g)\\n p = np.array([g['phi1']+40, g['phi2']])\\n q = np.array([xi_alt.wrap_at(wangle).to(u.deg).value, eta_alt])\\n idmin = np.empty(Ngd1, dtype='int')\\n \\n for i in range(Ngd1):\\n dist = np.sqrt((p[0,i]-q[0])**2 + (p[1,i]-q[1])**2)\\n idmin[i] = np.argmin(dist)\\n\\n # mask stream, mask spur\\n onstream_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>-0.2) & (g['phi2']<0.2))\\n spur_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>1) & (g['phi2']<1.4))\\n all_mask = np.ones(Ngd1, dtype='bool')\\n \\n # plot scaled data uncertainties on model pm drawn from a corresponding obs uncertainty\\n #np.random.seed(seed+3)\\n phi1 = xi_alt[idmin].wrap_at(wangle).to(u.deg).value\\n phi2 = eta_alt[idmin]\\n pmra = dv_alt[0][idmin] + g['pmra_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n pmdec = dv_alt[1][idmin] + g['pmdec_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n \\n colors = ['tab:red', 'tab:blue', '0.4']\\n labels = ['Gaia DR4', '']\\n \\n for e, mask in enumerate([onstream_mask, spur_mask]):\\n plt.sca(ax[1][0])\\n plt.plot(phi1[mask], phi2[mask], 'o', color=colors[e], mec='none', alpha=alpha, label=labels[e])\\n \\n plt.sca(ax[1][1])\\n plt.errorbar(phi1[mask], pmra[mask].value, yerr=g['pmra_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n plt.sca(ax[1][2])\\n plt.errorbar(phi1[mask], pmdec[mask].value, yerr=g['pmdec_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n Nfield = 2\\n p2 = np.array([np.array([-32.77,-32.77])+40, [1.167,0]])\\n q = np.array([xi_alt.wrap_at(wangle).to(u.deg).value, eta_alt])\\n idmin2 = np.empty(Nfield, dtype='int')\\n \\n for i in range(Nfield):\\n dist = np.sqrt((p2[0,i]-q[0])**2 + (p2[1,i]-q[1])**2)\\n idmin2[i] = np.argmin(dist)\\n \\n pmerr = np.array([0.11, 0.08])\\n \\n np.random.seed(seed+6)\\n phi1 = xi_alt[idmin2].wrap_at(wangle).to(u.deg).value\\n phi2 = eta_alt[idmin2]\\n pmra = dv_alt[0][idmin2].value + pmerr*np.random.randn(Nfield)\\n pmdec = dv_alt[1][idmin2].value + pmerr*np.random.randn(Nfield)\\n \\n plt.sca(ax[1][0])\\n plt.errorbar(phi1, phi2, color='k', fmt='o', label='HST')\\n \\n plt.sca(ax[1][1])\\n plt.errorbar(phi1, pmra, yerr=pmerr, color='k', fmt='o')\\n \\n plt.sca(ax[1][2])\\n plt.errorbar(phi1, pmdec, yerr=pmerr, color='k', fmt='o')\\n \\n \\n plt.sca(ax[0][0])\\n plt.text(0.1,0.85, '$\\\\\\\\theta_{roll}$ = 60$^\\\\circ$', fontsize='small', transform=plt.gca().transAxes)\\n\\n plt.sca(ax[1][0])\\n plt.text(0.1,0.85, '$\\\\\\\\theta_{roll}$ = -45$^\\\\circ$', fontsize='small', transform=plt.gca().transAxes)\\n plt.legend(fontsize='small', loc=3, handlelength=0.2)\\n \\n plt.suptitle('Expected astrometric performance', fontsize='medium')\\n plt.tight_layout(rect=[0,0,1,0.94])\\n plt.savefig('../plots/astrometric_performance.png')\",\n \"def get_D_C3H8_air(self, T):\\n\\n self.set_TempPres_dependents(T)\\n\\n self.D_C3H8_air = (\\n 2. / 3. * np.sqrt(const.k_B * T / np.pi * 0.5 * (1. /\\n self.air.m + 1. / self.fuel.m)) / (np.pi * (0.5 *\\n (self.air.d + self.fuel.d)) ** 2.) / self.air.n\\n )\\n\\n return self.D_C3H8_air\",\n \"def useKineticEnergy(self):\\n return self.params['useKinetic']\",\n \"def sensitivity(gas, surf, old_data, temp, dk, thermo=False):\\n rxns = []\\n sens1 = []\\n sens2 = []\\n sens3 = []\\n sens4 = []\\n sens5 = []\\n sens6 = []\\n sens7 = []\\n sens8 = []\\n sens9 = []\\n sens10 = []\\n sens11 = []\\n sens12 = []\\n\\n gas_out_data, gas_names_data, dist_array_data, T_array_data = old_data\\n\\n reference = []\\n for a in range(len(gas_names_data)):\\n reference.append([gas_names_data[a], [gas_out_data[:, a]]])\\n\\n # getting the ratio\\n for x in reference:\\n if x[0] == 'CH4(2)':\\n ch4_in = x[1][0][0]\\n if x[0] == 'O2(3)':\\n o2_in = x[1][0][0]\\n if x[0] == 'Ar':\\n ar_in = x[1][0][0]\\n ratio = ch4_in / (2 * o2_in)\\n moles_in = [ch4_in, o2_in, ar_in]\\n\\n for x in reference:\\n if x[0] == 'CH4(2)':\\n ch4_in = x[1][0][0]\\n ch4_out = x[1][0][-1]\\n if ch4_out < 0:\\n ch4_out = 0.\\n ch4_depletion = ch4_in - ch4_out\\n if ch4_depletion <= 1.0e-8:\\n ch4_depletion = 1.0e-8\\n reference_ch4_conv = 1.0e-8\\n else:\\n reference_ch4_conv = ch4_depletion / ch4_in # Sensitivity definition 7: CH4 conversion\\n if x[0] == 'Ar':\\n ar = x[1][0][-1]\\n if x[0] == 'O2(3)':\\n o2_out = x[1][0][-1]\\n if o2_out < 0:\\n o2_out = 1.0e-15 # O2 can't be negative\\n elif o2_out > o2_in:\\n o2_out = o2_in # O2 can't be created, to make it equal to O2 in\\n if x[0] == 'CO(7)':\\n co_out = x[1][0][-1]\\n if x[0] == 'H2(6)':\\n h2_out = x[1][0][-1]\\n if x[0] == 'H2O(5)':\\n h2o_out = x[1][0][-1]\\n if x[0] == 'CO2(4)':\\n co2_out = x[1][0][-1]\\n\\n if reference_ch4_conv <= 1.0e-8:\\n reference_h2_sel = 1.0e-8\\n reference_co_sel = 1.0e-8\\n reference_syngas_selectivity = 1.0e-8\\n reference_syngas_yield = 1.0e-8\\n reference_co_yield = 1.0e-8\\n reference_h2_yield = 1.0e-8\\n reference_full_oxidation_selectivity = 1.0e-8\\n reference_full_oxidation_yield = 1.0e-8\\n else:\\n # negative sensitivity is higher selectivity\\n reference_h2_sel = h2_out / (ch4_depletion * 2) # Sensitivity definition 5: H2 selectivity\\n if reference_h2_sel <= 0:\\n reference_h2_sel = 1.0e-15 # selectivity can't be 0\\n\\n reference_co_sel = co_out / ch4_depletion # Sensitivity definition 3: CO selectivity\\n if reference_co_sel <= 0:\\n reference_co_sel = 1.0e-15 # selectivity can't be 0\\n\\n reference_syngas_selectivity = reference_co_sel + reference_h2_sel # Sensitivity definition 1: SYNGAS selectivity\\n\\n reference_syngas_yield = reference_syngas_selectivity * reference_ch4_conv # Sensitivity definition 2: SYNGAS yield\\n if reference_syngas_yield <= 0:\\n reference_syngas_yield = 1.0e-15 # yield can't be 0\\n\\n reference_co_yield = co_out / ch4_in # Sensitivity definition 4: CO % yield\\n # reference_co_yield = reference_co_sel * reference_ch4_conv\\n\\n reference_h2_yield = h2_out / (2 * ch4_in) # Sensitivity definition 6: H2 % yield\\n # reference_h2_yield = reference_h2_sel * reference_ch4_conv\\n\\n # Sensitivity definition 8: H2O + CO2 selectivity\\n reference_h2o_sel = h2o_out / (ch4_depletion * 2)\\n reference_co2_sel = co2_out / ch4_depletion\\n if reference_h2o_sel <= 0:\\n reference_h2o_sel = 1.0e-15 # H2O selectivity can't be 0\\n if reference_co2_sel <= 0:\\n reference_co2_sel = 1.0e-15 # CO2 selectivity can't be 0\\n reference_full_oxidation_selectivity = reference_h2o_sel + reference_co2_sel\\n\\n # Sensitivity definition 9: H2O + CO2 yield\\n reference_full_oxidation_yield = reference_full_oxidation_selectivity * reference_ch4_conv\\n\\n # Sensitivity definition 10: exit temperature\\n reference_exit_temp = T_array_data[-1]\\n\\n # Sensitivity definition 11: peak temperature\\n reference_peak_temp = max(T_array_data)\\n\\n # Sensitivity definition 12: distance to peak temperautre\\n reference_peak_temp_dist = dist_array_data[T_array_data.index(max(T_array_data))]\\n\\n # run the simulations\\n if thermo is True:\\n for m in range(surf.n_species):\\n s = surf.species(m)\\n original_coeffs = s.thermo.coeffs\\n perturbed_coeffs = np.ones_like(original_coeffs)\\n perturbed_coeffs[0] = original_coeffs[0]\\n perturbed_coeffs[1:6] = original_coeffs[1:6]\\n perturbed_coeffs[7:13] = original_coeffs[7:13]\\n perturbed_coeffs[14] = original_coeffs[14]\\n # perturbed_coeffs[6] = original_coeffs[6] + original_coeffs[6]*dk\\n # perturbed_coeffs[13] = original_coeffs[13] + original_coeffs[13]*dk\\n perturbed_coeffs[6] = original_coeffs[6] + dk\\n perturbed_coeffs[13] = original_coeffs[13] + dk\\n s.thermo = ct.NasaPoly2(100.000, 5000.000, ct.one_atm, perturbed_coeffs)\\n surf.modify_species(m, s)\\n c = monolithFull(gas, surf, temp, moles_in)\\n\\n gas_out, surf_out, gas_names, surf_names, dist_array, T_array = c\\n\\n new_amts = []\\n for a in range(len(gas_names)):\\n new_amts.append([gas_names[a], [gas_out[:, a]]])\\n\\n for x in new_amts:\\n if x[0] == 'CH4(2)':\\n new_ch4_in = x[1][0][0]\\n new_ch4_out = x[1][0][-1]\\n if new_ch4_out < 0:\\n new_ch4_out = 0.\\n new_ch4_depletion = new_ch4_in - new_ch4_out\\n if new_ch4_depletion <= 1e-8:\\n new_ch4_depletion = 1e-8\\n new_ch4_conv = 1e-8\\n else:\\n new_ch4_conv = new_ch4_depletion / new_ch4_in # Sensitivity definition 7: CH4 conversion\\n if x[0] == 'Ar':\\n ar = x[1][0][-1]\\n if x[0] == 'O2(3)':\\n new_o2_in = x[1][0][0]\\n new_o2_out = x[1][0][-1]\\n if new_o2_out < 0:\\n new_o2_out = 1.0e-15\\n elif new_o2_out > new_o2_in:\\n new_o2_out = new_o2_in\\n if x[0] == 'CO(7)':\\n new_co_out = x[1][0][-1]\\n if x[0] == 'H2(6)':\\n new_h2_out = x[1][0][-1]\\n if x[0] == 'H2O(5)':\\n new_h2o_out = x[1][0][-1]\\n if x[0] == 'CO2(4)':\\n new_co2_out = x[1][0][-1]\\n\\n if new_ch4_conv <= 1e-8:\\n new_h2_sel = 1.0e-8\\n new_co_sel = 1.0e-8\\n new_syngas_selectivity = 1.0e-8\\n new_syngas_yield = 1.0e-8\\n new_co_yield = 1.0e-8\\n new_h2_yield = 1.0e-8\\n new_full_oxidation_selectivity = 1.0e-8\\n new_full_oxidation_yield = 1.0e-8\\n else:\\n new_h2_sel = new_h2_out / (new_ch4_depletion * 2) # Sensitivity definition 5: H2 selectivity\\n new_co_sel = new_co_out / new_ch4_depletion # Sensitivity definition 3: CO selectivity\\n new_syngas_selectivity = new_co_sel + new_h2_sel # Sensitivity definition 1: SYNGAS selectivity\\n new_syngas_yield = new_syngas_selectivity * new_ch4_conv # Sensitivity definition 2: SYNGAS yield\\n new_co_yield = new_co_out / new_ch4_in # Sensitivity definition 4: CO % yield\\n new_h2_yield = new_h2_out / (2 * new_ch4_in) # Sensitivity definition 6: H2 % yield\\n new_h2o_sel = new_h2o_out / (new_ch4_depletion * 2) # Sensitivity definition 8: H2O + CO2 selectivity\\n new_co2_sel = new_co2_out / new_ch4_depletion\\n new_full_oxidation_selectivity = new_h2o_sel + new_co2_sel\\n new_full_oxidation_yield = new_full_oxidation_selectivity * new_ch4_conv # Sensitivity definition 9: C2O + CO2 yield\\n\\n Sens5 = (new_h2_sel - reference_h2_sel) / (reference_h2_sel * dk)\\n sens5.append(Sens5)\\n\\n Sens3 = (new_co_sel - reference_co_sel) / (reference_co_sel * dk)\\n sens3.append(Sens3)\\n\\n Sens1 = (new_syngas_selectivity - reference_syngas_selectivity) / (reference_syngas_selectivity * dk)\\n sens1.append(Sens1)\\n\\n Sens2 = (new_syngas_yield - reference_syngas_yield) / (reference_syngas_yield * dk)\\n sens2.append(Sens2)\\n\\n Sens4 = (new_co_yield - reference_co_yield) / (reference_co_yield * dk)\\n sens4.append(Sens4)\\n\\n Sens6 = (new_h2_yield - reference_h2_yield) / (reference_h2_yield * dk)\\n sens6.append(Sens6)\\n\\n Sens7 = (new_ch4_conv - reference_ch4_conv) / (\\n reference_ch4_conv * dk)\\n sens7.append(Sens7)\\n\\n Sens8 = (new_full_oxidation_selectivity - reference_full_oxidation_selectivity) / (\\n reference_full_oxidation_selectivity * dk)\\n sens8.append(Sens8)\\n\\n Sens9 = (new_full_oxidation_yield - reference_full_oxidation_yield) / (reference_full_oxidation_yield * dk)\\n sens9.append(Sens9)\\n\\n new_exit_temp = T_array[-1] # Sensitivity definition 10: exit temperature\\n Sens10 = (new_exit_temp - reference_exit_temp) / (reference_exit_temp * dk)\\n sens10.append(Sens10)\\n\\n new_peak_temp = max(T_array) # Sensitivity definition 11: peak temperature\\n Sens11 = (new_peak_temp - reference_peak_temp) / (reference_peak_temp * dk)\\n sens11.append(Sens11)\\n\\n new_peak_temp_dist = dist_array[\\n T_array.index(max(T_array))] # Sensitivity definition 12: dist to peak temperature\\n Sens12 = (new_peak_temp_dist - reference_peak_temp_dist) / (reference_peak_temp_dist * dk)\\n sens12.append(Sens12)\\n\\n print \\\"%d %s %.3F %.3F\\\" % (m, surf.species_name(m), Sens1, Sens2)\\n rxns.append(surf.species_name(m))\\n\\n # this step is essential, otherwise mechanism will have been altered\\n s.thermo = ct.NasaPoly2(100.000, 5000.000, ct.one_atm, original_coeffs)\\n surf.modify_species(m, s)\\n else:\\n for rxn in range(surf.n_reactions):\\n c = monolithFull(gas, surf, temp, moles_in, sens=[dk, rxn])\\n gas_out, surf_out, gas_names, surf_names, dist_array, T_array = c\\n\\n new_amts = []\\n for a in range(len(gas_names)):\\n new_amts.append([gas_names[a], [gas_out[:, a]]])\\n\\n for x in new_amts:\\n if x[0] == 'CH4(2)':\\n new_ch4_in = x[1][0][0]\\n new_ch4_out = x[1][0][-1]\\n if new_ch4_out < 0:\\n new_ch4_out = 0.\\n new_ch4_depletion = new_ch4_in - new_ch4_out\\n if new_ch4_depletion <= 1e-8:\\n new_ch4_depletion = 1e-8\\n new_ch4_conv = 1e-8\\n else:\\n new_ch4_conv = new_ch4_depletion / new_ch4_in # Sensitivity definition 7: CH4 conversion\\n if x[0] == 'Ar':\\n ar = x[1][0][-1]\\n if x[0] == 'O2(3)':\\n new_o2_in = x[1][0][0]\\n new_o2_out = x[1][0][-1]\\n if new_o2_out < 0:\\n new_o2_out = 1.0e-15\\n elif new_o2_out > new_o2_in:\\n new_o2_out = new_o2_in\\n if x[0] == 'CO(7)':\\n new_co_out = x[1][0][-1]\\n if x[0] == 'H2(6)':\\n new_h2_out = x[1][0][-1]\\n if x[0] == 'H2O(5)':\\n new_h2o_out = x[1][0][-1]\\n if x[0] == 'CO2(4)':\\n new_co2_out = x[1][0][-1]\\n\\n if new_ch4_conv <= 1e-8:\\n new_h2_sel = 1.0e-8\\n new_co_sel = 1.0e-8\\n new_syngas_selectivity = 1.0e-8\\n new_syngas_yield = 1.0e-8\\n new_co_yield = 1.0e-8\\n new_h2_yield = 1.0e-8\\n new_full_oxidation_selectivity = 1.0e-8\\n new_full_oxidation_yield = 1.0e-8\\n else:\\n new_h2_sel = new_h2_out / (new_ch4_depletion * 2) # Sensitivity definition 5: H2 selectivity\\n new_co_sel = new_co_out / new_ch4_depletion # Sensitivity definition 3: CO selectivity\\n new_syngas_selectivity = new_co_sel + new_h2_sel # Sensitivity definition 1: SYNGAS selectivity\\n new_syngas_yield = new_syngas_selectivity * new_ch4_conv # Sensitivity definition 2: SYNGAS yield\\n new_co_yield = new_co_out / new_ch4_in # Sensitivity definition 4: CO % yield\\n new_h2_yield = new_h2_out / (2 * new_ch4_in) # Sensitivity definition 6: H2 % yield\\n new_h2o_sel = new_h2o_out / (new_ch4_depletion * 2) # Sensitivity definition 8: H2O + CO2 selectivity\\n new_co2_sel = new_co2_out / new_ch4_depletion\\n new_full_oxidation_selectivity = new_h2o_sel + new_co2_sel\\n new_full_oxidation_yield = new_full_oxidation_selectivity * new_ch4_conv # Sensitivity definition 9: C2O + CO2 yield\\n\\n Sens5 = (new_h2_sel - reference_h2_sel) / (reference_h2_sel * dk)\\n sens5.append(Sens5)\\n\\n Sens3 = (new_co_sel - reference_co_sel) / (reference_co_sel * dk)\\n sens3.append(Sens3)\\n\\n Sens1 = (new_syngas_selectivity - reference_syngas_selectivity) / (reference_syngas_selectivity * dk)\\n sens1.append(Sens1)\\n\\n Sens2 = (new_syngas_yield - reference_syngas_yield) / (reference_syngas_yield * dk)\\n sens2.append(Sens2)\\n\\n Sens4 = (new_co_yield - reference_co_yield) / (reference_co_yield * dk)\\n sens4.append(Sens4)\\n\\n Sens6 = (new_h2_yield - reference_h2_yield) / (reference_h2_yield * dk)\\n sens6.append(Sens6)\\n\\n Sens7 = (new_ch4_conv - reference_ch4_conv) / (\\n reference_ch4_conv * dk)\\n sens7.append(Sens7)\\n\\n Sens8 = (new_full_oxidation_selectivity - reference_full_oxidation_selectivity) / (\\n reference_full_oxidation_selectivity * dk)\\n sens8.append(Sens8)\\n\\n Sens9 = (new_full_oxidation_yield - reference_full_oxidation_yield) / (reference_full_oxidation_yield * dk)\\n sens9.append(Sens9)\\n\\n new_exit_temp = T_array[-1] # Sensitivity definition 10: exit temperature\\n Sens10 = (new_exit_temp - reference_exit_temp) / (reference_exit_temp * dk)\\n sens10.append(Sens10)\\n\\n new_peak_temp = max(T_array) # Sensitivity definition 11: peak temperature\\n Sens11 = (new_peak_temp - reference_peak_temp) / (reference_peak_temp * dk)\\n sens11.append(Sens11)\\n\\n new_peak_temp_dist = dist_array[T_array.index(max(T_array))] # Sensitivity definition 12: dist to peak temperature\\n Sens12 = (new_peak_temp_dist - reference_peak_temp_dist) / (reference_peak_temp_dist * dk)\\n sens12.append(Sens12)\\n\\n print \\\"%d %s %.3F %.3F\\\" % (rxn, surf.reaction_equations()[rxn], Sens1, Sens2)\\n rxns.append(surf.reaction_equations()[rxn])\\n\\n return rxns, sens1, sens2, sens3, sens4, sens5, sens6, sens7, sens8, sens9, sens10, sens11, sens12\",\n \"def _iso_ic_on_planck(electron_energy, soft_photon_temperature, gamma_energy):\\n Ktomec2 = 1.6863699549e-10\\n soft_photon_temperature *= Ktomec2\\n\\n def G34(x, a):\\n \\\"\\\"\\\"\\n Eqs 20, 24, 25\\n \\\"\\\"\\\"\\n alpha, a, beta, b, c = a\\n pi26 = np.pi ** 2 / 6.0\\n tmp = (1 + c * x) / (1 + pi26 * c * x)\\n G = pi26 * tmp * np.exp(-x)\\n tmp = 1 + b * x ** beta\\n g = 1. / (a * x ** alpha / tmp + 1.)\\n return G * g\\n\\n gamma_energy = np.vstack(gamma_energy)\\n # Parameters from Eqs 26, 27\\n a3 = [0.606, 0.443, 1.481, 0.540, 0.319]\\n a4 = [0.461, 0.726, 1.457, 0.382, 6.620]\\n z = gamma_energy / electron_energy\\n x = z / (1 - z) / (4. * electron_energy * soft_photon_temperature)\\n # Eq. 14\\n cross_section = z ** 2 / (2 * (1 - z)) * G34(x, a3) + G34(x, a4)\\n tmp = (soft_photon_temperature / electron_energy) ** 2\\n # r0 = (e**2 / m_e / c**2).to('cm')\\n # (2 * r0 ** 2 * m_e ** 3 * c ** 4 / (pi * hbar ** 3)).cgs\\n tmp *= 2.6318735743809104e+16\\n cross_section = tmp * cross_section\\n cc = ((gamma_energy < electron_energy) * (electron_energy > 1))\\n return np.where(cc, cross_section,\\n np.zeros_like(cross_section))\",\n \"def get_vcond(lambdam, taum):\\n return 2 * lambdam / taum\",\n \"def calc_metric3(K_tilda):\\n trace = np.trace(K_tilda)\\n # determinant = np.linalg.det(K_tilda)\\n _, log_determinant = np.linalg.slogdet(K_tilda)\\n diff = trace - log_determinant\\n print(trace, log_determinant, diff)\\n return diff\",\n \"def TM_fluid(layer, kx, om):\\n\\n h = layer.d\\n rho = layer.medium.rho\\n K = layer.medium.K\\n k = om*np.sqrt(rho/K)\\n ky = np.sqrt(k**2-kx**2)\\n T = np.zeros((2, 2), dtype=complex)\\n T[0, 0] = np.cos(ky*h)\\n T[1, 0] = (om**2*rho/ky)*np.sin(ky*h)\\n T[0, 1] = -(ky/(om**2*rho))*np.sin(ky*h)\\n T[1, 1] = np.cos(ky*h)\\n return T\",\n \"def isKelvin(self):\\n return _libsbml.Unit_isKelvin(self)\",\n \"def neutrino_thermal_velocity(self, z):\\n fac = 5./3.*5.*ss.zeta(5.)/ss.zeta(3.)\\n vel = np.zeros(self.N_nu)\\n vel = fac**.5*(const.kB*self.T_nu/self.M_nu)*(1.+z)*const.c\\n return vel\",\n \"def func_d23_318(n, series):\\n if series == \\\"3D3\\\":\\n try: \\n return np.sqrt((3*os_3D3[str(n)]*wl_3D3[str(n)]*1e-9*hbar*e**2)/(4*np.pi*m_e*c))\\n except:\\n return 0\",\n \"def kn(self):\\n if self.enginePos == 'wing': # wing mounted engines\\n return -4.0\\n elif self.enginePos == 'fuselage': # tail mounted engines\\n return -2.5\",\n \"def _leg_kinematics(self, knee_pos):\\n knee_pos = np.abs(knee_pos)\\n sign = np.sign(knee_pos)\\n t = np.sqrt(self.p ** 2 + self.q ** 2 + self.r ** 2 - 2 * np.sqrt(self.p ** 2 + self.q ** 2) * self.r * np.cos(knee_pos))\\n phi = np.arccos((self.c ** 2 + t ** 2 - self.s **2) / (2 * self.c * t))\\n delta = np.arcsin((self.c * np.sin(phi)) / self.s)\\n beta = np.arcsin((self.r * np.sin(knee_pos)) / t)\\n epsilon = np.pi - (delta + beta)\\n Ax = self.r * np.cos(knee_pos) + self.u\\n Ay = self.r * np.sin(knee_pos) + self.v\\n Bx = self.s * np.cos(epsilon) + self.u + self.p\\n By = self.s * np.cos(epsilon) + self.v + self.q\\n Cx = Ax + ((Bx - Ax) * self.e + (Ay - By) * self.h) / self.c\\n Cy = Ay + ((By - Ay) * self.e + (Ax - Bx) * self.h) / self.c\\n alpha = np.arctan((Cy - Ay) / (Cx - Ax))\\n return alpha * sign\",\n \"def test_vertical_velocity_pressure_dry_air():\\n w = 1 * units('cm/s')\\n omega_truth = -1.25073619 * units('microbar/second')\\n omega_test = vertical_velocity_pressure(w, 1000. * units.mbar, 273.15 * units.K)\\n assert_almost_equal(omega_test, omega_truth, 6)\",\n \"def variance(self):\\n return self.k * self.theta ** 2\",\n \"def _kvol(sunAz, sunZen, viewAz, viewZen):\\n\\t\\t\\t\\n\\t\\t\\trelative_azimuth = sunAz.subtract(viewAz).rename(['relAz'])\\n\\t\\t\\tpa1 = viewZen.cos() \\\\\\n\\t\\t\\t\\t.multiply(sunZen.cos())\\n\\t\\t\\tpa2 = viewZen.sin() \\\\\\n\\t\\t\\t\\t.multiply(sunZen.sin()) \\\\\\n\\t\\t\\t\\t.multiply(relative_azimuth.cos())\\n\\t\\t\\tphase_angle1 = pa1.add(pa2)\\n\\t\\t\\tphase_angle = phase_angle1.acos()\\n\\t\\t\\tp1 = ee.Image(PI().divide(2)).subtract(phase_angle)\\n\\t\\t\\tp2 = p1.multiply(phase_angle1)\\n\\t\\t\\tp3 = p2.add(phase_angle.sin())\\n\\t\\t\\tp4 = sunZen.cos().add(viewZen.cos())\\n\\t\\t\\tp5 = ee.Image(PI().divide(4))\\n\\n\\t\\t\\tkvol = p3.divide(p4).subtract(p5).rename(['kvol'])\\n\\n\\t\\t\\tviewZen0 = ee.Image(0)\\n\\t\\t\\tpa10 = viewZen0.cos() \\\\\\n\\t\\t\\t\\t.multiply(sunZen.cos())\\n\\t\\t\\tpa20 = viewZen0.sin() \\\\\\n\\t\\t\\t\\t.multiply(sunZen.sin()) \\\\\\n\\t\\t\\t\\t.multiply(relative_azimuth.cos())\\n\\t\\t\\tphase_angle10 = pa10.add(pa20)\\n\\t\\t\\tphase_angle0 = phase_angle10.acos()\\n\\t\\t\\tp10 = ee.Image(PI().divide(2)).subtract(phase_angle0)\\n\\t\\t\\tp20 = p10.multiply(phase_angle10)\\n\\t\\t\\tp30 = p20.add(phase_angle0.sin())\\n\\t\\t\\tp40 = sunZen.cos().add(viewZen0.cos())\\n\\t\\t\\tp50 = ee.Image(PI().divide(4))\\n\\n\\t\\t\\tkvol0 = p30.divide(p40).subtract(p50).rename(['kvol0'])\\n\\n\\t\\t\\treturn (kvol, kvol0)\",\n \"def aliveness(self, physics):\\n return 0.\",\n \"def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\\n k = get_kappa(K_medium, rho_medium, c_medium)\\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial\",\n \"def fuel_cond(T):\\n\\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\\n \\n return kc\",\n \"def Local_Kinetic(Walker):\\n\\n # laplacian -0.5 \\\\nabla^2 \\\\Psi / \\\\Psi\\n h = 0.001\\n h2 = h*h\\n K = 0.0\\n Psi_R = wfs(Walker)\\n for i in range(Walker.Ne):\\n for j in range(Walker.sys_dim):\\n Y=Walker.Re[i][j]\\n Walker.Re[i][j]-=h\\n wfs1 = wfs(Walker)\\n Walker.Re[i][j]+=2.0*h\\n wfs2 = wfs(Walker)\\n K -= 0.5*(wfs1+wfs2-2.0*Psi_R)/h2\\n Walker.Re[i][j]=Y\\n return K/Psi_R\",\n \"def wind_chill(T_a, v):\\r\\n return 13.12 + 0.6215*(T_a) - 11.37*(v)**0.16 + 0.3965*(T_a)*(v)**0.16\",\n \"def calculateTemperature(self):\\n \\n # CIE XYZ space\\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\\n\\n # CIE Chromaticities xy\\n self.x = self.X/(self.X + self.Y + self.Z)\\n self.y = self.Y/(self.X + self.Y + self.Z)\\n \\n # CIE Chromaticities uv\\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n \\n # constant for McCamy's/Hernandez-Andrés formula\\n n = (self.x - self.x_e)/(self.y - self.y_e)\\n \\n # Correlated color temperature according to Hernández-Andrés (1999)\\n self.color_temp = ( self.A_0 + \\n self.A_1*np.exp(-n/self.t_1) + \\n self.A_2*np.exp(-n/self.t_2) + \\n self.A_3*np.exp(-n/self.t_3) )\\n \\n # Delete too high values\\n self.color_temp[self.color_temp > 30000] = 0\\n \\n # Affichage de la CCT\\n self.mean_temp = int(round(self.color_temp.mean()))\\n self.mean_temp_label.setText(\\\"Temperature moyenne = \\\"+str(self.mean_temp))\\n self.mean_temp_label.adjustSize()\\n \\t\\n # Affichage de l'illuminance (Y)\\n self.mean_illu = int(round((self.Y.mean())))\\n self.illuminance_label.setText(\\\"Illuminance moyenne = \\\"+str(self.mean_illu))\\n self.illuminance_label.adjustSize()\",\n \"def a_realization(self):\\n if self.t==1:\\n return self.kmonomial()\\n else:\\n return self.kHallLittlewoodP()\",\n \"def get_variables(self, z0, u_inf):\\n # Get the ambient data from the CTD profile\\n Ta, Sa, P = self.profile.get_values(z0, ['temperature', 'salinity',\\n 'pressure'])\\n rho = seawater.density(Ta, Sa, P)\\n \\n # Compute the properties of each dispersed-phase particle\\n us = np.zeros(len(self.particles))\\n rho_p = np.zeros(len(self.particles))\\n m_p = np.zeros(len(self.particles))\\n B_p = np.zeros(len(self.particles))\\n for i in range(len(self.particles)):\\n m0 = self.particles[i].m0\\n T0 = self.particles[i].T0\\n m_p[i] = np.sum(m0) * self.particles[i].nb0\\n if m_p[i] > 0.:\\n # Particles exist, get properties. Make sure the algorithm \\n # uses the dirty bubble properties since this is supposed\\n # to be the rise velocity averaged over the whole plume.\\n us[i], rho_p[i]= self.particles[i].properties(m0, T0, P, Sa, \\n Ta, np.inf)[0:2]\\n B_p[i] = (rho - rho_p[i]) / rho * 9.81 * (m_p[i] / rho_p[i])\\n else:\\n # Particles dissolved, set to ambient conditions\\n us[i] = 0.\\n rho_p[i] = rho\\n B_p[i] = 0.\\n \\n # Select the correct slip velocity\\n u_slip = us[0]\\n for i in range(len(self.particles) - 1):\\n if B_p[i+1] > B_p[i]:\\n u_slip = us[i+1]\\n \\n # Compute the total buoyancy flux\\n B = np.sum(B_p)\\n \\n # Get the ambient buoyancy frequency\\n N = self.profile.buoyancy_frequency(z0)\\n \\n # Return the governing parameters\\n return (B, N, u_slip, u_inf)\",\n \"def get_sky_data_flag(self,data):\\n sky_data_flag = ~Calibration.get_vane_flag(data['level1']) \\n features = self.getFeatures(data)\\n features = np.log10(features)/np.log10(2)\\n sky_data_flag = sky_data_flag & np.isfinite(features) & (features != 16)\\n\\n return sky_data_flag\",\n \"def dvdt(self, args: Dict) -> float:\\n if self.channel_bool['leak']:\\n i_leak: float = self.leak.i(args['v'])\\n else:\\n i_leak: float = 0.\\n \\n if self.channel_bool['nav']:\\n i_nav: float = self.nav.i(args['v'], h=args['h_nav'])\\n else:\\n i_nav: float = 0.\\n\\n if self.channel_bool['kvhh']:\\n i_kvhh: float = self.kvhh.i(args['v'], n=args['n_kvhh'])\\n else:\\n i_kvhh: float = 0.\\n\\n if self.channel_bool['kva']:\\n i_kva: float = self.kva.i(args['v'], h=args['h_kva'])\\n else:\\n i_kva: float = 0.\\n\\n if self.channel_bool['kvsi']:\\n i_kvsi: float = self.kvsi.i(args['v'], m=args['m_kvsi'])\\n else:\\n i_kvsi: float = 0.\\n\\n if self.channel_bool['cav']:\\n i_cav: float = self.cav.i(args['v'])\\n else:\\n i_cav: float = 0.\\n\\n if self.channel_bool['kca']:\\n i_kca: float = self.kca.i(args['v'], ca=args['ca'])\\n else:\\n i_kca: float = 0.\\n \\n if self.channel_bool['nap']:\\n i_nap: float = self.nap.i(args['v'])\\n else:\\n i_nap: float = 0.\\n\\n if self.channel_bool['kir']:\\n i_kir: float = self.kir.i(args['v'])\\n else:\\n i_kir: float = 0.\\n\\n if self.channel_bool['ampar']:\\n i_ampar: float = self.ampar.i(args['v'], s=args['s_ampar'])\\n else:\\n i_ampar: float = 0.\\n\\n if self.channel_bool['nmdar']:\\n i_nmdar: float = self.nmdar.i(args['v'], s=args['s_nmdar'])\\n else:\\n i_nmdar: float = 0.\\n\\n if self.channel_bool['gabar']:\\n i_gabar: float = self.gabar.i(args['v'], s=args['s_gabar'])\\n else:\\n i_gabar: float = 0.\\n\\n return ((-10.0*self.params.area \\n * (i_leak\\n + i_nav \\n + i_kvhh \\n + i_kva \\n + i_kvsi \\n + i_cav \\n + i_kca \\n + i_nap \\n + i_kir) \\n - (i_ampar \\n + i_nmdar \\n + i_gabar))\\n / (10.0*self.params.cm*self.params.area))\",\n \"def is_solvated(molecular_system):\\n\\n from molsysmt.basic import get\\n\\n output = False\\n\\n n_waters, volume = get(molecular_system, element='system', n_waters=True, box_volume=True)\\n\\n if (n_waters>0) and (volume is not None):\\n\\n density_number = puw.get_value((n_waters/volume), to_unit='1/nm**3')\\n\\n if (density_number)>15:\\n\\n output = True\\n\\n return output\",\n \"def kinetic_energy(vel):\\r\\n return 0.5 * (vel ** 2).sum(axis=1)\",\n \"def get_force(self):\\n displ = self.get_displ()\\n equil = displ / np.linalg.norm(displ) * self.L0\\n return self.k * (displ - equil)\",\n \"def test_vertical_velocity_dry_air():\\n omega = 1 * units('microbar/second')\\n w_truth = -0.7995291 * units('cm/s')\\n w_test = vertical_velocity(omega, 1000. * units.mbar, 273.15 * units.K)\\n assert_almost_equal(w_test, w_truth, 6)\",\n \"def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\\n\\n # Compute constants.\\n kT = kB * temperature\\n gamma = collision_rate\\n\\n # Create a new custom integrator.\\n super(VVVRIntegrator, self).__init__(timestep)\\n\\n #\\n # Integrator initialization.\\n #\\n self.addGlobalVariable(\\\"kT\\\", kT) # thermal energy\\n self.addGlobalVariable(\\\"b\\\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\\n self.addPerDofVariable(\\\"sigma\\\", 0)\\n self.addPerDofVariable(\\\"x1\\\", 0) # position before application of constraints\\n\\n #\\n # Allow context updating here.\\n #\\n self.addUpdateContextState()\\n\\n #\\n # Pre-computation.\\n # This only needs to be done once, but it needs to be done for each degree of freedom.\\n # Could move this to initialization?\\n #\\n self.addComputePerDof(\\\"sigma\\\", \\\"sqrt(kT/m)\\\")\\n\\n #\\n # Velocity perturbation.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Metropolized symplectic step.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m\\\")\\n self.addComputePerDof(\\\"x\\\", \\\"x + v*dt\\\")\\n self.addComputePerDof(\\\"x1\\\", \\\"x\\\")\\n self.addConstrainPositions()\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m + (x-x1)/dt\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Velocity randomization\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def compute_ground_truth_volume(self, display_opt):\\n\\n self.meshActor.GetProperty().SetOpacity(0.2)\\n self.meshActor.GetProperty().SetColor(1, 0, 0)\\n\\n clean = vtk.vtkCleanPolyData()\\n clean.SetInputData(self.endo_poly)\\n\\n d3 = vtk.vtkDelaunay3D()\\n d3.SetInputConnection(clean.GetOutputPort())\\n d3.SetTolerance(0.01)\\n d3.SetAlpha(0.0)\\n d3.Update()\\n\\n surfaceFilter = vtk.vtkDataSetSurfaceFilter() # output is triangular mesh\\n surfaceFilter.SetInputConnection(d3.GetOutputPort())\\n surfaceFilter.Update()\\n\\n Mass = vtk.vtkMassProperties()\\n Mass.SetInputConnection(surfaceFilter.GetOutputPort())\\n Mass.Update()\\n\\n self.ground_truth_vol = Mass.GetVolume()/1000.0\\n\\n if display_opt:\\n\\n m = vtk.vtkDataSetMapper()\\n m.SetInputConnection(d3.GetOutputPort())\\n\\n a = vtk.vtkActor()\\n a.SetMapper(m)\\n\\n # set mapper for epi for visualization\\n m2 = vtk.vtkDataSetMapper()\\n m2.SetInputData(self.epi_poly)\\n\\n epi_actor = vtk.vtkActor()\\n epi_actor.SetMapper(m2)\\n epi_actor.GetProperty().SetOpacity(0.3)\\n epi_actor.GetProperty().SetColor(1,0,0)\\n\\n ren = vtk.vtkRenderer()\\n ren.SetBackground(0.0, 0.0, 0.0)\\n ren.AddActor(epi_actor)\\n ren.AddActor(a)\\n\\n vtk_show(ren)\",\n \"def acc_visc(j,rA,vA,mA,rhoA,PA,hA,dW=kernel.dW_M4):\\n assert rA.shape[0] == vA.shape[0] == mA.shape[0] == rhoA.shape[0] == hA.shape[0], \\\"arrays are not matched\\\"\\n N = len(mA)\\n c_j = c_gas(j,rhoA,PA)\\n\\n tot = 0\\n for i in range(N):\\n if i != j:\\n\\n r_ij = rA[j,:] - rA[i,:]\\n r_ij1 = np.linalg.norm(r_ij)\\n v_ij = vA[j,:] - vA[i,:]\\n m_i = mA[i]\\n c_i = c_gas(i,rhoA,PA)\\n c_ij = 0.5 * (c_i + c_j)\\n h_ij = 0.5 * (hA[i] + hA[j])\\n rho_ij = 0.5 * (rhoA[i] + rhoA[j])\\n\\n c = np.dot(v_ij,r_ij)\\n mu_ij = ( c * h_ij ) / ( r_ij1**2 + 0.01*h_ij**2 )\\n\\n a = ( -alpha * mu_ij * c_ij + beta * mu_ij**2 ) / rho_ij\\n b = 0\\n Pi_ij = a*dm.heavi(-c) + b*dm.heavi(c)\\n\\n # if Pi_ij == 0:\\n # print(\\\"i,j:\\\",i,j)\\n # print(\\\"c:\\\",c)\\n # print(\\\"c_ij\\\",c_ij)\\n # print(\\\"\\\")\\n # assert Pi_ij != 0\\n\\n tot += m_i * h_ij**(-4) * Pi_ij * dW(r_ij1,h_ij) * (r_ij/r_ij1)\\n\\n return - tot\",\n \"def var(self):\\n\\n return self.scale ** -2 \\\\\\n * (m.gamma(1 + 2 * self.shape ** -1) - m.gamma(1 + self.shape ** -1) ** 2)\",\n \"def simulated_reflectivity(pressure, temperature, vapor_mixing_ratio, liquid_mixing_ratio, snow_mixing_ratio=None,\\n graupel_mixing_ratio=None, use_varint=False, use_liqskin=False):\\n # Set values for constants with variable intercept\\n R1 = 1e-15\\n RON = 8e6\\n RON2 = 1e10\\n SON = 2e7\\n GON = 5e7\\n RON_MIN = 8e6\\n RON_QR0 = 0.00010\\n RON_DELQR0 = 0.25*RON_QR0\\n RON_CONST1R = (RON2-RON_MIN)*0.5\\n RON_CONST2R = (RON2+RON_MIN)*0.5\\n\\n # set constant intercepts\\n rno_l = 8e6\\n rno_s = 2e7\\n rno_g = 4e6\\n\\n qvapor = da.clip(vapor_mixing_ratio, 0., None)\\n qliquid = da.clip(liquid_mixing_ratio, 0., None)\\n\\n # If qgraupel but not qsnow, set qgraupel = qsnow\\n if snow_mixing_ratio is None:\\n if graupel_mixing_ratio is None:\\n qsnow = da.zeros_like(qliquid)\\n qgraupel = da.zeros_like(qliquid)\\n else:\\n qgraupel = da.clip(graupel_mixing_ratio, 0., None)\\n qsnow = da.zeros_like(graupel_mixing_ratio)\\n qsnow[temperature <= 273.15] = qgraupel[temperature <= 273.15]\\n else:\\n qsnow = da.clip(snow_mixing_ratio, 0., None)\\n qgraupel = da.clip(graupel_mixing_ratio, 0., None)\\n\\n # density for liquid, snow, and graupel (kg m-3)\\n rho_l = 1000. # liquid\\n rho_i = 100. # snow\\n rho_g = 400. # graupel\\n\\n # constant evaluation of gamma distribution\\n gamma = 720.\\n\\n # Alpha constant\\n alpha = 0.224\\n\\n # constant multiplication factors\\n factor_l = gamma * 1e18 * (1./(np.pi*rho_l))**1.75\\n s = gamma * 1e18 * (1./(np.pi*rho_i))**1.75 * (rho_i/rho_l)**2 * alpha\\n g = gamma * 1e18 * (1./(np.pi*rho_g))**1.75 * (rho_g/rho_l)**2 * alpha\\n\\n # calculate virtual temperature\\n virtual_t = virtual_temperature(temperature, qvapor)\\n\\n # dry gas constant\\n Rd = 287.\\n rho_air = pressure/(Rd*virtual_t)\\n\\n # adjust for brightband if use_liqskin=True\\n if use_liqskin:\\n raise NotImplementedError('Liquid skin correction not implemented')\\n # factor_s = da.full_like(temperature, s)\\n # factor_g = da.full_like(temperature, g)\\n # try:\\n # factor_s[temperature >= 273.15] = factor_s[temperature >= 273.15] / da.array([alpha])\\n # factor_g[temperature >= 273.15] = factor_g[temperature >= 273.15] / da.array([alpha])\\n # except ValueError:\\n # factor_s = s\\n # factor_g = g\\n else:\\n factor_s = s\\n factor_g = g\\n\\n # calculate variable intercept if use_varint=True\\n if use_varint:\\n raise NotImplementedError('Variable intercepts not yet implemented')\\n # temp_c = da.clip(temperature-273.15, temperature.min(), -0.001)\\n # sonv = MIN(2.0D8, 2.0D6*EXP(-0.12D0*temp_c))\\n #\\n # gonv = gon\\n # IF (qgr(i,j,k) .GT. R1) THEN\\n # gonv = 2.38D0 * (PI*RHO_G/(rhoair*qgr(i,j,k)))**0.92D0\\n # gonv = MAX(1.D4, MIN(gonv,GON))\\n # END IF\\n #\\n # ronv = RON2\\n # IF (qra(i,j,k) .GT. R1) THEN\\n # ronv = RON_CONST1R*TANH((RON_QR0 - qra(i,j,k))/RON_DELQR0) + RON_CONST2R\\n # END IF\\n else:\\n ronv = rno_l\\n sonv = rno_s\\n gonv = rno_g\\n\\n # Total equivalent reflectivity factor (z_e, in mm^6 m^-3) is\\n # the sum of z_e for each hydrometeor species:\\n z_e = (((factor_l*(rho_air*qliquid)**1.75)/(ronv**.75)) +\\n ((factor_s*(rho_air*qsnow)**1.75)/(sonv**.75)) +\\n ((factor_g*(rho_air*qgraupel)**1.75)/(gonv**.75)))\\n\\n # Adjust small values of Z_e so that dBZ is no lower than -30\\n z_e = da.clip(z_e, .001, None)\\n\\n # Convert to dBZ\\n dbz = 10.*da.log10(z_e)\\n return dbz\",\n \"def fGasAcousticVelocity(GasGravity, Temperature, Pressure):\\n\\tGasBulkModulus = fGasBulkModulus(GasGravity, Temperature, Pressure) # Pascals\\n\\tGasDensity = fGasDensity(GasGravity, Temperature, Pressure) * 1000 # Kg\\n\\treturn (GasBulkModulus / GasDensity)**0.5 # m/s\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7809844","0.62890047","0.62317854","0.6194581","0.61644995","0.61163557","0.600697","0.5984969","0.5790677","0.575207","0.5744783","0.5729746","0.5714137","0.57019544","0.5691392","0.56809","0.56597465","0.56572974","0.5655333","0.56213343","0.56118655","0.55981666","0.55919814","0.5574342","0.55607146","0.5530427","0.55266887","0.55231625","0.55113393","0.5509306","0.54993814","0.5494139","0.54788834","0.54775524","0.54595804","0.54469913","0.5445236","0.5445171","0.54366094","0.5420351","0.5416981","0.540632","0.53993636","0.5398881","0.53863126","0.53668475","0.5353126","0.5333249","0.5328219","0.53188753","0.53187746","0.53184503","0.5317413","0.5314278","0.5308887","0.5305678","0.5291435","0.52860075","0.5283745","0.5280399","0.527981","0.52765036","0.52747774","0.52712065","0.5267032","0.5252972","0.5241843","0.52387714","0.5236489","0.5236283","0.52278775","0.52248365","0.5221702","0.5216384","0.5211454","0.5206013","0.52000856","0.5188656","0.5185088","0.51814246","0.5170094","0.5166184","0.51649916","0.5160163","0.51592225","0.5157207","0.5143566","0.5138126","0.5138095","0.5136418","0.51359534","0.513296","0.51313585","0.51312256","0.51271766","0.5125665","0.5123816","0.51138973","0.5111476","0.51096165"],"string":"[\n \"0.7809844\",\n \"0.62890047\",\n \"0.62317854\",\n \"0.6194581\",\n \"0.61644995\",\n \"0.61163557\",\n \"0.600697\",\n \"0.5984969\",\n \"0.5790677\",\n \"0.575207\",\n \"0.5744783\",\n \"0.5729746\",\n \"0.5714137\",\n \"0.57019544\",\n \"0.5691392\",\n \"0.56809\",\n \"0.56597465\",\n \"0.56572974\",\n \"0.5655333\",\n \"0.56213343\",\n \"0.56118655\",\n \"0.55981666\",\n \"0.55919814\",\n \"0.5574342\",\n \"0.55607146\",\n \"0.5530427\",\n \"0.55266887\",\n \"0.55231625\",\n \"0.55113393\",\n \"0.5509306\",\n \"0.54993814\",\n \"0.5494139\",\n \"0.54788834\",\n \"0.54775524\",\n \"0.54595804\",\n \"0.54469913\",\n \"0.5445236\",\n \"0.5445171\",\n \"0.54366094\",\n \"0.5420351\",\n \"0.5416981\",\n \"0.540632\",\n \"0.53993636\",\n \"0.5398881\",\n \"0.53863126\",\n \"0.53668475\",\n \"0.5353126\",\n \"0.5333249\",\n \"0.5328219\",\n \"0.53188753\",\n \"0.53187746\",\n \"0.53184503\",\n \"0.5317413\",\n \"0.5314278\",\n \"0.5308887\",\n \"0.5305678\",\n \"0.5291435\",\n \"0.52860075\",\n \"0.5283745\",\n \"0.5280399\",\n \"0.527981\",\n \"0.52765036\",\n \"0.52747774\",\n \"0.52712065\",\n \"0.5267032\",\n \"0.5252972\",\n \"0.5241843\",\n \"0.52387714\",\n \"0.5236489\",\n \"0.5236283\",\n \"0.52278775\",\n \"0.52248365\",\n \"0.5221702\",\n \"0.5216384\",\n \"0.5211454\",\n \"0.5206013\",\n \"0.52000856\",\n \"0.5188656\",\n \"0.5185088\",\n \"0.51814246\",\n \"0.5170094\",\n \"0.5166184\",\n \"0.51649916\",\n \"0.5160163\",\n \"0.51592225\",\n \"0.5157207\",\n \"0.5143566\",\n \"0.5138126\",\n \"0.5138095\",\n \"0.5136418\",\n \"0.51359534\",\n \"0.513296\",\n \"0.51313585\",\n \"0.51312256\",\n \"0.51271766\",\n \"0.5125665\",\n \"0.5123816\",\n \"0.51138973\",\n \"0.5111476\",\n \"0.51096165\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8016766"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2392,"cells":{"query":{"kind":"string","value":"Determines the radiative temperature of the sky. The \"sky,\" as a black body, has a radiative temperature different to that of the surrounding air, or the ambient temperature. This function converts between them and outputs the sky's radiative temperature."},"document":{"kind":"string","value":"def sky_temperature(self) -> float:\n\n return 0.0552 * (self.ambient_temperature**1.5)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_sky_ir_temperature(self) -> float:\n self.serial.write(b\"S!\")\n sky_ir_temp = self.__extract_int(self.__read_response(1)[0], b\"!1\")\n\n return round(sky_ir_temp / 100, 2)","def temperature() -> float:","def temperature(self):\n names = ['anc_air_temperature']\n return self.sensor.get_with_fallback('temperature', names)","def temperature(self):\n return self.read_short(65) / 340.0 + 36.53","def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\n self.digital_temp_data() \n dT = self.tempadc-(C_5*(2**8))\n temperature=(2000+(dT*(C_6/(2**23))))/100\n return temperature, dT","def temperature(self):\r\n self._read_temperature()\r\n return self._t_fine / 5120.0","def Tsky(self, source):\n\n if not _usePyGSM:\n raise ImportError('PyGSM is not available: cannot access sky temperatures')\n if not isinstance(source, astropy.coordinates.sky_coordinate.SkyCoord):\n if isinstance(source,str):\n # assume .par file\n source=parfile2SkyCoord(source)\n else:\n raise TypeError('Do not know how to interpret an object of type %s' % source.__class__)\n\n source=source.galactic\n T=healpy.pixelfunc.get_interp_val(self.map,\n source.l.value,\n source.b.value,\n lonlat=True)\n return T*u.K","def temperature():\n from .imperial import deg_F as F\n from .imperial import deg_R as R\n\n K = si.K\n C = si.deg_C\n\n return Equivalency(\n [\n (K, C, lambda x: x - 273.15, lambda x: x + 273.15),\n (C, F, lambda x: x * 1.8 + 32.0, lambda x: (x - 32.0) / 1.8),\n (K, F, lambda x: x * 1.8 - 459.67, lambda x: (x + 459.67) / 1.8),\n (R, F, lambda x: x - 459.67, lambda x: x + 459.67),\n (R, C, lambda x: (x - 491.67) * (5 / 9), lambda x: x * 1.8 + 491.67),\n (R, K, lambda x: x * (5 / 9), lambda x: x * 1.8),\n ],\n \"temperature\",\n )","def temperature(self):\n return _cantera.reactor_temperature(self.__reactor_id)","def get_capacitive_rain_sensor_temp(\n self, rain_sensor_temp: Optional[int] = None\n ) -> float:\n # TODO: these values were hardcoded but now are taken from the CW.\n # Check which way is the \"true\" way based on the sensor type (capacitive vs Hydredon)\n # rain_pull_up_resistance = 1\n # rain_res_at_25 = 1\n # rain_beta = 3450\n absolute_zero = 273.15\n\n if rain_sensor_temp is None:\n rain_sensor_temp = self.raw_rain_sensor_temp\n\n if rain_sensor_temp < 1:\n rain_sensor_temp = 1\n elif rain_sensor_temp > 1022:\n rain_sensor_temp = 1022\n\n r = self.rain_pull_up_resistance / ((1023 / rain_sensor_temp) - 1)\n r = math.log(r / self.rain_res_at_25)\n\n return 1 / (r / self.rain_beta + 1 / (absolute_zero + 25)) - absolute_zero","def get_temperature(self):\n \n # Get temp readings from both sensors\n humidity_temp = self._sense_hat.get_temperature_from_humidity()\n pressure_temp = self._sense_hat.get_temperature_from_pressure()\n \n # avg_temp becomes the average of the temperatures from both sensors\n # We need to check for pressure_temp value is not 0, to not ruin avg_temp calculation\n avg_temp = (humidity_temp + pressure_temp) / 2 if pressure_temp else humidity_temp\n \n # Get the CPU temperature\n cpu_temp = self._get_cpu_temp()\n \n # Calculate temperature compensating for CPU heating\n adj_temp = avg_temp - (cpu_temp - avg_temp) / 1.5\n \n # Average out value across the last three readings\n return self._get_smooth(adj_temp)","def compute_surface_temperature(heat_flux):\n\n return 1.1e-4*heat_flux + 323","def temperature(self) -> float:\n # Start a measurement then poll the measurement finished bit.\n self.temp_start = 1\n while self.temp_running > 0:\n pass\n # Grab the temperature value and convert it to Celsius.\n # This uses the same observed value formula from the Radiohead library.\n temp = self._read_u8(_REG_TEMP2)\n return 166.0 - temp","def calculateTemperature(self):\n \n # CIE XYZ space\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\n\n # CIE Chromaticities xy\n self.x = self.X/(self.X + self.Y + self.Z)\n self.y = self.Y/(self.X + self.Y + self.Z)\n \n # CIE Chromaticities uv\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\n \n # constant for McCamy's/Hernandez-Andrés formula\n n = (self.x - self.x_e)/(self.y - self.y_e)\n \n # Correlated color temperature according to Hernández-Andrés (1999)\n self.color_temp = ( self.A_0 + \n self.A_1*np.exp(-n/self.t_1) + \n self.A_2*np.exp(-n/self.t_2) + \n self.A_3*np.exp(-n/self.t_3) )\n \n # Delete too high values\n self.color_temp[self.color_temp > 30000] = 0\n \n # Affichage de la CCT\n self.mean_temp = int(round(self.color_temp.mean()))\n self.mean_temp_label.setText(\"Temperature moyenne = \"+str(self.mean_temp))\n self.mean_temp_label.adjustSize()\n \t\n # Affichage de l'illuminance (Y)\n self.mean_illu = int(round((self.Y.mean())))\n self.illuminance_label.setText(\"Illuminance moyenne = \"+str(self.mean_illu))\n self.illuminance_label.adjustSize()","def temperature(altitude):\n if altitude <= 36152:\n t = 59-0.00356*altitude # deg F\n else:\n t = -70 # deg F\n t = t + 459.7 # R\n return t","def temp(self):\n if self.temp_sensor is None:\n return None\n else:\n if self.temp_scale.lower() in ['f', 'fahrenheit']:\n return self.temp_sensor.temp_f\n elif self.temp_scale.lower() in ['c', 'celsius']:\n return self.temp_sensor.temp_c","def read_ambient_temperatureF(self, ):\n return self.read_ambient_temperatureC() * (9.0/5.0) + 32.0","def ambient_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_f\"))\r\n return celsius_to_fahrenheit(self.ambient_temperature_c)","def temperature() -> FlowFieldVal:\n return [\n self._t_s - self._delta_t * tf.math.tanh(z / self._height) for z in zz\n ]","def tempAir(sample):\n sample *= 1.0\n sample /= 1000\n celsius = (sample - 0.5) * 100\n return round(celsius,2)","def temperatures(self):\r\n return self._arm.temperatures","def temperature(self):\n return float(self._current_observation['temp_c'])","def get_temperature(\n self, sensitivity: Optional[str] = None, temp_sensor: Optional[int] = None\n ) -> float:\n if sensitivity is None or temp_sensor is None:\n sensitivity, temp_sensor = self.get_temperature_sensor()\n if sensitivity == \"th\":\n temp = temp_sensor * 175.72 / 65536 - 46.85\n elif sensitivity == \"t\":\n temp = temp_sensor * 1.7572 - 46.85\n else:\n raise CloudWatcherException(\n f\"Unknown temperature sensor type {sensitivity}\"\n )\n\n return temp","def ambient_temperature(self) -> int:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature\"))\r\n # TODO: Force this to return an int.\r\n if self.temperature_scale == \"F\":\r\n return self.ambient_temperature_f\r\n elif self.temperature_scale == \"C\":\r\n return self.ambient_temperature_c\r\n else:\r\n return self._ambient_temperature","def tempWater(sample):\n sample *= .0009\n sample *= 1000\n celsius = (sample - 20.5128) * 0.0512\n return round(celsius,2)","def get_temperature(self):\n pass","def getTemperature(self):\n return self.temperature","def ambient_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_c\"))\r\n return kelvin_to_celsius(self._ambient_temperature)","def temperatures():\n\n return station_9281","def get_temperature(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. .*? .*? (.*?) .*? .*? . .*? .*? . . . .*?'\n temperature = float(re.findall(pattern,summary).pop())\n return temperature","def target_temperature(self):\n if self._client.mode == self._client.MODE_HEAT:\n return self._client.heattemp\n if self._client.mode == self._client.MODE_COOL:\n return self._client.cooltemp\n return None","def read_temperature(self):\n tRaw = self._read_multiple_bytes_as_array(self.BME280_TEMP_MSB, 3)\n\n return float(self._compensate_temperature((tRaw[0] << 12) + (tRaw[1] << 4) + (tRaw[2] >> 4)))","def raw_rain_sensor_temp(self) -> int:\n self._update_analog_value_cache()\n return self.analog_cache.rain_sensor_temp","def temperature(self):\n return self._temperature","def temperature(self):\n return self._temperature","def get_attic_temperature(theta_sat: np.ndarray, theta_ac: np.ndarray) -> np.ndarray:\n\n # temperature difference coefficient\n h = 1.0\n\n return theta_sat * h + theta_ac * (1 - h)","def _calculate_temperature(c, h):\n\n return (c - 331.4 - 0.0124 * h) / 0.6","def get_temperature(elevation, sea_level):\n if elevation <= sea_level:\n return 0.8\n else:\n return (-1.0 / (1.0 - sea_level)) * (elevation - sea_level) + 1.0","def getTemperature(self):\n return self.json_state.get(\"temperature\")","def current_temperature(self) -> float:\n return self._thermostat.current_temperatue","def temperature(self, alt):\n T = self.altitude_profile(alt)[1]\n return T","def temperature(self) -> SmartSsdTemperature:\n return self._temperature","def target_temperature(self) -> int:\r\n # TODO: Find a better way to do this. This is ugly.\r\n if self._hvac_mode == \"cool\":\r\n return self.target_temperature_low\r\n elif self._hvac_mode == \"heat\":\r\n return self.target_temperature_high\r\n elif self._hvac_mode == \"heat-cool\":\r\n # TODO: Fix this so that heat or cool is chosen.\r\n if self._ambient_temperature >= self._target_temperature:\r\n return self.target_temperature_low\r\n elif self._ambient_temperature <= self._target_temperature:\r\n return self.target_temperature_high\r\n elif self._hvac_mode == \"eco\":\r\n if self._ambient_temperature >= self._target_temperature:\r\n return self.eco_temperature_low\r\n elif self._ambient_temperature <= self._target_temperature:\r\n return self.eco_temperature_high\r\n elif self._hvac_mode == \"off\":\r\n return self.ambient_temperature\r\n\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature\"))","def calculateTemperature(self, R):\n rRuOx2005 = self._busToRuOx2005(R)\n T = self.RuOx2005.calculateTemperature(rRuOx2005)\n return T","def soil_temp_factor(self, project_day):\n tsoil = self.met_data['tsoil'][project_day]\n\n if float_gt(tsoil, 0.0):\n tfac = (0.0326 + 0.00351 * tsoil**1.652 - (tsoil / 41.748)**7.19)\n if float_lt(tfac, 0.0):\n tfac = 0.0\n else:\n # negative number cannot be raised to a fractional power\n # number would need to be complex\n tfac = 0.0\n\n return tfac","def temperature(self):\r\n try:\r\n return str(self.connect()['main']['temp'])\r\n except:\r\n return '@weather_temperature'","def temperature(self):\n temp = ct.c_float()\n self.lib.GetTemperatureF(ct.pointer(temp))\n return temp.value","def read_object_temperatureF(self, ):\n return self.read_object_temperatureC() * (9.0/5.0) + 32.0","def tskypy(self, psr):\n # ensure l is in range 0 -> 360\n b = psr.gb\n if psr.gl < 0.:\n l = 360 + psr.gl\n else:\n l = psr.gl\n\n # convert from l and b to list indices\n j = b + 90.5\n if j > 179:\n j = 179\n\n nl = l - 0.5\n if l < 0.5:\n nl = 359\n i = float(nl) / 4.\n \n tsky_haslam = self.tskylist[180*int(i) + int(j)]\n # scale temperature before returning\n return tsky_haslam * (self.freq/408.0)**(-2.6)","def get_temperature(self):\n # Fake a random temperature change\n temperature = random.randint(20, 25)\n self.set_temperature(temperature)","def read_temperature(self):\n self._force_read(False)\n\n tempADC = (self._read_register_1ubyte(self.BME680_TEMP_MSB) << 12) | (self._read_register_1ubyte(self.BME680_TEMP_LSB) << 4) | (self._read_register_1ubyte(self.BME680_TEMP_XLSB) >> 4)\n\n return float(self._compensate_temperature(tempADC))","def target_temperature(self) -> float:\n return self._thermostat.setpoint_temperature","def temperature(self, state: State):\n if self.is_self_play and state.number_of_stones < 16:\n return self._temperature\n return None","def color_temp(self):\n return kelvin_to_mired(self._color_temp)","def get_temp(self):\n\t\traw_temp = self.read_i2c_word(self.TEMP_OUT0)\n\n\t\t# Get the actual temperature using the formule given in the\n\t\t# MPU-6050 Register Map and Descriptions revision 4.2, page 30\n\t\tactual_temp = (raw_temp / 340.0) + 36.53\n\n\t\treturn actual_temp","def __getRawTemperature(self):\n t1 = self.read_byte_data(self.address, 0x03)\n t2 = self.read_byte_data(self.address, 0x04)\n t3 = self.read_byte_data(self.address, 0x05)\n t = (t1 << 16) | (t2 << 8) | t3\n t = getTwosComplement(t, 24)\n return t","def temperature(self):\n self.convert_window(\"Temperature\", \"Celsius\", [\"Celsius\", \"Fahrenheit\", \"Kelvin\", \"Rankine\", \"Reaumur\", \"Newton\", \"Romer\", \"Delisle\"])","def temperature(self):\n done, data = self._request('GP')\n if done:\n return {\n 'ds3231temp': float(data[0])/10,\n 'mcp9808temp': float(data[1])/10,\n 'tmp007temp': float(data[2])/10\n }\n\n raise EvseError","def get_weather(self):\n\n city = self.user_data[\"weatherSettings\"][\"weatherCity\"]\n country = self.user_data[\"weatherSettings\"][\"weatherCountry\"]\n\n host = \"weather.mios.com\"\n temp_scale = \"C\"\n url = \"http://%s/?tempFormat=%s&cityWeather=%s&countryWeather=%s\" % \\\n (host, temp_scale, Vera.urlencode(city), Vera.urlencode(country))\n\n weather = self.proxy_get(url)\n\n return (float(weather[\"temp\"]), weather[\"text\"])","def thermodynamic_temperature(frequency, T_cmb=None):\n nu = frequency.to(si.GHz, spectral())\n\n if T_cmb is None:\n from astropy.cosmology import default_cosmology\n\n T_cmb = default_cosmology.get().Tcmb0\n\n def f(nu, T_cmb=T_cmb):\n x = _si.h * nu / _si.k_B / T_cmb\n return x**2 * np.exp(x) / np.expm1(x) ** 2\n\n def convert_Jy_to_K(x_jybm):\n factor = (f(nu) * 2 * _si.k_B * si.K * nu**2 / _si.c**2).to_value(\n astrophys.Jy\n )\n return x_jybm / factor\n\n def convert_K_to_Jy(x_K):\n factor = (astrophys.Jy / (f(nu) * 2 * _si.k_B * nu**2 / _si.c**2)).to_value(\n si.K\n )\n return x_K / factor\n\n return Equivalency(\n [(astrophys.Jy / si.sr, si.K, convert_Jy_to_K, convert_K_to_Jy)],\n \"thermodynamic_temperature\",\n {\"frequency\": frequency, \"T_cmb\": T_cmb},\n )","def convert_temp(self, temperature):\n return 1.8 * (temperature - 273) + 32","def temperature(self) -> Optional[float]:\n return self.data.get(\"temp\")","def get_tank_temperature():\n if sensor is None:\n return DEFAULT_TEMPERATURE\n\n try:\n # Read the temperature from the I2C sensor.\n return sensor.read_temperature(True)\n except OSError:\n return DEFAULT_TEMPERATURE","def formation_temperature(surface_temperature, gradient, depth):\n form_temp = surface_temperature + gradient * depth\n return form_temp","def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference suggests this equation is accurate to 1%.\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646","def current_temperature(self):\n return self.atag.dhw_temperature","def target_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_f\"))\r\n return celsius_to_fahrenheit(self.target_temperature_c)","def get_brightnesstemperature(self, channel):\n K1 = {\n \"10\": 3040.136402, # Constant K1 [W m-2 um-1].\n \"11\": 2482.375199,\n \"12\": 1935.060183,\n \"13\": 866.468575,\n \"14\": 641.326517,\n }\n\n K2 = {\n \"10\": 1735.337945, # Constant K2 [K].\n \"11\": 1666.398761,\n \"12\": 1585.420044,\n \"13\": 1350.069147,\n \"14\": 1271.221673,\n }\n\n return K2[channel] / np.log((K1[channel] / self.get_radiance(channel)) + 1)","def get_chip_temperature(self):\n self.check_validity()\n\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_CHIP_TEMPERATURE, (), '', 10, 'h')","def read_temp(self):\n return 19.0\n data = self.read(_TEMP_REG, 2)\n temp = ((data[0] * 256) + data[1]) / 16\n if temp > 2047:\n temp -= 4096\n return temp * 0.0625","def temperature_const_theta() -> FlowFieldVal:\n return self._potential_temperature_to_temperature([\n self._const_theta,\n ] * self._params.nz, zz)","def get_temperature(self):\n self.temperature = self.temperature_sensors.get_temperature(\n self.channel)\n return self.temperature","def current_temperature(self):\n if self._device.temp is not None and self._device.temp > -460:\n return self._device.temp\n return None","def temperature_sensor():\n\n\tsensor_name = \"humiture\"\n\treg_addr = 26\n\tdata_len = 4\n\tregist_sensor(sensor_name, reg_addr, data_len)\n\n\t# get sensor data\n\tdata = rospy.wait_for_message('MediumSize/SensorHub/Temperature', Temperature, 2)\n\ttemperature = data.temperature\n\n\tdelete_sensor(sensor_name)\n\treturn temperature","def update_rain_temp(self, day_of_week, departure_time_seconds):\n\n current_time = t.time()\n today = datetime.today().weekday()\n\n if (departure_time_seconds < (current_time + 3600) \\\n and day_of_week == today):\n\n self.temp = self.current_temperature\n self.rain = self.current_rainfall\n\n elif (day_of_week == today):\n for i in range(24):\n if (departure_time_seconds > self.weather_forecast_json \\\n [\"hourly\"][\"data\"][i][\"time\"] and departure_time_seconds \\\n < self.weather_forecast_json[\"hourly\"][\"data\"][i + 1][\"time\"]):\n\n self.temp = self.weather_forecast_json \\\n ['hourly']['data'][i]['temperature']\n\n self.rain = self.weather_forecast_json['hourly'] \\\n ['data'][i]['precipIntensity']\n break\n else:\n continue\n else:\n day_difference = int((departure_time_seconds - current_time) / 86400)\n\n self.temp = (self.weather_forecast_json['daily']['data'] \\\n [day_difference]['temperatureMax'] + \\\n self.weather_forecast_json['daily']['data'] \\\n [day_difference]['temperatureMin']) / 2\n\n self.rain = self.weather_forecast_json['daily'] \\\n ['data'][day_difference]['precipIntensity']","def env_temperature(v3: \"float\", v4: \"float\") -> \"float\":","def current_temperature(self):\n temperature = float('NaN')\n while math.isnan(temperature) or temperature < MINIMUM_BELIEVABLE_TEMPERATURE:\n temperature = float(self._sensor.readTempC())\n return temperature","def read_temperature():\n temp = 0.0\n with open(\"daily_temp.txt\", \"r\") as f:\n temp = float(f.readline())\n\n return temp","def target_temperature(self) -> float | None:\n return self.vera_device.get_current_goal_temperature()","def get_temperature(self):\r\n\r\n\t# get current resolution\r\n\r\n\tconf = self.read_config()\r\n\tmask = 0x60 # 0110 0000\r\n\tres = conf & mask # extract resolution from config register\r\n\t# get temperature from register\r\n \r\n self.write('\\x00')\r\n data = self.read(2)\r\n t_raw = struct.unpack('>h', data)\r\n\tt_raw = t_raw[0]\r\n\r\n#\tmsb = 0b11110101\r\n#\tlsb = 0b11100000\r\n#\tdata = struct.pack('BB', msb, lsb)\r\n # t_raw = struct.unpack('>h', data)\r\n#\tt_raw = t_raw[0]\r\n#\tprint t_raw\r\n\t\r\n # return t_raw\r\n\t# t_raw = ((msb << 8) + lsb) # convert to 2 Byte Integer\r\n\r\n\tif (res == 0x00): # 9 bit resolution 0.5 degree\r\n\t print \"res: 0.5\"\r\n\t return (t_raw >> 7) * 0.5\r\n\r\n\tif (res == 0x20): # 10 bit resolution 0.25 degree\r\n\t print \"res: 0.25\"\r\n\t return (t_raw >> 6) * 0.25\r\n\r\n\tif (res == 0x40): # 11 bit resolution 0.125 degree\r\n\t print \"res: 0.125\"\r\n\t return (t_raw >> 5) * 0.125\r\n\r\n\tif (res == 0x60): # l2 bit resolution 0.0625 degree\r\n\t print \"res: 0.0625\"\r\n\t return (t_raw >> 4) * 0.0625","def get_rain():\n global rain\n\n # Report rain only if the condition is 'rainy' (and not always).\n if weather_condition == CONDITION_RAINY and random.random() > 0.7:\n rain += round(random.random(), 2)\n return rain","def get_ir_sensor_temperature(self) -> float:\n self.serial.write(b\"T!\")\n ir_sensor_temp = self.__extract_int(self.__read_response(1)[0], b\"!2\")\n\n return round(ir_sensor_temp / 100, 2)","def get_temperature(self, sensor: int = 0) -> float:\n\n return self.send(self.cmd.GET_HEATING_ACT)","def read_ambient_temperatureC(self, ):\n return self._read_temperature(MLX90614_TA)","def target_temperature(self):\n return self._target_temp","def temperature(wair,pres,entr=None,temp=None,airf=None,dhum=None,\n chkvals=False,chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,\n chkbnd=False,mathargs=None):\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\n return temp","def getTempMedia(self):\n temp_media = float(self.soup.media.text)\n temperature = str(int(round(temp_media)))\n return temperature","def temp(self):\n\t\ttemp_out = self.read16(MPU9250_ADDRESS, TEMP_DATA)\n\t\ttemp = temp_out / 333.87 + 21.0 # these are from the datasheets\n\t\treturn temp","def readtemperature(self, cTemp):\r\n\t\tdata = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_STATUS)\r\n\t\twhile (data & 0x01) != 0 :\r\n\t\t\tdata = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_STATUS)\r\n\t\tdata1 = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_DATAH)\r\n\t\tdata2 = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_DATAH)\r\n\t\t\r\n\t\t# Convert the data to 14-bits\r\n\t\tcTemp = (((data1 * 256.0) + data2) / 4.0)\r\n\t\t\r\n\t\tif cTemp < 0x0140 :\r\n\t\t\tcTemp = 0x0140\r\n\t\telif cTemp > 0x12C0 :\r\n\t\t\tcTemp = 0x12C0\r\n\t\telse :\r\n\t\t\tcTemp = cTemp\r\n\t\t\r\n\t\tcTemp = (cTemp / 32.0) - 50.0\r\n\t\tfTemp = cTemp * 1.8 + 32\r\n\t\t\r\n\t\treturn {'c' : cTemp, 'f' : fTemp}","def target_temperature(self):\n return self.atag.dhw_target_temperature","def read_temperature(self):\n data = self.ag.read_bytes(Register.OUT_TEMP_L, 2)\n return lsm9ds1.to_int16(data)","def rain(self, json):\n rain = str(json['forecast']['txt_forecast']['forecastday'][0]['pop'])\n return rain","def get_temperature(self):\n\n svc = \"urn:upnp-org:serviceId:TemperatureSensor1\"\n if not svc in self.services:\n raise RuntimeError, \"Device doesn't support the service\"\n\n return self.get_variable(svc, \"CurrentTemperature\")","def high_temperature(self):\r\n return self._yesterdays_weather.get_high_temperature()","def calculate_color_temperature(r: int, g: int, b: int) -> float:\n\n # 1. Map RGB values to their XYZ counterparts.\n # Based on 6500K fluorescent, 3000K fluorescent\n # and 60W incandescent values for a wide range.\n # Note: Y = Illuminance or lux\n x = (-0.14282 * r) + (1.54924 * g) + (-0.95641 * b)\n y = (-0.32466 * r) + (1.57837 * g) + (-0.73191 * b)\n z = (-0.68202 * r) + (0.77073 * g) + (0.56332 * b)\n\n # 2. Calculate the chromaticity co-ordinates\n xchrome = x / (x + y + z)\n ychrome = y / (x + y + z)\n\n # 3. Use to determine the CCT\n n = (xchrome - 0.3320) / (0.1858 - ychrome)\n\n # 4. Calculate the final CCT\n cct = (449.0 * pow(n, 3)) + (3525.0 * pow(n, 2)) + (6823.3 * n) + 5520.33\n\n # Return the results in degrees Kelvin\n return cct","def get_temperature(data):\n celcius = 0\n celcius = [i for i in data if re.search(r'\\d+[/]', i)]\n \n if celcius == []:\n return \"N/A\"\n celcius = celcius[0].split('/')[0]\n celcius = celcius.replace('M', '-')\n \n try:\n celcius = int(celcius)\n except ValueError:\n return \"N/A\"\n\n farenheit = round((celcius * 9/5) + 32) # formula to get farenheit from celcius\n temperature = \"{0} C ({1} F)\".format(celcius, farenheit)\n return temperature","def target_temperature(self):\n return self._target_temperature","def target_temperature(self):\n return self._target_temperature","def target_temperature(self):\n return self._target_temperature","def target_temperature(self):\n return self._target_temperature"],"string":"[\n \"def get_sky_ir_temperature(self) -> float:\\n self.serial.write(b\\\"S!\\\")\\n sky_ir_temp = self.__extract_int(self.__read_response(1)[0], b\\\"!1\\\")\\n\\n return round(sky_ir_temp / 100, 2)\",\n \"def temperature() -> float:\",\n \"def temperature(self):\\n names = ['anc_air_temperature']\\n return self.sensor.get_with_fallback('temperature', names)\",\n \"def temperature(self):\\n return self.read_short(65) / 340.0 + 36.53\",\n \"def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\\n self.digital_temp_data() \\n dT = self.tempadc-(C_5*(2**8))\\n temperature=(2000+(dT*(C_6/(2**23))))/100\\n return temperature, dT\",\n \"def temperature(self):\\r\\n self._read_temperature()\\r\\n return self._t_fine / 5120.0\",\n \"def Tsky(self, source):\\n\\n if not _usePyGSM:\\n raise ImportError('PyGSM is not available: cannot access sky temperatures')\\n if not isinstance(source, astropy.coordinates.sky_coordinate.SkyCoord):\\n if isinstance(source,str):\\n # assume .par file\\n source=parfile2SkyCoord(source)\\n else:\\n raise TypeError('Do not know how to interpret an object of type %s' % source.__class__)\\n\\n source=source.galactic\\n T=healpy.pixelfunc.get_interp_val(self.map,\\n source.l.value,\\n source.b.value,\\n lonlat=True)\\n return T*u.K\",\n \"def temperature():\\n from .imperial import deg_F as F\\n from .imperial import deg_R as R\\n\\n K = si.K\\n C = si.deg_C\\n\\n return Equivalency(\\n [\\n (K, C, lambda x: x - 273.15, lambda x: x + 273.15),\\n (C, F, lambda x: x * 1.8 + 32.0, lambda x: (x - 32.0) / 1.8),\\n (K, F, lambda x: x * 1.8 - 459.67, lambda x: (x + 459.67) / 1.8),\\n (R, F, lambda x: x - 459.67, lambda x: x + 459.67),\\n (R, C, lambda x: (x - 491.67) * (5 / 9), lambda x: x * 1.8 + 491.67),\\n (R, K, lambda x: x * (5 / 9), lambda x: x * 1.8),\\n ],\\n \\\"temperature\\\",\\n )\",\n \"def temperature(self):\\n return _cantera.reactor_temperature(self.__reactor_id)\",\n \"def get_capacitive_rain_sensor_temp(\\n self, rain_sensor_temp: Optional[int] = None\\n ) -> float:\\n # TODO: these values were hardcoded but now are taken from the CW.\\n # Check which way is the \\\"true\\\" way based on the sensor type (capacitive vs Hydredon)\\n # rain_pull_up_resistance = 1\\n # rain_res_at_25 = 1\\n # rain_beta = 3450\\n absolute_zero = 273.15\\n\\n if rain_sensor_temp is None:\\n rain_sensor_temp = self.raw_rain_sensor_temp\\n\\n if rain_sensor_temp < 1:\\n rain_sensor_temp = 1\\n elif rain_sensor_temp > 1022:\\n rain_sensor_temp = 1022\\n\\n r = self.rain_pull_up_resistance / ((1023 / rain_sensor_temp) - 1)\\n r = math.log(r / self.rain_res_at_25)\\n\\n return 1 / (r / self.rain_beta + 1 / (absolute_zero + 25)) - absolute_zero\",\n \"def get_temperature(self):\\n \\n # Get temp readings from both sensors\\n humidity_temp = self._sense_hat.get_temperature_from_humidity()\\n pressure_temp = self._sense_hat.get_temperature_from_pressure()\\n \\n # avg_temp becomes the average of the temperatures from both sensors\\n # We need to check for pressure_temp value is not 0, to not ruin avg_temp calculation\\n avg_temp = (humidity_temp + pressure_temp) / 2 if pressure_temp else humidity_temp\\n \\n # Get the CPU temperature\\n cpu_temp = self._get_cpu_temp()\\n \\n # Calculate temperature compensating for CPU heating\\n adj_temp = avg_temp - (cpu_temp - avg_temp) / 1.5\\n \\n # Average out value across the last three readings\\n return self._get_smooth(adj_temp)\",\n \"def compute_surface_temperature(heat_flux):\\n\\n return 1.1e-4*heat_flux + 323\",\n \"def temperature(self) -> float:\\n # Start a measurement then poll the measurement finished bit.\\n self.temp_start = 1\\n while self.temp_running > 0:\\n pass\\n # Grab the temperature value and convert it to Celsius.\\n # This uses the same observed value formula from the Radiohead library.\\n temp = self._read_u8(_REG_TEMP2)\\n return 166.0 - temp\",\n \"def calculateTemperature(self):\\n \\n # CIE XYZ space\\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\\n\\n # CIE Chromaticities xy\\n self.x = self.X/(self.X + self.Y + self.Z)\\n self.y = self.Y/(self.X + self.Y + self.Z)\\n \\n # CIE Chromaticities uv\\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n \\n # constant for McCamy's/Hernandez-Andrés formula\\n n = (self.x - self.x_e)/(self.y - self.y_e)\\n \\n # Correlated color temperature according to Hernández-Andrés (1999)\\n self.color_temp = ( self.A_0 + \\n self.A_1*np.exp(-n/self.t_1) + \\n self.A_2*np.exp(-n/self.t_2) + \\n self.A_3*np.exp(-n/self.t_3) )\\n \\n # Delete too high values\\n self.color_temp[self.color_temp > 30000] = 0\\n \\n # Affichage de la CCT\\n self.mean_temp = int(round(self.color_temp.mean()))\\n self.mean_temp_label.setText(\\\"Temperature moyenne = \\\"+str(self.mean_temp))\\n self.mean_temp_label.adjustSize()\\n \\t\\n # Affichage de l'illuminance (Y)\\n self.mean_illu = int(round((self.Y.mean())))\\n self.illuminance_label.setText(\\\"Illuminance moyenne = \\\"+str(self.mean_illu))\\n self.illuminance_label.adjustSize()\",\n \"def temperature(altitude):\\n if altitude <= 36152:\\n t = 59-0.00356*altitude # deg F\\n else:\\n t = -70 # deg F\\n t = t + 459.7 # R\\n return t\",\n \"def temp(self):\\n if self.temp_sensor is None:\\n return None\\n else:\\n if self.temp_scale.lower() in ['f', 'fahrenheit']:\\n return self.temp_sensor.temp_f\\n elif self.temp_scale.lower() in ['c', 'celsius']:\\n return self.temp_sensor.temp_c\",\n \"def read_ambient_temperatureF(self, ):\\n return self.read_ambient_temperatureC() * (9.0/5.0) + 32.0\",\n \"def ambient_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.ambient_temperature_c)\",\n \"def temperature() -> FlowFieldVal:\\n return [\\n self._t_s - self._delta_t * tf.math.tanh(z / self._height) for z in zz\\n ]\",\n \"def tempAir(sample):\\n sample *= 1.0\\n sample /= 1000\\n celsius = (sample - 0.5) * 100\\n return round(celsius,2)\",\n \"def temperatures(self):\\r\\n return self._arm.temperatures\",\n \"def temperature(self):\\n return float(self._current_observation['temp_c'])\",\n \"def get_temperature(\\n self, sensitivity: Optional[str] = None, temp_sensor: Optional[int] = None\\n ) -> float:\\n if sensitivity is None or temp_sensor is None:\\n sensitivity, temp_sensor = self.get_temperature_sensor()\\n if sensitivity == \\\"th\\\":\\n temp = temp_sensor * 175.72 / 65536 - 46.85\\n elif sensitivity == \\\"t\\\":\\n temp = temp_sensor * 1.7572 - 46.85\\n else:\\n raise CloudWatcherException(\\n f\\\"Unknown temperature sensor type {sensitivity}\\\"\\n )\\n\\n return temp\",\n \"def ambient_temperature(self) -> int:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature\\\"))\\r\\n # TODO: Force this to return an int.\\r\\n if self.temperature_scale == \\\"F\\\":\\r\\n return self.ambient_temperature_f\\r\\n elif self.temperature_scale == \\\"C\\\":\\r\\n return self.ambient_temperature_c\\r\\n else:\\r\\n return self._ambient_temperature\",\n \"def tempWater(sample):\\n sample *= .0009\\n sample *= 1000\\n celsius = (sample - 20.5128) * 0.0512\\n return round(celsius,2)\",\n \"def get_temperature(self):\\n pass\",\n \"def getTemperature(self):\\n return self.temperature\",\n \"def ambient_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._ambient_temperature)\",\n \"def temperatures():\\n\\n return station_9281\",\n \"def get_temperature(self):\\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. .*? .*? (.*?) .*? .*? . .*? .*? . . . .*?'\\n temperature = float(re.findall(pattern,summary).pop())\\n return temperature\",\n \"def target_temperature(self):\\n if self._client.mode == self._client.MODE_HEAT:\\n return self._client.heattemp\\n if self._client.mode == self._client.MODE_COOL:\\n return self._client.cooltemp\\n return None\",\n \"def read_temperature(self):\\n tRaw = self._read_multiple_bytes_as_array(self.BME280_TEMP_MSB, 3)\\n\\n return float(self._compensate_temperature((tRaw[0] << 12) + (tRaw[1] << 4) + (tRaw[2] >> 4)))\",\n \"def raw_rain_sensor_temp(self) -> int:\\n self._update_analog_value_cache()\\n return self.analog_cache.rain_sensor_temp\",\n \"def temperature(self):\\n return self._temperature\",\n \"def temperature(self):\\n return self._temperature\",\n \"def get_attic_temperature(theta_sat: np.ndarray, theta_ac: np.ndarray) -> np.ndarray:\\n\\n # temperature difference coefficient\\n h = 1.0\\n\\n return theta_sat * h + theta_ac * (1 - h)\",\n \"def _calculate_temperature(c, h):\\n\\n return (c - 331.4 - 0.0124 * h) / 0.6\",\n \"def get_temperature(elevation, sea_level):\\n if elevation <= sea_level:\\n return 0.8\\n else:\\n return (-1.0 / (1.0 - sea_level)) * (elevation - sea_level) + 1.0\",\n \"def getTemperature(self):\\n return self.json_state.get(\\\"temperature\\\")\",\n \"def current_temperature(self) -> float:\\n return self._thermostat.current_temperatue\",\n \"def temperature(self, alt):\\n T = self.altitude_profile(alt)[1]\\n return T\",\n \"def temperature(self) -> SmartSsdTemperature:\\n return self._temperature\",\n \"def target_temperature(self) -> int:\\r\\n # TODO: Find a better way to do this. This is ugly.\\r\\n if self._hvac_mode == \\\"cool\\\":\\r\\n return self.target_temperature_low\\r\\n elif self._hvac_mode == \\\"heat\\\":\\r\\n return self.target_temperature_high\\r\\n elif self._hvac_mode == \\\"heat-cool\\\":\\r\\n # TODO: Fix this so that heat or cool is chosen.\\r\\n if self._ambient_temperature >= self._target_temperature:\\r\\n return self.target_temperature_low\\r\\n elif self._ambient_temperature <= self._target_temperature:\\r\\n return self.target_temperature_high\\r\\n elif self._hvac_mode == \\\"eco\\\":\\r\\n if self._ambient_temperature >= self._target_temperature:\\r\\n return self.eco_temperature_low\\r\\n elif self._ambient_temperature <= self._target_temperature:\\r\\n return self.eco_temperature_high\\r\\n elif self._hvac_mode == \\\"off\\\":\\r\\n return self.ambient_temperature\\r\\n\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature\\\"))\",\n \"def calculateTemperature(self, R):\\n rRuOx2005 = self._busToRuOx2005(R)\\n T = self.RuOx2005.calculateTemperature(rRuOx2005)\\n return T\",\n \"def soil_temp_factor(self, project_day):\\n tsoil = self.met_data['tsoil'][project_day]\\n\\n if float_gt(tsoil, 0.0):\\n tfac = (0.0326 + 0.00351 * tsoil**1.652 - (tsoil / 41.748)**7.19)\\n if float_lt(tfac, 0.0):\\n tfac = 0.0\\n else:\\n # negative number cannot be raised to a fractional power\\n # number would need to be complex\\n tfac = 0.0\\n\\n return tfac\",\n \"def temperature(self):\\r\\n try:\\r\\n return str(self.connect()['main']['temp'])\\r\\n except:\\r\\n return '@weather_temperature'\",\n \"def temperature(self):\\n temp = ct.c_float()\\n self.lib.GetTemperatureF(ct.pointer(temp))\\n return temp.value\",\n \"def read_object_temperatureF(self, ):\\n return self.read_object_temperatureC() * (9.0/5.0) + 32.0\",\n \"def tskypy(self, psr):\\n # ensure l is in range 0 -> 360\\n b = psr.gb\\n if psr.gl < 0.:\\n l = 360 + psr.gl\\n else:\\n l = psr.gl\\n\\n # convert from l and b to list indices\\n j = b + 90.5\\n if j > 179:\\n j = 179\\n\\n nl = l - 0.5\\n if l < 0.5:\\n nl = 359\\n i = float(nl) / 4.\\n \\n tsky_haslam = self.tskylist[180*int(i) + int(j)]\\n # scale temperature before returning\\n return tsky_haslam * (self.freq/408.0)**(-2.6)\",\n \"def get_temperature(self):\\n # Fake a random temperature change\\n temperature = random.randint(20, 25)\\n self.set_temperature(temperature)\",\n \"def read_temperature(self):\\n self._force_read(False)\\n\\n tempADC = (self._read_register_1ubyte(self.BME680_TEMP_MSB) << 12) | (self._read_register_1ubyte(self.BME680_TEMP_LSB) << 4) | (self._read_register_1ubyte(self.BME680_TEMP_XLSB) >> 4)\\n\\n return float(self._compensate_temperature(tempADC))\",\n \"def target_temperature(self) -> float:\\n return self._thermostat.setpoint_temperature\",\n \"def temperature(self, state: State):\\n if self.is_self_play and state.number_of_stones < 16:\\n return self._temperature\\n return None\",\n \"def color_temp(self):\\n return kelvin_to_mired(self._color_temp)\",\n \"def get_temp(self):\\n\\t\\traw_temp = self.read_i2c_word(self.TEMP_OUT0)\\n\\n\\t\\t# Get the actual temperature using the formule given in the\\n\\t\\t# MPU-6050 Register Map and Descriptions revision 4.2, page 30\\n\\t\\tactual_temp = (raw_temp / 340.0) + 36.53\\n\\n\\t\\treturn actual_temp\",\n \"def __getRawTemperature(self):\\n t1 = self.read_byte_data(self.address, 0x03)\\n t2 = self.read_byte_data(self.address, 0x04)\\n t3 = self.read_byte_data(self.address, 0x05)\\n t = (t1 << 16) | (t2 << 8) | t3\\n t = getTwosComplement(t, 24)\\n return t\",\n \"def temperature(self):\\n self.convert_window(\\\"Temperature\\\", \\\"Celsius\\\", [\\\"Celsius\\\", \\\"Fahrenheit\\\", \\\"Kelvin\\\", \\\"Rankine\\\", \\\"Reaumur\\\", \\\"Newton\\\", \\\"Romer\\\", \\\"Delisle\\\"])\",\n \"def temperature(self):\\n done, data = self._request('GP')\\n if done:\\n return {\\n 'ds3231temp': float(data[0])/10,\\n 'mcp9808temp': float(data[1])/10,\\n 'tmp007temp': float(data[2])/10\\n }\\n\\n raise EvseError\",\n \"def get_weather(self):\\n\\n city = self.user_data[\\\"weatherSettings\\\"][\\\"weatherCity\\\"]\\n country = self.user_data[\\\"weatherSettings\\\"][\\\"weatherCountry\\\"]\\n\\n host = \\\"weather.mios.com\\\"\\n temp_scale = \\\"C\\\"\\n url = \\\"http://%s/?tempFormat=%s&cityWeather=%s&countryWeather=%s\\\" % \\\\\\n (host, temp_scale, Vera.urlencode(city), Vera.urlencode(country))\\n\\n weather = self.proxy_get(url)\\n\\n return (float(weather[\\\"temp\\\"]), weather[\\\"text\\\"])\",\n \"def thermodynamic_temperature(frequency, T_cmb=None):\\n nu = frequency.to(si.GHz, spectral())\\n\\n if T_cmb is None:\\n from astropy.cosmology import default_cosmology\\n\\n T_cmb = default_cosmology.get().Tcmb0\\n\\n def f(nu, T_cmb=T_cmb):\\n x = _si.h * nu / _si.k_B / T_cmb\\n return x**2 * np.exp(x) / np.expm1(x) ** 2\\n\\n def convert_Jy_to_K(x_jybm):\\n factor = (f(nu) * 2 * _si.k_B * si.K * nu**2 / _si.c**2).to_value(\\n astrophys.Jy\\n )\\n return x_jybm / factor\\n\\n def convert_K_to_Jy(x_K):\\n factor = (astrophys.Jy / (f(nu) * 2 * _si.k_B * nu**2 / _si.c**2)).to_value(\\n si.K\\n )\\n return x_K / factor\\n\\n return Equivalency(\\n [(astrophys.Jy / si.sr, si.K, convert_Jy_to_K, convert_K_to_Jy)],\\n \\\"thermodynamic_temperature\\\",\\n {\\\"frequency\\\": frequency, \\\"T_cmb\\\": T_cmb},\\n )\",\n \"def convert_temp(self, temperature):\\n return 1.8 * (temperature - 273) + 32\",\n \"def temperature(self) -> Optional[float]:\\n return self.data.get(\\\"temp\\\")\",\n \"def get_tank_temperature():\\n if sensor is None:\\n return DEFAULT_TEMPERATURE\\n\\n try:\\n # Read the temperature from the I2C sensor.\\n return sensor.read_temperature(True)\\n except OSError:\\n return DEFAULT_TEMPERATURE\",\n \"def formation_temperature(surface_temperature, gradient, depth):\\n form_temp = surface_temperature + gradient * depth\\n return form_temp\",\n \"def thermal_conductivity_of_air(self) -> float:\\n\\n # This more accurate equation is not used by the paper.\\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\\n # )\\n\\n # The reference suggests this equation is accurate to 1%.\\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646\",\n \"def current_temperature(self):\\n return self.atag.dhw_temperature\",\n \"def target_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.target_temperature_c)\",\n \"def get_brightnesstemperature(self, channel):\\n K1 = {\\n \\\"10\\\": 3040.136402, # Constant K1 [W m-2 um-1].\\n \\\"11\\\": 2482.375199,\\n \\\"12\\\": 1935.060183,\\n \\\"13\\\": 866.468575,\\n \\\"14\\\": 641.326517,\\n }\\n\\n K2 = {\\n \\\"10\\\": 1735.337945, # Constant K2 [K].\\n \\\"11\\\": 1666.398761,\\n \\\"12\\\": 1585.420044,\\n \\\"13\\\": 1350.069147,\\n \\\"14\\\": 1271.221673,\\n }\\n\\n return K2[channel] / np.log((K1[channel] / self.get_radiance(channel)) + 1)\",\n \"def get_chip_temperature(self):\\n self.check_validity()\\n\\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_CHIP_TEMPERATURE, (), '', 10, 'h')\",\n \"def read_temp(self):\\n return 19.0\\n data = self.read(_TEMP_REG, 2)\\n temp = ((data[0] * 256) + data[1]) / 16\\n if temp > 2047:\\n temp -= 4096\\n return temp * 0.0625\",\n \"def temperature_const_theta() -> FlowFieldVal:\\n return self._potential_temperature_to_temperature([\\n self._const_theta,\\n ] * self._params.nz, zz)\",\n \"def get_temperature(self):\\n self.temperature = self.temperature_sensors.get_temperature(\\n self.channel)\\n return self.temperature\",\n \"def current_temperature(self):\\n if self._device.temp is not None and self._device.temp > -460:\\n return self._device.temp\\n return None\",\n \"def temperature_sensor():\\n\\n\\tsensor_name = \\\"humiture\\\"\\n\\treg_addr = 26\\n\\tdata_len = 4\\n\\tregist_sensor(sensor_name, reg_addr, data_len)\\n\\n\\t# get sensor data\\n\\tdata = rospy.wait_for_message('MediumSize/SensorHub/Temperature', Temperature, 2)\\n\\ttemperature = data.temperature\\n\\n\\tdelete_sensor(sensor_name)\\n\\treturn temperature\",\n \"def update_rain_temp(self, day_of_week, departure_time_seconds):\\n\\n current_time = t.time()\\n today = datetime.today().weekday()\\n\\n if (departure_time_seconds < (current_time + 3600) \\\\\\n and day_of_week == today):\\n\\n self.temp = self.current_temperature\\n self.rain = self.current_rainfall\\n\\n elif (day_of_week == today):\\n for i in range(24):\\n if (departure_time_seconds > self.weather_forecast_json \\\\\\n [\\\"hourly\\\"][\\\"data\\\"][i][\\\"time\\\"] and departure_time_seconds \\\\\\n < self.weather_forecast_json[\\\"hourly\\\"][\\\"data\\\"][i + 1][\\\"time\\\"]):\\n\\n self.temp = self.weather_forecast_json \\\\\\n ['hourly']['data'][i]['temperature']\\n\\n self.rain = self.weather_forecast_json['hourly'] \\\\\\n ['data'][i]['precipIntensity']\\n break\\n else:\\n continue\\n else:\\n day_difference = int((departure_time_seconds - current_time) / 86400)\\n\\n self.temp = (self.weather_forecast_json['daily']['data'] \\\\\\n [day_difference]['temperatureMax'] + \\\\\\n self.weather_forecast_json['daily']['data'] \\\\\\n [day_difference]['temperatureMin']) / 2\\n\\n self.rain = self.weather_forecast_json['daily'] \\\\\\n ['data'][day_difference]['precipIntensity']\",\n \"def env_temperature(v3: \\\"float\\\", v4: \\\"float\\\") -> \\\"float\\\":\",\n \"def current_temperature(self):\\n temperature = float('NaN')\\n while math.isnan(temperature) or temperature < MINIMUM_BELIEVABLE_TEMPERATURE:\\n temperature = float(self._sensor.readTempC())\\n return temperature\",\n \"def read_temperature():\\n temp = 0.0\\n with open(\\\"daily_temp.txt\\\", \\\"r\\\") as f:\\n temp = float(f.readline())\\n\\n return temp\",\n \"def target_temperature(self) -> float | None:\\n return self.vera_device.get_current_goal_temperature()\",\n \"def get_temperature(self):\\r\\n\\r\\n\\t# get current resolution\\r\\n\\r\\n\\tconf = self.read_config()\\r\\n\\tmask = 0x60 # 0110 0000\\r\\n\\tres = conf & mask # extract resolution from config register\\r\\n\\t# get temperature from register\\r\\n \\r\\n self.write('\\\\x00')\\r\\n data = self.read(2)\\r\\n t_raw = struct.unpack('>h', data)\\r\\n\\tt_raw = t_raw[0]\\r\\n\\r\\n#\\tmsb = 0b11110101\\r\\n#\\tlsb = 0b11100000\\r\\n#\\tdata = struct.pack('BB', msb, lsb)\\r\\n # t_raw = struct.unpack('>h', data)\\r\\n#\\tt_raw = t_raw[0]\\r\\n#\\tprint t_raw\\r\\n\\t\\r\\n # return t_raw\\r\\n\\t# t_raw = ((msb << 8) + lsb) # convert to 2 Byte Integer\\r\\n\\r\\n\\tif (res == 0x00): # 9 bit resolution 0.5 degree\\r\\n\\t print \\\"res: 0.5\\\"\\r\\n\\t return (t_raw >> 7) * 0.5\\r\\n\\r\\n\\tif (res == 0x20): # 10 bit resolution 0.25 degree\\r\\n\\t print \\\"res: 0.25\\\"\\r\\n\\t return (t_raw >> 6) * 0.25\\r\\n\\r\\n\\tif (res == 0x40): # 11 bit resolution 0.125 degree\\r\\n\\t print \\\"res: 0.125\\\"\\r\\n\\t return (t_raw >> 5) * 0.125\\r\\n\\r\\n\\tif (res == 0x60): # l2 bit resolution 0.0625 degree\\r\\n\\t print \\\"res: 0.0625\\\"\\r\\n\\t return (t_raw >> 4) * 0.0625\",\n \"def get_rain():\\n global rain\\n\\n # Report rain only if the condition is 'rainy' (and not always).\\n if weather_condition == CONDITION_RAINY and random.random() > 0.7:\\n rain += round(random.random(), 2)\\n return rain\",\n \"def get_ir_sensor_temperature(self) -> float:\\n self.serial.write(b\\\"T!\\\")\\n ir_sensor_temp = self.__extract_int(self.__read_response(1)[0], b\\\"!2\\\")\\n\\n return round(ir_sensor_temp / 100, 2)\",\n \"def get_temperature(self, sensor: int = 0) -> float:\\n\\n return self.send(self.cmd.GET_HEATING_ACT)\",\n \"def read_ambient_temperatureC(self, ):\\n return self._read_temperature(MLX90614_TA)\",\n \"def target_temperature(self):\\n return self._target_temp\",\n \"def temperature(wair,pres,entr=None,temp=None,airf=None,dhum=None,\\n chkvals=False,chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,\\n chkbnd=False,mathargs=None):\\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\\n return temp\",\n \"def getTempMedia(self):\\n temp_media = float(self.soup.media.text)\\n temperature = str(int(round(temp_media)))\\n return temperature\",\n \"def temp(self):\\n\\t\\ttemp_out = self.read16(MPU9250_ADDRESS, TEMP_DATA)\\n\\t\\ttemp = temp_out / 333.87 + 21.0 # these are from the datasheets\\n\\t\\treturn temp\",\n \"def readtemperature(self, cTemp):\\r\\n\\t\\tdata = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_STATUS)\\r\\n\\t\\twhile (data & 0x01) != 0 :\\r\\n\\t\\t\\tdata = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_STATUS)\\r\\n\\t\\tdata1 = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_DATAH)\\r\\n\\t\\tdata2 = bus.read_byte_data(SI7015_DEFAULT_ADDRESS, SI7015_REG_DATAH)\\r\\n\\t\\t\\r\\n\\t\\t# Convert the data to 14-bits\\r\\n\\t\\tcTemp = (((data1 * 256.0) + data2) / 4.0)\\r\\n\\t\\t\\r\\n\\t\\tif cTemp < 0x0140 :\\r\\n\\t\\t\\tcTemp = 0x0140\\r\\n\\t\\telif cTemp > 0x12C0 :\\r\\n\\t\\t\\tcTemp = 0x12C0\\r\\n\\t\\telse :\\r\\n\\t\\t\\tcTemp = cTemp\\r\\n\\t\\t\\r\\n\\t\\tcTemp = (cTemp / 32.0) - 50.0\\r\\n\\t\\tfTemp = cTemp * 1.8 + 32\\r\\n\\t\\t\\r\\n\\t\\treturn {'c' : cTemp, 'f' : fTemp}\",\n \"def target_temperature(self):\\n return self.atag.dhw_target_temperature\",\n \"def read_temperature(self):\\n data = self.ag.read_bytes(Register.OUT_TEMP_L, 2)\\n return lsm9ds1.to_int16(data)\",\n \"def rain(self, json):\\n rain = str(json['forecast']['txt_forecast']['forecastday'][0]['pop'])\\n return rain\",\n \"def get_temperature(self):\\n\\n svc = \\\"urn:upnp-org:serviceId:TemperatureSensor1\\\"\\n if not svc in self.services:\\n raise RuntimeError, \\\"Device doesn't support the service\\\"\\n\\n return self.get_variable(svc, \\\"CurrentTemperature\\\")\",\n \"def high_temperature(self):\\r\\n return self._yesterdays_weather.get_high_temperature()\",\n \"def calculate_color_temperature(r: int, g: int, b: int) -> float:\\n\\n # 1. Map RGB values to their XYZ counterparts.\\n # Based on 6500K fluorescent, 3000K fluorescent\\n # and 60W incandescent values for a wide range.\\n # Note: Y = Illuminance or lux\\n x = (-0.14282 * r) + (1.54924 * g) + (-0.95641 * b)\\n y = (-0.32466 * r) + (1.57837 * g) + (-0.73191 * b)\\n z = (-0.68202 * r) + (0.77073 * g) + (0.56332 * b)\\n\\n # 2. Calculate the chromaticity co-ordinates\\n xchrome = x / (x + y + z)\\n ychrome = y / (x + y + z)\\n\\n # 3. Use to determine the CCT\\n n = (xchrome - 0.3320) / (0.1858 - ychrome)\\n\\n # 4. Calculate the final CCT\\n cct = (449.0 * pow(n, 3)) + (3525.0 * pow(n, 2)) + (6823.3 * n) + 5520.33\\n\\n # Return the results in degrees Kelvin\\n return cct\",\n \"def get_temperature(data):\\n celcius = 0\\n celcius = [i for i in data if re.search(r'\\\\d+[/]', i)]\\n \\n if celcius == []:\\n return \\\"N/A\\\"\\n celcius = celcius[0].split('/')[0]\\n celcius = celcius.replace('M', '-')\\n \\n try:\\n celcius = int(celcius)\\n except ValueError:\\n return \\\"N/A\\\"\\n\\n farenheit = round((celcius * 9/5) + 32) # formula to get farenheit from celcius\\n temperature = \\\"{0} C ({1} F)\\\".format(celcius, farenheit)\\n return temperature\",\n \"def target_temperature(self):\\n return self._target_temperature\",\n \"def target_temperature(self):\\n return self._target_temperature\",\n \"def target_temperature(self):\\n return self._target_temperature\",\n \"def target_temperature(self):\\n return self._target_temperature\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.677967","0.65615","0.64276695","0.6416784","0.64141804","0.6408829","0.62878555","0.6222289","0.6220975","0.61991245","0.6182677","0.61740917","0.61678636","0.6091844","0.60883605","0.6079088","0.6072627","0.6015334","0.60076296","0.59943837","0.59905624","0.5988029","0.5980697","0.59772134","0.5975562","0.59164184","0.5863152","0.586078","0.5853003","0.5832522","0.5827362","0.58265156","0.5820179","0.58153296","0.58153296","0.58105195","0.58070904","0.5787698","0.5771034","0.5770008","0.5768418","0.575893","0.57132816","0.5709572","0.5702031","0.5700625","0.56975675","0.56973493","0.5694243","0.5688774","0.5675132","0.5662157","0.56581","0.56546646","0.5645749","0.56297594","0.5628357","0.56259096","0.5620268","0.56078464","0.5603357","0.55883086","0.5586931","0.55809605","0.5578597","0.55726355","0.557004","0.5568196","0.5538022","0.5534543","0.5519602","0.5511703","0.5499495","0.54924357","0.5485852","0.54829025","0.5480603","0.5477799","0.54719996","0.5470503","0.54703283","0.54624283","0.5459717","0.5456861","0.5447461","0.5445308","0.5443012","0.543805","0.54202324","0.5418282","0.54056865","0.5402589","0.53981","0.53954524","0.5376369","0.5375557","0.5370513","0.5370513","0.5370513","0.5370513"],"string":"[\n \"0.677967\",\n \"0.65615\",\n \"0.64276695\",\n \"0.6416784\",\n \"0.64141804\",\n \"0.6408829\",\n \"0.62878555\",\n \"0.6222289\",\n \"0.6220975\",\n \"0.61991245\",\n \"0.6182677\",\n \"0.61740917\",\n \"0.61678636\",\n \"0.6091844\",\n \"0.60883605\",\n \"0.6079088\",\n \"0.6072627\",\n \"0.6015334\",\n \"0.60076296\",\n \"0.59943837\",\n \"0.59905624\",\n \"0.5988029\",\n \"0.5980697\",\n \"0.59772134\",\n \"0.5975562\",\n \"0.59164184\",\n \"0.5863152\",\n \"0.586078\",\n \"0.5853003\",\n \"0.5832522\",\n \"0.5827362\",\n \"0.58265156\",\n \"0.5820179\",\n \"0.58153296\",\n \"0.58153296\",\n \"0.58105195\",\n \"0.58070904\",\n \"0.5787698\",\n \"0.5771034\",\n \"0.5770008\",\n \"0.5768418\",\n \"0.575893\",\n \"0.57132816\",\n \"0.5709572\",\n \"0.5702031\",\n \"0.5700625\",\n \"0.56975675\",\n \"0.56973493\",\n \"0.5694243\",\n \"0.5688774\",\n \"0.5675132\",\n \"0.5662157\",\n \"0.56581\",\n \"0.56546646\",\n \"0.5645749\",\n \"0.56297594\",\n \"0.5628357\",\n \"0.56259096\",\n \"0.5620268\",\n \"0.56078464\",\n \"0.5603357\",\n \"0.55883086\",\n \"0.5586931\",\n \"0.55809605\",\n \"0.5578597\",\n \"0.55726355\",\n \"0.557004\",\n \"0.5568196\",\n \"0.5538022\",\n \"0.5534543\",\n \"0.5519602\",\n \"0.5511703\",\n \"0.5499495\",\n \"0.54924357\",\n \"0.5485852\",\n \"0.54829025\",\n \"0.5480603\",\n \"0.5477799\",\n \"0.54719996\",\n \"0.5470503\",\n \"0.54703283\",\n \"0.54624283\",\n \"0.5459717\",\n \"0.5456861\",\n \"0.5447461\",\n \"0.5445308\",\n \"0.5443012\",\n \"0.543805\",\n \"0.54202324\",\n \"0.5418282\",\n \"0.54056865\",\n \"0.5402589\",\n \"0.53981\",\n \"0.53954524\",\n \"0.5376369\",\n \"0.5375557\",\n \"0.5370513\",\n \"0.5370513\",\n \"0.5370513\",\n \"0.5370513\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77121866"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2393,"cells":{"query":{"kind":"string","value":"The thermal conductivity of air varies as a function of temperature."},"document":{"kind":"string","value":"def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference suggests this equation is accurate to 1%.\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature","def thermal_conductivity(temperature):\n a0 = -4.1236\n a1 = 13.788\n a2 = -26.068\n a3 = 26.272\n a4 = -14.663\n a5 = 4.4954\n a6 = -0.6905\n a7 = 0.0397\n log_t = math.log10(temperature)\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\n g10_thermal_conductivity = 10.0**f_exp\n return g10_thermal_conductivity","def temperature() -> float:","def thermalConductivity(self, Tk=None, Tc=None):\n Tk = getTk(Tc, Tk)\n self.checkPropertyTempRange(\"thermal conductivity\", Tk)\n thermalConductivity = (\n 2.13014e-08 * Tk**3\n - 6.31916e-05 * Tk**2\n + 1.11629e-01 * Tk\n - 2.00043e00\n )\n return thermalConductivity * 1e-3","def temperature(self):\n names = ['anc_air_temperature']\n return self.sensor.get_with_fallback('temperature', names)","def sky_temperature(self) -> float:\n\n return 0.0552 * (self.ambient_temperature**1.5)","def temperature(self):\r\n self._read_temperature()\r\n return self._t_fine / 5120.0","def temperature(self):\n return float(self._current_observation['temp_c'])","def latent_heat_vapourisation(self, tair):\n return (2.501 - 0.00237 * tair) * 1E06","def dynamic_viscosity_of_air(self) -> float:\n\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\n self.ambient_temperature + 110.4\n )","def thermal_i(mu,Ti):\n return 9.79*1.e5/np.sqrt(mu/Ti)/1.e2","def ambient_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_c\"))\r\n return kelvin_to_celsius(self._ambient_temperature)","def compute_surface_temperature(heat_flux):\n\n return 1.1e-4*heat_flux + 323","def cooled_surface_temp(T:np.ndarray) -> float:\n \n return T.dot(cs_temp_weights)","def env_temperature(v3: \"float\", v4: \"float\") -> \"float\":","def thermal(isatom, freq, scalfac,linnonlin,T):\n if isatom != \"true\":\n nfreq = len(freq)\n\n vib_temp = []\n for ifreq in range(nfreq):\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\n vib_temp.append(vib_temp_new)\n\n dE_vib = 0\n for ifreq in range(nfreq):\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\n\n dE_ZPE = 0.5 * sum(freq) * cmi2au\n\n if linnonlin == \"L\":\n dE_rot = kB * T * j2au\n elif linnonlin == \"NL\":\n dE_rot = kB * T * j2au * (3.0/2.0)\n else:\n with open(\"Thermochemistry.out\", \"a\") as ther_chem:\n ther_chem.write(\"ERROR: unknown entry for linear/nonlinear\")\n else:\n dE_ZPE = 0\n dE_vib = 0\n dE_rot = 0\n\n dE_tra = kB * T * j2au * (3.0/2.0)\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\n\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)","def tempAir(sample):\n sample *= 1.0\n sample /= 1000\n celsius = (sample - 0.5) * 100\n return round(celsius,2)","def temperature() -> FlowFieldVal:\n return [\n self._t_s - self._delta_t * tf.math.tanh(z / self._height) for z in zz\n ]","def ambient_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_f\"))\r\n return celsius_to_fahrenheit(self.ambient_temperature_c)","def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\n k = get_kappa(K_medium, rho_medium, c_medium)\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial","def temperature(self) -> float:\n # Start a measurement then poll the measurement finished bit.\n self.temp_start = 1\n while self.temp_running > 0:\n pass\n # Grab the temperature value and convert it to Celsius.\n # This uses the same observed value formula from the Radiohead library.\n temp = self._read_u8(_REG_TEMP2)\n return 166.0 - temp","def __t_fine__(self, adc_temperature):\n var1 = (((adc_temperature >> 3) -\n (self._calibration_t[0] << 1)) * self._calibration_t[1]) >> 11\n var2 = (((\n ((adc_temperature >> 4) - self._calibration_t[0]) *\n ((adc_temperature >> 4) - self._calibration_t[0])) >> 12)\n * self._calibration_t[2]) >> 14\n return var1 + var2","def airfoilEffT(self):\n return float(Importer(Component='Evaluations',\n VariableName='Wing airfoil efficiency factor',\n Default=.95,\n Path=self.filePath).getValue)","def get_temperature(self):\n pass","def current_temperature(self) -> float:\n return self._thermostat.current_temperatue","def temperature(self):\n return self.read_short(65) / 340.0 + 36.53","def air_density(self):\n return self.flow_field.air_density","def temperature():\n from .imperial import deg_F as F\n from .imperial import deg_R as R\n\n K = si.K\n C = si.deg_C\n\n return Equivalency(\n [\n (K, C, lambda x: x - 273.15, lambda x: x + 273.15),\n (C, F, lambda x: x * 1.8 + 32.0, lambda x: (x - 32.0) / 1.8),\n (K, F, lambda x: x * 1.8 - 459.67, lambda x: (x + 459.67) / 1.8),\n (R, F, lambda x: x - 459.67, lambda x: x + 459.67),\n (R, C, lambda x: (x - 491.67) * (5 / 9), lambda x: x * 1.8 + 491.67),\n (R, K, lambda x: x * (5 / 9), lambda x: x * 1.8),\n ],\n \"temperature\",\n )","def getTemperature(self):\n return self.temperature","def temperature(wair,pres,entr=None,temp=None,airf=None,dhum=None,\n chkvals=False,chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,\n chkbnd=False,mathargs=None):\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\n return temp","def temp(self):\n if self.temp_sensor is None:\n return None\n else:\n if self.temp_scale.lower() in ['f', 'fahrenheit']:\n return self.temp_sensor.temp_f\n elif self.temp_scale.lower() in ['c', 'celsius']:\n return self.temp_sensor.temp_c","def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\n self.digital_temp_data() \n dT = self.tempadc-(C_5*(2**8))\n temperature=(2000+(dT*(C_6/(2**23))))/100\n return temperature, dT","def target_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_c\"))\r\n return kelvin_to_celsius(self._target_temperature)","def get_cold_junction_temperature(self):\n return self._mcp9600.get('COLD_JUNCTION').temperature","def get_chiller_temperature(self) -> float:\n\n return self.send(self.cmd.GET_COOLING_ACT)","def temperature_energy():\n e = _si.e.value\n k_B = _si.k_B.value\n return Equivalency(\n [(si.K, si.eV, lambda x: x / (e / k_B), lambda x: x * (e / k_B))],\n \"temperature_energy\",\n )","def get_D_C3H8_air_eff(self, T):\n\n Kn = self.get_Kn(T)\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\n\n if np.isscalar(Kn):\n if Kn <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n else:\n if Kn.any() <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n self.D_C3H8_air_eff = D_C3H8_air_eff\n\n return D_C3H8_air_eff","def temperature(self, alt):\n T = self.altitude_profile(alt)[1]\n return T","def target_temperature(self) -> int:\r\n # TODO: Find a better way to do this. This is ugly.\r\n if self._hvac_mode == \"cool\":\r\n return self.target_temperature_low\r\n elif self._hvac_mode == \"heat\":\r\n return self.target_temperature_high\r\n elif self._hvac_mode == \"heat-cool\":\r\n # TODO: Fix this so that heat or cool is chosen.\r\n if self._ambient_temperature >= self._target_temperature:\r\n return self.target_temperature_low\r\n elif self._ambient_temperature <= self._target_temperature:\r\n return self.target_temperature_high\r\n elif self._hvac_mode == \"eco\":\r\n if self._ambient_temperature >= self._target_temperature:\r\n return self.eco_temperature_low\r\n elif self._ambient_temperature <= self._target_temperature:\r\n return self.eco_temperature_high\r\n elif self._hvac_mode == \"off\":\r\n return self.ambient_temperature\r\n\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature\"))","def get_D_C3H8_air(self, T):\n\n self.set_TempPres_dependents(T)\n\n self.D_C3H8_air = (\n 2. / 3. * np.sqrt(const.k_B * T / np.pi * 0.5 * (1. /\n self.air.m + 1. / self.fuel.m)) / (np.pi * (0.5 *\n (self.air.d + self.fuel.d)) ** 2.) / self.air.n\n )\n\n return self.D_C3H8_air","def calculateTemperature(self):\n \n # CIE XYZ space\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\n\n # CIE Chromaticities xy\n self.x = self.X/(self.X + self.Y + self.Z)\n self.y = self.Y/(self.X + self.Y + self.Z)\n \n # CIE Chromaticities uv\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\n \n # constant for McCamy's/Hernandez-Andrés formula\n n = (self.x - self.x_e)/(self.y - self.y_e)\n \n # Correlated color temperature according to Hernández-Andrés (1999)\n self.color_temp = ( self.A_0 + \n self.A_1*np.exp(-n/self.t_1) + \n self.A_2*np.exp(-n/self.t_2) + \n self.A_3*np.exp(-n/self.t_3) )\n \n # Delete too high values\n self.color_temp[self.color_temp > 30000] = 0\n \n # Affichage de la CCT\n self.mean_temp = int(round(self.color_temp.mean()))\n self.mean_temp_label.setText(\"Temperature moyenne = \"+str(self.mean_temp))\n self.mean_temp_label.adjustSize()\n \t\n # Affichage de l'illuminance (Y)\n self.mean_illu = int(round((self.Y.mean())))\n self.illuminance_label.setText(\"Illuminance moyenne = \"+str(self.mean_illu))\n self.illuminance_label.adjustSize()","def ambient_temperature(self) -> int:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature\"))\r\n # TODO: Force this to return an int.\r\n if self.temperature_scale == \"F\":\r\n return self.ambient_temperature_f\r\n elif self.temperature_scale == \"C\":\r\n return self.ambient_temperature_c\r\n else:\r\n return self._ambient_temperature","def read_ambient_temperatureF(self, ):\n return self.read_ambient_temperatureC() * (9.0/5.0) + 32.0","def internal_temp_c(self) -> int:\n return int(self._device_info[\"Temperature\"])","def soil_temp_factor(self, project_day):\n tsoil = self.met_data['tsoil'][project_day]\n\n if float_gt(tsoil, 0.0):\n tfac = (0.0326 + 0.00351 * tsoil**1.652 - (tsoil / 41.748)**7.19)\n if float_lt(tfac, 0.0):\n tfac = 0.0\n else:\n # negative number cannot be raised to a fractional power\n # number would need to be complex\n tfac = 0.0\n\n return tfac","def temperature(self):\n return self._temperature","def temperature(self):\n return self._temperature","def target_temperature(self):\n if self.current_operation == 'Heat & Cool':\n return None\n if self.current_operation == 'Heat only':\n return int(self._api._heatto)\n elif self.current_operation == 'Cool only':\n return int(self._api._coolto)\n return None","def target_temperature(self) -> float | None:\n if self.hvac_mode == HVACMode.COOL:\n return self.target_temperature_high\n if self.hvac_mode == HVACMode.HEAT:\n return self.target_temperature_low\n return None","def wind_heat_transfer_coefficient(self) -> float:\n\n return 3.8 + 2 * self.wind_speed\n # return 4.5 + 2.9 * self.wind_speed","def heat_capacity_of_air(self) -> float:\n\n return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2","def current_temperature(self):\n return self.atag.dhw_temperature","def get_compensated_temperature() -> float:\n comp_factor = 2.25\n cpu_temp = get_cpu_temperature()\n raw_temp = bme280.get_temperature()\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\n # print(\"\"\"\n # Compensated_Temperature: {:05.2f} *C\n # Pressure: {:05.2f} hPa\n # Relative humidity: {:05.2f} %\n # \"\"\".format(temperature, pressure, humidity))\n return comp_temp","def get_temperature(self):\n \n # Get temp readings from both sensors\n humidity_temp = self._sense_hat.get_temperature_from_humidity()\n pressure_temp = self._sense_hat.get_temperature_from_pressure()\n \n # avg_temp becomes the average of the temperatures from both sensors\n # We need to check for pressure_temp value is not 0, to not ruin avg_temp calculation\n avg_temp = (humidity_temp + pressure_temp) / 2 if pressure_temp else humidity_temp\n \n # Get the CPU temperature\n cpu_temp = self._get_cpu_temp()\n \n # Calculate temperature compensating for CPU heating\n adj_temp = avg_temp - (cpu_temp - avg_temp) / 1.5\n \n # Average out value across the last three readings\n return self._get_smooth(adj_temp)","def thermal_state(self, beta: float = .1) -> numpy.ndarray:\n rho = numpy.exp(-beta * self.cost)\n return rho / numpy.sum(rho)","def temperature(self):\n return _cantera.reactor_temperature(self.__reactor_id)","def target_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_f\"))\r\n return celsius_to_fahrenheit(self.target_temperature_c)","def temperature(self):\n temp = ct.c_float()\n self.lib.GetTemperatureF(ct.pointer(temp))\n return temp.value","def density_of_air(self) -> float:\n\n return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature)","def get_chip_temperature(self):\n self.check_validity()\n\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_CHIP_TEMPERATURE, (), '', 10, 'h')","def temperatures(self):\r\n return self._arm.temperatures","def get_temp(self) -> float:\n return np.round(np.mean(self.temp_data), 1)","def eco_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_low)","def tempWater(sample):\n sample *= .0009\n sample *= 1000\n celsius = (sample - 20.5128) * 0.0512\n return round(celsius,2)","async def air_density(self, temperature, station_pressure):\n if temperature is not None and station_pressure is not None:\n kelvin = temperature + 273.15\n pressure = station_pressure\n r_specific = 287.058\n decimals = 2\n\n air_dens = (pressure * 100) / (r_specific * kelvin)\n\n if self._unit_system == UNITS_IMPERIAL:\n air_dens = air_dens * 0.06243\n decimals = 4\n\n return round(air_dens, decimals)\n\n _LOGGER.error(\"FUNC: air_density ERROR: Temperature or Pressure value was reported as NoneType. Check the sensor\")","def target_temperature(self):\n if self._client.mode == self._client.MODE_HEAT:\n return self._client.heattemp\n if self._client.mode == self._client.MODE_COOL:\n return self._client.cooltemp\n return None","def thermal_state(self, T):\n return unvectorize(\n np.diag(thermal_dist(t, self.ev)) \\\n .astype(settings.DTYPE_COMPLEX)\n for t in vectorize(T)\n )","def get_production_factor(self, temp_atmosphere):\n return 1.","def thermal_velocity(charge, temperature, mass):\n return np.sqrt(2*abs(charge)*temperature/mass)","def constant_temp(self, numIterations):\n return 1 + self.alpha","def target_temperature(self) -> float:\n return self._thermostat.setpoint_temperature","def eco_temperature_low_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_f\"))\r\n return celsius_to_fahrenheit(self.eco_temperature_low_c)","def _therm_cond(self):\n xy = dict() # used to label the components e.g 1->CO2,2->N2\n for (i, j) in enumerate(self.component_list, 1):\n xy[i] = j\n\n k_vap = 0\n for i in range(1, len(self.component_list) + 1):\n sumij = 0\n for j in range(1, len(self.component_list) + 1):\n Aij = (1 + (self.visc_d_comp[xy[i]] / self.visc_d_comp[xy[j]])**0.5 *\n (self.mw_comp[xy[j]] / self.mw_comp[xy[i]])**0.25)**2 *\\\n (8 * (1 + self.mw_comp[xy[i]] / self.mw_comp[xy[j]]))**-0.5\n sumij += self.mole_frac_comp[xy[j]] * Aij\n k_vap += self.mole_frac_comp[xy[i]] * self.therm_cond_comp[xy[i]] / sumij\n\n try:\n self.therm_cond = Expression(expr=k_vap,\n doc='Vapor thermal'\n 'conductivity [J/(m.K.s)]')\n except AttributeError:\n self.del_component(self.therm_cond)\n raise","def dielectric_constant_water(temperature=298.15):\n tabulated_data = np.array([[263.15, 92.10],\n [268.15, 89.96],\n [273.15, 87.90],\n [278.15, 85.90],\n [283.15, 83.96],\n [288.15, 82.06],\n [293.15, 80.20],\n [298.15, 78.38],\n [303.15, 76.60],\n [308.15, 74.86],\n [313.15, 73.17],\n [318.15, 71.50],\n [323.15, 69.88],\n [328.15, 68.29],\n [333.15, 66.74],\n [338.15, 65.22],\n [343.15, 63.73],\n [348.15, 62.28],\n [353.15, 60.87],\n [358.15, 59.48],\n [363.15, 58.13],\n [368.15, 56.81],\n [373.15, 55.51]])\n polynomal_degree = 5\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\n polynomal_degree)\n fitfunction = np.poly1d(fitdata)\n return fitfunction(temperature)","def current_temperature(self):\n temperature = float('NaN')\n while math.isnan(temperature) or temperature < MINIMUM_BELIEVABLE_TEMPERATURE:\n temperature = float(self._sensor.readTempC())\n return temperature","def current_temperature(self) -> float:\n return self._device.scaled_temperature","def _temperature_policy(self, N: np.ndarray) -> np.ndarray:\n t = 1 / self.temperature\n pi = N ** t + np.finfo(np.float32).eps # Add tiny value to remove 0s\n pi = pi / pi.sum()\n return pi","def get_actual_air_conditioned_temperature(\n hc_period: np.ndarray,\n theta_ac: np.ndarray, v_supply: np.ndarray, theta_supply_h: np.ndarray, theta_supply_c: np.ndarray,\n l_d_h: np.ndarray, l_d_cs: np.ndarray,\n u_prt: float, a_prt: np.ndarray, a_hcz: np.ndarray, q: float) -> np.ndarray:\n\n rho = get_air_density()\n c = get_specific_heat()\n\n a_prt = a_prt.reshape(1, 5).T\n a_hcz = a_hcz[0:5].reshape(1, 5).T\n\n theta_ac_act_h = np.maximum(theta_ac + (c * rho * v_supply * (theta_supply_h - theta_ac) - l_d_h * 10 ** 6)\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\n\n theta_ac_act_c = np.minimum(theta_ac - (c * rho * v_supply * (theta_ac - theta_supply_c) - l_d_cs * 10 ** 6)\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\n\n return theta_ac_act_h * (hc_period == 'h') + theta_ac_act_c * (hc_period == 'c') + theta_ac * (hc_period == 'm')","def get_sky_ir_temperature(self) -> float:\n self.serial.write(b\"S!\")\n sky_ir_temp = self.__extract_int(self.__read_response(1)[0], b\"!1\")\n\n return round(sky_ir_temp / 100, 2)","def get_attic_temperature(theta_sat: np.ndarray, theta_ac: np.ndarray) -> np.ndarray:\n\n # temperature difference coefficient\n h = 1.0\n\n return theta_sat * h + theta_ac * (1 - h)","def temperatures():\n\n return station_9281","def volumetric_heat_capacity(temperature):\n a = -2.4083\n b = 7.6006\n c = -8.2982\n d = 7.3301\n e = -4.2386\n f = 1.4294\n g = -0.24396\n h = 0.015236\n i = 0.0\n log_t = math.log10(temperature)\n f_exp = a + b*log_t + c*log_t**2.0 + d*log_t**3.0 + e*log_t**4.0 + f*log_t**5.0 + g*log_t**6.0 + \\\n h*log_t**7.0 + i*log_t**8.0\n g10_cp = 10.0**f_exp\n return g10_cp * G10NISTMaterialProperties.density","def eco_temperature_high_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_high)","def temperature(self) -> Optional[float]:\n return self.data.get(\"temp\")","def conductivity(self):\n m = 1.67296736e-02 # Determined from optimisation\n c = 8.54665149e-05 # Determined from optimisation\n return m * self.concentration + c","def airfoilEffW(self):\n return float(Importer(Component='Evaluations',\n VariableName='Wing airfoil efficiency factor',\n Default=.95,\n Path=self.filePath).getValue)","def read_object_temperatureF(self, ):\n return self.read_object_temperatureC() * (9.0/5.0) + 32.0","def fuel_cond(T):\n\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\n \n return kc","def get_specific_heat() -> float:\n return 1006.0","def compute_td_spectral_function(self):\n nomegase = self.nomegase\n nkpt = self.nkpt\n nband = self.nband\n ntemp = self.ntemp\n\n self.spectral_function_T = np.zeros((nomegase, ntemp, nkpt, nband),\n dtype=float)\n\n omega = np.einsum('ijt,l->ijlt',\n np.ones((nkpt, nband, ntemp)), self.omegase)\n\n self.spectral_function_T = (\n (1 / np.pi) * np.abs(self.self_energy_T.imag) /\n ((omega - self.self_energy_T.real) ** 2\n + self.self_energy_T.imag ** 2)\n )","def get_temperature(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. .*? .*? (.*?) .*? .*? . .*? .*? . . . .*?'\n temperature = float(re.findall(pattern,summary).pop())\n return temperature","def water_vapour(t):\n T_0 = 273.15\n T_rw = 35.86 # over water\n a = 17.269\n # cdo -mulc,610.78 -exp -div -mulc,17.5 -subc,273.15 a\n return 610.78 * np.exp(a * (t - T_0) / (t - T_rw))","def get_convection_vent(self,T_i,el):\n\n rad = radiation.Radiation()\n T_atm = rad.getTemp(el)\n\n Q_vent = self.mdot*self.Cp_air0*(T_i-T_atm) # Convection due to released air\n return Q_vent","def current_temperature(self) -> float | None:\n return self.vera_device.get_current_temperature()","def target_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_low_c\"))\r\n return kelvin_to_celsius(self._target_temperature_low)","def _calculate_temperature(c, h):\n\n return (c - 331.4 - 0.0124 * h) / 0.6","def get_ir_sensor_temperature(self) -> float:\n self.serial.write(b\"T!\")\n ir_sensor_temp = self.__extract_int(self.__read_response(1)[0], b\"!2\")\n\n return round(ir_sensor_temp / 100, 2)","def eco_temperature_high_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_f\"))\r\n return celsius_to_fahrenheit(self.eco_temperature_high_c)","def current_temperature(self):\n if self._device.temp is not None and self._device.temp > -460:\n return self._device.temp\n return None","def temperature_unit(self):\n return TEMP_FAHRENHEIT"],"string":"[\n \"def thermal_expansivity_of_air(self) -> float:\\n\\n return 1 / self.ambient_temperature\",\n \"def thermal_conductivity(temperature):\\n a0 = -4.1236\\n a1 = 13.788\\n a2 = -26.068\\n a3 = 26.272\\n a4 = -14.663\\n a5 = 4.4954\\n a6 = -0.6905\\n a7 = 0.0397\\n log_t = math.log10(temperature)\\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\\\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\\n g10_thermal_conductivity = 10.0**f_exp\\n return g10_thermal_conductivity\",\n \"def temperature() -> float:\",\n \"def thermalConductivity(self, Tk=None, Tc=None):\\n Tk = getTk(Tc, Tk)\\n self.checkPropertyTempRange(\\\"thermal conductivity\\\", Tk)\\n thermalConductivity = (\\n 2.13014e-08 * Tk**3\\n - 6.31916e-05 * Tk**2\\n + 1.11629e-01 * Tk\\n - 2.00043e00\\n )\\n return thermalConductivity * 1e-3\",\n \"def temperature(self):\\n names = ['anc_air_temperature']\\n return self.sensor.get_with_fallback('temperature', names)\",\n \"def sky_temperature(self) -> float:\\n\\n return 0.0552 * (self.ambient_temperature**1.5)\",\n \"def temperature(self):\\r\\n self._read_temperature()\\r\\n return self._t_fine / 5120.0\",\n \"def temperature(self):\\n return float(self._current_observation['temp_c'])\",\n \"def latent_heat_vapourisation(self, tair):\\n return (2.501 - 0.00237 * tair) * 1E06\",\n \"def dynamic_viscosity_of_air(self) -> float:\\n\\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\\n self.ambient_temperature + 110.4\\n )\",\n \"def thermal_i(mu,Ti):\\n return 9.79*1.e5/np.sqrt(mu/Ti)/1.e2\",\n \"def ambient_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._ambient_temperature)\",\n \"def compute_surface_temperature(heat_flux):\\n\\n return 1.1e-4*heat_flux + 323\",\n \"def cooled_surface_temp(T:np.ndarray) -> float:\\n \\n return T.dot(cs_temp_weights)\",\n \"def env_temperature(v3: \\\"float\\\", v4: \\\"float\\\") -> \\\"float\\\":\",\n \"def thermal(isatom, freq, scalfac,linnonlin,T):\\n if isatom != \\\"true\\\":\\n nfreq = len(freq)\\n\\n vib_temp = []\\n for ifreq in range(nfreq):\\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\\n vib_temp.append(vib_temp_new)\\n\\n dE_vib = 0\\n for ifreq in range(nfreq):\\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\\n\\n dE_ZPE = 0.5 * sum(freq) * cmi2au\\n\\n if linnonlin == \\\"L\\\":\\n dE_rot = kB * T * j2au\\n elif linnonlin == \\\"NL\\\":\\n dE_rot = kB * T * j2au * (3.0/2.0)\\n else:\\n with open(\\\"Thermochemistry.out\\\", \\\"a\\\") as ther_chem:\\n ther_chem.write(\\\"ERROR: unknown entry for linear/nonlinear\\\")\\n else:\\n dE_ZPE = 0\\n dE_vib = 0\\n dE_rot = 0\\n\\n dE_tra = kB * T * j2au * (3.0/2.0)\\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\\n\\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)\",\n \"def tempAir(sample):\\n sample *= 1.0\\n sample /= 1000\\n celsius = (sample - 0.5) * 100\\n return round(celsius,2)\",\n \"def temperature() -> FlowFieldVal:\\n return [\\n self._t_s - self._delta_t * tf.math.tanh(z / self._height) for z in zz\\n ]\",\n \"def ambient_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.ambient_temperature_c)\",\n \"def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\\n k = get_kappa(K_medium, rho_medium, c_medium)\\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial\",\n \"def temperature(self) -> float:\\n # Start a measurement then poll the measurement finished bit.\\n self.temp_start = 1\\n while self.temp_running > 0:\\n pass\\n # Grab the temperature value and convert it to Celsius.\\n # This uses the same observed value formula from the Radiohead library.\\n temp = self._read_u8(_REG_TEMP2)\\n return 166.0 - temp\",\n \"def __t_fine__(self, adc_temperature):\\n var1 = (((adc_temperature >> 3) -\\n (self._calibration_t[0] << 1)) * self._calibration_t[1]) >> 11\\n var2 = (((\\n ((adc_temperature >> 4) - self._calibration_t[0]) *\\n ((adc_temperature >> 4) - self._calibration_t[0])) >> 12)\\n * self._calibration_t[2]) >> 14\\n return var1 + var2\",\n \"def airfoilEffT(self):\\n return float(Importer(Component='Evaluations',\\n VariableName='Wing airfoil efficiency factor',\\n Default=.95,\\n Path=self.filePath).getValue)\",\n \"def get_temperature(self):\\n pass\",\n \"def current_temperature(self) -> float:\\n return self._thermostat.current_temperatue\",\n \"def temperature(self):\\n return self.read_short(65) / 340.0 + 36.53\",\n \"def air_density(self):\\n return self.flow_field.air_density\",\n \"def temperature():\\n from .imperial import deg_F as F\\n from .imperial import deg_R as R\\n\\n K = si.K\\n C = si.deg_C\\n\\n return Equivalency(\\n [\\n (K, C, lambda x: x - 273.15, lambda x: x + 273.15),\\n (C, F, lambda x: x * 1.8 + 32.0, lambda x: (x - 32.0) / 1.8),\\n (K, F, lambda x: x * 1.8 - 459.67, lambda x: (x + 459.67) / 1.8),\\n (R, F, lambda x: x - 459.67, lambda x: x + 459.67),\\n (R, C, lambda x: (x - 491.67) * (5 / 9), lambda x: x * 1.8 + 491.67),\\n (R, K, lambda x: x * (5 / 9), lambda x: x * 1.8),\\n ],\\n \\\"temperature\\\",\\n )\",\n \"def getTemperature(self):\\n return self.temperature\",\n \"def temperature(wair,pres,entr=None,temp=None,airf=None,dhum=None,\\n chkvals=False,chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,\\n chkbnd=False,mathargs=None):\\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\\n return temp\",\n \"def temp(self):\\n if self.temp_sensor is None:\\n return None\\n else:\\n if self.temp_scale.lower() in ['f', 'fahrenheit']:\\n return self.temp_sensor.temp_f\\n elif self.temp_scale.lower() in ['c', 'celsius']:\\n return self.temp_sensor.temp_c\",\n \"def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\\n self.digital_temp_data() \\n dT = self.tempadc-(C_5*(2**8))\\n temperature=(2000+(dT*(C_6/(2**23))))/100\\n return temperature, dT\",\n \"def target_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature)\",\n \"def get_cold_junction_temperature(self):\\n return self._mcp9600.get('COLD_JUNCTION').temperature\",\n \"def get_chiller_temperature(self) -> float:\\n\\n return self.send(self.cmd.GET_COOLING_ACT)\",\n \"def temperature_energy():\\n e = _si.e.value\\n k_B = _si.k_B.value\\n return Equivalency(\\n [(si.K, si.eV, lambda x: x / (e / k_B), lambda x: x * (e / k_B))],\\n \\\"temperature_energy\\\",\\n )\",\n \"def get_D_C3H8_air_eff(self, T):\\n\\n Kn = self.get_Kn(T)\\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\\n\\n if np.isscalar(Kn):\\n if Kn <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n else:\\n if Kn.any() <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n self.D_C3H8_air_eff = D_C3H8_air_eff\\n\\n return D_C3H8_air_eff\",\n \"def temperature(self, alt):\\n T = self.altitude_profile(alt)[1]\\n return T\",\n \"def target_temperature(self) -> int:\\r\\n # TODO: Find a better way to do this. This is ugly.\\r\\n if self._hvac_mode == \\\"cool\\\":\\r\\n return self.target_temperature_low\\r\\n elif self._hvac_mode == \\\"heat\\\":\\r\\n return self.target_temperature_high\\r\\n elif self._hvac_mode == \\\"heat-cool\\\":\\r\\n # TODO: Fix this so that heat or cool is chosen.\\r\\n if self._ambient_temperature >= self._target_temperature:\\r\\n return self.target_temperature_low\\r\\n elif self._ambient_temperature <= self._target_temperature:\\r\\n return self.target_temperature_high\\r\\n elif self._hvac_mode == \\\"eco\\\":\\r\\n if self._ambient_temperature >= self._target_temperature:\\r\\n return self.eco_temperature_low\\r\\n elif self._ambient_temperature <= self._target_temperature:\\r\\n return self.eco_temperature_high\\r\\n elif self._hvac_mode == \\\"off\\\":\\r\\n return self.ambient_temperature\\r\\n\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature\\\"))\",\n \"def get_D_C3H8_air(self, T):\\n\\n self.set_TempPres_dependents(T)\\n\\n self.D_C3H8_air = (\\n 2. / 3. * np.sqrt(const.k_B * T / np.pi * 0.5 * (1. /\\n self.air.m + 1. / self.fuel.m)) / (np.pi * (0.5 *\\n (self.air.d + self.fuel.d)) ** 2.) / self.air.n\\n )\\n\\n return self.D_C3H8_air\",\n \"def calculateTemperature(self):\\n \\n # CIE XYZ space\\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\\n\\n # CIE Chromaticities xy\\n self.x = self.X/(self.X + self.Y + self.Z)\\n self.y = self.Y/(self.X + self.Y + self.Z)\\n \\n # CIE Chromaticities uv\\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n \\n # constant for McCamy's/Hernandez-Andrés formula\\n n = (self.x - self.x_e)/(self.y - self.y_e)\\n \\n # Correlated color temperature according to Hernández-Andrés (1999)\\n self.color_temp = ( self.A_0 + \\n self.A_1*np.exp(-n/self.t_1) + \\n self.A_2*np.exp(-n/self.t_2) + \\n self.A_3*np.exp(-n/self.t_3) )\\n \\n # Delete too high values\\n self.color_temp[self.color_temp > 30000] = 0\\n \\n # Affichage de la CCT\\n self.mean_temp = int(round(self.color_temp.mean()))\\n self.mean_temp_label.setText(\\\"Temperature moyenne = \\\"+str(self.mean_temp))\\n self.mean_temp_label.adjustSize()\\n \\t\\n # Affichage de l'illuminance (Y)\\n self.mean_illu = int(round((self.Y.mean())))\\n self.illuminance_label.setText(\\\"Illuminance moyenne = \\\"+str(self.mean_illu))\\n self.illuminance_label.adjustSize()\",\n \"def ambient_temperature(self) -> int:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature\\\"))\\r\\n # TODO: Force this to return an int.\\r\\n if self.temperature_scale == \\\"F\\\":\\r\\n return self.ambient_temperature_f\\r\\n elif self.temperature_scale == \\\"C\\\":\\r\\n return self.ambient_temperature_c\\r\\n else:\\r\\n return self._ambient_temperature\",\n \"def read_ambient_temperatureF(self, ):\\n return self.read_ambient_temperatureC() * (9.0/5.0) + 32.0\",\n \"def internal_temp_c(self) -> int:\\n return int(self._device_info[\\\"Temperature\\\"])\",\n \"def soil_temp_factor(self, project_day):\\n tsoil = self.met_data['tsoil'][project_day]\\n\\n if float_gt(tsoil, 0.0):\\n tfac = (0.0326 + 0.00351 * tsoil**1.652 - (tsoil / 41.748)**7.19)\\n if float_lt(tfac, 0.0):\\n tfac = 0.0\\n else:\\n # negative number cannot be raised to a fractional power\\n # number would need to be complex\\n tfac = 0.0\\n\\n return tfac\",\n \"def temperature(self):\\n return self._temperature\",\n \"def temperature(self):\\n return self._temperature\",\n \"def target_temperature(self):\\n if self.current_operation == 'Heat & Cool':\\n return None\\n if self.current_operation == 'Heat only':\\n return int(self._api._heatto)\\n elif self.current_operation == 'Cool only':\\n return int(self._api._coolto)\\n return None\",\n \"def target_temperature(self) -> float | None:\\n if self.hvac_mode == HVACMode.COOL:\\n return self.target_temperature_high\\n if self.hvac_mode == HVACMode.HEAT:\\n return self.target_temperature_low\\n return None\",\n \"def wind_heat_transfer_coefficient(self) -> float:\\n\\n return 3.8 + 2 * self.wind_speed\\n # return 4.5 + 2.9 * self.wind_speed\",\n \"def heat_capacity_of_air(self) -> float:\\n\\n return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2\",\n \"def current_temperature(self):\\n return self.atag.dhw_temperature\",\n \"def get_compensated_temperature() -> float:\\n comp_factor = 2.25\\n cpu_temp = get_cpu_temperature()\\n raw_temp = bme280.get_temperature()\\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\\n # print(\\\"\\\"\\\"\\n # Compensated_Temperature: {:05.2f} *C\\n # Pressure: {:05.2f} hPa\\n # Relative humidity: {:05.2f} %\\n # \\\"\\\"\\\".format(temperature, pressure, humidity))\\n return comp_temp\",\n \"def get_temperature(self):\\n \\n # Get temp readings from both sensors\\n humidity_temp = self._sense_hat.get_temperature_from_humidity()\\n pressure_temp = self._sense_hat.get_temperature_from_pressure()\\n \\n # avg_temp becomes the average of the temperatures from both sensors\\n # We need to check for pressure_temp value is not 0, to not ruin avg_temp calculation\\n avg_temp = (humidity_temp + pressure_temp) / 2 if pressure_temp else humidity_temp\\n \\n # Get the CPU temperature\\n cpu_temp = self._get_cpu_temp()\\n \\n # Calculate temperature compensating for CPU heating\\n adj_temp = avg_temp - (cpu_temp - avg_temp) / 1.5\\n \\n # Average out value across the last three readings\\n return self._get_smooth(adj_temp)\",\n \"def thermal_state(self, beta: float = .1) -> numpy.ndarray:\\n rho = numpy.exp(-beta * self.cost)\\n return rho / numpy.sum(rho)\",\n \"def temperature(self):\\n return _cantera.reactor_temperature(self.__reactor_id)\",\n \"def target_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.target_temperature_c)\",\n \"def temperature(self):\\n temp = ct.c_float()\\n self.lib.GetTemperatureF(ct.pointer(temp))\\n return temp.value\",\n \"def density_of_air(self) -> float:\\n\\n return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature)\",\n \"def get_chip_temperature(self):\\n self.check_validity()\\n\\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_CHIP_TEMPERATURE, (), '', 10, 'h')\",\n \"def temperatures(self):\\r\\n return self._arm.temperatures\",\n \"def get_temp(self) -> float:\\n return np.round(np.mean(self.temp_data), 1)\",\n \"def eco_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_low)\",\n \"def tempWater(sample):\\n sample *= .0009\\n sample *= 1000\\n celsius = (sample - 20.5128) * 0.0512\\n return round(celsius,2)\",\n \"async def air_density(self, temperature, station_pressure):\\n if temperature is not None and station_pressure is not None:\\n kelvin = temperature + 273.15\\n pressure = station_pressure\\n r_specific = 287.058\\n decimals = 2\\n\\n air_dens = (pressure * 100) / (r_specific * kelvin)\\n\\n if self._unit_system == UNITS_IMPERIAL:\\n air_dens = air_dens * 0.06243\\n decimals = 4\\n\\n return round(air_dens, decimals)\\n\\n _LOGGER.error(\\\"FUNC: air_density ERROR: Temperature or Pressure value was reported as NoneType. Check the sensor\\\")\",\n \"def target_temperature(self):\\n if self._client.mode == self._client.MODE_HEAT:\\n return self._client.heattemp\\n if self._client.mode == self._client.MODE_COOL:\\n return self._client.cooltemp\\n return None\",\n \"def thermal_state(self, T):\\n return unvectorize(\\n np.diag(thermal_dist(t, self.ev)) \\\\\\n .astype(settings.DTYPE_COMPLEX)\\n for t in vectorize(T)\\n )\",\n \"def get_production_factor(self, temp_atmosphere):\\n return 1.\",\n \"def thermal_velocity(charge, temperature, mass):\\n return np.sqrt(2*abs(charge)*temperature/mass)\",\n \"def constant_temp(self, numIterations):\\n return 1 + self.alpha\",\n \"def target_temperature(self) -> float:\\n return self._thermostat.setpoint_temperature\",\n \"def eco_temperature_low_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_f\\\"))\\r\\n return celsius_to_fahrenheit(self.eco_temperature_low_c)\",\n \"def _therm_cond(self):\\n xy = dict() # used to label the components e.g 1->CO2,2->N2\\n for (i, j) in enumerate(self.component_list, 1):\\n xy[i] = j\\n\\n k_vap = 0\\n for i in range(1, len(self.component_list) + 1):\\n sumij = 0\\n for j in range(1, len(self.component_list) + 1):\\n Aij = (1 + (self.visc_d_comp[xy[i]] / self.visc_d_comp[xy[j]])**0.5 *\\n (self.mw_comp[xy[j]] / self.mw_comp[xy[i]])**0.25)**2 *\\\\\\n (8 * (1 + self.mw_comp[xy[i]] / self.mw_comp[xy[j]]))**-0.5\\n sumij += self.mole_frac_comp[xy[j]] * Aij\\n k_vap += self.mole_frac_comp[xy[i]] * self.therm_cond_comp[xy[i]] / sumij\\n\\n try:\\n self.therm_cond = Expression(expr=k_vap,\\n doc='Vapor thermal'\\n 'conductivity [J/(m.K.s)]')\\n except AttributeError:\\n self.del_component(self.therm_cond)\\n raise\",\n \"def dielectric_constant_water(temperature=298.15):\\n tabulated_data = np.array([[263.15, 92.10],\\n [268.15, 89.96],\\n [273.15, 87.90],\\n [278.15, 85.90],\\n [283.15, 83.96],\\n [288.15, 82.06],\\n [293.15, 80.20],\\n [298.15, 78.38],\\n [303.15, 76.60],\\n [308.15, 74.86],\\n [313.15, 73.17],\\n [318.15, 71.50],\\n [323.15, 69.88],\\n [328.15, 68.29],\\n [333.15, 66.74],\\n [338.15, 65.22],\\n [343.15, 63.73],\\n [348.15, 62.28],\\n [353.15, 60.87],\\n [358.15, 59.48],\\n [363.15, 58.13],\\n [368.15, 56.81],\\n [373.15, 55.51]])\\n polynomal_degree = 5\\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\\n polynomal_degree)\\n fitfunction = np.poly1d(fitdata)\\n return fitfunction(temperature)\",\n \"def current_temperature(self):\\n temperature = float('NaN')\\n while math.isnan(temperature) or temperature < MINIMUM_BELIEVABLE_TEMPERATURE:\\n temperature = float(self._sensor.readTempC())\\n return temperature\",\n \"def current_temperature(self) -> float:\\n return self._device.scaled_temperature\",\n \"def _temperature_policy(self, N: np.ndarray) -> np.ndarray:\\n t = 1 / self.temperature\\n pi = N ** t + np.finfo(np.float32).eps # Add tiny value to remove 0s\\n pi = pi / pi.sum()\\n return pi\",\n \"def get_actual_air_conditioned_temperature(\\n hc_period: np.ndarray,\\n theta_ac: np.ndarray, v_supply: np.ndarray, theta_supply_h: np.ndarray, theta_supply_c: np.ndarray,\\n l_d_h: np.ndarray, l_d_cs: np.ndarray,\\n u_prt: float, a_prt: np.ndarray, a_hcz: np.ndarray, q: float) -> np.ndarray:\\n\\n rho = get_air_density()\\n c = get_specific_heat()\\n\\n a_prt = a_prt.reshape(1, 5).T\\n a_hcz = a_hcz[0:5].reshape(1, 5).T\\n\\n theta_ac_act_h = np.maximum(theta_ac + (c * rho * v_supply * (theta_supply_h - theta_ac) - l_d_h * 10 ** 6)\\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\\n\\n theta_ac_act_c = np.minimum(theta_ac - (c * rho * v_supply * (theta_ac - theta_supply_c) - l_d_cs * 10 ** 6)\\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\\n\\n return theta_ac_act_h * (hc_period == 'h') + theta_ac_act_c * (hc_period == 'c') + theta_ac * (hc_period == 'm')\",\n \"def get_sky_ir_temperature(self) -> float:\\n self.serial.write(b\\\"S!\\\")\\n sky_ir_temp = self.__extract_int(self.__read_response(1)[0], b\\\"!1\\\")\\n\\n return round(sky_ir_temp / 100, 2)\",\n \"def get_attic_temperature(theta_sat: np.ndarray, theta_ac: np.ndarray) -> np.ndarray:\\n\\n # temperature difference coefficient\\n h = 1.0\\n\\n return theta_sat * h + theta_ac * (1 - h)\",\n \"def temperatures():\\n\\n return station_9281\",\n \"def volumetric_heat_capacity(temperature):\\n a = -2.4083\\n b = 7.6006\\n c = -8.2982\\n d = 7.3301\\n e = -4.2386\\n f = 1.4294\\n g = -0.24396\\n h = 0.015236\\n i = 0.0\\n log_t = math.log10(temperature)\\n f_exp = a + b*log_t + c*log_t**2.0 + d*log_t**3.0 + e*log_t**4.0 + f*log_t**5.0 + g*log_t**6.0 + \\\\\\n h*log_t**7.0 + i*log_t**8.0\\n g10_cp = 10.0**f_exp\\n return g10_cp * G10NISTMaterialProperties.density\",\n \"def eco_temperature_high_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_high)\",\n \"def temperature(self) -> Optional[float]:\\n return self.data.get(\\\"temp\\\")\",\n \"def conductivity(self):\\n m = 1.67296736e-02 # Determined from optimisation\\n c = 8.54665149e-05 # Determined from optimisation\\n return m * self.concentration + c\",\n \"def airfoilEffW(self):\\n return float(Importer(Component='Evaluations',\\n VariableName='Wing airfoil efficiency factor',\\n Default=.95,\\n Path=self.filePath).getValue)\",\n \"def read_object_temperatureF(self, ):\\n return self.read_object_temperatureC() * (9.0/5.0) + 32.0\",\n \"def fuel_cond(T):\\n\\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\\n \\n return kc\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def compute_td_spectral_function(self):\\n nomegase = self.nomegase\\n nkpt = self.nkpt\\n nband = self.nband\\n ntemp = self.ntemp\\n\\n self.spectral_function_T = np.zeros((nomegase, ntemp, nkpt, nband),\\n dtype=float)\\n\\n omega = np.einsum('ijt,l->ijlt',\\n np.ones((nkpt, nband, ntemp)), self.omegase)\\n\\n self.spectral_function_T = (\\n (1 / np.pi) * np.abs(self.self_energy_T.imag) /\\n ((omega - self.self_energy_T.real) ** 2\\n + self.self_energy_T.imag ** 2)\\n )\",\n \"def get_temperature(self):\\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. .*? .*? (.*?) .*? .*? . .*? .*? . . . .*?'\\n temperature = float(re.findall(pattern,summary).pop())\\n return temperature\",\n \"def water_vapour(t):\\n T_0 = 273.15\\n T_rw = 35.86 # over water\\n a = 17.269\\n # cdo -mulc,610.78 -exp -div -mulc,17.5 -subc,273.15 a\\n return 610.78 * np.exp(a * (t - T_0) / (t - T_rw))\",\n \"def get_convection_vent(self,T_i,el):\\n\\n rad = radiation.Radiation()\\n T_atm = rad.getTemp(el)\\n\\n Q_vent = self.mdot*self.Cp_air0*(T_i-T_atm) # Convection due to released air\\n return Q_vent\",\n \"def current_temperature(self) -> float | None:\\n return self.vera_device.get_current_temperature()\",\n \"def target_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature_low)\",\n \"def _calculate_temperature(c, h):\\n\\n return (c - 331.4 - 0.0124 * h) / 0.6\",\n \"def get_ir_sensor_temperature(self) -> float:\\n self.serial.write(b\\\"T!\\\")\\n ir_sensor_temp = self.__extract_int(self.__read_response(1)[0], b\\\"!2\\\")\\n\\n return round(ir_sensor_temp / 100, 2)\",\n \"def eco_temperature_high_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_f\\\"))\\r\\n return celsius_to_fahrenheit(self.eco_temperature_high_c)\",\n \"def current_temperature(self):\\n if self._device.temp is not None and self._device.temp > -460:\\n return self._device.temp\\n return None\",\n \"def temperature_unit(self):\\n return TEMP_FAHRENHEIT\"\n]","isConversational":false},"negative_scores":{"kind":"list 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temperature."},"document":{"kind":"string","value":"def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference suggests this equation is accurate to 1%.\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646","def thermal_conductivity(temperature):\n a0 = -4.1236\n a1 = 13.788\n a2 = -26.068\n a3 = 26.272\n a4 = -14.663\n a5 = 4.4954\n a6 = -0.6905\n a7 = 0.0397\n log_t = math.log10(temperature)\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\n g10_thermal_conductivity = 10.0**f_exp\n return g10_thermal_conductivity","def temperature() -> float:","def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\n k = get_kappa(K_medium, rho_medium, c_medium)\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial","def get_D_C3H8_air(self, T):\n\n self.set_TempPres_dependents(T)\n\n self.D_C3H8_air = (\n 2. / 3. * np.sqrt(const.k_B * T / np.pi * 0.5 * (1. /\n self.air.m + 1. / self.fuel.m)) / (np.pi * (0.5 *\n (self.air.d + self.fuel.d)) ** 2.) / self.air.n\n )\n\n return self.D_C3H8_air","def temperature(wair,pres,entr=None,temp=None,airf=None,dhum=None,\n chkvals=False,chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,\n chkbnd=False,mathargs=None):\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\n return temp","def get_D_C3H8_air_eff(self, T):\n\n Kn = self.get_Kn(T)\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\n\n if np.isscalar(Kn):\n if Kn <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n else:\n if Kn.any() <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n self.D_C3H8_air_eff = D_C3H8_air_eff\n\n return D_C3H8_air_eff","def helmholtzenergy(temp,pres):\n g = liq_g(0,0,temp,pres)\n g_p = liq_g(0,1,temp,pres)\n f = g - pres*g_p\n return f","def thermal(isatom, freq, scalfac,linnonlin,T):\n if isatom != \"true\":\n nfreq = len(freq)\n\n vib_temp = []\n for ifreq in range(nfreq):\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\n vib_temp.append(vib_temp_new)\n\n dE_vib = 0\n for ifreq in range(nfreq):\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\n\n dE_ZPE = 0.5 * sum(freq) * cmi2au\n\n if linnonlin == \"L\":\n dE_rot = kB * T * j2au\n elif linnonlin == \"NL\":\n dE_rot = kB * T * j2au * (3.0/2.0)\n else:\n with open(\"Thermochemistry.out\", \"a\") as ther_chem:\n ther_chem.write(\"ERROR: unknown entry for linear/nonlinear\")\n else:\n dE_ZPE = 0\n dE_vib = 0\n dE_rot = 0\n\n dE_tra = kB * T * j2au * (3.0/2.0)\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\n\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)","def thermalConductivity(self, Tk=None, Tc=None):\n Tk = getTk(Tc, Tk)\n self.checkPropertyTempRange(\"thermal conductivity\", Tk)\n thermalConductivity = (\n 2.13014e-08 * Tk**3\n - 6.31916e-05 * Tk**2\n + 1.11629e-01 * Tk\n - 2.00043e00\n )\n return thermalConductivity * 1e-3","def temperature(self):\n names = ['anc_air_temperature']\n return self.sensor.get_with_fallback('temperature', names)","def tempAir(sample):\n sample *= 1.0\n sample /= 1000\n celsius = (sample - 0.5) * 100\n return round(celsius,2)","def wind_heat_transfer_coefficient(self) -> float:\n\n return 3.8 + 2 * self.wind_speed\n # return 4.5 + 2.9 * self.wind_speed","def compute_surface_temperature(heat_flux):\n\n return 1.1e-4*heat_flux + 323","def sky_temperature(self) -> float:\n\n return 0.0552 * (self.ambient_temperature**1.5)","def temperature(self):\n return float(self._current_observation['temp_c'])","def dynamic_viscosity_of_air(self) -> float:\n\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\n self.ambient_temperature + 110.4\n )","def temperature_energy():\n e = _si.e.value\n k_B = _si.k_B.value\n return Equivalency(\n [(si.K, si.eV, lambda x: x / (e / k_B), lambda x: x * (e / k_B))],\n \"temperature_energy\",\n )","def heat_capacity_of_air(self) -> float:\n\n return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2","def thermal_i(mu,Ti):\n return 9.79*1.e5/np.sqrt(mu/Ti)/1.e2","def eco_temperature_low_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_f\"))\r\n return celsius_to_fahrenheit(self.eco_temperature_low_c)","def constant_temp(self, numIterations):\n return 1 + self.alpha","def latent_heat_vapourisation(self, tair):\n return (2.501 - 0.00237 * tair) * 1E06","def eco_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_low)","def ambient_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_c\"))\r\n return kelvin_to_celsius(self._ambient_temperature)","def energy_func(self):\n i = self.inl[0].to_flow()\n o = self.outl[0].to_flow()\n\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\n\n return (i[0] * (o[2] - i[2]) -\n self.A.val * (\n self.E.val * self.eta_opt.val -\n (T_m - self.Tamb.val_SI) * self.lkf_lin.val -\n self.lkf_quad.val * (T_m - self.Tamb.val_SI) ** 2))","def internalenergy(temp,pres):\n g = liq_g(0,0,temp,pres)\n g_t = liq_g(1,0,temp,pres)\n g_p = liq_g(0,1,temp,pres)\n u = g - temp*g_t - pres*g_p\n return u","def dielectric_constant_water(temperature=298.15):\n tabulated_data = np.array([[263.15, 92.10],\n [268.15, 89.96],\n [273.15, 87.90],\n [278.15, 85.90],\n [283.15, 83.96],\n [288.15, 82.06],\n [293.15, 80.20],\n [298.15, 78.38],\n [303.15, 76.60],\n [308.15, 74.86],\n [313.15, 73.17],\n [318.15, 71.50],\n [323.15, 69.88],\n [328.15, 68.29],\n [333.15, 66.74],\n [338.15, 65.22],\n [343.15, 63.73],\n [348.15, 62.28],\n [353.15, 60.87],\n [358.15, 59.48],\n [363.15, 58.13],\n [368.15, 56.81],\n [373.15, 55.51]])\n polynomal_degree = 5\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\n polynomal_degree)\n fitfunction = np.poly1d(fitdata)\n return fitfunction(temperature)","def get_actual_air_conditioned_temperature(\n hc_period: np.ndarray,\n theta_ac: np.ndarray, v_supply: np.ndarray, theta_supply_h: np.ndarray, theta_supply_c: np.ndarray,\n l_d_h: np.ndarray, l_d_cs: np.ndarray,\n u_prt: float, a_prt: np.ndarray, a_hcz: np.ndarray, q: float) -> np.ndarray:\n\n rho = get_air_density()\n c = get_specific_heat()\n\n a_prt = a_prt.reshape(1, 5).T\n a_hcz = a_hcz[0:5].reshape(1, 5).T\n\n theta_ac_act_h = np.maximum(theta_ac + (c * rho * v_supply * (theta_supply_h - theta_ac) - l_d_h * 10 ** 6)\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\n\n theta_ac_act_c = np.minimum(theta_ac - (c * rho * v_supply * (theta_ac - theta_supply_c) - l_d_cs * 10 ** 6)\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\n\n return theta_ac_act_h * (hc_period == 'h') + theta_ac_act_c * (hc_period == 'c') + theta_ac * (hc_period == 'm')","def energy_func(self):\n i = self.inl[0].to_flow()\n o = self.outl[0].to_flow()\n\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\n\n iam = (\n 1 - self.iam_1.val * abs(self.aoi.val) -\n self.iam_2.val * self.aoi.val ** 2)\n\n return (i[0] * (o[2] - i[2]) -\n self.A.val * (\n self.E.val * self.eta_opt.val * self.doc.val ** 1.5 * iam -\n (T_m - self.Tamb.val_SI) * self.c_1.val -\n self.c_2.val * (T_m - self.Tamb.val_SI) ** 2))","def get_mfp(self, T):\n\n self.air.T = T\n self.air.set_TempPres_dependents()\n\n self.mfp = (\n (np.sqrt(2.) * np.pi * self.air.d ** 2. * self.air.n) ** -1.\n )\n\n return self.mfp","def airfoilEffT(self):\n return float(Importer(Component='Evaluations',\n VariableName='Wing airfoil efficiency factor',\n Default=.95,\n Path=self.filePath).getValue)","def temperature(self):\n return self.read_short(65) / 340.0 + 36.53","def temperature(self):\r\n self._read_temperature()\r\n return self._t_fine / 5120.0","def get_compensated_temperature() -> float:\n comp_factor = 2.25\n cpu_temp = get_cpu_temperature()\n raw_temp = bme280.get_temperature()\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\n # print(\"\"\"\n # Compensated_Temperature: {:05.2f} *C\n # Pressure: {:05.2f} hPa\n # Relative humidity: {:05.2f} %\n # \"\"\".format(temperature, pressure, humidity))\n return comp_temp","def target_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_c\"))\r\n return kelvin_to_celsius(self._target_temperature)","def Fermi(En,T):\n ev = 1.60218e-19\n kb = 1.380e-23\n return 1/(1+np.exp(En/(kb*T/ev)))","def air_density(self):\n return self.flow_field.air_density","def temperature() -> FlowFieldVal:\n return [\n self._t_s - self._delta_t * tf.math.tanh(z / self._height) for z in zz\n ]","def heat(self, delta_temp):\n return self.heat_capacity * self.mass * delta_temp","def get_production_factor(self, temp_atmosphere):\n a1 = self.damages_terms[0]\n a2 = self.damages_terms[1]\n a3 = self.damages_terms[2]\n pf = self.params.prod_frac\n return ne.evaluate('1 - pf * (1 - 1 / (1 + a1 * temp_atmosphere + a2 * temp_atmosphere ** a3))')","def calculateTemperature(self):\n \n # CIE XYZ space\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\n\n # CIE Chromaticities xy\n self.x = self.X/(self.X + self.Y + self.Z)\n self.y = self.Y/(self.X + self.Y + self.Z)\n \n # CIE Chromaticities uv\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\n \n # constant for McCamy's/Hernandez-Andrés formula\n n = (self.x - self.x_e)/(self.y - self.y_e)\n \n # Correlated color temperature according to Hernández-Andrés (1999)\n self.color_temp = ( self.A_0 + \n self.A_1*np.exp(-n/self.t_1) + \n self.A_2*np.exp(-n/self.t_2) + \n self.A_3*np.exp(-n/self.t_3) )\n \n # Delete too high values\n self.color_temp[self.color_temp > 30000] = 0\n \n # Affichage de la CCT\n self.mean_temp = int(round(self.color_temp.mean()))\n self.mean_temp_label.setText(\"Temperature moyenne = \"+str(self.mean_temp))\n self.mean_temp_label.adjustSize()\n \t\n # Affichage de l'illuminance (Y)\n self.mean_illu = int(round((self.Y.mean())))\n self.illuminance_label.setText(\"Illuminance moyenne = \"+str(self.mean_illu))\n self.illuminance_label.adjustSize()","def energy_function(self, x):\n \n return -T.dot(T.transpose(x), T.dot(self.W, x)) -\\\n T.dot(T.transpose(self.b), x)","def fuel_cond(T):\n\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\n \n return kc","def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\n self.digital_temp_data() \n dT = self.tempadc-(C_5*(2**8))\n temperature=(2000+(dT*(C_6/(2**23))))/100\n return temperature, dT","def ambient_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_f\"))\r\n return celsius_to_fahrenheit(self.ambient_temperature_c)","def get_cold_junction_temperature(self):\n return self._mcp9600.get('COLD_JUNCTION').temperature","def test_getThermalExpansionFactorConservedMassByLinearExpansionPercent(self):\n hotTemp = 700.0\n dLL = self.component.material.linearExpansionFactor(\n Tc=hotTemp, T0=self._coldTemp\n )\n ref = 1.0 + dLL\n cur = self.component.getThermalExpansionFactor(Tc=hotTemp)\n self.assertAlmostEqual(cur, ref)","def volumetric_heat_capacity(temperature):\n a = -2.4083\n b = 7.6006\n c = -8.2982\n d = 7.3301\n e = -4.2386\n f = 1.4294\n g = -0.24396\n h = 0.015236\n i = 0.0\n log_t = math.log10(temperature)\n f_exp = a + b*log_t + c*log_t**2.0 + d*log_t**3.0 + e*log_t**4.0 + f*log_t**5.0 + g*log_t**6.0 + \\\n h*log_t**7.0 + i*log_t**8.0\n g10_cp = 10.0**f_exp\n return g10_cp * G10NISTMaterialProperties.density","def P(self, energy, newEnergy, temperature):\n \"\"\" This is the decision-rule, adapted from Nascimento, et al., 2009 (See references) \"\"\"\n \n delta = self.calcDelta(newEnergy, energy)\n\n minTemp = 0.00001 # use minimum to avoid div/0 and buffer overflow\n if temperature == 0:\n return minTemp\n elif temperature > minTemp:\n try:\n return math.exp(-1 * round(delta, 4) / round(temperature, 4))\n except OverflowError as detail:\n return minTemp\n else:\n return 1","def target_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_f\"))\r\n return celsius_to_fahrenheit(self.target_temperature_c)","def temperature():\n from .imperial import deg_F as F\n from .imperial import deg_R as R\n\n K = si.K\n C = si.deg_C\n\n return Equivalency(\n [\n (K, C, lambda x: x - 273.15, lambda x: x + 273.15),\n (C, F, lambda x: x * 1.8 + 32.0, lambda x: (x - 32.0) / 1.8),\n (K, F, lambda x: x * 1.8 - 459.67, lambda x: (x + 459.67) / 1.8),\n (R, F, lambda x: x - 459.67, lambda x: x + 459.67),\n (R, C, lambda x: (x - 491.67) * (5 / 9), lambda x: x * 1.8 + 491.67),\n (R, K, lambda x: x * (5 / 9), lambda x: x * 1.8),\n ],\n \"temperature\",\n )","def cooled_surface_temp(T:np.ndarray) -> float:\n \n return T.dot(cs_temp_weights)","def target_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_low_c\"))\r\n return kelvin_to_celsius(self._target_temperature_low)","def env_temperature(v3: \"float\", v4: \"float\") -> \"float\":","def farenheit(ctemp):\n return round(9.0/5.0 * ctemp + 32)","def soil_temp_factor(self, project_day):\n tsoil = self.met_data['tsoil'][project_day]\n\n if float_gt(tsoil, 0.0):\n tfac = (0.0326 + 0.00351 * tsoil**1.652 - (tsoil / 41.748)**7.19)\n if float_lt(tfac, 0.0):\n tfac = 0.0\n else:\n # negative number cannot be raised to a fractional power\n # number would need to be complex\n tfac = 0.0\n\n return tfac","def temperature(k, kmax):\n return 1.0 / 500 * (1.0 / k - 1.0 / kmax)","def _calculate_temperature(c, h):\n\n return (c - 331.4 - 0.0124 * h) / 0.6","def target_temperature_low_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_low_f\"))\r\n return celsius_to_fahrenheit(self.target_temperature_low_c)","def get_evaporation_latent_heat() -> float:\n theta = 28.0\n return 2500.8 - 2.3668 * theta","def temperature(self, alt):\n T = self.altitude_profile(alt)[1]\n return T","def liqtemperature(pres):\n psi = pres/_PTPE - 1\n temp = 0.\n for coeff in _C_TMELT[::-1]:\n temp = temp*psi + coeff\n temp = (1 + temp*psi)*_TTP\n return temp","def temperature(altitude):\n if altitude <= 36152:\n t = 59-0.00356*altitude # deg F\n else:\n t = -70 # deg F\n t = t + 459.7 # R\n return t","def get_production_factor(self, temp_atmosphere):\n return 1.","def linear_heat_transfer(x, t, K_medium, rho_medium, c_medium, T_medium_initial, H_heat_transfer, T_external_applied):\n k = get_kappa(K_medium, rho_medium, c_medium)\n\n h = H_heat_transfer/K_medium\n erfc_factor_1 = erfc(x/(2*np.sqrt(k*t)))\n\n #combine factors in logdomain, since the exp-factors quickly approach\n #infinity while erfc-factor goes to zero\n log_exp_factor_1 = h*x\n log_exp_factor_2 = k*t*h**2\n log_erfc_factor_2 = np.log(erfc(x/(2*np.sqrt(k*t)) + h*np.sqrt(k*t)))\n exp_erfc_factor = np.exp(log_exp_factor_1 + log_exp_factor_2 + log_erfc_factor_2)\n\n return (erfc_factor_1 - exp_erfc_factor)*(T_external_applied - T_medium_initial) + T_medium_initial","def eco_temperature_high_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_high)","def exponential(min_iterations, i, start = start_temp, final = final_temp):\n\n\ttemperature = (start * (final / start) ** (i / min_iterations))\n\n\treturn temperature","def __t_fine__(self, adc_temperature):\n var1 = (((adc_temperature >> 3) -\n (self._calibration_t[0] << 1)) * self._calibration_t[1]) >> 11\n var2 = (((\n ((adc_temperature >> 4) - self._calibration_t[0]) *\n ((adc_temperature >> 4) - self._calibration_t[0])) >> 12)\n * self._calibration_t[2]) >> 14\n return var1 + var2","def gas_zfactor(T_pr, P_pr):\n # T_pr : calculated pseudoreduced temperature\n # P_pr : calculated pseudoreduced pressure \n from scipy.optimize import fsolve # non-linear solver\n import numpy as np\n\n a1 = 0.3265; a2 = -1.0700; a3 = -0.5339; a4 = 0.01569; a5 = -0.05165; a6 = 0.5475\n a7 = -0.7361; a8 = 0.1844; a9 = 0.1056; a10 = 0.6134; a11 = 0.7210\n\n def f(y):\n rho_pr, z = y\n c1 = a1 + (a2/T_pr) + (a3/(T_pr**3))+ (a4/(T_pr**4))+ (a5/(T_pr**5))\n c2 = a6 + (a7/T_pr) + (a8/(T_pr**2))\n c3 = a9*((a7/T_pr) + (a8/(T_pr**2)))\n c4 = (a10)*(1+(a11*(rho_pr**2)))*((rho_pr**2)/(T_pr**3))*(np.exp(-a11*(rho_pr**2)))\n\n f1 = z + (c3*(rho_pr**5)) - (c2*(rho_pr**2)) - (c1*(rho_pr**1)) - c4 - 1\n f2 = rho_pr - ((0.27 * P_pr) / (z * T_pr))\n return[f1, f2]\n\n solve = fsolve(f, [1, 1]) # initial guess\n return(solve[0], solve[1]) # result is density, z-factor","def fahrenheit(T_in_celsius):\n return (T_in_celsius * 9 / 5) + 32","def expansion(temp,pres):\n g_p = liq_g(0,1,temp,pres)\n g_tp = liq_g(1,1,temp,pres)\n alpha = g_tp / g_p\n return alpha","def water_vapour(t):\n T_0 = 273.15\n T_rw = 35.86 # over water\n a = 17.269\n # cdo -mulc,610.78 -exp -div -mulc,17.5 -subc,273.15 a\n return 610.78 * np.exp(a * (t - T_0) / (t - T_rw))","def thermal_velocity(charge, temperature, mass):\n return np.sqrt(2*abs(charge)*temperature/mass)","def TM_fluid(layer, kx, om):\n\n h = layer.d\n rho = layer.medium.rho\n K = layer.medium.K\n k = om*np.sqrt(rho/K)\n ky = np.sqrt(k**2-kx**2)\n T = np.zeros((2, 2), dtype=complex)\n T[0, 0] = np.cos(ky*h)\n T[1, 0] = (om**2*rho/ky)*np.sin(ky*h)\n T[0, 1] = -(ky/(om**2*rho))*np.sin(ky*h)\n T[1, 1] = np.cos(ky*h)\n return T","def eco_temperature_high_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_f\"))\r\n return celsius_to_fahrenheit(self.eco_temperature_high_c)","def density_of_air(self) -> float:\n\n return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature)","def calcTempInEV(expFitCoeffWithVoltUnits):\n\t\t# constants\n\t\teV=1.60218e-19;\n\t\tq=1.6e-19\n\t\t\n\t\t# temperature in eV\n\t\treturn q*expFitCoeffWithVoltUnits/eV","def temperature(self) -> float:\n # Start a measurement then poll the measurement finished bit.\n self.temp_start = 1\n while self.temp_running > 0:\n pass\n # Grab the temperature value and convert it to Celsius.\n # This uses the same observed value formula from the Radiohead library.\n temp = self._read_u8(_REG_TEMP2)\n return 166.0 - temp","def temperature(self):\n value = float(self._parent.query('R{}'.format(self._idx))[1:])\n return pq.Quantity(value, pq.Kelvin)","def formation_temperature(surface_temperature, gradient, depth):\n form_temp = surface_temperature + gradient * depth\n return form_temp","def thermal_state(self, beta: float = .1) -> numpy.ndarray:\n rho = numpy.exp(-beta * self.cost)\n return rho / numpy.sum(rho)","def get_attic_temperature(theta_sat: np.ndarray, theta_ac: np.ndarray) -> np.ndarray:\n\n # temperature difference coefficient\n h = 1.0\n\n return theta_sat * h + theta_ac * (1 - h)","def temperature(self):\n return _cantera.reactor_temperature(self.__reactor_id)","def temperature(self):\n temp = ct.c_float()\n self.lib.GetTemperatureF(ct.pointer(temp))\n return temp.value","def temperature_from_potential_temperature(pressure, potential_temperature, reference_pressure=P0):\n return potential_temperature * exner_function(pressure, reference_pressure=reference_pressure)","def tempWater(sample):\n sample *= .0009\n sample *= 1000\n celsius = (sample - 20.5128) * 0.0512\n return round(celsius,2)","def read_ambient_temperatureF(self, ):\n return self.read_ambient_temperatureC() * (9.0/5.0) + 32.0","def _temperature(self, p_input:float) -> float:\n if self._unit_in == 'R':\n temp_K = p_input*5.0/9.0\n elif self._unit_in == 'F':\n temp_K = (p_input+459.67)/9.0*5.0\n elif self._unit_in == 'C':\n temp_K = p_input+273.15\n elif self._unit_in == 'K':\n temp_K = p_input\n \n if self._unit_out == 'R':\n return (temp_K*9.0/5.0)\n elif self._unit_out == 'F':\n return (temp_K*9.0/5.0-459.67) \n elif self._unit_out == 'C':\n return (temp_K-273.15)\n elif self._unit_out == 'K':\n return temp_K","def update_temperature(self):\n self.iteration += 1 \n self.T = self.T0 * 0.9935**self.iteration","def getTemperature(self):\n return self.temperature","def heat_flux_out(T_inf, T_old, hc_air, emmi):\n\n #nz = T_old.shape[0]\n ny = T_old.shape[0]\n nx = T_old.shape[1]\n\n Q_out = np.zeros((ny, nx))\n h_eff = np.zeros((ny, nx))\n T_eff = np.zeros((ny, nx))\n for i in range(nx):\n for j in range(ny):\n T_eff[j, i] = ((T_old[j, i]**3) + (T_inf * T_old[j, i]**2)\n + (T_old[j, i] * T_inf**2) + T_inf**3)\n\n h_eff[j, i] = hc_air + (emmi*STEF_BOL_C*T_eff[j, i])\n\n Q_out[j, i] = h_eff[j, i] * (T_old[j, i] - T_inf)\n\n return Q_out","def read_object_temperatureF(self, ):\n return self.read_object_temperatureC() * (9.0/5.0) + 32.0","def temp(self):\n if self.temp_sensor is None:\n return None\n else:\n if self.temp_scale.lower() in ['f', 'fahrenheit']:\n return self.temp_sensor.temp_f\n elif self.temp_scale.lower() in ['c', 'celsius']:\n return self.temp_sensor.temp_c","def get_temperature(self):\n pass","def ionization_constant_water(temperature=298.15, density=None):\n import numpy as np\n\n # using Model II from Bandura etal\n # model parameters\n n = 6\n alpha_0 = -0.864671\n alpha_1 = 8659.19\n alpha_2 = -22786.2\n beta_0 = 0.642044\n beta_1 = -56.8534\n beta_2 = -0.375754\n\n # Water parameters\n Mw = 18.01528\n\n # temperature\n T = temperature\n\n # density\n if density:\n D = density\n else:\n D = density_water(T)\n\n pKWG = 0.61415 \\\n + 48251.33 / T \\\n - 67707.93 / T**2.0 \\\n + 10102100.0 / T**3.0\n\n Z = D * np.exp(alpha_0 \\\n + alpha_1/T \\\n + alpha_2/T**2 *np.power(D,2.0/3.0)\n )\n\n pKw = -2*n*(\n np.log10(1 + Z) - (Z/(Z + 1)) * D * (\n beta_0 + beta_1/T + beta_2*D\n )\n ) + pKWG + 2 * np.log10(Mw/1000.0)\n\n return np.power(10, -pKw)","def calculate_celerity(period, depth, gravity):\r\n return geometry.gmCalculateCelerity(period, depth, gravity)","def _temperature_policy(self, N: np.ndarray) -> np.ndarray:\n t = 1 / self.temperature\n pi = N ** t + np.finfo(np.float32).eps # Add tiny value to remove 0s\n pi = pi / pi.sum()\n return pi","def effective_temperature(R,times):\n T_eff = np.empty((len(R),times))\n #T_c = ((3 * G * M_body) / (8 * np.pi * sigma)) #constants for a blackbody \n for i in range (len(R)):\n for t in range(times): \n T_eff[i][t] = ((m_dot[i][t])/(R[i]**3.0))**(1.0/4.0)\n return T_eff","def calc_supply_temp(tr, Q, m, cp, case):\n if m > 0:\n if case == \"DH\":\n ts = tr + Q / (m * cp)\n else:\n ts = tr - Q / (m * cp)\n else:\n ts = 0\n return ts"],"string":"[\n \"def thermal_conductivity_of_air(self) -> float:\\n\\n # This more accurate equation is not used by the paper.\\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\\n # )\\n\\n # The reference suggests this equation is accurate to 1%.\\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646\",\n \"def thermal_conductivity(temperature):\\n a0 = -4.1236\\n a1 = 13.788\\n a2 = -26.068\\n a3 = 26.272\\n a4 = -14.663\\n a5 = 4.4954\\n a6 = -0.6905\\n a7 = 0.0397\\n log_t = math.log10(temperature)\\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\\\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\\n g10_thermal_conductivity = 10.0**f_exp\\n return g10_thermal_conductivity\",\n \"def temperature() -> float:\",\n \"def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\\n k = get_kappa(K_medium, rho_medium, c_medium)\\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial\",\n \"def get_D_C3H8_air(self, T):\\n\\n self.set_TempPres_dependents(T)\\n\\n self.D_C3H8_air = (\\n 2. / 3. * np.sqrt(const.k_B * T / np.pi * 0.5 * (1. /\\n self.air.m + 1. / self.fuel.m)) / (np.pi * (0.5 *\\n (self.air.d + self.fuel.d)) ** 2.) / self.air.n\\n )\\n\\n return self.D_C3H8_air\",\n \"def temperature(wair,pres,entr=None,temp=None,airf=None,dhum=None,\\n chkvals=False,chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,\\n chkbnd=False,mathargs=None):\\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\\n return temp\",\n \"def get_D_C3H8_air_eff(self, T):\\n\\n Kn = self.get_Kn(T)\\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\\n\\n if np.isscalar(Kn):\\n if Kn <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n else:\\n if Kn.any() <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n self.D_C3H8_air_eff = D_C3H8_air_eff\\n\\n return D_C3H8_air_eff\",\n \"def helmholtzenergy(temp,pres):\\n g = liq_g(0,0,temp,pres)\\n g_p = liq_g(0,1,temp,pres)\\n f = g - pres*g_p\\n return f\",\n \"def thermal(isatom, freq, scalfac,linnonlin,T):\\n if isatom != \\\"true\\\":\\n nfreq = len(freq)\\n\\n vib_temp = []\\n for ifreq in range(nfreq):\\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\\n vib_temp.append(vib_temp_new)\\n\\n dE_vib = 0\\n for ifreq in range(nfreq):\\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\\n\\n dE_ZPE = 0.5 * sum(freq) * cmi2au\\n\\n if linnonlin == \\\"L\\\":\\n dE_rot = kB * T * j2au\\n elif linnonlin == \\\"NL\\\":\\n dE_rot = kB * T * j2au * (3.0/2.0)\\n else:\\n with open(\\\"Thermochemistry.out\\\", \\\"a\\\") as ther_chem:\\n ther_chem.write(\\\"ERROR: unknown entry for linear/nonlinear\\\")\\n else:\\n dE_ZPE = 0\\n dE_vib = 0\\n dE_rot = 0\\n\\n dE_tra = kB * T * j2au * (3.0/2.0)\\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\\n\\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)\",\n \"def thermalConductivity(self, Tk=None, Tc=None):\\n Tk = getTk(Tc, Tk)\\n self.checkPropertyTempRange(\\\"thermal conductivity\\\", Tk)\\n thermalConductivity = (\\n 2.13014e-08 * Tk**3\\n - 6.31916e-05 * Tk**2\\n + 1.11629e-01 * Tk\\n - 2.00043e00\\n )\\n return thermalConductivity * 1e-3\",\n \"def temperature(self):\\n names = ['anc_air_temperature']\\n return self.sensor.get_with_fallback('temperature', names)\",\n \"def tempAir(sample):\\n sample *= 1.0\\n sample /= 1000\\n celsius = (sample - 0.5) * 100\\n return round(celsius,2)\",\n \"def wind_heat_transfer_coefficient(self) -> float:\\n\\n return 3.8 + 2 * self.wind_speed\\n # return 4.5 + 2.9 * self.wind_speed\",\n \"def compute_surface_temperature(heat_flux):\\n\\n return 1.1e-4*heat_flux + 323\",\n \"def sky_temperature(self) -> float:\\n\\n return 0.0552 * (self.ambient_temperature**1.5)\",\n \"def temperature(self):\\n return float(self._current_observation['temp_c'])\",\n \"def dynamic_viscosity_of_air(self) -> float:\\n\\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\\n self.ambient_temperature + 110.4\\n )\",\n \"def temperature_energy():\\n e = _si.e.value\\n k_B = _si.k_B.value\\n return Equivalency(\\n [(si.K, si.eV, lambda x: x / (e / k_B), lambda x: x * (e / k_B))],\\n \\\"temperature_energy\\\",\\n )\",\n \"def heat_capacity_of_air(self) -> float:\\n\\n return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2\",\n \"def thermal_i(mu,Ti):\\n return 9.79*1.e5/np.sqrt(mu/Ti)/1.e2\",\n \"def eco_temperature_low_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_f\\\"))\\r\\n return celsius_to_fahrenheit(self.eco_temperature_low_c)\",\n \"def constant_temp(self, numIterations):\\n return 1 + self.alpha\",\n \"def latent_heat_vapourisation(self, tair):\\n return (2.501 - 0.00237 * tair) * 1E06\",\n \"def eco_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_low)\",\n \"def ambient_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._ambient_temperature)\",\n \"def energy_func(self):\\n i = self.inl[0].to_flow()\\n o = self.outl[0].to_flow()\\n\\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\\n\\n return (i[0] * (o[2] - i[2]) -\\n self.A.val * (\\n self.E.val * self.eta_opt.val -\\n (T_m - self.Tamb.val_SI) * self.lkf_lin.val -\\n self.lkf_quad.val * (T_m - self.Tamb.val_SI) ** 2))\",\n \"def internalenergy(temp,pres):\\n g = liq_g(0,0,temp,pres)\\n g_t = liq_g(1,0,temp,pres)\\n g_p = liq_g(0,1,temp,pres)\\n u = g - temp*g_t - pres*g_p\\n return u\",\n \"def dielectric_constant_water(temperature=298.15):\\n tabulated_data = np.array([[263.15, 92.10],\\n [268.15, 89.96],\\n [273.15, 87.90],\\n [278.15, 85.90],\\n [283.15, 83.96],\\n [288.15, 82.06],\\n [293.15, 80.20],\\n [298.15, 78.38],\\n [303.15, 76.60],\\n [308.15, 74.86],\\n [313.15, 73.17],\\n [318.15, 71.50],\\n [323.15, 69.88],\\n [328.15, 68.29],\\n [333.15, 66.74],\\n [338.15, 65.22],\\n [343.15, 63.73],\\n [348.15, 62.28],\\n [353.15, 60.87],\\n [358.15, 59.48],\\n [363.15, 58.13],\\n [368.15, 56.81],\\n [373.15, 55.51]])\\n polynomal_degree = 5\\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\\n polynomal_degree)\\n fitfunction = np.poly1d(fitdata)\\n return fitfunction(temperature)\",\n \"def get_actual_air_conditioned_temperature(\\n hc_period: np.ndarray,\\n theta_ac: np.ndarray, v_supply: np.ndarray, theta_supply_h: np.ndarray, theta_supply_c: np.ndarray,\\n l_d_h: np.ndarray, l_d_cs: np.ndarray,\\n u_prt: float, a_prt: np.ndarray, a_hcz: np.ndarray, q: float) -> np.ndarray:\\n\\n rho = get_air_density()\\n c = get_specific_heat()\\n\\n a_prt = a_prt.reshape(1, 5).T\\n a_hcz = a_hcz[0:5].reshape(1, 5).T\\n\\n theta_ac_act_h = np.maximum(theta_ac + (c * rho * v_supply * (theta_supply_h - theta_ac) - l_d_h * 10 ** 6)\\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\\n\\n theta_ac_act_c = np.minimum(theta_ac - (c * rho * v_supply * (theta_ac - theta_supply_c) - l_d_cs * 10 ** 6)\\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\\n\\n return theta_ac_act_h * (hc_period == 'h') + theta_ac_act_c * (hc_period == 'c') + theta_ac * (hc_period == 'm')\",\n \"def energy_func(self):\\n i = self.inl[0].to_flow()\\n o = self.outl[0].to_flow()\\n\\n T_m = (T_mix_ph(i, T0=self.inl[0].T.val_SI) +\\n T_mix_ph(o, T0=self.outl[0].T.val_SI)) / 2\\n\\n iam = (\\n 1 - self.iam_1.val * abs(self.aoi.val) -\\n self.iam_2.val * self.aoi.val ** 2)\\n\\n return (i[0] * (o[2] - i[2]) -\\n self.A.val * (\\n self.E.val * self.eta_opt.val * self.doc.val ** 1.5 * iam -\\n (T_m - self.Tamb.val_SI) * self.c_1.val -\\n self.c_2.val * (T_m - self.Tamb.val_SI) ** 2))\",\n \"def get_mfp(self, T):\\n\\n self.air.T = T\\n self.air.set_TempPres_dependents()\\n\\n self.mfp = (\\n (np.sqrt(2.) * np.pi * self.air.d ** 2. * self.air.n) ** -1.\\n )\\n\\n return self.mfp\",\n \"def airfoilEffT(self):\\n return float(Importer(Component='Evaluations',\\n VariableName='Wing airfoil efficiency factor',\\n Default=.95,\\n Path=self.filePath).getValue)\",\n \"def temperature(self):\\n return self.read_short(65) / 340.0 + 36.53\",\n \"def temperature(self):\\r\\n self._read_temperature()\\r\\n return self._t_fine / 5120.0\",\n \"def get_compensated_temperature() -> float:\\n comp_factor = 2.25\\n cpu_temp = get_cpu_temperature()\\n raw_temp = bme280.get_temperature()\\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\\n # print(\\\"\\\"\\\"\\n # Compensated_Temperature: {:05.2f} *C\\n # Pressure: {:05.2f} hPa\\n # Relative humidity: {:05.2f} %\\n # \\\"\\\"\\\".format(temperature, pressure, humidity))\\n return comp_temp\",\n \"def target_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature)\",\n \"def Fermi(En,T):\\n ev = 1.60218e-19\\n kb = 1.380e-23\\n return 1/(1+np.exp(En/(kb*T/ev)))\",\n \"def air_density(self):\\n return self.flow_field.air_density\",\n \"def temperature() -> FlowFieldVal:\\n return [\\n self._t_s - self._delta_t * tf.math.tanh(z / self._height) for z in zz\\n ]\",\n \"def heat(self, delta_temp):\\n return self.heat_capacity * self.mass * delta_temp\",\n \"def get_production_factor(self, temp_atmosphere):\\n a1 = self.damages_terms[0]\\n a2 = self.damages_terms[1]\\n a3 = self.damages_terms[2]\\n pf = self.params.prod_frac\\n return ne.evaluate('1 - pf * (1 - 1 / (1 + a1 * temp_atmosphere + a2 * temp_atmosphere ** a3))')\",\n \"def calculateTemperature(self):\\n \\n # CIE XYZ space\\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\\n\\n # CIE Chromaticities xy\\n self.x = self.X/(self.X + self.Y + self.Z)\\n self.y = self.Y/(self.X + self.Y + self.Z)\\n \\n # CIE Chromaticities uv\\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n \\n # constant for McCamy's/Hernandez-Andrés formula\\n n = (self.x - self.x_e)/(self.y - self.y_e)\\n \\n # Correlated color temperature according to Hernández-Andrés (1999)\\n self.color_temp = ( self.A_0 + \\n self.A_1*np.exp(-n/self.t_1) + \\n self.A_2*np.exp(-n/self.t_2) + \\n self.A_3*np.exp(-n/self.t_3) )\\n \\n # Delete too high values\\n self.color_temp[self.color_temp > 30000] = 0\\n \\n # Affichage de la CCT\\n self.mean_temp = int(round(self.color_temp.mean()))\\n self.mean_temp_label.setText(\\\"Temperature moyenne = \\\"+str(self.mean_temp))\\n self.mean_temp_label.adjustSize()\\n \\t\\n # Affichage de l'illuminance (Y)\\n self.mean_illu = int(round((self.Y.mean())))\\n self.illuminance_label.setText(\\\"Illuminance moyenne = \\\"+str(self.mean_illu))\\n self.illuminance_label.adjustSize()\",\n \"def energy_function(self, x):\\n \\n return -T.dot(T.transpose(x), T.dot(self.W, x)) -\\\\\\n T.dot(T.transpose(self.b), x)\",\n \"def fuel_cond(T):\\n\\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\\n \\n return kc\",\n \"def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\\n self.digital_temp_data() \\n dT = self.tempadc-(C_5*(2**8))\\n temperature=(2000+(dT*(C_6/(2**23))))/100\\n return temperature, dT\",\n \"def ambient_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.ambient_temperature_c)\",\n \"def get_cold_junction_temperature(self):\\n return self._mcp9600.get('COLD_JUNCTION').temperature\",\n \"def test_getThermalExpansionFactorConservedMassByLinearExpansionPercent(self):\\n hotTemp = 700.0\\n dLL = self.component.material.linearExpansionFactor(\\n Tc=hotTemp, T0=self._coldTemp\\n )\\n ref = 1.0 + dLL\\n cur = self.component.getThermalExpansionFactor(Tc=hotTemp)\\n self.assertAlmostEqual(cur, ref)\",\n \"def volumetric_heat_capacity(temperature):\\n a = -2.4083\\n b = 7.6006\\n c = -8.2982\\n d = 7.3301\\n e = -4.2386\\n f = 1.4294\\n g = -0.24396\\n h = 0.015236\\n i = 0.0\\n log_t = math.log10(temperature)\\n f_exp = a + b*log_t + c*log_t**2.0 + d*log_t**3.0 + e*log_t**4.0 + f*log_t**5.0 + g*log_t**6.0 + \\\\\\n h*log_t**7.0 + i*log_t**8.0\\n g10_cp = 10.0**f_exp\\n return g10_cp * G10NISTMaterialProperties.density\",\n \"def P(self, energy, newEnergy, temperature):\\n \\\"\\\"\\\" This is the decision-rule, adapted from Nascimento, et al., 2009 (See references) \\\"\\\"\\\"\\n \\n delta = self.calcDelta(newEnergy, energy)\\n\\n minTemp = 0.00001 # use minimum to avoid div/0 and buffer overflow\\n if temperature == 0:\\n return minTemp\\n elif temperature > minTemp:\\n try:\\n return math.exp(-1 * round(delta, 4) / round(temperature, 4))\\n except OverflowError as detail:\\n return minTemp\\n else:\\n return 1\",\n \"def target_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.target_temperature_c)\",\n \"def temperature():\\n from .imperial import deg_F as F\\n from .imperial import deg_R as R\\n\\n K = si.K\\n C = si.deg_C\\n\\n return Equivalency(\\n [\\n (K, C, lambda x: x - 273.15, lambda x: x + 273.15),\\n (C, F, lambda x: x * 1.8 + 32.0, lambda x: (x - 32.0) / 1.8),\\n (K, F, lambda x: x * 1.8 - 459.67, lambda x: (x + 459.67) / 1.8),\\n (R, F, lambda x: x - 459.67, lambda x: x + 459.67),\\n (R, C, lambda x: (x - 491.67) * (5 / 9), lambda x: x * 1.8 + 491.67),\\n (R, K, lambda x: x * (5 / 9), lambda x: x * 1.8),\\n ],\\n \\\"temperature\\\",\\n )\",\n \"def cooled_surface_temp(T:np.ndarray) -> float:\\n \\n return T.dot(cs_temp_weights)\",\n \"def target_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature_low)\",\n \"def env_temperature(v3: \\\"float\\\", v4: \\\"float\\\") -> \\\"float\\\":\",\n \"def farenheit(ctemp):\\n return round(9.0/5.0 * ctemp + 32)\",\n \"def soil_temp_factor(self, project_day):\\n tsoil = self.met_data['tsoil'][project_day]\\n\\n if float_gt(tsoil, 0.0):\\n tfac = (0.0326 + 0.00351 * tsoil**1.652 - (tsoil / 41.748)**7.19)\\n if float_lt(tfac, 0.0):\\n tfac = 0.0\\n else:\\n # negative number cannot be raised to a fractional power\\n # number would need to be complex\\n tfac = 0.0\\n\\n return tfac\",\n \"def temperature(k, kmax):\\n return 1.0 / 500 * (1.0 / k - 1.0 / kmax)\",\n \"def _calculate_temperature(c, h):\\n\\n return (c - 331.4 - 0.0124 * h) / 0.6\",\n \"def target_temperature_low_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_low_f\\\"))\\r\\n return celsius_to_fahrenheit(self.target_temperature_low_c)\",\n \"def get_evaporation_latent_heat() -> float:\\n theta = 28.0\\n return 2500.8 - 2.3668 * theta\",\n \"def temperature(self, alt):\\n T = self.altitude_profile(alt)[1]\\n return T\",\n \"def liqtemperature(pres):\\n psi = pres/_PTPE - 1\\n temp = 0.\\n for coeff in _C_TMELT[::-1]:\\n temp = temp*psi + coeff\\n temp = (1 + temp*psi)*_TTP\\n return temp\",\n \"def temperature(altitude):\\n if altitude <= 36152:\\n t = 59-0.00356*altitude # deg F\\n else:\\n t = -70 # deg F\\n t = t + 459.7 # R\\n return t\",\n \"def get_production_factor(self, temp_atmosphere):\\n return 1.\",\n \"def linear_heat_transfer(x, t, K_medium, rho_medium, c_medium, T_medium_initial, H_heat_transfer, T_external_applied):\\n k = get_kappa(K_medium, rho_medium, c_medium)\\n\\n h = H_heat_transfer/K_medium\\n erfc_factor_1 = erfc(x/(2*np.sqrt(k*t)))\\n\\n #combine factors in logdomain, since the exp-factors quickly approach\\n #infinity while erfc-factor goes to zero\\n log_exp_factor_1 = h*x\\n log_exp_factor_2 = k*t*h**2\\n log_erfc_factor_2 = np.log(erfc(x/(2*np.sqrt(k*t)) + h*np.sqrt(k*t)))\\n exp_erfc_factor = np.exp(log_exp_factor_1 + log_exp_factor_2 + log_erfc_factor_2)\\n\\n return (erfc_factor_1 - exp_erfc_factor)*(T_external_applied - T_medium_initial) + T_medium_initial\",\n \"def eco_temperature_high_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_high)\",\n \"def exponential(min_iterations, i, start = start_temp, final = final_temp):\\n\\n\\ttemperature = (start * (final / start) ** (i / min_iterations))\\n\\n\\treturn temperature\",\n \"def __t_fine__(self, adc_temperature):\\n var1 = (((adc_temperature >> 3) -\\n (self._calibration_t[0] << 1)) * self._calibration_t[1]) >> 11\\n var2 = (((\\n ((adc_temperature >> 4) - self._calibration_t[0]) *\\n ((adc_temperature >> 4) - self._calibration_t[0])) >> 12)\\n * self._calibration_t[2]) >> 14\\n return var1 + var2\",\n \"def gas_zfactor(T_pr, P_pr):\\n # T_pr : calculated pseudoreduced temperature\\n # P_pr : calculated pseudoreduced pressure \\n from scipy.optimize import fsolve # non-linear solver\\n import numpy as np\\n\\n a1 = 0.3265; a2 = -1.0700; a3 = -0.5339; a4 = 0.01569; a5 = -0.05165; a6 = 0.5475\\n a7 = -0.7361; a8 = 0.1844; a9 = 0.1056; a10 = 0.6134; a11 = 0.7210\\n\\n def f(y):\\n rho_pr, z = y\\n c1 = a1 + (a2/T_pr) + (a3/(T_pr**3))+ (a4/(T_pr**4))+ (a5/(T_pr**5))\\n c2 = a6 + (a7/T_pr) + (a8/(T_pr**2))\\n c3 = a9*((a7/T_pr) + (a8/(T_pr**2)))\\n c4 = (a10)*(1+(a11*(rho_pr**2)))*((rho_pr**2)/(T_pr**3))*(np.exp(-a11*(rho_pr**2)))\\n\\n f1 = z + (c3*(rho_pr**5)) - (c2*(rho_pr**2)) - (c1*(rho_pr**1)) - c4 - 1\\n f2 = rho_pr - ((0.27 * P_pr) / (z * T_pr))\\n return[f1, f2]\\n\\n solve = fsolve(f, [1, 1]) # initial guess\\n return(solve[0], solve[1]) # result is density, z-factor\",\n \"def fahrenheit(T_in_celsius):\\n return (T_in_celsius * 9 / 5) + 32\",\n \"def expansion(temp,pres):\\n g_p = liq_g(0,1,temp,pres)\\n g_tp = liq_g(1,1,temp,pres)\\n alpha = g_tp / g_p\\n return alpha\",\n \"def water_vapour(t):\\n T_0 = 273.15\\n T_rw = 35.86 # over water\\n a = 17.269\\n # cdo -mulc,610.78 -exp -div -mulc,17.5 -subc,273.15 a\\n return 610.78 * np.exp(a * (t - T_0) / (t - T_rw))\",\n \"def thermal_velocity(charge, temperature, mass):\\n return np.sqrt(2*abs(charge)*temperature/mass)\",\n \"def TM_fluid(layer, kx, om):\\n\\n h = layer.d\\n rho = layer.medium.rho\\n K = layer.medium.K\\n k = om*np.sqrt(rho/K)\\n ky = np.sqrt(k**2-kx**2)\\n T = np.zeros((2, 2), dtype=complex)\\n T[0, 0] = np.cos(ky*h)\\n T[1, 0] = (om**2*rho/ky)*np.sin(ky*h)\\n T[0, 1] = -(ky/(om**2*rho))*np.sin(ky*h)\\n T[1, 1] = np.cos(ky*h)\\n return T\",\n \"def eco_temperature_high_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_f\\\"))\\r\\n return celsius_to_fahrenheit(self.eco_temperature_high_c)\",\n \"def density_of_air(self) -> float:\\n\\n return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature)\",\n \"def calcTempInEV(expFitCoeffWithVoltUnits):\\n\\t\\t# constants\\n\\t\\teV=1.60218e-19;\\n\\t\\tq=1.6e-19\\n\\t\\t\\n\\t\\t# temperature in eV\\n\\t\\treturn q*expFitCoeffWithVoltUnits/eV\",\n \"def temperature(self) -> float:\\n # Start a measurement then poll the measurement finished bit.\\n self.temp_start = 1\\n while self.temp_running > 0:\\n pass\\n # Grab the temperature value and convert it to Celsius.\\n # This uses the same observed value formula from the Radiohead library.\\n temp = self._read_u8(_REG_TEMP2)\\n return 166.0 - temp\",\n \"def temperature(self):\\n value = float(self._parent.query('R{}'.format(self._idx))[1:])\\n return pq.Quantity(value, pq.Kelvin)\",\n \"def formation_temperature(surface_temperature, gradient, depth):\\n form_temp = surface_temperature + gradient * depth\\n return form_temp\",\n \"def thermal_state(self, beta: float = .1) -> numpy.ndarray:\\n rho = numpy.exp(-beta * self.cost)\\n return rho / numpy.sum(rho)\",\n \"def get_attic_temperature(theta_sat: np.ndarray, theta_ac: np.ndarray) -> np.ndarray:\\n\\n # temperature difference coefficient\\n h = 1.0\\n\\n return theta_sat * h + theta_ac * (1 - h)\",\n \"def temperature(self):\\n return _cantera.reactor_temperature(self.__reactor_id)\",\n \"def temperature(self):\\n temp = ct.c_float()\\n self.lib.GetTemperatureF(ct.pointer(temp))\\n return temp.value\",\n \"def temperature_from_potential_temperature(pressure, potential_temperature, reference_pressure=P0):\\n return potential_temperature * exner_function(pressure, reference_pressure=reference_pressure)\",\n \"def tempWater(sample):\\n sample *= .0009\\n sample *= 1000\\n celsius = (sample - 20.5128) * 0.0512\\n return round(celsius,2)\",\n \"def read_ambient_temperatureF(self, ):\\n return self.read_ambient_temperatureC() * (9.0/5.0) + 32.0\",\n \"def _temperature(self, p_input:float) -> float:\\n if self._unit_in == 'R':\\n temp_K = p_input*5.0/9.0\\n elif self._unit_in == 'F':\\n temp_K = (p_input+459.67)/9.0*5.0\\n elif self._unit_in == 'C':\\n temp_K = p_input+273.15\\n elif self._unit_in == 'K':\\n temp_K = p_input\\n \\n if self._unit_out == 'R':\\n return (temp_K*9.0/5.0)\\n elif self._unit_out == 'F':\\n return (temp_K*9.0/5.0-459.67) \\n elif self._unit_out == 'C':\\n return (temp_K-273.15)\\n elif self._unit_out == 'K':\\n return temp_K\",\n \"def update_temperature(self):\\n self.iteration += 1 \\n self.T = self.T0 * 0.9935**self.iteration\",\n \"def getTemperature(self):\\n return self.temperature\",\n \"def heat_flux_out(T_inf, T_old, hc_air, emmi):\\n\\n #nz = T_old.shape[0]\\n ny = T_old.shape[0]\\n nx = T_old.shape[1]\\n\\n Q_out = np.zeros((ny, nx))\\n h_eff = np.zeros((ny, nx))\\n T_eff = np.zeros((ny, nx))\\n for i in range(nx):\\n for j in range(ny):\\n T_eff[j, i] = ((T_old[j, i]**3) + (T_inf * T_old[j, i]**2)\\n + (T_old[j, i] * T_inf**2) + T_inf**3)\\n\\n h_eff[j, i] = hc_air + (emmi*STEF_BOL_C*T_eff[j, i])\\n\\n Q_out[j, i] = h_eff[j, i] * (T_old[j, i] - T_inf)\\n\\n return Q_out\",\n \"def read_object_temperatureF(self, ):\\n return self.read_object_temperatureC() * (9.0/5.0) + 32.0\",\n \"def temp(self):\\n if self.temp_sensor is None:\\n return None\\n else:\\n if self.temp_scale.lower() in ['f', 'fahrenheit']:\\n return self.temp_sensor.temp_f\\n elif self.temp_scale.lower() in ['c', 'celsius']:\\n return self.temp_sensor.temp_c\",\n \"def get_temperature(self):\\n pass\",\n \"def ionization_constant_water(temperature=298.15, density=None):\\n import numpy as np\\n\\n # using Model II from Bandura etal\\n # model parameters\\n n = 6\\n alpha_0 = -0.864671\\n alpha_1 = 8659.19\\n alpha_2 = -22786.2\\n beta_0 = 0.642044\\n beta_1 = -56.8534\\n beta_2 = -0.375754\\n\\n # Water parameters\\n Mw = 18.01528\\n\\n # temperature\\n T = temperature\\n\\n # density\\n if density:\\n D = density\\n else:\\n D = density_water(T)\\n\\n pKWG = 0.61415 \\\\\\n + 48251.33 / T \\\\\\n - 67707.93 / T**2.0 \\\\\\n + 10102100.0 / T**3.0\\n\\n Z = D * np.exp(alpha_0 \\\\\\n + alpha_1/T \\\\\\n + alpha_2/T**2 *np.power(D,2.0/3.0)\\n )\\n\\n pKw = -2*n*(\\n np.log10(1 + Z) - (Z/(Z + 1)) * D * (\\n beta_0 + beta_1/T + beta_2*D\\n )\\n ) + pKWG + 2 * np.log10(Mw/1000.0)\\n\\n return np.power(10, -pKw)\",\n \"def calculate_celerity(period, depth, gravity):\\r\\n return geometry.gmCalculateCelerity(period, depth, gravity)\",\n \"def _temperature_policy(self, N: np.ndarray) -> np.ndarray:\\n t = 1 / self.temperature\\n pi = N ** t + np.finfo(np.float32).eps # Add tiny value to remove 0s\\n pi = pi / pi.sum()\\n return pi\",\n \"def effective_temperature(R,times):\\n T_eff = np.empty((len(R),times))\\n #T_c = ((3 * G * M_body) / (8 * np.pi * sigma)) #constants for a blackbody \\n for i in range (len(R)):\\n for t in range(times): \\n T_eff[i][t] = ((m_dot[i][t])/(R[i]**3.0))**(1.0/4.0)\\n return T_eff\",\n \"def calc_supply_temp(tr, Q, m, cp, case):\\n if m > 0:\\n if case == \\\"DH\\\":\\n ts = tr + Q / (m * cp)\\n else:\\n ts = tr - Q / (m * cp)\\n else:\\n ts = 0\\n return ts\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7279272","0.68165576","0.6797786","0.6684487","0.66219014","0.6579864","0.6570214","0.6559898","0.649577","0.64194864","0.63722163","0.6345614","0.6337939","0.6337814","0.6305888","0.6286312","0.6284374","0.6271602","0.6268423","0.62625694","0.62529576","0.62480354","0.62434256","0.6219022","0.6205769","0.62008363","0.61886775","0.61633605","0.6153172","0.61523736","0.6142886","0.6138658","0.61331356","0.6131802","0.60888356","0.60715157","0.60712886","0.60671556","0.60627663","0.6032802","0.6031146","0.6026677","0.602504","0.6020692","0.6011433","0.6008431","0.60076565","0.60003465","0.59979975","0.59821904","0.59781516","0.59618366","0.595415","0.5940936","0.59350663","0.5928525","0.5927561","0.59241194","0.59234256","0.5919663","0.59142226","0.5905145","0.5902006","0.5891895","0.5888309","0.5886067","0.5882062","0.5881698","0.58715725","0.5868934","0.58674747","0.5867025","0.58495915","0.5848728","0.58425426","0.5840377","0.58343774","0.5826279","0.58240813","0.58238375","0.58200395","0.5818309","0.5797296","0.57965916","0.57850564","0.5758969","0.57580245","0.5756962","0.57553506","0.5755176","0.5753814","0.57532537","0.57518065","0.5751042","0.57456774","0.574134","0.5729222","0.57270586","0.5725671","0.57193094"],"string":"[\n \"0.7279272\",\n \"0.68165576\",\n \"0.6797786\",\n \"0.6684487\",\n \"0.66219014\",\n \"0.6579864\",\n \"0.6570214\",\n \"0.6559898\",\n \"0.649577\",\n \"0.64194864\",\n \"0.63722163\",\n \"0.6345614\",\n \"0.6337939\",\n \"0.6337814\",\n \"0.6305888\",\n \"0.6286312\",\n \"0.6284374\",\n \"0.6271602\",\n \"0.6268423\",\n \"0.62625694\",\n \"0.62529576\",\n \"0.62480354\",\n \"0.62434256\",\n \"0.6219022\",\n \"0.6205769\",\n \"0.62008363\",\n \"0.61886775\",\n \"0.61633605\",\n \"0.6153172\",\n \"0.61523736\",\n \"0.6142886\",\n \"0.6138658\",\n \"0.61331356\",\n \"0.6131802\",\n \"0.60888356\",\n \"0.60715157\",\n \"0.60712886\",\n \"0.60671556\",\n \"0.60627663\",\n \"0.6032802\",\n \"0.6031146\",\n \"0.6026677\",\n \"0.602504\",\n \"0.6020692\",\n \"0.6011433\",\n \"0.6008431\",\n \"0.60076565\",\n \"0.60003465\",\n \"0.59979975\",\n \"0.59821904\",\n \"0.59781516\",\n \"0.59618366\",\n \"0.595415\",\n \"0.5940936\",\n \"0.59350663\",\n \"0.5928525\",\n \"0.5927561\",\n \"0.59241194\",\n \"0.59234256\",\n \"0.5919663\",\n \"0.59142226\",\n \"0.5905145\",\n \"0.5902006\",\n \"0.5891895\",\n \"0.5888309\",\n \"0.5886067\",\n \"0.5882062\",\n \"0.5881698\",\n \"0.58715725\",\n \"0.5868934\",\n \"0.58674747\",\n \"0.5867025\",\n \"0.58495915\",\n \"0.5848728\",\n \"0.58425426\",\n \"0.5840377\",\n \"0.58343774\",\n \"0.5826279\",\n \"0.58240813\",\n \"0.58238375\",\n \"0.58200395\",\n \"0.5818309\",\n \"0.5797296\",\n \"0.57965916\",\n \"0.57850564\",\n \"0.5758969\",\n \"0.57580245\",\n \"0.5756962\",\n \"0.57553506\",\n \"0.5755176\",\n \"0.5753814\",\n \"0.57532537\",\n \"0.57518065\",\n \"0.5751042\",\n \"0.57456774\",\n \"0.574134\",\n \"0.5729222\",\n \"0.57270586\",\n \"0.5725671\",\n \"0.57193094\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76827806"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2395,"cells":{"query":{"kind":"string","value":"Determines the convective heat transfer coefficient, either free, or forced. In the absence of any wind, the \"free\" wind_heat_transfer_coefficient is returned. If there is wind present, then this parameter is known as the \"forced\" wind_heat_transfer_coefficient."},"document":{"kind":"string","value":"def wind_heat_transfer_coefficient(self) -> float:\n\n return 3.8 + 2 * self.wind_speed\n # return 4.5 + 2.9 * self.wind_speed"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def thermalConductivity(self, Tk=None, Tc=None):\n Tk = getTk(Tc, Tk)\n self.checkPropertyTempRange(\"thermal conductivity\", Tk)\n thermalConductivity = (\n 2.13014e-08 * Tk**3\n - 6.31916e-05 * Tk**2\n + 1.11629e-01 * Tk\n - 2.00043e00\n )\n return thermalConductivity * 1e-3","def get_chiller_temperature(self) -> float:\n\n return self.send(self.cmd.GET_COOLING_ACT)","def getHeatFlux(self, T):\n\t\tQ = self.heat_transfer_coefficient * (self.T_wall - T)\n\t\treturn Q","def conductive_heat_flux(discr, eos, cv, grad_t):\n transport = eos.transport_model()\n return -transport.thermal_conductivity(eos, cv)*grad_t","def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference suggests this equation is accurate to 1%.\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646","def setHeatTransferCoeff(self, u):\n return _cantera.wall_setHeatTransferCoeff(self.__wall_id, u)","def calc_cogen_const(q_heat_Wh, thermal_eff, electrical_eff):\n q_fuel_Wh = q_heat_Wh / thermal_eff\n p_el_Wh = q_fuel_Wh * electrical_eff\n q_anth_Wh = q_fuel_Wh - (q_heat_Wh + p_el_Wh)\n return q_fuel_Wh, p_el_Wh, q_anth_Wh","def heatFlowRate(self):\n return _cantera.wall_Q(self.__wall_id)","def calc_maintenance_cost (self):\n\n if str(self.comp_specs['operational costs']) \\\n != 'UNKNOWN':\n self.maintenance_cost = \\\n self.comp_specs['operational costs']\n else:\n self.maintenance_cost = \\\n (self.comp_specs['percent o&m'] / 100.0) * self.capital_costs\n #~ print 'self.maintenance_cost',self.maintenance_cost","def conductivity(self):\n m = 1.67296736e-02 # Determined from optimisation\n c = 8.54665149e-05 # Determined from optimisation\n return m * self.concentration + c","def target_temperature(self):\n if self.current_operation == 'Heat & Cool':\n return None\n if self.current_operation == 'Heat only':\n return int(self._api._heatto)\n elif self.current_operation == 'Cool only':\n return int(self._api._coolto)\n return None","def fuel_cost(self, update=False):\n if update or self._dfs['fuel_cost'] is None:\n self._dfs['fuel_cost'] = pudl.analysis.mcoe.fuel_cost(self)\n return self._dfs['fuel_cost']","def target_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_c\"))\r\n return kelvin_to_celsius(self._target_temperature)","def thermal_conductivity(temperature):\n a0 = -4.1236\n a1 = 13.788\n a2 = -26.068\n a3 = 26.272\n a4 = -14.663\n a5 = 4.4954\n a6 = -0.6905\n a7 = 0.0397\n log_t = math.log10(temperature)\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\n g10_thermal_conductivity = 10.0**f_exp\n return g10_thermal_conductivity","def target_temperature(self) -> float | None:\n if self.hvac_mode == HVACMode.COOL:\n return self.target_temperature_high\n if self.hvac_mode == HVACMode.HEAT:\n return self.target_temperature_low\n return None","def get_compensated_temperature() -> float:\n comp_factor = 2.25\n cpu_temp = get_cpu_temperature()\n raw_temp = bme280.get_temperature()\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\n # print(\"\"\"\n # Compensated_Temperature: {:05.2f} *C\n # Pressure: {:05.2f} hPa\n # Relative humidity: {:05.2f} %\n # \"\"\".format(temperature, pressure, humidity))\n return comp_temp","def transport_cost_per_t(self):\n return safe_divide(self.reseller.operating_expenses(), self.quantity_fieldside)","def fuel_cond(T):\n\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\n \n return kc","def best_coupling(self):\n\n return self.coupling().max()","def target_temperature_high_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_high_c\"))\r\n return kelvin_to_celsius(self._target_temperature_high)","def coupling(self):\n couple = coupling_parameter(\n self.T_e, self.n_e, (self.particle, self.particle), self.Z\n )\n if couple < 0.01:\n warnings.warn(\n f\"Coupling parameter is {couple}, you might have strong coupling effects\",\n CouplingWarning,\n )\n\n return couple","def cooled_surface_temp(T:np.ndarray) -> float:\n \n return T.dot(cs_temp_weights)","def get_chiller_temperature_setpoint(self) -> float:\n\n return self.send(self.cmd.GET_COOLING_SET)","def distribute(self, date_time, air_temp, vapor_pressure=None,\n dew_point=None, cloud_factor=None):\n\n self._logger.debug('%s Distributing thermal' % date_time)\n\n # calculate clear sky thermal\n if self.clear_sky_method == 'marks1979':\n cth = np.zeros_like(air_temp, dtype=np.float64)\n envphys_c.ctopotherm(\n air_temp, dew_point,\n self.dem,\n self.sky_view_factor,\n cth,\n self.config['marks1979_nthreads'])\n\n elif self.clear_sky_method == 'dilley1998':\n cth = clear_sky.Dilly1998(air_temp, vapor_pressure/1000)\n\n elif self.clear_sky_method == 'prata1996':\n cth = clear_sky.Prata1996(air_temp, vapor_pressure/1000)\n\n elif self.clear_sky_method == 'angstrom1918':\n cth = clear_sky.Angstrom1918(air_temp, vapor_pressure/1000)\n\n # terrain factor correction\n if (self.sky_view_factor is not None) and \\\n (self.clear_sky_method != 'marks1979'):\n # apply (emiss * skvfac) + (1.0 - skvfac) to the longwave\n cth = cth * self.sky_view_factor + (1.0 - self.sky_view_factor) * \\\n STEF_BOLTZ * air_temp**4\n\n # make output variable\n self.thermal_clear = cth.copy()\n\n # correct for the cloud factor\n # ratio of measured/modeled solar indicates the thermal correction\n if self.correct_cloud:\n if self.cloud_method == 'garen2005':\n cth = cloud.Garen2005(cth,\n cloud_factor)\n\n elif self.cloud_method == 'unsworth1975':\n cth = cloud.Unsworth1975(cth,\n air_temp,\n cloud_factor)\n\n elif self.cloud_method == 'kimball1982':\n cth = cloud.Kimball1982(cth,\n air_temp,\n vapor_pressure/1000,\n cloud_factor)\n\n elif self.cloud_method == 'crawford1999':\n cth = cloud.Crawford1999(cth,\n air_temp,\n cloud_factor)\n\n # make output variable\n self.thermal_cloud = cth.copy()\n\n # correct for vegetation\n if self.correct_veg:\n cth = vegetation.thermal_correct_canopy(cth,\n air_temp,\n self.veg_tau,\n self.veg_height)\n\n # make output variable\n self.thermal_veg = cth.copy()\n\n self.thermal = utils.set_min_max(cth, self.min, self.max)","def target_temperature(self) -> float | None:\n if self._device.mode == ThermostatMode.COOL and self._device.cooling_setpoint:\n return self._device.scaled_cooling_setpoint\n\n if self._device.heating_setpoint:\n return self._device.scaled_heating_setpoint\n\n return None","def get_correction_factor(self, temperature, humidity):\n\n if temperature < 20:\n return self.CORA * temperature * temperature - self.CORB * temperature + self.CORC - (humidity - 33.) * self.CORD\n\n return self.CORE * temperature + self.CORF * humidity + self.CORG","def get_correction_factor(self, temperature, humidity):\n\n if temperature < 20:\n return self.CORA * temperature * temperature - self.CORB * temperature + self.CORC - (humidity - 33.) * self.CORD\n\n return self.CORE * temperature + self.CORF * humidity + self.CORG","def eco_temperature_high_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_high)","def adjust_cost(self) -> None:\n\n n_iterations = self.array.shape[-1]\n n_year = len(self.array.year.values)\n\n # If uncertainty is not considered, the cost factor equals 1.\n # Otherwise, a variability of +/-30% is added.\n\n if n_iterations == 1:\n cost_factor = 1\n else:\n if \"reference\" in self.array.value.values.tolist():\n cost_factor = np.ones((n_iterations, 1))\n else:\n cost_factor = np.random.triangular(0.7, 1, 1.3, (n_iterations, 1))\n\n # Correction of hydrogen tank cost, per kg\n # Correction of fuel cell stack cost, per kW\n if \"FCEV\" in self.array.powertrain:\n self.array.loc[\n dict(powertrain=\"FCEV\", parameter=\"fuel tank cost per kg\")\n ] = np.reshape(\n (1.078e58 * np.exp(-6.32e-2 * self.array.year.values) + 3.43e2)\n * cost_factor,\n (1, n_year, n_iterations),\n )\n\n self.array.loc[\n dict(powertrain=\"FCEV\", parameter=\"fuel tank cost per kg\")\n ] = np.reshape(\n (3.15e66 * np.exp(-7.35e-2 * self.array.year.values) + 2.39e1)\n * cost_factor,\n (1, n_year, n_iterations),\n )\n\n # Correction of energy battery system cost, per kWh\n list_batt = [\n i\n for i in [\"BEV\", \"PHEV-e\", \"PHEV-c-p\", \"PHEV-c-d\"]\n if i in self.array.powertrain\n ]\n if len(list_batt) > 0:\n self.array.loc[\n dict(powertrain=list_batt, parameter=\"energy battery cost per kWh\")\n ] = np.reshape(\n (2.75e86 * np.exp(-9.61e-2 * self.array.year.values) + 5.059e1)\n * cost_factor,\n (1, 1, n_year, n_iterations),\n )\n\n # Correction of power battery system cost, per kW\n list_pwt = [\n i\n for i in [\n \"ICEV-p\",\n \"ICEV-d\",\n \"ICEV-g\",\n \"PHEV-c-p\",\n \"PHEV-c-d\",\n \"FCEV\",\n \"HEV-p\",\n \"HEV-d\",\n ]\n if i in self.array.powertrain\n ]\n\n if len(list_pwt) > 0:\n self.array.loc[\n dict(powertrain=list_pwt, parameter=\"power battery cost per kW\")\n ] = np.reshape(\n (8.337e40 * np.exp(-4.49e-2 * self.array.year.values) + 11.17)\n * cost_factor,\n (1, 1, n_year, n_iterations),\n )\n\n # Correction of combustion powertrain cost for ICEV-g\n if \"ICEV-g\" in self.array.powertrain:\n self.array.loc[\n dict(powertrain=\"ICEV-g\", parameter=\"combustion powertrain cost per kW\")\n ] = np.clip(\n np.reshape(\n (5.92e160 * np.exp(-0.1819 * self.array.year.values) + 26.76)\n * cost_factor,\n (1, n_year, n_iterations),\n ),\n None,\n 100,\n )","def discharge_coefficient(self) -> _VectorisedFloat:\n window_ratio = np.array(self.window_width / self.window_height)\n coefs = np.empty(window_ratio.shape + (2, ), dtype=np.float64)\n\n coefs[window_ratio < 0.5] = (0.06, 0.612)\n coefs[np.bitwise_and(0.5 <= window_ratio, window_ratio < 1)] = (0.048, 0.589)\n coefs[np.bitwise_and(1 <= window_ratio, window_ratio < 2)] = (0.04, 0.563)\n coefs[window_ratio >= 2] = (0.038, 0.548)\n M, cd_max = coefs.T\n\n window_angle = 2.*np.rad2deg(np.arcsin(self.opening_length/(2.*self.window_height)))\n return cd_max*(1-np.exp(-M*window_angle))","def CalcForceDistribution(self):\n\t\t\n\t\tself.F = self.s * (self.Tether - self.X)\n\t\t\n\t\treturn self.F","def DragCoeff(h,Vc,Temp_m,Thrust,S):\n T,p,rho = isa(h)\n return Thrust/(0.5*rho*VTrue(h,Vc,p,Temp_m)**2*S)","def min_background_concentration(self) -> _VectorisedFloat:\n return self.CO2_atmosphere_concentration","def eco_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_low)","def coherence(self):\r\n return np.abs(self.coherency) ** 2","def gradient_cf(self, potential, get_energy=True):\n xn, xe, lpn, lpe, alpha, o1, o2 = self(None)\n fn_, fe_ = potential((xn, xe))\n fn_ = (fn_ + self.tw * lpn) * self.wn\n fe_ = (fe_ - lpe) * self.we\n fn = fn_ * alpha\n fe = fe_ * alpha\n dmu = tf.math.divide_no_nan(tf.reduce_sum(fn * self.xn, axis=-1, keepdims=True), self.sigma)\n dsg = tf.math.divide_no_nan(tf.reduce_sum(fn * self.x22, axis=-1, keepdims=True), self.sigma)\n dmu1 = tf.math.divide_no_nan(tf.reduce_sum(fe * self.xi, -1, keepdims=True), o1)\n dmu2 = tf.reduce_sum(fe * self.xj, -1, keepdims=True) / o2\n dsg1 = tf.math.divide_no_nan(tf.reduce_sum(fe * self.xi22, -1, keepdims=True), o1)\n dsg2 = tf.reduce_sum(fe * self.xj22, -1, keepdims=True) / o2\n\n dmu += (tf.concat([dmu1, self.top0], 2) + tf.concat(\n [self.btm0, tf.reshape(tf.reduce_sum(tf.reshape(dmu2[:, :, :729, ...], self.s1), [3, 5]), self.s3),\n tf.reshape(tf.reduce_sum(tf.reshape(dmu2[:, :, 729:810, ...], self.s2), [3, 5]), self.s3),\n tf.reduce_sum(dmu2[:, :, 810:, ...], 2, True)], 2))\n\n dsg += (tf.concat([dsg1, self.top0], 2) + tf.concat(\n [self.btm0, tf.reshape(tf.reduce_sum(tf.reshape(dsg2[:, :, :729, ...], self.s1), [3, 5]), self.s3),\n tf.reshape(tf.reduce_sum(tf.reshape(dsg2[:, :, 729:810, ...], self.s2), [3, 5]), self.s3),\n tf.reduce_sum(dsg2[:, :, 810:, ...], 2, True)], 2))\n\n dalpha = (tf.reduce_sum(fn_, [2, 4], keepdims=True) + tf.reduce_sum(fe_, [2, 4], keepdims=True))\n dw = alpha * (dalpha - tf.reduce_sum(dalpha * alpha, 3, keepdims=True))\n energy = tf.zeros(fn.shape[:2], tf.float64) if not get_energy else \\\n -(tf.reduce_sum(fn, [2, 3, 4]) + tf.reduce_sum(fe, [2, 3, 4]))\n return (-dmu * sqrt2, -dsg, -dw), energy","def target_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_low_c\"))\r\n return kelvin_to_celsius(self._target_temperature_low)","def cost(distance, highway, bicycle, incline, preferences):\n\n #unpack preferences\n (flatness_pref, bicycle_pref, distance_pref,\n motorway_pref, highway_pref, residential_pref) = preferences\n multiplier = 1 + bike_multiplier(bicycle, bicycle_pref) + road_multiplier(highway, bicycle_pref, motorway_pref, highway_pref, residential_pref)\n if multiplier <= 0:\n multiplier = 0.01\n incl = incline_multiplier(float(incline))*flatness_pref\n cost = float(distance) * multiplier + incl\n if cost <= 0:\n cost = 0.01\n return cost","def target_temperature_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_f\"))\r\n return celsius_to_fahrenheit(self.target_temperature_c)","def net_alchemical_force(self):\n return self._cpp_obj.net_force","def target_temperature(self):\n if self._client.mode == self._client.MODE_HEAT:\n return self._client.heattemp\n if self._client.mode == self._client.MODE_COOL:\n return self._client.cooltemp\n return None","def LiftCoeff(h,Vc,Temp_m,W,S):\n T,p,rho = isa(h)\n return W*g0/(0.5*rho*VTrue(h,Vc,p,Temp_m)**2*S)","def get_specific_heat() -> float:\n return 1006.0","def forcing(self):\n if not self._fr or not self._frstar:\n raise ValueError('Need to compute Fr, Fr* first.')\n return -Matrix([self._f_d, self._f_dnh])","def cash_flow(self):\n _cash_flow = self.after_tax_profit() + self.depreciation()\n return _cash_flow","def thermal_operating_costs_rule(_m, y, s):\r\n\r\n return sum((m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])) * m.p[g, y, s, t]\r\n for g in m.G_THERM for t in m.T)","def computeSmoothEnergy(self):\n _cgco.gcoComputeSmoothEnergy(self.handle, self.energyTempArray)\n return self._convertEnergyBack(self.energyTempArray[0])","def local_cost(self) -> Number:\n mu_E = self._payoff_weight_energy\n mu_T = self._payoff_weight_time\n D_loc = SIMULATION_PARAMETERS['LOCAL_CPU_CYCLES']\n F_loc = self.cpu_frequency\n T_loc = D_loc / F_loc\n E_loc = D_loc / self.cpu_effeciency\n return mu_T * T_loc + mu_E * E_loc","def calc_cash_flow(self):\n s = self # shortcut variable\n\n # determine the changes caused by the heat pump on an annual basis.\n # First calculate annual totals for base case and heat pump case and\n # then calculate the change.\n ann_base = s.df_mo_dol_base.sum()\n ann_hp = s.df_mo_dol_hp.sum()\n ann_chg = ann_hp - ann_base\n initial_cost = np.zeros(s.hp_life+1)\n \n # Am not automatically adding sales tax to the initial cost as the user was\n # supposed to includes sales tax in their input.\n initial_cost[0] = -s.capital_cost * (1 - s.pct_financed) + s.rebate_dol\n loan_pmt = npf.pmt(s.loan_interest, s.loan_term, s.capital_cost * s.pct_financed)\n if loan_pmt < -0.01: # loan payment is negative\n loan_cost = [0.0] + [loan_pmt] * s.loan_term + [0.0] * (s.hp_life - s.loan_term)\n loan_cost = np.array(loan_cost)\n else:\n loan_cost = 0.0\n op_cost = -s.op_cost_chg * make_pattern(s.inflation_rate, s.hp_life)\n fuel_cost = -ann_chg.secondary_fuel_dol * make_pattern(s.fuel_esc_rate, s.hp_life)\n elec_cost = -ann_chg.elec_dol * make_pattern(s.elec_esc_rate, s.hp_life)\n cash_flow = initial_cost + loan_cost + op_cost + fuel_cost + elec_cost\n\n # calculate cumulative, discounted cash flow.\n disc_factor = np.ones(s.hp_life) * (1 + s.discount_rate)\n disc_factor = np.insert(disc_factor.cumprod(), 0, 1.0)\n cum_disc_cash_flow = np.cumsum(cash_flow / disc_factor)\n \n s.df_cash_flow = pd.DataFrame(\n {'initial_cost': initial_cost,\n 'loan_cost': loan_cost,\n 'op_cost': op_cost,\n 'fuel_cost': fuel_cost,\n 'elec_cost': elec_cost,\n 'cash_flow': cash_flow,\n 'cum_disc_cash_flow': cum_disc_cash_flow,\n }\n )\n s.df_cash_flow.index.name = 'year'\n \n # Calculate IRR and NPV for w/ and w/o PCE.\n s.summary['irr'] = npf.irr(s.df_cash_flow.cash_flow)\n s.summary['npv'] = npf.npv(s.discount_rate, s.df_cash_flow.cash_flow)\n \n # Add some summary fuel and electric usage and unit cost info\n s.summary['fuel_use_base'] = ann_base.secondary_fuel_units\n s.summary['fuel_use_hp'] = ann_hp.secondary_fuel_units\n s.summary['fuel_use_chg'] = ann_chg.secondary_fuel_units\n if ann_chg.secondary_fuel_units != 0.0:\n s.summary['fuel_price_incremental'] = ann_chg.secondary_fuel_dol / ann_chg.secondary_fuel_units\n else:\n s.summary['fuel_price_incremental'] = np.nan\n s.summary['elec_use_base'] = ann_base.elec_kwh\n s.summary['elec_use_hp'] = ann_hp.elec_kwh\n s.summary['elec_use_chg'] = ann_chg.elec_kwh\n s.summary['elec_rate_avg_base'] = ann_base.elec_dol / ann_base.elec_kwh\n s.summary['elec_rate_avg_hp'] = ann_hp.elec_dol / ann_hp.elec_kwh\n s.summary['elec_rate_incremental'] = ann_chg.elec_dol / ann_chg.elec_kwh","def get_cold_junction_temperature(self):\n return self._mcp9600.get('COLD_JUNCTION').temperature","def costFunction(self):\n priorDiff = np.matrix(self.model.stateVector - self.model.prior).T\n measurementDiff = np.matrix(self.model.observation\n - self.model.modelCalculation).T\n chisq = measurementDiff.T * self.errSinv * measurementDiff\n chisq += priorDiff.T * self.priorSinv * priorDiff\n \n return chisq[0,0]","def calc_chromatic_coupling(self):\n raise NotImplementedError('Chromatic Coupling is not Implemented yet.')","def heat(self, delta_temp):\n return self.heat_capacity * self.mass * delta_temp","def _get_concentration(self, state):\n return self.fc(state.float_features).exp() + self.EPSILON","def get_dar_coupling_throughput(self, wvs, wvs0=None):\n if wvs0 is None:\n wvs0 = np.mean(wvs)\n\n n = self.telescope.get_nair(wvs)\n n0 = self.telescope.get_nair(wvs0)\n\n dar = np.abs(nair.compute_dar(n, n0, np.radians(self.zenith)))\n\n lam_d = (wvs * u.um) / (self.telescope.diameter.to(u.um)) * 206265 * 1000\n\n coupling_th = np.interp(dar/lam_d, self.fiber_coupling['sep'], self.fiber_coupling['eta'], right=0)\n\n return coupling_th","def coherence(self):\r\n coherence = np.abs(self.coherency ** 2)\r\n\r\n return coherence","def calc_capital_costs (self):\n powerhouse_control_cost = 0\n if not self.cd['switchgear suitable for renewables']:\n powerhouse_control_cost = self.cd['switchgear cost']\n\n #~ road_needed = self.comp_specs['road needed for transmission line']\n\n\n if str(self.comp_specs['transmission capital cost'])\\\n != 'UNKNOWN':\n transmission_line_cost = \\\n int(self.comp_specs['transmission capital cost'])\n else:\n if str(self.comp_specs['distance to resource']) \\\n != 'UNKNOWN':\n distance = \\\n float(self.comp_specs\\\n ['distance to resource'])\n transmission_line_cost = \\\n distance*self.comp_specs['est. transmission line cost']\n\n secondary_load_cost = 0\n if self.comp_specs['secondary load']:\n secondary_load_cost = self.comp_specs['secondary load cost']\n\n if str(self.comp_specs['generation capital cost']) \\\n != 'UNKNOWN':\n wind_cost = \\\n int(self.comp_specs['generation capital cost'])\n self.cost_per_kw = np.nan\n else:\n for i in range(len(self.comp_specs['estimated costs'])):\n if int(self.comp_specs['estimated costs'].iloc[i].name) < \\\n self.load_offset_proposed:\n if i == len(self.comp_specs['estimated costs']) - 1:\n cost = float(self.comp_specs['estimated costs'].iloc[i])\n break\n continue\n\n cost = float(self.comp_specs['estimated costs'].iloc[i])\n break\n\n wind_cost = self.load_offset_proposed * cost\n self.cost_per_kw = cost\n\n #~ print powerhouse_control_cost\n #~ print transmission_line_cost\n #~ print secondary_load_cost\n #~ print wind_cost\n self.capital_costs = powerhouse_control_cost + transmission_line_cost +\\\n secondary_load_cost + wind_cost\n\n #~ print 'self.capital_costs',self.capital_costs","def get_force(self):\n # @todo: Probably need to check the state of the landing gear for this (e.g. are they on the track?).\n # Note: you can get the state of the landing gear by going through self.sim \n return 0.0","def get_force(self):\n displ = self.get_displ()\n equil = displ / np.linalg.norm(displ) * self.L0\n return self.k * (displ - equil)","def calc_maintenance_cost(self):\n\n self.maintenance_cost = self.capital_costs * .01","def cond_boiler_op_cost(Q_therm_W, Q_design_W, T_return_to_boiler_K):\n if Q_therm_W > 0.0:\n\n # boiler efficiency\n eta_boiler = cond_boiler_operation(Q_therm_W, Q_design_W, T_return_to_boiler_K)\n\n E_aux_Boiler_req_W = BOILER_P_AUX * Q_therm_W\n\n Q_primary_W = Q_therm_W / eta_boiler\n else:\n Q_primary_W = 0.0\n E_aux_Boiler_req_W = 0.0\n\n return Q_primary_W, E_aux_Boiler_req_W","def linear_heat_transfer(x, t, K_medium, rho_medium, c_medium, T_medium_initial, H_heat_transfer, T_external_applied):\n k = get_kappa(K_medium, rho_medium, c_medium)\n\n h = H_heat_transfer/K_medium\n erfc_factor_1 = erfc(x/(2*np.sqrt(k*t)))\n\n #combine factors in logdomain, since the exp-factors quickly approach\n #infinity while erfc-factor goes to zero\n log_exp_factor_1 = h*x\n log_exp_factor_2 = k*t*h**2\n log_erfc_factor_2 = np.log(erfc(x/(2*np.sqrt(k*t)) + h*np.sqrt(k*t)))\n exp_erfc_factor = np.exp(log_exp_factor_1 + log_exp_factor_2 + log_erfc_factor_2)\n\n return (erfc_factor_1 - exp_erfc_factor)*(T_external_applied - T_medium_initial) + T_medium_initial","def infer(self, potential, iterations, cf=True, x0=None):\n energy = tf.zeros(shape=[x0.shape[0], 1 + x0.shape[1]], dtype=self.dtype)\n x0 = tf.tile(x0, [1, 1, 1, self.mu.shape[-2], 1])\n for i in tf.range(iterations):\n self.conditioned(x0)\n verbose = tf.equal(tf.math.mod(i, 50), 0) or tf.equal(i + 1, iterations)\n if cf:\n grd, energy = self.gradient_cf(potential, verbose)\n else:\n with tf.GradientTape(watch_accessed_variables=False) as tape:\n tape.watch(self.trainable_variables)\n energy = self.bethe_free_energy(potential)\n grd = tape.gradient(energy, self.trainable_variables)\n\n self.optimizer.apply_gradients(zip(grd, self.trainable_variables))\n # reasonable clip for the distribution of bottom layer\n self.mu[:, 0, :729, ...].assign(tf.clip_by_value(self.mu[:, 0, :729, ...], 0., 1.))\n self.sigma[:, 0, :729, ...].assign(tf.clip_by_value(self.sigma[:, 0, :729, ...], 0.02, 1.))\n\n if verbose:\n tf.print(tf.strings.format('iter: {} dmu = {}, dsigma = {}, dw={}, Energy = {}', (\n i, tf.reduce_mean(tf.abs(grd[0])), tf.reduce_mean(tf.abs(grd[1])), tf.reduce_mean(tf.abs(grd[2])),\n tf.reduce_mean(energy))))\n return energy","def gtf(self):\n\t #if tank is empty, conductance is 0\n\t if self.tx <= 0:\n\t return 0.\n\t\t#returns 0.5, as a function of TAI\n\t else:\n\t return 0.5","def mcoe(self, update=False,\n min_heat_rate=5.5, min_fuel_cost_per_mwh=0.0,\n min_cap_fact=0.0, max_cap_fact=1.5):\n if update or self._dfs['mcoe'] is None:\n self._dfs['mcoe'] = pudl.analysis.mcoe.mcoe(\n self,\n min_heat_rate=min_heat_rate,\n min_fuel_cost_per_mwh=min_fuel_cost_per_mwh,\n min_cap_fact=min_cap_fact,\n max_cap_fact=max_cap_fact)\n return self._dfs['mcoe']","def _therm_cond(self):\n xy = dict() # used to label the components e.g 1->CO2,2->N2\n for (i, j) in enumerate(self.component_list, 1):\n xy[i] = j\n\n k_vap = 0\n for i in range(1, len(self.component_list) + 1):\n sumij = 0\n for j in range(1, len(self.component_list) + 1):\n Aij = (1 + (self.visc_d_comp[xy[i]] / self.visc_d_comp[xy[j]])**0.5 *\n (self.mw_comp[xy[j]] / self.mw_comp[xy[i]])**0.25)**2 *\\\n (8 * (1 + self.mw_comp[xy[i]] / self.mw_comp[xy[j]]))**-0.5\n sumij += self.mole_frac_comp[xy[j]] * Aij\n k_vap += self.mole_frac_comp[xy[i]] * self.therm_cond_comp[xy[i]] / sumij\n\n try:\n self.therm_cond = Expression(expr=k_vap,\n doc='Vapor thermal'\n 'conductivity [J/(m.K.s)]')\n except AttributeError:\n self.del_component(self.therm_cond)\n raise","def investment_decision_thermal_rule(_m, g, y):\r\n\r\n return (((m.DELTA[y] / m.INTEREST_RATE) * m.GAMMA[g] * m.I_C[g, y])\r\n + sum(m.DELTA[j] * m.C_FOM[g] for j in m.Y if j >= y)\r\n - m.mu_1[g, y]\r\n + ((m.DELTA[m.Y.last()] / m.INTEREST_RATE) * m.C_FOM[g])\r\n + sum(- m.sigma_3[g, j, s, t] for s in m.S for t in m.T for j in m.Y if j >= y)\r\n == 0)","def diffuse_coefficient(self):\n return self._diffuse_coefficient","def dielectric_constant_water(temperature=298.15):\n tabulated_data = np.array([[263.15, 92.10],\n [268.15, 89.96],\n [273.15, 87.90],\n [278.15, 85.90],\n [283.15, 83.96],\n [288.15, 82.06],\n [293.15, 80.20],\n [298.15, 78.38],\n [303.15, 76.60],\n [308.15, 74.86],\n [313.15, 73.17],\n [318.15, 71.50],\n [323.15, 69.88],\n [328.15, 68.29],\n [333.15, 66.74],\n [338.15, 65.22],\n [343.15, 63.73],\n [348.15, 62.28],\n [353.15, 60.87],\n [358.15, 59.48],\n [363.15, 58.13],\n [368.15, 56.81],\n [373.15, 55.51]])\n polynomal_degree = 5\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\n polynomal_degree)\n fitfunction = np.poly1d(fitdata)\n return fitfunction(temperature)","def _cost_route_fine(self):\n return self.fine","def estimate_diffusion_coefficient(n_CFx: int) -> t.Diffusivity:\n return t.Diffusivity(10 ** (-4.5360 + -0.1088 * n_CFx), \"cm^2/s\")","def _calc_ft(Tci, Thi, Tco, Tho, N_shells) -> 'ft':\n if (Tco - Tci)/Tco < 0.01 or (Thi-Tho)/Tho < 0.01:\n return 1\n try:\n return ht.F_LMTD_Fakheri(Thi, Tho, Tci, Tco,\n shells=N_shells)\n except ValueError:\n return 0.6 # Accounts for worst case scenario","def get_hcost(self):\n hvcost = self.get_hvcost()\n dcost = self.get_dcost()\n hcost = hvcost + dcost\n return hcost","def discharge_coefficient(self) -> _VectorisedFloat:\n return 0.6","def inflatedCost(self):\n\t\tinflated = self.cost\n\n\t\t# https://www.in2013dollars.com/Wine-at-home/price-inflation/2020-to-2021?amount=10000\n\t\tif self.acquisition.year <= 2018: # 2018-to-2019\n\t\t\tinflated *= 1.010727\n\n\t\tif self.acquisition.year <= 2019: # 2019-to-2020\n\t\t\tinflated *= 1.002446\n\n\t\tif self.acquisition.year <= 2020: # 2020-to-2021\n\t\t\tinflated *= 1.010612\n\n\t\tif self.acquisition.year <= 2021: # 2021-to-2022\n\t\t\tinflated *= 1.011850\n\n\t\treturn round(inflated, 2)","def target_temperature_high_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperatue_high_f\"))\r\n return celsius_to_fahrenheit(self.target_temperature_high_c)","def eco_temperature_high_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_f\"))\r\n return celsius_to_fahrenheit(self.eco_temperature_high_c)","def _normed_concentration(self, time: float) -> _VectorisedFloat:\n # The model always starts at t=0, but we avoid running concentration calculations\n # before the first presence as an optimisation.\n if time <= self._first_presence_time():\n return self.min_background_concentration()/self.normalization_factor()\n \n next_state_change_time = self._next_state_change(time)\n\n RR = self.removal_rate(next_state_change_time)\n # If RR is 0, conc_limit does not play a role but its computation \n # would raise an error -> we set it to zero.\n try:\n conc_limit = self._normed_concentration_limit(next_state_change_time)\n except ZeroDivisionError:\n conc_limit = 0.\n\n t_last_state_change = self.last_state_change(time)\n conc_at_last_state_change = self._normed_concentration_cached(t_last_state_change)\n\n delta_time = time - t_last_state_change\n fac = np.exp(-RR * delta_time)\n\n return conc_limit * (1 - fac) + conc_at_last_state_change * fac","def eco_temperature_low_f(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_f\"))\r\n return celsius_to_fahrenheit(self.eco_temperature_low_c)","def thermal(isatom, freq, scalfac,linnonlin,T):\n if isatom != \"true\":\n nfreq = len(freq)\n\n vib_temp = []\n for ifreq in range(nfreq):\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\n vib_temp.append(vib_temp_new)\n\n dE_vib = 0\n for ifreq in range(nfreq):\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\n\n dE_ZPE = 0.5 * sum(freq) * cmi2au\n\n if linnonlin == \"L\":\n dE_rot = kB * T * j2au\n elif linnonlin == \"NL\":\n dE_rot = kB * T * j2au * (3.0/2.0)\n else:\n with open(\"Thermochemistry.out\", \"a\") as ther_chem:\n ther_chem.write(\"ERROR: unknown entry for linear/nonlinear\")\n else:\n dE_ZPE = 0\n dE_vib = 0\n dE_rot = 0\n\n dE_tra = kB * T * j2au * (3.0/2.0)\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\n\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)","def locked_temp_min_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"locked_temp_min_c\"))\r\n return kelvin_to_celsius(self._locked_temp_min)","def target_temperature_high(self):\n if self._client.mode == self._client.MODE_AUTO:\n return self._client.cooltemp\n return None","def compute_windchill(t,v):\n a = 35.74\n b = 0.6215\n c = 35.75\n d = 0.4275\n v16 = v**0.16\n wci = a+(b*t)-(c*v16)+(d*t*v16)\n return wci","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def define_ufl_stress_work_diff(self):\n\n if hasattr(self, 'ufl_stress_work_diff'):\n return None\n\n if self.displacement != 0:\n # Derivative of stress term w.r.t. to displacement.\n self.ufl_stress_work_du = dlf.derivative(self.ufl_stress_work,\n self.displacement,\n self.trial_vector)\n else:\n self.ufl_stress_work_du = 0\n\n if self.velocity != 0:\n self.ufl_stress_work_dv = dlf.derivative(self.ufl_stress_work,\n self.velocity,\n self.trial_vector)\n else:\n self.ufl_stress_work_dv = 0\n\n if self.pressure != 0:\n self.ufl_stress_work_dp = dlf.derivative(self.ufl_stress_work,\n self.pressure,\n self.trial_scalar)\n else:\n self.ufl_stress_work_dp = 0\n\n return None","def calcCostFun(self):\n\n self.start()\n F, K = self.model()\n \n return self.costFunction","def get_fc3(supercell,\n primitive,\n displacements,\n forces,\n fc_calculator=None,\n fc_calculator_options=None,\n is_compact_fc=False,\n log_level=0):\n\n if fc_calculator == 'alm':\n from phono3py.interface.alm import get_fc3\n return get_fc3(supercell,\n primitive,\n displacements,\n forces,\n options=fc_calculator_options,\n is_compact_fc=is_compact_fc,\n log_level=log_level)\n else:\n msg = (\"Force constants calculator of %s was not found .\"\n % fc_calculator)\n raise RuntimeError(msg)","def get_cruft(self):\n\n raise computerjanitor.UnimplementedMethod(self.get_cruft)","def get_cheap_conformer(self):\n num_confs = min(500, max(50, len(self.mol.atoms) * 3))\n rd_mol, rd_index_map = conformers.embed_rdkit(label=self.label, mol=self.mol, num_confs=num_confs)\n xyzs, energies = conformers.rdkit_force_field(label=self.label, rd_mol=rd_mol, rd_index_map=rd_index_map,\n mol=self.mol, force_field='MMFF94', return_xyz_strings=True)\n if energies:\n min_energy = min(energies)\n min_energy_index = energies.index(min_energy)\n self.cheap_conformer = xyzs[min_energy_index]\n elif xyzs:\n self.cheap_conformer = xyzs[0]\n else:\n logger.warning('Could not generate a cheap conformer for {0}'.format(self.label))\n self.cheap_conformer = None","def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature","def getCapitalChargeFactor(self):\n return 1 / self._interestRate - 1 / (pow(1 + self._interestRate, self._economicLifetime) * self._interestRate)","def get_remaining_fuel(self):\n return min(self.liquid_fuel, self.oxidizer)","def _cost_load_theft(self):\n theft_prob = self.theft_probability\n theft = np.random.choice([0, 1], p=[1-theft_prob, theft_prob])\n if theft == 1:\n cost = self._calculate_profit_weight() * PRICE_PER_KG\n return cost\n else:\n return 0","def _get_coeffs(self):\n # lift (Clmax) and parasitic drag (Cd0max)\n self.cl = 0.0\n self.cd = 0.0\n kpp = 0.0\n\n for sail in self.sails:\n\n self.cl += sail.cl(self.awa) * sail.area * sail.bk\n self.cd += sail.cd(self.awa) * sail.area * sail.bk\n kpp += sail.cl(self.awa) ** 2 * sail.area * sail.bk * sail.kp\n\n self.cl /= self.area\n self.cd /= self.area\n\n # viscous quadratic parasitic drag and induced drag\n devisor_1 = self.area * self.cl ** 2\n devisor_2 = np.pi * self._heff(self.awa) ** 2\n self.CE = (kpp / devisor_1 if devisor_1 else 0.0) + (self.area / devisor_2 if devisor_2 else 0.0)\n\n # fraction of parasitic drag due to jib\n self.fcdj = 0.0\n for sail in self.sails:\n if sail.type == \"jib\":\n self.fcdj = (\n sail.bk * sail.cd(self.awa) * sail.area / (self.cd * self.area)\n )\n\n # final lift and drag\n self.cd = self.cd * (\n self.flat * self.fcdmult(self.flat) * self.fcdj + (1 - self.fcdj)\n ) + self.CE * self.cl ** 2 * self.flat ** 2 * self.fcdmult(self.flat)\n self.cl = self.flat * self.cl","def get_conductivity(self) -> float:\n try:\n datalist = self.get_data()\n data = datalist[0]\n if data.endswith('\\x00'):\n data = data.rstrip('\\x00')\n return float(data)\n else:\n return float(data)\n except Exception as err:\n print(f'get_conductivity error: {err}')\n return -1","def temperature(self) -> float:\n # Start a measurement then poll the measurement finished bit.\n self.temp_start = 1\n while self.temp_running > 0:\n pass\n # Grab the temperature value and convert it to Celsius.\n # This uses the same observed value formula from the Radiohead library.\n temp = self._read_u8(_REG_TEMP2)\n return 166.0 - temp","def cost(self):\n abs_cost = sum(f['price'] * f['qty'] for f in self.fills)\n return -abs_cost if self.is_ask() else abs_cost","def get_duct_linear_heat_loss_coefficient() -> float:\n return 0.49","def fpct(self):\n # 1 is probably the best number in most cases because the game is often CPU-bound.\n # the following number could be chosen instead someday\n tps = self.real_speed * 1000 / VIRTUAL_TIME_INTERVAL\n # Avoid unrealistic ping values.\n ping = min(self.max_ping, self.ping)\n result = int(tps * ping * config.fpct_coef) + 1\n return min(config.fpct_max, result)","def best_coupling_frequency(self):\n\n idx_best = self.coupling().argmax()\n\n return self.freq.f[idx_best]"],"string":"[\n \"def thermalConductivity(self, Tk=None, Tc=None):\\n Tk = getTk(Tc, Tk)\\n self.checkPropertyTempRange(\\\"thermal conductivity\\\", Tk)\\n thermalConductivity = (\\n 2.13014e-08 * Tk**3\\n - 6.31916e-05 * Tk**2\\n + 1.11629e-01 * Tk\\n - 2.00043e00\\n )\\n return thermalConductivity * 1e-3\",\n \"def get_chiller_temperature(self) -> float:\\n\\n return self.send(self.cmd.GET_COOLING_ACT)\",\n \"def getHeatFlux(self, T):\\n\\t\\tQ = self.heat_transfer_coefficient * (self.T_wall - T)\\n\\t\\treturn Q\",\n \"def conductive_heat_flux(discr, eos, cv, grad_t):\\n transport = eos.transport_model()\\n return -transport.thermal_conductivity(eos, cv)*grad_t\",\n \"def thermal_conductivity_of_air(self) -> float:\\n\\n # This more accurate equation is not used by the paper.\\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\\n # )\\n\\n # The reference suggests this equation is accurate to 1%.\\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646\",\n \"def setHeatTransferCoeff(self, u):\\n return _cantera.wall_setHeatTransferCoeff(self.__wall_id, u)\",\n \"def calc_cogen_const(q_heat_Wh, thermal_eff, electrical_eff):\\n q_fuel_Wh = q_heat_Wh / thermal_eff\\n p_el_Wh = q_fuel_Wh * electrical_eff\\n q_anth_Wh = q_fuel_Wh - (q_heat_Wh + p_el_Wh)\\n return q_fuel_Wh, p_el_Wh, q_anth_Wh\",\n \"def heatFlowRate(self):\\n return _cantera.wall_Q(self.__wall_id)\",\n \"def calc_maintenance_cost (self):\\n\\n if str(self.comp_specs['operational costs']) \\\\\\n != 'UNKNOWN':\\n self.maintenance_cost = \\\\\\n self.comp_specs['operational costs']\\n else:\\n self.maintenance_cost = \\\\\\n (self.comp_specs['percent o&m'] / 100.0) * self.capital_costs\\n #~ print 'self.maintenance_cost',self.maintenance_cost\",\n \"def conductivity(self):\\n m = 1.67296736e-02 # Determined from optimisation\\n c = 8.54665149e-05 # Determined from optimisation\\n return m * self.concentration + c\",\n \"def target_temperature(self):\\n if self.current_operation == 'Heat & Cool':\\n return None\\n if self.current_operation == 'Heat only':\\n return int(self._api._heatto)\\n elif self.current_operation == 'Cool only':\\n return int(self._api._coolto)\\n return None\",\n \"def fuel_cost(self, update=False):\\n if update or self._dfs['fuel_cost'] is None:\\n self._dfs['fuel_cost'] = pudl.analysis.mcoe.fuel_cost(self)\\n return self._dfs['fuel_cost']\",\n \"def target_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature)\",\n \"def thermal_conductivity(temperature):\\n a0 = -4.1236\\n a1 = 13.788\\n a2 = -26.068\\n a3 = 26.272\\n a4 = -14.663\\n a5 = 4.4954\\n a6 = -0.6905\\n a7 = 0.0397\\n log_t = math.log10(temperature)\\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\\\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\\n g10_thermal_conductivity = 10.0**f_exp\\n return g10_thermal_conductivity\",\n \"def target_temperature(self) -> float | None:\\n if self.hvac_mode == HVACMode.COOL:\\n return self.target_temperature_high\\n if self.hvac_mode == HVACMode.HEAT:\\n return self.target_temperature_low\\n return None\",\n \"def get_compensated_temperature() -> float:\\n comp_factor = 2.25\\n cpu_temp = get_cpu_temperature()\\n raw_temp = bme280.get_temperature()\\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\\n # print(\\\"\\\"\\\"\\n # Compensated_Temperature: {:05.2f} *C\\n # Pressure: {:05.2f} hPa\\n # Relative humidity: {:05.2f} %\\n # \\\"\\\"\\\".format(temperature, pressure, humidity))\\n return comp_temp\",\n \"def transport_cost_per_t(self):\\n return safe_divide(self.reseller.operating_expenses(), self.quantity_fieldside)\",\n \"def fuel_cond(T):\\n\\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\\n \\n return kc\",\n \"def best_coupling(self):\\n\\n return self.coupling().max()\",\n \"def target_temperature_high_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_high_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature_high)\",\n \"def coupling(self):\\n couple = coupling_parameter(\\n self.T_e, self.n_e, (self.particle, self.particle), self.Z\\n )\\n if couple < 0.01:\\n warnings.warn(\\n f\\\"Coupling parameter is {couple}, you might have strong coupling effects\\\",\\n CouplingWarning,\\n )\\n\\n return couple\",\n \"def cooled_surface_temp(T:np.ndarray) -> float:\\n \\n return T.dot(cs_temp_weights)\",\n \"def get_chiller_temperature_setpoint(self) -> float:\\n\\n return self.send(self.cmd.GET_COOLING_SET)\",\n \"def distribute(self, date_time, air_temp, vapor_pressure=None,\\n dew_point=None, cloud_factor=None):\\n\\n self._logger.debug('%s Distributing thermal' % date_time)\\n\\n # calculate clear sky thermal\\n if self.clear_sky_method == 'marks1979':\\n cth = np.zeros_like(air_temp, dtype=np.float64)\\n envphys_c.ctopotherm(\\n air_temp, dew_point,\\n self.dem,\\n self.sky_view_factor,\\n cth,\\n self.config['marks1979_nthreads'])\\n\\n elif self.clear_sky_method == 'dilley1998':\\n cth = clear_sky.Dilly1998(air_temp, vapor_pressure/1000)\\n\\n elif self.clear_sky_method == 'prata1996':\\n cth = clear_sky.Prata1996(air_temp, vapor_pressure/1000)\\n\\n elif self.clear_sky_method == 'angstrom1918':\\n cth = clear_sky.Angstrom1918(air_temp, vapor_pressure/1000)\\n\\n # terrain factor correction\\n if (self.sky_view_factor is not None) and \\\\\\n (self.clear_sky_method != 'marks1979'):\\n # apply (emiss * skvfac) + (1.0 - skvfac) to the longwave\\n cth = cth * self.sky_view_factor + (1.0 - self.sky_view_factor) * \\\\\\n STEF_BOLTZ * air_temp**4\\n\\n # make output variable\\n self.thermal_clear = cth.copy()\\n\\n # correct for the cloud factor\\n # ratio of measured/modeled solar indicates the thermal correction\\n if self.correct_cloud:\\n if self.cloud_method == 'garen2005':\\n cth = cloud.Garen2005(cth,\\n cloud_factor)\\n\\n elif self.cloud_method == 'unsworth1975':\\n cth = cloud.Unsworth1975(cth,\\n air_temp,\\n cloud_factor)\\n\\n elif self.cloud_method == 'kimball1982':\\n cth = cloud.Kimball1982(cth,\\n air_temp,\\n vapor_pressure/1000,\\n cloud_factor)\\n\\n elif self.cloud_method == 'crawford1999':\\n cth = cloud.Crawford1999(cth,\\n air_temp,\\n cloud_factor)\\n\\n # make output variable\\n self.thermal_cloud = cth.copy()\\n\\n # correct for vegetation\\n if self.correct_veg:\\n cth = vegetation.thermal_correct_canopy(cth,\\n air_temp,\\n self.veg_tau,\\n self.veg_height)\\n\\n # make output variable\\n self.thermal_veg = cth.copy()\\n\\n self.thermal = utils.set_min_max(cth, self.min, self.max)\",\n \"def target_temperature(self) -> float | None:\\n if self._device.mode == ThermostatMode.COOL and self._device.cooling_setpoint:\\n return self._device.scaled_cooling_setpoint\\n\\n if self._device.heating_setpoint:\\n return self._device.scaled_heating_setpoint\\n\\n return None\",\n \"def get_correction_factor(self, temperature, humidity):\\n\\n if temperature < 20:\\n return self.CORA * temperature * temperature - self.CORB * temperature + self.CORC - (humidity - 33.) * self.CORD\\n\\n return self.CORE * temperature + self.CORF * humidity + self.CORG\",\n \"def get_correction_factor(self, temperature, humidity):\\n\\n if temperature < 20:\\n return self.CORA * temperature * temperature - self.CORB * temperature + self.CORC - (humidity - 33.) * self.CORD\\n\\n return self.CORE * temperature + self.CORF * humidity + self.CORG\",\n \"def eco_temperature_high_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_high)\",\n \"def adjust_cost(self) -> None:\\n\\n n_iterations = self.array.shape[-1]\\n n_year = len(self.array.year.values)\\n\\n # If uncertainty is not considered, the cost factor equals 1.\\n # Otherwise, a variability of +/-30% is added.\\n\\n if n_iterations == 1:\\n cost_factor = 1\\n else:\\n if \\\"reference\\\" in self.array.value.values.tolist():\\n cost_factor = np.ones((n_iterations, 1))\\n else:\\n cost_factor = np.random.triangular(0.7, 1, 1.3, (n_iterations, 1))\\n\\n # Correction of hydrogen tank cost, per kg\\n # Correction of fuel cell stack cost, per kW\\n if \\\"FCEV\\\" in self.array.powertrain:\\n self.array.loc[\\n dict(powertrain=\\\"FCEV\\\", parameter=\\\"fuel tank cost per kg\\\")\\n ] = np.reshape(\\n (1.078e58 * np.exp(-6.32e-2 * self.array.year.values) + 3.43e2)\\n * cost_factor,\\n (1, n_year, n_iterations),\\n )\\n\\n self.array.loc[\\n dict(powertrain=\\\"FCEV\\\", parameter=\\\"fuel tank cost per kg\\\")\\n ] = np.reshape(\\n (3.15e66 * np.exp(-7.35e-2 * self.array.year.values) + 2.39e1)\\n * cost_factor,\\n (1, n_year, n_iterations),\\n )\\n\\n # Correction of energy battery system cost, per kWh\\n list_batt = [\\n i\\n for i in [\\\"BEV\\\", \\\"PHEV-e\\\", \\\"PHEV-c-p\\\", \\\"PHEV-c-d\\\"]\\n if i in self.array.powertrain\\n ]\\n if len(list_batt) > 0:\\n self.array.loc[\\n dict(powertrain=list_batt, parameter=\\\"energy battery cost per kWh\\\")\\n ] = np.reshape(\\n (2.75e86 * np.exp(-9.61e-2 * self.array.year.values) + 5.059e1)\\n * cost_factor,\\n (1, 1, n_year, n_iterations),\\n )\\n\\n # Correction of power battery system cost, per kW\\n list_pwt = [\\n i\\n for i in [\\n \\\"ICEV-p\\\",\\n \\\"ICEV-d\\\",\\n \\\"ICEV-g\\\",\\n \\\"PHEV-c-p\\\",\\n \\\"PHEV-c-d\\\",\\n \\\"FCEV\\\",\\n \\\"HEV-p\\\",\\n \\\"HEV-d\\\",\\n ]\\n if i in self.array.powertrain\\n ]\\n\\n if len(list_pwt) > 0:\\n self.array.loc[\\n dict(powertrain=list_pwt, parameter=\\\"power battery cost per kW\\\")\\n ] = np.reshape(\\n (8.337e40 * np.exp(-4.49e-2 * self.array.year.values) + 11.17)\\n * cost_factor,\\n (1, 1, n_year, n_iterations),\\n )\\n\\n # Correction of combustion powertrain cost for ICEV-g\\n if \\\"ICEV-g\\\" in self.array.powertrain:\\n self.array.loc[\\n dict(powertrain=\\\"ICEV-g\\\", parameter=\\\"combustion powertrain cost per kW\\\")\\n ] = np.clip(\\n np.reshape(\\n (5.92e160 * np.exp(-0.1819 * self.array.year.values) + 26.76)\\n * cost_factor,\\n (1, n_year, n_iterations),\\n ),\\n None,\\n 100,\\n )\",\n \"def discharge_coefficient(self) -> _VectorisedFloat:\\n window_ratio = np.array(self.window_width / self.window_height)\\n coefs = np.empty(window_ratio.shape + (2, ), dtype=np.float64)\\n\\n coefs[window_ratio < 0.5] = (0.06, 0.612)\\n coefs[np.bitwise_and(0.5 <= window_ratio, window_ratio < 1)] = (0.048, 0.589)\\n coefs[np.bitwise_and(1 <= window_ratio, window_ratio < 2)] = (0.04, 0.563)\\n coefs[window_ratio >= 2] = (0.038, 0.548)\\n M, cd_max = coefs.T\\n\\n window_angle = 2.*np.rad2deg(np.arcsin(self.opening_length/(2.*self.window_height)))\\n return cd_max*(1-np.exp(-M*window_angle))\",\n \"def CalcForceDistribution(self):\\n\\t\\t\\n\\t\\tself.F = self.s * (self.Tether - self.X)\\n\\t\\t\\n\\t\\treturn self.F\",\n \"def DragCoeff(h,Vc,Temp_m,Thrust,S):\\n T,p,rho = isa(h)\\n return Thrust/(0.5*rho*VTrue(h,Vc,p,Temp_m)**2*S)\",\n \"def min_background_concentration(self) -> _VectorisedFloat:\\n return self.CO2_atmosphere_concentration\",\n \"def eco_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_low)\",\n \"def coherence(self):\\r\\n return np.abs(self.coherency) ** 2\",\n \"def gradient_cf(self, potential, get_energy=True):\\n xn, xe, lpn, lpe, alpha, o1, o2 = self(None)\\n fn_, fe_ = potential((xn, xe))\\n fn_ = (fn_ + self.tw * lpn) * self.wn\\n fe_ = (fe_ - lpe) * self.we\\n fn = fn_ * alpha\\n fe = fe_ * alpha\\n dmu = tf.math.divide_no_nan(tf.reduce_sum(fn * self.xn, axis=-1, keepdims=True), self.sigma)\\n dsg = tf.math.divide_no_nan(tf.reduce_sum(fn * self.x22, axis=-1, keepdims=True), self.sigma)\\n dmu1 = tf.math.divide_no_nan(tf.reduce_sum(fe * self.xi, -1, keepdims=True), o1)\\n dmu2 = tf.reduce_sum(fe * self.xj, -1, keepdims=True) / o2\\n dsg1 = tf.math.divide_no_nan(tf.reduce_sum(fe * self.xi22, -1, keepdims=True), o1)\\n dsg2 = tf.reduce_sum(fe * self.xj22, -1, keepdims=True) / o2\\n\\n dmu += (tf.concat([dmu1, self.top0], 2) + tf.concat(\\n [self.btm0, tf.reshape(tf.reduce_sum(tf.reshape(dmu2[:, :, :729, ...], self.s1), [3, 5]), self.s3),\\n tf.reshape(tf.reduce_sum(tf.reshape(dmu2[:, :, 729:810, ...], self.s2), [3, 5]), self.s3),\\n tf.reduce_sum(dmu2[:, :, 810:, ...], 2, True)], 2))\\n\\n dsg += (tf.concat([dsg1, self.top0], 2) + tf.concat(\\n [self.btm0, tf.reshape(tf.reduce_sum(tf.reshape(dsg2[:, :, :729, ...], self.s1), [3, 5]), self.s3),\\n tf.reshape(tf.reduce_sum(tf.reshape(dsg2[:, :, 729:810, ...], self.s2), [3, 5]), self.s3),\\n tf.reduce_sum(dsg2[:, :, 810:, ...], 2, True)], 2))\\n\\n dalpha = (tf.reduce_sum(fn_, [2, 4], keepdims=True) + tf.reduce_sum(fe_, [2, 4], keepdims=True))\\n dw = alpha * (dalpha - tf.reduce_sum(dalpha * alpha, 3, keepdims=True))\\n energy = tf.zeros(fn.shape[:2], tf.float64) if not get_energy else \\\\\\n -(tf.reduce_sum(fn, [2, 3, 4]) + tf.reduce_sum(fe, [2, 3, 4]))\\n return (-dmu * sqrt2, -dsg, -dw), energy\",\n \"def target_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature_low)\",\n \"def cost(distance, highway, bicycle, incline, preferences):\\n\\n #unpack preferences\\n (flatness_pref, bicycle_pref, distance_pref,\\n motorway_pref, highway_pref, residential_pref) = preferences\\n multiplier = 1 + bike_multiplier(bicycle, bicycle_pref) + road_multiplier(highway, bicycle_pref, motorway_pref, highway_pref, residential_pref)\\n if multiplier <= 0:\\n multiplier = 0.01\\n incl = incline_multiplier(float(incline))*flatness_pref\\n cost = float(distance) * multiplier + incl\\n if cost <= 0:\\n cost = 0.01\\n return cost\",\n \"def target_temperature_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_f\\\"))\\r\\n return celsius_to_fahrenheit(self.target_temperature_c)\",\n \"def net_alchemical_force(self):\\n return self._cpp_obj.net_force\",\n \"def target_temperature(self):\\n if self._client.mode == self._client.MODE_HEAT:\\n return self._client.heattemp\\n if self._client.mode == self._client.MODE_COOL:\\n return self._client.cooltemp\\n return None\",\n \"def LiftCoeff(h,Vc,Temp_m,W,S):\\n T,p,rho = isa(h)\\n return W*g0/(0.5*rho*VTrue(h,Vc,p,Temp_m)**2*S)\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def forcing(self):\\n if not self._fr or not self._frstar:\\n raise ValueError('Need to compute Fr, Fr* first.')\\n return -Matrix([self._f_d, self._f_dnh])\",\n \"def cash_flow(self):\\n _cash_flow = self.after_tax_profit() + self.depreciation()\\n return _cash_flow\",\n \"def thermal_operating_costs_rule(_m, y, s):\\r\\n\\r\\n return sum((m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])) * m.p[g, y, s, t]\\r\\n for g in m.G_THERM for t in m.T)\",\n \"def computeSmoothEnergy(self):\\n _cgco.gcoComputeSmoothEnergy(self.handle, self.energyTempArray)\\n return self._convertEnergyBack(self.energyTempArray[0])\",\n \"def local_cost(self) -> Number:\\n mu_E = self._payoff_weight_energy\\n mu_T = self._payoff_weight_time\\n D_loc = SIMULATION_PARAMETERS['LOCAL_CPU_CYCLES']\\n F_loc = self.cpu_frequency\\n T_loc = D_loc / F_loc\\n E_loc = D_loc / self.cpu_effeciency\\n return mu_T * T_loc + mu_E * E_loc\",\n \"def calc_cash_flow(self):\\n s = self # shortcut variable\\n\\n # determine the changes caused by the heat pump on an annual basis.\\n # First calculate annual totals for base case and heat pump case and\\n # then calculate the change.\\n ann_base = s.df_mo_dol_base.sum()\\n ann_hp = s.df_mo_dol_hp.sum()\\n ann_chg = ann_hp - ann_base\\n initial_cost = np.zeros(s.hp_life+1)\\n \\n # Am not automatically adding sales tax to the initial cost as the user was\\n # supposed to includes sales tax in their input.\\n initial_cost[0] = -s.capital_cost * (1 - s.pct_financed) + s.rebate_dol\\n loan_pmt = npf.pmt(s.loan_interest, s.loan_term, s.capital_cost * s.pct_financed)\\n if loan_pmt < -0.01: # loan payment is negative\\n loan_cost = [0.0] + [loan_pmt] * s.loan_term + [0.0] * (s.hp_life - s.loan_term)\\n loan_cost = np.array(loan_cost)\\n else:\\n loan_cost = 0.0\\n op_cost = -s.op_cost_chg * make_pattern(s.inflation_rate, s.hp_life)\\n fuel_cost = -ann_chg.secondary_fuel_dol * make_pattern(s.fuel_esc_rate, s.hp_life)\\n elec_cost = -ann_chg.elec_dol * make_pattern(s.elec_esc_rate, s.hp_life)\\n cash_flow = initial_cost + loan_cost + op_cost + fuel_cost + elec_cost\\n\\n # calculate cumulative, discounted cash flow.\\n disc_factor = np.ones(s.hp_life) * (1 + s.discount_rate)\\n disc_factor = np.insert(disc_factor.cumprod(), 0, 1.0)\\n cum_disc_cash_flow = np.cumsum(cash_flow / disc_factor)\\n \\n s.df_cash_flow = pd.DataFrame(\\n {'initial_cost': initial_cost,\\n 'loan_cost': loan_cost,\\n 'op_cost': op_cost,\\n 'fuel_cost': fuel_cost,\\n 'elec_cost': elec_cost,\\n 'cash_flow': cash_flow,\\n 'cum_disc_cash_flow': cum_disc_cash_flow,\\n }\\n )\\n s.df_cash_flow.index.name = 'year'\\n \\n # Calculate IRR and NPV for w/ and w/o PCE.\\n s.summary['irr'] = npf.irr(s.df_cash_flow.cash_flow)\\n s.summary['npv'] = npf.npv(s.discount_rate, s.df_cash_flow.cash_flow)\\n \\n # Add some summary fuel and electric usage and unit cost info\\n s.summary['fuel_use_base'] = ann_base.secondary_fuel_units\\n s.summary['fuel_use_hp'] = ann_hp.secondary_fuel_units\\n s.summary['fuel_use_chg'] = ann_chg.secondary_fuel_units\\n if ann_chg.secondary_fuel_units != 0.0:\\n s.summary['fuel_price_incremental'] = ann_chg.secondary_fuel_dol / ann_chg.secondary_fuel_units\\n else:\\n s.summary['fuel_price_incremental'] = np.nan\\n s.summary['elec_use_base'] = ann_base.elec_kwh\\n s.summary['elec_use_hp'] = ann_hp.elec_kwh\\n s.summary['elec_use_chg'] = ann_chg.elec_kwh\\n s.summary['elec_rate_avg_base'] = ann_base.elec_dol / ann_base.elec_kwh\\n s.summary['elec_rate_avg_hp'] = ann_hp.elec_dol / ann_hp.elec_kwh\\n s.summary['elec_rate_incremental'] = ann_chg.elec_dol / ann_chg.elec_kwh\",\n \"def get_cold_junction_temperature(self):\\n return self._mcp9600.get('COLD_JUNCTION').temperature\",\n \"def costFunction(self):\\n priorDiff = np.matrix(self.model.stateVector - self.model.prior).T\\n measurementDiff = np.matrix(self.model.observation\\n - self.model.modelCalculation).T\\n chisq = measurementDiff.T * self.errSinv * measurementDiff\\n chisq += priorDiff.T * self.priorSinv * priorDiff\\n \\n return chisq[0,0]\",\n \"def calc_chromatic_coupling(self):\\n raise NotImplementedError('Chromatic Coupling is not Implemented yet.')\",\n \"def heat(self, delta_temp):\\n return self.heat_capacity * self.mass * delta_temp\",\n \"def _get_concentration(self, state):\\n return self.fc(state.float_features).exp() + self.EPSILON\",\n \"def get_dar_coupling_throughput(self, wvs, wvs0=None):\\n if wvs0 is None:\\n wvs0 = np.mean(wvs)\\n\\n n = self.telescope.get_nair(wvs)\\n n0 = self.telescope.get_nair(wvs0)\\n\\n dar = np.abs(nair.compute_dar(n, n0, np.radians(self.zenith)))\\n\\n lam_d = (wvs * u.um) / (self.telescope.diameter.to(u.um)) * 206265 * 1000\\n\\n coupling_th = np.interp(dar/lam_d, self.fiber_coupling['sep'], self.fiber_coupling['eta'], right=0)\\n\\n return coupling_th\",\n \"def coherence(self):\\r\\n coherence = np.abs(self.coherency ** 2)\\r\\n\\r\\n return coherence\",\n \"def calc_capital_costs (self):\\n powerhouse_control_cost = 0\\n if not self.cd['switchgear suitable for renewables']:\\n powerhouse_control_cost = self.cd['switchgear cost']\\n\\n #~ road_needed = self.comp_specs['road needed for transmission line']\\n\\n\\n if str(self.comp_specs['transmission capital cost'])\\\\\\n != 'UNKNOWN':\\n transmission_line_cost = \\\\\\n int(self.comp_specs['transmission capital cost'])\\n else:\\n if str(self.comp_specs['distance to resource']) \\\\\\n != 'UNKNOWN':\\n distance = \\\\\\n float(self.comp_specs\\\\\\n ['distance to resource'])\\n transmission_line_cost = \\\\\\n distance*self.comp_specs['est. transmission line cost']\\n\\n secondary_load_cost = 0\\n if self.comp_specs['secondary load']:\\n secondary_load_cost = self.comp_specs['secondary load cost']\\n\\n if str(self.comp_specs['generation capital cost']) \\\\\\n != 'UNKNOWN':\\n wind_cost = \\\\\\n int(self.comp_specs['generation capital cost'])\\n self.cost_per_kw = np.nan\\n else:\\n for i in range(len(self.comp_specs['estimated costs'])):\\n if int(self.comp_specs['estimated costs'].iloc[i].name) < \\\\\\n self.load_offset_proposed:\\n if i == len(self.comp_specs['estimated costs']) - 1:\\n cost = float(self.comp_specs['estimated costs'].iloc[i])\\n break\\n continue\\n\\n cost = float(self.comp_specs['estimated costs'].iloc[i])\\n break\\n\\n wind_cost = self.load_offset_proposed * cost\\n self.cost_per_kw = cost\\n\\n #~ print powerhouse_control_cost\\n #~ print transmission_line_cost\\n #~ print secondary_load_cost\\n #~ print wind_cost\\n self.capital_costs = powerhouse_control_cost + transmission_line_cost +\\\\\\n secondary_load_cost + wind_cost\\n\\n #~ print 'self.capital_costs',self.capital_costs\",\n \"def get_force(self):\\n # @todo: Probably need to check the state of the landing gear for this (e.g. are they on the track?).\\n # Note: you can get the state of the landing gear by going through self.sim \\n return 0.0\",\n \"def get_force(self):\\n displ = self.get_displ()\\n equil = displ / np.linalg.norm(displ) * self.L0\\n return self.k * (displ - equil)\",\n \"def calc_maintenance_cost(self):\\n\\n self.maintenance_cost = self.capital_costs * .01\",\n \"def cond_boiler_op_cost(Q_therm_W, Q_design_W, T_return_to_boiler_K):\\n if Q_therm_W > 0.0:\\n\\n # boiler efficiency\\n eta_boiler = cond_boiler_operation(Q_therm_W, Q_design_W, T_return_to_boiler_K)\\n\\n E_aux_Boiler_req_W = BOILER_P_AUX * Q_therm_W\\n\\n Q_primary_W = Q_therm_W / eta_boiler\\n else:\\n Q_primary_W = 0.0\\n E_aux_Boiler_req_W = 0.0\\n\\n return Q_primary_W, E_aux_Boiler_req_W\",\n \"def linear_heat_transfer(x, t, K_medium, rho_medium, c_medium, T_medium_initial, H_heat_transfer, T_external_applied):\\n k = get_kappa(K_medium, rho_medium, c_medium)\\n\\n h = H_heat_transfer/K_medium\\n erfc_factor_1 = erfc(x/(2*np.sqrt(k*t)))\\n\\n #combine factors in logdomain, since the exp-factors quickly approach\\n #infinity while erfc-factor goes to zero\\n log_exp_factor_1 = h*x\\n log_exp_factor_2 = k*t*h**2\\n log_erfc_factor_2 = np.log(erfc(x/(2*np.sqrt(k*t)) + h*np.sqrt(k*t)))\\n exp_erfc_factor = np.exp(log_exp_factor_1 + log_exp_factor_2 + log_erfc_factor_2)\\n\\n return (erfc_factor_1 - exp_erfc_factor)*(T_external_applied - T_medium_initial) + T_medium_initial\",\n \"def infer(self, potential, iterations, cf=True, x0=None):\\n energy = tf.zeros(shape=[x0.shape[0], 1 + x0.shape[1]], dtype=self.dtype)\\n x0 = tf.tile(x0, [1, 1, 1, self.mu.shape[-2], 1])\\n for i in tf.range(iterations):\\n self.conditioned(x0)\\n verbose = tf.equal(tf.math.mod(i, 50), 0) or tf.equal(i + 1, iterations)\\n if cf:\\n grd, energy = self.gradient_cf(potential, verbose)\\n else:\\n with tf.GradientTape(watch_accessed_variables=False) as tape:\\n tape.watch(self.trainable_variables)\\n energy = self.bethe_free_energy(potential)\\n grd = tape.gradient(energy, self.trainable_variables)\\n\\n self.optimizer.apply_gradients(zip(grd, self.trainable_variables))\\n # reasonable clip for the distribution of bottom layer\\n self.mu[:, 0, :729, ...].assign(tf.clip_by_value(self.mu[:, 0, :729, ...], 0., 1.))\\n self.sigma[:, 0, :729, ...].assign(tf.clip_by_value(self.sigma[:, 0, :729, ...], 0.02, 1.))\\n\\n if verbose:\\n tf.print(tf.strings.format('iter: {} dmu = {}, dsigma = {}, dw={}, Energy = {}', (\\n i, tf.reduce_mean(tf.abs(grd[0])), tf.reduce_mean(tf.abs(grd[1])), tf.reduce_mean(tf.abs(grd[2])),\\n tf.reduce_mean(energy))))\\n return energy\",\n \"def gtf(self):\\n\\t #if tank is empty, conductance is 0\\n\\t if self.tx <= 0:\\n\\t return 0.\\n\\t\\t#returns 0.5, as a function of TAI\\n\\t else:\\n\\t return 0.5\",\n \"def mcoe(self, update=False,\\n min_heat_rate=5.5, min_fuel_cost_per_mwh=0.0,\\n min_cap_fact=0.0, max_cap_fact=1.5):\\n if update or self._dfs['mcoe'] is None:\\n self._dfs['mcoe'] = pudl.analysis.mcoe.mcoe(\\n self,\\n min_heat_rate=min_heat_rate,\\n min_fuel_cost_per_mwh=min_fuel_cost_per_mwh,\\n min_cap_fact=min_cap_fact,\\n max_cap_fact=max_cap_fact)\\n return self._dfs['mcoe']\",\n \"def _therm_cond(self):\\n xy = dict() # used to label the components e.g 1->CO2,2->N2\\n for (i, j) in enumerate(self.component_list, 1):\\n xy[i] = j\\n\\n k_vap = 0\\n for i in range(1, len(self.component_list) + 1):\\n sumij = 0\\n for j in range(1, len(self.component_list) + 1):\\n Aij = (1 + (self.visc_d_comp[xy[i]] / self.visc_d_comp[xy[j]])**0.5 *\\n (self.mw_comp[xy[j]] / self.mw_comp[xy[i]])**0.25)**2 *\\\\\\n (8 * (1 + self.mw_comp[xy[i]] / self.mw_comp[xy[j]]))**-0.5\\n sumij += self.mole_frac_comp[xy[j]] * Aij\\n k_vap += self.mole_frac_comp[xy[i]] * self.therm_cond_comp[xy[i]] / sumij\\n\\n try:\\n self.therm_cond = Expression(expr=k_vap,\\n doc='Vapor thermal'\\n 'conductivity [J/(m.K.s)]')\\n except AttributeError:\\n self.del_component(self.therm_cond)\\n raise\",\n \"def investment_decision_thermal_rule(_m, g, y):\\r\\n\\r\\n return (((m.DELTA[y] / m.INTEREST_RATE) * m.GAMMA[g] * m.I_C[g, y])\\r\\n + sum(m.DELTA[j] * m.C_FOM[g] for j in m.Y if j >= y)\\r\\n - m.mu_1[g, y]\\r\\n + ((m.DELTA[m.Y.last()] / m.INTEREST_RATE) * m.C_FOM[g])\\r\\n + sum(- m.sigma_3[g, j, s, t] for s in m.S for t in m.T for j in m.Y if j >= y)\\r\\n == 0)\",\n \"def diffuse_coefficient(self):\\n return self._diffuse_coefficient\",\n \"def dielectric_constant_water(temperature=298.15):\\n tabulated_data = np.array([[263.15, 92.10],\\n [268.15, 89.96],\\n [273.15, 87.90],\\n [278.15, 85.90],\\n [283.15, 83.96],\\n [288.15, 82.06],\\n [293.15, 80.20],\\n [298.15, 78.38],\\n [303.15, 76.60],\\n [308.15, 74.86],\\n [313.15, 73.17],\\n [318.15, 71.50],\\n [323.15, 69.88],\\n [328.15, 68.29],\\n [333.15, 66.74],\\n [338.15, 65.22],\\n [343.15, 63.73],\\n [348.15, 62.28],\\n [353.15, 60.87],\\n [358.15, 59.48],\\n [363.15, 58.13],\\n [368.15, 56.81],\\n [373.15, 55.51]])\\n polynomal_degree = 5\\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\\n polynomal_degree)\\n fitfunction = np.poly1d(fitdata)\\n return fitfunction(temperature)\",\n \"def _cost_route_fine(self):\\n return self.fine\",\n \"def estimate_diffusion_coefficient(n_CFx: int) -> t.Diffusivity:\\n return t.Diffusivity(10 ** (-4.5360 + -0.1088 * n_CFx), \\\"cm^2/s\\\")\",\n \"def _calc_ft(Tci, Thi, Tco, Tho, N_shells) -> 'ft':\\n if (Tco - Tci)/Tco < 0.01 or (Thi-Tho)/Tho < 0.01:\\n return 1\\n try:\\n return ht.F_LMTD_Fakheri(Thi, Tho, Tci, Tco,\\n shells=N_shells)\\n except ValueError:\\n return 0.6 # Accounts for worst case scenario\",\n \"def get_hcost(self):\\n hvcost = self.get_hvcost()\\n dcost = self.get_dcost()\\n hcost = hvcost + dcost\\n return hcost\",\n \"def discharge_coefficient(self) -> _VectorisedFloat:\\n return 0.6\",\n \"def inflatedCost(self):\\n\\t\\tinflated = self.cost\\n\\n\\t\\t# https://www.in2013dollars.com/Wine-at-home/price-inflation/2020-to-2021?amount=10000\\n\\t\\tif self.acquisition.year <= 2018: # 2018-to-2019\\n\\t\\t\\tinflated *= 1.010727\\n\\n\\t\\tif self.acquisition.year <= 2019: # 2019-to-2020\\n\\t\\t\\tinflated *= 1.002446\\n\\n\\t\\tif self.acquisition.year <= 2020: # 2020-to-2021\\n\\t\\t\\tinflated *= 1.010612\\n\\n\\t\\tif self.acquisition.year <= 2021: # 2021-to-2022\\n\\t\\t\\tinflated *= 1.011850\\n\\n\\t\\treturn round(inflated, 2)\",\n \"def target_temperature_high_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperatue_high_f\\\"))\\r\\n return celsius_to_fahrenheit(self.target_temperature_high_c)\",\n \"def eco_temperature_high_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_f\\\"))\\r\\n return celsius_to_fahrenheit(self.eco_temperature_high_c)\",\n \"def _normed_concentration(self, time: float) -> _VectorisedFloat:\\n # The model always starts at t=0, but we avoid running concentration calculations\\n # before the first presence as an optimisation.\\n if time <= self._first_presence_time():\\n return self.min_background_concentration()/self.normalization_factor()\\n \\n next_state_change_time = self._next_state_change(time)\\n\\n RR = self.removal_rate(next_state_change_time)\\n # If RR is 0, conc_limit does not play a role but its computation \\n # would raise an error -> we set it to zero.\\n try:\\n conc_limit = self._normed_concentration_limit(next_state_change_time)\\n except ZeroDivisionError:\\n conc_limit = 0.\\n\\n t_last_state_change = self.last_state_change(time)\\n conc_at_last_state_change = self._normed_concentration_cached(t_last_state_change)\\n\\n delta_time = time - t_last_state_change\\n fac = np.exp(-RR * delta_time)\\n\\n return conc_limit * (1 - fac) + conc_at_last_state_change * fac\",\n \"def eco_temperature_low_f(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_f\\\"))\\r\\n return celsius_to_fahrenheit(self.eco_temperature_low_c)\",\n \"def thermal(isatom, freq, scalfac,linnonlin,T):\\n if isatom != \\\"true\\\":\\n nfreq = len(freq)\\n\\n vib_temp = []\\n for ifreq in range(nfreq):\\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\\n vib_temp.append(vib_temp_new)\\n\\n dE_vib = 0\\n for ifreq in range(nfreq):\\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\\n\\n dE_ZPE = 0.5 * sum(freq) * cmi2au\\n\\n if linnonlin == \\\"L\\\":\\n dE_rot = kB * T * j2au\\n elif linnonlin == \\\"NL\\\":\\n dE_rot = kB * T * j2au * (3.0/2.0)\\n else:\\n with open(\\\"Thermochemistry.out\\\", \\\"a\\\") as ther_chem:\\n ther_chem.write(\\\"ERROR: unknown entry for linear/nonlinear\\\")\\n else:\\n dE_ZPE = 0\\n dE_vib = 0\\n dE_rot = 0\\n\\n dE_tra = kB * T * j2au * (3.0/2.0)\\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\\n\\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)\",\n \"def locked_temp_min_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"locked_temp_min_c\\\"))\\r\\n return kelvin_to_celsius(self._locked_temp_min)\",\n \"def target_temperature_high(self):\\n if self._client.mode == self._client.MODE_AUTO:\\n return self._client.cooltemp\\n return None\",\n \"def compute_windchill(t,v):\\n a = 35.74\\n b = 0.6215\\n c = 35.75\\n d = 0.4275\\n v16 = v**0.16\\n wci = a+(b*t)-(c*v16)+(d*t*v16)\\n return wci\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def define_ufl_stress_work_diff(self):\\n\\n if hasattr(self, 'ufl_stress_work_diff'):\\n return None\\n\\n if self.displacement != 0:\\n # Derivative of stress term w.r.t. to displacement.\\n self.ufl_stress_work_du = dlf.derivative(self.ufl_stress_work,\\n self.displacement,\\n self.trial_vector)\\n else:\\n self.ufl_stress_work_du = 0\\n\\n if self.velocity != 0:\\n self.ufl_stress_work_dv = dlf.derivative(self.ufl_stress_work,\\n self.velocity,\\n self.trial_vector)\\n else:\\n self.ufl_stress_work_dv = 0\\n\\n if self.pressure != 0:\\n self.ufl_stress_work_dp = dlf.derivative(self.ufl_stress_work,\\n self.pressure,\\n self.trial_scalar)\\n else:\\n self.ufl_stress_work_dp = 0\\n\\n return None\",\n \"def calcCostFun(self):\\n\\n self.start()\\n F, K = self.model()\\n \\n return self.costFunction\",\n \"def get_fc3(supercell,\\n primitive,\\n displacements,\\n forces,\\n fc_calculator=None,\\n fc_calculator_options=None,\\n is_compact_fc=False,\\n log_level=0):\\n\\n if fc_calculator == 'alm':\\n from phono3py.interface.alm import get_fc3\\n return get_fc3(supercell,\\n primitive,\\n displacements,\\n forces,\\n options=fc_calculator_options,\\n is_compact_fc=is_compact_fc,\\n log_level=log_level)\\n else:\\n msg = (\\\"Force constants calculator of %s was not found .\\\"\\n % fc_calculator)\\n raise RuntimeError(msg)\",\n \"def get_cruft(self):\\n\\n raise computerjanitor.UnimplementedMethod(self.get_cruft)\",\n \"def get_cheap_conformer(self):\\n num_confs = min(500, max(50, len(self.mol.atoms) * 3))\\n rd_mol, rd_index_map = conformers.embed_rdkit(label=self.label, mol=self.mol, num_confs=num_confs)\\n xyzs, energies = conformers.rdkit_force_field(label=self.label, rd_mol=rd_mol, rd_index_map=rd_index_map,\\n mol=self.mol, force_field='MMFF94', return_xyz_strings=True)\\n if energies:\\n min_energy = min(energies)\\n min_energy_index = energies.index(min_energy)\\n self.cheap_conformer = xyzs[min_energy_index]\\n elif xyzs:\\n self.cheap_conformer = xyzs[0]\\n else:\\n logger.warning('Could not generate a cheap conformer for {0}'.format(self.label))\\n self.cheap_conformer = None\",\n \"def thermal_expansivity_of_air(self) -> float:\\n\\n return 1 / self.ambient_temperature\",\n \"def getCapitalChargeFactor(self):\\n return 1 / self._interestRate - 1 / (pow(1 + self._interestRate, self._economicLifetime) * self._interestRate)\",\n \"def get_remaining_fuel(self):\\n return min(self.liquid_fuel, self.oxidizer)\",\n \"def _cost_load_theft(self):\\n theft_prob = self.theft_probability\\n theft = np.random.choice([0, 1], p=[1-theft_prob, theft_prob])\\n if theft == 1:\\n cost = self._calculate_profit_weight() * PRICE_PER_KG\\n return cost\\n else:\\n return 0\",\n \"def _get_coeffs(self):\\n # lift (Clmax) and parasitic drag (Cd0max)\\n self.cl = 0.0\\n self.cd = 0.0\\n kpp = 0.0\\n\\n for sail in self.sails:\\n\\n self.cl += sail.cl(self.awa) * sail.area * sail.bk\\n self.cd += sail.cd(self.awa) * sail.area * sail.bk\\n kpp += sail.cl(self.awa) ** 2 * sail.area * sail.bk * sail.kp\\n\\n self.cl /= self.area\\n self.cd /= self.area\\n\\n # viscous quadratic parasitic drag and induced drag\\n devisor_1 = self.area * self.cl ** 2\\n devisor_2 = np.pi * self._heff(self.awa) ** 2\\n self.CE = (kpp / devisor_1 if devisor_1 else 0.0) + (self.area / devisor_2 if devisor_2 else 0.0)\\n\\n # fraction of parasitic drag due to jib\\n self.fcdj = 0.0\\n for sail in self.sails:\\n if sail.type == \\\"jib\\\":\\n self.fcdj = (\\n sail.bk * sail.cd(self.awa) * sail.area / (self.cd * self.area)\\n )\\n\\n # final lift and drag\\n self.cd = self.cd * (\\n self.flat * self.fcdmult(self.flat) * self.fcdj + (1 - self.fcdj)\\n ) + self.CE * self.cl ** 2 * self.flat ** 2 * self.fcdmult(self.flat)\\n self.cl = self.flat * self.cl\",\n \"def get_conductivity(self) -> float:\\n try:\\n datalist = self.get_data()\\n data = datalist[0]\\n if data.endswith('\\\\x00'):\\n data = data.rstrip('\\\\x00')\\n return float(data)\\n else:\\n return float(data)\\n except Exception as err:\\n print(f'get_conductivity error: {err}')\\n return -1\",\n \"def temperature(self) -> float:\\n # Start a measurement then poll the measurement finished bit.\\n self.temp_start = 1\\n while self.temp_running > 0:\\n pass\\n # Grab the temperature value and convert it to Celsius.\\n # This uses the same observed value formula from the Radiohead library.\\n temp = self._read_u8(_REG_TEMP2)\\n return 166.0 - temp\",\n \"def cost(self):\\n abs_cost = sum(f['price'] * f['qty'] for f in self.fills)\\n return -abs_cost if self.is_ask() else abs_cost\",\n \"def get_duct_linear_heat_loss_coefficient() -> float:\\n return 0.49\",\n \"def fpct(self):\\n # 1 is probably the best number in most cases because the game is often CPU-bound.\\n # the following number could be chosen instead someday\\n tps = self.real_speed * 1000 / VIRTUAL_TIME_INTERVAL\\n # Avoid unrealistic ping values.\\n ping = min(self.max_ping, self.ping)\\n result = int(tps * ping * config.fpct_coef) + 1\\n return min(config.fpct_max, result)\",\n \"def best_coupling_frequency(self):\\n\\n idx_best = self.coupling().argmax()\\n\\n return self.freq.f[idx_best]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6343963","0.59971076","0.59249985","0.5791971","0.576443","0.5683115","0.565232","0.55878526","0.5570194","0.54607165","0.5381237","0.52986056","0.5273817","0.5269059","0.5203606","0.5199913","0.5169069","0.5163147","0.5161178","0.51067317","0.510497","0.5098822","0.50977695","0.5091799","0.5088686","0.50850314","0.50850314","0.5050838","0.50430024","0.50383663","0.50196457","0.5007527","0.5007485","0.5006707","0.5004865","0.49978605","0.4989394","0.49838457","0.49735674","0.49537858","0.49493426","0.494904","0.49486387","0.49470538","0.49460146","0.49382645","0.49359253","0.49297574","0.4929614","0.49166504","0.49098557","0.4904686","0.48983502","0.48855397","0.4885508","0.4877309","0.4875149","0.48748937","0.48642212","0.48590156","0.48560908","0.4849536","0.4840836","0.4839072","0.48343676","0.4831373","0.48298457","0.48266608","0.48263085","0.48221284","0.48103663","0.48067504","0.48061502","0.4798044","0.47948518","0.47915915","0.47873503","0.47783795","0.4768591","0.4763097","0.4762324","0.47588003","0.4756652","0.47473717","0.47465208","0.47408658","0.47375646","0.47322574","0.4731408","0.4729779","0.47288495","0.47267315","0.4725189","0.4721651","0.47205853","0.47191757","0.4713861","0.47127703","0.47111037","0.4710614"],"string":"[\n \"0.6343963\",\n \"0.59971076\",\n \"0.59249985\",\n \"0.5791971\",\n \"0.576443\",\n \"0.5683115\",\n \"0.565232\",\n \"0.55878526\",\n \"0.5570194\",\n \"0.54607165\",\n \"0.5381237\",\n \"0.52986056\",\n \"0.5273817\",\n \"0.5269059\",\n \"0.5203606\",\n \"0.5199913\",\n \"0.5169069\",\n \"0.5163147\",\n \"0.5161178\",\n \"0.51067317\",\n \"0.510497\",\n \"0.5098822\",\n \"0.50977695\",\n \"0.5091799\",\n \"0.5088686\",\n \"0.50850314\",\n \"0.50850314\",\n \"0.5050838\",\n \"0.50430024\",\n \"0.50383663\",\n \"0.50196457\",\n \"0.5007527\",\n \"0.5007485\",\n \"0.5006707\",\n \"0.5004865\",\n \"0.49978605\",\n \"0.4989394\",\n \"0.49838457\",\n \"0.49735674\",\n \"0.49537858\",\n \"0.49493426\",\n \"0.494904\",\n \"0.49486387\",\n \"0.49470538\",\n \"0.49460146\",\n \"0.49382645\",\n \"0.49359253\",\n \"0.49297574\",\n \"0.4929614\",\n \"0.49166504\",\n \"0.49098557\",\n \"0.4904686\",\n \"0.48983502\",\n \"0.48855397\",\n \"0.4885508\",\n \"0.4877309\",\n \"0.4875149\",\n \"0.48748937\",\n \"0.48642212\",\n \"0.48590156\",\n \"0.48560908\",\n \"0.4849536\",\n \"0.4840836\",\n \"0.4839072\",\n \"0.48343676\",\n \"0.4831373\",\n \"0.48298457\",\n \"0.48266608\",\n \"0.48263085\",\n \"0.48221284\",\n \"0.48103663\",\n \"0.48067504\",\n \"0.48061502\",\n \"0.4798044\",\n \"0.47948518\",\n \"0.47915915\",\n \"0.47873503\",\n \"0.47783795\",\n \"0.4768591\",\n \"0.4763097\",\n \"0.4762324\",\n \"0.47588003\",\n \"0.4756652\",\n \"0.47473717\",\n \"0.47465208\",\n \"0.47408658\",\n \"0.47375646\",\n \"0.47322574\",\n \"0.4731408\",\n \"0.4729779\",\n \"0.47288495\",\n \"0.47267315\",\n \"0.4725189\",\n \"0.4721651\",\n \"0.47205853\",\n \"0.47191757\",\n \"0.4713861\",\n \"0.47127703\",\n \"0.47111037\",\n \"0.4710614\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69990456"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2396,"cells":{"query":{"kind":"string","value":"Return a nice representation of the weather conditions."},"document":{"kind":"string","value":"def __repr__(self) -> str:\n\n return (\n \"WeatherConditions(\"\n f\"ambient_temperature: {self.ambient_temperature:.3f}K, \"\n f\"azimuthal_angle: {self.azimuthal_angle}deg, \"\n f\"declination: {self.declination}deg, \"\n f\"density: {self.density_of_air:.3f}kg/m^3, \"\n f\"dynamic_viscosity: {self.dynamic_viscosity_of_air:.3f}kg/m*s, \"\n f\"heat_capacity: {self.heat_capacity_of_air}:.3fJ/kg*K, \"\n f\"irradiance: {self.irradiance:.3f}W/m^2, \"\n f\"kinematic_viscosity: {self.kinematic_viscosity_of_air:.3f}m^2/s, \"\n f\"sky_temperature: {self.sky_temperature:.3f}K, \"\n f\"thermal_conductivity: {self.thermal_conductivity_of_air:.3f}W/m*K, \"\n f\"thermal_expansion_coefficient: {self.thermal_expansivity_of_air:.3f}K^-1, \"\n f\"wind_heat_transfer_coefficient: {self.wind_heat_transfer_coefficient:2f}W/m*K, \"\n f\"wind_speed: {self.wind_speed:.3f}m/s, \"\n \")\"\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def conditions(self, json):\n conditions = str(json['forecast']['simpleforecast']['forecastday'][0]['conditions'])\n return conditions","def genWeather():\n\n weather = random.choice(weather_conditions.keys())\n condition = weather_conditions[weather]\n (tMax, tMin) = condition[\"temperature\"]\n (pMax, pMin) = condition[\"pressure\"]\n (hMax, hMin) = condition[\"humidity\"]\n\n return weather + \"|\" + str(round(random.uniform(tMax, tMin), 1)) + \"|\" + \\\n str(round(random.uniform(pMax, pMin), 1)) + \"|\" + \\\n str(random.randrange(hMax, hMin, -1))","def convert_weather(self, description):\n conditions = {\n 'clear sky': 'clear',\n 'few clouds': 'clouds with some sunshine',\n 'scattered clouds': 'cloudy',\n 'broken clouds': 'cloudy',\n 'shower rain': 'showers',\n 'thunderstorm': 'thunder and lightning',\n 'mist': 'fog'\n }\n\n if description in conditions:\n return conditions.get(description, \"Look out of the window.\")\n else:\n return description","def conditions(self) -> pulumi.Output[Mapping[str, str]]:\n return pulumi.get(self, \"conditions\")","def __str__(self):\n temperature = None\n offset = ' ' * 4\n if self._forecast_type == ForecastType.TODAY:\n temperature = (f'{offset}{self._current_temp}\\xb0\\n'\n f'{offset}High {self._high_temp}\\xb0 / '\n f'Low {self._low_temp}\\xb0 ')\n else:\n temperature = (f'{offset}High {self._high_temp}\\xb0 / '\n f'Low {self._low_temp}\\xb0 ')\n return (f'>> {self.forecast_date}\\n'\n f'{temperature}'\n f'({self._description})\\n'\n f'{offset}Wind: '\n f'{self._wind} / Humidity: {self._humidity}\\n')","def weather(self):\r\n try:\r\n return str(self.connect()['weather'][0]['description'])\r\n except:\r\n return '@weather'","def __str__(self):\n astr = ' variables:\\t[ '\n for var in self.variables:\n astr += str(var) + ', '\n astr = astr[:-2] + ' ]\\n assumptions :\\t[ '\n for assumption in self.assumptions.cnf:\n astr += assumption.formula + ', '\n astr = astr[:-2] + ' ]\\n guarantees :\\t[ '\n for guarantee in self.guarantees.cnf:\n astr += guarantee.formula + ', '\n # astr = astr[:-2] + ' ]\\n guarantees_unsat :\\t[ '\n # for guarantee in self.guarantees.cnf:\n # astr += guarantee.unsaturated + ', '\n return astr[:-2] + ' ]\\n'","def __str__(self):\n msg = '- Window size: ' + str(self.window_size) + \" by \" + str(self.window_size)\n msg += '\\n'\n msg += ' - Expression for r.mapcalc to determine column water vapor: '\n return msg + str(self.column_water_vapor_expression)","def getData():\n\t\n\ttry:\n\t\tgoogleWeather = pywapi.get_weather_from_google(location)\n\t\tcondition = googleWeather['current_conditions']['condition']\n\t\ttemp = googleWeather['current_conditions']['temp_c']\n\t\treturn \"<weather location=\\\"\" + location + \"\\\" condition=\\\"\" + condition + \"\\\" temp=\\\"\" + temp + \"c\" + \"\\\"/>\"\n\texcept:\n\t\treturn \"\"","def __str__(self):\n s = ''\n for i, (k, v) in enumerate(self.meters.items()):\n if i > 0:\n s += ' '\n s += k + ' ' + str(v)\n return s","def weather_helper():\n\n weather = get_weather('Chicago')\n conditions = weather['weather'][0]['description']\n temperature = weather['main']['temp']\n location = weather['name']\n\n curr_weather = 'It is currently %s degrees with %s in %s' % (temperature, conditions, location)\n return curr_weather","def getWaterConditions(self):\n return self._getConditions(restrict=['CS-Eau'])","def __str__(self):\r\n s = ''\r\n for i, (k, v) in enumerate(self.meters.items()):\r\n if i > 0:\r\n s += ' '\r\n s += k + ' ' + str(v)\r\n return s","def details(weather):\n\treturn \"\"\"<table class=\"forecast bg-success\"><tr><th colspan=\"2\" class=\"text-center lead\">Weather for {location} at {time}<th></tr>\n\t<tr><td>Temp: {temperature}<i class=\"wi wi-celsius\"></i> Feels Like: {feelsLike}<i class=\"wi wi-celsius\"></i></td><td rowspan=\"9\"><img src=\"map.gif?{latitude},{longitude}\" width=\"600\" height=\"371\"/><td></tr>\n\t<tr><td>Low: {low}<i class=\"wi wi-celsius\"></i> High: {high}<i class=\"wi wi-celsius\"></i></td></tr>\n\t<tr><td>Sunrise <i class=\"wi wi-sunrise\"></i>: {sunrise} Sunset <i class=\"wi wi-sunset\"></i>: {sunset}</td></tr>\n\t<tr><td>Wind: {windSpeed} kph from {windBearing} <i class=\"wi wi-wind.towards-{windDirection}-deg\"></i></td></tr>\n\t<tr><td>Summary <i class=\"wi wi-{icon}\"></i>: {summary}</td></tr>\n\t<tr><td></td></tr>\n\t<tr><td></td></tr>\n\t<tr><td></td></tr>\n\t<tr><td></td></tr>\n\t<tr><td> </td><td> </td></tr>\n\t</table>\"\"\".format(**weather)","def __str__(self):\n s = ''\n for i, (k, v) in enumerate(self.meters.iteritems()):\n if i > 0:\n s += ' '\n s += k + ' ' + str(v)\n return s","def current_weather(self):\n weather = DarkSky(self.coordinates)\n current_weather = weather.current_weather\n reply = [f\"*Current Weather in {self.format_location(self.location)}*\"]\n summary = current_weather['summary']\n temp = current_weather['temperature']\n feels_like = current_weather['apparentTemperature']\n rain_chance = current_weather['precipProbability']\n wind_speed = current_weather['windSpeed']\n cloud_cover = current_weather['cloudCover']\n icon = current_weather['icon']\n emoji = self.get_emoji(icon)\n logging.info(f\"Weather Icon: {icon}\")\n weather_message = [\n f\"{emoji} *{summary}*\",\n f\">*Temperature: `{temp}`*\",\n f\">*Feels Like: `{feels_like}`*\",\n f\">*Chance of Rain: `{rain_chance}`*\",\n f\">*Wind Speed: `{wind_speed}`*\",\n f\">*Cloud Cover: `{cloud_cover}`*\"\n ]\n reply.append(\"\\n\".join(weather_message))\n return \"\\n\".join(reply)","def __str__(self):\n status = (\"\\na: %.2f \\n\" % self.a +\n \"e: %.2f \\n\" % self.e +\n \"inc: %.2f deg \\n\" % (self.inc * 180/math.pi) +\n \"om: %.2f deg \\n\" % (self.om * 180/math.pi) +\n \"Om: %.2f deg \\n\" % (self.Om * 180/math.pi) +\n \"H: %.2f \\n\" % self.H\n )\n return status","def generate_weather_conditions(temperature, temp_type):\n\n if temp_type == \"MIN\" or temperature < 5:\n if temperature > 10:\n return 0\n elif temperature >= 0:\n return (10.-temperature)/10.\n else:\n return 1\n\n elif temp_type == \"AVG\":\n\n if temperature > 25:\n return 0\n elif temperature >= 15:\n return (25.-temperature)/(25.-15)\n elif temperature >= 5:\n return (temperature-5.)/(15-5.)\n\n elif temp_type == \"MAX\":\n if temperature > 40:\n return 1\n elif temperature >= 20:\n return (temperature-20)/(40.-20)\n else:\n return 0","def get_conditions(self):\n return (self.temp, self.humid)","def get_weather_violation(weather,minimums):\n # Implement this function\n #print(weather)\n #print(minimums)\n \n result = ''\n #print(weather['wind'])\n if weather == None:\n# result = 'Unknown'\n return 'Unknown'\n #elif bad_visibility(weather['visibility'],minimums[1]) == False and bad_winds(weather['wind'], minimums[2], minimums[3]) == False and bad_ceiling(weather['sky'],minimums[0]) == False:\n #result = ''\n \n if bad_winds(weather['wind'], minimums[2], minimums[3]) == True:\n result = 'Winds' if result == '' else 'Weather'\n \n if bad_visibility(weather['visibility'], minimums[1]) == True:\n result = 'Visibility' if result == '' else 'Weather'\n \n if bad_ceiling(weather['sky'], minimums[0]) == True:\n result = 'Ceiling' if result == '' else 'Weather'\n \n #elif \n \n return result","def format_status(self) -> str:\n if not self.ready_to_trade:\n return \"Market connectors are not ready.\"\n lines = []\n\n if len(self.stored_executors) > 0:\n lines.extend([\n \"\\n########################################## Closed Executors ##########################################\"])\n\n for executor in self.stored_executors:\n lines.extend([f\"|Signal id: {executor.timestamp}\"])\n lines.extend(executor.to_format_status())\n lines.extend([\n \"-----------------------------------------------------------------------------------------------------------\"])\n\n if len(self.active_executors) > 0:\n lines.extend([\n \"\\n########################################## Active Executors ##########################################\"])\n\n for executor in self.active_executors:\n lines.extend([f\"|Signal id: {executor.timestamp}\"])\n lines.extend(executor.to_format_status())\n if self.candles.is_ready:\n lines.extend([\n \"\\n############################################ Market Data ############################################\\n\"])\n signal, take_profit, stop_loss, indicators = self.get_signal_tp_and_sl()\n lines.extend([f\"Signal: {signal} | Take Profit: {take_profit} | Stop Loss: {stop_loss}\"])\n lines.extend([f\"BB%: {indicators[0]} | MACDh: {indicators[1]} | MACD: {indicators[2]}\"])\n lines.extend([\"\\n-----------------------------------------------------------------------------------------------------------\\n\"])\n else:\n lines.extend([\"\", \" No data collected.\"])\n\n return \"\\n\".join(lines)","def all_characteristics_as_string(self):\n chars1 = _all_characteristics_\n chars2 = [ch.title() for ch in chars1]\n chars2[chars2.index('Internalstructure')] = 'InternalStructure'\n\n s = ('%-18s %-24s %-2s'%('Characteristic', 'Semantic value','#'))\n s+= '\\n'\n s+= ('%-18s %-24s %-2s' % ('-', '-', '-')) + '\\n'\n\n for i in range(len(chars1)):\n attrs = (chars2[i],\\\n getattr(self,chars2[i])(),\n getattr(self,chars1[i]))\n s += '%-18s | %-24s | %-2d' % attrs\n s += '\\n'\n return s[:-1] # cut the trailing newline character","def device_state_attributes(self):\n # TODO: convert RH from Elk to AH ?\n #if self.current_humidity > 0:\n # humidity = self.current_humidity\n data = {\n 'hidden': self._hidden,\n 'temp_unit' : self.temperature_unit,\n }\n if self._device.temp_outside is not None and self._device.temp_outside > -460:\n data['temp_outside'] = self._device.temp_outside\n if self._device.temp_3 is not None and self._device.temp_3 > -460:\n data['temp_3'] = self._device.temp_3\n if self._device.temp_4 is not None and self._device.temp_4 > -460:\n data['temp_4'] = self._device.temp_4\n return data","def generate_condition_data(self):\n # set 'Conditions' column to NA\n self.output['Conditions'] = 'NA'\n\n # instantiate new MarkovChain object\n MC = MarkovChain()\n\n # apply forecast function on 'Conditions' column based on temperature\n # and humidity values for each observation period\n params = self.output[[\"Temperature\", \"Humidity\"]]\n self.output[['Conditions']] = params.apply(\n lambda x: MC.forecast_weather(x.values[0], x.values[1]), axis=1)","def state(self):\n result = \"\"\n if self._type == \"weather\":\n result = self._connector.get_condition()\n elif self._type == \"weather_report\":\n result = re.search(\n \"\\w+, \\d{2}\\.\\d{2}\\.\\d{2}, \\d{2}:\\d{2}\",\n self._connector.get_weather_report(),\n ).group()\n elif self._type == \"temperature\":\n result = self._connector.get_temperature()\n elif self._type == \"dewpoint\":\n result = self._connector.get_dewpoint()\n elif self._type == \"pressure\":\n result = self._connector.get_pressure()\n elif self._type == \"wind_speed\":\n result = self._connector.get_wind_speed()\n elif self._type == \"wind_direction\":\n result = self._connector.get_wind_direction()\n elif self._type == \"wind_gusts\":\n result = self._connector.get_wind_gusts()\n elif self._type == \"precipitation\":\n result = self._connector.get_precipitation()\n elif self._type == \"precipitation_probability\":\n result = self._connector.get_precipitation_probability()\n elif self._type == \"precipitation_duration\":\n result = self._connector.get_precipitation_duration()\n elif self._type == \"cloud_coverage\":\n result = self._connector.get_cloud_coverage()\n elif self._type == \"visibility\":\n result = self._connector.get_visibility()\n elif self._type == \"sun_duration\":\n result = self._connector.get_sun_duration()\n elif self._type == \"sun_irradiance\":\n result = self._connector.get_sun_irradiance()\n elif self._type == \"fog_probability\":\n result = self._connector.get_fog_probability()\n elif self._type == \"humidity\":\n result = self._connector.get_humidity()\n return result","def __str__(self):\n state_1 = \"Time: \" + str(self._time)\n state_2 = \"Current Cookies: \" + str(self._current_cookies)\n state_3 = \"CPS: \" + str(self._cps)\n state_4 = \"Total Cookies: \" + str(self._total_cookies)\n return state_1 + \" \" + state_2 + \" \" + state_3 + \" \" + state_4","def PrintWeather(Weather):\n print('Temperature : {}°C'.format(Weather[0]))\n print('Humidity : {} %'.format(Weather[1]))\n print('Description : {}'.format(Weather[2])+'\\n')\n return 1","def wattsString(self):\n return self.watts is None and \"unknown\" or str(self.watts)","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"architecture = {}\\n\".format(self.architecture)\n status += \"activations = {}\\n\".format(self.activations)\n status += \"conv_activations = {}\\n\".format(self.conv_activations)\n status += \"conv_architecture = {}\\n\".format(self.conv_architecture)\n status += \"kernel_sizes = {}\\n\".format(self.kernel_sizes)\n status += \"pool_kernel = {}\\n\".format(self.pool_kernel)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def to_string(self):\n if self.is_power_onoff():\n return 'Power On/Off'\n else:\n gain = str(hex(int(self['gain_speed'])))\n out = self['target'].ljust(20) + ' ' + self['filters'].ljust(11) + ' ' + self['x_bin'] + 'x' + self['y_bin'] + ' ' + gain[2:].upper()\n \n \n if self.number_windows() > 0:\n out += ' ' + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + ' ' + self['x1_start'].ljust(3) + ' ' + self['y1_start'].ljust(4)\n if self.number_windows() > 1:\n out += ' ' + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + ' ' + self['x2_start'].ljust(3) + ' ' + self['y2_start'].ljust(4)\n \n if 'Comment' in self:\n out += ' ' + self['Comment']\n return out","def format(self):\r\n\r\n earth = \"???\" if self.maskearth else self.earth\r\n air = \"???\" if self.maskair else self.air\r\n fire = \"???\" if self.maskfire else self.fire\r\n water = \"???\" if self.maskwater else self.water\r\n\r\n if any((self.earth, self.fire, self.water)):\r\n statsline = f'Stats: {earth}/{air}/{fire}/{water}'\r\n elif self.air:\r\n statsline = f'Air: {air}'\r\n else:\r\n statsline = ''\r\n\r\n return (\r\n f'Character {self.name}, [{self.token}]. '\r\n f'Init: {self.init} {statsline} Owner: {self.user.name}'\r\n )","def getZaptelConf(self):\n output = []\n for portInd, portLine in enumerate(self.portLines):\n if self[portInd]['type'] != 'na':\n values = self[portInd]\n values['type'] = values['type'] == 'fxs' and \"fxo\" or 'fxs' #Hmm crazy zaptel idea that your fxo is your fxs in zapata but the correct way around in zaptel\n output.append(\"%(type)s%(signalling)s=\" % self[portInd] + str(portLine[0]))\n return output","def __str__(self) -> str:\n return (\n f\"GlacierFlowModel '{self.model_name}' \"\n f\"{'' if self.steady_state else 'not '}in steady state with:\"\n f\"\\n - m: {self.m:20.5f} [m/m]\"\n f\"\\n - ela: {self.ela:20.2f} [m MSL]\"\n f\"\\n - resolution: {self.res:20.2f} [m]\"\n f\"\\n - extent: min max\"\n f\"\\n {self.extent[0]:10.1f} \"\n f\"{self.extent[1]:10.1f} [x]\"\n f\"\\n {self.extent[2]:10.1f} \"\n f\"{self.extent[3]:10.1f} [y]\"\n )","def conditions(self) -> Optional[Sequence['_meta.v1.outputs.ConditionPatch']]:\n return pulumi.get(self, \"conditions\")","def conditions(self) -> Sequence['outputs.StatusConditionResponse']:\n return pulumi.get(self, \"conditions\")","def mountain_weather_assessment(backcast):\n \n if backcast.total_precip < 6:\n return 'Less than 6 inches of precipitation in the past 7 days, climb on.'\n if backcast.total_precip > 36:\n return f\"There's been {backcast.total_precip} of precip in the past 7 days, you probably shouldn't climb.\"\n if backcast.sun_count > 72 and backcast.high_temp > 40:\n return f\"It's precipitated {backcast.total_precip} inches recently here, but also been sunny for {backcast.sun_count} hours and reached {backcast.high_temp} degrees F. Use your discretion.\"\n if backcast.precip_count > 30 and backcast.avg_temp <= 40:\n return f\"It's precipitated {backcast.total_precip} inches recently here, over {backcast.precip_count} hours out of the last 7 days, with an average temp of {backcast.avg_temp}F. Use your discretion and please stay safe.\"\n return f\"Not sure how to assess this information.\"","def __str__(self):\n #Get an ordered list of the elements strings so it outputs always the same\n #string given a mass function.\n elements = []\n for element in self.focals:\n elements.append((element, str(element)))\n sortedList = sorted(elements, key=lambda x:x[1])\n \n result = \"\"\n first = True\n for t in sortedList:\n if first:\n result += t[1] + \":\" + \"{:.4f}\".format(self.focals[t[0]])\n first = False\n else:\n result += \", \" + t[1] + \":\" + \"{:.4f}\".format(self.focals[t[0]])\n return \"{\" + result + \"}\"","def print_weather(self, days):\n if days == 1:\n open_weather = urlopen(self.full_url).read().decode(\"utf8\")\n read_json = json.loads(open_weather)\n outside = self.get_outside_outlook(read_json[\"weather\"])\n wind_speed = read_json[\"wind\"][\"speed\"]\n wind_direction = self.deg_to_compass(read_json[\"wind\"][\"deg\"])\n current_temp = self.convert_temp(read_json[\"main\"][\"temp\"])\n print(\"Current Temperature: {:.2f}\\xb0\\n\"\n \"Sky: {}\\n\"\n \"Wind speed: {} MPH\\n\"\n \"Wind direction: {}\".format(current_temp, outside, wind_speed, wind_direction))\n else:\n open_weather = urlopen(self.full_url).read().decode(\"utf8\")\n read_json = json.loads(open_weather)\n outside = read_json[\"list\"]\n \"\"\"\n Should be:\n for temp in outside:\n stuff = temp[\"weather\"]\n for i in stuff:\n print(i['description'])\n\n Each of these will need to be added to a list or a dictionary to print relationally\n \"\"\"\n print(outside)","def __str__(self):\n struct_repr = \", \".join([\n \"is_gyrometer_calibration_ok: \" + str(self.is_gyrometer_calibration_ok),\n \"is_accelerometer_calibration_ok: \" + str(self.is_accelerometer_calibration_ok),\n \"is_magnetometer_calibration_ok: \" + str(self.is_magnetometer_calibration_ok),\n \"is_level_calibration_ok: \" + str(self.is_level_calibration_ok),\n \"is_local_position_ok: \" + str(self.is_local_position_ok),\n \"is_global_position_ok: \" + str(self.is_global_position_ok),\n \"is_home_position_ok: \" + str(self.is_home_position_ok)\n ])\n\n return f\"Health: [{struct_repr}]\"","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"input_dim = {}\\n\".format(self.input_dim)\n status += \"architecture = {}\\n\".format(self.architecture)\n status += \"activations = {}\\n\".format(self.activations)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def print_info(self):\n outstr = '================================================= Ambient Noise Cross-correlation Database =================================================\\n'\n outstr += self.__str__()+'\\n'\n outstr += '--------------------------------------------------------------------------------------------------------------------------------------------\\n'\n if 'NoiseXcorr' in self.auxiliary_data.list():\n outstr += 'NoiseXcorr - Cross-correlation seismogram\\n'\n if 'StaInfo' in self.auxiliary_data.list():\n outstr += 'StaInfo - Auxiliary station information\\n'\n if 'DISPbasic1' in self.auxiliary_data.list():\n outstr += 'DISPbasic1 - Basic dispersion curve, no jump correction\\n'\n if 'DISPbasic2' in self.auxiliary_data.list():\n outstr += 'DISPbasic2 - Basic dispersion curve, with jump correction\\n'\n if 'DISPpmf1' in self.auxiliary_data.list():\n outstr += 'DISPpmf1 - PMF dispersion curve, no jump correction\\n'\n if 'DISPpmf2' in self.auxiliary_data.list():\n outstr += 'DISPpmf2 - PMF dispersion curve, with jump correction\\n'\n if 'DISPbasic1interp' in self.auxiliary_data.list():\n outstr += 'DISPbasic1interp - Interpolated DISPbasic1\\n'\n if 'DISPbasic2interp' in self.auxiliary_data.list():\n outstr += 'DISPbasic2interp - Interpolated DISPbasic2\\n'\n if 'DISPpmf1interp' in self.auxiliary_data.list():\n outstr += 'DISPpmf1interp - Interpolated DISPpmf1\\n'\n if 'DISPpmf2interp' in self.auxiliary_data.list():\n outstr += 'DISPpmf2interp - Interpolated DISPpmf2\\n'\n if 'FieldDISPbasic1interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPbasic1interp - Field data of DISPbasic1\\n'\n if 'FieldDISPbasic2interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPbasic2interp - Field data of DISPbasic2\\n'\n if 'FieldDISPpmf1interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPpmf1interp - Field data of DISPpmf1\\n'\n if 'FieldDISPpmf2interp' in self.auxiliary_data.list():\n outstr += 'FieldDISPpmf2interp - Field data of DISPpmf2\\n'\n outstr += '============================================================================================================================================\\n'\n print outstr\n return","def __str__(self):\n\n string = ''\n string += \"Battery Voltage: \" + \"{0:.2f}\".format(self.getBatteryVoltage()) + '\\n'\n string += \"Data Logging: \" + str(self.getDataLoggingStatus()) + '\\n'\n string += \"Data Filename: \" + self.getFilename() +'\\n'\n\n string += \"Time Since Instrument Reset (s): \" + \"{0:.2f}\".format(self.getResetTime()) + '\\n'\n string += \"Data Age (s): \" + \"{0:.2f}\".format(self.getDataAge()) + '\\n'\n string += '\\n'\n string += \"Interior Temperature 1 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature1()) + '\\n'\n string += \"Interior Temperature 2 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature2()) + '\\n'\n string += \"Interior Temperature 3 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature3()) + '\\n'\n string += \"Exterior Temperature (F): \" + \"{0:.2f}\".format(self.getExteriorTemperature()) + '\\n'\n string += \"Pressure (PSI): \" + \"{0:.2f}\".format(self.getPressure()) + '\\n'\n string += \"Humidity (%): \" + \"{0:.2f}\".format(self.getHumidity()) + '\\n'\n string += '\\n'\n\n string += \"GPS Time: \" + str(self.getGpsTime1()) + '\\n'\n string += \"Latitude: \" + \"{0:.9f}\".format(self.getLatitude1()) + '\\n'\n string += \"Longitude: \" + \"{0:.9f}\".format(self.getLongitude1()) + '\\n'\n string += \"Altitude (ft): \" + \"{0:.2f}\".format(self.getAltitude1()) + '\\n'\n string += \"Speed (MPH): \" + \"{0:.2f}\".format(self.getSpeed()) + '\\n'\n string += '\\n'\n\n string += \"GPS Time: \" + str(self.getGpsTime2()) + '\\n'\n string += \"Latitude: \" + \"{0:.9f}\".format(self.getLatitude2()) + '\\n'\n string += \"Longitude: \" + \"{0:.9f}\".format(self.getLongitude2()) + '\\n'\n string += \"Altitude (ft): \" + \"{0:.2f}\".format(self.getAltitude2()) + '\\n'\n string += '\\n'\n\n ax, ay, az = self.getAcceleration()\n string += \"Acceleration (x, y, z): \"\n string += \"{0:.2f}\".format(ax) + \", \"\n string += \"{0:.2f}\".format(ay) + \", \"\n string += \"{0:.2f}\".format(az) + '\\n'\n\n gx, gy, gz = self.getRates()\n string += \"Rates (x, y, z): \"\n string += \"{0:.2f}\".format(gx) + \", \"\n string += \"{0:.2f}\".format(gy) + \", \"\n string += \"{0:.2f}\".format(gz) + '\\n'\n\n mx, my, mz = self.getMagneticReading()\n string += \"Magnetic Field (x, y, z): \"\n string += \"{0:.2f}\".format(mx) + \", \"\n string += \"{0:.2f}\".format(my) + \", \"\n string += \"{0:.2f}\".format(mz) + '\\n'\n\n roll, pitch, yaw = self.getAttitude()\n string += \"Roll (deg): \" + \"{0:.2f}\".format(roll) + '\\n'\n string += \"Pitch (deg): \" + \"{0:.2f}\".format(pitch) + '\\n'\n string += \"Yaw (deg): \" + \"{0:.2f}\".format(yaw) + '\\n'\n string += '\\n'\n relayStates = self.getRelayStates()\n \n\n string += \"Relay States: \" \n string += (( \"ON \") if relayStates[0] else ( \"OFF \")) \n string += (( \"ON \") if relayStates[1] else ( \"OFF \"))\n string += (( \"ON \") if relayStates[2] else ( \"OFF \"))\n string += (( \"ON \") if relayStates[3] else ( \"OFF \"))\n string += '\\n'\n\n\n return string","def __str__(self):\n struct_repr = \", \".join([\n \"active: \" + str(self.active),\n \"actuator: \" + str(self.actuator)\n ])\n\n return f\"ActuatorOutputStatus: [{struct_repr}]\"","def description(self):\r\n try:\r\n return str(self.connect()['weather'][0]['description'])\r\n except:\r\n return '@weather_description'","def format_status(self) -> str:\n if not self.ready_to_trade:\n return \"Market connectors are not ready.\"\n lines = []\n\n balance_df = self.get_balance_df()\n lines.extend([\"\", \" Balances:\"] + [\" \" + line for line in balance_df.to_string(index=False).split(\"\\n\")])\n\n exchanges_df = self.exchanges_df()\n lines.extend([\"\", \" Exchanges:\"] + [\" \" + line for line in exchanges_df.to_string(index=False).split(\"\\n\")])\n\n try:\n orders_df = self.active_orders_df()\n lines.extend([\"\", \" Active Orders:\"] + [\" \" + line for line in orders_df.to_string(index=False).split(\"\\n\")])\n except ValueError:\n lines.extend([\"\", \" No active maker orders.\"])\n\n return \"\\n\".join(lines)","def conditions(self) -> Optional[Sequence['_meta.v1.outputs.Condition']]:\n return pulumi.get(self, \"conditions\")","def __repr__(self):\n values = ', '.join(f'{k}={v}' for k, v in self.variables.items())\n return f'D({values})'","def get_weather_info(forecast):\n day_forecast = {}\n day_forecast['condition_text'] = forecast['day']['condition']['text']\n #this icon is a url to an image that describes the weather condition\n day_forecast['condition_icon'] = forecast['day']['condition']['icon']\n day_forecast['max_temp'] = forecast['day']['maxtemp_c']\n day_forecast['min_temp'] = forecast['day']['mintemp_c']\n day_forecast['avg_temp'] = forecast['day']['avgtemp_c']\n date = datetime.strptime(forecast['date'], \"%Y-%m-%d\").strftime(\"%b %d:%a\")\n date_format = date.split(':')\n day_forecast['day'] = date_format[0]\n day_forecast['weekday'] = date_format[1]\n return day_forecast","def __repr__(self) -> str:\n s = \"\\n\"\n fmt = \"{:7.3f}\"\n for i in range(len(self.w)):\n s += \" \".join(fmt.format(w) for w in self.w[i])\n s += \" | \" + fmt.format(self.b[i]) + \"\\n\"\n return s","def get_weather(self):\n return self.__weather","def __str__(self):\n s=''\n for r in range(self.n):\n for c in range(self.n):\n s += str(self.state[r][c]) + ' '\n s += '\\n'\n s += str('hcost') + ' : ' + str(self.hcost)\n s += '\\n'\n return s","def output(self):\n val = \"\"\n if self.home:\n val = \"1\"\n if self.away:\n if val != \"\": val = val + \",\"\n val = val + \"2\"\n if self.night:\n if val != \"\": val = val + \",\"\n val = val + \"3\"\n if self.vacation:\n if val != \"\": val = val + \",\"\n val = val + \"4\"\n return val","def _truth_condition(self, state):\n return '{models}'.format(\n models=models(state, self.rewrite(), '$'),\n )","def __str__(self):\n # First obtain a string describing the underlying data model.\n strg = super(MiriTelescopeEmissionModel, self).__str__()\n \n # Add the extras\n if self.meta.instrument.filter is not None:\n strg += \"Data valid for filter=\\'%s\\' \" % \\\n self.meta.instrument.filter\n else:\n strg += \"Data valid for UNKNOWN filter \"\n if self.meta.telescope_temperature is not None:\n strg += \"and telescope temperature=%.2fK\" % \\\n self.meta.telescope_temperature\n else:\n strg += \"and UNKNOWN telescope temperature\"\n return strg","def __repr__(self):\n s = \"s = $%.2f, x = $%.2f, t = %.2f (years), sigma = %.3f, rf = %.3f\" %(self.s, self.x, self.t, self.sigma, self.rf)\n return s","def cloudiness(self):\r\n try:\r\n return str(self.connect()['clouds']['all'])\r\n except:\r\n return '@weather_cloudiness'","def get_weather(self):\n\n city = self.user_data[\"weatherSettings\"][\"weatherCity\"]\n country = self.user_data[\"weatherSettings\"][\"weatherCountry\"]\n\n host = \"weather.mios.com\"\n temp_scale = \"C\"\n url = \"http://%s/?tempFormat=%s&cityWeather=%s&countryWeather=%s\" % \\\n (host, temp_scale, Vera.urlencode(city), Vera.urlencode(country))\n\n weather = self.proxy_get(url)\n\n return (float(weather[\"temp\"]), weather[\"text\"])","def __str__(self, quiet: bool = True) -> str:\n\n q_type = self.q_type\n\n if quiet:\n q_type = \"\"\n\n string = \"\"\n\n if self.representation == \"\":\n string = f\"({self.t}, {self.x}, {self.y}, {self.z}) {q_type}\"\n\n elif self.representation == \"polar\":\n rep = self.txyz_2_representation(\"polar\")\n string = f\"({rep[0]} A, {rep[1]} 𝜈x, {rep[2]} 𝜈y, {rep[3]} 𝜈z) {q_type}\"\n\n elif self.representation == \"spherical\":\n rep = self.txyz_2_representation(\"spherical\")\n string = f\"({rep[0]} t, {rep[1]} R, {rep[2]} θ, {rep[3]} φ) {q_type}\"\n\n return string","def strings(self, sensor_data=None):\r\n disc = self.discrete(sensor_data)\r\n def toString(val):\r\n if val==self.far:\r\n return 'far'\r\n elif val==self.close:\r\n return 'close'\r\n else: \r\n return 'collision'\r\n return list(map(toString, disc))","def report(self):\n print(f\"Water: {self.resources['water']}ml\")\n print(f\"Milk: {self.resources['milk']}ml\")\n print(f\"Coffee: {self.resources['coffee']}g\")","def print_info(self):\n print('Condition list: (Cell definitions)')\n if len(list(self.condition_dict.keys())) > 0:\n for ID in list(self.condition_dict.keys()):\n ident = self.condition_dict[ID][0]['identifier']\n print(\n f'\\t[{ident}]: {len(self.condition_dict[ID])} definition(s)')\n else:\n print('\\tNo instances.')\n print()\n print('Modcell types: (Cell mappings on module)')\n if len(list(self.modcells.keys())) > 0:\n for ident in list(self.modcells.keys()):\n print(f'\\t[{ident}]: {len(self.modcells[ident])} definition(s)')\n else:\n print('\\tNo instances.')\n print()\n print('String definitions (Series of modcells)')\n passed = True\n if len(list(self.string_cond.keys())) > 0:\n for str_key in self.string_cond:\n try:\n print(\n f\"\\t[{str_key}]: {len(self.multilevel_ivdata['string'][str_key]['V'])} definition(s)\")\n except:\n passed = False\n continue\n if not passed:\n print('String definitions are defined by deducing the combination of module definitions. So, for an accurate display of the string-level definitions, call this module after enacting .simulate()')\n else:\n print('\\tNo instances.')\n print()","def status(self):\n res = \"\"\n for tlight in self.trafficLights:\n res += \"Traffic light {} status: {}\\n\".format(self.trafficLights[tlight].id,self.trafficLights[tlight].getState())\n return res","def __repr__(self):\n\n values = (\n f\"tickers: {tuple(self._tickers)}\\n\"\n f\"filters: {tuple(self._filters)}\\n\"\n f\"rows: {self._rows}\\n\"\n f\"order: {self._order}\\n\"\n f\"signal: {self._signal}\\n\"\n f\"table: {self._table}\\n\"\n f\"table: {self._custom}\"\n )\n\n return values","def get_condition(self) -> dict:\n url = self.base_url + \"/condition\"\n condition = self._session.get(url).json()\n keys = [\"bandwidth\", \"latency\", \"jitter\", \"loss\"]\n result = {k: v for (k, v) in condition.items() if k in keys}\n return result","def __str__(self):\n _str = \"Variables:\\n\"\n for variable in self.variables:\n _str += \" {}\\n\".format(str(variable))\n _str += \"\\nConstraints:\\n\"\n for constraint in self.constraints:\n _str += \" {}\\n\".format(str(constraint))\n return _str","def __str__(self):\n return self.designation + ' ' +self.winery + ' wine'","def __str__(self):\n return \"{}, fuel={}, odometer={} reliability= {}%\".format(self.name, self.fuel,\n self.odometer, self.reliability)","def getDebugText(self):\n timeDifference = time.time() - self.time_created\n hours = math.floor(timeDifference / 3600)\n minutes = math.floor((timeDifference % 3600) / 60)\n seconds = math.floor(timeDifference % 3600 % 60)\n\n output = \"\\n\" * 50\n output += \"Time started: %s\\n\" % time.ctime(self.time_created)\n output += \"Time now: %s\\n\" % time.ctime()\n output += \"Time elapsed: %02d:%02d:%02d\\n\" % (hours, minutes, seconds)\n output += (\"=\" * 80) + \"\\n\"\n output += \"Health potions used: %d\\n\" % self.hp_pots_used\n output += \"Health potions per hour: %d\\n\" % (self.hp_pots_used / (\n timeDifference / 3600))\n output += \"Mana potions used: %d\\n\" % self.mana_pots_used\n output += \"Mana potions per hour: %d\\n\" % (self.mana_pots_used / (\n timeDifference / 3600))\n return output","def toQif(self):\n out=list();\n if 'date' in self:\n out.append(\"D{}\".format(self['date']));\n if 'amount' in self:\n out.append(\"T{}\".format(self['amount']));\n if 'memo' in self and len(self['memo'])>3:\n out.append(\"M{}\".format(self['memo']));\n if 'payee' in self and len(self['payee'])>3:\n out.append(\"P{}\".format(self['payee']));\n out.append(\"^\");\n return \"\\n\".join(out);","def getConditionNames():\n return _conditions.keys()","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"classes = {}\\n\".format(self.classes)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def summarize_observing_conditions(fitsFiles):\n count = len(fitsFiles)\n\n # Here is the data we are going to collect from the fits headers\n year = np.zeros(count, dtype=int)\n month = np.zeros(count, dtype=int)\n day = np.zeros(count, dtype=int)\n hour = np.zeros(count, dtype=int)\n minute = np.zeros(count, dtype=int)\n airmass = np.zeros(count, dtype=float)\n water_column = np.zeros(count, dtype=float)\n \n for ii in range(len(fitsFiles)):\n # Get header info\n hdr = pyfits.getheader(fitsFiles[ii])\n\n airmass[ii] = float(hdr['AIRMASS'])\n\n date = hdr['DATE-OBS'].split('-')\n _year = int(date[0])\n _month = int(date[1])\n _day = int(date[2])\n\n utc = hdr['UTC'].split(':')\n _hour = int(utc[0])\n _minute = int(utc[1])\n _second = int(math.floor(float(utc[2])))\n\n utc = datetime.datetime(_year, _month, _day, _hour, _minute, _second)\n utc2hst = datetime.timedelta(hours=-10)\n hst = utc + utc2hst\n\n year[ii] = hst.year\n month[ii] = hst.month\n day[ii] = hst.day\n hour[ii] = hst.hour\n minute[ii] = hst.minute\n\n # Get the water column in mm of H2O\n water_column[ii] = weather.cso_water_column(_year, _month, _day, \n _hour, _minute)\n\n # Now lets fetch the CFHT weather data\n (temperature, pressure, humidity, wind_speed, wind_dir) = \\\n weather.cfht_weather_data(year, month, day, hour, minute)\n\n # Print out a nicely formatted table\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\n ('Filename', 'Year', 'M', 'D', 'h', 'm', 'AirM', 'H2O', 'Temp', \n 'Press', 'Humi', 'Wind', 'Dir'))\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\n ('HST', '', '', '', '', '', '', 'mm', 'C', 'mbar', '%', 'km/h', 'deg'))\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\n ('--------', '----', '--', '--', '--', '--', '----', '----', '-----', \n '-----', '----', '----', '----'))\n\n for ii in range(len(fitsFiles)):\n print('%-20s %4d %2d %2d %2d %2d ' % \\\n (fitsFiles[ii], year[ii], month[ii], day[ii], hour[ii], minute[ii]),)\n print('%4.2f %4.2f %5.1f %5.1f %4.1f %4.1f %4d' % \\\n (airmass[ii], water_column[ii], temperature[ii], pressure[ii],\n humidity[ii], wind_speed[ii], wind_dir[ii]))\n\n # Print out the average values\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\n ('--------', '----', '--', '--', '--', '--', '----', '----', '-----', \n '-----', '----', '----', '----'))\n print('%-20s %4d %2d %2d %2d %2d ' % \\\n ('Average', year.mean(), month.mean(), day.mean(), hour.mean(), \n minute.mean()),)\n print('%4.2f %4.2f %5.1f %5.1f %4.1f %4.1f %4d' % \\\n (airmass.mean(), water_column.mean(), temperature.mean(), \n pressure.mean(), humidity.mean(), wind_speed.mean(), wind_dir.mean()))\n print('%-20s %4d %2d %2d %2d %2d ' % \\\n ('Std. Dev.', year.std(), month.std(), day.std(), hour.std(), \n minute.std()),)\n print('%4.2f %4.2f %5.1f %5.1f %4.1f %4.1f %4d' % \\\n (airmass.std(), water_column.std(), temperature.std(), \n pressure.std(), humidity.std(), wind_speed.std(), wind_dir.std()))","def __str__(self):\n #{{{ Nicely print values\n text = 'Null values for databases: %s' % self.dbcentral.list()\n\n for value in self.null_vals.keys():\n text += \"\\t%s: %s\" % (value,self.null_vals[value])\n\n return text","def formated_print(dic):\n print dic['Year'] + '/' + get_month_number(dic['Month']) + '/' + \\\n dic['Day'] + ' | ' + dic['Hour'] + ':' + dic['Min'] + ':' + \\\n dic['Seg'] + ' | ' + dic['Energy'] + ' Watts'","def __str__(self):\r\n\r\n retval = self.__class__.__name__ + ' ('\r\n for val in self.VALUES:\r\n value = getattr(self, val, None)\r\n if value is not None:\r\n retval += '%s:%.4f ' % (val, getattr(self, val))\r\n return retval.strip() + ')'","def __repr__(self):\n string = ''\n for key, val in self.setting().items():\n string += '{}({})\\n'.format(key, val)\n return string","def __str__(self):\n r = []\n for item in sorted(self._data.keys()):\n correct, incorrect = self._data[item][True], self._data[item][False]\n acc = correct / (correct + incorrect)\n s = f\"{item:4} | Accuracy: {acc:.2f}% (diff {'+' if acc-item >=0 else ''}{acc-item:.2f}%) | correct: {correct:2}, incorrect: {incorrect:2}\" \n r.append(s)\n\n return \"\\n\".join(r)","def __str__(self):\n return f'{self._name} has {self._calories} calories, {self._carbohydrates}' +\\\n f'g. carbohydrates, {self._fat}g. of fat and {self._proteins}g. of proteins'","def __str__(self) -> str:\n\n return (\n f\"The {self.name} temperature sensor has a value of \"\n f\"{self.value} degrees {self.unit}.\"\n )","def __str__(self):\n out_str = \"\\n\".join(`\"%.5f, %.5f, %.1f, %s, %s\" % (point[0], point[1], point[2], point[3], point[4])` for point in self.__traectory_list)\n return \"\\'x, y, altitude, capture time, capture date'\\n\"+out_str","def get_status(self):\n request_format = \"{oscillating:01d} {initialising:01d} {initialised:01d} {width:03d} \" \\\n \"{offset:+04d} {speed:02d} {acceleration:03d} {cycles:05d} {backlash:03d}\"\n status_string = request_format.format(\n oscillating=int(self.device.is_oscillating()),\n initialising=int(self.device.is_initialising()),\n initialised=int(self.device.has_been_initialised()),\n width=int(self.device.get_window_width()),\n offset=int(self.device.get_offset()),\n speed=int(self.device.get_speed()),\n acceleration=int(self.device.get_acceleration()),\n cycles=int(self.device.get_complete_cycles()),\n backlash=int(self.device.get_backlash())\n )\n return status_string","def __str__(self, output=[]):\n\n output = [['only heat', self.only_heat],\n ] + output\n\n class_str = 'Phonon simulation properties:\\n\\n'\n class_str += super().__str__(output)\n\n return class_str","def condition(self) -> str:\r\n condition = self._first_timeserie[\"data\"][\"next_1_hours\"][\"summary\"][\r\n \"symbol_code\"\r\n ]\r\n return format_condition(condition)","def status(self):\n \n tmpl1 = \"\"\"%-20s%-52s[%s]\"\"\"\n tmpl2 = \"\"\"%-20s%-52s\\n\"\"\"\n # print tmpl1 % (\"Machine Name\", \"IP Addresses\", \"Status\")\n # print 80 * \"-\"\n # print self.get_image()\n if self.cloudserver:\n # let's build the IPs first\n status = self.cloudserver.status\n \n else:\n status = \"OFF\"\n\n res2=\"\"\n ip1 = \"%s:%s\" % (self.networks[0], self.ip_addresses[self.networks[0]])\n if len(self.networks) > 1:\n res2 += \"\\n\"\n for network in self.networks[1:]:\n ipstr = \"%s:%s\" % (network, self.ip_addresses[network])\n res2+=tmpl2 % (\"-\", ipstr)\n # print res2\n # if len(self.ip_addresses.keys()) > 1:\n # ip1 = self.ip_addresses.values()[0]\n res1 = tmpl1 % (self.machine_name, ip1, status)\n return res1 + res2","def generate_watch_output():\r\n values = H.unicode_string('')\r\n if S.WATCH is None:\r\n return values\r\n for watch_data in S.WATCH:\r\n watch_entry = ''\r\n if watch_data and isinstance(watch_data, dict):\r\n # Whether watch expression is enabled or disabled\r\n if 'enabled' in watch_data.keys():\r\n if watch_data['enabled']:\r\n watch_entry += '|+|'\r\n else:\r\n watch_entry += '|-|'\r\n # Watch expression\r\n if 'expression' in watch_data.keys():\r\n watch_entry += ' \"%s\"' % watch_data['expression']\r\n # Evaluated value\r\n if watch_data['value'] is not None:\r\n watch_entry += ' = ' + generate_context_output(watch_data['value'])\r\n else:\r\n watch_entry += \"\\n\"\r\n values += H.unicode_string(watch_entry)\r\n return values","def __str__(self):\n state = ''\n state += ' '.join([str(x) for x in self.pos]) + ' '\n state += ''.join([str(x) + ' ' + str(y) + ' ' for x,\n y in zip(self.BU, self.BD)])\n for e in self.BF:\n state += ' '.join([str(x) for x in e])\n state += ' '\n state += ' '.join([str(x) for x in self.LU]) + ' '\n state += ' '.join([str(x) for x in self.LD]) + ' '\n\n return state","def reaction_str(self):\n\n def format(number):\n return str(number).rstrip(\".0\") + \" \"\n\n reactant_bits = []\n product_bits = []\n for met in sorted(self._metabolites, key=attrgetter(\"id\")):\n coefficient = self._metabolites[met]\n if coefficient >= 0:\n product_bits.append(format(coefficient) + met.id)\n else:\n reactant_bits.append(format(abs(coefficient)) + met.id)\n\n reaction_string = ' + '.join(reactant_bits)\n if self.gapfill_direction == '=':\n reaction_string += ' <=> '\n elif self.gapfill_direction == '<':\n reaction_string += ' <-- '\n elif self.gapfill_direction == '>':\n reaction_string += ' --> '\n reaction_string += ' + '.join(product_bits)\n return reaction_string","def __repr__(self):\n string = \"Current state: \\n\"\n if self.state[0] == 0: # We're on the left side\n string += \"M: \"\n string += str(self.state[1]).ljust(10)\n string += \"M: \"\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\n string += \"\\n\"\n\n string += \"C: \"\n string += str(self.state[2]).ljust(10)\n string += \"C: \"\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\n string += \"\\n\"\n\n string += \"Boat position: left\\n\"\n else: # We're on the right side\n string += \"M: \"\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\n string += \"M: \"\n string += str(self.state[1])\n string += \"\\n\"\n\n string += \"C: \"\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\n string += \"C: \"\n string += str(self.state[2]).ljust(10)\n string += \"\\n\"\n\n string += \"Boat position: right\\n\"\n string += \"\\n\"\n return string","def encode(self):\n color_str = []\n if self.brightness is not None:\n color_str.append(f\"brightness:{self.brightness}\")\n if self.hue is not None:\n color_str.append(f\"hue:{self.hue}\")\n if self.saturation is not None:\n color_str.append(f\"saturation:{self.saturation}\")\n if self.kelvin is not None:\n color_str.append(f\"kelvin:{self.kelvin}\")\n\n return ' '.join(color_str)","def __repr__(self):\n\n repme = \"delta0= {!r}, c_upd= {!r}, n_upd= {!r}, smooth= {!r}\"\\\n .format(self.delta0, self.c_upd, self.n_upd, self.smooth)\n\n return \"WangLandau({!s})\".format(repme)","def __str__(self):\n s = ''\n for i in range(5):\n for j in range(5):\n ani = False\n if self[i][j] == 0:\n s += ' 0 '\n elif self[i][j].species == 'Elephant':\n s += ' E'\n ani = True\n elif self[i][j].species == 'Rhinoceros':\n s += ' R'\n ani = True\n else:\n s += ' B '\n if ani:\n if self[i][j].direction[0] == 0 and self[i][j].direction[1] == 1:\n d = '> '\n elif self[i][j].direction[0] == -1 and self[i][j].direction[1] == 0:\n d = '∧ '\n elif self[i][j].direction[0] == 0 and self[i][j].direction[1] == -1:\n d = '< '\n else:\n d = '∨ '\n s += d\n s += '\\n \\n'\n return s","def __str__(self):\n s = 'hit '+str(self.hit)+'\\n'\n s+= 'states '+str(self.states)+'\\n'\n s+= 'chi2 '+str(self.chi2)\n return s","def device_state_attributes(self) -> str:\n return {\n \"remo_device_id\": self._remo_device.id,\n \"remo_device_name\": self._remo_device.name,\n \"remo_firmware_version\": self._remo_device.firmware_version,\n \"remo_temperature_offset\": self._remo_device.temperature_offset,\n \"remo_humidity_offset\": self._remo_device.humidity_offset\n }","def describe(self) -> str:\r\n env = str(self.env)\r\n observation_space = str(self.observations)\r\n action_space = str(self.actions)\r\n policy = str(self.policy)\r\n gamma = f\"Gamma({self.gamma})\"\r\n\r\n return env, observation_space, action_space, policy, gamma","def __str__(self):\n\t\n\t\tresult = \"\"\n\t\tresult += \"Torsional Spring Specs: \\n\"\n\t\tresult += \"Shape Eq. Slope: {0}\\n\".format(str(self.shape_slope))\n\t\tresult += \"Z Thickness: {0}\\n\".format(str(self.z_thick))\n\t\tresult += \"In-Plane Thickness: {0}\\n\".format(str(self.thick))\n\t\tresult += \"Spiral Length: {0}\\n\".format(str(self.length))\n\n\t\treturn result","def full_broadcast(city_name):\n today_weather(city_name)\n for i in range(1, 4):\n output = (\n (datetime.date.today() + datetime.timedelta(days=i)).strftime(\"%d/%m/%Y\")\n + \"Temperature: \"\n + \"{}\".format(\n get_weather(\n city_name,\n (datetime.date.today() + datetime.timedelta(days=i)).strftime(\n \"%d/%m/%Y\"\n ),\n )\n )\n )\n print(output)","def __str__(self):\n return f'''\n {super().__str__()}\n Brand: {self._breand}\n Pover: {self._power} (W)\n Nozzle: {self._nozzle} (pieces)\n '''","def __str__(self) -> str:\n if self.scalar_vector:\n return f\"({self.w:-.4f} {self.x:+.4f}i {self.y:+.4f}j {self.z:+.4f}k)\"\n return f\"({self.x:-.4f}i {self.y:+.4f}j {self.z:+.4f}k {self.w:+.4f})\"","def __str__(self):\n output = \"There are {!s} cities with {!s} connections in {}.\".format(\n self.__railways.vertex_count(), self.__railways.edge_count(),\n self.__timeTable.split('.')[0])\n return output","def __repr__(self):\n\n output = list()\n output.append('{resonance_id:6s}'.format(**self.par))\n output.append('{h_larmor_frq:6.1f}'.format(**self.par))\n output.append('{temperature:4.1f}'.format(**self.par))\n output.append('{:10.5f}'.format(self.val))\n output.append('{:10.5f}'.format(self.err))\n\n if self.cal:\n output.append('{:10.5f}'.format(self.cal))\n\n return ' '.join(output)"],"string":"[\n \"def conditions(self, json):\\n conditions = str(json['forecast']['simpleforecast']['forecastday'][0]['conditions'])\\n return conditions\",\n \"def genWeather():\\n\\n weather = random.choice(weather_conditions.keys())\\n condition = weather_conditions[weather]\\n (tMax, tMin) = condition[\\\"temperature\\\"]\\n (pMax, pMin) = condition[\\\"pressure\\\"]\\n (hMax, hMin) = condition[\\\"humidity\\\"]\\n\\n return weather + \\\"|\\\" + str(round(random.uniform(tMax, tMin), 1)) + \\\"|\\\" + \\\\\\n str(round(random.uniform(pMax, pMin), 1)) + \\\"|\\\" + \\\\\\n str(random.randrange(hMax, hMin, -1))\",\n \"def convert_weather(self, description):\\n conditions = {\\n 'clear sky': 'clear',\\n 'few clouds': 'clouds with some sunshine',\\n 'scattered clouds': 'cloudy',\\n 'broken clouds': 'cloudy',\\n 'shower rain': 'showers',\\n 'thunderstorm': 'thunder and lightning',\\n 'mist': 'fog'\\n }\\n\\n if description in conditions:\\n return conditions.get(description, \\\"Look out of the window.\\\")\\n else:\\n return description\",\n \"def conditions(self) -> pulumi.Output[Mapping[str, str]]:\\n return pulumi.get(self, \\\"conditions\\\")\",\n \"def __str__(self):\\n temperature = None\\n offset = ' ' * 4\\n if self._forecast_type == ForecastType.TODAY:\\n temperature = (f'{offset}{self._current_temp}\\\\xb0\\\\n'\\n f'{offset}High {self._high_temp}\\\\xb0 / '\\n f'Low {self._low_temp}\\\\xb0 ')\\n else:\\n temperature = (f'{offset}High {self._high_temp}\\\\xb0 / '\\n f'Low {self._low_temp}\\\\xb0 ')\\n return (f'>> {self.forecast_date}\\\\n'\\n f'{temperature}'\\n f'({self._description})\\\\n'\\n f'{offset}Wind: '\\n f'{self._wind} / Humidity: {self._humidity}\\\\n')\",\n \"def weather(self):\\r\\n try:\\r\\n return str(self.connect()['weather'][0]['description'])\\r\\n except:\\r\\n return '@weather'\",\n \"def __str__(self):\\n astr = ' variables:\\\\t[ '\\n for var in self.variables:\\n astr += str(var) + ', '\\n astr = astr[:-2] + ' ]\\\\n assumptions :\\\\t[ '\\n for assumption in self.assumptions.cnf:\\n astr += assumption.formula + ', '\\n astr = astr[:-2] + ' ]\\\\n guarantees :\\\\t[ '\\n for guarantee in self.guarantees.cnf:\\n astr += guarantee.formula + ', '\\n # astr = astr[:-2] + ' ]\\\\n guarantees_unsat :\\\\t[ '\\n # for guarantee in self.guarantees.cnf:\\n # astr += guarantee.unsaturated + ', '\\n return astr[:-2] + ' ]\\\\n'\",\n \"def __str__(self):\\n msg = '- Window size: ' + str(self.window_size) + \\\" by \\\" + str(self.window_size)\\n msg += '\\\\n'\\n msg += ' - Expression for r.mapcalc to determine column water vapor: '\\n return msg + str(self.column_water_vapor_expression)\",\n \"def getData():\\n\\t\\n\\ttry:\\n\\t\\tgoogleWeather = pywapi.get_weather_from_google(location)\\n\\t\\tcondition = googleWeather['current_conditions']['condition']\\n\\t\\ttemp = googleWeather['current_conditions']['temp_c']\\n\\t\\treturn \\\"<weather location=\\\\\\\"\\\" + location + \\\"\\\\\\\" condition=\\\\\\\"\\\" + condition + \\\"\\\\\\\" temp=\\\\\\\"\\\" + temp + \\\"c\\\" + \\\"\\\\\\\"/>\\\"\\n\\texcept:\\n\\t\\treturn \\\"\\\"\",\n \"def __str__(self):\\n s = ''\\n for i, (k, v) in enumerate(self.meters.items()):\\n if i > 0:\\n s += ' '\\n s += k + ' ' + str(v)\\n return s\",\n \"def weather_helper():\\n\\n weather = get_weather('Chicago')\\n conditions = weather['weather'][0]['description']\\n temperature = weather['main']['temp']\\n location = weather['name']\\n\\n curr_weather = 'It is currently %s degrees with %s in %s' % (temperature, conditions, location)\\n return curr_weather\",\n \"def getWaterConditions(self):\\n return self._getConditions(restrict=['CS-Eau'])\",\n \"def __str__(self):\\r\\n s = ''\\r\\n for i, (k, v) in enumerate(self.meters.items()):\\r\\n if i > 0:\\r\\n s += ' '\\r\\n s += k + ' ' + str(v)\\r\\n return s\",\n \"def details(weather):\\n\\treturn \\\"\\\"\\\"<table class=\\\"forecast bg-success\\\"><tr><th colspan=\\\"2\\\" class=\\\"text-center lead\\\">Weather for {location} at {time}<th></tr>\\n\\t<tr><td>Temp: {temperature}<i class=\\\"wi wi-celsius\\\"></i> Feels Like: {feelsLike}<i class=\\\"wi wi-celsius\\\"></i></td><td rowspan=\\\"9\\\"><img src=\\\"map.gif?{latitude},{longitude}\\\" width=\\\"600\\\" height=\\\"371\\\"/><td></tr>\\n\\t<tr><td>Low: {low}<i class=\\\"wi wi-celsius\\\"></i> High: {high}<i class=\\\"wi wi-celsius\\\"></i></td></tr>\\n\\t<tr><td>Sunrise <i class=\\\"wi wi-sunrise\\\"></i>: {sunrise} Sunset <i class=\\\"wi wi-sunset\\\"></i>: {sunset}</td></tr>\\n\\t<tr><td>Wind: {windSpeed} kph from {windBearing} <i class=\\\"wi wi-wind.towards-{windDirection}-deg\\\"></i></td></tr>\\n\\t<tr><td>Summary <i class=\\\"wi wi-{icon}\\\"></i>: {summary}</td></tr>\\n\\t<tr><td></td></tr>\\n\\t<tr><td></td></tr>\\n\\t<tr><td></td></tr>\\n\\t<tr><td></td></tr>\\n\\t<tr><td> </td><td> </td></tr>\\n\\t</table>\\\"\\\"\\\".format(**weather)\",\n \"def __str__(self):\\n s = ''\\n for i, (k, v) in enumerate(self.meters.iteritems()):\\n if i > 0:\\n s += ' '\\n s += k + ' ' + str(v)\\n return s\",\n \"def current_weather(self):\\n weather = DarkSky(self.coordinates)\\n current_weather = weather.current_weather\\n reply = [f\\\"*Current Weather in {self.format_location(self.location)}*\\\"]\\n summary = current_weather['summary']\\n temp = current_weather['temperature']\\n feels_like = current_weather['apparentTemperature']\\n rain_chance = current_weather['precipProbability']\\n wind_speed = current_weather['windSpeed']\\n cloud_cover = current_weather['cloudCover']\\n icon = current_weather['icon']\\n emoji = self.get_emoji(icon)\\n logging.info(f\\\"Weather Icon: {icon}\\\")\\n weather_message = [\\n f\\\"{emoji} *{summary}*\\\",\\n f\\\">*Temperature: `{temp}`*\\\",\\n f\\\">*Feels Like: `{feels_like}`*\\\",\\n f\\\">*Chance of Rain: `{rain_chance}`*\\\",\\n f\\\">*Wind Speed: `{wind_speed}`*\\\",\\n f\\\">*Cloud Cover: `{cloud_cover}`*\\\"\\n ]\\n reply.append(\\\"\\\\n\\\".join(weather_message))\\n return \\\"\\\\n\\\".join(reply)\",\n \"def __str__(self):\\n status = (\\\"\\\\na: %.2f \\\\n\\\" % self.a +\\n \\\"e: %.2f \\\\n\\\" % self.e +\\n \\\"inc: %.2f deg \\\\n\\\" % (self.inc * 180/math.pi) +\\n \\\"om: %.2f deg \\\\n\\\" % (self.om * 180/math.pi) +\\n \\\"Om: %.2f deg \\\\n\\\" % (self.Om * 180/math.pi) +\\n \\\"H: %.2f \\\\n\\\" % self.H\\n )\\n return status\",\n \"def generate_weather_conditions(temperature, temp_type):\\n\\n if temp_type == \\\"MIN\\\" or temperature < 5:\\n if temperature > 10:\\n return 0\\n elif temperature >= 0:\\n return (10.-temperature)/10.\\n else:\\n return 1\\n\\n elif temp_type == \\\"AVG\\\":\\n\\n if temperature > 25:\\n return 0\\n elif temperature >= 15:\\n return (25.-temperature)/(25.-15)\\n elif temperature >= 5:\\n return (temperature-5.)/(15-5.)\\n\\n elif temp_type == \\\"MAX\\\":\\n if temperature > 40:\\n return 1\\n elif temperature >= 20:\\n return (temperature-20)/(40.-20)\\n else:\\n return 0\",\n \"def get_conditions(self):\\n return (self.temp, self.humid)\",\n \"def get_weather_violation(weather,minimums):\\n # Implement this function\\n #print(weather)\\n #print(minimums)\\n \\n result = ''\\n #print(weather['wind'])\\n if weather == None:\\n# result = 'Unknown'\\n return 'Unknown'\\n #elif bad_visibility(weather['visibility'],minimums[1]) == False and bad_winds(weather['wind'], minimums[2], minimums[3]) == False and bad_ceiling(weather['sky'],minimums[0]) == False:\\n #result = ''\\n \\n if bad_winds(weather['wind'], minimums[2], minimums[3]) == True:\\n result = 'Winds' if result == '' else 'Weather'\\n \\n if bad_visibility(weather['visibility'], minimums[1]) == True:\\n result = 'Visibility' if result == '' else 'Weather'\\n \\n if bad_ceiling(weather['sky'], minimums[0]) == True:\\n result = 'Ceiling' if result == '' else 'Weather'\\n \\n #elif \\n \\n return result\",\n \"def format_status(self) -> str:\\n if not self.ready_to_trade:\\n return \\\"Market connectors are not ready.\\\"\\n lines = []\\n\\n if len(self.stored_executors) > 0:\\n lines.extend([\\n \\\"\\\\n########################################## Closed Executors ##########################################\\\"])\\n\\n for executor in self.stored_executors:\\n lines.extend([f\\\"|Signal id: {executor.timestamp}\\\"])\\n lines.extend(executor.to_format_status())\\n lines.extend([\\n \\\"-----------------------------------------------------------------------------------------------------------\\\"])\\n\\n if len(self.active_executors) > 0:\\n lines.extend([\\n \\\"\\\\n########################################## Active Executors ##########################################\\\"])\\n\\n for executor in self.active_executors:\\n lines.extend([f\\\"|Signal id: {executor.timestamp}\\\"])\\n lines.extend(executor.to_format_status())\\n if self.candles.is_ready:\\n lines.extend([\\n \\\"\\\\n############################################ Market Data ############################################\\\\n\\\"])\\n signal, take_profit, stop_loss, indicators = self.get_signal_tp_and_sl()\\n lines.extend([f\\\"Signal: {signal} | Take Profit: {take_profit} | Stop Loss: {stop_loss}\\\"])\\n lines.extend([f\\\"BB%: {indicators[0]} | MACDh: {indicators[1]} | MACD: {indicators[2]}\\\"])\\n lines.extend([\\\"\\\\n-----------------------------------------------------------------------------------------------------------\\\\n\\\"])\\n else:\\n lines.extend([\\\"\\\", \\\" No data collected.\\\"])\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def all_characteristics_as_string(self):\\n chars1 = _all_characteristics_\\n chars2 = [ch.title() for ch in chars1]\\n chars2[chars2.index('Internalstructure')] = 'InternalStructure'\\n\\n s = ('%-18s %-24s %-2s'%('Characteristic', 'Semantic value','#'))\\n s+= '\\\\n'\\n s+= ('%-18s %-24s %-2s' % ('-', '-', '-')) + '\\\\n'\\n\\n for i in range(len(chars1)):\\n attrs = (chars2[i],\\\\\\n getattr(self,chars2[i])(),\\n getattr(self,chars1[i]))\\n s += '%-18s | %-24s | %-2d' % attrs\\n s += '\\\\n'\\n return s[:-1] # cut the trailing newline character\",\n \"def device_state_attributes(self):\\n # TODO: convert RH from Elk to AH ?\\n #if self.current_humidity > 0:\\n # humidity = self.current_humidity\\n data = {\\n 'hidden': self._hidden,\\n 'temp_unit' : self.temperature_unit,\\n }\\n if self._device.temp_outside is not None and self._device.temp_outside > -460:\\n data['temp_outside'] = self._device.temp_outside\\n if self._device.temp_3 is not None and self._device.temp_3 > -460:\\n data['temp_3'] = self._device.temp_3\\n if self._device.temp_4 is not None and self._device.temp_4 > -460:\\n data['temp_4'] = self._device.temp_4\\n return data\",\n \"def generate_condition_data(self):\\n # set 'Conditions' column to NA\\n self.output['Conditions'] = 'NA'\\n\\n # instantiate new MarkovChain object\\n MC = MarkovChain()\\n\\n # apply forecast function on 'Conditions' column based on temperature\\n # and humidity values for each observation period\\n params = self.output[[\\\"Temperature\\\", \\\"Humidity\\\"]]\\n self.output[['Conditions']] = params.apply(\\n lambda x: MC.forecast_weather(x.values[0], x.values[1]), axis=1)\",\n \"def state(self):\\n result = \\\"\\\"\\n if self._type == \\\"weather\\\":\\n result = self._connector.get_condition()\\n elif self._type == \\\"weather_report\\\":\\n result = re.search(\\n \\\"\\\\w+, \\\\d{2}\\\\.\\\\d{2}\\\\.\\\\d{2}, \\\\d{2}:\\\\d{2}\\\",\\n self._connector.get_weather_report(),\\n ).group()\\n elif self._type == \\\"temperature\\\":\\n result = self._connector.get_temperature()\\n elif self._type == \\\"dewpoint\\\":\\n result = self._connector.get_dewpoint()\\n elif self._type == \\\"pressure\\\":\\n result = self._connector.get_pressure()\\n elif self._type == \\\"wind_speed\\\":\\n result = self._connector.get_wind_speed()\\n elif self._type == \\\"wind_direction\\\":\\n result = self._connector.get_wind_direction()\\n elif self._type == \\\"wind_gusts\\\":\\n result = self._connector.get_wind_gusts()\\n elif self._type == \\\"precipitation\\\":\\n result = self._connector.get_precipitation()\\n elif self._type == \\\"precipitation_probability\\\":\\n result = self._connector.get_precipitation_probability()\\n elif self._type == \\\"precipitation_duration\\\":\\n result = self._connector.get_precipitation_duration()\\n elif self._type == \\\"cloud_coverage\\\":\\n result = self._connector.get_cloud_coverage()\\n elif self._type == \\\"visibility\\\":\\n result = self._connector.get_visibility()\\n elif self._type == \\\"sun_duration\\\":\\n result = self._connector.get_sun_duration()\\n elif self._type == \\\"sun_irradiance\\\":\\n result = self._connector.get_sun_irradiance()\\n elif self._type == \\\"fog_probability\\\":\\n result = self._connector.get_fog_probability()\\n elif self._type == \\\"humidity\\\":\\n result = self._connector.get_humidity()\\n return result\",\n \"def __str__(self):\\n state_1 = \\\"Time: \\\" + str(self._time)\\n state_2 = \\\"Current Cookies: \\\" + str(self._current_cookies)\\n state_3 = \\\"CPS: \\\" + str(self._cps)\\n state_4 = \\\"Total Cookies: \\\" + str(self._total_cookies)\\n return state_1 + \\\" \\\" + state_2 + \\\" \\\" + state_3 + \\\" \\\" + state_4\",\n \"def PrintWeather(Weather):\\n print('Temperature : {}°C'.format(Weather[0]))\\n print('Humidity : {} %'.format(Weather[1]))\\n print('Description : {}'.format(Weather[2])+'\\\\n')\\n return 1\",\n \"def wattsString(self):\\n return self.watts is None and \\\"unknown\\\" or str(self.watts)\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"architecture = {}\\\\n\\\".format(self.architecture)\\n status += \\\"activations = {}\\\\n\\\".format(self.activations)\\n status += \\\"conv_activations = {}\\\\n\\\".format(self.conv_activations)\\n status += \\\"conv_architecture = {}\\\\n\\\".format(self.conv_architecture)\\n status += \\\"kernel_sizes = {}\\\\n\\\".format(self.kernel_sizes)\\n status += \\\"pool_kernel = {}\\\\n\\\".format(self.pool_kernel)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def to_string(self):\\n if self.is_power_onoff():\\n return 'Power On/Off'\\n else:\\n gain = str(hex(int(self['gain_speed'])))\\n out = self['target'].ljust(20) + ' ' + self['filters'].ljust(11) + ' ' + self['x_bin'] + 'x' + self['y_bin'] + ' ' + gain[2:].upper()\\n \\n \\n if self.number_windows() > 0:\\n out += ' ' + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + ' ' + self['x1_start'].ljust(3) + ' ' + self['y1_start'].ljust(4)\\n if self.number_windows() > 1:\\n out += ' ' + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + ' ' + self['x2_start'].ljust(3) + ' ' + self['y2_start'].ljust(4)\\n \\n if 'Comment' in self:\\n out += ' ' + self['Comment']\\n return out\",\n \"def format(self):\\r\\n\\r\\n earth = \\\"???\\\" if self.maskearth else self.earth\\r\\n air = \\\"???\\\" if self.maskair else self.air\\r\\n fire = \\\"???\\\" if self.maskfire else self.fire\\r\\n water = \\\"???\\\" if self.maskwater else self.water\\r\\n\\r\\n if any((self.earth, self.fire, self.water)):\\r\\n statsline = f'Stats: {earth}/{air}/{fire}/{water}'\\r\\n elif self.air:\\r\\n statsline = f'Air: {air}'\\r\\n else:\\r\\n statsline = ''\\r\\n\\r\\n return (\\r\\n f'Character {self.name}, [{self.token}]. '\\r\\n f'Init: {self.init} {statsline} Owner: {self.user.name}'\\r\\n )\",\n \"def getZaptelConf(self):\\n output = []\\n for portInd, portLine in enumerate(self.portLines):\\n if self[portInd]['type'] != 'na':\\n values = self[portInd]\\n values['type'] = values['type'] == 'fxs' and \\\"fxo\\\" or 'fxs' #Hmm crazy zaptel idea that your fxo is your fxs in zapata but the correct way around in zaptel\\n output.append(\\\"%(type)s%(signalling)s=\\\" % self[portInd] + str(portLine[0]))\\n return output\",\n \"def __str__(self) -> str:\\n return (\\n f\\\"GlacierFlowModel '{self.model_name}' \\\"\\n f\\\"{'' if self.steady_state else 'not '}in steady state with:\\\"\\n f\\\"\\\\n - m: {self.m:20.5f} [m/m]\\\"\\n f\\\"\\\\n - ela: {self.ela:20.2f} [m MSL]\\\"\\n f\\\"\\\\n - resolution: {self.res:20.2f} [m]\\\"\\n f\\\"\\\\n - extent: min max\\\"\\n f\\\"\\\\n {self.extent[0]:10.1f} \\\"\\n f\\\"{self.extent[1]:10.1f} [x]\\\"\\n f\\\"\\\\n {self.extent[2]:10.1f} \\\"\\n f\\\"{self.extent[3]:10.1f} [y]\\\"\\n )\",\n \"def conditions(self) -> Optional[Sequence['_meta.v1.outputs.ConditionPatch']]:\\n return pulumi.get(self, \\\"conditions\\\")\",\n \"def conditions(self) -> Sequence['outputs.StatusConditionResponse']:\\n return pulumi.get(self, \\\"conditions\\\")\",\n \"def mountain_weather_assessment(backcast):\\n \\n if backcast.total_precip < 6:\\n return 'Less than 6 inches of precipitation in the past 7 days, climb on.'\\n if backcast.total_precip > 36:\\n return f\\\"There's been {backcast.total_precip} of precip in the past 7 days, you probably shouldn't climb.\\\"\\n if backcast.sun_count > 72 and backcast.high_temp > 40:\\n return f\\\"It's precipitated {backcast.total_precip} inches recently here, but also been sunny for {backcast.sun_count} hours and reached {backcast.high_temp} degrees F. Use your discretion.\\\"\\n if backcast.precip_count > 30 and backcast.avg_temp <= 40:\\n return f\\\"It's precipitated {backcast.total_precip} inches recently here, over {backcast.precip_count} hours out of the last 7 days, with an average temp of {backcast.avg_temp}F. Use your discretion and please stay safe.\\\"\\n return f\\\"Not sure how to assess this information.\\\"\",\n \"def __str__(self):\\n #Get an ordered list of the elements strings so it outputs always the same\\n #string given a mass function.\\n elements = []\\n for element in self.focals:\\n elements.append((element, str(element)))\\n sortedList = sorted(elements, key=lambda x:x[1])\\n \\n result = \\\"\\\"\\n first = True\\n for t in sortedList:\\n if first:\\n result += t[1] + \\\":\\\" + \\\"{:.4f}\\\".format(self.focals[t[0]])\\n first = False\\n else:\\n result += \\\", \\\" + t[1] + \\\":\\\" + \\\"{:.4f}\\\".format(self.focals[t[0]])\\n return \\\"{\\\" + result + \\\"}\\\"\",\n \"def print_weather(self, days):\\n if days == 1:\\n open_weather = urlopen(self.full_url).read().decode(\\\"utf8\\\")\\n read_json = json.loads(open_weather)\\n outside = self.get_outside_outlook(read_json[\\\"weather\\\"])\\n wind_speed = read_json[\\\"wind\\\"][\\\"speed\\\"]\\n wind_direction = self.deg_to_compass(read_json[\\\"wind\\\"][\\\"deg\\\"])\\n current_temp = self.convert_temp(read_json[\\\"main\\\"][\\\"temp\\\"])\\n print(\\\"Current Temperature: {:.2f}\\\\xb0\\\\n\\\"\\n \\\"Sky: {}\\\\n\\\"\\n \\\"Wind speed: {} MPH\\\\n\\\"\\n \\\"Wind direction: {}\\\".format(current_temp, outside, wind_speed, wind_direction))\\n else:\\n open_weather = urlopen(self.full_url).read().decode(\\\"utf8\\\")\\n read_json = json.loads(open_weather)\\n outside = read_json[\\\"list\\\"]\\n \\\"\\\"\\\"\\n Should be:\\n for temp in outside:\\n stuff = temp[\\\"weather\\\"]\\n for i in stuff:\\n print(i['description'])\\n\\n Each of these will need to be added to a list or a dictionary to print relationally\\n \\\"\\\"\\\"\\n print(outside)\",\n \"def __str__(self):\\n struct_repr = \\\", \\\".join([\\n \\\"is_gyrometer_calibration_ok: \\\" + str(self.is_gyrometer_calibration_ok),\\n \\\"is_accelerometer_calibration_ok: \\\" + str(self.is_accelerometer_calibration_ok),\\n \\\"is_magnetometer_calibration_ok: \\\" + str(self.is_magnetometer_calibration_ok),\\n \\\"is_level_calibration_ok: \\\" + str(self.is_level_calibration_ok),\\n \\\"is_local_position_ok: \\\" + str(self.is_local_position_ok),\\n \\\"is_global_position_ok: \\\" + str(self.is_global_position_ok),\\n \\\"is_home_position_ok: \\\" + str(self.is_home_position_ok)\\n ])\\n\\n return f\\\"Health: [{struct_repr}]\\\"\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"input_dim = {}\\\\n\\\".format(self.input_dim)\\n status += \\\"architecture = {}\\\\n\\\".format(self.architecture)\\n status += \\\"activations = {}\\\\n\\\".format(self.activations)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def print_info(self):\\n outstr = '================================================= Ambient Noise Cross-correlation Database =================================================\\\\n'\\n outstr += self.__str__()+'\\\\n'\\n outstr += '--------------------------------------------------------------------------------------------------------------------------------------------\\\\n'\\n if 'NoiseXcorr' in self.auxiliary_data.list():\\n outstr += 'NoiseXcorr - Cross-correlation seismogram\\\\n'\\n if 'StaInfo' in self.auxiliary_data.list():\\n outstr += 'StaInfo - Auxiliary station information\\\\n'\\n if 'DISPbasic1' in self.auxiliary_data.list():\\n outstr += 'DISPbasic1 - Basic dispersion curve, no jump correction\\\\n'\\n if 'DISPbasic2' in self.auxiliary_data.list():\\n outstr += 'DISPbasic2 - Basic dispersion curve, with jump correction\\\\n'\\n if 'DISPpmf1' in self.auxiliary_data.list():\\n outstr += 'DISPpmf1 - PMF dispersion curve, no jump correction\\\\n'\\n if 'DISPpmf2' in self.auxiliary_data.list():\\n outstr += 'DISPpmf2 - PMF dispersion curve, with jump correction\\\\n'\\n if 'DISPbasic1interp' in self.auxiliary_data.list():\\n outstr += 'DISPbasic1interp - Interpolated DISPbasic1\\\\n'\\n if 'DISPbasic2interp' in self.auxiliary_data.list():\\n outstr += 'DISPbasic2interp - Interpolated DISPbasic2\\\\n'\\n if 'DISPpmf1interp' in self.auxiliary_data.list():\\n outstr += 'DISPpmf1interp - Interpolated DISPpmf1\\\\n'\\n if 'DISPpmf2interp' in self.auxiliary_data.list():\\n outstr += 'DISPpmf2interp - Interpolated DISPpmf2\\\\n'\\n if 'FieldDISPbasic1interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPbasic1interp - Field data of DISPbasic1\\\\n'\\n if 'FieldDISPbasic2interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPbasic2interp - Field data of DISPbasic2\\\\n'\\n if 'FieldDISPpmf1interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPpmf1interp - Field data of DISPpmf1\\\\n'\\n if 'FieldDISPpmf2interp' in self.auxiliary_data.list():\\n outstr += 'FieldDISPpmf2interp - Field data of DISPpmf2\\\\n'\\n outstr += '============================================================================================================================================\\\\n'\\n print outstr\\n return\",\n \"def __str__(self):\\n\\n string = ''\\n string += \\\"Battery Voltage: \\\" + \\\"{0:.2f}\\\".format(self.getBatteryVoltage()) + '\\\\n'\\n string += \\\"Data Logging: \\\" + str(self.getDataLoggingStatus()) + '\\\\n'\\n string += \\\"Data Filename: \\\" + self.getFilename() +'\\\\n'\\n\\n string += \\\"Time Since Instrument Reset (s): \\\" + \\\"{0:.2f}\\\".format(self.getResetTime()) + '\\\\n'\\n string += \\\"Data Age (s): \\\" + \\\"{0:.2f}\\\".format(self.getDataAge()) + '\\\\n'\\n string += '\\\\n'\\n string += \\\"Interior Temperature 1 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature1()) + '\\\\n'\\n string += \\\"Interior Temperature 2 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature2()) + '\\\\n'\\n string += \\\"Interior Temperature 3 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature3()) + '\\\\n'\\n string += \\\"Exterior Temperature (F): \\\" + \\\"{0:.2f}\\\".format(self.getExteriorTemperature()) + '\\\\n'\\n string += \\\"Pressure (PSI): \\\" + \\\"{0:.2f}\\\".format(self.getPressure()) + '\\\\n'\\n string += \\\"Humidity (%): \\\" + \\\"{0:.2f}\\\".format(self.getHumidity()) + '\\\\n'\\n string += '\\\\n'\\n\\n string += \\\"GPS Time: \\\" + str(self.getGpsTime1()) + '\\\\n'\\n string += \\\"Latitude: \\\" + \\\"{0:.9f}\\\".format(self.getLatitude1()) + '\\\\n'\\n string += \\\"Longitude: \\\" + \\\"{0:.9f}\\\".format(self.getLongitude1()) + '\\\\n'\\n string += \\\"Altitude (ft): \\\" + \\\"{0:.2f}\\\".format(self.getAltitude1()) + '\\\\n'\\n string += \\\"Speed (MPH): \\\" + \\\"{0:.2f}\\\".format(self.getSpeed()) + '\\\\n'\\n string += '\\\\n'\\n\\n string += \\\"GPS Time: \\\" + str(self.getGpsTime2()) + '\\\\n'\\n string += \\\"Latitude: \\\" + \\\"{0:.9f}\\\".format(self.getLatitude2()) + '\\\\n'\\n string += \\\"Longitude: \\\" + \\\"{0:.9f}\\\".format(self.getLongitude2()) + '\\\\n'\\n string += \\\"Altitude (ft): \\\" + \\\"{0:.2f}\\\".format(self.getAltitude2()) + '\\\\n'\\n string += '\\\\n'\\n\\n ax, ay, az = self.getAcceleration()\\n string += \\\"Acceleration (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(ax) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(ay) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(az) + '\\\\n'\\n\\n gx, gy, gz = self.getRates()\\n string += \\\"Rates (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(gx) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(gy) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(gz) + '\\\\n'\\n\\n mx, my, mz = self.getMagneticReading()\\n string += \\\"Magnetic Field (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(mx) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(my) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(mz) + '\\\\n'\\n\\n roll, pitch, yaw = self.getAttitude()\\n string += \\\"Roll (deg): \\\" + \\\"{0:.2f}\\\".format(roll) + '\\\\n'\\n string += \\\"Pitch (deg): \\\" + \\\"{0:.2f}\\\".format(pitch) + '\\\\n'\\n string += \\\"Yaw (deg): \\\" + \\\"{0:.2f}\\\".format(yaw) + '\\\\n'\\n string += '\\\\n'\\n relayStates = self.getRelayStates()\\n \\n\\n string += \\\"Relay States: \\\" \\n string += (( \\\"ON \\\") if relayStates[0] else ( \\\"OFF \\\")) \\n string += (( \\\"ON \\\") if relayStates[1] else ( \\\"OFF \\\"))\\n string += (( \\\"ON \\\") if relayStates[2] else ( \\\"OFF \\\"))\\n string += (( \\\"ON \\\") if relayStates[3] else ( \\\"OFF \\\"))\\n string += '\\\\n'\\n\\n\\n return string\",\n \"def __str__(self):\\n struct_repr = \\\", \\\".join([\\n \\\"active: \\\" + str(self.active),\\n \\\"actuator: \\\" + str(self.actuator)\\n ])\\n\\n return f\\\"ActuatorOutputStatus: [{struct_repr}]\\\"\",\n \"def description(self):\\r\\n try:\\r\\n return str(self.connect()['weather'][0]['description'])\\r\\n except:\\r\\n return '@weather_description'\",\n \"def format_status(self) -> str:\\n if not self.ready_to_trade:\\n return \\\"Market connectors are not ready.\\\"\\n lines = []\\n\\n balance_df = self.get_balance_df()\\n lines.extend([\\\"\\\", \\\" Balances:\\\"] + [\\\" \\\" + line for line in balance_df.to_string(index=False).split(\\\"\\\\n\\\")])\\n\\n exchanges_df = self.exchanges_df()\\n lines.extend([\\\"\\\", \\\" Exchanges:\\\"] + [\\\" \\\" + line for line in exchanges_df.to_string(index=False).split(\\\"\\\\n\\\")])\\n\\n try:\\n orders_df = self.active_orders_df()\\n lines.extend([\\\"\\\", \\\" Active Orders:\\\"] + [\\\" \\\" + line for line in orders_df.to_string(index=False).split(\\\"\\\\n\\\")])\\n except ValueError:\\n lines.extend([\\\"\\\", \\\" No active maker orders.\\\"])\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def conditions(self) -> Optional[Sequence['_meta.v1.outputs.Condition']]:\\n return pulumi.get(self, \\\"conditions\\\")\",\n \"def __repr__(self):\\n values = ', '.join(f'{k}={v}' for k, v in self.variables.items())\\n return f'D({values})'\",\n \"def get_weather_info(forecast):\\n day_forecast = {}\\n day_forecast['condition_text'] = forecast['day']['condition']['text']\\n #this icon is a url to an image that describes the weather condition\\n day_forecast['condition_icon'] = forecast['day']['condition']['icon']\\n day_forecast['max_temp'] = forecast['day']['maxtemp_c']\\n day_forecast['min_temp'] = forecast['day']['mintemp_c']\\n day_forecast['avg_temp'] = forecast['day']['avgtemp_c']\\n date = datetime.strptime(forecast['date'], \\\"%Y-%m-%d\\\").strftime(\\\"%b %d:%a\\\")\\n date_format = date.split(':')\\n day_forecast['day'] = date_format[0]\\n day_forecast['weekday'] = date_format[1]\\n return day_forecast\",\n \"def __repr__(self) -> str:\\n s = \\\"\\\\n\\\"\\n fmt = \\\"{:7.3f}\\\"\\n for i in range(len(self.w)):\\n s += \\\" \\\".join(fmt.format(w) for w in self.w[i])\\n s += \\\" | \\\" + fmt.format(self.b[i]) + \\\"\\\\n\\\"\\n return s\",\n \"def get_weather(self):\\n return self.__weather\",\n \"def __str__(self):\\n s=''\\n for r in range(self.n):\\n for c in range(self.n):\\n s += str(self.state[r][c]) + ' '\\n s += '\\\\n'\\n s += str('hcost') + ' : ' + str(self.hcost)\\n s += '\\\\n'\\n return s\",\n \"def output(self):\\n val = \\\"\\\"\\n if self.home:\\n val = \\\"1\\\"\\n if self.away:\\n if val != \\\"\\\": val = val + \\\",\\\"\\n val = val + \\\"2\\\"\\n if self.night:\\n if val != \\\"\\\": val = val + \\\",\\\"\\n val = val + \\\"3\\\"\\n if self.vacation:\\n if val != \\\"\\\": val = val + \\\",\\\"\\n val = val + \\\"4\\\"\\n return val\",\n \"def _truth_condition(self, state):\\n return '{models}'.format(\\n models=models(state, self.rewrite(), '$'),\\n )\",\n \"def __str__(self):\\n # First obtain a string describing the underlying data model.\\n strg = super(MiriTelescopeEmissionModel, self).__str__()\\n \\n # Add the extras\\n if self.meta.instrument.filter is not None:\\n strg += \\\"Data valid for filter=\\\\'%s\\\\' \\\" % \\\\\\n self.meta.instrument.filter\\n else:\\n strg += \\\"Data valid for UNKNOWN filter \\\"\\n if self.meta.telescope_temperature is not None:\\n strg += \\\"and telescope temperature=%.2fK\\\" % \\\\\\n self.meta.telescope_temperature\\n else:\\n strg += \\\"and UNKNOWN telescope temperature\\\"\\n return strg\",\n \"def __repr__(self):\\n s = \\\"s = $%.2f, x = $%.2f, t = %.2f (years), sigma = %.3f, rf = %.3f\\\" %(self.s, self.x, self.t, self.sigma, self.rf)\\n return s\",\n \"def cloudiness(self):\\r\\n try:\\r\\n return str(self.connect()['clouds']['all'])\\r\\n except:\\r\\n return '@weather_cloudiness'\",\n \"def get_weather(self):\\n\\n city = self.user_data[\\\"weatherSettings\\\"][\\\"weatherCity\\\"]\\n country = self.user_data[\\\"weatherSettings\\\"][\\\"weatherCountry\\\"]\\n\\n host = \\\"weather.mios.com\\\"\\n temp_scale = \\\"C\\\"\\n url = \\\"http://%s/?tempFormat=%s&cityWeather=%s&countryWeather=%s\\\" % \\\\\\n (host, temp_scale, Vera.urlencode(city), Vera.urlencode(country))\\n\\n weather = self.proxy_get(url)\\n\\n return (float(weather[\\\"temp\\\"]), weather[\\\"text\\\"])\",\n \"def __str__(self, quiet: bool = True) -> str:\\n\\n q_type = self.q_type\\n\\n if quiet:\\n q_type = \\\"\\\"\\n\\n string = \\\"\\\"\\n\\n if self.representation == \\\"\\\":\\n string = f\\\"({self.t}, {self.x}, {self.y}, {self.z}) {q_type}\\\"\\n\\n elif self.representation == \\\"polar\\\":\\n rep = self.txyz_2_representation(\\\"polar\\\")\\n string = f\\\"({rep[0]} A, {rep[1]} 𝜈x, {rep[2]} 𝜈y, {rep[3]} 𝜈z) {q_type}\\\"\\n\\n elif self.representation == \\\"spherical\\\":\\n rep = self.txyz_2_representation(\\\"spherical\\\")\\n string = f\\\"({rep[0]} t, {rep[1]} R, {rep[2]} θ, {rep[3]} φ) {q_type}\\\"\\n\\n return string\",\n \"def strings(self, sensor_data=None):\\r\\n disc = self.discrete(sensor_data)\\r\\n def toString(val):\\r\\n if val==self.far:\\r\\n return 'far'\\r\\n elif val==self.close:\\r\\n return 'close'\\r\\n else: \\r\\n return 'collision'\\r\\n return list(map(toString, disc))\",\n \"def report(self):\\n print(f\\\"Water: {self.resources['water']}ml\\\")\\n print(f\\\"Milk: {self.resources['milk']}ml\\\")\\n print(f\\\"Coffee: {self.resources['coffee']}g\\\")\",\n \"def print_info(self):\\n print('Condition list: (Cell definitions)')\\n if len(list(self.condition_dict.keys())) > 0:\\n for ID in list(self.condition_dict.keys()):\\n ident = self.condition_dict[ID][0]['identifier']\\n print(\\n f'\\\\t[{ident}]: {len(self.condition_dict[ID])} definition(s)')\\n else:\\n print('\\\\tNo instances.')\\n print()\\n print('Modcell types: (Cell mappings on module)')\\n if len(list(self.modcells.keys())) > 0:\\n for ident in list(self.modcells.keys()):\\n print(f'\\\\t[{ident}]: {len(self.modcells[ident])} definition(s)')\\n else:\\n print('\\\\tNo instances.')\\n print()\\n print('String definitions (Series of modcells)')\\n passed = True\\n if len(list(self.string_cond.keys())) > 0:\\n for str_key in self.string_cond:\\n try:\\n print(\\n f\\\"\\\\t[{str_key}]: {len(self.multilevel_ivdata['string'][str_key]['V'])} definition(s)\\\")\\n except:\\n passed = False\\n continue\\n if not passed:\\n print('String definitions are defined by deducing the combination of module definitions. So, for an accurate display of the string-level definitions, call this module after enacting .simulate()')\\n else:\\n print('\\\\tNo instances.')\\n print()\",\n \"def status(self):\\n res = \\\"\\\"\\n for tlight in self.trafficLights:\\n res += \\\"Traffic light {} status: {}\\\\n\\\".format(self.trafficLights[tlight].id,self.trafficLights[tlight].getState())\\n return res\",\n \"def __repr__(self):\\n\\n values = (\\n f\\\"tickers: {tuple(self._tickers)}\\\\n\\\"\\n f\\\"filters: {tuple(self._filters)}\\\\n\\\"\\n f\\\"rows: {self._rows}\\\\n\\\"\\n f\\\"order: {self._order}\\\\n\\\"\\n f\\\"signal: {self._signal}\\\\n\\\"\\n f\\\"table: {self._table}\\\\n\\\"\\n f\\\"table: {self._custom}\\\"\\n )\\n\\n return values\",\n \"def get_condition(self) -> dict:\\n url = self.base_url + \\\"/condition\\\"\\n condition = self._session.get(url).json()\\n keys = [\\\"bandwidth\\\", \\\"latency\\\", \\\"jitter\\\", \\\"loss\\\"]\\n result = {k: v for (k, v) in condition.items() if k in keys}\\n return result\",\n \"def __str__(self):\\n _str = \\\"Variables:\\\\n\\\"\\n for variable in self.variables:\\n _str += \\\" {}\\\\n\\\".format(str(variable))\\n _str += \\\"\\\\nConstraints:\\\\n\\\"\\n for constraint in self.constraints:\\n _str += \\\" {}\\\\n\\\".format(str(constraint))\\n return _str\",\n \"def __str__(self):\\n return self.designation + ' ' +self.winery + ' wine'\",\n \"def __str__(self):\\n return \\\"{}, fuel={}, odometer={} reliability= {}%\\\".format(self.name, self.fuel,\\n self.odometer, self.reliability)\",\n \"def getDebugText(self):\\n timeDifference = time.time() - self.time_created\\n hours = math.floor(timeDifference / 3600)\\n minutes = math.floor((timeDifference % 3600) / 60)\\n seconds = math.floor(timeDifference % 3600 % 60)\\n\\n output = \\\"\\\\n\\\" * 50\\n output += \\\"Time started: %s\\\\n\\\" % time.ctime(self.time_created)\\n output += \\\"Time now: %s\\\\n\\\" % time.ctime()\\n output += \\\"Time elapsed: %02d:%02d:%02d\\\\n\\\" % (hours, minutes, seconds)\\n output += (\\\"=\\\" * 80) + \\\"\\\\n\\\"\\n output += \\\"Health potions used: %d\\\\n\\\" % self.hp_pots_used\\n output += \\\"Health potions per hour: %d\\\\n\\\" % (self.hp_pots_used / (\\n timeDifference / 3600))\\n output += \\\"Mana potions used: %d\\\\n\\\" % self.mana_pots_used\\n output += \\\"Mana potions per hour: %d\\\\n\\\" % (self.mana_pots_used / (\\n timeDifference / 3600))\\n return output\",\n \"def toQif(self):\\n out=list();\\n if 'date' in self:\\n out.append(\\\"D{}\\\".format(self['date']));\\n if 'amount' in self:\\n out.append(\\\"T{}\\\".format(self['amount']));\\n if 'memo' in self and len(self['memo'])>3:\\n out.append(\\\"M{}\\\".format(self['memo']));\\n if 'payee' in self and len(self['payee'])>3:\\n out.append(\\\"P{}\\\".format(self['payee']));\\n out.append(\\\"^\\\");\\n return \\\"\\\\n\\\".join(out);\",\n \"def getConditionNames():\\n return _conditions.keys()\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"classes = {}\\\\n\\\".format(self.classes)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def summarize_observing_conditions(fitsFiles):\\n count = len(fitsFiles)\\n\\n # Here is the data we are going to collect from the fits headers\\n year = np.zeros(count, dtype=int)\\n month = np.zeros(count, dtype=int)\\n day = np.zeros(count, dtype=int)\\n hour = np.zeros(count, dtype=int)\\n minute = np.zeros(count, dtype=int)\\n airmass = np.zeros(count, dtype=float)\\n water_column = np.zeros(count, dtype=float)\\n \\n for ii in range(len(fitsFiles)):\\n # Get header info\\n hdr = pyfits.getheader(fitsFiles[ii])\\n\\n airmass[ii] = float(hdr['AIRMASS'])\\n\\n date = hdr['DATE-OBS'].split('-')\\n _year = int(date[0])\\n _month = int(date[1])\\n _day = int(date[2])\\n\\n utc = hdr['UTC'].split(':')\\n _hour = int(utc[0])\\n _minute = int(utc[1])\\n _second = int(math.floor(float(utc[2])))\\n\\n utc = datetime.datetime(_year, _month, _day, _hour, _minute, _second)\\n utc2hst = datetime.timedelta(hours=-10)\\n hst = utc + utc2hst\\n\\n year[ii] = hst.year\\n month[ii] = hst.month\\n day[ii] = hst.day\\n hour[ii] = hst.hour\\n minute[ii] = hst.minute\\n\\n # Get the water column in mm of H2O\\n water_column[ii] = weather.cso_water_column(_year, _month, _day, \\n _hour, _minute)\\n\\n # Now lets fetch the CFHT weather data\\n (temperature, pressure, humidity, wind_speed, wind_dir) = \\\\\\n weather.cfht_weather_data(year, month, day, hour, minute)\\n\\n # Print out a nicely formatted table\\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\\\\n ('Filename', 'Year', 'M', 'D', 'h', 'm', 'AirM', 'H2O', 'Temp', \\n 'Press', 'Humi', 'Wind', 'Dir'))\\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\\\\n ('HST', '', '', '', '', '', '', 'mm', 'C', 'mbar', '%', 'km/h', 'deg'))\\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\\\\n ('--------', '----', '--', '--', '--', '--', '----', '----', '-----', \\n '-----', '----', '----', '----'))\\n\\n for ii in range(len(fitsFiles)):\\n print('%-20s %4d %2d %2d %2d %2d ' % \\\\\\n (fitsFiles[ii], year[ii], month[ii], day[ii], hour[ii], minute[ii]),)\\n print('%4.2f %4.2f %5.1f %5.1f %4.1f %4.1f %4d' % \\\\\\n (airmass[ii], water_column[ii], temperature[ii], pressure[ii],\\n humidity[ii], wind_speed[ii], wind_dir[ii]))\\n\\n # Print out the average values\\n print('%-20s %4s %2s %2s %2s %2s %4s %4s %5s %5s %4s %4s %4s' % \\\\\\n ('--------', '----', '--', '--', '--', '--', '----', '----', '-----', \\n '-----', '----', '----', '----'))\\n print('%-20s %4d %2d %2d %2d %2d ' % \\\\\\n ('Average', year.mean(), month.mean(), day.mean(), hour.mean(), \\n minute.mean()),)\\n print('%4.2f %4.2f %5.1f %5.1f %4.1f %4.1f %4d' % \\\\\\n (airmass.mean(), water_column.mean(), temperature.mean(), \\n pressure.mean(), humidity.mean(), wind_speed.mean(), wind_dir.mean()))\\n print('%-20s %4d %2d %2d %2d %2d ' % \\\\\\n ('Std. Dev.', year.std(), month.std(), day.std(), hour.std(), \\n minute.std()),)\\n print('%4.2f %4.2f %5.1f %5.1f %4.1f %4.1f %4d' % \\\\\\n (airmass.std(), water_column.std(), temperature.std(), \\n pressure.std(), humidity.std(), wind_speed.std(), wind_dir.std()))\",\n \"def __str__(self):\\n #{{{ Nicely print values\\n text = 'Null values for databases: %s' % self.dbcentral.list()\\n\\n for value in self.null_vals.keys():\\n text += \\\"\\\\t%s: %s\\\" % (value,self.null_vals[value])\\n\\n return text\",\n \"def formated_print(dic):\\n print dic['Year'] + '/' + get_month_number(dic['Month']) + '/' + \\\\\\n dic['Day'] + ' | ' + dic['Hour'] + ':' + dic['Min'] + ':' + \\\\\\n dic['Seg'] + ' | ' + dic['Energy'] + ' Watts'\",\n \"def __str__(self):\\r\\n\\r\\n retval = self.__class__.__name__ + ' ('\\r\\n for val in self.VALUES:\\r\\n value = getattr(self, val, None)\\r\\n if value is not None:\\r\\n retval += '%s:%.4f ' % (val, getattr(self, val))\\r\\n return retval.strip() + ')'\",\n \"def __repr__(self):\\n string = ''\\n for key, val in self.setting().items():\\n string += '{}({})\\\\n'.format(key, val)\\n return string\",\n \"def __str__(self):\\n r = []\\n for item in sorted(self._data.keys()):\\n correct, incorrect = self._data[item][True], self._data[item][False]\\n acc = correct / (correct + incorrect)\\n s = f\\\"{item:4} | Accuracy: {acc:.2f}% (diff {'+' if acc-item >=0 else ''}{acc-item:.2f}%) | correct: {correct:2}, incorrect: {incorrect:2}\\\" \\n r.append(s)\\n\\n return \\\"\\\\n\\\".join(r)\",\n \"def __str__(self):\\n return f'{self._name} has {self._calories} calories, {self._carbohydrates}' +\\\\\\n f'g. carbohydrates, {self._fat}g. of fat and {self._proteins}g. of proteins'\",\n \"def __str__(self) -> str:\\n\\n return (\\n f\\\"The {self.name} temperature sensor has a value of \\\"\\n f\\\"{self.value} degrees {self.unit}.\\\"\\n )\",\n \"def __str__(self):\\n out_str = \\\"\\\\n\\\".join(`\\\"%.5f, %.5f, %.1f, %s, %s\\\" % (point[0], point[1], point[2], point[3], point[4])` for point in self.__traectory_list)\\n return \\\"\\\\'x, y, altitude, capture time, capture date'\\\\n\\\"+out_str\",\n \"def get_status(self):\\n request_format = \\\"{oscillating:01d} {initialising:01d} {initialised:01d} {width:03d} \\\" \\\\\\n \\\"{offset:+04d} {speed:02d} {acceleration:03d} {cycles:05d} {backlash:03d}\\\"\\n status_string = request_format.format(\\n oscillating=int(self.device.is_oscillating()),\\n initialising=int(self.device.is_initialising()),\\n initialised=int(self.device.has_been_initialised()),\\n width=int(self.device.get_window_width()),\\n offset=int(self.device.get_offset()),\\n speed=int(self.device.get_speed()),\\n acceleration=int(self.device.get_acceleration()),\\n cycles=int(self.device.get_complete_cycles()),\\n backlash=int(self.device.get_backlash())\\n )\\n return status_string\",\n \"def __str__(self, output=[]):\\n\\n output = [['only heat', self.only_heat],\\n ] + output\\n\\n class_str = 'Phonon simulation properties:\\\\n\\\\n'\\n class_str += super().__str__(output)\\n\\n return class_str\",\n \"def condition(self) -> str:\\r\\n condition = self._first_timeserie[\\\"data\\\"][\\\"next_1_hours\\\"][\\\"summary\\\"][\\r\\n \\\"symbol_code\\\"\\r\\n ]\\r\\n return format_condition(condition)\",\n \"def status(self):\\n \\n tmpl1 = \\\"\\\"\\\"%-20s%-52s[%s]\\\"\\\"\\\"\\n tmpl2 = \\\"\\\"\\\"%-20s%-52s\\\\n\\\"\\\"\\\"\\n # print tmpl1 % (\\\"Machine Name\\\", \\\"IP Addresses\\\", \\\"Status\\\")\\n # print 80 * \\\"-\\\"\\n # print self.get_image()\\n if self.cloudserver:\\n # let's build the IPs first\\n status = self.cloudserver.status\\n \\n else:\\n status = \\\"OFF\\\"\\n\\n res2=\\\"\\\"\\n ip1 = \\\"%s:%s\\\" % (self.networks[0], self.ip_addresses[self.networks[0]])\\n if len(self.networks) > 1:\\n res2 += \\\"\\\\n\\\"\\n for network in self.networks[1:]:\\n ipstr = \\\"%s:%s\\\" % (network, self.ip_addresses[network])\\n res2+=tmpl2 % (\\\"-\\\", ipstr)\\n # print res2\\n # if len(self.ip_addresses.keys()) > 1:\\n # ip1 = self.ip_addresses.values()[0]\\n res1 = tmpl1 % (self.machine_name, ip1, status)\\n return res1 + res2\",\n \"def generate_watch_output():\\r\\n values = H.unicode_string('')\\r\\n if S.WATCH is None:\\r\\n return values\\r\\n for watch_data in S.WATCH:\\r\\n watch_entry = ''\\r\\n if watch_data and isinstance(watch_data, dict):\\r\\n # Whether watch expression is enabled or disabled\\r\\n if 'enabled' in watch_data.keys():\\r\\n if watch_data['enabled']:\\r\\n watch_entry += '|+|'\\r\\n else:\\r\\n watch_entry += '|-|'\\r\\n # Watch expression\\r\\n if 'expression' in watch_data.keys():\\r\\n watch_entry += ' \\\"%s\\\"' % watch_data['expression']\\r\\n # Evaluated value\\r\\n if watch_data['value'] is not None:\\r\\n watch_entry += ' = ' + generate_context_output(watch_data['value'])\\r\\n else:\\r\\n watch_entry += \\\"\\\\n\\\"\\r\\n values += H.unicode_string(watch_entry)\\r\\n return values\",\n \"def __str__(self):\\n state = ''\\n state += ' '.join([str(x) for x in self.pos]) + ' '\\n state += ''.join([str(x) + ' ' + str(y) + ' ' for x,\\n y in zip(self.BU, self.BD)])\\n for e in self.BF:\\n state += ' '.join([str(x) for x in e])\\n state += ' '\\n state += ' '.join([str(x) for x in self.LU]) + ' '\\n state += ' '.join([str(x) for x in self.LD]) + ' '\\n\\n return state\",\n \"def reaction_str(self):\\n\\n def format(number):\\n return str(number).rstrip(\\\".0\\\") + \\\" \\\"\\n\\n reactant_bits = []\\n product_bits = []\\n for met in sorted(self._metabolites, key=attrgetter(\\\"id\\\")):\\n coefficient = self._metabolites[met]\\n if coefficient >= 0:\\n product_bits.append(format(coefficient) + met.id)\\n else:\\n reactant_bits.append(format(abs(coefficient)) + met.id)\\n\\n reaction_string = ' + '.join(reactant_bits)\\n if self.gapfill_direction == '=':\\n reaction_string += ' <=> '\\n elif self.gapfill_direction == '<':\\n reaction_string += ' <-- '\\n elif self.gapfill_direction == '>':\\n reaction_string += ' --> '\\n reaction_string += ' + '.join(product_bits)\\n return reaction_string\",\n \"def __repr__(self):\\n string = \\\"Current state: \\\\n\\\"\\n if self.state[0] == 0: # We're on the left side\\n string += \\\"M: \\\"\\n string += str(self.state[1]).ljust(10)\\n string += \\\"M: \\\"\\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"C: \\\"\\n string += str(self.state[2]).ljust(10)\\n string += \\\"C: \\\"\\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"Boat position: left\\\\n\\\"\\n else: # We're on the right side\\n string += \\\"M: \\\"\\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\\n string += \\\"M: \\\"\\n string += str(self.state[1])\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"C: \\\"\\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\\n string += \\\"C: \\\"\\n string += str(self.state[2]).ljust(10)\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"Boat position: right\\\\n\\\"\\n string += \\\"\\\\n\\\"\\n return string\",\n \"def encode(self):\\n color_str = []\\n if self.brightness is not None:\\n color_str.append(f\\\"brightness:{self.brightness}\\\")\\n if self.hue is not None:\\n color_str.append(f\\\"hue:{self.hue}\\\")\\n if self.saturation is not None:\\n color_str.append(f\\\"saturation:{self.saturation}\\\")\\n if self.kelvin is not None:\\n color_str.append(f\\\"kelvin:{self.kelvin}\\\")\\n\\n return ' '.join(color_str)\",\n \"def __repr__(self):\\n\\n repme = \\\"delta0= {!r}, c_upd= {!r}, n_upd= {!r}, smooth= {!r}\\\"\\\\\\n .format(self.delta0, self.c_upd, self.n_upd, self.smooth)\\n\\n return \\\"WangLandau({!s})\\\".format(repme)\",\n \"def __str__(self):\\n s = ''\\n for i in range(5):\\n for j in range(5):\\n ani = False\\n if self[i][j] == 0:\\n s += ' 0 '\\n elif self[i][j].species == 'Elephant':\\n s += ' E'\\n ani = True\\n elif self[i][j].species == 'Rhinoceros':\\n s += ' R'\\n ani = True\\n else:\\n s += ' B '\\n if ani:\\n if self[i][j].direction[0] == 0 and self[i][j].direction[1] == 1:\\n d = '> '\\n elif self[i][j].direction[0] == -1 and self[i][j].direction[1] == 0:\\n d = '∧ '\\n elif self[i][j].direction[0] == 0 and self[i][j].direction[1] == -1:\\n d = '< '\\n else:\\n d = '∨ '\\n s += d\\n s += '\\\\n \\\\n'\\n return s\",\n \"def __str__(self):\\n s = 'hit '+str(self.hit)+'\\\\n'\\n s+= 'states '+str(self.states)+'\\\\n'\\n s+= 'chi2 '+str(self.chi2)\\n return s\",\n \"def device_state_attributes(self) -> str:\\n return {\\n \\\"remo_device_id\\\": self._remo_device.id,\\n \\\"remo_device_name\\\": self._remo_device.name,\\n \\\"remo_firmware_version\\\": self._remo_device.firmware_version,\\n \\\"remo_temperature_offset\\\": self._remo_device.temperature_offset,\\n \\\"remo_humidity_offset\\\": self._remo_device.humidity_offset\\n }\",\n \"def describe(self) -> str:\\r\\n env = str(self.env)\\r\\n observation_space = str(self.observations)\\r\\n action_space = str(self.actions)\\r\\n policy = str(self.policy)\\r\\n gamma = f\\\"Gamma({self.gamma})\\\"\\r\\n\\r\\n return env, observation_space, action_space, policy, gamma\",\n \"def __str__(self):\\n\\t\\n\\t\\tresult = \\\"\\\"\\n\\t\\tresult += \\\"Torsional Spring Specs: \\\\n\\\"\\n\\t\\tresult += \\\"Shape Eq. Slope: {0}\\\\n\\\".format(str(self.shape_slope))\\n\\t\\tresult += \\\"Z Thickness: {0}\\\\n\\\".format(str(self.z_thick))\\n\\t\\tresult += \\\"In-Plane Thickness: {0}\\\\n\\\".format(str(self.thick))\\n\\t\\tresult += \\\"Spiral Length: {0}\\\\n\\\".format(str(self.length))\\n\\n\\t\\treturn result\",\n \"def full_broadcast(city_name):\\n today_weather(city_name)\\n for i in range(1, 4):\\n output = (\\n (datetime.date.today() + datetime.timedelta(days=i)).strftime(\\\"%d/%m/%Y\\\")\\n + \\\"Temperature: \\\"\\n + \\\"{}\\\".format(\\n get_weather(\\n city_name,\\n (datetime.date.today() + datetime.timedelta(days=i)).strftime(\\n \\\"%d/%m/%Y\\\"\\n ),\\n )\\n )\\n )\\n print(output)\",\n \"def __str__(self):\\n return f'''\\n {super().__str__()}\\n Brand: {self._breand}\\n Pover: {self._power} (W)\\n Nozzle: {self._nozzle} (pieces)\\n '''\",\n \"def __str__(self) -> str:\\n if self.scalar_vector:\\n return f\\\"({self.w:-.4f} {self.x:+.4f}i {self.y:+.4f}j {self.z:+.4f}k)\\\"\\n return f\\\"({self.x:-.4f}i {self.y:+.4f}j {self.z:+.4f}k {self.w:+.4f})\\\"\",\n \"def __str__(self):\\n output = \\\"There are {!s} cities with {!s} connections in {}.\\\".format(\\n self.__railways.vertex_count(), self.__railways.edge_count(),\\n self.__timeTable.split('.')[0])\\n return output\",\n \"def __repr__(self):\\n\\n output = list()\\n output.append('{resonance_id:6s}'.format(**self.par))\\n output.append('{h_larmor_frq:6.1f}'.format(**self.par))\\n output.append('{temperature:4.1f}'.format(**self.par))\\n output.append('{:10.5f}'.format(self.val))\\n output.append('{:10.5f}'.format(self.err))\\n\\n if self.cal:\\n output.append('{:10.5f}'.format(self.cal))\\n\\n return ' '.join(output)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63701195","0.6170547","0.6153412","0.6075571","0.59051156","0.5875144","0.582113","0.5815576","0.5801642","0.5736014","0.5721063","0.5704934","0.56886274","0.5640063","0.56320167","0.5610608","0.5609194","0.55756634","0.5571646","0.5566451","0.5524659","0.5514211","0.5509786","0.55024344","0.54777163","0.5442844","0.54310066","0.5416631","0.5409348","0.5381201","0.53624815","0.53510803","0.5347678","0.5343814","0.532623","0.5316304","0.5313363","0.53050655","0.5290525","0.5289795","0.52775407","0.527598","0.52743644","0.52648294","0.5261644","0.5258245","0.52527606","0.5251696","0.524947","0.5248626","0.5247982","0.52384645","0.52331436","0.52323276","0.52113813","0.5208211","0.5205113","0.5203182","0.5203057","0.5200823","0.5200325","0.51986223","0.51972926","0.5197267","0.5196014","0.51855606","0.51725596","0.5171399","0.5168826","0.5168691","0.5162104","0.51562935","0.51413375","0.5139421","0.5138165","0.5136719","0.51361555","0.5128181","0.51195925","0.51118","0.5111716","0.5110758","0.510889","0.51077485","0.5105787","0.5104485","0.51041925","0.5101914","0.509739","0.5094452","0.5091404","0.50840515","0.508364","0.5078986","0.5077018","0.50751925","0.5070547","0.5070371","0.5068583","0.5067776"],"string":"[\n \"0.63701195\",\n \"0.6170547\",\n \"0.6153412\",\n \"0.6075571\",\n \"0.59051156\",\n \"0.5875144\",\n \"0.582113\",\n \"0.5815576\",\n \"0.5801642\",\n \"0.5736014\",\n \"0.5721063\",\n \"0.5704934\",\n \"0.56886274\",\n \"0.5640063\",\n \"0.56320167\",\n \"0.5610608\",\n \"0.5609194\",\n \"0.55756634\",\n \"0.5571646\",\n \"0.5566451\",\n \"0.5524659\",\n \"0.5514211\",\n \"0.5509786\",\n \"0.55024344\",\n \"0.54777163\",\n \"0.5442844\",\n \"0.54310066\",\n \"0.5416631\",\n \"0.5409348\",\n \"0.5381201\",\n \"0.53624815\",\n \"0.53510803\",\n \"0.5347678\",\n \"0.5343814\",\n \"0.532623\",\n \"0.5316304\",\n \"0.5313363\",\n \"0.53050655\",\n \"0.5290525\",\n \"0.5289795\",\n \"0.52775407\",\n \"0.527598\",\n \"0.52743644\",\n \"0.52648294\",\n \"0.5261644\",\n \"0.5258245\",\n \"0.52527606\",\n \"0.5251696\",\n \"0.524947\",\n \"0.5248626\",\n \"0.5247982\",\n \"0.52384645\",\n \"0.52331436\",\n \"0.52323276\",\n \"0.52113813\",\n \"0.5208211\",\n \"0.5205113\",\n \"0.5203182\",\n \"0.5203057\",\n \"0.5200823\",\n \"0.5200325\",\n \"0.51986223\",\n \"0.51972926\",\n \"0.5197267\",\n \"0.5196014\",\n \"0.51855606\",\n \"0.51725596\",\n \"0.5171399\",\n \"0.5168826\",\n \"0.5168691\",\n \"0.5162104\",\n \"0.51562935\",\n \"0.51413375\",\n \"0.5139421\",\n \"0.5138165\",\n \"0.5136719\",\n \"0.51361555\",\n \"0.5128181\",\n \"0.51195925\",\n \"0.51118\",\n \"0.5111716\",\n \"0.5110758\",\n \"0.510889\",\n \"0.51077485\",\n \"0.5105787\",\n \"0.5104485\",\n \"0.51041925\",\n \"0.5101914\",\n \"0.509739\",\n \"0.5094452\",\n \"0.5091404\",\n \"0.50840515\",\n \"0.508364\",\n \"0.5078986\",\n \"0.5077018\",\n \"0.50751925\",\n \"0.5070547\",\n \"0.5070371\",\n \"0.5068583\",\n \"0.5067776\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79582655"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2397,"cells":{"query":{"kind":"string","value":"A generator function for looping through various times."},"document":{"kind":"string","value":"def time_iterator(\n *,\n first_time: datetime.datetime,\n last_time: datetime.datetime,\n resolution: int,\n timezone: datetime.timezone,\n) -> Generator[datetime.datetime, None, None]:\n\n current_time = first_time\n while current_time < last_time:\n yield current_time.replace(tzinfo=timezone)\n current_time += relativedelta(\n hours=resolution // 3600,\n minutes=(resolution // 60) % 60,\n seconds=resolution % 60,\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def times(self):\n \n class IterTimes:\n def __init__(self, st):\n self.c = 0\n self.st = st\n \n def __iter__(self):\n return self\n \n def next(self):\n t = self.st.time(self.c)\n if t == None or self.c == self.st.longitud:\n raise StopIteration\n else:\n self.c += 1\n return t\n \n return IterTimes(self)","def exercise_gen(ret_val, times):","def time_processor(self):\n while True:\n rexp = (yield)\n self.time = float(rexp.group(1))\n self.time_str = rexp.group(1)\n # Reset subIteration counters\n for k in self.subiter_map:\n self.subiter_map[k] = 0\n self._tick = False","def getTimes():","def getTimes():","def getTimes():","def iterate(func, x):\n while True:\n x = func(x)\n yield x","def repeat(fun, n):\n for i in range(n):\n yield fun()","def _fill_time(beats, shard, *args, **kwargs):\n total = 0\n for delay in shard(*args, **kwargs):\n total += delay.count\n yield delay\n yield Beat(beats - total)","def get_time(self):\n for _ in range(5):\n current_time = datetime.datetime.now().strftime('%H:%M:%S')\n self.logger.info(current_time)\n num: int = self.rand_num_gen.get_number()\n display = current_time + f\": {num}\\n\"\n yield display.encode()\n sleep(1)\n try:\n raise RuntimeError(\"something unexpected happened!\")\n except:\n trace_info = traceback.format_exc()\n self.logger.error(\"uncaught exception: %s\", trace_info)\n yield trace_info","def countdown():\n for i in range(100, 0, -1):\n yield i","def _mc_gen():\r\n n = 1\r\n while True:\r\n yield n\r\n n += 1","def test_generator_method(self):\n for i in range(0, 4):\n yield self.try_odd, i","def random_values():\n while True:\n yield random()","def task5(count):\n number_1, number_2 = 1, 1\n for _ in range(count):\n yield number_1\n number_1, number_2 = number_2, number_1 + number_2","def data_repeated(data):\n\n def gen(count):\n for _ in range(count):\n yield data\n\n yield gen","def counter_wrapper(generator):\n for value in generator:\n yield value","def _iter_items(data_sequence):\n for time, element in data_sequence:\n for item in element:\n yield time, item","def __iter__(self):\n\n # Wanted to do this, but count() only accepts numbers:\n # return count(start, timedelta(days=self.step))\n\n next = self.start\n while (True):\n yield next\n next = next + timedelta(days=self.step)","def multiple_gen(modulus):\n count = 1\n while True:\n yield modulus * count\n count += 1","def repeatedly(func, /, *args, **kwargs):\n func = to_callable(func)\n try:\n while True:\n yield func(*args, **kwargs)\n except StopIteration as e:\n yield from stop_seq(e)","def counter_wrapper_2(generator):\n yield from generator","def __iter__(self):\n start_times = (start for start, end in self.tss)\n names = (name.rstrip() for name in self.inps)\n for ind, (c, t) in enumerate(zip(names, start_times)):\n yield (c, t, ind)","def repeat(obj, times=None):\n if times is None:\n return Iter(itertools.repeat(obj))\n return Iter(itertools.repeat(obj, times))","def __iter__(self):\n if len(self) == 0:\n return\n current = self.first_timestamp\n delta = datetime.timedelta(0, self.interval)\n while current <= self.last_timestamp:\n yield current\n current += delta","def timeit_context() -> Generator:\n result = TimeItResult()\n started_time = time.time()\n try:\n yield result\n finally:\n result.time_passed = time.time() - started_time","def static_trajectory(Tinit, n):\n for i in xrange(n):\n yield Tinit","def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:","def customer_generator(env, inventory_stock):\n for i in itertools.count():\n yield env.timeout(random.randint(*T_INTER))\n env.process(customer(env, inventory_stock, 'Customer_'+str(i+1)))","def itime(iterable, seconds):\n items = iter(iterable)\n\n end = time.time() + seconds\n yield items.next()\n\n for item in itertools.takewhile(lambda _: time.time() < end, items):\n yield item","def counter():\n for value in range(5):\n yield \"<{}>\".format(value)","def test_generator_method_name(self):\n for i in range(0, 4):\n yield 'try_odd', i","def forever():\n\n def animate(thing):\n thing = list(thing)\n yield from repeat(thing)\n return animate","def times(self, fn):\n for i in range(0, self._):\n fn()\n return self","def simple():\n yield 1\n yield 2\n yield 3","def iterate(f, x):\n while True:\n yield x\n x = f(x)","def generator():\n mygenerator = (x for x in range(3))\n for element in mygenerator:\n print 'poprve = ', element\n\n for element in mygenerator:\n print 'podruhe = ', element","def wait(self, fps=2):\n i = 1\n t = clock()\n while 1:\n free_count = 0\n skip = 0\n while free_count == 0:\n while clock()-t < float(i)/fps:\n free_count += 1\n if free_count == 0:\n i += 1\n skip += 1\n yield i-1, skip\n i += 1","def generator_fn():\n for thumbs, counts in data_dir.hotspot_data(num_timestamps=num_timestamps):\n for thumb, count in zip(thumbs, counts):\n yield thumb, count","def source_deterministic(env, number, interval, counter):\n for i in range(number):\n bus_arrival = 12.0 # deterministic - fixed frequency\n c = customer(env, 'Customer_%02d' % (i + 1), counter, time_in_stop=bus_arrival)\n env.process(c)\n t = random.expovariate(1.0 / interval)\n yield env.timeout(t)","def cycle(obj):\r\n while True:\r\n for item in obj:\r\n yield item","def repeat(seq, n):\n for e in seq:\n for _ in range(n):\n yield e","def times(self):\n return list(range(self._max_time + 1))","def test_func_generator():\n def test_odd(v):\n assert v % 2\n for i in range(0, 4):\n yield test_odd, i","def step_sequence_gen(track, click=False, fillvalue=0.0, t=None, srate=None):\n if t is None:\n t = time_gen(srate=srate)\n else:\n t = iter(t)\n t0 = next(t)\n t1 = t0\n duration = 1\n for tple in track:\n if hasattr(tple,'__getitem__'):\n value = tple[0]\n if len(tple) > 1:\n duration = tple[1]\n else:\n value = tple\n while t0 + duration > t1:\n yield value\n if click:\n value = fillvalue\n t1 = next(t)\n t0 = t1","def record_time(times, enabled, *args):\n if not enabled:\n yield\n else:\n start = time.time()\n yield\n end = time.time()\n times.append((' '.join(args), start, end))","def test_func_generator_name():\n for i in range(0, 4):\n yield 'try_odd', i","def anim():\n i = 0\n while 1:\n\n for r in Reprs:\n r.draw(i)\n i = i+ 1\n i = i % len(t)\n yield","def _ids(self):\n prev_values = set()\n while True:\n next_value = self._time_ns()\n while True:\n if next_value not in prev_values:\n break\n next_value += 1000\n prev_values.add(next_value)\n yield next_value","def repeatfunc(cls, func, times=None, *args):\n if times is None:\n return starmap(func, repeat(args))\n return starmap(func, repeat(args, times))","def e_seq():\n yield 2;\n for n in count(2, 2):\n yield 1\n yield n\n yield 1","def __iter__(self):\n self._loop_idx = 0\n self._target_time = time.time()\n return self","def inference_generator(env, storage, pipe, arrival_rate):\n\n global num_clients, trace, last_inf_times, request_times\n for i in itertools.count():\n random_request_time = random.expovariate(arrival_rate)\n cumulative_request_time = last_inf_times + random_request_time\n last_inf_times = cumulative_request_time\n request_times.append(cumulative_request_time)\n yield env.timeout(random_request_time)\n num_clients +=1\n d = {'idx' : num_clients, 'request_time' : env.now}\n pipe.put(d)","def timed(name):\n t0 = time.time()\n yield\n t1 = time.time()\n print(\"..%-24s: %8.4f\" % (name, t1 - t0))","def iterate(self, start, end):\n if not self.db:\n self.db = self.get_db()\n\n p = start[:-4]\n s = struct.unpack(\"!L\", start[-4:])[0]\n e = struct.unpack(\"!L\", end[-4:])[0]\n time.sleep(self.READ_DELAY)\n while s <= e:\n v = random.random() * 100\n yield p + struct.pack(\"!L\", s), struct.pack(\"!d\", v)\n s += self.STEP","def vytvorit_generator():\n mylist = range(3)\n print 'mylist = ', mylist\n for element in mylist:\n yield element","def forever(shard):\n def repeat(*args, **kwargs):\n while True:\n for delay in shard(*args, **kwargs):\n yield delay\n return repeat","def repeat(value: T, times: int) -> List[T]:\n return [value] * times","def repeatfunc(func, times=None, *args):\n if times is None:\n return starmap(func, repeat(args))\n return starmap(func, repeat(args, times))","def repeatfunc(func, times=None, *args):\n if times is None:\n return starmap(func, repeat(args))\n return starmap(func, repeat(args, times))","def stream():\n while True:\n yield random_point()","def repeat(iterable, count=None):\n if count is None:\n while True:\n for sample in iterable:\n yield sample\n else:\n for i in range(count):\n for sample in iterable:\n yield sample","def comp_pattern_generator(iterable):\n for chord in iterable:\n yield (600, chord)","async def sleep_generator(pos: int):\n if pos <= 0:\n raise ValueError(f\"none_zero must be some positive integer, got {pos}\")\n scale = 1 / pos / 10\n for i in range(pos):\n t = scale * (pos - i)\n LOGGER.info(f\"Number {i}, going to sleep {t} s\")\n await asyncio.sleep(t)\n LOGGER.info(f\"Number {i}, slept {t} s\")\n yield i","def floating_point_generator():\n i = 0\n while True:\n yield str((i % 5) * 1.1)\n i += 1","def make_iter(capture, channel):\n\n def cycle():\n threading.Timer(INTERVAL, cycle).start()\n publish_frame(capture, channel)\n\n return cycle","def test_generator_inline(self):\n def test_odd(v):\n assert v % 2\n for i in range(0, 4):\n yield test_odd, i","def numbers():\n for number in range(1, 76):\n yield number","def id_generator():\n start_value = 0\n while True:\n yield start_value\n start_value += 1","def _exponential_timeout_generator(initial, maximum, multiplier, deadline):\n if deadline is not None:\n deadline_datetime = (\n datetime_helpers.utcnow() +\n datetime.timedelta(seconds=deadline))\n else:\n deadline_datetime = datetime.datetime.max\n\n timeout = initial\n while True:\n now = datetime_helpers.utcnow()\n yield min(\n # The calculated timeout based on invocations.\n timeout,\n # The set maximum timeout.\n maximum,\n # The remaining time before the deadline is reached.\n float((deadline_datetime - now).seconds))\n timeout = timeout * multiplier","def timer():\n start = time.time()\n\n yield\n\n end = time.time()\n\n print('Elapsed: {:.2f}s'.format(end - start))","def cycle(iterator: Iterable[Any]) -> Iterable[Any]:\n while True:\n yield from iterator","def changefreqs_generator():\n changefreqs = ['weekly', 'daily']\n yield from itertools.chain(changefreqs, itertools.repeat('monthly'))","def sleeper(self):\n for waittime in (.01, .02, .05, .1, .2, .5):\n yield waittime\n while True:\n waittime = min(waittime + .2, 5)\n yield waittime","def __iter__(self): # for each frequency, yield a QTile\n for freq in self._iter_frequencies():\n yield snrQTile(self.q, freq, self.duration, self.sampling,\n mismatch=self.mismatch, shift = self.freq_timeshift(freq))","def test_generator_continuous():\n RANGE_MAX = 100\n prev_value = RANGE_MAX // 2\n for msg in it.islice(generate_msgs(0, RANGE_MAX), 0, 42):\n curr_value = Message.parse(msg).power\n assert curr_value - prev_value <= 1\n prev_value = curr_value","def generate(th1):\n pro = 1\n while True:\n if th1.is_alive() and pro < 99:\n pro = pro + 1\n time.sleep(1)\n yield \"data:\" + str(pro) + \"\\n\\n\"\n else:\n if not th1.is_alive():\n yield \"data:\" + str(100) + \"\\n\\n\"","def convergence_processor(self):\n while True:\n rexp = (yield)\n self.converged = True\n self.converged_time = int(rexp.group(2))","def cycle(start, times):\n current_gen = start\n for _ in range(times):\n next_gen = defaultdict(int)\n all_locs = get_all_neighbors(current_gen.keys())\n all_locs.update(current_gen.keys())\n for loc in all_locs:\n neighbors = get_neighbors(loc)\n count = sum(current_gen[n] for n in neighbors)\n if count in (2, 3) and current_gen[loc] == 1:\n next_gen[loc] = 1\n elif count == 3 and current_gen[loc] == 0:\n next_gen[loc] = 1\n current_gen = next_gen\n return current_gen","def color_cycle():\n while True:\n for color in colors:\n yield color","def gen_sequence(a, b, c):\n i = 1\n while True:\n yield a * i**2 + b * i + c\n i += 1","def test_nested_gen(n):\n for a in range(n):\n yield (b for b in range(a))","def sequences(self):\n # i am one\n yield self\n # nothing further\n return","def __iter__(self):\n for run in self.runs:\n yield run","def timer(description):\n t0 = time.time()\n yield\n print(f'[{description}] done in {time.time() - t0:.0f} s')","def static(fps, duration):\n\n frames = int(duration * fps)\n def animate(thing):\n thing = list(thing)\n yield from repeat(thing, frames)\n return animate","def iterwhite():\n while True:\n for n in rng.randn(100):\n yield n","def gen_next_time(intervals, start_time=[6, 0, 0], end_time=[23, 0, 0]):\n now = datetime.now()\n year = now.year\n month = now.month\n day = now.day\n\n starttime, endtime = gen_start_end_times(\n start_time=start_time, end_time=end_time\n )\n\n next_datetime = starttime\n\n while next_datetime < endtime:\n\n if next_datetime < now:\n while next_datetime < now:\n next_datetime += timedelta(seconds=intervals)\n else:\n next_datetime += timedelta(seconds=intervals)\n\n yield next_datetime","def very_simple():\n yield 1","def __iter__(self):\n for i in range(self.n):\n yield self.get(i, i + 1)","def perf_timer():\n start_time = datetime.now()\n yield\n end_time = datetime.now()\n log.info(end_time - start_time)","def generator(func):\n\n @fn\n @wraps(func)\n def gen(*args, **kwargs):\n return Iter(func(*args, **kwargs))\n\n return gen","def __iter__(self):\n for x in self.seq: yield x","def get_times(self):\n raise NotImplementedError(\"Abstract method not implemented.\")","def __iter__(self):\n yield from self.gen","def __iter__(self):\n for value in dict.__iter__(self):\n for count in range(self[value]):\n yield value","def ticker_generator():\n return (v for v in load_equities().values)","def id_generator():\n\t\tcount = 0\n\t\twhile True:\n\t\t\tyield count\n\t\t\tcount += 1","def __iter__(self) -> Generator:\r\n yield from self.sequence","def tempo_gen(tempo=120, srate=None):\n dt = tempo / (60 * get_srate(srate))\n return (i * dt for i in count())"],"string":"[\n \"def times(self):\\n \\n class IterTimes:\\n def __init__(self, st):\\n self.c = 0\\n self.st = st\\n \\n def __iter__(self):\\n return self\\n \\n def next(self):\\n t = self.st.time(self.c)\\n if t == None or self.c == self.st.longitud:\\n raise StopIteration\\n else:\\n self.c += 1\\n return t\\n \\n return IterTimes(self)\",\n \"def exercise_gen(ret_val, times):\",\n \"def time_processor(self):\\n while True:\\n rexp = (yield)\\n self.time = float(rexp.group(1))\\n self.time_str = rexp.group(1)\\n # Reset subIteration counters\\n for k in self.subiter_map:\\n self.subiter_map[k] = 0\\n self._tick = False\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def iterate(func, x):\\n while True:\\n x = func(x)\\n yield x\",\n \"def repeat(fun, n):\\n for i in range(n):\\n yield fun()\",\n \"def _fill_time(beats, shard, *args, **kwargs):\\n total = 0\\n for delay in shard(*args, **kwargs):\\n total += delay.count\\n yield delay\\n yield Beat(beats - total)\",\n \"def get_time(self):\\n for _ in range(5):\\n current_time = datetime.datetime.now().strftime('%H:%M:%S')\\n self.logger.info(current_time)\\n num: int = self.rand_num_gen.get_number()\\n display = current_time + f\\\": {num}\\\\n\\\"\\n yield display.encode()\\n sleep(1)\\n try:\\n raise RuntimeError(\\\"something unexpected happened!\\\")\\n except:\\n trace_info = traceback.format_exc()\\n self.logger.error(\\\"uncaught exception: %s\\\", trace_info)\\n yield trace_info\",\n \"def countdown():\\n for i in range(100, 0, -1):\\n yield i\",\n \"def _mc_gen():\\r\\n n = 1\\r\\n while True:\\r\\n yield n\\r\\n n += 1\",\n \"def test_generator_method(self):\\n for i in range(0, 4):\\n yield self.try_odd, i\",\n \"def random_values():\\n while True:\\n yield random()\",\n \"def task5(count):\\n number_1, number_2 = 1, 1\\n for _ in range(count):\\n yield number_1\\n number_1, number_2 = number_2, number_1 + number_2\",\n \"def data_repeated(data):\\n\\n def gen(count):\\n for _ in range(count):\\n yield data\\n\\n yield gen\",\n \"def counter_wrapper(generator):\\n for value in generator:\\n yield value\",\n \"def _iter_items(data_sequence):\\n for time, element in data_sequence:\\n for item in element:\\n yield time, item\",\n \"def __iter__(self):\\n\\n # Wanted to do this, but count() only accepts numbers:\\n # return count(start, timedelta(days=self.step))\\n\\n next = self.start\\n while (True):\\n yield next\\n next = next + timedelta(days=self.step)\",\n \"def multiple_gen(modulus):\\n count = 1\\n while True:\\n yield modulus * count\\n count += 1\",\n \"def repeatedly(func, /, *args, **kwargs):\\n func = to_callable(func)\\n try:\\n while True:\\n yield func(*args, **kwargs)\\n except StopIteration as e:\\n yield from stop_seq(e)\",\n \"def counter_wrapper_2(generator):\\n yield from generator\",\n \"def __iter__(self):\\n start_times = (start for start, end in self.tss)\\n names = (name.rstrip() for name in self.inps)\\n for ind, (c, t) in enumerate(zip(names, start_times)):\\n yield (c, t, ind)\",\n \"def repeat(obj, times=None):\\n if times is None:\\n return Iter(itertools.repeat(obj))\\n return Iter(itertools.repeat(obj, times))\",\n \"def __iter__(self):\\n if len(self) == 0:\\n return\\n current = self.first_timestamp\\n delta = datetime.timedelta(0, self.interval)\\n while current <= self.last_timestamp:\\n yield current\\n current += delta\",\n \"def timeit_context() -> Generator:\\n result = TimeItResult()\\n started_time = time.time()\\n try:\\n yield result\\n finally:\\n result.time_passed = time.time() - started_time\",\n \"def static_trajectory(Tinit, n):\\n for i in xrange(n):\\n yield Tinit\",\n \"def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:\",\n \"def customer_generator(env, inventory_stock):\\n for i in itertools.count():\\n yield env.timeout(random.randint(*T_INTER))\\n env.process(customer(env, inventory_stock, 'Customer_'+str(i+1)))\",\n \"def itime(iterable, seconds):\\n items = iter(iterable)\\n\\n end = time.time() + seconds\\n yield items.next()\\n\\n for item in itertools.takewhile(lambda _: time.time() < end, items):\\n yield item\",\n \"def counter():\\n for value in range(5):\\n yield \\\"<{}>\\\".format(value)\",\n \"def test_generator_method_name(self):\\n for i in range(0, 4):\\n yield 'try_odd', i\",\n \"def forever():\\n\\n def animate(thing):\\n thing = list(thing)\\n yield from repeat(thing)\\n return animate\",\n \"def times(self, fn):\\n for i in range(0, self._):\\n fn()\\n return self\",\n \"def simple():\\n yield 1\\n yield 2\\n yield 3\",\n \"def iterate(f, x):\\n while True:\\n yield x\\n x = f(x)\",\n \"def generator():\\n mygenerator = (x for x in range(3))\\n for element in mygenerator:\\n print 'poprve = ', element\\n\\n for element in mygenerator:\\n print 'podruhe = ', element\",\n \"def wait(self, fps=2):\\n i = 1\\n t = clock()\\n while 1:\\n free_count = 0\\n skip = 0\\n while free_count == 0:\\n while clock()-t < float(i)/fps:\\n free_count += 1\\n if free_count == 0:\\n i += 1\\n skip += 1\\n yield i-1, skip\\n i += 1\",\n \"def generator_fn():\\n for thumbs, counts in data_dir.hotspot_data(num_timestamps=num_timestamps):\\n for thumb, count in zip(thumbs, counts):\\n yield thumb, count\",\n \"def source_deterministic(env, number, interval, counter):\\n for i in range(number):\\n bus_arrival = 12.0 # deterministic - fixed frequency\\n c = customer(env, 'Customer_%02d' % (i + 1), counter, time_in_stop=bus_arrival)\\n env.process(c)\\n t = random.expovariate(1.0 / interval)\\n yield env.timeout(t)\",\n \"def cycle(obj):\\r\\n while True:\\r\\n for item in obj:\\r\\n yield item\",\n \"def repeat(seq, n):\\n for e in seq:\\n for _ in range(n):\\n yield e\",\n \"def times(self):\\n return list(range(self._max_time + 1))\",\n \"def test_func_generator():\\n def test_odd(v):\\n assert v % 2\\n for i in range(0, 4):\\n yield test_odd, i\",\n \"def step_sequence_gen(track, click=False, fillvalue=0.0, t=None, srate=None):\\n if t is None:\\n t = time_gen(srate=srate)\\n else:\\n t = iter(t)\\n t0 = next(t)\\n t1 = t0\\n duration = 1\\n for tple in track:\\n if hasattr(tple,'__getitem__'):\\n value = tple[0]\\n if len(tple) > 1:\\n duration = tple[1]\\n else:\\n value = tple\\n while t0 + duration > t1:\\n yield value\\n if click:\\n value = fillvalue\\n t1 = next(t)\\n t0 = t1\",\n \"def record_time(times, enabled, *args):\\n if not enabled:\\n yield\\n else:\\n start = time.time()\\n yield\\n end = time.time()\\n times.append((' '.join(args), start, end))\",\n \"def test_func_generator_name():\\n for i in range(0, 4):\\n yield 'try_odd', i\",\n \"def anim():\\n i = 0\\n while 1:\\n\\n for r in Reprs:\\n r.draw(i)\\n i = i+ 1\\n i = i % len(t)\\n yield\",\n \"def _ids(self):\\n prev_values = set()\\n while True:\\n next_value = self._time_ns()\\n while True:\\n if next_value not in prev_values:\\n break\\n next_value += 1000\\n prev_values.add(next_value)\\n yield next_value\",\n \"def repeatfunc(cls, func, times=None, *args):\\n if times is None:\\n return starmap(func, repeat(args))\\n return starmap(func, repeat(args, times))\",\n \"def e_seq():\\n yield 2;\\n for n in count(2, 2):\\n yield 1\\n yield n\\n yield 1\",\n \"def __iter__(self):\\n self._loop_idx = 0\\n self._target_time = time.time()\\n return self\",\n \"def inference_generator(env, storage, pipe, arrival_rate):\\n\\n global num_clients, trace, last_inf_times, request_times\\n for i in itertools.count():\\n random_request_time = random.expovariate(arrival_rate)\\n cumulative_request_time = last_inf_times + random_request_time\\n last_inf_times = cumulative_request_time\\n request_times.append(cumulative_request_time)\\n yield env.timeout(random_request_time)\\n num_clients +=1\\n d = {'idx' : num_clients, 'request_time' : env.now}\\n pipe.put(d)\",\n \"def timed(name):\\n t0 = time.time()\\n yield\\n t1 = time.time()\\n print(\\\"..%-24s: %8.4f\\\" % (name, t1 - t0))\",\n \"def iterate(self, start, end):\\n if not self.db:\\n self.db = self.get_db()\\n\\n p = start[:-4]\\n s = struct.unpack(\\\"!L\\\", start[-4:])[0]\\n e = struct.unpack(\\\"!L\\\", end[-4:])[0]\\n time.sleep(self.READ_DELAY)\\n while s <= e:\\n v = random.random() * 100\\n yield p + struct.pack(\\\"!L\\\", s), struct.pack(\\\"!d\\\", v)\\n s += self.STEP\",\n \"def vytvorit_generator():\\n mylist = range(3)\\n print 'mylist = ', mylist\\n for element in mylist:\\n yield element\",\n \"def forever(shard):\\n def repeat(*args, **kwargs):\\n while True:\\n for delay in shard(*args, **kwargs):\\n yield delay\\n return repeat\",\n \"def repeat(value: T, times: int) -> List[T]:\\n return [value] * times\",\n \"def repeatfunc(func, times=None, *args):\\n if times is None:\\n return starmap(func, repeat(args))\\n return starmap(func, repeat(args, times))\",\n \"def repeatfunc(func, times=None, *args):\\n if times is None:\\n return starmap(func, repeat(args))\\n return starmap(func, repeat(args, times))\",\n \"def stream():\\n while True:\\n yield random_point()\",\n \"def repeat(iterable, count=None):\\n if count is None:\\n while True:\\n for sample in iterable:\\n yield sample\\n else:\\n for i in range(count):\\n for sample in iterable:\\n yield sample\",\n \"def comp_pattern_generator(iterable):\\n for chord in iterable:\\n yield (600, chord)\",\n \"async def sleep_generator(pos: int):\\n if pos <= 0:\\n raise ValueError(f\\\"none_zero must be some positive integer, got {pos}\\\")\\n scale = 1 / pos / 10\\n for i in range(pos):\\n t = scale * (pos - i)\\n LOGGER.info(f\\\"Number {i}, going to sleep {t} s\\\")\\n await asyncio.sleep(t)\\n LOGGER.info(f\\\"Number {i}, slept {t} s\\\")\\n yield i\",\n \"def floating_point_generator():\\n i = 0\\n while True:\\n yield str((i % 5) * 1.1)\\n i += 1\",\n \"def make_iter(capture, channel):\\n\\n def cycle():\\n threading.Timer(INTERVAL, cycle).start()\\n publish_frame(capture, channel)\\n\\n return cycle\",\n \"def test_generator_inline(self):\\n def test_odd(v):\\n assert v % 2\\n for i in range(0, 4):\\n yield test_odd, i\",\n \"def numbers():\\n for number in range(1, 76):\\n yield number\",\n \"def id_generator():\\n start_value = 0\\n while True:\\n yield start_value\\n start_value += 1\",\n \"def _exponential_timeout_generator(initial, maximum, multiplier, deadline):\\n if deadline is not None:\\n deadline_datetime = (\\n datetime_helpers.utcnow() +\\n datetime.timedelta(seconds=deadline))\\n else:\\n deadline_datetime = datetime.datetime.max\\n\\n timeout = initial\\n while True:\\n now = datetime_helpers.utcnow()\\n yield min(\\n # The calculated timeout based on invocations.\\n timeout,\\n # The set maximum timeout.\\n maximum,\\n # The remaining time before the deadline is reached.\\n float((deadline_datetime - now).seconds))\\n timeout = timeout * multiplier\",\n \"def timer():\\n start = time.time()\\n\\n yield\\n\\n end = time.time()\\n\\n print('Elapsed: {:.2f}s'.format(end - start))\",\n \"def cycle(iterator: Iterable[Any]) -> Iterable[Any]:\\n while True:\\n yield from iterator\",\n \"def changefreqs_generator():\\n changefreqs = ['weekly', 'daily']\\n yield from itertools.chain(changefreqs, itertools.repeat('monthly'))\",\n \"def sleeper(self):\\n for waittime in (.01, .02, .05, .1, .2, .5):\\n yield waittime\\n while True:\\n waittime = min(waittime + .2, 5)\\n yield waittime\",\n \"def __iter__(self): # for each frequency, yield a QTile\\n for freq in self._iter_frequencies():\\n yield snrQTile(self.q, freq, self.duration, self.sampling,\\n mismatch=self.mismatch, shift = self.freq_timeshift(freq))\",\n \"def test_generator_continuous():\\n RANGE_MAX = 100\\n prev_value = RANGE_MAX // 2\\n for msg in it.islice(generate_msgs(0, RANGE_MAX), 0, 42):\\n curr_value = Message.parse(msg).power\\n assert curr_value - prev_value <= 1\\n prev_value = curr_value\",\n \"def generate(th1):\\n pro = 1\\n while True:\\n if th1.is_alive() and pro < 99:\\n pro = pro + 1\\n time.sleep(1)\\n yield \\\"data:\\\" + str(pro) + \\\"\\\\n\\\\n\\\"\\n else:\\n if not th1.is_alive():\\n yield \\\"data:\\\" + str(100) + \\\"\\\\n\\\\n\\\"\",\n \"def convergence_processor(self):\\n while True:\\n rexp = (yield)\\n self.converged = True\\n self.converged_time = int(rexp.group(2))\",\n \"def cycle(start, times):\\n current_gen = start\\n for _ in range(times):\\n next_gen = defaultdict(int)\\n all_locs = get_all_neighbors(current_gen.keys())\\n all_locs.update(current_gen.keys())\\n for loc in all_locs:\\n neighbors = get_neighbors(loc)\\n count = sum(current_gen[n] for n in neighbors)\\n if count in (2, 3) and current_gen[loc] == 1:\\n next_gen[loc] = 1\\n elif count == 3 and current_gen[loc] == 0:\\n next_gen[loc] = 1\\n current_gen = next_gen\\n return current_gen\",\n \"def color_cycle():\\n while True:\\n for color in colors:\\n yield color\",\n \"def gen_sequence(a, b, c):\\n i = 1\\n while True:\\n yield a * i**2 + b * i + c\\n i += 1\",\n \"def test_nested_gen(n):\\n for a in range(n):\\n yield (b for b in range(a))\",\n \"def sequences(self):\\n # i am one\\n yield self\\n # nothing further\\n return\",\n \"def __iter__(self):\\n for run in self.runs:\\n yield run\",\n \"def timer(description):\\n t0 = time.time()\\n yield\\n print(f'[{description}] done in {time.time() - t0:.0f} s')\",\n \"def static(fps, duration):\\n\\n frames = int(duration * fps)\\n def animate(thing):\\n thing = list(thing)\\n yield from repeat(thing, frames)\\n return animate\",\n \"def iterwhite():\\n while True:\\n for n in rng.randn(100):\\n yield n\",\n \"def gen_next_time(intervals, start_time=[6, 0, 0], end_time=[23, 0, 0]):\\n now = datetime.now()\\n year = now.year\\n month = now.month\\n day = now.day\\n\\n starttime, endtime = gen_start_end_times(\\n start_time=start_time, end_time=end_time\\n )\\n\\n next_datetime = starttime\\n\\n while next_datetime < endtime:\\n\\n if next_datetime < now:\\n while next_datetime < now:\\n next_datetime += timedelta(seconds=intervals)\\n else:\\n next_datetime += timedelta(seconds=intervals)\\n\\n yield next_datetime\",\n \"def very_simple():\\n yield 1\",\n \"def __iter__(self):\\n for i in range(self.n):\\n yield self.get(i, i + 1)\",\n \"def perf_timer():\\n start_time = datetime.now()\\n yield\\n end_time = datetime.now()\\n log.info(end_time - start_time)\",\n \"def generator(func):\\n\\n @fn\\n @wraps(func)\\n def gen(*args, **kwargs):\\n return Iter(func(*args, **kwargs))\\n\\n return gen\",\n \"def __iter__(self):\\n for x in self.seq: yield x\",\n \"def get_times(self):\\n raise NotImplementedError(\\\"Abstract method not implemented.\\\")\",\n \"def __iter__(self):\\n yield from self.gen\",\n \"def __iter__(self):\\n for value in dict.__iter__(self):\\n for count in range(self[value]):\\n yield value\",\n \"def ticker_generator():\\n return (v for v in load_equities().values)\",\n \"def id_generator():\\n\\t\\tcount = 0\\n\\t\\twhile True:\\n\\t\\t\\tyield count\\n\\t\\t\\tcount += 1\",\n \"def __iter__(self) -> Generator:\\r\\n yield from self.sequence\",\n \"def tempo_gen(tempo=120, srate=None):\\n dt = tempo / (60 * get_srate(srate))\\n return (i * dt for i in count())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7211171","0.7095168","0.676974","0.66167104","0.66167104","0.66167104","0.6530617","0.6517885","0.64912164","0.64652574","0.6435925","0.64267135","0.6413881","0.6370479","0.6354844","0.63290936","0.62823224","0.62550485","0.6254462","0.62441665","0.622235","0.62171364","0.617532","0.6170421","0.61660296","0.61528873","0.61487913","0.6146654","0.6145423","0.6127929","0.60942054","0.6067145","0.60662895","0.6037557","0.60282916","0.6019171","0.6007657","0.59970874","0.59839386","0.5971751","0.5954244","0.59488374","0.5941522","0.5941312","0.59118944","0.59109956","0.5903925","0.5903162","0.5903144","0.5878092","0.5861819","0.5843706","0.58397096","0.58381337","0.5837873","0.5834509","0.5834459","0.582228","0.58073133","0.58073133","0.5792741","0.5778669","0.57713056","0.5745666","0.57446265","0.5716468","0.5712249","0.5707731","0.5706086","0.5701153","0.57005215","0.5700209","0.5698443","0.5696942","0.5692017","0.56857663","0.56822","0.5679249","0.5674118","0.567319","0.567132","0.56678385","0.5667513","0.56622714","0.5651936","0.5649722","0.5646785","0.56455207","0.5645516","0.5632664","0.5632296","0.56309676","0.56276304","0.56180185","0.56177086","0.5616168","0.56055856","0.5590438","0.558428","0.55803823"],"string":"[\n \"0.7211171\",\n \"0.7095168\",\n \"0.676974\",\n \"0.66167104\",\n \"0.66167104\",\n \"0.66167104\",\n \"0.6530617\",\n \"0.6517885\",\n \"0.64912164\",\n \"0.64652574\",\n \"0.6435925\",\n \"0.64267135\",\n \"0.6413881\",\n \"0.6370479\",\n \"0.6354844\",\n \"0.63290936\",\n \"0.62823224\",\n \"0.62550485\",\n \"0.6254462\",\n \"0.62441665\",\n \"0.622235\",\n \"0.62171364\",\n \"0.617532\",\n \"0.6170421\",\n \"0.61660296\",\n \"0.61528873\",\n \"0.61487913\",\n \"0.6146654\",\n \"0.6145423\",\n \"0.6127929\",\n \"0.60942054\",\n \"0.6067145\",\n \"0.60662895\",\n \"0.6037557\",\n \"0.60282916\",\n \"0.6019171\",\n \"0.6007657\",\n \"0.59970874\",\n \"0.59839386\",\n \"0.5971751\",\n \"0.5954244\",\n \"0.59488374\",\n \"0.5941522\",\n \"0.5941312\",\n \"0.59118944\",\n \"0.59109956\",\n \"0.5903925\",\n \"0.5903162\",\n \"0.5903144\",\n \"0.5878092\",\n \"0.5861819\",\n \"0.5843706\",\n \"0.58397096\",\n \"0.58381337\",\n \"0.5837873\",\n \"0.5834509\",\n \"0.5834459\",\n \"0.582228\",\n \"0.58073133\",\n \"0.58073133\",\n \"0.5792741\",\n \"0.5778669\",\n \"0.57713056\",\n \"0.5745666\",\n \"0.57446265\",\n \"0.5716468\",\n \"0.5712249\",\n \"0.5707731\",\n \"0.5706086\",\n \"0.5701153\",\n \"0.57005215\",\n \"0.5700209\",\n \"0.5698443\",\n \"0.5696942\",\n \"0.5692017\",\n \"0.56857663\",\n \"0.56822\",\n \"0.5679249\",\n \"0.5674118\",\n \"0.567319\",\n \"0.567132\",\n \"0.56678385\",\n \"0.5667513\",\n \"0.56622714\",\n \"0.5651936\",\n \"0.5649722\",\n \"0.5646785\",\n \"0.56455207\",\n \"0.5645516\",\n \"0.5632664\",\n \"0.5632296\",\n \"0.56309676\",\n \"0.56276304\",\n \"0.56180185\",\n \"0.56177086\",\n \"0.5616168\",\n \"0.56055856\",\n \"0.5590438\",\n \"0.558428\",\n \"0.55803823\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.597769"},"document_rank":{"kind":"string","value":"39"}}},{"rowIdx":2398,"cells":{"query":{"kind":"string","value":"Names of endogenous variables"},"document":{"kind":"string","value":"def endog_names(self):\n return self.data.ynames"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getMeteorologicalVariableNames(self, product):\r\n return []","def variables(self):\n return [i.name for i in self.inputs + self.outputs]","def getOthVarNames( self ):\n\n if self.othVarNames:\n return self.othVarNames.keys()\n\n n = self.adb.get( \"nOthVars\" )\n for indx in range( n ):\n name = self.adb.get( \"othVarName\",\n indx ) \n self.othVarNames[ name ] = indx\n\n return self.othVarNames.keys()","def getVariableNames(self, product):\r\n\r\n variable_names = self.getDataVariableNames(product) + \\\r\n self.getMaskVariableNames(product) + \\\r\n self.getMeteorologicalVariableNames(product) + \\\r\n self.getSensorVariableNames(product) + \\\r\n self.getInfoVariableNames(product)\r\n\r\n return variable_names","def get_all_variables_names(self):\n return self.project.get_variable_names() + self.design.get_variable_names()","def get_variable_names(self):\n return [var[1] for var in self.variables]","def name(self):\n return '{} {} {}'.format(self.var_period, self.var_type,\n self.var_detail)","def variables_used (self) :\r\n\t\t## These names possibly contain dimension specification!\r\n\t\treturn self.variable_names","def var_name(i, j):\n return \"x_\" + str(i) + \",\" + str(j)","def variable_names(self):\n \n return [x['variable'] for x in self.variable_dicts()]","def getInfoVariableNames(self, product):\r\n return []","def getOeiVarNames( self ):\n\n if self.oeiVarNames:\n return self.oeiVarNames.keys()\n\n n = self.adb.get( \"nOeiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oeiVarName\",\n indx ) \n self.oeiVarNames[name] = indx\n\n return self.oeiVarNames.keys()","def getOqiVarNames( self ):\n\n if self.oqiVarNames:\n return self.oqiVarNames.keys()\n\n n = self.adb.get( \"nOqiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oqiVarName\",\n indx ) \n self.oqiVarNames[name] = indx\n\n return self.oqiVarNames.keys()","def get_variable_names(self):\n return [VariableString(s) for s in\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]","def getSensorVariableNames(self, product):\r\n return []","def get_variables(self):\n return [self.g_t, self.m_t]","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def varNames(self):\n return self.__varNames","def getDataVariableNames(self, product):\r\n return []","def getSensorVariableNames(self, product):\r\n\r\n return []","def getOhcVarNames( self ):\n\n if self.ohcVarNames:\n return self.ohcVarNames.keys()\n \n n = self.adb.get( \"nOhcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ohcVarName\",\n indx ) \n self.ohcVarNames[name] = indx\n\n return self.ohcVarNames.keys()","def variables(names, **kwargs):\n return symbols(names, cls=Variable, seq=True, **kwargs)","def name(self):\n base_str = 'd{}{}_'.format(self.derivative_count if\n self.derivative_count > 1 else '', self.expr)\n for var, count in self.variable_count:\n base_str += 'd{}{}'.format(var, count if count > 1 else '')\n return base_str","def getMeteorologicalVariableNames(self, product):\r\n\r\n meteorological_variable_names = []\r\n\r\n return meteorological_variable_names","def get_all_variables(self):\n return self.start.get_all_variables() + self.end.get_all_variables()","def variables(self):\n return ()","def variables(self):\r\n return self.get_field('variable')","def variables(self):\n return {u for u in self if u.type == 'var'}","def get_variable_names(self):\n varNames = []\n for var in self.variables:\n # EstimationVariable\n varNames.append(var.name)\n return varNames","def getLinIterVarNames( self ):\n\n self.updateAdb( )\n\n return self.iterNames.keys()","def vars(cls):\n for key in dir(cls):\n if key.startswith('var_'):\n yield key[4:]","def getOfcVarNames( self ):\n\n if self.ofcVarNames:\n return self.ofcVarNames.keys()\n \n n = self.adb.get( \"nOfcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ofcVarName\",\n indx ) \n self.ofcVarNames[name] = indx\n\n return self.ofcVarNames.keys()","def get_layer_var_names(self):\n return(self.params)","def getOriVarNames( self ):\n\n if self.oriVarNames:\n return self.oriVarNames.keys()\n\n n = self.adb.get( \"nOriVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oriVarName\",\n indx ) \n self.oriVarNames[name] = indx\n\n return self.oriVarNames.keys()","def vars(cls):\n raise NotImplementedError(\"This is an abstract function that needs to be implemented for each value function\")","def printing_vars(self):\n print(\"Name is \", self.name)","def getVariables(self):\n statVars = [self[vn] for vn in self.statVars]\n timeVars = [self[vn] for vn in self.timeVars]\n return statVars + timeVars","def data_variable_names(self):\n data_names = []\n mesh = self.mesh_names()\n prefix = mesh[0]+'_'\n for vname in self.nc.variables.keys():\n if vname.startswith(prefix):\n if self.nc.dimensions.has_key(vname):\n continue\n if hasattr(self.nc.variables[vname],'cf_role'):\n continue\n data_names.append( vname[len(prefix):] )\n return data_names","def names():\n pass","def vars(self):\n return self.v","def var(self, name):\n raise NotImplementedError","def getSolutionExportVariableNames(cls):\n return {}","def get_element_variable_names(self):\n return [b\"\".join(_i).strip().decode()\n for _i in self._f.variables[\"name_elem_var\"][:]]","def variable_names(self):\n\n status, stdout, stderr = self.__xcall__(['--print-variables'])\n\n if status != 0:\n raise RuntimeError(\"error querying --print-variables for package `%s': %s\" % (self.name, stderr))\n\n return stdout.strip().split()","def __setVarNames(self):\n result = set()\n\n # detecting variables\n for templatePart in self.inputString().split(\"{\"):\n if templatePart is '' or \"}\" not in templatePart:\n continue\n\n endIndex = templatePart.find('}')\n result.add(templatePart[:endIndex])\n\n self.__varNames = list(result)","def variable(self):","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def var_names(self):\n return self._var_names","def get_all_variables(self):\n raise NotImplementedError()","def variables_declared (self) :\r\n\t\tresult = {}\r\n\r\n\t\tfor var in self.variables :\r\n\t\t\tresult[var.name.upper()] = var\r\n\t\t\r\n\t\treturn result","def __str__(self):\n return f\"Variable(type={self._type}, id={self._id}, value={self.status}, init={self.init})\"","def nvar(self):\n return len(self.__vars)","def get_vars(self):\n return [self.mu, self.var]","def _variable_types(self):\n return self._variable_single_types + self._variable_array_types","def __splitVariableNames(self, name, indexes):\n if name == 'x':\n var = self.xCoordinates[indexes[0]][indexes[1]]\n elif name == 'y':\n var = self.yCoordinates[indexes[0]][indexes[1]]\n elif name == 'z':\n var = self.zCoordinates[indexes[0]][indexes[1]]\n elif name == 'colorMap':\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\n elif name == 'clusterLabels':\n var = self.clusterLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureLabels':\n var = self.mixtureLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureMeans':\n var = self.mixtureMeans[indexes[0]][indexes[1]]\n elif name == 'mixtureCovars':\n var = self.mixtureCovars[indexes[0]][indexes[1]]\n\n # The variable can contain brackets {} (when the symbol \"|\" is present in\n # the variable name), e.g.:\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\n # or it can look like:\n # DataName|Input|variableName\n\n if var is not None:\n result = [None] * 3\n if '|input|' in var.lower():\n match = re.search(r\"(\\|input\\|)\", var.lower())\n elif '|output|' in var.lower():\n match = re.search(r\"(\\|output\\|)\", var.lower())\n else:\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \"Input\" or \"Output\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\n startLoc, endLoc = match.start(), match.end()\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\n if '{' in result[-1] and '}' in result[-1]:\n locLower, locUpper = result[-1].find(\"{\"), result[-1].rfind(\"}\")\n result[-1] = result[-1][locLower + 1:locUpper].strip()\n else:\n result = None\n\n return result","def variables(self) -> OrderedDict:\n pass","def r_vars(size, used=None):\n return r_symbols(size, VAR_SYMBOLS, ARGS.variable_length, used)","def give_variables_names(variables):\r\n names = map(lambda var: var.name, variables)\r\n h = hist(names)\r\n bad_var = lambda var: not var.name or h[var.name] > 1\r\n\r\n for i, var in enumerate(filter(bad_var, variables)):\r\n var.name = (var.name or \"\") + \"_%d\" % i\r\n\r\n if not unique(map(str, variables)):\r\n raise ValueError(\"Not all variables have unique names.\"\r\n \"Maybe you've named some of the variables identically\")\r\n\r\n return variables","def get_node_variable_names(self):\n return [b\"\".join(_i).strip().decode()\n for _i in self._f.variables[\"name_nod_var\"][:]]","def variables(self):\n return self._.vars","def getVariables(self)->Dict[str,str]:\n pass","def name(self):","def name(self):","def name(self):","def name(self):","def getBindedNames(self):\n names = []\n for function in self.functions:\n names.append(function.__name__)\n return \", \".join(names)","def getDataVariableNames(self, product):\r\n\r\n h = product.getSceneRasterHeight()\r\n\r\n # 10m resolution\r\n if h == 10980:\r\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_10m)\r\n\r\n # 20m resolution\r\n elif h == 5490:\r\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_20m)\r\n\r\n # 20m resolution\r\n elif h == 1830:\r\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_60m)","def _var_name_sub(self, sprintf, quote=False):\n q = ''\n if quote:\n q = \"'\"\n name_list = map(lambda x: q + self.cdict[x][0] + q, sprintf[\"vars\"] )\n return sprintf[\"text\"] % tuple(name_list)","def stats_variable_names(res):\n def varname(s):\n pos = s.find(':')\n return s if pos==-1 else s[0:pos]\n return set( [ varname(key) for key in res.keys()] )","def get_variable_full_name(var):\n if var._save_slice_info:\n return var._save_slice_info.full_name\n else:\n return var.op.name","def generate_variable_names():\n while True:\n name = uuid.uuid4()\n yield f\"_{name.hex}\"","def variables(self):\n return [term.variable for term in self.terms]","def print_all_variables():\n for idx, v in enumerate(tf.all_variables()):\n print(\" var %d: %s %s\" % (idx, v.get_shape(), v.name))","def get_output_names(self):\n outputNames = []\n for outVar in self.outputs:\n # outVar is of type InOutVar and the object that it contains is a PyFMI variable\n outputNames.append(outVar.get_object().name)\n return outputNames","def names(self):\n\t\treturn","def variables(self):\n return tuple(flatten([a.variables for a in self.args]))","def visit_varname(self, node, children):\n # Return only dict nodes\n return {'type':'var','val':str(node)}","def getOsiVarNames( self ):\n\n if self.osiVarNames:\n return self.osiVarNames.keys()\n \n n = self.adb.get( \"nOsiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"osiVarName\",\n indx ) \n self.osiVarNames[ name ]= indx\n\n return self.osiVarNames.keys()","def get_all_variables(self):\n return []","def show_variables(self):\r\n\r\n variablelist = [(x_temp,self.variables[x_temp]) for x_temp in sorted(self.variables.keys())]\r\n display.noteprint(('/C/ '+labels.VARIABLES.upper(), EOL.join([x_temp[0]+BLANK\r\n +COLON+BLANK\r\n +abridge(str(x_temp[1]),40)\r\n for x_temp in variablelist])))","def variables(self):\n for name in self._nodes:\n if isinstance(self._nodes[name], RandomVariable):\n yield name","def names(self):\n return self.dark_name, self.light_name","def variables_used (self) :\r\n\t\treturn []","def vars(self) -> {(EVar, Pool)}:\n raise NotImplementedError()","def var():\n def _var(quoted_name):\n name = quoted_name.subexpression.name\n if (value := get_name(name)) is not None:\n return value\n else:\n raise TypeError(f\"Binding {name} not found\")\n yield (\"(λ &[name] . any)\", _var)","def get_global_variable_names(self):\n return [b\"\".join(_i).strip().decode()\n for _i in self._f.variables[\"name_glo_var\"][:]]","def get_variables(self) -> np.array:\n pass","def variables(self):\n return self._variablesDef","def _get_vars(symbol: Union[str, int]) -> str:\n if isinstance(symbol, str):\n return {\n 'circle': 'var b1=n.round(t,2);',\n 'square': 'var b1=n.round(t,2);',\n 'diamond': 'var b1=n.round(t*1.3,2);',\n 'hexagram': 'var b1=n.round(t,2);var b2=n.round(t/2,2);var b3=n.round(t*Math.sqrt(3)/2,2);'\n }[symbol]\n return {\n 37: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 38: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 39: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 40: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 34: 'var d1=n.round(t,2);',\n 33: 'var d1=n.round(t*1.4,2);',\n 35: 'var d1=n.round(t*1.2,2);var d2=n.round(t*0.85,2);',\n 36: 'var d1=n.round(t/2,2);var d2=n.round(t,2);'\n }[symbol]","def get_variables_by_name(given_name, scope=None):\n suffix = '/' + given_name + ':|^' + given_name + ':'\n return get_variables(scope=scope, suffix=suffix)","def variables(self) -> OrderedDict:\n return OrderedDict({'mu': self.mu, 'sig': self.sig})","def getvarname(self,j_): # 3\n sizename_ = (1 + self.getvarnamelen((j_)))\n arr_name = array.array(\"b\",[0]*((sizename_)))\n memview_arr_name = memoryview(arr_name)\n res,resargs = self.__obj.getvarname(j_,sizename_,memview_arr_name)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_name = resargs\n retarg_name = arr_name.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_name","def _var_name_generator():\n count = itertools.count()\n while True:\n yield '_var_' + str(count.next())","def variable_string(self, name):\n return \"$(\" + name + \")\"","def __str__(self):\n return '{}.{} >> {}'.format(self.scope, self.name,\n '/'.join(map(str, self.variables)))","def put_var_names(self, var_type, num_vars, var_names):\n if var_type.upper() not in EX_VAR_TYPES:\n raise ExodusIIWriterError(\n \"var_type {0} not recognized\".format(var_type))\n # var names must all be of same length due to Fortran restrictions\n var_names = [\"{0:{1}s}\".format(x, MAX_STR_LENGTH)[:MAX_STR_LENGTH]\n for x in var_names]\n ierr = exolib.py_expvan(self.exoid, var_type.lower(), var_names)\n if ierr:\n raise ExodusIIWriterError(\"Error putting var names\")","def getMaskVariableNames(self, product):\r\n return []","def getbarvarname(self,i_): # 3\n sizename_ = (1 + self.getbarvarnamelen((i_)))\n arr_name = array.array(\"b\",[0]*((sizename_)))\n memview_arr_name = memoryview(arr_name)\n res,resargs = self.__obj.getbarvarname(i_,sizename_,memview_arr_name)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_name = resargs\n retarg_name = arr_name.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_name","def get_variables(self):\n\t\treturn self.variables","def _setup_special_names(self):\n special_names = []\n dynamic_params = tuple(set(self._fget_params_list + self._fset_params_list))\n # Check whether class variables of DynamicProperty type are present\n for attr_name, attr in getmembers(self.__class__):\n if isinstance(attr, DynamicProperty):\n special_names += [attr_name + \"_\" + key for key in dynamic_params]\n # Check if special variables are defined at class level\n for attr, value in getmembers(self.__class__):\n if attr in special_names:\n # Copy class special variable at instance level, prefixing reserved_prefix\n setattr(self, self.__reserved_prefix + attr, value)\n return special_names"],"string":"[\n \"def getMeteorologicalVariableNames(self, product):\\r\\n return []\",\n \"def variables(self):\\n return [i.name for i in self.inputs + self.outputs]\",\n \"def getOthVarNames( self ):\\n\\n if self.othVarNames:\\n return self.othVarNames.keys()\\n\\n n = self.adb.get( \\\"nOthVars\\\" )\\n for indx in range( n ):\\n name = self.adb.get( \\\"othVarName\\\",\\n indx ) \\n self.othVarNames[ name ] = indx\\n\\n return self.othVarNames.keys()\",\n \"def getVariableNames(self, product):\\r\\n\\r\\n variable_names = self.getDataVariableNames(product) + \\\\\\r\\n self.getMaskVariableNames(product) + \\\\\\r\\n self.getMeteorologicalVariableNames(product) + \\\\\\r\\n self.getSensorVariableNames(product) + \\\\\\r\\n self.getInfoVariableNames(product)\\r\\n\\r\\n return variable_names\",\n \"def get_all_variables_names(self):\\n return self.project.get_variable_names() + self.design.get_variable_names()\",\n \"def get_variable_names(self):\\n return [var[1] for var in self.variables]\",\n \"def name(self):\\n return '{} {} {}'.format(self.var_period, self.var_type,\\n self.var_detail)\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names possibly contain dimension specification!\\r\\n\\t\\treturn self.variable_names\",\n \"def var_name(i, j):\\n return \\\"x_\\\" + str(i) + \\\",\\\" + str(j)\",\n \"def variable_names(self):\\n \\n return [x['variable'] for x in self.variable_dicts()]\",\n \"def getInfoVariableNames(self, product):\\r\\n return []\",\n \"def getOeiVarNames( self ):\\n\\n if self.oeiVarNames:\\n return self.oeiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOeiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oeiVarName\\\",\\n indx ) \\n self.oeiVarNames[name] = indx\\n\\n return self.oeiVarNames.keys()\",\n \"def getOqiVarNames( self ):\\n\\n if self.oqiVarNames:\\n return self.oqiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOqiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oqiVarName\\\",\\n indx ) \\n self.oqiVarNames[name] = indx\\n\\n return self.oqiVarNames.keys()\",\n \"def get_variable_names(self):\\n return [VariableString(s) for s in\\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]\",\n \"def getSensorVariableNames(self, product):\\r\\n return []\",\n \"def get_variables(self):\\n return [self.g_t, self.m_t]\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def varNames(self):\\n return self.__varNames\",\n \"def getDataVariableNames(self, product):\\r\\n return []\",\n \"def getSensorVariableNames(self, product):\\r\\n\\r\\n return []\",\n \"def getOhcVarNames( self ):\\n\\n if self.ohcVarNames:\\n return self.ohcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOhcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ohcVarName\\\",\\n indx ) \\n self.ohcVarNames[name] = indx\\n\\n return self.ohcVarNames.keys()\",\n \"def variables(names, **kwargs):\\n return symbols(names, cls=Variable, seq=True, **kwargs)\",\n \"def name(self):\\n base_str = 'd{}{}_'.format(self.derivative_count if\\n self.derivative_count > 1 else '', self.expr)\\n for var, count in self.variable_count:\\n base_str += 'd{}{}'.format(var, count if count > 1 else '')\\n return base_str\",\n \"def getMeteorologicalVariableNames(self, product):\\r\\n\\r\\n meteorological_variable_names = []\\r\\n\\r\\n return meteorological_variable_names\",\n \"def get_all_variables(self):\\n return self.start.get_all_variables() + self.end.get_all_variables()\",\n \"def variables(self):\\n return ()\",\n \"def variables(self):\\r\\n return self.get_field('variable')\",\n \"def variables(self):\\n return {u for u in self if u.type == 'var'}\",\n \"def get_variable_names(self):\\n varNames = []\\n for var in self.variables:\\n # EstimationVariable\\n varNames.append(var.name)\\n return varNames\",\n \"def getLinIterVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.iterNames.keys()\",\n \"def vars(cls):\\n for key in dir(cls):\\n if key.startswith('var_'):\\n yield key[4:]\",\n \"def getOfcVarNames( self ):\\n\\n if self.ofcVarNames:\\n return self.ofcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOfcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ofcVarName\\\",\\n indx ) \\n self.ofcVarNames[name] = indx\\n\\n return self.ofcVarNames.keys()\",\n \"def get_layer_var_names(self):\\n return(self.params)\",\n \"def getOriVarNames( self ):\\n\\n if self.oriVarNames:\\n return self.oriVarNames.keys()\\n\\n n = self.adb.get( \\\"nOriVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oriVarName\\\",\\n indx ) \\n self.oriVarNames[name] = indx\\n\\n return self.oriVarNames.keys()\",\n \"def vars(cls):\\n raise NotImplementedError(\\\"This is an abstract function that needs to be implemented for each value function\\\")\",\n \"def printing_vars(self):\\n print(\\\"Name is \\\", self.name)\",\n \"def getVariables(self):\\n statVars = [self[vn] for vn in self.statVars]\\n timeVars = [self[vn] for vn in self.timeVars]\\n return statVars + timeVars\",\n \"def data_variable_names(self):\\n data_names = []\\n mesh = self.mesh_names()\\n prefix = mesh[0]+'_'\\n for vname in self.nc.variables.keys():\\n if vname.startswith(prefix):\\n if self.nc.dimensions.has_key(vname):\\n continue\\n if hasattr(self.nc.variables[vname],'cf_role'):\\n continue\\n data_names.append( vname[len(prefix):] )\\n return data_names\",\n \"def names():\\n pass\",\n \"def vars(self):\\n return self.v\",\n \"def var(self, name):\\n raise NotImplementedError\",\n \"def getSolutionExportVariableNames(cls):\\n return {}\",\n \"def get_element_variable_names(self):\\n return [b\\\"\\\".join(_i).strip().decode()\\n for _i in self._f.variables[\\\"name_elem_var\\\"][:]]\",\n \"def variable_names(self):\\n\\n status, stdout, stderr = self.__xcall__(['--print-variables'])\\n\\n if status != 0:\\n raise RuntimeError(\\\"error querying --print-variables for package `%s': %s\\\" % (self.name, stderr))\\n\\n return stdout.strip().split()\",\n \"def __setVarNames(self):\\n result = set()\\n\\n # detecting variables\\n for templatePart in self.inputString().split(\\\"{\\\"):\\n if templatePart is '' or \\\"}\\\" not in templatePart:\\n continue\\n\\n endIndex = templatePart.find('}')\\n result.add(templatePart[:endIndex])\\n\\n self.__varNames = list(result)\",\n \"def variable(self):\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def var_names(self):\\n return self._var_names\",\n \"def get_all_variables(self):\\n raise NotImplementedError()\",\n \"def variables_declared (self) :\\r\\n\\t\\tresult = {}\\r\\n\\r\\n\\t\\tfor var in self.variables :\\r\\n\\t\\t\\tresult[var.name.upper()] = var\\r\\n\\t\\t\\r\\n\\t\\treturn result\",\n \"def __str__(self):\\n return f\\\"Variable(type={self._type}, id={self._id}, value={self.status}, init={self.init})\\\"\",\n \"def nvar(self):\\n return len(self.__vars)\",\n \"def get_vars(self):\\n return [self.mu, self.var]\",\n \"def _variable_types(self):\\n return self._variable_single_types + self._variable_array_types\",\n \"def __splitVariableNames(self, name, indexes):\\n if name == 'x':\\n var = self.xCoordinates[indexes[0]][indexes[1]]\\n elif name == 'y':\\n var = self.yCoordinates[indexes[0]][indexes[1]]\\n elif name == 'z':\\n var = self.zCoordinates[indexes[0]][indexes[1]]\\n elif name == 'colorMap':\\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\\n elif name == 'clusterLabels':\\n var = self.clusterLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureLabels':\\n var = self.mixtureLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureMeans':\\n var = self.mixtureMeans[indexes[0]][indexes[1]]\\n elif name == 'mixtureCovars':\\n var = self.mixtureCovars[indexes[0]][indexes[1]]\\n\\n # The variable can contain brackets {} (when the symbol \\\"|\\\" is present in\\n # the variable name), e.g.:\\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\\n # or it can look like:\\n # DataName|Input|variableName\\n\\n if var is not None:\\n result = [None] * 3\\n if '|input|' in var.lower():\\n match = re.search(r\\\"(\\\\|input\\\\|)\\\", var.lower())\\n elif '|output|' in var.lower():\\n match = re.search(r\\\"(\\\\|output\\\\|)\\\", var.lower())\\n else:\\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \\\"Input\\\" or \\\"Output\\\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\\n startLoc, endLoc = match.start(), match.end()\\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\\n if '{' in result[-1] and '}' in result[-1]:\\n locLower, locUpper = result[-1].find(\\\"{\\\"), result[-1].rfind(\\\"}\\\")\\n result[-1] = result[-1][locLower + 1:locUpper].strip()\\n else:\\n result = None\\n\\n return result\",\n \"def variables(self) -> OrderedDict:\\n pass\",\n \"def r_vars(size, used=None):\\n return r_symbols(size, VAR_SYMBOLS, ARGS.variable_length, used)\",\n \"def give_variables_names(variables):\\r\\n names = map(lambda var: var.name, variables)\\r\\n h = hist(names)\\r\\n bad_var = lambda var: not var.name or h[var.name] > 1\\r\\n\\r\\n for i, var in enumerate(filter(bad_var, variables)):\\r\\n var.name = (var.name or \\\"\\\") + \\\"_%d\\\" % i\\r\\n\\r\\n if not unique(map(str, variables)):\\r\\n raise ValueError(\\\"Not all variables have unique names.\\\"\\r\\n \\\"Maybe you've named some of the variables identically\\\")\\r\\n\\r\\n return variables\",\n \"def get_node_variable_names(self):\\n return [b\\\"\\\".join(_i).strip().decode()\\n for _i in self._f.variables[\\\"name_nod_var\\\"][:]]\",\n \"def variables(self):\\n return self._.vars\",\n \"def getVariables(self)->Dict[str,str]:\\n pass\",\n \"def name(self):\",\n \"def name(self):\",\n \"def name(self):\",\n \"def name(self):\",\n \"def getBindedNames(self):\\n names = []\\n for function in self.functions:\\n names.append(function.__name__)\\n return \\\", \\\".join(names)\",\n \"def getDataVariableNames(self, product):\\r\\n\\r\\n h = product.getSceneRasterHeight()\\r\\n\\r\\n # 10m resolution\\r\\n if h == 10980:\\r\\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_10m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 5490:\\r\\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_20m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 1830:\\r\\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_60m)\",\n \"def _var_name_sub(self, sprintf, quote=False):\\n q = ''\\n if quote:\\n q = \\\"'\\\"\\n name_list = map(lambda x: q + self.cdict[x][0] + q, sprintf[\\\"vars\\\"] )\\n return sprintf[\\\"text\\\"] % tuple(name_list)\",\n \"def stats_variable_names(res):\\n def varname(s):\\n pos = s.find(':')\\n return s if pos==-1 else s[0:pos]\\n return set( [ varname(key) for key in res.keys()] )\",\n \"def get_variable_full_name(var):\\n if var._save_slice_info:\\n return var._save_slice_info.full_name\\n else:\\n return var.op.name\",\n \"def generate_variable_names():\\n while True:\\n name = uuid.uuid4()\\n yield f\\\"_{name.hex}\\\"\",\n \"def variables(self):\\n return [term.variable for term in self.terms]\",\n \"def print_all_variables():\\n for idx, v in enumerate(tf.all_variables()):\\n print(\\\" var %d: %s %s\\\" % (idx, v.get_shape(), v.name))\",\n \"def get_output_names(self):\\n outputNames = []\\n for outVar in self.outputs:\\n # outVar is of type InOutVar and the object that it contains is a PyFMI variable\\n outputNames.append(outVar.get_object().name)\\n return outputNames\",\n \"def names(self):\\n\\t\\treturn\",\n \"def variables(self):\\n return tuple(flatten([a.variables for a in self.args]))\",\n \"def visit_varname(self, node, children):\\n # Return only dict nodes\\n return {'type':'var','val':str(node)}\",\n \"def getOsiVarNames( self ):\\n\\n if self.osiVarNames:\\n return self.osiVarNames.keys()\\n \\n n = self.adb.get( \\\"nOsiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"osiVarName\\\",\\n indx ) \\n self.osiVarNames[ name ]= indx\\n\\n return self.osiVarNames.keys()\",\n \"def get_all_variables(self):\\n return []\",\n \"def show_variables(self):\\r\\n\\r\\n variablelist = [(x_temp,self.variables[x_temp]) for x_temp in sorted(self.variables.keys())]\\r\\n display.noteprint(('/C/ '+labels.VARIABLES.upper(), EOL.join([x_temp[0]+BLANK\\r\\n +COLON+BLANK\\r\\n +abridge(str(x_temp[1]),40)\\r\\n for x_temp in variablelist])))\",\n \"def variables(self):\\n for name in self._nodes:\\n if isinstance(self._nodes[name], RandomVariable):\\n yield name\",\n \"def names(self):\\n return self.dark_name, self.light_name\",\n \"def variables_used (self) :\\r\\n\\t\\treturn []\",\n \"def vars(self) -> {(EVar, Pool)}:\\n raise NotImplementedError()\",\n \"def var():\\n def _var(quoted_name):\\n name = quoted_name.subexpression.name\\n if (value := get_name(name)) is not None:\\n return value\\n else:\\n raise TypeError(f\\\"Binding {name} not found\\\")\\n yield (\\\"(λ &[name] . any)\\\", _var)\",\n \"def get_global_variable_names(self):\\n return [b\\\"\\\".join(_i).strip().decode()\\n for _i in self._f.variables[\\\"name_glo_var\\\"][:]]\",\n \"def get_variables(self) -> np.array:\\n pass\",\n \"def variables(self):\\n return self._variablesDef\",\n \"def _get_vars(symbol: Union[str, int]) -> str:\\n if isinstance(symbol, str):\\n return {\\n 'circle': 'var b1=n.round(t,2);',\\n 'square': 'var b1=n.round(t,2);',\\n 'diamond': 'var b1=n.round(t*1.3,2);',\\n 'hexagram': 'var b1=n.round(t,2);var b2=n.round(t/2,2);var b3=n.round(t*Math.sqrt(3)/2,2);'\\n }[symbol]\\n return {\\n 37: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 38: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 39: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 40: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 34: 'var d1=n.round(t,2);',\\n 33: 'var d1=n.round(t*1.4,2);',\\n 35: 'var d1=n.round(t*1.2,2);var d2=n.round(t*0.85,2);',\\n 36: 'var d1=n.round(t/2,2);var d2=n.round(t,2);'\\n }[symbol]\",\n \"def get_variables_by_name(given_name, scope=None):\\n suffix = '/' + given_name + ':|^' + given_name + ':'\\n return get_variables(scope=scope, suffix=suffix)\",\n \"def variables(self) -> OrderedDict:\\n return OrderedDict({'mu': self.mu, 'sig': self.sig})\",\n \"def getvarname(self,j_): # 3\\n sizename_ = (1 + self.getvarnamelen((j_)))\\n arr_name = array.array(\\\"b\\\",[0]*((sizename_)))\\n memview_arr_name = memoryview(arr_name)\\n res,resargs = self.__obj.getvarname(j_,sizename_,memview_arr_name)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n retarg_name = resargs\\n retarg_name = arr_name.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return retarg_name\",\n \"def _var_name_generator():\\n count = itertools.count()\\n while True:\\n yield '_var_' + str(count.next())\",\n \"def variable_string(self, name):\\n return \\\"$(\\\" + name + \\\")\\\"\",\n \"def __str__(self):\\n return '{}.{} >> {}'.format(self.scope, self.name,\\n '/'.join(map(str, self.variables)))\",\n \"def put_var_names(self, var_type, num_vars, var_names):\\n if var_type.upper() not in EX_VAR_TYPES:\\n raise ExodusIIWriterError(\\n \\\"var_type {0} not recognized\\\".format(var_type))\\n # var names must all be of same length due to Fortran restrictions\\n var_names = [\\\"{0:{1}s}\\\".format(x, MAX_STR_LENGTH)[:MAX_STR_LENGTH]\\n for x in var_names]\\n ierr = exolib.py_expvan(self.exoid, var_type.lower(), var_names)\\n if ierr:\\n raise ExodusIIWriterError(\\\"Error putting var names\\\")\",\n \"def getMaskVariableNames(self, product):\\r\\n return []\",\n \"def getbarvarname(self,i_): # 3\\n sizename_ = (1 + self.getbarvarnamelen((i_)))\\n arr_name = array.array(\\\"b\\\",[0]*((sizename_)))\\n memview_arr_name = memoryview(arr_name)\\n res,resargs = self.__obj.getbarvarname(i_,sizename_,memview_arr_name)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n retarg_name = resargs\\n retarg_name = arr_name.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return retarg_name\",\n \"def get_variables(self):\\n\\t\\treturn self.variables\",\n \"def _setup_special_names(self):\\n special_names = []\\n dynamic_params = tuple(set(self._fget_params_list + self._fset_params_list))\\n # Check whether class variables of DynamicProperty type are present\\n for attr_name, attr in getmembers(self.__class__):\\n if isinstance(attr, DynamicProperty):\\n special_names += [attr_name + \\\"_\\\" + key for key in dynamic_params]\\n # Check if special variables are defined at class level\\n for attr, value in getmembers(self.__class__):\\n if attr in special_names:\\n # Copy class special variable at instance level, prefixing reserved_prefix\\n setattr(self, self.__reserved_prefix + attr, value)\\n return special_names\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64263195","0.6372972","0.63582456","0.6340898","0.6314623","0.625726","0.62262017","0.62185633","0.61736417","0.61590856","0.61525357","0.61390096","0.6113262","0.6112568","0.6102501","0.60962504","0.6091112","0.6083438","0.6079277","0.6068774","0.60612804","0.6044552","0.60366374","0.60131043","0.6000316","0.5997138","0.5987385","0.59729964","0.5970381","0.59599495","0.5950197","0.5948254","0.59304345","0.5922799","0.5919998","0.58687663","0.5864936","0.58380824","0.5838037","0.5830587","0.58292884","0.5806649","0.57983327","0.5759079","0.57569134","0.57284945","0.5725828","0.5725764","0.5720664","0.56934005","0.56866896","0.5686396","0.5668344","0.56661433","0.5649939","0.56449616","0.5643111","0.56412464","0.56224203","0.5595914","0.55921084","0.5589689","0.5589689","0.5589689","0.5589689","0.5586577","0.55528677","0.553553","0.5533948","0.5532035","0.55314094","0.55286884","0.5528151","0.55280584","0.55261135","0.5524591","0.55244446","0.5521872","0.551207","0.5510684","0.550892","0.5508908","0.55034995","0.55014247","0.55012506","0.5497999","0.5497491","0.54862165","0.54826796","0.5480371","0.5475101","0.5465996","0.54585564","0.5448978","0.54481894","0.54480624","0.54462653","0.5445449","0.5444748","0.5440367"],"string":"[\n \"0.64263195\",\n \"0.6372972\",\n \"0.63582456\",\n \"0.6340898\",\n \"0.6314623\",\n \"0.625726\",\n \"0.62262017\",\n \"0.62185633\",\n \"0.61736417\",\n \"0.61590856\",\n \"0.61525357\",\n \"0.61390096\",\n \"0.6113262\",\n \"0.6112568\",\n \"0.6102501\",\n \"0.60962504\",\n \"0.6091112\",\n \"0.6083438\",\n \"0.6079277\",\n \"0.6068774\",\n \"0.60612804\",\n \"0.6044552\",\n \"0.60366374\",\n \"0.60131043\",\n \"0.6000316\",\n \"0.5997138\",\n \"0.5987385\",\n \"0.59729964\",\n \"0.5970381\",\n \"0.59599495\",\n \"0.5950197\",\n \"0.5948254\",\n \"0.59304345\",\n \"0.5922799\",\n \"0.5919998\",\n \"0.58687663\",\n \"0.5864936\",\n \"0.58380824\",\n \"0.5838037\",\n \"0.5830587\",\n \"0.58292884\",\n \"0.5806649\",\n \"0.57983327\",\n \"0.5759079\",\n \"0.57569134\",\n \"0.57284945\",\n \"0.5725828\",\n \"0.5725764\",\n \"0.5720664\",\n \"0.56934005\",\n \"0.56866896\",\n \"0.5686396\",\n \"0.5668344\",\n \"0.56661433\",\n \"0.5649939\",\n \"0.56449616\",\n \"0.5643111\",\n \"0.56412464\",\n \"0.56224203\",\n \"0.5595914\",\n \"0.55921084\",\n \"0.5589689\",\n \"0.5589689\",\n \"0.5589689\",\n \"0.5589689\",\n \"0.5586577\",\n \"0.55528677\",\n \"0.553553\",\n \"0.5533948\",\n \"0.5532035\",\n \"0.55314094\",\n \"0.55286884\",\n \"0.5528151\",\n \"0.55280584\",\n \"0.55261135\",\n \"0.5524591\",\n \"0.55244446\",\n \"0.5521872\",\n \"0.551207\",\n \"0.5510684\",\n \"0.550892\",\n \"0.5508908\",\n \"0.55034995\",\n \"0.55014247\",\n \"0.55012506\",\n \"0.5497999\",\n \"0.5497491\",\n \"0.54862165\",\n \"0.54826796\",\n \"0.5480371\",\n \"0.5475101\",\n \"0.5465996\",\n \"0.54585564\",\n \"0.5448978\",\n \"0.54481894\",\n \"0.54480624\",\n \"0.54462653\",\n \"0.5445449\",\n \"0.5444748\",\n \"0.5440367\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6065987"},"document_rank":{"kind":"string","value":"20"}}},{"rowIdx":2399,"cells":{"query":{"kind":"string","value":"Names of exogenous variables"},"document":{"kind":"string","value":"def exog_names(self):\n return self.data.xnames"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def variable_names(self):\n \n return [x['variable'] for x in self.variable_dicts()]","def get_all_variables_names(self):\n return self.project.get_variable_names() + self.design.get_variable_names()","def get_variable_names(self):\n return [var[1] for var in self.variables]","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def get_variable_names(self):\n varNames = []\n for var in self.variables:\n # EstimationVariable\n varNames.append(var.name)\n return varNames","def get_variable_names(self):\n return [VariableString(s) for s in\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]","def getVariableNames(self, product):\r\n\r\n variable_names = self.getDataVariableNames(product) + \\\r\n self.getMaskVariableNames(product) + \\\r\n self.getMeteorologicalVariableNames(product) + \\\r\n self.getSensorVariableNames(product) + \\\r\n self.getInfoVariableNames(product)\r\n\r\n return variable_names","def getOthVarNames( self ):\n\n if self.othVarNames:\n return self.othVarNames.keys()\n\n n = self.adb.get( \"nOthVars\" )\n for indx in range( n ):\n name = self.adb.get( \"othVarName\",\n indx ) \n self.othVarNames[ name ] = indx\n\n return self.othVarNames.keys()","def variables_used (self) :\r\n\t\t## These names possibly contain dimension specification!\r\n\t\treturn self.variable_names","def variables(self):\n return {u for u in self if u.type == 'var'}","def getOeiVarNames( self ):\n\n if self.oeiVarNames:\n return self.oeiVarNames.keys()\n\n n = self.adb.get( \"nOeiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oeiVarName\",\n indx ) \n self.oeiVarNames[name] = indx\n\n return self.oeiVarNames.keys()","def getInfoVariableNames(self, product):\r\n return []","def variables(names, **kwargs):\n return symbols(names, cls=Variable, seq=True, **kwargs)","def vars(cls):\n for key in dir(cls):\n if key.startswith('var_'):\n yield key[4:]","def getOqiVarNames( self ):\n\n if self.oqiVarNames:\n return self.oqiVarNames.keys()\n\n n = self.adb.get( \"nOqiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oqiVarName\",\n indx ) \n self.oqiVarNames[name] = indx\n\n return self.oqiVarNames.keys()","def getMeteorologicalVariableNames(self, product):\r\n return []","def var_name(i, j):\n return \"x_\" + str(i) + \",\" + str(j)","def variables(self):\n return [i.name for i in self.inputs + self.outputs]","def varNames(self):\n return self.__varNames","def getOhcVarNames( self ):\n\n if self.ohcVarNames:\n return self.ohcVarNames.keys()\n \n n = self.adb.get( \"nOhcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ohcVarName\",\n indx ) \n self.ohcVarNames[name] = indx\n\n return self.ohcVarNames.keys()","def getSensorVariableNames(self, product):\r\n return []","def getSensorVariableNames(self, product):\r\n\r\n return []","def variable_names(self):\n\n status, stdout, stderr = self.__xcall__(['--print-variables'])\n\n if status != 0:\n raise RuntimeError(\"error querying --print-variables for package `%s': %s\" % (self.name, stderr))\n\n return stdout.strip().split()","def give_variables_names(variables):\r\n names = map(lambda var: var.name, variables)\r\n h = hist(names)\r\n bad_var = lambda var: not var.name or h[var.name] > 1\r\n\r\n for i, var in enumerate(filter(bad_var, variables)):\r\n var.name = (var.name or \"\") + \"_%d\" % i\r\n\r\n if not unique(map(str, variables)):\r\n raise ValueError(\"Not all variables have unique names.\"\r\n \"Maybe you've named some of the variables identically\")\r\n\r\n return variables","def name(self):\n return '{} {} {}'.format(self.var_period, self.var_type,\n self.var_detail)","def getSolutionExportVariableNames(cls):\n return {}","def data_variable_names(self):\n data_names = []\n mesh = self.mesh_names()\n prefix = mesh[0]+'_'\n for vname in self.nc.variables.keys():\n if vname.startswith(prefix):\n if self.nc.dimensions.has_key(vname):\n continue\n if hasattr(self.nc.variables[vname],'cf_role'):\n continue\n data_names.append( vname[len(prefix):] )\n return data_names","def getOriVarNames( self ):\n\n if self.oriVarNames:\n return self.oriVarNames.keys()\n\n n = self.adb.get( \"nOriVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oriVarName\",\n indx ) \n self.oriVarNames[name] = indx\n\n return self.oriVarNames.keys()","def get_element_variable_names(self):\n return [b\"\".join(_i).strip().decode()\n for _i in self._f.variables[\"name_elem_var\"][:]]","def getDataVariableNames(self, product):\r\n return []","def get_variables(self):\n return [self.g_t, self.m_t]","def name(self):\n base_str = 'd{}{}_'.format(self.derivative_count if\n self.derivative_count > 1 else '', self.expr)\n for var, count in self.variable_count:\n base_str += 'd{}{}'.format(var, count if count > 1 else '')\n return base_str","def getVariableInfo(self, variables, name):\r\n\r\n return [var.return_variable_dict() for var in variables if var.name == name][0]","def getOsiVarNames( self ):\n\n if self.osiVarNames:\n return self.osiVarNames.keys()\n \n n = self.adb.get( \"nOsiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"osiVarName\",\n indx ) \n self.osiVarNames[ name ]= indx\n\n return self.osiVarNames.keys()","def getOfcVarNames( self ):\n\n if self.ofcVarNames:\n return self.ofcVarNames.keys()\n \n n = self.adb.get( \"nOfcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ofcVarName\",\n indx ) \n self.ofcVarNames[name] = indx\n\n return self.ofcVarNames.keys()","def vars(cls):\n raise NotImplementedError(\"This is an abstract function that needs to be implemented for each value function\")","def variables(self):\r\n return self.get_field('variable')","def get_layer_var_names(self):\n return(self.params)","def var(self, name):\n raise NotImplementedError","def __setVarNames(self):\n result = set()\n\n # detecting variables\n for templatePart in self.inputString().split(\"{\"):\n if templatePart is '' or \"}\" not in templatePart:\n continue\n\n endIndex = templatePart.find('}')\n result.add(templatePart[:endIndex])\n\n self.__varNames = list(result)","def var_names(self):\n return self._var_names","def _variable_types(self):\n return self._variable_single_types + self._variable_array_types","def getMeteorologicalVariableNames(self, product):\r\n\r\n meteorological_variable_names = []\r\n\r\n return meteorological_variable_names","def __splitVariableNames(self, name, indexes):\n if name == 'x':\n var = self.xCoordinates[indexes[0]][indexes[1]]\n elif name == 'y':\n var = self.yCoordinates[indexes[0]][indexes[1]]\n elif name == 'z':\n var = self.zCoordinates[indexes[0]][indexes[1]]\n elif name == 'colorMap':\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\n elif name == 'clusterLabels':\n var = self.clusterLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureLabels':\n var = self.mixtureLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureMeans':\n var = self.mixtureMeans[indexes[0]][indexes[1]]\n elif name == 'mixtureCovars':\n var = self.mixtureCovars[indexes[0]][indexes[1]]\n\n # The variable can contain brackets {} (when the symbol \"|\" is present in\n # the variable name), e.g.:\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\n # or it can look like:\n # DataName|Input|variableName\n\n if var is not None:\n result = [None] * 3\n if '|input|' in var.lower():\n match = re.search(r\"(\\|input\\|)\", var.lower())\n elif '|output|' in var.lower():\n match = re.search(r\"(\\|output\\|)\", var.lower())\n else:\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \"Input\" or \"Output\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\n startLoc, endLoc = match.start(), match.end()\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\n if '{' in result[-1] and '}' in result[-1]:\n locLower, locUpper = result[-1].find(\"{\"), result[-1].rfind(\"}\")\n result[-1] = result[-1][locLower + 1:locUpper].strip()\n else:\n result = None\n\n return result","def variables_declared (self) :\r\n\t\tresult = {}\r\n\r\n\t\tfor var in self.variables :\r\n\t\t\tresult[var.name.upper()] = var\r\n\t\t\r\n\t\treturn result","def stats_variable_names(res):\n def varname(s):\n pos = s.find(':')\n return s if pos==-1 else s[0:pos]\n return set( [ varname(key) for key in res.keys()] )","def get_input_names(self):\n inputNames = []\n for inVar in self.inputs:\n # inVar is of type InOutVar and the object that it contains is a PyFMI variable\n inputNames.append(inVar.get_object().name)\n return inputNames","def put_var_names(self, var_type, num_vars, var_names):\n if var_type.upper() not in EX_VAR_TYPES:\n raise ExodusIIWriterError(\n \"var_type {0} not recognized\".format(var_type))\n # var names must all be of same length due to Fortran restrictions\n var_names = [\"{0:{1}s}\".format(x, MAX_STR_LENGTH)[:MAX_STR_LENGTH]\n for x in var_names]\n ierr = exolib.py_expvan(self.exoid, var_type.lower(), var_names)\n if ierr:\n raise ExodusIIWriterError(\"Error putting var names\")","def printing_vars(self):\n print(\"Name is \", self.name)","def _setup_special_names(self):\n special_names = []\n dynamic_params = tuple(set(self._fget_params_list + self._fset_params_list))\n # Check whether class variables of DynamicProperty type are present\n for attr_name, attr in getmembers(self.__class__):\n if isinstance(attr, DynamicProperty):\n special_names += [attr_name + \"_\" + key for key in dynamic_params]\n # Check if special variables are defined at class level\n for attr, value in getmembers(self.__class__):\n if attr in special_names:\n # Copy class special variable at instance level, prefixing reserved_prefix\n setattr(self, self.__reserved_prefix + attr, value)\n return special_names","def get_vars(self):\n return [self.mu, self.var]","def names():\n pass","def use_vars():\n obj = Subclass(\"Austin\", \"Red\")\n print(vars(obj))\n print(obj.__dict__)\n if vars(obj) == obj.__dict__:\n print(True)\n print(type(vars(obj)))\n \"\"\"This throws a TypeError because built-in types do not have a __dict__ attribute.\"\"\"\n # print(vars(0))","def vars(self):\n return self.v","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def getVariables(self):\n statVars = [self[vn] for vn in self.statVars]\n timeVars = [self[vn] for vn in self.timeVars]\n return statVars + timeVars","def get_input_var_names(self):\n return self._input_var_names","def variables(self):\n return self._.vars","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def get_all_variables(self):\n raise NotImplementedError()","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def getLinIterVarNames( self ):\n\n self.updateAdb( )\n\n return self.iterNames.keys()","def get_output_names(self):\n outputNames = []\n for outVar in self.outputs:\n # outVar is of type InOutVar and the object that it contains is a PyFMI variable\n outputNames.append(outVar.get_object().name)\n return outputNames","def variables(self) -> OrderedDict:\n return OrderedDict({'mu': self.mu, 'sig': self.sig})","def vars(self) -> {(EVar, Pool)}:\n raise NotImplementedError()","def var():\n def _var(quoted_name):\n name = quoted_name.subexpression.name\n if (value := get_name(name)) is not None:\n return value\n else:\n raise TypeError(f\"Binding {name} not found\")\n yield (\"(λ &[name] . any)\", _var)","def _get_vars(symbol: Union[str, int]) -> str:\n if isinstance(symbol, str):\n return {\n 'circle': 'var b1=n.round(t,2);',\n 'square': 'var b1=n.round(t,2);',\n 'diamond': 'var b1=n.round(t*1.3,2);',\n 'hexagram': 'var b1=n.round(t,2);var b2=n.round(t/2,2);var b3=n.round(t*Math.sqrt(3)/2,2);'\n }[symbol]\n return {\n 37: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 38: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 39: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 40: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 34: 'var d1=n.round(t,2);',\n 33: 'var d1=n.round(t*1.4,2);',\n 35: 'var d1=n.round(t*1.2,2);var d2=n.round(t*0.85,2);',\n 36: 'var d1=n.round(t/2,2);var d2=n.round(t,2);'\n }[symbol]","def getMaskVariableNames(self, product):\r\n return []","def variables(self):\n return [term.variable for term in self.terms]","def get_global_variable_names(self):\n return [b\"\".join(_i).strip().decode()\n for _i in self._f.variables[\"name_glo_var\"][:]]","def variables(self):\n for name in self._nodes:\n if isinstance(self._nodes[name], RandomVariable):\n yield name","def variables(model: Model) -> AbstractSet[str]:\r\n assert is_model(model)\r\n return model.keys()","def print_all_variables():\n for idx, v in enumerate(tf.all_variables()):\n print(\" var %d: %s %s\" % (idx, v.get_shape(), v.name))","def get_node_variable_names(self):\n return [b\"\".join(_i).strip().decode()\n for _i in self._f.variables[\"name_nod_var\"][:]]","def variables(model: Model) -> AbstractSet[str]:\n assert is_model(model)\n return model.keys()","def variables(s):\n result = set([])\n def walk(s):\n if is_variable(s):\n result.add(s)\n else:\n for arg in s.args:\n walk(arg)\n walk(s)\n return result","def _var_name_sub(self, sprintf, quote=False):\n q = ''\n if quote:\n q = \"'\"\n name_list = map(lambda x: q + self.cdict[x][0] + q, sprintf[\"vars\"] )\n return sprintf[\"text\"] % tuple(name_list)","def show_variables(self):\r\n\r\n variablelist = [(x_temp,self.variables[x_temp]) for x_temp in sorted(self.variables.keys())]\r\n display.noteprint(('/C/ '+labels.VARIABLES.upper(), EOL.join([x_temp[0]+BLANK\r\n +COLON+BLANK\r\n +abridge(str(x_temp[1]),40)\r\n for x_temp in variablelist])))","def getSolRatioVarNames( self ):\n\n self.updateAdb( )\n\n return self.solNames.keys()","def get_name_list(msh, varname):\n return [str(chartostring(v)) for v in msh.variables[varname]]","def vars(*tensor_types):\n return map(var, tensor_types)","def var(self, name):\n return self.get_ground_vector('!Var:{}'.format(name))","def var(self, name):\n return self.get_ground_vector('!Var:{}'.format(name))","def var(self, name):\n return self.get_ground_vector('!Var:{}'.format(name))","def getBindedNames(self):\n names = []\n for function in self.functions:\n names.append(function.__name__)\n return \", \".join(names)","def getVariables(self)->Dict[str,str]:\n pass","def get_all_variables(instance):\n return [v for v in dir(instance) if not callable(getattr(instance, v))]","def vars(self):\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)","def getDataVariableNames(self, product):\r\n\r\n h = product.getSceneRasterHeight()\r\n\r\n # 10m resolution\r\n if h == 10980:\r\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_10m)\r\n\r\n # 20m resolution\r\n elif h == 5490:\r\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_20m)\r\n\r\n # 20m resolution\r\n elif h == 1830:\r\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_60m)","def dictOfVariables(self):\n return {x.name: x for x in self.variables}","def get_all_var_names(self):\n\n if hasattr(self, \"all_var_names\"):\n return self.all_var_names\n\n # Append all variables in model (defined in YAML).\n aux_all_var_names = []\n aux_all_var_names.extend(self.sim_config_params)\n aux_all_var_names.extend(self.sim_inputs)\n aux_all_var_names.extend(self.sim_outputs)\n aux_all_var_names.extend(self.sim_other_vars)\n\n # Remove duplicates (if any) -- Keeping initial order\n all_var_names = [aux_all_var_names[i] for i in range(len(aux_all_var_names)) \\\n if aux_all_var_names[i] not in aux_all_var_names[:i]]\n\n # Store for following calls\n self.all_var_names = all_var_names\n return self.all_var_names","def retrieve_name(self, var):\r\n\t\tfor fi in reversed(inspect.stack()):\r\n\t\t\tnames = [var_name for var_name, var_val in fi.frame.f_locals.items() if var_val is var]\r\n\t\t\tif len(names) > 0:\r\n\t\t\t\treturn names[0]\r\n\t\treturn \"<unknown>\"","def vars(svars):\n return np.array([pm.var(var) for var in svars.split()])","def generate_variable_names():\n while True:\n name = uuid.uuid4()\n yield f\"_{name.hex}\"","def visit_varname(self, node, children):\n # Return only dict nodes\n return {'type':'var','val':str(node)}","def getInfoVariableNames(self, product):\r\n\r\n h = product.getSceneRasterHeight()\r\n\r\n # 10m resolution\r\n if h == 10980:\r\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_10m)\r\n\r\n # 20m resolution\r\n elif h == 5490:\r\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_20m)\r\n\r\n # 20m resolution\r\n elif h == 1830:\r\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_60m)\r\n return []","def get_variable_values(self, vars):\n raise NotImplementedError()","def get_variable_full_name(var):\n if var._save_slice_info:\n return var._save_slice_info.full_name\n else:\n return var.op.name","def get_variable_names(text):\n names = []\n if '@@' in text:\n matches = _property_pattern.findall(text)\n for token, key in matches:\n names.append(key)\n\n return names","def get_all_variables(self):\n return []"],"string":"[\n \"def variable_names(self):\\n \\n return [x['variable'] for x in self.variable_dicts()]\",\n \"def get_all_variables_names(self):\\n return self.project.get_variable_names() + self.design.get_variable_names()\",\n \"def get_variable_names(self):\\n return [var[1] for var in self.variables]\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def get_variable_names(self):\\n varNames = []\\n for var in self.variables:\\n # EstimationVariable\\n varNames.append(var.name)\\n return varNames\",\n \"def get_variable_names(self):\\n return [VariableString(s) for s in\\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]\",\n \"def getVariableNames(self, product):\\r\\n\\r\\n variable_names = self.getDataVariableNames(product) + \\\\\\r\\n self.getMaskVariableNames(product) + \\\\\\r\\n self.getMeteorologicalVariableNames(product) + \\\\\\r\\n self.getSensorVariableNames(product) + \\\\\\r\\n self.getInfoVariableNames(product)\\r\\n\\r\\n return variable_names\",\n \"def getOthVarNames( self ):\\n\\n if self.othVarNames:\\n return self.othVarNames.keys()\\n\\n n = self.adb.get( \\\"nOthVars\\\" )\\n for indx in range( n ):\\n name = self.adb.get( \\\"othVarName\\\",\\n indx ) \\n self.othVarNames[ name ] = indx\\n\\n return self.othVarNames.keys()\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names possibly contain dimension specification!\\r\\n\\t\\treturn self.variable_names\",\n \"def variables(self):\\n return {u for u in self if u.type == 'var'}\",\n \"def getOeiVarNames( self ):\\n\\n if self.oeiVarNames:\\n return self.oeiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOeiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oeiVarName\\\",\\n indx ) \\n self.oeiVarNames[name] = indx\\n\\n return self.oeiVarNames.keys()\",\n \"def getInfoVariableNames(self, product):\\r\\n return []\",\n \"def variables(names, **kwargs):\\n return symbols(names, cls=Variable, seq=True, **kwargs)\",\n \"def vars(cls):\\n for key in dir(cls):\\n if key.startswith('var_'):\\n yield key[4:]\",\n \"def getOqiVarNames( self ):\\n\\n if self.oqiVarNames:\\n return self.oqiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOqiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oqiVarName\\\",\\n indx ) \\n self.oqiVarNames[name] = indx\\n\\n return self.oqiVarNames.keys()\",\n \"def getMeteorologicalVariableNames(self, product):\\r\\n return []\",\n \"def var_name(i, j):\\n return \\\"x_\\\" + str(i) + \\\",\\\" + str(j)\",\n \"def variables(self):\\n return [i.name for i in self.inputs + self.outputs]\",\n \"def varNames(self):\\n return self.__varNames\",\n \"def getOhcVarNames( self ):\\n\\n if self.ohcVarNames:\\n return self.ohcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOhcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ohcVarName\\\",\\n indx ) \\n self.ohcVarNames[name] = indx\\n\\n return self.ohcVarNames.keys()\",\n \"def getSensorVariableNames(self, product):\\r\\n return []\",\n \"def getSensorVariableNames(self, product):\\r\\n\\r\\n return []\",\n \"def variable_names(self):\\n\\n status, stdout, stderr = self.__xcall__(['--print-variables'])\\n\\n if status != 0:\\n raise RuntimeError(\\\"error querying --print-variables for package `%s': %s\\\" % (self.name, stderr))\\n\\n return stdout.strip().split()\",\n \"def give_variables_names(variables):\\r\\n names = map(lambda var: var.name, variables)\\r\\n h = hist(names)\\r\\n bad_var = lambda var: not var.name or h[var.name] > 1\\r\\n\\r\\n for i, var in enumerate(filter(bad_var, variables)):\\r\\n var.name = (var.name or \\\"\\\") + \\\"_%d\\\" % i\\r\\n\\r\\n if not unique(map(str, variables)):\\r\\n raise ValueError(\\\"Not all variables have unique names.\\\"\\r\\n \\\"Maybe you've named some of the variables identically\\\")\\r\\n\\r\\n return variables\",\n \"def name(self):\\n return '{} {} {}'.format(self.var_period, self.var_type,\\n self.var_detail)\",\n \"def getSolutionExportVariableNames(cls):\\n return {}\",\n \"def data_variable_names(self):\\n data_names = []\\n mesh = self.mesh_names()\\n prefix = mesh[0]+'_'\\n for vname in self.nc.variables.keys():\\n if vname.startswith(prefix):\\n if self.nc.dimensions.has_key(vname):\\n continue\\n if hasattr(self.nc.variables[vname],'cf_role'):\\n continue\\n data_names.append( vname[len(prefix):] )\\n return data_names\",\n \"def getOriVarNames( self ):\\n\\n if self.oriVarNames:\\n return self.oriVarNames.keys()\\n\\n n = self.adb.get( \\\"nOriVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oriVarName\\\",\\n indx ) \\n self.oriVarNames[name] = indx\\n\\n return self.oriVarNames.keys()\",\n \"def get_element_variable_names(self):\\n return [b\\\"\\\".join(_i).strip().decode()\\n for _i in self._f.variables[\\\"name_elem_var\\\"][:]]\",\n \"def getDataVariableNames(self, product):\\r\\n return []\",\n \"def get_variables(self):\\n return [self.g_t, self.m_t]\",\n \"def name(self):\\n base_str = 'd{}{}_'.format(self.derivative_count if\\n self.derivative_count > 1 else '', self.expr)\\n for var, count in self.variable_count:\\n base_str += 'd{}{}'.format(var, count if count > 1 else '')\\n return base_str\",\n \"def getVariableInfo(self, variables, name):\\r\\n\\r\\n return [var.return_variable_dict() for var in variables if var.name == name][0]\",\n \"def getOsiVarNames( self ):\\n\\n if self.osiVarNames:\\n return self.osiVarNames.keys()\\n \\n n = self.adb.get( \\\"nOsiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"osiVarName\\\",\\n indx ) \\n self.osiVarNames[ name ]= indx\\n\\n return self.osiVarNames.keys()\",\n \"def getOfcVarNames( self ):\\n\\n if self.ofcVarNames:\\n return self.ofcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOfcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ofcVarName\\\",\\n indx ) \\n self.ofcVarNames[name] = indx\\n\\n return self.ofcVarNames.keys()\",\n \"def vars(cls):\\n raise NotImplementedError(\\\"This is an abstract function that needs to be implemented for each value function\\\")\",\n \"def variables(self):\\r\\n return self.get_field('variable')\",\n \"def get_layer_var_names(self):\\n return(self.params)\",\n \"def var(self, name):\\n raise NotImplementedError\",\n \"def __setVarNames(self):\\n result = set()\\n\\n # detecting variables\\n for templatePart in self.inputString().split(\\\"{\\\"):\\n if templatePart is '' or \\\"}\\\" not in templatePart:\\n continue\\n\\n endIndex = templatePart.find('}')\\n result.add(templatePart[:endIndex])\\n\\n self.__varNames = list(result)\",\n \"def var_names(self):\\n return self._var_names\",\n \"def _variable_types(self):\\n return self._variable_single_types + self._variable_array_types\",\n \"def getMeteorologicalVariableNames(self, product):\\r\\n\\r\\n meteorological_variable_names = []\\r\\n\\r\\n return meteorological_variable_names\",\n \"def __splitVariableNames(self, name, indexes):\\n if name == 'x':\\n var = self.xCoordinates[indexes[0]][indexes[1]]\\n elif name == 'y':\\n var = self.yCoordinates[indexes[0]][indexes[1]]\\n elif name == 'z':\\n var = self.zCoordinates[indexes[0]][indexes[1]]\\n elif name == 'colorMap':\\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\\n elif name == 'clusterLabels':\\n var = self.clusterLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureLabels':\\n var = self.mixtureLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureMeans':\\n var = self.mixtureMeans[indexes[0]][indexes[1]]\\n elif name == 'mixtureCovars':\\n var = self.mixtureCovars[indexes[0]][indexes[1]]\\n\\n # The variable can contain brackets {} (when the symbol \\\"|\\\" is present in\\n # the variable name), e.g.:\\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\\n # or it can look like:\\n # DataName|Input|variableName\\n\\n if var is not None:\\n result = [None] * 3\\n if '|input|' in var.lower():\\n match = re.search(r\\\"(\\\\|input\\\\|)\\\", var.lower())\\n elif '|output|' in var.lower():\\n match = re.search(r\\\"(\\\\|output\\\\|)\\\", var.lower())\\n else:\\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \\\"Input\\\" or \\\"Output\\\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\\n startLoc, endLoc = match.start(), match.end()\\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\\n if '{' in result[-1] and '}' in result[-1]:\\n locLower, locUpper = result[-1].find(\\\"{\\\"), result[-1].rfind(\\\"}\\\")\\n result[-1] = result[-1][locLower + 1:locUpper].strip()\\n else:\\n result = None\\n\\n return result\",\n \"def variables_declared (self) :\\r\\n\\t\\tresult = {}\\r\\n\\r\\n\\t\\tfor var in self.variables :\\r\\n\\t\\t\\tresult[var.name.upper()] = var\\r\\n\\t\\t\\r\\n\\t\\treturn result\",\n \"def stats_variable_names(res):\\n def varname(s):\\n pos = s.find(':')\\n return s if pos==-1 else s[0:pos]\\n return set( [ varname(key) for key in res.keys()] )\",\n \"def get_input_names(self):\\n inputNames = []\\n for inVar in self.inputs:\\n # inVar is of type InOutVar and the object that it contains is a PyFMI variable\\n inputNames.append(inVar.get_object().name)\\n return inputNames\",\n \"def put_var_names(self, var_type, num_vars, var_names):\\n if var_type.upper() not in EX_VAR_TYPES:\\n raise ExodusIIWriterError(\\n \\\"var_type {0} not recognized\\\".format(var_type))\\n # var names must all be of same length due to Fortran restrictions\\n var_names = [\\\"{0:{1}s}\\\".format(x, MAX_STR_LENGTH)[:MAX_STR_LENGTH]\\n for x in var_names]\\n ierr = exolib.py_expvan(self.exoid, var_type.lower(), var_names)\\n if ierr:\\n raise ExodusIIWriterError(\\\"Error putting var names\\\")\",\n \"def printing_vars(self):\\n print(\\\"Name is \\\", self.name)\",\n \"def _setup_special_names(self):\\n special_names = []\\n dynamic_params = tuple(set(self._fget_params_list + self._fset_params_list))\\n # Check whether class variables of DynamicProperty type are present\\n for attr_name, attr in getmembers(self.__class__):\\n if isinstance(attr, DynamicProperty):\\n special_names += [attr_name + \\\"_\\\" + key for key in dynamic_params]\\n # Check if special variables are defined at class level\\n for attr, value in getmembers(self.__class__):\\n if attr in special_names:\\n # Copy class special variable at instance level, prefixing reserved_prefix\\n setattr(self, self.__reserved_prefix + attr, value)\\n return special_names\",\n \"def get_vars(self):\\n return [self.mu, self.var]\",\n \"def names():\\n pass\",\n \"def use_vars():\\n obj = Subclass(\\\"Austin\\\", \\\"Red\\\")\\n print(vars(obj))\\n print(obj.__dict__)\\n if vars(obj) == obj.__dict__:\\n print(True)\\n print(type(vars(obj)))\\n \\\"\\\"\\\"This throws a TypeError because built-in types do not have a __dict__ attribute.\\\"\\\"\\\"\\n # print(vars(0))\",\n \"def vars(self):\\n return self.v\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def getVariables(self):\\n statVars = [self[vn] for vn in self.statVars]\\n timeVars = [self[vn] for vn in self.timeVars]\\n return statVars + timeVars\",\n \"def get_input_var_names(self):\\n return self._input_var_names\",\n \"def variables(self):\\n return self._.vars\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def get_all_variables(self):\\n raise NotImplementedError()\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def getLinIterVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.iterNames.keys()\",\n \"def get_output_names(self):\\n outputNames = []\\n for outVar in self.outputs:\\n # outVar is of type InOutVar and the object that it contains is a PyFMI variable\\n outputNames.append(outVar.get_object().name)\\n return outputNames\",\n \"def variables(self) -> OrderedDict:\\n return OrderedDict({'mu': self.mu, 'sig': self.sig})\",\n \"def vars(self) -> {(EVar, Pool)}:\\n raise NotImplementedError()\",\n \"def var():\\n def _var(quoted_name):\\n name = quoted_name.subexpression.name\\n if (value := get_name(name)) is not None:\\n return value\\n else:\\n raise TypeError(f\\\"Binding {name} not found\\\")\\n yield (\\\"(λ &[name] . any)\\\", _var)\",\n \"def _get_vars(symbol: Union[str, int]) -> str:\\n if isinstance(symbol, str):\\n return {\\n 'circle': 'var b1=n.round(t,2);',\\n 'square': 'var b1=n.round(t,2);',\\n 'diamond': 'var b1=n.round(t*1.3,2);',\\n 'hexagram': 'var b1=n.round(t,2);var b2=n.round(t/2,2);var b3=n.round(t*Math.sqrt(3)/2,2);'\\n }[symbol]\\n return {\\n 37: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 38: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 39: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 40: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 34: 'var d1=n.round(t,2);',\\n 33: 'var d1=n.round(t*1.4,2);',\\n 35: 'var d1=n.round(t*1.2,2);var d2=n.round(t*0.85,2);',\\n 36: 'var d1=n.round(t/2,2);var d2=n.round(t,2);'\\n }[symbol]\",\n \"def getMaskVariableNames(self, product):\\r\\n return []\",\n \"def variables(self):\\n return [term.variable for term in self.terms]\",\n \"def get_global_variable_names(self):\\n return [b\\\"\\\".join(_i).strip().decode()\\n for _i in self._f.variables[\\\"name_glo_var\\\"][:]]\",\n \"def variables(self):\\n for name in self._nodes:\\n if isinstance(self._nodes[name], RandomVariable):\\n yield name\",\n \"def variables(model: Model) -> AbstractSet[str]:\\r\\n assert is_model(model)\\r\\n return model.keys()\",\n \"def print_all_variables():\\n for idx, v in enumerate(tf.all_variables()):\\n print(\\\" var %d: %s %s\\\" % (idx, v.get_shape(), v.name))\",\n \"def get_node_variable_names(self):\\n return [b\\\"\\\".join(_i).strip().decode()\\n for _i in self._f.variables[\\\"name_nod_var\\\"][:]]\",\n \"def variables(model: Model) -> AbstractSet[str]:\\n assert is_model(model)\\n return model.keys()\",\n \"def variables(s):\\n result = set([])\\n def walk(s):\\n if is_variable(s):\\n result.add(s)\\n else:\\n for arg in s.args:\\n walk(arg)\\n walk(s)\\n return result\",\n \"def _var_name_sub(self, sprintf, quote=False):\\n q = ''\\n if quote:\\n q = \\\"'\\\"\\n name_list = map(lambda x: q + self.cdict[x][0] + q, sprintf[\\\"vars\\\"] )\\n return sprintf[\\\"text\\\"] % tuple(name_list)\",\n \"def show_variables(self):\\r\\n\\r\\n variablelist = [(x_temp,self.variables[x_temp]) for x_temp in sorted(self.variables.keys())]\\r\\n display.noteprint(('/C/ '+labels.VARIABLES.upper(), EOL.join([x_temp[0]+BLANK\\r\\n +COLON+BLANK\\r\\n +abridge(str(x_temp[1]),40)\\r\\n for x_temp in variablelist])))\",\n \"def getSolRatioVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.solNames.keys()\",\n \"def get_name_list(msh, varname):\\n return [str(chartostring(v)) for v in msh.variables[varname]]\",\n \"def vars(*tensor_types):\\n return map(var, tensor_types)\",\n \"def var(self, name):\\n return self.get_ground_vector('!Var:{}'.format(name))\",\n \"def var(self, name):\\n return self.get_ground_vector('!Var:{}'.format(name))\",\n \"def var(self, name):\\n return self.get_ground_vector('!Var:{}'.format(name))\",\n \"def getBindedNames(self):\\n names = []\\n for function in self.functions:\\n names.append(function.__name__)\\n return \\\", \\\".join(names)\",\n \"def getVariables(self)->Dict[str,str]:\\n pass\",\n \"def get_all_variables(instance):\\n return [v for v in dir(instance) if not callable(getattr(instance, v))]\",\n \"def vars(self):\\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)\",\n \"def getDataVariableNames(self, product):\\r\\n\\r\\n h = product.getSceneRasterHeight()\\r\\n\\r\\n # 10m resolution\\r\\n if h == 10980:\\r\\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_10m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 5490:\\r\\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_20m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 1830:\\r\\n return self.return_available_variables(product, DATA_VARIABLE_NAMES_60m)\",\n \"def dictOfVariables(self):\\n return {x.name: x for x in self.variables}\",\n \"def get_all_var_names(self):\\n\\n if hasattr(self, \\\"all_var_names\\\"):\\n return self.all_var_names\\n\\n # Append all variables in model (defined in YAML).\\n aux_all_var_names = []\\n aux_all_var_names.extend(self.sim_config_params)\\n aux_all_var_names.extend(self.sim_inputs)\\n aux_all_var_names.extend(self.sim_outputs)\\n aux_all_var_names.extend(self.sim_other_vars)\\n\\n # Remove duplicates (if any) -- Keeping initial order\\n all_var_names = [aux_all_var_names[i] for i in range(len(aux_all_var_names)) \\\\\\n if aux_all_var_names[i] not in aux_all_var_names[:i]]\\n\\n # Store for following calls\\n self.all_var_names = all_var_names\\n return self.all_var_names\",\n \"def retrieve_name(self, var):\\r\\n\\t\\tfor fi in reversed(inspect.stack()):\\r\\n\\t\\t\\tnames = [var_name for var_name, var_val in fi.frame.f_locals.items() if var_val is var]\\r\\n\\t\\t\\tif len(names) > 0:\\r\\n\\t\\t\\t\\treturn names[0]\\r\\n\\t\\treturn \\\"<unknown>\\\"\",\n \"def vars(svars):\\n return np.array([pm.var(var) for var in svars.split()])\",\n \"def generate_variable_names():\\n while True:\\n name = uuid.uuid4()\\n yield f\\\"_{name.hex}\\\"\",\n \"def visit_varname(self, node, children):\\n # Return only dict nodes\\n return {'type':'var','val':str(node)}\",\n \"def getInfoVariableNames(self, product):\\r\\n\\r\\n h = product.getSceneRasterHeight()\\r\\n\\r\\n # 10m resolution\\r\\n if h == 10980:\\r\\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_10m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 5490:\\r\\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_20m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 1830:\\r\\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_60m)\\r\\n return []\",\n \"def get_variable_values(self, vars):\\n raise NotImplementedError()\",\n \"def get_variable_full_name(var):\\n if var._save_slice_info:\\n return var._save_slice_info.full_name\\n else:\\n return var.op.name\",\n \"def get_variable_names(text):\\n names = []\\n if '@@' in text:\\n matches = _property_pattern.findall(text)\\n for token, key in matches:\\n names.append(key)\\n\\n return names\",\n \"def get_all_variables(self):\\n return []\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69048613","0.6785922","0.67740816","0.6711591","0.6687262","0.6663156","0.6635133","0.66310126","0.6589906","0.6581961","0.6580228","0.6575236","0.6552368","0.6544824","0.6535504","0.6525284","0.6511582","0.64991194","0.64609987","0.641673","0.6381078","0.6379418","0.6365293","0.6352937","0.63271374","0.6306018","0.62861323","0.62565476","0.6240985","0.6227237","0.62216944","0.620346","0.62016004","0.61982244","0.6197064","0.6185238","0.617523","0.61743015","0.6171904","0.6116352","0.6103282","0.60806125","0.6057325","0.60489136","0.603551","0.6034992","0.60331947","0.6026331","0.60188305","0.5999864","0.59965295","0.59958863","0.5988674","0.5960137","0.59338677","0.59249157","0.5922055","0.58924544","0.5892304","0.5885361","0.58795","0.586849","0.5846989","0.58311594","0.58290035","0.5825151","0.5818875","0.5810105","0.5809393","0.580774","0.58058137","0.57959306","0.5779041","0.5775341","0.57750213","0.57699573","0.5769692","0.57675976","0.576251","0.57568246","0.5748901","0.57463074","0.57463074","0.57463074","0.5743074","0.57425624","0.5740519","0.5732459","0.5725715","0.5718498","0.57123667","0.57079047","0.57073474","0.57052594","0.570229","0.569894","0.5698264","0.569772","0.5697154","0.5693202"],"string":"[\n \"0.69048613\",\n \"0.6785922\",\n \"0.67740816\",\n \"0.6711591\",\n \"0.6687262\",\n \"0.6663156\",\n \"0.6635133\",\n \"0.66310126\",\n \"0.6589906\",\n \"0.6581961\",\n \"0.6580228\",\n \"0.6575236\",\n \"0.6552368\",\n \"0.6544824\",\n \"0.6535504\",\n \"0.6525284\",\n \"0.6511582\",\n \"0.64991194\",\n \"0.64609987\",\n \"0.641673\",\n \"0.6381078\",\n \"0.6379418\",\n \"0.6365293\",\n \"0.6352937\",\n \"0.63271374\",\n \"0.6306018\",\n \"0.62861323\",\n \"0.62565476\",\n \"0.6240985\",\n \"0.6227237\",\n \"0.62216944\",\n \"0.620346\",\n \"0.62016004\",\n \"0.61982244\",\n \"0.6197064\",\n \"0.6185238\",\n \"0.617523\",\n \"0.61743015\",\n \"0.6171904\",\n \"0.6116352\",\n \"0.6103282\",\n \"0.60806125\",\n \"0.6057325\",\n \"0.60489136\",\n \"0.603551\",\n \"0.6034992\",\n \"0.60331947\",\n \"0.6026331\",\n \"0.60188305\",\n \"0.5999864\",\n \"0.59965295\",\n \"0.59958863\",\n \"0.5988674\",\n \"0.5960137\",\n \"0.59338677\",\n \"0.59249157\",\n \"0.5922055\",\n \"0.58924544\",\n \"0.5892304\",\n \"0.5885361\",\n \"0.58795\",\n \"0.586849\",\n \"0.5846989\",\n \"0.58311594\",\n \"0.58290035\",\n \"0.5825151\",\n \"0.5818875\",\n \"0.5810105\",\n \"0.5809393\",\n \"0.580774\",\n \"0.58058137\",\n \"0.57959306\",\n \"0.5779041\",\n \"0.5775341\",\n \"0.57750213\",\n \"0.57699573\",\n \"0.5769692\",\n \"0.57675976\",\n \"0.576251\",\n \"0.57568246\",\n \"0.5748901\",\n \"0.57463074\",\n \"0.57463074\",\n \"0.57463074\",\n \"0.5743074\",\n \"0.57425624\",\n \"0.5740519\",\n \"0.5732459\",\n \"0.5725715\",\n \"0.5718498\",\n \"0.57123667\",\n \"0.57079047\",\n \"0.57073474\",\n \"0.57052594\",\n \"0.570229\",\n \"0.569894\",\n \"0.5698264\",\n \"0.569772\",\n \"0.5697154\",\n \"0.5693202\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5873568"},"document_rank":{"kind":"string","value":"61"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":23,"numItemsPerPage":100,"numTotalItems":95772,"offset":2300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MjcxMjc1Niwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzI3MTYzNTYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.T7iH8nxIy6m4NcCQmxU4YFV-iGcQmUKuMt8qOJhFJrWRqJgHHYG1Ywcm1hK1NfgYBxJjwsP4u1r3D_d7L1E6Ag","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Simulate printing each design, until there are none left. Move each design to completed_models after printing. | def print_models(unprinted_designs, completed_models):
while unprinted_designs:
current_design = unprinted_designs.pop()
# Simulate creating a 3d print from the design.
print("Printing model: " + current_design)
completed_models.append(current_design) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_models (unprinted_designs,completed_models):\n \n while unprinted_designs:\n current_design = unprinted_designs.pop()\n #Simulate creating a 3D print from the desig.\n print (\"printing model: \" + current_design)\n completed_models.append (current_design)",
"def print... | [
"0.83719826",
"0.83719486",
"0.82855046",
"0.8230399",
"0.80913585",
"0.79925144",
"0.74432874",
"0.690036",
"0.68810475",
"0.68694586",
"0.68694586",
"0.68694586",
"0.67919457",
"0.6589304",
"0.61238146",
"0.58934677",
"0.57532626",
"0.57419646",
"0.57194996",
"0.5676091",
"... | 0.8240983 | 4 |
Show all the models that were printed. | def show_completed_models(completed_models):
print("\nThe following models have been printed:")
for completed_model in completed_models:
print(completed_model) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_completed_models(completed_models):\r\n print(\"\\nThe following models have been printed:\")\r\n for completed_model in completed_models:\r\n print(completed_model)",
"def show_completed_models(completed_models):\n\tprint(\"\\n The following models has been printed \")\n\tfor completed_mod... | [
"0.7550581",
"0.7505565",
"0.74779797",
"0.7463235",
"0.7299305",
"0.689944",
"0.6806887",
"0.67993057",
"0.6793462",
"0.67158437",
"0.6685057",
"0.6670043",
"0.6669607",
"0.6669607",
"0.663256",
"0.66235834",
"0.66213685",
"0.66103745",
"0.6587718",
"0.6491061",
"0.6472574",... | 0.75561774 | 2 |
Build a dictionary containing everything we know about a user. | def build_profile(first, last, **user_info):
profile = {}
profile['first_name'] = first
profile['last_name'] = last
for key, value in user_info.items():
profile[key] = value
return profile | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_user_info(user):\r\n return {\r\n 'username': user.username,\r\n 'email': user.email,\r\n 'first_name': user.first_name,\r\n 'last_name': user.last_name,\r\n }",
"def extract_user_info(user):\r\n return {\r\n 'username': user... | [
"0.739899",
"0.739899",
"0.73685396",
"0.7349614",
"0.7152368",
"0.7149312",
"0.6997632",
"0.68651915",
"0.6837776",
"0.68375385",
"0.68306404",
"0.68195456",
"0.67827326",
"0.6781717",
"0.67752063",
"0.6750619",
"0.6731149",
"0.6635023",
"0.6614327",
"0.6569148",
"0.65338075... | 0.6014076 | 69 |
Register the message handlers that every journal should support. | def register_message_handlers(journal):
journal.dispatcher.register_message_handler(
DumpQuorumMessage, _dumpquorumhandler) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _register_handlers(self):\n DBG(\"\\nregister handlers\")\n for hook, handler in self.handlers:\n g.registerHandler(hook, handler)\n\n signal_manager.connect(self.c, 'body_changed', self._after_body_key)",
"def _register_handlers(self):\n import handlers as th\n ... | [
"0.72620213",
"0.70435107",
"0.66157293",
"0.65716064",
"0.6314305",
"0.62207043",
"0.6180045",
"0.60054207",
"0.6003531",
"0.5998502",
"0.5990488",
"0.59491366",
"0.5929773",
"0.5925832",
"0.5805171",
"0.58019876",
"0.5768111",
"0.575978",
"0.5712927",
"0.5671215",
"0.566424... | 0.8429424 | 0 |
Constructor for DumpQuorumMessage class. | def __init__(self, minfo=None):
if minfo is None:
minfo = {}
super(DumpQuorumMessage, self).__init__(minfo)
self.IsSystemMessage = False
self.IsForward = True
self.IsReliable = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump(self):\n result = super(DumpQuorumMessage, self).dump()\n return result",
"def __init__(self):\n super().__init__()\n\n self.__encoded_msg = ''",
"def __init__(self, msg):\n\n self.msg = msg",
"def __init__(self, msg):\n self.msg = msg",
"def __init__(self... | [
"0.7168633",
"0.57381815",
"0.5708409",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.56985074",
"0.569133",
"0.5681516",
"0.5638713",
"0.5632342",
"0.5632342",
"0.5... | 0.7561885 | 0 |
Returns a dict with information about the dump quorum message. | def dump(self):
result = super(DumpQuorumMessage, self).dump()
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dumps(self) -> Dict[str, Any]:\n return {\n \"commitId\": self.commit_id,\n \"parentCommitId\": self.parent_commit_id,\n \"message\": self.message,\n \"committer\": self.committer.dumps(),\n }",
"def __init__(self, minfo=None):\n if minfo is No... | [
"0.6006111",
"0.567213",
"0.5670017",
"0.5648441",
"0.5578808",
"0.53971803",
"0.5394207",
"0.5387473",
"0.53841007",
"0.5374053",
"0.5371038",
"0.53614724",
"0.53580964",
"0.5316283",
"0.5305731",
"0.52922034",
"0.52715415",
"0.51807743",
"0.5176574",
"0.51549894",
"0.513852... | 0.7622692 | 0 |
The entry endpoint of our API. | def api_root(request, format=None):
return Response({
'users': reverse('user-list', request=request),
'groups': reverse('group-list', request=request),
'pools': reverse('pool-list', request=request),
}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def api():\n\treturn \"The API call\"",
"def home():\n return(\n f\"Available Routes: <br/>\"\n\n f\"For Precipitation: /api/v1.0/precipitation<br/>\"\n f\"Returns Jsonify dictionary of dates and Precepitation<br/><br/>\"\n\n f\"For list of Stations: /api/v1.0/stations<br/>\"\n ... | [
"0.6402737",
"0.6304572",
"0.6296278",
"0.6259337",
"0.61859983",
"0.6181998",
"0.6150856",
"0.6121735",
"0.6036392",
"0.6036392",
"0.6036392",
"0.5975089",
"0.59478664",
"0.5932474",
"0.5926881",
"0.59074867",
"0.5892754",
"0.5886604",
"0.5877651",
"0.58573645",
"0.5808704",... | 0.0 | -1 |
List all pools, or create a new pool. | def pool_list(request, format=None):
if request.method == 'GET':
pools = storage.models.Pool.objects.all()
serializer = serializers.PoolSerializer(pools)
return Response(serializer.data)
elif request.method == 'POST':
serializer = serializers.PoolSerializer(data=request.DATA)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pool_list(call=None):\n if call == \"action\":\n raise SaltCloudSystemExit(\n \"This function must be called with -f, --function argument.\"\n )\n ret = {}\n session = _get_session()\n pools = session.xenapi.pool.get_all()\n for pool in pools:\n pool_record = sess... | [
"0.70111126",
"0.6758204",
"0.6567126",
"0.65345985",
"0.645883",
"0.6388487",
"0.6305644",
"0.62771875",
"0.61912704",
"0.61358166",
"0.6108212",
"0.6096144",
"0.6054042",
"0.6049037",
"0.604506",
"0.6033518",
"0.602757",
"0.59318095",
"0.59190464",
"0.5901486",
"0.5883244",... | 0.64396614 | 5 |
Retrieve a known stored filter object from the db | def retrieve_filter(self, filter_id):
LOG.debug("Retrieve filter {}".format(filter_id))
filter_obj = self.filter_collection.find_one({"_id": ObjectId(filter_id)})
# use _id to preselect the currently loaded filter, and drop it while we are at it
filter_obj.update([("filters", filter_obj.pop("_id", None))])
return filter_obj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter():\n return get_filter_data(db, MyTable)",
"def get_instance(self, data):\n if self.transient:\n return None\n props = get_primary_keys(self.opts.model)\n filters = {prop.key: data.get(prop.key) for prop in props}\n if None not in filters.values():\n ... | [
"0.6960243",
"0.62703",
"0.62410784",
"0.61514443",
"0.6141893",
"0.61203706",
"0.60491437",
"0.60114807",
"0.585763",
"0.5800049",
"0.578962",
"0.5782984",
"0.5733101",
"0.5730981",
"0.571787",
"0.5717332",
"0.5716527",
"0.5708957",
"0.5699338",
"0.5699338",
"0.5699338",
"... | 0.6990458 | 0 |
Store away filter settings for later use. | def stash_filter(
self, filter_obj, institute_obj, case_obj, user_obj, category="snv", link=None
):
LOG.info(
"Stashing filter for user '%s' and institute %s.",
user_obj.get("email"),
institute_obj.get("display_name"),
)
LOG.info("Filter object {}".format(filter_obj))
institute_id = institute_obj.get("_id")
filter_dict = {"institute_id": institute_id, "category": category}
# make up a default display name
filter_dict["display_name"] = (
institute_obj.get("display_name") + "-" + case_obj.get("display_name")
)
for (element, value) in filter_obj.lists():
if value == [""]:
continue
if element in ["save_filter", "filters", "csrf_token"]:
continue
if element == "filter_display_name":
# filter display_name if given
# will appear as the only element in an array
filter_dict["display_name"] = value[0]
continue
filter_dict[element] = value
result = self.filter_collection.insert_one(filter_dict)
filter_id = result.inserted_id
# log event
subject = institute_obj["display_name"]
# link e.g. to the variants view where filter was created
if link is None:
variants_target_from_category = {
"sv": "variants.sv_variants",
"cancer": "variants.cancer_variants",
"snv": "variants.variants",
}
target = variants_target_from_category.get(category)
case_name = case_obj.get("display_name")
# filter dict already contains institute_id=institute_id,
link = url_for(target, case_name=case_name, **filter_dict)
self.create_event(
institute=institute_obj,
case=case_obj,
user=user_obj,
link=link,
category="case",
verb="filter_stash",
subject=subject,
level="global",
)
return filter_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_filters(self, options):\n if options.keywords:\n self.filters[\"keywords\"] = string_to_list(options.keywords)\n if options.features:\n self.filters[\"features\"] = string_to_list(options.features)\n if options.authors:\n self.filters[\"authors\"] = st... | [
"0.702972",
"0.6695293",
"0.6549355",
"0.6472333",
"0.6450351",
"0.6393664",
"0.63646764",
"0.62200606",
"0.616542",
"0.61460763",
"0.6126508",
"0.61021346",
"0.6078377",
"0.60579336",
"0.6051252",
"0.60247004",
"0.60106236",
"0.60073197",
"0.59548503",
"0.59348184",
"0.59262... | 0.0 | -1 |
Obtain a cursor for all filters available to an institute in a category. | def filters(self, institute_id, category="snv"):
filters_res = self.filter_collection.find(
{"institute_id": institute_id, "category": category}
)
return filters_res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select(self, eng_category):\r\n sql_select_query = \"SELECT Name, URL, Ingredients FROM \"+ str(eng_category)\r\n self.mycursor.execute(sql_select_query)\r\n records = self.mycursor.fetchall()\r\n \r\n return records",
"def filter():\n return get_filter_data(db, MyTable)... | [
"0.5364717",
"0.53382677",
"0.5266764",
"0.5199028",
"0.51203734",
"0.51203734",
"0.5113741",
"0.508854",
"0.5086552",
"0.5083298",
"0.50145316",
"0.49857625",
"0.4980039",
"0.49589002",
"0.49563545",
"0.49544093",
"0.49255437",
"0.48955005",
"0.48900995",
"0.48809904",
"0.48... | 0.6988995 | 0 |
Synchronize IDA's cursor with GDB | def j(*args):
try:
pc = int(gdb.selected_frame().pc())
pwndbg.ida.Jump(pc)
except Exception:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_pdb():\r\n import ctypes\r\n ctypes.windll.kernel32.AllocConsole()\r\n import sys\r\n sys.stdout = open('CONOUT$', 'wt')\r\n sys.stdin = open('CONIN$', 'rt')\r\n import pdb\r\n pdb.set_trace()",
"def gdb_breakpoint():\n _gdb_python_call_gen('gdb_breakpoint')()",
"def set_trace... | [
"0.618987",
"0.61721236",
"0.5961415",
"0.5940629",
"0.5920072",
"0.59150904",
"0.58346677",
"0.5826086",
"0.58243257",
"0.5765665",
"0.56860536",
"0.56628925",
"0.56359243",
"0.5516654",
"0.5512976",
"0.55124795",
"0.5501486",
"0.5466048",
"0.5450095",
"0.5408604",
"0.539628... | 0.48803896 | 61 |
Select and print stack frame that called this one. An argument says how many frames up to go. | def up(n=1):
f = gdb.selected_frame()
for i in range(n):
o = f.older()
if o:
o.select()
bt = pwndbg.commands.context.context_backtrace(with_banner=False)
print('\n'.join(bt))
j() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_frame(self, arg):\n if not arg:\n # Just display the frame, without handling sticky.\n self.print_stack_entry(self.stack[self.curindex])\n return\n\n try:\n arg = int(arg)\n except (ValueError, TypeError):\n print(\n ... | [
"0.73913187",
"0.68238056",
"0.6653886",
"0.65766746",
"0.6376097",
"0.63759094",
"0.6302292",
"0.627555",
"0.625705",
"0.6184294",
"0.6113973",
"0.60998887",
"0.6060529",
"0.60272187",
"0.5915152",
"0.5864522",
"0.5844503",
"0.57457596",
"0.5740299",
"0.5735211",
"0.5721572"... | 0.668176 | 2 |
Select and print stack frame called by this one. An argument says how many frames down to go. | def down(n=1):
f = gdb.selected_frame()
for i in range(n):
o = f.newer()
if o:
o.select()
bt = pwndbg.commands.context.context_backtrace(with_banner=False)
print('\n'.join(bt))
j() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_frame(self, arg):\n if not arg:\n # Just display the frame, without handling sticky.\n self.print_stack_entry(self.stack[self.curindex])\n return\n\n try:\n arg = int(arg)\n except (ValueError, TypeError):\n print(\n ... | [
"0.74253505",
"0.6774561",
"0.6765047",
"0.6575413",
"0.65482455",
"0.6473951",
"0.630916",
"0.6290664",
"0.6155866",
"0.6132151",
"0.6068907",
"0.60600847",
"0.60045964",
"0.5957227",
"0.5937258",
"0.59002703",
"0.5870945",
"0.58447737",
"0.58314973",
"0.5798143",
"0.5762033... | 0.6162764 | 8 |
Copy `in_tree` to `out_tree`, checking selection(in_tree) for each event. | def tree_copy_selection(in_tree, out_tree, selection):
for entry in in_tree:
if selection(entry):
out_tree.Fill() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_tree_checker(src, dst):\n copy_tree(src, dst)\n return True",
"def execute(self, context):\n\n # go to subtree, select all except input and output groups and mark nodes to be copied\n group_node = context.active_node\n sub_tree = group_node.group_tree\n\n ... | [
"0.62684405",
"0.5746463",
"0.5593152",
"0.5558629",
"0.5449127",
"0.5425309",
"0.53680193",
"0.5225137",
"0.51527005",
"0.5107789",
"0.5051202",
"0.504769",
"0.5014795",
"0.50122386",
"0.49738657",
"0.49728838",
"0.49718148",
"0.496103",
"0.49524227",
"0.4934227",
"0.4934111... | 0.78454643 | 0 |
Copy `in` to `out` for events where event.`key` does not exist in `keys` `keys` is the set of keys seen so far. | def tree_copy_duplicate_removal(in_tree, out_tree, key, keys):
for entry in in_tree:
key_value = getattr(entry, key)
if not key_value in keys:
out_tree.Fill()
keys.add(key_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_missing_values(events):\n ret = deepcopy(events)\n srchd, key_events = [], []\n for evt in events:\n _tmp = [(j, e) for j, e in enumerate(events) if e['key']\n == evt['key'] and not e['key'] in srchd]\n if _tmp != []:\n key_events.append(_tmp)\n sr... | [
"0.623754",
"0.6082424",
"0.5918844",
"0.59167224",
"0.56955945",
"0.5677415",
"0.5673033",
"0.5609741",
"0.55079687",
"0.55027145",
"0.5486954",
"0.5394884",
"0.53932744",
"0.5390138",
"0.53668404",
"0.53537357",
"0.533538",
"0.52792567",
"0.5232413",
"0.5232282",
"0.5213723... | 0.663589 | 0 |
Returns a list of all the keys in a directory, only taking the most recent TKey.GetCycle() per TKey.GetName(). | def most_recent_cycle_keys(directory):
most_recent = {}
# Generate a list of (name, (cyclenum, keyobject)) values
all_keys = list(directory.GetListOfKeys())
keys = ((k.GetName(), (k.GetCycle(), k)) for k in all_keys)
for name, cyclekey in keys:
most_recent[name] = (max(cyclekey, most_recent[name])
if name in most_recent else cyclekey)
# Just the key objects, sorted in name order.
recent_keys = [most_recent[name][-1] for name in sorted(most_recent.keys())]
# Return all_keys so that we don't lose anything
return all_keys, recent_keys | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _keys(self):\n for name in listdir(abspath(self._path)):\n key, ext = splitext(name)\n if ext == \".pkl\":\n yield key",
"def list(self):\n entries = []\n regex = re.compile('^%s' % self.directory)\n\n for obj in self.driver.list(self.directory... | [
"0.701094",
"0.67127836",
"0.6352646",
"0.6343178",
"0.63064986",
"0.6274958",
"0.6232864",
"0.6204033",
"0.6202406",
"0.6179034",
"0.61675316",
"0.6141725",
"0.61349463",
"0.6060003",
"0.60563046",
"0.603784",
"0.6035911",
"0.6027133",
"0.59946287",
"0.5978249",
"0.59718513"... | 0.6699988 | 2 |
Convert the numpy array representing the GOL grid to a QImage. | def numpy_to_qimage(np_array: np.ndarray, show_age: bool):
# Only support 2D array of bytes
assert len(np_array.shape) == 2 and np_array.dtype == np.uint8
width = np_array.shape[1]
height = np_array.shape[0]
bytes_per_line = width
image = QImage(np_array, width, height, bytes_per_line, QImage.Format_Indexed8)
# Maps array values to color
if show_age:
image.setColorTable(colors.AGE_COLOR_TABLE)
else:
image.setColorTable(colors.BINARY_COLOR_TABLE)
return image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convertNumpy2Image(self, array):\n cv2image = cv2.cvtColor(array, cv2.COLOR_BGR2RGBA)\n img = Image.fromarray(cv2image)\n imgtk = ImageTk.PhotoImage(image=img)\n return imgtk",
"def make_image(self, save=False):\n\n # image_grid = np.full((self.size_x, self.size_y), '#88888... | [
"0.6397902",
"0.6196134",
"0.60192525",
"0.59758794",
"0.5931983",
"0.5765702",
"0.5721111",
"0.5687924",
"0.56588805",
"0.5628664",
"0.55947673",
"0.55882084",
"0.5583067",
"0.55810326",
"0.55731523",
"0.5548744",
"0.5506583",
"0.54962564",
"0.54832053",
"0.54686344",
"0.546... | 0.68053734 | 0 |
my_map list of lists specifying obstacle positions path [(x1, y1), (x2, y2), ...] list of the steps take by the agent goals [(x1, y1), (x2, y2), ...] list of goal locations | def __init__(self, my_map, paths, starts, goals, agent_goals, predictions):
self.my_map = my_map
self.paths = paths
self.starts = starts
self.goals = goals
self.agent_goals = agent_goals
self.num_of_agents = len(starts)
self.predictions = predictions
self.CPU_time = 0
# compute heuristics for the low-level search
self.heuristics = []
for goal in self.goals:
self.heuristics.append(compute_heuristics(my_map, goal)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_goal(self):\r\n\t\t# Creates a flat list of correct values\r\n\t\ttempList = [x for x in range(self.n**2)]\r\n\r\n\t\t# Nests those lists into a NxN\r\n\t\tBoardClass.goal = [tempList[self.n*i:self.n*(i+1)] for i in range(self.n)]\r\n\r\n\t\t# Creates a dictionary for the intended location of any spec... | [
"0.6306571",
"0.62811714",
"0.62330747",
"0.6082783",
"0.6031487",
"0.59971946",
"0.59853303",
"0.59514654",
"0.5945449",
"0.58769524",
"0.5866137",
"0.5830052",
"0.57840186",
"0.5759361",
"0.57322335",
"0.5720972",
"0.57009214",
"0.56796384",
"0.56514007",
"0.5645225",
"0.56... | 0.5904296 | 9 |
Prepare paths specified as config. The input is a list of either strings, or 2tuples (source, target). Where single strings are supplied, the basenames are used as targets. Where targets are given explicitly, they must not be absolute paths. Returns a list of 2tuples, or throws ConfigError if something is wrong in the input. | def process_path_specs(specs):
processedSpecs = []
for spec in specs:
if not isinstance(spec, (list, tuple)):
source = spec
target = None
elif len(spec) != 2:
raise ConfigError("path spec must be a list or tuple of "
"length two")
else:
source, target = spec
source = os.path.normpath(source)
if not target:
target = os.path.basename(source)
elif os.path.isabs(target):
raise ConfigError("target path for include file may not be "
"an absolute path")
processedSpecs.append((source, target))
return processedSpecs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_paths(src, dst, paths, *, exclude=None):\n files = []\n\n for path in paths:\n if isinstance(path, tuple):\n files += copy_path(src, dst, path[0], path[1], exclude=exclude)\n else:\n files += copy_path(src, dst, path, exclude=exclude)\n\n return files",
"def ... | [
"0.5550079",
"0.5392351",
"0.5171191",
"0.5132501",
"0.5108284",
"0.5076208",
"0.5054015",
"0.5015645",
"0.4951827",
"0.4932448",
"0.49286622",
"0.49249643",
"0.48797044",
"0.48406097",
"0.48093775",
"0.4735485",
"0.47257975",
"0.47036657",
"0.47026363",
"0.46804345",
"0.4679... | 0.6430958 | 0 |
Return the path to a resource file shipped with cx_Freeze. This is used to find our base executables and initscripts when they are just specified by name. | def get_resource_file_path(dirName, name, ext):
if os.path.isabs(name):
return name
name = os.path.normcase(name)
fullDir = os.path.join(os.path.dirname(cx_Freeze.__file__), dirName)
if os.path.isdir(fullDir):
for fileName in os.listdir(fullDir):
checkName, checkExt = \
os.path.splitext(os.path.normcase(fileName))
if name == checkName and ext == checkExt:
return os.path.join(fullDir, fileName) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resource_filename(name):\n return pkg_resources.resource_filename(__name__, name)",
"def resourcePath(relative):\r\n try:\r\n # PyInstaller creates a temp folder and stores path in _MEIPASS\r\n base_path = sys._MEIPASS\r\n except Exception:\r\n base_path = os.path.abspath(os.pat... | [
"0.7262757",
"0.72428066",
"0.7147131",
"0.7135437",
"0.70946133",
"0.70946133",
"0.70946133",
"0.70946133",
"0.70946133",
"0.70946133",
"0.70946133",
"0.7087055",
"0.7067677",
"0.70555377",
"0.705534",
"0.705534",
"0.705534",
"0.705534",
"0.705534",
"0.705534",
"0.705534",
... | 0.69520795 | 40 |
Return the file names of libraries that need not be included because they would normally be expected to be found on the target system or because they are part of a package which requires independent installation anyway. | def _GetDefaultBinExcludes(self):
if sys.platform == "win32":
return ["comctl32.dll", "oci.dll", "cx_Logging.pyd"]
else:
return ["libclntsh.so", "libwtc9.so"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def library_directories(self):\n\n status, stdout, stderr = self.__xcall__(['--libs-only-L'])\n\n if status != 0:\n raise RuntimeError(\"error querying --libs-only-L for package `%s': %s\" % (self.name, stderr))\n\n retval = []\n for token in stdout.split():\n retval.append(token[2:])\n\n ... | [
"0.7460289",
"0.73898304",
"0.71631336",
"0.7133726",
"0.70093113",
"0.68778294",
"0.67150843",
"0.6555115",
"0.6427538",
"0.6418846",
"0.6387556",
"0.6331555",
"0.62914765",
"0.6284729",
"0.62831557",
"0.6266173",
"0.622727",
"0.616253",
"0.6143471",
"0.6061587",
"0.6038263"... | 0.6424925 | 9 |
Return the file names of libraries which must be included for the frozen executable to work. | def _GetDefaultBinIncludes(self):
if sys.platform == "win32":
pythonDll = "python%s%s.dll" % sys.version_info[:2]
return [pythonDll, "gdiplus.dll", "mfc71.dll", "msvcp71.dll",
"msvcr71.dll"]
else:
soName = distutils.sysconfig.get_config_var("INSTSONAME")
if soName is None:
return []
pythonSharedLib = self._RemoveVersionNumbers(soName)
return [pythonSharedLib] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def libraries(self):\n\n status, stdout, stderr = self.__xcall__(['--libs-only-l'])\n\n if status != 0:\n raise RuntimeError(\"error querying --libs-only-l for package `%s': %s\" % (self.name, stderr))\n\n retval = []\n for token in stdout.split():\n retval.append(token[2:])\n\n return uni... | [
"0.6991022",
"0.6678766",
"0.6654032",
"0.66060007",
"0.6492413",
"0.6385442",
"0.6274169",
"0.62589043",
"0.62560844",
"0.62152314",
"0.6175629",
"0.61445343",
"0.61295563",
"0.61234623",
"0.61047596",
"0.6061235",
"0.6051311",
"0.6039806",
"0.6035648",
"0.603107",
"0.601471... | 0.6333758 | 6 |
Return the paths of directories which contain files that should not be included, generally because they contain standard system libraries. | def _GetDefaultBinPathExcludes(self):
if sys.platform == "win32":
import cx_Freeze.util
systemDir = cx_Freeze.util.GetSystemDir()
windowsDir = cx_Freeze.util.GetWindowsDir()
return [windowsDir, systemDir, os.path.join(windowsDir, "WinSxS")]
elif sys.platform == "darwin":
return ["/lib", "/usr/lib", "/System/Library/Frameworks"]
else:
return ["/lib", "/lib32", "/lib64", "/usr/lib", "/usr/lib32",
"/usr/lib64"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeduppaths():\r\n # This ensures that the initial path provided by the interpreter contains\r\n # only absolute pathnames, even if we're running from the build directory.\r\n L = []\r\n known_paths = set()\r\n for dir in sys.path:\r\n # Filter out duplicate paths (on case-insensitive ... | [
"0.68841964",
"0.66146666",
"0.6608054",
"0.6544238",
"0.6524269",
"0.65076166",
"0.64406294",
"0.64142877",
"0.63882494",
"0.6343896",
"0.6316815",
"0.6312666",
"0.6243478",
"0.6181976",
"0.6178829",
"0.6160177",
"0.615914",
"0.61474144",
"0.61427253",
"0.61281586",
"0.61181... | 0.7017395 | 0 |
Return the file's dependencies using platformspecific tools (the imagehlp library on Windows, otool on Mac OS X and ldd on Linux); limit this list by the exclusion lists as needed | def _GetDependentFiles(self, path):
dependentFiles = self.dependentFiles.get(path)
if dependentFiles is None:
if sys.platform == "win32":
origPath = os.environ["PATH"]
os.environ["PATH"] = origPath + os.pathsep + \
os.pathsep.join(sys.path)
import cx_Freeze.util
try:
dependentFiles = cx_Freeze.util.GetDependentFiles(path)
except cx_Freeze.util.BindError:
# Sometimes this gets called when path is not actually a library
# See issue 88
dependentFiles = []
os.environ["PATH"] = origPath
else:
dependentFiles = []
if sys.platform == "darwin":
command = 'otool -L "%s"' % path
splitString = " (compatibility"
dependentFileIndex = 0
else:
command = 'ldd "%s"' % path
splitString = " => "
dependentFileIndex = 1
for line in os.popen(command):
parts = line.expandtabs().strip().split(splitString)
if len(parts) != 2:
continue
dependentFile = parts[dependentFileIndex].strip()
if dependentFile == os.path.basename(path):
continue
if dependentFile in ("not found", "(file not found)"):
fileName = parts[0]
if fileName not in self.linkerWarnings:
self.linkerWarnings[fileName] = None
message = "WARNING: cannot find %s\n" % fileName
sys.stdout.write(message)
continue
if dependentFile.startswith("("):
continue
pos = dependentFile.find(" (")
if pos >= 0:
dependentFile = dependentFile[:pos].strip()
if dependentFile:
dependentFiles.append(dependentFile)
if sys.platform == "darwin":
# Make library paths absolute. This is needed to use
# cx_Freeze on OSX in e.g. a conda-based distribution.
# Note that with @rpath we just assume Python's lib dir,
# which should work in most cases.
dirname = os.path.dirname(path)
dependentFiles = [p.replace('@loader_path', dirname)
for p in dependentFiles]
dependentFiles = [p.replace('@rpath', sys.prefix + '/lib')
for p in dependentFiles]
dependentFiles = self.dependentFiles[path] = \
[f for f in dependentFiles if self._ShouldCopyFile(f)]
return dependentFiles | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetFilesForTool(self):\n return ['tools/valgrind/android/vg-chrome-wrapper.sh',\n 'tools/valgrind/memcheck/suppressions.txt',\n 'tools/valgrind/memcheck/suppressions_android.txt']",
"def GetFilesForTool(self):\n return ['tools/valgrind/android/vg-chrome-wrapper-tsan.sh',\n ... | [
"0.6188307",
"0.60172987",
"0.594588",
"0.58179677",
"0.57686013",
"0.57123476",
"0.56695175",
"0.5656404",
"0.56266695",
"0.56120116",
"0.5609849",
"0.5595345",
"0.5572678",
"0.55723906",
"0.5521513",
"0.55099297",
"0.5491542",
"0.5476667",
"0.5474204",
"0.54685295",
"0.5443... | 0.5495714 | 16 |
Return true if the file should be copied to the target machine. This is done by checking the binPathIncludes, binPathExcludes, binIncludes and binExcludes configuration variables using first the full file name, then just the base file name, then the file name without any version numbers. Files are included unless specifically excluded but inclusions take precedence over exclusions. | def _ShouldCopyFile(self, path):
# check for C runtime, if desired
path = os.path.normcase(path)
dirName, fileName = os.path.split(path)
if fileName.startswith("msvcr") and fileName.endswith(".dll"):
self.msvcRuntimeDir = dirName
return self.includeMSVCR
# check the full path
if path in self.binIncludes:
return True
if path in self.binExcludes:
return False
# check the file name by itself (with any included version numbers)
if fileName in self.binIncludes:
return True
if fileName in self.binExcludes:
return False
# check the file name by itself (version numbers removed)
name = self._RemoveVersionNumbers(fileName)
if name in self.binIncludes:
return True
if name in self.binExcludes:
return False
# check the path for inclusion/exclusion
for path in self.binPathIncludes:
if dirName.startswith(path):
return True
for path in self.binPathExcludes:
if dirName.startswith(path):
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conditional_copy(asciitest_out_dir, doc_file):\n # path join uses backslash win32 which is not cmake compatible\n\n filename = save_cmake_filename(doc_file)\n\n filename1 = os.path.join(asciitest_out_dir, filename + \".temp\").replace(\"\\\\\",\"/\")\n filename2 = os.path.join(asciitest_out_dir, fi... | [
"0.5926794",
"0.576537",
"0.5734732",
"0.563512",
"0.55802953",
"0.5525144",
"0.5376832",
"0.5344218",
"0.5341193",
"0.5339224",
"0.5329194",
"0.53072923",
"0.5279975",
"0.5243441",
"0.5227413",
"0.5205067",
"0.51915103",
"0.51590645",
"0.5157132",
"0.51411957",
"0.512988",
... | 0.80116713 | 0 |
Create the module which consists of declaration statements for each of the values. | def Create(self, finder):
today = datetime.datetime.today()
sourceTimestamp = 0
for module in finder.modules:
if module.file is None:
continue
if module.inZipFile:
continue
if not os.path.exists(module.file):
raise ConfigError("no file named %s (for module %s)",
module.file, module.name)
timestamp = os.stat(module.file).st_mtime
sourceTimestamp = max(sourceTimestamp, timestamp)
sourceTimestamp = datetime.datetime.fromtimestamp(sourceTimestamp)
self.values["BUILD_TIMESTAMP"] = today.strftime(self.timeFormat)
self.values["BUILD_HOST"] = socket.gethostname().split(".")[0]
self.values["SOURCE_TIMESTAMP"] = \
sourceTimestamp.strftime(self.timeFormat)
module = finder._AddModule(self.moduleName)
sourceParts = []
names = list(self.values.keys())
names.sort()
for name in names:
value = self.values[name]
sourceParts.append("%s = %r" % (name, value))
source = "\n".join(sourceParts)
module.code = compile(source, "%s.py" % self.moduleName, "exec")
return module | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_module(self, body: list, **kwargs):\n return ast.Module(body=body)",
"def genH(self,fp):\n id = 0\n for nm in GetOsekObjects('NM'):\n if(self == nm):\n break\n else:\n id += 1\n fp.write('\\n#define %s %s\\n'%(self.name,id... | [
"0.5886545",
"0.58752614",
"0.585057",
"0.57920617",
"0.5724532",
"0.5722173",
"0.56763613",
"0.5671061",
"0.5638687",
"0.55701363",
"0.556386",
"0.55578065",
"0.5548401",
"0.553911",
"0.5494058",
"0.54860514",
"0.54860514",
"0.54849756",
"0.5464117",
"0.5413439",
"0.54076755... | 0.48852018 | 95 |
Test that a dotted path is properly converted to a file address. | def test_get_file_path(self):
path = self.corpus.get_file_path('chatterbot.corpus.english')
self.assertIn(
os.path.join('chatterbot_corpus', 'data', 'english'),
path
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_valid_pathname(self):\n self.assertTrue(Util.is_pathname_valid('./myrandomvalidfilename.dat'))\n self.assertTrue(Util.is_pathname_valid('myrandomvalidfilename.dat'))",
"def test_expand_path_3(self):\n partial_path = \"/fake/path\"\n input_path = \".\" + partial_path\n ... | [
"0.64686894",
"0.63700265",
"0.63254786",
"0.6183657",
"0.6174094",
"0.6157679",
"0.6133528",
"0.60470575",
"0.6045977",
"0.6009044",
"0.5981257",
"0.5977913",
"0.5938566",
"0.5925104",
"0.5923361",
"0.5874661",
"0.5874289",
"0.5859754",
"0.58453584",
"0.5831364",
"0.58010995... | 0.0 | -1 |
Read and convert to example, returns None if no data is available. | def read_and_convert(self, result_file):
if self._example_pointer == self._num_examples:
return None
path_to_image_file = self._path_to_image_files[self._example_pointer]
# Get image index
index = int(path_to_image_file.split('/')[-1].split('.')[0])
self._example_pointer += 1
label_of_digits = result_file[index].strip().split(' ')
# for digits: 10 represents no digit, for letters: 0 represents no letter
digits = [10, 10, 10, 10]
letters = [0, 0, 0, 0, 0]
idd = 0
idl = 0
for i in range(len(label_of_digits)):
if i in [0, 4, 5, 6]:
digits[idd] = int(label_of_digits[i]) # label 10 is essentially digit zero
idd += 1
if i in [1, 2, 3, 7, 8]:
letters[idl] = int(label_of_digits[i])
idl += 1
image = Image.open(path_to_image_file)
image = image.resize([96, 24])
image = np.array(image).tobytes()
example = tf.train.Example(features=tf.train.Features(feature={
'image': ExampleReader._bytes_feature(image),
'digits': tf.train.Feature(int64_list=tf.train.Int64List(value=digits)),
'letters': tf.train.Feature(int64_list=tf.train.Int64List(value=letters))
}))
return example | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def example_to_data(self, example):\n raise NotImplementedError",
"def _read_one_example(\n top_example_dir_name, full_storm_id_string, storm_time_unix_sec,\n source_name, radar_field_name, include_sounding):\n\n if source_name == radar_utils.GRIDRAD_SOURCE_ID:\n num_radar_rows = N... | [
"0.7003179",
"0.6128065",
"0.59333533",
"0.562596",
"0.560513",
"0.559558",
"0.5577824",
"0.5567824",
"0.55473655",
"0.55461675",
"0.5537794",
"0.55172545",
"0.55114156",
"0.55060756",
"0.5497584",
"0.5480115",
"0.5461704",
"0.54357123",
"0.5398757",
"0.53854465",
"0.5373124"... | 0.6061454 | 2 |
Print the list of toppings that have been requested. | def make_pizza(*toppings):
print(toppings) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toppings(request, pizza_id):\r\n pizza = Pizza.objects.get(id=pizza_id)\r\n toppings = pizza.topping_set.order_by('name')\r\n context = {'pizza': pizza, 'toppings': toppings}\r\n return render(request, 'pizzas/toppings.html', context)",
"def printResults(self):\n for tweet in self.tweets:\... | [
"0.6459545",
"0.6434612",
"0.64327085",
"0.6416531",
"0.61851096",
"0.61739594",
"0.60970765",
"0.6081574",
"0.60734904",
"0.60210615",
"0.60160345",
"0.5975071",
"0.59174913",
"0.5913153",
"0.5871116",
"0.58542",
"0.58345544",
"0.58035886",
"0.57819474",
"0.5701928",
"0.5669... | 0.5833372 | 18 |
Summarize the pizza we are about to make. | def make_pizza(*toppings):
print("\nMaking a pizza with the following toppings:")
for topping in toppings:
print(f"- {topping}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_summarize_recipe(self):\n pass",
"def summarize_food_data(unprocessed_food_list: List[str]) -> List[Dict[str, str]]:\n summary: List[Dict[str, str]] = []\n item_count_data: Dict[str, int] = {}\n\n for item in unprocessed_food_list:\n if item not in item_count_data:\n it... | [
"0.600868",
"0.6002266",
"0.593303",
"0.59114504",
"0.5896875",
"0.5879344",
"0.58790183",
"0.5830151",
"0.58242345",
"0.58242345",
"0.58150595",
"0.5803258",
"0.57938284",
"0.57802445",
"0.57720184",
"0.57685405",
"0.57685405",
"0.5758228",
"0.5754923",
"0.57318443",
"0.5714... | 0.59282035 | 3 |
Summarize the pizza we are about to make. | def make_pizza(size, *toppings):
print(f"\nMaking {size}-inch pizza with the following toppings:")
for topping in toppings:
print(f"- {topping}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_summarize_recipe(self):\n pass",
"def summarize_food_data(unprocessed_food_list: List[str]) -> List[Dict[str, str]]:\n summary: List[Dict[str, str]] = []\n item_count_data: Dict[str, int] = {}\n\n for item in unprocessed_food_list:\n if item not in item_count_data:\n it... | [
"0.600868",
"0.6002266",
"0.59282035",
"0.59114504",
"0.5896875",
"0.5879344",
"0.58790183",
"0.5830151",
"0.58242345",
"0.58242345",
"0.58150595",
"0.5803258",
"0.57938284",
"0.57802445",
"0.57720184",
"0.57685405",
"0.57685405",
"0.5758228",
"0.5754923",
"0.57318443",
"0.57... | 0.593303 | 2 |
Takes a user and a group name, and returns `True` if the user is in that group. | def is_in_group(user, group_name):
return is_in_group_user_id(user.id, group_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_user_in_group(user, group):\n users = group.get_users()\n if user in users:\n return True\n return False",
"def is_in_group(user, group_name):\n return user.groups.filter(name__exact=group_name).exists()",
"def is_in_group(user, group_name):\n return Group.objects.get(name=group_na... | [
"0.90404195",
"0.89909446",
"0.8921993",
"0.88956964",
"0.8691311",
"0.8651488",
"0.85226196",
"0.8471016",
"0.8424121",
"0.83981097",
"0.83976525",
"0.8325658",
"0.8266118",
"0.8199991",
"0.81931895",
"0.7539715",
"0.7505139",
"0.7332905",
"0.726839",
"0.7217727",
"0.7149777... | 0.90948594 | 0 |
Takes a user id and a group name, and returns `True` if the user is in that group. | def is_in_group_user_id(user_id, group_name):
try:
return Group.objects.get(name=group_name).user_set.filter(id=user_id).exists()
except Group.DoesNotExist:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_in_group(user, group_name):\n return is_in_group_user_id(user.id, group_name)",
"def is_in_group(user, group_name):\n return Group.objects.get(name=group_name).user_set.filter(id=user.id).exists()",
"def is_user_in_group(user, group):\n users = group.get_users()\n if user in users:\n ... | [
"0.9006803",
"0.8860701",
"0.8772584",
"0.87357795",
"0.85706574",
"0.85022044",
"0.8340125",
"0.8216126",
"0.8146766",
"0.810608",
"0.8100358",
"0.803607",
"0.7980639",
"0.79746544",
"0.7930329",
"0.7888287",
"0.74050176",
"0.7149647",
"0.71302086",
"0.70612216",
"0.70261055... | 0.8786698 | 2 |
Perform single crossovers on a given population. | def __init__(self, population_fraction, max_crossover_probability,
**kwargs):
if not (0 < population_fraction <= 1.0):
raise ValueError("num_crossover_fraction must be in ]0. 1]")
for val in max_crossover_probability:
if not (0 < val <= 1.0):
raise ValueError(
"max_crossover_probability values must be between ]0. 1]")
self.population_fraction = population_fraction
self.max_crossover_probability = max_crossover_probability
super(SingleCrossover, self).__init__(**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def crossover(self):\n self.sort_population()\n elite_amount = round(self.elite_rate * self.population_size)\n # preserve from the top\n new_population = [ele for ele in self.population if ele.ttl > 0]\n for individual in new_population:\n if individual.ttl > 0:\n ... | [
"0.736195",
"0.72073233",
"0.7123464",
"0.705394",
"0.6893509",
"0.6868745",
"0.6859749",
"0.6791421",
"0.67872",
"0.66507554",
"0.6649494",
"0.66126937",
"0.6332303",
"0.63217306",
"0.6302329",
"0.6289073",
"0.62873167",
"0.62827295",
"0.6282539",
"0.624641",
"0.6236538",
... | 0.55216956 | 55 |
'If you create a Lambda function that processes events from streambased services (Amazon Kinesis Streams), the number of shards per stream is the unit of concurrency. If your stream has 100 active shards, there will be 100 Lambda functions running concurrently. Then, each Lambda function processes events on a shard in the order that they arrive.' Therefore, for checkpointing logic, we should make the primary | def handler(event, context):
debug = False
rewind = False
dry_run = False
table = _ensure_dynamo_table()
consumer_id = 'test-consumer'
if debug:
state = table.scan()
print "Active leases in Dynamo:", state["Count"]
for item in state["Items"]:
print json.dumps(item, indent=4, sort_keys=True)
lease = None
shard = None
try:
visitors = set()
last_timestamp = None
for i, record in enumerate(event.get('Records', [])):
event_id, data = (record['eventID'], record['kinesis']['data'])
shard, checkpoint = event_id.split(u':')
if rewind:
print "Rewinding to checkpoint 0"
_clear_consumer_lease(table, consumer_id, shard)
rewind = False
if lease is None:
lease = _get_consumer_lease(table, consumer_id, shard) \
or {"checkpoint": "0"}
if checkpoint <= lease["checkpoint"]:
# replayed event, we should skip it
print "Replayed event; skipping"
continue
# => decode from b64
raw_event = base64.b64decode(data)
# => parse from JSON
json_event = json.loads(raw_event)
# => extract out visitor id and timestamp if present
visitor = json_event.get("visitor_site_id", "N/A")
visitors.add(visitor)
last_timestamp = json_event.get("ts_action", "N/A")
# => do something with the data
result = process(json_event)
if result:
pass
# => checkpoint the shard
lease["checkpoint"] = checkpoint
logger.info("Saw {} unique visitors in batch ending with {}".format(
len(visitors), last_timestamp))
if not dry_run:
_put_consumer_lease(table, consumer_id, shard, lease)
except Exception as ex:
# do not save consumer checkpoints because error happened
# instead, we should probably log something about the error
# in the consumer lease, to allow the Lambda to retry a fixed
# number of times, before finally "giving up" and skipping
# the records
raise
"^ some form of error handling required"
if ex:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lambda_handler(event, context):\n\n mytime, lambda_name, env_vars = lambda_init.init_lambda(context)\n stage = env_vars[\"stage\"]\n consumer_master_past_lambda = env_vars[\"consumer_master_past_name\"]\n\n apps, test_params = init_apps_from_test_params(event)\n filters = init_filters()\n\n s... | [
"0.6036465",
"0.5968453",
"0.5582644",
"0.54831797",
"0.5460034",
"0.54361176",
"0.5364517",
"0.53256035",
"0.52649206",
"0.5256149",
"0.5254377",
"0.5188891",
"0.5165099",
"0.5165099",
"0.5149605",
"0.51353747",
"0.51282066",
"0.51216805",
"0.5100538",
"0.50952226",
"0.50934... | 0.61084247 | 0 |
Create a Render object. | def __init__(self, *args, **kwargs):
super(ShotRenderCrawler, self).__init__(*args, **kwargs)
parts = self.var("name").split("_")
locationParts = parts[0].split("-")
# Add the job var once job names on disk match job code names in shotgun
self.setVar('seq', locationParts[1], True)
self.setVar('shot', parts[0], True)
self.setVar('step', parts[1], True)
self.setVar('pass', parts[2], True)
self.setVar('renderName', '{}-{}'.format(
self.var('step'),
self.var('pass')
),
True
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(self):\n raise NotImplementedError(\"Renderer is an abstract class\")",
"def make(self):\n\t\tif RENDER_VIEWS > 1:\n\t\t\tself._make()",
"def render(self):\n return self",
"def render(self):\n raise RenderNotImplemented('Render function is not implemented.')",
"def SyncRende... | [
"0.6394629",
"0.621124",
"0.61963266",
"0.6114999",
"0.5978222",
"0.5978222",
"0.5938133",
"0.593024",
"0.5887697",
"0.56696737",
"0.5659791",
"0.5651973",
"0.5642207",
"0.5623252",
"0.55697376",
"0.55697376",
"0.55697376",
"0.55697376",
"0.55697376",
"0.55697376",
"0.553905"... | 0.0 | -1 |
Test if the path holder contains a shot render. | def test(cls, pathHolder, parentCrawler):
if not super(ShotRenderCrawler, cls).test(pathHolder, parentCrawler):
return False
renderType = pathHolder.baseName().split(".")[0].split("_")[-1]
return renderType == "sr" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def point_is_shot(self, point: Point):\n return point in self.shot_locations",
"def test(cls, pathHolder, parentCrawler):\n if not super(TurntableCrawler, cls).test(pathHolder, parentCrawler):\n return False\n\n renderType = pathHolder.baseName().split(\".\")[0].split(\"_\")[-1]\n... | [
"0.66429377",
"0.6288864",
"0.6145428",
"0.6053248",
"0.6007993",
"0.5842656",
"0.5803181",
"0.5795196",
"0.57627773",
"0.5747576",
"0.56485415",
"0.56485415",
"0.5615542",
"0.55867237",
"0.5571235",
"0.55255395",
"0.55035883",
"0.54886705",
"0.5453938",
"0.545361",
"0.542327... | 0.7443225 | 0 |
Find links in jsoncompatible data. | def find_links(obj):
if isinstance(obj, dict):
for key, value in obj.iteritems():
for url in find_links(value):
yield url
elif isinstance(obj, list):
for item in obj:
for url in find_links(item):
yield url
else:
try:
if is_link(str(obj)):
yield obj
except Exception:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_urls(json_dict):\n url_list = []\n count = 0\n for i in json_dict[\"items\"]:\n if i[\"is_answered\"]:\n url_list.append(i[\"link\"])\n count += 1\n if count == 3 or count == len(i):\n break\n \n for i in url_list:\n wb.open(i)",
"def g... | [
"0.64437044",
"0.6328608",
"0.6288369",
"0.62582314",
"0.5989727",
"0.5896046",
"0.58893055",
"0.58317786",
"0.58317786",
"0.5802547",
"0.5788355",
"0.57476854",
"0.5663333",
"0.5635389",
"0.5627574",
"0.5625349",
"0.56044173",
"0.5590741",
"0.55799675",
"0.5561591",
"0.55544... | 0.66384387 | 0 |
Load the correct backend driver for data persistent. | def _load_driver(backend, **kargs):
bk_module = importlib.import_module('backend', __package__)
driver_cls = getattr(bk_module, str.capitalize(backend) + 'Backend')
return driver_cls(**kargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_backend(cls) -> IBackend:\n cls.Lock.acquire()\n try:\n return cls._load_backend()\n finally:\n cls.Lock.release()",
"def set_backend(self, backend):\n if backend not in AVAILABLE_BACKENDS:\n raise StorageError(f'Unrecognized backend {backend}... | [
"0.64767873",
"0.6454533",
"0.6352563",
"0.6303405",
"0.6164173",
"0.59922636",
"0.5975386",
"0.58862835",
"0.58696896",
"0.5819013",
"0.58041245",
"0.5792222",
"0.5769137",
"0.575706",
"0.5755538",
"0.5722598",
"0.5693296",
"0.5691711",
"0.5683557",
"0.5676043",
"0.5659401",... | 0.7439424 | 0 |
Get the table name to save data from the url. | def _get_table_name(url):
try:
return urlparse(url).path.strip('/').split('/')[1]
except IndexError:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tablename(self):\n _, tail = os.path.split(self.url)\n return tail[:-4]",
"def table_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"table_name\")",
"def table_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"table_name\")",
"def table_name(self) -> pu... | [
"0.72730315",
"0.6656085",
"0.6656085",
"0.6656085",
"0.65448517",
"0.64656055",
"0.6451091",
"0.6374937",
"0.6281148",
"0.6230515",
"0.61168766",
"0.60657483",
"0.6009189",
"0.5981553",
"0.5945678",
"0.5933731",
"0.5901814",
"0.5891688",
"0.5882904",
"0.58783686",
"0.5870253... | 0.7577692 | 0 |
Check if this url contains an item detail. | def item_url(url):
return all(map(lambda x: str.isdigit(x), str(url.strip('/').split('/')[-1]))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_details(self):\n return hasattr(self, 'details')",
"def is_view_price_list_detail_present(self):\n return self.is_element_present(self.view_price_list_detail_locator)",
"def is_item_complete(self, item):\n return (item.get('id') and\n item.get('name') and\n ... | [
"0.6802034",
"0.6584066",
"0.64352536",
"0.62934774",
"0.6240942",
"0.6176948",
"0.612433",
"0.61073184",
"0.59965223",
"0.59712005",
"0.5829178",
"0.5820094",
"0.58197546",
"0.58129585",
"0.5806847",
"0.5757746",
"0.57442445",
"0.57396597",
"0.572949",
"0.5726727",
"0.571683... | 0.0 | -1 |
Save data from response to backend persistent driver. Only save the detail item from a url, filter out the overall items like | def save(self, response):
url = response.url
if self.item_url(url):
table_name = self._get_table_name(url)
if table_name:
data = response.json()
self.backend.save(table_name, data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_detail(self, response):\n\n self.logger.log(self.log_lvl, 'scraping data @ {}'.format(response.url))\n\n item_list = list()\n image_urls = list()\n # extract image\n try:\n pattern = re.compile(r\"(.*imagearray:)(.*)(,.*displaymode.*)\", re.MULTILINE | re.DOT... | [
"0.5922734",
"0.5857825",
"0.5833559",
"0.5798279",
"0.5555864",
"0.55542606",
"0.5488991",
"0.5482613",
"0.54276925",
"0.5380093",
"0.53528607",
"0.5344608",
"0.53070605",
"0.5304953",
"0.5301043",
"0.5283759",
"0.52696395",
"0.5248865",
"0.52482927",
"0.5235908",
"0.5224081... | 0.690983 | 0 |
Tests basic whitelist functionality | def test_whitelist(self):
p = self.load_policy({
'name': 'test-key-vault',
'resource': 'azure.keyvault',
'filters': [
{'type': 'value',
'key': 'name',
'op': 'glob',
'value_type': 'normalize',
'value': 'cckeyvault1*'},
{'not': [
{'type': 'whitelist',
'key': 'principalName',
'users': ['account1@sample.com']}
]}
]
})
resources = p.run()
self.assertEqual(len(resources), 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_add_to_whitelist(self):\n\n self.feature_test.add_to_whitelist(3)\n self.assertTrue(3 in Feature(\"testing\").whitelist)",
"def test_add_to_whitelist_with_string(self):\n email = 'example@example.com'\n self.feature_test.add_to_whitelist(email)\n self.assertTrue(email ... | [
"0.7257275",
"0.69541496",
"0.6669898",
"0.6634256",
"0.63836783",
"0.61440814",
"0.607428",
"0.6058461",
"0.60226995",
"0.60136276",
"0.59704053",
"0.59617037",
"0.5960837",
"0.59211046",
"0.59204555",
"0.59204555",
"0.5909409",
"0.59081924",
"0.5890749",
"0.5881475",
"0.587... | 0.67256504 | 2 |
Tests that a keyvault with 0 access policies is processed properly and doesn't raise an exception. | def test_whitelist_zero_access_policies(self):
p = self.load_policy({
'name': 'test-key-vault',
'resource': 'azure.keyvault',
'filters': [
{'type': 'value',
'key': 'name',
'op': 'glob',
'value_type': 'normalize',
'value': 'cckeyvault2*'},
{'not': [
{'type': 'whitelist',
'key': 'principalName',
'users': ['account1@sample.com']}
]}
]
})
resources = p.run()
self.assertEqual(len(resources), 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_authz_file_empty_raises(self):\n self.env.config.set('authz_policy', 'authz_file', '')\n self.assertRaises(ConfigurationError, self.check_permission,\n 'WIKI_VIEW', 'änon', None, None)",
"def noaccess(self):\n self.assertEqual(self.client.get(self.ENDPOINT).... | [
"0.615517",
"0.6075056",
"0.6071155",
"0.6068709",
"0.59903747",
"0.5970702",
"0.59659946",
"0.5951002",
"0.5945882",
"0.5927157",
"0.59155506",
"0.5856525",
"0.58519167",
"0.5801739",
"0.5789592",
"0.5782919",
"0.5781346",
"0.577808",
"0.5775319",
"0.576997",
"0.57695985",
... | 0.7272632 | 0 |
Filters a list of elements. 'viewer' is the viewer that we are filtering elements for. 'parent' is the parent element. 'elements' is the list of elements to filter. Returns a list containing only those elements for which 'select' returns True. | def filter(self, viewer, parent, elements):
return [e for e in elements if self.select(viewer, parent, e)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select(self, viewer, parent, element):\n\n return True",
"def filter_by_reviewers(reviews, selected_reviewers):\n return [x for x in reviews if x.reviewer in selected_reviewers]",
"def validate(elements):\n return list(filter(lambda el: el.is_valid, elements))",
"def select(elements, val... | [
"0.53416806",
"0.5286004",
"0.516275",
"0.51596725",
"0.5059366",
"0.5055007",
"0.50266373",
"0.49372914",
"0.4907756",
"0.47648945",
"0.47609642",
"0.46878317",
"0.46518713",
"0.46268824",
"0.46157223",
"0.4608182",
"0.4597628",
"0.45550662",
"0.45305058",
"0.45274553",
"0.4... | 0.87938285 | 0 |
Returns True if the element is 'allowed' (ie. NOT filtered). 'viewer' is the viewer that we are filtering elements for. 'parent' is the parent element. 'element' is the element to select. By default we return True. | def select(self, viewer, parent, element):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_element_in_view(self, element: Element) -> bool:\n return self.find_element_view(element=element) is not None",
"def tag_visible(element):\n\n if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:\n return False\n if isinstance(element, Comment):\n ... | [
"0.59139556",
"0.57015985",
"0.5638367",
"0.54528904",
"0.5326502",
"0.52207935",
"0.5189183",
"0.51695454",
"0.5162016",
"0.51491517",
"0.50781363",
"0.49656478",
"0.49402714",
"0.49008185",
"0.48731193",
"0.48402408",
"0.4839491",
"0.48392522",
"0.48301572",
"0.48110285",
"... | 0.6605754 | 0 |
Is the filter affected by changes to an element's trait? 'element' is the element. 'trait_name' is the name of the trait. Returns True if the filter would be affected by changes to the trait named 'trait_name' on the specified element. By default we return False. | def is_filter_trait(self, element, trait_name):
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_element_in_view(self, element: Element) -> bool:\n return self.find_element_view(element=element) is not None",
"def has_visibility(trait, visibility_name):\n\n return trait.visibility == getattr(schema.Trait.Visibility, visibility_name)",
"def contains(self, element) -> bool:\n\n return ... | [
"0.5432807",
"0.5380321",
"0.5154928",
"0.50004315",
"0.49823684",
"0.4972386",
"0.49546346",
"0.49431932",
"0.4915069",
"0.4896147",
"0.48712415",
"0.48191318",
"0.48169592",
"0.47890756",
"0.4781882",
"0.47721133",
"0.47471988",
"0.47195056",
"0.47182444",
"0.46942788",
"0.... | 0.7937278 | 0 |
A BFS solution that runs in O(N) | def bfs(self, root: TreeNode) -> int:
if not root:
return 0
queue = deque([(root, 1)])
while queue:
node, level = queue.popleft()
if not node.left and not node.right:
return level
if node.left:
queue.append((node.left, level + 1))
if node.right:
queue.append((node.right, level + 1))
return -1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bfs(graph,start):\n #keeps track of nodes to be visited\n queue = []\n #keeps track of nodes already visited\n explored = []\n queue.append(start)\n while queue:\n #remove first node from queue\n curr_node = queue.pop(0)\n #check if node is visited\n if curr_node not in explored:\n explo... | [
"0.7630348",
"0.7485004",
"0.7482269",
"0.746009",
"0.73893636",
"0.7359191",
"0.7313912",
"0.72900593",
"0.7280733",
"0.7267169",
"0.72345245",
"0.72265714",
"0.71806186",
"0.71715397",
"0.7168454",
"0.7154696",
"0.713683",
"0.7130648",
"0.7121028",
"0.706812",
"0.706548",
... | 0.0 | -1 |
A DFS solution that runs in O(N) in time and space | def dfs(self, root: TreeNode) -> int:
if not root:
return 0
def dfs(node):
if not node:
return float('inf')
if not node.left and not node.right:
return 1
return min(dfs(node.left), dfs(node.right)) + 1
return dfs(root) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iterativeDeepeningSearch(problem):\n from util import Stack\n \n for max_depth in range(0, 10000000):\n # print max_depth\n st = Stack()\n mapper = {}\n mapper[(problem.getStartState(), 0)] = None #map of (childpos, depth): (parentpos, direction, depth)\n st.push((pr... | [
"0.7127771",
"0.71177804",
"0.70137775",
"0.6997336",
"0.6967545",
"0.6946851",
"0.6918456",
"0.69104403",
"0.68680465",
"0.68369263",
"0.68138105",
"0.6810762",
"0.6806383",
"0.6798006",
"0.6774225",
"0.675221",
"0.673416",
"0.67276394",
"0.6717568",
"0.66977406",
"0.6696019... | 0.0 | -1 |
Given a binary tree, find its minimum depth. The minimum depth is the number of nodes along the shortest path from the root node down to the nearest leaf node. | def minDepth(self, root: TreeNode) -> int:
return self.bfs(root) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def min_depth(node):\n if not node:\n return 0\n elif (not node.left) and (not node.right):\n # found leaf\n return 1\n elif not node.left:\n # if the root has only 1 child, this prevents the minimum depth from\n # equaling zero\n return min_depth(node.right) + 1\... | [
"0.8461286",
"0.8181243",
"0.71641576",
"0.7144373",
"0.6993832",
"0.6950117",
"0.6907654",
"0.6858466",
"0.68347263",
"0.6743698",
"0.67391497",
"0.6727257",
"0.6615435",
"0.6594133",
"0.657416",
"0.6555998",
"0.6510495",
"0.64694285",
"0.646406",
"0.6413745",
"0.6391296",
... | 0.86733794 | 0 |
Converts the complex number `c` to a string in Fortranformat, i.e. (Re c, Im c). If c is iterable, it returns a string of the form [(Re c_1, Im c_1), ...]. | def str_complex(c, kindstr=''):
if hasattr(c, '__iter__'):
return '[' + ', '.join([str_complex(c_i, kindstr) for c_i in c]) + ']'
else:
c = complex(c)
return '({}{}, {}{})'.format(c.real, kindstr, c.imag, kindstr) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complex_vct_str ( vct , format = '%.5g%-+.5gj' ) :\n try :\n lst = [] \n for c in vct :\n cc = complex ( c )\n item = format % ( cc.real , cc.imag )\n lst.append ( cc ) \n return '[ ' + ', '.join ( lst ) + ' ]' \n except TypeError :\n ... | [
"0.7047887",
"0.58852553",
"0.5754803",
"0.5726591",
"0.57228047",
"0.56523186",
"0.56442934",
"0.56111693",
"0.5576192",
"0.55657303",
"0.5538955",
"0.55268145",
"0.5501663",
"0.54070234",
"0.5394753",
"0.53852344",
"0.536029",
"0.535686",
"0.53531355",
"0.53468204",
"0.5337... | 0.7722821 | 0 |
Select PORT update events, notify the observers upon a port update in APPL_DB/CONFIG_DB or a XCVR insertion/removal in STATE_DB | def handle_port_update_event(sel, asic_context, stop_event, logger, port_change_event_handler):
if not stop_event.is_set():
(state, _) = sel.select(SELECT_TIMEOUT_MSECS)
if state == swsscommon.Select.TIMEOUT:
return
if state != swsscommon.Select.OBJECT:
logger.log_warning('sel.select() did not return swsscommon.Select.OBJECT')
return
for port_tbl in asic_context.keys():
while True:
(key, op, fvp) = port_tbl.pop()
if not key:
break
if not validate_port(key):
continue
fvp = dict(fvp) if fvp is not None else {}
if 'index' not in fvp:
fvp['index'] = '-1'
port_index = int(fvp['index'])
port_change_event = None
if op == swsscommon.SET_COMMAND:
port_change_event = PortChangeEvent(key,
port_index,
asic_context[port_tbl],
PortChangeEvent.PORT_SET,
fvp)
elif op == swsscommon.DEL_COMMAND:
port_change_event = PortChangeEvent(key,
port_index,
asic_context[port_tbl],
PortChangeEvent.PORT_DEL,
fvp)
if port_change_event is not None:
port_change_event_handler(port_change_event) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _port_status_handler(self, ev):\n msg = ev.msg\n reason = msg.reason\n port_no = msg.desc.port_no\n dpid = msg.datapath.id\n ofproto = msg.datapath.ofproto\n\n reason_dict = {ofproto.OFPPR_ADD: \"added\",\n ofproto.OFPPR_DELETE: \"deleted\",\n ... | [
"0.62168676",
"0.6123209",
"0.60724276",
"0.60613334",
"0.60579395",
"0.6057897",
"0.60528636",
"0.59052724",
"0.58132577",
"0.5790811",
"0.5715928",
"0.5668344",
"0.56622344",
"0.5567299",
"0.55424833",
"0.54548216",
"0.5438808",
"0.5418422",
"0.5405286",
"0.5364919",
"0.534... | 0.6788266 | 0 |
Select CONFIG_DB PORT table changes, once there is a port configuration add/remove, notify observers | def handle_port_config_change(sel, asic_context, stop_event, port_mapping, logger, port_change_event_handler):
if not stop_event.is_set():
(state, _) = sel.select(SELECT_TIMEOUT_MSECS)
if state == swsscommon.Select.TIMEOUT:
return
if state != swsscommon.Select.OBJECT:
logger.log_warning('sel.select() did not return swsscommon.Select.OBJECT')
return
read_port_config_change(asic_context, port_mapping, logger, port_change_event_handler) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_port( self ):\n # disconnect and delete controller\n self.delete_controller()\n \n # update port\n self.update_port()",
"def check_device_changes(self):\n\n #---------------------------------------------------------------------------\n # USB ports\n ... | [
"0.60486573",
"0.601688",
"0.6003362",
"0.5990358",
"0.5919747",
"0.5897559",
"0.5824499",
"0.5805411",
"0.57939553",
"0.5755642",
"0.5738589",
"0.57356256",
"0.56507355",
"0.5590035",
"0.55844575",
"0.5562933",
"0.5554786",
"0.55488783",
"0.5544349",
"0.5528714",
"0.55249166... | 0.6089049 | 0 |
Get port mapping from CONFIG_DB | def get_port_mapping(namespaces):
port_mapping = PortMapping()
for namespace in namespaces:
asic_id = multi_asic.get_asic_index_from_namespace(namespace)
config_db = daemon_base.db_connect("CONFIG_DB", namespace=namespace)
port_table = swsscommon.Table(config_db, swsscommon.CFG_PORT_TABLE_NAME)
for key in port_table.getKeys():
if not validate_port(key):
continue
_, port_config = port_table.get(key)
port_config_dict = dict(port_config)
port_change_event = PortChangeEvent(key, port_config_dict['index'], asic_id, PortChangeEvent.PORT_ADD)
port_mapping.handle_port_change_event(port_change_event)
return port_mapping | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDbPort():\n\n if \"DB_PORT\" in controller.CONF.keys():\n return controller.CONF[\"DB_PORT\"]\n\n return basedefs.DB_PORT",
"def db_port(self) -> Optional[int]:\n return pulumi.get(self, \"db_port\")",
"def get_all_port(self, conf, dpid):\n\t\tpass",
"def get_port_binding():\n ... | [
"0.6964796",
"0.6683577",
"0.653418",
"0.65336806",
"0.6416702",
"0.628645",
"0.6283446",
"0.6275742",
"0.6270378",
"0.6266905",
"0.62222916",
"0.6177558",
"0.60762554",
"0.60760283",
"0.597032",
"0.59239864",
"0.5915565",
"0.59000075",
"0.58872294",
"0.5862604",
"0.5836161",... | 0.6833719 | 1 |
Add a class that doesn't descend from Pickleable to the pickle whitelist | def addClassToPickleWhitelist(cls):
unpickleWhitelist_.add(cls) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_class(self, module, name):\n raise pickle.UnpicklingError(\"global '%s.%s' is forbidden\" %\n (module, name))",
"def test__pickle_unpickle(self):\n pass",
"def __reduce__(self) -> NoReturn:\n raise TypeError(\n \"can't... | [
"0.5848995",
"0.5714878",
"0.5440713",
"0.5331448",
"0.53248143",
"0.5274852",
"0.5263136",
"0.5262969",
"0.5250038",
"0.52005446",
"0.5171152",
"0.515845",
"0.5151149",
"0.51253116",
"0.51061636",
"0.51053673",
"0.5073575",
"0.5070191",
"0.5066985",
"0.5048813",
"0.50263953"... | 0.8570413 | 0 |
Override the serializer to use 'override' as the identifier for instances of 'cls' This is primarily to shorted the amount of data in the representation and to allow the representation to remain constant even if classes are moving around or changing names. override may not be a tuple | def addOverride(cls, override):
assert cls not in locationTypeOverrides_
assert not isinstance(override, tuple)
locationTypeOverrides_[cls] = override
locationTypes_[override] = cls | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize(cls, *args, **kwargs):\n return serialize_cls(cls)(*args, **kwargs)",
"def serialize_cls(cls):\n return _create_wrapper_cls(cls)",
"def default_serializer(_cls: Type[Any], obj: Any) -> Any:",
"def __call__(\n self, cls: Type, key: Optional[str] = None, override: bool = False, show_... | [
"0.6116893",
"0.6029395",
"0.5879258",
"0.5812591",
"0.5711086",
"0.55567217",
"0.55458784",
"0.5513836",
"0.5463761",
"0.5428988",
"0.5375427",
"0.5358711",
"0.5357488",
"0.5266866",
"0.5182065",
"0.5126839",
"0.51204216",
"0.5094683",
"0.5066752",
"0.5049762",
"0.5035309",
... | 0.5931994 | 2 |
Convert 'x' from a simplified form to the full CG form. | def toComplex(simpleObject):
if simpleObject[0] == ENCODING_SIMPLE_PYTHON:
return simpleObject[1]
if simpleObject[0] == ENCODING_INT:
return int(simpleObject[1])
if simpleObject[0] == ENCODING_UNICODE:
return unicode(simpleObject[1], 'utf-8')
if simpleObject[0] == ENCODING_LONG:
return long(simpleObject[1])
if simpleObject[0] == ENCODING_TUPLE:
return tuple([toComplex(x) for x in simpleObject[1]])
if simpleObject[0] == ENCODING_LIST:
return [toComplex(x) for x in simpleObject[1]]
if simpleObject[0] == ENCODING_DICT:
return dict((toComplex(k), toComplex(v)) for k,v in simpleObject[1])
elif simpleObject[0] == ENCODING_OBJECT:
clsModuleAndName = simpleObject[1][0]
args = simpleObject[1][1]
cls = classFromModuleAndName(clsModuleAndName)
kwds = toComplex(args)
try:
return cls(**kwds)
except:
raise UserWarning("Failed to construct instance of %s with %s" % (cls, kwds))
raise UserWarning("Badly encoded object") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _to_clips_value(cls, x):\n return x",
"def _canon_cg(expr):\n return expr.replace(CG, _canon_cg_core)",
"def reconstruct(self, x):\n return self.inverse_transform(self.transform(x))",
"def construct(self, x):\n return self.double_conv(x)",
"def call(self, x):\n self._chec... | [
"0.62322056",
"0.5949095",
"0.5794452",
"0.57718503",
"0.56907374",
"0.5687841",
"0.56126106",
"0.5525381",
"0.55237037",
"0.55102",
"0.5448745",
"0.5415053",
"0.5387446",
"0.53741324",
"0.5301165",
"0.517558",
"0.5163078",
"0.515195",
"0.5121617",
"0.51215816",
"0.5120705",
... | 0.0 | -1 |
`sam list stackoutputs` command entry point | def cli(self, stack_name, output, config_file, config_env):
do_cli(stack_name=stack_name, output=output, region=self.region, profile=self.profile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_cli(stack_name, output, region, profile):\n from samcli.commands.list.stack_outputs.stack_outputs_context import StackOutputsContext\n\n with StackOutputsContext(\n stack_name=stack_name, output=output, region=region, profile=profile\n ) as stack_output_context:\n stack_output_context... | [
"0.7447414",
"0.7092905",
"0.6619107",
"0.6294256",
"0.62495506",
"0.6235097",
"0.6223217",
"0.5894003",
"0.58861333",
"0.58443314",
"0.57637775",
"0.5655446",
"0.56269765",
"0.56182945",
"0.5560416",
"0.5491101",
"0.5430899",
"0.5425569",
"0.5410986",
"0.54088354",
"0.538655... | 0.5937543 | 7 |
Implementation of the ``cli`` method | def do_cli(stack_name, output, region, profile):
from samcli.commands.list.stack_outputs.stack_outputs_context import StackOutputsContext
with StackOutputsContext(
stack_name=stack_name, output=output, region=region, profile=profile
) as stack_output_context:
stack_output_context.run() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():"... | [
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233",
"0.85499233"... | 0.0 | -1 |
Plot an image along with its histogram and cumulative histogram. | def plot_img_and_hist(image, axes, bins=256):
image = img_as_float(image)
ax_img, ax_hist = axes
ax_cdf = ax_hist.twinx()
# Display image
ax_img.imshow(image, cmap=plt.cm.gray)
ax_img.set_axis_off()
# Display histogram
ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')
ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
ax_hist.set_xlabel('Pixel intensity')
ax_hist.set_xlim(0, 1)
ax_hist.set_yticks([])
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(image, bins)
ax_cdf.plot(bins, img_cdf, 'r')
ax_cdf.set_yticks([])
return ax_img, ax_hist, ax_cdf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_img_and_hist(image, axes, bins=256):\n# image = img_as_float(image)\n ax_img, ax_hist = axes\n ax_cdf = ax_hist.twinx()\n\n # Display image\n ax_img.imshow(image, cmap=plt.cm.gray);\n ax_img.set_axis_off()\n\n # Display histogram\n ax_hist.hist(image.ravel(), bins=bins, histtype='... | [
"0.7969631",
"0.78266644",
"0.7776819",
"0.74996084",
"0.7412575",
"0.72977114",
"0.68852156",
"0.6873014",
"0.6869999",
"0.668876",
"0.66544735",
"0.66544735",
"0.66544735",
"0.66200167",
"0.652162",
"0.6493563",
"0.64923745",
"0.6483739",
"0.64781225",
"0.6413293",
"0.63585... | 0.7809336 | 3 |
Download metadata catalogues necessary for downloading via FORCE if user confirms. | def download_catalogues(directory):
while True:
answer = input(f"\nTo download datasets via FORCE, it is necessary to have "
f"metadata catalogues stored in a local directory (size ~9 GB).\n "
f"More information: https://force-eo.readthedocs.io/en/latest/howto/level1-csd.html#downloading-the-metadata-catalogues \n"
f"Do you want to download the latest catalogues into {directory}? (y/n)")
if answer in ['y', 'yes']:
print("\n#### Starting download...")
utils.isdir_mkdir(directory)
out = Client.execute(FORCE_PATH, ["force-level1-csd", "-u", directory],
options=["--cleanenv"], stream=True)
for line in out:
print(line, end='')
elif answer in ['n', 'no']:
print("\n#### Download cancelled...")
sys.exit()
else:
print(f"\n{answer} is not a valid answer!")
continue | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _download_metadata():\n if not os.path.isfile(L1000FWD_METADATA):\n if not os.path.exists('L1000FWD'):\n os.mkdir('L1000FWD')\n response = requests.get('https://amp.pharm.mssm.edu/l1000fwd/download/Drugs_metadata.csv', stream=True)\n if response.status_code != 200:\n ... | [
"0.6125075",
"0.6057573",
"0.5919949",
"0.59092546",
"0.58205396",
"0.57917017",
"0.5775783",
"0.5760028",
"0.5726218",
"0.56873673",
"0.5681924",
"0.5636815",
"0.5625458",
"0.5623649",
"0.5623649",
"0.56185186",
"0.55663484",
"0.55158645",
"0.5502782",
"0.54930496",
"0.54860... | 0.5666497 | 11 |
Recursively searches for 'datacubedefinition.prj' in a level2 directory and returns its parent directory. | def _get_datacubeprj_dir(directory):
prj_path = []
for path in Path(directory).rglob('datacube-definition.prj'):
prj_path.append(path)
if len(prj_path) < 1:
raise FileNotFoundError(f"'datacube-definition.prj' not found in {directory}")
elif len(prj_path) > 1:
raise RuntimeError(f"'datacube-definition.prj' multiple copies found in {directory}")
else:
return prj_path[0].parent | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_parent_path(self):\n return os.path.join(os.getcwd(), \"src\", \"data\", \"genes\")",
"def find_in_parent_dir(fname):\n p = os.path.abspath(os.path.curdir)\n \n while not os.path.exists(os.path.join(p, project_conf_name)):\n oldp, p = p, os.path.dirname(p)\n if p == oldp:\n... | [
"0.59310746",
"0.5910224",
"0.5907544",
"0.56730086",
"0.567043",
"0.54829234",
"0.5468787",
"0.5349158",
"0.53045815",
"0.52922255",
"0.52142084",
"0.52010685",
"0.519746",
"0.51958925",
"0.51642823",
"0.5158747",
"0.51506376",
"0.51362514",
"0.5133889",
"0.51276433",
"0.511... | 0.72337234 | 0 |
send 1 to enable motor | def timer_callback(self):
self.i += 1
ms=MotorState()
ms.state=1
self.motor_state_pub.publish(ms) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enable_motor():\n print('Enabling motor')\n start_motor = '{\"id\" : \"Motor1\", \"enabled\" : \"1\"}'\n SERIAL_PARENT.send(start_motor)\n OUTGOING.append(start_motor)",
"def enable_motor(self, enabled):\r\n self.enabled = enabled\r\n\r\n # Set motors in neutral if disabling.\r\n ... | [
"0.8308927",
"0.72755456",
"0.71145564",
"0.69568515",
"0.68919075",
"0.6866074",
"0.6863435",
"0.68514323",
"0.683486",
"0.6831353",
"0.68029517",
"0.6771648",
"0.6737354",
"0.6695286",
"0.66666657",
"0.6659766",
"0.66348124",
"0.66312325",
"0.6606162",
"0.65834403",
"0.6577... | 0.0 | -1 |
Create the list of dictionaries | def load_training_data(vocab, directory):
top_level = os.listdir(directory)
dataset = []
for d in top_level:
if d[-1] == '/':
label = d[:-1]
subdir = d
else:
label = d
subdir = d+"/"
files = os.listdir(directory+subdir)
for f in files:
bow = create_bow(vocab, directory+subdir+f)
dataset.append({'label': label, 'bow': bow})
return dataset | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_list_dict(list_of_nodes: List['Part']):\n list_of_part_dict = list()\n\n return list_of_part_dict",
"def codebook_json_data_factory() -> List[Dict[str, Any]]:\n codebook_data = [\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 0... | [
"0.69087183",
"0.67974454",
"0.6580766",
"0.65108544",
"0.64887786",
"0.6461656",
"0.64098716",
"0.64027894",
"0.64027894",
"0.6329244",
"0.63245296",
"0.6318974",
"0.62794995",
"0.62457955",
"0.6242187",
"0.6208534",
"0.61998814",
"0.6194605",
"0.6188527",
"0.61769027",
"0.6... | 0.0 | -1 |
Create a vocabulary from the training directory return a sorted vocabulary list | def create_vocabulary(directory, cutoff):
top_level = os.listdir(directory)
a = cutoff
vocab = {}
for d in top_level:
subdir = d if d[-1] == '/' else d+'/'
files = os.listdir(directory+subdir)
for f in files:
with open(directory+subdir+f,'r', encoding="utf-8") as doc:
for word in doc:
word = word.strip()
if not word in vocab and len(word) > 0:
vocab[word] = 1
elif len(word) > 0:
vocab[word] += 1
return sorted([word for word in vocab if vocab[word] >= cutoff]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_vocabulary(vocabulary_path, json_vocab_path):\n if not gfile.Exists(vocabulary_path):\n print(\"Transform vocabulary to %s\" % vocabulary_path)\n with gfile.GFile(json_vocab_path, mode=\"rb\") as f:\n jvocab = json.load(f)\n vocab = jvocab['w2id']\n voca... | [
"0.72303003",
"0.7164037",
"0.71459424",
"0.7054131",
"0.7033178",
"0.700483",
"0.68919396",
"0.68904585",
"0.68769383",
"0.6866679",
"0.6859989",
"0.6854887",
"0.6839105",
"0.6836058",
"0.68341655",
"0.6810794",
"0.6810794",
"0.6805193",
"0.6802589",
"0.6783411",
"0.6731821"... | 0.7643122 | 0 |
Create a single dictionary for the data | def create_bow(vocab, filepath):
bow = {}
# TODO: add your code here
wordcount = 0
wordcountnone = 0
c = 0
for i in vocab:
c+=1
with open(filepath, 'r', encoding="utf-8") as doc: ###############################################
for word in doc:
word = word.strip()
if(c==1):
if (word not in vocab):
wordcountnone += 1
if(i == str(word)):
wordcount += 1
#print(wordcount)
if(wordcount > 0):
bow[i] = wordcount
wordcount = 0
if(wordcountnone != 0):
bow[None] = wordcountnone
return bow | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data_to_create_object(self):\n return {}",
"def as_dict(self) -> dict[str, Any]:\n return {\n \"type\": self.type,\n \"timestamp\": self.timestamp,\n \"data\": self.data or {},\n }",
"def dict(self):\n d = {}\n d['template_id'] = self.... | [
"0.74594545",
"0.6839548",
"0.675466",
"0.66805357",
"0.666756",
"0.6639752",
"0.6629121",
"0.65938157",
"0.658765",
"0.6585759",
"0.6554827",
"0.6547288",
"0.65392995",
"0.65266854",
"0.651302",
"0.64879346",
"0.6485962",
"0.64473516",
"0.6439832",
"0.6439223",
"0.64320755",... | 0.0 | -1 |
return the prior probability of the label in the training set => frequency of DOCUMENTS | def prior(training_data, label_list):
smooth = 1 # smoothing factor
logprob = {}
# TODO: add your code here
numfile1 = 0
numfile2 = 0
for dic in training_data:
if(dic["label"] == label_list[0]):
numfile1 += 1
elif(dic["label"] == label_list[1]):
numfile2 += 1
numtotal = numfile1 + numfile2
prob1 = (numfile1+smooth)/(numtotal+2)
prob2 = (numfile2 + smooth) / (numtotal + 2)
logprob[label_list[0]] = math.log(prob1)
logprob[label_list[1]] = math.log(prob2)
return logprob | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predictability(self):\n temp = self.probs\n for n in range(10):\n temp = temp.dot(temp)\n final = temp[0,:]\n #Let's assume that all words have unique initial letters\n probs = map(len, self.words)\n probs = array(probs)\n probs = (probs + self.probs.... | [
"0.71765524",
"0.7150891",
"0.71340966",
"0.6940001",
"0.6911364",
"0.68786913",
"0.6726276",
"0.6663634",
"0.66374254",
"0.6611508",
"0.6575516",
"0.6557912",
"0.6525762",
"0.65140253",
"0.64985853",
"0.64976215",
"0.64904463",
"0.6479281",
"0.64610314",
"0.6453354",
"0.6435... | 0.71356946 | 2 |
return the class conditional probability of label over all words, with smoothing | def p_word_given_label(vocab, training_data, label):
smooth = 1 # smoothing factor
word_prob = {}
# TODO: add your code here
total_word = 0
word_prob[None] = 0
for dic in training_data:
for index0, i0 in enumerate(dic['bow']):
if (list(dic['bow'])[index0] in word_prob):
continue;
word_prob[list(dic['bow'])[index0]] = 0
#word_prob[None] = 0
if(dic["label"] == label):
for index, i in enumerate(dic["bow"]):
if(list(dic['bow'])[index] in vocab):
if(list(dic['bow'])[index] in word_prob):
word_prob[list(dic['bow'])[index]] += dic["bow"][i]
else:
word_prob[list(dic['bow'])[index]] = dic["bow"][i]
else:
if(None in word_prob):
word_prob[None] += dic["bow"][i]
else:
word_prob[None] = 0
total_word += dic["bow"][i]
#word_prob [None] = 5
for h in word_prob:
word_prob[h] = math.log((word_prob[h] + smooth*1)) - math.log((total_word + smooth*(len(vocab) +1)))
return word_prob | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predictability(self):\n temp = self.probs\n for n in range(10):\n temp = temp.dot(temp)\n final = temp[0,:]\n #Let's assume that all words have unique initial letters\n probs = map(len, self.words)\n probs = array(probs)\n probs = (probs + self.probs.... | [
"0.7158563",
"0.68635947",
"0.6840666",
"0.66153693",
"0.6609253",
"0.6576637",
"0.6564863",
"0.653211",
"0.6521681",
"0.6498006",
"0.6471072",
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"0.6352886",
"0.6348521",
"0.6348173",
"0.634401",
"0.634401",
"0.6340316",
"0.63331866",
"0.6293823",
"... | 0.7195733 | 0 |
Check if the string is palindrome. | def isPalindrome(string):
for i in range(len(string)//2):
if string[i] != string[(i*-1)-1]:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_palindrome(string):\n return",
"def check_palindrome(s):\n return s[::-1] == s",
"def is_palindrome(string):\n return string[::-1] == string",
"def is_palindrome_v1(s):\n return reverse(s) == s",
"def is_palindrome(string):\n if string == string[::-1]:\n return True\n return... | [
"0.89631575",
"0.88805836",
"0.8844723",
"0.87995744",
"0.8799044",
"0.8768949",
"0.8720113",
"0.87144065",
"0.86752564",
"0.8644568",
"0.8638425",
"0.8585656",
"0.85773665",
"0.8561977",
"0.8518276",
"0.8494618",
"0.84669435",
"0.84236455",
"0.8422641",
"0.8409156",
"0.84011... | 0.84398353 | 17 |
Find all pairs of unique indices which form a palindrome. | def palindromePairs(lst):
results = []
for i, e1 in enumerate(lst):
for j, e2 in enumerate(lst):
if i != j:
if isPalindrome(e1+e2):
results.append((i, j))
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def palindromePairs(self, words: List[str]) -> List[List[int]]:\n d = {w : i for i, w in enumerate(words)}\n \n res = []\n for idx, word in enumerate(words):\n for i in range(len(word)+1):\n str1 = word[:i]\n str2 = word[i:]\n # fi... | [
"0.7363634",
"0.7076098",
"0.70694715",
"0.6878793",
"0.67712194",
"0.67707276",
"0.67490494",
"0.65257967",
"0.6466228",
"0.63992494",
"0.63456815",
"0.633291",
"0.6321037",
"0.62712246",
"0.6258766",
"0.6231323",
"0.6217285",
"0.62059194",
"0.6202977",
"0.6194959",
"0.61816... | 0.78059846 | 0 |
Sets the properties mean, count and sum for an object if a reduceRegion operation returns no values in earth engine one cannot join the result list with a list of the zones without setting a nodata (9999) value first | def ensure_default_properties(obj):
obj = ee.Dictionary(obj)
default_properties = ee.Dictionary({"mean": -9999,"count": -9999,"sum":-9999})
return default_properties.combine(obj) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def region_stats(ds, mask, region_name):\n agg = ds.where(mask == arctic_mask_region[region_name]).mean(dim=['x','y'])\n if 'latitude' in agg:\n agg = agg.drop('latitude')\n if 'longitude' in agg:\n agg = agg.drop('longitude')\n return agg",
"def addOverallMeans(results, fieldNames, fie... | [
"0.58719265",
"0.5710536",
"0.5359844",
"0.5348019",
"0.5338033",
"0.52508754",
"0.5220636",
"0.5190192",
"0.5181222",
"0.5081943",
"0.5053021",
"0.5022049",
"0.5003427",
"0.5002751",
"0.4982933",
"0.496246",
"0.496002",
"0.49582618",
"0.49564335",
"0.4926023",
"0.49112692",
... | 0.5071645 | 10 |
filter results by key server side function. | def mapList(results, key):
newResult = results.map(lambda x: ee.Dictionary(x).get(key))
return newResult | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_by(self, key: str, *args, **kwargs):\n filter_ = self.filters.get(key)\n if not filter_:\n raise ValueError(key)\n return filter_(*args, **kwargs)",
"def filter_keys_c(func):\n return partial(filter_keys, func)",
"def filter(self, key):\n with suppress(KeyEr... | [
"0.7041496",
"0.67481804",
"0.66853714",
"0.6602866",
"0.6594346",
"0.6538373",
"0.62875813",
"0.625645",
"0.6235937",
"0.61752564",
"0.617185",
"0.6147107",
"0.61328727",
"0.6030898",
"0.59337604",
"0.5894746",
"0.58756953",
"0.58703166",
"0.5859899",
"0.5856352",
"0.5783209... | 0.0 | -1 |
Convert a dictionary to an earth engine feature server side | def dict_to_feature(d):
f = ee.Feature(None,ee.Dictionary(d))
return f | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _preprocess(self, feature_dict):\n return feature_dict",
"def from_dict(cls, dikt) -> 'Features':\n return util.deserialize_model(dikt, cls)",
"def dict_to_feature(feature_dict, keys, max_value=None):\n feature = []\n for key, val in feature_dict.items(): # First level\n if key not ... | [
"0.6012967",
"0.5905541",
"0.58329165",
"0.57612747",
"0.5738102",
"0.5726723",
"0.5687374",
"0.56813467",
"0.5624164",
"0.56068397",
"0.5599818",
"0.5524402",
"0.548304",
"0.5466543",
"0.5398618",
"0.53919345",
"0.53797644",
"0.5355627",
"0.53457856",
"0.5333529",
"0.5318250... | 0.78169143 | 0 |
Convert volume to flux | def volumeToFlux(volume_image):
image = ee.Image(volume_image)
flux_image = image.divide(ee.Image(AREA_PFAF6_30MIN)).multiply(1e6).copyProperties(image)
flux_image = flux_image.set("units","m")
flux_image = flux_image.set("convertedToFlux", 1)
return flux_image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convertflux(self, *args, **kwargs):\n return _image.image_convertflux(self, *args, **kwargs)",
"def flux(source, freq=0.0, deltafreq=0.0, daysback=0.0) :\n x = queryFlux(source,freq,deltafreq,daysback)\n return x.flux",
"def flux(self, x):\n return self.cal_spec.get_flux(self(x))",
"d... | [
"0.6863947",
"0.65460765",
"0.6410462",
"0.63754797",
"0.6337502",
"0.63291806",
"0.63291806",
"0.62562454",
"0.61814946",
"0.61583227",
"0.60439867",
"0.60325944",
"0.592126",
"0.5912541",
"0.5898983",
"0.5894703",
"0.58924603",
"0.58740014",
"0.5857131",
"0.58522546",
"0.58... | 0.8054486 | 0 |
filters an imagecollection based on year and month | def filter_ic(ic,year,month):
ic_filtered = (ic.filter(ee.Filter.eq("month",month))
.filter(ee.Filter.eq("year",year)))
image = ee.Image(ic_filtered.first())
return(image) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _filter_temporal(self, start_date: str, end_date: str) -> 'ImageCollection':\n process_id = 'filter_daterange'\n args = {\n 'imagery': self.graph,\n 'extent': [start_date, end_date]\n }\n\n return self.graph_add_process(process_id, args)",
"def fi... | [
"0.6298117",
"0.5903462",
"0.58848464",
"0.5747459",
"0.5735303",
"0.5665092",
"0.54974794",
"0.5486667",
"0.5385302",
"0.5352185",
"0.5291872",
"0.5263054",
"0.5232077",
"0.52078956",
"0.519673",
"0.51886106",
"0.5142435",
"0.510914",
"0.50958353",
"0.50748765",
"0.50723386"... | 0.77574176 | 0 |
Zonal statistics with rasters as input and rasters and lists as output | def zonalStatsToRaster(image,zonesImage,geometry,maxPixels,reducerType):
# reducertype can be mean, max, sum, first. Count is always included for QA
# the resolution of the zonesimage is used for scale
reducer = ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"mean"),ee.Reducer.mean(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"max"),ee.Reducer.max(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"sum"),ee.Reducer.sum(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"first"),ee.Reducer.first(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"mode"),ee.Reducer.mode(),"error"))))
)
reducer = ee.Reducer(reducer).combine(reducer2= ee.Reducer.count(), sharedInputs= True).group(groupField=1, groupName="zones")
scale = zonesImage.projection().nominalScale().getInfo()
zonesImage = zonesImage.select(zonesImage.bandNames(),["zones"])
totalImage = ee.Image(image).addBands(zonesImage)
resultsList = ee.List(totalImage.reduceRegion(
geometry= geometry,
reducer= reducer,
scale= scale,
maxPixels=maxPixels
).get("groups"))
resultsList = resultsList.map(ensure_default_properties);
zoneList = mapList(resultsList, 'zones');
countList = mapList(resultsList, 'count');
valueList = mapList(resultsList, reducerType);
valueImage = zonesImage.remap(zoneList, valueList).select(["remapped"],[reducerType])
countImage = zonesImage.remap(zoneList, countList).select(["remapped"],["count"])
newImage = zonesImage.addBands(countImage).addBands(valueImage)
return newImage,zoneList,valueList,countList | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zonal_stats(src_poly, src_raster, operator=['mean'], features=None):\n assert src_raster.geo_transform is not None, \"src_raster.geo_transform should not be None\"\n assert isinstance(operator, list), \"operator should be a list of string. ex: ['mean']\"\n features = list(range(src_raster.bands)) if f... | [
"0.68493986",
"0.6522732",
"0.62442964",
"0.62096614",
"0.6187095",
"0.60611725",
"0.60029554",
"0.5962458",
"0.5880883",
"0.58487236",
"0.5846595",
"0.5792119",
"0.57755446",
"0.57681596",
"0.5670428",
"0.55730975",
"0.5560811",
"0.5551571",
"0.55226356",
"0.55010265",
"0.54... | 0.71548915 | 0 |
Zonal statistics with rasters as input and rasters and lists as output | def zonalStatsToFeatureCollection(image,zonesImage,geometry,maxPixels,reducerType):
# reducertype can be mean, max, sum, first. Count is always included for QA
# the resolution of the zonesimage is used for scale
reducer = ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"mean"),ee.Reducer.mean(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"max"),ee.Reducer.max(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"sum"),ee.Reducer.sum(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"first"),ee.Reducer.first(),
ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,"mode"),ee.Reducer.mode(),"error"))))
)
reducer = ee.Reducer(reducer).combine(reducer2= ee.Reducer.count(), sharedInputs= True).group(groupField=1, groupName="zones")
scale = zonesImage.projection().nominalScale().getInfo()
zonesImage = zonesImage.select(zonesImage.bandNames(),["zones"])
totalImage = ee.Image(image).addBands(zonesImage)
resultsList = ee.List(totalImage.reduceRegion(
geometry= geometry,
reducer= reducer,
scale= scale,
maxPixels=maxPixels
).get("groups"))
resultsList = resultsList.map(ensure_default_properties);
fc = ee.FeatureCollection(resultsList.map(dict_to_feature))
return fc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zonalStatsToRaster(image,zonesImage,geometry,maxPixels,reducerType):\n # reducertype can be mean, max, sum, first. Count is always included for QA\n # the resolution of the zonesimage is used for scale\n\n reducer = ee.Algorithms.If(ee.Algorithms.IsEqual(reducerType,\"mean\"),ee.Reducer.mean(),\n e... | [
"0.71548915",
"0.68493986",
"0.6522732",
"0.62442964",
"0.62096614",
"0.60611725",
"0.60029554",
"0.5962458",
"0.5880883",
"0.58487236",
"0.5846595",
"0.5792119",
"0.57755446",
"0.57681596",
"0.5670428",
"0.55730975",
"0.5560811",
"0.5551571",
"0.55226356",
"0.55010265",
"0.5... | 0.6187095 | 5 |
Export a google earth engine featureCollection to an asset folder | def export_table_to_cloudstorage(fc,description,fileNamePrefix):
task = ee.batch.Export.table.toCloudStorage(
collection = ee.FeatureCollection(fc),
description = description,
bucket = GCS_BUCKET,
fileNamePrefix = GCS_OUTPUT_PATH + fileNamePrefix,
fileFormat = "CSV"
)
task.start() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_geojson(self):\n outputfile = tkinter.filedialog.asksaveasfilename(\n defaultextension=\".geojson\",\n filetypes=((\"geo json\", \"*.geojson\"),\n (\"All Files\", \"*.*\")))\n if outputfile:\n self.tabs.window.aistracker.create_geojson... | [
"0.61912423",
"0.61882675",
"0.59939873",
"0.5933762",
"0.5790404",
"0.5758867",
"0.5716076",
"0.5666679",
"0.56646466",
"0.56428987",
"0.5607236",
"0.5524524",
"0.5473312",
"0.5465263",
"0.54639006",
"0.54507273",
"0.54334444",
"0.53373575",
"0.533101",
"0.5320749",
"0.53174... | 0.539379 | 17 |
Initializing an instance of Square | def __init__(self, size):
self.__size = size | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_init(self):\n s1 = Square(4)\n self.assertEqual(s1.width, 4)\n self.assertEqual(s1.height, 4)\n self.assertEqual(s1.id, 1)\n self.assertEqual(s1.x, 0)\n self.assertEqual(s1.y, 0)\n\n s2 = Square(5, 2, 3, 47)\n self.assertEqual(s2.width, 5)\n s... | [
"0.731118",
"0.7260903",
"0.70362157",
"0.7032253",
"0.7006199",
"0.68428224",
"0.6766883",
"0.67145604",
"0.6702565",
"0.6646152",
"0.65749985",
"0.63614845",
"0.6321423",
"0.63113105",
"0.624146",
"0.6238611",
"0.62014264",
"0.60820174",
"0.6076275",
"0.6062393",
"0.6061413... | 0.0 | -1 |
Instantiate an Invalid Data Error. | def __init__(self, data_file_name: str, message: str) -> None:
super().__init__(
"Invalid data error. "
"The file '{}' contained data of the wrong format: {}".format(
data_file_name, message
)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, message=\"\"):\n super(DataError, self).__init__(message)",
"def from_data(cls, data):\n errors = data.get('errors')\n if not errors or len(errors) == 0:\n return cls('Unknown error!')\n\n # For simplicity, we'll just include the first error.\n err... | [
"0.7352192",
"0.6801983",
"0.65621036",
"0.646361",
"0.64199716",
"0.6389275",
"0.6367765",
"0.63524276",
"0.63038003",
"0.6254848",
"0.62355447",
"0.6224742",
"0.6218904",
"0.6123961",
"0.61025405",
"0.6082676",
"0.6062875",
"0.6062328",
"0.60509044",
"0.6037686",
"0.6018284... | 0.64582944 | 4 |
Instantiate a Missing Data Error. | def __init__(self, message: str) -> None:
super().__init__(
"Data requested from a class could not be found: {}".format(message)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, message=\"\"):\n super(DataError, self).__init__(message)",
"def data_missing(data):\n return type(data)._from_sequence([None, data[0]])",
"def test_create_data_lookup_error(self):\n with self.assertRaises(LookupError):\n _ = create_data({\"name\": \"fake_data\"})... | [
"0.7335149",
"0.65505475",
"0.65356326",
"0.62742686",
"0.62742686",
"0.62742686",
"0.6204105",
"0.6189645",
"0.6147461",
"0.6143845",
"0.6142353",
"0.6132027",
"0.60576224",
"0.60308665",
"0.6008944",
"0.5981049",
"0.5965471",
"0.5946241",
"0.5938257",
"0.59336597",
"0.58896... | 0.60894585 | 12 |
Instantiate a resolution mismatch error. | def __init__(self, message: str) -> None:
super().__init__(
"Due to the nature of the load data, an integer multiple of resolutions, "
"or divsions of resolutions, must be supplied with the '--resolution' or "
"'-r' flag.\nI appreciate that this is poor coding, but at least I took "
"the time to write a custom exception for it :p .\n Error message: "
f"{message}"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, name, pattern_factory):\n\t\tsuper(AlreadyRegisteredError, self).__init__(name, pattern_factory)",
"def __init__(self, error = ''):\n IPRO_Error.__init__(self, error)",
"def __init__(self, error = ''):\n IPRO_Error.__init__(self, error)",
"def __init__(self, error = ''):\n ... | [
"0.64293504",
"0.62001663",
"0.62001663",
"0.62001663",
"0.61242396",
"0.61132437",
"0.60633713",
"0.5971559",
"0.589876",
"0.5896457",
"0.58818203",
"0.587032",
"0.5862494",
"0.5797569",
"0.57908887",
"0.57661104",
"0.57357174",
"0.57350785",
"0.57019705",
"0.5677138",
"0.56... | 0.5793322 | 14 |
Instantiate the daily profile class.. | def __init__(self, profile: Dict[datetime.time, float] = None) -> None:
if profile is None:
profile = dict()
if not isinstance(profile, dict):
raise ProgrammerJudgementFault(
"The input daily profile provided is not a mapping of the correct type."
)
self._profile = profile | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, name):\n self.__username = name\n self.__startDate = datetime.now().date().today() # This attributes will not be change ever once it has been initialized.",
"def __init__(self, dt=60*60*24):\n pass",
"def __init__(self, student, start_date, day_periods):\n self... | [
"0.6303617",
"0.6276865",
"0.61957604",
"0.6129965",
"0.60512453",
"0.5994838",
"0.59334636",
"0.5906946",
"0.57980454",
"0.57551837",
"0.5752836",
"0.57355404",
"0.5726834",
"0.5710793",
"0.5709311",
"0.56886977",
"0.5673901",
"0.5655608",
"0.5648033",
"0.5615956",
"0.559050... | 0.6949065 | 0 |
Return an irradiance value from the profile. | def __getitem__(self, index: datetime.time) -> float:
# If the index is in the profile, return the index.
if index in self._profile:
return self._profile[index]
# If the index is not in the profile, then the closest value needs to be
# determined. If there is a tie, this does not matter.
delta_t_to_t_map = {
(
abs(
time.hour * 3600
+ time.minute * 60
+ time.second
- (index.hour * 3600 + index.minute * 60 + index.second)
)
): time
for time in self._profile
}
return self._profile[delta_t_to_t_map[min(delta_t_to_t_map)]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def irradiance(self) -> float:\n\n if self.declination > 0:\n return self._irradiance\n return 0",
"def internal_rate_of_return(proforma):\n return np.irr(proforma['Yearly Net Value'].values)",
"def getIR2() -> int:\n pass",
"def emissivity_profile(R):\n E = R**(-3.0)*(1... | [
"0.68686956",
"0.5816468",
"0.5590658",
"0.5589434",
"0.5456386",
"0.5432771",
"0.5328536",
"0.5325384",
"0.52494484",
"0.52432454",
"0.5228382",
"0.5184835",
"0.5157474",
"0.5141578",
"0.5102126",
"0.5093269",
"0.5086786",
"0.5064944",
"0.5061839",
"0.5061839",
"0.50400925",... | 0.0 | -1 |
Sets an item in the profile. | def __setitem__(self, index: datetime.time, value: float) -> None:
self._profile[index] = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_item(self, item):\n self.item = item",
"def set_item(self, item):\n self.item = item",
"def SetItem(self, item):\r\n\r\n self._item = item",
"def setItem(self, item):\n self.setItem(0, item)",
"def set(self, item, value):\r\n raise NotImplementedError",
"def set... | [
"0.77816904",
"0.77816904",
"0.7673996",
"0.7493998",
"0.72386533",
"0.68001693",
"0.66733706",
"0.6579954",
"0.6452752",
"0.6452752",
"0.6386416",
"0.62725997",
"0.6261532",
"0.6182471",
"0.616033",
"0.6158893",
"0.6145247",
"0.6110622",
"0.60548663",
"0.6047665",
"0.6031433... | 0.5924909 | 24 |
Updates the internal profile with the mapping provided. | def update(self, profile: Dict[datetime.time, float]) -> None:
if self._profile is None:
self._profile = profile
else:
self._profile.update(profile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, mapping):\n if not ismapping(mapping):\n raise TypeError(\"mapping type required\")\n field_names = getpyattr(type(self), 'field_names')\n for key, value in mapping.items():\n if key in field_names:\n setattr(self, key, value)",
"def upda... | [
"0.63624704",
"0.609009",
"0.60749924",
"0.5952606",
"0.59126085",
"0.5860821",
"0.5851326",
"0.58165675",
"0.57689905",
"0.5755722",
"0.57045287",
"0.56417894",
"0.5599517",
"0.55890405",
"0.5579434",
"0.55684793",
"0.55682325",
"0.5558063",
"0.55190516",
"0.5518738",
"0.549... | 0.7125943 | 0 |
The density of air varies as a function of temperature. | def density_of_air(self) -> float:
return self.pressure / (SPECIFIC_GAS_CONSTANT_OF_AIR * self.ambient_temperature) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def air_density(self):\n return self.flow_field.air_density",
"async def air_density(self, temperature, station_pressure):\n if temperature is not None and station_pressure is not None:\n kelvin = temperature + 273.15\n pressure = station_pressure\n r_specific = 287... | [
"0.7871237",
"0.77620345",
"0.73548687",
"0.7349471",
"0.6937895",
"0.6883913",
"0.6820493",
"0.6713705",
"0.6696069",
"0.66546506",
"0.6654112",
"0.6648508",
"0.6641304",
"0.6609871",
"0.65910405",
"0.6577765",
"0.6577765",
"0.6577765",
"0.6552875",
"0.654293",
"0.65178937",... | 0.7765341 | 1 |
The dynamic viscosity of air varies as a function of temperature. | def dynamic_viscosity_of_air(self) -> float:
return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (
self.ambient_temperature + 110.4
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kinematic_viscosity_of_air(self) -> float:\n\n return self.dynamic_viscosity_of_air / self.density_of_air",
"def latent_heat_vapourisation(self, tair):\n return (2.501 - 0.00237 * tair) * 1E06",
"def air_density(self):\n return self.flow_field.air_density",
"def thermal_expansivity_o... | [
"0.7093741",
"0.6370206",
"0.617513",
"0.6122823",
"0.61151516",
"0.6091893",
"0.6021482",
"0.59835947",
"0.592721",
"0.5878499",
"0.58544457",
"0.5826623",
"0.58215696",
"0.5811718",
"0.58073586",
"0.56892794",
"0.56720847",
"0.56465936",
"0.5620597",
"0.5558093",
"0.5547346... | 0.82398015 | 0 |
Return the heat capacity of air in Joules perkilogram Kelvin. The heat capacity of air varies with a function of temperature and is given by an empiricallyderived formula. | def heat_capacity_of_air(self) -> float:
return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def heatCapacity(self, Tk=None, Tc=None):\n Tk = getTk(Tc, Tk)\n self.checkPropertyTempRange(\"heat capacity\", Tk)\n return (\n sum(\n [\n +1.38642e-13 * Tk**4,\n -6.47481e-10 * Tk**3,\n +1.02345e-06 * Tk**2,\n... | [
"0.72807765",
"0.70755357",
"0.67571175",
"0.6657788",
"0.6539134",
"0.63971967",
"0.63434315",
"0.6203374",
"0.62022215",
"0.61694735",
"0.61694306",
"0.6157651",
"0.61571056",
"0.61204106",
"0.6098233",
"0.60910946",
"0.6065617",
"0.6051415",
"0.60473984",
"0.6033305",
"0.6... | 0.77720433 | 0 |
The irradiance should only be definied if the sun is above the horizon. | def irradiance(self) -> float:
if self.declination > 0:
return self._irradiance
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rad_field_initial_condition(self):\n\n # revert in viewing direct\n angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)\n # Looking at the sky\n if angle < 90:\n I_init = (\n self.sun_intensity\n * f.delta_func(self... | [
"0.634991",
"0.565729",
"0.5482073",
"0.5471151",
"0.543911",
"0.5316347",
"0.52773964",
"0.5272393",
"0.52664614",
"0.52653426",
"0.52644473",
"0.519665",
"0.51890105",
"0.5169418",
"0.50999045",
"0.5082697",
"0.5082697",
"0.508089",
"0.5074152",
"0.50740224",
"0.5064365",
... | 0.5284681 | 6 |
The kinematic viscosity of air varies as a function of temperature. | def kinematic_viscosity_of_air(self) -> float:
return self.dynamic_viscosity_of_air / self.density_of_air | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dynamic_viscosity_of_air(self) -> float:\n\n return (1.458 * (10 ** (-6)) * (self.ambient_temperature**1.5)) / (\n self.ambient_temperature + 110.4\n )",
"def latent_heat_vapourisation(self, tair):\n return (2.501 - 0.00237 * tair) * 1E06",
"def dispersion(self, k):\n ... | [
"0.7809844",
"0.62890047",
"0.62317854",
"0.6194581",
"0.61644995",
"0.61163557",
"0.600697",
"0.5984969",
"0.5790677",
"0.575207",
"0.5744783",
"0.5729746",
"0.5714137",
"0.57019544",
"0.5691392",
"0.56809",
"0.56597465",
"0.56572974",
"0.5655333",
"0.56213343",
"0.56118655"... | 0.8016766 | 0 |
Determines the radiative temperature of the sky. The "sky," as a black body, has a radiative temperature different to that of the surrounding air, or the ambient temperature. This function converts between them and outputs the sky's radiative temperature. | def sky_temperature(self) -> float:
return 0.0552 * (self.ambient_temperature**1.5) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sky_ir_temperature(self) -> float:\n self.serial.write(b\"S!\")\n sky_ir_temp = self.__extract_int(self.__read_response(1)[0], b\"!1\")\n\n return round(sky_ir_temp / 100, 2)",
"def temperature() -> float:",
"def temperature(self):\n names = ['anc_air_temperature']\n ... | [
"0.677967",
"0.65615",
"0.64276695",
"0.6416784",
"0.64141804",
"0.6408829",
"0.62878555",
"0.6222289",
"0.6220975",
"0.61991245",
"0.6182677",
"0.61740917",
"0.61678636",
"0.6091844",
"0.60883605",
"0.6079088",
"0.6072627",
"0.6015334",
"0.60076296",
"0.59943837",
"0.5990562... | 0.77121866 | 0 |
The thermal conductivity of air varies as a function of temperature. | def thermal_conductivity_of_air(self) -> float:
# This more accurate equation is not used by the paper.
# return (0.02646 * self.ambient_temperature ** 1.5) / (
# self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))
# )
# The reference suggests this equation is accurate to 1%.
return 0.02646 * (self.ambient_temperature / 300) ** 0.8646 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature",
"def thermal_conductivity(temperature):\n a0 = -4.1236\n a1 = 13.788\n a2 = -26.068\n a3 = 26.272\n a4 = -14.663\n a5 = 4.4954\n a6 = -0.6905\n a7 = 0.0397\n ... | [
"0.79880536",
"0.764283",
"0.7523666",
"0.75130093",
"0.7090662",
"0.70669293",
"0.7042304",
"0.6954361",
"0.6936385",
"0.6905874",
"0.6894696",
"0.68758714",
"0.6788131",
"0.6774004",
"0.677208",
"0.67583066",
"0.6724294",
"0.67135936",
"0.66866106",
"0.66677165",
"0.6657772... | 0.8359033 | 0 |
The thermal expansion coefficient of air varies as a function of temperature. | def thermal_expansivity_of_air(self) -> float:
return 1 / self.ambient_temperature | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference sug... | [
"0.7279272",
"0.68165576",
"0.6797786",
"0.6684487",
"0.66219014",
"0.6579864",
"0.6570214",
"0.6559898",
"0.649577",
"0.64194864",
"0.63722163",
"0.6345614",
"0.6337939",
"0.6337814",
"0.6305888",
"0.6286312",
"0.6284374",
"0.6271602",
"0.6268423",
"0.62625694",
"0.62529576"... | 0.76827806 | 0 |
Determines the convective heat transfer coefficient, either free, or forced. In the absence of any wind, the "free" wind_heat_transfer_coefficient is returned. If there is wind present, then this parameter is known as the "forced" wind_heat_transfer_coefficient. | def wind_heat_transfer_coefficient(self) -> float:
return 3.8 + 2 * self.wind_speed
# return 4.5 + 2.9 * self.wind_speed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def thermalConductivity(self, Tk=None, Tc=None):\n Tk = getTk(Tc, Tk)\n self.checkPropertyTempRange(\"thermal conductivity\", Tk)\n thermalConductivity = (\n 2.13014e-08 * Tk**3\n - 6.31916e-05 * Tk**2\n + 1.11629e-01 * Tk\n - 2.00043e00\n )\n... | [
"0.6343963",
"0.59971076",
"0.59249985",
"0.5791971",
"0.576443",
"0.5683115",
"0.565232",
"0.55878526",
"0.5570194",
"0.54607165",
"0.5381237",
"0.52986056",
"0.5273817",
"0.5269059",
"0.5203606",
"0.5199913",
"0.5169069",
"0.5163147",
"0.5161178",
"0.51067317",
"0.510497",
... | 0.69990456 | 0 |
Return a nice representation of the weather conditions. | def __repr__(self) -> str:
return (
"WeatherConditions("
f"ambient_temperature: {self.ambient_temperature:.3f}K, "
f"azimuthal_angle: {self.azimuthal_angle}deg, "
f"declination: {self.declination}deg, "
f"density: {self.density_of_air:.3f}kg/m^3, "
f"dynamic_viscosity: {self.dynamic_viscosity_of_air:.3f}kg/m*s, "
f"heat_capacity: {self.heat_capacity_of_air}:.3fJ/kg*K, "
f"irradiance: {self.irradiance:.3f}W/m^2, "
f"kinematic_viscosity: {self.kinematic_viscosity_of_air:.3f}m^2/s, "
f"sky_temperature: {self.sky_temperature:.3f}K, "
f"thermal_conductivity: {self.thermal_conductivity_of_air:.3f}W/m*K, "
f"thermal_expansion_coefficient: {self.thermal_expansivity_of_air:.3f}K^-1, "
f"wind_heat_transfer_coefficient: {self.wind_heat_transfer_coefficient:2f}W/m*K, "
f"wind_speed: {self.wind_speed:.3f}m/s, "
")"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conditions(self, json):\n conditions = str(json['forecast']['simpleforecast']['forecastday'][0]['conditions'])\n return conditions",
"def genWeather():\n\n weather = random.choice(weather_conditions.keys())\n condition = weather_conditions[weather]\n (tMax, tMin) = condition[\"temperat... | [
"0.63701195",
"0.6170547",
"0.6153412",
"0.6075571",
"0.59051156",
"0.5875144",
"0.582113",
"0.5815576",
"0.5801642",
"0.5736014",
"0.5721063",
"0.5704934",
"0.56886274",
"0.5640063",
"0.56320167",
"0.5610608",
"0.5609194",
"0.55756634",
"0.5571646",
"0.5566451",
"0.5524659",... | 0.79582655 | 0 |
A generator function for looping through various times. | def time_iterator(
*,
first_time: datetime.datetime,
last_time: datetime.datetime,
resolution: int,
timezone: datetime.timezone,
) -> Generator[datetime.datetime, None, None]:
current_time = first_time
while current_time < last_time:
yield current_time.replace(tzinfo=timezone)
current_time += relativedelta(
hours=resolution // 3600,
minutes=(resolution // 60) % 60,
seconds=resolution % 60,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def times(self):\n \n class IterTimes:\n def __init__(self, st):\n self.c = 0\n self.st = st\n \n def __iter__(self):\n return self\n \n def next(self):\n t = self.st.time(self.c)\n ... | [
"0.7211171",
"0.7095168",
"0.676974",
"0.66167104",
"0.66167104",
"0.66167104",
"0.6530617",
"0.6517885",
"0.64912164",
"0.64652574",
"0.6435925",
"0.64267135",
"0.6413881",
"0.6370479",
"0.6354844",
"0.63290936",
"0.62823224",
"0.62550485",
"0.6254462",
"0.62441665",
"0.6222... | 0.597769 | 39 |
Names of endogenous variables | def endog_names(self):
return self.data.ynames | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMeteorologicalVariableNames(self, product):\r\n return []",
"def variables(self):\n return [i.name for i in self.inputs + self.outputs]",
"def getOthVarNames( self ):\n\n if self.othVarNames:\n return self.othVarNames.keys()\n\n n = self.adb.get( \"nOthVars\" )\n... | [
"0.64263195",
"0.6372972",
"0.63582456",
"0.6340898",
"0.6314623",
"0.625726",
"0.62262017",
"0.62185633",
"0.61736417",
"0.61590856",
"0.61525357",
"0.61390096",
"0.6113262",
"0.6112568",
"0.6102501",
"0.60962504",
"0.6091112",
"0.6083438",
"0.6079277",
"0.6068774",
"0.60612... | 0.6065987 | 20 |
Names of exogenous variables | def exog_names(self):
return self.data.xnames | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def variable_names(self):\n \n return [x['variable'] for x in self.variable_dicts()]",
"def get_all_variables_names(self):\n return self.project.get_variable_names() + self.design.get_variable_names()",
"def get_variable_names(self):\n return [var[1] for var in self.variables]",
"... | [
"0.69048613",
"0.6785922",
"0.67740816",
"0.6711591",
"0.6687262",
"0.6663156",
"0.6635133",
"0.66310126",
"0.6589906",
"0.6581961",
"0.6580228",
"0.6575236",
"0.6552368",
"0.6544824",
"0.6535504",
"0.6525284",
"0.6511582",
"0.64991194",
"0.64609987",
"0.641673",
"0.6381078",... | 0.5873568 | 61 |
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