xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

527f504c64c7073761090a2b2887d81bf25e99e8

160

py

Python

project/app/admin.py

diegoinacio/basic-app-django

e993909449bb1f86272233d7f7dda40516886606

[ "MIT" ]

null

project/app/admin.py

diegoinacio/basic-app-django

e993909449bb1f86272233d7f7dda40516886606

[ "MIT" ]

1

2020-02-12T12:59:29.000Z

project/app/admin.py

diegoinacio/basic-app-django

e993909449bb1f86272233d7f7dda40516886606

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import Person, MailList admin.site.register(Person) admin.site.register(MailList)

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bffc660e93ebf92dfe8f9df706f9fa2592e2c227

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py

Python

visualization_tool/visulization_npy/opt/__init__.py

nschor/G2LGAN

e690c0c0d0d4a2077715749b15a91553e04a76b0

[ "MIT" ]

24

2019-01-04T14:08:05.000Z

2021-10-13T09:26:52.000Z

visualization_tool/visulization_npy/opt/__init__.py

nschor/G2LGAN

e690c0c0d0d4a2077715749b15a91553e04a76b0

[ "MIT" ]

9

2019-07-29T03:07:42.000Z

2021-05-22T10:40:24.000Z

visualization_tool/visulization_npy/opt/__init__.py

nschor/G2LGAN

e690c0c0d0d4a2077715749b15a91553e04a76b0

[ "MIT" ]

4

2019-04-15T00:30:26.000Z

2020-06-03T18:35:06.000Z

import constrained_opt ConstrainedOpt = constrained_opt.ConstrainedOpt

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py

Python

pyeccodes/defs/grib2/tables/14/5_8_table.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

7

2020-04-14T09:41:17.000Z

2021-08-06T09:38:19.000Z

pyeccodes/defs/grib2/tables/9/5_8_table.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

null

pyeccodes/defs/grib2/tables/9/5_8_table.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

3

2020-04-30T12:44:48.000Z

2020-12-15T08:40:26.000Z

def load(h): return ({'abbr': 'no', 'code': 0, 'title': 'no compression method'}, {'abbr': None, 'code': 255, 'title': 'Missing'})

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5

873515f1a2bafec184a7e007d7ef1f26daefc6f8

190

py

Python

rabbit2ev/exception.py

lovelle/rabbit2ev

e3703760813753565bf445f2af12101c82366b30

[ "MIT" ]

10

2016-07-05T04:02:31.000Z

2020-12-15T12:48:57.000Z

rabbit2ev/exception.py

lovelle/rabbit2ev

e3703760813753565bf445f2af12101c82366b30

[ "MIT" ]

null

rabbit2ev/exception.py

lovelle/rabbit2ev

e3703760813753565bf445f2af12101c82366b30

[ "MIT" ]

null

class NullMailerErrorPool(Exception): pass class NullMailerErrorPipe(Exception): pass class NullMailerErrorQueue(Exception): pass class RabbitMQError(Exception): pass

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5

873eaca1165136ecd3aa329aa39ee953f27ceb70

218

py

Python

lib/JumpScale/baselib/jsdeveltools/__init__.py

Jumpscale/jumpscale6_core

0502ddc1abab3c37ed982c142d21ea3955d471d3

[ "BSD-2-Clause" ]

1

2015-10-26T10:38:13.000Z

lib/JumpScale/baselib/jsdeveltools/__init__.py

Jumpscale/jumpscale6_core

0502ddc1abab3c37ed982c142d21ea3955d471d3

[ "BSD-2-Clause" ]

null

lib/JumpScale/baselib/jsdeveltools/__init__.py

Jumpscale/jumpscale6_core

0502ddc1abab3c37ed982c142d21ea3955d471d3

[ "BSD-2-Clause" ]

null

from JumpScale import j from JSDevelToolsInstaller import JSDevelToolsInstaller from JSDevelTools import JSDevelTools class Empty(): pass j.develtools=JSDevelTools() j.develtools.installer=JSDevelToolsInstaller()

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5

875312474c2ccfcdbfca2e90171c26ac2a0cf769

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py

Python

src/clearskies/column_types/__init__.py

cmancone/clearskies

aaa33fef6d03205faf26f123183a46adc1dbef9c

[ "MIT" ]

4

2021-04-23T18:13:06.000Z

2022-03-26T01:51:01.000Z

src/clearskies/column_types/__init__.py

cmancone/clearskies

aaa33fef6d03205faf26f123183a46adc1dbef9c

[ "MIT" ]

null

src/clearskies/column_types/__init__.py

cmancone/clearskies

aaa33fef6d03205faf26f123183a46adc1dbef9c

[ "MIT" ]

null

from .belongs_to import BelongsTo from .column import Column from .created import Created from .datetime import DateTime from .email import Email from .float import Float from .has_many import HasMany from .integer import Integer from .json import JSON from .many_to_many import ManyToMany from .string import String from .updated import Updated def build_column_config(name, column_class, **kwargs): return ( name, { **{"class": column_class}, **kwargs } ) def belongs_to(name, **kwargs): return build_column_config(name, BelongsTo, **kwargs) def created(name, **kwargs): return build_column_config(name, Created, **kwargs) def datetime(name, **kwargs): return build_column_config(name, DateTime, **kwargs) def email(name, **kwargs): return build_column_config(name, Email, **kwargs) def float_column(name, **kwargs): return build_column_config(name, Float, **kwargs) def has_many(name, **kwargs): return build_column_config(name, HasMany, **kwargs) def integer(name, **kwargs): return build_column_config(name, Integer, **kwargs) def json(name, **kwargs): return build_column_config(name, JSON, **kwargs) def many_to_many(name, **kwargs): return build_column_config(name, ManyToMany, **kwargs) def string(name, **kwargs): return build_column_config(name, String, **kwargs) def updated(name, **kwargs): return build_column_config(name, Updated, **kwargs)

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5

8760f6d3df69853806420feb4a900c1607d7f005

116

py

Python

simoni/training/adversarial/trainer.py

yamad07/simoni

7c0f18667ced093a86e5e5d875b734c4e018015e

[ "MIT" ]

null

simoni/training/adversarial/trainer.py

yamad07/simoni

7c0f18667ced093a86e5e5d875b734c4e018015e

[ "MIT" ]

null

simoni/training/adversarial/trainer.py

yamad07/simoni

7c0f18667ced093a86e5e5d875b734c4e018015e

[ "MIT" ]

null

from simoni.models import Model class AdversarialTrainer(Trainer): def _train_step(self, batch): pass

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5

8771add59849a0bd75f778d53acac4427b3c594f

17,148

py

Python

polypy/plotting.py

symmy596/Polypy

9e9ce03b3e2c287f6d7efb04b31a4e9be7dea396

[ "MIT" ]

8

2020-11-08T20:08:39.000Z

2022-03-24T06:36:40.000Z

polypy/plotting.py

symmy596/Polypy

9e9ce03b3e2c287f6d7efb04b31a4e9be7dea396

[ "MIT" ]

4

2018-09-02T05:08:45.000Z

2020-09-09T12:58:30.000Z

polypy/plotting.py

symmy596/Polypy

9e9ce03b3e2c287f6d7efb04b31a4e9be7dea396

[ "MIT" ]

2

2021-03-03T17:20:15.000Z

2021-11-22T09:37:32.000Z

""" Plotting functions included with `polypy`. """ # Copyright (c) Adam R. Symington # Distributed under the terms of the MIT License # author: Adam R. Symington import numpy as np import matplotlib.pyplot as plt from polypy import fig_params from matplotlib.gridspec import GridSpec from matplotlib import ticker def line_plot(x, y, xlab, ylab, figsize=(10, 6)): """ Simple line plotting function. Designed to be generic and used in several different applications. Args: x (:py:attr:`array like`): x axis points. y (:py:attr:`array like`): y axis points. xlab (:py:attr:`str`): x axis label. ylab (:py:attr:`str`): y axis label. fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = plt.subplots(figsize=figsize)[1] ax.plot(x, y) ax.set_xlabel(xlab) ax.set_ylabel(ylab) ax.tick_params() plt.tight_layout() return ax def msd_plot(msd_data, show_all_dimensions=True, figsize=(10, 6)): """ Plotting function for the mean squared displacements (MSD). Args: msd_data ():py:class:`polypy.msd.MSDContainer`): MSD data. show_all_dimensions (:py:attr:`bool`): Display all MSD data or the total MSD. Default is :py:attr:`bool` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = plt.subplots(figsize=figsize)[1] ax.set_ylim(ymin=0, ymax=np.amax(msd_data.msd)) ax.set_xlim(xmin=0, xmax=np.amax(msd_data.time)) ax.plot(msd_data.time, msd_data.msd, label="XYZMSD") if show_all_dimensions: ax.plot(msd_data.time, msd_data.xymsd, label="XYMSD") ax.plot(msd_data.time, msd_data.xzmsd, label="XZMSD") ax.plot(msd_data.time, msd_data.yzmsd, label="YZMSD") ax.plot(msd_data.time, msd_data.xmsd, label="XMSD") ax.plot(msd_data.time, msd_data.ymsd, label="YMSD") ax.plot(msd_data.time, msd_data.zmsd, label="ZMSD") ax.set_xlabel("Time (ps)") ax.set_ylabel("MSD ($\AA$)") ax.legend(frameon=True, edgecolor="black", loc=2) return ax def volume_plot(x, y, xlab="Timestep (ps)", ylab="System Volume ($\AA$)", figsize=(10, 6)): """ Gathers the data and creates a line plot for the system volume as a function of simulation timesteps Args: x (:py:attr:`array like`): x axis points - simulation timesteps y (:py:attr:`array like`): y axis points - Volume xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"Timestep (ps)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"System Volume ($\AA$)"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = line_plot(x, y, xlab, ylab, figsize) return ax def electric_field_plot(x, y, xlab="X Coordinate ($\AA$)", ylab="Electric Field (V)", figsize=(10, 6)): """ Gathers the data and creates a line plot for the electric field in one dimension. Args: x (:py:attr:`array like`): x axis points - position in simulation cell y (:py:attr:`array like`): y axis points - electric field xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Electric Field (V)"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = line_plot(x, y, xlab, ylab, figsize) return ax def electrostatic_potential_plot(x, y, xlab="X Coordinate ($\AA$)", ylab="Electrostatic Potential (V)", figsize=(10, 6)): """ Gathers the data and creates a line plot for the electrostatic potential in one dimension. Args: x (:py:attr:`array like`): x axis points - position in simulation cell y (:py:attr:`array like`): y axis points - electrostatic potential xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Electrostatic Potential (V)"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = line_plot(x, y, xlab, ylab, figsize) return ax def one_dimensional_charge_density_plot(x, y, xlab="X Coordinate ($\AA$)", ylab="Charge Density", figsize=(10, 6)): """ Gathers the data and creates a line plot for the charge density in one dimension. Args: x (:py:attr:`array like`): x axis points - position in simulation cell y (:py:attr:`array like`): y axis points - charge density xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Charge Density"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = line_plot(x, y, xlab, ylab, figsize) return ax def one_dimensional_density_plot(x, y, data_labels, xlab="X Coordinate ($\AA$)", ylab="Particle Density", figsize=(10, 6)): """ Plots the number density of all given species in one dimension. Args: x (:py:attr:`list`): x axis points - list of numpy arrays containing x axis coordinates. y (:py:attr:`list`): y axis points - list of numpy arrays containing y axis coordinates. data_labels (:py:attr:`list`): List of labels for legend. xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Particle Density"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ ax = plt.subplots(figsize=figsize)[1] for i in range(len(x)): ax.plot(x[i], y[i], label=data_labels[i]) ax.set_xlabel(xlab) ax.set_ylabel(ylab) ax.tick_params() ax.legend(frameon=True, edgecolor="black") plt.tight_layout() return ax def two_dimensional_charge_density_plot(x, y, z, xlab="X Coordinate ($\AA$)", ylab="Y Coordinate ($\AA$)", palette="viridis", figsize=(10, 6), colorbar=True, log=False): """ Plots the charge density in two dimensions. Args: x (:py:attr:`array like`): x axis points - x axis coordinates. y (:py:attr:`array like`): y axis points - y axis coordinates. z (:py:attr:`array like`): z axis points - 2D array of points. xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Y Coordinate ($\AA$)"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. colorbar (:py:class:`bool`): Include the colorbar or not. Returns: (:py:class:`matplotlib.Fig`): Figure object (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ fig, ax = plt.subplots(figsize=figsize) if log: CM = ax.contourf(x, y, z, cmap=palette, locator=ticker.LogLocator()) else: CM = ax.contourf(x, y, z, cmap=palette) ax.set_xlabel(xlab) ax.set_ylabel(ylab) ax.tick_params() if colorbar: cbar = fig.colorbar(CM) cbar.set_label('Charge Density', labelpad=-40, y=1.1, rotation=0) return fig, ax def two_dimensional_density_plot(x, y, z, xlab="X Coordinate ($\AA$)", ylab="Y Coordinate ($\AA$)", palette="viridis", figsize=(10, 6), colorbar=True, log=False): """ Plots the distribution of an atom species in two dimensions. Args: x (:py:attr:`array like`): x axis points - x axis coordinates. y (:py:attr:`array like`): y axis points - y axis coordinates. z (:py:attr:`array like`): z axis points - 2D array of points. xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Y Coordinate ($\AA$)"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. colorbar (:py:class:`bool`): Include the colorbar or not. log (:py:class:`bool`): Log the z data or not? This can sometimes be useful but obviously one needs to be careful when drawing conclusions from the data. Returns: (:py:class:`matplotlib.Fig`): Figure object (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ fig, ax = plt.subplots(figsize=figsize) if log: CM = ax.contourf(x, y, z, cmap=palette, locator=ticker.LogLocator()) else: CM = ax.contourf(x, y, z, cmap=palette) ax.set_xlabel(xlab) ax.set_ylabel(ylab) ax.tick_params() if colorbar: cbar = fig.colorbar(CM) cbar.set_label('Particle Density', labelpad=-40, y=1.1, rotation=0) plt.tight_layout() return fig, ax def combined_density_plot(x, y, z, xlab="X Coordinate ($\AA$)", ylab="Y Coordinate ($\AA$)", y2_lab="Number Density", palette="viridis", linecolor="black", figsize=(10, 6), log=False): """ Plots the distribution of an atom species in two dimensions and overlays the one dimensional density on top. Think of it as a combination of the two_dimensional_density_plot and one_dimensional_density_plot functions Args: x (:py:attr:`array like`): x axis points - x axis coordinates. y (:py:attr:`array like`): y axis points - y axis coordinates. z (:py:attr:`array like`): z axis points - 2D array of points. xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Y Coordinate ($\AA$)"` y2_lab (:py:attr:`str`): second y axis label. Default is :py:attr:`"Particle Density"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. log (:py:class:`bool`): Log the z data or not? This can sometimes be useful but obviously one needs to be careful when drawing conclusions from the data. Returns: (:py:class:`matplotlib.Fig`): Figure object (:py:attr:`list`): List of axes objects. """ y2 = np.sum(z, axis=0) fig = plt.figure(constrained_layout=True, figsize=figsize) gs = GridSpec(5, 2, figure=fig) gs.update(wspace=0.025, hspace=0.05) ax2 = fig.add_subplot(gs[0,:]) ax1 = fig.add_subplot(gs[1:, :]) ax = [ax1, ax2] if log: ax1.contourf(x, y, z, cmap=palette, locator=ticker.LogLocator()) else: ax1.contourf(x, y, z, cmap=palette) ax1.set_xlabel(xlab) ax1.set_ylabel(ylab) ax1.set_xlim([np.amin(x), np.amax(x)]) ax1.tick_params() ax2.plot(x, y2, color=linecolor) ax2.set_xlim([np.amin(x), np.amax(x)]) ax2.axis('off') plt.tight_layout() return fig, ax def two_dimensional_density_plot_multiple_species(x_list, y_list, z_list, palette_list, xlab="X Coordinate ($\AA$)", ylab="Y Coordinate ($\AA$)", y2_lab="Number Density", figsize=(10, 6), log=False): """ Plots the distribution of a list of atom species in two dimensions. Returns heatmaps for each species stacking on top of one another. This is limited to four species. Args: x_list (:py:attr:`array like`): x axis points - x axis coordinates. y_list (:py:attr:`array like`): y axis points - y axis coordinates. z_list (:py:attr:`array like`): z axis points - 2D array of points. palette_list (:py:attr:`array like`): Color palletes for each atom species. xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Y Coordinate ($\AA$)"` y2_lab (:py:attr:`str`): second y axis label. Default is :py:attr:`"Particle Density"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. log (:py:class:`bool`): Log the z data or not? This can sometimes be useful but obviously one needs to be careful when drawing conclusions from the data. Returns: (:py:class:`matplotlib.Fig`): Figure object (:py:class:`matplotlib.axes.Axes`): The axes with new plots. """ fig, ax1 = plt.subplots(figsize=figsize) alphas = [1.0, 0.7, 0.5, 0.3] if log: for i in range(len(x_list)): ax1.contourf(x_list[i], y_list[i], z_list[i], cmap=palette_list[i], locator=ticker.LogLocator()) else: for i in range(len(x_list)): ax1.contourf(x_list[i], y_list[i], z_list[i], cmap=palette_list[i], alpha=alphas[i]) ax1.set_xlabel(xlab) ax1.set_ylabel(ylab) ax1.tick_params() plt.tight_layout() return fig, ax1 def combined_density_plot_multiple_species(x_list, y_list, z_list, palette_list, label_list, color_list, xlab="X Coordinate ($\AA$)", ylab="Y Coordinate ($\AA$)", figsize=(10, 6), log=False): """ Plots the distribution of a list of atom species in two dimensions. Returns heatmaps for each species stacking on top of one another. It also plots the same density in one dimension on top of the heatmaps. Args: x (:py:attr:`list`): x axis points - x axis coordinates. y (:py:attr:`list`): y axis points - y axis coordinates. z (:py:attr:`list`): z axis points - 2D array of points. palette_list (:py:attr:`list`): Color palletes for each atom species. label_list (:py:attr:`list`): List of species labels. color_list (:py:attr:`list`): List of colors for one dimensional plot. xlab (:py:attr:`str`): x axis label. Default is :py:attr:`"X Coordinate ($\AA$)"` ylab (:py:attr:`str`): y axis label. Default is :py:attr:`"Y Coordinate ($\AA$)"` fig_size (:py:class:`tuple`): Horizontal and veritcal size for figure (in inches). Default is :py:attr:`(10, 6)`. Returns: (:py:class:`matplotlib.Fig`): Figure object (:py:attr:`list`): List of axes objects. """ fig = plt.figure(constrained_layout=True, figsize=figsize) gs = GridSpec(5, 2, figure=fig) gs.update(wspace=0.025, hspace=0.05) ax2 = fig.add_subplot(gs[0,:]) ax1 = fig.add_subplot(gs[1:, :]) ax = [ax1, ax2] alphas = [1.0, 0.7, 0.5, 0.3] if log: for i in range(len(x_list)): ax1.contourf(x_list[i], y_list[i], z_list[i], cmap=palette_list[i], locator=ticker.LogLocator()) else: for i in range(len(x_list)): ax1.contourf(x_list[i], y_list[i], z_list[i], cmap=palette_list[i], alpha=alphas[i]) ax1.set_xlabel(xlab) ax1.set_ylabel(ylab) ax1.set_xlim([np.amin(x_list[0]), np.amax(x_list[0])]) ax1.tick_params() for i in range(len(x_list)): ax2.plot(x_list[i], np.sum(z_list[i], axis=0), label=label_list[i], color=color_list[i]) ax2.axis('off') ax2.set_ylim(np.amin(z_list[0]), np.amax(np.sum(z_list[0], axis=0)) * 1.4) ax2.set_xlim(np.amin(x_list[0]), np.amax(x_list[0])) ax2.legend(loc=2, ncol=len(label_list), frameon=False, fontsize=12) plt.tight_layout() return fig, ax

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py

Python

util/data/gen/wintypes.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/wintypes.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/wintypes.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'DllCanUnloadNow', 'address': '0x7ffb385047c0'}, {'type': 'function', 'name': 'DllGetActivationFactory', 'address': '0x7ffb384f9100'}, {'type': 'function', 'name': 'DllGetClassObject', 'address': '0x7ffb384f95a0'}, {'type': 'function', 'name': 'RoCreateNonAgilePropertySet', 'address': '0x7ffb3850aca0'}, {'type': 'function', 'name': 'RoCreatePropertySetSerializer', 'address': '0x7ffb385115c0'}, {'type': 'function', 'name': 'RoGetBufferMarshaler', 'address': '0x7ffb38516110'}, {'type': 'function', 'name': 'RoGetMetaDataFile', 'address': '0x7ffb38517620'}, {'type': 'function', 'name': 'RoIsApiContractMajorVersionPresent', 'address': '0x7ffb38536eb0'}, {'type': 'function', 'name': 'RoIsApiContractPresent', 'address': '0x7ffb38536f00'}, {'type': 'function', 'name': 'RoParseTypeName', 'address': '0x7ffb384f8810'}, {'type': 'function', 'name': 'RoResolveNamespace', 'address': '0x7ffb385171c0'}]

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5e845e2739cc0fb1f05c1e2ae4f3e00ccd2d7629

316

py

Python

transactions/models.py

MattatPath/Path-backend

c8fde0f39162ff020c475badc76b191f9fec600c

[ "Apache-2.0" ]

null

transactions/models.py

MattatPath/Path-backend

c8fde0f39162ff020c475badc76b191f9fec600c

[ "Apache-2.0" ]

null

transactions/models.py

MattatPath/Path-backend

c8fde0f39162ff020c475badc76b191f9fec600c

[ "Apache-2.0" ]

null

from django.db import models class Transaction(models.Model): amount = models.CharField(max_length=20, default="0.00") date = models.CharField(max_length=8, default="DATE") identification = models.CharField(max_length=20, default="ID LENGTH?") status = models.CharField(max_length=20, default="status pending")

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5e9ee150ade809ec68574bf7e4eeaaec6cfcda7a

211

py

Python

models/registry.py

liaojh1998/RNW

412764400a4a555fb8245ab51047019429d1141f

[ "MIT" ]

46

2021-07-28T11:09:24.000Z

2022-03-05T07:48:50.000Z

models/registry.py

liaojh1998/RNW

412764400a4a555fb8245ab51047019429d1141f

[ "MIT" ]

10

2021-09-27T00:26:26.000Z

2022-03-31T13:28:12.000Z

models/registry.py

liaojh1998/RNW

412764400a4a555fb8245ab51047019429d1141f

[ "MIT" ]

6

2021-11-18T08:06:12.000Z

2022-03-22T13:46:53.000Z

import mmcv from mmcv.utils import Registry def _build_func(name: str, option: mmcv.ConfigDict, registry: Registry): return registry.get(name)(option) MODELS = Registry('models', build_func=_build_func)

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5eb3d554c92266401685af3b17e7a868588a643d

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py

Python

apps/info/routers.py

ycheng-aa/gated_launch_backend

cbb9e7e530ab28d5914276e9607ebfcf84be6433

[ "MIT" ]

null

apps/info/routers.py

ycheng-aa/gated_launch_backend

cbb9e7e530ab28d5914276e9607ebfcf84be6433

[ "MIT" ]

null

apps/info/routers.py

ycheng-aa/gated_launch_backend

cbb9e7e530ab28d5914276e9607ebfcf84be6433

[ "MIT" ]

null

from apps.common.routers import routers from .views import MyTasksViewSet routers.register(r'myTasks', MyTasksViewSet, 'mytasks')

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py

Python

ASS/__init__.py

rik2803/aws-ass

31c89f888cb30124ecaedda66e36766bcd0f51c8

[ "Apache-2.0" ]

1

2019-01-19T05:53:49.000Z

ASS/__init__.py

rik2803/aws-delete-tagged-cfn-stacks

19fd32913a21801b3cecc19337f0252dce5f86b5

[ "Apache-2.0" ]

1

2021-03-08T12:50:30.000Z

ASS/__init__.py

rik2803/aws-delete-tagged-cfn-stacks

19fd32913a21801b3cecc19337f0252dce5f86b5

[ "Apache-2.0" ]

1

2020-10-30T14:45:02.000Z

from .Config import Config from .AWS import AWS from .Notification import Notification

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py

Python

src/ecs/components/deathcomponent.py

joehowells/critical-keep

4aba3322a8582a2d06ab0d4b67028738249669e9

[ "MIT" ]

1

2019-04-27T22:39:33.000Z

src/ecs/components/deathcomponent.py

joehowells/critical-keep

4aba3322a8582a2d06ab0d4b67028738249669e9

[ "MIT" ]

null

src/ecs/components/deathcomponent.py

joehowells/critical-keep

4aba3322a8582a2d06ab0d4b67028738249669e9

[ "MIT" ]

null

class DeathComponent: pass

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0d741519f1be347f6a56aa5a1b2abbf52c55d16e

59

py

Python

enthought/traits/ui/editors/title_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/traits/ui/editors/title_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/traits/ui/editors/title_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from traitsui.editors.title_editor import *

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5

0d89bcfaf81de261279d6492ef9f45b7e7d598fe

130

py

Python

privacy_evaluator/datasets/tf/__init__.py

chen-yuxuan/privacy-evaluator

ed4852408108c3e6a01216af4183261945fd7e67

[ "MIT" ]

7

2021-04-10T15:01:19.000Z

2022-02-08T14:45:21.000Z

privacy_evaluator/datasets/tf/__init__.py

chen-yuxuan/privacy-evaluator

ed4852408108c3e6a01216af4183261945fd7e67

[ "MIT" ]

175

2021-04-13T08:32:27.000Z

2021-08-30T09:44:51.000Z

privacy_evaluator/datasets/tf/__init__.py

chen-yuxuan/privacy-evaluator

ed4852408108c3e6a01216af4183261945fd7e67

[ "MIT" ]

21

2021-04-13T08:03:36.000Z

2021-10-05T15:35:01.000Z

""" Module providing TensorFlow datasets. """ from .cifar10 import TFCIFAR10 from .mnist import TFMNIST from .tf import TFDataset

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py

Python

server/api/resources/user/__init__.py

NUS-CS-MComp/cs-cloud-computing-music-personality

35cc926bef83fb8be3c6af680862343a67cd6e1c

[ "Apache-2.0" ]

2

2021-07-13T07:57:48.000Z

2021-11-18T08:20:38.000Z

server/api/resources/user/__init__.py

NUS-CS-MComp/cs-cloud-computing-music-personality

35cc926bef83fb8be3c6af680862343a67cd6e1c

[ "Apache-2.0" ]

null

server/api/resources/user/__init__.py

NUS-CS-MComp/cs-cloud-computing-music-personality

35cc926bef83fb8be3c6af680862343a67cd6e1c

[ "Apache-2.0" ]

null

from .user import UserAuthentication, UserLogout, UserRecord __all__ = ["UserAuthentication", "UserLogout", "UserRecord"]

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62

py

Python

HMMs/__init__.py

hiaoxui/D2T_Grounding

4c46f8a8d2867712399ac7c0e7f7f34ef911a69a

[ "MIT" ]

15

2018-11-16T08:59:12.000Z

2021-02-06T10:57:16.000Z

HMMs/__init__.py

hiaoxui/D2T_Grounding

4c46f8a8d2867712399ac7c0e7f7f34ef911a69a

[ "MIT" ]

2

2019-01-18T09:36:53.000Z

2019-05-01T15:06:03.000Z

HMMs/__init__.py

hiaoxui/D2T-Grounding

4c46f8a8d2867712399ac7c0e7f7f34ef911a69a

[ "MIT" ]

null

from .HMM import HMMs from .PR import PosteriorRegularization

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5

216caa0ab0a33fc950ecdeacd26e0da54e4f4336

90

py

Python

python/ovs/PaxHeaders.47482/process.py

xiaobinglu/openvswitch

b206a49997a51909d73fd5c11784c17aa885f76b

[ "Apache-2.0" ]

null

python/ovs/PaxHeaders.47482/process.py

xiaobinglu/openvswitch

b206a49997a51909d73fd5c11784c17aa885f76b

[ "Apache-2.0" ]

null

python/ovs/PaxHeaders.47482/process.py

xiaobinglu/openvswitch

b206a49997a51909d73fd5c11784c17aa885f76b

[ "Apache-2.0" ]

null

30 mtime=1365496689.478878594 30 atime=1440176559.397245608 30 ctime=1440177385.057309404

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216f6f3c20006bd0212410104a98f5b48814caab

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py

Python

dexp/datasets/__init__.py

haesleinhuepf/dexp

2ea84f3db323724588fac565fae56f0d522bc5ca

[ "BSD-3-Clause" ]

16

2021-04-21T14:09:19.000Z

2022-03-22T02:30:59.000Z

dexp/datasets/__init__.py

haesleinhuepf/dexp

2ea84f3db323724588fac565fae56f0d522bc5ca

[ "BSD-3-Clause" ]

28

2021-04-15T17:43:08.000Z

2022-03-29T16:08:35.000Z

dexp/datasets/__init__.py

haesleinhuepf/dexp

2ea84f3db323724588fac565fae56f0d522bc5ca

[ "BSD-3-Clause" ]

3

2022-02-08T17:41:30.000Z

2022-03-18T15:32:27.000Z

from dexp.datasets.base_dataset import BaseDataset from dexp.datasets.clearcontrol_dataset import CCDataset from dexp.datasets.joined_dataset import JoinedDataset from dexp.datasets.zarr_dataset import ZDataset

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217055e057f35ada80335a508123ab98c467c868

84

py

Python

Lib/js/polyfill/__init__.py

jeamick/ares-visual

3cf5068f874b3f6fe898968b2a7efa86fadca99d

[ "MIT" ]

null

Lib/js/polyfill/__init__.py

jeamick/ares-visual

3cf5068f874b3f6fe898968b2a7efa86fadca99d

[ "MIT" ]

2

2019-03-27T00:36:09.000Z

2019-04-09T00:39:12.000Z

Lib/js/polyfill/__init__.py

jeamick/ares-visual

3cf5068f874b3f6fe898968b2a7efa86fadca99d

[ "MIT" ]

null

from . import AresJsGlobals from . import AresJsData from . import AresJsDataChart

28

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0

1

0

null

0

1

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1

0

5

21ad445d2031f20659ba6e5c5005633e3e71339d

400

py

Python

src/domain/domainmodel/userstatemachine/states/user_state.py

nhmendes/pythonrestapi

9c99cb5f9d6e180a69b2d2158d6046817cf8c5fd

[ "MIT" ]

null

src/domain/domainmodel/userstatemachine/states/user_state.py

nhmendes/pythonrestapi

9c99cb5f9d6e180a69b2d2158d6046817cf8c5fd

[ "MIT" ]

null

src/domain/domainmodel/userstatemachine/states/user_state.py

nhmendes/pythonrestapi

9c99cb5f9d6e180a69b2d2158d6046817cf8c5fd

[ "MIT" ]

null

from abc import ABC, abstractmethod class UserState(ABC): @abstractmethod def get_name(self) -> str: pass def invited(self): raise Exception("Invalid operation") def active(self): raise Exception("Invalid operation") def disabled(self): raise Exception("Invalid operation") def deleted(self): raise Exception("Invalid operation")

20

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1

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21c1201c93a8b62355a6ebce7016a2c44d288b22

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py

Python

Chapter 01/Chap01_Example1.183.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

Chapter 01/Chap01_Example1.183.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

Chapter 01/Chap01_Example1.183.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

n1 = 3 print(id(n1)) print(id(3))

8.5

13

0.588235

8

34

2.5

0.5

0.7

0

0.137931

0.147059

34

3

14

11.333333

0.551724

0

1

0

false

0

0.666667

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null

1

0

1

0

null

0

1

0

5

1d062ced8f1e39dd005011887a20e8ea634159e7

145

py

Python

python.io/jindu-py2exe/setup.py

cnzht/grit

eab457a0a9b216f5a6026669095b8126bf8a9e1d

[ "MIT" ]

1

2018-04-04T09:26:21.000Z

python.io/jindu-py2exe/setup.py

cnzht/grit

eab457a0a9b216f5a6026669095b8126bf8a9e1d

[ "MIT" ]

null

python.io/jindu-py2exe/setup.py

cnzht/grit

eab457a0a9b216f5a6026669095b8126bf8a9e1d

[ "MIT" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- import py2exe from distutils.core import setup setup(console=["jindu.py"]) #console可改为windows

24.166667

46

0.696552

19

145

5.315789

0.894737

0

0.016

0.137931

145

5

47

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0.792

0.441379

0

0.102564

0

1

0

true

0

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0.666667

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

df6a229294ac476e8a58b4da1ec91a0522131c7b

53

py

Python

model/__init__.py

WillyChen123/CDFNet

12d6b288aa2a8301683395a75bd44a7be44b7f2a

[ "MIT" ]

null

model/__init__.py

WillyChen123/CDFNet

12d6b288aa2a8301683395a75bd44a7be44b7f2a

[ "MIT" ]

null

model/__init__.py

WillyChen123/CDFNet

12d6b288aa2a8301683395a75bd44a7be44b7f2a

[ "MIT" ]

null

from .RTFNet import RTFNet from .CDFNet import CDFNet

26.5

26

0.830189

8

53

5.5

0.5

0

0.132075

53

2

27

26.5

0.956522

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1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

10c325bd4fe7a6a51df2daf2afdcb26325450ed1

123

py

Python

recommender/admin.py

mfrancisco927/djangotutorial

580a002845f3438012209b2d88ca7112e2e96cf1

[ "Apache-2.0" ]

null

recommender/admin.py

mfrancisco927/djangotutorial

580a002845f3438012209b2d88ca7112e2e96cf1

[ "Apache-2.0" ]

null

recommender/admin.py

mfrancisco927/djangotutorial

580a002845f3438012209b2d88ca7112e2e96cf1

[ "Apache-2.0" ]

3

2021-02-22T01:49:11.000Z

2022-02-09T01:44:13.000Z

from django.contrib import admin from .models import Musicdata # Register your models here. admin.site.register(Musicdata)

24.6

32

0.821138

17

123

5.941176

0.647059

0

0.113821

123

5

33

24.6

0.926606

0.211382

0

1

0

true

0

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0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

10d30fdc876a8c39d46c4d7536dc311b643c0bda

20

py

Python

src/test/resources/files/singleQuotes_after.py

rendner/py-prefix-fstring-plugin

c2e2ca7cca1b3833e988543fda5bce05c6860309

[ "MIT" ]

null

src/test/resources/files/singleQuotes_after.py

rendner/py-prefix-fstring-plugin

c2e2ca7cca1b3833e988543fda5bce05c6860309

[ "MIT" ]

null

src/test/resources/files/singleQuotes_after.py

rendner/py-prefix-fstring-plugin

c2e2ca7cca1b3833e988543fda5bce05c6860309

[ "MIT" ]

1

2021-05-24T09:32:06.000Z

x = f'test {<caret>'

20

0.55

4

20

2.75

1

0

0.15

20

1

20

0.647059

0

0.619048

0

null

0

null

0

1

0

null

0

1

0

1

0

null

0

1

0

5

10dd3c1b1637f97fb4f6f2cf5742305eab1a032b

58

py

Python

contact_book/src/contact_book/gui/__init__.py

marianne-manaog/ContactBookPythonApplication

7adcdeb1f7664c1867e8b9939792ed0325ca20c8

[ "MIT" ]

null

contact_book/src/contact_book/gui/__init__.py

marianne-manaog/ContactBookPythonApplication

7adcdeb1f7664c1867e8b9939792ed0325ca20c8

[ "MIT" ]

null

contact_book/src/contact_book/gui/__init__.py

marianne-manaog/ContactBookPythonApplication

7adcdeb1f7664c1867e8b9939792ed0325ca20c8

[ "MIT" ]

null

from . import application_window, contacts_user_interface

29

57

0.87931

7

58

6.857143

1

0

0.086207

58

1

58

0.90566

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

10ebabd6d7dfb02a6809dbca4f237334f0ace00e

31

py

Python

run.py

Bloomca/github-users-explorer

f2cd60e508bfaf584aea456bea9a5eb5e0299c3e

[ "MIT" ]

null

run.py

Bloomca/github-users-explorer

f2cd60e508bfaf584aea456bea9a5eb5e0299c3e

[ "MIT" ]

null

run.py

Bloomca/github-users-explorer

f2cd60e508bfaf584aea456bea9a5eb5e0299c3e

[ "MIT" ]

null

# start with "flask run" in CLI

31

0.709677

6

31

3.666667

1

0

0.193548

31

1

31

0.88

0.935484

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

10fb55662ca4fe78c086b3ec07a127ec774e28a5

633

py

Python

8kyu/test_exclamation_marks_series_1.py

adun/codewars.py

89e7d81e9ca05a432007d634892c1cba28f5b715

[ "MIT" ]

null

8kyu/test_exclamation_marks_series_1.py

adun/codewars.py

89e7d81e9ca05a432007d634892c1cba28f5b715

[ "MIT" ]

null

8kyu/test_exclamation_marks_series_1.py

adun/codewars.py

89e7d81e9ca05a432007d634892c1cba28f5b715

[ "MIT" ]

null

# Remove a exclamation mark from the end of string. For a beginner kata, you can assume # that the input data is always a string, no need to verify it. # Examples # remove("Hi!") === "Hi" # remove("Hi!!!") === "Hi!!" # remove("!Hi") === "!Hi" # remove("!Hi!") === "!Hi" # remove("Hi! Hi!") === "Hi! Hi" # remove("Hi") === "Hi" def remove(s): return s[:-1] if s.endswith('!') else s def test_remove(): assert remove("Hi!") == "Hi" assert remove("Hi!!!") == "Hi!!" assert remove("!Hi") == "!Hi" assert remove("!Hi!") == "!Hi" assert remove("Hi! Hi!") == "Hi! Hi" assert remove("Hi") == "Hi"

27.521739

87

0.529226

90

633

3.711111

0.4

0.191617

0.359281

0.287425

0.491018

0.449102

0.389222

0

0.002024

0.219589

633

22

88

28.772727

0.674089

0.505529

0

0.148515

0

0.666667

1

0.222222

false

0

0.111111

0.333333

0

null

0

1

0

null

0

1

0

1

0

1

0

5

804377ef5839cc7f7012e69b3558f91d9cd16aa4

55

py

Python

yourapp/tests/tests.py

kevinhowbrook/wagtail-pypi-template

99273f4c1614fc60ccee8d669fadb92494f0ca4b

[ "MIT" ]

3

2020-04-20T05:35:57.000Z

2022-03-22T09:40:57.000Z

yourapp/tests/tests.py

kevinhowbrook/wagtail-pypi-template

99273f4c1614fc60ccee8d669fadb92494f0ca4b

[ "MIT" ]

1

2021-01-13T22:50:42.000Z

yourapp/tests/tests.py

kevinhowbrook/wagtail-pypi-template

99273f4c1614fc60ccee8d669fadb92494f0ca4b

[ "MIT" ]

null

from django.test import TestCase # Add your tests here

18.333333

32

0.8

9

55

4.888889

1

0

0.163636

55

3

33

18.333333

0.956522

0.345455

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

337f06d7edd2330edd549193eea0b1c4f038600a

39

py

Python

backend/blog/model/__init__.py

o8oo8o/blog

2a6f44f86469bfbb472dfd1bec4238587d8402bf

[ "MIT" ]

null

backend/blog/model/__init__.py

o8oo8o/blog

2a6f44f86469bfbb472dfd1bec4238587d8402bf

[ "MIT" ]

null

backend/blog/model/__init__.py

o8oo8o/blog

2a6f44f86469bfbb472dfd1bec4238587d8402bf

[ "MIT" ]

null

#!/usr/bin/evn python3 # coding=utf-8

9.75

22

0.666667

7

39

3.714286

1

0

0.058824

0.128205

39

3

23

13

0.705882

0.871795

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

d500436ee3e22327fc75e120dd9fb9a3c9518a54

285

py

Python

Curso de Cisco/Actividades/Intercambio de valores.py

tomasfriz/Curso-de-Cisco

a50ee5fa96bd86d468403e29ccdc3565a181af60

[ "MIT" ]

null

Curso de Cisco/Actividades/Intercambio de valores.py

tomasfriz/Curso-de-Cisco

a50ee5fa96bd86d468403e29ccdc3565a181af60

[ "MIT" ]

null

Curso de Cisco/Actividades/Intercambio de valores.py

tomasfriz/Curso-de-Cisco

a50ee5fa96bd86d468403e29ccdc3565a181af60

[ "MIT" ]

null

variable1 = 1 variable2 = 2 auxiliar = variable1 variable1 = variable2 variable2 = auxiliar print(variable1) print(variable2) #------------------------------------------- variable1 = 1 variable2 = 2 variable1, variable2 = variable2, variable1 print(variable1) print(variable2)

15

44

0.645614

27

285

6.851852

0.259259

0.227027

0.205405

0.216216

0

0.080972

0.133333

285

19

45

15

0.663968

0.150877

0

0.666667

0

null

0

null

0.333333

1

0

null

1

0

null

0

1

0

5

1d7d9b249c8e1648ec9fa11cdd645e65d7948d32

133

py

Python

rltorch/scheduler/__init__.py

Brandon-Rozek/rltorch

cb87105305315c5df4cae91fee0ee54b981bc04b

[ "MIT" ]

null

rltorch/scheduler/__init__.py

Brandon-Rozek/rltorch

cb87105305315c5df4cae91fee0ee54b981bc04b

[ "MIT" ]

null

rltorch/scheduler/__init__.py

Brandon-Rozek/rltorch

cb87105305315c5df4cae91fee0ee54b981bc04b

[ "MIT" ]

null

from .Scheduler import Scheduler from .LinearScheduler import LinearScheduler from .ExponentialScheduler import ExponentialScheduler

33.25

54

0.887218

12

133

9.833333

0.416667

0

0.090226

133

3

55

44.333333

0.975207

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1d863a2ea2286ab6af13fbb9ae5f9628e8e3cf2b

247

py

Python

dad_torch/__init__.py

trendscenter/easytorch

0faf6c7f09701c8f73ed4061214ca724c83d82aa

[ "MIT" ]

1

2021-07-04T15:37:24.000Z

dad_torch/__init__.py

trendscenter/easytorch

0faf6c7f09701c8f73ed4061214ca724c83d82aa

[ "MIT" ]

null

dad_torch/__init__.py

trendscenter/easytorch

0faf6c7f09701c8f73ed4061214ca724c83d82aa

[ "MIT" ]

1

2021-09-29T18:17:25.000Z

from dad_torch.config import default_ap, default_args from dad_torch.data import DTDataset, DTDataHandle from dad_torch.metrics import DADMetrics, DADAverages, Prf1a, ConfusionMatrix from .dad_torch import DADTorch from .trainer import NNTrainer

35.285714

77

0.854251

34

247

6.029412

0.558824

0.136585

0.234146

0

0.004525

0.105263

247

6

78

41.166667

0.923077

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

d51f3a4b1b4d23e3c75a85467b9d1625865cb146

123

py

Python

src/__init__.py

ValentinVignal/MusicLoop

0831522249b368826aa06b2b8994e443a5a5d548

[ "MIT" ]

2

2020-08-11T01:41:38.000Z

2020-09-08T03:43:44.000Z

src/__init__.py

ValentinVignal/MusicLoop

0831522249b368826aa06b2b8994e443a5a5d548

[ "MIT" ]

1

2020-10-31T12:08:47.000Z

src/__init__.py

ValentinVignal/MusicWebLooper

0831522249b368826aa06b2b8994e443a5a5d548

[ "MIT" ]

null

from .Languages import Languages from .MusicWebLooper import MusicWebLooper from .Urls import Urls from . import Buttons

17.571429

42

0.821138

15

123

6.733333

0.4

0

0.146341

123

6

43

20.5

0.961905

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d52a1bb8c68437cc62e38f0b8ef596af6652f32f

243

py

Python

mockseries/interaction/__init__.py

cyrilou242/mockseries

4dc892f971160bf31cfc7e2a60a1c5acc25be1de

[ "BSD-3-Clause" ]

18

2021-06-28T01:24:18.000Z

2022-01-24T04:54:49.000Z

mockseries/interaction/__init__.py

cyrilou242/mockseries

4dc892f971160bf31cfc7e2a60a1c5acc25be1de

[ "BSD-3-Clause" ]

23

2021-06-20T23:34:24.000Z

2022-01-24T08:14:36.000Z

mockseries/interaction/__init__.py

cyrilou242/mockseries

4dc892f971160bf31cfc7e2a60a1c5acc25be1de

[ "BSD-3-Clause" ]

null

from mockseries.interaction.additive_interaction import AdditiveInteraction from mockseries.interaction.multiplicative_interaction import MultiplicativeInteraction ADDITIVE = AdditiveInteraction() MULTIPLICATIVE = MultiplicativeInteraction()

40.5

87

0.897119

18

243

12

0.444444

0.12963

0.231481

0

0.061728

243

5

88

48.6

0.947368

0

1

0

false

0

0.5

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0.5

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1

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1

null

0

1

0

1

0

null

0

1

0

5

d595ccf07bc6eeaeaa97ad2872b7a04bb563d9ed

30,599

py

Python

sds/initial.py

hanyas/sds

3c195fb9cbd88a9284287d62c0eacb6afc4598a7

[ "MIT" ]

12

2019-09-21T13:52:09.000Z

2022-02-14T06:48:46.000Z

sds/initial.py

hanyas/sds

3c195fb9cbd88a9284287d62c0eacb6afc4598a7

[ "MIT" ]

1

2020-01-22T12:34:52.000Z

2020-01-26T21:14:11.000Z

sds/initial.py

hanyas/sds

3c195fb9cbd88a9284287d62c0eacb6afc4598a7

[ "MIT" ]

5

2019-09-18T15:11:26.000Z

2021-12-10T14:04:53.000Z

import numpy as np import numpy.random as npr import scipy as sc from scipy import stats from scipy.special import logsumexp from scipy.stats import multivariate_normal as mvn from scipy.stats import invwishart from sds.utils.stats import multivariate_normal_logpdf as lg_mvn from sds.utils.general import linear_regression, one_hot from sds.distributions.categorical import Categorical from sds.distributions.gaussian import StackedGaussiansWithPrecision from sds.distributions.gaussian import StackedGaussiansWithDiagonalPrecision from sds.distributions.lingauss import StackedLinearGaussiansWithPrecision from sds.distributions.gaussian import GaussianWithPrecision from sds.distributions.gaussian import GaussianWithDiagonalPrecision from sklearn.preprocessing import PolynomialFeatures from functools import partial from operator import mul import copy class InitCategoricalState: def __init__(self, nb_states, **kwargs): self.nb_states = nb_states self.pi = 1. / self.nb_states * np.ones(self.nb_states) @property def params(self): return self.pi @params.setter def params(self, value): self.pi = value def permute(self, perm): self.pi = self.pi[perm] def initialize(self): pass def likeliest(self): return np.argmax(self.pi) def sample(self): return npr.choice(self.nb_states, p=self.pi) def log_init(self): return np.log(self.pi) def mstep(self, p, **kwargs): eps = kwargs.get('eps', 1e-8) pi = sum([_p[0, :] for _p in p]) + eps self.pi = pi / sum(pi) class InitGaussianObservation: def __init__(self, nb_states, obs_dim, act_dim, nb_lags=1, **kwargs): assert nb_lags > 0 self.nb_states = nb_states self.obs_dim = obs_dim self.act_dim = act_dim self.nb_lags = nb_lags # self.mu = npr.randn(self.nb_states, self.obs_dim) # self._sigma_chol = 5. * npr.randn(self.nb_states, self.obs_dim, self.obs_dim) self.mu = np.zeros((self.nb_states, self.obs_dim)) self._sigma_chol = np.zeros((self.nb_states, self.obs_dim, self.obs_dim)) for k in range(self.nb_states): _sigma = invwishart.rvs(self.obs_dim + 1, np.eye(self.obs_dim)) self._sigma_chol[k] = np.linalg.cholesky(_sigma * np.eye(self.obs_dim)) self.mu[k] = mvn.rvs(mean=None, cov=1e2 * _sigma, size=(1, )) @property def sigma(self): return np.matmul(self._sigma_chol, np.swapaxes(self._sigma_chol, -1, -2)) @sigma.setter def sigma(self, value): self._sigma_chol = np.linalg.cholesky(value + 1e-8 * np.eye(self.obs_dim)) @property def params(self): return self.mu, self._sigma_chol @params.setter def params(self, value): self.mu, self._sigma_chol = value def permute(self, perm): self.mu = self.mu[perm] self._sigma_chol = self._sigma_chol[perm] def initialize(self, x, **kwargs): x0 = np.vstack([_x[:self.nb_lags] for _x in x]) self.mu = np.array([np.mean(x0, axis=0) for k in range(self.nb_states)]) self.sigma = np.array([np.cov(x0, rowvar=False) for k in range(self.nb_states)]) def mean(self, z): return self.mu[z] def sample(self, z): x = mvn(mean=self.mean(z), cov=self.sigma[z]).rvs() return np.atleast_1d(x) def log_likelihood(self, x): if isinstance(x, np.ndarray): x0 = x[:self.nb_lags] log_lik = np.zeros((x0.shape[0], self.nb_states)) for k in range(self.nb_states): log_lik[:, k] = lg_mvn(x0, self.mean(k), self.sigma[k]) return log_lik else: return list(map(self.log_likelihood, x)) def mstep(self, p, x, **kwargs): x0, p0 = [], [] for _x, _p in zip(x, p): x0.append(_x[:self.nb_lags]) p0.append(_p[:self.nb_lags]) J = np.zeros((self.nb_states, self.obs_dim)) h = np.zeros((self.nb_states, self.obs_dim)) for _x, _p in zip(x0, p0): J += np.sum(_p[:, :, None], axis=0) h += np.sum(_p[:, :, None] * _x[:, None, :], axis=0) self.mu = h / J sqerr = np.zeros((self.nb_states, self.obs_dim, self.obs_dim)) norm = np.zeros((self.nb_states, )) for _x, _p in zip(x0, p0): resid = _x[:, None, :] - self.mu sqerr += np.sum(_p[:, :, None, None] * resid[:, :, None, :] * resid[:, :, :, None], axis=0) norm += np.sum(_p, axis=0) self.sigma = sqerr / norm[:, None, None] def smooth(self, p, x): if all(isinstance(i, np.ndarray) for i in [p, x]): p0 = p[:self.nb_lags] return p0.dot(self.mu) else: return list(map(self.smooth, p, x)) class InitGaussianControl: def __init__(self, nb_states, obs_dim, act_dim, nb_lags=1, degree=1, **kwargs): assert nb_lags > 0 self.nb_states = nb_states self.obs_dim = obs_dim self.act_dim = act_dim self.nb_lags = nb_lags self.degree = degree self.feat_dim = int(sc.special.comb(self.degree + self.obs_dim, self.degree)) - 1 self.basis = PolynomialFeatures(self.degree, include_bias=False) # self.K = npr.randn(self.nb_states, self.act_dim, self.feat_dim) # self.kff = npr.randn(self.nb_states, self.act_dim) # self._sigma_chol = 5. * npr.randn(self.nb_states, self.act_dim, self.act_dim) self.K = np.zeros((self.nb_states, self.act_dim, self.feat_dim)) self.kff = np.zeros((self.nb_states, self.act_dim)) self._sigma_chol = np.zeros((self.nb_states, self.act_dim, self.act_dim)) for k in range(self.nb_states): _sigma = invwishart.rvs(self.act_dim + 1, np.eye(self.act_dim)) self._sigma_chol[k] = np.linalg.cholesky(_sigma * np.eye(self.act_dim)) self.K[k] = mvn.rvs(mean=None, cov=1e2 * _sigma, size=(self.feat_dim, )).T self.kff[k] = mvn.rvs(mean=None, cov=1e2 * _sigma, size=(1, )) @property def sigma(self): return np.matmul(self._sigma_chol, np.swapaxes(self._sigma_chol, -1, -2)) @sigma.setter def sigma(self, value): self._sigma_chol = np.linalg.cholesky(value + 1e-8 * np.eye(self.act_dim)) @property def params(self): return self.K, self.kff, self._sigma_chol @params.setter def params(self, value): self.K, self.kff, self._sigma_chol = value def permute(self, perm): self.K = self.K[perm] self.kff = self.kff[perm] self._sigma_chol = self._sigma_chol[perm] def initialize(self, x, u, **kwargs): mu0 = kwargs.get('mu0', 0.) sigma0 = kwargs.get('sigma0', 1e64) psi0 = kwargs.get('psi0', 1.) nu0 = kwargs.get('nu0', self.act_dim + 1) x0 = np.vstack([_x[:self.nb_lags] for _x in x]) u0 = np.vstack([_u[:self.nb_lags] for _u in u]) f0 = self.featurize(x0) K, kff, sigma = linear_regression(f0, u0, weights=None, fit_intercept=True, mu0=mu0, sigma0=sigma0, psi0=psi0, nu0=nu0) self.K = np.array([K for _ in range(self.nb_states)]) self.kff = np.array([kff for _ in range(self.nb_states)]) self.sigma = np.array([sigma for _ in range(self.nb_states)]) def featurize(self, x): feat = self.basis.fit_transform(np.atleast_2d(x)) return np.squeeze(feat) if x.ndim == 1\ else np.reshape(feat, (x.shape[0], -1)) def mean(self, z, x): feat = self.featurize(x) u = np.einsum('kh,...h->...k', self.K[z], feat) + self.kff[z] return np.atleast_1d(u) def sample(self, z, x): u = mvn(mean=self.mean(z, x), cov=self.sigma[z]).rvs() return np.atleast_1d(u) def log_likelihood(self, x, u): if isinstance(x, np.ndarray): x0 = x[:self.nb_lags] u0 = u[:self.nb_lags] log_lik = np.zeros((u0.shape[0], self.nb_states)) for k in range(self.nb_states): log_lik[:, k] = lg_mvn(u0, self.mean(k, x0), self.sigma[k]) return log_lik else: return list(map(self.log_likelihood, x, u)) def mstep(self, p, x, u, **kwargs): mu0 = kwargs.get('mu0', 0.) sigma0 = kwargs.get('sigma0', 1e64) psi0 = kwargs.get('psi0', 1.) nu0 = kwargs.get('nu0', self.act_dim + 1) x0, u0, p0 = [], [], [] for _x, _u, _p in zip(x, u, p): x0.append(_x[:self.nb_lags]) u0.append(_u[:self.nb_lags]) p0.append(_p[:self.nb_lags]) f0 = list(map(self.featurize, x0)) _sigma = np.zeros((self.nb_states, self.act_dim, self.act_dim)) for k in range(self.nb_states): coef, intercept, sigma = linear_regression(Xs=np.vstack(f0), ys=np.vstack(u0), weights=np.vstack(p0)[:, k], fit_intercept=True, mu0=mu0, sigma0=sigma0, psi0=psi0, nu0=nu0) self.K[k] = coef self.kff[k] = intercept _sigma[k] = sigma self.sigma = _sigma def smooth(self, p, x, u): if all(isinstance(i, np.ndarray) for i in [p, x, u]): x0 = x[:self.nb_lags] u0 = u[:self.nb_lags] p0 = p[:self.nb_lags] mu = np.zeros((len(u0), self.nb_states, self.act_dim)) for k in range(self.nb_states): mu[:, k, :] = self.mean(k, x0) return np.einsum('nk,nkl->nl', p, mu) else: return list(map(self.smooth, p, x, u)) class BayesianInitCategoricalState: def __init__(self, nb_states, prior, likelihood=None): self.nb_states = nb_states # Dirichlet prior self.prior = prior # Dirichlet posterior self.posterior = copy.deepcopy(prior) # Categorical likelihood if likelihood is not None: self.likelihood = likelihood else: pi = self.prior.rvs() self.likelihood = Categorical(dim=nb_states, pi=pi) @property def params(self): return self.likelihood.pi @params.setter def params(self, value): self.likelihood.pi = value def permute(self, perm): self.likelihood.pi = self.likelihood.pi[perm] def initialize(self): pass def likeliest(self): return np.argmax(self.likelihood.pi) def sample(self): return npr.choice(self.nb_states, p=self.likelihood.pi) def log_init(self): return np.log(self.likelihood.pi) def mstep(self, p, **kwargs): p0 = [_p[0, :] for _p in p] stats = self.likelihood.weighted_statistics(None, p0) self.posterior.nat_param = self.prior.nat_param + stats try: self.likelihood.params = self.posterior.mode() except AssertionError: self.likelihood.params = self.posterior.mean() self.empirical_bayes(**kwargs) def empirical_bayes(self, lr=1e-3): grad = self.prior.log_likelihood_grad(self.likelihood.params) self.prior.params = self.prior.params + lr * grad class _BayesianInitGaussianObservationBase: def __init__(self, nb_states, obs_dim, act_dim, nb_lags, prior, likelihood=None): assert nb_lags > 0 self.nb_states = nb_states self.obs_dim = obs_dim self.act_dim = act_dim self.nb_lags = nb_lags self.prior = prior self.posterior = copy.deepcopy(prior) self.likelihood = likelihood @property def params(self): return self.likelihood.params @params.setter def params(self, values): self.likelihood.params = values def permute(self, perm): raise NotImplementedError def initialize(self, x, **kwargs): kmeans = kwargs.get('kmeans', True) x0 = [_x[:self.nb_lags] for _x in x] t = list(map(len, x0)) if kmeans: from sklearn.cluster import KMeans km = KMeans(self.nb_states) km.fit(np.vstack(x0)) z0 = np.split(km.labels_, np.cumsum(t)[:-1]) else: z0 = list(map(partial(npr.choice, self.nb_states), t)) z0 = list(map(partial(one_hot, self.nb_states), z0)) stats = self.likelihood.weighted_statistics(x0, z0) self.posterior.nat_param = self.prior.nat_param + stats self.likelihood.params = self.posterior.rvs() def mean(self, z): x = self.likelihood.dists[z].mean() return np.atleast_1d(x) def sample(self, z): x = self.likelihood.dists[z].rvs() return np.atleast_1d(x) def log_likelihood(self, x): if isinstance(x, np.ndarray): x0 = x[:self.nb_lags] return self.likelihood.log_likelihood(x0) else: return list(map(self.log_likelihood, x)) def mstep(self, p, x, **kwargs): x0, p0 = [], [] for _x, _p in zip(x, p): x0.append(_x[:self.nb_lags]) p0.append(_p[:self.nb_lags]) stats = self.likelihood.weighted_statistics(x0, p0) self.posterior.nat_param = self.prior.nat_param + stats self.likelihood.params = self.posterior.mode() self.empirical_bayes(**kwargs) def empirical_bayes(self, lr=np.array([0., 0., 1e-3, 1e-3])): raise NotImplementedError def smooth(self, p, x): if all(isinstance(i, np.ndarray) for i in [p, x]): p0 = p[:self.nb_lags] return p0.dot(self.likelihood.mus) else: return list(map(self.smooth, p, x)) class BayesianInitGaussianObservation(_BayesianInitGaussianObservationBase): # mu = np.zeros((obs_dim,)) # kappa = 1e-64 # psi = 1e8 * np.eye(obs_dim) / (obs_dim + 1) # nu = (obs_dim + 1) + obs_dim + 1 # # from sds.distributions.composite import StackedNormalWishart # prior = StackedNormalWishart(nb_states, obs_dim, # mus=np.array([mu for _ in range(nb_states)]), # kappas=np.array([kappa for _ in range(nb_states)]), # psis=np.array([psi for _ in range(nb_states)]), # nus=np.array([nu for _ in range(nb_states)])) def __init__(self, nb_states, obs_dim, act_dim, nb_lags, prior, likelihood=None): super(BayesianInitGaussianObservation, self).__init__(nb_states, obs_dim, act_dim, nb_lags, prior, likelihood) # Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: mus, lmbdas = self.prior.rvs() self.likelihood = StackedGaussiansWithPrecision(size=self.nb_states, dim=self.obs_dim, mus=mus, lmbdas=lmbdas) def permute(self, perm): self.likelihood.mus = self.likelihood.mus[perm] self.likelihood.lmbdas = self.likelihood.lmbdas[perm] def empirical_bayes(self, lr=np.array([0., 0., 1e-3, 1e-3])): grad = self.prior.log_likelihood_grad(self.likelihood.params) self.prior.params = [p + r * g for p, g, r in zip(self.prior.params, grad, lr)] class BayesianInitDiagonalGaussianObservation(_BayesianInitGaussianObservationBase): # mu = np.zeros((obs_dim,)) # kappa = 1e-64 * np.ones((obs_dim,)) # alpha = ((obs_dim + 1) + obs_dim + 1) / 2. * np.ones((obs_dim,)) # beta = 1. / (2. * 1e8 * np.ones((obs_dim,)) / (obs_dim + 1)) # # from sds.distributions.composite import StackedNormalGamma # prior = StackedNormalGamma(nb_states, obs_dim, # mus=np.array([mu for _ in range(nb_states)]), # kappas=np.array([kappa for _ in range(nb_states)]), # alphas=np.array([alpha for _ in range(nb_states)]), # betas=np.array([beta for _ in range(nb_states)])) def __init__(self, nb_states, obs_dim, act_dim, nb_lags, prior, likelihood=None): super(BayesianInitDiagonalGaussianObservation, self).__init__(nb_states, obs_dim, act_dim, nb_lags, prior, likelihood) # Diagonal Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: mus, lmbdas_diag = self.prior.rvs() self.likelihood = StackedGaussiansWithDiagonalPrecision(size=self.nb_states, dim=self.obs_dim, mus=mus, lmbdas_diag=lmbdas_diag) def permute(self, perm): self.likelihood.mus = self.likelihood.mus[perm] self.likelihood.lmbdas_diag = self.likelihood.lmbdas_diag[perm] def empirical_bayes(self, lr=np.array([0., 0., 1e-3, 1e-3])): pass class BayesianInitGaussianControl: def __init__(self, nb_states, obs_dim, act_dim, nb_lags, prior, degree=1, likelihood=None): assert nb_lags > 0 self.nb_states = nb_states self.obs_dim = obs_dim self.act_dim = act_dim self.nb_lags = nb_lags self.degree = degree self.feat_dim = int(sc.special.comb(self.degree + self.obs_dim, self.degree)) - 1 self.basis = PolynomialFeatures(self.degree, include_bias=False) self.input_dim = self.feat_dim + 1 self.output_dim = self.act_dim self.prior = prior self.posterior = copy.deepcopy(prior) # Linear-Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: As, lmbdas = self.prior.rvs() self.likelihood = StackedLinearGaussiansWithPrecision(size=self.nb_states, column_dim=self.input_dim, row_dim=self.output_dim, As=As, lmbdas=lmbdas, affine=True) @property def params(self): return self.likelihood.params @params.setter def params(self, values): self.likelihood.params = values def permute(self, perm): self.likelihood.As = self.likelihood.As[perm] self.likelihood.lmbdas = self.likelihood.lmbdas[perm] def initialize(self, x, u, **kwargs): kmeans = kwargs.get('kmeans', False) x0, u0 = [], [] for _x, _u in zip(x, u): x0.append(_x[:self.nb_lags]) u0.append(_u[:self.nb_lags]) f0 = list(map(self.featurize, x0)) t = list(map(len, f0)) if kmeans: from sklearn.cluster import KMeans km = KMeans(self.nb_states) km.fit(np.vstack(f0)) z0 = np.split(km.labels_, np.cumsum(t)[:-1]) else: z0 = list(map(partial(npr.choice, self.nb_states), t)) z0 = list(map(partial(one_hot, self.nb_states), z0)) stats = self.likelihood.weighted_statistics(f0, u0, z0) self.posterior.nat_param = self.prior.nat_param + stats self.likelihood.params = self.posterior.rvs() def featurize(self, x): feat = self.basis.fit_transform(np.atleast_2d(x)) return np.squeeze(feat) if x.ndim == 1\ else np.reshape(feat, (x.shape[0], -1)) def mean(self, z, x): feat = self.featurize(x) u = self.likelihood.dists[z].mean(feat) return np.atleast_1d(u) def sample(self, z, x): feat = self.featurize(x) u = self.likelihood.dists[z].rvs(feat) return np.atleast_1d(u) def log_likelihood(self, x, u): if isinstance(x, np.ndarray): x0 = x[:self.nb_lags] u0 = u[:self.nb_lags] f0 = self.featurize(x0) return self.likelihood.log_likelihood(f0, u0) else: return list(map(self.log_likelihood, x, u)) def mstep(self, p, x, u, **kwargs): x0, u0, p0 = [], [], [] for _x, _u, _p in zip(x, u, p): x0.append(_x[:self.nb_lags]) u0.append(_u[:self.nb_lags]) p0.append(_p[:self.nb_lags]) f0 = list(map(self.featurize, x0)) stats = self.likelihood.weighted_statistics(f0, u0, p0) self.posterior.nat_param = self.prior.nat_param + stats self.likelihood.params = self.posterior.mode() def smooth(self, p, x, u): if all(isinstance(i, np.ndarray) for i in [p, x, u]): x0 = x[:self.nb_lags] u0 = u[:self.nb_lags] p0 = p[:self.nb_lags] mu = np.zeros((len(u0), self.nb_states, self.obs_dim)) for k in range(self.nb_states): mu[:, k, :] = self.mean(k, x0) return np.einsum('nk,nkl->nl', p0, mu) else: return list(map(self.smooth, p, x, u)) class BayesianInitGaussianControlWithAutomaticRelevance: def __init__(self, nb_states, obs_dim, act_dim, nb_lags, prior, degree=1): assert nb_lags > 0 self.nb_states = nb_states self.obs_dim = obs_dim self.act_dim = act_dim self.nb_lags = nb_lags self.degree = degree self.feat_dim = int(sc.special.comb(self.degree + self.obs_dim, self.degree)) - 1 self.basis = PolynomialFeatures(self.degree, include_bias=False) self.input_dim = self.feat_dim + 1 self.output_dim = self.act_dim likelihood_precision_prior = prior['likelihood_precision_prior'] parameter_precision_prior = prior['parameter_precision_prior'] from sds.distributions.composite import StackedMultiOutputLinearGaussianWithAutomaticRelevance self.object = StackedMultiOutputLinearGaussianWithAutomaticRelevance(self.nb_states, self.input_dim, self.output_dim, likelihood_precision_prior, parameter_precision_prior) @property def params(self): return self.object.params @params.setter def params(self, values): self.object.params = values def permute(self, perm): self.object.As = self.object.As[perm] self.object.lmbdas = self.object.lmbdas[perm] def initialize(self, x, u, **kwargs): pass def featurize(self, x): feat = self.basis.fit_transform(np.atleast_2d(x)) return np.squeeze(feat) if x.ndim == 1\ else np.reshape(feat, (x.shape[0], -1)) def mean(self, z, x): feat = self.featurize(x) u = self.object.mean(z, feat) return np.atleast_1d(u) def sample(self, z, x): feat = self.featurize(x) u = self.object.rvs(z, feat) return np.atleast_1d(u) def log_likelihood(self, x, u): if isinstance(x, np.ndarray) and isinstance(u, np.ndarray): x0 = x[:self.nb_lags] u0 = u[:self.nb_lags] f0 = self.featurize(x0) return self.object.log_likelihood(f0, u0) else: def inner(x, u): return self.log_likelihood(x, u) return list(map(inner, x, u)) def mstep(self, p, x, u, **kwargs): x0, u0, p0 = [], [], [] for _x, _u, _p in zip(x, u, p): x0.append(_x[:self.nb_lags]) u0.append(_u[:self.nb_lags]) p0.append(_p[:self.nb_lags]) f0 = list(map(self.featurize, x0)) f0, u0, p0 = list(map(np.vstack, (f0, u0, p0))) self.object.em(f0, u0, p0, **kwargs) def smooth(self, p, x, u): if all(isinstance(i, np.ndarray) for i in [p, x, u]): x0 = x[:self.nb_lags] u0 = u[:self.nb_lags] p0 = p[:self.nb_lags] mu = np.zeros((len(x), self.nb_states, self.act_dim)) for k in range(self.nb_states): mu[:, k, :] = self.mean(k, x0) return np.einsum('nk,nkl->nl', p0, mu) else: return list(map(self.smooth, p, x, u)) class _BayesianInitGaussianLatentBase: def __init__(self, ltn_dim, act_dim, nb_lags, prior, likelihood=None): assert nb_lags > 0 self.ltn_dim = ltn_dim self.act_dim = act_dim self.nb_lags = nb_lags self.prior = prior self.posterior = copy.deepcopy(prior) self.likelihood = likelihood @property def params(self): return self.likelihood.params @params.setter def params(self, values): self.likelihood.params = values def initialize(self, x, **kwargs): pass def mstep(self, stats, **kwargs): self.posterior.nat_param = self.prior.nat_param + stats self.likelihood.params = self.posterior.mode() class SingleBayesianInitGaussianLatent(_BayesianInitGaussianLatentBase): # mu = np.zeros((ltn_dim,)) # kappa = 1e-64 # psi = 1e8 * np.eye(ltn_dim) / (ltn_dim + 1) # nu = (ltn_dim + 1) + ltn_dim + 1 # # from sds.distributions.composite import NormalWishart # prior = NormalWishart(ltn_dim, # mu=mu, kappa=kappa, # psi=psi, nu=nu) def __init__(self, ltn_dim, act_dim, nb_lags, prior, likelihood=None): super(SingleBayesianInitGaussianLatent, self).__init__(ltn_dim, act_dim, nb_lags, prior, likelihood) # Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: mu, lmbda = self.prior.rvs() self.likelihood = GaussianWithPrecision(dim=self.ltn_dim, mu=mu, lmbda=lmbda) class SingleBayesianInitDiagonalGaussianLatent(_BayesianInitGaussianLatentBase): # mu = np.zeros((ltn_dim,)) # kappa = 1e-64 * np.ones((ltn_dim,)) # alpha = ((ltn_dim + 1) + ltn_dim + 1) / 2. * np.ones((ltn_dim,)) # beta = 1. / (2. * 1e8 * np.ones((ltn_dim,)) / (ltn_dim + 1)) # # from sds.distributions.composite import NormalGamma # prior = NormalGamma(ltn_dim, # mu=mu, kappa=kappa, # alphas=alpha, betas=beta) def __init__(self, ltn_dim, act_dim, nb_lags, prior, likelihood=None): super(SingleBayesianInitDiagonalGaussianLatent, self).__init__(ltn_dim, act_dim, nb_lags, prior, likelihood) # Diagonal Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: mu, lmbda_diag = self.prior.rvs() self.likelihood = GaussianWithDiagonalPrecision(dim=self.ltn_dim, mu=mu, lmbda_diag=lmbda_diag) class BayesianInitGaussianLatent(_BayesianInitGaussianLatentBase): # mu = np.zeros((ltn_dim,)) # kappa = 1e-64 # psi = 1e8 * np.eye(ltn_dim) / (ltn_dim + 1) # nu = (ltn_dim + 1) + ltn_dim + 1 # # from sds.distributions.composite import StackedNormalWishart # prior = StackedNormalWishart(nb_states, ltn_dim, # mus=np.array([mu for _ in range(nb_states)]), # kappas=np.array([kappa for _ in range(nb_states)]), # psis=np.array([psi for _ in range(nb_states)]), # nus=np.array([nu for _ in range(nb_states)])) def __init__(self, nb_states, ltn_dim, act_dim, nb_lags, prior, likelihood=None): super(BayesianInitGaussianLatent, self).__init__(ltn_dim, act_dim, nb_lags, prior, likelihood) self.nb_states = nb_states # Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: mus, lmbdas = self.prior.rvs() self.likelihood = StackedGaussiansWithPrecision(size=self.nb_states, dim=self.ltn_dim, mus=mus, lmbdas=lmbdas) def permute(self, perm): pass class BayesianInitDiagonalGaussianLatent(_BayesianInitGaussianLatentBase): # mu = np.zeros((ltn_dim,)) # kappa = 1e-64 * np.ones((ltn_dim,)) # alpha = ((ltn_dim + 1) + ltn_dim + 1) / 2. * np.ones((ltn_dim,)) # beta = 1. / (2. * 1e8 * np.ones((ltn_dim,)) / (ltn_dim + 1)) # # from sds.distributions.composite import StackedNormalGamma # prior = StackedNormalGamma(nb_states, ltn_dim, # mus=np.array([mu for _ in range(nb_states)]), # kappas=np.array([kappa for _ in range(nb_states)]), # alphas=np.array([alpha for _ in range(nb_states)]), # betas=np.array([beta for _ in range(nb_states)])) def __init__(self, nb_states, ltn_dim, act_dim, nb_lags, prior, likelihood=None): super(BayesianInitDiagonalGaussianLatent, self).__init__(ltn_dim, act_dim, nb_lags, prior, likelihood) self.nb_states = nb_states # Diagonal Gaussian likelihood if likelihood is not None: self.likelihood = likelihood else: mus, lmbdas_diag = self.prior.rvs() self.likelihood = StackedGaussiansWithDiagonalPrecision(size=self.nb_states, dim=self.ltn_dim, mus=mus, lmbdas_diag=lmbdas_diag) def permute(self, perm): pass

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0.56296

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null

0

5

637fa1b344c6a8d338258e9d43afcac58596c8d9

125

py

Python

destinator/handlers/base_handler.py

PJUllrich/distributed-systems

b362c1c6783fbd1659448277aab6c158485d7c3c

[ "MIT" ]

null

destinator/handlers/base_handler.py

PJUllrich/distributed-systems

b362c1c6783fbd1659448277aab6c158485d7c3c

[ "MIT" ]

null

destinator/handlers/base_handler.py

PJUllrich/distributed-systems

b362c1c6783fbd1659448277aab6c158485d7c3c

[ "MIT" ]

null

from abc import ABC class BaseHandler(ABC): def __init__(self, message_handler): self.parent = message_handler

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0.72

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125

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0.6875

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125

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0

1

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5

63ac1b97b49fdc0fe55229a76ab0810e2a0c624a

245

py

Python

setup.py

azarabdulla/GaraPhishik

142b5e43b538060f4b4e5df0450497927a58c78a

[ "CC0-1.0" ]

null

setup.py

azarabdulla/GaraPhishik

142b5e43b538060f4b4e5df0450497927a58c78a

[ "CC0-1.0" ]

null

setup.py

azarabdulla/GaraPhishik

142b5e43b538060f4b4e5df0450497927a58c78a

[ "CC0-1.0" ]

null

import os os.system('sudo apt install php curl python-is-python3 python3 python3-pip') os.system('pip3 install pyfiglet') os.system('sudo unzip ngrok.zip') os.system('sudo chmod +x ngrok') os.system('sudo mv ngrok /usr/bin') os.system('ngrok')

27.222222

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0.742857

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245

4.333333

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0.106122

245

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77

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null

0

1

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5

63bca27accdb49de80527711111e24a436600ec5

7,777

py

Python

tests/vns3ms/test_backups_api.py

cohesive/python-cohesivenet-sdk

5620acfa669ff97c94d9aa04a16facda37d648c1

[ "MIT" ]

null

tests/vns3ms/test_backups_api.py

cohesive/python-cohesivenet-sdk

5620acfa669ff97c94d9aa04a16facda37d648c1

[ "MIT" ]

null

tests/vns3ms/test_backups_api.py

cohesive/python-cohesivenet-sdk

5620acfa669ff97c94d9aa04a16facda37d648c1

[ "MIT" ]

null

# coding: utf-8 """ VNS3:ms API Cohesive networks VNS3 API providing complete control of your network's addresses, routes, rules and edge # noqa: E501 Contact: solutions@cohesive.net Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import pytest import cohesivenet from cohesivenet.api.vns3ms import backups_api # noqa: E501 from cohesivenet.rest import ApiException from tests.openapi import generate_method_test from tests.vns3ms.stub_data import BackupsApiData class TestMSBackupsApi(object): """VNS3:ms Backups API unit tests""" def test_get_backups(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for get_backups""" generate_method_test( ms_client, ms_api_schema, "GET", "/backups", rest_mocker, mock_request_from_schema=True, mock_response={ "response_type": "success", "response": {"backup_files": [], "failed_backups": []}, }, )(backups_api.get_backups) def test_delete_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for delete_backup""" generate_method_test( ms_client, ms_api_schema, "DELETE", "/backups", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.delete_backup) def test_get_download_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for get_download_backup""" generate_method_test( ms_client, ms_api_schema, "GET", "/backups/download", rest_mocker, mock_request_from_schema=True, mock_response="imafileimafileimafileimafileimafileimafileimafile", )(backups_api.get_download_backup) def test_post_upload_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for post_upload_backup""" generate_method_test( ms_client, ms_api_schema, "POST", "/backups/upload", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.post_upload_backup) def test_post_create_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for post_create_backup""" generate_method_test( ms_client, ms_api_schema, "POST", "/backups/create_backup", rest_mocker, mock_request_from_schema=True, mock_response=BackupsApiData.CreateBackupResponse, )(backups_api.post_create_backup) def test_get_backup_job(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for get_backup_job""" generate_method_test( ms_client, ms_api_schema, "GET", "/backups/create_backup/{uuid}", rest_mocker, mock_request_from_schema=True, mock_response=BackupsApiData.BackupJobStatusResponse, )(backups_api.get_backup_job) def test_post_restore_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for post_restore_backup""" generate_method_test( ms_client, ms_api_schema, "POST", "/backups/restore_backup", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.post_restore_backup) def test_get_snapshots_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for get_snapshots_backup""" generate_method_test( ms_client, ms_api_schema, "GET", "/snapshots_backup", rest_mocker, mock_request_from_schema=True, mock_response={ "response_type": "success", "response": { "snapshot_backup_file": { "backup_name": "snapshots.tgz", "create_time": "2020-01-01T10:10:10.000", "size": 112312312, } }, }, )(backups_api.get_snapshots_backup) def test_delete_snapshots_backup(self, rest_mocker, ms_client, ms_api_schema: dict): """Test case for delete_snapshots_backup""" generate_method_test( ms_client, ms_api_schema, "DELETE", "/snapshots_backup", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.delete_snapshots_backup) def test_get_download_snapshots_backup( self, rest_mocker, ms_client, ms_api_schema: dict ): """Test case for get_download_snapshots_backup""" generate_method_test( ms_client, ms_api_schema, "GET", "/snapshots_backup/download", rest_mocker, mock_request_from_schema=True, mock_response="imafileimafileimafileimafileimafileimafileimafile", )(backups_api.get_download_snapshots_backup) def test_post_upload_snapshots_backup( self, rest_mocker, ms_client, ms_api_schema: dict ): """Test case for post_upload_snapshots_backup""" generate_method_test( ms_client, ms_api_schema, "POST", "/snapshots_backup/upload_backup", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.post_upload_snapshots_backup) def test_get_snapshots_upload_status( self, rest_mocker, ms_client, ms_api_schema: dict ): """Test case for get_snapshots_upload_status""" generate_method_test( ms_client, ms_api_schema, "GET", "/snapshots_backup/upload_backup/{uuid}", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.get_snapshots_upload_status) def test_post_create_snapshots_backup( self, rest_mocker, ms_client, ms_api_schema: dict ): """Test case for post_create_snapshots_backup""" generate_method_test( ms_client, ms_api_schema, "POST", "/snapshots_backup/create_backup", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.post_create_snapshots_backup) def test_get_snapshots_backup_status( self, rest_mocker, ms_client, ms_api_schema: dict ): """Test case for get_snapshots_backup_status""" generate_method_test( ms_client, ms_api_schema, "GET", "/snapshots_backup/create_backup/{uuid}", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.get_snapshots_backup_status) def test_post_restore_snapshots_backup( self, rest_mocker, ms_client, ms_api_schema: dict ): """Test case for post_restore_snapshots_backup""" generate_method_test( ms_client, ms_api_schema, "POST", "/snapshots_backup/restore_backup", rest_mocker, mock_request_from_schema=True, mock_response_from_schema=True, )(backups_api.post_restore_snapshots_backup)

33.666667

123

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844

7,777

5.17891

0.112559

0.035461

0.068634

0.089224

0.792954

0.761611

0.74102

0

0.007066

0.308474

7,777

230

124

33.813043

0.80569

0.104796

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0.097856

0.057022

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0.082418

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0.126374

0

null

0

1

0

null

0

5

63c0e5257460e80b12d15b1b3793994f1d4fcda7

4,447

py

Python

db/data/generateProductImages.py

lorneez/mini-amazon

0b707cec7e8e704fa40c537f39274ba4a50e6c36

[ "MIT" ]

2

2021-10-21T02:30:38.000Z

2021-10-21T22:17:15.000Z

db/data/generateProductImages.py

lorneez/mini-amazon

0b707cec7e8e704fa40c537f39274ba4a50e6c36

[ "MIT" ]

null

db/data/generateProductImages.py

lorneez/mini-amazon

0b707cec7e8e704fa40c537f39274ba4a50e6c36

[ "MIT" ]

null

""" generateProductImages.py 50 products --> 50 images 10 images per category Categories: 1. Books 2. Electronics 3. Clothing 4. Games 5. Groceries """ def getProductImage(category, idx): bookImages = ["https://tinyurl.com/bdew8ewr", "https://tinyurl.com/ycktxfjs", "https://tinyurl.com/4uuw7d7c", "https://m.media-amazon.com/images/I/61rFgbqlcrL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71QVHBdFzAL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71QVHBdFzAL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/61q1IqrlhyL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71nSoeWRPNL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/710aLBwkr7L._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71BkEu-sVzL._AC_UL640_FMwebp_QL65_.jpg"] electronicImages = ["https://m.media-amazon.com/images/I/71hhgeQCrOL._AC_SX960_SY720_.jpg", "https://m.media-amazon.com/images/I/71+2H96GHZL._AC_SX960_SY720_.jpg", "https://m.media-amazon.com/images/I/71zRcqRQGOL._AC_SX960_SY720_.jpg", "https://m.media-amazon.com/images/I/81eRAX3sB6L._AC_SX960_SY720_.jpg", "https://m.media-amazon.com/images/I/81w3miL-DHL._AC_SX960_SY720_.jpg", "https://m.media-amazon.com/images/I/81suFCdoD6L._AC_SX960_SY720_.jpg", "https://m.media-amazon.com/images/I/61xT7CDtrwS._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/51utxdpV8cS._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/61Kq-Pz8d-L._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71LJJrKbezL._AC_UY436_FMwebp_QL65_.jpg"] clothingImages = ["https://m.media-amazon.com/images/I/91z1+DpSXIL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71A6F+t7AoL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81Sv3Z2suBL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/812m6InUlzL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/61s+ft92apL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81te-8XgnlL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/61y-w4bewQL._MCnd_AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81ZAKQ1jzeL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71VQsF2tupL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71F2tjW19JS._AC_UL640_FMwebp_QL65_.jpg"] gameImages = ["https://m.media-amazon.com/images/I/814IztfQ5LL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/817zvXdCgSL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/818iETWG-aL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/611sCBctXuL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/51PfIGfAutL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71H2kx9wTqL._AC_UY436_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71PIucvgepL._AC_UY436_QL65_.jpg", "https://m.media-amazon.com/images/I/71YC7GYYKAL._AC_UY436_QL65_.jpg", "https://m.media-amazon.com/images/I/81-FD3tzUlL._AC_UY436_QL65_.jpg", "https://m.media-amazon.com/images/I/61+AtsQkQ6S._AC_UY436_FMwebp_QL65_.jpg"] groceryImages = ["https://m.media-amazon.com/images/I/71kQY4DDlNL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81u6mQeOSmL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81DF9tHWcbL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81p6f569R0L._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/71luFg8EBjS._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81te0dgkN4L._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/61jTpExBMAL._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/81I75zgQ1-L._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/817x89Wnc5S._AC_UL640_FMwebp_QL65_.jpg", "https://m.media-amazon.com/images/I/816flmsx1JL._AC_UL640_FMwebp_QL65_.jpg"] categoryMap = {"Books": bookImages, "Electronics": electronicImages, "Clothing": clothingImages, "Games": gameImages, "Groceries": groceryImages} return categoryMap[category][idx]

37.369748

149

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null

0

1

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1

0

1

0

null

0

5

8944db986de8d470c49b60ae27db8d00296db70e

46

py

Python

commodities/exceptions.py

uktrade/tamato

4ba2ffb25eea2887e4e081c81da7634cd7b4f9ca

[ "MIT" ]

14

2020-03-25T11:11:29.000Z

2022-03-08T20:41:33.000Z

commodities/exceptions.py

uktrade/tamato

4ba2ffb25eea2887e4e081c81da7634cd7b4f9ca

[ "MIT" ]

352

2020-03-25T10:42:09.000Z

2022-03-30T15:32:26.000Z

commodities/exceptions.py

uktrade/tamato

4ba2ffb25eea2887e4e081c81da7634cd7b4f9ca

[ "MIT" ]

3

2020-08-06T12:22:41.000Z

2022-01-16T11:51:12.000Z

class InvalidIndentError(Exception): pass

15.333333

36

0.782609

4

46

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46

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0

1

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null

0

1

0

null

0

1

0

5

98521c28f15b03084ca47f442337a62bd25056d4

23

py

Python

app/tests/v1/views/__init__.py

Dave-mash/flask-blog-api

30a070020b1a784e71558bc0991246894dfeceed

[ "MIT" ]

1

2019-03-23T11:36:19.000Z

app/tests/v1/views/__init__.py

Dave-mash/flask-blog-api

30a070020b1a784e71558bc0991246894dfeceed

[ "MIT" ]

null

app/tests/v1/views/__init__.py

Dave-mash/flask-blog-api

30a070020b1a784e71558bc0991246894dfeceed

[ "MIT" ]

null

# pytest -p no:warnings

23

0.73913

4

23

4.25

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0.130435

23

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0.85

0.913043

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null

0

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null

true

0

null

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null

0

1

0

1

0

null

0

1

0

5

985734a778f3533ccfe15a02cbcaadfbbfb5a6b2

2,325

py

Python

tests/test_networkml.py

lostminty/NetML-arff

11e933f8772f8502f5b45acf226eaab08abfb090

[ "Apache-2.0" ]

null

tests/test_networkml.py

lostminty/NetML-arff

11e933f8772f8502f5b45acf226eaab08abfb090

[ "Apache-2.0" ]

3

2020-09-25T21:03:43.000Z

2022-02-10T00:40:31.000Z

tests/test_networkml.py

lostminty/NetML-arff

11e933f8772f8502f5b45acf226eaab08abfb090

[ "Apache-2.0" ]

null

import os import sys import pytest from networkml.NetworkML import NetworkML def test_networml_nofiles(): sys.argv = ['bin/networkml'] netml = NetworkML() assert netml.model.feature_list def test_networkml_eval_onelayer(): sys.argv = ['bin/networkml', '-p', 'tests/'] netml = NetworkML() assert netml.model.feature_list def test_networkml_eval_randomforest(): sys.argv = ['bin/networkml', '-p', 'tests/', '-a', 'randomforest'] os.environ['POSEIDON_PUBLIC_SESSIONS'] = '' netml = NetworkML() assert netml.model.feature_list def test_networkml_eval_sos(): sys.argv = ['bin/networkml', '-p', 'tests/trace_ab12_2001-01-01_02_03-client-ip-1-2-3-4.pcap', '-a', 'sos'] netml = NetworkML() assert netml.model.feature_list def test_networkml_train_onelayer(): sys.argv = ['bin/networkml', '-p', 'tests/', '-o', 'train'] with pytest.raises(SystemExit) as pytest_wrapped_e: netml = NetworkML() assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 1 def test_networkml_train_randomforest(): sys.argv = ['bin/networkml', '-p', 'tests/', '-o', 'train', '-a', 'randomforest', '-m', 'networkml/trained_models/randomforest/RandomForestModel.pkl'] os.environ['POSEIDON_PUBLIC_SESSIONS'] = '' with pytest.raises(SystemExit) as pytest_wrapped_e: netml = NetworkML() assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 1 def test_networkml_train_sos(): sys.argv = ['bin/networkml', '-p', 'tests/', '-o', 'train', '-a', 'sos', '-m', 'networkml/trained_models/sos/SoSmodel'] netml = NetworkML() assert not netml.model.feature_list def test_networkml_test_onelayer(): sys.argv = ['bin/networkml', '-p', 'tests/', '-o', 'test'] with pytest.raises(SystemExit) as pytest_wrapped_e: netml = NetworkML() assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 1 def test_networkml_test_randomforest(): sys.argv = ['bin/networkml', '-p', 'tests/', '-o', 'test', '-a', 'randomforest', '-m', 'networkml/trained_models/randomforest/RandomForestModel.pkl'] os.environ['POSEIDON_PUBLIC_SESSIONS'] = '' netml = NetworkML() assert netml.model.feature_list

31.849315

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false

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0.25

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null

0

1

0

1

0

null

0

5

985a0e556a7cd9ed43feff7ed1cb903e7edfddf8

203

py

Python

strainrec_app/routes/howto_routes.py

bw-ft-med-cabinet-4/DS

d718120cf7544240f914557f5a2473968e68dd33

[ "MIT" ]

null

strainrec_app/routes/howto_routes.py

bw-ft-med-cabinet-4/DS

d718120cf7544240f914557f5a2473968e68dd33

[ "MIT" ]

null

strainrec_app/routes/howto_routes.py

bw-ft-med-cabinet-4/DS

d718120cf7544240f914557f5a2473968e68dd33

[ "MIT" ]

1

2020-10-15T21:54:04.000Z

from flask import Blueprint, render_template howto_routes = Blueprint("howto_routes", __name__) @howto_routes.route("/quickstart") def getting_started(): return render_template("quickstart.html")

22.555556

50

0.788177

24

203

6.25

0.666667

0.22

0

0.103448

203

8

51

25.375

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0

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986322316ccf4a134373e5bd48fb642dc2b73996

3,552

py

Python

tests/torchgan/test_layers.py

avik-pal/torchgan

5644d519581d751605df9763c16c4d6bd7282295

[ "MIT" ]

1

2022-01-27T19:24:03.000Z

tests/torchgan/test_layers.py

avik-pal/torchgan

5644d519581d751605df9763c16c4d6bd7282295

[ "MIT" ]

null

tests/torchgan/test_layers.py

avik-pal/torchgan

5644d519581d751605df9763c16c4d6bd7282295

[ "MIT" ]

null

import os import sys import unittest import torch from torchgan.layers import * sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) class TestLayers(unittest.TestCase): def match_layer_outputs(self, layer, input, output_shape): z = layer(input) self.assertEqual(z.shape, output_shape) def test_residual_block2d(self): input = torch.rand(16, 3, 10, 10) layer = ResidualBlock2d([3, 16, 32, 3], [3, 3, 1], paddings=[1, 1, 0]) self.match_layer_outputs(layer, input, (16, 3, 10, 10)) layer = ResidualBlock2d( [3, 16, 32, 1], [3, 3, 1], paddings=[1, 1, 0], shortcut=torch.nn.Conv2d(3, 1, 3, padding=1), ) self.match_layer_outputs(layer, input, (16, 1, 10, 10)) layer = ResidualBlock2d([3, 16, 3], [1, 1]) self.match_layer_outputs(layer, input, (16, 3, 10, 10)) def test_transposed_residula_block2d(self): input = torch.rand(16, 3, 10, 10) layer = ResidualBlockTranspose2d([3, 16, 32, 3], [3, 3, 1], paddings=[1, 1, 0]) self.match_layer_outputs(layer, input, (16, 3, 10, 10)) layer = ResidualBlockTranspose2d( [3, 16, 32, 1], [3, 3, 1], paddings=[1, 1, 0], shortcut=torch.nn.Conv2d(3, 1, 3, padding=1), ) self.match_layer_outputs(layer, input, (16, 1, 10, 10)) layer = ResidualBlockTranspose2d([3, 16, 3], [1, 1]) self.match_layer_outputs(layer, input, (16, 3, 10, 10)) def test_basic_block2d(self): input = torch.rand(16, 3, 10, 10) layer = BasicBlock2d(3, 13, 3, 1, 1) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) layer = BasicBlock2d(3, 13, 3, 1, 1, batchnorm=False) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) def test_bottleneck_block2d(self): input = torch.rand(16, 3, 10, 10) layer = BottleneckBlock2d(3, 13, 3, 1, 1) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) layer = BottleneckBlock2d(3, 13, 3, 1, 1, batchnorm=False) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) def test_transition_block2d(self): input = torch.rand(16, 3, 10, 10) layer = TransitionBlock2d(3, 16, 3, 1, 1) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) layer = TransitionBlock2d(3, 16, 3, 1, 1, batchnorm=False) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) def test_transition_block_transpose2d(self): input = torch.rand(16, 3, 10, 10) layer = TransitionBlockTranspose2d(3, 16, 3, 1, 1) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) layer = TransitionBlockTranspose2d(3, 16, 3, 1, 1, batchnorm=False) self.match_layer_outputs(layer, input, (16, 16, 10, 10)) def test_dense_block2d(self): input = torch.rand(16, 3, 10, 10) layer = DenseBlock2d(5, 3, 16, BottleneckBlock2d, 3, padding=1) self.match_layer_outputs(layer, input, (16, 83, 10, 10)) def test_self_attention2d(self): input = torch.rand(16, 88, 10, 10) layer = SelfAttention2d(88) self.match_layer_outputs(layer, input, (16, 88, 10, 10)) def test_spectral_norm2d(self): input = torch.rand(16, 3, 10, 10) layer = SpectralNorm2d( torch.nn.Conv2d(3, 10, 3, padding=1), power_iterations=10 ) self.match_layer_outputs(layer, input, (16, 10, 10, 10))

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null

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5

988f1674c666f023bffde15493c2ee33c45abfa0

390

py

Python

Lib/site-packages/nbformat/corpus/tests/test_words.py

edupyter/EDUPYTER38

396183cea72987506f1ef647c0272a2577c56218

[ "bzip2-1.0.6" ]

null

Lib/site-packages/nbformat/corpus/tests/test_words.py

edupyter/EDUPYTER38

396183cea72987506f1ef647c0272a2577c56218

[ "bzip2-1.0.6" ]

null

Lib/site-packages/nbformat/corpus/tests/test_words.py

edupyter/EDUPYTER38

396183cea72987506f1ef647c0272a2577c56218

[ "bzip2-1.0.6" ]

null

"""Tests for nbformat corpus""" # Copyright (c) Jupyter Development Team. # Distributed under the terms of the Modified BSD License. from .. import words def test_generate_corpus_id(recwarn): assert len(words.generate_corpus_id()) > 7 # 1 in 4294967296 (2^32) times this will fail assert words.generate_corpus_id() != words.generate_corpus_id() assert len(recwarn) == 0

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0

null

0

1

0

5

7f576ecc55e4bdf8d47e967f49b6b003e4eb5f3b

205

py

Python

project-1.py

weasal/python_coursework

1a8d48e9d2e36f47f5147ae4ead7cae15425acdf

[ "MIT" ]

null

project-1.py

weasal/python_coursework

1a8d48e9d2e36f47f5147ae4ead7cae15425acdf

[ "MIT" ]

null

project-1.py

weasal/python_coursework

1a8d48e9d2e36f47f5147ae4ead7cae15425acdf

[ "MIT" ]

null

# program template for mini-project 0 # Modify the print statement according to # the mini-project instructions #This program prints the phrase "We want... a shrubbery!" print "We want... a shrubbery!"

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1

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0

5

7f6271e2defecbd50f012a60d71227e4f90f789e

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py

Python

tests/flytekit/unit/sdk/test_workflow.py

flytehub/flytekit

f8f53567594069b29fcd3f99abd1da71a5ef0e22

[ "Apache-2.0" ]

1

2019-10-22T05:22:16.000Z

tests/flytekit/unit/sdk/test_workflow.py

chixcode/flytekit

f901aee721847c6264d44079d4fa31a75b8876e1

[ "Apache-2.0" ]

null

tests/flytekit/unit/sdk/test_workflow.py

chixcode/flytekit

f901aee721847c6264d44079d4fa31a75b8876e1

[ "Apache-2.0" ]

1

2019-08-28T22:27:07.000Z

from __future__ import absolute_import import pytest from flytekit.common import constants from flytekit.common.exceptions import user as _user_exceptions from flytekit.common.types import primitives, base_sdk_types, containers from flytekit.sdk.tasks import python_task, inputs, outputs from flytekit.sdk.types import Types from flytekit.sdk.workflow import workflow, Input, Output, workflow_class def test_input(): i = Input(primitives.Integer, help="blah", default=None) assert i.name == '' assert i.sdk_default is None assert i.default == base_sdk_types.Void() assert i.sdk_required is False assert i.required is None assert i.help == "blah" assert i.var.description == "blah" assert i.sdk_type == primitives.Integer i = i.rename_and_return_reference('new_name') assert i.name == 'new_name' assert i.sdk_default is None assert i.default == base_sdk_types.Void() assert i.sdk_required is False assert i.required is None assert i.help == "blah" assert i.var.description == "blah" assert i.sdk_type == primitives.Integer i = Input(primitives.Integer, default=1) assert i.name == '' assert i.sdk_default is 1 assert i.default == primitives.Integer(1) assert i.sdk_required is False assert i.required is None assert i.help is None assert i.var.description == "" assert i.sdk_type == primitives.Integer i = i.rename_and_return_reference('new_name') assert i.name == 'new_name' assert i.sdk_default is 1 assert i.default == primitives.Integer(1) assert i.sdk_required is False assert i.required is None assert i.help is None assert i.var.description == "" assert i.sdk_type == primitives.Integer with pytest.raises(_user_exceptions.FlyteAssertion): Input(primitives.Integer, required=True, default=1) i = Input([primitives.Integer], default=[1, 2]) assert i.name == '' assert i.sdk_default == [1, 2] assert i.default == containers.List(primitives.Integer)([primitives.Integer(1), primitives.Integer(2)]) assert i.sdk_required is False assert i.required is None assert i.help is None assert i.var.description == "" assert i.sdk_type == containers.List(primitives.Integer) i = i.rename_and_return_reference('new_name') assert i.name == 'new_name' assert i.sdk_default == [1, 2] assert i.default == containers.List(primitives.Integer)([primitives.Integer(1), primitives.Integer(2)]) assert i.sdk_required is False assert i.required is None assert i.help is None assert i.var.description == "" assert i.sdk_type == containers.List(primitives.Integer) def test_output(): o = Output(1, sdk_type=primitives.Integer, help="blah") assert o.name == '' assert o.var.description == "blah" assert o.var.type == primitives.Integer.to_flyte_literal_type() assert o.binding_data.scalar.primitive.integer == 1 o = o.rename_and_return_reference('new_name') assert o.name == 'new_name' assert o.var.description == "blah" assert o.var.type == primitives.Integer.to_flyte_literal_type() assert o.binding_data.scalar.primitive.integer == 1 def _get_node_by_id(wf, nid): for n in wf.nodes: if n.id == nid: return n assert False def test_workflow_no_node_dependencies_or_outputs(): @inputs(a=Types.Integer) @outputs(b=Types.Integer) @python_task def my_task(wf_params, a, b): b.set(a + 1) i1 = Input(Types.Integer) i2 = Input(Types.Integer, default=5, help='Not required.') input_dict = { 'input_1': i1, 'input_2': i2 } nodes = { 'a': my_task(a=input_dict['input_1']), 'b': my_task(a=input_dict['input_2']), 'c': my_task(a=100) } w = workflow(inputs=input_dict, outputs={}, nodes=nodes) assert w.interface.inputs['input_1'].type == Types.Integer.to_flyte_literal_type() assert w.interface.inputs['input_2'].type == Types.Integer.to_flyte_literal_type() assert _get_node_by_id(w, 'a').inputs[0].var == 'a' assert _get_node_by_id(w, 'a').inputs[0].binding.promise.node_id == constants.GLOBAL_INPUT_NODE_ID assert _get_node_by_id(w, 'a').inputs[0].binding.promise.var == 'input_1' assert _get_node_by_id(w, 'b').inputs[0].binding.promise.node_id == constants.GLOBAL_INPUT_NODE_ID assert _get_node_by_id(w, 'b').inputs[0].binding.promise.var == 'input_2' assert _get_node_by_id(w, 'c').inputs[0].binding.scalar.primitive.integer == 100 def test_workflow_metaclass_no_node_dependencies_or_outputs(): @inputs(a=Types.Integer) @outputs(b=Types.Integer) @python_task def my_task(wf_params, a, b): b.set(a + 1) @workflow_class class sup(object): input_1 = Input(Types.Integer) input_2 = Input(Types.Integer, default=5, help='Not required.') a = my_task(a=input_1) b = my_task(a=input_2) c = my_task(a=100) assert sup.interface.inputs['input_1'].type == Types.Integer.to_flyte_literal_type() assert sup.interface.inputs['input_2'].type == Types.Integer.to_flyte_literal_type() assert _get_node_by_id(sup, 'a').inputs[0].var == 'a' assert _get_node_by_id(sup, 'a').inputs[0].binding.promise.node_id == constants.GLOBAL_INPUT_NODE_ID assert _get_node_by_id(sup, 'a').inputs[0].binding.promise.var == 'input_1' assert _get_node_by_id(sup, 'b').inputs[0].binding.promise.node_id == constants.GLOBAL_INPUT_NODE_ID assert _get_node_by_id(sup, 'b').inputs[0].binding.promise.var == 'input_2' assert _get_node_by_id(sup, 'c').inputs[0].binding.scalar.primitive.integer == 100

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0

1

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0

null

0

1

0

5

f6b8e3059ac52402c615ca0121ea96a496835ac6

27

py

Python

slack_api/__init__.py

EndoHizumi/channel-cleaner

8eeb95598d161108e1bcd0ae16c8ff02227a7174

[ "MIT" ]

1

2020-03-13T09:09:04.000Z

slack_api/__init__.py

challenge-every-month/channel-cleaner

c4dbb86a218b7eae7ef8905e757a2c5a4e919a14

[ "MIT" ]

null

slack_api/__init__.py

challenge-every-month/channel-cleaner

c4dbb86a218b7eae7ef8905e757a2c5a4e919a14

[ "MIT" ]

1

2020-03-03T11:57:41.000Z

from slack_api.api import *

27

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4.2

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1

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5

f6bde9b8e5fb0549a77a404c76a88cda7d940fda

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py

Python

01-HelloWorld/hello.py

MrNiebieski/boost-python-test

a0330a04c241575d6700a6b1d3f18a0567055afe

[ "BSL-1.0" ]

null

01-HelloWorld/hello.py

MrNiebieski/boost-python-test

a0330a04c241575d6700a6b1d3f18a0567055afe

[ "BSL-1.0" ]

null

01-HelloWorld/hello.py

MrNiebieski/boost-python-test

a0330a04c241575d6700a6b1d3f18a0567055afe

[ "BSL-1.0" ]

null

#!/usr/bin/env python import hello print hello.greet()

9.5

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null

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null

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1

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1

0

1

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5

63f20884c11ce07356a0003f24206d33168f5c45

104

py

Python

tia/bbg/__init__.py

AmarisAI/tia

a7043b6383e557aeea8fc7112bbffd6e36a230e9

[ "BSD-3-Clause" ]

366

2015-01-21T21:57:23.000Z

2022-03-29T09:11:24.000Z

tia/bbg/__init__.py

AmarisAI/tia

a7043b6383e557aeea8fc7112bbffd6e36a230e9

[ "BSD-3-Clause" ]

51

2015-03-01T14:20:44.000Z

2021-08-19T15:46:51.000Z

tia/bbg/__init__.py

AmarisAI/tia

a7043b6383e557aeea8fc7112bbffd6e36a230e9

[ "BSD-3-Clause" ]

160

2015-02-22T07:16:17.000Z

2022-03-29T13:41:15.000Z

from tia.bbg.v3api import * LocalTerminal = Terminal('localhost', 8194) from tia.bbg.datamgr import *

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12250a0bdfb1c0dfcfca3d037201528d0d7fe055

153

py

Python

app/routes.py

andrewmaximoff/aiohttp-redis-docker

152c296c24446ca5d3387a4e54a2f3a120899319

[ "MIT" ]

null

app/routes.py

andrewmaximoff/aiohttp-redis-docker

152c296c24446ca5d3387a4e54a2f3a120899319

[ "MIT" ]

null

app/routes.py

andrewmaximoff/aiohttp-redis-docker

152c296c24446ca5d3387a4e54a2f3a120899319

[ "MIT" ]

null

from aiohttp import web from app.views import index def init_routes(app: web.Application) -> None: app.add_routes([web.route('GET', '/', index)])

19.125

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1

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1

0

5

d6033b7edeaccb11e007400d13b5f56a4b5b4a6b

356

py

Python

services/common/domain/game_queue_interface.py

tkblackbelt/Asteroids-Multiplayer-Backend

e09b110ec8698657d8f22f2600e95acc663b1ba0

[ "Apache-2.0" ]

null

services/common/domain/game_queue_interface.py

tkblackbelt/Asteroids-Multiplayer-Backend

e09b110ec8698657d8f22f2600e95acc663b1ba0

[ "Apache-2.0" ]

null

services/common/domain/game_queue_interface.py

tkblackbelt/Asteroids-Multiplayer-Backend

e09b110ec8698657d8f22f2600e95acc663b1ba0

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod from typing import List from common.domain.game import Player class GameQueueInterface(ABC): @abstractmethod def push(self, game: Player) -> bool: pass @abstractmethod def pop(self, max_size: int = 1) -> List[Player]: pass @abstractmethod def size(self) -> int: pass

18.736842

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18

54

19.777778

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null

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1

0

1

0

1

0

5

d60a1fc6f3117cfd8aa648ca817c65523a8a5f5d

173

py

Python

Reto_Back_Adrian_Velazquez/ExpLab/admin.py

Velazquezadrian/hackthatstartup

e8845fb4e0f22232c4a143e2c76cc20e1087a01d

[ "MIT" ]

null

Reto_Back_Adrian_Velazquez/ExpLab/admin.py

Velazquezadrian/hackthatstartup

e8845fb4e0f22232c4a143e2c76cc20e1087a01d

[ "MIT" ]

null

Reto_Back_Adrian_Velazquez/ExpLab/admin.py

Velazquezadrian/hackthatstartup

e8845fb4e0f22232c4a143e2c76cc20e1087a01d

[ "MIT" ]

null

from django.contrib import admin from ExpLab.models import Employee, Experience # Register your models here. admin.site.register(Employee) admin.site.register(Experience)

21.625

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6.173913

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null

0

1

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1

0

5

d64a351c631025507a07a5a3f35359dd0a53c779

47

py

Python

hello.py

PES-Coding-for-OOP/Unit-0-Setup-Environment

a6c78259fa87b135f8caed6d6d6ed1bea6030851

[ "MIT" ]

null

hello.py

PES-Coding-for-OOP/Unit-0-Setup-Environment

a6c78259fa87b135f8caed6d6d6ed1bea6030851

[ "MIT" ]

null

hello.py

PES-Coding-for-OOP/Unit-0-Setup-Environment

a6c78259fa87b135f8caed6d6d6ed1bea6030851

[ "MIT" ]

null

def main(): ### add your code here main()

9.4

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0

null

0

1

0

1

0

5

c3a60a735a3afaa6f22f14ef1a2b192eb4ca3c5f

2,251

py

Python

CV_adv/examples/penul_guided_defect.py

ziqi-zhang/ReMoS_artifact

9cbac09333aeb0891cc54d287d6829fdf4bd5d23

[ "MIT" ]

4

2022-03-14T06:11:19.000Z

2022-03-16T09:21:59.000Z

CV_adv/examples/penul_guided_defect.py

ziqi-zhang/ReMoS_artifact

9cbac09333aeb0891cc54d287d6829fdf4bd5d23

[ "MIT" ]

null

CV_adv/examples/penul_guided_defect.py

ziqi-zhang/ReMoS_artifact

9cbac09333aeb0891cc54d287d6829fdf4bd5d23

[ "MIT" ]

2

2022-03-14T22:58:24.000Z

2022-03-16T05:29:37.000Z

import os, sys import os.path as osp import pandas as pd from pdb import set_trace as st import numpy as np np.set_printoptions(precision = 1) datasets = ["mit67", "cub200", "flower102", "sdog120", "stanford40"] dataset_names = ["Scenes", "Birds", "Flowers", "Dogs", "Actions"] methods = ["finetune", "delta", "weight", "retrain", "deltar", "renofeation", "remos"] method_names = ["Finetune", "DELTA", "Magprune", "Retrain", "DELTA-R", "Renofeation", "ReMoS"] model = "resnet18" root = "results/res18_models" m_indexes = pd.MultiIndex.from_product([dataset_names, method_names], names=["Dataset", "Techniques"]) result = pd.DataFrame(np.random.randn(2, 5*7), index=["Acc", "DIR"], columns=m_indexes) for dataset_name, dataset in zip(dataset_names, datasets): for method_name, method in zip(method_names, methods): path = osp.join(root, method, f"{model}_{dataset}", "posttrain_eval.txt") with open(path) as f: line = f.readline() info = line.split("|") acc = float(info[0].split()[-1]) dir = float(info[-1].split()[-1]) result[(dataset_name, method_name)]["Acc"] = acc result[(dataset_name, method_name)]["DIR"] = dir print("="*30, " ResNet18 ", "="*30) for dataset_name in dataset_names: print(f"Dataset {dataset_name}:") print(result[dataset_name]) model = "resnet50" root = "results/res50_models" m_indexes = pd.MultiIndex.from_product([dataset_names, method_names], names=["Dataset", "Techniques"]) result = pd.DataFrame(np.random.randn(2, 5*7), index=["Acc", "DIR"], columns=m_indexes) for dataset_name, dataset in zip(dataset_names, datasets): for method_name, method in zip(method_names, methods): path = osp.join(root, method, f"{model}_{dataset}", "posttrain_eval.txt") with open(path) as f: line = f.readline() info = line.split("|") acc = float(info[0].split()[-1]) dir = float(info[-1].split()[-1]) result[(dataset_name, method_name)]["Acc"] = acc result[(dataset_name, method_name)]["DIR"] = dir print("="*30, " ResNet50 ", "="*30) for dataset_name in dataset_names: print(f"Dataset {dataset_name}:") print(result[dataset_name])

43.288462

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null

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null

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5

c3aaef1e6fddaa49b9b115939e0b2234c541f740

53

py

Python

src/attrbench/metrics/runtime/__init__.py

zoeparman/benchmark

96331b7fa0db84f5f422b52cae2211b41bbd15ce

[ "MIT" ]

null

src/attrbench/metrics/runtime/__init__.py

zoeparman/benchmark

96331b7fa0db84f5f422b52cae2211b41bbd15ce

[ "MIT" ]

7

2020-03-02T13:03:50.000Z

2022-03-12T00:16:20.000Z

src/attrbench/metrics/runtime/__init__.py

zoeparman/benchmark

96331b7fa0db84f5f422b52cae2211b41bbd15ce

[ "MIT" ]

null

from .runtime import runtime, RuntimeResult, Runtime

26.5

52

0.830189

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1

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1

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5

c3afddfc4ad61ae96a832636766c90c1f1ee9e8c

408

py

Python

classification_models/__init__.py

MinuteswithMetrics/classification_models

3caf45b4d565fd786a019518a69373a04fae90c5

[ "MIT" ]

null

classification_models/__init__.py

MinuteswithMetrics/classification_models

3caf45b4d565fd786a019518a69373a04fae90c5

[ "MIT" ]

null

classification_models/__init__.py

MinuteswithMetrics/classification_models

3caf45b4d565fd786a019518a69373a04fae90c5

[ "MIT" ]

null

from .resnet.models import ResNet18 from .resnet.models import ResNet34 from .resnet.models import ResNet50 from .resnet.models import ResNet101 from .resnet.models import ResNet152 from .resnext.models import ResNeXt50 from .resnext.models import ResNeXt101 from .__version__ import __version__ __all__ = ['ResNet18', 'ResNet34', 'ResNet50', 'ResNet101', 'ResNet152', 'ResNeXt50', 'ResNeXt101']

34

72

0.776961

47

408

6.489362

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0.262295

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34

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null

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null

0

1

0

1

0

5

c3e8b06e48359aab5c5f6c93475275e85519af70

2,539

py

Python

perks.py

LonlyGamerX/Dead-By-Daylight-Randomizer

9295fa44893b1ea898515124cb97089639b0c2bb

[ "MIT" ]

null

perks.py

LonlyGamerX/Dead-By-Daylight-Randomizer

9295fa44893b1ea898515124cb97089639b0c2bb

[ "MIT" ]

null

perks.py

LonlyGamerX/Dead-By-Daylight-Randomizer

9295fa44893b1ea898515124cb97089639b0c2bb

[ "MIT" ]

null

survivorPerks = ('Repressed Alliance','Blood Pact','Soul Guard','Dark Sense','Deja Vu', 'Hope','Lightweight','No One Left Behind',"Plunderer's Instinct", 'Premonition', 'Resilience', 'Slippery Meat', 'Small Game', 'Spine Chill', 'This is Not Happening', "We'll Make It",'Open-Handed', 'Up the Ante', 'Ace in the Hole', 'Deliverance', 'Autodidact', 'Diversion', 'Flip-Flop', 'Buckle Up', 'Mettle of Man', 'Self Care', 'Empathy', 'Botany Knowledge', 'Leader', 'Bond', 'Prove Thyself','Dead Hard', 'No Mither', "We're Gonna Live Forever", 'Stake Out', 'Tenacity', "Detective's Hunch", 'Technician', 'Lithe', 'Alert', 'Calm Spirit', 'Iron Will', 'Saboteur', 'Head On', 'Poised', 'Solidarity', 'Aftercare', 'Breakdown', 'Distortion','Windows Of Opportunity', 'Boil Over', 'Dance With Me', 'Decisive Strike','Sole Survivor', 'Object of Obsession', 'Adrenaline', 'Sprint Burst', 'Quick and Quiet', 'Better Together','Fixated', 'Inner Strength', 'Balanced Landing', 'Urban Evasion', 'Streetwise', 'Pharmacy','Wake Up!', 'Vigil', 'Lucky Break', 'Any Means Necessary', 'Breakout', 'Babysitter', 'Camaraderie', 'Second Wind','Borrowed Time', 'Left Behind', 'Unbreakable') killerPerks = ( 'Save the Best for Last', 'Dying Light', 'Play With your Food', 'Beast of Prey', 'Hex: Huntress Lullaby', 'Territorial Imperative', 'Remember Me', 'Blood Warden', 'Fire Up', 'Knock Out', 'Barbecue & Chilli', "Franklin's Demise", 'Make Your Choice', "Hangman's Trick", 'Surveillance', 'Bamboozle', 'Coulrophobia', 'Pop Goes The Weasel', 'Monitor & Abuse', 'Overcharge', 'Overwhelming Presence','Shadowborn', 'Bloodhound', 'Predator', 'Enduring', 'Lightborn' 'Tinkerer', 'Agitation', 'Unnerving Presence', 'Brutal Strength', "Nurse's Calling", 'Stridor', 'Thanatophobia', 'Hex: Devour Hope', 'Hex: Ruin', 'Hex: The Third Seal', 'Spirit Fury', 'Hex: Haunted Ground', 'Rancor', 'Discordance', 'Iron Maiden', 'Mad Grit', 'Corrupt Intervention', 'Dark Devotion', 'Infectious Fright', 'Furtive Chase', "I'm All Ears", 'Thrilling Tremors', 'Cruel Limits', 'Mindbreaker', 'Surge', 'Blood Echo', 'Nemesis', 'Zanshin Tactics', 'Gear Head', "Dead Man's Switch", 'Hex: Retribution', 'Bitter Murmur', 'Deerstalker', 'Distressing', 'Insidious', 'Iron Grasp', 'Monstrous Shrine', 'No One Escapes Death', 'Sloppy Butcher', 'Spies From The Shadows', 'Thrill of the Hunt', 'Unrelenting', 'Whispers', 'Forced Penance', 'Trail of Torment', 'Deathbound')

846.333333

1,318

0.658133

293

2,539

5.703072

0.83959

0.005984

0

0.157936

2,539

3

1,318

846.333333

0.781572

0

0.709055

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1

0

1

0

null

0

1

0

5

c3f65d9d2284b07bd11ce0c3a66e52992713e78f

183

py

Python

mmcif/io/__init__.py

epeisach/py-mmcif

f44cb8e4a1699c1f254f873fadced1f4461763f6

[ "Apache-2.0" ]

9

2019-08-29T09:43:02.000Z

2022-01-11T01:00:39.000Z

mmcif/io/__init__.py

epeisach/py-mmcif

f44cb8e4a1699c1f254f873fadced1f4461763f6

[ "Apache-2.0" ]

7

2018-07-03T16:04:38.000Z

2022-03-23T05:54:37.000Z

mmcif/io/__init__.py

epeisach/py-mmcif

f44cb8e4a1699c1f254f873fadced1f4461763f6

[ "Apache-2.0" ]

10

2019-03-05T18:06:59.000Z

2022-01-27T03:32:19.000Z

# # try: from mmcif.io.IoAdapterCore import IoAdapterCore as IoAdapter # noqa: F401 except Exception: from mmcif.io.IoAdapterPy import IoAdapterPy as IoAdapter # noqa: F401

26.142857

79

0.754098

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183

6

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0.15942

0.275362

0

0.04

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183

6

80

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1

0

1

0

null

0

1

0

1

0

5

c3f9cc5cf08f6904dabebf5c7adedd608de225a7

224

py

Python

drl/agents/preprocessing/vision.py

lucaslingle/pytorch_drl

6b2c1142a36553ce5dcb0a5768767579676d5791

[ "MIT" ]

null

drl/agents/preprocessing/vision.py

lucaslingle/pytorch_drl

6b2c1142a36553ce5dcb0a5768767579676d5791

[ "MIT" ]

null

drl/agents/preprocessing/vision.py

lucaslingle/pytorch_drl

6b2c1142a36553ce5dcb0a5768767579676d5791

[ "MIT" ]

null

from drl.agents.preprocessing.abstract import Preprocessing class ToChannelMajor(Preprocessing): def __init__(self): super().__init__() def forward(self, x, **kwargs): return x.permute(0, 3, 1, 2)

22.4

59

0.678571

27

224

5.333333

0.777778

0

0.022346

0.200893

224

9

60

24.888889

0.782123

0

1

0.333333

false

0

0.166667

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7f0dca7fdbd0281664930931d7f628d043814816

139

py

Python

votaciones/admin.py

marthalilianamd/SIVORE

7f0d6c2c79aa909e6cecbf5f562ebe64f40a560d

[ "Apache-2.0" ]

1

2016-02-11T05:01:49.000Z

votaciones/admin.py

marthalilianamd/SIVORE

7f0d6c2c79aa909e6cecbf5f562ebe64f40a560d

[ "Apache-2.0" ]

89

2016-01-29T00:04:48.000Z

2016-07-05T15:52:30.000Z

votaciones/admin.py

Jorgesolis1989/SIVORE

7f0d6c2c79aa909e6cecbf5f562ebe64f40a560d

[ "Apache-2.0" ]

2

2016-02-08T15:16:12.000Z

2016-05-14T02:33:06.000Z

from django.contrib import admin from votaciones.models import Votacion_Log # Register your models here. admin.site.register(Votacion_Log)

27.8

42

0.841727

20

139

5.75

0.65

0.191304

0

0.100719

139

5

43

27.8

0.92

0.18705

0

1

0

true

0

0.666667

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0.666667

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

7f10daa357e73830e86e176429f62ebb1165e399

208

py

Python

packageit/test.py

hendrikdutoit/PackageIt

c9358c97f3bc99fd33a8ff9abea23dc26e538d4f

[ "MIT" ]

null

packageit/test.py

hendrikdutoit/PackageIt

c9358c97f3bc99fd33a8ff9abea23dc26e538d4f

[ "MIT" ]

39

2021-12-25T01:02:44.000Z

2022-01-24T08:17:37.000Z

packageit/test.py

hendrikdutoit/PackageIt

c9358c97f3bc99fd33a8ff9abea23dc26e538d4f

[ "MIT" ]

null

class Pizza: def __init__(self, ingredients): self.ingredients = ingredients def __repr__(self): return f"Pizza({self.ingredients!r})" print(Pizza(["cheese", "tomatoes"]))

20.8

46

0.620192

22

208

5.5

0.590909

0.371901

0

0.240385

208

9

47

23.111111

0.765823

0

0.20603

0.135678

0

1

0.333333

false

0

0.166667

0.666667

0.166667

1

0

null

1

0

1

0

null

0

1

0

1

0

5

617d43d8af6955c11c6f523d89e880f6dcf8fe9f

150

py

Python

mail/admin.py

tomwhross/web50-mail

0538e30e7f73e82bbbcf2ff6ee260b9858d3d1ed

[ "MIT" ]

null

mail/admin.py

tomwhross/web50-mail

0538e30e7f73e82bbbcf2ff6ee260b9858d3d1ed

[ "MIT" ]

null

mail/admin.py

tomwhross/web50-mail

0538e30e7f73e82bbbcf2ff6ee260b9858d3d1ed

[ "MIT" ]

null

from django.contrib import admin from .models import Email, User admin.site.register(Email) admin.site.register(User) # Register your models here.

16.666667

32

0.786667

22

150

5.363636

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0.152542

0.288136

0

0.126667

150

8

33

18.75

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0

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true

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1

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null

0

1

0

1

0

5

61ca9ed02e81f6f0e43616801ee5d05cca3b95bd

158

py

Python

base_django_api/router.py

aeasringnar/-django-RESTfulAPI

3065f7617dc3534005ab94cd08324c2b51526634

[ "MIT" ]

242

2019-07-05T06:15:26.000Z

2022-03-30T17:51:06.000Z

base_django_api/router.py

aeasringnar/-django-RESTfulAPI

3065f7617dc3534005ab94cd08324c2b51526634

[ "MIT" ]

11

2019-10-16T09:17:26.000Z

2022-03-15T05:51:29.000Z

base_django_api/router.py

aeasringnar/-django-RESTfulAPI

3065f7617dc3534005ab94cd08324c2b51526634

[ "MIT" ]

37

2019-09-29T01:11:27.000Z

2022-03-29T07:08:25.000Z

class Router(object): def db_for_read(self, model, **hints): return 'read' def db_for_write(self, model, **hints): return 'default'

19.75

43

0.613924

21

158

4.428571

0.619048

0.107527

0.172043

0.430108

0

0.253165

158

7

44

22.571429

0.788136

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0

1

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1

0

null

0

1

0

1

0

5

4ee04c61ab5038b8025544054098927c47d7ea89

213

py

Python

torchreid/engine/image/__init__.py

kirillProkofiev/deep-object-reid

2abc96ec49bc0005ed556e203925354fdf12165c

[ "MIT" ]

null

torchreid/engine/image/__init__.py

kirillProkofiev/deep-object-reid

2abc96ec49bc0005ed556e203925354fdf12165c

[ "MIT" ]

null

torchreid/engine/image/__init__.py

kirillProkofiev/deep-object-reid

2abc96ec49bc0005ed556e203925354fdf12165c

[ "MIT" ]

null

from __future__ import absolute_import from .softmax import ImageSoftmaxEngine from .am_softmax import ImageAMSoftmaxEngine from .triplet import ImageTripletEngine from .contrastive import ImageContrastiveEngine

30.428571

47

0.882629

22

213

8.272727

0.545455

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0

0.098592

213

6

48

35.5

0.947917

0

1

0

true

0

1

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1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

5

4ee2a35140aa9bb5cb70f05c9841e1208ccf37df

176

py

Python

challenges/00_introduction/test_A.py

deniederhut/workshop_pyintensive

f8f494081c6daabeae0724aa058c2b80fe42878b

[ "BSD-2-Clause" ]

1

2016-10-04T00:04:56.000Z

challenges/00_introduction/test_A.py

deniederhut/workshop_pyintensive

f8f494081c6daabeae0724aa058c2b80fe42878b

[ "BSD-2-Clause" ]

8

2015-12-26T05:49:39.000Z

2016-05-26T00:10:57.000Z

challenges/00_introduction/test_A.py

deniederhut/workshop_pyintensive

f8f494081c6daabeae0724aa058c2b80fe42878b

[ "BSD-2-Clause" ]

null

#!/bin/env python import pytest import sys import A_objects as A def test_version(): assert A.version > (3,4) def test_dillon(): assert isinstance(A.dillon, float)

13.538462

38

0.704545

28

176

4.321429

0.642857

0.115702

0

0.013986

0.1875

176

12

39

14.666667

0.832168

0.090909

0

0.285714

1

0.285714

true

0

0.428571

0

0.714286

0

1

0

null

0

null

0

1

0

1

0

1

0

5

4ee909de79c62bc6f80a30df33bd9647bff7a7f1

155

py

Python

tests/test_imports.py

bukowa/django-georangefilter

a2437b0a54d0098b903eae886c2d5a2ec26e6ffa

[ "Unlicense" ]

1

2018-06-04T16:17:02.000Z

tests/test_imports.py

bukowa/django-georangefilter

a2437b0a54d0098b903eae886c2d5a2ec26e6ffa

[ "Unlicense" ]

null

tests/test_imports.py

bukowa/django-georangefilter

a2437b0a54d0098b903eae886c2d5a2ec26e6ffa

[ "Unlicense" ]

null

from django.test import SimpleTestCase class PackageImportTestCase(SimpleTestCase): def test_can_import_package(self): import georangefilter

22.142857

44

0.8

16

155

7.5625

0.75

0

0.154839

155

6

45

25.833333

0.923664

0

1

0.25

false

0

1

0

1.5

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f600db522dbd7410cab1aa5e42e6f1f10af68bd0

82

py

Python

samplepack/__init__.py

jensqin/samplepack

49e0799154968ac9fc6eb2510d6972f091fd1676

[ "MIT" ]

1

2021-06-11T14:20:01.000Z

samplepack/__init__.py

jensqin/samplepack

49e0799154968ac9fc6eb2510d6972f091fd1676

[ "MIT" ]

null

samplepack/__init__.py

jensqin/samplepack

49e0799154968ac9fc6eb2510d6972f091fd1676

[ "MIT" ]

1

2021-06-11T14:20:03.000Z

from samplepack.sample01 import sample01 from samplepack.sample02 import sample02

27.333333

40

0.878049

10

82

7.2

0.5

0.388889

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82

2

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0

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0

true

0

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1

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1

0

null

0

1

0

1

0

5

f60adcc891304e34ac9d85d108b6a232b4bf0c93

172

py

Python

scipy/optimize/_lsq/__init__.py

Ennosigaeon/scipy

2d872f7cf2098031b9be863ec25e366a550b229c

[ "BSD-3-Clause" ]

9,095

2015-01-02T18:24:23.000Z

2022-03-31T20:35:31.000Z

scipy/optimize/_lsq/__init__.py

Ennosigaeon/scipy

2d872f7cf2098031b9be863ec25e366a550b229c

[ "BSD-3-Clause" ]

11,500

2015-01-01T01:15:30.000Z

2022-03-31T23:07:35.000Z

scipy/optimize/_lsq/__init__.py

Ennosigaeon/scipy

2d872f7cf2098031b9be863ec25e366a550b229c

[ "BSD-3-Clause" ]

5,838

2015-01-05T11:56:42.000Z

2022-03-31T23:21:19.000Z

"""This module contains least-squares algorithms.""" from .least_squares import least_squares from .lsq_linear import lsq_linear __all__ = ['least_squares', 'lsq_linear']

28.666667

52

0.790698

23

172

5.478261

0.478261

0.380952

0

0.104651

172

5

53

34.4

0.818182

0.267442

0

0.191667

0

1

0

false

0

0.666667

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0.666667

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0

1

0

null

0

1

0

1

0

5

f62ed672364b9c1e481236017d1eb7da5c0ce403

7,322

py

Python

Finance2Go/solvers/ContinuousInterest.py

DenManokhin/Finance2Go

d8ad58ad8d6957e3b1c5809e44105552f6cfa05c

[ "MIT" ]

null

Finance2Go/solvers/ContinuousInterest.py

DenManokhin/Finance2Go

d8ad58ad8d6957e3b1c5809e44105552f6cfa05c

[ "MIT" ]

4

2021-11-19T23:36:19.000Z

2021-12-07T22:41:43.000Z

Finance2Go/solvers/ContinuousInterest.py

DenManokhin/Finance2Go

d8ad58ad8d6957e3b1c5809e44105552f6cfa05c

[ "MIT" ]

null

import numpy as np # 1 def get_accumulated_value_with_growth_force(n: int, p: float, delta: float) -> float: """" Повертає нарощена суму у випадку використання сили росту. Parameters ---------- n : int Термін угоди, виражений у роках p : float Сума грошей (капітал), що даються в борг delta : float Неперервна ставка нарощення (сила росту) Returns ------- S : float Нарощена сума. """ return p * np.exp(n * delta) # 2 def get_continuous_interest(i: float) -> float: """" Повертає неперервну ставку нарощення. Parameters ---------- i : float Складна дискретна ставка нарощення Returns ------- delta : float Неперервна ставка нарощення. """ return np.log(1 + i) # 3 def get_discrete_interest(delta: float) -> float: """" Повертає складну дискретна ставка нарощення. Parameters ---------- delta : float Складна неперервна ставку нарощення Returns ------- i : float Дискретна ставка нарощення. """ return np.exp(delta) - 1 # 4 def get_mathematical_discounting_with_growth_force(n: int, s: float, delta: float) -> float: """" Повертає визначення теперішньої суми боргу P за відомою кінцевою сумою S. Parameters ---------- n : int Термін кредиту у роках s : float Нарощена сума боргу delta : float Неперервна ставка нарощення (сила росту) Returns ------- P : float Математична дисконтована вартість. """ return s * np.exp(-delta * n) # 5 def get_build_up_multiplier_for_linear_function(n: int, a: float, delta: float) -> float: """" Повертає множник нарощення у випадку використання змінної сили росту для лінійної залежності. Parameters ---------- n : int Термін кредиту у роках a : float Приріст сили росту за одиницю часу delta : float Початкове значення сили росту Returns ------- : float Множник нарощення. """ return np.exp(delta * n + (a * n**2) / 2) # 6 def get_build_up_multiplier_for_exp_function(n: int, a: float, delta: float) -> float: """" Повертає множник нарощення у випадку використання змінної сили росту для експоненціальної залежності. Parameters ---------- n : int Термін кредиту у роках a : float Приріст сили росту за одиницю часу delta : float Початкове значення сили росту Returns ------- : float Множник нарощення. """ return (delta / np.log(a)) * (a**n - 1) # 7 def get_accumulated_value_with_changeable_growth_force(n: int, a: float, p: float, delta: float, func_type: str) -> \ float: """" Повертає нарощена суму у випадку використання змінної сили росту. Parameters ---------- n : int Термін кредиту у роках a : float Приріст сили росту за одиницю часу p : float Сума грошей (капітал), що даються в борг delta : float Неперервна ставка нарощення (сила росту) func_type: str Тип неперервної функції (linear або exp) Returns ------- S : float Нарощена сума. """ if func_type == "linear": return p * get_build_up_multiplier_for_linear_function(n, a, delta) elif func_type == "exp": return p * get_build_up_multiplier_for_exp_function(n, a, delta) else: pass # 8 def get_mathematical_discounting_with_changeable_growth_force( n: int, a: float, s: float, delta: float, func_type: str) -> float: """" Повертає визначення теперішньої суми боргу P за відомою кінцевою сумою S. Parameters ---------- n : int Термін кредиту у роках a : float Приріст сили росту за одиницю часу s : float Нарощена сума боргу delta : float Неперервна ставка нарощення (сила росту) func_type: str Тип неперервної функції (linear або exp) Returns ------- P : float Математична дисконтована вартість. """ if func_type == "linear": return s * get_build_up_multiplier_for_linear_function(n, a, delta) elif func_type == "exp": return s * get_build_up_multiplier_for_exp_function(n, a, delta) else: pass # 9 def get_loan_term_with_growth_force(s: float, p: float, delta: float) -> float: """" Повертає термін кредиту у роках для нарощення з постійно силою росту. Parameters ---------- s : float Нарощена сума боргу p : float Сума грошей (капітал), що даються в борг delta : float Неперервна ставка нарощення (сила росту) Returns ------- n : float Термін кредиту. """ return np.log(s / p) / delta # 10 def get_continuous_rate_with_growth_force(n: int, p: float, s: float) -> float: """" Повертає неперервну ставку для нарощення з постійно силою росту. Parameters ---------- n : int Термін угоди, виражений у роках p : float Сума грошей (капітал), що даються в борг s : float Нарощена сума боргу Returns ------- delta : float Неперервна ставка. """ return np.log(s / p) / n # 11 def get_loan_term_with_changeable_growth_force(a: float, p: float, s: float, delta: float) -> float: """" Повертає термін кредиту у роках для нарощення зі змінною силою росту. Parameters ---------- a : float Приріст сили росту за одиницю часу p : float Сума грошей (капітал), що даються в борг s : float Нарощена сума боргу delta : float Неперервна ставка нарощення (сила росту) Returns ------- n : float Термін кредиту. """ return np.log(1 + (np.log(a) * np.log(s / p) / delta)) / np.log(a) # 12 def get_continuous_rate_with_changeable_growth_force(n: int, a: float, p: float, s: float) -> float: """" Повертає неперервну ставку для нарощення зі змінною силою росту. Parameters ---------- n : int Термін угоди, виражений у роках a : float Приріст сили росту за одиницю часу p : float Сума грошей (капітал), що даються в борг s : float Нарощена сума боргу Returns ------- delta : float Неперервна ставка. """ return (np.log(a) * np.log(s / p)) / (a**n - 1)

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f65bbe07fb11515923271c806e235a306382c761

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py

Python

coordinator/SwitchOracles/__init__.py

TotalKnob/muse

3c4a05d2a3ffc3bceb6827207a2a4a1a63aba503

[ "Apache-2.0" ]

122

2019-11-12T18:43:32.000Z

2022-01-30T15:11:43.000Z

coordinator/SwitchOracles/__init__.py

TotalKnob/muse

3c4a05d2a3ffc3bceb6827207a2a4a1a63aba503

[ "Apache-2.0" ]

14

2020-04-02T07:31:46.000Z

2022-03-25T07:35:59.000Z

coordinator/SwitchOracles/__init__.py

TotalKnob/muse

3c4a05d2a3ffc3bceb6827207a2a4a1a63aba503

[ "Apache-2.0" ]

34

2019-11-12T18:43:34.000Z

2021-12-22T07:46:03.000Z

from oracle import get_switch_oracle

18.5

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py

Python

python/testlint/test/example.py

mpsonntag/snippets

fc3cc42ea49b885c1f29c0aef1379055a931a978

[ "BSD-3-Clause" ]

null

python/testlint/test/example.py

mpsonntag/snippets

fc3cc42ea49b885c1f29c0aef1379055a931a978

[ "BSD-3-Clause" ]

null

python/testlint/test/example.py

mpsonntag/snippets

fc3cc42ea49b885c1f29c0aef1379055a931a978

[ "BSD-3-Clause" ]

null

def add_up(a, b): return a + b def test_add_up_succeed(): assert add_up(1, 2) == 3 def test_add_up_fail(): assert add_up(1, 2) == 4 def ignore_me(): assert "I will be" == "completely ignored"

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py

Python

e.g._mocking_decorator/target.py

thinkAmi-sandbox/python_mock-sample

ba8714e5fe375bf2f74810eb0bb99e996adac09e

[ "Unlicense" ]

6

2017-03-09T02:14:47.000Z

2021-03-17T09:29:51.000Z

e.g._mocking_decorator/target.py

thinkAmi-sandbox/python_mock-sample

ba8714e5fe375bf2f74810eb0bb99e996adac09e

[ "Unlicense" ]

null

e.g._mocking_decorator/target.py

thinkAmi-sandbox/python_mock-sample

ba8714e5fe375bf2f74810eb0bb99e996adac09e

[ "Unlicense" ]

null

from deco.my_decorator import countup, countdown, add, calculate class Target: def __init__(self, value=0): self.value = value @countup def execute_count_up(self): return self.value @countdown def execute_count_down(self): return self.value @add(2) def execute_add(self): return self.value @calculate(1, 2, 3) def execute_calculate_increment(self): return self.value @calculate(1, 2, 3, is_decrement=True) def execute_calculate_decrement(self): return self.value if __name__ == '__main__': t = Target() print(t.execute_add())

20.451613

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0.180879

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5

9c9af43acf8c7654d51d3a0d0e83a218ca4e79ff

576

py

Python

spec/python/test_expr_int_div.py

DarkShadow44/kaitai_struct_tests

4bb13cef82965cca66dda2eb2b77cd64e9f70a12

[ "MIT" ]

11

2018-04-01T03:58:15.000Z

2021-08-14T09:04:55.000Z

spec/python/test_expr_int_div.py

DarkShadow44/kaitai_struct_tests

4bb13cef82965cca66dda2eb2b77cd64e9f70a12

[ "MIT" ]

73

2016-07-20T10:27:15.000Z

2020-12-17T18:56:46.000Z

spec/python/test_expr_int_div.py

DarkShadow44/kaitai_struct_tests

4bb13cef82965cca66dda2eb2b77cd64e9f70a12

[ "MIT" ]

37

2016-08-15T08:25:56.000Z

2021-08-28T14:48:46.000Z

# Autogenerated from KST: please remove this line if doing any edits by hand! import unittest from expr_int_div import ExprIntDiv class TestExprIntDiv(unittest.TestCase): def test_expr_int_div(self): with ExprIntDiv.from_file('src/fixed_struct.bin') as r: self.assertEqual(r.int_u, 1262698832) self.assertEqual(r.int_s, -52947) self.assertEqual(r.div_pos_const, 756) self.assertEqual(r.div_neg_const, -757) self.assertEqual(r.div_pos_seq, 97130679) self.assertEqual(r.div_neg_seq, -4073)

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null

0

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5

9c9e279f927cb808bcf51c6fa389d60b40e0b05e

560

py

Python

symbol.py

adamchau/mas_sim

42eca43915ebf0df4e3842e657cf4ecb26802a6b

[ "MIT" ]

null

symbol.py

adamchau/mas_sim

42eca43915ebf0df4e3842e657cf4ecb26802a6b

[ "MIT" ]

null

symbol.py

adamchau/mas_sim

42eca43915ebf0df4e3842e657cf4ecb26802a6b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Dec 26 16:09:52 2014 @author: ydzhao """ import sympy as spy spy.init_printing(use_unicode=True) a1=spy.symbols('a1') a2=spy.symbols('a2') a3=spy.symbols('a3') a4=spy.symbols('a4') a5=spy.symbols('a5') deltaA=spy.Matrix([[-0.04743*a1,0,0,0,0,0,0,0,0,0],\ [0,-0.0763*a2,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,0],\ [0,-0.017408*a3,0,0,0,0,0,0,0,0],\ [-0.008981*a4,-0.28926*a5,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,0]])

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py

Python

application/mod_map/__init__.py

hieusydo/Voyage

2a98118131fad927326d318ae1766e64bbb5add8

[ "MIT" ]

1

2018-04-23T05:16:49.000Z

application/mod_map/__init__.py

hieusydo/Voyage

2a98118131fad927326d318ae1766e64bbb5add8

[ "MIT" ]

null

application/mod_map/__init__.py

hieusydo/Voyage

2a98118131fad927326d318ae1766e64bbb5add8

[ "MIT" ]

null

# Needed to import mod_map

26

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9cabb06e7d0e0fed11ec8eb8f94403bcecb84e44

35

py

Python

__init__.py

cj-mclaughlin/segmentation_research

6d59ffccdb274430b2ef02258d120f65db9004d5

[ "MIT" ]

1

2021-07-19T04:46:46.000Z

__init__.py

cj-mclaughlin/segmentation_research

6d59ffccdb274430b2ef02258d120f65db9004d5

[ "MIT" ]

null

__init__.py

cj-mclaughlin/segmentation_research

6d59ffccdb274430b2ef02258d120f65db9004d5

[ "MIT" ]

null

from segmentation_research import *

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0

null

0

1

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1

0

5

9cb65c1ea4ee70b1665df0fc35f246a1cac57ba3

95

py

Python

app/routes/__init__.py

UtkarshMish/ondc_text_parsing

3fa2f8153d40ee12b462b22ac40f8b62cbcf2533

[ "MIT" ]

null

app/routes/__init__.py

UtkarshMish/ondc_text_parsing

3fa2f8153d40ee12b462b22ac40f8b62cbcf2533

[ "MIT" ]

null

app/routes/__init__.py

UtkarshMish/ondc_text_parsing

3fa2f8153d40ee12b462b22ac40f8b62cbcf2533

[ "MIT" ]

null

from .api import api_route from .home import home_route __all__ = ("home_route", "api_route")

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py

Python

catboost/python-package/catboost/version.py

notimesea/catboost

1d3e0744f1d6c6d74d724878dc9fe92076c8b1ce

[ "Apache-2.0" ]

1

2021-10-30T05:50:36.000Z

catboost/python-package/catboost/version.py

notimesea/catboost

1d3e0744f1d6c6d74d724878dc9fe92076c8b1ce

[ "Apache-2.0" ]

null

catboost/python-package/catboost/version.py

notimesea/catboost

1d3e0744f1d6c6d74d724878dc9fe92076c8b1ce

[ "Apache-2.0" ]

null

VERSION = '0.24.2'

9.5

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null

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142698335ad500bf03082f9bcef56d7ce13f5469

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py

Python

fpga/myhdl/simple/__init__.py

wingel/sds7102

77d85533d2ddfd26a0fb45f3ceff4cf8e6ff447a

[ "MIT" ]

47

2016-07-16T20:03:19.000Z

2021-12-21T03:35:41.000Z

fpga/myhdl/simple/__init__.py

wingel/sds7102

77d85533d2ddfd26a0fb45f3ceff4cf8e6ff447a

[ "MIT" ]

null

fpga/myhdl/simple/__init__.py

wingel/sds7102

77d85533d2ddfd26a0fb45f3ceff4cf8e6ff447a

[ "MIT" ]

15

2016-07-29T08:10:11.000Z

2020-11-28T15:49:55.000Z

#! /usr/bin/python from __future__ import absolute_import

19.333333

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142c7a477611360c6f9df8bb9e1e2818e0cee22e

41

py

Python

tests/__init__.py

yukihiko-shinoda/asyncffmpeg

1980aec3065b19cd9d0fffacf1953327062818b8

[ "MIT" ]

8

2021-04-14T13:39:25.000Z

2022-03-10T06:51:50.000Z

tests/__init__.py

yukihiko-shinoda/asyncffmpeg

1980aec3065b19cd9d0fffacf1953327062818b8

[ "MIT" ]

4

2021-03-18T14:13:27.000Z

2021-12-26T13:16:32.000Z

tests/__init__.py

yukihiko-shinoda/asyncffmpeg

1980aec3065b19cd9d0fffacf1953327062818b8

[ "MIT" ]

2

2021-08-02T18:32:19.000Z

2021-12-23T19:51:31.000Z

"""Unit test package for asyncffmpeg."""

20.5

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null

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1

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5

144918db93ef7dc8b44344aaebc017c20af5f234

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py

Python

tests/pipelining_test.py

NunoEdgarGFlowHub/poptorch

2e69b81c7c94b522d9f57cc53d31be562f5e3749

[ "MIT" ]

null

tests/pipelining_test.py

NunoEdgarGFlowHub/poptorch

2e69b81c7c94b522d9f57cc53d31be562f5e3749

[ "MIT" ]

null

tests/pipelining_test.py

NunoEdgarGFlowHub/poptorch

2e69b81c7c94b522d9f57cc53d31be562f5e3749

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2020 Graphcore Ltd. All rights reserved. import json import torch import poptorch import pytest import helpers def test_missing_block(): poptorch.setLogLevel(1) # Force debug logging class Model(torch.nn.Module): def forward(self, x): poptorch.Block.useAutoId() with poptorch.Block(ipu_id=0): x = x * 4 x = x * 4 return x m = Model() opts = poptorch.Options() opts.deviceIterations(2) opts.setExecutionStrategy( poptorch.PipelinedExecution(poptorch.AutoStage.AutoIncrement)) m = poptorch.inferenceModel(m, opts) with pytest.raises(RuntimeError, match="No active Block"): m.compile(torch.randn(2, 5)) def test_api_inline(capfd): poptorch.setLogLevel(1) # Force debug logging class Model(torch.nn.Module): def forward(self, x): poptorch.Block.useAutoId() with poptorch.Block(ipu_id=0): x = x * 4 with poptorch.Block(ipu_id=1): x = x * 2 return x m = Model() opts = poptorch.Options() opts.deviceIterations(2) m = poptorch.inferenceModel(m, opts) m(torch.randn(2, 5)) log = helpers.LogChecker(capfd) log.assert_contains("enablePipelining set to value 1") log.assert_contains(" Mul:0 ", " mode(Pipelined), ipu(0), stage(0)") log.assert_contains(" Mul:0/1 ", " mode(Pipelined), ipu(1), stage(1)") def test_recomputation_checkpoint(): poptorch.setLogLevel(1) # Force debug logging size = 3 class Model(torch.nn.Module): def __init__(self, checkpoint=False): super().__init__() self.checkpoint = checkpoint weight = torch.nn.Parameter(torch.rand(size, size), requires_grad=True) self.register_parameter("weight", weight) def forward(self, x, target): poptorch.Block.useAutoId() with poptorch.Block(ipu_id=0): x = torch.matmul(self.weight, x) if self.checkpoint: x = poptorch.recomputationCheckpoint(x) x = torch.matmul(self.weight, x) with poptorch.Block(ipu_id=1): x = x * 2 return x, torch.nn.functional.l1_loss(x, target) m = Model() opts = poptorch.Options() opts.deviceIterations(6) opts.Popart.set("autoRecomputation", 3) # All forward pipeline stages. m = poptorch.trainingModel(Model(), opts) m.compile(torch.randn(size * 6, 1), torch.randn(size * 6, 1)) ir = json.loads(m._debugGetPopartIR()) # pylint: disable=protected-access assert not any(["Checkpoint" in node["name"] for node in ir["maingraph"] ]), ("Popart IR shouldn't contain any checkpoint") assert sum(["Stash" in node["type"] for node in ir["maingraph"] ]) == 1, ("Only the graph input should be stashed") m = poptorch.trainingModel(Model(True), opts) m.compile(torch.randn(size * 6, 1), torch.randn(size * 6, 1)) ir = json.loads(m._debugGetPopartIR()) # pylint: disable=protected-access assert any(["Checkpoint" in node["name"] for node in ir["maingraph"] ]), ("Popart IR should contain a checkpoint") assert sum([ "Stash" in node["type"] for node in ir["maingraph"] ]) == 2, ("Both the graph input and the checkpoint should be stashed") def test_api_wrap(capfd): """ stage "0" ipu(0) stage(0) l0 l1 l2 """ poptorch.setLogLevel(1) # Force debug logging class Block(torch.nn.Module): def forward(self, x): return x * 6 class Model(torch.nn.Module): def __init__(self): super().__init__() self.l1 = Block() self.l2 = Block() def forward(self, x): x = self.l1(x) x = self.l2(x) return x m = Model() m.l1 = poptorch.BeginBlock(m.l1, ipu_id=0) m.l2 = poptorch.BeginBlock(m.l2, ipu_id=0) opts = poptorch.Options() opts.deviceIterations(2) m = poptorch.inferenceModel(m, opts) m(torch.randn(2, 5)) log = helpers.LogChecker(capfd) log.assert_contains("enablePipelining set to value 0") log.assert_contains(" Mul:0 ", " mode(Pipelined), ipu(0), stage(0)") log.assert_contains(" Mul:0/1 ", " mode(Pipelined), ipu(0), stage(0)") def test_api_wrap_2stages(capfd): """ stage "0" ipu(0) stage(0) l0 stage "1" ipu(1) stage(1) l1 / l2 """ poptorch.setLogLevel(1) # Force debug logging class Block(torch.nn.Module): def forward(self, x): return x * 6 class Model(torch.nn.Module): def __init__(self): super().__init__() self.l0 = Block() self.l1 = Block() self.l2 = Block() def forward(self, x): x = self.l0(x) x = self.l1(x) x = self.l2(x) return x m = Model() m.l1 = poptorch.BeginBlock(m.l1, ipu_id=1) m.l2 = poptorch.BeginBlock(m.l2, ipu_id=1) opts = poptorch.Options() opts.deviceIterations(2) m = poptorch.inferenceModel(m, opts) m(torch.randn(2, 5)) log = helpers.LogChecker(capfd) log.assert_contains("enablePipelining set to value 1") log.assert_contains(" Mul:0 ", " mode(Pipelined), ipu(0), stage(0)") log.assert_contains(" Mul:0/1 ", " mode(Pipelined), ipu(1), stage(1)") log.assert_contains(" Mul:0/2 ", " mode(Pipelined), ipu(1), stage(1)") def test_inline_AutoIncrement(capfd): poptorch.setLogLevel(1) # Force debug logging class Model(torch.nn.Module): def forward(self, x): poptorch.Block.useAutoId() with poptorch.Block(ipu_id=0): x = x * 2 with poptorch.Block(ipu_id=1): x = x * 3 with poptorch.Block(ipu_id=2): x = x * 4 with poptorch.Block(ipu_id=1): x = x * 5 return x m = Model() opts = poptorch.Options() opts.deviceIterations(4).autoRoundNumIPUs(True) opts.setExecutionStrategy( poptorch.PipelinedExecution(poptorch.AutoStage.AutoIncrement)) m = poptorch.inferenceModel(m, opts) m.compile(torch.randn(4, 5)) log = helpers.LogChecker(capfd) log.assert_contains("enablePipelining set to value 1") log.assert_contains(" Mul:0 ", " mode(Pipelined), ipu(0), stage(1)") log.assert_contains(" Mul:0/1 ", " mode(Pipelined), ipu(1), stage(2)") log.assert_contains(" Mul:0/2 ", " mode(Pipelined), ipu(2), stage(3)") log.assert_contains(" Mul:0/3 ", " mode(Pipelined), ipu(1), stage(4)") def test_api_AutoIncrement(capfd): poptorch.setLogLevel(1) # Force debug logging class Block(torch.nn.Module): def forward(self, x): return x * 6 class Model(torch.nn.Module): def __init__(self): super().__init__() self.l1 = Block() self.l2 = Block() self.l3 = Block() self.l4 = Block() def forward(self, x): x = self.l1(x) x = self.l2(x) x = self.l3(x) x = self.l4(x) return x m = Model() m.l2 = poptorch.BeginBlock(m.l2, ipu_id=1) m.l3 = poptorch.BeginBlock(m.l3, ipu_id=2) m.l4 = poptorch.BeginBlock(m.l4, ipu_id=1) opts = poptorch.Options() opts.deviceIterations(4).autoRoundNumIPUs(True) opts.setExecutionStrategy( poptorch.PipelinedExecution(poptorch.AutoStage.AutoIncrement)) m = poptorch.inferenceModel(m, opts) m(torch.randn(4, 5)) log = helpers.LogChecker(capfd) log.assert_contains("enablePipelining set to value 1") log.assert_contains(" Mul:0 ", " mode(Pipelined), ipu(0), stage(0)") log.assert_contains(" Mul:0/1 ", " mode(Pipelined), ipu(1), stage(1)") log.assert_contains(" Mul:0/2 ", " mode(Pipelined), ipu(2), stage(2)") log.assert_contains(" Mul:0/3 ", " mode(Pipelined), ipu(1), stage(3)") @pytest.mark.skipif(not poptorch.ipuHardwareIsAvailable(), reason="Round up only needed for IPU Hardware") def test_ipu_round_up_error(): class Block(torch.nn.Module): def forward(self, x): return x * 6 class Model(torch.nn.Module): def __init__(self): super().__init__() self.l1 = Block() self.l2 = Block() self.l3 = Block() def forward(self, x): x = self.l1(x) x = self.l2(x) x = self.l3(x) return x m = Model() m.l1 = poptorch.BeginBlock(m.l2, ipu_id=0) m.l2 = poptorch.BeginBlock(m.l2, ipu_id=1) m.l3 = poptorch.BeginBlock(m.l3, ipu_id=2) opts = poptorch.Options() opts.setExecutionStrategy( poptorch.PipelinedExecution(poptorch.AutoStage.AutoIncrement)) m = poptorch.inferenceModel(m, opts) error_msg = ( ".+The model specifies the use of 3 IPUs, however PopTorch must " "reserve a minimum of 4 in order to allow the model to run, " "because PopTorch must reserve a power of 2 or a multiple of 64" r" IPUs\. Please reconfigure your model to use a different " r"number of IPUs or set poptorch\.Options\." r"autoRoundNumIPUs$True$\.") with pytest.raises(RuntimeError, match=error_msg): m(torch.randn(4, 5))

31.460265

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0.588991

1,234

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4.449757

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0.06192

0.054635

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0.763067

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0.236842

0

null

0

1

0

null

0

5

147dc3d6ab144dc6640c46ec6afe5b850b2affbf

259

py

Python

Darlington/phase1/python Basic 2/day 24 solution/qtn2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

Darlington/phase1/python Basic 2/day 24 solution/qtn2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

Darlington/phase1/python Basic 2/day 24 solution/qtn2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

#program to check whether a given integer is a palindrome or not. def is_Palindrome(n): return str(n) == str(n)[::-1] print(is_Palindrome(100)) print(is_Palindrome(252)) print(is_Palindrome(-838)) print(is_Palindrome('swims')) print(is_Palindrome(1001))

32.375

65

0.741313

42

259

4.428571

0.52381

0.387097

0.456989

0

0.060606

0.108108

259

8

66

32.375

0.744589

0.247104

0

0.025641

0

1

0.142857

false

0

0.142857

0.285714

0.714286

0

null

1

0

1

0

null

0

1

0

1

0

5

14b85ff66f1e4b6db22b5f9f4a49ec7d948b29f1

113

py

Python

configuracao/admin.py

Moisestuli/karrata

962ce0c573214bfc83720727c9cacae823a8c372

[ "MIT" ]

null

configuracao/admin.py

Moisestuli/karrata

962ce0c573214bfc83720727c9cacae823a8c372

[ "MIT" ]

null

configuracao/admin.py

Moisestuli/karrata

962ce0c573214bfc83720727c9cacae823a8c372

[ "MIT" ]

null

from django.contrib import admin from configuracao.models import Configuracao admin.site.register(Configuracao)

22.6

44

0.858407

14

113

6.928571

0.642857

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0.088496

113

4

45

28.25

0.941748

0

1

0

true

0

0.666667

0

0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

1ad9652ad4a77a8a5daa6198d53d4bfdb4bc5d77

33

py

Python

spikeforest/spikesorters/ironclust/__init__.py

mhhennig/spikeforest

5b4507ead724af3de0be5d48a3b23aaedb0be170

[ "Apache-2.0" ]

1

2021-09-23T01:07:19.000Z

spikeforest/spikesorters/ironclust/__init__.py

mhhennig/spikeforest

5b4507ead724af3de0be5d48a3b23aaedb0be170

[ "Apache-2.0" ]

null

spikeforest/spikesorters/ironclust/__init__.py

mhhennig/spikeforest

5b4507ead724af3de0be5d48a3b23aaedb0be170

[ "Apache-2.0" ]

1

2021-09-23T01:07:21.000Z

from .ironclust import IronClust

16.5

32

0.848485

4

33

7

0.75

0

0.121212

33

1

33

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

1ae8b0c77774dbc030e11b3b332110a8d7be41ca

97

py

Python

kube_hunter/modules/report/__init__.py

wongearl/kube-hunter

00eb0dfa87d8a1b2932f2fa0bfc67c57e7a4ed03

[ "Apache-2.0" ]

3,521

2018-08-15T15:43:57.000Z

2022-03-31T07:17:39.000Z

kube_hunter/modules/report/__init__.py

wongearl/kube-hunter

00eb0dfa87d8a1b2932f2fa0bfc67c57e7a4ed03

[ "Apache-2.0" ]

350

2018-08-16T16:13:12.000Z

2022-03-22T16:22:36.000Z

kube_hunter/modules/report/__init__.py

wongearl/kube-hunter

00eb0dfa87d8a1b2932f2fa0bfc67c57e7a4ed03

[ "Apache-2.0" ]

551

2018-08-15T15:53:27.000Z

2022-03-30T02:53:40.000Z

# flake8: noqa: E402 from kube_hunter.modules.report.factory import get_reporter, get_dispatcher

32.333333

75

0.835052

14

97

5.571429

0.928571

0

0.045455

0.092784

97

2

76

48.5

0.840909

0.185567

0

1

0

true

0

1

0

1

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0

null

0

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0

null

0

1

0

1

0

1

0

5

1af6858bbb903592f72ae3591f7ca22c13bc3a84

1,451

py

Python

tests/lib/locations/test_check_file_extension.py

Kilobyte22/ffflash

44bdecf24065fc37fe189ba2e683f767711e4dcf

[ "BSD-3-Clause" ]

2

2016-04-20T14:13:46.000Z

2017-01-27T23:43:01.000Z

tests/lib/locations/test_check_file_extension.py

spookey/changeffapi

44bdecf24065fc37fe189ba2e683f767711e4dcf

[ "BSD-3-Clause" ]

1

2021-05-10T20:34:36.000Z

tests/lib/locations/test_check_file_extension.py

Kilobyte22/ffflash

44bdecf24065fc37fe189ba2e683f767711e4dcf

[ "BSD-3-Clause" ]

2

2016-01-21T19:22:37.000Z

2021-01-19T13:18:16.000Z

from ffflash.lib.locations import check_file_extension def test_check_file_extension_no_ext(): assert check_file_extension('file') == (None, None) assert check_file_extension('file', '') == (None, None) assert check_file_extension('file.txt') == (None, None) assert check_file_extension('file.txt', '') == (None, None) assert check_file_extension('file.txt', '.') == (None, None) assert check_file_extension('file.txt', '..') == (None, None) assert check_file_extension('file.txt', 'file') == (None, None) assert check_file_extension('file', 'file') == (None, None) def test_check_file_extension(): for ext in ['txt', '.txt', 'TXT', '.TXT', 'tXt', '.tXt', 'TxT', '.TxT']: assert check_file_extension('file.txt', ext) == ('file', '.txt') assert check_file_extension( 'file.json', 'txt', 'yaml' ) == (None, None) assert check_file_extension( 'file.json', 'txt', 'yaml', 'json' ) == ('file', '.json') def test_check_file_extension_dir(tmpdir): assert check_file_extension( str(tmpdir.join('file.txt')), 'txt' ) == ('file', '.txt') assert check_file_extension( str(tmpdir.join('file.txt')), 'json' ) == (None, None) assert check_file_extension( str(tmpdir.join('file.txt')) ) == (None, None) assert check_file_extension( str(tmpdir.join('out')), 'txt' ) == (None, None) assert tmpdir.remove() is None

32.977273

76

0.619573

182

1,451

4.697802

0.142857

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0.4

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0.65731

0.519298

0.302924

0

0.19366

1,451

43

77

33.744186

0.730769

0

0.30303

0

0.135768

0

0.484848

1

0.090909

false

0

0.030303

0

0.121212

0

null

0

1

0

null

0

1

0

5

211af4df5dbb683b5ebdc4a57a47c20bcdbfc597

61

py

Python

rouver/__init__.py

srittau/rouver

29f3b9ca3e96eafc8ceb60e64c40308a5de6c9f6

[ "MIT" ]

1

2018-02-27T00:31:15.000Z

rouver/__init__.py

srittau/rouver

29f3b9ca3e96eafc8ceb60e64c40308a5de6c9f6

[ "MIT" ]

69

2017-10-21T15:57:55.000Z

2022-03-29T06:56:26.000Z

rouver/__init__.py

srittau/rouver

29f3b9ca3e96eafc8ceb60e64c40308a5de6c9f6

[ "MIT" ]

null

from .util import absolute_url as absolute_url # noqa: F401

30.5

60

0.786885

10

61

4.6

0.8

0.478261

0

0.058824

0.163934

61

1

61

0.843137

0.163934

0

1

0

true

0

1

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1

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1

0

null

1

0

1

0

null

0

1

0

1

0

5

21528a55a5f288c108c86fcd21208d94e7b0885f

82

py

Python

tests/utils/test_utils.py

Jimmy-INL/pysensors

62b79a233a551ae01125e20e06fde0c96b4dffd2

[ "MIT" ]

43

2020-10-26T14:43:56.000Z

2022-03-03T16:03:15.000Z

tests/utils/test_utils.py

Jimmy-INL/pysensors

62b79a233a551ae01125e20e06fde0c96b4dffd2

[ "MIT" ]

4

2020-11-10T11:15:15.000Z

2022-01-07T16:05:11.000Z

tests/utils/test_utils.py

Jimmy-INL/pysensors

62b79a233a551ae01125e20e06fde0c96b4dffd2

[ "MIT" ]

13

2020-10-14T10:38:38.000Z

2022-01-03T09:05:15.000Z

# TODO: include some unit tests once there are more functions # in this submodule

27.333333

61

0.780488

13

82

4.923077

1

0

0.182927

82

2

62

41

0.955224

0.939024

0

null

0

null

0

null

0

0.5

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

dcca08805697066c5d40a74bd183ef98c1ca0a79

57

py

Python

admin_email_sender/tests/__init__.py

stasfilin/django-admin-email-sender

8abb82f45ffebf47e4abcc1a2eda8225e3448668

[ "Apache-2.0" ]

8

2018-01-22T00:48:40.000Z

2020-05-24T11:40:22.000Z

admin_email_sender/tests/__init__.py

stasfilin/django-admin-email-sender

8abb82f45ffebf47e4abcc1a2eda8225e3448668

[ "Apache-2.0" ]

6

2018-01-20T19:18:49.000Z

2020-06-27T11:38:59.000Z

admin_email_sender/tests/__init__.py

stasfilin/django-admin-email-sender

8abb82f45ffebf47e4abcc1a2eda8225e3448668

[ "Apache-2.0" ]

2

2018-09-14T19:27:30.000Z

2020-05-24T11:40:28.000Z

from .test_models import * from .test_validators import *

28.5

30

0.807018

8

57

5.5

0.625

0.363636

0

0.122807

57

2

30

28.5

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0

true

0

1

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

5

dcf022812996a768686e6ff59b91ea7868f113fb

223

py

Python

scvelo/preprocessing/__init__.py

saksham219/scvelo

41fb2a90ae6a71577cf2c55b80e1ade4407891b7

[ "BSD-3-Clause" ]

null

scvelo/preprocessing/__init__.py

saksham219/scvelo

41fb2a90ae6a71577cf2c55b80e1ade4407891b7

[ "BSD-3-Clause" ]

null

scvelo/preprocessing/__init__.py

saksham219/scvelo

41fb2a90ae6a71577cf2c55b80e1ade4407891b7

[ "BSD-3-Clause" ]

null

from .utils import show_proportions, cleanup, filter_genes, filter_genes_dispersion, normalize_per_cell, log1p, \ filter_and_normalize, recipe_velocity from .neighbors import pca, neighbors from .moments import moments

44.6

113

0.834081

29

223

6.103448

0.655172

0.124294

0

0.005051

0.112108

223

4

114

55.75

0.888889

0

1

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true

0

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0.75

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null

0

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0

null

0

1

0

1

0

1

0

5

0d0bc21e63c2c002f3303402dca6101e048f5d46

28,924

py

Python

app/util/nanote.py

bbedward/monkeytalks

2f40a0cf7f9a9b8690e2c13fe86113a43ceeca09

[ "MIT" ]

9

2019-04-01T13:42:41.000Z

2021-12-30T20:32:48.000Z

app/util/nanote.py

bbedward/monkeytalks

2f40a0cf7f9a9b8690e2c13fe86113a43ceeca09

[ "MIT" ]

1

2021-08-01T00:00:06.000Z

app/util/nanote.py

bbedward/monkeytalks

2f40a0cf7f9a9b8690e2c13fe86113a43ceeca09

[ "MIT" ]

8

2019-04-26T15:32:56.000Z

2021-08-09T11:52:53.000Z

class Nanote(): """Implementation of some Nanote logic in python for server-side validation""" sikrit = 895175784877 charsets = [" etaoinsrhl"," 1234567890"," 1234567890'"," etaoinsrhl'"," 1234567890'"," etaoinsrhl'"," 1234567890'"," etaoinsrhl'"," 1234567890]"," etaoinsrhl]"," 1234567890["," etaoinsrhl["," 1234567890:"," etaoinsrhl:"," 1234567890;"," etaoinsrhl;"," 1234567890>"," etaoinsrhld"," 1234567890<"," etaoinsrhl<"," 1234567890/"," etaoinsrhl/"," 1234567890?"," etaoinsrhl?"," etaoinsrhl>"," etaoinsrhl~"," 1234567890."," etaoinsrhl."," 1234567890,"," etaoinsrhl,"," 1234567890="," etaoinsrhl="," 1234567890+"," etaoinsrhl+"," 1234567890_"," etaoinsrhl_"," 1234567890-"," etaoinsrhl-"," 1234567890)"," etaoinsrhl)"," 1234567890("," etaoinsrhl("," 1234567890*"," etaoinsrhl*"," 1234567890&"," etaoinsrhl&"," 1234567890%"," etaoinsrhl%"," 1234567890$"," 1234567890~"," etaoinsrhl!"," etaoinsrhl$"," 1234567890#"," etaoinsrhl#"," 1234567890@"," etaoinsrhl@"," 1234567890!"," etaoinsrhld$"," etaoinsrhld_"," etaoinsrhld'"," etaoinsrhld@"," etaoinsrhld-"," etaoinsrhld="," etaoinsrhld'"," etaoinsrhld,"," etaoinsrhld#"," etaoinsrhld)"," etaoinsrhld."," etaoinsrhld~"," etaoinsrhld?"," etaoinsrhld!"," etaoinsrhld+"," etaoinsrhld("," etaoinsrhld/"," etaoinsrhld<"," etaoinsrhld>"," etaoinsrhld*"," etaoinsrhld%"," etaoinsrhld;"," etaoinsrhld:"," etaoinsrhld'"," etaoinsrhld["," etaoinsrhldc"," etaoinsrhld]"," etaoinsrhld&"," etaoinsrhldc_"," etaoinsrhldc'"," etaoinsrhldc+"," etaoinsrhldc$"," etaoinsrhldc'"," etaoinsrhldc,"," etaoinsrhldc/"," etaoinsrhldc)"," etaoinsrhldc<"," etaoinsrhldc@"," etaoinsrhldc>"," etaoinsrhldc*"," etaoinsrhldc#"," etaoinsrhldc-"," etaoinsrhldc%"," etaoinsrhldc~"," etaoinsrhldc;"," etaoinsrhldc["," etaoinsrhldc'"," etaoinsrhldc."," etaoinsrhldc:"," etaoinsrhldcu"," etaoinsrhldc="," etaoinsrhldc]"," etaoinsrhldc?"," etaoinsrhldc&"," etaoinsrhldc("," etaoinsrhldc!"," etaoinsrhldcu$"," etaoinsrhldcu!"," etaoinsrhldcu;"," etaoinsrhldcu'"," etaoinsrhl()-_"," 1234567890$%&*"," etaoinsrhldcu/"," etaoinsrhldcu#"," etaoinsrhldcu="," etaoinsrhl$%&*"," 1234567890~!@#"," etaoinsrhldcu<"," etaoinsrhldcu~"," etaoinsrhldcu'"," etaoinsrhl~!@#"," etaoinsrhldcu>"," etaoinsrhldcu*"," etaoinsrhldcu@"," etaoinsrhldcu-"," etaoinsrhldcu."," etaoinsrhl?/<>"," etaoinsrhldcu%"," 1234567890+=,."," etaoinsrhldcu_"," 1234567890;:[]"," etaoinsrhldcu'"," etaoinsrhldcu+"," etaoinsrhl+=,."," 1234567890()-_"," etaoinsrhldcu:"," etaoinsrhldcu?"," etaoinsrhldcu("," etaoinsrhldcu]"," etaoinsrhldcu,"," etaoinsrhldcu)"," etaoinsrhldcu&"," etaoinsrhl;:[]"," 1234567890?/<>"," etaoinsrhldcu["," etaoinsrhldcum"," etaoinsrhldcum["," etaoinsrhldcum&"," etaoinsrhldcum_"," etaoinsrhldcum]"," etaoinsrhldcum'"," etaoinsrhldcum+"," etaoinsrhldcum-"," etaoinsrhldcum@"," etaoinsrhldcum'"," etaoinsrhldcum:"," etaoinsrhldcum="," etaoinsrhldcum~"," etaoinsrhldcumf"," etaoinsrhldcum,"," etaoinsrhldcum)"," etaoinsrhldcum;"," etaoinsrhld;:[]"," etaoinsrhldcum%"," etaoinsrhldcum!"," etaoinsrhldcum#"," etaoinsrhldcum>"," etaoinsrhldcum'"," etaoinsrhld?/<>"," etaoinsrhldcum."," etaoinsrhldcum*"," etaoinsrhld+=,."," etaoinsrhldcum?"," etaoinsrhldcum("," etaoinsrhld~!@#"," etaoinsrhld()-_"," etaoinsrhldcum$"," etaoinsrhldcum/"," etaoinsrhldcum<"," etaoinsrhld$%&*"," etaoinsrhldcumf%"," etaoinsrhldcumf)"," etaoinsrhldcumf<"," etaoinsrhldcumf$"," etaoinsrhldc~!@#"," etaoinsrhldc()-_"," etaoinsrhldcumfp"," etaoinsrhldcumf("," etaoinsrhldcumf?"," etaoinsrhldcumf["," etaoinsrhldcumf*"," etaoinsrhldc+=,."," etaoinsrhldcumf."," etaoinsrhldcumf'"," etaoinsrhldc?/<>"," etaoinsrhldcumf#"," etaoinsrhldcumf>"," etaoinsrhldcumf/"," etaoinsrhldcumf;"," etaoinsrhldc$%&*"," etaoinsrhldcumf,"," etaoinsrhldc;:[]"," etaoinsrhldcumf~"," etaoinsrhldcumf="," etaoinsrhldcumf:"," etaoinsrhldcumf'"," etaoinsrhldcumf@"," etaoinsrhldcumf]"," etaoinsrhldcumf-"," etaoinsrhldcumf+"," etaoinsrhldcumf!"," etaoinsrhldcumf&"," etaoinsrhldcumf_"," etaoinsrhldcumf'"," etaoinsrhldcumfp$"," etaoinsrhldcumfp)"," etaoinsrhldcu+=,."," etaoinsrhldcumfp;"," etaoinsrhldcumfp."," etaoinsrhldcu;:[]"," etaoinsrhldcumfp%"," etaoinsrhldcumfpg"," etaoinsrhldcu~!@#"," etaoinsrhldcumfp="," etaoinsrhldcumfp'"," etaoinsrhldcumfp]"," etaoinsrhldcumfp("," etaoinsrhldcumfp?"," etaoinsrhldcumfp'"," etaoinsrhldcumfp~"," etaoinsrhldcumfp+"," etaoinsrhldcumfp@"," etaoinsrhldcu?/<>"," etaoinsrhldcumfp:"," etaoinsrhldcumfp#"," etaoinsrhldcumfp<"," etaoinsrhldcumfp-"," etaoinsrhldcumfp,"," etaoinsrhldcumfp&"," etaoinsrhldcumfp*"," etaoinsrhldcumfp!"," etaoinsrhldcumfp>"," etaoinsrhldcumfp_"," etaoinsrhldcumfp/"," etaoinsrhldcumfp["," etaoinsrhldcu$%&*"," etaoinsrhldcumfp'"," etaoinsrhldcu()-_"," etaoinsrhldcumfpg;"," etaoinsrhl?/<>;:[]"," etaoinsrhldcumfpg)"," 1234567890()-_+=,."," etaoinsrhl()-_+=,."," etaoinsrhldcumfpg?"," etaoinsrhldcumfpg'"," etaoinsrhldcumfpg["," etaoinsrhldcumfpg>"," etaoinsrhldcum+=,."," etaoinsrhldcumfpg="," 1234567890~!@#$%&*"," etaoinsrhl~!@#$%&*"," etaoinsrhldcumfpg("," etaoinsrhldcumfpg$"," etaoinsrhldcumfpg@"," etaoinsrhldcum;:[]"," etaoinsrhldcum$%&*"," etaoinsrhldcumfpg~"," etaoinsrhldcumfpg+"," etaoinsrhldcumfpg'"," etaoinsrhldcum()-_"," etaoinsrhldcumfpg*"," etaoinsrhldcumfpg."," etaoinsrhldcumfpg%"," etaoinsrhldcumfpg'"," etaoinsrhldcumfpg&"," etaoinsrhldcumfpg-"," etaoinsrhldcumfpg<"," etaoinsrhldcumfpg#"," etaoinsrhldcumfpg:"," etaoinsrhldcum?/<>"," etaoinsrhldcum~!@#"," etaoinsrhldcumfpg_"," etaoinsrhldcumfpg,"," etaoinsrhldcumfpgw"," etaoinsrhldcumfpg]"," etaoinsrhldcumfpg/"," etaoinsrhldcumfpg!"," 1234567890?/<>;:[]"," etaoinsrhldcumfpgw'"," etaoinsrhldcumfpgw<"," etaoinsrhldcumfpgw_"," etaoinsrhldcumf?/<>"," etaoinsrhldcumfpgw#"," 1234567890?/<>;:[]'"," etaoinsrhldcumfpgw~"," etaoinsrhldcumf+=,."," etaoinsrhldcumfpgw-"," etaoinsrhldcumfpgw+"," etaoinsrhldcumf()-_"," etaoinsrhldcumfpgw$"," etaoinsrhldcumfpgw'"," etaoinsrhldcumf$%&*"," etaoinsrhldcumfpgw="," etaoinsrhldcumf~!@#"," etaoinsrhld?/<>;:[]"," etaoinsrhldcumfpgw)"," etaoinsrhldcumfpgw'"," etaoinsrhldcumfpgw,"," etaoinsrhldcumf;:[]"," etaoinsrhldcumfpgw%"," etaoinsrhldcumfpgw@"," etaoinsrhld~!@#$%&*"," etaoinsrhldcumfpgw."," etaoinsrhldcumfpgw]"," etaoinsrhldcumfpgwy"," etaoinsrhl?/<>;:[]'"," etaoinsrhldcumfpgw["," etaoinsrhldcumfpgw("," etaoinsrhldcumfpgw?"," etaoinsrhldcumfpgw&"," etaoinsrhldcumfpgw:"," etaoinsrhld()-_+=,."," etaoinsrhldcumfpgw/"," etaoinsrhldcumfpgw;"," etaoinsrhldcumfpgw!"," etaoinsrhldcumfpgw>"," etaoinsrhldcumfpgw*"," etaoinsrhldcumfpgwy,"," etaoinsrhldcumfp;:[]"," etaoinsrhldcumfpgwy_"," etaoinsrhldcumfp+=,."," etaoinsrhldcumfpgwy%"," etaoinsrhldcumfpgwy$"," etaoinsrhldcumfpgwy!"," etaoinsrhldcumfpgwy]"," etaoinsrhldcumfpgwy@"," etaoinsrhldcumfpgwy."," etaoinsrhldcumfpgwy~"," etaoinsrhldc~!@#$%&*"," etaoinsrhldc?/<>;:[]"," etaoinsrhldcumfpgwyb"," etaoinsrhldcumfp$%&*"," etaoinsrhldcumfpgwy["," etaoinsrhldcumfp?/<>"," etaoinsrhld?/<>;:[]'"," etaoinsrhldcumfpgwy("," etaoinsrhldcumfpgwy<"," etaoinsrhldcumfpgwy="," etaoinsrhldcumfpgwy?"," etaoinsrhldcumfpgwy'"," etaoinsrhldcumfpgwy:"," etaoinsrhldcumfp~!@#"," etaoinsrhldcumfpgwy&"," tnsrhldcmfpgwbvkxjqz"," etaoinsrhldc()-_+=,."," etaoinsrhldcumfpgwy-"," etaoinsrhldcumfpgwy#"," etaoinsrhldcumfpgwy/"," etaoinsrhldcumfpgwy;"," etaoinsrhldcumfpgwy'"," etaoinsrhldcumfpgwy)"," etaoinsrhldcumfpgwy>"," etaoinsrhldcumfpgwy'"," etaoinsrhldcumfpgwy+"," etaoinsrhldcumfpgwy*"," etaoinsrhl1234567890"," etaoinsrhldcumfp()-_"," etaoinsrhl1234567890;"," etaoinsrhldcumfpgwyb,"," etaoinsrhldcumfpg+=,."," tnsrhldcmfpgwbvkxjqz'"," tnsrhldcmfpgwbvkxjqz~"," tnsrhldcmfpgwbvkxjqz'"," etaoinsrhl1234567890+"," etaoinsrhldcumfpgwyb%"," etaoinsrhl1234567890,"," tnsrhldcmfpgwbvkxjqz_"," tnsrhldcmfpgwbvkxjqz]"," tnsrhldcmfpgwbvkxjqz,"," etaoinsrhl1234567890!"," tnsrhldcmfpgwbvkxjqz("," etaoinsrhldcumfpgwyb@"," etaoinsrhl1234567890]"," etaoinsrhl1234567890("," etaoinsrhldcumfpgwyb]"," etaoinsrhl1234567890-"," tnsrhldcmfpgwbvkxjqz+"," etaoinsrhldcumfpgwyb_"," etaoinsrhldcumfpgwyb."," etaoinsrhldcu?/<>;:[]"," etaoinsrhl1234567890%"," tnsrhldcmfpgwbvkxjqz)"," etaoinsrhldcumfpgwyb~"," etaoinsrhldcumfpg$%&*"," tnsrhldcmfpgwbvkxjqz-"," etaoinsrhl1234567890."," tnsrhldcmfpgwbvkxjqz["," etaoinsrhl1234567890)"," etaoinsrhldcu~!@#$%&*"," tnsrhldcmfpgwbvkxjqz."," etaoinsrhl1234567890["," etaoinsrhldcumfpgwyb["," tnsrhldcmfpgwbvkxjqz@"," etaoinsrhl1234567890#"," etaoinsrhldcumfpgwyb-"," tnsrhldcmfpgwbvkxjqz%"," etaoinsrhldcumfpgwyb("," etaoinsrhldcumfpg?/<>"," etaoinsrhldcumfpgwyb="," etaoinsrhldc?/<>;:[]'"," etaoinsrhldcumfpgwyb$"," etaoinsrhldcumfpgwyb#"," etaoinsrhldcumfpgwyb?"," etaoinsrhldcumfpgwyb'"," etaoinsrhldcumfpgwybv"," tnsrhldcmfpgwbvkxjqz:"," etaoinsrhl1234567890:"," etaoinsrhldcumfpgwyb:"," etaoinsrhldcumfpg~!@#"," etaoinsrhl1234567890?"," etaoinsrhl1234567890@"," tnsrhldcmfpgwbvkxjqz'"," etaoinsrhldcumfpgwyb&"," tnsrhldcmfpgwbvkxjqz?"," etaoinsrhldcu()-_+=,."," etaoinsrhl1234567890="," etaoinsrhl1234567890$"," etaoinsrhl1234567890~"," etaoinsrhl1234567890'"," tnsrhldcmfpgwbvkxjqz*"," tnsrhldcmfpgwbvkxjqz;"," etaoinsrhldcumfpgwyb;"," tnsrhldcmfpgwbvkxjqz="," etaoinsrhl1234567890'"," etaoinsrhldcumfpgwyb/"," etaoinsrhl1234567890'"," etaoinsrhldcumfpgwyb'"," etaoinsrhl1234567890&"," etaoinsrhldcumfpgwyb'"," tnsrhldcmfpgwbvkxjqz>"," etaoinsrhldcumfpgwyb!"," etaoinsrhl1234567890/"," etaoinsrhl1234567890>"," tnsrhldcmfpgwbvkxjqz!"," etaoinsrhldcumfpgwyb>"," tnsrhldcmfpgwbvkxjqz/"," etaoinsrhldcumfpgwyb)"," etaoinsrhl1234567890*"," etaoinsrhldcumfpgwyb+"," tnsrhldcmfpgwbvkxjqz&"," etaoinsrhldcumfpg()-_"," etaoinsrhl1234567890_"," etaoinsrhldcumfpgwyb*"," tnsrhldcmfpgwbvkxjqz$"," etaoinsrhldcumfpg;:[]"," tnsrhldcmfpgwbvkxjqz#"," etaoinsrhldcumfpgwyb<"," tnsrhldcmfpgwbvkxjqz<"," etaoinsrhl1234567890<"," etaoinsrhldcumfpgwybv)"," etaoinsrhldcumfpgwybv%"," etaoinsrhldcumfpgwybv,"," etaoinsrhldcumfpgw;:[]"," etaoinsrhldcumfpgwybv'"," etaoinsrhldcumfpgwybv<"," etaoinsrhldcumfpgw~!@#"," etaoinsrhldcumfpgwybv*"," etaoinsrhldcumfpgwybv]"," etaoinsrhldcumfpgwybv!"," etaoinsrhldcumfpgwybv'"," etaoinsrhldcumfpgwybv>"," etaoinsrhldcumfpgwybv."," etaoinsrhldcumfpgwybv'"," etaoinsrhldcumfpgw?/<>"," etaoinsrhldcumfpgwybv_"," etaoinsrhldcumfpgwybv$"," etaoinsrhldcumfpgwybv#"," etaoinsrhldcumfpgwybv="," etaoinsrhldcumfpgw$%&*"," etaoinsrhldcum?/<>;:[]"," etaoinsrhldcumfpgwybv/"," etaoinsrhldcumfpgw+=,."," etaoinsrhldcumfpgwybv;"," etaoinsrhldcumfpgwybv["," etaoinsrhldcumfpgwybv("," etaoinsrhldcumfpgwybv-"," etaoinsrhldcum~!@#$%&*"," etaoinsrhldcumfpgwybv~"," etaoinsrhldcumfpgwybv?"," etaoinsrhldcumfpgwybvk"," etaoinsrhldcu?/<>;:[]'"," etaoinsrhldcumfpgwybv@"," etaoinsrhldcumfpgwybv&"," etaoinsrhldcum()-_+=,."," etaoinsrhldcumfpgwybv:"," etaoinsrhldcumfpgw()-_"," etaoinsrhldcumfpgwybv+"," etaoinsrhldcumfpgwybvk#"," etaoinsrhldcumfpgwy;:[]"," etaoinsrhldcumfpgwybvk:"," etaoinsrhldcumfpgwybvk@"," etaoinsrhldcumfpgwybvk&"," etaoinsrhldcumfpgwy()-_"," etaoinsrhldcumf~!@#$%&*"," etaoinsrhldcumfpgwybvkx"," etaoinsrhldcumfpgwybvk?"," etaoinsrhldcum?/<>;:[]'"," etaoinsrhldcumfpgwybvk~"," etaoinsrhldcumfpgwybvk-"," etaoinsrhldcumfpgwybvk("," etaoinsrhldcumf?/<>;:[]"," etaoinsrhldcumfpgwybvk;"," etaoinsrhldcumfpgwybvk["," etaoinsrhldcumfpgwybvk/"," etaoinsrhldcumfpgwy$%&*"," etaoinsrhldcumfpgwy+=,."," etaoinsrhldcumf()-_+=,."," etaoinsrhldcumfpgwybvk_"," etaoinsrhldcumfpgwybvk$"," etaoinsrhldcumfpgwybvk="," etaoinsrhldcumfpgwy?/<>"," etaoinsrhldcumfpgwybvk."," etaoinsrhldcumfpgwybvk'"," etaoinsrhldcumfpgwybvk>"," etaoinsrhldcumfpgwy~!@#"," etaoinsrhldcumfpgwybvk*"," etaoinsrhldcumfpgwybvk]"," etaoinsrhldcumfpgwybvk!"," etaoinsrhldcumfpgwybvk'"," etaoinsrhldcumfpgwybvk'"," etaoinsrhldcumfpgwybvk)"," etaoinsrhldcumfpgwybvk<"," etaoinsrhldcumfpgwybvk,"," etaoinsrhldcumfpgwybvk%"," etaoinsrhldcumfpgwybvk+"," etaoinsrhldcumfpgwybvkx>"," etaoinsrhldcumfpgwybvkx?"," etaoinsrhldcumfpgwybvkx*"," etaoinsrhldcumfpgwybvkx;"," etaoinsrhldcumfpgwybvkx'"," etaoinsrhldcumfpgwybvkx-"," etaoinsrhl1234567890$%&*"," tnsrhldcmfpgwbvkxjqz?/<>"," etaoinsrhldcumfpgwybvkx!"," tnsrhldcmfpgwbvkxjqz()-_"," etaoinsrhldcumfpgwybvkx("," etaoinsrhldcumfp()-_+=,."," etaoinsrhldcumfpgwybvkx~"," etaoinsrhldcumfpgwybvkx_"," etaoinsrhl1234567890()-_"," etaoinsrhldcumfpgwybvkx["," etaoinsrhldcumfpgwybvkx@"," etaoinsrhldcumfpgwyb$%&*"," etaoinsrhldcumfpgwybvkxj"," etaoinsrhl1234567890;:[]"," etaoinsrhldcumfpgwyb()-_"," etaoinsrhl1234567890?/<>"," etaoinsrhldcumfpgwybvkx&"," etaoinsrhldcumfpgwybvkx$"," tnsrhldcmfpgwbvkxjqz~!@#"," etaoinsrhldcumfpgwybvkx#"," etaoinsrhldcumfpgwybvkx="," etaoinsrhldcumfpgwybvkx."," tnsrhldcmfpgwbvkxjqz+=,."," etaoinsrhldcumf?/<>;:[]'"," etaoinsrhl1234567890~!@#"," etaoinsrhl1234567890+=,."," etaoinsrhldcumfpgwybvkx'"," etaoinsrhldcumfpgwybvkx%"," tnsrhldcmfpgwbvkxjqz;:[]"," etaoinsrhldcumfpgwyb~!@#"," etaoinsrhldcumfpgwyb+=,."," etaoinsrhldcumfpgwybvkx/"," etaoinsrhldcum1234567890"," etaoinsrhldcumfpgwybvkx]"," etaoinsrhldcumfpgwyb?/<>"," etaoinsrhldcumfpgwybvkx)"," etaoinsrhldcumfpgwybvkx'"," etaoinsrhldcumfpgwybvkx:"," etaoinsrhldcumfpgwybvkx+"," etaoinsrhldcumfpgwyb;:[]"," etaoinsrhldcumfpgwybvkx<"," tnsrhldcmfpgwbvkxjqz$%&*"," etaoinsrhldcumfpgwybvkx,"," etaoinsrhldcumfp?/<>;:[]"," etaoinsrhldcumfp~!@#$%&*"," etaoinsrhldcumfpgwybvkxj!"," etaoinsrhldcum1234567890'"," etaoinsrhldcum1234567890,"," etaoinsrhldcum1234567890!"," etaoinsrhldcumfpgwybvkxj~"," etaoinsrhldcumfpgwybvkxj<"," etaoinsrhldcumfpgwybv;:[]"," etaoinsrhldcum1234567890]"," etaoinsrhldcum1234567890("," etaoinsrhldcumfpgwybvkxj'"," etaoinsrhldcum1234567890<"," etaoinsrhldcum1234567890'"," etaoinsrhldcum1234567890'"," etaoinsrhldcumfpgwybvkxj)"," etaoinsrhldcum1234567890-"," etaoinsrhldcumfpgwybvkxj'"," etaoinsrhldcum1234567890$"," etaoinsrhldcum1234567890="," etaoinsrhldcum1234567890@"," etaoinsrhldcumfpgwybvkxj="," etaoinsrhldcumfpgwybvkxj]"," etaoinsrhldcumfpgwybv~!@#"," etaoinsrhldcumfpgwybvkxj*"," etaoinsrhldcumfpgwybvkxj%"," etaoinsrhldcum1234567890~"," etaoinsrhldcum1234567890%"," etaoinsrhldcumfpgwybvkxj."," etaoinsrhldcumfpgwybvkxj'"," etaoinsrhldcumfpgwybvkxj$"," etaoinsrhldcum1234567890."," etaoinsrhldcum1234567890["," etaoinsrhldcum1234567890)"," etaoinsrhldcumfpg()-_+=,."," etaoinsrhldcumfpgwybvkxj,"," etaoinsrhldcumfpgwybv$%&*"," etaoinsrhldcumfpgwybvkxj["," etaoinsrhldcumfpgwybvkxj("," etaoinsrhldcumfpgwybv?/<>"," etaoinsrhldcum1234567890:"," etaoinsrhldcumfpgwybvkxj>"," etaoinsrhldcumfpgwybvkxj?"," etaoinsrhldcum1234567890+"," etaoinsrhldcumfpgwybvkxj-"," etaoinsrhldcumfpgwybvkxj+"," etaoinsrhldcumfpgwybvkxj:"," etaoinsrhldcum1234567890/"," etaoinsrhldcum1234567890?"," etaoinsrhldcumfpgwybvkxj&"," etaoinsrhldcumfpgwybv()-_"," etaoinsrhldcum1234567890>"," etaoinsrhldcumfpg~!@#$%&*"," etaoinsrhldcumfpgwybvkxj@"," etaoinsrhldcum1234567890;"," etaoinsrhldcumfpgwybvkxj_"," etaoinsrhldcumfp?/<>;:[]'"," etaoinsrhldcum1234567890#"," etaoinsrhldcumfpgwybvkxj#"," etaoinsrhldcumfpgwybvkxj;"," etaoinsrhldcumfpg?/<>;:[]"," etaoinsrhldcum1234567890*"," etaoinsrhldcum1234567890&"," etaoinsrhldcumfpgwybv+=,."," etaoinsrhldcumfpgwybvkxjq"," etaoinsrhldcum1234567890_"," etaoinsrhldcumfpgwybvkxj/"," etaoinsrhldcumfpgwybvkxjq?"," etaoinsrhldcumfpgwybvkxjq+"," etaoinsrhldcumfpgwybvkxjq$"," etaoinsrhldcumfpgwybvkxjq~"," etaoinsrhldcumfpgwybvkxjq:"," etaoinsrhldcumfpgwybvkxjq'"," etaoinsrhldcumfpgwybvkxjq,"," etaoinsrhldcumfpgwybvkxjq'"," etaoinsrhldcumfpgw()-_+=,."," etaoinsrhldcumfpgwybvkxjq-"," etaoinsrhldcumfpgwybvkxjq&"," etaoinsrhldcumfpgwybvkxjq)"," etaoinsrhldcumfpgwybvk()-_"," etaoinsrhldcumfpgwybvkxjq*"," etaoinsrhldcumfpgwybvkxjq("," etaoinsrhldcumfpgwybvkxjq'"," etaoinsrhldcumfpgwybvkxjq!"," etaoinsrhldcumfpgwybvkxjq@"," etaoinsrhldcumfpgw~!@#$%&*"," etaoinsrhldcumfpgwybvk~!@#"," etaoinsrhldcumfpgwybvkxjq["," etaoinsrhldcumfpgwybvk;:[]"," etaoinsrhldcumfpgwybvk$%&*"," etaoinsrhldcumfpgwybvkxjq;"," etaoinsrhldcumfpgwybvkxjq/"," etaoinsrhldcumfpgwybvkxjq#"," etaoinsrhldcumfpgwybvk?/<>"," etaoinsrhldcumfpgwybvkxjq]"," etaoinsrhldcumfpgwybvkxjq%"," etaoinsrhldcumfpgw?/<>;:[]"," 1234567890~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjqz"," etaoinsrhldcumfpgwybvkxjq<"," etaoinsrhldcumfpg?/<>;:[]'"," etaoinsrhldcumfpgwybvk+=,."," etaoinsrhl~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjq."," etaoinsrhldcumfpgwybvkxjq_"," etaoinsrhldcumfpgwybvkxjq="," etaoinsrhldcumfpgwybvkxjq>"," etaoinsrhldcumfpgwybvkxjqz["," etaoinsrhldcumfpgwybvkxjqz_"," etaoinsrhldcumfpgwybvkx+=,."," etaoinsrhldcumfpgwybvkxjqz'"," etaoinsrhldcumfpgwybvkxjqz&"," etaoinsrhldcumfpgwybvkxjqz#"," etaoinsrhldcumfpgwybvkxjqz;"," etaoinsrhldcumfpgwybvkxjqz@"," etaoinsrhldcumfpgwybvkxjqz/"," etaoinsrhldcumfpgwybvkx()-_"," etaoinsrhldcumfpgwybvkxjqz?"," etaoinsrhldcumfpgwybvkxjqz>"," etaoinsrhldcumfpgwybvkxjqz:"," etaoinsrhldcumfpgwybvkxjqz+"," etaoinsrhld~!@#$%&*()-_+=,."," etaoinsrhldcumfpgw?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz~"," etaoinsrhldcumfpgwybvkx$%&*"," etaoinsrhldcumfpgwybvkx?/<>"," etaoinsrhldcumfpgwy~!@#$%&*"," etaoinsrhldcumfpgwybvkxjqz*"," etaoinsrhldcumfpgwybvkxjqz)"," etaoinsrhldcumfpgwy()-_+=,."," etaoinsrhldcumfpgwybvkxjqz."," etaoinsrhldcumfpgwybvkxjqz%"," etaoinsrhldcumfpgwybvkx~!@#"," etaoinsrhldcumfpgwybvkxjqz("," etaoinsrhldcumfpgwybvkxjqz="," etaoinsrhldcumfpgwybvkx;:[]"," etaoinsrhldcumfpgwybvkxjqz$"," etaoinsrhldcumfpgwybvkxjqz-"," etaoinsrhldcumfpgwybvkxjqz'"," etaoinsrhldcumfpgwy?/<>;:[]"," etaoinsrhldcumfpgwybvkxjqz'"," etaoinsrhldcumfpgwybvkxjqz<"," etaoinsrhldcumfpgwybvkxjqz]"," etaoinsrhldcumfpgwybvkxjqz!"," etaoinsrhldcumfpgwybvkxjqz,"," etaoinsrhldcum1234567890+=,."," etaoinsrhldcumfpgwybvkxj?/<>"," etaoinsrhldcumfpgwyb()-_+=,."," etaoinsrhldcum1234567890;:[]"," etaoinsrhldcumfpgwybvkxj$%&*"," etaoinsrhldcum1234567890?/<>"," etaoinsrhldcumfpgwy?/<>;:[]'"," tnsrhldcmfpgwbvkxjqz()-_+=,."," etaoinsrhldcumfpgwyb?/<>;:[]"," etaoinsrhldcum1234567890$%&*"," etaoinsrhldcumfpgwybvkxj~!@#"," etaoinsrhldcumfpgwybvkxj;:[]"," etaoinsrhl1234567890()-_+=,."," etaoinsrhldcumfpgwyb~!@#$%&*"," etaoinsrhldcumfpgwybvkxj()-_"," tnsrhldcmfpgwbvkxjqz~!@#$%&*"," etaoinsrhldcum1234567890~!@#"," etaoinsrhldcumfpgw1234567890"," etaoinsrhldcum1234567890()-_"," tnsrhldcmfpgwbvkxjqz?/<>;:[]"," etaoinsrhldcumfpgwybvkxj+=,."," etaoinsrhl1234567890?/<>;:[]"," etaoinsrhl1234567890~!@#$%&*"," etaoinsrhldc~!@#$%&*()-_+=,."," etaoinsrhldcumfpgw1234567890["," etaoinsrhldcumfpgw1234567890@"," etaoinsrhl1234567890?/<>;:[]'"," etaoinsrhldcumfpgw1234567890,"," etaoinsrhldcumfpgw1234567890="," etaoinsrhldcumfpgwybvkxjq?/<>"," etaoinsrhldcumfpgwybv?/<>;:[]"," etaoinsrhldcumfpgw1234567890)"," etaoinsrhldcumfpgwybvkxjq+=,."," etaoinsrhldcumfpgw1234567890#"," etaoinsrhldcumfpgw1234567890'"," etaoinsrhldcumfpgwybv()-_+=,."," etaoinsrhldcumfpgwybvkxjq()-_"," etaoinsrhldcumfpgw1234567890-"," etaoinsrhldcumfpgw1234567890("," etaoinsrhldcumfpgwybvkxjq$%&*"," etaoinsrhldcu~!@#$%&*()-_+=,."," etaoinsrhldcumfpgw1234567890."," etaoinsrhldcumfpgw1234567890_"," etaoinsrhldcumfpgw1234567890?"," etaoinsrhldcumfpgwybvkxjq~!@#"," etaoinsrhldcumfpgw1234567890$"," etaoinsrhldcumfpgw1234567890'"," etaoinsrhldcumfpgw1234567890'"," etaoinsrhldcumfpgw1234567890]"," etaoinsrhldcumfpgw1234567890%"," etaoinsrhldcumfpgwybvkxjq;:[]"," etaoinsrhldcumfpgw1234567890:"," etaoinsrhldcumfpgw1234567890;"," etaoinsrhldcumfpgw1234567890&"," etaoinsrhldcumfpgwybv~!@#$%&*"," etaoinsrhldcumfpgwyb?/<>;:[]'"," etaoinsrhldcumfpgw1234567890+"," etaoinsrhldcumfpgw1234567890>"," tnsrhldcmfpgwbvkxjqz?/<>;:[]'"," etaoinsrhldcumfpgw1234567890~"," etaoinsrhldcumfpgw1234567890<"," etaoinsrhldcumfpgw1234567890/"," etaoinsrhldcumfpgw1234567890*"," etaoinsrhldcumfpgw1234567890!"," etaoinsrhldcumfpgwybvk~!@#$%&*"," etaoinsrhldcumfpgwybvkxjqz~!@#"," tnsrhldcmfpgwbvkxjqz1234567890"," etaoinsrhldcum~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjqz$%&*"," etaoinsrhldcumfpgwybv?/<>;:[]'"," etaoinsrhldcumfpgwybvk()-_+=,."," etaoinsrhldcumfpgwybvkxjqz+=,."," etaoinsrhldcumfpgwybvk?/<>;:[]"," etaoinsrhldcumfpgwybvkxjqz?/<>"," etaoinsrhldcumfpgwybvkxjqz;:[]"," etaoinsrhldcumfpgwybvkxjqz()-_"," tnsrhldcmfpgwbvkxjqz1234567890&"," tnsrhldcmfpgwbvkxjqz1234567890'"," tnsrhldcmfpgwbvkxjqz1234567890]"," tnsrhldcmfpgwbvkxjqz1234567890<"," tnsrhldcmfpgwbvkxjqz1234567890!"," tnsrhldcmfpgwbvkxjqz1234567890+"," tnsrhldcmfpgwbvkxjqz1234567890/"," tnsrhldcmfpgwbvkxjqz1234567890["," etaoinsrhldcumfpgwybvkx?/<>;:[]"," tnsrhldcmfpgwbvkxjqz1234567890*"," tnsrhldcmfpgwbvkxjqz1234567890'"," tnsrhldcmfpgwbvkxjqz1234567890?"," tnsrhldcmfpgwbvkxjqz1234567890%"," tnsrhldcmfpgwbvkxjqz1234567890:"," etaoinsrhldcumf~!@#$%&*()-_+=,."," tnsrhldcmfpgwbvkxjqz1234567890."," etaoinsrhldcumfpgwybvk?/<>;:[]'"," tnsrhldcmfpgwbvkxjqz1234567890;"," tnsrhldcmfpgwbvkxjqz1234567890'"," tnsrhldcmfpgwbvkxjqz1234567890_"," tnsrhldcmfpgwbvkxjqz1234567890>"," etaoinsrhldcumfpgwybvkx()-_+=,."," tnsrhldcmfpgwbvkxjqz1234567890)"," tnsrhldcmfpgwbvkxjqz1234567890#"," tnsrhldcmfpgwbvkxjqz1234567890("," tnsrhldcmfpgwbvkxjqz1234567890-"," tnsrhldcmfpgwbvkxjqz1234567890,"," tnsrhldcmfpgwbvkxjqz1234567890@"," tnsrhldcmfpgwbvkxjqz1234567890="," tnsrhldcmfpgwbvkxjqz1234567890$"," tnsrhldcmfpgwbvkxjqz1234567890~"," etaoinsrhldcumfpgwybvkx~!@#$%&*"," etaoinsrhldcum1234567890?/<>;:[]"," etaoinsrhldcumfpgwybvkx?/<>;:[]'"," etaoinsrhldcumfpgwybvkxj()-_+=,."," etaoinsrhldcumfpgwybvk1234567890"," etaoinsrhldcum1234567890~!@#$%&*"," etaoinsrhldcumfpgwybvkxj~!@#$%&*"," etaoinsrhldcumfp~!@#$%&*()-_+=,."," etaoinsrhldcum1234567890()-_+=,."," etaoinsrhldcumfpgw1234567890$%&*"," etaoinsrhldcumfpgw1234567890()-_"," etaoinsrhldcumfpgw1234567890+=,."," etaoinsrhldcumfpgw1234567890;:[]"," etaoinsrhldcumfpgw1234567890?/<>"," etaoinsrhldcumfpgwybvkxj?/<>;:[]"," etaoinsrhldcumfpgw1234567890~!@#"," etaoinsrhldcumfpgwybvk1234567890]"," etaoinsrhldcumfpgwybvk1234567890/"," etaoinsrhldcumfpgwybvk1234567890&"," etaoinsrhldcumfpgwybvk1234567890'"," etaoinsrhldcumfpgwybvk1234567890'"," etaoinsrhldcumfpgwybvk1234567890@"," etaoinsrhldcumfpgwybvkxjq()-_+=,."," etaoinsrhldcumfpg~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvk1234567890_"," etaoinsrhldcumfpgwybvk1234567890;"," etaoinsrhldcumfpgwybvk1234567890%"," etaoinsrhldcumfpgwybvk1234567890$"," etaoinsrhldcumfpgwybvkxjq~!@#$%&*"," etaoinsrhldcum1234567890?/<>;:[]'"," etaoinsrhldcumfpgwybvk1234567890)"," etaoinsrhldcumfpgwybvk1234567890!"," etaoinsrhldcumfpgwybvk1234567890+"," etaoinsrhldcumfpgwybvk1234567890#"," etaoinsrhldcumfpgwybvk1234567890-"," etaoinsrhldcumfpgwybvk1234567890."," etaoinsrhldcumfpgwybvk1234567890("," etaoinsrhldcumfpgwybvk1234567890="," etaoinsrhldcumfpgwybvk1234567890:"," etaoinsrhldcumfpgwybvk1234567890'"," etaoinsrhldcumfpgwybvk1234567890>"," etaoinsrhldcumfpgwybvk1234567890,"," etaoinsrhldcumfpgwybvk1234567890*"," etaoinsrhldcumfpgwybvkxj?/<>;:[]'"," etaoinsrhldcumfpgwybvk1234567890["," etaoinsrhldcumfpgwybvk1234567890<"," etaoinsrhldcumfpgwybvk1234567890?"," etaoinsrhldcumfpgwybvkxjq?/<>;:[]"," etaoinsrhldcumfpgwybvk1234567890~"," etaoinsrhldcumfpgwybvkxjqz()-_+=,."," etaoinsrhldcumfpgwybvkxjqz?/<>;:[]"," etaoinsrhldcumfpgw~!@#$%&*()-_+=,."," tnsrhldcmfpgwbvkxjqz1234567890~!@#"," tnsrhldcmfpgwbvkxjqz1234567890$%&*"," etaoinsrhldcumfpgwybvkxjq?/<>;:[]'"," tnsrhldcmfpgwbvkxjqz1234567890()-_"," tnsrhldcmfpgwbvkxjqz1234567890+=,."," tnsrhldcmfpgwbvkxjqz1234567890?/<>"," tnsrhldcmfpgwbvkxjqz1234567890;:[]"," etaoinsrhldcumfpgwybvkxjqz~!@#$%&*"," etaoinsrhldcumfpgwybvkxjqz?/<>;:[]'"," 1234567890~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwy~!@#$%&*()-_+=,."," etaoinsrhl~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgw1234567890()-_+=,."," etaoinsrhldcumfpgw1234567890~!@#$%&*"," etaoinsrhldcumfpgwybvk1234567890;:[]"," etaoinsrhldcumfpgw1234567890?/<>;:[]"," etaoinsrhldcumfpgwybvk1234567890?/<>"," etaoinsrhldcumfpgwybvk1234567890+=,."," etaoinsrhldcumfpgwybvk1234567890()-_"," etaoinsrhld~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvk1234567890$%&*"," etaoinsrhldcumfpgwybvk1234567890~!@#"," etaoinsrhldcumfpgwyb~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjqz1234567890"," etaoinsrhl1234567890~!@#$%&*()-_+=,."," tnsrhldcmfpgwbvkxjqz~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjqz1234567890'"," etaoinsrhldcumfpgwybvkxjqz1234567890:"," etaoinsrhldcumfpgwybvkxjqz1234567890#"," etaoinsrhldcumfpgwybvkxjqz1234567890_"," etaoinsrhldcumfpgwybvkxjqz1234567890="," etaoinsrhldc~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890,"," etaoinsrhldcumfpgwybvkxjqz1234567890("," etaoinsrhldcumfpgwybvkxjqz1234567890+"," etaoinsrhldcumfpgwybvkxjqz1234567890$"," etaoinsrhldcumfpgwybvkxjqz1234567890-"," etaoinsrhldcumfpgwybvkxjqz1234567890'"," etaoinsrhldcumfpgwybvkxjqz1234567890."," etaoinsrhldcumfpgwybvkxjqz1234567890?"," etaoinsrhldcumfpgw1234567890?/<>;:[]'"," etaoinsrhldcumfpgwybv~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjqz1234567890@"," etaoinsrhldcumfpgwybvkxjqz1234567890*"," etaoinsrhldcumfpgwybvkxjqz1234567890/"," etaoinsrhldcumfpgwybvkxjqz1234567890<"," etaoinsrhldcumfpgwybvkxjqz1234567890>"," etaoinsrhldcumfpgwybvkxjqz1234567890!"," etaoinsrhldcumfpgwybvkxjqz1234567890&"," etaoinsrhldcumfpgwybvkxjqz1234567890;"," etaoinsrhldcumfpgwybvkxjqz1234567890'"," etaoinsrhldcumfpgwybvkxjqz1234567890~"," etaoinsrhldcumfpgwybvkxjqz1234567890)"," etaoinsrhldcumfpgwybvkxjqz1234567890%"," etaoinsrhldcumfpgwybvkxjqz1234567890]"," etaoinsrhldcumfpgwybvkxjqz1234567890["," etaoinsrhldcumfpgwybvk~!@#$%&*()-_+=,."," tnsrhldcmfpgwbvkxjqz1234567890?/<>;:[]"," tnsrhldcmfpgwbvkxjqz1234567890~!@#$%&*"," tnsrhldcmfpgwbvkxjqz1234567890()-_+=,."," etaoinsrhldcu~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcum~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkx~!@#$%&*()-_+=,."," tnsrhldcmfpgwbvkxjqz1234567890?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890$%&*"," etaoinsrhldcumfpgwybvkxjqz1234567890()-_"," etaoinsrhldcumfpgwybvkxj~!@#$%&*()-_+=,."," etaoinsrhldcumf~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890+=,."," etaoinsrhldcumfpgwybvkxjqz1234567890~!@#"," etaoinsrhldcumfpgwybvkxjqz1234567890?/<>"," etaoinsrhldcumfpgwybvk1234567890()-_+=,."," etaoinsrhldcumfpgwybvkxjqz1234567890;:[]"," etaoinsrhldcumfpgwybvk1234567890~!@#$%&*"," etaoinsrhldcumfpgwybvk1234567890?/<>;:[]"," etaoinsrhldcum1234567890~!@#$%&*()-_+=,."," etaoinsrhldcumfp~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjq~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvk1234567890?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz~!@#$%&*()-_+=,."," etaoinsrhldcumfpg~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgw~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890()-_+=,."," etaoinsrhldcumfpgwy~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890?/<>;:[]"," etaoinsrhldcumfpgwybvkxjqz1234567890~!@#$%&*"," etaoinsrhldcumfpgw1234567890~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvkxjqz1234567890?/<>;:[]'"," tnsrhldcmfpgwbvkxjqz~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwyb~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhl1234567890~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybv~!@#$%&*()-_+=,.?/<>;:[]'"," tnsrhldcmfpgwbvkxjqz1234567890~!@#$%&*()-_+=,."," etaoinsrhldcumfpgwybvk~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkx~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvk1234567890~!@#$%&*()-_+=,."," etaoinsrhldcum1234567890~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxj~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjq~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890~!@#$%&*()-_+=,."," etaoinsrhldcumfpgw1234567890~!@#$%&*()-_+=,.?/<>;:[]'"," tnsrhldcmfpgwbvkxjqz1234567890~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvk1234567890~!@#$%&*()-_+=,.?/<>;:[]'"," etaoinsrhldcumfpgwybvkxjqz1234567890~!@#$%&*()-_+=,.?/<>;:[]'"] def shortest_charset(self, input_str : str) -> int: """Find shortest suitable charset for input""" unique = "".join(set(input_str)) # Remove duplicates for index,charset in enumerate(self.charsets): good_charset = True for char in unique: if char not in charset: good_charset = False break if good_charset: return index return -1 def calculate_checksum(self, digits : int) -> int: sum = 0 for d in str(digits): sum += int(d) sum+=1 return sum % 10 def validate_checksum(self, digits : int, expected : int) -> bool: try: int(digits) int(expected) except ValueError: return False if self.calculate_checksum(digits) == expected: return True return False def validate_message(self, message : str) -> bool: if len(message) > 29: message = message[-29:] shortest = self.shortest_charset(message) if shortest == -1: return False try: message = str(int(message) ^ self.sikrit) except Exception: return False expected = int(message[-1:]) charset_index = int(message[-4:-1]) return self.validate_checksum(charset_index, expected)

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0d143ff2211a060d0994832159c312c4435375c5

877

py

Python

dml/maths/gquad.py

FCDM/py-dml

3e753e543644211ba42c8e048f46f956af1c5f8c

[ "MIT" ]

null

dml/maths/gquad.py

FCDM/py-dml

3e753e543644211ba42c8e048f46f956af1c5f8c

[ "MIT" ]

null

dml/maths/gquad.py

FCDM/py-dml

3e753e543644211ba42c8e048f46f956af1c5f8c

[ "MIT" ]

null

def gaussianQuadrature(function, a, b): """ Perform a Gaussian quadrature approximation of the integral of a function from a to b. """ # Coefficient values can be found at pomax.github.io/bezierinfo/legendre-gauss.html A = (b - a)/2 B = (b + a)/2 return A * ( 0.2955242247147529*function(-A*0.1488743389816312 + B) + \ 0.2955242247147529*function(+A*0.1488743389816312 + B) + \ 0.2692667193099963*function(-A*0.4333953941292472 + B) + \ 0.2692667193099963*function(+A*0.4333953941292472 + B) + \ 0.2190863625159820*function(-A*0.6794095682990244 + B) + \ 0.2190863625159820*function(+A*0.6794095682990244 + B) + \ 0.1494513491505806*function(-A*0.8650633666889845 + B) + \ 0.1494513491505806*function(+A*0.8650633666889845 + B) + \ 0.0666713443086881*function(-A*0.9739065285171717 + B) + \ 0.0666713443086881*function(+A*0.9739065285171717 + B) )

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1

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null

0

5

b4a69cab73fcca029c2617b364e28799bbe378ab

137

py

Python

lib/__init__.py

retr0-13/skype_ip_resolver

4f9ec2f05ddff28cc90cb631f8eae4882ee2d293

[ "MIT" ]

11

2015-09-03T12:07:59.000Z

2022-03-06T06:59:10.000Z

lib/__init__.py

retr0-13/skype_ip_resolver

4f9ec2f05ddff28cc90cb631f8eae4882ee2d293

[ "MIT" ]

null

lib/__init__.py

retr0-13/skype_ip_resolver

4f9ec2f05ddff28cc90cb631f8eae4882ee2d293

[ "MIT" ]

4

2018-04-01T09:03:30.000Z

2021-04-08T19:13:47.000Z

15.222222

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null

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5

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55

py

Python

melior_transformers/ner/__init__.py

MeliorAI/meliorTransformers

b2936e1aac23e63e0b737d03975124c31a960812

[ "Apache-2.0" ]

1

2020-08-06T10:48:49.000Z

melior_transformers/ner/__init__.py

MeliorAI/meliorTransformers

b2936e1aac23e63e0b737d03975124c31a960812

[ "Apache-2.0" ]

2

2020-02-13T12:45:57.000Z

2020-04-14T11:30:33.000Z

melior_transformers/ner/__init__.py

MeliorAI/meliorTransformers

b2936e1aac23e63e0b737d03975124c31a960812

[ "Apache-2.0" ]

2

2020-07-21T12:43:51.000Z

2021-08-13T15:21:22.000Z

from melior_transformers.ner.ner_model import NERModel

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5

37037620fc1660cb192fa230b828f7a68ad5534f

473

py

Python

components/Actuators/LowLevel/limelight.py

Raptacon/Robot-2022

f59c6a6ebd5779a2fd91181b65cbcd677507ca5d

[ "MIT" ]

4

2022-01-31T14:05:31.000Z

2022-03-26T14:12:45.000Z

components/Actuators/LowLevel/limelight.py

Raptacon/Robot-2022

f59c6a6ebd5779a2fd91181b65cbcd677507ca5d

[ "MIT" ]

57

2022-01-13T02:41:31.000Z

2022-03-26T14:50:42.000Z

components/Actuators/LowLevel/limelight.py

Raptacon/Robot-2022

f59c6a6ebd5779a2fd91181b65cbcd677507ca5d

[ "MIT" ]

null

from networktables import NetworkTables class Limelight(): compatString = ["teapot"] limeTable = NetworkTables.getTable("limelight") def LEDOff(self): self.limeTable.putNumber('ledMode',1) def LEDOn(self): self.limeTable.putNumber('ledMode',3) def resetLED(self): """ Resets the LED to whatever the setting is """ self.limeTable.putNumber('ledMode',0) def execute(self): pass

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473

16

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5

3703d1153db76a59529d150705210dd3bf130f20

375

py

Python

app/repository/setting_repository.py

tch1bo/viaduct

bfd37b0a8408b2dd66fb01138163b80ce97699ff

[ "MIT" ]

11

2015-04-23T21:57:56.000Z

2019-04-28T12:48:58.000Z

app/repository/setting_repository.py

tch1bo/viaduct

bfd37b0a8408b2dd66fb01138163b80ce97699ff

[ "MIT" ]

1

2016-10-05T14:10:58.000Z

2016-10-05T14:12:23.000Z

app/repository/setting_repository.py

tch1bo/viaduct

bfd37b0a8408b2dd66fb01138163b80ce97699ff

[ "MIT" ]

3

2016-10-05T14:00:42.000Z

2019-01-16T14:33:43.000Z

from app import db from app.models.setting_model import Setting def create_setting(): return Setting() def save(setting): db.session.add(setting) db.session.commit() return setting def find_by_key(key): return db.session.query(Setting).filter_by(key=key).first() def delete_setting(setting): db.session.delete(setting) db.session.commit()

17.045455

63

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375

4.888889

0.388889

0.170455

0.242424

0.166667

0

0.165333

375

21

64

17.857143

0.84345

0

0.153846

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false

0

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0

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