xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

2346b7d4b689aedf70be90e22366c7d461f0ff5d

1,479

py

Python

mupub/tests/test_utils.py

MutopiaProject/mupub

8c59ae15ea13af14139570fcccfef850e1363548

[ "MIT" ]

null

mupub/tests/test_utils.py

MutopiaProject/mupub

8c59ae15ea13af14139570fcccfef850e1363548

[ "MIT" ]

1

2017-02-22T17:33:23.000Z

2017-02-23T10:02:48.000Z

mupub/tests/test_utils.py

MutopiaProject/mupub

8c59ae15ea13af14139570fcccfef850e1363548

[ "MIT" ]

null

"""Util module tests """ import os.path from unittest import TestCase import mupub from clint.textui.validators import ValidationError from .tutils import PREFIX _SIMPLE_PATH = os.path.join(PREFIX, 'SorF', 'O77', 'sorf-o77-01',) _LYS_PATH = os.path.join(PREFIX, 'PaganiniN', 'O1', 'Caprice_1',) class UtilsTest(TestCase): """Utils testing""" def test_find(self): """Find files (for zipping ly files)""" here = os.getcwd() try: os.chdir(_SIMPLE_PATH) flist = mupub.utils.find_files('.') self.assertEqual(len(flist), 2) finally: os.chdir(here) def test_resolve(self): """Resolving file input""" here = os.getcwd() try: for test_path in [_SIMPLE_PATH, _LYS_PATH,]: os.chdir(test_path) base,infile = mupub.utils.resolve_input() self.assertEqual(base, os.path.basename(test_path)) self.assertIsNotNone(infile) finally: os.chdir(here) def test_bools(self): boolv = mupub.utils.BooleanValidator('some message') boolv_nom = mupub.utils.BooleanValidator() self.assertTrue(boolv('y'), 'y is True') self.assertFalse(boolv('n'), 'n is False') self.assertTrue(not boolv_nom('N'), 'not N is True') with self.assertRaises(ValidationError): if boolv('x'): self.assertFail('should not be here!')

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2,736

py

Python

routemaster/cli.py

thread/routemaster

1fd997a3bcee5e6760e9f7a60cb54323c3dfdc41

[ "MIT" ]

13

2018-01-16T14:26:27.000Z

2022-03-19T12:43:17.000Z

routemaster/cli.py

thread/routemaster

1fd997a3bcee5e6760e9f7a60cb54323c3dfdc41

[ "MIT" ]

86

2018-01-03T17:00:56.000Z

2021-12-06T12:58:06.000Z

routemaster/cli.py

thread/routemaster

1fd997a3bcee5e6760e9f7a60cb54323c3dfdc41

[ "MIT" ]

3

2018-02-21T23:13:45.000Z

2022-03-19T12:43:23.000Z

"""CLI handling for `routemaster`.""" import logging import yaml import click import layer_loader from routemaster.app import App from routemaster.cron import CronThread from routemaster.config import ConfigError, load_config from routemaster.server import server from routemaster.middleware import wrap_application from routemaster.validation import ValidationError, validate_config from routemaster.gunicorn_application import GunicornWSGIApplication logger = logging.getLogger(__name__) @click.group() @click.option( '-c', '--config-file', 'config_files', help="Path to the service config file.", type=click.File(encoding='utf-8'), required=True, multiple=True, ) @click.pass_context def main(ctx, config_files): """Shared entrypoint configuration.""" logging.getLogger('schedule').setLevel(logging.CRITICAL) config_data = layer_loader.load_files( config_files, loader=yaml.load, ) try: config = load_config(config_data) except ConfigError: logger.exception("Configuration Error") click.get_current_context().exit(1) ctx.obj = App(config) _validate_config(ctx.obj) @main.command() @click.pass_context def validate(ctx): """ Entrypoint for validation of configuration files. Validation is done by the main handler in order to cover all code paths, so this function is a stub so that `serve` does not have to be called. """ pass @main.command() @click.option( '-b', '--bind', help="Bind address and port.", type=str, default='[::]:2017', ) @click.option( '--debug/--no-debug', help="Enable debugging mode.", default=False, ) @click.option( '--workers', help="Number of gunicorn workers to run.", type=int, default=1, ) @click.pass_context def serve(ctx, bind, debug, workers): # pragma: no cover """Entrypoint for serving the Routemaster HTTP service.""" app = ctx.obj server.config.app = app if debug: server.config['DEBUG'] = True cron_thread = CronThread(app) cron_thread.start() wrapped_server = wrap_application(app, server) def post_fork(): app.initialise() app.logger.init_flask(server) try: instance = GunicornWSGIApplication( wrapped_server, bind=bind, debug=debug, workers=workers, post_fork=post_fork, ) instance.run() finally: cron_thread.stop() def _validate_config(app: App): try: validate_config(app, app.config) except ValidationError as e: msg = f"Validation Error: {e}" logger.exception(msg) click.get_current_context().exit(1)

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2348e1dd77f2ba0e869197de55900d212aa3c556

965

py

Python

grid_sticky_example_3.py

crazcalm/learn_tkinter_canvas

b798a6f2217a478e9222bb6eaa2afec3d28a2758

[ "MIT" ]

null

grid_sticky_example_3.py

crazcalm/learn_tkinter_canvas

b798a6f2217a478e9222bb6eaa2afec3d28a2758

[ "MIT" ]

2

2020-02-14T02:14:26.000Z

2020-02-14T02:15:58.000Z

grid_sticky_example_3.py

crazcalm/learn_tkinter_canvas

b798a6f2217a478e9222bb6eaa2afec3d28a2758

[ "MIT" ]

1

2021-11-24T13:00:34.000Z

""" When a widget is positioned with sticky, the size of the widget itself is just big enough to contain any text and other contents inside of it. It won’t fill the entire grid cell. In order to fill the grid, you can specify "ns" to force the widget to fill the cell in the vertical direction, or "ew" to fill the cell in the vertical direction. To fill the entire cell, set sticky to "nsew". The following example illustrates each of these options: """ import tkinter as tk window = tk.Tk() window.rowconfigure(0, minsize=50) window.columnconfigure([0, 1, 2, 3], minsize=50) label1 = tk.Label(text="1", bg="black", fg="white") label2 = tk.Label(text="2", bg="black", fg="white") label3 = tk.Label(text="3", bg="black", fg="white") label4 = tk.Label(text="4", bg="black", fg="white") label1.grid(row=0, column=0) label2.grid(row=0, column=1, sticky="ew") label3.grid(row=0, column=2, sticky="ns") label4.grid(row=0, column=3, sticky="nsew") window.mainloop()

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234e920fdc139ffec693a188e6071590ea84ef74

20,151

py

Python

praatio/pitch_and_intensity.py

timmahrt/praatIO

000d0477fffb033b63d54311fac5c913157a59a6

[ "MIT" ]

208

2016-04-20T12:42:05.000Z

2022-03-25T13:44:03.000Z

praatio/pitch_and_intensity.py

timmahrt/praatIO

000d0477fffb033b63d54311fac5c913157a59a6

[ "MIT" ]

37

2017-10-31T15:22:59.000Z

2022-01-02T02:55:46.000Z

praatio/pitch_and_intensity.py

timmahrt/praatIO

000d0477fffb033b63d54311fac5c913157a59a6

[ "MIT" ]

33

2016-05-09T07:34:22.000Z

2022-03-30T09:00:58.000Z

# coding: utf-8 """ Functions for working with pitch data This file depends on the praat script get_pitch_and_intensity.praat (which depends on praat) to extract pitch and intensity values from audio data. Once the data is extracted, there are functions for data normalization and calculating various measures from the time stamped output of the praat script (ie **generatePIMeasures()**) For brevity, 'pitch_and_intensity' is referred to as 'PI' see **examples/get_pitch_and_formants.py** """ import os from os.path import join import io import math from typing import List, Tuple, Optional, cast from praatio import data_points from praatio import praatio_scripts from praatio import textgrid from praatio.utilities import errors from praatio.utilities import my_math from praatio.utilities import utils from praatio.utilities.constants import Point HERTZ = "Hertz" UNSPECIFIED = "unspecified" _PITCH_ERROR_TIER_NAME = "pitch errors" def _extractPIPiecewise( inputFN: str, outputFN: str, praatEXE: str, minPitch: float, maxPitch: float, tgFN: str, tierName: str, tmpOutputPath: str, sampleStep: float = 0.01, silenceThreshold: float = 0.03, pitchUnit: str = HERTZ, forceRegenerate: bool = True, undefinedValue: float = None, medianFilterWindowSize: int = 0, pitchQuadInterp: bool = False, ) -> List[Tuple[float, ...]]: """ Extracts pitch and int from each labeled interval in a textgrid This has the benefit of being faster than using _extractPIFile if only labeled regions need to have their pitch values sampled, particularly for longer files. Returns the result as a list. Will load the serialized result if this has already been called on the appropriate files before """ outputPath = os.path.split(outputFN)[0] utils.makeDir(outputPath) windowSize = medianFilterWindowSize if not os.path.exists(inputFN): raise errors.ArgumentError(f"Required folder does not exist: f{inputFN}") firstTime = not os.path.exists(outputFN) if firstTime or forceRegenerate is True: utils.makeDir(tmpOutputPath) splitAudioList = praatio_scripts.splitAudioOnTier( inputFN, tgFN, tierName, tmpOutputPath, False ) allPIList: List[Tuple[str, str, str]] = [] for start, _, fn in splitAudioList: tmpTrackName = os.path.splitext(fn)[0] + ".txt" piList = _extractPIFile( join(tmpOutputPath, fn), join(tmpOutputPath, tmpTrackName), praatEXE, minPitch, maxPitch, sampleStep, silenceThreshold, pitchUnit, forceRegenerate=True, medianFilterWindowSize=windowSize, pitchQuadInterp=pitchQuadInterp, ) convertedPiList = [ ("%0.3f" % (float(time) + start), str(pV), str(iV)) for time, pV, iV in piList ] allPIList.extend(convertedPiList) outputData = [",".join(row) for row in allPIList] with open(outputFN, "w") as fd: fd.write("\n".join(outputData) + "\n") return loadTimeSeriesData(outputFN, undefinedValue=undefinedValue) def _extractPIFile( inputFN: str, outputFN: str, praatEXE: str, minPitch: float, maxPitch: float, sampleStep: float = 0.01, silenceThreshold: float = 0.03, pitchUnit: str = HERTZ, forceRegenerate: bool = True, undefinedValue: float = None, medianFilterWindowSize: int = 0, pitchQuadInterp: bool = False, ) -> List[Tuple[float, ...]]: """ Extracts pitch and intensity values from an audio file Returns the result as a list. Will load the serialized result if this has already been called on the appropriate files before """ outputPath = os.path.split(outputFN)[0] utils.makeDir(outputPath) if not os.path.exists(inputFN): raise errors.ArgumentError(f"Required folder does not exist: f{inputFN}") firstTime = not os.path.exists(outputFN) if firstTime or forceRegenerate is True: # The praat script uses append mode, so we need to clear any prior # result if os.path.exists(outputFN): os.remove(outputFN) if pitchQuadInterp is True: doInterpolation = 1 else: doInterpolation = 0 argList = [ inputFN, outputFN, sampleStep, minPitch, maxPitch, silenceThreshold, pitchUnit, -1, -1, medianFilterWindowSize, doInterpolation, ] scriptName = "get_pitch_and_intensity.praat" scriptFN = join(utils.scriptsPath, scriptName) utils.runPraatScript(praatEXE, scriptFN, argList) return loadTimeSeriesData(outputFN, undefinedValue=undefinedValue) def extractIntensity( inputFN: str, outputFN: str, praatEXE: str, minPitch: float, sampleStep: float = 0.01, forceRegenerate: bool = True, undefinedValue: float = None, ) -> List[Tuple[float, ...]]: """ Extract the intensity for an audio file Calculates intensity using the following praat command: https://www.fon.hum.uva.nl/praat/manual/Sound__To_Intensity___.html """ outputPath = os.path.split(outputFN)[0] utils.makeDir(outputPath) if not os.path.exists(inputFN): raise errors.ArgumentError(f"Required folder does not exist: f{inputFN}") firstTime = not os.path.exists(outputFN) if firstTime or forceRegenerate is True: # The praat script uses append mode, so we need to clear any prior # result if os.path.exists(outputFN): os.remove(outputFN) argList = [inputFN, outputFN, sampleStep, minPitch, -1, -1] scriptName = "get_intensity.praat" scriptFN = join(utils.scriptsPath, scriptName) utils.runPraatScript(praatEXE, scriptFN, argList) return loadTimeSeriesData(outputFN, undefinedValue=undefinedValue) def extractPitchTier( wavFN: str, outputFN: str, praatEXE: str, minPitch: float, maxPitch: float, sampleStep: float = 0.01, silenceThreshold: float = 0.03, forceRegenerate: bool = True, medianFilterWindowSize: int = 0, pitchQuadInterp: bool = False, ) -> data_points.PointObject2D: """ Extract pitch at regular intervals from the input wav file Data is output to a text file and then returned in a list in the form [(timeV1, pitchV1), (timeV2, pitchV2), ...] sampleStep - the frequency to sample pitch at silenceThreshold - segments with lower intensity won't be analyzed for pitch forceRegenerate - if running this function for the same file, if False just read in the existing pitch file pitchQuadInterp - if True, quadratically interpolate pitch Calculates pitch using the following praat command: https://www.fon.hum.uva.nl/praat/manual/Sound__To_Pitch___.html """ outputPath = os.path.split(outputFN)[0] utils.makeDir(outputPath) if pitchQuadInterp is True: doInterpolation = 1 else: doInterpolation = 0 if not os.path.exists(wavFN): raise errors.ArgumentError(f"Required file does not exist: f{wavFN}") firstTime = not os.path.exists(outputFN) if firstTime or forceRegenerate is True: if os.path.exists(outputFN): os.remove(outputFN) argList = [ wavFN, outputFN, sampleStep, minPitch, maxPitch, silenceThreshold, medianFilterWindowSize, doInterpolation, ] scriptName = "get_pitchtier.praat" scriptFN = join(utils.scriptsPath, scriptName) utils.runPraatScript(praatEXE, scriptFN, argList) return data_points.open2DPointObject(outputFN) def extractPitch( wavFN: str, outputFN: str, praatEXE: str, minPitch: float, maxPitch: float, sampleStep: float = 0.01, silenceThreshold: float = 0.03, forceRegenerate: bool = True, undefinedValue: float = None, medianFilterWindowSize: int = 0, pitchQuadInterp: bool = False, ) -> List[Tuple[float, ...]]: """ Extract pitch at regular intervals from the input wav file Data is output to a text file and then returned in a list in the form [(timeV1, pitchV1), (timeV2, pitchV2), ...] sampleStep - the frequency to sample pitch at silenceThreshold - segments with lower intensity won't be analyzed for pitch forceRegenerate - if running this function for the same file, if False just read in the existing pitch file undefinedValue - if None remove from the dataset, otherset set to undefinedValue pitchQuadInterp - if True, quadratically interpolate pitch Calculates pitch using the following praat command: https://www.fon.hum.uva.nl/praat/manual/Sound__To_Pitch___.html """ outputPath = os.path.split(outputFN)[0] utils.makeDir(outputPath) if pitchQuadInterp is True: doInterpolation = 1 else: doInterpolation = 0 if not os.path.exists(wavFN): raise errors.ArgumentError(f"Required file does not exist: f{wavFN}") firstTime = not os.path.exists(outputFN) if firstTime or forceRegenerate is True: if os.path.exists(outputFN): os.remove(outputFN) argList = [ wavFN, outputFN, sampleStep, minPitch, maxPitch, silenceThreshold, -1, -1, medianFilterWindowSize, doInterpolation, ] scriptName = "get_pitch.praat" scriptFN = join(utils.scriptsPath, scriptName) utils.runPraatScript(praatEXE, scriptFN, argList) return loadTimeSeriesData(outputFN, undefinedValue=undefinedValue) def extractPI( inputFN: str, outputFN: str, praatEXE: str, minPitch: float, maxPitch: float, sampleStep: float = 0.01, silenceThreshold: float = 0.03, pitchUnit: str = HERTZ, forceRegenerate: bool = True, tgFN: str = None, tierName: str = None, tmpOutputPath: str = None, undefinedValue: float = None, medianFilterWindowSize: int = 0, pitchQuadInterp: bool = False, ) -> List[Tuple[float, ...]]: """ Extracts pitch and intensity from a file wholesale or piecewise If the parameters for a tg are passed in, this will only extract labeled segments in a tier of the tg. Otherwise, pitch will be extracted from the entire file. male: minPitch=50; maxPitch=350 female: minPitch=75; maxPitch=450 pitchUnit: "Hertz", "semitones re 100 Hz", etc Calculates pitch and intensity using the following praat command: https://www.fon.hum.uva.nl/praat/manual/Sound__To_Pitch___.html https://www.fon.hum.uva.nl/praat/manual/Sound__To_Intensity___.html """ outputPath = os.path.split(outputFN)[0] windowSize = medianFilterWindowSize if tgFN is None or tierName is None: piList = _extractPIFile( inputFN, outputFN, praatEXE, minPitch, maxPitch, sampleStep, silenceThreshold, pitchUnit, forceRegenerate, undefinedValue=undefinedValue, medianFilterWindowSize=windowSize, pitchQuadInterp=pitchQuadInterp, ) else: if tmpOutputPath is None: tmpOutputPath = join(outputPath, "piecewise_output") piList = _extractPIPiecewise( inputFN, outputFN, praatEXE, minPitch, maxPitch, tgFN, tierName, tmpOutputPath, sampleStep, silenceThreshold, pitchUnit, forceRegenerate, undefinedValue=undefinedValue, medianFilterWindowSize=windowSize, pitchQuadInterp=pitchQuadInterp, ) return piList def loadTimeSeriesData( fn: str, undefinedValue: float = None ) -> List[Tuple[float, ...]]: """ For reading the output of get_pitch_and_intensity or get_intensity Data should be of the form [(time1, value1a, value1b, ...), (time2, value2a, value2b, ...), ] """ name = os.path.splitext(os.path.split(fn)[1])[0] try: with io.open(fn, "r", encoding="utf-8") as fd: data = fd.read() except IOError: print(f"No pitch track for: {name}") raise dataList = [row.split(",") for row in data.splitlines() if row != ""] # The new praat script includes a header if dataList[0][0] == "time": dataList = dataList[1:] newDataList = [] for row in dataList: time = float(row.pop(0)) entry = [ time, ] doSkip = False for value in row: if "--" in value: if undefinedValue is not None: appendValue = undefinedValue else: doSkip = True break else: appendValue = float(value) entry.append(appendValue) if doSkip is True: continue newDataList.append(tuple(entry)) return newDataList def generatePIMeasures( dataList: List[Tuple[float, float, float]], tgFN: str, tierName: str, doPitch: bool, medianFilterWindowSize: int = None, globalZNormalization: bool = False, localZNormalizationWindowSize: int = 0, ) -> List[Tuple[float, ...]]: """ Generates processed values for the labeled intervals in a textgrid nullLabelList - labels to ignore in the textgrid. Defaults to ["",] if 'doPitch'=true get pitch measures; if =false get rms intensity medianFilterWindowSize: if none, no filtering is done globalZNormalization: if True, values are normalized with the mean and stdDev of the data in dataList localZNormalization: if greater than 1, values are normalized with the mean and stdDev of the local context (for a window of 5, it would consider the current value, 2 values before and 2 values after) """ # Warn user that normalizing a second time nullifies the first normalization if globalZNormalization is True and localZNormalizationWindowSize > 0: raise errors.NormalizationException() castDataList = cast(List[Tuple[float, ...]], dataList) if globalZNormalization is True: if doPitch: castDataList = my_math.znormalizeSpeakerData(castDataList, 1, True) else: castDataList = my_math.znormalizeSpeakerData(castDataList, 2, True) # Raw values should have 0 filtered; normalized values are centered around 0, so don't filter filterZeroFlag = not globalZNormalization tg = textgrid.openTextgrid(tgFN, False) if not isinstance(tg.tierDict[tierName], textgrid.IntervalTier): raise errors.IncompatibleTierError(tg.tierDict[tierName]) tier = cast(textgrid.IntervalTier, tg.tierDict[tierName]) piData = tier.getValuesInIntervals(castDataList) outputList: List[List[float]] = [] for interval, entryList in piData: label = interval[0] if doPitch: tmpValList = [f0Val for _, f0Val, _ in entryList] f0Measures = getPitchMeasures( tmpValList, tgFN, label, medianFilterWindowSize, filterZeroFlag ) outputList.append(list(f0Measures)) else: tmpValList = [intensityVal for _, _, intensityVal in entryList] if filterZeroFlag: tmpValList = [ intensityVal for intensityVal in tmpValList if intensityVal != 0.0 ] rmsIntensity = 0.0 if len(tmpValList) != 0: rmsIntensity = my_math.rms(tmpValList) outputList.append( [ rmsIntensity, ] ) # Locally normalize the output if localZNormalizationWindowSize > 0 and len(outputList) > 0: for colI in range(len(outputList[0])): featValList = [row[colI] for row in outputList] featValList = my_math.znormWindowFilter( featValList, localZNormalizationWindowSize, True, True ) if len(featValList) != len(outputList): # This should hopefully not happen raise errors.UnexpectedError( "Lists must be of the same length but are not: " f"({len(featValList)}), ({len(outputList)})" ) for i, val in enumerate(featValList): outputList[i][colI] = val return [tuple(row) for row in outputList] def getPitchMeasures( f0Values: List[float], name: str = None, label: str = None, medianFilterWindowSize: int = None, filterZeroFlag: bool = False, ) -> Tuple[float, float, float, float, float, float]: """ Get various measures (min, max, etc) for the passed in list of pitch values name is the name of the file. Label is the label of the current interval. Both of these labels are only used debugging and can be ignored if desired. medianFilterWindowSize: None -> no median filtering filterZeroFlag:True -> zero values are removed """ if name is None: name = UNSPECIFIED if label is None: label = UNSPECIFIED if medianFilterWindowSize is not None: f0Values = my_math.medianFilter( f0Values, medianFilterWindowSize, useEdgePadding=True ) if filterZeroFlag: f0Values = [f0Val for f0Val in f0Values if int(f0Val) != 0] if len(f0Values) == 0: myStr = f"No pitch data for file: {name}, label: {label}" print(myStr.encode("ascii", "replace")) counts = 0.0 meanF0 = 0.0 maxF0 = 0.0 minF0 = 0.0 rangeF0 = 0.0 variance = 0.0 std = 0.0 else: counts = float(len(f0Values)) meanF0 = sum(f0Values) / counts maxF0 = max(f0Values) minF0 = min(f0Values) rangeF0 = maxF0 - minF0 variance = sum([(val - meanF0) ** 2 for val in f0Values]) / counts std = math.sqrt(variance) return (meanF0, maxF0, minF0, rangeF0, variance, std) def detectPitchErrors( pitchList: List[Tuple[float, float]], maxJumpThreshold: float = 0.70, tgToMark: Optional[textgrid.Textgrid] = None, ) -> Tuple[List[Point], Optional[textgrid.Textgrid]]: """ Detect pitch halving and doubling errors. If a textgrid is passed in, it adds the markings to the textgrid """ if maxJumpThreshold < 0 or maxJumpThreshold > 1: raise errors.ArgumentError( f"'maxJumpThreshold' must be between 0 and 1. Was given ({maxJumpThreshold})" ) tierName = _PITCH_ERROR_TIER_NAME if tgToMark is not None and tierName in tgToMark.tierNameList: raise errors.ArgumentError( f"Tier name '{tierName}' is already in provided textgrid" ) errorList = [] for i in range(1, len(pitchList)): lastPitch = pitchList[i - 1][1] currentPitch = pitchList[i][1] ceilingCutoff = currentPitch / maxJumpThreshold floorCutoff = currentPitch * maxJumpThreshold if (lastPitch <= floorCutoff) or (lastPitch >= ceilingCutoff): currentTime = pitchList[i][0] errorList.append(Point(currentTime, str(currentPitch / lastPitch))) if tgToMark is not None: pointTier = textgrid.PointTier( tierName, errorList, tgToMark.minTimestamp, tgToMark.maxTimestamp ) tgToMark.addTier(pointTier) return errorList, tgToMark

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234efbd93d84cd1c579cc2b9b03be2e426d9604e

1,488

py

Python

keras_classifier.py

03pie/SMPCUP2017

956f97fce8620b3b0c35e6b3757347ede30c64ba

[ "MIT" ]

25

2017-11-08T08:56:45.000Z

2021-11-24T20:24:37.000Z

keras_classifier.py

03pie/SMPCUP2017

956f97fce8620b3b0c35e6b3757347ede30c64ba

[ "MIT" ]

null

keras_classifier.py

03pie/SMPCUP2017

956f97fce8620b3b0c35e6b3757347ede30c64ba

[ "MIT" ]

13

2017-12-11T05:47:52.000Z

2021-03-04T13:53:41.000Z

import pandas as pd from keras.models import Sequential from keras.layers import Dense, Dropout from keras.wrappers.scikit_learn import KerasClassifier from keras.utils import np_utils # return the best three results def top_n(matrix_prob, label_map): ans = [] for line in matrix_prob: rank = [label_map[item[0]] for item in sorted(enumerate(line), key=lambda v:v[1], reverse=True)] ans.append(rank[:3]) return ans # basic neural network model def basic_model(): model = Sequential() model.add(Dense(output_dim=500, input_dim=100, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(output_dim=42, input_dim=500, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model if __name__ == '__main__': X = pd.read_csv('./data/triple_train_x_mean.txt', header=None, encoding='utf-8') Y = pd.read_csv('./data/triple_train_y.txt', header=None, encoding='utf-8') X_test = pd.read_csv('./data/triple_test_x_mean.txt', header=None, encoding='utf-8') matrix_y = np_utils.to_categorical(Y,42) # KerasClassifier analysis classifier = KerasClassifier(build_fn=basic_model, nb_epoch=10, batch_size=500) classifier.fit(X, Y) pred_prob = classifier.predict_proba(X_test) with open('./model/task2_label_space.txt', encoding='utf-8') as flabel: label_map = flabel.read().split() pd.DataFrame(top_n(pred_prob, label_map)).to_csv('./data/task2_ans_int_index.txt', index=None, header=None, encoding='utf-8')

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126

0.755376

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1,488

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0.462185

0.051354

0.056022

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1,488

36

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0.310345

0

null

0

null

0

1

0

234f3d49dc75338604b163336e34c3247e009fb7

2,012

py

Python

greening/get_tiles_from_google_maps.py

uchr/Hackathon-Urbaton

83362fec9777054050c858eda87905c8b512372a

[ "MIT" ]

null

greening/get_tiles_from_google_maps.py

uchr/Hackathon-Urbaton

83362fec9777054050c858eda87905c8b512372a

[ "MIT" ]

null

greening/get_tiles_from_google_maps.py

uchr/Hackathon-Urbaton

83362fec9777054050c858eda87905c8b512372a

[ "MIT" ]

null

import numpy as np import cv2 import os import time import requests import shutil def get_route_tile(x, y, out_file): #http://mt1.google.com/vt/lyrs=y&x=5975&y=2598&z=13 url = 'http://mt1.google.com/vt/lyrs=y&x={}&y={}&z=13'.format(x, y) response = requests.get(url, stream=True) with open(out_file, 'wb') as file: shutil.copyfileobj(response.raw, file) del response def union(all_x, all_y, path): x_layers = [] for x_index in range(all_x): file_path = os.path.join(path, "_".join(map(str, [x_index, 0]))) print(file_path) img = cv2.imread(file_path) for y_index in range(1, all_y): file_path = os.path.join(path, "_".join(map(str, [x_index, y_index]))) print(file_path) if os.path.exists(file_path) and os.path.isfile(file_path): print(img.shape) img = np.concatenate((img, cv2.imread(file_path)), axis=0) else: print("fail") break x_layers.append(img) final_image = x_layers[0] for layer in range(1, all_x): final_image = np.concatenate((final_image, x_layers[layer]), axis=1) cv2.imwrite(os.path.join(path, 'map.png'), final_image) return final_image def main(): """ https://api.openstreetmap.org/api/0.6/map?bbox=82.54715,54.839455,83.182984,55.103517 https://sat02.maps.yandex.net/tiles?l=sat&v=3.465.0&x=2989&y=1297&z=12&lang=ru_RU """ city_min_x = 5975 city_max_x = 5989 city_min_y = 2582 city_max_y = 2597 all_x = city_max_x - city_min_x + 1 all_y = city_max_y - city_min_y + 1 path = './google_tiles_' + str(13) + '/' for x_index in range(5975, 5990): for y_index in range(2582, 2598): file_name = os.path.join(path, "_".join(map(str, [x_index, y_index])) + '.png') get_route_tile(x_index, y_index, file_name) time.sleep(0.1) final_image = union(all_x, all_y, path) if __name__ == '__main__': main()

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0.081633

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null

0

null

0

1

0

23549ec1228d9e42823643453e7b9895b370ca45

1,933

py

Python

reVX/utilities/cluster_methods.py

NREL/reVX

4d62eb2c003c3b53b959f7a58bdc342d18098884

[ "BSD-3-Clause" ]

7

2020-04-06T00:29:55.000Z

2022-01-23T20:00:14.000Z

reVX/utilities/cluster_methods.py

NREL/reVX

4d62eb2c003c3b53b959f7a58bdc342d18098884

[ "BSD-3-Clause" ]

67

2020-02-28T20:15:35.000Z

2022-03-31T21:34:52.000Z

reVX/utilities/cluster_methods.py

NREL/reVX

4d62eb2c003c3b53b959f7a58bdc342d18098884

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Clustering Methods """ import numpy as np from sklearn.cluster import KMeans from sklearn.preprocessing import normalize class ClusteringMethods: """ Base class of clustering methods """ @staticmethod def _normalize_values(arr, norm=None, axis=None): """ Normalize values in array by column Parameters ---------- arr : ndarray ndarray of values extracted from meta shape (n samples, with m features) norm : str Normalization method to use (see sklearn.preprocessing.normalize) if None range normalize axis : int Axis to normalize along Returns --------- arr : ndarray array with values normalized by column shape (n samples, with m features) """ if norm: arr = normalize(arr, norm=norm, axis=axis) else: if np.issubdtype(arr.dtype, np.integer): arr = arr.astype(float) min_all = arr.min(axis=axis) max_all = arr.max(axis=axis) range_all = max_all - min_all if axis is not None: pos = range_all == 0 range_all[pos] = 1 arr -= min_all arr /= range_all return arr @staticmethod def kmeans(data, **kwargs): """ Cluster based on kmeans methodology """ kmeans = KMeans(random_state=0, **kwargs) results = kmeans.fit(data) labels = results.labels_ # Create deterministic cluster labels based on size label_n, l_size = np.unique(labels, return_counts=True) idx = np.argsort(l_size) l_mapping = dict(zip(label_n[idx], label_n)) sorted_labels = labels.copy() for k, v in l_mapping.items(): sorted_labels[labels == k] = v return sorted_labels

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0.25

0

null

0

null

0

1

0

23585aa3fd91ad92d3f8755c7797b9e71281a6bc

918

py

Python

Unit3/Lesson7.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

Unit3/Lesson7.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

Unit3/Lesson7.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

#Python的内建模块itertools提供了非常有用的用于操作迭代对象的函数。 import itertools #从10开始数自然数 naturals =itertools.count(10) from collections import Iterator #判断naturals的类型 print(isinstance(naturals,Iterator)) for x in naturals: if x>70: break print(x) #cycle()会把传入的一个序列无限重复下去： cycles =itertools.cycle("szhualeilei") print(isinstance(cycles,Iterator)) n =0 for x in cycles : #print(x) n+=1 if n >100: break #repeat 重复 repeats =itertools.repeat("szhua",10) for x in repeats: print(x) inter =(x**2 for x in range(100) if x%2==0and x%3==0) #使用take while对Iterrator进行过滤： ns =itertools.takewhile(lambda x :x<1000,inter) print(list(ns)) #chain() #chain()可以把一组迭代对象串联起来，形成一个更大的迭代器： print(list(itertools.chain("fjksjdfk","abcdefghijklmn"))) #groupby() #groupby()把迭代器中相邻的重复元素挑出来放在一起： for key ,value in itertools.groupby("aaajjjfdsfkkkfffff"): print(str(key).upper(),list(value))

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null

0

null

0

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0

23593360ab941b0e68d201d7be4b82afc1cc2f9c

8,536

py

Python

flaskr/databaseCURD.py

Ln-Yangzl/yukiyu-webpage

f9aaf71dca18067ecbe43faccb74a7f8d4cf56b7

[ "Apache-2.0" ]

null

flaskr/databaseCURD.py

Ln-Yangzl/yukiyu-webpage

f9aaf71dca18067ecbe43faccb74a7f8d4cf56b7

[ "Apache-2.0" ]

null

flaskr/databaseCURD.py

Ln-Yangzl/yukiyu-webpage

f9aaf71dca18067ecbe43faccb74a7f8d4cf56b7

[ "Apache-2.0" ]

2

2021-03-23T12:22:04.000Z

2021-05-24T13:56:26.000Z

# 该模块提供了一个数据库的通用CURD接口 # 通过该接口能够快速进行数据库的增删查改功能 # 该模块还提供了获取数据库所有表表名，各表表头的接口 import traceback import pymysql from userManage import commmitChangeToUserlist, privilegeOfUser, ifManage global db # TODO: improve the robustness def checkValibleTableName(targetTable, user): if user != None and targetTable == 'user_list': return user in getSuperUser() return targetTable != None def commitChangeToDatabase(oldInfo, newInfo, targetTable, user = None): returnStatu = changeProcess(oldInfo, newInfo, targetTable, user) if returnStatu == 0: info = '错误的数据格式！' elif returnStatu == -1: info = '该表不存在！' elif returnStatu == -2: info = '非法访问：未经过用户认证' elif returnStatu == -3: info = '非法访问：用户无该权限' elif returnStatu == -4: info = '错误的数据格式：管理员用户拥有增删查改所有权限' elif returnStatu == -5: info = '用户名重复' elif returnStatu == 1: info = '运行成功！' else: info = '未知错误！' return {'statu': returnStatu, 'info': info} # this function call updataItem, insertItem, deleteItem # according to the oldInfo and newInfo # if oldInfo is None, call insert # if newInfo is None, call delete # else, call updata # # OK code: return 1 # error code: # 0 : sql run time error # -1 : invalid target table # -2 : user is None # -3 : user has not target privilege # -4 : manager's privilege is not 'YYYY' # -5 : user name chongfu def changeProcess(oldInfo, newInfo, targetTable, user = None): if user == None: return -2 userPrivilege = privilegeOfUser(user).get('privilege') global db db = pymysql.connect(host="localhost", port=3306, db="yukiyu", user="jhchen", password="123456",charset='utf8') if oldInfo == None and newInfo == None or not checkValibleTableName(targetTable, user): print('error ! invalid change!') print('oldInfo:', oldInfo) print('newInfo:', newInfo) print('targetTable:', targetTable) return -1 returnStatus = 0 if targetTable == 'user_list': if ifManage(user) == 'Y': return commmitChangeToUserlist(oldInfo, newInfo) else: return -3 if oldInfo == None: if userPrivilege[1] == 'Y': returnStatus = insertItem(newInfo, targetTable) else: returnStatus = -3 elif newInfo == None: if userPrivilege[3] == 'Y': returnStatus = deleteItem(oldInfo, targetTable) else: returnStatus = -3 else: if userPrivilege[1] == 'Y': returnStatus = updateItem(oldInfo, newInfo, targetTable) else: returnStatus = -3 return returnStatus # shuffle : ((a,),(b,),(c,)) --> (a, b, c) def signColumnsShuffle(input): res = [] for i in input: res.append(i[0]) return res # shuffle datetime.date to str: 2021-02-20 def datetimeShffle(input): res = [] for i in input: temp = [] for k in i: temp.append(str(k)) res.append(temp) return res def getTableHead(tableName): print('start to get table head from ' + tableName) cursor = db.cursor() sql = "select column_name from information_schema.columns as col where col.table_name='%s'"%tableName print('start to execute:') print(sql) cursor.execute(sql) res = cursor.fetchall() res = signColumnsShuffle(res) print('success ! \nget result: ') print(res) cursor.close() return res def getTableData(tableName): cursor = db.cursor() print('start to get table data from ' + tableName) sql = "select * from %s"%tableName # print('start to execute:') # print(sql) cursor.execute(sql) res = cursor.fetchall() res = datetimeShffle(res) print(res) cursor.close() return res def getSuperUser(): cursor = db.cursor() sql = "select name from user_list where if_manager = 'Y'" print('start to execute:') print(sql) cursor.execute(sql) res = cursor.fetchall() res = signColumnsShuffle(res) print('execute success!') print('result:' ,res) cursor.close() return res def getTableNames(user): cursor = db.cursor() print('start to get table names from yukiyu') sql = "select table_name from information_schema.tables as tb where tb.table_schema = 'yukiyu'" cursor.execute(sql) res = cursor.fetchall() res = signColumnsShuffle(res) print('success ! \nget result: ') print(res) cursor.close() # 非超级用户不允许查看user列表 if user not in getSuperUser(): res.remove('user_list') # 将主表放在最前面 res.remove('bangumi_list') res.insert(0, 'bangumi_list') return res # get all tables, including table names and data def getDatabase(target, user): global db db = pymysql.connect(host="localhost", port=3306, db="yukiyu", user="jhchen", password="123456",charset='utf8') print('get url args:') print(target) res = {} selectPriv = privilegeOfUser(user).get('privilege')[0] for key in target: if target[key] != 'tables': # 获取数据表中的表头 res[target[key]+'Header'] = getTableHead(target[key]) # 获取数据表中的所有数据 if selectPriv == 'Y': res[target[key]] = getTableData(target[key]) else: res[target[key]] = None else: # 获取数据库中的所有数据表名 res['tableList'] = getTableNames(user) return res # return the string: key1=value1 seperate key2=valuue2... def getKeyValueString(name, data, seperate=','): res = '' seperate = ' ' + seperate + ' ' length = len(name) for i in range(length): res += (name[i] + '=' + "'" + str(data[i]) + "'") if i != length - 1: res += seperate return res # return the string: value1 seperate value2... # if strlization is True, when the data[i] is str, the value will be: 'value' def getValueString(data, seperate=',', strlization = False): seperate = ' ' + seperate + ' ' res = '' strlize = '' if strlization == True: strlize = "'" length = len(data) for i in range(length): res += (strlize + str(data[i]) + strlize) if i != length - 1: res += seperate return res def updateItem(oldInfo, newInfo, targetTable): tableHead = getTableHead(targetTable) setField = getKeyValueString(tableHead, newInfo, ',') whereField = getKeyValueString(tableHead, oldInfo, 'and') cursor = db.cursor() returnStatus = 0 sql = """ update %s set %s where %s """%(targetTable, setField, whereField) try: print('start to execute:') print(sql) cursor.execute(sql) db.commit() print('success !') returnStatus = 1 except: print('updata error !') db.rollback() traceback.print_exception() returnStatus = 0 db.close() return returnStatus def insertItem(newInfo, targetTable): tableHeadStr = getValueString(getTableHead(targetTable)) valueStr = getValueString(newInfo,strlization=True) cursor = db.cursor() sql = """ insert into %s (%s) values (%s) """%(targetTable, tableHeadStr, valueStr) returnStatus = 0 try: print('start to execute:') print(sql) cursor.execute(sql) db.commit() print('success !') returnStatus = 1 except: print('insert error !') db.rollback() traceback.print_exc() returnStatus = 0 db.close() return returnStatus def deleteItem(oldInfo, targetTable): tableHead = getTableHead(targetTable) whereField = getKeyValueString(tableHead, oldInfo, 'and') cursor = db.cursor() sql = """ delete from %s where %s """%(targetTable, whereField) returnStatus = 0 try: print('start to execute:') print(sql) cursor.execute(sql) db.commit() print('success !') returnStatus = 1 except: print('delete error !') db.rollback() traceback.print_exc() returnStatus = 0 db.close() return returnStatus def getUserList(): db = pymysql.connect(host="localhost", port=3306, db="yukiyu", user="jhchen", password="123456",charset='utf8') cursor = db.cursor() sql = 'select name, password, user_id from user_list' cursor.execute(sql) res = cursor.fetchall() return res

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null

0

null

0

1

0

236087aea9a609e4effde96065112e3417f806cd

3,864

py

Python

src/imreg_dft/show.py

GCBallesteros/imreg_dft

3eb7137403dd0689711ff1dae78200b0fbdcedfb

[ "BSD-3-Clause" ]

167

2015-02-28T19:14:52.000Z

2022-03-30T03:42:33.000Z

src/imreg_dft/show.py

GCBallesteros/imreg_dft

3eb7137403dd0689711ff1dae78200b0fbdcedfb

[ "BSD-3-Clause" ]

40

2015-01-18T23:58:41.000Z

2021-08-02T13:36:48.000Z

src/imreg_dft/show.py

GCBallesteros/imreg_dft

3eb7137403dd0689711ff1dae78200b0fbdcedfb

[ "BSD-3-Clause" ]

51

2015-02-27T21:19:55.000Z

2022-03-24T12:28:45.000Z

# -*- coding: utf-8 -*- # show.py # Copyright (c) 2016-?, Matěj Týč # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the copyright holders nor the names of any # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import argparse as ap from imreg_dft import cli from imreg_dft import reporting TOSHOW = ( "filtered input (I)mages", "filtered input images (S)pectra", "spectra (L)ogpolar transform", "(1) angle-scale phase correlation", "angle-scale transform (A)pplied", "(2) translation phase correlation", "(T)ile info", ) TOSHOW_ABBR = "isl1a2t" def create_parser(): parser = ap.ArgumentParser() cli.update_parser_imreg(parser) parser.add_argument("--prefix", default="reports") parser.add_argument("--ftype", choices=("png", "pdf"), default="png") parser.add_argument("--dpi", default=150, type=float) parser.add_argument("--terse", default=False, action="store_true", help="Don't show every smallest thing.") parser.add_argument("--tex", default=False, action="store_true", help="Use TeX to typeset labels (if applicable).") parser.add_argument("--size", default=5, type=float, help="Base image element size [in]") parser.add_argument( "--display", type=_show_valid, default=TOSHOW_ABBR, help="String composing of '{}', meaning respectively: {}." .format(TOSHOW_ABBR, ", ".join(TOSHOW))) return parser def _show_valid(stri): stripped = stri.rstrip(TOSHOW_ABBR) if len(stripped) > 0: raise ap.ArgumentError("Argument contains invalid characters: {}" .format(stripped)) return stri def main(): parser = create_parser() args = parser.parse_args() opts = cli.args2dict(args) reports = reporting.ReportsWrapper(args.display) usetex = args.ftype == "pdf" and args.tex from matplotlib import rc if usetex: rc("text", usetex=True) rc("text.latex", unicode=True) reporting.TEXT_MODE = "tex" reports.set_global("dpi", args.dpi) reports.set_global("ftype", args.ftype) reports.set_global("size", args.size) reports.set_global("usetex", usetex) reports.set_global("terse", args.terse) opts["show"] = False opts["reports"] = reports opts["prefix"] = args.prefix cli.run(args.template, args.subject, opts) reporting.report_tile(reports, args.prefix) if __name__ == "__main__": main()

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0

null

0

null

0

1

0

236634d05aadb9d36762574305057814f7a3b99e

3,939

py

Python

tests/unit/transport/pecan/models/response/test_health.py

jqxin2006/poppy

10636e6255c7370172422afece4a5c3d95c1e937

[ "Apache-2.0" ]

null

tests/unit/transport/pecan/models/response/test_health.py

jqxin2006/poppy

10636e6255c7370172422afece4a5c3d95c1e937

[ "Apache-2.0" ]

null

tests/unit/transport/pecan/models/response/test_health.py

jqxin2006/poppy

10636e6255c7370172422afece4a5c3d95c1e937

[ "Apache-2.0" ]

null

# Copyright (c) 2014 Rackspace, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import ddt from poppy.common import util from poppy.transport.pecan.models.response import health from tests.unit import base class TestDNSModel(base.TestCase): def setUp(self): super(TestDNSModel, self).setUp() def test_dns_is_alive(self): dns_model = health.DNSModel(True) self.assertEqual('true', dns_model['online']) def test_dns_is_not_alive(self): dns_model = health.DNSModel(False) self.assertEqual('false', dns_model['online']) class TestStorageModel(base.TestCase): def setUp(self): super(TestStorageModel, self).setUp() def test_storage_is_alive(self): storage_model = health.StorageModel(True) self.assertEqual('true', storage_model['online']) def test_storage_is_not_alive(self): storage_model = health.StorageModel(False) self.assertEqual('false', storage_model['online']) class TestProviderModel(base.TestCase): def setUp(self): super(TestProviderModel, self).setUp() def test_provider_is_alive(self): provider_model = health.ProviderModel(True) self.assertEqual('true', provider_model['online']) def test_provider_is_not_alive(self): provider_model = health.ProviderModel(False) self.assertEqual('false', provider_model['online']) @ddt.ddt class TestHealthModel(base.TestCase): def setUp(self): super(TestHealthModel, self).setUp() self.mock_controller = util.dict2obj( {'base_url': 'https://www.poppycdn.io/'}) @ddt.file_data('health_map.json') def test_health(self, health_map): health_model = health.HealthModel(self.mock_controller, health_map) storage_name = health_map['storage']['storage_name'] self.assertEqual('true', health_model['storage'][storage_name]['online']) dns_name = health_map['dns']['dns_name'] self.assertEqual('true', health_model['dns'][dns_name]['online']) @ddt.file_data('health_map_dns_not_available.json') def test_health_dns_not_available(self, health_map): health_model = health.HealthModel(self.mock_controller, health_map) dns_name = health_map['dns']['dns_name'] self.assertEqual('false', health_model['dns'][dns_name]['online']) @ddt.file_data('health_map_storage_not_available.json') def test_health_storage_not_available(self, health_map): health_model = health.HealthModel(self.mock_controller, health_map) storage_name = health_map['storage']['storage_name'] self.assertEqual('false', health_model['storage'][storage_name]['online']) @ddt.file_data('health_map_provider_not_available.json') def test_health_provider_not_available(self, health_map): health_model = health.HealthModel(self.mock_controller, health_map) providers = health_map['providers'] for provider in providers: provider_name = provider['provider_name'] provider_is_alive = provider['is_alive'] provider_model = health_model['providers'][provider_name] if provider_is_alive: self.assertEqual('true', provider_model['online']) else: self.assertEqual('false', provider_model['online'])

35.809091

75

0.68393

482

3,939

5.352697

0.242739

0.059302

0.044186

0.031008

0.502713

0.494961

0.24031

0.228682

0.196899

0

0.002876

0.205636

3,939

109

76

36.137615

0.821668

0.140899

0

0.342857

0

0.11873

0.032057

0

0.171429

1

0.2

false

0

0.057143

0

0.314286

0

null

0

null

0

1

0

236931ea9461223fe34c99e295340ff93405cc67

229

py

Python

Src/Squar-root/squar-root.py

MadushikaPerera/Python

b7919b252c02b5e1017273a65dd022ac9d13f3e4

[ "MIT" ]

null

Src/Squar-root/squar-root.py

MadushikaPerera/Python

b7919b252c02b5e1017273a65dd022ac9d13f3e4

[ "MIT" ]

null

Src/Squar-root/squar-root.py

MadushikaPerera/Python

b7919b252c02b5e1017273a65dd022ac9d13f3e4

[ "MIT" ]

null

#1 number = int(input("Enter a number to find the square root : ")) #2 if number < 0 : print("Please enter a valid number.") else : #3 sq_root = number ** 0.5 #4 print("Square root of {} is {} ".format(number,sq_root))

20.818182

64

0.624454

39

229

3.615385

0.641026

0.085106

0

0.039106

0.218341

229

10

65

22.9

0.748603

0.017467

0

0.420814

0

1

0

false

0

0.333333

0

null

0

null

0

1

0

2369a4c986708b3067b08b2725a7bdc63e4b378b

12,141

py

Python

Tools/resultsdbpy/resultsdbpy/model/mock_model_factory.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

6

2021-07-05T16:09:39.000Z

2022-03-06T22:44:42.000Z

Tools/resultsdbpy/resultsdbpy/model/mock_model_factory.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

7

2022-03-15T13:25:39.000Z

2022-03-15T13:25:44.000Z

Tools/resultsdbpy/resultsdbpy/model/mock_model_factory.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

null

# Copyright (C) 2019 Apple Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY APPLE INC. AND ITS CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR ITS CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import base64 import io import time import calendar from resultsdbpy.controller.configuration import Configuration from resultsdbpy.model.configuration_context_unittest import ConfigurationContextTest from resultsdbpy.model.mock_repository import MockStashRepository, MockSVNRepository from resultsdbpy.model.model import Model class MockModelFactory(object): ARCHIVE_ZIP = """UEsDBAoAAAAAAAtSBU8AAAAAAAAAAAAAAAAIABAAYXJjaGl2ZS9VWAwAZ2RIXWZkSF31ARQAUEsDBBQACAAIAA9SBU8AAAAAAAAAAAAAAAAQABAAYXJjaGl2ZS9maWxlLnR4dFVYDABovU1d bmRIXfUBFABLSSxJBABQSwcIY/PzrQYAAAAEAAAAUEsDBAoAAAAAABRdCU8AAAAAAAAAAAAAAAAJABAAX19NQUNPU1gvVVgMACi+TV0ovk1d9QEUAFBLAwQKAAAAAAAUXQlPAAAAAAAA AAAAAAAAEQAQAF9fTUFDT1NYL2FyY2hpdmUvVVgMACi+TV0ovk1d9QEUAFBLAwQUAAgACAAPUgVPAAAAAAAAAAAAAAAAGwAQAF9fTUFDT1NYL2FyY2hpdmUvLl9maWxlLnR4dFVYDABo vU1dbmRIXfUBFABjYBVjZ2BiYPBNTFbwD1aIUIACkBgDJxAbMTAwegFpIJ+xhoEo4BgSEgRhgXXcAeIFaEqYoeICDAxSyfm5eokFBTmpejmJxSWlxakpKYklqcoBwVC1b4DYg4GBH6Eu NzE5B2K+CUROFCFXWJpYlJhXkpmXypCd4hELUsUaKK4AVs0w95H9l352x+37375yVmg4n0+cf9BBob6BgYWxtWmKSUpSipGxtWNRckZmWWpMhZFBaElmTmZJpbWBs6GzkbOzpa6FpamF romRm6Wuk7mFi66FqZuxiamLhauriSsDAFBLBwjEE3dr4AAAAHwBAABQSwMEFAAIAAgAzFwJTwAAAAAAAAAAAAAAABIAEABhcmNoaXZlL2luZGV4Lmh0bWxVWAwAor1NXaC9TV31ARQA tVNdb+IwEHz3r9j2qZUCvfbt7hCSSQxYCnHOdsrxmBK3tRRilJj2+u9vbajKfejeDgliMruzM7PJ5GI0IpC6/Vtvn549XKXXcPfp9jPQ/b41wLvtGGjbQkQH6M1g+hfTjAkBaRo7+N4+ HLx1HdRdA4fBgO1gcId+a+KdB9vV/Rs8un43JPBq/TO4Pl7dwRPYucY+2m0dGBKoewN70++s96aBfe9ebIMH/1x7/DHI0rbu1XZPsHVdY0PTQGLXzvgvBG7Hv4kawD2+q9m6BusOg0cT vkaVgbF+cC8BOtkngJ/Oebs1CeJ2gBbZAsnHwGjrVzU4ctvWdmf6MYG7P0XgsLMc3kWgv+aAwv6HDjj6izyN2x52pvP1+5pucAMO0R52tTe9rdvhI+y4okB7biGsWy+5AiXmek0lAzyX UtzzjGUw2wAtyxxvFukYLqlC9BJokeF3Q4B9LyVTCoQEvipzjh1IIWmhOVNJaMqrjBeLBGaVhkJoyPmKayzTIsGxjPylD8QcVkymS/xLZzznehMnzrkuwrA5TqNQUql5WuVUEigrWQrF IKjPuEpzylcsGwMKwKHA7lmhQS1pnp+7EdiZikJLjuKEVDBjKI/OEI8jig2SSZbqYOTjlGIwKCxPQJUs5XgIOTC0QeUmCVEgqWLfKqxCFDK6ogt0dfXvNEgIPa0kWwWxGIGqZkpzXWkG CyGyGLJi8p6nTH2FXKgYVKVYgiM0TaIf5MCYEMfiWaV4DIwXmklZlZqL4hqWYo2BoEqKvVlMVhTRLe5DSNwq0oYcYvIJrJcMARmyjGnREIPC1FJ9XoYDMURNznxCwRY5X7AiZQEWgWbN FbvGRXEVCvhx8JpuQFTRNdYET+R4Pnssk7hG4HOg2T0Pyk/VuHnFT49JjC1dnjIfk9FoSsjk2e/aKV5M3Zh+OvHWt2Zqu8b8GAdocnO8M7k5VZDJg2vepvENWxp8A+HV9W1zQSY3RwAr A+VPUEsHCPbdMMviAgAAYQUAAFBLAwQUAAgACADMXAlPAAAAAAAAAAAAAAAAHQAQAF9fTUFDT1NYL2FyY2hpdmUvLl9pbmRleC5odG1sVVgMAKK9TV2gvU1d9QEUAGNgFWNnYGJg8E1M VvAPVohQgAKQGAMnEBsxMDB6AWkgn7GGgSjgGBISBGGBddwB4gVoSpih4gIMDFLJ+bl6iQUFOal6OYnFJaXFqSkpiSWpygHBULVvgNiDgYEfoS43MTkHYr4JRE4UIVdYmliUmFeSmZfK UL/XNxak6qLfEiGwaoa5j+y/9LM7bt//9pWzQsP5fOL8gw4K9Q0MLIytTVNMUpJSjIytHYuSMzLLUmMqjAxCSzJzMksqrQ2cDZ2NnJ0tdS0sTS10TYzcLHWdzC1cdC1M3YxNTF0sXF1N XBkAUEsHCLRBGwrgAAAAfAEAAFBLAwQUAAgACAALUgVPAAAAAAAAAAAAAAAAEgAQAF9fTUFDT1NYLy5fYXJjaGl2ZVVYDABnZEhdZmRIXfUBFABjYBVjZ2BiYPBNTFbwD1aIUIACkBgD JxAbMTAwCgFpIJ/RhYEo4BgSEgRhgXVsAeIJaEqYoOIeDAz8yfm5eokFBTmpermJyTkQ+T8QOVGEXGFpYlFiXklmXioDI0Ntye3fifMcHKZ8fXTEZauLLSPD3Ef2X/rZHbfvf/vKWaHh fD4x7izUNzCwMLY2gAJrx6LkjMyy1JgKI4PQksyczJJKawNnQ2cjZ2dLXQtLUwtdEyM3S10ncwsXXQtTN2MTUxcLV1cTVwYAUEsHCAAolTbHAAAARAEAAFBLAQIVAwoAAAAAAAtSBU8A AAAAAAAAAAAAAAAIAAwAAAAAAAAAAEDtQQAAAABhcmNoaXZlL1VYCABnZEhdZmRIXVBLAQIVAxQACAAIAA9SBU9j8/OtBgAAAAQAAAAQAAwAAAAAAAAAAECkgTYAAABhcmNoaXZlL2Zp bGUudHh0VVgIAGi9TV1uZEhdUEsBAhUDCgAAAAAAFF0JTwAAAAAAAAAAAAAAAAkADAAAAAAAAAAAQP1BigAAAF9fTUFDT1NYL1VYCAAovk1dKL5NXVBLAQIVAwoAAAAAABRdCU8AAAAA AAAAAAAAAAARAAwAAAAAAAAAAED9QcEAAABfX01BQ09TWC9hcmNoaXZlL1VYCAAovk1dKL5NXVBLAQIVAxQACAAIAA9SBU/EE3dr4AAAAHwBAAAbAAwAAAAAAAAAAECkgQABAABfX01B Q09TWC9hcmNoaXZlLy5fZmlsZS50eHRVWAgAaL1NXW5kSF1QSwECFQMUAAgACADMXAlP9t0wy+ICAABhBQAAEgAMAAAAAAAAAABApIE5AgAAYXJjaGl2ZS9pbmRleC5odG1sVVgIAKK9 TV2gvU1dUEsBAhUDFAAIAAgAzFwJT7RBGwrgAAAAfAEAAB0ADAAAAAAAAAAAQKSBawUAAF9fTUFDT1NYL2FyY2hpdmUvLl9pbmRleC5odG1sVVgIAKK9TV2gvU1dUEsBAhUDFAAIAAgA C1IFTwAolTbHAAAARAEAABIADAAAAAAAAAAAQKSBpgYAAF9fTUFDT1NYLy5fYXJjaGl2ZVVYCABnZEhdZmRIXVBLBQYAAAAACAAIAF4CAAC9BwAAAAA=""" THREE_WEEKS = 60 * 60 * 24 * 21 @classmethod def create(cls, redis, cassandra, async_processing=False): oldest_commit = time.time() for repo in [MockStashRepository.safari(), MockSVNRepository.webkit()]: for commits in repo.commits.values(): for commit in commits: oldest_commit = min(oldest_commit, calendar.timegm(commit.timestamp.timetuple())) model = Model( redis=redis, cassandra=cassandra, repositories=[ MockStashRepository.safari(redis=redis), MockSVNRepository.webkit(redis=redis), ], default_ttl_seconds=time.time() - oldest_commit + Model.TTL_WEEK, archive_ttl_seconds=time.time() - oldest_commit + Model.TTL_WEEK, async_processing=async_processing, ) with model.commit_context, model.commit_context.cassandra.batch_query_context(): for repository in model.commit_context.repositories.values(): for branch_commits in repository.commits.values(): for commit in branch_commits: model.commit_context.register_commit(commit) return model @classmethod def layout_test_results(cls): default_result = {'expected': 'PASS', 'modifiers': '', 'actual': 'PASS', 'time': 1.2} return dict( details=dict(link='dummy-link'), run_stats=dict(tests_skipped=0), results={ 'fast': { 'encoding': { 'css-cached-bom.html': default_result, 'css-charset-default.xhtml': default_result, 'css-charset.html': default_result, 'css-link-charset.html': default_result, } } }, ) @classmethod def iterate_all_commits(cls, model, callback): repos = ('webkit', 'safari') branches = (None, 'safari-606-branch') for branch in branches: commit_index = {repo: 0 for repo in repos} commits_for_repo = {repo: sorted(model.commit_context.find_commits_in_range(repo, branch)) for repo in repos} for repo in repos: while max([commits_for_repo[r][commit_index[r]] for r in repos]) > commits_for_repo[repo][commit_index[repo]]: if commit_index[repo] + 1 >= len(commits_for_repo[repo]): break commit_index[repo] += 1 while True: commits = [] for repo in repos: commits.append(commits_for_repo[repo][commit_index[repo]]) callback(commits) youngest_next_repo = None for repo in repos: if commit_index[repo] + 1 >= len(commits_for_repo[repo]): continue if not youngest_next_repo: youngest_next_repo = repo continue if commits_for_repo[youngest_next_repo][commit_index[youngest_next_repo] + 1] > commits_for_repo[repo][commit_index[repo] + 1]: youngest_next_repo = repo if not youngest_next_repo: break commit_index[youngest_next_repo] += 1 @classmethod def add_mock_results(cls, model, configuration=Configuration(), suite='layout-tests', test_results=None): if test_results is None: test_results = cls.layout_test_results() configurations = [configuration] if configuration.is_complete() else ConfigurationContextTest.CONFIGURATIONS with model.upload_context: current = time.time() old = current - cls.THREE_WEEKS for complete_configuration in configurations: if complete_configuration != configuration: continue timestamp_to_use = current if (complete_configuration.platform == 'Mac' and complete_configuration.version <= Configuration.version_to_integer('10.13')) \ or (complete_configuration.platform == 'iOS' and complete_configuration.version <= Configuration.version_to_integer('11')): timestamp_to_use = old cls.iterate_all_commits(model, lambda commits: model.upload_context.upload_test_results(complete_configuration, commits, suite=suite, test_results=test_results, timestamp=timestamp_to_use)) @classmethod def process_results(cls, model, configuration=Configuration(), suite='layout-tests'): configurations = [configuration] if configuration.is_complete() else ConfigurationContextTest.CONFIGURATIONS with model.upload_context: for complete_configuration in configurations: if complete_configuration != configuration: continue for branch in (None, 'safari-606-branch'): results_dict = model.upload_context.find_test_results( configurations=[complete_configuration], suite=suite, branch=branch, recent=False, ) for config, results in results_dict.items(): for result in results: model.upload_context.process_test_results( configuration=config, commits=result['commits'], suite=suite, test_results=result['test_results'], timestamp=result['timestamp'], ) @classmethod def add_mock_archives(cls, model, configuration=Configuration(), suite='layout-tests', archive=None): archive = archive or io.BytesIO(base64.b64decode(cls.ARCHIVE_ZIP)) configurations = [configuration] if configuration.is_complete() else ConfigurationContextTest.CONFIGURATIONS with model.upload_context: current = time.time() old = current - cls.THREE_WEEKS for complete_configuration in configurations: if complete_configuration != configuration: continue timestamp_to_use = current if (complete_configuration.platform == 'Mac' and complete_configuration.version <= Configuration.version_to_integer('10.13')) \ or (complete_configuration.platform == 'iOS' and complete_configuration.version <= Configuration.version_to_integer('11')): timestamp_to_use = old cls.iterate_all_commits(model, lambda commits: model.archive_context.register(archive, complete_configuration, commits, suite=suite, timestamp=timestamp_to_use))

61.318182

205

0.730335

980

12,141

8.883673

0.312245

0.041006

0.014473

0.012405

0.247645

0.215369

0.205261

0.188146

0.175052

0.165403

0

0.041006

0.210609

12,141

197

206

61.629442

0.867383

0.105757

0

0.298701

0

0.357545

0.325427

0

1

0.038961

false

0.006494

0.051948

0

0.123377

0

null

0

null

0

1

0

236f461f8b6d07d3beef17a23e616ee5fd033b61

3,488

py

Python

02_Flask_REST/04_MongoDB_REST/app/main.py

CrispenGari/python-flask

3e7896f401920b8dd045d807212ec24b8353a75a

[ "Apache-2.0" ]

2

2021-11-08T07:37:18.000Z

2021-11-13T09:23:46.000Z

02_Flask_REST/04_MongoDB_REST/app/main.py

CrispenGari/Flask

3e7896f401920b8dd045d807212ec24b8353a75a

[ "Apache-2.0" ]

null

02_Flask_REST/04_MongoDB_REST/app/main.py

CrispenGari/Flask

3e7896f401920b8dd045d807212ec24b8353a75a

[ "Apache-2.0" ]

null

from keys.keys import pwd import pymongo from flask import Flask, request, abort from flask_restful import Resource, Api, reqparse, marshal_with, fields """ DATABASE CONFIGURATION """ databaseName = "students" connection_url = f'mongodb+srv://crispen:{pwd}@cluster0.3zay8.mongodb.net/{databaseName}?retryWrites=true&w=majority' client = pymongo.MongoClient(connection_url) cursor = client.list_database_names() db = client.blob """ Student post args """ student_post_args = reqparse.RequestParser() student_post_args.add_argument("name", type=str, help="name required", required=True) student_post_args.add_argument("surname", type=str, help="surname required", required=True) student_post_args.add_argument("student_number", type=int, help="student number required", required=True) student_post_args.add_argument("course", type=str, help="name required", required=True) student_post_args.add_argument("mark", type=int, help="surname required", required=True) """ Student patch args * We want to be able only to update student course and mark """ """ Resource Fields """ resource_fields = { '_id': fields.String, 'name': fields.String, 'surname': fields.String, 'course': fields.String, 'mark': fields.Integer, "student_number":fields.Integer, } app = Flask(__name__) app.config["ENV"] = "development" api = Api(app) class GetPatchDeleteStudent(Resource): @marshal_with(resource_fields) def get(self, id): cursor = db.students.find_one({"student_number": id}) if cursor is None: abort(404, f"Student with student number {id} not found.") return cursor, 200 def delete(self, id): cursor = db.students.find_one({"student_number": id}) if cursor is None: abort(404, f"Student with student number {id} not found.") db.students.delete_one({"student_number": id}) return "", 204 @marshal_with(resource_fields) def patch(self, id): args = student_post_args.parse_args() cursor = db.students.find_one({"student_number": id}) if cursor is None: abort(404, f"Student with student number {id} not found.") if args["mark"]: db.students.update_one({"student_number": id}, {"$set": {"mark": args["mark"]} }) if args["course"]: db.students.update_one({"student_number": id}, { "$set": {"course": args["course"]} }) return db.students.find_one({"student_number": id}), 204 class PostStudent(Resource): @marshal_with(resource_fields) def post(self): args = student_post_args.parse_args() cursor = db.students.find_one({"student_number": args["student_number"]}) if cursor is None: """ Insert the students to the database. """ res = db.students.insert_one({ "name": args["name"], "surname": args["surname"], "student_number": args["student_number"], "course": args["course"], "mark": args["mark"] }) print(res, type(res)) else: abort(409, "Student number taken by another student") return db.students.find_one({"student_number": args["student_number"]}), 201 api.add_resource(PostStudent, '/student') api.add_resource(GetPatchDeleteStudent, '/student/<int:id>') if __name__ == "__main__": app.run(debug=True)

33.219048

117

0.641628

424

3,488

5.099057

0.245283

0.120259

0.06938

0.058279

0.457909

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0.265957

0.236355

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0.009934

0.220757

3,488

105

118

33.219048

0.785504

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0.24

0

0.013333

0.226253

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1

0.053333

false

0

0.053333

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0.186667

0.013333

0

null

0

null

0

1

0

236fe878b484e34a105ad050281a3bd06899f1d7

4,703

py

Python

data/validate_possession.py

lpraat/scep2019

f120ee20397648e708cce41a7949c70b523b6e56

[ "MIT" ]

1

2021-11-02T20:34:22.000Z

data/validate_possession.py

lpraat/scep2019

f120ee20397648e708cce41a7949c70b523b6e56

[ "MIT" ]

null

data/validate_possession.py

lpraat/scep2019

f120ee20397648e708cce41a7949c70b523b6e56

[ "MIT" ]

1

2021-11-02T20:34:29.000Z

import csv import math import datetime def build_target_possession(player_file, till): possessions = [] to_skip = 1 # first line with open(player_file) as csv_file: reader = csv.reader(csv_file, delimiter=';') for row in reader: if to_skip: to_skip -= 1 continue if not row: continue if row[0] == 'Statistic:' or row[0] == '': break t = datetime.datetime.strptime(row[2], "%H:%M:%S.%f") t = float(t.minute * 60 + t.hour * 60 * 60 + t.second) * math.pow(10, 12) + t.microsecond * math.pow(10, 6) if t <= till: possessions.append(t) possession_time = 0 # always match begin end if len(possessions) % 2 != 0: possessions = possessions[-1:] for i in range(0, len(possessions) - 1, 2): possession_time += possessions[i + 1] - possessions[i] return possession_time * 10 ** -12 def build_target_possessions_first_half(): players = ( "Nick Gertje", "Dennis Dotterweich", "Willi Sommer", "Philipp Harlass", "Roman Hartleb", "Erik Engelhardt", "Sandro Schneider", "Leon Krapf", "Kevin Baer", "Luca Ziegler", "Ben Mueller", "Vale Reitstetter", "Christopher Lee", "Leon Heinze", "Leo Langhans", ) possessions = {} for player in players: file_name = f"oracle/Ball Possession/1st Half/{player}.csv" # [(12397999951273772 - 10753295594424116L) * 10 ** -12 + 3.092 + 0.9885] * 10**12 player_possession = build_target_possession(file_name, 1648784856849656) possessions[player] = player_possession return possessions def build_target_possessions_second_half(): players = ( "Nick Gertje", "Dennis Dotterweich", "Niklas Welzlein", "Willi Sommer", "Philipp Harlass", "Roman Hartleb", "Erik Engelhardt", "Sandro Schneider", "Leon Krapf", "Kevin Baer", "Luca Ziegler", "Ben Mueller", "Vale Reitstetter", "Christopher Lee", "Leon Heinze", "Leo Langhans", ) possessions = {} for player in players: file_name = f"oracle/Ball Possession/2nd Half/{player}.csv" # [(14879639049922641 - 13086639146403495) * 10 ** -12 + 0.455 + 0.84795] * 10**12 player_possession = build_target_possession(file_name, 1794302853519146) possessions[player] = player_possession return possessions def compute_errors_first_half(): target_posssessions = build_target_possessions_first_half() predicted_possessions = {} with open('../results/to_validate/first_half/ball_possession.txt') as f: possessions = [] for row in f: possessions.append(row) possessions = possessions[::-1] already_checked = set() for event in possessions: event_split = event.split(",") player = event_split[1] time = int(event_split[2]) if player not in already_checked: predicted_possessions[player] = time * 10**-12 already_checked.add(player) errors = {} for player, possession in target_posssessions.items(): # I'm too lazy to rename where needed if player == 'Willi Sommer': player = 'Wili Sommer' if player not in predicted_possessions: continue errors[player] = abs(possession - predicted_possessions[player]) return errors def compute_errors_second_half(): target_posssessions = build_target_possessions_second_half() predicted_possessions = {} with open('../results/to_validate/second_half/ball_possession.txt') as f: possessions = [] for row in f: possessions.append(row) possessions = possessions[::-1] already_checked = set() for event in possessions: event_split = event.split(",") player = event_split[1] time = int(event_split[2]) if player not in already_checked: predicted_possessions[player] = time * 10**-12 already_checked.add(player) errors = {} for player, possession in target_posssessions.items(): # I'm too lazy to rename where needed if player == 'Willi Sommer': player = 'Wili Sommer' if player not in predicted_possessions: continue errors[player] = abs(possession - predicted_possessions[player]) return errors

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0.011981

0.032946

0.019468

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0

null

0

null

0

1

0

2370cb70aa4ccbe33c76c9f8fc510ffbcf707f15

6,065

py

Python

directory_components/context_processors.py

uktrade/directory-components

f5f52ceeecd2975bff07d1bd3afa7a84046fdd50

[ "MIT" ]

2

2019-06-24T20:22:23.000Z

2019-07-26T12:51:31.000Z

directory_components/context_processors.py

uktrade/directory-components

f5f52ceeecd2975bff07d1bd3afa7a84046fdd50

[ "MIT" ]

278

2018-02-21T11:49:46.000Z

2021-09-16T08:27:54.000Z

directory_components/context_processors.py

uktrade/directory-components

f5f52ceeecd2975bff07d1bd3afa7a84046fdd50

[ "MIT" ]

3

2019-05-02T15:26:26.000Z

2020-02-18T17:47:57.000Z

from directory_constants import urls from django.conf import settings from django.utils import translation from directory_components import helpers def ga360(request): user = helpers.get_user(request) is_logged_in = helpers.get_is_authenticated(request) context = {'ga360': {'site_language': translation.get_language()}} if is_logged_in and hasattr(user, 'hashed_uuid'): context['ga360']['user_id'] = user.hashed_uuid else: context['ga360']['user_id'] = None context['ga360']['login_status'] = is_logged_in if hasattr(settings, 'GA360_BUSINESS_UNIT'): context['ga360']['business_unit'] = settings.GA360_BUSINESS_UNIT return context def sso_processor(request): url = request.build_absolute_uri() sso_register_url = helpers.add_next(settings.SSO_PROXY_SIGNUP_URL, url) return { 'sso_user': helpers.get_user(request), 'sso_is_logged_in': helpers.get_is_authenticated(request), 'sso_login_url': helpers.add_next(settings.SSO_PROXY_LOGIN_URL, url), 'sso_register_url': sso_register_url, 'sso_logout_url': helpers.add_next(settings.SSO_PROXY_LOGOUT_URL, url), 'sso_profile_url': settings.SSO_PROFILE_URL, } def analytics(request): return { 'directory_components_analytics': { 'GOOGLE_TAG_MANAGER_ID': settings.GOOGLE_TAG_MANAGER_ID, 'GOOGLE_TAG_MANAGER_ENV': settings.GOOGLE_TAG_MANAGER_ENV, 'UTM_COOKIE_DOMAIN': settings.UTM_COOKIE_DOMAIN, } } def cookie_notice(request): return { 'directory_components_cookie_notice': { 'PRIVACY_COOKIE_DOMAIN': settings.PRIVACY_COOKIE_DOMAIN } } def header_footer_processor(request): magna_header = settings.MAGNA_HEADER or False magna_urls = { 'magna_home': urls.magna.HOME, 'magna_where_to_export': urls.magna.WHERE_TO_EXPORT, 'magna_learn_to_export': urls.magna.LEARN_TO_EXPORT, 'magna_exportplan_dashboard': urls.magna.EXPORT_PLAN_DASHBOARD, 'magna_search': urls.magna.SEARCH, 'magna_privacy_and_cookies': urls.magna.PRIVACY_AND_COOKIES, 'magna_terms_and_conditions': urls.magna.TERMS_AND_CONDITIONS, 'magna_accessibility': urls.magna.ACCESSIBILITY, 'magna_cookie_preference_settings': urls.magna.COOKIE_PREFERENCE_SETTINGS, 'magna_contact_us': urls.magna.CONTACT_US, 'magna_performance': urls.magna.PERFORMANCE_DASHBOARD, 'magna_account': urls.magna.ACCOUNT, 'magna_advice': urls.magna.ADVICE, 'magna_markets': urls.magna.MARKETS, 'magna_services': urls.magna.SERVICES, 'magna_international': urls.magna.INTERNATIONAL, } advice_urls = { 'create_an_export_plan': urls.domestic.ADVICE_CREATE_AN_EXPORT_PLAN, 'find_an_export_market': urls.domestic.ADVICE_FIND_AN_EXPORT_MARKET, 'define_route_to_market': urls.domestic.ADVICE_DEFINE_ROUTE_TO_MARKET, 'get_export_finance_and_funding': urls.domestic.ADVICE_GET_EXPORT_FINANCE_AND_FUNDING, 'manage_payment_for_export_orders': urls.domestic.ADVICE_MANAGE_PAYMENT_FOR_EXPORT_ORDERS, 'prepare_to_do_business_in_a_foreign_country': urls.domestic.ADVICE_PREPARE_TO_DO_BUSINESS_IN_A_FOREIGN_COUNTRY, 'manage_legal_and_ethical_compliance': urls.domestic.ADVICE_MANAGE_LEGAL_AND_ETHICAL_COMPLIANCE, 'prepare_for_export_procedures_and_logistics': urls.domestic.ADVICE_PREPARE_FOR_EXPORT_PROCEDURES_AND_LOGISTICS, } header_footer_urls = { 'about': urls.domestic.ABOUT, 'dit': urls.domestic.DIT, 'get_finance': urls.domestic.GET_FINANCE, 'ukef': urls.domestic.GET_FINANCE, 'performance': urls.domestic.PERFORMANCE_DASHBOARD, 'privacy_and_cookies': urls.domestic.PRIVACY_AND_COOKIES, 'terms_and_conditions': urls.domestic.TERMS_AND_CONDITIONS, 'accessibility': urls.domestic.ACCESSIBILITY, 'cookie_preference_settings': urls.domestic.COOKIE_PREFERENCE_SETTINGS, 'fas': urls.international.TRADE_FAS, 'advice': urls.domestic.ADVICE, 'markets': urls.domestic.MARKETS, 'search': urls.domestic.SEARCH, 'services': urls.domestic.SERVICES, 'domestic_news': urls.domestic.GREAT_DOMESTIC_NEWS, 'international_news': urls.international.NEWS, 'how_to_do_business_with_the_uk': urls.international.EXPAND_HOW_TO_DO_BUSINESS, 'industries': urls.international.ABOUT_UK_INDUSTRIES, 'market_access': urls.domestic.HOME / 'report-trade-barrier' } header_footer_urls = {**header_footer_urls, **advice_urls, **magna_urls} return {'magna_header': magna_header, 'header_footer_urls': header_footer_urls} def invest_header_footer_processor(request): invest_header_footer_urls = { 'industries': urls.international.ABOUT_UK_INDUSTRIES, 'uk_setup_guide': urls.international.EXPAND_HOW_TO_SETUP, } return {'invest_header_footer_urls': invest_header_footer_urls} def urls_processor(request): return { 'services_urls': { 'contact_us': urls.domestic.CONTACT_US, 'contact_us_international': urls.international.CONTACT_US, 'events': urls.domestic.EVENTS, 'exopps': urls.domestic.EXPORT_OPPORTUNITIES, 'exred': urls.domestic.HOME, 'great_domestic': urls.domestic.HOME, 'great_international': urls.international.HOME, 'fab': urls.domestic.FIND_A_BUYER, 'fas': urls.international.TRADE_FAS, 'feedback': urls.domestic.FEEDBACK, 'office_finder': urls.domestic.OFFICE_FINDER, 'invest': urls.international.EXPAND_HOME, 'soo': urls.domestic.SELLING_OVERSEAS, 'sso': urls.domestic.SINGLE_SIGN_ON, 'uk_setup_guide': urls.international.EXPAND_HOW_TO_SETUP, 'isd': urls.international.EXPAND_ISD_HOME, } } def feature_flags(request): return {'features': settings.FEATURE_FLAGS}

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0

null

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null

0

1

0

237138c111b7235bbb0b60fb326edee46f57fa80

1,962

py

Python

src/leetcodepython/string/remove_duplicate_letters_316.py

zhangyu345293721/leetcode

1aa5bcb984fd250b54dcfe6da4be3c1c67d14162

[ "MIT" ]

90

2018-12-25T06:01:30.000Z

2022-01-03T14:01:26.000Z

src/leetcodepython/string/remove_duplicate_letters_316.py

zhangyu345293721/leetcode

1aa5bcb984fd250b54dcfe6da4be3c1c67d14162

[ "MIT" ]

1

2020-08-27T09:53:49.000Z

2020-08-28T08:57:49.000Z

src/leetcodepython/string/remove_duplicate_letters_316.py

zhangyu345293721/leetcode

1aa5bcb984fd250b54dcfe6da4be3c1c67d14162

[ "MIT" ]

27

2019-01-02T01:41:32.000Z

2022-01-03T14:01:30.000Z

# encoding='utf-8' ''' /** * This is the solution of No.316 problem in the LeetCode, * the website of the problem is as follow: * https://leetcode-cn.com/problems/smallest-subsequence-of-distinct-characters * * The description of problem is as follow: * ========================================================================================================== * 返回字符串 text 中按字典序排列最小的子序列，该子序列包含 text 中所有不同字符一次。 * * 示例 1： * * 输入："cdadabcc" * 输出："adbc" * 示例 2： * * 输入："abcd" * 输出："abcd" * * 来源：力扣（LeetCode） * 链接：https://leetcode-cn.com/problems/smallest-subsequence-of-distinct-characters * 著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。 * ========================================================================================================== * * @author zhangyu (zhangyuyu417@gmail.com) */ ''' class Solution: def remove_duplicate_letters(self, s: str) -> str: ''' 移除重复字符 Args: s: 字符串 Returns: 字典排序表 ''' nums_map = self.get_num_map(s) in_stack_map = {} stack = [] for ch in s: nums_map[ch] -= 1 if ch in in_stack_map and in_stack_map[ch]: continue while len(stack) > 0 and ord(ch) < ord(stack[-1]) and nums_map[ch] > 0: in_stack_map[stack[-1]] = False stack.pop() stack.append(ch) in_stack_map[ch] = True return ''.join(stack) def get_num_map(self, s: str): ''' 统计字符出现个数 Args: s: 字符串 Returns: map ''' num_map = {} for ch in s: if ch in num_map: num_map[ch] += 1 else: num_map[ch] = 1 return num_map if __name__ == '__main__': s = 'cdadabcc' solution = Solution() result = solution.remove_duplicate_letters(s) assert result == 'adbc'

25.480519

109

0.469929

217

1,962

4.092166

0.40553

0.047297

0.056306

0.038288

0.146396

0

0.011826

0.310398

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0.071429

false

0

0.178571

0

null

0

null

0

1

0

23723b37428721d547ab23434d036479e7a2836c

1,055

py

Python

setup.py

julienvaslet/interactive-shell

9ae800f2d9bb3365b5e68b2beef577fb39264f10

[ "MIT" ]

null

setup.py

julienvaslet/interactive-shell

9ae800f2d9bb3365b5e68b2beef577fb39264f10

[ "MIT" ]

null

setup.py

julienvaslet/interactive-shell

9ae800f2d9bb3365b5e68b2beef577fb39264f10

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os from setuptools import setup current_directory = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(current_directory, "VERSION"), "r", encoding="utf-8") as f: version = f.read() with open(os.path.join(current_directory, "README.rst"), "r", encoding="utf-8") as f: long_description = f.read() setup( name="interactive-shell", version=version, description="Interactive shell classes to easily integrate a terminal in application.", long_description=long_description, license="MIT License", author="Julien Vaslet", author_email="julien.vaslet@gmail.com", url="https://github.com/julienvaslet/interactive-shell", packages=["interactive_shell"], install_requires=[], scripts=[], classifiers=[ "Development Status :: 1 - Planning", "Environment :: Console", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.7", "Topic :: Software Development", "Topic :: Terminals" ] )

31.029412

91

0.660664

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0.035088

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0.040936

0.146199

0.099415

0

0.007026

0.190521

1,055

33

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0

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0

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0

0.071429

0

0.071429

0

null

0

null

0

1

0

2381759676c1a13a9190cbf2cbe7006518dd9448

1,093

py

Python

behaviour/models.py

red-and-black/friendly

f453344ad1e9173ad3545e4ea0c825b65190b3c5

[ "Apache-2.0" ]

2

2020-01-28T12:56:56.000Z

2021-07-02T03:07:39.000Z

behaviour/models.py

red-and-black/friendly

f453344ad1e9173ad3545e4ea0c825b65190b3c5

[ "Apache-2.0" ]

5

2021-03-18T23:02:11.000Z

2021-09-17T11:02:08.000Z

behaviour/models.py

red-and-black/goodchat

1a391a04d4edfbcefaf87663f08308dd58578634

[ "Apache-2.0" ]

null

from django.db import models class BehaviourReport(models.Model): NOT_REVIEWED = 'not_reviewed' UNDER_REVIEW = 'under_review' COMPLETED = 'completed' STATUS_CHOICES = ( (NOT_REVIEWED, 'Not reviewed'), (UNDER_REVIEW, 'Under review'), (COMPLETED, 'Completed') ) # Automatic timestamping fields. created = models.DateTimeField(auto_now_add=True) modified = models.DateTimeField(auto_now=True) # Report reporter = models.ForeignKey( 'auth.User', on_delete=models.CASCADE, related_name='reporter' ) reportee = models.ForeignKey( 'auth.User', on_delete=models.CASCADE, related_name='reportee' ) report = models.TextField(max_length=2000) # Outcome/handling public_outcome = models.CharField(max_length=255, blank=True) private_outcome = models.CharField(max_length=255, blank=True) status = models.CharField( max_length=50, choices=STATUS_CHOICES, default=NOT_REVIEWED ) class Meta: ordering = ['-modified']

25.418605

66

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114

1,093

6.052632

0.447368

0.07971

0.078261

0.104348

0.466667

0.342029

0

0.014563

0.246112

1,093

42

67

26.02381

0.822816

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0

1

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false

0

0.03125

0

0.46875

0

null

0

null

0

1

0

88bb3f59329f873d5176f1525a62f453fd0b978d

2,879

py

Python

dockermap/map/runner/cmd.py

merll/docker-map

54e325595fc0b6b9d154dacc790a222f957895da

[ "MIT" ]

85

2015-01-02T01:05:14.000Z

2022-03-23T22:23:12.000Z

dockermap/map/runner/cmd.py

merll/docker-map

54e325595fc0b6b9d154dacc790a222f957895da

[ "MIT" ]

21

2015-02-10T18:25:03.000Z

2020-10-28T08:38:39.000Z

dockermap/map/runner/cmd.py

merll/docker-map

54e325595fc0b6b9d154dacc790a222f957895da

[ "MIT" ]

15

2015-02-27T12:19:35.000Z

2021-09-29T06:20:14.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals import logging from ..action import ContainerUtilAction from ..input import ItemType log = logging.getLogger(__name__) class ExecMixin(object): """ Utility mixin for executing configured commands inside containers. """ action_method_names = [ (ItemType.CONTAINER, ContainerUtilAction.EXEC_COMMANDS, 'exec_commands'), (ItemType.CONTAINER, ContainerUtilAction.EXEC_ALL, 'exec_container_commands'), ] def exec_commands(self, action, c_name, run_cmds, **kwargs): """ Runs a single command inside a container. :param action: Action configuration. :type action: dockermap.map.runner.ActionConfig :param c_name: Container name. :type c_name: unicode | str :param run_cmds: Commands to run. :type run_cmds: list[dockermap.map.input.ExecCommand] :return: List of exec command return values (e.g. containing the command id), if applicable, or ``None`` if either no commands have been run or no values have been returned from the API. :rtype: list[dict] | NoneType """ client = action.client exec_results = [] for run_cmd in run_cmds: cmd = run_cmd.cmd cmd_user = run_cmd.user log.debug("Creating exec command in container %s with user %s: %s.", c_name, cmd_user, cmd) ec_kwargs = self.get_exec_create_kwargs(action, c_name, cmd, cmd_user) create_result = client.exec_create(**ec_kwargs) if create_result: e_id = create_result['Id'] log.debug("Starting exec command with id %s.", e_id) es_kwargs = self.get_exec_start_kwargs(action, c_name, e_id) client.exec_start(**es_kwargs) exec_results.append(create_result) else: log.debug("Exec command was created, but did not return an id. Assuming that it has been started.") if exec_results: return exec_results return None def exec_container_commands(self, action, c_name, **kwargs): """ Runs all configured commands of a container configuration inside the container instance. :param action: Action configuration. :type action: dockermap.map.runner.ActionConfig :param c_name: Container name. :type c_name: unicode | str :return: List of exec command return values (e.g. containing the command id), if applicable, or ``None`` if either no commands have been run or no values have been returned from the API. :rtype: list[dict] | NoneType """ config_cmds = action.config.exec_commands if not config_cmds: return None return self.exec_commands(action, c_name, run_cmds=config_cmds)

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0.307091

0

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0.271969

2,879

70

116

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0.854008

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0.013914

0

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0.057143

false

0

0.114286

0

0.342857

0

null

0

null

0

1

0

88bc403d25f54bbc912895c21b6786cdfc90a30c

3,678

py

Python

main.py

PWN0N/Working-Time-lapse

1ebe4cb1a669a1b77528b4f2583e27fdd4e5953b

[ "MIT" ]

null

main.py

PWN0N/Working-Time-lapse

1ebe4cb1a669a1b77528b4f2583e27fdd4e5953b

[ "MIT" ]

null

main.py

PWN0N/Working-Time-lapse

1ebe4cb1a669a1b77528b4f2583e27fdd4e5953b

[ "MIT" ]

null

import signal import numpy as np from PIL import ImageGrab import cv2 import time import sys import os flips_time_mins = 30 interval = 5 # seconds num_frames = flips_time_mins*60/interval num_frames = int(num_frames) year = -1 month = -1 day = -1 out_fps = 24 cammode = 0 shutdown_msg = False def signal_handler(signal,frame): print('You Pressed Ctrl+C, The Program Will Be Shutdown') global shutdown_msg shutdown_msg = True print('Saving Videos') def add_timestamp(img): time_str= time.strftime("%Y-%m-%d %H:%M:%S") color=(255,255,255) if np.mean( img[700:780,900:950])>128: color=(0,0,0) cv2.putText(img, time_str, (900, 700) ,cv2.FONT_HERSHEY_SIMPLEX ,0.8, color ,2) return img capture = cv2.VideoCapture(0) capture1 = cv2.VideoCapture(1) cam, _ = capture.read() cam1, _ = capture1.read() if(cam and cam1): print('Dual Camera Mode') cammode = 1 elif(cam): print('Single Camera Mode') cammode = 2 else: print('No Camera Detect!') sys.exit(0) signal.signal(signal.SIGINT,signal_handler) # capture frames to video while True: if(day != time.strftime("%d")): year = time.strftime("%Y") month = time.strftime("%m") day = time.strftime("%d") hour = time.strftime("%H") save_dir = "{0}/{1}/{2}".format(year, month, day) if not os.path.isdir(save_dir): os.makedirs(save_dir) # innner camera init size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))) codec = cv2.VideoWriter.fourcc('M', 'J', 'P', 'G') cam_filename = save_dir+"/cam_{:4}.avi".format(time.strftime("%H%M")) video = cv2.VideoWriter(cam_filename, codec, out_fps, size) # for low quality webcams, discard the starting unstable frames for i in range(20): capture.read() # desktop screen init desktopim = np.array(ImageGrab.grab().convert('RGB')) # desktopFrame =np.array(desktopim.getdata(),dtype='uint8')\ # .reshape((desktopim.size[1],desktopim.size[0],3)) sp = desktopim.shape sz1 = sp[0] # height(rows) of image sz2 = sp[1] # width(colums) of image desktopsize = (int(sz2),int(sz1)) codec = cv2.VideoWriter.fourcc('M', 'J', 'P', 'G') desktop_filename = save_dir+"/desktop_{:4}.avi".format(time.strftime("%H%M")) desktopvideo = cv2.VideoWriter(desktop_filename, codec, out_fps, desktopsize) # outter camera init if (cammode == 1): size1 = (int(capture1.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture1.get(cv2.CAP_PROP_FRAME_HEIGHT))) cam1_filename = save_dir+"/cam1_{:4}.avi".format(time.strftime("%H%M")) video1 = cv2.VideoWriter(cam1_filename, codec, out_fps, size1) # for low quality webcams, discard the starting unstable frames for i in range(20): capture1.read() for i in range(num_frames): if (shutdown_msg): break _, frame = capture.read() video.write(add_timestamp(frame)) desktopim = np.array(ImageGrab.grab().convert('RGB')) # ImageGrab and OpenCV have different color space desktopFrame = cv2.cvtColor(desktopim, cv2.COLOR_BGR2RGB) desktopvideo.write(add_timestamp(desktopFrame)) if (cammode == 1): _, frame1 = capture1.read() video1.write(add_timestamp(frame1)) time.sleep(interval) video.release() desktopvideo.release() if (cammode == 1): video1.release() if (shutdown_msg): break capture.release() if(cammode ==1): capture1.release() print('Done!') print('Exit The Program') sys.exit(0)

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83

0.637575

504

3,678

4.535714

0.337302

0.047244

0.022747

0.207349

0.2021

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0

0.039387

0.219957

3,678

134

84

27.447761

0.757407

0.115552

0

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0

0.074383

0

1

0.020408

false

0

0.071429

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0.071429

0

null

0

null

0

1

0

88bd61d6346e9f097545fab6de60f3909f62dcdf

1,823

py

Python

tests/test_tokenizers.py

BMarcin/MordinezNLP

884f6c2ccade8ac796d40d3081560021e96765ca

[ "MIT" ]

1

2021-02-03T19:38:05.000Z

tests/test_tokenizers.py

BMarcin/MordinezNLP

884f6c2ccade8ac796d40d3081560021e96765ca

[ "MIT" ]

13

2020-11-30T21:01:56.000Z

2021-03-12T21:23:45.000Z

tests/test_tokenizers.py

BMarcin/MordinezNLP

884f6c2ccade8ac796d40d3081560021e96765ca

[ "MIT" ]

null

import unittest import spacy from spacy.language import Language try: from src.MordinezNLP.tokenizers import spacy_tokenizer except: from MordinezNLP.tokenizers import spacy_tokenizer class TestTokenizers(unittest.TestCase): nlp: Language = spacy.load("en_core_web_sm") nlp.tokenizer = spacy_tokenizer(nlp) def test_spacy_tokenizer_case1(self): tokenized_data = self.nlp("Hello today is <date>, tomorrow it will be <number> degrees of celcius. I don't like him.") self.assertEqual( [str(token) for token in tokenized_data], [ "Hello", "today", "is", "<date>", ",", "tomorrow", "it", "will", "be", "<number>", "degrees", "of", "celcius", ".", "I", "do", "n't", "like", "him", "." ] ) def test_spacy_tokenizer_case2(self): tokenized_data = self.nlp('Punkt wir haben extra um <number> : <number> Uhr noch ein Event') self.assertEqual( [str(token) for token in tokenized_data], [ "Punkt", "wir", "haben", "extra", "um", "<number>", ":", "<number>", "be", "<number>", "Uhr", "noch", "ein", "Event" ] ) if __name__ == '__main__': unittest.main()

26.808824

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0.397696

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1,823

4.902098

0.440559

0.099857

0.077033

0.091298

0.616262

0.379458

0.28816

0.156919

0

0.002174

0.495337

1,823

67

127

27.208955

0.759783

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0.15

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null

0

null

0

1

0

88bdb402bf1da07ef8a27f4a47f88d7c557aae53

3,905

py

Python

scripts/autopost/image_maker.py

sahawaee/quotes-indonesia

ef6f0dc5afa460d8da6266f5df89d2a350cc9835

[ "MIT" ]

6

2019-11-02T06:04:37.000Z

2022-03-27T14:41:45.000Z

scripts/autopost/image_maker.py

sahawaee/quotes-indonesia

ef6f0dc5afa460d8da6266f5df89d2a350cc9835

[ "MIT" ]

1

2021-09-29T08:33:14.000Z

2021-11-06T02:10:38.000Z

scripts/autopost/image_maker.py

sahawaee/quotes-indonesia

ef6f0dc5afa460d8da6266f5df89d2a350cc9835

[ "MIT" ]

8

2020-03-21T20:09:38.000Z

2022-03-11T19:14:24.000Z

import random import requests import tempfile from io import BytesIO from PIL import Image, ImageDraw, ImageFont FONTS = [ 'https://cdn.statically.io/gh/google/fonts/main/ofl/neucha/Neucha.ttf', # 'https://cdn.statically.io/gh/google/fonts/main/ofl/catamaran/Catamaran%5Bwght%5D.ttf', # font_base_url + 'lobstertwo.ttf', # font_base_url + 'underdog.ttf', # font_base_url + 'specialelite.ttf', # font_base_url + 'abrilfatface.ttf', # font_base_url + 'merienda.ttf', # font_base_url + 'poiretone.ttf', # font_base_url + 'shadowsintolight.ttf', # font_base_url + 'caveatbrush.ttf', # font_base_url + 'gochihand.ttf', # font_base_url + 'itim.ttf', # font_base_url + 'rancho.ttf' ] # thanks to https://clrs.cc COLORS = [ {'bg': (255, 255, 255), 'fg': (100, 100, 100)} # { 'bg': (0, 31, 63), 'fg': (128, 191, 255) }, # { 'bg': (0, 116, 217), 'fg': (179, 219, 255) }, # { 'bg': (127, 219, 255), 'fg': (0, 73, 102) }, # { 'bg': (57, 204, 204), 'fg': (0, 0, 0) }, # { 'bg': (61, 153, 112), 'fg': (22, 55, 40) }, # { 'bg': (46, 204, 64), 'fg': (14, 62, 20) }, # { 'bg': (1, 255, 112), 'fg': (0, 102, 44) }, # { 'bg': (255, 220, 0), 'fg': (102, 88, 0) }, # { 'bg': (255, 133, 27), 'fg': (102, 48, 0) }, # { 'bg': (255, 65, 54), 'fg': (128, 6, 0) }, # { 'bg': (133, 20, 75), 'fg': (235, 122, 177) }, # { 'bg': (240, 18, 190), 'fg': (101, 6, 79) }, # { 'bg': (177, 13, 201), 'fg': (239, 169, 249) }, # { 'bg': (17, 17, 17), 'fg': (221, 221, 221) }, # { 'bg': (170, 170, 170), 'fg': (0, 0, 0) }, # { 'bg': (221, 221, 221), 'fg': (0, 0, 0) } ] def image_maker(quote_by: str, quote_body: str) -> BytesIO: # image configuration img_width = 612 img_height = 612 # font configuration font_selected = random.choice(FONTS) fontfile = requests.get(font_selected, stream=True) font = ImageFont.truetype(BytesIO(fontfile.content), 35) # color configuration color = random.choice(COLORS) # draw image image = Image.new('RGB', (img_width, img_height), color=color['bg']) document = ImageDraw.Draw(image) # find the average size of the letter in quote_body sum = 0 for letter in quote_body: sum += document.textsize(letter, font=font)[0] average_length_of_letter = sum/len(quote_body) # find the number of letters to be put on each linex number_of_letters_for_each_line = ( img_width / 1.818) / average_length_of_letter # build new text to put on the image incrementer = 0 fresh_quote = '' for letter in quote_body: if (letter == '-'): # fresh_quote += '\n\n' + letter #add some line breaks fresh_quote += '' + letter elif (incrementer < number_of_letters_for_each_line): fresh_quote += letter else: if(letter == ' '): fresh_quote += '\n' incrementer = 0 else: fresh_quote += letter incrementer += 1 fresh_quote += '\n\n--' + quote_by # render the text in the center of the box dim = document.textsize(fresh_quote, font=font) x2 = dim[0] y2 = dim[1] qx = (img_width / 2 - x2 / 2) qy = (img_height / 2 - y2 / 2) document.text((qx, qy), fresh_quote, align="center", font=font, fill=color['fg']) # save image to bytes image_io = BytesIO() image.save(image_io, 'JPEG', quality=100) image_io.seek(0) return image_io def image_maker_make_file(quote_by: str, quote_body: str) -> str: image_io = image_maker(quote_by, quote_body) fd, image_path = tempfile.mkstemp(suffix='.jpg') image_file = open(image_path, 'wb') image_file.write(image_io.getbuffer()) image_file.close() return image_path

33.956522

93

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0

0.152542

0

null

0

null

0

1

0

88beff2251e1c3db657b53d33c4f8b3982f9a861

5,093

py

Python

metashade/hlsl/sm5/profile.py

ppenenko/metashade

7148e808e47bace59e61e1483da9ddf3f9daa1cc

[ "Apache-2.0" ]

3

2020-04-02T13:29:06.000Z

2020-09-07T17:43:09.000Z

metashade/hlsl/sm5/profile.py

ppenenko/metashade

7148e808e47bace59e61e1483da9ddf3f9daa1cc

[ "Apache-2.0" ]

null

metashade/hlsl/sm5/profile.py

ppenenko/metashade

7148e808e47bace59e61e1483da9ddf3f9daa1cc

[ "Apache-2.0" ]

null

# Copyright 2017 Pavlo Penenko # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import metashade.rtsl.profile as rtsl import metashade.clike.struct as struct from . import data_types from . import samplers import sys, inspect class UniformBuffer: def __init__(self, sh, register : int, name : str = None): self._sh = sh self._name = name self._register = register def __enter__(self): self._sh._emit('cbuffer') if self._name is not None: self._sh._emit(' ') self._sh._emit(self._name) self._sh._emit( ' : register(b{register})\n{{\n'.format(register = self._register) ) self._sh._push_indent() return self def __exit__(self, exc_type, exc_value, traceback): self._sh._pop_indent() self._sh._emit('};\n\n') class Generator(rtsl.Generator): _is_pixel_shader = False class _UsedRegisterSet(set): def __init__(self, category : str): self._category = category def check_candidate(self, register : int): if register < 0: raise RuntimeError('Invalid register value') if register in self: raise RuntimeError(self._category + ' register already in use') def __init__(self, file_): super(Generator, self).__init__(file_) self._uniforms_by_semantic = dict() self._used_uniform_buffer_registers = \ self.__class__._UsedRegisterSet('Uniform buffer') self._used_texture_registers = \ self.__class__._UsedRegisterSet('Texture') self._used_sampler_registers = \ self.__class__._UsedRegisterSet('Sampler') def uniform_buffer(self, register : int, name : str = None): self._used_uniform_buffer_registers.check_candidate(register) return UniformBuffer(self, register = register, name = name) # TODO: registers, packoffset def uniform( self, name : str, dtype, semantic : str = None, annotations = None ): self._check_public_name(name) if not self._check_global_scope(): raise RuntimeError( "Uniforms can only be defined at the global scope" ) if semantic is not None: existing = self._uniforms_by_semantic.get(semantic) if existing is not None: raise RuntimeError( "Can't define uniform '{name}' with semantic '{semantic}' " "because uniform '{existing_name}' already uses that " "semantic.".format( name = name, semantic = semantic, existing_name = existing._name ) ) value = dtype() #TODO: make it immutable self._set_global(name, value) self._emit_indent() value._define(self, name, semantic, annotations = annotations) self._emit(';\n') def combined_sampler_2d( self, texture_name : str, texture_register : int, sampler_name : str, sampler_register : int ): self._check_public_name(texture_name) self._check_public_name(sampler_name) if not self._check_global_scope(): raise RuntimeError( "Uniform textures and samplers " "can only be defined at the global scope" ) self._used_texture_registers.check_candidate(texture_register) self._used_sampler_registers.check_candidate(sampler_register) texture = samplers.Texture2d(self, texture_name, texture_register) self._set_global(texture_name, texture) self._used_texture_registers.add(texture_register) sampler = samplers.Sampler( self, sampler_name, sampler_register, texture ) self._set_global(sampler_name, sampler) self._used_sampler_registers.add(sampler_register) def vs_input(self, name): return stage_interface.VsInputDef(self, name) def vs_output(self, name): return stage_interface.VsOutputDef(self, name) def ps_output(self, name): return stage_interface.PsOutputDef(self, name) # Reference all the data types from the generator class for name, cls in inspect.getmembers( sys.modules[data_types.__name__], lambda member: (inspect.isclass(member) and member.__module__ == data_types.__name__ and not member.__name__.startswith('_') )): setattr(Generator, name, cls)

34.181208

79

0.634204

582

5,093

5.24055

0.293814

0.028852

0.016393

0.032459

0.118033

0.090492

0.051148

0.030164

0

0.003009

0.282152

5,093

148

80

34.412162

0.831236

0.128608

0

0.082569

0

0.080751

0.006107

0

0.006757

0

1

0.110092

false

0

0.045872

0.027523

0.238532

0

null

0

null

0

1

0

88c000e9e02c415df05d77f3582eae21f519869a

8,256

py

Python

backend/views.py

johnzhang1999/Pop

284cc1c5195efdc676759d8494965b2dfb44cf78

[ "MIT" ]

1

2019-02-10T06:50:25.000Z

backend/views.py

johnzhang1999/Pop

284cc1c5195efdc676759d8494965b2dfb44cf78

[ "MIT" ]

null

backend/views.py

johnzhang1999/Pop

284cc1c5195efdc676759d8494965b2dfb44cf78

[ "MIT" ]

null

from django.http import JsonResponse, Http404 from django.views.decorators.csrf import csrf_exempt from exponent_server_sdk import PushClient, PushMessage, DeviceNotRegisteredError from .models import Group, User, Event import hashlib, uuid def getParams(request, tags): print(request.POST) return [request.POST[i] for i in tags] def getHash(name, pwd): return hashlib.sha256((name+pwd).encode()).digest() # Create your views here. @csrf_exempt def getUid(request):#done, tested [name] = getParams(request, ['name']) q = User.objects.filter(pk=name) if len(q) > 0: return JsonResponse({'uid': q[0].uid}) else: raise Http404("you done fked up") @csrf_exempt def joinOpenGroup(request):#done, tested [uid, gid] = getParams(request, ['uid', 'gid']) g = Group.objects.get(pk=gid) u = User.objects.get(pk=uid) if g.groupType == 'public' or g.groupType == 'private': g.members.add(u) g.save() return JsonResponse({'success': 'true'}) else: raise Http404("Invalid group or invalid user!") @csrf_exempt def addEvent(request):#done, tested [uid, gid, name, desc, loc] = getParams(request, ['uid', 'gid', 'name', 'desc', 'loc']) newEvent = Event(name=name, eid=str(uuid.uuid4()), desc=desc, loc=loc, owner=User.objects.get(pk=uid)) newEvent.save() q = Group.objects.get(pk=gid) q.events.add(newEvent) q.save() if q.groupType == 'private' or q.groupType == 'public': responses = PushClient().publish_multiple([PushMessage(to=u.expoPushToken, title='{} happening at {}!'.format(name, loc), body=newEvent.desc, ttl=3, priority='high', sound='default') for u in q.members.all()]) for i in range(len(responses)): try: responses[i].validate_response() except DeviceNotRegisteredError: u = q.members.all()[i] u.expoPushToken = '' u.save() return JsonResponse({'eid': newEvent.eid}) @csrf_exempt def deleteEvent(request):#done, BUGGY [uid, eid] = getParams(request, ['uid', 'eid']) q = Event.objects.get(pk=eid) g = q.group_events.all()[0] if uid == q.owner.uid or uid == g.owner.uid: g.events.remove(q) q.delete() q.save() return JsonResponse({'success': 'true'}) else: raise Http404("Restricted access!") @csrf_exempt def getGroupList(request):#done, tested [uid] = getParams(request, ['uid']) gList = User.objects.get(pk=uid).group_members.all() return JsonResponse({'groupList': [g.gid for g in gList]}) @csrf_exempt def getGroupInfo(request):#done, tested [gid] = getParams(request, ['gid']) g = Group.objects.get(pk=gid) return JsonResponse({'gid': gid,'name': g.name, 'type': g.groupType, 'memberList': [u.uid for u in g.members.all()], 'owner': g.owner.uid, 'unconfirmed': 0}) @csrf_exempt def getEventList(request):#done, should be ok [gid] = getParams(request, ['gid']) eList = Group.objects.get(gid=gid).events.all() return JsonResponse({'eventList': [e.eid for e in eList]}) @csrf_exempt def getEventInfo(request):#done, tested [eid, uid] = getParams(request, ['eid', 'uid']) q = Event.objects.get(pk=eid) return JsonResponse({'eid': eid, 'name': q.name,'desc': q.desc, 'loc': q.loc, 'status': q.confirmed, 'initTime': q.initTime.strftime('%b-%d %I:%M %p'), 'owner': q.owner.uid, 'isOwner': uid == q.owner.uid or uid == q.group_events.all()[0].owner.uid}) @csrf_exempt def register(request):#done, tested [name, pwd] = getParams(request, ['name', 'pwd']) if len(User.objects.filter(name=name)) > 0: raise Http404("Try another name!") newUser = User(name=name, uid=str(uuid.uuid4()), pwdHash=getHash(name, pwd)) newUser.save() return JsonResponse({'uid': newUser.uid}) @csrf_exempt def login(request):#done, tested [name, pwd] = getParams(request, ['name', 'pwd']) u = User.objects.get(name=name) if u.pwdHash == getHash(name, pwd): for otheruser in User.objects.all(): if otheruser.expoPushToken == u.expoPushToken: otheruser.expoPushToken = '' return JsonResponse({'uid': u.uid}) else: raise Http404("Restricted access!") @csrf_exempt def createGroup(request):#done, tested [uid, name, gtype] = getParams(request, ['uid', 'name', 'type']) newGroup = Group(name=name, gid=str(uuid.uuid4()), owner=User.objects.get(uid=uid), groupType=gtype) newGroup.save() newGroup.members.add(User.objects.get(uid=uid)) newGroup.save() return JsonResponse({'gid': newGroup.gid}) @csrf_exempt def removeMember(request):#done, tested [m_uid, uid, gid] = getParams(request, ['m_uid', 'uid', 'gid']) if m_uid == Group.objects.get(pk=gid).owner.uid or m_uid == uid: q = Group.objects.get(pk=gid) q.members.remove(User.objects.get(pk=uid)) q.save() return JsonResponse({'status': 'success'}) else: raise Http404("Restricted access!") @csrf_exempt def addMember(request):#done, tested [m_uid, uid, gid] = getParams(request, ['m_uid', 'uid', 'gid']) if m_uid == Group.objects.get(pk=gid).owner.uid: q = Group.objects.get(pk=gid) q.members.add(User.objects.get(pk=uid)) q.save() return JsonResponse({'status': 'success'}) else: raise Http404("Restricted access!") @csrf_exempt def deleteGroup(request):#done, BUGGY [gid, uid] = getParams(request, ['gid', 'uid']) q = Group.objects.get(pk=gid) if uid == q.owner.uid: q.delete() return JsonResponse({'status': 'success'}) else: raise Http404("Restricted access!") @csrf_exempt def getUserInfo(request):#done, tested [uid] = getParams(request, ['uid']) name = User.objects.get(pk=uid).name return JsonResponse({'name': name}) @csrf_exempt def confirmEvent(request):#done, tested [uid, eid] = getParams(request, ['uid', 'eid']) e = Event.objects.get(pk=eid) if len(e.confirmedMembers.filter(pk=uid)) == 0: e.confirmed += 1 e.confirmedMembers.add(User.objects.get(pk=uid)) e.save() if e.confirmed == 1: g = e.group_events.all()[0] if g.groupType == 'public': responses = PushClient().publish_multiple([PushMessage(to=u.expoPushToken, title="You'll never believe what you're missing out on!", body="This is a test notification", ttl=30, priority='high', sound='default') for u in g.members.all()]) for i in range(len(responses)): try: responses[i].validate_response() except DeviceNotRegisteredError: u = g.members.all()[i] u.expoPushToken = '' u.save() return JsonResponse({'status': 'success'}) else: raise Http404("Multiple confirmation") @csrf_exempt def search(request):#done, tested [query] = getParams(request, ['q']) return JsonResponse({'list': [g.gid for g in Group.objects.all() if query in g.name and g.groupType == 'public']}) @csrf_exempt def updateToken(request): [token, uid] = getParams(request, ['token', 'uid']) u = User.objects.get(uid=uid) print("before: "+u.expoPushToken) u.expoPushToken = token u.save() print("after: "+u.expoPushToken) return JsonResponse({'status': 'success'})

38.222222

128

0.56468

969

8,256

4.773994

0.177503

0.049719

0.050584

0.029399

0.428448

0.358193

0.320363

0.270212

0.226762

0.172503

0

0.008311

0.285853

8,256

216

129

38.222222

0.776289

0.02798

0

0.426316

0

0.085051

0

1

0.105263

false

0

0.026316

0.005263

0.236842

0.015789

0

null

0

null

0

1

0

88c03f34e857962f5d9b4b18e80b0b7a54e0e36b

3,572

py

Python

SDFConv/code/utils/vis/unet_vis.py

zshyang/FieldConvolution

ca88df568a6f2143dcb85d22c005fce4562a7523

[ "MIT" ]

1

2021-01-03T18:53:06.000Z

SDFConv/code/utils/vis/unet_vis.py

zshyang/FieldConvolution

ca88df568a6f2143dcb85d22c005fce4562a7523

[ "MIT" ]

null

SDFConv/code/utils/vis/unet_vis.py

zshyang/FieldConvolution

ca88df568a6f2143dcb85d22c005fce4562a7523

[ "MIT" ]

null

"""Implement this function across different project. ----ZY.2020.Oct. """ import os from easydict import EasyDict from torchvision.utils import save_image from logging import Logger from subprocess import call def create_save_folders(root_folder, folder_list: list): """Create folders to save visualization image. :param root_folder: The root folder. :param folder_list: The list of folders """ for folder in folder_list: os.makedirs(os.path.join(root_folder, folder), exist_ok=True) def unet_vis( in_batch: dict, out_batch: tuple, training: bool, epoch: int, step: int, options: EasyDict, logger: Logger ): """The visualization function of UNet. :param in_batch: The input batch. :param out_batch: The output batch. :param training: Whether it is training stage. :param epoch: The epoch number start with 1. :param step: The step. :param logger: The logger. :param options: The options for visualization. """ # Folders if training: vis_dir = os.path.join(options.vis.dir, "train_vis") else: vis_dir = os.path.join(options.vis.dir, "val_vis") out_dir = os.path.join(vis_dir, "epoch-{:04d}".format(epoch)) # Customize the list of folders. dir_list = ["input_image", "info"] # Create the list folders. create_save_folders(out_dir, dir_list) # The list of key in input and output batch. key_list = ["input_image", ["loss"]] batch = {} batch.update(in_batch) batch.update(out_batch[0]) batch.update(out_batch[1]) # Get the batch size. if training: batch_size = options.train.batch_size else: batch_size = options.test.batch_size # Get number of steps each epoch. if training: # Update the number of training samples in options. num_step_each_epoch = options.dataset.len_train // (options.train.batch_size * options.num_gpus) else: # Update the number of validation samples in options. num_step_each_epoch = options.dataset.len_test // (options.test.batch_size * options.num_gpus) # Save images and info. for i in range(batch_size): batch_id = step % num_step_each_epoch fn = "data-{:04d}.png".format(batch_id * batch_size + i) # file name. for key, folder in zip(key_list, dir_list): if folder == "info": with open(os.path.join(out_dir, folder, fn.replace('.png', '.txt')), 'w') as file: for loss_item in key: file.write("{}: {}\n".format(loss_item, batch[loss_item][i].item())) else: save_image(batch[key][i], os.path.join(out_dir, folder, fn)) # Get the KC step interval. if training: kc_steps = options.train.kc_steps else: kc_steps = options.test.kc_steps # Generate HTML file. mod_step = step % num_step_each_epoch # step starts ar 1. extra_step = (mod_step + kc_steps) / num_step_each_epoch if mod_step == 0 or extra_step > 1.0: # Visualize HTML. logger.info("Generating html visualization ...") sublist = ",".join(dir_list) script_path = os.path.join(os.path.abspath(os.getcwd()), "utils", "gen_html_hierarchy_local.py") if not os.path.exists(script_path): raise ValueError("{} this python script does not exist!".format(script_path)) cmd = "cd {} && python {} . 10 htmls {} {} > /dev/null".format( out_dir, script_path, sublist, sublist ) call(cmd, shell=True) logger.info("DONE")

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3,572

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0.275449

0.024368

0.031588

0.036101

0.130866

0.092058

0.070397

0.044224

0

0.006268

0.240761

3,572

95

115

37.6

0.810841

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0

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0

0.086207

0

0.12069

0

null

0

null

0

1

0

88c151ffa4679f358142c0fae2020059a35ad3a9

1,465

py

Python

tests/test_models/test_metric_report.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

6

2015-01-28T05:59:08.000Z

2018-01-09T07:48:57.000Z

tests/test_models/test_metric_report.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

2

2020-05-09T16:36:43.000Z

2020-05-09T16:52:35.000Z

tests/test_models/test_metric_report.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

1

2016-01-13T07:19:44.000Z

from nose.tools import assert_equals, assert_true from wikimetrics.metrics import metric_classes from wikimetrics.models import ( MetricReport ) from ..fixtures import DatabaseTest class MetricReportTest(DatabaseTest): def setUp(self): DatabaseTest.setUp(self) self.common_cohort_1() def test_basic_response(self): metric = metric_classes['NamespaceEdits']( name='NamespaceEdits', namespaces=[0, 1, 2], start_date='2013-01-01 00:00:00', end_date='2013-01-02 00:00:00', ) mr = MetricReport( metric, self.cohort.id, [ self.editors[0].user_id, self.editors[1].user_id, self.editors[2].user_id, ], 'wiki' ) result = mr.run() assert_equals(result[self.editor(0)]['edits'], 2) def test_repr(self): metric = metric_classes['NamespaceEdits']( name='NamespaceEdits', namespaces=[0, 1, 2], start_date='2013-05-01 00:00:00', end_date='2013-09-01 00:00:00', ) mr = MetricReport( metric, self.cohort.id, [ self.editors[0].user_id, self.editors[1].user_id, self.editors[2].user_id, ], 'wiki' ) assert_true(str(mr).find('MetricReport') >= 0)

28.173077

57

0.524915

159

1,465

4.698113

0.327044

0.042838

0.104418

0.091031

0.524766

0.473896

0

0.076433

0.356997

1,465

51

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0

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0.177778

0

null

0

null

0

1

0

88c3e3d167bb3169d56f9fd93e05df1be55709b1

1,903

py

Python

xugrid/data/synthetic.py

Deltares/xugrid

41881977e5e49d0f87a90dd995960283b812b921

[ "MIT" ]

15

2021-10-04T15:18:33.000Z

2022-03-14T13:58:27.000Z

xugrid/data/synthetic.py

Deltares/xugrid

41881977e5e49d0f87a90dd995960283b812b921

[ "MIT" ]

10

2021-11-10T15:12:02.000Z

2022-02-10T14:35:57.000Z

xugrid/data/synthetic.py

Deltares/xugrid

41881977e5e49d0f87a90dd995960283b812b921

[ "MIT" ]

null

import meshzoo import numpy as np import xarray as xr import xugrid def transform(vertices, minx, maxx, miny): """ Transform vertices to fit within minx to maxx. Maintains x:y aspect ratio. """ x, y = vertices.T xmin = x.min() xmax = x.max() ymin = y.min() ymax = y.max() dx = xmax - xmin dy = ymax - ymin new_dx = maxx - minx new_dy = dy / dx * new_dx x = (x - xmin) * new_dx / dx + minx y = (y - ymin) * new_dy / dy + miny return np.column_stack([x, y]) def disk(): def function_z(x, y): """ from https://matplotlib.org/stable/gallery/images_contours_and_fields/tricontour_smooth_user.html """ r1 = np.sqrt((0.5 - x) ** 2 + (0.5 - y) ** 2) theta1 = np.arctan2(0.5 - x, 0.5 - y) r2 = np.sqrt((-x - 0.2) ** 2 + (-y - 0.2) ** 2) theta2 = np.arctan2(-x - 0.2, -y - 0.2) z = -( 2 * (np.exp((r1 / 10) ** 2) - 1) * 30.0 * np.cos(7.0 * theta1) + (np.exp((r2 / 10) ** 2) - 1) * 30.0 * np.cos(11.0 * theta2) + 0.7 * (x ** 2 + y ** 2) ) zmin = z.min() zmax = z.max() return (zmax - z) / (zmax - zmin) * 10.0 vertices, triangles = meshzoo.disk(6, 8) vertices = transform(vertices, 0.0, 10.0, 0.0) grid = xugrid.Ugrid2d( node_x=vertices[:, 0], node_y=vertices[:, 1], fill_value=-1, face_node_connectivity=triangles, ) ds = xr.Dataset() ds["node_z"] = xr.DataArray( data=function_z(*grid.node_coordinates.T), dims=[grid.node_dimension], ) ds["face_z"] = xr.DataArray( data=function_z(*grid.face_coordinates.T), dims=[grid.face_dimension], ) ds["edge_z"] = xr.DataArray( data=function_z(*grid.edge_coordinates.T), dims=[grid.edge_dimension], ) return xugrid.UgridDataset(ds, grid)

27.985294

105

0.527063

280

1,903

3.478571

0.317857

0.008214

0.036961

0.049281

0.113963

0

0.053071

0.306884

1,903

67

106

28.402985

0.685368

0.090384

0

0.010664

0

1

0.056604

false

0

0.075472

0

0.188679

0

null

0

null

0

1

0

88c4cf9f6f8d805d5af7d3c164350e7934f1fcde

2,492

py

Python

ckanext/tess/group.py

ElixirUK/ckanext-tess

01725ff81b74f31d906875cb0cf493e7d3533615

[ "BSD-3-Clause" ]

1

2015-05-18T08:31:28.000Z

ckanext/tess/group.py

ElixirUK/ckanext-tess

01725ff81b74f31d906875cb0cf493e7d3533615

[ "BSD-3-Clause" ]

null

ckanext/tess/group.py

ElixirUK/ckanext-tess

01725ff81b74f31d906875cb0cf493e7d3533615

[ "BSD-3-Clause" ]

null

import ckan.plugins as plugins import ckan.model as model import ckan.logic as logic import ckan.plugins.toolkit as toolkit import ckan.lib.plugins as plugs from pylons import c NotFound = logic.NotFound get_action = logic.get_action class GroupPlugin(plugins.SingletonPlugin, plugs.DefaultGroupForm): plugins.implements(plugins.IGroupForm, inherit=False) plugins.implements(plugins.interfaces.IGroupController, inherit=True) def before_view(self, group): if c.controller == 'group': group['owner'] = group_owner(group) if c.userobj and c.userobj.id: group['display'] = True else: group['display'] = False return group def group_types(self): return ['group'] def is_fallback(self): return True def form_to_db_schema(self): schema = super(GroupPlugin, self).form_to_db_schema() schema = self._modify_group_schema(schema) return schema def db_to_form_schema(self): schema = super(GroupPlugin, self).form_to_db_schema() _convert_from_extras = toolkit.get_converter('convert_from_extras') _ignore_missing = toolkit.get_validator('ignore_missing') _boolean = toolkit.get_validator('boolean_validator') default_validators = [_convert_from_extras, _ignore_missing, _boolean] schema.update({ 'private': default_validators }) return schema def _modify_group_schema(self, schema): #Import core converters and validators _convert_to_extras = toolkit.get_converter('convert_to_extras') _ignore_missing = toolkit.get_validator('ignore_missing') _boolean = toolkit.get_validator('boolean_validator') default_validators = [_ignore_missing, _boolean, _convert_to_extras] schema.update({ 'private': default_validators }) return schema def group_owner(group): context = {'model': model, 'session': model.Session, 'user': c.user or c.author, 'for_view': True} admin = logic.get_action('member_list')(context, {'id': group.get('name'), 'object_type': 'user', 'capacity': 'admin'}) if admin and isinstance(admin, list) and admin[0][0]: user = logic.get_action('user_show')(context, {'id': admin[0][0]}) return {'name': user.get('display_name'), 'link': user.get('id')} else: return {'name': 'unknown', 'link': '--'}

33.675676

123

0.656902

292

2,492

5.35274

0.273973

0.038388

0.048624

0.026871

0.332694

0.269994

0.204734

0

0.002088

0.23114

2,492

73

124

34.136986

0.813674

0.014848

0

0.303571

0

0.106036

0

1

0.125

false

0

0.107143

0.035714

0.392857

0

null

0

null

0

1

0

88c6dfcf5b1c5b830035587feb18704990928ca6

2,087

py

Python

package/spack-discovardenovo/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

1

2018-07-17T07:45:09.000Z

package/spack-discovardenovo/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

null

package/spack-discovardenovo/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

null

############################################################################## # Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class Discovardenovo(AutotoolsPackage): """DISCOVAR de novo is a large (and small) de novo genome assembler. It quickly generates highly accurate and complete assemblies using the same single library data as used by DISCOVAR. It currently doesn't support variant calling, for that, please use DISCOVAR instead.""" homepage = "https://software.broadinstitute.org/software/discovar/blog/" url = "ftp://ftp.broadinstitute.org/pub/crd/DiscovarDeNovo/latest_source_code/discovardenovo-52488.tar.gz" version('52488', '2b08c77b1b998d85be8048e5efb10358') # lots of compiler errors with GCC7, works with 4.8.5 # and devs claim it works with 4.7 so I'm assuming 4.7-4.8'll work conflicts('%gcc@5:') conflicts('%gcc@:4.7.0') depends_on('samtools') depends_on('jemalloc')

45.369565

115

0.689506

290

2,087

4.948276

0.617241

0.022997

0.016725

0.029268

0.07108

0

0.045195

0.162434

2,087

45

116

46.377778

0.775744

0.679444

0

0.111111

0.492441

0.280778

0

1

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false

0

0.111111

0

0.444444

0

null

0

null

0

1

0

88c7e5dd6da0bf02bd4f18142f4b9f76bc68b52c

10,379

py

Python

s2_convert.py

uscensusbureau/SABLE

883d449e4e6b75636d2589f540e86a5401e09932

[ "CC0-1.0" ]

27

2017-11-06T22:55:24.000Z

2021-06-11T12:56:03.000Z

s2_convert.py

uscensusbureau/SABLE

883d449e4e6b75636d2589f540e86a5401e09932

[ "CC0-1.0" ]

1

2018-01-31T18:26:23.000Z

s2_convert.py

uscensusbureau/SABLE

883d449e4e6b75636d2589f540e86a5401e09932

[ "CC0-1.0" ]

8

2017-10-05T19:17:05.000Z

2020-10-21T23:08:34.000Z

#Name: s2_convert.py #Purpose: Convert PDFs to TXT format #Invocation: python3 s2_convert.py <projName> <lng> <clss> import codecs import os import re import sys #Name: valid_arguments #Purpose: Check whether the command-line arguments are valid #Parameters: sys.argv (globally defined list of command-line arguments) #Returns: True (arguments are valid) or False (arguments are invalid) def valid_arguments(): lngValid = set(["danish", "dutch", "english", "finnish", "french", "german", "hungarian", "italian", "norwegian", "portuguese", "spanish", "swedish", "turkish"]) clssValid = set(["neg", "pos", "pred"]) if len(sys.argv) == 4 and re.search(r"^[a-zA-Z][a-zA-Z_-]*$", sys.argv[1]) and sys.argv[2] in lngValid and sys.argv[3] in clssValid: return True return False #Name: match_page #Purpose: Match line to an XML page tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object def match_page(line): return re.search(r"<page id=\"(\d+)\"", line) #Name: match_textbox #Purpose: Match line to an XML textbox tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object def match_textbox(line): return re.search(r"<textbox id=\"(\d+)\"", line) #Name: match_textline #Purpose: Match line to an XML textline tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object def match_textline(line): return re.search(r"<textline", line) #Name: match_text #Purpose: Match line to an XML text tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object def match_text(line): return re.search(r"<text.*font=\"(.*)\".*bbox=\"([0-9]+\.[0-9]+),([0-9]+\.[0-9]+),([0-9]+\.[0-9]+),([0-9]+\.[0-9]+)\".*size=\"([0-9]+\.[0-9]+)\">(.*)</text>", line) #Name: clean_char #Purpose: Clean character to deal with punctuation, numbers, and foreign accent marks #Parameters: old (character) #Returns: Cleaned character def clean_char(old): #Check the length of the argument if len(old) == 0: new = "" elif len(old) >= 2: new = " " else: #The function "ord" returns the integer representing the Unicode code point of a character ucp = ord(old) #Control codes if (0 <= ucp <= 31): new = " " #Punctuation elif (32 <= ucp <= 38) or (40 <= ucp <= 47) or (58 <= ucp <= 64) or (91 <= ucp <= 96) or (123 <= ucp <= 126) or ucp == 8221: new = " " #Apostrophe elif ucp == 39 or ucp == 8217: new = "" #Numbers elif (48 <= ucp <= 57): new = " " #Letters elif (192 <= ucp <= 198) or (224 <= ucp <= 230): new = "a" elif ucp == 199 or ucp == 231: new = "c" elif (200 <= ucp <= 203) or (232 <= ucp <= 235): new = "e" elif (204 <= ucp <= 207) or (236 <= ucp <= 239): new = "i" elif ucp == 209 or ucp == 241: new = "n" elif (210 <= ucp <= 214) or ucp == 216 or (242 <= ucp <= 246) or ucp == 248: new = "o" elif ucp == 223: new = "ss" elif (217 <= ucp <= 220) or (249 <= ucp <= 252): new = "u" elif ucp == 221 or ucp == 253 or ucp == 255: new = "y" elif ucp >= 128: new = " " else: new = old return new #Name: get_chars #Purpose: Extract the character values, coordinates, hierarchy, and font information from XML file #Parameters: xmlFile (location of XML file) #Returns: List of tuples (one for each character) containing character data def get_chars(xmlFile): chars = [] page = 0 textbox = 0 textline = 0 #Open XML file and use regular expressions to parse contents f = codecs.open(xmlFile, "rU", encoding="utf8") for l in f: line = l.strip() pageMatch = match_page(line) textboxMatch = match_textbox(line) textlineMatch = match_textline(line) textMatch = match_text(line) if pageMatch: page = int(pageMatch.group(1)) elif textboxMatch: textline = 0 textbox = int(textboxMatch.group(1)) elif textlineMatch: textline += 1 elif textMatch: font = textMatch.group(1) x1 = float(textMatch.group(2)) y1 = float(textMatch.group(3)) x2 = float(textMatch.group(4)) y2 = float(textMatch.group(5)) size = float(textMatch.group(6)) value = clean_char(textMatch.group(7)) chars.append((page, textbox, textline, x1, y1, x2, y2, size, font, value)) f.close() return chars #Name: clean_text #Purpose: Clean string of text and check each word against a list of stop words #Parameters: text (string of text) #Returns: Cleaned text def clean_text(text): text = text.lower() text = re.sub("\s+", " ", text) #Remove stop words textClean = [] text = text.split(" ") global stopWords for word in text: word = word.strip() if word not in stopWords: textClean.append(word) textClean = " ".join(textClean) return textClean #Name: write_text #Purpose: Construct words character by character #Parameters: chars (list of tuples) # txtFile (location of TXT file) #Returns: def write_text(chars, txtFile): text = [] #Sort characters according to page, textbox, textline, y1, and x1 chars = sorted(chars, key = lambda z: (z[0], z[1], z[2], -z[4], z[3])) pageCur = chars[0][0] textboxCur = chars[0][1] textlineCur = chars[0][2] for char in chars: spaceFlag = 0 pageNew = char[0] textboxNew = char[1] textlineNew = char[2] if pageNew != pageCur: pageCur = pageNew spaceFlag = 1 if textboxNew != textboxCur: textboxCur = textboxNew spaceFlag = 1 if textlineNew != textlineCur: textlineCur = textlineNew spaceFlag = 1 if spaceFlag == 1: text.append(" ") text.append(char[9]) text = "".join(text) f = codecs.open(txtFile, "w") f.write(clean_text(text)) f.close() return #Name: create_output #Purpose: Convert a PDF document of a given class to TXT format #Parameters: projName (project name) # clss ("pos" or "neg") # docName (document name) #Returns: def create_output(projName, clss, docName): #Create file locations pdfFile = "/" + projName + "/" + clss + "_pdf/" + docName + ".pdf" xmlFile = "/" + projName + "/" + clss + "_xml/" + docName + ".xml" txtFile = "/" + projName + "/" + clss + "_txt/" + docName + ".txt" probFile = "/" + projName + "/" + clss + "_prob/" + docName + ".pdf" #probFlag indicates whether there is a problem extracting text from the PDF #The problem PDFs are moved to separate folders where they can be inspected probFlag = 0 chars = [] #If the TXT file does not already exist, then try creating it if not os.path.isfile(txtFile): try: #The pdf2txt.py program comes with the PDFMiner module os.system("pdf2txt.py -o " + xmlFile + " -t xml " + pdfFile) except PDFTextExtractionNotAllowed: #Exception indicates that text cannot be extracted from the PDF probFlag = 1 if not os.path.isfile(xmlFile): probFlag = 1 elif os.stat(xmlFile).st_size == 0: probFlag = 1 if probFlag == 0: chars = get_chars(xmlFile) if len(chars) == 0: probFlag = 1 #Check probFlag value and act accordingly if probFlag == 0: write_text(chars, txtFile) if os.path.isfile(xmlFile): #The intermediate XML file is deleted because it tends to be large os.remove(xmlFile) print(docName) elif probFlag == 1: if os.path.isfile(xmlFile): #The intermediate XML file is deleted because it tends to be large os.remove(xmlFile) if os.path.isfile(txtFile): #Any text that has been extracted from the problem PDF is deleted os.remove(txtFile) os.system("mv " + pdfFile + " " + probFile) print("!!! PROBLEM: " + docName) return #Name: convert_files #Purpose: Convert PDFs to TXT format #Parameters: projName (project name) # lng (language) # clss ("neg", "pos", or "pred") #Returns: def convert_files(projName, lng, clss): #Read in stop words stopWordsList = [] f = codecs.open("stop_" + lng + ".txt", "rU") for word in f: if word.strip() != "": stopWordsList.append(word.strip()) f.close() global stopWords stopWords = set(stopWordsList) #Iterate through PDFs of a given class, extract text, and create output files print("\n***** " + clss + " *****\n") pdfs = sorted(os.listdir("/" + projName + "/" + clss + "_pdf/")) for pdf in pdfs: pdfMatch = re.search(r"^(\S+)\.([pP][dD][fF])$", pdf) if pdfMatch: docName = pdfMatch.group(1) if pdfMatch.group(2) != "pdf": oldFile = "/" + projName + "/" + clss + "_pdf/" + docName + "." + pdfMatch.group(2) newFile = "/" + projName + "/" + clss + "_pdf/" + docName + ".pdf" os.system("mv " + oldFile + " " + newFile) create_output(projName, clss, docName) print("") return def main(): if valid_arguments(): convert_files(sys.argv[1], sys.argv[2], sys.argv[3]) else: print("\nInvalid arguments\n") return if __name__ == "__main__": main()

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0.544561

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null

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null

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88cc892509c73d91def940743039ba5fd8a12e2f

5,818

py

Python

copy-net3_main0_1_2_20-01-10_14-12-14_epoch600_lr0-3_decay0-0_decay20-0_decay39e-05_seed0/main0.py

ninfueng/nsn

a214eafbcf5cf6dedb57131bc6eb1d307797f2ab

[ "MIT" ]

null

copy-net3_main0_1_2_20-01-10_14-12-14_epoch600_lr0-3_decay0-0_decay20-0_decay39e-05_seed0/main0.py

ninfueng/nsn

a214eafbcf5cf6dedb57131bc6eb1d307797f2ab

[ "MIT" ]

null

copy-net3_main0_1_2_20-01-10_14-12-14_epoch600_lr0-3_decay0-0_decay20-0_decay39e-05_seed0/main0.py

ninfueng/nsn

a214eafbcf5cf6dedb57131bc6eb1d307797f2ab

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """The code implementation of SharedGradNet. main0.py is for neural networks without hidden layer. Some part from: https://jhui.github.io/2018/02/09/PyTorch-Variables-functionals-and-Autograd/ 2019/06/17: Update with hyper-parameter tuning script. 2019/06/25: Committed main0.py. """ __author__ = 'Ninnart Fuengfusin' __version__ = '0.0.1' __email__ = 'ninnart.fuengfusin@yahoo.com' import os import time import logging import argparse import torch import torch.nn as nn import model from weight_decay import * from dataset import load_dataset from utils import * from recorder import Recorder from updater import UpdateMomentum from namer import namer parser = argparse.ArgumentParser(description='PyTorch implementation of SharedGradNet.') parser.add_argument('--epoch', '-e', type=int, default=600, help='Number of training epoch.') parser.add_argument('--learning_rate', '-lr', type=float, default=3e-1, help='A floating for initial learning rate.') parser.add_argument('--train_batch', type=int, default=128, help='A integer for train batch amount.') parser.add_argument('--test_batch', type=int, default=128, help='A integer for test batch amount') parser.add_argument('--num_neuron', type=int, default=784, help='Number of neurons in fully connected layer for produce codes') parser.add_argument('--weight_decay', type=float, default=0, help='A floating for weight decay.') parser.add_argument('--load', type=str2bool, default=False, help='A boolean for loading weights from load_location or not.') parser.add_argument('--load_location', type=str, default='model1-baseline', help='A string of location for loading weights.') parser.add_argument('--seed', '-s', type=int, default=0, help='An integer for initialization randomness.') args = parser.parse_args() if __name__ == '__main__': save_loc = namer( f'epoch{args.epoch}', f'lr{args.learning_rate}', f'decay{args.weight_decay}', f'seed{args.seed}') set_logger(os.path.join(os.getcwd(), save_loc), 'info.log') logging.info(__doc__) logging.info(args) set_printoptions() seed_everywhere_torch(args.seed) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') record = Recorder('test_acc', 'test_acc2', 'test_acc3', 'test_loss', 'test_loss2', 'test_loss3') train_loader, test_loader, img_size = load_dataset( num_train_batch=args.train_batch, num_test_batch=args.test_batch, num_extra_batch=0, num_worker=8, dataset='mnist') model1 = model.NetworkWithSub1() updaterW1_1 = UpdateMomentum() updaterB1_1 = UpdateMomentum() model1.to(device) BETA = 0.9 t1 = time.time() for i in range(args.epoch): # Accumulating variables. total_train_loss = 0 train_correct = 0 train_total = 0 total_test_loss = 0 test_correct = 0 test_total = 0 model1.train() args.learning_rate = args.learning_rate/3 if i % 200 == 0 and i != 0 else args.learning_rate for train_data, train_label in train_loader: model1.zero_grad() train_data, train_label = train_data.to(device), train_label.to(device) train_output = model1.forward(train_data) train_loss = nn.CrossEntropyLoss()( train_output, train_label) #+ l2_weight_decay(args.weight_decay2, model2.w1) train_loss.backward() total_train_loss += train_loss.item() _, train_predicted = torch.max(train_output.data, 1) train_correct += (train_predicted == train_label).sum().item() train_total += train_label.data.size(0) model1.w1.data = updaterW1_1.update( model1.w1.data, BETA, args.learning_rate, model1.w1.grad.data) model1.b1.data = updaterB1_1.update( model1.b1.data, BETA, args.learning_rate, model1.b1.grad.data) logging.info(f'Epoch: {i + 1}') logging.info(f'Train Accuracy: {train_correct/train_total}, \nLoss: {total_train_loss/train_total}') with torch.no_grad(): model1.eval() for test_data, test_label in test_loader: test_data, test_label = test_data.to(device), test_label.to(device) test_output = model1.forward(test_data) test_loss = nn.CrossEntropyLoss()(test_output, test_label) total_test_loss += test_loss.item() _, test_predicted = torch.max(test_output.data, 1) test_correct += (test_predicted == test_label).sum().item() test_total += test_label.data.size(0) if record.more_than_highest('test_acc', test_correct/test_total): save_model(model1, os.path.join(os.getcwd(), save_loc, 'checkpoint.pth')) logging.info(f'Save model') t2 = time.time() - t1 logging.info(f'Test Accuracy: {test_correct/test_total}, \nLoss: {total_test_loss/test_total}') record.record('test_acc', test_correct/test_total) logging.info(f'Learning rate {args.learning_rate}') logging.info(f'Timer: {to_hhmmss(t2)}') logging.info(f'=====================================================================================') record.save_all(os.path.join(os.getcwd(), save_loc)) logging.info(f'best test_acc: {record.highest("test_acc")}') logging.info(f'model1:w1 = {model1.w1.data}') record.plot( 'test_acc', save=True, save_loc=os.path.join(os.getcwd(), save_loc, 'test_acc.png')) np.savetxt( os.path.join(os.getcwd(), save_loc, f'{record.highest("test_acc")}.txt'), record.highest("test_acc"), delimiter=',')

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0.27871

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false

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0.009524

0

null

0

null

0

1

0

88cfb3f12d8dd1b1dd5417858d82ba8a891b227e

7,694

py

Python

etc/dbus-serialbattery/battery.py

Carstijn/dbus-serialbattery

23afec33c2fd87fd4d4c53516f0a25f290643c82

[ "MIT" ]

null

etc/dbus-serialbattery/battery.py

Carstijn/dbus-serialbattery

23afec33c2fd87fd4d4c53516f0a25f290643c82

[ "MIT" ]

null

etc/dbus-serialbattery/battery.py

Carstijn/dbus-serialbattery

23afec33c2fd87fd4d4c53516f0a25f290643c82

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals import utils class Protection(object): # 2 = Alarm, 1 = Warning, 0 = Normal def __init__(self): self.voltage_high = None self.voltage_low = None self.voltage_cell_low = None self.soc_low = None self.current_over = None self.current_under = None self.cell_imbalance = None self.internal_failure = None self.temp_high_charge = None self.temp_low_charge = None self.temp_high_discharge = None self.temp_low_discharge = None class Cell: voltage = None balance = None def __init__(self, balance): self.balance = balance class Battery(object): def __init__(self, port, baud): self.port = port self.baud_rate = baud self.role = 'battery' self.type = 'Generic' self.poll_interval = 1000 self.hardware_version = None self.voltage = None self.current = None self.capacity_remain = None self.capacity = None self.cycles = None self.total_ah_drawn = None self.production = None self.protection = Protection() self.version = None self.soc = None self.charge_fet = None self.discharge_fet = None self.cell_count = None self.temp_sensors = None self.temp1 = None self.temp2 = None self.cells = [] self.control_charging = None self.control_voltage = None self.control_current = None self.control_previous_total = None self.control_previous_max = None self.control_discharge_current = None self.control_charge_current = None self.control_allow_charge = None # max battery charge/discharge current self.max_battery_current = None self.max_battery_discharge_current = None def test_connection(self): # Each driver must override this function to test if a connection can be made # return false when fail, true if successful return false def get_settings(self): # Each driver must override this function to read/set the battery settings # It is called once after a successful connection by DbusHelper.setup_vedbus() # Values: battery_type, version, hardware_version, min_battery_voltage, max_battery_voltage, # MAX_BATTERY_CURRENT, MAX_BATTERY_DISCHARGE_CURRENT, cell_count, capacity # return false when fail, true if successful return false def refresh_data(self): # Each driver must override this function to read battery data and populate this class # It is called each poll just before the data is published to vedbus # return false when fail, true if successful return false def to_temp(self, sensor, value): # Keep the temp value between -20 and 100 to handle sensor issues or no data. # The BMS should have already protected before those limits have been reached. if sensor == 1: self.temp1 = min(max(value, -20), 100) if sensor == 2: self.temp2 = min(max(value, -20), 100) def manage_charge_current(self): # Start with the current values # Change depending on the SOC values if self.soc > 99: self.control_allow_charge = False else: self.control_allow_charge = True # Change depending on the SOC values if 98 < self.soc <= 100: self.control_charge_current = 1 elif 95 < self.soc <= 97: self.control_charge_current = 4 elif 91 < self.soc <= 95: self.control_charge_current = self.max_battery_current/2 else: self.control_charge_current = self.max_battery_current # Change depending on the SOC values if self.soc <= 20: self.control_discharge_current = 5 elif 20 < self.soc <= 30: self.control_discharge_current = self.max_battery_discharge_current/4 elif 30 < self.soc <= 35: self.control_discharge_current = self.max_battery_discharge_current/2 else: self.control_discharge_current = self.max_battery_discharge_current def get_min_cell(self): min_voltage = 9999 min_cell = None if len(self.cells) == 0 and hasattr(self, 'cell_min_no'): return self.cell_min_no for c in range(min(len(self.cells), self.cell_count)): if self.cells[c].voltage is not None and min_voltage > self.cells[c].voltage: min_voltage = self.cells[c].voltage min_cell = c return min_cell def get_max_cell(self): max_voltage = 0 max_cell = None if len(self.cells) == 0 and hasattr(self, 'cell_max_no'): return self.cell_max_no for c in range(min(len(self.cells), self.cell_count)): if self.cells[c].voltage is not None and max_voltage < self.cells[c].voltage: max_voltage = self.cells[c].voltage max_cell = c return max_cell def get_min_cell_desc(self): cell_no = self.get_min_cell() if cell_no is None: return None return 'C' + str(cell_no + 1) def get_max_cell_desc(self): cell_no = self.get_max_cell() if cell_no is None: return None return 'C' + str(cell_no + 1) def get_min_cell_voltage(self): min_voltage = 9999 if len(self.cells) == 0 and hasattr(self, 'cell_min_voltage'): return self.cell_min_voltage for c in range(min(len(self.cells), self.cell_count)): if self.cells[c].voltage is not None and min_voltage > self.cells[c].voltage: min_voltage = self.cells[c].voltage return min_voltage def get_max_cell_voltage(self): max_voltage = 0 if len(self.cells) == 0 and hasattr(self, 'cell_max_voltage'): return self.cell_max_voltage for c in range(min(len(self.cells), self.cell_count)): if self.cells[c].voltage is not None and max_voltage < self.cells[c].voltage: max_voltage = self.cells[c].voltage return max_voltage def get_balancing(self): for c in range(min(len(self.cells), self.cell_count)): if self.cells[c].balance is not None and self.cells[c].balance: return 1 return 0 def get_temp(self): if self.temp1 is not None and self.temp2 is not None: return round((float(self.temp1) + float(self.temp2)) / 2, 2) if self.temp1 is not None and self.temp2 is None: return round(float(self.temp1) , 2) if self.temp1 is None and self.temp2 is not None: return round(float(self.temp2) , 2) else: return None def get_min_temp(self): if self.temp1 is not None and self.temp2 is not None: return min(self.temp1, self.temp2) if self.temp1 is not None and self.temp2 is None: return self.temp1 if self.temp1 is None and self.temp2 is not None: return self.temp2 else: return None def get_max_temp(self): if self.temp1 is not None and self.temp2 is not None: return max(self.temp1, self.temp2) if self.temp1 is not None and self.temp2 is None: return self.temp1 if self.temp1 is None and self.temp2 is not None: return self.temp2 else: return None

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101

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1,049

7,694

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0

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7,694

215

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false

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0.006024

0

null

0

null

0

1

0

88d2d07a327a91f5956c30f18f5820061fc0b593

16,227

py

Python

lumicks/pylake/kymotracker/detail/binding_times.py

lumicks/pylake

b5875d156d6416793a371198f3f2590fca2be4cd

[ "Apache-2.0" ]

8

2019-02-18T07:56:39.000Z

2022-03-19T01:14:48.000Z

lumicks/pylake/kymotracker/detail/binding_times.py

lumicks/pylake

b5875d156d6416793a371198f3f2590fca2be4cd

[ "Apache-2.0" ]

42

2018-11-30T14:40:35.000Z

2022-03-29T11:43:45.000Z

lumicks/pylake/kymotracker/detail/binding_times.py

lumicks/pylake

b5875d156d6416793a371198f3f2590fca2be4cd

[ "Apache-2.0" ]

4

2019-01-09T13:45:53.000Z

2021-07-06T14:06:52.000Z

import numpy as np from scipy.special import logsumexp from scipy.optimize import minimize from functools import partial from dataclasses import dataclass, field import matplotlib.pyplot as plt @dataclass class BindingDwelltimesBootstrap: """Bootstrap distributions for a binding dwelltime model. This class is stored in the `BindingDwelltime.bootstrap` attribute and should not be constructed manually. Attributes ---------- _samples : np.ndarray array of optimized model parameters for each bootstrap sample pull; shape is [number of parameters, number of samples] """ _samples: np.ndarray = field(default_factory=lambda: np.array([]), repr=False) def _sample_distributions(self, optimized, iterations): """Construct bootstrap distributions for parameters. For each iteration, a dataset is randomly selected (with replacement) with the same size as the data used to optimize the model. Model parameters are then optimized for this new sampled dataset. Parameters ---------- optimized : BindingDwelltimes optimized model results iterations : int number of iterations (random samples) to use for the bootstrap """ n_data = optimized.dwelltimes_sec.size self._samples = np.empty((optimized._parameters.size, iterations)) for itr in range(iterations): sample = np.random.choice(optimized.dwelltimes_sec, size=n_data, replace=True) result = _kinetic_mle_optimize( optimized.n_components, sample, *optimized.observation_limits, initial_guess=optimized._parameters, ) self._samples[:, itr] = result._parameters @property def n_samples(self): """Number of samples in the bootstrap.""" return self._samples.shape[1] @property def n_components(self): """Number of components in the model.""" return int(self._samples.shape[0] / 2) @property def amplitude_distributions(self): """Array of sample optimized amplitude parameters; shape is [number of components, number of samples]""" return self._samples[: self.n_components] @property def lifetime_distributions(self): """Array of sample optimized lifetime parameters; shape is [number of components, number of samples]""" return self._samples[self.n_components :] def calculate_stats(self, key, component, alpha=0.05): """Calculate the mean and confidence intervals of the bootstrap distribution for a parameter. *NOTE*: the `100*(1-alpha)` % confidence intervals calculated here correspond to the `100*(alpha/2)` and `100*(1-(alpha/2))` quantiles of the distribution. For distributions which are not well approximated by a normal distribution these values are not reliable confidence intervals. Parameters ---------- key : {'amplitude', 'lifetime'} name of the parameter to be analyzed component : int index of the component to be analyzed alpha : float confidence intervals are calculated as 100*(1-alpha)% """ if key not in ("amplitude", "lifetime"): raise KeyError("key must be either 'amplitude' or 'lifetime'") data = getattr(self, f"{key}_distributions")[component] mean = np.mean(data) lower = np.quantile(data, alpha / 2) upper = np.quantile(data, 1 - (alpha / 2)) return mean, (lower, upper) def plot(self, alpha=0.05, n_bins=25, hist_kwargs={}, span_kwargs={}, line_kwargs={}): """Plot the bootstrap distributions for the parameters of a model. Parameters ---------- alpha : float confidence intervals are calculated as 100*(1-alpha)% n_bins : int number of bins in the histogram hist_kwargs : dict dictionary of plotting kwargs applied to histogram span_kwargs : dict dictionary of plotting kwargs applied to the patch indicating the area spanned by the confidence intervals line_kwargs : dict dictionary of plotting kwargs applied to the line indicating the distribution means """ hist_kwargs = {"facecolor": "#c5c5c5", "edgecolor": "#888888", **hist_kwargs} span_kwargs = {"facecolor": "tab:red", "alpha": 0.3, **span_kwargs} line_kwargs = {"color": "k", **line_kwargs} def plot_axes(data, key, component, use_index): plt.hist(data, bins=n_bins, **hist_kwargs) mean, (lower, upper) = self.calculate_stats(key, component, alpha) plt.axvspan(lower, upper, **span_kwargs) plt.axvline(mean, **line_kwargs) plt.xlabel(f"{key}" if key == "amplitude" else f"{key} (sec)") plt.ylabel("counts") label = "a" if key == "amplitude" else r"\tau" unit = "" if key == "amplitude" else "sec" prefix = fr"${label}_{component+1}$" if use_index else fr"${label}$" plt.title(f"{prefix} = {mean:0.2g} ({lower:0.2g}, {upper:0.2g}) {unit}") if self.n_components == 1: data = self.lifetime_distributions.squeeze() plot_axes(data, "lifetime", 0, False) else: for component in range(2): for column, key in enumerate(("amplitude", "lifetime")): data = getattr(self, f"{key}_distributions")[component] column += 1 plt.subplot(self.n_components, 2, 2 * component + column) plot_axes(data, key, component, True) plt.tight_layout() @dataclass(frozen=True) class BindingDwelltimes: """Results of exponential mixture model optimization for binding dwelltimes. This class is returned from `_kinetic_mle_optimize()` and should not be constructed manually. Attributes ---------- n_components : int number of components in the model. dwelltimes_sec : np.ndarray observations on which the model was trained. observations_limits : tuple tuple of (`min`, `max`) values of the experimental observation time. _parameters : np.ndarray optimized parameters in the order [amplitudes, lifetimes] log_likelihood : float log likelihood of the trained model bootstrap : BindingDwelltimesBootstrap object containing information about the bootstrapping analysis. """ n_components: int dwelltimes_sec: np.ndarray = field(repr=False) observation_limits: list = field(repr=False) _parameters: np.ndarray = field(repr=False) log_likelihood: float bootstrap: BindingDwelltimesBootstrap = field( default_factory=BindingDwelltimesBootstrap, init=False, repr=False ) @property def amplitudes(self): """Fractional amplitude of each model component""" return self._parameters[: self.n_components] @property def lifetimes(self): """Lifetime parameter (in seconds) of each model component.""" return self._parameters[self.n_components :] @property def aic(self): """Akaike Information Criterion.""" k = (2 * self.n_components) - 1 # number of parameters return 2 * k - 2 * self.log_likelihood @property def bic(self): """Bayesian Information Criterion.""" k = (2 * self.n_components) - 1 # number of parameters n = self.dwelltimes_sec.size # number of observations return k * np.log(n) - 2 * self.log_likelihood def calculate_bootstrap(self, iterations=500): self.bootstrap._sample_distributions(self, iterations) def plot( self, n_bins=25, bin_spacing="linear", hist_kwargs={}, component_kwargs={}, fit_kwargs={}, xscale=None, yscale=None, ): """Plot the dwelltime distribution histogram and overlayed model density. Parameters ---------- n_bins : int number of bins in the histogram bin_spacing : {"log", "linear"} determines how bin edges are spaced apart hist_kwargs : dict dictionary of plotting kwargs applied to histogram component_kwargs : dict dictionary of plotting kwargs applied to the line plot for each component fit_kwargs : dict dictionary of plotting kwargs applied to line plot for the total fit xscale : {"log", "linear", None} scaling for the x-axis; when `None` default is "linear" yscale : {"log", "linear", None} scaling for the y-axis; when `None` default is same as `bin_spacing` """ if bin_spacing == "log": scale = np.logspace limits = (np.log10(self.dwelltimes_sec.min()), np.log10(self.dwelltimes_sec.max())) xscale = "linear" if xscale is None else xscale yscale = "log" if yscale is None else yscale elif bin_spacing == "linear": scale = np.linspace limits = (self.dwelltimes_sec.min(), self.dwelltimes_sec.max()) xscale = "linear" if xscale is None else xscale yscale = "linear" if yscale is None else yscale else: raise ValueError("spacing must be either 'log' or 'linear'") bins = scale(*limits, n_bins) centers = bins[:-1] + (bins[1:] - bins[:-1]) / 2 hist_kwargs = {"facecolor": "#cdcdcd", "edgecolor": "#aaaaaa", **hist_kwargs} component_kwargs = {"marker": "o", "ms": 3, **component_kwargs} fit_kwargs = {"color": "k", **fit_kwargs} components = np.exp( exponential_mixture_log_likelihood_components( self.amplitudes, self.lifetimes, centers, *self.observation_limits ) ) def label_maker(a, t, n): if self.n_components == 1: amplitude = "" lifetime_label = r"$\tau$" else: amplitude = f"($a_{n}$ = {a:0.2g}) " lifetime_label = fr"$\tau_{n}$" return f"{amplitude}{lifetime_label} = {t:0.2g} sec" # plot histogram density, _, _ = plt.hist(self.dwelltimes_sec, bins=bins, density=True, **hist_kwargs) # plot individual components for n in range(self.n_components): label = label_maker(self.amplitudes[n], self.lifetimes[n], n + 1) plt.plot(centers, components[n], label=label, **component_kwargs) # plot total fit label = r"$\ln \mathcal{L} $" + f"= {self.log_likelihood:0.3f}" plt.plot(centers, np.sum(components, axis=0), label=label, **fit_kwargs) # rearrange legend entries so that total fit is first legend_components = [[c[-1], *c[:-1]] for c in plt.gca().get_legend_handles_labels()] plt.legend(*legend_components, loc="upper right") # format axes plt.xscale(xscale) plt.yscale(yscale) if yscale == "log": ylim = (np.min(density[density != 0] * 0.5), np.max(density[density != 0] * 1.5)) plt.ylim(ylim) plt.ylabel("density") plt.xlabel("dwelltime (sec)") plt.tight_layout() def exponential_mixture_log_likelihood_components( amplitudes, lifetimes, t, min_observation_time, max_observation_time ): """Calculate each component of the log likelihood of an exponential mixture distribution. The full log likelihood for a single observation is given by: log(L) = log( sum_i( component_i ) ) with the output of this function being log(component_i) defined as: log(component_i) = log(a_i) - log(N) + log(tau_i) - t/tau_i where a_i and tau_i are the amplitude and lifetime of component i and N is a normalization factor that takes into account the minimum and maximum observation times of the experiment: N = sum_i { a_i * [ exp(-t_min / tau_i) - exp(-t_max / tau_i) ] } Therefore, the full log likelihood is calculated from the output of this function by applying logsumexp(output, axis=0) where the summation is taken over the components. Parameters ---------- amplitudes : array_like fractional amplitude parameters for each component lifetimes : array_like lifetime parameters for each component in seconds t : array_like dwelltime observations in seconds min_observation_time : float minimum observation time in seconds max_observation_time : float maximum observation time in seconds """ amplitudes = amplitudes[:, np.newaxis] lifetimes = lifetimes[:, np.newaxis] t = t[np.newaxis, :] norm_factor = np.log(amplitudes) + np.log( np.exp(-min_observation_time / lifetimes) - np.exp(-max_observation_time / lifetimes) ) log_norm_factor = logsumexp(norm_factor, axis=0) return -log_norm_factor + np.log(amplitudes) - np.log(lifetimes) - t / lifetimes def exponential_mixture_log_likelihood(params, t, min_observation_time, max_observation_time): """Calculate the log likelihood of an exponential mixture distribution. The full log likelihood for a single observation is given by: log(L) = log( sum_i( exp( log(component_i) ) ) ) where log(component_i) is output from `exponential_mixture_log_likelihood_components()` Parameters ---------- amplitudes : array_like fractional amplitude parameters for each component lifetimes : array_like lifetime parameters for each component in seconds t : array_like dwelltime observations in seconds min_observation_time : float minimum observation time in seconds max_observation_time : float maximum observation time in seconds """ params = np.reshape(params, (2, -1)) components = exponential_mixture_log_likelihood_components( params[0], params[1], t, min_observation_time, max_observation_time ) log_likelihood = logsumexp(components, axis=0) return -np.sum(log_likelihood) def _kinetic_mle_optimize( n_components, t, min_observation_time, max_observation_time, initial_guess=None ): """Calculate the maximum likelihood estimate of the model parameters given measured dwelltimes. Parameters ---------- n_components : int number of components in the mixture model t : array_like dwelltime observations in seconds min_observation_time : float minimum observation time in seconds max_observation_time : float maximum observation time in seconds initial_guess : array_like initial guess for the model parameters ordered as [amplitude1, amplitude2, ..., lifetime1, lifetime2, ...] """ if np.any(np.logical_or(t < min_observation_time, t > max_observation_time)): raise ValueError( "some data is outside of the bounded region. Please choose" "appropriate values for `min_observation_time` and/or `max_observation_time`." ) cost_fun = partial( exponential_mixture_log_likelihood, t=t, min_observation_time=min_observation_time, max_observation_time=max_observation_time, ) if initial_guess is None: initial_guess_amplitudes = np.ones(n_components) / n_components initial_guess_lifetimes = np.mean(t) * np.arange(1, n_components + 1) initial_guess = np.hstack([initial_guess_amplitudes, initial_guess_lifetimes]) bounds = ( *[(np.finfo(float).eps, 1) for _ in range(n_components)], *[(min_observation_time * 0.1, max_observation_time * 1.1) for _ in range(n_components)], ) constraints = {"type": "eq", "fun": lambda x, n: 1 - sum(x[:n]), "args": [n_components]} result = minimize( cost_fun, initial_guess, method="SLSQP", bounds=bounds, constraints=constraints ) return BindingDwelltimes( n_components, t, (min_observation_time, max_observation_time), result.x, -result.fun )

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88d788b313f88688621166e29c634f6bf47ff41a

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py

Python

GUI/crab.py

31337H4X0R/crab-tracker

c822a40010d172ba797b5de8c340931d0feea6e4

[ "MIT" ]

1

2019-07-31T01:32:17.000Z

GUI/crab.py

31337H4X0R/crab-tracker

c822a40010d172ba797b5de8c340931d0feea6e4

[ "MIT" ]

47

2017-11-04T02:04:42.000Z

2018-06-16T01:00:48.000Z

GUI/crab.py

31337H4X0R/crab-tracker

c822a40010d172ba797b5de8c340931d0feea6e4

[ "MIT" ]

2

2018-06-10T21:58:49.000Z

2019-06-18T17:21:03.000Z

class Crab: def __init__(self, crab_id, sex, species, color, damage, carapace, mass, epibiont, molt): self.id = crab_id self.sex = sex self.species = species self.color = color self.damage = damage self.carapace = carapace self.mass = mass self.epibiont = epibiont self.molt = molt def get_tuple(self): return self.id, self.sex, self.species, self.color, self.damage, self.carapace, self.mass, self.epibiont, self.molt

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88da52cb71a753a4cfdc782a27d2b76618927365

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py

Python

build/fbcode_builder_config.py

YangKian/LogDevice

e5c2168c11e9de867a1bcf519f95016e1c879b5c

[ "BSD-3-Clause" ]

1,831

2018-09-12T15:41:52.000Z

2022-01-05T02:38:03.000Z

build/fbcode_builder_config.py

YangKian/LogDevice

e5c2168c11e9de867a1bcf519f95016e1c879b5c

[ "BSD-3-Clause" ]

183

2018-09-12T16:14:59.000Z

2021-12-07T15:49:43.000Z

build/fbcode_builder_config.py

YangKian/LogDevice

e5c2168c11e9de867a1bcf519f95016e1c879b5c

[ "BSD-3-Clause" ]

228

2018-09-12T15:41:51.000Z

2022-01-05T08:12:09.000Z

#!/usr/bin/env python # Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. from __future__ import absolute_import, division, print_function, unicode_literals import specs.fizz as fizz import specs.fmt as fmt import specs.folly as folly import specs.sodium as sodium import specs.wangle as wangle import specs.zstd as zstd from shell_quoting import ShellQuoted "fbcode_builder steps to build & test LogDevice" """ Running this script from the command line on a dev-server: 1. Ensure you have the HTTP proxy configured in environment 2. This is env items is not compatible with the scutil create call, so must not be permenently exported. git config --global http.proxy http://fwdproxy:8080 cd .../fbcode/logdevice/public_tld/build HTTP_PROXY=http://fwdproxy:8080 HTTPS_PROXY=http://fwdproxy:8080 \ fbcode/opensource/fbcode_builder/facebook_make_legocastle_job.py \ | scutil create Which outputs a legocastle job to stdout; to be fed into scutil create ... """ class FakeClangModule: """ fbcode_builder doesn't allow us to inject build stuff before building dependencies. This is a hack to point set the compiler to clang by injecting it as a fake module that runs before any other dependency. """ @staticmethod def fbcode_builder_spec(builder): return { "depends_on": [], "steps": [ builder.set_env("CC", "clang-9"), builder.set_env("CXX", "clang++-9"), ], } def fbcode_builder_spec(builder): # This API should change rarely, so build the latest tag instead of master. builder.add_option( "no1msd/mstch:git_hash", ShellQuoted("$(git describe --abbrev=0 --tags)") ) builder.add_option("PYTHON_VENV", "ON") builder.add_option( "LogDevice/logdevice/_build:cmake_defines", {"BUILD_SUBMODULES": "OFF"} ) builder.add_option( "facebook/folly:cmake_defines", {"BUILD_SHARED_LIBS": "ON", "BUILD_TESTS": "OFF", "FOLLY_USE_JEMALLOC": "OFF"}, ) return { "depends_on": [FakeClangModule, zstd, fmt, folly, fizz, wangle, sodium], "steps": [ # This isn't a separete spec, since only fbthrift uses mstch. builder.github_project_workdir("no1msd/mstch", "build"), builder.cmake_install("no1msd/mstch"), builder.fb_github_cmake_install("fbthrift/thrift"), builder.fb_github_cmake_install( "LogDevice/logdevice/_build", github_org="facebookincubator" ), ], } config = { "github_project": "facebookincubator/LogDevice", "fbcode_builder_spec": fbcode_builder_spec, }

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88dac5f28a975211597a7acd699981246fdfddd1

1,883

py

Python

Python-Programs/Discord-bot-Motivation Bot/main.py

adityaverma121/Simple-Programs

8450560b97f89e0fa3da16a623ad35c0b26409c9

[ "MIT" ]

71

2021-09-30T11:25:12.000Z

2021-10-03T11:33:22.000Z

Python-Programs/Discord-bot-Motivation Bot/main.py

adityaverma121/Simple-Programs

8450560b97f89e0fa3da16a623ad35c0b26409c9

[ "MIT" ]

186

2021-09-30T12:25:16.000Z

2021-10-03T13:45:04.000Z

Python-Programs/Discord-bot-Motivation Bot/main.py

adityaverma121/Simple-Programs

8450560b97f89e0fa3da16a623ad35c0b26409c9

[ "MIT" ]

385

2021-09-30T11:34:23.000Z

2021-10-03T13:41:00.000Z

import json import os import random import string import requests from keep_alive import keep_alive from nltk.sentiment.vader import SentimentIntensityAnalyzer import discord client = discord.Client() starter_motivator = [ "Cheer Up!", "Always remember, I am here for you!", "You are a great person. Remember this!", "Think positive man! There is always a bright side!", "What about you watching a funny video to swing the mood?", ] def get_quote(): response = requests.get("https://zenquotes.io/api/random") json_data = json.loads(response.text) quote = f"`{json_data[0]['q']}`" + " -" + json_data[0]["a"] return quote @client.event async def on_ready(): print("Logged in as {0.user}".format(client)) @client.event async def on_message(message): if message.author == client.user: return msg = message.content.lower() if ( (msg.startswith("$hello")) or (msg.startswith("$hi")) or (msg.startswith("$hey")) ): await message.channel.send( "Hello there! Nice to see you !!\nHow are you feeling?" ) if msg.startswith("$motivate"): quote = get_quote() await message.channel.send(quote) if msg.startswith("$help"): await message.channel.send( "This is bot help.\nCommands:\n*` $hey, $hello, $hi `:- Bot responds.\n*` $motivate `:- Generates motivating quotes.\n*` $help `:- Bot help." ) cleaned_text = msg.translate(str.maketrans("", "", string.punctuation)) score = SentimentIntensityAnalyzer().polarity_scores(cleaned_text) neg = score["neg"] pos = score["pos"] if neg > pos: await message.channel.send( "I am sensing `Negative Sentiment` from you.\n" + f"`{random.choice(starter_motivator)}`" ) keep_alive() client.run(os.environ["TOKEN"])

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null

0

null

0

1

0

88df26756f4d2511b8925b4ee5ec1ed8cec09d0b

1,234

py

Python

utils.py

wangke0809/learn-statistical-learning-method

10772659ff52ef64e7ff36dd3b701615e58de335

[ "MIT" ]

null

utils.py

wangke0809/learn-statistical-learning-method

10772659ff52ef64e7ff36dd3b701615e58de335

[ "MIT" ]

null

utils.py

wangke0809/learn-statistical-learning-method

10772659ff52ef64e7ff36dd3b701615e58de335

[ "MIT" ]

null

import os import numpy as np def load_mnist(path='mnist'): data_dir = os.path.join("./data", path) fd = open(os.path.join(data_dir,'train-images-idx3-ubyte')) loaded = np.fromfile(file=fd,dtype=np.uint8) trX = loaded[16:].reshape((60000,28,28,1)).astype(np.float) fd = open(os.path.join(data_dir,'train-labels-idx1-ubyte')) loaded = np.fromfile(file=fd,dtype=np.uint8) trY = loaded[8:].reshape((60000)).astype(np.float) fd = open(os.path.join(data_dir,'t10k-images-idx3-ubyte')) loaded = np.fromfile(file=fd,dtype=np.uint8) teX = loaded[16:].reshape((10000,28,28,1)).astype(np.float) fd = open(os.path.join(data_dir,'t10k-labels-idx1-ubyte')) loaded = np.fromfile(file=fd,dtype=np.uint8) teY = loaded[8:].reshape((10000)).astype(np.float) trY = np.asarray(trY) teY = np.asarray(teY) # X = np.concatenate((trX, teX), axis=0) # y = np.concatenate((trY, teY), axis=0).astype(np.int) # seed = 547 # np.random.seed(seed) # np.random.shuffle(X) # np.random.seed(seed) # np.random.shuffle(y) seed = 200 np.random.seed(seed) np.random.shuffle(trX) np.random.seed(seed) np.random.shuffle(trY) return (trX, trY, teX, teY)

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null

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0

88e15f87d60572f7354fdd0f31ccffeddc289a42

2,210

py

Python

src/analysis.py

aliFrancis/mars-crater-catalogue

5e6ac4e1f7967b1d37d95e436edaa31ef2f2ed55

[ "CC-BY-4.0" ]

null

src/analysis.py

aliFrancis/mars-crater-catalogue

5e6ac4e1f7967b1d37d95e436edaa31ef2f2ed55

[ "CC-BY-4.0" ]

null

src/analysis.py

aliFrancis/mars-crater-catalogue

5e6ac4e1f7967b1d37d95e436edaa31ef2f2ed55

[ "CC-BY-4.0" ]

null

import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sn from utils import convert, iou def average_pairwise_IOU(IOU_mat): n = IOU_mat.shape[0] mean_IOU = (np.sum(IOU_mat)-n)/(np.size(IOU_mat)-n) return mean_IOU def group_IOU_matrices(paths): survey_names = [p.replace('.xml','')[-6:] for p in paths] surveys = [convert.xml2df(p) for p in paths] binary_IOUs = [] IOUs = [] for i,s_i in enumerate(surveys): iou_i = [] for j,s_j in enumerate(surveys): if j!=i: iou_i.append(iou.all_ious_np(s_i,s_j)) iou_i = np.concatenate(iou_i,axis=1) #Compare 1 person's annotations to everyone else's iou_max_i = np.max(iou_i,axis=1) binary_IOU_i = iou_max_i>=0.5 binary_IOUs.append(np.mean(binary_IOU_i)) IOUs.append(np.mean(iou_max_i)) return binary_IOUs, IOUs if __name__ == '__main__': import os import sys survey_dir = sys.argv[1] paths = [os.path.join(survey_dir,path) for path in os.listdir(survey_dir)] surveys = [convert.xml2df(p) for p in paths] print('\nANALYSIS OF {}'.format(os.path.basename(survey_dir)),'\n') print(' NO. OF ANNOTATIONS') print(' ------------------') for survey,path in zip(surveys,paths): print(' ',os.path.basename(path).replace('.xml','')+':',len(survey)) total_survey = convert.dfs2df(surveys) print(' ____________') print(' TOTAL :',len(total_survey)) print('\n') group_binary_IOUs, group_IOUs = group_IOU_matrices(paths) print(' MEAN IoU') print(' --------') for i,path in enumerate(paths): print(' ',os.path.basename(path).replace('.xml','')+':',np.round(group_IOUs[i],4)) print(' ____________') print(' MEAN :',np.round(np.mean(group_IOUs),4)) print('\n') print('\n MEAN BINARY IoU (IoU treated as 1 if above 0.5)') print(' -----------------------------------------------') for i,path in enumerate(paths): print(' ',os.path.basename(path).replace('.xml','')+':',np.round(group_binary_IOUs[i],4)) print(' ____________') print(' MEAN :',np.round(np.mean(group_binary_IOUs),4)) print('\n')

34.53125

97

0.608597

326

2,210

3.831288

0.251534

0.022418

0.044836

0.026421

0.261009

0.179343

0

0.01021

0.202262

2,210

63

98

35.079365

0.698242

0.022172

0

0.181818

0

0.128241

0.021759

0

1

0.036364

false

0

0.127273

0

0.2

0.345455

0

null

0

null

0

1

0

88e45fe24cb4ac33e12b90a494c738b76fd18630

3,033

py

Python

scripts/test_tensorflow_spectrogram.py

RichardLitt/Vesper

5360844f42a06942e7684121c650b08cf8616285

[ "MIT" ]

29

2017-07-10T14:49:15.000Z

2022-02-02T23:14:38.000Z

scripts/test_tensorflow_spectrogram.py

Tubbz-alt/Vesper

76e5931ca0c7fbe070c53b1362ec246ec9007beb

[ "MIT" ]

167

2015-03-17T14:45:22.000Z

2022-03-30T21:00:05.000Z

scripts/test_tensorflow_spectrogram.py

Tubbz-alt/Vesper

76e5931ca0c7fbe070c53b1362ec246ec9007beb

[ "MIT" ]

4

2015-02-06T03:30:27.000Z

2020-12-27T08:38:52.000Z

""" Compares spectrogram computations with TensorFlow and Vesper. As of 2018-11-09, Vesper is a little more than three times faster than TensorFlow at computing spectrograms with a DFT size of 128. """ import functools import time import numpy as np import tensorflow as tf import vesper.util.data_windows as data_windows import vesper.util.time_frequency_analysis_utils as tfa_utils SHOW_SPECTROGRAMS = False SAMPLE_RATE = 24000 # Hertz AMPLITUDE = 1 FREQUENCY = 3000 # Hertz DURATION = 1000 # seconds WINDOW_SIZE = .005 # seconds HOP_SIZE = .5 # fraction of window size if SHOW_SPECTROGRAMS: SAMPLE_RATE = 1 FREQUENCY = .25 DURATION = 8 WINDOW_SIZE = 8 HOP_SIZE = 1 def main(): waveform = create_waveform() window_size = int(round(WINDOW_SIZE * SAMPLE_RATE)) print('Window size is {} samples.'.format(window_size)) hop_size = int(round(window_size * HOP_SIZE)) print('Hop size is {} samples.'.format(hop_size)) gram = compute_tensorflow_spectrogram(waveform, window_size, hop_size) if SHOW_SPECTROGRAMS: print(gram) gram = compute_vesper_spectrogram(waveform, window_size, hop_size) if SHOW_SPECTROGRAMS: print(gram) def create_waveform(): length = int(round(DURATION * SAMPLE_RATE)) print('Waveform length is {} samples.'.format(length)) phases = 2 * np.pi * FREQUENCY / SAMPLE_RATE * np.arange(length) return AMPLITUDE * np.cos(phases) def compute_tensorflow_spectrogram(waveform, window_size, hop_size): waveform_ = tf.placeholder(tf.float32) window_fn = functools.partial(tf.signal.hann_window, periodic=True) stft = tf.signal.stft( waveform_, window_size, hop_size, window_fn=window_fn) gram = tf.real(stft * tf.conj(stft)) with tf.Session() as sess: print('Computing TensorFlow spectrogram...') start_time = time.time() g = sess.run(gram, feed_dict={waveform_: waveform}) end_time = time.time() print('Done.') report_performance(g, start_time, end_time) return g def report_performance(gram, start_time, end_time): num_spectra = len(gram) delta = end_time - start_time print('Computed {} spectra in {:.1f} seconds.'.format(num_spectra, delta)) micros = int(round(1000000 * delta / num_spectra)) speedup = DURATION / delta print(( "That's {} microseconds per spectrum, or {} times faster than " "real time.").format(micros, speedup)) def compute_vesper_spectrogram(waveform, window_size, hop_size): window = data_windows.create_window('Hann', window_size).samples print('Computing Vesper spectrogram...') start_time = time.time() gram = tfa_utils.compute_spectrogram(waveform, window, hop_size) end_time = time.time() print('Done.') report_performance(gram, start_time, end_time) return gram if __name__ == '__main__': main()

26.146552

78

0.672272

386

3,033

5.059585

0.300518

0.071685

0.046595

0.060932

0.287762

0.222222

0.203277

0.174091

0.064516

0

0.019692

0.229805

3,033

115

79

26.373913

0.816353

0.081438

0

0.157143

0

0.099495

0

1

0.071429

false

0

0.085714

0

0.2

0.157143

0

null

0

null

0

1

0

88e644ee347cd6bc7d3ce925d9db807476d778e2

2,770

py

Python

Station A/Pooling M300/v1_station_a_S30_pooling.py

Opentrons/covid19-system-30

4db5980a93e87f9f607b727678b7ea6d528109ba

[ "Apache-2.0" ]

null

Station A/Pooling M300/v1_station_a_S30_pooling.py

Opentrons/covid19-system-30

4db5980a93e87f9f607b727678b7ea6d528109ba

[ "Apache-2.0" ]

null

Station A/Pooling M300/v1_station_a_S30_pooling.py

Opentrons/covid19-system-30

4db5980a93e87f9f607b727678b7ea6d528109ba

[ "Apache-2.0" ]

1

2020-07-29T14:52:28.000Z

from opentrons import protocol_api import json import os import math # metadata metadata = { 'protocolName': 'V1 S14 Station A MagMax', 'author': 'Nick <protocols@opentrons.com>', 'source': 'Custom Protocol Request', 'apiLevel': '2.4' } NUM_SAMPLES = 64 SAMPLE_VOLUME = 100 TIP_TRACK = False def run(ctx: protocol_api.ProtocolContext): # load labware dest_plate = ctx.load_labware( 'nest_96_wellplate_2ml_deep', '2', '96-deepwell sample plate') tipracks300 = [ctx.load_labware('opentrons_96_filtertiprack_200ul', '1', '200µl filter tiprack')] # load pipette m300 = ctx.load_instrument( 'p300_multi_gen2', 'right', tip_racks=tipracks300) tip_log = {'count': {}} folder_path = '/data/A' tip_file_path = folder_path + '/tip_log.json' if TIP_TRACK and not ctx.is_simulating(): if os.path.isfile(tip_file_path): with open(tip_file_path) as json_file: data = json.load(json_file) if 'tips1000' in data: tip_log['count'][m300] = data['tips1000'] else: tip_log['count'][m300] = 0 else: tip_log['count'] = {m300: 0} tip_log['tips'] = { m300: [tip for rack in tipracks300 for tip in rack.rows()[0]] } tip_log['max'] = { pip: len(tip_log['tips'][pip]) for pip in [m300] } def pick_up(pip): nonlocal tip_log if tip_log['count'][pip] == tip_log['max'][pip]: ctx.pause('Replace ' + str(pip.max_volume) + 'µl tipracks before \ resuming.') pip.reset_tipracks() tip_log['count'][pip] = 0 pip.pick_up_tip(tip_log['tips'][pip][tip_log['count'][pip]]) tip_log['count'][pip] += 1 # pool samples num_cols = math.ceil(NUM_SAMPLES/8) for i in range(math.ceil(num_cols/2)): if num_cols % 2 != 0 and i == math.ceil(num_cols/2) - 1: pool_source_set = [dest_plate.rows()[0][num_cols]] vol = SAMPLE_VOLUME*2 else: pool_source_set = dest_plate.rows()[0][i*2:i*2+2] vol = SAMPLE_VOLUME for s in pool_source_set: pick_up(m300) m300.transfer(vol, s, dest_plate.rows()[0][i+8], air_gap=20, new_tip='never') m300.air_gap(20) m300.drop_tip() ctx.comment('Move deepwell plate (slot 2) to Station B for RNA \ extraction.') # track final used tip if not ctx.is_simulating(): if not os.path.isdir(folder_path): os.mkdir(folder_path) data = {'tips1000': tip_log['count'][m300]} with open(tip_file_path, 'w') as outfile: json.dump(data, outfile)

31.123596

78

0.574729

376

2,770

4.023936

0.345745

0.06345

0.065433

0.039656

0.179114

0.088566

0.035691

0

0.054592

0.292419

2,770

88

79

31.477273

0.717347

0.024549

0

0.042254

0

0.132047

0.030786

0

1

0.028169

false

0

0.056338

0

0.084507

0

null

0

null

0

1

0

88e9ebaa162edc8b9d8c063256bea5900e94971c

5,101

py

Python

Reuters/reuters.py

dheeraj7596/SCDV

e83fc81e1b59bebfa2fa1e334097caa44f9e7f48

[ "MIT" ]

60

2017-05-25T14:08:50.000Z

2022-02-04T19:29:44.000Z

Reuters/reuters.py

vgupta123/SCDV

329b13a413318262f1888d872d8e33b30217cbc7

[ "MIT" ]

2

2020-03-27T14:01:12.000Z

2020-07-16T14:33:31.000Z

Reuters/reuters.py

vgupta123/SCDV

329b13a413318262f1888d872d8e33b30217cbc7

[ "MIT" ]

19

2017-11-10T01:06:28.000Z

2021-09-25T19:31:25.000Z

# Reuters-21578 dataset downloader and parser # # Author: Eustache Diemert <eustache@diemert.fr> # http://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html # # Modified by @herrfz, get pandas DataFrame from the orig SGML # License: BSD 3 clause from __future__ import print_function import re import os.path import fnmatch import sgmllib import urllib import tarfile import itertools from pandas import DataFrame ############################################################################### # Reuters Dataset related routines ############################################################################### def _not_in_sphinx(): # Hack to detect whether we are running by the sphinx builder return '__file__' in globals() class ReutersParser(sgmllib.SGMLParser): """Utility class to parse a SGML file and yield documents one at a time.""" def __init__(self, verbose=0): sgmllib.SGMLParser.__init__(self, verbose) self._reset() def _reset(self): self.in_title = 0 self.in_body = 0 self.in_topics = 0 self.in_topic_d = 0 self.title = "" self.body = "" self.topics = [] self.topic_d = "" def parse(self, fd): self.docs = [] try: for chunk in fd: self.feed(chunk) for doc in self.docs: yield doc self.docs = [] except: pass self.close() def handle_data(self, data): if self.in_body: self.body += data elif self.in_title: self.title += data elif self.in_topic_d: self.topic_d += data def start_reuters(self, attributes): pass def end_reuters(self): self.body = re.sub(r'\s+', r' ', self.body) self.docs.append({'title': self.title, 'body': self.body, 'topics': self.topics}) self._reset() def start_title(self, attributes): self.in_title = 1 def end_title(self): self.in_title = 0 def start_body(self, attributes): self.in_body = 1 def end_body(self): self.in_body = 0 def start_topics(self, attributes): self.in_topics = 1 def end_topics(self): self.in_topics = 0 def start_d(self, attributes): self.in_topic_d = 1 def end_d(self): self.in_topic_d = 0 self.topics.append(self.topic_d) self.topic_d = "" class ReutersStreamReader(): """Iterate over documents of the Reuters dataset. The Reuters archive will automatically be downloaded and uncompressed if the `data_path` directory does not exist. Documents are represented as dictionaries with 'body' (str), 'title' (str), 'topics' (list(str)) keys. """ DOWNLOAD_URL = ('http://archive.ics.uci.edu/ml/machine-learning-databases/' 'reuters21578-mld/reuters21578.tar.gz') ARCHIVE_FILENAME = 'reuters21578.tar.gz' def __init__(self, data_path): self.data_path = data_path if not os.path.exists(self.data_path): self.download_dataset() def download_dataset(self): """Download the dataset.""" print("downloading dataset (once and for all) into %s" % self.data_path) os.mkdir(self.data_path) def progress(blocknum, bs, size): total_sz_mb = '%.2f MB' % (size / 1e6) current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6) if _not_in_sphinx(): print('\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb), end='') urllib.urlretrieve(self.DOWNLOAD_URL, filename=os.path.join(self.data_path, self.ARCHIVE_FILENAME), reporthook=progress) if _not_in_sphinx(): print('\r', end='') print("untaring data ...") tfile = tarfile.open(os.path.join(self.data_path, self.ARCHIVE_FILENAME), 'r:gz') tfile.extractall(self.data_path) print("done !") def iterdocs(self): """Iterate doc by doc, yield a dict.""" for root, _dirnames, filenames in os.walk(self.data_path): for filename in fnmatch.filter(filenames, '*.sgm'): path = os.path.join(root, filename) parser = ReutersParser() for doc in parser.parse(open(path)): yield doc def get_minibatch(doc_iter, size): """Extract a minibatch of examples, return a tuple X, y. Note: size is before excluding invalid docs with no topics assigned. """ data = [('{title}\n\n{body}'.format(**doc), doc['topics']) for doc in itertools.islice(doc_iter, size) if doc['topics']] if not len(data): return DataFrame([]) else: return DataFrame(data, columns=['text', 'tags'])

30.183432

96

0.556558

606

5,101

4.516502

0.325083

0.032883

0.039459

0.017537

0.078919

0.042382

0.02996

0

0.011338

0.308371

5,101

168

97

30.363095

0.764456

0.172123

0

0.19469

0

0.072482

0.008998

0

1

0.176991

false

0.017699

0.079646

0.00885

0.318584

0.053097

0

null

0

null

0

1

0

88ebd6984c24756abffb46e10ea57a9b5e6af63f

485

py

Python

ratemyprof_api/professor.py

nananananate/ratemyprof-api

c037e68e763154cc60812393538c7aa380fbb90e

[ "MIT" ]

7

2021-09-29T22:48:56.000Z

2022-02-23T16:54:10.000Z

ratemyprof_api/professor.py

nananananate/ratemyprof-api

c037e68e763154cc60812393538c7aa380fbb90e

[ "MIT" ]

null

ratemyprof_api/professor.py

nananananate/ratemyprof-api

c037e68e763154cc60812393538c7aa380fbb90e

[ "MIT" ]

1

2021-11-19T02:48:08.000Z

class Professor: def __init__(self, ratemyprof_id: int, first_name: str, last_name: str, num_of_ratings: int, overall_rating): self.ratemyprof_id = ratemyprof_id self.name = f"{first_name} {last_name}" self.first_name = first_name self.last_name = last_name self.num_of_ratings = num_of_ratings if self.num_of_ratings < 1: self.overall_rating = 0 else: self.overall_rating = float(overall_rating)

30.3125

113

0.65567

66

485

4.409091

0.348485

0.123711

0.164948

0.109966

0

0.005602

0.263918

485

15

114

32.333333

0.809524

0

0.049587

0

1

0.090909

false

0

0.181818

0

null

0

null

0

1

0

88ec6c26cd7a2f727e00f467fdd178e22cb46386

810

py

Python

hello/hello_sqlite.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

1

2017-10-23T14:58:47.000Z

hello/hello_sqlite.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

null

hello/hello_sqlite.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

1

2018-04-06T07:49:18.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # 导入SQLite驱动: import sqlite3 # 连接到SQLite数据库 # 数据库文件是test.db # 如果文件不存在，会自动在当前目录创建: conn = sqlite3.connect('hello.db') # 创建一个Cursor: cursor = conn.cursor() cursor.execute('drop table user') # 执行一条SQL语句，创建user表: cursor.execute('create table user (id varchar(20) primary key, name varchar(20))') # 继续执行一条SQL语句，插入一条记录: cursor.execute('insert into user (id, name) values (\'1\', \'Michael\')') cursor.execute('insert into user (id, name) values (\'2\', \'Jackson\')') # 通过rowcount获得插入的行数: print(cursor.rowcount) # 查询: print(cursor.execute('select * from user').fetchall()) print(cursor.execute('select * from user').fetchmany(size=1)) print(cursor.execute('select * from user').fetchone()) # 关闭Cursor: cursor.close() # 提交事务: conn.commit() # 关闭Connection: conn.close()

26.129032

82

0.707407

103

810

5.563107

0.553398

0.158813

0.094241

0.125654

0.303665

0.136126

0

0.013793

0.104938

810

31

83

26.129032

0.776552

0.253086

0

0.376689

0

1

0

false

0

0.071429

0

0.071429

0.285714

0

null

0

null

0

1

0

88edaae7baa65ef0737db43dff89261e7016c55e

1,324

py

Python

ig_data/InstaSearch.py

swapnanildutta/instagram-search

919a3383f0f7789671108f899d9ba9092a69009f

[ "MIT" ]

1

2022-01-04T16:51:50.000Z

ig_data/InstaSearch.py

swapnanildutta/instagram-search

919a3383f0f7789671108f899d9ba9092a69009f

[ "MIT" ]

3

2020-10-26T13:31:05.000Z

2022-01-05T23:11:42.000Z

ig_data/InstaSearch.py

swapnanildutta/instagram-search

919a3383f0f7789671108f899d9ba9092a69009f

[ "MIT" ]

2

2020-04-07T09:24:07.000Z

2020-04-14T06:38:49.000Z

# imports import requests, json # beautifulsoup4 from bs4 import BeautifulSoup def searchDisplay(username): # base url for the data url = 'https://www.instagram.com/{}/'.format(username) try: req = requests.get(url).content soup=BeautifulSoup(req,"html.parser") row=soup.find_all('script') details=str(row[3]).strip("<script type=></")[22:].strip() account=json.loads(details) try: if len(account['description'])<1: account['description']="" except: account['description']="" print("Name : ",account['name'],'\t',"Username : ",account['alternateName'], '\t',"Followers : ",account['mainEntityofPage']['interactionStatistic']['userInteractionCount'],'\n', "Bio : ",account['description']) except: print('Not found or no internet connection') def getDetails(username): url = 'https://www.instagram.com/{}/'.format(username) try: req = requests.get(url).content soup=BeautifulSoup(req,"html.parser") row=soup.find_all('script') details=row[3].text account=json.loads(details) return account except: print('Not found or no internet connection') return {}

33.948718

116

0.58006

135

1,324

5.674074

0.474074

0.093995

0.028721

0.052219

0.422977

0.315927

0

0.007202

0.265861

1,324

38

117

34.842105

0.780864

0.033233

0

0.625

0

0.272433

0

1

0.0625

false

0

0.0625

0

0.1875

0.09375

0

null

0

null

0

1

0

88ef1b2b45df53d3ae9e2451d75c76436af81011

2,369

py

Python

tests/jax_ops_test.py

ita9naiwa/fast-soft-sort

72cbd93ecc229736f9e05bfdfd0f48c09432904f

[ "Apache-2.0" ]

389

2020-06-08T22:30:18.000Z

2022-03-25T23:04:28.000Z

tests/jax_ops_test.py

ita9naiwa/fast-soft-sort

72cbd93ecc229736f9e05bfdfd0f48c09432904f

[ "Apache-2.0" ]

14

2020-06-21T13:21:51.000Z

2021-10-18T18:02:07.000Z

tests/jax_ops_test.py

ita9naiwa/fast-soft-sort

72cbd93ecc229736f9e05bfdfd0f48c09432904f

[ "Apache-2.0" ]

32

2020-06-20T17:25:10.000Z

2022-03-26T13:34:23.000Z

# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for jax_ops.py.""" import functools import itertools import unittest from absl.testing import absltest from absl.testing import parameterized import numpy as np import jax.numpy as jnp import jax from jax.config import config config.update("jax_enable_x64", True) from fast_soft_sort import jax_ops GAMMAS = (0.1, 1, 10.0) DIRECTIONS = ("ASCENDING", "DESCENDING") REGULARIZERS = ("l2", ) class JaxOpsTest(parameterized.TestCase): def _test(self, func, regularization_strength, direction, regularization): def loss_func(values): soft_values = func(values, regularization_strength=regularization_strength, direction=direction, regularization=regularization) return jnp.sum(soft_values ** 2) rng = np.random.RandomState(0) values = jnp.array(rng.randn(5, 10)) mat = jnp.array(rng.randn(5, 10)) unitmat = mat / np.sqrt(np.vdot(mat, mat)) eps = 1e-5 numerical = (loss_func(values + 0.5 * eps * unitmat) - loss_func(values - 0.5 * eps * unitmat)) / eps autodiff = jnp.vdot(jax.grad(loss_func)(values), unitmat) np.testing.assert_almost_equal(numerical, autodiff) @parameterized.parameters(itertools.product(GAMMAS, DIRECTIONS, REGULARIZERS)) def test_soft_rank(self, regularization_strength, direction, regularization): self._test(jax_ops.soft_rank, regularization_strength, direction, regularization) @parameterized.parameters(itertools.product(GAMMAS, DIRECTIONS, REGULARIZERS)) def test_soft_sort(self, regularization_strength, direction, regularization): self._test(jax_ops.soft_sort, regularization_strength, direction, regularization) if __name__ == "__main__": absltest.main()

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88f18a67e803424dde5d28eb3302913d647a3a2f

27,163

py

Python

src/pages/pe_cuencas.py

ValentinSilvestri/cammesa

33ff17ad4a0447fd4668b6adad1c4bbfd88aba8e

[ "MIT" ]

null

src/pages/pe_cuencas.py

ValentinSilvestri/cammesa

33ff17ad4a0447fd4668b6adad1c4bbfd88aba8e

[ "MIT" ]

null

src/pages/pe_cuencas.py

ValentinSilvestri/cammesa

33ff17ad4a0447fd4668b6adad1c4bbfd88aba8e

[ "MIT" ]

null

import os import re import pymongo import pandas as pd import numpy as np import streamlit as st from bokeh.plotting import figure from bokeh.palettes import Set1_9, Set3_12, Inferno256 @st.cache(suppress_st_warning=True, allow_output_mutation=True) def get_caudales(): """Function to obtain the rivers basin flows from MongoDB Atlas. Returns: DataFrame: Pandas DataFrame with the query result. """ st.spinner("Obteniendo los datos de caudales...") client = pymongo.MongoClient(os.environ['MONGO']) try: collection_name = client['publicaciones-especiales']['cuencas-datos-hidraulicos'] project={ '_id': 0, 'fecha': 1, 'situacionCuencaComahue': { 'Caudal Collon Cura': 1, 'Caudal Neuquen': 1, 'Caudal Limay': 1, 'Caudal Río Negro': 1, 'Caudal Limay despues desembocadura de Collon Cura': 1 }, 'situacionYacyretaSaltoGrande': { 'Caudal Río Uruguay': 1, 'Caudal Río Paraná': 1 }, 'situacionCuencaPatagonica': { 'Caudal Río Chubut': 1, 'Caudal Río Futaleufu': 1 }, 'situacionCuencaRioGrande': { 'Caudal Río Grande': 1 }, 'situacionCuencaRioSanJuan': { 'Caudal Inicial Río San Juan': 1, 'Caudal Final Río San Juan': 1 } } df = pd.DataFrame(collection_name.find(projection=project)) return df except Exception as e: st.error(f'Opps, algo fallo\n{e}') finally: client.close() @st.cache(suppress_st_warning=True, allow_output_mutation=True) def get_cotas(): """Function to obtai the rivers basin levels from MongoDB Atlas. Returns: DataFrame: Pandas DataFrame with the query result. """ st.spinner("Obteniendo los datos de cotas...") client = pymongo.MongoClient(os.environ['MONGO']) try: collection_name = client['publicaciones-especiales']['cuencas-datos-hidraulicos'] project={ '_id': 0, 'fecha': 1, 'situacionCuencaComahue': { 'Cota Hoy Alicura': 1, 'Cota Min Alicura': 1, 'Cota Max Alicura': 1, 'Cota Hoy Mari Menuco': 1, 'Cota Min Mari Menuco': 1, 'Cota Max Mari Menuco': 1, 'Cota Hoy Piedra del Aguila': 1, 'Cota Min Piedra del Aguila': 1, 'Cota Max Piedra del Aguila': 1, 'Cota Hoy Planicie Banderita Barreales': 1, 'Cota Min Planicie Banderita Barreales': 1, 'Cota Max Planicie Banderita Barreales': 1, 'Cota Hoy Arroyito': 1, 'Cota Min Arroyito': 1, 'Cota Max Arroyito': 1, 'Cota Hoy El Chocon': 1, 'Cota Min El Chocon': 1, 'Cota Max El Chocon': 1, 'Cota Hoy P': { 'P': { 'Leufu': 1 } } }, 'situacionYacyretaSaltoGrande': { 'Cota Hoy Yacyreta': 1, 'Cota Min Yacyreta': 1, 'Cota Max Yacyreta': 1, 'Cota Hoy Salto Grande': 1, 'Cota Min Salto Grande': 1, 'Cota Max Salto Grande': 1 }, 'situacionCuencaPatagonica': { 'Cota Hoy Futaleufu': 1, 'Cota Min Futaleufu': 1, 'Cota Max Futaleufu': 1, 'Cota Hoy Ameghino': 1, 'Cota Min Ameghino': 1, 'Cota Max Ameghino': 1 }, 'situacionCuencaRioGrande': { 'Cota Hoy Río Grande': 1, 'Cota Min Río Grande': 1, 'Cota Max Río Grande': 1 }, 'situacionCuencaRioSanJuan': { 'Cota Hoy Quebrada de Ullum': 1, 'Cota Min Quebrada de Ullum': 1, 'Cota Max Quebrada de Ullum': 1, 'Cota Hoy Los Caracole': 1, 'Cota Min Los Caracoles': 1, 'Cota Max Los Caracoles': 1, 'Cota Hoy Punta Negra': 1, 'Cota Min Punta Negra': 1, 'Cota Max Punta Negra': 1 } } df = pd.DataFrame(collection_name.find(projection=project)) return df except Exception as e: st.error(f'Opps, algo fallo\n{e}') finally: client.close() @st.cache(suppress_st_warning=True, allow_output_mutation=True) def get_turbinado(): """Function to obtain the rivers basin turbinate from MongoDB Atlas. Returns: DataFrame: Pandas DataFrame with the query result. """ st.spinner("Obteniendo los datos de turbinado...") client = pymongo.MongoClient(os.environ['MONGO']) try: collection_name = client['publicaciones-especiales']['cuencas-datos-hidraulicos'] project={ '_id': 0, 'fecha': 1, 'situacionCuencaComahue': { 'Turbinado Alicura': 1, 'Turbinado Piedra del Aguila': 1, 'Turbinado Arroyito': 1, 'Turbinado El Chocon': 1, 'Turbinado Mari Menuco': 1, 'Turbinado P': { 'P': { 'Leufu': 1 } } }, 'situacionYacyretaSaltoGrande': { 'Turbinado Salto Grande': 1, 'Turbinado Yacyreta': 1 }, 'situacionCuencaPatagonica': { 'Turbinado Futaleufu': 1, 'Turbinado Ameghino': 1 }, 'situacionCuencaRioGrande': { 'Turbinado Río Grande': 1 }, 'situacionCuencaRioSanJuan': { 'Turbinado Punta Negra': 1, 'Turbinado Ullum': 1, 'Turbinado Los Caracoles': 1, 'Turbinado Quebrada de Ullum': 1 } } df = pd.DataFrame(collection_name.find(projection=project)) return df except Exception as e: st.error(f'Opps, algo fallo\n{e}') finally: client.close() @st.cache(suppress_st_warning=True, allow_output_mutation=True) def get_vertido(): """Function to obtain the rivers basin discharge from MongoDB Atlas. Returns: DataFrame: Pandas DataFrame with the query result. """ st.spinner("Obteniendo los datos de turbinado...") client = pymongo.MongoClient(os.environ['MONGO']) try: collection_name = client['publicaciones-especiales']['cuencas-datos-hidraulicos'] project={ '_id': 0, 'fecha': 1, 'situacionCuencaComahue': { 'Vertido El Chañar': 1, 'Vertido Arroyito': 1, 'Vertido Piedra del Aguila': 1, 'Vertido P': { 'P': { 'Leufu': 1 } } }, 'situacionYacyretaSaltoGrande': { 'Vertido Salto Grande': 1, 'Vertido Yacyreta': 1 }, 'situacionCuencaPatagonica': { 'Vertido Futaleufu': 1, 'Vertido Ameghino': 1 }, 'situacionCuencaRioGrande': { 'Bombeo Río Grande': 1 }, 'situacionCuencaRioSanJuan': { 'Vertido Punta Negra': 1, 'Vertido Los Caracoles': 1, 'Vertido Quebrada de Ullum': 1 } } df = pd.DataFrame(collection_name.find(projection=project)) return df except Exception as e: st.error(f'Opps, algo fallo\n{e}') finally: client.close() def caudales(): """Get the rivers basin flows and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_caudales() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ "fecha": "Fecha", "Caudal Collon Cura": "Cuenca Comahue - Caudal Collon Cura", "Caudal Neuquen": "Cuenca Comahue - Caudal Neuquen", "Caudal Limay": "Cuenca Comahue - Caudal Limay", "Caudal Río Negro": "Cuenca Comahue - Caudal Río Negro", "Caudal Limay despues desembocadura de Collon Cura": "Cuenca Comahue - Caudal Limay despues desembocadura de Collon Cura", "Caudal Río Uruguay": "Yacyreta Salto Grande - Caudal Río Uruguay", "Caudal Río Paraná": "Yacyreta Salto Grande - Caudal Río Paraná", "Caudal Río Chubut": "Cuenca Patagónica - Caudal Río Chubut", "Caudal Río Futaleufu": "Cuenca Patagónica - Caudal Río Futaleufu", "Caudal Río Grande": "Cuenca Río Grande - Caudal Río Grande", "Caudal Inicial Río San Juan": "Cuenca Río San Juan - Caudal Inicial Río San Juan", "Caudal Final Río San Juan": "Cuenca Río San Juan - Caudal Final Río San Juan" }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Caudales cuencas", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Caudal [m\u00b3/s]' p.legend.location = "top_left" return p, df def cotas(): """Get the rivers basin levels and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_cotas() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ 'fecha': 'Fecha', 'Cota Hoy Alicura': 'Cuenca Comahue - Alicura', 'Cota Min Alicura': 'Cuenca Comahue - Min Alicura', 'Cota Max Alicura': 'Cuenca Comahue - Max Alicura', 'Cota Hoy Piedra del Aguila': 'Cuenca Comahue - Piedra del Aguil', 'Cota Min Piedra del Aguila': 'Cuenca Comahue - Min Piedra del Aguila', 'Cota Max Piedra del Aguila': 'Cuenca Comahue - Max Piedra del Aguila', 'Cota Hoy Arroyito': 'Cuenca Comahue - Arroyito', 'Cota Min Arroyito': 'Cuenca Comahue - Min Arroyito', 'Cota Max Arroyito': 'Cuenca Comahue - Max Arroyito', 'Cota Hoy Mari Menuco': 'Cuenca Comahue - Mari Menuco', 'Cota Min Mari Menuco': 'Cuenca Comahue - Min Mari Menuco', 'Cota Max Mari Menuco': 'Cuenca Comahue - Max Mari Menuco', 'Cota Hoy Planicie Banderita Barreales': 'Cuenca Comahue - Planicie Banderita Barreales', 'Cota Min Planicie Banderita Barreales': 'Cuenca Comahue - Min Planicie Banderita Barreales', 'Cota Max Planicie Banderita Barreales': 'Cuenca Comahue - Max Planicie Banderita Barreales', 'Cota Hoy El Chocon': 'Cuenca Comahue - El Chocon', 'Cota Min El Chocon': 'Cuenca Comahue - Min El Chocon', 'Cota Max El Chocon': 'Cuenca Comahue - Max El Chocon', 'Cota Hoy P.P.Leufu': 'Cuenca Comahue - Leufu', 'Cota Hoy Yacyreta': 'Cuenca Yacyreta - Yacyreta', 'Cota Min Yacyreta': 'Cuenca Yacyreta - Min Yacyreta', 'Cota Max Yacyreta': 'Cuenca Yacyreta - Max Yacyreta', 'Cota Hoy Salto Grande': 'Cuenca Yacyreta - Salto Grande', 'Cota Min Salto Grande': 'Cuenca Yacyreta - Min Salto Grande', 'Cota Max Salto Grande': 'Cuenca Yacyreta - Max Salto Grande', 'Cota Hoy Futaleufu': 'Cuenca Patagónica - Futaleufu', 'Cota Min Futaleufu': 'Cuenca Patagónica - Min Futaleufu', 'Cota Max Futaleufu': 'Cuenca Patagónica - Max Futaleufu', 'Cota Hoy Ameghino': 'Cuenca Patagónica - Ameghino', 'Cota Min Ameghino': 'Cuenca Patagónica - Min Ameghino', 'Cota Max Ameghino': 'Cuenca Patagónica - Max Ameghino', 'Cota Hoy Río Grande': 'Cuenca Río Grande - Río Grande', 'Cota Min Río Grande': 'Cuenca Río Grande - Min Río Grande', 'Cota Max Río Grande': 'Cuenca Río Grande - Max Río Grande', 'Cota Hoy Quebrada de Ullum': 'Cuenca Río San Juan - Quebrada de Ullum', 'Cota Min Quebrada de Ullum': 'Cuenca Río San Juan - Min Quebrada de Ullum', 'Cota Max Quebrada de Ullum': 'Cuenca Río San Juan - Max Quebrada de Ullum', 'Cota Hoy Punta Negra': 'Cuenca Río San Juan - Punta Negra', 'Cota Min Punta Negra': 'Cuenca Río San Juan - Min Punta Negra', 'Cota Max Punta Negra': 'Cuenca Río San Juan - Max Punta Negra' }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Cotas cuencas", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Cota [cm]' p.legend.location = "top_left" return p, df def turbinado(): """Get the rivers basin discharge and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_turbinado() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ 'fecha': 'Fecha', 'Turbinado Alicura': 'Cuenca Comahue - Alicura', 'Turbinado Piedra del Aguila': 'Cuenca Comahue - Piedra del Aguila', 'Turbinado Arroyito': 'Cuenca Comahue - Arroyito', 'Turbinado El Chocon': 'Cuenca Comahue - El Chocon', 'Turbinado Mari Menuco': 'Cuenca Comahue - Mari Menuco', 'Turbinado P.P.Leufu': 'Cuenca Comahue - Leufu', 'Turbinado Salto Grande': 'Cuenca Yacyreta - Salto Grande', 'Turbinado Yacyreta': 'Cuenca Yacyreta - Yacyreta', 'Turbinado Futaleufu': 'Cuenca Patagónica - Futaleufu', 'Turbinado Ameghino': 'Cuenca Patagónica - Ameghino', 'Turbinado Río Grande': 'Cuenca Río Grande - Río Grande', 'Turbinado Punta Negra': 'Cuenca Río San Juan - Punta Negra', 'Turbinado Ullum': 'Cuenca Río San Juan - Ullum', 'Turbinado Los Caracoles': 'Cuenca Río San Juan - Los Caracoles', 'Turbinado Quebrada de Ullum': 'Cuenca Río San Juan - Quebrada de Ullum' }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) # df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Turbinado", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Turbinado' p.legend.location = "top_left" return p, df def vertido(): """Get the rivers basin discharge and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_vertido() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ 'fecha': 'Fecha', 'Vertido El Chañar': 'Cuenca Comahue - El Chañar', 'Vertido Arroyito': 'Cuenca Comahue - Arroyito', 'Vertido Piedra del Aguila': 'Cuenca Comahue - Piedra del Aguila', 'Vertido P.P.Leufu': 'Cuenca Comahue - Leufu', 'Vertido Salto Grande': 'Cuenca Yacyreta - Salto Grande', 'Vertido Yacyreta': 'Cuenca Yacyreta - Yacyreta', 'Vertido Futaleufu': 'Cuenca Patagónica - Futaleufu', 'Vertido Ameghino': 'Cuenca Patagónica - Ameghino', 'Bombeo Río Grande': 'Cuenca Río Grande - Bombeo Río Grande', 'Vertido Punta Negra': 'Cuenca Río San Juan - Punta Negra', 'Vertido Los Caracoles': 'Cuenca Río San Juan - Los Caracoles', 'Vertido Quebrada de Ullum': 'Cuenca Río San Juan - Quebrada de Ullum' }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) # df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Vertido", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Vertido' p.legend.location = "top_left" return p, df def write(): """Function to write the Streamlit content of the page pe_cuencas """ p_caudales, df_caudales = caudales() p_cotas, df_cotas = cotas() p_turbinado, df_turbinado = turbinado() p_vertido, df_vertido = vertido() st.header("Publicaciones especiales - Cuencas/Datos Hidráulicos 🌊", anchor=None) with st.container(): st.subheader("Análisis de caudales", anchor=None) options = st.multiselect( "Seleccionar datos a graficar.", options=[ "Cuenca Comahue - Caudal Collon Cura", "Cuenca Comahue - Caudal Neuquen", "Cuenca Comahue - Caudal Limay", "Cuenca Comahue - Caudal Río Negro", "Cuenca Comahue - Caudal Limay despues desembocadura de Collon Cura", "Yacyreta Salto Grande - Caudal Río Uruguay", "Yacyreta Salto Grande - Caudal Río Paraná", "Cuenca Patagónica - Caudal Río Chubut", "Cuenca Patagónica - Caudal Río Futaleufu", "Cuenca Río Grande - Caudal Río Grande", "Cuenca Río San Juan - Caudal Inicial Río San Juan", "Cuenca Río San Juan - Caudal Final Río San Juan" ], default=[ "Yacyreta Salto Grande - Caudal Río Paraná", "Yacyreta Salto Grande - Caudal Río Uruguay" ] ) if len(options)>9: col = Set3_12 else: col = Set1_9 for index, value in enumerate(options): p_caudales.line( df_caudales['Fecha'], df_caudales[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_caudales) with st.expander("Ver datos"): st.write("Datos de los caudales de las cuencas en [m\u00b3/s].") st.dataframe(df_caudales) st.download_button( label="Descargar dataset como .CSV", data=df_caudales.to_csv(index=False).encode('utf-8'), file_name='Caudales.csv', mime='text/csv', ) with st.container(): st.subheader("Análisis de cotas", anchor=None) options_cotas = st.multiselect( "Seleccionar datos a graficar.", options=[ 'Cuenca Comahue - Alicura', 'Cuenca Comahue - Min Alicura', 'Cuenca Comahue - Max Alicura', 'Cuenca Comahue - Piedra del Aguil', 'Cuenca Comahue - Min Piedra del Aguila', 'Cuenca Comahue - Max Piedra del Aguila', 'Cuenca Comahue - Arroyito', 'Cuenca Comahue - Min Arroyito', 'Cuenca Comahue - Max Arroyito', 'Cuenca Comahue - Mari Menuco', 'Cuenca Comahue - Min Mari Menuco', 'Cuenca Comahue - Max Mari Menuco', 'Cuenca Comahue - Planicie Banderita Barreales', 'Cuenca Comahue - Min Planicie Banderita Barreales', 'Cuenca Comahue - Max Planicie Banderita Barreales', 'Cuenca Comahue - El Chocon', 'Cuenca Comahue - Min El Chocon', 'Cuenca Comahue - Max El Chocon', 'Cuenca Comahue - Leufu', 'Cuenca Yacyreta - Yacyreta', 'Cuenca Yacyreta - Min Yacyreta', 'Cuenca Yacyreta - Max Yacyreta', 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Min Salto Grande', 'Cuenca Yacyreta - Max Salto Grande', 'Cuenca Patagónica - Futaleufu', 'Cuenca Patagónica - Min Futaleufu', 'Cuenca Patagónica - Max Futaleufu', 'Cuenca Patagónica - Ameghino', 'Cuenca Patagónica - Min Ameghino', 'Cuenca Patagónica - Max Ameghino', 'Cuenca Río Grande - Río Grande', 'Cuenca Río Grande - Min Río Grande', 'Cuenca Río Grande - Max Río Grande', 'Cuenca Río San Juan - Quebrada de Ullum', 'Cuenca Río San Juan - Min Quebrada de Ullum', 'Cuenca Río San Juan - Max Quebrada de Ullum', 'Cuenca Río San Juan - Punta Negra', 'Cuenca Río San Juan - Min Punta Negra', 'Cuenca Río San Juan - Max Punta Negra' ], default=[ 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Min Salto Grande', 'Cuenca Yacyreta - Max Salto Grande' ] ) if len(options_cotas)<=9: col = Set1_9 elif len(options_cotas) <=12: col = Set3_12 else: col = Inferno256 for index, value in enumerate(options_cotas): p_cotas.line( df_cotas['Fecha'], df_cotas[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_cotas) with st.expander("Ver datos"): st.write("Datos de los Cotas de las cuencas en [cm].") st.dataframe(df_cotas) st.download_button( label="Descargar dataset como .CSV", data=df_cotas.to_csv(index=False).encode('utf-8'), file_name='Cotas.csv', mime='text/csv', ) with st.container(): st.subheader("Análisis del turbinado", anchor=None) options_turbinado = st.multiselect( "Seleccionar datos a graficar.", options=[ 'Cuenca Comahue - Alicura', 'Cuenca Comahue - Piedra del Aguila', 'Cuenca Comahue - Arroyito', 'Cuenca Comahue - El Chocon', 'Cuenca Comahue - Mari Menuco', 'Cuenca Comahue - Leufu', 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Yacyreta', 'Cuenca Patagónica - Futaleufu', 'Cuenca Patagónica - Ameghino', 'Cuenca Río Grande - Río Grande', 'Cuenca Río San Juan - Punta Negra', 'Cuenca Río San Juan - Ullum', 'Cuenca Río San Juan - Los Caracoles', 'Cuenca Río San Juan - Quebrada de Ullum' ], default=[ 'Cuenca Yacyreta - Yacyreta', 'Cuenca Yacyreta - Salto Grande' ] ) if len(options_turbinado)<=9: col = Set1_9 elif len(options_turbinado) <=12: col = Set3_12 else: col = Inferno256 for index, value in enumerate(options_turbinado): p_turbinado.line( df_turbinado['Fecha'], df_turbinado[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_turbinado) with st.expander("Ver datos"): st.write("Datos del turbinado.") st.dataframe(df_turbinado) st.download_button( label="Descargar dataset como .CSV", data=df_turbinado.to_csv(index=False).encode('utf-8'), file_name='Turbinado.csv', mime='text/csv', ) with st.container(): st.subheader("Análisis del vertido", anchor=None) options_vertido = st.multiselect( "Seleccionar datos a graficar.", options=[ 'Cuenca Comahue - El Chañar', 'Cuenca Comahue - Arroyito', 'Cuenca Comahue - Piedra del Aguila', 'Cuenca Comahue - Leufu', 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Yacyreta', 'Cuenca Patagónica - Futaleufu', 'Cuenca Patagónica - Ameghino', 'Cuenca Río Grande - Bombeo Río Grande', 'Cuenca Río San Juan - Punta Negra', 'Cuenca Río San Juan - Los Caracoles', 'Cuenca Río San Juan - Quebrada de Ullum' ], default=[ 'Cuenca Yacyreta - Yacyreta', 'Cuenca Yacyreta - Salto Grande' ] ) if len(options_vertido)>9: col = Set3_12 else: col = Set1_9 for index, value in enumerate(options_vertido): p_vertido.line( df_vertido['Fecha'], df_vertido[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_vertido) with st.expander("Ver datos"): st.write("Datos del vertido.") st.dataframe(df_vertido) st.download_button( label="Descargar dataset como .CSV", data=df_vertido.to_csv(index=False).encode('utf-8'), file_name='Vertido.csv', mime='text/csv', )

39.538574

130

0.555793

2,864

27,163

5.208101

0.085196

0.059265

0.025476

0.03218

0.758045

0.65661

0.60244

0.553366

0.506034

0.434902

0

0.009018

0.338622

27,163

686

131

39.59621

0.821208

0.045945

0

0.424191

0

0.438753

0.04614

0

1

0.015332

false

0

0.013629

0

0.042589

0

null

0

null

0

1

0

88f1cc3699cf5781999a9874993e5299f3224a9d

5,930

py

Python

utils/gen-vowel-constraints.py

ctrlcctrlv/fontFeatures

76d68586da2c1c42bb3cd79f92d583e63f52cf02

[ "BSD-3-Clause" ]

51

2020-01-15T09:28:51.000Z

2022-03-30T06:48:36.000Z

utils/gen-vowel-constraints.py

ctrlcctrlv/fontFeatures

76d68586da2c1c42bb3cd79f92d583e63f52cf02

[ "BSD-3-Clause" ]

51

2020-05-11T18:51:25.000Z

2021-12-20T12:55:08.000Z

utils/gen-vowel-constraints.py

ctrlcctrlv/fontFeatures

76d68586da2c1c42bb3cd79f92d583e63f52cf02

[ "BSD-3-Clause" ]

8

2020-08-28T20:03:14.000Z

2021-12-08T01:22:28.000Z

#!/usr/bin/env python3 """Generator of the function to prohibit certain vowel sequences. It creates ``_hb_preprocess_text_vowel_constraints``, which inserts dotted circles into sequences prohibited by the USE script development spec. This function should be used as the ``preprocess_text`` of an ``hb_ot_complex_shaper_t``. usage: ./gen-vowel-constraints.py ms-use/IndicShapingInvalidCluster.txt """ import collections import youseedee def write (s): sys.stdout.flush () sys.stdout.buffer.write (s.encode ('utf-8')) import sys if len (sys.argv) != 2: sys.exit (__doc__) script_order = {} scripts = {} for start, end,script in youseedee.parse_file_ranges("Scripts.txt"): for u in range (start, end + 1): scripts[u] = script if script not in script_order: script_order[script] = start class ConstraintSet (object): """A set of prohibited code point sequences. Args: constraint (List[int]): A prohibited code point sequence. """ def __init__ (self, constraint): # Either a list or a dictionary. As a list of code points, it # represents a prohibited code point sequence. As a dictionary, # it represents a set of prohibited sequences, where each item # represents the set of prohibited sequences starting with the # key (a code point) concatenated with any of the values # (ConstraintSets). self._c = constraint def add (self, constraint): """Add a constraint to this set.""" if not constraint: return first = constraint[0] rest = constraint[1:] if isinstance (self._c, list): if constraint == self._c[:len (constraint)]: self._c = constraint elif self._c != constraint[:len (self._c)]: self._c = {self._c[0]: ConstraintSet (self._c[1:])} if isinstance (self._c, dict): if first in self._c: self._c[first].add (rest) else: self._c[first] = ConstraintSet (rest) @staticmethod def _indent (depth): return (' ' * depth) @staticmethod def _cp_accessor(index): if index: return "buffer.items[i+{}].codepoint".format(index) return "buffer.items[i].codepoint" def __str__ (self, index=0, depth=2): s = [] indent = self._indent (depth) if isinstance (self._c, list): if len (self._c) == 0: assert index == 2, 'Cannot use `matched` for this constraint; the general case has not been implemented' s.append ('{}matched = True\n'.format (indent)) elif len (self._c) == 1: assert index == 1, 'Cannot use `matched` for this constraint; the general case has not been implemented' s.append ('{}matched = 0x{:04X} == {}\n'.format (indent, next (iter (self._c)), self._cp_accessor(index))) else: s.append ('{}if (0x{:04X} == {} and\n'.format (indent, self._c[0], self._cp_accessor(index))) if index: s.append ('{}i + {} < len(buffer.items)-1 and\n'.format (self._indent (depth + 2), index + 1)) for i, cp in enumerate (self._c[1:], start=1): s.append ('{}0x{:04X} == {}{}\n'.format ( self._indent (depth + 2), cp, self._cp_accessor(index + i), '):' if i == len (self._c) - 1 else 'and') ) s.append ('{}matched = True\n'.format (self._indent (depth + 1))) else: cases = collections.defaultdict (set) for first, rest in sorted (self._c.items ()): cases[rest.__str__ (index + 1, depth + 2)].add (first) for body, labels in sorted (cases.items (), key=lambda b_ls: sorted (b_ls[1])[0]): if len(labels) == 1: s.append (self._indent (depth + 1) + "if {} == 0x{:04X}:\n".format(self._cp_accessor(index), list(labels)[0])) else: points = ", ".join(['0x{:04X}'.format(cp) for cp in sorted(labels)]) s.append (self._indent (depth + 1) + "if {} in [{}]:\n".format(self._cp_accessor(index), points)) s.append (body) return ''.join (s) constraints = {} with open (sys.argv[1], encoding='utf-8') as f: constraints_header = [] while True: line = f.readline ().strip () if line == '#': break constraints_header.append(line) for line in f: j = line.find ('#') if j >= 0: line = line[:j] constraint = [int (cp, 16) for cp in line.split (';')[0].split ()] if not constraint: continue assert 2 <= len (constraint), 'Prohibited sequence is too short: {}'.format (constraint) script = scripts[constraint[0]] if script in constraints: constraints[script].add (constraint) else: constraints[script] = ConstraintSet (constraint) assert constraints, 'No constraints found' print ('# The following functions are generated by running:') print ('# %s ms-use/IndicShapingInvalidCluster.txt' % sys.argv[0]) print(""" from fontFeatures.shaperLib.Buffer import BufferItem DOTTED_CIRCLE = 0x25CC def _insert_dotted_circle(buf, index): dotted_circle = BufferItem.new_unicode(DOTTED_CIRCLE) buf.items.insert(index, dotted_circle) """) print ('def preprocess_text_vowel_constraints(buffer):') for script, constraints in sorted (constraints.items (), key=lambda s_c: script_order[s_c[0]]): print(f' if buffer.script == "{script}":') print (' i = 0') print (' while i < len(buffer.items)-1:') print (' matched = False') write (str (constraints)) print (' i = i + 1') print (' if matched: _insert_dotted_circle(buffer, i)')

37.770701

130

0.579764

738

5,930

4.533875

0.257453

0.031381

0.026898

0.028392

0.187986

0.13688

0.063957

0.049014

0

0.014654

0.286509

5,930

156

131

38.012821

0.776176

0.143508

0

0.117117

0

0.208135

0.054762

0

0.00119

0

0.036036

1

0.054054

false

0

0.036036

0.009009

0.144144

0.09009

0

null

0

null

0

1

0

88f5b3a545f8379f5c6bd871ff166dd1442dd335

1,263

py

Python

solutions/validate-binary-search-tree.py

edab/-LC_StudyPlan_Python

e065f0ced68d23800d7b5001102c2e930ee35e23

[ "MIT" ]

null

solutions/validate-binary-search-tree.py

edab/-LC_StudyPlan_Python

e065f0ced68d23800d7b5001102c2e930ee35e23

[ "MIT" ]

1

2022-02-22T15:42:54.000Z

2022-02-25T00:10:04.000Z

solutions/validate-binary-search-tree.py

edab/-LC_StudyPlan_Python

e065f0ced68d23800d7b5001102c2e930ee35e23

[ "MIT" ]

null

# Leetcode 98. Validate Binary Search Tree # # Link: https://leetcode.com/problems/validate-binary-search-tree/ # Difficulty: Medium # Complexity: # O(N) time | where N represent the number of elements in the input tree # O(N) space | where N represent the number of elements in the input tree # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def isValidBST(self, root: Optional[TreeNode]) -> bool: def is_valid(node, left_limit, right_limit): if not node: return True if not (left_limit < node.val < right_limit): return False return (is_valid(node.left, left_limit, node.val) and is_valid(node.right, node.val, right_limit)) def dfs_bfs_check_iterative(node): if not node: return True stack = [] previous = None while node or stack: while node: stack.append(node) node = node.left node = stack.pop() if previous and node.val <= previous.val: return False previous = node node = node.right return True return dfs_bfs_check_iterative(root)

30.071429

104

0.63658

173

1,263

4.537572

0.364162

0.035669

0.042038

0.061147

0.170701

0.122293

0

0.003264

0.272367

1,263

41

105

30.804878

0.850925

0.366587

0

0.291667

0

1

0.125

false

0

0.458333

0

null

0

null

0

1

0

88f8317bf62d16d93d0f7dd37a85760c1a1014e1

763

py

Python

setup.py

stanford-ccb/ccb

ba75d490663958703f19e7a13f72001b050da229

[ "MIT" ]

3

2020-02-13T00:49:06.000Z

2020-06-24T23:53:25.000Z

setup.py

stanford-ccb/ccb

ba75d490663958703f19e7a13f72001b050da229

[ "MIT" ]

null

setup.py

stanford-ccb/ccb

ba75d490663958703f19e7a13f72001b050da229

[ "MIT" ]

4

2020-01-29T17:21:59.000Z

2021-01-27T01:53:05.000Z

from setuptools import setup version = open("ccb/__version__.py").read().strip('"\n') setup_args = { "name": "ccb", "version": version, "url": "https://github.com/earth-chris/ccb", "license": "MIT", "author": "Christopher Anderson", "author_email": "cbanders@stanford.edu", "description": "Species distribution modeling support tools", "keywords": ["maxent", "biogeography", "SDM", "species distribution modeling", "ecologyy", "conservation"], "packages": ["ccb"], "include_package_data": True, "platforms": "any", "scripts": ["bin/gbif-to-vector.py", "bin/vector-to-maxent.py"], "data_files": [("maxent", ["ccb/maxent/maxent.jar", "ccb/maxent/README.txt", "ccb/maxent/LICENSE.txt"])], } setup(**setup_args)

34.681818

111

0.643512

87

763

5.528736

0.643678

0.056133

0.112266

0

0.142857

763

21

112

36.333333

0.735474

0

0.585845

0.169069

0

1

0

false

0

0.055556

0

0.055556

0

null

0

null

0

1

0

88f8e7ad2c848fa7633da12c05df70cdb4d3835a

1,576

py

Python

Unit4/Lesson8.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

Unit4/Lesson8.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

Unit4/Lesson8.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

# import time # # def reader(): # """A generator that fakes a read from a file, socket, etc.""" # for i in range(101): # yield '<< %s' % i # # def consumer(): # r = '' # while True: # #但是Python的yield不但可以返回一个值，它还可以接收调用者发出的参数。 # #此处的n是接受参数 # n = yield from reader() # print("===",n) # if not n: # return # print('[CONSUMER] Consuming %s...' % n) # r = '200 OK' # # def produce(c): # c.send(None) # n = 0 # while n < 100: # n = n + 1 # print('[PRODUCER] Producing %s...' % n) # r = c.send(n) # print('[PRODUCER] Consumer return: %s' % r) # c.close() # # c = consumer() # produce(c) # def getIN(): # for x in range(1000): # n = yield x # print(n,"--rer",x) # # ge =getIN() # # #开始 # ge.send(None) # ge.send("11") # ge.send("222") def accumulate(): # 子生成器，将传进的非None值累加，传进的值若为None，则返回累加结果 tally = 0 while 1: next = yield if next is None: return tally tally += next def gather_tallies(tallies): # 外部生成器，将累加操作任务委托给子生成器 while 1: tally = yield from accumulate() tallies.append(tally) tallies = [] acc = gather_tallies(tallies) next(acc) # 使累加生成器准备好接收传入值 for i in range(4): acc.send(i) acc.send(None) # 结束第一次累加 for i in range(5): acc.send(i) acc.send(None) # 结束第二次累加 print(tallies) def get(): n =1 while True: n+=1 if n>10: break yield for x in get(): print(x)

17.909091

68

0.498096

198

1,576

3.954545

0.378788

0.03576

0.022989

0.042146

0.048531

0

0.028155

0.346447

1,576

87

69

18.114943

0.732039

0.564721

0

0.2

0

1

0.1

false

0

0.133333

0.066667

0

null

0

null

0

1

0

88fb6794a48a5fc109dca145fcd71d6498bacc28

1,288

py

Python

tools/transferComponentSelection.py

fsanges/glTools

8ff0899de43784a18bd4543285655e68e28fb5e5

[ "MIT" ]

165

2015-01-26T05:22:04.000Z

2022-03-22T02:50:41.000Z

tools/transferComponentSelection.py

qeeji/glTools

8ff0899de43784a18bd4543285655e68e28fb5e5

[ "MIT" ]

5

2015-12-02T02:39:44.000Z

2020-12-09T02:45:54.000Z

tools/transferComponentSelection.py

qeeji/glTools

8ff0899de43784a18bd4543285655e68e28fb5e5

[ "MIT" ]

83

2015-02-10T17:18:24.000Z

2022-02-10T07:16:47.000Z

import maya.cmds as mc import maya.OpenMaya as OpenMaya import glTools.utils.base def transferComponentSelection(sourceSelection,targetMesh,threshold=0.0001): ''' ''' # Check selection target mesh if not mc.objExists(targetMesh): raise Exception('Target mesh "'+targetMesh+'" does not exist!') # Flatten selection sourceSelection = mc.ls(sourceSelection,fl=True) # Get mesh points tPtArray = glTools.utils.base.getMPointArray(targetMesh) tPtLen = tPtArray.length() # Initialize component selection transfer list tPtBool = [False for i in range(tPtLen)] # Initialize selection list tSel = [] # Transfer selection for sel in sourceSelection: # Get selection point pt = mc.pointPosition(sel) pt = OpenMaya.MPoint(pt[0],pt[1],pt[2],1.0) # Find closest component cDist = 99999 cIndex = -1 for i in range(tPtLen): # Check component selection transfer list if tPtBool[i]: continue # Check distance to current point dist = (pt-tPtArray[i]).length() if dist < cDist: cDist = dist cIndex = i # Test threshold if dist < threshold: break # Append selection tSel.append(targetMesh+'.vtx['+str(cIndex)+']') # Update component selection transfer list tPtBool[i] = True # Return result return tSel

22.596491

76

0.699534

162

1,288

5.561728

0.450617

0.059933

0.08657

0.099889

0.119867

0

0.015504

0.198758

1,288

56

77

23

0.857558

0.274068

0

0.039474

0

1

0.038462

false

0

0.115385

0

0.192308

0

null

0

null

0

1

0

88fe054080de49b8785340e2f3ce23ac82e4a3fa

324

py

Python

the_office/test.py

zubyjaved/reddit-bots

9f15f5ee9eede5223c975c29527c9e58d68bb517

[ "MIT" ]

2

2019-09-07T09:40:23.000Z

2021-06-19T08:40:00.000Z

the_office/test.py

zubyjaved/reddit-bots

9f15f5ee9eede5223c975c29527c9e58d68bb517

[ "MIT" ]

2

2019-09-05T04:42:23.000Z

2019-09-05T04:44:37.000Z

the_office/test.py

zubyjaved/reddit-bots

9f15f5ee9eede5223c975c29527c9e58d68bb517

[ "MIT" ]

null

import json import praw reddit = praw.Reddit("dwight-schrute-bot") for submission in reddit.subreddit('all').rising(limit=15): submission.comments.replace_more(limit=None) print(submission.subreddit.display_name) if not submission.over_18: for comment in submission.comments.list(): print()

29.454545

59

0.722222

42

324

5.5

0.666667

0.08658

0

0.014815

0.166667

324

11

60

29.454545

0.840741

0

0.064615

0

1

0

false

0

0.222222

0

0.222222

0

null

0

null

0

1

0

88fe820e78b74b43c84647fdd224db13efd8f585

1,311

py

Python

Scripts/Client/ManualControlTest.py

Fzeak/sauvc-2019

573dcb351d0f87f9b7605667c570a5003bedb224

[ "MIT" ]

null

Scripts/Client/ManualControlTest.py

Fzeak/sauvc-2019

573dcb351d0f87f9b7605667c570a5003bedb224

[ "MIT" ]

null

Scripts/Client/ManualControlTest.py

Fzeak/sauvc-2019

573dcb351d0f87f9b7605667c570a5003bedb224

[ "MIT" ]

null

from pymavlink import mavutil import time # Create the connection master = mavutil.mavlink_connection('udpin:0.0.0.0:14550') # Wait a heartbeat before sending commands master.wait_heartbeat() # Send a positive x value, negative y, negative z, # positive rotation and no button. # http://mavlink.org/messages/common#MANUAL_CONTROL # Warning: Because of some legacy workaround, z will work between [0-1000] # where 0 is full reverse, 500 is no output and 1000 is full throttle. # x,y and r will be between [-1000 and 1000]. master.mav.manual_control_send( master.target_system, 500, -500, 250, 500, 0) # To active button 0 (first button), 3 (fourth button) and 7 (eighth button) # It's possible to check and configure this buttons in the Joystick menu of QGC buttons = 1 + 1 << 3 + 1 << 7 master.mav.manual_control_send( master.target_system, 0, 0, 0, 0, buttons) # Request all parameters master.mav.param_request_list_send( master.target_system, master.target_component ) while True: time.sleep(0.01) try: message = master.recv_match(type='PARAM_VALUE', blocking=True).to_dict() print('name: %s\tvalue: %d' % (message['param_id'].decode("utf-8"), message['param_value'])) except Exception as e: print(e) exit(0)

28.5

100

0.695652

200

1,311

4.465

0.54

0.013438

0.073908

0.098544

0

0.05698

0.196796

1,311

45

101

29.133333

0.791073

0.423341

0

0.333333

0

0.09825

0

1

0

false

0

0.066667

0

0.066667

0

null

0

null

0

1

0

00002c1133ee1a3e69c2c023cddb9b34c36440ca

1,634

py

Python

setup.py

DNA-and-Natural-Algorithms-Group/peppercompiler

effbcdedfb17534300fb3504a552e46c1ead41e4

[ "MIT" ]

3

2019-06-10T18:44:03.000Z

2021-11-17T10:57:09.000Z

setup.py

DNA-and-Natural-Algorithms-Group/peppercompiler

effbcdedfb17534300fb3504a552e46c1ead41e4

[ "MIT" ]

2

2017-12-15T01:09:49.000Z

2021-03-25T20:42:23.000Z

setup.py

DNA-and-Natural-Algorithms-Group/peppercompiler

effbcdedfb17534300fb3504a552e46c1ead41e4

[ "MIT" ]

4

2017-08-21T03:32:51.000Z

2019-10-18T04:09:38.000Z

#!/usr/bin/env python from setuptools import setup from distutils.command.build import build from setuptools.command.develop import develop class build_with_spurious(build): def run(self): import os if "CC" in os.environ: cc = os.environ['CC'] else: cc = "cc" os.system( "{} -Wall -O3 peppercompiler/SpuriousDesign/spuriousSSM.c -o peppercompiler/_spuriousSSM -lm". format(cc)) build.run(self) class develop_with_spurious(develop): def run(self): import os os.system( "cc -Wall -O3 peppercompiler/SpuriousDesign/spuriousSSM.c -o peppercompiler/_spuriousSSM -lm" ) develop.run(self) setup( name="peppercompiler", version="0.1.3", packages=['peppercompiler', 'peppercompiler.design'], install_requires=["pyparsing", "six"], include_package_data=True, package_data={ 'peppercompiler': ['_spuriousSSM', 'SpuriousDesign/spuriousSSM.c'] }, test_suite='peppercompiler.tests', cmdclass={'build': build_with_spurious, 'develop': develop_with_spurious}, entry_points={ 'console_scripts': [ 'pepper-compiler = peppercompiler.compiler:main', 'pepper-design-spurious = peppercompiler.design.spurious_design:main', 'pepper-finish = peppercompiler.finish:main', 'spuriousSSM = peppercompiler._spuriousSSM_wrapper:main' ] }, author="Constantine Evans et al (this version)", author_email="cge@dna.caltech.edu", description="PepperCompiler in a pythonic form")

29.709091

106

0.641983

170

1,634

6.041176

0.452941

0.046738

0.075949

0.031159

0.179163

0.144109

0

0.004036

0.241738

1,634

54

107

30.259259

0.824859

0.01224

0

0.136364

0

0.405456

0.215127

0

1

0.045455

false

0

0.113636

0

0.204545

0

null

0

null

0

1

0

00004b28f6ae2b9a9b673b26fbf0fba70c90416d

1,126

py

Python

client/client.py

flavioribeiro/playmobil

d104b80fd666158e7ae3d1e28ce8d3ba68e93a68

[ "Apache-2.0" ]

1

2016-10-27T21:30:30.000Z

client/client.py

flavioribeiro/playmobil

d104b80fd666158e7ae3d1e28ce8d3ba68e93a68

[ "Apache-2.0" ]

null

client/client.py

flavioribeiro/playmobil

d104b80fd666158e7ae3d1e28ce8d3ba68e93a68

[ "Apache-2.0" ]

null

import sys sys.path.append("/Library/Frameworks/GStreamer.framework/Versions/0.10/lib/python2.7/site-packages/") import gobject gobject.threads_init() import pygst pygst.require("0.10") import gst class Client(object): def __init__(self): self.pipeline = gst.Pipeline('client') self.videotestsrc = self.create_element('videotestsrc', 'video') self.theoraenc = self.create_element('theoraenc', 'encoder') self.oggmux = self.create_element('oggmux', 'muxer') self.tcpserversink = self.create_element('tcpserversink', 'serversink') self.tcpserversink.set_property('host', '0.0.0.0') self.tcpserversink.set_property('port', 8080) self.pipeline.add(self.videotestsrc, self.theoraenc, self.oggmux, self.tcpserversink) gst.element_link_many(self.videotestsrc, self.theoraenc, self.oggmux, self.tcpserversink) def create_element(self, element, name): return gst.element_factory_make(element, name) def start(self): self.pipeline.set_state(gst.STATE_PLAYING) client = Client() client.start() loop = gobject.MainLoop() loop.run()

34.121212

101

0.71492

139

1,126

5.661871

0.402878

0.082592

0.086404

0.071156

0.142313

0

0.016736

0.150977

1,126

32

102

35.1875

0.806485

0

0.038462

0.154529

0.072824

0

1

0.115385

false

0

0.153846

0.038462

0.346154

0

null

0

null

0

1

0

00005f4a70c5144076952dbbb1c77de24a5e43d7

3,852

py

Python

InternetSemLimites/api/tests/test_edit_view.py

InternetSemLimites/PublicAPI

3dd0f17fe66688ef2895de540950f45d69bcd9d8

[ "MIT" ]

18

2016-04-14T17:03:29.000Z

2020-01-01T00:54:03.000Z

InternetSemLimites/api/tests/test_edit_view.py

InternetSemLimites/PublicAPI

3dd0f17fe66688ef2895de540950f45d69bcd9d8

[ "MIT" ]

48

2016-04-15T12:33:33.000Z

2018-01-25T16:01:45.000Z

InternetSemLimites/api/tests/test_edit_view.py

InternetSemLimites/PublicAPI

3dd0f17fe66688ef2895de540950f45d69bcd9d8

[ "MIT" ]

4

2016-04-15T07:57:04.000Z

2017-09-10T18:10:40.000Z

from django.contrib.auth.models import User from django.core import mail from django.shortcuts import resolve_url from django.test import TestCase from InternetSemLimites.core.forms import ProviderForm from InternetSemLimites.core.models import Provider, State class TestPostValid(TestCase): def setUp(self): User.objects.create_superuser(username='two', password='', email='42@xp.to') sc, *_ = State.objects.get_or_create(abbr='SC', name='Santa Catarina') go, *_ = State.objects.get_or_create(abbr='GO', name='Goiás') self.provider = Provider.objects.create( name='Xpto', url='http://xp.to', source='http://twitter.com/xpto', category=Provider.SHAME, other='Lorem ipsum' ) self.provider.coverage.add(sc) self.provider.coverage.add(go) self.data = { 'name': 'XptoEdited', 'url': 'http://xpedited.to', 'source': 'http://twitter.com/xptoedited', 'coverage': [sc.pk], 'category': Provider.FAME, 'other': 'Lorem ipsum dolor' } self.resp = self.client.post(resolve_url('api:provider', self.provider.pk), self.data) self.edited_provider = Provider.objects.last() def test_not_allowed_methods(self): url = resolve_url('api:provider', self.provider.pk) for r in (self.client.delete(url), self.client.patch(url, self.data)): with self.subTest(): self.assertEqual(405, r.status_code) def test_post(self): self.assertRedirects(self.resp, resolve_url('api:provider', self.edited_provider.pk)) def test_send_email(self): self.assertEqual(1, len(mail.outbox)) def test_edit(self): edited_provider_coverage_ids = [state.id for state in self.edited_provider.coverage.all()] self.assertEqual(self.edited_provider.name, self.data['name']) self.assertEqual(self.edited_provider.url, self.data['url']) self.assertEqual(self.edited_provider.source, self.data['source']) self.assertEqual(self.edited_provider.category, self.data['category']) self.assertEqual(self.edited_provider.other, self.data['other']) self.assertEqual(edited_provider_coverage_ids, self.data['coverage']) class TestPostInvalid(TestCase): def setUp(self): User.objects.create_superuser(username='two', password='', email='42@xp.to') sc, *_ = State.objects.get_or_create(abbr='SC', name='Santa Catarina') go, *_ = State.objects.get_or_create(abbr='GO', name='Goiás') self.provider = Provider.objects.create( name='Xpto', url='http://xp.to', source='http://twitter.com/xpto', category=Provider.SHAME, other='Lorem ipsum' ) self.provider.coverage.add(sc) self.provider.coverage.add(go) self.resp = self.client.post(resolve_url('api:provider', self.provider.pk), dict()) def test_post(self): self.assertEqual(422, self.resp.status_code) def test_has_errors_on_empty_form(self): json_resp = self.resp.json() self.assertTrue(json_resp['errors']) def test_has_errors_on_non_empty_form(self): invalid_data = {'name': 'Xpto', 'coverage': ['xp', 'to'], 'url': ''} resp = self.client.post(resolve_url('api:provider', self.provider.pk), invalid_data) json_resp = resp.json() errors = json_resp['errors'] with self.subTest(): self.assertEqual('Este campo é obrigatório.', errors['category'][0]) self.assertEqual('Este campo é obrigatório.', errors['source'][0]) self.assertEqual('Este campo é obrigatório.', errors['url'][0]) self.assertIn('não é um valor válido', errors['coverage'][0])

39.306122

98

0.637072

474

3,852

5.048523

0.236287

0.075219

0.067697

0.043878

0.550355

0.412035

0.379858

0.34392

0

0.004988

0.219367

3,852

97

99

39.71134

0.790821

0

0.363636

0

0.136552

0

0.194805

1

0.116883

false

0.025974

0.077922

0

0.220779

0

null

0

null

0

1

0

000069dbeffca39b13535cbec664af30b8b425d2

351

py

Python

algorithm-study/therory/selectionSort.py

Seongkyun-Yu/TIL

2be6a2a68246bc98996b1421e2cc20e025c876ed

[ "MIT" ]

1

2020-02-17T15:15:55.000Z

algorithm-study/therory/selectionSort.py

Seongkyun-Yu/TIL

2be6a2a68246bc98996b1421e2cc20e025c876ed

[ "MIT" ]

6

2020-07-31T17:03:56.000Z

2022-02-27T04:17:57.000Z

algorithm-study/therory/selectionSort.py

Seongkyun-Yu/TIL

2be6a2a68246bc98996b1421e2cc20e025c876ed

[ "MIT" ]

null

import random data_list = random.sample(range(100), 50) def selectionSort(arr): for index1 in range(len(arr) - 1): lowestIndex = index1 for index2 in range(index1, len(arr)): if(arr[lowestIndex] > arr[index2]): lowestIndex = index2 arr[index1] = arr[lowestIndex] return arr print(selectionSort(data_list))

18.473684

42

0.65812

45

351

5.088889

0.466667

0.069869

0

0.047794

0.225071

351

18

43

19.5

0.794118

0

1

0.090909

false

0

0.090909

0

0.272727

0.090909

0

null

0

null

0

1

0

00021f532b1a8ddd0a8c15783c8737edde030453

12,045

py

Python

platform/core/polyaxon/monitor_statuses/monitor.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/monitor_statuses/monitor.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/monitor_statuses/monitor.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

import logging from typing import Any, Mapping import redis import conf import ocular import workers from constants.experiment_jobs import get_experiment_job_uuid from db.redis.containers import RedisJobContainers from db.redis.statuses import RedisStatuses from lifecycles.jobs import JobLifeCycle from options.registry.container_names import ( CONTAINER_NAME_BUILD_JOBS, CONTAINER_NAME_EXPERIMENT_JOBS, CONTAINER_NAME_JOBS, CONTAINER_NAME_PLUGIN_JOBS, CONTAINER_NAME_PYTORCH_JOBS, CONTAINER_NAME_TF_JOBS ) from options.registry.spawner import ( APP_LABELS_DOCKERIZER, APP_LABELS_EXPERIMENT, APP_LABELS_JOB, APP_LABELS_NOTEBOOK, APP_LABELS_TENSORBOARD, ROLE_LABELS_DASHBOARD, ROLE_LABELS_WORKER, TYPE_LABELS_RUNNER ) from options.registry.ttl import TTL_WATCH_STATUSES from polyaxon.settings import K8SEventsCeleryTasks logger = logging.getLogger('polyaxon.monitors.statuses') def update_job_containers(event: Mapping, status: str, job_container_name: str) -> None: job_containers = RedisJobContainers() if JobLifeCycle.is_done(status): # Remove the job monitoring job_uuid = event['metadata']['labels']['job_uuid'] logger.info('Stop monitoring job_uuid: %s', job_uuid) job_containers.remove_job(job_uuid) if event['status']['container_statuses'] is None: return def get_container_id(container_id): if not container_id: return None if container_id.startswith('docker://'): return container_id[len('docker://'):] return container_id for container_status in event['status']['container_statuses']: if container_status['name'] != job_container_name: continue container_id = get_container_id(container_status['container_id']) if container_id: job_uuid = event['metadata']['labels']['job_uuid'] if container_status['state']['running'] is not None: logger.info('Monitoring (container_id, job_uuid): (%s, %s)', container_id, job_uuid) job_containers.monitor(container_id=container_id, job_uuid=job_uuid) else: job_containers.remove_container(container_id=container_id) def get_restart_count(event: Mapping, job_container_name: str) -> int: if event['status']['container_statuses'] is None: return 0 for container_status in event['status']['container_statuses']: if container_status['name'] != job_container_name: continue return container_status['restart_count'] or 0 return 0 def get_label_selector() -> str: return 'role in ({},{}),type={}'.format( conf.get(ROLE_LABELS_WORKER), conf.get(ROLE_LABELS_DASHBOARD), conf.get(TYPE_LABELS_RUNNER)) def should_handle_job_status(pod_state: Any, status: str) -> bool: job_uuid = pod_state['details']['labels']['job_uuid'] current_status = RedisStatuses.get_status(job=job_uuid) if not current_status: # If the status does not exist or is evicted return True try: return JobLifeCycle.can_transition(status_from=RedisStatuses.get_status(job=job_uuid), status_to=status) except redis.connection.ConnectionError: return True def handle_job_condition(event_object, pod_state, status, labels, container_name, task_name, update_containers): if update_containers: try: update_job_containers(event_object, status, container_name) except redis.connection.ConnectionError: pass # Handle experiment job statuses if should_handle_job_status(pod_state=pod_state, status=status): logger.debug("Sending state to handler %s, %s", status, labels) restart_count = get_restart_count(event_object, container_name) pod_state['restart_count'] = restart_count or 0 workers.send(task_name, kwargs={'payload': pod_state}, countdown=None) def run(k8s_manager: 'K8SManager') -> None: # pylint:disable=too-many-branches # Local cache label_selector = get_label_selector() container_name_experiment_job = conf.get(CONTAINER_NAME_EXPERIMENT_JOBS) container_name_tf_job = conf.get(CONTAINER_NAME_TF_JOBS) container_name_pytorch_job = conf.get(CONTAINER_NAME_PYTORCH_JOBS) container_name_plugin_job = conf.get(CONTAINER_NAME_PLUGIN_JOBS) container_name_job = conf.get(CONTAINER_NAME_JOBS) container_name_build_job = conf.get(CONTAINER_NAME_BUILD_JOBS) watch_ttl = conf.get(TTL_WATCH_STATUSES) app_labels_experiment = conf.get(APP_LABELS_EXPERIMENT) app_labels_job = conf.get(APP_LABELS_JOB) app_labels_build_job = conf.get(APP_LABELS_DOCKERIZER) app_labels_tensorboard = conf.get(APP_LABELS_TENSORBOARD) app_labels_notebook = conf.get(APP_LABELS_NOTEBOOK) for (event_object, pod_state) in ocular.monitor(k8s_manager.k8s_api, namespace=k8s_manager.namespace, container_names=( container_name_experiment_job, container_name_tf_job, container_name_pytorch_job, container_name_plugin_job, container_name_job, container_name_build_job), label_selector=label_selector, return_event=True, watch_ttl=watch_ttl): logger.debug('-------------------------------------------\n%s\n', pod_state) if not pod_state: continue status = pod_state['status'] labels = None if pod_state['details'] and pod_state['details']['labels']: labels = pod_state['details']['labels'] logger.info("Updating job container %s, %s", status, labels) experiment_condition = status and labels['app'] == app_labels_experiment experiment_job_condition = ( container_name_experiment_job in pod_state['details']['container_statuses'] or 'job_uuid' in labels ) tf_job_condition = ( container_name_tf_job in pod_state['details']['container_statuses'] or 'tf-replica-index' in labels ) mpi_job_condition = 'mpi_job_name' in labels pytorch_job_condition = ( container_name_pytorch_job in pod_state['details']['container_statuses'] or 'pytroch-replica-index' in labels ) job_condition = ( container_name_job in pod_state['details']['container_statuses'] or (status and labels['app'] == app_labels_job) ) plugin_job_condition = ( container_name_plugin_job in pod_state['details']['container_statuses'] or (status and labels['app'] in (app_labels_tensorboard, app_labels_notebook)) ) dockerizer_job_condition = ( container_name_build_job in pod_state['details']['container_statuses'] or (status and labels['app'] == app_labels_build_job) ) if experiment_condition: if tf_job_condition: # We augment the payload with standard Polyaxon requirement pod_state['details']['labels']['job_uuid'] = get_experiment_job_uuid( experiment_uuid=labels['experiment_uuid'], task_type=labels['task_type'], task_index=labels['tf-replica-index'] ) handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_tf_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_EXPERIMENT_JOB_STATUSES, update_containers=False ) elif pytorch_job_condition: # We augment the payload with standard Polyaxon requirement pod_state['details']['labels']['job_uuid'] = get_experiment_job_uuid( experiment_uuid=labels['experiment_uuid'], task_type=labels['task_type'], task_index=labels['pytorch-replica-index'] ) handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_pytorch_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_EXPERIMENT_JOB_STATUSES, update_containers=False ) elif mpi_job_condition: job_name = pod_state['details']['pod_name'] parts = job_name.split('-') if len(parts) != 4: continue # We augment the payload with standard Polyaxon requirement pod_state['details']['labels']['job_uuid'] = get_experiment_job_uuid( experiment_uuid=labels['experiment_uuid'], task_type=labels['task_type'], task_index=parts[-1] ) handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_experiment_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_EXPERIMENT_JOB_STATUSES, update_containers=False ) elif experiment_job_condition: handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_experiment_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_EXPERIMENT_JOB_STATUSES, update_containers=False ) elif job_condition: handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_JOB_STATUSES, update_containers=False ) elif plugin_job_condition: handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_plugin_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_PLUGIN_JOB_STATUSES, update_containers=False ) elif dockerizer_job_condition: handle_job_condition( event_object=event_object, pod_state=pod_state, status=status, labels=labels, container_name=container_name_build_job, task_name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_BUILD_JOB_STATUSES, update_containers=False ) else: logger.info("Lost state %s, %s", status, pod_state)

39.491803

94

0.594687

1,234

12,045

5.423825

0.120746

0.099059

0.031376

0.025549

0.514269

0.466906

0.37457

0.344091

0.314059

0

0.003225

0.330677

12,045

304

95

39.621711

0.826966

0.026401

0

0.313492

0

0.080901

0.009985

0

1

0.027778

false

0.003968

0.055556

0.003968

0.126984

0

null

0

null

0

1

0

0003b8ab877e0ee926932cb4211e0799fd7d5511

14,636

py

Python

flow65/wing_tool.py

corygoates/Flow65

148eddaeeed8711eae37a16820215c89f93f01d5

[ "MIT" ]

null

flow65/wing_tool.py

corygoates/Flow65

148eddaeeed8711eae37a16820215c89f93f01d5

[ "MIT" ]

null

flow65/wing_tool.py

corygoates/Flow65

148eddaeeed8711eae37a16820215c89f93f01d5

[ "MIT" ]

null

import sys import json import numpy as np import matplotlib.pyplot as plt class Wing: """A class for modeling a finite wing using the sine-series solution to Prandtl's lifting-line equation. Parameters ---------- planform : str May be "elliptic" or "tapered". b : float Wingspan. AR : float Aspect ratio. RT : float Taper ratio. Only required for "tapered" planform. CL_a_section : float, optional Section lift slope. Defaults to 2 pi. washout : str May be "none", "linear", or "optimum". washout_mag : float Magnitude of the washout at the tip in degrees. washout_CLd : float Design lift coefficient for washout. Only required if "washout" is "optimum". aileron_lims : list, optional Aileron limits as a fraction of the span. Defaults to entire span. aileron_cf: list, optional Aileron chord fractions at the root and tip of the ailerons. Defaults to 0.0. aileron_hinge_eff : float, optional Aileron hinge efficiency. Defaults to 1.0. """ def __init__(self, **kwargs): # Get planform parameters self._planform_type = kwargs["planform"] self._AR = kwargs["AR"] self._b = kwargs["b"] if self._planform_type == "tapered": self._RT = kwargs["RT"] self._CL_a_s = kwargs.get("CL_a_section", 2.0*np.pi) # Get washout parameters self._washout_type = kwargs.get("washout", "none") if self._washout_type != "none": self._W = np.radians(kwargs.get("washout_mag", 0.0)) else: self._W = 0.0 if self._washout_type == "optimum": self._CL_d = kwargs["washout_CLd"] # Get aileron parameters self._aln_lims = kwargs.get("aileron_lims", [0.0, 0.5]) self._aln_cf = kwargs.get("aileron_cf", [0.0, 0.0]) self._aln_e_hinge = kwargs.get("aileron_hinge_eff", 1.0) def set_grid(self, N): """Sets the spanwise grid for the wing. Uses cosine clustering Parameters ---------- N : int Number of nodes per semispan to specify. Note that one node will be placed at the root, making the total number of nodes 2N-1. """ np.set_printoptions(linewidth=np.inf, precision=12) # Create theta and z distributions self._N = 2*N-1 self._theta = np.linspace(0, np.pi, self._N) self._z = -0.5*self._b*np.cos(self._theta) # Calculate control point trig values self._N_range = np.arange(1, self._N+1) self._S_theta = np.sin(self._theta) # Calculate chord distribution if self._planform_type == "elliptic": self._c_b = 4.0*self._S_theta/(np.pi*self._AR) else: self._c_b = 2.0*(1.0-(1.0-self._RT)*np.abs(np.cos(self._theta)))/(self._AR*(1.0+self._RT)) self._c_b = np.where(self._c_b==0.0, 1e-6, self._c_b) # Calculate washout distribution if self._washout_type == "none": self._w = np.zeros(self._N) elif self._washout_type == "linear": self._w = np.abs(np.cos(self._theta)) elif self._washout_type == "optimum": self._w = 1.0-self._S_theta*self._c_b[self._N//2]/self._c_b self._W = 4.0*self._CL_d/(np.pi*self._AR*self._CL_a_s*self._c_b[self._N//2]) # Determine aileron chord fractions self._cf = np.zeros(self._N) z_in_aileron = ((self._z>self._aln_lims[0]) & (self._z<self._aln_lims[1])) | ((self._z>-self._aln_lims[1]) & (self._z<-self._aln_lims[0])) if self._planform_type == "elliptic": self._c_aln_tip = (4.0/(np.pi*self._AR)*np.sqrt(1.0-(2.0*self._aln_lims[1])**2)) self._c_aln_root = (4.0/(np.pi*self._AR)*np.sqrt(1.0-(2.0*self._aln_lims[0])**2)) else: self._c_aln_tip = (2.0/(self._AR*(1.0+self._RT))*(1.0-(1.0-self._RT)*2.0*self._aln_lims[1])) self._c_aln_root = (2.0/(self._AR*(1.0+self._RT))*(1.0-(1.0-self._RT)*2.0*self._aln_lims[0])) self._x_h_tip = -(1.0-self._aln_cf[1]-0.25)*self._c_aln_tip self._x_h_root = -(1.0-self._aln_cf[0]-0.25)*self._c_aln_root aln_b = (self._x_h_tip-self._x_h_root)/(self._aln_lims[1]-self._aln_lims[0]) x_h = z_in_aileron[self._N//2:]*(self._x_h_root+(self._z[self._N//2:]-self._aln_lims[0])*aln_b) self._cf[self._N//2:] = 1.0-(-x_h/self._c_b[self._N//2:]+0.25) self._cf[self._N//2::-1] = 1.0-(-x_h/self._c_b[self._N//2:]+0.25) self._cf *= z_in_aileron # Determine flap efficiency theta_f = np.arccos(2.0*self._cf-1.0) self._e_f = (1.0-(theta_f-np.sin(theta_f))/np.pi)*self._aln_e_hinge self._e_f[self._N//2:] *= -1.0 # Get C matrix self._C = np.zeros((self._N, self._N)) self._C[0,:] = self._N_range**2 self._C[1:-1,:] = (4.0/(self._CL_a_s*self._c_b[1:-1,np.newaxis])+self._N_range[np.newaxis,:]/self._S_theta[1:-1,np.newaxis])*np.sin(self._N_range[np.newaxis,:]*self._theta[1:-1,np.newaxis]) self._C[-1,:] = (-1.0)**(self._N_range+1)*self._N_range**2 # Get C inverse (why on earth, I have no idea...) self._C_inv = np.linalg.inv(self._C) # Determine the Fourier coefficients self._a_n = np.linalg.solve(self._C, np.ones(self._N)) self._b_n = np.linalg.solve(self._C, self._w) self._c_n = np.linalg.solve(self._C, self._e_f) self._d_n = np.linalg.solve(self._C, np.cos(self._theta)) # Determine coefficient slopes self.CL_a = np.pi*self._AR*self._a_n[0] # Determine the kappa factors due to planform self.K_D = np.sum(np.arange(2, self._N+1)*self._a_n[1:]**2/self._a_n[0]**2) A = (1+np.pi*self._AR/self._CL_a_s)*self._a_n[0] self.K_L = (1.0-A)/A # Determine span efficiency factor self.e_s = 1.0/(1.0+self.K_D) # Determine kappa factors due to washout if self._washout_type != "none": self.e_omega = self._b_n[0]/self._a_n[0] self.K_DL = 2.0*self._b_n[0]/self._a_n[0]*np.sum(self._N_range[1:]*self._a_n[1:]/self._a_n[0]*(self._b_n[1:]/self._b_n[0]-self._a_n[1:]/self._a_n[0])) self.K_Domega = (self._b_n[0]/self._a_n[0])**2*np.sum(self._N_range[1:]*(self._b_n[1:]/self._b_n[0]-self._a_n[1:]/self._a_n[0])**2) self.K_Do = self.K_D-0.25*self.K_DL**2/self.K_Domega else: self.e_omega = 0.0 self.K_DL = 0.0 self.K_Domega = 0.0 self.K_Do = 0.0 # Determine aileron and roll derivatives self.Cl_da = -0.25*np.pi*self._AR*self._c_n[1] self.Cl_p = -0.25*np.pi*self._AR*self._d_n[1] def set_condition(self, **kwargs): """Sets atmospheric condition for the wing. Parameters ---------- alpha : float Angle of attack in degrees. V_inf : float Freestream velocity. da : float, optional Aileron deflection in degrees. Defaults to 0.0. p_bar : float or string, optional Nondimensional rolling rate. May be "steady" to imply the steady roll rate should be solved for. Defaults to 0.0. """ # Store condition self._alpha = np.radians(kwargs["alpha"]) self._V_inf = kwargs["V_inf"] self._da = np.radians(kwargs.get("da", 0.0)) self._p_bar = kwargs.get("p_bar", 0.0) def solve(self): """Solves for the aerodynamic coefficients at the current condition.""" # Determine rolling moment/rate if self._p_bar == "steady": self.Cl = 0.0 self.p_bar = -self.Cl_da*self._da/self.Cl_p else: self.p_bar = self._p_bar self.Cl = self.Cl_da*self._da+self.Cl_p*self.p_bar # Determine Fourier coefficients dependent on condition self._A_n = self._a_n*(self._alpha)-self._b_n*self._W+self._c_n*self._da+self._d_n*self.p_bar # Determine lift coefficient self.CL = np.pi*self._AR*self._A_n[0] # Calculate gamma distribution An_sin_n0 = self._A_n[np.newaxis,:]*np.sin(self._N_range[np.newaxis,:]*self._theta[:,np.newaxis]) self.gamma = 2.0*self._b*self._V_inf*np.sum(An_sin_n0, axis=1).flatten() # Determine drag coefficient with and without rolling and aileron effects self.CD_i = np.pi*self._AR*np.sum(self._N_range*self._A_n**2) # With self.CD_i_simp = (self.CL**2*(1.0+self.K_D)-self.K_DL*self.CL*self.CL_a*self._W+self.K_Domega*(self.CL_a*self._W)**2)/(np.pi*self._AR) # Determine yawing moment C = 0.0 for i in range(3, self._N): C += (2.0*i+1)*self._A_n[i-1]*self._A_n[i] self.Cn = 0.125*self.CL*(6.0*self._A_n[1]-self.p_bar)+0.125*np.pi*self._AR*(10.0*self._A_n[1]-self.p_bar)*self._A_n[2]+0.25*np.pi*self._AR*C def plot_planform(self): """Shows a plot of the planform.""" # Get leading and trailing edge points x_le = np.zeros(self._N+2) x_te = np.zeros(self._N+2) x_le[1:-1] = 0.25*self._c_b x_te[1:-1] = -0.75*self._c_b z = np.zeros(self._N+2) z[0] = self._z[0] z[1:-1] = self._z z[-1] = self._z[-1] # Plot outline and LQC plt.figure() plt.plot(z, x_le, 'k-') plt.plot(z, x_te, 'k-') plt.plot(z, np.zeros(self._N+2), 'b-', label='c/4') # Plot spanwise stations for i in range(self._N): plt.plot([z[i+1], z[i+1]], [x_le[i+1], x_te[i+1]], 'b--') # Plot ailerons plt.plot([self._aln_lims[0], self._aln_lims[0], self._aln_lims[1], self._aln_lims[1]], [-0.75*self._c_aln_root, self._x_h_root, self._x_h_tip, -0.75*self._c_aln_tip], 'k-') plt.plot([-self._aln_lims[0], -self._aln_lims[0], -self._aln_lims[1], -self._aln_lims[1]], [-0.75*self._c_aln_root, self._x_h_root, self._x_h_tip, -0.75*self._c_aln_tip], 'k-') # Plot labels plt.xlabel('z/b') plt.ylabel('x/b') plt.title('Planform') plt.gca().set_aspect('equal', adjustable='box') plt.legend(loc='upper right') plt.show() def plot_washout(self): """Plots the washout distribution on the wing.""" plt.figure() plt.plot(self._z, self._w, 'k-') plt.xlabel("z/b") plt.ylabel("Washout [deg]") plt.title("Washout Distribution") plt.show() def plot_aileron(self): """Plots the aileron deflection distribution on the wing.""" plt.figure() plt.plot(self._z, self._e_f, 'k-') plt.xlabel("z/b") plt.ylabel("Aileron Effectiveness") plt.title("Aileron Effectiveness") plt.show() if __name__=="__main__": # Read in input input_file = sys.argv[-1] with open(input_file, 'r') as input_handle: input_dict = json.load(input_handle) # Initialize wing wing_dict = input_dict["wing"] washout_dict = input_dict["wing"]["washout"] aileron_dict = input_dict["wing"]["aileron"] wing = Wing(planform=wing_dict["planform"]["type"], AR=wing_dict["planform"]["aspect_ratio"], RT=wing_dict["planform"].get("taper_ratio"), CL_a_section=wing_dict["airfoil_lift_slope"], washout=washout_dict["distribution"], washout_mag=washout_dict["magnitude[deg]"], washout_CLd=washout_dict["CL_design"], aileron_lims=[aileron_dict["begin[z/b]"], aileron_dict["end[z/b]"]], aileron_cf=[aileron_dict["begin[cf/c]"], aileron_dict["end[cf/c]"]], aileron_hinge_eff=aileron_dict["hinge_efficiency"]) # Set up grid wing.set_grid(wing_dict["nodes_per_semispan"]) # Set condition cond_dict = input_dict["condition"] wing.set_condition(alpha=cond_dict["alpha_root[deg]"], da=cond_dict["aileron_deflection[deg]"], p_bar=cond_dict["pbar"]) # Solve wing.solve() print() print("Wing") print(" Type: {0}".format(wing._planform_type)) print(" Aspect Ratio: {0}".format(wing._AR)) try: print(" Taper Ratio: {0}".format(wing._RT)) except AttributeError: pass print(" Nodes: {0}".format(wing._N)) print() print("Condition") print(" Alpha: {0} deg".format(np.degrees(wing._alpha))) print(" p_bar: {0}".format(wing.p_bar)) print() print("Aerodynamic Coefficients") print(" CL: {0}".format(wing.CL)) print(" CD_i (without roll and aileron effects): {0}".format(wing.CD_i_simp)) print(" CD_i (with roll and airleron effects): {0}".format(wing.CD_i)) print(" Cl: {0}".format(wing.Cl)) print(" Cn: {0}".format(wing.Cn)) print() print("Planform Effects") print(" CL,a: {0}".format(wing.CL_a)) print(" K_L: {0}".format(wing.K_L)) print(" K_D: {0}".format(wing.K_D)) print(" Span efficiency: {0}".format(wing.e_s)) print() print("Washout Effects") print(" Washout effectiveness: {0}".format(wing.e_omega)) print(" K_DL: {0}".format(wing.K_DL)) print(" Washout contribution to induced drag: {0}".format(wing.K_Domega)) print(" K_Do: {0}".format(wing.K_Do)) print() print("Aileron Effects") print(" Cl,da: {0}".format(wing.Cl_da)) print() print("Roll Effects") print(" Cl,p: {0}".format(wing.Cl_p)) # Check for plot requests if input_dict["view"]["planform"]: wing.plot_planform() if input_dict["view"]["washout_distribution"]: wing.plot_washout() if input_dict["view"]["aileron_distribution"]: wing.plot_aileron() # Write solution with open("Solution.txt", 'w') as f: C_str = np.array2string(wing._C) C_inv_str = np.array2string(wing._C_inv) a_n_str = np.array2string(wing._a_n) b_n_str = np.array2string(wing._b_n) c_n_str = np.array2string(wing._c_n) d_n_str = np.array2string(wing._d_n) print("C array", file=f) print(C_str, file=f) print("C_inv array", file=f) print(C_inv_str, file=f) print("a_n", file=f) print(a_n_str, file=f) print("b_n", file=f) print(b_n_str, file=f) print("c_n", file=f) print(c_n_str, file=f) print("d_n", file=f) print(d_n_str, file=f)

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0.205128

0

null

0

null

0

1

0

0005a7c39d8aea447086a691df1fb17b38ca23eb

927

py

Python

UAC.py

weareblahs/wsa-auto-install

0633d2b4e36ba50ddbe5b16505b8a09ff764df26

[ "MIT" ]

76

2021-10-29T23:41:26.000Z

2021-12-09T06:31:04.000Z

UAC.py

weareblahs/wsa-auto-install

0633d2b4e36ba50ddbe5b16505b8a09ff764df26

[ "MIT" ]

7

2021-11-10T19:05:26.000Z

2021-12-07T15:53:43.000Z

UAC.py

weareblahs/wsa-auto-install

0633d2b4e36ba50ddbe5b16505b8a09ff764df26

[ "MIT" ]

16

2021-11-06T06:17:58.000Z

2021-12-08T22:08:24.000Z

def elevate(): import ctypes, win32com, win32event, win32process, os, sys outpath = r'%s\%s.out' % (os.environ["TEMP"], os.path.basename(__file__)) if ctypes.windll.shell32.IsUserAnAdmin(): if os.path.isfile(outpath): sys.stderr = sys.stdout = open(outpath, 'w', 0) return with open(outpath, 'w+', 0) as outfile: hProc = win32com.shell.shell.ShellExecuteEx(lpFile=sys.executable, \ lpVerb='runas', lpParameters=' '.join(sys.argv), fMask=64, nShow=0)['hProcess'] while True: hr = win32event.WaitForSingleObject(hProc, 40) while True: line = outfile.readline() if not line: break sys.stdout.write(line) if hr != 0x102: break os.remove(outpath) sys.stderr = '' sys.exit(win32process.GetExitCodeProcess(hProc)) if __name__ == '__main__': elevate() main()

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927

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false

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0

0.130435

0

null

0

null

0

1

0

0006b94d0b62d69c3ab03500298cb1fb2775bd17

629

py

Python

etikihead/urls.py

hodeld/etiki-prototype1

bcae893423519f6ddfa4f67b980066e04062d9f3

[ "MIT" ]

1

2019-08-31T18:04:39.000Z

etikihead/urls.py

hodeld/etiki-prototype1

bcae893423519f6ddfa4f67b980066e04062d9f3

[ "MIT" ]

19

2019-12-12T01:38:49.000Z

2022-03-12T00:26:14.000Z

etikihead/urls.py

hodeld/etiki-prototype1

bcae893423519f6ddfa4f67b980066e04062d9f3

[ "MIT" ]

null

from django.urls import path from django.conf import settings from django.conf.urls.static import static from . import views app_name = 'etikihead' urlpatterns = [ path('', views.entry_mask, name='entrymask'), path('contact/', views.contact, name='contact'), path('privacy/', views.privacy, name='privacy'), path('terms/', views.legal, name='legal'), path('impressum/', views.impressum, name='impressum'), path('about/', views.about, name='about'), path('faq/', views.faq, name='faq'), path('todo/', views.todo, name='todo'), path('startinfo/', views.startinfo, name='startinfo'), # ]

26.208333

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0.072816

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0.25

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null

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null

0

1

0

0007473466f54c5bf5a586f0d058eba177c13018

1,070

py

Python

test_proj/test_tcA002.py

leeltib/vizsgamunka_ref

59dc64d499c32a548a6e83c251cf16e2787e8672

[ "MIT" ]

null

test_proj/test_tcA002.py

leeltib/vizsgamunka_ref

59dc64d499c32a548a6e83c251cf16e2787e8672

[ "MIT" ]

null

test_proj/test_tcA002.py

leeltib/vizsgamunka_ref

59dc64d499c32a548a6e83c251cf16e2787e8672

[ "MIT" ]

null

# TC002 test case - Login in with new user data - exit import data.data_tcA002 as da02 import func.func_01 as fu01 from selenium import webdriver from selenium.webdriver.common.by import By import time from selenium.webdriver.chrome.options import Options from webdriver_manager.chrome import ChromeDriverManager options = Options() options.headless = True driver = webdriver.Chrome(executable_path=ChromeDriverManager().install(), options=options) driver.get("http://localhost:1667") # Wait for loading fu01.wait(driver, By.ID, "app", 2) # *** TC-A002 ************************************** def test_A002(): fu01.cookie_ok(driver) fu01.sign_in(driver, da02.mail, da02.passw) usern_text = fu01.login_check(driver) fu01.out_close_driver(driver) return usern_text username_text = test_A002() # *************************************************** # Normal run if __name__ == "__main__": print(username_text) try: assert da02.name == username_text except: print("Hiba, az ellenőrző feltételnél nincs egyezés.")

23.26087

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1,070

5.216418

0.544776

0.051502

0.060086

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0.36

0.08

0

null

0

null

0

1

0

000803fab8613a18bfb601cb4e0f3433d97e4dce

966

py

Python

lisc/tests/test_data_utils.py

jasongfleischer/lisc

ed30be957d7ce13ccbac51092990869840e6f176

[ "Apache-2.0" ]

1

2020-05-11T18:36:16.000Z

lisc/tests/test_data_utils.py

jasongfleischer/lisc

ed30be957d7ce13ccbac51092990869840e6f176

[ "Apache-2.0" ]

null

lisc/tests/test_data_utils.py

jasongfleischer/lisc

ed30be957d7ce13ccbac51092990869840e6f176

[ "Apache-2.0" ]

null

"""Tests for the data utilities from lisc.""" from lisc.data.utils import * ################################################################################################### ################################################################################################### def test_count_elements(): tdat = ['a', 'b', 'a', None] out = count_elements(tdat) assert out['a'] == 2 assert out['b'] == 1 assert None not in out def test_combine_lists(): tdat = [['a', 'b'], None, ['c', 'd']] out = combine_lists(tdat) assert out == ['a', 'b', 'c', 'd'] def test_convert_string(): string_words = 'The Last wOrd, in the bRain!' words_out = convert_string(string_words) expected = ['last', 'word', 'brain'] assert words_out == expected def test_lower_list(): words = ['The', 'Cool', 'Project'] words_out = lower_list(words) expected = ['the', 'cool', 'project'] assert words_out == expected

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0.064073

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966

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false

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0

0.238095

0

null

0

null

0

1

0

0008876f23a1dced29f967f65132c7d09b0756dc

2,316

py

Python

repos/system_upgrade/el8toel9/actors/firewalldcheckallowzonedrifting/actor.py

tmds/leapp-repository

7c9ea115a68530eb25f5c23d3fcadd60c501bf78

[ "Apache-2.0" ]

null

repos/system_upgrade/el8toel9/actors/firewalldcheckallowzonedrifting/actor.py

tmds/leapp-repository

7c9ea115a68530eb25f5c23d3fcadd60c501bf78

[ "Apache-2.0" ]

1

2022-03-07T15:34:11.000Z

2022-03-07T15:35:15.000Z

repos/system_upgrade/el8toel9/actors/firewalldcheckallowzonedrifting/actor.py

tmds/leapp-repository

7c9ea115a68530eb25f5c23d3fcadd60c501bf78

[ "Apache-2.0" ]

null

from leapp import reporting from leapp.actors import Actor from leapp.models import FirewalldGlobalConfig, FirewallsFacts from leapp.reporting import create_report, Report from leapp.tags import ChecksPhaseTag, IPUWorkflowTag class FirewalldCheckAllowZoneDrifting(Actor): """ This actor will check if AllowZoneDrifiting=yes in firewalld.conf. This option has been removed in RHEL-9 and behavior is as if AllowZoneDrifiting=no. """ name = 'firewalld_check_allow_zone_drifting' consumes = (FirewallsFacts, FirewalldGlobalConfig) produces = (Report,) tags = (ChecksPhaseTag, IPUWorkflowTag) def process(self): # If firewalld is not enabled then don't bother the user about its # configuration. This Report keys off a _default_ value and as such # will trigger for all users that have not done one of the following: # - disabled firewalld # - manually set AllowZoneDrifting=no (as firewalld logs suggests) # for facts in self.consume(FirewallsFacts): if not facts.firewalld.enabled: return for facts in self.consume(FirewalldGlobalConfig): if not facts.allowzonedrifting: return create_report([ reporting.Title('Firewalld Configuration AllowZoneDrifting Is Unsupported'), reporting.Summary('Firewalld has enabled configuration option ' '"{conf_key}" which has been removed in RHEL-9. ' 'New behavior is as if "{conf_key}" was set to "no".'.format( conf_key='AllowZoneDrifiting')), reporting.Severity(reporting.Severity.HIGH), reporting.Tags([reporting.Tags.SANITY, reporting.Tags.FIREWALL]), reporting.Flags([reporting.Flags.INHIBITOR]), reporting.ExternalLink( url='https://access.redhat.com/articles/4855631', title='Changes in firewalld related to Zone Drifting'), reporting.Remediation( hint='Set AllowZoneDrifting=no in /etc/firewalld/firewalld.conf', commands=[['sed -i "s/^AllowZoneDrifting=.*/AllowZoneDrifting=no/" ' '/etc/firewalld/firewalld.conf']]), ])

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0

0.382353

0

null

0

null

0

1

0

0009620a33b624fda2004552df089e8ea26f0972

665

py

Python

eeve/eeve actions/list_dir.py

vMarcelino/eeve

7dcfa17d34480f5c120ce963680babffff8ab412

[ "Apache-2.0" ]

1

2019-10-11T18:42:48.000Z

eeve/eeve actions/list_dir.py

vMarcelino/eeve

7dcfa17d34480f5c120ce963680babffff8ab412

[ "Apache-2.0" ]

null

eeve/eeve actions/list_dir.py

vMarcelino/eeve

7dcfa17d34480f5c120ce963680babffff8ab412

[ "Apache-2.0" ]

1

2019-10-11T18:42:49.000Z

import os def run(path: str, return_full_path: bool = False): """Gets all files and folders from a path and stores them into $file_list Arguments: path {str} -- The path to get files and folders from Keyword Arguments: return_full_path {bool} -- True to return the full path of the file instead of just the file name (default: {False}) Returns: file_list {List[str]} -- list of files and folders """ result = os.listdir(path) if return_full_path: for i, f in enumerate(result): result[i] = os.path.join(path, f) return {'file_list': result} actions = {"list dir": run}

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665

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false

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0.375

0

null

0

null

0

1

0

0009cacc81bd5d1ceb8972e6ec2ff4235cfdb2ad

11,938

py

Python

tests/test_workspaces.py

jeokrohn/wxc_sdk

e28b7e0f870d17b7f9a79ad9a4b8af221e58f8e9

[ "MIT" ]

null

tests/test_workspaces.py

jeokrohn/wxc_sdk

e28b7e0f870d17b7f9a79ad9a4b8af221e58f8e9

[ "MIT" ]

null

tests/test_workspaces.py

jeokrohn/wxc_sdk

e28b7e0f870d17b7f9a79ad9a4b8af221e58f8e9

[ "MIT" ]

1

2022-03-29T18:56:59.000Z

""" Test for workspaces API """ # TODO: tests for authorization codes import random from collections.abc import Generator from concurrent.futures import ThreadPoolExecutor from contextlib import contextmanager from wxc_sdk.rest import RestError from wxc_sdk.all_types import * from .base import TestCaseWithLog TEST_WORKSPACES_PREFIX = 'workspace test ' class TestList(TestCaseWithLog): def test_001_list(self): workspaces = list(self.api.workspaces.list()) print(f'got {len(workspaces)} workspaces') print('\n'.join(w.json() for w in workspaces)) class TestDetails(TestCaseWithLog): def test_001_all(self): """ details for all workspaces """ ws = self.api.workspaces ws_list = ws.list() with ThreadPoolExecutor() as pool: details = list(pool.map(lambda w: ws.details(workspace_id=w.workspace_id), ws_list)) print(f'got details for {len(details)} workspaces') class TestOutgoingPermissionsAutoTransferNumbers(TestCaseWithLog): def test_001_get_all(self): """ get outgoing permissions auto transfer numbers for all workspaces """ wsa = self.api.workspaces tna = self.api.workspace_settings.permissions_out.transfer_numbers targets = [ws for ws in wsa.list() if ws.calling == CallingType.webex] if not targets: self.skipTest('Need some WxC enabled workspaces to run this test') with ThreadPoolExecutor() as pool: _ = list(pool.map(lambda ws: tna.read(person_id=ws.workspace_id), targets)) print(f'outgoing permissions auto transfer numbers for {len(targets)} workspaces') @contextmanager def target_ws_context(self, use_custom_enabled: bool = True) -> Workspace: """ pick a random workspace and make sure that the outgoing permission settings are restored :return: """ po = self.api.workspace_settings.permissions_out targets = [ws for ws in self.api.workspaces.list() if ws.calling == CallingType.webex] if not targets: self.skipTest('Need some WxC enabled workspaces to run this test') random.shuffle(targets) # if enable == False then we need a workspace where custom_enabled is not set. Else setting it to False # will clear all existing customer settings and we want to avoid that side effect of the test po_settings = None target_ws = next((ws for ws in targets if use_custom_enabled or not (po_settings := po.read(person_id=ws.workspace_id)).use_custom_enabled), None) if target_ws is None: self.skipTest('No WxC enabled workspace with use_custom_enabled==False') if po_settings is None: po_settings = po.read(person_id=target_ws.workspace_id) try: if use_custom_enabled: # enable custom settings: else auto transfer numbers can't be set po.configure(person_id=target_ws.workspace_id, settings=OutgoingPermissions(use_custom_enabled=use_custom_enabled)) yield target_ws finally: # restore old settings if use_custom_enabled: po.configure(person_id=target_ws.workspace_id, settings=po_settings) po_restored = po.read(person_id=target_ws.workspace_id) self.assertEqual(po_settings, po_restored) def test_002_update_wo_custom_enabled(self): """ updating auto transfer numbers requires use_custom_enabled to be set :return: """ tna = self.api.workspace_settings.permissions_out.transfer_numbers with self.target_ws_context(use_custom_enabled=False) as target_ws: target_ws: Workspace numbers = tna.read(person_id=target_ws.workspace_id) try: # change auto transfer number 1 update = numbers.copy(deep=True) transfer = f'+4961007739{random.randint(0, 999):03}' update.auto_transfer_number1 = transfer tna.configure(person_id=target_ws.workspace_id, settings=update) # verify update updated = tna.read(person_id=target_ws.workspace_id) # update should not work with use_custom_enabled == False self.assertEqual(numbers, updated) finally: # restore old settings tna.configure(person_id=target_ws.workspace_id, settings=numbers.configure_unset_numbers) restored = tna.read(person_id=target_ws.workspace_id) self.assertEqual(numbers, restored) # try # with def test_003_update_one_number(self): """ try to update auto transfer numbers for a workspace """ tna = self.api.workspace_settings.permissions_out.transfer_numbers with self.target_ws_context() as target_ws: target_ws: Workspace numbers = tna.read(person_id=target_ws.workspace_id) try: # change auto transfer number 1 update = numbers.copy(deep=True) transfer = f'+496100773{random.randint(0, 9999):03}' update.auto_transfer_number1 = transfer tna.configure(person_id=target_ws.workspace_id, settings=update) # verify update updated = tna.read(person_id=target_ws.workspace_id) # number should be equal; ignore hyphens in number returned by API self.assertEqual(transfer, updated.auto_transfer_number1.replace('-', '')) # other than that the updated numbers should be identical to the numbers before updated.auto_transfer_number1 = numbers.auto_transfer_number1 self.assertEqual(numbers, updated) finally: # restore old settings tna.configure(person_id=target_ws.workspace_id, settings=numbers.configure_unset_numbers) restored = tna.read(person_id=target_ws.workspace_id) self.assertEqual(numbers, restored) # try # with def test_002_update_one_number_no_effect_on_other_numbers(self): """ try to update auto transfer numbers for a workspace. Verify that updating a single number doesn't affect the other numbers """ tna = self.api.workspace_settings.permissions_out.transfer_numbers with self.target_ws_context() as target_ws: target_ws: Workspace numbers = tna.read(person_id=target_ws.workspace_id) try: all_numbers_set = AutoTransferNumbers(auto_transfer_number1='+4961007738001', auto_transfer_number2='+4961007738002', auto_transfer_number3='+4961007738003') tna.configure(person_id=target_ws.workspace_id, settings=all_numbers_set) all_numbers_set = tna.read(person_id=target_ws.workspace_id) # change auto transfer number 1 transfer = f'+496100773{random.randint(0, 9999):03}' update = AutoTransferNumbers(auto_transfer_number1=transfer) tna.configure(person_id=target_ws.workspace_id, settings=update) # verify update updated = tna.read(person_id=target_ws.workspace_id) # number should be equal; ignore hyphens in number returned by API self.assertEqual(transfer, updated.auto_transfer_number1.replace('-', '')) # other than that the updated numbers should be identical to the numbers before updated.auto_transfer_number1 = all_numbers_set.auto_transfer_number1 self.assertEqual(all_numbers_set, updated) finally: # restore old settings tna.configure(person_id=target_ws.workspace_id, settings=numbers.configure_unset_numbers) restored = tna.read(person_id=target_ws.workspace_id) self.assertEqual(numbers, restored) # try # with class TestCreateUpdate(TestCaseWithLog): def new_names(self) -> Generator[str, None, None]: ws_list = list(self.api.workspaces.list()) ws_names = set(w.display_name for w in ws_list) new_gen = (name for i in range(1000) if (name := f'{TEST_WORKSPACES_PREFIX}{i:03}') not in ws_names) return new_gen @contextmanager def target(self, no_edge: bool = False): ws = self.api.workspaces ws_list = list(ws.list()) if no_edge: ws_list = [ws for ws in ws_list if ws.calling != CallingType.edge_for_devices] targat_ws = random.choice(ws_list) targat_ws = ws.details(workspace_id=targat_ws.workspace_id) try: yield targat_ws finally: ws.update(workspace_id=targat_ws.workspace_id, settings=targat_ws) restored = ws.details(workspace_id=targat_ws.workspace_id) self.assertEqual(targat_ws, restored) def test_001_trivial(self): """ create workspace with minimal settings """ ws = self.api.workspaces name = next(self.new_names()) settings = Workspace.create(display_name=name) workspace = ws.create(settings=settings) print(f'new worksspace: {workspace.json()}') self.assertEqual(name, workspace.display_name) def test_002_edge_for_devices(self): """ create workspace with edge_for_devices """ ws = self.api.workspaces name = next(self.new_names()) settings = Workspace(display_name=name, calling=CallingType.edge_for_devices) workspace = ws.create(settings=settings) print(f'new worksspace: {workspace.json()}') self.assertEqual(name, workspace.display_name) def test_003_change_name_full(self): """ change name of a workspace, full settings """ ws = self.api.workspaces with self.target(no_edge=True) as target_ws: target_ws: Workspace settings: Workspace = target_ws.copy(deep=True) new_name = next(self.new_names()) settings.display_name = new_name after = ws.update(workspace_id=target_ws.workspace_id, settings=settings) self.assertEqual(new_name, after.display_name) def test_004_change_name_name_only(self): """ change name of a workspace, only name update """ ws = self.api.workspaces with self.target(no_edge=True) as target_ws: target_ws: Workspace new_name = next(self.new_names()) settings = Workspace(display_name=new_name) after = ws.update(workspace_id=target_ws.workspace_id, settings=settings) self.assertEqual(new_name, after.display_name) class TestDelete(TestCaseWithLog): def test_001_delete_one(self): """ delete a random workspace """ ws = self.api.workspaces ws_list = list(ws.list(display_name=TEST_WORKSPACES_PREFIX)) if not ws_list: self.skipTest('No test workspace to delete') target = random.choice(ws_list) ws.delete_workspace(workspace_id=target.workspace_id) with self.assertRaises(RestError) as exc: ws.details(workspace_id=target.workspace_id) rest_error: RestError = exc.exception self.assertEqual(404, rest_error.response.status_code)

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1,399

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5.13867

0.149392

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0.060787

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0

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11,938

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0

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0

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0.087432

1

0.076503

false

0

0.038251

0

0.147541

0.032787

0

null

0

null

0

1

0

000aa2371b1616577368d0ba5de43105bfebe942

1,935

py

Python

openpose/data/parse_tfrecord.py

calmisential/Pose_Estimation

f3546fcfdc81ef60708fbda5fc1eb499679fff2f

[ "MIT" ]

null

openpose/data/parse_tfrecord.py

calmisential/Pose_Estimation

f3546fcfdc81ef60708fbda5fc1eb499679fff2f

[ "MIT" ]

null

openpose/data/parse_tfrecord.py

calmisential/Pose_Estimation

f3546fcfdc81ef60708fbda5fc1eb499679fff2f

[ "MIT" ]

null

import tensorflow as tf import glob from configuration import OpenPoseCfg as cfg from openpose.data.augmentation import Transformer def get_tfrecord_filenames(path): print("从"+path+"中提取TFRecords文件：") tfrecord_files = glob.glob(path + "*") tfrecord_files.sort() if not tfrecord_files: raise ValueError("未找到TFRecords文件!") for filename in tfrecord_files: print(filename) return tfrecord_files def place_label_func(label): paf_tr = label["pafs"] kpt_tr = label["kpts"] image = label["image"] return image, (paf_tr, paf_tr, paf_tr, paf_tr, kpt_tr, kpt_tr) class TFRecordDataset: def __init__(self, tfrecord_filenames, label_placement_func): self.AUTOTUNE = tf.data.AUTOTUNE self.label_place = label_placement_func self.tfrecords = tfrecord_filenames self.transformer = Transformer() self.img_aug = cfg.image_aug_on self.batch_size = cfg.batch_size def generate(self): dataset = tf.data.TFRecordDataset(filenames=self.tfrecords) dataset = dataset.map(self.transformer.read_tfrecord, num_parallel_calls=self.AUTOTUNE) dataset = dataset.map(self.transformer.read_image, num_parallel_calls=self.AUTOTUNE) dataset = dataset.map(self.transformer.convert_label_to_tensors, num_parallel_calls=self.AUTOTUNE) dataset = dataset.batch(self.batch_size) if self.img_aug: dataset = dataset.map(self.transformer.image_aug, num_parallel_calls=self.AUTOTUNE) dataset = dataset.map(self.transformer.apply_mask, num_parallel_calls=self.AUTOTUNE) dataset = dataset.map(self.label_place, num_parallel_calls=self.AUTOTUNE) # dataset = dataset.repeat() return dataset def get_dataset(): tfrecord_files = get_tfrecord_filenames(cfg.train_tfrecords) dataset = TFRecordDataset(tfrecord_files, place_label_func).generate() return dataset

34.553571

106

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1,935

5.533058

0.27686

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0.0941

0.302465

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0.04878

0

null

0

null

0

1

0

000df0f38e45fa3a6986ef9af7fbf9e539d0a092

689

py

Python

gbdxtools/images/quickbird.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

81

2016-04-05T23:32:46.000Z

2022-01-02T21:21:09.000Z

gbdxtools/images/quickbird.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

624

2016-04-06T22:22:01.000Z

2022-01-03T17:48:50.000Z

gbdxtools/images/quickbird.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

66

2016-04-13T22:45:37.000Z

2022-01-03T18:03:26.000Z

from gbdxtools.images.worldview import WorldViewImage from gbdxtools.images.geoeye01 import GeoEyeDriver from gbdxtools.images.util import vector_services_query band_types = { 'MS': 'BGRN', 'Panchromatic': 'PAN', 'Pan': 'PAN', 'pan': 'PAN' } class QB02Driver(GeoEyeDriver): pass class QB02(WorldViewImage): __Driver__ = QB02Driver @property def _rgb_bands(self): return [2,1,0] @staticmethod def _find_parts(cat_id, band_type): query = "item_type:IDAHOImage AND attributes.catalogID:{} " \ "AND attributes.colorInterpretation:{}".format(cat_id, band_types[band_type]) return vector_services_query(query)

25.518519

93

0.690856

78

689

5.858974

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0.052516

0.124726

0.052516

0

0.019964

0.20029

689

26

94

26.5

0.809437

0

0.172714

0.081277

0

1

0.095238

false

0.047619

0.142857

0.047619

0.47619

0

null

0

null

0

1

0

000f226aca878e3b01ee23b36d3e3744fe747d69

1,137

py

Python

fastISM/models/bpnet.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

12

2020-09-20T17:03:48.000Z

2022-03-16T06:51:52.000Z

fastISM/models/bpnet.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

5

2020-10-24T20:43:45.000Z

2022-02-25T19:40:47.000Z

fastISM/models/bpnet.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

2

2020-10-14T05:18:55.000Z

2022-02-21T07:34:14.000Z

import tensorflow as tf def bpnet_model(seqlen=1000, numchars=4, num_dilated_convs=9, num_tasks=1, name='bpnet_model'): # original as per https://www.biorxiv.org/content/10.1101/737981v1.full.pdf inp = tf.keras.layers.Input(shape=(seqlen, 4)) x = tf.keras.layers.Conv1D( 64, kernel_size=25, padding='same', activation='relu')(inp) for i in range(num_dilated_convs): conv_x = tf.keras.layers.Conv1D( 64, kernel_size=3, padding='same', activation='relu', dilation_rate=2**i)(x) x = tf.keras.layers.Add()([conv_x, x]) bottleneck = x # heads outputs = [] for _ in range(num_tasks): # profile shape head px = tf.keras.layers.Reshape((-1, 1, 64))(bottleneck) px = tf.keras.layers.Conv2DTranspose( 1, kernel_size=(25, 1), padding='same')(px) outputs.append(tf.keras.layers.Flatten()(px)) # total counts head cx = tf.keras.layers.GlobalAvgPool1D()(bottleneck) outputs.append(tf.keras.layers.Dense(1)(cx)) model = tf.keras.Model(inputs=inp, outputs=outputs) return model

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0.142857

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null

0

null

0

1

0

000fb685fc9f26f073890df0d180e999e12bb012

801

py

Python

leetcode/108-Convert-Sorted-Array-To-Binary-Search-Tree/answer.py

vaishali-bariwal/Practice-Coding-Questions

747bfcb1cb2be5340daa745f2b9938f0ee87c9ac

[ "Unlicense" ]

25

2018-05-22T15:18:50.000Z

2022-01-08T02:41:46.000Z

leetcode/108-Convert-Sorted-Array-To-Binary-Search-Tree/answer.py

vaishali-bariwal/Practice-Coding-Questions

747bfcb1cb2be5340daa745f2b9938f0ee87c9ac

[ "Unlicense" ]

1

2019-05-24T16:55:27.000Z

leetcode/108-Convert-Sorted-Array-To-Binary-Search-Tree/answer.py

vaishali-bariwal/Practice-Coding-Questions

747bfcb1cb2be5340daa745f2b9938f0ee87c9ac

[ "Unlicense" ]

18

2018-09-20T15:39:26.000Z

2022-03-02T21:38:22.000Z

#!/usr/bin/env python3 #------------------------------------------------------------------------------- # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def sortedArrayToBST(self, nums): """ :type nums: List[int] :rtype: TreeNode """ def help(left, right): if left > right: return None mid = (left + right) // 2 root = TreeNode(nums[mid]) root.left = help(left, mid-1) root.right = help(mid+1, right) return root return help(0, len(nums)-1) #------------------------------------------------------------------------------- # Testing

27.62069

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0.085174

0

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0.2

false

0

0.5

0

null

0

null

0

1

0

0011bad4390288b5a901919fe11d0ebf83273af9

596

py

Python

tests/test_filter.py

rdemaria/xpart

35fe06eeb508991dfe1dd23685331f8347d0b603

[ "MIT" ]

1

2021-09-07T14:34:10.000Z

tests/test_filter.py

rdemaria/xpart

35fe06eeb508991dfe1dd23685331f8347d0b603

[ "MIT" ]

null

tests/test_filter.py

rdemaria/xpart

35fe06eeb508991dfe1dd23685331f8347d0b603

[ "MIT" ]

5

2021-11-04T08:23:43.000Z

2022-03-16T10:34:23.000Z

import numpy as np import xobjects as xo import xpart as xp def test_basics(): for context in xo.context.get_test_contexts(): print(f"Test {context.__class__}") p1 = xp.Particles(x=[1,2,3], px=[10, 20, 30], mass0=xp.ELECTRON_MASS_EV, _context=context) mask = p1.x > 1 p2 = p1.filter(mask) assert p2._buffer.context == context assert p2._capacity == 2 dct = p2.to_dict() assert dct['mass0'] == xp.ELECTRON_MASS_EV assert np.all(dct['px'] == np.array([20., 30.]))

25.913043

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0.318792

596

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0

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0.25

0.0625

0

null

0

null

0

1

0

001513d4c6890aca681f0ade18ed556b34353f85

4,550

py

Python

main.py

NimaVahdat/Image-Categorization

4addce895b14c0c663e3ee317ffcd802b774452b

[ "MIT" ]

null

main.py

NimaVahdat/Image-Categorization

4addce895b14c0c663e3ee317ffcd802b774452b

[ "MIT" ]

null

main.py

NimaVahdat/Image-Categorization

4addce895b14c0c663e3ee317ffcd802b774452b

[ "MIT" ]

null

from utils.loader import Loader from utils.model import DeepSNN import torch import os def feature_extraction(prop): name = prop["name"] epochs_l1 = prop["epochs_l1"] epochs_l2 = prop["epochs_l2"] trainset, testset = Loader(name) model = DeepSNN(prop) # Training The First Layer print("-------Training the first layer-------") if os.path.isfile(name+"_Layer1.net"): model.load_state_dict(torch.load(name+"_Layer1.net")) print("Loaded from disck!") else: for epoch in range(epochs_l1): print("Epoch:", epoch) for data,_ in trainset: model.train_model(data, 1) print("\nDone!") torch.save(model.state_dict(), name+"_Layer1.net") # Training The Second Layer print("-------Training the second layer-------") if os.path.isfile(name+"_Layer2.net"): model.load_state_dict(torch.load(name+"_Layer2.net")) print("Loaded from disck!") else: for epoch in range(epochs_l2): print("Epoch:", epoch) for data,_ in trainset: model.train_model(data, 2) print("\nDone!") torch.save(model.state_dict(), name+"_Layer2.net") # Classification on trainset and testset # Get train data for data,target in trainset: train_X, train_y = model.test(data, target, 2) # Get test data for data,target in testset: test_X, test_y = model.test(data, target, 2) return train_X, train_y, test_X, test_y, (model.conv1.weight, model.conv2.weight) def Classification(train_X, train_y, test_X, test_y, C=2.4): # SVM from sklearn.svm import LinearSVC clf = LinearSVC(C=C) clf.fit(train_X, train_y) predicted_train = clf.predict(train_X) predicted_test = clf.predict(test_X) return predicted_train, predicted_test def performance(x, y, predict): correct = 0 silence = 0 for i in range(len(predict)): if x[i].sum() == 0: silence += 1 else: if predict[i] == y[i]: correct += 1 return (correct/len(x), (len(x)-(correct+silence))/len(x), silence/len(x)) def confussion_matrix(test_y, predicted_test, labels): import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay cm = confusion_matrix(test_y, predicted_test) cmd_obj = ConfusionMatrixDisplay(cm, display_labels=labels) # print(cm) cmd_obj.plot() plt.show() # %% Caltech = { "name" : "Caltech", "epochs_l1" : 20, "epochs_l2" : 100, "weight_mean" : 0.8, "weight_std" : 0.05, "lr" : (0.005, -0.0025), "in_channel1" : 4, "in_channel2" : 40, "out_channel" : 150, "k1" : 10, "k2" : 25, "r1" : 0, "r2" : 2,} train_X, train_y, test_X, test_y, weights = feature_extraction(Caltech) predicted_train, predicted_test = Classification(train_X, train_y, test_X, test_y) n = performance(train_X, train_y, predicted_train) m = performance(test_X, test_y, predicted_test) print(n) print(m) labels = ['Airplane', 'Car_side', 'Faces_easy', 'Motorbikes'] confussion_matrix(test_y, predicted_test, labels) # %% MNIST = {"name" : "MNIST", "epochs_l1":2, "epochs_l2":20, "weight_mean" : 0.8, "weight_std" : 0.05, "lr" : (0.004, -0.003), "in_channel1" : 2, "in_channel2" : 32, "out_channel" : 150, "k1" : 5, "k2" : 8, "r1" : 2, "r2" : 1,} train_X, train_y, test_X, test_y, weights = feature_extraction(MNIST) predicted_train, predicted_test = Classification(train_X, train_y, test_X, test_y) n = performance(train_X, train_y, predicted_train) m = performance(test_X, test_y, predicted_test) print(n) print(m) labels = ['0','1','2','3','4','5','6','7','8','9'] confussion_matrix(test_y, predicted_test, labels) # %% # import cv2 # import numpy as np # w1, w2 = weights # w1 = torch.reshape(w1, (160, 5, 5)) # # w2 = torch.reshape(w2, (6000, 2, 2)) # def features_pic(w, i): # # w = torch.squeeze(w) # w -= w.min() # w = (w/w.max()) * 255 # pic = cv2.resize(np.array(w), (100, 100)) # cv2.imwrite("features/feature" + str(i) + ".jpg", pic) # for i in range(len(w1)): # features_pic(w1[i], i)

28.980892

86

0.575824

605

4,550

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0.247934

0.025907

0.043842

0.047828

0.479075

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4,550

157

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1

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false

0

0.067961

0

0.135922

0.116505

0

null

0

null

0

1

0

001659507468ba211846a086bb3af6d259d15e23

409

py

Python

1704-determine-if-string-halves-are-alike/1704-determine-if-string-halves-are-alike.py

hyeseonko/LeetCode

48dfc93f1638e13041d8ce1420517a886abbdc77

[ "MIT" ]

2

2021-12-05T14:29:06.000Z

2022-01-01T05:46:13.000Z

1704-determine-if-string-halves-are-alike/1704-determine-if-string-halves-are-alike.py

hyeseonko/LeetCode

48dfc93f1638e13041d8ce1420517a886abbdc77

[ "MIT" ]

null

1704-determine-if-string-halves-are-alike/1704-determine-if-string-halves-are-alike.py

hyeseonko/LeetCode

48dfc93f1638e13041d8ce1420517a886abbdc77

[ "MIT" ]

null

class Solution: def halvesAreAlike(self, s: str) -> bool: vowel = {'a', 'e', 'i', 'o', 'u', 'A', 'E', 'I', 'O', 'U'} first = s[:int(len(s)/2)] second = s[int(len(s)/2):] firstsum = sum([1 for f in first if f in vowel]) secondsum = sum([1 for s in second if s in vowel]) if firstsum==secondsum: return True else: return False

37.181818

66

0.484108

61

409

3.245902

0.508197

0.020202

0.030303

0.040404

0.141414

0

0.014815

0.339853

409

11

67

37.181818

0.718519

0

0.02439

0

1

0.090909

false

0

0.363636

0

null

0

null

0

1

0

00198cc9e3c841bb01a56c333dd3c279b3334a56

9,789

py

Python

honeycomb/worker_bee.py

agrc/honeycomb

a4227221759541b007c2d2a8dcfca5a40192eeff

[ "MIT" ]

1

2018-06-07T13:17:40.000Z

honeycomb/worker_bee.py

agrc/honeycomb

a4227221759541b007c2d2a8dcfca5a40192eeff

[ "MIT" ]

24

2017-08-28T19:53:15.000Z

2022-03-28T21:36:37.000Z

honeycomb/worker_bee.py

agrc/honeycomb

a4227221759541b007c2d2a8dcfca5a40192eeff

[ "MIT" ]

null

#!/usr/bin/env python # * coding: utf8 * ''' worker_bee.py A module that contains logic for building traditional image-based caches. ''' import os import socket import time from os.path import join, dirname, realpath from shutil import rmtree import pygsheets from datetime import date import arcpy from . import config, settings, update_data from .messaging import send_email spot_cache_name = 'spot cache' error_001470_message = 'ERROR 001470: Failed to retrieve the job status from server. The Job is running on the server, please use the above URL to check the job status.\nFailed to execute (ManageMapServerCacheTiles).\n' # noqa def parse_levels(levels_txt): #: parse the levels parameter text into an array of scales min, max = map(int, levels_txt.split('-')) return settings.SCALES[min:max + 1] def intersect_scales(scales, restrict_scales): #: return the intersection of between scales and restrict_scales intersection = set(scales) & set(restrict_scales) return list(intersection) class WorkerBee(object): def __init__(self, s_name, missing_only=False, skip_update=False, skip_test=False, spot_path=False, levels=False): print('caching {}'.format(s_name)) self.errors = [] self.start_time = time.time() self.service_name = s_name if not levels: self.restrict_scales = settings.SCALES else: self.restrict_scales = parse_levels(levels) try: print('deleting previous *_GCS folder, if any') rmtree(os.path.join(settings.CACHE_DIR, s_name + '_GCS')) except Exception: pass if config.is_dev(): self.complete_num_bundles = 19 else: self.complete_num_bundles = settings.COMPLETE_NUM_BUNDLES_LU[self.service_name] ip = socket.gethostbyname(socket.gethostname()) self.preview_url = settings.PREVIEW_URL.format(ip, self.service_name) self.service = os.path.join(config.get_ags_connection(), '{}.MapServer'.format(self.service_name)) self.email_subject = 'Cache Update ({})'.format(self.service_name) if skip_update: print('skipping data update...') else: update_data.main() send_email(self.email_subject, 'Data update complete. Proceeding with caching...') if skip_test: print('skipping test cache...') else: self.cache_test_extent() if missing_only: print('caching empty tiles only...') self.missing_only = missing_only self.start_bundles = self.get_bundles_count() if self.missing_only: self.update_mode = 'RECREATE_EMPTY_TILES' print('Caching empty tiles only') else: self.update_mode = 'RECREATE_ALL_TILES' print('Caching all tiles') if not spot_path: self.cache(not levels) else: #: levels 0-17 include the entire state print('spot caching levels 0-17...') self.cache_extent(settings.SCALES[:18], spot_path, spot_cache_name) #: levels 18-19 intersect with cache extent print('intersecting spot cache polygon with level 18-19 cache extent...') intersect = arcpy.analysis.Intersect([spot_path, join(settings.EXTENTSFGDB, settings.EXTENT_18_19)], 'in_memory/spot_cache_intersect', join_attributes='ONLY_FID') print('spot caching levels 18-19...') self.cache_extent(settings.SCALES[18:], intersect, spot_cache_name) def cache_extent(self, scales, aoi, name): cache_scales = intersect_scales(scales, self.restrict_scales) if len(cache_scales) == 0: return print('caching {} at {}'.format(name, cache_scales)) if config.is_dev() and name != spot_cache_name: aoi = settings.TEST_EXTENT try: arcpy.server.ManageMapServerCacheTiles(self.service, cache_scales, self.update_mode, settings.NUM_INSTANCES, aoi) except arcpy.ExecuteError as e: if e.message == error_001470_message: msg = 'ERROR 001470 thrown. Moving on and hoping the job completes successfully.' print(msg) send_email('Cache Warning (ERROR 001470)', 'e.message\n\narcpy.GetMessages:\n{}'.format(arcpy.GetMessages().encode('utf-8'))) else: self.errors.append([cache_scales, aoi, name]) print(arcpy.GetMessages().encode('utf-8')) send_email('Cache Update ({}) - arcpy.ExecuteError'.format(self.service_name), arcpy.GetMessages().encode('utf-8')) def get_progress(self): total_bundles = self.get_bundles_count() bundles_per_hour = (total_bundles - self.start_bundles) / ((time.time() - self.start_time) / 60 / 60) if bundles_per_hour != 0 and total_bundles > self.start_bundles: hours_remaining = (self.complete_num_bundles - total_bundles) / bundles_per_hour else: self.start_time = time.time() hours_remaining = '??' percent = int(round(float(total_bundles) / self.complete_num_bundles * 100.00)) msg = '{} of {} ({}%) bundle files created.\nEstimated hours remaining: {}'.format( total_bundles, self.complete_num_bundles, percent, hours_remaining) print(msg) return msg def get_bundles_count(self): totalfiles = 0 basefolder = os.path.join(settings.CACHE_DIR, self.service_name.replace('/', '_'), 'Layers', '_alllayers') for d in os.listdir(basefolder): if d != 'missing.jpg': totalfiles += len(os.listdir(os.path.join(basefolder, d))) return totalfiles def cache_test_extent(self): print('caching test extent') cache_scales = intersect_scales(settings.SCALES, self.restrict_scales) try: arcpy.server.ManageMapServerCacheTiles(self.service, cache_scales, 'RECREATE_ALL_TILES', settings.NUM_INSTANCES, settings.TEST_EXTENT) send_email('Cache Test Extent Complete ({})'.format(self.service_name), self.preview_url) # if raw_input('Recache test extent (T) or continue with full cache (F): ') == 'T': # self.cache_test_extent() except arcpy.ExecuteError: print(arcpy.GetMessages().encode('utf-8')) send_email('Cache Test Extent Error ({}) - arcpy.ExecuteError'.format(self.service_name), arcpy.GetMessages().encode('utf-8')) raise arcpy.ExecuteError def cache(self, run_all_levels): arcpy.env.workspace = settings.EXTENTSFGDB for fc_name, scales in settings.CACHE_EXTENTS: self.cache_extent(scales, fc_name, fc_name) send_email(self.email_subject, 'Levels 0-9 completed.\n{}\n{}'.format(self.get_progress(), self.preview_url)) if config.is_dev(): settings.GRIDS = settings.GRIDS[:-4] for grid in settings.GRIDS: total_grids = int(arcpy.management.GetCount(grid[0])[0]) grid_count = 0 progress = '' with arcpy.da.SearchCursor(grid[0], ['SHAPE@', 'OID@']) as cur: for row in cur: grid_count += 1 grid_percent = int(round((float(grid_count) / total_grids) * 100)) self.cache_extent([grid[1]], row[0], '{}: OBJECTID: {}'.format(grid[0], row[1])) grit_percent_msg = 'Grids for this level completed: {}%'.format(grid_percent) print(grit_percent_msg) progress = self.get_progress() send_email(self.email_subject, 'Level {} completed.\n{}\n{}\nNumber of errors: {}'.format(grid[0], progress, self.preview_url, len(self.errors))) while (len(self.errors) > 0): msg = 'Recaching errors. Errors left: {}'.format(len(self.errors)) print(msg) send_email(self.email_subject, msg) self.cache_extent(*self.errors.pop()) bundles = self.get_bundles_count() if bundles < self.complete_num_bundles and run_all_levels: msg = 'Only {} out of {} bundles completed. Recaching...'.format(bundles, self.complete_num_bundles) print(msg) send_email(self.email_subject, msg) self.cache(True) send_email(self.email_subject + ' Finished', 'Caching complete!\n\n{}'.format(self.preview_url)) print('updating google spreadsheets') client = pygsheets.authorize(service_file=join(dirname(realpath(__file__)), 'service_account.json')) sgid_sheet = client.open_by_key('11ASS7LnxgpnD0jN4utzklREgMf1pcvYjcXcIcESHweQ') sgid_worksheet = sgid_sheet[0] base_maps_sheet = client.open_by_key('1XnncmhWrIjntlaMfQnMrlcCTyl9e2i-ztbvqryQYXDc') base_maps_worksheet = base_maps_sheet[0] #: update sgid changelog today = date.today().strftime(r'%m/%d/%Y') matrix = sgid_worksheet.get_all_values(include_tailing_empty_rows=False, include_tailing_empty=False) row = [today, 'Complete', self.service_name, 'Recache', 'Statewide cache rebuild and upload to GCP', 'stdavis', 'no', 'no', 'no', 'no', 'no', 'no', 'yes'] sgid_worksheet.insert_rows(len(matrix), values=row, inherit=True) #: update base maps spreadsheet embedded in gis.utah.gov page this_month = date.today().strftime(r'%b %Y') results = base_maps_worksheet.find(self.service_name, matchEntireCell=True) cell = results[0] base_maps_worksheet.update_value((cell.row + 1, cell.col), this_month)

42.934211

227

0.635611

1,186

9,789

5.043845

0.250422

0.025744

0.027583

0.025744

0.194584

0.111668

0.071882

0.051488

0.036443

0

0.014606

0.251609

9,789

227

228

43.123348

0.801938

0.053632

0

0.138554

0

0.006024

0.167856

0.022604

0

1

0.048193

false

0.006024

0.060241

0

0.144578

0.120482

0

null

0

null

0

1

0

00214dc954dea8b8ef76726b14c1872ccfd1e59a

799

py

Python

robotarm/armservice/app.py

AmidBidee/Robot-Arm

cfacfc779b2f025846e9748167bcfb15ce207923

[ "MIT" ]

1

2022-03-27T20:09:10.000Z

robotarm/armservice/app.py

AmidBidee/Robot-Arm

cfacfc779b2f025846e9748167bcfb15ce207923

[ "MIT" ]

4

2022-03-25T03:45:10.000Z

2022-03-29T14:31:16.000Z

robotarm/armservice/app.py

AmidBidee/RobotArm

cfacfc779b2f025846e9748167bcfb15ce207923

[ "MIT" ]

null

#!/usr/bin/python3 """ RobotArm API service config file """ import pathlib from robotarm.armservice.views import api_views from flask import ( Flask, make_response, jsonify ) from robotarm.armservice import getenv # initialize flask app app = Flask(__name__) # register/mount blueprint app.register_blueprint(api_views) # allow missing trailing app.url_map.strict_slashes = False @app.errorhandler(404) def not_found(error): """ Handle non existing objects Args: error: [description] Returns: JSON: json object """ e = { "error": "Not Found" } return make_response(jsonify(e), 404) if __name__ == '__main__': host = getenv("ARM_API_HOST", "0.0.0.0") port = getenv("ARM_API_PORT", "5555") app.run(host=host, port=port)

17.755556

47

0.677096

104

799

4.971154

0.548077

0.011605

0.085106

0

0.023659

0.206508

799

44

48

18.159091

0.791798

0.254068

0

0.102888

0

1

0.047619

false

0

0.190476

0

0.285714

0.047619

0

null

0

null

0

1

0

0023e5b95c57b280f82e4d979d1eecb37cba4ae9

4,685

py

Python

xbrr/edinet/reader/element_schema.py

5laps2go/xbrr

4c0824b53bfe971111d60e6c1ff4e36f4f4845a3

[ "MIT" ]

null

xbrr/edinet/reader/element_schema.py

5laps2go/xbrr

4c0824b53bfe971111d60e6c1ff4e36f4f4845a3

[ "MIT" ]

null

xbrr/edinet/reader/element_schema.py

5laps2go/xbrr

4c0824b53bfe971111d60e6c1ff4e36f4f4845a3

[ "MIT" ]

null

from xbrr.base.reader.base_element_schema import BaseElementSchema from bs4.element import NavigableString, Tag import bs4 class ElementSchema(BaseElementSchema): def __init__(self, name="", reference="", label="", alias="", abstract="", data_type="", period_type="", balance=""): super().__init__() self.name = name self.reference = reference self.label = label self.alias = alias self.abstract = abstract self.period_type = period_type self.balance = balance self.verbose_label = "" # data types: # domain, textBlock, percent, perShare, boolean, date, decimal, # monetary, nonNegativeInteger, shares, string self.data_type = data_type if data_type is not None and ':' in data_type: self.data_type = data_type.split(':')[-1].replace('ItemType','') def set_alias(self, alias): self.alias = alias return self @classmethod def create_from_reference(cls, reader, reference): if not reader.xbrl_doc.has_schema: # for test purpose only name = reference.split("#")[-1] instance = cls(name=name, reference=reference) return instance instance = reader.get_schema_by_link(reference) instance.reference = reference return instance @classmethod def read_schema(cls, reader, xsduri): xsd_dic = {} xml = reader.read_uri(xsduri) for element in xml.find_all("element"): # <xsd:element id="jpcrp030000-asr_E00436-000_Subsidy" xbrli:balance="credit" xbrli:periodType="duration" abstract="false" name="Subsidy" nillable="true" substitutionGroup="xbrli:item" type="xbrli:monetaryItemType" /> instance = cls(name=element["id"], alias=element["name"], data_type=element["type"], period_type=element["xbrli:periodType"], abstract=element["abstract"] if element.get("abstract") else "", balance=element.get("xbrli:balance") if element.get("xbrli:balance") else "") xsd_dic[element["id"]] = instance return xsd_dic @classmethod def read_label_taxonomy(cls, reader, xsduri, xsd_dic): label_xml = reader.read_label_of_xsd(xsduri) loc_dic = {} resource_dic = {} def read_label(elem: bs4.element.Tag): if elem.name == "loc": attrs = elem.attrs assert 'xlink:href' in attrs and 'xlink:label' in attrs # href = jpcrp040300-q1r-001_E04251-000_2016-06-30_01_2016-08-12.xsd#jpcrp040300-q1r_E04251-000_ProvisionForLossOnCancellationOfContractEL # label = ProvisionForLossOnCancellationOfContractEL v = elem['xlink:href'].split('#') assert len(v) == 2 loc_dic[elem['xlink:label']] = v[1] elif elem.name == "label": attrs = elem.attrs if 'xlink:label' in attrs and 'xlink:role' in attrs: label_role = "http://www.xbrl.org/2003/role/label" verboseLabel_role = "http://www.xbrl.org/2003/role/verboseLabel" if elem['xlink:role'] in [label_role, verboseLabel_role]: resource_dic[elem['xlink:label']] = {'role': elem['xlink:role'], 'text': elem.text} elif elem.name == "labelArc": attrs = elem.attrs if 'xlink:from' in attrs and 'xlink:to' in attrs and elem['xlink:to'] in resource_dic: if elem['xlink:from'] in loc_dic and loc_dic[elem['xlink:from']] in xsd_dic: ele = xsd_dic[loc_dic[elem['xlink:from']]] res = resource_dic[elem['xlink:to']] ele.set_label(**res) # Label(res['role'], res['text']) for elem in label_xml.find_all('labelLink'): # "link:labelLink" for child in elem.children: if isinstance(child, Tag): read_label(child) def set_label(self, role, text): if role.endswith('label'): self.label = text elif role.endswith('verboseLabel'): self.verbose_label = text def to_dict(self): return { "name": self.name, "reference": self.reference, "label": self.label, "abstract": self.abstract, "data_type": self.data_type, "period_type": self.period_type, "balance": self.balance }

41.096491

229

0.56841

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4.998066

0.253385

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0.020124

0

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0.313554

4,685

113

230

41.460177

0.778607

0.127001

0

0.11236

0

0.104683

0

0.022472

1

0.089888

false

0

0.033708

0.011236

0.191011

0

null

0

null

0

1

0

0024cde788e4fc1c63bad501dddfdfc712994f43

817

py

Python

app/models.py

james-muriithi/news-hub

6f0fee2ab6be5bba86c4309050592e000859f8db

[ "Unlicense" ]

null

app/models.py

james-muriithi/news-hub

6f0fee2ab6be5bba86c4309050592e000859f8db

[ "Unlicense" ]

null

app/models.py

james-muriithi/news-hub

6f0fee2ab6be5bba86c4309050592e000859f8db

[ "Unlicense" ]

null

from datetime import datetime class Sources: """ Sources class to define sources object """ def __init__(self, id, name, description, url, category, country): self.id = id self.name = name self.description = description self.url = url self.category = category self.country = country class Articles: """ Articles class to define articles object """ def __init__(self, author, title, description, url, url_to_Image, published_at, content): self.author = author self.title = title self.description = description self.url = url self.url_to_Image = url_to_Image self.content = content self.published_at = datetime.strptime(published_at, "%Y-%m-%dT%H:%M:%SZ").strftime("%B %d, %Y")

27.233333

103

0.623011

99

817

4.969697

0.333333

0.042683

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002d82da21503f07067ace5a4397c6b6011e7cc0

4,709

py

Python

pybps/preprocess/trnsys.py

dtavan/PyBPS

92bd063daed78a7fcff1af954d7d90d0cde8dcfc

[ "BSD-3-Clause" ]

9

2015-03-12T15:23:42.000Z

2021-12-21T13:01:42.000Z

pybps/preprocess/trnsys.py

dtavan/PyBPS

92bd063daed78a7fcff1af954d7d90d0cde8dcfc

[ "BSD-3-Clause" ]

3

2015-09-20T17:31:09.000Z

2018-02-26T13:11:53.000Z

pybps/preprocess/trnsys.py

dtavan/PyBPS

92bd063daed78a7fcff1af954d7d90d0cde8dcfc

[ "BSD-3-Clause" ]

3

2019-02-14T08:13:03.000Z

2020-12-10T07:04:41.000Z

""" A set of functions required to pre-process TRNSYS simulation input files """ # Common imports import os import sys import shutil import re # Custom imports from pybps import util # Handle Python 2/3 compatibility from six.moves import configparser import six if six.PY2: ConfigParser = configparser.SafeConfigParser else: ConfigParser = configparser.ConfigParser def parse_deck_const(deck_abspath): """Parse constants in control cards and equations from TRNSYS deck file Finds all constants in a TRNSYS deck file and stores constant name and value in a dict. Args: deck_abspath: absolute path to TRNSYS deck file Returns: A dict containing all found constants and their values """ const_dict = {} f = open(deck_abspath, 'r') split_blocks_pat = re.compile(r'[*][-]+') equa_pat = re.compile(r'[*]\sEQUATIONS\s"(.+?)"') const_pat = re.compile(r'\b(\w+)\b\s=\s(\d+\.*\d*)\s') with f: data = f.read() blocks = split_blocks_pat.split(data) for block in blocks: if block[0] == 'V': match_par = const_pat.findall(block) if match_par: group = "Control Cards" const_dict[group] = {} for (m,v) in match_par: const_dict[group][m] = v else: match_eq = equa_pat.findall(block) if match_eq: group = match_eq[0] match_par = const_pat.findall(block) if match_par: const_dict[group] = {} for (m,v) in match_par: const_dict[group][m] = v return const_dict def prepare_deck_template(deck_abspath, param_list): """Prepare a template TRNSYS deck file for parametric analysis Transforms a TRNSYS deck in a template file valid for parametric analysis by replacing constant values with parameter search strings (the name of the constant surrounded by '%' signs). That parameters are given in a list. Args: deck_abspath: absolute path to TRNSYS deck file param_list: list of parameters to be included in template Returns: A valid template file for parametric analysis with PyBPS """ templ_deck_abspath = os.path.splitext(deck_abspath)[0] + "_Template.dck" shutil.copyfile(deck_abspath, templ_deck_abspath) f = open(templ_deck_abspath, 'r+') with f: data = f.read() for par in param_list: data = re.sub(r'(' + par + r')\s=\s(\d+\.*\d*)', r'\g<1> = %\g<1>%', data) f.seek(0) f.write(data) f.truncate() def gen_type56(model_abspath, select='all'): """Generate Type56 matrices and idf files Calls TRNBUILD.exe with flags to generate matrices and IDF files. Args: model_abspath: absolute path to Type56 model file select: selects which files should by generated by TRNBUILD. 'masks' generates insolation matrix, 'vfm' generates de view factor matrix, 'matrices' generates both 'idf' generates the IDF file (similar to TRNBUILD 'export' funtion) 'all' generates everything Returns: Generated files. """ # Get information from config file conf = SafeConfigParser() conf_file = os.path.join(os.path.abspath(os.path.dirname(__file__)), '..\config.ini') conf.read(conf_file) trnbuild_path = os.path.abspath(conf.get('TRNSYS', 'TRNBuild_Path')) trnsidf_path = os.path.abspath(conf.get('TRNSYS', 'trnsIDF_Path')) # Get b17 file path from deck file pattern = re.compile(r'ASSIGN "(.*b17)"') with open(model_abspath, 'rU') as m_f: temp = m_f.read() match = pattern.search(temp) # TRNBUILD is only called if Type56 is found in deck file. if match: b17_relpath = match.group(1) b17_abspath = os.path.join(os.path.dirname(model_abspath), b17_relpath) # Generate shading/insolation matrix if select == 'all' or select == 'matrices' or select == 'masks': cmd = [trnbuild_path, b17_abspath, '/N', '/masks'] util.run_cmd(cmd) # Generate view factor matrix if select == 'all' or select == 'matrices' or select == 'vfm': cmd = [trnbuild_path, b17_abspath, '/N', '/vfm'] util.run_cmd(cmd) # Generate trnsys3D idf file, to view geometry in Sketchup if select == 'all' or select == 'idf': cmd = [trnsidf_path, b17_abspath] util.run_cmd(cmd)

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null

0

null

0

1

0

002fac18e96ba58a8fc58e5945a2e85038de81e9

1,650

py

Python

gof/console.py

jul/game_of_life

0ffb798679f50dea27b55f8a630c437b6ee4d8f9

[ "Python-2.0" ]

1

2015-01-13T13:42:32.000Z

gof/console.py

jul/game_of_life

0ffb798679f50dea27b55f8a630c437b6ee4d8f9

[ "Python-2.0" ]

null

gof/console.py

jul/game_of_life

0ffb798679f50dea27b55f8a630c437b6ee4d8f9

[ "Python-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """Game of Life console. small play ground for leanning python, or just having fun. To use the console with an empty grid: python -i -mgof.console or bpython -i -mgof.console To use the console with a pseudo animation: python -i -mgof.demo Avalailble variable : * grid your playground a matrix of cellular automata * patterns : pixel, still, oscillator, glider they are singular patterns to play with in game of life. * all_pattern : a list of all patterns (except pixel) * matrix : the class name of grid is imported for educationnal purpose * DEAD, ALIVE Available functions: * intro() : a short summary of all available functions * bleach(...) a function to init the grid * at(...) a function to draw a pattern on the grid * rand_pattern() : a function to add random pattern in your grid * evolve(...) make the game evolve for some time. If your terminal and/or interactive python supports it, it will make a continuous animation * use help(function_name) to know more yes it is a builtin ^_^ """ from .matrix import matrix from time import sleep from .gof import glider, oscillator, still, pixel, all_pattern from .gof import evolve, bleach,dirty, DEAD, ALIVE, at #### Constants and globals __all__ = [ "matrix","at", "grid", "intro", "glider", "oscillator", "still","pixel","all_pattern", "evolve", "bleach", "dirty", "DEAD", "ALIVE" ] x=10 y=30 def intro(): print(__doc__) print(""" you are left with an empty grid of %dx%d to play with, have fun""" % (x,y)) grid=matrix(x,y,x*y*[DEAD]) if '__main__' == __name__: print(__doc__)

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1,650

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0.166667

0

null

0

null

0

1

0

00316d362ddde2a4019229bccffbafd03e33e693

1,129

py

Python

covid19uk/data/tier_data_test.py

sdwfrost/covid19uk

ffd59342d9daee2d819d2f7211afbe9713880612

[ "MIT" ]

10

2020-03-21T22:36:24.000Z

2021-05-23T22:47:13.000Z

covid19uk/data/tier_data_test.py

sdwfrost/covid19uk

ffd59342d9daee2d819d2f7211afbe9713880612

[ "MIT" ]

14

2020-03-27T19:24:51.000Z

2021-07-21T12:41:23.000Z

covid19uk/data/tier_data_test.py

sdwfrost/covid19uk

ffd59342d9daee2d819d2f7211afbe9713880612

[ "MIT" ]

13

2020-03-21T17:17:20.000Z

2021-05-06T22:50:18.000Z

"""Tests Tier Data""" import numpy as np from covid.data import TierData def test_url_tier_data(): config = { "AreaCodeData": { "input": "json", "address": "https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LAD_APR_2019_UK_NC/FeatureServer/0/query?where=1%3D1&outFields=LAD19CD,LAD19NM&returnGeometry=false&returnDistinctValues=true&orderByFields=LAD19CD&outSR=4326&f=json", "format": "ons", "output": "processed_data/processed_lad19cd.csv", "regions": ["E"], }, "TierData": { "input": "api", "address": None, "format": "api", }, "GenerateOutput": { "storeInputs": True, "scrapedDataDir": "scraped_data", "storeProcessedInputs": True, }, "Global": { "prependID": False, "prependDate": False, "inference_period": ["2020-10-12", "2021-01-04"], }, } xarr = TierData.process(config) print("xarr", xarr) np.testing.assert_array_equal(xarr.shape, [315, 84, 6])

30.513514

258

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5.688073

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0

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0.033333

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null

0

null

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1

0

00333d9d6b82d73d347ef774e208f7a11fd780ab

1,550

py

Python

Baixar um arquivo Excel e mandar o resultado para emails.py

GustavooBueno/Projetos-Python-Automacao

43ec53040cd543746d88e8523fcffbdb69112ab7

[ "MIT" ]

null

Baixar um arquivo Excel e mandar o resultado para emails.py

GustavooBueno/Projetos-Python-Automacao

43ec53040cd543746d88e8523fcffbdb69112ab7

[ "MIT" ]

null

Baixar um arquivo Excel e mandar o resultado para emails.py

GustavooBueno/Projetos-Python-Automacao

43ec53040cd543746d88e8523fcffbdb69112ab7

[ "MIT" ]

null

import pyautogui import time import pyperclip import pandas as pd #pyautogui.displayMousePosition() pyautogui.PAUSE = 1 #Passo 1 #Abrir uma nova aba time.sleep(2) pyautogui.hotkey('ctrl', 't') #Entrar no link do sistema link = "https://drive.google.com/drive/folders/149xknr9JvrlEnhNWO49zPcw0PW5icxga" pyperclip.copy(link) pyautogui.hotkey('ctrl', 'v') pyautogui.press('enter') #Passo 2 time.sleep(5) pyautogui.click(389, 270, clicks = 2) time.sleep(2) #Passo 3 pyautogui.click(401, 337) #clicar no arquivo pyautogui.click(1713, 157) #clicar nos 3 pontos pyautogui.click(1525, 561) #clicar no fazer download time.sleep(10) #Passo 4 tabela = pd.read_excel(r'C:\Users\Pichau\Downloads\Vendas - Dez.xlsx') faturamento = tabela['Valor Final'].sum() quantidade = tabela['Quantidade'].sum() #Passo 5 time.sleep(2) pyautogui.hotkey('ctrl', 't') #Entrar no link do sistema link = "https://mail.google.com/mail/u/0/#inbox" pyperclip.copy(link) pyautogui.hotkey('ctrl', 'v') pyautogui.press('enter') time.sleep(7) pyautogui.click(33, 170) pyautogui.write('gustavo.ibis.gb+diretoria@gmail.com') pyautogui.press('tab') pyautogui.press('tab') assunto = 'Relatório de Vendas' pyperclip.copy(assunto) pyautogui.hotkey('ctrl', 'v') pyautogui.press('tab') texto_email = f""" Prezados, bom dia O faturamento de ontem foi de: R${faturamento:,.2f} A quantidade de produtos foi de: R${quantidade:,.2f} Abs """ pyperclip.copy(texto_email) pyautogui.hotkey('ctrl', 'v') pyautogui.hotkey('ctrl', 'enter')

25

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0.267452

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0

0.042411

0.132903

1,550

62

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0.126452

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0.052262

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1

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false

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0

0.088889

0

null

0

null

0

1

0

00341cca0d8a5cb36317216f72b4964ad336e187

34,289

py

Python

brain_class.py

featureCreacle/Baka-battle

0588216ff08bec1f6d4e0679daa8ac70e7f3d83a

[ "MIT" ]

1

2017-12-23T11:16:35.000Z

brain_class.py

featureCreacle/Baka-battle

0588216ff08bec1f6d4e0679daa8ac70e7f3d83a

[ "MIT" ]

null

brain_class.py

featureCreacle/Baka-battle

0588216ff08bec1f6d4e0679daa8ac70e7f3d83a

[ "MIT" ]

null

import tkinter import json from math import * from random import * from tkinter import * class brain_abstract(): ''' # один слой это лист туплов: колво нейронов в группе, # плюс(True) или минус(False) на выходе нейронов группы, # номер функции нейронов группы, # дискретный выход у нейронов или нет (True - да) # тулпа с номерами inputs-групп, с которыми связанна нейронная группа # длинна входного input-вектора группы # коэф нормализации вывода группы laysConfigs = [ [(15, True, 1, False, (0,), 25, 10), (5, True, 1, False, (1,), 6, 10)], [( 7, True, 0, True, (0,1), 20, 10), (4, True, 1, False, (0,1), 20, 10)], [( 5, True, 1, False, (0,1), 11, 5), (15, False, 0, False, (0,1), 11, 5)], [( 6, True, 1, False, (0,1), 20, 10)], [( 2, True, 1, True, (0,), 6, 0)] ]''' def __init__(self, laysConfigs = [], NNtemp = 70, cooldownTemp = 50): self.lays = [] self.lay_counts = 0 self.NN_learning_temp = NNtemp self.NN_cooldown_temp = cooldownTemp if not laysConfigs: # один слой это лист туплов: колво нейронов в группе, # плюс(True) или минус(False) на выходе нейронов группы, # номер функции нейронов группы, # дискретный выход у нейронов или нет (True - да) # тулпа с номерами inputs-групп, с которыми связанна нейронная группа # длинна входного input-вектора группы # коэф нормализации вывода группы laysConfigs = [ [(10, True, 1, False, (0,), 25, 10), (7, False, 1, False, (0,), 25, 10), (3, True, 1, False, (1,), 3, 10)], [(10, False, 0, True, (0,1),17, 0), (7, True, 1, True, (0,1,2), 20, 0)], #[( 5, True, 1, False, (0,1), 14, 5), (15, False, 0, False, (0,1), 14, 5)], [( 5, True, 1, False, (0,1), 17, 10)], [( 2, True, 1, True, (0,), 5, 0)] ] for lay_conf in laysConfigs: self.lays.append(lay_abstact(lay_conf)) self.frozen_mind = {'scheme':laysConfigs, 'weights' :self.get_all_synapse_weight()} def train(self, input = [], output = []): lays_output = [] lays_output.append(input) lay_input = input for lay in self.lays: lays_output.append( lay.get_excited(lay_input) ) lay_input = lays_output[-1] desire_lay_output = output i = len(self.lays) j = 0 while i > 0: lay_out = lays_output[i] lay_in = lays_output[i-1] lay_temp = self.NN_learning_temp - (j*self.NN_cooldown_temp) changingNeuronsCount = round(self.lays[i-1].neuron_count * lay_temp / 100) Mu = lay_temp / ( len(self.lays) * 100 ) changingNeuronsWeightCount = round(self.lays[i-2].neuron_count * (self.NN_learning_temp - (j*self.NN_cooldown_temp) ) / 100) cool_inp = self.lays[i-1].fcingCooldown(lay_in, lay_out, desire_lay_output, changingNeuronsCount, changingNeuronsWeightCount, Mu) desire_lay_output = cool_inp i-=1 j+=1 def get_err_out(self, input = [[],], output = [[],]): return (1,1) def guess(self, input = []): lay_output = [] lay_input = input for lay in self.lays: lay_output = lay.get_excited(lay_input) lay_input = lay_output return lay_output def learn(self, input = [], output = [], maxSteps = 3): desire_lay_output = output net_output = self.guess(input) i = 0 while not self.isEqualOuts(net_output, desire_lay_output) and i < maxSteps: self.train(input, output) net_output = self.guess(input) i+=1 def think(self): pass def isEqualOuts(self, out1, out2): try: len1 = len(out1) i = 0 while i < len1: len2 = len(out1[i]) j = 0 while j < len2: if out1[i][j] != out2[i][j]: return False j+=1 i+=1 finally: return False return True def get_lay_ref(self, layNum = 0): return self.lays[layNum] def get_all_synapse_weight(self): synapse_weight = [] for lay in self.lays: for lay_group in lay.neuronGroups_list: for neuron in lay_group: synapse_weight.extend(neuron.get_weights()) return synapse_weight def load_consciousness(self, consciousness = [0]*100): neuron_weights = [] shift = 0 for lay in self.lays: for lay_group in lay.neuronGroups_list: for neuron in lay_group: neuron_weights = consciousness[shift:shift+neuron.weightsCount] neuron.set_weights(neuron_weights) shift += neuron.weightsCount def get_draw_scheme(self): draw_scheme = [] for lay in self.lays: draw_scheme.append(lay.get_draw_scheme()) return draw_scheme def draw_brain_scheme(self, root_win = None, width = 800, height = 600): worm_brain = self brain_scheme_width = width brain_scheme_height = height if root_win == None: self.scheme_window = Tk() self.scheme_window.title("Brain scheme") else: self.scheme_window = Toplevel(root_win) self.canvas_brain = Canvas(self.scheme_window, width=brain_scheme_width + 60, height=brain_scheme_height + 40, bg='white') self.canvas_brain.pack() # draw_scheme pattern [[ (Ncount, sign, links), ],] brain_draw_scheme = worm_brain.get_draw_scheme() color_lay = self.get_color(200, 200, 200)[1] color_pos_group = self.get_color(150, 250, 150)[1] color_neg_group = self.get_color(250, 150, 150)[1] color_digital_neuron = self.get_color(120, 110, 120)[1] color_analog_neuron = self.get_color(130, 170, 130)[1] color_input = self.get_color(250, 250, 60)[1] color_output = self.get_color(60, 250, 250)[1] color_inside = self.get_color(250, 200, 120)[1] lay_width = round(brain_scheme_width / len(brain_draw_scheme)) lay_height = brain_scheme_height layNum = 0 dec_lay_height = brain_scheme_height / (2.5 * len(brain_draw_scheme) ) group_out_cord = [] prev_lay_group_out_cord = [] for lay_scheme in brain_draw_scheme: group_count = len(lay_scheme) group_height = round(lay_height / group_count) groups_heights = [] for group in lay_scheme: groups_heights.append(group[0]) groups_heights = self.get_proportion(groups_heights) max_group_height = max(groups_heights) min_group_height = min(groups_heights) while (max_group_height - min_group_height) > 2 * min_group_height: minNum = groups_heights.index(min_group_height) maxNum = groups_heights.index(max_group_height) delta = groups_heights[maxNum] * 0.1 groups_heights[maxNum] -= delta groups_heights[minNum] += delta max_group_height = max(groups_heights) min_group_height = min(groups_heights) x_preset = 30 + layNum * lay_width y_preset = 20 + layNum * dec_lay_height lay_thicc = lay_width/3 self.canvas_brain.create_rectangle(x_preset+lay_thicc, y_preset, x_preset+lay_thicc*2, lay_height-y_preset, fill=color_lay, outline=color_lay) gr_bound = 10 lay_h = lay_height - 2 * y_preset gr_preset = y_preset + gr_bound gNum = 0 for group_h in groups_heights: group_h *= lay_h color_group = color_pos_group if lay_scheme[gNum][1] > 0 else color_neg_group n_count = lay_scheme[gNum][0] #lay x_group_preset = x_preset + lay_thicc + gr_bound self.canvas_brain.create_rectangle(x_group_preset, gr_preset, x_group_preset + lay_thicc - 2 * gr_bound, group_h+gr_preset - 2 * gr_bound, fill=color_group, outline=color_group)#"#000000") #in x_input_vec_preset = x_preset + lay_thicc/2 x_inp_vec_width = lay_thicc/10 self.canvas_brain.create_rectangle(x_input_vec_preset, gr_preset, x_input_vec_preset + x_inp_vec_width, group_h + gr_preset - 2 * gr_bound, fill=color_input, outline="#000000") #in vector weight_count = lay_scheme[gNum][4] weight_height = (group_h - 2 * gr_bound)/weight_count for i in range(0,weight_count): self.canvas_brain.create_line(x_input_vec_preset, gr_preset + i*weight_height, x_input_vec_preset + x_inp_vec_width, gr_preset + i*weight_height, fill='#000000') #in link for link in lay_scheme[gNum][3]: if brain_draw_scheme.index(lay_scheme) == 0: break cord = prev_lay_group_out_cord[link] self.canvas_brain.create_line(cord[0], cord[1], x_input_vec_preset, gr_preset + (group_h - 2 * gr_bound) / 2, fill='#000000', arrow=LAST) #out x_output_vec_preset = x_input_vec_preset + 2* lay_thicc x_out_vec_width = lay_thicc/10 self.canvas_brain.create_rectangle(x_output_vec_preset, gr_preset, x_output_vec_preset + x_out_vec_width, group_h + gr_preset - 2 * gr_bound, fill=color_output, outline="#000000") group_out_cord.append((x_output_vec_preset + x_out_vec_width, gr_preset + (group_h - 2 * gr_bound) / 2)) #out vector out_height = (group_h - 2 * gr_bound)/n_count for i in range(0,n_count): self.canvas_brain.create_line(x_output_vec_preset, gr_preset + i*out_height, x_output_vec_preset + x_inp_vec_width, gr_preset + i*out_height, fill='#000000') nr_preset = gr_preset nr_bound_w = 4 nr_bound_h = 4 neuron_draw_size = (group_h - 2 * gr_bound) / n_count neuron_area_width = lay_thicc - 2 * gr_bound min_neuron_draw_size = (lay_thicc - 2 * gr_bound)/2 gr_h = (group_h - 2 * gr_bound) column_count = 1 n_in_col = n_count neuron_area = 0 down_shift = 0 if neuron_draw_size > neuron_area_width: nr_bound_w = 4 neuron_draw_size = neuron_area_width - 2 * nr_bound_w nr_bound_h = (gr_h - (neuron_draw_size * n_count)) / (2 * n_count) elif neuron_draw_size < neuron_area_width: if neuron_draw_size < min_neuron_draw_size: neuron_area = sqrt((neuron_area_width * gr_h) / (n_count*1.5) ) column_count = ceil((n_count * neuron_area) / gr_h) n_in_col = round(n_count / column_count) column_count += 0 if n_count % column_count == 0 else 1 if neuron_area_width/column_count < neuron_area: neuron_draw_size = neuron_area_width/column_count column_count = ceil((n_count * neuron_draw_size) / gr_h) n_in_col = round(n_count / column_count) column_count += 0 if n_count % column_count == 0 else 1 else: neuron_draw_size = neuron_area nr_bound_w = (neuron_area_width - (neuron_draw_size * column_count)) / (column_count + 1) nr_bound_h = (gr_h - (neuron_draw_size * n_in_col)) / (2 * n_in_col) if nr_bound_w < 1: nr_bound_w = 1 nr_bound_h = 1 neuron_draw_size = (neuron_area_width - (nr_bound_w * (column_count + 1))) / column_count if neuron_draw_size < 2: nr_bound_w = 1 nr_bound_h = 1 neuron_draw_size = 2 neuron_area = neuron_draw_size+nr_bound_w+nr_bound_h n_count = round((neuron_area_width/neuron_area) * (gr_h/neuron_area)) column_count = ceil((n_count * neuron_area) / gr_h) n_in_col = round(n_count / column_count) n_count = column_count * n_in_col down_shift = (gr_h - ((neuron_draw_size + 2*nr_bound_h) * (n_in_col)))/2 else: nr_bound_h = neuron_draw_size*0.1 neuron_draw_size = neuron_draw_size - 2 * nr_bound_h nr_bound_w = (neuron_area_width - neuron_draw_size ) / 2 colNum = 0 nrNum = 0 draw_neuron = 0 while draw_neuron < n_count: nrNum+=1 if lay_scheme[gNum][2]: color_neuron = color_digital_neuron else: color_neuron = color_analog_neuron # neuron self.canvas_brain.create_arc(x_group_preset + nr_bound_w + colNum * (neuron_draw_size + nr_bound_w), nr_preset + down_shift + nr_bound_h, x_group_preset + nr_bound_w + colNum * ( neuron_draw_size + nr_bound_w) + neuron_draw_size, nr_preset + down_shift + neuron_draw_size + nr_bound_h, start=1, extent=359, fill=color_neuron, outline=color_neuron) if lay_scheme[gNum][2]: #center self.canvas_brain.create_rectangle(x_group_preset + nr_bound_w + colNum * ( neuron_draw_size + nr_bound_w) + neuron_draw_size / 3, nr_preset + down_shift + nr_bound_h + neuron_draw_size / 3, x_group_preset + nr_bound_w + colNum * ( neuron_draw_size + nr_bound_w) + 2 * neuron_draw_size / 3, nr_preset + down_shift + 2 * neuron_draw_size / 3 + nr_bound_h, fill=color_inside, outline=color_inside) else: #center self.canvas_brain.create_arc(x_group_preset + nr_bound_w + colNum * ( neuron_draw_size + nr_bound_w) + neuron_draw_size / 3, nr_preset + down_shift + nr_bound_h + neuron_draw_size / 3, x_group_preset + nr_bound_w + colNum * ( neuron_draw_size + nr_bound_w) + 2 * neuron_draw_size / 3, nr_preset + down_shift + 2 * neuron_draw_size / 3 + nr_bound_h, start=1, extent=359, fill=color_inside, outline=color_inside) self.canvas_brain.create_arc(x_group_preset + nr_bound_w + colNum * (neuron_draw_size + nr_bound_w) + neuron_draw_size / 3, nr_preset + down_shift + nr_bound_h + neuron_draw_size / 3, x_group_preset + nr_bound_w + colNum * ( neuron_draw_size + nr_bound_w) + 2 * neuron_draw_size / 3, nr_preset + down_shift + 2 * neuron_draw_size / 3 + nr_bound_h, start=1, extent=359, fill=color_inside, outline=color_inside) if colNum == 0: #input self.canvas_brain.create_line(x_input_vec_preset + x_inp_vec_width, gr_preset + (group_h - 2 * gr_bound) / 2, x_group_preset, nr_preset + down_shift + nr_bound_h + neuron_draw_size/2, fill='#000000', arrow=LAST) #output self.canvas_brain.create_line(x_group_preset + lay_thicc - 2 * gr_bound, nr_preset + down_shift + nr_bound_h + neuron_draw_size/2, x_output_vec_preset, nr_preset + down_shift + nr_bound_h + neuron_draw_size/2, fill='#000000', arrow=LAST) #self.canvas_brain.create_line(x_output_vec_preset, # nr_preset, # x_output_vec_preset + x_out_vec_width, # nr_preset, # fill='#000000') nr_preset = nr_preset + neuron_draw_size + 2 * nr_bound_h if nrNum == n_in_col: colNum+=1 nrNum = 0 if colNum % 2 == 0: nr_preset = gr_preset else: nr_preset = gr_preset draw_neuron+=1 gr_preset = group_h+gr_preset gNum+=1 layNum+=1 prev_lay_group_out_cord = group_out_cord group_out_cord = [] self.scheme_window.mainloop() def get_proportion(self, vector = []): sum = 0 for el in vector: sum += el out_vector = [] for el in vector: out_vector.append(el / sum) return out_vector def get_color(self, r=0, g=0, b=0): clr = ((r * 1.0, g * 1.0, b * 1.0), '#' + r.to_bytes(1, 'little').hex().__str__() + g.to_bytes(1, 'little').hex().__str__() + b.to_bytes(1, 'little').hex().__str__()) return clr def frozed_mind(self): build_scheme = [] for lay in self.lays: build_scheme.append(lay.get_build_scheme()) consciousness = self.get_all_synapse_weight() self.frozen_mind = {'scheme': build_scheme, 'weights': consciousness} return self.frozen_mind def unfrozed_mind(self, ice_piece): self.__init__(laysConfigs = ice_piece['scheme']) self.load_consciousness(consciousness = ice_piece['weights']) def save_to_file(self, filename = 'frozen_mind.txt'): self.frozed_mind() f = open(filename, 'w') f.write(json.dumps(self.frozen_mind)) f.close() def load_from_file(self, filename = 'frozen_mind.txt'): f = open(filename, 'r') json_mind = f.read() self.unfrozed_mind(ice_piece = json.loads(json_mind)) f.close() def __gt__(self, other): return self.NN_learning_temp > other.NN_learning_temp def __lt__(self, other): return self.NN_learning_temp < other.NN_learning_temp def __ge__(self, other): return self.NN_learning_temp >= other.NN_learning_temp def __le__(self, other): return self.NN_learning_temp <= other.NN_learning_temp class lay_abstact(): '''layConfig = [ (0, True, 0, (0,), 25, 0), ] лист туплов: колво нейронов в группе, # плюс(True) или минус(False) на выходе нейронов группы, # номер функции нейронов группы, # дискретный выход у нейронов или нет (True - да) # тулпа с номерами inputs-групп, с которыми связанна нейронная группа # длинна входного input-вектора группы # коэф нормализации вывода группы''' def __init__(self, layConfig = [ (0, True, 0, True, (0,), 25, 0), ]): self.neuronGroups_count = len(layConfig) self.neuronGroups_list = [] self.neuronGroups_inputs_link = [] self.groupsNormalization_coeff = [] self.neuron_count = 0 if layConfig[0][0] == 0: pass else: for neuron_group in layConfig: neuton_with_pos_out = neuron_group[1] lay_temp = [] i = 0 while i < neuron_group[0]: neuron = neuron_abstact(generateWeightsCount=neuron_group[5], positiveOutput=neuton_with_pos_out, funcNum=neuron_group[2], digitalOut=neuron_group[3] ) lay_temp.append(neuron) self.neuron_count+=1 i+=1 self.neuronGroups_list.append(lay_temp) self.neuronGroups_inputs_link.append(neuron_group[4]) self.groupsNormalization_coeff.append(neuron_group[6]) def get_excited(self, inputsGoups = [[],]): output_groups = [] i=0 while i<self.neuronGroups_count: group_input = [] neurou_group_output = [] for inputgroupNum in self.neuronGroups_inputs_link[i]: group_input.extend(inputsGoups[inputgroupNum]) for neuron in self.neuronGroups_list[i]: neurou_group_output.append(neuron.spike( group_input )) if self.groupsNormalization_coeff[i] != 0: neurou_group_output = self.normalize_vector(neurou_group_output, self.groupsNormalization_coeff[i]) output_groups.append(neurou_group_output) i+=1 return output_groups def normalize_vector(self, inputVector = [], norm_coeff = 1): min_val = min(inputVector) max_val = max(inputVector) delitel = max_val - min_val normalOutputVector = [] if delitel == 0: for x_val in inputVector: norm_x_val = 0 normalOutputVector.append(norm_x_val) else: for x_val in inputVector: norm_x_val = ( (x_val - min_val) * norm_coeff )/delitel normalOutputVector.append(norm_x_val) return normalOutputVector def fcingCooldown(self, inputGroups = [[],], output = [[],], desire_out = [[],], changingNeuronsCount = 1, changingNeuronsWeightCount = 1, Lwa = 1): errOuts = self.get_err_out(output, desire_out) errOuts.sort() changing_errOuts = [] changing_neuron_weights_info = [] inputs = [] len_groupinput = [] i = 0 while i < self.neuronGroups_count: group_input = [] for inputgroupNum in self.neuronGroups_inputs_link[i]: group_input.extend(inputGroups[inputgroupNum]) inputs.append(group_input) i+=1 i = 0 while i < changingNeuronsCount and i < len(errOuts): changing_errOuts.append(errOuts[i]) i+=1 for err_out in changing_errOuts: err_neuron = self.neuron_at(err_out[2]) err_neuron_vector = err_neuron.learning_spike(inputs[err_out[2][0]]) err_neuron_vector.sort() i = 0 Nwa = Lwa while i < changingNeuronsWeightCount: if err_out[1] == 1:#надо увеличить выход нейрона if err_neuron_vector[i][1] < err_out[1]: #вес нейрона находится на отрицательном ребре Nwa = - Lwa self.neuron_at(err_out[2]).adjust_weight(err_neuron_vector[i][2], Nwa) else: #вес нейрона находится на положительном ребре Nwa = Lwa self.neuron_at(err_out[2]).adjust_weight(err_neuron_vector[i][2], Nwa) else: #надо уменьшить выход if err_neuron_vector[i][1] < err_out[1]: #вес нейрона находится на отрицательном ребре Nwa = Lwa self.neuron_at(err_out[2]).adjust_weight(err_neuron_vector[i][2], Nwa) else: #вес нейрона находится на положительном ребре Nwa = - Lwa self.neuron_at(err_out[2]).adjust_weight(err_neuron_vector[i][2], Nwa) changing_neuron_weights_info.append( (err_out, err_neuron_vector[i], Nwa) ) i+=1 input_group_len = [] for input in inputGroups: input_group_len.append(len(input)) for ch_neur in changing_neuron_weights_info: #изменить инпут пропорионально аджастам весов нейронов Gnum = 0 Nnum = ch_neur[1][2] for link in self.neuronGroups_inputs_link[Gnum]: if Nnum >= input_group_len[link]: Nnum -= input_group_len[link] else: Gnum = link break inputGroups[Gnum][Nnum] += inputGroups[Gnum][Nnum] * ch_neur[2] return inputGroups def get_err_out(self, list, example_list): '''На входе лист листов с интами''' err_elements = [] try: len1 = len(list) i = 0 while i < len1: len2 = len(list[i]) j = 0 while j < len2: if list[i][j] != example_list[i][j]: diff = example_list[i][j] - list[i][j] delta = abs(diff) sign = 1 if diff > 0 else -1 er_element = (delta, sign, (i,j)) err_elements.append(er_element) j+=1 i+=1 finally: return err_elements return err_elements def neuron_at(self, coord): '''coord = (groupNum, neuronNum)''' return self.neuronGroups_list[coord[0]][coord[1]] def get_neuron_group_ref(self, NgNum = 0): return self.neuronGroups_list[NgNum] def get_draw_scheme(self): draw_scheme = [] for group in self.neuronGroups_list: neuron_count = len(group) sigh = group[0].output_sign digital_out = group[0].digital_out links = self.neuronGroups_inputs_link[self.neuronGroups_list.index(group)] weights_count = group[0].weightsCount draw_scheme.append((neuron_count, sigh, digital_out, links, weights_count)) return draw_scheme def get_build_scheme(self): build_scheme = [] for group in self.neuronGroups_list: neuron_count = len(group) sigh = True if group[0].output_sign == 1 else False digital_out = group[0].digital_out links = self.neuronGroups_inputs_link[self.neuronGroups_list.index(group)] weights_count = group[0].weightsCount func_num = group[0].funcNum normal_coef = self.groupsNormalization_coeff[self.neuronGroups_list.index(group)] build_scheme.append((neuron_count, sigh, func_num, digital_out, links, weights_count, normal_coef)) return build_scheme class neuron_abstact(): '''funcNum: 0 - сумматор (если цифровой выход, то пороговый сумматор 1 - рациональная сигмоида threshold порог срабатывания для цифрового выхода''' def __init__(self, weights = [], generateWeightsCount = 0, positiveOutput = True, funcNum = 0, digitalOut = True): if generateWeightsCount > 0: self.weights = [] self.set_random_weights(weights, generateWeightsCount) else: self.weights = weights self.weightsCount = len(self.weights) self.output_sign = 1 if positiveOutput else -1 self.digital_out = digitalOut self.funcNum = funcNum self.threshold = round(generateWeightsCount/2) self.recurrent_mem = [] def set_random_weights(self, weights = [], weightsCount = 25,): if len(weights) == 0: self.weights = [(9.9 + x - x) / randint(1, 100) + 0.1 for x in range(weightsCount)] else: self.weights = weights.copy() self.weightsCount = len(self.weights) def set_funcNum(self,funcNum): self.funcNum = funcNum def set_weights(self, weights = []): self.weights = weights self.weightsCount = len(self.weights) if self.funcNum == 1: for weight in self.weights: if weight == 0: weights = self.get_random_weight(from_=0.1,to=10) def get_weights(self): return self.weights def get_random_weight(self, from_ = 0, to = 10): return (to - from_) / randint(1, 100) + from_ def spike(self, input = []): if self.funcNum == 0: #linear sum output = 0 i = 0 while i < self.weightsCount: output += ( input[i] * self.weights[i] ) i+=1 if self.digital_out: return 1 if output > self.threshold else 0 else: return output * self.output_sign elif self.funcNum == 1: #rational sig output = 0 i = 0 while i < self.weightsCount: abs_inp = abs(input[i]) output += abs_inp / ( abs_inp + abs(self.weights[i]) ) i+=1 if self.digital_out: return 1 if output > self.threshold else 0 else: return output * self.output_sign elif self.funcNum == 2: #RelU output = 0 i = 0 while i < self.weightsCount: output += ( input[i] + self.weights[i] ) * self.weights[i] #закоментить, если черви сойдут с ума i+=1 output = max([0, output]) if self.digital_out: return 1 if output > self.threshold else 0 else: return output * self.output_sign else: pass def learning_spike(self, input = []): '''output = [(x,y,z),] x - input*weight, y - output sigh, z - weight number''' output = [] i = 0 if self.funcNum == 1: while i < self.weightsCount: abs_inp = abs(input[i]) output.append( ( abs_inp/(abs_inp + self.weights[i]), self.output_sign, i ) ) i+=1 else: while i < self.weightsCount: output.append( (input[i] * self.weights[i], self.output_sign, i) ) i+=1 return output def adjust_weight(self, weightNum = 0, Nwa = 0 ): if self.funcNum == 1: self.weights[weightNum] -= Nwa * self.weights[weightNum] if self.weights[weightNum] == 0: self.weights[weightNum] = self.get_random_weight(from_=0.1,to=10) else: self.weights[weightNum] += Nwa * self.weights[weightNum]

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0.018529

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0

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34,289

730

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1

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false

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0.135452

0

null

0

null

0

1

0

0034f805a844662bdcbee5246fde715c74fdfb46

761

py

Python

xiamiu/urls.py

Vida42/xiamiu

9249a20746d1da050546e3fcdfafbc5ff49ab4d0

[ "Apache-2.0" ]

null

xiamiu/urls.py

Vida42/xiamiu

9249a20746d1da050546e3fcdfafbc5ff49ab4d0

[ "Apache-2.0" ]

null

xiamiu/urls.py

Vida42/xiamiu

9249a20746d1da050546e3fcdfafbc5ff49ab4d0

[ "Apache-2.0" ]

null

from django.urls import path from django.conf.urls import url from . import views urlpatterns = [ path('', views.index, name='home'), url(r'^artist/(?P<inputID>.*?)/$', views.showArtistPage, name='showArtistPage'), url(r'^album/(?P<inputID>.*?)/$', views.showAlbumPage, name='showAlbumPage'), url(r'^song/(?P<inputID>.*?)/$', views.showSongPage, name='showSongPage'), url(r'^genre/(?P<inputID>.*?)/$', views.showGenrePage, name='showGenrePage'), url(r'^search/$', views.search, name='search'), url(r'^search/artist/$', views.searchArtistByName, name='searchArtistByName'), url(r'^search/album/$', views.searchAlbumByName, name='searchAlbumByName'), url(r'^search/song/$', views.searchSongByName, name='searchSongByName') ]

44.764706

84

0.672799

87

761

5.885057

0.310345

0.0625

0.101563

0

0.106439

761

16

85

47.5625

0.752941

0

0.350854

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0

1

0

false

0

0.214286

0

0.214286

0

null

0

null

0

1

0

00350a7f72c371d77bedf708f8a55456f2d37c38

19,030

py

Python

lib/python/treadmill_aws/formatter.py

Morgan-Stanley/treadmill-aws

4c3d25c477422d83f0cd8dc6851fd02ffa48dcbb

[ "Apache-2.0" ]

6

2018-05-24T17:17:51.000Z

2020-06-06T02:21:59.000Z

lib/python/treadmill_aws/formatter.py

Morgan-Stanley/treadmill-aws

4c3d25c477422d83f0cd8dc6851fd02ffa48dcbb

[ "Apache-2.0" ]

93

2018-04-16T16:14:40.000Z

2019-09-17T22:10:28.000Z

lib/python/treadmill_aws/formatter.py

Morgan-Stanley/treadmill-aws

4c3d25c477422d83f0cd8dc6851fd02ffa48dcbb

[ "Apache-2.0" ]

17

2017-09-29T10:30:47.000Z

2019-01-28T21:52:03.000Z

"""Table CLI formatter. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import yaml from treadmill.formatter import tablefmt def _sort(unsorted): """Sort list.""" unsorted.sort() return '\n'.join(unsorted) def _state(state): """Get status from instance.""" return state['Name'] def _name_from_tags(tags): """Get name from tags.""" for tag in tags: if tag['Key'] == 'Name': return tag['Value'] return None def _fmt_tags(): """Output formatter tags.""" def _fmt(items): """Format tags, discard cloudformation tags.""" filtered = [ item for item in items if not item['Key'].startswith('aws:cloudformation:') ] schema = [ ('key', 'Key', None), ('value', 'Value', None), ] return tablefmt.list_to_table( filtered, schema, header=False, align=None ) return _fmt def _fmt_secgroups(): """Output formatter security groups.""" def _fmt(items): """Format tags, discard cloudformation tags.""" schema = [ ('name', 'GroupName', None), ('id', 'GroupId', None), ] return tablefmt.list_to_table( items, schema, header=False, align=None ) return _fmt def _fmt_list(): """Output formatter list.""" def _fmt(items): """Format list.""" schema = [ ('item', None, None), ] return tablefmt.list_to_table( items, schema, header=False, align=None ) return _fmt def _fmt_trusted_entities(policy): def _statement_principals(statement): entities = [] if (statement['Action'] == 'sts:AssumeRole' and statement['Effect'] == 'Allow' and 'AWS' in statement['Principal']): principals = statement['Principal']['AWS'] if isinstance(principals, str): principals = [principals] principals.sort() for principal in principals: parts = principal.split(':') parts[5] = parts[5].replace('/', ':') entities.append({'Entity': parts[5], 'Arn': principal}) return entities def _statement_saml_providers(statement): entities = [] if (statement['Action'] == 'sts:AssumeRoleWithSAML' and statement['Effect'] == 'Allow'): saml_providers = statement['Principal']['Federated'] if isinstance(saml_providers, str): saml_providers = [saml_providers] saml_providers.sort() for saml_provider in saml_providers: parts = saml_provider.split(':') parts[5] = parts[5].replace('/', ':') entities.append({'Entity': parts[5], 'Arn': saml_provider}) return entities def _statement_services(statement): entities = [] if (statement['Action'] == 'sts:AssumeRole' and statement['Effect'] == 'Allow' and 'Service' in statement['Principal']): services = statement['Principal']['Service'] if isinstance(services, str): services = [services] services.sort() for service in services: entities.append({'Entity': 'service:%s' % service, 'Arn': service}) return entities # pylint: disable=R0912 def _trusted_entities(pol): entities = [] for statement in pol['Statement']: principals = _statement_principals(statement) if principals: for principal in principals: entities.append(principal) saml_providers = _statement_saml_providers(statement) if saml_providers: for saml_provider in saml_providers: entities.append(saml_provider) services = _statement_services(statement) if services: for service in services: entities.append(service) return entities items = _trusted_entities(policy) schema = [ ('Entity', 'Entity', None), ('Arn', 'Arn', None) ] return tablefmt.list_to_table(items, schema, header=False, align=None) def _fmt_attached_policies(policies): def _fpolicies(policies): fpolicies = [] for policy in policies: if policy['PolicyArn']. startswith('arn:aws:iam::aws:policy/'): pn = policy['PolicyArn'].replace('arn:aws:iam::aws:policy/', '') fpolicies.append({ 'Type': 'global', 'PolicyName': pn, 'PolicyArn': policy['PolicyArn'] }) else: fpolicies.append({ 'Type': 'local', 'PolicyName': policy['PolicyName'], 'PolicyArn': policy['PolicyArn'] }) return fpolicies items = _fpolicies(policies) schema = [ ('Type', 'Type', None), ('PolicyName', 'PolicyName', None), ('PolicyArn', 'PolicyArn', None), ] return tablefmt.list_to_table(items, schema, header=False, align=None, sortby='PolicyName') def _fmt_policy_version(policy_version): return yaml.dump(policy_version, default_flow_style=False, indent=4) class SubnetPrettyFormatter: """Pretty table formatter for AWS subnets.""" @staticmethod def format(item): """Return pretty-formatted item.""" schema = [ ('id', 'SubnetId', None), ('state', 'State', None), ('zone', 'AvailabilityZone', None), ('cidr_block', 'CidrBlock', None), ('vpc', 'VpcId', None), ('tags', 'Tags', _fmt_tags()), ] format_item = tablefmt.make_dict_to_table(schema) format_list = tablefmt.make_list_to_table(schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class VpcPrettyFormatter: """Pretty table formatter for AWS vpcs.""" @staticmethod def format(item): """Return pretty-formatted item.""" schema = [ ('id', 'VpcId', None), ('default', 'IsDefault', None), ('state', 'State', None), ('cidr_block', 'CidrBlock', None), ('tags', 'Tags', _fmt_tags()), ] format_item = tablefmt.make_dict_to_table(schema) format_list = tablefmt.make_list_to_table(schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class InstancePrettyFormatter: """Pretty table formatter for AWS instances.""" @staticmethod def format(item): """Return pretty-formatted item.""" item_schema = [ ('hostname', 'Tags', _name_from_tags), ('id', 'InstanceId', None), ('arch', 'Architecture', None), ('image', 'ImageId', None), ('type', 'InstanceType', None), ('key', 'KeyName', None), ('launch', 'LaunchTime', None), ('state', 'State', _state), ('vpc', 'VpcId', None), ('subnet', 'SubnetId', None), ('secgroups', 'SecurityGroups', _fmt_secgroups()), ('tags', 'Tags', _fmt_tags()), ] list_schema = [ ('hostname', 'Tags', _name_from_tags), ('id', 'InstanceId', None), ('image', 'ImageId', None), ('type', 'InstanceType', None), ('key', 'KeyName', None), ('vpc', 'VpcId', None), ('subnet', 'SubnetId', None), ('tags', 'Tags', _fmt_tags()), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class SpotPrettyFormatter: """Pretty table formatter for Spot Instance Requests.""" @staticmethod def format(item): """Return pretty-formatted item.""" item_schema = [ ('id', 'id', None), ('status', 'state', None), ('code', 'status_code', None), ('changed', 'status_timestamp', None), ('zone', 'az', None), ('subnet', 'subnet', None), ('type', 'instance_type', None), ('instance_id', 'instance_id', None), ('ami_id', 'ami_id', None), ('hostname', 'hostname', None), ('launch', 'instance_launch', None), ('state', 'instance_status', None), ('duration', 'duration', None), ] list_schema = item_schema format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class IamRolePrettyFormatter: """Pretty table formatter for AWS IAM roles.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('RoleName', 'RoleName', None), ('Arn', 'Arn', None), ('MaxSessionDuration', 'MaxSessionDuration', None), ('CreateDate', 'CreateDate', None), ] item_schema = [ ('RoleName', 'RoleName', None), ('Path', 'Path', None), ('Arn', 'Arn', None), ('MaxSessionDuration', 'MaxSessionDuration', None), ('CreateDate', 'CreateDate', None), ('RoleId', 'RoleId', None), ('TrustedEntities', 'AssumeRolePolicyDocument', _fmt_trusted_entities), ('InlinePolicies', 'RolePolicies', None), ('AttachedPolicies', 'AttachedPolicies', _fmt_attached_policies), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class IamPolicyPrettyFormatter: """Pretty table formatter for AWS IAM policies.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('AttachmentCount', 'DefaultVersionId', None), ('DefaultVersionId', 'DefaultVersionId', None), ('Arn', 'Arn', None), ('MaxSessionDuration', 'MaxSessionDuration', None), ('CreateDate', 'CreateDate', None), ] item_schema = [ ('Arn', 'Arn', None), ('PolicyName', 'PolicyName', None), ('Path', 'Path', None), ('DefaultVersionId', 'DefaultVersionId', None), ('IsAttachable', 'IsAttachable', None), ('AttachmentCount', 'AttachmentCount', None), ('Description', 'Description', None), ('CreateDate', 'CreateDate', None), ('UpdateDate', 'UpdateDate', None), ('PolicyVersion', 'PolicyVersion', _fmt_policy_version) ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class SnapshotPrettyFormatter: """Pretty table formatter for AWS snaphots.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('Name', 'Tags', _name_from_tags), ('SnapshotId', 'SnapshotId', None), ('VolumeId', 'VolumeId', None), ('State', 'State', None), ('Progress', 'Progress', None), ('VolumeSize', 'VolumeSize', None), ('StartTime', 'StartTime', None), ('Description', 'Description', None), ] item_schema = [ ('Name', 'Tags', _name_from_tags), ('Description', 'Description', None), ('SnapshotId', 'SnapshotId', None), ('VolumeId', 'VolumeId', None), ('State', 'State', None), ('Progress', 'Progress', None), ('VolumeSize', 'VolumeSize', None), ('StartTime', 'StartTime', None), ('Encrypted', 'Encrypted', None), ('KmsKeyId', 'KmsKeyId', None), ('tags', 'Tags', _fmt_tags()), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class ImagePrettyFormatter: """Pretty table formatter for AWS images.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('id', 'ImageId', None), ('name', 'Name', None), ('owner', 'OwnerId', None), ('created', 'CreationDate', None), ('public', 'Public', lambda v: 'yes' if v else 'no'), ('state', 'State', None), ] item_schema = list_schema + [ ('tags', 'Tags', _fmt_tags()), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class SecgroupPrettyFormatter: """Pretty table formatter for AWS security groups.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('id', 'GroupId', None), ('owner', 'OwnerId', None), ('vpc', 'VpcId', None), ('tags', 'Tags', _fmt_tags()), ] # TODO: add ip ingress/egress permissions to the output. item_schema = [ ('id', 'GroupId', None), ('owner', 'OwnerId', None), ('vpc', 'VpcId', None), ('tags', 'Tags', _fmt_tags()), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class IpaUserPrettyFormatter: """Pretty table formatter for AWS user.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('username', 'uid', lambda _: _[0]), ] item_schema = [ ('username', 'uid', lambda _: _[0]), ('class', 'userclass', lambda _: _[0]), ('groups', 'memberof_group', _sort), ('indirect-groups', 'memberofindirect_group', '\n'.join), ('hbac-rule', 'memberofindirect_hbacrule', '\n'.join), ('sudo-rule', 'memberofindirect_sudorule', '\n'.join), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class IamUserPrettyFormatter: """Pretty table formatter for AWS users.""" @staticmethod def format(item): """Return pretty-formatted item.""" list_schema = [ ('UserName', 'UserName', None), ('Arn', 'Arn', None), ] item_schema = [ ('UserName', 'UserName', None), ('Path', 'Path', None), ('Arn', 'Arn', None), ('CreateDate', 'CreateDate', None), ('UserId', 'UserId', None), ('InlinePolicies', 'UserPolicies', None), ('AttachedPolicies', 'AttachedPolicies', _fmt_attached_policies), ] format_item = tablefmt.make_dict_to_table(item_schema) format_list = tablefmt.make_list_to_table(list_schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class CellDataFormatter: """Pretty table formatter for cell data.""" @staticmethod def format(item): """Return pretty-formatted item.""" schema = [ ('aws_account', 'aws_account', None), ('aws_admin', 'aws_admin', None), ('aws_region', 'aws_region', None), ('docker-registries', 'docker_registries', ','.join), ('disk-size', 'disk_size', None), ('hostgroups', 'hostgroups', ','.join), ('image', 'image', None), ('image-accounts', 'image_accounts', ','.join), ('instance-profile', 'instance_profile', None), ('realm', 'realm', None), ('secgroup', 'secgroup', None), ('size', 'size', None), ('sns-topic', 'sns_topic', None), ('subnets', 'subnets', ','.join), ('s3_registry_bucket', 's3_registry_bucket', None), ('tls_certs', 'tls_certs', None), ] format_item = tablefmt.make_dict_to_table(schema) format_list = tablefmt.make_list_to_table(schema) if isinstance(item, list): return format_list(item) else: return format_item(item) class PartDataFormatter: """Pretty table formatter for partition data.""" @staticmethod def format(item): """Return pretty-formatted item.""" schema = [ ('autoscale', 'autoscale', None), ('image', 'image', None), ('image-accounts', 'image_accounts', ','.join), ('instance-types', 'instance_types', ','.join), ('spot-instance-types', 'spot_instance_types', ','.join), ('spot-duration', 'spot_duration', None), ('disk-size', 'disk_size', None), ('hostgroups', 'hostgroups', ','.join), ('secgroup', 'secgroup', None), ('instance-profile', 'instance_profile', None), ('subnets', 'subnets', ','.join), ('s3_registry_bucket', 's3_registry_bucket', None), ] format_item = tablefmt.make_dict_to_table(schema) format_list = tablefmt.make_list_to_table(schema) if isinstance(item, list): return format_list(item) else: return format_item(item)

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0035d39504b6fcd873cb06e3d139aa8135704401

10,156

py

Python

src/newt/db/updater.py

bmjjr/db

39d3833f4458fcd20d09f383711745842b5db4f2

[ "MIT" ]

153

2017-01-24T16:55:00.000Z

2022-03-21T08:24:13.000Z

src/newt/db/updater.py

bmjjr/db

39d3833f4458fcd20d09f383711745842b5db4f2

[ "MIT" ]

14

2017-01-25T17:04:49.000Z

2021-12-05T19:26:35.000Z

src/newt/db/updater.py

bmjjr/db

39d3833f4458fcd20d09f383711745842b5db4f2

[ "MIT" ]

16

2017-01-25T07:25:17.000Z

2022-03-21T08:24:16.000Z

from __future__ import print_function """Updates database json representation """ import argparse import itertools import logging import relstorage.adapters.postgresql import relstorage.options import sys from . import pg_connection from . import follow from .jsonpickle import Jsonifier from ._adapter import DELETE_TRIGGER from ._util import closing, table_exists, trigger_exists logger = logging.getLogger(__name__) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('connection_string', help='Postgresql connection string') parser.add_argument('-t', '--poll-timeout', type=int, default=300, help='Change-poll timeout, in seconds') parser.add_argument('-m', '--transaction-size-limit', type=int, default=100000, help='Transaction size limit (aproximate)') parser.add_argument( '-l', '--logging-configuration', default='info', help='Logging configuration file path, or a logging level name') parser.add_argument( '-d', '--driver', default='auto', help='Provide an explicit Postgres driver name (e.g. psycopg2)') parser.add_argument( '-T', '--remove-delete-trigger', action="store_true", help="""\ Remove the Newt DB delete trigger, if it exists. The Newt DB delete trigger is incompatible with the updater. It can cause deadlock errors is packed while the updater is running. """) gc_sql = """ delete from newt n where not exists ( select from object_state s where n.zoid = s.zoid) """ parser.add_argument( '-g', '--gc-only', action="store_true", help="""\ Collect garbage and exit. This removes Newt DB records that don't have corresponding database records. This is done by executing: %s Note that garbage collection is normally performed on startup unless the -G option is used. """ % gc_sql) parser.add_argument( '-G', '--no-gc', action="store_true", help="Don't perform garbage collection on startup.") parser.add_argument( '--compute-missing', action='store_true', help="""\ Compute missing newt records. Rather than processing new records, process records written up through the current time and stop. Only missing records are updated. This option requires PostgreSQL 9.5. This is used to compute newt records after adding Newt DB to an existing PostgreSQL RelStorage application. """) parser.add_argument( '--nagios', help="""\ Check the status of the updater. The status is checked by checking the updater lag, which is the difference between the last transaction committed to the database, and the last transaction processed by the updater. The option takes 2 numbers, separated by commas. The first number is the lag, in seconds, for the updater to be considered to be OK. The second number is the maximum lag for which the updater isn't considered to be in error. For example, 1,99 indicates OK if 1 or less, WARNING if more than 1 and less than or equal to 99 and ERROR of more than 99 seconds. """) parser.add_argument( '-x', '--transform', help = """\ The dotted name of a function (or callable object) to transform generated JSON data. This provides a way to control how your JSON data are generated and also provides a mechanism for ignoring some objects. See the Newt DB transform option. """) def _update_newt(conn, cursor, jsonifier, Binary, batch): ex = cursor.execute mogrify = cursor.mogrify tid = None while True: data = list(itertools.islice(batch, 0, 100)) if not data: break tid = data[-1][0] # Delete any existing records for the values. 2 reasons: # a) Make sire that new invalid data removes old valid data, and # b) Don't depend on upsert. ex("delete from newt where zoid = any(%s)", ([d[1] for d in data], )) # Convert, filtering out null conversions (uninteresting classes) to_save = [] for tid, zoid, state in data: class_name, ghost_pickle, state = jsonifier((tid, zoid), state) if state is not None: to_save.append((zoid, class_name, Binary(ghost_pickle), state)) if to_save: ex("insert into newt (zoid, class_name, ghost_pickle, state)" " values " + ', '.join(mogrify('(%s, %s, %s, %s)', d).decode('ascii') for d in to_save) ) if tid is not None: follow.set_progress_tid(conn, __name__, tid) conn.commit() def _compute_missing(conn, cursor, jsonifier, Binary, batch): ex = cursor.execute mogrify = cursor.mogrify tid = None while True: data = list(itertools.islice(batch, 0, 100)) if not data: break tid = data[-1][0] # Convert, filtering out null conversions (uninteresting classes) to_save = [] for tid, zoid, state in data: class_name, ghost_pickle, state = jsonifier((tid, zoid), state) if state is not None: to_save.append((zoid, class_name, Binary(ghost_pickle), state)) if to_save: ex("insert into newt (zoid, class_name, ghost_pickle, state)" " values %s on conflict do nothing" % ', '.join(mogrify('(%s, %s, %s, %s)', d).decode('ascii') for d in to_save) ) conn.commit() logging_levels = 'DEBUG INFO WARNING ERROR CRITICAL'.split() def main(args=None): options = parser.parse_args(args) if options.logging_configuration.upper() in logging_levels: logging.basicConfig(level=options.logging_configuration.upper()) else: with open(options.logging_configuration) as f: from ZConfig import configureLoggers configureLoggers(f.read()) transform = options.transform if transform is not None: from .component import global_by_name transform = global_by_name(transform) jsonifier = Jsonifier(transform=transform) driver = relstorage.adapters.postgresql.select_driver( relstorage.options.Options(driver=options.driver)) Binary = driver.Binary dsn = options.connection_string with closing(pg_connection(dsn)) as conn: with closing(conn.cursor()) as cursor: if options.nagios: if not table_exists(cursor, 'newt_follow_progress'): print("Updater has not run") return 2 cursor.execute("select max(tid) from object_state") [[stid]] = cursor utid = follow.get_progress_tid(conn, __name__) if stid is None: if utid == -1: print("No transactions") return 0 else: print("Updater saw data but there was None") return 2 elif utid < 0: print("Updater hasn't done anything") return 2 else: from ZODB.utils import p64 from ZODB.TimeStamp import TimeStamp lag = (TimeStamp(p64(stid)).timeTime() - TimeStamp(p64(utid)).timeTime()) if lag < 0: print("Updater is ahead") return 2 warn, error = map(int, options.nagios.split(',')) flag = lambda : ("%99.3f" % lag).strip() if lag > error: print("Updater is too far behind | %s" % flag()) return 2 elif lag > warn: print("Updater is behind | %s" % flag()) return 1 else: print("OK | %s" % flag()) return 0 compute_missing = options.compute_missing if (compute_missing and not table_exists(cursor, follow.PROGRESS_TABLE) ): if not table_exists(cursor, 'newt'): raise AssertionError("newt table doesn't exist") cursor.execute("select max(tid) from object_state") [[tid]] = cursor else: tid = follow.get_progress_tid(conn, __name__) if tid < 0 and not table_exists(cursor, 'newt'): from ._adapter import _newt_ddl cursor.execute(_newt_ddl) elif trigger_exists(cursor, DELETE_TRIGGER): if options.remove_delete_trigger: cursor.execute("drop trigger %s on object_state" % DELETE_TRIGGER) else: logger.error( "The Newt DB delete trigger exists.\n" "It is incompatible with the updater.\n" "Use -T to remove it.") return 1 if not options.no_gc: cursor.execute(gc_sql) conn.commit() if options.gc_only: if options.no_gc: logger.warn( "Exiting after garbage collection,\n" "but garbage collection was suppressed.") return 0 if options.compute_missing: start_tid = -1 end_tid = tid logger.info("Compute_missing through %s", tid) process = _compute_missing else: logger.info("Starting updater at %s", tid) start_tid = tid end_tid = None process = _update_newt for batch in follow.updates( dsn, start_tid=start_tid, end_tid=end_tid, batch_limit=options.transaction_size_limit, poll_timeout=options.poll_timeout, ): process(conn, cursor, jsonifier, Binary, batch) if __name__ == '__main__': sys.exit(main())

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79

0.581824

1,198

10,156

4.809683

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0

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0.329756

10,156

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0

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1

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false

0

0.072961

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0.038627

0

null

0

null

0

1

0

00372000fecac8f61456e1d5394b1e5bd0e18f9f

3,538

py

Python

medtype-trainer/neleval/scripts/merge_evaluations.py

vsocrates/medtype

16c6f39d38a73c4c44258bbdf78074a81e07b1c7

[ "Apache-2.0" ]

113

2015-01-07T14:12:25.000Z

2022-01-21T12:23:57.000Z

medtype-trainer/neleval/scripts/merge_evaluations.py

vsocrates/medtype

16c6f39d38a73c4c44258bbdf78074a81e07b1c7

[ "Apache-2.0" ]

27

2015-02-02T02:45:38.000Z

2018-09-08T10:33:25.000Z

medtype-trainer/neleval/scripts/merge_evaluations.py

vsocrates/medtype

16c6f39d38a73c4c44258bbdf78074a81e07b1c7

[ "Apache-2.0" ]

24

2015-02-16T18:26:48.000Z

2021-05-25T13:23:53.000Z

#!/usr/bin/env python """Merge multiple evaluation files into one with prefixed measure names If directories are given, and --out-dir, will group by filename. Example usage: ./scripts/merge_evaluations.py --label-re='[^/]+/?$' -x eval_merged -l =TEDL2015_neleval-no1331 --out-dir /tmp/foobar tac15data/TEDL2015_neleval-no1331 $(find tac15data/TEDL2015_neleval-no1331/00filtered/ -type d ) """ from __future__ import print_function import argparse import os import glob import collections import sys import re ap = argparse.ArgumentParser(description=__doc__) ap.add_argument('-o', '--out-dir', default=None) ap.add_argument('-x', '--out-extension', default=None) ap.add_argument('-l', '--label', dest='labels', action='append', type=lambda s: s.split('=', 1)) ap.add_argument('-r', '--label-re', default=None, type=re.compile) ap.add_argument('--fmt', default='{label}/{{}}') ap.add_argument('paths', nargs='+') args = ap.parse_args() def _swap_ext(name, new_ext): if new_ext is None: return name name, ext = os.path.splitext(name) return name + '.' + new_ext nonexist = [path for path in args.paths if not os.path.exists(path)] if nonexist: ap.error('Paths do not exist: %r' % nonexist) is_dir = [os.path.isdir(path) for path in args.paths] if all(is_dir): if args.out_dir is None: ap.error('Must specify --out-dir in path mode') input_paths = collections.defaultdict(list) for dir_path in args.paths: for path in glob.glob(os.path.join(dir_path, '*.evaluation')): input_paths[os.path.basename(path)].append(path) outputs = {name: os.path.join(args.out_dir, _swap_ext(name, args.out_extension)) for name in input_paths} elif not any(is_dir): if args.out_dir is not None or args.out_extension is not None: ap.error('--out-dir and --out-extension not used in files mode; output is STDOUT') input_paths = {'all': args.paths} outputs = {'all': sys.stdout} else: ap.error('Got mixture of directories (e.g. %r) and files (e.g. %r)' % (args.paths[is_dir.index(True)], args.paths[is_dir.index(False)])) seen_labels = set() labels = {src: dst for dst, src in args.labels or []} def get_label(path): name = os.path.dirname(path) if args.label_re: match = args.label_re.search(name) if match is not None: name = match.group() seen_labels.add(name) return labels.get(name, name) for name in input_paths: fout = outputs[name] if not hasattr(fout, 'read'): opened = True fout = open(fout, 'w') else: opened = False print('Processing', name, 'to', fout.name, file=sys.stderr) for i, path in enumerate(input_paths[name]): label = get_label(path) if label: fmt = args.fmt.format(label=label) else: fmt = '{}' fmt = '{{}}\t{}'.format(fmt) with open(path) as fin: fin = iter(fin) try: header = next(fin) except StopIteration: print('Found empty file at', path, file=sys.stderr) if i == 0: fout.write(header) for l in fin: l, measure = l.rstrip('\n\r').rsplit('\t', 1) print(fmt.format(l, measure), file=fout) if opened: fout.close() unseen_labels = set(labels) - seen_labels if unseen_labels: print('WARNING: did not see labels %r' % sorted(unseen_labels), file=sys.stderr)

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null

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null

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0

00373487473f078500f863d310d10f7cf34f3397

11,276

py

Python

IQA_BIECON_release/models/BIECON_base.py

lionzhnn/IQA_BIECON_release

9b9452681460cbd3b670aff62f18c6661a724997

[ "MIT" ]

96

2017-07-25T07:54:59.000Z

2022-01-09T03:33:07.000Z

IQA_BIECON_release/models/BIECON_base.py

lionzhnn/IQA_BIECON_release

9b9452681460cbd3b670aff62f18c6661a724997

[ "MIT" ]

4

2018-04-25T09:46:05.000Z

2019-11-08T12:44:39.000Z

IQA_BIECON_release/models/BIECON_base.py

jongyookim/IQA_BIECON_release

9b9452681460cbd3b670aff62f18c6661a724997

[ "MIT" ]

35

2017-07-25T02:51:22.000Z

2022-02-05T03:05:40.000Z

from __future__ import absolute_import, division, print_function import os import numpy as np import theano.tensor as T from .model_basis import ModelBasis from .model_record import Record from ..layers import layers class Model(ModelBasis): def __init__(self, model_config, rng=None): super(Model, self).__init__(model_config, rng) self.set_configs(model_config) self.layers['feat'] = [] self.layers['feat_fc'] = [] self.layers['reg_loc'] = [] self.layers['reg_mos'] = [] print('\nBIECON base model') print(' - Model file: %s' % (os.path.split(__file__)[1])) self.init_model() def set_configs(self, model_config): self.set_opt_configs(model_config) self.wl_loc = float(model_config.get('wl_loc', 1e2)) self.wl_mos = float(model_config.get('wl_mos', 1e2)) self.wr_l2 = float(model_config.get('wr_l2', 1e-4)) self.dropout = model_config.get('use_dropout', False) self.update_wrt_loc = model_config.get( 'update_wrt_loc', ['feat', 'feat_fc', 'reg_loc']) self.update_wrt_iqa = model_config.get( 'update_wrt_iqa', ['feat', 'feat_fc', 'reg_mos']) def init_model(self): print(' - Feature conv layers') cur_key = 'feat' self.layers[cur_key] = [] # Conv. layers self.layers[cur_key].append(layers.ConvLayer( input_shape=self.get_input_shape(), num_filts=64, filt_size=(5, 5), layer_name=cur_key + '/conv1', activation=layers.relu, )) self.layers[cur_key].append(layers.Pool2DLayer( input_shape=self.get_out_shape(cur_key), pool_size=(2, 2), mode='max')) self.layers[cur_key].append(layers.ConvLayer( input_shape=self.get_out_shape(cur_key), num_filts=64, filt_size=(5, 5), layer_name=cur_key + '/conv2', activation=layers.relu, )) self.layers[cur_key].append(layers.Pool2DLayer( input_shape=self.get_out_shape(cur_key), pool_size=(2, 2), mode='max')) # Reshaping layer self.layers[cur_key].append( layers.TensorToVectorLayer(self.get_out_shape(cur_key))) # Fully connected layers cur_key = 'feat_fc' self.layers[cur_key] = [] self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape('feat'), n_out=1024, layer_name=cur_key + '/fc1', activation=layers.relu, )) if self.dropout: self.layers[cur_key].append(layers.DropoutLayer(p=0.5)) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape(cur_key), n_out=512, layer_name=cur_key + '/fc2', activation=layers.relu, )) if self.dropout: self.layers[cur_key].append(layers.DropoutLayer(p=0.5)) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape(cur_key), n_out=256, layer_name=cur_key + '/fc3', activation=layers.relu, )) if self.dropout: self.layers[cur_key].append(layers.DropoutLayer(p=0.5)) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape(cur_key), n_out=128, layer_name=cur_key + '/fc4', activation=layers.relu, )) ####################################################################### print(' - Regression metric layers') cur_key = 'reg_loc' self.layers[cur_key] = [] if self.dropout: self.layers[cur_key].append(layers.DropoutLayer(p=0.5)) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape('feat_fc'), n_out=128, layer_name=cur_key + '/fc1', activation=layers.relu, )) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape('feat_fc'), n_out=1, layer_name=cur_key + '/fc2', b_init=np.ones((1,), dtype='float32') * 0.5, )) ####################################################################### print(' - Regression mos layers') cur_key = 'reg_mos' self.layers[cur_key] = [] if self.dropout: self.layers[cur_key].append(layers.DropoutLayer(p=0.5)) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape('feat_fc'), n_out=128, layer_name=cur_key + '/fc1', activation=layers.relu, )) self.layers[cur_key].append(layers.FCLayer( n_in=self.get_out_shape(cur_key), n_out=1, layer_name=cur_key + '/fc2', b_init=np.ones((1,), dtype='float32') * 0.5, )) ####################################################################### super(Model, self).make_param_list() super(Model, self).show_num_params() def aggregation_fn(self, feat_vec): feat_avg = T.mean(feat_vec, axis=0, keepdims=True) return feat_avg # feat_std = T.std(feat_vec, axis=0, keepdims=True) # return T.concatenate([feat_avg, feat_std], axis=1) def feat_fn(self, x): out = self.get_key_layers_output(x, 'feat') return self.get_key_layers_output(out, 'feat_fc') def regress_loc_fn(self, feat_vec): return self.get_key_layers_output(feat_vec, 'reg_loc') def regress_mos_fn(self, feat_vec): return self.get_key_layers_output(feat_vec, 'reg_mos') def cost_reg_loc(self, x_c, met_s, n_img=None, bat2img_idx_set=None): """Get cost: regression onto local metroc scores """ records = Record() # concatenate the image patches if bat2img_idx_set: # if dummy data with fixed size is given and current data is # overwritten on dummy data with size of n_patches, # pick current dataset with size of n_patches n_patches = bat2img_idx_set[n_img - 1][1] x_c_set = x_c[:n_patches] met_s_set = met_s[:n_patches] else: # if input is current data x_c_set = x_c met_s_set = met_s ###################################################################### x_c_im = self.image_vec_to_tensor(x_c_set) met_s_im = self.image_vec_to_tensor(met_s_set) feat_vec = self.feat_fn(x_c_im) met_s_p = self.regress_loc_fn(feat_vec).flatten() met_s_mean = T.mean(met_s_set, axis=[1, 2, 3]) loc_cost = self.get_cost_mse_mae(met_s_mean, met_s_p) # regularization l2_reg = self.get_l2_regularization( ['feat', 'feat_fc', 'reg_loc'], mode='sum') cost = self.add_all_losses_with_weight( [loc_cost, l2_reg], [self.wl_loc, self.wr_l2]) # Parameters to record records.add_data('loc_mse', self.wl_loc * loc_cost) records.add_data('l2_reg', self.wr_l2 * l2_reg) # records.add_im_data('met_s_p', met_s_p_set) # records.add_im_data('met_s', met_s_set) records.add_imgs('x_c', x_c_im, caxis=[-0.25, 0.25]) if bat2img_idx_set: def score_to_img(score, repeat=1): tmp = score.dimshuffle(0, 'x', 'x', 'x') tmp = T.extra_ops.repeat(tmp, repeat, axis=2) return T.extra_ops.repeat(tmp, repeat, axis=3) met_s_img = score_to_img(met_s_mean, 10) records.add_imgs('met_s', met_s_img, caxis='auto') met_s_p_img = score_to_img(met_s_p, 10) records.add_imgs('met_s_p', met_s_p_img, caxis='auto') return cost, records def cost_updates_reg_loc(self, x_c, met_s, n_img=None, bat2img_idx_set=None): cost, records = self.cost_reg_loc( x_c, met_s, n_img=n_img, bat2img_idx_set=bat2img_idx_set) updates = self.get_updates_keys(cost, self.update_wrt_loc) return cost, updates, records def cost_nr_iqa(self, x_c, mos, n_img=None, bat2img_idx_set=None): records = Record() # concatenate the image patches if bat2img_idx_set: # if dummy data with fixed size is given and current data is # overwritten on dummy data with size of n_patches, # pick current dataset with size of n_patches n_patches = bat2img_idx_set[n_img - 1][1] x_c_set = x_c[:n_patches] else: # if input is current data x_c_set = x_c ###################################################################### x_c_im = self.image_vec_to_tensor(x_c_set) # x_c_im = normalize_lowpass_subt(x_c_im, 3) feat_vec = self.feat_fn(x_c_im) # get feature vector and concatenate the mos_p set if bat2img_idx_set: # if patch based aggr_feat_list = [] for idx in range(n_img): idx_from = bat2img_idx_set[idx][0] idx_to = bat2img_idx_set[idx][1] cur_feat_vec = feat_vec[idx_from: idx_to] cur_aggr_feat = self.aggregation_fn(cur_feat_vec) aggr_feat_list.append(cur_aggr_feat) aggr_feat = T.concatenate(aggr_feat_list, axis=0).flatten(2) # aggr_feat = T.stack(aggr_feat_list).flatten() else: # aggr_feat = self.regress_mos_fn(feat_vec).flatten() raise NotImplementedError ###################################################################### # regress onto MOS mos_p = self.regress_mos_fn(aggr_feat).flatten() # MOS loss subj_loss = self.get_cost_mse_mae(mos, mos_p) # L2 regularization l2_reg = self.get_l2_regularization( ['feat', 'feat_fc', 'reg_mos'], mode='sum') cost = self.add_all_losses_with_weight( [subj_loss, l2_reg], [self.wl_mos, self.wr_l2]) # Parameters to record records.add_data('subj', self.wl_mos * subj_loss) records.add_data('l2_reg', self.wr_l2 * l2_reg) records.add_im_data('mos_p', mos_p) records.add_im_data('mos_gt', mos) records.add_imgs('x_c', x_c_im, caxis=[-0.25, 0.25]) return cost, records def cost_updates_nr_iqa(self, x_c, mos, n_img=None, bat2img_idx_set=None): cost, records = self.cost_nr_iqa( x_c, mos, n_img=n_img, bat2img_idx_set=bat2img_idx_set) updates = self.get_updates_keys(cost, self.update_wrt_iqa) return cost, updates, records def set_training_mode(self, training): # Decide behaviors of the model during training # Dropout self.set_dropout_on(training)

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11,276

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null

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null

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1

0

00388d39e13165d5d68a97cebd954314afba1ff8

908

py

Python

pi4_software/Examples/WSClient.py

stuckatmarine/srauv-sim

30f4bae5d22a4529233ffa2705d7631d048a8130

[ "MIT" ]

1

2020-11-01T13:39:42.000Z

pi4_software/Examples/WSClient.py

stuckatmarine/srauv-sim

30f4bae5d22a4529233ffa2705d7631d048a8130

[ "MIT" ]

null

pi4_software/Examples/WSClient.py

stuckatmarine/srauv-sim

30f4bae5d22a4529233ffa2705d7631d048a8130

[ "MIT" ]

null

#!/usr/bin/env python # WS client example to test server import asyncio import websockets import json import time async def hello(): uri = "ws://localhost:8001" async with websockets.connect(uri) as websocket: inp = input("Input msg number? ") obj = { "source" : "sim", "msgNum" : inp, "msgType" : "telemetry", "timestamp" : time.strftime("%Y-%m-%d %H:%M.%S"), "cardDist" : [6.0,7.0,8.0,9.0], "depth" : 10.0, "alt" : 11.0, "assetDistances" : { "cage" : 12.0, "tree1" : 13.0, "tree2" : 14.0 } } msg = json.dumps(obj) await websocket.send(msg) print(f"> {msg}") resp = await websocket.recv() print(f"< {resp}") asyncio.get_event_loop().run_until_complete(hello())

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0.137931

0.068966

0

null

0

null

0

1

0

00389d2165f30aa4a1d559c1fcdd0bbb4c1ad957

1,834

py

Python

scripts/trends.py

iamDyeus/KnickAI

c17d808c949cb3467031498e7252bd2095c04699

[ "MIT" ]

31

2021-11-08T18:42:17.000Z

2022-03-25T07:45:46.000Z

scripts/trends.py

iamDyeus/KnickAI

c17d808c949cb3467031498e7252bd2095c04699

[ "MIT" ]

6

2021-12-20T14:15:44.000Z

2022-03-28T16:19:12.000Z

scripts/trends.py

iamDyeus/KnickAI

c17d808c949cb3467031498e7252bd2095c04699

[ "MIT" ]

3

2021-11-13T09:38:12.000Z

2022-03-25T07:44:17.000Z

#NOT YET WORKING PROPERLY #NOT INTEGRATED WITH THE ASSISTANT YET from pytrends.request import TrendReq # Only need to run this once, the rest of requests will use the same session. pytrend = TrendReq() def trending_searches(): # Get Google Hot Trends data trending_searches = pytrend.trending_searches() print(trending_searches.head()) def todays_trends(): # Get Google Today's Trend data today_searches = pytrend.today_searches() print(today_searches.head()) def top_charts(): # Get Google Top Charts top_charts = pytrend.top_charts(2018, hl='en-US', tz=300, geo='GLOBAL') print(top_charts.head()) def keyword_suggestions(): # Get Google Keyword Suggestions kw=input("please enter the keyword you want to search:") suggestions_dict = pytrend.suggestions(keyword=kw) if suggestions_dict==[]: print("No suggestions found for the keyword: " + kw) else: print(suggestions_dict) def console_trends(): print("Available Google Trends Research Options :\n1. Trending Searches\n2. Today's Trends\n3. Top Charts\n4. Keyword Suggestions\n\n type Exit to leave the trends Research console") while True: choice = input("\n\nPlease enter your choice: ") if choice == "1" or choice == "Trending Searches": trending_searches() elif choice == "2" or choice == "Today's Trends": todays_trends() elif choice == "3" or choice == "Top Charts": top_charts() elif choice == "4" or choice == "Keyword Suggestions": keyword_suggestions() elif choice == "Exit" or choice == "exit" or choice == "quit": break else: print("\n\nInvalid choice, please try again, Dumbass!") continue if __name__ == '__main__': console_trends()

35.960784

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5.064655

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0.025532

0.030638

0

0.010737

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1,834

51

188

35.960784

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0.212121

0

null

0

null

0

1

0

00394b6eff0f3c670ab13b288147b361f3297dc6

4,902

py

Python

Speedo/helpers/convert.py

aviskumar/speedo

758e8ac1fdeeb0b72c3a57742032ca5c79f0b2fa

[ "BSD-3-Clause" ]

null

Speedo/helpers/convert.py

aviskumar/speedo

758e8ac1fdeeb0b72c3a57742032ca5c79f0b2fa

[ "BSD-3-Clause" ]

null

Speedo/helpers/convert.py

aviskumar/speedo

758e8ac1fdeeb0b72c3a57742032ca5c79f0b2fa

[ "BSD-3-Clause" ]

3

2021-10-12T08:17:01.000Z

2021-12-21T01:17:54.000Z

import os import asyncio import re import requests import time import lottie import PIL.ImageOps from os.path import basename from PIL import Image from typing import Optional from .. import LOGS from ..config import Config from ..utils.extras import edit_or_reply as eor from .progress import * from .runner import runcmd dwlpath = Config.TMP_DOWNLOAD_DIRECTORY # convertions are done here... # make a image async def convert_to_image(event, bot): speedo = await event.get_reply_message() if not ( speedo.gif or speedo.audio or speedo.voice or speedo.video or speedo.video_note or speedo.photo or speedo.sticker or speedo.media ): await eor(event, "`Format Not Supported.`") return else: try: c_time = time.time() downloaded_file_name = await bot.download_media( speedo.media, dwlpath, progress_callback=lambda d, t: asyncio.get_event_loop().create_task( progress(d, t, event, c_time, "`Downloading...`") ), ) except Exception as e: # pylint:disable=C0103,W0703 await eor(event, str(e)) else: await eor(event, "Downloaded to `{}` successfully.".format(downloaded_file_name) ) if not os.path.exists(downloaded_file_name): await eor(event, "Download Unsucessfull :(") return if speedo and speedo.photo: speedo_final = downloaded_file_name elif speedo.sticker and speedo.sticker.mime_type == "application/x-tgsticker": rpath = downloaded_file_name image_name20 = os.path.join(dwlpath, "omk.png") cmd = f"lottie_convert.py --frame 0 -if lottie -of png {downloaded_file_name} {image_name20}" stdout, stderr = (await runcmd(cmd))[:2] os.remove(rpath) speedo_final = image_name20 elif speedo.sticker and speedo.sticker.mime_type == "image/webp": pathofsticker2 = downloaded_file_name image_new_path = dwlpath + "image.png" im = Image.open(pathofsticker2) im.save(image_new_path, "PNG") if not os.path.exists(image_new_path): await eor(event, "`Unable To Fetch Shot.`") return speedo_final = image_new_path elif speedo.audio: omk_p = downloaded_file_name hmmyes = dwlpath + "semx.mp3" imgpath = dwlpath + "semxy.jpg" os.rename(omk_p, hmmyes) await runcmd(f"ffmpeg -i {hmmyes} -filter:v scale=500:500 -an {imgpath}") os.remove(omk_p) if not os.path.exists(imgpath): await eor(event, "`Unable To Fetch Shot.`") return speedo_final = imgpath elif speedo.gif or speedo.video or speedo.video_note: omk_p2 = downloaded_file_name jpg_file = os.path.join(dwlpath, "image.jpg") await take_screen_shot(omk_p2, 0, jpg_file) os.remove(omk_p2) if not os.path.exists(jpg_file): await eor(event, "`Couldn't Fetch shot`") return speedo_final = jpg_file return speedo_final async def take_ss( video_file: str, duration: int, path: str = "" ) -> Optional[str]: LOGS.info( "[[[Extracting a frame from %s ||| Video duration => %s]]]", video_file, duration, ) ttl = duration // 2 thumb_image_path = path or os.path.join(dwlpath, f"{basename(video_file)}.jpg") command = f'''ffmpeg -ss {ttl} -i "{video_file}" -vframes 1 "{thumb_image_path}"''' err = (await runcmd(command))[1] if err: LOGS.error(err) return thumb_image_path if os.path.exists(thumb_image_path) else None def tgs_to_gif(sticker_path: str, quality: int = 256) -> str: semx = os.path.join(dwlpath, "Speedotgs.gif") with open(semx, 'wb') as t_g: lottie.exporters.gif.export_gif(lottie.parsers.tgs.parse_tgs(sticker_path), t_g, quality, 1) os.remove(sticker_path) return semx # deal with it... EMOJI_PATTERN = re.compile( "[" "\U0001F1E0-\U0001F1FF" # flags (iOS) "\U0001F300-\U0001F5FF" # symbols & pictographs "\U0001F600-\U0001F64F" # emoticons "\U0001F680-\U0001F6FF" # transport & map symbols "\U0001F700-\U0001F77F" # alchemical symbols "\U0001F780-\U0001F7FF" # Geometric Shapes Extended "\U0001F800-\U0001F8FF" # Supplemental Arrows-C "\U0001F900-\U0001F9FF" # Supplemental Symbols and Pictographs "\U0001FA00-\U0001FA6F" # Chess Symbols "\U0001FA70-\U0001FAFF" # Symbols and Pictographs Extended-A "\U00002702-\U000027B0" # Dingbats "]+" ) def deEmojify(inputString: str) -> str: """Remove emojis and other non-safe characters from string""" return re.sub(EMOJI_PATTERN, "", inputString) # Speedo

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611

4,902

4.870704

0.346972

0.020161

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0.014785

0.130376

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0

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0.206349

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null

0

null

0

1

0

003c4c33a5e695c65aafeb6d426e17f7228d37ef

12,953

py

Python

assemblyline_service_server/api/v1/task.py

CybercentreCanada/assemblyline-service-server

f4fbc7dcab122fa63fcc598db1c23a770c10145f

[ "MIT" ]

6

2020-06-29T14:32:24.000Z

2022-01-03T19:40:39.000Z

assemblyline_service_server/api/v1/task.py

CybercentreCanada/assemblyline-service-server

f4fbc7dcab122fa63fcc598db1c23a770c10145f

[ "MIT" ]

null

assemblyline_service_server/api/v1/task.py

CybercentreCanada/assemblyline-service-server

f4fbc7dcab122fa63fcc598db1c23a770c10145f

[ "MIT" ]

2

2021-01-15T18:31:17.000Z

2021-05-29T15:57:08.000Z

import time from typing import cast, Dict, Any from flask import request from assemblyline.common import forge from assemblyline.common.constants import SERVICE_STATE_HASH, ServiceStatus from assemblyline.common.dict_utils import flatten, unflatten from assemblyline.common.forge import CachedObject from assemblyline.common.heuristics import HeuristicHandler, InvalidHeuristicException from assemblyline.common.isotime import now_as_iso from assemblyline.odm import construct_safe from assemblyline.odm.messages.service_heartbeat import Metrics from assemblyline.odm.messages.task import Task as ServiceTask from assemblyline.odm.models.error import Error from assemblyline.odm.models.heuristic import Heuristic from assemblyline.odm.models.result import Result from assemblyline.odm.models.tagging import Tagging from assemblyline.remote.datatypes.exporting_counter import export_metrics_once from assemblyline.remote.datatypes.hash import ExpiringHash from assemblyline_core.dispatching.client import DispatchClient from assemblyline_service_server.api.base import make_subapi_blueprint, make_api_response, api_login from assemblyline_service_server.config import FILESTORE, LOGGER, STORAGE, config from assemblyline_service_server.helper.heuristics import get_heuristics status_table = ExpiringHash(SERVICE_STATE_HASH, ttl=60*30) dispatch_client = DispatchClient(STORAGE) heuristics = cast(Dict[str, Heuristic], CachedObject(get_heuristics, refresh=300)) heuristic_hander = HeuristicHandler(STORAGE) tag_safelister = CachedObject(forge.get_tag_safelister, kwargs=dict(log=LOGGER, config=config, datastore=STORAGE), refresh=300) SUB_API = 'task' task_api = make_subapi_blueprint(SUB_API, api_version=1) task_api._doc = "Perform operations on service tasks" @task_api.route("/", methods=["GET"]) @api_login() def get_task(client_info): """ Header: {'container_id': abcd...123 'service_name': 'Extract', 'service_version': '4.0.1', 'service_tool_version': ' 'timeout': '30'} Result example: {'keep_alive': true} """ service_name = client_info['service_name'] service_version = client_info['service_version'] service_tool_version = client_info['service_tool_version'] client_id = client_info['client_id'] remaining_time = timeout = int(float(request.headers.get('timeout', 30))) try: service_data = dispatch_client.service_data[service_name] except KeyError: return make_api_response({}, "The service you're asking task for does not exist, try later", 404) start_time = time.time() stats = { "execute": 0, "cache_miss": 0, "cache_hit": 0, "cache_skipped": 0, "scored": 0, "not_scored": 0 } try: while remaining_time > 0: cache_found = False # Set the service status to Idle since we will be waiting for a task status_table.set(client_id, (service_name, ServiceStatus.Idle, start_time + timeout)) # Getting a new task task = dispatch_client.request_work(client_id, service_name, service_version, timeout=remaining_time) if not task: # We've reached the timeout and no task found in service queue return make_api_response(dict(task=False)) # We've got a task to process, consider us busy status_table.set(client_id, (service_name, ServiceStatus.Running, time.time() + service_data.timeout)) stats['execute'] += 1 result_key = Result.help_build_key(sha256=task.fileinfo.sha256, service_name=service_name, service_version=service_version, service_tool_version=service_tool_version, is_empty=False, task=task) # If we are allowed, try to see if the result has been cached if not task.ignore_cache and not service_data.disable_cache: # Checking for previous results for this key result = STORAGE.result.get_if_exists(result_key) if result: stats['cache_hit'] += 1 if result.result.score: stats['scored'] += 1 else: stats['not_scored'] += 1 result.archive_ts = now_as_iso(config.datastore.ilm.days_until_archive * 24 * 60 * 60) if task.ttl: result.expiry_ts = now_as_iso(task.ttl * 24 * 60 * 60) dispatch_client.service_finished(task.sid, result_key, result) cache_found = True if not cache_found: # Checking for previous empty results for this key result = STORAGE.emptyresult.get_if_exists(f"{result_key}.e") if result: stats['cache_hit'] += 1 stats['not_scored'] += 1 result = STORAGE.create_empty_result_from_key(result_key) dispatch_client.service_finished(task.sid, f"{result_key}.e", result) cache_found = True if not cache_found: stats['cache_miss'] += 1 else: stats['cache_skipped'] += 1 if not cache_found: # No luck with the cache, lets dispatch the task to a client return make_api_response(dict(task=task.as_primitives())) # Recalculating how much time we have left before we reach the timeout remaining_time = start_time + timeout - time.time() # We've been processing cache hit for the length of the timeout... bailing out! return make_api_response(dict(task=False)) finally: export_metrics_once(service_name, Metrics, stats, host=client_id, counter_type='service') @task_api.route("/", methods=["POST"]) @api_login() def task_finished(client_info): """ Header: {'container_id': abcd...123 'service_name': 'Extract', 'service_version': '4.0.1', 'service_tool_version': ' } Data Block: { "exec_time": 300, "task": <Original Task Dict>, "result": <AL Result Dict>, "freshen": true } """ data = request.json exec_time = data.get('exec_time') try: task = ServiceTask(data['task']) if 'result' in data: # Task created a result missing_files = handle_task_result(exec_time, task, data['result'], client_info, data['freshen']) if missing_files: return make_api_response(dict(success=False, missing_files=missing_files)) return make_api_response(dict(success=True)) elif 'error' in data: # Task created an error error = data['error'] handle_task_error(exec_time, task, error, client_info) return make_api_response(dict(success=True)) else: return make_api_response("", "No result or error provided by service.", 400) except ValueError as e: # Catch errors when building Task or Result model return make_api_response("", e, 400) def handle_task_result(exec_time: int, task: ServiceTask, result: Dict[str, Any], client_info: Dict[str, str], freshen: bool): archive_ts = now_as_iso(config.datastore.ilm.days_until_archive * 24 * 60 * 60) if task.ttl: expiry_ts = now_as_iso(task.ttl * 24 * 60 * 60) else: expiry_ts = None # Check if all files are in the filestore if freshen: missing_files = [] for f in result['response']['extracted'] + result['response']['supplementary']: cur_file_info = STORAGE.file.get_if_exists(f['sha256'], as_obj=False) if cur_file_info is None or not FILESTORE.exists(f['sha256']): missing_files.append(f['sha256']) else: cur_file_info['archive_ts'] = archive_ts cur_file_info['expiry_ts'] = expiry_ts cur_file_info['classification'] = f['classification'] STORAGE.save_or_freshen_file(f['sha256'], cur_file_info, cur_file_info['expiry_ts'], cur_file_info['classification'], is_section_image=f.get('is_section_image', False)) if missing_files: return missing_files service_name = client_info['service_name'] client_id = client_info['client_id'] # Add scores to the heuristics, if any section set a heuristic total_score = 0 for section in result['result']['sections']: zeroize_on_sig_safe = section.pop('zeroize_on_sig_safe', True) section['tags'] = flatten(section['tags']) if section.get('heuristic'): heur_id = f"{client_info['service_name'].upper()}.{str(section['heuristic']['heur_id'])}" section['heuristic']['heur_id'] = heur_id try: section['heuristic'], new_tags = heuristic_hander.service_heuristic_to_result_heuristic( section['heuristic'], heuristics, zeroize_on_sig_safe) for tag in new_tags: section['tags'].setdefault(tag[0], []) if tag[1] not in section['tags'][tag[0]]: section['tags'][tag[0]].append(tag[1]) total_score += section['heuristic']['score'] except InvalidHeuristicException: section['heuristic'] = None # Update the total score of the result result['result']['score'] = total_score # Add timestamps for creation, archive and expiry result['created'] = now_as_iso() result['archive_ts'] = archive_ts result['expiry_ts'] = expiry_ts # Pop the temporary submission data temp_submission_data = result.pop('temp_submission_data', None) # Process the tag values for section in result['result']['sections']: # Perform tag safelisting tags, safelisted_tags = tag_safelister.get_validated_tag_map(section['tags']) section['tags'] = unflatten(tags) section['safelisted_tags'] = safelisted_tags section['tags'], dropped = construct_safe(Tagging, section.get('tags', {})) # Set section score to zero and lower total score if service is set to zeroize score # and all tags were safelisted if section.pop('zeroize_on_tag_safe', False) and \ section.get('heuristic') and \ len(tags) == 0 and \ len(safelisted_tags) != 0: result['result']['score'] -= section['heuristic']['score'] section['heuristic']['score'] = 0 if dropped: LOGGER.warning(f"[{task.sid}] Invalid tag data from {client_info['service_name']}: {dropped}") result = Result(result) result_key = result.build_key(service_tool_version=result.response.service_tool_version, task=task) dispatch_client.service_finished(task.sid, result_key, result, temp_submission_data) # Metrics if result.result.score > 0: export_metrics_once(service_name, Metrics, dict(scored=1), host=client_id, counter_type='service') else: export_metrics_once(service_name, Metrics, dict(not_scored=1), host=client_id, counter_type='service') LOGGER.info(f"[{task.sid}] {client_info['client_id']} - {client_info['service_name']} " f"successfully completed task {f' in {exec_time}ms' if exec_time else ''}") def handle_task_error(exec_time: int, task: ServiceTask, error: Dict[str, Any], client_info: Dict[str, str]) -> None: service_name = client_info['service_name'] client_id = client_info['client_id'] LOGGER.info(f"[{task.sid}] {client_info['client_id']} - {client_info['service_name']} " f"failed to complete task {f' in {exec_time}ms' if exec_time else ''}") # Add timestamps for creation, archive and expiry error['created'] = now_as_iso() error['archive_ts'] = now_as_iso(config.datastore.ilm.days_until_archive * 24 * 60 * 60) if task.ttl: error['expiry_ts'] = now_as_iso(task.ttl * 24 * 60 * 60) error = Error(error) error_key = error.build_key(service_tool_version=error.response.service_tool_version, task=task) dispatch_client.service_failed(task.sid, error_key, error) # Metrics if error.response.status == 'FAIL_RECOVERABLE': export_metrics_once(service_name, Metrics, dict(fail_recoverable=1), host=client_id, counter_type='service') else: export_metrics_once(service_name, Metrics, dict(fail_nonrecoverable=1), host=client_id, counter_type='service')

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119

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0.032168

0.019072

0.024031

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0.188938

0.138843

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12,953

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0.009852

0

null

0

null

0

1

0

003c669ec9b5a285ed0283853c1db390654e252e

3,627

py

Python

install/app_store/tk-framework-qtwidgets/v2.6.5/python/search_completer/global_search_result_delegate.py

JoanAzpeitia/lp_sg

e0ee79555e419dd2ae3a5f31e5515b3f40b22a62

[ "MIT" ]

null

install/app_store/tk-framework-qtwidgets/v2.6.5/python/search_completer/global_search_result_delegate.py

JoanAzpeitia/lp_sg

e0ee79555e419dd2ae3a5f31e5515b3f40b22a62

[ "MIT" ]

null

install/app_store/tk-framework-qtwidgets/v2.6.5/python/search_completer/global_search_result_delegate.py

JoanAzpeitia/lp_sg

e0ee79555e419dd2ae3a5f31e5515b3f40b22a62

[ "MIT" ]

1

2020-02-15T10:42:56.000Z

# Copyright (c) 2017 Shotgun Software Inc. # # CONFIDENTIAL AND PROPRIETARY # # This work is provided "AS IS" and subject to the Shotgun Pipeline Toolkit # Source Code License included in this distribution package. See LICENSE. # By accessing, using, copying or modifying this work you indicate your # agreement to the Shotgun Pipeline Toolkit Source Code License. All rights # not expressly granted therein are reserved by Shotgun Software Inc. import sgtk from sgtk.platform.qt import QtCore from .search_result_delegate import SearchResultDelegate # import the shotgun_model and view modules from the shotgun utils framework shotgun_model = sgtk.platform.import_framework("tk-framework-shotgunutils", "shotgun_model") shotgun_globals = sgtk.platform.import_framework("tk-framework-shotgunutils", "shotgun_globals") views = sgtk.platform.current_bundle().import_module("views") class GlobalSearchResultDelegate(SearchResultDelegate): """ Delegate which renders search match entries in the global search completer. """ def _render_result(self, widget, model_index): """ Renders a result from the model into the provided widget. :param widget: Widget used to render the result. :type widget: ``SearchResultWidget`` :param model_index: Index of the item to render. :type model_index: :class:`~PySide.QtCore.QModelIndex` """ from .global_search_completer import GlobalSearchCompleter icon = shotgun_model.get_sanitized_data(model_index, QtCore.Qt.DecorationRole) if icon: thumb = icon.pixmap(512) widget.set_thumbnail(thumb) else: # probably won't hit here, but just in case, use default/empty # thumbnail widget.set_thumbnail(self._pixmaps.no_thumbnail) data = shotgun_model.get_sanitized_data(model_index, GlobalSearchCompleter.SG_DATA_ROLE) # Example of data stored in the data role: # {'status': 'vwd', # 'name': 'bunny_010_0050_comp_v001', # 'links': ['Shot', 'bunny_010_0050'], # 'image': 'https://xxx', # 'project_id': 65, # 'type': 'Version', # 'id': 99} entity_type_display_name = shotgun_globals.get_type_display_name(data["type"]) content = "" et_url = shotgun_globals.get_entity_type_icon_url(data["type"]) underlined_name = self._underline_search_term(data["name"]) if et_url: # present thumbnail icon and name content += "<img src='%s'/>  %s" % ( et_url, underlined_name ) else: # present type name name content += "%s" % underlined_name content += " %s" % entity_type_display_name links = data["links"] # note users return weird data so ignore it. if links and links[0] != "" and links[0] != "HumanUser" and links[0] != "ClientUser": underlined_link = self._underline_search_term(links[1]) # there is a referenced entity et_url = shotgun_globals.get_entity_type_icon_url(links[0]) if et_url: # present thumbnail icon and name content += " on <img align=absmiddle src='%s'/> %s" % (et_url, underlined_link) else: # present type name name link_entity_type = links[0] content += " on %s %s" % (shotgun_globals.get_type_display_name(link_entity_type), underlined_link) widget.set_text(content)

38.585106

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0.243243

0

null

0

null

0

1

0

003e8e2269ee664c19971a3c8f0ad03c45a56847

2,485

py

Python

preprocessing/generate_diffs.py

windysavage/dfdc_deepfake_challenge

d10b54cf933282366157a031954b046d87d57009

[ "MIT" ]

null

preprocessing/generate_diffs.py

windysavage/dfdc_deepfake_challenge

d10b54cf933282366157a031954b046d87d57009

[ "MIT" ]

null

preprocessing/generate_diffs.py

windysavage/dfdc_deepfake_challenge

d10b54cf933282366157a031954b046d87d57009

[ "MIT" ]

null

import numpy as np import cv2 from preprocessing.utils import get_original_with_fakes from tqdm import tqdm from multiprocessing.pool import Pool from functools import partial # from skimage.measure import compare_ssim from skimage import metrics import argparse import os os.environ["MKL_NUM_THREADS"] = "1" os.environ["NUMEXPR_NUM_THREADS"] = "1" os.environ["OMP_NUM_THREADS"] = "1" cv2.ocl.setUseOpenCL(False) cv2.setNumThreads(0) cache = {} def save_diffs(pair, root_dir): ori_id, fake_id = pair ori_dir = os.path.join(root_dir, "crops", ori_id) fake_dir = os.path.join(root_dir, "crops", fake_id) diff_dir = os.path.join(root_dir, "diffs", fake_id) os.makedirs(diff_dir, exist_ok=True) for frame in range(320): if frame % 10 != 0: continue for actor in range(2): image_id = "{}_{}.png".format(frame, actor) diff_image_id = "{}_{}_diff.png".format(frame, actor) ori_path = os.path.join(ori_dir, image_id) fake_path = os.path.join(fake_dir, image_id) diff_path = os.path.join(diff_dir, diff_image_id) # some frames didn't exist... if os.path.exists(ori_path) and os.path.exists(fake_path): img1 = cv2.imread(ori_path, cv2.IMREAD_COLOR) img2 = cv2.imread(fake_path, cv2.IMREAD_COLOR) try: d, a = metrics.structural_similarity( img1, img2, multichannel=True, full=True) a = 1 - a diff = (a * 255).astype(np.uint8) diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) cv2.imwrite(diff_path, diff) except Exception as e: print(e) def parse_args(): parser = argparse.ArgumentParser( description="Extract image diffs") parser.add_argument("--root-dir", help="root directory", default="/mnt/sota/datasets/deepfake") args = parser.parse_args() return args def main(): args = parse_args() pairs = get_original_with_fakes(args.root_dir) os.makedirs(os.path.join(args.root_dir, "diffs"), exist_ok=True) with Pool(processes=os.cpu_count() - 2) as p: with tqdm(total=len(pairs)) as pbar: func = partial(save_diffs, root_dir=args.root_dir) for v in p.imap_unordered(func, pairs): pbar.update() if __name__ == '__main__': main()

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0

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2,485

77

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0.786349

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0

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0.016667

0

null

0

null

0

1

0

003fa9552b48b33e85beb33eaf261aef70d7ae40

18,905

py

Python

Model/model_v2.py

Insper-Data/data_bcg_news

49986db18095759adea00bb0dedc149acebb683b

[ "MIT" ]

null

Model/model_v2.py

Insper-Data/data_bcg_news

49986db18095759adea00bb0dedc149acebb683b

[ "MIT" ]

null

Model/model_v2.py

Insper-Data/data_bcg_news

49986db18095759adea00bb0dedc149acebb683b

[ "MIT" ]

null

import ast import community import datetime import lightgbm as lgb import math import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd import pickle import plotly.express as px import os from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score from tqdm import tqdm from make_boruta import * class Zeus: """ Class criada para construir modelo """ def __init__(self, termo, user, treino_id, test_id): """ Metodo construtor, aqui serão armazenadas as informações padrões do modelo: - User - run_id da base """ self.term = termo self.data = datetime.date.today() self.user = str(user).upper() self.path_user = '' self.treino_id = treino_id self.test_id = test_id self.var_treino = '' self.var_teste = '' self.filtro_local = False self.filtro_data = False self.local = '' self.data_start = '' self.data_end = '' self.filtro = '' self.random_state = 101 self.base_sintetica = '' self.data_active = False self.data_local = False self.mm = '' self.ids = '' self.train = '' self.numero_de_amostras_sinteticas_para_criar = '' self.porcentagem_para_criacao_de_amostras = '' self.df_cluster = '' self.clusters = '' self.var_teste_original = '' self.pega_variaveis() self.agregado = '' self.df_agregado = '' self.informacoes = '' self.sentimento = '' self.data_df = '' self.load_df = '' def pega_path_user(self): """ Metodo que pega o path de acordo com o usuario que inicializou a class """ os.chdir(os.path.dirname( r'C:\Users\wilgn\Desktop\Faculdade\3° Semestre\Insper Data\Projeto\Git projeto\Data_BCG_News\Model\\')) path_atual = os.getcwd() #print(os.listdir()) if self.user == 'WILGNER': path_aux_funcs = path_atual.replace('Model', r'aux_funcs\\') else: path_aux_funcs = path_atual.replace('Model', r'aux_funcs/') os.chdir(os.path.dirname(path_aux_funcs)) #print(os.listdir()) with open('set_path.py', 'r') as arquivo_path: ler_arquivo = arquivo_path.read() dicionario = ast.literal_eval(ler_arquivo) lista_users = list(dicionario.keys()) if self.user in lista_users: print('USUARIO VALIDO !') self.path_user = dicionario[self.user] else: raise TypeError( 'O USUARIO SELECIONADO NÃO TEM UM ENDEREÇO VALIDO CADASTRADO') os.chdir(os.path.dirname( r'C:\Users\wilgn\Desktop\Faculdade\3° Semestre\Insper Data\Projeto\Git projeto\Data_BCG_News\Model\\')) #print(os.listdir()) # arquivo_path.close() def valida_acesso_path_user(self): self.pega_path_user() try: os.path.exists(self.path_user) print('PATH VALIDO PARA ACESSO') except: raise TypeError('IMPOSSIVEL ACESSAR O PATH') def pega_variaveis(self, teste=False, load_df=False, file_name_load=False): if teste: path_name = f'{self.path_user}Variables/{self.term}/{self.test_id}.parquet' os.chdir(os.path.dirname(path_name)) self.var_teste = pd.read_parquet(os.path.basename(path_name)) elif load_df: path_name = f'{self.path_user}Model/{file_name_load}' os.chdir(os.path.dirname(path_name)) self.load_df = pd.read_parquet(os.path.basename(path_name)) else: self.valida_acesso_path_user() path_name = f'{self.path_user}Variables/{self.term}/{self.treino_id}.parquet' os.chdir(os.path.dirname(path_name)) self.var_treino = pd.read_parquet(os.path.basename(path_name)) def seleciona_filtros(self, local=False, data_start=False, data_end=False): """ Isinstance verifica se houve uma solicitação de filtro em alguma das variaveis """ estado_local = isinstance(local, bool) estado_data_start = isinstance(data_start, bool) if not estado_local: self.local = local self.filtro_local = True if not estado_data_start: self.data_start = data_start self.data_end = data_end self.filtro_data = True def construir_filtro(self, teste=False): self.filtro = '' if not teste: if self.filtro_local and self.filtro_data: self.filtro = (self.var_treino.sigla == self.local.upper()) self.data_local = True self.data_active = True elif self.filtro_data and not self.filtro_local: self.data_active = True elif self.filtro_local and not self.filtro_data: self.filtro = (self.var_treino.sigla == self.local.upper()) else: if self.filtro_local and self.filtro_data: self.filtro = (self.var_teste.sigla == self.local.upper()) self.data_local = True self.data_active = True elif self.filtro_data and not self.filtro_local: self.data_active = True elif self.filtro_local and not self.filtro_data: self.filtro = (self.var_teste.sigla == self.local.upper()) def filtrar_treino(self, local=False, data_start=False, data_end=False): self.seleciona_filtros( local=local, data_start=data_start, data_end=data_end) self.construir_filtro() self.var_treino.data = pd.to_datetime(self.var_treino.data) if self.data_active and self.data_local: self.var_treino = self.var_treino[self.filtro] self.var_treino = self.var_treino[(self.var_treino.data > self.data_start) & ( self.var_treino.data < self.data_end)] elif self.data_active and not self.data_local: self.var_treino = self.var_treino[ (self.var_treino.data > self.data_start) & (self.var_treino.data < self.data_end)] else: self.var_treino = self.var_treino[self.filtro] def filtrar_teste(self, local=False, data_start=False, data_end=False): self.seleciona_filtros( local=local, data_start=data_start, data_end=data_end) self.construir_filtro(teste=True) self.var_teste.data = pd.to_datetime(self.var_teste.data) if self.data_active and self.data_local: self.var_teste = self.var_teste[self.filtro] self.var_teste = self.var_teste[ (self.var_teste.data > self.data_start) & (self.var_teste.data < self.data_end)] elif self.data_active and not self.data_local: self.var_teste = self.var_teste[ (self.var_teste.data > self.data_start) & (self.var_teste.data < self.data_end)] else: self.var_teste = self.var_teste[self.filtro] def criar_base_sintetica(self, numero_de_amostras=3, porcentagem_para_criacao=.25): bases_sinteticas = [] self.numero_de_amostras_sinteticas_para_criar = numero_de_amostras self.porcentagem_para_criacao_de_amostras = porcentagem_para_criacao colunas_pro_drop = ['unique_identifier', 'sigla', 'data'] for i in range(numero_de_amostras): unique_identifier = self.var_treino['unique_identifier'] df_com_drop = self.var_treino.drop(columns=colunas_pro_drop) df_com_colunas_sorteadas = df_com_drop.sample( frac=porcentagem_para_criacao, replace=True, random_state=self.random_state, axis=1) amostra = pd.concat( [unique_identifier, df_com_colunas_sorteadas], axis=1) amostra_sintetica = pd.DataFrame() amostra = amostra.loc[:, ~amostra.columns.duplicated()] for coluna in amostra.columns.tolist(): amostra_sintetica[coluna] = amostra[coluna].sample(frac=1, replace=True, random_state=self.random_state).tolist() amostra_sintetica['label'] = 1 amostra['label'] = 0 amostra_concluida = pd.concat([amostra, amostra_sintetica]) amostra_concluida.reset_index(inplace=True, drop=True) bases_sinteticas.append(amostra_concluida) self.base_sintetica = bases_sinteticas def treina_lightGBM(self, boruta_percs=[10], thr_bor_good=.5, thr_bor_ok=.9): numero_de_amostras = len(self.base_sintetica) x_list = [] y_list = [] col_lists = [] model_list = [] trained_models = [] dfs = self.base_sintetica for i in range(numero_de_amostras): numero_de_colunas = dfs[i].shape[1] self.Y = dfs[i]['label'] self.X = dfs[i].drop(columns=['unique_identifier', 'label']) self.take_out_cols_0 = [] self.take_out_cols = [] self.full_cols = self.X.columns.tolist() self.thr_bor_good = thr_bor_good self.thr_bor_ok = thr_bor_ok self.boruta_percs = boruta_percs self.boruta_res = boruta_select( X_df=self.X[[ col for col in self.full_cols if col not in self.take_out_cols]], Y=self.Y, perc_list=self.boruta_percs, allowed_perc_good=self.thr_bor_good, allowed_perc_med=self.thr_bor_ok) self.take_out_cols_irrelevant = self.boruta_res[0].loc[~self.boruta_res[0]['use']].index.tolist( ) self.take_out_cols += self.take_out_cols_irrelevant self.use_cols = self.X[[col for col in self.X.columns.tolist( ) if col not in self.take_out_cols]].columns.tolist() y_list += [dfs[i]['label'].values] x_list += [dfs[i].drop(columns=['unique_identifier', 'label'])] col_lists += [self.use_cols] model_list += [{'type': 'LGBM', 'params': {'num_leaves': 25, 'n_estimators': 300, 'boosting_type': 'rf', 'bagging_fraction': .8, 'bagging_freq': 1, 'random_state': self.random_state}}] # Treinando modelo for (model, x, y, cols) in zip(model_list, x_list, y_list, col_lists): X = x[cols] Y = y if model['type'] == 'LGBM': model_to_train = lgb.LGBMClassifier(**model['params']) trained_models += [model_to_train.fit(X=X.values, y=Y)] self.models = trained_models self.rf_models = self.models self.col_lists = col_lists def coleta_folhas(self, porcentagem_do_sample=0.1): self.df_random = self.var_treino.sample( frac=porcentagem_do_sample, replace=True, random_state=self.random_state, axis=0).copy() print(self.df_random.shape) # frame_list = [] model_c = 0 self.mm = set() self.ids = self.df_random['unique_identifier'].tolist() print('start with list values') # Pegando o resultado das folhas do model for (model, cols) in zip(self.rf_models, self.col_lists): if cols == 'label': continue else: raw_leafs = model.predict( self.df_random[cols].values, pred_leaf=True) # return raw_leafs if model_c == 0: full_leafs = raw_leafs else: full_leafs = np.concatenate( (full_leafs, raw_leafs), axis=1) model_c += 1 self.raw = raw_leafs def criando_matriz_de_similaridade(self, porcentagem_do_sample=0.1): self.porcentagem_para_matriz = porcentagem_do_sample self.coleta_folhas(porcentagem_do_sample=porcentagem_do_sample) print('CRIANDO EDGES') edges = [] # Criando matriz de similaridade for cc1, i in tqdm(enumerate(self.raw), 'FOLHAS:'): if cc1 % 100 == 0: print(cc1, datetime.datetime.now()) for cc2_, j in enumerate(self.raw[cc1 + 1:]): cc2 = cc2_ + cc1 + 1 if (cc1, cc2) not in self.mm and (cc2, cc1) not in self.mm: leaf_count = sum(i == j) # TODO: Fix similarity matrix with the square root edges += [(self.ids[cc1], self.ids[cc2], math.sqrt(leaf_count / len(self.raw[0])))] self.mm.add((cc1, cc2)) print('done with list values') # YOU ARE HERE G = nx.Graph() G.add_weighted_edges_from(edges) self.G = G def rodando_louvain(self, porcentagem_do_sample): self.criando_matriz_de_similaridade( porcentagem_do_sample=porcentagem_do_sample) self.clusters = (community.best_partition( self.G, weight='weight', randomize=True)) def desenha_cluster_no_edges(self): plt.figure(figsize=(12, 8), dpi=150) plt.title('Louvain Tets', fontsize=20, loc='left', pad=15) self.pos = nx.spring_layout(self.G) nx.draw_networkx_nodes(self.G, self.pos, self.clusters.keys(), node_size=150, node_color=list(self.clusters.values())) plt.show() def classifica_agrupamento(self, boruta_percs=[10], thr_bor_good=.5, thr_bor_ok=.9, take_out_cols=False): self.df_cluster = pd.DataFrame({'Rotulo': self.clusters.keys(), 'Label': self.clusters.values()}) print(f'Tamanho dos dados de cluster {self.df_cluster.shape}') self.train = self.var_treino[self.var_treino['unique_identifier'].isin( self.df_cluster.Rotulo.values.tolist())] self.train['label'] = self.df_cluster.Label.values.tolist() self.train.reset_index(drop=True) colunas_pro_drop = ['unique_identifier', 'sigla', 'data', 'artigo_original'] self.var_teste_original = self.var_teste self.sentimento = self.var_teste_original['sentimento'] self.data_df = self.var_teste_original['data'] self.var_teste = self.var_teste.drop(columns=colunas_pro_drop) self.train = self.train.drop(columns=['sigla', 'data', 'artigo_original']) self.var_teste.reset_index(drop=True) self.train.reset_index(drop=True) print(f'Tamanho dos dados de treinamento {self.train.shape}') print(f'Tamanho dos dados de teste {self.var_teste.shape}') self.x_list = [] self.y_list = [] self.col_lists = [] model_list = [] trained_models = 0 self.Y = self.train['label'] self.X = self.train.drop(columns=['unique_identifier', 'label']) self.take_out_cols_0 = [] self.take_out_cols = [] self.full_cols = self.X.columns.tolist() self.thr_bor_good = thr_bor_good self.thr_bor_ok = thr_bor_ok self.boruta_percs = boruta_percs self.boruta_res = boruta_select(X_df=self.X[[col for col in self.full_cols if col not in self.take_out_cols]], Y=self.Y, perc_list=self.boruta_percs, allowed_perc_good=self.thr_bor_good, allowed_perc_med=self.thr_bor_ok) self.take_out_cols_irrelevant = self.boruta_res[0].loc[~self.boruta_res[0]['use']].index.tolist( ) self.take_out_cols += self.take_out_cols_irrelevant self.use_cols = self.X[[col for col in self.X.columns.tolist( ) if col not in self.take_out_cols]].columns.tolist() if len(self.use_cols) < 1: self.use_cols = self.X.columns.tolist() self.y_list += [self.train['label'].values] self.x_list += [self.train.drop(columns=['unique_identifier', 'label'])] self.col_lists += [self.use_cols] model_list += [{'type': 'LGBM', 'params': {'num_leaves': 30, 'n_estimators': 500, 'boosting_type': 'rf', 'bagging_fraction': .8, 'bagging_freq': 1, 'random_state': self.random_state}}] for (model, x, y, cols) in zip(model_list, self.x_list, self.y_list, self.col_lists): X = x[cols] print(X.shape) Y = y print(Y.shape) if model['type'] == 'LGBM': model_to_train = lgb.LGBMClassifier(**model['params']) trained_models = model_to_train.fit(X=X.values, y=Y) self.models = trained_models self.previsão = trained_models.predict( self.var_teste[self.col_lists[0]]) self.resultado = self.previsão self.var_teste['label'] = self.resultado self.faz_agregacao() print('FREQUENCIA CLUSTER') print(self.df_cluster.Label.value_counts(sort=False)) print('********************') print('FREQUENCIA CLASSIFICADO') print(self.var_teste.label.value_counts(sort=False)) def plota_palavras_maiores(self, numero): for i in range(len(self.var_teste.label.unique())): df_data = pd.DataFrame({'word': self.var_teste[self.var_teste.label == i].drop( columns=['label']).sum(axis=0).nlargest(numero).index.tolist(), 'value': self.var_teste[self.var_teste.label == i].drop( columns=['label']).sum(axis=0).nlargest( numero).values.tolist()}) fig = px.bar(df_data, x='word', y='value', color='value', color_continuous_scale='Blues') fig.show() def salva_parametros(self): self.informacoes = { 'user': self.user, 'data': self.data, 'run_id_treino': self.treino_id, 'run_id_teste': self.test_id, 'path_user': self.path_user, 'filtro_nome': self.term, 'filtro_data': self.data, 'filtro_local': self.local, 'numero_de_amostras_bases_sinteticas': self.numero_de_amostras_sinteticas_para_criar, 'porcentagem_para_criacao_de_amostras': self.porcentagem_para_criacao_de_amostras, 'porcentagem_para_matriz': self.porcentagem_para_matriz } def faz_agregacao(self): # Agrega os resultados self.var_teste_original['label'] = self.var_teste['label'] self.agregado = self.var_teste_original[['unique_identifier', 'sigla', 'data', 'label']] self.df_agregado = pd.crosstab(self.agregado.sigla, self.agregado.label, normalize='index') return self.df_agregado

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0044e564b42a943f2371101ac16d4cb0e1aee8d7

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py

Python

roosterize/data/DataMiner.py

EngineeringSoftware/roosterize

2990f7bdef8889045a26f3e9aaaca96d9c92e0bc

[ "MIT" ]

16

2020-06-05T20:01:56.000Z

2022-02-09T16:10:09.000Z

roosterize/data/DataMiner.py

EngineeringSoftware/roosterize

2990f7bdef8889045a26f3e9aaaca96d9c92e0bc

[ "MIT" ]

6

2020-07-02T15:22:36.000Z

2020-12-16T13:04:16.000Z

roosterize/data/DataMiner.py

EngineeringSoftware/roosterize

2990f7bdef8889045a26f3e9aaaca96d9c92e0bc

[ "MIT" ]

3

2020-07-21T17:37:51.000Z

2020-12-10T05:36:32.000Z

from typing import * import collections import copy import hashlib import math import numpy as np from pathlib import Path import random import re from tqdm import tqdm import traceback import sys from seutil import LoggingUtils, IOUtils, BashUtils from seutil.project import Project from roosterize.data.CoqDocument import CoqDocument from roosterize.FilesManager import FilesManager from roosterize.data.Definition import Definition from roosterize.data.Lemma import Lemma from roosterize.data.LemmaBackendSexpTransformers import LemmaBackendSexpTransformers from roosterize.data.LemmaForeendSexpTransformers import LemmaForeendSexpTransformers from roosterize.Environment import Environment from roosterize.Macros import Macros from roosterize.parser.CoqParser import CoqParser from roosterize.parser.ParserUtils import ParserUtils from roosterize.parser.SexpAnalyzer import SexpAnalyzer, SexpInfo from roosterize.sexp import * from roosterize.Utils import Utils class DataMiner: logger = LoggingUtils.get_logger(__name__, LoggingUtils.DEBUG) from roosterize.Debug import Debug if Debug.is_debug: logger.setLevel(LoggingUtils.DEBUG) Project.set_downloads_dir(Macros.downloads_dir) TASK_COQ_DOCUMENTS = FilesManager.COQ_DOCUMENTS # "coq-documents" TASK_DATA_INDEXES = FilesManager.DATA_INDEXES # "data-indexes" TASK_DEFINITIONS = FilesManager.DEFINITIONS # "definitions" TASK_INSTALL_COQ_PROJECTS = "install-coq-projects" TASK_LEMMA = FilesManager.LEMMAS # "lemmas" TASK_LEMMA_BACKEND_SEXP_TRANSFORMATIONS = FilesManager.LEMMAS_BACKEND_SEXP_TRANSFORMATIONS # "lemmas-bsexp-transformations" TASK_LEMMA_FILTERED = FilesManager.LEMMAS_FILTERED # "lemmas-filtered" TASK_LEMMA_FOREEND_SEXP_TRANSFORMATIONS = FilesManager.LEMMAS_FOREEND_SEXP_TRANSFORMATIONS # "lemmas-fsexp-transformations" dataset_dir = Macros.project_dir.parent / "math-comp-corpus" @classmethod def collect_data(cls, **options) -> NoReturn: data_mgr = FilesManager(cls.dataset_dir) task = options["task"] projects_path = Path(options.get("corpus", cls.dataset_dir / "projects-standalone-8.10.yml")) projects: List[Project] = IOUtils.dejsonfy(IOUtils.load(projects_path, "json"), Project) if task == cls.TASK_COQ_DOCUMENTS: files = Utils.get_option_as_list(options, "files", None) is_verifying_tokenizer = Utils.get_option_as_boolean(options, "verify-tokenizer") cls.collect_coq_documents_projects(data_mgr, projects, files, is_verifying_tokenizer) elif task == cls.TASK_DATA_INDEXES: cls.collect_data_indexes(data_mgr, projects) elif task == cls.TASK_DEFINITIONS: cls.collect_definitions(data_mgr) elif task == cls.TASK_INSTALL_COQ_PROJECTS: cls.install_coq_projects(projects) elif task == cls.TASK_LEMMA: files = Utils.get_option_as_list(options, "files", None) cls.collect_lemmas(data_mgr, projects, files) elif task == cls.TASK_LEMMA_BACKEND_SEXP_TRANSFORMATIONS: cls.collect_lemmas_backend_sexp_transformations(data_mgr) elif task == cls.TASK_LEMMA_FILTERED: cls.filter_lemmas(data_mgr) elif task == cls.TASK_LEMMA_FOREEND_SEXP_TRANSFORMATIONS: cls.collect_lemmas_foreend_sexp_transformations(data_mgr) else: LoggingUtils.log_and_raise(cls.logger, f"Unknown task {task}", ValueError) # end if return @classmethod def collect_coq_documents_projects(cls, data_mgr: FilesManager, projects: List[Project], files: List[str] = None, is_verifying_tokenizer: bool = False, ) -> NoReturn: # Prepare the used directories (coq-documents, raw-files, original-files) for rel_path in [ [FilesManager.COQ_DOCUMENTS], [FilesManager.RAW_FILES], [FilesManager.ORIGINAL_FILES], ]: data_mgr.clean_path(rel_path) data_mgr.resolve(rel_path).mkdir(parents=True) # end for coq_documents: List[CoqDocument] = list() names_projects = {p.full_name: p for p in projects} for i, project in enumerate(projects): try: cls.logger.info(f"Project {i + 1}/{len(projects)}: {project.full_name}") coq_documents_project = cls.collect_coq_documents_project(data_mgr, project, names_projects=names_projects, files=files, is_verifying_tokenizer=is_verifying_tokenizer) except KeyboardInterrupt: raise except: cls.logger.warning(f"Error while processing project {project.full_name}: {traceback.format_exc()}") continue else: coq_documents.extend(coq_documents_project) # end try # end for # Save datasets data_mgr.dump_data([FilesManager.COQ_DOCUMENTS, FilesManager.COQ_DOCUMENTS], coq_documents, IOUtils.Format.json, is_batched=True) return @classmethod def load_coq_documents(cls, data_mgr: FilesManager) -> List[CoqDocument]: return data_mgr.load_data([FilesManager.COQ_DOCUMENTS, FilesManager.COQ_DOCUMENTS], IOUtils.Format.json, is_batched=True, clz=CoqDocument) @classmethod def collect_coq_documents_project(cls, data_mgr: FilesManager, project: Project, names_projects: Dict[str, Project], files: List[str] = None, is_verifying_tokenizer: bool = False, ) -> List[CoqDocument]: coq_documents: List[CoqDocument] = list() # Clone and checkout repo project.clone() project.checkout(project.data["sha"], is_forced=True) # Build the project cls.install_coq_project(project, names_projects) # For each file, parse code to tokens with IOUtils.cd(project.checkout_dir): coq_files: List[str] = BashUtils.run(f"find -name '*.v' -type f").stdout.split("\n")[:-1] if files is not None: coq_files = [f for f in coq_files if f[2:] in files] # [2:] is to remove the ./ # end if re_ignore_path = re.compile(project.data["ignore_path_regex"]) if "ignore_path_regex" in project.data else None for i, coq_file in enumerate(coq_files): try: coq_file = coq_file[2:] cls.logger.debug(f"File {i + 1}/{len(coq_files)}: {coq_file}") # Check if file is ignored if re_ignore_path is not None and re_ignore_path.fullmatch(coq_file): cls.logger.info(f"Ignoring file {coq_file}") continue # end if # Read file with open(coq_file, "r", newline="") as f: source_code = f.read() # end with # Get unicode offsets unicode_offsets = ParserUtils.get_unicode_offsets(source_code) # Save original file to original_files data_mgr.dump_data([FilesManager.ORIGINAL_FILES,project.full_name, coq_file], source_code, IOUtils.Format.txt) # Call SerAPI serapi_options = project.data.get("serapi_options", "") ast_sexp_str: str = BashUtils.run(f"sercomp {serapi_options} --mode=sexp -- {coq_file}", expected_return_code=0).stdout tok_sexp_str: str = BashUtils.run(f"sertok {serapi_options} -- {coq_file}", expected_return_code=0).stdout # Save ast sexp to dataset (.ast.sexp) data_mgr.dump_data([FilesManager.RAW_FILES,project.full_name, coq_file[:-2] + ".ast.sexp"], ast_sexp_str, IOUtils.Format.txt) # Save tok sexp to dataset (.tok.sexp) data_mgr.dump_data([FilesManager.RAW_FILES, project.full_name, coq_file[:-2] + ".tok.sexp"], tok_sexp_str, IOUtils.Format.txt) # Parse ast sexp ast_sexp_list: List[SexpNode] = SexpParser.parse_list(ast_sexp_str) tok_sexp_list: List[SexpNode] = SexpParser.parse_list(tok_sexp_str) # Verify the tokenizer if requested if is_verifying_tokenizer: if not cls.verify_tokenizer(tok_sexp_list, source_code, unicode_offsets): LoggingUtils.log_and_raise(cls.logger, "Tokenized content doesn't match original file!", Exception) # end if # end if # Parse the document coq_document = CoqParser.parse_document(source_code, ast_sexp_list, tok_sexp_list, unicode_offsets=unicode_offsets) # Save the parsed document (printed format) to raw_files data_mgr.dump_data([FilesManager.RAW_FILES, project.full_name, coq_file], coq_document.str_with_space(), IOUtils.Format.txt) # Set meta data coq_document.file_name = coq_file coq_document.project_name = project.full_name coq_document.revision = project.revision coq_documents.append(coq_document) except KeyboardInterrupt: cls.logger.warning("Keyboard interrupt!") raise except: cls.logger.warning(f"File {coq_file} failed! Exception was: {traceback.format_exc()}") continue # end try # end for # end with return coq_documents @classmethod def verify_tokenizer(cls, tok_sexp_list: List[SexpNode], source_code: str, unicode_offsets: List[int]) -> bool: sertok_sentences = SexpAnalyzer.analyze_sertok_sentences(tok_sexp_list, unicode_offsets) vernac_sentences = CoqParser.parse_sertok_sentences(sertok_sentences, source_code) code_i = 0 has_error: bool = False for sent_i, sentence in enumerate(vernac_sentences): for token_i, token in enumerate(sentence.tokens): # Check space/comment if token.beg_charno != code_i: if not ParserUtils.is_ws_or_comment(source_code[code_i:token.beg_charno]): cls.logger.error(f"Unresolved characters at charno {code_i} to {token.beg_charno}; next expect token {token.content} beginning at charno {token.beg_charno} (lineno {token.lineno}); file content {source_code[code_i:token.beg_charno]};") cls.logger.error(f"assotiated sexp: \n{tok_sexp_list[sent_i][1][token_i].pretty_format()}") has_error = True # end if # end if # Check token code_i = token.beg_charno if token.content != source_code[code_i:token.end_charno]: cls.logger.error(f"Mismatch token at charno {code_i} to {token.end_charno}; expect token {token.content} beginning at charno {token.beg_charno} (lineno {token.lineno}); file content {source_code[code_i:token.end_charno]};") cls.logger.error(f"assotiated sexp: \n{tok_sexp_list[sent_i][1][token_i].pretty_format()}") has_error = True # end if code_i = token.end_charno # end for, for # Check space/comment at end of file if code_i != len(source_code): if not ParserUtils.is_ws_or_comment(source_code[code_i:len(source_code)]): cls.logger.error(f"Unresolved characters at charno {code_i} to {len(source_code)} (end of file); file content {source_code[code_i:len(source_code)]}") has_error = True # end if # end if return not has_error @classmethod def install_coq_projects(cls, projects: List[Project]) -> None: names_projects = {p.full_name: p for p in projects} for i, p in enumerate(projects): cls.logger.info(f"Installing {p.full_name} ({i}/{len(projects)})") cls.install_coq_project(p, names_projects) # end for return @classmethod def install_coq_project(cls, project: Project, names_projects: Dict[str, Project]) -> None: """ :requires: the project is cloned and checked-out to the desired version. """ if not project.is_cloned: project.clone() project.checkout(project.data["sha"], is_forced=True) # end if # Check if the project is already compiled confirmation_file = "lpc-installed.txt" confirmation_content = project.revision + " " + BashUtils.run("opam list coq -s", expected_return_code=0).stdout.strip() if (project.checkout_dir/confirmation_file).is_file() and IOUtils.load(project.checkout_dir/confirmation_file, "txt") == confirmation_content: cls.logger.debug(f"Project {project.full_name} already installed") return # end if project.clean() # Install dependencies for dependency in project.data.get("dependencies", []): dependency_project = names_projects.get(dependency) if dependency_project is None: raise Exception(f"Cannot find dependency {dependency}") cls.logger.info(f"For Project {project.full_name}, installing dependency {dependency}") cls.install_coq_project(dependency_project, names_projects) # end for if "build_cmd" not in project.data: raise Exception(f"Project {project.full_name} does not have build_cmd") if "install_cmd" not in project.data: raise Exception(f"Project {project.full_name} does not have install_cmd") with IOUtils.cd(project.checkout_dir): # Build cls.logger.info(f"Project {project.full_name}: Building with {project.data['build_cmd']}") r = BashUtils.run(project.data["build_cmd"]) if r.return_code != 0: raise Exception(f"Compilation failed! Return code is {r.return_code}! stdout:\n{r.stdout}\n; stderr:\n{r.stderr}") else: cls.logger.debug(f"Compilation finished. Return code is {r.return_code}. stdout:\n{r.stdout}\n; stderr:\n{r.stderr}") # end if # Install cls.logger.info(f"Project {project.full_name}: Installing with {project.data['install_cmd']}") r = BashUtils.run(project.data["install_cmd"]) if r.return_code != 0: raise Exception(f"Installation failed! Return code is {r.return_code}! stdout:\n{r.stdout}\n; stderr:\n{r.stderr}") else: cls.logger.debug(f"Installation finished. Return code is {r.return_code}. stdout:\n{r.stdout}\n; stderr:\n{r.stderr}") # end if IOUtils.dump(project.checkout_dir / confirmation_file, confirmation_content, "txt") # end with return @classmethod def collect_data_indexes(cls, data_mgr: FilesManager, projects: List[Project]) -> NoReturn: """ Split the dataset and record the data indexes for {t1, t2, t3, lo, ta, allgroup} * {train, val, test, all} dataset parts. """ data_mgr.clean_path([FilesManager.DATA_INDEXES]) data_mgr.resolve([FilesManager.DATA_INDEXES]).mkdir(parents=True) # (Random) Split by train/val/test cls.logger.info(f"Splitting regular dataset info train/val/test sets with ratio of {Macros.DS_TRAIN_RATIO}/{Macros.DS_VAL_RATIO}/{Macros.DS_TEST_RATIO}") cls.logger.info(f"Splitting leave-out dataset info train/val/test sets with ratio of {Macros.DS_LO_TRAIN_RATIO}/{Macros.DS_LO_VAL_RATIO}/{Macros.DS_LO_TEST_RATIO}") # Load and sort coq-documents data coq_documents: List[CoqDocument] = cls.load_coq_documents(data_mgr) coq_documents.sort(key=lambda d: d.get_data_index()) cls.logger.info(f"Total dataset #doc = {len(coq_documents)}") if len(coq_documents) < 10: cls.logger.warning(f"Dataset is probably too small: {len(coq_documents)}") # end if trainevals_data_indexes: Dict[str, Set[str]] = collections.defaultdict(set) # Split data for each project, using the same random seed salted with the project name for project in projects: documents_this_project: List[CoqDocument] = sorted([d for d in coq_documents if d.project_name == project.full_name]) hasher = hashlib.sha256() hasher.update(str.encode(project.full_name)) hasher.update(str.encode(str(Environment.random_seed))) salted_seed = int.from_bytes(hasher.digest(), "big") random.seed(salted_seed) random.shuffle(documents_this_project) if project.data["group"] in [Macros.DS_GROUP_T1, Macros.DS_GROUP_T2, Macros.DS_GROUP_T3]: train_ratio, val_ratio, test_ratio = Macros.DS_TRAIN_RATIO, Macros.DS_VAL_RATIO, Macros.DS_TEST_RATIO elif project.data["group"] in [Macros.DS_GROUP_LO]: train_ratio, val_ratio, test_ratio = Macros.DS_LO_TRAIN_RATIO, Macros.DS_LO_VAL_RATIO, Macros.DS_LO_TEST_RATIO else: LoggingUtils.log_and_raise(cls.logger, f"Invalid group name {project.data['group']} for {project.full_name}", Exception) # end if train_val_split_point = int(math.ceil(train_ratio * len(documents_this_project))) val_test_split_point = int(math.ceil((train_ratio + val_ratio) * len(documents_this_project))) trainevals_data_indexes[Macros.DS_TRAIN].update(set([d.get_data_index() for d in documents_this_project[:train_val_split_point]])) trainevals_data_indexes[Macros.DS_VAL].update(set([d.get_data_index() for d in documents_this_project[train_val_split_point:val_test_split_point]])) trainevals_data_indexes[Macros.DS_TEST].update(set([d.get_data_index() for d in documents_this_project[val_test_split_point:]])) # end for trainevals_data_indexes[Macros.DS_TRAINEVAL_ALL] = set.union(*trainevals_data_indexes.values()) cls.logger.info(f"Train/eval split #doc:\n" + ";\n".join([ f"{traineval}: {len(data_indexes)}" for traineval, data_indexes in trainevals_data_indexes.items() ])) # Split by groups groups_project_names: Dict[str, List[str]] = {group: [p.full_name for p in projects if p.data["group"] == group] for group in Macros.DS_GROUPS} groups_data_indexes: Dict[str, Set[str]] = dict() for group, project_names in groups_project_names.items(): documents_this_group: List[CoqDocument] = [d for d in coq_documents if d.project_name in project_names] groups_data_indexes[group] = set([d.get_data_index() for d in documents_this_group]) # end for groups_data_indexes[Macros.DS_GROUP_TA] = set.union(groups_data_indexes[Macros.DS_GROUP_T1], groups_data_indexes[Macros.DS_GROUP_T2], groups_data_indexes[Macros.DS_GROUP_T3]) groups_data_indexes[Macros.DS_GROUP_ALL] = set.union(groups_data_indexes[Macros.DS_GROUP_T1], groups_data_indexes[Macros.DS_GROUP_T2], groups_data_indexes[Macros.DS_GROUP_T3], groups_project_names[Macros.DS_GROUP_LO]) cls.logger.info(f"Groups split #doc:\n" + ";\n".join([ f"{group}: {len(data_indexes)}" for group, data_indexes in groups_data_indexes.items() ])) # The final data indexes is cross product of the two splits for traineval in Macros.DS_TRAINEVALS + [Macros.DS_TRAINEVAL_ALL]: for group in Macros.DS_GROUPS + [Macros.DS_GROUP_TA, Macros.DS_GROUP_ALL]: data_indexes = list(set.intersection(groups_data_indexes[group], trainevals_data_indexes[traineval])) cls.logger.info(f"{group}-{traineval} #doc = {len(data_indexes)}") data_mgr.dump_data([FilesManager.DATA_INDEXES, f"{group}-{traineval}.json"], data_indexes, IOUtils.Format.jsonPretty) # end for # end for return RE_PATH_TO_QUALIFIED_PREFIX = re.compile(r"-[QR] (?P<path>[^,]+),(?P<qprefix>\S+)") @classmethod def collect_lemmas(cls, data_mgr: FilesManager, projects: List[Project], files: List[str] = None): data_mgr.clean_path([FilesManager.LEMMAS]) data_mgr.resolve([FilesManager.LEMMAS]).mkdir(parents=True) # Increase recursion limit because the backend sexps are CRAZZZZY deep sys.setrecursionlimit(10000) # Load coq-documents coq_documents: List[CoqDocument] = cls.load_coq_documents(data_mgr) if files is not None: coq_documents = [d for d in coq_documents if d.file_name in files] lemmas: List[Lemma] = list() # Prepare serapi_options project_2_serapi_options: Dict[str, str] = {p.full_name: p.data["serapi_options"] for p in projects} errors: List[Tuple[str, str]] = list() for doc_i, doc in enumerate(tqdm(coq_documents)): try: cls.logger.info(f"Collecting from file {doc.get_data_index()} ({doc_i}/{len(coq_documents)}). Collected: {len(lemmas)}") # Load AST sexp ast_sexp_list: List[SexpNode] = SexpParser.parse_list(data_mgr.load_data([FilesManager.RAW_FILES, doc.get_data_index()[:-2] + ".ast.sexp"], IOUtils.Format.txt)) # Collect lemmas from this doc lemmas_doc: List[Lemma] = cls.collect_lemmas_doc(doc, ast_sexp_list, project_2_serapi_options[doc.project_name]) lemmas.extend(lemmas_doc) except KeyboardInterrupt: cls.logger.warning(f"Keyboard Interrupt!") raise except: cls.logger.warning(f"Error while parsing {doc.get_data_index()}: {traceback.format_exc()}") cls.logger.warning(f"The script will continue on other files before it returns with failure. Use Ctrl+C to cut it early.") errors.append((doc.get_data_index(), traceback.format_exc())) continue # end try # end for if len(errors) > 0: LoggingUtils.log_and_raise(cls.logger, f"There were {len(errors)} errors during collection.", Exception) data_mgr.dump_data([FilesManager.LEMMAS, "errors.txt"], errors, IOUtils.Format.jsonPretty) # end if # Assign uids for lemma_i, lemma in enumerate(lemmas): lemma.uid = lemma_i data_mgr.dump_data([FilesManager.LEMMAS], lemmas, IOUtils.Format.json, is_batched=True, per_batch=5000) return @classmethod def filter_lemmas(cls, data_mgr: FilesManager): # Increase recursion limit because the backend sexps are CRAZZZZY deep sys.setrecursionlimit(10000) data_mgr.clean_path([FilesManager.LEMMAS_FILTERED]) data_mgr.resolve([FilesManager.LEMMAS_FILTERED]).mkdir(parents=True) # Load lemmas lemmas: List[Lemma] = data_mgr.load_data([FilesManager.LEMMAS], IOUtils.Format.json, is_batched=True, clz=Lemma) heights: List[int] = [l.backend_sexp.height() for l in lemmas] depth_cutoff_point = sorted(heights)[int(np.ceil(Macros.LEMMAS_DEPTH_CUTOFF * len(lemmas)))] data_indexes_names: List[Tuple[str, str]] = [(l.data_index, l.name) for l in lemmas if l.backend_sexp.height() <= depth_cutoff_point] cls.logger.info(f"Cutoff depth is {depth_cutoff_point}, and {len(data_indexes_names)} data are included") lemmas_filtered: List[Lemma] = [l for l in lemmas if (l.data_index, l.name) in data_indexes_names] # Assign uids for lemma_i, lemma in enumerate(lemmas_filtered): lemma.uid = lemma_i data_mgr.dump_data([FilesManager.LEMMAS_FILTERED], lemmas_filtered, IOUtils.Format.json, is_batched=True, per_batch=5000) return @classmethod def collect_definitions(cls, data_mgr: FilesManager): data_mgr.clean_path([FilesManager.DEFINITIONS]) data_mgr.resolve([FilesManager.DEFINITIONS]).mkdir(parents=True) # Load coq-documents coq_documents: List[CoqDocument] = cls.load_coq_documents(data_mgr) definitions: List[Definition] = list() errors: List[Tuple[str, str]] = list() for doc_i, doc in enumerate(tqdm(coq_documents)): try: # Load AST sexp ast_sexp_list: List[SexpNode] = SexpParser.parse_list(data_mgr.load_data([FilesManager.RAW_FILES, doc.get_data_index()[:-2] + ".ast.sexp"], IOUtils.Format.txt)) definitions_doc: List[Definition] = cls.collect_definitions_doc(doc, ast_sexp_list) definitions.extend(definitions_doc) except KeyboardInterrupt: cls.logger.warning(f"Keyboard Interrupt!") raise except: cls.logger.warning(f"Error while parsing {doc.get_data_index()}: {traceback.format_exc()}") cls.logger.warning(f"The script will continue on other files before it returns with failure. Use Ctrl+C to cut it early.") errors.append((doc.get_data_index(), traceback.format_exc())) continue # end try # end for if len(errors) > 0: LoggingUtils.log_and_raise(cls.logger, f"There were {len(errors)} errors during collection.", Exception) data_mgr.dump_data([FilesManager.DEFINITIONS, "errors.txt"], errors, IOUtils.Format.jsonPretty) # end if data_mgr.dump_data([FilesManager.DEFINITIONS, "definitions.json"], definitions, IOUtils.Format.json) return @classmethod def collect_lemmas_backend_sexp_transformations(cls, data_mgr: FilesManager): data_mgr.clean_path([cls.TASK_LEMMA_BACKEND_SEXP_TRANSFORMATIONS]) data_mgr.resolve([cls.TASK_LEMMA_BACKEND_SEXP_TRANSFORMATIONS]).mkdir(parents=True) # Increase recursion limit because the backend sexps are CRAZZZZY deep sys.setrecursionlimit(10000) lemmas_filtered: List[Lemma] = data_mgr.load_data([FilesManager.LEMMAS_FILTERED], IOUtils.Format.json, is_batched=True, clz=Lemma) # Main stream transformations, applied one after another levels_lemmas_bsexp_transformed: Dict[str, List[SexpNode]] = dict() last_level: Optional[str] = None # None means original for level in LemmaBackendSexpTransformers.LEVELS: cls.logger.info(f"Doing {last_level if last_level is not None else 'orig'} -> {level} transformation") levels_lemmas_bsexp_transformed[level] = list() for lemma_i, lemma in enumerate(tqdm(lemmas_filtered)): orig_sexp = lemma.backend_sexp if last_level is None else levels_lemmas_bsexp_transformed[last_level][lemma_i] bsexp_transformed = LemmaBackendSexpTransformers.transform(level, copy.deepcopy(orig_sexp)) levels_lemmas_bsexp_transformed[level].append(bsexp_transformed) # end for last_level = level data_mgr.dump_data([cls.TASK_LEMMA_BACKEND_SEXP_TRANSFORMATIONS, level, "transformed"], levels_lemmas_bsexp_transformed[level], IOUtils.Format.json, is_batched=True, per_batch=5000) # end for # Other special transformation, directly applied on original trees for tr_name in LemmaBackendSexpTransformers.SPECIALS: cls.logger.info(f"Doing orig -> {tr_name} transformation") bsexp_transformed_list = list() for lemma_i, lemma in enumerate(tqdm(lemmas_filtered)): orig_sexp = lemma.backend_sexp bsexp_transformed = LemmaBackendSexpTransformers.transform(tr_name, copy.deepcopy(orig_sexp)) bsexp_transformed_list.append(bsexp_transformed) # end for data_mgr.dump_data([cls.TASK_LEMMA_BACKEND_SEXP_TRANSFORMATIONS, tr_name, "transformed"], bsexp_transformed_list, IOUtils.Format.json, is_batched=True, per_batch=5000) # end for return @classmethod def collect_lemmas_foreend_sexp_transformations(cls, data_mgr: FilesManager): data_mgr.clean_path([cls.TASK_LEMMA_FOREEND_SEXP_TRANSFORMATIONS]) data_mgr.resolve([cls.TASK_LEMMA_FOREEND_SEXP_TRANSFORMATIONS]).mkdir(parents=True) # Increase recursion limit because the backend sexps are CRAZZZZY deep sys.setrecursionlimit(10000) lemmas_filtered: List[Lemma] = data_mgr.load_data([FilesManager.LEMMAS_FILTERED], IOUtils.Format.json, is_batched=True, clz=Lemma) # Main stream transformations, applied one after another levels_lemmas_fsexp_transformed: Dict[str, List[SexpNode]] = dict() last_level: Optional[str] = None # None means original for level in LemmaForeendSexpTransformers.LEVELS: cls.logger.info(f"Doing {last_level if last_level is not None else 'orig'} -> {level} transformation") levels_lemmas_fsexp_transformed[level] = list() for lemma_i, lemma in enumerate(tqdm(lemmas_filtered)): orig_sexp = lemma.ast_sexp if last_level is None else levels_lemmas_fsexp_transformed[last_level][lemma_i] fsexp_transformed = LemmaForeendSexpTransformers.transform(level, copy.deepcopy(orig_sexp)) levels_lemmas_fsexp_transformed[level].append(fsexp_transformed) # end for last_level = level data_mgr.dump_data([cls.TASK_LEMMA_FOREEND_SEXP_TRANSFORMATIONS, level, "transformed"], levels_lemmas_fsexp_transformed[level], IOUtils.Format.json, is_batched=True, per_batch=5000) # end for # Other special transformation, directly applied on level 0 trees for tr_name in LemmaForeendSexpTransformers.SPECIALS: cls.logger.info(f"Doing {LemmaForeendSexpTransformers.LEVEL_0} -> {tr_name} transformation") fsexp_transformed_list = list() for lemma_i, lemma in enumerate(tqdm(lemmas_filtered)): orig_sexp = levels_lemmas_fsexp_transformed[LemmaForeendSexpTransformers.LEVEL_0][lemma_i] fsexp_transformed = LemmaForeendSexpTransformers.transform(tr_name, copy.deepcopy(orig_sexp)) fsexp_transformed_list.append(fsexp_transformed) # end for data_mgr.dump_data([cls.TASK_LEMMA_FOREEND_SEXP_TRANSFORMATIONS, tr_name, "transformed"], fsexp_transformed_list, IOUtils.Format.json, is_batched=True, per_batch=5000) # end for return VTYPES_LEMMA = [SexpInfo.VernacConsts.type_start_theorem_proof] VTYPES_MODULE_BEG = [SexpInfo.VernacConsts.type_define_module] VTYPES_MODULE_END = [SexpInfo.VernacConsts.type_end_segment] VTYPES_DEFINITIONS = [SexpInfo.VernacConsts.type_definition] @classmethod def collect_lemmas_doc( cls, doc: CoqDocument, ast_sexp_list: List[SexpNode], serapi_options: str, ) -> List[Lemma]: lemmas_doc: List[Lemma] = list() data_index = doc.get_data_index() # Maintain a stack of module modules: List[str] = list() # Prepare qualified name prefix qprefix_this_doc = "./" + doc.file_name[:-2] # Remove .v for m in cls.RE_PATH_TO_QUALIFIED_PREFIX.finditer(serapi_options): path = m.group("path") if path != ".": path = "./" + path qprefix = m.group("qprefix") if qprefix_this_doc.startswith(path): qprefix_this_doc = qprefix + qprefix_this_doc[len(path):] break # end if # end for if qprefix_this_doc.startswith("./"): qprefix_this_doc = qprefix_this_doc[len("./"):] qprefix_this_doc = qprefix_this_doc.replace("/", ".") for sent_i, sent in enumerate(doc.sentences): ast_sexp = ast_sexp_list[sent_i] vernac = SexpAnalyzer.analyze_vernac(ast_sexp) if vernac.vernac_type in cls.VTYPES_MODULE_BEG: # (VernacExpr()(VernacDefineModule() ( ( v ( Id <module name>)) ... # 0 1 2 20 21 22 220 2201 22011 module_name = vernac.vernac_sexp[2][2][0][1][1].content_no_quote modules.append(module_name) elif vernac.vernac_type in cls.VTYPES_MODULE_END: # (VernacExpr()(VernacEndSegment ( ( v ( Id <module name>)) ... # 0 1 2 20 21 210 2101 21011 try: module_name = vernac.vernac_sexp[2][1][0][1][1].content_no_quote except: print(vernac.vernac_sexp.pretty_format()) raise # end try if len(modules) > 0 and module_name == modules[-1]: modules.pop() # EndModule and EndSection share the same vernac type elif vernac.vernac_type in cls.VTYPES_LEMMA: # (VernacExpr()(VernacStartTheoremProof Lemma ( ( ( ( ( v ( Id <lemma name>)) # 0 1 2 20 21 22 2200000 2200001 22000011 lemma = Lemma() lemma.data_index = data_index lemma.name = vernac.vernac_sexp[2][2][0][0][0][0][1][1].content_no_quote lemma.qname = qprefix_this_doc + "." + ".".join(modules + [lemma.name]) # Find lemma content, after the first token matching the lemma name tok_i = 0 for tok in sent.tokens: if tok.content == lemma.name: break tok_i += 1 # end for if tok_i == len(sent.tokens): LoggingUtils.log_and_raise(cls.logger, f"Lemma name {lemma.name} didn't appear in the source code {sent.str_with_space()}", Exception) lemma.vernac_command = sent.tokens[:tok_i] lemma.statement = sent.tokens[tok_i + 1:] lemma.ast_sexp = vernac.vernac_sexp lemmas_doc.append(lemma) # end if # end for # Use sername to get the backend representations lemma_qnames: str = "".join([l.qname + "\n" for l in lemmas_doc]) lemma_qnames_file = BashUtils.get_temp_file() IOUtils.dump(lemma_qnames_file, lemma_qnames, IOUtils.Format.txt) lemma_qnames_backend_sexps_str: str = BashUtils.run(f"sername {serapi_options} --require-lib={qprefix_this_doc} {lemma_qnames_file}", expected_return_code=0).stdout IOUtils.rm(lemma_qnames_file) for qname_backend_sexp_str in lemma_qnames_backend_sexps_str.splitlines(): qname, backend_sexp_str = qname_backend_sexp_str.split(":", 1) backend_sexp = SexpParser.parse(backend_sexp_str) for lemma in lemmas_doc: if lemma.qname == qname: lemma.backend_sexp = backend_sexp break # end if # end for # end for lemmas_doc = [l for l in lemmas_doc if l.backend_sexp is not None] return lemmas_doc @classmethod def collect_definitions_doc(cls, doc: CoqDocument, ast_sexp_list: List[SexpNode], ) -> List[Definition]: definitions_doc: List[Definition] = list() data_index = doc.get_data_index() for sent_i, sent in enumerate(doc.sentences): ast_sexp = ast_sexp_list[sent_i] vernac = SexpAnalyzer.analyze_vernac(ast_sexp) if vernac.vernac_type in cls.VTYPES_DEFINITIONS: # (VernacExpr()( VernacDefinition ( NoDischarge Definition) ( ( ( v ( Name ( Id codom ))) ... # 0 1 2 20 21 210 211 22 220 2200 22000 22001 220010 220011 2200110 2200111 try: if vernac.vernac_sexp[2][1][0].content == "NoDischarge" and vernac.vernac_sexp[2][1][1].content == "Definition": definition = Definition() definition.data_index = data_index definition.name = vernac.vernac_sexp[2][2][0][0][1][1][1].content_no_quote definitions_doc.append(definition) # end if except IllegalSexpOperationException: continue # end try # end if # end for return definitions_doc @classmethod def extract_data_project(cls, project_path: Path, files: Optional[List[str]], exclude_files: Optional[List[str]], exclude_pattern: Optional[str], serapi_options: str, output_path: Path, ): # 1. Prepare output path if output_path.is_dir(): cls.logger.warning(f"{output_path} already exists, will overwrite the files.") elif output_path.is_file(): LoggingUtils.log_and_raise(cls.logger, f"{output_path} already exists as a file. Aborting.", Exception) else: IOUtils.mk_dir(output_path) # end if # 2. Extract documents, tok.sexp and ast.sexp coq_documents: Dict[str, CoqDocument] = collections.OrderedDict() ast_sexp_lists: Dict[str, List[SexpNode]] = dict() tok_sexp_lists: Dict[str, List[SexpNode]] = dict() with IOUtils.cd(project_path): coq_files: List[str] = BashUtils.run(f"find -name '*.v' -type f").stdout.split("\n")[:-1] coq_files = [coq_file[2:] for coq_file in coq_files] if files is not None: coq_files = [f for f in coq_files if f in files] # end if if exclude_files is not None: coq_files = [f for f in coq_files if f not in exclude_files] # end if if exclude_pattern is not None: re_exclude_pattern = re.compile(exclude_pattern) coq_files = [f for f in coq_files if not re_exclude_pattern.fullmatch(f)] # end if for i, coq_file in enumerate(tqdm(coq_files)): try: # Read file with open(coq_file, "r", newline="") as f: source_code = f.read() # end with # Get unicode offsets unicode_offsets = ParserUtils.get_unicode_offsets(source_code) # Call SerAPI ast_sexp_str: str = BashUtils.run(f"sercomp {serapi_options} --mode=sexp -- {coq_file}", expected_return_code=0).stdout tok_sexp_str: str = BashUtils.run(f"sertok {serapi_options} -- {coq_file}", expected_return_code=0).stdout # Parse ast sexp ast_sexp_list: List[SexpNode] = SexpParser.parse_list(ast_sexp_str) tok_sexp_list: List[SexpNode] = SexpParser.parse_list(tok_sexp_str) # Parse the document coq_document = CoqParser.parse_document(source_code, ast_sexp_list, tok_sexp_list, unicode_offsets=unicode_offsets) # Set meta data coq_document.file_name = coq_file coq_document.project_name = project_path.name coq_documents[coq_file] = coq_document ast_sexp_lists[coq_file] = ast_sexp_list tok_sexp_lists[coq_file] = tok_sexp_list except KeyboardInterrupt: cls.logger.warning("Keyboard interrupt!") raise except: cls.logger.warning(f"File {coq_file} failed! Exception was: {traceback.format_exc()}") continue # end try # end for # 3. Extract and save lemmas and definitions lemmas: List[Lemma] = list() definitions: List[Definition] = list() # Increase recursion limit because the backend sexps are CRAZZZZY deep sys.setrecursionlimit(10000) for file_path, doc in tqdm(coq_documents.items()): ast_sexp_list = ast_sexp_lists[file_path] lemmas_doc = cls.collect_lemmas_doc(doc, ast_sexp_list, serapi_options) lemmas.extend(lemmas_doc) definitions_doc = cls.collect_definitions_doc(doc, ast_sexp_list) definitions.extend(definitions_doc) # end for IOUtils.dump(output_path/"lemmas.json", IOUtils.jsonfy(lemmas), IOUtils.Format.json) IOUtils.dump(output_path/"definitions.json", IOUtils.jsonfy(definitions), IOUtils.Format.json) # end with return @classmethod def extract_data_from_corpus(cls, corpus_path: Path, trainevals: List[str], groups: List[str], output_path: Path, ): # 1. Prepare output path if output_path.is_dir(): cls.logger.warning(f"{output_path} already exists, will overwrite the files.") elif output_path.is_file(): LoggingUtils.log_and_raise(cls.logger, f"{output_path} already exists as a file. Aborting.", Exception) else: IOUtils.mk_dir(output_path) # end if assert all([traineval in Macros.DS_TRAINEVALS for traineval in trainevals]) assert all([group in Macros.DS_GROUPS+[Macros.DS_GROUP_TA] for group in groups]) data_mgr = FilesManager(corpus_path) # 2. Load lemmas and definitions lemmas_filtered: List[Lemma] = data_mgr.load_data([FilesManager.LEMMAS_FILTERED], IOUtils.Format.json, is_batched=True, clz=Lemma) definitions: List[Definition] = data_mgr.load_data([FilesManager.DEFINITIONS, "definitions.json"], IOUtils.Format.json, clz=Definition) # 3. Output to output_path for each combination of traineval and group for traineval in trainevals: for group in groups: IOUtils.mk_dir(output_path/f"{group}-{traineval}") data_indexes = IOUtils.load(Macros.project_dir/"training"/f"{group}-{traineval}.json", IOUtils.Format.json) IOUtils.dump(output_path/f"{group}-{traineval}/lemmas.json", IOUtils.jsonfy([l for l in lemmas_filtered if l.data_index in data_indexes]), IOUtils.Format.json) IOUtils.dump(output_path/f"{group}-{traineval}/definitions.json", IOUtils.jsonfy([d for d in definitions if d.data_index in data_indexes]), IOUtils.Format.json) # end for # end for return

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null

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null

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00464d29c3ee1cf1c9de61907d49f9253edbd2f3

965

py

Python

venv/Lib/site-packages/gensim/similarities/__init__.py

saritmaitra/nlp_ner_topic_modeling

70914b4ae4cd7d3b9cb10776161132216394883c

[ "MIT" ]

3

2021-03-29T19:21:08.000Z

2021-12-31T09:30:11.000Z

VisionAPI/lib/python3.8/site-packages/gensim/similarities/__init__.py

aniruddhakj/AnswerScriptEvaluation

7b039b84355ecda1d55dc037ccfc4a4d661ad5e3

[ "BSD-3-Clause" ]

1

2021-08-30T08:53:09.000Z

venv/Lib/site-packages/gensim/similarities/__init__.py

saritmaitra/nlp_ner_topic_modeling

70914b4ae4cd7d3b9cb10776161132216394883c

[ "MIT" ]

1

2021-03-30T05:02:53.000Z

""" This package contains implementations of pairwise similarity queries. """ # bring classes directly into package namespace, to save some typing import warnings try: import Levenshtein # noqa:F401 except ImportError: msg = ( "The gensim.similarities.levenshtein submodule is disabled, because the optional " "Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. " "Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning." ) warnings.warn(msg) LevenshteinSimilarityIndex = None else: from .levenshtein import LevenshteinSimilarityIndex # noqa:F401 from .docsim import ( # noqa:F401 Similarity, MatrixSimilarity, SparseMatrixSimilarity, SoftCosineSimilarity, WmdSimilarity) from .termsim import ( # noqa:F401 TermSimilarityIndex, UniformTermSimilarityIndex, WordEmbeddingSimilarityIndex, SparseTermSimilarityMatrix)

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null

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004abcc62f87b11013e726a8b69a3c514744935d

1,858

py

Python

src/PIDController.py

methylDragon/momo-emotions

137161632cc45227884d1a7a46dbd75d261de371

[ "BSD-2-Clause" ]

11

2019-05-24T00:25:59.000Z

2021-05-17T07:08:58.000Z

src/PIDController.py

methylDragon/momo-emotions

137161632cc45227884d1a7a46dbd75d261de371

[ "BSD-2-Clause" ]

null

src/PIDController.py

methylDragon/momo-emotions

137161632cc45227884d1a7a46dbd75d261de371

[ "BSD-2-Clause" ]

10

2019-06-21T02:38:45.000Z

2021-07-07T04:50:39.000Z

import time class PIDController: def __init__(self, Kp=0.25, Ki=0.0, Kd=0.0, anti_windup=10.0, cmd_freq=0.0): self.Kp = Kp self.Ki = Ki self.Kd = Kd # Set max integral correction per timestep self.anti_windup = anti_windup # Set delay between updates (seconds) self.cmd_freq = cmd_freq self.current_time = time.time() self.prev_time = self.current_time self.reset() def reset(self): self.setpoint = 0.0 self.p_ = 0.0 self.i_ = 0.0 self.d_ = 0.0 self.prev_error = 0.0 def compute(self, setpoint, measured_value): ''' Compute PID correction wrt. measured_value - setpoint ''' self.current_time = time.time() delta_time = self.current_time - self.prev_time if delta_time >= self.cmd_freq: self.setpoint = setpoint error = self.setpoint - measured_value delta_error = error - self.prev_error self.accumulated_error = error * delta_time # Limit the integration to prevent absolutely wrecking yourself if self.accumulated_error < -self.anti_windup: self.accumulated_error = -self.anti_windup if self.accumulated_error > self.anti_windup: self.accumulated_error = self.anti_windup self.i_ = self.i_ + self.accumulated_error self.d_ = delta_error / delta_time self.prev_error = error self.prev_time = self.current_time return self.Kp * error + self.Ki * self.i_ + self.Kd * self.d_ def set_kp(self, kp): self.Kp = kp def set_ki(self, ki): self.Ki = ki def set_kd(self, kd): self.Kd = kd def set_anti_windup(self, anti_windup): self.anti_windup = anti_windup

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null

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null

0

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0

004b29aa8664031730012b088eba2ecb4c151bb6

1,420

py

Python

tilty/emitters/sqlite.py

heresurpizza/tilty

758fc2513b5fb660ac11163941340e4c10f61081

[ "MIT" ]

13

2020-02-27T03:07:50.000Z

2022-01-02T20:01:57.000Z

tilty/emitters/sqlite.py

heresurpizza/tilty

758fc2513b5fb660ac11163941340e4c10f61081

[ "MIT" ]

10

2020-03-04T14:57:59.000Z

2021-07-23T03:54:17.000Z

tilty/emitters/sqlite.py

heresurpizza/tilty

758fc2513b5fb660ac11163941340e4c10f61081

[ "MIT" ]

8

2020-03-15T02:23:10.000Z

2020-11-25T12:42:37.000Z

# -*- coding: utf-8 -*- """ SQLite emitter """ import logging import sqlite3 LOGGER = logging.getLogger() def __type__() -> str: return 'SQLite' class SQLite: # pylint: disable=too-few-public-methods """ SQLite wrapper class """ def __init__(self, config: dict) -> None: """ Initializer Args: config: (dict) represents the configuration for the emitter """ # <start config sample> # [sqlite] # file = /etc/tilty/tilt.sqlite self.conn = sqlite3.connect(config['file']) self.conn.execute(''' CREATE TABLE IF NOT EXISTS data( id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, gravity INTEGER, temp INTEGER, color VARCHAR(16), mac VARCHAR(17), timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL) ''') def emit(self, tilt_data: dict) -> None: """ Initializer Args: tilt_data (dict): data returned from valid tilt device scan """ LOGGER.info('[sqlite] creating row') self.conn.execute( "insert into data (gravity,temp,color,mac) values (?,?,?,?)", ( tilt_data['gravity'], tilt_data['temp'], tilt_data['color'], tilt_data['mac'] ) ) self.conn.commit()

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null

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null

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004de3fdbd877d9dca8c67922ef30d1cf30e4c3c

6,043

py

Python

IFishFarm.py

HussamElden/IFishFarm

c49acc997229b9ae0649d9e4765255cb2db02bfc

[ "CECILL-B" ]

1

2021-08-03T13:24:38.000Z

IFishFarm.py

HussamElden/IFishFarm

c49acc997229b9ae0649d9e4765255cb2db02bfc

[ "CECILL-B" ]

null

IFishFarm.py

HussamElden/IFishFarm

c49acc997229b9ae0649d9e4765255cb2db02bfc

[ "CECILL-B" ]

2

2021-01-12T11:25:11.000Z

2022-03-11T21:25:53.000Z

import cv2 import numpy as np from numpy.linalg import norm import math import csv from operator import itemgetter from datetime import datetime import VideoEnhancement import fishpredictor import detector import kmeancluster import preproccesing import randomforst cluster = kmeancluster.kmeans() classifier = randomforst.randomforst() samak = [] framenum = 0 sum = 0 max = 0 mylist = [[]] yolo = detector.detector() cap = cv2.VideoCapture('chaos1.avi') ret, frame = cap.read() fheight, fwidth, channels = frame.shape resize = False if (fheight > 352 or fwidth > 640): resize = True fwidth = 640 fheight = 352 frame = cv2.resize(frame, (640, 352)) mask = np.zeros_like(frame) # Needed for saving video fps = cap.get(cv2.CAP_PROP_FPS) fourcc = cv2.VideoWriter_fourcc(*'DIVX') dt_string = datetime.now().strftime("%H_%M_%S_%d_%m_%y") num_seconds = 10 video = cv2.VideoWriter('videonormal/' +str(num_seconds*round(fps))+'_'+str(dt_string)+'.avi', fourcc, fps, (fwidth, fheight)) # Read until video is completed counter = 0 buffer = [[]] apperance = [[]] last_changed = [] top = 0 frms = 0 # Needed to track objects n_frame = 8 ref_n_frame_axies = [] ref_n_frame_label = [] ref_n_frame_axies_flatten = [] ref_n_frame_label_flatten = [] frm_num = 1 coloredLine = np.random.randint(0, 255, (10000, 3)) arr = [] label_cnt = 1 min_distance = 50 while (cap.isOpened()): ret, img = cap.read() if ret == True: if frms % 2 == 0: img = VideoEnhancement.enhanceVideo(img, resize) v = 0 cur_frame_axies = [] cur_frame_label = [] height, width, channels = img.shape boxes, confidences, centers, colors = yolo.detect(img) counter += 1 indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.1, 0.4) font = cv2.FONT_HERSHEY_PLAIN fishcounter = 1 for i in range(len(boxes)): if i in indexes: lbl = float('nan') x, y, w, h, = boxes[i] center_x, center_y = centers[i] color = colors[0] if (len(ref_n_frame_label_flatten) > 0): b = np.array([(center_x, center_y)]) a = np.array(ref_n_frame_axies_flatten) distance = norm(a - b, axis=1) min_value = distance.min() if (min_value < min_distance): idx = np.where(distance == min_value)[0][0] lbl = ref_n_frame_label_flatten[idx] points = (int(ref_n_frame_axies_flatten[idx][0]), int(ref_n_frame_axies_flatten[idx][1])) mask = cv2.line(mask, (center_x, center_y), points, coloredLine[lbl].tolist(), 2) cv2.circle(img, points, 5, coloredLine[lbl].tolist(), -1) if (math.isnan(lbl)): lbl = label_cnt label_cnt += 1 arr.append([counter, lbl, center_x, center_y]) cur_frame_label.append(lbl) cur_frame_axies.append((center_x, center_y)) samak.append([lbl, x, y, w, h]) cv2.rectangle(img, (x, y), (x + w, y + h), color, 2) cv2.putText(img, '{}{}'.format("Fish", lbl), (x, y - 5), font, 1, (255, 255, 255), 2) if (len(ref_n_frame_axies) == n_frame): del ref_n_frame_axies[0] del ref_n_frame_label[0] ref_n_frame_label.append(cur_frame_label) ref_n_frame_axies.append(cur_frame_axies) ref_n_frame_axies_flatten = [a for ref_n_frame_axie in ref_n_frame_axies for a in ref_n_frame_axie] ref_n_frame_label_flatten = [b for ref_n_frame_lbl in ref_n_frame_label for b in ref_n_frame_lbl] z = sorted(samak, key=itemgetter(0)) samak = [] if (len(z) != 0): fishpredictor.predictfish(z, apperance, buffer, last_changed, top, img, color, mylist, framenum) img = cv2.add(img, mask) # cv2.imshow("Image", img) mylist.append([]) framenum += 1 print(frms) print("----------") # cap.set(1,frms) video.write(img) if (frms % (round(fps) * num_seconds) == 0 and frms!=0): result = cluster.classify(mask) print(classifier.classify(z, mask,fps)) if (result == 1): with open('exceltext/' + str(frms)+'_'+str(dt_string)+ '.csv', 'w', newline='') as file: writer = csv.writer(file) writer.writerows(mylist) # writer.writerows(preproccesing.featuresCalc(mylist)) cv2.imwrite("trajecstest" + str(frms)+'_'+str(dt_string) + ".png", mask) video.release() dt_string = datetime.now().strftime("%H_%M_%S_%d_%m_%y") video = cv2.VideoWriter('videotest/' + str(frms+(num_seconds*round(fps)))+'_'+str(dt_string)+'.avi', fourcc, fps, (fwidth, fheight)) print("result " + str(result)) mask = np.zeros_like(frame) ref_n_frame_axies = [] ref_n_frame_label = [] ref_n_frame_axies_flatten = [] ref_n_frame_label_flatten = [] buffer = [[]] apperance = [[]] last_changed = [] # frms = 0 counter = 0 mylist = [[]] framenum = 0 fishcounter = 1 label_cnt = 1 top = 0 if cv2.waitKey(25) & 0xFF == ord('q'): break # Break the loop else: break frms += 1 cap.release() cv2.destroyAllWindows() video.release()

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