xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

293a1bcbc6d90a969408d79aa6adc718e9ac7de4

92

py

Python

run.py

b0nbon1/stack-overflow-lite-API

fccb7b9b7bf39434e6a26ffd5d8db99d27d4f680

[ "MIT" ]

5

2019-04-08T21:12:46.000Z

2019-04-16T08:12:31.000Z

run.py

b0nbon1/stack-overflow-lite-API

fccb7b9b7bf39434e6a26ffd5d8db99d27d4f680

[ "MIT" ]

null

run.py

b0nbon1/stack-overflow-lite-API

fccb7b9b7bf39434e6a26ffd5d8db99d27d4f680

[ "MIT" ]

null

"""Runn the app""" # local import from app import create_app app = create_app('development')

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2952e64663e3b517bbf6c69b4c9be78e790cfb65

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py

Python

acquisitions/migrations/0022_auto_20160726_2026.py

18F/acqstackdb

7d939e7deb1cb8749f16fe6b6bc092f5db5c4469

[ "CC0-1.0" ]

2

2016-06-03T16:33:34.000Z

2016-07-22T12:10:31.000Z

acquisitions/migrations/0022_auto_20160726_2026.py

18F/acqstackdb

7d939e7deb1cb8749f16fe6b6bc092f5db5c4469

[ "CC0-1.0" ]

26

2016-06-02T11:21:15.000Z

2016-07-18T14:10:03.000Z

acquisitions/migrations/0022_auto_20160726_2026.py

18F/acqstackdb

7d939e7deb1cb8749f16fe6b6bc092f5db5c4469

[ "CC0-1.0" ]

2

2017-07-14T08:33:32.000Z

2021-02-15T10:16:18.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.9.6 on 2016-07-26 20:26 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('acquisitions', '0021_auto_20160712_2135'), ] operations = [ migrations.AlterModelOptions( name='agency', options={'ordering': ('name',), 'verbose_name_plural': 'Agencies'}, ), migrations.AlterModelOptions( name='step', options={'ordering': ('steptrackthroughmodel__order',)}, ), migrations.AlterField( model_name='acquisition', name='competition_strategy', field=models.CharField(blank=True, choices=[('A/E Procedures', 'A/E Procedures'), ('Competed under SAP', 'Competed under SAP'), ('Competitive Delivery Order Fair Opportunity Provided', 'Competitive Delivery Order Fair Opportunity Provided'), ('Competitive Schedule Buy', 'Competitive Schedule Buy'), ('Fair Opportunity', 'Fair Opportunity'), ('Follow On to Competed Action (FAR 6.302-1)', 'Follow On to Competed Action (FAR 6.302-1)'), ('Follow On to Competed Action', 'Follow On to Competed Action'), ('Full and Open after exclusion of sources (competitive small business set-asides, competitive 8a)', 'Full and Open after exclusion of sources (competitive small business set-asides, competitive 8a)'), ('Full and Open Competition Unrestricted', 'Full and Open Competition Unrestricted'), ('Full and Open Competition', 'Full and Open Competition'), ('Limited Sources FSS Order', 'Limited Sources FSS Order'), ('Limited Sources', 'Limited Sources'), ('Non-Competitive Delivery Order', 'Non-Competitive Delivery Order'), ('Not Available for Competition (e.g., 8a sole source, HUBZone & SDVOSB sole source, Ability One, all > SAT)', 'Not Available for Competition (e.g., 8a sole source, HUBZone & SDVOSB sole source, Ability One, all > SAT)'), ('Not Competed (e.g., sole source, urgency, etc., all > SAT)', 'Not Competed (e.g., sole source, urgency, etc., all > SAT)'), ('Not Competed under SAP (e.g., Urgent, Sole source, Logical Follow-On, 8a, HUBZone & SDVOSB sole source, all < SAT)', 'Not Competed under SAP (e.g., Urgent, Sole source, Logical Follow-On, 8a, HUBZone & SDVOSB sole source, all < SAT)'), ('Partial Small Business Set-Aside', 'Partial Small Business Set-Aside'), ('Set-Aside', 'Set-Aside'), ('Sole Source', 'Sole Source')], max_length=100, null=True), ), migrations.AlterField( model_name='acquisition', name='contract_type', field=models.CharField(blank=True, choices=[('Cost No Fee', 'Cost No Fee'), ('Cost Plus Award Fee', 'Cost Plus Award Fee'), ('Cost Plus Fixed Fee', 'Cost Plus Fixed Fee'), ('Cost Plus Incentive Fee', 'Cost Plus Incentive Fee'), ('Cost Sharing', 'Cost Sharing'), ('Fixed Price Award Fee', 'Fixed Price Award Fee'), ('Fixed Price Incentive', 'Fixed Price Incentive'), ('Fixed Price Labor Hours', 'Fixed Price Labor Hours'), ('Fixed Price Level of Effort', 'Fixed Price Level of Effort'), ('Fixed Price Time and Materials', 'Fixed Price Time and Materials'), ('Fixed Price with Economic Price Adjustment', 'Fixed Price with Economic Price Adjustment'), ('Fixed Price', 'Fixed Price'), ('Interagency Agreement', 'Interagency Agreement'), ('Labor Hours and Time and Materials', 'Labor Hours and Time and Materials'), ('Labor Hours', 'Labor Hours'), ('Order Dependent', 'Order Dependent'), ('Time and Materials', 'Time and Materials')], max_length=100, null=True), ), migrations.AlterField( model_name='acquisition', name='procurement_method', field=models.CharField(blank=True, choices=[('Ability One', 'Ability One'), ('Basic Ordering Agreement', 'Basic Ordering Agreement'), ('Blanket Purchase Agreement-BPA', 'Blanket Purchase Agreement-BPA'), ('BPA Call', 'BPA Call'), ('Call Order under GSA Schedules BPA', 'Call Order under GSA Schedules BPA'), ('Commercial Item Contract', 'Commercial Item Contract'), ('Contract modification', 'Contract modification'), ('Contract', 'Contract'), ('Definitive Contract other than IDV', 'Definitive Contract other than IDV'), ('Definitive Contract', 'Definitive Contract'), ('Government-wide Agency Contract-GWAC', 'Government-wide Agency Contract-GWAC'), ('GSA Schedule Contract', 'GSA Schedule Contract'), ('GSA Schedule', 'GSA Schedule'), ('GSA Schedules Program BPA', 'GSA Schedules Program BPA'), ('Indefinite Delivery Indefinite Quantity-IDIQ', 'Indefinite Delivery Indefinite Quantity-IDIQ'), ('Indefinite Delivery Vehicle (IDV)', 'Indefinite Delivery Vehicle (IDV)'), ('Indefinite Delivery Vehicle Base Contract', 'Indefinite Delivery Vehicle Base Contract'), ('Multi-Agency Contract', 'Multi-Agency Contract'), ('Negotiated', 'Negotiated'), ('Order under GSA Federal Supply Schedules Program', 'Order under GSA Federal Supply Schedules Program'), ('Order under GSA Schedules Program BPA', 'Order under GSA Schedules Program BPA'), ('Order under GSA Schedules Program', 'Order under GSA Schedules Program'), ('Order under IDV', 'Order under IDV'), ('Purchase Order', 'Purchase Order'), ('Sealed Bid', 'Sealed Bid')], max_length=100, null=True), ), ]

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4623fc8082a8955099775873e9b30efb4c7473c2

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py

Python

tests/geometry.py

Pithikos/python-rectangles

3dcee1786b876117674c0a1371b0a0f1d5fc313f

[ "MIT" ]

25

2015-05-29T21:12:00.000Z

2022-02-16T06:29:29.000Z

tests/geometry.py

Pithikos/python-rectangles

3dcee1786b876117674c0a1371b0a0f1d5fc313f

[ "MIT" ]

3

2016-11-22T12:42:36.000Z

2017-05-22T10:26:23.000Z

tests/geometry.py

Pithikos/python-rectangles

3dcee1786b876117674c0a1371b0a0f1d5fc313f

[ "MIT" ]

9

2018-03-07T16:44:30.000Z

2021-01-13T00:26:53.000Z

import sys, os if os.getcwd().endswith('python-rectangles'): sys.path.insert(0, os.path.abspath('.')) elif os.getcwd().endswith('python-rectangles/tests'): sys.path.insert(0, os.path.abspath('..')) from geometry import * import math # ---------------------------- Tools ----------------------------- def Line(x1, y1, x2, y2): return Point(x1, y1), Point(x2, y2) # ---------------------------- Test Points ----------------------------- p1=Point(10, 20) p2=Point(10, 20) p3=Point(20, 30) p4=Point(20, 30) assert p1==p2 assert p1!=p3 assert p2!=p3 # --- Point in rect --- r=Rect(0, 0, 1, 1) assert r.is_point_inside_rect(Point(1, 0)) assert r.is_point_inside_rect(Point(1, 1)) assert not r.is_point_inside_rect(Point(-1, -1)) assert not r.is_point_inside_rect(Point(-1, 0)) assert not r.is_point_inside_rect(Point(-1, 1)) assert not r.is_point_inside_rect(Point(0, -1)) assert r.is_point_inside_rect(Point(0, 0)) assert r.is_point_inside_rect(Point(0, 1)) assert not r.is_point_inside_rect(Point(1, -1)) assert r.is_point_inside_rect(Point(1, 0)) assert r.is_point_inside_rect(Point(1, 1)) assert not r.is_point_inside_rect(Point(2, -1)) assert not r.is_point_inside_rect(Point(2, 0)) assert not r.is_point_inside_rect(Point(2, 1)) assert not r.is_point_inside_rect(Point(-1, -1)) assert not r.is_point_inside_rect(Point(-1, 0)) assert r.is_point_inside_rect(Point(0.5, 0.5)) # --- Distance --- assert Point(0, 0).distance_to_point(Point(0, 1))==1 assert Point(0, 0).distance_to_point(Point(1, 0))==1 assert Point(0, 0).distance_to_point(Point(1, 1))==math.sqrt(2) assert Point(0, 2).distance_to_point(Point(1, 1))==math.sqrt(2) assert Point(0, 0).distance_to_point(Point(20, 20))==math.sqrt(20**2+20**2) assert Point(20, 20).distance_to_point(Point(0, 0))==math.sqrt(20**2+20**2) assert Point(-20, -20).distance_to_point(Point(0, 0))==math.sqrt(20**2+20**2) assert Point(0, 0).distance_to_point(Point(-20, -20))==math.sqrt(20**2+20**2) # --- Area of triangle assert triangle_area_at_points(Point(0, 0), Point(0, 0), Point(0, 0))==0 assert round(triangle_area_at_points(Point(0, 0), Point(1, 1), Point(1, 0)), 2)==round(0.5, 2) assert round(triangle_area_at_points(Point(0, 0), Point(1, 2), Point(1, 0)), 2)==round(1, 2) # --- Point faces edge edge = (Point(0, 0), Point(1, 1)) assert point_faces_edge(edge, Point(0, 2)) assert point_faces_edge(edge, Point(-1, 1)) assert point_faces_edge(edge, Point(0, 1)) assert point_faces_edge(edge, Point(1, -1)) assert point_faces_edge(edge, Point(5, -5)) assert not point_faces_edge(edge, Point(5, -7)) assert not point_faces_edge(edge, Point(0, 3)) edge = (Point(-5, -10), Point(-6, -11)) assert point_faces_edge(edge, Point(-5, -10)) assert point_faces_edge(edge, Point(-6, -11)) assert point_faces_edge(edge, Point(-6, -10)) assert not point_faces_edge(edge, Point(0, 0)) edge = (Point(-5, -100), Point(-5, 100)) assert point_faces_edge(edge, Point(-100, 0)) assert point_faces_edge(edge, Point(-100, 100)) assert not point_faces_edge(edge, Point(-100, 101)) # --- Distance between edge and point edge = (Point(0, 0), Point(1, 1)) assert round(distance_between_edge_and_point(edge, Point(0, 1)), 2)==0.71 assert round(distance_between_edge_and_point(edge, Point(0, 2)), 2)==1.41 assert distance_between_edge_and_point(edge, Point(0, 0))==0 edge = (Point(0, 1), Point(2, 6)) # slope = 2, offset = (y=1) assert distance_between_edge_and_point(edge, Point(0, 1))==0 assert distance_between_edge_and_point(edge, Point(2, 6))==0 edge = (Point(0, 0), Point(1, 2)) # slope = 2, offset = 0 assert round(distance_between_edge_and_point(edge, Point(0, 2)), 2)==0.89 edge = (Point(0.5, 0.5), Point(2.5, 0.5)) assert round(distance_between_edge_and_point(edge, Point(0, 1)), 2)==0.71 assert round(distance_between_edge_and_point(edge, Point(0, 0)), 2)==0.71 assert round(distance_between_edge_and_point(edge, Point(1, 1)), 2)==0.5 assert round(distance_between_edge_and_point(edge, Point(1, 0)), 2)==0.5 # -------------------------- Test properties --------------------------- # Positive numbers r1=Rect(100, 100, 30, 20) assert r1.l_top == Point(100.0, 100.0) assert r1.r_top == Point(130.0, 100.0) assert r1.l_bot == Point(100.0, 120.0) assert r1.r_bot == Point(130.0, 120.0) assert r1.center == Point(100+30/2, 100+20/2) assert r1.width == 30.0 assert r1.height == 20.0 assert r1.is_point_inside_rect(Point(100, 100)) assert r1.is_point_inside_rect(Point(130, 100)) assert r1.is_point_inside_rect(Point(130, 120)) assert r1.is_point_inside_rect(Point(100, 120)) assert not r1.is_point_inside_rect(Point(131, 100)) # ---------------- Test relations with other rectangles ---------------- # --- Alignment r1=Rect(0, 0, 50, 50) r2=Rect(40, 40, 20, 20) r1.align_with_top_edge_of(r2) assert r1.l_top.y==r2.l_top.y assert r1.r_top.y==r2.r_top.y assert r1.l_bot.y==r1.l_top.y+r1.height assert r1.r_bot.y==r1.l_top.y+r1.height r1=Rect(0, 0, 50, 50) r2=Rect(40, 40, 20, 20) r1.align_with_left_edge_of(r2) assert r1.l_top.x==r2.l_top.x assert r1.l_bot.x==r2.l_bot.x assert r1.r_bot.x==r1.l_top.x+r1.width assert r1.r_top.x==r1.l_top.x+r1.width # --- Overlapping r1=Rect(100, 100, 30, 20) r2=Rect(110, 100, 30, 20) # a bit to the right compared to r1 r3=Rect(100, 110, 30, 20) # a bit to the bottom compared to r1 r4=Rect(150, 150, 50, 50) # doesn't overlap at all assert r1.overlaps_with(r2) assert r1.overlaps_with(r3) assert not r1.overlaps_with(r4) # (commutative property) assert r2.overlaps_with(r1) assert r3.overlaps_with(r1) assert not r4.overlaps_with(r1) # --- on x axis r1=Rect(0, 0, 50, 50) r2=Rect(0, 10, 50, 50) r3=Rect(0, 500, 50, 50) r4=Rect(500, 0, 50, 50) assert r1.overlaps_on_x_axis_with(r2) assert r1.overlaps_on_x_axis_with(r3) assert not r1.overlaps_on_x_axis_with(r4) # (commutative property) assert r2.overlaps_on_x_axis_with(r1) assert r3.overlaps_on_x_axis_with(r1) assert not r4.overlaps_on_x_axis_with(r1) # --- y axis r1=Rect(0, 0, 50, 50) r2=Rect(10, 0, 50, 50) r3=Rect(50, 0, 50, 50) r4=Rect(100, 0, 50, 50) r5=Rect( 0, 100, 50, 50) assert r1.overlaps_on_y_axis_with(r2) assert r1.overlaps_on_y_axis_with(r3) assert r1.overlaps_on_y_axis_with(r4) assert not r1.overlaps_on_y_axis_with(r5) # (commutative property) assert r2.overlaps_on_y_axis_with(r1) assert r3.overlaps_on_y_axis_with(r1) assert r4.overlaps_on_y_axis_with(r1) assert not r5.overlaps_on_y_axis_with(r1) # other cases rect1 = Rect(1, 0, 1, 1) rect2 = Rect(0, 0, 6, 1) assert rect1.overlaps_with(rect2) # EDGE OVERLAPPING # edge overlap on x axis e1 = Line(0, 0, 100, 0) assert lines_overlap_on_x_axis(e1, Line(50, 0, 150, 0)) assert lines_overlap_on_x_axis(Line(50, 0, 150, 0), e1) assert lines_overlap_on_x_axis(Line(50, 0, 60, 0), e1) assert lines_overlap_on_x_axis(e1, Line(50, 0, 60, 0)) assert lines_overlap_on_x_axis(e1, Line(50, 100, 150, 200)) assert lines_overlap_on_x_axis(Line(50, -190, 150, -200), e1) assert not lines_overlap_on_x_axis(e1, Line(-1, 0, -100, 0)) # edge overlap on y axis e1 = Line(0, 0, 0, 100) assert lines_overlap_on_y_axis(e1, Line(0, 50, 0, 150)) assert lines_overlap_on_y_axis(Line(0, 50, 0, 150), e1) assert lines_overlap_on_y_axis(e1, Line(0, 50, 0, 60)) assert lines_overlap_on_y_axis(Line(0, 50, 0, 60), e1) assert not lines_overlap_on_y_axis(e1, Line(0, 101, 0, 110)) assert not lines_overlap_on_y_axis(e1, Line(0, -1, 0, -10)) # edge intersect detection e1 = Line(0, 0, 1, 1) assert lines_intersect(e1, Line(0, 0, 0, 1)) assert lines_intersect(e1, Line(0, 0, 1, 0)) assert lines_intersect(e1, Line(0, 0, 1, 1)) assert lines_intersect(e1, Line(1, 1, 1, 1)) assert lines_intersect(e1, Line(2, 2, 1, 1)) assert not lines_intersect(e1, Line(1.1, 1.1, 1.2, 1.2)) assert lines_intersect(e1, Line(1, 0, 0, 1)) assert not lines_intersect(e1, Line(-0.1, -0.1, -1, 1)) # more complex cases r1 = Rect(0, 5, 50, 50) r2 = Rect(35, 0, 1, 1) assert r1.overlaps_on_x_axis_with(r2) assert not r1.overlaps_with(r2) assert round(r1.distance_to_rect(r2)) == 4.0 # --- Distance between rectangles w, h = 1, 1 # positives r1=Rect( 0, 0, w, h) r2=Rect( 1, 0, w, h) r3=Rect( 2, 0, w, h) r4=Rect( 0, 1, w, h) r5=Rect( 1, 1, w, h) r6=Rect( 2, 1, w, h) r7=Rect( 0, 2, w, h) r8=Rect( 1, 2, w, h) r9=Rect( 2, 2, w, h) r0=Rect( 0.5, 0.5, w, h) assert r1.distance_to_rect(r2)==0.0 assert r1.distance_to_rect(r3)==1.0 assert r1.distance_to_rect(r4)==0.0 assert r1.distance_to_rect(r5)==0.0 assert r1.distance_to_rect(r6)==1.0 assert r1.distance_to_rect(r7)==1.0 assert r1.distance_to_rect(r8)==1.0 assert round(r1.distance_to_rect(r9), 2)==1.41 assert r1.distance_to_rect(r0)==0 #negative x r1=Rect( 0, 0, w, h) r2=Rect(-1, 0, w, h) r3=Rect(-2, 0, w, h) r4=Rect( 0, 1, w, h) r5=Rect(-1, 1, w, h) r6=Rect(-2, 1, w, h) r7=Rect( 0, 2, w, h) r8=Rect(-1, 2, w, h) r9=Rect(-2, 2, w, h) r0=Rect(-0.5, 0.5, w, h) assert r1.distance_to_rect(r2)==0.0 assert r1.distance_to_rect(r3)==1.0 assert r1.distance_to_rect(r4)==0.0 assert r1.distance_to_rect(r5)==0.0 assert r1.distance_to_rect(r6)==1.0 assert r1.distance_to_rect(r7)==1.0 assert r1.distance_to_rect(r8)==1.0 assert round(r1.distance_to_rect(r9), 2)==1.41 assert r1.distance_to_rect(r0)==0 # negative y r1=Rect( 0, 0, w, h) r2=Rect( 1, 0, w, h) r3=Rect( 2, 0, w, h) r4=Rect( 0,-1, w, h) r5=Rect( 1,-1, w, h) r6=Rect( 2,-1, w, h) r7=Rect( 0,-2, w, h) r8=Rect( 1,-2, w, h) r9=Rect( 2,-2, w, h) r0=Rect( 0.5,-0.5, w, h) assert r1.distance_to_rect(r2)==0.0 assert r1.distance_to_rect(r3)==1.0 assert r1.distance_to_rect(r4)==0.0 assert r1.distance_to_rect(r5)==0.0 assert r1.distance_to_rect(r6)==1.0 assert r1.distance_to_rect(r7)==1.0 assert r1.distance_to_rect(r8)==1.0 assert round(r1.distance_to_rect(r9), 2)==1.41 assert r1.distance_to_rect(r0)==0 #negative x and y r1=Rect( 0, 0, w, h) r2=Rect(-1, 0, w, h) r3=Rect(-2, 0, w, h) r4=Rect( 0,-1, w, h) r5=Rect(-1,-1, w, h) r6=Rect(-2,-1, w, h) r7=Rect( 0,-2, w, h) r8=Rect(-1,-2, w, h) r9=Rect(-2,-2, w, h) r0=Rect(-0.5,-0.5, w, h) assert r1.distance_to_rect(r2)==0.0 assert r1.distance_to_rect(r3)==1.0 assert r1.distance_to_rect(r4)==0.0 assert r1.distance_to_rect(r5)==0.0 assert r1.distance_to_rect(r6)==1.0 assert r1.distance_to_rect(r7)==1.0 assert r1.distance_to_rect(r8)==1.0 assert round(r1.distance_to_rect(r9), 2)==1.41 assert r1.distance_to_rect(r0)==0 # overlap r1=Rect( 0, 0, 50, 50) r2=Rect( 10, 10, 50, 50) assert r1.distance_to_rect(r2)==0 # overlap on x axis r1=Rect( 0, 0, 50, 50) # __ r2=Rect( 0, 60, 50, 50) # __ assert round(r1.distance_to_rect(r2), 2)==10 r1=Rect( 0, 0, 50, 50) # __ r2=Rect( 10, 60, 50, 50) # __ assert round(r1.distance_to_rect(r2), 2)==10 r1=Rect( 10, 60, 50, 50) # __ r2=Rect( 0, 0, 50, 50) # __ assert round(r1.distance_to_rect(r2), 2)==10 # overlap on y axis r1=Rect( 0, 0, 50, 50) # || r2=Rect( 60, 0, 50, 50) assert round(r1.distance_to_rect(r2), 2)==10 r1=Rect( 0, 0, 50, 50) # |. r2=Rect( 60, 10, 50, 50) # ' assert round(r1.distance_to_rect(r2), 2)==10 r1=Rect( 0, 20, 50, 50) # . r2=Rect( 60, 0, 50, 50) # |' assert round(r1.distance_to_rect(r2), 2)==10 # ---------------------------------------------------------------------- print("No errors")

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4627c1074de5ba034558d3930e787f37d27fd104

83

py

Python

cntm_backend/cntm/apps.py

dzimmerer/cntm2018

c61987d882a53cc9f428e49f7d91dd0a1413d2e5

[ "Apache-2.0" ]

2

2018-02-08T21:45:52.000Z

2018-03-19T14:45:01.000Z

cntm_backend/cntm/apps.py

dzimmerer/cntm2018

c61987d882a53cc9f428e49f7d91dd0a1413d2e5

[ "Apache-2.0" ]

null

cntm_backend/cntm/apps.py

dzimmerer/cntm2018

c61987d882a53cc9f428e49f7d91dd0a1413d2e5

[ "Apache-2.0" ]

null

from django.apps import AppConfig class CntmConfig(AppConfig): name = 'cntm'

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462aa5c3346a5f2bd03cfb570251e723c51a3093

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py

Python

dependencies/extrae/src/others/pyextrae/profile/__init__.py

TANGO-Project/compss-tango

d9e007b6fe4f8337d4f267f95f383d8962602ab8

[ "Apache-2.0" ]

3

2018-03-05T14:52:22.000Z

2019-02-08T09:58:24.000Z

dependencies/extrae/src/others/pyextrae/profile/__init__.py

TANGO-Project/compss-tango

d9e007b6fe4f8337d4f267f95f383d8962602ab8

[ "Apache-2.0" ]

1

2019-11-13T14:30:21.000Z

dependencies/extrae/src/others/pyextrae/profile/__init__.py

TANGO-Project/compss-tango

d9e007b6fe4f8337d4f267f95f383d8962602ab8

[ "Apache-2.0" ]

null

from pyextrae.common.extrae import * startProfiling()

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4

46823f6e46ef7fe88a8c8762e9b0d3b640fbb629

274

py

Python

tests/__init__.py

questionlp/libwwdtm

02b667cefd6c3bf971dc626110cd80fd32e61096

[ "Apache-2.0" ]

2

2019-07-24T20:06:48.000Z

2019-11-13T04:12:34.000Z

tests/__init__.py

questionlp/libwwdtm

02b667cefd6c3bf971dc626110cd80fd32e61096

[ "Apache-2.0" ]

1

2021-04-20T18:45:49.000Z

tests/__init__.py

questionlp/libwwdtm

02b667cefd6c3bf971dc626110cd80fd32e61096

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2018-2019 Linh Pham # wwdtm is relased under the terms of the Apache License 2.0 """Explicitly listing all modules in this package""" from tests import test_guest, test_host, test_location, test_panelist, test_scorekeeper, test_show

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6

99

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1

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4

46a38efa5d90121cc3e5a1b4b3c935b5c10e04a0

50

py

Python

setup.py

Murabei-OpenSource-Codes/pybats-detection

a5c7ad4bd4bd5be6d25ebb9665cf3637d21ca831

[ "Apache-2.0" ]

1

2022-01-19T01:08:18.000Z

setup.py

Murabei-OpenSource-Codes/pybats-detection

a5c7ad4bd4bd5be6d25ebb9665cf3637d21ca831

[ "Apache-2.0" ]

null

setup.py

Murabei-OpenSource-Codes/pybats-detection

a5c7ad4bd4bd5be6d25ebb9665cf3637d21ca831

[ "Apache-2.0" ]

null

"""Setup.""" import setuptools setuptools.setup()

12.5

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46aac7f635101386e3b4e9f6808ea2d46aad7d65

216

py

Python

terra_sdk/client/lcd/__init__.py

fabio-nukui/terra.py

adee2e1abf41a05a1c39d52b664bd7cf7c9bc975

[ "MIT" ]

66

2021-10-21T23:29:38.000Z

2022-03-30T15:58:13.000Z

terra_sdk/client/lcd/__init__.py

fabio-nukui/terra.py

adee2e1abf41a05a1c39d52b664bd7cf7c9bc975

[ "MIT" ]

50

2021-10-19T06:11:56.000Z

2022-03-31T17:06:57.000Z

terra_sdk/client/lcd/__init__.py

fabio-nukui/terra.py

adee2e1abf41a05a1c39d52b664bd7cf7c9bc975

[ "MIT" ]

39

2021-11-07T17:28:31.000Z

2022-03-31T15:03:57.000Z

from .lcdclient import AsyncLCDClient, LCDClient from .params import PaginationOptions from .wallet import AsyncWallet, Wallet __all__ = ["AsyncLCDClient", "LCDClient", "AsyncWallet", "Wallet", "PaginationOptions"]

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null

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d3c865164eedb320086fc6d02fe41ee6b9f04a86

101

py

Python

dummyauthenticator/__init__.py

Naba7/dummyauthenticator

4d2c39fc99a5665691e5d5dbc6156e5f29151e3e

[ "BSD-3-Clause" ]

18

2019-03-22T10:50:42.000Z

2021-12-10T03:46:24.000Z

dummyauthenticator/__init__.py

Naba7/dummyauthenticator

4d2c39fc99a5665691e5d5dbc6156e5f29151e3e

[ "BSD-3-Clause" ]

7

2018-10-08T07:46:40.000Z

2021-02-12T08:19:56.000Z

dummyauthenticator/__init__.py

Naba7/dummyauthenticator

4d2c39fc99a5665691e5d5dbc6156e5f29151e3e

[ "BSD-3-Clause" ]

8

2018-12-13T08:30:54.000Z

2021-03-15T07:27:23.000Z

from dummyauthenticator.dummyauthenticator import DummyAuthenticator __all__ = [DummyAuthenticator]

25.25

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d3e328f383f79dcadab88566ca65a72bcb3a7ede

201

py

Python

tripadvisor/apps.py

baajarmeh/tripadvisor-scraper

e5dd7bf0864e4f87ff909e57e1ed531eeb30f9dd

[ "Apache-2.0" ]

7

2018-06-26T14:02:32.000Z

2022-01-14T01:42:19.000Z

tripadvisor/apps.py

baajarmeh/tripadvisor-scraper

e5dd7bf0864e4f87ff909e57e1ed531eeb30f9dd

[ "Apache-2.0" ]

null

tripadvisor/apps.py

baajarmeh/tripadvisor-scraper

e5dd7bf0864e4f87ff909e57e1ed531eeb30f9dd

[ "Apache-2.0" ]

1

2020-03-27T15:48:11.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.apps import AppConfig from suit.apps import DjangoSuitConfig class TripadvisorConfig(AppConfig): name = 'tripadvisor'

20.1

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201

9

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4

d3fa1d7c1de446e1123a9f727600d6971b849d3c

94

py

Python

pixiv_crawler/__init__.py

Akaisorani/pixiv-crawl

b661109b631e9bb0462f7219c39243cd40afbe6d

[ "MIT" ]

40

2017-04-06T14:20:24.000Z

2021-10-31T10:09:13.000Z

pixiv_crawler/__init__.py

Akaisorani/pixiv-crawl

b661109b631e9bb0462f7219c39243cd40afbe6d

[ "MIT" ]

6

2018-11-20T14:41:44.000Z

2020-08-03T07:58:14.000Z

pixiv_crawler/__init__.py

Akaisorani/pixiv-crawl

b661109b631e9bb0462f7219c39243cd40afbe6d

[ "MIT" ]

7

2018-04-15T06:03:25.000Z

2021-05-25T19:03:42.000Z

name = "pixiv_crawler" __author__ = "Akaisora" from pixiv_crawler.scraper_manga import *

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31095f2f12ef1570530e7745cf913677450025bc

17

py

Python

subscriptions/__version__.py

primal100/stripe-subscriptions

05b3c9a1253cf09e7ef17ef4c2ed872e16812641

[ "MIT" ]

null

subscriptions/__version__.py

primal100/stripe-subscriptions

05b3c9a1253cf09e7ef17ef4c2ed872e16812641

[ "MIT" ]

null

subscriptions/__version__.py

primal100/stripe-subscriptions

05b3c9a1253cf09e7ef17ef4c2ed872e16812641

[ "MIT" ]

null

version = "0.5.3"

17

0.588235

4

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null

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1

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null

0

4

31125bbb21aa7c647603e26b14a5f723059b0245

15,252

py

Python

blender/2.79/scripts/addons/space_view3d_brush_menus/texture_menu.py

uzairakbar/bpy2.79

3a3e0004ac6783c4e4b89d939e4432de99026a85

[ "MIT" ]

2

2019-11-27T09:05:42.000Z

2020-02-20T01:25:23.000Z

space_view3d_brush_menus/texture_menu.py

1-MillionParanoidTterabytes/blender-addons-master

acc8fc23a38e6e89099c3e5079bea31ce85da06a

[ "Unlicense" ]

null

space_view3d_brush_menus/texture_menu.py

1-MillionParanoidTterabytes/blender-addons-master

acc8fc23a38e6e89099c3e5079bea31ce85da06a

[ "Unlicense" ]

4

2020-02-19T20:02:26.000Z

2022-02-11T18:47:56.000Z

# gpl author: Ryan Inch (Imaginer) import bpy from bpy.types import Menu from . import utils_core class TextureMenu(Menu): bl_label = "Texture Options" bl_idname = "VIEW3D_MT_sv3_texture_menu" @classmethod def poll(self, context): return utils_core.get_mode() in ( 'SCULPT', 'VERTEX_PAINT', 'TEXTURE_PAINT' ) def draw(self, context): layout = self.layout if utils_core.get_mode() == 'SCULPT': self.sculpt(layout, context) elif utils_core.get_mode() == 'VERTEX_PAINT': self.vertpaint(layout, context) else: self.texpaint(layout, context) def sculpt(self, layout, context): has_brush = utils_core.get_brush_link(context, types="brush") tex_slot = has_brush.texture_slot if has_brush else None # Menus layout.row().menu(Textures.bl_idname) layout.row().menu(TextureMapMode.bl_idname) layout.row().separator() # Checkboxes if tex_slot: if tex_slot.map_mode != '3D': if tex_slot.map_mode in ('RANDOM', 'VIEW_PLANE', 'AREA_PLANE'): layout.row().prop(tex_slot, "use_rake", toggle=True) layout.row().prop(tex_slot, "use_random", toggle=True) # Sliders layout.row().prop(tex_slot, "angle", text=utils_core.PIW + "Angle", slider=True) if tex_slot.tex_paint_map_mode in ('RANDOM', 'VIEW_PLANE') and tex_slot.use_random: layout.row().prop(tex_slot, "random_angle", text=utils_core.PIW + "Random Angle", slider=True) # Operator if tex_slot.tex_paint_map_mode == 'STENCIL': if has_brush.texture and has_brush.texture.type == 'IMAGE': layout.row().operator("brush.stencil_fit_image_aspect") layout.row().operator("brush.stencil_reset_transform") else: layout.row().label("No Texture Slot available", icon="INFO") def vertpaint(self, layout, context): has_brush = utils_core.get_brush_link(context, types="brush") tex_slot = has_brush.texture_slot if has_brush else None # Menus layout.row().menu(Textures.bl_idname) layout.row().menu(TextureMapMode.bl_idname) # Checkboxes if tex_slot: if tex_slot.tex_paint_map_mode != '3D': if tex_slot.tex_paint_map_mode in ('RANDOM', 'VIEW_PLANE'): layout.row().prop(tex_slot, "use_rake", toggle=True) layout.row().prop(tex_slot, "use_random", toggle=True) # Sliders layout.row().prop(tex_slot, "angle", text=utils_core.PIW + "Angle", slider=True) if tex_slot.tex_paint_map_mode in ('RANDOM', 'VIEW_PLANE') and tex_slot.use_random: layout.row().prop(tex_slot, "random_angle", text=utils_core.PIW + "Random Angle", slider=True) # Operator if tex_slot.tex_paint_map_mode == 'STENCIL': if has_brush.texture and has_brush.texture.type == 'IMAGE': layout.row().operator("brush.stencil_fit_image_aspect") layout.row().operator("brush.stencil_reset_transform") else: layout.row().label("No Texture Slot available", icon="INFO") def texpaint(self, layout, context): has_brush = utils_core.get_brush_link(context, types="brush") tex_slot = has_brush.texture_slot if has_brush else None mask_tex_slot = has_brush.mask_texture_slot if has_brush else None # Texture Section layout.row().label(text="Texture", icon='TEXTURE') # Menus layout.row().menu(Textures.bl_idname) layout.row().menu(TextureMapMode.bl_idname) # Checkboxes if tex_slot: if tex_slot.tex_paint_map_mode != '3D': if tex_slot.tex_paint_map_mode in ('RANDOM', 'VIEW_PLANE'): layout.row().prop(tex_slot, "use_rake", toggle=True) layout.row().prop(tex_slot, "use_random", toggle=True) # Sliders layout.row().prop(tex_slot, "angle", text=utils_core.PIW + "Angle", slider=True) if tex_slot.tex_paint_map_mode in ('RANDOM', 'VIEW_PLANE') and tex_slot.use_random: layout.row().prop(tex_slot, "random_angle", text=utils_core.PIW + "Random Angle", slider=True) # Operator if tex_slot.tex_paint_map_mode == 'STENCIL': if has_brush.texture and has_brush.texture.type == 'IMAGE': layout.row().operator("brush.stencil_fit_image_aspect") layout.row().operator("brush.stencil_reset_transform") else: layout.row().label("No Texture Slot available", icon="INFO") layout.row().separator() # Texture Mask Section layout.row().label(text="Texture Mask", icon='MOD_MASK') # Menus layout.row().menu(MaskTextures.bl_idname) layout.row().menu(MaskMapMode.bl_idname) layout.row().menu(MaskPressureModeMenu.bl_idname) # Checkboxes if mask_tex_slot: if mask_tex_slot.mask_map_mode in ('RANDOM', 'VIEW_PLANE'): layout.row().prop(mask_tex_slot, "use_rake", toggle=True) layout.row().prop(mask_tex_slot, "use_random", toggle=True) # Sliders layout.row().prop(mask_tex_slot, "angle", text=utils_core.PIW + "Angle", icon_value=5, slider=True) if mask_tex_slot.mask_map_mode in ('RANDOM', 'VIEW_PLANE') and mask_tex_slot.use_random: layout.row().prop(mask_tex_slot, "random_angle", text=utils_core.PIW + "Random Angle", slider=True) # Operator if mask_tex_slot.mask_map_mode == 'STENCIL': if has_brush.mask_texture and has_brush.mask_texture.type == 'IMAGE': layout.row().operator("brush.stencil_fit_image_aspect") prop = layout.row().operator("brush.stencil_reset_transform") prop.mask = True else: layout.row().label("Mask Texture not available", icon="INFO") class Textures(Menu): bl_label = "Brush Texture" bl_idname = "VIEW3D_MT_sv3_texture_list" def init(self): if utils_core.get_mode() == 'SCULPT': datapath = "tool_settings.sculpt.brush.texture" elif utils_core.get_mode() == 'VERTEX_PAINT': datapath = "tool_settings.vertex_paint.brush.texture" elif utils_core.get_mode() == 'TEXTURE_PAINT': datapath = "tool_settings.image_paint.brush.texture" else: datapath = "" return datapath def draw(self, context): datapath = self.init() has_brush = utils_core.get_brush_link(context, types="brush") current_texture = eval("bpy.context.{}".format(datapath)) if \ has_brush else None layout = self.layout # get the current texture's name if current_texture: current_texture = current_texture.name layout.row().label(text="Brush Texture") layout.row().separator() # add an item to set the texture to None utils_core.menuprop(layout.row(), "None", "None", datapath, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON', custom_disable_exp=(None, current_texture), path=True) # add the menu items for item in bpy.data.textures: utils_core.menuprop(layout.row(), item.name, 'bpy.data.textures["%s"]' % item.name, datapath, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON', custom_disable_exp=(item.name, current_texture), path=True) class TextureMapMode(Menu): bl_label = "Brush Mapping" bl_idname = "VIEW3D_MT_sv3_texture_map_mode" def draw(self, context): layout = self.layout has_brush = utils_core.get_brush_link(context, types="brush") layout.row().label(text="Brush Mapping") layout.row().separator() if has_brush: if utils_core.get_mode() == 'SCULPT': path = "tool_settings.sculpt.brush.texture_slot.map_mode" # add the menu items for item in has_brush. \ texture_slot.bl_rna.properties['map_mode'].enum_items: utils_core.menuprop( layout.row(), item.name, item.identifier, path, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON' ) elif utils_core.get_mode() == 'VERTEX_PAINT': path = "tool_settings.vertex_paint.brush.texture_slot.tex_paint_map_mode" # add the menu items for item in has_brush. \ texture_slot.bl_rna.properties['tex_paint_map_mode'].enum_items: utils_core.menuprop( layout.row(), item.name, item.identifier, path, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON' ) else: path = "tool_settings.image_paint.brush.texture_slot.tex_paint_map_mode" # add the menu items for item in has_brush. \ texture_slot.bl_rna.properties['tex_paint_map_mode'].enum_items: utils_core.menuprop( layout.row(), item.name, item.identifier, path, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON' ) else: layout.row().label("No brushes available", icon="INFO") class MaskTextures(Menu): bl_label = "Mask Texture" bl_idname = "VIEW3D_MT_sv3_mask_texture_list" def draw(self, context): layout = self.layout datapath = "tool_settings.image_paint.brush.mask_texture" has_brush = utils_core.get_brush_link(context, types="brush") current_texture = eval("bpy.context.{}".format(datapath)) if \ has_brush else None layout.row().label(text="Mask Texture") layout.row().separator() if has_brush: # get the current texture's name if current_texture: current_texture = current_texture.name # add an item to set the texture to None utils_core.menuprop( layout.row(), "None", "None", datapath, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON', custom_disable_exp=(None, current_texture), path=True ) # add the menu items for item in bpy.data.textures: utils_core.menuprop( layout.row(), item.name, 'bpy.data.textures["%s"]' % item.name, datapath, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON', custom_disable_exp=(item.name, current_texture), path=True ) else: layout.row().label("No brushes available", icon="INFO") class MaskMapMode(Menu): bl_label = "Mask Mapping" bl_idname = "VIEW3D_MT_sv3_mask_map_mode" def draw(self, context): layout = self.layout path = "tool_settings.image_paint.brush.mask_texture_slot.mask_map_mode" has_brush = utils_core.get_brush_link(context, types="brush") layout.row().label(text="Mask Mapping") layout.row().separator() if has_brush: items = has_brush. \ mask_texture_slot.bl_rna.properties['mask_map_mode'].enum_items # add the menu items for item in items: utils_core.menuprop( layout.row(), item.name, item.identifier, path, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON' ) else: layout.row().label("No brushes available", icon="INFO") class TextureAngleSource(Menu): bl_label = "Texture Angle Source" bl_idname = "VIEW3D_MT_sv3_texture_angle_source" def draw(self, context): layout = self.layout has_brush = utils_core.get_brush_link(context, types="brush") if has_brush: if utils_core.get_mode() == 'SCULPT': items = has_brush. \ bl_rna.properties['texture_angle_source_random'].enum_items path = "tool_settings.sculpt.brush.texture_angle_source_random" elif utils_core.get_mode() == 'VERTEX_PAINT': items = has_brush. \ bl_rna.properties['texture_angle_source_random'].enum_items path = "tool_settings.vertex_paint.brush.texture_angle_source_random" else: items = has_brush. \ bl_rna.properties['texture_angle_source_random'].enum_items path = "tool_settings.image_paint.brush.texture_angle_source_random" # add the menu items for item in items: utils_core.menuprop( layout.row(), item[0], item[1], path, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON' ) else: layout.row().label("No brushes available", icon="INFO") class MaskPressureModeMenu(Menu): bl_label = "Mask Pressure Mode" bl_idname = "VIEW3D_MT_sv3_mask_pressure_mode_menu" def draw(self, context): layout = self.layout path = "tool_settings.image_paint.brush.use_pressure_masking" layout.row().label(text="Mask Pressure Mode") layout.row().separator() # add the menu items for item in context.tool_settings.image_paint.brush. \ bl_rna.properties['use_pressure_masking'].enum_items: utils_core.menuprop( layout.row(), item.name, item.identifier, path, icon='RADIOBUT_OFF', disable=True, disable_icon='RADIOBUT_ON' )

38.321608

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0.68015

0.676018

0.659487

0

0.002003

0.345463

15,252

397

101

38.418136

0.797856

0.032586

0

0.647687

0

0.171104

0.081103

0

1

0.042705

false

0

0.010676

0.003559

0.135231

0

null

0

1

0

1

0

null

0

4

31139178ff5be5fe8dd99e7fdd7cdf1b6de74c36

9,974

py

Python

util/unauth/weakPassScan.py

Shinpachi8/SubDomainsResultDeal

0303d95bd96b8f1e696c6534f686f30809763970

[ "Apache-2.0" ]

1

2020-04-23T08:01:36.000Z

util/unauth/weakPassScan.py

Shinpachi8/SubDomainsResultDeal

0303d95bd96b8f1e696c6534f686f30809763970

[ "Apache-2.0" ]

null

util/unauth/weakPassScan.py

Shinpachi8/SubDomainsResultDeal

0303d95bd96b8f1e696c6534f686f30809763970

[ "Apache-2.0" ]

5

2017-09-24T15:54:03.000Z

2020-05-01T15:33:03.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- 'this script can bruter ftp/ssh/mysql' __author__ = 'reber' import Queue import threading import time import logging import socket from optparse import OptionParser import paramiko from ftplib import FTP import MySQLdb #################公有类################# class CommonFun(object): """docstring for CommonFun""" def __init__(self): super(CommonFun, self).__init__() def set_log(self,lname): logger = logging.getLogger(lname) logger.setLevel(logging.DEBUG) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) def show_log(self, lname, msg): a = logging.getLogger(lname) a.debug(msg) def show_result(self, lname, rlist): if rlist: print "###################################################################" for x in rlist: self.show_log(lname,x) else: print "not found..." #################SSH爆破模块################# class SshBruter(CommonFun): """docstring for SshBruter""" def __init__(self, *args): super(SshBruter, self).__init__() (options,arg) = args self.host = options.host self.userfile = options.userfile self.passfile = options.passfile self.threadnum = options.threadnum self.timeout = options.timeout self.result = [] self.set_log(self.host) self.qlist = Queue.Queue() self.is_exit = False print self.host,self.userfile,self.passfile,self.threadnum def get_queue(self): with open(self.userfile, 'r') as f: ulines = f.readlines() with open(self.passfile, 'r') as f: plines = f.readlines() for name in ulines: for pwd in plines: name = name.strip() pwd = pwd.strip() self.qlist.put(name + ':' + pwd) def thread(self): while not self.qlist.empty(): if not self.is_exit: name,pwd = self.qlist.get().split(':') try: ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) ssh.connect(hostname=self.host,port=22,username=name,password=pwd,timeout=self.timeout) time.sleep(0.05) ssh.close() s = "[OK] %s:%s" % (name,pwd) self.show_log(self.host,s) self.result.append(s) except socket.timeout: self.show_log(self.host,"Timeout...") self.qlist.put(name + ':' + pwd) time.sleep(3) except Exception, e: error = "[Error] %s:%s" % (name,pwd) self.show_log(self.host,error) pass else: break def run(self): self.get_queue() starttime = time.time() threads = [] for x in xrange(1,self.threadnum+1): t = threading.Thread(target=self.thread) threads.append(t) t.setDaemon(True) #主线程完成后不管子线程有没有结束，直接退出 t.start() try: while True: if self.qlist.empty(): break else: time.sleep(1) except KeyboardInterrupt: self.is_exit = True print "Exit the program..." print "Waiting..." time.sleep(5) self.show_result(self.host,self.result) finishetime = time.time() print "Used time: %f" % (finishetime-starttime) #################FTP爆破模块################# class FtpBruter(CommonFun): """docstring for FtpBruter""" def __init__(self, *args): super(FtpBruter, self).__init__() (options,arg) = args self.host = options.host self.userfile = options.userfile self.passfile = options.passfile self.threadnum = options.threadnum self.timeout = options.timeout self.result = [] self.set_log(self.host) self.qlist = Queue.Queue() print self.host,self.userfile,self.passfile,self.threadnum def get_queue(self): with open(self.userfile, 'r') as f: ulines = f.readlines() with open(self.passfile, 'r') as f: plines = f.readlines() for name in ulines: for pwd in plines: name = name.strip() pwd = pwd.strip() self.qlist.put(name + ':' + pwd) def thread(self): while not self.qlist.empty(): name,pwd = self.qlist.get().split(':') try: ftp = FTP() ftp.connect(self.host, 21, self.timeout) ftp.login(name, pwd) time.sleep(0.05) ftp.quit() s = "[OK] %s:%s" % (name,pwd) self.show_log(self.host,s) self.result.append(s) except socket.timeout: self.show_log(self.host,"Timeout...") self.qlist.put(name + ':' + pwd) time.sleep(1) except Exception, e: error = "[Error] %s:%s" % (name,pwd) self.show_log(self.host,error) pass def run(self): self.get_queue() starttime = time.time() threads = [] for x in xrange(1,self.threadnum+1): t = threading.Thread(target=self.thread) threads.append(t) t.setDaemon(True) #主线程完成后不管子线程有没有结束，直接退出 t.start() try: while True: if self.qlist.empty(): break else: time.sleep(1) except KeyboardInterrupt: self.is_exit = True print "Exit the program..." print "Waiting..." time.sleep(5) self.show_result(self.host,self.result) finishetime = time.time() print "Used time: %f" % (finishetime-starttime) #################MySQL爆破模块################# class MysqlBruter(CommonFun): """docstring for MysqlBruter""" def __init__(self, *args): super(MysqlBruter, self).__init__() (options,arg) = args self.host = options.host self.userfile = options.userfile self.passfile = options.passfile self.threadnum = options.threadnum self.timeout = options.timeout self.result = [] self.set_log(self.host) self.qlist = Queue.Queue() print self.host,self.userfile,self.passfile,self.threadnum def get_queue(self): with open(self.userfile, 'r') as f: ulines = f.readlines() with open(self.passfile, 'r') as f: plines = f.readlines() for name in ulines: for pwd in plines: name = name.strip() pwd = pwd.strip() self.qlist.put(name + ':' + pwd) def thread(self): while not self.qlist.empty(): name,pwd = self.qlist.get().split(':') try: conn = MySQLdb.connect(host=self.host, user=name, passwd=pwd, db='mysql', port=3306) if conn: # time.sleep(0.05) conn.close() s = "[OK] %s:%s" % (name,pwd) self.show_log(self.host,s) self.result.append(s) except socket.timeout: self.show_log(self.host,"Timeout") self.qlist.put(name + ':' + pwd) time.sleep(3) except Exception, e: error = "[Error] %s:%s" % (name,pwd) self.show_log(self.host,error) pass def run(self): self.get_queue() starttime = time.time() threads = [] for x in xrange(1,self.threadnum+1): t = threading.Thread(target=self.thread) threads.append(t) t.setDaemon(True) #主线程完成后不管子线程有没有结束，直接退出 t.start() try: while True: if self.qlist.empty(): break else: time.sleep(1) except KeyboardInterrupt: self.is_exit = True print "Exit the program..." print "Waiting..." time.sleep(5) self.show_result(self.host,self.result) finishetime = time.time() print "Used time: %f" % (finishetime-starttime) def main(): parser = OptionParser(usage='Usage: python %prog [options] type') parser.add_option('-i','--host',dest='host',help='target ip') parser.add_option('-o','--timeout',type=int,dest='timeout',default=5,help='timeout') parser.add_option('-t','--thread',type=int,dest='threadnum',default=10,help='threadnum') parser.add_option('-L','--userfile',dest='userfile',default='username.txt',help='userfile') parser.add_option('-P','--passfile',dest='passfile',default='password.txt',help='passfile') (options, args) = parser.parse_args() if not args: parser.print_help() exit() if args[0]=='ssh': if options.host: ssh = SshBruter(options, args) ssh.run() else: parser.print_help() elif args[0]=='ftp': if options.host: ftp = FtpBruter(options, args) ftp.run() else: parser.print_help() elif args[0]=='mysql': if options.host: mysql = MysqlBruter(options, args) mysql.run() else: parser.print_help() else: print "type must be ssh or ftp or mysql" if __name__ == '__main__': main()

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312b726b7e5ecb26482fbfae18fa550d78818cd1

435

py

Python

Python/PlusOneTest.py

TonnyL/Windary

39f85cdedaaf5b85f7ce842ecef975301fc974cf

[ "MIT" ]

205

2017-11-16T08:38:46.000Z

2022-03-06T05:50:03.000Z

Python/PlusOneTest.py

santosh241/Windary

39f85cdedaaf5b85f7ce842ecef975301fc974cf

[ "MIT" ]

3

2018-04-10T10:17:52.000Z

2020-12-11T08:00:09.000Z

Python/PlusOneTest.py

santosh241/Windary

39f85cdedaaf5b85f7ce842ecef975301fc974cf

[ "MIT" ]

28

2018-04-10T06:42:42.000Z

2021-09-14T14:15:39.000Z

from unittest import TestCase from PlusOne import PlusOne class TestPlusOne(TestCase): def test_plusOne(self): po = PlusOne() self.assertEqual(po.plusOne([1]), [2]) self.assertEqual(po.plusOne([9]), [1, 0]) self.assertEqual(po.plusOne([9, 9]), [1, 0, 0]) self.assertEqual(po.plusOne([2, 8, 9, 9, 9]), [2, 9, 0, 0, 0]) self.assertEqual(po.plusOne([2, 8, 8, 9]), [2, 8, 9, 0])

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31404d6a297c546b7e49eff01d6b4e30888bf2a2

149

py

Python

app/config.py

shakedmanes/mandolin-cloud

faf6fe2d583d3d654ff0dc92b3f0389bbd5b7c4d

[ "MIT" ]

3

2019-08-19T09:10:23.000Z

2020-12-02T09:45:50.000Z

app/config.py

shakedmanes/mandolin-cloud

faf6fe2d583d3d654ff0dc92b3f0389bbd5b7c4d

[ "MIT" ]

null

app/config.py

shakedmanes/mandolin-cloud

faf6fe2d583d3d654ff0dc92b3f0389bbd5b7c4d

[ "MIT" ]

null

import os class BaseConfig(object): """Default configuration options for flask""" SITE_NAME = os.environ.get('APP_NAME', 'mandolin-cloud')

21.285714

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19

149

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0.154362

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null

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4

314cb1f81db06f5646dd964ee4351b26d94c250e

3,286

py

Python

wandb/vendor/prompt_toolkit/contrib/regular_languages/__init__.py

dreamflasher/client

c8267f1c6b8b6970172d622bb8fbf7cc773d78b2

[ "MIT" ]

6,989

2017-07-18T06:23:18.000Z

2022-03-31T15:58:36.000Z

wandb/vendor/prompt_toolkit/contrib/regular_languages/__init__.py

dreamflasher/client

c8267f1c6b8b6970172d622bb8fbf7cc773d78b2

[ "MIT" ]

1,978

2017-07-18T09:17:58.000Z

2022-03-31T14:28:43.000Z

wandb/vendor/prompt_toolkit/contrib/regular_languages/__init__.py

dreamflasher/client

c8267f1c6b8b6970172d622bb8fbf7cc773d78b2

[ "MIT" ]

1,228

2017-07-18T09:03:13.000Z

2022-03-29T05:57:40.000Z

r""" Tool for expressing the grammar of an input as a regular language. ================================================================== The grammar for the input of many simple command line interfaces can be expressed by a regular language. Examples are PDB (the Python debugger); a simple (bash-like) shell with "pwd", "cd", "cat" and "ls" commands; arguments that you can pass to an executable; etc. It is possible to use regular expressions for validation and parsing of such a grammar. (More about regular languages: http://en.wikipedia.org/wiki/Regular_language) Example ------- Let's take the pwd/cd/cat/ls example. We want to have a shell that accepts these three commands. "cd" is followed by a quoted directory name and "cat" is followed by a quoted file name. (We allow quotes inside the filename when they're escaped with a backslash.) We could define the grammar using the following regular expression:: grammar = \s* ( pwd | ls | (cd \s+ " ([^"]|\.)+ ") | (cat \s+ " ([^"]|\.)+ ") ) \s* What can we do with this grammar? --------------------------------- - Syntax highlighting: We could use this for instance to give file names different colour. - Parse the result: .. We can extract the file names and commands by using a regular expression with named groups. - Input validation: .. Don't accept anything that does not match this grammar. When combined with a parser, we can also recursively do filename validation (and accept only existing files.) - Autocompletion: .... Each part of the grammar can have its own autocompleter. "cat" has to be completed using file names, while "cd" has to be completed using directory names. How does it work? ----------------- As a user of this library, you have to define the grammar of the input as a regular expression. The parts of this grammar where autocompletion, validation or any other processing is required need to be marked using a regex named group. Like ``(?P<varname>...)`` for instance. When the input is processed for validation (for instance), the regex will execute, the named group is captured, and the validator associated with this named group will test the captured string. There is one tricky bit: Ofter we operate on incomplete input (this is by definition the case for autocompletion) and we have to decide for the cursor position in which possible state the grammar it could be and in which way variables could be matched up to that point. To solve this problem, the compiler takes the original regular expression and translates it into a set of other regular expressions which each match prefixes of strings that would match the first expression. (We translate it into multiple expression, because we want to have each possible state the regex could be in -- in case there are several or-clauses with each different completers.) TODO: some examples of: - How to create a highlighter from this grammar. - How to create a validator from this grammar. - How to create an autocompleter from this grammar. - How to create a parser from this grammar. """ from .compiler import compile

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0

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1

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1

0

4

3169e58d073baf05bd3b85c464514dfcc235a90e

65

py

Python

lambda.py

ricardodarocha/Pyjamas

8b12f3966740655c3a7bb1cad782689973059869

[ "MIT" ]

1

2019-12-14T16:27:30.000Z

lambda.py

ricardodarocha/Pyjamas

8b12f3966740655c3a7bb1cad782689973059869

[ "MIT" ]

null

lambda.py

ricardodarocha/Pyjamas

8b12f3966740655c3a7bb1cad782689973059869

[ "MIT" ]

2

2019-12-14T06:54:38.000Z

2021-11-08T10:31:48.000Z

reajuste = lambda salario, taxareajuste : salario += taxareajuste

65

0.8

6

65

8.666667

0.666667

0.730769

0

0.123077

65

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null

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null

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0

null

1

0

1

0

null

0

1

0

4

316ad913d19aa1e5367045049f8d28a22b8bda37

463

py

Python

core/net_errors.py

nikon-petr/perceptron

40509070e1d5c2407e5778af9bccde1eda284efb

[ "MIT" ]

null

core/net_errors.py

nikon-petr/perceptron

40509070e1d5c2407e5778af9bccde1eda284efb

[ "MIT" ]

null

core/net_errors.py

nikon-petr/perceptron

40509070e1d5c2407e5778af9bccde1eda284efb

[ "MIT" ]

null

class IncorrectInputVectorLength(Exception): pass class NetIsNotInitialized(Exception): pass class IncorrectFactorValue(Exception): pass class NetIsNotCalculated(Exception): pass class IncorrectExpectedOutputVectorLength(Exception): pass class NetConfigIndefined(Exception): pass class NetConfigIncorrect(Exception): pass class JsonFileNotFound(Exception): pass class JsonFileStructureIncorrect(Exception): pass

13.617647

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36

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0.402235

0

0.168467

463

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13.617647

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0

0.5

0

1

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true

0.5

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0.5

0

null

1

0

null

0

1

0

4

31802c55dbfe8b8397bbd035c7ff9bff835d52d0

2,641

py

Python

graphpipe/graphpipefb/IOMetadata.py

LaudateCorpus1/graphpipe-py

eb474b29860d4a0dc713b834586dec527028e688

[ "UPL-1.0" ]

38

2018-08-15T15:56:15.000Z

2020-09-18T09:19:45.000Z

graphpipe/graphpipefb/IOMetadata.py

LaudateCorpus1/graphpipe-py

eb474b29860d4a0dc713b834586dec527028e688

[ "UPL-1.0" ]

3

2018-08-16T04:56:53.000Z

2019-02-21T09:16:36.000Z

graphpipe/graphpipefb/IOMetadata.py

LaudateCorpus1/graphpipe-py

eb474b29860d4a0dc713b834586dec527028e688

[ "UPL-1.0" ]

11

2018-08-16T09:10:05.000Z

2022-02-18T04:45:20.000Z

# automatically generated by the FlatBuffers compiler, do not modify # namespace: graphpipe import flatbuffers class IOMetadata(object): __slots__ = ['_tab'] @classmethod def GetRootAsIOMetadata(cls, buf, offset): n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) x = IOMetadata() x.Init(buf, n + offset) return x # IOMetadata def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) # IOMetadata def Name(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.String(o + self._tab.Pos) return bytes() # IOMetadata def Description(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return self._tab.String(o + self._tab.Pos) return bytes() # IOMetadata def Shape(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8)) return 0 # IOMetadata def ShapeAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o) return 0 # IOMetadata def ShapeLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return self._tab.VectorLen(o) return 0 # IOMetadata def Type(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) return 0 def IOMetadataStart(builder): builder.StartObject(4) def IOMetadataAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) def IOMetadataAddDescription(builder, description): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(description), 0) def IOMetadataAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) def IOMetadataStartShapeVector(builder, numElems): return builder.StartVector(8, numElems, 8) def IOMetadataAddType(builder, type): builder.PrependUint8Slot(3, type, 0) def IOMetadataEnd(builder): return builder.EndObject()

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false

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null

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1

0

1

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null

0

1

0

1

0

4

318586b0ce470220bf289f9c66a976db9e14c909

376

py

Python

Ranmath/MatrixReconstructors/SingleMatrixReconstructor.py

pawel-ta/ranmath

f52a15b10bdb5830a50c43da11fed5f182026587

[ "MIT" ]

null

Ranmath/MatrixReconstructors/SingleMatrixReconstructor.py

pawel-ta/ranmath

f52a15b10bdb5830a50c43da11fed5f182026587

[ "MIT" ]

null

Ranmath/MatrixReconstructors/SingleMatrixReconstructor.py

pawel-ta/ranmath

f52a15b10bdb5830a50c43da11fed5f182026587

[ "MIT" ]

null

from .AbstractReconstructor import AbstractReconstructor import numpy.linalg as la import numpy as np from copy import deepcopy class SingleMatrixReconstructor(AbstractReconstructor): def __init__(self): super().__init__() def reconstruct(self, eigenvectors, eigenvalues): return (eigenvectors @ np.diag(eigenvalues) @ la.inv(eigenvectors)).real

23.5

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376

15

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0

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0.222222

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0.888889

0

null

0

null

0

1

0

1

0

4

3189a7de210069fc59f46d79f74a30607865397b

243

py

Python

plugin.video.iwn/resources/lib/__init__.py

TheWardoctor/wardoctors-repo

893f646d9e27251ffc00ca5f918e4eb859a5c8f0

[ "Apache-2.0" ]

1

2019-03-05T09:38:10.000Z

plugin.video.iwn/resources/lib/__init__.py

TheWardoctor/wardoctors-repo

893f646d9e27251ffc00ca5f918e4eb859a5c8f0

[ "Apache-2.0" ]

null

plugin.video.iwn/resources/lib/__init__.py

TheWardoctor/wardoctors-repo

893f646d9e27251ffc00ca5f918e4eb859a5c8f0

[ "Apache-2.0" ]

1

2021-11-05T20:48:09.000Z

# #!/usr/bin/env python #################################################################################################### # Blank Init File ####################################################################################################

60.75

100

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243

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null

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null

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0

null

0

1

0

4

3195516bba3c002d11bed7aa2adaeb584b55db98

305

py

Python

pyawx/exceptions/__init__.py

irunasroot/pyawx-client

ad2cdae2997d3026774ed89506c5fc5ac15f0002

[ "Apache-2.0" ]

null

pyawx/exceptions/__init__.py

irunasroot/pyawx-client

ad2cdae2997d3026774ed89506c5fc5ac15f0002

[ "Apache-2.0" ]

null

pyawx/exceptions/__init__.py

irunasroot/pyawx-client

ad2cdae2997d3026774ed89506c5fc5ac15f0002

[ "Apache-2.0" ]

null

""" exceptions/__init__.py Comments: Author: Dennis Whitney Email: dennis@runasroot.com Copyright (c) 2021, iRunAsRoot """ class ValueReadOnly(Exception): pass class ValueNotAllowed(Exception): pass class UnauthorizedAccess(Exception): pass class UnknownEndpoint(Exception): pass

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7.193548

0.677419

0.233184

0.242152

0

0.015686

0.163934

305

23

37

13.26087

0.858824

0.377049

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1

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true

0.5

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0.5

0

null

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null

0

1

0

4

31b41fab4a40f5e996c206128d9b5d1175495eba

59,387

py

Python

Leg-UP/models/attacker/aushplus_helper.py

sharanmayank/ShillingAttack

783f135a4fcc709e7ce478c2e6f2e7e6c5ad2ace

[ "MIT" ]

null

Leg-UP/models/attacker/aushplus_helper.py

sharanmayank/ShillingAttack

783f135a4fcc709e7ce478c2e6f2e7e6c5ad2ace

[ "MIT" ]

null

Leg-UP/models/attacker/aushplus_helper.py

sharanmayank/ShillingAttack

783f135a4fcc709e7ce478c2e6f2e7e6c5ad2ace

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Time : 2020/12/3 20:03 # @Author : chensi # @File : tkde.py # @Software : PyCharm # @Desciption : None import random import numpy as np import torch from torch import nn from utils.utils import * from utils.loss import * import higher # tf = None # try: # import tensorflow.compat.v1 as tf # # tf.disable_v2_behavior() # except: # import tensorflow as tf seed = 1234 random.seed(seed) np.random.seed(seed) # tf.set_random_seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) import time import torch.nn.functional as F import math import torch.optim as optim # ============================================================================================= # ============================================================================================= # ============================================================================================= # ============================================================================================= # ============================================================================================= class BaseGenerator(nn.Module): def __init__(self, device, input_dim): super(BaseGenerator, self).__init__() # self.input_dim = input_dim self.device = device """helper_tensor""" self.epsilon = torch.tensor(1e-4).to(self.device) # 计算boundary self.helper_tensor = torch.tensor(2.5).to(device) pass def project(self, fake_tensor): fake_tensor.data = torch.round(fake_tensor) # fake_tensor.data = torch.where(fake_tensor < 1, torch.ones_like(fake_tensor).to(self.device), fake_tensor) fake_tensor.data = torch.where(fake_tensor < 0, torch.zeros_like(fake_tensor).to(self.device), fake_tensor) fake_tensor.data = torch.where(fake_tensor > 5, torch.tensor(5.).to(self.device), fake_tensor) # return fake_tensor def forward(self, input): raise NotImplementedError class BaseDiscretGenerator(BaseGenerator): def __init__(self, device, input_dim): super(BaseDiscretGenerator, self).__init__(device, input_dim) self.min_boundary_value = torch.nn.Parameter(torch.rand([self.input_dim]), requires_grad=True) self.register_parameter("min_boundary_value", self.min_boundary_value) self.interval_lengths = torch.nn.Parameter(torch.rand([self.input_dim, 4]), requires_grad=True) self.register_parameter("interval_lengths", self.interval_lengths) pass def forward(self, input): # fake_tensor = (self.main(input) * self.helper_tensor) + self.helper_tensor # # project # fake_dsct_distribution, fake_dsct_value = self.project(fake_tensor) # return fake_dsct_value raise NotImplementedError def project(self, fake_tensor): Heaviside = HeaviTanh.apply boundary_values = self.get_boundary_values() cnt_ratings = fake_tensor.flatten() iids = np.expand_dims(np.arange(self.input_dim), 0).repeat(fake_tensor.shape[0], axis=0).flatten() boundary_values_per_rating = boundary_values[iids] def _project_helper(ratings, boundary_values_input): def get_target_dst_rating_prob(target_dst_rating, input_cnt_rating, boundary_values, device): # boundary_values = boundary_values.reshape([-1, 4]) # input_cnt_rating = input_cnt_rating.reshape([-1]) rating_prob = torch.ones(input_cnt_rating.shape[0]).to(self.device) for boundary_idx in range(5): """ :param target_dst_rating: r_i_j :param boundary_idx: k :param input_cnt_rating: a_i_j :param boundary_value: b_j_k :return: """ # p_1 = torch.sign(target_dst_rating - boundary_idx - torch.tensor(0.5).to(device)) # p_2 = input_cnt_rating - boundary_values[:, boundary_idx] # rating_prob *= Heaviside(p_1 * p_2, torch.tensor(1.).to(device)) return rating_prob cur_dsct_distribution = [] for rating_dsct in range(6): p = get_target_dst_rating_prob(rating_dsct, ratings, boundary_values_input, self.device) cur_dsct_distribution += [p] dsct_distribution = torch.cat([torch.unsqueeze(p, 1) for p in cur_dsct_distribution], 1) return dsct_distribution fake_dsct_distribution = _project_helper(cnt_ratings, boundary_values_per_rating).reshape( [-1, self.input_dim, 6]) fake_dsct_value = torch.matmul(fake_dsct_distribution, torch.tensor(np.arange(0., 6.)).type(torch.float32).to(self.device)) return fake_dsct_distribution, fake_dsct_value def project_old(self, fake_tensor): boundary_values = self.get_boundary_values() fake_dsct_distribution = [] for iid in range(self.input_dim): cur_dsct_distribution = [] for rating_dsct in range(6): rating_prob = torch.ones(fake_tensor.shape[0]).to(self.device) for boundary_idx in range(5): rating_prob *= self.is_in_interval(rating_dsct, boundary_idx, fake_tensor[:, iid], boundary_values[iid][boundary_idx]) cur_dsct_distribution += [rating_prob] fake_dsct_distribution += [torch.cat([torch.unsqueeze(p, 1) for p in cur_dsct_distribution], 1)] fake_dsct_distribution = torch.cat([torch.unsqueeze(p, 1) for p in fake_dsct_distribution], 1) fake_dsct_value = torch.matmul(fake_dsct_distribution, torch.tensor(np.arange(6.0)).type(torch.float32).to(self.device)) return fake_dsct_distribution, fake_dsct_value def get_boundary_values(self): boundary_values = torch.zeros([self.input_dim, 5]).to(self.device) boundary_values[:, 0] = self.min_boundary_value for i in range(1, 5): cur_interval_length = torch.relu(self.interval_lengths[:, i - 1]) + self.epsilon boundary_values[:, i] = boundary_values[:, i - 1] + cur_interval_length return boundary_values def is_in_interval(self, rating_dsct, boundary_idx, rating_cnt, boundary_value): tensor_aux_0_5 = torch.tensor(0.5).to(self.device) tensor_aux_1 = torch.tensor(1.).to(self.device) Heaviside = HeaviTanh.apply """ :param rating_dsct: r_i_j :param boundary_idx: k :param rating_cnt: a_i_j :param boundary_value: b_j_k :return: """ # p_1 = torch.sign(rating_dsct - boundary_idx - tensor_aux_0_5) # p_2 = rating_cnt - boundary_value # return Heaviside(p_1 * p_2, tensor_aux_1) class RecsysGenerator(BaseGenerator): def __init__(self, device, init_tensor): super(RecsysGenerator, self).__init__(device, init_tensor.shape[1]) """ fake_parameter """ fake_tensor = init_tensor.clone().detach().requires_grad_(True) self.fake_parameter = torch.nn.Parameter(fake_tensor, requires_grad=True) self.register_parameter("fake_tensor", self.fake_parameter) pass def forward(self, input=None): return None, self.project(self.fake_parameter * (input > 0)) class DiscretGenerator_AE(BaseDiscretGenerator): def __init__(self, device, p_dims, q_dims=None): super(DiscretGenerator_AE, self).__init__(device, input_dim=p_dims[0]) self.p_dims = p_dims if q_dims: assert q_dims[0] == p_dims[-1], "In and Out dimensions must equal to each other" assert q_dims[-1] == p_dims[0], "Latent dimension for p- and q- network mismatches." self.q_dims = q_dims else: self.q_dims = p_dims[::-1] self.dims = self.p_dims + self.q_dims[1:] self.layers = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(self.dims[:-1], self.dims[1:])]) # self.drop = nn.Dropout(dropout) self.init_weights() def forward(self, input): h = F.normalize(input) # h = self.drop(h) for i, layer in enumerate(self.layers): h = layer(h) if i != len(self.layers) - 1: h = F.relu(h) else: h = torch.nn.Tanh()(h) fake_tensor = (h * self.helper_tensor) + self.helper_tensor # project fake_dsct_distribution, fake_dsct_value = self.project(fake_tensor) sampled_filler = (input > 0) return fake_dsct_distribution, fake_dsct_value * sampled_filler def init_weights(self): for layer in self.layers: # Xavier Initialization for weights size = layer.weight.size() fan_out = size[0] fan_in = size[1] std = np.sqrt(2.0 / (fan_in + fan_out)) layer.weight.data.normal_(0.0, std) # Normal Initialization for Biases layer.bias.data.normal_(0.0, 0.001) class RoundGenerator_AE(BaseGenerator): def __init__(self, device, p_dims, q_dims=None): super(RoundGenerator_AE, self).__init__(device, input_dim=p_dims[0]) self.p_dims = p_dims if q_dims: assert q_dims[0] == p_dims[-1], "In and Out dimensions must equal to each other" assert q_dims[-1] == p_dims[0], "Latent dimension for p- and q- network mismatches." self.q_dims = q_dims else: self.q_dims = p_dims[::-1] self.dims = self.p_dims + self.q_dims[1:] self.layers = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(self.dims[:-1], self.dims[1:])]) # self.drop = nn.Dropout(dropout) self.init_weights() def forward(self, input): h = F.normalize(input) # h = self.drop(h) for i, layer in enumerate(self.layers): h = layer(h) if i != len(self.layers) - 1: h = F.relu(h) else: h = torch.nn.Tanh()(h) fake_tensor = (h * self.helper_tensor) + self.helper_tensor # project fake_dsct_value = self.project(fake_tensor) return None, fake_dsct_value * (input > 0) def init_weights(self): for layer in self.layers: # Xavier Initialization for weights size = layer.weight.size() fan_out = size[0] fan_in = size[1] std = np.sqrt(2.0 / (fan_in + fan_out)) layer.weight.data.normal_(0.0, std) # Normal Initialization for Biases layer.bias.data.normal_(0.0, 0.001) # ============================================================================================= # ============================================================================================= # ============================================================================================= # ============================================================================================= # ============================================================================================= # 最小值为1 class BaseGenerator_1(nn.Module): def __init__(self, device, input_dim): super(BaseGenerator_1, self).__init__() # self.input_dim = input_dim self.device = device """helper_tensor""" self.epsilon = torch.tensor(1e-4).to(self.device) # 计算boundary self.helper_tensor = torch.tensor(2.5).to(device) pass def project(self, fake_tensor): fake_tensor.data = torch.round(fake_tensor) fake_tensor.data = torch.where(fake_tensor < 1, torch.ones_like(fake_tensor).to(self.device), fake_tensor) # fake_tensor.data = torch.where(fake_tensor < 0, torch.zeros_like(fake_tensor).to(self.device), fake_tensor) fake_tensor.data = torch.where(fake_tensor > 5, torch.tensor(5.).to(self.device), fake_tensor) # return fake_tensor def forward(self, input): raise NotImplementedError class BaseDiscretGenerator_1(BaseGenerator): def __init__(self, device, input_dim): super(BaseDiscretGenerator_1, self).__init__(device, input_dim) # self.min_boundary_value = torch.nn.Parameter(torch.rand([self.input_dim]), requires_grad=True) self.min_boundary_value = torch.nn.Parameter(torch.ones([self.input_dim]), requires_grad=True) self.register_parameter("min_boundary_value", self.min_boundary_value) # self.interval_lengths = torch.nn.Parameter(torch.rand([self.input_dim, 3]), requires_grad=True) self.interval_lengths = torch.nn.Parameter(torch.ones([self.input_dim, 3]), requires_grad=True) self.register_parameter("interval_lengths", self.interval_lengths) pass def forward(self, input): # fake_tensor = (self.main(input) * self.helper_tensor) + self.helper_tensor # # project # fake_dsct_distribution, fake_dsct_value = self.project(fake_tensor) # return fake_dsct_value raise NotImplementedError def project_old(self, fake_tensor): boundary_values = self.get_boundary_values() fake_dsct_distribution = [] for iid in range(self.input_dim): cur_dsct_distribution = [] for rating_dsct in range(5): rating_prob = torch.ones(fake_tensor.shape[0]).to(self.device) for boundary_idx in range(4): rating_prob *= self.is_in_interval(rating_dsct, boundary_idx, fake_tensor[:, iid], boundary_values[iid][boundary_idx]) cur_dsct_distribution += [rating_prob] fake_dsct_distribution += [torch.cat([torch.unsqueeze(p, 1) for p in cur_dsct_distribution], 1)] fake_dsct_distribution = torch.cat([torch.unsqueeze(p, 1) for p in fake_dsct_distribution], 1) fake_dsct_value = torch.matmul(fake_dsct_distribution, torch.tensor(np.arange(1., 6.)).type(torch.float32).to(self.device)) return fake_dsct_distribution, fake_dsct_value def project(self, fake_tensor): Heaviside = HeaviTanh.apply boundary_values = self.get_boundary_values() cnt_ratings = fake_tensor.flatten() iids = np.expand_dims(np.arange(self.input_dim), 0).repeat(fake_tensor.shape[0], axis=0).flatten() boundary_values_per_rating = boundary_values[iids] def _project_helper(ratings, boundary_values_input): def get_target_dst_rating_prob(target_dst_rating, input_cnt_rating, boundary_values, device): # boundary_values = boundary_values.reshape([-1, 4]) # input_cnt_rating = input_cnt_rating.reshape([-1]) rating_prob = torch.ones(input_cnt_rating.shape[0]).to(self.device) for boundary_idx in range(4): """ :param target_dst_rating: r_i_j :param boundary_idx: k :param input_cnt_rating: a_i_j :param boundary_value: b_j_k :return: """ # p_1 = torch.sign(target_dst_rating - boundary_idx - torch.tensor(0.5).to(device)) # p_2 = input_cnt_rating - boundary_values[:, boundary_idx] # rating_prob *= Heaviside(p_1 * p_2, torch.tensor(1.).to(device)) return rating_prob cur_dsct_distribution = [] for rating_dsct in range(5): p = get_target_dst_rating_prob(rating_dsct, ratings, boundary_values_input, self.device) cur_dsct_distribution += [p] dsct_distribution = torch.cat([torch.unsqueeze(p, 1) for p in cur_dsct_distribution], 1) return dsct_distribution fake_dsct_distribution = _project_helper(cnt_ratings, boundary_values_per_rating).reshape( [-1, self.input_dim, 5]) fake_dsct_value = torch.matmul(fake_dsct_distribution, torch.tensor(np.arange(1., 6.)).type(torch.float32).to(self.device)) return fake_dsct_distribution, fake_dsct_value def get_boundary_values(self): boundary_values = torch.zeros([self.input_dim, 4]).to(self.device) boundary_values[:, 0] = self.min_boundary_value for i in range(1, 4): cur_interval_length = torch.relu(self.interval_lengths[:, i - 1]) + self.epsilon boundary_values[:, i] = boundary_values[:, i - 1] + cur_interval_length return boundary_values def is_in_interval(self, rating_dsct, boundary_idx, rating_cnt, boundary_value): tensor_aux_0_5 = torch.tensor(0.5).to(self.device) tensor_aux_1 = torch.tensor(1.).to(self.device) Heaviside = HeaviTanh.apply """ :param rating_dsct: r_i_j :param boundary_idx: k :param rating_cnt: a_i_j :param boundary_value: b_j_k :return: """ # p_1 = torch.sign(rating_dsct - boundary_idx - tensor_aux_0_5) # p_2 = rating_cnt - boundary_value # return Heaviside(p_1 * p_2, tensor_aux_1) class DiscretGenerator_AE_1(BaseDiscretGenerator_1): def __init__(self, device, p_dims, q_dims=None): super(DiscretGenerator_AE_1, self).__init__(device, input_dim=p_dims[0]) self.p_dims = p_dims if q_dims: assert q_dims[0] == p_dims[-1], "In and Out dimensions must equal to each other" assert q_dims[-1] == p_dims[0], "Latent dimension for p- and q- network mismatches." self.q_dims = q_dims else: self.q_dims = p_dims[::-1] self.dims = self.p_dims + self.q_dims[1:] self.layers = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(self.dims[:-1], self.dims[1:])]) # self.drop = nn.Dropout(dropout) self.init_weights() def forward(self, input): h = F.normalize(input) # h = self.drop(h) for i, layer in enumerate(self.layers): h = layer(h) if i != len(self.layers) - 1: h = F.relu(h) else: h = torch.nn.Tanh()(h) fake_tensor = (h * self.helper_tensor) + self.helper_tensor # project fake_dsct_distribution, fake_dsct_value = self.project(fake_tensor) sampled_filler = (input > 0) # sampled_filler = (torch.rand(fake_dsct_value.shape) < (90 / 1924)).float() # filler_num = np.sum(sampled_filler.detach().cpu().numpy()*(fake_dsct_value.detach().cpu().numpy()>0),1).mean() # if filler_num<90: return fake_dsct_distribution, fake_dsct_value * sampled_filler def init_weights(self): for layer in self.layers: # Xavier Initialization for weights size = layer.weight.size() fan_out = size[0] fan_in = size[1] std = np.sqrt(2.0 / (fan_in + fan_out)) layer.weight.data.normal_(0.0, std) # Normal Initialization for Biases layer.bias.data.normal_(0.0, 0.001) class RoundGenerator_AE_1(BaseGenerator_1): def __init__(self, device, p_dims, q_dims=None): super(RoundGenerator_AE_1, self).__init__(device, input_dim=p_dims[0]) self.p_dims = p_dims if q_dims: assert q_dims[0] == p_dims[-1], "In and Out dimensions must equal to each other" assert q_dims[-1] == p_dims[0], "Latent dimension for p- and q- network mismatches." self.q_dims = q_dims else: self.q_dims = p_dims[::-1] self.dims = self.p_dims + self.q_dims[1:] self.layers = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(self.dims[:-1], self.dims[1:])]) # self.drop = nn.Dropout(dropout) self.init_weights() def forward(self, input): h = F.normalize(input) # h = self.drop(h) for i, layer in enumerate(self.layers): h = layer(h) if i != len(self.layers) - 1: h = F.relu(h) else: h = torch.nn.Tanh()(h) fake_tensor = (h * self.helper_tensor) + self.helper_tensor # project fake_dsct_value = self.project(fake_tensor) sampled_filler = (input > 0) return None, fake_dsct_value * sampled_filler def init_weights(self): for layer in self.layers: # Xavier Initialization for weights size = layer.weight.size() fan_out = size[0] fan_in = size[1] std = np.sqrt(2.0 / (fan_in + fan_out)) layer.weight.data.normal_(0.0, std) # Normal Initialization for Biases layer.bias.data.normal_(0.0, 0.001) class DiscretRecsysGenerator_1(BaseDiscretGenerator_1): def __init__(self, device, init_tensor): super(DiscretRecsysGenerator_1, self).__init__(device, init_tensor.shape[1]) """ fake_parameter """ fake_tensor = init_tensor.clone().detach().requires_grad_(True) self.fake_parameter = torch.nn.Parameter(fake_tensor, requires_grad=True) self.register_parameter("fake_tensor", self.fake_parameter) pass def forward(self, input=None): fake_dsct_distribution, fake_dsct_value = self.project(self.fake_parameter) sampled_filler = (input > 0) return fake_dsct_distribution, fake_dsct_value * sampled_filler # ============================================================================================= # ============================================================================================= # ============================================================================================= # ============================================================================================= # ============================================================================================= class HeaviTanh(torch.autograd.Function): """ Approximation of the heaviside step function as h(x,k) = \frac{1}{2} + \frac{1}{2} \text{tanh}(k x) """ @staticmethod def forward(ctx, x, k): ctx.save_for_backward(x, k) def heaviside(data): """ A `heaviside step function <https://en.wikipedia.org/wiki/Heaviside_step_function>`_ that truncates numbers <= 0 to 0 and everything else to 1. .. math:: H[n]=\\begin{cases} 0, & n <= 0, \\ 1, & n \g 0, \end{cases} """ return torch.where( data <= torch.zeros_like(data), torch.zeros_like(data), torch.ones_like(data), ) return heaviside(x) # 0.5 + 0.5 * torch.tanh(k * x) @staticmethod def backward(ctx, dy): x, k, = ctx.saved_tensors dtanh = 1 - (x * k).tanh().pow(2) return dy * dtanh, None class Discriminator(nn.Module): def __init__(self, input_dim): super(Discriminator, self).__init__() self.main = nn.Sequential( nn.Linear(input_dim, 512), nn.ReLU(True), nn.Linear(512, 128), nn.ReLU(True), nn.Linear(128, 1), nn.Sigmoid() ) def forward(self, input): return self.main(input) class BaseTrainer(object): def __init__(self): self.args = None self.n_users = None self.n_items = None self.net = None self.optimizer = None self.metrics = None self.golden_metric = "Recall@50" @staticmethod def minibatch(*tensors, **kwargs): """Mini-batch generator for pytorch tensor.""" batch_size = kwargs.get('batch_size', 128) if len(tensors) == 1: tensor = tensors[0] for i in range(0, len(tensor), batch_size): yield tensor[i:i + batch_size] else: for i in range(0, len(tensors[0]), batch_size): yield tuple(x[i:i + batch_size] for x in tensors) @staticmethod def mult_ce_loss(data, logits): # ======================================== # surrogate network loss function # ======================================== # Func_WeightedMSELoss = lambda weight, input, target: \ # torch.where(target > 2, torch.tensor(weight), torch.tensor(1.)) \ # * MSELoss(reduce=False, size_average=False)(input, target) # # Func_MSELoss = MSELoss(reduce=False, size_average=False) # adv_grads = torch.autograd.grad(adv_loss, data_tensor)[0] # # Copy fmodel's parameters to default trainer.net(). # model.load_state_dict(fmodel.state_dict()) """Multi-class cross-entropy loss.""" log_probs = F.log_softmax(logits, dim=-1) loss = -log_probs * data instance_data = data.sum(1) instance_loss = loss.sum(1) # Avoid divide by zeros. res = instance_loss / (instance_data + 0.1) # PSILON) return res @staticmethod def weighted_mse_loss(data, logits, weight_pos=1, weight_neg=0): """Mean square error loss.""" weights = torch.ones_like(data) * weight_neg weights[data > 0] = weight_pos res = weights * (data - logits) ** 2 return res.sum(1) @staticmethod def _array2sparsediag(x): values = x indices = np.vstack([np.arange(x.size), np.arange(x.size)]) i = torch.LongTensor(indices) v = torch.FloatTensor(values) shape = [x.size, x.size] return torch.sparse.FloatTensor(i, v, torch.Size(shape)) @property def _initialized(self): return self.net is not None def _initialize(self): """Initialize model and optimizer.""" # See actual implementation in each trainer. raise NotImplementedError def recommend(self, data, top_k, return_preds=False, allow_repeat=False): """Generate a top-k recommendation (ranked) list.""" # See actual implementation in each trainer. raise NotImplementedError def train_epoch(self, data): """Train model for one epoch""" # See actual implementation in each trainer. raise NotImplementedError def train_epoch_wrapper(self, train_data, epoch_num): """Wrapper for train_epoch with some logs.""" time_st = time.time() epoch_loss = self.train_epoch(train_data) print("Training [{:.1f} s], epoch: {}, loss: {:.4f}".format( time.time() - time_st, epoch_num, epoch_loss)) def evaluate_epoch(self, train_data, test_data, epoch_num): """Evaluate model performance on test data.""" t1 = time.time() n_rows = train_data.shape[0] n_evaluate_users = test_data.shape[0] total_metrics_len = sum(len(x) for x in self.metrics) total_val_metrics = np.zeros([n_rows, total_metrics_len], dtype=np.float32) recommendations = self.recommend(train_data, top_k=100) valid_rows = list() for i in range(train_data.shape[0]): # Ignore augmented users, evaluate only on real users. if i >= n_evaluate_users: continue targets = test_data[i].indices if targets.size <= 0: continue recs = recommendations[i].tolist() metric_results = list() for metric in self.metrics: result = metric(targets, recs) metric_results.append(result) total_val_metrics[i, :] = np.concatenate(metric_results) valid_rows.append(i) # Average evaluation results by user. total_val_metrics = total_val_metrics[valid_rows] avg_val_metrics = (total_val_metrics.mean(axis=0)).tolist() # Summary evaluation results into a dict. # ind, result = 0, OrderedDict() # for metric in self.metrics: # values = avg_val_metrics[ind:ind + len(metric)] # if len(values) <= 1: # result[str(metric)] = values[0] # else: # for name, value in zip(str(metric).split(','), values): # result[name] = value # ind += len(metric) # # print("Evaluation [{:.1f} s], epoch: {}, {} ".format( # time.time() - t1, epoch_num, str(result))) # return result def fit(self, train_data, test_data): """Full model training loop.""" if not self._initialized: self._initialize() if self.args.save_feq > self.args.epochs: raise ValueError("Model save frequency should be smaller than" " total training epochs.") start_epoch = 1 best_checkpoint_path = "" best_perf = 0.0 for epoch_num in range(start_epoch, self.args.epochs + 1): # Train the model. self.train_epoch_wrapper(train_data, epoch_num) if epoch_num % self.args.save_feq == 0: result = self.evaluate_epoch(train_data, test_data, epoch_num) # Save model checkpoint if it has better performance. # if result[self.golden_metric] > best_perf: # str_metric = "{}={:.4f}".format(self.golden_metric, # result[self.golden_metric]) # print("Having better model checkpoint with" # " performance {}".format(str_metric)) # checkpoint_path = os.path.join( # self.args.output_dir, # self.args.model['model_name']) # save_checkpoint(self.net, self.optimizer, # checkpoint_path, # epoch=epoch_num) # # best_perf = result[self.golden_metric] # best_checkpoint_path = checkpoint_path # Load best model and evaluate on test data. print("Loading best model checkpoint.") self.restore(best_checkpoint_path) self.evaluate_epoch(train_data, test_data, -1) return def restore(self, path): return class WeightedMF(nn.Module): def __init__(self, n_users, n_items, hidden_dim): super(WeightedMF, self).__init__() self.n_users = n_users self.n_items = n_items self.dim = hidden_dim self.Q = nn.Parameter( torch.zeros([self.n_items, self.dim]).normal_(mean=0, std=0.1)) self.P = nn.Parameter( torch.zeros([self.n_users, self.dim]).normal_(mean=0, std=0.1)) self.params = nn.ParameterList([self.Q, self.P]) def forward(self, user_id=None, item_id=None): if user_id is None and item_id is None: return torch.mm(self.P, self.Q.t()) if user_id is not None: return torch.mm(self.P[[user_id]], self.Q.t()) if item_id is not None: return torch.mm(self.P, self.Q[[item_id]].t()) class WMFTrainer(BaseTrainer): def __init__(self, n_users, n_items, device, hidden_dim, lr, weight_decay, batch_size, weight_pos, weight_neg, verbose=False): super(WMFTrainer, self).__init__() self.device = device # self.n_users = n_users self.n_items = n_items # self.hidden_dim = hidden_dim # self.lr = lr self.weight_decay = weight_decay self.batch_size = batch_size # self.weight_pos = weight_pos self.weight_neg = weight_neg # self.verbose = verbose pass def _initialize(self): self.net = WeightedMF(n_users=self.n_users, n_items=self.n_items, hidden_dim=self.hidden_dim).to(self.device) self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr, weight_decay=self.weight_decay) self.dim = self.net.dim def fit_adv(self, data_tensor, epoch_num, unroll_steps): self._initialize() import higher if not data_tensor.requires_grad: raise ValueError("To compute adversarial gradients, data_tensor " "should have requires_grad=True.") # data_tensor = data_tensor.to(self.device) n_rows = data_tensor.shape[0] idx_list = np.arange(n_rows) # model = self.net.to(self.device) # for i in range(1, epoch_num - unroll_steps + 1): t1 = time.time() np.random.shuffle(idx_list) model.train() epoch_loss = 0.0 for batch_idx in self.minibatch(idx_list, batch_size=self.batch_size): # Compute loss # TODO detach() loss = self.weighted_mse_loss(data=data_tensor[batch_idx].detach(), logits=model(user_id=batch_idx), weight_pos=self.weight_pos, weight_neg=self.weight_neg).sum() epoch_loss += loss.item() self.optimizer.zero_grad() loss.backward() self.optimizer.step() if self.verbose: print("Training [{:.1f} s], epoch: {}, loss: {:.4f}".format( time.time() - t1, i, epoch_loss), flush=True) with higher.innerloop_ctx(model, self.optimizer) as (fmodel, diffopt): if self.verbose: print("Switching to higher mode...") for i in range(epoch_num - unroll_steps + 1, epoch_num + 1): t1 = time.time() np.random.shuffle(idx_list) fmodel.train() epoch_loss = 0.0 for batch_idx in self.minibatch(idx_list, batch_size=self.batch_size): # Compute loss # ===========warning================= loss = self.weighted_mse_loss(data=data_tensor[batch_idx], logits=fmodel(user_id=batch_idx), weight_pos=self.weight_pos, weight_neg=self.weight_neg).sum() # ==================================== epoch_loss += loss.item() diffopt.step(loss) if self.verbose: print("Training (higher mode) [{:.1f} s]," " epoch: {}, loss: {:.4f}".format(time.time() - t1, i, epoch_loss), flush=True) # if self.verbose: print("Finished surrogate model training," " {} copies of surrogate model params.".format(len(fmodel._fast_params)), flush=True) fmodel.eval() predictions = fmodel() return predictions # adv_loss # .item(), adv_grads[-n_fakes:, ] def recommend(self, data, top_k, return_preds=False, allow_repeat=False): # Set model to eval mode model = self.net.to(self.device) model.eval() n_rows = data.shape[0] idx_list = np.arange(n_rows) recommendations = np.empty([n_rows, top_k], dtype=np.int64) all_preds = list() with torch.no_grad(): for batch_idx in self.minibatch( idx_list, batch_size=self.args.valid_batch_size): batch_data = data[batch_idx].toarray() preds = model(user_id=batch_idx) if return_preds: all_preds.append(preds) if not allow_repeat: preds[batch_data.nonzero()] = -np.inf if top_k > 0: _, recs = preds.topk(k=top_k, dim=1) recommendations[batch_idx] = recs.cpu().numpy() if return_preds: return recommendations, torch.cat(all_preds, dim=0).cpu() else: return recommendations class ItemAE(nn.Module): def __init__(self, input_dim, hidden_dims): super(ItemAE, self).__init__() self.q_dims = [input_dim] + [hidden_dims] self.p_dims = self.q_dims[::-1] self.q_layers = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(self.q_dims[:-1], self.q_dims[1:])]) self.p_layers = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(self.p_dims[:-1], self.p_dims[1:])]) def encode(self, input): h = input for i, layer in enumerate(self.q_layers): h = layer(h) h = torch.tanh(h) return h def decode(self, z): h = z for i, layer in enumerate(self.p_layers): h = layer(h) if i != len(self.p_layers) - 1: h = torch.tanh(h) return h def forward(self, input): z = self.encode(input) return self.decode(z) def loss(self, data, outputs): return BaseTrainer.weighted_mse_loss(data=data, logits=outputs) class ItemAETrainer(BaseTrainer): def __init__(self, n_users, n_items, hidden_dims, device, lr, l2, batch_size, weight_pos, weight_neg, verbose=False): super(ItemAETrainer, self).__init__() self.n_users = n_users self.n_items = n_items self.hidden_dims = hidden_dims self.device = device self.lr = lr self.l2 = l2 self.batch_size = batch_size self.weight_pos = weight_pos self.weight_neg = weight_neg self.device = device self.verbose = verbose pass def _initialize(self): self.net = ItemAE(self.n_users, self.hidden_dims).to(self.device) self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr, weight_decay=self.l2) def train_epoch(self, data): # Transpose the data first for ItemVAE. data = data.transpose() n_rows = data.shape[0] n_cols = data.shape[1] idx_list = np.arange(n_rows) # Set model to training mode. model = self.net.to(self.device) model.train() np.random.shuffle(idx_list) epoch_loss = 0.0 batch_size = (self.args.batch_size if self.args.batch_size > 0 else len(idx_list)) for batch_idx in self.minibatch(idx_list, batch_size=batch_size): batch_tensor = data[batch_idx].to(self.device) # Compute loss outputs = model(batch_tensor) loss = model.loss(data=batch_tensor, outputs=outputs).sum() epoch_loss += loss.item() self.optimizer.zero_grad() loss.backward() self.optimizer.step() return epoch_loss def fit_adv(self, data_tensor, epoch_num, unroll_steps, ): import higher if not data_tensor.requires_grad: raise ValueError("To compute adversarial gradients, data_tensor " "should have requires_grad=True.") self._initialize() data_tensor = data_tensor.to(self.device) # target_tensor = torch.zeros_like(data_tensor) # target_tensor[:, target_items] = 1.0 data_tensor = data_tensor.t() n_rows = data_tensor.shape[0] n_cols = data_tensor.shape[1] idx_list = np.arange(n_rows) # Set model to training mode. model = self.net.to(self.device) optimizer = self.optimizer batch_size = (self.batch_size if self.batch_size > 0 else len(idx_list)) for i in range(1, epoch_num - unroll_steps + 1): t1 = time.time() np.random.shuffle(idx_list) model.train() epoch_loss = 0.0 for batch_idx in self.minibatch(idx_list, batch_size=batch_size): # TODO detach() batch_tensor = data_tensor[batch_idx].detach() # Compute loss outputs = model(batch_tensor) loss = model.loss(data=batch_tensor, outputs=outputs).sum() epoch_loss += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() if self.verbose: # and i%20==0: print("Training [{:.1f} s], epoch: {}, loss: {:.4f}".format( time.time() - t1, i, epoch_loss)) with higher.innerloop_ctx(model, optimizer) as (fmodel, diffopt): if self.verbose: print("Switching to higher mode...") for i in range(epoch_num - unroll_steps + 1, epoch_num + 1): t1 = time.time() np.random.shuffle(idx_list) epoch_loss = 0.0 fmodel.train() for batch_idx in self.minibatch(idx_list, batch_size=batch_size): batch_tensor = data_tensor[batch_idx] # Compute loss outputs = fmodel(batch_tensor) loss = fmodel.loss(data=batch_tensor, outputs=outputs).sum() epoch_loss += loss.item() diffopt.step(loss) if self.verbose: print("Training (higher mode) [{:.1f} s]," " epoch: {}, loss: {:.4f}".format(time.time() - t1, i, epoch_loss)) if self.verbose: print("Finished surrogate model training," " {} copies of surrogate model params.".format(len(fmodel._fast_params))) fmodel.eval() all_preds = list() for batch_idx in self.minibatch(np.arange(n_rows), batch_size=batch_size): all_preds += [fmodel(data_tensor[batch_idx])] predictions = torch.cat(all_preds, dim=0).t() # # Compute adversarial (outer) loss. # adv_loss = self.mult_ce_loss( # logits=predictions[:-n_fakes, ], # data=target_tensor[:-n_fakes, ]).sum() # adv_grads = torch.autograd.grad(adv_loss, data_tensor)[0] # # Copy fmodel's parameters to default trainer.net(). # model.load_state_dict(fmodel.state_dict()) return predictions # adv_loss.item(), adv_grads.t()[-n_fakes:, :] class SVDpp(nn.Module): def __init__(self, n_users, n_items, hidden_dims, data): super(SVDpp, self).__init__() self.n_users = n_users self.n_items = n_items self.dim = hidden_dims[0] self.data = data self.Q1 = nn.Parameter( torch.zeros([self.n_items, self.dim]).normal_(mean=0, std=0.1)) self.Q2 = nn.Parameter( torch.zeros([self.n_items, self.dim]).normal_(mean=0, std=0.1)) self.P = nn.Parameter( torch.zeros([self.n_users, self.dim]).normal_(mean=0, std=0.1)) self.bu = nn.Parameter(torch.zeros(self.n_users)) self.bi = nn.Parameter(torch.zeros(self.n_items)) # store each users' interaction history self.Ni = list() for user in self.data: self.Ni.append(user.nonzero().squeeze(1)) self.u = self.data.float().mean() def forward(self, user_id=None, item_id=None): # bias computing bu = self.bu.expand((self.n_items, self.n_users)).t() bi = self.bi.expand((self.n_users, self.n_items)) b = bu + bi # user features computing P = list() for i in self.Ni: yi = self.Q2[i] Yi = self.Q2[i].sum(dim=0) length = len(yi) P.append(Yi / math.sqrt(length)) P = torch.cat(P).view((self.n_users, self.dim)) P = P + self.P if user_id is None and item_id is None: return torch.sigmoid(torch.mm(P, self.Q1.t()) + b[[user_id]] + self.u) * 5 if user_id is not None: return torch.sigmoid(torch.mm(P[[user_id]], self.Q1.t()) + b[[user_id]] + self.u) * 5 if item_id is not None: return torch.sigmoid(torch.mm(P, self.Q1[[item_id]].t()) + b[[user_id]] + self.u) * 5 class SVDppTrainer(BaseTrainer): def __init__(self, n_users, n_items, hidden_dims, device, lr, l2, batch_size, weight_alpha): super(SVDppTrainer, self).__init__() self.n_users = n_users self.n_items = n_items self.hidden_dims = hidden_dims self.device = device self.lr = lr self.l2 = l2 self.batch_size = batch_size self.weight_alpha = weight_alpha def _initialize(self, data): self.net = SVDpp( n_users=self.n_users, n_items=self.n_items, hidden_dims=self.hidden_dims, data=data ).to(self.device) self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr, weight_decay=self.l2) def fit_adv(self, data_tensor, epoch_num, unroll_steps): self._initialize(data_tensor) import higher if not data_tensor.requires_grad: raise ValueError("To compute adversarial gradients, data_tensor " "should have requires_grad=True.") data_tensor = data_tensor.to(self.device) n_rows = data_tensor.shape[0] n_cols = data_tensor.shape[1] idx_list = np.arange(n_rows) model = self.net.to(self.device) optimizer = self.optimizer batch_size = (self.batch_size if self.batch_size > 0 else len(idx_list)) for i in range(1, epoch_num - unroll_steps + 1): t1 = time.time() np.random.shuffle(idx_list) model.train() epoch_loss = 0.0 for batch_idx in minibatch( idx_list, batch_size=batch_size): # Compute loss loss = mse_loss(data=data_tensor[batch_idx], logits=model(user_id=batch_idx), weight=self.weight_alpha).sum() epoch_loss += loss.item() optimizer.zero_grad() loss.backward(retain_graph=True) optimizer.step() print("Training [{:.1f} s], epoch: {}, loss: {:.4f}".format( time.time() - t1, i, epoch_loss)) with higher.innerloop_ctx(model, optimizer) as (fmodel, diffopt): print("Switching to higher mode...") for i in range(epoch_num - unroll_steps + 1, epoch_num + 1): t1 = time.time() np.random.shuffle(idx_list) fmodel.train() epoch_loss = 0.0 for batch_idx in minibatch( idx_list, batch_size=batch_size): # Compute loss loss = mse_loss(data=data_tensor[batch_idx], logits=fmodel(user_id=batch_idx), weight=self.weight_alpha).sum() epoch_loss += loss.item() diffopt.step(loss) print("Training (higher mode) [{:.1f} s]," " epoch: {}, loss: {:.4f}".format(time.time() - t1, i, epoch_loss)) print("Finished surrogate model training," " {} copies of surrogate model params.".format(len(fmodel._fast_params))) fmodel.eval() predictions = fmodel() return predictions.squeeze(0) class NMF(nn.Module): def __init__(self, n_users, n_items, hidden_dim, data): super(NMF, self).__init__() self.n_users, self.n_items = n_users, n_items self.hideen_dim = hidden_dim self.data = data self.scale = torch.sqrt(torch.mean(self.data.detach()) / self.hideen_dim) W = torch.abs(torch.rand([self.n_users, self.hideen_dim]) * self.scale) H = torch.abs(torch.rand([self.hideen_dim, self.n_items]) * self.scale) self.W = torch.nn.Parameter(W, requires_grad=True) self.H = torch.nn.Parameter(H, requires_grad=True) def forward(self, user_id=None, item_id=None): if user_id is None and item_id is None: return torch.mm(self.W, self.H) if user_id is not None: return torch.mm(self.W[[user_id]], self.H) if item_id is not None: return torch.mm(self.W, self.H[[item_id]]) class NMFTrainer(BaseTrainer): def __init__(self, n_users, n_items, batch_size, device, k=128, solver='autograd', eps=1e-7, alpha=0.99, loss='l2', lr=1e-2): super(NMFTrainer, self).__init__() self.n_users = n_users self.n_items = n_items self.batch_size = batch_size self.k = k self.loss = loss self.lr = lr self.alpha = alpha self.solver = solver self.eps = eps self.device = device @staticmethod def weighted_mse_loss(data, logits, weight_pos=1.0, weight_neg=0.0): """Mean square error loss.""" weights = torch.ones_like(data) * weight_neg weights[data > 0] = weight_pos res = weights * (data - logits) ** 2 return res.sum(1) @staticmethod def l2(x, y): return torch.nn.MSELoss()(x, y) @staticmethod def kl_dev(x, y): return (x * torch.log(x / y) - x + y).mean() def _initialize(self, data_tensor): self.net = NMF(self.n_users, self.n_items, self.k, data_tensor) # for autograd solver self.opt = torch.optim.RMSprop(self.net.parameters(), alpha=self.alpha, lr=self.lr, weight_decay=1e-6) def plus(self, X): X[X < 0] = self.eps return X def fit_adv(self, data_tensor, epoch_num, unroll_steps): self._initialize(data_tensor) import higher if not data_tensor.requires_grad: raise ValueError("To compute adversarial gradients, data_tensor " "should have requires_grad=True.") data_tensor = data_tensor.to(self.device) n_rows = data_tensor.shape[0] n_cols = data_tensor.shape[1] idx_list = np.arange(n_rows) model = self.net.to(self.device) optimizer = self.opt batch_size = (self.batch_size if self.batch_size > 0 else len(idx_list)) for i in range(1, epoch_num - unroll_steps + 1): t1 = time.time() np.random.shuffle(idx_list) model.train() epoch_loss = 0.0 for batch_idx in minibatch( idx_list, batch_size=self.batch_size): # Compute loss # loss = NMFTrainer.weighted_mse_loss(data=data_tensor[batch_idx], # logits=model(user_id=batch_idx), # weight_pos=1.0, # weight_neg=-1.0 * self.eps).sum() loss = NMFTrainer.l2(data_tensor[batch_idx], model(user_id=batch_idx)) epoch_loss += loss.item() optimizer.zero_grad() loss.backward(retain_graph=True) optimizer.step() for p in model.parameters(): p.data = self.plus(p.data) print("Training [{:.1f} s], epoch: {}, loss: {:.4f}".format( time.time() - t1, i, epoch_loss)) with higher.innerloop_ctx(model, optimizer) as (fmodel, diffopt): print("Switching to higher mode...") for i in range(epoch_num - unroll_steps + 1, epoch_num + 1): t1 = time.time() np.random.shuffle(idx_list) fmodel.train() epoch_loss = 0.0 for batch_idx in minibatch( idx_list, batch_size=self.batch_size): # Compute loss # loss = NMFTrainer.weighted_mse_loss(data=data_tensor[batch_idx], # logits=fmodel(user_id=batch_idx), # weight_pos=1.0, # weight_neg=-1.0*self.eps).sum() loss = NMFTrainer.l2(data_tensor[batch_idx], fmodel(user_id=batch_idx)) epoch_loss += loss.item() diffopt.step(loss) for p in fmodel.parameters(): p.data = self.plus(p.data) print("Training (higher mode) [{:.1f} s]," " epoch: {}, loss: {:.4f}".format(time.time() - t1, i, epoch_loss)) print("Finished surrogate model training," " {} copies of surrogate model params.".format(len(fmodel._fast_params))) fmodel.eval() predictions = fmodel() return predictions from numpy.random import RandomState class PMF(nn.Module): def __init__(self, n_users, n_items, n_factors=128, is_sparse=False, no_cuda=None): super(PMF, self).__init__() self.n_users = n_users self.n_items = n_items self.n_factors = n_factors self.no_cuda = no_cuda self.random_state = RandomState(1) self.user_embeddings = nn.Embedding(n_users, n_factors, sparse=is_sparse) self.user_embeddings.weight.data = torch.from_numpy(0.1 * self.random_state.rand(n_users, n_factors)).float() self.item_embeddings = nn.Embedding(n_items, n_factors, sparse=is_sparse) self.item_embeddings.weight.data = torch.from_numpy(0.1 * self.random_state.rand(n_items, n_factors)).float() self.ub = nn.Embedding(n_users, 1) self.ib = nn.Embedding(n_items, 1) self.ub.weight.data.uniform_(-.01, .01) self.ib.weight.data.uniform_(-.01, .01) def forward(self, user_id, item_id): user_h1 = self.user_embeddings(user_id) item_h1 = self.item_embeddings(item_id).T R_h = torch.mm(user_h1, item_h1) + self.ub(user_id) + self.ib(item_id).T return R_h class PMFTrainer(BaseTrainer): def __init__(self, n_users, n_items, device, hidden_dim, lr, weight_decay, batch_size, momentum, verbose=False): super(PMFTrainer, self).__init__() self.device = device # self.n_users = n_users self.n_items = n_items # self.hidden_dim = hidden_dim # self.lr = lr self.weight_decay = weight_decay self.momentum = momentum self.batch_size = batch_size # self.verbose = verbose pass def _initialize(self): self.net = PMF(n_users=self.n_users, n_items=self.n_items, n_factors=self.hidden_dim).to(self.device) # self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr, weight_decay=self.weight_decay) self.optimizer = optim.SGD(self.net.parameters(), lr=self.lr, weight_decay=self.weight_decay, momentum=self.momentum) for name, param in self.net.named_parameters(): print(name) def fit_adv(self, data_tensor, epoch_num, unroll_steps): self._initialize() import higher if not data_tensor.requires_grad: raise ValueError("To compute adversarial gradients, data_tensor " "should have requires_grad=True.") # data_tensor = data_tensor.to(self.device) n_rows = data_tensor.shape[0] idx_list = np.arange(n_rows) # model = self.net.to(self.device) # user_idx = np.array(range(self.n_users), dtype=np.int16) item_idx = np.array(range(self.n_items), dtype=np.int16) for i in range(1, epoch_num - unroll_steps + 1): t1 = time.time() np.random.shuffle(idx_list) model.train() epoch_loss = 0.0 for batch_idx in self.minibatch(idx_list, batch_size=self.batch_size): # Compute loss loss = mse_loss(data_tensor[batch_idx].float(), model(user_id=torch.tensor(batch_idx).long(), item_id=torch.tensor(item_idx).long()).float(), 1).sum() epoch_loss += loss.item() self.optimizer.zero_grad() loss.backward(retain_graph=True) self.optimizer.step() if self.verbose: print("Training [{:.1f} s], epoch: {}, loss: {:.4f}".format( time.time() - t1, i, epoch_loss), flush=True) with higher.innerloop_ctx(model, self.optimizer) as (fmodel, diffopt): if self.verbose: print("Switching to higher mode...") for i in range(epoch_num - unroll_steps + 1, epoch_num + 1): t1 = time.time() np.random.shuffle(idx_list) fmodel.train() epoch_loss = 0.0 for batch_idx in self.minibatch(idx_list, batch_size=self.batch_size): # Compute loss # ===========warning================= loss = mse_loss(data_tensor[batch_idx].float(), fmodel(user_id=torch.tensor(batch_idx).long(), item_id=torch.tensor(item_idx).long()).float(), 1).sum() # ==================================== epoch_loss += loss.item() diffopt.step(loss) if self.verbose: print("Training (higher mode) [{:.1f} s]," " epoch: {}, loss: {:.4f}".format(time.time() - t1, i, epoch_loss), flush=True) # if self.verbose: print("Finished surrogate model training," " {} copies of surrogate model params.".format(len(fmodel._fast_params)), flush=True) fmodel.eval() predictions = fmodel(torch.tensor(user_idx).long(), torch.tensor(item_idx).long()) print(predictions) return predictions

38.840419

125

0.551383

7,163

59,387

4.333938

0.069664

0.019327

0.015075

0.007087

0.76543

0.742881

0.725905

0.710443

0.694144

0.674655

0

0.013682

0.315709

59,387

1,528

126

38.865838

0.750234

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0.089374

false

0.008937

0.016882

0.006951

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0

null

0

1

0

1

0

null

0

4

31b44a654f4e853f7c24f653ac8a36ad960df7df

75

py

Python

BOJ_Python/24266.py

tnsgh9603/BOJ_CPP

432b1350f6c67cce83aec3e723e30a3c6b5dbfda

[ "MIT" ]

null

BOJ_Python/24266.py

tnsgh9603/BOJ_CPP

432b1350f6c67cce83aec3e723e30a3c6b5dbfda

[ "MIT" ]

null

BOJ_Python/24266.py

tnsgh9603/BOJ_CPP

432b1350f6c67cce83aec3e723e30a3c6b5dbfda

[ "MIT" ]

null

import sys num = int(sys.stdin.readline()) print(num * num * num) print(3)

15

31

0.68

13

75

3.923077

0.615385

0.235294

0

0.015625

0.146667

75

5

32

15

0.78125

0

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false

0

0.25

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0.25

0.5

1

0

null

1

0

1

0

null

0

1

0

4

9ed5992090eeb7bba5868b5ccaff9c66e4e62254

610

py

Python

lib/utils.py

LeDernier/avalanche_simulation

9a73b9862356270add9adf0319ac92d910827ac1

[ "Unlicense" ]

null

lib/utils.py

LeDernier/avalanche_simulation

9a73b9862356270add9adf0319ac92d910827ac1

[ "Unlicense" ]

null

lib/utils.py

LeDernier/avalanche_simulation

9a73b9862356270add9adf0319ac92d910827ac1

[ "Unlicense" ]

null

######################################################################################################################################################################### # Author : Remi Monthiller, remi.monthiller@etu.enseeiht.fr # Adapted from the code of Raphael Maurin, raphael.maurin@imft.fr # 30/10/2018 # # Incline plane simulations # ######################################################################################################################################################################### def lengthVector3(vect): return (vect[0] ** 2.0 + vect[1] ** 2.0 + vect[2] ** 2.0) ** (1.0/2.0)

46.923077

169

0.296721

45

610

4.022222

0.6

0.044199

0.033149

0

0.038869

0.072131

610

12

170

50.833333

0.280919

0.259016

0

1

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0

0.5

1

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null

0

1

0

null

0

1

0

1

0

4

7328c8e72a13ab852db005ef4c10aa743256ffb8

344

py

Python

src/FFEAT/ffeat/__init__.py

PatrikValkovic/MasterThesis

6e9f3b186541db6c8395ebc96ace7289d01c805b

[ "MIT" ]

null

src/FFEAT/ffeat/__init__.py

PatrikValkovic/MasterThesis

6e9f3b186541db6c8395ebc96ace7289d01c805b

[ "MIT" ]

null

src/FFEAT/ffeat/__init__.py

PatrikValkovic/MasterThesis

6e9f3b186541db6c8395ebc96ace7289d01c805b

[ "MIT" ]

null

############################### # # Created by Patrik Valkovic # 3/9/2021 # ############################### from .Pipe import Pipe, STANDARD_REPRESENTATION from .NormalizedPipe import NormalizedPipe from . import flow from . import measure from . import utils from . import strategies from . import genetic from . import pso from . import cma

20.235294

47

0.630814

38

344

5.684211

0.526316

0.324074

0

0.020339

0.142442

344

16

48

21.5

0.711864

0.101744

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

4

732fa2f9aa6599c1773b284aa81c6537453cf733

439

py

Python

FirebaseLoginScreen/main.py

solointer11/KivyBusApp

2eaa0158e3594c4b2ffc81affd0ec177adc60d8d

[ "MIT" ]

1

2020-07-18T22:07:39.000Z

FirebaseLoginScreen/main.py

solointer11/KivyBusApp

2eaa0158e3594c4b2ffc81affd0ec177adc60d8d

[ "MIT" ]

null

FirebaseLoginScreen/main.py

solointer11/KivyBusApp

2eaa0158e3594c4b2ffc81affd0ec177adc60d8d

[ "MIT" ]

null

if __name__ == "__main__": from kivy.app import App from kivy import utils # -- This import can be done in kv lang or python class MainApp(App): #login_primary_color = utils.get_color_from_hex("#ABCDEF")#(1, 0, 0, 1) #login_secondary_color = utils.get_color_from_hex("#060809")#(1, 1, 0, 1) #login_tertiary_color = utils.get_color_from_hex("#434343")#(0,0, 1, 1) pass MainApp().run()

31.357143

81

0.637813

67

439

3.835821

0.492537

0.116732

0.151751

0.210117

0.291829

0

0.070796

0.22779

439

14

82

31.357143

0.687316

0.57631

0

0.045455

0

1

0

true

0.166667

0.333333

0

0.5

0

null

0

1

0

null

0

1

0

4

b407ea921989eec447bc1d366976486f8c6fcc21

1,228

py

Python

ciperpus_test_client.py

R-N/ciperpus_test

6075133fbd6cf1b7dd16434a9eb9611d3bd72929

[ "MIT" ]

null

ciperpus_test_client.py

R-N/ciperpus_test

6075133fbd6cf1b7dd16434a9eb9611d3bd72929

[ "MIT" ]

null

ciperpus_test_client.py

R-N/ciperpus_test

6075133fbd6cf1b7dd16434a9eb9611d3bd72929

[ "MIT" ]

null

from ciperpus_exception import * from ciperpus_test_exception import * from ciperpus_test_context import * from ciperpus_client import ciperpus_client class ciperpus_test_client: def __init__(self, client=None): if client is None: self.client = ciperpus_client() else: self.client = client def login(self, username, password, expect_error=None, use_button=True): with ciperpus_test_context(expect_error) as context: self.client.login(username, password) def logout(self, expect_error=None): with ciperpus_test_context(expect_error) as context: self.client.logout() def dashboard(self, expect_error=None): with ciperpus_test_context(expect_error) as context: self.client.logout() @property def url(self): return self.client.url def check_url(self, endpoint): return self.client.check_url(endpoint) def close(self): return self.client.close() def quit(self): return self.client.quit() def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.quit() test_client_instance = None def get_test_client(): global test_client_instance test_client_instance = test_client_instance or ciperpus_test_client() return test_client_instance

24.56

73

0.773616

173

1,228

5.184971

0.254335

0.111483

0.100334

0.076923

0.362319

0.2932

0.232999

0

0.140879

1,228

50

74

24.56

0.850237

0

0.135135

0

1

0.297297

false

0.054054

0.108108

0.135135

0.594595

0

null

0

null

0

1

0

1

0

1

0

4

b412227b154ac29c5efea1e06da298740ad7c046

130

py

Python

test.py

canyoupleasecreateanaccount/pytest-slack

1894d6168394a1daffdeff5ced05814a68594cdd

[ "MIT" ]

null

test.py

canyoupleasecreateanaccount/pytest-slack

1894d6168394a1daffdeff5ced05814a68594cdd

[ "MIT" ]

null

test.py

canyoupleasecreateanaccount/pytest-slack

1894d6168394a1daffdeff5ced05814a68594cdd

[ "MIT" ]

null

z = "Tests were ran for service: matchService \n" \ "Passed: \n" \ "Failed: \n" \ "Error: \n" \ "Skipped: \n"

21.666667

51

0.492308

16

130

4

0.75

0

0.315385

130

5

52

26

0.719101

0

0.669231

0

1

0

false

0.2

0

1

0

null

0

1

0

1

0

null

0

1

0

4

b4179e48e94d2df8b9be7d67d89d129fb24f7f10

41

py

Python

Day_1_Scientific_Python/snippets/02-pandas_introduction65.py

Morisset/python-workshop

ec8b0c4f08a24833e53a22f6b52566a08715c9d0

[ "BSD-3-Clause" ]

183

2016-08-24T12:32:07.000Z

2022-03-26T14:05:04.000Z

Day_1_Scientific_Python/snippets/02-pandas_introduction65.py

Morisset/python-workshop

ec8b0c4f08a24833e53a22f6b52566a08715c9d0

[ "BSD-3-Clause" ]

100

2016-12-15T03:44:06.000Z

2022-03-07T08:14:07.000Z

Day_1_Scientific_Python/snippets/02-pandas_introduction65.py

Morisset/python-workshop

ec8b0c4f08a24833e53a22f6b52566a08715c9d0

[ "BSD-3-Clause" ]

204

2016-08-24T14:22:58.000Z

2022-03-29T15:09:03.000Z

df.loc[df['Sex'] == 'male', 'Age'].mean()

41

0.512195

7

41

3

0.857143

0

0.073171

41

1

41

0.552632

0

0.238095

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

b43ff936903c6eb6884da38660988e5cfb3d3c3a

342

py

Python

py17track/errors.py

ofalvai/py17track

4e2705adcebf2a5ea6d9da2e39d6886013afd9b4

[ "MIT" ]

23

2018-07-28T17:44:03.000Z

2022-03-14T19:30:27.000Z

py17track/errors.py

ofalvai/py17track

4e2705adcebf2a5ea6d9da2e39d6886013afd9b4

[ "MIT" ]

62

2018-10-31T03:58:05.000Z

2022-03-14T20:18:41.000Z

py17track/errors.py

ofalvai/py17track

4e2705adcebf2a5ea6d9da2e39d6886013afd9b4

[ "MIT" ]

9

2020-10-16T10:49:42.000Z

2022-02-17T04:24:26.000Z

"""Define module exceptions.""" class SeventeenTrackError(Exception): """Define a base error.""" pass class InvalidTrackingNumberError(SeventeenTrackError): """Define an error for an invalid tracking number.""" pass class RequestError(SeventeenTrackError): """Define an error for HTTP request errors.""" pass

17.1

57

0.704678

34

342

7.088235

0.588235

0.074689

0.224066

0.26556

0.290456

0

0.187135

342

19

58

18

0.866906

0.394737

0

0.5

0

1

0

true

0.5

0

0.5

0

null

0

1

0

null

0

1

0

4

b45d024c5f144ec7d9bd572d324d3eea4f2b4820

317

py

Python

backend/rumergy_backend/rumergy/models/__init__.py

Firefly-Tech/rumergy-webapp

859054bd9ee710a11b393027bb9cb1bad55d0f00

[ "MIT" ]

1

2021-11-08T00:28:37.000Z

backend/rumergy_backend/rumergy/models/__init__.py

Firefly-Tech/rumergy-webapp

859054bd9ee710a11b393027bb9cb1bad55d0f00

[ "MIT" ]

1

2021-11-02T02:17:37.000Z

backend/rumergy_backend/rumergy/models/__init__.py

Firefly-Tech/rumergy-webapp

859054bd9ee710a11b393027bb9cb1bad55d0f00

[ "MIT" ]

1

2021-10-18T22:27:04.000Z

from .user_profile import UserProfile from .access_request import AccessRequest from .data_log import DataLog from .meter_model import MeterModel from .building import Building from .meter import Meter from .meter_data import MeterData from .data_log_measures import DataLogMeasures from .data_point import DataPoint

31.7

46

0.858044

44

317

6

0.454545

0.090909

0.083333

0

0.113565

317

9

47

35.222222

0.939502

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c30b201d7ad864cdb0e856d23ae2e8b0ca901a7f

101

py

Python

python/testData/inspections/PyUnresolvedReferencesInspection/InstanceAttributeCreatedThroughWithStatementInAnotherFile/foo.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyUnresolvedReferencesInspection/InstanceAttributeCreatedThroughWithStatementInAnotherFile/foo.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyUnresolvedReferencesInspection/InstanceAttributeCreatedThroughWithStatementInAnotherFile/foo.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class Foo(object): def __init__(self): with open('scope') as self.scope: pass

25.25

41

0.564356

13

101

4.076923

0.846154

0

0.316832

101

4

42

25.25

0.768116

0

0.04902

0

1

0.25

false

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c3161a1bfc0ebbf49c7e875757b2ef002e247b84

953

py

Python

lib/systems/anisole.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

lib/systems/anisole.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

lib/systems/anisole.py

pulsar-chem/BPModule

f8e64e04fdb01947708f098e833600c459c2ff0e

[ "BSD-3-Clause" ]

null

import pulsar as psr def load_ref_system(): """ Returns anisole as found in the IQMol fragment library. All credit to https://github.com/nutjunkie/IQmol """ return psr.make_system(""" C 0.6504 1.1978 -0.0297 C 1.9454 0.7011 -0.0190 C 2.1752 -0.6726 0.0122 C 1.1032 -1.5553 0.0323 C -0.2051 -1.0813 0.0222 C -0.4284 0.2985 -0.0085 H 0.4708 2.2786 -0.0544 H 2.7923 1.3949 -0.0349 H 3.2006 -1.0551 0.0208 H 1.2845 -2.6349 0.0565 H -1.0364 -1.7948 0.0383 O -1.6672 0.9093 -0.0224 C -2.8002 0.0778 0.0128 H -2.8636 -0.5772 -0.8639 H -3.6200 0.8011 -0.0005 H -2.8459 -0.5255 0.9269 """)

39.708333

63

0.41553

141

953

2.787234

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0.015267

0

0.486815

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953

23

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41.434783

0.310345

0.109129

0

0.898673

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0.05

true

0

0.05

0

0.15

0

null

0

1

0

1

0

1

0

null

0

1

0

4

c31c4347b4c4487f06bb4cfdc0a77ff446995c75

64

py

Python

FWCore/Integration/python/forbidden_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2020-08-12T08:37:04.000Z

FWCore/Integration/python/forbidden_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

null

FWCore/Integration/python/forbidden_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-03-19T13:44:54.000Z

import FWCore.ParameterSet.Config as cms import restricted_cff

16

40

0.859375

9

64

6

0.888889

0

0.109375

64

3

41

21.333333

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c334e1370343a51072d6b6f11e1cdd14b02fd88f

361

py

Python

preprocessing/cleaning/FilterSpaces.py

mrForest13/sentiment-analysis

f747800b78dbf5d9c598d52cbe9f92a44b90cc42

[ "Apache-2.0" ]

null

preprocessing/cleaning/FilterSpaces.py

mrForest13/sentiment-analysis

f747800b78dbf5d9c598d52cbe9f92a44b90cc42

[ "Apache-2.0" ]

null

preprocessing/cleaning/FilterSpaces.py

mrForest13/sentiment-analysis

f747800b78dbf5d9c598d52cbe9f92a44b90cc42

[ "Apache-2.0" ]

null

from preprocessing.Processor import Processor class FilterSpacesProcessor(Processor): def process(self, data): data['text'] = self.remove_unnecessary_spaces(data) return self.next_processor.process(data) @staticmethod def remove_unnecessary_spaces(data): return data["text"].swifter.apply(lambda x: " ".join(x.split()))

25.785714

72

0.711911

41

361

6.146341

0.560976

0.063492

0.18254

0.214286

0.261905

0

0.174515

361

13

73

27.769231

0.845638

0

0.024931

0

1

0.25

false

0

0.125

0.75

0

null

0

1

0

null

0

1

0

1

0

4

c339ce8297c0aad1a314edcb843f427becbadd79

1,548

py

Python

src/niveristand/clientapi/__init__.py

ioancornea/niveristand-python

a7fd578aefa904e9eb0bab00762af0ebba21ada0

[ "MIT" ]

6

2018-07-04T10:59:43.000Z

2022-03-24T13:34:33.000Z

src/niveristand/clientapi/__init__.py

ioancornea/niveristand-python

a7fd578aefa904e9eb0bab00762af0ebba21ada0

[ "MIT" ]

14

2018-11-05T20:05:33.000Z

2022-03-10T12:54:58.000Z

src/niveristand/clientapi/__init__.py

ioancornea/niveristand-python

a7fd578aefa904e9eb0bab00762af0ebba21ada0

[ "MIT" ]

15

2018-07-04T07:58:49.000Z

2022-02-22T16:35:26.000Z

from niveristand.clientapi._datatypes import BooleanValue from niveristand.clientapi._datatypes import BooleanValueArray from niveristand.clientapi._datatypes import ChannelReference from niveristand.clientapi._datatypes import DoubleValue from niveristand.clientapi._datatypes import DoubleValueArray from niveristand.clientapi._datatypes import I32Value from niveristand.clientapi._datatypes import I32ValueArray from niveristand.clientapi._datatypes import I64Value from niveristand.clientapi._datatypes import I64ValueArray from niveristand.clientapi._datatypes import U32Value from niveristand.clientapi._datatypes import U32ValueArray from niveristand.clientapi._datatypes import U64Value from niveristand.clientapi._datatypes import U64ValueArray from niveristand.clientapi._datatypes import VectorChannelReference from niveristand.clientapi._realtimesequencedefinitionapi.erroraction import ErrorAction from niveristand.clientapi.realtimesequence import RealTimeSequence from niveristand.clientapi.stimulusprofileapi import StimulusProfileState __all__ = ["BooleanValue", "BooleanValueArray", "ChannelReference", "DoubleValue", "DoubleValueArray", "I32Value", "I32ValueArray", "I64Value", "I64ValueArray", "U32Value", "U32ValueArray", "U64Value", "U64ValueArray", "VectorChannelReference", "ErrorAction", "RealTimeSequence", "StimulusProfileState", ]

40.736842

88

0.763566

121

1,548

9.61157

0.206612

0.219261

0.350817

0.397249

0.469475

0

0.025177

0.178941

1,548

37

89

41.837838

0.889851

0

0.145349

0.014212

0

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0

false

0

0.485714

0

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0

null

1

0

null

0

1

0

4

c33d4eca6c592b71e641e5dcbdadf82d15d2244f

4,788

py

Python

release/stubs.min/Autodesk/Revit/DB/Structure/__init___parts/RebarContainerParameterManager.py

htlcnn/ironpython-stubs

780d829e2104b2789d5f4d6f32b0ec9f2930ca03

[ "MIT" ]

182

2017-06-27T02:26:15.000Z

2022-03-30T18:53:43.000Z

release/stubs.min/Autodesk/Revit/DB/Structure/__init___parts/RebarContainerParameterManager.py

htlcnn/ironpython-stubs

780d829e2104b2789d5f4d6f32b0ec9f2930ca03

[ "MIT" ]

28

2017-06-27T13:38:23.000Z

2022-03-15T11:19:44.000Z

release/stubs.min/Autodesk/Revit/DB/Structure/__init___parts/RebarContainerParameterManager.py

htlcnn/ironpython-stubs

780d829e2104b2789d5f4d6f32b0ec9f2930ca03

[ "MIT" ]

67

2017-06-28T09:43:59.000Z

2022-03-20T21:17:10.000Z

class RebarContainerParameterManager(object,IDisposable): """ Provides implementation of RebarContainer parameters overrides. """ def AddOverride(self,paramId,value): """ AddOverride(self: RebarContainerParameterManager,paramId: ElementId,value: int) Adds an override for the given parameter as its value will be displayed for the Rebar Container element. paramId: The id of the parameter value: The override value of the parameter. AddOverride(self: RebarContainerParameterManager,paramId: ElementId,value: float) Adds an override for the given parameter as its value will be displayed for the Rebar Container element. paramId: The id of the parameter value: The override value of the parameter. AddOverride(self: RebarContainerParameterManager,paramId: ElementId,value: ElementId) Adds an override for the given parameter as its value will be displayed for the Rebar Container element. paramId: The id of the parameter value: The override value of the parameter. AddOverride(self: RebarContainerParameterManager,paramId: ElementId,value: str) Adds an override for the given parameter as its value will be displayed for the Rebar Container element. paramId: The id of the parameter value: The override value of the parameter. """ pass def AddSharedParameterAsOverride(self,paramId): """ AddSharedParameterAsOverride(self: RebarContainerParameterManager,paramId: ElementId) Adds a shared parameter as one of the parameter overrides stored by this Rebar Container element. paramId: The id of the shared parameter element """ pass def ClearOverrides(self): """ ClearOverrides(self: RebarContainerParameterManager) Clears any overridden values from all parameters of the associated RebarContainer element. """ pass def Dispose(self): """ Dispose(self: RebarContainerParameterManager) """ pass def IsOverriddenParameterModifiable(self,paramId): """ IsOverriddenParameterModifiable(self: RebarContainerParameterManager,paramId: ElementId) -> bool Checks if overridden parameter is modifiable. paramId: Overridden parameter id Returns: True if the parameter is modifiable,false if the parameter is readonly. """ pass def IsParameterOverridden(self,paramId): """ IsParameterOverridden(self: RebarContainerParameterManager,paramId: ElementId) -> bool Checks if the parameter has an override paramId: The id of the parameter element Returns: True if the parameter has an override """ pass def IsRebarContainerParameter(self,paramId): """ IsRebarContainerParameter(self: RebarContainerParameterManager,paramId: ElementId) -> bool Checks if the parameter is a Rebar Container parameter paramId: The id of the parameter element Returns: True if the parameter is a Rebar Container parameter """ pass def ReleaseUnmanagedResources(self,*args): """ ReleaseUnmanagedResources(self: RebarContainerParameterManager,disposing: bool) """ pass def RemoveOverride(self,paramId): """ RemoveOverride(self: RebarContainerParameterManager,paramId: ElementId) Removes an overridden value from the given parameter. paramId: The id of the parameter """ pass def SetOverriddenParameterModifiable(self,paramId): """ SetOverriddenParameterModifiable(self: RebarContainerParameterManager,paramId: ElementId) Sets this overridden parameter to be modifiable. paramId: Overridden parameter id """ pass def SetOverriddenParameterReadonly(self,paramId): """ SetOverriddenParameterReadonly(self: RebarContainerParameterManager,paramId: ElementId) Sets this overridden parameter to be readonly. paramId: Overridden parameter id """ pass def __enter__(self,*args): """ __enter__(self: IDisposable) -> object """ pass def __exit__(self,*args): """ __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) """ pass def __init__(self,*args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass def __repr__(self,*args): """ __repr__(self: object) -> str """ pass IsValidObject=property(lambda self: object(),lambda self,v: None,lambda self: None) """Specifies whether the .NET object represents a valid Revit entity. Get: IsValidObject(self: RebarContainerParameterManager) -> bool """

24.680412

215

0.71533

503

4,788

6.675944

0.198807

0.064324

0.05003

0.163788

0.525015

0.506552

0.450268

0.421382

0.39458

0.350506

0

0.211571

4,788

193

216

24.80829

0.889536

0.694862

0

0.46875

0

1

0.46875

false

0.46875

0

0.53125

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c346e7dcb789d9a57933e9423921d7e66c3c59a8

2,010

py

Python

data_structures/linked-list/test_ll_merge.py

zarkle/data-structures-and-algorithms

0485b95f5aabc0ee255cd7e50b48a6ccec851e00

[ "MIT" ]

1

2021-01-28T07:32:17.000Z

data_structures/linked-list/test_ll_merge.py

zarkle/data-structures-and-algorithms

0485b95f5aabc0ee255cd7e50b48a6ccec851e00

[ "MIT" ]

null

data_structures/linked-list/test_ll_merge.py

zarkle/data-structures-and-algorithms

0485b95f5aabc0ee255cd7e50b48a6ccec851e00

[ "MIT" ]

1

2020-04-10T08:01:50.000Z

from ll_merge import merge_lists as ml def test_merge_list(short_ll, long_ll): """test merged list with lists of varying lengths""" assert ml(short_ll, long_ll) == 8 assert len(long_ll) == 10 def test_merge_list_values(short_ll, long_ll): """test merged list with lists of varying lengths""" ml(short_ll, long_ll) assert long_ll.head.val == 8 assert long_ll.head._next.val == 16 def test_merge_list_two(long_ll, short_ll): """test merged list with lists of varying lengths""" assert ml(long_ll, short_ll) == 16 assert len(long_ll) == 10 def test_merge_list_two_values(long_ll, short_ll): """test merged list with lists of varying lengths""" ml(long_ll, short_ll) assert long_ll.head.val == 16 assert long_ll.head._next.val == 8 def test_merge_list_same(short_ll, small_ll): """test merged list with lists of same length""" assert ml(short_ll, small_ll) == 8 assert len(small_ll) == 8 def test_merge_list_same_values(short_ll, small_ll): """test merged list with lists of same length""" ml(short_ll, small_ll) assert small_ll.head.val == 8 assert small_ll.head._next.val == 4 assert small_ll.head._next._next._next._next._next._next._next.val == 1 def test_merge_list_empty(short_ll, empty_ll): """test merged list when one list is empty""" assert ml(short_ll, empty_ll) == 8 assert len(short_ll) == 4 def test_merge_list_empty_values(short_ll, empty_ll): """test merged list when one list is empty""" ml(short_ll, empty_ll) assert short_ll.head.val == 8 assert short_ll.head._next.val == 7 def test_merge_list_empty_first(empty_ll, short_ll): """test merged list when one list is empty""" assert ml(empty_ll, short_ll) == 8 assert len(short_ll) == 4 def test_merge_list_empty_first_values(empty_ll, short_ll): """test merged list when one list is empty""" ml(empty_ll, short_ll) assert short_ll.head.val == 8 assert short_ll.head._next.val == 7

29.130435

75

0.703483

346

2,010

3.783237

0.109827

0.139037

0.091673

0.122231

0.903743

0.735676

0.591291

0.540871

0

0.015951

0.189055

2,010

68

76

29.558824

0.787117

0.215423

0

0.216216

0

0.567568

1

0.27027

false

0

0.027027

0

0.297297

0

null

0

1

0

null

0

1

0

1

0

4

c365c5f6a562d4aede79aea0f69f00400ee84f7a

38

py

Python

CanvasSync/settings/__init__.py

jnbli/CanvasSync

763eeb91d092aaaf225ea46abcfc5dd0a4a0f8c0

[ "MIT" ]

34

2017-08-28T23:35:11.000Z

2022-03-29T00:09:50.000Z

CanvasSync/settings/__init__.py

jnbli/CanvasSync

763eeb91d092aaaf225ea46abcfc5dd0a4a0f8c0

[ "MIT" ]

23

2017-02-07T16:42:46.000Z

2022-03-13T07:49:35.000Z

CanvasSync/settings/__init__.py

jnbli/CanvasSync

763eeb91d092aaaf225ea46abcfc5dd0a4a0f8c0

[ "MIT" ]

26

2017-02-11T08:59:31.000Z

2022-03-15T09:20:05.000Z

""" CanvasSync by Mathias Perslev """

19

37

0.684211

4

38

6.5

1

0

0.157895

38

1

38

0.8125

0.763158

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

5ef4215a969655dd8506fb5e4bf815ff31aa21e9

145

py

Python

chap7/even_or_odd.py

wikilike7/python-crash-course

85cd7a2ab6e43a554c282b6e0c1c44c415cca3a3

[ "MIT" ]

null

chap7/even_or_odd.py

wikilike7/python-crash-course

85cd7a2ab6e43a554c282b6e0c1c44c415cca3a3

[ "MIT" ]

null

chap7/even_or_odd.py

wikilike7/python-crash-course

85cd7a2ab6e43a554c282b6e0c1c44c415cca3a3

[ "MIT" ]

1

2019-03-05T09:31:27.000Z

number = input('Enter a number: ') number = int(number) if number % 2 == 0: print('The number is even') else: print('The number is odd')

20.714286

34

0.627586

23

145

3.956522

0.608696

0.175824

0.307692

0.351648

0

0.017699

0.22069

145

7

35

20.714286

0.787611

0

0.349315

0

1

0

false

0

0.333333

1

0

null

0

1

0

1

0

null

0

4

6f0ce1607f3e882dea2f9fb9d0c7dd0008bbdd70

136

py

Python

doc/architecture/examples/attribute-control/proxied-attr.py

elmordo/Py3AMF

ac12211459d6e11de3fb4f03a43bc0e688c6c1f6

[ "MIT" ]

87

2015-01-25T14:54:00.000Z

2021-11-16T13:12:40.000Z

doc/architecture/examples/attribute-control/proxied-attr.py

thijstriemstra/pyamf

d13915dfc68d06eb69ffc3e4e2a23257383568cc

[ "MIT" ]

36

2015-01-05T01:24:59.000Z

2021-09-15T20:40:33.000Z

doc/architecture/examples/attribute-control/proxied-attr.py

thijstriemstra/pyamf

d13915dfc68d06eb69ffc3e4e2a23257383568cc

[ "MIT" ]

37

2015-01-04T03:31:28.000Z

2022-01-20T04:38:49.000Z

import pyamf class Person(object): class __amf__: proxy = ('address',) pyamf.register_class(Person, 'com.acme.app.Person')

19.428571

51

0.683824

17

136

5.176471

0.705882

0.25

0

0.176471

136

7

51

19.428571

0.785714

0

0.189781

0

1

0

false

0

0.2

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

4

6f39e4ffcd734d85bd6e06a14af21c8cd63041b3

1,229

py

Python

opencontrail/files/test_files/conftest.py

casek14/salt-formula-opencontrail

65d4ca1356cbb52341c94fdd44d5b79fc77f9392

[ "Apache-2.0" ]

3

2017-06-15T15:29:03.000Z

2018-07-19T11:35:20.000Z

opencontrail/files/test_files/conftest.py

casek14/salt-formula-opencontrail

65d4ca1356cbb52341c94fdd44d5b79fc77f9392

[ "Apache-2.0" ]

13

2017-02-15T06:28:22.000Z

2018-05-04T14:57:18.000Z

opencontrail/files/test_files/conftest.py

casek14/salt-formula-opencontrail

65d4ca1356cbb52341c94fdd44d5b79fc77f9392

[ "Apache-2.0" ]

12

2017-02-05T23:11:33.000Z

2017-10-05T01:17:08.000Z

# pytest settings and fixtures from stepler import * from stepler.conftest import * # noqa from stepler.conftest import __all__ from stepler.conftest import pytest_plugins from stepler.glance.fixtures import * # noqa from stepler.keystone.fixtures import * # noqa from stepler.neutron.fixtures import * # noqa from stepler.nova.fixtures import * # noqa from vapor.fixtures.contrail import * # noqa from vapor.fixtures.contrail_resources import * # noqa from vapor.fixtures.different_tenants_resources import * # noqa from vapor.fixtures.dns import * # noqa from vapor.fixtures.images import * # noqa from vapor.fixtures.instance_ip import * # noqa from vapor.fixtures.ipams import * # noqa from vapor.fixtures.lbaas import * # noqa from vapor.fixtures.networks import * # noqa from vapor.fixtures.nodes import * # noqa from vapor.fixtures.policies import * # noqa from vapor.fixtures.security_groups import * # noqa from vapor.fixtures.service_chain import * # noqa from vapor.fixtures.skip import * # noqa from vapor.fixtures.subnets import * # noqa from vapor.fixtures.system_services import * # noqa from vapor.fixtures.virtual_interface import * # noqa pytest_plugins = [ 'vapor.plugins.xfail', ]

38.40625

64

0.771359

162

1,229

5.765432

0.246914

0.235546

0.314775

0.345824

0.620985

0.152034

0

0.148088

1,229

31

65

39.645161

0.892073

0.112286

0

0.017807

0

1

0

false

0

0.892857

0

0.892857

0

null

1

0

null

0

1

0

4

6f67d691806c52489c531270cc293cfdb5b2a538

211

py

Python

appspider/spiders/wenshucourt/__init__.py

SullivanLin/appspider

116457ac93bd90e79c9eb4e9db37fcb3427fbd35

[ "MIT" ]

10

2018-09-17T07:45:12.000Z

2020-08-01T18:33:32.000Z

appspider/spiders/wenshucourt/__init__.py

pingfangx/appspider

d974cfbf9d926b686e4e5f550f55d045955bb370

[ "MIT" ]

null

appspider/spiders/wenshucourt/__init__.py

pingfangx/appspider

d974cfbf9d926b686e4e5f550f55d045955bb370

[ "MIT" ]

6

2018-07-25T16:30:40.000Z

2020-08-01T18:36:12.000Z

# -*- coding: utf-8 -*- # @Time : 2018/4/12 20:16 # @Author : ddvv # @Site : http://ddvv.life # @File : __init__.py.py # @Software: PyCharm def main(): pass if __name__ == "__main__": main()

16.230769

29

0.545024

28

211

3.678571

0.857143

0

0.075949

0.251185

211

13

30

16.230769

0.575949

0.649289

0

0.117647

0

1

0.25

true

0.25

0

0.25

0

1

0

null

0

null

0

1

0

4

488e6f11e1a5bd7e3145a73f5e24ca20e637d24c

73

py

Python

tests/rest_framework/__init__.py

A-Ashiq/django-filter

5a2548afebba2d30f5fede12e7bf4e3f6ef16920

[ "BSD-3-Clause" ]

2,512

2016-02-19T11:48:54.000Z

2022-03-30T03:26:15.000Z

tests/rest_framework/__init__.py

A-Ashiq/django-filter

5a2548afebba2d30f5fede12e7bf4e3f6ef16920

[ "BSD-3-Clause" ]

979

2015-11-23T08:14:39.000Z

2022-03-26T02:54:18.000Z

tests/rest_framework/__init__.py

A-Ashiq/django-filter

5a2548afebba2d30f5fede12e7bf4e3f6ef16920

[ "BSD-3-Clause" ]

572

2016-02-25T16:07:00.000Z

2022-02-24T20:49:47.000Z

default_app_config = 'tests.rest_framework.apps.RestFrameworkTestConfig'

36.5

72

0.876712

8

73

7.625

1

0

0.041096

73

1

73

0.871429

0

0.671233

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

48a12d2bbb12271a0fabea408592390227552cb9

39,329

py

Python

parserutils/tests/collection_tests.py

consbio/parserutils

50e0e4b6afd807a7cf230b2b2fccfe0b287bc2ab

[ "BSD-3-Clause" ]

null

parserutils/tests/collection_tests.py

consbio/parserutils

50e0e4b6afd807a7cf230b2b2fccfe0b287bc2ab

[ "BSD-3-Clause" ]

1

2021-03-03T23:02:44.000Z

2021-03-17T19:20:32.000Z

parserutils/tests/collection_tests.py

consbio/parserutils

50e0e4b6afd807a7cf230b2b2fccfe0b287bc2ab

[ "BSD-3-Clause" ]

null

import unittest from copy import deepcopy from ..collections import accumulate_items, setdefaults from ..collections import filter_empty, flatten_items from ..collections import remove_duplicates, rfind, rindex, reduce_value, wrap_value class DictsTestCase(unittest.TestCase): def test_accumulate_items(self): """ Tests accumulate_items with general inputs """ # Test with empty items self.assertEqual(accumulate_items(None), {}) self.assertEqual(accumulate_items(b''), {}) self.assertEqual(accumulate_items(''), {}) self.assertEqual(accumulate_items(dict()), {}) self.assertEqual(accumulate_items(list()), {}) self.assertEqual(accumulate_items(set()), {}) self.assertEqual(accumulate_items(tuple()), {}) self.assertEqual(accumulate_items(x for x in b''), {}) self.assertEqual(accumulate_items((x for x in '')), {}) # Test with items containing single key/val self.assertEqual(accumulate_items({(None, None)}), {None: [None]}) self.assertEqual(accumulate_items([(b'', '')]), {b'': ['']}) self.assertEqual(accumulate_items((['', b''],)), {'': [b'']}) self.assertEqual(accumulate_items((k, v) for k, v in [['key', 'val']]), {'key': ['val']}) self.assertEqual(accumulate_items(((k, v) for k, v in [(0, 1)])), {0: [1]}) # Test with items containing single key/val, reducing each self.assertEqual(accumulate_items({(None, None)}, reduce_each=True), {None: None}) self.assertEqual(accumulate_items([(b'', '')], reduce_each=True), {b'': ''}) self.assertEqual(accumulate_items((['', b''],), reduce_each=True), {'': b''}) self.assertEqual(accumulate_items(((k, v) for k, v in [['key', 'val']]), reduce_each=True), {'key': 'val'}) self.assertEqual(accumulate_items(((k, v) for k, v in [(0, 1)]), reduce_each=True), {0: 1}) # Test with items containing single vals under multiple keys, with and without reduction self.assertEqual( accumulate_items([('key1', 'val'), ('key2', 'val'), ('key3', 'val')]), {'key1': ['val'], 'key2': ['val'], 'key3': ['val']} ) self.assertEqual( accumulate_items([('key1', 'val'), ('key2', 'val'), ('key3', 'val')], reduce_each=True), {'key1': 'val', 'key2': 'val', 'key3': 'val'} ) # Test with items containing multiple vals under a single key, with and without reduction self.assertEqual( accumulate_items([('key', 'val1'), ('key', 'val2'), ('key', 'val3')]), {'key': ['val1', 'val2', 'val3']} ) self.assertEqual( accumulate_items([('key', 'val1'), ('key', 'val2'), ('key', 'val3')], reduce_each=True), {'key': ['val1', 'val2', 'val3']} ) self.assertEqual( accumulate_items( [('key', 'val1'), ('key', 'val2'), ('key2', ['val1', 'val2']), ('key3', 'val3')], reduce_each=True ), {'key': ['val1', 'val2'], 'key2': ['val1', 'val2'], 'key3': 'val3'} ) # Test with items containing multiple vals under multiple keys, with and without reduction self.assertEqual( accumulate_items([('key3', 'val1'), ('key2', 'val2'), ('key1', 'val3')]), {'key1': ['val3'], 'key2': ['val2'], 'key3': ['val1']} ) self.assertEqual( accumulate_items([('key3', 'val1'), ('key2', 'val2'), ('key1', 'val3')], reduce_each=True), {'key1': 'val3', 'key2': 'val2', 'key3': 'val1'} ) def test_setdefaults(self): """ Tests setdefaults with general inputs """ # Test with invalid dict and empty defaults self.assertEqual(setdefaults(None, None), None) self.assertEqual(setdefaults(b'', None), b'') self.assertEqual(setdefaults('', None), '') self.assertEqual(setdefaults({}, None), {}) self.assertEqual([x for x in setdefaults((c for c in 'abc'), None)], [c for c in 'abc']) # Test with invalid dict and valid defaults self.assertEqual(setdefaults(None, 'x'), None) self.assertEqual(setdefaults(b'', 'y'), b'') self.assertEqual(setdefaults('', 'z'), '') self.assertEqual([x for x in setdefaults((c for c in 'abc'), 'xyz')], [c for c in 'abc']) # Test with empty dict and valid defaults self.assertEqual(setdefaults({}, 'a'), {'a': None}) self.assertEqual(setdefaults({}, ['b']), {'b': None}) self.assertEqual(setdefaults({}, {'c': None}), {'c': None}) self.assertEqual(setdefaults({}, {'c': False}), {'c': False}) self.assertEqual(setdefaults({}, {'c': True}), {'c': True}) self.assertEqual(setdefaults({}, {'c': 0}), {'c': 0}) self.assertEqual(setdefaults({}, {'c': 1}), {'c': 1}) self.assertEqual(setdefaults({}, {'c': 2.3}), {'c': 2.3}) self.assertEqual(setdefaults({}, {'d': 'ddd'}), {'d': 'ddd'}) self.assertEqual(setdefaults({}, [{'e': 'eee'}, {'f': 'fff'}]), {'e': 'eee', 'f': 'fff'}) self.assertEqual(setdefaults({}, {'x': 'xxx', 'y': 'yyy'}), {'x': 'xxx', 'y': 'yyy'}) self.assertEqual(setdefaults({'z': 'zzz'}, None), {'z': 'zzz'}) def test_setdefaults_str(self): """ Tests setdefaults with defaults specified as strings """ inputs = 'a.b' d = {} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) # Output should equal input self.assertEqual(o, {'a': {'b': None}}) # Test against a hard value self.assertEqual(setdefaults(d, inputs), o) # Test unchanged with multiple runs d = {'a': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': 'xxx'}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'b': 'xxx'}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': {'b': 'xxx'}}) self.assertEqual(setdefaults(d, inputs), o) d = {'c': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': {'b': None}, 'c': 'xxx'}) self.assertEqual(setdefaults(d, inputs), o) inputs = 'a.b.c' d = {} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': {'b': {'c': None}}}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'b': {'c': 'xxx'}}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': {'b': {'c': 'xxx'}}}) self.assertEqual(setdefaults(d, inputs), o) def test_setdefaults_dict_nested(self): """ Tests setdefaults with nested defaults specified as dicts """ inputs = {'a.b': 'bbb', 'a.c': 'ccc'} d = {} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) # Output should equal input self.assertEqual(o, {'a': {'b': 'bbb', 'c': 'ccc'}}) # Test against a hard value self.assertEqual(setdefaults(d, inputs), o) # Test unchanged with multiple runs d = {'a': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': 'xxx'}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'b': 'xxx'}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a']['c'], 'ccc') self.assertEqual(o['a']['b'], 'xxx') self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'c': 'xxx'}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a']['b'], 'bbb') self.assertEqual(o['a']['c'], 'xxx') self.assertEqual(setdefaults(d, inputs), o) d = {'c': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a'], {'b': 'bbb', 'c': 'ccc'}) self.assertEqual(o['a']['c'], 'ccc') self.assertEqual(o['c'], 'xxx') self.assertEqual(setdefaults(d, inputs), o) inputs = {'a.b.c': True, 'd.e.f': [123.456]} d = {} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a'], {'b': {'c': True}}) self.assertEqual(o['d'], {'e': {'f': [123.456]}}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'b': {'c': 'xxx'}}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a'], {'b': {'c': 'xxx'}}) self.assertEqual(o['d'], {'e': {'f': [123.456]}}) self.assertEqual(setdefaults(d, inputs), o) d = {'d': {'e': {'f': 'xxx'}}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a'], {'b': {'c': True}}) self.assertEqual(o['d'], {'e': {'f': 'xxx'}}) self.assertEqual(setdefaults(d, inputs), o) def test_setdefaults_dict_overlapping(self): """ Tests setdefaults with overlapping defaults specified as dicts """ inputs = {'a.b.c': 'ccc', 'a.c.d.e': 'eee'} d = {} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) # Output should equal input self.assertEqual(o, {'a': {'b': {'c': 'ccc'}, 'c': {'d': {'e': 'eee'}}}}) # Test against a hard value self.assertEqual(setdefaults(d, inputs), o) # Test unchanged with multiple runs d = {'a': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o, {'a': 'xxx'}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'b': 'xxx'}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a']['b'], 'xxx') self.assertEqual(o['a']['c'], {'d': {'e': 'eee'}}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'c': 'xxx'}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a']['b'], {'c': 'ccc'}) self.assertEqual(o['a']['c'], 'xxx') self.assertEqual(setdefaults(d, inputs), o) d = {'c': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(o['a'], {'b': {'c': 'ccc'}, 'c': {'d': {'e': 'eee'}}}) self.assertEqual(o['c'], 'xxx') self.assertEqual(setdefaults(d, inputs), o) def test_setdefaults_other(self): """ Tests setdefaults with defaults specified as list, set, and tuple """ inputs = ['a.b', 'a.c'] d = {} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) # Output should equal input self.assertEqual(setdefaults(d, inputs), {'a': {'b': None, 'c': None}}) # Test against a hard value self.assertEqual(setdefaults(d, inputs), o) # Test unchanged with multiple runs d = {'a': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(setdefaults(d, inputs), {'a': 'xxx'}) self.assertEqual(setdefaults(d, inputs), o) d = {'a': {'b': 'xxx'}} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(setdefaults(d, inputs)['a']['b'], 'xxx') self.assertEqual(setdefaults(d, inputs)['a']['c'], None) self.assertEqual(setdefaults(d, inputs), o) d = {'c': 'xxx'} o = deepcopy(setdefaults(d, inputs)) self.assertEqual(d, o) self.assertEqual(setdefaults(d, inputs)['a'], {'b': None, 'c': None}) self.assertEqual(setdefaults(d, inputs)['c'], 'xxx') self.assertEqual(setdefaults(d, inputs), o) class ListTupleSetTestCase(unittest.TestCase): def test_filter_empty(self): """ Tests filter_empty with general inputs """ # Test None case: nothing to filter but default applies self.assertEqual(filter_empty(None), None) self.assertEqual(filter_empty(None, 'None'), 'None') # Test empty string case: nothing to filter but default applies self.assertEqual(filter_empty(b''), None) self.assertEqual(filter_empty(b'', 'None'), 'None') self.assertEqual(filter_empty(''), None) self.assertEqual(filter_empty('', 'None'), 'None') # Test empty collections case: nothing to filter but default applies self.assertEqual(filter_empty(list()), None) self.assertEqual(filter_empty(list(), 'None'), 'None') self.assertEqual(filter_empty(set()), None) self.assertEqual(filter_empty(set(), 'None'), 'None') self.assertEqual(filter_empty(tuple()), None) self.assertEqual(filter_empty(tuple(), 'None'), 'None') self.assertEqual(filter_empty(x for x in ''), None) self.assertEqual(filter_empty((x for x in ''), 'None'), 'None') # Test when there's nothing to filter self.assertEqual(filter_empty(False), False) self.assertEqual(filter_empty(True), True) self.assertEqual(filter_empty(0), 0) self.assertEqual(filter_empty(1), 1) self.assertEqual(filter_empty('a'), 'a') self.assertEqual(filter_empty('abc'), 'abc') self.assertEqual(filter_empty({'a': 'aaa'}), {'a': 'aaa'}) self.assertEqual(filter_empty({'b': 'bbb', 'c': 'ccc'}), {'b': 'bbb', 'c': 'ccc'}) self.assertEqual(filter_empty(c for c in 'abc'), ['a', 'b', 'c']) self.assertEqual(filter_empty((c for c in 'abc')), ['a', 'b', 'c']) # Test when there's nothing to filter, but with unused default self.assertEqual(filter_empty(0, '0'), 0) self.assertEqual(filter_empty(1, '1'), 1) self.assertEqual(filter_empty('a', 'None'), 'a') self.assertEqual(filter_empty('abc', 'None'), 'abc') self.assertEqual(filter_empty((c for c in 'abc'), 'None'), ['a', 'b', 'c']) # Test with filterable values self.assertEqual(filter_empty([None]), None) self.assertEqual(filter_empty({None}), None) self.assertEqual(filter_empty((None,)), None) self.assertEqual(filter_empty([b'']), None) self.assertEqual(filter_empty({b''}), None) self.assertEqual(filter_empty((b'',)), None) self.assertEqual(filter_empty(['']), None) self.assertEqual(filter_empty({''}), None) self.assertEqual(filter_empty(('',)), None) self.assertEqual(filter_empty(x for x in (None, b'', '')), None) self.assertEqual(filter_empty((x for x in (None, b'', ''))), None) # Test with filterable values and defaults self.assertEqual(filter_empty([None, b'', ''], {}), {}) self.assertEqual(filter_empty({b'', None, ''}, []), []) self.assertEqual(filter_empty((b'', '', None), []), []) self.assertEqual(filter_empty([list(), set(), tuple(), dict()], {}), {}) self.assertEqual(filter_empty((tuple(), dict(), list(), set()), []), []) self.assertEqual(filter_empty(x for x in (None, b'', '')), None) self.assertEqual(filter_empty((x for x in (tuple(), dict(), list(), set())), {}), {}) # Test with values that should not be filtered self.assertEqual(filter_empty([0]), [0]) self.assertEqual(filter_empty([1]), [1]) self.assertEqual(filter_empty(['x']), ['x']) self.assertEqual(filter_empty({'y'}), {'y'}) self.assertEqual(filter_empty(('z',)), ('z',)) self.assertEqual(filter_empty(c for c in '0'), ['0']) self.assertEqual(filter_empty((c for c in '1')), ['1']) # Test with combinations of values self.assertEqual(filter_empty([None, 0, '', 1]), [0, 1]) self.assertEqual(filter_empty(['a', None, 'b', b'', 'c']), ['a', 'b', 'c']) self.assertEqual(filter_empty({None, '', 'a', 'b', 'c'}), {'a', 'b', 'c'}) self.assertEqual(filter_empty(('a', 'b', None, 'c', b'')), ('a', 'b', 'c')) self.assertEqual(filter_empty(t for t in ('a', 'b', tuple(), 'c', set())), ['a', 'b', 'c']) self.assertEqual(filter_empty((t for t in ('a', 'b', 'c', set(), list()))), ['a', 'b', 'c']) # Test with non-filterable collections self.assertEqual(filter_empty({'a': 'aaa'}), {'a': 'aaa'}) self.assertEqual([x for x in filter_empty(c for c in 'abc')], ['a', 'b', 'c']) self.assertEqual([x for x in filter_empty((c for c in 'xyz'))], ['x', 'y', 'z']) def test_flatten_items(self): """ Tests flatten_items with general inputs """ # Test None case: nothing to filter but default applies self.assertEqual(flatten_items(None), None) self.assertEqual(flatten_items(None, True), None) # Test empty string case: nothing to filter but default applies self.assertEqual(flatten_items(b''), b'') self.assertEqual(flatten_items(b'', True), b'') self.assertEqual(flatten_items(''), '') self.assertEqual(flatten_items('', True), '') self.assertEqual(flatten_items(dict()), dict()) self.assertEqual(flatten_items(dict(), True), dict()) # Test empty collections case: nothing to flatten but default applies self.assertEqual(flatten_items(list()), list()) self.assertEqual(flatten_items(list(), True), list()) self.assertEqual(flatten_items(set()), set()) self.assertEqual(flatten_items(set(), True), set()) self.assertEqual(flatten_items(tuple()), tuple()) self.assertEqual(flatten_items(tuple(), True), tuple()) # Test when there's nothing to flatten self.assertEqual(flatten_items(False), False) self.assertEqual(flatten_items(False, True), False) self.assertEqual(flatten_items(True), True) self.assertEqual(flatten_items(True, True), True) self.assertEqual(flatten_items(0), 0) self.assertEqual(flatten_items(0, True), 0) self.assertEqual(flatten_items(1), 1) self.assertEqual(flatten_items(1, True), 1) self.assertEqual(flatten_items('a'), 'a') self.assertEqual(flatten_items('a', True), 'a') self.assertEqual(flatten_items('abc'), 'abc') self.assertEqual(flatten_items('abc', True), 'abc') self.assertEqual(flatten_items({'a': 'aaa'}), {'a': 'aaa'}) self.assertEqual(flatten_items({'a': 'aaa'}, True), {'a': 'aaa'}) self.assertEqual(flatten_items({'b': 'bbb', 'c': 'ccc'}), {'b': 'bbb', 'c': 'ccc'}) self.assertEqual(flatten_items({'b': 'bbb', 'c': 'ccc'}, True), {'b': 'bbb', 'c': 'ccc'}) # Test with single value collections with nothing to flatten, without defaults for flat in (None, b'', '', 'abc', 0, 1, True, False): self.assertEqual(flatten_items([flat]), [flat]) self.assertEqual(flatten_items([flat], True), [flat]) self.assertEqual(flatten_items({flat}), {flat}) self.assertEqual(flatten_items({flat}, True), {flat}) self.assertEqual(flatten_items((flat,)), (flat,)) self.assertEqual(flatten_items((flat,), True), (flat,)) self.assertEqual(flatten_items(f for f in [flat]), [flat]) self.assertEqual(flatten_items((f for f in (flat,)), True), [flat]) # Test with multiple values with nothing to flatten for flat in ([None, b'', ''], (False, True, 0, 1, 'a'), {'False', 'True', '0', '1', 'a'}): for flat_type in (list, tuple, set): flat_in = flat_type(flat) flat_out = flat_in self.assertEqual(flatten_items(flat_in), flat_out) self.assertEqual(flatten_items(flat_in, True), flat_out) self.assertEqual(flatten_items(f for f in flat_in), list(flat_out)) self.assertEqual(flatten_items((f for f in flat_in), True), list(flat_out)) # Test with collection values (some unhashable) that should be flattened, but not recursed for flat_type in (list, tuple): flat_in = flat_type([tuple(), 'a', set(), 'bc', list(), b'xyz', dict()]) flat_out = flat_in self.assertEqual(flatten_items(flat_in), flat_out) self.assertEqual(flatten_items(flat_in, True), flat_out) self.assertEqual(flatten_items(f for f in flat_in), list(flat_out)) self.assertEqual(flatten_items((f for f in flat_in), True), list(flat_out)) # Test with values that should be flattened and recursed in many combinations self.assertEqual(flatten_items([('a', 'b', 'c'), 'd', {'e'}, ['f', 'g']]), ['a', 'b', 'c', 'd', 'e', 'f', 'g']) self.assertEqual(flatten_items((0, [1, 2, 3], 4, 5, {6}, 7)), (0, 1, 2, 3, 4, 5, 6, 7)) self.assertEqual( flatten_items(x for x in ((False, True), {'xyz'}, 7, 8, 9, ['10'])), [False, True, 'xyz', 7, 8, 9, '10'] ) not_yet_flat = [tuple(c for c in 'abc'), 'd', list(c for c in '123'), [None, {False}, {True}]] for flat_type in (list, tuple): flat_to_recurse = flat_type(not_yet_flat) flat_no_recurse = flat_type(['a', 'b', 'c', 'd', '1', '2', '3', None, {False}, {True}]) flat_after_recurse = flat_type(['a', 'b', 'c', 'd', '1', '2', '3', None, False, True]) self.assertEqual(flatten_items(flat_to_recurse), flat_no_recurse) self.assertEqual(flatten_items(flat_to_recurse, True), flat_after_recurse) self.assertEqual(flatten_items(f for f in flat_to_recurse), list(flat_no_recurse)) self.assertEqual(flatten_items((f for f in flat_to_recurse), True), list(flat_after_recurse)) def test_remove_duplicates(self): """ Tests remove_duplicates with general inputs """ # Test with non-iterable values self.assertEqual(remove_duplicates(None), None) self.assertEqual(remove_duplicates(b''), b'') self.assertEqual(remove_duplicates(''), '') self.assertEqual(remove_duplicates(0), 0) self.assertEqual(remove_duplicates(1), 1) self.assertEqual(remove_duplicates(False), False) self.assertEqual(remove_duplicates(True), True) self.assertEqual(remove_duplicates([]), []) self.assertEqual(remove_duplicates({}), {}) self.assertEqual(remove_duplicates(tuple()), tuple()) self.assertEqual(remove_duplicates(set()), set()) # Test with iterable values with nothing to remove self.assertEqual(remove_duplicates('abc'), 'abc') self.assertEqual(remove_duplicates(b'abc'), b'abc') self.assertEqual(remove_duplicates(['a', 'b', 'c']), ['a', 'b', 'c']) self.assertEqual(remove_duplicates(('a', 'b', 'c')), ('a', 'b', 'c')) self.assertEqual(remove_duplicates({'a', 'b', 'c'}), {'a', 'b', 'c'}) self.assertEqual(remove_duplicates(x for x in 'abc'), ['a', 'b', 'c']) self.assertEqual(remove_duplicates({'a': 'aaa'}), {'a': 'aaa'}) self.assertEqual(remove_duplicates({'b': 'bbb', 'c': 'ccc'}), {'b': 'bbb', 'c': 'ccc'}) self.assertEqual(remove_duplicates([('a',), ('b', 'c')]), [('a',), ('b', 'c')]) # Test with iterable unhashable values with nothing to remove self.assertEqual(remove_duplicates([{'a', 'b', 'c'}], is_unhashable=True), [{'a', 'b', 'c'}]) self.assertEqual(remove_duplicates([{'a': 'bc'}, {'d': 'ef'}], is_unhashable=True), [{'a': 'bc'}, {'d': 'ef'}]) # Test that unexpected unhashable values raise TypeError with self.assertRaises(TypeError): remove_duplicates([{'a', 'b', 'c'}], is_unhashable=False) with self.assertRaises(TypeError): remove_duplicates([{'a': 'bc'}, {'d': 'ef'}], is_unhashable=False) # Test with iterable values with duplicates to remove str_test = u'abcabcdefdefghiabcdef' self.assertEqual(remove_duplicates(str_test), u'abcdefghi') self.assertEqual(remove_duplicates(str_test, in_reverse=True), u'ghiabcdef') bin_test = b'abcabcdefdefghiabcdef' self.assertEqual(remove_duplicates(bin_test), b'abcdefghi') self.assertEqual(remove_duplicates(bin_test, in_reverse=True), b'ghiabcdef') list_test = [x for x in 'abcabcdefdefghiabcdef'] self.assertEqual(remove_duplicates(list_test), ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']) self.assertEqual(remove_duplicates(list_test, in_reverse=True), ['g', 'h', 'i', 'a', 'b', 'c', 'd', 'e', 'f']) tuple_test = tuple(x for x in 'abcabcdefdefghiabcdef') self.assertEqual(remove_duplicates(tuple_test), ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i')) self.assertEqual(remove_duplicates(tuple_test, in_reverse=True), ('g', 'h', 'i', 'a', 'b', 'c', 'd', 'e', 'f')) gen_test = (x for x in 'abcabcdefdefghiabcdef') self.assertEqual(remove_duplicates(x for x in gen_test), ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']) gen_test = (x for x in 'abcabcdefdefghiabcdef') self.assertEqual(remove_duplicates(gen_test, in_reverse=True), ['g', 'h', 'i', 'a', 'b', 'c', 'd', 'e', 'f']) # Test with iterable values with all unhashable duplicates to remove list_test = [set(x) for x in 'abcdefabc'] self.assertEqual(remove_duplicates(list_test, is_unhashable=True), [{'a'}, {'b'}, {'c'}, {'d'}, {'e'}, {'f'}]) self.assertEqual( remove_duplicates(list_test, in_reverse=True, is_unhashable=True), [{'d'}, {'e'}, {'f'}, {'a'}, {'b'}, {'c'}] ) tuple_test = tuple(set(x) for x in 'abcdefabc') self.assertEqual(remove_duplicates(tuple_test, is_unhashable=True), ({'a'}, {'b'}, {'c'}, {'d'}, {'e'}, {'f'})) self.assertEqual( remove_duplicates(tuple_test, in_reverse=True, is_unhashable=True), ({'d'}, {'e'}, {'f'}, {'a'}, {'b'}, {'c'}) ) gen_test = (set(x) for x in 'abcdefabc') self.assertEqual( remove_duplicates((x for x in gen_test), is_unhashable=True), [{'a'}, {'b'}, {'c'}, {'d'}, {'e'}, {'f'}] ) gen_test = (set(x) for x in 'abcdefabc') self.assertEqual( remove_duplicates(gen_test, in_reverse=True, is_unhashable=True), [{'d'}, {'e'}, {'f'}, {'a'}, {'b'}, {'c'}] ) # Test with iterable values with some unhashable duplicates to remove list_test = [{'a'}, 'b', {'c'}, 'b', {'a'}] self.assertEqual(remove_duplicates(list_test, is_unhashable=True), [{'a'}, 'b', {'c'}]) self.assertEqual(remove_duplicates(list_test, in_reverse=True, is_unhashable=True), [{'c'}, 'b', {'a'}]) tuple_test = ({'a'}, 'b', {'c'}, 'b', {'a'}) self.assertEqual(remove_duplicates(tuple_test, is_unhashable=True), ({'a'}, 'b', {'c'})) self.assertEqual(remove_duplicates(tuple_test, in_reverse=True, is_unhashable=True), ({'c'}, 'b', {'a'})) gen_test = (x for x in ({'a'}, 'b', {'c'}, 'b', {'a'})) self.assertEqual(remove_duplicates(gen_test, is_unhashable=True), [{'a'}, 'b', {'c'}]) gen_test = (x for x in ({'a'}, 'b', {'c'}, 'b', {'a'})) self.assertEqual(remove_duplicates(gen_test, in_reverse=True, is_unhashable=True), [{'c'}, 'b', {'a'}]) def test_rfind(self): """ Tests rfind with general inputs """ # Test empty cases: nothing to find self.assertEqual(rfind(None, 'x'), -1) self.assertEqual(rfind(b'', 'x'), -1) self.assertEqual(rfind(b'', b'x'), -1) self.assertEqual(rfind('', 'x'), -1) self.assertEqual(rfind('', b'x'), -1) self.assertEqual(rfind(list(), 'x'), -1) self.assertEqual(rfind(tuple(), 'x'), -1) self.assertEqual(rfind(set(), 'x'), -1) self.assertEqual(rfind(dict(), 'x'), -1) # Test missing cases: still nothing to find self.assertEqual(rfind(b'abc', 'x'), -1) self.assertEqual(rfind(b'abc', b'x'), -1) self.assertEqual(rfind(u'abc', 'x'), -1) self.assertEqual(rfind(u'abc', b'x'), -1) self.assertEqual(rfind(['a', 'b', 'c'], 'x'), -1) self.assertEqual(rfind(('a', 'b', 'c'), 'x'), -1) self.assertEqual(rfind({'a', 'b', 'c'}, 'x'), -1) self.assertEqual(rfind({'a': 'aaa', 'b': 'bbb', 'c': 'ccc'}, 'x'), -1) # Test invalid cases: still nothing to find self.assertEqual(rfind({'x', 'y', 'z'}, 'x'), -1) self.assertEqual(rfind({'x': 'xxx', 'y': 'yyy', 'z': 'zzz'}, 'x'), -1) # Test one match cases: find at first, middle and last self.assertEqual(rfind(b'xyz', 'x'), 0) self.assertEqual(rfind(b'yxz', 'x'), 1) self.assertEqual(rfind(b'zyx', 'x'), 2) self.assertEqual(rfind(b'xyz', b'x'), 0) self.assertEqual(rfind(b'yxz', b'x'), 1) self.assertEqual(rfind(b'zyx', b'x'), 2) self.assertEqual(rfind(u'xyz', 'x'), 0) self.assertEqual(rfind(u'yxz', 'x'), 1) self.assertEqual(rfind(u'zyx', 'x'), 2) self.assertEqual(rfind(u'xyz', b'x'), 0) self.assertEqual(rfind(u'yxz', b'x'), 1) self.assertEqual(rfind(u'zyx', b'x'), 2) self.assertEqual(rfind(['x', 'y', 'z'], 'x'), 0) self.assertEqual(rfind(['y', 'x', 'z'], 'x'), 1) self.assertEqual(rfind(['z', 'y', 'x'], 'x'), 2) self.assertEqual(rfind(('x', 'y', 'z'), 'x'), 0) self.assertEqual(rfind(('y', 'x', 'z'), 'x'), 1) self.assertEqual(rfind(('z', 'y', 'x'), 'x'), 2) # Test multiple match cases: find at middle and last self.assertEqual(rfind(b'xxz', 'x'), 1) self.assertEqual(rfind(b'xyx', 'x'), 2) self.assertEqual(rfind(b'xxz', b'x'), 1) self.assertEqual(rfind(b'xyx', b'x'), 2) self.assertEqual(rfind(u'xxz', 'x'), 1) self.assertEqual(rfind(u'xyx', 'x'), 2) self.assertEqual(rfind(u'xxz', b'x'), 1) self.assertEqual(rfind(u'xyx', b'x'), 2) self.assertEqual(rfind(['x', 'x', 'z'], 'x'), 1) self.assertEqual(rfind(['x', 'y', 'x'], 'x'), 2) self.assertEqual(rfind(('x', 'x', 'z'), 'x'), 1) self.assertEqual(rfind(('x', 'y', 'x'), 'x'), 2) def test_rindex(self): """ Tests rindex with general inputs """ # Test valid empty cases: raise ValueError for empty in (b'', '', list(), tuple()): with self.assertRaises(ValueError): rindex(empty, b'x') with self.assertRaises(ValueError): rindex(empty, u'x') # Test invalid empty cases: raise TypeError for empty in (None, set(), dict()): with self.assertRaises(TypeError): rindex(empty, b'x') with self.assertRaises(TypeError): rindex(empty, u'x') # Test valid missing cases: raise ValueError for empty in (b'abc', u'abc', ['a', 'b', 'c'], ('a', 'b', 'c')): with self.assertRaises(ValueError): rindex(empty, b'x') with self.assertRaises(ValueError): rindex(empty, u'x') # Test invalid missing cases: raise TypeError for empty in ({'a', 'b', 'c'}, {'a': 'aaa', 'b': 'bbb', 'c': 'ccc'}): with self.assertRaises(TypeError): rindex(empty, 'x') # Test invalid matching cases: raise TypeError for empty in ({'x', 'y', 'z'}, {'x': 'xxx', 'y': 'yyy', 'z': 'zzz'}): with self.assertRaises(TypeError): rindex(empty, 'x') # Test one match cases: find at first, middle and last self.assertEqual(rindex(b'xyz', 'x'), 0) self.assertEqual(rindex(b'yxz', 'x'), 1) self.assertEqual(rindex(b'zyx', 'x'), 2) self.assertEqual(rindex(b'xyz', b'x'), 0) self.assertEqual(rindex(b'yxz', b'x'), 1) self.assertEqual(rindex(b'zyx', b'x'), 2) self.assertEqual(rindex(u'xyz', 'x'), 0) self.assertEqual(rindex(u'yxz', 'x'), 1) self.assertEqual(rindex(u'zyx', 'x'), 2) self.assertEqual(rindex(u'xyz', b'x'), 0) self.assertEqual(rindex(u'yxz', b'x'), 1) self.assertEqual(rindex(u'zyx', b'x'), 2) self.assertEqual(rindex(['x', 'y', 'z'], 'x'), 0) self.assertEqual(rindex(['y', 'x', 'z'], 'x'), 1) self.assertEqual(rindex(['z', 'y', 'x'], 'x'), 2) self.assertEqual(rindex(('x', 'y', 'z'), 'x'), 0) self.assertEqual(rindex(('y', 'x', 'z'), 'x'), 1) self.assertEqual(rindex(('z', 'y', 'x'), 'x'), 2) # Test multiple match cases: find at middle and last self.assertEqual(rfind(b'xxz', 'x'), 1) self.assertEqual(rfind(b'xyx', 'x'), 2) self.assertEqual(rfind(b'xxz', b'x'), 1) self.assertEqual(rfind(b'xyx', b'x'), 2) self.assertEqual(rfind(u'xxz', 'x'), 1) self.assertEqual(rfind(u'xyx', 'x'), 2) self.assertEqual(rfind(u'xxz', b'x'), 1) self.assertEqual(rfind(u'xyx', b'x'), 2) self.assertEqual(rfind(['x', 'x', 'z'], 'x'), 1) self.assertEqual(rfind(['x', 'y', 'x'], 'x'), 2) self.assertEqual(rfind(('x', 'x', 'z'), 'x'), 1) self.assertEqual(rfind(('x', 'y', 'x'), 'x'), 2) def test_reduce_value(self): """ Tests reduce_value with general inputs """ # Test None case: nothing to reduce but default applies self.assertEqual(reduce_value(None), '') self.assertEqual(reduce_value(None, 'None'), 'None') # Test empty string case: nothing to reduce but default applies self.assertEqual(reduce_value(b''), '') self.assertEqual(reduce_value(b'', 'None'), 'None') self.assertEqual(reduce_value(''), '') self.assertEqual(reduce_value('', 'None'), 'None') # Test empty collections case: nothing to reduce but default applies self.assertEqual(reduce_value(list()), '') self.assertEqual(reduce_value(list(), 'None'), 'None') self.assertEqual(reduce_value(set()), '') self.assertEqual(reduce_value(set(), 'None'), 'None') self.assertEqual(reduce_value(tuple()), '') self.assertEqual(reduce_value(tuple(), 'None'), 'None') # Test when there's nothing to reduce self.assertEqual(reduce_value(0), 0) self.assertEqual(reduce_value(1), 1) self.assertEqual(reduce_value('a'), 'a') self.assertEqual(reduce_value('abc'), 'abc') self.assertEqual(reduce_value({'a': 'aaa'}), {'a': 'aaa'}) self.assertEqual(reduce_value({'b': 'bbb', 'c': 'ccc'}), {'b': 'bbb', 'c': 'ccc'}) # Test when there's nothing to reduce, but with unused default self.assertEqual(reduce_value(0, None), 0) self.assertEqual(reduce_value(1, None), 1) self.assertEqual(reduce_value('a', None), 'a') self.assertEqual(reduce_value('abc', None), 'abc') # Test with reducible values self.assertEqual(reduce_value([None]), None) self.assertEqual(reduce_value([b'']), b'') self.assertEqual(reduce_value(['']), '') self.assertEqual(reduce_value([0]), 0) self.assertEqual(reduce_value([1]), 1) self.assertEqual(reduce_value(['x']), 'x') self.assertEqual(reduce_value({'y'}), 'y') self.assertEqual(reduce_value(('z',)), 'z') # Test with non-reducible values self.assertEqual(reduce_value([None, None]), [None, None]) self.assertEqual(reduce_value([b'', '']), [b'', '']) self.assertEqual(reduce_value([0, 0]), [0, 0]) self.assertEqual(reduce_value([1, 1]), [1, 1]) self.assertEqual(reduce_value(['a', 'b', 'c']), ['a', 'b', 'c']) self.assertEqual(reduce_value({'a', 'b', 'c'}), {'a', 'b', 'c'}) self.assertEqual(reduce_value(('a', 'b', 'c')), ('a', 'b', 'c')) # Test with non-reducible collections self.assertEqual(reduce_value({'a': 'aaa'}), {'a': 'aaa'}) self.assertEqual([x for x in reduce_value(c for c in 'abc')], [c for c in 'abc']) def test_wrap_value(self): """ Tests wrap_value with general inputs """ # Test when there's nothing to wrap self.assertEqual(wrap_value(None), []) self.assertEqual(wrap_value(b''), []) self.assertEqual(wrap_value(''), []) # Test with wrappable values self.assertEqual(wrap_value(0), [0]) self.assertEqual(wrap_value(1), [1]) self.assertEqual(wrap_value('a'), ['a']) self.assertEqual(wrap_value('abc'), ['abc']) self.assertEqual(wrap_value({'a': 'aaa'}), [{'a': 'aaa'}]) self.assertEqual(wrap_value({'b': 'bbb', 'c': 'ccc'}), [{'b': 'bbb', 'c': 'ccc'}]) # Test with already wrapped values self.assertEqual(wrap_value([0]), [0]) self.assertEqual(wrap_value([1]), [1]) self.assertEqual(wrap_value(['x']), ['x']) self.assertEqual(wrap_value({'y'}), {'y'}) self.assertEqual(wrap_value(('z',)), ('z',)) # Test with empty collections self.assertEqual(wrap_value(dict()), []) self.assertEqual(wrap_value(list()), []) self.assertEqual(wrap_value(set()), []) self.assertEqual(wrap_value(tuple()), []) # Test with non-empty collections, filtering out empty self.assertEqual(wrap_value([None]), []) self.assertEqual(wrap_value([b'']), []) self.assertEqual(wrap_value(['']), []) self.assertEqual(wrap_value([None, None]), []) self.assertEqual(wrap_value([b'', '']), []) # Test with non-empty collections, preserving empty self.assertEqual(wrap_value([None], include_empty=True), [None]) self.assertEqual(wrap_value([b''], include_empty=True), [b'']) self.assertEqual(wrap_value([''], include_empty=True), ['']) self.assertEqual(wrap_value([None, None], include_empty=True), [None, None]) self.assertEqual(wrap_value([b'', ''], include_empty=True), [b'', '']) # Test with non-empty collections self.assertEqual(wrap_value([0, 1, 2]), [0, 1, 2]) self.assertEqual(wrap_value({0, 1, 2}), {0, 1, 2}) self.assertEqual(wrap_value((0, 1, 2)), (0, 1, 2)) self.assertEqual(wrap_value(['a', 'b', 'c']), ['a', 'b', 'c']) self.assertEqual(wrap_value({'a', 'b', 'c'}), {'a', 'b', 'c'}) self.assertEqual(wrap_value(('a', 'b', 'c')), ('a', 'b', 'c')) # Test with non-wrappable collections self.assertEqual([x for x in wrap_value(c for c in 'abc')], [c for c in 'abc']) def test_reduce_wrap_value(self): """ Tests reduce_value after wrapping """ values = ([0], [1], ['a'], ['abc'], [{'a': 'aaa'}], [{'b': 'bbb', 'c': 'ccc'}]) for value in values: self.assertEqual(wrap_value(reduce_value(value)), value) def test_wrap_reduce_value(self): """ Tests wrap_value after reducing """ values = (0, 1, 'a', 'abc', {'a': 'aaa'}, {'b': 'bbb', 'c': 'ccc'}) for value in values: self.assertEqual(reduce_value(wrap_value(value)), value)

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48bd61142d021f69ef1afbee8fc0c9bcc1de6924

97

py

Python

Trakttv.bundle/Contents/Libraries/Shared/elapsed/__init__.py

disrupted/Trakttv.bundle

24712216c71f3b22fd58cb5dd89dad5bb798ed60

[ "RSA-MD" ]

1,346

2015-01-01T14:52:24.000Z

2022-03-28T12:50:48.000Z

Trakttv.bundle/Contents/Libraries/Shared/elapsed/__init__.py

alcroito/Plex-Trakt-Scrobbler

4f83fb0860dcb91f860d7c11bc7df568913c82a6

[ "RSA-MD" ]

474

2015-01-01T10:27:46.000Z

2022-03-21T12:26:16.000Z

Trakttv.bundle/Contents/Libraries/Shared/elapsed/__init__.py

alcroito/Plex-Trakt-Scrobbler

4f83fb0860dcb91f860d7c11bc7df568913c82a6

[ "RSA-MD" ]

191

2015-01-02T18:27:22.000Z

2022-03-29T10:49:48.000Z

from elapsed.main import setup, reset, clock, format_report, print_report __version__ = '1.0.0'

24.25

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48caca64ece5ddf28eb6e7ebfd6c164999bd9e1a

201

py

Python

Python/count_digit.py

OluSure/Hacktoberfest2021-1

ad1bafb0db2f0cdeaae8f87abbaa716638c5d2ea

[ "MIT" ]

215

2021-10-01T08:18:16.000Z

2022-03-29T04:12:03.000Z

Python/count_digit.py

OluSure/Hacktoberfest2021-1

ad1bafb0db2f0cdeaae8f87abbaa716638c5d2ea

[ "MIT" ]

51

2021-10-01T08:16:42.000Z

2021-10-31T13:51:51.000Z

Python/count_digit.py

OluSure/Hacktoberfest2021-1

ad1bafb0db2f0cdeaae8f87abbaa716638c5d2ea

[ "MIT" ]

807

2021-10-01T08:11:45.000Z

2021-11-21T18:57:09.000Z

# python program to find how many digit in given integer numbers e.g. 123->3 , 737327->6 digit first we take log of base 10 then add 1. from math import log,floor print(floor(log(int(input()),10)+1))

40.2

135

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0.164179

201

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136

50.25

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1

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4

48da7762aaf7b80b67e6478bb0ffad66cc035fe0

114

py

Python

BasicPythonPrograms/test.py

Pushkar745/PythonProgramming

ea60e97b70d46fb63ef203913c8b3f9570232dd3

[ "Apache-2.0" ]

null

BasicPythonPrograms/test.py

Pushkar745/PythonProgramming

ea60e97b70d46fb63ef203913c8b3f9570232dd3

[ "Apache-2.0" ]

null

BasicPythonPrograms/test.py

Pushkar745/PythonProgramming

ea60e97b70d46fb63ef203913c8b3f9570232dd3

[ "Apache-2.0" ]

null

#name=input("Enter the name ") phoneBook={(sam,99912222),(tom,11122222),(harry,12299933)} print(phoneBook[0])

28.5

58

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114

5.333333

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114

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null

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48fd834792ae8e326fe94bde2c5771cb50359178

882

py

Python

it_2/soluzione_it2/conto.py

StefanoExc/bank_repo

82a3da2ce804ce67726d650d92cc594d3009c495

[ "MIT" ]

null

it_2/soluzione_it2/conto.py

StefanoExc/bank_repo

82a3da2ce804ce67726d650d92cc594d3009c495

[ "MIT" ]

null

it_2/soluzione_it2/conto.py

StefanoExc/bank_repo

82a3da2ce804ce67726d650d92cc594d3009c495

[ "MIT" ]

null

class Conto: def __init__(self, numero_conto, cliente, saldo=0.00): self.__numero_conto = numero_conto self.__cliente = cliente self.__saldo = saldo # Definizione di getter e setter # @property def numero_conto(self): return self.__numero_conto @numero_conto.setter def numero_conto(self, numero_conto): self.__numero_conto = numero_conto @property def cliente(self): return self.__cliente @cliente.setter def cliente(self, cliente): self.__cliente = cliente @property def saldo(self): return self.__saldo @saldo.setter def saldo(self, saldo): self.__saldo = saldo def __repr__(self): return "Conto " + self.numero_conto + " intestato a cliente " + self.cliente.nome_cliente + " con saldo " + str(self.saldo) + "€"

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d299c2f39a30abf842318418f8031bff36cc4d16

56

py

Python

nest_py/knoweng/flask/accounts/__init__.py

KnowEnG/platform

7356fabf5e2db4171ef1f910514436b69ecaa701

[ "MIT" ]

2

2020-02-12T22:20:51.000Z

2020-07-31T03:19:51.000Z

nest_py/knoweng/flask/accounts/__init__.py

KnowEnG/platform

7356fabf5e2db4171ef1f910514436b69ecaa701

[ "MIT" ]

1

2021-06-02T00:29:02.000Z

nest_py/knoweng/flask/accounts/__init__.py

KnowEnG/platform

7356fabf5e2db4171ef1f910514436b69ecaa701

[ "MIT" ]

1

2018-01-03T22:56:27.000Z

"""This package contains code for KnowEnG accounts. """

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56

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52

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true

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null

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null

0

1

0

4

d29cf2f8335f9f98296972e767763a003e28a942

259

py

Python

toontown/estate/DistributedGardenPlotAI.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

99

2019-11-02T22:25:00.000Z

2022-02-03T03:48:00.000Z

toontown/estate/DistributedGardenPlotAI.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

42

2019-11-03T05:31:08.000Z

2022-03-16T22:50:32.000Z

toontown/estate/DistributedGardenPlotAI.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

57

2019-11-03T07:47:37.000Z

2022-03-22T00:41:49.000Z

from direct.directnotify import DirectNotifyGlobal from direct.distributed.DistributedObjectAI import DistributedObjectAI class DistributedGardenPlotAI(DistributedObjectAI): notify = DirectNotifyGlobal.directNotify.newCategory('DistributedGardenPlotAI')

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259

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259

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null

0

1

0

4

d29fc6d65e348fc08e6fc81315621b39f17a5d32

609

py

Python

Aulas/Aula08/poli_ex1.py

matheusmenezs/com220

d699f00892df1259249ae012aa2a02f63ae0f06f

[ "MIT" ]

null

Aulas/Aula08/poli_ex1.py

matheusmenezs/com220

d699f00892df1259249ae012aa2a02f63ae0f06f

[ "MIT" ]

null

Aulas/Aula08/poli_ex1.py

matheusmenezs/com220

d699f00892df1259249ae012aa2a02f63ae0f06f

[ "MIT" ]

null

from abc import ABC, abstractmethod class Documento(ABC): def __init__(self, nome): self.__nome = nome def getNome(self): return self.__nome @abstractmethod def visualizar(self): pass class Pdf(Documento): def visualizar(self): return 'Mostra no Adobe Acrobat' class Word(Documento): def visualizar(self): return 'Mostra no Word' if __name__ == "__main__": documentos = [Pdf('PDF1'), Word('DOC1'), Pdf('PDF2')] for documento in documentos: print('{}: {}'.format(documento.getNome(), documento.visualizar()))

24.36

75

0.62069

67

609

5.402985

0.462687

0.066298

0.140884

0.143646

0.220994

0

0.006608

0.254516

609

25

75

24.36

0.790749

0

0.157895

0

0.103279

0

1

0.263158

false

0.052632

0.157895

0.631579

0.052632

0

null

0

null

0

1

0

1

0

1

0

4

d2c6a60bce75b3b4e4f4c2c3aa919a6c52104d3b

333

py

Python

glycowork/motif.py

Old-Shatterhand/glycowork

544fde03dd38cf95fb97792e050d7ff68f5637b1

[ "MIT" ]

22

2021-04-22T23:53:26.000Z

2022-03-21T00:36:32.000Z

glycowork/motif.py

Old-Shatterhand/glycowork

544fde03dd38cf95fb97792e050d7ff68f5637b1

[ "MIT" ]

3

2021-04-23T13:01:07.000Z

2022-03-16T19:13:12.000Z

glycowork/motif.py

Old-Shatterhand/glycowork

544fde03dd38cf95fb97792e050d7ff68f5637b1

[ "MIT" ]

2

2021-07-06T14:13:40.000Z

2021-12-15T15:12:37.000Z

# AUTOGENERATED! DO NOT EDIT! File to edit: 04_motif.ipynb (unless otherwise specified). __all__ = [] # Cell from .motif.analysis import * from .motif.annotate import * from .motif.graph import * from .motif.processing import * from .motif.query import * from .motif.tokenization import * from .glycan_data.loader import df_species

27.75

88

0.765766

46

333

5.391304

0.586957

0.217742

0.302419

0

0.006969

0.138138

333

12

89

27.75

0.857143

0.273273

0

1

0

1

0

false

0

0.875

0

0.875

0

null

1

0

1

0

null

0

1

0

4

d2cbe87acfd56206b5acbacd66973eb038ef52e7

2,196

py

Python

core/users/serializers.py

sharebears/pulsar-core

430e879c7a4c4a758641af56da552355e6c76a45

[ "MIT" ]

null

core/users/serializers.py

sharebears/pulsar-core

430e879c7a4c4a758641af56da552355e6c76a45

[ "MIT" ]

null

core/users/serializers.py

sharebears/pulsar-core

430e879c7a4c4a758641af56da552355e6c76a45

[ "MIT" ]

null

from core.mixins import Attribute, Serializer from core.users.permissions import ( ApikeyPermissions, InvitePermissions, UserPermissions, ) class UserSerializer(Serializer): id = Attribute() username = Attribute() enabled = Attribute() user_class = Attribute() secondary_classes = Attribute() uploaded = Attribute() downloaded = Attribute() email = Attribute(permission=UserPermissions.MODERATE) locked = Attribute(permission=UserPermissions.MODERATE) invites = Attribute(permission=UserPermissions.MODERATE) inviter = Attribute( permission=UserPermissions.MODERATE, self_access=False, nested=False ) api_keys = Attribute(permission=UserPermissions.MODERATE, nested=False) basic_permissions = Attribute( permission=UserPermissions.MODERATE, self_access=False, nested=False ) permissions = Attribute( permission=UserPermissions.MODERATE_ADVANCED, nested=False ) class InviteSerializer(Serializer): code = Attribute(permission=InvitePermissions.VIEW_OTHERS) email = Attribute(permission=InvitePermissions.VIEW_OTHERS) time_sent = Attribute(permission=InvitePermissions.VIEW_OTHERS) expired = Attribute(permission=InvitePermissions.VIEW_OTHERS) invitee = Attribute(permission=InvitePermissions.VIEW_OTHERS) from_ip = Attribute( permission=InvitePermissions.VIEW_OTHERS, self_access=False ) inviter = Attribute( permission=InvitePermissions.VIEW_OTHERS, nested=False, self_access=False, ) class APIKeySerializer(Serializer): hash = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) user_id = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) last_used = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) ip = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) user_agent = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) revoked = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) permanent = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) timeout = Attribute(permission=ApikeyPermissions.VIEW_OTHERS) permissions = Attribute(permission=ApikeyPermissions.VIEW_OTHERS)

37.862069

76

0.765483

200

2,196

8.255

0.26

0.264688

0.196245

0.21805

0.571775

0.142944

0.082374

0

0.155738

2,196

57

77

38.526316

0.890507

0

0.078431

0

1

0

false

0

0.039216

0

0.686275

0

null

1

0

null

0

1

0

4

960c425b50d6d2abbe01bf06fae09064e31c3bb4

13,578

py

Python

naoqi_proxy_python_classes/ALPeoplePerception.py

FabianGroeger96/hslu-roblab-hs18

60fca783609f04dee785a96356646a586a63b768

[ "MIT" ]

null

naoqi_proxy_python_classes/ALPeoplePerception.py

FabianGroeger96/hslu-roblab-hs18

60fca783609f04dee785a96356646a586a63b768

[ "MIT" ]

null

naoqi_proxy_python_classes/ALPeoplePerception.py

FabianGroeger96/hslu-roblab-hs18

60fca783609f04dee785a96356646a586a63b768

[ "MIT" ]

null

#!/usr/bin/env python # Class autogenerated from /home/sam/Downloads/aldebaran_sw/nao/naoqi-sdk-2.1.4.13-linux64/include/alproxies/alpeopleperceptionproxy.h # by Sammy Pfeiffer's <Sammy.Pfeiffer at student.uts.edu.au> generator # You need an ALBroker running from naoqi import ALProxy class ALPeoplePerception(object): def __init__(self, session): self.proxy = None self.session = session def force_connect(self): self.proxy = self.session.service("ALPeoplePerception") def getCurrentPeriod(self): """Gets the current period. :returns int: Refresh period (in milliseconds). """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getCurrentPeriod() def getCurrentPrecision(self): """Gets the current precision. :returns float: Precision of the extractor. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getCurrentPrecision() def getEventList(self): """Get the list of events updated in ALMemory. :returns std::vector<std::string>: Array of events updated by this extractor in ALMemory """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getEventList() def getMaximumBodyHeight(self): """Gets the current maximum body height used for human detection (3D mode only). :returns float: Maximum height in meters. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getMaximumBodyHeight() def getMaximumDetectionRange(self): """Gets the current maximum detection and tracking range. :returns float: Maximum range in meters. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getMaximumDetectionRange() def getMemoryKeyList(self): """Get the list of events updated in ALMemory. :returns std::vector<std::string>: Array of events updated by this extractor in ALMemory """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getMemoryKeyList() def getMinimumBodyHeight(self): """Gets the current minimum body height used for human detection (3D mode only). :returns float: Minimum height in meters. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getMinimumBodyHeight() def getMyPeriod(self, name): """Gets the period for a specific subscription. :param str name: Name of the module which has subscribed. :returns int: Refresh period (in milliseconds). """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getMyPeriod(name) def getMyPrecision(self, name): """Gets the precision for a specific subscription. :param str name: name of the module which has subscribed :returns float: precision of the extractor """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getMyPrecision(name) def getOutputNames(self): """Get the list of values updated in ALMemory. :returns std::vector<std::string>: Array of values updated by this extractor in ALMemory """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getOutputNames() def getSubscribersInfo(self): """Gets the parameters given by the module. :returns AL::ALValue: Array of names and parameters of all subscribers. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getSubscribersInfo() def getTimeBeforePersonDisappears(self): """Gets the time after which a person, supposed not to be in the field of view of the camera disappears if it has not been detected. :returns float: Time in seconds. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getTimeBeforePersonDisappears() def getTimeBeforeVisiblePersonDisappears(self): """Gets the time after which a person, supposed to be in the field of view of the camera disappears if it has not been detected. :returns float: Time in seconds. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.getTimeBeforeVisiblePersonDisappears() def isFaceDetectionEnabled(self): """Gets the current status of face detection. :returns bool: True if face detection is enabled, False otherwise. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.isFaceDetectionEnabled() def isFastModeEnabled(self): """Gets the current status of fast mode. :returns bool: True if fast mode is enabled, False otherwise. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.isFastModeEnabled() def isGraphicalDisplayEnabled(self): """Gets the current status of graphical display in Choregraphe. :returns bool: True if graphical display is enabled, False otherwise. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.isGraphicalDisplayEnabled() def isMovementDetectionEnabled(self): """Gets the current status of movement detection in Choregraphe. :returns bool: True if movement detection is enabled, False otherwise. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.isMovementDetectionEnabled() def isPaused(self): """Gets extractor pause status :returns bool: True if the extractor is paused, False if not """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.isPaused() def isProcessing(self): """Gets extractor running status :returns bool: True if the extractor is currently processing images, False if not """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.isProcessing() def pause(self, status): """Changes the pause status of the extractor :param bool status: New pause satus """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.pause(status) def ping(self): """Just a ping. Always returns true :returns bool: returns true """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.ping() def resetPopulation(self): """Empties the tracked population. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.resetPopulation() def setFaceDetectionEnabled(self, enable): """Turns face detection on or off. :param bool enable: True to turn it on, False to turn it off. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setFaceDetectionEnabled(enable) def setFastModeEnabled(self, enable): """Turns fast mode on or off. :param bool enable: True to turn it on, False to turn it off. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setFastModeEnabled(enable) def setGraphicalDisplayEnabled(self, enable): """Turns graphical display in Choregraphe on or off. :param bool enable: True to turn it on, False to turn it off. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setGraphicalDisplayEnabled(enable) def setMaximumBodyHeight(self, height): """Sets the maximum human body height (3D mode only). :param float height: Maximum height in meters. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setMaximumBodyHeight(height) def setMaximumDetectionRange(self, range): """Sets the maximum range for human detection and tracking. :param float range: Maximum range in meters. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setMaximumDetectionRange(range) def setMinimumBodyHeight(self, height): """Sets the minimum human body height (3D mode only). :param float height: Minimum height in meters. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setMinimumBodyHeight(height) def setMovementDetectionEnabled(self, enable): """Turns movement detection on or off. :param bool enable: True to turn it on, False to turn it off. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setMovementDetectionEnabled(enable) def setTimeBeforePersonDisappears(self, seconds): """Sets the time after which a person, supposed not to be in the field of view of the camera disappears if it has not been detected. :param float seconds: Time in seconds. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setTimeBeforePersonDisappears(seconds) def setTimeBeforeVisiblePersonDisappears(self, seconds): """Sets the time after which a person, supposed to be in the field of view of the camera disappears if it has not been detected. :param float seconds: Time in seconds. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.setTimeBeforeVisiblePersonDisappears(seconds) def subscribe(self, name, period, precision): """Subscribes to the extractor. This causes the extractor to start writing information to memory using the keys described by getOutputNames(). These can be accessed in memory using ALMemory.getData("keyName"). In many cases you can avoid calling subscribe on the extractor by just calling ALMemory.subscribeToEvent() supplying a callback method. This will automatically subscribe to the extractor for you. :param str name: Name of the module which subscribes. :param int period: Refresh period (in milliseconds) if relevant. :param float precision: Precision of the extractor if relevant. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.subscribe(name, period, precision) def subscribe2(self, name): """Subscribes to the extractor. This causes the extractor to start writing information to memory using the keys described by getOutputNames(). These can be accessed in memory using ALMemory.getData("keyName"). In many cases you can avoid calling subscribe on the extractor by just calling ALMemory.subscribeToEvent() supplying a callback method. This will automatically subscribe to the extractor for you. :param str name: Name of the module which subscribes. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.subscribe(name) def unsubscribe(self, name): """Unsubscribes from the extractor. :param str name: Name of the module which had subscribed. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.unsubscribe(name) def updatePeriod(self, name, period): """Updates the period if relevant. :param str name: Name of the module which has subscribed. :param int period: Refresh period (in milliseconds). """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.updatePeriod(name, period) def updatePrecision(self, name, precision): """Updates the precision if relevant. :param str name: Name of the module which has subscribed. :param float precision: Precision of the extractor. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.updatePrecision(name, precision) def version(self): """Returns the version of the module. :returns str: A string containing the version of the module. """ if not self.proxy: self.proxy = self.session.service("ALPeoplePerception") return self.proxy.version()

38.355932

413

0.657608

1,550

13,578

5.756774

0.143871

0.113975

0.109268

0.085173

0.673092

0.660988

0.637566

0.613359

0.602376

0

0.00119

0.257107

13,578

353

414

38.464589

0.883414

0.390264

0

0.483871

1

0

0.092059

0

1

0.251613

false

0

0.006452

0

0.503226

0

null

0

1

0

1

0

null

0

1

0

1

0

4

8251c5b3dc0d72ef24d09797185a3b2bc3b9df0b

488

py

Python

src/python/psic/common/tests/test_h.py

UNCG-CSE/Poststorm_Imagery

f963d451050b793d7e8350137e0b145c80e739b5

[ "MIT" ]

7

2019-09-21T15:14:58.000Z

2019-11-04T18:52:37.000Z

src/python/psic/common/tests/test_h.py

UNCG-CSE/Poststorm_Imagery

f963d451050b793d7e8350137e0b145c80e739b5

[ "MIT" ]

744

2019-09-11T01:15:44.000Z

2020-06-09T23:56:16.000Z

src/python/psic/common/tests/test_h.py

UNCG-CSE/Poststorm_Imagery

f963d451050b793d7e8350137e0b145c80e739b5

[ "MIT" ]

6

2019-10-09T11:08:15.000Z

2020-09-16T06:57:33.000Z

from unittest import TestCase from psic.common import h class TestHelper(TestCase): def test_to_readable_bytes(self): self.assertIn('???', h.to_readable_bytes('taco')) self.assertIn('???', h.to_readable_bytes(None)) self.assertIn('KiB', h.to_readable_bytes(1)) self.assertIn('KiB', h.to_readable_bytes(1024 ** 1 + 1)) self.assertIn('MiB', h.to_readable_bytes(1024 ** 2 + 1)) self.assertIn('GiB', h.to_readable_bytes(1024 ** 3 + 1))

32.533333

64

0.651639

70

488

4.328571

0.357143

0.231023

0.346535

0.316832

0.534653

0.389439

0.20462

0

0.04798

0.188525

488

14

65

34.857143

0.717172

0

0.045082

0

0.6

1

0.1

false

0

0.2

0

0.4

0

null

1

0

null

0

1

0

4

82572d225631b3591fc4f4fb9e4b55a355626503

10,418

py

Python

ecl/tests/unit/compute/v2/test_limits.py

keiichi-hikita/eclsdk

c43afb982fd54eb1875cdc22d46044644d804c4a

[ "Apache-2.0" ]

5

2017-04-07T06:23:04.000Z

2019-11-19T00:52:34.000Z

ecl/tests/unit/compute/v2/test_limits.py

keiichi-hikita/eclsdk

c43afb982fd54eb1875cdc22d46044644d804c4a

[ "Apache-2.0" ]

16

2018-09-12T11:14:40.000Z

2021-04-19T09:02:44.000Z

ecl/tests/unit/compute/v2/test_limits.py

keiichi-hikita/eclsdk

c43afb982fd54eb1875cdc22d46044644d804c4a

[ "Apache-2.0" ]

14

2017-05-11T14:26:26.000Z

2021-07-14T14:00:06.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock import testtools from ecl.compute.v2 import limits ABSOLUTE_LIMITS = { "maxImageMeta": 128, "maxPersonality": 5, "maxPersonalitySize": 10240, "maxSecurityGroupRules": 20, "maxSecurityGroups": 10, "maxServerMeta": 128, "maxTotalCores": 20, "maxTotalFloatingIps": 10, "maxTotalInstances": 10, "maxTotalKeypairs": 100, "maxTotalRAMSize": 51200, "maxServerGroups": 10, "maxServerGroupMembers": 10, "totalFloatingIpsUsed": 1, "totalSecurityGroupsUsed": 2, "totalRAMUsed": 4, "totalInstancesUsed": 5, "totalServerGroupsUsed": 6, "totalCoresUsed": 7, #: New missing attributes "totalSnapshotsUsed": 0, "maxTotalBackups": 0, "maxTotalVolumeGigabytes": 0, "maxTotalSnapshots": 0, "maxTotalBackupGigabytes": 0, "totalBackupGigabytesUsed": 0, "maxTotalVolumes": 0, "totalVolumesUsed": 0, "totalBackupsUsed": 0, "totalGigabytesUsed": 0, } RATE_LIMIT = { "limit": [ { "next-available": "2012-11-27T17:22:18Z", "remaining": 120, "unit": "MINUTE", "value": 120, "verb": "POST" }, ], "regex": ".*", "uri": "*" } LIMITS_BODY = { "limits": { "absolute": ABSOLUTE_LIMITS, "rate": [RATE_LIMIT] } } class TestAbsoluteLimits(testtools.TestCase): def test_basic(self): sot = limits.AbsoluteLimits() self.assertIsNone(sot.resource_key) self.assertIsNone(sot.resources_key) self.assertEqual("", sot.base_path) self.assertIsNone(sot.service) self.assertFalse(sot.allow_create) self.assertFalse(sot.allow_get) self.assertFalse(sot.allow_update) self.assertFalse(sot.allow_delete) self.assertFalse(sot.allow_list) def test_make_it(self): sot = limits.AbsoluteLimits(**ABSOLUTE_LIMITS) self.assertEqual(ABSOLUTE_LIMITS["maxImageMeta"], sot.image_meta) self.assertEqual(ABSOLUTE_LIMITS["maxPersonality"], sot.personality) self.assertEqual(ABSOLUTE_LIMITS["maxPersonalitySize"], sot.personality_size) self.assertEqual(ABSOLUTE_LIMITS["maxSecurityGroupRules"], sot.security_group_rules) self.assertEqual(ABSOLUTE_LIMITS["maxSecurityGroups"], sot.security_groups) self.assertEqual(ABSOLUTE_LIMITS["maxServerMeta"], sot.server_meta) self.assertEqual(ABSOLUTE_LIMITS["maxTotalCores"], sot.cores) self.assertEqual(ABSOLUTE_LIMITS["maxTotalFloatingIps"], sot.floating_ips) self.assertEqual(ABSOLUTE_LIMITS["maxTotalInstances"], sot.instances) self.assertEqual(ABSOLUTE_LIMITS["maxTotalKeypairs"], sot.keypairs) self.assertEqual(ABSOLUTE_LIMITS["maxTotalRAMSize"], sot.ram) self.assertEqual(ABSOLUTE_LIMITS["maxServerGroups"], sot.server_groups) self.assertEqual(ABSOLUTE_LIMITS["maxServerGroupMembers"], sot.server_group_members) self.assertEqual(ABSOLUTE_LIMITS["totalFloatingIpsUsed"], sot.floating_ips_used) self.assertEqual(ABSOLUTE_LIMITS["totalSecurityGroupsUsed"], sot.security_groups_used) self.assertEqual(ABSOLUTE_LIMITS["totalRAMUsed"], sot.ram_used) self.assertEqual(ABSOLUTE_LIMITS["totalInstancesUsed"], sot.instances_used) self.assertEqual(ABSOLUTE_LIMITS["totalServerGroupsUsed"], sot.server_groups_used) self.assertEqual(ABSOLUTE_LIMITS["totalCoresUsed"], sot.cores_used) #: new missing attributes self.assertEqual(ABSOLUTE_LIMITS["totalSnapshotsUsed"], sot.snapshots_used) self.assertEqual(ABSOLUTE_LIMITS["maxTotalBackups"], sot.backups) self.assertEqual(ABSOLUTE_LIMITS["maxTotalVolumeGigabytes"], sot.volume_gigabytes) self.assertEqual(ABSOLUTE_LIMITS["maxTotalSnapshots"], sot.snapshots) self.assertEqual(ABSOLUTE_LIMITS["maxTotalBackupGigabytes"], sot.backup_gigabytes) self.assertEqual(ABSOLUTE_LIMITS["totalBackupGigabytesUsed"], sot.backup_gigabytes_used) self.assertEqual(ABSOLUTE_LIMITS["maxTotalVolumes"], sot.volumes) self.assertEqual(ABSOLUTE_LIMITS["totalVolumesUsed"], sot.volumes_used) self.assertEqual(ABSOLUTE_LIMITS["totalBackupsUsed"], sot.backups_used) self.assertEqual(ABSOLUTE_LIMITS["totalGigabytesUsed"], sot.gigabytes_used) class TestRateLimit(testtools.TestCase): def test_basic(self): sot = limits.RateLimit() self.assertIsNone(sot.resource_key) self.assertIsNone(sot.resources_key) self.assertEqual("", sot.base_path) self.assertIsNone(sot.service) self.assertFalse(sot.allow_create) self.assertFalse(sot.allow_get) self.assertFalse(sot.allow_update) self.assertFalse(sot.allow_delete) self.assertFalse(sot.allow_list) def test_make_it(self): sot = limits.RateLimit(**RATE_LIMIT) self.assertEqual(RATE_LIMIT["regex"], sot.regex) self.assertEqual(RATE_LIMIT["uri"], sot.uri) self.assertEqual(RATE_LIMIT["limit"], sot.limits) class TestLimits(testtools.TestCase): def test_basic(self): sot = limits.Limits() self.assertEqual("limits", sot.resource_key) self.assertEqual("/limits", sot.base_path) self.assertEqual("compute", sot.service.service_type) self.assertTrue(sot.allow_get) self.assertFalse(sot.allow_create) self.assertFalse(sot.allow_update) self.assertFalse(sot.allow_delete) self.assertFalse(sot.allow_list) def test_get(self): sess = mock.Mock() resp = mock.Mock() sess.get.return_value = resp resp.json.return_value = LIMITS_BODY sot = limits.Limits().get(sess) self.assertEqual(ABSOLUTE_LIMITS["maxImageMeta"], sot.absolute.image_meta) self.assertEqual(ABSOLUTE_LIMITS["maxPersonality"], sot.absolute.personality) self.assertEqual(ABSOLUTE_LIMITS["maxPersonalitySize"], sot.absolute.personality_size) self.assertEqual(ABSOLUTE_LIMITS["maxSecurityGroupRules"], sot.absolute.security_group_rules) self.assertEqual(ABSOLUTE_LIMITS["maxSecurityGroups"], sot.absolute.security_groups) self.assertEqual(ABSOLUTE_LIMITS["maxServerMeta"], sot.absolute.server_meta) self.assertEqual(ABSOLUTE_LIMITS["maxTotalCores"], sot.absolute.cores) self.assertEqual(ABSOLUTE_LIMITS["maxTotalFloatingIps"], sot.absolute.floating_ips) self.assertEqual(ABSOLUTE_LIMITS["maxTotalInstances"], sot.absolute.instances) self.assertEqual(ABSOLUTE_LIMITS["maxTotalKeypairs"], sot.absolute.keypairs) self.assertEqual(ABSOLUTE_LIMITS["maxTotalRAMSize"], sot.absolute.ram) self.assertEqual(ABSOLUTE_LIMITS["maxServerGroups"], sot.absolute.server_groups) self.assertEqual(ABSOLUTE_LIMITS["maxServerGroupMembers"], sot.absolute.server_group_members) self.assertEqual(ABSOLUTE_LIMITS["totalFloatingIpsUsed"], sot.absolute.floating_ips_used) self.assertEqual(ABSOLUTE_LIMITS["totalSecurityGroupsUsed"], sot.absolute.security_groups_used) self.assertEqual(ABSOLUTE_LIMITS["totalRAMUsed"], sot.absolute.ram_used) self.assertEqual(ABSOLUTE_LIMITS["totalInstancesUsed"], sot.absolute.instances_used) self.assertEqual(ABSOLUTE_LIMITS["totalServerGroupsUsed"], sot.absolute.server_groups_used) self.assertEqual(ABSOLUTE_LIMITS["totalCoresUsed"], sot.absolute.cores_used) self.assertEqual(ABSOLUTE_LIMITS["totalSnapshotsUsed"], sot.absolute.snapshots_used) self.assertEqual(ABSOLUTE_LIMITS["maxTotalBackups"], sot.absolute.backups) self.assertEqual(ABSOLUTE_LIMITS["maxTotalVolumeGigabytes"], sot.absolute.volume_gigabytes) self.assertEqual(ABSOLUTE_LIMITS["maxTotalSnapshots"], sot.absolute.snapshots) self.assertEqual(ABSOLUTE_LIMITS["maxTotalBackupGigabytes"], sot.absolute.backup_gigabytes) self.assertEqual(ABSOLUTE_LIMITS["totalBackupGigabytesUsed"], sot.absolute.backup_gigabytes_used) self.assertEqual(ABSOLUTE_LIMITS["maxTotalVolumes"], sot.absolute.volumes) self.assertEqual(ABSOLUTE_LIMITS["totalVolumesUsed"], sot.absolute.volumes_used) self.assertEqual(ABSOLUTE_LIMITS["totalBackupsUsed"], sot.absolute.backups_used) self.assertEqual(ABSOLUTE_LIMITS["totalGigabytesUsed"], sot.absolute.gigabytes_used) self.assertEqual(RATE_LIMIT["uri"], sot.rate[0].uri) self.assertEqual(RATE_LIMIT["regex"], sot.rate[0].regex) self.assertEqual(RATE_LIMIT["limit"], sot.rate[0].limits)

41.177866

79

0.629775

939

10,418

6.816826

0.200213

0.161693

0.208405

0.262771

0.707077

0.703953

0.645212

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0.23012

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0.270109

10,418

252

80

41.34127

0.831536

0.054713

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0.341122

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0.168599

0.04759

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null

0

1

0

1

0

null

0

1

0

4

825f522e3e610055105d809ea60488f5782849c8

2,636

py

Python

tests/test_2d.py

John-Hennig/KDE-diffusion

f0daee3294533808786ad6287fc1d70211bcc6dd

[ "MIT" ]

5

2020-05-13T00:57:08.000Z

2021-08-29T12:39:03.000Z

tests/test_2d.py

john-hen/KDE-diffusion

5a540fe863fb022e6cc68b5737f732e8bee99f96

[ "MIT" ]

1

2021-05-16T09:41:13.000Z

tests/test_2d.py

john-hen/KDE-diffusion

5a540fe863fb022e6cc68b5737f732e8bee99f96

[ "MIT" ]

2

2022-01-03T15:03:57.000Z

2022-02-07T08:43:46.000Z

"""Tests the 2d kernel density estimation.""" ######################################## # Dependencies # ######################################## from kde_diffusion import kde2d from pathlib import Path from numpy import isclose, load from pytest import raises ######################################## # Fixtures # ######################################## reference = None def setup_module(): global reference here = Path(__file__).parent reference = load(here/'reference2d.npz') ######################################## # Test # ######################################## def test_reference(): x = reference['x'] y = reference['y'] N = reference['N'] assert N == len(x) n = reference['n'] xmin = reference['xmin'] xmax = reference['xmax'] ymin = reference['ymin'] ymax = reference['ymax'] (density, grid, bandwidth) = kde2d(x, y, n, ((xmin, xmax), (ymin, ymax))) assert isclose(grid[0].min(), xmin) assert isclose(grid[0].max(), xmax - (xmax-xmin)/n) assert isclose(grid[1].min(), ymin) assert isclose(grid[1].max(), ymax - (ymax-ymin)/n) assert isclose(density, reference['density']).all() assert isclose(grid, reference['grid']).all() assert isclose(bandwidth, reference['bandwidth']).all() def test_arguments(): samples = [-2, -1, 0, +1, +2] (density, grid, bandwith) = kde2d(samples*5, samples*5, 16) assert len(grid[0]) == 16 assert len(grid[1]) == 16 assert isclose(grid[0].min(), -3.0) assert isclose(grid[0].max(), +2.625) assert isclose(grid[1].min(), -3.0) assert isclose(grid[1].max(), +2.625) (density, grid, bandwidth) = kde2d(samples*5, samples*5, 16, (2, None)) assert isclose(grid[0].min(), -2) assert isclose(grid[0].max(), +1.75) assert isclose(grid[1].min(), -3) assert isclose(grid[1].max(), +2.625) (density, grid, bandwidth) = kde2d(samples*5, samples*5, 16, (None, 2)) assert isclose(grid[0].min(), -3) assert isclose(grid[0].max(), +2.625) assert isclose(grid[1].min(), -2) assert isclose(grid[1].max(), +1.75) (density, grid, bandwidth) = kde2d(samples*5, samples*5, 16, 2) assert isclose(grid[0].min(), -2) assert isclose(grid[0].max(), +1.75) assert isclose(grid[1].min(), -2) assert isclose(grid[1].max(), +1.75) with raises(ValueError): kde2d(samples, samples*2, 16) with raises(ValueError): kde2d(samples, samples, 16)

34.233766

78

0.518589

315

2,636

4.31746

0.190476

0.219853

0.2625

0.132353

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0.336029

0

0.050914

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2,636

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79

34.684211

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0.472727

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null

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null

0

null

1

0

null

0

1

0

4

827ebef698412f9042922d3f464e790e6b1e0672

470

py

Python

Python/euler006/euler006_test.py

troberson/exercises-euler

03ffafb1016d252ca297f2ab6f02552df1377496

[ "BSD-3-Clause" ]

1

2020-02-12T20:40:39.000Z

Python/euler006/euler006_test.py

troberson/exercises-euler

03ffafb1016d252ca297f2ab6f02552df1377496

[ "BSD-3-Clause" ]

null

Python/euler006/euler006_test.py

troberson/exercises-euler

03ffafb1016d252ca297f2ab6f02552df1377496

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 import euler006 def test_sum_of_squares_of_1_to_10_is_385(): assert euler006.sum_of_squares(range(1, 11)) == 385 def test_square_of_sum_of_1_to_10_is_3025(): assert euler006.square_of_sum(range(1, 11)) == 3025 def test_sum_square_difference_of_1_to_10_is_2640(): assert euler006.sum_square_difference(range(1, 11)) == 2640 def test_final_sum_square_difference_of_1_to_100_is_25164150(): assert euler006.main() == 25164150

23.5

63

0.782979

82

470

3.963415

0.329268

0.086154

0.061538

0.064615

0.215385

0.147692

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0.183575

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470

19

64

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0.444444

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null

0

null

0

1

0

1

0

1

0

4

82941e7ba7132e8fb1137a78c937ea721d640dd5

890

py

Python

tests/test_alphabetmatcher.py

natemarks/alphabetmatcher

9620b36c4e4ed702e339ea3d701b19745b2c2554

[ "MIT" ]

null

tests/test_alphabetmatcher.py

natemarks/alphabetmatcher

9620b36c4e4ed702e339ea3d701b19745b2c2554

[ "MIT" ]

null

tests/test_alphabetmatcher.py

natemarks/alphabetmatcher

9620b36c4e4ed702e339ea3d701b19745b2c2554

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for `alphabetmatcher` package.""" import pytest def test_empty_string(): from alphabetmatcher.alphabetmatcher import Matcher dd = Matcher('') assert not dd.success() def test_exact_match(): from alphabetmatcher.alphabetmatcher import Matcher dd = Matcher('abcdefghijklmnopqrstuvwxyz') assert dd.success() def test_case_mixing(): from alphabetmatcher.alphabetmatcher import Matcher dd = Matcher('abcdefGhijklmnopqrStuvwxyz') assert dd.success() def test_reorder_and_junk(): from alphabetmatcher.alphabetmatcher import Matcher dd = Matcher('CBAhjsvdf734y4tu9820h%$%$&%defGhijklmnopqrStuvwxyz') assert dd.success() def test_repeats(): from alphabetmatcher.alphabetmatcher import Matcher dd = Matcher('aaaaaaaaaabcdefGhijklmnopqrStuvwxyz') assert dd.success()

20.227273

70

0.733708

89

890

7.224719

0.393258

0.054432

0.264386

0.311042

0.618974

0.584759

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0

0.012129

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890

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71

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0.238095

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0.238095

false

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null

0

1

0

null

0

1

0

1

0

4

829e162fc8ce3fb260c319fe7d7b1789e33d5ae3

185

py

Python

addons/dropbox/settings/defaults.py

gaybro8777/osf.io

30408511510a40bc393565817b343ef5fd76ab14

[ "Apache-2.0" ]

628

2015-01-15T04:33:22.000Z

2022-03-30T06:40:10.000Z

addons/dropbox/settings/defaults.py

gaybro8777/osf.io

30408511510a40bc393565817b343ef5fd76ab14

[ "Apache-2.0" ]

4,712

2015-01-02T01:41:53.000Z

2022-03-30T14:18:40.000Z

addons/dropbox/settings/defaults.py

Johnetordoff/osf.io

de10bf249c46cede04c78f7e6f7e352c69e6e6b5

[ "Apache-2.0" ]

371

2015-01-12T16:14:08.000Z

2022-03-31T18:58:29.000Z

# OAuth app keys DROPBOX_KEY = None DROPBOX_SECRET = None DROPBOX_AUTH_CSRF_TOKEN = 'dropbox-auth-csrf-token' # Max file size permitted by frontend in megabytes MAX_UPLOAD_SIZE = 150

20.555556

51

0.794595

29

185

4.827586

0.689655

0.157143

0.214286

0.285714

0

0.018987

0.145946

185

8

52

23.125

0.867089

0.340541

0

0.193277

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

82bc386f8ea6078c89409c1bed3f32733e9ec8ce

269

py

Python

tests/test_test1.py

inTestiGator/pytest-focus

5c136a8c7af3f3a4a149aae5e5e2512c5bf2d9ea

[ "MIT" ]

3

2019-04-03T01:29:27.000Z

2021-10-01T06:33:01.000Z

tests/test_test1.py

inTestiGator/pytest-focus

5c136a8c7af3f3a4a149aae5e5e2512c5bf2d9ea

[ "MIT" ]

26

2019-04-02T19:12:22.000Z

2019-05-05T01:03:34.000Z

tests/test_test1.py

inTestiGator/pytest-focus

5c136a8c7af3f3a4a149aae5e5e2512c5bf2d9ea

[ "MIT" ]

1

2019-05-04T21:52:23.000Z

""" practice test cases for testing plugin """ def test_iequals1(): """ practice test case 1 for testing plugin """ i = 1 assert i == 1 def test_iequals2(): """ practice test case 2 for testing plugin """ i = 2 assert i == 2

13.45

43

0.561338

36

269

4.138889

0.388889

0.241611

0.322148

0.228188

0

0.044199

0.327138

269

19

44

14.157895

0.779006

0.438662

0

0.333333

1

0.333333

false

0

0.333333

0

null

1

0

null

0

1

0

4

7d7836806c92e26e325f2b91aaef484d06b69690

659

py

Python

tests/models.py

joebowen/channelwormdjango

b5d940c5ca3c48afaac328de75971f2dc9f35956

[ "MIT" ]

null

tests/models.py

joebowen/channelwormdjango

b5d940c5ca3c48afaac328de75971f2dc9f35956

[ "MIT" ]

null

tests/models.py

joebowen/channelwormdjango

b5d940c5ca3c48afaac328de75971f2dc9f35956

[ "MIT" ]

null

import sciunit import capabilities class IonChannelModel(sciunit.Model, capabilities.Generates_IV_Curve, capabilities.Receives_Current, capabilities.Generates_Membrane_Potential): """A generic ion channel model.""" def __init__(self, name=None, iv_curve=None, current=None, voltage=None): super(IonChannelModel, self).__init__(name=name) def generate_iv_curve(self): return self.iv_curve def receive_current(self): #Not clear what this should return pass def generate_membrane_potential(self): #Not clear what this should return pass

31.380952

82

0.672231

74

659

5.716216

0.445946

0.066194

0.056738

0.07565

0.170213

0

0.256449

659

20

83

32.95

0.863265

0.144158

0

0.166667

1

0

1

0.333333

false

0.166667

0.083333

0.666667

0

null

0

1

0

null

0

1

0

1

0

1

0

4

7d892d9a0b5782d58d6d5b58b028fd88a0001c8c

48

py

Python

settings.py

mdgrotheer/twitter-intelligence

a256081be6ca5a38c5e34a019438792175374317

[ "MIT" ]

202

2018-07-06T11:56:32.000Z

2022-02-16T22:19:26.000Z

settings.py

mdgrotheer/twitter-intelligence

a256081be6ca5a38c5e34a019438792175374317

[ "MIT" ]

7

2018-09-28T09:47:21.000Z

2021-10-01T15:05:10.000Z

settings.py

mdgrotheer/twitter-intelligence

a256081be6ca5a38c5e34a019438792175374317

[ "MIT" ]

56

2018-08-19T18:56:05.000Z

2022-03-26T11:41:36.000Z

GOOGLE_MAP_API_KEY = 'YOUR_API_KEY' PORT = 5000

16

35

0.791667

9

48

3.666667

0.777778

0.363636

0

0.095238

0.125

48

2

36

24

0.690476

0

0.25

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

7d9c91e5e12108694668a3c18558e8f0bebe6b19

154

py

Python

gui.py

bsivanantham/WorldModels

188a58f6e056bec2320eb5ae45dac1bcb109af8a

[ "MIT" ]

null

gui.py

bsivanantham/WorldModels

188a58f6e056bec2320eb5ae45dac1bcb109af8a

[ "MIT" ]

null

gui.py

bsivanantham/WorldModels

188a58f6e056bec2320eb5ae45dac1bcb109af8a

[ "MIT" ]

null

from tkinter import * master = Tk() w = Scale(master, from_=0, to=42) w.pack() w = Scale(master, from_=0, to=200, orient=HORIZONTAL) w.pack() mainloop()

17.111111

53

0.675325

26

154

3.923077

0.576923

0.117647

0.235294

0.313725

0.372549

0

0.05303

0.142857

154

9

54

17.111111

0.719697

0

0.285714

0

1

0

false

0

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0

0.142857

0

1

0

null

0

1

0

1

0

null

0

4

7dcfe03e6c432ecac8cc4f5820a66a8051e09fed

79

py

Python

instance/config.py

amiinegal/News-app

0c793604f097167fae53ff91e0d296d2e893108f

[ "MIT", "Unlicense" ]

null

instance/config.py

amiinegal/News-app

0c793604f097167fae53ff91e0d296d2e893108f

[ "MIT", "Unlicense" ]

null

instance/config.py

amiinegal/News-app

0c793604f097167fae53ff91e0d296d2e893108f

[ "MIT", "Unlicense" ]

null

export NEWS_API_KEY='cb4e9f2ecd7343a19992a9b5043a14db' export SECRET_KEY='2030'

39.5

54

0.886076

9

79

7.444444

0.777778

0

0.302632

0.037975

79

2

55

39.5

0.578947

0

0.45

0.4

0

null

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null

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1

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null

0

1

0

1

0

null

0

1

0

4

7dd5457bc86e8c6ca79122b74de713080dc741a7

458

py

Python

src/encoded/tests/test_schema_computational_model.py

procha2/encoded

e9f122362b71f3b8641023b8d2d5ad531d3484b7

[ "MIT" ]

102

2015-05-20T01:17:43.000Z

2022-03-07T06:03:55.000Z

src/encoded/tests/test_schema_computational_model.py

procha2/encoded

e9f122362b71f3b8641023b8d2d5ad531d3484b7

[ "MIT" ]

901

2015-01-07T23:11:57.000Z

2022-03-18T13:56:12.000Z

src/encoded/tests/test_schema_computational_model.py

procha2/encoded

e9f122362b71f3b8641023b8d2d5ad531d3484b7

[ "MIT" ]

65

2015-02-06T23:00:26.000Z

2022-01-22T07:58:44.000Z

import pytest def test_unique_software(testapp, computational_model_unique_software): res = testapp.post_json('/computational_model', computational_model_unique_software, expect_errors=True) assert res.status_code == 201 def test_non_unique_software(testapp, computational_model_non_unique_software): res = testapp.post_json('/computational_model',computational_model_non_unique_software, expect_errors=True) assert res.status_code == 422

41.636364

111

0.829694

59

458

5.983051

0.355932

0.23796

0.144476

0.192635

0.90085

0.66289

0.385269

0

0.014493

0.09607

458

10

112

45.8

0.838164

0

0.087336

0

0.285714

1

0.285714

false

0

0.142857

0

0.428571

0

null

1

0

1

0

null

0

1

0

4

815cddf45be731d8cf2f66e4d25f571763a19a3c

818

py

Python

game/khet/model/pieces/piece.py

xelahalo/khet

c4aca94703c24c01d106959849240b890fa6744b

[ "MIT" ]

null

game/khet/model/pieces/piece.py

xelahalo/khet

c4aca94703c24c01d106959849240b890fa6744b

[ "MIT" ]

null

game/khet/model/pieces/piece.py

xelahalo/khet

c4aca94703c24c01d106959849240b890fa6744b

[ "MIT" ]

1

2022-03-19T22:25:54.000Z

from abc import ABC, abstractclassmethod from game.util.constants import Color class Piece(ABC): def __init__(self, color, rotation): self._color = color self._rotation = rotation @property def color(self): return self._color @property def rotation(self): return self._rotation @rotation.setter def rotation(self, value): self._rotation = value @abstractclassmethod def get_value(self): pass @abstractclassmethod def copy(self): pass @abstractclassmethod def on_hit(self, source_dir): """ Returns: True if it should be destroyed, reflected direction otherwise """ pass def __str__(self, char): return self._color.value + str(char) + Color.RESET.value

21.526316

69

0.627139

90

818

5.511111

0.422222

0.072581

0.080645

0.120968

0

0.292176

818

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false

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0.115385

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0

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0

null

0

1

0

1

0

1

0

4

c4a70002d1dd7fd344e6e1d96e9a073cf2e15074

50

py

Python

reinforcement_learning/ppoc/__init__.py

mizolotu/izi

d2d00813919259aad3dcdbc54039c30cbb16b125

[ "MIT" ]

null

reinforcement_learning/ppoc/__init__.py

mizolotu/izi

d2d00813919259aad3dcdbc54039c30cbb16b125

[ "MIT" ]

null

reinforcement_learning/ppoc/__init__.py

mizolotu/izi

d2d00813919259aad3dcdbc54039c30cbb16b125

[ "MIT" ]

null

from reinforcement_learning.ppo2.ppo2 import PPO2

25

49

0.88

7

50

6.142857

0.714286

0

0.065217

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50

1

50

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true

0

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null

0

1

0

null

0

1

0

1

0

4

c4caccc931cd14eb8356fedc07f913f67eece1a6

759

py

Python

data/smart_garden/exceptions.py

linxaddict/pytomatoes

fe281c6ba1a1f7d8ba87a5286afde9d1bd4f4d58

[ "MIT" ]

null

data/smart_garden/exceptions.py

linxaddict/pytomatoes

fe281c6ba1a1f7d8ba87a5286afde9d1bd4f4d58

[ "MIT" ]

3

2020-04-24T21:18:24.000Z

2020-05-21T19:58:55.000Z

data/smart_garden/exceptions.py

linxaddict/pytomatoes

fe281c6ba1a1f7d8ba87a5286afde9d1bd4f4d58

[ "MIT" ]

null

from typing import Optional from aiohttp import ClientResponse class SmartGardenException(Exception): def __init__(self, internal_error: Optional[Exception] = None, response: Optional[ClientResponse] = None, *args: object) -> None: super().__init__(*args) self.internal_error = internal_error self.response = response def __str__(self) -> str: return str(self.response) class SmartGardenResponseError(SmartGardenException): pass class SmartGardenUnauthorizedError(SmartGardenException): pass class SmartGardenConnectionError(SmartGardenException): pass class SmartGardenPayloadError(SmartGardenException): pass class SmartGardenInvalidUrl(SmartGardenException): pass

21.083333

109

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759

8.4

0.415385

0.21978

0.212454

0

0.189723

759

35

110

21.685714

0.887805

0

0.25

0

1

0.1

false

0.25

0.1

0.05

0.55

0

null

1

0

null

0

1

0

1

0

4

f218cb7db1e92faf46e8fa3abb1e13c80a412b8d

63

py

Python

tf_netbuilder/__init__.py

thunfischtoast/tf_netbuilder

728826ac5e4e58a39ea862cecc86ad249a19e278

[ "Apache-2.0" ]

10

2020-11-06T13:44:44.000Z

2021-11-20T11:20:23.000Z

tf_netbuilder/__init__.py

thunfischtoast/tf_netbuilder

728826ac5e4e58a39ea862cecc86ad249a19e278

[ "Apache-2.0" ]

2

2021-01-11T06:41:54.000Z

2021-03-31T10:41:49.000Z

tf_netbuilder/__init__.py

thunfischtoast/tf_netbuilder

728826ac5e4e58a39ea862cecc86ad249a19e278

[ "Apache-2.0" ]

10

2020-11-12T23:02:28.000Z

2022-01-29T12:07:41.000Z

from .builder_cfg import NetBuilderConfig from . import config

21

41

0.84127

8

63

6.5

0.75

0

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63

2

42

31.5

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0

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0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

480eec09f650fb02f729ef7913c9a06e9683a75b

177

py

Python

tests/unit/array/mixins/test_empty.py

sugatoray/docarray

e62c1ad045ea7236912c702aebe87c3a25db110d

[ "Apache-2.0" ]

null

tests/unit/array/mixins/test_empty.py

sugatoray/docarray

e62c1ad045ea7236912c702aebe87c3a25db110d

[ "Apache-2.0" ]

1

2022-01-11T00:59:52.000Z

tests/unit/array/mixins/test_empty.py

sugatoray/docarray

e62c1ad045ea7236912c702aebe87c3a25db110d

[ "Apache-2.0" ]

null

from docarray import DocumentArray def test_empty_non_zero(): da = DocumentArray.empty(10) assert len(da) == 10 da = DocumentArray.empty() assert len(da) == 0

19.666667

34

0.677966

24

177

4.875

0.583333

0.25641

0.34188

0

0.035971

0.214689

177

8

35

22.125

0.805755

0

0.333333

1

0.166667

false

0

0.166667

0

0.333333

0

1

0

null

1

0

1

0

null

0

4

481f03491bcab44bab66cdcc98df28fc68505154

275

py

Python

stl/utility/__init__.py

pieter-hendriks/STL-monitoring

114b73b1f4b0687b11b8842b3c4a1c8af7b0d9df

[ "MIT" ]

null

stl/utility/__init__.py

pieter-hendriks/STL-monitoring

114b73b1f4b0687b11b8842b3c4a1c8af7b0d9df

[ "MIT" ]

null

stl/utility/__init__.py

pieter-hendriks/STL-monitoring

114b73b1f4b0687b11b8842b3c4a1c8af7b0d9df

[ "MIT" ]

null

""" Utility functions / helpers for STL implementation. """ from .interval import Interval from .helpers import cm2inch, getTimeListIntersection from .singleton import Singleton from .plothelper import PlotHelper from .linesegment import LineSegment from .point import Point

34.375

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0.821818

31

275

7.290323

0.483871

0

0.004149

0.123636

275

7

60

39.285714

0.93361

0.185455

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

482af87c62026dbba8ed7391038adcded047cb33

3,471

py

Python

tests/contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/test_micheline_coding_KT1At3.py

juztin/pytezos-1

7e608ff599d934bdcf129e47db43dbdb8fef9027

[ "MIT" ]

1

2021-05-20T16:52:08.000Z

tests/contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/test_micheline_coding_KT1At3.py

juztin/pytezos-1

7e608ff599d934bdcf129e47db43dbdb8fef9027

[ "MIT" ]

1

2020-12-30T16:44:56.000Z

tests/contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/test_micheline_coding_KT1At3.py

tqtezos/pytezos

a4ac0b022d35d4c9f3062609d8ce09d584b5faa8

[ "MIT" ]

1

2022-03-20T19:01:00.000Z

from unittest import TestCase from tests import get_data from pytezos.michelson.converter import build_schema, decode_micheline, encode_micheline class MichelineCodingTestKT1At3(TestCase): @classmethod def setUpClass(cls): cls.maxDiff = None cls.code = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/code_KT1At3.json') cls.schema = dict( parameter=build_schema(cls.code[0]), storage=build_schema(cls.code[1]) ) def test_micheline_inverse_storage_KT1At3(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/storage_KT1At3.json') decoded = decode_micheline(expected, self.code[1], self.schema['storage']) actual = encode_micheline(decoded, self.schema['storage']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_oozfkT(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_oozfkT.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_oo2UMR(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_oo2UMR.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_onwvUM(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_onwvUM.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_opGhNz(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_opGhNz.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_oowDU8(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_oowDU8.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_onpSQy(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_onpSQy.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual) def test_micheline_inverse_parameter_oopBY6(self): expected = get_data( path='contracts/KT1At3oM7k94ccMmFCqjAZy42QyaDh2uNqhD/parameter_oopBY6.json') decoded = decode_micheline(expected, self.code[0], self.schema['parameter']) actual = encode_micheline(decoded, self.schema['parameter']) self.assertEqual(expected, actual)

46.905405

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0.715356

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3,471

6.868946

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0.066363

0.110328

0.074658

0.81377

0.790543

0.774782

0.729158

0.505599

0.480299

0

0.033958

0.185537

3,471

73

89

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0.818889

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false

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0.04918

0

0.213115

0

null

0

1

0

null

0

4

48462ff8c9477e614ea783c1bb87468aa265722b

703

py

Python

python3/lib/python3.6/site-packages/tensorflow/_api/v1/xla/experimental/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

3

2020-10-12T15:47:01.000Z

2022-01-14T19:51:26.000Z

python3/lib/python3.6/site-packages/tensorflow/_api/v1/xla/experimental/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

null

python3/lib/python3.6/site-packages/tensorflow/_api/v1/xla/experimental/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

2

2020-08-03T13:02:06.000Z

2020-11-04T03:15:44.000Z

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Public API for tf.xla.experimental namespace. """ from __future__ import print_function as _print_function from tensorflow.python.compiler.xla.jit import experimental_jit_scope as jit_scope from tensorflow.python.compiler.xla.xla import compile del _print_function import sys as _sys from tensorflow.python.util import deprecation_wrapper as _deprecation_wrapper if not isinstance(_sys.modules[__name__], _deprecation_wrapper.DeprecationWrapper): _sys.modules[__name__] = _deprecation_wrapper.DeprecationWrapper( _sys.modules[__name__], "xla.experimental")

37

83

0.825036

93

703

5.83871

0.462366

0.117864

0.110497

0.103131

0.324125

0.209945

0

0.099573

703

18

84

39.055556

0.85782

0.244666

0

1

0

0.030593

0

1

0

true

0

0.555556

0

0.555556

0.222222

0

null

0

1

0

null

0

1

0

1

0

1

0

4

485feaa51217869955fe58e3b35fdcdfdd385268

210

py

Python

python/problem2_better.py

mo/project-euler

7d6e59c0d82216b9a1d59e4f2472e53b8f330574

[ "MIT" ]

null

python/problem2_better.py

mo/project-euler

7d6e59c0d82216b9a1d59e4f2472e53b8f330574

[ "MIT" ]

null

python/problem2_better.py

mo/project-euler

7d6e59c0d82216b9a1d59e4f2472e53b8f330574

[ "MIT" ]

null

special_sum = 0 n_minus_1 = n_minus_2 = 1 fib_n = 0 while fib_n < 1000000: fib_n, n_minus_1, n_minus_2 = n_minus_1, n_minus_2, n_minus_1 + n_minus_2 if fib_n % 2 == 0: special_sum += fib_n print(special_sum)

26.25

74

0.733333

49

210

2.653061

0.244898

0.369231

0.215385

0.246154

0.430769

0.323077

0

0.114943

0.171429

210

7

75

30

0.632184

0

1

0

false

0

0.142857

0

null

1

0

1

0

null

0

4

48704e2e1ef7305aa5a6a78d426b35e7c88258f2

4,990

py

Python

tests/fixtures/rsa_pair.py

pdyba/lambdalizator

0371b8d3e25249096a9c7e7cf90fc590a99ad536

[ "MIT" ]

3

2020-09-26T11:05:32.000Z

2021-09-25T08:58:10.000Z

tests/fixtures/rsa_pair.py

pdyba/lambdalizator

0371b8d3e25249096a9c7e7cf90fc590a99ad536

[ "MIT" ]

15

2020-09-29T12:10:55.000Z

2021-11-17T10:42:21.000Z

tests/fixtures/rsa_pair.py

pdyba/lambdalizator

0371b8d3e25249096a9c7e7cf90fc590a99ad536

[ "MIT" ]

1

2020-09-26T11:05:38.000Z

EXPECTED_TOKEN = "eyJhbGciOiJSUzI1NiIsImtpZCI6Ijk0OTRhZDc1LTNmNTQtNDE1NS04NGZhLWMxYTE3ZGEyMmIzNSIsInR5cCI6IkpXVCJ9.eyJhbGxvdyI6eyIqIjoiKiJ9LCJkZW55Ijp7fX0.nDqCxO2Q1iXpxzbH7syxuyqw7kCY0sDfi9RX-VSUMTRN5aWTLt1bcPw4oN_jx89-YHBzDwnwBc07RsMgpFuo4zz2LU9PF0ciYxMNX-atTNsaIn05NkXT08au2AYb0DRCDS76MZ4QNi-4mRpLrj1SD4mSCwGtc2WNw9f0J0Vm4ZCYPVW6BqpcHcaFXzcFZ6EIoooaK6GvdTOjy498lWsAXjAen2U6Jles_BwFjqW1lW_ky4WV4J9NnK3v5wWKgR1Pg4R4LpnhIXe0dU_l64JHoJA3YcYxl-qilHfoBduc3La4kRKk7FAQDIqbOv4uN03BIoDXLH5t2uJ1Sm79Pe0ngGd5pSBmfUDKOGsHtx_3_9ZKfp-E2IVS0C7r36p4Ue0gKQzn0pXxa591bxm_puJAQ399SdbmlOJsM2cVFYAtlUQvWgErc57WcUJ0Qe4jEycury7hagNbP2fLn-7Gg4gZHiZ_Ul7L6GukbDfCHnhxSS4P3t3cVtWuslZi16hDhNbOTKD95y7PXvHePvI57ALV2v0RecQ5Blwurt1OuDRSjCYXyO6U4Y9MBHcd1wMtDoVW0jjvjXvqkEhuB52Zajh_yTNnJo0OAHpuK5wldVpECGFVx1rkW1ypKqlukGIgD--m6ElKnl6jw5VWSbdh2TJsZHnzjovbQUeqZOeMxwX6SE8" SAMPLE_PRIVATE_KEY = { "p": "5Cwk4zIvta07E37iZVlnzqeQX2jV1GaHKSUFtUVeaMVY_FBQ5Yr5ux7bxTKrikSs22QI8z1x6GVuEH3MGeh3qjOyUTfJJyOcS_RVmQxdYwjkxOsN953SWMvPRhkLd-svB8LI4Ylwo1NrlSBJOQqM2xvtoQ7KkEAJquvX_UHkTuMuSCcpuDyp-qUkvgfbSUgvCacmwNf-bXh2kKVM14YKt3el4vYITetyy96jzSLKf6V36AX8PFbSOS7oZbeO3dgfGp0MDcpF0flu8McaSipBXoHbrjtxx0MOJjpU3Fhpy6Q7o7TX3-OC7fKplseHol4dxkRCsiDoyCxjhUzPHShy8Q", "kty": "RSA", "q": "1PdEoZDjRNefrOxin6a4HEMnooopdLLbNrTmFcd3k3vIiKKK" "--8eg0zMQMYNe5QXDsiDuly3GTZkptDC93CxWQfjRftmC9gaz_pmdMhOPncgMfGfJAt0Ic57d023rmoZzBQecGKr_3lFxV29cs8bB1ppGxdIlweCXfvvbTubcU3CUyZrAjZqfXqxj4B78PQ96BIbFBTgtCpvSW0YhaswMMFpRnb8grMDQzkB0pXnq-GiBGc3wWKrWWrQjW5sLklwZUsQJz0GdZbLkHiq2nSM_wera6FgshyHvJFHSa9gpREq5rZdMsERp1C2Dd5h8W2cwohpuWqjQQVYGqSXgbN30Q", "d": "eYbYTGZ5uklUa2c2LUvbWyRLe8fL3fjFVG87xV76AwxkJZXmn_Mzv0c2F3rbiFjAL" "-BAWRwK2hrojzWMAztN0u3o13rQh4LasNrz9nPA" "-jCzIu1JnmBwwNBfY6x2LOOQPlrXI9SBbe94gB4xQUkb8yhzIlWk7jpPzbcKxi19r0SG06UVOEpB6z06cPGOEFrpKpgEaOulYx0H2G1s4vBADQaEFvamN94-sE_PhNjve-HjS3Lz3lhnm0ajFjpLsUqE0dIpeLWmEM3jMU7Zz6c9mI3V2aBUiuNYi9MeUvs9usmbX5krvijfJxDJsxmt1OK3EJSW_kju00hQYRi3jvmKqKnId7bHQSEaXa3FX5FTaardaaCUZSoEl6_WiypxyYg4PHp37HbXnZClBKWZot5lPWHn-Uf-2E2TXdqvysZRfc8NiBVrvVOzQDAkndR-wU3QgL5Efi6vrAFGkb5Ra7WvZ5fjkbQvb5VjKj0kKjJS3__CzLfAp_mQymy6U2wJE_YRxwwavFfaa53YhUdhiYM5P83Tj1UNG48ilVEFIzXWjgjfBMyNsgGF70mINJcc47GUXXh49GeWsHB9DqUMqyEbcVlEr_7tgkVHI7Dko8QlFjlLZRRa-JZApYlcRhaQGni0oUOMELP9ZRtArP3ib8hfdqqcBratNM_BB0bczBAJRQE", "e": "AQAB", "kid": "9494ad75-3f54-4155-84fa-c1a17da22b35", "qi": "znwFS129727wjq6Whq6wfD1cxkLs9hnS7" "-cEbZ8k_p7gE2vXucaK_AN1hIeU6HAGHgoP1RH7rUMN8" "-YzxfAsj8X0g0u8Qte7evf7jaj4YqPDGy7dTTA2ALLcWAD" "-djrDAU40ZJXnzM6hIIPbn_uMXAB5W0q_icV5xoIwxJog4ReUle0qMRS0iQCNocPBHydWt0zP" "-Cqz9MIPd0ctNEf8E55Go90_yAWibQlo4PgLF48UI5BG0NhmPa1nI9Kpt36TwAoyKwscGysSgPGNxgzj4t2PxatXO7Xm47dfNi_yhBSHq9TNcw2laMs9e77G9gluTAOPqYC06zRC7Rr3m9lXOA", "dp": "Z_3KjhW4ctfR_e" "-tZT2bNy9deG6CTjywS0tJT7We8qdHCC_evs9ZRDQrO7P9RJZKJe9wuNN_T8iyoieDVyeBKnxHQAbp0cHEIUXpoUhmY5WRFkJ-6iTu0nOJM0yE0pHIrIPVJB2MzZNei-fcF3g8fDw9UFM6dQYKofC9TvqyAFZAKLhYplRXsBmGJmnUQpD4hzC8U9Xdaq0ldIUyAWRhC_8nBsrVPBYcCtic1QiPPCABByl7LVDwnQlI99rx7R_sBSggb0SKD8ncCzbjP3wEsPsEUWNcVtGz6C5bsNVG2n4uhE0OukapzKL1MfgcVB8K-Orxbtfa4CiC7yTznDlsMQ", "dq": "Pl5f-hUNiea_" "-4uK4oiX2KcOH3ro4yVSL7ZQv8YXzdhthR5dJ6UCwZ8nHj0iS7O2AP1WHqjycm7MkVIIFyEovxMhSyhx3TwfthL2GHNk_sQyaI4DdjHog9INtIXNKkYmYe7ubylmh74DYeavCcV_e-rNZ0KtXpWzZ0TV_J59SnRkWaehpRc8npzlDUqqgYl169YJmhr3J6xZxR4vFU5qIY0zAJDuKHS2muRCFWMTYvIEWdfEq1zzI4-1ngXdpryZLwEJrQQhNSTBXwEHwExr0nBzkmTDhcX3NpExWHIFErJxZvm3V5rVSbPIbU1YT7UzOIFsvQFu6Cbhg4P6XuCpUQ", "n": "vdDyT3d33_NmNKBsF4OjIjtOsyOMrH8xhL9C8Jx6yvPcHNWkBHorIEyp7CcKp2gCo7jch6TlkH483cEwhw1GyyfrMPKh4P1uwNHpFI2eEDPqw_wyORVIIT8FPb_QhDxV5GFiWWecr_0DWNf9murqa_T7p5YUK3XVIhewFPDf0iHsV87OJLB8AoIsUfOCym5tvskTuxsMaIpYJZETe7upE_Xg-nVhyXhpFAJEw7RlYebrSEtoFpN6TwYuxutocZ4jNLn1x5t-YHWnyLYUIxN5_fuuzVGIAleY9T3WJXurYGnCUwQjgT7OvqM2K_xej0vFOp_P5C2YSxBX0SZG2322gDQiEqz7G2BCZ7I3PA4XVWV0KENwhxgz5GS7zjuZPIWm4oKwBRlYluTdWpc7A9w7LRs3tCJl6t_ReTlblnT9Dq7l5Na36IBOpesY77apE4BFlFdJZvhF_qrkHEQwo4ckOGrFlG8M3iV8UclHvVzBOPTi-sZoOOiytuSKbn5HikB2CV7k0GIfAJS6Q-RUHSYVFEH7IfmbvE0YkwglDggJWIfwtZG8IVuOFZunmWrYaKdMAvIJQjjv7oAwqrRD6HlXAPGHiTb_BRVaYqOD8ugjZ9ZO2tags12QyMOJq8XQ1mjegp0F-MNKlp_zff5xJbKETu2VUDFQmBqU6gLw_2r23cE", } SAMPLE_PUBLIC_KEY = { "kty": "RSA", "e": "AQAB", "kid": "9494ad75-3f54-4155-84fa-c1a17da22b35", "n": "vdDyT3d33_NmNKBsF4OjIjtOsyOMrH8xhL9C8Jx6yvPcHNWkBHorIEyp7CcKp2gCo7jch6TlkH483cEwhw1GyyfrMPKh4P1uwNHpFI2eEDPqw_wyORVIIT8FPb_QhDxV5GFiWWecr_0DWNf9murqa_T7p5YUK3XVIhewFPDf0iHsV87OJLB8AoIsUfOCym5tvskTuxsMaIpYJZETe7upE_Xg-nVhyXhpFAJEw7RlYebrSEtoFpN6TwYuxutocZ4jNLn1x5t-YHWnyLYUIxN5_fuuzVGIAleY9T3WJXurYGnCUwQjgT7OvqM2K_xej0vFOp_P5C2YSxBX0SZG2322gDQiEqz7G2BCZ7I3PA4XVWV0KENwhxgz5GS7zjuZPIWm4oKwBRlYluTdWpc7A9w7LRs3tCJl6t_ReTlblnT9Dq7l5Na36IBOpesY77apE4BFlFdJZvhF_qrkHEQwo4ckOGrFlG8M3iV8UclHvVzBOPTi-sZoOOiytuSKbn5HikB2CV7k0GIfAJS6Q-RUHSYVFEH7IfmbvE0YkwglDggJWIfwtZG8IVuOFZunmWrYaKdMAvIJQjjv7oAwqrRD6HlXAPGHiTb_BRVaYqOD8ugjZ9ZO2tags12QyMOJq8XQ1mjegp0F-MNKlp_zff5xJbKETu2VUDFQmBqU6gLw_2r23cE", }

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4880034d3f0a1caff60f4b886038b5b54c1a617f

97

py

Python

src/python/WMCore/Agent/Daemon/__init__.py

khurtado/WMCore

f74e252412e49189a92962945a94f93bec81cd1e

[ "Apache-2.0" ]

21

2015-11-19T16:18:45.000Z

2021-12-02T18:20:39.000Z

src/python/WMCore/Agent/Daemon/__init__.py

khurtado/WMCore

f74e252412e49189a92962945a94f93bec81cd1e

[ "Apache-2.0" ]

5,671

2015-01-06T14:38:52.000Z

2022-03-31T22:11:14.000Z

src/python/WMCore/Agent/Daemon/__init__.py

khurtado/WMCore

f74e252412e49189a92962945a94f93bec81cd1e

[ "Apache-2.0" ]

67

2015-01-21T15:55:38.000Z

2022-02-03T19:53:13.000Z

#!/usr/bin/env python """ __init__ Module containing methods for daemonizing applications. """

10.777778

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null

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6f9dd510493d691a79dc8b2b14bd6c8fae68f816

3,682

py

Python

test/distributed/pipeline/sync/skip/test_verify_skippables.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

60,067

2017-01-18T17:21:31.000Z

2022-03-31T21:37:45.000Z

test/distributed/pipeline/sync/skip/test_verify_skippables.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

66,955

2017-01-18T17:21:38.000Z

2022-03-31T23:56:11.000Z

test/distributed/pipeline/sync/skip/test_verify_skippables.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

19,210

2017-01-18T17:45:04.000Z

2022-03-31T23:51:56.000Z

# Copyright 2019 Kakao Brain # # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. # # This source code is licensed under the BSD license found in the # LICENSE file in the root directory of this source tree. import pytest from torch import nn from torch.distributed.pipeline.sync.skip import Namespace, skippable, verify_skippables def test_matching(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass @skippable(pop=["foo"]) class Layer2(nn.Module): pass verify_skippables(nn.Sequential(Layer1(), Layer2())) def test_stash_not_pop(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass with pytest.raises(TypeError) as e: verify_skippables(nn.Sequential(Layer1())) assert "no module declared 'foo' as poppable but stashed" in str(e.value) def test_pop_unknown(): @skippable(pop=["foo"]) class Layer1(nn.Module): pass with pytest.raises(TypeError) as e: verify_skippables(nn.Sequential(Layer1())) assert "'0' declared 'foo' as poppable but it was not stashed" in str(e.value) def test_stash_again(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass @skippable(stash=["foo"]) class Layer2(nn.Module): pass @skippable(pop=["foo"]) class Layer3(nn.Module): pass with pytest.raises(TypeError) as e: verify_skippables(nn.Sequential(Layer1(), Layer2(), Layer3())) assert "'1' redeclared 'foo' as stashable" in str(e.value) def test_pop_again(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass @skippable(pop=["foo"]) class Layer2(nn.Module): pass @skippable(pop=["foo"]) class Layer3(nn.Module): pass with pytest.raises(TypeError) as e: verify_skippables(nn.Sequential(Layer1(), Layer2(), Layer3())) assert "'2' redeclared 'foo' as poppable" in str(e.value) def test_stash_pop_together_different_names(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass @skippable(pop=["foo"], stash=["bar"]) class Layer2(nn.Module): pass @skippable(pop=["bar"]) class Layer3(nn.Module): pass verify_skippables(nn.Sequential(Layer1(), Layer2(), Layer3())) def test_stash_pop_together_same_name(): @skippable(stash=["foo"], pop=["foo"]) class Layer1(nn.Module): pass with pytest.raises(TypeError) as e: verify_skippables(nn.Sequential(Layer1())) assert "'0' declared 'foo' both as stashable and as poppable" in str(e.value) def test_double_stash_pop(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass @skippable(pop=["foo"]) class Layer2(nn.Module): pass @skippable(stash=["foo"]) class Layer3(nn.Module): pass @skippable(pop=["foo"]) class Layer4(nn.Module): pass with pytest.raises(TypeError) as e: verify_skippables(nn.Sequential(Layer1(), Layer2(), Layer3(), Layer4())) assert "'2' redeclared 'foo' as stashable" in str(e.value) assert "'3' redeclared 'foo' as poppable" in str(e.value) def test_double_stash_pop_but_isolated(): @skippable(stash=["foo"]) class Layer1(nn.Module): pass @skippable(pop=["foo"]) class Layer2(nn.Module): pass @skippable(stash=["foo"]) class Layer3(nn.Module): pass @skippable(pop=["foo"]) class Layer4(nn.Module): pass ns1 = Namespace() ns2 = Namespace() verify_skippables( nn.Sequential(Layer1().isolate(ns1), Layer2().isolate(ns1), Layer3().isolate(ns2), Layer4().isolate(ns2),) )

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null

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4

6fca82fb7a3cb4065768ef651df5a3e1e00bf4c5

253

py

Python

mailto/tasks.py

hckjck/django-mailto

3f36661a6a345ce2e87b16d14c3060f75f4da467

[ "BSD-3-Clause" ]

null

mailto/tasks.py

hckjck/django-mailto

3f36661a6a345ce2e87b16d14c3060f75f4da467

[ "BSD-3-Clause" ]

2

2015-04-01T09:44:30.000Z

2015-04-01T11:01:51.000Z

mailto/tasks.py

hckjck/django-mailto

3f36661a6a345ce2e87b16d14c3060f75f4da467

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from mailto.models import mailto try: from celery import shared_task @shared_task def task_mailto(args, kwargs): mailto(*args, **kwargs) except ImportError: pass

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null

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1

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1

0

4

6ff32bfc9f9631144f5ffe2cc6345bda2076d633

2,234

py

Python

timingCalculations/SET/GaussianPFAcalc.py

annafriebe/timing

685d05417bcd6bb1b640e30b37e52b33c7da66d8

[ "MIT" ]

null

timingCalculations/SET/GaussianPFAcalc.py

annafriebe/timing

685d05417bcd6bb1b640e30b37e52b33c7da66d8

[ "MIT" ]

null

timingCalculations/SET/GaussianPFAcalc.py

annafriebe/timing

685d05417bcd6bb1b640e30b37e52b33c7da66d8

[ "MIT" ]

null

#!/usr/bin/env python import math import numpy as np import scipy.stats as stats from matplotlib import pyplot as plt from PoissonPFAcalc import calcT nGenerated = 100 def calcPFAGaussian(z): zMean = np.mean(z) print("mean: ", zMean) zStdDev = math.sqrt(np.var(z)) print("stddev: ", zStdDev) generatedData = np.random.normal(zMean, zStdDev, (nGenerated, len(z))) dataProbabilities = stats.norm.pdf(z, zMean, zStdDev) logLikelihoodsData = np.log(dataProbabilities) generatedDataProbabilities =stats.norm.pdf(generatedData, zMean, zStdDev) logLikelihoodsGen = np.log(generatedDataProbabilities) expW = np.mean(logLikelihoodsGen) varW = np.var(logLikelihoodsGen) measuredT = calcT(logLikelihoodsData, expW, varW) generatedT = np.zeros(nGenerated) for k in range(nGenerated): generatedT[k] = calcT(logLikelihoodsGen[k], expW, varW) beta = np.count_nonzero(generatedT <= measuredT)/nGenerated print("Beta:", beta) PFA = min(beta, 1-beta) print("PFA:", PFA) return zMean, zStdDev def calcPFASkewNorm(z): a, loc, scale = stats.skewnorm.fit(z) print("a", a) print("loc", loc) print("scale", scale) zMean = np.mean(z) print("mean: ", zMean) zStdDev = math.sqrt(np.var(z)) print("stddev: ", zStdDev) generatedData = stats.skewnorm(a, loc, scale).rvs((nGenerated, len(z))) # np.random.normal(zMean, zStdDev, (nGenerated, len(z))) dataProbabilities = stats.skewnorm.pdf(z, a, loc, scale) logLikelihoodsData = np.log(dataProbabilities) generatedDataProbabilities =stats.skewnorm.pdf(generatedData, a, loc, scale) logLikelihoodsGen = np.log(generatedDataProbabilities) expW = np.mean(logLikelihoodsGen) varW = np.var(logLikelihoodsGen) measuredT = calcT(logLikelihoodsData, expW, varW) generatedT = np.zeros(nGenerated) for k in range(nGenerated): generatedT[k] = calcT(logLikelihoodsGen[k], expW, varW) beta = np.count_nonzero(generatedT <= measuredT)/nGenerated print("Beta:", beta) PFA = min(beta, 1-beta) print("PFA:", PFA) return a, loc, scale #TODO, draw z and probability distribution #print(generatedData)

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1

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null

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1

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4

b5003da2268625bc95d8dceef5949216a61e4057

87

py

Python

cancan/apps.py

pgorecki/django-cancango

c2859db27169af57862d7974b326140f253465f1

[ "MIT" ]

34

2020-09-02T11:28:03.000Z

2022-03-17T08:18:02.000Z

cancan/apps.py

pgorecki/django-cancango

c2859db27169af57862d7974b326140f253465f1

[ "MIT" ]

2

2020-09-23T12:51:20.000Z

2022-02-10T14:42:46.000Z

cancan/apps.py

pgorecki/django-cancango

c2859db27169af57862d7974b326140f253465f1

[ "MIT" ]

null

from django.apps import AppConfig class CanCanConfig(AppConfig): name = "cancan"

14.5

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0

1

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1

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4

82eb9e4a3ecf573f68cb616c0cb06f994216d93b

276

py

Python

osuapi/endpoints.py

khazhyk/osssss

6286e4c61cc9510f791256e8a2598bbee13cda7f

[ "MIT" ]

20

2017-03-21T06:04:32.000Z

2021-11-04T21:11:58.000Z

osuapi/endpoints.py

khazhyk/osuapi

6286e4c61cc9510f791256e8a2598bbee13cda7f

[ "MIT" ]

31

2016-08-05T02:12:20.000Z

2021-01-11T21:12:47.000Z

osuapi/endpoints.py

khazhyk/osuapi

6286e4c61cc9510f791256e8a2598bbee13cda7f

[ "MIT" ]

19

2016-08-03T18:34:02.000Z

2021-12-06T09:20:00.000Z

"""API endpoints.""" API_BASE = "https://osu.ppy.sh/api" USER = API_BASE + "/get_user" USER_BEST = API_BASE + "/get_user_best" USER_RECENT = API_BASE + "/get_user_recent" SCORES = API_BASE + "/get_scores" BEATMAPS = API_BASE + "/get_beatmaps" MATCH = API_BASE + "/get_match"

27.6

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1

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null

0

4

82f1bac901fd9d43bcbaf44c4d6bac9556909ab8

97

py

Python

mlm/subset_model/__main__.py

ririw/kaggle-bimbo-pymc3

fbf016751e2459b9fa6c8d058aad9c75fca57731

[ "MIT" ]

null

mlm/subset_model/__main__.py

ririw/kaggle-bimbo-pymc3

fbf016751e2459b9fa6c8d058aad9c75fca57731

[ "MIT" ]

null

mlm/subset_model/__main__.py

ririw/kaggle-bimbo-pymc3

fbf016751e2459b9fa6c8d058aad9c75fca57731

[ "MIT" ]

null

from mlm.subset_model import SubsetModelCLI if __name__ == '__main__': SubsetModelCLI.run()

19.4

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97

5.909091

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1

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82f6a0cbbf2b7e08a8c33dbf03ef202f8e47dd20

421

py

Python

tests/test_maybe/test_maybe_unwrap.py

ksurta/returns

9746e569303f214d035462ae3dffe5c49abdcfa7

[ "BSD-2-Clause" ]

null

tests/test_maybe/test_maybe_unwrap.py

ksurta/returns

9746e569303f214d035462ae3dffe5c49abdcfa7

[ "BSD-2-Clause" ]

null

tests/test_maybe/test_maybe_unwrap.py

ksurta/returns

9746e569303f214d035462ae3dffe5c49abdcfa7

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- import pytest from returns.maybe import Nothing, Some from returns.primitives.exceptions import UnwrapFailedError def test_unwrap_success(): """Ensures that unwrap works for Some container.""" assert Some(5).unwrap() == 5 def test_unwrap_failure(): """Ensures that unwrap works for Nothing container.""" with pytest.raises(UnwrapFailedError): assert Nothing.unwrap()

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421

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421

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0

1

0

1

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4

82fc2f9abb6c40b0c27c9515fdac1aac4a77ba41

131

py

Python

tests/conftest.py

nylas/mypy-tools

5c5a7a18fd38a469372a99fd64e84ef3851cf15f

[ "MIT" ]

27

2017-10-12T02:32:59.000Z

2021-07-01T04:38:17.000Z

tests/conftest.py

nylas/mypy-tools

5c5a7a18fd38a469372a99fd64e84ef3851cf15f

[ "MIT" ]

6

2017-10-16T23:20:47.000Z

2021-03-25T21:44:30.000Z

tests/conftest.py

nylas/mypy-tools

5c5a7a18fd38a469372a99fd64e84ef3851cf15f

[ "MIT" ]

5

2017-10-12T02:33:04.000Z

2018-12-13T05:57:12.000Z

import sys collect_ignore = [] if sys.version_info[0] > 2: collect_ignore.append("py2") else: collect_ignore.append("py3")

18.714286

32

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4.631579

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1

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null

0

4

82fd70d563323f0a082928808a18d3e295a11aeb

327

py

Python

utilities/__init__.py

jakeaylmer/ice_edge_latitude

327aecbfc742b8deb7c055ed57beab8b9bb931f6

[ "MIT" ]

null

utilities/__init__.py

jakeaylmer/ice_edge_latitude

327aecbfc742b8deb7c055ed57beab8b9bb931f6

[ "MIT" ]

null

utilities/__init__.py

jakeaylmer/ice_edge_latitude

327aecbfc742b8deb7c055ed57beab8b9bb931f6

[ "MIT" ]

null

""" --------------------------------------------------------- For sub-package documentation, refer the relevant function documentation. --------------------------------------------------------- """ import sys import os sys.path.append(os.path.dirname(__file__)) sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

25.153846

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63

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null

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1

0

1

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4

d206208dc77cb07161822176c5a7566e3dafea64

388

py

Python

src/SPH/SolidSPHHydroBaseInst.cc.py

markguozhiming/spheral

bbb982102e61edb8a1d00cf780bfa571835e1b61

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

1

2020-10-21T01:56:55.000Z

src/SPH/SolidSPHHydroBaseInst.cc.py

markguozhiming/spheral

bbb982102e61edb8a1d00cf780bfa571835e1b61

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

null

src/SPH/SolidSPHHydroBaseInst.cc.py

markguozhiming/spheral

bbb982102e61edb8a1d00cf780bfa571835e1b61

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

null

text = """ //------------------------------------------------------------------------------ // Explict instantiation. //------------------------------------------------------------------------------ #include "SolidSPHHydroBase.cc" #include "SolidSPHEvaluateDerivatives.cc" #include "Geometry/Dimension.hh" namespace Spheral { template class SolidSPHHydroBase< Dim< %(ndim)s > >; } """

29.846154

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388

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0.108434

0

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388

12

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0

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null

0

1

0

1

null

0

4

d221e61c12e17b97a96e4ce6b93655b302f747d2

690

py

Python

5kyu/last-digit-of-a-big-number.py

PabloCorbCon/codewars-examples

b792a73d78d764aeb3fc2231f97e06f976136854

[ "Apache-2.0" ]

1

2021-02-26T16:29:04.000Z

5kyu/last-digit-of-a-big-number.py

PabloCorbCon/codewars-examples

b792a73d78d764aeb3fc2231f97e06f976136854

[ "Apache-2.0" ]

null

5kyu/last-digit-of-a-big-number.py

PabloCorbCon/codewars-examples

b792a73d78d764aeb3fc2231f97e06f976136854

[ "Apache-2.0" ]

null

# Define a function that takes in two non-negative integers a and b and returns the last decimal digit of a^b. # Note that a and b may be very large! # For example, the last decimal digit of 9^7 is 9, since 9^7=4782969. # The last decimal digit of (2^200)^2300, which has over 10^92 decimal digits, is 6. # Also, please take 0^0 = 1 # # You may assume that the input will always be valid. # Examples # # last_digit(4, 1) # returns 4 # last_digit(4, 2) # returns 6 # last_digit(9, 7) # returns 9 # last_digit(10, 10 ** 10) # returns 0 # last_digit(2 ** 200, 2 ** 300) # returns 6 def last_digit(a, b): return pow(a, b, 10)

40.588235

111

0.62029

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690

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690

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4

d229d2c9dcb0d06fc42d31d488172e38cbb6d68b

144

py

Python

techtest/social_media/urls.py

vittoriozamboni/techtest-backend

783a55a8ea18738c92445ace3e218402b1731fa0

[ "MIT" ]

null

techtest/social_media/urls.py

vittoriozamboni/techtest-backend

783a55a8ea18738c92445ace3e218402b1731fa0

[ "MIT" ]

null

techtest/social_media/urls.py

vittoriozamboni/techtest-backend

783a55a8ea18738c92445ace3e218402b1731fa0

[ "MIT" ]

null

from django.conf.urls import include, url urlpatterns = [ url(r'^api/', include('social_media.api.urls', namespace='social_media_api')) ]

20.571429

81

0.715278

20

144

5

0.65

0.22

0.28

0

0.125

144

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82

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1

0

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null

0

4

d22a222c63dedf14c9a5365be0ce520b0e648da1

105

py

Python

bdpy/__init__.py

birkin/borrowdirect.py

98b7e605c1f2a97eea7ed049b6f04914197a48e8

[ "Unlicense", "MIT" ]

2

2015-11-17T15:27:22.000Z

2016-10-28T17:13:30.000Z

bdpy/__init__.py

birkin/borrowdirect.py

98b7e605c1f2a97eea7ed049b6f04914197a48e8

[ "Unlicense", "MIT" ]

1

2015-05-28T19:04:48.000Z

2015-11-02T21:14:15.000Z

bdpy/__init__.py

birkin/borrowdirect.py

98b7e605c1f2a97eea7ed049b6f04914197a48e8

[ "Unlicense", "MIT" ]

1

2015-03-27T20:52:45.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from .borrowdirect import BorrowDirect

17.5

39

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105

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1

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4

d234b6c93697c102ff4480721422bea295483173

2,500

py

Python

marlgrid/pz_envs/yummyyucky.py

aivaslab/marlgrid

10b53d27ce224fadeeb5830d6034350a69feb4b4

[ "Apache-2.0" ]

null

marlgrid/pz_envs/yummyyucky.py

aivaslab/marlgrid

10b53d27ce224fadeeb5830d6034350a69feb4b4

[ "Apache-2.0" ]

null

marlgrid/pz_envs/yummyyucky.py

aivaslab/marlgrid

10b53d27ce224fadeeb5830d6034350a69feb4b4

[ "Apache-2.0" ]

null

from ..base_AEC import * from ..objects import * from random import randrange import random import math class YummyYuckyEnv0(para_MultiGridEnv): """ """ mission = "yummy yucky simple: go to the correct color, of 2." metadata = {} def _gen_grid(self, width, height): # Create an empty grid self.grid = MultiGrid((width, height)) chosen = 0 # choose green as the good color mirror1 = random.choice([-1,1]) c = ['green', 'blue'] # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) for x in range(2): r = 1 if x == chosen else -1 self.put_obj(Goal(color=c[x], reward=r), width//2 + 1*(x*2-1)*mirror1, height//2) self.agent_spawn_kwargs = {"top":(1,1)} self.place_agents(**self.agent_spawn_kwargs) class YummyYuckyEnv1(para_MultiGridEnv): """ """ mission = "yummy yucky" metadata = {} def _gen_grid(self, width, height): # Create an empty grid self.grid = MultiGrid((width, height)) chosen = 0#random.choice([0,1]) mirror1 = random.choice([-1,1]) mirror2 = random.choice([-1,1]) c = ['green', 'blue'] # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) for x in range(2): r = 1 if x == chosen else -1 self.put_obj(Goal(color=c[x], reward=r), width//2 + 3*(x*2-1)*mirror1, height//2) for x in range(2): r = 1 if x == chosen else -1 self.put_obj(Goal(color=c[x], reward=r), width//2 + 3*(x*2-1), height//2-3*(x*2-1)*mirror2) self.put_obj(Goal(color=c[not x], reward=r), width//2 + 3*(x*2-1), height//2+3*(x*2-1)*mirror2) self.agent_spawn_kwargs = {"top":(1,1)} self.place_agents(**self.agent_spawn_kwargs) class YummyYuckyEnv3(para_MultiGridEnv): """ """ mission = "yummy yucky" metadata = {} def _gen_grid(self, width, height): # Create an empty grid self.grid = MultiGrid((width, height)) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) for x in range(4): r = 1 if x == chosen else -1 self.put_obj(Goal(color=c[x], reward=r), width//2 + int(3*math.cos(x*3.14/2)), height//2 + int(3*math.sin(x*3.14/2))) self.agent_spawn_kwargs = {"color":"green", "view_offset": 0} self.place_agents(**self.agent_spawn_kwargs)

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null

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d238445108b6a7a1476130f941cdfead6a1e2990

369

py

Python

Level1/Lessons76501/gamjapark.py

StudyForCoding/ProgrammersLevel

dc957b1c02cc4383a93b8cbf3d739e6c4d88aa25

[ "MIT" ]

null

Level1/Lessons76501/gamjapark.py

StudyForCoding/ProgrammersLevel

dc957b1c02cc4383a93b8cbf3d739e6c4d88aa25

[ "MIT" ]

null

Level1/Lessons76501/gamjapark.py

StudyForCoding/ProgrammersLevel

dc957b1c02cc4383a93b8cbf3d739e6c4d88aa25

[ "MIT" ]

1

2021-04-05T07:35:59.000Z

# 음양 더하기 def solution(absolutes, signs): return sum([x if y else -x for x, y in zip(absolutes, signs)]) ''' 테스트 1 〉 통과 (0.11ms, 10.2MB) 테스트 2 〉 통과 (0.12ms, 10.2MB) 테스트 3 〉 통과 (0.11ms, 10.2MB) 테스트 4 〉 통과 (0.11ms, 10.3MB) 테스트 5 〉 통과 (0.12ms, 10.3MB) 테스트 6 〉 통과 (0.11ms, 10.2MB) 테스트 7 〉 통과 (0.10ms, 10.2MB) 테스트 8 〉 통과 (0.12ms, 10.3MB) 테스트 9 〉 통과 (0.12ms, 10.2MB) '''

23.0625

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null

0

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1

0

4

d24ed0652ef6bf199feeca107d971a6eb055a99b

297

py

Python

maatpy/classifiers/__init__.py

sanzgiri/MaatPy

381a0d31f1afdd2c53b9ccbb410eb0df6b4b9965

[ "MIT" ]

11

2019-05-17T03:50:18.000Z

2021-08-23T22:18:23.000Z

maatpy/classifiers/__init__.py

sanzgiri/MaatPy

381a0d31f1afdd2c53b9ccbb410eb0df6b4b9965

[ "MIT" ]

3

2021-04-08T14:01:15.000Z

2021-06-21T15:41:31.000Z

maatpy/classifiers/__init__.py

sanzgiri/MaatPy

381a0d31f1afdd2c53b9ccbb410eb0df6b4b9965

[ "MIT" ]

7

2019-06-09T06:16:59.000Z

2021-11-12T01:45:52.000Z

from .smoteboost import SMOTEBoost from .smotebagging import SMOTEBagging from .adacost import AdaCost from .balanced_random_forest import BalancedRandomForestClassifier __all__ = ['BalancedBaggingClassifier', 'SMOTEBoost', 'SMOTEBagging', 'AdaCost', 'BalancedRandomForestClassifier']

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4

d264013af4cc5dd8eb56627916a5d6c9d605e8ff

871

py

Python

1.py

2spmohanty/Performance

5a86a56f40bad1e12654fe1bb737affb4e0edd26

[ "Apache-2.0" ]

null

1.py

2spmohanty/Performance

5a86a56f40bad1e12654fe1bb737affb4e0edd26

[ "Apache-2.0" ]

null

1.py

2spmohanty/Performance

5a86a56f40bad1e12654fe1bb737affb4e0edd26

[ "Apache-2.0" ]

null

from collections import OrderedDict, namedtuple, defaultdict import glob instance_data = {} instance_dict= {'1': ',PRIMARY_LDU_NAME:10.172.109.23,PRIMARY_LDU_USER_NAME:administrator@skyscraper.local,PRIMARY_LDU_PASSWD:vc_password,DATACENTER:Datacenter3,CLUSTER:cls,HOST_NAME:w1-hs4-n2203.eng.vmware.com,SRC_PNIC:vmnic1,DATASTORE:vsanDatastore,SRC_DISK:vmhba2,DEST_DATACENTER:Datacenter3,DEST_CLUSTER:cls,DEST_HOST_NAME:w1-hs4-n2204.eng.vmware.com,PNIC:vmnic1,DEST_DATASTORE:vsanDatastore,DEST_DISK:vmhba2,STAT_COLLLECTION_LIST:pnic,datastore,mem,disk'} """ for instance in instance_dict: print instance instance_data[instance] = dict( (x, y) for x, y in (item.split(":") for item in instance_dict[instance].strip(",").split(","))) instance = '1' for x,y in (item.split(":") for item in instance_dict[instance].strip(",").split(",")): """ z = dict(1,2)

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d272041d547cbdbd15af0ea5dd177119519b09bf

1,839

py

Python

sniffersapp/equipment/migrations/0005_auto_20180812_1938.py

jamesokane/Oneworksite-Application

1749ffa89430be75394ae0d43905f3dd30a24fc6

[ "MIT" ]

null

sniffersapp/equipment/migrations/0005_auto_20180812_1938.py

jamesokane/Oneworksite-Application

1749ffa89430be75394ae0d43905f3dd30a24fc6

[ "MIT" ]

7

2020-06-05T19:27:52.000Z

2022-03-11T23:34:52.000Z

sniffersapp/equipment/migrations/0005_auto_20180812_1938.py

jamesokane/Oneworksite-Application

1749ffa89430be75394ae0d43905f3dd30a24fc6

[ "MIT" ]

null

# Generated by Django 2.0.6 on 2018-08-12 09:38 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('equipment', '0004_auto_20180703_2320'), ] operations = [ migrations.RemoveField( model_name='equipment_additionalinfo', name='created_user', ), migrations.RemoveField( model_name='equipment_additionalinfo', name='equipment_id', ), migrations.RemoveField( model_name='equipment', name='fuel', ), migrations.RemoveField( model_name='equipment', name='height_restrictor', ), migrations.RemoveField( model_name='equipment', name='maintenance', ), migrations.RemoveField( model_name='equipment', name='make', ), migrations.RemoveField( model_name='equipment', name='model', ), migrations.RemoveField( model_name='equipment', name='purchase_amount', ), migrations.RemoveField( model_name='equipment', name='purchase_date', ), migrations.RemoveField( model_name='equipment', name='rubber_tracks', ), migrations.RemoveField( model_name='equipment', name='size', ), migrations.RemoveField( model_name='equipment', name='year', ), migrations.AddField( model_name='equipment', name='description', field=models.CharField(blank=True, max_length=120), ), migrations.DeleteModel( name='Equipment_AdditionalInfo', ), ]

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Python

pymatflow/cp2k/__init__.py

DeqiTang/pymatflow

bd8776feb40ecef0e6704ee898d9f42ded3b0186

[ "MIT" ]

6

2020-03-06T16:13:08.000Z

2022-03-09T07:53:34.000Z

pymatflow/cp2k/__init__.py

DeqiTang/pymatflow

bd8776feb40ecef0e6704ee898d9f42ded3b0186

[ "MIT" ]

1

2021-10-02T02:23:08.000Z

2021-11-08T13:29:37.000Z

pymatflow/cp2k/__init__.py

DeqiTang/pymatflow

bd8776feb40ecef0e6704ee898d9f42ded3b0186

[ "MIT" ]

1

2021-07-10T16:28:14.000Z

from .cp2k import Cp2k from .static import StaticRun from .opt import OptRun from .phonopy import PhonopyRun from .md import MdRun

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964897838ab34a87d4f61c62299d67615a7c424a

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py

Python

ds4se/infoxplainer/causality/eval/traceability.py

WM-CSCI-435-F19/data-science-4-software-engineering

3692163df710550d4ee5b399a2a184968a0f18c6

[ "Apache-2.0" ]

5

2020-12-08T00:38:24.000Z

2021-11-16T20:00:59.000Z

ds4se/infoxplainer/causality/eval/traceability.py

WM-CSCI-435-F19/data-science-4-software-engineering

3692163df710550d4ee5b399a2a184968a0f18c6

[ "Apache-2.0" ]

110

2020-09-26T18:36:35.000Z

2022-03-12T00:54:35.000Z

ds4se/infoxplainer/causality/eval/traceability.py

WM-CSCI-435-F19/data-science-4-software-engineering

3692163df710550d4ee5b399a2a184968a0f18c6

[ "Apache-2.0" ]

3

2020-12-09T19:23:10.000Z

2021-02-16T12:54:16.000Z

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/4.4_infoxplainer.causality.eval.traceability.ipynb (unless otherwise specified). __all__ = []

47.333333

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142

5.578947

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0.105634

142

3

129

47.333333

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