xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

9b350a79a6775424d2d8cb6ced758fe97fa60621

139

py

Python

exersices/hourglasses.py

gcvalderrama/python_foundations

5ac045085dcc6c906729b481f833fa6a7889bd19

[ "MIT" ]

null

exersices/hourglasses.py

gcvalderrama/python_foundations

5ac045085dcc6c906729b481f833fa6a7889bd19

[ "MIT" ]

null

exersices/hourglasses.py

gcvalderrama/python_foundations

5ac045085dcc6c906729b481f833fa6a7889bd19

[ "MIT" ]

null

if __name__ == "__main__": print(reverseArray([1, 4, 3, 2])) print(reverseArray([6676, 3216, 4063, 8373, 423, 586, 8850, 6762]))

23.166667

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19

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0.18705

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1

0

1

0

7

9b7ae1c4c5bd492fe513f1e4a6ec6b4635863b3b

146

py

Python

utils/__init__.py

itsnarsi/1WeStar

c3d0caa1079db6900a227ab51694faabd6f0eb2d

[ "MIT" ]

null

utils/__init__.py

itsnarsi/1WeStar

c3d0caa1079db6900a227ab51694faabd6f0eb2d

[ "MIT" ]

null

utils/__init__.py

itsnarsi/1WeStar

c3d0caa1079db6900a227ab51694faabd6f0eb2d

[ "MIT" ]

null

# @Author: Narsi Reddy <narsi> # @Date: 2019-12-17T20:51:47-06:00 # @Last modified by: narsi # @Last modified time: 2019-12-17T20:51:48-06:00

29.2

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0.671233

26

146

3.769231

0.615385

0.122449

0.22449

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null

0

1

0

7

32e01bf72683c9d183ccd04008d1a93cedc9f774

1,363

py

Python

Largest product in a series.py

cakmakaf/Project_Euler_Challenges

27364e2a924440a8e4bacde5d56b9de4ecbf9fe9

[ "MIT" ]

1

2019-02-21T22:27:28.000Z

Largest product in a series.py

cakmakaf/Project_Euler_Challenges

27364e2a924440a8e4bacde5d56b9de4ecbf9fe9

[ "MIT" ]

null

Largest product in a series.py

cakmakaf/Project_Euler_Challenges

27364e2a924440a8e4bacde5d56b9de4ecbf9fe9

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Nov 13 10:56:32 2018 @author: acakmak """ series = "7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450" LargestProduct = 0 for n in range(0, len(series) - 13): Product = 1 for i in range(n, n + 13): Product *= int(series[i: i + 1]) if Product > LargestProduct: LargestProduct = Product print (LargestProduct)

61.954545

1,012

0.879677

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0.011676

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1,363

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1

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7

fd47a797af2a59ffb9a07feea8b58efbc2b5798b

42,428

py

Python

src/framework/visualization/visualizer.py

securedataplane/mts

9ffe415ce586600e558e7a2855348c9cd1651f49

[ "MIT" ]

1

2022-03-10T13:00:25.000Z

src/framework/visualization/visualizer.py

securedataplane/mts

9ffe415ce586600e558e7a2855348c9cd1651f49

[ "MIT" ]

1

2019-07-23T08:49:09.000Z

src/framework/visualization/visualizer.py

securedataplane/mts

9ffe415ce586600e558e7a2855348c9cd1651f49

[ "MIT" ]

null

#!/usr/bin/env python # http://matplotlib.org/api/pyplot_api.html # https://gist.github.com/bilalhusain import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from numpy import arange from scipy.interpolate import spline from pylab import * import itertools import json import time import re from datetime import datetime, tzinfo, timedelta import glob import expLib labels = [u'44bytes', u'512bytes', u'1500bytes', u'2048bytes', u'9000bytes'] labels = [u'64bytes', u'512bytes', u'1500bytes', u'2048bytes', u'9000bytes'] lat_packet_start_index = 100 lat_packet_end_index = 10100 def plotLatency(pcapAnalysisPath,topology): baseline_noDpdk = {} baseline_Dpdk = {} sriov_dpdk = {} sriov_noDpdk = {} if topology == "phy2phy": baseline_noDpdk = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-Baseline_NoDPDK-') baseline_Dpdk = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-Baseline_DPDK-') sriov_dpdk = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-SRIOV_DPDK-') sriov_noDpdk = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-SRIOV_NoDPDK-') elif topology == "phy2vm2vm2phy": baseline_noDpdk = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-Baseline_NoDPDK-') baseline_Dpdk = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-Baseline_DPDK-') sriov_dpdk = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-SRIOV_DPDK-') sriov_noDpdk = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-SRIOV_NoDPDK-') # print baseline_noDpdk # print sriov_dpdk # print sriov_noDpdk fig = plt.figure(1, figsize = (8.75,4.6),frameon=True) fig.autofmt_xdate(bottom=0.1, rotation=90, ha='right') ax = plt.subplot(111) c = 0 data = [] xmark = [] data.append([]) xmark.append("") c = 0 for l in labels: data.append(baseline_noDpdk[l]) xmark.append('baseline-nodpdk') data.append(baseline_Dpdk[l]) xmark.append('baseline-dpdk') data.append(sriov_noDpdk[l]) xmark.append('sriov-nodpdk') data.append(sriov_dpdk[l]) xmark.append('sriov-dpdk') ax.text(2.0, 10000.05, u'64$B$') ax.text(5.0, 10000.05, u'512$B$') ax.text(8.0, 10000.05, u'1500$B$') ax.text(11.0, 10000.05, u'2048$B$') ax.text(14.0, 10000.05, u'9000$B$') bp_dict = plt.boxplot(data, patch_artist=False) plt.setp(bp_dict['whiskers'], color='black', linewidth=1, linestyle='-') plt.setp(bp_dict['fliers'], color='blue', linewidth=1, marker='+', markersize=2) plt.setp(bp_dict['boxes'], linewidth=1) plt.setp(bp_dict['medians'], linewidth=1, color='red') plt.xticks(range(1, 18), tuple(xmark), rotation='-45', ha='left') # Print median values for debug # medians=[] # for line in bp_dict['medians']: # # get position data for median line # x, y = line.get_xydata()[1] # top of median line # # overlay median value # text(x, y, '%.4f' % y, # horizontalalignment='center', fontsize=5) # draw above, centered # print "%.4f" % y # medians.append(y) # plt.grid(True) marker = itertools.cycle(('d', '*', 'o', '^')) plt.plot([1.0, 1.0], [-1, 10000], color='#000000') plt.plot([4.5, 4.5], [-1, 10000], color='#000000') plt.plot([7.5, 7.5], [-1, 10000], color='#000000') plt.plot([10.5, 10.5], [-1, 10000], color='#000000') plt.plot([13.5, 13.5], [-1, 10000], color='#000000') plt.ylim((0.001,10000)) plt.ylabel('Latency in millisecond') plt.xlabel("Scenario mode") box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.25, box.width * 1.0, box.height * 0.78]) ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=0.8) ax.set_axisbelow(True) ax.set_yscale('log') # ax.set_xscale('log') plt.savefig(pcapAnalysisPath+'plot_box_latency_'+topology+'.pdf', dpi=(2500), format='pdf') plt.savefig(pcapAnalysisPath+'plot_box_latency_'+topology+'.png', dpi=(250), format='png') plt.close() def plotLatencyMulti(pcapAnalysisPath,topology): Baseline_NoDPDK = {} Baseline_DPDK = {} SRIOV_NoDPDK_OneOvs = {} SRIOV_DPDK_OneOvs = {} SRIOV_NoDPDK_TwoOvs = {} SRIOV_DPDK_TwoOvs = {} SRIOV_NoDPDK_FourOvs = {} SRIOV_DPDK_FourOvs = {} if topology == "phy2phy": Baseline_NoDPDK = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-Baseline_NoDPDK-') Baseline_DPDK = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-Baseline_DPDK-') SRIOV_DPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-SRIOV_DPDK_OneOvs-') SRIOV_NoDPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-SRIOV_NoDPDK_OneOvs-') SRIOV_DPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_DPDK_TwoOvs-') SRIOV_NoDPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_TwoOvs-') SRIOV_DPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_DPDK_FourOvs-') SRIOV_NoDPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_FourOvs-') elif topology == "phy2vm2vm2phy": Baseline_NoDPDK = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-Baseline_NoDPDK-') Baseline_DPDK = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-Baseline_DPDK-') SRIOV_DPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-SRIOV_DPDK_OneOvs-') SRIOV_NoDPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-SRIOV_NoDPDK_OneOvs-') SRIOV_DPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_DPDK_TwoOvs-') SRIOV_NoDPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_TwoOvs-') SRIOV_DPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_DPDK_FourOvs-') SRIOV_NoDPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_FourOvs-') # print Baseline_NoDPDK # print SRIOV_DPDK_OneOvs # print SRIOV_NoDPDK_OneOvs # print SRIOV_DPDK_TwoOvs # print SRIOV_NoDPDK_TwoOvs fig = plt.figure(1, figsize = (8.75,4.6),frameon=True) fig.autofmt_xdate(bottom=0.1, rotation=90, ha='right') ax = plt.subplot(111) c = 0 data = [] xmark = [] data.append([]) xmark.append("") c = 0 for l in labels: data.append(Baseline_NoDPDK[l]) xmark.append('Baseline_NoDPDK') data.append(Baseline_DPDK[l]) xmark.append('baseline-dpdk') data.append(SRIOV_NoDPDK_OneOvs[l]) xmark.append('SRIOV_NoDPDK_OneOvs') data.append(SRIOV_DPDK_OneOvs[l]) xmark.append('SRIOV_NoDPDK_OneOvs') data.append(SRIOV_NoDPDK_TwoOvs[l]) xmark.append('SRIOV_NoDPDK_TwoOvs') data.append(SRIOV_DPDK_TwoOvs[l]) xmark.append('SRIOV_NoDPDK_TwoOvs') data.append(SRIOV_NoDPDK_FourOvs[l]) xmark.append('SRIOV_NoDPDK_FourOvs') data.append(SRIOV_DPDK_FourOvs[l]) xmark.append('SRIOV_NoDPDK_FourOvs') # ax.text(2.0, 10000.05, u'64$B$') # ax.text(7.0, 10000.05, u'512$B$') # ax.text(12.0, 10000.05, u'1500$B$') # ax.text(17.0, 10000.05, u'2048$B$') # ax.text(22.0, 10000.05, u'9000$B$') bp_dict = plt.boxplot(data, patch_artist=False) plt.setp(bp_dict['whiskers'], color='black', linewidth=1, linestyle='-') plt.setp(bp_dict['fliers'], color='blue', linewidth=1, marker='+', markersize=2) plt.setp(bp_dict['boxes'], linewidth=1) plt.setp(bp_dict['medians'], linewidth=1, color='red') plt.xticks(range(1, 22), tuple(xmark), rotation='-45', ha='left') # Print median values for debug # medians=[] # for line in bp_dict['medians']: # # get position data for median line # x, y = line.get_xydata()[1] # top of median line # # overlay median value # text(x, y, '%.4f' % y, # horizontalalignment='center', fontsize=5) # draw above, centered # print "%.4f" % y # medians.append(y) # plt.grid(True) marker = itertools.cycle(('d', '*', 'o', '^')) # plt.plot([1.0, 1.0], [-1, 10000], color='#000000') # plt.plot([6.5, 4.5], [-1, 10000], color='#000000') # plt.plot([11.5, 7.5], [-1, 10000], color='#000000') # plt.plot([16.5, 10.5], [-1, 10000], color='#000000') # plt.plot([21.5, 13.5], [-1, 10000], color='#000000') plt.ylim((0.001,10000)) plt.ylabel('Latency in millisecond') plt.xlabel("Scenario mode") box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.25, box.width * 1.0, box.height * 0.78]) ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=0.8) ax.set_axisbelow(True) ax.set_yscale('log') # ax.set_xscale('log') plt.savefig(pcapAnalysisPath+'plot_box_latency_'+topology+'-4Tenants.pdf', dpi=(2500), format='pdf') plt.savefig(pcapAnalysisPath+'plot_box_latency_'+topology+'-4Tenants.png', dpi=(250), format='png') plt.close() def plotLatencySplit(pcapAnalysisPath,topology): Baseline_NoDPDK = {} Baseline_DPDK = {} SRIOV_NoDPDK_OneOvs = {} SRIOV_DPDK_OneOvs = {} SRIOV_NoDPDK_TwoOvs = {} SRIOV_DPDK_TwoOvs = {} SRIOV_NoDPDK_FourOvs = {} SRIOV_DPDK_FourOvs = {} if topology == "phy2phy": Baseline_NoDPDK = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-Baseline_NoDPDK-') Baseline_DPDK = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-Baseline_DPDK-') SRIOV_DPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-SRIOV_DPDK_OneOvs-') SRIOV_NoDPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2phy-latency-SRIOV_NoDPDK_OneOvs-') SRIOV_DPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_DPDK_TwoOvs-') SRIOV_NoDPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_TwoOvs-') SRIOV_DPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_DPDK_FourOvs-') SRIOV_NoDPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_FourOvs-') elif topology == "phy2vm2vm2phy": Baseline_NoDPDK = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-Baseline_NoDPDK-') Baseline_DPDK = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-Baseline_DPDK-') SRIOV_DPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-SRIOV_DPDK_OneOvs-') SRIOV_NoDPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'phy2vm2vm2phy-latency-SRIOV_NoDPDK_OneOvs-') SRIOV_DPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_DPDK_TwoOvs-') SRIOV_NoDPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_TwoOvs-') SRIOV_DPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_DPDK_FourOvs-') SRIOV_NoDPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_FourOvs-') elif topology == "vm2vm": Baseline_NoDPDK = read_lat_dict(pcapAnalysisPath+'vm2vm-latency-Baseline_NoDPDK-') Baseline_DPDK = read_lat_dict(pcapAnalysisPath+'vm2vm-latency-Baseline_DPDK-') SRIOV_DPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'vm2vm-latency-SRIOV_DPDK_OneOvs-') SRIOV_NoDPDK_OneOvs = read_lat_dict(pcapAnalysisPath+'vm2vm-latency-SRIOV_NoDPDK_OneOvs-') SRIOV_DPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-SRIOV_DPDK_TwoOvs-') SRIOV_NoDPDK_TwoOvs = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-SRIOV_NoDPDK_TwoOvs-') SRIOV_DPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-SRIOV_DPDK_FourOvs-') SRIOV_NoDPDK_FourOvs = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-SRIOV_NoDPDK_FourOvs-') # print Baseline_NoDPDK # print SRIOV_DPDK_OneOvs # print SRIOV_NoDPDK_OneOvs # print SRIOV_DPDK_TwoOvs # print SRIOV_NoDPDK_TwoOvs fig = plt.figure(1, figsize = (8.75, 4.6),frameon=True) fig.autofmt_xdate(bottom=0.1, rotation=90, ha='right') ax = plt.subplot(1, 2, 1) plt.tight_layout() c = 0 data = [] xmark = [] # data.append([]) # xmark.append("") c = 0 labels = ["64bytes", "512bytes", "1500bytes", "2048bytes"] for l in labels: data.append(Baseline_NoDPDK[l]) xmark.append('Baseline') data.append(SRIOV_NoDPDK_OneOvs[l]) xmark.append('1\nvswitch\nVM') data.append(SRIOV_NoDPDK_TwoOvs[l]) xmark.append('2\nvswitch\nVM') data.append(SRIOV_NoDPDK_FourOvs[l]) xmark.append('4\nvswitch\nVM') ax.text(2.0, 1000000.05, u'64$B$') ax.text(6.0, 1000000.05, u'512$B$') ax.text(10.0, 1000000.05, u'1500$B$') ax.text(14.0, 1000000.05, u'2048$B$') bp_dict = plt.boxplot(data, patch_artist=False) colors = ['black', '#1F77B4', '#FF7F0E', '#2CA02C'] colors = ['black'] for color in colors: plt.setp(bp_dict['whiskers'], color=color, linewidth=1, linestyle='-') plt.setp(bp_dict['fliers'], color=color, linewidth=1, marker='+', markersize=1) plt.setp(bp_dict['boxes'], color=color, linewidth=1) plt.setp(bp_dict['medians'], linewidth=1, color='red') plt.xticks(range(1, 17), tuple(["B", "1", "2", "4", "B", "1", "2", "4", "B", "1", "2", "4", "B", "1", "2", "4"])) # plt.xticks(range(1, 5), tuple(xmark)) plt.plot([1.5, 1.5], [-1, 1000000], color='#000000') plt.plot([2.5, 2.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([3.5, 3.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([4.5, 4.5], [-1, 1000000], color='#000000') plt.plot([5.5, 5.5], [-1, 1000000], color='#000000') plt.plot([6.5, 6.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([7.5, 7.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([8.5, 8.5], [-1, 1000000], color='#000000') plt.plot([9.5, 9.5], [-1, 1000000], color='#000000') plt.plot([10.5, 10.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([11.5, 11.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([12.5, 12.5], [-1, 1000000], color='#000000') plt.plot([13.5, 13.5], [-1, 1000000], color='#000000') plt.plot([14.5, 14.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([15.5, 15.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([16.5, 16.5], [-1, 1000000], color='#000000') # plt.axvspan(1.5, 5.0, facecolor='0.6', alpha=0.5) plt.ylim((1,1000000)) plt.ylabel('Latency (microsecond)') # ax.add_patch(Rectangle((1.49, .9), 1, 10002, alpha=0.2, color='#1F77B4')) # ax.add_patch(Rectangle((2.49, .9), 1, 10002, alpha=0.2, color='#FF7F0E')) # ax.add_patch(Rectangle((3.49, .9), 1, 10002, alpha=0.2, color='#2CA02C')) box = ax.get_position() ax.set_position([box.x0 + 0.05, box.y0 + box.height * 0.25, box.width * 0.91, box.height * 0.75]) ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=0.8) ax.set_axisbelow(True) ax.set_yscale('log') ### Second plot with dpdk ax = plt.subplot(1, 2, 2) c = 0 data = [] xmark = [] # data.append([]) # xmark.append("") c = 0 labels = ["64bytes", "512bytes", "1500bytes", "2048bytes"] for l in labels: data.append(Baseline_DPDK[l]) xmark.append('Baseline') data.append(SRIOV_DPDK_OneOvs[l]) xmark.append('1\nvswitch\nVM') data.append(SRIOV_DPDK_TwoOvs[l]) xmark.append('2\nvswitch\nVM') data.append(SRIOV_DPDK_FourOvs[l]) xmark.append('4\nvswitch\nVM') ax.text(2.0, 1000000.05, u'64$B$') ax.text(6.0, 1000000.05, u'512$B$') ax.text(10.0, 1000000.05, u'1500$B$') ax.text(14.0, 1000000.05, u'2048$B$') bp_dict = plt.boxplot(data, patch_artist=False) plt.setp(bp_dict['whiskers'], color='black', linewidth=1, linestyle='-') plt.setp(bp_dict['fliers'], color='blue', linewidth=1, marker='+', markersize=1) plt.setp(bp_dict['boxes'], linewidth=1) plt.setp(bp_dict['medians'], linewidth=1, color='red') plt.xticks(range(1, 17), tuple(["B", "1", "2", "4", "B", "1", "2", "4", "B", "1", "2", "4", "B", "1", "2", "4"])) # plt.xticks(range(1, 5), tuple(xmark)) plt.plot([1.5, 1.5], [-1, 1000000], color='#000000') plt.plot([2.5, 2.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([3.5, 3.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([4.5, 4.5], [-1, 1000000], color='#000000') plt.plot([5.5, 5.5], [-1, 1000000], color='#000000') plt.plot([6.5, 6.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([7.5, 7.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([8.5, 8.5], [-1, 1000000], color='#000000') plt.plot([9.5, 9.5], [-1, 1000000], color='#000000') plt.plot([10.5, 10.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([11.5, 11.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([12.5, 12.5], [-1, 1000000], color='#000000') plt.plot([13.5, 13.5], [-1, 1000000], color='#000000') plt.plot([14.5, 14.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([15.5, 15.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([16.5, 16.5], [-1, 1000000], color='#000000') # plt.axvspan(1.5, 5.0, facecolor='0.6', alpha=0.5) plt.ylim((1,1000000)) # ax.add_patch(Rectangle((1.49, .9), 1, 10002, alpha=0.01, color='#1F77B4')) # ax.add_patch(Rectangle((2.49, .9), 1, 10002, alpha=0.01, color='#FF7F0E')) # ax.add_patch(Rectangle((3.49, .9), 1, 10002, alpha=0.01, color='#2CA02C')) box = ax.get_position() ax.set_position([box.x0 + 0.05, box.y0 + box.height * 0.25, box.width * 0.91, box.height * 0.75]) ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=0.8) ax.set_axisbelow(True) ax.set_yscale('log') plt.figtext(0.26, 0.209, "No DPDK", color="black") plt.figtext(0.72, 0.209, "With DPDK", color="black") # ax.legend(['B: Baseline', '1: 1 vswitch VM', '2: 2 vswitch VM', '4: 4 vswitch VM'], handletextpad=-0.1, handlelength=0, markerscale=0, loc='lower center', ncol=2, bbox_to_anchor=(-0.315, -0.5), numpoints=1) plt.savefig(pcapAnalysisPath+'plot_box_latency_'+topology+'-4Tenants-Split.pdf', dpi=(2500), format='pdf') plt.savefig(pcapAnalysisPath+'plot_box_latency_'+topology+'-4Tenants-Split.png', dpi=(250), format='png') plt.close() def plotLatencyPerTenant(pcapAnalysisPath, resourceType="shared", topology="p2p", throughput=10000, time=50, startOffset=10, endOffset=10, tenantCount=4, packetSize="64bytes"): tenants = ["tenant1", "tenant2", "tenant3", "tenant4"] # tenants = ["tenant1"] # dicts to store the per tenant latency b_tLat_p2p = dict.fromkeys(tenants, {}) o_tLat_p2p = dict.fromkeys(tenants, {}) t_tLat_p2p = dict.fromkeys(tenants, {}) f_tLat_p2p = dict.fromkeys(tenants, {}) b_tLat_p2v = dict.fromkeys(tenants, {}) o_tLat_p2v = dict.fromkeys(tenants, {}) t_tLat_p2v = dict.fromkeys(tenants, {}) f_tLat_p2v = dict.fromkeys(tenants, {}) b_tLat_v2v = dict.fromkeys(tenants, {}) o_tLat_v2v = dict.fromkeys(tenants, {}) t_tLat_v2v = dict.fromkeys(tenants, {}) f_tLat_v2v = dict.fromkeys(tenants, {}) if resourceType == "shared": print "tenants" + str(tenants) for t in tenants: if topology == "p2p" or topology == "phy2phy": b_tLat_p2p[t] = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-Baseline_NoDPDK-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) o_tLat_p2p[t] = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_OneOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) t_tLat_p2p[t] = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_TwoOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) f_tLat_p2p[t] = read_lat_dict(pcapAnalysisPath + 'phy2phy-latency-SRIOV_NoDPDK_FourOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) elif topology == "p2v" or topology == "phy2vm2vm2phy": b_tLat_p2v[t] = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-Baseline_NoDPDK-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) o_tLat_p2v[t] = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_OneOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) t_tLat_p2v[t] = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_TwoOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) f_tLat_p2v[t] = read_lat_dict(pcapAnalysisPath + 'phy2vm2vm2phy-latency-SRIOV_NoDPDK_FourOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) elif topology == "v2v" or topology == "vm2vm": if t == "tenant3" or t == "tenant4": continue else: b_tLat_v2v[t] = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-Baseline_NoDPDK-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) o_tLat_v2v[t] = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-SRIOV_NoDPDK_OneOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) t_tLat_v2v[t] = read_lat_dict(pcapAnalysisPath + 'vm2vm-latency-SRIOV_NoDPDK_TwoOvs-', True, t, topo=topology, throughput=throughput, time=time, startOffset=startOffset, endOffset=endOffset, tenantCount=tenantCount) # pprint.pprint(two_tLat_p2p[tenants[0]]["64bytes"]) fig = plt.figure(1, figsize = (8.75, 4.6),frameon=True) fig.autofmt_xdate(bottom=0.1, rotation=90, ha='right') ax = plt.subplot(1, 1, 1) # plt.tight_layout() c = 0 data = [] xmark = [] c = 0 if topology == "p2p" or topology == "phy2phy": for t in tenants: data.append(b_tLat_p2p[t][packetSize]) for t in tenants: data.append(o_tLat_p2p[t][packetSize]) for t in tenants: data.append(t_tLat_p2p[t][packetSize]) for t in tenants: data.append(f_tLat_p2p[t][packetSize]) elif topology == "p2v" or topology == "phy2vm2vm2phy": for t in tenants: data.append(b_tLat_p2v[t][packetSize]) for t in tenants: data.append(o_tLat_p2v[t][packetSize]) for t in tenants: data.append(t_tLat_p2v[t][packetSize]) for t in tenants: data.append(f_tLat_p2v[t][packetSize]) elif topology == "v2v" or topology == "vm2vm": for t in ["tenant1", "tenant2"]: data.append(b_tLat_v2v[t][packetSize]) for t in ["tenant1", "tenant2"]: data.append(o_tLat_v2v[t][packetSize]) for t in ["tenant1", "tenant2"]: data.append(t_tLat_v2v[t][packetSize]) # pprint.pprint(data) print "len(data): " + str(len(data)) plotType = "boxplot" if plotType == "histogram": # For histogram print "histogram" plt.hist(x=data[8], bins=20, density=False, range=[2,32]) print len(data[0]) elif plotType == "cdf": # For cdf print "cdf" values, base = np.histogram(data[0], bins=20, normed=True) cumulative = np.cumsum(values) print cumulative plt.plot(base[1:], cumulative, c='blue') elif plotType == "sortedcdf": # For sorted cdf print "Sortedcdf" sorted_data = np.sort(data[0]) yvals = np.arange(len(sorted_data))/float(len(sorted_data)-1) plt.plot(sorted_data, yvals) for d in data: for q in [50, 90, 95, 100]: print ("{}%% percentile: {}".format(q, np.percentile(d, q))) elif plotType == "boxplot": # For boxplot bp_dict = plt.boxplot(data, patch_artist=False) colors = ['black', '#1F77B4', '#FF7F0E', '#2CA02C'] colors = ['black'] for color in colors: plt.setp(bp_dict['whiskers'], color=color, linewidth=1, linestyle='-') plt.setp(bp_dict['fliers'], color=color, linewidth=1, marker='+', markersize=1) plt.setp(bp_dict['boxes'], color=color, linewidth=1) plt.setp(bp_dict['medians'], linewidth=1, color='red') plt.xticks(range(1, 17), tuple(["B-T1", "B-T2", "B-T3", "B-T4", "1-T1", "1-T2", "1-T3", "1-T4", "2-T1", "2-T2", "2-T3", "2-T4", "4-T1", "4-T2", "4-T3", "4-T4"])) # plt.xticks(range(1, 5), tuple(xmark)) # plt.plot([1.5, 1.5], [-1, 1000000], color='#000000') # plt.plot([2.5, 2.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) # plt.plot([3.5, 3.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([4.5, 4.5], [-1, 1000000], color='#000000') # plt.plot([5.5, 5.5], [-1, 1000000], color='#000000') # plt.plot([6.5, 6.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) # plt.plot([7.5, 7.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([8.5, 8.5], [-1, 1000000], color='#000000') # plt.plot([9.5, 9.5], [-1, 1000000], color='#000000') # plt.plot([10.5, 10.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) # plt.plot([11.5, 11.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) plt.plot([12.5, 12.5], [-1, 1000000], color='#000000') # plt.plot([13.5, 13.5], [-1, 1000000], color='#000000') # plt.plot([14.5, 14.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) # plt.plot([15.5, 15.5], [-1, 1000000], color='#000000', alpha=0.1, linewidth=0.5) # plt.plot([16.5, 16.5], [-1, 1000000], color='#000000') plt.ylim((1,10000)) plt.ylabel('Latency ($\mu$s)') # box = ax.get_position() # ax.set_position([box.x0 + 0.05, box.y0 + box.height * 0.25, box.width * 0.91, box.height * 0.75]) ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=0.8) ax.set_axisbelow(True) ax.set_yscale('log') plt.title("Per tenant latency analysis for "+topology+ "@" + str(throughput) + "pps" + " for " + packetSize + " packets") figureName = "plot_lat_pertenant_"+plotType+"_"+resourceType+"_"+topology+"_"+str(throughput)+"pps"+"_"+packetSize+".png" plt.savefig(pcapAnalysisPath + figureName) return figureName def read_lat_dict(path, isTenant=False, tenant="tenant1", topo="p2p", throughput=10000, time=50, startOffset=10, endOffset=10, tenantCount=4): # print "read_lat_dict()" # import ast tenantTput = throughput/tenantCount startIndex = tenantTput*startOffset ret = {} for i in labels: # print "i: " + str(i) ret[i] = [] try: # print "printing the combo: " # print (str(path+i+'.res')) # data = ast.literal_eval(open(path+i+'.res').read()) if isTenant is False: data = json.loads(open(path+i+'.res').read()) else: data = json.loads(open(path+i+'-'+tenant+'.res').read()) # continue if isTenant is False: for j in range(0, len(data.keys())): ret[i].append(data[unicode(str(j))] * 1000000.0) #in microsecond # if data[unicode(str(j))] * 1000.0 < 1: # ret[i].append(data[unicode(str(j))] * 1000.0) elif isTenant is True: if topo != "v2v": print "len(data.keys(): " + str(len(data.keys())) # for j in range(2500, 2500+len(data.keys())-5): for j in range(startIndex, startIndex + len(data.keys()) - 5): if data[unicode(str(j))] * 1000000.0 > 1000000: print str(j) + " has latency greater than 1 second!!" + str(data[unicode(str(j))] * 1000000.0) else: ret[i].append(data[unicode(str(j))] * 1000000.0) #in microsecond elif topo == "v2v": for j in range(startIndex, startIndex + len(data.keys())): if data[unicode(str(j))] * 1000000.0 > 1000000: print str(j) + " has latency greater than 1 second!!" else: ret[i].append(data[unicode(str(j))] * 1000000.0) #in microsecond print "len of ret is:" + str(len(ret[i])) except Exception as e: print (e) # print ret return ret def plotThroughput(pcapAnalysisPath, topology): baseline_noDpdk_tx, baseline_noDpdk_rx = [], [] baseline_Dpdk_tx, baseline_Dpdk_rx = [], [] sriov_dpdk_tx, sriov_dpdk_rx = [], [] sriov_noDpdk_tx, sriov_noDpdk_rx = [], [] if topology == "phy2phy": baseline_noDpdk_tx, baseline_noDpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-Baseline_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-Baseline_NoDPDK-planeelbe-*') baseline_Dpdk_tx, baseline_Dpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-Baseline_DPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-Baseline_DPDK-planeelbe-*') sriov_dpdk_tx, sriov_dpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-SRIOV_DPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-SRIOV_DPDK-planeelbe-*') sriov_noDpdk_tx, sriov_noDpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-SRIOV_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-SRIOV_NoDPDK-planeelbe-*') elif topology == "phy2vm2vm2phy": baseline_noDpdk_tx, baseline_noDpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_NoDPDK-planeelbe-*') baseline_Dpdk_tx, baseline_Dpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_DPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_DPDK-planeelbe-*') sriov_dpdk_tx, sriov_dpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_DPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_DPDK-planeelbe-*') sriov_noDpdk_tx, sriov_noDpdk_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_NoDPDK-planeelbe-*') print baseline_noDpdk_tx, baseline_noDpdk_rx print baseline_Dpdk_tx, baseline_Dpdk_rx print sriov_dpdk_tx, sriov_dpdk_rx print sriov_noDpdk_tx, sriov_noDpdk_rx fig = plt.figure(1, figsize=(8.75, 4.6), frameon=True) ax = plt.subplot(111) plt.grid(True) marker = itertools.cycle(('d', '*', 'o', '^')) plt.plot(baseline_noDpdk_tx, baseline_noDpdk_rx, marker=marker.next(), color='#79c36a', linestyle='', label='baseline_nodpdk', markersize=9) plt.plot(baseline_Dpdk_tx, baseline_Dpdk_rx, marker=marker.next(), color='#79c36a', linestyle='', label='baseline_dpdk', markersize=9) plt.plot(sriov_noDpdk_tx, sriov_noDpdk_rx, marker=marker.next(), color='#599ad3', linestyle='', label='sriov_nodpdk', markersize=9) plt.plot(sriov_dpdk_tx, sriov_dpdk_rx, marker=marker.next(), color='#727272', linestyle='', label='sriov_dpdk', markersize=9) # plt.ylim((300000, 700000 + 20000)) # plt.xlim((300000, 1500000 + 20000)) plt.ylabel('Packets/s Forwarded') plt.xlabel("Packets/s Sent") ax.legend(loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.45), numpoints=1) box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.25, box.width * 1.0, box.height * 0.75]) ax.set_axisbelow(True) plt.savefig(pcapAnalysisPath+'plot_tput_'+topology+'.pdf', dpi=(2500), format='pdf') plt.savefig(pcapAnalysisPath+'plot_tput_'+topology+'.png', dpi=(250), format='png') plt.close() def plotThroughputMulti(pcapAnalysisPath, topology): Baseline_MultiTenant_NoDPDK_tx, Baseline_MultiTenant_NoDPDK_rx = [], [] Baseline_MultiTenant_DPDK_tx, Baseline_MultiTenant_DPDK_rx = [], [] SRIOV_MultiTenant_NoDPDK_tx, SRIOV_MultiTenant_NoDPDK_rx = [], [] SRIOV_MultiTenant_DPDK_tx, SRIOV_MultiTenant_DPDK_rx = [], [] SRIOV_MultiOvs_DPDK_tx, SRIOV_MultiOvs_DPDK_rx = [], [] if topology == "phy2phy": Baseline_MultiTenant_NoDPDK_tx, Baseline_MultiTenant_NoDPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-Baseline_MultiTenant_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-Baseline_MultiTenant_NoDPDK-planeelbe-*') Baseline_MultiTenant_DPDK_tx, Baseline_MultiTenant_DPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-Baseline_MultiTenant_DPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-Baseline_MultiTenant_DPDK-planeelbe-*') SRIOV_MultiTenant_NoDPDK_tx, SRIOV_MultiTenant_NoDPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiTenant_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiTenant_NoDPDK-planeelbe-*') SRIOV_MultiTenant_DPDK_tx, SRIOV_MultiTenant_DPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiTenant_DPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiTenant_DPDK-planeelbe-*') SRIOV_MultiOvs_NoDPDK_tx, SRIOV_MultiOvs_NoDPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiOvs_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiOvs_NoDPDK-planeelbe-*') SRIOV_MultiOvs_DPDK_tx, SRIOV_MultiOvs_DPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiOvs_DPDK-elbeplane-*', pcapAnalysisPath+'phy2phy-throughput-SRIOV_MultiOvs_DPDK-planeelbe-*') elif topology == "phy2vm2vm2phy": Baseline_MultiTenant_NoDPDK_tx, Baseline_MultiTenant_NoDPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_MultiTenant_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_MultiTenant_NoDPDK-planeelbe-*') Baseline_MultiTenant_DPDK_tx, Baseline_MultiTenant_DPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_MultiTenant_DPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-Baseline_MultiTenant_DPDK-planeelbe-*') SRIOV_MultiTenant_NoDPDK_tx, SRIOV_MultiTenant_NoDPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiTenant_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiTenant_NoDPDK-planeelbe-*') SRIOV_MultiTenant_DPDK_tx, SRIOV_MultiTenant_DPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiTenant_DPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiTenant_DPDK-planeelbe-*') SRIOV_MultiOvs_NoDPDK_tx, SRIOV_MultiOvs_NoDPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiOvs_NoDPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiOvs_NoDPDK-planeelbe-*') SRIOV_MultiOvs_DPDK_tx, SRIOV_MultiOvs_DPDK_rx = get_tput_dict( pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiOvs_DPDK-elbeplane-*', pcapAnalysisPath+'phy2vm2vm2phy-throughput-SRIOV_MultiOvs_DPDK-planeelbe-*') print Baseline_MultiTenant_NoDPDK_tx, Baseline_MultiTenant_NoDPDK_rx print SRIOV_MultiTenant_NoDPDK_tx, SRIOV_MultiTenant_NoDPDK_rx print SRIOV_MultiTenant_DPDK_tx, SRIOV_MultiTenant_DPDK_rx print SRIOV_MultiOvs_NoDPDK_tx, SRIOV_MultiOvs_NoDPDK_rx print SRIOV_MultiOvs_DPDK_tx, SRIOV_MultiOvs_DPDK_rx fig = plt.figure(1, figsize=(8.75, 4.6), frameon=True) ax = plt.subplot(111) plt.grid(True) marker = itertools.cycle(('d', '*', 'o', '^', 'p')) plt.plot(Baseline_MultiTenant_NoDPDK_tx, Baseline_MultiTenant_NoDPDK_rx, marker=marker.next(), color='#79c36a', linestyle='', label='Baseline_MultiTenant_NoDPDK', markersize=9) plt.plot(Baseline_MultiTenant_DPDK_tx, Baseline_MultiTenant_DPDK_rx, marker=marker.next(), color='#727272', linestyle='', label='Baseline_MultiTenant_DPDK', markersize=9) plt.plot(SRIOV_MultiTenant_DPDK_tx, SRIOV_MultiTenant_DPDK_rx, marker=marker.next(), color='#599ad3', linestyle='', label='SRIOV_MultiTenant_DPDK', markersize=9) plt.plot(SRIOV_MultiTenant_NoDPDK_tx, SRIOV_MultiTenant_NoDPDK_rx, marker=marker.next(), color='#727272', linestyle='', label='SRIOV_MultiTenant_NoDPDK', markersize=9) plt.plot(SRIOV_MultiOvs_DPDK_tx, SRIOV_MultiOvs_DPDK_rx, marker=marker.next(), color='#599ad3', linestyle='', label='SRIOV_MultiOvs_DPDK', markersize=9) plt.plot(SRIOV_MultiOvs_NoDPDK_tx, SRIOV_MultiOvs_NoDPDK_rx, marker=marker.next(), color='#727272', linestyle='', label='SRIOV_MultiOvs_NoDPDK', markersize=9) # plt.ylim((300000, 1400000 + 20000)) # plt.xlim((300000, 1400000 + 20000)) plt.ylabel('Packets/s Forwarded (k pps)') plt.xlabel("Packets/s Sent (k pps)") ax.legend(loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.45), numpoints=1) box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.25, box.width * 1.0, box.height * 0.75]) ax.set_axisbelow(True) plt.savefig(pcapAnalysisPath+'plot_tput_'+topology+'-Multi.pdf', dpi=(2500), format='pdf') plt.savefig(pcapAnalysisPath+'plot_tput_'+topology+'-Multi.png', dpi=(320), format='png') plt.close() def tabulateTput(pcapAnalysisPath, topology, vswitchMode, isIsolated, txrxFiles, txThroughput): print "tabulateTput()" import os.path import csv if os.path.isfile(pcapAnalysisPath+"tput.csv") is False: print "create the tput table csv file." headings = ["topology", "vswitchMode", "isIsolated", "tx (Mpps)", "rx (Mpps)"] with open(pcapAnalysisPath+"tput.csv", 'a') as tputFile: wr = csv.writer(tputFile, quoting=csv.QUOTE_NONE) wr.writerow(headings) else: print "tput table csv already exits. Will append data." if len(txrxFiles) == 1: txTput = txThroughput * 1.0 / 1000000 rx = pcapAnalysisPath + txrxFiles[0] rxTput = parse_tput_dict_dagbits(rx) elif len(txrxFiles) == 2: tx = pcapAnalysisPath + txrxFiles[0] rx = pcapAnalysisPath + txrxFiles[1] txTput = parse_tput_dict_dagbits(tx) rxTput = parse_tput_dict_dagbits(rx) data = [topology, vswitchMode, isIsolated, txTput, rxTput] with open(pcapAnalysisPath+"tput.csv", 'a') as tputFile: wr = csv.writer(tputFile, quoting=csv.QUOTE_NONE) wr.writerow(data) def get_tput_dict(txPath, rxPath): print "get_tput_dict()" x1 = [] y1 = [] try: d = glob.glob(rxPath) d.sort() for i in d: # print "y1 parsedicts:" y1.append(parse_tput_dict(i)) print parse_tput_dict(i) d = glob.glob(txPath) d.sort() for i in d: # print "x1 parsedicts:" x1.append(parse_tput_dict(i)) print parse_tput_dict(i) # exit() return x1, y1 except: x1 = [] y1 = [] def parse_tput_dict(dict_data): try: for l in open(dict_data): if l.split()[0] == 'Average': return int(float(l.split()[3])/1000) except: print "Looks like its empty!" return 0 def parse_tput_dict_dagbits(tputFile): try: foundRate = False for l in open(tputFile): if l.split(':')[0] == ' Rate' and foundRate is False: foundRate = True if l.split(':')[0] == ' Rate' and foundRate is True: rateLine = l.split(':') rate = rateLine[1].split('(')[1].split()[0] return float(float(rate)/1000000.0) except: print "Looks like its empty!" return 0

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0.634958

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0.076099

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0.061383

0.864982

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0.705032

0.642918

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839

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0

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null

0

1

0

7

fd5b5dccf615119cdf9d53f8d764b52ee8d44ae9

10,079

py

Python

csdl/examples/ex_bracketed_search.py

LSDOlab/csdl

04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3

[ "MIT" ]

null

csdl/examples/ex_bracketed_search.py

LSDOlab/csdl

04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3

[ "MIT" ]

null

csdl/examples/ex_bracketed_search.py

LSDOlab/csdl

04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3

[ "MIT" ]

1

2021-10-04T19:40:32.000Z

from csdl import Model, ScipyKrylov, NewtonSolver, NonlinearBlockGS import numpy as np class ExampleBracketedScalar(Model): """ :param var: x """ def define(self): # NOTE: Importing definitions within a method is bad practice. # This is only done here to automate example/test case # generation more easily. # When defining CSDL models, please put the import statements at # the top of your Python file(s). from csdl.examples.models.quadratic_function import QuadraticFunction solve_quadratic = self.create_implicit_operation( QuadraticFunction(shape=(1, ))) solve_quadratic.declare_state('x', residual='y', bracket=(0, 2)) a = self.declare_variable('a', val=1) b = self.declare_variable('b', val=-4) c = self.declare_variable('c', val=3) x = solve_quadratic(a, b, c) class ExampleBracketedArray(Model): """ :param var: x """ def define(self): # NOTE: Importing definitions within a method is bad practice. # This is only done here to automate example/test case # generation more easily. # When defining CSDL models, please put the import statements at # the top of your Python file(s). from csdl.examples.models.quadratic_function import QuadraticFunction solve_quadratic = self.create_implicit_operation( QuadraticFunction(shape=(2, ))) solve_quadratic.declare_state('x', residual='y', bracket=( np.array([0, 2.]), np.array([2, np.pi], ), )) a = self.declare_variable('a', val=[1, -1]) b = self.declare_variable('b', val=[-4, 4]) c = self.declare_variable('c', val=[3, -3]) x = solve_quadratic(a, b, c) class ExampleWithSubsystemsBracketedScalar(Model): """ :param var: y """ def define(self): # NOTE: Importing definitions within a method is bad practice. # This is only done here to automate example/test case # generation more easily. # When defining CSDL models, please put the import statements at # the top of your Python file(s). from csdl.examples.models.quadratic_wih_extra_term import QuadraticWithExtraTerm from csdl.examples.models.simple_add import SimpleAdd from csdl.examples.models.fixed_point import FixedPoint2 self.add(SimpleAdd(p=7, q=8), name='R') solve_fixed_point_iteration = self.create_implicit_operation( FixedPoint2(name='a')) solve_fixed_point_iteration.declare_state('a', residual='r') solve_fixed_point_iteration.nonlinear_solver = NonlinearBlockGS( maxiter=100) solve_quadratic = self.create_implicit_operation( QuadraticWithExtraTerm(shape=(1, ))) solve_quadratic.declare_state('y', residual='z', bracket=(0, 2)) solve_quadratic.nonlinear_solver = NonlinearBlockGS(maxiter=100) a = solve_fixed_point_iteration() b = self.declare_variable('b', val=-4) c = self.declare_variable('c', val=18) r = self.declare_variable('r') y = solve_quadratic(a, b, c, r) class ExampleWithSubsystemsBracketedArray(Model): """ :param var: y """ def define(self): # NOTE: Importing definitions within a method is bad practice. # This is only done here to automate example/test case # generation more easily. # When defining CSDL models, please put the import statements at # the top of your Python file(s). from csdl.examples.models.quadratic_wih_extra_term import QuadraticWithExtraTerm from csdl.examples.models.simple_add import SimpleAdd from csdl.examples.models.fixed_point import FixedPoint2 self.add(SimpleAdd(p=[7, -7], q=[8, -8]), name='R') solve_fixed_point_iteration = self.create_implicit_operation( FixedPoint2(name='ap')) solve_fixed_point_iteration.declare_state('ap', residual='r') solve_fixed_point_iteration.nonlinear_solver = NonlinearBlockGS( maxiter=100) solve_quadratic = self.create_implicit_operation( QuadraticWithExtraTerm(shape=(2, ))) solve_quadratic.declare_state('y', residual='z', bracket=( np.array([0, 2.]), np.array([2, np.pi], ), )) solve_quadratic.nonlinear_solver = NonlinearBlockGS(maxiter=100) ap = solve_fixed_point_iteration() a = self.create_output('a', shape=(2, )) a[0] = ap a[1] = -ap b = self.declare_variable('b', val=[-4, 4]) c = self.declare_variable('c', val=[18, -18]) r = self.declare_variable('r', shape=(2, )) y = solve_quadratic(a, b, c, r) # ---------------------------------------------------------------------- class ExampleBracketedScalarDefineModelInline(Model): """ :param var: x """ def define(self): model = Model() a = model.declare_variable('a') b = model.declare_variable('b') c = model.declare_variable('c') x = model.declare_variable('x') y = a * x**2 + b * x + c model.register_output('y', y) solve_quadratic = self.create_implicit_operation(model) solve_quadratic.declare_state('x', residual='y', bracket=(0, 2)) a = self.declare_variable('a', val=1) b = self.declare_variable('b', val=-4) c = self.declare_variable('c', val=3) x = solve_quadratic(a, b, c) class ExampleBracketedArrayDefineModelInline(Model): """ :param var: x """ def define(self): model = Model() a = model.declare_variable('a', shape=(2, )) b = model.declare_variable('b', shape=(2, )) c = model.declare_variable('c', shape=(2, )) x = model.declare_variable('x', shape=(2, )) y = a * x**2 + b * x + c model.register_output('y', y) solve_quadratic = self.create_implicit_operation(model) solve_quadratic.declare_state('x', residual='y', bracket=( np.array([0, 2.]), np.array([2, np.pi], ), )) a = self.declare_variable('a', val=[1, -1]) b = self.declare_variable('b', val=[-4, 4]) c = self.declare_variable('c', val=[3, -3]) x = solve_quadratic(a, b, c) class ExampleWithSubsystemsBracketedScalarDefineModelInline(Model): """ :param var: y """ def define(self): with self.create_submodel('R') as model: p = model.create_input('p', val=7) q = model.create_input('q', val=8) r = p + q model.register_output('r', r) m2 = Model() x = m2.declare_variable('a') r = m2.register_output('r', x - ((x + 3 - x**4) / 2)**(1 / 4)) m3 = Model() a = m3.declare_variable('a') b = m3.declare_variable('b') c = m3.declare_variable('c') r = m3.declare_variable('r') y = m3.declare_variable('y') m3.register_output('z', a * y**2 + b * y + c - r) solve_fixed_point_iteration = self.create_implicit_operation(m2) solve_fixed_point_iteration.declare_state('a', residual='r') solve_fixed_point_iteration.nonlinear_solver = NonlinearBlockGS( maxiter=100) solve_quadratic = self.create_implicit_operation(m3) solve_quadratic.declare_state('y', residual='z', bracket=(0, 2)) solve_quadratic.nonlinear_solver = NonlinearBlockGS(maxiter=100) a = solve_fixed_point_iteration() b = self.declare_variable('b', val=-4) c = self.declare_variable('c', val=18) r = self.declare_variable('r') y = solve_quadratic(a, b, c, r) class ExampleWithSubsystemsBracketedArrayDefineModelInline(Model): """ :param var: y """ def define(self): with self.create_submodel('R') as model: p = model.create_input('p', val=[7, -7]) q = model.create_input('q', val=[8, -8]) r = p + q model.register_output('r', r) m2 = Model() x = m2.declare_variable('ap') r = m2.register_output('r', x - ((x + 3 - x**4) / 2)**(1 / 4)) m3 = Model() a = m3.declare_variable('a', shape=(2, )) b = m3.declare_variable('b', shape=(2, )) c = m3.declare_variable('c', shape=(2, )) r = m3.declare_variable('r', shape=(2, )) y = m3.declare_variable('y', shape=(2, )) m3.register_output('z', a * y**2 + b * y + c - r) solve_fixed_point_iteration = self.create_implicit_operation(m2) solve_fixed_point_iteration.declare_state('ap', residual='r') solve_fixed_point_iteration.nonlinear_solver = NonlinearBlockGS( maxiter=100) solve_quadratic = self.create_implicit_operation(m3) solve_quadratic.declare_state('y', residual='z', bracket=( np.array([0, 2.]), np.array([2, np.pi], ), )) solve_quadratic.nonlinear_solver = NonlinearBlockGS(maxiter=100) ap = solve_fixed_point_iteration() a = self.create_output('a', shape=(2, )) a[0] = ap a[1] = -ap b = self.declare_variable('b', val=[-4, 4]) c = self.declare_variable('c', val=[18, -18]) r = self.declare_variable('r', shape=(2, )) y = solve_quadratic(a, b, c, r)

38.17803

88

0.560671

1,173

10,079

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0.098892

0.120901

0.083532

0.070343

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0.882946

0.867009

0.858033

0

0.022283

0.309852

10,079

263

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0.762507

0.110626

0

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0

1

0.045455

false

0

0.056818

0

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0

null

0

1

0

1

0

null

0

7

b5dced6d67f40fde1df3f32e546d64dbe1f829f0

3,437

py

Python

prep/join_student_question_index.py

mack-the-psych/vdok3

f3ab898c1815d0d54822dbb18b82723a48fc71f7

[ "MIT" ]

null

prep/join_student_question_index.py

mack-the-psych/vdok3

f3ab898c1815d0d54822dbb18b82723a48fc71f7

[ "MIT" ]

null

prep/join_student_question_index.py

mack-the-psych/vdok3

f3ab898c1815d0d54822dbb18b82723a48fc71f7

[ "MIT" ]

null

#!/usr/bin/env python import pandas as pd def bas_join_student_question_index(csv_file_src, csv_file_trg, data_dir=r'./'): df_ac_src = pd.read_csv(data_dir + csv_file_src, encoding= 'latin1') df_ac_src = df_ac_src.set_index(r'AC_Doc_ID') df_ac_trg = pd.read_csv(data_dir + csv_file_trg, encoding= 'latin1') df_ac_trg = df_ac_trg.set_index(r'AC_Doc_ID') df_ac_trg[r'Student_Question_Index'] = df_ac_src[r'Student_Question_Index'] return df_ac_trg if __name__ == "__main__": data_dir = r'../data/' df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Def-PRE.csv', r'RF_Classified-Prediction-Def-PRE-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF_Classified-Prediction-Def-PRE-All.csv', encoding= 'latin1') df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Sen-PRE.csv', r'RF-Classified-Prediction-Sen-PRE-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF-Classified-Prediction-Sen-PRE-All.csv', encoding= 'latin1') df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Def-PRE-POST.csv', r'RF-Classified-Prediction-Def-PRE-POST-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF-Classified-Prediction-Def-PRE-POST-All.csv', encoding= 'latin1') df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Sen-PRE-POST.csv', r'RF-Classified-Prediction-Sen-PRE-POST-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF-Classified-Prediction-Sen-PRE-POST-All.csv', encoding= 'latin1') data_dir = r'../data/wo_DK_NR/' df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Def-PRE_wo_DK_NR.csv', r'RF_wo_DK_NR_Classified-Prediction-Def-PRE-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF_wo_DK_NR_Classified-Prediction-Def-PRE-All.csv', encoding= 'latin1') df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Sen-PRE_wo_DK_NR.csv', r'RF_wo_DK_NR-Classified-Prediction-Sen-PRE-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF_wo_DK_NR-Classified-Prediction-Sen-PRE-All.csv', encoding= 'latin1') df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Def-PRE-POST_wo_DK_NR.csv', r'RF-Classified-Prediction-Def-PRE-POST_wo_DK_NR-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF-Classified-Prediction-Def-PRE-POST_wo_DK_NR-All.csv', encoding= 'latin1') df_ac_merge_trg = bas_join_student_question_index(r'Key-Stem-Passage-Aggregate_plim-Sen-PRE-POST_wo_DK_NR.csv', r'RF-Classified-Prediction-Sen-PRE-POST_wo_DK_NR-All.csv', data_dir) df_ac_merge_trg.to_csv(data_dir + r'Refmt_RF-Classified-Prediction-Sen-PRE-POST_wo_DK_NR-All.csv', encoding= 'latin1')

59.258621

115

0.656677

529

3,437

3.856333

0.096408

0.04902

0.088235

0.094118

0.885784

0.872549

0.865686

0.837255

0.805882

0.776471

0

0.003795

0.233343

3,437

57

116

60.298246

0.770398

0.005819

0

0.186047

0

0.397248

0.364169

0

1

0.023256

false

0.186047

0.023256

0

0.069767

0

null

0

1

0

null

0

1

0

7

bd069c43f21971c6b3bbae50776b33b9ecce3825

570

py

Python

ss5/4-5.py

DuongVu39/C4E10_Duong

60ec59bddbb3397b5a1804930d5bdfd81107dcae

[ "MIT" ]

null

ss5/4-5.py

DuongVu39/C4E10_Duong

60ec59bddbb3397b5a1804930d5bdfd81107dcae

[ "MIT" ]

null

ss5/4-5.py

DuongVu39/C4E10_Duong

60ec59bddbb3397b5a1804930d5bdfd81107dcae

[ "MIT" ]

null

<<<<<<< HEAD from turtle import * bgcolor('lightgreen') color('blue') speed (-1) def draw_pattern(n): m = 5 for i in range (n): for j in range (4): right(90) fd(m) m += 4 left (5) draw_pattern(100) ======= from turtle import * bgcolor('lightgreen') color('blue') speed (-1) def draw_pattern(n): m = 5 for i in range (n): for j in range (4): right(90) fd(m) m += 4 left (5) draw_pattern(100) >>>>>>> 687005e51286e9522a42a2d33dcef452fb0a05b2

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0

9

1fbf6a553d223f7bed9e04af16513a82f835904b

27,510

py

Python

sdk/python/pulumi_alicloud/datahub/topic.py

pulumi/pulumi-alicloud

9c34d84b4588a7c885c6bec1f03b5016e5a41683

[ "ECL-2.0", "Apache-2.0" ]

42

2019-03-18T06:34:37.000Z

2022-03-24T07:08:57.000Z

sdk/python/pulumi_alicloud/datahub/topic.py

pulumi/pulumi-alicloud

9c34d84b4588a7c885c6bec1f03b5016e5a41683

[ "ECL-2.0", "Apache-2.0" ]

152

2019-04-15T21:03:44.000Z

2022-03-29T18:00:57.000Z

sdk/python/pulumi_alicloud/datahub/topic.py

pulumi/pulumi-alicloud

9c34d84b4588a7c885c6bec1f03b5016e5a41683

[ "ECL-2.0", "Apache-2.0" ]

3

2020-08-26T17:30:07.000Z

2021-07-05T01:37:45.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = ['TopicArgs', 'Topic'] @pulumi.input_type class TopicArgs: def __init__(__self__, *, project_name: pulumi.Input[str], comment: Optional[pulumi.Input[str]] = None, life_cycle: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, record_schema: Optional[pulumi.Input[Mapping[str, Any]]] = None, record_type: Optional[pulumi.Input[str]] = None, shard_count: Optional[pulumi.Input[int]] = None): """ The set of arguments for constructing a Topic resource. :param pulumi.Input[str] project_name: The name of the datahub project that this topic belongs to. It is case-insensitive. :param pulumi.Input[str] comment: Comment of the datahub topic. It cannot be longer than 255 characters. :param pulumi.Input[int] life_cycle: How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. :param pulumi.Input[str] name: The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. :param pulumi.Input[Mapping[str, Any]] record_schema: Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP :param pulumi.Input[str] record_type: The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. :param pulumi.Input[int] shard_count: The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ pulumi.set(__self__, "project_name", project_name) if comment is not None: pulumi.set(__self__, "comment", comment) if life_cycle is not None: pulumi.set(__self__, "life_cycle", life_cycle) if name is not None: pulumi.set(__self__, "name", name) if record_schema is not None: pulumi.set(__self__, "record_schema", record_schema) if record_type is not None: pulumi.set(__self__, "record_type", record_type) if shard_count is not None: pulumi.set(__self__, "shard_count", shard_count) @property @pulumi.getter(name="projectName") def project_name(self) -> pulumi.Input[str]: """ The name of the datahub project that this topic belongs to. It is case-insensitive. """ return pulumi.get(self, "project_name") @project_name.setter def project_name(self, value: pulumi.Input[str]): pulumi.set(self, "project_name", value) @property @pulumi.getter def comment(self) -> Optional[pulumi.Input[str]]: """ Comment of the datahub topic. It cannot be longer than 255 characters. """ return pulumi.get(self, "comment") @comment.setter def comment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "comment", value) @property @pulumi.getter(name="lifeCycle") def life_cycle(self) -> Optional[pulumi.Input[int]]: """ How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. """ return pulumi.get(self, "life_cycle") @life_cycle.setter def life_cycle(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "life_cycle", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="recordSchema") def record_schema(self) -> Optional[pulumi.Input[Mapping[str, Any]]]: """ Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP """ return pulumi.get(self, "record_schema") @record_schema.setter def record_schema(self, value: Optional[pulumi.Input[Mapping[str, Any]]]): pulumi.set(self, "record_schema", value) @property @pulumi.getter(name="recordType") def record_type(self) -> Optional[pulumi.Input[str]]: """ The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. """ return pulumi.get(self, "record_type") @record_type.setter def record_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "record_type", value) @property @pulumi.getter(name="shardCount") def shard_count(self) -> Optional[pulumi.Input[int]]: """ The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ return pulumi.get(self, "shard_count") @shard_count.setter def shard_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "shard_count", value) @pulumi.input_type class _TopicState: def __init__(__self__, *, comment: Optional[pulumi.Input[str]] = None, create_time: Optional[pulumi.Input[str]] = None, last_modify_time: Optional[pulumi.Input[str]] = None, life_cycle: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, project_name: Optional[pulumi.Input[str]] = None, record_schema: Optional[pulumi.Input[Mapping[str, Any]]] = None, record_type: Optional[pulumi.Input[str]] = None, shard_count: Optional[pulumi.Input[int]] = None): """ Input properties used for looking up and filtering Topic resources. :param pulumi.Input[str] comment: Comment of the datahub topic. It cannot be longer than 255 characters. :param pulumi.Input[str] create_time: Create time of the datahub topic. It is a human-readable string rather than 64-bits UTC. :param pulumi.Input[str] last_modify_time: Last modify time of the datahub topic. It is the same as *create_time* at the beginning. It is also a human-readable string rather than 64-bits UTC. :param pulumi.Input[int] life_cycle: How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. :param pulumi.Input[str] name: The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. :param pulumi.Input[str] project_name: The name of the datahub project that this topic belongs to. It is case-insensitive. :param pulumi.Input[Mapping[str, Any]] record_schema: Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP :param pulumi.Input[str] record_type: The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. :param pulumi.Input[int] shard_count: The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ if comment is not None: pulumi.set(__self__, "comment", comment) if create_time is not None: pulumi.set(__self__, "create_time", create_time) if last_modify_time is not None: pulumi.set(__self__, "last_modify_time", last_modify_time) if life_cycle is not None: pulumi.set(__self__, "life_cycle", life_cycle) if name is not None: pulumi.set(__self__, "name", name) if project_name is not None: pulumi.set(__self__, "project_name", project_name) if record_schema is not None: pulumi.set(__self__, "record_schema", record_schema) if record_type is not None: pulumi.set(__self__, "record_type", record_type) if shard_count is not None: pulumi.set(__self__, "shard_count", shard_count) @property @pulumi.getter def comment(self) -> Optional[pulumi.Input[str]]: """ Comment of the datahub topic. It cannot be longer than 255 characters. """ return pulumi.get(self, "comment") @comment.setter def comment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "comment", value) @property @pulumi.getter(name="createTime") def create_time(self) -> Optional[pulumi.Input[str]]: """ Create time of the datahub topic. It is a human-readable string rather than 64-bits UTC. """ return pulumi.get(self, "create_time") @create_time.setter def create_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "create_time", value) @property @pulumi.getter(name="lastModifyTime") def last_modify_time(self) -> Optional[pulumi.Input[str]]: """ Last modify time of the datahub topic. It is the same as *create_time* at the beginning. It is also a human-readable string rather than 64-bits UTC. """ return pulumi.get(self, "last_modify_time") @last_modify_time.setter def last_modify_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_modify_time", value) @property @pulumi.getter(name="lifeCycle") def life_cycle(self) -> Optional[pulumi.Input[int]]: """ How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. """ return pulumi.get(self, "life_cycle") @life_cycle.setter def life_cycle(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "life_cycle", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="projectName") def project_name(self) -> Optional[pulumi.Input[str]]: """ The name of the datahub project that this topic belongs to. It is case-insensitive. """ return pulumi.get(self, "project_name") @project_name.setter def project_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project_name", value) @property @pulumi.getter(name="recordSchema") def record_schema(self) -> Optional[pulumi.Input[Mapping[str, Any]]]: """ Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP """ return pulumi.get(self, "record_schema") @record_schema.setter def record_schema(self, value: Optional[pulumi.Input[Mapping[str, Any]]]): pulumi.set(self, "record_schema", value) @property @pulumi.getter(name="recordType") def record_type(self) -> Optional[pulumi.Input[str]]: """ The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. """ return pulumi.get(self, "record_type") @record_type.setter def record_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "record_type", value) @property @pulumi.getter(name="shardCount") def shard_count(self) -> Optional[pulumi.Input[int]]: """ The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ return pulumi.get(self, "shard_count") @shard_count.setter def shard_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "shard_count", value) class Topic(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, comment: Optional[pulumi.Input[str]] = None, life_cycle: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, project_name: Optional[pulumi.Input[str]] = None, record_schema: Optional[pulumi.Input[Mapping[str, Any]]] = None, record_type: Optional[pulumi.Input[str]] = None, shard_count: Optional[pulumi.Input[int]] = None, __props__=None): """ The topic is the basic unit of Datahub data source and is used to define one kind of data or stream. It contains a set of subscriptions. You can manage the datahub source of an application by using topics. [Refer to details](https://help.aliyun.com/document_detail/47440.html). ## Example Usage Basic Usage - BLob Topic ```python import pulumi import pulumi_alicloud as alicloud example = alicloud.datahub.Topic("example", comment="created by terraform", life_cycle=7, project_name="tf_datahub_project", record_type="BLOB", shard_count=3) ``` - Tuple Topic ```python import pulumi import pulumi_alicloud as alicloud example = alicloud.datahub.Topic("example", comment="created by terraform", life_cycle=7, project_name="tf_datahub_project", record_schema={ "bigint_field": "BIGINT", "boolean_field": "BOOLEAN", "double_field": "DOUBLE", "string_field": "STRING", "timestamp_field": "TIMESTAMP", }, record_type="TUPLE", shard_count=3) ``` ## Import Datahub topic can be imported using the ID, e.g. ```sh $ pulumi import alicloud:datahub/topic:Topic example tf_datahub_project:tf_datahub_topic ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] comment: Comment of the datahub topic. It cannot be longer than 255 characters. :param pulumi.Input[int] life_cycle: How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. :param pulumi.Input[str] name: The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. :param pulumi.Input[str] project_name: The name of the datahub project that this topic belongs to. It is case-insensitive. :param pulumi.Input[Mapping[str, Any]] record_schema: Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP :param pulumi.Input[str] record_type: The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. :param pulumi.Input[int] shard_count: The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ ... @overload def __init__(__self__, resource_name: str, args: TopicArgs, opts: Optional[pulumi.ResourceOptions] = None): """ The topic is the basic unit of Datahub data source and is used to define one kind of data or stream. It contains a set of subscriptions. You can manage the datahub source of an application by using topics. [Refer to details](https://help.aliyun.com/document_detail/47440.html). ## Example Usage Basic Usage - BLob Topic ```python import pulumi import pulumi_alicloud as alicloud example = alicloud.datahub.Topic("example", comment="created by terraform", life_cycle=7, project_name="tf_datahub_project", record_type="BLOB", shard_count=3) ``` - Tuple Topic ```python import pulumi import pulumi_alicloud as alicloud example = alicloud.datahub.Topic("example", comment="created by terraform", life_cycle=7, project_name="tf_datahub_project", record_schema={ "bigint_field": "BIGINT", "boolean_field": "BOOLEAN", "double_field": "DOUBLE", "string_field": "STRING", "timestamp_field": "TIMESTAMP", }, record_type="TUPLE", shard_count=3) ``` ## Import Datahub topic can be imported using the ID, e.g. ```sh $ pulumi import alicloud:datahub/topic:Topic example tf_datahub_project:tf_datahub_topic ``` :param str resource_name: The name of the resource. :param TopicArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(TopicArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, comment: Optional[pulumi.Input[str]] = None, life_cycle: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, project_name: Optional[pulumi.Input[str]] = None, record_schema: Optional[pulumi.Input[Mapping[str, Any]]] = None, record_type: Optional[pulumi.Input[str]] = None, shard_count: Optional[pulumi.Input[int]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = TopicArgs.__new__(TopicArgs) __props__.__dict__["comment"] = comment __props__.__dict__["life_cycle"] = life_cycle __props__.__dict__["name"] = name if project_name is None and not opts.urn: raise TypeError("Missing required property 'project_name'") __props__.__dict__["project_name"] = project_name __props__.__dict__["record_schema"] = record_schema __props__.__dict__["record_type"] = record_type __props__.__dict__["shard_count"] = shard_count __props__.__dict__["create_time"] = None __props__.__dict__["last_modify_time"] = None super(Topic, __self__).__init__( 'alicloud:datahub/topic:Topic', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, comment: Optional[pulumi.Input[str]] = None, create_time: Optional[pulumi.Input[str]] = None, last_modify_time: Optional[pulumi.Input[str]] = None, life_cycle: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, project_name: Optional[pulumi.Input[str]] = None, record_schema: Optional[pulumi.Input[Mapping[str, Any]]] = None, record_type: Optional[pulumi.Input[str]] = None, shard_count: Optional[pulumi.Input[int]] = None) -> 'Topic': """ Get an existing Topic resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] comment: Comment of the datahub topic. It cannot be longer than 255 characters. :param pulumi.Input[str] create_time: Create time of the datahub topic. It is a human-readable string rather than 64-bits UTC. :param pulumi.Input[str] last_modify_time: Last modify time of the datahub topic. It is the same as *create_time* at the beginning. It is also a human-readable string rather than 64-bits UTC. :param pulumi.Input[int] life_cycle: How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. :param pulumi.Input[str] name: The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. :param pulumi.Input[str] project_name: The name of the datahub project that this topic belongs to. It is case-insensitive. :param pulumi.Input[Mapping[str, Any]] record_schema: Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP :param pulumi.Input[str] record_type: The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. :param pulumi.Input[int] shard_count: The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _TopicState.__new__(_TopicState) __props__.__dict__["comment"] = comment __props__.__dict__["create_time"] = create_time __props__.__dict__["last_modify_time"] = last_modify_time __props__.__dict__["life_cycle"] = life_cycle __props__.__dict__["name"] = name __props__.__dict__["project_name"] = project_name __props__.__dict__["record_schema"] = record_schema __props__.__dict__["record_type"] = record_type __props__.__dict__["shard_count"] = shard_count return Topic(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def comment(self) -> pulumi.Output[Optional[str]]: """ Comment of the datahub topic. It cannot be longer than 255 characters. """ return pulumi.get(self, "comment") @property @pulumi.getter(name="createTime") def create_time(self) -> pulumi.Output[str]: """ Create time of the datahub topic. It is a human-readable string rather than 64-bits UTC. """ return pulumi.get(self, "create_time") @property @pulumi.getter(name="lastModifyTime") def last_modify_time(self) -> pulumi.Output[str]: """ Last modify time of the datahub topic. It is the same as *create_time* at the beginning. It is also a human-readable string rather than 64-bits UTC. """ return pulumi.get(self, "last_modify_time") @property @pulumi.getter(name="lifeCycle") def life_cycle(self) -> pulumi.Output[Optional[int]]: """ How many days this topic lives. The permitted range of values is [1, 7]. The default value is 3. """ return pulumi.get(self, "life_cycle") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the datahub topic. Its length is limited to 1-128 and only characters such as letters, digits and '_' are allowed. It is case-insensitive. """ return pulumi.get(self, "name") @property @pulumi.getter(name="projectName") def project_name(self) -> pulumi.Output[str]: """ The name of the datahub project that this topic belongs to. It is case-insensitive. """ return pulumi.get(self, "project_name") @property @pulumi.getter(name="recordSchema") def record_schema(self) -> pulumi.Output[Optional[Mapping[str, Any]]]: """ Schema of this topic, required only for TUPLE topic. Supported data types (case-insensitive) are: - BIGINT - STRING - BOOLEAN - DOUBLE - TIMESTAMP """ return pulumi.get(self, "record_schema") @property @pulumi.getter(name="recordType") def record_type(self) -> pulumi.Output[Optional[str]]: """ The type of this topic. Its value must be one of {BLOB, TUPLE}. For BLOB topic, data will be organized as binary and encoded by BASE64. For TUPLE topic, data has fixed schema. The default value is "TUPLE" with a schema {STRING}. """ return pulumi.get(self, "record_type") @property @pulumi.getter(name="shardCount") def shard_count(self) -> pulumi.Output[Optional[int]]: """ The number of shards this topic contains. The permitted range of values is [1, 10]. The default value is 1. """ return pulumi.get(self, "shard_count")

44.80456

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1fd0d9e214842fc979e7e0b052b2a321bd60b783

44

py

Python

tase/telegram/methods/__init__.py

soran-ghaderi/Chromusic_search_engine

e811401fee39ff4cb184750fcbde55053c69453d

[ "Apache-2.0" ]

4

2022-02-21T06:56:16.000Z

2022-03-07T21:10:19.000Z

tase/telegram/methods/__init__.py

soran-ghaderi/Chromusic_search_engine

e811401fee39ff4cb184750fcbde55053c69453d

[ "Apache-2.0" ]

null

tase/telegram/methods/__init__.py

soran-ghaderi/Chromusic_search_engine

e811401fee39ff4cb184750fcbde55053c69453d

[ "Apache-2.0" ]

1

2022-03-07T21:10:02.000Z

from .search_messages import search_messages

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1f45503f400da41e3109736d2448566ba2d55fbd

16,127

py

Python

tests/test_main.py

Bag-of-Party/python-website

50e6135ade7311aa66e229857f531a4d52c85d1d

[ "Apache-2.0" ]

null

tests/test_main.py

Bag-of-Party/python-website

50e6135ade7311aa66e229857f531a4d52c85d1d

[ "Apache-2.0" ]

1

2021-07-24T11:18:27.000Z

tests/test_main.py

Bag-of-Party/python-website

50e6135ade7311aa66e229857f531a4d52c85d1d

[ "Apache-2.0" ]

null

from flask import session import psycopg2 import psycopg2.extras import pytest import uuid from psycopg2 import Error from app.main import home, signup, login, create_party, party, add_items, terms, action, contact, login_data_check, app, DATABASE_URL, bcrypt from unittest.mock import Mock @pytest.fixture def db_conn_parties(): db_conn = psycopg2.connect(DATABASE_URL) cur = db_conn.cursor() cur.execute("TRUNCATE TABLE parties") db_conn.commit() cur.close() yield db_conn db_conn.close() @pytest.fixture def db_conn_items(): db_conn = psycopg2.connect(DATABASE_URL) cur = db_conn.cursor() cur.execute("TRUNCATE TABLE items") db_conn.commit() cur.close() yield db_conn db_conn.close() def test_home(monkeypatch): with app.test_request_context('/'): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = home() render_template.assert_called_with('home.html', page_class="home") def test_contacts_page(monkeypatch): with app.test_request_context('/contact'): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = contact() render_template.assert_called_with('contact.html', page_class="contact") def test_terms_page(monkeypatch): with app.test_request_context('/terms'): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = terms() render_template.assert_called_with('terms.html', page_class="terms") def test_signup_get(monkeypatch): with app.test_request_context('/signup'): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = signup() render_template.assert_called_with('signup.html', page_class="signup") def test_signup_post_redirect(db_conn_items, db_conn_parties, monkeypatch): with app.test_request_context('/signup', method = "POST", data = { "party_name": "test", "generated_url": "4u3u/test", "user_email": "test", "party_password": "test" }): response = signup() assert response.status_code == 303 assert response.headers["location"] == "/4u3u/test" def test_create_party_database_insertion(db_conn_parties): uniqid = uuid.uuid4() create_party(uniqid, "test_name", "2u3u/test", "test_email", "test_password") cur = db_conn_parties.cursor() cur.execute("SELECT * from parties where url = '2u3u/test'") data = cur.fetchone() assert data == (str(uniqid), 'test_name', '2u3u/test', 'test_email', "test_password") def test_signup_POSTreq_creates_party_input(monkeypatch): uniqid = uuid.uuid4() with app.test_request_context('/signup', method = "POST", data = { "party_name": "test_name", "generated_url": "2u3u/test", "user_email": "test_email", "party_password": "test" }): create_party = Mock() monkeypatch.setattr("app.main.create_party", create_party) # uniqid = uuid.uuid4() testuniqid = Mock(return_value = uniqid) monkeypatch.setattr("uuid.uuid4", testuniqid) hash_password = b"1234" test_password = Mock(return_value = hash_password) monkeypatch.setattr("app.main.bcrypt.generate_password_hash", test_password) signup() create_party.assert_called_with(uniqid, "test_name", "2u3u/test", "test_email", "1234") def test_login_sucsess_routing(monkeypatch, db_conn_parties): uniqid = uuid.uuid4() with app.test_request_context('/login', method = "POST", data = { "login_group_email": "test_email", "login_password": "test_password" }): test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) response = login() print("response") print(response) assert response.status_code == 303 assert response.headers["location"] == "/2u3u/test" def test_login_fail_routing(monkeypatch, db_conn_parties): uniqid = uuid.uuid4() with app.test_request_context('/login', method = "POST", data = { "login_group_email": "test_email", "login_password": "fail_password" }): test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = login() render_template.assert_called_with('login.html') def test_login_bypass_routing(monkeypatch): with app.test_request_context('/login'): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = login() render_template.assert_called_with('login.html') def test_login_data_check(db_conn_parties): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) data = login_data_check("test_email") assert data == (str(uniqid), "test_name", '2u3u/test', 'test_email', test_password) def test_login_fail_session_empty(monkeypatch, db_conn_parties): uniqid = uuid.uuid4() with app.test_request_context('/login', method = "POST", data = { "login_group_email": "test_email", "login_password": "fail_password" }): test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = login() assert 'group_id' not in session def test_party_page_routing_out_of_session(db_conn_parties, monkeypatch): with app.test_request_context("/1j5p/party_name"): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) response = party("1j5p", "party_name") render_template.assert_called_with('login.html') def test_party_page_routing_in_session_no_items(db_conn_items, db_conn_parties, monkeypatch): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) with app.test_request_context("/2u3u/test"): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) session['group_id'] = str(uniqid) party("2u3u", "test") render_template.assert_called_with('partypage.html', data= [str(uniqid), 'test_name', '2u3u/test', 'test_email', test_password], page_items=[], root_items=[], names=[]) def test_party_page_routing_in_session_with_items_no_contents(db_conn_items, db_conn_parties, monkeypatch): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) uniqid_container = uuid.uuid4() string_id = str(uniqid) add_items(str(uniqid_container), str(uniqid), "test_item", "test_info", None) with app.test_request_context("/2u3u/test"): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) session['group_id'] = str(uniqid) party("2u3u", "test") render_template.assert_called_with('partypage.html', data= [str(uniqid), 'test_name', '2u3u/test', 'test_email', test_password], page_items=[[str(uniqid_container), str(uniqid), "test_item", "test_info", None]], root_items=[{'id': str(uniqid_container), 'party_id': str(uniqid), 'name': 'test_item', 'info': 'test_info', 'container_id': None, 'contents': [], 'length': 0}], names=['test_item']) def test_party_page_routing_in_session_with_items_with_contents(db_conn_items, db_conn_parties, monkeypatch): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) uniqid_container = uuid.uuid4() uniqid_container_inside = uuid.uuid4() uniqid_group = uuid.uuid4() uniqid_item_inside = uuid.uuid4() add_items(str(uniqid_container), str(uniqid), "test_item", "test_info", None) add_items(str(uniqid_container_inside), str(uniqid), "test_item_2", "test_info_2", str(uniqid_container)) cur = db_conn_items.cursor() cur.execute("SELECT * from items where party_id = %s",(str(uniqid_group),)) item_data = cur.fetchone() with app.test_request_context("/2u3u/test"): render_template = Mock() monkeypatch.setattr("app.main.render_template", render_template) session['group_id'] = str(uniqid) party("2u3u", "test") render_template.assert_called_with('partypage.html', data=[str(uniqid), 'test_name', '2u3u/test', 'test_email', test_password], page_items=[[str(uniqid_container), str(uniqid), 'test_item', 'test_info', None], [str(uniqid_container_inside), str(uniqid), 'test_item_2', 'test_info_2', str(uniqid_container)]], root_items=[{'id': str(uniqid_container), 'party_id': str(uniqid), 'name': 'test_item', 'info': 'test_info', 'container_id': None, 'contents': [{'id': str(uniqid_container_inside), 'party_id': str(uniqid), 'name': 'test_item_2', 'info': 'test_info_2', 'container_id': str(uniqid_container), 'contents': []}], 'length': 1}], names=['test_item', 'test_item_2']) # def test_party_page_routing_in_session_with_items(db_conn_items, db_conn_parties, monkeypatch): # uniqid = uuid.uuid4() # test_password = bcrypt.generate_password_hash("test_password").decode() # create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) # with app.test_request_context("/2u3u/test"): # render_template = Mock() # monkeypatch.setattr("app.main.render_template", render_template) # session['group_id'] = str(uniqid) # party("2u3u", "test") # render_template.assert_called_with('partypage.html', data= [str(uniqid), 'test_name', '2u3u/test', 'test_email', test_password], page_items=[], root_items=[], names=[]) # def test_add_items(db_conn_items, db_conn_parties): # create_party(uniqid, "test_name", "1j5p/party_name", "test_email", "test_password") # db_conn = psycopg2.connect(DATABASE_URL) # cur = db_conn.cursor() # # cur = db_conn_parties.cursor() # cur.execute("SELECT * from parties where url = '1j5p/party_name'") # data = cur.fetchone() # print("data") # print(data) # # with app.test_request_context('/1j5p/party_name', method = "POST", data = { # # "add_item": "test_item", # # "add_item_info": "test_info", # # "container_id": uniqid_container # # }): # add_items(str(uniqid_container), str(uniqid), "test_item", "test_info", None) # cur.execute("SELECT * from parties where url = '1j5p/party_name'") # item_data = cur.fetchone() # assert data == (str(uniqid), 'test_name', '1j5p/party_name', 'test_email', "test_password") # assert item_data == (str(uniqid_container), str(uniqid), "test_item", "test_info") def test_add_items_no_contents(db_conn_parties, db_conn_items): uniqid = uuid.uuid4() uniqid_container = uuid.uuid4() string_id = str(uniqid) add_items(str(uniqid_container), str(uniqid), "test_item", "test_info", None) cur = db_conn_items.cursor() cur.execute("SELECT * from items where party_id = %s",(str(uniqid),)) item_data = cur.fetchone() assert item_data == (str(uniqid_container), str(uniqid), "test_item", "test_info", None) # FIXME this is not testing all the data, only paretnt level bing pulled def test_add_items_with_contents(db_conn_parties, db_conn_items): uniqid_container = uuid.uuid4() uniqid_container_inside = uuid.uuid4() uniqid_group = uuid.uuid4() uniqid_item_inside = uuid.uuid4() add_items(str(uniqid_container), str(uniqid_group), "test_item", "test_info", None) add_items(str(uniqid_container_inside), str(uniqid_group), "test_item_2", "test_info_2", str(uniqid_container)) cur = db_conn_items.cursor() cur.execute("SELECT * from items where party_id = %s",(str(uniqid_group),)) item_data = cur.fetchone() # print("item_data") # print(item_data) assert item_data == (str(uniqid_container), str(uniqid_group), "test_item", "test_info", None) def test_delete_item_no_contents_delete_redirect(db_conn_parties, db_conn_items): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) # uniqid_item = uuid.uuid4() uniqid_container = uuid.uuid4() string_id = str(uniqid) add_items(str(uniqid_container), str(uniqid), "test_item", "test_info", None) with app.test_request_context(f'/2u3u/test/?delete={uniqid}'): session['group_id'] = str(uniqid) response = party("2u3u", "test") print("response") print(response) assert response.status_code == 303 assert response.headers["location"] == "/2u3u/test" def test_delete_item_no_contents(db_conn_parties, db_conn_items): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) # uniqid_item = uuid.uuid4() uniqid_container = uuid.uuid4() string_id = str(uniqid) add_items(str(uniqid_container), str(uniqid), "test_item", "test_info", None) with app.test_request_context(f'/2u3u/test/?delete={uniqid_container}'): session['group_id'] = str(uniqid) # party("2u3u", "test") party("2u3u", "test") cur = db_conn_items.cursor() cur.execute("SELECT * from items where party_id = %s",(str(uniqid),)) item_data = cur.fetchone() print("item_data") print(item_data) assert item_data != (str(uniqid_container), str(uniqid), 'test_item', 'test_info', None) def test_partpage_item_added_page_redirect(db_conn_parties, db_conn_items, monkeypatch): uniqid = uuid.uuid4() test_password = bcrypt.generate_password_hash("test_password").decode() create_party(uniqid, "test_name", "2u3u/test", "test_email", test_password) with app.test_request_context("/2u3u/test", method = "POST", data = { "add_item": "test_item", "add_item_info": "test_info", "container_id": None }): session['group_id'] = str(uniqid) response = party("2u3u", "test") # print("response") # print(response) RETURNIUNG PAGE HTML assert response.status_code == 303 assert response.headers["location"] == "/2u3u/test" def test_add_items_no_contents_action_modal(db_conn_items, db_conn_parties, monkeypatch): uniqid_container = uuid.uuid4() uniqid_container_inside = uuid.uuid4() uniqid_group = uuid.uuid4() uniqid_item_inside = uuid.uuid4() with app.test_request_context('/action', method = 'POST', data = { "itemName": "item_name", "infoDetails": "item_info", "container": None, }): session['group_url'] = '2u3u/test' session['group_id'] = str(uniqid_group) uniqid = uuid.uuid4() testuniqid = Mock(return_value = uniqid) monkeypatch.setattr("uuid.uuid4", testuniqid) action() cur = db_conn_items.cursor() cur.execute("SELECT * from items where party_id = %s",(str(uniqid_group),)) item_data = cur.fetchone() assert item_data == (str(uniqid), str(uniqid_group), "item_name", "item_info", None)

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false

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0

null

0

1

0

null

0

1

0

7

1f46a0696fe0e67b4d126c647f72efa69c7ef748

82

py

Python

nodes/IGResizeImage/__init__.py

bertrandboudaud/imagegraph

7c95b645edeb1a68d56c6c3b19f1ff6fde413afc

[ "MIT" ]

null

nodes/IGResizeImage/__init__.py

bertrandboudaud/imagegraph

7c95b645edeb1a68d56c6c3b19f1ff6fde413afc

[ "MIT" ]

null

nodes/IGResizeImage/__init__.py

bertrandboudaud/imagegraph

7c95b645edeb1a68d56c6c3b19f1ff6fde413afc

[ "MIT" ]

null

from . import IGResizeImage def get(): return IGResizeImage.IGResizeImage()

13.666667

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0.75

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null

0

1

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1

0

7

2f43cd59944830caf0f22ae2cbbdb8d706a340dc

78

py

Python

snnw/nn/activation/relu.py

juliustao/SNNW

2051c81b4013030d67fdfdcb1dd2973ba550ddd9

[ "MIT" ]

null

snnw/nn/activation/relu.py

juliustao/SNNW

2051c81b4013030d67fdfdcb1dd2973ba550ddd9

[ "MIT" ]

null

snnw/nn/activation/relu.py

juliustao/SNNW

2051c81b4013030d67fdfdcb1dd2973ba550ddd9

[ "MIT" ]

null

def function(x): return x * (x > 0) def derivative(x): return x > 0

11.142857

22

0.551282

13

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3.307692

0.461538

0.325581

0.372093

0

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null

0

1

0

1

0

7

2f9ef5ffb24d82460cddcb1e37246e51b4d8a70d

198

py

Python

image_base/__init__.py

ryanfeather/image_base

6aa56eaa59abd0c62bebd06f26a801e6ba62ddb4

[ "MIT" ]

null

image_base/__init__.py

ryanfeather/image_base

6aa56eaa59abd0c62bebd06f26a801e6ba62ddb4

[ "MIT" ]

null

image_base/__init__.py

ryanfeather/image_base

6aa56eaa59abd0c62bebd06f26a801e6ba62ddb4

[ "MIT" ]

null

from . import parallel from . import filter from . import io_utils from . import sloth_utils from . import net_base from . import extraction from . import data_generators from . import augmentation

22

29

0.79798

28

198

5.5

0.464286

0.519481

0.194805

0

0.161616

198

9

30

22

0.927711

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1

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true

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null

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1

0

1

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1

0

7

85dcb00c4841f36f2abbfee23a51d4109d33c34f

8,054

py

Python

conformal_blocks/test/test_f_curve_random.py

mjschust/conformal-blocks

7f6c551f506b3ebf5e0f73eb9b4cadaeee2a02b9

[ "MIT" ]

null

conformal_blocks/test/test_f_curve_random.py

mjschust/conformal-blocks

7f6c551f506b3ebf5e0f73eb9b4cadaeee2a02b9

[ "MIT" ]

1

2017-04-20T23:15:32.000Z

conformal_blocks/test/test_f_curve_random.py

mjschust/conformal-blocks

7f6c551f506b3ebf5e0f73eb9b4cadaeee2a02b9

[ "MIT" ]

null

import unittest import conformal_blocks.cbbundle as cbd class MyTestCase(unittest.TestCase): def test_A2N6(self): liealg = cbd.TypeALieAlgebra(2) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0), (0, 1), (2, 0), (1, 0), (0, 0), (0, 2)], 2) f_curve = [(1,), (5,), (3, 6), (2, 4)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 2), (0, 2), (0, 0), (2, 0), (0, 2), (0, 2)], 2) f_curve = [(4,), (2,), (3, 6), (1, 5)] self.assertEqual(2, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0), (1, 1), (2, 0), (0, 1), (0, 1), (2, 0)], 2) f_curve = [(2,), (3, 6), (5,), (1, 4)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 2), (0, 0), (0, 0), (0, 0), (0, 2), (0, 2)], 2) f_curve = [(2,), (3, 4, 5), (1,), (6,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0), (2, 0), (1, 1), (0, 2), (0, 0), (1, 0)], 2) f_curve = [(1, 3), (5,), (2, 4), (6,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 1), (0, 1), (2, 0), (0, 2), (1, 0), (0, 2)], 2) f_curve = [(3, 6), (1,), (4,), (2, 5)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0), (2, 0), (2, 0), (2, 0), (0, 1), (1, 1)], 2) f_curve = [(4,), (1, 5, 6), (2,), (3,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0), (0, 1), (0, 0), (2, 0), (1, 1), (0, 1)], 2) f_curve = [(4,), (2,), (1, 5), (3, 6)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 1), (0, 1), (1, 1), (0, 2), (1, 0), (0, 0)], 2) f_curve = [(2,), (4, 5), (3, 6), (1,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0), (1, 1), (1, 1), (1, 1), (0, 2), (0, 0)], 2) f_curve = [(1, 4), (2, 5), (6,), (3,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0), (1, 1), (1, 0), (1, 0), (1, 0), (0, 2)], 2) f_curve = [(1,), (5,), (4, 6), (2, 3)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0), (1, 0), (0, 1), (0, 0), (1, 0), (1, 1)], 2) f_curve = [(1, 4, 6), (5,), (2,), (3,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0), (2, 0), (1, 0), (1, 0), (1, 1), (0, 1)], 2) f_curve = [(4,), (2, 3), (1, 6), (5,)] self.assertEqual(1, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0), (0, 2), (2, 0), (1, 1), (0, 2), (0, 0)], 2) f_curve = [(3, 6), (1,), (2, 5), (4,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 1), (0, 2), (2, 0), (1, 0), (0, 1), (1, 1)], 2) f_curve = [(5,), (1,), (4, 6), (2, 3)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0), (0, 0), (0, 2), (0, 0), (0, 0), (0, 2)], 2) f_curve = [(3,), (2, 4), (5, 6), (1,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) def test_A3N6(self): liealg = cbd.TypeALieAlgebra(3) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 2, 0), (0, 1, 1), (0, 0, 1), (1, 1, 0), (0, 0, 1), (0, 1, 1)], 2) f_curve = [(6,), (1,), (2, 3, 4), (5,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0, 0), (0, 0, 2), (0, 0, 1), (2, 0, 0), (1, 0, 0), (0, 0, 1)], 2) f_curve = [(5, 6), (2, 3), (4,), (1,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 1, 1), (1, 0, 1), (0, 1, 0), (1, 1, 0), (0, 0, 2), (0, 0, 1)], 2) f_curve = [(2, 3), (4,), (1, 6), (5,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0, 1), (0, 1, 1), (1, 0, 0), (1, 1, 0), (0, 0, 2), (0, 2, 0)], 2) f_curve = [(4,), (5,), (1, 3), (2, 6)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0, 0), (0, 1, 1), (0, 0, 2), (0, 1, 1), (1, 0, 1), (0, 1, 1)], 2) f_curve = [(6,), (1,), (3, 4, 5), (2,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 1, 0), (0, 2, 0), (0, 0, 2), (1, 0, 1), (0, 1, 1), (0, 0, 1)], 2) f_curve = [(1, 3), (2,), (5,), (4, 6)] self.assertEqual(3, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(2, 0, 0), (0, 1, 0), (1, 0, 1), (1, 0, 1), (1, 0, 0), (0, 0, 1)], 2) f_curve = [(3,), (4,), (1, 2), (5, 6)] self.assertEqual(3, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0, 1), (0, 1, 1), (1, 0, 0), (0, 0, 0), (0, 0, 0), (0, 2, 0)], 2) f_curve = [(1, 4), (3,), (2, 5), (6,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 1, 0), (0, 1, 1), (2, 0, 0), (0, 0, 0), (1, 1, 0), (0, 2, 0)], 2) f_curve = [(2, 4), (1,), (3, 5), (6,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0, 1), (0, 0, 0), (0, 0, 0), (0, 0, 2), (0, 1, 1), (0, 2, 0)], 2) f_curve = [(3, 5), (1, 2), (6,), (4,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) def test_C2N6(self): liealg = cbd.TypeCLieAlgebra(2) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 1), (2, 0), (0, 0), (2, 0), (1, 0), (0, 1)], 2) f_curve = [(6,), (2, 4), (3,), (1, 5)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 1), (2, 0), (0, 2), (0, 1), (1, 0), (0, 2)], 2) f_curve = [(3,), (4,), (1, 2, 6), (5,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 1), (0, 0), (0, 2), (0, 2), (0, 2), (0, 1)], 2) f_curve = [(1, 4), (6,), (3, 5), (2,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 1), (2, 0), (0, 1), (0, 0), (1, 0), (0, 0)], 2) f_curve = [(2, 3), (6,), (1, 5), (4,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0), (2, 0), (1, 0), (0, 1), (1, 0), (1, 0)], 2) f_curve = [(2,), (4, 6), (1,), (3, 5)] self.assertEqual(3, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0), (0, 0), (0, 1), (0, 1), (1, 1), (0, 0)], 2) f_curve = [(1,), (4, 5, 6), (3,), (2,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0), (1, 1), (0, 1), (1, 0), (0, 2), (0, 1)], 2) f_curve = [(3, 5, 6), (2,), (1,), (4,)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(1, 0), (0, 2), (1, 0), (0, 0), (1, 1), (0, 2)], 2) f_curve = [(1,), (5,), (3, 4), (2, 6)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 2), (1, 0), (0, 0), (1, 1), (0, 2), (2, 0)], 2) f_curve = [(5,), (2,), (1,), (3, 4, 6)] self.assertEqual(1, cbb.intersect_F_curve(f_curve)) cbb = cbd.ConformalBlocksBundle(liealg, [(0, 0), (1, 1), (0, 1), (1, 1), (1, 1), (0, 0)], 2) f_curve = [(1,), (2,), (3,), (4, 5, 6)] self.assertEqual(0, cbb.intersect_F_curve(f_curve)) if __name__ == '__main__': unittest.main()

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null

0

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1

0

null

0

8

c85b898dd362377012d9b6eb6ea45062cf82eec6

5,843

py

Python

debug_logging/migrations/0001_initial.py

lincolnloop/django-debug-logging

dc2f60b5abaf5f344de45a791f3a3473873ae304

[ "BSD-3-Clause" ]

14

2015-02-13T08:23:59.000Z

2021-09-01T22:38:48.000Z

debug_logging/migrations/0001_initial.py

lincolnloop/django-debug-logging

dc2f60b5abaf5f344de45a791f3a3473873ae304

[ "BSD-3-Clause" ]

null

debug_logging/migrations/0001_initial.py

lincolnloop/django-debug-logging

dc2f60b5abaf5f344de45a791f3a3473873ae304

[ "BSD-3-Clause" ]

2

2016-04-18T03:07:47.000Z

2017-05-24T08:35:27.000Z

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'DebugLogRecord' db.create_table('debug_logging_debuglogrecord', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('timestamp', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), ('project_name', self.gf('django.db.models.fields.CharField')(max_length=255)), ('hostname', self.gf('django.db.models.fields.CharField')(max_length=255)), ('request_path', self.gf('django.db.models.fields.CharField')(max_length=255)), ('revision', self.gf('django.db.models.fields.CharField')(max_length=40, null=True, blank=True)), ('settings_pickled', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('timer_utime', self.gf('django.db.models.fields.FloatField')(null=True, blank=True)), ('timer_stime', self.gf('django.db.models.fields.FloatField')(null=True, blank=True)), ('timer_cputime', self.gf('django.db.models.fields.FloatField')(null=True, blank=True)), ('timer_total', self.gf('django.db.models.fields.FloatField')(null=True, blank=True)), ('timer_vcsw', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('timer_ivcsw', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('sql_num_queries', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('sql_time', self.gf('django.db.models.fields.FloatField')(null=True, blank=True)), ('sql_queries_pickled', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('cache_num_calls', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_time', self.gf('django.db.models.fields.FloatField')(null=True, blank=True)), ('cache_hits', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_misses', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_sets', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_gets', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_get_many', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_deletes', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('cache_calls_pickled', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), )) db.send_create_signal('debug_logging', ['DebugLogRecord']) def backwards(self, orm): # Deleting model 'DebugLogRecord' db.delete_table('debug_logging_debuglogrecord') models = { 'debug_logging.debuglogrecord': { 'Meta': {'object_name': 'DebugLogRecord'}, 'cache_calls_pickled': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'cache_deletes': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_get_many': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_gets': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_hits': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_misses': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_num_calls': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_sets': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'cache_time': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'hostname': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'project_name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'request_path': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'revision': ('django.db.models.fields.CharField', [], {'max_length': '40', 'null': 'True', 'blank': 'True'}), 'settings_pickled': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'sql_num_queries': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'sql_queries_pickled': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'sql_time': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'timer_cputime': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'timer_ivcsw': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'timer_stime': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'timer_total': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'timer_utime': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'timer_vcsw': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'timestamp': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}) } } complete_apps = ['debug_logging']

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null

0

8

23b1bf0f450ff95a144de5045f5b3bc8d76d99ad

4,689

py

Python

matplotcheck/tests/test_base.py

earthlab/matPlotCheck

c1b6a3bf805b1b813b9bdf98752f2bf4baa9cdcf

[ "BSD-3-Clause" ]

12

2019-03-01T15:07:10.000Z

2022-03-04T08:23:26.000Z

matplotcheck/tests/test_base.py

earthlab/matPlotCheck

c1b6a3bf805b1b813b9bdf98752f2bf4baa9cdcf

[ "BSD-3-Clause" ]

382

2018-12-20T23:05:47.000Z

2022-01-13T02:34:44.000Z

matplotcheck/tests/test_base.py

earthlab/matPlotCheck

c1b6a3bf805b1b813b9bdf98752f2bf4baa9cdcf

[ "BSD-3-Clause" ]

8

2019-01-23T23:10:51.000Z

2021-11-03T16:17:51.000Z

"""Tests for the base module""" import pytest import matplotlib.pyplot as plt def test_line_plot(pt_line_plt): """Test that the line plot returns true for line but false for bar or scatter.""" pt_line_plt.assert_plot_type("line") with pytest.raises(AssertionError, match="Plot is not of type bar"): pt_line_plt.assert_plot_type("bar") with pytest.raises(AssertionError, match="Plot is not of type scatter"): pt_line_plt.assert_plot_type("scatter") plt.close() def test_scatter_plot(pt_scatter_plt): """Test that the scatter plot returns true for line but false for bar or line.""" pt_scatter_plt.assert_plot_type("scatter") with pytest.raises(AssertionError, match="Plot is not of type bar"): pt_scatter_plt.assert_plot_type("bar") with pytest.raises(AssertionError, match="Plot is not of type line"): pt_scatter_plt.assert_plot_type("line") plt.close() def test_bar_plot(pt_bar_plt): """Test that the scatter plot returns true for line but false for bar or line.""" pt_bar_plt.assert_plot_type("bar") with pytest.raises(AssertionError, match="Plot is not of type scatter"): pt_bar_plt.assert_plot_type("scatter") with pytest.raises(AssertionError, match="Plot is not of type line"): pt_bar_plt.assert_plot_type("line") plt.close() def test_options(pt_line_plt): """Test that a ValueError is raised if an incorrect plot type is provided.""" with pytest.raises( ValueError, match="Plot_type to test must be either: scatter, bar or line", ): pt_line_plt.assert_plot_type("foo") plt.close() """ assert_string_contains Tests """ def test_assert_string_contains(pt_line_plt): """Tests that assert_string_contains passes with correct expected strings.""" test_string = "this is a test string" string_expected = ["this", "is", "a", "test"] pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_fails(pt_line_plt): """Tests that assert_string_contains fails with incorrect expected strings.""" test_string = "this is a test string" string_expected = ["this", "is", "not", "a", "test"] with pytest.raises( AssertionError, match="String does not contain expected string: not" ): pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_or(pt_line_plt): """Tests that assert_string_contains correctly passes when using OR logic.""" test_string = "this is a test string" string_expected = ["this", ["is", "not"], "a", "test"] pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_or_fails(pt_line_plt): """Tests that assert_string_contains correctly fails when using OR logic.""" test_string = "this is a test string" string_expected = ["this", "is", ["not", "jambalaya"], "a", "test"] with pytest.raises( AssertionError, match="String does not contain at least one of: " ): pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_handles_short_list_passes(pt_line_plt): """Tests that assert_string_contains correctly passes in the case that strings_expected conains a list of length 1.""" test_string = "this is a test string" string_expected = [["this"], ["is"]] pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_handles_short_list_fails(pt_line_plt): """Tests that assert_string_contains correctly fails in the case that strings_expected conains a list of length 1.""" test_string = "this is a test string" string_expected = [["this"], ["is"], ["not"]] with pytest.raises( AssertionError, match="String does not contain expected string: not" ): pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_passes_with_none(pt_line_plt): """Tests that assert_string_contains passes when strings_expected is None""" test_string = "this is a test string" string_expected = None pt_line_plt.assert_string_contains(test_string, string_expected) plt.close() def test_assert_string_contains_passes_with_empty(pt_line_plt): """Tests that assert_string_contains passes when strings_expected is empty""" test_string = "this is a test string" string_expected = [] pt_line_plt.assert_string_contains(test_string, string_expected) plt.close()

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null

0

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0

null

0

1

0

7

23de52878cb9dc5002da8a2e6ff233024eab8cb6

16,679

py

Python

platform/core/tests/test_auditor/test_auditor_notebook.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/tests/test_auditor/test_auditor_notebook.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/tests/test_auditor/test_auditor_notebook.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

# pylint:disable=ungrouped-imports from unittest.mock import patch import pytest import auditor from events.registry import notebook as notebook_events from factories.factory_plugins import NotebookJobFactory from factories.factory_projects import ProjectFactory from tests.test_auditor.utils import AuditorBaseTest @pytest.mark.auditor_mark class AuditorNotebookTest(AuditorBaseTest): """Testing subscribed events""" EVENTS = notebook_events.EVENTS def setUp(self): super().setUp() self.notebook = NotebookJobFactory(project=ProjectFactory()) self.tested_events = { notebook_events.NOTEBOOK_STARTED, notebook_events.NOTEBOOK_STARTED_TRIGGERED, notebook_events.NOTEBOOK_STOPPED, notebook_events.NOTEBOOK_STOPPED_TRIGGERED, notebook_events.NOTEBOOK_CLEANED_TRIGGERED, notebook_events.NOTEBOOK_VIEWED, notebook_events.NOTEBOOK_NEW_STATUS, notebook_events.NOTEBOOK_FAILED, notebook_events.NOTEBOOK_SUCCEEDED, notebook_events.NOTEBOOK_STATUSES_VIEWED, notebook_events.NOTEBOOK_UPDATED, notebook_events.NOTEBOOK_DELETED, notebook_events.NOTEBOOK_DELETED_TRIGGERED, notebook_events.NOTEBOOK_ARCHIVED, notebook_events.NOTEBOOK_RESTORED, notebook_events.NOTEBOOK_UNBOOKMARKED, notebook_events.NOTEBOOK_BOOKMARKED, } @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_started(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_STARTED, instance=self.notebook, target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 1 assert executor_record.call_count == 1 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_started_triggered(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_STARTED_TRIGGERED, instance=self.notebook, target='project', actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_stopped(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_STOPPED, instance=self.notebook, target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 0 assert executor_record.call_count == 1 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_stopped_triggered(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_STOPPED_TRIGGERED, instance=self.notebook, target='project', actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_cleaned_triggered(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_CLEANED_TRIGGERED, instance=self.notebook, target='project', actor_id=1, actor_name='foo') assert tracker_record.call_count == 0 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 0 assert executor_record.call_count == 1 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_viewed(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_VIEWED, instance=self.notebook, target='project', actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_experiment_new_status(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_NEW_STATUS, instance=self.notebook, target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 0 assert executor_record.call_count == 1 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_experiment_failed(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_FAILED, instance=self.notebook, target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 1 assert executor_record.call_count == 1 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_experiment_succeeded(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_SUCCEEDED, instance=self.notebook, target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 1 assert executor_record.call_count == 1 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_statuses_viewed(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_STATUSES_VIEWED, instance=self.notebook, target='project', actor_name='foo', actor_id=1) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_updated(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_UPDATED, instance=self.notebook, actor_name='foo', actor_id=1) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_deleted(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_DELETED, instance=self.notebook) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_triggered_deleted(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_DELETED_TRIGGERED, instance=self.notebook, actor_name='foo', actor_id=1) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_job_archived(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_ARCHIVED, instance=self.notebook, actor_name='foo', actor_id=1) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_job_restored(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_RESTORED, instance=self.notebook, actor_name='foo', actor_id=1) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_unbookmarked(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_UNBOOKMARKED, instance=self.notebook, actor_id=1, actor_name='foo', target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 @patch('executor.executor_service.ExecutorService.record_event') @patch('notifier.service.NotifierService.record_event') @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_notebook_bookmarked(self, activitylogs_record, tracker_record, notifier_record, executor_record): auditor.record(event_type=notebook_events.NOTEBOOK_BOOKMARKED, instance=self.notebook, actor_id=1, actor_name='foo', target='project') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 assert notifier_record.call_count == 0 assert executor_record.call_count == 0 del AuditorBaseTest

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1

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0

7

f1a9175ad1549a1fc92beaf4a95975ff2d9f908a

3,761

py

Python

personal_portfolio/vendor/migrations/0027_auto_20210302_1031.py

numbstudent/ghvms

6d5f93c76498e0fa167b0ce7dfed53764242626f

[ "MIT" ]

null

personal_portfolio/vendor/migrations/0027_auto_20210302_1031.py

numbstudent/ghvms

6d5f93c76498e0fa167b0ce7dfed53764242626f

[ "MIT" ]

null

personal_portfolio/vendor/migrations/0027_auto_20210302_1031.py

numbstudent/ghvms

6d5f93c76498e0fa167b0ce7dfed53764242626f

[ "MIT" ]

null

# Generated by Django 3.1.6 on 2021-03-02 03:31 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('vendor', '0026_pekerjaanberjalanperusahaan_pengalamanmitrakerjaperusahaan'), ] operations = [ migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='bidang_usaha', new_name='b_bidang_usaha', ), migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='dokumen', new_name='b_dokumen', ), migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='lokasi', new_name='b_lokasi', ), migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='mata_uang', new_name='b_mata_uang', ), migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='mulai_kerjasama', new_name='b_mulai_kerjasama', ), migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='nama', new_name='b_nama', ), migrations.RenameField( model_name='pekerjaanberjalanperusahaan', old_name='nilai_kontrak', new_name='b_nilai_kontrak', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='bidang_usaha', new_name='m_bidang_usaha', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='dokumen', new_name='m_dokumen', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='lokasi', new_name='m_lokasi', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='mata_uang', new_name='m_mata_uang', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='mulai_kerjasama', new_name='m_mulai_kerjasama', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='nama', new_name='m_nama', ), migrations.RenameField( model_name='pengalamanmitrakerjaperusahaan', old_name='nilai_kontrak', new_name='m_nilai_kontrak', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='bidang_usaha', new_name='p_bidang_usaha', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='dokumen', new_name='p_dokumen', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='lokasi', new_name='p_lokasi', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='mata_uang', new_name='p_mata_uang', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='mulai_kerjasama', new_name='p_mulai_kerjasama', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='nama', new_name='p_nama', ), migrations.RenameField( model_name='pengalamanperusahaan', old_name='nilai_kontrak', new_name='p_nilai_kontrak', ), ]

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null

1

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1

0

null

0

7

f1bffc93c5bfe3d5eb8359d7140dbed4fd729a6c

36,051

py

Python

simple_salesforce/tests/test_api.py

cmitzel/simple-salesforce

c210452ef64425e95129ed7cca36d146e722fe00

[ "Apache-2.0" ]

1

2020-11-19T22:12:23.000Z

simple_salesforce/tests/test_api.py

cmitzel/simple-salesforce

c210452ef64425e95129ed7cca36d146e722fe00

[ "Apache-2.0" ]

null

simple_salesforce/tests/test_api.py

cmitzel/simple-salesforce

c210452ef64425e95129ed7cca36d146e722fe00

[ "Apache-2.0" ]

null

"""Tests for api.py""" import http.client as http import re import unittest from collections import OrderedDict from datetime import datetime from unittest.mock import Mock, patch import requests import responses from simple_salesforce import tests from simple_salesforce.api import PerAppUsage, Salesforce, SFType, Usage def _create_sf_type(object_name="Case", session_id="5", sf_instance="my.salesforce.com"): """Creates SFType instances""" return SFType(object_name=object_name, session_id=session_id, sf_instance=sf_instance, session=requests.Session()) class TestSFType(unittest.TestCase): """Tests for the SFType instance""" def setUp(self): request_patcher = patch("simple_salesforce.api.requests") self.mockrequest = request_patcher.start() self.addCleanup(request_patcher.stop) @responses.activate def test_metadata_with_additional_request_headers(self): """Ensure custom headers are used for metadata requests""" responses.add(responses.GET, re.compile(r"^https://.*$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.metadata(headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_metadata_without_additional_request_headers(self): """Ensure metadata requests without additional headers""" responses.add(responses.GET, re.compile(r"^https://.*$"), body="{}", status=http.OK) sf_type = _create_sf_type() self.assertEqual(sf_type.metadata(), {}) @responses.activate def test_describe_with_additional_request_headers(self): """Ensure custom headers are used for describe requests""" responses.add(responses.GET, re.compile(r"^https://.*/Case/describe$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.describe(headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_describe_without_additional_request_headers(self): """Ensure describe requests without additional headers""" responses.add(responses.GET, re.compile(r"^https://.*/Case/describe$"), body="{}", status=http.OK) sf_type = _create_sf_type() self.assertEqual(sf_type.describe(), {}) @responses.activate def test_describe_layout_with_additional_request_headers(self): """Ensure custom headers are used for describe_layout requests""" responses.add(responses.GET, re.compile(r"^https://.*/Case/describe/layouts/444$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.describe_layout(record_id="444", headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_describe_layout_without_additional_request_headers(self): """Ensure describe_layout requests without additional headers""" responses.add(responses.GET, re.compile(r"^https://.*/Case/describe/layouts/444$"), body="{}", status=http.OK) sf_type = _create_sf_type() self.assertEqual(sf_type.describe_layout(record_id="444"), {}) @responses.activate def test_get_with_additional_request_headers(self): """Ensure custom headers are used for get requests""" responses.add(responses.GET, re.compile(r"^https://.*/Case/444$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.get(record_id="444", headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_get_without_additional_request_headers(self): """Ensure get requests without additional headers""" responses.add(responses.GET, re.compile(r"^https://.*/Case/444$"), body="{}", status=http.OK) sf_type = _create_sf_type() self.assertEqual(sf_type.get(record_id="444"), {}) @responses.activate def test_get_by_custom_id_with_additional_request_headers(self): """Ensure custom headers are used for get_by_custom_id requests""" responses.add(responses.GET, re.compile(r"^https://.*/Case/some-field/444$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.get_by_custom_id( custom_id_field="some-field", custom_id="444", headers={"Sforce-Auto-Assign": "FALSE"} ) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_get_by_custom_id_without_additional_request_headers(self): """Ensure get_by_custom_id requests without additional headers""" responses.add(responses.GET, re.compile(r"^https://.*/Case/some-field/444$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.get_by_custom_id(custom_id_field="some-field", custom_id="444") self.assertEqual(result, {}) @responses.activate def test_create_with_additional_request_headers(self): """Ensure custom headers are used for create requests""" responses.add(responses.POST, re.compile(r"^https://.*/Case/$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.create(data={"some": "data"}, headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_create_without_additional_request_headers(self): """Ensure create requests without additional headers""" responses.add(responses.POST, re.compile(r"^https://.*/Case/$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.create(data={"some": "data"}) self.assertEqual(result, {}) @responses.activate def test_update_with_additional_request_headers(self): """Ensure custom headers are used for updates""" responses.add(responses.PATCH, re.compile(r"^https://.*/Case/some-case-id$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.update( record_id="some-case-id", data={"some": "data"}, headers={"Sforce-Auto-Assign": "FALSE"} ) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, http.OK) @responses.activate def test_update_without_additional_request_headers(self): """Ensure updates work without custom headers""" responses.add(responses.PATCH, re.compile(r"^https://.*/Case/some-case-id$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.update(record_id="some-case-id", data={"some": "data"}) self.assertEqual(result, http.OK) @responses.activate def test_upsert_with_additional_request_headers(self): """Ensure custom headers are used for upserts""" responses.add(responses.PATCH, re.compile(r"^https://.*/Case/some-case-id$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.upsert( record_id="some-case-id", data={"some": "data"}, headers={"Sforce-Auto-Assign": "FALSE"} ) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, http.OK) @responses.activate def test_upsert_without_additional_request_headers(self): """Ensure upserts work without custom headers""" responses.add(responses.PATCH, re.compile(r"^https://.*/Case/some-case-id$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.upsert(record_id="some-case-id", data={"some": "data"}) self.assertEqual(result, http.OK) @responses.activate def test_delete_with_additional_request_headers(self): """Ensure custom headers are used for deletes""" responses.add(responses.DELETE, re.compile(r"^https://.*/Case/some-case-id$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.delete(record_id="some-case-id", headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, http.OK) @responses.activate def test_delete_without_additional_request_headers(self): """Ensure deletes work without custom headers""" responses.add(responses.DELETE, re.compile(r"^https://.*/Case/some-case-id$"), body="{}", status=http.OK) sf_type = _create_sf_type() result = sf_type.delete(record_id="some-case-id") self.assertEqual(result, http.OK) @responses.activate def test_deleted_with_additional_request_headers(self): """Ensure custom headers are used for deleted""" responses.add( responses.GET, re.compile(r"^https://.*/Case/deleted/\?start=.+&end=.+$"), body="{}", status=http.OK ) sf_type = _create_sf_type() result = sf_type.deleted(start=datetime.now(), end=datetime.now(), headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_deleted_without_additional_request_headers(self): """Ensure deleted works without custom headers""" responses.add( responses.GET, re.compile(r"^https://.*/Case/deleted/\?start=.+&end=.+$"), body="{}", status=http.OK ) sf_type = _create_sf_type() result = sf_type.deleted(start=datetime.now(), end=datetime.now()) self.assertEqual(result, {}) @responses.activate def test_updated_with_additional_request_headers(self): """Ensure custom headers are used for updated""" responses.add( responses.GET, re.compile(r"^https://.*/Case/updated/\?start=.+&end=.+$"), body="{}", status=http.OK ) sf_type = _create_sf_type() result = sf_type.updated(start=datetime.now(), end=datetime.now(), headers={"Sforce-Auto-Assign": "FALSE"}) request_headers = responses.calls[0].request.headers additional_request_header = request_headers["Sforce-Auto-Assign"] self.assertEqual(additional_request_header, "FALSE") self.assertEqual(result, {}) @responses.activate def test_updated_without_additional_request_headers(self): """Ensure updated works without custom headers""" responses.add( responses.GET, re.compile(r"^https://.*/Case/updated/\?start=.+&end=.+$"), body="{}", status=http.OK ) sf_type = _create_sf_type() result = sf_type.updated(start=datetime.now(), end=datetime.now()) self.assertEqual(result, {}) class TestSalesforce(unittest.TestCase): """Tests for the Salesforce instance""" def setUp(self): """Setup the SalesforceLogin tests""" request_patcher = patch("simple_salesforce.api.requests") self.mockrequest = request_patcher.start() self.addCleanup(request_patcher.stop) @responses.activate def test_custom_session_success(self): """Ensure custom session is used""" responses.add(responses.POST, re.compile(r"^https://.*$"), body=tests.LOGIN_RESPONSE_SUCCESS, status=http.OK) session_state = { "called": False, } # pylint: disable=unused-argument,missing-docstring def on_response(*args, **kwargs): session_state["called"] = True session = requests.Session() session.hooks = { "response": on_response, } client = Salesforce(session=session, username="foo@bar.com", password="password", security_token="token") self.assertEqual(tests.SESSION_ID, client.session_id) self.assertEqual(session, client.session) @responses.activate def test_custom_version_success(self): """Test custom version""" responses.add(responses.POST, re.compile(r"^https://.*$"), body=tests.LOGIN_RESPONSE_SUCCESS, status=http.OK) # Use an invalid version that is guaranteed to never be used expected_version = "4.2" client = Salesforce( session=requests.Session(), username="foo@bar.com", password="password", security_token="token", version=expected_version, ) self.assertEqual(client.base_url.split("/")[-2], "v%s" % expected_version) def test_shared_session_to_sftype(self): """Test Salesforce and SFType instances share default `Session`""" client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL) self.assertIs(client.session, client.Contact.session) def test_shared_custom_session_to_sftype(self): """Test Salesforce and SFType instances share custom `Session`""" session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) self.assertIs(session, client.session) self.assertIs(session, client.Contact.session) def test_proxies_inherited_default(self): """Test Salesforce and SFType use same proxies""" session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) self.assertIs(session.proxies, client.session.proxies) self.assertIs(session.proxies, client.Contact.session.proxies) def test_proxies_inherited_set_on_session(self): """Test Salesforce and SFType use same custom proxies""" session = requests.Session() session.proxies = tests.PROXIES client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) self.assertIs(tests.PROXIES, client.session.proxies) self.assertIs(tests.PROXIES, client.Contact.session.proxies) def test_proxies_ignored(self): """Test overridden proxies are ignored""" session = requests.Session() session.proxies = tests.PROXIES with patch("simple_salesforce.api.logger.warning") as mock_log: client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session, proxies={}) self.assertIn("ignoring proxies", mock_log.call_args[0][0]) self.assertIs(tests.PROXIES, client.session.proxies) @responses.activate def test_api_usage_simple(self): """Make sure a header response is recorded""" responses.add( responses.GET, re.compile(r"^https://.*$"), body='{"example": 1}', adding_headers={"Sforce-Limit-Info": "api-usage=18/5000"}, status=http.OK, ) client = Salesforce.__new__(Salesforce) client.session = requests.Session() client.headers = {} client.base_url = "https://localhost" client.query("q") self.assertDictEqual(client.api_usage, {"api-usage": Usage(18, 5000)}) @responses.activate def test_api_usage_per_app(self): """Make sure a header response is recorded""" pau = "api-usage=25/5000; per-app-api-usage=17/250(appName=sample-app)" responses.add( responses.GET, re.compile(r"^https://.*$"), body='{"example": 1}', adding_headers={"Sforce-Limit-Info": pau}, status=http.OK, ) client = Salesforce.__new__(Salesforce) client.session = requests.Session() client.headers = {} client.base_url = "https://localhost" client.query("q") self.assertDictEqual( client.api_usage, {"api-usage": Usage(25, 5000), "per-app-api-usage": PerAppUsage(17, 250, "sample-app")} ) @responses.activate def test_query(self): """Test querying generates the expected request""" responses.add( responses.GET, re.compile(r"^https://.*/query/\?q=SELECT\+ID\+FROM\+Account$"), body="{}", status=http.OK ) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query("SELECT ID FROM Account") self.assertEqual(result, {}) @responses.activate def test_query_include_deleted(self): """Test querying for all records generates the expected request""" responses.add( responses.GET, re.compile(r"^https://.*/queryAll/\?q=SELECT\+ID\+FROM\+Account$"), body="{}", status=http.OK ) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query("SELECT ID FROM Account", include_deleted=True) self.assertEqual(result, {}) @responses.activate def test_query_more_id_not_url(self): """ Test fetching additional results by ID generates the expected request """ responses.add(responses.GET, re.compile(r"^https://.*/query/next-records-id$"), body="{}", status=http.OK) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query_more("next-records-id", identifier_is_url=False) self.assertEqual(result, {}) @responses.activate def test_query_more_id_not_url_include_deleted(self): """ Test fetching additional results by ID generates the expected request """ responses.add(responses.GET, re.compile(r"^https://.*/queryAll/next-records-id$"), body="{}", status=http.OK) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query_more("next-records-id", identifier_is_url=False, include_deleted=True) self.assertEqual(result, {}) @responses.activate def test_query_all_iter(self): """ Test that we get data only once we ask for them (lazily). """ responses.add( responses.GET, re.compile(r"^https://.*/query/\?q=SELECT\+ID\+FROM\+Account$"), body='{"records": [{"ID": "1"}], "done": false, "nextRecordsUrl": ' '"https://example.com/query/next-records-id", "totalSize": 2}', status=http.OK, ) responses.add( responses.GET, re.compile(r"^https://.*/query/next-records-id$"), body='{"records": [{"ID": "2"}], "done": true, "totalSize": 2}', status=http.OK, ) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query_all_iter("SELECT ID FROM Account") self.assertEqual(next(result), OrderedDict([(u"ID", u"1")])) self.assertEqual(next(result), OrderedDict([(u"ID", u"2")])) with self.assertRaises(StopIteration): next(result) @responses.activate def test_query_all(self): """ Test that we query and fetch additional result sets automatically. """ responses.add( responses.GET, re.compile(r"^https://.*/query/\?q=SELECT\+ID\+FROM\+Account$"), body='{"records": [{"ID": "1"}], "done": false, "nextRecordsUrl": ' '"https://example.com/query/next-records-id", "totalSize": 2}', status=http.OK, ) responses.add( responses.GET, re.compile(r"^https://.*/query/next-records-id$"), body='{"records": [{"ID": "2"}], "done": true, "totalSize": 2}', status=http.OK, ) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query_all("SELECT ID FROM Account") self.assertEqual( result, OrderedDict( [ ("records", [OrderedDict([("ID", "1")]), OrderedDict([("ID", "2")])]), ("done", True), ("totalSize", 2), ] ), ) @responses.activate def test_query_all_include_deleted(self): """ Test that we query all and fetch additional result sets automatically. """ responses.add( responses.GET, re.compile(r"^https://.*/queryAll/\?q=SELECT\+ID\+FROM\+Account$"), body='{"records": [{"ID": "1"}], "done": false, "nextRecordsUrl": ' '"https://example.com/queryAll/next-records-id",' '"totalSize": 2}', status=http.OK, ) responses.add( responses.GET, re.compile(r"^https://.*/queryAll/next-records-id$"), body='{"records": [{"ID": "2"}], "done": true, "totalSize": 2}', status=http.OK, ) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.query_all("SELECT ID FROM Account", include_deleted=True) self.assertEqual( result, OrderedDict( [ ("records", [OrderedDict([("ID", "1")]), OrderedDict([("ID", "2")])]), ("done", True), ("totalSize", 2), ] ), ) @responses.activate def test_api_limits(self): """Test method for getting Salesforce organization limits""" responses.add( responses.GET, re.compile(r"^https://.*/limits/$"), json=tests.ORGANIZATION_LIMITS_RESPONSE, status=http.OK ) session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance_url=tests.SERVER_URL, session=session) result = client.limits() self.assertEqual(result, tests.ORGANIZATION_LIMITS_RESPONSE) @patch("simple_salesforce.sfdc_session.SfdcSession.post") @patch("simple_salesforce.metadata.SfdcMetadataApi._read_deploy_zip") def test_md_deploy_success(self, mock_read_zip, mock_post): """ " Test method for metadata deployment """ mock_response = Mock() mock_response.status_code = 200 mock_response.text = '<?xml version="1.0" encoding="UTF-8"?><soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns="http://soap.sforce.com/2006/04/metadata"><soapenv:Body><deployResponse><result><done>false</done><id>0Af3B00001CMyfASAT</id><state>Queued</state></result></deployResponse></soapenv:Body></soapenv:Envelope>' mock_post.return_value = mock_response session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance=tests.SERVER_URL, session=session) pid, state = client.deploy("path/to/fake/zip.zip", {}) self.assertEqual(pid, "0Af3B00001CMyfASAT") self.assertEqual(state, "Queued") @patch("simple_salesforce.sfdc_session.SfdcSession.post") @patch("simple_salesforce.metadata.SfdcMetadataApi._read_deploy_zip") def test_md_deploy_failed_status_code(self, mock_read_zip, mock_post): """ " Test method for metadata deployment """ mock_response = Mock() mock_response.status_code = 2599 mock_response.text = "Unrecognized Error" mock_post.return_value = mock_response session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance=tests.SERVER_URL, session=session) with self.assertRaises(Exception): client.deploy("path/to/fake/zip.zip", {}) @patch("simple_salesforce.sfdc_session.SfdcSession.post") def test_check_status_pending(self, mock_post): """ " Test method for metadata deployment """ mock_response = Mock() mock_response.status_code = 200 mock_response.text = '<?xml version="1.0" encoding="UTF-8"?><soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns="http://soap.sforce.com/2006/04/metadata"><soapenv:Body><checkDeployStatusResponse><result><checkOnly>true</checkOnly><createdBy>0053D0000052Xaq</createdBy><createdByName>User User</createdByName><createdDate>2020-10-28T15:38:34.000Z</createdDate><details><runTestResult><numFailures>0</numFailures><numTestsRun>0</numTestsRun><totalTime>0.0</totalTime></runTestResult></details><done>false</done><id>0Af3D00001NViC1SAL</id><ignoreWarnings>false</ignoreWarnings><lastModifiedDate>2020-10-28T15:38:34.000Z</lastModifiedDate><numberComponentErrors>0</numberComponentErrors><numberComponentsDeployed>0</numberComponentsDeployed><numberComponentsTotal>0</numberComponentsTotal><numberTestErrors>0</numberTestErrors><numberTestsCompleted>0</numberTestsCompleted><numberTestsTotal>0</numberTestsTotal><rollbackOnError>true</rollbackOnError><runTestsEnabled>false</runTestsEnabled><status>Pending</status><success>false</success></result></checkDeployStatusResponse></soapenv:Body></soapenv:Envelope>' mock_post.return_value = mock_response session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance=tests.SERVER_URL, session=session) state, state_detail, deployment_detail, unit_test_detail = client.checkDeployStatus("abdcefg") self.assertEqual(state, "Pending") self.assertEqual(state_detail, None) self.assertEqual( deployment_detail, {"total_count": "0", "failed_count": "0", "deployed_count": "0", "errors": []} ) self.assertEqual( unit_test_detail, {"total_count": "0", "failed_count": "0", "completed_count": "0", "errors": []} ) @patch("simple_salesforce.sfdc_session.SfdcSession.post") def test_check_status_success(self, mock_post): """ " Test method for metadata deployment """ mock_response = Mock() mock_response.status_code = 200 mock_response.text = '<?xml version="1.0" encoding="UTF-8"?><soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns="http://soap.sforce.com/2006/04/metadata"><soapenv:Body><checkDeployStatusResponse><result><checkOnly>false</checkOnly><completedDate>2020-10-28T13:33:29.000Z</completedDate><createdBy>0053D0000052Xaq</createdBy><createdByName>User User</createdByName><createdDate>2020-10-28T13:33:25.000Z</createdDate><details><componentSuccesses><changed>true</changed><componentType>ApexSettings</componentType><created>false</created><createdDate>2020-10-28T13:33:29.000Z</createdDate><deleted>false</deleted><fileName>shape/settings/Apex.settings</fileName><fullName>Apex</fullName><success>true</success></componentSuccesses><componentSuccesses><changed>true</changed><componentType>ChatterSettings</componentType><created>false</created><createdDate>2020-10-28T13:33:29.000Z</createdDate><deleted>false</deleted><fileName>shape/settings/Chatter.settings</fileName><fullName>Chatter</fullName><success>true</success></componentSuccesses><componentSuccesses><changed>true</changed><componentType></componentType><created>false</created><createdDate>2020-10-28T13:33:29.000Z</createdDate><deleted>false</deleted><fileName>shape/package.xml</fileName><fullName>package.xml</fullName><success>true</success></componentSuccesses><componentSuccesses><changed>true</changed><componentType>LightningExperienceSettings</componentType><created>false</created><createdDate>2020-10-28T13:33:29.000Z</createdDate><deleted>false</deleted><fileName>shape/settings/LightningExperience.settings</fileName><fullName>LightningExperience</fullName><success>true</success></componentSuccesses><componentSuccesses><changed>true</changed><componentType>LanguageSettings</componentType><created>false</created><createdDate>2020-10-28T13:33:29.000Z</createdDate><deleted>false</deleted><fileName>shape/settings/Language.settings</fileName><fullName>Language</fullName><success>true</success></componentSuccesses><runTestResult><numFailures>0</numFailures><numTestsRun>0</numTestsRun><totalTime>0.0</totalTime></runTestResult></details><done>true</done><id>0Af3D00001NVCnwSAH</id><ignoreWarnings>false</ignoreWarnings><lastModifiedDate>2020-10-28T13:33:29.000Z</lastModifiedDate><numberComponentErrors>0</numberComponentErrors><numberComponentsDeployed>4</numberComponentsDeployed><numberComponentsTotal>4</numberComponentsTotal><numberTestErrors>0</numberTestErrors><numberTestsCompleted>0</numberTestsCompleted><numberTestsTotal>0</numberTestsTotal><rollbackOnError>true</rollbackOnError><runTestsEnabled>false</runTestsEnabled><startDate>2020-10-28T13:33:26.000Z</startDate><status>Succeeded</status><success>true</success></result></checkDeployStatusResponse></soapenv:Body></soapenv:Envelope>' mock_post.return_value = mock_response session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance=tests.SERVER_URL, session=session) state, state_detail, deployment_detail, unit_test_detail = client.checkDeployStatus("abdcefg") self.assertEqual(state, "Succeeded") self.assertEqual(state_detail, None) self.assertEqual( deployment_detail, {"total_count": "4", "failed_count": "0", "deployed_count": "4", "errors": []} ) self.assertEqual( unit_test_detail, {"total_count": "0", "failed_count": "0", "completed_count": "0", "errors": []} ) @patch("simple_salesforce.sfdc_session.SfdcSession.post") def test_check_status_payload_error(self, mock_post): """ " Test method for metadata deployment """ mock_response = Mock() mock_response.status_code = 200 mock_response.text = '<?xml version="1.0" encoding="UTF-8"?><soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns="http://soap.sforce.com/2006/04/metadata"><soapenv:Body><checkDeployStatusResponse><result><checkOnly>true</checkOnly><completedDate>2020-10-28T13:37:48.000Z</completedDate><createdBy>0053D0000052Xaq</createdBy><createdByName>User User</createdByName><createdDate>2020-10-28T13:37:46.000Z</createdDate><details><componentFailures><changed>false</changed><componentType></componentType><created>false</created><createdDate>2020-10-28T13:37:47.000Z</createdDate><deleted>false</deleted><fileName>package.xml</fileName><fullName>package.xml</fullName><problem>No package.xml found</problem><problemType>Error</problemType><success>false</success></componentFailures><runTestResult><numFailures>0</numFailures><numTestsRun>0</numTestsRun><totalTime>0.0</totalTime></runTestResult></details><done>true</done><id>0Af3D00001NVD0TSAX</id><ignoreWarnings>false</ignoreWarnings><lastModifiedDate>2020-10-28T13:37:48.000Z</lastModifiedDate><numberComponentErrors>0</numberComponentErrors><numberComponentsDeployed>0</numberComponentsDeployed><numberComponentsTotal>0</numberComponentsTotal><numberTestErrors>0</numberTestErrors><numberTestsCompleted>0</numberTestsCompleted><numberTestsTotal>0</numberTestsTotal><rollbackOnError>true</rollbackOnError><runTestsEnabled>false</runTestsEnabled><startDate>2020-10-28T13:37:47.000Z</startDate><status>Failed</status><success>false</success></result></checkDeployStatusResponse></soapenv:Body></soapenv:Envelope>' mock_post.return_value = mock_response session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance=tests.SERVER_URL, session=session) state, state_detail, deployment_detail, unit_test_detail = client.checkDeployStatus("abdcefg") self.assertEqual(state, "Failed") self.assertEqual(state_detail, None) self.assertEqual( deployment_detail, { "total_count": "0", "failed_count": "0", "deployed_count": "0", "errors": [{"type": None, "file": "package.xml", "status": "Error", "message": "No package.xml found"}], }, ) self.assertEqual( unit_test_detail, {"total_count": "0", "failed_count": "0", "completed_count": "0", "errors": []} ) @patch("simple_salesforce.sfdc_session.SfdcSession.post") def test_check_status_in_progress(self, mock_post): """ " Test method for metadata deployment """ mock_response = Mock() mock_response.status_code = 200 mock_response.text = '<?xml version="1.0" encoding="UTF-8"?><soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns="http://soap.sforce.com/2006/04/metadata"><soapenv:Body><checkDeployStatusResponse><result><checkOnly>false</checkOnly><createdBy>0053D0000052Xaq</createdBy><createdByName>User User</createdByName><createdDate>2020-10-28T17:24:30.000Z</createdDate><details><runTestResult><numFailures>0</numFailures><numTestsRun>0</numTestsRun><totalTime>0.0</totalTime></runTestResult></details><done>false</done><id>0Af3D00001NW8mnSAD</id><ignoreWarnings>false</ignoreWarnings><lastModifiedDate>2020-10-28T17:37:08.000Z</lastModifiedDate><numberComponentErrors>0</numberComponentErrors><numberComponentsDeployed>2</numberComponentsDeployed><numberComponentsTotal>3</numberComponentsTotal><numberTestErrors>0</numberTestErrors><numberTestsCompleted>0</numberTestsCompleted><numberTestsTotal>0</numberTestsTotal><rollbackOnError>true</rollbackOnError><runTestsEnabled>false</runTestsEnabled><startDate>2020-10-28T17:24:30.000Z</startDate><status>InProgress</status><success>false</success></result></checkDeployStatusResponse></soapenv:Body></soapenv:Envelope>' mock_post.return_value = mock_response session = requests.Session() client = Salesforce(session_id=tests.SESSION_ID, instance=tests.SERVER_URL, session=session) state, state_detail, deployment_detail, unit_test_detail = client.checkDeployStatus("abdcefg") self.assertEqual(state, "InProgress") self.assertEqual(state_detail, None) self.assertEqual( deployment_detail, {"total_count": "3", "failed_count": "0", "deployed_count": "2", "errors": []} ) self.assertEqual( unit_test_detail, {"total_count": "0", "failed_count": "0", "completed_count": "0", "errors": []} )

51.13617

2,812

0.678289

4,067

36,051

5.845832

0.084829

0.016909

0.032681

0.023344

0.874238

0.854721

0.81897

0.782166

0.754658

0.75041

0

0.021892

0.177693

36,051

704

2,813

51.208807

0.780098

0.06746

0

0.602

0

0.012

0.329585

0.181766

0

0.152

1

0.098

false

0.004

0.02

0

0.124

0

null

0

1

0

1

0

null

0

7

9e75a0c226b95475266faab0ff0122199498d7df

3,131

py

Python

Lib/site-packages/psycopg/_wrappers.py

RosaSineSpinis/twitter_bitcon_tag_analyser

3311022b6fd629ce85f0c4fa0516e310bed05d74

[ "bzip2-1.0.6" ]

5

2021-12-14T03:33:39.000Z

2022-01-11T14:13:21.000Z

ch16-deployment/.venv/lib/python3.10/site-packages/psycopg/_wrappers.py

wsvincent/djangoforbeginners_32

aba7c99aa6050cfe8fb9d588af58c9f67411ae8a

[ "MIT" ]

null

ch16-deployment/.venv/lib/python3.10/site-packages/psycopg/_wrappers.py

wsvincent/djangoforbeginners_32

aba7c99aa6050cfe8fb9d588af58c9f67411ae8a

[ "MIT" ]

null

""" Wrappers for numeric types. """ # Copyright (C) 2020-2021 The Psycopg Team # Wrappers to force numbers to be cast as specific PostgreSQL types # These types are implemented here but exposed by `psycopg.types.numeric`. # They are defined here to avoid a circular import. _MODULE = "psycopg.types.numeric" class Int2(int): """ Force dumping a Python `!int` as a PostgreSQL :sql:`smallint/int2`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: int) -> "Int2": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})" class Int4(int): """ Force dumping a Python `!int` as a PostgreSQL :sql:`integer/int4`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: int) -> "Int4": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})" class Int8(int): """ Force dumping a Python `!int` as a PostgreSQL :sql:`bigint/int8`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: int) -> "Int8": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})" class IntNumeric(int): """ Force dumping a Python `!int` as a PostgreSQL :sql:`numeric/decimal`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: int) -> "IntNumeric": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})" class Float4(float): """ Force dumping a Python `!float` as a PostgreSQL :sql:`float4/real`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: float) -> "Float4": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})" class Float8(float): """ Force dumping a Python `!float` as a PostgreSQL :sql:`float8/double precision`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: float) -> "Float8": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})" class Oid(int): """ Force dumping a Python `!int` as a PostgreSQL :sql:`oid`. """ __module__ = _MODULE __slots__ = () def __new__(cls, arg: int) -> "Oid": return super().__new__(cls, arg) def __str__(self) -> str: return super().__repr__() def __repr__(self) -> str: return f"{self.__class__.__name__}({super().__repr__()})"

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0.597892

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0.751263

0.607323

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3,131

137

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null

0

1

0

1

0

8

9e8cbc089e0c1d05479fc8a52790f0be45db7c8d

10,481

py

Python

consequent/alignment.py

pjmartel/consequent

fa0a41e056a6b240d41819a8e5c310a9057fa7e9

[ "MIT" ]

null

consequent/alignment.py

pjmartel/consequent

fa0a41e056a6b240d41819a8e5c310a9057fa7e9

[ "MIT" ]

null

consequent/alignment.py

pjmartel/consequent

fa0a41e056a6b240d41819a8e5c310a9057fa7e9

[ "MIT" ]

1

2019-10-24T18:25:59.000Z

import numpy as np import matplotlib.pyplot as plt from sys import exit def plotMat(mat, fs): fig, axs = plt.subplots() axs.axis("tight") axs.axis("off") the_table = axs.table(cellText=mat, cellLoc='center', loc='center') for key, cell in the_table.get_celld().items(): row, col = key cell.set_fontsize(fs) fig.show() def plotTraceMat(tmat, smat, fs): fig, axs = plt.subplots() axs.axis("tight") axs.axis("off") arrows = [8598, 8593, 8592] plotMat = [['' for c in range(tmat.shape[1])] for r in range(tmat.shape[0])] for i in range(tmat.shape[0]): for j in range(tmat.shape[1]): # if(smat[i, j] == 0): # plotMat[i][j] = ' ' # else: if smat[i, j] != 0: plotMat[i][j] = chr(arrows[tmat[i, j]]) # plotMat = np.fromfunction(lambda i, j: arrows[mat[i, j]], mat.shape) t1 = axs.table(cellText=plotMat, cellLoc='center', loc='center') t1.set_fontsize(fs) for key, cell in t1.get_celld().items(): cell.set_linewidth(0) # t2 = axs.table(cellText=smat, cellLoc='center', loc='center') # t2.set_fontsize(fs) # t2.set_alpha(0.5) fig.show() def needlemanWunsch(seqA, seqB, scoreMat, gapO=-10, gapE=-0.5, ScoreOnly=False): """ Computes the optimal global aligment of two amino acid sequences using the Needleman-Wunsch algorithm. seqA, seqB - aminoacid sequences scoreMat - scoring matrix gapO - gap opening penalty gapE - gap extension penalty ScoreOnly - if True, only the optimal score is computed (aligment is returned as "None") """ # Check for valid score matrix argument if not isinstance(scoreMat, np.ndarray): print("Error in 'smitWaterman': scoring matrix has wrong data type.") exit(0) elif scoreMat.shape != (26, 26): print("Error in 'smitWaterman': scoring matrix has wrong shape.") exit(0) # print(type(scoreMat) # Convert seqs to numerical seqA = np.array(list(map(ord, seqA)), dtype=np.int8) seqB = np.array(list(map(ord, seqB)), dtype=np.int8) seqA -= ord("A") seqB -= ord("A") matAli = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int32) matTrace = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int8) # init aligment and trace matrices matAli[1:, 0] = np.fromfunction( lambda i: gapO+gapE*i, (len(seqA),)) matTrace[1:, 0] = 1 # # (ix,iy) = np.meshgrid(range(len(seqB)), range(len(seqA))) # matAli[1: matAli[0, 1:] = np.fromfunction( lambda j: gapO+gapE*j, (len(seqB),)) matTrace[0, 1:] = 2 # Let'a initialize the rest of the aligment matrix with # the comparison scores from the scoring matrix. # Note the meshgrid trick to avoid loops ;-) (ix, iy) = np.meshgrid(range(len(seqB)), range(len(seqA))) matAli[1:, 1:] = scoreMat[seqB[ix], seqA[iy]] # for i in range(len(A)+1): # matAli[i, 0] = (gapOpen+gapExtend*(i-1))*(i > 0) # matTrace[i, 0] = 1 # for j in range(len(B)+1): # matAli[0, j] = (gapOpen+gapExtend*(j-1))*(j > 0) # matTrace[0, j] = 2 # matTrace[0, 0] = 0 # Fill aligment matrix for i in range(1, len(seqA)+1): for j in range(1, len(seqB)+1): # tempScores = (matAli[i-1, j-1] + matAli[i, j], tempScores = (matAli[i-1, j-1] + scoreMat[seqA[i-1], seqB[j-1]], matAli[i-1, j] + (gapE if (matTrace[i-1, j] == 1) else gapO), matAli[i, j-1] + (gapE if (matTrace[i, j-1] == 2) else gapO)) idx = np.argmax(tempScores) matTrace[i, j] = idx matAli[i, j] = tempScores[idx] Score = matAli[-1, -1] # if ScoreOnly is True, return before computing alignment if ScoreOnly: return(Score, None, matAli, matTrace) # Backtrack aligment alB = [] alA = [] i = len(seqA) j = len(seqB) cur = matTrace[i, j] while(i > 0 or j > 0): # print(i, j) cur = matTrace[i, j] if cur == 0: alA.append(chr(seqA[i-1]+65)) alB.append(chr(seqB[j-1]+65)) i -= 1 j -= 1 elif cur == 1: alA.append(chr(seqA[i-1]+65)) alB.append("-") i -= 1 elif cur == 2: alB.append(chr(seqB[j-1]+65)) alA.append("-") j -= 1 else: print("Invalid value in aligment matrix.") exit(0) Alignment = np.array(["".join(alA[::-1]), "".join(alB[::-1])]) return(Score, Alignment, matAli, matTrace) def smithWaterman(seqA, seqB, scoreMat, gapO=-10, gapE=-0.5, ScoreOnly=False): """ Computer the optimal local aligment of two aminoacid sequences using the Smith-Waterman algoritm. seqA, seqB - aminoacid sequences scoreMat - scoring matrix gapO - gap opening penalty gapE - gap extension penalty ScoreOnly - if True, only the optimal score is computed and no aligment is returned. """ # Check for valid score matrix argument if not isinstance(scoreMat, np.ndarray): print("Error in 'smitWaterman': scoring matrix has wrong data type.") exit(1) elif scoreMat.shape != (26, 26): print("Error in 'smitWaterman': scoring matrix has wrong shape.") exit(1) # Convert seqs to numerical seqA = np.array(list(map(ord, seqA)), dtype=np.int8) seqB = np.array(list(map(ord, seqB)), dtype=np.int8) seqA -= ord("A") seqB -= ord("A") matAli = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int32) matTrace = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int8) # matTrace = np.zeros((len(A)+1,len(B)+1),dtype=(np.int8,2)) # matTrace = np.empty((len(A)+1,len(B)+1),dtype=object) # init aligment and trace matrices # matAli[1:, 0] = np.fromfunction( # lambda i: gapO+gapE*i, (len(seqA),)) # matTrace[1:, 0] = 1 # matAli[0, 1:] = np.fromfunction( # lambda j: gapO+gapE*j, (len(seqB),)) # matTrace[0, 1:] = 2 # for i in range(len(A)+1): # matAli[i, 0] = (gapOpen+gapExtend*(i-1))*(i > 0) # matTrace[i, 0] = 1 # for j in range(len(B)+1): # matAli[0, j] = (gapOpen+gapExtend*(j-1))*(j > 0) # matTrace[0, j] = 2 # matTrace[0, 0] = 0 # Fill aligment matrix for i in range(1, len(seqA)+1): for j in range(1, len(seqB)+1): tempScores = (matAli[i-1, j-1] + scoreMat[seqA[i-1], seqB[j-1]], matAli[i-1, j] + (gapE if (matTrace[i-1, j] == 1) else gapO), matAli[i, j-1] + (gapE if (matTrace[i, j-1] == 2) else gapO), 0) idx = np.argmax(tempScores) matAli[i, j] = tempScores[idx] if idx == 3: idx = 2 matTrace[i, j] = idx Score = matAli.max() if ScoreOnly: return(Score, None, matAli, matTrace) # Backtrack aligment alB = [] alA = [] # (i, j) are the indices of max score in matAli i, j = np.unravel_index(np.argmax(matAli), matAli.shape) # print("--->", Score, i, j) # print(matAli) while(matAli[i, j] != 0): # print(i, j) cur = matTrace[i, j] if cur == 0: alA.append(chr(seqA[i-1]+65)) alB.append(chr(seqB[j-1]+65)) i -= 1 j -= 1 elif cur == 1: alA.append(chr(seqA[i-1]+65)) alB.append("-") i -= 1 elif cur == 2: alB.append(chr(seqB[j-1]+65)) alA.append("-") j -= 1 else: print("Invalid value in trace matrix:", cur) exit(0) Alignment = np.array(["".join(alA[::-1]), "".join(alB[::-1])]) return(Score, Alignment, matAli, matTrace) def needleFast(seqA, seqB, scoreMat, gapO=-8, gapE=-8): # Convert seqs to numerical seqA = np.array(list(map(ord, seqA)), dtype=np.int8) seqB = np.array(list(map(ord, seqB)), dtype=np.int8) seqA -= ord("A") seqB -= ord("A") matAli = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int32) matTrace = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int8) # init aligment and trace matrices matAli[1:, 0] = np.fromfunction( lambda i: gapO+gapE*i, (len(seqA),)) matTrace[1:, 0] = 1 # # (ix,iy) = np.meshgrid(range(len(seqB)), range(len(seqA))) # matAli[1: matAli[0, 1:] = np.fromfunction( lambda j: gapO+gapE*j, (len(seqB),)) matTrace[0, 1:] = 2 # Let'a initialize the rest of the aligment matrix with # the comparison scores from the scoring matrix. # Note the meshgrid trick to avoid loops ;-) (ix, iy) = np.meshgrid(range(len(seqB)), range(len(seqA))) matAli[1:, 1:] = scoreMat[seqB[ix], seqA[iy]] # Fill aligment matrix for i in range(1, len(seqA)+1): for j in range(1, len(seqB)+1): # tempScores = (matAli[i-1, j-1] + matAli[i, j], tempScores = (matAli[i-1, j-1] + scoreMat[seqA[i-1], seqB[j-1]], matAli[i-1, j] + (gapE if (matTrace[i-1, j] == 1) else gapO), matAli[i, j-1] + (gapE if (matTrace[i, j-1] == 2) else gapO)) idx = np.argmax(tempScores) matTrace[i, j] = idx matAli[i, j] = tempScores[idx] return(matAli[-1, -1], None, matAli, matTrace) def smithFast(seqA, seqB, scoreMat, gapO=-8, gapE=-8): # Convert seqs to numerical seqA = np.array(list(map(ord, seqA)), dtype=np.int8) seqB = np.array(list(map(ord, seqB)), dtype=np.int8) seqA -= ord("A") seqB -= ord("A") matAli = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int32) matTrace = np.zeros((len(seqA)+1, len(seqB)+1), dtype=np.int8) # Fill aligment matrix for i in range(1, len(seqA)+1): for j in range(1, len(seqB)+1): tempScores = (matAli[i-1, j-1] + scoreMat[seqA[i-1], seqB[j-1]], matAli[i-1, j] + (gapE if (matTrace[i-1, j] == 1) else gapO), matAli[i, j-1] + (gapE if (matTrace[i, j-1] == 2) else gapO), 0) idx = np.argmax(tempScores) matAli[i, j] = tempScores[idx] if idx == 3: idx = 2 matTrace[i, j] = idx Score = matAli.max() return(Score, None, matAli, matTrace)

33.273016

90

0.540025

1,513

10,481

3.734303

0.124256

0.012035

0.008496

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0.293865

10,481

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0.726118

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null

0

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1

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null

0

7

9ead3d147e570bf234ba8c0f909578ebabb04e30

9,447

py

Python

graphAttack/operations/twoInputOperations.py

jgolebiowski/graphAttack

ec8488444b44d0bd54498bf917ee42d821643ee8

[ "MIT" ]

51

2017-08-16T13:04:43.000Z

2022-03-30T09:10:30.000Z

graphAttack/operations/twoInputOperations.py

jgolebiowski/graphAttack

ec8488444b44d0bd54498bf917ee42d821643ee8

[ "MIT" ]

null

graphAttack/operations/twoInputOperations.py

jgolebiowski/graphAttack

ec8488444b44d0bd54498bf917ee42d821643ee8

[ "MIT" ]

12

2017-09-27T01:10:02.000Z

2021-05-05T09:44:56.000Z

"""This where implementations of individual operations live""" from ..coreOperation import * from ..coreNode import broadcast_shape, reduce_shape from ..gaUtilities import graphAttackFunctions as gaf import numpy as np class MultiplyOperation(TwoInputOperation): """Multiply two inputs Attributes ---------- name : str Name of the operation result : np.array Output of the operation testing : bool Flag specifying if the operation is in testing (making prefictions: True) or training (optimizing parameters: False) mode gradA : np.array gradient with respect to inputA gradB : np.array gradient with respect to inputB inputA : ga.Operation Operation feeding data A into this operation inputB : ga.Operation Operation feeding data B into this operation shape : tuple shape of the output """ name = "MultiplyOperation" def perform(self, a, b): """Multiply two together Parameters ---------- a : np.array first set of input data b : np.array second set pf input data Returns ------- np.aarray Result of the operation """ return np.multiply(a, b) def performGradient(self, input): """Find out the gradient with respect to the parameter Parameters ---------- input : int Specify an input operation with respect to which the gradient is calculated the key is: inputA => 0 inputB => 1 Returns ------- np.array Gradient propagated through this operation Raises ------ ValueError input has ot be either 0 or 1 """ if (self.endNode): if (input == 0): grad = np.ones(self.inputA.shape) elif (input == 1): grad = np.ones(self.inputB.shape) else: raise ValueError else: if (input == 0): grad = np.zeros(self.inputA.shape) elif (input == 1): grad = np.zeros(self.inputB.shape) else: raise ValueError for out in self.outputs: grad += reduce_shape(out.getGradient(self), grad) if (input == 0): grad = grad * self.inputB.getValue() elif (input == 1): grad = grad * self.inputA.getValue() return grad class DivideOperation(TwoInputOperation): """Divide two inputs Attributes ---------- name : str Name of the operation result : np.array Output of the operation testing : bool Flag specifying if the operation is in testing (making prefictions: True) or training (optimizing parameters: False) mode gradA : np.array gradient with respect to inputA gradB : np.array gradient with respect to inputB inputA : ga.Operation Operation feeding data A into this operation inputB : ga.Operation Operation feeding data B into this operation shape : tuple shape of the output """ name = "DivideOperation" def perform(self, a, b): """Multiply two together Parameters ---------- a : np.array first set of input data b : np.array second set pf input data Returns ------- np.aarray Result of the operation """ return np.divide(a, b) def performGradient(self, input): """Find out the gradient with respect to the parameter Parameters ---------- input : int Specify an input operation with respect to which the gradient is calculated the key is: inputA => 0 inputB => 1 Returns ------- np.array Gradient propagated through this operation Raises ------ ValueError input has ot be either 0 or 1 """ if (self.endNode): if (input == 0): grad = np.ones(self.inputA.shape) elif (input == 1): grad = np.ones(self.inputB.shape) else: raise ValueError else: if (input == 0): grad = np.zeros(self.inputA.shape) elif (input == 1): grad = np.zeros(self.inputB.shape) else: raise ValueError for out in self.outputs: grad += reduce_shape(out.getGradient(self), grad) if (input == 0): grad = np.divide(grad, self.inputB.getValue()) elif (input == 1): grad = np.divide(grad, np.square(self.inputA.getValue())) return grad class AddOperation(TwoInputOperation): """add two inputs Attributes ---------- name : str Name of the operation result : np.array Output of the operation testing : bool Flag specifying if the operation is in testing (making prefictions: True) or training (optimizing parameters: False) mode gradA : np.array gradient with respect to inputA gradB : np.array gradient with respect to inputB inputA : ga.Operation Operation feeding data A into this operation inputB : ga.Operation Operation feeding data B into this operation shape : tuple shape of the output """ name = "AddOperation" def perform(self, a, b): """add two together Parameters ---------- a : np.array first set of input data b : np.array second set pf input data Returns ------- np.aarray Result of the operation """ return np.add(a, b) def performGradient(self, input): """Find out the gradient with respect to the parameter Parameters ---------- input : int Specify an input operation with respect to which the gradient is calculated the key is: inputA => 0 inputB => 1 Returns ------- np.array Gradient propagated through this operation Raises ------ ValueError input has ot be either 0 or 1 """ if (self.endNode): if (input == 0): grad = np.ones(self.inputA.shape) elif (input == 1): grad = np.ones(self.inputB.shape) else: raise ValueError else: if (input == 0): grad = np.zeros(self.inputA.shape) elif (input == 1): grad = np.zeros(self.inputB.shape) else: raise ValueError for out in self.outputs: grad += reduce_shape(out.getGradient(self), grad) return grad class MatMatmulOperation(TwoInputOperation): """MatrixMultiplication for 2d matrices Attributes ---------- name : str Name of the operation result : np.array Output of the operation testing : bool Flag specifying if the operation is in testing (making prefictions: True) or training (optimizing parameters: False) mode gradA : np.array gradient with respect to inputA gradB : np.array gradient with respect to inputB inputA : ga.Operation Operation feeding data A into this operation inputB : ga.Operation Operation feeding data B into this operation shape : tuple shape of the output """ name = "MatMatmulOperation" def setShape(self): """Set the output shape""" if not (len(self.inputA.shape) == 2 == len(self.inputB.shape)): raise ValueError("This should be used on arrays of ndims == 2") self.shape = (self.inputA.shape[0], self.inputB.shape[1]) def perform(self, a, b): """Perform MatMul Parameters ---------- a : np.array first set of input data b : np.array second set pf input data Returns ------- np.aarray Result of the operation """ return gaf.matmul(a, b) def performGradient(self, input): """Find out the gradient with respect to the parameter Parameters ---------- input : int Specify an input operation with respect to which the gradient is calculated the key is: inputA => 0 inputB => 1 Returns ------- np.array Gradient propagated through this operation Raises ------ ValueError input has ot be either 0 or 1 """ if (self.endNode): grad = np.ones(self.shape) else: grad = np.zeros(self.shape) for out in self.outputs: grad += out.getGradient(self) if (input == 0): grad = gaf.matmul(grad, self.inputB.getValue().T) elif (input == 1): grad = gaf.matmul(self.inputA.getValue().T, grad) return grad

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null

0

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0

1

0

null

0

7

7ba21bdc5c3c20d756bec632b059838dba7c1299

24,199

py

Python

sqlalchemy_mptt/tests/cases/edit_node.py

AngelLiang/sqlalchemy_mptt

ce7c8ceea915f4bc2716aafdc7cafe075d862942

[ "MIT" ]

123

2016-01-10T16:46:09.000Z

2022-02-23T10:44:30.000Z

sqlalchemy_mptt/tests/cases/edit_node.py

AngelLiang/sqlalchemy_mptt

ce7c8ceea915f4bc2716aafdc7cafe075d862942

[ "MIT" ]

23

2016-01-21T06:12:31.000Z

2021-11-10T15:21:49.000Z

sqlalchemy_mptt/tests/cases/edit_node.py

AngelLiang/sqlalchemy_mptt

ce7c8ceea915f4bc2716aafdc7cafe075d862942

[ "MIT" ]

23

2016-01-19T20:26:11.000Z

2021-04-06T13:56:34.000Z

class Changes(object): def test_update_wo_move(self): """ Update node w/o move initial state of the tree :mod:`sqlalchemy_mptt.tests.add_mptt_tree` .. code:: level Nested sets example 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 """ node = self.session.query(self.model)\ .filter(self.model.get_pk_column() == 4).one() node.visible = True self.session.add(node) _level = node.get_default_level() self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 22, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 11, _level + 1, 1, 1), (5, 7, 8, _level + 2, 4, 1), (6, 9, 10, _level + 2, 4, 1), (7, 12, 21, _level + 1, 1, 1), (8, 13, 16, _level + 2, 7, 1), (9, 14, 15, _level + 3, 8, 1), (10, 17, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2) ], self.result.all()) # flake8: noqa def test_update_wo_move_like_sacrud_save(self): """ Just change attr from node w/o move initial state of the tree :mod:`sqlalchemy_mptt.tests.add_mptt_tree` .. code:: level Nested sets example 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 """ node = self.session.query(self.model)\ .filter(self.model.get_pk_column() == 4).one() node.parent_id = '1' node.visible = True self.session.add(node) _level = node.get_default_level() # id lft rgt lvl parent tree self.assertEqual([(1, 1, 22, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 11, _level + 1, 1, 1), (5, 7, 8, _level + 2, 4, 1), (6, 9, 10, _level + 2, 4, 1), (7, 12, 21, _level + 1, 1, 1), (8, 13, 16, _level + 2, 7, 1), (9, 14, 15, _level + 3, 8, 1), (10, 17, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2)], self.result.all()) def test_insert_node(self): """ Insert node with parent==6 initial state of the tree :mod:`sqlalchemy_mptt.tests.add_mptt_tree` .. code:: level Nested sets example 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 level Insert node with parent_id == 6 1 1(1)24 _______________|_________________ | | | 2 2(2)5 6(4)13 14(7)23 | ____|____ ___|____ | | | | | 3 3(3)4 7(5)8 9(6)12 15(8)18 19(10)22 | | | 4 10(23)11 16(9)17 20(11)21 """ node = self.model(parent_id=6) self.session.add(node) _level = node.get_default_level() self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 24, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 13, _level + 1, 1, 1), (5, 7, 8, _level + 2, 4, 1), (6, 9, 12, _level + 2, 4, 1), (7, 14, 23, _level + 1, 1, 1), (8, 15, 18, _level + 2, 7, 1), (9, 16, 17, _level + 3, 8, 1), (10, 19, 22, _level + 2, 7, 1), (11, 20, 21, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2), (23, 10, 11, _level + 3, 6, 1) ], self.result.all()) def test_insert_node_near_subtree(self): """ Insert node with parent==4 initial state of the tree :mod:`sqlalchemy_mptt.tests.add_mptt_tree` .. code:: level Nested sets example 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 level Insert node with parent_id == 4 1 1(1)24 _______________|_____________________ | | | 2 2(2)5 6(4)13 14(7)23 | ______|________ __|______ | | | | | | 3 3(3)4 7(5)8 9(6)10 11(23)12 15(8)18 19(10)22 | | 4 16(9)17 20(11)21 """ node = self.model(parent_id=4) self.session.add(node) _level = node.get_default_level() self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 24, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 13, _level + 1, 1, 1), (5, 7, 8, _level + 2, 4, 1), (6, 9, 10, _level + 2, 4, 1), (7, 14, 23, _level + 1, 1, 1), (8, 15, 18, _level + 2, 7, 1), (9, 16, 17, _level + 3, 8, 1), (10, 19, 22, _level + 2, 7, 1), (11, 20, 21, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2), (23, 11, 12, _level + 2, 4, 1) ], self.result.all()) def test_insert_after_node(self): pass def test_delete_node(self): """ Delete node(4) initial state of the tree :mod:`sqlalchemy_mptt.tests.add_mptt_tree` .. code:: level Test delete node 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 level Delete node == 4 1 1(1)16 _______________|_____ | | 2 2(2)5 6(7)15 | ^ 3 3(3)4 7(8)10 11(10)14 | | 4 8(9)9 12(11)13 """ node = self.session.query(self.model)\ .filter(self.model.get_pk_column() == 4).one() self.session.delete(node) _level = node.get_default_level() self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 16, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (7, 6, 15, _level + 1, 1, 1), (8, 7, 10, _level + 2, 7, 1), (9, 8, 9, _level + 3, 8, 1), (10, 11, 14, _level + 2, 7, 1), (11, 12, 13, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2) ], self.result.all()) def test_update_node(self): """ Set parent_id==5 for node(8) initial state of the tree :mod:`sqlalchemy_mptt.tests.add_mptt_tree` .. code:: level Test update node 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 level Move 8 - > 5 1 1(1)22 _______________|__________________ | | | 2 2(2)5 6(4)15 16(7)21 | ^ | 3 3(3)4 7(5)12 13(6)14 17(10)20 | | 4 8(8)11 18(11)19 | 5 9(9)10 """ node = self.session.query(self.model)\ .filter(self.model.get_pk_column() == 8).one() node.parent_id = 5 self.session.add(node) _level = node.get_default_level() self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 22, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 15, _level + 1, 1, 1), (5, 7, 12, _level + 2, 4, 1), (6, 13, 14, _level + 2, 4, 1), (7, 16, 21, _level + 1, 1, 1), (8, 8, 11, _level + 3, 5, 1), (9, 9, 10, _level + 4, 8, 1), (10, 17, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2) ], self.result.all()) """ level Move 8 - > 5 1 1(1)22 _______________|__________________ | | | 2 2(2)5 6(4)15 16(7)21 | ^ | 3 3(3)4 7(5)12 13(6)14 17(10)20 | | 4 8(8)11 18(11)19 | 5 9(9)10 level Move 4 - > 2 1 1(1)22 ________|_____________ | | 2 2(2)15 16(7)21 ____|_____ | | | | 3 3(4)12 13(3)14 17(10)20 ^ | 4 4(5)9 10(6)11 18(11)19 | 5 5(8)8 | 6 6(9)7 """ node = self.session.query(self.model)\ .filter(self.model.get_pk_column() == 4).one() node.parent_id = 2 self.session.add(node) self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 22, _level + 0, None, 1), (2, 2, 15, _level + 1, 1, 1), (3, 13, 14, _level + 2, 2, 1), (4, 3, 12, _level + 2, 2, 1), (5, 4, 9, _level + 3, 4, 1), (6, 10, 11, _level + 3, 4, 1), (7, 16, 21, _level + 1, 1, 1), (8, 5, 8, _level + 4, 5, 1), (9, 6, 7, _level + 5, 8, 1), (10, 17, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2) ], self.result.all()) """ level Move 4 - > 2 1 1(1)22 ________|_____________ | | 2 2(2)15 16(7)21 ____|_____ | | | | 3 3(4)12 13(3)14 17(10)20 ^ | 4 4(5)9 10(6)11 18(11)19 | 5 5(8)8 | 6 6(9)7 level Move 8 - > 10 1 1(1)22 ________|_____________ | | 2 2(2)11 12(7)21 ______|_____ | | | | 3 3(4)8 9(3)10 13(10)20 __|____ _|______ | | | | 4 4(5)5 6(6)7 14(8)17 18(11)19 | 5 15(9)16 """ node = self.session.query(self.model)\ .filter(self.model.get_pk_column() == 8).one() node.parent_id = 10 self.session.add(node) self.assertEqual( [ # id lft rgt lvl parent tree (1, 1, 22, _level + 0, None, 1), (2, 2, 11, _level + 1, 1, 1), (3, 9, 10, _level + 2, 2, 1), (4, 3, 8, _level + 2, 2, 1), (5, 4, 5, _level + 3, 4, 1), (6, 6, 7, _level + 3, 4, 1), (7, 12, 21, _level + 1, 1, 1), (8, 14, 17, _level + 3, 10, 1), (9, 15, 16, _level + 4, 8, 1), (10, 13, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2) ], self.result.all()) def test_rebuild(self): """ Rebuild tree with tree_id==1 .. code:: level Nested sets w/o left & right (or broken left & right) 1 (1) _______________|___________________ | | | 2 (2) (4) (7) | ^ ^ 3 (3) (5) (6) (8) (10) | | 4 (9) (11) level Nested sets after rebuild 1 1(1)22 _______________|___________________ | | | 2 2(2)5 6(4)11 12(7)21 | ^ ^ 3 3(3)4 7(5)8 9(6)10 13(8)16 17(10)20 | | 4 14(9)15 18(11)19 """ self.session.query(self.model).update({ self.model.left: 0, self.model.right: 0, self.model.level: 0 }) self.model.rebuild(self.session, 1) _level = self.model.get_default_level() self.assertEqual( self.result.all(), [ # id lft rgt lvl parent tree (1, 1, 22, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 11, _level + 1, 1, 1), (5, 7, 8, _level + 2, 4, 1), (6, 9, 10, _level + 2, 4, 1), (7, 12, 21, _level + 1, 1, 1), (8, 13, 16, _level + 2, 7, 1), (9, 14, 15, _level + 3, 8, 1), (10, 17, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 0, 0, 0, None, 2), (13, 0, 0, 0, 12, 2), (14, 0, 0, 0, 13, 2), (15, 0, 0, 0, 12, 2), (16, 0, 0, 0, 15, 2), (17, 0, 0, 0, 15, 2), (18, 0, 0, 0, 12, 2), (19, 0, 0, 0, 18, 2), (20, 0, 0, 0, 19, 2), (21, 0, 0, 0, 18, 2), (22, 0, 0, 0, 21, 2) ] ) self.model.rebuild(self.session) self.assertEqual( self.result.all(), [ # id lft rgt lvl parent tree (1, 1, 22, _level + 0, None, 1), (2, 2, 5, _level + 1, 1, 1), (3, 3, 4, _level + 2, 2, 1), (4, 6, 11, _level + 1, 1, 1), (5, 7, 8, _level + 2, 4, 1), (6, 9, 10, _level + 2, 4, 1), (7, 12, 21, _level + 1, 1, 1), (8, 13, 16, _level + 2, 7, 1), (9, 14, 15, _level + 3, 8, 1), (10, 17, 20, _level + 2, 7, 1), (11, 18, 19, _level + 3, 10, 1), (12, 1, 22, _level + 0, None, 2), (13, 2, 5, _level + 1, 12, 2), (14, 3, 4, _level + 2, 13, 2), (15, 6, 11, _level + 1, 12, 2), (16, 7, 8, _level + 2, 15, 2), (17, 9, 10, _level + 2, 15, 2), (18, 12, 21, _level + 1, 12, 2), (19, 13, 16, _level + 2, 18, 2), (20, 14, 15, _level + 3, 19, 2), (21, 17, 20, _level + 2, 18, 2), (22, 18, 19, _level + 3, 21, 2) ] )

44.647601

78

0.290508

2,670

24,199

2.310861

0.035581

0.030794

0.020421

0.035008

0.894814

0.835494

0.820421

0.808104

0.803566

0

0.261551

0.585892

24,199

541

79

44.730129

0.354156

0.267945

0

0.7411

0

0.000072

0

0.032362

1

0.02589

false

0.003236

0

0.029126

0

null

0

1

0

1

0

1

0

1

0

null

0

10

7bcc1145ed49c7ff4c515e172774f0110005813b

16,729

py

Python

app/main/views.py

ergauravsoni/final-year-frontend

0529236a3fd36bbc364e8f5c93d102fcdf557a6a

[ "MIT" ]

null

app/main/views.py

ergauravsoni/final-year-frontend

0529236a3fd36bbc364e8f5c93d102fcdf557a6a

[ "MIT" ]

null

app/main/views.py

ergauravsoni/final-year-frontend

0529236a3fd36bbc364e8f5c93d102fcdf557a6a

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponseRedirect from django.urls import reverse from django.views.decorators.csrf import csrf_exempt from django.views.static import serve import os import requests from datetime import datetime, timedelta from pathlib import Path ENDPOINT_URL = 'https://1d74196f58f8.ngrok.io/api/' BASE_DIR = Path(__file__).resolve().parent.parent def download(request): try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: r2 = requests.get(ENDPOINT_URL + 'games/score_list/',headers=request.session['auth_payload']) print(r2.json()) content = {'email': r.json()['email'],'name': r.json()['name']} file = open("file.txt", "w") #convert variable to string str1 = repr(r.json()) str2 = repr(r2.json()) str3 = "" content ={} if r2.json()[0]['inventory_score']==0: str3 = "Unable to evaluate. Try Again." elif r2.json()[0]['inventory_score']>0 and r2.json()[0]['inventory_score']<11: str3 = "These ups and downs are considered normal." elif r2.json()[0]['inventory_score']>10 and r2.json()[0]['inventory_score']<17: str3 = "Mild Mood Disturbance" elif r2.json()[0]['inventory_score']>16 and r2.json()[0]['inventory_score']<21: str3 = "Borderline Clinical Depression" elif r2.json()[0]['inventory_score']>20 and r2.json()[0]['inventory_score']<31: str3 = "Moderate Depression" elif r2.json()[0]['inventory_score']>30 and r2.json()[0]['inventory_score']<41: str3 = "Severe Depression" else: str3 = "Extreme Depression" if r2.json()[0]['tower_blocks_score']>40: content['tb_score_factor'] = 'High' content['tb_color'] = 'success' elif r2.json()[0]['tower_blocks_score']<21: content['tb_score_factor'] = 'Low' content['tb_color'] = 'danger' else: content['tb_score_factor'] = 'Average' content['tb_color'] = 'warning' content['bounce_score'] = r2.json()[0]['bounce_score'] if r2.json()[0]['bounce_score']==0: content['b_score_factor'] = 'Low' content['b_color'] = 'danger' elif r2.json()[0]['bounce_score']<41: content['b_score_factor'] = 'High' content['b_color'] = 'success' elif r2.json()[0]['bounce_score']>50: content['b_score_factor'] = 'Low' content['b_color'] = 'danger' else: content['b_score_factor'] = 'Average' content['b_color'] = 'warning' content['kill_birds_score'] = r2.json()[0]['kill_birds_score'] if r2.json()[0]['kill_birds_score']>4: content['kb_score_factor'] = 'High' content['kb_color'] = 'success' elif r2.json()[0]['kill_birds_score']<3: content['kb_score_factor'] = 'Low' content['kb_color'] = 'danger' else: content['kb_score_factor'] = 'Average' content['kb_color'] = 'warning' content['snake_score'] = r2.json()[0]['snake_score'] if r2.json()[0]['snake_score']>14: content['s_score_factor'] = 'High' content['s_color'] = 'success' elif r2.json()[0]['snake_score']<5: content['s_score_factor'] = 'Low' content['s_color'] = 'danger' else: content['s_score_factor'] = 'Average' content['s_color'] = 'warning' tbn=0 if content['tb_score_factor']=='High': tbn=3 elif content['tb_score_factor']=='Average': tbn=2 else: tbn=1 bn=0 if content['b_score_factor']=='High': bn=3 elif content['b_score_factor']=='Average': bn=2 else: bn=1 kbn=0 if content['kb_score_factor']=='High': kbn=3 elif content['kb_score_factor']=='Average': kbn=2 else: kbn=1 sn=0 if content['s_score_factor']=='High': sn=3 elif content['s_score_factor']=='Average': sn=2 else: sn=1 if r2.json()[0]['tower_blocks_score']==0: tbn=0 if r2.json()[0]['bounce_score']==0: bn=0 if r2.json()[0]['kill_birds_score']==0: kbn=0 if r2.json()[0]['snake_score']==0: sn=0 content['perception_percent'] = str(round(23.33*tbn + 10*kbn)) content['attention_percent'] = str(round(20*sn + 13.33*bn)) content['learning_percent'] = str(round(33.33*sn)) str4 = "Perception = " + content['perception_percent'] + "% \n" + "Attention = " + content['attention_percent'] + "%\nLearning = " + content['learning_percent'] + "%\n" r3 = requests.get("https://thingspeak.com/channels/1408511/fields/3/last.json?api_key=0IQJRAN4L6IQO10B") print("BPM:",r3.json()) str5 = r3.json()['field3'] r4 = requests.get("https://thingspeak.com/channels/1408511/fields/1/last.json?api_key=0IQJRAN4L6IQO10B") print("Steps:",r4.json()) str6 = r4.json()['field1'] r5 = requests.get("https://thingspeak.com/channels/1408511/fields/2/last.json?api_key=0IQJRAN4L6IQO10B") print("Calories:",r5.json()) str7 = r5.json()['field2'] file.write("Overall Health Analytical Report\n\nUser Details = " + str1 + "\n\n" + "Game Analytics Report = " + str2 + "\n\n" + "Neuropsychological Report :\n" + str4 + "\n" + "Depression Detection Inventory Report = " + str3 + "\n\nHeartrate (BPM) = " + str5 + "\n\nStep Count = " + str6 + "\n\nEnergy Consumed(Calories) = " + str7) #close file file.close() filepath = str(BASE_DIR) + "/file.txt" return serve(request, os.path.basename(filepath), os.path.dirname(filepath)) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) def dashboard(request): try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: content = {'email': r.json()['email'],'name': r.json()['name']} return render(request, "main/dashboard.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) @csrf_exempt def tower_blocks(request): if request.method == 'POST': print('Tower Blocks Score:',request.POST['score']) payload={ 'tower_blocks_score':request.POST['score'], 'user':request.POST['email'] } r = requests.put(ENDPOINT_URL + 'games/score_update/',data=payload,headers=request.session['auth_payload']) print(r.json()) print(r.status_code) return HttpResponseRedirect(reverse('main:dashboard')) else: try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: content = {'email': r.json()['email'],'name': r.json()['name']} return render(request, "main/tower_blocks.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) @csrf_exempt def bounce(request): if request.method == 'POST': print('Bounce Score:',request.POST['score']) payload={ 'bounce_score':request.POST['score'], 'user':request.POST['email'] } r = requests.put(ENDPOINT_URL + 'games/score_update/',data=payload,headers=request.session['auth_payload']) print(r.json()) print(r.status_code) return HttpResponseRedirect(reverse('main:dashboard')) else: try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: content = {'email': r.json()['email'],'name': r.json()['name']} return render(request, "main/bounce.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) @csrf_exempt def kill_birds(request): if request.method == 'POST': print('Kill Birds Score:',request.POST['score']) payload={ 'kill_birds_score':request.POST['score'], 'user':request.POST['email'] } r = requests.put(ENDPOINT_URL + 'games/score_update/',data=payload,headers=request.session['auth_payload']) print(r.json()) print(r.status_code) return HttpResponseRedirect(reverse('main:dashboard')) else: try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: content = {'email': r.json()['email'],'name': r.json()['name']} return render(request, "main/kill_birds.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) @csrf_exempt def snake(request): if request.method == 'POST': print('Snake Score:',request.POST['score']) payload={ 'snake_score':request.POST['score'], 'user':request.POST['email'] } r = requests.put(ENDPOINT_URL + 'games/score_update/',data=payload,headers=request.session['auth_payload']) print(r.json()) print(r.status_code) return HttpResponseRedirect(reverse('main:dashboard')) else: try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: content = {'email': r.json()['email'],'name': r.json()['name']} return render(request, "main/snake.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) def game_analytics(request): try: r1 = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) r2 = requests.get(ENDPOINT_URL + 'games/score_list/',headers=request.session['auth_payload']) print(r1.json()) print(r1.status_code) print(r2.json()) print(r2.status_code) if r1.status_code >= 200 and r1.status_code < 300: content = {'email': r1.json()['email'],'name': r1.json()['name']} content['tower_blocks_score'] = r2.json()[0]['tower_blocks_score'] if r2.json()[0]['tower_blocks_score']>40: content['tb_score_factor'] = 'High' content['tb_color'] = 'success' elif r2.json()[0]['tower_blocks_score']<21: content['tb_score_factor'] = 'Low' content['tb_color'] = 'danger' else: content['tb_score_factor'] = 'Average' content['tb_color'] = 'warning' content['bounce_score'] = r2.json()[0]['bounce_score'] if r2.json()[0]['bounce_score']==0: content['b_score_factor'] = 'Low' content['b_color'] = 'danger' elif r2.json()[0]['bounce_score']<41: content['b_score_factor'] = 'High' content['b_color'] = 'success' elif r2.json()[0]['bounce_score']>50: content['b_score_factor'] = 'Low' content['b_color'] = 'danger' else: content['b_score_factor'] = 'Average' content['b_color'] = 'warning' content['kill_birds_score'] = r2.json()[0]['kill_birds_score'] if r2.json()[0]['kill_birds_score']>4: content['kb_score_factor'] = 'High' content['kb_color'] = 'success' elif r2.json()[0]['kill_birds_score']<3: content['kb_score_factor'] = 'Low' content['kb_color'] = 'danger' else: content['kb_score_factor'] = 'Average' content['kb_color'] = 'warning' content['snake_score'] = r2.json()[0]['snake_score'] if r2.json()[0]['snake_score']>14: content['s_score_factor'] = 'High' content['s_color'] = 'success' elif r2.json()[0]['snake_score']<5: content['s_score_factor'] = 'Low' content['s_color'] = 'danger' else: content['s_score_factor'] = 'Average' content['s_color'] = 'warning' tbn=0 if content['tb_score_factor']=='High': tbn=3 elif content['tb_score_factor']=='Average': tbn=2 else: tbn=1 bn=0 if content['b_score_factor']=='High': bn=3 elif content['b_score_factor']=='Average': bn=2 else: bn=1 kbn=0 if content['kb_score_factor']=='High': kbn=3 elif content['kb_score_factor']=='Average': kbn=2 else: kbn=1 sn=0 if content['s_score_factor']=='High': sn=3 elif content['s_score_factor']=='Average': sn=2 else: sn=1 if r2.json()[0]['tower_blocks_score']==0: tbn=0 if r2.json()[0]['bounce_score']==0: bn=0 if r2.json()[0]['kill_birds_score']==0: kbn=0 if r2.json()[0]['snake_score']==0: sn=0 content['perception_percent'] = round(23.33*tbn + 10*kbn) content['attention_percent'] = round(20*sn + 13.33*bn) content['learning_percent'] = round(33.33*sn) date_obj = datetime.fromisoformat(r2.json()[0]['last_updated']) print(date_obj.tzinfo,date_obj.microsecond) date_p = datetime.fromisoformat((date_obj.strftime("%Y-%m-%d %H:%M:%S"))) print(date_p) print(date_p+timedelta(hours=3,minutes=30,microseconds=int(date_obj.microsecond))) date_f=date_p+timedelta(hours=3,minutes=30,microseconds=int(date_obj.microsecond)) content['last_updated'] = date_f return render(request, "main/game_analytics.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin')) @csrf_exempt def inventory(request): if request.method == 'POST': print('Inventory Score:',request.POST['score']) payload={ 'inventory_score':request.POST['score'], 'user':request.POST['email'] } r = requests.put(ENDPOINT_URL + 'games/score_update/',data=payload,headers=request.session['auth_payload']) print(r.json()) print(r.status_code) return HttpResponseRedirect(reverse('main:dashboard')) else: try: r = requests.get(ENDPOINT_URL + 'users/me/',headers=request.session['auth_payload']) print(r.json()) print(r.status_code) if r.status_code >= 200 and r.status_code < 300: content = {'email': r.json()['email'],'name': r.json()['name']} return render(request, "main/inventory.html",content) except KeyError: pass return HttpResponseRedirect(reverse('users:signin'))

40.604369

345

0.5347

1,874

16,729

4.614194

0.115261

0.033306

0.036429

0.017694

0.814965

0.796114

0.728692

0.720597

0.70325

0.673528

0

0.031937

0.314962

16,729

411

346

40.703163

0.7226

0.002152

0

0.745902

0

0.008197

0.238032

0.002756

0.005464

0

1

0.021858

false

0.021858

0.02459

0

0.103825

0.10929

0

null

0

1

0

1

0

null

0

7

c8bc3d8f7ad248b964ece9bfde44698d9c384d12

160

py

Python

ssheepdog/settings/test.py

SheepDogInc/ssheepdog

12d54d7f94b137fb76d997486d98b6df8d25d0f0

[ "BSD-3-Clause" ]

1

2015-09-30T19:10:14.000Z

ssheepdog/settings/test.py

SheepDogInc/ssheepdog

12d54d7f94b137fb76d997486d98b6df8d25d0f0

[ "BSD-3-Clause" ]

2

2020-06-05T16:51:34.000Z

2021-06-10T17:33:57.000Z

ssheepdog/settings/test.py

SheepDogInc/ssheepdog

12d54d7f94b137fb76d997486d98b6df8d25d0f0

[ "BSD-3-Clause" ]

2

2015-02-21T21:08:56.000Z

2015-05-30T10:18:50.000Z

print "You can configure SKIP_TESTS_WITH_FLAGS in ssheepdog/test.py to skip those tests that require a VM" SKIP_TESTS_WITH_FLAGS = [] # or ['requires_server']

40

106

0.7875

27

160

4.407407

0.777778

0.151261

0.218487

0.302521

0

0.1375

160

3

107

53.333333

0.862319

0.1375

0

0.720588

0.154412

0

null

0

null

0.5

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

a810bcaa11c10b03a06c0435b38bbc87d7fdbae9

204

py

Python

src/clikit/io/input_stream/__init__.py

finswimmer/clikit

206198eb60d53c5daefa715f8a93e1fe85ffcf7e

[ "MIT" ]

78

2018-12-06T16:18:08.000Z

2022-03-18T01:44:06.000Z

src/clikit/io/input_stream/__init__.py

finswimmer/clikit

206198eb60d53c5daefa715f8a93e1fe85ffcf7e

[ "MIT" ]

24

2019-05-11T16:40:06.000Z

2022-02-27T01:11:04.000Z

src/clikit/io/input_stream/__init__.py

finswimmer/clikit

206198eb60d53c5daefa715f8a93e1fe85ffcf7e

[ "MIT" ]

17

2019-01-14T18:18:01.000Z

2022-03-07T23:05:46.000Z

from .null_input_stream import NullInputStream from .standard_input_stream import StandardInputStream from .stream_input_stream import StreamInputStream from .string_input_stream import StringInputStream

40.8

54

0.901961

24

204

7.333333

0.458333

0.25

0.386364

0

0.078431

204

4

55

51

0.93617

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

b558451abbef440c32cff971440b1eda1e006ef8

22,540

py

Python

circuit_figure_generation.py

mar-be/master-thesis-code

79c102a2b685eb1c54ace12cb6df31f32d4a51ae

[ "Apache-2.0" ]

null

circuit_figure_generation.py

mar-be/master-thesis-code

79c102a2b685eb1c54ace12cb6df31f32d4a51ae

[ "Apache-2.0" ]

null

circuit_figure_generation.py

mar-be/master-thesis-code

79c102a2b685eb1c54ace12cb6df31f32d4a51ae

[ "Apache-2.0" ]

null

import os from matplotlib import pyplot as plt from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit from numpy import pi dir = "circuit_drawings" file_ending = ".pdf" output = "mpl" scale = 1 with_layout=False plot_barriers=False fold = 11 try: os.makedirs("circuit_drawings") except FileExistsError: pass def save_figure(circuit:QuantumCircuit, scale, fold, with_layout, plot_barriers, filename): circuit.draw(output="mpl", scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, style={"margin":[1.7,0,0,0], "figwidth":8}, filename=filename) def count(circuit:QuantumCircuit, name): print('#'*40) print(f"Counts for {name}:") print(f"Depth: {circuit.depth()-1}") one_qubit_ops = 0 for op, count in circuit.count_ops().items(): if op in ["measure", "cx", "barrier"]: continue one_qubit_ops += count print(f"One Qubit Ops: {one_qubit_ops}") print(f"CNOTs: {circuit.num_nonlocal_gates()}") bv_qreg_q = QuantumRegister(2, 'q') bv_creg_meas = ClassicalRegister(2, 'meas') bv = QuantumCircuit(bv_qreg_q, bv_creg_meas) bv.rz(1.5707963267948961, bv_qreg_q[0]) bv.sx(bv_qreg_q[0]) bv.rz(1.5707963267948966, bv_qreg_q[0]) bv.rz(1.5707963267948966, bv_qreg_q[1]) bv.sx(bv_qreg_q[1]) bv.rz(4.71238898038469, bv_qreg_q[1]) bv.cx(bv_qreg_q[0], bv_qreg_q[1]) bv.rz(1.5707963267948961, bv_qreg_q[0]) bv.sx(bv_qreg_q[0]) bv.rz(1.5707963267948966, bv_qreg_q[0]) bv.rz(1.5707963267948961, bv_qreg_q[1]) bv.sx(bv_qreg_q[1]) bv.rz(1.5707963267948966, bv_qreg_q[1]) bv.barrier(bv_qreg_q[0], bv_qreg_q[1]) bv.measure(bv_qreg_q[0], bv_creg_meas[0]) bv.measure(bv_qreg_q[1], bv_creg_meas[1]) bv.draw(output=output, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/bv{file_ending}") qft_qreg_q = QuantumRegister(2, 'q') qft_creg_meas = ClassicalRegister(2, 'meas') qft = QuantumCircuit(qft_qreg_q, qft_creg_meas) qft.rz(1.5707963267948966, qft_qreg_q[0]) qft.sx(qft_qreg_q[0]) qft.rz(2.356194490192344, qft_qreg_q[0]) qft.cx(qft_qreg_q[0], qft_qreg_q[1]) qft.rz(5.497787143782137, qft_qreg_q[1]) qft.cx(qft_qreg_q[0], qft_qreg_q[1]) qft.rz(2.356194490192344, qft_qreg_q[1]) qft.sx(qft_qreg_q[1]) qft.rz(1.5707963267948966, qft_qreg_q[1]) qft.barrier(qft_qreg_q[0], qft_qreg_q[1]) qft.measure(qft_qreg_q[0], qft_creg_meas[0]) qft.measure(qft_qreg_q[1], qft_creg_meas[1]) qft.draw(output=output, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/qft{file_ending}") hwea_qreg_q = QuantumRegister(2, 'q') hwea_creg_meas = ClassicalRegister(2, 'meas') hwea = QuantumCircuit(hwea_qreg_q, hwea_creg_meas) hwea.rz(4.71238898038469, hwea_qreg_q[0]) hwea.sx(hwea_qreg_q[0]) hwea.rz(1.5707963267948966, hwea_qreg_q[0]) hwea.cx(hwea_qreg_q[0], hwea_qreg_q[1]) hwea.sx(hwea_qreg_q[0]) hwea.sx(hwea_qreg_q[0]) hwea.rz(3.141592653589793, hwea_qreg_q[0]) hwea.barrier(hwea_qreg_q[0], hwea_qreg_q[1]) hwea.measure(hwea_qreg_q[0], hwea_creg_meas[0]) hwea.measure(hwea_qreg_q[1], hwea_creg_meas[1]) hwea.draw(output=output, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/hwea{file_ending}") uccsd_qreg_q = QuantumRegister(2, 'q') uccsd_creg_meas = ClassicalRegister(2, 'meas') uccsd = QuantumCircuit(uccsd_qreg_q, uccsd_creg_meas) uccsd.rz(1.5707963267948961, uccsd_qreg_q[0]) uccsd.sx(uccsd_qreg_q[0]) uccsd.rz(1.5707963267948966, uccsd_qreg_q[0]) uccsd.rz(1.5707963267948961, uccsd_qreg_q[1]) uccsd.sx(uccsd_qreg_q[1]) uccsd.rz(1.5707963267948966, uccsd_qreg_q[1]) uccsd.cx(uccsd_qreg_q[1], uccsd_qreg_q[0]) uccsd.rz(-1.4877468618698795, uccsd_qreg_q[0]) uccsd.cx(uccsd_qreg_q[1], uccsd_qreg_q[0]) uccsd.sx(uccsd_qreg_q[0]) uccsd.rz(1.5707963267948948, uccsd_qreg_q[0]) uccsd.sx(uccsd_qreg_q[1]) uccsd.rz(1.5707963267948948, uccsd_qreg_q[1]) uccsd.cx(uccsd_qreg_q[1], uccsd_qreg_q[0]) uccsd.rz(-1.4877468618698795, uccsd_qreg_q[0]) uccsd.cx(uccsd_qreg_q[1], uccsd_qreg_q[0]) uccsd.rz(3.141592653589793, uccsd_qreg_q[0]) uccsd.sx(uccsd_qreg_q[0]) uccsd.rz(3.141592653589793, uccsd_qreg_q[0]) uccsd.rz(3.141592653589793, uccsd_qreg_q[1]) uccsd.sx(uccsd_qreg_q[1]) uccsd.rz(3.141592653589793, uccsd_qreg_q[1]) uccsd.barrier(uccsd_qreg_q[0], uccsd_qreg_q[1]) uccsd.measure(uccsd_qreg_q[0], uccsd_creg_meas[0]) uccsd.measure(uccsd_qreg_q[1], uccsd_creg_meas[1]) aqft_qreg_q = QuantumRegister(2, 'q') aqft_creg_meas = ClassicalRegister(2, 'meas') aqft = QuantumCircuit(aqft_qreg_q, aqft_creg_meas) aqft.rz(1.5707963267948966, aqft_qreg_q[0]) aqft.sx(aqft_qreg_q[0]) aqft.rz(2.356194490192344, aqft_qreg_q[0]) aqft.cx(aqft_qreg_q[0], aqft_qreg_q[1]) aqft.rz(5.497787143782137, aqft_qreg_q[1]) aqft.cx(aqft_qreg_q[0], aqft_qreg_q[1]) aqft.rz(2.356194490192344, aqft_qreg_q[1]) aqft.sx(aqft_qreg_q[1]) aqft.rz(1.5707963267948966, aqft_qreg_q[1]) aqft.barrier(aqft_qreg_q[0], aqft_qreg_q[1]) aqft.measure(aqft_qreg_q[0], aqft_creg_meas[0]) aqft.measure(aqft_qreg_q[1], aqft_creg_meas[1]) sl_qreg_q = QuantumRegister(2, 'q') sl_creg_meas = ClassicalRegister(2, 'meas') supremacy_linear = QuantumCircuit(sl_qreg_q, sl_creg_meas) supremacy_linear.rz(1.5707963267948961, sl_qreg_q[0]) supremacy_linear.sx(sl_qreg_q[0]) supremacy_linear.rz(1.5707963267948966, sl_qreg_q[0]) supremacy_linear.cx(sl_qreg_q[0], sl_qreg_q[1]) supremacy_linear.rz(3.1415926535897936, sl_qreg_q[0]) supremacy_linear.sx(sl_qreg_q[0]) supremacy_linear.rz(3.9269908169872423, sl_qreg_q[0]) supremacy_linear.sx(sl_qreg_q[0]) supremacy_linear.rz(4.71238898038469, sl_qreg_q[0]) supremacy_linear.rz(3.1415926535897936, sl_qreg_q[1]) supremacy_linear.sx(sl_qreg_q[1]) supremacy_linear.rz(3.9269908169872405, sl_qreg_q[1]) supremacy_linear.sx(sl_qreg_q[1]) supremacy_linear.rz(1.5707963267948943, sl_qreg_q[1]) supremacy_linear.barrier(sl_qreg_q[0], sl_qreg_q[1]) supremacy_linear.measure(sl_qreg_q[0], sl_creg_meas[0]) supremacy_linear.measure(sl_qreg_q[1], sl_creg_meas[1]) grover_qreg_q = QuantumRegister(2, 'q') grover_creg_meas = ClassicalRegister(2, 'meas') grover = QuantumCircuit(grover_qreg_q, grover_creg_meas) grover.rz(1.5707963267948961, grover_qreg_q[0]) grover.sx(grover_qreg_q[0]) grover.rz(1.5707963267948966, grover_qreg_q[0]) grover.cx(grover_qreg_q[0], grover_qreg_q[1]) grover.rz(4.71238898038469, grover_qreg_q[1]) grover.sx(grover_qreg_q[1]) grover.rz(1.5707963267948966, grover_qreg_q[1]) grover.barrier(grover_qreg_q[0], grover_qreg_q[1]) grover.rz(1.5707963267948961, grover_qreg_q[0]) grover.sx(grover_qreg_q[0]) grover.rz(1.5707963267948966, grover_qreg_q[0]) grover.rz(1.5707963267948961, grover_qreg_q[1]) grover.sx(grover_qreg_q[1]) grover.rz(1.5707963267948966, grover_qreg_q[1]) grover.barrier(grover_qreg_q[0], grover_qreg_q[1]) grover.x(grover_qreg_q[0]) grover.rz(1.5707963267948966, grover_qreg_q[1]) grover.sx(grover_qreg_q[1]) grover.rz(4.71238898038469, grover_qreg_q[1]) grover.cx(grover_qreg_q[0], grover_qreg_q[1]) grover.x(grover_qreg_q[0]) grover.rz(4.71238898038469, grover_qreg_q[1]) grover.sx(grover_qreg_q[1]) grover.rz(1.5707963267948966, grover_qreg_q[1]) grover.barrier(grover_qreg_q[0], grover_qreg_q[1]) grover.rz(1.5707963267948961, grover_qreg_q[0]) grover.sx(grover_qreg_q[0]) grover.rz(1.5707963267948966, grover_qreg_q[0]) grover.rz(1.5707963267948961, grover_qreg_q[1]) grover.sx(grover_qreg_q[1]) grover.rz(1.5707963267948966, grover_qreg_q[1]) grover.barrier(grover_qreg_q[0], grover_qreg_q[1]) grover.measure(grover_qreg_q[0], grover_creg_meas[0]) grover.measure(grover_qreg_q[1], grover_creg_meas[1]) part_bv_qreg_q = QuantumRegister(5, 'q') part_bv_creg_meas = ClassicalRegister(5, 'meas') part_bv = QuantumCircuit(part_bv_qreg_q, part_bv_creg_meas) part_bv.rz(1.5707963267948966, part_bv_qreg_q[0]) part_bv.sx(part_bv_qreg_q[0]) part_bv.rz(4.71238898038469, part_bv_qreg_q[0]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[1]) part_bv.sx(part_bv_qreg_q[1]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[0], part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[1], part_bv_qreg_q[0]) part_bv.cx(part_bv_qreg_q[0], part_bv_qreg_q[1]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[2]) part_bv.sx(part_bv_qreg_q[2]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[2]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[3]) part_bv.sx(part_bv_qreg_q[3]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[3]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[4]) part_bv.sx(part_bv_qreg_q[4]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[4], part_bv_qreg_q[3]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[2], part_bv_qreg_q[3]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[2]) part_bv.cx(part_bv_qreg_q[2], part_bv_qreg_q[3]) part_bv.cx(part_bv_qreg_q[2], part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[1], part_bv_qreg_q[2]) part_bv.cx(part_bv_qreg_q[2], part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[1], part_bv_qreg_q[2]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[1]) part_bv.sx(part_bv_qreg_q[1]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[0], part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[1], part_bv_qreg_q[0]) part_bv.cx(part_bv_qreg_q[0], part_bv_qreg_q[1]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[4], part_bv_qreg_q[3]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[2]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[4], part_bv_qreg_q[3]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[4]) part_bv.cx(part_bv_qreg_q[3], part_bv_qreg_q[2]) part_bv.cx(part_bv_qreg_q[1], part_bv_qreg_q[2]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[1]) part_bv.sx(part_bv_qreg_q[1]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[1]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[2]) part_bv.sx(part_bv_qreg_q[2]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[2]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[3]) part_bv.sx(part_bv_qreg_q[3]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[3]) part_bv.rz(1.5707963267948961, part_bv_qreg_q[4]) part_bv.sx(part_bv_qreg_q[4]) part_bv.rz(1.5707963267948966, part_bv_qreg_q[4]) part_bv.barrier(part_bv_qreg_q[1], part_bv_qreg_q[0], part_bv_qreg_q[2], part_bv_qreg_q[4], part_bv_qreg_q[3]) part_bv.measure(part_bv_qreg_q[0], part_bv_creg_meas[0]) part_bv.measure(part_bv_qreg_q[4], part_bv_creg_meas[1]) part_bv.measure(part_bv_qreg_q[3], part_bv_creg_meas[2]) part_bv.measure(part_bv_qreg_q[1], part_bv_creg_meas[3]) part_bv.measure(part_bv_qreg_q[2], part_bv_creg_meas[4]) part_sl_qreg_q = QuantumRegister(5, 'q') part_sl_creg_meas = ClassicalRegister(5, 'meas') part_supremacy_linear = QuantumCircuit(part_sl_qreg_q, part_sl_creg_meas) part_supremacy_linear.rz(1.5707963267948961, part_sl_qreg_q[0]) part_supremacy_linear.sx(part_sl_qreg_q[0]) part_supremacy_linear.rz(1.5707963267948966, part_sl_qreg_q[0]) part_supremacy_linear.cx(part_sl_qreg_q[0], part_sl_qreg_q[1]) part_supremacy_linear.rz(3.1415926535897936, part_sl_qreg_q[0]) part_supremacy_linear.sx(part_sl_qreg_q[0]) part_supremacy_linear.rz(3.9269908169872423, part_sl_qreg_q[0]) part_supremacy_linear.sx(part_sl_qreg_q[0]) part_supremacy_linear.rz(4.71238898038469, part_sl_qreg_q[0]) part_supremacy_linear.rz(3.141592653589793, part_sl_qreg_q[1]) part_supremacy_linear.sx(part_sl_qreg_q[1]) part_supremacy_linear.rz(1.5707963267948966, part_sl_qreg_q[1]) part_supremacy_linear.rz(1.5707963267948963, part_sl_qreg_q[2]) part_supremacy_linear.sx(part_sl_qreg_q[2]) part_supremacy_linear.rz(3.9269908169872396, part_sl_qreg_q[2]) part_supremacy_linear.sx(part_sl_qreg_q[2]) part_supremacy_linear.rz(1.570796326794896, part_sl_qreg_q[2]) part_supremacy_linear.cx(part_sl_qreg_q[1], part_sl_qreg_q[2]) part_supremacy_linear.rz(-1.5707963267948968, part_sl_qreg_q[1]) part_supremacy_linear.sx(part_sl_qreg_q[1]) part_supremacy_linear.rz(3.9269908169872405, part_sl_qreg_q[1]) part_supremacy_linear.sx(part_sl_qreg_q[1]) part_supremacy_linear.rz(1.5707963267948966, part_sl_qreg_q[1]) part_supremacy_linear.rz(3.141592653589793, part_sl_qreg_q[2]) part_supremacy_linear.sx(part_sl_qreg_q[2]) part_supremacy_linear.rz(1.5707963267948966, part_sl_qreg_q[2]) part_supremacy_linear.rz(1.5707963267948963, part_sl_qreg_q[3]) part_supremacy_linear.sx(part_sl_qreg_q[3]) part_supremacy_linear.rz(3.9269908169872396, part_sl_qreg_q[3]) part_supremacy_linear.sx(part_sl_qreg_q[3]) part_supremacy_linear.rz(1.570796326794896, part_sl_qreg_q[3]) part_supremacy_linear.cx(part_sl_qreg_q[2], part_sl_qreg_q[3]) part_supremacy_linear.rz(3.1415926535897936, part_sl_qreg_q[2]) part_supremacy_linear.sx(part_sl_qreg_q[2]) part_supremacy_linear.rz(3.9269908169872423, part_sl_qreg_q[2]) part_supremacy_linear.sx(part_sl_qreg_q[2]) part_supremacy_linear.rz(4.71238898038469, part_sl_qreg_q[2]) part_supremacy_linear.rz(3.141592653589793, part_sl_qreg_q[3]) part_supremacy_linear.sx(part_sl_qreg_q[3]) part_supremacy_linear.rz(1.5707963267948966, part_sl_qreg_q[3]) part_supremacy_linear.rz(1.5707963267948963, part_sl_qreg_q[4]) part_supremacy_linear.sx(part_sl_qreg_q[4]) part_supremacy_linear.rz(3.9269908169872396, part_sl_qreg_q[4]) part_supremacy_linear.sx(part_sl_qreg_q[4]) part_supremacy_linear.rz(1.570796326794896, part_sl_qreg_q[4]) part_supremacy_linear.cx(part_sl_qreg_q[3], part_sl_qreg_q[4]) part_supremacy_linear.rz(3.141592653589795, part_sl_qreg_q[3]) part_supremacy_linear.rz(1.5707963267948974, part_sl_qreg_q[4]) part_supremacy_linear.sx(part_sl_qreg_q[4]) part_supremacy_linear.rz(4.712388980384688, part_sl_qreg_q[4]) part_supremacy_linear.barrier(part_sl_qreg_q[0], part_sl_qreg_q[1], part_sl_qreg_q[2], part_sl_qreg_q[3], part_sl_qreg_q[4]) part_supremacy_linear.measure(part_sl_qreg_q[0], part_sl_creg_meas[0]) part_supremacy_linear.measure(part_sl_qreg_q[1], part_sl_creg_meas[1]) part_supremacy_linear.measure(part_sl_qreg_q[2], part_sl_creg_meas[2]) part_supremacy_linear.measure(part_sl_qreg_q[3], part_sl_creg_meas[3]) part_supremacy_linear.measure(part_sl_qreg_q[4], part_sl_creg_meas[4]) part_adder_qreg_q = QuantumRegister(5, 'q') part_adder_creg_meas = ClassicalRegister(5, 'meas') part_adder = QuantumCircuit(part_adder_qreg_q, part_adder_creg_meas) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.x(part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.rz(1.5707963267948961, part_adder_qreg_q[2]) part_adder.sx(part_adder_qreg_q[2]) part_adder.rz(1.5707963267948966, part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.rz(5.497787143782137, part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.rz(5.497787143782137, part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.rz(2.356194490192344, part_adder_qreg_q[2]) part_adder.sx(part_adder_qreg_q[2]) part_adder.rz(1.5707963267948966, part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[2]) part_adder.rz(1.5707963267948961, part_adder_qreg_q[1]) part_adder.sx(part_adder_qreg_q[1]) part_adder.rz(1.5707963267948966, part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.rz(5.497787143782137, part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[0]) part_adder.rz(5.497787143782137, part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.rz(0.7853981633974474, part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.rz(5.497787143782137, part_adder_qreg_q[1]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.x(part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.rz(5.497787143782137, part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[0]) part_adder.rz(5.497787143782137, part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.rz(2.356194490192344, part_adder_qreg_q[0]) part_adder.sx(part_adder_qreg_q[0]) part_adder.rz(1.5707963267948966, part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.rz(5.497787143782137, part_adder_qreg_q[1]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[0], part_adder_qreg_q[1]) part_adder.x(part_adder_qreg_q[0]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[1], part_adder_qreg_q[0]) part_adder.rz(1.5707963267948961, part_adder_qreg_q[2]) part_adder.sx(part_adder_qreg_q[2]) part_adder.rz(1.5707963267948966, part_adder_qreg_q[2]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[3]) part_adder.rz(5.497787143782137, part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.x(part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.rz(5.497787143782137, part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[2]) part_adder.rz(5.497787143782137, part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.rz(5.497787143782137, part_adder_qreg_q[2]) part_adder.rz(0.7853981633974487, part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.x(part_adder_qreg_q[2]) part_adder.rz(2.356194490192344, part_adder_qreg_q[4]) part_adder.sx(part_adder_qreg_q[4]) part_adder.rz(1.5707963267948966, part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[4], part_adder_qreg_q[3]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[4]) part_adder.cx(part_adder_qreg_q[3], part_adder_qreg_q[2]) part_adder.barrier(part_adder_qreg_q[0], part_adder_qreg_q[4], part_adder_qreg_q[1], part_adder_qreg_q[2], part_adder_qreg_q[3]) part_adder.measure(part_adder_qreg_q[4], part_adder_creg_meas[0]) part_adder.measure(part_adder_qreg_q[2], part_adder_creg_meas[1]) part_adder.measure(part_adder_qreg_q[3], part_adder_creg_meas[2]) part_adder.measure(part_adder_qreg_q[0], part_adder_creg_meas[3]) part_adder.measure(part_adder_qreg_q[1], part_adder_creg_meas[4]) save_figure(circuit=bv, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/bv{file_ending}") count(bv, "bv") save_figure(circuit=qft, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/qft{file_ending}") count(qft, "qft") save_figure(circuit=hwea, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/hwea{file_ending}") count(hwea, "hwea") save_figure(circuit=grover, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/grover{file_ending}") count(grover, "grover") save_figure(circuit=uccsd, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/uccsd{file_ending}") count(uccsd, "uccsd") save_figure(circuit=supremacy_linear, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/supremacy_linear{file_ending}") count(supremacy_linear, "supremacy_linear") save_figure(circuit=part_bv, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/part_bv{file_ending}") count(part_bv, "part_bv") save_figure(circuit=part_supremacy_linear, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/part_supremacy_linear{file_ending}") count(part_supremacy_linear, "part_supremacy_linear") save_figure(circuit=part_adder, scale=scale, fold = fold, with_layout=with_layout, plot_barriers=plot_barriers, filename=f"{dir}/part_adder{file_ending}") count(part_adder, "part_adder")

47.653277

177

0.811624

4,410

22,540

3.7161

0.025397

0.155297

0.149134

0.160605

0.881621

0.815292

0.793019

0.750549

0.718819

0.687393

0

0.13666

0.048802

22,540

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178

47.754237

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0.531818

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false

0.002273

0.009091

0

0.013636

0.011364

0

null

0

1

0

null

0

7

b59ae08472a885d6e26020cd4c416a3030db9025

2,933

py

Python

tests/contract/test_entity.py

alucardxh/zvt

02a2c64828146f4e15e702150f26a5ab6647a91c

[ "MIT" ]

1

2021-09-08T17:00:23.000Z

tests/contract/test_entity.py

alucardxh/zvt

02a2c64828146f4e15e702150f26a5ab6647a91c

[ "MIT" ]

1

2021-12-02T11:21:52.000Z

tests/contract/test_entity.py

alucardxh/zvt

02a2c64828146f4e15e702150f26a5ab6647a91c

[ "MIT" ]

null

# -*- coding: utf-8 -*- from zvt.contract import TradableEntity, IntervalLevel from zvt.utils.time_utils import to_pd_timestamp def test_get_1min_timestamps(): timestamps = [] for timestamp in TradableEntity.get_interval_timestamps(start_date='2020-06-17', end_date='2020-06-18', level=IntervalLevel.LEVEL_1MIN): timestamps.append(timestamp) assert to_pd_timestamp('2020-06-17 09:31:00') in timestamps assert to_pd_timestamp('2020-06-17 11:30:00') in timestamps assert to_pd_timestamp('2020-06-17 13:01:00') in timestamps assert to_pd_timestamp('2020-06-17 15:00:00') in timestamps assert to_pd_timestamp('2020-06-17 09:31:00') in timestamps assert to_pd_timestamp('2020-06-17 11:30:00') in timestamps assert to_pd_timestamp('2020-06-17 13:01:00') in timestamps assert to_pd_timestamp('2020-06-18 15:00:00') in timestamps def test_get_1h_timestamps(): timestamps = [] for timestamp in TradableEntity.get_interval_timestamps(start_date='2020-06-17', end_date='2020-06-18', level=IntervalLevel.LEVEL_1HOUR): timestamps.append(timestamp) assert to_pd_timestamp('2020-06-17 10:30:00') in timestamps assert to_pd_timestamp('2020-06-17 11:30:00') in timestamps assert to_pd_timestamp('2020-06-17 14:00:00') in timestamps assert to_pd_timestamp('2020-06-17 15:00:00') in timestamps assert to_pd_timestamp('2020-06-17 10:30:00') in timestamps assert to_pd_timestamp('2020-06-17 11:30:00') in timestamps assert to_pd_timestamp('2020-06-17 14:00:00') in timestamps assert to_pd_timestamp('2020-06-18 15:00:00') in timestamps def test_is_finished_kdata_timestamp(): assert TradableEntity.is_finished_kdata_timestamp('2020-06-17 10:30', IntervalLevel.LEVEL_30MIN) assert not TradableEntity.is_finished_kdata_timestamp('2020-06-17 10:30', IntervalLevel.LEVEL_1DAY) assert TradableEntity.is_finished_kdata_timestamp('2020-06-17 11:30', IntervalLevel.LEVEL_30MIN) assert not TradableEntity.is_finished_kdata_timestamp('2020-06-17 11:30', IntervalLevel.LEVEL_1DAY) assert TradableEntity.is_finished_kdata_timestamp('2020-06-17 13:30', IntervalLevel.LEVEL_30MIN) assert not TradableEntity.is_finished_kdata_timestamp('2020-06-17 13:30', IntervalLevel.LEVEL_1DAY) def test_open_close(): assert TradableEntity.is_open_timestamp('2020-06-17 09:30') assert TradableEntity.is_close_timestamp('2020-06-17 15:00') timestamps = [] for timestamp in TradableEntity.get_interval_timestamps(start_date='2020-06-17', end_date='2020-06-18', level=IntervalLevel.LEVEL_1HOUR): timestamps.append(timestamp) assert TradableEntity.is_open_timestamp(timestamps[0]) assert TradableEntity.is_close_timestamp(timestamps[-1])

47.306452

107

0.717354

426

2,933

4.71831

0.126761

0.089552

0.099502

0.18607

0.90597

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0.827363

0.824378

0

0.159354

0.17627

2,933

61

108

48.081967

0.672599

0.00716

0

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false

0

0.045455

0

0.136364

0

null

0

1

0

null

0

1

0

8

a914974ca99d76ada501cb7b4318972d2e3f6032

8,104

py

Python

Train/Train_TPC_module.py

giussepi/cyto_CRLM

4489d5d81c4270ec7b6048ceb2f2a02bfa699177

[ "Apache-2.0" ]

null

Train/Train_TPC_module.py

giussepi/cyto_CRLM

4489d5d81c4270ec7b6048ceb2f2a02bfa699177

[ "Apache-2.0" ]

6

2020-03-24T18:11:41.000Z

2022-03-12T00:16:18.000Z

Train/Train_TPC_module.py

giussepi/cyto_CRLM

4489d5d81c4270ec7b6048ceb2f2a02bfa699177

[ "Apache-2.0" ]

null

import torch import time import copy import logging def train_model(model, dataloaders,criterion, optimizer, scheduler,config): num_epochs= config.num_epochs display_size= config.display_size save_epoch= config.save_epoch save_path= config.save_path device_ids = config.device_ids device = torch.device('cuda:{}'.format(','.join([str(i) for i in device_ids])) \ if torch.cuda.device_count()>0 else torch.device('cpu')) since = time.time() dataset_sizes = {x: len(dataloaders[x].dataset) for x in ['train', 'val']} train_loss = [] train_acc = [] eval_loss = [] eval_acc = [] best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 #model.cuda() for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) logging.info('Epoch {}/{}'.format(epoch, num_epochs - 1)) logging.info('-' * 10) # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': scheduler.step() model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode running_loss = 0.0 running_corrects = 0 # Iterate over data. for batch_index,(inputs, labels) in enumerate(dataloaders[phase]): inputs = inputs.to(device).float() labels = labels.to(device).long() # zero the parameter gradients optimizer.zero_grad() # forward # track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) # backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() # statistics running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data).float() if phase == 'train' and batch_index% display_size==0: print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, \ running_loss/(batch_index+1)/inputs.shape[0], \ running_corrects.double()/(batch_index+1)/inputs.shape[0])) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] if phase=='train': train_loss.append(epoch_loss) train_acc.append(epoch_acc) torch.save(train_loss,save_path + 'train_loss.pth') torch.save(train_acc,save_path + 'train_acc.pth') if phase=='val': #print(eval_acc) eval_loss.append(epoch_loss) eval_acc.append(epoch_acc) torch.save(eval_loss,save_path + 'eval_loss.pth') torch.save(eval_acc,save_path + 'eval_acc.pth') if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc)) logging.info('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc)) if (epoch+1)%save_epoch == 0: torch.save(model.state_dict(),save_path + 'TPC_%03d.pth'%(epoch+1)) print("model saved %d"%(epoch+1)) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) logging.info('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) logging.info('Best val Acc: {:4f}'.format(best_acc)) # load best model weights model.load_state_dict(best_model_wts) torch.save(model.state_dict(),save_path+'TPC_best.pth') def train_model_bak(model, dataloaders,criterion, optimizer, scheduler, num_epochs=25,\ display_size=100,save_epoch=5,save_path='./'): since = time.time() dataset_sizes = {x: len(dataloaders[x].dataset) for x in ['train', 'val']} train_loss = [] train_acc = [] eval_loss = [] eval_acc = [] #model.cuda() for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) logging.info('Epoch {}/{}'.format(epoch, num_epochs - 1)) logging.info('-' * 10) # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': scheduler.step() model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode running_loss = 0.0 running_corrects = 0 # Iterate over data. for batch_index,(inputs, labels) in enumerate(dataloaders[phase]): inputs = inputs.cuda().float() labels = labels.cuda().long() # zero the parameter gradients optimizer.zero_grad() # forward # track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) # backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() # statistics running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data).float() if phase == 'train' and batch_index% display_size==0: print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, \ running_loss/(batch_index+1)/inputs.shape[0], \ running_corrects.double()/(batch_index+1)/inputs.shape[0])) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] train_loss.append(epoch_loss) train_acc.append(epoch_acc) torch.save(train_loss,save_path + 'train_loss.pth') torch.save(train_acc,save_path + 'train_acc.pth') print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc)) logging.info('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc)) if phase=='val': eval_loss.append(epoch_loss) eval_acc.append(epoch_acc) torch.save(epoch_loss,save_path + 'eval_loss.pth') torch.save(epoch_acc,save_path + 'eval_acc.pth') if (epoch+1)%save_epoch == 0: torch.save(model.state_dict(),save_path + 'TCP_%03d.pth'%(epoch+1)) print("model saved %d"%(epoch+1)) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) logging.info('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) logging.info('Best val Acc: {:4f}'.format(best_acc))

40.318408

122

0.522458

908

8,104

4.480176

0.129956

0.027532

0.02704

0.016224

0.866765

0.8412

0.830383

0.805556

0.786136

0

0.017724

0.352542

8,104

200

123

40.52

0.757576

0.068238

0

0.786765

0

0.0926

0

1

0.014706

false

0

0.029412

0

0.044118

0.102941

0

null

0

1

0

1

0

null

0

7

a956257e4b85a4db3727b31a5fedaac1bbaa36c6

1,590

py

Python

kooplex-proxy/proxy/crud.py

enasequence/covid-workflow-manager

185899b236f925dadb45ea5f224713202c4e7b00

[ "Apache-2.0" ]

5

2020-06-29T19:50:36.000Z

2022-01-31T09:16:29.000Z

kooplex-proxy/proxy/crud.py

enasequence/covid-workflow-manager

185899b236f925dadb45ea5f224713202c4e7b00

[ "Apache-2.0" ]

10

2020-06-29T19:48:57.000Z

2022-02-13T11:54:06.000Z

kooplex-proxy/proxy/crud.py

enasequence/covid-workflow-manager

185899b236f925dadb45ea5f224713202c4e7b00

[ "Apache-2.0" ]

2

2020-06-25T13:40:52.000Z

2021-02-03T20:23:09.000Z

from sqlalchemy.orm import Session import models def get_covid_country_weekly(db: Session, skip: int = 0, limit: int = 100): return db.query(models.CovidCountryWeekly).offset(skip).limit(limit).all() def get_unique_vcf_append(db: Session, skip: int = 0, limit: int = 100): return db.query(models.UniqueVCFAppend).offset(skip).limit(limit).all() def get_vcf_all(db: Session, skip: int = 0, limit: int = 100): return db.query(models.VCFAll).offset(skip).limit(limit).all() def get_cov(db: Session, skip: int = 0, limit: int = 100): return db.query(models.Cov).offset(skip).limit(limit).all() def get_meta(db: Session, skip: int = 0, limit: int = 100): return db.query(models.Meta).offset(skip).limit(limit).all() def get_unique_cov_append(db: Session, skip: int = 0, limit: int = 100): return db.query(models.UniqueCovAppend).offset(skip).limit(limit).all() def get_lineage_def(db: Session, skip: int = 0, limit: int = 100): return db.query(models.LineageDef).offset(skip).limit(limit).all() def get_operation(db: Session, skip: int = 0, limit: int = 100): return db.query(models.Operation).offset(skip).limit(limit).all() def get_unique_cov(db: Session, skip: int = 0, limit: int = 100): return db.query(models.UniqueCov).offset(skip).limit(limit).all() def get_unique_vcf(db: Session, skip: int = 0, limit: int = 100): return db.query(models.UniqueVCF).offset(skip).limit(limit).all() def get_vcf_all_append(db: Session, skip: int = 0, limit: int = 100): return db.query(models.VCFAllAppend).offset(skip).limit(limit).all()

33.829787

78

0.705031

250

1,590

4.392

0.144

0.060109

0.130237

0.160291

0.818761

0.797814

0.692168

0.492714

0

0.032

0.13522

1,590

46

79

34.565217

0.766545

0

1

0.458333

false

0

0.083333

0.458333

1

0

null

0

1

0

null

0

1

0

1

0

7

a996c8357d85160b00c7d3a4653b061b5bb8e38a

124

py

Python

pose_format/__init__.py

yairc2223/pose-format

6556433193582f8a7ed80d58d19ec11749e8606b

[ "MIT" ]

11

2020-09-02T02:58:23.000Z

2022-01-20T09:17:26.000Z

pose_format/__init__.py

yairc2223/pose-format

6556433193582f8a7ed80d58d19ec11749e8606b

[ "MIT" ]

5

2021-12-10T15:48:59.000Z

2022-02-21T15:53:20.000Z

pose_format/__init__.py

yairc2223/pose-format

6556433193582f8a7ed80d58d19ec11749e8606b

[ "MIT" ]

6

2020-09-21T02:21:26.000Z

2022-02-05T17:18:44.000Z

from pose_format.pose import Pose from pose_format.pose_body import PoseBody from pose_format.pose_header import PoseHeader

31

46

0.879032

20

124

5.2

0.4

0.230769

0.403846

0.519231

0

0.096774

124

3

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41.333333

0.928571

0

1

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true

0

1

0

1

0

1

0

null

1

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1

0

null

0

1

0

1

0

1

0

8

8d3cc3c2be883a0b058735a525362761810d51a9

8,007

py

Python

test.py

natalierpayne/join_replicate_fqs

ad560963100e6b4a98927e2f4ef9f317b53adb71

[ "MIT" ]

null

test.py

natalierpayne/join_replicate_fqs

ad560963100e6b4a98927e2f4ef9f317b53adb71

[ "MIT" ]

null

test.py

natalierpayne/join_replicate_fqs

ad560963100e6b4a98927e2f4ef9f317b53adb71

[ "MIT" ]

null

""" Tests for join_replicate_fqs.py """ import os import re import gzip from subprocess import getstatusoutput from shutil import rmtree PRG = './join_replicate_fqs.py' # pylint:disable=consider-using-with,unspecified-encoding # -------------------------------------------------- def test_exists(): """ Program exists """ assert os.path.isfile(PRG) # -------------------------------------------------- def test_usage(): """ Program prints usage statement with help flag """ for flag in ['-h', '--help']: rv, out = getstatusoutput(f'{PRG} {flag}') assert rv == 0 assert out.lower().startswith('usage') # -------------------------------------------------- def test_dies_bad_file(): """ Program fails with nonexistent file """ rv, out = getstatusoutput(f'{PRG} oo -f foo') assert rv != 0 assert out.lower().startswith('usage:') assert re.search("foo does not exist. Please provide valid filename.", out) # ------------------------------------------------- def test_dies_no_task(): """ Program fails without -c or -e""" suffix = '_WR' rv, out = getstatusoutput(f'{PRG} {suffix} -f inputs/*.fq') assert rv != 0 assert out.lower().startswith('usage:') assert re.search('join_replicate_fqs.py: error:', out) # ------------------------------------------------- def test_dies_o_without_c(): """ Program fails when -o used without -c""" suffix = '_WR' rv, out = getstatusoutput(f'{PRG} {suffix} -f inputs/*.fq -o') assert rv != 0 assert out.lower().startswith('usage:') assert re.search('join_replicate_fqs.py: error:', out) # ------------------------------------------------- def test_dies_d_without_e(): """ Program fails when -d used without -e""" suffix = '_WR' rv, out = getstatusoutput(f'{PRG} {suffix} -f inputs/*.fq -d') assert rv != 0 assert out.lower().startswith('usage:') assert re.search('join_replicate_fqs.py: error:', out) # ------------------------------------------------- def test_dies_e_without_d(): """ Program fails when -e used without -d""" suffix = '_WR' rv, out = getstatusoutput(f'{PRG} {suffix} -f inputs/*.fq -e') assert rv != 0 assert out.lower().startswith('usage:') assert re.search('join_replicate_fqs.py: error:', out) # -------------------------------------------------- def test_dies_bad_pattern(): """ Program fails to find replicates with bad pattern """ rv, out = getstatusoutput(f'{PRG} foo -f inputs/*.fq -ed exdir') assert rv != 0 assert out == ("No replicates were found! Check ['foo'] is correct.") if os.path.isdir('exdir'): rmtree('exdir') # ------------------------------------------------- def test_fqs_concat(): """ Program concatenates replicate fq files """ suffix = '_WR' rv, out = getstatusoutput(f'{PRG} {suffix} -f inputs/*.fq -c -o outdir') assert rv == 0 assert out == '' assert os.path.isdir('outdir') assert os.path.isfile('outdir/ind_1.1.fq') assert os.path.isfile('outdir/ind_1.2.fq') with open('outdir/ind_1.1.fq', encoding='utf-8') as fh: assert fh.read() == open('expected/concat/ind_1.1.fq').read() with open('outdir/ind_1.2.fq', encoding='utf-8') as fh: assert fh.read() == open('expected/concat/ind_1.2.fq').read() if os.path.isdir('outdir'): rmtree('outdir') # ------------------------------------------------- def test_fqs_extract(): """ Program extracts replicate fq files """ suffix = '_WR' rv, out = getstatusoutput(f'{PRG} {suffix} -f inputs/*.fq -e -d outdir') assert rv == 0 assert out == '' assert os.path.isdir('outdir') assert os.path.isfile('outdir/ind_1.1.fq') assert os.path.isfile('outdir/ind_1_WR.1.fq') assert os.path.isfile('outdir/ind_1.2.fq') assert os.path.isfile('outdir/ind_1_WR.2.fq') with open('outdir/ind_1.1.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1.1.fq').read().strip() with open('outdir/ind_1_WR.1.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1_WR.1.fq').read().strip() with open('outdir/ind_1.2.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1.2.fq').read().strip() with open('outdir/ind_1_WR.2.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1_WR.2.fq').read().strip() if os.path.isdir('outdir'): rmtree('outdir') # ------------------------------------------------- def test_fqs_concat_extract(): """ Program concatenates and extracts replicate files """ rv, out = getstatusoutput(f'{PRG} _WR -f inputs/*.fq -co catdir -ed exdir') assert rv == 0 assert out == '' assert os.path.isdir('catdir') assert os.path.isfile('catdir/ind_1.1.fq') assert os.path.isfile('catdir/ind_1.2.fq') with open('catdir/ind_1.1.fq', encoding='utf-8') as fh: assert fh.read() == open('expected/concat/ind_1.1.fq').read() with open('catdir/ind_1.2.fq', encoding='utf-8') as fh: assert fh.read() == open('expected/concat/ind_1.2.fq').read() if os.path.isdir('catdir'): rmtree('catdir') assert os.path.isdir('exdir') assert os.path.isfile('exdir/ind_1.1.fq') assert os.path.isfile('exdir/ind_1_WR.1.fq') assert os.path.isfile('exdir/ind_1.2.fq') assert os.path.isfile('exdir/ind_1_WR.2.fq') with open('exdir/ind_1.1.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1.1.fq').read().strip() with open('exdir/ind_1_WR.1.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1_WR.1.fq').read().strip() with open('exdir/ind_1.2.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1.2.fq').read().strip() with open('exdir/ind_1_WR.2.fq', encoding='utf-8') as fh: assert fh.read().strip() == \ open('expected/extract/ind_1_WR.2.fq').read().strip() if os.path.isdir('exdir'): rmtree('exdir') # ------------------------------------------------- def test_gz_concat_extract(): """ Program concatenates and extracts gzipped replicate files """ rv, out = getstatusoutput(f'{PRG} _WR -f inputs/*.gz -co catdir -ed exdir') assert rv == 0 assert out == '' assert os.path.isdir('catdir') assert os.path.isfile('catdir/ind_1.1.fq.gz') assert os.path.isfile('catdir/ind_1.2.fq.gz') with gzip.open('catdir/ind_1.1.fq.gz', 'rt') as fh: assert fh.read() == \ gzip.open('expected/gz_concat/ind_1.1.fq.gz', 'rt').read() with gzip.open('catdir/ind_1.2.fq.gz', 'rt') as fh: assert fh.read() == \ gzip.open('expected/gz_concat/ind_1.2.fq.gz', 'rt').read() if os.path.isdir('catdir'): rmtree('catdir') assert os.path.isdir('exdir') assert os.path.isfile('exdir/ind_1.1.fq.gz') assert os.path.isfile('exdir/ind_1_WR.1.fq.gz') assert os.path.isfile('exdir/ind_1.2.fq.gz') assert os.path.isfile('exdir/ind_1_WR.2.fq.gz') with gzip.open('exdir/ind_1.1.fq.gz', 'rt') as fh: assert fh.read() == \ gzip.open('expected/gz_extract/ind_1.1.fq.gz', 'rt').read() with gzip.open('exdir/ind_1_WR.1.fq.gz', 'rt') as fh: assert fh.read() == \ gzip.open('expected/gz_extract/ind_1_WR.1.fq.gz', 'rt').read() with gzip.open('exdir/ind_1.2.fq.gz', 'rt') as fh: assert fh.read() == \ gzip.open('expected/gz_extract/ind_1.2.fq.gz', 'rt').read() with gzip.open('exdir/ind_1_WR.2.fq.gz', 'rt') as fh: assert fh.read() == \ gzip.open('expected/gz_extract/ind_1_WR.2.fq.gz', 'rt').read() if os.path.isdir('exdir'): rmtree('exdir')

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5714f73ce724fa6a48760a262fe40938aac86506

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py

Python

eval/survey_utils.py

CristianViorelPopa/BART-TL-topic-label-generation

d2cee2438bb855e426548b39de1ed89c6db08840

[ "MIT" ]

6

2021-05-01T08:01:49.000Z

2022-03-13T11:54:17.000Z

eval/survey_utils.py

CristianViorelPopa/BART-TL-topic-label-generation

d2cee2438bb855e426548b39de1ed89c6db08840

[ "MIT" ]

3

2021-05-07T09:05:45.000Z

2021-06-30T15:32:21.000Z

eval/survey_utils.py

CristianViorelPopa/BART-TL-topic-label-generation

d2cee2438bb855e426548b39de1ed89c6db08840

[ "MIT" ]

1

2021-11-18T09:10:45.000Z

import numpy as np import pandas as pd import nltk from nltk.corpus import stopwords nltk.download('stopwords') # run once stop_words = stopwords.words('english') invalid_tokens = stop_words + \ ['we', 'once', 'that', 'you', 'i', 'an', 'yet', 'whom', 'was', 'being', 'my', 'our', 'their', 'he', 'she', 'us', 'them', 'him', 'her', 'twice', 'had', 'the', 'your', 'his', 'would', 'mr.', 'sir', 'not', 'ok', 'also', 'mr', 'one', 'no', 'while', 'as'] invalid_tokens = list(set(invalid_tokens)) num_minimum_annotators = 2 total_annotated_labels = 0 insufficiently_annotated_labels = 0 sufficiently_annotated_labels = 0 def preprocess_label(label): return label.split('(')[0].replace('_', ' ').strip() def get_model_scores(survey_csv_files, topics_csv_file, model_hypos_file, start=2, end=None, ignore_validation=True): answer_values = { 'Not relevant at all': 0, 'A bit relevant': 1, # 'Somehow relevant': 2, # 'Relevant': 3, 'Relevant': 2, # 'Very relevant': 4 'Very relevant': 3 } topic_labels_scores = {} target_topic_terms = [] for csv_file in survey_csv_files: # if csv_file == sys.argv[2]: # import pdb # pdb.set_trace() responses = pd.read_csv(csv_file) invalid_tokens_scores = [] for _ in range(responses.shape[0]): invalid_tokens_scores.append([]) # skip timestamp and nickname columns if end is None: end = len(responses.columns) for question in responses.columns[start:end]: # topic_terms = ' '.join(question.split('\n')[1].split('[')[0][:-1].split(', ')) # bugged question if len(question.split('\n')) == 1: continue topic_terms = ' '.join(question .split('Select how relevant the terms below are for describing the following word sequence:')[1] .split('Here are two sample paragraphs')[0] .split(', ')) else: topic_terms = ' '.join(question.split('\n')[1].split('[')[0][:].split(', ')) label = question.split('[')[1].split(']')[0] if topic_terms not in topic_labels_scores: topic_labels_scores[topic_terms] = {} answers = responses[question].values if label in invalid_tokens: for idx in range(len(answers)): if not pd.isnull(answers[idx]): invalid_tokens_scores[idx].append(answer_values[answers[idx]]) answers = answers[~pd.isnull(answers)] answers = [answer_values[answer] for answer in answers] if len(answers) > 0: # topic_labels_scores[topic_terms][label] = np.mean(answers) if label not in invalid_tokens: # if len(answers) < num_minimum_annotators: # insufficiently_annotated_labels += 1 # else: if label not in topic_labels_scores[topic_terms]: topic_labels_scores[topic_terms][label] = [] # sufficiently_annotated_labels += 1 topic_labels_scores[topic_terms][label].extend(answers) # sufficiently_annotated_labels += 1 else: if label not in topic_labels_scores[topic_terms] and label not in invalid_tokens: topic_labels_scores[topic_terms][label] = [] # topic_labels_scores[topic_terms][label] = -1.0 # print(topic_terms) # only account for the topics in the last survey if csv_file == survey_csv_files[-1]: target_topic_terms.append(topic_terms) valid_answers = [sum(np.array(user_answers) <= 1) >= 0.25 * len(user_answers) for user_answers in invalid_tokens_scores] if not ignore_validation and not np.all(valid_answers): print('WARNING: Not all answers passed the validation!') import pdb pdb.set_trace() # topic_labels_scores['voice direction speed signal button format technique light command procedure']['accelerometer'] = 3.0 # topic_labels_scores['voice direction speed signal button format technique light command procedure']['addition'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['observation'] = 2.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['metabolism'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['circadian rhythm'] = 3.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['contradiction'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['proteus'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['hypnotism'] = 3.5 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['subjective experience'] = 4.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['vibration'] = 1.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['unconsciousness'] = 4.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['signal processing'] = 1.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['principle'] = 4.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['formalism'] = 4.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['argument ad fallacy'] = 2.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['interrogation'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['confidentiality'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['prosecutor'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['apprehension'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['public enquiry'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['crime and punishment'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['observation'] = 1.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['propos inquiry'] = 1.0 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['head of state'] = 3.5 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['freedom of speech'] = 4.0 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['imperialism'] = 4.0 # topic_labels_scores['cloud virus amazon trail frankenstein addicted deployed crumb maze othello']['viruses'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['passenger vehicle'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['passenger car'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['point of sale'] = 1.0 global total_annotated_labels global insufficiently_annotated_labels global sufficiently_annotated_labels for _, labels_scores in topic_labels_scores.items(): for label in labels_scores.keys(): if len(labels_scores[label]) >= num_minimum_annotators: labels_scores[label] = np.mean(labels_scores[label]) sufficiently_annotated_labels += 1 total_annotated_labels += 1 elif labels_scores[label]: labels_scores[label] = -1.0 insufficiently_annotated_labels += 1 total_annotated_labels += 1 else: labels_scores[label] = -1.0 total_annotated_labels += 1 model_scores = [] with open(topics_csv_file) as topics_csv, open(model_hypos_file) as model_hypos: # ignore header topics_csv.readline() while True: topic_terms = topics_csv.readline() if topic_terms.strip() == '': break topic_terms = ' '.join(topic_terms.strip().split(',')[2:]) labels = [preprocess_label(label) for label in model_hypos.readline().strip().split(' ')] # the topic was not included in the survey # if topic_terms not in topic_labels_scores: # continue # the topic is not in the last survey if topic_terms not in target_topic_terms: continue label_scores = [] for label in labels: if label not in topic_labels_scores[topic_terms]: # this is no longer applicable, since every label that doesn't exist has a score # of -1 and is treated correctly print('Not all labels of the model were included in the survey!') continue # label_scores.append(topic_labels_scores[topic_terms][label]) label_scores.append((label, topic_labels_scores[topic_terms][label])) # label_scores = list(filter(lambda x: x != -1.0, label_scores)) label_scores = list(filter(lambda x: x[1] != -1.0, label_scores)) for label in labels: if label in invalid_tokens: label_scores.append((label, 0.0)) if len(label_scores) > 0: model_scores.append(label_scores) else: model_scores.append([]) model_scores = np.array(model_scores) return model_scores, sufficiently_annotated_labels, insufficiently_annotated_labels, total_annotated_labels def get_model_scores_with_topics(survey_csv_files, topics_csv_file, model_hypos_file, start=2, end=None, ignore_validation=True): answer_values = { 'Not relevant at all': 0, 'A bit relevant': 1, # 'Somehow relevant': 2, # 'Relevant': 3, 'Relevant': 2, # 'Very relevant': 4 'Very relevant': 3 } topic_labels_scores = {} target_topic_terms = [] for csv_file in survey_csv_files: # if csv_file == sys.argv[2]: # import pdb # pdb.set_trace() responses = pd.read_csv(csv_file) invalid_tokens_scores = [] for _ in range(responses.shape[0]): invalid_tokens_scores.append([]) # skip timestamp and nickname columns if end is None: end = len(responses.columns) for question in responses.columns[start:end]: # topic_terms = ' '.join(question.split('\n')[1].split('[')[0][:-1].split(', ')) # bugged question if len(question.split('\n')) == 1: continue topic_terms = ' '.join(question .split('Select how relevant the terms below are for describing the following word sequence:')[1] .split('Here are two sample paragraphs')[0] .split(', ')) else: topic_terms = ' '.join(question.split('\n')[1].split('[')[0][:].split(', ')) label = question.split('[')[1].split(']')[0] if topic_terms not in topic_labels_scores: topic_labels_scores[topic_terms] = {} answers = responses[question].values if label in invalid_tokens: for idx in range(len(answers)): if not pd.isnull(answers[idx]): invalid_tokens_scores[idx].append(answer_values[answers[idx]]) answers = answers[~pd.isnull(answers)] answers = [answer_values[answer] for answer in answers] if len(answers) > 0: # topic_labels_scores[topic_terms][label] = np.mean(answers) if label not in invalid_tokens: # if len(answers) < num_minimum_annotators: # insufficiently_annotated_labels += 1 # else: if label not in topic_labels_scores[topic_terms]: topic_labels_scores[topic_terms][label] = [] # sufficiently_annotated_labels += 1 topic_labels_scores[topic_terms][label].extend(answers) # sufficiently_annotated_labels += 1 else: if label not in topic_labels_scores[topic_terms] and label not in invalid_tokens: topic_labels_scores[topic_terms][label] = [] # topic_labels_scores[topic_terms][label] = -1.0 # print(topic_terms) # only account for the topics in the last survey if csv_file == survey_csv_files[-1]: target_topic_terms.append(topic_terms) valid_answers = [sum(np.array(user_answers) <= 1) >= 0.25 * len(user_answers) for user_answers in invalid_tokens_scores] if not ignore_validation and not np.all(valid_answers): print('WARNING: Not all answers passed the validation!') import pdb pdb.set_trace() # topic_labels_scores['voice direction speed signal button format technique light command procedure']['accelerometer'] = 3.0 # topic_labels_scores['voice direction speed signal button format technique light command procedure']['addition'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['observation'] = 2.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['metabolism'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['circadian rhythm'] = 3.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['contradiction'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['proteus'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['hypnotism'] = 3.5 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['subjective experience'] = 4.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['vibration'] = 1.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['unconsciousness'] = 4.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['signal processing'] = 1.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['principle'] = 4.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['formalism'] = 4.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['argument ad fallacy'] = 2.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['interrogation'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['confidentiality'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['prosecutor'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['apprehension'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['public enquiry'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['crime and punishment'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['observation'] = 1.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['propos inquiry'] = 1.0 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['head of state'] = 3.5 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['freedom of speech'] = 4.0 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['imperialism'] = 4.0 # topic_labels_scores['cloud virus amazon trail frankenstein addicted deployed crumb maze othello']['viruses'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['passenger vehicle'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['passenger car'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['point of sale'] = 1.0 global total_annotated_labels global insufficiently_annotated_labels global sufficiently_annotated_labels for _, labels_scores in topic_labels_scores.items(): for label in labels_scores.keys(): if len(labels_scores[label]) >= num_minimum_annotators: labels_scores[label] = np.mean(labels_scores[label]) sufficiently_annotated_labels += 1 total_annotated_labels += 1 elif labels_scores[label]: labels_scores[label] = -1.0 insufficiently_annotated_labels += 1 total_annotated_labels += 1 else: labels_scores[label] = -1.0 total_annotated_labels += 1 model_scores = {} with open(topics_csv_file) as topics_csv, open(model_hypos_file) as model_hypos: # ignore header topics_csv.readline() while True: topic_terms = topics_csv.readline() if topic_terms.strip() == '': break topic_terms = ' '.join(topic_terms.strip().split(',')[2:]) labels = [preprocess_label(label) for label in model_hypos.readline().strip().split(' ')] # the topic was not included in the survey # if topic_terms not in topic_labels_scores: # continue # the topic is not in the last survey if topic_terms not in target_topic_terms: continue label_scores = [] for label in labels: if label not in topic_labels_scores[topic_terms]: # this is no longer applicable, since every label that doesn't exist has a score # of -1 and is treated correctly print('Not all labels of the model were included in the survey!') continue # label_scores.append(topic_labels_scores[topic_terms][label]) label_scores.append((label, topic_labels_scores[topic_terms][label])) # label_scores = list(filter(lambda x: x != -1.0, label_scores)) label_scores = list(filter(lambda x: x[1] != -1.0, label_scores)) for label in labels: if label in invalid_tokens: label_scores.append((label, 0.0)) if len(label_scores) > 0: model_scores[topic_terms] = label_scores else: model_scores[topic_terms] = [] # model_scores = np.array(model_scores) return model_scores, sufficiently_annotated_labels, insufficiently_annotated_labels, total_annotated_labels def get_model_stddev(survey_csv_files, topics_csv_file, model_hypos_file, start=2, end=None, ignore_validation=True): answer_values = { 'Not relevant at all': 0, 'A bit relevant': 1, # 'Somehow relevant': 2, # 'Relevant': 3, 'Relevant': 2, # 'Very relevant': 4 'Very relevant': 3 } topic_labels_scores = {} target_topic_terms = [] for csv_file in survey_csv_files: # if csv_file == sys.argv[2]: # import pdb # pdb.set_trace() responses = pd.read_csv(csv_file) invalid_tokens_scores = [] for _ in range(responses.shape[0]): invalid_tokens_scores.append([]) # skip timestamp and nickname columns if end is None: end = len(responses.columns) for question in responses.columns[start:end]: # topic_terms = ' '.join(question.split('\n')[1].split('[')[0][:-1].split(', ')) # bugged question if len(question.split('\n')) == 1: continue topic_terms = ' '.join(question .split('Select how relevant the terms below are for describing the following word sequence:')[1] .split('Here are two sample paragraphs')[0] .split(', ')) else: topic_terms = ' '.join(question.split('\n')[1].split('[')[0][:].split(', ')) label = question.split('[')[1].split(']')[0] if topic_terms not in topic_labels_scores: topic_labels_scores[topic_terms] = {} answers = responses[question].values if label in invalid_tokens: for idx in range(len(answers)): if not pd.isnull(answers[idx]): invalid_tokens_scores[idx].append(answer_values[answers[idx]]) answers = answers[~pd.isnull(answers)] answers = [answer_values[answer] for answer in answers] if len(answers) > 0: # topic_labels_scores[topic_terms][label] = np.mean(answers) if label not in invalid_tokens: # if len(answers) < num_minimum_annotators: # insufficiently_annotated_labels += 1 # else: if label not in topic_labels_scores[topic_terms]: topic_labels_scores[topic_terms][label] = [] # sufficiently_annotated_labels += 1 topic_labels_scores[topic_terms][label].extend(answers) # sufficiently_annotated_labels += 1 else: if label not in topic_labels_scores[topic_terms] and label not in invalid_tokens: topic_labels_scores[topic_terms][label] = [] # topic_labels_scores[topic_terms][label] = -1.0 # print(topic_terms) # only account for the topics in the last survey if csv_file == survey_csv_files[-1]: target_topic_terms.append(topic_terms) valid_answers = [sum(np.array(user_answers) <= 1) >= 0.25 * len(user_answers) for user_answers in invalid_tokens_scores] if not ignore_validation and not np.all(valid_answers): print('WARNING: Not all answers passed the validation!') import pdb pdb.set_trace() # topic_labels_scores['voice direction speed signal button format technique light command procedure']['accelerometer'] = 3.0 # topic_labels_scores['voice direction speed signal button format technique light command procedure']['addition'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['observation'] = 2.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['metabolism'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['circadian rhythm'] = 3.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['contradiction'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['proteus'] = 0.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['hypnotism'] = 3.5 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['subjective experience'] = 4.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['vibration'] = 1.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['unconsciousness'] = 4.0 # topic_labels_scores['suffix ending brain eye perception visual experienced breath ic historic']['signal processing'] = 1.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['principle'] = 4.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['formalism'] = 4.0 # topic_labels_scores['argument ad logical attack fallacy toilet beer visited delivery accusative']['argument ad fallacy'] = 2.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['interrogation'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['confidentiality'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['prosecutor'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['apprehension'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['public enquiry'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['crime and punishment'] = 4.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['observation'] = 1.0 # topic_labels_scores['mid sudden inquiry newly beard ford flavour investigate apropos inquire']['propos inquiry'] = 1.0 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['head of state'] = 3.5 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['freedom of speech'] = 4.0 # topic_labels_scores['power government kill approved ruler democracy dictator chauvinism unlimited authoritarian']['imperialism'] = 4.0 # topic_labels_scores['cloud virus amazon trail frankenstein addicted deployed crumb maze othello']['viruses'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['passenger vehicle'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['passenger car'] = 3.0 # topic_labels_scores['plate vehicle state license motor shall registration law apostille issued']['point of sale'] = 1.0 global total_annotated_labels global insufficiently_annotated_labels global sufficiently_annotated_labels for _, labels_scores in topic_labels_scores.items(): for label in labels_scores.keys(): if len(labels_scores[label]) >= num_minimum_annotators: labels_scores[label] = np.std(labels_scores[label]) sufficiently_annotated_labels += 1 total_annotated_labels += 1 elif labels_scores[label]: labels_scores[label] = -1.0 insufficiently_annotated_labels += 1 total_annotated_labels += 1 else: labels_scores[label] = -1.0 total_annotated_labels += 1 model_scores = [] with open(topics_csv_file) as topics_csv, open(model_hypos_file) as model_hypos: # ignore header topics_csv.readline() while True: topic_terms = topics_csv.readline() if topic_terms.strip() == '': break topic_terms = ' '.join(topic_terms.strip().split(',')[2:]) labels = [preprocess_label(label) for label in model_hypos.readline().strip().split(' ')] # the topic was not included in the survey # if topic_terms not in topic_labels_scores: # continue # the topic is not in the last survey if topic_terms not in target_topic_terms: continue label_scores = [] for label in labels: if label not in topic_labels_scores[topic_terms]: # this is no longer applicable, since every label that doesn't exist has a score # of -1 and is treated correctly print('Not all labels of the model were included in the survey!') continue # label_scores.append(topic_labels_scores[topic_terms][label]) label_scores.append((label, topic_labels_scores[topic_terms][label])) # label_scores = list(filter(lambda x: x != -1.0, label_scores)) label_scores = list(filter(lambda x: x[1] != -1.0, label_scores)) for label in labels: if label in invalid_tokens: label_scores.append((label, 0.0)) if len(label_scores) > 0: model_scores.append(label_scores) else: model_scores.append([]) model_scores = np.array(model_scores) return model_scores, sufficiently_annotated_labels, insufficiently_annotated_labels, total_annotated_labels

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5721c553325c0bc54b57c405497ba212019b1190

3,681

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Python

data/types.py

wuhoodude/Bappybot

c7f0bf42678758d2d042b48f843c8c341b737e70

[ "MIT" ]

7

2015-06-08T17:57:16.000Z

2017-12-14T09:09:01.000Z

data/types.py

wuhoodude/Bappybot

c7f0bf42678758d2d042b48f843c8c341b737e70

[ "MIT" ]

8

2015-06-08T19:51:50.000Z

2021-12-13T19:46:10.000Z

data/types.py

wuhoodude/Bappybot

c7f0bf42678758d2d042b48f843c8c341b737e70

[ "MIT" ]

21

2015-06-08T17:06:42.000Z

2020-07-23T07:21:12.000Z

Types = { "Bug":{"Bug":1,"Dark":1,"Dragon":1,"Electric":1,"Fairy":1,"Fighting":0.5,"Fire":2,"Flying":2,"Ghost":1,"Grass":0.5,"Ground":0.5,"Ice":1,"Normal":1,"Poison":1,"Psychic":1,"Rock":2,"Steel":1,"Water":1}, "Dark":{"Bug":2,"Dark":0.5,"Dragon":1,"Electric":1,"Fairy":2,"Fighting":2,"Fire":1,"Flying":1,"Ghost":0.5,"Grass":1,"Ground":1,"Ice":1,"Normal":1,"Poison":1,"Psychic":0,"Rock":1,"Steel":1,"Water":1}, "Dragon":{"Bug":1,"Dark":1,"Dragon":2,"Electric":0.5,"Fairy":2,"Fighting":1,"Fire":0.5,"Flying":1,"Ghost":1,"Grass":0.5,"Ground":1,"Ice":2,"Normal":1,"Poison":1,"Psychic":1,"Rock":1,"Steel":1,"Water":0.5}, "Electric":{"Bug":1,"Dark":1,"Dragon":1,"Electric":0.5,"Fairy":1,"Fighting":1,"Fire":1,"Flying":0.5,"Ghost":1,"Grass":1,"Ground":2,"Ice":1,"Normal":1,"Poison":1,"Psychic":1,"Rock":1,"Steel":0.5,"Water":1}, "Fairy":{"Bug":0.5,"Dark":0.5,"Dragon":0,"Electric":1,"Fairy":1,"Fighting":0.5,"Fire":1,"Flying":1,"Ghost":1,"Grass":1,"Ground":1,"Ice":1,"Normal":1,"Poison":2,"Psychic":1,"Rock":1,"Steel":2,"Water":1}, "Fighting":{"Bug":0.5,"Dark":0.5,"Dragon":1,"Electric":1,"Fairy":2,"Fighting":1,"Fire":1,"Flying":2,"Ghost":1,"Grass":1,"Ground":1,"Ice":1,"Normal":1,"Poison":1,"Psychic":2,"Rock":1,"Steel":1,"Water":1}, "Fire":{"Bug":0.5,"Dark":1,"Dragon":1,"Electric":1,"Fairy":0.5,"Fighting":1,"Fire":0.5,"Flying":1,"Ghost":1,"Grass":0.5,"Ground":2,"Ice":0.5,"Normal":1,"Poison":1,"Psychic":1,"Rock":2,"Steel":0.5,"Water":2}, "Flying":{"Bug":0.5,"Dark":1,"Dragon":1,"Electric":2,"Fairy":1,"Fighting":0.5,"Fire":1,"Flying":1,"Ghost":1,"Grass":0.5,"Ground":0,"Ice":2,"Normal":1,"Poison":1,"Psychic":1,"Rock":2,"Steel":1,"Water":1}, "Ghost":{"Bug":1,"Dark":2,"Dragon":1,"Electric":1,"Fairy":1,"Fighting":0,"Fire":1,"Flying":1,"Ghost":2,"Grass":1,"Ground":1,"Ice":1,"Normal":0,"Poison":0.5,"Psychic":1,"Rock":1,"Steel":1,"Water":1}, "Grass":{"Bug":2,"Dark":1,"Dragon":1,"Electric":0.5,"Fairy":1,"Fighting":1,"Fire":2,"Flying":2,"Ghost":1,"Grass":0.5,"Ground":0.5,"Ice":2,"Normal":1,"Poison":2,"Psychic":1,"Rock":1,"Steel":1,"Water":0.5}, "Ground":{"Bug":1,"Dark":1,"Dragon":1,"Electric":0,"Fairy":1,"Fighting":1,"Fire":1,"Flying":1,"Ghost":1,"Grass":2,"Ground":1,"Ice":2,"Normal":1,"Poison":0.5,"Psychic":1,"Rock":0.5,"Steel":1,"Water":2}, "Ice":{"Bug":1,"Dark":1,"Dragon":1,"Electric":1,"Fairy":1,"Fighting":2,"Fire":2,"Flying":1,"Ghost":1,"Grass":1,"Ground":1,"Ice":0.5,"Normal":1,"Poison":1,"Psychic":1,"Rock":2,"Steel":2,"Water":1}, "Normal":{"Bug":1,"Dark":1,"Dragon":1,"Electric":1,"Fairy":1,"Fighting":2,"Fire":1,"Flying":1,"Ghost":0,"Grass":1,"Ground":1,"Ice":1,"Normal":1,"Poison":1,"Psychic":1,"Rock":1,"Steel":1,"Water":1}, "Poison":{"Bug":0.5,"Dark":1,"Dragon":1,"Electric":1,"Fairy":0.5,"Fighting":0.5,"Fire":1,"Flying":1,"Ghost":1,"Grass":0.5,"Ground":2,"Ice":1,"Normal":1,"Poison":0.5,"Psychic":2,"Rock":1,"Steel":1,"Water":1}, "Psychic":{"Bug":2,"Dark":2,"Dragon":1,"Electric":1,"Fairy":1,"Fighting":0.5,"Fire":1,"Flying":1,"Ghost":2,"Grass":1,"Ground":1,"Ice":1,"Normal":1,"Poison":1,"Psychic":0.5,"Rock":1,"Steel":1,"Water":1}, "Rock":{"Bug":1,"Dark":1,"Dragon":1,"Electric":1,"Fairy":1,"Fighting":2,"Fire":0.5,"Flying":0.5,"Ghost":1,"Grass":2,"Ground":2,"Ice":1,"Normal":0.5,"Poison":0.5,"Psychic":1,"Rock":1,"Steel":2,"Water":2}, "Steel":{"Bug":0.5,"Dark":1,"Dragon":0.5,"Electric":1,"Fairy":0.5,"Fighting":2,"Fire":2,"Flying":0.5,"Ghost":1,"Grass":0.5,"Ground":2,"Ice":0.5,"Normal":0.5,"Poison":0,"Psychic":0.5,"Rock":0.5,"Steel":0.5,"Water":1}, "Water":{"Bug":1,"Dark":1,"Dragon":1,"Electric":2,"Fairy":1,"Fighting":1,"Fire":0.5,"Flying":1,"Ghost":1,"Grass":2,"Ground":1,"Ice":0.5,"Normal":1,"Poison":1,"Psychic":1,"Rock":1,"Steel":0.5,"Water":0.5} }

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22,653

py

Python

tests/quara/protocol/qtomography/standard/test_loss_minimization_estimator.py

tknrsgym/quara

8f3337af83cdd02bb85632bb1e297902b1fff8fb

[ "Apache-2.0" ]

3

2021-05-19T11:44:30.000Z

2022-03-30T07:13:49.000Z

tests/quara/protocol/qtomography/standard/test_loss_minimization_estimator.py

tknrsgym/quara

8f3337af83cdd02bb85632bb1e297902b1fff8fb

[ "Apache-2.0" ]

2

2021-06-02T01:24:59.000Z

2021-06-02T12:20:31.000Z

tests/quara/protocol/qtomography/standard/test_loss_minimization_estimator.py

tknrsgym/quara

8f3337af83cdd02bb85632bb1e297902b1fff8fb

[ "Apache-2.0" ]

1

2021-10-14T13:21:27.000Z

import time from typing import List import numpy as np import numpy.testing as npt import pytest from quara.loss_function.weighted_probability_based_squared_error import ( WeightedProbabilityBasedSquaredError, WeightedProbabilityBasedSquaredErrorOption, ) from quara.minimization_algorithm.minimization_algorithm import MinimizationResult from quara.minimization_algorithm.projected_gradient_descent_backtracking import ( ProjectedGradientDescentBacktracking, ProjectedGradientDescentBacktrackingOption, ) from quara.math import func_proj from quara.objects.composite_system import CompositeSystem from quara.objects.elemental_system import ElementalSystem from quara.objects.matrix_basis import get_normalized_pauli_basis from quara.objects.povm import ( get_x_povm, get_y_povm, get_z_povm, ) from quara.objects.state import convert_var_to_state, get_z0_1q from quara.protocol.qtomography.standard.standard_qst import StandardQst from quara.protocol.qtomography.standard.loss_minimization_estimator import ( LossMinimizationEstimator, LossMinimizationEstimationResult, ) def get_test_data(on_para_eq_constraint=False): e_sys = ElementalSystem(0, get_normalized_pauli_basis()) c_sys = CompositeSystem([e_sys]) povm_x = get_x_povm(c_sys) povm_y = get_y_povm(c_sys) povm_z = get_z_povm(c_sys) povms = [povm_x, povm_y, povm_z] qst = StandardQst(povms, on_para_eq_constraint=on_para_eq_constraint, seed_data=7) return qst, c_sys class TestLossMinimizationEstimator: def test_calc_estimate__is_computation_time_required(self): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True ) estimator = LossMinimizationEstimator() # is_computation_time_required=True actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option, is_computation_time_required=True, ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) assert type(actual.computation_time) == float # is_computation_time_required=False actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) assert actual.computation_time == None def test_calc_estimate__is_detailed_results_required(self): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True ) estimator = LossMinimizationEstimator() # is_detailed_results_required=True actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option, is_detailed_results_required=True, ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) assert type(actual.detailed_results) == list assert len(actual.detailed_results) == 1 assert isinstance(actual.detailed_results[0], MinimizationResult) == True # is_detailed_results_required=False actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) assert actual.detailed_results == None def test_calc_estimate__on_algo_xx_constraint(self): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") # case1: on_algo_eq_constraint=True, on_algo_ineq_constraint=True qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, var_start=var_start, ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) # case2: on_algo_eq_constraint=True, on_algo_ineq_constraint=False qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=False, var_start=var_start, ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 0.707106764760052] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=15) # case3: on_algo_eq_constraint=False, on_algo_ineq_constraint=True qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=False, on_algo_ineq_constraint=True, var_start=var_start, ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [0.707106790461949, 0, 0, 0.707106713475754] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=15) # case4: on_algo_eq_constraint=False, on_algo_ineq_constraint=False qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=False, on_algo_ineq_constraint=False, var_start=var_start, ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [ 7.071067772680395e-01, -3.291237808416748e-26, -3.291237808416748e-26, 7.071067976130435e-01, ] npt.assert_almost_equal(actual.estimated_var, expected, decimal=10) def test_calc_estimate__func_proj(self): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") # case1: func_proj=auto setting qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, var_start=var_start, ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) # case2: func_proj=func_proj.proj_to_self qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, var_start=var_start, ) loss = WeightedProbabilityBasedSquaredError( 4, func_prob_dists=func_proj.proj_to_self ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) # case3: func_proj=func_proj.proj_to_hyperplane qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, var_start=var_start, ) loss = WeightedProbabilityBasedSquaredError( 4, func_prob_dists=func_proj.proj_to_hyperplane(var_start) ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) # case4: func_proj=func_proj.proj_to_nonnegative qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() obj_start = ( qst.generate_empty_estimation_obj_with_setting_info().generate_origin_obj() ) var_start = obj_start.to_var() algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, var_start=var_start, ) loss = WeightedProbabilityBasedSquaredError( 4, func_prob_dists=func_proj.proj_to_nonnegative ) estimator = LossMinimizationEstimator() actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=7) def test_calc_estimate_sequence(self): qst, _ = get_test_data() empi_dists_seq = [ [ (100, np.array([0.5, 0.5], dtype=np.float64)), (100, np.array([0.5, 0.5], dtype=np.float64)), (100, np.array([1, 0], dtype=np.float64)), ], [ (10000, np.array([1, 0], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), ], ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() algo_option = ProjectedGradientDescentBacktrackingOption() estimator = LossMinimizationEstimator() actual = estimator.calc_estimate_sequence( qst, empi_dists_seq, loss, loss_option, algo, algo_option, ) expected = [ [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)], [1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0], ] for a, e in zip(actual.estimated_qoperation_sequence, expected): assert a.is_physical() npt.assert_almost_equal(a.to_stacked_vector(), e, decimal=7) def test_calc_estimate_sequence_value_error(self): qst, _ = get_test_data() empi_dists_seq = [ [ (100, np.array([0.5, 0.5], dtype=np.float64)), (100, np.array([0.5, 0.5], dtype=np.float64)), (100, np.array([1, 0], dtype=np.float64)), ], [ (10000, np.array([1, 0], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), ], ] # loss.is_option_sufficient() is False loss = WeightedProbabilityBasedSquaredError(4) def _is_option_sufficient(): return False loss.is_option_sufficient = _is_option_sufficient loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() algo_option = ProjectedGradientDescentBacktrackingOption() estimator = LossMinimizationEstimator() with pytest.raises(ValueError): estimator.calc_estimate_sequence( qst, empi_dists_seq, loss, loss_option, algo, algo_option, ) # algo.is_loss_sufficient() is False loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() def _is_loss_sufficient(): return False algo.is_loss_sufficient = _is_loss_sufficient algo_option = ProjectedGradientDescentBacktrackingOption() estimator = LossMinimizationEstimator() with pytest.raises(ValueError): estimator.calc_estimate_sequence( qst, empi_dists_seq, loss, loss_option, algo, algo_option, ) # algo.is_option_sufficient() is False loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() def _is_option_sufficient(): return False algo.is_option_sufficient = _is_option_sufficient algo_option = ProjectedGradientDescentBacktrackingOption() estimator = LossMinimizationEstimator() with pytest.raises(ValueError): estimator.calc_estimate_sequence( qst, empi_dists_seq, loss, loss_option, algo, algo_option, ) # algo.is_loss_and_option_sufficient() is False loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() def _is_loss_and_option_sufficient(): return False algo.is_loss_and_option_sufficient = _is_loss_and_option_sufficient algo_option = ProjectedGradientDescentBacktrackingOption() estimator = LossMinimizationEstimator() with pytest.raises(ValueError): estimator.calc_estimate_sequence( qst, empi_dists_seq, loss, loss_option, algo, algo_option, ) def test_calc_estimate__pgdb_opton__sum_absolute_difference_loss(self): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() # mode_stopping_criterion_gradient_descent="sum_absolute_difference_loss" # num_history_stopping_criterion_gradient_descent=10 algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, mode_stopping_criterion_gradient_descent="sum_absolute_difference_loss", num_history_stopping_criterion_gradient_descent=10, ) estimator = LossMinimizationEstimator() # is_computation_time_required=True actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option, is_computation_time_required=True, ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=11) def test_calc_estimate__pgdb_opton__sum_absolute_difference_variable(self): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() # mode_stopping_criterion_gradient_descent="sum_absolute_difference_variable" # num_history_stopping_criterion_gradient_descent=10 algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, mode_stopping_criterion_gradient_descent="sum_absolute_difference_variable", num_history_stopping_criterion_gradient_descent=10, ) estimator = LossMinimizationEstimator() # is_computation_time_required=True actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option, is_computation_time_required=True, ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=15) def test_calc_estimate__pgdb_opton__sum_absolute_difference_projected_gradient( self, ): empi_dists = [ (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([0.5, 0.5], dtype=np.float64)), (10000, np.array([1, 0], dtype=np.float64)), ] loss = WeightedProbabilityBasedSquaredError(4) loss_option = WeightedProbabilityBasedSquaredErrorOption(mode_weight="identity") qst, _ = get_test_data() algo = ProjectedGradientDescentBacktracking() # mode_stopping_criterion_gradient_descent="sum_absolute_difference_projected_gradient" # num_history_stopping_criterion_gradient_descent=10 algo_option = ProjectedGradientDescentBacktrackingOption( on_algo_eq_constraint=True, on_algo_ineq_constraint=True, mode_stopping_criterion_gradient_descent="sum_absolute_difference_projected_gradient", num_history_stopping_criterion_gradient_descent=10, ) estimator = LossMinimizationEstimator() # is_computation_time_required=True actual = estimator.calc_estimate( qst, empi_dists, loss, loss_option, algo, algo_option, is_computation_time_required=True, ) expected = [1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)] assert actual.estimated_qoperation.is_physical() npt.assert_almost_equal(actual.estimated_var, expected, decimal=15)

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null

0

1

0

1

0

null

0

7

939ab1fbb68d176522f954e28d7d1b5645071bd2

3,898

py

Python

agent/test/test_agent_unittest.py

rampopat/charje

3af178bd72800e339c45637356440780c3b0563a

[ "MIT" ]

1

2021-12-22T02:04:40.000Z

agent/test/test_agent_unittest.py

rampopat/charje

3af178bd72800e339c45637356440780c3b0563a

[ "MIT" ]

null

agent/test/test_agent_unittest.py

rampopat/charje

3af178bd72800e339c45637356440780c3b0563a

[ "MIT" ]

null

import unittest from unittest import TestCase, mock from agents.prediction_market_adapter import NUM_PREDICTIONS from agents.agent import Agent class TestAgent(TestCase): def test_place_bet(self): with mock.patch('agents.agent.PredictionMarketAdapter', autospec=True) \ as MockPredictionMarket: mock_prediction_market = MockPredictionMarket.return_value account = '42' agent = Agent(account, logging=False) predictions = [agent.DEFAULT_PREDICTION] * NUM_PREDICTIONS agent.place_bet() mock_prediction_market.place_bet.assert_called_with(account, Agent.DEFAULT_BETTING_AMOUNT, predictions) agent.place_bet() mock_prediction_market.place_bet.assert_called_with(account, Agent.DEFAULT_BETTING_AMOUNT, predictions) def test_rank_bet(self): with mock.patch('agents.agent.PredictionMarketAdapter', autospec=True) \ as MockPredictionMarket: mock_prediction_market = MockPredictionMarket.return_value account = '42' agent = Agent(account, logging=False) agent.prediction_history = [[75] * NUM_PREDICTIONS, None, None] agent.aggregate_history = [75] * NUM_PREDICTIONS agent.rank_bet() mock_prediction_market.rank.assert_called_once_with(account) def test_collect_reward(self): with mock.patch('agents.agent.PredictionMarketAdapter', autospec=True) \ as MockPredictionMarket: mock_prediction_market = MockPredictionMarket.return_value account = '42' agent = Agent(account, logging=False) agent.prediction_history = [[75] * NUM_PREDICTIONS, None, None] agent.aggregate_history = [75] * NUM_PREDICTIONS mock_prediction_market.get_winning_tier.return_value = 'top' agent.collect_reward() mock_prediction_market.transfer_reward.assert_called_once_with(account) def test_no_rank_if_no_bet(self): with mock.patch('agents.agent.PredictionMarketAdapter', autospec=True) \ as MockPredictionMarket: mock_prediction_market = MockPredictionMarket.return_value account = '42' agent = Agent(account, logging=False) agent.prediction_history = [None, None, None] agent.rank_bet() mock_prediction_market.rank.assert_not_called() def test_no_rank_if_lost(self): with mock.patch('agents.agent.PredictionMarketAdapter', autospec=True) \ as MockPredictionMarket: mock_prediction_market = MockPredictionMarket.return_value account = '42' agent = Agent(account, logging=False) agent.prediction_history = [[1000] * NUM_PREDICTIONS, [75] * NUM_PREDICTIONS, [75] * NUM_PREDICTIONS] agent.aggregate_history = [75] * NUM_PREDICTIONS agent.rank_bet() mock_prediction_market.rank.assert_not_called() def test_no_transfer_if_no_bet(self): with mock.patch('agents.agent.PredictionMarketAdapter', autospec=True) \ as MockPredictionMarket: mock_prediction_market = MockPredictionMarket.return_value account = '42' agent = Agent(account, logging=False) agent.prediction_history = [None, None, None] agent.collect_reward() mock_prediction_market.transfer_reward.assert_not_called() if __name__ == '__main__': unittest.main()

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3,898

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0

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false

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null

0

1

0

1

0

null

0

7

93f4cfeccc5304a876f560c6ae8ea2f84d5f2a91

62,017

py

Python

scripts/PortalFilesTester.py

vkozareva/single_cell_portal

d5c5d4f6de5cce97ddc30d2e80068155c11cd818

[ "BSD-3-Clause" ]

42

2016-07-27T22:25:24.000Z

2021-07-29T12:31:05.000Z

scripts/PortalFilesTester.py

vkozareva/single_cell_portal

d5c5d4f6de5cce97ddc30d2e80068155c11cd818

[ "BSD-3-Clause" ]

80

2016-08-16T18:39:22.000Z

2021-11-27T07:01:53.000Z

scripts/PortalFilesTester.py

vkozareva/single_cell_portal

d5c5d4f6de5cce97ddc30d2e80068155c11cd818

[ "BSD-3-Clause" ]

25

2017-02-22T13:27:12.000Z

2021-07-21T02:05:50.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os import PortalFiles import SortSparseMatrix import unittest __author__ = "Timothy Tickle" __copyright__ = "Copyright 2016" __credits__ = ["Timothy Tickle", "Brian Haas"] __license__ = "MIT" __maintainer__ = "Timothy Tickle" __email__ = "ttickle@broadinstitute.org" __status__ = "Development" def files_are_equivalent(file_path_1, file_path_2): """ Returns if the contents of two files are the same. Errors if the files are missing. * str_file_path_1 : String Path of the first of the files to compare to each other * str_file_path_2 : String Path of the second of files to compare to each other * Return : Boolean True indicates the contents of the files are exact. """ if not os.path.exists(file_path_1): raise IOError("Missing file: " + file_path_1) if not os.path.exists(file_path_2): raise IOError("Missing file: " + file_path_2) # Check if file lines counts are not the same. with open(file_path_1) as count_file_1: with open(file_path_2) as count_file_2: count_1 = sum(1 for line in count_file_1) count_2 = sum(1 for line in count_file_2) if count_1 != count_2: print("::Expected files to be the same size::") print("::File 1 line count::"+ str(count_1)) print("::File 2 line count::"+ str(count_2)) return(False) # Compare line by line and short circuit on a mismatched line. with open(file_path_1) as check_file_1: with open(file_path_2) as check_file_2: for line in check_file_1: compare_line = next(check_file_2) if not line == compare_line: print("::"+line+"::") print("::"+compare_line+"::") return(False) return(True) class CoordinatesFileTester(unittest.TestCase): """ Tests the Coordinates File object. """ def test_init(self): """ Make sure init can occur """ test_file = os.path.join("test_files", "coordinates.txt") PortalFiles.CoordinatesFile(test_file) self.assertTrue(True, 'Coordinates file can init.') def test_init_details(self): """ Check internals on init. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) has_error = False error_message = [] if test_file.file_has_error: has_error = True error_message.append("Started with a false error state.") if not test_file.delimiter == PortalFiles.c_DEFAULT_DELIM: has_error = True error_message.append("Delimiter was not set correctly.") if not test_file.demo_file == PortalFiles.c_COORDINATES_DEMO_LINK: has_error = True error_message.append("Demo link was not set correctly.") if(not ".".join(test_file.expected_header) == ".".join(PortalFiles.c_COORDINATES_HEADER)): has_error = True error_message.append("Started with a wrong expected header.") if not test_file.expected_header_length == PortalFiles.c_COORDINATES_HEADER_LENGTH: has_error = True error_message.append("Started with a false error state.") if not test_file.file_name == test_file_name: has_error = True error_message.append("File name incorrect.") if not test_file.header_length == 6: has_error = True error_message.append("File length incorrect.") if not test_file.line_number == 1: has_error = True error_message.append("Wrong initial file number.") if test_file.cell_names is None: has_error = True error_message.append("Incorrect cell names on init.") error_message.append(str(test_file)) self.assertTrue(not has_error, error_message) def test_init_str(self): """ Check str on init. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) actual_str = str(test_file) correct_str = "; ".join(["Error:"+str(False), "Delim:"+PortalFiles.c_DEFAULT_DELIM, "Demo:"+PortalFiles.c_COORDINATES_DEMO_LINK, "ExpectedHeader:"+",".join(PortalFiles.c_COORDINATES_HEADER), "ExpectedHeaderLen:"+str(PortalFiles.c_COORDINATES_HEADER_LENGTH), "FileName:"+test_file_name, "Header:"+",".join(["NAME","X","Y","Z","Category","Intensity"]), "HeaderLen:"+str(6), "LineNumber:"+str(1), "CellNames:"+str(test_file.cell_names)]) self.assertTrue(correct_str == actual_str, "Expected="+correct_str+"\nReceived="+actual_str) def test_check_header_correct(self): """ Check the header is called correct when it is. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_header_incorrect(self): """ Check the header is called incorrect when position 00 is incorrect. """ test_file_name = os.path.join("test_files", "coordinates_bad_header.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_header_for_duplicates(self): """ Check the header is called incorrect when duplicates exist. """ test_file_name = os.path.join("test_files", "coordinates_bad_header_duplicates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_correct(self): """ Check the body is called correct when it is. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_body_incorrect_type(self): """ Check the body is called incorrect when it is incorrect. This file has a bad entry, wrong type. """ test_file_name = os.path.join("test_files", "coordinates_bad_body.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_incorrect_type_2(self): """ Check the body is called incorrect when it is incorrect. This file has a missing entry. """ test_file_name = os.path.join("test_files", "coordinates_bad_body_2.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_get_duplicates_no_duplicates(self): """ Get duplicates from file, no duplicates. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) duplicates = test_file.get_duplicates(test_file.cell_names) self.assertTrue(len(duplicates) == 0, "Should be empty with no duplicates.") def test_get_duplicates_with_duplicates(self): """ Get duplicates from file, with 2 duplicates. """ test_file_name = os.path.join("test_files", "coordinates_duplicates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) duplicates = test_file.get_duplicates(test_file.cell_names) self.assertTrue(len(duplicates) == 2, "Should have 2 duplicates.") def test_check_duplicate_cell_names_no_duplicates(self): """ Check duplicates cell names in file, no duplicates. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) original_len = len(test_file.cell_names) test_file.check_duplicate_cell_names() self.assertTrue(not test_file.file_has_error, " ".join(["There are no duplicates,", "no error detected."])) def test_check_duplicate_cell_names_with_duplicates(self): """ Check duplicates cell names in file, wirh 2 duplicates. """ test_file_name = os.path.join("test_files", "coordinates_duplicates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) original_len = len(test_file.cell_names) test_file.check_duplicate_cell_names() self.assertTrue(test_file.file_has_error, " ".join(["There are 2 duplicates,", "error detected."])) def test_check_correct(self): """ Check the file is called correct when it is. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_correct_bad_header(self): """ Check the file is called incorrect when a header is bad. """ test_file_name = os.path.join("test_files", "coordinates_bad_header.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_correct_bad_body(self): """ Check the file is called incorrect when the body is bad. """ test_file_name = os.path.join("test_files", "coordinates_bad_body.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_correct_bad_body_duplicates(self): """ Check the file is called incorrect when the body has duplicates. """ test_file_name = os.path.join("test_files", "coordinates_duplicates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_compare_cell_names_for_identical(self): """ Check comparing cell names for two identical files. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file_name_2 = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) test_file_2 = PortalFiles.CoordinatesFile(test_file_name_2) self.assertTrue((not test_file.file_has_error) and (not test_file_2.file_has_error), "Did not start test with a no error state.") compare_error = test_file.compare_cell_names(test_file_2) self.assertTrue(not compare_error, "Should not have reached an error state.") def test_compare_cell_names_for_correct_files(self): """ Check comparing cell names for two files with identical cell names. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file_name_2 = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) test_file_2 = PortalFiles.MetadataFile(test_file_name_2) self.assertTrue((not test_file.file_has_error) and (not test_file_2.file_has_error), "Did not start test with a no error state.") compare_error = test_file.compare_cell_names(test_file_2) self.assertTrue(not compare_error, "Should not have reached an error state.") def test_compare_cell_names_for_incorrect_files(self): """ Check comparing cell names for two files, this one with duplicate names. """ test_file_name = os.path.join("test_files", "coordinates_duplicates.txt") test_file_name_2 = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) test_file_2 = PortalFiles.MetadataFile(test_file_name_2) self.assertTrue((not test_file.file_has_error) and (not test_file_2.file_has_error), "Did not start test with a no error state.") compare_error = test_file.compare_cell_names(test_file_2) self.assertTrue(compare_error, "Should have reached an error state.") def test_compare_cell_names_for_incorrect_compare_files(self): """ Check comparing cell names for two files, the comparing file with duplicate names. """ test_file_name = os.path.join("test_files", "coordinates.txt") test_file_name_2 = os.path.join("test_files", "metadata_duplicates.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) test_file_2 = PortalFiles.MetadataFile(test_file_name_2) self.assertTrue((not test_file.file_has_error) and (not test_file_2.file_has_error), "Did not start test with a no error state.") compare_error = test_file.compare_cell_names(test_file_2) self.assertTrue(compare_error, "Should have reached an error state.") def test_update_cell_names_from_none(self): """ Update cell names from a None value. """ test_file = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file) names = ",".join(["CELL_0001","CELL_00010","CELL_00011", "CELL_00012","CELL_00013","CELL_00014", "CELL_00015","CELL_0002","CELL_0003", "CELL_0004","CELL_0005","CELL_0006", "CELL_0007","CELL_0008","CELL_0009"]) test_file.cell_names = None test_file.update_cell_names() names_after = ",".join(sorted(test_file.cell_names)) self.assertTrue(names == names_after, "Updated cell names are correct.") def test_update_cell_names_from_init(self): """ Update cell names from an init value. """ test_file = os.path.join("test_files", "coordinates.txt") test_file = PortalFiles.CoordinatesFile(test_file) names = ",".join(["CELL_0001","CELL_00010","CELL_00011", "CELL_00012","CELL_00013","CELL_00014", "CELL_00015","CELL_0002","CELL_0003", "CELL_0004","CELL_0005","CELL_0006", "CELL_0007","CELL_0008","CELL_0009"]) test_file.update_cell_names() test_file.update_cell_names() test_file.update_cell_names() names_after = ",".join(sorted(test_file.cell_names)) self.assertTrue(names == names_after, "Updated cell names are correct.") def test_subset_cells_one(self): """ Test subset cells to 1 cell. """ test_file_name = os.path.join("test_files", "coordinates.txt") correct_file = os.path.join("test_files", "coordinates_subset_1_correct.txt") keep_cells = ["CELL_0009"] test_file = PortalFiles.CoordinatesFile(test_file_name) subset_file_name = test_file.subset_cells(keep_cells) # Test files pass_test = files_are_equivalent(file_path_1=subset_file_name, file_path_2=correct_file) # Remove the test files if(os.path.exists(subset_file_name)): os.remove(subset_file_name) self.assertTrue(pass_test, "Can not subset file.") def test_subset_cells_many(self): """ Test subset cells to 3 cell. """ test_file_name = os.path.join("test_files", "coordinates.txt") correct_file = os.path.join("test_files", "coordinates_subset_3_correct.txt") keep_cells = ["CELL_0009","CELL_0002","CELL_0005"] test_file = PortalFiles.CoordinatesFile(test_file_name) subset_file_name = test_file.subset_cells(keep_cells) # Test files pass_test = files_are_equivalent(file_path_1=subset_file_name, file_path_2=correct_file) # Remove the test files if(os.path.exists(subset_file_name)): os.remove(subset_file_name) self.assertTrue(pass_test, "Can not subset file.") def test_deidentify_cells(self): """ Test deidentify file. """ test_file_name = os.path.join("test_files", "coordinates.txt") correct_file = os.path.join("test_files", "coordinates_deidentifed_correct.txt") correct_map = os.path.join("test_files", "coordinates_mapping_correct.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) deid_return = test_file.deidentify_cell_names() deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] # Test files pass_test = files_are_equivalent(file_path_1=deid_file_name, file_path_2=correct_file) pass_test = pass_test and files_are_equivalent(file_path_1=map_file_name, file_path_2=correct_map) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(pass_test, "Can not deidentify file.") def test_deidentify_cells_check_created_names(self): """ Test deidentify file. Check to make sure cell names are created and passed. """ test_file_name = os.path.join("test_files", "coordinates.txt") cell_mappings_truth = ["CELL_0001:cell_0", "CELL_0002:cell_1", "CELL_0003:cell_2", "CELL_0004:cell_3", "CELL_0005:cell_4", "CELL_0006:cell_5", "CELL_0007:cell_6", "CELL_0008:cell_7", "CELL_0009:cell_8", "CELL_00010:cell_9", "CELL_00011:cell_10", "CELL_00012:cell_11", "CELL_00013:cell_12", "CELL_00014:cell_13", "CELL_00015:cell_14"] cell_mappings_truth = ",".join(sorted(cell_mappings_truth)) test_file = PortalFiles.CoordinatesFile(test_file_name) deid_return = test_file.deidentify_cell_names() deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] deid_file_mapping = deid_return["mapping"] # Test files cell_mappings = [] for sorted_key in deid_file_mapping.keys(): cell_mappings.append(str(sorted_key)+":"+str(deid_file_mapping[sorted_key])) cell_mappings = ",".join(sorted(cell_mappings)) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(cell_mappings == cell_mappings_truth, "Returned mappings were not expected.="+cell_mappings) def test_deidentify_cells_precreated_names(self): """ Test deidentify file with cell already made. """ test_file_name = os.path.join("test_files", "coordinates.txt") correct_file = os.path.join("test_files", "coordinates_deidentifed_precreated_correct.txt") correct_map = os.path.join("test_files", "coordinates_mapping_precreated_correct.txt") test_file = PortalFiles.CoordinatesFile(test_file_name) names = dict(zip(test_file.cell_names, [str(i) for i in range(len(test_file.cell_names))])) deid_return = test_file.deidentify_cell_names(cell_names_change=names) deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] # Test files pass_test = files_are_equivalent(file_path_1=deid_file_name, file_path_2=correct_file) pass_test = pass_test and files_are_equivalent(file_path_1=map_file_name, file_path_2=correct_map) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(pass_test, "Can not deidentify file.") class ExpressionFileTester(unittest.TestCase): """ Tests the expression file object. """ def test_init(self): """ Make sure init can occur. """ test_file = os.path.join("test_files", "expression.txt") PortalFiles.ExpressionFile(test_file) self.assertTrue(True, "Expression File can init.") def test_check_header_correct(self): """ Check the header is called correct when it is. """ test_file_name = os.path.join("test_files", "expression.txt") test_file = PortalFiles.ExpressionFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_header_incorrect_00(self): """ Check the header is called incorrect when element 00 is wrong. """ test_file_name = os.path.join("test_files", "expression_bad_header.txt") test_file = PortalFiles.ExpressionFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_header_incorrect_element_count(self): """ Check the header is called incorrect when 00 is blank. """ test_file_name = os.path.join("test_files", "expression_bad_header_2.txt") test_file = PortalFiles.ExpressionFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_correct(self): """ Check the body is called correct when it is. """ test_file_name = os.path.join("test_files", "expression.txt") test_file = PortalFiles.ExpressionFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_body_incorrect_1(self): """ Check the body is called incorrect when it has a missing element. """ test_file_name = os.path.join("test_files", "expression_bad_body_1.txt") test_file = PortalFiles.ExpressionFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_incorrect_2(self): """ Check the body is called incorrect when it has a wrong type element. """ test_file_name = os.path.join("test_files", "expression_bad_body_2.txt") test_file = PortalFiles.ExpressionFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_update_cell_names_from_none(self): """ Update cell names from a None value. """ test_file = os.path.join("test_files", "expression.txt") test_file = PortalFiles.ExpressionFile(test_file) names = ",".join(sorted(test_file.cell_names)) test_file.cell_names = None test_file.update_cell_names() names_after = ",".join(sorted(test_file.cell_names)) self.assertTrue(names == names_after, "Updated cell names are correct.") def test_update_cell_names_from_init(self): """ Update cell names from an init value. """ test_file = os.path.join("test_files", "expression.txt") test_file = PortalFiles.ExpressionFile(test_file) names = "".join(["CELL_0001,CELL_00010,CELL_00011,", "CELL_00012,CELL_00013,CELL_00014,", "CELL_00015,CELL_0002,CELL_0003,", "CELL_0004,CELL_0005,CELL_0006,", "CELL_0007,CELL_0008,CELL_0009"]) test_file.update_cell_names() test_file.update_cell_names() test_file.update_cell_names() names_after = ",".join(sorted(test_file.cell_names)) self.assertTrue(names == names_after, "Updated cell names are correct.") def test_deidentify_cells(self): """ Test deidentify file. """ test_file_name = os.path.join("test_files", "expression.txt") correct_file = os.path.join("test_files", "expression_deidentifed_correct.txt") correct_map = os.path.join("test_files", "expression_mapping_correct.txt") test_file = PortalFiles.ExpressionFile(test_file_name) deid_return = test_file.deidentify_cell_names() deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] # Test files pass_test = files_are_equivalent(file_path_1=deid_file_name, file_path_2=correct_file) pass_test = pass_test and files_are_equivalent(file_path_1=map_file_name, file_path_2=correct_map) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(pass_test, "Can not deidentify file.") def test_deidentify_cells_check_created_names(self): """ Test deidentify file. Check to make sure cell names are created and passed. """ test_file_name = os.path.join("test_files", "expression.txt") deid_file = os.path.join("test_files", "expression"+PortalFiles.c_DEID_POSTFIX+".txt") map_file = os.path.join("test_files", "expression"+PortalFiles.c_MAP_POSTFIX+".txt") cell_mappings_truth = sorted(["CELL_0001:cell_0", "CELL_0002:cell_1", "CELL_0003:cell_2", "CELL_0004:cell_3", "CELL_0005:cell_4", "CELL_0006:cell_5", "CELL_0007:cell_6", "CELL_0008:cell_7", "CELL_0009:cell_8", "CELL_00010:cell_9", "CELL_00011:cell_10", "CELL_00012:cell_11", "CELL_00013:cell_12", "CELL_00014:cell_13", "CELL_00015:cell_14"]) cell_mappings_truth = ",".join(cell_mappings_truth) test_file = PortalFiles.ExpressionFile(test_file_name) deid_return = test_file.deidentify_cell_names() deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] deid_file_mapping = deid_return["mapping"] # Test files cell_mappings = [] for sorted_key in deid_file_mapping.keys(): cell_mappings.append(str(sorted_key)+":"+str(deid_file_mapping[sorted_key])) cell_mappings = ",".join(sorted(cell_mappings)) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(cell_mappings == cell_mappings_truth, "Returned mappings were not expected.="+cell_mappings) def test_deidentify_cells_precreated_names(self): """ Test deidentify file with cell already made. """ test_file_name = os.path.join("test_files", "expression.txt") correct_file = os.path.join("test_files", "expression_deidentifed_precreated_correct.txt") correct_map = os.path.join("test_files", "expression_mapping_precreated_correct.txt") test_file = PortalFiles.ExpressionFile(test_file_name) names = dict(zip(test_file.cell_names, [str(i) for i in range(len(test_file.cell_names))])) deid_return = test_file.deidentify_cell_names(cell_names_change=names) deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] # Test files pass_test = files_are_equivalent(file_path_1=deid_file_name, file_path_2=correct_file) pass_test = pass_test and files_are_equivalent(file_path_1=map_file_name, file_path_2=correct_map) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(pass_test, "Can not deidentify file.") def test_get_gene_names(self): """ Confirm that genes names are returned correctly from am good file. """ expected_gene_names = ["Itm2a", "Sergef", "Chil5", "Fam109a", "Dhx9", "Ssu72", "Olfr1018", "Fam71e2", "Eif2b2", "1700061E18Rik", "Mks1", "Gm12000", "Hebp2", "Gm14444", "Vps28", "Setd6", "Gstm2", "Spn-ps", "Psma4"] test_file_name = os.path.join("test_files", "expression.txt") test_file = PortalFiles.ExpressionFile(test_file_name) str_truth = ",".join(sorted(expected_gene_names)) str_received = ",".join(sorted(test_file.get_gene_names())) self.assertTrue(str_truth == str_received, "Did not receive the expected gene names.") def test_subset_cells_one(self): """ Test subset cells to 1 cell. """ test_file_name = os.path.join("test_files", "expression.txt") correct_file = os.path.join("test_files", "expression_subset_1_correct.txt") keep_cells = ["CELL_0002"] test_file = PortalFiles.ExpressionFile(test_file_name) subset_file_name = test_file.subset_cells(keep_cells) # Test files pass_test = files_are_equivalent(file_path_1=subset_file_name, file_path_2=correct_file) # Remove the test files if(os.path.exists(subset_file_name)): os.remove(subset_file_name) self.assertTrue(pass_test, "Can not subset file.") def test_subset_cells_many(self): """ Test subset cells to 4 cell. """ test_file_name = os.path.join("test_files", "expression.txt") correct_file = os.path.join("test_files", "expression_subset_4_correct.txt") keep_cells = ["CELL_0002","CELL_0004","CELL_0006","CELL_0008"] test_file = PortalFiles.ExpressionFile(test_file_name) subset_file_name = test_file.subset_cells(keep_cells) # Test files pass_test = files_are_equivalent(file_path_1=subset_file_name, file_path_2=correct_file) # Remove the test files if(os.path.exists(subset_file_name)): os.remove(subset_file_name) self.assertTrue(pass_test, "Can not subset file.") class MetadataFileTester(unittest.TestCase): """ Tests the Metadata File object. """ def test_init(self): """ Make sure init can occur """ test_file = os.path.join("test_files", "metadata.txt") PortalFiles.MetadataFile(test_file) self.assertTrue(True, 'Metadata file can init.') def test_check_header_correct(self): """ Check the header is called correct when it is. """ test_file_name = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_header_incorrect_00(self): """ Check the header is called incorrect when element 00 is wrong. """ test_file_name = os.path.join("test_files", "metadata_bad_header.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_header_incorrect_element_count(self): """ Check the header is called incorrect when 00 is blank. """ test_file_name = os.path.join("test_files", "metadata_bad_header_2.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_header_duplicate_id(self): """ Check the header is called incorrect when a column is duplicate. """ test_file_name = os.path.join("test_files", "metadata_bad_header_dup.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_header_no_data_column(self): """ Check the header is called incorrect when there are only id columns. """ test_file_name = os.path.join("test_files", "metadata_bad_no_data.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_type_row_correct(self): """ Check the type row is called correct when it is. """ test_file_name = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_type_row() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_type_row_bad_type(self): """ Check the type row is called error when one of the types are incorrect. """ test_file_name = os.path.join("test_files", "metadata_bad_type.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_type_row() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_type_row_bad_type_id(self): """ Check the type row is called error when the type id is not correct """ test_file_name = os.path.join("test_files", "metadata_bad_type_id.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_type_row() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_type_row_bad_row_length(self): """ Check the type row is called error when the type row is a wrong length """ test_file_name = os.path.join("test_files", "metadata_bad_type_row_length.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_type_row() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_correct(self): """ Check the body is called correct when it is """ test_file_name = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(not test_file.file_has_error, "Should have not reached an error state.") def test_check_body_incorrect_1(self): """ Check the body is called incorrect when it has a missing element. """ test_file_name = os.path.join("test_files", "metadata_bad_body_1.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_incorrect_2(self): """ Check the body is called incorrect when it has a wrong type element. """ test_file_name = os.path.join("test_files", "metadata_bad_body_2.txt") test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_update_cell_names_from_none(self): """ Update cell names from a None value. """ test_file = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.MetadataFile(test_file) names = ",".join(sorted(test_file.cell_names)) test_file.cell_names = None test_file.update_cell_names() names_after = ",".join(sorted(test_file.cell_names)) self.assertTrue(names == names_after, "Updated cell names are correct.") def test_update_cell_names_from_init(self): """ Update cell names from an init value. """ test_file = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.MetadataFile(test_file) names = "".join(["CELL_0001,CELL_00010,CELL_00011,", "CELL_00012,CELL_00013,CELL_00014,", "CELL_00015,CELL_0002,CELL_0003,", "CELL_0004,CELL_0005,CELL_0006,", "CELL_0007,CELL_0008,CELL_0009"]) test_file.update_cell_names() test_file.update_cell_names() test_file.update_cell_names() names_after = ",".join(sorted(test_file.cell_names)) self.assertTrue(names == names_after, "Updated cell names are correct.") def test_deidentify_cells(self): """ Test deidentify file. """ test_file_name = os.path.join("test_files", "metadata.txt") correct_file = os.path.join("test_files", "metadata_deidentifed_correct.txt") correct_map = os.path.join("test_files", "metadata_mapping_correct.txt") test_file = PortalFiles.MetadataFile(test_file_name) deid_return = test_file.deidentify_cell_names() deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] # Test files pass_test = files_are_equivalent(file_path_1=deid_file_name, file_path_2=correct_file) pass_test = pass_test and files_are_equivalent(file_path_1=map_file_name, file_path_2=correct_map) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(pass_test, "Can not deidentify file.") def test_deidentify_cells_check_created_names(self): """ Test deidentify file. Check to make sure cell names are created and passed. """ test_file_name = os.path.join("test_files", "metadata.txt") cell_mappings_truth = sorted(["CELL_0001:cell_0", "CELL_0002:cell_1", "CELL_0003:cell_2", "CELL_0004:cell_3", "CELL_0005:cell_4", "CELL_0006:cell_5", "CELL_0007:cell_6", "CELL_0008:cell_7", "CELL_0009:cell_8", "CELL_00010:cell_9", "CELL_00011:cell_10", "CELL_00012:cell_11", "CELL_00013:cell_12", "CELL_00014:cell_13", "CELL_00015:cell_14"]) cell_mappings_truth = ",".join(cell_mappings_truth) test_file = PortalFiles.MetadataFile(test_file_name) deid_return = test_file.deidentify_cell_names() deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] deid_file_mapping = deid_return["mapping"] # Test files cell_mappings = [] for sorted_key in deid_file_mapping.keys(): cell_mappings.append(str(sorted_key)+":"+str(deid_file_mapping[sorted_key])) cell_mappings = ",".join(sorted(cell_mappings)) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(cell_mappings == cell_mappings_truth, "Returned mappings were not expected.="+cell_mappings) def test_deidentify_cells_precreated_names(self): """ Test deidentify file with cell already made. """ test_file_name = os.path.join("test_files", "metadata.txt") correct_file = os.path.join("test_files", "metadata_deidentifed_precreated_correct.txt") correct_map = os.path.join("test_files", "metadata_mapping_precreated_correct.txt") test_file = PortalFiles.MetadataFile(test_file_name) names = dict(zip(test_file.cell_names, [str(i) for i in range(len(test_file.cell_names))])) deid_return = test_file.deidentify_cell_names(cell_names_change=names) deid_file_name = deid_return["name"] map_file_name = deid_return["mapping_file"] # Test files pass_test = files_are_equivalent(file_path_1=deid_file_name, file_path_2=correct_file) pass_test = pass_test and files_are_equivalent(file_path_1=map_file_name, file_path_2=correct_map) # Remove the test files if(os.path.exists(deid_file_name)): os.remove(deid_file_name) if(os.path.exists(map_file_name)): os.remove(map_file_name) self.assertTrue(pass_test, "Can not deidentify file.") def test_get_labels_correct(self): """ Check to make sure labels are returned correctly with a good file """ test_file_name = os.path.join("test_files", "metadata.txt") truth = ["CLST_A", "CLST_B", "CLST_C", "CLST_A_1", "CLST_A_2", "CLST_B_1", "CLST_B_2", "CLST_C_1", "CLST_C_2"] test_file = PortalFiles.MetadataFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") received_labels = test_file.get_labels() truth_str = ",".join(sorted(truth)) received_str = ",".join(sorted(received_labels)) self.assertTrue(truth_str == received_str, "Did not receive the expected labels.") def test_subset_cells_one(self): """ Test subset cells to 1 cell. """ test_file_name = os.path.join("test_files", "metadata.txt") correct_file = os.path.join("test_files", "metadata_subset_1_correct.txt") keep_cells = ["CELL_0001"] test_file = PortalFiles.MetadataFile(test_file_name) subset_file_name = test_file.subset_cells(keep_cells) # Test files pass_test = files_are_equivalent(file_path_1=subset_file_name, file_path_2=correct_file) # Remove the test files if(os.path.exists(subset_file_name)): os.remove(subset_file_name) self.assertTrue(pass_test, "Can not subset file.") def test_subset_cells_many(self): """ Test subset cells to 2 cell. """ test_file_name = os.path.join("test_files", "metadata.txt") correct_file = os.path.join("test_files", "metadata_subset_2_correct.txt") keep_cells = ["CELL_0001","CELL_00015"] test_file = PortalFiles.MetadataFile(test_file_name) subset_file_name = test_file.subset_cells(keep_cells) # Test files pass_test = files_are_equivalent(file_path_1=subset_file_name, file_path_2=correct_file) # Remove the test files if(os.path.exists(subset_file_name)): os.remove(subset_file_name) self.assertTrue(pass_test, "Can not subset file.") class GeneListFileTester(unittest.TestCase): """ Tests the Gene list file object. """ def test_get_gene_names(self): """ Confirm that genes names are returned correctly from am good file. """ expected_gene_names = ["Itm2a", "Sergef", "Chil5", "Fam109a", "Dhx9", "Ssu72", "Olfr1018", "Fam71e2", "Eif2b2", "1700061E18Rik", "Mks1", "Gm12000", "Hebp2", "Gm14444", "Vps28", "Setd6", "Gstm2", "Spn-ps", "Psma4"] test_file_name = os.path.join("test_files", "gene_list.txt") test_file = PortalFiles.GeneListFile(test_file_name) str_truth = ",".join(sorted(expected_gene_names)) str_received = ",".join(sorted(test_file.get_gene_names())) self.assertTrue(str_truth == str_received, "Did not receive the expected gene names.") def test_check_header_correct(self): """ Check the header is called correct when it is. """ test_file_name = os.path.join("test_files", "gene_list.txt") test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_check_header_bad_00(self): """ Check the header is called error when the 00 element is wrong. """ test_file_name = os.path.join("test_files", "gene_list_bad_header.txt") test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_header() self.assertTrue(test_file.file_has_error, "Should not have reached an error state.") def test_check_body_correct(self): """ Check the body is called correct when it is correct. """ test_file_name = os.path.join("test_files", "gene_list.txt") test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(not test_file.file_has_error, "Should have reached an error state.") def test_check_body_incorrect_type(self): """ Check the body is called correct when it is correct. """ test_file_name = os.path.join("test_files", "gene_list_bad_type.txt") test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_incorrect_value_empty(self): """ Check the body is called correct when one value is empty. """ test_file_name = os.path.join("test_files", "gene_list_bad_empty_value.txt") test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_check_body_incorrect_row_length(self): """ Check the body is called correct when one row is too long. """ test_file_name = os.path.join("test_files", "gene_list_bad_row_length.txt") test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.check_body() self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_deidentify_cells(self): """ Test deidentify file. """ test_file_name = os.path.join("test_files", "gene_list.txt") test_file = PortalFiles.GeneListFile(test_file_name) deid_result = test_file.deidentify_cell_names() self.assertTrue((deid_result["name"] is None) and (deid_result["mapping"] is None), "Should not have attempted to deid file, there are no cell names.") def test_compare_gene_names_for_good(self): """ Check compare gene names for a good file. """ test_file_name = os.path.join("test_files", "gene_list.txt") test_expression_file_name = os.path.join("test_files", "expression.txt") test_file = PortalFiles.GeneListFile(test_file_name) expr_file = PortalFiles.ExpressionFile(test_expression_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.compare_gene_names(expr_file) self.assertTrue(not test_file.file_has_error, "Should have reached an error state.") def test_compare_gene_names_for_good_2(self): """ Check compare gene names for a good file, expression file having more genes then gene list. """ test_file_name = os.path.join("test_files", "gene_list_minus_one.txt") test_expression_file_name = os.path.join("test_files", "expression.txt") test_file = PortalFiles.GeneListFile(test_file_name) expr_file = PortalFiles.ExpressionFile(test_expression_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.compare_gene_names(expr_file) self.assertTrue(not test_file.file_has_error, "Should have reached an error state.") def test_compare_gene_names_for_none_expression(self): """ Check compare gene names for a none expression file. """ test_file_name = os.path.join("test_files", "gene_list.txt") test_file = PortalFiles.GeneListFile(test_file_name) expr_file = None if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.compare_gene_names(expr_file) self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_compare_gene_names_for_missing_in_expression(self): """ Check compare gene names for an expression file missing a gene in the gene list. """ test_file_name = os.path.join("test_files", "gene_list_plus_one.txt") test_expression_file_name = os.path.join("test_files", "expression.txt") test_file = PortalFiles.GeneListFile(test_file_name) expr_file = PortalFiles.ExpressionFile(test_expression_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.compare_gene_names(expr_file) self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_compare_cluster_labels_for_good(self): """ Check compare cluster labels for good files """ test_file_name = os.path.join("test_files", "gene_list.txt") test_metadata_file_name = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.GeneListFile(test_file_name) meta_file = PortalFiles.MetadataFile(test_metadata_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.compare_cluster_labels(meta_file) self.assertTrue(not test_file.file_has_error, "Should not have reached an error state.") def test_compare_cluster_labels_for_missing_label(self): """ Check compare cluster labels for a missing label that is only found in the gene list file. """ test_file_name = os.path.join("test_files", "gene_list_additional_label.txt") test_metadata_file_name = os.path.join("test_files", "metadata.txt") test_file = PortalFiles.GeneListFile(test_file_name) meta_file = PortalFiles.MetadataFile(test_metadata_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") test_file.compare_cluster_labels(meta_file) self.assertTrue(test_file.file_has_error, "Should have reached an error state.") def test_get_labels_correct(self): """ Check to make sure labels are returned correctly with a good file """ test_file_name = os.path.join("test_files", "gene_list.txt") truth = ["CLST_B", "CLST_C", "CLST_A"] test_file = PortalFiles.GeneListFile(test_file_name) if test_file.file_has_error: self.assertTrue(False, "Did not start test with a no error state.") received_labels = test_file.get_labels() truth_str = ",".join(sorted(truth)) received_str = ",".join(sorted(received_labels)) self.assertTrue(truth_str == received_str, "Did not receive the expected labels.") class SortSparseMatrixTester(unittest.TestCase): """ Tests the Sparse Matrix Sorting function. """ def test_check_sort_sparse_matrix(self): """ Check that the sparse matrix sorting function outputs a correctely sorted file. """ SortSparseMatrix.sort_sparse_matrix(os.path.join("test_files", "test_sparse_matrix.mtx"), "sorted_test.mtx") self.assertTrue(files_are_equivalent(os.path.join("test_files", "gene_sorted-test_sparse_matrix.mtx"), "sorted_test.mtx"), "Generated file and validation file are not equal.") # Creates a suite of tests def suite(): loader = unittest.TestLoader() tests = loader.loadTestsFromTestCase(CoordinatesFileTester) tests.addTests(loader.loadTestsFromTestCase(ExpressionFileTester)) tests.addTests(loader.loadTestsFromTestCase(MetadataFileTester)) tests.addTests(loader.loadTestsFromTestCase(GeneListFileTester)) tests.addTests(loader.loadTestsFromTestCase(SortSparseMatrixTester)) return(tests)

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9e1a7658136e2fad1bc312db9620bc87bb1fdb18

5,384

py

Python

tests/itach.py

rlsgit/pytach

8ee7ee427ec4bf6f78992adce0cfdfb9f3e94509

[ "MIT" ]

null

tests/itach.py

rlsgit/pytach

8ee7ee427ec4bf6f78992adce0cfdfb9f3e94509

[ "MIT" ]

null

tests/itach.py

rlsgit/pytach

8ee7ee427ec4bf6f78992adce0cfdfb9f3e94509

[ "MIT" ]

null

""" The itach driver test function. """ import logging import logging.config import time from pytach import iTach from pytach import const logging.config.fileConfig("logging.ini",disable_existing_loggers=False) _LOGGER = logging.getLogger(__name__) _LOGGER.info("iTach test starting...") configIR = { "type": const.TYPE_IR, "name": "ip2ir01", "host": "192.168.1.32", # "devices": [ # { # "name": "proj", # "port": 2, # "commands": [ # { # "name": "on", # "data": "0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0040 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A" # }, # { # "name": "off", # "data": "0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0015 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A" # } # ] # } # ] } itach = iTach.createHost(configIR) _LOGGER.info("iTach created: %s", itach.toString()) itach.addDevice({ const.CONF_NAME: "projector", const.CONF_PORT: "2" }) _LOGGER.info("iTach with projector: %s", itach.toString()) itach.removeDevice("projector") _LOGGER.info("iTach without projector: %s", itach.toString()) projector = itach.addDevice({ const.CONF_NAME: "projector", const.CONF_PORT: "2" }) _LOGGER.info("iTach with projector: %s", itach.toString()) projector.addCommand({ const.CONF_NAME: "blank", const.CONF_DATA: "0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0040 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A" }) response = projector.sendCommand("blank") _LOGGER.info("projector on returned: %s", response) # print(prontoToGC(1,1,"0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0040 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A")) # print(prontoToGC(1,1,"0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0015 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A")) # configIR = { # "type": TYPE_IR, # "name": "ip2ir01", # "host": "192.168.1.32", # "devices": [ # { # "name": "proj", # "port": 2, # "commands": [ # { # "name": "on", # "data": "0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0040 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A" # }, # { # "name": "off", # "data": "0000 006C 0000 0022 0157 00AB 0015 0040 0015 0040 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0015 0040 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0040 0015 0015 0015 0015 0015 0015 0015 0040 0015 0015 0015 0015 0015 0040 0015 0015 0015 0040 0015 0040 0015 0040 0015 0015 0015 0040 0015 0040 0015 0015 0015 060A" # } # ] # } # ] # } # itachIR = iTach.createHost(configIR) # print(itachIR.getDevice("proj").sendData("sendir,1:1,1,38000,1,1,343,171,21,64,21,64,21,21,21,21,21,21,21,21,21,21,21,64,21,64,21,21,21,64,21,21,21,64,21,21,21,64,21,21,21,21,21,21,21,21,21,21,21,64,21,21,21,21,21,64,21,64,21,64,21,64,21,64,21,21,21,64,21,64,21,21,21,1546")) # configSL = { # "type": TYPE_SL, # "name": "ip2sl01", # "host": "192.168.1.31", # "devices": [ # { # "name": "mca", # "port": 1, # "commands": [ # { # "name": "zone1_status", # "data": "?11\r" # }, # { # "name": "zone1_on", # "data": "<11PR01\r" # }, # { # "name": "zone1_off", # "data": "<11PR00\r" # } # ] # } # ] # } # itachSL = iTach.createHost(configSL) #print(itachSL.getDevice("mca").sendCommand("zone1_status")) #print(itachSL.getDevice("mca").sendCommand("zone1_on")) #time.sleep(5) #print(itachSL.getDevice("mca").sendCommand("zone1_off")) #print(itachSL.getDevice("mca").sendCommand("zone1_status"))

47.646018

431

0.651932

830

5,384

4.193976

0.107229

0.546969

0.606722

0.523987

0.786843

0.775065

0.752083

0.724504

0.711864

0

0.534742

0.235513

5,384

112

432

48.071429

0.310982

0.698366

0

0.083333

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0.041667

0.404612

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1

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false

0

0.208333

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0.208333

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0

1

0

1

0

1

0

null

0

10

f564b74111b159142fbbba3b27d775d0df390d39

11,560

py

Python

script/swift_sqlite_helper.py

NightDrivers/PythonStudy

25c4eafc0c68d0faac06af0e27622c74ff9dac22

[ "MIT" ]

null

script/swift_sqlite_helper.py

NightDrivers/PythonStudy

25c4eafc0c68d0faac06af0e27622c74ff9dac22

[ "MIT" ]

null

script/swift_sqlite_helper.py

NightDrivers/PythonStudy

25c4eafc0c68d0faac06af0e27622c74ff9dac22

[ "MIT" ]

null

# -*- coding: utf-8 -*- import re import argparse def parse_argv(): __parse = argparse.ArgumentParser() __parse.add_argument("file", help="文件名") __parse.add_argument("--tab-identation-width", type=int, default=4, help="代码Tab缩进宽度") __parse.add_argument("--connection", default="DBConnection", help="DBConnection实例名称") return __parse.parse_args() if __name__ == "__main__": argv = parse_argv() source_file_path = argv.file source_file = open(source_file_path) tab_identation_width = argv.tab_identation_width connection = argv.connection print(tab_identation_width) file_prefix_text = "" class_name = "" super_class_name = "" identation_component_text = " " * tab_identation_width variables = [] variable_declaration_codes = [] for item in source_file: if len(class_name) == 0: match = re.match("class .+: .+{\n", item, re.RegexFlag.DOTALL) file_prefix_text += item if match is not None: # 这里可能没有父类 temp = item[6:-3].split(": ") class_name = temp[0] super_class_name = temp[1] print(repr(temp)) print(class_name) print(super_class_name) else: if item.startswith(identation_component_text + "var "): print(repr(item)) if item.endswith("{\n"): continue variable_declaration_codes.append(item) temp = item[8:-1] elif item.startswith(identation_component_text + "@objc var "): print(repr(item)) if item.endswith("{\n"): continue variable_declaration_codes.append(item) temp = item[14:-1] else: continue temp = temp.replace(" ", "") temp = "".join(re.split("//.+", temp)) if temp.__contains__("="): if temp.__contains__(":"): temp = temp.split("=")[0] items = temp.split(":") variables.append((items[0], items[1])) else: items = temp.split("=") variable_type = "" if re.match("\\d+", items[1]) is not None: variable_type = "Int" elif re.match("\".?\"", items[1], re.RegexFlag.DOTALL): variable_type = "String" elif re.match(".+\$\$", items[1], re.RegexFlag.DOTALL): variable_type = items[1][:-2] elif re.match("true|false", items[1]): variable_type = "Bool" else: print("暂不支持的变量声明格式: {0}".format(item)) continue variables.append((items[0], variable_type)) elif temp.__contains__(":"): items = temp.split(":") variables.append((items[0], items[1])) else: print("暂不支持的变量声明格式: {0}".format(item)) continue source_file.close() if len(variables) == 0: print("没有找到声明的变量") exit(0) source_file = open(source_file_path, "wt") source_file.write(file_prefix_text) source_file.write("\n") for item in variable_declaration_codes: source_file.write(item) # TableColum source_file.write("\n") source_file.write(identation_component_text + "struct TableColumn {\n") for item in variables: print(item[0]) print(item[1]) source_file.write(identation_component_text*2 + "static let {0} = Expression<{1}>(\"{0}\")\n".format(item[0], item[1])) source_file.write(identation_component_text + "}\n") source_file.write("\n") source_file.write( identation_component_text + "static let table = Table(\"{0}\")\n".format(class_name)) # create source_file.write("\n") source_file.write(identation_component_text + "static var create = {\n") source_file.write(identation_component_text*2 + "\n") source_file.write(identation_component_text*2 + "return table.create(temporary: false, ifNotExists: true, withoutRowid: false) {\n") for item in variables: source_file.write(identation_component_text*3 + "$0.column(TableColumn.{0})\n".format(item[0])) source_file.write(identation_component_text*2 + "}\n") source_file.write(identation_component_text + "}()\n") # all source_file.write("\n") source_file.write(identation_component_text + "static func all(_ predicate: Expression<Bool> = Expression<Bool>.init(value: true)) -> [{0}] ".format(class_name) + "{\n") source_file.write(identation_component_text * 2 + "\n") source_file.write(identation_component_text * 2 + "do {\n") source_file.write(identation_component_text * 3 + "let items = try {0}.prepare({1}.table.filter(predicate))\n".format(connection, class_name)) source_file.write(identation_component_text * 3 + "let models = items.map({ " + "row -> {0} in\n".format(class_name)) source_file.write(identation_component_text * 4 + "let item = {0}()\n".format(class_name)) for item in variables: source_file.write(identation_component_text * 4 + "item.{0} = row[TableColumn.{0}]\n".format(item[0])) source_file.write(identation_component_text * 4 + "return item\n") source_file.write(identation_component_text * 3 + "})\n") source_file.write(identation_component_text * 3 + "return models\n") source_file.write(identation_component_text * 2 + "} catch {\n") source_file.write(identation_component_text * 3 + "print(error)\n") source_file.write(identation_component_text * 3 + "return []\n") source_file.write(identation_component_text * 2 + "}\n") source_file.write(identation_component_text + "}\n") # insert source_file.write("\n") source_file.write(identation_component_text + "func insert() throws {\n") source_file.write(identation_component_text * 2 + "\n") source_file.write(identation_component_text * 2 + "do {\n") source_file.write(identation_component_text * 3 + "let insert = {0}.table.insert(\n".format(class_name)) for i in range(len(variables)): item = variables[i] source_file.write(identation_component_text * 4 + "TableColumn.{0} <- self.{0}".format(item[0])) if i == len(variables) - 1: source_file.write("\n") else: source_file.write(",\n") source_file.write(identation_component_text * 3 + ")\n") source_file.write(identation_component_text * 3 + "try {0}.run(insert)\n".format(connection)) source_file.write(identation_component_text * 2 + "} catch {\n") source_file.write(identation_component_text * 3 + "print(error)\n") source_file.write(identation_component_text * 3 + "throw error\n") source_file.write(identation_component_text * 2 + "}\n") source_file.write(identation_component_text + "}\n") # delete source_file.write("\n") source_file.write(identation_component_text + "static func delete(_ predicate: Expression<Bool> = Expression<Bool>.init(value: true)) throws {\n") source_file.write(identation_component_text * 2 + "\n") source_file.write(identation_component_text * 2 + "do {\n") source_file.write(identation_component_text * 3 + "let delete = {0}.table.filter(predicate).delete()\n".format(class_name)) source_file.write(identation_component_text * 3 + "try {0}.run(delete)\n".format(connection)) source_file.write(identation_component_text * 2 + "} catch {\n") source_file.write(identation_component_text * 3 + "print(error)\n") source_file.write(identation_component_text * 3 + "throw error\n") source_file.write(identation_component_text * 2 + "}\n") source_file.write(identation_component_text + "}\n") # update source_file.write("\n") source_file.write(identation_component_text + "func update(_ predicate: Expression<Bool> = Expression<Bool>.init(value: true)) throws {\n") source_file.write(identation_component_text * 2 + "\n") source_file.write(identation_component_text * 2 + "do {\n") source_file.write( identation_component_text * 3 + "let update = {0}.table.filter(predicate).update(\n".format(class_name)) for i in range(len(variables)): item = variables[i] source_file.write(identation_component_text * 4 + "TableColumn.{0} <- self.{0}".format(item[0])) if i == len(variables) - 1: source_file.write("\n") else: source_file.write(",\n") source_file.write(identation_component_text * 3 + ")\n") source_file.write(identation_component_text * 3 + "try {0}.run(update)\n".format(connection)) source_file.write(identation_component_text * 2 + "} catch {\n") source_file.write(identation_component_text * 3 + "print(error)\n") source_file.write(identation_component_text * 3 + "throw error\n") source_file.write(identation_component_text * 2 + "}\n") source_file.write(identation_component_text + "}\n") # exist source_file.write("\n") source_file.write(identation_component_text + "static func exist(_ predicate: Expression<Bool> = Expression<Bool>.init(value: true)) -> Bool {\n") source_file.write(identation_component_text * 2 + "\n") source_file.write(identation_component_text * 2 + "do {\n") source_file.write( identation_component_text * 3 + "let items = try {0}.prepare({1}.table.filter(predicate))\n".format(connection, class_name)) source_file.write(identation_component_text * 3 + "return items.first(where: { _ in true }) != nil\n") source_file.write(identation_component_text * 2 + "} catch {\n") source_file.write(identation_component_text * 3 + "print(error)\n") source_file.write(identation_component_text * 3 + "return false\n") source_file.write(identation_component_text * 2 + "}\n") source_file.write(identation_component_text + "}\n") # first item source_file.write("\n") source_file.write(identation_component_text + "static func firstItem(_ predicate: Expression<Bool> = Expression<Bool>.init(value: true)) -> {0}? ".format(class_name) + "{\n") source_file.write(identation_component_text * 2 + "\n") source_file.write(identation_component_text * 2 + "do {\n") source_file.write(identation_component_text * 3 + "let items = try {0}.prepare({1}.table.filter(predicate))\n".format(connection, class_name)) source_file.write(identation_component_text * 3 + "if let item = items.first(where: { _ in true }) {\n") source_file.write(identation_component_text * 4 + "let model = {0}()\n".format(class_name)) for item in variables: source_file.write(identation_component_text * 4 + "model.{0} = item[TableColumn.{0}]\n".format(item[0])) source_file.write(identation_component_text * 4 + "return model\n") source_file.write(identation_component_text * 3 + "}else {\n") source_file.write(identation_component_text * 4 + "return nil\n") source_file.write(identation_component_text * 3 + "}\n") source_file.write(identation_component_text * 2 + "} catch {\n") source_file.write(identation_component_text * 3 + "print(error)\n") source_file.write(identation_component_text * 3 + "return nil\n") source_file.write(identation_component_text * 2 + "}\n") source_file.write(identation_component_text + "}\n") source_file.write("}\n") source_file.close()

50.26087

178

0.640398

1,431

11,560

4.906359

0.090846

0.156673

0.217918

0.302663

0.780088

0.756445

0.748469

0.737644

0.704316

0.701325

0

0.015225

0.215917

11,560

229

179

50.480349

0.759378

0.007785

0

0.5

0

0.025

0.178113

0.040056

0

1

0.005

false

0

0.01

0

0.02

0.085

0

null

0

1

0

1

0

null

0

7

f565bb055ff6913f93cf3590772a4d10b7878b81

187

py

Python

Aulas/aula07a.py

yurixf/Python

d1f91d52235dcba73deff281d80af0b44f028735

[ "MIT" ]

null

Aulas/aula07a.py

yurixf/Python

d1f91d52235dcba73deff281d80af0b44f028735

[ "MIT" ]

null

Aulas/aula07a.py

yurixf/Python

d1f91d52235dcba73deff281d80af0b44f028735

[ "MIT" ]

null

n = input('Digite algo:') print ('Seu nome é: {:20}!'.format(n)) print ('Seu nome é: {:=>20}!'.format(n)) print ('Seu nome é: {:=<20}!'.format(n)) print ('Seu nome é: {:=^20}!'.format(n))

37.4

40

0.55615

32

187

3.25

0.3125

0.307692

0.461538

0.5

0.846154

0

0.04878

0.122995

187

5

41

37.4

0.585366

0

0.478723

0

1

0

false

0

0.8

0

null

1

0

1

0

null

0

1

0

11

f5762aca8eacbf3ebfa63be00a7c87c48a29c37c

4,993

py

Python

gyoithon/migrations/0005_auto_20210506_1358.py

gyoisamurai/GyoiBoard

aebd29820a39d1d88b9e5874b56f923b1c88d170

[ "MIT" ]

3

2021-07-03T12:41:39.000Z

2021-11-18T17:23:08.000Z

gyoithon/migrations/0005_auto_20210506_1358.py

gyoisamurai/GyoiBoard

aebd29820a39d1d88b9e5874b56f923b1c88d170

[ "MIT" ]

null

gyoithon/migrations/0005_auto_20210506_1358.py

gyoisamurai/GyoiBoard

aebd29820a39d1d88b9e5874b56f923b1c88d170

[ "MIT" ]

3

2021-06-12T13:25:48.000Z

2021-09-30T19:06:49.000Z

# Generated by Django 3.1.7 on 2021-05-06 04:58 import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('gyoithon', '0004_auto_20210506_0955'), ] operations = [ migrations.CreateModel( name='Domain', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255, verbose_name='Domain Name')), ('registrar', models.CharField(default='N/A', max_length=255, verbose_name='Registrar')), ('administrative_contact', models.CharField(default='N/A', max_length=255, verbose_name='Administrative Contact')), ('registrant_name', models.CharField(default='N/A', max_length=255, verbose_name='Registrant Name')), ('registrant_organization', models.CharField(default='N/A', max_length=255, verbose_name='Registrant Organization')), ('registrant_email', models.CharField(default='N/A', max_length=255, verbose_name='Registrant Email')), ('admin_name', models.CharField(default='N/A', max_length=255, verbose_name='Admin Name')), ('admin_organization', models.CharField(default='N/A', max_length=255, verbose_name='Admin Organization')), ('admin_email', models.CharField(default='N/A', max_length=255, verbose_name='Admin Email')), ('tech_name', models.CharField(default='N/A', max_length=255, verbose_name='Tech Name')), ('tech_organization', models.CharField(default='N/A', max_length=255, verbose_name='Tech Organization')), ('tech_email', models.CharField(default='N/A', max_length=255, verbose_name='Tech Email')), ('name_server', models.CharField(default='N/A', max_length=1000, verbose_name='Name Server')), ('registration_date', models.DateTimeField(default=datetime.datetime(2021, 5, 6, 4, 58, 40, 765699, tzinfo=utc))), ], ), migrations.CreateModel( name='Subdomain', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('related_domain', models.CharField(max_length=255, verbose_name='Domain Name')), ('name', models.CharField(max_length=255, verbose_name='Subdomain Name')), ('ip_address', models.CharField(default='N/A', max_length=15, verbose_name='IP Address')), ('http_accessible', models.CharField(default='N/A', max_length=255, verbose_name='Accessible (HTTP)')), ('http_location', models.CharField(default='N/A', max_length=1000, verbose_name='Location (HTTP)')), ('http_page_title', models.CharField(default='N/A', max_length=1000, verbose_name='Page Title (HTTP)')), ('http_screenshot_url', models.CharField(default='N/A', max_length=1000, verbose_name='Screenshot Captured URL (HTTP)')), ('http_screenshot_path', models.CharField(default='N/A', max_length=1000, verbose_name='Path to save the Screenshot (HTTP)')), ('https_accessible', models.CharField(default='N/A', max_length=255, verbose_name='Accessible (HTTPS)')), ('https_location', models.CharField(default='N/A', max_length=1000, verbose_name='Location (HTTPS)')), ('https_page_title', models.CharField(default='N/A', max_length=1000, verbose_name='Page Title (HTTPS)')), ('https_screenshot_url', models.CharField(default='N/A', max_length=1000, verbose_name='Screenshot Captured URL (HTTPS)')), ('https_screenshot_path', models.CharField(default='N/A', max_length=1000, verbose_name='Path to save the Screenshot (HTTPS)')), ('dns_a_record', models.CharField(default='N/A', max_length=5000, verbose_name='DNS A Record')), ('dns_cname_record', models.CharField(default='N/A', max_length=5000, verbose_name='DNS CNAME Record')), ('dns_ns_record', models.CharField(default='N/A', max_length=5000, verbose_name='DNS NS Record')), ('dns_mx_record', models.CharField(default='N/A', max_length=5000, verbose_name='DNS MX Record')), ('dns_soa_record', models.CharField(default='N/A', max_length=5000, verbose_name='DNS SOA Record')), ('dns_txt_record', models.CharField(default='N/A', max_length=5000, verbose_name='DNS TXT Record')), ('registration_date', models.DateTimeField(default=datetime.datetime(2021, 5, 6, 4, 58, 40, 766259, tzinfo=utc))), ], ), migrations.AlterField( model_name='organization', name='registration_date', field=models.DateTimeField(default=datetime.datetime(2021, 5, 6, 4, 58, 40, 765144, tzinfo=utc)), ), ]

74.522388

144

0.641498

597

4,993

5.167504

0.167504

0.121232

0.206807

0.216207

0.738736

0.728039

0.713128

0.68201

0

0.048302

0.203885

4,993

66

145

75.651515

0.727799

0.009013

0

0.183333

1

0

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0.017994

0

1

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false

0

0.05

0

0.1

0

null

0

1

0

1

0

1

0

null

0

8

193845c5e689c2b8f7193220a7d2b44803cb3528

2,751

py

Python

tests/test_binder.py

jonathanslenders/cycli

288f098dd9b96df65d4f0d517a6fe0da4126afe4

[ "MIT" ]

3

2019-02-07T14:26:11.000Z

2021-12-15T09:34:34.000Z

tests/test_binder.py

jonathanslenders/cycli

288f098dd9b96df65d4f0d517a6fe0da4126afe4

[ "MIT" ]

1

2018-04-05T21:47:42.000Z

2018-04-05T22:08:55.000Z

tests/test_binder.py

jonathanslenders/cycli

288f098dd9b96df65d4f0d517a6fe0da4126afe4

[ "MIT" ]

null

from cycli.binder import curly_left, curly_right, paren_left, paren_right, bracket_left, bracket_right, apostrophe, \ quote from mock import call import pytest @pytest.fixture def binder(): from mock import MagicMock return MagicMock() def test_curly_left(binder): curly_left(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("{"), call("}", move_cursor=False)]) def test_curly_right_curly_current(binder): binder.cli.current_buffer.document.current_char = "}" curly_right(binder) binder.cli.current_buffer.cursor_right.assert_called_once_with() def test_curly_right_not_curly_current(binder): binder.cli.current_buffer.document.current_char = "n" curly_right(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("}")]) def test_paren_left(binder): paren_left(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("("), call(")", move_cursor=False)]) def test_paren_right_paren_current(binder): binder.cli.current_buffer.document.current_char = ")" paren_right(binder) binder.cli.current_buffer.cursor_right.assert_called_once_with() def test_paren_right_not_paren_current(binder): binder.cli.current_buffer.document.current_char = "n" paren_right(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call(")")]) def test_bracket_left(binder): bracket_left(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("["), call("]", move_cursor=False)]) def test_bracket_right_bracket_current(binder): binder.cli.current_buffer.document.current_char = "]" bracket_right(binder) binder.cli.current_buffer.cursor_right.assert_called_once_with() def test_bracket_right_not_bracket_current(binder): binder.cli.current_buffer.document.current_char = "n" bracket_right(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("]")]) def test_apostrophe_current(binder): binder.cli.current_buffer.document.current_char = "'" apostrophe(binder) binder.cli.current_buffer.cursor_right.assert_called_once_with() def test_apostrophe_not_current(binder): binder.cli.current_buffer.document.current_char = "n" apostrophe(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("'"), call("'", move_cursor=False)]) def test_quote_current(binder): binder.cli.current_buffer.document.current_char = "\"" quote(binder) binder.cli.current_buffer.cursor_right.assert_called_once_with() def test_quote_not_current(binder): binder.cli.current_buffer.document.current_char = "n" quote(binder) binder.cli.current_buffer.insert_text.assert_has_calls([call("\""), call("\"", move_cursor=False)])

31.62069

117

0.768448

376

2,751

5.25

0.098404

0.139818

0.174772

0.256332

0.817123

0.801925

0.784701

0.72999

0

0.110505

2,751

86

118

31.988372

0.806702

0

0.350877

0

0.010905

0

0.22807

1

0.245614

false

0

0.070175

0

0.333333

0

null

0

1

0

null

0

1

0

8

1944ff3bd6f343b5071ddfa6717eded1e30f0968

91

py

Python

LuoguCodes/AT3674.py

Anguei/OI-Codes

0ef271e9af0619d4c236e314cd6d8708d356536a

[ "MIT" ]

null

LuoguCodes/AT3674.py

Anguei/OI-Codes

0ef271e9af0619d4c236e314cd6d8708d356536a

[ "MIT" ]

null

LuoguCodes/AT3674.py

Anguei/OI-Codes

0ef271e9af0619d4c236e314cd6d8708d356536a

[ "MIT" ]

null

s = raw_input() print ';Yes'; if (s[0] == s[1] == s[2] or s[1] == s[2] == s[3]) else ';No';

45.5

75

0.43956

21

91

1.857143

0.619048

0.102564

0.153846

0.205128

0

0.082192

0.197802

91

2

75

45.5

0.452055

0

0.076087

0

null

0

null

0.5

1

0

1

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

5fd7be8e5a9dc5f8bdc443871e7f64125d142528

157

py

Python

torch_discounted_cumsum/__init__.py

toshas/torch-discounted-cumsum

43e94d1475d62ded4dcb60001c98c10cee63c6c4

[ "CC-BY-4.0", "BSD-3-Clause" ]

56

2020-12-31T14:15:36.000Z

2022-02-28T14:53:07.000Z

torch_discounted_cumsum/__init__.py

toshas/torch-discounted-cumsum

43e94d1475d62ded4dcb60001c98c10cee63c6c4

[ "CC-BY-4.0", "BSD-3-Clause" ]

7

2021-04-16T07:55:06.000Z

2022-03-30T10:00:54.000Z

torch_discounted_cumsum/__init__.py

toshas/torch-discounted-cumsum

43e94d1475d62ded4dcb60001c98c10cee63c6c4

[ "CC-BY-4.0", "BSD-3-Clause" ]

3

2021-08-18T10:02:38.000Z

2021-11-22T08:23:19.000Z

from torch_discounted_cumsum.discounted_cumsum import discounted_cumsum_left, discounted_cumsum_right from torch_discounted_cumsum.version import __version__

78.5

101

0.929936

20

157

6.65

0.4

0.601504

0.285714

0.37594

0

0.050955

157

2

102

78.5

0.892617

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

27480e46d085b8f7d8afb3da497719d04a1ec04a

255

py

Python

files/telling.py

janbodnar/Python-Course

51705ab5a2adef52bcdb99a800e94c0d67144a38

[ "BSD-2-Clause" ]

13

2017-08-22T12:26:07.000Z

2021-07-29T16:13:50.000Z

files/telling.py

janbodnar/Python-Course

51705ab5a2adef52bcdb99a800e94c0d67144a38

[ "BSD-2-Clause" ]

1

2021-02-08T10:24:33.000Z

files/telling.py

janbodnar/Python-Course

51705ab5a2adef52bcdb99a800e94c0d67144a38

[ "BSD-2-Clause" ]

17

2018-08-13T11:10:33.000Z

2021-07-29T16:14:02.000Z

#!/usr/bin/python with open('works.txt', 'r') as f: print(f'The current file position is {f.tell()}') f.read(22) print(f'The current file position is {f.tell()}') f.seek(0, 0) print(f'The current file position is {f.tell()}')

19.615385

53

0.592157

44

255

3.431818

0.477273

0.119205

0.178808

0.317881

0.708609

0.476821

0

0.020101

0.219608

255

12

54

21.25

0.738693

0.062745

0

0.5

0

0.533613

0

1

0

true

0

0.5

0

null

0

1

0

1

0

null

0

1

0

1

0

7

27c5e4d18f144f26ff246e1ae8eb3204eac3bb93

209

py

Python

toppra/solverwrapper/__init__.py

indivisibleatom/toppra

a5634163c99155ae633ae399d5cac753eef4c80c

[ "MIT" ]

null

toppra/solverwrapper/__init__.py

indivisibleatom/toppra

a5634163c99155ae633ae399d5cac753eef4c80c

[ "MIT" ]

null

toppra/solverwrapper/__init__.py

indivisibleatom/toppra

a5634163c99155ae633ae399d5cac753eef4c80c

[ "MIT" ]

null

from .cvxpy_solverwrapper import cvxpyWrapper from .qpoases_solverwrapper import qpOASESSolverWrapper from .hot_qpoases_solverwrapper import hotqpOASESSolverWrapper from .ecos_solverwrapper import ecosWrapper

41.8

62

0.904306

21

209

8.761905

0.52381

0.413043

0.282609

0

0.076555

209

4

63

52.25

0.953368

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

7e1113ed11dc33d96ff85fa409dbf6d3b916cc7c

156

py

Python

stateful_simulator/time_delays/__init__.py

pboueri/stateful-simulator

19a85138e11fd566b88be0a8446030d4143a90dd

[ "BSD-3-Clause" ]

2

2018-10-28T06:41:34.000Z

2018-10-30T04:19:05.000Z

stateful_simulator/time_delays/__init__.py

pboueri/stateful-simulator

19a85138e11fd566b88be0a8446030d4143a90dd

[ "BSD-3-Clause" ]

null

stateful_simulator/time_delays/__init__.py

pboueri/stateful-simulator

19a85138e11fd566b88be0a8446030d4143a90dd

[ "BSD-3-Clause" ]

null

import stateful_simulator.time_delays.UniformDelayer import stateful_simulator.time_delays.ExponentialDelayer import stateful_simulator.time_delays.Delayer

39

56

0.923077

18

156

7.666667

0.444444

0.304348

0.5

0.586957

0.717391

0

0.038462

156

3

57

52

0.92

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

fd72780f3fc0149b45d960f39de2c24173c2ebdc

168

py

Python

z2/process/__init__.py

mpenning/zz

b8448e39f0ad34285c40144bd0f55f506c5a7f2b

[ "BSD-3-Clause" ]

null

z2/process/__init__.py

mpenning/zz

b8448e39f0ad34285c40144bd0f55f506c5a7f2b

[ "BSD-3-Clause" ]

null

z2/process/__init__.py

mpenning/zz

b8448e39f0ad34285c40144bd0f55f506c5a7f2b

[ "BSD-3-Clause" ]

null

# Expose run_cmd() and iter_cmd_realtime... from z2.process.control import zrun from z2.process.control import iter_cmd_realtime from z2.process.control import run_cmd

33.6

48

0.827381

28

168

4.75

0.428571

0.135338

0.293233

0.451128

0.81203

0.616541

0

0.019868

0.10119

168

4

49

42

0.860927

0.244048

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

7

fd9e4d44e5d042682c756c876a38a69ae974d50c

24,771

py

Python

djfrontend/tests/tests_templatetags.py

mitjanikolaus/django-frontend

9573c3968d082bd2f1dfa733075115ec94975539

[ "MIT" ]

null

djfrontend/tests/tests_templatetags.py

mitjanikolaus/django-frontend

9573c3968d082bd2f1dfa733075115ec94975539

[ "MIT" ]

null

djfrontend/tests/tests_templatetags.py

mitjanikolaus/django-frontend

9573c3968d082bd2f1dfa733075115ec94975539

[ "MIT" ]

null

from djfrontend.templatetags.settings import * from django.conf import settings from django.test import TestCase, LiveServerTestCase from django.template import Template, Context class DjfrontendStaticTest(LiveServerTestCase): """ Ensure the files actually exist. """ # Change settings until StaticLiveServerCase in 1.7! settings.DEBUG = True def test_djfrontend_h5bp_css(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/h5bp/%s/h5bp.css' % DJFRONTEND_H5BP_CSS_DEFAULT) self.assertEqual(file.status_code, 200) def test_djfrontend_normalize(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/normalize/%s/normalize.css' % DJFRONTEND_NORMALIZE_DEFAULT) self.assertEqual(file.status_code, 200) def test_djfrontend_fontawesome(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/fontawesome/%s/font-awesome.css' % DJFRONTEND_FONTAWESOME_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/css/fontawesome/%s/font-awesome.min.css' % DJFRONTEND_FONTAWESOME_DEFAULT) file_font_eot = self.client.get(self.live_server_url + '/static/djfrontend/fonts/fontawesome/%s/fontawesome-webfont.eot' % DJFRONTEND_FONTAWESOME_DEFAULT) file_font_svg = self.client.get(self.live_server_url + '/static/djfrontend/fonts/fontawesome/%s/fontawesome-webfont.svg' % DJFRONTEND_FONTAWESOME_DEFAULT) file_font_ttf = self.client.get(self.live_server_url + '/static/djfrontend/fonts/fontawesome/%s/fontawesome-webfont.ttf' % DJFRONTEND_FONTAWESOME_DEFAULT) file_font_woff = self.client.get(self.live_server_url + '/static/djfrontend/fonts/fontawesome/%s/fontawesome-webfont.woff' % DJFRONTEND_FONTAWESOME_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) self.assertEqual(file_font_eot.status_code, 200) self.assertEqual(file_font_svg.status_code, 200) self.assertEqual(file_font_ttf.status_code, 200) self.assertEqual(file_font_woff.status_code, 200) def test_djfrontend_modernizr(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/modernizr/%s/modernizr.js' % DJFRONTEND_MODERNIZR_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/modernizr/%s/modernizr.min.js' % DJFRONTEND_MODERNIZR_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_jquery(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/%s/jquery.js' % DJFRONTEND_JQUERY_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/%s/jquery.min.js' % DJFRONTEND_JQUERY_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_jqueryui(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jqueryui/%s/jquery-ui.js' % DJFRONTEND_JQUERYUI_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jqueryui/%s/jquery-ui.min.js' % DJFRONTEND_JQUERYUI_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_jquery_datatables(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.dataTables/%s/jquery.dataTables.js' % DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.dataTables/%s/jquery.dataTables.min.js' % DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_jquery_datatables_css(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/jquery/jquery.dataTables/%s/jquery.dataTables.css' % DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/css/jquery/jquery.dataTables/%s/jquery.dataTables.min.css' % DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file.status_code, 200) def test_djfrontend_jquery_datatables_themeroller(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/jquery/jquery.dataTables/%s/jquery.dataTables_themeroller.css' % DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT) self.assertEqual(file.status_code, 200) def test_djfrontend_jquery_formset(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.formset/%s/jquery.formset.js' % DJFRONTEND_JQUERY_FORMSET_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.formset/%s/jquery.formset.min.js' % DJFRONTEND_JQUERY_FORMSET_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_jquery_scrollto(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.scrollTo/%s/jquery.scrollTo.js' % DJFRONTEND_JQUERY_SCROLLTO_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.scrollTo/%s/jquery.scrollTo.min.js' % DJFRONTEND_JQUERY_SCROLLTO_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_jquery_smoothscroll(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.smooth-scroll/%s/jquery.smooth-scroll.js' % DJFRONTEND_JQUERY_SMOOTHSCROLL_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/jquery/jquery.smooth-scroll/%s/jquery.smooth-scroll.min.js' % DJFRONTEND_JQUERY_SMOOTHSCROLL_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_twbs_css(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/twbs/%s/bootstrap.css' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/css/twbs/%s/bootstrap.min.css' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_font_eot = self.client.get(self.live_server_url + '/static/djfrontend/fonts/twbs/%s/glyphicons-halflings-regular.eot' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_font_svg = self.client.get(self.live_server_url + '/static/djfrontend/fonts/twbs/%s/glyphicons-halflings-regular.svg' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_font_ttf = self.client.get(self.live_server_url + '/static/djfrontend/fonts/twbs/%s/glyphicons-halflings-regular.ttf' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_font_woff = self.client.get(self.live_server_url + '/static/djfrontend/fonts/twbs/%s/glyphicons-halflings-regular.woff' % DJFRONTEND_TWBS_VERSION_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) self.assertEqual(file_font_eot.status_code, 200) self.assertEqual(file_font_svg.status_code, 200) self.assertEqual(file_font_ttf.status_code, 200) self.assertEqual(file_font_woff.status_code, 200) def test_djfrontend_twbs_theme_css(self): file = self.client.get(self.live_server_url + '/static/djfrontend/css/twbs/%s/bootstrap-theme.css' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/css/twbs/%s/bootstrap-theme.min.css' % DJFRONTEND_TWBS_VERSION_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) def test_djfrontend_twbs_js(self): file = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/bootstrap.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_min = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/bootstrap.min.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_affix = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/affix.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_alert = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/alert.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_button = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/button.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_carousel = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/carousel.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_collapse = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/collapse.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_dropdown = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/dropdown.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_modal = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/modal.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_popover = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/popover.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_scrollspy = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/scrollspy.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_tab = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/tab.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_tooltip = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/tooltip.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) file_transition = self.client.get(self.live_server_url + '/static/djfrontend/js/twbs/%s/transition.js' % DJFRONTEND_TWBS_VERSION_DEFAULT) self.assertEqual(file.status_code, 200) self.assertEqual(file_min.status_code, 200) self.assertEqual(file_affix.status_code, 200) self.assertEqual(file_alert.status_code, 200) self.assertEqual(file_button.status_code, 200) self.assertEqual(file_carousel.status_code, 200) self.assertEqual(file_collapse.status_code, 200) self.assertEqual(file_dropdown.status_code, 200) self.assertEqual(file_modal.status_code, 200) self.assertEqual(file_popover.status_code, 200) self.assertEqual(file_scrollspy.status_code, 200) self.assertEqual(file_tab.status_code, 200) self.assertEqual(file_tooltip.status_code, 200) self.assertEqual(file_transition.status_code, 200) class DjfrontendDefaultTestCase(TestCase): """ Ensure the default values are being returned. """ def test_djfrontend_h5bp_html(self): attr = 'lang="%s"' % DJFRONTEND_H5BP_HTML_DEFAULT t = Template( "{% load djfrontend %}" "{% djfrontend_h5bp_html %}" ).render(Context()) self.assertTrue(attr in t) def test_djfrontend_h5bp_css(self): t = Template( "{% load djfrontend %}" "{% djfrontend_h5bp_css %}" ).render(Context()) self.assertTrue(DJFRONTEND_H5BP_CSS_DEFAULT in t) def test_djfrontend_normalize(self): t = Template( "{% load djfrontend %}" "{% djfrontend_normalize %}" ).render(Context()) self.assertTrue(DJFRONTEND_NORMALIZE_DEFAULT in t) def test_djfrontend_fontawesome(self): t = Template( "{% load djfrontend %}" "{% djfrontend_fontawesome %}" ).render(Context()) self.assertTrue(DJFRONTEND_FONTAWESOME_DEFAULT in t) def test_djfrontend_modernizr(self): t = Template( "{% load djfrontend %}" "{% djfrontend_modernizr %}" ).render(Context()) self.assertTrue(DJFRONTEND_MODERNIZR_DEFAULT in t) def test_djfrontend_jquery(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jquery %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERY_DEFAULT in t) def test_djfrontend_jqueryui(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jqueryui %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERYUI_DEFAULT in t) def test_djfrontend_jquery_datatables(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) def test_djfrontend_jquery_datatables_css(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables_css %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) def test_djfrontend_jquery_formset(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_formset %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERY_FORMSET_DEFAULT in t) def test_djfrontend_jquery_scrollto(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_scrollto %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERY_SCROLLTO_DEFAULT in t) def test_djfrontend_jquery_smoothscroll(self): t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_smoothscroll %}" ).render(Context()) self.assertTrue(DJFRONTEND_JQUERY_SMOOTHSCROLL_DEFAULT in t) def test_djfrontend_twbs_css(self): t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_css %}" ).render(Context()) self.assertTrue(DJFRONTEND_TWBS_VERSION_DEFAULT in t) def test_djfrontend_twbs_theme_css(self): t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_theme_css %}" ).render(Context()) self.assertTrue(DJFRONTEND_TWBS_VERSION_DEFAULT in t) def test_djfrontend_twbs_js(self): t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_js %}" ).render(Context()) self.assertTrue(DJFRONTEND_TWBS_VERSION_DEFAULT in t) def test_djfrontend_twbs_js_popover(self): t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_js files='popover' %}" ).render(Context()) self.assertTrue('popover.js' in t) self.assertTrue('tooltip.js' in t) class DjfrontendSettingsTestCase(TestCase): """ Ensure the settings override the defaults. """ def test_djfrontend_h5bp_html(self): attr = 'lang="%s"' % DJFRONTEND_H5BP_HTML_DEFAULT settings.DJFRONTEND_H5BP_HTML = 'fr' t = Template( "{% load djfrontend %}" "{% djfrontend_h5bp_html %}" ).render(Context()) self.assertFalse(attr in t) def test_djfrontend_h5bp_css(self): settings.DJFRONTEND_H5BP_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_h5bp_css %}" ).render(Context()) self.assertFalse(DJFRONTEND_H5BP_CSS_DEFAULT in t) def test_djfrontend_normalize(self): settings.DJFRONTEND_NORMALIZE = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_normalize %}" ).render(Context()) self.assertFalse(DJFRONTEND_NORMALIZE_DEFAULT in t) def test_djfrontend_fontawesome(self): settings.DJFRONTEND_FONTAWESOME = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_fontawesome %}" ).render(Context()) self.assertFalse(DJFRONTEND_FONTAWESOME_DEFAULT in t) def test_djfrontend_modernizr(self): settings.DJFRONTEND_MODERNIZR = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_modernizr %}" ).render(Context()) self.assertFalse(DJFRONTEND_MODERNIZR_DEFAULT in t) def test_djfrontend_jquery(self): settings.DJFRONTEND_JQUERY = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DEFAULT in t) def test_djfrontend_jqueryui(self): settings.DJFRONTEND_JQUERYUI = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jqueryui %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERYUI_DEFAULT in t) def test_djfrontend_jquery_datatables(self): settings.DJFRONTEND_JQUERY_DATATABLES = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) def test_djfrontend_jquery_datatables_css(self): settings.DJFRONTEND_JQUERY_DATATABLES_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables_css %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) def test_djfrontend_jquery_formset(self): settings.DJFRONTEND_JQUERY_FORMSET = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_formset %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_FORMSET_DEFAULT in t) def test_djfrontend_jquery_scrollto(self): settings.DJFRONTEND_JQUERY_SCROLLTO = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_scrollto %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_SCROLLTO_DEFAULT in t) def test_djfrontend_jquery_smoothscroll(self): settings.DJFRONTEND_JQUERY_SMOOTHSCROLL = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_smoothscroll %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_SMOOTHSCROLL_DEFAULT in t) def test_djfrontend_twbs_css(self): settings.DJFRONTEND_TWBS_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_css %}" ).render(Context()) self.assertFalse(DJFRONTEND_TWBS_VERSION_DEFAULT in t) def test_djfrontend_twbs_theme_css(self): settings.DJFRONTEND_TWBS_THEME_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_theme_css %}" ).render(Context()) self.assertFalse(DJFRONTEND_TWBS_VERSION_DEFAULT in t) def test_djfrontend_twbs_js(self): settings.DJFRONTEND_TWBS_JS_VERSION = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_js %}" ).render(Context()) self.assertFalse(DJFRONTEND_TWBS_VERSION_DEFAULT in t) class DjfrontendSettingsGranularityTestCase(TestCase): """ Ensure the template tag argument is the final override. """ def test_djfrontend_h5bp_html(self): attr_default = 'lang="%s"' % DJFRONTEND_H5BP_HTML_DEFAULT attr = 'lang="de"' settings.DJFRONTEND_H5BP_HTML = 'fr' t = Template( "{% load djfrontend %}" "{% djfrontend_h5bp_html 'de' %}" ).render(Context()) self.assertFalse(attr_default in t) self.assertTrue(attr in t) def test_djfrontend_h5bp_css(self): settings.DJFRONTEND_H5BP_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_h5bp_css 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_H5BP_CSS_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_normalize(self): settings.DJFRONTEND_NORMALIZE = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_normalize 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_NORMALIZE_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_fontawesome(self): settings.DJFRONTEND_FONTAWESOME = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_fontawesome 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_FONTAWESOME_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_modernizr(self): settings.DJFRONTEND_MODERNIZR = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_modernizr 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_MODERNIZR_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery(self): settings.DJFRONTEND_JQUERY = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jqueryui(self): settings.DJFRONTEND_JQUERYUI = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jqueryui 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERYUI_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery_datatables(self): settings.DJFRONTEND_JQUERY_DATATABLES = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery_datatables_css(self): settings.DJFRONTEND_JQUERY_DATATABLES_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables_css 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery_datatables_themeroller(self): settings.DJFRONTEND_JQUERY_DATATABLES_THEMEROLLER = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_datatables_css 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_DATATABLES_VERSION_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery_formset(self): settings.DJFRONTEND_JQUERY_FORMSET = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_formset 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_FORMSET_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery_scrollto(self): settings.DJFRONTEND_JQUERY_SCROLLTO = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_scrollto 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_SCROLLTO_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_jquery_smoothscroll(self): settings.DJFRONTEND_JQUERY_SMOOTHSCROLL = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_jquery_smoothscroll 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_JQUERY_SMOOTHSCROLL_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_twbs_css(self): settings.DJFRONTEND_TWBS_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_css 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_TWBS_VERSION_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_twbs_theme_css(self): settings.DJFRONTEND_TWBS_THEME_CSS = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_theme_css 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_TWBS_VERSION_DEFAULT in t) self.assertTrue('x.x.x' in t) def test_djfrontend_twbs_js(self): settings.DJFRONTEND_TWBS_JS_VERSION = '0.0.0' t = Template( "{% load djfrontend %}" "{% djfrontend_twbs_js 'x.x.x' %}" ).render(Context()) self.assertFalse(DJFRONTEND_TWBS_VERSION_DEFAULT in t) self.assertTrue('x.x.x' in t)

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7

e304282704b5488f6d5bc6b1f025772390c40fe9

152

py

Python

src/jax_limbdark/__init__.py

dfm/gpu-limbdark

05d246316cc3b96d7b62a4d0f14a50c725d4ed7a

[ "MIT" ]

1

2021-08-24T13:38:04.000Z

src/jax_limbdark/__init__.py

dfm/gpu-limbdark

05d246316cc3b96d7b62a4d0f14a50c725d4ed7a

[ "MIT" ]

null

src/jax_limbdark/__init__.py

dfm/gpu-limbdark

05d246316cc3b96d7b62a4d0f14a50c725d4ed7a

[ "MIT" ]

null

# -*- coding: utf-8 -*- __all__ = ["__version__", "kepler"] from .jax_limbdark import kepler from .jax_limbdark_version import version as __version__

21.714286

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null

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e30897aafcd1fff9999ceae4f564483cacbf364d

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py

Python

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_0/_pkg0_0_1/_pkg0_0_1_1/_pkg0_0_1_1_1/_mod0_0_1_1_1_4.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_0/_pkg0_0_1/_pkg0_0_1_1/_pkg0_0_1_1_1/_mod0_0_1_1_1_4.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_0/_pkg0_0_1/_pkg0_0_1_1/_pkg0_0_1_1_1/_mod0_0_1_1_1_4.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

name0_0_1_1_1_4_0 = None name0_0_1_1_1_4_1 = None name0_0_1_1_1_4_2 = None name0_0_1_1_1_4_3 = None name0_0_1_1_1_4_4 = None

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1

0

null

0

10

e34ed88cd1e7c246026b7c8ea7a442a0aa7364e7

1,341

py

Python

test/test_del_project.py

dinyt/mantis_training

ca61bc895a2d1b93a4742d64b23e31f3f7f368a5

[ "Apache-2.0" ]

null

test/test_del_project.py

dinyt/mantis_training

ca61bc895a2d1b93a4742d64b23e31f3f7f368a5

[ "Apache-2.0" ]

null

test/test_del_project.py

dinyt/mantis_training

ca61bc895a2d1b93a4742d64b23e31f3f7f368a5

[ "Apache-2.0" ]

null

from model.project import Project import random def test_delete_some_project_with_db(app, db): if db.get_project_list() == 0: app.project.create(Project(name="New project")) old_projects = db.get_project_list() project = random.choice(old_projects) app.project.delete_by_id(project.id) new_projects = db.get_project_list() assert len(old_projects) - 1 == app.project.count() old_projects.remove(project) assert old_projects == new_projects assert sorted(new_projects, key=Project.id_or_max) == sorted(app.project.get_project_list(), key=Project.id_or_max) def test_delete_some_project_with_soap(app): user = app.config['webadmin'] old_projects = app.soap.get_all_projects(user["username"], user["password"]) if old_projects == 0: app.project.create(Project(name="New project")) old_projects = app.soap.get_all_projects(user["username"], user["password"]) project = random.choice(old_projects) app.project.delete_by_id(project.id) new_projects = app.soap.get_all_projects(user["username"], user["password"]) assert len(old_projects) - 1 == app.project.count() old_projects.remove(project) assert old_projects == new_projects assert sorted(new_projects, key=Project.id_or_max) == sorted(app.project.get_project_list(), key=Project.id_or_max)

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47.892857

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0

null

0

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null

0

1

0

7

e37227ab12d63205c689794286c9ebcd0941bd4d

9,970

py

Python

tests/src/smiley/smiley/tests/presentation/test_trace.py

incognitoRepo/hdlogger

c738161ef3144469ba0f47caf89770613031e96e

[ "BSD-2-Clause" ]

null

tests/src/smiley/smiley/tests/presentation/test_trace.py

incognitoRepo/hdlogger

c738161ef3144469ba0f47caf89770613031e96e

[ "BSD-2-Clause" ]

null

tests/src/smiley/smiley/tests/presentation/test_trace.py

incognitoRepo/hdlogger

c738161ef3144469ba0f47caf89770613031e96e

[ "BSD-2-Clause" ]

null

import testtools from smiley import db from smiley.presentation import trace class CollapseTraceTest(testtools.TestCase): def test_consecutive_lines(self): trace_data = [ db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=2, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=3, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), ] collapsed = list(trace.collapse_trace(trace_data)) self.assertEqual(len(collapsed), 1) line_nos = [r.line_no for r in collapsed] self.assertEqual(line_nos, [(1, 3)]) def test_non_consecutive_lines(self): trace_data = [ db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=20, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=40, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), ] collapsed = list(trace.collapse_trace(trace_data)) self.assertEqual(len(collapsed), 1) line_nos = [r.line_no for r in collapsed] self.assertEqual(line_nos, [(1, 40)]) def test_call_lines_return(self): trace_data = [ db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='call', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=2, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='return', filename='filename.py', line_no=2, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), ] collapsed = list(trace.collapse_trace(trace_data)) self.assertEqual(len(collapsed), 3) line_nos = [r.line_no for r in collapsed] self.assertEqual(line_nos, [(1, 1), (1, 2), (2, 2)]) def test_new_var(self): trace_data = [ db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={ 'v1': 1, }, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=2, func_name='func', trace_arg={}, local_vars={ 'v1': 1, 'v2': 2, }, timestamp=1, ), ] collapsed = list(trace.collapse_trace(trace_data)) self.assertEqual(len(collapsed), 1) self.assertEqual(collapsed[0].local_vars, {'v1': 1, 'v2': 2}) def test_changed_var(self): trace_data = [ db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={ 'v1': 1, 'v3': 3, }, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=2, func_name='func', trace_arg={}, local_vars={ 'v1': 1, 'v2': 2, 'v3': 4, }, timestamp=1, ), ] collapsed = list(trace.collapse_trace(trace_data)) self.assertEqual(len(collapsed), 2) self.assertEqual(collapsed[0].local_vars, {'v1': 1, 'v3': 3}) self.assertEqual(collapsed[1].local_vars, {'v1': 1, 'v2': 2, 'v3': 4}) def test_changed_var2(self): trace_data = [ db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=1, func_name='func', trace_arg={}, local_vars={ 'v1': 1, 'v3': 3, }, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=2, func_name='func', trace_arg={}, local_vars={ 'v1': 1, 'v2': 2, 'v3': 4, }, timestamp=1, ), # Force the previous data to be emitted, with the next set # of local variables being completely different to verify # that the variable change detection resets db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='call', filename='filename.py', line_no=3, func_name='func', trace_arg={}, local_vars={}, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=4, func_name='func', trace_arg={}, local_vars={ 'V1': 1, }, timestamp=1, ), db.Trace( id='1', run_id='1', thread_id='t1', call_id='1', event='line', filename='filename.py', line_no=5, func_name='func', trace_arg={}, local_vars={ 'V1': 1, 'V2': 2, }, timestamp=1, ), ] collapsed = list(trace.collapse_trace(trace_data)) self.assertEqual(len(collapsed), 4) self.assertEqual(collapsed[0].local_vars, {'v1': 1, 'v3': 3}) self.assertEqual(collapsed[1].local_vars, {'v1': 1, 'v2': 2, 'v3': 4}) self.assertEqual(collapsed[3].local_vars, {'V1': 1, 'V2': 2})

29.497041

70

0.350552

871

9,970

3.80597

0.089552

0.051584

0.057315

0.890498

0.885068

0.873906

0

0.042717

0.530391

9,970

337

71

29.58457

0.665314

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0.873846

0

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false

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0

1

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1

0

1

0

null

0

8

e3770caf225a524dfd209a49e73ad29480af766b

4,164

py

Python

app/looptest2.py

ThebiggunSeeoil/VIS-MASTER

a54a5f321cfe8b258bacc25458490c5b154edf19

[ "MIT" ]

null

app/looptest2.py

ThebiggunSeeoil/VIS-MASTER

a54a5f321cfe8b258bacc25458490c5b154edf19

[ "MIT" ]

null

app/looptest2.py

ThebiggunSeeoil/VIS-MASTER

a54a5f321cfe8b258bacc25458490c5b154edf19

[ "MIT" ]

null

from itertools import groupby INFO = [{'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '11', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '11', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '11', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '12', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '12', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '12', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '13', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '13', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '13', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '14', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '14', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '14', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '15', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '15', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '11', 'NOZZLE_pump_log_address': '15', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '16', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '16', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '16', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '17', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '17', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '17', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '18', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '18', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '18', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '19', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '19', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '19', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '20', 'NOZZLE_num': '1', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '20', 'NOZZLE_num': '2', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '20', 'NOZZLE_num': '3', 'dcount': 1}, {'name_id': 1224, 'Unit_log_address': '12', 'NOZZLE_pump_log_address': '23', 'NOZZLE_num': '1', 'dcount': 1}] # dictionary # INFO = [ # {'employee': 'XYZ_1', 'company': 'ABC_1'}, # {'employee': 'XYZ_2', 'company': 'ABC_2'}, # {'employee': 'XYZ_3', 'company': 'ABC_3'}, # {'employee': 'XYZ_4', 'company': 'ABC_3'}, # {'employee': 'XYZ_5', 'company': 'ABC_2'}, # {'employee': 'XYZ_6', 'company': 'ABC_3'}, # {'employee': 'XYZ_7', 'company': 'ABC_1'}, # {'employee': 'XYZ_8', 'company': 'ABC_2'}, # {'employee': 'XYZ_9', 'company': 'ABC_1'} # ] # define a fuction for key def key_func(k): return k['name_id'] # sort INFO data by 'company' key. INFO = sorted(INFO, key=key_func) for key, value in groupby(INFO, key_func): print(key) print(list(value))

154.222222

3,418

0.636407

626

4,164

3.851438

0.091054

0.257155

0.128577

0.180008

0.907922

0.827872

0

0.088937

0.114313

4,164

27

3,419

154.222222

0.564805

0.120317

0

0.575214

0.196798

0

1

0.125

false

0

0.125

0.375

0.25

0

null

1

0

1

0

1

0

null

0

1

0

11

473b93be5f9db35539699f971be4e5c4d05c0fbb

63,090

py

Python

cinder/tests/test_oodrive_eguan.py

oodrive/cinder

40b43d6698128ab26b056586e0a5c5e818e21c11

[ "Apache-2.0" ]

1

2015-11-25T10:18:28.000Z

cinder/tests/test_oodrive_eguan.py

oodrive/cinder

40b43d6698128ab26b056586e0a5c5e818e21c11

[ "Apache-2.0" ]

null

cinder/tests/test_oodrive_eguan.py

oodrive/cinder

40b43d6698128ab26b056586e0a5c5e818e21c11

[ "Apache-2.0" ]

null

# Copyright (c) 2012 - 2014 Oodrive # All Rights Reserved. # # 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. """ Test the eguan block storage backend driver. @author: pwehrle """ from cinder import exception from cinder.openstack.common import log from cinder import test from cinder.tests.volume.drivers.eguan import mocks from cinder import units from cinder.volume.configuration import Configuration from cinder.volume.drivers.eguan.driver import eguan_opts from cinder.volume.drivers.eguan.driver import EguanDriver from cinder.volume.drivers.eguan.driver import KeystoneNotificationHandler from cinder.volume.drivers.eguan.driver import map_tenant_to_vvr_id from cinder.volume.drivers.eguan import resource_proxies from httmock import all_requests from httmock import HTTMock from os import environ from oslo.config import cfg from oslo.messaging.notify.dispatcher import NotificationResult from requests.exceptions import ConnectionError from requests import Session from threading import Thread from uuid import UUID from uuid import uuid4 LOG = log.getLogger(__name__) CONF = cfg.CONF INTEGR_BACKEND = environ.get('EGUAN_INT_BACKEND') INT_SPLIT = INTEGR_BACKEND.split('|') if INTEGR_BACKEND else None _VOLD_BACKENDS = ({UUID(hex=INT_SPLIT[0]): INT_SPLIT[1]} if INTEGR_BACKEND else {uuid4(): uri + '/storage' for uri in ['http://192.0.2.130:2345', 'http://198.51.100.130:2345', 'http://203.0.113.130:2345']}) _MOCK_VOLD_REST_SERVERS = ({t_id: mocks.MockVvrsRes(str(t_id), url, {}) for t_id, url in _VOLD_BACKENDS.iteritems()}) @all_requests def respond_all(url, request): """Intercept all calls, raise common exceptions and forward to mock volds. """ LOG.debug("Analyzing call to URL %(url)s with request %(request)s" % {'url': url, 'request': request}) # checks if the requested host is reachable target_hosts = [host_url for host_url in _MOCK_VOLD_REST_SERVERS.itervalues() if host_url.base_url.netloc == url.netloc] LOG.debug('Found matching hosts; %s' % target_hosts) if len(target_hosts) == 0: raise ConnectionError() # checks for a valid tenant ID tenant_id = UUID(mocks.extract_tenant_id(url)) target_server = _MOCK_VOLD_REST_SERVERS.get(tenant_id) if target_server is None: return {'status_code': 403} # forward request to next level server_mock = HTTMock(target_server.respond_all) with server_mock: s = Session() return s.send(request) ROOT_MOCK = (HTTMock() if INTEGR_BACKEND else HTTMock(respond_all)) def _new_test_volume(tenant_id, snapshot_id=None, **kw_args): volume_id = uuid4().get_hex() display_name = 'test_vol_' + volume_id volume_size_gb = kw_args.get('size') or 4 test_vol = {'name': display_name, 'size': volume_size_gb, 'display_name': display_name, 'id': volume_id, 'provider_auth': None, 'project_id': tenant_id.get_hex(), 'display_description': 'test volume for owner ID ' + str(tenant_id), 'snapshot_id': snapshot_id, 'volume_type_id': None} test_vol.update(kw_args) return test_vol def _new_test_snapshot(tenant_id, volume_id, **kw_args): snapshot_id = uuid4().get_hex() test_snap = {'display_name': 'testSnap_' + str(tenant_id), 'id': snapshot_id, 'volume_id': volume_id, 'volume_size': 1, 'project_id': tenant_id.get_hex()} test_snap.update(kw_args) return test_snap class TestEguanDriver(test.TestCase): """Main test class for the eguan block storage backend driver.""" def setUp(self): super(TestEguanDriver, self).setUp() self._driver = EguanDriver() CONF.import_group('eguan', 'cinder.volume.drivers.eguan') CONF.import_opt('vold_rest_uri', 'cinder.volume.drivers.eguan', 'eguan') CONF.eguan.vold_rest_uri = ([INTEGR_BACKEND] if INTEGR_BACKEND else [t_id.get_hex() + '|' + url for t_id, url in _VOLD_BACKENDS.iteritems()]) # initialize backend by creating the appropriate VVR(s) for t_id, url in _VOLD_BACKENDS.iteritems(): vvrs_res = resource_proxies.VvrsRes(url + '/vvrs', t_id) vvr_id = map_tenant_to_vvr_id(t_id) with ROOT_MOCK: try: vvr_res = vvrs_res.get_vvr(vvr_id) except exception.NotFound: vvr_res = vvrs_res.create_vvr(vvr_id) # start the VVR if it's not yet started if not vvr_res.started: vvr_res.start() def test_do_setup(self): """Test successful setup using the test client configuration.""" with HTTMock(respond_all): self._driver.do_setup({}) def test_do_setup_fail_no_backend(self): """Test setup failure due to a missing backend address.""" CONF.eguan.vold_rest_uri = [] self.assertRaises(exception.VolumeBackendAPIException, self._driver.do_setup, {}) def test_do_setup_fail_invalid_tenant(self): """Test setup failure due to an invalid tenant ID.""" CONF.eguan.vold_rest_uri.append("%s|http://192.0.2.134:4536/storage" % '1337-dead-beef') self.assertRaises(exception.VolumeBackendAPIException, self._driver.do_setup, {}) def test_check_for_setup_error(self): """Test successful post-setup checking.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() def test_check_for_setup_error_fail_unreachable(self): """Test post-setup check failure due to an unreachable vold server.""" CONF.eguan.vold_rest_uri.append("%s|http://192.0.2.134:4536/storage" % uuid4().get_hex()) with ROOT_MOCK: self._driver.do_setup({}) self.assertRaises(exception.VolumeBackendAPIException, self._driver.check_for_setup_error) def test_create_volume(self): """Test creating volumes on multiple REST backends.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) display_name = test_vol.get('display_name') volume_size_gb = test_vol.get('size') self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertEqual(volume_id, UUID(hex=getattr(read_volume, 'uuid'))) self.assertEqual(display_name, getattr(read_volume, 'name')) self.assertEqual(volume_size_gb * units.GiB, long(getattr(read_volume, 'size'))) def test_create_volume_with_description(self): """Test creating volumes with optional descriptions.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) description = 'Test volume description' test_vol['display_description'] = description volume_id = UUID(hex=test_vol.get('id')) display_name = test_vol.get('display_name') volume_size_gb = test_vol.get('size') self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertEqual(volume_id, UUID(hex=getattr(read_volume, 'uuid'))) self.assertEqual(display_name, getattr(read_volume, 'name')) self.assertEqual(volume_size_gb * units.GiB, long(getattr(read_volume, 'size'))) self.assertEqual(description, getattr(read_volume, 'description')) def test_create_volume_no_name(self): """Test creation of volumes without a name.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) test_vol.pop('display_name') self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertEqual(volume_id, UUID(hex=getattr(read_volume, 'uuid'))) def test_create_volume_fail_missing_project_id(self): """Test failure to create volumes due to missing project IDs.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.pop('project_id') self.assertIsNone(test_vol.get('project_id')) self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver.create_volume(test_vol)) def test_create_volume_fail_bad_project_id(self): """Test failure to create volumes due to a bad project ID.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update({'project_id': uuid4().get_hex()}) self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver.create_volume(test_vol)) def test_create_volume_fail_invalid_size(self): """Test failure to create volumes due to a negative size.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol['size'] = -1 self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver.create_volume(test_vol)) def test_create_volume_fail_no_size(self): """Test failure to create volumes due to a missing size.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.pop('size') self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver.create_volume(test_vol)) def test_create_volume_from_snapshot(self): """Test creating volumes from existing snapshots.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) test_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id'], display_name='test_snap') snapshot_id = UUID(hex=test_snap.get('id')) snapshot_name = test_snap.get('display_name') self._driver.create_snapshot(test_snap) test_snap_vol = _new_test_volume(tenant_id, snapshot_id=test_snap['id']) self._driver.create_volume_from_snapshot(test_snap_vol, test_snap) read_snap = self._driver.get_snapshot(tenant_id, snapshot_id) self.assertEqual(snapshot_id, UUID(hex=getattr(read_snap, 'uuid'))) self.assertEqual(snapshot_name, getattr(read_snap, 'name')) def test_create_volume_from_snapshot_fail_no_snap(self): """Test volume creation failure if given an invalid snapshot ID.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): vol_id = uuid4().get_hex() bad_snap = _new_test_snapshot(tenant_id, volume_id=vol_id) bad_snap_id = bad_snap['id'] test_vol = _new_test_volume(tenant_id, snapshot_id=bad_snap_id) test_vol['id'] = vol_id self.assertRaises(exception.NotFound, lambda: self._driver. create_volume_from_snapshot(test_vol, bad_snap)) def test_create_cloned_volume(self): """Test volume creation as a clone of an existing volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): orig_vol = _new_test_volume(tenant_id, size=6) self._driver.create_volume(orig_vol) cloned_vol = _new_test_volume(tenant_id, size=6) self._driver.create_cloned_volume(cloned_vol, orig_vol) read_cloned_vol = (self._driver. get_volume(tenant_id, UUID(hex=cloned_vol['id']))) self.assertNotEqual(UUID(hex=orig_vol['id']), UUID(hex=getattr(read_cloned_vol, read_cloned_vol.id_attr))) self.assertEqual(orig_vol['size'] * units.GiB, long(getattr(read_cloned_vol, 'size'))) def test_create_cloned_volume_with_description(self): """Test volume creation as a clone with a description.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): orig_vol = _new_test_volume(tenant_id, size=6) self._driver.create_volume(orig_vol) cloned_vol = _new_test_volume(tenant_id, size=6) clone_desc = 'volume cloned from ' + orig_vol['id'] cloned_vol['display_description'] = clone_desc self._driver.create_cloned_volume(cloned_vol, orig_vol) read_cloned_vol = (self._driver. get_volume(tenant_id, UUID(hex=cloned_vol['id']))) self.assertNotEqual(UUID(hex=orig_vol['id']), UUID(hex=getattr(read_cloned_vol, read_cloned_vol.id_attr))) self.assertEqual(orig_vol['size'] * units.GiB, long(getattr(read_cloned_vol, 'size'))) self.assertEqual(clone_desc, getattr(read_cloned_vol, 'description')) def test_create_cloned_volume_fail_no_src_volume(self): """Test volume cloning failure if given a non-existent source.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): orig_vol = _new_test_volume(tenant_id) cloned_vol = _new_test_volume(tenant_id) self.assertRaises(exception.NotFound, lambda: self._driver. create_cloned_volume(cloned_vol, orig_vol)) def test_create_cloned_volume_fail_diff_tenant_ids(self): """Test volume creation failure due to different tenant IDs.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): orig_vol = _new_test_volume(tenant_id) self._driver.create_volume(orig_vol) cloned_vol = _new_test_volume(uuid4()) self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver. create_cloned_volume(cloned_vol, orig_vol)) def test_extend_volume(self): """Test deletion of a volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) old_size = test_vol.get('size') self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertEqual(old_size * units.GiB, long(getattr(read_volume, 'size'))) new_size = 2 * old_size self._driver.extend_volume(test_vol, new_size) new_read_vol = self._driver.get_volume(tenant_id, volume_id) self.assertEqual(new_size * units.GiB, long(getattr(new_read_vol, 'size'))) def test_extend_volume_fail_negative_size(self): """Test deletion of a volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) old_size = test_vol.get('size') self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertEqual(old_size * units.GiB, long(getattr(read_volume, 'size'))) new_size = -2 * old_size self._driver.extend_volume(test_vol, new_size) # check the volume's size was not set to the negative value reread_volume = self._driver.get_volume(tenant_id, volume_id) self.assertGreater(getattr(reread_volume, 'size'), 0) def test_delete_volume(self): """Test deletion of a volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) self._driver.create_volume(test_vol) self._driver.delete_volume(test_vol) self.assertRaises(exception.NotFound, lambda: self._driver.get_volume(tenant_id, volume_id)) def test_delete_volume_fail_no_volume(self): """Test failed deletion of an inexistent volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) self.assertRaises(exception.NotFound, lambda: self._driver.get_volume(tenant_id, volume_id)) self.assertRaises(exception.NotFound, lambda: self._driver.delete_volume(test_vol)) def test_create_snapshot(self): """Test creation of a snapshot.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) test_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id']) snap_id = UUID(hex=test_snap['id']) self._driver.create_snapshot(test_snap) read_snap = self._driver.get_snapshot(tenant_id, snap_id) self.assertEqual(snap_id, UUID(hex=getattr(read_snap, 'uuid'))) self.assertEqual(test_snap['display_name'], getattr(read_snap, 'name')) read_vol = self._driver.get_volume(tenant_id, UUID(hex=test_vol['id'])) self.assertEqual(UUID(hex=test_snap['volume_id']), UUID(hex=getattr(read_vol, 'uuid'))) self.assertEqual(snap_id, UUID(hex=getattr(read_vol, 'parent'))) def test_create_snapshot_with_description(self): """Test creation of a snapshot with optional description.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) tst_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id']) description = 'Test snapshot description' tst_snap['display_description'] = description snap_id = UUID(hex=tst_snap['id']) self._driver.create_snapshot(tst_snap) read_snap = self._driver.get_snapshot(tenant_id, snap_id) self.assertEqual(snap_id, UUID(hex=getattr(read_snap, 'uuid'))) self.assertEqual(tst_snap['display_name'], getattr(read_snap, 'name')) read_vol = self._driver.get_volume(tenant_id, UUID(hex=test_vol['id'])) self.assertEqual(UUID(hex=tst_snap['volume_id']), UUID(hex=getattr(read_vol, 'uuid'))) self.assertEqual(snap_id, UUID(hex=getattr(read_vol, 'parent'))) read_snap = self._driver.get_snapshot(tenant_id, UUID(hex=tst_snap['id'])) self.assertEqual(snap_id, UUID(hex=getattr(read_snap, 'uuid'))) self.assertEqual(description, getattr(read_snap, 'description')) def test_create_snapshot_no_name(self): """Test snapshot creation without a name.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) test_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id']) test_snap.pop('display_name') snap_id = UUID(hex=test_snap['id']) self._driver.create_snapshot(test_snap) read_snap = self._driver.get_snapshot(tenant_id, snap_id) self.assertEqual(snap_id, UUID(hex=getattr(read_snap, 'uuid'))) def test_create_snapshot_fail_no_volume(self): """Test snapshot creation failure if given an invalid volume ID.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): bad_vol_id = uuid4().get_hex() test_snap = _new_test_snapshot(tenant_id, volume_id=bad_vol_id) self.assertRaises(exception.NotFound, lambda: self._driver. create_snapshot(test_snap)) def test_create_snapshot_fail_missing_project_id(self): """Test failure to create snapshots due to missing project IDs.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) vol_id = test_vol['id'] test_snap = _new_test_snapshot(tenant_id, volume_id=vol_id) self._driver.create_volume(test_vol) test_snap.pop('project_id') self.assertIsNone(test_snap.get('project_id')) self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver. create_snapshot(test_snap)) def test_create_snapshot_fail_bad_project_id(self): """Test failure to create snapshots due to a bad project ID.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) vol_id = test_vol['id'] test_snap = _new_test_snapshot(tenant_id, volume_id=vol_id) self._driver.create_volume(test_vol) test_snap.update({'project_id': uuid4().get_hex()}) self.assertRaises(exception.VolumeBackendAPIException, lambda: self. _driver.create_snapshot(test_snap)) def test_create_snapshot_fail_read_only(self): """Test failure creating a snapshot on a read-only volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) self._driver.create_volume(test_vol) test_vol['readOnly'] = True self._driver.create_export({}, test_vol) read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertTrue(getattr(read_volume, 'readOnly')) test_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id']) self.assertRaises(exception.VolumeBackendAPIException, lambda: self. _driver.create_snapshot(test_snap)) def test_delete_snapshot(self): """Test deletion of a snapshot.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) test_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id']) snap_id = UUID(hex=test_snap['id']) self._driver.create_snapshot(test_snap) self._driver.delete_snapshot(test_snap) self.assertRaises(exception.NotFound, lambda: self._driver.get_volume(tenant_id, snap_id)) def test_delete_snapshot_fail_no_snapshot(self): """Test deletion of a snapshot.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) test_snap = _new_test_snapshot(tenant_id, volume_id=test_vol['id']) snap_id = UUID(hex=test_snap.get('id')) self.assertRaises(exception.NotFound, lambda: self._driver.get_snapshot(tenant_id, snap_id)) self.assertRaises(exception.NotFound, lambda: self._driver. delete_snapshot(test_snap)) def test_create_export(self): """Test export creation.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self._driver.create_volume(test_vol) update = self._driver.create_export({}, test_vol) self.assertTrue(update['provider_location']) #TODO(pwehrle): check for valid values def test_create_export_read_only(self): """Test export creation.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol.get('id')) self._driver.create_volume(test_vol) test_vol['readOnly'] = True update = self._driver.create_export({}, test_vol) self.assertTrue(update['provider_location']) #TODO(pwehrle): check for valid values read_volume = self._driver.get_volume(tenant_id, volume_id) self.assertTrue(getattr(read_volume, 'readOnly')) def test_create_export_fail_no_volume(self): """Test export creation failure due to a missing volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) self.assertRaises(exception.NotFound, lambda: self._driver. create_export({}, test_vol)) def test_ensure_export(self): """Test ensuring export existence.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol['id']) self._driver.create_volume(test_vol) vol_res = self._driver.get_volume(tenant_id, volume_id) self.assertFalse(hasattr(vol_res, 'provider_location')) self._driver.ensure_export({}, test_vol) exp_vol_res = self._driver.get_volume(tenant_id, volume_id) self.assertTrue(hasattr(exp_vol_res, 'provider_location')) def test_remove_export(self): """Test export removal.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol['id']) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol['provider_location']) self._driver.remove_export({}, test_vol) read_vol_res = self._driver.get_volume(tenant_id, volume_id) self.assertFalse(hasattr(read_vol_res, 'provider_location')) def test_initialize_connection(self): """Test connection initialization for an unspecified client.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol.get('provider_location')) test_conn = {'ip': '192.0.2.232'} connection_info = (self._driver. initialize_connection(test_vol, test_conn)) vol_type = connection_info['driver_volume_type'] self.assertEqual('iscsi', vol_type) self.assertTrue('data' in connection_info) conn_data = connection_info['data'] self.assertTrue('access_mode' in conn_data) self.assertTrue('target_portal' in conn_data) #iscsi-specific part self.assertTrue('target_iqn' in conn_data) self.assertTrue('volume_id' in conn_data) self.assertTrue('target_lun' in conn_data) self.assertFalse('nbd_dev_name' in conn_data) def test_initialize_connection_iscsi(self): """Test connection initialization for an iSCSI client.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol.get('provider_location')) test_conn = {'ip': '192.0.2.232', 'initiator': 'iqn.1993-08.org.debian:01:80373e17099'} connection_info = (self._driver. initialize_connection(test_vol, test_conn)) vol_type = connection_info['driver_volume_type'] self.assertEqual('iscsi', vol_type) self.assertTrue('data' in connection_info) conn_data = connection_info['data'] self.assertTrue('access_mode' in conn_data) self.assertTrue('target_portal' in conn_data) #iscsi-specific part self.assertTrue('target_iqn' in conn_data) self.assertTrue('volume_id' in conn_data) self.assertTrue('target_lun' in conn_data) self.assertFalse('nbd_dev_name' in conn_data) def test_initialize_connection_nbd(self): """Test connection initialization for an NBD client.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol.get('provider_location')) test_conn = {'ip': '192.0.2.232', 'nbd_client_id': 'nbd-client version 2.9.25'} connection_info = (self._driver. initialize_connection(test_vol, test_conn)) vol_type = connection_info['driver_volume_type'] self.assertEqual('nbd', vol_type) self.assertTrue('data' in connection_info) conn_data = connection_info['data'] self.assertTrue('access_mode' in conn_data) self.assertTrue('target_portal' in conn_data) # nbd-specific part self.assertTrue('nbd_dev_name' in conn_data) self.assertFalse('target_iqn' in conn_data) def test_initialize_connection_not_exported(self): """Test connection initialization on a non-exported volume.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) volume_id = UUID(hex=test_vol['id']) test_vol.update(self._driver.create_volume(test_vol) or {}) self.assertFalse(test_vol.get('provider_location')) test_conn = {'ip': '192.0.2.232', 'initiator': 'iqn.1993-08.org.debian:01:80373e17099'} connection_info = self._driver.initialize_connection(test_vol, test_conn) vol_res = self._driver.get_volume(tenant_id, volume_id) self.assertTrue(hasattr(vol_res, 'provider_location')) vol_type = connection_info['driver_volume_type'] self.assertTrue(vol_type in {'iscsi', 'nbd'}) self.assertTrue('data' in connection_info) conn_data = connection_info['data'] self.assertTrue('access_mode' in conn_data) self.assertTrue('target_portal' in conn_data) if vol_type == 'iscsi': self.assertTrue('target_iqn' in conn_data) self.assertTrue('volume_id' in conn_data) self.assertTrue('target_lun' in conn_data) elif vol_type == 'nbd': self.assertTrue('nbd_dev_name' in conn_data) def test_initialize_connection_fail_no_client_ip(self): """Test connection init failure due to a missing client IP.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol['provider_location']) test_conn = {'initiator': 'iqn.1993-08.org.debian:01:80373e17099'} self.assertRaises(exception.VolumeBackendAPIException, lambda: self. _driver.initialize_connection(test_vol, test_conn)) def test_validate_connector(self): """Test the connector validation function.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol['provider_location']) test_conn = {'ip': '192.0.2.232', 'initiator': 'iqn.1993-08.org.debian:01:80373e17099'} self._driver.validate_connector(test_conn) def test_validate_connector_fail_no_ip(self): """Test the connector validation function.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol['provider_location']) test_conn = {'initiator': 'iqn.1993-08.org.debian:01:80373e17099'} self.assertRaises(exception.VolumeBackendAPIException, lambda: self._driver. validate_connector(test_conn)) def test_terminate_connection(self): """Test connection termination.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) test_vol.update(self._driver.create_export({}, test_vol)) self.assertTrue(test_vol.get('provider_location')) test_conn = {'ip': '192.0.2.232', 'initiator': 'iqn.1993-08.org.debian:01:80373e17099'} self._driver.initialize_connection(test_vol, test_conn) self._driver.terminate_connection(test_vol, test_conn) #TODO(pwehrle): find some smart thing(s) to check def test_attach_volume(self): """Test attaching volumes to instances.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) instance_uuid = uuid4() self._driver.attach_volume({}, test_vol, instance_uuid, 'test_host.example.com', '/mnt/vol1') def test_attach_volume_fail_no_volume(self): """Test failure to attach non-existent volumes to instances.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) instance_uuid = uuid4() self.assertRaises(exception.NotFound, lambda: self._driver. attach_volume({}, test_vol, instance_uuid, 'test_host.example.com', '/mnt/vol1')) def test_detach_volume(self): """Test detaching volumes from instances.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) instance_uuid = uuid4() test_host = 'test_host.example.com' self._driver.attach_volume({}, test_vol, instance_uuid, test_host, '/mnt/vol1') # add attached_host manually test_vol['attached_host'] = test_host self._driver.detach_volume({}, test_vol) def test_detach_volume_not_attached(self): """Test detaching non-attached volumes from instances.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol.update(self._driver.create_volume(test_vol) or {}) self._driver.detach_volume({}, test_vol) def test_detach_volume_fail_no_volume(self): """Test failure detaching non-existent volumes from instances.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() for tenant_id in _VOLD_BACKENDS.iterkeys(): test_vol = _new_test_volume(tenant_id) test_vol['attached_host'] = 'test_host.example.com' self.assertRaises(exception.NotFound, lambda: self._driver.detach_volume({}, test_vol)) def test_get_volume_stats(self): """Test getting volume statistics.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() cap_values = ['unknown', 'infinite'] stats_data = self._driver.get_volume_stats() self.assertTrue(stats_data.get('driver_version')) free_cap = stats_data.get('free_capacity_gb') self.assertTrue(free_cap in cap_values or long(free_cap) >= 0) self.assertTrue(long(stats_data. get('reserved_percentage')) >= 0) self.assertTrue(stats_data.get('storage_protocol') in ['iSCSI', 'nbd']) total_cap = stats_data.get('total_capacity_gb') self.assertTrue(total_cap in cap_values or long(total_cap) >= 0) self.assertEqual(stats_data.get('vendor_name'), 'Oodrive') self.assertEqual(stats_data.get('volume_backend_name'), 'eguan') def test_get_volume_stats_with_conf_backend_name(self): """Test getting volume statistics with a configured backend name.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() cap_values = ['unknown', 'infinite'] custom_backend_name = 'Custom backend' new_conf = Configuration(eguan_opts) new_conf.local_conf.set_override('volume_backend_name', custom_backend_name) try: new_driver = EguanDriver(configuration=new_conf) stats_data = new_driver.get_volume_stats(refresh=True) self.assertTrue(stats_data.get('driver_version')) free_cap = stats_data.get('free_capacity_gb') self.assertTrue(free_cap in cap_values or long(free_cap) >= 0) self.assertTrue(long(stats_data. get('reserved_percentage')) >= 0) self.assertTrue(stats_data.get('storage_protocol') in ['iSCSI', 'nbd']) total_cap = stats_data.get('total_capacity_gb') self.assertTrue(total_cap == 'infinite' or long(total_cap) >= 0) self.assertEqual(stats_data.get('vendor_name'), 'Oodrive') self.assertEqual(stats_data.get('volume_backend_name'), custom_backend_name) finally: new_conf.local_conf.clear_override('volume_backend_name') def test_get_volume_stats_with_refresh(self): """Test getting volume statistics with forced refresh.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() cap_values = ['unknown', 'infinite'] stats_data = self._driver.get_volume_stats(refresh=True) self.assertTrue(stats_data.get('driver_version')) free_cap = stats_data.get('free_capacity_gb') self.assertTrue(free_cap in cap_values or long(free_cap) >= 0) self.assertTrue(long(stats_data. get('reserved_percentage')) >= 0) self.assertTrue(stats_data.get('storage_protocol') in ['iSCSI', 'nbd']) total_cap = stats_data.get('total_capacity_gb') self.assertTrue(total_cap == 'infinite' or long(total_cap) >= 0) self.assertEqual(stats_data.get('vendor_name'), 'Oodrive') self.assertEqual(stats_data.get('volume_backend_name'), 'eguan') def test_keystone_notification_create(self): """Test creating a VVR following a tenant creation notification.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) target_vvr = vvrs_proxy.get_vvr(vvr_id) self.assertEqual(vvr_id, UUID(hex=getattr(target_vvr, 'uuid'))) test_vol = _new_test_volume(new_tenant_id) volume_id = UUID(hex=test_vol.get('id')) self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(new_tenant_id, volume_id) self.assertEqual(volume_id, UUID(hex=getattr(read_volume, 'uuid'))) def test_keystone_notification_create_existing(self): """Test creating a VVR following a tenant creation notification.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) target_vvr = vvrs_proxy.get_vvr(vvr_id) self.assertEqual(vvr_id, UUID(hex=getattr(target_vvr, 'uuid'))) notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) def test_keystone_notification_create_async_vol_request(self): """Test volume creation requests while the VVR is being created.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_thread = Thread(target=notif_handler.info, args=({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {})) notif_thread.start() test_vol = _new_test_volume(new_tenant_id) volume_id = UUID(hex=test_vol.get('id')) self._driver.create_volume(test_vol) read_volume = self._driver.get_volume(new_tenant_id, volume_id) self.assertEqual(volume_id, UUID(hex=getattr(read_volume, 'uuid'))) notif_thread.join(10) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) target_vvr = vvrs_proxy.get_vvr(vvr_id) self.assertEqual(vvr_id, UUID(hex=getattr(target_vvr, 'uuid'))) def test_keystone_notification_delete(self): """Test deleting a VVR following a tenant deletion notification.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) target_vvr = vvrs_proxy.get_vvr(vvr_id) self.assertEqual(vvr_id, UUID(hex=getattr(target_vvr, 'uuid'))) notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.deleted', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) self.assertRaises(exception.NotFound, lambda: vvrs_proxy.get_vvr(vvr_id)) def test_keystone_notification_delete_exported_device(self): """Test deleting a VVR in the presence of an exported device.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) target_vvr = vvrs_proxy.get_vvr(vvr_id) self.assertEqual(vvr_id, UUID(hex=getattr(target_vvr, 'uuid'))) for dev_nb in range(1, 5): test_vol = _new_test_volume(new_tenant_id) test_vol['display_name'] = 'Vol-' + str(dev_nb) self._driver.create_volume(test_vol) if (dev_nb % 2) == 0: continue update = self._driver.create_export({}, test_vol) self.assertTrue(update['provider_location']) notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.deleted', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) self.assertRaises(exception.NotFound, lambda: vvrs_proxy.get_vvr(vvr_id)) def test_keystone_notification_delete_stopped_vvr(self): """Test deleting a stopped VVR.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.created', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) target_vvr = vvrs_proxy.get_vvr(vvr_id) self.assertEqual(vvr_id, UUID(hex=getattr(target_vvr, 'uuid'))) updated_vvr = target_vvr.stop() self.assertFalse(updated_vvr.started) notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.deleted', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) self.assertRaises(exception.NotFound, lambda: vvrs_proxy.get_vvr(vvr_id)) def test_keystone_notification_delete_ignore_missing(self): """Test handling a deletion notification for a non-existent VVR.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) new_tenant_id = uuid4() notif_result = notif_handler.info({}, 'identity.localhost', 'identity.project.deleted', {'resource_info': new_tenant_id.get_hex()}, {}) self.assertEqual(NotificationResult.HANDLED, notif_result) vvr_id = map_tenant_to_vvr_id(new_tenant_id) vvrs_proxy = self._driver._checked_vvrs_proxy(new_tenant_id) self.assertRaises(exception.NotFound, lambda: vvrs_proxy.get_vvr(vvr_id)) def test_keystone_notification_ignore(self): """Test miscellaneous ignored notifications.""" with ROOT_MOCK: self._driver.do_setup({}) self._driver.check_for_setup_error() notif_handler = KeystoneNotificationHandler(self._driver) tenant_id_hex = uuid4().get_hex() # missing resource ID self.assertIsNone(notif_handler.info({}, 'identity.localhost', 'identity.project.created', {}, {})) # identity service, but not on project topic self.assertIsNone(notif_handler.info({}, 'identity.localhost', 'identity.user', {'resource_info': tenant_id_hex}, {})) # identity service, project topic, but unknown operation self.assertIsNone(notif_handler.info({}, 'identity.localhost', 'identity.project.unknown', {'resource_info': tenant_id_hex}, {})) # another service entirely self.assertIsNone(notif_handler.info({}, 'image.localhost', 'image', {}, {}))

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7

474ed78bf0cbcfa9b4f30d0a83025be484fdec02

46

py

Python

tests/test_pycounts-klv-524.py

Kingslin0810/pycounts-klv-524

db0700031ac7b8ea5ca5f247174de87d8f108a2e

[ "MIT" ]

null

tests/test_pycounts-klv-524.py

Kingslin0810/pycounts-klv-524

db0700031ac7b8ea5ca5f247174de87d8f108a2e

[ "MIT" ]

null

tests/test_pycounts-klv-524.py

Kingslin0810/pycounts-klv-524

db0700031ac7b8ea5ca5f247174de87d8f108a2e

[ "MIT" ]

null

from pycounts-klv-524 import pycounts-klv-524

23

45

0.826087

8

46

4.75

0.625

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7

9a2611bb44033b45ce5a6f2385b600a31e2621ed

134

py

Python

gyakujinton/__init__.py

mamerisawesome/gyakujinton

835ffe8ddf61b638db50a6ff15f764bee19917bd

[ "MIT" ]

null

gyakujinton/__init__.py

mamerisawesome/gyakujinton

835ffe8ddf61b638db50a6ff15f764bee19917bd

[ "MIT" ]

null

gyakujinton/__init__.py

mamerisawesome/gyakujinton

835ffe8ddf61b638db50a6ff15f764bee19917bd

[ "MIT" ]

null

from .functions.draw import generate_superimposition from .functions.draw import draw_on_image from .functions.skew import skew_image

33.5

52

0.865672

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134

5.894737

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0.303571

0.410714

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9

7be88ff979a0af19aa00c283f466fe83d0c188fe

515

py

Python

backends/paltoodl/rev20150404/odl_types/odl_abstract_map.py

snlab/alto-server

1d8b8530a8948930c267298801b0e2dcd78dd71a

[ "Apache-2.0" ]

null

backends/paltoodl/rev20150404/odl_types/odl_abstract_map.py

snlab/alto-server

1d8b8530a8948930c267298801b0e2dcd78dd71a

[ "Apache-2.0" ]

null

backends/paltoodl/rev20150404/odl_types/odl_abstract_map.py

snlab/alto-server

1d8b8530a8948930c267298801b0e2dcd78dd71a

[ "Apache-2.0" ]

1

2015-04-25T17:03:18.000Z

class AbstractODLMap: def load_from_odl(self, odl_map): raise NotImplementedError( "Should have implemented this" ) def load_from_rfc(self, rfc_map): raise NotImplementedError( "Should have implemented this" ) def resource_id(self): raise NotImplementedError( "Should have implemented this" ) def odl_map(self): raise NotImplementedError( "Should have implemented this" ) def rfc_map(self): raise NotImplementedError( "Should have implemented this" )

32.1875

67

0.706796

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515

6.12069

0.293103

0.338028

0.422535

0.478873

0.791549

0

0.219417

515

15

68

34.333333

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0.454545

false

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null

1

0

1

0

null

0

1

0

1

0

8

d0030f4a8a791164ed114ddfc0a4f6deed6f74dd

3,158

py

Python

src/errors.py

altmirai/altpiggybank

751590642e0a2a572310923fbd971acd0fdf8527

[ "MIT" ]

null

src/errors.py

altmirai/altpiggybank

751590642e0a2a572310923fbd971acd0fdf8527

[ "MIT" ]

6

2020-08-13T13:45:12.000Z

2020-08-14T15:08:41.000Z

src/errors.py

altmirai/altpiggybank

751590642e0a2a572310923fbd971acd0fdf8527

[ "MIT" ]

1

2020-08-13T16:16:00.000Z

import requests import click import ecdsa import json def addr(error, context, config, **kwargs): # Connection Timeout Error if isinstance(error, requests.exceptions.ConnectTimeout): raise click.UsageError( 'API connection timeout, please try again later', context) # API status_code NOT 200 elif isinstance(error, AssertionError): raise click.UsageError(error.args[0], context) # VerifyingKey.from_pem failed due to bad file format elif isinstance(error, ecdsa.der.UnexpectedDER): raise click.UsageError( 'Incorrect file, please provide a PEM formatted public key file', context) else: unkown_error(error, context) def refresh(error, context, config, **kwargs): # Connection Timeout Error if isinstance(error, requests.exceptions.ConnectTimeout): raise click.UsageError( 'API connection timeout, please try again later', context) # API status_code NOT 200, PEM not present in JSON file elif isinstance(error, AssertionError): raise click.UsageError(error.args[0], context) # File not JSON elif isinstance(error, json.decoder.JSONDecodeError): raise click.UsageError( 'Incorrect file, please provide a JSON formatted addr file created by piggy addr', context) else: unkown_error(error, context) def fee(error, context, config, **kwargs): # Connection Timeout Error if isinstance(error, requests.exceptions.ConnectTimeout): raise click.UsageError( 'API connection timeout, please try again later', context) # API status_code NOT 200, PEM not present in JSON file elif isinstance(error, AssertionError): raise click.UsageError(error.args[0], context) # File not JSON elif isinstance(error, json.decoder.JSONDecodeError): raise click.UsageError( 'Incorrect file, please provide a JSON formatted addr file created by piggy addr', context) else: unkown_error(error, context) def tx(error, context, config, **kwargs): # Connection Timeout Error if isinstance(error, requests.exceptions.ConnectTimeout): raise click.UsageError( 'API connection timeout, please try again later', context) # API status_code NOT 200 elif isinstance(error, AssertionError): raise click.UsageError(error.args[0], context) else: unkown_error(error, context) def signed(error, context, config, **kwargs): # Connection Timeout Error if isinstance(error, requests.exceptions.ConnectTimeout): raise click.UsageError( 'API connection timeout, please try again later', context) # API status_code NOT 200 elif isinstance(error, AssertionError): raise click.UsageError(error.args[0], context) else: unkown_error(error, context) def unkown_error(error, context): file_name = error.__traceback__.tb_frame.f_code.co_filename line_num = error.__traceback__.tb_lineno raise click.UsageError( f'This is embarrassing, There was a {type(error).__name__}({error}) on line {line_num} in {file_name}', context)

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120

0.69696

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5.722955

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0.064546

0.129092

0.063624

0.828953

0.811895

0.790226

0

0.008104

0.218493

3,158

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121

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0

0.741379

0

0.017241

0.198913

0.011232

0

0.086207

1

0.103448

false

0

0.068966

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0.172414

0

null

0

1

0

1

0

null

0

7

d0f5caf6fc0dceb31966caa47a9b31b570968943

18,273

py

Python

training/config/training.py

vhrspvl/Minda-Training

6d54d44b718506d6fe460abe5796bdee9e74d0ad

[ "MIT" ]

null

training/config/training.py

vhrspvl/Minda-Training

6d54d44b718506d6fe460abe5796bdee9e74d0ad

[ "MIT" ]

null

training/config/training.py

vhrspvl/Minda-Training

6d54d44b718506d6fe460abe5796bdee9e74d0ad

[ "MIT" ]

1

2020-02-14T12:56:53.000Z

from __future__ import unicode_literals from frappe import _ import frappe def get_data(): system_manager = frappe.get_doc("User", frappe.session.user).get( "roles", {"role": "System Manager"}) training_manager = frappe.get_doc("User", frappe.session.user).get( "roles", {"role": "Training Manager"}) employee = frappe.get_doc("User", frappe.session.user).get( "roles", {"role": "Employee"}) hr_manager = frappe.get_doc("User", frappe.session.user).get( "roles", {"role": "HR Manager"}) if system_manager: return [ { "label": _("Evaluation Forms"), "items": [ { "type": "doctype", "name": "Auto Cutting and Crimping", "description": _("Auto Cutting and Crimping"), }, { "type": "doctype", "name": "Semi Auto Crimping", "description": _("Semi Auto Crimping"), }, { "type": "doctype", "name": "Assembly Evaluation", "description": _("Assembly Evaluation"), }, { "type": "doctype", "name": "Quality Evaluation", "description": _("Quality Evaluation"), }, ] }, { "label": _("Monitoring Sheets"), "items": [ { "type": "doctype", "name": "Associate Performance Monitoring", "description": _("Associate Performance Monitoring"), }, { "type": "doctype", "name": "Associate Performance Monitoring Check Sheet", "description": _("Associate Performance Monitoring Check Sheet"), }, { "type": "doctype", "name": "Knowledge Verification Sheet", "description": _("Knowledge Verification Sheet"), }, { "type": "doctype", "name": "Employee Performance Summary Report", "description": _("Employee Performance Summary Report"), } ] }, { "label": _("Tests & Questions"), "items": [ { "type": "doctype", "name": "Color Blindness Test", "description": _("Color Blindness Test"), }, { "type": "doctype", "name": "Selection Test", "description": _("Selection Test"), }, { "type": "doctype", "name": "New Joinees Practical Knowledge Verification", "description": _("New Joinees Practical Knowledge Verification"), }, { "type": "doctype", "name": "Induction Training Assembly Area", "description": _("Induction Training Assembly Area"), }, { "type": "doctype", "name": "Induction Training Machine Area Crimping", "description": _("Induction Training Machine Area Crimping"), }, { "type": "doctype", "name": "Induction Training Machine Area Operator", "description": _("Induction Training Machine Area Operator"), }, { "type": "doctype", "name": "Crimping Questions", "description": _("Crimping Questions"), } ] }, { "label": _("Tools & Reports"), "items": [ { "type": "report", "is_query_report": True, "name": "New Joinees Details", "label": _("New Joinees Test Details"), "description": _("New Joinees Details"), "doctype": "Employee" }, { "type": "report", "is_query_report": True, "name": "Pending List KVS", "label": _("Pending List KVS"), "description": _("Pending List KVS"), "doctype": "Employee" }, { "type": "report", "is_query_report": True, "name": "Practical Knowledge Verification Pending List", "label": _("Pending List Practical Knowledge Verification"), "description": _("Pending List KVS"), "doctype": "New Joinees Practical Knowledge Verification" }, { "type": "doctype", "name": "KVS Type Assignment", "description": _("Line and Shift Assignment Details"), }, { "type": "doctype", "name": "Line and Shift Assignment Tool", "description": _("Line and Shift Assignment Tool"), }, { "type": "doctype", "name": "Line and Shift Assignment Details", "description": _("Line and Shift Assignment Details"), }, { "type": "report", "is_query_report": True, "name": "Skill Evaluation Due Report", "label": _("Skill Evaluation Due Report"), "description": _("Skill Evaluation Due Report") # "doctype": "New Joinees Practical Knowledge Verification" } ] }, { "label": _("Supervisor Evaluation Marks"), "items": [ { "type": "doctype", "name": "Auto Cutting and Crimping Marks", "description": _("Auto Cutting and Crimping Marks"), }, { "type": "doctype", "name": "Semi Auto Crimping Marks", "description": _("Semi Auto Crimping Marks"), }, { "type": "doctype", "name": "Assembly Evaluation Marks", "description": _("Assembly Evaluation Marks"), }, { "type": "doctype", "name": "Quality Evaluation Marks", "description": _("Quality Evaluation Marks"), }, ] }, ] elif training_manager: return [ { "label": _("Evaluation Forms"), "items": [ { "type": "doctype", "name": "Auto Cutting and Crimping", "description": _("Auto Cutting and Crimping"), }, { "type": "doctype", "name": "Semi Auto Crimping", "description": _("Semi Auto Crimping"), }, { "type": "doctype", "name": "Assembly Evaluation", "description": _("Assembly Evaluation"), }, { "type": "doctype", "name": "Quality Evaluation", "description": _("Quality Evaluation"), }, ] }, { "label": _("Monitoring Sheets"), "items": [ { "type": "doctype", "name": "Associate Performance Monitoring", "description": _("Associate Performance Monitoring"), }, { "type": "doctype", "name": "Associate Performance Monitoring Check Sheet", "description": _("Associate Performance Monitoring Check Sheet"), }, { "type": "doctype", "name": "Knowledge Verification Sheet", "description": _("Knowledge Verification Sheet"), }, { "type": "doctype", "name": "Employee Performance Summary Report", "description": _("Employee Performance Summary Report"), } ] }, { "label": _("Tests & Questions"), "items": [ { "type": "doctype", "name": "Color Blindness Test", "description": _("Color Blindness Test"), }, { "type": "doctype", "name": "Selection Test", "description": _("Selection Test"), }, { "type": "doctype", "name": "New Joinees Practical Knowledge Verification", "description": _("New Joinees Practical Knowledge Verification"), }, { "type": "doctype", "name": "Induction Training Assembly Area", "description": _("Induction Training Assembly Area"), }, { "type": "doctype", "name": "Induction Training Machine Area Crimping", "description": _("Induction Training Machine Area Crimping"), }, { "type": "doctype", "name": "Induction Training Machine Area Operator", "description": _("Induction Training Machine Area Operator"), }, { "type": "doctype", "name": "Crimping Questions", "description": _("Crimping Questions"), } ] }, { "label": _("Tools & Reports"), "items": [ { "type": "report", "is_query_report": True, "name": "New Joinees Details", "label": _("New Joinees Test Details"), "description": _("New Joinees Details"), "doctype": "Employee" }, { "type": "report", "is_query_report": True, "name": "Pending List KVs", "label": _("Pending List KVS"), "description": _("Pending List KVS"), "doctype": "Employee" }, { "type": "report", "is_query_report": True, "name": "Practical Knowledge Verificatio Pending List", "label": _("Pending List Practical Knowledge Verification"), "description": _("Pending List KVS"), "doctype": "New Joinees Practical Knowledge Verification" }, { "type": "doctype", "name": "KVS Type Assignment", "description": _("Line and Shift Assignment Details"), }, { "type": "doctype", "name": "Line and Shift Assignment Tool", "description": _("Line and Shift Assignment Tool"), }, { "type": "doctype", "name": "Line and Shift Assignment Details", "description": _("Line and Shift Assignment Details"), }, { "type": "report", "is_query_report": True, "name": "Skill Evaluation Due Report", "label": _("Skill Evaluation Due Report"), "description": _("Skill Evaluation Due Report") # "doctype": "New Joinees Practical Knowledge Verification" } ] }, { "label": _("Supervisor Evaluation Marks"), "items": [ { "type": "doctype", "name": "Auto Cutting and Crimping Marks", "description": _("Auto Cutting and Crimping Marks"), }, { "type": "doctype", "name": "Semi Auto Crimping Marks", "description": _("Semi Auto Crimping Marks"), }, { "type": "doctype", "name": "Assembly Evaluation Marks", "description": _("Assembly Evaluation Marks"), }, { "type": "doctype", "name": "Quality Evaluation Marks", "description": _("Quality Evaluation Marks"), }, ] }, ] elif hr_manager: return [ { "label": _("Evaluation Forms"), "items": [ { "type": "doctype", "name": "Auto Cutting and Crimping", "description": _("Auto Cutting and Crimping"), }, { "type": "doctype", "name": "Semi Auto Crimping", "description": _("Semi Auto Crimping"), }, { "type": "doctype", "name": "Assembly Evaluation", "description": _("Assembly Evaluation"), }, { "type": "doctype", "name": "Quality Evaluation", "description": _("Quality Evaluation"), }, ] }, { "label": _("Monitoring Sheets"), "items": [ { "type": "doctype", "name": "Associate Performance Monitoring", "description": _("Associate Performance Monitoring Tool"), }, { "type": "doctype", "name": "Associate Performance Monitoring Check Sheet", "description": _("Associate Performance Monitoring Check Sheet"), }, { "type": "doctype", "name": "Knowledge Verification Sheet", "description": _("Knowledge Verification Sheet"), }, { "type": "doctype", "name": "Employee Performance Summary Report", "description": _("Employee Performance Summary Report"), } ] }, { "label": _("Tests & Questions"), "items": [ { "type": "doctype", "name": "Induction Training Assembly Area", "description": _("Induction Training Assembly Area"), }, { "type": "doctype", "name": "Induction Training Machine Area Crimping", "description": _("Induction Training Machine Area Crimping"), }, { "type": "doctype", "name": "Induction Training Machine Area Operator", "description": _("Induction Training Machine Area Operator"), }, { "type": "doctype", "name": "Crimping Questions", "description": _("Crimping Questions"), } ] }, ]

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0

1

0

1

0

null

0

7

ef8b45f32d03ee64bdd830071f72a1ecd4f9b70a

8,912

py

Python

tests/test_plugin.py

rad96/pytest-telegram

3e1fbf46c30eda0366060c897367e4f47cd48bf1

[ "MIT" ]

1

2021-06-11T07:22:06.000Z

tests/test_plugin.py

rad96/pytest-telegram

3e1fbf46c30eda0366060c897367e4f47cd48bf1

[ "MIT" ]

11

2020-12-06T00:35:45.000Z

2020-12-10T20:19:08.000Z

tests/test_plugin.py

rad96/pytest-telegram

3e1fbf46c30eda0366060c897367e4f47cd48bf1

[ "MIT" ]

null

import mock import pytest def test_pytest_telegram(testdir): """Make sure plugin works.""" testdir.makepyfile( """ import pytest def test_pass(): assert 1 == 1 def test_fail(): assert 1 == 2 @pytest.mark.skip() def test_skip(): assert 1 == 1 def test_error(test): assert 1 == "" @pytest.mark.xfail() def test_xfail(): assert 1 == 2 @pytest.mark.xfail() def test_xpass(): assert 1 == 1 """ ) telegram_token = 'Token' telegram_chat_id = '130559633' telegram_report_url = 'http://report_link.com' fail_sticker_id = 'CAACAgIAAxkBAAMIX8rohSxoNbodB1D38VZx9HI2CDwAAmIBAAIQGm0izcITZBkXtbceBA' expected_text = 'Passed=1 Failed=1 Skipped=1 Error=1 XFailed=1 XPassed=1' \ '\nTime taken: 00:00:00' \ '\nhttp://report_link.com\n' with mock.patch('requests.post') as mock_post: testdir.runpytest('--telegram_id', telegram_chat_id, '--telegram_token', telegram_token, '--telegram_report_url', telegram_report_url) sticker_request = mock_post.mock_calls[0] sticker_called_url = sticker_request[1][0] send_sticker = sticker_request[2]['json']['sticker'] message_request = mock_post.mock_calls[4] message_called_url = message_request[1][0] message_text = message_request[2]['json']['text'] message_chat_id = message_request[2]['json']['chat_id'] assert sticker_called_url == f'https://api.telegram.org/bot{telegram_token}/sendSticker' assert message_called_url == f'https://api.telegram.org/bot{telegram_token}/sendMessage' assert message_text == expected_text assert message_chat_id == telegram_chat_id assert send_sticker == fail_sticker_id def test_success_sticker_telegram(testdir): """Make sure plugin sends success sticker.""" testdir.makepyfile( """ import pytest def test_pass(): assert 1 == 1 """ ) telegram_token = 'Token' telegram_chat_id = '130559633' telegram_report_url = 'http://report_link.com' success_sticker_id = 'CAACAgIAAxkBAAMHX8roD4u8f7DCsRobma1dZNuCeBwAAlkBAAIQGm0iHZVsOV_OQB8eBA' expected_text = 'Passed=1 Failed=0 Skipped=0 Error=0 XFailed=0 XPassed=0' \ '\nTime taken: 00:00:00' \ '\nhttp://report_link.com\n' with mock.patch('requests.post') as mock_post: testdir.runpytest('--telegram_id', telegram_chat_id, '--telegram_token', telegram_token, '--telegram_report_url', telegram_report_url) sticker_request = mock_post.mock_calls[0] sticker_called_url = sticker_request[1][0] send_sticker = sticker_request[2]['json']['sticker'] message_request = mock_post.mock_calls[4] message_called_url = message_request[1][0] message_text = message_request[2]['json']['text'] message_chat_id = message_request[2]['json']['chat_id'] assert sticker_called_url == f'https://api.telegram.org/bot{telegram_token}/sendSticker' assert message_called_url == f'https://api.telegram.org/bot{telegram_token}/sendMessage' assert message_text == expected_text assert message_chat_id == telegram_chat_id assert send_sticker == success_sticker_id def test_list_failed_telegram(testdir): """Make sure plugin sends failed tests.""" testdir.makepyfile( """ import pytest def test_fail(): assert 1 != 1 """ ) telegram_token = 'Token' telegram_chat_id = '130559633' telegram_report_url = 'http://report_link.com' fail_sticker_id = 'CAACAgIAAxkBAAMIX8rohSxoNbodB1D38VZx9HI2CDwAAmIBAAIQGm0izcITZBkXtbceBA' expected_text = 'Passed=0 Failed=1 Skipped=0 Error=0 XFailed=0 XPassed=0' \ '\nTime taken: 00:00:00' \ '\nhttp://report_link.com\n' \ '\nFailed tests:' \ '\ntest_list_failed_telegram.py::test_fail\n' with mock.patch('requests.post') as mock_post: testdir.runpytest('--telegram_id', telegram_chat_id, '--telegram_token', telegram_token, '--telegram_report_url', telegram_report_url, '--telegram_list_failed') sticker_request = mock_post.mock_calls[0] sticker_called_url = sticker_request[1][0] send_sticker = sticker_request[2]['json']['sticker'] message_request = mock_post.mock_calls[4] message_called_url = message_request[1][0] message_text = message_request[2]['json']['text'] message_chat_id = message_request[2]['json']['chat_id'] assert sticker_called_url == f'https://api.telegram.org/bot{telegram_token}/sendSticker' assert message_called_url == f'https://api.telegram.org/bot{telegram_token}/sendMessage' assert message_text == expected_text assert message_chat_id == telegram_chat_id assert send_sticker == fail_sticker_id def test_list_failed_with_dots_telegram(testdir): """Make sure plugin sends limited amount of failed tests.""" testdir.makepyfile( """ import pytest @pytest.mark.parametrize('id', range(10)) def test_fail(id): assert 1 != 1 """ ) telegram_token = 'Token' telegram_chat_id = '130559633' telegram_report_url = 'http://report_link.com' fail_sticker_id = 'CAACAgIAAxkBAAMIX8rohSxoNbodB1D38VZx9HI2CDwAAmIBAAIQGm0izcITZBkXtbceBA' expected_text = 'Passed=0 Failed=10 Skipped=0 Error=0 XFailed=0 XPassed=0' \ '\nTime taken: 00:00:00' \ '\nhttp://report_link.com\n' \ '\nFailed tests:' \ '\ntest_list_failed_with_dots_telegram.py::test_fail[0]' \ '\ntest_list_failed_with_dots_telegram.py::test_fail[1]' \ '\ntest_list_failed_with_dots_telegram.py::test_fail[2]' \ '\n...' with mock.patch('requests.post') as mock_post: testdir.runpytest('--telegram_id', telegram_chat_id, '--telegram_token', telegram_token, '--telegram_report_url', telegram_report_url, '--telegram_list_failed') sticker_request = mock_post.mock_calls[0] sticker_called_url = sticker_request[1][0] send_sticker = sticker_request[2]['json']['sticker'] message_request = mock_post.mock_calls[4] message_called_url = message_request[1][0] message_text = message_request[2]['json']['text'] message_chat_id = message_request[2]['json']['chat_id'] assert sticker_called_url == f'https://api.telegram.org/bot{telegram_token}/sendSticker' assert message_called_url == f'https://api.telegram.org/bot{telegram_token}/sendMessage' assert message_text == expected_text assert message_chat_id == telegram_chat_id assert send_sticker == fail_sticker_id def test_time_taken(testdir): """Make sure plugin sends limited amount of failed tests.""" testdir.makepyfile( """ import pytest import time def test_fail(): time.sleep(1) assert 1 != 1 """ ) telegram_token = 'Token' telegram_chat_id = '130559633' telegram_report_url = 'http://report_link.com' fail_sticker_id = 'CAACAgIAAxkBAAMIX8rohSxoNbodB1D38VZx9HI2CDwAAmIBAAIQGm0izcITZBkXtbceBA' expected_text = 'Passed=0 Failed=1 Skipped=0 Error=0 XFailed=0 XPassed=0' \ '\nTime taken: 00:00:01' \ '\nhttp://report_link.com\n' with mock.patch('requests.post') as mock_post: testdir.runpytest('--telegram_id', telegram_chat_id, '--telegram_token', telegram_token, '--telegram_report_url', telegram_report_url) sticker_request = mock_post.mock_calls[0] sticker_called_url = sticker_request[1][0] send_sticker = sticker_request[2]['json']['sticker'] message_request = mock_post.mock_calls[4] message_called_url = message_request[1][0] message_text = message_request[2]['json']['text'] message_chat_id = message_request[2]['json']['chat_id'] assert sticker_called_url == f'https://api.telegram.org/bot{telegram_token}/sendSticker' assert message_called_url == f'https://api.telegram.org/bot{telegram_token}/sendMessage' assert message_text == expected_text assert message_chat_id == telegram_chat_id assert send_sticker == fail_sticker_id

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1,029

8,912

5.123421

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0.034143

0.039833

0.030349

0.935319

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0.873103

0.852618

0

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0.260435

8,912

228

98

39.087719

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0

0.30305

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0

0.177305

1

0.035461

false

0.035461

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0

0.049645

0

null

0

1

0

1

0

null

0

7

ef953047179fd7b98c1cbf2c6b4cae1f4b186e5c

173

py

Python

mythril/laser/ethereum/transaction/__init__.py

yrashk/mythril

0cea8f562726da468ab4761ff1ff3746ab0d747a

[ "MIT" ]

1

2018-09-07T10:17:35.000Z

mythril/laser/ethereum/transaction/__init__.py

yrashk/mythril

0cea8f562726da468ab4761ff1ff3746ab0d747a

[ "MIT" ]

null

mythril/laser/ethereum/transaction/__init__.py

yrashk/mythril

0cea8f562726da468ab4761ff1ff3746ab0d747a

[ "MIT" ]

1

2018-06-14T08:36:03.000Z

from mythril.laser.ethereum.transaction.transaction_models import * from mythril.laser.ethereum.transaction.symbolic import execute_message_call, execute_contract_creation

57.666667

104

0.884393

21

173

7.047619

0.619048

0.148649

0.216216

0.324324

0.472973

0

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173

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true

0

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0

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0

null

0

1

0

1

0

1

0

7

eff8387559d57b894ff6cf641e0a956fe92744bf

9,860

py

Python

src/architectures.py

axemasquelin/WaveletsDNN

e9b1ca426aa36733194304d775142e3e84c268c4

[ "MIT" ]

null

src/architectures.py

axemasquelin/WaveletsDNN

e9b1ca426aa36733194304d775142e3e84c268c4

[ "MIT" ]

null

src/architectures.py

axemasquelin/WaveletsDNN

e9b1ca426aa36733194304d775142e3e84c268c4

[ "MIT" ]

null

# coding: utf-8 """ MIT License """ ''' Project: Wavelet DNN Authors: Axel Masquelin Description: ''' # Libraries # ---------------------------------------------------------------------------- # import torchvision.models as models import torch.nn as nn import torchvision import torch import preprocessing as pre import numpy as np import utils import cv2 # ---------------------------------------------------------------------------- # class Wave_1(nn.Module): def __init__(self): super(Wave_1, self).__init__() self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(4*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): x = pre.singlelvl_wd(x, device, int(x.size()[2]/2)) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_wave2(nn.Module): def __init__(self): super(incept_wave2, self).__init__() self.nonlin = nn.ReLU(inplace = True) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(8*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): LL, x1 = pre.multiscale_wd(x, device, int(x.size()[2]/2)) LL, x2 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) x = utils.tensor_cat(x1,x2, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_wave3(nn.Module): def __init__(self): super(incept_wave3, self).__init__() self.nonlin = nn.ReLU(inplace = True) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(12*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): LL, x1 = pre.multiscale_wd(x, device, int(x.size()[2]/2)) LL, x2 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x3 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) x = utils.tensor_cat(x1,x2,x3, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_wave4(nn.Module): def __init__(self): super(incept_wave4, self).__init__() self.nonlin = nn.ReLU(inplace = True) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(16*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): LL, x1 = pre.multiscale_wd(x, device, int(x.size()[2]/2)) LL, x2 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x3 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x4 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) x = utils.tensor_cat(x1,x2,x3,x4, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_wave5(nn.Module): def __init__(self): super(incept_wave5, self).__init__() self.nonlin = nn.ReLU(inplace = True) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(20*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): LL, x1 = pre.multiscale_wd(x, device, int(x.size()[2]/2)) LL, x2 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x3 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x4 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x5 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) x = utils.tensor_cat(x1,x2,x3,x4,x5, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_wave6(nn.Module): def __init__(self): super(incept_wave6, self).__init__() self.nonlin = nn.ReLU(inplace = True) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(24*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): LL, x1 = pre.multiscale_wd(x, device, int(x.size()[2]/2)) LL, x2 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x3 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x4 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x5 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) LL, x6 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2)) x = utils.tensor_cat(x1,x2,x3,x4,x5,x6, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class Conv_1 (nn.Module): def __init__(self): super(Conv_1, self).__init__() self.features = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 2, stride= 2, padding = 1), nn.ReLU(inplace = True), nn.MaxPool2d(kernel_size = 3, stride = 2), ) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(4*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): # print(x.size()) x = self.features(x) # print(x.size()) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_conv(nn.Module): def __init__(self): super(incept_conv, self).__init__() self.block2x2 = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 2, stride= 2, padding = 0, dilation = 1), # nn.Conv2d(1, 4, kernel_size= 3, stride= 2, padding = 1, dilation = 1), nn.ReLU(inplace = True), # nn.MaxPool2d(kernel_size = 3, stride = 2), ) self.block3x3 = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 4, stride= 4, padding = 2, dilation = 2), # nn.Conv2d(1, 4, kernel_size= 5, stride= 2, padding = 2, dilation = 1), nn.ReLU(inplace = True), # nn.MaxPool2d(kernel_size = 4, stride = 2), ) self.block5x5 = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 8, stride= 8, padding = 8, dilation = 3), # nn.Conv2d(1, 4, kernel_size= 7, stride= 2, padding = 3, dilation = 1), nn.ReLU(inplace = True), # nn.MaxPool2d(kernel_size = 8, stride = 2), ) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(12*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): x2 = self.block2x2(x) x3 = self.block3x3(x) x5 = self.block5x5(x) x = utils.tensor_cat(x2,x3,x5, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x class incept_dilationconv(nn.Module): def __init__(self): super(incept_dilationconv, self).__init__() self.block2x2 = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 2, stride= 2, padding = 0, dilation = 1), nn.ReLU(inplace = True), # nn.MaxPool2d(kernel_size = 3, stride = 2), ) self.block3x3 = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 2, stride= 4, padding = 1, dilation = 2), nn.ReLU(inplace = True), # nn.MaxPool2d(kernel_size = 4, stride = 2), ) self.block5x5 = nn.Sequential( nn.Conv2d(1, 4, kernel_size= 2, stride= 8, padding = 2, dilation = 4), nn.ReLU(inplace = True), # nn.MaxPool2d(kernel_size = 8, stride = 2), ) self.avgpool = nn.AdaptiveAvgPool2d((6,6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(12*6*6, 150), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(150, 50), nn.ReLU(inplace=True), nn.Linear(50, 2), ) def forward(self, x, device): x2 = self.block2x2(x) x3 = self.block3x3(x) x5 = self.block5x5(x) x = utils.tensor_cat(x2,x3,x5, padding = False) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x

31.909385

84

0.513692

1,276

9,860

3.863636

0.074451

0.036511

0.079108

0.103448

0.897769

0.891075

0.87931

0.826572

0.822718

0

0.058912

0.325152

9,860

308

85

32.012987

0.681996

0.072515

0

0.74477

0

1

0.075314

false

0

0.033473

0

0.1841

0

null

0

1

0

1

0

null

0

7

4bd284fd198fa6cebd148b6dbfe8086c8993947e

38

py

Python

aula11cores.py

mario-nobre/python-guanabara

2048d833f61c8545ced2163ae1b6b0d844494ada

[ "MIT" ]

null

aula11cores.py

mario-nobre/python-guanabara

2048d833f61c8545ced2163ae1b6b0d844494ada

[ "MIT" ]

null

aula11cores.py

mario-nobre/python-guanabara

2048d833f61c8545ced2163ae1b6b0d844494ada

[ "MIT" ]

null

print('\33[1;30;43mOlá, mundo!\33[m')

19

37

0.631579

8

38

3

0.875

0

0.25

0.052632

38

1

38

0.416667

0

0.736842

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

1

0

null

0

1

0

1

0

8

ef24051d2041cdd1cf75ecbeaed36bf8b2cc0807

14,328

py

Python

tests/ut/python/adv_robustness/attacks/test_iterative_gradient_method.py

hboshnak/mindarmour

0609a4eaea875a84667bed279add9305752880cc

[ "Apache-2.0" ]

null

tests/ut/python/adv_robustness/attacks/test_iterative_gradient_method.py

hboshnak/mindarmour

0609a4eaea875a84667bed279add9305752880cc

[ "Apache-2.0" ]

null

tests/ut/python/adv_robustness/attacks/test_iterative_gradient_method.py

hboshnak/mindarmour

0609a4eaea875a84667bed279add9305752880cc

[ "Apache-2.0" ]

null

# Copyright 2019 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. """ Iterative-gradient Attack test. """ import gc import numpy as np import pytest from mindspore.ops import operations as P from mindspore.nn import Cell from mindspore import context from mindspore.nn import SoftmaxCrossEntropyWithLogits from mindarmour.adv_robustness.attacks import BasicIterativeMethod from mindarmour.adv_robustness.attacks import MomentumIterativeMethod from mindarmour.adv_robustness.attacks import ProjectedGradientDescent from mindarmour.adv_robustness.attacks import IterativeGradientMethod from mindarmour.adv_robustness.attacks import DiverseInputIterativeMethod from mindarmour.adv_robustness.attacks import MomentumDiverseInputIterativeMethod # for user class Net(Cell): """ Construct the network of target model. Examples: >>> net = Net() """ def __init__(self): super(Net, self).__init__() self._softmax = P.Softmax() def construct(self, inputs): """ Construct network. Args: inputs (Tensor): Input data. """ out = self._softmax(inputs) return out @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour def test_basic_iterative_method_ascend(): """ Feature: Basic iterative method unit test for ascend. Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) for i in range(5): net = Net() attack = BasicIterativeMethod(net, nb_iter=i + 1, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any( ms_adv_x != input_np), 'Basic iterative method: generate value' \ ' must not be equal to original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_card @pytest.mark.component_mindarmour def test_basic_iterative_method_cpu(): """ Feature: Basic iterative method unit test for cpu. Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) for i in range(5): net = Net() attack = BasicIterativeMethod(net, nb_iter=i + 1, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any( ms_adv_x != input_np), 'Basic iterative method: generate value' \ ' must not be equal to original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour def test_momentum_iterative_method_ascend(): """ Feature: Momentum iterative method unit test for ascend Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) for i in range(5): attack = MomentumIterativeMethod(Net(), nb_iter=i + 1, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any(ms_adv_x != input_np), 'Momentum iterative method: generate' \ ' value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_card @pytest.mark.component_mindarmour def test_momentum_iterative_method_cpu(): """ Feature: Momentum iterative method unit test for cpu Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) for i in range(5): attack = MomentumIterativeMethod(Net(), nb_iter=i + 1, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any(ms_adv_x != input_np), 'Momentum iterative method: generate' \ ' value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour def test_projected_gradient_descent_method_ascend(): """ Feature: Projected gradient descent method unit test for ascend Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) for i in range(5): attack = ProjectedGradientDescent(Net(), nb_iter=i + 1, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any( ms_adv_x != input_np), 'Projected gradient descent method: ' \ 'generate value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_card @pytest.mark.component_mindarmour def test_projected_gradient_descent_method_cpu(): """ Feature: Projected gradient descent method unit test for cpu Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) for i in range(5): attack = ProjectedGradientDescent(Net(), nb_iter=i + 1, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any( ms_adv_x != input_np), 'Projected gradient descent method: ' \ 'generate value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour def test_diverse_input_iterative_method_ascend(): """ Feature: Diverse input iterative method unit test for ascend Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) attack = DiverseInputIterativeMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any(ms_adv_x != input_np), 'Diverse input iterative method: generate' \ ' value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_card @pytest.mark.component_mindarmour def test_diverse_input_iterative_method_cpu(): """ Feature: Diverse input iterative method unit test for cpu Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) attack = DiverseInputIterativeMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any(ms_adv_x != input_np), 'Diverse input iterative method: generate' \ ' value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour def test_momentum_diverse_input_iterative_method_ascend(): """ Feature: Momentum diverse input iterative method unit test for ascend Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) attack = MomentumDiverseInputIterativeMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any(ms_adv_x != input_np), 'Momentum diverse input iterative method: ' \ 'generate value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_card @pytest.mark.component_mindarmour def test_momentum_diverse_input_iterative_method_cpu(): """ Feature: Momentum diverse input iterative method unit test for cpu Description: Given multiple images, we want to make sure the adversarial examples generated are different from the images Expectation: input_np != ms_adv_x """ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) attack = MomentumDiverseInputIterativeMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) ms_adv_x = attack.generate(input_np, label) assert np.any(ms_adv_x != input_np), 'Momentum diverse input iterative method: ' \ 'generate value must not be equal to' \ ' original value.' del input_np, label, ms_adv_x gc.collect() @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour def test_error_ascend(): """ Feature: test error for ascend Description: test error Expectation: error detected or attach.generate works properly """ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") attack = IterativeGradientMethod(Net(), bounds=(0.0, 1.0), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) with pytest.raises(NotImplementedError): input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) assert attack.generate(input_np, label) del input_np, label gc.collect() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_card @pytest.mark.component_mindarmour def test_error_cpu(): """ Feature: test error for cpu Description: test error Expectation: error detected or attach.generate works properly """ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") attack = IterativeGradientMethod(Net(), bounds=(0.0, 1.0), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) with pytest.raises(NotImplementedError): input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32) label = np.asarray([2], np.int32) label = np.eye(3)[label].astype(np.float32) assert attack.generate(input_np, label) del input_np, label gc.collect()

40.134454

116

0.680276

1,885

14,328

4.995756

0.098674

0.041627

0.025486

0.028034

0.88542

0.884464

0.857067

0.851333

0.843262

0.840076

0

0.020798

0.221454

14,328

356

117

40.247191

0.823398

0.226689

0

0.857143

0

0.082584

0

0.053571

1

0.0625

false

0

0.058036

0

0.129464

0

null

0

1

0

1

0

null

0

7

ef5cbe1117b36804aeaea897f8915ff559ab6958

15,844

py

Python

discO/plugin/gala/tests/test_wrapper.py

GalOrrery/discO

3b17b6ead65908c053b09a1a967e8a1819a06209

[ "BSD-3-Clause" ]

1

2020-11-17T14:40:51.000Z

discO/plugin/gala/tests/test_wrapper.py

nstarman/discO

3b17b6ead65908c053b09a1a967e8a1819a06209

[ "BSD-3-Clause" ]

45

2020-11-17T14:30:13.000Z

2021-02-26T18:47:37.000Z

discO/plugin/gala/tests/test_wrapper.py

nstarman/discO

3b17b6ead65908c053b09a1a967e8a1819a06209

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """Testing :mod:`~discO.plugin.gala.wrapper`.""" __all__ = [ "Test_GalaPotentialWrapperMeta", "Test_GalaPotentialWrapper", ] ############################################################################## # IMPORTS # BUILT-IN from abc import abstractmethod # THIRD PARTY import astropy.coordinates as coord import astropy.units as u import gala.potential as gpot import numpy as np import pytest from gala.units import galactic # PROJECT-SPECIFIC from discO.core.tests.test_wrapper import ( Test_PotentialWrapper as PotentialWrapper_Test, ) from discO.core.tests.test_wrapper import ( Test_PotentialWrapperMeta as PotentialWrapperMeta_Test, ) from discO.plugin.gala import wrapper from discO.utils import resolve_framelike, vectorfield ############################################################################## # TESTS ############################################################################## class Test_GalaPotentialWrapperMeta( PotentialWrapperMeta_Test, obj=wrapper.GalaPotentialMeta, ): @classmethod def setup_class(cls): """Setup fixtures for testing.""" super().setup_class() # now gala stuff # override super cls.potential = gpot.KeplerPotential( m=1 * u.solMass, units=galactic, ) # /def ################################################################# # Method Tests def test_total_mass(self): """Test method ``total_mass``.""" # TODO! upstream fix assert np.isnan(self.subclass.total_mass(self.potential)) # assert np.allclose( # self.subclass.total_mass(self.potential), # 1 * u.solMass, # ) # /def def test_density(self): """Test method ``density``.""" # --------------- # when there isn't a frame with pytest.raises(TypeError, match="must have a frame."): self.subclass.density(self.potential, self.points) # --------------- # basic points, values = self.subclass.density( self.potential, self.points.data, ) # the points are unchanged assert points is self.points.data # check data types assert isinstance(points, coord.BaseRepresentation) # and on the values assert u.allclose(values, [0.0, 0.0, 0.0] * u.solMass / u.pc ** 3) # --------------- # frame # test the different inputs for frame in ( coord.Galactocentric, coord.Galactocentric(), "galactocentric", ): points, values = self.subclass.density( self.potential, self.points, frame=frame, ) assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, resolve_framelike(frame).__class__) assert u.allclose(values, [0.0, 0.0, 0.0] * u.solMass / u.pc ** 3) # TODO! test the specific values # --------------- # representation_type points, values = self.subclass.density( self.potential, self.points, frame=self.points.frame.replicate_without_data(), representation_type=coord.CartesianRepresentation, ) assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.frame) assert isinstance(points.data, coord.CartesianRepresentation) assert u.allclose(values, [0.0, 0.0, 0.0] * u.solMass / u.pc ** 3) # TODO! test the specific values # /def def test_potential(self): """Test method ``potential``.""" # --------------- # when there isn't a frame with pytest.raises(TypeError, match="must have a frame."): self.subclass.potential(self.potential, self.points) # --------------- # basic points, values = self.subclass.potential( self.potential, self.points.data, ) # the points are unchanged assert points is self.points.data # check data types assert isinstance(points, coord.BaseRepresentation) # and on the values assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # --------------- # frame # test the different inputs for frame in ( coord.Galactocentric, coord.Galactocentric(), "galactocentric", ): points, values = self.subclass.potential( self.potential, self.points, frame=frame, ) assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, resolve_framelike(frame).__class__) assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # --------------- # representation_type points, values = self.subclass.potential( self.potential, self.points, frame=self.points.frame.replicate_without_data(), representation_type=coord.CartesianRepresentation, ) assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.frame) assert isinstance(points.data, coord.CartesianRepresentation) assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # /def def test_specific_force(self): """Test method ``specific_force``.""" # --------------- # when there isn't a frame with pytest.raises(TypeError, match="must have a frame."): self.subclass.specific_force(self.potential, self.points) # --------------- # basic vf = self.subclass.specific_force(self.potential, self.points.data) assert isinstance(vf, vectorfield.BaseVectorField) assert isinstance(vf.points, coord.CartesianRepresentation) assert hasattr(vf, "vf_x") assert hasattr(vf, "vf_y") assert hasattr(vf, "vf_z") assert vf.frame is None # TODO! test the specific values # --------------- # frame # test the different inputs for frame in ( coord.Galactocentric, coord.Galactocentric(), "galactocentric", ): vf = self.subclass.specific_force( self.potential, self.points, frame=frame, ) assert isinstance(vf, vectorfield.BaseVectorField) assert isinstance(vf.points, coord.CartesianRepresentation) assert hasattr(vf, "vf_x") assert hasattr(vf, "vf_y") assert hasattr(vf, "vf_z") assert isinstance(vf.frame, resolve_framelike(frame).__class__) # TODO! test the specific values # --------------- # representation_type vf = self.subclass.specific_force( self.potential, self.points, frame=self.points.frame.replicate_without_data(), representation_type=coord.CartesianRepresentation, ) assert isinstance(vf, vectorfield.BaseVectorField) assert isinstance(vf.points, coord.CartesianRepresentation) assert hasattr(vf, "vf_x") assert hasattr(vf, "vf_y") assert hasattr(vf, "vf_z") assert isinstance(vf.frame, self.frame) # TODO! test the specific values # /def def test_acceleration(self): """Test method ``acceleration``.""" assert self.subclass.acceleration == self.subclass.specific_force # /def ################################################################# # Usage Tests # /class ##################################################################### class Test_GalaPotentialWrapper( PotentialWrapper_Test, obj=wrapper.GalaPotentialWrapper, ): @classmethod def setup_class(cls): """Setup fixtures for testing.""" cls.potential = gpot.KeplerPotential( m=1 * u.solMass, units=galactic, ) # set up the rest super().setup_class() cls.inst = cls.obj( cls.potential, frame="galactocentric", representation_type="cylindrical", ) # /def ################################################################# # Method Tests def test_total_mass(self): """Test method ``total_mass``.""" # TODO! upstream fix assert np.isnan(self.subclass.total_mass(self.potential)) # assert np.allclose(self.inst.total_mass(), 1 * u.solMass) # /def def test_density(self): """Test method ``specific_force``.""" # --------------- # basic points, values = self.inst.density(self.points.data) # check data types assert isinstance(points, self.inst.frame.__class__) # and on the values assert u.allclose(values, [0.0, 0.0, 0.0] * u.solMass / u.pc ** 3) # TODO! test the specific values # --------------- # with a frame points, values = self.inst.density(self.points) # check data types assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.inst.frame.__class__) # and on the values assert u.allclose(values, [0.0, 0.0, 0.0] * u.solMass / u.pc ** 3) # TODO! test the specific values # --------------- # can't pass frame # test the different inputs with pytest.raises(TypeError, match="keyword argument 'frame'"): points, values = self.inst.potential( self.points, frame=coord.Galactocentric(), ) # --------------- # representation_type points, values = self.inst.density( self.points, representation_type=coord.CartesianRepresentation, ) assert points is not self.points assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.inst.frame.__class__) assert isinstance(points.data, coord.CartesianRepresentation) assert u.allclose(values, [0.0, 0.0, 0.0] * u.solMass / u.pc ** 3) # TODO! test the specific values # /def def test_potential(self): """Test method ``potential``.""" # --------------- # basic points, values = self.inst.potential(self.points.data) # check data types assert isinstance(points, self.inst.frame.__class__) # and on the values assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # --------------- # with a frame points, values = self.inst.potential(self.points) # check data types assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.inst.frame.__class__) # and on the values assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # --------------- # can't pass frame # test the different inputs with pytest.raises(TypeError, match="keyword argument 'frame'"): points, values = self.inst.potential( self.points, frame=coord.Galactocentric(), ) # --------------- # representation_type points, values = self.inst.potential( self.points, representation_type=coord.CartesianRepresentation, ) assert points is not self.points assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.inst.frame.__class__) assert isinstance(points.data, coord.CartesianRepresentation) assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # /def @abstractmethod def test___call__(self): """Test method ``specific_force``.""" # --------------- # basic points, values = self.inst(self.points.data) # check data types assert isinstance(points, self.inst.frame.__class__) # and on the values assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # --------------- # with a frame points, values = self.inst(self.points) # check data types assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.inst.frame.__class__) # and on the values assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # --------------- # can't pass frame # test the different inputs with pytest.raises(TypeError, match="keyword argument 'frame'"): points, values = self.inst( self.points, frame=coord.Galactocentric(), ) # --------------- # representation_type points, values = self.inst( self.points, representation_type=coord.CartesianRepresentation, ) assert points is not self.points assert isinstance(points, coord.SkyCoord) assert isinstance(points.frame, self.inst.frame.__class__) assert isinstance(points.data, coord.CartesianRepresentation) assert isinstance(values, u.Quantity) assert values.unit == u.kpc ** 2 / u.Myr ** 2 # TODO! test the specific values # /def def test_specific_force(self): """Test method ``specific_force``.""" # --------------- # basic vf = self.inst.specific_force(self.points) assert isinstance(vf, vectorfield.BaseVectorField) assert isinstance(vf.points, coord.CylindricalRepresentation) assert hasattr(vf, "vf_rho") assert hasattr(vf, "vf_phi") assert hasattr(vf, "vf_z") assert vf.vf_rho.unit == u.kpc / u.Myr ** 2 assert isinstance(vf.frame, coord.Galactocentric) # TODO! test the specific values # --------------- # frame # test the different inputs with pytest.raises(TypeError, match="keyword argument 'frame'"): vf = self.inst.specific_force( self.points, frame=coord.Galactocentric(), ) # TODO! test the specific values # --------------- # representation_type vf = self.inst.specific_force( self.points, representation_type=coord.CartesianRepresentation, ) assert isinstance(vf, vectorfield.BaseVectorField) assert isinstance(vf.points, coord.CartesianRepresentation) assert hasattr(vf, "vf_x") assert hasattr(vf, "vf_y") assert hasattr(vf, "vf_z") assert vf.vf_x.unit == u.kpc / u.Myr ** 2 assert isinstance(vf.frame, coord.Galactocentric) # TODO! test the specific values # /def @abstractmethod def test_acceleration(self): """Test method ``acceleration``.""" assert self.subclass.acceleration == self.subclass.specific_force # /def # /class ############################################################################## # END

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Python

skforecast/model_selection/model_selection.py

JavierEscobarOrtiz/skforecast

a3af4a1dd4201c582f159d4e3a1734ed6d29b6c5

[ "MIT" ]

1

2021-12-01T09:21:21.000Z

skforecast/model_selection/model_selection.py

JavierEscobarOrtiz/skforecast

a3af4a1dd4201c582f159d4e3a1734ed6d29b6c5

[ "MIT" ]

null

skforecast/model_selection/model_selection.py

JavierEscobarOrtiz/skforecast

a3af4a1dd4201c582f159d4e3a1734ed6d29b6c5

[ "MIT" ]

null

################################################################################ # skforecast.model_selection # # # # This work by Joaquin Amat Rodrigo is licensed under a Creative Commons # # Attribution 4.0 International License. # ################################################################################ # coding=utf-8 from typing import Union, Tuple, Optional, Any import numpy as np import pandas as pd import warnings import logging from copy import deepcopy from tqdm import tqdm from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_absolute_percentage_error from sklearn.metrics import mean_squared_log_error from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler import optuna from optuna.samplers import TPESampler, RandomSampler optuna.logging.set_verbosity(optuna.logging.WARNING) # disable optuna logs from skopt.utils import use_named_args from skopt import gp_minimize from ..ForecasterAutoreg import ForecasterAutoreg from ..ForecasterAutoregCustom import ForecasterAutoregCustom from ..ForecasterAutoregDirect import ForecasterAutoregDirect from ..ForecasterAutoregMultiOutput import ForecasterAutoregMultiOutput logging.basicConfig( format = '%(name)-10s %(levelname)-5s %(message)s', level = logging.INFO, ) def time_series_splitter( y: Union[np.ndarray, pd.Series], initial_train_size: int, steps: int, allow_incomplete_fold: bool=True, verbose: bool=True ) -> Union[np.ndarray, np.ndarray]: ''' Split indices of a time series into multiple train-test pairs. The order of is maintained and the training set increases in each iteration. Parameters ---------- y : 1d numpy ndarray, pandas Series Training time series values. initial_train_size: int Number of samples in the initial train split. steps : int Number of steps to predict. allow_incomplete_fold : bool, default `True` The last test set is allowed to be incomplete if it does not reach `steps` observations. Otherwise, the latest observations are discarded. verbose : bool, default `True` Print number of splits created. Yields ------ train : 1d numpy ndarray Training indices. test : 1d numpy ndarray Test indices. ''' if not isinstance(y, (np.ndarray, pd.Series)): raise Exception('`y` must be `1D np.ndarray` o `pd.Series`.') elif isinstance(y, np.ndarray) and y.ndim != 1: raise Exception( f"`y` must be `1D np.ndarray` o `pd.Series`, " f"got `np.ndarray` with {y.ndim} dimensions." ) if initial_train_size > len(y): raise Exception( '`initial_train_size` must be smaller than length of `y`.' ' Try to reduce `initial_train_size` or `steps`.' ) if isinstance(y, pd.Series): y = y.to_numpy().copy() folds = (len(y) - initial_train_size) // steps + 1 # +1 fold is needed to allow including the remainder in the last iteration. remainder = (len(y) - initial_train_size) % steps if verbose: if folds == 1: print(f"Number of folds: {folds - 1}") print("Not enough observations in `y` to create even a complete fold." " Try to reduce `initial_train_size` or `steps`." ) elif remainder == 0: print(f"Number of folds: {folds - 1}") elif remainder != 0 and allow_incomplete_fold: print(f"Number of folds: {folds}") print( f"Since `allow_incomplete_fold=True`, " f"last fold only includes {remainder} observations instead of {steps}." ) print( 'Incomplete folds with few observations could overestimate or ', 'underestimate validation metrics.' ) elif remainder != 0 and not allow_incomplete_fold: print(f"Number of folds: {folds - 1}") print( f"Since `allow_incomplete_fold=False`, " f"last {remainder} observations are descarted." ) if folds == 1: # There are no observations to create even a complete fold return [] for i in range(folds): if i < folds - 1: train_end = initial_train_size + i * steps train_indices = range(train_end) test_indices = range(train_end, train_end + steps) else: if remainder != 0 and allow_incomplete_fold: train_end = initial_train_size + i * steps train_indices = range(train_end) test_indices = range(train_end, len(y)) else: break yield train_indices, test_indices def _get_metric(metric:str) -> callable: ''' Get the corresponding scikitlearn function to calculate the metric. Parameters ---------- metric : {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_log_error'} Metric used to quantify the goodness of fit of the model. Returns ------- metric : callable scikitlearn function to calculate the desired metric. ''' if metric not in ['mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_log_error']: raise Exception( f"Allowed metrics are: 'mean_squared_error', 'mean_absolute_error', " f"'mean_absolute_percentage_error' and 'mean_squared_log_error'. Got {metric}." ) metrics = { 'mean_squared_error': mean_squared_error, 'mean_absolute_error': mean_absolute_error, 'mean_absolute_percentage_error': mean_absolute_percentage_error, 'mean_squared_log_error': mean_squared_log_error } metric = metrics[metric] return metric def cv_forecaster( forecaster, y: pd.Series, initial_train_size: int, steps: int, metric: Union[str, callable], exog: Optional[Union[pd.Series, pd.DataFrame]]=None, allow_incomplete_fold: bool=True, verbose: bool=True ) -> Tuple[np.array, pd.DataFrame]: ''' Cross-validation of forecaster. The order of data is maintained and the training set increases in each iteration. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forecaster model. y : pandas Series Training time series values. initial_train_size: int Number of samples in the initial train split. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} It callable: Function with arguments y_true, y_pred that returns a float. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. allow_incomplete_fold : bool, default `True` The last test partition is allowed to be incomplete if it does not reach `steps` observations. Otherwise, the latest observations are discarded. verbose : bool, default `True` Print number of folds used for cross validation. Returns ------- cv_metrics: 1d numpy ndarray Value of the metric for each fold. cv_predictions: pandas DataFrame Predictions. ''' if initial_train_size > len(y): raise Exception( '`initial_train_size` must be smaller than length of `y`.' ) if initial_train_size is not None and initial_train_size < forecaster.window_size: raise Exception( f"`initial_train_size` must be greater than " f"forecaster's window_size ({forecaster.window_size})." ) forecaster = deepcopy(forecaster) if isinstance(metric, str): metric = _get_metric(metric=metric) splits = time_series_splitter( y = y, initial_train_size = initial_train_size, steps = steps, allow_incomplete_fold = allow_incomplete_fold, verbose = verbose ) cv_predictions = [] cv_metrics = [] for train_index, test_index in splits: if exog is None: forecaster.fit(y=y.iloc[train_index]) pred = forecaster.predict(steps=len(test_index)) else: forecaster.fit(y=y.iloc[train_index], exog=exog.iloc[train_index,]) pred = forecaster.predict(steps=len(test_index), exog=exog.iloc[test_index]) metric_value = metric( y_true = y.iloc[test_index], y_pred = pred ) cv_predictions.append(pred) cv_metrics.append(metric_value) cv_predictions = pd.concat(cv_predictions) cv_predictions = pd.DataFrame(cv_predictions) cv_metrics = np.array(cv_metrics) return cv_metrics, cv_predictions def _backtesting_forecaster_refit( forecaster, y: pd.Series, steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, interval: Optional[list]=None, n_boot: int=500, random_state: int=123, in_sample_residuals: bool=True, verbose: bool=False ) -> Tuple[float, pd.DataFrame]: ''' Backtesting of forecaster model with a re-fitting strategy. A copy of the original forecaster is created so it is not modified during the process. In each iteration: - Fit forecaster with the training set. - A number of `steps` ahead are predicted. - The training set increases with `steps` observations. - The model is re-fitted using the new training set. In order to apply backtesting with re-fit, an initial training set must be available, otherwise it would not be possible to increase the training set after each iteration. `initial_train_size` must be provided. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forecaster model. y : pandas Series Training time series values. initial_train_size: int Number of samples in the initial train split. The backtest forecaster is trained using the first `initial_train_size` observations. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. exog :panda Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. interval: list, default `None` Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. If `None`, no intervals are estimated. Only available for forecaster of type ForecasterAutoreg and ForecasterAutoregCustom. n_boot: int, default `500` Number of bootstrapping iterations used to estimate prediction intervals. random_state: int, default `123` Sets a seed to the random generator, so that boot intervals are always deterministic. in_sample_residuals: bool, default `True` If `True`, residuals from the training data are used as proxy of prediction error to create prediction intervals. If `False`, out_sample_residuals are used if they are already stored inside the forecaster. verbose : bool, default `False` Print number of folds and index of training and validation sets used for backtesting. Returns ------- metric_value: float Value of the metric. backtest_predictions: pandas Dataframe Value of predictions and their estimated interval if `interval` is not `None`. column pred = predictions. column lower_bound = lower bound of the interval. column upper_bound = upper bound interval of the interval. ''' forecaster = deepcopy(forecaster) if isinstance(metric, str): metric = _get_metric(metric=metric) backtest_predictions = [] folds = int(np.ceil((len(y) - initial_train_size) / steps)) remainder = (len(y) - initial_train_size) % steps if verbose: print(f"Information of backtesting process") print(f"----------------------------------") print(f"Number of observations used for initial training: {initial_train_size}") print(f"Number of observations used for backtesting: {len(y) - initial_train_size}") print(f" Number of folds: {folds}") print(f" Number of steps per fold: {steps}") if remainder != 0: print(f" Last fold only includes {remainder} observations.") print("") for i in range(folds): if fixed_train_size: # The train size doesn't increase but moves by `steps` in each iteration. train_idx_start = i * steps train_idx_end = initial_train_size + i * steps else: # The train size increases by `steps` in each iteration. train_idx_start = 0 train_idx_end = initial_train_size + i * steps print(f"Data partition in fold: {i}") if i < folds - 1: print(f" Training: {y.index[train_idx_start]} -- {y.index[train_idx_end - 1]} (n={len(y.index[train_idx_start:train_idx_end])})") print(f" Validation: {y.index[train_idx_end]} -- {y.index[train_idx_end + steps - 1]} (n={len(y.index[train_idx_end:train_idx_end + steps])})") else: print(f" Training: {y.index[train_idx_start]} -- {y.index[train_idx_end - 1]} (n={len(y.index[train_idx_start:train_idx_end])})") print(f" Validation: {y.index[train_idx_end]} -- {y.index[-1]} (n={len(y.index[train_idx_end:])})") print("") if folds > 50: print( f"Forecaster will be fit {folds} times. This can take substantial amounts of time. " f"If not feasible, try with `refit = False`. \n" ) for i in range(folds): # In each iteration (except the last one) the model is fitted before making predictions. if fixed_train_size: # The train size doesn't increases but moves by `steps` in each iteration. train_idx_start = i * steps train_idx_end = initial_train_size + i * steps else: # The train size increases by `steps` in each iteration. train_idx_start = 0 train_idx_end = initial_train_size + i * steps if exog is not None: next_window_exog = exog.iloc[train_idx_end:train_idx_end + steps, ] if interval is None: if i < folds - 1: if exog is None: forecaster.fit(y=y.iloc[train_idx_start:train_idx_end]) pred = forecaster.predict(steps=steps) else: forecaster.fit( y = y.iloc[train_idx_start:train_idx_end], exog = exog.iloc[train_idx_start:train_idx_end, ] ) pred = forecaster.predict(steps=steps, exog=next_window_exog) else: if remainder == 0: if exog is None: forecaster.fit(y=y.iloc[train_idx_start:train_idx_end]) pred = forecaster.predict(steps=steps) else: forecaster.fit( y = y.iloc[train_idx_start:train_idx_end], exog = exog.iloc[train_idx_start:train_idx_end, ] ) pred = forecaster.predict(steps=steps, exog=next_window_exog) else: # Only the remaining steps need to be predicted steps = remainder if exog is None: forecaster.fit(y=y.iloc[train_idx_start:train_idx_end]) pred = forecaster.predict(steps=steps) else: forecaster.fit( y = y.iloc[train_idx_start:train_idx_end], exog = exog.iloc[train_idx_start:train_idx_end, ] ) pred = forecaster.predict(steps=steps, exog=next_window_exog) else: if i < folds - 1: if exog is None: forecaster.fit(y=y.iloc[train_idx_start:train_idx_end]) pred = forecaster.predict_interval( steps = steps, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: forecaster.fit( y = y.iloc[train_idx_start:train_idx_end], exog = exog.iloc[train_idx_start:train_idx_end, ] ) pred = forecaster.predict_interval( steps = steps, exog = next_window_exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: if remainder == 0: if exog is None: forecaster.fit(y=y.iloc[train_idx_start:train_idx_end]) pred = forecaster.predict_interval( steps = steps, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: forecaster.fit( y = y.iloc[train_idx_start:train_idx_end], exog = exog.iloc[train_idx_start:train_idx_end, ] ) pred = forecaster.predict_interval( steps = steps, exog = next_window_exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: # Only the remaining steps need to be predicted steps = remainder if exog is None: forecaster.fit(y=y.iloc[train_idx_start:train_idx_end]) pred = forecaster.predict_interval( steps = steps, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: forecaster.fit( y = y.iloc[train_idx_start:train_idx_end], exog = exog.iloc[train_idx_start:train_idx_end, ] ) pred = forecaster.predict_interval( steps = steps, exog = next_window_exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) backtest_predictions.append(pred) backtest_predictions = pd.concat(backtest_predictions) if isinstance(backtest_predictions, pd.Series): backtest_predictions = pd.DataFrame(backtest_predictions) metric_value = metric( y_true = y.iloc[initial_train_size: initial_train_size + len(backtest_predictions)], y_pred = backtest_predictions['pred'] ) return metric_value, backtest_predictions def _backtesting_forecaster_no_refit( forecaster, y: pd.Series, steps: int, metric: Union[str, callable], initial_train_size: Optional[int]=None, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, interval: Optional[list]=None, n_boot: int=500, random_state: int=123, in_sample_residuals: bool=True, verbose: bool=False ) -> Tuple[float, pd.DataFrame]: ''' Backtesting of forecaster without iterative re-fitting. In each iteration, a number of `steps` are predicted. A copy of the original forecaster is created so it is not modified during the process. If `forecaster` is already trained and `initial_train_size` is `None`, no initial train is done and all data is used to evaluate the model. However, the first `len(forecaster.last_window)` observations are needed to create the initial predictors, so no predictions are calculated for them. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forecaster model. y : pandas Series Training time series values. initial_train_size: int, default `None` Number of samples in the initial train split. If `None` and `forecaster` is already trained, no initial train is done and all data is used to evaluate the model. However, the first `len(forecaster.last_window)` observations are needed to create the initial predictors, so no predictions are calculated for them. steps : int, None Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. exog :panda Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. interval: list, default `None` Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. If `None`, no intervals are estimated. Only available for forecaster of type ForecasterAutoreg and ForecasterAutoregCustom. n_boot: int, default `500` Number of bootstrapping iterations used to estimate prediction intervals. random_state: int, default `123` Sets a seed to the random generator, so that boot intervals are always deterministic. in_sample_residuals: bool, default `True` If `True`, residuals from the training data are used as proxy of prediction error to create prediction intervals. If `False`, out_sample_residuals are used if they are already stored inside the forecaster. verbose : bool, default `False` Print number of folds and index of training and validation sets used for backtesting. Returns ------- metric_value: float Value of the metric. backtest_predictions: pandas DataFrame Value of predictions and their estimated interval if `interval` is not `None`. column pred = predictions. column lower_bound = lower bound of the interval. column upper_bound = upper bound interval of the interval. ''' forecaster = deepcopy(forecaster) if isinstance(metric, str): metric = _get_metric(metric=metric) backtest_predictions = [] if initial_train_size is not None: if exog is None: forecaster.fit(y=y.iloc[:initial_train_size]) else: forecaster.fit( y = y.iloc[:initial_train_size], exog = exog.iloc[:initial_train_size, ] ) window_size = forecaster.window_size else: # Although not used for training, first observations are needed to create # the initial predictors window_size = forecaster.window_size initial_train_size = window_size folds = int(np.ceil((len(y) - initial_train_size) / steps)) remainder = (len(y) - initial_train_size) % steps if verbose: print(f"Information of backtesting process") print(f"----------------------------------") print(f"Number of observations used for initial training or as initial window: {initial_train_size}") print(f"Number of observations used for backtesting: {len(y) - initial_train_size}") print(f" Number of folds: {folds}") print(f" Number of steps per fold: {steps}") if remainder != 0: print(f" Last fold only includes {remainder} observations") print("") for i in range(folds): last_window_end = initial_train_size + i * steps print(f"Data partition in fold: {i}") if i < folds - 1: print(f" Training: {y.index[0]} -- {y.index[initial_train_size - 1]} (n={len(y.index[:initial_train_size])})") print(f" Validation: {y.index[last_window_end]} -- {y.index[last_window_end + steps -1]} (n={len(y.index[last_window_end:last_window_end + steps])})") else: print(f" Training: {y.index[0]} -- {y.index[initial_train_size - 1]} (n={len(y.index[:initial_train_size])})") print(f" Validation: {y.index[last_window_end]} -- {y.index[-1]} (n={len(y.index[last_window_end:])})") print("") for i in range(folds): # Since the model is only fitted with the initial_train_size, last_window # and next_window_exog must be updated to include the data needed to make # predictions. last_window_end = initial_train_size + i * steps last_window_start = last_window_end - window_size last_window_y = y.iloc[last_window_start:last_window_end] if exog is not None: next_window_exog = exog.iloc[last_window_end:last_window_end + steps, ] if interval is None: if i < folds - 1: if exog is None: pred = forecaster.predict( steps = steps, last_window = last_window_y ) else: pred = forecaster.predict( steps = steps, last_window = last_window_y, exog = next_window_exog ) else: if remainder == 0: if exog is None: pred = forecaster.predict( steps = steps, last_window = last_window_y ) else: pred = forecaster.predict( steps = steps, last_window = last_window_y, exog = next_window_exog ) else: # Only the remaining steps need to be predicted steps = remainder if exog is None: pred = forecaster.predict( steps = steps, last_window = last_window_y ) else: pred = forecaster.predict( steps = steps, last_window = last_window_y, exog = next_window_exog ) backtest_predictions.append(pred) else: if i < folds - 1: if exog is None: pred = forecaster.predict_interval( steps = steps, last_window = last_window_y, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: pred = forecaster.predict_interval( steps = steps, last_window = last_window_y, exog = next_window_exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: if remainder == 0: if exog is None: pred = forecaster.predict_interval( steps = steps, last_window = last_window_y, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: pred = forecaster.predict_interval( steps = steps, last_window = last_window_y, exog = next_window_exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: # Only the remaining steps need to be predicted steps = remainder if exog is None: pred = forecaster.predict_interval( steps = steps, last_window = last_window_y, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) else: pred = forecaster.predict_interval( steps = steps, last_window = last_window_y, exog = next_window_exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals ) backtest_predictions.append(pred) backtest_predictions = pd.concat(backtest_predictions) if isinstance(backtest_predictions, pd.Series): backtest_predictions = pd.DataFrame(backtest_predictions) metric_value = metric( y_true = y.iloc[initial_train_size : initial_train_size + len(backtest_predictions)], y_pred = backtest_predictions['pred'] ) return metric_value, backtest_predictions def backtesting_forecaster( forecaster, y: pd.Series, steps: int, metric: Union[str, callable], initial_train_size: Optional[int], fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, refit: bool=False, interval: Optional[list]=None, n_boot: int=500, random_state: int=123, in_sample_residuals: bool=True, verbose: bool=False ) -> Tuple[float, pd.DataFrame]: ''' Backtesting of forecaster model. If `refit` is False, the model is trained only once using the `initial_train_size` first observations. If `refit` is True, the model is trained in each iteration increasing the training set. A copy of the original forecaster is created so it is not modified during the process. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forecaster model. y : pandas Series Training time series values. initial_train_size: int, default `None` Number of samples in the initial train split. If `None` and `forecaster` is already trained, no initial train is done and all data is used to evaluate the model. However, the first `len(forecaster.last_window)` observations are needed to create the initial predictors, so no predictions are calculated for them. `None` is only allowed when `refit` is `False`. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. exog :panda Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. refit: bool, default `False` Whether to re-fit the forecaster in each iteration. interval: list, default `None` Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. If `None`, no intervals are estimated. Only available for forecaster of type ForecasterAutoreg and ForecasterAutoregCustom. n_boot: int, default `500` Number of bootstrapping iterations used to estimate prediction intervals. random_state: int, default `123` Sets a seed to the random generator, so that boot intervals are always deterministic. in_sample_residuals: bool, default `True` If `True`, residuals from the training data are used as proxy of prediction error to create prediction intervals. If `False`, out_sample_residuals are used if they are already stored inside the forecaster. verbose : bool, default `False` Print number of folds and index of training and validation sets used for backtesting. Returns ------- metric_value: float Value of the metric. backtest_predictions: pandas DataFrame Value of predictions and their estimated interval if `interval` is not `None`. column pred = predictions. column lower_bound = lower bound of the interval. column upper_bound = upper bound interval of the interval. ''' if initial_train_size is not None and initial_train_size > len(y): raise Exception( 'If used, `initial_train_size` must be smaller than length of `y`.' ) if initial_train_size is not None and initial_train_size < forecaster.window_size: raise Exception( f"`initial_train_size` must be greater than " f"forecaster's window_size ({forecaster.window_size})." ) if initial_train_size is None and not forecaster.fitted: raise Exception( '`forecaster` must be already trained if no `initial_train_size` is provided.' ) if not isinstance(refit, bool): raise Exception( f'`refit` must be boolean: True, False.' ) if initial_train_size is None and refit: raise Exception( f'`refit` is only allowed when there is a initial_train_size.' ) if interval is not None and isinstance(forecaster, (ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): raise Exception( ('Interval prediction is only available when forecaster is of type ' 'ForecasterAutoreg or ForecasterAutoregCustom.') ) if refit: metric_value, backtest_predictions = _backtesting_forecaster_refit( forecaster = forecaster, y = y, steps = steps, metric = metric, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, exog = exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals, verbose = verbose ) else: metric_value, backtest_predictions = _backtesting_forecaster_no_refit( forecaster = forecaster, y = y, steps = steps, metric = metric, initial_train_size = initial_train_size, exog = exog, interval = interval, n_boot = n_boot, random_state = random_state, in_sample_residuals = in_sample_residuals, verbose = verbose ) return metric_value, backtest_predictions def grid_search_forecaster( forecaster, y: pd.Series, param_grid: dict, steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, lags_grid: Optional[list]=None, refit: bool=False, return_best: bool=True, verbose: bool=True ) -> pd.DataFrame: ''' Exhaustive search over specified parameter values for a Forecaster object. Validation is done using time series backtesting. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forcaster model. y : pandas Series Training time series values. param_grid : dict Dictionary with parameters names (`str`) as keys and lists of parameter settings to try as values. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. initial_train_size: int Number of samples in the initial train split. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. lags_grid : list of int, lists, np.narray or range, default `None` Lists of `lags` to try. Only used if forecaster is an instance of `ForecasterAutoreg`, `ForecasterAutoregDirect` or `ForecasterAutoregMultiOutput`. refit: bool, default `False` Whether to re-fit the forecaster in each iteration of backtesting. return_best : bool, default `True` Refit the `forecaster` using the best found parameters on the whole data. verbose : bool, default `True` Print number of folds used for cv or backtesting. Returns ------- results: pandas DataFrame Results for each combination of parameters. column lags = predictions. column params = lower bound of the interval. column metric = metric value estimated for the combination of parameters. additional n columns with param = value. ''' param_grid = list(ParameterGrid(param_grid)) results = _evaluate_grid_hyperparameters( forecaster = forecaster, y = y, param_grid = param_grid, steps = steps, metric = metric, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, exog = exog, lags_grid = lags_grid, refit = refit, return_best = return_best, verbose = verbose ) return results def random_search_forecaster( forecaster, y: pd.Series, param_distributions: dict, steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, lags_grid: Optional[list]=None, refit: bool=False, n_iter: int=10, random_state: int=123, return_best: bool=True, verbose: bool=True ) -> pd.DataFrame: ''' Random search over specified parameter values or distributions for a Forecaster object. Validation is done using time series backtesting. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forcaster model. y : pandas Series Training time series values. param_distributions : dict Dictionary with parameters names (`str`) as keys and distributions or lists of parameters to try. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. initial_train_size: int Number of samples in the initial train split. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. lags_grid : list of int, lists, np.narray or range, default `None` Lists of `lags` to try. Only used if forecaster is an instance of `ForecasterAutoreg`, `ForecasterAutoregDirect` or `ForecasterAutoregMultiOutput`. refit: bool, default `False` Whether to re-fit the forecaster in each iteration of backtesting. n_iter: int, default `10` Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. random_state: int, default `123` Sets a seed to the random sampling for reproducible output. return_best : bool, default `True` Refit the `forecaster` using the best found parameters on the whole data. verbose : bool, default `True` Print number of folds used for cv or backtesting. Returns ------- results: pandas DataFrame Results for each combination of parameters. column lags = predictions. column params = lower bound of the interval. column metric = metric value estimated for the combination of parameters. additional n columns with param = value. ''' param_grid = list(ParameterSampler(param_distributions, n_iter=n_iter, random_state=random_state)) results = _evaluate_grid_hyperparameters( forecaster = forecaster, y = y, param_grid = param_grid, steps = steps, metric = metric, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, exog = exog, lags_grid = lags_grid, refit = refit, return_best = return_best, verbose = verbose ) return results def _evaluate_grid_hyperparameters( forecaster, y: pd.Series, param_grid: dict, steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, lags_grid: Optional[list]=None, refit: bool=False, return_best: bool=True, verbose: bool=True ) -> pd.DataFrame: ''' Evaluate parameter values for a Forecaster object using time series backtesting. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forcaster model. y : pandas Series Training time series values. param_grid : dict Dictionary with parameters names (`str`) as keys and lists of parameter settings to try as values. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. initial_train_size: int Number of samples in the initial train split. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. lags_grid : list of int, lists, np.narray or range, default `None` Lists of `lags` to try. Only used if forecaster is an instance of `ForecasterAutoreg`, `ForecasterAutoregDirect` or `ForecasterAutoregMultiOutput`. refit: bool, default `False` Whether to re-fit the forecaster in each iteration of backtesting. return_best : bool, default `True` Refit the `forecaster` using the best found parameters on the whole data. verbose : bool, default `True` Print number of folds used for cv or backtesting. Returns ------- results: pandas DataFrame Results for each combination of parameters. column lags = predictions. column params = lower bound of the interval. column metric = metric value estimated for the combination of parameters. additional n columns with param = value. ''' if isinstance(forecaster, ForecasterAutoregCustom): if lags_grid is not None: warnings.warn( '`lags_grid` ignored if forecaster is an instance of `ForecasterAutoregCustom`.' ) lags_grid = ['custom predictors'] elif lags_grid is None: lags_grid = [forecaster.lags] lags_list = [] params_list = [] metric_list = [] print( f"Number of models compared: {len(param_grid)*len(lags_grid)}." ) for lags in tqdm(lags_grid, desc='loop lags_grid', position=0, ncols=90): if isinstance(forecaster, (ForecasterAutoreg, ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): forecaster.set_lags(lags) lags = forecaster.lags.copy() for params in tqdm(param_grid, desc='loop param_grid', position=1, leave=False, ncols=90): forecaster.set_params(**params) metrics = backtesting_forecaster( forecaster = forecaster, y = y, exog = exog, steps = steps, metric = metric, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, refit = refit, interval = None, verbose = verbose )[0] lags_list.append(lags) params_list.append(params) metric_list.append(metrics) results = pd.DataFrame({ 'lags' : lags_list, 'params': params_list, 'metric': metric_list}) results = results.sort_values(by='metric', ascending=True) results = pd.concat([results, results['params'].apply(pd.Series)], axis=1) if return_best: best_lags = results['lags'].iloc[0] best_params = results['params'].iloc[0] best_metric = results['metric'].iloc[0] if isinstance(forecaster, (ForecasterAutoreg, ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): forecaster.set_lags(best_lags) forecaster.set_params(**best_params) forecaster.fit(y=y, exog=exog) print( f"`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n" f" Lags: {best_lags} \n" f" Parameters: {best_params}\n" f" Backtesting metric: {best_metric}\n" ) return results def bayesian_search_forecaster( forecaster, y: pd.Series, search_space: Union[callable, dict], steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, lags_grid: Optional[list]=None, refit: bool=False, n_trials: int=10, random_state: int=123, return_best: bool=True, verbose: bool=True, engine: str='skopt', kwargs_create_study: dict={}, kwargs_study_optimize: dict={}, kwargs_gp_minimize: dict={}, ) -> Tuple[pd.DataFrame, object]: ''' Bayesian optimization for a Forecaster object using time series backtesting and optuna or skopt library. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forcaster model. y : pandas Series Training time series values. search_space : callable (optuna), dict (skopt) If optuna engine: callable Function with argument `trial` which returns a dictionary with parameters names (`str`) as keys and Trial object from optuna (trial.suggest_float, trial.suggest_int, trial.suggest_categorical) as values. If skopt engine: dict Dictionary with parameters names (`str`) as keys and Space object from skopt (Real, Integer, Categorical) as values. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. initial_train_size: int Number of samples in the initial train split. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. lags_grid : list of int, lists, np.narray or range, default `None` Lists of `lags` to try. Only used if forecaster is an instance of `ForecasterAutoreg`, `ForecasterAutoregDirect` or `ForecasterAutoregMultiOutput`. refit: bool, default `False` Whether to re-fit the forecaster in each iteration of backtesting. n_trials: int, default `10` Number of parameter settings that are sampled in each lag configuration. random_state: int, default `123` Sets a seed to the sampling for reproducible output. return_best : bool, default `True` Refit the `forecaster` using the best found parameters on the whole data. verbose : bool, default `True` Print number of folds used for cv or backtesting. engine : str, default `'skopt'` If 'optuna': Bayesian optimization runs through the optuna library If 'skopt': Bayesian optimization runs through the skopt library kwargs_create_study : dict, default `{'direction':'minimize', 'sampler':TPESampler(seed=123)}` Only applies to engine='optuna'. Keyword arguments (key, value mappings) to pass to optuna.create_study. kwargs_study_optimize : dict, default `{}` Only applies to engine='optuna'. Other keyword arguments (key, value mappings) to pass to study.optimize(). kwargs_gp_minimize : dict, default `{}` Only applies to engine='skopt'. Other keyword arguments (key, value mappings) to pass to skopt.gp_minimize(). Returns ------- results: pandas DataFrame Results for each combination of parameters. column lags = predictions. column params = lower bound of the interval. column metric = metric value estimated for the combination of parameters. additional n columns with param = value. results_opt_best: optuna object (optuna), scipy object (skopt) If optuna engine: The best optimization result returned as a FrozenTrial optuna object. If skopt engine: The best optimization result returned as a OptimizeResult object. ''' if engine not in ['optuna', 'skopt']: raise Exception( f'''`engine` only allows 'optuna' or 'skopt', got {engine}.''' ) if engine == 'optuna': results, results_opt_best = _bayesian_search_optuna( forecaster = forecaster, y = y, exog = exog, lags_grid = lags_grid, search_space = search_space, steps = steps, metric = metric, refit = refit, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, n_trials = n_trials, random_state = random_state, return_best = return_best, verbose = verbose, kwargs_create_study = kwargs_create_study, kwargs_study_optimize = kwargs_study_optimize ) else: results, results_opt_best = _bayesian_search_skopt( forecaster = forecaster, y = y, exog = exog, lags_grid = lags_grid, search_space = search_space, steps = steps, metric = metric, refit = refit, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, n_trials = n_trials, random_state = random_state, return_best = return_best, verbose = verbose, kwargs_gp_minimize = kwargs_gp_minimize ) return results, results_opt_best def _bayesian_search_optuna( forecaster, y: pd.Series, search_space: callable, steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, lags_grid: Optional[list]=None, refit: bool=False, n_trials: int=10, random_state: int=123, return_best: bool=True, verbose: bool=True, kwargs_create_study: dict={}, kwargs_study_optimize: dict={} ) -> Tuple[pd.DataFrame, object]: ''' Bayesian optimization for a Forecaster object using time series backtesting and optuna library. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forcaster model. y : pandas Series Training time series values. search_space : callable Function with argument `trial` which returns a dictionary with parameters names (`str`) as keys and Trial object from optuna (trial.suggest_float, trial.suggest_int, trial.suggest_categorical) as values. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} If callable: Function with arguments y_true, y_pred that returns a float. initial_train_size: int Number of samples in the initial train split. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. lags_grid : list of int, lists, np.narray or range, default `None` Lists of `lags` to try. Only used if forecaster is an instance of `ForecasterAutoreg`, `ForecasterAutoregDirect` or `ForecasterAutoregMultiOutput`. refit: bool, default `False` Whether to re-fit the forecaster in each iteration of backtesting. n_trials: int, default `10` Number of parameter settings that are sampled in each lag configuration. random_state: int, default `123` Sets a seed to the sampling for reproducible output. return_best : bool, default `True` Refit the `forecaster` using the best found parameters on the whole data. verbose : bool, default `True` Print number of folds used for cv or backtesting. kwargs_create_study : dict, default `{'direction':'minimize', 'sampler':TPESampler(seed=123)}` Keyword arguments (key, value mappings) to pass to optuna.create_study. kwargs_study_optimize : dict, default `{}` Other keyword arguments (key, value mappings) to pass to study.optimize(). Returns ------- results: pandas DataFrame Results for each combination of parameters. column lags = predictions. column params = lower bound of the interval. column metric = metric value estimated for the combination of parameters. additional n columns with param = value. results_opt_best: optuna object The best optimization result returned as a FrozenTrial optuna object. ''' if isinstance(forecaster, ForecasterAutoregCustom): if lags_grid is not None: warnings.warn( '`lags_grid` ignored if forecaster is an instance of `ForecasterAutoregCustom`.' ) lags_grid = ['custom predictors'] elif lags_grid is None: lags_grid = [forecaster.lags] lags_list = [] params_list = [] metric_list = [] results_opt_best = None # Objective function using backtesting_forecaster def _objective( trial, forecaster = forecaster, y = y, exog = exog, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, steps = steps, metric = metric, refit = refit, verbose = verbose, search_space = search_space, ) -> float: forecaster.set_params(**search_space(trial)) metric, _ = backtesting_forecaster( forecaster = forecaster, y = y, exog = exog, steps = steps, metric = metric, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, refit = refit, verbose = verbose ) return abs(metric) print( f'''Number of models compared: {n_trials*len(lags_grid)}, {n_trials} bayesian search in each lag configuration.''' ) for lags in tqdm(lags_grid, desc='loop lags_grid', position=0, ncols=90): if isinstance(forecaster, (ForecasterAutoreg, ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): forecaster.set_lags(lags) lags = forecaster.lags.copy() if 'sampler' in kwargs_create_study.keys(): kwargs_create_study['sampler']._rng = np.random.RandomState(random_state) kwargs_create_study['sampler']._random_sampler = RandomSampler(seed=random_state) study = optuna.create_study(**kwargs_create_study) if 'sampler' not in kwargs_create_study.keys(): study.sampler = TPESampler(seed=random_state) study.optimize(_objective, n_trials=n_trials, **kwargs_study_optimize) best_trial = study.best_trial if search_space(best_trial).keys() != best_trial.params.keys(): raise Exception( f'''Some of the key values do not match the search_space key names. Dict keys : {list(search_space(best_trial).keys())} Trial objects : {list(best_trial.params.keys())}.''' ) for trial in study.get_trials(): params_list.append(trial.params) lags_list.append(lags) metric_list.append(trial.value) if results_opt_best is None: results_opt_best = best_trial else: if best_trial.value < results_opt_best.value: results_opt_best = best_trial results = pd.DataFrame({ 'lags' : lags_list, 'params': params_list, 'metric': metric_list}) results = results.sort_values(by='metric', ascending=True) results = pd.concat([results, results['params'].apply(pd.Series)], axis=1) if return_best: best_lags = results['lags'].iloc[0] best_params = results['params'].iloc[0] best_metric = results['metric'].iloc[0] if isinstance(forecaster, (ForecasterAutoreg, ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): forecaster.set_lags(best_lags) forecaster.set_params(**best_params) forecaster.fit(y=y, exog=exog) print( f"`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n" f" Lags: {best_lags} \n" f" Parameters: {best_params}\n" f" Backtesting metric: {best_metric}\n" ) return results, results_opt_best def _bayesian_search_skopt( forecaster, y: pd.Series, search_space: dict, steps: int, metric: Union[str, callable], initial_train_size: int, fixed_train_size: bool=True, exog: Optional[Union[pd.Series, pd.DataFrame]]=None, lags_grid: Optional[list]=None, refit: bool=False, n_trials: int=10, random_state: int=123, return_best: bool=True, verbose: bool=True, kwargs_gp_minimize: dict={} ) -> Tuple[pd.DataFrame, object]: ''' Bayesian optimization for a Forecaster object using time series backtesting and skopt library. Parameters ---------- forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect, ForecasterAutoregMultiOutput Forcaster model. y : pandas Series Training time series values. search_space : dict Dictionary with parameters names (`str`) as keys and Space object from skopt (Real, Integer, Categorical) as values. steps : int Number of steps to predict. metric : str, callable Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'} It callable: Function with arguments y_true, y_pred that returns a float. initial_train_size: int Number of samples in the initial train split. fixed_train_size: bool, default `True` If True, train size doesn't increases but moves by `steps` in each iteration. exog : pandas Series, pandas DataFrame, default `None` Exogenous variable/s included as predictor/s. Must have the same number of observations as `y` and should be aligned so that y[i] is regressed on exog[i]. lags_grid : list of int, lists, np.narray or range, default `None` Lists of `lags` to try. Only used if forecaster is an instance of `ForecasterAutoreg`, `ForecasterAutoregDirect` or `ForecasterAutoregMultiOutput`. refit: bool, default `False` Whether to re-fit the forecaster in each iteration of backtesting. n_trials: int, default `10` Number of parameter settings that are sampled in each lag configuration. random_state: int, default `123` Sets a seed to the sampling for reproducible output. return_best : bool, default `True` Refit the `forecaster` using the best found parameters on the whole data. verbose : bool, default `True` Print number of folds used for cv or backtesting. kwargs_gp_minimize : dict, default `{}` Other keyword arguments (key, value mappings) to pass to skopt.gp_minimize(). Returns ------- results: pandas DataFrame Results for each combination of parameters. column lags = predictions. column params = lower bound of the interval. column metric = metric value estimated for the combination of parameters. additional n columns with param = value. results_opt_best: scipy object The best optimization result returned as a OptimizeResult object. ''' if isinstance(forecaster, ForecasterAutoregCustom): if lags_grid is not None: warnings.warn( '`lags_grid` ignored if forecaster is an instance of `ForecasterAutoregCustom`.' ) lags_grid = ['custom predictors'] elif lags_grid is None: lags_grid = [forecaster.lags] lags_list = [] params_list = [] metric_list = [] results_opt_best = None for key in search_space.keys(): if key != search_space[key].name: raise Exception( f'''Some of the key values do not match the Space object name from skopt. {key} != {search_space[key].name}.''' ) search_space = list(search_space.values()) # Objective function using backtesting_forecaster @use_named_args(search_space) def _objective( forecaster = forecaster, y = y, exog = exog, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, steps = steps, metric = metric, refit = refit, verbose = verbose, **params ) -> float: forecaster.set_params(**params) metric, _ = backtesting_forecaster( forecaster = forecaster, y = y, exog = exog, steps = steps, metric = metric, initial_train_size = initial_train_size, fixed_train_size = fixed_train_size, refit = refit, verbose = verbose ) return abs(metric) print( f'''Number of models compared: {n_trials*len(lags_grid)}, {n_trials} bayesian search in each lag configuration.''' ) for lags in tqdm(lags_grid, desc='loop lags_grid', position=0, ncols=90): if isinstance(forecaster, (ForecasterAutoreg, ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): forecaster.set_lags(lags) lags = forecaster.lags.copy() results_opt = gp_minimize( func = _objective, dimensions = search_space, n_calls = n_trials, random_state = random_state, **kwargs_gp_minimize ) for i, x in enumerate(results_opt.x_iters): params = {} for j, x in enumerate(search_space): params[x.name] = results_opt.x_iters[i][j] params_list.append(params) lags_list.append(lags) metric_list.append(results_opt.func_vals[i]) if results_opt_best is None: results_opt_best = results_opt else: if results_opt.fun < results_opt_best.fun: results_opt_best = results_opt results = pd.DataFrame({ 'lags' : lags_list, 'params': params_list, 'metric': metric_list}) results = results.sort_values(by='metric', ascending=True) results = pd.concat([results, results['params'].apply(pd.Series)], axis=1) if return_best: best_lags = results['lags'].iloc[0] best_params = results['params'].iloc[0] best_metric = results['metric'].iloc[0] if isinstance(forecaster, (ForecasterAutoreg, ForecasterAutoregDirect, ForecasterAutoregMultiOutput)): forecaster.set_lags(best_lags) forecaster.set_params(**best_params) forecaster.fit(y=y, exog=exog) print( f"`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n" f" Lags: {best_lags} \n" f" Parameters: {best_params}\n" f" Backtesting metric: {best_metric}\n" ) return results, results_opt_best

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08b592c8e9a247efee8f940c2e10cffd4147c4c4

4,948

py

Python

pizza_cutter/coadding/_lanczos.py

beckermr/pizza-cutter

04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6

[ "BSD-3-Clause" ]

null

pizza_cutter/coadding/_lanczos.py

beckermr/pizza-cutter

04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6

[ "BSD-3-Clause" ]

194

2018-10-24T23:40:47.000Z

2021-11-17T16:02:35.000Z

pizza_cutter/coadding/_lanczos.py

beckermr/pizza-cutter

04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6

[ "BSD-3-Clause" ]

null

import numpy as np from numba import njit if False: from ._sinc import sinc else: from ._sinc import sinc_pade as sinc @njit(fastmath=True) def lanczos_resample(im, rows, cols, a=3): """Lanczos resample one image at the input row and column positions. Parameters ---------- im : np.ndarray A two-dimensional array with the image values. rows : np.ndarray A one-dimensional array of input row/y values. These denote the location to sample on the first, slowest moving axis of the image. cols : np.ndarray A one-dimensional array of input column/x values. These denote the location to sample on the second, fastest moving axis of the image. a : int, optional The size of the Lanczos kernel. The default of 3 is a good choice for many applications. Returns ------- values, edge : np.ndarray The resampled value for each row, column pair. Points whose interpolation kernal was truncated because it extended beyond the input image have edge=True """ assert a <= 3, ( "Due to the pade approximate sinc function, Lanczos interpolation " "can only be done up to order 3." ) ny, nx = im.shape outsize = rows.shape[0] res1 = np.zeros(outsize, dtype=np.float64) edge = np.zeros(outsize, dtype=np.bool_) for i in range(rows.shape[0]): y = rows[i] x = cols[i] # get range for kernel x_s = int(np.floor(x)) - a + 1 x_f = int(np.floor(x)) + a y_s = int(np.floor(y)) - a + 1 y_f = int(np.floor(y)) + a out_of_bounds = ( x_f < 0 or x_s > nx-1 or y_f < 0 or y_s > ny-1 ) if out_of_bounds: edge[i] = True # now sum over the cells in the kernel val1 = 0.0 for y_pix in range(y_s, y_f+1): if y_pix < 0 or y_pix > ny-1: continue dy = y - y_pix sy = sinc(dy) * sinc(dy/a) for x_pix in range(x_s, x_f+1): if x_pix < 0 or x_pix > nx-1: continue dx = x - x_pix sx = sinc(dx) * sinc(dx/a) kernel = sx*sy val1 += im[y_pix, x_pix] * kernel res1[i] = val1 return res1, edge @njit(fastmath=True) def lanczos_resample_two(im1, im2, rows, cols, a=3): """Lanczos resample two images at the input row and column positions. Unlike lanczos_resample, points whose interpolation kernel would be truncated because it extends beyond the input image do not get NaN values. Instead they get edge=True in the output edge boolean array Parameters ---------- im1 : np.ndarray A two-dimensional array with the image values. im2 : np.ndarray A two-dimensional array with the image values. rows : np.ndarray A one-dimensional array of input row/y values. These denote the location to sample on the first, slowest moving axis of the image. cols : np.ndarray A one-dimensional array of input column/x values. These denote the location to sample on the second, fastest moving axis of the image. a : int, optional The size of the Lanczos kernel. The default of 3 is a good choice for many applications. Returns ------- values1, values2, edge : np.ndarray The resampled value for each row, column pair. Points whose interpolation kernal was truncated because it extended beyond the input image have edge=True """ ny, nx = im1.shape outsize = rows.shape[0] res1 = np.zeros(outsize, dtype=np.float64) res2 = np.zeros(outsize, dtype=np.float64) edge = np.zeros(outsize, dtype=np.bool_) for i in range(rows.shape[0]): y = rows[i] x = cols[i] # get range for kernel x_s = int(np.floor(x)) - a + 1 x_f = int(np.floor(x)) + a y_s = int(np.floor(y)) - a + 1 y_f = int(np.floor(y)) + a out_of_bounds = ( x_f < 0 or x_s > nx-1 or y_f < 0 or y_s > ny-1 ) if out_of_bounds: edge[i] = True # now sum over the cells in the kernel val1 = 0.0 val2 = 0.0 for y_pix in range(y_s, y_f+1): if y_pix < 0 or y_pix > ny-1: continue dy = y - y_pix sy = sinc(dy) * sinc(dy/a) for x_pix in range(x_s, x_f+1): if x_pix < 0 or x_pix > nx-1: continue dx = x - x_pix sx = sinc(dx) * sinc(dx/a) kernel = sx*sy val1 += im1[y_pix, x_pix] * kernel val2 += im2[y_pix, x_pix] * kernel res1[i] = val1 res2[i] = val2 return res1, res2, edge

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3eceafa0071f796b23c861bcc6fe737d5af19511

389,826

pyt

Python

eran/NNet/nnet/ACASXU_run2a_1_9_batch_2000_16bit.pyt

pauls658/ReluDiff-ICSE2020-Artifact

212854fe04f482183c239e5dfec70106a9a83df8

[ "Apache-2.0" ]

7

2020-01-27T21:25:49.000Z

2022-01-07T04:37:37.000Z

eran/NNet/nnet/ACASXU_run2a_1_9_batch_2000_16bit.pyt

yqtianust/ReluDiff-ICSE2020-Artifact

149f6efe4799602db749faa576980c36921a07c7

[ "Apache-2.0" ]

1

2022-01-25T17:41:54.000Z

2022-01-26T02:27:51.000Z

eran/NNet/nnet/ACASXU_run2a_1_9_batch_2000_16bit.pyt

yqtianust/ReluDiff-ICSE2020-Artifact

149f6efe4799602db749faa576980c36921a07c7

[ "Apache-2.0" ]

3

2020-03-14T17:12:17.000Z

2022-03-16T09:50:46.000Z

ReLU [[-0.0791752, -0.805586, -0.0231979, 0.649247, -0.397765], [0.0831648, -0.00536296, -0.0106399, 0.275283, -1.11862], [-0.0121543, -0.0225282, 0.00307771, 0.173961, -0.989381], [-0.0188319, -0.591901, 0.574422, 0.0133294, 0.157198], [0.00761831, -0.68365, 0.769109, 0.0194096, 0.0625865], [-0.0146541, 0.448687, -0.00930574, 0.0767494, -0.125253], [0.025496, -0.122473, 0.284814, -0.163065, 0.37846], [-0.0175174, -0.688039, -0.752055, 0.150861, 0.0586098], [-0.837946, 0.00508856, 0.00254875, 0.000298656, 0.000478515], [-0.0304969, 0.37302, 0.323848, 0.206257, -0.0738219], [0.0110145, 0.203139, -0.00684705, 0.00534789, -0.177648], [0.183578, -0.159554, -0.0217952, -0.0760196, -0.431161], [-0.601081, -0.374512, 0.12108, 0.111087, 0.0674687], [0.438981, -0.0260015, 0.00487298, 0.00945177, -0.102797], [-0.256736, 0.0414723, 0.025076, 0.0229962, -0.624653], [0.0429594, -0.233959, 0.709691, 0.3906, -0.0460539], [-0.0273826, 0.394765, 0.0318927, -0.130928, 0.0309174], [0.0388179, 0.874781, 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f5c5dd3fe29a357e1374dce8b83e9b857310946e

3,453

py

Python

unitary_tests/test_implemented_schedulers.py

llpilla/study-load-balancing

873b4e633c2e290a099887f2cb8d54ea0b94161f

[ "CC0-1.0" ]

null

unitary_tests/test_implemented_schedulers.py

llpilla/study-load-balancing

873b4e633c2e290a099887f2cb8d54ea0b94161f

[ "CC0-1.0" ]

null

unitary_tests/test_implemented_schedulers.py

llpilla/study-load-balancing

873b4e633c2e290a099887f2cb8d54ea0b94161f

[ "CC0-1.0" ]

1

2022-01-22T17:48:07.000Z

#!/usr/bin/env python3 import unittest import sys # Add the parent directory to the path so we can import # code from our simulator sys.path.append('../') from simulator.schedulers import round_robin, compact # noqa from simulator.schedulers import uniformly_random, list_scheduler # noqa class RRTest(unittest.TestCase): def test_five_tasks(self): num_tasks = 5 num_resources = 2 mapping = round_robin(num_tasks, num_resources) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 1) self.assertEqual(mapping[2], 0) self.assertEqual(mapping[3], 1) self.assertEqual(mapping[4], 0) def test_more_resources(self): num_tasks = 5 num_resources = 10 mapping = round_robin(num_tasks, num_resources) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 1) self.assertEqual(mapping[2], 2) self.assertEqual(mapping[3], 3) self.assertEqual(mapping[4], 4) class CompactTest(unittest.TestCase): def test_five_tasks(self): num_tasks = 5 num_resources = 2 mapping = compact(num_tasks, num_resources) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 0) self.assertEqual(mapping[2], 0) self.assertEqual(mapping[3], 1) self.assertEqual(mapping[4], 1) def test_more_resources(self): num_tasks = 5 num_resources = 10 mapping = compact(num_tasks, num_resources) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 1) self.assertEqual(mapping[2], 2) self.assertEqual(mapping[3], 3) self.assertEqual(mapping[4], 4) class RandomTest(unittest.TestCase): def test_seed_one(self): num_tasks = 5 num_resources = 3 mapping = uniformly_random(num_tasks, num_resources, 1) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 2) self.assertEqual(mapping[2], 0) self.assertEqual(mapping[3], 1) self.assertEqual(mapping[4], 0) def test_seed_two(self): num_tasks = 5 num_resources = 3 mapping = uniformly_random(num_tasks, num_resources, 2) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 0) self.assertEqual(mapping[2], 0) self.assertEqual(mapping[3], 1) self.assertEqual(mapping[4], 0) class LSTest(unittest.TestCase): def test_five_tasks(self): num_resources = 3 task_loads = [5, 3, 2, 7, 4] mapping = list_scheduler(task_loads, num_resources) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 1) self.assertEqual(mapping[2], 2) self.assertEqual(mapping[3], 2) self.assertEqual(mapping[4], 1) def test_ten_tasks(self): num_resources = 5 task_loads = [5, 3, 2, 7, 4, 1, 9, 3, 2, 7] mapping = list_scheduler(task_loads, num_resources) self.assertEqual(mapping[0], 0) self.assertEqual(mapping[1], 1) self.assertEqual(mapping[2], 2) self.assertEqual(mapping[3], 3) self.assertEqual(mapping[4], 4) self.assertEqual(mapping[5], 2) self.assertEqual(mapping[6], 1) self.assertEqual(mapping[7], 2) self.assertEqual(mapping[8], 4) self.assertEqual(mapping[9], 0) if __name__ == '__main__': unittest.main()

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f5d46eb73208a993d09c50513d84ae411e3e923e

200

py

Python

applications/popart/bert/bert_data/__init__.py

payoto/graphcore_examples

46d2b7687b829778369fc6328170a7b14761e5c6

[ "MIT" ]

260

2019-11-18T01:50:00.000Z

2022-03-28T23:08:53.000Z

applications/popart/bert/bert_data/__init__.py

payoto/graphcore_examples

46d2b7687b829778369fc6328170a7b14761e5c6

[ "MIT" ]

27

2020-01-28T23:07:50.000Z

2022-02-14T15:37:06.000Z

applications/popart/bert/bert_data/__init__.py

payoto/graphcore_examples

46d2b7687b829778369fc6328170a7b14761e5c6

[ "MIT" ]

56

2019-11-18T02:13:12.000Z

2022-02-28T14:36:09.000Z

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eb35ffdf6dca49418e2977de911d007264720c84

38

py

Python

core/myapp/settings/__init__.py

myworkshopproject/myworkshop

44946bda1e454f784d6b86b0748a5fc0c80f257f

[ "MIT" ]

null

core/myapp/settings/__init__.py

myworkshopproject/myworkshop

44946bda1e454f784d6b86b0748a5fc0c80f257f

[ "MIT" ]

7

2020-03-21T09:03:26.000Z

2021-03-22T15:21:50.000Z

core/myapp/settings/__init__.py

myworkshopproject/myworkshop

44946bda1e454f784d6b86b0748a5fc0c80f257f

[ "MIT" ]

null

from .base import * # noqa F401 F403

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1

0

1

0

7

deac47a9cb02cb2ca307a43d852262ac5368a6df

465

py

Python

gtfs_util/static/models/__init__.py

jwoos/gtfs_util

995ce529d09ba8dc693d3cf4b6cabf395a173997

[ "MIT" ]

1

2018-09-18T02:14:50.000Z

gtfs_util/static/models/__init__.py

jwoos/gtfs_util

995ce529d09ba8dc693d3cf4b6cabf395a173997

[ "MIT" ]

17

2018-10-17T03:00:19.000Z

2018-11-12T03:46:40.000Z

gtfs_util/static/models/__init__.py

jwoos/python_gtfs-util

995ce529d09ba8dc693d3cf4b6cabf395a173997

[ "MIT" ]

null

__all__ = [ 'base', 'agency', 'fare_attribute', 'fare_rule', 'feed_info', 'frequency', 'route', 'service', 'service_update', 'point', 'stop', 'stop_time', 'transfer', 'trip', ] from gtfs_util.static.models import ( base, agency, fare_attribute, fare_rule, feed_info, frequency, route, service, service_update, point, stop, stop_time, transfer, trip, )

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def04f9dab501ae464aeeeaac30f1037e715829f

1,792

py

Python

Settings/independent_experiments/compare_expanded.py

previtus/MGR-Project-Code

1126215059eb3f731dcf78ec24d9a480e73abce6

[ "MIT" ]

null

Settings/independent_experiments/compare_expanded.py

previtus/MGR-Project-Code

1126215059eb3f731dcf78ec24d9a480e73abce6

[ "MIT" ]

null

Settings/independent_experiments/compare_expanded.py

previtus/MGR-Project-Code

1126215059eb3f731dcf78ec24d9a480e73abce6

[ "MIT" ]

null

def Setup(Settings,DefaultModel): # compare_expanded.py Settings["experiment_name"] = "CompareExpandedVsRegular" Settings["graph_histories"] = ['together', [0,1], [1,2], [0,2]] #['all','together',[],[1,0],[0,0,0],[]] n=0 # 5556x_reslen30_299px 5556x_reslen20_299px Settings["models"][n]["dataset_name"] = "1200x_markable_299x299" Settings["models"][n]["dump_file_override"] = 'SegmentsData_marked_R100_4Tables.dump' Settings["models"][n]["pixels"] = 299 Settings["models"][n]["model_type"] = 'img_osm_mix' Settings["models"][n]["unique_id"] = 'original' Settings["models"][n]["top_repeat_FC_block"] = 2 Settings["models"][n]["epochs"] = 1000 Settings["models"].append(DefaultModel.copy()) n=1 Settings["models"][n]["dataset_pointer"] = -1 # 0 - reuse the first dataset Settings["models"][n]["dataset_name"] = "1200x_markable_299x299_1x_expanded" Settings["models"][n]["dump_file_override"] = 'SegmentsData_marked_R100_4Tables_expanded.dump' Settings["models"][n]["pixels"] = 299 Settings["models"][n]["model_type"] = 'img_osm_mix' Settings["models"][n]["unique_id"] = 'expanded' Settings["models"][n]["top_repeat_FC_block"] = 2 Settings["models"][n]["epochs"] = 1000 Settings["models"].append(DefaultModel.copy()) n=2 Settings["models"][n]["dataset_name"] = "1200x_markable_299x299_1x_agressive_expanded" Settings["models"][n]["dump_file_override"] = 'SegmentsData_marked_R100_4Tables_expanded.dump' Settings["models"][n]["pixels"] = 299 Settings["models"][n]["model_type"] = 'img_osm_mix' Settings["models"][n]["unique_id"] = 'agressively_expanded' Settings["models"][n]["top_repeat_FC_block"] = 2 Settings["models"][n]["epochs"] = 1000 return Settings

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def688e1bc01c38cb3b2f6f6a7d55b1ec172165d

68,606

py

Python

benchmarks/SimResults/_bigLittle_hrrs_splash_tugberk_ml/cmp_barnes/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/_bigLittle_hrrs_splash_tugberk_ml/cmp_barnes/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/_bigLittle_hrrs_splash_tugberk_ml/cmp_barnes/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0661699, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.254661, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.359599, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.281194, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.486927, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.279266, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.04739, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.222818, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 5.94896, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.067936, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0101935, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution 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'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.363742, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction 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'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0651723, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.253878, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.352313, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction 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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.161354, 'Execution Unit/Register Files/Runtime Dynamic': 0.0817466, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.216001, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.545725, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 2.08229, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 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Predictor/L2_Local Predictor/Runtime Dynamic': 0.000457547, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00103443, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00488756, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold 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'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.4033, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.206212, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.235119, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 6.83551, 'Instruction Fetch Unit/Runtime Dynamic': 0.528071, 'Instruction Fetch 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Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000453124, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00101234, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00482305, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.012459, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch 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0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0944939, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0944938, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 4.60411, 'Load Store Unit/Runtime Dynamic': 1.96423, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.233006, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.466011, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591321, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283293, 'Memory Management Unit/Area': 0.4339, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0826945, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0832742, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00808595, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.267934, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0331418, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.566098, 'Memory Management Unit/Runtime Dynamic': 0.116416, 'Memory Management Unit/Subthreshold Leakage': 0.0766103, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333, 'Peak Dynamic': 20.3676, 'Renaming Unit/Area': 0.303608, 'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.170441, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.0123239, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.113013, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.295778, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461, 'Runtime Dynamic': 4.95142, 'Subthreshold Leakage': 6.16288, 'Subthreshold Leakage with power gating': 2.55328}], 'DRAM': {'Area': 0, 'Gate Leakage': 0, 'Peak Dynamic': 0.4550974874861491, 'Runtime Dynamic': 0.4550974874861491, 'Subthreshold Leakage': 4.252, 'Subthreshold Leakage with power gating': 4.252}, 'L3': [{'Area': 61.9075, 'Gate Leakage': 0.0484137, 'Peak Dynamic': 0.0392187, 'Runtime Dynamic': 0.0220209, 'Subthreshold Leakage': 6.80085, 'Subthreshold Leakage with power gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 84.2883, 'Peak Power': 117.401, 'Runtime Dynamic': 20.5457, 'Subthreshold Leakage': 31.5774, 'Subthreshold Leakage with power gating': 13.9484, 'Total Cores/Area': 128.669, 'Total Cores/Gate Leakage': 1.4798, 'Total Cores/Peak Dynamic': 84.2491, 'Total Cores/Runtime Dynamic': 20.5236, 'Total Cores/Subthreshold Leakage': 24.7074, 'Total Cores/Subthreshold Leakage with power gating': 10.2429, 'Total L3s/Area': 61.9075, 'Total L3s/Gate Leakage': 0.0484137, 'Total L3s/Peak Dynamic': 0.0392187, 'Total L3s/Runtime Dynamic': 0.0220209, 'Total L3s/Subthreshold Leakage': 6.80085, 'Total L3s/Subthreshold Leakage with power gating': 3.32364, 'Total Leakage': 33.1122, 'Total NoCs/Area': 1.33155, 'Total NoCs/Gate Leakage': 0.00662954, 'Total NoCs/Peak Dynamic': 0.0, 'Total NoCs/Runtime Dynamic': 0.0, 'Total NoCs/Subthreshold Leakage': 0.0691322, 'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}

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a0c7ae34f367dfca0707a0e84c64920804ba41a8

448

py

Python

semantic_aware_models/models/recommendation/__init__.py

ITAINNOVA/SAME

d46dda98753fcb3606e04c3db2d20c9e700140e8

[ "OML" ]

null

semantic_aware_models/models/recommendation/__init__.py

ITAINNOVA/SAME

d46dda98753fcb3606e04c3db2d20c9e700140e8

[ "OML" ]

null

semantic_aware_models/models/recommendation/__init__.py

ITAINNOVA/SAME

d46dda98753fcb3606e04c3db2d20c9e700140e8

[ "OML" ]

1

2020-03-19T12:41:54.000Z

from __future__ import absolute_import from semantic_aware_models.models.recommendation.abstract_recommender import * from semantic_aware_models.models.recommendation.bert_recommender import * from semantic_aware_models.models.recommendation.collaborative_filtering_recommender import * from semantic_aware_models.models.recommendation.content_based_recommender import * from semantic_aware_models.models.recommendation.random_recommender import *

64

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448

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null

0

1

0

1

0

1

0

8

9d2330a31e5e8c5a3ba6fcd9b80aa1d412ecca68

20,653

py

Python

test/integration_test/test_auction_contract.py

heshu-by/likelib-ws

85987d328dc274622f4b758afa1b6af43d15564f

[ "Apache-2.0" ]

null

test/integration_test/test_auction_contract.py

heshu-by/likelib-ws

85987d328dc274622f4b758afa1b6af43d15564f

[ "Apache-2.0" ]

null

test/integration_test/test_auction_contract.py

heshu-by/likelib-ws

85987d328dc274622f4b758afa1b6af43d15564f

[ "Apache-2.0" ]

null

import os import datetime import time from tester import test_case, Env, NodeConfig, Id, TEST_CHECK, TEST_CHECK_EQUAL, get_distributor_address_path, \ ClientType, TransactionStatusCode, TEST_CHECK_NOT_EQUAL CONTRACTS_FOLDER = os.path.realpath( os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", "doc", "contracts")) if not os.path.exists(CONTRACTS_FOLDER): print("Contracts folder was not found", flush=True) exit(1) TRANSACTION_WAIT = 10 @test_case("auction_contract_legacy_grpc") def main(env: Env): contract_file_path = os.path.join(CONTRACTS_FOLDER, "auction", "contract.sol") if not os.path.exists(contract_file_path): TEST_CHECK(False, message="Contracts file was not found") node_id = Id(20401, grpc_port=50401) env.start_node(NodeConfig(node_id)) client = env.get_client(ClientType.LEGACY_GRPC, node_id) TEST_CHECK(client.connection_test()) contracts = client.compile_file(code=contract_file_path) auction_compiled_contract = contracts[0] beneficiary_keys = client.generate_keys(keys_path="user_1") beneficiary_init_balance = client.get_balance(address=beneficiary_keys.address) account_seconds_duration = 200 auction_init_message = client.encode_message(code=auction_compiled_contract, message=f"constructor({account_seconds_duration}, Address({beneficiary_keys.address}))") start_time = datetime.datetime.now() distributor_keys = client.load_address(keys_path=get_distributor_address_path()) env.logger.info("Deploy contract") deployed_contract_status = client.push_contract(from_address=distributor_keys, code=auction_compiled_contract, amount=0, fee=10000000, init_message=auction_init_message, wait=TRANSACTION_WAIT) TEST_CHECK_EQUAL(deployed_contract_status.status_code, TransactionStatusCode.PENDING) TEST_CHECK(client.transaction_success_wait(transaction=deployed_contract_status)) deployed_contract_status = client.get_transaction_status(tx_hash=deployed_contract_status.tx_hash) # TEST_CHECK_EQUAL(deployed_contract_status.status_code, TransactionStatusCode.SUCCESS) deployed_contract = deployed_contract_status.data client_1 = client.generate_keys(keys_path="client_1") client_2 = client.generate_keys(keys_path="client_2") client_1_init_amount = 5000000 client_2_init_amount = 5000000 client.transfer(from_address=distributor_keys, to_address=client_1.address, amount=client_1_init_amount, fee=5, wait=TRANSACTION_WAIT) client.transfer(from_address=distributor_keys, to_address=client_2.address, amount=client_2_init_amount, fee=5, wait=TRANSACTION_WAIT) bid_message = client.encode_message(code=auction_compiled_contract, message="bid()") max_bid_message = client.encode_message(code=auction_compiled_contract, message="highestBid()") client_1_bid_1 = 10000 client_1_bid_1_gas = 100000 env.logger.info("Bid 1 from client 1") bid_status = client.message_call(from_address=client_1, to_address=deployed_contract, fee=client_1_bid_1_gas, amount=client_1_bid_1, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) client_1_bid_2 = 15000 client_1_bid_2_gas = 100000 env.logger.info("Bid 2 from client 1") bid_status = client.message_call(from_address=client_1, to_address=deployed_contract, fee=client_1_bid_2_gas, amount=client_1_bid_2, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_1_bid_2) client_2_bid_1 = 10000 client_2_bid_1_gas = 100000 env.logger.info("Bid 1 from client 2") bid_status = client.message_call(from_address=client_2, to_address=deployed_contract, fee=client_2_bid_1_gas, amount=client_2_bid_1, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_NOT_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_1_bid_2) client_2_bid_2 = 20000 client_2_bid_2_gas = 100000 env.logger.info("Bid 2 from client 2") bid_status = client.message_call(from_address=client_2, to_address=deployed_contract, fee=client_2_bid_2_gas, amount=client_2_bid_2, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_2_bid_2) time.sleep(account_seconds_duration - (datetime.datetime.now() - start_time).total_seconds()) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) auction_end_message = client.encode_message(code=auction_compiled_contract, message="auctionEnd()") env.logger.info("End auction") auction_end_status = client.message_call(from_address=distributor_keys, to_address=deployed_contract, fee=100000, amount=0, message=auction_end_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=auction_end_status)) # auction_end_status = client.get_transaction_status(tx_hash=auction_end_status.tx_hash) # TEST_CHECK_EQUAL(auction_end_status.status_code, TransactionStatusCode.SUCCESS) TEST_CHECK( client.decode_message(code=auction_compiled_contract, method="auctionEnd", message=auction_end_status.data)[ 'is_end']) beneficiary_current_balance = client.get_balance(address=beneficiary_keys.address) TEST_CHECK(beneficiary_init_balance + current_max_bid[''] == beneficiary_current_balance) return 0 @test_case("auction_contract_legacy_http") def main(env: Env): contract_file_path = os.path.join(CONTRACTS_FOLDER, "auction", "contract.sol") if not os.path.exists(contract_file_path): TEST_CHECK(False, message="Contracts file was not found") node_id = Id(20402, http_port=50402) env.start_node(NodeConfig(node_id)) client = env.get_client(ClientType.LEGACY_HTTP, node_id) TEST_CHECK(client.connection_test()) contracts = client.compile_file(code=contract_file_path) auction_compiled_contract = contracts[0] beneficiary_keys = client.generate_keys(keys_path="user_1") beneficiary_init_balance = client.get_balance(address=beneficiary_keys.address) account_seconds_duration = 200 auction_init_message = client.encode_message(code=auction_compiled_contract, message=f"constructor({account_seconds_duration}, Address({beneficiary_keys.address}))") start_time = datetime.datetime.now() distributor_keys = client.load_address(keys_path=get_distributor_address_path()) deployed_contract_status = client.push_contract(from_address=distributor_keys, code=auction_compiled_contract, amount=0, fee=10000000, init_message=auction_init_message, wait=TRANSACTION_WAIT) TEST_CHECK_EQUAL(deployed_contract_status.status_code, TransactionStatusCode.PENDING) TEST_CHECK(client.transaction_success_wait(transaction=deployed_contract_status)) deployed_contract_status = client.get_transaction_status(tx_hash=deployed_contract_status.tx_hash) # TEST_CHECK_EQUAL(deployed_contract_status.status_code, TransactionStatusCode.SUCCESS) deployed_contract = deployed_contract_status.data client_1 = client.generate_keys(keys_path="client_1") client_2 = client.generate_keys(keys_path="client_2") client_1_init_amount = 5000000 client_2_init_amount = 5000000 client.transfer(from_address=distributor_keys, to_address=client_1.address, amount=client_1_init_amount, fee=5, wait=TRANSACTION_WAIT) client.transfer(from_address=distributor_keys, to_address=client_2.address, amount=client_2_init_amount, fee=5, wait=TRANSACTION_WAIT) bid_message = client.encode_message(code=auction_compiled_contract, message="bid()") max_bid_message = client.encode_message(code=auction_compiled_contract, message="highestBid()") client_1_bid_1 = 10000 client_1_bid_1_gas = 100000 bid_status = client.message_call(from_address=client_1, to_address=deployed_contract, fee=client_1_bid_1_gas, amount=client_1_bid_1, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) client_1_bid_2 = 15000 client_1_bid_2_gas = 100000 bid_status = client.message_call(from_address=client_1, to_address=deployed_contract, fee=client_1_bid_2_gas, amount=client_1_bid_2, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_1_bid_2) client_2_bid_1 = 10000 client_2_bid_1_gas = 100000 bid_status = client.message_call(from_address=client_2, to_address=deployed_contract, fee=client_2_bid_1_gas, amount=client_2_bid_1, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_NOT_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_1_bid_2) client_2_bid_2 = 20000 client_2_bid_2_gas = 100000 bid_status = client.message_call(from_address=client_2, to_address=deployed_contract, fee=client_2_bid_2_gas, amount=client_2_bid_2, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_2_bid_2) time.sleep(account_seconds_duration - (datetime.datetime.now() - start_time).total_seconds()) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) auction_end_message = client.encode_message(code=auction_compiled_contract, message="auctionEnd()") auction_end_status = client.message_call(from_address=distributor_keys, to_address=deployed_contract, fee=100000, amount=0, message=auction_end_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=auction_end_status_status)) # auction_end_status = client.get_transaction_status(tx_hash=auction_end_status.tx_hash) # TEST_CHECK_EQUAL(auction_end_status.status_code, TransactionStatusCode.SUCCESS) TEST_CHECK( client.decode_message(code=auction_compiled_contract, method="auctionEnd", message=auction_end_status.data)[ 'is_end']) beneficiary_current_balance = client.get_balance(address=beneficiary_keys.address) TEST_CHECK(beneficiary_init_balance + current_max_bid[''] == beneficiary_current_balance) return 0 @test_case("auction_contract_python_grpc") def main(env: Env): contract_file_path = os.path.join(CONTRACTS_FOLDER, "auction", "contract.sol") if not os.path.exists(contract_file_path): TEST_CHECK(False, message="Contracts file was not found") node_id = Id(20403, http_port=50403) env.start_node(NodeConfig(node_id)) client = env.get_client(ClientType.PYTHON_HTTP, node_id) TEST_CHECK(client.connection_test()) contracts = client.compile_file(code=contract_file_path) auction_compiled_contract = contracts[0] beneficiary_keys = client.generate_keys(keys_path="user_1") beneficiary_init_balance = client.get_balance(address=beneficiary_keys.address) account_seconds_duration = 200 auction_init_message = client.encode_message(code=auction_compiled_contract, message=f"constructor({account_seconds_duration}, Address({beneficiary_keys.address}))") start_time = datetime.datetime.now() distributor_keys = client.load_address(keys_path=get_distributor_address_path()) deployed_contract_status = client.push_contract(from_address=distributor_keys, code=auction_compiled_contract, amount=0, fee=10000000, init_message=auction_init_message, wait=TRANSACTION_WAIT) TEST_CHECK_EQUAL(deployed_contract_status.status_code, TransactionStatusCode.PENDING) TEST_CHECK(client.transaction_success_wait(transaction=deployed_contract_status)) deployed_contract_status = client.get_transaction_status(tx_hash=deployed_contract_status.tx_hash) # TEST_CHECK_EQUAL(deployed_contract_status.status_code, TransactionStatusCode.SUCCESS) deployed_contract = deployed_contract_status.data client_1 = client.generate_keys(keys_path="client_1") client_2 = client.generate_keys(keys_path="client_2") client_1_init_amount = 5000000 client_2_init_amount = 5000000 client.transfer(from_address=distributor_keys, to_address=client_1.address, amount=client_1_init_amount, fee=5, wait=TRANSACTION_WAIT) client.transfer(from_address=distributor_keys, to_address=client_2.address, amount=client_2_init_amount, fee=5, wait=TRANSACTION_WAIT) bid_message = client.encode_message(code=auction_compiled_contract, message="bid()") max_bid_message = client.encode_message(code=auction_compiled_contract, message="highestBid()") client_1_bid_1 = 10000 client_1_bid_1_gas = 100000 bid_status = client.message_call(from_address=client_1, to_address=deployed_contract, fee=client_1_bid_1_gas, amount=client_1_bid_1, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) client_1_bid_2 = 15000 client_1_bid_2_gas = 100000 bid_status = client.message_call(from_address=client_1, to_address=deployed_contract, fee=client_1_bid_2_gas, amount=client_1_bid_2, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_1_bid_2) client_2_bid_1 = 10000 client_2_bid_1_gas = 100000 bid_status = client.message_call(from_address=client_2, to_address=deployed_contract, fee=client_2_bid_1_gas, amount=client_2_bid_1, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_NOT_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_1_bid_2) client_2_bid_2 = 20000 client_2_bid_2_gas = 100000 bid_status = client.message_call(from_address=client_2, to_address=deployed_contract, fee=client_2_bid_2_gas, amount=client_2_bid_2, message=bid_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=bid_status)) # bid_status = client.get_transaction_status(tx_hash=bid_status.tx_hash) # TEST_CHECK_EQUAL(bid_status.status_code, TransactionStatusCode.SUCCESS) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) TEST_CHECK(current_max_bid[''] == client_2_bid_2) time.sleep(account_seconds_duration - (datetime.datetime.now() - start_time).total_seconds()) result = client.call_view(from_address=distributor_keys, to_address=deployed_contract, message=max_bid_message) current_max_bid = client.decode_message(code=auction_compiled_contract, method="highestBid", message=result) auction_end_message = client.encode_message(code=auction_compiled_contract, message="auctionEnd()") auction_end_status = client.message_call(from_address=distributor_keys, to_address=deployed_contract, fee=100000, amount=0, message=auction_end_message, wait=TRANSACTION_WAIT) TEST_CHECK(client.transaction_success_wait(transaction=auction_end_status)) # auction_end_status = client.get_transaction_status(tx_hash=auction_end_status.tx_hash) # TEST_CHECK_EQUAL(auction_end_status.status_code, TransactionStatusCode.SUCCESS) TEST_CHECK( client.decode_message(code=auction_compiled_contract, method="auctionEnd", message=auction_end_status.data)[ 'is_end']) beneficiary_current_balance = client.get_balance(address=beneficiary_keys.address) TEST_CHECK(beneficiary_init_balance + current_max_bid[''] == beneficiary_current_balance) return 0

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1

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null

0

7

9d3414ae5ee27af3f713d763fc5709e209039dc1

6,706

py

Python

app/core/migrations/0013_auto_20200820_2140.py

dodge-ttu/gohan-api

bf3d4a4e7a93c699a00865c769975d6cfb3ec8cf

[ "MIT" ]

null

app/core/migrations/0013_auto_20200820_2140.py

dodge-ttu/gohan-api

bf3d4a4e7a93c699a00865c769975d6cfb3ec8cf

[ "MIT" ]

null

app/core/migrations/0013_auto_20200820_2140.py

dodge-ttu/gohan-api

bf3d4a4e7a93c699a00865c769975d6cfb3ec8cf

[ "MIT" ]

null

# Generated by Django 2.1.15 on 2020-08-20 21:40 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0012_raingaugereading_soilprobereading_tankmonitorreading'), ] operations = [ migrations.AddField( model_name='soilprobereading', name='dailyuse', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='datetime', field=models.DateTimeField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth1', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth10', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth10temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth11', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth11temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth12', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth12temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth13', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth13temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth14', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth14temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth15', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth15temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth16', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth16temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth1temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth2', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth2temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth3', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth3temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth4', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth4temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth5', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth5temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth6', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth6temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth7', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth7temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth8', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth8temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth9', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='depth9temp', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='humidity', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='irrigation_due_actual', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='irrigation_due_default', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='rainfall', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='soiltotal', field=models.FloatField(null=True), ), migrations.AddField( model_name='soilprobereading', name='temp', field=models.FloatField(null=True), ), ]

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9d3bf3c03f34d145a3b8665e1f20fd85772faffa

101,428

py

Python

sdk/python/pulumi_oci/bds/outputs.py

EladGabay/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

[ "ECL-2.0", "Apache-2.0" ]

5

2021-08-17T11:14:46.000Z

2021-12-31T02:07:03.000Z

sdk/python/pulumi_oci/bds/outputs.py

pulumi-oci/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

[ "ECL-2.0", "Apache-2.0" ]

1

2021-09-06T11:21:29.000Z

sdk/python/pulumi_oci/bds/outputs.py

pulumi-oci/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

[ "ECL-2.0", "Apache-2.0" ]

2

2021-08-24T23:31:30.000Z

2022-01-02T19:26:54.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'AutoScalingConfigurationPolicy', 'AutoScalingConfigurationPolicyRule', 'AutoScalingConfigurationPolicyRuleMetric', 'AutoScalingConfigurationPolicyRuleMetricThreshold', 'BdsInstanceCloudSqlDetails', 'BdsInstanceCloudSqlDetailsKerberosDetail', 'BdsInstanceClusterDetails', 'BdsInstanceMasterNode', 'BdsInstanceNetworkConfig', 'BdsInstanceNode', 'BdsInstanceNodeAttachedBlockVolume', 'BdsInstanceUtilNode', 'BdsInstanceWorkerNode', 'GetAutoScalingConfigurationPolicyResult', 'GetAutoScalingConfigurationPolicyRuleResult', 'GetAutoScalingConfigurationPolicyRuleMetricResult', 'GetAutoScalingConfigurationPolicyRuleMetricThresholdResult', 'GetBdsInstanceCloudSqlDetailsResult', 'GetBdsInstanceCloudSqlDetailsKerberosDetailResult', 'GetBdsInstanceClusterDetailsResult', 'GetBdsInstanceMasterNodeResult', 'GetBdsInstanceNetworkConfigResult', 'GetBdsInstanceNodeResult', 'GetBdsInstanceNodeAttachedBlockVolumeResult', 'GetBdsInstanceUtilNodeResult', 'GetBdsInstanceWorkerNodeResult', 'GetBdsInstancesBdsInstanceResult', 'GetBdsInstancesBdsInstanceCloudSqlDetailsResult', 'GetBdsInstancesBdsInstanceCloudSqlDetailsKerberosDetailResult', 'GetBdsInstancesBdsInstanceClusterDetailsResult', 'GetBdsInstancesBdsInstanceMasterNodeResult', 'GetBdsInstancesBdsInstanceNetworkConfigResult', 'GetBdsInstancesBdsInstanceNodeResult', 'GetBdsInstancesBdsInstanceNodeAttachedBlockVolumeResult', 'GetBdsInstancesBdsInstanceUtilNodeResult', 'GetBdsInstancesBdsInstanceWorkerNodeResult', 'GetBdsInstancesFilterResult', ] @pulumi.output_type class AutoScalingConfigurationPolicy(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "policyType": suggest = "policy_type" if suggest: pulumi.log.warn(f"Key '{key}' not found in AutoScalingConfigurationPolicy. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: AutoScalingConfigurationPolicy.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: AutoScalingConfigurationPolicy.__key_warning(key) return super().get(key, default) def __init__(__self__, *, policy_type: str, rules: Sequence['outputs.AutoScalingConfigurationPolicyRule']): """ :param str policy_type: (Updatable) Types of autoscale policies. Options are SCHEDULE-BASED or THRESHOLD-BASED. (Only THRESHOLD-BASED is supported in this release.) :param Sequence['AutoScalingConfigurationPolicyRuleArgs'] rules: (Updatable) The list of rules for autoscaling. If an action has multiple rules, the last rule in the array will be applied. """ pulumi.set(__self__, "policy_type", policy_type) pulumi.set(__self__, "rules", rules) @property @pulumi.getter(name="policyType") def policy_type(self) -> str: """ (Updatable) Types of autoscale policies. Options are SCHEDULE-BASED or THRESHOLD-BASED. (Only THRESHOLD-BASED is supported in this release.) """ return pulumi.get(self, "policy_type") @property @pulumi.getter def rules(self) -> Sequence['outputs.AutoScalingConfigurationPolicyRule']: """ (Updatable) The list of rules for autoscaling. If an action has multiple rules, the last rule in the array will be applied. """ return pulumi.get(self, "rules") @pulumi.output_type class AutoScalingConfigurationPolicyRule(dict): def __init__(__self__, *, action: str, metric: 'outputs.AutoScalingConfigurationPolicyRuleMetric'): """ :param str action: (Updatable) The valid value are CHANGE_SHAPE_SCALE_UP or CHANGE_SHAPE_SCALE_DOWN. :param 'AutoScalingConfigurationPolicyRuleMetricArgs' metric: (Updatable) Metric and threshold details for triggering an autoscale action. """ pulumi.set(__self__, "action", action) pulumi.set(__self__, "metric", metric) @property @pulumi.getter def action(self) -> str: """ (Updatable) The valid value are CHANGE_SHAPE_SCALE_UP or CHANGE_SHAPE_SCALE_DOWN. """ return pulumi.get(self, "action") @property @pulumi.getter def metric(self) -> 'outputs.AutoScalingConfigurationPolicyRuleMetric': """ (Updatable) Metric and threshold details for triggering an autoscale action. """ return pulumi.get(self, "metric") @pulumi.output_type class AutoScalingConfigurationPolicyRuleMetric(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "metricType": suggest = "metric_type" if suggest: pulumi.log.warn(f"Key '{key}' not found in AutoScalingConfigurationPolicyRuleMetric. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: AutoScalingConfigurationPolicyRuleMetric.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: AutoScalingConfigurationPolicyRuleMetric.__key_warning(key) return super().get(key, default) def __init__(__self__, *, metric_type: str, threshold: 'outputs.AutoScalingConfigurationPolicyRuleMetricThreshold'): """ :param str metric_type: (Updatable) Allowed value is CPU_UTILIZATION. :param 'AutoScalingConfigurationPolicyRuleMetricThresholdArgs' threshold: (Updatable) An autoscale action is triggered when a performance metric meets or exceeds a threshold. """ pulumi.set(__self__, "metric_type", metric_type) pulumi.set(__self__, "threshold", threshold) @property @pulumi.getter(name="metricType") def metric_type(self) -> str: """ (Updatable) Allowed value is CPU_UTILIZATION. """ return pulumi.get(self, "metric_type") @property @pulumi.getter def threshold(self) -> 'outputs.AutoScalingConfigurationPolicyRuleMetricThreshold': """ (Updatable) An autoscale action is triggered when a performance metric meets or exceeds a threshold. """ return pulumi.get(self, "threshold") @pulumi.output_type class AutoScalingConfigurationPolicyRuleMetricThreshold(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "durationInMinutes": suggest = "duration_in_minutes" if suggest: pulumi.log.warn(f"Key '{key}' not found in AutoScalingConfigurationPolicyRuleMetricThreshold. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: AutoScalingConfigurationPolicyRuleMetricThreshold.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: AutoScalingConfigurationPolicyRuleMetricThreshold.__key_warning(key) return super().get(key, default) def __init__(__self__, *, duration_in_minutes: int, operator: str, value: int): """ :param int duration_in_minutes: (Updatable) This value is the minimum period of time the metric value meets or exceeds the threshold value before the action is triggered. The value is in minutes. :param str operator: (Updatable) The comparison operator to use. Options are greater than (GT) or less than (LT). :param int value: (Updatable) Integer non-negative value. 0 < value < 100 """ pulumi.set(__self__, "duration_in_minutes", duration_in_minutes) pulumi.set(__self__, "operator", operator) pulumi.set(__self__, "value", value) @property @pulumi.getter(name="durationInMinutes") def duration_in_minutes(self) -> int: """ (Updatable) This value is the minimum period of time the metric value meets or exceeds the threshold value before the action is triggered. The value is in minutes. """ return pulumi.get(self, "duration_in_minutes") @property @pulumi.getter def operator(self) -> str: """ (Updatable) The comparison operator to use. Options are greater than (GT) or less than (LT). """ return pulumi.get(self, "operator") @property @pulumi.getter def value(self) -> int: """ (Updatable) Integer non-negative value. 0 < value < 100 """ return pulumi.get(self, "value") @pulumi.output_type class BdsInstanceCloudSqlDetails(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "blockVolumeSizeInGbs": suggest = "block_volume_size_in_gbs" elif key == "ipAddress": suggest = "ip_address" elif key == "isKerberosMappedToDatabaseUsers": suggest = "is_kerberos_mapped_to_database_users" elif key == "kerberosDetails": suggest = "kerberos_details" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceCloudSqlDetails. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceCloudSqlDetails.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceCloudSqlDetails.__key_warning(key) return super().get(key, default) def __init__(__self__, *, block_volume_size_in_gbs: str, shape: str, ip_address: Optional[str] = None, is_kerberos_mapped_to_database_users: Optional[bool] = None, kerberos_details: Optional[Sequence['outputs.BdsInstanceCloudSqlDetailsKerberosDetail']] = None): """ :param str block_volume_size_in_gbs: The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. :param str shape: (Updatable) Shape of the node. :param str ip_address: IP address of the node. :param bool is_kerberos_mapped_to_database_users: Boolean flag specifying whether or not Kerberos principals are mapped to database users. :param Sequence['BdsInstanceCloudSqlDetailsKerberosDetailArgs'] kerberos_details: Details about the Kerberos principals. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "shape", shape) if ip_address is not None: pulumi.set(__self__, "ip_address", ip_address) if is_kerberos_mapped_to_database_users is not None: pulumi.set(__self__, "is_kerberos_mapped_to_database_users", is_kerberos_mapped_to_database_users) if kerberos_details is not None: pulumi.set(__self__, "kerberos_details", kerberos_details) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter def shape(self) -> str: """ (Updatable) Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> Optional[str]: """ IP address of the node. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="isKerberosMappedToDatabaseUsers") def is_kerberos_mapped_to_database_users(self) -> Optional[bool]: """ Boolean flag specifying whether or not Kerberos principals are mapped to database users. """ return pulumi.get(self, "is_kerberos_mapped_to_database_users") @property @pulumi.getter(name="kerberosDetails") def kerberos_details(self) -> Optional[Sequence['outputs.BdsInstanceCloudSqlDetailsKerberosDetail']]: """ Details about the Kerberos principals. """ return pulumi.get(self, "kerberos_details") @pulumi.output_type class BdsInstanceCloudSqlDetailsKerberosDetail(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "keytabFile": suggest = "keytab_file" elif key == "principalName": suggest = "principal_name" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceCloudSqlDetailsKerberosDetail. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceCloudSqlDetailsKerberosDetail.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceCloudSqlDetailsKerberosDetail.__key_warning(key) return super().get(key, default) def __init__(__self__, *, keytab_file: Optional[str] = None, principal_name: Optional[str] = None): """ :param str keytab_file: Location of the keytab file :param str principal_name: Name of the Kerberos principal. """ if keytab_file is not None: pulumi.set(__self__, "keytab_file", keytab_file) if principal_name is not None: pulumi.set(__self__, "principal_name", principal_name) @property @pulumi.getter(name="keytabFile") def keytab_file(self) -> Optional[str]: """ Location of the keytab file """ return pulumi.get(self, "keytab_file") @property @pulumi.getter(name="principalName") def principal_name(self) -> Optional[str]: """ Name of the Kerberos principal. """ return pulumi.get(self, "principal_name") @pulumi.output_type class BdsInstanceClusterDetails(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "ambariUrl": suggest = "ambari_url" elif key == "bdCellVersion": suggest = "bd_cell_version" elif key == "bdaVersion": suggest = "bda_version" elif key == "bdmVersion": suggest = "bdm_version" elif key == "bdsVersion": suggest = "bds_version" elif key == "bigDataManagerUrl": suggest = "big_data_manager_url" elif key == "clouderaManagerUrl": suggest = "cloudera_manager_url" elif key == "csqlCellVersion": suggest = "csql_cell_version" elif key == "dbVersion": suggest = "db_version" elif key == "hueServerUrl": suggest = "hue_server_url" elif key == "osVersion": suggest = "os_version" elif key == "timeCreated": suggest = "time_created" elif key == "timeRefreshed": suggest = "time_refreshed" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceClusterDetails. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceClusterDetails.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceClusterDetails.__key_warning(key) return super().get(key, default) def __init__(__self__, *, ambari_url: Optional[str] = None, bd_cell_version: Optional[str] = None, bda_version: Optional[str] = None, bdm_version: Optional[str] = None, bds_version: Optional[str] = None, big_data_manager_url: Optional[str] = None, cloudera_manager_url: Optional[str] = None, csql_cell_version: Optional[str] = None, db_version: Optional[str] = None, hue_server_url: Optional[str] = None, os_version: Optional[str] = None, time_created: Optional[str] = None, time_refreshed: Optional[str] = None): """ :param str ambari_url: The URL of Ambari :param str bd_cell_version: Cloud SQL cell version. :param str bda_version: BDA version installed in the cluster :param str bdm_version: Big Data Manager version installed in the cluster. :param str bds_version: Big Data Service version installed in the cluster. :param str big_data_manager_url: The URL of Big Data Manager. :param str cloudera_manager_url: The URL of Cloudera Manager :param str csql_cell_version: Big Data SQL version. :param str db_version: Cloud SQL query server database version. :param str hue_server_url: The URL of the Hue server. :param str os_version: Oracle Linux version installed in the cluster. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. :param str time_refreshed: The time the cluster was automatically or manually refreshed, shown as an RFC 3339 formatted datetime string. """ if ambari_url is not None: pulumi.set(__self__, "ambari_url", ambari_url) if bd_cell_version is not None: pulumi.set(__self__, "bd_cell_version", bd_cell_version) if bda_version is not None: pulumi.set(__self__, "bda_version", bda_version) if bdm_version is not None: pulumi.set(__self__, "bdm_version", bdm_version) if bds_version is not None: pulumi.set(__self__, "bds_version", bds_version) if big_data_manager_url is not None: pulumi.set(__self__, "big_data_manager_url", big_data_manager_url) if cloudera_manager_url is not None: pulumi.set(__self__, "cloudera_manager_url", cloudera_manager_url) if csql_cell_version is not None: pulumi.set(__self__, "csql_cell_version", csql_cell_version) if db_version is not None: pulumi.set(__self__, "db_version", db_version) if hue_server_url is not None: pulumi.set(__self__, "hue_server_url", hue_server_url) if os_version is not None: pulumi.set(__self__, "os_version", os_version) if time_created is not None: pulumi.set(__self__, "time_created", time_created) if time_refreshed is not None: pulumi.set(__self__, "time_refreshed", time_refreshed) @property @pulumi.getter(name="ambariUrl") def ambari_url(self) -> Optional[str]: """ The URL of Ambari """ return pulumi.get(self, "ambari_url") @property @pulumi.getter(name="bdCellVersion") def bd_cell_version(self) -> Optional[str]: """ Cloud SQL cell version. """ return pulumi.get(self, "bd_cell_version") @property @pulumi.getter(name="bdaVersion") def bda_version(self) -> Optional[str]: """ BDA version installed in the cluster """ return pulumi.get(self, "bda_version") @property @pulumi.getter(name="bdmVersion") def bdm_version(self) -> Optional[str]: """ Big Data Manager version installed in the cluster. """ return pulumi.get(self, "bdm_version") @property @pulumi.getter(name="bdsVersion") def bds_version(self) -> Optional[str]: """ Big Data Service version installed in the cluster. """ return pulumi.get(self, "bds_version") @property @pulumi.getter(name="bigDataManagerUrl") def big_data_manager_url(self) -> Optional[str]: """ The URL of Big Data Manager. """ return pulumi.get(self, "big_data_manager_url") @property @pulumi.getter(name="clouderaManagerUrl") def cloudera_manager_url(self) -> Optional[str]: """ The URL of Cloudera Manager """ return pulumi.get(self, "cloudera_manager_url") @property @pulumi.getter(name="csqlCellVersion") def csql_cell_version(self) -> Optional[str]: """ Big Data SQL version. """ return pulumi.get(self, "csql_cell_version") @property @pulumi.getter(name="dbVersion") def db_version(self) -> Optional[str]: """ Cloud SQL query server database version. """ return pulumi.get(self, "db_version") @property @pulumi.getter(name="hueServerUrl") def hue_server_url(self) -> Optional[str]: """ The URL of the Hue server. """ return pulumi.get(self, "hue_server_url") @property @pulumi.getter(name="osVersion") def os_version(self) -> Optional[str]: """ Oracle Linux version installed in the cluster. """ return pulumi.get(self, "os_version") @property @pulumi.getter(name="timeCreated") def time_created(self) -> Optional[str]: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @property @pulumi.getter(name="timeRefreshed") def time_refreshed(self) -> Optional[str]: """ The time the cluster was automatically or manually refreshed, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_refreshed") @pulumi.output_type class BdsInstanceMasterNode(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "blockVolumeSizeInGbs": suggest = "block_volume_size_in_gbs" elif key == "numberOfNodes": suggest = "number_of_nodes" elif key == "subnetId": suggest = "subnet_id" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceMasterNode. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceMasterNode.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceMasterNode.__key_warning(key) return super().get(key, default) def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: (Updatable) Shape of the node. :param str subnet_id: The OCID of the subnet in which the node will be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ (Updatable) Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node will be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class BdsInstanceNetworkConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "cidrBlock": suggest = "cidr_block" elif key == "isNatGatewayRequired": suggest = "is_nat_gateway_required" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceNetworkConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceNetworkConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceNetworkConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, cidr_block: Optional[str] = None, is_nat_gateway_required: Optional[bool] = None): """ :param str cidr_block: The CIDR IP address block of the VCN. :param bool is_nat_gateway_required: A boolean flag whether to configure a NAT gateway. """ if cidr_block is not None: pulumi.set(__self__, "cidr_block", cidr_block) if is_nat_gateway_required is not None: pulumi.set(__self__, "is_nat_gateway_required", is_nat_gateway_required) @property @pulumi.getter(name="cidrBlock") def cidr_block(self) -> Optional[str]: """ The CIDR IP address block of the VCN. """ return pulumi.get(self, "cidr_block") @property @pulumi.getter(name="isNatGatewayRequired") def is_nat_gateway_required(self) -> Optional[bool]: """ A boolean flag whether to configure a NAT gateway. """ return pulumi.get(self, "is_nat_gateway_required") @pulumi.output_type class BdsInstanceNode(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "attachedBlockVolumes": suggest = "attached_block_volumes" elif key == "availabilityDomain": suggest = "availability_domain" elif key == "displayName": suggest = "display_name" elif key == "faultDomain": suggest = "fault_domain" elif key == "imageId": suggest = "image_id" elif key == "instanceId": suggest = "instance_id" elif key == "ipAddress": suggest = "ip_address" elif key == "nodeType": suggest = "node_type" elif key == "sshFingerprint": suggest = "ssh_fingerprint" elif key == "subnetId": suggest = "subnet_id" elif key == "timeCreated": suggest = "time_created" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceNode. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceNode.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceNode.__key_warning(key) return super().get(key, default) def __init__(__self__, *, attached_block_volumes: Optional[Sequence['outputs.BdsInstanceNodeAttachedBlockVolume']] = None, availability_domain: Optional[str] = None, display_name: Optional[str] = None, fault_domain: Optional[str] = None, hostname: Optional[str] = None, image_id: Optional[str] = None, instance_id: Optional[str] = None, ip_address: Optional[str] = None, node_type: Optional[str] = None, shape: Optional[str] = None, ssh_fingerprint: Optional[str] = None, state: Optional[str] = None, subnet_id: Optional[str] = None, time_created: Optional[str] = None): """ :param Sequence['BdsInstanceNodeAttachedBlockVolumeArgs'] attached_block_volumes: The list of block volumes attached to a given node. :param str availability_domain: The name of the availability domain in which the node is running. :param str display_name: (Updatable) Name of the Big Data Service cluster. :param str fault_domain: The name of the fault domain in which the node is running. :param str hostname: The fully-qualified hostname (FQDN) of the node. :param str image_id: The OCID of the image from which the node was created. :param str instance_id: The OCID of the underlying Oracle Cloud Infrastructure Compute instance. :param str ip_address: IP address of the node. :param str node_type: The Big Data Service cluster node type. :param str shape: (Updatable) Shape of the node. :param str ssh_fingerprint: The fingerprint of the SSH key used for node access. :param str state: The state of the cluster. :param str subnet_id: The OCID of the subnet in which the node will be created. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ if attached_block_volumes is not None: pulumi.set(__self__, "attached_block_volumes", attached_block_volumes) if availability_domain is not None: pulumi.set(__self__, "availability_domain", availability_domain) if display_name is not None: pulumi.set(__self__, "display_name", display_name) if fault_domain is not None: pulumi.set(__self__, "fault_domain", fault_domain) if hostname is not None: pulumi.set(__self__, "hostname", hostname) if image_id is not None: pulumi.set(__self__, "image_id", image_id) if instance_id is not None: pulumi.set(__self__, "instance_id", instance_id) if ip_address is not None: pulumi.set(__self__, "ip_address", ip_address) if node_type is not None: pulumi.set(__self__, "node_type", node_type) if shape is not None: pulumi.set(__self__, "shape", shape) if ssh_fingerprint is not None: pulumi.set(__self__, "ssh_fingerprint", ssh_fingerprint) if state is not None: pulumi.set(__self__, "state", state) if subnet_id is not None: pulumi.set(__self__, "subnet_id", subnet_id) if time_created is not None: pulumi.set(__self__, "time_created", time_created) @property @pulumi.getter(name="attachedBlockVolumes") def attached_block_volumes(self) -> Optional[Sequence['outputs.BdsInstanceNodeAttachedBlockVolume']]: """ The list of block volumes attached to a given node. """ return pulumi.get(self, "attached_block_volumes") @property @pulumi.getter(name="availabilityDomain") def availability_domain(self) -> Optional[str]: """ The name of the availability domain in which the node is running. """ return pulumi.get(self, "availability_domain") @property @pulumi.getter(name="displayName") def display_name(self) -> Optional[str]: """ (Updatable) Name of the Big Data Service cluster. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="faultDomain") def fault_domain(self) -> Optional[str]: """ The name of the fault domain in which the node is running. """ return pulumi.get(self, "fault_domain") @property @pulumi.getter def hostname(self) -> Optional[str]: """ The fully-qualified hostname (FQDN) of the node. """ return pulumi.get(self, "hostname") @property @pulumi.getter(name="imageId") def image_id(self) -> Optional[str]: """ The OCID of the image from which the node was created. """ return pulumi.get(self, "image_id") @property @pulumi.getter(name="instanceId") def instance_id(self) -> Optional[str]: """ The OCID of the underlying Oracle Cloud Infrastructure Compute instance. """ return pulumi.get(self, "instance_id") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> Optional[str]: """ IP address of the node. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="nodeType") def node_type(self) -> Optional[str]: """ The Big Data Service cluster node type. """ return pulumi.get(self, "node_type") @property @pulumi.getter def shape(self) -> Optional[str]: """ (Updatable) Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="sshFingerprint") def ssh_fingerprint(self) -> Optional[str]: """ The fingerprint of the SSH key used for node access. """ return pulumi.get(self, "ssh_fingerprint") @property @pulumi.getter def state(self) -> Optional[str]: """ The state of the cluster. """ return pulumi.get(self, "state") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> Optional[str]: """ The OCID of the subnet in which the node will be created. """ return pulumi.get(self, "subnet_id") @property @pulumi.getter(name="timeCreated") def time_created(self) -> Optional[str]: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @pulumi.output_type class BdsInstanceNodeAttachedBlockVolume(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "volumeAttachmentId": suggest = "volume_attachment_id" elif key == "volumeSizeInGbs": suggest = "volume_size_in_gbs" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceNodeAttachedBlockVolume. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceNodeAttachedBlockVolume.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceNodeAttachedBlockVolume.__key_warning(key) return super().get(key, default) def __init__(__self__, *, volume_attachment_id: Optional[str] = None, volume_size_in_gbs: Optional[str] = None): """ :param str volume_attachment_id: The OCID of the volume attachment. :param str volume_size_in_gbs: The size of the volume in GBs. """ if volume_attachment_id is not None: pulumi.set(__self__, "volume_attachment_id", volume_attachment_id) if volume_size_in_gbs is not None: pulumi.set(__self__, "volume_size_in_gbs", volume_size_in_gbs) @property @pulumi.getter(name="volumeAttachmentId") def volume_attachment_id(self) -> Optional[str]: """ The OCID of the volume attachment. """ return pulumi.get(self, "volume_attachment_id") @property @pulumi.getter(name="volumeSizeInGbs") def volume_size_in_gbs(self) -> Optional[str]: """ The size of the volume in GBs. """ return pulumi.get(self, "volume_size_in_gbs") @pulumi.output_type class BdsInstanceUtilNode(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "blockVolumeSizeInGbs": suggest = "block_volume_size_in_gbs" elif key == "numberOfNodes": suggest = "number_of_nodes" elif key == "subnetId": suggest = "subnet_id" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceUtilNode. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceUtilNode.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceUtilNode.__key_warning(key) return super().get(key, default) def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: (Updatable) Shape of the node. :param str subnet_id: The OCID of the subnet in which the node will be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ (Updatable) Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node will be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class BdsInstanceWorkerNode(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "blockVolumeSizeInGbs": suggest = "block_volume_size_in_gbs" elif key == "numberOfNodes": suggest = "number_of_nodes" elif key == "subnetId": suggest = "subnet_id" if suggest: pulumi.log.warn(f"Key '{key}' not found in BdsInstanceWorkerNode. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: BdsInstanceWorkerNode.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: BdsInstanceWorkerNode.__key_warning(key) return super().get(key, default) def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: (Updatable) Shape of the node. :param str subnet_id: The OCID of the subnet in which the node will be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB to be attached to a given node. All the details needed for attaching the block volume are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ (Updatable) Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node will be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetAutoScalingConfigurationPolicyResult(dict): def __init__(__self__, *, policy_type: str, rules: Sequence['outputs.GetAutoScalingConfigurationPolicyRuleResult']): """ :param str policy_type: Types of autoscale policies. Options are SCHEDULE-BASED or THRESHOLD-BASED. (Only THRESHOLD-BASED is supported in this release.) :param Sequence['GetAutoScalingConfigurationPolicyRuleArgs'] rules: The list of rules for autoscaling. If an action has multiple rules, the last rule in the array will be applied. """ pulumi.set(__self__, "policy_type", policy_type) pulumi.set(__self__, "rules", rules) @property @pulumi.getter(name="policyType") def policy_type(self) -> str: """ Types of autoscale policies. Options are SCHEDULE-BASED or THRESHOLD-BASED. (Only THRESHOLD-BASED is supported in this release.) """ return pulumi.get(self, "policy_type") @property @pulumi.getter def rules(self) -> Sequence['outputs.GetAutoScalingConfigurationPolicyRuleResult']: """ The list of rules for autoscaling. If an action has multiple rules, the last rule in the array will be applied. """ return pulumi.get(self, "rules") @pulumi.output_type class GetAutoScalingConfigurationPolicyRuleResult(dict): def __init__(__self__, *, action: str, metric: 'outputs.GetAutoScalingConfigurationPolicyRuleMetricResult'): """ :param str action: The valid value are CHANGE_SHAPE_SCALE_UP or CHANGE_SHAPE_SCALE_DOWN. :param 'GetAutoScalingConfigurationPolicyRuleMetricArgs' metric: Metric and threshold details for triggering an autoscale action. """ pulumi.set(__self__, "action", action) pulumi.set(__self__, "metric", metric) @property @pulumi.getter def action(self) -> str: """ The valid value are CHANGE_SHAPE_SCALE_UP or CHANGE_SHAPE_SCALE_DOWN. """ return pulumi.get(self, "action") @property @pulumi.getter def metric(self) -> 'outputs.GetAutoScalingConfigurationPolicyRuleMetricResult': """ Metric and threshold details for triggering an autoscale action. """ return pulumi.get(self, "metric") @pulumi.output_type class GetAutoScalingConfigurationPolicyRuleMetricResult(dict): def __init__(__self__, *, metric_type: str, threshold: 'outputs.GetAutoScalingConfigurationPolicyRuleMetricThresholdResult'): """ :param str metric_type: Allowed value is CPU_UTILIZATION. :param 'GetAutoScalingConfigurationPolicyRuleMetricThresholdArgs' threshold: An autoscale action is triggered when a performance metric meets or exceeds a threshold. """ pulumi.set(__self__, "metric_type", metric_type) pulumi.set(__self__, "threshold", threshold) @property @pulumi.getter(name="metricType") def metric_type(self) -> str: """ Allowed value is CPU_UTILIZATION. """ return pulumi.get(self, "metric_type") @property @pulumi.getter def threshold(self) -> 'outputs.GetAutoScalingConfigurationPolicyRuleMetricThresholdResult': """ An autoscale action is triggered when a performance metric meets or exceeds a threshold. """ return pulumi.get(self, "threshold") @pulumi.output_type class GetAutoScalingConfigurationPolicyRuleMetricThresholdResult(dict): def __init__(__self__, *, duration_in_minutes: int, operator: str, value: int): """ :param int duration_in_minutes: This value is the minimum period of time the metric value meets or exceeds the threshold value before the action is triggered. The value is in minutes. :param str operator: The comparison operator to use. Options are greater than (GT) or less than (LT). :param int value: Integer non-negative value. 0 < value < 100 """ pulumi.set(__self__, "duration_in_minutes", duration_in_minutes) pulumi.set(__self__, "operator", operator) pulumi.set(__self__, "value", value) @property @pulumi.getter(name="durationInMinutes") def duration_in_minutes(self) -> int: """ This value is the minimum period of time the metric value meets or exceeds the threshold value before the action is triggered. The value is in minutes. """ return pulumi.get(self, "duration_in_minutes") @property @pulumi.getter def operator(self) -> str: """ The comparison operator to use. Options are greater than (GT) or less than (LT). """ return pulumi.get(self, "operator") @property @pulumi.getter def value(self) -> int: """ Integer non-negative value. 0 < value < 100 """ return pulumi.get(self, "value") @pulumi.output_type class GetBdsInstanceCloudSqlDetailsResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, ip_address: str, is_kerberos_mapped_to_database_users: bool, kerberos_details: Sequence['outputs.GetBdsInstanceCloudSqlDetailsKerberosDetailResult'], shape: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param str ip_address: IP address of the node. :param bool is_kerberos_mapped_to_database_users: Boolean flag specifying whether or not Kerberos principals are mapped to database users. :param Sequence['GetBdsInstanceCloudSqlDetailsKerberosDetailArgs'] kerberos_details: Details about the Kerberos principals. :param str shape: Shape of the node. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "ip_address", ip_address) pulumi.set(__self__, "is_kerberos_mapped_to_database_users", is_kerberos_mapped_to_database_users) pulumi.set(__self__, "kerberos_details", kerberos_details) pulumi.set(__self__, "shape", shape) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> str: """ IP address of the node. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="isKerberosMappedToDatabaseUsers") def is_kerberos_mapped_to_database_users(self) -> bool: """ Boolean flag specifying whether or not Kerberos principals are mapped to database users. """ return pulumi.get(self, "is_kerberos_mapped_to_database_users") @property @pulumi.getter(name="kerberosDetails") def kerberos_details(self) -> Sequence['outputs.GetBdsInstanceCloudSqlDetailsKerberosDetailResult']: """ Details about the Kerberos principals. """ return pulumi.get(self, "kerberos_details") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @pulumi.output_type class GetBdsInstanceCloudSqlDetailsKerberosDetailResult(dict): def __init__(__self__, *, keytab_file: str, principal_name: str): """ :param str keytab_file: Location of the keytab file :param str principal_name: Name of the Kerberos principal. """ pulumi.set(__self__, "keytab_file", keytab_file) pulumi.set(__self__, "principal_name", principal_name) @property @pulumi.getter(name="keytabFile") def keytab_file(self) -> str: """ Location of the keytab file """ return pulumi.get(self, "keytab_file") @property @pulumi.getter(name="principalName") def principal_name(self) -> str: """ Name of the Kerberos principal. """ return pulumi.get(self, "principal_name") @pulumi.output_type class GetBdsInstanceClusterDetailsResult(dict): def __init__(__self__, *, ambari_url: str, bd_cell_version: str, bda_version: str, bdm_version: str, bds_version: str, big_data_manager_url: str, cloudera_manager_url: str, csql_cell_version: str, db_version: str, hue_server_url: str, os_version: str, time_created: str, time_refreshed: str): """ :param str ambari_url: The URL of Ambari :param str bd_cell_version: Cloud SQL cell version. :param str bda_version: BDA version installed in the cluster :param str bdm_version: Big Data Manager version installed in the cluster. :param str bds_version: Big Data Service version installed in the cluster. :param str big_data_manager_url: The URL of Big Data Manager. :param str cloudera_manager_url: The URL of Cloudera Manager :param str csql_cell_version: Big Data SQL version. :param str db_version: Cloud SQL query server database version. :param str hue_server_url: The URL of the Hue server. :param str os_version: Oracle Linux version installed in the cluster. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. :param str time_refreshed: The time the cluster was automatically or manually refreshed, shown as an RFC 3339 formatted datetime string. """ pulumi.set(__self__, "ambari_url", ambari_url) pulumi.set(__self__, "bd_cell_version", bd_cell_version) pulumi.set(__self__, "bda_version", bda_version) pulumi.set(__self__, "bdm_version", bdm_version) pulumi.set(__self__, "bds_version", bds_version) pulumi.set(__self__, "big_data_manager_url", big_data_manager_url) pulumi.set(__self__, "cloudera_manager_url", cloudera_manager_url) pulumi.set(__self__, "csql_cell_version", csql_cell_version) pulumi.set(__self__, "db_version", db_version) pulumi.set(__self__, "hue_server_url", hue_server_url) pulumi.set(__self__, "os_version", os_version) pulumi.set(__self__, "time_created", time_created) pulumi.set(__self__, "time_refreshed", time_refreshed) @property @pulumi.getter(name="ambariUrl") def ambari_url(self) -> str: """ The URL of Ambari """ return pulumi.get(self, "ambari_url") @property @pulumi.getter(name="bdCellVersion") def bd_cell_version(self) -> str: """ Cloud SQL cell version. """ return pulumi.get(self, "bd_cell_version") @property @pulumi.getter(name="bdaVersion") def bda_version(self) -> str: """ BDA version installed in the cluster """ return pulumi.get(self, "bda_version") @property @pulumi.getter(name="bdmVersion") def bdm_version(self) -> str: """ Big Data Manager version installed in the cluster. """ return pulumi.get(self, "bdm_version") @property @pulumi.getter(name="bdsVersion") def bds_version(self) -> str: """ Big Data Service version installed in the cluster. """ return pulumi.get(self, "bds_version") @property @pulumi.getter(name="bigDataManagerUrl") def big_data_manager_url(self) -> str: """ The URL of Big Data Manager. """ return pulumi.get(self, "big_data_manager_url") @property @pulumi.getter(name="clouderaManagerUrl") def cloudera_manager_url(self) -> str: """ The URL of Cloudera Manager """ return pulumi.get(self, "cloudera_manager_url") @property @pulumi.getter(name="csqlCellVersion") def csql_cell_version(self) -> str: """ Big Data SQL version. """ return pulumi.get(self, "csql_cell_version") @property @pulumi.getter(name="dbVersion") def db_version(self) -> str: """ Cloud SQL query server database version. """ return pulumi.get(self, "db_version") @property @pulumi.getter(name="hueServerUrl") def hue_server_url(self) -> str: """ The URL of the Hue server. """ return pulumi.get(self, "hue_server_url") @property @pulumi.getter(name="osVersion") def os_version(self) -> str: """ Oracle Linux version installed in the cluster. """ return pulumi.get(self, "os_version") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @property @pulumi.getter(name="timeRefreshed") def time_refreshed(self) -> str: """ The time the cluster was automatically or manually refreshed, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_refreshed") @pulumi.output_type class GetBdsInstanceMasterNodeResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: Shape of the node. :param str subnet_id: The OCID of the subnet in which the node is to be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetBdsInstanceNetworkConfigResult(dict): def __init__(__self__, *, cidr_block: str, is_nat_gateway_required: bool): """ :param str cidr_block: The CIDR IP address block of the VCN. :param bool is_nat_gateway_required: A boolean flag whether to configure a NAT gateway. """ pulumi.set(__self__, "cidr_block", cidr_block) pulumi.set(__self__, "is_nat_gateway_required", is_nat_gateway_required) @property @pulumi.getter(name="cidrBlock") def cidr_block(self) -> str: """ The CIDR IP address block of the VCN. """ return pulumi.get(self, "cidr_block") @property @pulumi.getter(name="isNatGatewayRequired") def is_nat_gateway_required(self) -> bool: """ A boolean flag whether to configure a NAT gateway. """ return pulumi.get(self, "is_nat_gateway_required") @pulumi.output_type class GetBdsInstanceNodeResult(dict): def __init__(__self__, *, attached_block_volumes: Sequence['outputs.GetBdsInstanceNodeAttachedBlockVolumeResult'], availability_domain: str, display_name: str, fault_domain: str, hostname: str, image_id: str, instance_id: str, ip_address: str, node_type: str, shape: str, ssh_fingerprint: str, state: str, subnet_id: str, time_created: str): """ :param Sequence['GetBdsInstanceNodeAttachedBlockVolumeArgs'] attached_block_volumes: The list of block volumes attached to a given node. :param str availability_domain: The name of the availability domain in which the node is running. :param str display_name: The name of the node. :param str fault_domain: The name of the fault domain in which the node is running. :param str hostname: The fully-qualified hostname (FQDN) of the node. :param str image_id: The OCID of the image from which the node was created. :param str instance_id: The OCID of the underlying Oracle Cloud Infrastructure Compute instance. :param str ip_address: IP address of the node. :param str node_type: Cluster node type. :param str shape: Shape of the node. :param str ssh_fingerprint: The fingerprint of the SSH key used for node access. :param str state: The state of the cluster. :param str subnet_id: The OCID of the subnet in which the node is to be created. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ pulumi.set(__self__, "attached_block_volumes", attached_block_volumes) pulumi.set(__self__, "availability_domain", availability_domain) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "fault_domain", fault_domain) pulumi.set(__self__, "hostname", hostname) pulumi.set(__self__, "image_id", image_id) pulumi.set(__self__, "instance_id", instance_id) pulumi.set(__self__, "ip_address", ip_address) pulumi.set(__self__, "node_type", node_type) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "ssh_fingerprint", ssh_fingerprint) pulumi.set(__self__, "state", state) pulumi.set(__self__, "subnet_id", subnet_id) pulumi.set(__self__, "time_created", time_created) @property @pulumi.getter(name="attachedBlockVolumes") def attached_block_volumes(self) -> Sequence['outputs.GetBdsInstanceNodeAttachedBlockVolumeResult']: """ The list of block volumes attached to a given node. """ return pulumi.get(self, "attached_block_volumes") @property @pulumi.getter(name="availabilityDomain") def availability_domain(self) -> str: """ The name of the availability domain in which the node is running. """ return pulumi.get(self, "availability_domain") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ The name of the node. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="faultDomain") def fault_domain(self) -> str: """ The name of the fault domain in which the node is running. """ return pulumi.get(self, "fault_domain") @property @pulumi.getter def hostname(self) -> str: """ The fully-qualified hostname (FQDN) of the node. """ return pulumi.get(self, "hostname") @property @pulumi.getter(name="imageId") def image_id(self) -> str: """ The OCID of the image from which the node was created. """ return pulumi.get(self, "image_id") @property @pulumi.getter(name="instanceId") def instance_id(self) -> str: """ The OCID of the underlying Oracle Cloud Infrastructure Compute instance. """ return pulumi.get(self, "instance_id") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> str: """ IP address of the node. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="nodeType") def node_type(self) -> str: """ Cluster node type. """ return pulumi.get(self, "node_type") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="sshFingerprint") def ssh_fingerprint(self) -> str: """ The fingerprint of the SSH key used for node access. """ return pulumi.get(self, "ssh_fingerprint") @property @pulumi.getter def state(self) -> str: """ The state of the cluster. """ return pulumi.get(self, "state") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @pulumi.output_type class GetBdsInstanceNodeAttachedBlockVolumeResult(dict): def __init__(__self__, *, volume_attachment_id: str, volume_size_in_gbs: str): """ :param str volume_attachment_id: The OCID of the volume attachment. :param str volume_size_in_gbs: The size of the volume in GBs. """ pulumi.set(__self__, "volume_attachment_id", volume_attachment_id) pulumi.set(__self__, "volume_size_in_gbs", volume_size_in_gbs) @property @pulumi.getter(name="volumeAttachmentId") def volume_attachment_id(self) -> str: """ The OCID of the volume attachment. """ return pulumi.get(self, "volume_attachment_id") @property @pulumi.getter(name="volumeSizeInGbs") def volume_size_in_gbs(self) -> str: """ The size of the volume in GBs. """ return pulumi.get(self, "volume_size_in_gbs") @pulumi.output_type class GetBdsInstanceUtilNodeResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: Shape of the node. :param str subnet_id: The OCID of the subnet in which the node is to be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetBdsInstanceWorkerNodeResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: Shape of the node. :param str subnet_id: The OCID of the subnet in which the node is to be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetBdsInstancesBdsInstanceResult(dict): def __init__(__self__, *, cloud_sql_details: 'outputs.GetBdsInstancesBdsInstanceCloudSqlDetailsResult', cluster_admin_password: str, cluster_details: 'outputs.GetBdsInstancesBdsInstanceClusterDetailsResult', cluster_public_key: str, cluster_version: str, compartment_id: str, created_by: str, defined_tags: Mapping[str, Any], display_name: str, freeform_tags: Mapping[str, Any], id: str, is_cloud_sql_configured: bool, is_high_availability: bool, is_secure: bool, master_node: 'outputs.GetBdsInstancesBdsInstanceMasterNodeResult', network_config: 'outputs.GetBdsInstancesBdsInstanceNetworkConfigResult', nodes: Sequence['outputs.GetBdsInstancesBdsInstanceNodeResult'], number_of_nodes: int, state: str, time_created: str, time_updated: str, util_node: 'outputs.GetBdsInstancesBdsInstanceUtilNodeResult', worker_node: 'outputs.GetBdsInstancesBdsInstanceWorkerNodeResult'): """ :param 'GetBdsInstancesBdsInstanceCloudSqlDetailsArgs' cloud_sql_details: The information about added Cloud SQL capability :param 'GetBdsInstancesBdsInstanceClusterDetailsArgs' cluster_details: Specific info about a Hadoop cluster :param str cluster_version: Version of the Hadoop distribution. :param str compartment_id: The OCID of the compartment. :param str created_by: The user who created the cluster. :param Mapping[str, Any] defined_tags: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example, `{"foo-namespace": {"bar-key": "value"}}` :param str display_name: A filter to return only resources that match the entire display name given. :param Mapping[str, Any] freeform_tags: Simple key-value pair that is applied without any predefined name, type, or scope. Exists for cross-compatibility only. For example, `{"bar-key": "value"}` :param str id: The OCID of the Big Data Service resource. :param bool is_cloud_sql_configured: Boolean flag specifying whether or not Cloud SQL should be configured. :param bool is_high_availability: Boolean flag specifying whether or not the cluster is highly available (HA) :param bool is_secure: Boolean flag specifying whether or not the cluster should be set up as secure. :param 'GetBdsInstancesBdsInstanceNetworkConfigArgs' network_config: Additional configuration of the user's network. :param Sequence['GetBdsInstancesBdsInstanceNodeArgs'] nodes: The list of nodes in the cluster. :param int number_of_nodes: The number of nodes that form the cluster. :param str state: The state of the cluster. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. :param str time_updated: The time the cluster was updated, shown as an RFC 3339 formatted datetime string. """ pulumi.set(__self__, "cloud_sql_details", cloud_sql_details) pulumi.set(__self__, "cluster_admin_password", cluster_admin_password) pulumi.set(__self__, "cluster_details", cluster_details) pulumi.set(__self__, "cluster_public_key", cluster_public_key) pulumi.set(__self__, "cluster_version", cluster_version) pulumi.set(__self__, "compartment_id", compartment_id) pulumi.set(__self__, "created_by", created_by) pulumi.set(__self__, "defined_tags", defined_tags) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "freeform_tags", freeform_tags) pulumi.set(__self__, "id", id) pulumi.set(__self__, "is_cloud_sql_configured", is_cloud_sql_configured) pulumi.set(__self__, "is_high_availability", is_high_availability) pulumi.set(__self__, "is_secure", is_secure) pulumi.set(__self__, "master_node", master_node) pulumi.set(__self__, "network_config", network_config) pulumi.set(__self__, "nodes", nodes) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "state", state) pulumi.set(__self__, "time_created", time_created) pulumi.set(__self__, "time_updated", time_updated) pulumi.set(__self__, "util_node", util_node) pulumi.set(__self__, "worker_node", worker_node) @property @pulumi.getter(name="cloudSqlDetails") def cloud_sql_details(self) -> 'outputs.GetBdsInstancesBdsInstanceCloudSqlDetailsResult': """ The information about added Cloud SQL capability """ return pulumi.get(self, "cloud_sql_details") @property @pulumi.getter(name="clusterAdminPassword") def cluster_admin_password(self) -> str: return pulumi.get(self, "cluster_admin_password") @property @pulumi.getter(name="clusterDetails") def cluster_details(self) -> 'outputs.GetBdsInstancesBdsInstanceClusterDetailsResult': """ Specific info about a Hadoop cluster """ return pulumi.get(self, "cluster_details") @property @pulumi.getter(name="clusterPublicKey") def cluster_public_key(self) -> str: return pulumi.get(self, "cluster_public_key") @property @pulumi.getter(name="clusterVersion") def cluster_version(self) -> str: """ Version of the Hadoop distribution. """ return pulumi.get(self, "cluster_version") @property @pulumi.getter(name="compartmentId") def compartment_id(self) -> str: """ The OCID of the compartment. """ return pulumi.get(self, "compartment_id") @property @pulumi.getter(name="createdBy") def created_by(self) -> str: """ The user who created the cluster. """ return pulumi.get(self, "created_by") @property @pulumi.getter(name="definedTags") def defined_tags(self) -> Mapping[str, Any]: """ Defined tags for this resource. Each key is predefined and scoped to a namespace. For example, `{"foo-namespace": {"bar-key": "value"}}` """ return pulumi.get(self, "defined_tags") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ A filter to return only resources that match the entire display name given. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="freeformTags") def freeform_tags(self) -> Mapping[str, Any]: """ Simple key-value pair that is applied without any predefined name, type, or scope. Exists for cross-compatibility only. For example, `{"bar-key": "value"}` """ return pulumi.get(self, "freeform_tags") @property @pulumi.getter def id(self) -> str: """ The OCID of the Big Data Service resource. """ return pulumi.get(self, "id") @property @pulumi.getter(name="isCloudSqlConfigured") def is_cloud_sql_configured(self) -> bool: """ Boolean flag specifying whether or not Cloud SQL should be configured. """ return pulumi.get(self, "is_cloud_sql_configured") @property @pulumi.getter(name="isHighAvailability") def is_high_availability(self) -> bool: """ Boolean flag specifying whether or not the cluster is highly available (HA) """ return pulumi.get(self, "is_high_availability") @property @pulumi.getter(name="isSecure") def is_secure(self) -> bool: """ Boolean flag specifying whether or not the cluster should be set up as secure. """ return pulumi.get(self, "is_secure") @property @pulumi.getter(name="masterNode") def master_node(self) -> 'outputs.GetBdsInstancesBdsInstanceMasterNodeResult': return pulumi.get(self, "master_node") @property @pulumi.getter(name="networkConfig") def network_config(self) -> 'outputs.GetBdsInstancesBdsInstanceNetworkConfigResult': """ Additional configuration of the user's network. """ return pulumi.get(self, "network_config") @property @pulumi.getter def nodes(self) -> Sequence['outputs.GetBdsInstancesBdsInstanceNodeResult']: """ The list of nodes in the cluster. """ return pulumi.get(self, "nodes") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def state(self) -> str: """ The state of the cluster. """ return pulumi.get(self, "state") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @property @pulumi.getter(name="timeUpdated") def time_updated(self) -> str: """ The time the cluster was updated, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_updated") @property @pulumi.getter(name="utilNode") def util_node(self) -> 'outputs.GetBdsInstancesBdsInstanceUtilNodeResult': return pulumi.get(self, "util_node") @property @pulumi.getter(name="workerNode") def worker_node(self) -> 'outputs.GetBdsInstancesBdsInstanceWorkerNodeResult': return pulumi.get(self, "worker_node") @pulumi.output_type class GetBdsInstancesBdsInstanceCloudSqlDetailsResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, ip_address: str, is_kerberos_mapped_to_database_users: bool, kerberos_details: Sequence['outputs.GetBdsInstancesBdsInstanceCloudSqlDetailsKerberosDetailResult'], shape: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param str ip_address: IP address of the node. :param bool is_kerberos_mapped_to_database_users: Boolean flag specifying whether or not Kerberos principals are mapped to database users. :param Sequence['GetBdsInstancesBdsInstanceCloudSqlDetailsKerberosDetailArgs'] kerberos_details: Details about the Kerberos principals. :param str shape: Shape of the node. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "ip_address", ip_address) pulumi.set(__self__, "is_kerberos_mapped_to_database_users", is_kerberos_mapped_to_database_users) pulumi.set(__self__, "kerberos_details", kerberos_details) pulumi.set(__self__, "shape", shape) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> str: """ IP address of the node. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="isKerberosMappedToDatabaseUsers") def is_kerberos_mapped_to_database_users(self) -> bool: """ Boolean flag specifying whether or not Kerberos principals are mapped to database users. """ return pulumi.get(self, "is_kerberos_mapped_to_database_users") @property @pulumi.getter(name="kerberosDetails") def kerberos_details(self) -> Sequence['outputs.GetBdsInstancesBdsInstanceCloudSqlDetailsKerberosDetailResult']: """ Details about the Kerberos principals. """ return pulumi.get(self, "kerberos_details") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @pulumi.output_type class GetBdsInstancesBdsInstanceCloudSqlDetailsKerberosDetailResult(dict): def __init__(__self__, *, keytab_file: str, principal_name: str): """ :param str keytab_file: Location of the keytab file :param str principal_name: Name of the Kerberos principal. """ pulumi.set(__self__, "keytab_file", keytab_file) pulumi.set(__self__, "principal_name", principal_name) @property @pulumi.getter(name="keytabFile") def keytab_file(self) -> str: """ Location of the keytab file """ return pulumi.get(self, "keytab_file") @property @pulumi.getter(name="principalName") def principal_name(self) -> str: """ Name of the Kerberos principal. """ return pulumi.get(self, "principal_name") @pulumi.output_type class GetBdsInstancesBdsInstanceClusterDetailsResult(dict): def __init__(__self__, *, ambari_url: str, bd_cell_version: str, bda_version: str, bdm_version: str, bds_version: str, big_data_manager_url: str, cloudera_manager_url: str, csql_cell_version: str, db_version: str, hue_server_url: str, os_version: str, time_created: str, time_refreshed: str): """ :param str ambari_url: The URL of Ambari :param str bd_cell_version: Cloud SQL cell version. :param str bda_version: BDA version installed in the cluster :param str bdm_version: Big Data Manager version installed in the cluster. :param str bds_version: Big Data Service version installed in the cluster. :param str big_data_manager_url: The URL of Big Data Manager. :param str cloudera_manager_url: The URL of Cloudera Manager :param str csql_cell_version: Big Data SQL version. :param str db_version: Cloud SQL query server database version. :param str hue_server_url: The URL of the Hue server. :param str os_version: Oracle Linux version installed in the cluster. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. :param str time_refreshed: The time the cluster was automatically or manually refreshed, shown as an RFC 3339 formatted datetime string. """ pulumi.set(__self__, "ambari_url", ambari_url) pulumi.set(__self__, "bd_cell_version", bd_cell_version) pulumi.set(__self__, "bda_version", bda_version) pulumi.set(__self__, "bdm_version", bdm_version) pulumi.set(__self__, "bds_version", bds_version) pulumi.set(__self__, "big_data_manager_url", big_data_manager_url) pulumi.set(__self__, "cloudera_manager_url", cloudera_manager_url) pulumi.set(__self__, "csql_cell_version", csql_cell_version) pulumi.set(__self__, "db_version", db_version) pulumi.set(__self__, "hue_server_url", hue_server_url) pulumi.set(__self__, "os_version", os_version) pulumi.set(__self__, "time_created", time_created) pulumi.set(__self__, "time_refreshed", time_refreshed) @property @pulumi.getter(name="ambariUrl") def ambari_url(self) -> str: """ The URL of Ambari """ return pulumi.get(self, "ambari_url") @property @pulumi.getter(name="bdCellVersion") def bd_cell_version(self) -> str: """ Cloud SQL cell version. """ return pulumi.get(self, "bd_cell_version") @property @pulumi.getter(name="bdaVersion") def bda_version(self) -> str: """ BDA version installed in the cluster """ return pulumi.get(self, "bda_version") @property @pulumi.getter(name="bdmVersion") def bdm_version(self) -> str: """ Big Data Manager version installed in the cluster. """ return pulumi.get(self, "bdm_version") @property @pulumi.getter(name="bdsVersion") def bds_version(self) -> str: """ Big Data Service version installed in the cluster. """ return pulumi.get(self, "bds_version") @property @pulumi.getter(name="bigDataManagerUrl") def big_data_manager_url(self) -> str: """ The URL of Big Data Manager. """ return pulumi.get(self, "big_data_manager_url") @property @pulumi.getter(name="clouderaManagerUrl") def cloudera_manager_url(self) -> str: """ The URL of Cloudera Manager """ return pulumi.get(self, "cloudera_manager_url") @property @pulumi.getter(name="csqlCellVersion") def csql_cell_version(self) -> str: """ Big Data SQL version. """ return pulumi.get(self, "csql_cell_version") @property @pulumi.getter(name="dbVersion") def db_version(self) -> str: """ Cloud SQL query server database version. """ return pulumi.get(self, "db_version") @property @pulumi.getter(name="hueServerUrl") def hue_server_url(self) -> str: """ The URL of the Hue server. """ return pulumi.get(self, "hue_server_url") @property @pulumi.getter(name="osVersion") def os_version(self) -> str: """ Oracle Linux version installed in the cluster. """ return pulumi.get(self, "os_version") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @property @pulumi.getter(name="timeRefreshed") def time_refreshed(self) -> str: """ The time the cluster was automatically or manually refreshed, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_refreshed") @pulumi.output_type class GetBdsInstancesBdsInstanceMasterNodeResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: Shape of the node. :param str subnet_id: The OCID of the subnet in which the node is to be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetBdsInstancesBdsInstanceNetworkConfigResult(dict): def __init__(__self__, *, cidr_block: str, is_nat_gateway_required: bool): """ :param str cidr_block: The CIDR IP address block of the VCN. :param bool is_nat_gateway_required: A boolean flag whether to configure a NAT gateway. """ pulumi.set(__self__, "cidr_block", cidr_block) pulumi.set(__self__, "is_nat_gateway_required", is_nat_gateway_required) @property @pulumi.getter(name="cidrBlock") def cidr_block(self) -> str: """ The CIDR IP address block of the VCN. """ return pulumi.get(self, "cidr_block") @property @pulumi.getter(name="isNatGatewayRequired") def is_nat_gateway_required(self) -> bool: """ A boolean flag whether to configure a NAT gateway. """ return pulumi.get(self, "is_nat_gateway_required") @pulumi.output_type class GetBdsInstancesBdsInstanceNodeResult(dict): def __init__(__self__, *, attached_block_volumes: Sequence['outputs.GetBdsInstancesBdsInstanceNodeAttachedBlockVolumeResult'], availability_domain: str, display_name: str, fault_domain: str, hostname: str, image_id: str, instance_id: str, ip_address: str, node_type: str, shape: str, ssh_fingerprint: str, state: str, subnet_id: str, time_created: str): """ :param Sequence['GetBdsInstancesBdsInstanceNodeAttachedBlockVolumeArgs'] attached_block_volumes: The list of block volumes attached to a given node. :param str availability_domain: The name of the availability domain in which the node is running. :param str display_name: A filter to return only resources that match the entire display name given. :param str fault_domain: The name of the fault domain in which the node is running. :param str hostname: The fully-qualified hostname (FQDN) of the node. :param str image_id: The OCID of the image from which the node was created. :param str instance_id: The OCID of the underlying Oracle Cloud Infrastructure Compute instance. :param str ip_address: IP address of the node. :param str node_type: Cluster node type. :param str shape: Shape of the node. :param str ssh_fingerprint: The fingerprint of the SSH key used for node access. :param str state: The state of the cluster. :param str subnet_id: The OCID of the subnet in which the node is to be created. :param str time_created: The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ pulumi.set(__self__, "attached_block_volumes", attached_block_volumes) pulumi.set(__self__, "availability_domain", availability_domain) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "fault_domain", fault_domain) pulumi.set(__self__, "hostname", hostname) pulumi.set(__self__, "image_id", image_id) pulumi.set(__self__, "instance_id", instance_id) pulumi.set(__self__, "ip_address", ip_address) pulumi.set(__self__, "node_type", node_type) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "ssh_fingerprint", ssh_fingerprint) pulumi.set(__self__, "state", state) pulumi.set(__self__, "subnet_id", subnet_id) pulumi.set(__self__, "time_created", time_created) @property @pulumi.getter(name="attachedBlockVolumes") def attached_block_volumes(self) -> Sequence['outputs.GetBdsInstancesBdsInstanceNodeAttachedBlockVolumeResult']: """ The list of block volumes attached to a given node. """ return pulumi.get(self, "attached_block_volumes") @property @pulumi.getter(name="availabilityDomain") def availability_domain(self) -> str: """ The name of the availability domain in which the node is running. """ return pulumi.get(self, "availability_domain") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ A filter to return only resources that match the entire display name given. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="faultDomain") def fault_domain(self) -> str: """ The name of the fault domain in which the node is running. """ return pulumi.get(self, "fault_domain") @property @pulumi.getter def hostname(self) -> str: """ The fully-qualified hostname (FQDN) of the node. """ return pulumi.get(self, "hostname") @property @pulumi.getter(name="imageId") def image_id(self) -> str: """ The OCID of the image from which the node was created. """ return pulumi.get(self, "image_id") @property @pulumi.getter(name="instanceId") def instance_id(self) -> str: """ The OCID of the underlying Oracle Cloud Infrastructure Compute instance. """ return pulumi.get(self, "instance_id") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> str: """ IP address of the node. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="nodeType") def node_type(self) -> str: """ Cluster node type. """ return pulumi.get(self, "node_type") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="sshFingerprint") def ssh_fingerprint(self) -> str: """ The fingerprint of the SSH key used for node access. """ return pulumi.get(self, "ssh_fingerprint") @property @pulumi.getter def state(self) -> str: """ The state of the cluster. """ return pulumi.get(self, "state") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @property @pulumi.getter(name="timeCreated") def time_created(self) -> str: """ The time the cluster was created, shown as an RFC 3339 formatted datetime string. """ return pulumi.get(self, "time_created") @pulumi.output_type class GetBdsInstancesBdsInstanceNodeAttachedBlockVolumeResult(dict): def __init__(__self__, *, volume_attachment_id: str, volume_size_in_gbs: str): """ :param str volume_attachment_id: The OCID of the volume attachment. :param str volume_size_in_gbs: The size of the volume in GBs. """ pulumi.set(__self__, "volume_attachment_id", volume_attachment_id) pulumi.set(__self__, "volume_size_in_gbs", volume_size_in_gbs) @property @pulumi.getter(name="volumeAttachmentId") def volume_attachment_id(self) -> str: """ The OCID of the volume attachment. """ return pulumi.get(self, "volume_attachment_id") @property @pulumi.getter(name="volumeSizeInGbs") def volume_size_in_gbs(self) -> str: """ The size of the volume in GBs. """ return pulumi.get(self, "volume_size_in_gbs") @pulumi.output_type class GetBdsInstancesBdsInstanceUtilNodeResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: Shape of the node. :param str subnet_id: The OCID of the subnet in which the node is to be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetBdsInstancesBdsInstanceWorkerNodeResult(dict): def __init__(__self__, *, block_volume_size_in_gbs: str, number_of_nodes: int, shape: str, subnet_id: str): """ :param str block_volume_size_in_gbs: The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. :param int number_of_nodes: The number of nodes that form the cluster. :param str shape: Shape of the node. :param str subnet_id: The OCID of the subnet in which the node is to be created. """ pulumi.set(__self__, "block_volume_size_in_gbs", block_volume_size_in_gbs) pulumi.set(__self__, "number_of_nodes", number_of_nodes) pulumi.set(__self__, "shape", shape) pulumi.set(__self__, "subnet_id", subnet_id) @property @pulumi.getter(name="blockVolumeSizeInGbs") def block_volume_size_in_gbs(self) -> str: """ The size of block volume in GB that needs to be attached to a given node. All the necessary details needed for attachment are managed by service itself. """ return pulumi.get(self, "block_volume_size_in_gbs") @property @pulumi.getter(name="numberOfNodes") def number_of_nodes(self) -> int: """ The number of nodes that form the cluster. """ return pulumi.get(self, "number_of_nodes") @property @pulumi.getter def shape(self) -> str: """ Shape of the node. """ return pulumi.get(self, "shape") @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: """ The OCID of the subnet in which the node is to be created. """ return pulumi.get(self, "subnet_id") @pulumi.output_type class GetBdsInstancesFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str], regex: Optional[bool] = None): pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) if regex is not None: pulumi.set(__self__, "regex", regex) @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @property @pulumi.getter def regex(self) -> Optional[bool]: return pulumi.get(self, "regex")

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0.767083

0.756926

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101,428

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37.30342

0.829735

0.268594

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false

0.003163

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1

0

null

0

8

9d40bf35a8e6c1e796232b9e8d11cb961144ce9b

47

py

Python

hello.py

think-jin/osscap2020

ac87df1dd11d078fe5e40bec6815ee1e6f9e1de5

[ "Apache-2.0" ]

null

hello.py

think-jin/osscap2020

ac87df1dd11d078fe5e40bec6815ee1e6f9e1de5

[ "Apache-2.0" ]

null

hello.py

think-jin/osscap2020

ac87df1dd11d078fe5e40bec6815ee1e6f9e1de5

[ "Apache-2.0" ]

null

print("Hello, World!") print("Hello, WOrld!!")

15.666667

23

0.638298

6

47

5

0.5

0.666667

1

0

0.085106

47

2

24

23.5

0.697674

0

0.574468

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

19b424aa7317824338ae81ea103b38d707b1b16a

66

py

Python

task5.py

m4rn0v/pyRosalind

0e24f82b8e7fefdadbdc394190818c33f3432bd5

[ "MIT" ]

null

task5.py

m4rn0v/pyRosalind

0e24f82b8e7fefdadbdc394190818c33f3432bd5

[ "MIT" ]

null

task5.py

m4rn0v/pyRosalind

0e24f82b8e7fefdadbdc394190818c33f3432bd5

[ "MIT" ]

null

from libs.io import from_fasta from libs.io import to_fasta pass

13.2

30

0.818182

13

66

4

0.538462

0.307692

0.384615

0.615385

0

0.151515

66

4

31

16.5

0.928571

0

1

0

true

0.333333

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

9

19c3b447dc6702e08a6db2133af2622f0d889138

109

py

Python

output/elements/__init__.py

moehrenzahn/worktimer

ab92e8625652d94987c7da8ccdbf29be72bf3612

[ "MIT" ]

3

2018-07-29T20:48:15.000Z

2019-03-29T10:42:19.000Z

output/elements/__init__.py

moehrenzahn/worktimer

ab92e8625652d94987c7da8ccdbf29be72bf3612

[ "MIT" ]

null

output/elements/__init__.py

moehrenzahn/worktimer

ab92e8625652d94987c7da8ccdbf29be72bf3612

[ "MIT" ]

null

from output.elements.button import * from output.elements.spacer import * from output.elements.menu import *

27.25

36

0.807339

15

109

5.866667

0.466667

0.340909

0.613636

0.545455

0

0.110092

109

3

37

36.333333

0.907216

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

c2385c791bb7ea5999b7e544d96645efe4c1cb0f

119

py

Python

app/context_processors.py

callmewind/django-celery-bootstrap-heroku

cba6c3cc0b87ebd0ccb8ac5acf5e1cb71491f044

[ "MIT" ]

null

app/context_processors.py

callmewind/django-celery-bootstrap-heroku

cba6c3cc0b87ebd0ccb8ac5acf5e1cb71491f044

[ "MIT" ]

null

app/context_processors.py

callmewind/django-celery-bootstrap-heroku

cba6c3cc0b87ebd0ccb8ac5acf5e1cb71491f044

[ "MIT" ]

null

from django.conf import settings as django_settings def settings(request): return { 'settings' : django_settings }

29.75

51

0.773109

15

119

6

0.6

0.311111

0

0.151261

119

4

52

29.75

0.891089

0

0.066667

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

a07cae83cc98715c6a799942782a2aa6a9e9b473

382

py

Python

tests/formatters/test__format_currency.py

LCBRU/lbrc_flask

f5f6c3f3832a9040e941c6398b7f150e567d4762

[ "MIT" ]

null

tests/formatters/test__format_currency.py

LCBRU/lbrc_flask

f5f6c3f3832a9040e941c6398b7f150e567d4762

[ "MIT" ]

null

tests/formatters/test__format_currency.py

LCBRU/lbrc_flask

f5f6c3f3832a9040e941c6398b7f150e567d4762

[ "MIT" ]

null

from lbrc_flask.formatters import format_currency def test__format_currency__None(): assert format_currency(None) == '' def test__format_currency__NoPounds(): assert format_currency(0.39) == '£0.39' def test__format_currency__JustPennies(): assert format_currency(0.02) == '£0.02' def test__format_currency__Pounds(): assert format_currency(1.02) == '£1.02'

21.222222

49

0.743455

55

382

4.745455

0.345455

0.482759

0.199234

0.321839

0

0.054545

0.136126

382

17

50

22.470588

0.727273

0

0.039267

0

0.444444

1

0.444444

true

0

0.111111

0

0.555556

0

null

1

0

null

0

1

0

1

0

1

0

7

262932e35056e60acd09449bdfea1f13762365fc

112

py

Python

crypton/utils/__init__.py

batuhaninan/Crypton

cb3de3dccb79c49524b594a23709a8ae0c8fd555

[ "MIT" ]

null

crypton/utils/__init__.py

batuhaninan/Crypton

cb3de3dccb79c49524b594a23709a8ae0c8fd555

[ "MIT" ]

null

crypton/utils/__init__.py

batuhaninan/Crypton

cb3de3dccb79c49524b594a23709a8ae0c8fd555

[ "MIT" ]

null

import crypton.utils.math as math import crypton.utils.space as space import crypton.utils.cryption as cryption

28

41

0.839286

18

112

5.222222

0.388889

0.414894

0.574468

0

0.107143

112

3

42

37.333333

0.94

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

268d7134610c61ea214643a3735e34c7075f8e88

3,778

py

Python

tests/test_sequences_cutting.py

krzjoa/sciquence

6a5f758c757200fffeb0fdc9206462f1f89e2444

[ "MIT" ]

8

2017-10-23T17:59:35.000Z

2021-05-10T03:01:30.000Z

tests/test_sequences_cutting.py

krzjoa/sciquence

6a5f758c757200fffeb0fdc9206462f1f89e2444

[ "MIT" ]

2

2019-08-25T19:24:12.000Z

2019-09-05T12:16:10.000Z

tests/test_sequences_cutting.py

krzjoa/sciquence

6a5f758c757200fffeb0fdc9206462f1f89e2444

[ "MIT" ]

2

2018-02-28T09:47:53.000Z

2019-08-25T19:24:16.000Z

import unittest import numpy as np import sciquence.sequences as sq class TestSequencesCutting(unittest.TestCase): def test_seq(self): x = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0]) expected = [np.array([1, 1, 1]), np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]), np.array([0, 0, 0, 0]), np.array([1, 1, 1, 1]), np.array([0, 0, 0])] assert sq.lseq_equal(sq.seq(x), expected) def test_specseq(self): x = np.array([2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2]) expected = [np.array([2, 2, 2]), np.array([2, 2, 2])] assert sq.lseq_equal(sq.specseq(x, element=2), expected) def test_nseq(self): ''' All the sequences consisting of zeros only''' x = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0]) expected = [np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), np.array([0, 0, 0, 0]), np.array([0, 0, 0])] assert sq.lseq_equal(sq.nseq(x), expected) def test_pseq(self): ''' All the sequences consisting of ones only''' x = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0]) expected = [np.array([1, 1, 1]), np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]), np.array([1, 1, 1, 1])] assert sq.lseq_equal(sq.pseq(x), expected) ############ SEQUENCE INDICES ############ # def test_seqi(self): # x = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, # 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0]) # # expected = [np.array([1, 1, 1]), np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), # np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]), # np.array([0, 0, 0, 0]), np.array([1, 1, 1, 1]), np.array([0, 0, 0])] # # assert sq.lseq_equal(sq.seq(x), expected) # def test_specseqi(self): # x = np.array([2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, # 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2]) # # expected = [np.array([1, 1, 1]), np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), # np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]), # np.array([0, 0, 0, 0]), np.array([1, 1, 1, 1]), np.array([0, 0, 0])] # # assert sq.lseq_equal(sq.specseq(x, element=2), expected) # # def test_nseqi(self): # ''' All the sequences consisting of zeros only''' # x = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, # 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0]) # # expected = [np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), # np.array([0, 0, 0, 0]), np.array([0, 0, 0])] # # assert sq.lseq_equal(sq.nseq(x), expected) # # def test_pseqi(self): # ''' All the sequences consisting of ones only''' # x = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, # 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0]) # # expected = [np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), # np.array([0, 0, 0, 0]), np.array([0, 0, 0])] # # assert sq.lseq_equal(sq.pseq(x), expected) if __name__ == '__main__': unittest.main()

40.623656

93

0.4063

724

3,778

2.087017

0.060773

0.309729

0.385175

0.407677

0.874917

0.866314

0.836532

0.833885

0

0.199005

0.361567

3,778

92

94

41.065217

0.427446

0.47459

0

0.258065

0

0.004188

0

0.129032

1

0.129032

false

0

0.096774

0

0.258065

0

null

1

0

1

0

null

0

10

269d3809429899039149870122f3aec250516845

286

py

Python

06/01/LocaleTextCalendar/formatmonth.py

pylangstudy/201709

53d868786d7327a83bfa7f4149549c6f9855a6c6

[ "CC0-1.0" ]

null

06/01/LocaleTextCalendar/formatmonth.py

pylangstudy/201709

53d868786d7327a83bfa7f4149549c6f9855a6c6

[ "CC0-1.0" ]

32

2017-09-01T00:52:17.000Z

2017-10-01T00:30:02.000Z

06/01/LocaleTextCalendar/formatmonth.py

pylangstudy/201709

53d868786d7327a83bfa7f4149549c6f9855a6c6

[ "CC0-1.0" ]

null

import calendar for firstweekday in [0, 6]:#月,日 c = calendar.LocaleTextCalendar(firstweekday=firstweekday) print(c.formatmonth(2017, 9)) for firstweekday in [0, 6]:#月,日 c = calendar.LocaleTextCalendar(firstweekday=firstweekday) print(c.formatmonth(2017, 9, w=4, l=2))

28.6

62

0.72028

40

286

5.15

0.475

0.145631

0.165049

0.174757

0.912621

0

0.065574

0.146853

286

9

63

31.777778

0.778689

0.020979

0

0.571429

0

1

0

false

0

0.142857

0

0.142857

0.285714

0

null

0

1

0

1

0

null

0

8

26c502514907f39b5de98ce2ee04769591bf4c31

25,452

py

Python

ultracache/tests/test_base.py

hedleyroos/django-ultracache

b39a4df13882255bbe3c349b726331e0b51b5424

[ "BSD-3-Clause" ]

5

2020-06-23T14:53:14.000Z

2022-01-17T14:11:47.000Z

ultracache/tests/test_base.py

hedleyroos/django-ultracache

b39a4df13882255bbe3c349b726331e0b51b5424

[ "BSD-3-Clause" ]

null

ultracache/tests/test_base.py

hedleyroos/django-ultracache

b39a4df13882255bbe3c349b726331e0b51b5424

[ "BSD-3-Clause" ]

1

2020-12-03T19:29:14.000Z

## -*- coding: utf-8 -*- from django import template from django.conf import settings from django.core.cache import cache from django.http.cookie import SimpleCookie from django.test import TestCase from django.test.client import Client, RequestFactory from django.test.utils import override_settings from django.urls import reverse from ultracache.tests.models import DummyModel, DummyForeignModel, \ DummyOtherModel from ultracache.tests import views from ultracache.tests.utils import dummy_proxy class TemplateTagsTestCase(TestCase): if "django.contrib.sites" in settings.INSTALLED_APPS: fixtures = ["sites.json"] @classmethod def setUpClass(cls): super(TemplateTagsTestCase, cls).setUpClass() cls.factory = RequestFactory() cls.request = cls.factory.get('/') cache.clear() dummy_proxy.clear() if "django.contrib.sites" in settings.INSTALLED_APPS: def test_sites(self): from django.contrib.sites.models import Site first_site = Site.objects.all().first() second_site = Site.objects.all().last() # Caching on same site t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache' %}1{% endultracache %}" ) context = template.Context({'request' : self.request}) result1 = t.render(context) t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache' %}2{% endultracache %}" ) context = template.Context({'request' : self.request}) result2 = t.render(context) self.failUnlessEqual(result1, result2) # Caching on different sites t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache' %}1{% endultracache %}" ) context = template.Context({'request' : self.request}) result1 = t.render(context) with override_settings(SITE_ID=second_site.id): t = template.Template("{%% load ultracache_tags %%}\ {%% ultracache 1200 'test_ultracache' %%}%s{%% endultracache %%}" % second_site.id ) context = template.Context({'request' : self.request}) result2 = t.render(context) self.failIfEqual(result1, result2) def test_variables(self): # Check that undefined variables do not break caching t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_undefined' aaa %}1{% endultracache %}" ) context = template.Context({'request' : self.request}) result1 = t.render(context) t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_undefined' bbb %}2{% endultracache %}" ) context = template.Context({'request' : self.request}) result2 = t.render(context) self.failUnlessEqual(result1, result2) # Check that translation proxies are valid variables t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_xlt' _('aaa') %}1{% endultracache %}" ) context = template.Context({'request' : self.request}) result1 = t.render(context) t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_xlt' _('aaa') %}2{% endultracache %}" ) context = template.Context({'request' : self.request}) result2 = t.render(context) self.failUnlessEqual(result1, result2) # Check that large integer variables do not break caching t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_large' 565417614189797377 %}abcde{% endultracache %}" ) context = template.Context({'request' : self.request}) result1 = t.render(context) t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_large' 565417614189797377 %}abcde{% endultracache %}" ) context = template.Context({'request' : self.request}) result2 = t.render(context) self.failUnlessEqual(result1, result2) def test_context_without_request(self): t = template.Template("{% load ultracache_tags %}\ {% ultracache 1200 'test_ultracache_undefined' aaa %}abcde{% endultracache %}" ) context = template.Context() self.assertRaises(KeyError, t.render, context) def test_invalidation(self): """Directly render template """ one = DummyModel.objects.create(title='One', code='one') two = DummyModel.objects.create(title='Two', code='two') three = DummyForeignModel.objects.create(title='Three', points_to=one, code='three') four = DummyOtherModel.objects.create(title='Four', code='four') # The counter is used to track the iteration that a cached block was # last rendered. t = template.Template("""{% load ultracache_tags ultracache_test_tags %} {% ultracache 1200 'test_ultracache_invalidate_outer' %} counter outer = {{ counter }} {% ultracache 1200 'test_ultracache_invalidate_one' %} title = {{ one.title }} counter one = {{ counter }} {% endultracache %} {% ultracache 1200 'test_ultracache_invalidate_two' %} title = {{ two.title }} counter two = {{ counter }} {% endultracache %} {% ultracache 1200 'test_ultracache_invalidate_three' %} title = {{ three.title }} {{ three.points_to.title }} counter three = {{ counter }} {% endultracache %} {% ultracache 1200 'test_ultracache_invalidate_render_view' %} {% render_view 'render-view' %} {% endultracache %} {% ultracache 1200 'test_ultracache_invalidate_include %} {% include "ultracache/include_me.html" %} {% endultracache %} {% endultracache %}""" ) # Initial render request = self.factory.get('/aaa/') context = template.Context({ 'request' : request, 'one': one, 'two': two, 'three': three, 'counter': 1 }) result = t.render(context) dummy_proxy.cache(request, result) self.failUnless('title = One' in result) self.failUnless('title = Two' in result) self.failUnless('counter outer = 1' in result) self.failUnless('counter one = 1' in result) self.failUnless('counter two = 1' in result) self.failUnless('counter three = 1' in result) self.failUnless('render_view = One' in result) self.failUnless('include = One' in result) self.failUnless(dummy_proxy.is_cached('/aaa/')) # Change object one one.title = 'Onxe' one.save() request = self.factory.get('/bbb/') context = template.Context({ 'request' : request, 'one': one, 'two': two, 'three': three, 'counter': 2 }) result = t.render(context) dummy_proxy.cache(request, result) self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Two' in result) self.failUnless('counter outer = 2' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 1' in result) self.failUnless('counter three = 2' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failIf(dummy_proxy.is_cached('/aaa/')) # Change object two two.title = 'Twxo' two.save() request = self.factory.get('/ccc/') context = template.Context({ 'request' : request, 'one': one, 'two': two, 'three': three, 'counter': 3 }) result = t.render(context) dummy_proxy.cache(request, result) self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('counter outer = 3' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 2' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failIf(dummy_proxy.is_cached('/bbb/')) # Change object three three.title = 'Threxe' three.save() request = self.factory.get('/ddd/') context = template.Context({ 'request' : request, 'one': one, 'two': two, 'three': three, 'counter': 4 }) result = t.render(context) dummy_proxy.cache(request, result) self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('counter outer = 4' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failIf(dummy_proxy.is_cached('/ccc/')) # Add a DummyOtherModel object five five = DummyOtherModel.objects.create(title='Five', code='five') request = self.factory.get('/eee/') context = template.Context({ 'request' : request, 'one': one, 'two': two, 'three': three, 'counter': 5 }) result = t.render(context) dummy_proxy.cache(request, result) # RenderView is only view aware of DummyOtherModel. That means # test_ultracache_invalidate_outer and # test_ultracache_invalidate_render_view are expired. self.failUnless('render_view = Five' in result) self.failUnless('counter outer = 5' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failIf(dummy_proxy.is_cached('/ddd/')) # Delete object two two.delete() request = self.factory.get('/fff/') context = template.Context({ 'request' : request, 'one': one, 'two': None, 'three': three, 'counter': 6 }) result = t.render(context) dummy_proxy.cache(request, result) self.failUnless('title = Onxe' in result) self.failIf('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('counter outer = 6' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 6' in result) self.failUnless('counter three = 4' in result) self.failIf(dummy_proxy.is_cached('/eee/')) class DecoratorTestCase(TestCase): if "django.contrib.sites" in settings.INSTALLED_APPS: fixtures = ["sites.json"] def setUp(self): super(DecoratorTestCase, self).setUp() cache.clear() dummy_proxy.clear() def test_method(self): """Render template through a view with get method decorated with cached_get.""" one = DummyModel.objects.create(title='One', code='one') two = DummyModel.objects.create(title='Two', code='two') three = DummyForeignModel.objects.create(title='Three', points_to=one, code='three') four = DummyModel.objects.create(title='Four', code='four') five = DummyModel.objects.create(title='Five', code='five') url = reverse('method-cached-view') # Initial render cache.set("counter", 1) response = self.client.get(url) result = response.content.decode("utf-8") self.assertEqual(response.status_code, 200) self.failUnless('title = One' in result) self.failUnless('title = Two' in result) self.failUnless('title = Three' in result) self.failUnless('render_view = One' in result) self.failUnless('include = One' in result) self.failUnless('counter one = 1' in result) self.failUnless('counter two = 1' in result) self.failUnless('counter three = 1' in result) self.failUnless('counter four = 1' in result) self.failUnless('title = Four' in result) # Change object one cache.set("counter", 2) one.title = 'Onxe' one.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Two' in result) self.failUnless('title = Three' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 1' in result) self.failUnless('counter three = 2' in result) self.failUnless('counter four = 2' in result) self.failUnless('title = Four' in result) # Change object two cache.set("counter", 3) two.title = 'Twxo' two.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Three' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 2' in result) self.failUnless('counter four = 3' in result) self.failUnless('title = Four' in result) # Change object three cache.set("counter", 4) three.title = 'Threxe' three.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('counter four = 4' in result) self.failUnless('title = Four' in result) # Change object four cache.set("counter", 5) four.title = 'Fouxr' four.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('counter four = 5' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('title = Fouxr' in result) self.failIf('title = Four' in result) # Change object five. This object is never accessed in the template, # only get_context_data of CachedView. "counter four" and "counter # five" are under cached_get and not in any ultracache tag, and are # thus the only counters incremented. cache.set("counter", 6) five.title = 'Fivxe' five.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('counter four = 6' in result) self.failUnless('counter five = 6' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('title = Fouxr' in result) self.failIf('title = Four' in result) def test_class(self): """Render template through a view decorated with ultracache """ one = DummyModel.objects.create(title='One', code='one') two = DummyModel.objects.create(title='Two', code='two') three = DummyForeignModel.objects.create(title='Three', points_to=one, code='three') four = DummyModel.objects.create(title='Four', code='four') five = DummyModel.objects.create(title='Five', code='five') url = reverse('class-cached-view') # Initial render cache.set("counter", 1) response = self.client.get(url) result = response.content.decode("utf-8") self.assertEqual(response.status_code, 200) self.failUnless('title = One' in result) self.failUnless('title = Two' in result) self.failUnless('title = Three' in result) self.failUnless('render_view = One' in result) self.failUnless('include = One' in result) self.failUnless('counter one = 1' in result) self.failUnless('counter two = 1' in result) self.failUnless('counter three = 1' in result) self.failUnless('counter four = 1' in result) self.failUnless('title = Four' in result) # Change object one cache.set("counter", 2) one.title = 'Onxe' one.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Two' in result) self.failUnless('title = Three' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 1' in result) self.failUnless('counter three = 2' in result) self.failUnless('counter four = 2' in result) self.failUnless('title = Four' in result) # Change object two cache.set("counter", 3) two.title = 'Twxo' two.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Three' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 2' in result) self.failUnless('counter four = 3' in result) self.failUnless('title = Four' in result) # Change object three cache.set("counter", 4) three.title = 'Threxe' three.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('counter four = 4' in result) self.failUnless('title = Four' in result) # Change object four cache.set("counter", 5) four.title = 'Fouxr' four.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('counter four = 5' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('title = Fouxr' in result) self.failIf('title = Four' in result) # Change object five. This object is never accessed in the template, # only get_context_data of CachedView. "counter four" and "counter # five" are under cached_get and not in any ultracache tag, and are # thus the only counters incremented. cache.set("counter", 6) five.title = 'Fivxe' five.save() response = self.client.get(url) result = response.content.decode("utf-8") self.failUnless('title = Onxe' in result) self.failIf('title = One' in result) self.failUnless('title = Twxo' in result) self.failIf('title = Two' in result) self.failUnless('title = Threxe' in result) self.failIf('title = Three' in result) self.failUnless('counter one = 2' in result) self.failUnless('counter two = 3' in result) self.failUnless('counter three = 4' in result) self.failUnless('counter four = 6' in result) self.failUnless('counter five = 6' in result) self.failUnless('render_view = Onxe' in result) self.failUnless('include = Onxe' in result) self.failUnless('title = Fouxr' in result) self.failIf('title = Four' in result) def test_header(self): """Test that decorator preserves headers """ url = reverse('cached-header-view') # Initial render response = self.client.get(url) # Django 4 deprecates _headers and introduces headers if hasattr(response, "headers"): self.assertEqual(response.headers['content-type'], 'application/json') self.assertEqual(response.headers['foo'], 'bar') else: self.assertEqual(response._headers['content-type'], ('Content-Type', 'application/json')) self.assertEqual(response._headers['foo'], ('foo', 'bar')) # Second pass is cached response = self.client.get(url) # Django 4 deprecates _headers and introduces headers if hasattr(response, "headers"): self.assertEqual(response.headers['content-type'], 'application/json') self.assertEqual(response.headers['foo'], 'bar') else: self.assertEqual(response._headers['content-type'], ('Content-Type', 'application/json')) self.assertEqual(response._headers['foo'], ('foo', 'bar')) def test_cache_busting(self): """Test cache busting with and without random querystring param """ url = reverse('bustable-cached-view') response = self.client.get(url + '?aaa=1') self.failUnless('aaa=1' in response.content.decode("utf-8")) response = self.client.get(url + '?aaa=2') self.failUnless('aaa=2' in response.content.decode("utf-8")) url = reverse('non-bustable-cached-view') response = self.client.get(url + '?aaa=1') self.failUnless('aaa=1' in response.content.decode("utf-8")) response = self.client.get(url + '?aaa=2') self.failIf('aaa=2' in response.content.decode("utf-8"))

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0.15003

0.215387

0.8675

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0.812992

0.803069

0.799894

0

0.013943

0.278603

25,452

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0

null

0

1

0

1

0

null

0

8

f81d8d71dadcb6b3ecbf1bdea8546c1a8b863378

47

py

Python

venv/lib/python3.6/site-packages/weather/helpers/__init__.py

HackBots1111/flask-server-bot

683932802d689d21b0b8397acd3f072197dea208

[ "MIT" ]

null

venv/lib/python3.6/site-packages/weather/helpers/__init__.py

HackBots1111/flask-server-bot

683932802d689d21b0b8397acd3f072197dea208

[ "MIT" ]

null

venv/lib/python3.6/site-packages/weather/helpers/__init__.py

HackBots1111/flask-server-bot

683932802d689d21b0b8397acd3f072197dea208

[ "MIT" ]

null

from .add_leading_zero import add_leading_zero

23.5

46

0.893617

8

47

4.75

0.625

0.526316

0.736842

0

0.085106

47

1

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true

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null

1

0

1

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null

0

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0

1

0

7

f8a782903b5645d5106647458483d8a8957894ad

81

py

Python

CodeWars/8 Kyu/Invalid Login - Bug Fixing #11.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/8 Kyu/Invalid Login - Bug Fixing #11.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/8 Kyu/Invalid Login - Bug Fixing #11.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

def validate(username, password): return Database().login(username, password)

40.5

47

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9

81

6.888889

0.777778

0.516129

0

0.111111

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f8b56c2d9beacc33b21504897b5ee5a327d6d7d1

22,583

py

Python

nytimes/nytimes_scrape.py

alexmill/covid19demographics-fall

b53708883a0c091f7e4eaef3d270153d88fe11f9

[ "MIT" ]

null

nytimes/nytimes_scrape.py

alexmill/covid19demographics-fall

b53708883a0c091f7e4eaef3d270153d88fe11f9

[ "MIT" ]

null

nytimes/nytimes_scrape.py

alexmill/covid19demographics-fall

b53708883a0c091f7e4eaef3d270153d88fe11f9

[ "MIT" ]

2

2020-09-24T01:01:11.000Z

2020-10-06T19:17:43.000Z

from bs4 import BeautifulSoup import csv from datetime import datetime, timedelta import re import sys from urllib.request import urlopen, Request import re import os import pandas as pd # Example inputs: # About 39.6 million people # About 293,000 people def getPop(x): spl = x.split() if spl[0] == "About" and spl[2] == "million" and spl[3] == "people": return int(float(spl[1]) * 1000000) elif spl[0] == "About" and spl[2] == "people": return int(spl[1].replace(",", "")) else: print("Unexpected population:", x) exit(0) # Get order type and effective date/time def getInfo(x): if x.span.text.find(", effective ") != 0: print("Unexpected order info format:", x) exit(0) dateText = x.span.text[12:].replace(".", "").replace("am", "AM").replace("pm", "PM") if dateText.find(" at ") < 0: dateText += " at 12 AM" if dateText.find(":") >= 0: dt = datetime.strptime("2020 " + dateText, "%Y %B %d at %I:%M %p") else: dt = datetime.strptime("2020 " + dateText, "%Y %B %d at %I %p") return x.find(text=True), dt # Given place-wrap div, return place, pop, order, dt, orderLink, newsLink def getPlace(x): placeP = x.find("p", class_="l-place") if placeP is not None: place = placeP.find(text=True).strip() else: place = None popSpan = x.find("span", class_="l-population", recursive=True) if popSpan is not None: pop = getPop(popSpan.text) else: pop = None order, dt = getInfo(x.find("p", class_="l-order")) orderSpan = x.find("span", class_="linkorder", recursive=True) if orderSpan is not None: orderLink = orderSpan.find("a")["href"] else: orderLink = None newsSpan = x.find("span", class_="linklocalnews", recursive=True) if newsSpan is not None: newsLink = newsSpan.find("a")["href"] else: newsLink = None return place, pop, order, dt, orderLink, newsLink def scrape(): data = "nytimes.csv" dates = pd.read_csv(data)["Report Date"] dates = set(dates.unique()) # Get last updated url = "https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html" headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'} req = Request(url=url, headers=headers) html = urlopen(req).read() soup = BeautifulSoup(html, "html.parser") last_updated = (soup.find("time", class_="css-wcxsge").text).replace("Updated", "").strip() if last_updated in dates: print("Already collected most recent data for nytimes") else: rows = [] print("Getting data for " + last_updated) # Getting each status div try: reopened = ("Reopened", (soup.find("div", class_="g-list-container g-cat-reopened")).find_all("div", class_="g-state g-cat-reopened")) except: try: reopened = ("Reopened", (soup.find("div", class_="g-list-container g-cat-reopened")).find_all("div", class_="g-state g-cat-reopened ")) except: reopened = ("",[]) try: forward = ("Forward", (soup.find("div", class_="g-list-container g-cat-forward")).find_all("div", class_="g-state g-cat-forward")) except: try: forward = ("Forward", (soup.find("div", class_="g-list-container g-cat-forward")).find_all("div", class_="g-state g-cat-forward ")) except: forward = ("",[]) try: pausing = ("Pausing", (soup.find("div", class_="g-list-container g-cat-pausing")).find_all("div", class_="g-state g-cat-pausing")) except: try: pausing = ("Pausing", (soup.find("div", class_="g-list-container g-cat-pausing")).find_all("div", class_="g-state g-cat-pausing ")) except: pausing = ("",[]) try: reversing = ("Reversing", (soup.find("div", class_="g-list-container g-cat-reversing")).find_all("div", class_="g-state g-cat-reversing")) except: try: reversing = ("Reversing", (soup.find("div", class_="g-list-container g-cat-reversing")).find_all("div", class_="g-state g-cat-reversing ")) except: reversing = ("",[]) try: reopening = ("Reopening", (soup.find("div", class_="g-list-container g-cat-reopening")).find_all("div", class_="g-state g-cat-reopening")) except: try: reopening = ("Reopening", (soup.find("div", class_="g-list-container g-cat-reopening")).find_all("div", class_="g-state g-cat-reopening ")) except: reopening = ("",[]) try: soon = ("Order Lifting or Reopening in the Next Week", (soup.find("div", class_="g-list-container g-cat-soon")).find_all("div", class_="g-state g-cat-soon")) except: try: soon = ("Order Lifting or Reopening in the Next Week", (soup.find("div", class_="g-list-container g-cat-soon")).find_all("div", class_="g-state g-cat-soon ")) except: soon = ("",[]) try: shutdown = ("Shut down or restricted", (soup.find("div", class_="g-list-container g-cat-shutdown-restricted")).find_all("div", class_="g-state g-cat-shutdown-restricted")) except: try: shutdown = ("Shut down or restricted", (soup.find("div", class_="g-list-container g-cat-shutdown-restricted")).find_all("div", class_="g-state g-cat-shutdown-restricted ")) except: shutdown = ("",[]) try: regional = ("Regional Reopening", (soup.find("div", class_="g-list-container g-cat-regional")).find_all("div", class_="g-state g-cat-regional")) except: try: regional = ("Regional Reopening", (soup.find("div", class_="g-list-container g-cat-regional")).find_all("div", class_="g-state g-cat-regional ")) except: regional = ("",[]) if not reopened[1] and not forward[1] and not pausing[1] and not reversing[1] and not reopening[1] and not soon[1] and not shutdown[1] and not regional[1]: print("No data collected for " + last_updated) all_status = [reopened, forward, pausing, reversing, reopening, soon, shutdown, regional] # Iterating through all status for status in all_status: status_val, div = status # Iterate through states in status for state in div: st_ab = state["data-state"] try: open_ests = (state.find("div", class_="g-details-wrap g-details")).find_all("div", class_="g-cat-details-wrap") except: try: open_ests = (state.find("div", class_="g-details-wrap g-details")).find_all("div", class_="g-cat-name-text-wrap") except: open_ests = [] try: closed_ests = (state.find("div", class_="g-details-wrap g-details_closed")).find_all("div", class_="g-cat-details-wrap") except: try: closed_ests = (state.find("div", class_="g-details-wrap g-details_closed")).find_all("div", class_="g-cat-name-text-wrap") except: closed_ests = [] try: reopening_ests = (state.find("div", class_="g-details-wrap")).find_all("div", class_="g-cat-details-wrap") except: try: reopening_ests = (state.find("div", class_="g-details-wrap-outer")).find_all("div", class_="g-cat-name-text-wrap") except: reopening_ests = [] try: soon_ests = (state.find("div", class_="g-details-wrap g-details_soon")).find_all("div", class_="g-cat-details-wrap") except: try: soon_ests = (state.find("div", class_="g-details-wrap g-details_soon")).find_all("div", class_="g-cat-name-text-wrap") except: soon_ests = [] if not open_ests and not closed_ests and not reopening_ests and not soon_ests: print("No establishment data collected for " + date + ": " + st_ab) continue # Get open ests for est in open_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Open", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Get closed ests for est in closed_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Closed", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Get reopening ests for est in reopening_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Reopened", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Get soon ests for est in soon_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Reopening soon", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Output for row in rows: fields = sorted([x for x in row]) exists = os.path.exists(data) with open(data, "a") as fp: writer = csv.writer(fp) if not exists: writer.writerow(fields) writer.writerow([row[x] for x in fields]) def get_hist(): # Getting all dates data = "nytimes.csv" base_dt = datetime.now() - timedelta(days=1) date_list = [base_dt - timedelta(days=x) for x in range(83)] date_list = [x.strftime("%Y%m%d") for x in date_list] rows = [] collected = set() # Going through all dates for date in date_list: url = ("https://web.archive.org/web/%s/https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html" % date) headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'} req = Request(url=url, headers=headers) html = urlopen(req).read() soup = BeautifulSoup(html, "html.parser") # Get last updated last_updated = (soup.find("time", class_="css-wcxsge").text).replace("Updated", "").strip() if last_updated in collected: continue else: collected.add(last_updated) print(last_updated) # Getting each status div try: reopened = ("Reopened", (soup.find("div", class_="g-list-container g-cat-reopened")).find_all("div", class_="g-state g-cat-reopened")) except: try: reopened = ("Reopened", (soup.find("div", class_="g-list-container g-cat-reopened")).find_all("div", class_="g-state g-cat-reopened ")) except: reopened = ("",[]) try: forward = ("Forward", (soup.find("div", class_="g-list-container g-cat-forward")).find_all("div", class_="g-state g-cat-forward")) except: try: forward = ("Forward", (soup.find("div", class_="g-list-container g-cat-forward")).find_all("div", class_="g-state g-cat-forward ")) except: forward = ("",[]) try: pausing = ("Pausing", (soup.find("div", class_="g-list-container g-cat-pausing")).find_all("div", class_="g-state g-cat-pausing")) except: try: pausing = ("Pausing", (soup.find("div", class_="g-list-container g-cat-pausing")).find_all("div", class_="g-state g-cat-pausing ")) except: pausing = ("",[]) try: reversing = ("Reversing", (soup.find("div", class_="g-list-container g-cat-reversing")).find_all("div", class_="g-state g-cat-reversing")) except: try: reversing = ("Reversing", (soup.find("div", class_="g-list-container g-cat-reversing")).find_all("div", class_="g-state g-cat-reversing ")) except: reversing = ("",[]) try: reopening = ("Reopening", (soup.find("div", class_="g-list-container g-cat-reopening")).find_all("div", class_="g-state g-cat-reopening")) except: try: reopening = ("Reopening", (soup.find("div", class_="g-list-container g-cat-reopening")).find_all("div", class_="g-state g-cat-reopening ")) except: reopening = ("",[]) try: soon = ("Order Lifting or Reopening in the Next Week", (soup.find("div", class_="g-list-container g-cat-soon")).find_all("div", class_="g-state g-cat-soon")) except: try: soon = ("Order Lifting or Reopening in the Next Week", (soup.find("div", class_="g-list-container g-cat-soon")).find_all("div", class_="g-state g-cat-soon ")) except: soon = ("",[]) try: shutdown = ("Shut down or restricted", (soup.find("div", class_="g-list-container g-cat-shutdown-restricted")).find_all("div", class_="g-state g-cat-shutdown-restricted")) except: try: shutdown = ("Shut down or restricted", (soup.find("div", class_="g-list-container g-cat-shutdown-restricted")).find_all("div", class_="g-state g-cat-shutdown-restricted ")) except: shutdown = ("",[]) try: regional = ("Regional Reopening", (soup.find("div", class_="g-list-container g-cat-regional")).find_all("div", class_="g-state g-cat-regional")) except: try: regional = ("Regional Reopening", (soup.find("div", class_="g-list-container g-cat-regional")).find_all("div", class_="g-state g-cat-regional ")) except: regional = ("",[]) if not reopened[1] and not forward[1] and not pausing[1] and not reversing[1] and not reopening[1] and not soon[1] and not shutdown[1] and not regional[1]: print("No data collected for " + date) continue all_status = [reopened, forward, pausing, reversing, reopening, soon, shutdown, regional] # Iterating through all status for status in all_status: status_val, div = status # Iterate through states in status for state in div: st_ab = state["data-state"] try: open_ests = (state.find("div", class_="g-details-wrap g-details")).find_all("div", class_="g-cat-details-wrap") except: try: open_ests = (state.find("div", class_="g-details-wrap g-details")).find_all("div", class_="g-cat-name-text-wrap") except: open_ests = [] try: closed_ests = (state.find("div", class_="g-details-wrap g-details_closed")).find_all("div", class_="g-cat-details-wrap") except: try: closed_ests = (state.find("div", class_="g-details-wrap g-details_closed")).find_all("div", class_="g-cat-name-text-wrap") except: closed_ests = [] try: reopening_ests = (state.find("div", class_="g-details-wrap")).find_all("div", class_="g-cat-details-wrap") except: try: reopening_ests = (state.find("div", class_="g-details-wrap-outer")).find_all("div", class_="g-cat-name-text-wrap") except: reopening_ests = [] try: soon_ests = (state.find("div", class_="g-details-wrap g-details_soon")).find_all("div", class_="g-cat-details-wrap") except: try: soon_ests = (state.find("div", class_="g-details-wrap g-details_soon")).find_all("div", class_="g-cat-name-text-wrap") except: soon_ests = [] if not open_ests and not closed_ests and not reopening_ests and not soon_ests: print("No establishment data collected for " + date + ": " + st_ab) continue # Get open ests for est in open_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Open", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Get closed ests for est in closed_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Closed", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Get reopening ests for est in reopening_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Reopened", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Get soon ests for est in soon_ests: row = { "Report Date": last_updated, "State": st_ab, "State Status": status_val, "Establishment Category": est.find("div", class_="g-cat-name").text, "Establishment Status": "Reopening soon", "Establishment Details": est.find("div", class_="g-cat-text").text } rows.append(row) # Output for row in rows: fields = sorted([x for x in row]) exists = os.path.exists(data) with open(data, "a") as fp: writer = csv.writer(fp) if not exists: writer.writerow(fields) writer.writerow([row[x] for x in fields]) def get_masks(): url = "https://www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html" headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'} req = Request(url=url, headers=headers) html = urlopen(req).read() soup = BeautifulSoup(html, "html.parser") states = soup.find_all("h3") dates = soup.find_all("strong") dates = [x for x in dates if x.text != " "] rows = [] for state, date in zip(states, dates): row = { "State": state.text, "Requiring Masks As Of": ((date.text).replace("As of ", "")).replace(":", "") } rows.append(row) data = "cnn_masks.csv" # Output for row in rows: fields = sorted([x for x in row]) exists = os.path.exists(data) with open(data, "a") as fp: writer = csv.writer(fp) if not exists: writer.writerow(fields) writer.writerow([row[x] for x in fields]) # get_masks() # exit() scrape() # # Get historical # get_hist() # exit() # writer = csv.writer(sys.stdout) # writer.writerow(["state", "stateAbbrev", "place", "wholeState", "pop", # "order", "orderEff", "orderLink", "newsLink", "pullTime"]) # html = urlopen("https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html").read() # soup = BeautifulSoup(html, "html.parser") # for x in soup.find_all("div", class_="state-wrap"): # stateAbbrev = x["data-state"].strip() # state = x.h3.find(text=True).strip() # if "statewide" in x["class"]: # place, pop, order, dt, orderLink, newsLink = getPlace(x.div) # pop = getPop(x.h3.span.text) # writer.writerow([state, stateAbbrev, state, 1, pop, # order, dt, orderLink, newsLink, now]) # else: # for place in x.findChildren("div", class_="place-wrap"): # place, pop, order, dt, orderLink, newsLink = getPlace(place) # writer.writerow([state, stateAbbrev, place, 0, pop, # order, dt, orderLink, newsLink, now])

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3e14a88cb562c913c02f8adb522038a78bdaf02a

104

py

Python

docs/tutorials/depth/train_monodepth2.py

Neutron3529/gluon-cv

1c41c89c414cc9e447bbc9d2f96f3b6ac519c07c

[ "Apache-2.0" ]

null

docs/tutorials/depth/train_monodepth2.py

Neutron3529/gluon-cv

1c41c89c414cc9e447bbc9d2f96f3b6ac519c07c

[ "Apache-2.0" ]

null

docs/tutorials/depth/train_monodepth2.py

Neutron3529/gluon-cv

1c41c89c414cc9e447bbc9d2f96f3b6ac519c07c

[ "Apache-2.0" ]

null

"""02. Monodepth2 training on KITTI dataset ================================================== TODO"""

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e417844820a1f10ac90a63e47a9dbba83f290365

2,054

py

Python

usaspending_api/common/tests/unit/test_business_categories_helper.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

217

2016-11-03T17:09:53.000Z

2022-03-10T04:17:54.000Z

usaspending_api/common/tests/unit/test_business_categories_helper.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

622

2016-09-02T19:18:23.000Z

2022-03-29T17:11:01.000Z

usaspending_api/common/tests/unit/test_business_categories_helper.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

93

2016-09-07T20:28:57.000Z

2022-02-25T00:25:27.000Z

# Imports from your apps from usaspending_api.common.helpers.business_categories_helper import ( BUSINESS_CATEGORIES_LOOKUP_DICT, get_business_category_display_names, ) def _get_all_business_category_field_names(): return list(BUSINESS_CATEGORIES_LOOKUP_DICT.keys()) def _get_first_ten_business_category_field_names(): return list(BUSINESS_CATEGORIES_LOOKUP_DICT.keys())[:10] def _get_all_business_category_display_names(): return list(BUSINESS_CATEGORIES_LOOKUP_DICT.values()) def _get_first_ten_business_category_display_names(): return list(BUSINESS_CATEGORIES_LOOKUP_DICT.values())[:10] def test_get_ten_valid_business_category_display_names(): business_category_field_names_list = _get_first_ten_business_category_field_names() business_category_display_names_list = _get_first_ten_business_category_display_names() assert ( get_business_category_display_names(business_category_field_names_list) == business_category_display_names_list ) def test_get_all_valid_business_category_display_names(): business_category_field_names_list = _get_all_business_category_field_names() business_category_display_names_list = _get_all_business_category_display_names() assert ( get_business_category_display_names(business_category_field_names_list) == business_category_display_names_list ) def test_get_invalid_business_category_display_names(): business_category_field_names_list = _get_all_business_category_field_names() business_category_field_names_list.insert(0, "invalid_name_1") business_category_field_names_list.append("invalid_name_2") business_category_display_names_list = _get_all_business_category_display_names() assert ( get_business_category_display_names(business_category_field_names_list) == business_category_display_names_list ) def test_get_empty_business_category_display_names(): business_category_field_names_list = [] assert get_business_category_display_names(business_category_field_names_list) == []

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e435065b56e7532fdfb54d0681d9559522db4c55

185

py

Python

learnGrammar/builtwith_learn.py

HelloHuDi/learn-python-demo

dd0419e93e96e67f86aaeba5fe8de9a1043ae1e7

[ "Apache-2.0" ]

1

2017-11-28T02:13:52.000Z

learnGrammar/builtwith_learn.py

HelloHuDi/learn-python-demo

dd0419e93e96e67f86aaeba5fe8de9a1043ae1e7

[ "Apache-2.0" ]

null

learnGrammar/builtwith_learn.py

HelloHuDi/learn-python-demo

dd0419e93e96e67f86aaeba5fe8de9a1043ae1e7

[ "Apache-2.0" ]

null

# -*- coding:utf-8 -*- # Created by hd on 2017/12/5 . import builtwith as bw """ 识别网站所用技术""" print(bw.parse("http://www.u9yy.com")) print(bw.parse("http://example.webscraping.com"))

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e44bb39b93cbdfd8626fa508237f75182682460a

4,042

py

Python

libs/ma/tests/paired_io.py

ITBE-Lab/ma

039e2833dd2e50df9285f183ff774bd87bbae710

[ "MIT" ]

40

2019-04-28T21:16:45.000Z

2022-02-05T05:54:47.000Z

libs/ma/tests/paired_io.py

ITBE-Lab/ma

039e2833dd2e50df9285f183ff774bd87bbae710

[ "MIT" ]

11

2019-04-28T22:29:12.000Z

2022-02-21T14:07:10.000Z

libs/ma/tests/paired_io.py

ITBE-Lab/ma

039e2833dd2e50df9285f183ff774bd87bbae710

[ "MIT" ]

2

2019-05-06T15:29:23.000Z

2021-01-08T13:22:17.000Z

from MA import * from random import choice import tempfile import random # create a reference string reference = "" for _ in range(1000): reference += choice(['C', 'T', 'G', 'A']) # create pack and fmd index from that string reference_pack = Pack() reference_pack.append("random pack", "sequence description", NucSeq(reference)) for _ in range(100): query1 = "" qual1 = "" for _ in range(100): query1 += choice(['C', 'T', 'G', 'A']) qual1 += str(chr(random.randint(33, 126))) # random quality query2 = "" qual2 = "" for _ in range(100): query2 += choice(['C', 'T', 'G', 'A']) qual2 += str(chr(random.randint(33, 126))) # random quality # write a fasta file fasta_1_file_name = tempfile.gettempdir() + "/.tmp1.fasta" fasta_2_file_name = tempfile.gettempdir() + "/.tmp2.fasta" sam_file_name = tempfile.gettempdir() + "/.tmp.p.sam" with open(fasta_1_file_name, "w") as out_file: out_file.write("@name desc\n") out_file.write(query1) out_file.write("\n+\n") out_file.write(qual1) out_file.write("\n") with open(fasta_2_file_name, "w") as out_file: out_file.write("@name desc\n") out_file.write(query2) out_file.write("\n+\n") out_file.write(qual2) out_file.write("\n") file_reader = PairedFileReader(ParameterSetManager()) get_first = GetFirstQuery(ParameterSetManager()) get_second = GetSecondQuery(ParameterSetManager()) stream1 = FileStreamFromPath(fasta_1_file_name) stream2 = FileStreamFromPath(fasta_2_file_name) paired_stream = PairedFileStream(stream1, stream2) query_vec = file_reader.execute(paired_stream) alignment_vec = AlignmentVector() file_writer = PairedFileWriter(ParameterSetManager(), sam_file_name, reference_pack) file_writer.execute(get_first.execute(query_vec), get_second.execute(query_vec), alignment_vec, reference_pack) del file_writer fst = True with open(sam_file_name, "r") as in_file: for line in in_file: if line[0] != '@': #print("|", line, "|", sep="") fields = line[:-1].split("\t") #print("|" + fields[10] + "|", "|" + qual + "|", sep='\n') assert fields[9] == query1 if fst else query2 assert fields[10] == qual1 if fst else query2 fst = False for _ in range(100): query1 = "" for _ in range(100): query1 += choice(['C', 'T', 'G', 'A']) query2 = "" for _ in range(100): query2 += choice(['C', 'T', 'G', 'A']) # write a fasta file fasta_1_file_name = tempfile.gettempdir() + "/.tmp.1.fasta" fasta_2_file_name = tempfile.gettempdir() + "/.tmp.2.fasta" sam_file_name = tempfile.gettempdir() + "/.tmp.p.sam" with open(fasta_1_file_name, "w") as out_file: out_file.write(">name desc\n") out_file.write(query1) out_file.write("\n") with open(fasta_2_file_name, "w") as out_file: out_file.write(">name desc\n") out_file.write(query2) out_file.write("\n") stream1 = FileStreamFromPath(fasta_1_file_name) stream2 = FileStreamFromPath(fasta_2_file_name) paired_stream = PairedFileStream(stream1, stream2) query_vec = file_reader.execute(paired_stream) alignment_vec = AlignmentVector() file_writer = PairedFileWriter(ParameterSetManager(), sam_file_name, reference_pack) file_writer.execute(get_first.execute(query_vec), get_second.execute(query_vec), alignment_vec, reference_pack) del file_writer fst = True with open(sam_file_name, "r") as in_file: for line in in_file: if line[0] != '@': #print("|", line, "|", sep="") fields = line[:-1].split("\t") #print("|" + fields[10] + "|", "|" + qual + "|", sep='\n') assert fields[9] == query1 if fst else query2 assert fields[10] == "*" fst = False

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5f806205190cb20c7aab3a76f58715a0a85d365c

6,118

py

Python

builder/build_libwebp/windows.py

kdschlosser/wxAnimation

ad472719a77a081da5e51280d469cfd5d5bfcd3c

[ "MIT" ]

2

2020-03-23T11:29:56.000Z

2021-11-24T22:10:07.000Z

builder/build_libwebp/windows.py

kdschlosser/wxAnimation

ad472719a77a081da5e51280d469cfd5d5bfcd3c

[ "MIT" ]

null

builder/build_libwebp/windows.py

kdschlosser/wxAnimation

ad472719a77a081da5e51280d469cfd5d5bfcd3c

[ "MIT" ]

null

from build_framework import PLATFORM from ..sources import WEBP_DECODER_SOURCES, WEBP_SOURCES, WEBP_MUX_SOURCES, WEBP_DEMUX_SOURCES from ..dep_versions import WEBP_VERSION import build_framework.library.windows MACROS = [ ('_CRT_SECURE_NO_WARNINGS', ''), ('_UNICODE', ''), ('UNICODE', ''), ('WIN32_LEAN_AND_MEAN', ''), ('HAVE_WINCODEC_H', ''), ('WEBP_USE_THREAD', '') ] LIBS = ['zlib.lib', 'libpng'] LIB_DIRS = [] UDEF_MACROS = [] CFLAGS = ['/nologo', '/O2', '/MT', '/W3'] LDFLAGS = ['/LARGEADDRESSAWARE', '/MANIFEST', '/NXCOMPAT', '/DYNAMICBASE'] INCLUDES = [ 'webp/src/dec', 'webp/src/demux', 'webp/src/dsp', 'webp/src/enc', 'webp/src/mux', 'webp/src/utils', 'webp/src/webp', 'libpng', 'zlib' ] if PLATFORM == 'x64': LDFLAGS += ['/SAFESEH:NO'] else: LDFLAGS += ['/SAFESEH'] class WebPDecoderLibrary(build_framework.library.windows.Library): def __init__(self): name = 'libwebpdecoder' sources = WEBP_DECODER_SOURCES include_dirs = INCLUDES define_macros = MACROS undef_macros = UDEF_MACROS library_dirs = LIB_DIRS libraries = LIBS runtime_library_dirs = () extra_objects = () extra_compile_args = CFLAGS extra_link_args = LDFLAGS export_symbols = () depends = () static_lib = True version = WEBP_VERSION build_framework.library.windows.Library.__init__( self, name=name, sources=sources, include_dirs=include_dirs, define_macros=define_macros, undef_macros=undef_macros, library_dirs=library_dirs, libraries=libraries, runtime_library_dirs=runtime_library_dirs, extra_objects=extra_objects, extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, export_symbols=export_symbols, depends=depends, static_lib=static_lib, version=version, ) # this enables the use of the build_framework multi threaded compiler self.build = self._build class WebPLibrary(build_framework.library.windows.Library): def __init__(self): name = 'libwebp' sources = WEBP_SOURCES include_dirs = INCLUDES define_macros = MACROS undef_macros = UDEF_MACROS library_dirs = LIB_DIRS libraries = LIBS runtime_library_dirs = () extra_objects = () extra_compile_args = CFLAGS extra_link_args = LDFLAGS export_symbols = () depends = () static_lib = True version = WEBP_VERSION build_framework.library.windows.Library.__init__( self, name=name, sources=sources, include_dirs=include_dirs, define_macros=define_macros, undef_macros=undef_macros, library_dirs=library_dirs, libraries=libraries, runtime_library_dirs=runtime_library_dirs, extra_objects=extra_objects, extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, export_symbols=export_symbols, depends=depends, static_lib=static_lib, version=version, ) # this enables the use of the build_framework multi threaded compiler self.build = self._build class WebPMuxLibrary(build_framework.library.windows.Library): def __init__(self): name = 'libwebpmux' sources = WEBP_MUX_SOURCES include_dirs = INCLUDES define_macros = MACROS undef_macros = UDEF_MACROS library_dirs = LIB_DIRS libraries = LIBS runtime_library_dirs = () extra_objects = () extra_compile_args = CFLAGS extra_link_args = LDFLAGS export_symbols = () depends = () static_lib = True version = WEBP_VERSION build_framework.library.windows.Library.__init__( self, name=name, sources=sources, include_dirs=include_dirs, define_macros=define_macros, undef_macros=undef_macros, library_dirs=library_dirs, libraries=libraries, runtime_library_dirs=runtime_library_dirs, extra_objects=extra_objects, extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, export_symbols=export_symbols, depends=depends, static_lib=static_lib, version=version, ) # this enables the use of the build_framework multi threaded compiler self.build = self._build class WebPDemuxLibrary(build_framework.library.windows.Library): def __init__(self): name = 'libwebpdemux' sources = WEBP_DEMUX_SOURCES include_dirs = INCLUDES define_macros = MACROS undef_macros = UDEF_MACROS library_dirs = LIB_DIRS libraries = LIBS runtime_library_dirs = () extra_objects = () extra_compile_args = CFLAGS extra_link_args = LDFLAGS export_symbols = () depends = () static_lib = True version = WEBP_VERSION build_framework.library.windows.Library.__init__( self, name=name, sources=sources, include_dirs=include_dirs, define_macros=define_macros, undef_macros=undef_macros, library_dirs=library_dirs, libraries=libraries, runtime_library_dirs=runtime_library_dirs, extra_objects=extra_objects, extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, export_symbols=export_symbols, depends=depends, static_lib=static_lib, version=version, ) # this enables the use of the build_framework multi threaded compiler self.build = self._build

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5f8a14c3c1cab62f87911be8919b6d3e87ff7d36

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py

Python

tests/unit/test_delineate_intersecting_shards.py

etta-trust/PolicyGlass

72157189a9af3172e6efbdcc2050969796cfa99f

[ "MIT" ]

49

2021-12-21T23:15:55.000Z

2022-03-28T09:38:30.000Z

tests/unit/test_delineate_intersecting_shards.py

etta-trust/PolicyGlass

72157189a9af3172e6efbdcc2050969796cfa99f

[ "MIT" ]

3

2021-12-23T22:02:02.000Z

2022-01-10T14:16:24.000Z

tests/unit/test_delineate_intersecting_shards.py

etta-trust/PolicyGlass

72157189a9af3172e6efbdcc2050969796cfa99f

[ "MIT" ]

1

2022-02-22T11:03:27.000Z

from policyglass import PolicyShard from policyglass.action import Action, EffectiveAction from policyglass.condition import Condition, EffectiveCondition from policyglass.policy_shard import dedupe_policy_shards from policyglass.principal import EffectivePrincipal, Principal from policyglass.resource import EffectiveResource, Resource def test_dedupe_policy_shards_simple(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Deny", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Deny", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Deny", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), ] def test_dedupe_policy_shards_complex_overlap(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:get*")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:getobject*")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [shards[1]] def test_larger_after_smaller(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [shards[1]] def test_identical(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [shards[1]] def test_identical_except_one_with_one_without_condition(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset({Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"])}) ), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [shards[1]] def test_matching_subset_conditions(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset({Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"])}) ), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset( { Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"]), Condition(key="s3:x-amz-server-side-encryption", operator="StringEquals", values=["AES256"]), } ) ), ), ] assert dedupe_policy_shards(shards) == [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:PutObject"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset( { Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"]), Condition(key="s3:x-amz-server-side-encryption", operator="StringEquals", values=["AES256"]), } ) ), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset({Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"])}) ), ), ] def test_matching_subset_conditions_and_condition_exclusions(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset({Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"])}) ), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( inclusions=frozenset( { Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"]), } ), exclusions=frozenset( { Condition(key="s3:x-amz-server-side-encryption", operator="StringEquals", values=["AES256"]), } ), ), ), ] assert dedupe_policy_shards(shards) == [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:PutObject"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( inclusions=frozenset( {Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"])} ), exclusions=frozenset( { Condition(key="s3:x-amz-server-side-encryption", operator="StringEquals", values=["AES256"]), } ), ), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset( { Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"]), } ) ), ), ] def test_subset_arps_differing_conditions(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset({Condition(key="aws:PrincipalOrgId", operator="StringNotEquals", values=["o-123456"])}) ), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("s3:*"), exclusions=frozenset()), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition( frozenset( { Condition(key="s3:x-amz-server-side-encryption", operator="StringEquals", values=["AES256"]), } ) ), ), ] assert dedupe_policy_shards(shards) == list(reversed(shards)) def test_result_of_denying_action_and_resource(): shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("*"), exclusions=frozenset({Resource("arn:aws:s3:::examplebucket/*")}) ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset({Action("S3:PutObject")})), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("*"), exclusions=frozenset({Resource("arn:aws:s3:::examplebucket/*")}) ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), ] def test_result_of_difference_of_deny_action_and_resource_subsets(): # This corresponds to the output of difference/test_policyshard::deny_action_and_resource_subsets # And demonstrates that counterintuitive output is resolved by dedupe_policy_shards shards = [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("*"), exclusions=frozenset({Resource("arn:aws:s3:::examplebucket/*")}) ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource(inclusion=Resource("*"), exclusions=frozenset()), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), ] assert dedupe_policy_shards(shards) == [ PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset({Action("s3:PutObject")})), effective_resource=EffectiveResource( inclusion=Resource("arn:aws:s3:::examplebucket/*"), exclusions=frozenset() ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), PolicyShard( effect="Allow", effective_action=EffectiveAction(inclusion=Action("*"), exclusions=frozenset()), effective_resource=EffectiveResource( inclusion=Resource("*"), exclusions=frozenset({Resource("arn:aws:s3:::examplebucket/*")}) ), effective_principal=EffectivePrincipal(inclusion=Principal(type="AWS", value="*"), exclusions=frozenset()), effective_condition=EffectiveCondition(inclusions=frozenset(), exclusions=frozenset()), ), ]

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9,254

py

Python

jet-python/build/lib.linux-x86_64-2.7/jnpr/jet/jnxBase_jnx_addr/ttypes.py

Juniper/vmx-docker-lwaftr

ebbedad67d8acd74f4a305a0a4ad6bf2ed716e7e

[ "Apache-2.0" ]

31

2016-11-22T06:17:37.000Z

2022-01-10T05:41:02.000Z

jet-python/lib/jnpr/jet/jnxBase_jnx_addr/ttypes.py

Juniper/vmx-docker-lwaftr

ebbedad67d8acd74f4a305a0a4ad6bf2ed716e7e

[ "Apache-2.0" ]

2

2016-12-20T13:53:57.000Z

2017-03-27T20:10:07.000Z

jet-python/build/lib.linux-x86_64-2.7/jnpr/jet/jnxBase_jnx_addr/ttypes.py

Juniper/vmx-docker-lwaftr

ebbedad67d8acd74f4a305a0a4ad6bf2ed716e7e

[ "Apache-2.0" ]

8

2017-01-31T22:18:18.000Z

2020-02-05T10:33:23.000Z

# # Autogenerated by Thrift Compiler (0.9.1) # # DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING # # options string: py # from thrift.Thrift import TType, TMessageType, TException, TApplicationException from thrift.transport import TTransport from thrift.protocol import TBinaryProtocol, TProtocol try: from thrift.protocol import fastbinary except: fastbinary = None class JnxBaseAddressFormat: ADDRESS_STRING = 0 ADDRESS_BYTES = 1 _VALUES_TO_NAMES = { 0: "ADDRESS_STRING", 1: "ADDRESS_BYTES", } _NAMES_TO_VALUES = { "ADDRESS_STRING": 0, "ADDRESS_BYTES": 1, } class JnxBaseAfType: AF_UNSPECIFIED = 0 AF_INET = 1 AF_INET6 = 2 AF_MAC = 3 _VALUES_TO_NAMES = { 0: "AF_UNSPECIFIED", 1: "AF_INET", 2: "AF_INET6", 3: "AF_MAC", } _NAMES_TO_VALUES = { "AF_UNSPECIFIED": 0, "AF_INET": 1, "AF_INET6": 2, "AF_MAC": 3, } class IpAddressAddrFormat: """ Attributes: - addr_string - addr_bytes """ thrift_spec = ( None, # 0 (1, TType.STRING, 'addr_string', None, None, ), # 1 (2, TType.STRING, 'addr_bytes', None, None, ), # 2 ) def __init__(self, addr_string=None, addr_bytes=None,): self.addr_string = addr_string self.addr_bytes = addr_bytes def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRING: self.addr_string = iprot.readString(); else: iprot.skip(ftype) elif fid == 2: if ftype == TType.STRING: self.addr_bytes = iprot.readString(); else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('IpAddressAddrFormat') if self.addr_string is not None: oprot.writeFieldBegin('addr_string', TType.STRING, 1) oprot.writeString(self.addr_string) oprot.writeFieldEnd() if self.addr_bytes is not None: oprot.writeFieldBegin('addr_bytes', TType.STRING, 2) oprot.writeString(self.addr_bytes) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class JnxBaseIpAddress: """ Attributes: - AddrFormat """ thrift_spec = ( None, # 0 (1, TType.STRUCT, 'AddrFormat', (IpAddressAddrFormat, IpAddressAddrFormat.thrift_spec), None, ), # 1 ) def __init__(self, AddrFormat=None,): self.AddrFormat = AddrFormat def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRUCT: self.AddrFormat = IpAddressAddrFormat() self.AddrFormat.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('JnxBaseIpAddress') if self.AddrFormat is not None: oprot.writeFieldBegin('AddrFormat', TType.STRUCT, 1) self.AddrFormat.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class MacAddressAddrFormat: """ Attributes: - addr_string - addr_bytes """ thrift_spec = ( None, # 0 (1, TType.STRING, 'addr_string', None, None, ), # 1 (2, TType.STRING, 'addr_bytes', None, None, ), # 2 ) def __init__(self, addr_string=None, addr_bytes=None,): self.addr_string = addr_string self.addr_bytes = addr_bytes def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRING: self.addr_string = iprot.readString(); else: iprot.skip(ftype) elif fid == 2: if ftype == TType.STRING: self.addr_bytes = iprot.readString(); else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('MacAddressAddrFormat') if self.addr_string is not None: oprot.writeFieldBegin('addr_string', TType.STRING, 1) oprot.writeString(self.addr_string) oprot.writeFieldEnd() if self.addr_bytes is not None: oprot.writeFieldBegin('addr_bytes', TType.STRING, 2) oprot.writeString(self.addr_bytes) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class JnxBaseMacAddress: """ Attributes: - AddrFormat """ thrift_spec = ( None, # 0 (1, TType.STRUCT, 'AddrFormat', (MacAddressAddrFormat, MacAddressAddrFormat.thrift_spec), None, ), # 1 ) def __init__(self, AddrFormat=None,): self.AddrFormat = AddrFormat def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRUCT: self.AddrFormat = MacAddressAddrFormat() self.AddrFormat.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('JnxBaseMacAddress') if self.AddrFormat is not None: oprot.writeFieldBegin('AddrFormat', TType.STRUCT, 1) self.AddrFormat.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other)

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39b6d542902b78c2561b9a7f78ded67e4c8b30b2

123

py

Python

core/admin.py

uktrade/export-wins-data

46caa444812e89abe504bec8c15aa7f7ba1a247e

[ "MIT" ]

5

2016-09-12T12:52:45.000Z

2020-03-24T14:43:13.000Z

core/admin.py

uktrade/export-wins-data

46caa444812e89abe504bec8c15aa7f7ba1a247e

[ "MIT" ]

435

2016-10-18T12:51:39.000Z

2021-06-09T17:22:08.000Z

core/admin.py

uktrade/export-wins-data

46caa444812e89abe504bec8c15aa7f7ba1a247e

[ "MIT" ]

2

2016-12-06T10:37:21.000Z

2017-02-22T17:27:43.000Z

from django.contrib import admin admin.site.site_header = 'Export Wins Admin' admin.site.site_title = 'Export Wins Admin'

24.6

44

0.788618

19

123

5

0.526316

0.210526

0.294737

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123

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null

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1

0

1

0

7

f2f9dc6db61bafbab20364b4cb3c5740b2221f09

68

py

Python

tests/test_mod.py

dreki/py-fodder

bff5338d0d532ccb1a06ab6edb3e0d34cba27a7e

[ "MIT" ]

1

2017-02-10T04:36:43.000Z

tests/test_mod.py

dreki/py-fodder

bff5338d0d532ccb1a06ab6edb3e0d34cba27a7e

[ "MIT" ]

null

tests/test_mod.py

dreki/py-fodder

bff5338d0d532ccb1a06ab6edb3e0d34cba27a7e

[ "MIT" ]

null

import fodder def test_has_legs(): assert not fodder.has_legs

11.333333

30

0.75

11

68

4.363636

0.727273

0.291667

0

0.191176

68

5

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0.333333

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0.333333

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1

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null

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