xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

45502c36194120822d47a489bb35792d7a4b090d

754

py

Python

matplotlib/axessetting.py

mk43/python-practice-project

4260456c1006c1f3e2a6f00bcb2639d6e8a71e5e

[ "Apache-2.0" ]

7

2018-05-29T07:14:22.000Z

2020-03-05T06:45:04.000Z

matplotlib/axessetting.py

mk43/python-practice-project

4260456c1006c1f3e2a6f00bcb2639d6e8a71e5e

[ "Apache-2.0" ]

null

matplotlib/axessetting.py

mk43/python-practice-project

4260456c1006c1f3e2a6f00bcb2639d6e8a71e5e

[ "Apache-2.0" ]

5

2018-11-08T04:03:48.000Z

2020-03-05T06:45:06.000Z

import matplotlib.pyplot as plt import numpy as np x = np.linspace(-10, 10, 40) y1 = 10 * x + 50 y2 = x**2 plt.figure() plt.plot(x, y1, 'b-') plt.plot(x, y2, 'b--') plt.xlim((-20, 20)) plt.ylim((-60, 160)) plt.xlabel('I am x') plt.ylabel('I am y') plt.xticks(np.linspace(-20, 20, 5)) plt.yticks([0, 50, 100], [r'$bad$', r'$normal$', r'$good$']) boderparameter = plt.gca() boderparameter.spines['right'].set_color('none') boderparameter.spines['top'].set_color('none') boderparameter.xaxis.set_ticks_position('top') boderparameter.spines['left'].set_position(('data',0)) boderparameter.spines['bottom'].set_position(('data',0)) boderparameter.xaxis.set_ticks_position('bottom') boderparameter.set_xlabel('') boderparameter.set_ylabel('') plt.show()

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py

Python

Xgam/__init__.py

aurelio-amerio/Xgam

fb65ed009bb35984eadd0c576aa385ca3702c8ce

[ "MIT" ]

1

2021-06-14T20:27:30.000Z

Xgam/__init__.py

aurelio-amerio/Xgam

fb65ed009bb35984eadd0c576aa385ca3702c8ce

[ "MIT" ]

null

Xgam/__init__.py

aurelio-amerio/Xgam

fb65ed009bb35984eadd0c576aa385ca3702c8ce

[ "MIT" ]

1

2021-06-14T20:27:55.000Z

#!/usr/bin/env python # # # # Autor: Michela Negro, GSFC/CRESST/UMBC . # # On behalf of the Fermi-LAT Collaboration. # # # # This program is free software; you can redistribute it and/or modify # # it under the terms of the GNU GengReral Public License as published by # # the Free Software Foundation; either version 3 of the License, or # # (at your option) any later version. # # # #------------------------------------------------------------------------------# """Xgam: Framework for Gamma-ray X-correlation Analysis """ import os PACKAGE_NAME = 'Xgam' """Basic folder structure of the package. """ X_ROOT = os.path.abspath(os.path.dirname(__file__)) X_BIN = os.path.join(X_ROOT, 'bin') X_CONFIG = os.path.join(X_ROOT, 'config') X_UTILS = os.path.join(X_ROOT, 'utils') """ This is where we put the actual (FT1 and FT2) data sets. """ from Xgam.utils.logging_ import logger try: FT_DATA_FOLDER = os.environ['P8_DATA'] logger.info('Base data folder set to $P8_DATA = %s...' % FT_DATA_FOLDER) except KeyError: FT_DATA_FOLDER = '/Users/mnegro/data/Fermi-LAT' logger.info('$P8_DATA not set, base data folder set to %s...' %\ FT_DATA_FOLDER) """ This is the output directory. """ try: X_OUT = os.environ['X_OUT'] X_OUT_FIG = os.environ['X_OUT_FIG'] except: X_OUT = os.path.join(X_ROOT, 'output') X_OUT_FIG = os.path.join(X_ROOT, 'output/figures') if __name__ == '__main__': from Xgam.utils.logging_ import startmsg startmsg() print(('X_ROOT: %s' % X_ROOT))

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py

Python

day_10.py

JeffHanna/Advent_of_Code_2018

a47f7c5dc1ef28df41a26a21fc16626cb2a9c922

[ "MIT" ]

null

day_10.py

JeffHanna/Advent_of_Code_2018

a47f7c5dc1ef28df41a26a21fc16626cb2a9c922

[ "MIT" ]

null

day_10.py

JeffHanna/Advent_of_Code_2018

a47f7c5dc1ef28df41a26a21fc16626cb2a9c922

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ --- Day 10: The Stars Align --- It's no use; your navigation system simply isn't capable of providing walking directions in the arctic circle, and certainly not in 1018. The Elves suggest an alternative. In times like these, North Pole rescue operations will arrange points of light in the sky to guide missing Elves back to base. Unfortunately, the message is easy to miss: the points move slowly enough that it takes hours to align them, but have so much momentum that they only stay aligned for a second. If you blink at the wrong time, it might be hours before another message appears. You can see these points of light floating in the distance, and record their position in the sky and their velocity, the relative change in position per second (your puzzle input). The coordinates are all given from your perspective; given enough time, those positions and velocities will move the points into a cohesive message! Rather than wait, you decide to fast-forward the process and calculate what the points will eventually spell. For example, suppose you note the following points: position=< 9, 1> velocity=< 0, 2> position=< 7, 0> velocity=<-1, 0> position=< 3, -2> velocity=<-1, 1> position=< 6, 10> velocity=<-2, -1> position=< 2, -4> velocity=< 2, 2> position=<-6, 10> velocity=< 2, -2> position=< 1, 8> velocity=< 1, -1> position=< 1, 7> velocity=< 1, 0> position=<-3, 11> velocity=< 1, -2> position=< 7, 6> velocity=<-1, -1> position=<-2, 3> velocity=< 1, 0> position=<-4, 3> velocity=< 2, 0> position=<10, -3> velocity=<-1, 1> position=< 5, 11> velocity=< 1, -2> position=< 4, 7> velocity=< 0, -1> position=< 8, -2> velocity=< 0, 1> position=<15, 0> velocity=<-2, 0> position=< 1, 6> velocity=< 1, 0> position=< 8, 9> velocity=< 0, -1> position=< 3, 3> velocity=<-1, 1> position=< 0, 5> velocity=< 0, -1> position=<-2, 2> velocity=< 2, 0> position=< 5, -2> velocity=< 1, 2> position=< 1, 4> velocity=< 2, 1> position=<-2, 7> velocity=< 2, -2> position=< 3, 6> velocity=<-1, -1> position=< 5, 0> velocity=< 1, 0> position=<-6, 0> velocity=< 2, 0> position=< 5, 9> velocity=< 1, -2> position=<14, 7> velocity=<-2, 0> position=<-3, 6> velocity=< 2, -1> Each line represents one point. Positions are given as <X, Y> pairs: X represents how far left (negative) or right (positive) the point appears, while Y represents how far up (negative) or down (positive) the point appears. At 0 seconds, each point has the position given. Each second, each point's velocity is added to its position. So, a point with velocity <1, -2> is moving to the right, but is moving upward twice as quickly. If this point's initial position were <3, 9>, after 3 seconds, its position would become <6, 3>. Over time, the points listed above would move like this: Initially: ........#............. ................#..... .........#.#..#....... ...................... #..........#.#.......# ...............#...... ....#................. ..#.#....#............ .......#.............. ......#............... ...#...#.#...#........ ....#..#..#.........#. .......#.............. ...........#..#....... #...........#......... ...#.......#.......... After 1 second: ...................... ...................... ..........#....#...... ........#.....#....... ..#.........#......#.. ...................... ......#............... ....##.........#...... ......#.#............. .....##.##..#......... ........#.#........... ........#...#.....#... ..#...........#....... ....#.....#.#......... ...................... ...................... After 2 seconds: ...................... ...................... ...................... ..............#....... ....#..#...####..#.... ...................... ........#....#........ ......#.#............. .......#...#.......... .......#..#..#.#...... ....#....#.#.......... .....#...#...##.#..... ........#............. ...................... ...................... ...................... After 3 seconds: ...................... ...................... ...................... ...................... ......#...#..###...... ......#...#...#....... ......#...#...#....... ......#####...#....... ......#...#...#....... ......#...#...#....... ......#...#...#....... ......#...#..###...... ...................... ...................... ...................... ...................... After 4 seconds: ...................... ...................... ...................... ............#......... ........##...#.#...... ......#.....#..#...... .....#..##.##.#....... .......##.#....#...... ...........#....#..... ..............#....... ....#......#...#...... .....#.....##......... ...............#...... ...............#...... ...................... ...................... After 3 seconds, the message appeared briefly: HI. Of course, your message will be much longer and will take many more seconds to appear. What message will eventually appear in the sky? """ from collections import namedtuple from itertools import count import numpy import re def _parse( filepath ): nums = re.compile( R'[+-]?\d+(?:\.\d+)?' ) Light = namedtuple( 'Light', 'p_x p_y v_x v_y' ) with open( filepath, 'r' ) as f: lines = f.readlines( ) lights = [ ] for line in lines: vals = [ int( x ) for x in nums.findall( line ) ] lights.append( Light( vals[ 0 ], vals[ 1 ], vals[ 2 ], vals[ 3 ] ) ) return lights def _simulate( lights ) -> tuple: sky_height = 0 Light_Position = namedtuple( 'Light_Position', 'x, y' ) light_positions = [ ] for time in count( ): new_time = time + 1 new_light_positions = [ Light_Position( x = l.p_x + l.v_x * new_time, y = l.p_y + l.v_y * new_time ) for l in lights ] new_light_positions = sorted( new_light_positions, key = lambda l: l.y ) min_y = new_light_positions[ 0 ].y max_y = new_light_positions[ -1 ].y new_sky_height = max_y - min_y if not sky_height or new_sky_height <= sky_height: sky_height = new_sky_height light_positions = new_light_positions else: break xs, ys = list( zip( *light_positions ) ) xs = sorted( xs ) min_x = xs[ 0 ] max_x = xs[ -1 ] x_range = range( min_x - 1, max_x + 2 ) ys = sorted( ys ) min_y = ys[ 0 ] max_y = ys[ -1 ] y_range = range( min_y - 1, max_y + 2 ) return '\n'.join( ''.join( '#' if ( i, j ) in light_positions else ' ' for i in x_range ) for j in y_range ), time if __name__ == '__main__': lights = _parse( r'day_10_input.txt' ) message, time = _simulate( lights ) print( 'The message {0} will appear after {1} seconds.'.format( message, time ))

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45563edf7177293ae810f1142fc2457d7d0c9f98

170,658

py

Python

TACT.py

CFARS/TACT

1b2bbf1f9d0a45cff232ec447286419faac66b58

[ "BSD-3-Clause" ]

1

2022-03-23T11:50:53.000Z

TACT.py

CFARS/TACT

1b2bbf1f9d0a45cff232ec447286419faac66b58

[ "BSD-3-Clause" ]

4

2021-12-18T04:01:41.000Z

2022-03-10T16:13:18.000Z

TACT.py

CFARS/TACT

1b2bbf1f9d0a45cff232ec447286419faac66b58

[ "BSD-3-Clause" ]

null

""" This is the main script to analyze projects without an NDA in place. Authors: Nikhil Kondabala, Alexandra Arntsen, Andrew Black, Barrett Goudeau, Nigel Swytink-Binnema, Nicolas Jolin Updated: 7/01/2021 Example command line execution: python TACT.py -in /Users/aearntsen/cfarsMASTER/CFARSPhase3/test/518Tower_Windcube_Filtered_subset.csv -config /Users/aearntsen/cfarsMASTER/CFARSPhase3/test/configuration_518Tower_Windcube_Filtered_subset_ex.xlsx -rtd /Volumes/New\ P/DataScience/CFARS/WISE_Phase3_Implementation/RTD_chunk -res /Users/aearntsen/cfarsMASTER/CFARSPhase3/test/out.xlsx --timetestFlag python phase3_implementation_noNDA.py -in /Users/aearntsen/cfarsMaster/cfarsMASTER/CFARSPhase3/test/NRG_canyonCFARS_data.csv -config /Users/aearntsen/cfarsMaster/CFARSPhase3/test/Configuration_template_phase3_NRG_ZX.xlsx -rtd /Volumes/New\ P/DataScience/CFARS/WISE_Phase3_Implementation/RTD_chunk -res /Users/aearntsen/cfarsMaster/CFARSPhase3/test/out.xlsx --timetestFlag """ try: from TACT import logger except ImportError: pass from TACT.computation.adjustments import Adjustments from TACT.computation.methods.GC import perform_G_C_adjustment # from TACT.computation.methods.GLTERRAWC1HZ import perform_G_LTERRA_WC_1HZ_adjustment from TACT.computation.methods.GSa import perform_G_Sa_adjustment from TACT.computation.methods.GSFc import perform_G_SFc_adjustment from TACT.computation.methods.SSLTERRAML import perform_SS_LTERRA_ML_adjustment from TACT.computation.methods.SSLTERRASML import perform_SS_LTERRA_S_ML_adjustment from TACT.computation.methods.SSNN import perform_SS_NN_adjustment from TACT.computation.methods.SSSS import perform_SS_SS_adjustment from TACT.computation.methods.SSWS import perform_SS_WS_adjustment from TACT.computation.methods.SSWSStd import perform_SS_WS_Std_adjustment from TACT.computation.match import perform_match, perform_match_input from TACT.computation.TI import get_count_per_WSbin, get_TI_MBE_Diff_j, get_TI_Diff_r, get_representative_TI, get_TI_bybin, get_TI_byTIrefbin, get_description_stats, Dist_stats, get_representative_TI from TACT.extrapolation.extrapolation import log_of_ratio, perform_TI_extrapolation, extrap_configResult from TACT.extrapolation.calculations import log_of_ratio from TACT.readers.windcube import import_WC_file_VAD, get_10min_spectrum_WC_raw from TACT.readers.config import Config from TACT.readers.data import Data from TACT.writers.files import write_all_resultstofile import pandas as pd import numpy as np import sys from sklearn import linear_model from sklearn.metrics import mean_squared_error, r2_score import os import math import datetime def get_modelRegression(inputdata, column1, column2, fit_intercept=True): ''' :param inputdata: input data (dataframe) :param column1: string, column name for x-variable :param column2: string, column name for y-variable :param columnNameOut: string, column name for predicted value :return: dict with output of regression ''' x = inputdata[column1].values.astype(float) y = inputdata[column2].values.astype(float) mask = ~np.isnan(x) & ~np.isnan(y) x = x[mask] y = y[mask] x = x.reshape(len(x), 1) y = y.reshape(len(y), 1) regr = linear_model.LinearRegression(fit_intercept=fit_intercept) regr.fit(x, y) slope = regr.coef_[0][0] intercept = regr.intercept_[0] predict = regr.predict(x) y = y.astype(np.float) r = np.corrcoef(x, y)[0, 1] r2 = r2_score(y, predict) # coefficient of determination, explained variance mse = mean_squared_error(y, predict, multioutput='raw_values')[0] rmse = np.sqrt(mse) difference = abs((x - y).mean()) resultsDict = {'c': intercept, 'm': slope, 'r': r, 'r2': r2, 'mse': mse, 'rmse': rmse, 'predicted': predict, 'difference': difference} results = [slope, intercept , r2 , difference, mse, rmse] return results def get_all_regressions(inputdata, title=None): # get the ws regression results for all the col required pairs. Title is the name of subset of data being evaluated # Note the order in input to regression function. x is reference. pairList = [['Ref_WS','RSD_WS'],['Ref_WS','Ane2_WS'],['Ref_TI','RSD_TI'],['Ref_TI','Ane2_TI'],['Ref_SD','RSD_SD'],['Ref_SD','Ane2_SD']] lenFlag = False if len(inputdata) < 2: lenFlag = True columns = [title, 'm', 'c', 'rsquared', 'mean difference', 'mse', 'rmse'] results = pd.DataFrame(columns=columns) logger.debug(f"getting regr for {title}") for p in pairList: res_name = str(p[0].split('_')[1] + '_regression_' + p[0].split('_')[0] + '_' + p[1].split('_')[0]) if p[1] in inputdata.columns and lenFlag == False: _adjuster = Adjustments(inputdata) results_regr = [res_name] + _adjuster.get_regression(inputdata[p[0]], inputdata[p[1]]) else: results_regr = [res_name, 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'] _results = pd.DataFrame(columns=columns, data=[results_regr]) results = pd.concat([results, _results], ignore_index=True, axis=0, join='outer') # labels not required labelsExtra = ['RSD_SD_Ht1','RSD_TI_Ht1', 'RSD_WS_Ht1','RSD_SD_Ht2', 'RSD_TI_Ht2', 'RSD_WS_Ht2', 'RSD_SD_Ht3', 'RSD_TI_Ht3', 'RSD_WS_Ht3', 'RSD_WS_Ht4', 'RSD_SD_Ht4', 'RSD_TI_Ht4'] labelsRef = ['Ref_WS', 'Ref_TI', 'Ref_SD'] labelsAne = ['Ane_SD_Ht1', 'Ane_TI_Ht1', 'Ane_WS_Ht1', 'Ane_SD_Ht2', 'Ane_TI_Ht2', 'Ane_WS_Ht2', 'Ane_SD_Ht3', 'Ane_TI_Ht3', 'Ane_WS_Ht3', 'Ane_WS_Ht4', 'Ane_SD_Ht4','Ane_TI_Ht4'] for l in labelsExtra: parts = l.split('_') reg_type = list(set(parts).intersection(['WS', 'TI', 'SD'])) if 'RSD' in l: ht_type = parts[2] ref_type = [s for s in labelsAne if reg_type[0] in s] ref_type = [s for s in ref_type if ht_type in s] res_name = str(reg_type[0] + '_regression_' + parts[0]) if 'Ht' in parts[2]: res_name = res_name + parts[2] + '_' + ref_type[0].split('_')[0] + ref_type[0].split('_')[2] else: res_name = res_name + '_Ref' logger.debug(res_name) if l in inputdata.columns and lenFlag == False: _adjuster = Adjustments(inputdata) res = [res_name] + _adjuster.get_regression(inputdata[ref_type[0]],inputdata[l]) else: res = [res_name, 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'] logger.debug(res) _results = pd.DataFrame(columns=columns, data=[res]) results = pd.concat([results, _results], ignore_index=True, axis=0, join='outer') return results def min_diff(array_orig,array_to_find,tol): #Finds indices in array_orig that correspond to values closest to numbers in array_to_find with tolerance tol #Inputs #array_orig: Original array where you want to find matching values #array_to_find: Array of numbers to find in array_orig #tol: Tolerance to find matching value #Outputs #found_indices: Indices corresponding to matching values. If no values matched with desired tolerance, index will be filled by NaN. import numpy as np found_indices = [] if not np.shape(array_to_find): array_to_find = [array_to_find] for i in array_to_find: min_difference = tol found_index_temp = np.nan for j in range(0,len(array_orig)): diff_temp = abs(i-array_orig[j]) if diff_temp < min_difference: min_difference = diff_temp found_index_temp = j found_indices.append(found_index_temp) return np.array(found_indices) def var_adjustment(vr_n,vr_e,vr_s,vr_w,vr_z,wd,U,height_needed,frequency_vert_beam,el_angle,mode): #Uses Taylor's frozen turbulence hypothesis with data from the vertically #pointing beam to estimate new values of the u and v variance. #Inputs #vr_n, vr_e, vr_s, vr_w, vr_z: Time series of radial velocity from north-, east-, south, west-, and #vertically pointing beams, respectively, at height of interest. #wd: 10-min. Mean wind direction #U: 10-min. Mean horizontal wind speed #height_needed: Measurement height corresponding to velocity data #frequency_vert_beam: Sampling frequency of data from vertically pointing beam #el_angle: Elevation angle of off-vertical beam positions (in degrees, measured from the ground) #mode: Type of variance contamination adjustment to be applied. Options are taylor_ws and taylor_var. #Outputs #var_diff: Estimate of increase in streamwise variance due to variance contamination import numpy as np w_N = np.zeros(len(vr_z)) w_N[:] = np.nan w_E = np.zeros(len(vr_z)) w_E[:] = np.nan w_S = np.zeros(len(vr_z)) w_S[:] = np.nan w_W = np.zeros(len(vr_z)) w_W[:] = np.nan u_bar = np.sin(np.radians(wd - 180))*U v_bar = np.cos(np.radians(wd - 180))*U delta_t_vert_beam = 1./frequency_vert_beam #Calculate the number of time steps needed for eddies to travel from one #side of the scanning circle to the other dist = height_needed/np.tan(np.radians(el_angle)) delta_t_u = dist/u_bar interval_u = round(delta_t_u/delta_t_vert_beam) delta_t_v = dist/v_bar interval_v = round(delta_t_v/delta_t_vert_beam) #Estimate values of w at different sides of the scanning circle by using #Taylor's frozen turbulence hypothesis for i in range(len(vr_z)): try: w_N[i] = vr_z[i-interval_v] w_E[i] = vr_z[i-interval_u] except: w_N[i] = np.nan w_E[i] = np.nan try: w_S[i] = vr_z[i+interval_v] w_W[i] = vr_z[i+interval_u] except: w_S[i] = np.nan w_W[i] = np.nan if "taylor_ws" in mode: #Use the new values of w to estimate the u and v components using the DBS technique #and calculate the variance u_DBS_new = ((vr_e-vr_w) - (w_E-w_W)*np.sin(np.radians(el_angle)))/(2*np.cos(np.radians(el_angle))) v_DBS_new = ((vr_n-vr_s) - (w_N-w_S)*np.sin(np.radians(el_angle)))/(2*np.cos(np.radians(el_angle))) u_var_lidar_new = get_10min_var(u_DBS_new,frequency_vert_beam) v_var_lidar_new = get_10min_var(v_DBS_new,frequency_vert_beam) else: #Calculate change in w across the scanning circle in north-south and east-west directions dw_est1 = w_S - w_N dw_est2 = w_W - w_E vr1_var = get_10min_var(vr_n,1./4) vr2_var = get_10min_var(vr_e,1./4) vr3_var = get_10min_var(vr_s,1./4) vr4_var = get_10min_var(vr_w,1./4) dw_var1 = get_10min_var(dw_est1,1./4) dw_var2 = get_10min_var(dw_est2,1./4) vr1_vr3_var = get_10min_covar(vr_n,vr_s,1./4) vr2_vr4_var = get_10min_covar(vr_e,vr_w,1./4) vr1_dw_var = get_10min_covar(vr_n,dw_est1,1./4) vr3_dw_var = get_10min_covar(vr_s,dw_est1,1./4) vr2_dw_var = get_10min_covar(vr_e,dw_est2,1./4) vr4_dw_var = get_10min_covar(vr_w,dw_est2,1./4) #These equations are adapted from Newman et al. (2016), neglecting terms involving #du or dv, as these terms are expected to be small compared to dw #Reference: Newman, J. F., P. M. Klein, S. Wharton, A. Sathe, T. A. Bonin, #P. B. Chilson, and A. Muschinski, 2016: Evaluation of three lidar scanning #strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993-2013. u_var_lidar_new = (1./(4*np.cos(np.radians(el_angle))**2))*(vr2_var + vr4_var- 2*vr2_vr4_var + 2*vr2_dw_var*np.sin(np.radians(el_angle)) \ - 2*vr4_dw_var*np.sin(np.radians(el_angle)) + dw_var2*np.sin(np.radians(el_angle))**2) v_var_lidar_new = (1./(4*np.cos(np.radians(el_angle))**2))*(vr1_var + vr3_var- 2*vr1_vr3_var + 2*vr1_dw_var*np.sin(np.radians(el_angle)) \ - 2*vr3_dw_var*np.sin(np.radians(el_angle)) + dw_var1*np.sin(np.radians(el_angle))**2) #Rotate the variance into the mean wind direction #Note: The rotation should include a term with the uv covariance, but the #covariance terms are also affected by variance contamination. In Newman #et al. (2016), it was found that the uv covariance is usually close to 0 and #can safely be neglected. #Reference: Newman, J. F., P. M. Klein, S. Wharton, A. Sathe, T. A. Bonin, #P. B. Chilson, and A. Muschinski, 2016: Evaluation of three lidar scanning #strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993-2013. u_rot_var_new = u_var_lidar_new*(np.sin(np.radians(wd)))**2 + v_var_lidar_new*(np.cos(np.radians(wd)))**2 #Calculate the wind speed and variance if w is assumed to be the same on all #sides of the scanning circle u_DBS = (vr_e-vr_w)/(2*np.cos(np.radians(el_angle))) v_DBS = (vr_n-vr_s)/(2*np.cos(np.radians(el_angle))) u_var_DBS = get_10min_var(u_DBS,frequency_vert_beam) v_var_DBS = get_10min_var(v_DBS,frequency_vert_beam) u_rot_var = u_var_DBS*(np.sin(np.radians(wd)))**2 + v_var_DBS*(np.cos(np.radians(wd)))**2 return u_rot_var-u_rot_var_new def acvf(ts): #Calculate autocovariance function for a time series #Inputs #ts: Time series of data #Outputs #ts_adj: Values of autovariance function starting from lag 0 import numpy as np lags = range(0,len(ts)) ts_adj = [] for i in lags: ts_subset_temp = ts[i:len(ts)] ts_subset_temp2 = ts[0:len(ts)-i] ts_adj.append(np.nanmean((ts_subset_temp-np.nanmean(ts_subset_temp))*(ts_subset_temp2-np.nanmean(ts_subset_temp2)))) return ts_adj def inertial_subrange_func(t, b, C): #Inertial subrange fit for autocovariance function #t is lag time, b is the variance at lag 0, and C is a parameter corresponding to eddy dissipation return -C*t**(2./3) + b def lenschow_technique(ts,frequency,mode_ws,option): #Apply different forms of the Lenschow et al. (2000) technique #Reference: Lenschow, D. H., V. Wulfmeyer, and C. Senff, 2000: Measuring second-through fourth-order moments in noisy data. J. Atmos. Oceanic Technol., 17, 1330–1347. #Inputs #ts: Time series of data #frequency: Sampling frequency of data #mode_ws: raw_WC, VAD, or raw_ZephIR #mode_noise: Type of Lenschow noise adjustment to be applied. Options are linear, subrange, and spectrum. #Outputs #new_ts_var: 10-min. variance after noise adjustment has been applied import numpy as np from scipy.optimize import curve_fit ts = fill_nan(ts) #Number of samples in a 10-min period ten_min_count = int(frequency*60*10) var_diff = [] var_orig = [] lags = np.arange(0,ten_min_count)/float(frequency) for i in np.arange(0,len(ts)-ten_min_count+1,ten_min_count): #10-min. window of data ts_window = ts[i:i+ten_min_count] ten_min_index = (i-1)/ten_min_count + 1 var_orig.append(get_10min_var(ts_window,frequency)) if 'linear' in option: #Use values of ACVF from first four non-zero lags to linearly extrpolate #ACVF to lag 0 ts_adj = acvf(ts_window) x_vals = lags[1:4]; y_vals = ts_adj[1:4] p = np.polyfit(x_vals,y_vals,1) var_diff.append(var_orig[ten_min_index]-p[1]) if 'subrange' in option: #Use values of ACVF from first four non-zero lags to produce fit #to inertial subrange function. Value of function at lag 0 is assumed #to be the true variance. ts_adj = acvf(ts_window) x_vals = lags[1:4]; y_vals = ts_adj[1:4] try: popt, pcov = curve_fit(inertial_subrange_func, x_vals, y_vals,\ p0 = [np.mean((ts_window-np.mean(ts_window))**2),0.002]) var_diff.append(var_orig[ten_min_index]-popt[0]) except: var_diff.append(np.nan) if 'spectrum' in option: #Assume spectral power at high frequencies is due only to noise. Average #the spectral power at the highest 20% of frequencies in the time series #and integrate the average power across all frequencies to estimate the #noise floor import numpy.ma as ma if "raw_WC" in mode_ws: [S,fr] = get_10min_spectrum_WC_raw(ts_window,frequency) else: [S,fr] = get_10min_spectrum(ts_window,frequency) x = ma.masked_inside(fr,0.8*fr[-1],fr[-1]) func_temp = [] for j in range(len(fr)): func_temp.append(np.mean(S[x.mask])) noise_floor = np.trapz(func_temp,fr) var_diff.append(noise_floor) var_diff = np.array(var_diff) #Only use var_diff values where the noise variance is positive var_diff[var_diff < 0] = 0 new_ts_var = np.array(var_orig)-var_diff return new_ts_var def get_10min_spectrum(ts,frequency): #Calculate power spectrum for 10-min. period #Inputs #ts: Time series of data #frequency: Sampling frequency of data #Outputs #S_A_fast: Spectral power #frequency_fft: Frequencies correspond to spectral power values import numpy as np N = len(ts) delta_f = float(frequency)/N frequency_fft = np.linspace(0,float(frequency)/2,float(N/2)) F_A_fast = np.fft.fft(ts)/N E_A_fast = 2*abs(F_A_fast[0:N/2]**2) S_A_fast = (E_A_fast)/delta_f return S_A_fast,frequency_fft def get_10min_covar(ts1,ts2,frequency): #Calculate the covariance of two variables #Inputs #ts1: Time series of variable 1 #ts2: Time series of variable 2 #frequency: Sampling frequency #Outputs #ts_covar: 10-min. covariance of variables 1 and 2 import numpy as np import functools #Number of samples in a 10-min period ten_min_count = int(frequency*60*10) ts_covar = [] for i in np.arange(0,len(ts1)-ten_min_count+1,ten_min_count): ts_temp1 = ts1[i:i+ten_min_count] ts_temp2 = ts2[i:i+ten_min_count] mask = [~np.isnan(ts_temp1),~np.isnan(ts_temp2)] total_mask = functools.reduce(np.logical_and, mask) ts_temp1 = ts_temp1[total_mask] ts_temp2 = ts_temp2[total_mask] ts_covar.append(np.nanmean((ts_temp1-np.nanmean(ts_temp1))*(ts_temp2-np.nanmean(ts_temp2)))) return np.array(ts_covar) def fill_nan(A): ''' interpolate to fill nan values ''' #Adapted from code posted on Stack Overflow: http://stackoverflow.com/a/9815522 #1-D linear interpolation to fill missing values #Inputs #A: Time series where NaNs need to be filled #Outputs #B: Time series with NaNs filled from scipy import interpolate import numpy as np inds = np.arange(A.shape[0]) good = np.where(np.isfinite(A)) #Only perform interpolation if more than 75% of the data are valid if float(len(np.array(good).ravel()))/len(A) > 0.75: f = interpolate.interp1d(inds[good], A[good],bounds_error=False,fill_value='extrapolate') B = np.where(np.isfinite(A),A,f(inds)) else: B = A return B def spike_filter(ts,frequency): #Spike filter based on procedure used in Wang et al. (2015) #Reference: Wang, H., R. J. Barthelmie, A. Clifton, and S. C. Pryor, 2015: #Wind measurements from arc scans with Doppler wind lidar, J. Atmos. #Ocean. Tech., 32, 2024–2040. #Inputs #ts: Time series of data #frequency: Sampling frequency of data #Outputs #ts_filtered_interp: Filtered time series with NaNs filled in import numpy as np #Number of samples in a 10-min period ten_min_count = int(frequency*60*10) ts_filtered = np.copy(ts) ts_filtered_interp = np.copy(ts) for i in np.arange(0,len(ts)-ten_min_count+1,ten_min_count): ts_window = ts_filtered[i:i+ten_min_count] #Calculate delta_v, difference between adjacent velocity values delta_v = np.zeros(len(ts_window)-1) for j in range(len(ts_window)-1): delta_v[j] = ts_window[j+1] - ts_window[j] q75, q25 = np.percentile(delta_v, [75 ,25]) IQR= q75 - q25 #If abs(delta_v) at times i and i-1 are larger than twice the interquartile #range (IQR) and the delta_v values at times i and i-1 are of opposite sign, #the velocity at time i is considered a spike and set to NaN. for j in range(1,len(ts_window)-1): if abs(delta_v[j]) > 2*IQR and abs(delta_v[j-1]) > 2*IQR: if np.sign(delta_v[j]) != np.sign(delta_v[j-1]): ts_window[j] = np.nan ts_filtered[i+j] = np.nan #Set entire 10-min. period to NaN if more than 40% of the velocity points #are already NaN. if (float(len(ts_window[np.isnan(ts_window)]))/len(ts_window)) > 0.4: ts_filtered[i:i+ten_min_count] = np.nan #Use a 1-D linear interpolation to fill in missing values ts_filtered_interp[i:i+ten_min_count] = fill_nan(ts_filtered[i:i+ten_min_count]) return ts_filtered_interp def lidar_processing_noise(ts,frequency,mode_ws,mode_noise): #Function to apply noise adjustment to time series. Outputs new variance after #noise adjustment has been applied. #Inputs #ts: Time series of data #frequency: Sampling frequency of data #mode_ws: raw_WC, VAD, or raw_ZephIR #mode_noise: Type of noise adjustment to be applied. Options are spike, lenschow_linear, lenschow_subrange, and lenschow_spectrum. #Outputs #new_ts_var: New 10-min. variance values after noise adjustment has been applied if "spike" in mode_noise: ts_filtered = spike_filter(ts,frequency) new_ts_var = get_10min_var(ts_filtered,frequency) if "lenschow_linear" in mode_noise: new_ts_var = lenschow_technique(ts,frequency,mode_ws,'linear') if "lenschow_subrange" in mode_noise: new_ts_var = lenschow_technique(ts,frequency,mode_ws,'subrange') if "lenschow_spectrum" in mode_noise: new_ts_var = lenschow_technique(ts,frequency,mode_ws,'spectrum') return new_ts_var def Kaimal_spectrum_func(X, L): #Given values of frequency (fr), mean horizontal wind speed (U), streamwise #variance (u_var), and length scale (L), calculate idealized Kaimal spectrum #This uses the form given by Eq. 2.24 in Burton et al. (2001) #Reference: Burton, T., D. Sharpe, N. Jenkins, N., and E. Bossanyi, 2001: #Wind Energy Handbook, John Wiley & Sons, Ltd., 742 pp. fr,U,u_var = X return u_var*fr*((4*(L/U)/((1+6*(fr*L/U))**(5./3)))) def Kaimal_spectrum_func2(pars, x, data=None): #Kaimal spectrum function for fitting. Trying to minimize the difference #between the actual spectrum (data) and the modeled spectrum (model) vals = pars.valuesdict() L = vals['L'] U = vals['U'] u_var = vals['u_var'] model = u_var*x*((4*(L/U)/((1+6*(x*L/U))**(5./3)))) if data is None: return model return model-data def spectral_adjustment(u_rot,frequency,mode_ws,option): #Estimates loss of variance due to volume averaging by extrapolating spectrum #out to higher frequencies and integrating spectrum over higher frequencies #Inputs #u_rot: Time series of streamwise wind speed #frequency: Sampling frequency of time series #mode_ws: raw_WC, VAD, or raw_ZephIR #option: Type of volume averaging adjustment to be applied. Options are spectral_adjustment_fit and acf. #Outputs #var_diff: Estimate of loss of streamwise variance due to volume averaging import numpy as np import scipy.signal from lmfit import minimize,Parameters ten_min_count = frequency*60*10 var_diff = [] for i in np.arange(0,len(u_rot)-ten_min_count+1,ten_min_count): u_temp = u_rot[i:i+ten_min_count] U = np.mean(u_temp) u_var = get_10min_var(u_temp,frequency) #Detrend time series before estimating parameters for modeled spectrum u_temp = scipy.signal.detrend(u_temp) if "raw_WC" in mode_ws: [S,fr] = get_10min_spectrum_WC_raw(u_temp,frequency) else: [S,fr] = get_10min_spectrum(u_temp,frequency) if "spectral_adjustment_fit" in option: #Find value of length scale that produces best fit to idealized #Kaimal spectrum fit_params = Parameters() fit_params.add('L', value=500,min=0,max=1500) fit_params.add('U', value=U,vary=False) fit_params.add('u_var', value=u_var,vary=False) out = minimize(Kaimal_spectrum_func2, fit_params, args=(fr,), kws={'data':fr*S}) L = out.params['L'].value else: #Otherwise, use ACF to estimate integral length scale and use this #value for the length scale in the Kaimal modeled spectrum lags = np.arange(0,ten_min_count)/float(frequency) u_adj = acvf(u_temp) u_acf = u_adj/u_adj[0] indices = np.arange(0,len(u_acf)) x = indices[np.array(u_acf)<=0] #ACF is integrated to the first zero crossing to esimate the integral #time scale and multipled by the mean horizontal wind speed to estimate #the integral length scale L = np.trapz(u_acf[:x[0]],lags[:x[0]])*U fr2 = np.linspace(0,float(10)/2,float(6000/2)) #Calculate Kaimal spectrum from 0 to 5 Hz S_model = Kaimal_spectrum_func((fr2,U,u_var),L) #Integrate spectrum for frequency values higher than those in the original #spectrum from the lidar var_diff.append(np.trapz((S_model[fr2 > fr[-1]]/fr2[fr2 > fr[-1]]),fr2[fr2 > fr[-1]])) return np.array(var_diff) def var_adjustment(vr_n,vr_e,vr_s,vr_w,vr_z,wd,U,height_needed,frequency_vert_beam,el_angle,mode): #Uses Taylor's frozen turbulence hypothesis with data from the vertically #pointing beam to estimate new values of the u and v variance. #Inputs #vr_n, vr_e, vr_s, vr_w, vr_z: Time series of radial velocity from north-, east-, south, west-, and #vertically pointing beams, respectively, at height of interest. #wd: 10-min. Mean wind direction #U: 10-min. Mean horizontal wind speed #height_needed: Measurement height corresponding to velocity data #frequency_vert_beam: Sampling frequency of data from vertically pointing beam #el_angle: Elevation angle of off-vertical beam positions (in degrees, measured from the ground) #mode: Type of variance contamination adjustment to be applied. Options are taylor_ws and taylor_var. #Outputs #var_diff: Estimate of increase in streamwise variance due to variance contamination import numpy as np w_N = np.zeros(len(vr_z)) w_N[:] = np.nan w_E = np.zeros(len(vr_z)) w_E[:] = np.nan w_S = np.zeros(len(vr_z)) w_S[:] = np.nan w_W = np.zeros(len(vr_z)) w_W[:] = np.nan u_bar = np.sin(np.radians(wd - 180))*U v_bar = np.cos(np.radians(wd - 180))*U delta_t_vert_beam = 1./frequency_vert_beam #Calculate the number of time steps needed for eddies to travel from one #side of the scanning circle to the other dist = height_needed/np.tan(np.radians(el_angle)) delta_t_u = dist/u_bar interval_u = np.round(delta_t_u/delta_t_vert_beam) delta_t_v = dist/v_bar interval_v = np.round(delta_t_v/delta_t_vert_beam) #Estimate values of w at different sides of the scanning circle by using #Taylor's frozen turbulence hypothesis for i in range(len(vr_z)): try: w_N[i] = vr_z[i-interval_v] w_E[i] = vr_z[i-interval_u] except: w_N[i] = np.nan w_E[i] = np.nan try: w_S[i] = vr_z[i+interval_v] w_W[i] = vr_z[i+interval_u] except: w_S[i] = np.nan w_W[i] = np.nan if "taylor_ws" in mode: #Use the new values of w to estimate the u and v components using the DBS technique #and calculate the variance u_DBS_new = ((vr_e-vr_w) - (w_E-w_W)*np.sin(np.radians(el_angle)))/(2*np.cos(np.radians(el_angle))) v_DBS_new = ((vr_n-vr_s) - (w_N-w_S)*np.sin(np.radians(el_angle)))/(2*np.cos(np.radians(el_angle))) u_var_lidar_new = get_10min_var(u_DBS_new,frequency_vert_beam) v_var_lidar_new = get_10min_var(v_DBS_new,frequency_vert_beam) else: #Calculate change in w across the scanning circle in north-south and east-west directions dw_est1 = w_S - w_N dw_est2 = w_W - w_E vr1_var = get_10min_var(vr_n,1./4) vr2_var = get_10min_var(vr_e,1./4) vr3_var = get_10min_var(vr_s,1./4) vr4_var = get_10min_var(vr_w,1./4) dw_var1 = get_10min_var(dw_est1,1./4) dw_var2 = get_10min_var(dw_est2,1./4) vr1_vr3_var = get_10min_covar(vr_n,vr_s,1./4) vr2_vr4_var = get_10min_covar(vr_e,vr_w,1./4) vr1_dw_var = get_10min_covar(vr_n,dw_est1,1./4) vr3_dw_var = get_10min_covar(vr_s,dw_est1,1./4) vr2_dw_var = get_10min_covar(vr_e,dw_est2,1./4) vr4_dw_var = get_10min_covar(vr_w,dw_est2,1./4) #These equations are adapted from Newman et al. (2016), neglecting terms involving #du or dv, as these terms are expected to be small compared to dw #Reference: Newman, J. F., P. M. Klein, S. Wharton, A. Sathe, T. A. Bonin, #P. B. Chilson, and A. Muschinski, 2016: Evaluation of three lidar scanning #strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993-2013. u_var_lidar_new = (1./(4*np.cos(np.radians(el_angle))**2))*(vr2_var + vr4_var- 2*vr2_vr4_var + 2*vr2_dw_var*np.sin(np.radians(el_angle)) \ - 2*vr4_dw_var*np.sin(np.radians(el_angle)) + dw_var2*np.sin(np.radians(el_angle))**2) v_var_lidar_new = (1./(4*np.cos(np.radians(el_angle))**2))*(vr1_var + vr3_var- 2*vr1_vr3_var + 2*vr1_dw_var*np.sin(np.radians(el_angle)) \ - 2*vr3_dw_var*np.sin(np.radians(el_angle)) + dw_var1*np.sin(np.radians(el_angle))**2) #Rotate the variance into the mean wind direction #Note: The rotation should include a term with the uv covariance, but the #covariance terms are also affected by variance contamination. In Newman #et al. (2016), it was found that the uv covariance is usually close to 0 and #can safely be neglected. #Reference: Newman, J. F., P. M. Klein, S. Wharton, A. Sathe, T. A. Bonin, #P. B. Chilson, and A. Muschinski, 2016: Evaluation of three lidar scanning #strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993-2013. u_rot_var_new = u_var_lidar_new*(np.sin(np.radians(wd)))**2 + v_var_lidar_new*(np.cos(np.radians(wd)))**2 #Calculate the wind speed and variance if w is assumed to be the same on all #sides of the scanning circle u_DBS = (vr_e-vr_w)/(2*np.cos(np.radians(el_angle))) v_DBS = (vr_n-vr_s)/(2*np.cos(np.radians(el_angle))) u_var_DBS = get_10min_var(u_DBS,frequency_vert_beam) v_var_DBS = get_10min_var(v_DBS,frequency_vert_beam) u_rot_var = u_var_DBS*(np.sin(np.radians(wd)))**2 + v_var_DBS*(np.cos(np.radians(wd)))**2 return u_rot_var-u_rot_var_new def lidar_processing_vol_averaging(u,frequency,mode_ws,mode_vol): #Function to estimate variance lost due to volume/temporal averaging #Inputs #u: Time series of streamwise wind speed #frequency: Sampling frequency of time series #mode_ws: raw_WC, VAD, or raw_ZephIR #mode_vol: Type of volume averaging adjustment to be applied. Options are spectral_adjustment_fit and acf. #Outputs #var_diff: Estimate of loss of streamwise variance due to volume averaging var_diff = spectral_adjustment(u,frequency,mode_ws,mode_vol) return var_diff def lidar_processing_var_contam(vr_n,vr_e,vr_s,vr_w,vr_z,wd,U,height_needed,frequency_vert_beam,el_angle,mode): #Function to estimate additional variance that results from variance contamination #Inputs #vr_n, vr_e, vr_s, vr_w, vr_z: Time series of radial velocity from north-, east-, south, west-, and #vertically pointing beams, respectively, at height of interest. #wd: 10-min. Mean wind direction #U: 10-min. Mean horizontal wind speed #height_needed: Measurement height corresponding to velocity data #frequency_vert_beam: Sampling frequency of data from vertically pointing beam #el_angle: Elevation angle of off-vertical beam positions (in degrees, measured from the ground) #mode: Type of variance contamination adjustment to be applied. Options are taylor_ws and taylor_var. #Outputs #var_diff: Estimate of increase in streamwise variance due to variance contamination var_diff = var_adjustment(vr_n,vr_e,vr_s,vr_w,vr_z,wd,U,height_needed,frequency_vert_beam,el_angle,mode) #Set negative values of var_diff to 0 as they would increase the corrected variance #Note: This is not the best procedure and should probably be fixed at some point. #It's possible that at times, the change in w across the scanning circle could #decrease, rather than increase, the u and v variance. try: if var_diff < 0: var_diff = 0. return var_diff except: var_diff = 0. def VAD_func(az, x1, x2, x3): import numpy as np return np.array(x3+x1*np.cos(np.radians(az)-x2)) def get_10min_var(ts,frequency): #Calculates variance for each 10-min. period #Inputs #ts: Time series of data #frequency: Sampling frequency of data #Outputs #ts_var: 10-min. variance values from time series import numpy as np #Number of samples in a 10-min period ten_min_count = int(frequency*60*10) ts_var = [] for i in np.arange(0,len(ts)-ten_min_count+1,ten_min_count): ts_temp = ts[i:i+ten_min_count] ts_var.append(np.nanmean((ts_temp-np.nanmean(ts_temp))**2)) return np.array(ts_var) def get_10min_spectrum(ts,frequency): #Calculate power spectrum for 10-min. period #Inputs #ts: Time series of data #frequency: Sampling frequency of data #Outputs #S_A_fast: Spectral power #frequency_fft: Frequencies correspond to spectral power values import numpy as np N = len(ts) delta_f = float(frequency)/N frequency_fft = np.linspace(0,float(frequency)/2,float(N/2)) F_A_fast = np.fft.fft(ts)/N E_A_fast = 2*abs(F_A_fast[0:N/2]**2) S_A_fast = (E_A_fast)/delta_f return S_A_fast,frequency_fft def rotate_ws(u,v,w,frequency): #Performs coordinate rotation according to Eqs. 22-29 in Wilczak et al. (2001) #Reference: Wilczak, J. M., S. P. Oncley, and S. A. Stage, 2001: Sonic anemometer tilt adjustment algorithms. #Bound.-Layer Meteor., 99, 127–150. #Inputs #u, v, w: Time series of east-west, north-south, and vertical wind speed components, respectively #frequency: Sampling frequency of velocity #Outputs #u_rot, v_rot, w_rot: Rotated u, v, and w wind speed, with u rotated into the 10-min. mean wind direction and #the 10-min. mean of v and w forced to 0 import numpy as np #Number of samples in a 10-min period ten_min_count = int(frequency*60*10) u_rot = [] v_rot = [] w_rot = [] #Perform coordinate rotation. First rotation rotates u into the mean wind direction and forces the mean v to 0. #Second rotation forces the mean w to 0. for i in np.arange(0,len(u)-ten_min_count+1,ten_min_count): u_temp = u[i:i+ten_min_count] v_temp = v[i:i+ten_min_count] w_temp = w[i:i+ten_min_count] phi_temp = np.arctan2(np.nanmean(v_temp),np.nanmean(u_temp)) u1_temp = u_temp*np.cos(phi_temp) + v_temp*np.sin(phi_temp) v1_temp = -u_temp*np.sin(phi_temp) + v_temp*np.cos(phi_temp) w1_temp = w_temp; phi_temp2 = np.arctan2(np.nanmean(w1_temp),np.nanmean(u1_temp)) u_rot.append(u1_temp*np.cos(phi_temp2) + w1_temp*np.sin(phi_temp2)) v_rot.append(v1_temp) w_rot.append(-u1_temp*np.sin(phi_temp2) + w1_temp*np.cos(phi_temp2)) return np.array(u_rot).ravel(),np.array(v_rot).ravel(),np.array(w_rot).ravel() def get_10min_mean_ws_wd(u,v,time,frequency): #Calculates the 10-min. scalar average wind speed and wind direction at all measurement heights #Inputs #u: East-west velocity time series #v: North-south velocity time series #time: Timestamps in datetime format #frequency: Sampling frequency of velocity data #Outputs #U: 10-min. mean horizontal wind speeds #wd: 10-min. mean wind direction #time_datenum_10min: Timestamp corresponding to the start of each 10-min. averaging period import numpy as np ten_min_count = int(frequency*60*10) U = [] wd = [] time_datenum_10min = [] for i in np.arange(0,len(u)-ten_min_count+1,ten_min_count): U_height = [] wd_height = [] #10-min. window of data if len(np.shape(u)) > 1: u_temp = u[i:i+ten_min_count,:] v_temp = v[i:i+ten_min_count,:] else: u_temp = u[i:i+ten_min_count] v_temp = v[i:i+ten_min_count] for j in range(np.shape(u_temp)[1]): U_height.append(np.nanmean((u_temp[:,j]**2 + v_temp[:,j]**2)**0.5,axis=0)); u_bar = np.nanmean(u_temp[:,j]) v_bar = np.nanmean(v_temp[:,j]) wd_height.append((180./np.pi)*(np.arctan2(u_bar,v_bar) + np.pi)) U.append(U_height) wd.append(wd_height) time_datenum_10min.append(time[i]) return np.array(U),np.array(wd),time_datenum_10min def get_10min_shear_parameter(U,heights,height_needed): import functools #Calculates the shear parameter for every 10-min. period of data by fitting power law equation to #10-min. mean wind speeds #Inputs #U: 10-min. mean horizontal wind speed at all measurement heights #heights: Measurement heights #height_needed: Height where TI is being extracted - values used to calculate shear parameter #should be centered around this height #Outputs #p: 10-min. values of shear parameter import warnings p = [] #Set heights for calculation of shear parameter and find corresponding indices zprofile = np.arange(0.5*height_needed,1.5*height_needed + 10,10) height_indices = np.unique(min_diff(heights,zprofile,5)) height_indices = height_indices[~np.isnan(height_indices)] #Arrays of height and mean wind speed to use for calculation heights_temp = np.array([heights[int(i)] for i in height_indices]) U_temp = np.array([U[:,int(i)] for i in height_indices]) mask = [~np.isnan(U_temp)] mask = functools.reduce(np.logical_and, mask) with warnings.catch_warnings(): warnings.filterwarnings('error') #For each set of 10-min. U values, use linear fit to determine value of shear parameter for i in range(0,len(U)): try: try: p_temp = np.polyfit(np.log(heights_temp[mask[:,i]]),np.log(U_temp[mask[:,i],i]),1) p.append(p_temp[0]) except np.RankWarning: p.append(np.nan) except: p.append(np.nan) return np.array(p) def interp_ts(ts,time_datenum,interval): #Interpolates time series ts with timestamps time_datenum to a grid with constant temporal spacing of "interval" #Inputs #ts: Time series for interpolation #time_datenum: Original timestamps for time series in datetime format #interval: Temporal interval to use for interpolation #Outputs #ts_interp: Interpolated time series #time_interp: Timestamps of interpolated time series in datetime format import numpy as np from datetime import datetime import calendar as cal #Convert timestamps to unix time (seconds after 1970 01-01) as it's easier to perform the interpolation unix_time = [] for i in range(0,len(time_datenum)): unix_time.append(cal.timegm(datetime.timetuple(time_datenum[i])) + (time_datenum[i].microsecond/1e6)) unix_time = np.array(unix_time) #Select the start and end time for the interpolation #The starting minute value of the interpolation should be the next multiple of 10 if time_datenum[0].minute%10 == 0: start_minute = str((time_datenum[0].minute//10)*10) else: start_minute = str((time_datenum[0].minute//10 + 1)*10) start_hour = str(time_datenum[0].hour) if int(start_minute) == 60: start_minute = '00' start_hour = str(time_datenum[0].hour + 1) end_hour = str(time_datenum[-1].hour) #The ending minute value of the interpolation should end with a 9 if (time_datenum[-1].minute-9)%10 == 0: end_minute = str((time_datenum[-1].minute//10*10) + 9) else: end_minute = str((time_datenum[-1].minute//10)*10 - 1) if int(end_minute) < 0: end_minute = '59' end_hour = str(time_datenum[-1].hour - 1) #Convert start and end times into unix time and get interpolation times in unix time timestamp_start = str(time_datenum[0].year) + "/" + str(time_datenum[0].month) + "/" + str(time_datenum[0].day) + \ " " + start_hour + ":" + start_minute + ":00" time_datenum_start = datetime.strptime(timestamp_start,"%Y/%m/%d %H:%M:%S") unix_time_start = cal.timegm(datetime.timetuple(time_datenum_start)) timestamp_end = str(time_datenum[-1].year) + "/" + str(time_datenum[-1].month) + "/" + str(time_datenum[-1].day) + \ " " + end_hour + ":" + end_minute + ":59" time_datenum_end = datetime.strptime(timestamp_end,"%Y/%m/%d %H:%M:%S") unix_time_end = cal.timegm(datetime.timetuple(time_datenum_end)) time_interp_unix = np.arange(unix_time_start,unix_time_end+1,interval) #Interpolate time series ts_interp = [] #If more than 75% of the data are valid, perform interpolation using only non-NaN data. (Every fifth point of the #u and v data will be NaNs because of the vertically pointing beam.) if float(len(ts[~np.isnan(ts)])/float(len(ts))) > 0.75: ts_temp = ts[~np.isnan(ts)] time_temp = unix_time[~np.isnan(ts)] else: ts_temp = ts time_temp = unix_time ts_interp = np.interp(time_interp_unix,time_temp,ts_temp) #If several points in a row have the same value, set these points to NaN. This can occur when the interpolation is #performed on a dataset with one valid value surrounded by several NaNs. for i in range(2,len(ts_interp)-2): if ts_interp[i-2] == ts_interp[i] and ts_interp[i+2] == ts_interp[i]: ts_interp[i-2:i+2] = np.nan time_interp = [datetime.utcfromtimestamp(int(i) + round(i-int(i),10)) for i in time_interp_unix] return np.transpose(ts_interp),time_interp def calculate_stability_alpha(inputdata, config_file, RSD_alphaFlag, Ht_1_rsd, Ht_2_rsd): ''' from Wharton and Lundquist 2012 stability class from shear exponent categories: [1] strongly stable -------- alpha > 0.3 [2] stable -------- 0.2 < alpha < 0.3 [3] near-neutral -------- 0.1 < TKE < 0.2 [4] convective -------- 0.0 < TKE < 0.1 [5] strongly convective -------- alpha < 0.0 ''' regimeBreakdown_ane = pd.DataFrame() #check for 2 anemometer heights (use furthest apart) for cup alpha calculation configHtData = pd.read_excel(config_file, usecols=[3, 4], nrows=17).iloc[[3,12,13,14,15]] primaryHeight = configHtData['Selection'].to_list()[0] all_heights, ane_heights, RSD_heights, ane_cols, RSD_cols = config.check_for_additional_heights(primaryHeight) if len(list(ane_heights))> 1: all_keys = list(all_heights.values()) max_key = list(all_heights.keys())[all_keys.index(max(all_heights.values()))] min_key = list(all_heights.keys())[all_keys.index(min(all_heights.values()))] if max_key == 'primary': max_cols = [s for s in inputdata.columns.to_list() if 'Ref' in s and 'WS' in s] else: subname = str('Ht' + str(max_key)) max_cols = [s for s in inputdata.columns.to_list() if subname in s and 'Ane' in s and 'WS' in s] if min_key == 'primary': min_cols = [s for s in inputdata.columns.to_list() if 'Ref' in s and 'WS' in s] else: subname = str('Ht' + str(min_key)) min_cols = [s for s in inputdata.columns.to_list() if subname in s and 'Ane' in s and 'WS' in s] # Calculate shear exponent tmp = pd.DataFrame(None) baseName = str(max_cols + min_cols) tmp[str(baseName + '_y')] = [val for sublist in log_of_ratio(inputdata[max_cols].values.astype(float), inputdata[min_cols].values.astype(float)) for val in sublist] tmp[str(baseName + '_alpha')] = tmp[str(baseName + '_y')] / (log_of_ratio(max(all_heights.values()), min(all_heights.values()))) stabilityMetric_ane = tmp[str(baseName + '_alpha')] Ht_2_ane = max(all_heights.values()) Ht_1_ane = min(all_heights.values()) tmp[str(baseName + 'stabilityClass')] = tmp[str(baseName + '_alpha')] tmp.loc[(tmp[str(baseName + '_alpha')] <= 0.4), str(baseName + 'stabilityClass')] = 1 tmp.loc[(tmp[str(baseName + '_alpha')] > 0.4) & (tmp[str(baseName + '_alpha')] <= 0.7), str(baseName + 'stabilityClass')] = 2 tmp.loc[(tmp[str(baseName + '_alpha')] > 0.7) & (tmp[str(baseName + '_alpha')] <= 1.0), str(baseName + 'stabilityClass')] = 3 tmp.loc[(tmp[str(baseName + '_alpha')] > 1.0) & (tmp[str(baseName + '_alpha')] <= 1.4), str(baseName + 'stabilityClass')] = 4 tmp.loc[(tmp[str(baseName + '_alpha')] > 1.4), str(baseName + 'stabilityClass')] = 5 # get count and percent of data in each class numNans = tmp[str(baseName) + '_alpha'].isnull().sum() totalCount = len(inputdata) - numNans name_class = str('stability_shear' + '_class') name_stabilityClass = str(baseName + 'stabilityClass') regimeBreakdown_ane[name_class] = ['1 (strongly stable)', '2 (stable)', '3 (near-neutral)', '4 (convective)', '5 (strongly convective)'] name_count = str('stability_shear_obs' + '_count') regimeBreakdown_ane[name_count] = [len(tmp[(tmp[name_stabilityClass] == 1)]), len(tmp[(tmp[name_stabilityClass] == 2)]), len(tmp[(tmp[name_stabilityClass] == 3)]), len(tmp[(tmp[name_stabilityClass] == 4)]), len(tmp[(tmp[name_stabilityClass] == 5)])] name_percent = str('stability_shear_obs' + '_percent') regimeBreakdown_ane[name_percent] = [len(tmp[(tmp[name_stabilityClass] == 1)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 2)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 3)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 4)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 5)])/totalCount] stabilityClass_ane = tmp[name_stabilityClass] cup_alphaFlag = True else: stabilityClass_ane = None stabilityMetric_ane = None regimeBreakdown_ane = None Ht_1_ane = None Ht_2_ane = None cup_alphaFlag = False # If possible, perform stability calculation with RSD data if RSD_alphaFlag: regimeBreakdown_rsd = pd.DataFrame() tmp = pd.DataFrame(None) baseName = str('WS_' + str(Ht_1_rsd) + '_' + 'WS_' + str(Ht_2_rsd)) max_col = 'RSD_alpha_lowHeight' min_col = 'RSD_alpha_highHeight' tmp[str(baseName + '_y')] = log_of_ratio(inputdata[max_col].values.astype(float),inputdata[min_col].values.astype(float)) tmp[str(baseName + '_alpha')] = tmp[str(baseName + '_y')] / (log_of_ratio(Ht_2_rsd, Ht_1_rsd)) stabilityMetric_rsd = tmp[str(baseName + '_alpha')] tmp[str(baseName + 'stabilityClass')] = tmp[str(baseName + '_alpha')] tmp.loc[(tmp[str(baseName + '_alpha')] <= 0.4), str(baseName + 'stabilityClass')] = 1 tmp.loc[(tmp[str(baseName + '_alpha')] > 0.4) & (tmp[str(baseName + '_alpha')] <= 0.7), str(baseName + 'stabilityClass')] = 2 tmp.loc[(tmp[str(baseName + '_alpha')] > 0.7) & (tmp[str(baseName + '_alpha')] <= 1.0), str(baseName + 'stabilityClass')] = 3 tmp.loc[(tmp[str(baseName + '_alpha')] > 1.0) & (tmp[str(baseName + '_alpha')] <= 1.4), str(baseName + 'stabilityClass')] = 4 tmp.loc[(tmp[str(baseName + '_alpha')] > 1.4), str(baseName + 'stabilityClass')] = 5 # get count and percent of data in each class numNans = tmp[str(baseName) + '_alpha'].isnull().sum() totalCount = len(inputdata) - numNans name_stabilityClass = str(baseName + 'stabilityClass') regimeBreakdown_rsd[name_class] = ['1 (strongly stable)', '2 (stable)', '3 (near-neutral)', '4 (convective)', '5 (strongly convective)'] name_count = str('stability_shear_obs' + '_count') regimeBreakdown_rsd[name_count] = [len(tmp[(tmp[name_stabilityClass] == 1)]), len(tmp[(tmp[name_stabilityClass] == 2)]), len(tmp[(tmp[name_stabilityClass] == 3)]), len(tmp[(tmp[name_stabilityClass] == 4)]), len(tmp[(tmp[name_stabilityClass] == 5)])] name_percent = str('stability_shear_obs' + '_percent') regimeBreakdown_rsd[name_percent] = [len(tmp[(tmp[name_stabilityClass] == 1)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 2)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 3)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 4)])/totalCount, len(tmp[(tmp[name_stabilityClass] == 5)])/totalCount] stabilityClass_rsd = tmp[name_stabilityClass] else: stabilityClass_rsd = None stabilityMetric_rsd = None regimeBreakdown_rsd = None Ht_1_rsd = None Ht_2_rsd = None return cup_alphaFlag,stabilityClass_ane, stabilityMetric_ane, regimeBreakdown_ane, Ht_1_ane, Ht_2_ane, stabilityClass_rsd, stabilityMetric_rsd, regimeBreakdown_rsd def calculate_stability_TKE(inputdata): ''' from Wharton and Lundquist 2012 stability class from TKE categories: [1] strongly stable -------- TKE < 0.4 m^(2)/s^(-2)) [2] stable -------- 0.4 < TKE < 0.7 m^(2)/s^(-2)) [3] near-neutral -------- 0.7 < TKE < 1.0 m^(2)/s^(-2)) [4] convective -------- 1.0 < TKE < 1.4 m^(2)/s^(-2)) [5] strongly convective -------- TKE > 1.4 m^(2)/s^(-2)) ''' regimeBreakdown = pd.DataFrame() # check to see if instrument type allows the calculation if RSDtype['Selection']=='Triton': print ('Triton TKE calc') elif 'ZX' in RSDtype['Selection']: # look for pre-calculated TKE column TKE_cols = [s for s in inputdata.columns.to_list() if 'TKE' in s or 'tke' in s] if len(TKE_cols) < 1: print ('!!!!!!!!!!!!!!!!!!!!!!!! Warning: Input data does not include calculated TKE. Exiting tool. Either add TKE to input data or contact aea@nrgsystems.com for assistence !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') sys.exit() else: for t in TKE_cols: name_stabilityClass = str(t + '_class') inputdata[name_stabilityClass] = inputdata[t] inputdata.loc[(inputdata[t] <= 0.4), name_stabilityClass] = 1 inputdata.loc[(inputdata[t] > 0.4) & (inputdata[t] <= 0.7), name_stabilityClass] = 2 inputdata.loc[(inputdata[t] > 0.7) & (inputdata[t] <= 1.0), name_stabilityClass] = 3 inputdata.loc[(inputdata[t] > 1.0) & (inputdata[t] <= 1.4), name_stabilityClass] = 4 inputdata.loc[(inputdata[t] > 1.4), name_stabilityClass] = 5 # get count and percent of data in each class numNans = inputdata[t].isnull().sum() totalCount = len(inputdata) - numNans regimeBreakdown[name_stabilityClass] = ['1 (strongly stable)', '2 (stable)', '3 (near-neutral)', '4 (convective)', '5 (strongly convective)'] name_count = str(name_stabilityClass.split('_class')[0] + '_count') regimeBreakdown[name_count] = [len(inputdata[(inputdata[name_stabilityClass] == 1)]), len(inputdata[(inputdata[name_stabilityClass] == 2)]), len(inputdata[(inputdata[name_stabilityClass] == 3)]), len(inputdata[(inputdata[name_stabilityClass] == 4)]), len(inputdata[(inputdata[name_stabilityClass] == 5)])] name_percent = str(name_stabilityClass.split('_class')[0] + '_percent') regimeBreakdown[name_percent] = [len(inputdata[(inputdata[name_stabilityClass] == 1)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 2)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 3)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 4)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 5)])/totalCount] elif 'WindCube' in RSDtype['Selection']: # convert to radians dir_cols = [s for s in inputdata.columns.to_list() if 'Direction' in s] if len(dir_cols)==0: stabilityClass = None stabilityMetric = None regimeBreakdown = None print ('Warning: Could not find direction columns in configuration key. TKE derived stability, check data.') sys.exit() else: for c in dir_cols: name_radians = str(c + '_radians') inputdata[name_radians] = inputdata[c] * (math.pi/180) if name_radians.split('_')[2] == 'radians': name_u_std = str(name_radians.split('_')[0] + '_u_std') name_v_std = str(name_radians.split('_')[0] + '_v_std') else: name_u_std = str(name_radians.split('_')[0] + '_' + name_radians.split('_')[2] + '_u_std') name_v_std = str(name_radians.split('_')[0] + '_' + name_radians.split('_')[2] + '_v_std') name_dispersion = None name_std = c.replace('Direction','SD') inputdata[name_u_std] = inputdata[name_std] * np.cos(inputdata[name_radians]) inputdata[name_v_std] = inputdata[name_std] * np.sin(inputdata[name_radians]) name_tke = str(name_u_std.split('_u')[0] + '_LidarTKE') inputdata[name_tke] = 0.5 * (inputdata[name_u_std]**2 + inputdata[name_v_std]**2 + inputdata[name_std]**2) name_stabilityClass = str(name_tke + '_class') inputdata[name_stabilityClass] = inputdata[name_tke] inputdata.loc[(inputdata[name_tke] <= 0.4), name_stabilityClass] = 1 inputdata.loc[(inputdata[name_tke] > 0.4) & (inputdata[name_tke] <= 0.7), name_stabilityClass] = 2 inputdata.loc[(inputdata[name_tke] > 0.7) & (inputdata[name_tke] <= 1.0), name_stabilityClass] = 3 inputdata.loc[(inputdata[name_tke] > 1.0) & (inputdata[name_tke] <= 1.4), name_stabilityClass] = 4 inputdata.loc[(inputdata[name_tke] > 1.4), name_stabilityClass] = 5 # get count and percent of data in each class numNans = inputdata[name_tke].isnull().sum() totalCount = len(inputdata) - numNans name_class = str(name_u_std.split('_u')[0] + '_class') regimeBreakdown[name_class] = ['1 (strongly stable)', '2 (stable)', '3 (near-neutral)', '4 (convective)', '5 (strongly convective)'] name_count = str(name_u_std.split('_u')[0] + '_count') regimeBreakdown[name_count] = [len(inputdata[(inputdata[name_stabilityClass] == 1)]), len(inputdata[(inputdata[name_stabilityClass] == 2)]), len(inputdata[(inputdata[name_stabilityClass] == 3)]), len(inputdata[(inputdata[name_stabilityClass] == 4)]), len(inputdata[(inputdata[name_stabilityClass] == 5)])] name_percent = str(name_u_std.split('_u')[0] + '_percent') regimeBreakdown[name_percent] = [len(inputdata[(inputdata[name_stabilityClass] == 1)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 2)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 3)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 4)])/totalCount, len(inputdata[(inputdata[name_stabilityClass] == 5)])/totalCount] else: print ('Warning: Due to senor type, TKE is not being calculated.') stabilityClass = None stabilityMetric = None regimeBreakdown = None classCols = [s for s in inputdata.columns.to_list() if '_class' in s] stabilityClass = inputdata[classCols] tkeCols = [s for s in inputdata.columns.to_list() if '_LidarTKE' in s or 'TKE' in s or 'tke' in s] tkeCols = [s for s in tkeCols if '_class' not in s] stabilityMetric = inputdata[tkeCols] return stabilityClass, stabilityMetric, regimeBreakdown def initialize_resultsLists(appendString): resultsLists = {} resultsLists[str('TI_MBEList' + '_' + appendString)] = [] resultsLists[str('TI_DiffList' + '_' + appendString)] = [] resultsLists[str('TI_DiffRefBinsList' + '_' + appendString)] = [] resultsLists[str('TI_RMSEList' + '_' + appendString)] = [] resultsLists[str('RepTI_MBEList' + '_' + appendString)] = [] resultsLists[str('RepTI_DiffList' + '_' + appendString)] = [] resultsLists[str('RepTI_DiffRefBinsList' + '_' + appendString)] = [] resultsLists[str('RepTI_RMSEList' + '_' + appendString)] = [] resultsLists[str('rep_TI_results_1mps_List' + '_' + appendString)] = [] resultsLists[str('rep_TI_results_05mps_List' + '_' + appendString)] = [] resultsLists[str('TIBinList' + '_' + appendString)] = [] resultsLists[str('TIRefBinList' + '_' + appendString)] = [] resultsLists[str('total_StatsList' + '_' + appendString)] = [] resultsLists[str('belownominal_statsList' + '_' + appendString)] = [] resultsLists[str('abovenominal_statsList' + '_' + appendString)] = [] resultsLists[str('lm_adjList' + '_' + appendString)] = [] resultsLists[str('adjustmentTagList' + '_' + appendString)] = [] resultsLists[str('Distribution_statsList' + '_' + appendString)] = [] resultsLists[str('sampleTestsLists' + '_' + appendString)] = [] return resultsLists def train_test_split(trainPercent, inputdata, stepOverride = False): ''' train is 'split' == True ''' import copy import numpy as np _inputdata = pd.DataFrame(columns=inputdata.columns, data=copy.deepcopy(inputdata.values)) if stepOverride: msk = [False] * len(inputdata) _inputdata['split'] = msk _inputdata.loc[stepOverride[0]:stepOverride[1], 'split'] = True else: msk = np.random.rand(len(_inputdata)) < float(trainPercent/100) train = _inputdata[msk] test = _inputdata[~msk] _inputdata['split'] = msk return _inputdata def quick_metrics(inputdata, results_df, lm_adj_dict, testID): """""" from TACT.computation.adjustments import Adjustments _adjuster = Adjustments(raw_data=inputdata) inputdata_train = inputdata[inputdata['split'] == True].copy() inputdata_test = inputdata[inputdata['split'] == False].copy() # baseline results results_ = get_all_regressions(inputdata_test, title='baselines') results_RSD_Ref = results_.loc[results_['baselines'].isin(['TI_regression_Ref_RSD'])].reset_index() results_Ane2_Ref = results_.loc[results_['baselines'].isin(['TI_regression_Ref_Ane2'])].reset_index() results_RSD_Ref_SD = results_.loc[results_['baselines'].isin(['SD_regression_Ref_RSD'])].reset_index() results_Ane2_Ref_SD = results_.loc[results_['baselines'].isin(['SD_regression_Ref_Ane2'])].reset_index() results_RSD_Ref_WS = results_.loc[results_['baselines'].isin(['WS_regression_Ref_RSD'])].reset_index() results_Ane2_Ref_WS = results_.loc[results_['baselines'].isin(['WS_regression_Ref_Ane2'])].reset_index() results_RSD_Ref.loc[0,'testID'] = [testID] results_Ane2_Ref.loc[0,'testID'] = [testID] results_RSD_Ref_SD.loc[0,'testID'] = [testID] results_Ane2_Ref_SD.loc[0,'testID'] = [testID] results_RSD_Ref_WS.loc[0,'testID'] = [testID] results_Ane2_Ref_WS.loc[0,'testID'] = [testID] results_df = pd.concat([results_df,results_RSD_Ref,results_Ane2_Ref,results_RSD_Ref_SD,results_Ane2_Ref_SD, results_RSD_Ref_WS,results_Ane2_Ref_WS],axis = 0) # Run a few adjustments with this timing test aswell inputdata_adj, lm_adj, m, c = _adjuster.perform_SS_S_adjustment(inputdata.copy()) lm_adj_dict[str(str(testID) + ' :SS_S' )] = lm_adj inputdata_adj, lm_adj, m, c = _adjuster.perform_SS_SF_adjustment(inputdata.copy()) lm_adj_dict[str(str(testID) + ' :SS_SF' )] = lm_adj inputdata_adj, lm_adj, m, c = perform_SS_WS_adjustment(inputdata.copy()) lm_adj_dict[str(str(testID) + ' :SS_WS-Std' )] = lm_adj inputdata_adj, lm_adj = perform_match(inputdata.copy()) lm_adj_dict[str(str(testID) + ' :Match' )] = lm_adj inputdata_adj, lm_adj = perform_match_input(inputdata.copy()) lm_adj_dict[str(str(testID) + ' :SS_Match_erforminput' )] = lm_adj override = False inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(inputdata.copy(),override,RSDtype) lm_adj_dict[str(str(testID) + ' :SS_G_SFa' )] = lm_adj return results_df, lm_adj_dict def block_print(): ''' disable print statements ''' sys.stdout = open(os.devnull, 'w') def enable_print(): ''' restore printing statements ''' sys.stdout = sys.__stdout__ def record_TIadj(adjustment_name, inputdata_adj, Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False): if isinstance(inputdata_adj, pd.DataFrame) == False: pass else: adj_cols = [s for s in inputdata_adj.columns.to_list() if 'adj' in s] adj_cols = [s for s in adj_cols if not ('diff' in s or 'Diff' in s or 'error' in s)] for c in adj_cols: TI_10minuteAdjusted[str(c + '_' + method)] = inputdata_adj[c] return TI_10minuteAdjusted def populate_resultsLists(resultDict, appendString, adjustment_name, lm_adj, inputdata_adj, Timestamps, method, emptyclassFlag = False): """""" if isinstance(inputdata_adj, pd.DataFrame) == False: emptyclassFlag = True elif inputdata_adj.empty: emptyclassFlag = True else: try: TI_MBE_j_, TI_Diff_j_, TI_RMSE_j_, RepTI_MBE_j_, RepTI_Diff_j_, RepTI_RMSE_j_ = get_TI_MBE_Diff_j(inputdata_adj) TI_Diff_r_, RepTI_Diff_r_ = get_TI_Diff_r(inputdata_adj) rep_TI_results_1mps, rep_TI_results_05mps = get_representative_TI(inputdata_adj) # char TI but at bin level TIbybin = get_TI_bybin(inputdata_adj) TIbyRefbin = get_TI_byTIrefbin(inputdata_adj) total_stats, belownominal_stats, abovenominal_stats = get_description_stats(inputdata_adj) except: emptyclassFlag = True if emptyclassFlag == True: resultDict[str('TI_MBEList' + '_' + appendString)].append(None) resultDict[str('TI_DiffList' + '_' + appendString)].append(None) resultDict[str('TI_DiffRefBinsList' + '_' + appendString)].append(None) resultDict[str('TI_RMSEList' + '_' + appendString)].append(None) resultDict[str('RepTI_MBEList' + '_' + appendString)].append(None) resultDict[str('RepTI_DiffList' + '_' + appendString)].append(None) resultDict[str('RepTI_DiffRefBinsList' + '_' + appendString)].append(None) resultDict[str('RepTI_RMSEList' + '_' + appendString)].append(None) resultDict[str('rep_TI_results_1mps_List' + '_' + appendString)].append(None) resultDict[str('rep_TI_results_05mps_List' + '_' + appendString)].append(None) resultDict[str('TIBinList' + '_' + appendString)].append(None) resultDict[str('TIRefBinList' + '_' + appendString)].append(None) resultDict[str('total_StatsList' + '_' + appendString)].append(None) resultDict[str('belownominal_statsList' + '_' + appendString)].append(None) resultDict[str('abovenominal_statsList' + '_' + appendString)].append(None) resultDict[str('lm_adjList' + '_' + appendString)].append(lm_adj) resultDict[str('adjustmentTagList' + '_' + appendString)].append(method) resultDict[str('Distribution_statsList' + '_' + appendString)].append(None) resultDict[str('sampleTestsLists' + '_' + appendString)].append(None) else: resultDict[str('TI_MBEList' + '_' + appendString)].append(TI_MBE_j_) resultDict[str('TI_DiffList' + '_' + appendString)].append(TI_Diff_j_) resultDict[str('TI_DiffRefBinsList' + '_' + appendString)].append(TI_Diff_r_) resultDict[str('TI_RMSEList' + '_' + appendString)].append(TI_RMSE_j_) resultDict[str('RepTI_MBEList' + '_' + appendString)].append(RepTI_MBE_j_) resultDict[str('RepTI_DiffList' + '_' + appendString)].append(RepTI_Diff_j_) resultDict[str('RepTI_DiffRefBinsList' + '_' + appendString)].append(RepTI_Diff_r_) resultDict[str('RepTI_RMSEList' + '_' + appendString)].append(RepTI_RMSE_j_) resultDict[str('rep_TI_results_1mps_List' + '_' + appendString)].append(rep_TI_results_1mps) resultDict[str('rep_TI_results_05mps_List' + '_' + appendString)].append(rep_TI_results_05mps) resultDict[str('TIBinList' + '_' + appendString)].append(TIbybin) resultDict[str('TIRefBinList' + '_' + appendString)].append(TIbyRefbin) resultDict[str('total_StatsList' + '_' + appendString)].append(total_stats) resultDict[str('belownominal_statsList' + '_' + appendString)].append(belownominal_stats) resultDict[str('abovenominal_statsList' + '_' + appendString)].append(abovenominal_stats) resultDict[str('lm_adjList' + '_' + appendString)].append(lm_adj) resultDict[str('adjustmentTagList' + '_' + appendString)].append(method) try: Distribution_stats, sampleTests = Dist_stats(inputdata_adj, Timestamps,adjustment_name) resultDict[str('Distribution_statsList' + '_' + appendString)].append(Distribution_stats) resultDict[str('sampleTestsLists' + '_' + appendString)].append(sampleTests) except: resultDict[str('Distribution_statsList' + '_' + appendString)].append(None) resultDict[str('sampleTestsLists' + '_' + appendString)].append(None) return resultDict def populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, appendString): ResultsLists_stability[str('TI_MBEList_stability' + '_' + appendString)].append(ResultsLists_class[str('TI_MBEList_class_' + appendString)]) ResultsLists_stability[str('TI_DiffList_stability' + '_' + appendString)].append(ResultsLists_class[str('TI_DiffList_class_' + appendString)]) ResultsLists_stability[str('TI_DiffRefBinsList_stability' + '_' + appendString)].append(ResultsLists_class[str('TI_DiffRefBinsList_class_' + appendString)]) ResultsLists_stability[str('TI_RMSEList_stability' + '_' + appendString)].append(ResultsLists_class[str('TI_RMSEList_class_' + appendString)]) ResultsLists_stability[str('RepTI_MBEList_stability' + '_' + appendString)].append(ResultsLists_class[str('RepTI_MBEList_class_' + appendString)]) ResultsLists_stability[str('RepTI_DiffList_stability' + '_' + appendString)].append(ResultsLists_class[str('RepTI_DiffList_class_' + appendString)]) ResultsLists_stability[str('RepTI_DiffRefBinsList_stability' + '_' + appendString)].append(ResultsLists_class[str('RepTI_DiffRefBinsList_class_' + appendString)]) ResultsLists_stability[str('RepTI_RMSEList_stability' + '_' + appendString)].append(ResultsLists_class[str('RepTI_RMSEList_class_' + appendString)]) ResultsLists_stability[str('rep_TI_results_1mps_List_stability' + '_' + appendString)].append(ResultsLists_class[str('rep_TI_results_1mps_List_class_' + appendString)]) ResultsLists_stability[str('rep_TI_results_05mps_List_stability' + '_' + appendString)].append(ResultsLists_class[str('rep_TI_results_05mps_List_class_' + appendString)]) ResultsLists_stability[str('TIBinList_stability' + '_' + appendString)].append(ResultsLists_class[str('TIBinList_class_' + appendString)]) ResultsLists_stability[str('TIRefBinList_stability' + '_' + appendString)].append(ResultsLists_class[str('TIRefBinList_class_' + appendString)]) ResultsLists_stability[str('total_StatsList_stability' + '_' + appendString)].append(ResultsLists_class[str('total_StatsList_class_' + appendString)]) ResultsLists_stability[str('belownominal_statsList_stability' + '_' + appendString)].append(ResultsLists_class[str('belownominal_statsList_class_' + appendString)]) ResultsLists_stability[str('abovenominal_statsList_stability' + '_' + appendString)].append(ResultsLists_class[str('abovenominal_statsList_class_' + appendString)]) ResultsLists_stability[str('lm_adjList_stability' + '_' + appendString)].append(ResultsLists_class[str('lm_adjList_class_' + appendString)]) ResultsLists_stability[str('adjustmentTagList_stability' + '_' + appendString)].append(ResultsLists_class[str('adjustmentTagList_class_' + appendString)]) ResultsLists_stability[str('Distribution_statsList_stability' + '_' + appendString)].append(ResultsLists_class[str('Distribution_statsList_class_' + appendString)]) ResultsLists_stability[str('sampleTestsLists_stability' + '_' + appendString)].append(ResultsLists_class[str('sampleTestsLists_class_' + appendString)]) return ResultsLists_stability if __name__ == '__main__': # Python 2 caveat: Only working for Python 3 currently if sys.version_info[0] < 3: raise Exception("Tool will not run at this time. You must be using Python 3, as running on Python 2 will encounter errors.") # ------------------------ # set up and configuration # ------------------------ """parser get_input_files""" config = Config() input_filename = config.input_filename config_file = config.config_file rtd_files = config.rtd_files results_filename = config.results_file saveModel = config.save_model_location timetestFlag = config.time_test_flag globalModel = config.global_model """config object assignments""" outpath_dir = config.outpath_dir outpath_file = config.outpath_file """metadata parser""" config.get_site_metadata() siteMetadata = config.site_metadata config.get_filtering_metadata() filterMetadata = config.config_metadata config.get_adjustments_metadata() adjustments_metadata = config.adjustments_metadata RSDtype = config.RSDtype extrap_metadata = config.extrap_metadata extrapolation_type = config.extrapolation_type """data object assignments""" data=Data(input_filename, config_file) data.get_inputdata() data.get_refTI_bins() # >> to data_file.py data.check_for_alphaConfig() inputdata = data.inputdata Timestamps = data.timestamps a = data.a lab_a = data.lab_a RSD_alphaFlag = data.RSD_alphaFlag Ht_1_rsd = data.Ht_1_rsd Ht_2_rsd = data.Ht_2_rsd """sensor, height""" sensor = config.model height = config.height print ('%%%%%%%%%%%%%%%%%%%%%%%%% Processing Data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%') # ------------------------------- # special handling for data types # ------------------------------- stabilityFlag = False if RSDtype['Selection'][0:4] == 'Wind': stabilityFlag = True if RSDtype['Selection']=='ZX': stabilityFlag = True TI_computed = inputdata['RSD_SD']/inputdata['RSD_WS'] RepTI_computed = TI_computed + 1.28 * inputdata['RSD_SD'] inputdata = inputdata.rename(columns={'RSD_TI':'RSD_TI_instrument'}) inputdata = inputdata.rename(columns={'RSD_RepTI':'RSD_RepTI_instrument'}) inputdata['RSD_TI'] = TI_computed inputdata['RSD_RepTI'] = RepTI_computed elif RSDtype['Selection']=='Triton': print ('RSD type is triton, not that output uncorrected TI is instrument corrected') # ------------------------ # Baseline Results # ------------------------ # Get all regressions available reg_results = get_all_regressions(inputdata, title='Full comparison') stabilityClass_tke, stabilityMetric_tke, regimeBreakdown_tke = calculate_stability_TKE(inputdata) cup_alphaFlag, stabilityClass_ane, stabilityMetric_ane, regimeBreakdown_ane, Ht_1_ane, Ht_2_ane, stabilityClass_rsd, stabilityMetric_rsd, regimeBreakdown_rsd = calculate_stability_alpha(inputdata, config_file, RSD_alphaFlag, Ht_1_rsd, Ht_2_rsd) #------------------------ # Time Sensivity Analysis #------------------------ # TimeTestA = pd.DataFrame() # TimeTestB = pd.DataFrame() # TimeTestC = pd.DataFrame() if timetestFlag == True: # A) increase % of test train split -- check for convergence --- basic metrics recorded baseline but also for every adjustments splitList = np.linspace(0.0, 100.0, num = 20, endpoint =False) print ('Testing model generation time period sensitivity...% of data') time_test_A_adjustment_df = {} TimeTestA_baseline_df = pd.DataFrame() for s in splitList[1:]: sys.stdout.write("\r") sys.stdout.write(f"{str(s).rjust(10, ' ')} % ") inputdata_test = train_test_split(s,inputdata.copy()) TimeTestA_baseline_df, time_test_A_adjustment_df = quick_metrics(inputdata_test, TimeTestA_baseline_df, time_test_A_adjustment_df,str(100-s)) sys.stdout.flush() print() # B) incrementally Add days to training set sequentially -- check for convergence numberofObsinOneDay = 144 numberofDaysInTest = int(round(len(inputdata)/numberofObsinOneDay)) print ('Testing model generation time period sensitivity...days to train model') print ('Number of days in the study ' + str(numberofDaysInTest)) time_test_B_adjustment_df = {} TimeTestB_baseline_df = pd.DataFrame() for i in range(0,numberofDaysInTest): sys.stdout.write("\r") sys.stdout.write(f"{str(i).rjust(10, ' ')} of {str(numberofDaysInTest)} days ") windowEnd = (i+1)*(numberofObsinOneDay) inputdata_test = train_test_split(i,inputdata.copy(), stepOverride = [0,windowEnd]) TimeTestB_baseline_df, time_test_B_adjustment_df = quick_metrics(inputdata_test,TimeTestB_baseline_df, time_test_B_adjustment_df,str(numberofDaysInTest-i)) sys.stdout.flush() print() # C) If experiment is greater than 3 months, slide a 6 week window (1 week step) if len(inputdata) > (numberofObsinOneDay*90): # check to see if experiment is greater than 3 months print ('Testing model generation time period sensitivity...6 week window pick') windowStart = 0 windowEnd = (numberofObsinOneDay*42) time_test_C_adjustment_df = {} TimeTestC_baseline_df = pd.DataFrame() while windowEnd < len(inputdata): print (str('After observation #' + str(windowStart) + ' ' + 'Before observation #' + str(windowEnd))) windowStart += numberofObsinOneDay*7 windowEnd = windowStart + (numberofObsinOneDay*42) inputdata_test = train_test_split(i,inputdata.copy(), stepOverride = [windowStart,windowEnd]) TimeTestC_baseline_df, time_test_C_adjustment_df = quick_metrics(inputdata_test, TimeTestC_baseline_df, time_test_C_adjustment_df, str('After_' + str(windowStart) + '_' + 'Before_' + str(windowEnd))) else: TimeTestA_baseline_df = pd.DataFrame() TimeTestB_baseline_df = pd.DataFrame() TimeTestC_baseline_df = pd.DataFrame() time_test_A_adjustment_df = {} time_test_B_adjustment_df = {} time_test_C_adjustment_df = {} #----------------------- # Test - Train split #----------------------- # random 80-20 split inputdata = train_test_split(80.0, inputdata.copy()) inputdata_train = inputdata[inputdata['split'] == True].copy().join(Timestamps) inputdata_test = inputdata[inputdata['split'] == False].copy().join(Timestamps) timestamp_train = inputdata_train['Timestamp'] timestamp_test = inputdata_test['Timestamp'] #----------------------------- # stability class subset lists #----------------------------- # get reg_results by stability class: list of df's for each height reg_results_class1 = [] reg_results_class2 = [] reg_results_class3 = [] reg_results_class4 = [] reg_results_class5 = [] reg_results_class1_alpha = {} reg_results_class2_alpha = {} reg_results_class3_alpha = {} reg_results_class4_alpha = {} reg_results_class5_alpha = {} if RSDtype['Selection'][0:4] == 'Wind' or 'ZX' in RSDtype['Selection']: inputdata_class1 = [] inputdata_class2 = [] inputdata_class3 = [] inputdata_class4 = [] inputdata_class5 = [] RSD_h = [] Alldata_inputdata = inputdata.copy() for h in stabilityClass_tke.columns.to_list(): RSD_h.append(h) inputdata_class1.append(Alldata_inputdata[Alldata_inputdata[h] == 1]) inputdata_class2.append(Alldata_inputdata[Alldata_inputdata[h] == 2]) inputdata_class3.append(Alldata_inputdata[Alldata_inputdata[h] == 3]) inputdata_class4.append(Alldata_inputdata[Alldata_inputdata[h] == 4]) inputdata_class5.append(Alldata_inputdata[Alldata_inputdata[h] == 5]) All_class_data = [inputdata_class1,inputdata_class2, inputdata_class3, inputdata_class4, inputdata_class5] All_class_data_clean = [inputdata_class1, inputdata_class2, inputdata_class3, inputdata_class4, inputdata_class5] for h in RSD_h: idx = RSD_h.index(h) df = inputdata_class1[idx] reg_results_class1.append(get_all_regressions(df, title = str('TKE_stability_' + h + 'class1'))) df = inputdata_class2[idx] reg_results_class2.append(get_all_regressions(df, title = str('TKE_stability_' + h + 'class2'))) df = inputdata_class3[idx] reg_results_class3.append(get_all_regressions(df, title = str('TKE_stability_' + h + 'class3'))) df = inputdata_class4[idx] reg_results_class4.append(get_all_regressions(df, title = str('TKE_stability_' + h + 'class4'))) df = inputdata_class5[idx] reg_results_class5.append(get_all_regressions(df, title = str('TKE_stability_' + h + 'class5'))) if RSD_alphaFlag: del inputdata_class1, inputdata_class2, inputdata_class3, inputdata_class4, inputdata_class5 Alldata_inputdata = inputdata.copy() colName = stabilityClass_rsd.name Alldata_inputdata[colName] = stabilityClass_rsd.values inputdata_class1=Alldata_inputdata[Alldata_inputdata[stabilityClass_rsd.name] == 1.0] inputdata_class2=Alldata_inputdata[Alldata_inputdata[stabilityClass_rsd.name] == 2.0] inputdata_class3=Alldata_inputdata[Alldata_inputdata[stabilityClass_rsd.name] == 3.0] inputdata_class4=Alldata_inputdata[Alldata_inputdata[stabilityClass_rsd.name] == 4.0] inputdata_class5=Alldata_inputdata[Alldata_inputdata[stabilityClass_rsd.name] == 5.0] All_class_data_alpha_RSD = [inputdata_class1,inputdata_class2, inputdata_class3, inputdata_class4, inputdata_class5] All_class_data_alpha_RSD_clean = [inputdata_class1.copy(),inputdata_class2.copy(), inputdata_class3.copy(), inputdata_class4.copy(), inputdata_class5.copy()] reg_results_class1_alpha['RSD'] = get_all_regressions(inputdata_class1, title = str('alpha_stability_RSD' + 'class1')) reg_results_class2_alpha['RSD'] = get_all_regressions(inputdata_class2, title = str('alpha_stability_RSD' + 'class2')) reg_results_class3_alpha['RSD'] = get_all_regressions(inputdata_class3, title = str('alpha_stability_RSD' + 'class3')) reg_results_class4_alpha['RSD'] = get_all_regressions(inputdata_class4, title = str('alpha_stability_RSD' + 'class4')) reg_results_class5_alpha['RSD'] = get_all_regressions(inputdata_class5, title = str('alpha_stability_RSD' + 'class5')) if cup_alphaFlag: del inputdata_class1, inputdata_class2, inputdata_class3, inputdata_class4, inputdata_class5 Alldata_inputdata = inputdata.copy() colName = stabilityClass_ane.name Alldata_inputdata[colName] = stabilityClass_ane.values inputdata_class1 = Alldata_inputdata[Alldata_inputdata[stabilityClass_ane.name] == 1.0] inputdata_class2 = Alldata_inputdata[Alldata_inputdata[stabilityClass_ane.name] == 2.0] inputdata_class3 = Alldata_inputdata[Alldata_inputdata[stabilityClass_ane.name] == 3.0] inputdata_class4 = Alldata_inputdata[Alldata_inputdata[stabilityClass_ane.name] == 4.0] inputdata_class5 = Alldata_inputdata[Alldata_inputdata[stabilityClass_ane.name] == 5.0] All_class_data_alpha_Ane = [inputdata_class1,inputdata_class2, inputdata_class3, inputdata_class4, inputdata_class5] All_class_data_alpha_Ane_clean = [inputdata_class1.copy(),inputdata_class2.copy(), inputdata_class3.copy(), inputdata_class4.copy(), inputdata_class5.copy()] reg_results_class1_alpha['Ane'] = get_all_regressions(inputdata_class1, title = str('alpha_stability_Ane' + 'class1')) reg_results_class2_alpha['Ane'] = get_all_regressions(inputdata_class2, title = str('alpha_stability_Ane' + 'class2')) reg_results_class3_alpha['Ane'] = get_all_regressions(inputdata_class3, title = str('alpha_stability_Ane' + 'class3')) reg_results_class4_alpha['Ane'] = get_all_regressions(inputdata_class4, title = str('alpha_stability_Ane' + 'class4')) reg_results_class5_alpha['Ane'] = get_all_regressions(inputdata_class5, title = str('alpha_stability_Ane' + 'class5')) # ------------------------ # TI Adjustments # ------------------------ from TACT.computation.adjustments import Adjustments baseResultsLists = initialize_resultsLists('') # get number of observations in each bin count_1mps, count_05mps = get_count_per_WSbin(inputdata, 'RSD_WS') inputdata_train = inputdata[inputdata['split'] == True].copy().join(Timestamps) inputdata_test = inputdata[inputdata['split'] == False].copy().join(Timestamps) timestamp_train = inputdata_train['Timestamp'] timestamp_test = inputdata_test['Timestamp'] count_1mps_train, count_05mps_train = get_count_per_WSbin(inputdata_train, 'RSD_WS') count_1mps_test, count_05mps_test = get_count_per_WSbin(inputdata_test, 'RSD_WS') if RSDtype['Selection'][0:4] == 'Wind' or 'ZX' in RSDtype['Selection']: primary_c = [h for h in RSD_h if 'Ht' not in h] primary_idx = RSD_h.index(primary_c[0]) ResultsLists_stability = initialize_resultsLists('stability_') if cup_alphaFlag: ResultsLists_stability_alpha_Ane = initialize_resultsLists('stability_alpha_Ane') if RSD_alphaFlag: ResultsLists_stability_alpha_RSD = initialize_resultsLists('stability_alpha_RSD') name_1mps_tke = [] name_1mps_alpha_Ane = [] name_1mps_alpha_RSD = [] name_05mps_tke = [] name_05mps_alpha_Ane = [] name_05mps_alpha_RSD = [] count_1mps_tke = [] count_1mps_alpha_Ane = [] count_1mps_alpha_RSD = [] count_05mps_tke = [] count_05mps_alpha_Ane = [] count_05mps_alpha_RSD = [] for c in range(0,len(All_class_data)): name_1mps_tke.append(str('count_1mps_class_' + str(c) + '_tke')) name_1mps_alpha_Ane.append(str('count_1mps_class_' + str(c) + '_alpha_Ane')) name_1mps_alpha_RSD.append(str('count_1mps_class_' + str(c) + '_alpha_RSD')) name_05mps_tke.append(str('count_05mps_class_' + str(c) + '_tke')) name_05mps_alpha_Ane.append(str('count_05mps_class_' + str(c) + '_alpha_Ane')) name_05mps_alpha_RSD.append(str('count_05mps_class_' + str(c) + '_alpha_RSD')) try: c_1mps_tke, c_05mps_tke = get_count_per_WSbin(All_class_data[c][primary_idx], 'RSD_WS') count_1mps_tke.append(c_1mps_tke) count_05mps_tke.append(c_05mps_tke) except: count_1mps_tke.append(None) count_05mps_tke.append(None) try: c_1mps_alpha_Ane, c_05mps_alpha_Ane = get_count_per_WSbin(All_class_data_alpha_Ane[c], 'RSD_WS') count_1mps_alpha_Ane.append(c_1mps_alpha_Ane) count_05mps_alpha_Ane.append(c_05mps_alpha_Ane) except: count_1mps_alpha_Ane.append(None) count_05mps_alpha_Ane.append(None) try: c_1mps_alpha_RSD, c_05mps_alpha_RSD = get_count_per_WSbin(All_class_data_alpha_RSD[c], 'RSD_WS') count_1mps_alpha_RSD.append(c_1mps_alpha_RSD) count_05mps_alpha_RSD.append(c_05mps_alpha_RSD) except: count_1mps_alpha_RSD.append(None) count_05mps_alpha_RSD.append(None) # intialize 10 minute output TI_10minuteAdjusted = pd.DataFrame() # initialize Adjustments object adjuster = Adjustments(inputdata.copy(), adjustments_metadata, baseResultsLists) for method in adjustments_metadata: # ************************************ # # Site Specific Simple Adjustment (SS-S) if method != 'SS-S': pass elif method == 'SS-S' and adjustments_metadata['SS-S'] == False: pass else: print('Applying Adjustment Method: SS-S') logger.info('Applying Adjustment Method: SS-S') inputdata_adj, lm_adj, m, c = adjuster.perform_SS_S_adjustment(inputdata.copy()) print("SS-S: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-S' adjustment_name = 'SS_S' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-S by stability class (TKE)') logger.info('Applying Adjustment Method: SS-S by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = adjuster.perform_SS_S_adjustment(item[primary_idx].copy()) print("SS-S: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-S' + '_TKE_' + 'class_' + str(className)) adjustment_name = str('SS-S'+ '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-S by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-S by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 print (str('class ' + str(className))) for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = adjuster.perform_SS_S_adjustment(item.copy()) print ("SS-S: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-S' + '_' + 'class_' + str(className)) adjustment_name = str('SS-S' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-S by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-S by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = adjuster.perform_SS_S_adjustment(item.copy()) print ("SS-S: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-S' + '_alphaCup_' + 'class_' + str(className)) adjustment_name = str('SS-S' + '_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ********************************************** # # Site Specific Simple + Filter Adjustment (SS-SF) if method != 'SS-SF': pass elif method == 'SS-SF' and adjustments_metadata['SS-SF'] == False: pass else: print('Applying Adjustment Method: SS-SF') logger.info('Applying Adjustment Method: SS-SF') # inputdata_adj, lm_adj, m, c = perform_SS_SF_adjustment(inputdata.copy()) inputdata_adj, lm_adj, m, c = adjuster.perform_SS_SF_adjustment(inputdata.copy()) print("SS-SF: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-SF' adjustment_name = 'SS_SF' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind' or 'ZX' in RSDtype['Selection']: print('Applying Adjustment Method: SS-SF by stability class (TKE)') logger.info('Applying Adjustment Method: SS-SF by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = adjuster.perform_SS_SF_adjustment(item[primary_idx].copy()) print("SS-SF: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-SF' + '_' + 'class_' + str(className)) adjustment_name = str('SS_SF' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-SF by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-SF by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = adjuster.perform_SS_SF_adjustment(item.copy()) print ("SS-SF: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-SF' + '_' + 'class_' + str(className)) adjustment_name = str('SS_SF' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-SF by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-SF by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = adjuster.perform_SS_SF_adjustment(item.copy()) print ("SS-SF: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-SF' + '_' + 'class_' + str(className)) adjustment_name = str('SS_SF' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************************ # # Site Specific Simple Adjustment (SS-SS) combining stability classes adjusted differently if method != 'SS-SS': pass elif method == 'SS-SS' and adjustments_metadata['SS-SS'] == False: pass elif RSDtype['Selection'][0:4] != 'Wind' and 'ZX' not in RSDtype['Selection']: pass else: print('Applying Adjustment Method: SS-SS') logger.info('Applying Adjustment Method: SS-SS') inputdata_adj, lm_adj, m, c = perform_SS_SS_adjustment(inputdata.copy(),All_class_data,primary_idx) print("SS-SS: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-SS' adjustment_name = 'SS_SS' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-SS by stability class (TKE). SAME as Baseline') logger.info('Applying Adjustment Method: SS-SS by stability class (TKE). SAME as Baseline') ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: print("SS-SS: y = " + str(m) + " * x + " + str(c)) adjustment_name = str('SS_SS' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-SS by stability class Alpha w/ RSD. SAEM as Baseline') logger.info('Applying Adjustment Method: SS-SS by stability class Alpha w/ RSD. SAEM as Baseline') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: print ("SS-SS: y = " + str(m) + "* x +" + str(c)) adjustment_name = str('SS_SS' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-SS by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-SS by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: print ("SS-SS: y = " + str(m) + "* x +" + str(c)) emptyclassFlag = False adjustment_name = str('SS_SS' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ******************************************* # # Site Specific WindSpeed Adjustment (SS-WS) if method != 'SS-WS': pass elif method == 'SS-WS' and adjustments_metadata['SS-WS'] == False: pass else: print('Applying Adjustment Method: SS-WS') logger.info('Applying Adjustment Method: SS-WS') inputdata_adj, lm_adj, m, c = perform_SS_WS_adjustment(inputdata.copy()) print("SS-WS: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-WS' adjustment_name = 'SS_WS' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind' or 'ZX' in RSDtype['Selection']: print('Applying Adjustment Method: SS-WS by stability class (TKE)') logger.info('Applying Adjustment Method: SS-WS by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = perform_SS_WS_adjustment(item[primary_idx].copy()) print("SS-WS: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-WS' + '_' + 'class_' + str(className)) adjustment_name = str('SS_WS' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-WS by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-WS by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_SS_WS_adjustment(item.copy()) print ("SS-WS: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-WS' + '_' + 'class_' + str(className)) adjustment_name = str('SS_WS' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-WS by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-WS by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_SS_WS_adjustment(item.copy()) print ("SS-WS: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-WS' + '_' + 'class_' + str(className)) emptyclassFlag = False adjustment_name = str('SS_WS' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ******************************************* # # Site Specific Comprehensive Adjustment (SS-WS-Std) if method != 'SS-WS-Std': pass elif method == 'SS-WS-Std' and adjustments_metadata['SS-WS-Std'] == False: pass else: print('Applying Adjustment Method: SS-WS-Std') logger.info('Applying Adjustment Method: SS-WS-Std') inputdata_adj, lm_adj, m, c = perform_SS_WS_Std_adjustment(inputdata.copy()) print("SS-WS-Std: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-WS-Std' adjustment_name = 'SS_WS_Std' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind' or 'ZX' in RSDtype['Selection']: print('Applying Adjustment Method: SS-WS-Std by stability class (TKE)') logger.info('Applying Adjustment Method: SS-WS-Std by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = perform_SS_WS_Std_adjustment(item[primary_idx].copy()) print("SS-WS-Std: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-WS-Std' + '_' + 'class_' + str(className)) adjustment_name = str('SS_WS_Std' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-WS-Std by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-WS-Std by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_SS_WS_Std_adjustment(item.copy()) print ("SS-WS-Std: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-WS-Std' + '_' + 'class_' + str(className)) adjustment_name = str('SS_WS_Std' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-WS-Std by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-WS-Std by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_SS_WS_Std_adjustment(item.copy()) print ("SS-WS-Std: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-WS-Std' + '_' + 'class_' + str(className)) emptyclassFlag = False adjustment_name = str('SS_WS_Std' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # **************************************************************** # # Site Specific LTERRA for WC 1HZ Data Adjustment (G-LTERRA_WC_1HZ) if method != 'SS-LTERRA-WC-1HZ': pass elif method == 'SS-LTERRA-WC-1HZ' and adjustments_metadata['SS-LTERRA-WC-1HZ'] == False: pass else: print('Applying Adjustment Method: SS-LTERRA-WC-1HZ') logger.info('Applying Adjustment Method: SS-LTERRA-WC-1HZ') # ******************************************************************* # # Site Specific LTERRA WC Machine Learning Adjustment (SS-LTERRA-MLa) # Random Forest Regression with now ancillary columns if method != 'SS-LTERRA-MLa': pass elif method == 'SS-LTERRA-MLa' and adjustments_metadata['SS-LTERRA-MLa'] == False: pass else: print('Applying Adjustment Method: SS-LTERRA-MLa') logger.info('Applying Adjustment Method: SS-LTERRA-MLa') inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_ML_adjustment(inputdata.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS_LTERRA_MLa' adjustment_name = 'SS_LTERRA_MLa' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-LTERRA MLa by stability class (TKE)') logger.info('Applying Adjustment Method: SS-LTERRA MLa by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c= perform_SS_LTERRA_ML_adjustment(item[primary_idx].copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS_LTERRA_MLa' + '_' + 'class_' + str(className)) adjustment_name = str('SS_LTERRA_MLa' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-LTERRA MLa by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-LTERRA MLa by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_ML_adjustment(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-LTERRA_MLa' + '_' + 'class_' + str(className)) adjustment_name = str('SS_LTERRA_ML' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-LTERRA_MLa by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-LTERRA_MLa by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_ML_adjustment(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS_LTERRA_MLa' + '_' + 'class_' + str(className)) emptyclassFlag = False adjustment_name = str('SS_LTERRA_MLa' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************************************************************************ # # Site Specific LTERRA WC (w/ stability) Machine Learning Adjustment (SS-LTERRA_MLc) if method != 'SS-LTERRA-MLc': pass elif method == 'SS-LTERRA-MLc' and adjustments_metadata['SS-LTERRA-MLc'] == False: pass else: print('Applying Adjustment Method: SS-LTERRA-MLc') logger.info('Applying Adjustment Method: SS-LTERRA-MLc') all_trainX_cols = ['x_train_TI', 'x_train_TKE','x_train_WS','x_train_DIR','x_train_Hour'] all_trainY_cols = ['y_train'] all_testX_cols = ['x_test_TI','x_test_TKE','x_test_WS','x_test_DIR','x_test_Hour'] all_testY_cols = ['y_test'] inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_S_ML_adjustment(inputdata.copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS_LTERRA_MLc' adjustment_name = 'SS_LTERRA_MLc' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-LTERRA_MLc by stability class (TKE)') logger.info('Applying Adjustment Method: SS-LTERRA_MLc by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c= perform_SS_LTERRA_S_ML_adjustment(item[primary_idx].copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS_LTERRA_MLc' + '_' + 'class_' + str(className)) adjustment_name = str('SS_LTERRA_MLc' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-LTERRA_MLc by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-LTERRA_MLc by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_S_ML_adjustment(item.copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-LTERRA_MLc' + '_' + 'class_' + str(className)) adjustment_name = str('SS_LTERRA_S_ML' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-LTERRA_MLc by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-LTERRA_MLc by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_S_ML_adjustment(item.copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS_LTERRA_MLc' + '_' + 'class_' + str(className)) emptyclassFlag = False adjustment_name = str('SS_LTERRA_MLc' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # *********************** # # Site Specific SS-LTERRA-MLb if method != 'SS-LTERRA-MLb': pass elif method == 'SS-LTERRA-MLb' and adjustments_metadata['SS-LTERRA-MLb'] == False: pass else: print('Applying Adjustment Method: SS-LTERRA-MLb') logger.info('Applying Adjustment Method: SS-LTERRA-MLb') all_trainX_cols = ['x_train_TI', 'x_train_TKE'] all_trainY_cols = ['y_train'] all_testX_cols = ['x_test_TI','x_test_TKE'] all_testY_cols = ['y_test'] inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_S_ML_adjustment(inputdata.copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS_LTERRA_MLb' adjustment_name = 'SS_LTERRA_MLb' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-LTERRA_MLb by stability class (TKE)') logger.info('Applying Adjustment Method: SS-LTERRA_MLb by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c= perform_SS_LTERRA_S_ML_adjustment(item[primary_idx].copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS_LTERRA_MLb' + '_' + 'class_' + str(className)) adjustment_name = str('SS_LTERRA_MLb' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-LTERRA_MLb by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-LTERRA_MLb by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_S_ML_adjustment(item.copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-LTERRA_MLb' + '_' + 'class_' + str(className)) adjustment_name = str('SS_LTERRA_MLb' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-LTERRA_MLb by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-LTERRA_MLb by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_SS_LTERRA_S_ML_adjustment(item.copy(),all_trainX_cols,all_trainY_cols,all_testX_cols,all_testY_cols) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS_LTERRA_MLb' + '_' + 'class_' + str(className)) emptyclassFlag = False adjustment_name = str('SS_LTERRA_MLb' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # *********************** # # TI Extrapolation (TI-Ext) if method != 'TI-Extrap': pass elif method == 'TI-Extrap' and adjustments_metadata['TI-Extrap'] == False: pass else: print ('Found enough data to perform extrapolation comparison') block_print() # Get extrapolation height height_extrap = float(extrap_metadata['height'][extrap_metadata['type'] == 'extrap']) # Extrapolate inputdata_adj, lm_adj, shearTimeseries= perform_TI_extrapolation(inputdata.copy(), extrap_metadata, extrapolation_type, height) adjustment_name = 'TI_EXTRAP' lm_adj['adjustment'] = adjustment_name inputdataEXTRAP = inputdata_adj.copy() inputdataEXTRAP, baseResultsLists = extrap_configResult(extrapolation_type, inputdataEXTRAP, baseResultsLists, method,lm_adj) if RSDtype['Selection'][0:4] == 'Wind': # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, shearTimeseries= perform_TI_extrapolation(item[primary_idx].copy(), extrap_metadata, extrapolation_type, height) lm_adj['adjustment'] = str('TI_EXT_class1' + '_TKE_' + 'class_' + str(className)) inputdataEXTRAP = inputdata_adj.copy() inputdataEXTRAP, ResultsLists_class = extrap_configResult(extrapolation_type, inputdataEXTRAP, ResultsLists_class, method, lm_adj, appendString = 'class_') className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if cup_alphaFlag: ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, shearTimeseries= perform_TI_extrapolation(item.copy(), extrap_metadata, extrapolation_type, height) lm_adj['adjustment'] = str('TI_Ane_class1' + '_alphaCup_' + 'class_' + str(className)) inputdataEXTRAP = inputdata_adj.copy() inputdataEXTRAP, ResultsLists_class_alpha_Ane = extrap_configResult(extrapolation_type, inputdataEXTRAP, ResultsLists_class_alpha_Ane, method, lm_adj, appendString = 'class_alpha_Ane') className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') if RSD_alphaFlag: ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, shearTimeseries= perform_TI_extrapolation(item.copy(), extrap_metadata, extrapolation_type, height) lm_adj['adjustment'] = str('TI_RSD_class1' + '_alphaRSD_' + 'class_' + str(className)) inputdataEXTRAP = inputdata_adj.copy() inputdataEXTRAP, ResultsLists_class_alpha_RSD = extrap_configResult(extrapolation_type, inputdataEXTRAP, ResultsLists_class_alpha_RSD, method, lm_adj, appendString = 'class_alpha_RSD') className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') # Add extra info to meta data and reformat if extrapolation_type == 'simple': desc = 'No truth measurement at extrapolation height' else: desc = 'Truth measurement available at extrapolation height' extrap_metadata = (extrap_metadata .append({'type': np.nan, 'height': np.nan, 'num': np.nan}, ignore_index=True) .append(pd.DataFrame([['extrapolation type', extrapolation_type, desc]], columns=extrap_metadata.columns)) .rename(columns={'type': 'Type', 'height': 'Height (m)', 'num': 'Comparison Height Number'})) enable_print() # ************************************************** # # Histogram Matching if method != 'SS-Match': pass elif method == 'SS-Match' and adjustments_metadata['SS-Match'] == False: pass else: print('Applying Match algorithm: SS-Match') logger.info('Applying Match algorithm: SS-Match') inputdata_adj, lm_adj = perform_match(inputdata.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-Match' adjustment_name = 'SS_Match' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-Match by stability class (TKE)') logger.info('Applying Adjustment Method: SS-Match by stability class (TKE)') ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj = perform_match(item[primary_idx].copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-Match' + '_' + 'class_' + str(className)) adjustment_name = str('SS_Match' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-Match by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-Match by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj = perform_match(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-Match' + '_' + 'class_' + str(className)) adjustment_name = str('SS_Match' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-Match by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-Match by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj = perform_match(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-Match' + '_' + 'class_' + str(className)) adjustment_name = str('SS_Match' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************************************** # # Histogram Matching Input Corrected if method != 'SS-Match2': pass elif method == 'SS-Match2' and adjustments_metadata['SS-Match2'] == False: pass else: print('Applying input match algorithm: SS-Match2') logger.info('Applying input match algorithm: SS-Match2') inputdata_adj, lm_adj = perform_match_input(inputdata.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'SS-Match2' adjustment_name = 'SS_Match2' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: SS-Match2 by stability class (TKE)') logger.info('Applying Adjustment Method: SS-Match2 by stability class (TKE)') ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj = perform_match_input(item[primary_idx].copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-Match2' + '_' + 'class_' + str(className)) adjustment_name = str('SS_Match2' + '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: SS-Match2 by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: SS-Match2 by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj = perform_match_input(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-Match2' + '_' + 'class_' + str(className)) adjustment_name = str('SS_Match2' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: SS-Match2 by stability class Alpha w/cup') logger.info('Applying Adjustment Method: SS-Match2 by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj = perform_match_input(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('SS-Match2' + '_' + 'class_' + str(className)) adjustment_name = str('SS_Match2' + '_alphaCup_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************************************** # # Global Simple Phase II mean Linear Reressions (G-Sa) + project ''' RSD_TI = .984993 * RSD_TI + .087916 ''' if method != 'G-Sa': pass elif method == 'G-Sa' and adjustments_metadata['G-Sa'] == False: pass else: print('Applying Adjustment Method: G-Sa') logger.info('Applying Adjustment Method: G-Sa') override = False inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(inputdata.copy(),override,RSDtype) print("G-Sa: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'G-Sa' adjustment_name = 'G_Sa' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: G-Sa by stability class (TKE)') logger.info('Applying Adjustment Method: G-Sa by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(item[primary_idx].copy(),override,RSDtype) print("G-Sa: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-Sa' + '_TKE_' + 'class_' + str(className)) adjustment_name = str('G-Sa'+ '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: G-Sa by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: G-Sa by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(item.copy(),override,RSDtype) print ("G-Sa: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-Sa' + '_' + 'class_' + str(className)) adjustment_name = str('G-Sa' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: G-Sa by stability class Alpha w/cup') logger.info('Applying Adjustment Method: G-Sa by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(item.copy(),override,RSDtype) print ("G-Sa: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-Sa' + '_alphaCup_' + 'class_' + str(className)) adjustment_name = str('G-Sa' + '_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ******************************************************** # # Global Simple w/filter Phase II Linear Regressions (G-SFa) + project # Check these values, but for WC m = 0.7086 and c = 0.0225 if method != 'G-SFa': pass elif method == 'G-SFa' and adjustments_metadata['G-SFa'] == False: pass elif RSDtype['Selection'][0:4] != 'Wind': pass else: print('Applying Adjustment Method: G-SFa') logger.info('Applying Adjustment Method: G-SFa') override = [0.7086, 0.0225] inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(inputdata.copy(),override,RSDtype) print("G-SFa: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'G-SFa' adjustment_name = 'G_SFa' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: G-SFa by stability class (TKE)') logger.info('Applying Adjustment Method: G-SFa by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(item[primary_idx].copy(),override,RSDtype) print("G-SFa: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-SFa' + '_TKE_' + 'class_' + str(className)) adjustment_name = str('G-SFa'+ '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: G-SFa by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: G-SFa by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(item.copy(),override,RSDtype) print ("G-SFa: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-Sa' + '_' + 'class_' + str(className)) adjustment_name = str('G-SFa' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: G-SFa by stability class Alpha w/cup') logger.info('Applying Adjustment Method: G-SFa by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_G_Sa_adjustment(item.copy(),override,RSDtype) print ("G-SFa: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-SFa' + '_alphaCup_' + 'class_' + str(className)) adjustment_name = str('G-SFa' + '_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************************************ # # Global Standard Deviation and WS adjustment (G-Sc) if method != 'G-SFc': pass elif method == 'G-SFc' and adjustments_metadata['G-SFc'] == False: pass elif RSDtype['Selection'][0:4] != 'Wind': pass else: print('Applying Adjustment Method: G-Sc') logger.info('Applying Adjustment Method: G-Sc') inputdata_adj, lm_adj, m, c = perform_G_SFc_adjustment(inputdata.copy()) print("G-SFc: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'G-SFc' adjustment_name = 'G_SFc' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: G-SFa by stability class (TKE)') logger.info('Applying Adjustment Method: G-SFa by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: inputdata_adj, lm_adj, m, c = perform_G_SFc_adjustment(item[primary_idx].copy()) print("G-SFc: y = " + str(m) + " * x + " + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-SFc' + '_TKE_' + 'class_' + str(className)) adjustment_name = str('G-SFc'+ '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: G-SFc by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: G-SFc by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: inputdata_adj, lm_adj, m, c = perform_G_SFc_adjustment(item.copy()) print ("G-SFc: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-SFc' + '_' + 'class_' + str(className)) adjustment_name = str('G-SFc' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: G-SFc by stability class Alpha w/cup') logger.info('Applying Adjustment Method: G-SFc by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: inputdata_adj, lm_adj, m, c = perform_G_SFc_adjustment(item.copy()) print ("G-SFc: y = " + str(m) + "* x +" + str(c)) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-SFc' + '_alphaCup_' + 'class_' + str(className)) adjustment_name = str('G-SFc' + '_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************ # # Global Comprehensive (G-C) ''' based on empirical calibrations by EON ''' if method != 'G-C': pass elif method == 'G-C' and adjustments_metadata['G-C'] == False: pass else: print('Applying Adjustment Method: G-C') logger.info('Applying Adjustment Method: G-C') inputdata_adj, lm_adj, m, c = perform_G_C_adjustment(inputdata.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = 'G-C' adjustment_name = 'G_C' baseResultsLists = populate_resultsLists(baseResultsLists, '', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) TI_10minuteAdjusted = record_TIadj(adjustment_name,inputdata_adj,Timestamps, method, TI_10minuteAdjusted, emptyclassFlag=False) if RSDtype['Selection'][0:4] == 'Wind': print('Applying Adjustment Method: G-C by stability class (TKE)') logger.info('Applying Adjustment Method: G-C by stability class (TKE)') # stability subset output for primary height (all classes) ResultsLists_class = initialize_resultsLists('class_') className = 1 for item in All_class_data: print (str('class ' + str(className))) inputdata_adj, lm_adj, m, c = perform_G_C_adjustment(item[primary_idx].copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-C' + '_TKE_' + 'class_' + str(className)) adjustment_name = str('G-C'+ '_TKE_' + str(className)) ResultsLists_class = populate_resultsLists(ResultsLists_class, 'class_', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsList_stability = populate_resultsLists_stability(ResultsLists_stability, ResultsLists_class, '') if RSD_alphaFlag: print('Applying Adjustment Method: G-C by stability class Alpha w/ RSD') logger.info('Applying Adjustment Method: G-C by stability class Alpha w/ RSD') ResultsLists_class_alpha_RSD = initialize_resultsLists('class_alpha_RSD') className = 1 for item in All_class_data_alpha_RSD: print (str('class ' + str(className))) inputdata_adj, lm_adj, m, c = perform_G_C_adjustment(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-C' + '_' + 'class_' + str(className)) adjustment_name = str('G-C' + '_alphaRSD_' + str(className)) ResultsLists_class_alpha_RSD = populate_resultsLists(ResultsLists_class_alpha_RSD, 'class_alpha_RSD', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_RSD = populate_resultsLists_stability(ResultsLists_stability_alpha_RSD, ResultsLists_class_alpha_RSD, 'alpha_RSD') if cup_alphaFlag: print('Applying Adjustment Method: G-C by stability class Alpha w/cup') logger.info('Applying Adjustment Method: G-C by stability class Alpha w/cup') ResultsLists_class_alpha_Ane = initialize_resultsLists('class_alpha_Ane') className = 1 for item in All_class_data_alpha_Ane: print (str('class ' + str(className))) inputdata_adj, lm_adj, m, c = perform_G_C_adjustment(item.copy()) lm_adj['sensor'] = sensor lm_adj['height'] = height lm_adj['adjustment'] = str('G-C' + '_alphaCup_' + 'class_' + str(className)) adjustment_name = str('G-C' + '_' + str(className)) ResultsLists_class_alpha_Ane = populate_resultsLists(ResultsLists_class_alpha_Ane, 'class_alpha_Ane', adjustment_name, lm_adj, inputdata_adj, Timestamps, method) className += 1 ResultsLists_stability_alpha_Ane = populate_resultsLists_stability(ResultsLists_stability_alpha_Ane, ResultsLists_class_alpha_Ane, 'alpha_Ane') # ************************ # # Global Comprehensive (G-Match) if method != 'G-Match': pass elif method == 'G-Match' and adjustments_metadata['G-Match'] == False: pass else: print('Applying Adjustment Method: G-Match') logger.info('Applying Adjustment Method: G-Match') # ************************ # # Global Comprehensive (G-Ref-S) if method != 'G-Ref-S': pass elif method == 'G-Ref-S' and adjustments_metadata['G-Ref-S'] == False: pass else: print('Applying Adjustment Method: G-Ref-S') logger.info('Applying Adjustment Method: G-Ref-S') # ************************ # # Global Comprehensive (G-Ref-Sf) if method != 'G-Ref-Sf': pass elif method == 'G-Ref-Sf' and adjustments_metadata['G-Ref-Sf'] == False: pass else: print('Applying Adjustment Method: G-Ref-Sf') logger.info('Applying Adjustment Method: G-Ref-Sf') # ************************ # # Global Comprehensive (G-Ref-SS) if method != 'G-Ref-SS': pass elif method == 'G-Ref-SS' and adjustments_metadata['G-Ref-SS'] == False: pass else: print('Applying Adjustment Method: G-Ref-SS') logger.info('Applying Adjustment Method: G-Ref-SS') # ************************ # # Global Comprehensive (G-Ref-SS-S) if method != 'G-Ref-SS-S': pass elif method == 'G-Ref-SS-S' and adjustments_metadata['G-Ref-SS-S'] == False: pass else: print('Applying Adjustment Method: G-Ref-SS-S') logger.info('Applying Adjustment Method: G-Ref-SS-S') # ************************ # # Global Comprehensive (G-Ref-WS-Std) if method != 'G-Ref-WS-Std': pass elif method == 'G-Ref-WS-Std' and adjustments_metadata['G-Ref-WS-Std'] == False: pass else: print('Applying Adjustment Method: G-Ref-WS-Std') logger.info('Applying Adjustment Method: G-Ref-WS-Std') # ***************************************** # # Global LTERRA WC 1Hz Data (G-LTERRA_WC_1Hz) if method != 'G-LTERRA_WC_1Hz': pass elif method == 'G-LTERRA_WC_1Hz' and adjustments_metadata['G-LTERRA_WC_1Hz'] == False: pass else: print('Applying Adjustment Method: G-LTERRA_WC_1Hz') logger.info('Applying Adjustment Method: G-LTERRA_WC_1Hz') # ************************************************ # # Global LTERRA ZX Machine Learning (G-LTERRA_ZX_ML) if method != 'G-LTERRA_ZX_ML': pass elif adjustments_metadata['G-LTERRA_ZX_ML'] == False: pass else: print('Applying Adjustment Method: G-LTERRA_ZX_ML') logger.info('Applying Adjustment Method: G-LTERRA_ZX_ML') # ************************************************ # # Global LTERRA WC Machine Learning (G-LTERRA_WC_ML) if method != 'G-LTERRA_WC_ML': pass elif adjustments_metadata['G-LTERRA_WC_ML'] == False: pass else: print('Applying Adjustment Method: G-LTERRA_WC_ML') logger.info('Applying Adjustment Method: G-LTERRA_WC_ML') # ************************************************** # # Global LTERRA WC w/Stability 1Hz (G-LTERRA_WC_S_1Hz) if method != 'G-LTERRA_WC_S_1Hz': pass elif method == 'G-LTERRA_WC_S_1Hz' and adjustments_metadata['G-LTERRA_WC_S_1Hz'] == False: pass else: print('Applying Adjustment Method: G-LTERRA_WC_S_1Hz') logger.info('Applying Adjustment Method: G-LTERRA_WC_S_1Hz') # ************************************************************** # # Global LTERRA WC w/Stability Machine Learning (G-LTERRA_WC_S_ML) if method != 'G-LTERRA_WC_S_ML': pass elif method == 'G-LTERRA_WC_S_ML' and adjustments_metadata['G-LTERRA_WC_S_ML'] == False: pass else: print('Applying Adjustment Method: G-LTERRA_WC_S_ML') logger.info('Applying Adjustment Method: G-LTERRA_WC_S_ML') if RSD_alphaFlag: pass else: ResultsLists_stability_alpha_RSD = ResultsList_stability if cup_alphaFlag: pass else: ResultsLists_stability_alpha_Ane = ResultsList_stability if RSDtype['Selection'][0:4] != 'Wind': reg_results_class1 = np.nan reg_results_class2 = np.nan reg_results_class3 = np.nan reg_results_class4 = np.nan reg_results_class5 = np.nan TI_MBEList_stability = np.nan TI_DiffList_stability = np.nan TI_DiffRefBinsList_stability = np.nan TI_RMSEList_stability = np.nan RepTI_MBEList_stability = np.nan RepTI_DiffList_stability = np.nan RepTI_DiffRefBinsList_stability = np.nan RepTI_RMSEList_stability = np.nan rep_TI_results_1mps_List_stability = np.nan rep_TI_results_05mps_List_stability = np.nan TIBinList_stability = np.nan TIRefBinList_stability = np.nan total_StatsList_stability = np.nan belownominal_statsList_stability = np.nan abovenominal_statsList_stability = np.nan lm_adjList_stability = np.nan adjustmentTagList_stability = np.nan Distibution_statsList_stability = np.nan sampleTestsLists_stability = np.nan # Write 10 minute Adjusted data to a csv file outpath_dir = os.path.dirname(results_filename) outpath_file = os.path.basename(results_filename) outpath_file = str('TI_10minuteAdjusted_' + outpath_file.split('.xlsx')[0] + '.csv') out_dir = os.path.join(outpath_dir,outpath_file) TI_10minuteAdjusted.to_csv(out_dir) write_all_resultstofile(reg_results, baseResultsLists, count_1mps, count_05mps, count_1mps_train, count_05mps_train, count_1mps_test, count_05mps_test, name_1mps_tke, name_1mps_alpha_Ane, name_1mps_alpha_RSD, name_05mps_tke, name_05mps_alpha_Ane, name_05mps_alpha_RSD, count_05mps_tke, count_05mps_alpha_Ane, count_05mps_alpha_RSD, count_1mps_tke, count_1mps_alpha_Ane, count_1mps_alpha_RSD,results_filename, siteMetadata, filterMetadata, Timestamps,timestamp_train,timestamp_test,regimeBreakdown_tke, regimeBreakdown_ane, regimeBreakdown_rsd, Ht_1_ane, Ht_2_ane, extrap_metadata, reg_results_class1, reg_results_class2, reg_results_class3, reg_results_class4, reg_results_class5,reg_results_class1_alpha, reg_results_class2_alpha, reg_results_class3_alpha, reg_results_class4_alpha, reg_results_class5_alpha, Ht_1_rsd, Ht_2_rsd, ResultsLists_stability, ResultsLists_stability_alpha_RSD, ResultsLists_stability_alpha_Ane, stabilityFlag, cup_alphaFlag, RSD_alphaFlag, TimeTestA_baseline_df, TimeTestB_baseline_df, TimeTestC_baseline_df,time_test_A_adjustment_df,time_test_B_adjustment_df,time_test_C_adjustment_df)

55.211258

372

0.607127

20,203

170,658

4.846854

0.054447

0.015523

0.033701

0.020711

0.733673

0.688953

0.636186

0.602935

0.569541

0.546042

0

0.015021

0.282993

170,658

3,090

373

55.229126

0.785205

0.12818

0

0.488698

0

0.000904

0.135432

0.010268

0

1

0.015823

false

0.027577

0.026221

0.000452

0.057414

0.055154

0

null

0

null

0

1

0

4557176f3d49e4358253305342218dd4750b9adc

1,343

py

Python

FileStorage/language/LanguagePack.py

Thiefxt/FileStorage

db2882b2ea861f4412cb453edef6439501b13705

[ "MIT" ]

1

2020-07-15T10:02:40.000Z

FileStorage/language/LanguagePack.py

Thiefxt/FileStorage

db2882b2ea861f4412cb453edef6439501b13705

[ "MIT" ]

null

FileStorage/language/LanguagePack.py

Thiefxt/FileStorage

db2882b2ea861f4412cb453edef6439501b13705

[ "MIT" ]

null

""" @Author : xiaotao @Email : 18773993654@163.com @Lost modifid : 2020/4/24 10:18 @Filename : LanguagePack.py @Description : @Software : PyCharm """ class RET: """ 语言类包 """ OK = "200" DBERR = "501" NODATA = "462" DATAEXIST = "433" DATAERR = "499" REQERR = "521" IPERR = "422" THIRDERR = "431" IOERR = "502" SERVERERR = "500" UNKNOWERR = "451" USER_STATUS = "465" # 元组中第一个为中文，第二个为英文，第三个为繁体 language_pack = { RET.OK: ("成功",), RET.DBERR: ("数据库查询错误",), RET.NODATA: ("数据不存在",), RET.DATAEXIST: ("数据已存在",), RET.DATAERR: ("数据格式错误",), RET.REQERR: ("非法请求或请求次数受限",), RET.IPERR: ("IP受限",), RET.THIRDERR: ("第三方系统错误",), RET.IOERR: ("文件读写错误",), RET.SERVERERR: ("内部错误",), RET.UNKNOWERR: ("未知错误",), RET.USER_STATUS: ("账号已被禁用，如有疑义请联系平台客服",), } class Language(object): _lang ='zh_cn' @classmethod def init(cls, lang): cls._lang = lang @classmethod def get(cls, value): lang = language_pack.get(value) if not lang: return None if cls._lang == 'zh_cn' and len(lang) > 0: return lang[0] elif cls._lang == 'en_US' and len(lang) > 1: return lang[1] elif cls._lang == 'zh_F' and len(lang) > 2: return lang[2]

19.463768

52

0.531646

155

1,343

4.522581

0.567742

0.049929

0.042796

0

0.071277

0.300074

1,343

68

53

19.75

0.674468

0.204021

0

0.046512

0

0.11943

0

1

0.046512

false

0

0.488372

0

null

0

null

0

1

0

4558b0a8efff5ece908e59c6b9303248612636c0

2,427

py

Python

validate_binary_tree/solution_2.py

nunezpaul/practice_problems

22449c014046b702a4284bb66548f3a70c265622

[ "MIT" ]

null

validate_binary_tree/solution_2.py

nunezpaul/practice_problems

22449c014046b702a4284bb66548f3a70c265622

[ "MIT" ]

null

validate_binary_tree/solution_2.py

nunezpaul/practice_problems

22449c014046b702a4284bb66548f3a70c265622

[ "MIT" ]

null

class TreeNode(object): def __str__(self): left = self.left.val if self.left else 'N' right = self.right.val if self.right else 'N' return "{left} {val} {right}".format(val=self.val, left=left, right=right) def __init__(self, val): self.val = val self.right = None self.left = None def is_valid_tree(root): flattened_tree = [] _traverse_tree(root, flattened_tree) return _is_in_order(flattened_tree) def _traverse_tree(root, flattened_tree): if root: _traverse_tree(root.left, flattened_tree) flattened_tree.append(root.val) _traverse_tree(root.right, flattened_tree) return def _is_in_order(flattened_tree): for idx, num in enumerate(flattened_tree): if idx == 0: prev_num = num continue if prev_num >= num: return False prev_num = num return True def construct_bad_tree(): root = TreeNode(4) root.left = TreeNode(3) root.left.left = TreeNode(1) root.left.right = TreeNode(2) root.right = TreeNode(10) root.right.left = TreeNode(6) root.right.right = TreeNode(12) root.right.left.left = TreeNode(5) root.right.left.right = TreeNode(8) root.right.right.right = TreeNode(14) root.right.right.left = TreeNode(11) return root def construct_good_tree(): root = TreeNode(4) root.left = TreeNode(3) root.left.left = TreeNode(1) # root.left.right = TreeNode(2) root.right = TreeNode(10) root.right.left = TreeNode(6) root.right.right = TreeNode(12) root.right.left.left = TreeNode(5) root.right.left.right = TreeNode(8) root.right.right.right = TreeNode(14) root.right.right.left = TreeNode(11) return root def construct_bad_tree2(): root = TreeNode(5) root.left = TreeNode(1) root.right = TreeNode(4) root.right.left = TreeNode(3) root.right.right = TreeNode(6) return root def construct_good_tree2(): root = TreeNode(2) root.left = TreeNode(1) root.right = TreeNode(3) if __name__ == '__main__': bad_root = construct_bad_tree() assert not is_valid_tree(bad_root) good_root = construct_good_tree() assert is_valid_tree(good_root) bad_root2 = construct_bad_tree2() assert not is_valid_tree(bad_root2) good_root2 = construct_good_tree2() assert is_valid_tree(good_root2)

22.682243

82

0.651834

337

2,427

4.468843

0.160237

0.113546

0.060425

0.045153

0.555113

0.439575

0.409031

0.363878

0

0.025405

0.237742

2,427

107

83

22.682243

0.788649

0.011949

0

0.369863

0

0.012516

0

0.054795

1

0.123288

false

0

0.246575

0

null

0

null

0

1

0

455cfcd1a4533c63cce20863058bc3230fd98394

2,076

py

Python

aoc_cqkh42/year_2020/day_12.py

cqkh42/advent-of-code

bcf31cf8973a5b6d67492c412dce10df742e04d1

[ "MIT" ]

null

aoc_cqkh42/year_2020/day_12.py

cqkh42/advent-of-code

bcf31cf8973a5b6d67492c412dce10df742e04d1

[ "MIT" ]

null

aoc_cqkh42/year_2020/day_12.py

cqkh42/advent-of-code

bcf31cf8973a5b6d67492c412dce10df742e04d1

[ "MIT" ]

null

""" Solutions for day 12 of 2020's Advent of Code """ from typing import Tuple def _rotate_right(n, e) -> Tuple[int, int]: return -e, n def _rotate_left(n, e) -> Tuple[int, int]: return e, -n def part_a(data) -> int: """ Solution for part a Parameters ---------- data: str Returns ------- answer: int """ directions = ['N', 'E', 'S', 'W'] direction = 'E' n = 0 e = 0 for instruction in data.split('\n'): action = instruction[0] number = int(instruction[1:]) if action == 'F': action = direction if action == 'N': n += number elif action == 'S': n -= number elif action == 'E': e += number elif action == 'W': e -= number elif action == 'R': turns = number / 90 new_index = (directions.index(direction) + turns) % 4 direction = directions[int(new_index)] elif action == 'L': turns = number / 90 new_index = (directions.index(direction) - turns) % 4 direction = directions[int(new_index)] return abs(n) + abs(e) def part_b(data, **_) -> int: """ Solution for part b Parameters ---------- data: str Returns ------- answer: int """ w_n = 1 w_e = 10 n = 0 e = 0 for instruction in data.split('\n'): action = instruction[0] number = int(instruction[1:]) if action == 'F': n += (w_n*number) e += (w_e*number) if action == 'N': w_n += number elif action == 'S': w_n -= number elif action == 'E': w_e += number elif action == 'W': w_e -= number elif action == 'R': for _ in range(number // 90): w_n, w_e = _rotate_right(w_n, w_e) elif action == 'L': for _ in range(number // 90): w_n, w_e = _rotate_left(w_n, w_e) return abs(n) + abs(e)

21.625

65

0.461464

255

2,076

3.627451

0.207843

0.108108

0.138378

0.073514

0.765405

0.568649

0.456216

0.404324

0

0.021395

0.3921

2,076

95

66

21.852632

0.711569

0.100674

0

0.551724

0

0.012936

0

1

0.068966

false

0

0.017241

0.034483

0.155172

0

null

0

null

0

1

0

455db22f99db07331fddab80325507e7476dc805

3,786

py

Python

src/main/resources/python/stack.py

VAlgoLang/ManimDSLCompiler

87020d135fa7360aaeccf7e2b9a453f6ffb0fb33

[ "BSD-3-Clause" ]

19

2020-11-05T13:55:45.000Z

2021-01-08T13:19:40.000Z

src/main/resources/python/stack.py

VAlgoLang/ManimDSLCompiler

87020d135fa7360aaeccf7e2b9a453f6ffb0fb33

[ "BSD-3-Clause" ]

29

2020-10-13T10:29:21.000Z

2021-01-10T18:34:06.000Z

src/main/resources/python/stack.py

VAlgoLang/ManimDSLCompiler

87020d135fa7360aaeccf7e2b9a453f6ffb0fb33

[ "BSD-3-Clause" ]

6

2021-03-20T07:04:11.000Z

2022-03-22T02:39:03.000Z

class Stack(DataStructure, ABC): def __init__(self, ul, ur, ll, lr, aligned_edge, color=WHITE, text_color=WHITE, text_weight=NORMAL, font="Times New Roman"): super().__init__(ul, ur, ll, lr, aligned_edge, color, text_color, text_weight, font) self.empty = None def create_init(self, text=None, creation_style=None): if not creation_style: creation_style = "ShowCreation" empty = InitStructure(text, 0, self.max_width - 2 * MED_SMALL_BUFF, color=self.color, text_color=self.text_color) self.empty = empty.all empty.all.move_to(np.array([self.width_center, self.lr[1], 0]), aligned_edge=self.aligned_edge) self.all.add(empty.all) creation_transform = globals()[creation_style] return [creation_transform(empty.text), ShowCreation(empty.shape)] def push(self, obj, creation_style=None): if not creation_style: creation_style = "FadeIn" animations = [] obj.all.move_to(np.array([self.width_center, self.ul[1] - 0.1, 0]), UP) shrink, scale_factor = self.shrink_if_cross_boundary(obj.all) if shrink: animations.append([shrink]) target_width = self.all.get_width() * (scale_factor if scale_factor else 1) obj.all.scale(target_width / obj.all.get_width()) creation_transform = globals()[creation_style] animations.append([creation_transform(obj.all)]) animations.append([ApplyMethod(obj.all.next_to, self.all, np.array([0, 0.25, 0]))]) return animations def pop(self, obj, fade_out=True): self.all.remove(obj.all) animation = [[ApplyMethod(obj.all.move_to, np.array([self.width_center, self.ul[1] - 0.1, 0]), UP)]] if fade_out: animation.append([FadeOut(obj.all)]) enlarge, scale_factor = self.shrink(new_width=self.all.get_width(), new_height=self.all.get_height() + 0.25) if enlarge: animation.append([enlarge]) return animation def shrink_if_cross_boundary(self, new_obj): height = new_obj.get_height() if self.will_cross_boundary(height, "TOP"): return self.shrink(new_width=self.all.get_width(), new_height=self.all.get_height() + height + 0.4) return 0, 1 def push_existing(self, obj): animation = [[ApplyMethod(obj.all.move_to, np.array([self.width_center, self.ul[1] - 0.1, 0]), UP)]] enlarge, scale_factor = obj.owner.shrink(new_width=obj.owner.all.get_width(), new_height=obj.owner.all.get_height() + 0.25) sim_list = list() if enlarge: sim_list.append(enlarge) scale_factor = self.all.get_width() / obj.all.get_width() if scale_factor != 1: sim_list.append(ApplyMethod(obj.all.scale, scale_factor, {"about_edge": UP})) if len(sim_list) != 0: animation.append(sim_list) animation.append([ApplyMethod(obj.all.next_to, self.all, np.array([0, 0.25, 0]))]) return animation def clean_up(self): return [FadeOut(self.all)] # Object representing a stack instantiation. class InitStructure: def __init__(self, text, angle, length=1.5, color=WHITE, text_color=WHITE, text_weight=NORMAL, font="Times New Roman"): self.shape = Line(color=color) self.shape.set_length(length) self.shape.set_angle(angle) if text is not None: self.text = Text(text, color=text_color, weight=text_weight, font=font) self.text.next_to(self.shape, DOWN, SMALL_BUFF) self.all = VGroup(self.text, self.shape) else: self.all = VGroup(self.shape)

46.740741

120

0.626519

513

3,786

4.423002

0.192982

0.040106

0.033936

0.019392

0.379022

0.304099

0.285148

0.227413

0

0.014376

0.246698

3,786

80

121

47.325

0.781206

0.011094

0

0.169014

0

0.016301

0

1

0.112676

false

0

0.014085

0.239437

0

null

0

null

0

1

0

455ecf77b284ae23c05aaa297b5a0ed55da0477f

1,519

py

Python

notebooks_for_development/phase_fold_bh_peg.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

null

notebooks_for_development/phase_fold_bh_peg.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

null

notebooks_for_development/phase_fold_bh_peg.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # Reads in photometry from different sources, normalizes them, and puts them # onto a BJD time scale # Created 2021 Dec. 28 by E.S. import numpy as np import pandas as pd from astropy.time import Time import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler file_name_photometry_input = "./all_photometry_program_stars/polished/bh_peg_aavso_polished_ingest.txt" period_input = 0.640993 # Monson 2017 period of BH Peg # read in photometry df_test2 = pd.read_csv(file_name_photometry_input, names=["jd","mag","error"], delim_whitespace=True) # phase-folded data df_test2["epoch_start_zero"] = np.subtract(df_test2["jd"],np.min(df_test2["jd"])) df_test2["baseline_div_period"] = np.divide(df_test2["epoch_start_zero"],period_input) df_phase_folded = pd.DataFrame(data = [t%1. for t in df_test2["baseline_div_period"]], columns=["phase"]) df_phase_folded["mag"] = df_test2["mag"] # find where maximum is, and set the phase there to be zero idx_max = df_phase_folded["mag"] == np.min(df_phase_folded["mag"]) df_phase_folded["phase"] = np.mod(np.subtract(df_phase_folded["phase"],df_phase_folded["phase"].loc[idx_max].values),1.) # quick plot plt.clf() plt.scatter(df_phase_folded["phase"], df_phase_folded["mag"], s=2) plt.title("Phase-folded curve using ") plt.gca().invert_yaxis() plt.show() # write out file_name_out = "./data/phase_folded_curves/junk.csv" df_phase_folded.to_csv(file_name_out) print("Wrote ", file_name_out)

33.021739

120

0.75181

251

1,519

4.290837

0.474104

0.132776

0.120706

0.059424

0.181987

0.057567

0

0.021545

0.113891

1,519

45

121

33.755556

0.778603

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1

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false

0

0.217391

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0.043478

0

null

0

null

0

1

0

456020c6f80bf546826faf9d3db7db5ef4399eed

4,012

py

Python

history/predict.py

Snipa22/pytrader

5a730435332a159e68ba13ec01b4b7bfa380ec82

[ "MIT" ]

3

2016-04-11T13:51:07.000Z

2022-03-10T15:42:24.000Z

history/predict.py

Snipa22/pytrader

5a730435332a159e68ba13ec01b4b7bfa380ec82

[ "MIT" ]

null

history/predict.py

Snipa22/pytrader

5a730435332a159e68ba13ec01b4b7bfa380ec82

[ "MIT" ]

null

from history.tools import normalization, filter_by_mins, create_sample_row from history.models import Price, PredictionTest import time from history.tools import print_and_log def predict_v2(ticker,hidden_layers=15,NUM_MINUTES_BACK=1000,NUM_EPOCHS=1000,granularity_minutes=15,datasetinputs=5,learningrate=0.005,bias=False,momentum=0.1,weightdecay=0.0,recurrent=False,timedelta_back_in_granularity_increments=0): #setup print_and_log( "(p)starting ticker:{} hidden:{} min:{} epoch:{} gran:{} dsinputs:{} learningrate:{} bias:{} momentum:{} weightdecay:{} recurrent:{}, timedelta_back_in_granularity_increments:{} ".format(ticker,hidden_layers,NUM_MINUTES_BACK,NUM_EPOCHS,granularity_minutes,datasetinputs,learningrate,bias,momentum,weightdecay,recurrent,timedelta_back_in_granularity_increments)) pt = PredictionTest() pt.type = 'mock' pt.symbol = ticker pt.datasetinputs = datasetinputs pt.hiddenneurons = hidden_layers pt.minutes_back = NUM_MINUTES_BACK pt.epochs = NUM_EPOCHS pt.momentum = momentum pt.granularity = granularity_minutes pt.bias = bias pt.bias_chart = -1 if pt.bias is None else ( 1 if pt.bias else 0 ) pt.learningrate = learningrate pt.weightdecay = weightdecay pt.recurrent = recurrent pt.recurrent_chart = -1 if pt.recurrent is None else ( 1 if pt.recurrent else 0 ) pt.timedelta_back_in_granularity_increments = timedelta_back_in_granularity_increments all_output = "" start_time = int(time.time()) #get neural network & data nn = pt.get_nn() sample_data, test_data = pt.get_train_and_test_data() #output / testing round_to = 2 num_times_directionally_correct = 0 num_times = 0 diffs = [] profitloss_pct = [] for i,val in enumerate(test_data): try: # get NN projection sample = create_sample_row(test_data,i,datasetinputs) recommend, nn_price, last_sample, projected_change_pct = pt.predict(sample) ## calculate profitability actual_price = test_data[i+datasetinputs] diff = nn_price - actual_price diff_pct = 100 * diff / actual_price directionally_correct = ( (actual_price - last_sample) > 0 and (nn_price - last_sample) > 0 ) or ( (actual_price - last_sample) < 0 and (nn_price - last_sample) < 0 ) if recommend != 'HOLD': profitloss_pct = profitloss_pct + [abs( (actual_price - last_sample) / last_sample ) * ( 1 if directionally_correct else -1 )] if directionally_correct: num_times_directionally_correct = num_times_directionally_correct + 1 num_times = num_times + 1 diffs.append(diff) output = "{}) seq ending in {} => {} (act {}, {}/{} pct off); Recommend: {}; Was Directionally Correct:{}".format(i,round(actual_price,round_to),round(nn_price,round_to),round(actual_price,round_to),round(diff,round_to),round(diff_pct,1),recommend,directionally_correct) all_output = all_output + "\n" + output except Exception as e: if "list index out of range" not in str(e): print_and_log("(p)"+str(e)) pass; avg_diff = sum([abs(diff[0]) for diff in diffs]) / num_times pct_correct = 100 * num_times_directionally_correct / num_times modeled_profit_loss = sum(profitloss_pct) / len(profitloss_pct) output = 'directionally correct {} of {} times. {}%. avg diff={}, profit={}'.format(num_times_directionally_correct,num_times,round(pct_correct,0),round(avg_diff,4),round(modeled_profit_loss,3)) print_and_log("(p)"+output) all_output = all_output + "\n" + output end_time = int(time.time()) pt.time = end_time - start_time pt.prediction_size = len(diffs) pt.output = all_output pt.percent_correct = pct_correct pt.avg_diff = avg_diff pt.profitloss = modeled_profit_loss pt.profitloss_int = int(pt.profitloss * 100) pt.save() return pt.pk

48.337349

380

0.692423

532

4,012

4.943609

0.255639

0.03346

0.034221

0.04943

0.242586

0.201521

0.093536

0

0.016886

0.202891

4,012

82

381

48.926829

0.805503

0.021934

0

0.029851

0

0.029851

0.096988

0.010975

0

1

0.014925

false

0.014925

0.059701

0

0.089552

0.059701

0

null

0

null

0

1

0

456368c28972d08c5777b27f3941bd69bf9f4b4c

2,786

py

Python

rl_teacher/selector.py

oguzserbetci/rl-teacher-atari

fd6c399921d347333d7c5b4b12c63f1a955cea5c

[ "MIT" ]

null

rl_teacher/selector.py

oguzserbetci/rl-teacher-atari

fd6c399921d347333d7c5b4b12c63f1a955cea5c

[ "MIT" ]

5

2018-10-15T11:52:05.000Z

2018-10-30T12:58:53.000Z

rl_teacher/selector.py

oguzserbetci/rl-teacher-atari

fd6c399921d347333d7c5b4b12c63f1a955cea5c

[ "MIT" ]

null

from rl_teacher.segment_sampling import segments_from_rand_rollout, sample_segment_from_path, basic_segment_from_null_action import numpy as np class Selector(object): def __init__(self): print("Selector initialized") def select(self, segments): print("Selector.select()") return segments[:2], 0 class MinMaxSelector(object): def __init__(self): print("Selector initialized") def select(self, segments): print("Selector.select()") sort = np.argsort([sum(segment['rewards']) for segment in segments]) return [segments[sort[0]], segments[sort[-1]]], 0 class VarianceSelector(object): def __init__(self): print("Selector initialized") def select(self, segments): print("Selector.select()") variances = [segment['variance'] for segment in segments] sort = np.argsort(variances) return [segments[sort[-1]]], 0 class ClipSelector(object): """ Wraps a reward model's path_callback to sample, select and record segments for human to annotate. """ def __init__(self, model, env_id, make_env, save_dir, paths_per_selection=500): self.model = model self.selector = VarianceSelector() self.env_id = env_id self.make_env = make_env self.save_dir = save_dir self.paths_per_wait = 1 self.clip_length = 90 self.stacked_frames = 4 self.workers = 4 self.paths_per_selection = paths_per_selection self._num_paths_seen = 0 # Internal counter of how many paths we've seen self.collected_paths = [] def path_callback(self, path): # Video recording to elicit human feedback every x steps. if (self._num_paths_seen % self.paths_per_wait <= self.paths_per_selection) and (self.clip_manager.total_number_of_clips < self.label_schedule.n_desired_labels): if (len(self.collected_paths) < self.paths_per_selection): self.collected_paths.append(path) elif (len(self.collected_paths) == self.paths_per_selection): selected_paths, selection_time = self.selector.select(self.collected_paths) for selected_path in selected_paths: segment = sample_segment_from_path(selected_path, int(self.model._frames_per_segment)) if segment: self.model.clip_manager.add(segment, source="on-policy callback") self.model.clip_manager.sort_clips(wait_until_database_fully_sorted=True) self.collected_paths = [] print("clips sorted.") self._num_paths_seen += 1 self.model.path_callback(path) def predict_reward(self, path): return self.model.predict_reward(path)

37.648649

169

0.66224

348

2,786

5.014368

0.321839

0.036676

0.058453

0.048138

0.209169

0.187393

0.139255

0

0.008057

0.242642

2,786

73

170

38.164384

0.818957

0.072146

0

0.259259

0

0.060924

0

1

0.166667

false

0

0.037037

0.018519

0.351852

0.12963

0

null

0

null

0

1

0

45636a74d98ccc7ab3ff22be9c83602f958559c0

9,474

py

Python

pynet/models/vae/vunet.py

CorentinAmbroise/pynet

c353e5f80e75f785a460422ab7b39fa8f776991a

[ "CECILL-B" ]

null

pynet/models/vae/vunet.py

CorentinAmbroise/pynet

c353e5f80e75f785a460422ab7b39fa8f776991a

[ "CECILL-B" ]

null

pynet/models/vae/vunet.py

CorentinAmbroise/pynet

c353e5f80e75f785a460422ab7b39fa8f776991a

[ "CECILL-B" ]

null

# -*- coding: utf-8 -*- ########################################################################## # NSAp - Copyright (C) CEA, 2020 # Distributed under the terms of the CeCILL-B license, as published by # the CEA-CNRS-INRIA. Refer to the LICENSE file or to # http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html # for details. ########################################################################## """ The Variational U-Net auto-encoder. """ # Imports import logging import collections import torch import torch.nn as nn import torch.nn.functional as func from pynet.interfaces import DeepLearningDecorator from pynet.utils import Networks import numpy as np from .base import BaseVAE from ..unet import Down, Up, Conv1x1x1 # Global parameters logger = logging.getLogger("pynet") @Networks.register @DeepLearningDecorator(family=("encoder", "vae")) class VUNet(BaseVAE): """ VUNet. The Variational U-Net is a convolutional encoder-decoder neural network. The convolutional encoding/decoding parts are the same as the UNet. The model is composed of two sub-networks: 1. Given x (image), encode it into a distribution over the latent space - referred to as Q(z|x). 2. Given z in latent space (code representation of an image), decode it into the image it represents - referred to as f(z). """ def __init__(self, latent_dim, in_channels=1, depth=5, start_filts=64, up_mode="transpose", batchnorm=True, dim="3d", input_shape=None, num_classes=None): """ Init class. Parameters ---------- latent_dim: int the latent dimension. in_channels: int, default 1 number of channels in the input tensor. depth: int, default 5 number of layers in the U-Net. start_filts: int, default 64 number of convolutional filters for the first conv. up_mode: string, default 'transpose' type of upconvolution. Choices: 'transpose' for transpose convolution, 'upsample' for nearest neighbour upsampling. batchnorm: bool, default False normalize the inputs of the activation function. dim: str, default '3d' '3d' or '2d' input data. input_shape: uplet the tensor data shape (X, Y, Z) used during upsample (by default use a scale factor of 2). num_classes: int, default None the number of classes for the conditioning. """ # Inheritance nn.Module.__init__(self) # Check inputs if dim in ("2d", "3d"): self.dim = dim else: raise ValueError( "'{}' is not a valid mode for merging up and down paths. Only " "'3d' and '2d' are allowed.".format(dim)) if up_mode in ("transpose", "upsample"): self.up_mode = up_mode else: raise ValueError( "'{}' is not a valid mode for upsampling. Only 'transpose' " "and 'upsample' are allowed.".format(up_mode)) # Declare class parameters self.latent_dim = latent_dim self.num_classes = num_classes self.in_channels = in_channels self.start_filts = start_filts self.depth = depth self.down = [] self.up = [] self.shapes = None if input_shape is not None: self.shapes = self.downsample_shape( input_shape, nb_iterations=(depth - 1)) self.shapes = self.shapes[::-1] # Create the encoder pathway self.hidden_dims = [] for cnt in range(depth): in_channels = self.in_channels if cnt == 0 else out_channels out_channels = self.start_filts * (2**cnt) self.hidden_dims.append(out_channels) pooling = False if cnt == 0 else True self.down.append( Down(in_channels, out_channels, self.dim, pooling=pooling, batchnorm=batchnorm)) # Create the decoder pathway # - careful! decoding only requires depth-1 blocks for cnt in range(depth - 1): in_channels = out_channels out_channels = in_channels // 2 shape = None if self.shapes is not None: shape = self.shapes[cnt + 1] self.up.append( Up(in_channels, out_channels, up_mode=up_mode, dim=self.dim, merge_mode="none", batchnorm=batchnorm, shape=shape)) # Add the list of modules to current module self.down = nn.Sequential(*self.down) hidden_dim = self.hidden_dims[-1] * np.prod(self.shapes[0]) self.mu = nn.Linear(hidden_dim, latent_dim) self.var = nn.Linear(hidden_dim, latent_dim) self.latent_to_hidden = nn.Linear(latent_dim, hidden_dim) self.up = nn.Sequential(*self.up) self.conv_final = Conv1x1x1(out_channels, self.in_channels, self.dim) self.logit = nn.Tanh() # Kernel initializer self.kernel_initializer() def encode(self, x): """ Encodes the input by passing through the encoder network and returns the latent codes. Parameters ---------- x: Tensor, (N, C, X, Y, Z) input tensor to encode. Returns ------- mu: Tensor (N, D) mean of the latent Gaussian. logvar: Tensor (N, D) standard deviation of the latent Gaussian. """ logger.debug("Encode...") self.debug("input", x) x = self.down(x) self.debug("down", x) x = torch.flatten(x, start_dim=1) self.debug("flatten", x) # Split x into mu and var components of the latent Gaussian # distribution z_mu = self.mu(x) z_logvar = self.var(x) self.debug("z_mu", z_mu) self.debug("z_logvar", z_logvar) return z_mu, z_logvar def decode(self, x_sample): """ Maps the given latent codes onto the image space. Parameters ---------- x_sample: Tensor (N, D) sample from the distribution having latent parameters mu, var. Returns ------- x: Tensor, (N, C, X, Y, Z) the prediction. """ logger.debug("Decode...") self.debug("x sample", x_sample) x = self.latent_to_hidden(x_sample) self.debug("hidden", x) x = x.view(-1, self.hidden_dims[-1], *self.shapes[0]) self.debug("view", x) x = self.up(x) self.debug("up", x) x = self.conv_final(x) self.debug("final", x) return self.logit(x) def reparameterize(self, z_mu, z_logvar): """ Reparameterization trick to sample from N(mu, var) from N(0,1). Parameters ---------- mu: Tensor (N, D) mean of the latent Gaussian. logvar: Tensor (N, D) standard deviation of the latent Gaussian. Returns ------- x_sample: Tensor (N, D) sample from the distribution having latent parameters mu, var. """ logger.debug("Reparameterize...") self.debug("z_mu", z_mu) self.debug("z_logvar", z_logvar) std = torch.exp(0.5 * z_logvar) eps = torch.randn_like(std) x_sample = eps.mul(std).add_(z_mu) self.debug("x sample", x_sample) return x_sample def forward(self, x): logger.debug("VUnet...") z_mu, z_logvar = self.encode(x) x_sample = self.reparameterize(z_mu, z_logvar) predicted = self.decode(x_sample) return predicted, {"z_mu": z_mu, "z_logvar": z_logvar} class DecodeLoss(object): """ VAE consists of two loss functions: 1. Reconstruction loss: how well we can reconstruct the image 2. KL divergence loss: how off the distribution over the latent space is from the prior. Given the prior is a standard Gaussian and the inferred distribution is a Gaussian with a diagonal covariance matrix, the KL-divergence becomes analytically solvable. loss = REC_loss + k1 * KL_loss. """ def __init__(self, k1=1, rec_loss="mse", nodecoding=False): super(DecodeLoss, self).__init__() if rec_loss not in ("mse", "bce"): raise ValueError("Requested loss not yet supported.") self.layer_outputs = None self.k1 = k1 self.rec_loss = rec_loss self.nodecoding = nodecoding def __call__(self, x_sample, x): if self.nodecoding: return -1 if self.layer_outputs is None: raise ValueError("The model needs to return the latent space " "distribution parameters z_mu, z_logvar.") z_mu = self.layer_outputs["z_mu"] z_logvar = self.layer_outputs["z_logvar"] if self.rec_loss == "bce": recon_loss = func.binary_cross_entropy( x_sample, x, reduction="sum") else: recon_loss = func.mse_loss( x_sample, x, reduction="mean") # kld_loss = 0.5 * torch.sum( # torch.exp(z_logvar) + z_mu**2 - 1.0 - z_logvar) kld_loss = torch.mean(-0.5 * torch.sum( 1 + z_logvar - z_mu ** 2 - z_logvar.exp(), dim=-1), dim=0) return recon_loss + self.k1 * kld_loss

34.830882

79

0.576947

1,212

9,474

4.378713

0.237624

0.025061

0.007537

0.01319

0.140946

0.10929

0.100622

0.084794

0.07085

0

0.010666

0.307262

9,474

271

80

34.95941

0.797958

0.329533

0

0.081481

0

0.088324

0

1

0.051852

false

0

0.074074

0

0.185185

0

null

0

null

0

1

0

456499cb6f4f56978c9d9e33ec8c47f7702e105f

1,161

py

Python

api/subparts/alpha/alpha.py

schana/swagger-based-api

964cd549e73a81a0a72037cb8f75271708d061db

[ "Apache-2.0" ]

null

api/subparts/alpha/alpha.py

schana/swagger-based-api

964cd549e73a81a0a72037cb8f75271708d061db

[ "Apache-2.0" ]

null

api/subparts/alpha/alpha.py

schana/swagger-based-api

964cd549e73a81a0a72037cb8f75271708d061db

[ "Apache-2.0" ]

null

import collections import flask_restplus from flask_restplus import fields from flask_restplus import reqparse from api import util api = util.build_api('alpha', __name__, url_prefix='/subparts/alpha') v1 = util.build_namespace(api, 'v1', description='Version 1') AlphaSpec = collections.namedtuple('Alpha', ['x_and_y', 'z']) alpha_model = v1.model(AlphaSpec.__name__, AlphaSpec( x_and_y=fields.Integer(description='x plus y', required=True), z=fields.String(description='z-e-d', required=True) )._asdict()) alpha_params = reqparse.RequestParser() alpha_params.add_argument('x', type=int, required=True) alpha_params.add_argument('y', type=int, required=False, default=0) alpha_params.add_argument('z', type=str, required=True) @v1.route('/alphas') class Alpha(flask_restplus.Resource): @v1.expect(alpha_params) @v1.marshal_with(alpha_model) @v1.doc(description='A super-helpful description as to what is going on', params={'x': 'The best x of them all'}) def get(self): args = alpha_params.parse_args() return AlphaSpec(x_and_y=args['x'] + args['y'], z=args['z'])._asdict()

32.25

77

0.707149

166

1,161

4.728916

0.439759

0.084076

0.019108

0.084076

0

0.009109

0.149009

1,161

35

78

33.171429

0.785425

0

0.12317

0

1

0.038462

false

0

0.192308

0

0.307692

0

null

0

null

0

1

0

456b9d36efdf097837bfdedc583dac092dcbd61c

4,927

py

Python

EDGAR/Generic_Parser.py

laurakchen/Intended-Use-Of_Proceeds

4d958fbeddb3eb20b6a3ab1166ad918673408ddc

[ "Apache-2.0" ]

null

EDGAR/Generic_Parser.py

laurakchen/Intended-Use-Of_Proceeds

4d958fbeddb3eb20b6a3ab1166ad918673408ddc

[ "Apache-2.0" ]

null

EDGAR/Generic_Parser.py

laurakchen/Intended-Use-Of_Proceeds

4d958fbeddb3eb20b6a3ab1166ad918673408ddc

[ "Apache-2.0" ]

4

2021-01-10T02:22:24.000Z

2021-01-29T07:01:16.000Z

""" Program to provide generic parsing for all files in user-specified directory. The program assumes the input files have been scrubbed, i.e., HTML, ASCII-encoded binary, and any other embedded document structures that are not intended to be analyzed have been deleted from the file. Dependencies: Python: Load_MasterDictionary.py Data: LoughranMcDonald_MasterDictionary_XXXX.csv The program outputs: 1. File name 2. File size (in bytes) 3. Number of words (based on LM_MasterDictionary 4. Proportion of positive words (use with care - see LM, JAR 2016) 5. Proportion of negative words 6. Proportion of uncertainty words 7. Proportion of litigious words 8. Proportion of modal-weak words 9. Proportion of modal-moderate words 10. Proportion of modal-strong words 11. Proportion of constraining words (see Bodnaruk, Loughran and McDonald, JFQA 2015) 12. Number of alphanumeric characters (a-z, A-Z) 13. Number of digits (0-9) 14. Number of numbers (collections of digits) 15. Average number of syllables 16. Average word length 17. Vocabulary (see Loughran-McDonald, JF, 2015) ND-SRAF McDonald 2016/06 : updated 2018/03 """ import csv import glob import re import string import sys import time sys.path.append('/Users/laurachen/Desktop/FinProject/') # sys.path.append('D:\GD\Python\TextualAnalysis\Modules') # Modify to identify path for custom modules import Load_MasterDictionary as LM # User defined directory for files to be parsed TARGET_FILES = r'/Users/laurachen/Desktop/FinProject/fbCleaned.txt' # User defined file pointer to LM dictionary MASTER_DICTIONARY_FILE = r'/Users/laurachen/Desktop/FinProject/' + \ 'LoughranMcDonald_MasterDictionary_2018.csv' # User defined output file OUTPUT_FILE = r'/Users/laurachen/Desktop/FinProject//Parser.csv' # Setup output OUTPUT_FIELDS = ['file name,', 'file size,', 'number of words,', '% positive,', '% negative,', '% uncertainty,', '% litigious,', '% modal-weak,', '% modal moderate,', '% modal strong,', '% constraining,', '# of alphabetic,', '# of digits,', '# of numbers,', 'avg # of syllables per word,', 'average word length,', 'vocabulary'] lm_dictionary = LM.load_masterdictionary(MASTER_DICTIONARY_FILE, True) def main(): f_out = open(OUTPUT_FILE, 'w') wr = csv.writer(f_out, lineterminator='\n') wr.writerow(OUTPUT_FIELDS) file_list = glob.glob(TARGET_FILES) for file in file_list: print(file) with open(file, 'r', encoding='UTF-8', errors='ignore') as f_in: doc = f_in.read() doc_len = len(doc) doc = re.sub('(May|MAY)', ' ', doc) # drop all May month references doc = doc.upper() # for this parse caps aren't informative so shift output_data = get_data(doc) output_data[0] = file output_data[1] = doc_len wr.writerow(output_data) def get_data(doc): vdictionary = {} _odata = [0] * 17 total_syllables = 0 word_length = 0 tokens = re.findall('\w+', doc) # Note that \w+ splits hyphenated words for token in tokens: if not token.isdigit() and len(token) > 1 and token in lm_dictionary: _odata[2] += 1 # word count word_length += len(token) if token not in vdictionary: vdictionary[token] = 1 if lm_dictionary[token].positive: _odata[3] += 1 if lm_dictionary[token].negative: _odata[4] += 1 if lm_dictionary[token].uncertainty: _odata[5] += 1 if lm_dictionary[token].litigious: _odata[6] += 1 if lm_dictionary[token].weak_modal: _odata[7] += 1 if lm_dictionary[token].moderate_modal: _odata[8] += 1 if lm_dictionary[token].strong_modal: _odata[9] += 1 if lm_dictionary[token].constraining: _odata[10] += 1 total_syllables += lm_dictionary[token].syllables _odata[11] = len(re.findall('[A-Z]', doc)) _odata[12] = len(re.findall('[0-9]', doc)) # drop punctuation within numbers for number count doc = re.sub('(?!=[0-9])(\.|,)(?=[0-9])', '', doc) doc = doc.translate(str.maketrans(string.punctuation, " " * len(string.punctuation))) _odata[13] = len(re.findall(r'\b[-+$]?[$€£]?[-+(]?\d+$?\b', doc)) _odata[14] = total_syllables / _odata[2] _odata[15] = word_length / _odata[2] _odata[16] = len(vdictionary) # Convert counts to % for i in range(3, 10 + 1): _odata[i] = (_odata[i] / _odata[2]) * 100 # Vocabulary return _odata if __name__ == '__main__': print('\n' + time.strftime('%c') + '\nGeneric_Parser.py\n') main() print('\n' + time.strftime('%c') + '\nNormal termination.')

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0.043478

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null

0

null

0

1

0

456d5f84b2506b512723f3a37ab5b953c8eded00

4,882

py

Python

Code/techne_library_code.py

uk-gov-mirror/nationalarchives.TechneTraining

aabb15f2bfe6bbbcc824dbdaa7f8c59632fea21a

[ "MIT" ]

null

Code/techne_library_code.py

uk-gov-mirror/nationalarchives.TechneTraining

aabb15f2bfe6bbbcc824dbdaa7f8c59632fea21a

[ "MIT" ]

null

Code/techne_library_code.py

uk-gov-mirror/nationalarchives.TechneTraining

aabb15f2bfe6bbbcc824dbdaa7f8c59632fea21a

[ "MIT" ]

null

import os from sklearn.model_selection import train_test_split import numpy as np from operator import itemgetter from math import log import random from gensim.summarization.summarizer import summarize from sklearn.naive_bayes import BernoulliNB from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.neighbors import KDTree from matplotlib import pyplot from sklearn.feature_extraction.text import TfidfVectorizer import seaborn as sns import pandas as pd import ipywidgets as widgets from matplotlib.colors import LogNorm def add_to_dict(D, k, v=1): if k in D: D[k] += v else: D[k] = v def clean_string(string): out_string = "" for c in string: if c.isalpha(): out_string += c else: if len(out_string) > 0 and out_string[-1] != " ": out_string += " " return out_string def read_topic_list(file_name): topic_words = {} topic_file = open(file_name, 'r') for row in topic_file: fields = row[:-1].split("|") topic_id = int(fields[0]) words = fields[1].split(",") topic_words[topic_id] = words topic_file.close() return topic_words def read_doc_topics(file_name): topics_per_doc = {} doc_topics = open(file_name, 'r') for row in doc_topics: fields = row[:-1].split("|") file_name = fields[0] topic_probs = [float(x) for x in fields[1:]] topics_per_doc[file_name] = topic_probs doc_topics.close() return topics_per_doc def normalise_vector(v): norm = np.linalg.norm(v, ord=1) if norm == 0: return v return v / norm def plot_doc_topics(doc_ids, doc_topic_lookup, topic_count, normalise=True): fig, ax = pyplot.subplots(2,2) fig.set_size_inches(8.5,5) for i, file_number in enumerate(doc_ids): topic_probs = doc_topic_lookup["file_" + str(file_number) + ".txt"] if normalise: topic_probs = normalise_vector(topic_probs) ax[int(i/2), i % 2].bar(x = [str(x) for x in range(topic_count)], height = topic_probs) return fig, ax def filter_topics_by_threshold(topic_dict, threshold): filtered_dict = {} for k,v in topic_dict.items(): scores = [x if x >= threshold else 0.0 for i,x in enumerate(v)] filtered_dict[k] = scores return filtered_dict def topic_to_class_scores(topic_scores, topic_class_map): file_class_scores = {} max_class = max([v for v in topic_class_map.values()]) for doc_id,scores in topic_scores.items(): class_scores = np.zeros(max_class+1) for t,s in enumerate(scores): class_scores[topic_class_map[t]] += s file_class_scores[doc_id] = class_scores return file_class_scores def load_content_file_map(file_name): file_domain = {} file_map = open(data_drive + "TM/content_file_map.txt","r") file_url = {} for row in file_map: fields = row[:-1].split("|") file_url[fields[0]] = fields[1] file_domain[fields[0]] = fields[1].split("/")[0] file_map.close() def load_content(file_name): content_file = open(file_name, "r") file_contents = {} for row in content_file: fields = row[:-1].split("|") file_contents[fields[0]] = fields[1] content_file.close() return file_contents def load_summaries(file_name): summary_file = open(file_name, 'r') file_summaries = {} for row in summary_file: fields = row[:-1].split("|") file_summaries[fields[0]] = fields[1] summary_file.close() return file_summaries def prepare_for_ml(tfidf_features, classes_per_doc, file_to_idx_map): training_files = [] training_features = [] training_class = [] feature_matrix = tfidf_features.todense() for filename, scores in classes_per_doc.items(): norm_scores = normalise_vector(scores) highest = np.argmax(norm_scores) training_files.append(filename) training_class.append(highest) training_features.append(feature_matrix[file_to_idx_map[filename]]) training_features = np.vstack(training_features) return training_files, training_features, training_class def draw_confusion(y_true, y_pred, model, class_names): fig, ax = pyplot.subplots(1,1,figsize=(7, 7)) N = len(model.classes_) sns.heatmap(pd.DataFrame(confusion_matrix(y_true, y_pred, normalize=None), range(N), range(N)), cmap='magma', annot=True, annot_kws={"size": 15}, fmt='g', ax = ax) #, norm=LogNorm()) #ax.table(cellText=topN[{'TaxonomyCategory','TAXID'}].sort_values(by='TAXID').values, colLabels=['TaxonomyCategory','TAXID'], loc='top') ax.set_xticklabels([class_names[c] for c in model.classes_]) ax.set_yticklabels([class_names[c] for c in model.classes_], rotation = 30) return fig, ax

33.902778

140

0.670012

704

4,882

4.396307

0.255682

0.028433

0.012924

0.024233

0.107593

0.087884

0.03231

0.01874

0

0.010694

0.214666

4,882

143

141

34.13986

0.796557

0.031135

0

0.071429

0

0.011844

0.004865

0

1

0.103175

false

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0.333333

0

null

0

null

0

1

0

456e07cc6bba172145470a41d9133e6c7f291230

2,701

py

Python

sensor/src/upload_metrics.py

tubone24/raspi_plant_checker

e80ccd61c50cbba883f4af8fafafc0404bdf8978

[ "MIT" ]

null

sensor/src/upload_metrics.py

tubone24/raspi_plant_checker

e80ccd61c50cbba883f4af8fafafc0404bdf8978

[ "MIT" ]

null

sensor/src/upload_metrics.py

tubone24/raspi_plant_checker

e80ccd61c50cbba883f4af8fafafc0404bdf8978

[ "MIT" ]

1

2021-12-03T05:28:20.000Z

from gql import gql, Client from gql.transport.requests import RequestsHTTPTransport import requests from datetime import datetime, timedelta, timezone import os from os.path import join, dirname from dotenv import load_dotenv dotenv_path = join(dirname(__file__), "../../", '.env') load_dotenv(dotenv_path) RASPI_URL = os.environ.get("RASPI_URL") HASURA_URL = os.environ.get("HASURA_URL") HASURA_SECRET = os.environ.get("HASURA_SECRET") def get_metrics(url: str): moisture = requests.get(url=f"{url}/moisture").json() light = requests.get(url=f"{url}/light").json() return {"moisture": moisture["value"], "light": light["value"]} def upload_metric_to_hasura(moisture, light): client = Client( transport=RequestsHTTPTransport( url=HASURA_URL, use_json=True, headers={ "Content-type": "application/json", "x-hasura-admin-secret": HASURA_SECRET }, retries=3, ), fetch_schema_from_transport=True, ) query = gql( """ mutation MyMutation ($light: numeric!, $moisture: numeric!){ insert_raspi_plant_checker_one(object: {light: $light, moisture: $moisture}) { id light moisture timestamp } } """ ) params = {"light": light, "moisture": moisture} result = client.execute(query, variable_values=params) print(result) def delete_old_metrics_to_hasura(days_before=7): dt_now = datetime.now(timezone.utc) before_day = dt_now - timedelta(days=days_before) dt = before_day.astimezone().isoformat(timespec='microseconds') client = Client( transport=RequestsHTTPTransport( url=HASURA_URL, use_json=True, headers={ "Content-type": "application/json", "x-hasura-admin-secret": HASURA_SECRET }, retries=3, ), fetch_schema_from_transport=True, ) query = gql( """ mutation MyMutation ($dt: timestamptz){ delete_raspi_plant_checker(where: {timestamp: {_lt: $dt}}) { returning { id light moisture timestamp } } } """ ) params = {"dt": dt} result = client.execute(query, variable_values=params) print(result) def main(): metrics = get_metrics(RASPI_URL) upload_metric_to_hasura(moisture=metrics["moisture"], light=metrics["light"]) delete_old_metrics_to_hasura() if __name__ == "__main__": main()

28.135417

90

0.583858

277

2,701

5.451264

0.306859

0.023841

0.02649

0.460927

0.328477

0

0.00159

0.30137

2,701

95

91

28.431579

0.798622

0

0.42623

0

0.112595

0.020038

0

1

0.065574

false

0

0.114754

0

0.196721

0.032787

0

null

0

null

0

1

0

4571c0f5a311380362a2ed94f464b2d2bf10f14f

1,162

py

Python

project/concept_recog.py

hskang9/HelloWorld

50b09f710176f082c5dac955bc7ea3578a42bd40

[ "MIT" ]

null

project/concept_recog.py

hskang9/HelloWorld

50b09f710176f082c5dac955bc7ea3578a42bd40

[ "MIT" ]

null

project/concept_recog.py

hskang9/HelloWorld

50b09f710176f082c5dac955bc7ea3578a42bd40

[ "MIT" ]

null

# -*- coding: utf-8 -*- import json from watson_developer_cloud import NaturalLanguageUnderstandingV1 as nlu import watson_developer_cloud.natural_language_understanding.features.v1 \ as Features def concept_recog(path='./test.txt'): #with open(path, 'rt', encoding='utf-8') as f: # inputs = f.read() with open(path, 'rb') as f: inputs = f.read().decode("UTF-8") natural_language_understanding = nlu( url=("https://gateway.aibril-watson.kr/" + "natural-language-understanding/api"), username="01fc633a-01c2-486e-a202-44a3b7653a1d", password="wwbFwHfLV4jK", version="2017-02-27") response = natural_language_understanding.analyze( text=inputs, features=[ Features.Concepts( # Concepts options limit=3 ) ] ) # print(json.dumps(response)) texts = response['concepts'] text_list = [] for text in texts: text_list.append(text['text']) for text in text_list: print(text) return ' '.join(text_list) if __name__ == '__main__': concept_recog()

28.341463

74

0.605852

128

1,162

5.3125

0.539063

0.088235

0.164706

0.029412

0.041176

0

0.043581

0.269363

1,162

40

75

29.05

0.757362

0.113597

0

0.15918

0.068359

0

1

0.033333

false

0.033333

0.1

0

0.166667

0.033333

0

null

0

null

0

1

0

4573153b3fe512c2a5e16ccae54eb2974cf42494

6,242

py

Python

i7app/__main__.py

eblade/images7

7fa7c961e046a178243c866fd1f3b82f7e58c73d

[ "BSD-3-Clause", "MIT" ]

null

i7app/__main__.py

eblade/images7

7fa7c961e046a178243c866fd1f3b82f7e58c73d

[ "BSD-3-Clause", "MIT" ]

null

i7app/__main__.py

eblade/images7

7fa7c961e046a178243c866fd1f3b82f7e58c73d

[ "BSD-3-Clause", "MIT" ]

null

#!/usr/bin/env python3 import sys import os import zmq import time import logging from PyQt5 import QtWidgets as W, QtCore as C, QtGui as G from qtzevents.bus import Pub, Push from qtzevents.background import Background from images7.config import Config from images7.system import System from .grid import ThumbView from .browser import BrowserWidget, DateItem from images7 import ( date, entry, files, importer, job, ) from images7.job import ( register, transcode, to_cut, to_main, calculate_hash, read_metadata, create_proxy, clean_cut, ) from images7.analyse import exif from images7.job.transcode import imageproxy # Logging FORMAT = '%(asctime)s [%(threadName)s] %(filename)s +%(levelno)s ' + \ '%(funcName)s %(levelname)s %(message)s' logging.basicConfig( format=FORMAT, level=logging.DEBUG if '-g' in sys.argv else logging.INFO, filename='log', filemode='w', ) class View(W.QMainWindow): def __init__(self): super().__init__() self.context = zmq.Context(1) self.command = Push(self.context, 'command') self.system = None self.control = Control(self.system) self.setWindowTitle('Images7') self.setup_menu() self.setup_layout() #self.setup_model_event_handler() self.setup_control_event_handler() self.on_open('images.ini') logging.getLogger().setLevel(logging.DEBUG) self.show() def setup_menu(self): import_action = W.QAction('&Import', self) import_action.setShortcut('Ctrl+I') import_action.setStatusTip('Triggers an import of files from known cards') import_action.triggered.connect(self.on_import) reload_action = W.QAction('&Reload', self) reload_action.setShortcut('Ctrl+R') reload_action.setStatusTip('Reload the data browser') reload_action.triggered.connect(self.on_reload) m = self.menuBar() file_menu = m.addMenu('&File') file_menu.addAction(import_action) file_menu.addAction(reload_action) def setup_layout(self): self.setGeometry(100, 100, 1000, 800) splitter = W.QSplitter(C.Qt.Horizontal) self.tree = BrowserWidget() self.tree.currentItemChanged.connect(self.on_browser_selection_changed) self.main = W.QStackedWidget() empty = W.QFrame() self.main.addWidget(empty) self.main.setCurrentWidget(empty) splitter.addWidget(self.tree) splitter.addWidget(self.main) splitter.setSizes([300, 700]) self.setCentralWidget(splitter) def setup_model_event_handler(self): self.model_event_handler = ModelEventHandler.as_thread( self.system.event.subscriber('state', 'system', 'error')) self.model_event_handler.message.connect(self.on_message) self.model_event_handler.error.connect(self.on_error) def setup_control_event_handler(self): self.control_event_handler = ControlEventHandler.as_thread(self.control, self.command.puller()) self.control_event_handler.error.connect(self.on_error) self.control_event_handler.model_changed.connect(self.on_model_changed) def new_main_frame(self, widget): self.main.removeWidget(self.main.currentWidget()) self.main.addWidget(widget) self.main.setCurrentWidget(widget) def on_open(self, path): self.command.send({ 'command': 'load', 'path': path, }) def on_import(self): self.command.send({ 'command': 'import', }) def on_reload(self): self.command.send({ 'command': 'reload', }) def on_model_changed(self): self.system = self.control.system self.tree.load() def on_browser_selection_changed(self, current, previous): if isinstance(current, DateItem): widget = ThumbView() self.new_main_frame(widget) query = entry.EntryQuery(date=current.date.date) feed = entry.get_entries(query) widget.populate(feed) def on_message(self, state): self.state_label.setText(state) def on_error(self, message): W.QMessageBox.information(self, 'Error', message) def closeEvent(self, event): self.command.send({'command': 'quit'}) class ModelEventHandler(Background): message = C.pyqtSignal(str) error = C.pyqtSignal(str) def on_state(self, message): self.message.emit(message) def on_system(self, message): if message == 'quit': self.running = False self.quit_and_wait() def on_error(self, message): self.error.emit(message) class ControlEventHandler(Background): enable = C.pyqtSignal(bool) error = C.pyqtSignal(str) model_changed = C.pyqtSignal() def __init__(self, control, *args): super().__init__(*args) self.control = control self.running = True def on_message(self, message): if message['command'] == 'load': try: self.control.load_config(message['path']) self.model_changed.emit() except ValueError as e: self.error.emit(str(e)) if message['command'] == 'reload': self.model_changed.emit() elif message['command'] == 'import': try: logging.info('Importing...') from images7.importer import trig_import trig_import() self.model_changed.emit() logging.info('Imported.') except ValueError as e: self.error.emit(str(e)) elif message['command'] == 'quit': self.running = False class Control: def __init__(self, system): self.system = system def load_config(self, path): config = Config(path) self.system = System(config) importer.App.run(workers=1) job.App.run(workers=4) if __name__ == '__main__': app = W.QApplication(sys.argv) main = View() logging.getLogger().setLevel(logging.DEBUG) sys.exit(app.exec_())

27.257642

103

0.630567

723

6,242

5.278008

0.272476

0.014413

0.023847

0.023061

0.138889

0.037212

0.018868

0

0.006892

0.256168

6,242

228

104

27.377193

0.81499

0.009773

0

0.132948

0

0.061347

0

1

0.121387

false

0

0.17341

0

0.346821

0

null

0

null

0

1

0

45754cc8939f2ca8f5551df60c1a102df61680b5

2,049

py

Python

week01/day01/shopping.py

GsQxZz/fullstack

9010c0c69aec901fd0e0b4434445e822f682c367

[ "Apache-2.0" ]

null

week01/day01/shopping.py

GsQxZz/fullstack

9010c0c69aec901fd0e0b4434445e822f682c367

[ "Apache-2.0" ]

null

week01/day01/shopping.py

GsQxZz/fullstack

9010c0c69aec901fd0e0b4434445e822f682c367

[ "Apache-2.0" ]

null

iphone_price = 5800 mac_price = 9000 coffee_price = 32 python_price = 80 bicyle_price = 1500 list = [] salary = int(input("Please input your salary:")) print("**********欢迎来到购物车系统**********") while True: print("1. iPhone 11 ————%d元" % iphone_price) print("2. Mac book ————%d" % mac_price) print("3. coffee ————%d元" % coffee_price) print("4. python book ————%d元" % python_price) print("5. bicyle ————%d元" % bicyle_price) print("0. 退出") operator = int(input("Please input your choose:")) if operator == 1: if salary >= iphone_price: list.append(["iPhone", iphone_price]) salary -= iphone_price print("iPhone 11已加入到你的购物车，当前余额剩余：%d" % salary) else: print("余额不足，剩余 %d" % salary) elif operator == 2: if salary >= mac_price: list.append(["Mac Book", mac_price]) salary -= mac_price print("Mac Book已加入到你的购物车，当前余额剩余：%d" % salary) else: print("余额不足，剩余 %d" % salary) elif operator == 3: if salary >= coffee_price: list.append(["Coffee", coffee_price]) salary -= coffee_price print("Coffee已加入到你的购物车，当前余额剩余：%d" % salary) else: print("余额不足，剩余 %d" % salary) elif operator == 4: if salary >= python_price: list.append(["Python Book", python_price]) salary -= python_price print("Python Book已加入到你的购物车，当前余额剩余：%d" % salary) else: print("余额不足，剩余 %d" % salary) elif operator == 5: if salary >= bicyle_price: list.append(["Bicyle", bicyle_price]) salary -= bicyle_price print("Mac Book已加入到你的购物车，当前余额剩余：%d" % salary) else: print("余额不足，剩余 %d" % salary) elif operator == 0: break exit() else: print("输入错误，请重新输入") if list is not None: print("您已购买以下商品：") for i in list: print("%s %d" % (i[0], i[1])) print("您的余额为：%d" % salary) print("欢迎下次光临")

28.859155

60

0.537823

244

2,049

4.495902

0.241803

0.070191

0.068368

0.077484

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0

null

0

1

0

45756f7f3335ea9abab94df669ff19ff707b1db9

1,079

py

Python

test/test_edit_contact.py

Zaichkov/python_training

be8aff0b38c5a93c5574762ce5c8c27e6fe11b5a

[ "Apache-2.0" ]

null

test/test_edit_contact.py

Zaichkov/python_training

be8aff0b38c5a93c5574762ce5c8c27e6fe11b5a

[ "Apache-2.0" ]

null

test/test_edit_contact.py

Zaichkov/python_training

be8aff0b38c5a93c5574762ce5c8c27e6fe11b5a

[ "Apache-2.0" ]

null

from model.contact import Contact import random def test_edit_some_contact(app, orm, check_ui): if len(orm.get_contact_list()) == 0: app.contact.create(Contact(firstname="St_Claus")) old_contacts = orm.get_contact_list() contact_for_edit = random.choice(old_contacts) contact = Contact(firstname="edited_firstname", lastname="edited_lastname", address="edited_address", mobile_phone="edited_phone", email="edited_email", title="new_title", bday="19", bmonth="October", byear="1988", id=contact_for_edit.id) app.contact.edit_contact_by_id(contact.id, contact) new_contacts = orm.get_contact_list() old_contacts.remove(contact_for_edit) old_contacts.append(contact) assert sorted(old_contacts, key=Contact.id_or_max) == sorted(new_contacts, key=Contact.id_or_max) if check_ui: ui_list = app.contact.get_contact_list() orm_list = app.contact.make_list_like_ui(new_contacts) assert sorted(orm_list, key=Contact.id_or_max) == sorted(ui_list, key=Contact.id_or_max)

46.913043

107

0.720111

154

1,079

4.701299

0.331169

0.075967

0.077348

0.212707

0.143646

0

0.007778

0.165894

1,079

22

108

49.045455

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false

0

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0

0.157895

0

null

0

null

0

1

0

457919564bdf80b71f716bf686e0ed54de0bb593

9,424

py

Python

pytype/overlays/typed_dict.py

hboshnak/pytype

b6b6448dc562a7200326c92e75efeed203984e16

[ "Apache-2.0" ]

null

pytype/overlays/typed_dict.py

hboshnak/pytype

b6b6448dc562a7200326c92e75efeed203984e16

[ "Apache-2.0" ]

null

pytype/overlays/typed_dict.py

hboshnak/pytype

b6b6448dc562a7200326c92e75efeed203984e16

[ "Apache-2.0" ]

null

"""Implementation of TypedDict.""" import dataclasses from typing import Any, Dict, Set from pytype.abstract import abstract from pytype.abstract import abstract_utils from pytype.abstract import function from pytype.overlays import classgen from pytype.pytd import pytd @dataclasses.dataclass class TypedDictProperties: """Collection of typed dict properties passed between various stages.""" name: str fields: Dict[str, Any] required: Set[str] total: bool @property def keys(self): return set(self.fields.keys()) @property def optional(self): return self.keys - self.required def add(self, k, v, total): self.fields[k] = v if total: self.required.add(k) def check_keys(self, keys): keys = set(keys) missing = (self.keys - keys) & self.required extra = keys - self.keys return missing, extra class TypedDictBuilder(abstract.PyTDClass): """Factory for creating typing.TypedDict classes.""" def __init__(self, ctx): typing_ast = ctx.loader.import_name("typing") pyval = typing_ast.Lookup("typing._TypedDict") pyval = pyval.Replace(name="typing.TypedDict") super().__init__("TypedDict", pyval, ctx) def call(self, node, *args): details = ("Use the class definition form of TypedDict instead.") self.ctx.errorlog.not_supported_yet( self.ctx.vm.frames, "TypedDict functional constructor", details) return node, self.ctx.new_unsolvable(node) def _validate_bases(self, cls_name, bases): """Check that all base classes are valid.""" for base_var in bases: for base in base_var.data: if not isinstance(base, (TypedDictClass, TypedDictBuilder)): details = (f"TypedDict {cls_name} cannot inherit from " "a non-TypedDict class.") self.ctx.errorlog.base_class_error( self.ctx.vm.frames, base_var, details) def _merge_base_class_fields(self, bases, props): """Add the merged list of base class fields to the fields dict.""" # Updates props in place, raises an error if a duplicate key is encountered. provenance = {k: props.name for k in props.fields} for base_var in bases: for base in base_var.data: if not isinstance(base, TypedDictClass): continue for k, v in base.props.fields.items(): if k in props.fields: classes = f"{base.name} and {provenance[k]}" details = f"Duplicate TypedDict key {k} in classes {classes}" self.ctx.errorlog.base_class_error( self.ctx.vm.frames, base_var, details) else: props.add(k, v, base.props.total) provenance[k] = base.name def make_class(self, node, bases, f_locals, total): # If BuildClass.call() hits max depth, f_locals will be [unsolvable] # See comment in NamedTupleClassBuilder.make_class(); equivalent logic # applies here. if isinstance(f_locals.data[0], abstract.Unsolvable): return node, self.ctx.new_unsolvable(node) f_locals = abstract_utils.get_atomic_python_constant(f_locals) # retrieve __qualname__ to get the name of class name_var = f_locals["__qualname__"] cls_name = abstract_utils.get_atomic_python_constant(name_var) if "." in cls_name: cls_name = cls_name.rsplit(".", 1)[-1] if total is None: total = True else: total = abstract_utils.get_atomic_python_constant(total, bool) props = TypedDictProperties( name=cls_name, fields={}, required=set(), total=total) # Collect the key types defined in the current class. cls_locals = classgen.get_class_locals( cls_name, allow_methods=False, ordering=classgen.Ordering.FIRST_ANNOTATE, ctx=self.ctx) for k, local in cls_locals.items(): assert local.typ props.add(k, local.typ, total) # Process base classes and generate the __init__ signature. self._validate_bases(cls_name, bases) self._merge_base_class_fields(bases, props) cls = TypedDictClass(props, self, self.ctx) cls_var = cls.to_variable(node) return node, cls_var def make_class_from_pyi(self, cls_name, pytd_cls, total=True): """Make a TypedDictClass from a pyi class.""" # NOTE: Returns the abstract class, not a variable. if total is None: total = True props = TypedDictProperties( name=cls_name, fields={}, required=set(), total=total) for c in pytd_cls.constants: typ = self.ctx.convert.constant_to_var(c.type) props.add(c.name, typ, total) # Process base classes and generate the __init__ signature. bases = [self.ctx.convert.constant_to_var(x) for x in pytd_cls.bases] self._validate_bases(cls_name, bases) self._merge_base_class_fields(bases, props) cls = TypedDictClass(props, self, self.ctx) return cls class TypedDictClass(abstract.PyTDClass): """A template for typed dicts.""" def __init__(self, props, base_cls, ctx): self.props = props self._base_cls = base_cls # TypedDictBuilder for constructing subclasses super().__init__(props.name, ctx.convert.dict_type.pytd_cls, ctx) self.init_method = self._make_init(props) def __repr__(self): return f"TypedDictClass({self.name})" def _make_init(self, props): # __init__ method for type checking signatures. # We construct this here and pass it to TypedDictClass because we need # access to abstract.SignedFunction. sig = function.Signature.from_param_names( f"{props.name}.__init__", props.fields.keys(), kind=pytd.ParameterKind.KWONLY) sig.annotations = {k: abstract_utils.get_atomic_value(v) for k, v in props.fields.items()} sig.defaults = {k: self.ctx.new_unsolvable(self.ctx.root_node) for k in props.optional} return abstract.SignedFunction(sig, self.ctx) def _new_instance(self, container, node, args): self.init_method.match_and_map_args(node, args, {}) ret = TypedDict(self.props, self.ctx) for (k, v) in args.namedargs.items(): ret.set_str_item(node, k, v) return ret def instantiate(self, node, container): del container return TypedDict(self.props, self.ctx).to_variable(node) def make_class(self, *args, **kwargs): return self._base_cls.make_class(*args, **kwargs) class TypedDict(abstract.Dict): """Representation of TypedDict instances. Internally, a TypedDict is a dict with a restricted set of string keys allowed, each with a fixed type. We implement it as a subclass of Dict, with some type checks wrapped around key accesses. If a check fails, we simply add an error to the logs and then continue processing the method as though it were a regular dict. """ def __init__(self, props, ctx): super().__init__(ctx) self.props = props self.set_slot("__delitem__", self.delitem_slot) @property def fields(self): return self.props.fields @property def class_name(self): return self.props.name def __repr__(self): return f"<TypedDict {self.class_name}>" def _check_str_key(self, name): if name not in self.fields: self.ctx.errorlog.typed_dict_error(self.ctx.vm.frames, self, name) return False return True def _check_str_key_value(self, node, name, value_var): if not self._check_str_key(name): return typ = abstract_utils.get_atomic_value(self.fields[name]) bad = self.ctx.matcher(node).bad_matches(value_var, typ) for view, error_details in bad: binding = view[value_var] self.ctx.errorlog.annotation_type_mismatch( self.ctx.vm.frames, typ, binding, name, error_details, typed_dict=self ) def _check_key(self, name_var): """Check that key is in the typed dict.""" try: name = abstract_utils.get_atomic_python_constant(name_var, str) except abstract_utils.ConversionError: self.ctx.errorlog.typed_dict_error(self.ctx.vm.frames, self, name=None) return False return self._check_str_key(name) def _check_value(self, node, name_var, value_var): """Check that value has the right type.""" # We have already called check_key so name is in fields name = abstract_utils.get_atomic_python_constant(name_var, str) self._check_str_key_value(node, name, value_var) def getitem_slot(self, node, name_var): # A typed dict getitem should have a concrete string arg. If we have a var # with multiple bindings just fall back to Any. if not self._check_key(name_var): return node, self.ctx.new_unsolvable(node) name = abstract_utils.get_atomic_python_constant(name_var, str) typ = self.fields[name] ret = [v.instantiate(node) for v in typ.data] return node, self.ctx.join_variables(node, ret) def setitem_slot(self, node, name_var, value_var): if self._check_key(name_var): self._check_value(node, name_var, value_var) return super().setitem_slot(node, name_var, value_var) def set_str_item(self, node, name, value_var): self._check_str_key_value(node, name, value_var) return super().set_str_item(node, name, value_var) def delitem_slot(self, node, name_var): self._check_key(name_var) return self.call_pytd(node, "__delitem__", name_var) def pop_slot(self, node, key_var, default_var=None): self._check_key(key_var) return super().pop_slot(node, key_var, default_var)

34.520147

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1,344

9,424

4.65253

0.194196

0.031345

0.02047

0.028146

0.303534

0.225972

0.184391

0.168079

0.149048

0

0.0004

0.205008

9,424

272

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0.834223

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0.005263

1

0.152632

false

0

0.042105

0.036842

0.373684

0

null

0

null

0

1

0

4579450f7ab9dcd55c2527f2db1b882e121fbb78

4,962

py

Python

esmvaltool/cmorizers/obs/cmorize_obs_aphro_ma.py

cffbots/ESMValTool

a9b6592a02f2085634a214ff5f36a736fa18ff47

[ "Apache-2.0" ]

148

2017-02-07T13:16:03.000Z

2022-03-26T02:21:56.000Z

esmvaltool/cmorizers/obs/cmorize_obs_aphro_ma.py

cffbots/ESMValTool

a9b6592a02f2085634a214ff5f36a736fa18ff47

[ "Apache-2.0" ]

2,026

2017-02-03T12:57:13.000Z

2022-03-31T15:11:51.000Z

esmvaltool/cmorizers/obs/cmorize_obs_aphro_ma.py

cffbots/ESMValTool

a9b6592a02f2085634a214ff5f36a736fa18ff47

[ "Apache-2.0" ]

113

2017-01-27T13:10:19.000Z

2022-02-03T13:42:11.000Z

"""ESMValTool CMORizer for APHRODITE Monsoon Asia (APHRO-MA) data. Tier Tier 3: restricted dataset. Source http://aphrodite.st.hirosaki-u.ac.jp/download/ Last access 20200306 Download and processing instructions Register at http://aphrodite.st.hirosaki-u.ac.jp/download/create/ Download the following files from http://aphrodite.st.hirosaki-u.ac.jp/product/: APHRO_V1808_TEMP/APHRO_MA 025deg_nc/APHRO_MA_TAVE_025deg_V1808.nc.tgz 050deg_nc/APHRO_MA_TAVE_050deg_V1808.nc.tgz APHRO_V1101/APHRO_MA 025deg_nc/APHRO_MA_025deg_V1101.1951-2007.nc.gz.tar 050deg_nc/APHRO_MA_050deg_V1101.1951-2007.nc.gz.tar APHRO_V1101EX_R1/APHRO_MA 025deg_nc/APHRO_MA_025deg_V1101_EXR1.nc.tgz 050deg_nc/APHRO_MA_050deg_V1101_EXR1.nc.tgz Please untar / unzip all *.tar *.tgz *.gz files in the same directory (no subdirectories!) prior to running the cmorizer! Issues: In input file APHRO_MA_TAVE_050deg_V1808.2015.nc the input variable is called ta instead of tave as in the other files. Currently resolved using raw_fallback: ta in case of thrown iris.exceptions.ConstraintMismatchError Refs: APHRO_V1101 and APHRO_V1101EX_R1 Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges. Bull. Amer. Meteor. Soc., 93, 1401–1415 https://doi.org/10.1175/BAMS-D-11-00122.1 APHRO_V1808_TEMP Yasutomi, N., Hamada, A., Yatagai, A. (2011) Development of a long-term daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation, Global Environmental Research 15 (2), 165-172 """ import logging from warnings import catch_warnings, filterwarnings from pathlib import Path import iris from esmvalcore.preprocessor import monthly_statistics from . import utilities as utils logger = logging.getLogger(__name__) def _extract_variable(short_name, var, cfg, filepath, out_dir, version): """Extract variable.""" logger.info("CMORizing variable '%s' from input file '%s'", short_name, filepath) with catch_warnings(): filterwarnings( action='ignore', message="Skipping global attribute 'calendar': 'calendar' is .*", category=UserWarning, module='iris', ) try: cube = iris.load_cube( str(filepath), constraint=utils.var_name_constraint(var['raw']), ) except iris.exceptions.ConstraintMismatchError: cube = iris.load_cube( str(filepath), constraint=utils.var_name_constraint(var['raw_fallback']), ) # Fix var units cmor_info = cfg['cmor_table'].get_variable(var['mip'], short_name) cube.units = var.get('raw_units', short_name) cube.convert_units(cmor_info.units) utils.fix_var_metadata(cube, cmor_info) # fix coordinates if 'height2m' in cmor_info.dimensions: utils.add_height2m(cube) utils.fix_coords(cube) # Fix metadata attrs = cfg['attributes'].copy() attrs['mip'] = var['mip'] attrs['version'] = version.replace('_', '-') attrs['reference'] = var['reference'] attrs['source'] = attrs['source'] utils.set_global_atts(cube, attrs) # Save variable utils.save_variable(cube, short_name, out_dir, attrs, unlimited_dimensions=['time']) if 'add_mon' in var.keys(): if var['add_mon']: logger.info("Building monthly means") # Calc monthly cube = monthly_statistics(cube) cube.remove_coord('month_number') cube.remove_coord('year') # Fix metadata attrs['mip'] = 'Amon' # Fix coordinates utils.fix_coords(cube) # Save variable utils.save_variable(cube, short_name, out_dir, attrs, unlimited_dimensions=['time']) def cmorization(in_dir, out_dir, cfg, _): """Cmorization func call.""" raw_filename = cfg['filename'] # Run the cmorization for (short_name, var) in cfg['variables'].items(): for version in var['version'].values(): logger.info("CMORizing variable '%s'", short_name) filenames = raw_filename.format(raw_file_var=var['raw_file_var'], version=version) for filepath in sorted(Path(in_dir).glob(filenames)): _extract_variable(short_name, var, cfg, filepath, out_dir, version)

33.08

77

0.620919

598

4,962

4.964883

0.386288

0.025935

0.018188

0.02324

0.274166

0.228697

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0.175817

0.129336

0

0.046963

0.283353

4,962

149

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33.302013

0.787683

0.403265

0

0.235294

0

0.11376

0

1

0.029412

false

0

0.088235

0

0.117647

0

null

0

null

0

1

0

457e2b38d8a451c8e775c1c394ef7b2670d5e8bd

5,074

py

Python

tests/walkers/enum_values_test.py

yyang08/swagger-spec-compatibility

e7a6ba6fc53c6a8a92ba26016219a595a8cecbbe

[ "Apache-2.0" ]

18

2019-04-30T21:07:30.000Z

2021-12-16T17:56:08.000Z

tests/walkers/enum_values_test.py

yyang08/swagger-spec-compatibility

e7a6ba6fc53c6a8a92ba26016219a595a8cecbbe

[ "Apache-2.0" ]

30

2019-02-26T11:25:44.000Z

2021-04-16T00:12:11.000Z

tests/walkers/enum_values_test.py

yyang08/swagger-spec-compatibility

e7a6ba6fc53c6a8a92ba26016219a595a8cecbbe

[ "Apache-2.0" ]

6

2019-02-25T22:12:29.000Z

2020-12-23T00:24:48.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals from copy import deepcopy import mock import pytest from swagger_spec_compatibility.spec_utils import load_spec_from_spec_dict from swagger_spec_compatibility.util import EntityMapping from swagger_spec_compatibility.walkers.enum_values import _different_enum_values_mapping from swagger_spec_compatibility.walkers.enum_values import EnumValuesDiff from swagger_spec_compatibility.walkers.enum_values import EnumValuesDifferWalker @pytest.mark.parametrize( 'left_dict, right_dict, expected_value', [ (None, None, None), ({}, {}, None), ({'type': 'object'}, {}, None), ({'enum': ['v1']}, {}, None), ({'type': 'string', 'enum': ['v1']}, {}, EntityMapping({'v1'}, set())), ({}, {'type': 'string', 'enum': ['v1']}, EntityMapping(set(), {'v1'})), ({'type': 'string', 'enum': ['v1']}, {'type': 'string', 'enum': ['v1']}, None), ({'type': 'string', 'enum': ['v1', 'v2']}, {'type': 'string', 'enum': ['v2', 'v1']}, None), ({'type': 'string', 'enum': ['old', 'common']}, {'type': 'string', 'enum': ['common', 'new']}, EntityMapping({'old'}, {'new'})), ], ) def test__different_enum_values_mapping(left_dict, right_dict, expected_value): assert _different_enum_values_mapping( left_spec=mock.sentinel.LEFT_SPEC, right_spec=mock.sentinel.RIGHT_SPEC, left_schema=left_dict, right_schema=right_dict, ) == expected_value def test_EnumValuesDifferWalker_returns_no_paths_if_no_endpoints_defined(minimal_spec): assert EnumValuesDifferWalker(minimal_spec, minimal_spec).walk() == [] def test_EnumValuesDifferWalker_returns_paths_of_endpoints_responses(minimal_spec_dict): old_spec_dict = dict( minimal_spec_dict, definitions={ 'enum_1': { 'type': 'string', 'enum': ['value_to_remove', 'E2', 'E3'], 'x-model': 'enum_1', }, 'enum_2': { 'type': 'string', 'enum': ['E1', 'E2', 'E3'], 'x-model': 'enum_2', }, 'object': { 'properties': { 'enum_1': {'$ref': '#/definitions/enum_1'}, 'enum_2': {'$ref': '#/definitions/enum_2'}, }, 'type': 'object', 'x-model': 'object', }, }, paths={ '/endpoint': { 'get': { 'parameters': [{ 'in': 'body', 'name': 'body', 'required': True, 'schema': { '$ref': '#/definitions/object', }, }], 'responses': { '200': { 'description': '', 'schema': { '$ref': '#/definitions/object', }, }, }, }, }, }, ) new_spec_dict = deepcopy(old_spec_dict) del new_spec_dict['definitions']['enum_1']['enum'][0] new_spec_dict['definitions']['enum_2']['enum'].append('new_value') old_spec = load_spec_from_spec_dict(old_spec_dict) new_spec = load_spec_from_spec_dict(new_spec_dict) assert sorted(EnumValuesDifferWalker(old_spec, new_spec).walk()) == sorted([ EnumValuesDiff( path=('definitions', 'enum_2'), mapping=EntityMapping(old=set(), new={'new_value'}), ), EnumValuesDiff( path=('definitions', 'object', 'properties', 'enum_2'), mapping=EntityMapping(old=set(), new={'new_value'}), ), EnumValuesDiff( path=('definitions', 'object', 'properties', 'enum_1'), mapping=EntityMapping(old={'value_to_remove'}, new=set()), ), EnumValuesDiff( path=('definitions', 'enum_1'), mapping=EntityMapping(old={'value_to_remove'}, new=set()), ), EnumValuesDiff( path=('paths', '/endpoint', 'get', 'responses', '200', 'schema', 'properties', 'enum_2'), mapping=EntityMapping(old=set(), new={'new_value'}), ), EnumValuesDiff( path=('paths', '/endpoint', 'get', 'responses', '200', 'schema', 'properties', 'enum_1'), mapping=EntityMapping(old={'value_to_remove'}, new=set()), ), EnumValuesDiff( path=('paths', '/endpoint', 'get', 'parameters', 0, 'schema', 'properties', 'enum_2'), mapping=EntityMapping(old=set(), new={'new_value'}), ), EnumValuesDiff( path=('paths', '/endpoint', 'get', 'parameters', 0, 'schema', 'properties', 'enum_1'), mapping=EntityMapping(old={'value_to_remove'}, new=set()), ), ])

38.732824

136

0.522862

467

5,074

5.379015

0.188437

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0.055732

0.52707

0.407245

0.364252

0.343551

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0

0.013383

0.307844

5,074

130

137

39.030769

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0.004139

0

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0

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0

0.02521

1

0.02521

false

0

0.092437

0

0.117647

0.008403

0

null

0

null

0

1

0

458209565dea8013a3f4eb68bbd3789f17400e4a

2,458

py

Python

igvjs/main.py

Sparktx-Data-Science/igv.js-flask

1fc3a6623b6a747830ea0b4c3636adb4e6134c9f

[ "MIT" ]

25

2017-07-20T07:41:30.000Z

2021-12-30T15:49:03.000Z

igvjs/main.py

kkapuria3/igv.js-flask

f3cde4547eaa29133535f95501778d9fe492532d

[ "MIT" ]

10

2017-09-07T23:30:26.000Z

2021-08-06T05:08:32.000Z

igvjs/main.py

kkapuria3/igv.js-flask

f3cde4547eaa29133535f95501778d9fe492532d

[ "MIT" ]

12

2017-10-05T15:00:13.000Z

2021-12-30T15:49:07.000Z

import requests import re import os from flask import Response, request, abort, render_template, url_for, Blueprint from igvjs._config import basedir seen_tokens = set() igvjs_blueprint = Blueprint('igvjs', __name__) # give blueprint access to app config @igvjs_blueprint.record def record_igvjs(setup_state): igvjs_blueprint.config = setup_state.app.config; # routes @igvjs_blueprint.route('/') def show_vcf(): return render_template('igv.html') @igvjs_blueprint.before_app_request def before_request(): if igvjs_blueprint.config['USES_OAUTH'] and (not igvjs_blueprint.config['PUBLIC_DIR'] or \ not os.path.exists('.'+igvjs_blueprint.config['PUBLIC_DIR']) or \ not request.path.startswith(igvjs_blueprint.config['PUBLIC_DIR'])): auth = request.headers.get("Authorization", None) #print auth if auth: token = auth.split()[1] if token not in seen_tokens: google_url = 'https://www.googleapis.com/oauth2/v1/userinfo' params = {'access_token':token} res = requests.get(google_url, params=params) email = res.json()['email'] if email in allowed_emails(): seen_tokens.add(token) else: abort(403) else: if "static/data" in request.path and "data/static/data" not in request.path: abort(401) return ranged_data_response(request.headers.get('Range', None), request.path[1:]) def allowed_emails(): emails = [] if os.path.isfile(app.config['ALLOWED_EMAILS']): with open(app.config['ALLOWED_EMAILS'], 'r') as f: for line in f: emails.append(line.strip()) return emails def ranged_data_response(range_header, rel_path): path = os.path.join(basedir, rel_path) if not range_header: return None m = re.search('(\d+)-(\d*)', range_header) if not m: return "Error: unexpected range header syntax: {}".format(range_header) size = os.path.getsize(path) offset = int(m.group(1)) length = int(m.group(2) or size) - offset + 1 data = None with open(path, 'rb') as f: f.seek(offset) data = f.read(length) rv = Response(data, 206, mimetype="application/octet-stream", direct_passthrough=True) rv.headers['Content-Range'] = 'bytes {0}-{1}/{2}'.format(offset, offset + length-1, size) return rv

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4584c9acdb19bbdce86f86b2d488f787d8a6387a

5,068

py

Python

kraken/shut_down/common_shut_down_func.py

Sau1506mya/kraken-1

4f7616a1508e0f1f64356512aeda265e1dce5144

[ "Apache-2.0" ]

83

2021-01-15T10:42:22.000Z

2022-03-23T16:01:22.000Z

kraken/shut_down/common_shut_down_func.py

Sau1506mya/kraken-1

4f7616a1508e0f1f64356512aeda265e1dce5144

[ "Apache-2.0" ]

126

2021-01-19T07:41:06.000Z

2022-03-31T16:27:02.000Z

kraken/shut_down/common_shut_down_func.py

Sau1506mya/kraken-1

4f7616a1508e0f1f64356512aeda265e1dce5144

[ "Apache-2.0" ]

26

2021-01-27T19:34:33.000Z

2022-03-18T21:18:35.000Z

#!/usr/bin/env python import sys import yaml import logging import time from multiprocessing.pool import ThreadPool import kraken.cerberus.setup as cerberus import kraken.kubernetes.client as kubecli import kraken.post_actions.actions as post_actions from kraken.node_actions.aws_node_scenarios import AWS from kraken.node_actions.openstack_node_scenarios import OPENSTACKCLOUD from kraken.node_actions.az_node_scenarios import Azure from kraken.node_actions.gcp_node_scenarios import GCP def multiprocess_nodes(cloud_object_function, nodes): try: # pool object with number of element pool = ThreadPool(processes=len(nodes)) logging.info("nodes type " + str(type(nodes[0]))) if type(nodes[0]) is tuple: node_id = [] node_info = [] for node in nodes: node_id.append(node[0]) node_info.append(node[1]) logging.info("node id " + str(node_id)) logging.info("node info" + str(node_info)) pool.starmap(cloud_object_function, zip(node_info, node_id)) else: logging.info("pool type" + str(type(nodes))) pool.map(cloud_object_function, nodes) pool.close() except Exception as e: logging.info("Error on pool multiprocessing: " + str(e)) # Inject the cluster shut down scenario def cluster_shut_down(shut_down_config): runs = shut_down_config["runs"] shut_down_duration = shut_down_config["shut_down_duration"] cloud_type = shut_down_config["cloud_type"] timeout = shut_down_config["timeout"] if cloud_type.lower() == "aws": cloud_object = AWS() elif cloud_type.lower() == "gcp": cloud_object = GCP() elif cloud_type.lower() == "openstack": cloud_object = OPENSTACKCLOUD() elif cloud_type.lower() in ["azure", "az"]: cloud_object = Azure() else: logging.error("Cloud type " + cloud_type + " is not currently supported for cluster shut down") sys.exit(1) nodes = kubecli.list_nodes() node_id = [] for node in nodes: instance_id = cloud_object.get_instance_id(node) node_id.append(instance_id) logging.info("node id list " + str(node_id)) for _ in range(runs): logging.info("Starting cluster_shut_down scenario injection") stopping_nodes = set(node_id) multiprocess_nodes(cloud_object.stop_instances, node_id) stopped_nodes = stopping_nodes.copy() while len(stopping_nodes) > 0: for node in stopping_nodes: if type(node) is tuple: node_status = cloud_object.wait_until_stopped(node[1], node[0], timeout) else: node_status = cloud_object.wait_until_stopped(node, timeout) # Only want to remove node from stopping list when fully stopped/no error if node_status: stopped_nodes.remove(node) stopping_nodes = stopped_nodes.copy() logging.info("Shutting down the cluster for the specified duration: %s" % (shut_down_duration)) time.sleep(shut_down_duration) logging.info("Restarting the nodes") restarted_nodes = set(node_id) multiprocess_nodes(cloud_object.start_instances, node_id) logging.info("Wait for each node to be running again") not_running_nodes = restarted_nodes.copy() while len(not_running_nodes) > 0: for node in not_running_nodes: if type(node) is tuple: node_status = cloud_object.wait_until_running(node[1], node[0], timeout) else: node_status = cloud_object.wait_until_running(node, timeout) if node_status: restarted_nodes.remove(node) not_running_nodes = restarted_nodes.copy() logging.info("Waiting for 150s to allow cluster component initialization") time.sleep(150) logging.info("Successfully injected cluster_shut_down scenario!") def run(scenarios_list, config, wait_duration): failed_post_scenarios = [] for shut_down_config in scenarios_list: if len(shut_down_config) > 1: pre_action_output = post_actions.run("", shut_down_config[1]) else: pre_action_output = "" with open(shut_down_config[0], "r") as f: shut_down_config_yaml = yaml.full_load(f) shut_down_config_scenario = shut_down_config_yaml["cluster_shut_down_scenario"] start_time = int(time.time()) cluster_shut_down(shut_down_config_scenario) logging.info("Waiting for the specified duration: %s" % (wait_duration)) time.sleep(wait_duration) failed_post_scenarios = post_actions.check_recovery( "", shut_down_config, failed_post_scenarios, pre_action_output ) end_time = int(time.time()) cerberus.publish_kraken_status(config, failed_post_scenarios, start_time, end_time)

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4588e1870c69bb48a27afabbf83f4786a23a64c3

16,539

py

Python

game_seq_embedder/app/app_utils.py

Anonymous9999999/Pretrain-via-MTC

bc47c162aecb68708b68d8ff7c9bfd54b0fc485e

[ "Artistic-1.0-Perl" ]

null

game_seq_embedder/app/app_utils.py

Anonymous9999999/Pretrain-via-MTC

bc47c162aecb68708b68d8ff7c9bfd54b0fc485e

[ "Artistic-1.0-Perl" ]

null

game_seq_embedder/app/app_utils.py

Anonymous9999999/Pretrain-via-MTC

bc47c162aecb68708b68d8ff7c9bfd54b0fc485e

[ "Artistic-1.0-Perl" ]

null

import h5py import collections import numpy as np import os import ipdb import math import time import datetime import random import torch from torch.utils.data.dataset import Dataset def hdf5_load_dataset(hdf5_file_path, all_indices, step_size, large_batch=1024, is_decode_utf8=False): random.shuffle(all_indices) for step_i in range(step_size): hdf5_file = h5py.File(hdf5_file_path, 'r') next_indices = all_indices[step_i * large_batch:(step_i + 1) * large_batch] next_data = collections.defaultdict(lambda: []) for x in next_indices: *dataset_name, index = x.split('_') dataset_name = '_'.join(dataset_name) next_data[dataset_name].append(int(index)) large_batch_data = [] print(f"Load dataset from hdf5 step {step_i}, size next indices: {len(next_indices)}") for dataset_name, dataset_indices in next_data.items(): if dataset_name == 'nsh_2020-04-04': print(f"Skip for {dataset_name}") continue else: print(f"Read from {dataset_name} done, size: {len(dataset_indices)}") if is_decode_utf8: temp_indices_data = hdf5_file[dataset_name][sorted(dataset_indices)] temp_indices_data_str = [] for i, temp_line in enumerate(temp_indices_data): temp_line = [x.decode('utf-8') for x in temp_line] temp_indices_data_str.append(temp_line) temp_indices_data_str = np.stack(temp_indices_data_str) large_batch_data.append(temp_indices_data_str) else: large_batch_data.append(hdf5_file[dataset_name][sorted(dataset_indices)]) large_batch_data = np.concatenate(large_batch_data).astype(str) hdf5_file.close() yield large_batch_data class TextDataset(Dataset): """ This will be superseded by a framework-agnostic approach soon. """ def __init__( self, hdf_data, tokenizer, block_size: int, use_time_embed: bool = False, use_bpe=False, debugN=None, max_time_gap=None, use_sinusoidal=False, behave_tokenizer=None, design_tokenizer=None, ): if tokenizer is None: assert use_time_embed assert behave_tokenizer and design_tokenizer assert not use_bpe self.tokenizer = tokenizer self.behave_tokenizer = behave_tokenizer self.design_tokenizer = design_tokenizer self.use_time_embed = use_time_embed self.examples = [] self.time_gaps = [] self.design_ids = [] for sample_i, sample in enumerate(hdf_data): if debugN: if sample_i >= debugN: print(f"[DEBUG N] Stop loading data, debug N is set to {debugN}") break sample = [x for x in sample if x != '[PAD]'] text_block = ' '.join(sample) # -------------------------------------------------------------------------------------------------- # CODE BY PJS # -------------------------------------------------------------------------------------------------- if use_time_embed: pure_text_block = [x for i, x in enumerate(text_block.split(' ')) if (i + 1) % 3 != 0] time_gap_block = text_block.split(' ')[2::3] # This is only for data assertion temp_test_time_gap = time_gap_block[0] temp_date_obj = datetime.datetime.fromtimestamp(int(temp_test_time_gap)) assert 2019 < temp_date_obj.year < 2022 # compute the time gap if sinusoidal is not used if not use_sinusoidal: # TODO, 这个地方我觉得还是要改一下，统一成最大值1024秒，最小单位是秒，但是最小的单位是1秒*100 time_gap_block = list(zip(time_gap_block, time_gap_block[1:] + [0])) time_gap_block = [math.ceil((int(t2) - int(t1)) / 100) for t1, t2 in time_gap_block] time_gap_block[-1] = 0 assert min(time_gap_block) >= 0 if tokenizer is not None: time_gap_block = [y for x in zip(time_gap_block, time_gap_block) for y in x][:block_size] else: time_gap_block = [y for x in zip(time_gap_block, time_gap_block) for y in x][:int(block_size * 2)] # assert len(pure_text_block) == len(time_gap_block) if use_bpe: text_block = ''.join(pure_text_block) else: text_block = ' '.join(pure_text_block) else: pure_text_block = [x for i, x in enumerate(text_block.split(' ')) if (i + 1) % 3 != 0] if use_bpe: text_block = ''.join(pure_text_block) else: text_block = ' '.join(pure_text_block) if tokenizer is not None: output = tokenizer.encode(text_block) tokenized_ids = output.ids tokenized_texts = output.tokens design_tokenized_ids = None else: # get behave token behave_output = behave_tokenizer.encode(' '.join(text_block.split()[::2])) behave_tokenized_ids = behave_output.ids behave_texts = behave_output.tokens # get design token design_output = design_tokenizer.encode(' '.join(text_block.split()[1::2])) design_tokenized_ids = design_output.ids design_texts = design_output.tokens # combine them all assert len(behave_tokenized_ids) == len(design_tokenized_ids) == len(behave_texts) == len(design_texts) tokenized_ids = behave_tokenized_ids tokenized_texts = None # if use_bpe: # # assert len(''.join([y for x in output.tokens for y in x]).replace('_', '').replace('▁', '')) == len( # # text_block), print(f"len of tokenized_texts no equal to origin, text: {tokenized_texts}") # assert len(''.join([y for x in output.tokens for y in x]).replace('_', '').replace('▁', '')) == len( # text_block), print(f"len of tokenized_texts no equal to origin, text: {tokenized_texts}") tokenized_ids = tokenized_ids[:block_size] if design_tokenized_ids: design_tokenized_ids = design_tokenized_ids[:block_size] if tokenized_texts: tokenized_texts = tokenized_texts[:block_size] example = np.array(tokenized_ids) if use_time_embed: time_gaps = np.array([int(x) for x in time_gap_block], dtype=int) if use_bpe: new_time_gaps = [] start_index = 0 for word in tokenized_texts: word = word.replace('_', '').replace('▁', '') new_time_gap = time_gaps[start_index:start_index + len(word)] new_time_gaps.append(sum(new_time_gap)) start_index += len(word) new_time_gaps = np.array(new_time_gaps) time_gaps = new_time_gaps # cut off max time gap if not use_sinusoidal: time_gaps = np.array([x if x <= max_time_gap - 1 else max_time_gap - 1 for x in time_gaps]) else: # 这里做一下转换，一天有86400秒 time_gap0 = datetime.datetime.fromtimestamp(time_gaps[0]) today_start = datetime.datetime(year=time_gap0.year, month=time_gap0.month, day=time_gap0.day) today_start_timestamp = int(time.mktime(today_start.timetuple())) time_gaps = time_gaps - today_start_timestamp # recover the length of time gaps for sperate ids if tokenizer is None: time_gaps = time_gaps[::2] assert example.shape == time_gaps.shape if tokenizer is None: assert example.shape == time_gaps.shape == np.array(design_tokenized_ids).shape # -------------------------------------------------------------------------------------------------- if len(example) < block_size: # pad example if tokenizer: all_pad_example = np.full(block_size, tokenizer.pad_token_id) else: all_pad_example = np.full(block_size, behave_tokenizer.pad_token_id) all_pad_example[:len(example)] = example example = all_pad_example # pad design_id if design_tokenized_ids: all_pad_design_ids = np.full(block_size, design_tokenizer.pad_token_id) all_pad_design_ids[:len(design_tokenized_ids)] = design_tokenized_ids design_tokenized_ids = all_pad_design_ids if use_time_embed: all_pad_time_gap = np.full(block_size, 0) all_pad_time_gap[:len(time_gaps)] = time_gaps time_gaps = all_pad_time_gap # add example self.examples.append(example) # add design id if not tokenizer: self.design_ids.append(np.array(design_tokenized_ids)) if use_time_embed: self.time_gaps.append(time_gaps) # Note that we are losing the last truncated example here for the sake of simplicity (no padding) # If your dataset is small, first you should loook for a bigger one :-) and second you # can change this behavior by adding (model specific) padding. # with open(cached_features_file, "wb") as handle: # pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL) # logger.info( # "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start # ) def __len__(self): return len(self.examples) def __getitem__(self, i): # Mode: three in one if self.tokenizer is None: cat_arr = np.concatenate([self.examples[i], self.design_ids[i], self.time_gaps[i]]) return torch.tensor(cat_arr, dtype=torch.long) else: # Mode: General if self.use_time_embed: cat_arr = np.concatenate([self.examples[i], self.time_gaps[i]]) return torch.tensor(cat_arr, dtype=torch.long) else: return torch.tensor(self.examples[i], dtype=torch.long) def get_dataset( tokenizer, block_size, behave_tokenizer=None, design_tokenizer=None, use_time_embed=False, debugN=None, hdf_data=None, use_bpe=False, max_time_gap=None, use_sinusoidal=False ): return TextDataset( hdf_data=hdf_data, tokenizer=tokenizer, behave_tokenizer=behave_tokenizer, design_tokenizer=design_tokenizer, block_size=block_size, use_time_embed=use_time_embed, debugN=debugN, use_bpe=use_bpe, max_time_gap=max_time_gap, use_sinusoidal=use_sinusoidal ) def get_all_indices(h5_data_file_path, debug_N): hdf5_file = h5py.File(h5_data_file_path, 'r') all_indices = [] all_keys = sorted(hdf5_file.keys()) total_num = 0 for key in all_keys: data = hdf5_file[key] shape = data.shape total_num += shape[0] all_indices.extend([f'{key}_{x}' for x in range(shape[0])]) if debug_N: all_indices, total_num = all_indices[:debug_N], debug_N return all_indices, total_num def load_dataset_from_hdf5_by_indices(hdf5_file_path, indices, is_decode_utf8=False): hdf5_file = h5py.File(hdf5_file_path, 'r') next_data = collections.defaultdict(lambda: []) for x in indices: *dataset_name, index = x.split('_') dataset_name = '_'.join(dataset_name) next_data[dataset_name].append(int(index)) large_batch_data = [] for dataset_name, dataset_indices in next_data.items(): if is_decode_utf8: temp_indices_data = hdf5_file[dataset_name][sorted(dataset_indices)] temp_indices_data_str = [] for i, temp_line in enumerate(temp_indices_data): temp_line = [x.decode('utf-8') for x in temp_line] temp_indices_data_str.append(temp_line) temp_indices_data_str = np.stack(temp_indices_data_str) large_batch_data.append(temp_indices_data_str) else: large_batch_data.append(hdf5_file[dataset_name][sorted(dataset_indices)]) large_batch_data = np.concatenate(large_batch_data).astype(str) hdf5_file.close() return large_batch_data def _convert_token_to_id_with_added_voc(token, added_tokens_encoder): if token is None: return None if token in added_tokens_encoder: return added_tokens_encoder[token] def create_func1(sep_token_id, cls_token_id): def get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods. Args: token_ids_0: list of ids (must not contain special tokens) token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids for sequence pairs already_has_special_tokens: (default False) Set to True if the token list is already formated with special tokens for the model Returns: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formated with special tokens for the model." ) return list(map(lambda x: 1 if x in [sep_token_id, cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] return get_special_tokens_mask def create_func2(added_tokens_encoder, mask_token_id): def convert_tokens_to_ids(tokens): """ Converts a single token, or a sequence of tokens, (str) in a single integer id (resp. a sequence of ids), using the vocabulary. """ if tokens == '[MASK]': return mask_token_id if tokens is None: return None if isinstance(tokens, str): return _convert_token_to_id_with_added_voc(tokens, added_tokens_encoder) ids = [] for token in tokens: ids.append(_convert_token_to_id_with_added_voc(token, added_tokens_encoder)) return ids return convert_tokens_to_ids def tokenizer_post_process(tokenizer, block_size, type): if type == 'whitespace': tokenizer.max_len = block_size tokenizer.get_special_tokens_mask = create_func1(tokenizer.pad_token_id, tokenizer.cls_token_id) tokenizer.convert_tokens_to_ids = create_func2(tokenizer.added_tokens_encoder, tokenizer.mask_token_id) elif type == 'bpe': tokenizer.max_len = block_size tokenizer.cls_token_id = 0 tokenizer.pad_token_id = 1 tokenizer.sep_token_id = 2 tokenizer.unk_token_id = 3 tokenizer.mask_token_id = 4 tokenizer.get_special_tokens_mask = create_func1(tokenizer.pad_token_id, tokenizer.cls_token_id) tokenizer.added_tokens_encoder = {} tokenizer.convert_tokens_to_ids = create_func2(tokenizer.added_tokens_encoder, tokenizer.mask_token_id) tokenizer.mask_token = '[MASK]' tokenizer._pad_token = '[PAD]' else: raise NotImplementedError return tokenizer

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null

0

null

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1

0

458a74a131096b9821f4d2a10c82a473fe5352fd

30,220

py

Python

src/pagure/forms.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

src/pagure/forms.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

src/pagure/forms.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- """ (c) 2014-2016 - Copyright Red Hat Inc Authors: Pierre-Yves Chibon <pingou@pingoured.fr> """ # pylint: disable=too-few-public-methods # pylint: disable=no-init # pylint: disable=super-on-old-class from __future__ import unicode_literals, absolute_import import datetime import re import flask import flask_wtf as wtf try: from flask_wtf import FlaskForm except ImportError: from flask_wtf import Form as FlaskForm import six import wtforms import pagure.lib.query import pagure.validators from pagure.config import config as pagure_config from pagure.utils import urlpattern, is_admin STRICT_REGEX = "^[a-zA-Z0-9-_]+$" # This regex is used when creating tags, there we do not want to allow ',' # as otherwise it breaks the UI. TAGS_REGEX = "^[a-zA-Z0-9][a-zA-Z0-9-_ .:]+$" TAGS_REGEX_RE = re.compile(TAGS_REGEX) # In the issue page tags are sent as a comma-separated list, so in order to # allow having multiple tags in an issue, we need to allow ',' in them. TAGS_REGEX_MULTI = "^[a-zA-Z0-9][a-zA-Z0-9-_, .:]+$" FALSE_VALUES = ("false", "", False, "False", 0, "0") WTF_VERSION = tuple() if hasattr(wtf, "__version__"): WTF_VERSION = tuple(int(v) for v in wtf.__version__.split(".")) class PagureForm(FlaskForm): """ Local form allowing us to form set the time limit. """ def __init__(self, *args, **kwargs): delta = pagure_config.get("WTF_CSRF_TIME_LIMIT", 3600) if delta and WTF_VERSION < (0, 10, 0): self.TIME_LIMIT = datetime.timedelta(seconds=delta) else: self.TIME_LIMIT = delta if "csrf_enabled" in kwargs and kwargs["csrf_enabled"] is False: kwargs["meta"] = {"csrf": False} if WTF_VERSION >= (0, 14, 0): kwargs.pop("csrf_enabled") super(PagureForm, self).__init__(*args, **kwargs) def convert_value(val): """ Convert the provided values to strings when possible. """ if val: if not isinstance(val, (list, tuple, six.text_type)): return val.decode("utf-8") elif isinstance(val, six.string_types): return val class MultipleEmail(wtforms.validators.Email): """Split the value by comma and run them through the email validator of wtforms. """ def __call__(self, form, field): message = field.gettext("One or more invalid email address.") for data in field.data.split(","): data = data.strip() if not self.regex.match(data or ""): raise wtforms.validators.ValidationError(message) def user_namespace_if_private(form, field): """Check if the data in the field is the same as in the password field.""" if form.private.data: field.data = flask.g.fas_user.username def file_virus_validator(form, field): """Checks for virus in the file from flask request object, raises wtf.ValidationError if virus is found else None.""" if not pagure_config["VIRUS_SCAN_ATTACHMENTS"]: return from pyclamd import ClamdUnixSocket if ( field.name not in flask.request.files or flask.request.files[field.name].filename == "" ): # If no file was uploaded, this field is correct return uploaded = flask.request.files[field.name] clam = ClamdUnixSocket() if not clam.ping(): raise wtforms.ValidationError( "Unable to communicate with virus scanner" ) results = clam.scan_stream(uploaded.stream.read()) if results is None: uploaded.stream.seek(0) return else: result = results.values() res_type, res_msg = result if res_type == "FOUND": raise wtforms.ValidationError("Virus found: %s" % res_msg) else: raise wtforms.ValidationError("Error scanning uploaded file") def ssh_key_validator(form, field): """ Form for ssh key validation """ if not pagure.lib.query.are_valid_ssh_keys(field.data): raise wtforms.ValidationError("Invalid SSH keys") class ProjectFormSimplified(PagureForm): """ Form to edit the description of a project. """ description = wtforms.StringField( "Description", [wtforms.validators.DataRequired()], ) url = wtforms.StringField( "URL", [ wtforms.validators.optional(), wtforms.validators.Regexp(urlpattern, flags=re.IGNORECASE), ], ) avatar_email = wtforms.StringField( "Avatar email", [ pagure.validators.EmailValidator("avatar_email must be an email"), wtforms.validators.optional(), ], ) tags = wtforms.StringField( "Project tags", [wtforms.validators.optional(), wtforms.validators.Length(max=255)], ) private = wtforms.BooleanField( "Private", [wtforms.validators.Optional()], false_values=FALSE_VALUES ) mirrored_from = wtforms.StringField( "Mirrored from", [wtforms.validators.optional(), wtforms.validators.Length(max=255)], ) class ProjectForm(ProjectFormSimplified): """ Form to create or edit project. """ name = wtforms.StringField("Project name") mirrored_from = wtforms.StringField( "Mirror from URL", [ wtforms.validators.optional(), wtforms.validators.Regexp(urlpattern, flags=re.IGNORECASE), ], ) create_readme = wtforms.BooleanField( "Create README", [wtforms.validators.optional()], false_values=FALSE_VALUES, ) namespace = wtforms.SelectField( "Project Namespace", [user_namespace_if_private, wtforms.validators.optional()], choices=[], coerce=convert_value, ) ignore_existing_repos = wtforms.BooleanField( "Ignore existing repositories", [wtforms.validators.optional()], false_values=FALSE_VALUES, ) repospanner_region = wtforms.SelectField( "repoSpanner Region", [wtforms.validators.optional()], choices=( [("none", "Disabled")] + [ (region, region) for region in pagure_config["REPOSPANNER_REGIONS"].keys() ] ), coerce=convert_value, default=pagure_config["REPOSPANNER_NEW_REPO"], ) default_branch = wtforms.StringField( "Default branch", [wtforms.validators.optional()], ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(ProjectForm, self).__init__(*args, **kwargs) # set the name validator regex = pagure_config.get( "PROJECT_NAME_REGEX", "^[a-zA-z0-9_][a-zA-Z0-9-_.+]*$" ) self.name.validators = [ wtforms.validators.DataRequired(), wtforms.validators.Regexp(regex, flags=re.IGNORECASE), ] # Set the list of namespace if "namespaces" in kwargs: self.namespace.choices = [ (namespace, namespace) for namespace in kwargs["namespaces"] ] if not pagure_config.get("USER_NAMESPACE", False): self.namespace.choices.insert(0, ("", "")) if not ( is_admin() and pagure_config.get("ALLOW_ADMIN_IGNORE_EXISTING_REPOS") ) and ( flask.g.fas_user.username not in pagure_config["USERS_IGNORE_EXISTING_REPOS"] ): self.ignore_existing_repos = None if not ( is_admin() and pagure_config.get("REPOSPANNER_NEW_REPO_ADMIN_OVERRIDE") ): self.repospanner_region = None class IssueFormSimplied(PagureForm): """ Form to create or edit an issue. """ title = wtforms.StringField( "Title", [wtforms.validators.DataRequired()], ) issue_content = wtforms.TextAreaField( "Content", [wtforms.validators.DataRequired()], ) private = wtforms.BooleanField( "Private", [wtforms.validators.optional()], false_values=FALSE_VALUES ) milestone = wtforms.SelectField( "Milestone", [wtforms.validators.Optional()], choices=[], coerce=convert_value, ) priority = wtforms.SelectField( "Priority", [wtforms.validators.Optional()], choices=[], coerce=convert_value, ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(IssueFormSimplied, self).__init__(*args, **kwargs) self.priority.choices = [] if "priorities" in kwargs: for key in sorted(kwargs["priorities"]): self.priority.choices.append((key, kwargs["priorities"][key])) self.milestone.choices = [] if "milestones" in kwargs and kwargs["milestones"]: for key in kwargs["milestones"]: self.milestone.choices.append((key, key)) self.milestone.choices.insert(0, ("", "")) class IssueForm(IssueFormSimplied): """ Form to create or edit an issue. """ status = wtforms.SelectField( "Status", [wtforms.validators.DataRequired()], choices=[] ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(IssueForm, self).__init__(*args, **kwargs) if "status" in kwargs: self.status.choices = [ (status, status) for status in kwargs["status"] ] class RequestPullForm(PagureForm): """ Form to create a pull request. """ title = wtforms.StringField( "Title", [wtforms.validators.DataRequired()], ) initial_comment = wtforms.TextAreaField( "Initial Comment", [wtforms.validators.Optional()] ) allow_rebase = wtforms.BooleanField( "Allow rebasing", [wtforms.validators.Optional()], false_values=FALSE_VALUES, ) class RequestPullEditForm(RequestPullForm): """ Form to edit a pull request. """ branch_to = wtforms.SelectField( "Target branch", [wtforms.validators.Required()], choices=[], coerce=convert_value, ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(RequestPullEditForm, self).__init__(*args, **kwargs) if "branches" in kwargs: self.branch_to.choices = [ (branch, branch) for branch in kwargs["branches"] ] class RemoteRequestPullForm(RequestPullForm): """ Form to create a remote pull request. """ git_repo = wtforms.StringField( "Git repo address", [ wtforms.validators.DataRequired(), wtforms.validators.Regexp(urlpattern, flags=re.IGNORECASE), ], ) branch_from = wtforms.StringField( "Git branch", [wtforms.validators.DataRequired()], ) branch_to = wtforms.StringField( "Git branch to merge in", [wtforms.validators.DataRequired()], ) class DeleteIssueTagForm(PagureForm): """ Form to remove a tag to from a project. """ tag = wtforms.StringField( "Tag", [ wtforms.validators.Optional(), wtforms.validators.Regexp(TAGS_REGEX, flags=re.IGNORECASE), wtforms.validators.Length(max=255), ], ) class AddIssueTagForm(DeleteIssueTagForm): """ Form to add a tag to a project. """ tag_description = wtforms.StringField( "Tag Description", [wtforms.validators.Optional()] ) tag_color = wtforms.StringField( "Tag Color", [wtforms.validators.DataRequired()] ) class ApiAddIssueTagForm(PagureForm): """ Form to add a tag to a project from the API endpoint """ tag = wtforms.StringField( "Tag", [ wtforms.validators.DataRequired(), wtforms.validators.Regexp(TAGS_REGEX, flags=re.IGNORECASE), wtforms.validators.Length(max=255), ], ) tag_description = wtforms.StringField( "Tag Description", [wtforms.validators.Optional()] ) tag_color = wtforms.StringField( "Tag Color", [wtforms.validators.DataRequired()] ) class StatusForm(PagureForm): """ Form to add/change the status of an issue. """ status = wtforms.SelectField( "Status", [wtforms.validators.DataRequired()], choices=[] ) close_status = wtforms.SelectField( "Closed as", [wtforms.validators.Optional()], choices=[] ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(StatusForm, self).__init__(*args, **kwargs) if "status" in kwargs: self.status.choices = [ (status, status) for status in kwargs["status"] ] self.close_status.choices = [] if "close_status" in kwargs: for key in sorted(kwargs["close_status"]): self.close_status.choices.append((key, key)) self.close_status.choices.insert(0, ("", "")) class MilestoneForm(PagureForm): """ Form to change the milestone of an issue. """ milestone = wtforms.SelectField( "Milestone", [wtforms.validators.Optional()], choices=[], coerce=convert_value, ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(MilestoneForm, self).__init__(*args, **kwargs) self.milestone.choices = [] if "milestones" in kwargs and kwargs["milestones"]: for key in kwargs["milestones"]: self.milestone.choices.append((key, key)) self.milestone.choices.insert(0, ("", "")) class NewTokenForm(PagureForm): """ Form to add a new token. """ description = wtforms.StringField( "description", [wtforms.validators.Optional()] ) expiration_date = wtforms.DateField( "expiration date", [wtforms.validators.DataRequired()], default=datetime.date.today() + datetime.timedelta(days=(30 * 6)), ) acls = wtforms.SelectMultipleField( "ACLs", [wtforms.validators.DataRequired()], choices=[] ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(NewTokenForm, self).__init__(*args, **kwargs) if "acls" in kwargs: self.acls.choices = [ (acl.name, acl.name) for acl in kwargs["acls"] ] if "sacls" in kwargs: self.acls.choices = [(acl, acl) for acl in kwargs["sacls"]] class UpdateIssueForm(PagureForm): """ Form to add a comment to an issue. """ tag = wtforms.StringField( "tag", [ wtforms.validators.Optional(), wtforms.validators.Regexp(TAGS_REGEX_MULTI, flags=re.IGNORECASE), wtforms.validators.Length(max=255), ], ) depending = wtforms.StringField( "depending issue", [wtforms.validators.Optional()] ) blocking = wtforms.StringField( "blocking issue", [wtforms.validators.Optional()] ) comment = wtforms.TextAreaField("Comment", [wtforms.validators.Optional()]) assignee = wtforms.TextAreaField( "Assigned to", [wtforms.validators.Optional()] ) status = wtforms.SelectField( "Status", [wtforms.validators.Optional()], choices=[] ) priority = wtforms.SelectField( "Priority", [wtforms.validators.Optional()], choices=[] ) milestone = wtforms.SelectField( "Milestone", [wtforms.validators.Optional()], choices=[], coerce=convert_value, ) private = wtforms.BooleanField( "Private", [wtforms.validators.optional()], false_values=FALSE_VALUES ) close_status = wtforms.SelectField( "Closed as", [wtforms.validators.Optional()], choices=[], coerce=convert_value, ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(UpdateIssueForm, self).__init__(*args, **kwargs) if "status" in kwargs: self.status.choices = [ (status, status) for status in kwargs["status"] ] self.priority.choices = [] if "priorities" in kwargs: for key in sorted(kwargs["priorities"]): self.priority.choices.append((key, kwargs["priorities"][key])) self.milestone.choices = [] if "milestones" in kwargs and kwargs["milestones"]: for key in kwargs["milestones"]: self.milestone.choices.append((key, key)) self.milestone.choices.insert(0, ("", "")) self.close_status.choices = [] if "close_status" in kwargs: for key in sorted(kwargs["close_status"]): self.close_status.choices.append((key, key)) self.close_status.choices.insert(0, ("", "")) class AddPullRequestCommentForm(PagureForm): """ Form to add a comment to a pull-request. """ commit = wtforms.HiddenField("commit identifier") filename = wtforms.HiddenField("file changed") row = wtforms.HiddenField("row") requestid = wtforms.HiddenField("requestid") tree_id = wtforms.HiddenField("treeid") comment = wtforms.TextAreaField( "Comment", [wtforms.validators.DataRequired()], ) class AddPullRequestFlagFormV1(PagureForm): """ Form to add a flag to a pull-request or commit. """ username = wtforms.StringField( "Username", [wtforms.validators.DataRequired()] ) percent = wtforms.StringField( "Percentage of completion", [wtforms.validators.optional()] ) comment = wtforms.TextAreaField( "Comment", [wtforms.validators.DataRequired()] ) url = wtforms.StringField( "URL", [ wtforms.validators.DataRequired(), wtforms.validators.Regexp(urlpattern, flags=re.IGNORECASE), ], ) uid = wtforms.StringField("UID", [wtforms.validators.optional()]) class AddPullRequestFlagForm(AddPullRequestFlagFormV1): """ Form to add a flag to a pull-request or commit. """ def __init__(self, *args, **kwargs): # we need to instantiate dynamically because the configuration # values may change during tests and we want to always respect # the currently set value super(AddPullRequestFlagForm, self).__init__(*args, **kwargs) self.status.choices = list( zip( pagure_config["FLAG_STATUSES_LABELS"].keys(), pagure_config["FLAG_STATUSES_LABELS"].keys(), ) ) status = wtforms.SelectField( "status", [wtforms.validators.DataRequired()], choices=[] ) class AddSSHKeyForm(PagureForm): """ Form to add a SSH key to a user. """ ssh_key = wtforms.StringField( "SSH Key", [wtforms.validators.DataRequired()] # TODO: Add an ssh key validator? ) class AddDeployKeyForm(AddSSHKeyForm): """ Form to add a deploy key to a project. """ pushaccess = wtforms.BooleanField( "Push access", [wtforms.validators.optional()], false_values=FALSE_VALUES, ) class AddUserForm(PagureForm): """ Form to add a user to a project. """ user = wtforms.StringField( "Username", [wtforms.validators.DataRequired()], ) access = wtforms.StringField( "Access Level", [wtforms.validators.DataRequired()], ) branches = wtforms.StringField( "Git branches", [wtforms.validators.Optional()], ) class AddUserToGroupForm(PagureForm): """ Form to add a user to a pagure group. """ user = wtforms.StringField( "Username", [wtforms.validators.DataRequired()], ) class AssignIssueForm(PagureForm): """ Form to assign an user to an issue. """ assignee = wtforms.StringField( "Assignee", [wtforms.validators.Optional()], ) class AddGroupForm(PagureForm): """ Form to add a group to a project. """ group = wtforms.StringField( "Group", [ wtforms.validators.DataRequired(), wtforms.validators.Regexp(STRICT_REGEX, flags=re.IGNORECASE), ], ) access = wtforms.StringField( "Access Level", [wtforms.validators.DataRequired()], ) branches = wtforms.StringField( "Git branches", [wtforms.validators.Optional()], ) class ConfirmationForm(PagureForm): """ Simple form used just for CSRF protection. """ pass class ModifyACLForm(PagureForm): """ Form to change ACL of a user or a group to a project. """ user_type = wtforms.SelectField( "User type", [wtforms.validators.DataRequired()], choices=[("user", "User"), ("group", "Group")], ) name = wtforms.StringField( "User- or Groupname", [wtforms.validators.DataRequired()], ) acl = wtforms.SelectField( "ACL type", [wtforms.validators.Optional()], choices=[ ("admin", "Admin"), ("ticket", "Ticket"), ("commit", "Commit"), (None, None), ], coerce=convert_value, ) class UploadFileForm(PagureForm): """ Form to upload a file. """ filestream = wtforms.FileField( "File", [wtforms.validators.DataRequired(), file_virus_validator] ) class UserEmailForm(PagureForm): """ Form to edit the description of a project. """ email = wtforms.StringField("email", [wtforms.validators.DataRequired()]) def __init__(self, *args, **kwargs): super(UserEmailForm, self).__init__(*args, **kwargs) if "emails" in kwargs: if kwargs["emails"]: self.email.validators.append( wtforms.validators.NoneOf(kwargs["emails"]) ) else: self.email.validators = [wtforms.validators.DataRequired()] class ProjectCommentForm(PagureForm): """ Form to represent project. """ objid = wtforms.StringField( "Ticket/Request id", [wtforms.validators.DataRequired()] ) useremail = wtforms.StringField( "Email", [wtforms.validators.DataRequired()] ) class CommentForm(PagureForm): """ Form to upload a file. """ comment = wtforms.FileField( "Comment", [wtforms.validators.DataRequired(), file_virus_validator] ) class EditGroupForm(PagureForm): """ Form to ask for a password change. """ display_name = wtforms.StringField( "Group name to display", [ wtforms.validators.DataRequired(), wtforms.validators.Length(max=255), ], ) description = wtforms.StringField( "Description", [ wtforms.validators.DataRequired(), wtforms.validators.Length(max=255), ], ) class NewGroupForm(EditGroupForm): """ Form to ask for a password change. """ group_name = wtforms.StringField( "Group name", [ wtforms.validators.DataRequired(), wtforms.validators.Length(max=255), wtforms.validators.Regexp(STRICT_REGEX, flags=re.IGNORECASE), ], ) group_type = wtforms.SelectField( "Group type", [wtforms.validators.DataRequired()], choices=[] ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(NewGroupForm, self).__init__(*args, **kwargs) if "group_types" in kwargs: self.group_type.choices = [ (grptype, grptype) for grptype in kwargs["group_types"] ] class EditFileForm(PagureForm): """ Form used to edit a file. """ content = wtforms.TextAreaField("content", [wtforms.validators.Optional()]) commit_title = wtforms.StringField( "Title", [wtforms.validators.DataRequired()] ) commit_message = wtforms.TextAreaField( "Commit message", [wtforms.validators.optional()] ) email = wtforms.SelectField( "Email", [wtforms.validators.DataRequired()], choices=[] ) branch = wtforms.StringField("Branch", [wtforms.validators.DataRequired()]) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(EditFileForm, self).__init__(*args, **kwargs) if "emails" in kwargs: self.email.choices = [ (email.email, email.email) for email in kwargs["emails"] ] class DefaultBranchForm(PagureForm): """Form to change the default branh for a repository""" branches = wtforms.SelectField( "default_branch", [wtforms.validators.DataRequired()], choices=[] ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(DefaultBranchForm, self).__init__(*args, **kwargs) if "branches" in kwargs: self.branches.choices = [ (branch, branch) for branch in kwargs["branches"] ] class DefaultPriorityForm(PagureForm): """Form to change the default priority for a repository""" priority = wtforms.SelectField( "default_priority", [wtforms.validators.optional()], choices=[] ) def __init__(self, *args, **kwargs): """Calls the default constructor with the normal argument but uses the list of collection provided to fill the choices of the drop-down list. """ super(DefaultPriorityForm, self).__init__(*args, **kwargs) if "priorities" in kwargs: self.priority.choices = [ (priority, priority) for priority in kwargs["priorities"] ] class EditCommentForm(PagureForm): """Form to verify that comment is not empty""" update_comment = wtforms.TextAreaField( "Comment ", [wtforms.validators.DataRequired()], ) class ForkRepoForm(PagureForm): """ Form to fork a project in the API. """ repo = wtforms.StringField( "The project name", [wtforms.validators.DataRequired()] ) username = wtforms.StringField( "User who forked the project", [wtforms.validators.optional()] ) namespace = wtforms.StringField( "The project namespace", [wtforms.validators.optional()] ) class AddReportForm(PagureForm): """Form to verify that comment is not empty""" report_name = wtforms.TextAreaField( "Report name", [wtforms.validators.DataRequired()], ) class PublicNotificationForm(PagureForm): """Form to verify that comment is not empty""" issue_notifs = wtforms.TextAreaField( "Public issue notification", [wtforms.validators.optional(), MultipleEmail()], ) pr_notifs = wtforms.TextAreaField( "Public PR notification", [wtforms.validators.optional(), MultipleEmail()], ) class SubscribtionForm(PagureForm): """ Form to subscribe to or unsubscribe from an issue or a PR. """ status = wtforms.BooleanField( "Subscription status", [wtforms.validators.optional()], false_values=FALSE_VALUES, ) class MergePRForm(PagureForm): delete_branch = wtforms.BooleanField( "Delete branch after merging", [wtforms.validators.optional()], false_values=FALSE_VALUES, ) class TriggerCIPRForm(PagureForm): def __init__(self, *args, **kwargs): # we need to instantiate dynamically because the configuration # values may change during tests and we want to always respect # the currently set value super(TriggerCIPRForm, self).__init__(*args, **kwargs) choices = [] trigger_ci = pagure_config["TRIGGER_CI"] if isinstance(trigger_ci, dict): # make sure to preserver compatibility with older configs # which had TRIGGER_CI as a list for comment, meta in trigger_ci.items(): if meta is not None: choices.append((comment, comment)) self.comment.choices = choices comment = wtforms.SelectField( "comment", [wtforms.validators.Required()], choices=[] ) class AddGitTagForm(PagureForm): """ Form to create a new git tag. """ tagname = wtforms.StringField( "Name of the tag", [wtforms.validators.DataRequired()], ) commit_hash = wtforms.StringField( "Hash of the commit to tag", [wtforms.validators.DataRequired()] ) message = wtforms.TextAreaField( "Annotation message", [wtforms.validators.Optional()] ) force = wtforms.BooleanField( "Force the creation of the git tag", [wtforms.validators.optional()], false_values=FALSE_VALUES, )

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0.013144

0.522537

0.4865

0.446903

0.402048

0.333096

0.296566

0

0.003344

0.26777

30,220

988

80

30.587045

0.821809

0.151621

0

0.363768

0

0.088881

0.007851

0

0.001012

0

1

0.030435

false

0.001449

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0.272464

0

null

0

null

0

1

0

458b9c87e0590b71665c205e005ed91d9aac38d7

5,285

py

Python

docknv/shell/handlers/service.py

sharingcloud/docknv

6eec6a576a32cb05278b7af045f90859066c9f1d

[ "MIT" ]

null

docknv/shell/handlers/service.py

sharingcloud/docknv

6eec6a576a32cb05278b7af045f90859066c9f1d

[ "MIT" ]

null

docknv/shell/handlers/service.py

sharingcloud/docknv

6eec6a576a32cb05278b7af045f90859066c9f1d

[ "MIT" ]

null

"""Service sub commands.""" from docknv.shell.common import exec_handler, load_project def _init(subparsers): cmd = subparsers.add_parser( "service", help="manage one service at a time (service mode)" ) cmd.add_argument( "-c", "--config", help="configuration name (swap)", default=None ) subs = cmd.add_subparsers(dest="service_cmd", metavar="") # Start start_cmd = subs.add_parser("start", help="start a container") start_cmd.add_argument("service", help="service name") # Stop stop_cmd = subs.add_parser("stop", help="stop a container") stop_cmd.add_argument("service", help="service name") # Restart restart_cmd = subs.add_parser("restart", help="restart a container") restart_cmd.add_argument("service", help="service name") restart_cmd.add_argument( "-f", "--force", action="store_true", help="force restart" ) # Run run_cmd = subs.add_parser("run", help="run a command on a container") run_cmd.add_argument("service", help="service name") run_cmd.add_argument("run_command", help="command to run") run_cmd.add_argument( "-d", "--daemon", action="store_true", help="run in background" ) # Exec exec_cmd = subs.add_parser( "exec", help="execute command on a running container" ) exec_cmd.add_argument("service", help="service name") exec_cmd.add_argument("run_command", help="command to run") # Shell shell_cmd = subs.add_parser("shell", help="run shell") shell_cmd.add_argument("service", help="service name") shell_cmd.add_argument( "shell", help="shell executable", default="/bin/bash", nargs="?" ) # Logs logs_cmd = subs.add_parser("logs", help="show container logs") logs_cmd.add_argument("service", help="service name") logs_cmd.add_argument( "-t", "--tail", type=int, help="tail logs", default=0 ) logs_cmd.add_argument( "-f", "--follow", help="follow logs", action="store_true", default=False, ) # Push push_cmd = subs.add_parser("push", help="push a file to a container") push_cmd.add_argument("service", help="service name") push_cmd.add_argument("host_path", help="host path") push_cmd.add_argument("container_path", help="container path") # Pull pull_cmd = subs.add_parser("pull", help="pull a file from a container") pull_cmd.add_argument("service", help="service name") pull_cmd.add_argument("container_path", help="container path") pull_cmd.add_argument("host_path", help="host path") # Build build_cmd = subs.add_parser("build", help="build a service") build_cmd.add_argument("service", help="service name") build_cmd.add_argument("-b", "--build-args", nargs="+", help="build args") build_cmd.add_argument( "--no-cache", help="build without cache", action="store_true" ) def _handle(args): return exec_handler("service", args, globals()) def _handle_build(args): project = load_project(args.project) project.lifecycle.service.build( args.service, config_name=args.config, build_args=args.build_args, no_cache=args.no_cache, dry_run=args.dry_run, ) def _handle_run(args): project = load_project(args.project) project.lifecycle.service.run( args.service, args.run_command, daemon=args.daemon, config_name=args.config, dry_run=args.dry_run, ) def _handle_exec(args): project = load_project(args.project) project.lifecycle.service.execute( args.service, cmds=[args.run_command], config_name=args.config, dry_run=args.dry_run, ) def _handle_shell(args): project = load_project(args.project) project.lifecycle.service.shell( args.service, config_name=args.config, shell=args.shell, dry_run=args.dry_run, ) def _handle_restart(args): project = load_project(args.project) project.lifecycle.service.restart( args.service, config_name=args.config, force=args.force, dry_run=args.dry_run, ) def _handle_stop(args): project = load_project(args.project) project.lifecycle.service.stop( args.service, config_name=args.config, dry_run=args.dry_run ) def _handle_start(args): project = load_project(args.project) project.lifecycle.service.start( args.service, config_name=args.config, dry_run=args.dry_run ) def _handle_push(args): project = load_project(args.project) project.lifecycle.service.push( args.service, args.host_path, args.container_path, config_name=args.config, dry_run=args.dry_run, ) def _handle_pull(args): project = load_project(args.project) project.lifecycle.service.pull( args.service, args.container_path, args.host_path, config_name=args.config, dry_run=args.dry_run, ) def _handle_logs(args): project = load_project(args.project) project.lifecycle.service.logs( args.service, config_name=args.config, tail=args.tail, follow=args.follow, dry_run=args.dry_run, )

28.111702

78

0.653359

686

5,285

4.80758

0.123907

0.045482

0.10188

0.048514

0.513341

0.50849

0.470891

0.360825

0.31413

0.090964

0

0.000241

0.216083

5,285

187

79

28.262032

0.7958

0.01457

0

0.297872

0

0.174658

0

1

0.085106

false

0

0.007092

0.099291

0

null

0

null

0

1

0

458efec9cad1dcaa401e2261be2ecb2c2d569528

1,466

py

Python

canvasaio/bookmark.py

spapadim/canvasaio

a17e60447acd45cdbd6e4f0f24f3c9ae03a58ca8

[ "MIT" ]

null

canvasaio/bookmark.py

spapadim/canvasaio

a17e60447acd45cdbd6e4f0f24f3c9ae03a58ca8

[ "MIT" ]

null

canvasaio/bookmark.py

spapadim/canvasaio

a17e60447acd45cdbd6e4f0f24f3c9ae03a58ca8

[ "MIT" ]

null

from canvasaio.canvas_object import CanvasObject from canvasaio.util import combine_kwargs class Bookmark(CanvasObject): def __str__(self): return "{} ({})".format(self.name, self.id) async def delete(self, **kwargs): """ Delete this bookmark. :calls: `DELETE /api/v1/users/self/bookmarks/:id \ <https://canvas.instructure.com/doc/api/bookmarks.html#method.bookmarks/bookmarks.destroy>`_ :rtype: :class:`canvasaio.bookmark.Bookmark` """ response = await self._requester.request( "DELETE", "users/self/bookmarks/{}".format(self.id), _kwargs=combine_kwargs(**kwargs), ) return Bookmark(self._requester, await response.json()) async def edit(self, **kwargs): """ Modify this bookmark. :calls: `PUT /api/v1/users/self/bookmarks/:id \ <https://canvas.instructure.com/doc/api/bookmarks.html#method.bookmarks/bookmarks.update>`_ :rtype: :class:`canvasaio.bookmark.Bookmark` """ response = await self._requester.request( "PUT", "users/self/bookmarks/{}".format(self.id), _kwargs=combine_kwargs(**kwargs), ) response_json = await response.json() if "name" in response_json and "url" in response_json: super(Bookmark, self).set_attributes(response_json) return Bookmark(self._requester, response_json)

32.577778

100

0.621419

157

1,466

5.66242

0.318471

0.094488

0.08099

0.031496

0.485939

0

0.001807

0.244884

1,466

44

101

33.318182

0.801265

0

0.272727

0

0.072632

0.048421

0

1

0.045455

false

0

0.090909

0.045455

0.318182

0

null

0

null

0

1

0

4590230328e5c1d8487fcc4dc18325b330d72349

2,712

py

Python

sublime/Packages/BracketHighlighter/bh_modules/tagattrselect.py

herove/dotfiles

5f08d4d6f518758b3ad0516d9e704edf251e8ff3

[ "MIT" ]

1

2018-06-23T08:07:39.000Z

sublime/Packages/BracketHighlighter/bh_modules/tagattrselect.py

herove/dotfiles

5f08d4d6f518758b3ad0516d9e704edf251e8ff3

[ "MIT" ]

null

sublime/Packages/BracketHighlighter/bh_modules/tagattrselect.py

herove/dotfiles

5f08d4d6f518758b3ad0516d9e704edf251e8ff3

[ "MIT" ]

null

import bh_plugin class SelectAttr(bh_plugin.BracketPluginCommand): def run(self, edit, name, direction='right'): """ Select next attribute in the given direction. Wrap when the end is hit. """ if self.left.size() <= 1: return tag_name = r'[\w\:\.\-]+' attr_name = r'''([\w\-\.:]+)(?:\s*=\s*(?:(?:"((?:\.|[^"])*)")|(?:'((?:\.|[^'])*)')|([^>\s]+)))?''' tname = self.view.find(tag_name, self.left.begin) current_region = self.selection[0] current_pt = self.selection[0].b region = self.view.find(attr_name, tname.b) selection = self.selection if direction == 'left': last = None # Keep track of last attr if region is not None and current_pt <= region.b and region.b < self.left.end: last = region while region is not None and region.b < self.left.end: # Select attribute until you have closest to the left of selection if ( current_pt > region.b or ( current_pt <= region.b and current_region.a >= region.a and not ( region.a == current_region.a and region.b == current_region.b ) ) ): selection = [region] last = None # Update last attr elif last is not None: last = region region = self.view.find(attr_name, region.b) # Wrap right if last is not None: selection = [last] else: first = None # Keep track of first attr if region is not None and region.b < self.left.end: first = region while region is not None and region.b < self.left.end: # Select closest attr to the right of the selection if( current_pt < region.b or ( current_pt <= region.b and current_region.a >= region.a and not ( region.a == current_region.a and region.b == current_region.b ) ) ): selection = [region] first = None break region = self.view.find(attr_name, region.b) # Wrap left if first is not None: selection = [first] self.selection = selection def plugin(): return SelectAttr

35.220779

106

0.450959

285

2,712

4.217544

0.224561

0.087354

0.052413

0.066556

0.481697

0.468386

0.429285

0.404326

0.342762

0

0.002004

0.448009

2,712

76

107

35.684211

0.800935

0.101032

0

0.357143

0

0.04125

0.032917

0

1

0.035714

false

0

0.017857

0.107143

0

null

0

null

0

1

0

4591e96ca8eecc10cdc680e19398cdebfb252e8a

5,189

py

Python

interpro7dw/interpro/oracle/clans.py

matthiasblum/i7dw

b40e5b9984dec2895956828ddf9db8af4a8ec932

[ "Apache-2.0" ]

null

interpro7dw/interpro/oracle/clans.py

matthiasblum/i7dw

b40e5b9984dec2895956828ddf9db8af4a8ec932

[ "Apache-2.0" ]

null

interpro7dw/interpro/oracle/clans.py

matthiasblum/i7dw

b40e5b9984dec2895956828ddf9db8af4a8ec932

[ "Apache-2.0" ]

null

import json import pickle from typing import Dict import cx_Oracle from interpro7dw import pfam from interpro7dw.utils import logger from interpro7dw.utils.oracle import lob_as_str from interpro7dw.utils.store import BasicStore def get_clans(cur: cx_Oracle.Cursor) -> Dict[str, dict]: cur.execute( """ SELECT C.CLAN_AC, C.NAME, C.DESCRIPTION, D.DBSHORT, CM.MEMBER_AC, M.NAME, M.DESCRIPTION, CM.LEN, CM.SCORE FROM INTERPRO.CLAN C INNER JOIN INTERPRO.CV_DATABASE D ON C.DBCODE = D.DBCODE INNER JOIN INTERPRO.CLAN_MEMBER CM ON C.CLAN_AC = CM.CLAN_AC INNER JOIN INTERPRO.METHOD M ON CM.MEMBER_AC = M.METHOD_AC """ ) clans = {} for row in cur: clan_acc = row[0] clan_name = row[1] clan_desc = row[2] database = row[3] member_acc = row[4] if row[5] and row[5] != member_acc: member_name = row[5] else: member_name = None member_desc = row[6] seq_length = row[7] score = row[8] try: c = clans[clan_acc] except KeyError: c = clans[clan_acc] = { "accession": clan_acc, "name": clan_name, "description": clan_desc, "database": database, "members": [] } finally: c["members"].append((member_acc, member_name, member_desc, score, seq_length)) return clans def iter_alignments(cur: cx_Oracle.Cursor): # Fetching DOMAINS (LOB object) as a string cur.outputtypehandler = lob_as_str cur.execute( """ SELECT QUERY_AC, TARGET_AC, EVALUE, DOMAINS FROM INTERPRO.CLAN_MATCH """ ) for query, target, evalue, json_domains in cur: domains = [] for start, end in json.loads(json_domains): domains.append({ "start": start, "end": end }) yield query, target, evalue, domains def export_clans(ipr_uri: str, pfam_uri: str, clans_file: str, alignments_file: str, **kwargs): threshold = kwargs.get("threshold", 1e-2) logger.info("loading clans") con = cx_Oracle.connect(ipr_uri) cur = con.cursor() clans = get_clans(cur) clan_links = {} entry2clan = {} for accession, clan in clans.items(): clan_links[accession] = {} for member_acc, _, _, _, seq_length in clan["members"]: entry2clan[member_acc] = (accession, seq_length) logger.info("exporting alignments") with BasicStore(alignments_file, "w") as store: alignments = iter_alignments(cur) for i, (query, target, evalue, domains) in enumerate(alignments): if evalue > threshold: continue try: query_clan_acc, seq_length = entry2clan[query] except KeyError: continue try: target_clan_acc, _ = entry2clan[target] except KeyError: target_clan_acc = None store.write((query_clan_acc, query, target, target_clan_acc, evalue, seq_length, json.dumps(domains))) if query_clan_acc == target_clan_acc: # Query and target from the same clan: update clan's links links = clan_links[query_clan_acc] if query > target: query, target = target, query try: targets = links[query] except KeyError: links[query] = {target: evalue} else: if target not in targets or evalue < targets[target]: targets[target] = evalue if (i + 1) % 1e7 == 0: logger.info(f"{i + 1:>15,}") logger.info(f"{i + 1:>15,}") cur.close() con.close() logger.info("loading additional details for Pfam clans") pfam_clans = pfam.get_clans(pfam_uri) logger.info("finalizing") for clan_acc, clan in clans.items(): nodes = [] for member_acc, member_name, member_desc, score, _ in clan["members"]: nodes.append({ "accession": member_acc, "short_name": member_name, "name": member_desc, "type": "entry", "score": score }) links = [] for query_acc, targets in clan_links[clan_acc].items(): for target_acc, score in targets.items(): links.append({ "source": query_acc, "target": target_acc, "score": score }) clan["relationships"] = { "nodes": nodes, "links": links } if clan_acc in pfam_clans: # Replace `description`, add `authors` and `literature` clan.update(pfam_clans[clan_acc]) with open(clans_file, "wb") as fh: pickle.dump(clans, fh) logger.info("complete")

28.827778

78

0.533629

582

5,189

4.582474

0.238832

0.041995

0.025497

0.021372

0.036745

0.025497

0

0.009457

0.368279

5,189

179

79

28.988827

0.804149

0.029293

0

0.166667

0

0.060372

0

1

0.02381

false

0

0.063492

0

0.095238

0

null

0

null

0

1

0

459361bbb100cff1efc28163721eb85c951bcf87

4,054

py

Python

tests/test_core/test_controller.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

tests/test_core/test_controller.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

tests/test_core/test_controller.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

import unittest from annotypes import add_call_types, Anno from malcolm.core import Controller, Part, PartRegistrar, StringMeta, \ Process, Queue, Get, Return, Put, Error, Post, Subscribe, Update, \ Unsubscribe with Anno("The return value"): AWorld = str class MyPart(Part): my_attribute = None exception = None context = None @add_call_types def method(self): # type: () -> AWorld return 'world' def setup(self, registrar): # type: (PartRegistrar) -> None self.my_attribute = StringMeta( description="MyString" ).create_attribute_model('hello_block') registrar.add_attribute_model( "myAttribute", self.my_attribute, self.my_attribute.set_value) registrar.add_method_model(self.method) class TestController(unittest.TestCase): maxDiff = None def setUp(self): self.process = Process("proc") self.part = MyPart("test_part") self.o = Controller("mri") self.o.add_part(self.part) self.process.add_controller(self.o) self.process.start() def tearDown(self): self.process.stop(timeout=1) def test_init(self): assert self.o.mri == "mri" assert self.o.process == self.process def test_make_view(self): b = self.process.block_view("mri") method_view = b.method attribute_view = b.myAttribute dict_view = b.method.returns.elements list_view = b.method.returns.required assert method_view() == 'world' assert attribute_view.value == "hello_block" assert dict_view['return'].description == "The return value" assert list_view[0] == "return" def test_handle_request(self): q = Queue() request = Get(id=41, path=["mri", "myAttribute"]) request.set_callback(q.put) self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Return) assert response.id == 41 assert response.value["value"] == "hello_block" self.part.my_attribute.meta.writeable = False request = Put( id=42, path=["mri", "myAttribute"], value='hello_block2', get=True) request.set_callback(q.put) self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Error) # not writeable assert response.id == 42 self.part.my_attribute.meta.writeable = True self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Return) assert response.id == 42 assert response.value == "hello_block2" request = Post(id=43, path=["mri", "method"]) request.set_callback(q.put) self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Return) assert response.id == 43 assert response.value == "world" # cover the controller._handle_post path for parameters request = Post(id=43, path=["mri", "method"], parameters={'dummy': 1}) request.set_callback(q.put) self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Error) assert response.id == 43 assert str(response.message) == "Method passed argument 'dummy' which is not in []" request = Subscribe(id=44, path=["mri", "myAttribute"], delta=False) request.set_callback(q.put) self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Update) assert response.id == 44 assert response.value["typeid"] == "epics:nt/NTScalar:1.0" assert response.value["value"] == "hello_block2" request = Unsubscribe(id=44) request.set_callback(q.put) self.o.handle_request(request) response = q.get(timeout=.1) self.assertIsInstance(response, Return) assert response.id == 44

33.783333

91

0.629008

483

4,054

5.165631

0.221532

0.024048

0.030862

0.050501

0.381162

0.34509

0.319439

0.296994

0

0.013509

0.251357

4,054

119

92

34.067227

0.808567

0.028614

0

0.333333

0

0.081363

0.005339

0

0.270833

1

0.072917

false

0.010417

0.03125

0.010417

0.177083

0

null

0

null

0

1

0

4594f72e405e0824b4be1fe0fefd39e46766217c

684

py

Python

dado.py

crysller/Dado

63c77bb81bec6fe9d2add2b0d14ba5d399b30018

[ "MIT" ]

null

dado.py

crysller/Dado

63c77bb81bec6fe9d2add2b0d14ba5d399b30018

[ "MIT" ]

null

dado.py

crysller/Dado

63c77bb81bec6fe9d2add2b0d14ba5d399b30018

[ "MIT" ]

null

from random import randint from time import sleep continuar = 'Ss' while continuar in 'Ss': print('\033[1;32mVamos jogar. Exemplo: 1d20 ou 5d10\033[m') opc = input() qntDados = int(opc[:1]) valorDado = int(opc[2:]) if qntDados > 10 or valorDado not in [4,6,8,10,12,20,100]: print('\033[1;31mErro! Quantidade da dados ou valor dos dados é inválido!\033[m') elif valorDado in [4,6,8,10,12,20,100]: dado = 1 for l in range(qntDados): sleep(0.5) print(f'Valor do dado 0{dado}: {randint(1, valorDado)}') dado = dado + 1 continuar = str(input('Jogar novamente? S ou N?')) sleep(1) print('Até! o/')

34.2

89

0.599415

110

684

3.727273

0.536364

0.039024

0.043902

0.02439

0.068293

0

0.115686

0.254386

684

20

90

34.2

0.688235

0

0.29635

0

1

0

false

0

0.105263

0

0.105263

0.210526

0

null

0

null

0

1

0

4595f169799444e02f534a9747ed945159901694

1,094

py

Python

test/signature.py

orbs-network/orbs-client-sdk-python

1e8d5699ee98e2ff59d36081eb569237949a558b

[ "MIT" ]

5

2018-08-10T15:39:46.000Z

2020-02-10T03:14:51.000Z

test/signature.py

orbs-network/orbs-client-sdk-python

1e8d5699ee98e2ff59d36081eb569237949a558b

[ "MIT" ]

3

2018-06-22T07:32:46.000Z

2018-12-13T14:16:56.000Z

test/signature.py

orbs-network/orbs-client-sdk-python

1e8d5699ee98e2ff59d36081eb569237949a558b

[ "MIT" ]

2

2018-07-01T12:45:38.000Z

2020-04-13T11:09:36.000Z

import unittest from os import sys, path from crypto.signature import Signature sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) class TestSignatureFunctions(unittest.TestCase): def test_sign_ed25519(self): private_key = bytes.fromhex('3b24b5f9e6b1371c3b5de2e402a96930eeafe52111bb4a1b003e5ecad3fab53892d469d7c004cc0b24a192d9457836bf38effa27536627ef60718b00b0f33152') public_key = bytes.fromhex('92d469d7c004cc0b24a192d9457836bf38effa27536627ef60718b00b0f33152') data = b'This is what we want to sign' sig = Signature.sign_ed25519(private_key, data) self.assertEqual(Signature.ED25519_SIGNATURE_SIZE_BYTES, len(sig), 'signature length should equal 64 bytes') self.assertEqual(Signature.verify_ed25519(public_key, data, sig), True, 'signature cannot be verified') modified_sig = bytearray(sig) modified_sig[0] += 1 # corrupt the signature sig = bytes(modified_sig) self.assertEqual(Signature.verify_ed25519(public_key, data, sig), False) if __name__ == '__main__': unittest.main()

42.076923

167

0.761426

116

1,094

6.939655

0.5

0.03354

0.089441

0.074534

0.131677

0

0.167568

0.154479

1,094

25

168

43.76

0.702703

0.019196

0

0.27451

0.179272

0

0.166667

1

0.055556

false

0

0.166667

0

0.277778

0

null

0

null

0

1

0

459719287cbd8d4d670931c7497d555a6c24e23b

318

py

Python

q3volume.py

tiwa2022/Lab2

22ebbe4367316a969c0a1642747516b606618ca5

[ "MIT" ]

1

2022-01-27T16:57:53.000Z

q3volume.py

tiwa2022/Lab2

22ebbe4367316a969c0a1642747516b606618ca5

[ "MIT" ]

null

q3volume.py

tiwa2022/Lab2

22ebbe4367316a969c0a1642747516b606618ca5

[ "MIT" ]

null

#input print("This program find the volume of a cylinder") PI= 3.14159265359 diameter= float(input("Enter diameter: ")) height= float(input("Enter height: ")) #processing volume= PI * diameter *height #output (print("The volume of a cyclinder with a diameter of",diameter, "and a height of", height, "is",volume))

26.5

105

0.72327

47

318

4.893617

0.489362

0.078261

0.095652

0.104348

0

0.043956

0.141509

318

11

106

28.909091

0.798535

0.069182

0

0.453925

0

1

0

false

0

0.333333

0

null

0

null

0

1

0

459b4482cc67e06b82631e6053118e1bd6455789

2,219

py

Python

ebay_accounts/test_settings.py

luke-dixon/django-ebay-accounts

54cf0e90b75dfbdd63bcd588f3c4771ebe1297c0

[ "BSD-3-Clause" ]

4

2018-01-28T20:10:11.000Z

2020-09-06T14:30:36.000Z

ebay_accounts/test_settings.py

luke-dixon/django-ebay-accounts

54cf0e90b75dfbdd63bcd588f3c4771ebe1297c0

[ "BSD-3-Clause" ]

7

2017-06-04T08:50:06.000Z

2020-09-06T16:03:53.000Z

ebay_accounts/test_settings.py

luke-dixon/django-ebay-accounts

54cf0e90b75dfbdd63bcd588f3c4771ebe1297c0

[ "BSD-3-Clause" ]

7

2017-06-01T09:51:35.000Z

2021-05-25T16:01:53.000Z

# -*- coding: utf-8 -*- """ Test Settings """ import django APP_NAME = 'ebay_accounts' INSTALLED_APPS = ( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'ebay_accounts', ) MIDDLEWARE = ( 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', ) if django.VERSION[:2] >= (1, 8): TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', }, } SECRET_KEY = '^&*TESTING123^&*' ROOT_URLCONF = APP_NAME + '.urls' USE_TZ = True LOGGING = { 'version': 1, 'formatters': { 'simple': { 'format': '%(levelname)s %(module)s: %(message)s' }, }, 'handlers': { 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', 'formatter': 'simple', } }, 'loggers': { 'ebay_accounts': { 'handlers': ['console'], 'level': 'DEBUG', } } } EBAY_SANDBOX_DEVID = 'TEST_SANDBOX_DEVID' EBAY_SANDBOX_APPID = 'TEST_SANDBOX_APPID' EBAY_SANDBOX_CERTID = 'TEST_SANDBOX_CERTID' EBAY_SANDBOX_RU_NAME = 'TEST_SANDBOX_RU_NAME' EBAY_PRODUCTION_DEVID = 'TEST_PRODUCTION_DEVID' EBAY_PRODUCTION_APPID = 'TEST_PRODUCTION_APPID' EBAY_PRODUCTION_CERTID = 'TEST_PRODUCTION_CERTID' EBAY_PRODUCTION_RU_NAME = 'TEST_PRODUCTION_RU_NAME' TIME_ZONE = 'Europe/London' USE_TZ = True # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 fileencoding=utf-8

26.105882

74

0.605228

206

2,219

6.262136

0.42233

0.08062

0.039535

0.048062

0

0.007884

0.256872

2,219

84

75

26.416667

0.774409

0.04822

0

0.084507

0

0.482644

0.308131

0

1

0

false

0

0.014085

0

0.014085

0

null

0

null

0

1

0

459db3d81ac904122652cb0bff922fb7f630a44a

5,475

py

Python

export/views.py

felix-engelmann/badgecc

5bc0ced339f18737e24cc34935a87e96ae14a825

[ "MIT" ]

null

export/views.py

felix-engelmann/badgecc

5bc0ced339f18737e24cc34935a87e96ae14a825

[ "MIT" ]

null

export/views.py

felix-engelmann/badgecc

5bc0ced339f18737e24cc34935a87e96ae14a825

[ "MIT" ]

null

from django.shortcuts import render from django.template.loader import render_to_string from django.http import HttpResponse from django.conf import settings from persons.models import Role, Right, Department, Person from math import floor import os from subprocess import Popen, PIPE import tempfile # Create your views here. def _render_tex(texcode): with tempfile.TemporaryDirectory(dir=os.path.join(settings.BASE_DIR, 'tex')) as tempdir: # Create subprocess, supress output with PIPE and # run latex twice to generate the TOC properly. # Finally read the generated pdf. env = os.environ.copy() #env["TEXINPUTS"] = env["TEXINPUTS"]+":"+os.path.join(settings.BASE_DIR, 'media') for i in range(1): process = Popen( ['pdflatex', '-output-directory', tempdir, '-halt-on-error'], stdin=PIPE, stdout=PIPE, stderr=PIPE, env=env, ) out, err = process.communicate(bytes(texcode, 'UTF-8')) if process.returncode: return HttpResponse(err.decode('UTF-8')+'\n\n\n'+out.decode('UTF-8')+'\n\n\n'+texcode) with open(os.path.join(tempdir, 'texput.pdf'), 'rb') as f: pdf = f.read() response = HttpResponse(content_type='application/pdf') response['Content-Disposition'] = 'filename="{}"'.format("badges.pdf") response.write(pdf) return response def _make_side(x,y,person,template): render_to_string("export/tex/front.tex",{'x':"felix"}) def _make_sheet(front,back): """ Arranges a double sided sheet Args: front: plain tikz commands for front sides back: plain tikz commands for back sides Returns: String of self contained TeX commands """ sheet="" sheet+="\\centering\\begin{tikzpicture}[font=\\sffamily]\n" sheet+=front sheet+="\\end{tikzpicture}\n" sheet+="\\newpage\n" sheet+="\\centering\\begin{tikzpicture}[font=\\sffamily]\n" sheet+=back sheet+="\\end{tikzpicture}\n" sheet+="\\newpage\n" return sheet def texit(persons): for p in persons: p.printed = True; p.save() r=set(p.extra_rights.all()) r=r|set(p.department.rights.all()) if(p.role): r=r|set(p.role.rights.all()) rslug=[] for ro in list(r): rslug.append(ro.slug) print(rslug) p.calc_rights=list(rslug) #sheet layout in cm badge_height=6 badge_width=9.5 #count of badges badge_rows=4 badge_cols=2 document="" evenpage="" oddpage="" for idx,person in enumerate(persons): evenpage+=render_to_string("export/tex/front.tex",{'x':(idx%badge_cols)*badge_width,'y':floor((idx%(badge_cols*badge_rows))/badge_cols)*badge_height,'person':person}) oddpage+=render_to_string("export/tex/back.tex",{'x':badge_width-(idx%badge_cols)*badge_width,'y':floor((idx%(badge_cols*badge_rows))/badge_cols)*badge_height,'person':person}) if idx%(badge_cols*badge_rows)==7: document+=_make_sheet(evenpage,oddpage) evenpage="" oddpage="" if evenpage!="": document+=_make_sheet(evenpage,oddpage) #return document return _render_tex(render_to_string("export/tex/wrapper.tex", {'content':document})) def index(request): if request.method == 'POST': persons = Person.objects.filter(id__in=request.POST.getlist('print')).order_by("department") return texit(persons) else: persons = Person.objects.order_by("department") for p in persons: r=set(p.extra_rights.all()) r=r|set(p.department.rights.all()) if(p.role): r=r|set(p.role.rights.all()) p.calc_rights=list(r) return render(request, "export/index.html", {'person':persons}) def update(request): if request.method == 'POST': persons = Person.objects.filter(id__in=request.POST.getlist('print')).order_by("department") return texit(persons) else: persons = Person.objects.order_by("department") for p in persons: r=set(p.extra_rights.all()) r=r|set(p.department.rights.all()) if(p.role): r=r|set(p.role.rights.all()) p.calc_rights=list(r) return render(request, "export/updates.html", {'person':persons}) def dep(request): if request.method == 'POST': persons = Person.objects.filter(id__in=request.POST.getlist('print')).order_by("department") #print(persons) #print(request.GET['id']) return texit(persons) #return render(request, "export/departments.html", {'departments':None}) else: departments = Department.objects.order_by("name") for d in departments: d.persons = Person.objects.filter(department=d) for p in d.persons: r=set(p.extra_rights.all()) r=r|set(p.department.rights.all()) if(p.role): r=r|set(p.role.rights.all()) p.calc_rights=list(r) return render(request, "export/departments.html", {'departments':departments})

29.755435

184

0.588493

673

5,475

4.692422

0.273403

0.015199

0.018999

0.015199

0.479417

0.437935

0.41387

0.368588

0.317923

0

0.002509

0.271963

5,475

183

185

29.918033

0.789764

0.103744

0

0.424779

0

0.117901

0.029835

0

1

0.061947

false

0

0.079646

0

0.230089

0.044248

0

null

0

null

0

1

0

459fe241fa9cf83ded4a40b6c2040315ffceaa98

7,118

py

Python

Gems/Atom/Feature/Common/Editor/Scripts/ColorGrading/lut_helper.py

fromasmtodisasm/o3de

0d728a76778cb0ca88caa5c07f17162fac668b2a

[ "Apache-2.0", "MIT" ]

null

Gems/Atom/Feature/Common/Editor/Scripts/ColorGrading/lut_helper.py

fromasmtodisasm/o3de

0d728a76778cb0ca88caa5c07f17162fac668b2a

[ "Apache-2.0", "MIT" ]

null

Gems/Atom/Feature/Common/Editor/Scripts/ColorGrading/lut_helper.py

fromasmtodisasm/o3de

0d728a76778cb0ca88caa5c07f17162fac668b2a

[ "Apache-2.0", "MIT" ]

null

# coding:utf-8 #!/usr/bin/python # # Copyright (c) Contributors to the Open 3D Engine Project. # For complete copyright and license terms please see the LICENSE at the root of this distribution. # # SPDX-License-Identifier: Apache-2.0 OR MIT # # # lut_helper.py import sys import os import argparse import math import site import pathlib from pathlib import Path import logging as _logging import numpy as np from pathlib import Path # ------------------------------------------------------------------------ _MODULENAME = 'ColorGrading.lut_helper' import ColorGrading.initialize ColorGrading.initialize.start() _LOGGER = _logging.getLogger(_MODULENAME) _LOGGER.debug('Initializing: {0}.'.format({_MODULENAME})) try: import OpenImageIO as oiio pass except ImportError as e: _LOGGER.error(f"invalid import: {e}") sys.exit(1) # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # Transform from high dynamic range to normalized from ColorGrading import inv_shaper_transform # Transform from normalized range to high dynamic range from ColorGrading import shaper_transform # utils from ColorGrading import get_uv_coord from ColorGrading import log2 from ColorGrading import is_power_of_two shaper_presets = {"Log2-48nits": (-6.5, 6.5), "Log2-1000nits": (-12.0, 10.0), "Log2-2000nits": (-12.0, 11.0), "Log2-4000nits": (-12.0, 12.0)} def transform_exr(image_buffer, out_image_buffer, op, out_path, write_exr): # Set the destination image pixels by applying the shaperfunction for y in range(out_image_buffer.ybegin, out_image_buffer.yend): for x in range(out_image_buffer.xbegin, out_image_buffer.xend): src_pixel = image_buffer.getpixel(x, y) # _LOGGER.debug(f'src_pixel is: {src_pixel}') if op == 0: dst_pixel = (inv_shaper_transform(bias, scale, src_pixel[0]), inv_shaper_transform(bias, scale, src_pixel[1]), inv_shaper_transform(bias, scale, src_pixel[2])) out_image_buffer.setpixel(x, y, dst_pixel) elif op == 1: dst_pixel = (shaper_transform(bias, scale, src_pixel[0]), shaper_transform(bias, scale, src_pixel[1]), shaper_transform(bias, scale, src_pixel[2])) out_image_buffer.setpixel(x, y, dst_pixel) else: # Unspecified operation. Just write zeroes out_image_buffer.setpixel(x, y, 0.0, 0.0, 0.0) if write_exr: _LOGGER.info(f"writing {out_path}.exr ...") out_image_buffer.write(out_path + '.exr', "float") return out_image_buffer ########################################################################### # Main Code Block, runs this script as main (testing) # ------------------------------------------------------------------------- if __name__ == '__main__': """Run this file as main""" operations = {"pre-grading": 0, "post-grading": 1} parser = argparse.ArgumentParser() parser.add_argument('--i', type=str, required=True, help='input file') parser.add_argument('--shaper', type=str, required=True, help='shaper preset. Should be one of \'Log2-48nits\', \'Log2-1000nits\', \'Log2-2000nits\', \'Log2-4000nits\'') parser.add_argument('--op', type=str, required=True, help='operation. Should be \'pre-grading\' or \'post-grading\'') parser.add_argument('--o', type=str, required=True, help='output file') parser.add_argument('-e', dest='writeExr', action='store_true', help='output lut as exr file (float)') parser.add_argument('-l', dest='write3dl', action='store_true', help='output lut as .3dl file') parser.add_argument('-a', dest='writeAsset', action='store_true', help='write out lut as O3dE .azasset file') args = parser.parse_args() # Check for valid shaper type invalid_shaper = (0, 0) invalid_op = -1 shaper_limits = shaper_presets.get(args.shaper, invalid_shaper) if shaper_limits == invalid_shaper: _LOGGER.error("invalid shaper") sys.exit(1) op = operations.get(args.op, invalid_op) if op == invalid_op: _LOGGER.error("invalid operation") sys.exit(1) # input validation input_file = Path(args.i) if input_file.is_file(): # file exists pass else: FILE_ERROR_MSG = f'File does not exist: {input_file}' _LOGGER.error(FILE_ERROR_MSG) #raise FileNotFoundError(FILE_ERROR_MSG) sys.exit(1) # Read input image #buf = oiio.ImageBuf("linear_lut.exr") image_buffer = oiio.ImageBuf(args.i) image_spec = image_buffer.spec() _LOGGER.info(f"Resolution is x:{image_spec.height}, y:{image_spec.width}") if image_spec.height < 16: _LOGGER.info(f"invalid input file dimensions: x is {image_spec.height}. Expected LUT with height dimension >= 16 pixels") sys.exit(1) if not is_power_of_two(image_buffer.spec().height): _LOGGER.info(f"invalid input file dimensions: {buf.spec().height}. Expected LUT dimensions power of 2: 16, 32, or 64 height") sys.exit(1) elif image_spec.width != image_spec.height * image_spec.height: _LOGGER.info("invalid input file dimensions. Expect lengthwise LUT with dimension W: s*s X H: s, where s is the size of the LUT") sys.exit(1) lut_size = image_spec.height middle_grey = 0.18 lower_stops = shaper_limits[0] upper_stops = shaper_limits[1] middle_grey = math.log(middle_grey, 2.0) log_min = middle_grey + lower_stops log_max = middle_grey + upper_stops scale = 1.0 / (log_max - log_min) bias = -scale * log_min _LOGGER.info("Shaper: range in stops %.1f -> %.1f (linear: %.3f -> %.3f) logMin %.3f logMax %.3f scale %.3f bias %.3f\n" % (lower_stops, upper_stops, middle_grey * math.pow(2.0, lower_stops), middle_grey * math.pow(2.0, upper_stops), log_min, log_max, scale, bias)) buffer_name = Path(args.o).name # Create a writing image out_image_spec = oiio.ImageSpec(image_buffer.spec().width, image_buffer.spec().height, 3, "float") out_image_buffer = oiio.ImageBuf(out_image_spec) # write out the modified exr file write_exr = False if args.writeExr: write_exr = True out_image_buffer = transform_exr(image_buffer, out_image_buffer, op, args.o, write_exr) from ColorGrading.exr_to_3dl_azasset import generate_lut_values lut_intervals, lut_values = generate_lut_values(image_spec, out_image_buffer) if args.write3dl: from ColorGrading.exr_to_3dl_azasset import write_3DL write_3DL(args.o, lut_size, lut_intervals, lut_values) if args.writeAsset: from ColorGrading import AZASSET_LUT from ColorGrading.from_3dl_to_azasset import write_azasset write_azasset(args.o, lut_intervals, lut_values, AZASSET_LUT)

38.064171

139

0.631779

944

7,118

4.54661

0.246822

0.056384

0.045666

0.033551

0.179171

0.146552

0.14096

0.048462

0.030289

0

0.023542

0.212279

7,118

186

140

38.268817

0.74193

0.147232

0

0.12605

0

0.02521

0.16807

0.007578

0

1

0.008403

false

0.016807

0.193277

0

0.210084

0

null

0

null

0

1

0

45a097cba2dbbe048aae9c8954d61af23516651a

7,560

py

Python

recon/nmap.py

bbhunter/recon-pipeline

234fe5e639c2a6ef7573410eb4765df07fb352d1

[ "MIT" ]

null

recon/nmap.py

bbhunter/recon-pipeline

234fe5e639c2a6ef7573410eb4765df07fb352d1

[ "MIT" ]

null

recon/nmap.py

bbhunter/recon-pipeline

234fe5e639c2a6ef7573410eb4765df07fb352d1

[ "MIT" ]

1

2020-01-30T10:27:30.000Z

import pickle import logging import subprocess import concurrent.futures from pathlib import Path import luigi from luigi.util import inherits from recon.config import defaults from recon.masscan import ParseMasscanOutput @inherits(ParseMasscanOutput) class ThreadedNmapScan(luigi.Task): """ Run nmap against specific targets and ports gained from the ParseMasscanOutput Task. nmap commands are structured like the example below. nmap --open -sT -sC -T 4 -sV -Pn -p 43,25,21,53,22 -oA htb-targets-nmap-results/nmap.10.10.10.155-tcp 10.10.10.155 The corresponding luigi command is shown below. PYTHONPATH=$(pwd) luigi --local-scheduler --module recon.nmap ThreadedNmap --target-file htb-targets --top-ports 5000 Args: threads: number of threads for parallel nmap command execution rate: desired rate for transmitting packets (packets per second) *--* Required by upstream Task interface: use the named raw network interface, such as "eth0" *--* Required by upstream Task top_ports: Scan top N most popular ports *--* Required by upstream Task ports: specifies the port(s) to be scanned *--* Required by upstream Task target_file: specifies the file on disk containing a list of ips or domains *--* Required by upstream Task results_dir: specifes the directory on disk to which all Task results are written *--* Required by upstream Task """ threads = luigi.Parameter(default=defaults.get("threads", "")) def requires(self): """ ThreadedNmap depends on ParseMasscanOutput to run. TargetList expects target_file as a parameter. Masscan expects rate, target_file, interface, and either ports or top_ports as parameters. Returns: luigi.Task - ParseMasscanOutput """ args = { "results_dir": self.results_dir, "rate": self.rate, "target_file": self.target_file, "top_ports": self.top_ports, "interface": self.interface, "ports": self.ports, } return ParseMasscanOutput(**args) def output(self): """ Returns the target output for this task. Naming convention for the output folder is TARGET_FILE-nmap-results. The output folder will be populated with all of the output files generated by any nmap commands run. Because the nmap command uses -oA, there will be three files per target scanned: .xml, .nmap, .gnmap. Returns: luigi.local_target.LocalTarget """ return luigi.LocalTarget(f"{self.results_dir}/nmap-{self.target_file}-results") def run(self): """ Parses pickled target info dictionary and runs targeted nmap scans against only open ports. """ try: self.threads = abs(int(self.threads)) except TypeError: return logging.error("The value supplied to --threads must be a non-negative integer.") ip_dict = pickle.load(open(self.input().path, "rb")) nmap_command = [ # placeholders will be overwritten with appropriate info in loop below "nmap", "--open", "PLACEHOLDER-IDX-2", "-n", "-sC", "-T", "4", "-sV", "-Pn", "-p", "PLACEHOLDER-IDX-10", "-oA", ] commands = list() """ ip_dict structure { "IP_ADDRESS": {'udp': {"161", "5000", ... }, ... i.e. {protocol: set(ports) } } """ for target, protocol_dict in ip_dict.items(): for protocol, ports in protocol_dict.items(): tmp_cmd = nmap_command[:] tmp_cmd[2] = "-sT" if protocol == "tcp" else "-sU" # arg to -oA, will drop into subdir off curdir tmp_cmd[9] = ",".join(ports) tmp_cmd.append(f"{self.output().path}/nmap.{target}-{protocol}") tmp_cmd.append(target) # target as final arg to nmap commands.append(tmp_cmd) # basically mkdir -p, won't error out if already there Path(self.output().path).mkdir(parents=True, exist_ok=True) with concurrent.futures.ThreadPoolExecutor(max_workers=self.threads) as executor: executor.map(subprocess.run, commands) @inherits(ThreadedNmapScan) class SearchsploitScan(luigi.Task): """ Run searchcploit against each nmap*.xml file in the TARGET-nmap-results directory and write results to disk. searchsploit commands are structured like the example below. searchsploit --nmap htb-targets-nmap-results/nmap.10.10.10.155-tcp.xml The corresponding luigi command is shown below. PYTHONPATH=$(pwd) luigi --local-scheduler --module recon.nmap Searchsploit --target-file htb-targets --top-ports 5000 Args: threads: number of threads for parallel nmap command execution *--* Required by upstream Task rate: desired rate for transmitting packets (packets per second) *--* Required by upstream Task interface: use the named raw network interface, such as "eth0" *--* Required by upstream Task top_ports: Scan top N most popular ports *--* Required by upstream Task ports: specifies the port(s) to be scanned *--* Required by upstream Task target_file: specifies the file on disk containing a list of ips or domains *--* Required by upstream Task results_dir: specifies the directory on disk to which all Task results are written *--* Required by upstream Task """ def requires(self): """ Searchsploit depends on ThreadedNmap to run. TargetList expects target_file as a parameter. Masscan expects rate, target_file, interface, and either ports or top_ports as parameters. ThreadedNmap expects threads Returns: luigi.Task - ThreadedNmap """ args = { "rate": self.rate, "ports": self.ports, "threads": self.threads, "top_ports": self.top_ports, "interface": self.interface, "target_file": self.target_file, "results_dir": self.results_dir, } return ThreadedNmapScan(**args) def output(self): """ Returns the target output for this task. Naming convention for the output folder is TARGET_FILE-searchsploit-results. The output folder will be populated with all of the output files generated by any searchsploit commands run. Returns: luigi.local_target.LocalTarget """ return luigi.LocalTarget(f"{self.results_dir}/searchsploit-{self.target_file}-results") def run(self): """ Grabs the xml files created by ThreadedNmap and runs searchsploit --nmap on each one, saving the output. """ for entry in Path(self.input().path).glob("nmap*.xml"): proc = subprocess.run(["searchsploit", "--nmap", str(entry)], stderr=subprocess.PIPE) if proc.stderr: Path(self.output().path).mkdir(parents=True, exist_ok=True) # change wall-searchsploit-results/nmap.10.10.10.157-tcp to 10.10.10.157 target = entry.stem.replace("nmap.", "").replace("-tcp", "").replace("-udp", "") Path( f"{self.output().path}/searchsploit.{target}-{entry.stem[-3:]}.txt" ).write_bytes(proc.stderr)

38.571429

121

0.626852

925

7,560

5.069189

0.261622

0.034122

0.049904

0.060994

0.517594

0.493495

0.489657

0.459373

0.441459

0

0.014585

0.274471

7,560

195

122

38.769231

0.840292

0.497884

0

0.275

0

0.156693

0.066934

0

1

0.075

false

0

0.1125

0

0.2875

0

null

0

null

0

1

0

45a34389a4196f702824ce54a8d761be8862131f

3,041

py

Python

docs/source/generate_cli_help.py

Conzel/CompressAI

55be017e93e25fc936fe0fb4fa5851b3c1032dfc

[ "BSD-3-Clause-Clear" ]

515

2020-06-24T23:48:02.000Z

2022-03-31T08:09:19.000Z

docs/source/generate_cli_help.py

Conzel/CompressAI

55be017e93e25fc936fe0fb4fa5851b3c1032dfc

[ "BSD-3-Clause-Clear" ]

102

2020-08-12T15:13:19.000Z

2022-03-30T22:28:16.000Z

docs/source/generate_cli_help.py

Conzel/CompressAI

55be017e93e25fc936fe0fb4fa5851b3c1032dfc

[ "BSD-3-Clause-Clear" ]

123

2020-06-25T00:32:29.000Z

2022-03-28T19:19:16.000Z

# Copyright (c) 2021-2022, InterDigital Communications, Inc # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted (subject to the limitations in the disclaimer # below) 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 InterDigital Communications, Inc nor the names of its # contributors may be used to endorse or promote products derived from this # software without specific prior written permission. # NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY # THIS LICENSE. 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 HOLDER 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. # Based on https://github.com/facebookresearch/ParlAI/tree/c06c40603f45918f58cb09122fa8c74dd4047057/docs/source import importlib import io from pathlib import Path import compressai.utils def get_utils(): rootdir = Path(compressai.utils.__file__).parent for d in rootdir.iterdir(): if d.is_dir() and (d / "__main__.py").is_file(): yield d def main(): fout = open("cli_usage.inc", "w") for p in get_utils(): try: m = importlib.import_module(f"compressai.utils.{p.name}.__main__") except ImportError: continue if not hasattr(m, "setup_args"): continue fout.write(p.name) fout.write("\n") fout.write("-" * len(p.name)) fout.write("\n") doc = m.__doc__ if doc: fout.write(doc) fout.write("\n") fout.write(".. code-block:: text\n\n") capture = io.StringIO() parser = m.setup_args() if isinstance(parser, tuple): parser = parser[0] parser.prog = f"python -m compressai.utils.{p.name}" parser.print_help(capture) for line in capture.getvalue().split("\n"): fout.write(f"\t{line}\n") fout.write("\n\n") fout.close() if __name__ == "__main__": main()

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null

0

null

0

1

0

45a3d4d8a1f127580265a0a3c5979a90f99be58c

2,219

py

Python

implementation/data_io.py

rpalo/masters-thesis

fcc0beb933634b17dbe41bde982e947204fd498b

[ "MIT" ]

null

implementation/data_io.py

rpalo/masters-thesis

fcc0beb933634b17dbe41bde982e947204fd498b

[ "MIT" ]

null

implementation/data_io.py

rpalo/masters-thesis

fcc0beb933634b17dbe41bde982e947204fd498b

[ "MIT" ]

null

"""Data I/O: Import and export data to other useable formats.""" import csv from pathlib import Path from model import Job def import_csv(filename, base_dir=Path("data/")): """Converts CSV files with the relevant data (see columns below) to a list of Jobs. """ datafile = base_dir / filename with open(datafile, "r", newline="", encoding="utf-8-sig") as csvfile: reader = csv.DictReader(csvfile) return [ Job( line["part number"], int(line["quantity"]), float(line["cycle"]), int(line["cavities"]), float(line["due date"]), line["mold"], line["material"], [int(num) for num in line["machines"].split(",")], float(line["setup"]), float(line["teardown"]) ) for i, line in enumerate(reader, start=2) ] def export_csv(schedule, fitness, time_elapsed, filename, base_dir=Path("results/")): """Exports a generated schedule to CSV in a format where each machine has its jobs listed with start and end dates in order of operation. Each machine separated by a blank line. """ outfile = base_dir / filename with open(outfile, "w") as csvfile: fieldnames = ["part number", "due date", "material", "start", "end"] writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for machine in schedule: writer.writerow({"part number": f"Machine {machine.number}"}) for assignment in machine.queue: writer.writerow({ "part number": assignment.job.number, "due date": assignment.job.due_date, "material": assignment.job.material, "start": assignment.start, "end": assignment.end, }) writer.writerow({}) writer.writerow({}) writer.writerow({ "part number": "Total fitness:", "due date": fitness }) writer.writerow({ "part number": "Time elapsed:", "due date": time_elapsed })

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45a6eb2cafb85abd876f4684bf56dbe0066463d9

16,694

py

Python

research/cv/eppmvsnet/src/networks.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

77

2021-10-15T08:32:37.000Z

2022-03-30T13:09:11.000Z

research/cv/eppmvsnet/src/networks.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

3

2021-10-30T14:44:57.000Z

2022-02-14T06:57:57.000Z

research/cv/eppmvsnet/src/networks.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

24

2021-10-15T08:32:45.000Z

2022-03-24T18:45:20.000Z

# Copyright 2021 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """sub-networks of EPP-MVSNet""" import numpy as np import mindspore import mindspore.ops as P from mindspore import nn from mindspore import Tensor, Parameter from src.modules import depth_regression, soft_argmin, entropy class BasicBlockA(nn.Cell): """BasicBlockA""" def __init__(self, in_channels, out_channels, stride): super(BasicBlockA, self).__init__() self.conv2d_0 = nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1, pad_mode="pad") self.conv2d_1 = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0, pad_mode="valid") self.batchnorm2d_2 = nn.BatchNorm2d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.batchnorm2d_3 = nn.BatchNorm2d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_4 = nn.ReLU() self.conv2d_5 = nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=(1, 1, 1, 1), pad_mode="pad") self.batchnorm2d_6 = nn.BatchNorm2d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_8 = nn.ReLU() def construct(self, x): """construct""" x1 = self.conv2d_0(x) x1 = self.batchnorm2d_2(x1) x1 = self.relu_4(x1) x1 = self.conv2d_5(x1) x1 = self.batchnorm2d_6(x1) res = self.conv2d_1(x) res = self.batchnorm2d_3(res) out = P.Add()(x1, res) out = self.relu_8(out) return out class BasicBlockB(nn.Cell): """BasicBlockB""" def __init__(self, in_channels, out_channels): super(BasicBlockB, self).__init__() self.conv2d_0 = nn.Conv2d(in_channels, out_channels, 3, stride=1, padding=1, pad_mode="pad") self.batchnorm2d_1 = nn.BatchNorm2d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_2 = nn.ReLU() self.conv2d_3 = nn.Conv2d(in_channels, out_channels, 3, stride=1, padding=1, pad_mode="pad") self.batchnorm2d_4 = nn.BatchNorm2d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_6 = nn.ReLU() def construct(self, x): """construct""" x1 = self.conv2d_0(x) x1 = self.batchnorm2d_1(x1) x1 = self.relu_2(x1) x1 = self.conv2d_3(x1) x1 = self.batchnorm2d_4(x1) res = x out = P.Add()(x1, res) out = self.relu_6(out) return out class UNet2D(nn.Cell): """UNet2D""" def __init__(self): super(UNet2D, self).__init__() self.conv2d_0 = nn.Conv2d(3, 16, 5, stride=2, padding=2, pad_mode="pad") self.batchnorm2d_1 = nn.BatchNorm2d(16, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.leakyrelu_2 = nn.LeakyReLU(alpha=0.009999999776482582) self.convblocka_0 = BasicBlockA(16, 32, 1) self.convblockb_0 = BasicBlockB(32, 32) self.convblocka_1 = BasicBlockA(32, 64, 2) self.convblockb_1 = BasicBlockB(64, 64) self.convblocka_2 = BasicBlockA(64, 128, 2) self.convblockb_2 = BasicBlockB(128, 128) self.conv2dbackpropinput_51 = P.Conv2DBackpropInput(64, 3, stride=2, pad=1, pad_mode="pad") self.conv2dbackpropinput_51_weight = Parameter(Tensor( np.random.uniform(0, 1, (128, 64, 3, 3)).astype(np.float32))) self.conv2d_54 = nn.Conv2d(128, 64, 3, stride=1, padding=1, pad_mode="pad") self.convblockb_3 = BasicBlockB(64, 64) self.conv2dbackpropinput_62 = P.Conv2DBackpropInput(32, 3, stride=2, pad=1, pad_mode="pad") self.conv2dbackpropinput_62_weight = Parameter(Tensor( np.random.uniform(0, 1, (64, 32, 3, 3)).astype(np.float32))) self.conv2d_65 = nn.Conv2d(64, 32, 3, stride=1, padding=1, pad_mode="pad") self.convblockb_4 = BasicBlockB(32, 32) self.conv2d_52 = nn.Conv2d(128, 32, 3, stride=1, padding=1, pad_mode="pad") self.conv2d_63 = nn.Conv2d(64, 32, 3, stride=1, padding=1, pad_mode="pad") self.conv2d_73 = nn.Conv2d(32, 32, 3, stride=1, padding=1, pad_mode="pad") self.concat = P.Concat(axis=1) param_dict = mindspore.load_checkpoint("./ckpts/feat_ext.ckpt") params_not_loaded = mindspore.load_param_into_net(self, param_dict, strict_load=True) print(params_not_loaded) def construct(self, imgs): """construct""" _, _, h, w = imgs.shape x = self.conv2d_0(imgs) x = self.batchnorm2d_1(x) x = self.leakyrelu_2(x) x1 = self.convblocka_0(x) x1 = self.convblockb_0(x1) x2 = self.convblocka_1(x1) x2 = self.convblockb_1(x2) x3 = self.convblocka_2(x2) x3 = self.convblockb_2(x3) x2_upsample = self.conv2dbackpropinput_51(x3, self.conv2dbackpropinput_51_weight, (x2.shape[0], x2.shape[1], h // 4, w // 4)) x2_upsample = self.concat((x2_upsample, x2,)) x2_upsample = self.conv2d_54(x2_upsample) x2_upsample = self.convblockb_3(x2_upsample) x1_upsample = self.conv2dbackpropinput_62(x2_upsample, self.conv2dbackpropinput_62_weight, (x1.shape[0], x1.shape[1], h // 2, w // 2)) x1_upsample = self.concat((x1_upsample, x1,)) x1_upsample = self.conv2d_65(x1_upsample) x1_upsample = self.convblockb_4(x1_upsample) x3_final = self.conv2d_52(x3) x2_final = self.conv2d_63(x2_upsample) x1_final = self.conv2d_73(x1_upsample) return x3_final, x2_final, x1_final class ConvBnReLu(nn.Cell): """ConvBnReLu""" def __init__(self, in_channels, out_channels): super(ConvBnReLu, self).__init__() self.conv3d_0 = nn.Conv3d(in_channels, out_channels, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.batchnorm3d_1 = nn.BatchNorm3d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.leakyrelu_2 = nn.LeakyReLU(alpha=0.009999999776482582) def construct(self, x): """construct""" x = self.conv3d_0(x) x = self.batchnorm3d_1(x) x = self.leakyrelu_2(x) return x class CostCompression(nn.Cell): """CostCompression""" def __init__(self): super(CostCompression, self).__init__() self.basicblock_0 = ConvBnReLu(8, 64) self.basicblock_1 = ConvBnReLu(64, 64) self.basicblock_2 = ConvBnReLu(64, 8) param_dict = mindspore.load_checkpoint("./ckpts/stage1_cost_compression.ckpt") params_not_loaded = mindspore.load_param_into_net(self, param_dict, strict_load=True) print(params_not_loaded) def construct(self, x): """construct""" x = self.basicblock_0(x) x = self.basicblock_1(x) x = self.basicblock_2(x) return x class Pseudo3DBlock_A(nn.Cell): """Pseudo3DBlock_A""" def __init__(self, in_channels, out_channels): super(Pseudo3DBlock_A, self).__init__() self.conv3d_0 = nn.Conv3d(in_channels, out_channels, (1, 3, 3), stride=1, padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_1 = nn.Conv3d(out_channels, out_channels, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.batchnorm3d_2 = nn.BatchNorm3d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_3 = nn.ReLU() self.conv3d_4 = nn.Conv3d(out_channels, out_channels, (1, 3, 3), stride=1, padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_5 = nn.Conv3d(out_channels, out_channels, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.batchnorm3d_6 = nn.BatchNorm3d(out_channels, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_8 = nn.ReLU() def construct(self, x): """construct""" x1 = self.conv3d_0(x) x1 = self.conv3d_1(x1) x1 = self.batchnorm3d_2(x1) x1 = self.relu_3(x1) x1 = self.conv3d_4(x1) x1 = self.conv3d_5(x1) x1 = self.batchnorm3d_6(x1) res = x out = P.Add()(x1, res) out = self.relu_8(out) return out class Pseudo3DBlock_B(nn.Cell): """Pseudo3DBlock_B""" def __init__(self): super(Pseudo3DBlock_B, self).__init__() self.conv3d_0 = nn.Conv3d(8, 8, (1, 3, 3), stride=(1, 2, 2), padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_1 = nn.Conv3d(8, 16, (1, 1, 1), stride=2, padding=0, pad_mode="valid") self.conv3d_2 = nn.Conv3d(8, 16, (3, 1, 1), stride=(2, 1, 1), padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.batchnorm3d_3 = nn.BatchNorm3d(16, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.batchnorm3d_4 = nn.BatchNorm3d(16, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_5 = nn.ReLU() self.conv3d_6 = nn.Conv3d(16, 16, (1, 3, 3), stride=1, padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_7 = nn.Conv3d(16, 16, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.batchnorm3d_8 = nn.BatchNorm3d(16, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.relu_10 = nn.ReLU() def construct(self, x): """construct""" x1 = self.conv3d_0(x) x1 = self.conv3d_2(x1) x1 = self.batchnorm3d_4(x1) x1 = self.relu_5(x1) x1 = self.conv3d_6(x1) x1 = self.conv3d_7(x1) x1 = self.batchnorm3d_8(x1) res = self.conv3d_1(x) res = self.batchnorm3d_3(res) out = P.Add()(x1, res) out = self.relu_10(out) return out class CoarseStageRegFuse(nn.Cell): """CoarseStageRegFuse""" def __init__(self): super(CoarseStageRegFuse, self).__init__() self.basicblocka_0 = Pseudo3DBlock_A(8, 8) self.basicblockb_0 = Pseudo3DBlock_B() self.conv3dtranspose_21 = nn.Conv3dTranspose(16, 8, 3, stride=2, padding=1, pad_mode="pad", output_padding=1) self.conv3d_23 = nn.Conv3d(16, 8, (1, 3, 3), stride=1, padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_24 = nn.Conv3d(8, 8, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.conv3d_25 = nn.Conv3d(8, 1, 3, stride=1, padding=1, pad_mode="pad") self.concat_1 = P.Concat(axis=1) self.squeeze_1 = P.Squeeze(axis=1) param_dict = mindspore.load_checkpoint("./ckpts/stage1_reg_fuse.ckpt") params_not_loaded = mindspore.load_param_into_net(self, param_dict, strict_load=True) print(params_not_loaded) def construct(self, fused_interim, depth_values): """construct""" x1 = self.basicblocka_0(fused_interim) x2 = self.basicblockb_0(x1) x1_upsample = self.conv3dtranspose_21(x2) cost_volume = self.concat_1((x1_upsample, x1)) cost_volume = self.conv3d_23(cost_volume) cost_volume = self.conv3d_24(cost_volume) score_volume = self.conv3d_25(cost_volume) score_volume = self.squeeze_1(score_volume) prob_volume, _, prob_map = soft_argmin(score_volume, dim=1, keepdim=True, window=2) est_depth = depth_regression(prob_volume, depth_values, keep_dim=True) return est_depth, prob_map, prob_volume class CoarseStageRegPair(nn.Cell): """CoarseStageRegPair""" def __init__(self): super(CoarseStageRegPair, self).__init__() self.basicblocka_0 = Pseudo3DBlock_A(8, 8) self.basicblockb_0 = Pseudo3DBlock_B() self.conv3dtranspose_21 = nn.Conv3dTranspose(16, 8, 3, stride=2, padding=1, pad_mode="pad", output_padding=1) self.concat_22 = P.Concat(axis=1) self.conv3d_23 = nn.Conv3d(16, 8, (1, 3, 3), stride=1, padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_24 = nn.Conv3d(8, 8, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.conv3d_25 = nn.Conv3d(8, 1, 3, stride=1, padding=1, pad_mode="pad") self.conv2d_38 = nn.Conv2d(1, 8, 3, stride=1, padding=1, pad_mode="pad") self.batchnorm2d_39 = nn.BatchNorm2d(num_features=8, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.leakyrelu_40 = nn.LeakyReLU(alpha=0.009999999776482582) self.conv2d_41 = nn.Conv2d(8, 8, 3, stride=1, padding=1, pad_mode="pad") self.batchnorm2d_42 = nn.BatchNorm2d(num_features=8, eps=9.999999747378752e-06, momentum=0.8999999761581421) self.leakyrelu_43 = nn.LeakyReLU(alpha=0.009999999776482582) self.conv2d_45 = nn.Conv2d(8, 1, 3, stride=1, padding=1, pad_mode="pad") self.conv2d_46 = nn.Conv2d(8, 1, 3, stride=1, padding=1, pad_mode="pad") self.concat_1 = P.Concat(axis=1) self.squeeze_1 = P.Squeeze(axis=1) param_dict = mindspore.load_checkpoint("./ckpts/stage1_reg_pair.ckpt") params_not_loaded = mindspore.load_param_into_net(self, param_dict, strict_load=True) print(params_not_loaded) def construct(self, cost_volume, depth_values): """construct""" x1 = self.basicblocka_0(cost_volume) x2 = self.basicblockb_0(x1) x1_upsample = self.conv3dtranspose_21(x2) interim = self.concat_1((x1_upsample, x1)) interim = self.conv3d_23(interim) interim = self.conv3d_24(interim) score_volume = self.conv3d_25(interim) score_volume = self.squeeze_1(score_volume) prob_volume, _ = soft_argmin(score_volume, dim=1, keepdim=True) est_depth = depth_regression(prob_volume, depth_values, keep_dim=True) entropy_ = entropy(prob_volume, dim=1, keepdim=True) x = self.conv2d_38(entropy_) x = self.batchnorm2d_39(x) x = self.leakyrelu_40(x) x = self.conv2d_41(x) x = self.batchnorm2d_42(x) x = self.leakyrelu_43(x) out = P.Add()(x, entropy_) uncertainty_map = self.conv2d_45(out) occ = self.conv2d_46(out) return interim, est_depth, uncertainty_map, occ class StageRegFuse(nn.Cell): """StageRegFuse""" def __init__(self, ckpt_path): super(StageRegFuse, self).__init__() self.basicblocka_0 = Pseudo3DBlock_A(8, 8) self.basicblocka_1 = Pseudo3DBlock_A(8, 8) self.basicblockb_0 = Pseudo3DBlock_B() self.basicblocka_2 = Pseudo3DBlock_A(16, 16) self.conv3dtranspose_38 = nn.Conv3dTranspose(16, 8, 3, stride=2, padding=1, pad_mode="pad", output_padding=1) self.concat_39 = P.Concat(axis=1) self.conv3d_40 = nn.Conv3d(16, 8, (1, 3, 3), stride=1, padding=(0, 0, 1, 1, 1, 1), pad_mode="pad") self.conv3d_41 = nn.Conv3d(8, 8, (3, 1, 1), stride=1, padding=(1, 1, 0, 0, 0, 0), pad_mode="pad") self.conv3d_42 = nn.Conv3d(8, 1, 3, stride=1, padding=1, pad_mode="pad") self.concat_1 = P.Concat(axis=1) self.squeeze_1 = P.Squeeze(axis=1) param_dict = mindspore.load_checkpoint(ckpt_path) params_not_loaded = mindspore.load_param_into_net(self, param_dict, strict_load=True) print(params_not_loaded) def construct(self, fused_interim, depth_values): """construct""" x1 = self.basicblocka_0(fused_interim) x1 = self.basicblocka_1(x1) x2 = self.basicblockb_0(x1) x2 = self.basicblocka_2(x2) x1_upsample = self.conv3dtranspose_38(x2) cost_volume = self.concat_1((x1_upsample, x1)) cost_volume = self.conv3d_40(cost_volume) cost_volume = self.conv3d_41(cost_volume) score_volume = self.conv3d_42(cost_volume) score_volume = self.squeeze_1(score_volume) prob_volume, _, prob_map = soft_argmin(score_volume, dim=1, keepdim=True, window=2) est_depth = depth_regression(prob_volume, depth_values, keep_dim=True) return est_depth, prob_map, prob_volume

41.424318

117

0.636157

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4.257915

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0.008526

0.036681

0.047189

0.651135

0.637752

0.608803

0.567067

0.531674

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0

0.120376

0.229184

16,694

402

118

41.527363

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0.017731

0

null

0

null

0

1

0

45aa13a44c60964639568833f156cf26339f8f91

9,162

py

Python

master-v0/todolist_seperated.py

kaixindelele/Study-System

d64ed15d425064445e2ca6a4bb89515dec68d19c

[ "Apache-2.0" ]

null

master-v0/todolist_seperated.py

kaixindelele/Study-System

d64ed15d425064445e2ca6a4bb89515dec68d19c

[ "Apache-2.0" ]

null

master-v0/todolist_seperated.py

kaixindelele/Study-System

d64ed15d425064445e2ca6a4bb89515dec68d19c

[ "Apache-2.0" ]

null

import tkinter from tkinter import messagebox import random WINDOW_HEIGHT=1200 WINDOW_WIDTH=600 BUTTON_HEIGHT=2 BUTTON_WIDTH=10 class Item: def __init__(self, id, content): self.index=id if len(content.split(" ")) == 1: self.text=content.split(" ")[0] elif not content.split(" ")[-1].isdigit(): self.text=content.split(" ")[:] else: self.text=content.split(" ")[:-1] self.text=''.join(self.text) score=content.split(" ")[-1] if score.isdigit(): self.score=score else: self.score=0 class Project: def __init__(self, root, start_column, title): self.root=root self.title=title self.items_list=[] self.total_score=0 try: self.read_file() except: pass self._build_window(start_column, start_column+1, start_column+2) self.update_listbox() def read_file(self): file_name=self.title+".txt" f=open(file_name, 'r', encoding='utf-8') sourceInLines=f.readlines() # 按行读出文件内容 f.close() new=[] # 定义一个空列表，用来存储结果 for line in sourceInLines: temp1=line.strip('\n') # 去掉每行最后的换行符'\n' temp2=temp1.split(' ') # 以','为标志，将每行分割成列表 self.total_score += int(temp2[-1]) new.append(temp2) # 将上一步得到的列表添加到new中 for n in new: content=str(n[1]) + " " + str(n[2]) new_item=Item(n[0],content) self.items_list.append(new_item) def _build_window(self, c1, c2, c3): self._build_index(column=c1) self._build_list(c2) self._build_score_list(column=c3) self._build_score_analysis(5) def _build_index(self, column): self.num_label=tkinter.Label(self.root, text="序号", width=3, bg="white") self.num_label.grid(row=1, column=column) # 显示序号 self.index_display=tkinter.Listbox(self.root, width=3, height=20) self.index_display.grid(row=2, column=column, rowspan=12) def _build_list(self, column): if self.title == "task": text="任务清单" default_text="输入模板：看一篇paper 5" if self.title == "wish": text="愿望商店" default_text="输入模板：看十分钟小说 2" self.top_display=tkinter.Label(self.root, width=36, text=text, padx=10, pady=0, bg="SkyBlue", ) self.top_display.grid(row=0, column=column) self.input=tkinter.Entry(self.root, width=36, bg="SkyBlue", ) self.input.bind('<Return>', self.event_Save) self.input.bind('<ButtonPress>', self.event_Disappear) self.input.bind("<FocusOut>", self.event_Leave) self.input.insert(0, default_text) self.input.grid(row=1, column=column) # 显示窗口 self.display=tkinter.Listbox(self.root, width=40, height=20) self.display.bind('<Delete>', self.eventDeleteOne) self.display.grid(row=2, column=column, rowspan=7) # lb for listbox def _build_score_list(self, column): self.score_label=tkinter.Label(self.root, text="对应积分", width=4, height=BUTTON_HEIGHT, padx=10, pady=0, bg="lightCyan") self.score_label.grid(row=1, column=column) # 显示序号 self.score_display=tkinter.Listbox(self.root, width=6, height=20) self.score_display.grid(row=2, column=column, rowspan=12) def _build_score_analysis(self, column): if self.title == "task": default_text="累计积分" label_row=1 if self.title == "wish": default_text="愿望积分" label_row=3 self.score_label=tkinter.Label(self.root, text=default_text, width=8, height=BUTTON_HEIGHT, padx=10, pady=0, bg="Violet") self.score_label.grid(row=label_row, column=column) self.score_value=tkinter.Label(self.root, text=self.total_score, width=8, height=BUTTON_HEIGHT, padx=10, pady=0, bg="cornsilk") self.score_value.grid(row=label_row+1, column=column) def add_item(self): # Get the task to add self.item_content=self.input.get() self.items_num=len(self.items_list) print("num:", self.items_num) self.item_id=self.items_num + 1 self.new_item=Item(self.item_id, self.item_content) # Make sure the task is not empty if self.new_item.text != "": # Append to the list self.items_list.append(self.new_item) self.total_score += int(self.new_item.score) self.score_value['text']=self.total_score # Update the listbox self.update_listbox() else: # tkinter.messagebox.showwarning("Warning", "Please enter a task.") pass self.input.delete(0, "end") # clears the input box after a new task is entered def eventDeleteOne(self, event): self.del_one() def event_Save(self, event): self.add_item() def event_Disappear(self, event): self.input.delete(0, last='end') def event_Leave(self, event): self.default_wish_text='输入模版：看十分钟小说 2' self.input.insert(0, self.default_wish_text) def clear_listbox(self): self.display.delete(0, "end") def clear_index(self): self.index_display.delete(0, 'end') def clear_score_index(self): self.score_display.delete(0, 'end') def update_listbox(self): # Clear the current list to keep from add the same tasks to the list over and over again self.clear_listbox() self.clear_index() self.clear_score_index() # Populate the Listbox for item in self.items_list: self.display.insert("end", item.text) self.index_display.insert("end", item.index) self.score_display.insert("end", item.score) def clear_listbox(self): self.display.delete(0, "end") def clear_wishes_index(self): self.index_display.delete(0, 'end') def clear_wishes_score_index(self): self.score_display.delete(0, 'end') def del_all(self): confirmed=tkinter.messagebox.askyesno("Please Confirm", "Do you really want to delete all?") if confirmed == True: # Since we are changing the list, it needs to be global. # Clear the tasks list self.tasks_list=[] self.total_score=0 self.score_value['text']=self.total_score # Update the listbox self.update_listbox() self.sort_asc() def del_one(self): # Get the text of the currently selected item text=self.display.get("active") for t in self.items_list: if text == t.text: self.total_score -= int(t.score) self.score_value['text']=self.total_score self.items_list.remove(t) self.sort_asc() # TODO 根据ID进行重新排序！ def sort_asc(self): # sort the list tem_list=[] for i in range(len(self.items_list)): tem_task=self.items_list[i] tem_task.index=i+1 tem_list.append(tem_task) self.items_list=tem_list #update the listbox self.update_listbox() def save_to_local(self): file_name=self.title + ".txt" f=open(file_name, 'w', encoding='utf-8') for t in self.items_list: f.write(str(t.index)) f.write(" ") f.write(t.text) f.write(" ") f.write(str(t.score)) f.write("\n") f.close() class TODO_list: def __init__(self): #Create root window self.root=tkinter.Tk() # Change root window background color self.root.configure(bg="white") # Change the title self.root.title("骆永乐的任务清单商店") # Change the window size self.root.geometry("1200x600") # Create an empty list self.project_list=[] self._build_window() def _build_window(self): self.task_project=Project(self.root, 2, "task") self.wish_project=Project(self.root, 7, "wish") self.project_list.append(self.task_project) self.project_list.append(self.wish_project) self.check=tkinter.Label(self.root, text="账单", width=8, height=BUTTON_HEIGHT, padx=10, pady=0, bg="White") self.check.grid(row=0, column=5) self.check.bind_all('<Escape>', self.eventEsc) # Start the main events loop self.root.mainloop() def save_to_all(self): for p in self.project_list: p.save_to_local() def eventEsc(self, event): self.save_to_all() exit() def main(): todo_list=TODO_list() if __name__ == "__main__": main()

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0.175168

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0.004975

0

null

0

null

0

1

0

45aafdc53dd12d0a6a957bd81175c72fb3ad70c0

2,665

py

Python

src/settings/base.py

Hammerstad/blog

3075e59c46321fd5fd5ccafbfb36e6d59b765259

[ "MIT" ]

null

src/settings/base.py

Hammerstad/blog

3075e59c46321fd5fd5ccafbfb36e6d59b765259

[ "MIT" ]

null

src/settings/base.py

Hammerstad/blog

3075e59c46321fd5fd5ccafbfb36e6d59b765259

[ "MIT" ]

null

import os, re from django.contrib.messages import constants as messages BASE_DIR = os.path.dirname(os.path.dirname(__file__)) makepath = lambda *f: os.path.join(BASE_DIR, *f) # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = ')2x-r4x+4oxdmvvxenj*dhq##uxrgl%f3=#+l*1s32y=f^51hz' ALLOWED_HOSTS = [] TIME_ZONE = 'Europe/Oslo' # Application definition INSTALLED_APPS = ( 'grappelli', 'django.contrib.admin', 'django.contrib.admindocs', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.staticfiles', 'app.blog', ) MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) TEMPLATE_CONTEXT_PROCESSORS = ( 'django.core.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.core.context_processors.static', 'django.contrib.messages.context_processors.messages' ) ROOT_URLCONF = 'urls' WSGI_APPLICATION = 'wsgi.application' LANGUAGE_CODE = 'en-gb' STANDARD_USER_LANGUAGE = 'en-gb' DATE_FORMAT = 'd.m.Y' TIME_FORMAT = 'H.i' # If you set this to False, Django will not use timezone-aware datetimes. USE_TZ = False USE_I18N = True USE_L10N = True TEMPLATE_LOADERS = ( 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', 'django.template.loaders.eggs.Loader', ) MEDIA_ROOT = makepath("media") MEDIA_URL = '/media/' STATIC_URL = '/static/' STATICFILES_DIRS = ( makepath("static"), ) # Used by collect static and nginx STATIC_ROOT = os.path.join(BASE_DIR, '../static') # List of finder classes that know how to find static files in # various locations. STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', 'django.contrib.staticfiles.finders.DefaultStorageFinder', ) GRAPPELLI_ADMIN_TITLE = "Eirik M Hammerstad - Blog" ## Translates messages tags into our correct CSS classes MESSAGE_TAGS = { messages.DEBUG: 'alert-debug', messages.INFO: 'alert-info', messages.SUCCESS: 'alert-success', messages.WARNING: 'alert-info', messages.ERROR: 'alert-error', } SITE_ID = 1 # Login specific LOGIN_URL = "/login/" LOGIN_REDIRECT_URL = "/" LOGOUT_URL = "/logout/"

26.386139

73

0.734709

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2,665

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0.048589

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0

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2,665

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26.386139

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1

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false

0

0.028169

0

0.028169

0

null

0

null

0

1

0

45af13113748b76dff6737ce6168b834565ba369

737

py

Python

cpias/cli/__init__.py

CellProfiling/cpias

e2d9426436573b40625287101570b849ce9f4a38

[ "Apache-2.0" ]

null

cpias/cli/__init__.py

CellProfiling/cpias

e2d9426436573b40625287101570b849ce9f4a38

[ "Apache-2.0" ]

null

cpias/cli/__init__.py

CellProfiling/cpias

e2d9426436573b40625287101570b849ce9f4a38

[ "Apache-2.0" ]

null

# type: ignore """Provide a CLI.""" import logging import click from cpias import __version__ from cpias.cli.client import run_client from cpias.cli.server import start_server SETTINGS = dict(help_option_names=["-h", "--help"]) @click.group( options_metavar="", subcommand_metavar="<command>", context_settings=SETTINGS ) @click.option("--debug", is_flag=True, help="Start server in debug mode.") @click.version_option(__version__) @click.pass_context def cli(ctx, debug): """Run CPIAS server.""" ctx.obj = {} ctx.obj["debug"] = debug if debug: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) cli.add_command(start_server) cli.add_command(run_client)

23.03125

81

0.717775

99

737

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0.434343

0.053254

0.047337

0.118343

0

0.143826

737

31

82

23.774194

0.803487

0.062415

0

0.082353

0

1

0.047619

false

0.047619

0.238095

0

0.285714

0

null

0

null

0

1

0

45b1387d07ded17b27c38254744ea03c4ab7dcf9

1,368

py

Python

server/config.py

knrdl/casa

411a71f2fcc7b2f7c6cd33973ce6f5919c9f7180

[ "MIT" ]

40

2022-01-08T18:33:33.000Z

2022-01-17T11:52:40.000Z

server/config.py

knrdl/casa

411a71f2fcc7b2f7c6cd33973ce6f5919c9f7180

[ "MIT" ]

null

server/config.py

knrdl/casa

411a71f2fcc7b2f7c6cd33973ce6f5919c9f7180

[ "MIT" ]

3

2022-01-10T03:05:45.000Z

2022-01-10T16:17:29.000Z

import os from typing import Literal, get_args, Final, Set, Dict AUTH_API_URL = os.getenv('AUTH_API_URL') AUTH_API_FIELD_USERNAME = os.getenv('AUTH_API_FIELD_USERNAME', 'username') AUTH_API_FIELD_PASSWORD = os.getenv('AUTH_API_FIELD_PASSWORD', 'password') if not AUTH_API_URL: raise Exception('please provide AUTH_API_URL env var') PermissionType = Literal[ 'info', 'info-annotations', 'state', 'logs', 'term', 'procs', 'files', 'files-read', 'files-write'] PERMISSIONS: Final[Set[PermissionType]] = set(get_args(PermissionType)) ROLES_PERMS: Dict[str, Set[PermissionType]] = {} for key, value in os.environ.items(): if key.startswith('ROLES_'): role_name = key.removeprefix('ROLES_').strip().replace('_', '.') if role_name: permissions = {p.strip() for p in value.split(',')} permissions = {p for p in permissions if p} unknown_permission = next((p for p in permissions if p not in PERMISSIONS), None) if unknown_permission: raise Exception(f'unknown permission "{unknown_permission}" for role "{role_name}"') ROLES_PERMS[role_name] = permissions if not ROLES_PERMS: raise Exception('no roles defined, please set ROLES_* env vars') print('Roles:') for role in sorted(ROLES_PERMS): print('*', role, '->', ', '.join(sorted(ROLES_PERMS[role]))) print()

41.454545

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1,368

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false

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0.111111

0

null

0

null

0

1

0

45b98152e1ec2d40f01789e515fcc68c070f6c42

2,452

py

Python

src/python/lessons/Pokedex/Club Leader Resources/pokedex-finished.py

arve0/example_lessons

56ce7f386df2478165f4583a86b462974f5e19ec

[ "CC0-1.0" ]

2

2017-02-19T21:31:34.000Z

2019-06-27T07:55:50.000Z

src/python/lessons/Pokedex/Club Leader Resources/pokedex-finished.py

arve0/example_lessons

56ce7f386df2478165f4583a86b462974f5e19ec

[ "CC0-1.0" ]

null

src/python/lessons/Pokedex/Club Leader Resources/pokedex-finished.py

arve0/example_lessons

56ce7f386df2478165f4583a86b462974f5e19ec

[ "CC0-1.0" ]

null

from random import * import tkinter from pokeapi import * smallFont = ["Ariel" , 10] mediumFont = ["Ariel" , 14] bigFont = ["Ariel" , 24] #function to display data for a pokemon number def showPokemonData(): #get the number typed into the entry box pokemonNumber = randint(1,178) #use the function above to get the pokemon data pokemonDictionary = getPokemonData(pokemonNumber) #get the data from the dictionary and add it to the labels lblNameValue.configure(text = pokemonDictionary["name"]) lblHPValue.configure(text = pokemonDictionary["hp"]) lblAttackValue.configure(text = pokemonDictionary["attack"]) lblDefenceValue.configure(text = pokemonDictionary["defense"]) lblSpeedValue.configure(text = pokemonDictionary["speed"]) #create the main window window = tkinter.Tk() window.config(bg="#e0e0ff") window.title("Pokedex") #button to show a random pokemon btnGo = tkinter.Button(window,text="Get Random Pokemon!", command=showPokemonData, font=smallFont) btnGo.pack() #pokemon name lblNameText = tkinter.Label(window,text="Name:", font=mediumFont) lblNameText.config(bg="#e0e0ff", fg="#111111") lblNameText.pack() lblNameValue = tkinter.Label(window,text="?", font=bigFont) lblNameValue.config(bg="#e0e0ff", fg="#111111") lblNameValue.pack() #pokemon hp lblHPText = tkinter.Label(window,text="HP:", font=mediumFont) lblHPText.config(bg="#e0e0ff", fg="#111111") lblHPText.pack() lblHPValue = tkinter.Label(window,text="?", font=bigFont) lblHPValue.config(bg="#e0e0ff", fg="#111111") lblHPValue.pack() #pokemon attack lblAttackText = tkinter.Label(window,text="Attack:", font=mediumFont) lblAttackText.config(bg="#e0e0ff", fg="#111111") lblAttackText.pack() lblAttackValue = tkinter.Label(window,text="?", font=bigFont) lblAttackValue.config(bg="#e0e0ff", fg="#111111") lblAttackValue.pack() #pokemon defence lblDefenceText = tkinter.Label(window,text="Defence:", font=mediumFont) lblDefenceText.config(bg="#e0e0ff", fg="#111111") lblDefenceText.pack() lblDefenceValue = tkinter.Label(window,text="?", font=bigFont) lblDefenceValue.config(bg="#e0e0ff", fg="#111111") lblDefenceValue.pack() #pokemon speed lblSpeedText = tkinter.Label(window,text="Speed:", font=mediumFont) lblSpeedText.config(bg="#e0e0ff", fg="#111111") lblSpeedText.pack() lblSpeedValue = tkinter.Label(window,text="?", font=bigFont) lblSpeedValue.config(bg="#e0e0ff", fg="#111111") lblSpeedValue.pack() window.mainloop()

31.844156

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0.084245

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0

0.041742

0.101142

2,452

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32.263158

0.787659

0.12398

0

0.115115

0

1

0.02

false

0

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0

0.08

0

null

0

null

0

1

0

45bb4339df10b2c504d23e3c5f87aad73571f0d8

551

py

Python

PythonExercicios/ex092.py

lordvinick/Python

c03fd08d4c204104bf0196b0bd129427fd2067ae

[ "MIT" ]

null

PythonExercicios/ex092.py

lordvinick/Python

c03fd08d4c204104bf0196b0bd129427fd2067ae

[ "MIT" ]

null

PythonExercicios/ex092.py

lordvinick/Python

c03fd08d4c204104bf0196b0bd129427fd2067ae

[ "MIT" ]

null

from datetime import date info = dict() info['nome'] = str(input('Nome: ')) ano = int(input('Ano de Nascimento: ')) info['idade'] = date.today().year - ano info['ctps'] = int(input('Carteira de Trabalho (0 não tem): ')) if info['ctps'] > 0: info['contratação'] = int(input('Ano de contratação: ')) info['Salário'] = float(input('Salário: R$ ')) anostrabalho = date.today().year - info['contratação'] info['aposentadoria'] = 35 - anostrabalho + info['idade'] print('-='*30) for k, v in info.items(): print(f'- {k} tem o valor {v}')

36.733333

63

0.618875

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0.070381

0.064516

0.076246

0

0.013015

0.163339

551

14

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0.726681

0

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0

1

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false

0

0.071429

0

0.071429

0.142857

0

null

0

null

0

1

0

45bb5f01972a0422e197939fbc86dcc3efd9e8cf

2,547

py

Python

src/words/compiler/compile.py

DavidStrootman/ATP

72005be0ac75339bb5da037a7e98573e338d16db

[ "MIT" ]

2

2021-08-20T17:56:15.000Z

2021-08-21T01:04:08.000Z

src/words/compiler/compile.py

DavidStrootman/Words

72005be0ac75339bb5da037a7e98573e338d16db

[ "MIT" ]

null

src/words/compiler/compile.py

DavidStrootman/Words

72005be0ac75339bb5da037a7e98573e338d16db

[ "MIT" ]

null

from pathlib import Path from typing import Callable, Dict, List, Type, Iterator from words.lexer.lex import Lexer from words.parser.parse import Parser from words.parser.parse_util import Program from words.token_types.lexer_token import LexerToken from words.token_types.parser_token import ParserToken, VariableParserToken class Compiler: """ The compiler is used to compile the code to run natively on a piece of hardware. The only supported hardware is the Arduino Due. """ @staticmethod def compile(ast: Program, target: str = "arduino_due") -> str: selected_target = target.lower() if selected_target not in platform_compilers: raise NotImplementedError(f"{target:} not supported") return platform_compilers[target](ast) @staticmethod def build_asm(sections: List[str]) -> str: return "\n".join(sections) + "\n" @staticmethod def find_token_in_ast(ast: List[ParserToken], token: Type[ParserToken]) -> List[ParserToken]: """Recursively find token in ast tree""" def _find_token_in_token(token: Type[ParserToken]) -> ParserToken: pass return [_find_token_in_token(token)] @staticmethod def compile_file(file_path: Path) -> str: """ Compile from a file, this is the most common entrypoint for the Compiler. :param file_path: Path to the file to interpret. :return: The return value of the program executed, if any. """ lexed_tokens: Iterator[LexerToken] = Lexer.lex_file(file_path) program = Parser.parse(lexed_tokens) return Compiler.compile(program) class M0Compiler: @staticmethod def _compile_cpu_directive(): return ".cpu cortex-m0" @staticmethod def _compile_bss_segment(ast: Program): bytes_to_reserve = len(Compiler.find_token_in_ast(ast.tokens, VariableParserToken)) bss_segment = ".bss \n" \ ".byte " + ",".join(["0" for byte in range(bytes_to_reserve)]) + "\n" \ "test:\n" \ ".byte 0" return bss_segment @staticmethod def compile(ast: Program): cpu_directive = M0Compiler._compile_cpu_directive() bss_segment = M0Compiler._compile_bss_segment(ast) return Compiler.build_asm( [ cpu_directive, bss_segment ] ) platform_compilers: Dict[str, Callable[[Program], str]] = { "arduino_due": M0Compiler.compile }

29.964706

97

0.650962

302

2,547

5.301325

0.307947

0.065584

0.068707

0.026234

0.087445

0

0.003704

0.257951

2,547

84

98

30.321429

0.843386

0.135846

0

0.134615

0

0.043987

0

1

0.153846

false

0.019231

0.134615

0.038462

0.461538

0

null

0

null

0

1

0

45bb63a16d11fc1ffe3d85d5f0903a0791235525

666

py

Python

digger/modules/load.py

fxxkrlab/Digger

b69e23aee1f2a8eac4989badd354bd128d35100e

[ "MIT" ]

null

digger/modules/load.py

fxxkrlab/Digger

b69e23aee1f2a8eac4989badd354bd128d35100e

[ "MIT" ]

null

digger/modules/load.py

fxxkrlab/Digger

b69e23aee1f2a8eac4989badd354bd128d35100e

[ "MIT" ]

null

import os, logging, toml _cfgFile_RAW = os.path.abspath(os.path.join("conf.toml")) _cfg = toml.load(_cfgFile_RAW) ''' log setting ''' if _cfg['Servers']['server']['CONSOLE'] == 'DEBUG': logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.DEBUG, ) else: logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO, ) logger = logging.getLogger(__name__) path_regex_1 = r'([\s\S]+)\/([^\/][\s\S]+)\/$' tv_folders = r'^[\s\S]+[\.|\s]([se]\d{1,2}|[se]\d{1,2}\-*[se]\d{1,2}|complete|ep\d{1,2}\-ep\d{1,2}|ep\d{1,2})[\.|\s][\s\S]+$'

28.956522

125

0.564565

102

666

3.558824

0.401961

0.038567

0.049587

0.041322

0.451791

0.369146

0

0.022928

0.148649

666

23

125

28.956522

0.617284

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0.25

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0.0625

0.424383

0.21142

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0

0.0625

0

0.0625

0

null

0

null

0

1

0

45bbcaa18c9d8b0af41194ae911dd0604064bd55

11,982

py

Python

rados_deploy/internal/remoto/modules/rados_install.py

MariskaIJpelaar/rados-deploy

4ffb467211c2b05d17d76c2423c72c0ee4d4ec99

[ "MIT" ]

null

rados_deploy/internal/remoto/modules/rados_install.py

MariskaIJpelaar/rados-deploy

4ffb467211c2b05d17d76c2423c72c0ee4d4ec99

[ "MIT" ]

null

rados_deploy/internal/remoto/modules/rados_install.py

MariskaIJpelaar/rados-deploy

4ffb467211c2b05d17d76c2423c72c0ee4d4ec99

[ "MIT" ]

1

2022-02-08T10:47:14.000Z

import os import subprocess import tempfile import urllib.request def _get_ceph_deploy(location, silent=False, retries=5): url = 'https://github.com/ceph/ceph-deploy/archive/refs/heads/master.zip' with tempfile.TemporaryDirectory() as tmpdir: # We use a tempfile to store the downloaded archive. archiveloc = join(tmpdir, 'ceph-deploy.zip') if not silent: print('Fetching ceph-deploy from {}'.format(url)) for x in range(retries): try: try: rm(archiveloc) except Exception as e: pass urllib.request.urlretrieve(url, archiveloc) break except Exception as e: if x == 0: printw('Could not download ceph-deploy. Retrying...') elif x == retries-1: printe('Could not download ceph-deploy: {}'.format(e)) return False try: extractloc = join(tmpdir, 'extracted') mkdir(extractloc, exist_ok=True) unpack(archiveloc, extractloc) extracted_dir = next(ls(extractloc, only_dirs=True, full_paths=True)) # find out what the extracted directory is called. There will be only 1 extracted directory. rm(location, ignore_errors=True) mkdir(location) for x in ls(extracted_dir, full_paths=True): # Move every file and directory to the final location. mv(x, location) return True except Exception as e: printe('Could not extract ceph-deploy zip file correctly: ', e) return False def _get_rados_dev(location, arrow_url, silent=False, retries=5): with tempfile.TemporaryDirectory() as tmpdir: # We use a tempfile to store the downloaded archive. archiveloc = join(tmpdir, 'rados-arrow.zip') if not silent: print('Fetching RADOS-arrow from {}'.format(arrow_url)) for x in range(retries): try: try: rm(archiveloc) except Exception as e: pass urllib.request.urlretrieve(arrow_url, archiveloc) break except Exception as e: if x == 0: printw('Could not download RADOS-arrow. Retrying...') elif x == retries-1: printe('Could not download RADOS-arrow: {}'.format(e)) return False try: extractloc = join(tmpdir, 'extracted') mkdir(extractloc, exist_ok=True) unpack(archiveloc, extractloc) extracted_dir = next(ls(extractloc, only_dirs=True, full_paths=True)) # find out what the extracted directory is called. There will be only 1 extracted directory. rm(location, ignore_errors=True) mkdir(location) for x in ls(extracted_dir, full_paths=True): # Move every file and directory to the final location. mv(x, location) return True except Exception as e: printe('Could not extract RADOS-arrow zip file correctly: {}'.format(e)) return False def install_ceph_deploy(location, silent=False): '''Install ceph-deploy on the admin node. Warning: Assumes `git` is installed and available. Warning: This only has to be executed on 1 node, which will be designated the `ceph admin node`. Args: location (str): Location to install ceph-deploy in. Ceph-deploy root will be`location/ceph-deploy`. Returns: `True` on success, `False` on failure.''' if library_exists('ceph_deploy'): return True if not pip_install(py='python3'): return False if not exists(location): if not _get_ceph_deploy(location, silent=silent): return False kwargs = {'shell': True} if silent: kwargs['stderr'] = subprocess.DEVNULL kwargs['stdout'] = subprocess.DEVNULL return subprocess.call('pip3 install . --user', cwd=location, **kwargs) == 0 def install_ceph(hosts_designations_mapping, silent=False): '''Installs required ceph daemons on all nodes. Requires updated package manager. Warning: This only has to be executed on 1 node, which will be designated the `ceph admin node`. Warning: Expects to find a 'designations' extra-info key, with as value a comma-separated string for each node in the reservation, listing its designations. Daemons for the given designations will be installed. E.g. node.extra_info['designations'] = 'mon,mds,osd,osd' will install the monitor, metadata-server and osd daemons. Note: Designations may be repeated, which will not change behaviour from listing designations once. Warning: We assume apt package manager. Note: If a host has an empty list as specification, we ignore it and do not install anything. Args: hosts_designations_mapping (dict(str, list(str))): Dict with key=hostname and value=list of hostname's `Designations` as strings. hosts_user_mapping (dict(str, str)): Dict with key=hostname and val=username for host. silent (optional bool): If set, does not print compilation progress, output, etc. Otherwise, all output will be available. Returns: `True` on success, `False` on failure.''' ceph_deploypath = join(os.path.expanduser('~/'), '.local', 'bin', 'ceph-deploy') kwargs = {'shell': True, 'stderr': subprocess.DEVNULL, 'stdout': subprocess.DEVNULL} if subprocess.call('sudo apt update -y', **kwargs) != 0: return False if subprocess.call('{} install --common localhost'.format(ceph_deploypath), **kwargs) != 0: return False executors = [] for hostname, designations in hosts_designations_mapping.items(): if not any(designations): # If no designation given for node X, we skip installation of Ceph for X. continue designation_out = '--'+' --'.join([x.lower() for x in set(designations)]) executors.append(Executor('{} --overwrite-conf install --release octopus {} {}'.format(ceph_deploypath, designation_out, hostname), shell=True)) Executor.run_all(executors) return Executor.wait_all(executors, print_on_error=True) def install_rados(location, hosts_designations_mapping, arrow_url, force_reinstall=False, debug=False, silent=False, cores=16): '''Installs RADOS-arrow, which we need for bridging with Arrow. This function should be executed from the admin node. Warning: This only has to be executed on 1 node, which will be designated the `ceph admin node`. Warning: Assumes apt package manager. Args: location (str): Location to install RADOS-arrow in. Ceph-deploy root will be`location/ceph-deploy`. hosts_designations_mapping (dict(str, list(str))): Dict with key=hostname and value=list of hostname's `Designations` as strings. arrow_url (str): Download URL for Arrow library to use with RADOS-Ceph. force_reinstall (optional bool): If set, we always will re-download and install Arrow. Otherwise, we will skip installing if we already have installed Arrow. debug (optional bool): If set, we compile Arrow using debug flags. silent (optional bool): If set, does not print compilation progress, output, etc. Otherwise, all output will be available. cores (optional int): Number of cores to use for compiling (default=4). Note: Do not set this to a higher value than the number of available cores, as it would only lead to slowdowns. If set too high, it may happen that RAM consumption is much too high, leading to kernel panic and termination of critical processes. Returns: `True` on success, `False` on failure.''' kwargs = {'shell': True} if silent: kwargs['stderr'] = subprocess.DEVNULL kwargs['stdout'] = subprocess.DEVNULL if force_reinstall or not (exists('{}/cpp/build/latest'.format(location)) and any(ls('{}/cpp/build/latest'.format(location)))): if subprocess.call('sudo rm -rf {}'.format(location), shell=True, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL) != 0: printe('Could not remove all files at {}'.format(location)) return False if not silent: print('Installing required libraries for RADOS-Ceph.\nPatience...') cmd = 'sudo apt install libradospp-dev rados-objclass-dev openjdk-8-jdk openjdk-11-jdk default-jdk libboost-all-dev automake bison flex g++ libevent-dev libssl-dev libtool make pkg-config maven cmake thrift-compiler -y' if subprocess.call(cmd, shell=True, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL) != 0: printe('Failed to install all required libraries. Command used: {}'.format(cmd)) return False if not silent: prints('Installed required libraries.') if (not isdir(location)) and not _get_rados_dev(location, arrow_url, silent=silent, retries=5): return False cmake_cmd = 'cmake . -DARROW_PARQUET=ON -DARROW_DATASET=ON -DARROW_JNI=ON -DARROW_ORC=ON -DARROW_CSV=ON -DARROW_CLS=ON' if debug: cmake_cmd += ' -DCMAKE_BUILD_TYPE=Debug' print ("!!!! " + cmake_cmd + " !!!!!") my_env = os.environ.copy() my_env["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64" print(my_env) subprocess.call(cmake_cmd+' 1>&2', cwd='{}/cpp'.format(location), env=my_env, **kwargs) if subprocess.call(cmake_cmd+' 1>&2', cwd='{}/cpp'.format(location), env=my_env, **kwargs) != 0: return False if subprocess.call('sudo make install -j{} 1>&2'.format(cores), cwd='{}/cpp'.format(location), **kwargs) != 0: return False hosts = [key for key, value in hosts_designations_mapping.items() if any(value)] # Only nodes joining the ceph cluster will receive the libraries executors = [Executor('ssh {} "mkdir -p ~/.arrow-libs/ && sudo mkdir -p /usr/lib/rados-classes/"'.format(x), shell=True, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL) for x in hosts] Executor.run_all(executors) if not Executor.wait_all(executors, print_on_error=True): printe('Could not create required directories on all nodes.') return False executors = [Executor('scp {}/cpp/build/latest/libcls* {}:~/.arrow-libs/'.format(location, x), **kwargs) for x in hosts] executors += [Executor('scp {}/cpp/build/latest/libarrow* {}:~/.arrow-libs/'.format(location, x), **kwargs) for x in hosts] executors += [Executor('scp {}/cpp/build/latest/libparquet* {}:~/.arrow-libs/'.format(location, x), **kwargs) for x in hosts] Executor.run_all(executors) if not Executor.wait_all(executors, print_on_error=True): printe('Could not scp Arrow libraries to all nodes.') return False executors = [Executor('ssh {} "sudo cp ~/.arrow-libs/libcls* /usr/lib/rados-classes/"'.format(x), **kwargs) for x in hosts] executors += [Executor('ssh {} "sudo cp ~/.arrow-libs/libarrow* /usr/lib/"'.format(x), **kwargs) for x in hosts] executors += [Executor('ssh {} "sudo cp ~/.arrow-libs/libparquet* /usr/lib/"'.format(x), **kwargs) for x in hosts] Executor.run_all(executors) if not Executor.wait_all(executors, print_on_error=True): printe('Could not copy libraries to destinations on all nodes.') return False env = Environment() env.load_to_env() libpath = env.get('LD_LIBRARY_PATH') if not libpath: libpath = '' if not libpath or not '/usr/local/lib' in libpath.strip().split(':'): env.set('LD_LIBRARY_PATH', '/usr/local/lib:'+libpath) os.environ['LD_LIBRARY_PATH'] = '/usr/local/lib:'+libpath return subprocess.call('sudo cp /usr/local/lib/libparq* /usr/lib/', **kwargs) == 0

54.963303

227

0.647304

1,562

11,982

4.898207

0.220871

0.022219

0.009411

0.010064

0.547641

0.509868

0.467521

0.424781

0.405176

0.381127

0

0.004078

0.242864

11,982

217

228

55.21659

0.839286

0.284176

0

0.509554

0

0.031847

0.235818

0.036554

0.006369

0

1

0.031847

false

0.012739

0.025478

0

0.197452

0.133758

0

null

0

null

0

1

0

45bc17edfd4d56c459c035506d016dde933b4b38

1,539

py

Python

segment.py

jorjao81/zh-learn

9d14033361918a759dd245984c35c1cb8aa3f24f

[ "MIT" ]

2

2020-12-04T16:23:02.000Z

2021-12-25T12:54:45.000Z

segment.py

jorjao81/zh-learn

9d14033361918a759dd245984c35c1cb8aa3f24f

[ "MIT" ]

null

segment.py

jorjao81/zh-learn

9d14033361918a759dd245984c35c1cb8aa3f24f

[ "MIT" ]

1

2021-12-22T20:17:08.000Z

from pysubparser import parser from pysubparser.util import time_to_millis from pydub import AudioSegment # find segments of conversation import sys FIVE_SECONDS = 5000 def get_segments(subtitles): segments = [] prev_end = -1000000 curr_segment = None for subtitle in subtitles: this_start = time_to_millis(subtitle.start) if this_start - prev_end > FIVE_SECONDS: if curr_segment != None: segments.append(curr_segment) curr_segment = [] curr_segment.append(subtitle) prev_end = time_to_millis(subtitle.end) # append last segment segments.append(curr_segment) return segments def print_segment(seg): print(seg[0].start) for sub in seg: print(sub.text) print(seg[-1].end) print("------------------------------------") print("Segment duration: " + str((time_to_millis(seg[-1].end) - time_to_millis(seg[0].start))/1000)) print("====================================") audio_filename = sys.argv[1] subtitle_filename = sys.argv[2] subtitles = parser.parse(subtitle_filename) segments = get_segments(subtitles) song = AudioSegment.from_mp3(audio_filename) folder = "out/" episode = "e01" n = 1 for seg in segments: start = time_to_millis(seg[0].start) - 1000 end = time_to_millis(seg[-1].end) + 1500 cut = song[start:end] cut.export(folder + episode + "_seg" + str(n) + ".mp3", format="mp3") print("===== Segment " + str(n) + " ========") print_segment(seg) n += 1

22.632353

104

0.622482

195

1,539

4.723077

0.312821

0.045603

0.091205

0.065147

0.098806

0.095548

0.054289

0

0.031379

0.213125

1,539

67

105

22.970149

0.729149

0.031839

0

0.046512

0

0.088156

0.048452

0

1

0.046512

false

0

0.093023

0

0.162791

0.209302

0

null

0

null

0

1

0

45bc4038c9107e889c840e27905e254cabf514d3

4,766

py

Python

bot.py

BinaryWorld0101201/RSS_Feederbot

177c175fc309645661d2dd804e76f5dc8c5d726e

[ "MIT" ]

1

2019-05-14T11:34:40.000Z

bot.py

BinaryWorld0101201/RSS_Feederbot

177c175fc309645661d2dd804e76f5dc8c5d726e

[ "MIT" ]

null

bot.py

BinaryWorld0101201/RSS_Feederbot

177c175fc309645661d2dd804e76f5dc8c5d726e

[ "MIT" ]

null

try: import feedparser, html2text, asyncio, json, datetime, telepot from loguru import logger from telepot.aio.loop import MessageLoop from telepot.aio.delegate import per_chat_id, create_open, pave_event_space except ImportError: print("Failed to import required modules.") class RSS(telepot.aio.helper.ChatHandler): def __init__(self, *args, **kwargs): super(RSS, self).__init__(*args, **kwargs) async def date_title(self, file_name, object_name, date_title: str): """Set the date/title of latest post from a source. file_name: File name to open. Object_name: Name of the object: feed name or twitter screen name. date_title: Date/title of the object being posted.""" try: with open(file_name, "r+") as data_file: # Load json structure into memory. items = json.load(data_file) for name, data in items.items(): if ((name) == (object_name)): # Replace value of date/title with date_title data["date_title"] = date_title # Go to the top of feeds.json file. data_file.seek(0) # Dump the new json structure to the file. json.dump(items, data_file, indent=2) data_file.truncate() data_file.close() except IOError: logger.debug("date_title(): Failed to open requested file.") async def feed_to_md(self, state, name, feed_data): """A Function for converting rss feeds into markdown text. state: Either `set` or `None`: To execute date_title() name: Name of RSS feed object: eg: hacker_news feed_data: Data of the feed: URL and post_date from feeds.json""" # Parse rss feed. d = feedparser.parse(feed_data["url"]) # Target the first post. first_post = d["entries"][0] title = first_post["title"] summary = first_post["summary"] post_date = first_post["published"] link = first_post["link"] h = html2text.HTML2Text() h.ignore_images = True h.ignore_links = True summary = h.handle(summary) if ((state) == ("set")): logger.debug(f"Running date_title for feeds.json at {datetime.datetime.now()}") # date_title() see utils.py await self.date_title("feeds.json", name, title) results = [] result = {"title": title, "summary": summary, "url": link, "post_date": post_date} results.append(result) # A list containing the dict object result. return results async def file_reader(self, path, mode): """Loads json data from path specified. path: Path to target_file. mode: Mode for file to be opened in.""" try: with open(path, mode) as target_file: data = json.load(target_file) target_file.close() return data except IOError: logger.debug(f"Failed to open {path}") async def on_chat_message(self, msg): if msg["text"] == "/start": logger.start("file_{time}.log", rotation="300 MB") while True: logger.debug("Checking Feeds!") feeds = await self.file_reader("feeds.json", "r") for name, feed_data in feeds.items(): results = await self.feed_to_md(None, name, feed_data) # Checking if title is the same as date in feeds.json file. # If the same, pass; do nothing. if ((feed_data["date_title"]) == (results[0]["title"])): pass elif ((feed_data["date_title"]) != (results[0]["title"])): results = await self.feed_to_md("set", name, feed_data) logger.debug(f"Running feed_to_md at {datetime.datetime.now()}") rss_msg = f"""[{results[0]["title"]}]({results[0]["url"]})\n{results[0]["summary"]}""" await self.bot.sendMessage(msg["chat"]["id"], rss_msg, parse_mode="Markdown") # Sleep for 30 mins before re-checking. logger.debug("Sleeping for 30 mins.") await asyncio.sleep(1800) if __name__ == "__main__": TOKEN = "Insert Key Here." bot = telepot.aio.DelegatorBot(TOKEN, [ pave_event_space()( per_chat_id(), create_open, RSS, timeout=10), ]) loop = asyncio.get_event_loop() loop.create_task(MessageLoop(bot).run_forever()) print('Listening ...') loop.run_forever()

42.936937

110

0.563785

585

4,766

4.42906

0.304274

0.055577

0.01235

0.011579

0.056349

0.041683

0.023157

0

0.007442

0.323332

4,766

110

111

43.327273

0.795969

0.080781

0

0.064935

0

0.012987

0.13869

0.031557

0

1

0.012987

false

0.012987

0.077922

0

0.12987

0.025974

0

null

0

null

0

1

0

45be769f411fd4437219b4961db9cd7cef98434f

1,423

py

Python

Scripts/005_pyo/scripts/tutorial/s042_hilbert_transform.py

OrangePeelFX/Python-Tutorial

0d47f194553666304765f5bbc928374b7aec8a48

[ "MIT" ]

null

Scripts/005_pyo/scripts/tutorial/s042_hilbert_transform.py

OrangePeelFX/Python-Tutorial

0d47f194553666304765f5bbc928374b7aec8a48

[ "MIT" ]

1

2021-06-02T00:28:17.000Z

Scripts/005_pyo/scripts/tutorial/s042_hilbert_transform.py

florianwns/python-scripts

0d47f194553666304765f5bbc928374b7aec8a48

[ "MIT" ]

1

2020-01-13T11:08:18.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Effet de déphasage à la Barberpole. Cet exemple utilise deux déphaseurs (basés sur une modulation complexe) décalant linéairement le contenu fréquentiel d'un son. Le décalage de fréquence est similaire à la modulation en anneaux, sauf que les bandes latérales supérieures et inférieures sont séparées en sorties individuelles. """ from pyo import * from random import random import os class ComplexMod: """ Complex modulation used to shift the frequency spectrum of the input sound. """ def __init__(self, hilb, freq): # Quadrature oscillator (sine, cosine). self._quad = Sine(freq, [0, 0.25]) # real * cosine. self._mod1 = hilb['real'] * self._quad[1] # imaginary * sine. self._mod2 = hilb['imag'] * self._quad[0] # Up shift corresponds to the sum frequencies. self._up = (self._mod1 + self._mod2) * 0.7 def out(self, chnl=0): self._up.out(chnl) return self s = Server().boot().start() # Large spectrum source. src = PinkNoise(.2) # Apply the Hilbert transform. hilb = Hilbert(src) # LFOs controlling the amount of frequency shifting. lf1 = Sine(.03, mul=6) lf2 = Sine(.05, mul=6) # Stereo Single-Sideband Modulation. wetl = ComplexMod(hilb, lf1).out() wetr = ComplexMod(hilb, lf2).out(1) # Mixed with the dry sound. dry = src.mix(2).out() s.gui(locals())

25.410714

79

0.668306

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1,423

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45bebfee1fc6c99b8d76516c463bc09140a29f6d

8,885

py

Python

odfdo/paragraph_base.py

mat-m/odfdo

a4a509a056517ecf91449e029b36fe9a8ffa8ed0

[ "Apache-2.0" ]

null

odfdo/paragraph_base.py

mat-m/odfdo

a4a509a056517ecf91449e029b36fe9a8ffa8ed0

[ "Apache-2.0" ]

null

odfdo/paragraph_base.py

mat-m/odfdo

a4a509a056517ecf91449e029b36fe9a8ffa8ed0

[ "Apache-2.0" ]

null

# Copyright 2018 Jérôme Dumonteil # Copyright (c) 2009-2013 Ars Aperta, Itaapy, Pierlis, Talend. # # 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. # # # Authors (odfdo project): jerome.dumonteil@gmail.com # The odfdo project is a derivative work of the lpod-python project: # https://github.com/lpod/lpod-python # Authors: David Versmisse <david.versmisse@itaapy.com> # Hervé Cauwelier <herve@itaapy.com> # Romain Gauthier <romain@itaapy.com> # Jerome Dumonteil <jerome.dumonteil@itaapy.com> import re from .element import Element, register_element_class, to_str from .element import Text _rsplitter = re.compile('(\n|\t| +)') _rspace = re.compile('^ +$') def _get_formatted_text(element, context, with_text=True): document = context.get('document', None) rst_mode = context.get('rst_mode', False) result = [] if with_text: objects = element.xpath('*|text()') else: objects = element.children for obj in objects: if isinstance(obj, Text): result.append(obj) continue tag = obj.tag # Good tags with text if tag in ('text:a', 'text:p'): result.append(_get_formatted_text(obj, context, with_text=True)) continue # Try to convert some styles in rst_mode if tag == 'text:span': text = _get_formatted_text(obj, context, with_text=True) if not rst_mode: result.append(text) continue if not text.strip(): result.append(text) continue style = obj.style if not style: result.append(text) continue if document: style = document.get_style('text', style) properties = style.get_properties() else: properties = None if properties is None: result.append(text) continue # Compute before, text and after before = '' for c in text: if c.isspace(): before += c else: break after = '' for c in reversed(text): if c.isspace(): after = c + after else: break text = text.strip() # Bold ? if properties.get('fo:font-weight') == 'bold': result.append(before) result.append('**') result.append(text) result.append('**') result.append(after) continue # Italic ? if properties.get('fo:font-style') == 'italic': result.append(before) result.append('*') result.append(text) result.append('*') result.append(after) continue # Unknown style, ... result.append(before) result.append(text) result.append(after) continue # Footnote or endnote if tag == 'text:note': note_class = obj.note_class container = { 'footnote': context['footnotes'], 'endnote': context['endnotes'] }[note_class] citation = obj.citation if not citation: # Would only happen with hand-made documents citation = len(container) body = obj.note_body container.append((citation, body)) if rst_mode: marker = { 'footnote': " [#]_ ", 'endnote': " [*]_ " }[note_class] else: marker = { 'footnote': "[{citation}]", 'endnote': "({citation})" }[note_class] result.append(marker.format(citation=citation)) continue # Annotations if tag == 'office:annotation': context['annotations'].append(obj.note_body) if rst_mode: result.append(' [#]_ ') else: result.append('[*]') continue # Tabulation if tag == 'text:tab': result.append('\t') continue # Line break if tag == 'text:line-break': if rst_mode: result.append('\n|') else: result.append('\n') continue # other cases: result.append(obj.get_formatted_text(context)) return ''.join(result) class Spacer(Element): """This element shall be used to represent the second and all following “ “ (U+0020, SPACE) characters in a sequence of “ “ (U+0020, SPACE) characters. Note: It is not an error if the character preceding the element is not a white space character, but it is good practice to use this element only for the second and all following SPACE characters in a sequence. """ _tag = 'text:s' _properties = (('number', 'text:c'), ) def __init__(self, number=1, **kwargs): """ Arguments: number -- int Return: Space """ super().__init__(**kwargs) if self._do_init: self.number = str(number) Spacer._define_attribut_property() class Tab(Element): """This element represents the [UNICODE] tab character (HORIZONTAL TABULATION, U+0009). The position attribute contains the number of the tab-stop to which a tab character refers. The position 0 marks the start margin of a paragraph. Note: The position attribute is only a hint to help non-layout oriented consumers to determine the tab/tab-stop association. Layout oriented consumers should determine the tab positions based on the style information """ _tag = 'text:tab' _properties = (('position', 'text:tab-ref'), ) def __init__(self, position=None, **kwargs): """ Arguments: position -- int Return: Tab """ super().__init__(**kwargs) if self._do_init: if position is not None: if position >= 0: self.position = str(position) Tab._define_attribut_property() class LineBreak(Element): """This element represents a line break "text:line-break" Return: LineBreak """ _tag = 'text:line-break' def __init__(self, **kwargs): super().__init__(**kwargs) class ParagraphBase(Element): """Base class for Paragraph like classes. """ _tag = 'text:p-odfdo-notodf' _properties = (('style', 'text:style-name'), ) def __init__(self, **kwargs): super().__init__(**kwargs) def get_formatted_text(self, context=None, simple=False): if not context: context = { 'document': None, 'footnotes': [], 'endnotes': [], 'annotations': [], 'rst_mode': False, 'img_counter': 0, 'images': [], 'no_img_level': 0 } content = _get_formatted_text(self, context, with_text=True) if simple: return content else: return content + '\n\n' def append_plain_text(self, text=''): """Append plain text to the paragraph, replacing <CR>, <TAB> and multiple spaces by ODF corresponding tags. """ text = to_str(text) blocs = _rsplitter.split(text) for b in blocs: if not b: continue if b == '\n': self.append(LineBreak()) continue if b == '\t': self.append(Tab()) continue if _rspace.match(b): # follow ODF standard : n spaces => one space + spacer(n-1) self.append(' ') self.append(Spacer(len(b) - 1)) continue # standard piece of text: self.append(b) ParagraphBase._define_attribut_property() register_element_class(Spacer) register_element_class(Tab) register_element_class(LineBreak) register_element_class(ParagraphBase)

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null

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null

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45c2d3a01088393fc4acdc16b9de54511df97676

17,834

py

Python

reveries/maya/pipeline.py

davidlatwe/reveries-config

4a282dd64a32a9b87bd1a070759b6425ff785d68

[ "MIT" ]

3

2020-04-01T10:51:17.000Z

2021-08-05T18:35:23.000Z

reveries/maya/pipeline.py

davidlatwe/reveries-config

4a282dd64a32a9b87bd1a070759b6425ff785d68

[ "MIT" ]

null

reveries/maya/pipeline.py

davidlatwe/reveries-config

4a282dd64a32a9b87bd1a070759b6425ff785d68

[ "MIT" ]

1

2020-07-05T12:06:30.000Z

import os import logging import avalon.maya import avalon.io from collections import defaultdict from avalon.maya.pipeline import ( AVALON_CONTAINER_ID, AVALON_CONTAINERS, containerise, ) from maya import cmds from . import lib from .vendor import sticker from .capsule import namespaced, nodes_locker from .. import REVERIES_ICONS, utils AVALON_GROUP_ATTR = "subsetGroup" AVALON_CONTAINER_ATTR = "container" log = logging.getLogger(__name__) _node_lock_state = {"_": None} def is_editable(): return _node_lock_state["_"] is None def reset_edit_lock(): _node_lock_state["_"] = None def lock_edit(): """Restrict scene modifications All nodes will be locked, except: * default nodes * startup cameras * renderLayerManager """ all_nodes = set(cmds.ls(objectsOnly=True, long=True)) defaults = set(cmds.ls(defaultNodes=True)) cameras = set(lib.ls_startup_cameras()) materials = set(cmds.ls(materials=True)) nodes_to_lock = list((all_nodes - defaults - cameras).union(materials)) nodes_to_lock.remove("renderLayerManager") # Save current lock state _node_lock_state["_"] = lib.acquire_lock_state(nodes_to_lock) # Lock lib.lock_nodes(nodes_to_lock) def unlock_edit(): """Unleash scene modifications Restore all nodes' previous lock states """ lib.restore_lock_state(_node_lock_state["_"]) reset_edit_lock() def env_embedded_path(path): """Embed environment var `$AVALON_PROJECTS` and `$AVALON_PROJECT` into path This will ensure reference or cache path resolvable when project root moves to other place. """ path = path.replace( avalon.api.registered_root(), "$AVALON_PROJECTS", 1 ) path = path.replace( avalon.Session["AVALON_PROJECT"], "$AVALON_PROJECT", 1 ) return path def subset_group_name(namespace, name): return "{}:{}".format(namespace, name) def container_naming(namespace, name, suffix): return "%s_%s_%s" % (namespace, name, suffix) def unique_root_namespace(asset_name, family_name, parent_namespace=""): unique = avalon.maya.lib.unique_namespace( asset_name + "_" + family_name + "_", prefix=parent_namespace + ("_" if asset_name[0].isdigit() else ""), suffix="_", ) return ":" + unique # Ensure in root def get_container_from_namespace(namespace): """Return container node from namespace Raise `RuntimeError` if getting none or more then one container. Arguments: namespace (str): Namespace string Returns a str """ nodes = lib.lsAttrs({"id": AVALON_CONTAINER_ID}, # (TODO) This `namespace` should be attribute, not # node's namespace. namespace=namespace) if "*" in namespace: return nodes if not nodes: raise RuntimeError("No matched container, this is a bug.") if len(nodes) > 1: cmds.warning("Has more then one matched container, " "returning first matched.") return nodes[0] def iter_containers_from_namespace(namespace): """Yield container nodes from namespace Arguments: namespace (str): Namespace string Yield str """ for node in lib.lsAttrs({"id": AVALON_CONTAINER_ID, "namespace": namespace}): yield node def get_container_from_group(group): """Return container node from subset group If the `group` is not a subset group node, return `None`. Args: group (str): Subset group node name Return: str or None """ if not cmds.objExists(group): return None nodes = list() for node in cmds.listConnections(group + ".message", destination=True, source=False, type="objectSet") or []: if not cmds.objExists(node + ".id"): continue if cmds.getAttr(node + ".id") == AVALON_CONTAINER_ID: nodes.append(node) assert len(nodes) == 1, ("Group node has non or more then one container, " "this is a bug.") return nodes[0] def get_group_from_container(container, long=True): """Get top group node name from container node Arguments: container (str): Name of container node """ try: group = cmds.listConnections(container + ".subsetGroup", source=True, destination=False, plugs=False) return cmds.ls(group, long=long)[0] except ValueError: # The subset of family 'look' does not have subsetGroup. return None except IndexError: raise Exception("Container '%s' exists but subsetGroup does not, " "possible dirty scene." % container) def apply_namespace_wrapper(namespace, nodes): """Put nodes into a namespace wrapper objectSet For nodes that could not or did not have namespace, by putting them into a special objectSet so those nodes can be found by the tools which require namespace to work with. That special objectSet node is the actual member of the namespace, and it's AvalonID value must be `lib.AVALON_NAMESPACE_WRAPPER_ID`. Those tools must use `.lib.ls_nodes_by_id` to find nodes, that's the function has the implementation of reading nodes inside the wrapper. (NOTE): Nodes that already has namespace will be ignored. Example use case: Currently used by XGen Legacy type subsets, since XGen Legacy did not fully namespace supported. Args: namespace (str): Namespace string that will apply to the wrapper nodes (list): A list of nodes that need to be wrapped Returns: (list): A list of wrapper nodes """ from .utils import get_id_namespace, id_namespace, upsert_id id_cache = dict() wrapper_group = defaultdict(list) for node in nodes: # Ignore nodes that already has namespace if lib.get_ns(node) != ":": continue asset_id = get_id_namespace(node) if asset_id is None: continue if asset_id not in id_cache: id_cache[asset_id] = avalon.io.find_one( {"_id": avalon.io.ObjectId(asset_id)}, projection={"name": True})["name"] asset_name = id_cache[asset_id] wrapper = namespace + ":wrapper_" + asset_name wrapper_group[(wrapper, asset_id)].append(node) wrappers = list() for (wrapper, asset_id), nodes in wrapper_group.items(): if not cmds.objExists(wrapper): cmds.createNode("objectSet", name=wrapper) with id_namespace(asset_id): upsert_id(wrapper, id=lib.AVALON_NAMESPACE_WRAPPER_ID) cmds.sets(nodes, forceElement=wrapper) wrappers.append(wrapper) return wrappers def container_metadata(container): """Get additional data from container node Arguments: container (str): Name of container node Returns: (dict) """ return {} def parse_container(container): """Parse data from container node with additional data Arguments: container (str): Name of container node Returns: data (dict) """ data = avalon.maya.pipeline.parse_container(container) data.update(container_metadata(container)) return data def update_container(container, asset, subset, version, representation, rename_group=True): """Update container node attributes' value and namespace Arguments: container (dict): container document asset (dict): asset document subset (dict): subset document version (dict): version document representation (dict): representation document rename_group (bool): rename group """ container_node = container["objectName"] namespace = container["namespace"] child_namespace = namespace.rsplit(":", 1)[-1] # This rely on unique namespace's naming rule origin_family = child_namespace.rsplit("_", 3)[1] if subset["schema"] == "avalon-core:subset-3.0": family = subset["data"]["families"][0] else: family = version["data"]["families"][0] family_name = family.split(".")[-1] log.info("Updating container '%s'..." % container_node) # Update namespace asset_changed = container["assetId"] != str(asset["_id"]) family_changed = origin_family != family_name if (asset_changed or family_changed): parent_namespace = namespace.rsplit(":", 1)[0] + ":" with namespaced(parent_namespace, new=False) as parent_namespace: parent_namespace = parent_namespace[1:] asset_name = asset["data"].get("shortName", asset["name"]) new_namespace = unique_root_namespace(asset_name, family_name, parent_namespace) cmds.namespace(parent=":" + parent_namespace, rename=(child_namespace, new_namespace[1:].rsplit(":", 1)[-1])) namespace = new_namespace # Update data for key, value in { "name": subset["name"], "namespace": namespace, "assetId": str(asset["_id"]), "subsetId": str(subset["_id"]), "versionId": str(version["_id"]), "representation": str(representation["_id"]), }.items(): cmds.setAttr(container_node + "." + key, value, type="string") name = subset["name"] # Rename group node if rename_group: group = get_group_from_container(container_node) new_name = subset_group_name(namespace, name) if group and group != new_name and cmds.objExists(group): group = cmds.rename(group, new_name) log.info("Subset group renamed to '%s'." % group) # Rename container container_node = cmds.rename( container_node, container_naming(namespace, name, "CON")) log.info("Container renamed to '%s'." % container_node) # Rename reference node members = cmds.sets(container_node, query=True) reference_node = next(iter(lib.get_reference_nodes(members)), None) if reference_node: with nodes_locker(reference_node, False, False, False): cmds.rename(reference_node, namespace + "RN") def subset_containerising(name, namespace, container_id, nodes, context, cls_name, group_name): """Containerise loaded subset Containerizing imported/referenced nodes and connect subset group node's `message` attribute to container node. Arguments: name (str): Name of resulting assembly namespace (str): Namespace under which to host container container_id (str): Container UUID nodes (list): Long names of imported/referenced nodes context (dict): Asset information cls_name (str): avalon Loader class name group_name (str): Top group node of imported/referenced new nodes """ container = containerise(name=name, namespace=namespace, nodes=nodes, context=context, loader=cls_name) # Add additional data for key, value in { "containerId": container_id, "assetId": str(context["asset"]["_id"]), "subsetId": str(context["subset"]["_id"]), "versionId": str(context["version"]["_id"]), }.items(): cmds.addAttr(container, longName=key, dataType="string") cmds.setAttr(container + "." + key, value, type="string") # Connect subset group if group_name and cmds.objExists(group_name): lib.connect_message(group_name, container, AVALON_GROUP_ATTR) # Put icon to main container main_container = cmds.ls(AVALON_CONTAINERS, type="objectSet")[0] _icon = os.path.join(REVERIES_ICONS, "container_main-01.png") sticker.put(main_container, _icon) # Apply icons container_icon = os.path.join(REVERIES_ICONS, "container-01.png") sticker.put(container, container_icon) if cmds.objExists(group_name): package_icon = os.path.join(REVERIES_ICONS, "package-01.png") sticker.put(group_name, package_icon) return parse_container(container) def put_instance_icon(instance): instance_icon = os.path.join(REVERIES_ICONS, "instance-01.png") sticker.put(instance, instance_icon) return instance def find_stray_textures(nodes=lib._no_val): """Find file nodes which pointing files that were not in published space """ stray = list() containers = lib.lsAttr("id", AVALON_CONTAINER_ID) args = (nodes, ) if nodes is not lib._no_val else () for file_node in cmds.ls(*args, type="file"): # Not in published space file_path = cmds.getAttr(file_node + ".fileTextureName") if file_path and not lib.is_versioned_texture_path(file_path): stray.append(file_node) continue # OR # Not containerized sets = cmds.listSets(object=file_node) or [] if not any(s in containers for s in sets): stray.append(file_node) return stray _uuid_required_node_types = { "reveries.model": { "transform", }, "reveries.rig": { "transform", }, "reveries.look": { "transform", # (TODO): Map shaders with shadingEngine id "shadingEngine", # "shadingDependNode", # "THdependNode", "uvChooser", }, "reveries.setdress": { "transform", }, "reveries.camera": { "transform", "camera", }, "reveries.lightset": { "transform", "light", "locator", }, "reveries.xgen": { "transform", # Listed from cmds.listNodeTypes("xgen/spline") # "xgmCurveToSpline", "xgmModifierClump", "xgmModifierCollision", "xgmModifierCut", "xgmModifierDisplacement", "xgmModifierGuide", "xgmModifierLinearWire", "xgmModifierNoise", "xgmModifierScale", "xgmModifierSculpt", "xgmSeExpr", "xgmSplineBase", "xgmSplineCache", "xgmSplineDescription", "xgmPalette", "xgmDescription", }, } def uuid_required_node_types(family): try: types = _uuid_required_node_types[family] except KeyError: if family == "reveries.mayashare": types = set() for typ in _uuid_required_node_types.values(): types.update(typ) else: raise return types def has_turntable(): """Return turntable asset name if scene has loaded one Returns: str: turntable asset name, if scene has truntable asset loaded, else `None` """ project = avalon.io.find_one({"type": "project"}, {"data.pipeline.maya": True}) turntable = project["data"]["pipeline"]["maya"].get("turntable") if turntable is None: return None if get_container_from_namespace(":{}_*".format(turntable)): return turntable _current_fps = {"_": None} def set_scene_timeline(project=None, asset_name=None, strict=True): """Set timeline to correct frame range for the asset Args: project (dict, optional): Project document, query from database if not provided. asset_name (str, optional): Asset name, get from `avalon.Session` if not provided. strict (bool, optional): Whether or not to set the exactly frame range that pre-defined for asset, or leave the scene start/end untouched as long as the start/end frame could cover the pre-defined range. Default `True`. """ log.info("Timeline setting...") current_fps = _current_fps["_"] or lib.current_fps() _current_fps["_"] = None start_frame, end_frame, fps = utils.compose_timeline_data(project, asset_name, current_fps) fps = lib.FPS_MAP.get(fps) if fps is None: raise ValueError("Unsupported FPS value: {}".format(fps)) cmds.currentUnit(time=fps) if not strict: scene_start = cmds.playbackOptions(query=True, minTime=True) if start_frame < scene_start: cmds.playbackOptions(animationStartTime=start_frame) cmds.playbackOptions(minTime=start_frame) scene_end = cmds.playbackOptions(query=True, maxTime=True) if end_frame > scene_end: cmds.playbackOptions(animationEndTime=end_frame) cmds.playbackOptions(maxTime=end_frame) else: cmds.playbackOptions(animationStartTime=start_frame) cmds.playbackOptions(minTime=start_frame) cmds.playbackOptions(animationEndTime=end_frame) cmds.playbackOptions(maxTime=end_frame) cmds.currentTime(start_frame) def set_resolution(project=None, asset_name=None): width, height = utils.get_resolution_data(project, asset_name) cmds.setAttr("defaultResolution.width", width) cmds.setAttr("defaultResolution.height", height) def set_linear_unit(project=None, asset_name=None): unit = utils.get_linear_unit_data(project, asset_name) cmds.currentUnit(linear=unit)

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null

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null

0

1

0

45c440eae6695f819733ad7666e871c73dcc0582

2,697

py

Python

app.py

kmr2/vefferk5

5fe8604781ed65f62a6be8e71381296693215343

[ "MIT" ]

null

app.py

kmr2/vefferk5

5fe8604781ed65f62a6be8e71381296693215343

[ "MIT" ]

null

app.py

kmr2/vefferk5

5fe8604781ed65f62a6be8e71381296693215343

[ "MIT" ]

null

from flask import Flask, render_template, request, session, redirect, url_for import pyrebase app = Flask(__name__) app.config['SECRET_KEY'] = 'covid_19' config = { "apiKey": "AIzaSyB6L9PLOIa-y8spupA2UFiegQGN7gmp12E", "authDomain": "vefferk5.firebaseapp.com", "databaseURL": "https://vefferk5.firebaseio.com", "projectId": "vefferk5", "storageBucket": "vefferk5.appspot.com", "messagingSenderId": "683227377425", "appId": "1:683227377425:web:1317e8ebac70100ee126b0", "measurementId": "G-0BBSXPYZF8" } fb = pyrebase.initialize_app(config) db = fb.database() # Test route til að setja gögn í db @app.route('/') def index(): return render_template("index.html") # Test route til að sækja öll gögn úr db @app.route('/login', methods=['GET', 'POST']) def login(): login = False if request.method == 'POST': notendanafn = request.form['uname'] lykilorð = request.form['psw'] u = db.child("notandi").get().val() lst = list(u.items()) for i in lst: if notendanafn == i[1]['notendanafn'] and lykilorð == i[1]['lykilorð']: login = True break if login: session['logged_in'] = notendanafn return redirect("/topsecret") else: return render_template("nologin.html") else: return render_template("no_method.html") @app.route('/register') def register(): return render_template('register.html') # Test route til að sækja öll gögn úr db @app.route('/doregister', methods=['GET', 'POST']) def doregister(): usernames = [] if request.method == 'POST': notendanafn = request.form['uname'] lykilorð = request.form['psw'] u = db.child("notandi").get().val() lst = list(u.items()) for i in lst: usernames.append(i[1]['notendanafn']) if notendanafn not in usernames: db.child("notandi").push({"notendanafn": notendanafn, "lykilorð": lykilorð}) return render_template("registered.html") else: return render_template("userexists.html") @app.route('/logout') def logout(): session.pop("logged_in", None) return render_template("index.html") @app.route('/topsecret') def topsecret(): if 'logged_in' in session: return render_template("topsecret.html") else: return redirect("/") if __name__ == "__main__": app.run(debug=True) # skrifum nýjan í grunn hnútur sem heitir notandi # db.child("notandi").push({"notendanafn":"dsg", "lykilorð":1234}) # # förum í grunn og sækjum allar raðir ( öll gögn ) # u = db.child("notandi").get().val() # lst = list(u.items())

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null

0

null

0

1

0

45c45593e29f001e140d37954419e1ef43c370f9

2,511

py

Python

pax/utils.py

jacr20/pax

d64d0ae4e4ec3e9bb3e61065ed92e9ea23328940

[ "BSD-3-Clause" ]

17

2016-04-24T12:02:03.000Z

2021-07-19T19:39:47.000Z

pax/utils.py

jacr20/pax

d64d0ae4e4ec3e9bb3e61065ed92e9ea23328940

[ "BSD-3-Clause" ]

300

2016-04-01T15:29:57.000Z

2021-01-03T23:59:45.000Z

pax/utils.py

jacr20/pax

d64d0ae4e4ec3e9bb3e61065ed92e9ea23328940

[ "BSD-3-Clause" ]

20

2016-04-14T15:11:26.000Z

2021-09-18T06:39:09.000Z

"""Helper routines needed in pax Please only put stuff here that you *really* can't find any other place for! e.g. a list clustering routine that isn't in some standard, library but several plugins depend on it """ import re import sys import inspect import random import string import logging import time import os import glob log = logging.getLogger('pax_utils') ## # Utilities for finding files inside pax. ## # Store the directory of pax (i.e. this file's directory) as PAX_DIR PAX_DIR = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) def data_file_name(filename): """Returns filename if a file exists there, else returns PAX_DIR/data/filename""" if os.path.exists(filename): return filename new_filename = os.path.join(PAX_DIR, 'data', filename) if os.path.exists(new_filename): return new_filename else: raise ValueError('File name or path %s not found!' % filename) def get_named_configuration_options(): """ Return the names of all working named configurations """ config_files = [] for filename in glob.glob(os.path.join(PAX_DIR, 'config', '*.ini')): filename = os.path.basename(filename) m = re.match(r'(\w+)\.ini', filename) if m is None: print("Weird file in config dir: %s" % filename) filename = m.group(1) # Config files starting with '_' won't appear in the usage list (they won't work by themselves) if filename[0] == '_': continue config_files.append(filename) return config_files # Caching decorator # Stolen from http://avinashv.net/2008/04/python-decorators-syntactic-sugar/ class Memoize: def __init__(self, function): self.function = function self.memoized = {} def __call__(self, *args): try: return self.memoized[args] except KeyError: self.memoized[args] = self.function(*args) return self.memoized[args] class Timer: """Simple stopwatch timer punch() returns ms since timer creation or last punch """ last_t = 0 def __init__(self): self.punch() def punch(self): now = time.time() result = (now - self.last_t) * 1000 self.last_t = now return result def randomstring(n): return ''.join(random.choice(string.ascii_letters) for _ in range(n)) def refresh_status_line(text): sys.stdout.write('\r') sys.stdout.write(text) sys.stdout.flush()

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0.017857

0

null

0

null

0

1

0

45c8bb81a03ef5a3614a21fdea7815af182bb4cc

674

py

Python

tock/session/memory.py

elebescond/tock-py

2addcaa671be4a7af7cdf9bc061c707bbcf7128a

[ "MIT" ]

4

2020-09-05T10:08:34.000Z

2021-10-05T05:38:59.000Z

tock/session/memory.py

elebescond/tock-py

2addcaa671be4a7af7cdf9bc061c707bbcf7128a

[ "MIT" ]

16

2020-09-03T14:13:24.000Z

2021-03-22T09:54:08.000Z

tock/session/memory.py

elebescond/tock-py

2addcaa671be4a7af7cdf9bc061c707bbcf7128a

[ "MIT" ]

3

2020-09-15T09:04:06.000Z

2021-03-04T12:40:27.000Z

# -*- coding: utf-8 -*- from typing import List from tock.session.storage import Storage from tock.session.session import Session from tock.models import UserId class MemoryStorage(Storage): def __init__(self): self.__sessions: List[Session] = [] def get_session(self, user_id: UserId) -> Session: for session in self.__sessions: if session.user_id == user_id: return session return Session(user_id) def save(self, session: Session): for item in self.__sessions: if item.user_id == session.user_id: self.__sessions.remove(item) self.__sessions.append(session)

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null

0

null

0

1

0

45c8f3a70c973964d6c049db2cb9fbfddac2a6fa

2,607

py

Python

tests/tests.py

hamedrb/pystripe

8ffd6f64f9074562d2c8b293b57cc795bfdcc196

[ "MIT" ]

null

tests/tests.py

hamedrb/pystripe

8ffd6f64f9074562d2c8b293b57cc795bfdcc196

[ "MIT" ]

null

tests/tests.py

hamedrb/pystripe

8ffd6f64f9074562d2c8b293b57cc795bfdcc196

[ "MIT" ]

null

import numpy as np import unittest import os import tempfile # import matplotlib.pyplot as plt from pystripe import core class TestWavedec(unittest.TestCase): def test(self): img = np.eye(5) coeffs = core.wavedec(img, wavelet='db1', level=None) approx = coeffs[0] self.assertEqual(len(coeffs), 3) self.assertTrue(np.allclose(approx, np.array([[1, 0], [0, 4]]))) class TestWaverec(unittest.TestCase): def test(self): img = np.eye(6) wavelet = 'db1' coeffs = core.wavedec(img, wavelet=wavelet, level=None) recon = core.waverec(coeffs, wavelet=wavelet) self.assertTrue(np.allclose(img, recon)) # def plot_fft(data, fdata): # plt.subplot(121) # plt.imshow(data) # plt.subplot(122) # plt.imshow(np.sqrt(np.real(fdata) ** 2 + np.imag(fdata) ** 2)) # plt.show() class TestFFT(unittest.TestCase): def setUp(self): self.data = np.zeros((64, 64)) self.data[12, :] = 10 # thin horizontal stripe, should show up as high frequency vertical component def test_shift(self): fdata = core.fft(self.data) self.assertAlmostEqual(fdata[44, 32], 640.0) def test_noshift(self): fdata = core.fft(self.data, shift=False) self.assertAlmostEqual(fdata[12, 0], 640.0) class TestFFT2(unittest.TestCase): def setUp(self): self.data = np.zeros((64, 64)) self.data[12, :] = 10 # thin horizontal stripe, should show up as high frequency vertical component def test_shift(self): fdata = core.fft2(self.data) self.assertAlmostEqual(fdata[44, 32], np.complex(0, -640.0)) class TestNotch(unittest.TestCase): def test(self): g = core.notch(n=4, sigma=1) self.assertTrue(np.allclose(g, np.array([0, 0.39346934, 0.86466472, 0.988891]))) g = core.notch(n=4, sigma=2) self.assertTrue(np.allclose(g, np.array([0, 0.1175031, 0.39346934, 0.67534753]))) def test_zero(self): with self.assertRaises(ValueError): g = core.notch(n=4, sigma=0.0) with self.assertRaises(ValueError): g = core.notch(n=0, sigma=1.0) class TestGaussianFilter(unittest.TestCase): def test(self): m = 10 res = core.gaussian_filter(shape=(m, 4), sigma=1) self.assertTrue(np.allclose(res, np.array(m*[[0, 0.39346934, 0.86466472, 0.988891]]))) res = core.gaussian_filter(shape=(m, 4), sigma=2) self.assertTrue(np.allclose(res, np.array(m*[[0, 0.1175031, 0.39346934, 0.67534753]]))) if __name__ == '__main__': unittest.main()

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0.375

0

null

0

null

0

1

0

45cd8cd79b7985a77b217cfc932114de4167b993

8,854

py

Python

third_party/lid_adversarial_subspace_detection/adaptive_attacks.py

ptrcarta/neural-fingerprinting

01fa8cb592f6fa7497c6884861adf7680ffa7f29

[ "BSD-3-Clause" ]

29

2018-03-10T04:33:25.000Z

2022-03-18T13:03:37.000Z

third_party/lid_adversarial_subspace_detection/adaptive_attacks.py

ptrcarta/neural-fingerprinting

01fa8cb592f6fa7497c6884861adf7680ffa7f29

[ "BSD-3-Clause" ]

2

2019-07-22T20:59:01.000Z

2019-11-17T07:00:00.000Z

third_party/lid_adversarial_subspace_detection/adaptive_attacks.py

StephanZheng/neural-fingerprinting

57e93e487ef324427456b14d1d81bc9e08483d27

[ "BSD-3-Clause" ]

20

2018-03-14T14:01:55.000Z

2021-09-17T19:19:56.000Z

from __future__ import absolute_import from __future__ import print_function import copy from collections import defaultdict import numpy as np import tensorflow as tf from tqdm import tqdm from six.moves import xrange import sys sys.path.append('../../.') from cleverhans.utils import other_classes from cleverhans.utils_tf import model_argmax from cleverhans.evaluation import batch_eval from cleverhans.attacks_tf import (jacobian_graph, jacobian, apply_perturbations, saliency_map) import keras.backend as K import os import pickle def adaptive_fgsm(x, predictions, eps, clip_min=None, clip_max=None, log_dir=None, y=None, model_logits = None, alpha = None, dataset=None ): """ Computes symbolic TF tensor for the adversarial samples. This must be evaluated with a session.run call. :param x: the input placeholder :param predictions: the model's output tensor :param eps: the epsilon (input variation parameter) :param clip_min: optional parameter that can be used to set a minimum value for components of the example returned :param clip_max: optional parameter that can be used to set a maximum value for components of the example returned :param y: the output placeholder. Use None (the default) to avoid the label leaking effect. :return: a tensor for the adversarial example """ # Compute loss] logits, = predictions.op.inputs fingerprint_dir = log_dir fixed_dxs = pickle.load(open(os.path.join(fingerprint_dir, "fp_inputs_dx.pkl"), "rb")) fixed_dys = pickle.load(open(os.path.join(fingerprint_dir, "fp_outputs.pkl"), "rb")) if y is None: # In this case, use model predictions as ground truth y = tf.to_float( tf.equal(predictions, tf.reduce_max(predictions, 1, keep_dims=True))) output = logits pred_class = tf.argmax(y,axis=1) loss_fp = 0 [a,b,c] = np.shape(fixed_dys) num_dx = b target_dys = tf.convert_to_tensor(fixed_dys) target_dys = (tf.gather(target_dys,pred_class)) norms = tf.sqrt(tf.reduce_sum(tf.square(output), axis=1, keep_dims=True)) norm_logits = output/norms for i in range(num_dx): logits_p = model_logits(x + fixed_dxs[i]) p_norm = tf.sqrt(tf.reduce_sum(tf.square(logits_p), axis=1, keep_dims=True)) logits_p_norm = logits_p/p_norm loss_fp = loss_fp + tf.losses.mean_squared_error((logits_p_norm - norm_logits),target_dys[:,i,:]) #self appropriate fingerprint y = y / tf.reduce_sum(y, 1, keep_dims=True) loss_ce = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y) ) ## Tune this alpha!! loss = loss_ce - alpha*loss_fp # Define gradient of loss wrt input grad, = tf.gradients(loss, x) # Take sign of gradient signed_grad = tf.sign(grad) # Multiply by constant epsilon scaled_signed_grad = eps * signed_grad # Add perturbation to original example to obtain adversarial example adv_x = tf.stop_gradient(x + scaled_signed_grad) # If clipping is needed, reset all values outside of [clip_min, clip_max] if (clip_min is not None) and (clip_max is not None): adv_x = tf.clip_by_value(adv_x, clip_min, clip_max) return adv_x def adaptive_fast_gradient_sign_method(sess, model, X, Y, eps, clip_min=None, clip_max=None, batch_size=256, log_dir = None, model_logits = None, binary_steps = 2, dataset="cifar"): """ TODO :param sess: :param model: predictions or after-softmax :param X: :param Y: :param eps: :param clip_min: :param clip_max: :param batch_size: :return: """ # Define TF placeholders for the input and output x = tf.placeholder(tf.float32, shape=(None,) + X.shape[1:]) y = tf.placeholder(tf.float32, shape=(None,) + Y.shape[1:]) alpha = tf.placeholder(tf.float32, shape=(None,) + (1,)) num_samples = np.shape(X)[0] ALPHA = 0.1*np.ones((num_samples,1)) ub = 10.0*np.ones(num_samples) lb = 0.0*np.ones(num_samples) Best_X_adv = None for i in range(binary_steps): adv_x = adaptive_fgsm( x, model(x), eps=eps, clip_min=clip_min, clip_max=clip_max, y=y, log_dir= log_dir, model_logits = model_logits, alpha = alpha ) X_adv = batch_eval( sess, [x, y, alpha], [adv_x], [X, Y, ALPHA], feed={}, args={'batch_size': batch_size} ) X_adv = np.array(X_adv[0]) if(i==0): Best_X_adv = X_adv ALPHA, Best_X_adv = binary_refinement(sess,Best_X_adv, X_adv, Y, ALPHA, ub, lb, model, dataset) return Best_X_adv def binary_refinement(sess,Best_X_adv, X_adv, Y, ALPHA, ub, lb, model, dataset='cifar'): num_samples = np.shape(X_adv)[0] print(dataset) if(dataset=="mnist"): X_place = tf.placeholder(tf.float32, shape=[1, 1, 28, 28]) else: X_place = tf.placeholder(tf.float32, shape=[1, 3, 32, 32]) pred = model(X_place) for i in range(num_samples): logits_op = sess.run(pred,feed_dict={X_place:X_adv[i:i+1,:,:,:]}) if(not np.argmax(logits_op) == np.argmax(Y[i,:])): # Success, increase alpha Best_X_adv[i,:,:,:] = X_adv[i,:,:,] lb[i] = ALPHA[i,0] else: ub[i] = ALPHA[i,0] ALPHA[i] = 0.5*(lb[i] + ub[i]) return ALPHA, Best_X_adv def adaptive_basic_iterative_method(sess, model, X, Y, eps, eps_iter, nb_iter=50, clip_min=None, clip_max=None, batch_size=256, log_dir = None, model_logits = None, binary_steps =2, attack_type = "bim-b", dataset="cifar"): """ TODO :param sess: :param model: predictions or after-softmax :param X: :param Y: :param eps: :param eps_iter: :param nb_iter: :param clip_min: :param clip_max: :param batch_size: :return: """ print("nb_iter",nb_iter) # Define TF placeholders for the input and output x = tf.placeholder(tf.float32, shape=(None,)+X.shape[1:]) y = tf.placeholder(tf.float32, shape=(None,)+Y.shape[1:]) alpha = tf.placeholder(tf.float32, shape=(None,) + (1,)) num_samples = np.shape(X)[0] ALPHA = 0.1*np.ones((num_samples,1)) ub = 10.0*np.ones(num_samples) lb = 0.0*np.ones(num_samples) Best_X_adv = None results = np.zeros((nb_iter, X.shape[0],) + X.shape[1:]) # Initialize adversarial samples as the original samples, set upper and # lower bounds X_adv = X X_min = X_adv - eps X_max = X_adv + eps print('Running BIM iterations...') # "its" is a dictionary that keeps track of the iteration at which each # sample becomes misclassified. The default value will be (nb_iter-1), the # very last iteration. def f(val): return lambda: val its = defaultdict(f(nb_iter-1)) # Out keeps track of which samples have already been misclassified out = set() for j in range(binary_steps): for i in tqdm(range(nb_iter)): adv_x = adaptive_fgsm( x, model(x), eps=eps_iter, clip_min=clip_min, clip_max=clip_max, y=y, log_dir= log_dir, model_logits = model_logits, alpha = alpha ) X_adv, = batch_eval( sess, [x, y, alpha], [adv_x], [X_adv, Y, ALPHA], feed={K.learning_phase(): 0}, args={'batch_size': batch_size} ) X_adv = np.maximum(np.minimum(X_adv, X_max), X_min) results[i] = X_adv # check misclassifieds predictions = model.predict_classes(X_adv, batch_size=512, verbose=0) misclassifieds = np.where(predictions != Y.argmax(axis=1))[0] for elt in misclassifieds: if elt not in out: its[elt] = i out.add(elt) print(i) X_adv = results[-1] if(j==0): Best_X_adv = X_adv ALPHA, Best_X_adv = binary_refinement(sess,Best_X_adv, X_adv, Y, ALPHA, ub, lb, model, dataset) return Best_X_adv

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8,854

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0.39797

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0.358376

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8,854

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0.793047

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false

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0.006329

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0.050633

0

null

0

null

0

1

0

45cdb841e19bbd7ee0c6ca21d0ef0accbb5b6f9d

2,009

py

Python

pykit/ir/pretty.py

ContinuumIO/pyk

1730d7b831e0cf12a641ac23b5cf03e17e0dc550

[ "BSD-3-Clause" ]

9

2015-06-23T00:13:49.000Z

2022-02-23T02:46:43.000Z

pykit/ir/pretty.py

ContinuumIO/pyk

1730d7b831e0cf12a641ac23b5cf03e17e0dc550

[ "BSD-3-Clause" ]

1

2017-08-30T08:13:12.000Z

2017-08-31T06:36:32.000Z

pykit/ir/pretty.py

ContinuumIO/pyk

1730d7b831e0cf12a641ac23b5cf03e17e0dc550

[ "BSD-3-Clause" ]

7

2015-05-08T10:17:47.000Z

2021-04-01T15:00:57.000Z

# -*- coding: utf-8 -*- """ Pretty print pykit IR. """ from __future__ import print_function, division, absolute_import from pykit.utils import hashable prefix = lambda s: '%' + s indent = lambda s: '\n'.join(' ' + s for s in s.splitlines()) ejoin = "".join sjoin = " ".join ajoin = ", ".join njoin = "\n".join parens = lambda s: '(' + s + ')' compose = lambda f, g: lambda x: f(g(x)) def pretty(value): formatter = formatters[type(value).__name__] return formatter(value) def fmod(mod): gs, fs = mod.globals.values(), mod.functions.values() return njoin([njoin(map(pretty, gs)), "", njoin(map(pretty, fs))]) def ffunc(f): restype = ftype(f.type.restype) types, names = map(ftype, f.type.argtypes), map(prefix, f.argnames) args = ajoin(map(sjoin, zip(types, names))) header = sjoin(["function", restype, f.name + parens(args)]) return njoin([header + " {", njoin(map(fblock, f.blocks)), "}"]) def farg(func_arg): return "%" + func_arg.result def fblock(block): body = njoin(map(compose(indent, fop), block)) return njoin([block.name + ':', body, '']) def _farg(oparg): from pykit import ir if isinstance(oparg, ir.Function): return oparg.name else: return str(oparg) def fop(op): return '%{0} = ({1}) {2}({3})'.format(op.result, ftype(op.type), op.opcode, ajoin(map(prefix, map(_farg, op.operands)))) def fconst(c): return 'const(%s, %s)' % (ftype(c.type), c.const) def fglobal(val): return "global %{0} = {1}".format(val.name, ftype(val.type)) def fundef(val): return 'Undef' def ftype(val): from pykit import types if hashable(val) and val in types.type2name: return types.type2name[val] return str(val) formatters = { 'Module': fmod, 'GlobalValue': fglobal, 'Function': ffunc, 'FuncArg': farg, 'Block': fblock, 'Operation': fop, 'Constant': fconst, 'Undef': fundef, }

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4.456604

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0

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0.229965

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false

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0.017544

0

null

0

null

0

1

0

45cea41fbca701da0133eaa849e6902c4fa1ad42

9,059

py

Python

parser.py

JonahSussman/closure-language

9cbe9e381e81dc645c8e82096366fd50a60a6d32

[ "MIT" ]

null

parser.py

JonahSussman/closure-language

9cbe9e381e81dc645c8e82096366fd50a60a6d32

[ "MIT" ]

null

parser.py

JonahSussman/closure-language

9cbe9e381e81dc645c8e82096366fd50a60a6d32

[ "MIT" ]

null

from expr import Expr from stmt import Stmt class Parser: class ParserError(Exception): pass def __init__(self, tokens): self.tokens = tokens self.c_tok = 0 def match(self, *kinds): if self.c_tok == len(self.tokens): return False for kind in kinds: if kind == self.tokens[self.c_tok].kind: return True return False def error(self, token): print('Parser Error! Invalid token: %s' % (token)) raise Parser.ParserError def declaration(self): try: if self.match('FUNCTION'): self.c_tok += 1 return self.function('function') elif self.match('LET'): self.c_tok += 1 return self.var_declaration() else: return self.statement() except Parser.ParserError: print(self.tokens) exit() def statement(self): if self.match('IF'): self.c_tok += 1 return self.if_statement() elif self.match('WHILE'): self.c_tok += 1 return self.while_statement() elif self.match('PRINT'): self.c_tok += 1 return self.print_statement() elif self.match('RETURN'): self.c_tok += 1 return self.return_statment() elif self.match('L_BRACE'): self.c_tok += 1 return Stmt.Block(self.block()) return self.expression_statment() def block(self): statements = [] while not self.match('R_BRACE') and self.c_tok < len(self.tokens): statements.append(self.declaration()) self.c_tok += 1 return statements def function(self, like): if not self.match('ID'): raise Parser.ParserError name = self.tokens[self.c_tok].value self.c_tok += 1 if not self.match('L_PAREN'): print('Expected \'(\' after function name') raise Parser.ParserError self.c_tok += 1 params = [] if not self.match('R_PAREN'): while True: if not self.match('ID'): print('Expected identifier in parameters.') raise Parser.ParserError params.append(self.tokens[self.c_tok]) self.c_tok += 1 if not self.match('COMMA'): break self.c_tok += 1 if not self.match('R_PAREN'): print('Expected \')\' after function params') raise Parser.ParserError self.c_tok += 1 if not self.match('L_BRACE'): print('Expected \'{\' before body') raise Parser.ParserError self.c_tok += 1 body = self.block() return Stmt.Fn(name, params, body) def print_statement(self): value = self.expression() if not self.match('ENDLINE'): raise Parser.ParserError self.c_tok += 1 return Stmt.Print(value) def return_statment(self): value = None if not self.match('ENDLINE'): value = self.expression() if not self.match('ENDLINE'): print('\\n must follow return value') raise Parser.ParserError self.c_tok += 1 return Stmt.Return('return', value) def if_statement(self): if not self.match('L_PAREN'): raise Parser.ParserError self.c_tok += 1 expression = self.expression() if not self.match('R_PAREN'): raise Parser.ParserError self.c_tok += 1 then_branch = self.statement() else_branch = None if self.match('ELSE'): self.c_tok += 1 else_branch = self.statement() return Stmt.If(expression, then_branch, else_branch) def while_statement(self): if not self.match('L_PAREN'): raise Parser.ParserError self.c_tok += 1 expression = self.expression() if not self.match('R_PAREN'): raise Parser.ParserError self.c_tok += 1 body = self.statement() return Stmt.While(expression, body) def expression_statment(self): value = self.expression() if not self.match('ENDLINE'): raise Parser.ParserError self.c_tok += 1 return Stmt.Expression(value) def var_declaration(self): if not self.match('ID'): raise Parser.ParserError name = self.tokens[self.c_tok].value self.c_tok += 1 initalizer = None if self.match('EQUAL'): self.c_tok += 1 initalizer = self.expression() if not self.match('ENDLINE'): raise Parser.ParserError self.c_tok += 1 return Stmt.Let(name, initalizer) def expression(self): return self.assignment() def assignment(self): expr = self.cast() if self.match('EQUAL'): self.c_tok += 1 value = self.assignment() if isinstance(expr, Expr.Variable): return Expr.Assign(expr.name, value) else: raise Parser.ParserError return expr def cast(self): expr = self.equality() if self.match('CAST'): self.c_tok += 1 kind = self.cast() return Expr.Cast(expr, kind) return expr def equality(self): expr = self.comparison() while self.match('BANG_EQUAL', 'EQUAL_EQUAL', 'AND', 'OR'): operator = self.tokens[self.c_tok].value self.c_tok += 1 right = self.comparison() expr = Expr.Listed(operator, [expr, right]) return expr def comparison(self): expr = self.addition() while self.match('LESS', 'GREATER', 'LESS_EQUAL', 'GREATER_EQUAL'): operator = self.tokens[self.c_tok].value self.c_tok += 1 right = self.addition() expr = Expr.Listed(operator, [expr, right]) return expr def addition(self): expr = self.multiplication() while self.match('PLUS', 'MINUS'): operator = self.tokens[self.c_tok].value self.c_tok += 1 right = self.multiplication() expr = Expr.Listed(operator, [expr, right]) return expr self.c_tok += 1 return Expr.Literal(self.tokens[self.c_tok - 1].value) def multiplication(self): expr = self.exponentiation() while self.match('STAR', 'SLASH', 'MOD'): operator = self.tokens[self.c_tok].value self.c_tok += 1 right = self.exponentiation() expr = Expr.Listed(operator, [expr, right]) return expr self.c_tok += 1 return Expr.Literal(self.tokens[self.c_tok - 1].value) def exponentiation(self): stack = [self.negation()] while self.match('CARET'): self.c_tok += 1 stack.append(self.negation()) while len(stack) > 1: right = stack.pop() left = stack.pop() stack.append(Expr.Listed('^', [left, right])) return stack[0] def negation(self): if self.match('MINUS', 'NOT', 'LN', 'LOG_10', 'SQRT', 'INPUT'): operator = self.tokens[self.c_tok].value self.c_tok += 1 right = self.negation() return Expr.Listed(operator, [right]) else: return self.custom_root() def custom_root(self): expr = self.logbase() while self.match('ROOT'): operator = self.tokens[self.c_tok].value self.c_tok += 1 right = self.logbase() expr = Expr.Listed(operator, [expr, right]) return expr def logbase(self): if self.match('LOG'): operator = self.tokens[self.c_tok].value self.c_tok += 1 base = self.logbase() argument = self.logbase() return Expr.Listed(operator, [base, argument]) else: return self.factorial() def factorial(self): expr = self.call() while self.match('BANG'): operator = self.tokens[self.c_tok].value self.c_tok += 1 expr = Expr.Listed(operator, [expr]) return expr def call(self): expr = self.primary() while True: if self.match('L_PAREN'): self.c_tok += 1 expr = self.finish_call(expr) else: break return expr def finish_call(self, callee): arguments = [] if not self.match('R_PAREN'): while True: arguments.append(self.expression()) if not self.match('COMMA'): break self.c_tok += 1 if not self.match('R_PAREN'): print('No \')\' after arguments!') raise Parser.ParserError paren = self.tokens[self.c_tok] self.c_tok += 1 return Expr.Call(callee, paren, arguments) def primary(self): expr = None token_value = self.tokens[self.c_tok].value if self.match('ENDLINE'): self.c_tok -= 1 expr = Expr.Literal(None) elif self.match('TRUE'): expr = Expr.Literal(True) elif self.match('FALSE'): expr = Expr.Literal(False) elif self.match('NIL'): expr = Expr.Literal(None) elif self.match('STRING'): expr = Expr.Literal(token_value[1:len(token_value)-1]) elif self.match('NUM'): expr = Expr.Literal(float(token_value)) elif self.match('KIND'): expr = Expr.Literal(token_value) elif self.match('ID'): expr = Expr.Variable(token_value) elif self.match('L_PAREN'): self.c_tok += 1 expr = Expr.Grouping(self.expression()) if not self.match('R_PAREN'): raise Parser.ParserError if not expr: raise Parser.ParserError self.c_tok += 1 return expr def parse(self): statements = [] while self.c_tok < len(self.tokens): statements.append(self.declaration()) return statements

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85

0.610001

1,196

9,059

4.524247

0.10786

0.059139

0.094622

0.076511

0.494548

0.445759

0.403068

0.381445

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0.279061

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0.258196

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false

0.00346

0.00692

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0.259516

0.034602

0

null

0

null

0

1

0

45d7d0388364df4712cd375ea072f369e100ecba

3,542

py

Python

oocran/django/operators/views.py

howls90/oocran

9951f3ff752f9f6517a4d016476c1d1e2bb44a4d

[ "Apache-2.0", "BSD-3-Clause" ]

3

2018-12-12T10:32:16.000Z

2022-02-07T19:46:10.000Z

oocran/django/operators/views.py

howls90/oocran

9951f3ff752f9f6517a4d016476c1d1e2bb44a4d

[ "Apache-2.0", "BSD-3-Clause" ]

1

2017-01-11T06:56:35.000Z

2017-01-11T06:58:44.000Z

oocran/django/operators/views.py

howls90/OOCRAN

9951f3ff752f9f6517a4d016476c1d1e2bb44a4d

[ "Apache-2.0", "BSD-3-Clause" ]

6

2017-05-29T03:34:23.000Z

2022-02-07T19:46:11.000Z

""" Open Orchestrator Cloud Radio Access Network 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. """ from django.shortcuts import render, get_object_or_404, redirect from .forms import OperatorForm, ChangeCredenForm from .models import Operator from vims.models import Vim from django.contrib import messages from django.contrib.auth.decorators import login_required from django.contrib.admin.views.decorators import staff_member_required from oocran.global_functions import paginator from scenarios.models import Scenario from django.http import HttpResponse def update_scenarios(id): operator = Operator.objects.get(id=id) scenarios = Scenario.objects.filter(operator__user__is_staff=True) for scenario in scenarios: scenario.update_operators(operator) @staff_member_required def add(request): form = OperatorForm(request.POST or None) if form.is_valid(): if form.cleaned_data['password'] == form.cleaned_data['password_confirmation']: operator = form.save(commit=False) if operator.check_used_name(): operator.create(form.cleaned_data['email']) update_scenarios(id=operator.id) operator.create_influxdb_user() messages.success(request, "Operator successfully created!", extra_tags="alert alert-success") return redirect("operators:list") else: messages.success(request, "Username is already in use!", extra_tags="alert alert-danger") else: messages.success(request, "Password and confirmation are differents!", extra_tags="alert alert-danger") return redirect("operators:list") if form.errors: messages.success(request, form.errors, extra_tags="alert alert-danger") return redirect("operators:list") context = { "user": request.user, "form": form, } return render(request, "operators/form.html", context) @staff_member_required def list(request): operators = Operator.objects.filter().exclude(user__is_staff=True) operators = paginator(request, operators) context = { "user": request.user, "object_list": operators, } return render(request, "operators/list.html", context) @staff_member_required def delete(request, id=None): operator = get_object_or_404(Operator, id=id) operator.delete_influxdb_user() operator.remove() operator.user.delete() messages.success(request, "Operator successfully deleted!", extra_tags="alert alert-success") return redirect("operators:list") @login_required(login_url='/login/') def home(request): operator = get_object_or_404(Operator, name=request.user.username) context = { "user": request.user, "operator": operator, } return render(request, "operators/home.html", context) @login_required(login_url='/login/') def state(request, id=None): operator = get_object_or_404(Operator, id=id) return HttpResponse(operator.state)

34.38835

115

0.709486

436

3,542

5.639908

0.341743

0.0244

0.044734

0.038634

0.230582

0.186255

0.123627

0.038227

0

0.005618

0.195935

3,542

103

116

34.38835

0.857795

0.160361

0

0.275362

0

0.142174

0.007177

0

1

0.086957

false

0.028986

0.144928

0

0.347826

0

null

0

null

0

1

0

45d7eeb536d9afa54b424f98a4bd81f25efce2fc

2,066

py

Python

ad2dispatch/news/views.py

cetaSYN/ad2dispatch

a3fa7585aaf18905d7914faaa5d1d6f584c2dab4

[ "Apache-2.0" ]

null

ad2dispatch/news/views.py

cetaSYN/ad2dispatch

a3fa7585aaf18905d7914faaa5d1d6f584c2dab4

[ "Apache-2.0" ]

29

2019-01-25T16:05:58.000Z

2020-03-21T21:18:58.000Z

ad2dispatch/news/views.py

cetaSYN/ad2dispatch

a3fa7585aaf18905d7914faaa5d1d6f584c2dab4

[ "Apache-2.0" ]

4

2019-01-25T15:55:04.000Z

2019-01-25T17:33:02.000Z

import markdown from django.http import Http404 from django.shortcuts import render from pages.models import get_top_pages from userprofiles.models import Volunteer from .models import Article def index(request): try: article_list = Article.objects.values( 'id', 'title', 'created_date').order_by('-created_date') selected = Article.objects.latest('created_date') except Article.DoesNotExist: selected = Article( created_by=None, created_date=None, title='Placeholder', content='You are seeing this page because you do not ' + 'have any other pages created.<br> Please add content in ' + 'the <a href="/admin/">admin panel</a>.') # Parse Markdown try: selected.content = markdown.markdown(selected.content) except AttributeError: pass if hasattr(selected, 'created_by'): creator = Volunteer.objects.get(user=selected.created_by) else: creator = None top_pages = get_top_pages() context = { 'top_pages': top_pages, 'article_list': article_list, 'selected': selected, 'creator': creator, 'loc': 'news:index', } return render(request, 'news/article.html', context) def article(request, article_id): try: article_list = Article.objects.values('id', 'title', 'created_date').order_by('-created_date') selected = Article.objects.get(id=article_id) # Parse Markdown try: selected.content = markdown.markdown(selected.content) except AttributeError: pass top_pages = get_top_pages() except Article.DoesNotExist: raise Http404("Article does not exist.") context = { 'top_pages': top_pages, 'article_list': article_list, 'selected': selected, 'creator': Volunteer.objects.get(user=selected.created_by), 'loc': 'news:article:' + str(article_id), } return render(request, 'news/article.html', context)

29.514286

102

0.631171

231

2,066

5.502165

0.311688

0.056648

0.033045

0.549961

0.520063

0.455547

0.387097

0

0.003924

0.259923

2,066

69

103

29.942029

0.827338

0.014037

0

0.436364

0

0.193215

0

1

0.036364

false

0.036364

0.109091

0

0.181818

0

null

0

null

0

1

0

45d824fccee32ba7826f0cbb0aaa47b2a5382c06

727

py

Python

src/python/zquantum/core/history/recording_functions_with_gradient_test.py

kottmanj/z-quantum-core

21752e92e79aafedbfeb6e7ae196bdc2fd5803e4

[ "Apache-2.0" ]

null

src/python/zquantum/core/history/recording_functions_with_gradient_test.py

kottmanj/z-quantum-core

21752e92e79aafedbfeb6e7ae196bdc2fd5803e4

[ "Apache-2.0" ]

null

src/python/zquantum/core/history/recording_functions_with_gradient_test.py

kottmanj/z-quantum-core

21752e92e79aafedbfeb6e7ae196bdc2fd5803e4

[ "Apache-2.0" ]

null

"""Test cases for recording functions with gradients.""" import pytest import numpy as np from .example_functions import function_1, Function2, Function5 from .recorder import recorder from ..interfaces.functions import CallableWithGradient @pytest.mark.parametrize( "function,params", [ (function_1, np.array([3, 4])), (Function2(5), np.array([-1, 0, 1])), (Function5(10), np.array([1, 2, 3])), ], ) def test_recorder_propagates_calls_to_wrapped_functions_and_its_gradient( function: CallableWithGradient, params: np.ndarray ): target = recorder(function) assert target(params) == function(params) assert np.array_equal(target.gradient(params), function.gradient(params))

31.608696

77

0.72077

89

727

5.741573

0.483146

0.054795

0.031311

0

0.027732

0.156809

727

22

78

33.045455

0.805873

0.068776

0

0.022355

0

0.105263

1

0.052632

false

0

0.263158

0

0.315789

0

null

0

null

0

1

0

45da00cb76f03be18a89a07dd9e8e2426fff0cf4

15,085

py

Python

optical/converter/utils.py

hashtagml/optical

1ed11c403b5041497e5b795681b962280c4723d8

[ "MIT" ]

10

2021-04-11T04:45:54.000Z

2022-02-17T07:49:48.000Z

optical/converter/utils.py

hashtagml/optical

1ed11c403b5041497e5b795681b962280c4723d8

[ "MIT" ]

9

2021-04-13T18:09:38.000Z

2021-05-05T13:21:20.000Z

optical/converter/utils.py

hashtagml/optical

1ed11c403b5041497e5b795681b962280c4723d8

[ "MIT" ]

null

""" __author__: HashTagML license: MIT Created: Sunday, 28th March 2021 """ import json import io import os import shutil import warnings from pathlib import Path, PosixPath from typing import Any, Callable, Dict, Optional, Union import pandas as pd from lxml import etree as xml from PIL import Image import xml.etree.ElementTree as ET _TF_INSTALLED = True try: import tensorflow as tf except ImportError: _TF_INSTALLED = False def ifnone(x: Any, y: Any, transform: Optional[Callable] = None, type_safe: bool = False): """if x is None return y otherwise x after applying transofrmation ``transform`` and casting the result back to original type if ``type_safe`` Args: x (Any): returns x if x is not none y (Any): returns y if x is none transform (Optional[Callable], optional): applies transform to the output. Defaults to None. type_safe (bool, optional): if true, tries casting the output to the original type. Defaults to False. """ if transform is not None: assert callable(transform), "`transform` should be either `None` or instance of `Callable`" else: def transform(x): return x if x is None: orig_type = type(y) out = transform(y) else: orig_type = type(x) out = transform(x) if type_safe: try: out = orig_type(out) except (ValueError, TypeError): warnings.warn(f"output could not be casted as type {orig_type.__name__}") pass return out def exists(path: Union[str, os.PathLike]): """checks for whether a directory or file exists in the specified path""" if Path(path).is_dir(): return "dir" if Path(path).is_file(): return "file" return def get_image_dir(root: Union[str, os.PathLike]): """returns image directory given a root directory""" return Path(root) / "images" def get_annotation_dir(root: Union[str, os.PathLike]): """returns annotation directory given a root directory""" return Path(root) / "annotations" def find_job_metadata_key(json_data: Dict): """finds metadata key for sagemaker manifest format""" for key in json_data.keys(): if key.split("-")[-1] == "metadata": return key def read_coco(coco_json: Union[str, os.PathLike]): """read a coco json and returns the images, annotations and categories dict separately""" with open(coco_json, "r") as f: coco = json.load(f) return coco["images"], coco["annotations"], coco["categories"] def write_json(data_dict: Dict, filename: Union[str, os.PathLike]): """writes json to disk""" with open(filename, "w") as f: json.dump(data_dict, f, indent=2) def filter_split_category( df: pd.DataFrame, split: Optional[str] = None, category: Optional[str] = None ) -> pd.DataFrame: """given the label df, filters the dataframe by split and/or label category Args: df (pd.DataFrame): the label dataframe. split (Optional[str], optional): the dataset split e.g., ``train``, ``test`` etc. Defaults to None. category (Optional[str], optional): the label category. Defaults to None. Raises: ValueError: if an unknown category is specified. Returns: pd.DataFrame: the filtered dataframe. """ if split is not None: df = df.query("split == @split").copy() if category is not None: if category not in df.category.unique(): raise ValueError(f"class `{category}` is not present in annotations") df = df.query("category == @category").copy() return df def copyfile( src: Union[str, os.PathLike], dest: Union[str, os.PathLike], filename: Optional[Union[str, os.PathLike]] = None ) -> None: """copies a file from one path to another Args: src (Union[str, os.PathLike]): either a directory containing files or any filepath. dest (Union[str, os.PathLike]): the output directory for the copy. filename (Optional[Union[str, os.PathLike]], optional): If ``src`` is a directory, the name of the file to copy. Defaults to None. """ if filename is not None: filename = Path(src) / filename else: filename = Path(src) dest = Path(dest) / filename.name try: shutil.copyfile(filename, dest) except FileNotFoundError: pass def write_xml( df: pd.DataFrame, image_root: Union[str, os.PathLike, PosixPath], output_dir: Optional[Union[str, os.PathLike, PosixPath]] = None, ) -> None: """write xml files in PASCAL VOC format given a label dataframe Args: df (pd.DataFrame): dataframe of the single image with multiple objects in it. image_root (Union[str, os.PathLike, PosixPath]): path to image directory. output_dir (Optional[Union[str, os.PathLike, PosixPath]], optional): output directory """ root = xml.Element("annotation") folder = xml.Element("folder") folder.text = "" root.append(folder) filename = xml.Element("filename") filename.text = df.iloc[0]["image_id"] root.append(filename) path = xml.Element("path") path.text = str(Path(image_root) / "images" / df.iloc[0]["split"] / df.iloc[0]["image_id"]) root.append(path) source = xml.Element("source") root.append(source) database = xml.Element("database") database.text = "UNKNOWN" source.append(database) size = xml.Element("size") root.append(size) width = xml.Element("width") width.text = str(df.iloc[0]["image_width"]) size.append(width) height = xml.Element("height") height.text = str(df.iloc[0]["image_height"]) size.append(height) depth = xml.Element("depth") depth.text = "3" size.append(depth) segmented = xml.Element("segmented") segmented.text = "0" root.append(segmented) for _, objec in df.iterrows(): obj = xml.Element("object") root.append(obj) name = xml.Element("name") name.text = objec["category"] obj.append(name) pose = xml.Element("pose") pose.text = "Unspecified" obj.append(pose) truncated = xml.Element("truncated") truncated.text = "0" obj.append(truncated) difficult = xml.Element("difficult") difficult.text = "0" obj.append(difficult) occluded = xml.Element("occluded") occluded.text = "0" obj.append(occluded) bndbox = xml.Element("bndbox") obj.append(bndbox) xmin = xml.Element("xmin") xmin.text = str(objec["x_min"]) bndbox.append(xmin) xmax = xml.Element("xmax") xmax.text = str(objec["x_max"]) bndbox.append(xmax) ymin = xml.Element("ymin") ymin.text = str(objec["y_min"]) bndbox.append(ymin) ymax = xml.Element("ymax") ymax.text = str(objec["y_max"]) bndbox.append(ymax) tree = xml.ElementTree(root) f_name = Path(output_dir).joinpath(df.iloc[0]["split"], Path(df.iloc[0]["image_id"]).stem + ".xml") with open(f_name, "wb") as files: tree.write(files, pretty_print=True) def get_id_to_class_map(df: pd.DataFrame): """This function return the class_id to class name mapping Args: df (pd.DataFrame): master dataframe Returns: Dict: mapping dictionary """ set_df = df.drop_duplicates(subset="class_id")[["category", "class_id"]] return set_df.set_index("class_id")["category"].to_dict() def find_splits(image_dir: Union[str, os.PathLike], annotation_dir: Union[str, os.PathLike], format: str): """find the splits in the dataset, will ignore splits for which no annotation is found""" # print(f"passed format: {format}") exts = { "coco": "json", "csv": "csv", "pascal": "xml", "yolo": "txt", "sagemaker": "manifest", "createml": "json", "simple_json": "json", } ext = exts[format] im_splits = [x.name for x in Path(image_dir).iterdir() if x.is_dir() and not x.name.startswith(".")] if format in ("yolo", "pascal"): ann_splits = [x.name for x in Path(annotation_dir).iterdir() if x.is_dir()] if not ann_splits: files = list(Path(annotation_dir).glob(f"*.{ext}")) if len(files): ann_splits = ["main"] else: raise ValueError("No annotation found. Please check the directory specified.") else: ann_splits = [x.stem for x in Path(annotation_dir).glob(f"*.{ext}")] no_anns = set(im_splits).difference(ann_splits) if no_anns: warnings.warn(f"no annotation found for {', '.join(list(no_anns))}") return ann_splits, len(im_splits) > 0 def _tf_parse_example(example): """parse tf examples""" features = { "image/height": tf.io.FixedLenFeature([], tf.int64), "image/width": tf.io.FixedLenFeature([], tf.int64), "image/filename": tf.io.FixedLenFeature([], tf.string), "image/encoded": tf.io.FixedLenFeature([], tf.string), "image/format": tf.io.FixedLenFeature([], tf.string), "image/object/bbox/xmin": tf.io.VarLenFeature(tf.float32), "image/object/bbox/xmax": tf.io.VarLenFeature(tf.float32), "image/object/bbox/ymin": tf.io.VarLenFeature(tf.float32), "image/object/bbox/ymax": tf.io.VarLenFeature(tf.float32), "image/object/class/text": tf.io.VarLenFeature(tf.string), "image/object/class/label": tf.io.VarLenFeature(tf.int64), } return tf.io.parse_single_example(example, features) def _tf_int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) def _tf_bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) def _tf_float_list_feature(value): return tf.train.Feature(float_list=tf.train.FloatList(value=value)) def _tf_bytes_list_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) def _tf_int64_list_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) def create_tf_example(df: pd.DataFrame, root: Union[str, os.PathLike, PosixPath]): """returns protobuf for a given image Args: df (pd.DataFrame): Dataframe of a single image with multiple records of objects root (Union[str, os.PathLike, PosixPath]): root of the Image path Returns: protobuf: protobuf of each Image """ img_path = str(df["image_path"].iloc[0]) with tf.io.gfile.GFile(img_path, "rb") as fid: encoded_jpg = fid.read() width = df.iloc[0]["image_width"] height = df.iloc[0]["image_height"] filename = df["image_id"].iloc[0].encode("utf8") image_format = b"jpg" xmins = list(df["x_min"] / width) xmaxs = list((df["x_max"]) / width) ymins = list(df["y_min"] / height) ymaxs = list((df["y_max"]) / height) classes_text = [s.encode("utf8") for s in df["category"]] classes = list(df["class_id"].astype(int)) tf_example = tf.train.Example( features=tf.train.Features( feature={ "image/height": _tf_int64_feature(height), "image/width": _tf_int64_feature(width), "image/filename": _tf_bytes_feature(filename), "image/source_id": _tf_bytes_feature(filename), "image/encoded": _tf_bytes_feature(encoded_jpg), "image/format": _tf_bytes_feature(image_format), "image/object/bbox/xmin": _tf_float_list_feature(xmins), "image/object/bbox/xmax": _tf_float_list_feature(xmaxs), "image/object/bbox/ymin": _tf_float_list_feature(ymins), "image/object/bbox/ymax": _tf_float_list_feature(ymaxs), "image/object/class/text": _tf_bytes_list_feature(classes_text), "image/object/class/label": _tf_int64_list_feature(classes), } ) ) return tf_example def write_label_map(id_to_class_map: Dict, output_dir: Union[str, os.PathLike, PosixPath]): """writes label_map used in tf object detection Args: id_to_class_map (Dict): mapping dictionary output_dir ([type]): output path """ with open(output_dir.joinpath("label_map.pbtxt"), "w") as f: for id, cl in id_to_class_map.items(): f.write("item\n") f.write("{\n") f.write("name :'{0}'".format(str(cl))) f.write("\n") f.write("id :{}".format(int(id))) f.write("\n") f.write("display_name:'{0}'".format(str(cl))) f.write("\n") f.write("}\n") def tf_decode_image(root: Union[str, os.PathLike, PosixPath], data, split: Union[str, os.PathLike, PosixPath]): """Decodes images and save in images folder under root Args: root (Union[str, os.PathLike, PosixPath]): path to root directory data (tf.train.Example): single image example split (Union[str, os.PathLike, PosixPath]): split directory """ img_filename = data["image/filename"].numpy().decode("utf-8") img = data["image/encoded"].numpy() im = Image.open(io.BytesIO(img)) im.save(str(Path(root) / "images" / split / img_filename)) def read_xml(xml_folder: Union[str, os.PathLike, PosixPath], img_path: Union[str, os.PathLike, PosixPath]): """read xml files in the folder and return list's of information used to construct master_df Args: xml_folder (Union[str, os.PathLike, PosixPath]): Xml file folder img_path (Union[str, os.PathLike, PosixPath]): Image Directory """ img_filenames = [] img_widths = [] img_heights = [] cls_names = [] x_mins = [] y_mins = [] box_widths = [] box_heights = [] img_paths = [] xml_files = [x for x in Path(xml_folder).glob("*.xml")] for fxml in xml_files: tree = ET.parse(fxml) root = tree.getroot() img_filename = root.find("filename").text img_width = root.find("size").find("width").text img_height = root.find("size").find("height").text for obj in root.findall("object"): cls_name = obj.find("name").text x_min = int(obj.find("bndbox").find("xmin").text) y_min = int(obj.find("bndbox").find("ymin").text) box_width = int(obj.find("bndbox").find("xmax").text) - int(x_min) box_height = int(obj.find("bndbox").find("ymax").text) - int(y_min) img_filenames.append(img_filename) img_widths.append(img_width) img_heights.append(img_height) cls_names.append(cls_name) x_mins.append(x_min) y_mins.append(y_min) box_widths.append(box_width) box_heights.append(box_height) img_paths.append(str(img_path.joinpath(img_filename))) return img_filenames, img_widths, img_heights, cls_names, x_mins, y_mins, box_widths, box_heights, img_paths

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115

0.628638

2,025

15,085

4.553086

0.157037

0.024295

0.030369

0.054664

0.272993

0.20705

0.142625

0.093601

0.053579

0

0.005372

0.234935

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0.793519

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false

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0.003509

0

null

0

null

0

1

0

45daf2ba13dd10d36a72a6b2b05dc29e34435812

2,960

py

Python

bad_requests/request.py

Ben435/BensLoadTestTool

039c28a6c46cc8b7d6284fffdbf3a5c95158daed

[ "Apache-2.0" ]

1

2018-03-08T07:09:12.000Z

bad_requests/request.py

Ben435/BensLoadTestTool

039c28a6c46cc8b7d6284fffdbf3a5c95158daed

[ "Apache-2.0" ]

null

bad_requests/request.py

Ben435/BensLoadTestTool

039c28a6c46cc8b7d6284fffdbf3a5c95158daed

[ "Apache-2.0" ]

null

import socket import ssl from bad_requests.response import Response HTTP_STANDARD_PORTS = [80] HTTPS_STANDARD_PORTS = [443, 8080] BUFFERSIZE = 1024 class Request: def __init__(self, host, message, get_body=True): self.host = host self.message = message self.get_body = get_body def send(self): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.settimeout(5) resource = self.host.split("/") # Enable SSL if needed. if "https" in resource[0].lower(): sock = ssl.wrap_socket(sock) https = True else: https = False # Connect and send message. worked = False for port in HTTPS_STANDARD_PORTS if https else HTTP_STANDARD_PORTS: try: sock.connect((self.host, port)) worked = True except socket.error as e: print(e) print("Failed on port: " + str(port)) raise e finally: if worked: break if not worked: print("Failed all ports.") return None # Send message. sock.send(self.message.encode("UTF-8")) # Get headers headers = "" body = "" received = sock.recv(BUFFERSIZE) while len(received) == BUFFERSIZE and "\r\n\r\n" not in received.decode("UTF-8"): headers += received.decode("UTF-8") received = sock.recv(BUFFERSIZE) snip = received.decode("UTF-8").split("\r\n\r\n") if len(snip) >= 2: headers += snip[0] body += "\r\n\r\n".join(snip[1:]) else: headers += "\r\n\r\n".join(snip) # Parse headers. lines = headers.split("\r\n") status = lines[0].split(" ") proto = status[0] status_code = status[1] status_message = status[2] dict_headers = {} for line in lines[1:]: # Skip "HTTP/1.1 NUM MSG" line. if len(line) <= 1: continue data = line.split(": ", 1) key = data[0] vals = data[1].strip() dict_headers[key] = vals if "Content-Length" in dict_headers: total_body = int(dict_headers["Content-Length"]) else: total_body = 0 # Get body. total_received = len(body) while total_received < total_body: body += received.decode("UTF-8") received = sock.recv(BUFFERSIZE) total_received += len(received) body += received.decode("UTF-8") return Response(status_code, status_message, proto, dict_headers, body, init_req=self) def __str__(self): return "Host: {}\nGet Body: {}\nMessage: {{\n{}\n}}".format( self.host, self.get_body, "\n".join(map(lambda line: "\t" + line, self.message.strip().split("\n"))))

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113

0.529392

352

2,960

4.338068

0.301136

0.011788

0.055665

0.058939

0.090373

0.073346

0.057629

0

0.018605

0.346284

2,960

96

114

30.833333

0.770543

0.043243

0

0.106667

0

0.06551

0

1

0.04

false

0

0.04

0.013333

0.133333

0.04

0

null

0

null

0

1

0

45db1ddc9bbef6c02d6ebbfcea0de3c07ec3bc2b

2,055

py

Python

tests/status_test.py

araines/supervisor-newrelic

8061ced419e1603367272a42b729414a7a51ac35

[ "MIT" ]

4

2019-02-11T03:17:45.000Z

2022-01-17T19:53:03.000Z

tests/status_test.py

araines/supervisor-newrelic

8061ced419e1603367272a42b729414a7a51ac35

[ "MIT" ]

3

2016-12-24T07:30:32.000Z

2017-07-11T11:12:51.000Z

tests/status_test.py

araines/supervisor-newrelic

8061ced419e1603367272a42b729414a7a51ac35

[ "MIT" ]

2

2018-11-27T08:31:23.000Z

2021-03-04T16:59:13.000Z

import mock import unittest from StringIO import StringIO from supervisor_newrelic.status import Status def mock_request(*args, **kwargs): class MockResponse: def __init__(self, json_data, status_code): self.json_data = json_data self.status_code = status_code def json(self): return self.json_data if (args[0] == 'https://insights-collector.newrelic.com/v1/accounts/123/events' and kwargs.get('headers').get('X-Insert-Key') == 'abc'): return MockResponse({'foo': 'bar'}, 200) return MockResponse({}, 404) class StatusTests(unittest.TestCase): def _get_mock(self, account='123', key='abc'): prog = Status(account, key) prog.stdin = StringIO() prog.stdout = StringIO() return prog def test_run_not_process_state(self): prog = self._get_mock() prog.stdin.write('eventname:TICK len:0\n') prog.stdin.seek(0) prog.run(runonce=True) self.assertEqual(prog.stdout.getvalue(), 'READY\nRESULT 2\nOK') @mock.patch('supervisor_newrelic.status.requests.post', side_effect=mock_request) def test_run_successful_fatal_state_report(self, m): payload = 'processname:foo groupname:bar from_state:BACKOFF' prog = self._get_mock() prog.stdin.write('eventname:PROCESS_STATE_FATAL len:%d\n' % len(payload)) prog.stdin.write(payload) prog.stdin.seek(0) prog.run(runonce=True) self.assertEqual(prog.stdout.getvalue(), 'READY\nRESULT 2\nOK') @mock.patch('supervisor_newrelic.status.requests.post', side_effect=mock_request) def test_run_unsuccessful_fatal_state_report(self, m): payload = 'processname:foo groupname:bar from_state:BACKOFF' prog = self._get_mock('234') prog.stdin.write('eventname:PROCESS_STATE_FATAL len:%d\n' % len(payload)) prog.stdin.write(payload) prog.stdin.seek(0) prog.run(runonce=True) self.assertEqual(prog.stdout.getvalue(), 'READY\nRESULT 4\nFAIL')

36.052632

87

0.66618

265

2,055

4.988679

0.316981

0.061271

0.05295

0.034039

0.568079

0.539334

0

0.014724

0.206813

2,055

56

88

36.696429

0.796319

0

0.4

0

0.210219

0.067153

0

0.066667

1

0.155556

false

0

0.088889

0.022222

0.377778

0

null

0

null

0

1

0

45ddecfc6b040befb28c16618390c57c59d78c8c

11,100

py

Python

tuning.py

grdavis/college-basketball-elo

b26518d3114012bac8b1994f82e7dc93099f15bf

[ "MIT" ]

6

2022-02-07T01:07:57.000Z

2022-03-28T05:49:37.000Z

tuning.py

grdavis/college-basketball-elo

b26518d3114012bac8b1994f82e7dc93099f15bf

[ "MIT" ]

4

2022-03-01T21:55:24.000Z

2022-03-29T18:49:45.000Z

tuning.py

grdavis/college-basketball-elo

b26518d3114012bac8b1994f82e7dc93099f15bf

[ "MIT" ]

2

2022-02-25T01:32:53.000Z

2022-03-03T03:45:36.000Z

import numpy as np from tqdm import tqdm import elo import utils import random import plotly.graph_objects as go from sklearn.metrics import r2_score from scipy.stats import linregress import pandas as pd from predictions import predict_tournament, ROUNDS ERRORS_START = 4 #after 4 seasons (starts counting errors 20141114) class Tuning_ELO_Sim(elo.ELO_Sim): ''' This class is an extension of ELO_Sim that allows us to keep track of errors and extra metrics through the simulation process which are useful for tuning. Errors tracked are... error1: calculated (1 - predicted win probability)^2 for each game and add them up. More commonly known as Brier score (https://en.wikipedia.org/wiki/Brier_score). This is the primary error of interest error2: calculated at the end of a simulation as the average absolute difference between predicted win probability and actual win probability for teams who were given that prediction ''' def __init__(self): super().__init__() self.predict_tracker = {} self.win_tracker = {0.0: 0} self.elo_margin_tracker = {} self.MoV_tracker = {} self.error1 = [] def update_errors(self, w_winp): if self.season_count >= ERRORS_START: rounded, roundedL = round(w_winp, 2), round(1 - w_winp, 2) self.error1.append((1 - w_winp)**2) self.win_tracker[rounded] = self.win_tracker.get(rounded, 0) + 1 self.predict_tracker[rounded] = self.predict_tracker.get(rounded, 0) + 1 self.predict_tracker[roundedL] = self.predict_tracker.get(roundedL, 0) + 1 def update_MoVs(self, elo_margin, MoV): if self.season_count >= ERRORS_START: rounded = round(elo_margin/25) * 25 #round to nearest 25 self.elo_margin_tracker[rounded] = self.elo_margin_tracker.get(rounded, 0) + 1 self.MoV_tracker[rounded] = self.MoV_tracker.get(rounded, 0) + MoV self.elo_margin_tracker[-rounded] = self.elo_margin_tracker.get(-rounded, 0) + 1 self.MoV_tracker[-rounded] = self.MoV_tracker.get(-rounded, 0) - MoV def get_errors(self): error2 = 0 total_games = sum(self.predict_tracker.values()) for i in sorted(self.predict_tracker): result = self.win_tracker.get(i, 0)/self.predict_tracker[i] error2 += self.predict_tracker[i] * abs(result - i) return (sum(self.error1), error2 / total_games) def tuning_sim(data, k_factor, new_season_carry, home_elo, new_team_elo): ''' This function runs through all of the data and updates elo and errors along the way It is a simplified version of the official sim function used in elo.py ''' this_sim = Tuning_ELO_Sim() this_month = data[0][-1][4:6] elo.NEW_ELO = new_team_elo for row in data: row_month = int(row[-1][4:6]) if this_month in [3, 4] and row_month == 11: this_sim.season_count += 1 this_sim.season_reset(new_season_carry) this_sim.date = row[-1] this_month = row_month elo_margin, MoV = elo.step_elo(this_sim, row, k_factor, home_elo) this_sim.update_errors(elo.winp(elo_margin)) this_sim.update_MoVs(elo_margin, MoV) return this_sim def random_tune(data, number): ''' Use this function to repeatedly narrow down tighter and tighter ranges of possible optimal values for k, carry, and home elo advantage Start with wide ranges, then use the outputs (which are sorted by their errors) to inform a tighter range for the next iteration Once windows are small enough, switch to brute_tune ''' k_range = [44, 45, 46, 47] carry_range = np.arange(.87, .93, .01) home_range = np.arange(77, 85, 1) new_team_range = [900] errors = [] for i in tqdm(range(number)): k_factor, new_season_carry, home_elo, nte = random.choice(k_range), random.choice(carry_range), random.choice(home_range), random.choice(new_team_range) error1, error2 = tuning_sim(data, k_factor, new_season_carry, home_elo, nte).get_errors() errors.append((error1, error2, k_factor, new_season_carry, home_elo, nte)) return errors def brute_tune(data): ''' Use this function to cycle through all possible combinations of the 4 variables within the defined ranges and find the optimal solution Since brute force can take some time to run, random_tune first to help narrow possible ranges ''' k_range = [42, 43, 44, 45]#[64, 65, 66] carry_range = [.9, .91, .92, .93]#np.arange(.7, 1.00, .05) home_range = [80, 81, 82, 83]#[110, 111, 112] new_team_range = [925, 950, 975, 1000] #np.arange(750, 1250, 50) errors = [] for k in tqdm(k_range): for c in tqdm(carry_range, leave = False): for h in tqdm(home_range, leave = False): for n in tqdm(new_team_range, leave = False): error1, error2 = tuning_sim(data, k, c, h, n).get_errors() errors.append((error1, error2, k, c, h, n)) return errors def tune(data): #start with random_tune, then switch to brute_tune when the ranges for values are tight enough so as not to take too long to run # errors = random_tune(data, 50) errors = brute_tune(data) print(sorted(errors, key = lambda x: x[0])) print(sorted(errors, key = lambda x: x[1])) filepath = utils.get_latest_data_filepath() data = utils.read_csv(filepath) explore = tuning_sim(data, elo.K_FACTOR, elo.SEASON_CARRY, elo.HOME_ADVANTAGE, elo.NEW_ELO) print(explore.get_errors()) ###########################TUNING############################ # tune(data) # # start measuring after season 3 (start fall 2014), errors as of games through 11/8/2021 # # best: (error1 = 6887, error2 = 0.0097, k_factor = 43, carryover = .9, home_elo = 81, new_team = 925) ################Brier (Error 1) Over Time#################### # size = 5700 #roughly the number of games per season if we have ~40K errors over the course of 7 seasons (Fall 2014 - Spring 2021) # leftover = len(explore.error1) % size # y_vals = [sum(explore.error1[i*size:(i*size)+size])/size for i in range(len(explore.error1)//size)] + [sum(explore.error1[-leftover:])/leftover] # sizes = [size for i in range(len(explore.error1)//size)] + [leftover] # x_vals = [i for i in range(len(sizes))] # fig = go.Figure([go.Bar(x = x_vals, y = y_vals, text = ['n = ' + str(size) for size in sizes], textposition = 'auto')]) # fig.update_layout(title_text = 'Brier Score Over Time: Fall 2014 - Fall 2021', xaxis_title = 'Bucket of Chronological Games', yaxis_title = 'Avg. Brier Score in Bucket') # fig.show() ###################Visualizing Error 2####################### # x_vals = [i for i in explore.predict_tracker] # y_vals = [explore.win_tracker[i]/explore.predict_tracker[i] for i in x_vals] # sizes = [explore.predict_tracker[i] for i in x_vals] # fig = go.Figure() # fig.add_trace(go.Scatter(x = x_vals, y = y_vals, mode = 'markers', name = 'Predictions', text = ['n = ' + str(size) for size in sizes])) # fig.add_trace(go.Scatter(x = [0, 1], y = [0, 1], mode = 'lines', name = 'Perfect Line')) # r2 = r2_score(x_vals, y_vals) # fig.update_layout(title_text = 'Predicted vs. Actual Win Probability (R^2 = 0.99)', xaxis_title = 'Predicted Win Probability', yaxis_title = 'Actual Win Probability') # fig.show() ##############Elo margin vs. Margin of Victory################ # x_vals = [i for i in explore.elo_margin_tracker] # y_vals = [explore.MoV_tracker[i]/explore.elo_margin_tracker[i] for i in x_vals] # sizes = [explore.elo_margin_tracker[i] for i in x_vals] # fig = go.Figure() # fig.add_trace(go.Scatter(x = x_vals, y = y_vals, mode = 'markers', name = 'Results', text = ['n = ' + str(size) for size in sizes], marker=dict(size=[s/120 for s in sizes]))) # #fit a line to middle 80% of data (Pareto principle) # target_points = sum(sizes)*.8*.5 #I want to reach 80% of points on the positive side. They are duplicated on the negative side, so really 40% of total points # points_reached = explore.elo_margin_tracker[0] # for i in range(1, int(max(x_vals)/25)): # points_reached += explore.elo_margin_tracker.get(i*25, 0) # if points_reached > target_points: break # x_trimmed = [j*25 for j in range(-i, i+1)] # y_trimmed = [explore.MoV_tracker[i]/explore.elo_margin_tracker[i] for i in x_trimmed] # slope, intercept, r, p, se = linregress(x_trimmed, y_trimmed) # # print(slope, r) # # slope: 0.03914846 -> 1/slope: 25.5 elo difference / point difference # fig.add_trace(go.Scatter(x = x_trimmed, y = [i*slope + intercept for i in x_trimmed], mode = 'lines', name = 'LSRL for Middle 80% of Games (R^2 > 0.99)')) # fig.update_layout(title_text = 'Elo Margin vs. Average Scoring Margin: 1 game point = 25.5 Elo points', xaxis_title = 'Elo Margin', yaxis_title = 'Average Actual Scoring Margin') # fig.show() #################Elo Season-over-Season######################## # season_totals = {} # season_teams = {} # years = ['20110404', '20120402', '20130408', '20140407', '20150406', '20160404', '20170403', '20180402', '20190408', '20200311', '20210405'] # for team in explore.teams: # for year in years: # for date, snap in explore.teams[team].snapshots: # if date == year: # season_totals[year] = season_totals.get(year, 0) + snap # season_teams[year] = season_teams.get(year, 0) + 1 # y_vals = [round(season_totals[i]/season_teams[i]) for i in season_teams] # x_vals = [i[:4] for i in season_teams] # sizes = ['teams = ' + str(season_teams[i]) for i in season_teams] # fig = go.Figure([go.Bar(x = x_vals, y = y_vals, text = sizes, textposition = 'auto')]) # fig.update_layout(title_text = 'Average End-of-Season Elo over Time: Spring 2011 - Spring 2021', xaxis_title = 'Year', yaxis_title = 'End of Season Elo') # fig.show() ##################LATEST DISTRIBUTION########################## # bucketing = {} # for team in explore.teams: # rounded = round(explore.get_elo(team) / 50) * 50 # bucketing[rounded] = bucketing.get(rounded, 0) + 1 # x_vals = [i for i in range(min(bucketing), max(bucketing) + 1, 10)] # y_vals = [bucketing.get(i, 0) for i in x_vals] # fig = go.Figure([go.Bar(x = x_vals, y = y_vals)]) # fig.update_layout(title_text = 'Elo Distribution through ' + explore.date, xaxis_title = 'Elo Rating', yaxis_title = 'Number of Teams') # fig.show() ###############HISTORICAL BRACKET PERFORMANCE################## # scores = [10, 20, 40, 80, 160, 320] #ESPN scoring system for correct game in round # def evaluate_brackets(predictions, real_results): # predictions_score = 0 # for index in range(len(ROUNDS)): # predictions_score += sum([scores[index] if predictions[ROUNDS[index]][i] == real_results[ROUNDS[index]][i] else 0 for i in range(len(predictions[ROUNDS[index]]))]) # return predictions_score # for stop_date, tourney_filepath in [('20190320', 'tournament_results_2019.csv'), ('20180314', 'tournament_results_2018.csv'), ('20170315', 'tournament_results_2017.csv')]: # elo_state = elo.main(stop_short = stop_date) # df = pd.read_csv(utils.DATA_FOLDER + tourney_filepath) # tournamant_teams = list(df['first'].dropna()) # results = {'first': tournamant_teams} # for r in ROUNDS: # results[r] = df[r].dropna().values # best_bracket = predict_tournament(elo_state, tournamant_teams, pick_mode = 1) # print(evaluate_brackets(best_bracket, results)) # remaining = [32, 16, 8, 4, 2, 1] # print(sum([remaining[index]*scores[index]*(.5**(index + 1)) for index in range(6)])) # #2019: 1260 # #2018: 830 # #2017: 720 # #Random: 315

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null

0

null

0

1

0

45dfa94f9d32503706cf8aacffa0d5d22fe3e19c

3,075

py

Python

aula4/classifica_junto.py

davidpvilaca/TEP

decbf61a96863d76e1b84dc097aa37b12038aa75

[ "MIT" ]

2

2017-08-28T18:24:47.000Z

2019-08-29T03:34:15.000Z

aula4/classifica_junto.py

davidpvilaca/TEP

decbf61a96863d76e1b84dc097aa37b12038aa75

[ "MIT" ]

null

aula4/classifica_junto.py

davidpvilaca/TEP

decbf61a96863d76e1b84dc097aa37b12038aa75

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Aug 19 13:21:45 2017 @author: davidpvilaca """ import matplotlib.pyplot as plt import numpy as np import cv2 def getHist(arr_img): hists = [] sm = [] for img in arr_img: hist = cv2.calcHist([img], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) hist = cv2.normalize(hist, hist).flatten() hists.append(hist) sm.append(np.average(hist[:])) return def compareHist(hist1, hist2): OPENCV_METHODS = ( ("Correlation", cv2.HISTCMP_CORREL), ("Intersection", cv2.HISTCMP_INTERSECT) ) return cv2.compareHist(hist1, hist2, OPENCV_METHODS[0][1]) # intersec def showImages(imgArr, titleArr): lenImgArr = len(imgArr) assert lenImgArr == len(titleArr) plt.figure(figsize = (8,8)) p = int(str(lenImgArr//2) + "20" if lenImgArr > 2 and ( (lenImgArr % 2) == 0 ) else str(lenImgArr//3) + "30") i = 0 for img in imgArr: plt.subplot(p + i + 1) plt.title(titleArr[i]) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) i = i + 1 def main(): imageSource = { 'Goblin Town': { 'images': [ cv2.imread('Goblin_town1.jpg'), cv2.imread('Goblin_town2.jpg'), cv2.imread('Goblin_town3.jpg'), cv2.imread('Goblin_town4.jpg') ], 'hist': None }, 'Mordor': { 'images': [ cv2.imread('mordor1.jpg'), cv2.imread('mordor2.jpg'), cv2.imread('Mordor3.jpg'), cv2.imread('Mordor4.jpg') ], 'hist': None }, 'Rivendell': { 'images': [ cv2.imread('Rivendell1.jpg'), cv2.imread('Rivendell2.jpg'), cv2.imread('Rivendell3.jpg'), cv2.imread('Rivendell4.jpg') ], 'hist': None }, 'Shire': { 'images': [ cv2.imread('Shire1.jpg'), cv2.imread('Shire2.jpg'), cv2.imread('Shire3.jpg'), cv2.imread('Shire4.jpg') ], 'hist': None } } # calc histogram for name,data in imageSource.items(): imageSource[name]['hist'] = getHist(imageSource[name]['images']) imagesOnde = [ cv2.imread('Onde1.jpg'), cv2.imread('Onde2.jpg'), cv2.imread('Onde3.jpg'), cv2.imread('Onde4.jpg') ] imgs,titles = [],[] i = 1 for ondeImg in imagesOnde: ondeHist = getHist([ondeImg]) results = [] for imgSrcName, srcData in imageSource.items(): results.append((compareHist(ondeHist, srcData['hist']), imgSrcName)) result = sorted(results, reverse = True)[0] imgs.append(ondeImg) titles.append(result[1] + " (" + "Onde" + str(i) + ")") i += 1 showImages(imgs, titles) plt.show() return 0 if __name__ == '__main__': main()

27.954545

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114

27.954545

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0

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1

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0.111111

0

null

0

null

0

1

0

45dfb1283861c5bba5c59b6a90a79e3eb6fa9f46

8,005

py

Python

package_management/package_manager.py

m-j/ziprepo-server

35c1f40c3ba5489fb8731e8d66b301333dc9f8b0

[ "MIT" ]

null

package_management/package_manager.py

m-j/ziprepo-server

35c1f40c3ba5489fb8731e8d66b301333dc9f8b0

[ "MIT" ]

null

package_management/package_manager.py

m-j/ziprepo-server

35c1f40c3ba5489fb8731e8d66b301333dc9f8b0

[ "MIT" ]

null

import copy import json import logging import os import shutil from distutils.version import LooseVersion from threading import Lock from typing import List, Dict, Optional from zipfile import ZipFile import aiofiles from tornado.ioloop import IOLoop from package_management.constants import zpspec_filename, package_name_key, version_key from package_management.data_scanning import scan_data_directory from errors.errors import PackageAlreadyExistsError, PackageDoesntExistError, MaliciousDataError from package_management.model import PackageMetadata, PackageInfo from package_management.package_validation import validate_package_name, validate_package_version from package_management.paths_util import PathsUtil from package_management.utils import fullname read_chunk_size = 3*1024*1024*10 def packages_metadata_from_versions(name: str, semvers: List[str]): return [PackageMetadata(name=name, semver=semver) for semver in semvers] def parse_zpfile(temp_file_path: str): with ZipFile(temp_file_path) as zip_file: print(zip_file.namelist()) zpspec_contents = zip_file.read(zpspec_filename) json_dict = json.loads(zpspec_contents) return json_dict class PackageManager: _paths_util: PathsUtil _data_dir_path: str _package_infos: Dict[str, PackageInfo] _packages_in_processing_fullnames: List[str] _package_infos_lock: Lock def __init__(self, data_dir_path: str, paths_util: PathsUtil): self._paths_util = paths_util self._data_dir_path = data_dir_path self._packages_in_processing_fullnames = [] self._package_infos_lock = Lock() def scan(self): package_infos = scan_data_directory(self._data_dir_path) # todo: validate integirty here in a future self._package_infos = package_infos def query_all(self) -> Dict[str, PackageInfo]: return self._package_infos def query(self, name: str) -> Optional[PackageInfo]: if name is None: raise ValueError('You have to provide package name') if name in self._package_infos: package_info = self._package_infos[name] return package_info else: return None def remove_package_sync(self, package_name: str, package_version: str): validate_package_name(package_name) validate_package_version(package_version) package_version_dir_path = self._paths_util.get_package_version_dir_path(package_name, package_version) try: dir_exists = os.path.isdir(package_version_dir_path) if dir_exists: print('Removing package ' + package_name + ' in version ' + package_version) shutil.rmtree(package_version_dir_path) with self._package_infos_lock: self._remove_version_to_package_info(package_name, package_version) logging.info(f'Successfully removed package: {fullname(package_name, package_version)}') except OSError as err: logging.error(f'Error occurred while removing package: {fullname(package_name, package_version)}!') def add_package_sync(self, temp_file_path: str): json_dict = parse_zpfile(temp_file_path) name = json_dict[package_name_key] version = json_dict[version_key] validate_package_name(name) validate_package_version(version) package_version_dir_path = self._paths_util.get_package_version_dir_path(name, version) package_version_file_path = self._paths_util.get_package_version_file_path(name, version) package_version_zpspec_path = self._paths_util.get_package_version_zpspec_path(name, version) if not self._paths_util.paths_are_valid([package_version_dir_path, package_version_file_path, package_version_zpspec_path]): logging.error(f'Tried to create package in folder {package_version_dir_path} which is outside data directory') raise MaliciousDataError() self._add_fullname_to_in_processing_or_raise_exception(name, version) try: try: os.makedirs(package_version_dir_path, exist_ok=False) except OSError as err: raise PackageAlreadyExistsError(package_name=name, package_version=version) shutil.move(temp_file_path, package_version_file_path) # what if we fail here? it will violate integrity with open(package_version_zpspec_path, mode='wt') as zpspec_file: json.dump(json_dict, zpspec_file) with self._package_infos_lock: self._add_version_to_package_info(name, version) logging.info(f'Successfully added new package: {fullname(name, version)}') finally: with self._package_infos_lock: self._packages_in_processing_fullnames.remove(fullname(name, version)) def _add_version_to_package_info(self, name, version): package_infos_clone = copy.deepcopy(self._package_infos) if name not in self._package_infos: package_infos_clone[name] = PackageInfo(name=name, versions=[]) package_infos_clone[name].versions.append(version) package_infos_clone[name].versions.sort(key=LooseVersion) self._package_infos = package_infos_clone def _remove_version_to_package_info(self, name, version): package_infos_clone = copy.deepcopy(self._package_infos) if name not in self._package_infos: package_infos_clone[name] = PackageInfo(name=name, versions=[]) package_infos_clone[name].versions.remove(version) package_infos_clone[name].versions.sort(key=LooseVersion) if len(package_infos_clone[name].versions) == 0: del package_infos_clone[name] self._package_infos = package_infos_clone def _add_fullname_to_in_processing_or_raise_exception(self, name, version): with self._package_infos_lock: if name in self._package_infos and version in self._package_infos[name].versions: raise PackageAlreadyExistsError(package_name=name, package_version=version) if fullname(name, version) in self._packages_in_processing_fullnames: raise PackageAlreadyExistsError(package_name=name, package_version=version) self._packages_in_processing_fullnames.append(fullname(name, version)) async def add_package(self, temp_file_path: str): return await IOLoop.current().run_in_executor(None, self.add_package_sync, temp_file_path) async def remove_package(self, package_name: str, package_version: str): return await IOLoop.current().run_in_executor(None, self.remove_package_sync, package_name, package_version) async def read_package(self, name: str, version: str): if name is None or version is None: raise ValueError('You have to specify both name and version') # todo: protect from deleting package when it is being read package_info = self.query(name=name) if (package_info is None) or (version not in package_info.versions): raise PackageDoesntExistError(name, version) package_file_path = self._paths_util.get_package_version_file_path(name, version) if not self._paths_util.path_is_valid(package_file_path): logging.error(f'Tried to read data from file {package_file_path} which is outside data directory') raise MaliciousDataError() try: async with aiofiles.open(package_file_path, mode='rb') as file: while True: chunk_bytes = await file.read(read_chunk_size) if len(chunk_bytes) > 0: yield chunk_bytes else: return except OSError as oserr: logging.exception(f'Failed to open file {package_file_path} for reading') raise PackageDoesntExistError(name, version)

41.262887

132

0.713679

1,008

8,005

5.314484

0.170635

0.086242

0.053761

0.035281

0.446892

0.326675

0.278141

0.234646

0.152697

0.132164

0

0.00208

0.219363

8,005

193

133

41.476684

0.855177

0.018364

0

0.207143

0

0.068518

0.00917

0

0.005181

0

1

0.078571

false

0

0.128571

0.014286

0.307143

0.014286

0

null

0

null

0

1

0

45e194e23cc8440b698a5029bc927b2512c3eb9c

17,667

py

Python

cell2location/models/base/_pyro_mixin.py

jjhong922/cell2location

2c2eb49aa3c0263fe8c6d45baf4ca0345baf21d9

[ "Apache-2.0" ]

null

cell2location/models/base/_pyro_mixin.py

jjhong922/cell2location

2c2eb49aa3c0263fe8c6d45baf4ca0345baf21d9

[ "Apache-2.0" ]

null

cell2location/models/base/_pyro_mixin.py

jjhong922/cell2location

2c2eb49aa3c0263fe8c6d45baf4ca0345baf21d9

[ "Apache-2.0" ]

null

from datetime import date from functools import partial import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import pyro import torch from pyro import poutine from pyro.infer.autoguide import AutoNormal, init_to_mean from scipy.sparse import issparse from scvi import _CONSTANTS from scvi.data._anndata import get_from_registry from scvi.dataloaders import AnnDataLoader from scvi.model._utils import parse_use_gpu_arg from ...distributions.AutoNormalEncoder import AutoGuideList, AutoNormalEncoder def init_to_value(site=None, values={}): if site is None: return partial(init_to_value, values=values) if site["name"] in values: return values[site["name"]] else: return init_to_mean(site) class AutoGuideMixinModule: """ This mixin class provides methods for: - initialising standard AutoNormal guides - initialising amortised guides (AutoNormalEncoder) - initialising amortised guides with special additional inputs """ def _create_autoguide( self, model, amortised, encoder_kwargs, data_transform, encoder_mode, init_loc_fn=init_to_mean, n_cat_list: list = [], encoder_instance=None, ): if not amortised: _guide = AutoNormal( model, init_loc_fn=init_loc_fn, create_plates=model.create_plates, ) else: encoder_kwargs = encoder_kwargs if isinstance(encoder_kwargs, dict) else dict() n_hidden = encoder_kwargs["n_hidden"] if "n_hidden" in encoder_kwargs.keys() else 200 init_param_scale = ( encoder_kwargs["init_param_scale"] if "init_param_scale" in encoder_kwargs.keys() else 1 / 50 ) if "init_param_scale" in encoder_kwargs.keys(): del encoder_kwargs["init_param_scale"] amortised_vars = self.list_obs_plate_vars _guide = AutoGuideList(model, create_plates=model.create_plates) _guide.append( AutoNormal( pyro.poutine.block(model, hide=list(amortised_vars["sites"].keys())), init_loc_fn=init_loc_fn, ) ) if isinstance(data_transform, np.ndarray): # add extra info about gene clusters to the network self.register_buffer("gene_clusters", torch.tensor(data_transform.astype("float32"))) n_in = model.n_vars + data_transform.shape[1] data_transform = self.data_transform_clusters() elif data_transform == "log1p": # use simple log1p transform data_transform = torch.log1p n_in = self.model.n_vars elif ( isinstance(data_transform, dict) and "var_std" in list(data_transform.keys()) and "var_mean" in list(data_transform.keys()) ): # use data transform by scaling n_in = model.n_vars self.register_buffer( "var_mean", torch.tensor(data_transform["var_mean"].astype("float32").reshape((1, n_in))), ) self.register_buffer( "var_std", torch.tensor(data_transform["var_std"].astype("float32").reshape((1, n_in))), ) data_transform = self.data_transform_scale() else: # use custom data transform data_transform = data_transform n_in = model.n_vars if len(amortised_vars["input"]) >= 2: encoder_kwargs["n_cat_list"] = n_cat_list amortised_vars["input_transform"][0] = data_transform _guide.append( AutoNormalEncoder( pyro.poutine.block(model, expose=list(amortised_vars["sites"].keys())), amortised_plate_sites=amortised_vars, n_in=n_in, n_hidden=n_hidden, init_param_scale=init_param_scale, encoder_kwargs=encoder_kwargs, encoder_mode=encoder_mode, encoder_instance=encoder_instance, ) ) return _guide def _data_transform_clusters(self): def _data_transform(x): return torch.log1p(torch.cat([x, x @ self.gene_clusters], dim=1)) return _data_transform def _data_transform_scale(self): def _data_transform(x): # return (x - self.var_mean) / self.var_std return x / self.var_std return _data_transform class QuantileMixin: """ This mixin class provides methods for: - computing median and quantiles of the posterior distribution using both direct and amortised inference """ def _optim_param( self, lr: float = 0.01, autoencoding_lr: float = None, clip_norm: float = 200, module_names: list = ["encoder", "hidden2locs", "hidden2scales"], ): # TODO implement custom training method that can use this function. # create function which fetches different lr for autoencoding guide def optim_param(module_name, param_name): # detect variables in autoencoding guide if autoencoding_lr is not None and np.any([n in module_name + "." + param_name for n in module_names]): return { "lr": autoencoding_lr, # limit the gradient step from becoming too large "clip_norm": clip_norm, } else: return { "lr": lr, # limit the gradient step from becoming too large "clip_norm": clip_norm, } return optim_param @torch.no_grad() def _posterior_quantile_amortised(self, q: float = 0.5, batch_size: int = 2048, use_gpu: bool = None): """ Compute median of the posterior distribution of each parameter, separating local (minibatch) variable and global variables, which is necessary when performing amortised inference. Note for developers: requires model class method which lists observation/minibatch plate variables (self.module.model.list_obs_plate_vars()). Parameters ---------- q quantile to compute batch_size number of observations per batch use_gpu Bool, use gpu? Returns ------- dictionary {variable_name: posterior median} """ gpus, device = parse_use_gpu_arg(use_gpu) self.module.eval() train_dl = AnnDataLoader(self.adata, shuffle=False, batch_size=batch_size) # sample local parameters i = 0 for tensor_dict in train_dl: args, kwargs = self.module._get_fn_args_from_batch(tensor_dict) args = [a.to(device) for a in args] kwargs = {k: v.to(device) for k, v in kwargs.items()} self.to_device(device) if i == 0: means = self.module.guide.quantiles([q], *args, **kwargs) means = { k: means[k].cpu().numpy() for k in means.keys() if k in self.module.model.list_obs_plate_vars()["sites"] } # find plate dimension trace = poutine.trace(self.module.model).get_trace(*args, **kwargs) # print(trace.nodes[self.module.model.list_obs_plate_vars()['name']]) obs_plate = { name: site["cond_indep_stack"][0].dim for name, site in trace.nodes.items() if site["type"] == "sample" if any(f.name == self.module.model.list_obs_plate_vars()["name"] for f in site["cond_indep_stack"]) } else: means_ = self.module.guide.quantiles([q], *args, **kwargs) means_ = { k: means_[k].cpu().numpy() for k in means_.keys() if k in list(self.module.model.list_obs_plate_vars()["sites"].keys()) } means = { k: np.concatenate([means[k], means_[k]], axis=list(obs_plate.values())[0]) for k in means.keys() } i += 1 # sample global parameters tensor_dict = next(iter(train_dl)) args, kwargs = self.module._get_fn_args_from_batch(tensor_dict) args = [a.to(device) for a in args] kwargs = {k: v.to(device) for k, v in kwargs.items()} self.to_device(device) global_means = self.module.guide.quantiles([q], *args, **kwargs) global_means = { k: global_means[k].cpu().numpy() for k in global_means.keys() if k not in list(self.module.model.list_obs_plate_vars()["sites"].keys()) } for k in global_means.keys(): means[k] = global_means[k] self.module.to(device) return means @torch.no_grad() def _posterior_quantile(self, q: float = 0.5, batch_size: int = 2048, use_gpu: bool = None): """ Compute median of the posterior distribution of each parameter pyro models trained without amortised inference. Parameters ---------- q quantile to compute use_gpu Bool, use gpu? Returns ------- dictionary {variable_name: posterior median} """ self.module.eval() gpus, device = parse_use_gpu_arg(use_gpu) train_dl = AnnDataLoader(self.adata, shuffle=False, batch_size=batch_size) # sample global parameters tensor_dict = next(iter(train_dl)) args, kwargs = self.module._get_fn_args_from_batch(tensor_dict) args = [a.to(device) for a in args] kwargs = {k: v.to(device) for k, v in kwargs.items()} self.to_device(device) means = self.module.guide.quantiles([q], *args, **kwargs) means = {k: means[k].cpu().detach().numpy() for k in means.keys()} return means def posterior_quantile(self, q: float = 0.5, batch_size: int = 2048, use_gpu: bool = None): """ Compute median of the posterior distribution of each parameter. Parameters ---------- q quantile to compute use_gpu Returns ------- """ if self.module.is_amortised: return self._posterior_quantile_amortised(q=q, batch_size=batch_size, use_gpu=use_gpu) else: return self._posterior_quantile(q=q, batch_size=batch_size, use_gpu=use_gpu) class PltExportMixin: r""" This mixing class provides methods for common plotting tasks and data export. """ @staticmethod def plot_posterior_mu_vs_data(mu, data): r"""Plot expected value of the model (e.g. mean of NB distribution) vs observed data :param mu: expected value :param data: data value """ plt.hist2d( np.log10(data.flatten() + 1), np.log10(mu.flatten() + 1), bins=50, norm=matplotlib.colors.LogNorm(), ) plt.gca().set_aspect("equal", adjustable="box") plt.xlabel("Data, log10") plt.ylabel("Posterior expected value, log10") plt.title("Reconstruction accuracy") plt.tight_layout() def plot_history(self, iter_start=0, iter_end=-1, ax=None): r"""Plot training history Parameters ---------- iter_start omit initial iterations from the plot iter_end omit last iterations from the plot ax matplotlib axis """ if ax is None: ax = plt ax.set_xlabel = plt.xlabel ax.set_ylabel = plt.ylabel if iter_end == -1: iter_end = len(self.history_["elbo_train"]) ax.plot( self.history_["elbo_train"].index[iter_start:iter_end], np.array(self.history_["elbo_train"].values.flatten())[iter_start:iter_end], label="train", ) ax.legend() ax.xlim(0, len(self.history_["elbo_train"])) ax.set_xlabel("Training epochs") ax.set_ylabel("-ELBO loss") plt.tight_layout() def _export2adata(self, samples): r""" Export key model variables and samples Parameters ---------- samples dictionary with posterior mean, 5%/95% quantiles, SD, samples, generated by ``.sample_posterior()`` Returns ------- Updated dictionary with additional details is saved to ``adata.uns['mod']``. """ # add factor filter and samples of all parameters to unstructured data results = { "model_name": str(self.module.__class__.__name__), "date": str(date.today()), "factor_filter": list(getattr(self, "factor_filter", [])), "factor_names": list(self.factor_names_), "var_names": self.adata.var_names.tolist(), "obs_names": self.adata.obs_names.tolist(), "post_sample_means": samples["post_sample_means"], "post_sample_stds": samples["post_sample_stds"], "post_sample_q05": samples["post_sample_q05"], "post_sample_q95": samples["post_sample_q95"], } return results def sample2df_obs( self, samples: dict, site_name: str = "w_sf", summary_name: str = "means", name_prefix: str = "cell_abundance", ): """Export posterior distribution summary for observation-specific parameters (e.g. spatial cell abundance) as Pandas data frame (means, 5%/95% quantiles or sd of posterior distribution). Parameters ---------- samples dictionary with posterior mean, 5%/95% quantiles, SD, samples, generated by ``.sample_posterior()`` site_name name of the model parameter to be exported summary_name posterior distribution summary to return ['means', 'stds', 'q05', 'q95'] name_prefix prefix to add to column names (f'{summary_name}{name_prefix}_{site_name}_{self\.factor_names_}') Returns ------- Pandas data frame corresponding to either means, 5%/95% quantiles or sd of the posterior distribution """ return pd.DataFrame( samples[f"post_sample_{summary_name}"].get(site_name, None), index=self.adata.obs_names, columns=[f"{summary_name}{name_prefix}_{site_name}_{i}" for i in self.factor_names_], ) def sample2df_vars( self, samples: dict, site_name: str = "gene_factors", summary_name: str = "means", name_prefix: str = "", ): r"""Export posterior distribution summary for variable-specific parameters as Pandas data frame (means, 5%/95% quantiles or sd of posterior distribution). Parameters ---------- samples dictionary with posterior mean, 5%/95% quantiles, SD, samples, generated by ``.sample_posterior()`` site_name name of the model parameter to be exported summary_name posterior distribution summary to return ('means', 'stds', 'q05', 'q95') name_prefix prefix to add to column names (f'{summary_name}{name_prefix}_{site_name}_{self\.factor_names_}') Returns ------- Pandas data frame corresponding to either means, 5%/95% quantiles or sd of the posterior distribution """ return pd.DataFrame( samples[f"post_sample_{summary_name}"].get(site_name, None), columns=self.adata.var_names, index=[f"{summary_name}{name_prefix}_{site_name}_{i}" for i in self.factor_names_], ).T def plot_QC(self, summary_name: str = "means", use_n_obs: int = 1000): """ Show quality control plots: 1. Reconstruction accuracy to assess if there are any issues with model training. The plot should be roughly diagonal, strong deviations signal problems that need to be investigated. Plotting is slow because expected value of mRNA count needs to be computed from model parameters. Random observations are used to speed up computation. Parameters ---------- summary_name posterior distribution summary to use ('means', 'stds', 'q05', 'q95') Returns ------- """ if getattr(self, "samples", False) is False: raise RuntimeError("self.samples is missing, please run self.export_posterior() first") if use_n_obs is not None: ind_x = np.random.choice(self.adata.n_obs, np.min((use_n_obs, self.adata.n_obs)), replace=False) else: ind_x = None self.expected_nb_param = self.module.model.compute_expected( self.samples[f"post_sample_{summary_name}"], self.adata, ind_x=ind_x ) x_data = get_from_registry(self.adata, _CONSTANTS.X_KEY)[ind_x, :] if issparse(x_data): x_data = np.asarray(x_data.toarray()) self.plot_posterior_mu_vs_data(self.expected_nb_param["mu"], x_data)

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119

0.582159

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17,667

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0.182785

0.03435

0.009756

0.011382

0.430996

0.367988

0.32002

0.302033

0.273984

0

0.009895

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35.334

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0.190813

0

null

0

null

0

1

0

45e2441449b8a57f4c898888fafc70417c101445

570

py

Python

mayan/apps/sources/literals.py

atitaya1412/Mayan-EDMS

bda9302ba4b743e7d829ad118b8b836221888172

[ "Apache-2.0" ]

343

2015-01-05T14:19:35.000Z

2018-12-10T19:07:48.000Z

mayan/apps/sources/literals.py

atitaya1412/Mayan-EDMS

bda9302ba4b743e7d829ad118b8b836221888172

[ "Apache-2.0" ]

191

2015-01-03T00:48:19.000Z

2018-11-30T09:10:25.000Z

mayan/apps/sources/literals.py

atitaya1412/Mayan-EDMS

bda9302ba4b743e7d829ad118b8b836221888172

[ "Apache-2.0" ]

114

2015-01-08T20:21:05.000Z

2018-12-10T19:07:53.000Z

import os from django.conf import settings DEFAULT_BINARY_SCANIMAGE_PATH = '/usr/bin/scanimage' DEFAULT_SOURCES_BACKEND_ARGUMENTS = { 'mayan.apps.sources.source_backends.SourceBackendSANEScanner': { 'scanimage_path': DEFAULT_BINARY_SCANIMAGE_PATH } } DEFAULT_SOURCES_CACHE_STORAGE_BACKEND = 'django.core.files.storage.FileSystemStorage' DEFAULT_SOURCES_CACHE_STORAGE_BACKEND_ARGUMENTS = { 'location': os.path.join(settings.MEDIA_ROOT, 'source_cache') } DEFAULT_SOURCES_LOCK_EXPIRE = 600 STORAGE_NAME_SOURCE_CACHE_FOLDER = 'sources__source_cache'

30

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0.815789

68

570

6.367647

0.5

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0.120092

0.152425

0

0.005859

0.101754

570

18

86

31.666667

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0

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false

0

0.142857

0

0.142857

0

null

0

null

0

1

0

45e3e43df6217404133f05bdb8876753107b0f86

3,050

py

Python

test/test14_advanced_search.py

kyu-su/pixplusPlus

2813771c8292ea05d0fd9e7e6b1f4a71e2aba1f8

[ "MIT" ]

null

test/test14_advanced_search.py

kyu-su/pixplusPlus

2813771c8292ea05d0fd9e7e6b1f4a71e2aba1f8

[ "MIT" ]

null

test/test14_advanced_search.py

kyu-su/pixplusPlus

2813771c8292ea05d0fd9e7e6b1f4a71e2aba1f8

[ "MIT" ]

null

import warnings import time import random import util from test_base import TestCase class Test_AdvancedSearch(TestCase): def get_radio(self, name): return self.q('#search-option .content form input[type="radio"][name="%s"]:checked' % name) def set_size(self, wlt, hlt, wgt, hgt): for name in 'wlt', 'hlt', 'wgt', 'hgt': value = locals()[name] e = self.q('#pp-search-size-custom-' + name) e.clear() if value is not None: e.send_keys(str(value)) radio = self.get_radio('size') value = '%sx%s-%sx%s' % tuple(map(lambda a: '' if a is None else str(a), [wlt, hlt, wgt, hgt])) self.assertEqual(radio.get_attribute('value'), value) def check_size(self, wlt, hlt, wgt, hgt): self.open('/search.php?s_mode=s_tag&word=pixiv') self.click(self.q('.search-option')) self.set_size(wlt, hlt, wgt, hgt) self.q('#search-option .content form').submit() self.wait_page_load() self.assertTrue(self.url.startswith('http://www.pixiv.net/search.php?')) url = util.urlparse(self.url) query = dict(util.parse_qsl(url.query)) for name in 'wlt', 'hlt', 'wgt', 'hgt': value = locals()[name] self.assertEqual(name in query, value is not None) if value is not None: self.assertEqual(query[name], str(value)) # def test_size(self): # r = lambda: random.randint(1, 2000) # self.check_size(*sorted(random.sample(range(2000), 4))) # self.check_size(r(), None, r(), None) # self.check_size(None, r(), None, r()) def check_slider(self, slider, knob, text): sx, sy, sw, sh = self.geom(slider) self.ac().click_and_hold(knob or slider).move_by_offset(-sw, 0).release().perform() self.assertEqual(text.get_attribute('value'), '-1.5') self.assertEqual(self.get_radio('ratio').get_attribute('value'), '-1.5') if knob: kx, ky, kw, kh = self.geom(knob) self.assertEqual(kx, sx) self.assertEqual(ky, sy) ac = self.ac() if knob: ac.click_and_hold(knob) else: ac.move_to_element_with_offset(slider, 4, int(sh / 2)).click_and_hold() ac.move_by_offset(sw * 2, 0).release().perform() self.assertEqual(text.get_attribute('value'), '1.5') self.assertEqual(self.get_radio('ratio').get_attribute('value'), '1.5') if knob: kx, ky, kw, kh = self.geom(knob) self.assertEqual(kx, sx + sw - kw) self.assertEqual(ky, sy) def test_ratio(self): self.open('/search.php?s_mode=s_tag&word=pixiv') self.click(self.q('.search-option')) slider = self.q('#pp-search-ratio-custom-slider') if slider.tag_name.lower() != 'input': self.skipTest('%s seems not supports <input type=range>' % self.b.name) return text = self.q('#pp-search-ratio-custom-text') self.assertEqual(slider.get_attribute('min'), '-1.5') self.assertEqual(slider.get_attribute('max'), '1.5') self.check_slider(slider, None, text) text.clear() text.send_keys('123') self.assertEqual(self.get_radio('ratio').get_attribute('value'), '123')

32.795699

99

0.639672

464

3,050

4.099138

0.262931

0.11041

0.028391

0.037855

0.445321

0.360147

0.284437

0.258675

0

0.013242

0.182951

3,050

92

100

33.152174

0.75

0.064262

0

0.253731

0

0.164326

0.066362

0

0.223881

1

0.074627

false

0

0.074627

0.014925

0.19403

0

null

0

null

0

1

0

45e52e60e920306770881bdab0e81dba6a9ab8d4

511

py

Python

cogs/help/onHelpMessage.py

Narzaru/AiShindou

01daa4e51c5d19b0fa39ab7dff24adad6d764976

[ "BSD-2-Clause" ]

null

cogs/help/onHelpMessage.py

Narzaru/AiShindou

01daa4e51c5d19b0fa39ab7dff24adad6d764976

[ "BSD-2-Clause" ]

null

cogs/help/onHelpMessage.py

Narzaru/AiShindou

01daa4e51c5d19b0fa39ab7dff24adad6d764976

[ "BSD-2-Clause" ]

null

import discord from discord.ext import commands from service.utils import TemplateColours class Help_command(commands.MinimalHelpCommand): async def send_pages(self): ctx = self.get_destination() self.paginator.suffix = "\nif something is wrong, he is to blame\n ---> <@202011264589758464>" for page in self.paginator.pages: embed = discord.Embed(description=page, color=TemplateColours("service\\templateColours.json").Yellow) await ctx.send(embed=embed)

36.5

114

0.716243

61

511

5.95082

0.655738

0.071625

0

0.043269

0.18591

511

13

115

39.307692

0.829327

0

0.189824

0.097847

0

1

0

false

0

0.3

0

0.4

0

null

0

null

0

1

0

afd77d47937da5ad76e9e4fe3eba28dc639a3d40

3,587

py

Python

tools/WBDSP/UI_InputSignal.py

ptracton/wb_dsp

73586b10141952e26bbbfb2213b2ccaa1ddbcd39

[ "MIT" ]

null

tools/WBDSP/UI_InputSignal.py

ptracton/wb_dsp

73586b10141952e26bbbfb2213b2ccaa1ddbcd39

[ "MIT" ]

null

tools/WBDSP/UI_InputSignal.py

ptracton/wb_dsp

73586b10141952e26bbbfb2213b2ccaa1ddbcd39

[ "MIT" ]

null

#! /usr/bin/env python3 from PyQt4 import QtGui import Signal class UI_InputSignal(QtGui.QDialog): """ """ def __init__(self, parent=None): super(UI_InputSignal, self).__init__(parent) vbox = QtGui.QVBoxLayout() label = QtGui.QLabel("Input Signal") vbox.addWidget(label) self.Signal = Signal.Signal() self.StartTimeLabel = QtGui.QLabel("Start Time:") self.StartTimeInput = QtGui.QLineEdit("-3") self.StartTimeHBox = QtGui.QHBoxLayout() self.StartTimeHBox.addWidget(self.StartTimeLabel) self.StartTimeHBox.addWidget(self.StartTimeInput) vbox.addLayout(self.StartTimeHBox) self.EndTimeLabel = QtGui.QLabel("End Time:") self.EndTimeInput = QtGui.QLineEdit("3") self.EndTimeHBox = QtGui.QHBoxLayout() self.EndTimeHBox.addWidget(self.EndTimeLabel) self.EndTimeHBox.addWidget(self.EndTimeInput) vbox.addLayout(self.EndTimeHBox) self.SampleFrequencyLabel = QtGui.QLabel("Sample Frequency:") self.SampleFrequencyInput = QtGui.QLineEdit("1000") self.SampleFrequencyHBox = QtGui.QHBoxLayout() self.SampleFrequencyHBox.addWidget(self.SampleFrequencyLabel) self.SampleFrequencyHBox.addWidget(self.SampleFrequencyInput) vbox.addLayout(self.SampleFrequencyHBox) self.SignalTypeLabel = QtGui.QLabel("Signal Type:") self.SignalTypeHBox = QtGui.QHBoxLayout() self.SignalTypeComboBox = QtGui.QComboBox() self.SignalTypeComboBox.addItem("sine") self.SignalTypeComboBox.addItem("square") self.SignalTypeComboBox.addItem("triangle") self.SignalTypeHBox.addWidget(self.SignalTypeLabel) self.SignalTypeHBox.addWidget(self.SignalTypeComboBox) vbox.addLayout(self.SignalTypeHBox) self.AmplitudeLabel = QtGui.QLabel("Amplitude:") self.AmplitudeInput = QtGui.QLineEdit("0.75") self.AmplitudeHBox = QtGui.QHBoxLayout() self.AmplitudeHBox.addWidget(self.AmplitudeLabel) self.AmplitudeHBox.addWidget(self.AmplitudeInput) vbox.addLayout(self.AmplitudeHBox) self.FrequencyLabel = QtGui.QLabel("Signal Frequency:") self.FrequencyInput = QtGui.QLineEdit("1") self.FrequencyHBox = QtGui.QHBoxLayout() self.FrequencyHBox.addWidget(self.FrequencyLabel) self.FrequencyHBox.addWidget(self.FrequencyInput) vbox.addLayout(self.FrequencyHBox) self.PhaseLabel = QtGui.QLabel("Phase:") self.PhaseInput = QtGui.QLineEdit("0") self.PhaseHBox = QtGui.QHBoxLayout() self.PhaseHBox.addWidget(self.PhaseLabel) self.PhaseHBox.addWidget(self.PhaseInput) vbox.addLayout(self.PhaseHBox) self.DataLabel = QtGui.QLabel("Data Size:") self.DataInput = QtGui.QLineEdit("0") self.DataHBox = QtGui.QHBoxLayout() self.DataHBox.addWidget(self.DataLabel) self.DataHBox.addWidget(self.DataInput) vbox.addLayout(self.DataHBox) self.GraphPushButton = QtGui.QPushButton("Graph It") vbox.addWidget(self.GraphPushButton) self.MixSignalsPushButton = QtGui.QPushButton("Mix Signals") vbox.addWidget(self.MixSignalsPushButton) self.RemovePushButton = QtGui.QPushButton("Remove Last Graph") vbox.addWidget(self.RemovePushButton) self.WriteDataPushButton = QtGui.QPushButton("Write File") vbox.addWidget(self.WriteDataPushButton) self.setLayout(vbox) return

39.417582

70

0.681628

328

3,587

7.42378

0.265244

0.106776

0.065708

0.015606

0

0.004954

0.212155

3,587

90

71

39.855556

0.856688

0.006133

0

0.051253

0

1

0.014286

false

0

0.028571

0

0.071429

0

null

0

null

0

1

0

afd967c3188fbb324693e3925d0624ce1f560347

1,044

py

Python

ichnaea/alembic/versions/cad2875fd8cb_extend_api_keys.py

crankycoder/ichnaea

fb54000e92c605843b7a41521e36fd648c11ae94

[ "Apache-2.0" ]

1

2018-01-18T16:02:43.000Z

ichnaea/alembic/versions/cad2875fd8cb_extend_api_keys.py

crankycoder/ichnaea

fb54000e92c605843b7a41521e36fd648c11ae94

[ "Apache-2.0" ]

null

ichnaea/alembic/versions/cad2875fd8cb_extend_api_keys.py

crankycoder/ichnaea

fb54000e92c605843b7a41521e36fd648c11ae94

[ "Apache-2.0" ]

1

2018-01-19T17:56:48.000Z

"""Extend api keys with sample_store columns. Revision ID: cad2875fd8cb Revises: 385f842b2526 Create Date: 2017-02-22 11:52:47.837989 """ import logging from alembic import op import sqlalchemy as sa log = logging.getLogger('alembic.migration') revision = 'cad2875fd8cb' down_revision = '385f842b2526' def upgrade(): log.info('Add store_sample_* columns to api_key table.') op.execute(sa.text( 'ALTER TABLE api_key ' 'ADD COLUMN `store_sample_locate` TINYINT(4) ' 'AFTER `fallback_cache_expire`, ' 'ADD COLUMN `store_sample_submit` TINYINT(4) ' 'AFTER `store_sample_locate`' )) op.execute(sa.text( 'UPDATE api_key SET store_sample_locate = 100' )) op.execute(sa.text( 'UPDATE api_key SET store_sample_submit = 100' )) def downgrade(): log.info('Drop store_sample_* columns from api_key table.') op.execute(sa.text( 'ALTER TABLE api_key ' 'DROP COLUMN `store_sample_locate`, ' 'DROP COLUMN `store_sample_submit`' ))

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null

0

null

0

1

0

afdbf4b87bacf33c9e03d4659cab0141ad61a3a5

1,870

py

Python

sdk/policyinsights/azure-mgmt-policyinsights/azure/mgmt/policyinsights/models/tracked_resource_modification_details_py3.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

1

2021-09-07T18:36:04.000Z

sdk/policyinsights/azure-mgmt-policyinsights/azure/mgmt/policyinsights/models/tracked_resource_modification_details_py3.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

2

2019-10-02T23:37:38.000Z

2020-10-02T01:17:31.000Z

sdk/policyinsights/azure-mgmt-policyinsights/azure/mgmt/policyinsights/models/tracked_resource_modification_details_py3.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

1

2019-06-17T22:18:23.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class TrackedResourceModificationDetails(Model): """The details of the policy triggered deployment that created or modified the tracked resource. Variables are only populated by the server, and will be ignored when sending a request. :ivar policy_details: The details of the policy that created or modified the tracked resource. :vartype policy_details: ~azure.mgmt.policyinsights.models.PolicyDetails :ivar deployment_id: The ID of the deployment that created or modified the tracked resource. :vartype deployment_id: str :ivar deployment_time: Timestamp of the deployment that created or modified the tracked resource. :vartype deployment_time: datetime """ _validation = { 'policy_details': {'readonly': True}, 'deployment_id': {'readonly': True}, 'deployment_time': {'readonly': True}, } _attribute_map = { 'policy_details': {'key': 'policyDetails', 'type': 'PolicyDetails'}, 'deployment_id': {'key': 'deploymentId', 'type': 'str'}, 'deployment_time': {'key': 'deploymentTime', 'type': 'iso-8601'}, } def __init__(self, **kwargs) -> None: super(TrackedResourceModificationDetails, self).__init__(**kwargs) self.policy_details = None self.deployment_id = None self.deployment_time = None

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null

0

null

0

1

0

afdccd4d644296589a91c7378dac649e2358df5a

11,059

py

Python

backUp/jd_jxz.py

ZelZhu/faker3

4d1f2aff532211da9f356119c9f8273c3d0796dd

[ "MIT" ]

68

2021-11-19T09:28:04.000Z

2022-03-25T07:06:01.000Z

backUp/jd_jxz.py

ZelZhu/faker3

4d1f2aff532211da9f356119c9f8273c3d0796dd

[ "MIT" ]

2

2022-03-09T12:26:22.000Z

2022-03-10T03:00:49.000Z

backUp/jd_jxz.py

ZelZhu/faker3

4d1f2aff532211da9f356119c9f8273c3d0796dd

[ "MIT" ]

83

2021-11-19T08:27:05.000Z

2022-03-23T07:32:01.000Z

#!/usr/bin/python3 # -*- coding: utf8 -*- """ cron: 30 9 * * * new Env('集勋章'); 活动入口：东东农场->东东乐园(点大风车）->集勋章 500豆 """ # 是否开启通知，Ture：发送通知，False：不发送 isNotice = True # UA 可自定义你的, 默认随机生成UA。 UserAgent = '' import asyncio import json import random import os, re, sys try: import requests except Exception as e: print(e, "\n缺少requests 模块，请执行命令安装：python3 -m pip install requests") exit(3) try: import aiohttp except Exception as e: print(e, "\n缺少requests 模块，请执行命令安装：python3 -m pip install requests") exit(3) ############## requests.packages.urllib3.disable_warnings() # host_api = 'https://api.m.jd.com/client.action' pwd = os.path.dirname(os.path.abspath(__file__)) + os.sep def userAgent(): """ 随机生成一个UA :return: jdapp;iPhone;9.4.8;14.3;xxxx;network/wifi;ADID/201EDE7F-5111-49E8-9F0D-CCF9677CD6FE;supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone13,4;addressid/2455696156;supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1 """ if not UserAgent: uuid = ''.join(random.sample('123456789abcdef123456789abcdef123456789abcdef123456789abcdef', 40)) addressid = ''.join(random.sample('1234567898647', 10)) iosVer = ''.join( random.sample(["14.5.1", "14.4", "14.3", "14.2", "14.1", "14.0.1", "13.7", "13.1.2", "13.1.1"], 1)) iosV = iosVer.replace('.', '_') iPhone = ''.join(random.sample(["8", "9", "10", "11", "12", "13"], 1)) ADID = ''.join(random.sample('0987654321ABCDEF', 8)) + '-' + ''.join( random.sample('0987654321ABCDEF', 4)) + '-' + ''.join(random.sample('0987654321ABCDEF', 4)) + '-' + ''.join( random.sample('0987654321ABCDEF', 4)) + '-' + ''.join(random.sample('0987654321ABCDEF', 12)) return f'jdapp;iPhone;10.0.4;{iosVer};{uuid};network/wifi;ADID/{ADID};supportApplePay/0;hasUPPay/0;hasOCPay/0;model/iPhone{iPhone},1;addressid/{addressid};supportBestPay/0;appBuild/167629;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS {iosV} like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148;supportJDSHWK/1' else: return UserAgent ## 获取通知服务 class msg(object): def __init__(self, m=''): self.str_msg = m self.message() def message(self): global msg_info print(self.str_msg) try: msg_info = "{}\n{}".format(msg_info, self.str_msg) except: msg_info = "{}".format(self.str_msg) sys.stdout.flush() def getsendNotify(self, a=0): if a == 0: a += 1 try: url = 'https://gitee.com/curtinlv/Public/raw/master/sendNotify.py' response = requests.get(url) if 'curtinlv' in response.text: with open('sendNotify.py', "w+", encoding="utf-8") as f: f.write(response.text) else: if a < 5: a += 1 return self.getsendNotify(a) else: pass except: if a < 5: a += 1 return self.getsendNotify(a) else: pass def main(self): global send cur_path = os.path.abspath(os.path.dirname(__file__)) sys.path.append(cur_path) if os.path.exists(cur_path + "/sendNotify.py"): try: from sendNotify import send except: self.getsendNotify() try: from sendNotify import send except: print("加载通知服务失败~") else: self.getsendNotify() try: from sendNotify import send except: print("加载通知服务失败~") ################### msg().main() # @logger.catch async def get_headers(): """ 获取请求头 :return: """ headers = { 'Host': 'api.m.jd.com', 'Connection': 'keep-alive', 'Accept': 'application/json, text/plain, */*', 'Origin': 'https://h5.m.jd.com', 'User-Agent': userAgent(), 'content-type': 'application/x-www-form-urlencoded', 'Referer': 'https://gongyi.m.jd.com/m/welfare/donate/index.html', 'Accept-Encoding': 'gzip, deflate', 'Accept-Language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7', 'X-Requested-With': 'com.jingdong.app.mall', } return headers async def post(session, url, body=None): try: if body is None: body = {} response = await session.post(url=url, data=body) await asyncio.sleep(1) text = await response.text() data = json.loads(text) return data except Exception as e: print('请求服务器错误, {}!'.format(e.args)) return { 'success': False } async def get(session, url): try: response = await session.get(url=url) await asyncio.sleep(1) text = await response.text() data = json.loads(text) return data except Exception as e: print('请求服务器错误, {}!'.format(e.args)) return { 'success': False } # POST 判断任务列表 async def collect_Init_task(session): """ 查询用户信息 :return: """ url = 'https://api.m.jd.com/client.action' body = {"channel": 1} params = f'functionId=collect_Init&body={json.dumps(body)}&client=wh5&clientVersion=1.0.0' data = await post(session, url, params) if data['code'] == '0' and data['success'] == True: if data['result']['activityStatus'] == 2: print(f"""完成任务得对应勋章""") taskInfo = data['result']['taskInfo'] for task in taskInfo: if task['status'] == 4: print(f"""勋章:{task['medalName']}, 完成情况{task['currentTaskCount']}/{task['maxTaskCount']},已点亮""") continue if task['status'] == 1: print(f"""勋章:{task['medalName']}, 完成情况{task['currentTaskCount']}/{task['maxTaskCount']},还没进度，要加油""") continue if task['status'] == 2: print(f"""勋章:{task['medalName']}, 完成情况{task['currentTaskCount']}/{task['maxTaskCount']},明天再来看""") continue if task['status'] == 3: print(f"""勋章:{task['medalName']}, 完成情况{task['currentTaskCount']}/{task['maxTaskCount']},去点亮""") await asyncio.sleep(1) await collect_taskAward(session, task) elif data['result']['activityStatus'] == 3: print(f"""勋章全部点亮了,去合成领奖""") await asyncio.sleep(1) await collect_getAwardInfo(session) elif data['result']['activityStatus'] == 4: msg(f"""勋章全部点亮了,合成领奖已完成！""") else: msg(f"""勋章状态异常{data}""") else: print(f"""获取勋章列表异常{data}""") return 999 # 查询合成项目 async def collect_getAwardInfo(session): """ 查询用户信息 :return: """ url = 'https://api.m.jd.com/client.action' body = {} params = f'functionId=collect_getAwardInfo&body={json.dumps(body)}&client=wh5&clientVersion=1.0.0' data = await post(session, url, params) if data['code'] == '0' and data['success'] == True: print(f"""合成领奖""") awardList = data['result']['awardList'] for i in awardList: if i['awardValue'] == '500': await collect_exchangeAward(session, i['awardType']) else: print(f"""获取勋章列表异常{data}""") return 999 # 执行合成 async def collect_exchangeAward(session, awardType): """ 查询用户信息 :return: """ url = 'https://api.m.jd.com/client.action' body = {"type": awardType} params = f'functionId=collect_exchangeAward&body={json.dumps(body)}&client=wh5&clientVersion=1.0.0' data = await post(session, url, params) print(data) if data['code'] == '1' and data['success'] == False: print(f"""合成领奖获得：{data['message']}""") elif data['code'] == '0' and data['success'] == True: msg(f"""合成领奖获得：{data['result']['awardValue']}京豆""") else: print(f"""合成领奖获得异常：{data}""") return 999 # POST 获取任务列表 async def collect_Init(session): """ 查询用户信息 :return: """ await asyncio.sleep(0.5) try: url = 'https://api.m.jd.com/client.action' body = {"channel": 1} params = f'functionId=collect_Init&body={json.dumps(body)}&client=wh5&clientVersion=1.0.0' data = await post(session, url, params) return data except Exception as e: print(e.args) # POST 点亮勋章 async def collect_taskAward(session, task): """ 查询用户信息 :return: """ taskType = task['taskType'] medalName = task['medalName'] url = 'https://api.m.jd.com/client.action' body = {"taskType": taskType} params = f'functionId=collect_taskAward&body={json.dumps(body)}&client=wh5&clientVersion=1.0.0' data = await post(session, url, params) if data['code'] == '1' and data['success'] == False: print(f"""点亮勋章{medalName}获得：{data['message']}""") elif data['code'] == '0' and data['success'] == True: msg(f"""点亮勋章{medalName}获得：水滴{data['result']['awardValue']}g""") else: print(f"""点亮勋章{medalName}异常：{data}""") return 999 # POST 领取新人奖励 async def collect_newUserAward(session): """ 查询用户信息 :return: """ url = 'https://api.m.jd.com/client.action' body = {} params = f'functionId=collect_newUserAward&body={json.dumps(body)}&client=wh5&clientVersion=1.0.0' data = await post(session, url, params) if data['code'] == '1' and data['success'] == False: print(f"""领取新人奖励：{data['msg']}""") elif data['code'] == '0' and data['success'] == True: msg(f"""领取新人奖励：{data['msg']}""") else: print(f"""领取新人奖励：{data}""") return 999 async def run(): """ 程序入口 :return: """ scriptName = '集勋章' print(scriptName) headers = await get_headers() cks = os.environ["JD_COOKIE"].split("&") for ck in cks: ptpin = re.findall(r"pt_pin=(.*?);", ck)[0] print("--------开始京东账号" + ptpin + "--------") ck = ck.rstrip(';') ck = dict(item.split("=") for item in ck.split(";")) async with aiohttp.ClientSession(headers=headers, cookies=ck) as session: await collect_Init(session) await asyncio.sleep(1) await collect_newUserAward(session) await asyncio.sleep(1) await collect_Init_task(session) if isNotice: send(scriptName, msg_info) else: print("\n", scriptName, "\n", msg_info) if __name__ == '__main__': # from config import JD_COOKIES # # app = JdDdWorld() # asyncio.run(run()) loop = asyncio.get_event_loop() loop.run_until_complete(run()) # from utils.process import process_start # process_start(JdDdWorld, '东东世界', code_key=CODE_KEY)

31.962428

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4.688361

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0.009799

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0.286089

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0.110204

0

null

0

null

0

1

0

afdcedd441d4f48bbd1a4d60e657e73de3e6409b

1,726

py

Python

SRTM/convert_hgt.py

iDigBio/guoda-datasets

abcba7b03b27e641cd96825dde64f2180a65d978

[ "MIT" ]

6

2016-06-24T09:47:22.000Z

2018-04-10T20:04:58.000Z

SRTM/convert_hgt.py

iDigBio/guoda-datasets

abcba7b03b27e641cd96825dde64f2180a65d978

[ "MIT" ]

14

2016-06-17T20:29:21.000Z

2019-06-13T13:17:21.000Z

SRTM/convert_hgt.py

iDigBio/guoda-datasets

abcba7b03b27e641cd96825dde64f2180a65d978

[ "MIT" ]

null

from __future__ import division, absolute_import, print_function import os import csv import re import sys import numpy as np import multiprocessing re_split = re.compile("([NS])(\d+)([EW])(\d+)") SAMPLES = 1201 def read_hgt_file(f): fname = os.path.split(f)[-1][:-4].upper() with open(f, "rb") as hgt_data: m = re_split.match(fname) if os.path.exists(fname + ".csv"): print(fname + " SKIP") return elif m is None: print(fname + " BAD MATCH") return print(fname) g = m.groups() base_lat = int(g[1]) base_lon = int(g[3]) if g[0] == "N": lat_sign = 1 else: lat_sign = -1 if g[2] == "E": lon_sign = 1 else: lon_sign = -1 try: elevations = np.fromfile( hgt_data, np.dtype('>i2'), SAMPLES*SAMPLES ).reshape((SAMPLES, SAMPLES)) with open(fname + ".csv", "w") as outf: cw = csv.writer(outf) for x in range(0, SAMPLES): for y in range(0, SAMPLES): lat = lat_sign * (base_lat + (1200-y)/1200) lon = lon_sign * (base_lon + x/1200) hgt = elevations[y, x].astype(int) cw.writerow([lat - 1/2400, lat + 1/2400, lon - 1/2400, lon + 1/2400, hgt]) except Exception: print(fname + "FAIL") def main(): p = multiprocessing.Pool() for root, dirs, files in os.walk("dem3"): p.map(read_hgt_file, [root + "/" + f for f in files]) if __name__ == '__main__': main()

25.382353

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0.479722

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3.634703

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0.027638

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0.09434

0

null

0

null

0

1

0

afe45f4906350be6c265277d8a549116790aeb73

895

py

Python

iv/Leetcode/easy/530_min_absolute_diff_binary_search_tree.py

iamsuman/iv

bf68d3fd45455b6041e74b09272f69503bf7a8ac

[ "MIT" ]

2

2020-09-19T22:28:15.000Z

2020-10-03T01:44:53.000Z

iv/Leetcode/easy/530_min_absolute_diff_binary_search_tree.py

iamsuman/iv

bf68d3fd45455b6041e74b09272f69503bf7a8ac

[ "MIT" ]

null

iv/Leetcode/easy/530_min_absolute_diff_binary_search_tree.py

iamsuman/iv

bf68d3fd45455b6041e74b09272f69503bf7a8ac

[ "MIT" ]

1

2020-10-03T01:43:30.000Z

class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def getMinimumDifference(self, root: TreeNode) -> int: nodes = [] def traversal(root, nodes: list): if not root: return if root.left: traversal(root.left, nodes) nodes.append(root.val) if root.right: traversal(root.right, nodes) traversal(root, nodes) # print(nodes) mindiff = 2 ** 31 - 1 for i in range(len(nodes) - 1): diff = abs(nodes[i] - nodes[i + 1]) if mindiff > diff: mindiff = diff return mindiff root = TreeNode(1) root.right = TreeNode(3) root.right.left = TreeNode(2) s = Solution() print(s.getMinimumDifference(root))

24.189189

58

0.532961

104

895

4.548077

0.336538

0.109937

0.07611

0

0.017422

0.358659

895

36

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24.861111

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0

1

0.107143

false

0

0.25

0.035714

0

null

0

null

0

1

0

afe5cfafb8008bc831b95a3a68e90c4bb129626a

2,964

py

Python

barbante/utils/tests/test_text.py

hypermindr/barbante

40056e9e4f4564461294b3a1d9afc855062350ac

[ "MIT" ]

10

2015-06-01T21:48:16.000Z

2021-08-20T20:18:48.000Z

barbante/utils/tests/test_text.py

hypermindr/barbante

40056e9e4f4564461294b3a1d9afc855062350ac

[ "MIT" ]

null

barbante/utils/tests/test_text.py

hypermindr/barbante

40056e9e4f4564461294b3a1d9afc855062350ac

[ "MIT" ]

2

2015-06-03T21:54:32.000Z

2015-11-24T23:13:05.000Z

""" Test module for barbante.text. """ import nose.tools import barbante.utils.text as text def test_calculate_tf_en(): """ Tests calculate_tf for English contents. """ language = "english" contents = "Cooks who don't love cooking don't cook well." results = text.calculate_tf(language, contents) nose.tools.eq_(results['cook'], 3, "Wrong TF") nose.tools.eq_(results['love'], 1, "Wrong TF") nose.tools.eq_(results['well'], 1, "Wrong TF") def test_calculate_tf_pt(): """ Tests calculate_tf for Portuguese contents. """ language = "portuguese" contents = "Eu não gostava do gosto gasto do gesto de agosto." results = text.calculate_tf(language, contents) nose.tools.eq_(results['gost'], 2, "Wrong TF") nose.tools.eq_(results['gast'], 1, "Wrong TF") nose.tools.eq_(results['gest'], 1, "Wrong TF") nose.tools.eq_(results['agost'], 1, "Wrong TF") def test_performance(): """ Tests calculate_tf for huge texts. """ import random palavras = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "R$1000.00"] contents = "" language = 'english' for _ in range(10000): # increase number and measure time when necessary contents += palavras[random.randint(0, len(palavras) - 1)] + " " text.calculate_tf(language, contents) def test_tokenize(): """ Tests tokenization. """ actual = text.tokenize("The car is going to Mountain View. You! You \ should go too... Or, maybe, shouldn't!?") expected = ["The", "car", "is", "going", "to", "Mountain", "View", "You", "You", "should", "go", "too", "Or", "maybe", "shouldn", "\'", "t"] nose.tools.eq_(actual, expected) def test_remove_stopwords(): """ Tests removal of stopwords. """ actual = text.remove_stopwords(["The", "car", "is", "going", "to", "crash", "or", "going", "to", "win"], "english", 3) expected = ['The', 'car', 'going', 'crash', 'going', 'win'] nose.tools.eq_(actual, expected) def test_count_common_terms_English(): """ Tests common terms counting. """ language = "english" text1 = "Just a test sentence for the purpose of just testing common terms counting." text2 = "This is just a sentence for tests purposes." text1_tokens = text.tokenize(text1) text2_tokens = text.tokenize(text2) text1_stems = text.get_stems(text1_tokens, language) text2_stems = text.get_stems(text2_tokens, language) text1_stems_no_stopwords = set(text.remove_stopwords(text1_stems, language)) text2_stems_no_stopwords = set(text.remove_stopwords(text2_stems, language)) nose.tools.eq_(text.count_common_terms(text1_stems_no_stopwords, text2_stems_no_stopwords), 3) # sentence, purpos3, tests

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null

0

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0

afe5dba68241cb5dd2453af805ca160811228622

3,365

py

Python

YouTubeDownloader/views.py

gabzin/django-ytdownloader

e59e728aeac459b73fd4fb9ca663560855af19fd

[ "MIT" ]

27

2021-11-18T22:01:26.000Z

2022-01-08T14:10:32.000Z

YouTubeDownloader/views.py

gabzin/django-ytdownloader

e59e728aeac459b73fd4fb9ca663560855af19fd

[ "MIT" ]

1

2021-11-21T13:28:00.000Z

2021-11-21T15:05:42.000Z

YouTubeDownloader/views.py

gabzin/django-ytdownloader

e59e728aeac459b73fd4fb9ca663560855af19fd

[ "MIT" ]

5

2021-11-20T07:16:54.000Z

2021-12-16T10:44:38.000Z

#Imports from django.http.response import HttpResponse from django.shortcuts import render from django.contrib import messages from .forms import DownloadForm from pytube import YouTube from math import pow, floor, log from datetime import timedelta from requests import get # Your YouTube V3 Api Key KEY = "" # Convert from bytes def convertsize(size_bytes): if size_bytes == 0: return "0B" size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB") i = int(floor(log(size_bytes, 1024))) p = pow(1024, i) s = round(size_bytes / p, 2) return "%s %s" % (s, size_name[i]) # Convert long numbers def humanformat(number): units = ['', 'K', 'M', 'B', 'T', 'Q'] k = 1000.0 magnitude = int(floor(log(number, k))) return '%.2f%s' % (number / k**magnitude, units[magnitude]) #When click search button def download_video(request, string=""): global video_url form = DownloadForm(request.POST or None) if form.is_valid(): video_url = form.cleaned_data.get("url") try: yt_obj = YouTube(video_url) videos = yt_obj.streams.filter(is_dash=False).desc() audios = yt_obj.streams.filter(only_audio=True).order_by('abr').desc() except Exception as e: #messages.error(request, 'Invalid URL.') messages.error(request, e) return render(request, 'home.html',{ 'form': form }) video_audio_streams = [] audio_streams = [] try: url = f"https://www.googleapis.com/youtube/v3/videos?id={yt_obj.video_id}&key={KEY}&part=statistics" video_stats = get(url).json() video_likes = video_stats['items'][0]['statistics']['likeCount'] video_favs = video_stats['items'][0]['statistics']['favoriteCount'] except: video_likes = 0 # List of video streams dictionaries for s in videos: video_audio_streams.append({ 'resolution' : s.resolution, 'extension' : s.mime_type.replace('video/',''), 'file_size' : convertsize(s.filesize), 'video_url' : s.url, 'file_name' : yt_obj.title + '.' + s.mime_type.replace('video/','') }) # List of audio streams dictionaries for s in audios: audio_streams.append({ 'resolution' : s.abr, 'extension' : s.mime_type.replace('audio/',''), 'file_size' : convertsize(s.filesize), 'video_url' : s.url, 'file_name' : yt_obj.title + '.' + s.mime_type.replace('video/','') }) if yt_obj.rating == None: rating = 5 else: rating = yt_obj.rating # Full content to render context = { 'form' : form,'title' : yt_obj.title, 'rating': humanformat(int(video_likes)), 'thumb' : yt_obj.thumbnail_url, 'author' : yt_obj.author, 'author_url' : yt_obj.channel_url, 'duration' : str(timedelta(seconds=yt_obj.length)), 'views' : humanformat(yt_obj.views) if yt_obj.views >= 1000 else yt_obj.views, 'stream_audio' : audio_streams, 'streams' : video_audio_streams } return render(request, 'home.html', context) return render(request, 'home.html',{ 'form': form })

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0.239437

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null

0

null

0

1

0

afe75e37643caa6a9af81c6d249b336b0e5aca17

45,429

py

Python

fhirzeug/fhirspec.py

skalarsystems/fhir-zeug

19973438823c41247e3efb5b1d35e8942ae01fdb

[ "Apache-2.0" ]

10

2020-04-23T18:13:13.000Z

2020-11-25T07:45:26.000Z

fhirzeug/fhirspec.py

skalarsystems/fhir-zeug

19973438823c41247e3efb5b1d35e8942ae01fdb

[ "Apache-2.0" ]

71

2020-05-20T09:11:22.000Z

2020-10-26T14:01:03.000Z

fhirzeug/fhirspec.py

skalarsystems/fhir-zeug

19973438823c41247e3efb5b1d35e8942ae01fdb

[ "Apache-2.0" ]

1

2020-06-03T11:55:47.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import io import os import re import json import datetime from pathlib import Path import stringcase # type: ignore from typing import Any, Dict, List, Optional, Union, TYPE_CHECKING from .logger import logger from . import fhirclass if TYPE_CHECKING: from .generators.yaml_model import GeneratorConfig # TODO: check # allow to skip some profiles by matching against their url (used while WiP) skip_because_unsupported = [ r"SimpleQuantity", ] class FHIRSpec(object): """ The FHIR specification. """ def __init__(self, directory: Path, generator_config: "GeneratorConfig"): assert directory.is_dir() self.directory = directory self.generator_config = generator_config self.info = FHIRVersionInfo(self, directory) # system-url: FHIRValueSet() self.valuesets: Dict[str, "FHIRValueSet"] = {} # system-url: FHIRCodeSystem() self.codesystems: Dict[str, "FHIRCodeSystem"] = {} # profile-name: FHIRStructureDefinition() self.profiles: Dict[str, "FHIRStructureDefinition"] = {} # Load profiles self.prepare() self.read_profiles() self.finalize() def prepare(self): """ Run actions before starting to parse profiles. """ self.read_valuesets() self.handle_manual_profiles() def read_bundle_resources(self, filename: str): """ Return an array of the Bundle's entry's "resource" elements. """ logger.info("Reading {}".format(filename)) filepath = os.path.join(self.directory, filename) with io.open(filepath, encoding="utf-8") as handle: parsed = json.load(handle) if "resourceType" not in parsed: raise Exception( 'Expecting "resourceType" to be present, but is not in {}'.format( filepath ) ) if "Bundle" != parsed["resourceType"]: raise Exception('Can only process "Bundle" resources') if "entry" not in parsed: raise Exception( "There are no entries in the Bundle at {}".format(filepath) ) return [e["resource"] for e in parsed["entry"]] # MARK: Managing ValueSets and CodeSystems def read_valuesets(self): resources = self.read_bundle_resources("valuesets.json") for resource in resources: if "ValueSet" == resource["resourceType"]: assert "url" in resource valueset = FHIRValueSet(self, resource) self.valuesets[valueset.url] = valueset if valueset.dstu2_inlined_codesystem: codesystem = FHIRCodeSystem(self, valueset.dstu2_inlined_codesystem) codesystem.valueset_url = valueset.url self.found_codesystem(codesystem) elif "CodeSystem" == resource["resourceType"]: assert "url" in resource if "content" in resource and "concept" in resource: codesystem = FHIRCodeSystem(self, resource) self.found_codesystem(codesystem) else: logger.warning(f"CodeSystem with no concepts: {resource['url']}") logger.info( f"Found {len(self.valuesets)} ValueSets and {len(self.codesystems)} CodeSystems" ) def found_codesystem(self, codesystem): if codesystem.url not in self.generator_config.mapping_rules.enum_ignore: self.codesystems[codesystem.url] = codesystem def valueset_with_uri(self, uri) -> Optional["FHIRValueSet"]: assert uri if uri not in self.valuesets: logger.warning(f"Valueset not found for URI : {uri}") return None return self.valuesets[uri] def codesystem_with_uri(self, uri) -> Optional["FHIRCodeSystem"]: assert uri if uri not in self.codesystems: logger.warning(f"Codesystem not found for URI : {uri}") return None return self.codesystems[uri] # MARK: Handling Profiles def read_profiles(self): """ Find all (JSON) profiles and instantiate into FHIRStructureDefinition. """ resources = [] for filename in [ "profiles-types.json", "profiles-resources.json", ]: # , 'profiles-others.json']: bundle_res = self.read_bundle_resources(filename) for resource in bundle_res: if "StructureDefinition" == resource["resourceType"]: resources.append(resource) else: logger.debug( "Not handling resource of type {}".format( resource["resourceType"] ) ) # create profile instances for resource in resources: profile = FHIRStructureDefinition(self, resource) for pattern in skip_because_unsupported: if re.search(pattern, profile.url) is not None: logger.info('Skipping "{}"'.format(resource["url"])) profile = None break if profile is not None and self.found_profile(profile): profile.process_profile() def found_profile(self, profile): if not profile or not profile.name: raise Exception("No name for profile {}".format(profile)) if profile.name.lower() in self.profiles: logger.debug('Already have profile "{}", discarding'.format(profile.name)) return False self.profiles[profile.name.lower()] = profile return True def handle_manual_profiles(self): """ Creates in-memory representations for all our manually defined profiles. """ for manual_profile in self.generator_config.manual_profiles: for contained in manual_profile.contains: profile = FHIRStructureDefinition(self, None) profile.manual_module = manual_profile.module prof_dict = { "name": contained, "differential": {"element": [{"path": contained}]}, } profile.structure = FHIRStructureDefinitionStructure(profile, prof_dict) if self.found_profile(profile): profile.process_profile() def finalize(self): """ Should be called after all profiles have been parsed and allows to perform additional actions, like looking up class implementations from different profiles. """ for _, prof in self.profiles.items(): prof.finalize() # MARK: Naming Utilities def as_module_name(self, name: str) -> str: if self.generator_config.naming_rules.resource_modules_lowercase: return name.lower() else: return name def as_class_name( self, classname: Optional[str], parent_name: Optional[str] = None ) -> Optional[str]: """ This method formulates a class name from the given arguments, applying formatting according to config. """ if classname is None or len(classname) == 0: return None classmap = self.generator_config.mapping_rules.classmap if parent_name is not None: # if we have a parent, do we have a mapped class? pathname = f"{parent_name}.{classname}" if pathname in classmap: return classmap[pathname] # is our plain class mapped? if classname in classmap: return classmap[classname] # CamelCase or just plain if self.generator_config.naming_rules.camelcase_classes: return stringcase.pascalcase(classname) # upper camelcase return classname def class_name_for_type( self, type_name: str, parent_name: Optional[str] = None ) -> Optional[str]: return self.as_class_name(type_name, parent_name) def class_name_for_type_if_property(self, type_name: str) -> Optional[str]: classname = self.class_name_for_type(type_name) if not classname: return None return self.generator_config.mapping_rules.replacemap.get(classname, classname) def class_name_for_profile( self, profile_name: Optional[Union[List[str], str]] ) -> Optional[Union[List[Optional[str]], str]]: if not profile_name: return None # TODO need to figure out what to do with this later. Annotation author supports multiples types that caused this to fail if isinstance(profile_name, (list,)): classnames = [] for name_part in profile_name: classnames.append( self.as_class_name(name_part.split("/")[-1]) ) # may be the full Profile URI, like http://hl7.org/fhir/Profile/MyProfile return classnames type_name = profile_name.split("/")[ -1 ] # may be the full Profile URI, like http://hl7.org/fhir/Profile/MyProfile return self.as_class_name(type_name) def class_name_is_native(self, class_name: str) -> bool: return class_name in self.generator_config.mapping_rules.natives def safe_property_name(self, prop_name: str) -> str: return self.generator_config.mapping_rules.reservedmap.get(prop_name, prop_name) def safe_enum_name(self, enum_name: str, ucfirst: bool = False) -> str: assert enum_name, "Must have a name" name = self.generator_config.mapping_rules.enum_map.get(enum_name, enum_name) parts = re.split(r"[\W_]+", name) # /!\ "CamelCase" term here is misleading. # "CamelCase" is not opposed to "snake_case", at least here. # See tests to see real cases. if self.generator_config.naming_rules.camelcase_enums: name = "".join([n[:1].upper() + n[1:] for n in parts]) if not ucfirst and name.upper() != name: name = name[:1].lower() + name[1:] else: # /!\ This is not a real snakecase. # Is it a problem ? Ex: HTTPVerb remains HTTPVerb name = "_".join(parts) if re.match(r"^\d", name): name = f"_{name}" return self.generator_config.mapping_rules.reservedmap.get(name, name) def json_class_for_class_name(self, class_name: str) -> str: return self.generator_config.mapping_rules.jsonmap.get( class_name, self.generator_config.mapping_rules.jsonmap_default ) # MARK: Writing Data def writable_profiles(self): """ Returns a list of `FHIRStructureDefinition` instances. """ return [ profile for profile in self.profiles.values() if profile.manual_module is None ] class FHIRVersionInfo(object): """ The version of a FHIR specification. """ def __init__(self, spec, directory): self.spec = spec now = datetime.date.today() self.date = now.isoformat() self.year = now.year infofile = os.path.join(directory, "version.info") self.version = self.read_version(infofile) def read_version(self, filepath): assert os.path.isfile(filepath) with io.open(filepath, "r", encoding="utf-8") as handle: for line in handle.readlines(): if line.startswith("FhirVersion"): return line.split("=", 2)[1].strip() class FHIRValueSetEnum(object): """ Holds on to parsed `FHIRValueSet` properties. """ def __init__( self, name: str, restricted_to: List[str], value_set: "FHIRValueSet", is_codesystem_known: bool, ): self.name = name self.restricted_to = restricted_to if len(restricted_to) > 0 else None self.value_set = value_set self.is_codesystem_known = is_codesystem_known self.represents_class = True # required for FHIRClass compatibility self.module = name # required for FHIRClass compatibility self.name_if_class = name # required for FHIRClass compatibility self.superclass_name = None # required for FHIRClass compatibility self.path = None # required for FHIRClass compatibility @property def definition(self) -> "FHIRValueSet": return self.value_set def name_of_resource(self) -> None: # required for FHIRClass compatibility return None class FHIRValueSet(object): """ Holds on to ValueSets bundled with the spec. """ def __init__(self, spec: "FHIRSpec", set_dict: Dict[str, Any]): self.spec = spec self.definition = set_dict self.url = set_dict.get("url") self.dstu2_inlined_codesystem = self.definition.get("codeSystem") if self.dstu2_inlined_codesystem is not None: self.dstu2_inlined_codesystem["url"] = self.dstu2_inlined_codesystem[ "system" ] self.dstu2_inlined_codesystem["content"] = "complete" self.dstu2_inlined_codesystem["name"] = self.definition.get("name") self.dstu2_inlined_codesystem["description"] = self.definition.get( "description" ) self._enum: Optional["FHIRValueSetEnum"] = None @property def short(self): return self.definition.get("title") @property def formal(self): return self.definition.get("description") @property def enum(self) -> Optional[FHIRValueSetEnum]: """ Returns FHIRValueSetEnum if this valueset can be represented by one. """ if self._enum is not None: return self._enum include = self.__safely_get_single_include() if include is None: return None system = include.get("system") if system is None: return None # alright, this is a ValueSet with 1 include and a system, is there a CodeSystem? cs = self.spec.codesystem_with_uri(system) is_codesystem_known = True if cs is None or not cs.generate_enum: # If no CodeSystem is found, we build an unofficial enum # Example : system = "http://unitsofmeasure.org" is not defined in FHIR is_codesystem_known = False cs_name = "unknown_codesystem_enum" else: cs_name = cs.name # Restrict CodeSystem to subset of concepts restricted_to = [] for concept in include.get("concept", []): assert "code" in concept restricted_to.append(concept["code"]) self._enum = FHIRValueSetEnum( name=cs_name, restricted_to=restricted_to, value_set=self, is_codesystem_known=is_codesystem_known, ) return self._enum def __safely_get_single_include(self) -> Optional[Dict[str, Any]]: include = None if self.dstu2_inlined_codesystem is not None: include = [self.dstu2_inlined_codesystem] else: compose = self.definition.get("compose") if compose is None: msg = f"Currently only composed ValueSets are supported. {self.definition}" raise Exception(msg) if "exclude" in compose: msg = "Not currently supporting 'exclude' on ValueSet" raise Exception(msg) # "import" is for DSTU-2 compatibility include = compose.get("include") or compose.get("import") or [] if len(include) != 1: logger.warning( f"Ignoring ValueSet with more than 1 includes ({len(include)}: {include})" ) return None return include[0] class FHIRCodeSystem(object): """ Holds on to CodeSystems bundled with the spec. """ def __init__(self, spec: FHIRSpec, resource): assert "content" in resource self.spec = spec self.definition = resource self.url = resource.get("url") if self.url in self.spec.generator_config.mapping_rules.enum_namemap: self.name = self.spec.generator_config.mapping_rules.enum_namemap[self.url] else: self.name = self.spec.safe_enum_name(resource.get("name"), ucfirst=True) if len(self.name) < 1: raise Exception( f"Unable to create a name for enum of system {self.url}. You may need to specify a name explicitly in mappings.enum_namemap. Code system content: {resource}" ) self.description = resource.get("description") self.valueset_url = resource.get("valueSet") self.codes = None self.generate_enum = False concepts = resource.get("concept", []) if resource.get("experimental"): return if resource["content"] == "complete": self.generate_enum = True if not self.generate_enum: logger.warning( f"Will not generate enum for CodeSystem '{self.url}' whose content is {resource['content']}" ) return assert concepts, 'Expecting at least one code for "complete" CodeSystem' if len(concepts) > 200: self.generate_enum = False logger.info( f"Will not generate enum for CodeSystem '{self.url}' because it has > 200 ({len(concepts)}) concepts" ) return self.codes = self.parsed_codes(concepts) def parsed_codes(self, codes, prefix=None): found = [] for c in codes: if c["code"][:1].isdigit(): self.generate_enum = False logger.info( f"Will not generate enum for CodeSystem '{self.url}' because at least one concept code starts with a number" ) return None cd = c["code"] # name = ( # "{}-{}".format(prefix, cd) # if prefix and not cd.startswith(prefix) # else cd # ) code_name = self.spec.safe_enum_name(cd) if len(code_name) < 1: raise Exception( f"Unable to create a member name for enum '{cd}' in {self.url}. You may need to add '{cd}' to mappings.enum_map" ) c["name"] = code_name c["definition"] = c.get("definition") or c["name"] found.append(c) # nested concepts? if "concept" in c: fnd = self.parsed_codes(c["concept"]) if fnd is None: return None found.extend(fnd) return found class FHIRStructureDefinition(object): """ One FHIR structure definition. """ def __init__(self, spec, profile): self.manual_module = None self.spec = spec self.url = None self.targetname = None self.structure = None self.elements = None self.main_element = None self._class_map = {} self.classes: List[fhirclass.FHIRClass] = [] self._did_finalize = False if profile is not None: self.parse_profile(profile) def __repr__(self): return f"<{self.__class__.__name__}> name: {self.name}, url: {self.url}" @property def name(self): return self.structure.name if self.structure is not None else None def read_profile(self, filepath): """ Read the JSON definition of a profile from disk and parse. Not currently used. """ profile = None with io.open(filepath, "r", encoding="utf-8") as handle: profile = json.load(handle) self.parse_profile(profile) def parse_profile(self, profile): """ Parse a JSON profile into a structure. """ assert profile assert "StructureDefinition" == profile["resourceType"] # parse structure self.url = profile.get("url") logger.info('Parsing profile "{}"'.format(profile.get("name"))) self.structure = FHIRStructureDefinitionStructure(self, profile) def process_profile(self): """ Extract all elements and create classes. """ struct = self.structure.differential # or self.structure.snapshot if struct is not None: mapped = {} self.elements = [] for elem_dict in struct: element = FHIRStructureDefinitionElement( self, elem_dict, self.main_element is None ) self.elements.append(element) mapped[element.path] = element # establish hierarchy (may move to extra loop in case elements are no longer in order) if element.is_main_profile_element: self.main_element = element parent = mapped.get(element.parent_name) if parent: parent.add_child(element) # resolve element dependencies for element in self.elements: element.resolve_dependencies() # run check: if n_min > 0 and parent is in summary, must also be in summary for element in self.elements: if element.n_min is not None and element.n_min > 0: if ( element.parent is not None and element.parent.is_summary and not element.is_summary ): logger.error( "n_min > 0 but not summary: `{}`".format(element.path) ) element.summary_n_min_conflict = True # create classes and class properties if self.main_element is not None: snap_class, subs = self.main_element.create_class() if snap_class is None: raise Exception( 'The main element for "{}" did not create a class'.format(self.url) ) self.found_class(snap_class) for sub in subs: self.found_class(sub) self.targetname = snap_class.name def element_with_id(self, ident): """ Returns a FHIRStructureDefinitionElementDefinition with the given id, if found. Used to retrieve elements defined via `contentReference`. """ if self.elements is not None: for element in self.elements: if element.definition.id == ident: return element return None def dstu2_element_with_name(self, name): """ Returns a FHIRStructureDefinitionElementDefinition with the given name, if found. Used to retrieve elements defined via `nameReference` used in DSTU-2. """ if self.elements is not None: for element in self.elements: if element.definition.name == name: return element return None # MARK: Class Handling def found_class(self, klass): self.classes.append(klass) def needed_external_classes(self): """ Returns a unique list of class items that are needed for any of the receiver's classes' properties and are not defined in this profile. :raises: Will raise if called before `finalize` has been called. """ if not self._did_finalize: raise Exception("Cannot use `needed_external_classes` before finalizing") internal = set([c.name for c in self.classes]) needed = set() needs = [] for klass in self.classes: # are there superclasses that we need to import? sup_cls = klass.superclass if ( sup_cls is not None and sup_cls.name not in internal and sup_cls.name not in needed ): needed.add(sup_cls.name) needs.append(sup_cls) # look at all properties' classes and assign their modules for prop in klass.properties: prop_cls_name = prop.class_name if prop.enum is not None: enum_cls, did_create = fhirclass.FHIRClass.for_element(prop.enum) enum_cls.module = prop.enum.name prop.module_name = enum_cls.module if enum_cls.name not in needed: needed.add(enum_cls.name) needs.append(enum_cls) elif ( prop_cls_name not in internal and not self.spec.class_name_is_native(prop_cls_name) ): prop_cls = fhirclass.FHIRClass.with_name(prop_cls_name) if prop_cls is None: raise Exception( 'There is no class "{}" for property "{}" on "{}" in {}'.format( prop_cls_name, prop.name, klass.name, self.name ) ) else: prop.module_name = prop_cls.module if prop_cls_name not in needed: needed.add(prop_cls_name) needs.append(prop_cls) return sorted(needs, key=lambda n: n.module or n.name) def referenced_classes(self): """ Returns a unique list of **external** class names that are referenced from at least one of the receiver's `Reference`-type properties. :raises: Will raise if called before `finalize` has been called. """ if not self._did_finalize: raise Exception("Cannot use `referenced_classes` before finalizing") references = set() for klass in self.classes: for prop in klass.properties: if len(prop.reference_to_names) > 0: references.update(prop.reference_to_names) # no need to list references to our own classes, remove them for klass in self.classes: references.discard(klass.name) return sorted(references) def writable_classes(self): return [klass for klass in self.classes if klass.should_write()] # MARK: Finalizing def finalize(self): """ Our spec object calls this when all profiles have been parsed. """ # assign all super-classes as objects for cls in self.classes: if cls.superclass is None: super_cls = fhirclass.FHIRClass.with_name(cls.superclass_name) if super_cls is None and cls.superclass_name is not None: raise Exception( 'There is no class implementation for class named "{}" in profile "{}"'.format( cls.superclass_name, self.url ) ) else: cls.superclass = super_cls self._did_finalize = True class FHIRStructureDefinitionStructure(object): """ The actual structure of a complete profile. """ def __init__(self, profile, profile_dict): self.profile = profile self.name = None self.base = None self.kind = None self.subclass_of = None self.snapshot = None self.differential = None self.parse_from(profile_dict) def parse_from(self, json_dict): name = json_dict.get("name") if not name: raise Exception("Must find 'name' in profile dictionary but found nothing") self.name = self.profile.spec.class_name_for_profile(name) self.base = json_dict.get("baseDefinition") self.kind = json_dict.get("kind") if self.base: self.subclass_of = self.profile.spec.class_name_for_profile(self.base) # find element definitions if "snapshot" in json_dict: self.snapshot = json_dict["snapshot"].get("element", []) if "differential" in json_dict: self.differential = json_dict["differential"].get("element", []) class FHIRStructureDefinitionElement(object): """ An element in a profile's structure. """ def __init__(self, profile, element_dict, is_main_profile_element=False): assert isinstance(profile, FHIRStructureDefinition) self.profile = profile self.path = None self.parent = None self.children = None self.parent_name = None self.definition = None self.n_min = None self.n_max = None self.is_summary = False # to mark conflicts, see #13215 (http://gforge.hl7.org/gf/project/fhir/tracker/?action=TrackerItemEdit&tracker_item_id=13125) self.summary_n_min_conflict = False self.valueset = None self.enum = None # assigned if the element has a binding to a ValueSet that is a CodeSystem generating an enum self.is_main_profile_element = is_main_profile_element self.represents_class = False self._superclass_name = None self._name_if_class = None self._did_resolve_dependencies = False if element_dict is not None: self.parse_from(element_dict) else: self.definition = FHIRStructureDefinitionElementDefinition(self, None) def parse_from(self, element_dict): self.path = element_dict["path"] parts = self.path.split(".") self.parent_name = ".".join(parts[:-1]) if len(parts) > 0 else None prop_name = parts[-1] if "-" in prop_name: prop_name = "".join([n[:1].upper() + n[1:] for n in prop_name.split("-")]) self.definition = FHIRStructureDefinitionElementDefinition(self, element_dict) self.definition.prop_name = prop_name self.n_min = element_dict.get("min") self.n_max = element_dict.get("max") self.is_summary = element_dict.get("isSummary") def resolve_dependencies(self): if self.is_main_profile_element: self.represents_class = True if ( not self.represents_class and self.children is not None and len(self.children) > 0 ): self.represents_class = True if self.definition is not None: self.definition.resolve_dependencies() self._did_resolve_dependencies = True # MARK: Hierarchy def add_child(self, element): assert isinstance(element, FHIRStructureDefinitionElement) element.parent = self if self.children is None: self.children = [element] else: self.children.append(element) def create_class(self, module=None): """ Creates a FHIRClass instance from the receiver, returning the created class as the first and all inline defined subclasses as the second item in the tuple. """ assert self._did_resolve_dependencies if not self.represents_class: return None, None subs = [] cls, did_create = fhirclass.FHIRClass.for_element(self) if did_create: # manual_profiles if module is None: if self.profile.manual_module is not None: module = self.profile.manual_module elif self.is_main_profile_element: module = self.profile.spec.as_module_name(cls.name) cls.module = module logger.debug('Created class "{}", module {}'.format(cls.name, module)) # child classes if self.children is not None: for child in self.children: properties = child.as_properties() if properties is not None: # collect subclasses sub, subsubs = child.create_class(module) if sub is not None: subs.append(sub) if subsubs is not None: subs.extend(subsubs) # add properties to class if did_create: for prop in properties: cls.add_property(prop) return cls, subs def as_properties(self): """ If the element describes a *class property*, returns a list of FHIRClassProperty instances, None otherwise. """ assert self._did_resolve_dependencies if self.is_main_profile_element or self.definition is None: return None # TODO: handle slicing information (not sure why these properties were # omitted previously) # if self.definition.slicing: # logger.debug('Omitting property "{}" for slicing'.format(self.definition.prop_name)) # return None # this must be a property if self.parent is None: raise Exception( 'Element reports as property but has no parent: "{}"'.format(self.path) ) # create a list of FHIRClassProperty instances (usually with only 1 item) if len(self.definition.types) > 0: props = [] for type_obj in self.definition.types: # an inline class if ( "BackboneElement" == type_obj.code or "Element" == type_obj.code ): # data types don't use "BackboneElement" props.append( fhirclass.FHIRClassProperty(self, type_obj, self.name_if_class) ) # TODO: look at http://hl7.org/fhir/StructureDefinition/structuredefinition-explicit-type-name ? else: props.append(fhirclass.FHIRClassProperty(self, type_obj)) return props # no `type` definition in the element: it's a property with an inline class definition type_obj = FHIRElementType() return [fhirclass.FHIRClassProperty(self, type_obj, self.name_if_class)] # MARK: Name Utils def name_of_resource(self): assert self._did_resolve_dependencies if ( not self.is_main_profile_element or self.profile.structure.kind is None or self.profile.structure.kind != "resource" ): return None return self.profile.name @property def name_if_class(self): if self._name_if_class is None: self._name_if_class = self.definition.name_if_class() return self._name_if_class @property def superclass_name(self): """ Determine the superclass for the element (used for class elements). """ if self._superclass_name is None: tps = self.definition.types if len(tps) > 1: raise Exception( 'Have more than one type to determine superclass in "{}": "{}"'.format( self.path, tps ) ) type_code = None if ( self.is_main_profile_element and self.profile.structure.subclass_of is not None ): type_code = self.profile.structure.subclass_of elif len(tps) > 0: type_code = tps[0].code elif self.profile.structure.kind: type_code = self.profile.spec.generator_config.default_base[ self.profile.structure.kind ] self._superclass_name = self.profile.spec.class_name_for_type(type_code) return self._superclass_name def __repr__(self): return f"<{self.__class__.__name__}> path: {self.path}" class FHIRStructureDefinitionElementDefinition(object): """ The definition of a FHIR element. """ def __init__(self, element, definition_dict): self.id = None self.element = element self.types = [] self.name = None self.prop_name = None self.content_reference = None self._content_referenced = None self.short = None self.formal = None self.comment = None self.binding = None self.constraint = None self.mapping = None self.slicing = None self.representation = None # TODO: extract "defaultValue[x]", "fixed[x]", "pattern[x]" # TODO: handle "slicing" if definition_dict is not None: self.parse_from(definition_dict) def parse_from(self, definition_dict): self.id = definition_dict.get("id") self.types = [] for type_dict in definition_dict.get("type", []): self.types.append(FHIRElementType(type_dict)) self.name = definition_dict.get("name") self.content_reference = definition_dict.get("contentReference") self.dstu2_name_reference = definition_dict.get("nameReference") self.short = definition_dict.get("short") self.formal = definition_dict.get("definition") if ( self.formal and self.short == self.formal[:-1] ): # formal adds a trailing period self.formal = None self.comment = definition_dict.get("comments") if "binding" in definition_dict: self.binding = FHIRElementBinding(definition_dict["binding"]) if "constraint" in definition_dict: self.constraint = FHIRElementConstraint(definition_dict["constraint"]) if "mapping" in definition_dict: self.mapping = FHIRElementMapping(definition_dict["mapping"]) if "slicing" in definition_dict: self.slicing = definition_dict["slicing"] self.representation = definition_dict.get("representation") def resolve_dependencies(self): # update the definition from a reference, if there is one if self.content_reference is not None: if "#" != self.content_reference[:1]: raise Exception( "Only relative 'contentReference' element definitions are supported right now" ) elem = self.element.profile.element_with_id(self.content_reference[1:]) if elem is None: raise Exception( f'There is no element definiton with id "{self.content_reference}", as referenced by {self.path} in {self.profile.url}' ) self._content_referenced = elem.definition elif self.dstu2_name_reference is not None: # DSTU-2 backwards-compatibility elem = self.element.profile.dstu2_element_with_name( self.dstu2_name_reference ) if elem is None: raise Exception( f'There is no element definiton with name "{self.dstu2_name_reference}", as referenced by {self.path} in {self.profile.url}' ) self._content_referenced = elem.definition # resolve bindings if ( self.binding is not None and self.binding.is_required and self.binding.has_valueset ): uri = self.binding.valueset_uri if not uri.startswith("http://hl7.org/fhir"): logger.debug('Ignoring foreign ValueSet "{}"'.format(uri)) return # remove version from canonical URI, if present, e.g. "http://hl7.org/fhir/ValueSet/name-use|4.0.0" uri = uri.split("|")[0] valueset = self.element.profile.spec.valueset_with_uri(uri) if valueset is None: logger.error( 'There is no ValueSet for required binding "{}" on {} in {}'.format( uri, self.name or self.prop_name, self.element.profile.name ) ) else: self.element.valueset = valueset self.element.enum = valueset.enum def name_if_class(self): """ Determines the class-name that the element would have if it was defining a class. This means it uses "name", if present, and the last "path" component otherwise. It also detects if the definition is a reference and will re-use the class name defined by the referenced element (such as `ValueSet.codeSystem.concept.concept`). """ # This Element is a reference, pick up the original name if self._content_referenced is not None: return self._content_referenced.name_if_class() with_name = self.name or self.prop_name parent_name = ( self.element.parent.name_if_class if self.element.parent is not None else None ) classname = self.element.profile.spec.class_name_for_type( with_name, parent_name ) if ( parent_name is not None and self.element.profile.spec.generator_config.naming_rules.backbone_class_adds_parent ): classname = parent_name + classname return classname class FHIRElementType(object): """Representing a type of an element. https://www.hl7.org/fhir/element.html """ def __init__(self, type_dict=None): self.code = None self.profile = None if type_dict is not None: self.parse_from(type_dict) def parse_from(self, type_dict): self.code = type_dict.get("code") # Look for the "structuredefinition-fhir-type" extension, introduced after R4 ext_type = type_dict.get("extension") # http://hl7.org/fhir/2020Feb/extensibility.html#Extension if ext_type is not None: fhir_ext = [ e for e in ext_type if e.get("url") == "http://hl7.org/fhir/StructureDefinition/structuredefinition-fhir-type" ] if len(fhir_ext) == 1: # This may hit after R4 if "valueUri" in fhir_ext[0]: self.code = fhir_ext[0].get("valueUri") if "valueUrl" in fhir_ext[0]: self.code = fhir_ext[0].get("valueUrl") # This may hit on R4 or earlier ext_code = type_dict.get("_code") if self.code is None and ext_code is not None: json_ext = [ e for e in ext_code.get("extension", []) if e.get("url") == "http://hl7.org/fhir/StructureDefinition/structuredefinition-json-type" ] if len(json_ext) < 1: raise Exception( f'Expecting either "code" or "_code" and a JSON type extension, found neither in {type_dict}' ) if len(json_ext) > 1: raise Exception( f"Found more than one structure definition JSON type in {type_dict}" ) self.code = json_ext[0].get("valueString") if self.code is None: raise Exception(f"No element type code found in {type_dict}") if not isinstance(self.code, str): raise Exception( "Expecting a string for 'code' definition of an element type, got {} as {}".format( self.code, type(self.code) ) ) if not isinstance(type_dict.get("targetProfile"), (list,)): self.profile = type_dict.get("targetProfile") if ( self.profile is not None and not isinstance(self.profile, str) and not isinstance(type_dict.get("targetProfile"), (list,)) ): # Added a check to make sure the targetProfile wasn't a list raise Exception( "Expecting a string for 'targetProfile' definition of an element type, got {} as {}".format( self.profile, type(self.profile) ) ) class FHIRElementBinding(object): """ The "binding" element in an element definition """ def __init__(self, binding_obj): self.strength = binding_obj.get("strength") self.description = binding_obj.get("description") self.valueset = binding_obj.get("valueSet") self.legacy_uri = binding_obj.get("valueSetUri") self.legacy_canonical = binding_obj.get("valueSetCanonical") self.dstu2_reference = binding_obj.get("valueSetReference", {}).get("reference") self.is_required = "required" == self.strength @property def has_valueset(self): return self.valueset_uri is not None @property def valueset_uri(self): return ( self.valueset or self.legacy_uri or self.legacy_canonical or self.dstu2_reference ) class FHIRElementConstraint(object): """ Constraint on an element. """ def __init__(self, constraint_arr): pass class FHIRElementMapping(object): """ Mapping FHIR to other standards. """ def __init__(self, mapping_arr): pass

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Python

src/sims4communitylib/dialogs/_common_ui_dialog_text_input_ok_cancel.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

118

2019-08-31T04:33:18.000Z

2022-03-28T21:12:14.000Z

src/sims4communitylib/dialogs/_common_ui_dialog_text_input_ok_cancel.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

15

2019-12-05T01:29:46.000Z

2022-02-18T17:13:46.000Z

src/sims4communitylib/dialogs/_common_ui_dialog_text_input_ok_cancel.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

28

2019-09-07T04:11:05.000Z

2022-02-07T18:31:40.000Z

""" The Sims 4 Community Library is licensed under the Creative Commons Attribution 4.0 International public license (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/legalcode Copyright (c) COLONOLNUTTY """ from typing import Tuple, Any, Callable from protocolbuffers.Localization_pb2 import LocalizedString from sims.sim_info import SimInfo from sims4communitylib.utils.localization.common_localization_utils import CommonLocalizationUtils from ui.ui_dialog_generic import UiDialogTextInputOkCancel class _CommonUiDialogTextInputOkCancel(UiDialogTextInputOkCancel): def __init__( self, sim_info: SimInfo, *args, title: Callable[..., LocalizedString]=None, text: Callable[..., LocalizedString]=None, **kwargs ): super().__init__( sim_info, *args, title=title, text=text, **kwargs ) self.text_input_responses = {} def on_text_input(self, text_input_name: str='', text_input: str='') -> bool: """A callback that occurs upon text being entered. """ self.text_input_responses[text_input_name] = text_input return False def build_msg(self, text_input_overrides=None, additional_tokens: Tuple[Any]=(), **kwargs): """Build the message. """ from sims4communitylib.dialogs.utils.common_dialog_utils import CommonDialogUtils msg = super().build_msg(additional_tokens=(), **kwargs) text_input_msg = msg.text_input.add() text_input_msg.text_input_name = CommonDialogUtils.TEXT_INPUT_NAME if additional_tokens and additional_tokens[0] is not None: text_input_msg.initial_value = CommonLocalizationUtils.create_localized_string(str(additional_tokens[0])) return msg

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afee559bb9619fc76eddbb0de5034b9cc836e90b

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Python

build_an_ai_startup_demo/app/views/main.py

bbueno5000/BuildAnAIStartUpDemo

f70371802a2546530c34b7f04e2b644cd1faec8a

[ "MIT" ]

null

build_an_ai_startup_demo/app/views/main.py

bbueno5000/BuildAnAIStartUpDemo

f70371802a2546530c34b7f04e2b644cd1faec8a

[ "MIT" ]

null

build_an_ai_startup_demo/app/views/main.py

bbueno5000/BuildAnAIStartUpDemo

f70371802a2546530c34b7f04e2b644cd1faec8a

[ "MIT" ]

null

""" DOCSTRING """ import app import flask import keras import numpy import os import random @app.app.route('/') #disease_list = [ # 'Atelectasis', # 'Consolidation', # 'Infiltration', # 'Pneumothorax', # 'Edema', # 'Emphysema', # 'Fibrosis', # 'Effusion', # 'Pneumonia', # 'Pleural_Thickening', # 'Cardiomegaly', # 'Nodule', # 'Mass', # 'Hernia'] @app.app.route('/contact') def contact(): return flask.render_template('contact.html', title='Contact') @app.app.route('/index') def index(): return flask.render_template('index.html', title='Home') @app.app.route('/map') def map(): return flask.render_template('map.html', title='Map') @app.app.route('/map/refresh', methods=['POST']) def map_refresh(): points = [( random.uniform(48.8434100, 48.8634100), random.uniform(2.3388000, 2.3588000)) for _ in range(random.randint(2, 9))] return flask.jsonify({'points': points}) @app.app.route('/uploaded', methods = ['GET', 'POST']) def upload_file(): if flask.request.method == 'POST': f = flask.request.files['file'] path = os.path.join(app.app.config['UPLOAD_FOLDER'], f.filename) model = keras.applications.resnet50.ResNet50(weights='imagenet') img = keras.preprocessing.image.load_img(path, target_size=(224, 224)) x = keras.preprocessing.image.img_to_array(img) x = numpy.expand_dims(x, axis=0) x = keras.applications.resnet50.preprocess_input(x) preds = model.predict(x) preds_decoded = keras.applications.resnet50.decode_predictions(preds, top=3)[0] print(keras.applications.resnet50.decode_predictions(preds, top=3)[0]) f.save(path) return flask.render_template( 'uploaded.html', title='Success', predictions=preds_decoded, user_image=f.filename) @app.app.route('/upload') def upload_file2(): return flask.render_template('index.html')

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aff5059c5098386517f47226d8e0e39141c4a8f9

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py

Python

MGCosmoPop/posteriors/prior.py

nicoborghi/MGCosmoPop

ebf07744caed1ac6694e7c750c1147ac30442fe5

[ "BSD-3-Clause" ]

4

2022-01-31T02:00:30.000Z

2022-03-22T08:00:00.000Z

MGCosmoPop/posteriors/prior.py

nicoborghi/MGCosmoPop

ebf07744caed1ac6694e7c750c1147ac30442fe5

[ "BSD-3-Clause" ]

null

MGCosmoPop/posteriors/prior.py

nicoborghi/MGCosmoPop

ebf07744caed1ac6694e7c750c1147ac30442fe5

[ "BSD-3-Clause" ]

5

2021-12-13T03:33:48.000Z

2022-03-22T08:00:02.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 4 11:31:43 2021 @author: Michi """ import numpy as np class Prior(object): ''' Class implementing the prior. At the moment it only supports disjoint priors. contains a method logPrior that returns the sum of log priors for each variable in the inference ''' def __init__(self, priorLimits, params_inference, priorNames, priorParams): ''' Parameters ---------- priorLimits : list list of max and min values for the prior range used for every parameter of the inference, in the correcto oder. Example: for inference on H0, lambda: [ (20, 140) , (-10, 10) ] params_inference : list list of names of parameters used in the inference . Example: ['H0', 'lambdaRedshift'] priorNames : dict Ditrionary specifying the type of prior used for each parameter. Supported so far are 'flat', 'flatLog', 'gauss' Example: gaussian prior on H0, flat on lambda: {'H0': gauss, 'lambdaRedshift':flat} priorParams : dict If any of the prior types requires parameters (e.g. mu and sigma for the gaussian) they are passed though this argument. Example: mu and sigma for gauss prior on H0 {'mu': 67.9, 'sigma': 0.1 } ''' self.priorLimits = priorLimits self.params_inference = params_inference self.priorNames = priorNames self.priorParams = priorParams def _logGauss(self, x, mu, sigma): ''' gaussian prior ''' if np.abs(x-mu)>7*sigma: return np.NINF return (-np.log(sigma)-(x-mu)**2/(2*sigma**2)) def _flatLog(self, x): ''' 1/x prior ''' return -np.log(x) def logPrior(self, Lambda_test): if np.isscalar(Lambda_test): limInf, limSup = self.priorLimits[self.params_inference[0]] condition = limInf < Lambda_test < limSup else: condition = True for i,param in enumerate(self.params_inference): limInf, limSup = self.priorLimits[param] condition &= limInf < Lambda_test[i] < limSup if not condition: return np.NINF lp = 0 for i,param in enumerate(self.params_inference): pname= self.priorNames[param] if np.isscalar(Lambda_test): x = Lambda_test else: x=Lambda_test[i] if pname=='flatLog': lp+=self._flatLog(x) elif pname=='gauss': mu, sigma = self.priorParams[param]['mu'], self.priorParams[param]['sigma'] lp += self._logGauss( x, mu, sigma) return lp

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aff8c4003c708639ad7c4f72b3b09d1135fb4817

4,058

py

Python

mmhuman3d/core/visualization/renderer/torch3d_renderer/depth_renderer.py

ykk648/mmhuman3d

26af92bcf6abbe1855e1a8a48308621410f9c047

[ "Apache-2.0" ]

472

2021-12-03T03:12:55.000Z

2022-03-31T01:33:13.000Z

mmhuman3d/core/visualization/renderer/torch3d_renderer/depth_renderer.py

ykk648/mmhuman3d

26af92bcf6abbe1855e1a8a48308621410f9c047

[ "Apache-2.0" ]

127

2021-12-03T05:00:14.000Z

2022-03-31T13:47:33.000Z

mmhuman3d/core/visualization/renderer/torch3d_renderer/depth_renderer.py

ykk648/mmhuman3d

26af92bcf6abbe1855e1a8a48308621410f9c047

[ "Apache-2.0" ]

37

2021-12-03T03:23:22.000Z

2022-03-31T08:41:58.000Z

from typing import Iterable, Optional, Tuple, Union import torch from pytorch3d.structures import Meshes from mmhuman3d.core.cameras import MMCamerasBase from .base_renderer import BaseRenderer from .builder import RENDERER, build_shader from .utils import normalize @RENDERER.register_module( name=['Depth', 'depth', 'depth_renderer', 'DepthRenderer']) class DepthRenderer(BaseRenderer): """Render depth map with the help of camera system.""" shader_type = 'DepthShader' def __init__( self, resolution: Tuple[int, int] = None, device: Union[torch.device, str] = 'cpu', output_path: Optional[str] = None, out_img_format: str = '%06d.png', depth_max: Union[int, float, torch.Tensor] = None, **kwargs, ) -> None: """Renderer for depth map of meshes. Args: resolution (Iterable[int]): (width, height) of the rendered images resolution. device (Union[torch.device, str], optional): You can pass a str or torch.device for cpu or gpu render. Defaults to 'cpu'. output_path (Optional[str], optional): Output path of the video or images to be saved. Defaults to None. out_img_format (str, optional): The image format string for saving the images. Defaults to '%06d.png'. depth_max (Union[int, float, torch.Tensor], optional): The max value for normalize depth range. Defaults to None. Returns: None """ super().__init__( resolution=resolution, device=device, output_path=output_path, out_img_format=out_img_format, **kwargs) self.depth_max = depth_max def _init_renderer(self, rasterizer=None, shader=None, materials=None, lights=None, blend_params=None, **kwargs): shader = build_shader(dict( type='DepthShader')) if shader is None else shader return super()._init_renderer(rasterizer, shader, materials, lights, blend_params, **kwargs) def forward(self, meshes: Optional[Meshes] = None, cameras: Optional[MMCamerasBase] = None, indexes: Optional[Iterable[int]] = None, backgrounds: Optional[torch.Tensor] = None, **kwargs): """Render depth map. Args: meshes (Optional[Meshes], optional): meshes to be rendered. Defaults to None. cameras (Optional[MMCamerasBase], optional): cameras for rendering. Defaults to None. indexes (Optional[Iterable[int]], optional): indexes for the images. Defaults to None. backgrounds (Optional[torch.Tensor], optional): background images. Defaults to None. Returns: Union[torch.Tensor, None]: return tensor or None. """ meshes = meshes.to(self.device) self._update_resolution(cameras, **kwargs) fragments = self.rasterizer(meshes_world=meshes, cameras=cameras) depth_map = self.shader( fragments=fragments, meshes=meshes, cameras=cameras) if self.output_path is not None: rgba = self.tensor2rgba(depth_map) if self.output_path is not None: self._write_images(rgba, backgrounds, indexes) return depth_map def tensor2rgba(self, tensor: torch.Tensor): rgbs, valid_masks = tensor.repeat(1, 1, 1, 3), (tensor > 0) * 1.0 depth_max = self.depth_max if self.depth_max is not None else rgbs.max( ) rgbs = normalize( rgbs, origin_value_range=(0, depth_max), out_value_range=(0, 1)) return torch.cat([rgbs, valid_masks], -1)

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null

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1

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aff8e664de9e8a592be6d93982dd50a72c78e151

7,131

py

Python

bot.py

0xsmoos/PMD

049ef60f9a4c44b635fc4dc88c5096685d78b5b7

[ "MIT" ]

3

2021-04-22T17:00:05.000Z

2021-08-19T05:33:37.000Z

bot.py

0xsmoos/PMD

049ef60f9a4c44b635fc4dc88c5096685d78b5b7

[ "MIT" ]

4

2021-04-24T10:46:03.000Z

2022-01-06T14:36:00.000Z

bot.py

0xsmoos/PMD

049ef60f9a4c44b635fc4dc88c5096685d78b5b7

[ "MIT" ]

1

2021-05-06T17:12:52.000Z

# -*- coding: utf-8 -*- # filename : bot.py # description : Discord bot interface for interacting with the server # author : LikeToAccess # email : liketoaccess@protonmail.com # date : 08-01-2021 # version : v2.0 # usage : python main.py # notes : # license : MIT # py version : 3.8.2 (must run on 3.6 or higher) #============================================================================== import time from threading import Thread import discord from requests.exceptions import MissingSchema from discord.ext import commands, tasks from scraper import Scraper from errors import NoResults import config as cfg import media import download credentials = media.read_file("credentials.md", filter=True) scraper = Scraper() token = credentials[0] allowed_users = credentials[1:] channel_id = { "commands": 776367990560129066, "log": 776354053222826004, "spam": 780948981299150888, } bot = commands.Bot(command_prefix= [ "beta ", "Beta ", "BETA ", "test ", ], help_command=None, case_insensitive=True) # | # Discord Functions | # V @bot.event async def on_ready(): check_logs.start() print(f"{bot.user} successfuly connected!") await set_status("Free Movies on Plex!", discord.Status.online) @bot.listen("on_message") async def on_message(message): if not message.content.startswith("https://gomovies-online."): return if message.channel.id != channel_id["commands"]: return if message.author == bot.user: return await send("Testing link...", silent=False) # if "--res=" in message.content: # forced_resolution = message.content.split("--res=")[1] # cfg.write_attempts(int(forced_resolution)) author = message.author source_url = message.content download_queue = scraper.get_download_link(source_url) for data in download_queue: target_url, metadata, *_ = data run_download(target_url, metadata, author.id) @tasks.loop(seconds=0.5) async def check_logs(filename="log.txt"): log_data = media.read_file(filename, filter=True) if log_data: media.write_file(filename, "### Beginning of message buffer from server ###\n") bulk_message = [] for message in log_data: if "--embed" in message: metadata = eval(message.replace("--embed","")) await create_embed(metadata) elif "--channel=" in message: message = message.split("--channel=") await send(message[0], channel=message[1]) elif "--file" in message: await send(message) # elif "--res=" in message: # forced_resolution = message.split("--res=")[1] # cfg.write_attempts(int(forced_resolution)) # bulk_message.append(message.split("--res=")[0]) else: bulk_message.append(message) if bulk_message: await send("\n".join(bulk_message)) # | # Discord Commands | # V @bot.command() async def downloads(ctx, user: discord.User, *flags): total_size = 0 # This is in MB movies = [] user_id = user.id lines = media.read_file(f"{user_id}.txt", filter=True) for line in lines: line = line.split("|") movies.append(line[0]) total_size += float(line[2]) if "--list" in flags: await send("{}".format("\n".join(movies))) author = user.display_name total_size = ( f"{int(round(total_size, 0))} MB" if total_size < 2048 else f"{round(total_size/1024, 2)} GB" ) await send( f"{author} has downloaded {len(movies)} movies/episodes totaling {total_size}." ) @bot.command(aliases=["add", "download"]) async def download_first_result(ctx, *movie_name): movie_name = " ".join(movie_name) author = ctx.author.id scraper.author = author if "https://gomovies-online." in movie_name: await send("Downloading via direct link...") download_queue = scraper.get_download_link(movie_name) # This would be a link not a query else: await send("Searching for matches...") try: download_queue = scraper.download_first_from_search(movie_name) # Searches using a movie title except NoResults: download_queue = None if download_queue: for data in download_queue: url, metadata, author = data if url: # If there were results and there is a valid URL, then download await send("Link found, downloading starting...") print(f"DEBUG: {metadata}") await create_embed(metadata[list(metadata)[0]]) run_download(url, metadata[list(metadata)[0]], author) else: await send("**ERROR**: No search results found!") else: await send("No results!", silent=False) @bot.command() async def search(ctx, *search_query): search_query = " ".join(search_query) author = ctx.author.id scraper.author = author start_time = time.time() if search_query: results, metadata = scraper.search( "https://gomovies-online.cam/search/" + \ "-".join(search_query.split()) ) print(f"Finished scraping search results in {round(time.time()-start_time,2)} seconds!") if results and metadata: for description in metadata: # print(description) await create_embed(metadata[description]) else: await send("**ERROR**: No search results found!") @bot.command() async def react(ctx): await ctx.message.add_reaction("\U0001F44D") @bot.command(aliases=["status", "validate"]) async def validate_url(ctx, *url): url = " ".join(url) try: status_code = download.validate_url(url)[0] await send(f"Status for URL: {status_code}") except MissingSchema as error: await send(str(error)) @bot.command() async def solve(ctx, captcha_solution): await ctx.message.delete() filename = "solved_captcha.txt" media.write_file(filename, captcha_solution) await ctx.send("Attempting captcha solve...") # | # Async Functions | # V async def create_embed(metadata, color=0xcbaf2f, channel="commands"): embed = discord.Embed( title=metadata["data-filmname"], description=metadata["data-genre"], color=color ) embed.set_footer(text=metadata["data-descript"]) embed.set_thumbnail(url=metadata["img"]) embed.add_field(name="\U0001F4C5", value=metadata["data-year"], inline=True) embed.add_field(name="IMDb", value=metadata["data-imdb"], inline=True) embed.add_field(name="\U0001F554", value=metadata["data-duration"], inline=True) await bot.get_channel(channel_id[channel]).send(embed=embed) async def send(msg, channel="commands", silent=True): channel = bot.get_channel(channel_id[channel]) if "--file" in msg: msg = msg.split("--file=") print(f"DEBUG: msg contains \"--file\" and the filename is \"{msg[1]}\"") await channel.send(msg[0].strip()) await channel.send(file=discord.File(msg[1])) else: await channel.send(msg) if not silent: print(msg) async def set_status(activity, status=discord.Status.online): await bot.change_presence(status=status, activity=discord.Game(activity)) # | # Functions | # V def run_download(url, metadata, author): download_function = download.Download(url, metadata, author) threaded_download = Thread(target=download_function.run) threaded_download.start() def run(): return bot.run(token) if __name__ == "__main__": run()

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null

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null

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1

0

affae23e9d7b59854ff8ab5e58706d9252734abf

12,277

py

Python

rq1and3_localness/happiness/compute_happiness.py

joh12041/chi-2016-localness

a7048015aac417217d23fccb5f49971922af6322

[ "MIT" ]

4

2016-11-06T21:55:51.000Z

2019-07-23T19:39:00.000Z

rq1and3_localness/happiness/compute_happiness.py

joh12041/chi-2016-localness

a7048015aac417217d23fccb5f49971922af6322

[ "MIT" ]

null

rq1and3_localness/happiness/compute_happiness.py

joh12041/chi-2016-localness

a7048015aac417217d23fccb5f49971922af6322

[ "MIT" ]

null

"""RQ3: Happiness algorithm as impacted by localness""" import csv import os import argparse import sys from collections import OrderedDict import numpy from scipy.stats import spearmanr from scipy.stats import wilcoxon sys.path.append("./utils") import bots LOCALNESS_METRICS = ['nday','plurality'] HAPPINESS_EVALUATIONS_FN = "../resources/happiness_evaluations.txt" def build_happiness_dict(): """Return dictionary containing word : happiness.""" with open(HAPPINESS_EVALUATIONS_FN, 'r') as fin: csvreader = csv.reader(fin, delimiter='\t') # Clear out metadata for i in range(0, 3): next(csvreader) assert next(csvreader) == ['word', 'happiness_rank', 'happiness_average', 'happiness_standard_deviation', 'twitter_rank', 'google_rank', 'nyt_rank', 'lyrics_rank'] happy_dict = {} for line in csvreader: word = line[0] h_avg = float(line[2]) if h_avg > 6 or h_avg < 4: happy_dict[word] = h_avg return happy_dict def compute_happiness(scale='counties'): """Compute happiness by county based on localness-processed CSV from localness.py.""" # generate word -> happiness dictionary happy_dict = build_happiness_dict() bots_filter = bots.build_bots_filter() # directory containing all of the tweets sorted by state or county depending on scale - one file for each region tweets_dir = './{0}'.format(scale) tweets_fns = os.listdir(tweets_dir) output_fn = "./raw_happiness_results_{0}.csv".format(scale) with open(output_fn, "w") as fout: csvwriter = csv.writer(fout) for localness in LOCALNESS_METRICS: csvwriter.writerow(['{0}_fips'.format(scale), '{0}_med_h'.format(localness), '{0}_avg_h'.format(localness), 'nonlocal_med_h', 'nonlocal_avg_h', 'unfiltered_med_h', 'unfiltered_avg_h', 'total_local', 'total_nonlocal', 'local_excluded', 'nonlocal_excluded']) local_filtered_out = 0 nonlocal_filtered_out = 0 for file in tweets_fns: with open(os.path.join(tweets_dir, file), 'r') as fin: fips = os.path.splitext(file)[0] # files named by <FIPS-CODE>.csv csvreader = csv.reader(fin) header = ['text','uid','nday','plurality'] txt_idx = header.index('text') uid_idx = header.index('uid') localness_idx = header.index(localness) assert next(csvreader) == header local_tweets = [] lt_no_happy_words = 0 non_local = [] nl_no_happy_words = 0 for line in csvreader: txt = line[txt_idx] uid = line[uid_idx] if not line[localness_idx]: continue local = (line[localness_idx] == 'True') if uid in bots_filter: if local: local_filtered_out += 1 else: nonlocal_filtered_out += 1 continue total_happ = 0.0 count_words = 0 for word in txt.split(): cleaned = word.lower().strip('?!.,;:()[]{}"\'') if cleaned in happy_dict: count_words += 1 total_happ += happy_dict[cleaned] if count_words > 0: h_avg_txt = total_happ / count_words if local: local_tweets.append(h_avg_txt) else: non_local.append(h_avg_txt) else: if local: lt_no_happy_words += 1 else: nl_no_happy_words += 1 local_med_h = numpy.median(local_tweets) local_avg_h = numpy.average(local_tweets) nonlocal_med_h = numpy.median(non_local) nonlocal_avg_h = numpy.average(non_local) unfiltered_med_h = numpy.median(local_tweets + non_local) unfiltered_avg_h = numpy.average(local_tweets + non_local) csvwriter.writerow([fips, local_med_h, local_avg_h, nonlocal_med_h, nonlocal_avg_h, unfiltered_med_h, unfiltered_avg_h, len(local_tweets), len(non_local), lt_no_happy_words, nl_no_happy_words]) print("{0} 'local' tweets and {1} 'nonlocal' tweets filtered out from organizations for {2}.".format(local_filtered_out, nonlocal_filtered_out, localness)) process_happiness_results(scale, output_fn) def process_happiness_results(scale, input_fn): """ Go through all counties/states happiness results and filter for counties with sufficient tweets to produce rankings :param scale: counties or states :return: writes rankings to CSV """ tweet_threshold = 3000 # minimum "happiness" tweets for county to be considered output_fn = "happiness_rankings_{0}_min{1}tweets.csv".format(scale, tweet_threshold) # include county/state names for easier evaluation of results fips_to_county = {} with open('../resources/fips_to_names.csv', 'r') as fin: csvreader = csv.reader(fin) assert next(csvreader) == ['FIPS','STATE','COUNTY'] for line in csvreader: fips = line[0] if scale == 'counties': if len(fips) == 4: fips = '0' + fips fips_to_county[fips] = '{0}, {1}'.format(line[2], line[1]) else: fips = fips[:2] fips_to_county[fips] = line[1] # read in raw results by county/state from analyzing all tweets - four tables in succession for each localness metric with open(input_fn, "r") as fin: csvreader = csv.reader(fin) idx = 0 localness = LOCALNESS_METRICS[idx] header = ['{0}_fips'.format(scale), '{0}_med_h'.format(localness), '{0}_avg_h'.format(localness), 'nonlocal_med_h', 'nonlocal_avg_h', 'unfiltered_med_h', 'unfiltered_avg_h', 'total_local', 'total_nonlocal', 'local_excluded', 'nonlocal_excluded'] assert next(csvreader) == header total_local_idx = header.index('total_local') total_nonlocal_idx = header.index('total_nonlocal') fips_idx = header.index('counties_fips') local_havg_idx = header.index('{0}_avg_h'.format(localness)) nonlocal_havg_idx = header.index('nonlocal_avg_h') unfiltered_havg_idx = header.index('unfiltered_avg_h') # aggregate unfiltered, local, and nonlocal happiness by county/state for generating rankings data = {} for line in csvreader: if line[0] == header[0]: # have reached next localness metric idx += 1 localness = LOCALNESS_METRICS[idx] else: total_local = float(line[total_local_idx]) total_nonlocal = float(line[total_nonlocal_idx]) fips = fips_to_county[line[fips_idx]] local_havg = line[local_havg_idx] nonlocal_havg = line[nonlocal_havg_idx] unfiltered_havg = line[unfiltered_havg_idx] if total_local + total_nonlocal >= tweet_threshold: # if sufficiently robust number of tweets for comparing to other counties/states pct_local = total_local / (total_local + total_nonlocal) if fips in data: data[fips]['{0}_local'.format(localness)] = local_havg data[fips]['{0}_nonlocal'.format(localness)] = nonlocal_havg data[fips]['{0}_pct_local'.format(localness)] = pct_local data[fips]['total_local_{0}'.format(localness)] = total_local data[fips]['total_nonlocal_{0}'.format(localness)] = total_nonlocal else: data[fips] = {'county' : fips, 'total_tweets' : total_local + total_nonlocal, 'total_local_{0}'.format(localness) : total_local, 'total_nonlocal_{0}'.format(localness) : total_nonlocal, '{0}_local'.format(localness) : local_havg, '{0}_nonlocal'.format(localness) : nonlocal_havg, 'unfiltered' : unfiltered_havg, '{0}_pct_local'.format(localness) : pct_local} ranks = [] unfiltered = {} for i in range(1, len(data) + 1): ranks.append({}) # sort results by unfiltered happiest to saddest sd = OrderedDict(sorted(data.items(), key=lambda x: x[1]['unfiltered'], reverse=True)) for i, fips in enumerate(sd): ranks[i]['county'] = fips ranks[i]['unfiltered'] = i + 1 ranks[i]['total_tweets'] = sd[fips]['total_tweets'] unfiltered[fips] = i for localness in LOCALNESS_METRICS: for property in ['local','nonlocal']: sd = {} for k in data: if '{0}_{1}'.format(localness, property) in data[k]: sd[k] = data[k] # sort happiest to saddest for localness metric + local or nonlocal sd = OrderedDict(sorted(sd.items(), key=lambda x: x[1]['{0}_{1}'.format(localness, property)], reverse=True)) # write ranking for that metric and (non)local to the row where the unfiltered county name is (so sorting any given column by rankings has the correct county labels to understand it) for i, fips in enumerate(sd): ranks[unfiltered[fips]]['{0}_{1}'.format(localness, property)] = i + 1 # write out rankings with open(output_fn, 'w') as fout: header = ['county', 'total_tweets', 'unfiltered'] for property in ['local','nonlocal']: for localness in LOCALNESS_METRICS: header.append('{0}_{1}'.format(localness, property)) csvwriter = csv.DictWriter(fout, fieldnames=header, extrasaction='ignore') csvwriter.writeheader() for rank in ranks: csvwriter.writerow(rank) # generate Spearman's rho comparing unfiltered to each localness metric and counting geographies that changed dramatically ten_pct_threshold = int(len(ranks) * 0.1) for localness in LOCALNESS_METRICS: for property in ['local','nonlocal']: metric = [] uf = [] ten_pct_diff = 0 name = '{0}_{1}'.format(localness, property) for rank in ranks: if name in rank: uf.append(rank['unfiltered']) metric.append(rank[name]) if abs(rank[name] - rank['unfiltered']) >= ten_pct_threshold: ten_pct_diff += 1 rho, pval = spearmanr(metric,uf) print('{0}:'.format(name)) print("Spearman's rho between {0} and unfiltered rankings is {1} with a p-value of {2}.".format(name, rho, pval)) print("{0} counties out of {1} were more than {2} rankings different than the unfiltered results.".format(ten_pct_diff, len(ranks), ten_pct_threshold)) stat, pval = wilcoxon(metric, uf, zero_method="pratt") print("Wilcoxon statistic between {0} and unfiltered rankings is {1} with a p-value of {2}.\n".format(name, stat, pval)) def main(): parser = argparse.ArgumentParser() parser.add_argument("--scale", default = "counties", help = "compute happiness by either 'states' or 'counties'") args = parser.parse_args() compute_happiness(scale = args.scale) if __name__ == "__main__": main()

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0

0.043902

0

0.068293

0.02439

0

null

0

null

0

1

0

affb5675ca38a4b1d3c3c74ee6838fd7720f8076

4,239

py

Python

rubix-stress/workload_runner.py

raunaqmorarka/presto-rubix

3149b6385f6685f5fe934551126b6593f59da9c8

[ "Apache-2.0" ]

162

2016-07-04T05:03:52.000Z

2022-03-29T03:31:59.000Z

rubix-stress/workload_runner.py

raunaqmorarka/presto-rubix

3149b6385f6685f5fe934551126b6593f59da9c8

[ "Apache-2.0" ]

381

2016-07-25T04:09:36.000Z

2022-02-11T11:39:27.000Z

rubix-stress/workload_runner.py

raunaqmorarka/presto-rubix

3149b6385f6685f5fe934551126b6593f59da9c8

[ "Apache-2.0" ]

64

2016-07-13T05:47:14.000Z

2022-03-10T09:03:35.000Z

import logging import random import time import threading from threading import Event from patched_commands import PrestoCommand class WorkloadRunner(threading.Thread): log = logging.getLogger(__name__) def __init__(self, exitEvent, silencePeriodEvent, tid, queries, cluster_label): threading.Thread.__init__(self) self.tid = "thread-" + str(tid) self.queries = queries self.cluster_label = cluster_label self.exitEvent = exitEvent self.silencePeriodEvent = silencePeriodEvent self.failures = [] self.backOffTime = 0 # This serves as wait time in case silence period is selected self.current_cmd = None # Kill ongoing command to start silence period def kill_ongoing_commmand(self): if self.current_cmd != None: # TODO add synchronization to assure 100% cancellations self.log.warning("Thread %s Cancelling %s" % (self.tid, self.current_cmd.id)) self.current_cmd.cancel() # Kill ongoing command for exit def interrupt(self): self.log.warning("Thread %s interrupted" %self.tid) self.kill_ongoing_commmand() # Sleep when main thread has started silence period # Or, Randomly backoff for some time to get random downscaling events # Or, Run some query def run(self): while not self.exitEvent.is_set(): while self.silencePeriodEvent.is_set(): self.exitEvent.wait(5) if self.exitEvent.is_set(): return should_wait = random.choice([True, False, True, False, False, False]) if (should_wait) and self.backOffTime != 0: # If decided to backoff then backoff between [10s, 120s] timeToBackOff = min(max(10, self.backOffTime), 120) self.log.warning("Thread %s backing off for %dseconds" %(self.tid, timeToBackOff)) self.exitEvent.wait(timeToBackOff) # reset backOffTime to avoid back to back backOffs self.backOffTime = 0 else: self.run_query() # Run a query randomly selected from the query pool # Sometimes cancel the submitted query after some random time # Collect failures def run_query(self): idx = random.randint(0, len(self.queries) - 1) queryName = self.queries[idx][0] queryString = self.queries[idx][1] start = time.time() shouldCancelQuery = random.randint(0, 500) < 25 # cancel with very less chance cancelTime = random.randint(10, 500) # lot of times query will finish before this time, so there will be even fewer cancellations queryStartTime = time.time() self.current_cmd = PrestoCommand.create(name="%s_%s" %(self.tid, queryName), label=self.cluster_label, query=queryString) self.log.warning("Thread %s running query %s via Command %s" % (self.tid, queryName, self.current_cmd.id)) while not self.current_cmd.is_done(self.current_cmd.status): if shouldCancelQuery and (time.time() - queryStartTime) > cancelTime: self.current_cmd.cancel() self.log.warning("Thread %s cancelled Command %s" % (self.tid, self.current_cmd.id)) self.exitEvent.wait(1) self.current_cmd = self.current_cmd.find(self.current_cmd.id) elapsed = time.time() - start if self.current_cmd.status == "cancelled": # expected pass elif not self.current_cmd.is_success(self.current_cmd.status): # TODO: get actual error code and classify failures as per codes self.failures.append(self.current_cmd.id) self.log.warning("Thread %s Command failed %s" %(self.tid, self.current_cmd.id)) else: self.backOffTime = elapsed self.current_cmd = None def log_failures(self): if len(self.failures) == 0: return message = "Failures in " + self.tid + ":\n" for failure in self.failures: message = message + str(failure) + "\n" message += "\n" self.log.warning(message)

39.616822

137

0.624912

510

4,239

5.094118

0.317647

0.080447

0.102386

0.046189

0.100077

0.030793

0.021555

0

0.011195

0.283557

4,239

106

138

39.990566

0.844254

0.176457

0

0.135135

0

0.062482

0

0.009434

0

1

0.081081

false

0.013514

0.081081

0

0.216216

0

null

0

null

0

1

0

b30543d6bc96edaac43450d48578207ded1283f4

2,585

py

Python

src/commands/debug.py

mdabessa/discordbot

37f605c5218f55365d4a82914ba604f8c62266e3

[ "MIT" ]

5

2021-03-11T01:47:12.000Z

2022-01-18T05:33:18.000Z

src/commands/debug.py

mdabessa/discordbot

37f605c5218f55365d4a82914ba604f8c62266e3

[ "MIT" ]

null

src/commands/debug.py

mdabessa/discordbot

37f605c5218f55365d4a82914ba604f8c62266e3

[ "MIT" ]

null

import modules.database as db import modules.entity as entity category = 'Depuração' entity.Command.newcategory(category, 'Depuração',is_visible=False) async def exe(message, commandpar, bot): if commandpar != None: cont = commandpar.split() text = f'Executando: {cont[0]}' if len(cont) > 1: text += ' [' + ' '.join(cont[1:]) + ']' m = await message.channel.send(text) await entity.Command.trycommand(m, commandpar, bot) else: raise entity.CommandError('Falta algo nesse comando!') entity.Command(name='exec', func=exe , category=category, desc=f'Executar um comando através do bot.', args=[['comando', '*'], ['parametros do comando', '']], perm=2) async def get_all_scripts(message, commandpar, bot): scripts = entity.Script.get_scripts() text = 'Scripts infos:\n' for script in scripts: text += f''' Nome: {script.name}\n Cache: {script.cache}\n''' text += '==========\n' await message.channel.send(text) entity.Command(name='get_all_scripts', func=get_all_scripts, category=category, desc='Listar todos os scripts rodando.', aliases=['gas'], perm=2) async def get_allowed_bots(message, commandpar, bot): bots = db.get_allowed_bots(bot.db_connection) await message.channel.send('Bots(ids) permitidos:\n'+' ,'.join(bots)) entity.Command(name='get_allowed_bots', func=get_allowed_bots, category=category, desc='Listar todos os bots permitidos.', aliases=['gab'], perm=2) async def add_allowed_bot(message, commandpar, bot): if commandpar != None: bots = db.get_allowed_bots(bot.db_connection) if str(commandpar) in bots: raise entity.CommandError('Esse id de bot, ja esta registrado como um `allowed_bot`') db.add_bot(commandpar, bot.db_connection) await message.add_reaction('✅') else: raise entity.CommandError('Falta parametros nesse comando!') entity.Command(name='add_allowed_bot', func=add_allowed_bot, category=category, desc='Permitir com que um bot especifico seja respondido.', aliases=['aab'], args=[['bot_id', '*']], perm=2) async def del_allowed_bot(message, commandpar, bot): if commandpar != None: db.del_bot(commandpar, bot.db_connection) await message.add_reaction('✅') else: raise entity.CommandError('Falta parametros nesse comando!') entity.Command(name='del_allowed_bot', func=del_allowed_bot, category=category, desc='Remover um bot especifico da lista de bots permitidos.', aliases=['dab'], args=[['bot_id', '*']], perm=2)

41.031746

191

0.677369

343

2,585

4.991254

0.28863

0.060748

0.058411

0.030374

0.442173

0.297897

0.238318

0.143692

0

0.003763

0.177563

2,585

62

192

41.693548

0.800564

0

0.272727

0

0.238298

0

0.022727

0

1

0

false

0

0.045455

0

0.045455

0

null

0

null

0

1

0

b30ec77ef09bdffed154fe1328bf919b56c53f19

2,877

py

Python

tests/test_amqp_transport.py

0x1EE7/tomodachi

8147b16d8be19b80b3bd7c5d8ed21c9863eaaa83

[ "MIT" ]

null

tests/test_amqp_transport.py

0x1EE7/tomodachi

8147b16d8be19b80b3bd7c5d8ed21c9863eaaa83

[ "MIT" ]

null

tests/test_amqp_transport.py

0x1EE7/tomodachi

8147b16d8be19b80b3bd7c5d8ed21c9863eaaa83

[ "MIT" ]

null

import os import signal import pytest from typing import Any from tomodachi.transport.amqp import AmqpTransport, AmqpException from run_test_service_helper import start_service def test_routing_key(monkeypatch: Any) -> None: routing_key = AmqpTransport.get_routing_key('test.topic', {}) assert routing_key == 'test.topic' routing_key = AmqpTransport.get_routing_key('test.topic', {'options': {'amqp': {'routing_key_prefix': 'prefix-'}}}) assert routing_key == 'prefix-test.topic' def test_encode_routing_key(monkeypatch: Any) -> None: routing_key = AmqpTransport.encode_routing_key('test-topic') assert routing_key == 'test-topic' routing_key = AmqpTransport.encode_routing_key('test.topic') assert routing_key == 'test.topic' def test_decode_routing_key(monkeypatch: Any) -> None: routing_key = AmqpTransport.decode_routing_key('test-topic') assert routing_key == 'test-topic' routing_key = AmqpTransport.decode_routing_key('test.topic') assert routing_key == 'test.topic' def test_queue_name(monkeypatch: Any) -> None: _uuid = '5d0b530f-5c44-4981-b01f-342801bd48f5' queue_name = AmqpTransport.get_queue_name('test.topic', 'func', _uuid, False, {}) assert queue_name == 'b444917b9b922e8c29235737c7775c823e092c2374d1bfde071d42c637e3b4fd' queue_name = AmqpTransport.get_queue_name('test.topic', 'func2', _uuid, False, {}) assert queue_name != 'b444917b9b922e8c29235737c7775c823e092c2374d1bfde071d42c637e3b4fd' queue_name = AmqpTransport.get_queue_name('test.topic', 'func', _uuid, False, {'options': {'amqp': {'queue_name_prefix': 'prefix-'}}}) assert queue_name == 'prefix-b444917b9b922e8c29235737c7775c823e092c2374d1bfde071d42c637e3b4fd' queue_name = AmqpTransport.get_queue_name('test.topic', 'func', _uuid, True, {}) assert queue_name == '540e8e5bc604e4ea618f7e0517a04f030ad1dcbff2e121e9466ddd1c811450bf' queue_name = AmqpTransport.get_queue_name('test.topic', 'func2', _uuid, True, {}) assert queue_name == '540e8e5bc604e4ea618f7e0517a04f030ad1dcbff2e121e9466ddd1c811450bf' queue_name = AmqpTransport.get_queue_name('test.topic', 'func', _uuid, True, {'options': {'amqp': {'queue_name_prefix': 'prefix-'}}}) assert queue_name == 'prefix-540e8e5bc604e4ea618f7e0517a04f030ad1dcbff2e121e9466ddd1c811450bf' def test_publish_invalid_credentials(monkeypatch: Any, capsys: Any, loop: Any) -> None: services, future = start_service('tests/services/dummy_service.py', monkeypatch) instance = services.get('test_dummy') with pytest.raises(AmqpException): loop.run_until_complete(AmqpTransport.publish(instance, 'data', 'test.topic', wait=True)) os.kill(os.getpid(), signal.SIGINT) loop.run_until_complete(future) out, err = capsys.readouterr() assert 'Unable to connect [amqp] to 127.0.0.1:54321' in err assert out == ''

42.308824

138

0.75113

323

2,877

6.427245

0.229102

0.105973

0.074181

0.100674

0.638728

0.575145

0

0.112306

0.127216

2,877

67

139

42.940299

0.714456

0

0.155556

0

0.295794

0.161627

0

0.311111

1

0.111111

false

0

0.133333

0

0.244444

0

null

0

null

0

1

0

b30f1e158c5e65ac948bbdec93ad290aa5c96a51

3,451

py

Python

enteletaor_lib/modules/redis/__init__.py

Seabreg/enteletaor

d1fbda5fcd68677fbce76e3ed4e79a886b8ad9db

[ "BSD-3-Clause" ]

159

2016-03-05T09:57:19.000Z

2022-02-20T02:45:03.000Z

enteletaor_lib/modules/redis/__init__.py

Seabreg/enteletaor

d1fbda5fcd68677fbce76e3ed4e79a886b8ad9db

[ "BSD-3-Clause" ]

8

2016-03-06T13:02:45.000Z

2020-06-12T08:19:16.000Z

enteletaor_lib/modules/redis/__init__.py

Seabreg/enteletaor

d1fbda5fcd68677fbce76e3ed4e79a886b8ad9db

[ "BSD-3-Clause" ]

30

2016-03-06T16:52:42.000Z

2021-03-31T09:46:39.000Z

# -*- coding: utf-8 -*- # # Enteletaor - https://github.com/cr0hn/enteletaor # # 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. # # 3. Neither the name of the copyright holder nor the names of its 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 HOLDER 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 logging from .. import IModule from ...libs.core.structs import CommonData from ...libs.core.models import StringField, IntegerField from .redis_dump import action_redis_dump from .redis_shell import action_redis_shell from .redis_info import action_redis_server_info from .redis_cache import action_redis_cache_poison from .redis_discover_db import action_redis_discover_dbs from .redis_clients import action_redis_server_connected from .redis_disconnect import action_redis_server_disconnect from .cmd_actions import parser_redis_dump, parser_redis_server_disconnect, parser_redis_server_cache_poison log = logging.getLogger() # ---------------------------------------------------------------------- class ModuleModel(CommonData): target = StringField(required=True) port = IntegerField(default=6379) db = IntegerField(default=0) # ---------------------------------------------------------------------- class RedisModule(IModule): """ Try to extract information from remote processes """ __model__ = ModuleModel __submodules__ = { 'dump': dict( help="dumps all keys in Redis database", cmd_args=parser_redis_dump, action=action_redis_dump ), 'info': dict( help="open a remote shell through the Redis server", action=action_redis_server_info ), 'connected': dict( help="get connected users to Redis server", action=action_redis_server_connected ), 'disconnect': dict( help="disconnect one or all users from Redis server", cmd_args=parser_redis_server_disconnect, action=action_redis_server_disconnect ), 'discover-dbs': dict( help="discover all Redis DBs at server", action=action_redis_discover_dbs ), 'cache': dict( help="poison remotes cache using Redis server", action=action_redis_cache_poison, cmd_args=parser_redis_server_cache_poison ), } name = "redis" description = "some attacks over Redis service"

38.344444

118

0.753115

460

3,451

5.48913

0.419565

0.06099

0.047129

0.036436

0.145347

0.080792

0.053861

0

0.003388

0.144596

3,451

89

119

38.775281

0.851965

0.488554

0

0.12

0

0.177457

0

1

0

false

0

0.24

0

0.42

0

null

0

null

0

1

0

b31211dcdebcd9d087794655b7c39c4690abb81c

1,110

py

Python

handler.py

altbdoor/legend-of-8ball-bot

d6be034bc6e440c2b99cfbf2c7f608f15af3f537

[ "WTFPL" ]

null

handler.py

altbdoor/legend-of-8ball-bot

d6be034bc6e440c2b99cfbf2c7f608f15af3f537

[ "WTFPL" ]

null

handler.py

altbdoor/legend-of-8ball-bot

d6be034bc6e440c2b99cfbf2c7f608f15af3f537

[ "WTFPL" ]

null

import random import time def send_bytes(con, channel, msg): con.send((f'PRIVMSG #{channel} : {msg}\r\n').encode('utf-8')) def sync_fn(con, channel, epoch, user, msg): pass def async_fn(con, channel, epoch, user, msg): if msg.startswith('!8ball'): answer_list = [ 'it is certain', 'it is decidedly so', 'without a doubt', 'yes - definitely', 'you may rely on it', 'as I see it, yes', 'most likely', 'outlook good', 'yes', 'signs point to yes', 'reply hazy, try again', 'ask again later', 'better not tell you now', 'cannot predict now', 'concentrate and ask again', "don't count on it", 'my reply is no', 'my sources say no', 'outlook not so good', 'very doubtful', ] time.sleep(1) answer_index = random.randint(0, len(answer_list) - 1) send_bytes(con, channel, f'[8ball] @{user}, {answer_list[answer_index]}')

27.073171

81

0.502703

136

1,110

4.036765

0.588235

0.07286

0.043716

0.069217

0.087432

0

0.008584

0.37027

1,110

40

82

27.75

0.776824

0

0.366667

0.024324

0

1

0.090909

false

0.030303

0.060606

0

0.151515

0

null

0

null

0

1

0

b3129e2827a5d1653cdcb5e64182818266d524f0

518

py

Python

main.py

gruporofex/alexa_rofex

338eb1e08da37a45f44eaab70a633e255d4b2be7

[ "Apache-2.0" ]

null

main.py

gruporofex/alexa_rofex

338eb1e08da37a45f44eaab70a633e255d4b2be7

[ "Apache-2.0" ]

null

main.py

gruporofex/alexa_rofex

338eb1e08da37a45f44eaab70a633e255d4b2be7

[ "Apache-2.0" ]

1

2019-06-02T14:17:14.000Z

import logging from configuration.config import LOGGING_LEVEL from alexa_handlers.AlexaForRFXHandler import AlexaForRFXHandler """ Main entry point for the Lambda function. """ logging.basicConfig(format='%(asctime)s %(message)s') logging.getLogger().setLevel(LOGGING_LEVEL) def lambda_handler(event, context): logging.info("Executing main lambda_handler for AlexaForRFXHandler class") alexa = AlexaForRFXHandler() alexa_response = alexa.process_request(event, context) return alexa_response

21.583333

78

0.787645

59

518

6.779661

0.576271

0.065

0

0.127413

518

23

79

22.521739

0.884956

0

0.17382

0

1

0.1

false

0

0.3

0

0.5

0

null

0

null

0

1

0

b314cd99dbfe61e94cec61ba01708bcc90cd79e7

236

py

Python

course/source/exercises/E001/test.py

sebastian-mutz/integrate

ce2a83358e2eb7f482d4fb70d167b1eba2abf2a8

[ "MIT" ]

2

2021-05-17T14:23:50.000Z

2021-08-24T13:07:42.000Z

course/source/exercises/E001/test.py

sebastian-mutz/integrate

ce2a83358e2eb7f482d4fb70d167b1eba2abf2a8

[ "MIT" ]

null

course/source/exercises/E001/test.py

sebastian-mutz/integrate

ce2a83358e2eb7f482d4fb70d167b1eba2abf2a8

[ "MIT" ]

1

2021-08-24T13:04:01.000Z

# wow. such script. many calculation. wow. # let's do some operations and save the results in variables a=20 + 22 b=2077 - 93 c=578 * 4 d=1332/2 e=16**2 print(a, b, c, d, e) # tell the computer to show us the values of each variable

21.454545

80

0.686441

49

236

3.306122

0.816327

0

0.117647

0.207627

236

10

81

23.6

0.748663

0.661017

0

1

0

false

0

0.166667

0

null

0

null

0

1

0

b3171b0e547151b36dca4c7d117c611b0459a447

5,070

py

Python

scripts/test_script.py

OmoooJ/gluon-facex

c5606fc9e2223c6d6dce2aaf2858d83f5eac1d54

[ "MIT" ]

257

2018-12-28T12:02:28.000Z

2021-11-25T08:43:52.000Z

scripts/test_script.py

OmoooJ/gluon-facex

c5606fc9e2223c6d6dce2aaf2858d83f5eac1d54

[ "MIT" ]

37

2019-01-10T02:31:12.000Z

2020-11-09T03:09:40.000Z

scripts/test_script.py

OmoooJ/gluon-facex

c5606fc9e2223c6d6dce2aaf2858d83f5eac1d54

[ "MIT" ]

57

2018-12-29T01:18:31.000Z

2021-09-14T14:41:35.000Z

# -*- coding: utf-8 -*- # Author: pistonyang@gmail.com import argparse import os import mxnet as mx import sklearn import numpy as np from mxnet import gluon, nd from mxnet.gluon.data import DataLoader from mxnet.gluon.data.vision import transforms from gluonfr.model_zoo import get_model from gluonfr.data import get_recognition_dataset from gluonfr.metrics.verification import FaceVerification parser = argparse.ArgumentParser(description='Test a model for face recognition.') parser.add_argument('--batch-size', type=int, default=512, help='Test batch size.') parser.add_argument('-n', '--model', type=str, default='l_se_resnet50v2', help='Model to test.') parser.add_argument('--model-params', type=str, required=True, help='Model params to load.') parser.add_argument('-t', '--val-dateset', dest='target', type=str, default='lfw', help='Val datasets, default is lfw.' 'Options are lfw, calfw, cplfw, agedb_30, cfp_ff, vgg2_fp.') parser.add_argument('--export', action='store_true', help='Whether to export model.') parser.add_argument('--export-path', type=str, default='', help='Path to save export files.') parser.add_argument('--dtype', type=str, default='float32', help='data type for training. default is float32') parser.add_argument('--ctx', type=str, default="0", help='Use GPUs to train.') parser.add_argument('--hybrid', action='store_true', help='Whether to use hybrid.') opt = parser.parse_args() assert opt.batch_size % len(opt.ctx.split(",")) == 0, "Per batch on each GPU must be same." assert opt.dtype in ('float32', 'float16'), "Data type only support FP16/FP32." transform_test = transforms.Compose([ transforms.ToTensor() ]) def transform_test_flip(data, isf=False): flip_data = nd.flip(data, axis=1) if isf: data = nd.transpose(data, (2, 0, 1)).astype('float32') flip_data = nd.transpose(flip_data, (2, 0, 1)).astype('float32') return data, flip_data return transform_test(data), transform_test(flip_data) export_path = os.path.dirname(opt.model_params) if opt.export_path == '' else opt.export_path ctx = [mx.gpu(int(i)) for i in opt.ctx.split(",")] batch_size = opt.batch_size targets = opt.target val_sets = [get_recognition_dataset(name, transform=transform_test_flip) for name in targets.split(",")] val_datas = [DataLoader(dataset, batch_size, last_batch='keep') for dataset in val_sets] test_net = get_model(opt.model, need_cls_layer=False) test_net.cast(opt.dtype) test_net.load_parameters(opt.model_params, ctx=ctx, ignore_extra=True) def validate(nfolds=10): metric = FaceVerification(nfolds) metric_flip = FaceVerification(nfolds) for loader, name in zip(val_datas, targets.split(",")): metric.reset() for i, batch in enumerate(loader): data0s = gluon.utils.split_and_load(batch[0][0][0], ctx, even_split=False) data1s = gluon.utils.split_and_load(batch[0][1][0], ctx, even_split=False) data0s_flip = gluon.utils.split_and_load(batch[0][0][1], ctx, even_split=False) data1s_flip = gluon.utils.split_and_load(batch[0][1][1], ctx, even_split=False) issame_list = gluon.utils.split_and_load(batch[1], ctx, even_split=False) embedding0s = [test_net(X) for X in data0s] embedding1s = [test_net(X) for X in data1s] embedding0s_flip = [test_net(X) for X in data0s_flip] embedding1s_flip = [test_net(X) for X in data1s_flip] emb0s = [nd.L2Normalization(e, mode='instance') for e in embedding0s] emb1s = [nd.L2Normalization(e, mode='instance') for e in embedding1s] for embedding0, embedding1, issame in zip(emb0s, emb1s, issame_list): metric.update(issame, embedding0, embedding1) emb0s_flip = [nd.L2Normalization(nd.concatenate([e, ef], 1), mode='instance') for e, ef in zip(embedding0s, embedding0s_flip)] emb1s_flip = [nd.L2Normalization(nd.concatenate([e, ef], 1), mode='instance') for e, ef in zip(embedding1s, embedding1s_flip)] for embedding0, embedding1, issame in zip(emb0s_flip, emb1s_flip, issame_list): metric_flip.update(issame, embedding0, embedding1) tpr, fpr, accuracy, val, val_std, far, accuracy_std = metric.get() print("{}: \t{:.6f}+-{:.6f}".format(name, accuracy, accuracy_std)) _, _, accuracy, _, _, _, accuracy_std = metric_flip.get() print("{}-flip: {:.6f}+-{:.6f}".format(name, accuracy, accuracy_std)) if __name__ == '__main__': if opt.hybrid: test_net.hybridize() validate() if opt.export: assert opt.hybrid is True, 'Export need --hybrid.' expot_name = os.path.join(export_path, opt.model) test_net.export(expot_name) print('export model is saved at {}'.format(expot_name))

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py

Python

gamepyd/readPad.py

Marceline/PyXinput

fa60e215e99b8c0fe95767c21fd9ba239a0719bd

[ "Unlicense" ]

2

2020-11-26T09:23:35.000Z

2020-11-27T13:36:46.000Z

gamepyd/readPad.py

Marceline/PyXinput

fa60e215e99b8c0fe95767c21fd9ba239a0719bd

[ "Unlicense" ]

7

2020-10-03T16:38:26.000Z

2020-10-03T17:17:00.000Z

gamepyd/readPad.py

Marceline/PyXinput

fa60e215e99b8c0fe95767c21fd9ba239a0719bd

[ "Unlicense" ]

1

2021-06-04T17:44:55.000Z

"""Read the current state of Xbox Controllers""" from ctypes import * import pandas as pd from time import time_ns # Xinput DLL try: _xinput = windll.xinput1_4 except OSError as err: _xinput = windll.xinput1_3 class _xinput_gamepad(Structure): """CType XInput Gamepad Object""" _fields_ = [ ("wButtons", c_ushort), #Contains all button information in one integer ("LT", c_ubyte), #Left Trigger ("RT", c_ubyte), #Right Trigger ("Lx", c_short), #Right stick horizontal movement ("Ly", c_short), #Right stick vertical movement ("Rx", c_short), #Left stick horizontal movement ("Ry", c_short) ] #Left stick vertical movement fields = [f[0] for f in _fields_] def __dict__(self): return {field: self.__getattribute__(field) for field in self.fields} def __str__(self): return str(self.__dict__()) def __getitem__(self, string): return self.__dict__()[string] class _xinput_state(Structure): """CType XInput State Object""" _fields_ = [("dwPacketNumber", c_uint), ("XINPUT_GAMEPAD", _xinput_gamepad)] fields = fields = [f[0] for f in _fields_] def __dict__(self): return {field: self.__getattribute__(field) for field in self.fields} def __str__(self): return str(self.__dict__()) def __getitem__(self, string): return self.__dict__()[string] class rPad(object): """XInput Controller State reading object""" _buttons = { # All possible button values 'UP': 0x0001, 'DOWN': 0x0002, 'LEFT': 0x0004, 'RIGHT': 0x0008, 'START': 0x0010, 'SELECT': 0x0020, 'L3': 0x0040, 'R3': 0x0080, 'LB': 0x0100, 'RB': 0x0200, 'A': 0x1000, 'B': 0x2000, 'X': 0x4000, 'Y': 0x8000 } def __init__(self, ControllerID: int = 1, absolute: bool = False): """ Initialise Controller object. ControllerID Int Position of gamepad. """ self.ControllerID = ControllerID self.dwPacketNumber = c_uint() self.absolute = absolute print(f"Now reading gamepad#{ControllerID} as ABSOLUTE values" ) if self.absolute else print( f"Now reading gamepad#{ControllerID}") self.dwPacketNumber = c_uint() @property def read(self): """ Returns the current gamepad state. """ """If you wanna optimize reading, this is THE method to look at first""" state = _xinput_state() _xinput.XInputGetState(self.ControllerID - 1, pointer(state)) self.dwPacketNumber = state.dwPacketNumber check = lambda x: (state.XINPUT_GAMEPAD.wButtons & x) == x buttons = {name: check(value) for name, value in rPad._buttons.items()} analogs = state.XINPUT_GAMEPAD.__dict__() del analogs['wButtons'] return {**analogs, **buttons} def __loop(self, line, start, wait_ns, i): #Provides an easy loop #foo=str(xbox.read) #jot.write(foo+"\n") if (time_ns() >= start[0] + wait_ns): moment = self.read # will return a dictionary for instantaneous state of the controller moment['time(ns)'] = time_ns() #store current time in nanoseconds moment['timeDelta(ms)'] = ( time_ns() - start[0]) / 10**6 #Store the time diffference in milliseconds moment['error(ms)'] = moment['timeDelta(ms)'] - wait_ns / 10**6 line.append(moment) i[0] += 1 #print(f"time elapsed={((time_ns()-start)/10**6)/1000}") start[0] = time_ns() def __write( self, line, type: str, dest: str): #Provides writing facility given a type and location supportedTypes = ["df"] if type not in supportedTypes: print( f"sorry, currently supported types are: {str(supportedTypes)[1:-1]}" ) if (type == "df"): output = pd.DataFrame(line) if not self.absolute: #The following line is technically inaccurate as Bryan says "Axis are -32768 to 32767" output[['Lx', 'Ly', 'Rx', 'Ry']] = output[['Lx', 'Ly', 'Rx', 'Ry']] / 32768 output[['LT', 'RT']] = output[['LT', 'RT']] / 255 #Save to disk if required if (len(dest) > 0 and type == "df"): (pd.DataFrame(line)).to_feather(dest) #elif(len(file) > 0 and type == "list"): return output #elif(type == "list"): def record(self, duration: float = 5, rate: float = float(1 / 120), file: str = "", type="df"): """ Records for a given duration at a fixed rate, possibly to a file """ #Setup loop parameters line = [] start = [time_ns()] count = duration // rate wait_ns = rate * 10**9 i = [0] #Time for the loop #pbar = tq(total=count, position=0, leave=True) while (i[0] < count): self.__loop(line, start, wait_ns, i) return self.__write(line, type, file) #write to disk if wanted def capture(self, stopper, rate: float = float(1 / 120), file: str = "", type="df"): """ Records till mentioned button is pressed at a fixed rate, possibly to a file """ if stopper not in self._buttons: print("Choose a button label to end recording please") print(f"Your choices are ${self._buttons}") return 1 #Setup loop parameters line = [self.read] start = [time_ns()] wait_ns = rate * 10**9 i = [0] while not bool((line[-1])[stopper]): self.__loop(line, start, wait_ns, i) #write to disk if wanted return self.__write(line, type, file) def main(): """Test the functionality of the rPad object""" from time import sleep print('Testing controller in position 1:') print( "This will just take a second. We'll look at the controller values in 200 milli-second intervals:" ) # Initialise Controller con = rPad(1) # Loop printing controller state and buttons held for i in range(5): print(f"{i}---------------------------------------------") print(f'State:{con.read}') print("---------------------------------------------") sleep(0.2) print( "Better yet, you can use prettyRead() to sample as many times as desired for any required duration." ) print( f"And then return it as a dataframe, can even write it to a file by supplying the filename.\n {con.prettyRead(1).head()}" ) print("Do note that the final three columns are metadata.") if __name__ == '__main__': main()

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