xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

21d1c69858e63fd0fa03a99fca3f0a286e9d9502

45

py

Python

shenfun/laguerre/__init__.py

jaisw7/shenfun

7482beb5b35580bc45f72704b69343cc6fc1d773

[ "BSD-2-Clause" ]

138

2017-06-17T13:30:27.000Z

2022-03-20T02:33:47.000Z

shenfun/laguerre/__init__.py

jaisw7/shenfun

7482beb5b35580bc45f72704b69343cc6fc1d773

[ "BSD-2-Clause" ]

73

2017-05-16T06:53:04.000Z

2022-02-04T10:40:44.000Z

shenfun/laguerre/__init__.py

jaisw7/shenfun

7482beb5b35580bc45f72704b69343cc6fc1d773

[ "BSD-2-Clause" ]

38

2018-01-31T14:37:01.000Z

2022-03-31T15:07:27.000Z

from .bases import * from .matrices import *

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21dbaafa9584719c2eb09101ae445c14289a272e

155

py

Python

archive/p/python/baklava.py

asharma13524/sample-programs

1e15059b92144991a2983112c0d8fe14111fd0a8

[ "MIT" ]

422

2018-08-14T11:57:47.000Z

2022-03-07T23:54:34.000Z

archive/p/python/baklava.py

asharma13524/sample-programs

1e15059b92144991a2983112c0d8fe14111fd0a8

[ "MIT" ]

1,498

2018-08-10T19:18:52.000Z

2021-12-14T03:02:00.000Z

archive/p/python/baklava.py

asharma13524/sample-programs

1e15059b92144991a2983112c0d8fe14111fd0a8

[ "MIT" ]

713

2018-08-12T21:37:49.000Z

2022-03-02T22:57:21.000Z

for i in range(0, 10, 1): print((" " * (10 - i)) + ("*" * (i * 2 + 1))) for i in range(10, -1, -1): print((" " * (10 - i)) + ("*" * (i * 2 + 1)))

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df5511bc676c3d380f605acfd03401177919aa4b

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py

Python

plugins/helpers/__init__.py

rmmoreira/udacity-dend-capstone

600bf61bc59e686a4d3b0b6f371681c084759663

[ "MIT" ]

1

2021-02-12T19:10:03.000Z

plugins/helpers/__init__.py

rmmoreira/udacity-dend-capstone

600bf61bc59e686a4d3b0b6f371681c084759663

[ "MIT" ]

314

2020-05-27T02:59:59.000Z

2021-08-03T02:43:42.000Z

plugins/helpers/__init__.py

rmmoreira/udacity-dend-capstone

600bf61bc59e686a4d3b0b6f371681c084759663

[ "MIT" ]

3

2020-05-31T13:08:33.000Z

2021-07-06T23:00:36.000Z

from helpers.sql_queries import (immigration_table, temperature_table, airport_table, demographics_table, dim_airport_table, dim_demographic_table, dim_visitor_table, fact_city_data_table, fact_city_table_insert, dim_airport_table_insert, dim_demographic_table_insert, dim_visitor_table_insert ) from helpers.table_dictionaries import (staging_tables, fact_dimension_tables, fact_dimension_insert) __all__ = [ 'staging_tables', 'fact_dimension_tables', 'immigration_table', 'temperature_table', 'airport_table', 'demographics_table', 'dim_airport_table', 'dim_demographic_table', 'dim_visitor_table', 'fact_city_data_table', 'fact_city_table_insert', 'dim_airport_table_insert', 'dim_demographic_table_insert', 'dim_visitor_table_insert', 'fact_dimension_insert' ]

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df7541ae2a5203a47616b3833f3471ad2b030cc8

13,810

py

Python

ultilities.py

xuan0802/state-load-placement

0a5ff41006c266001c6938dff64e4251d4f77f9b

[ "MIT" ]

null

ultilities.py

xuan0802/state-load-placement

0a5ff41006c266001c6938dff64e4251d4f77f9b

[ "MIT" ]

null

ultilities.py

xuan0802/state-load-placement

0a5ff41006c266001c6938dff64e4251d4f77f9b

[ "MIT" ]

null

import matplotlib.pyplot as plt import ujson from contants import * import numpy as np from ast import literal_eval # product function def prod(x): product = 1 for i in x: product = product * i return product def draw_pareto_front_ue_num(): # make figures plt.figure() for n_ in ue_num_list: # read data filename = "data/pareto_n_" + str(n_) + ".js" f = open(filename) perf_pareto_list = ujson.load(f) # make plots x_data = list() y_data = list() perf_pareto_list.sort(key=lambda x: x[0]) for i in perf_pareto_list: x_data.append(i[0]) y_data.append(i[1]) plt.plot(x_data, y_data, marker='D', markerfacecolor=line_style_map_ue[n_]['marker_color'], markersize=10, linestyle='dashed', color='olive', label=line_style_map_ue[n_]['label']) for a in annotate_map_ue[n_].keys(): plt.annotate(annotate_map_ue[n_][a], a) plt.xlabel('State Transfer Cost', fontsize='x-large') plt.ylabel('Traffic load', fontsize='x-large') plt.legend() plt.show() def draw_pareto_front_handover_frequency(): # make figures plt.figure() for h_ in handover_list: # read data filename = "data/pareto_h_" + str(h_) + ".js" f = open(filename) perf_pareto_list = ujson.load(f) # make plots x_data = list() y_data = list() perf_pareto_list.sort(key=lambda x: x[0]) for i in perf_pareto_list: x_data.append(i[0]) y_data.append(i[1]) plt.plot(x_data, y_data, marker='D', markerfacecolor=line_style_map_handover[h_]['marker_color'], markersize=10, linestyle='dashed', color='olive', label=line_style_map_handover[h_]['label']) for a in annotate_map_handover[h_].keys(): plt.annotate(annotate_map_handover[h_][a], a) plt.xlabel('State Transfer Cost', fontsize='x-large') plt.ylabel('Traffic load', fontsize='x-large') plt.legend() plt.show() def draw_pareto_front_request(): # make figures plt.figure() for u_ in ue_num_list: # read data filename = "data/pareto_u_" + str(u_) + ".js" f = open(filename) perf_pareto_list = ujson.load(f) # make plots x_data = list() y_data = list() perf_pareto_list.sort(key=lambda x: x[0]) for i in perf_pareto_list: x_data.append(i[0]) y_data.append(i[1]) plt.plot(x_data, y_data, marker='D', markerfacecolor=line_style_map_request[u_]['marker_color'], markersize=10, linestyle='dashed', color='olive', label=line_style_map_request[u_]['label']) for a in annotate_map_request[u_].keys(): plt.annotate(annotate_map_request[u_][a], a) plt.xlabel('State Transfer Cost', fontsize='x-large') plt.ylabel('Traffic load', fontsize='x-large') plt.legend() plt.show() def draw_running_time(): po = dict() po['label'] = 'PO with w=0.5' po['running_time'] = [0.882800817489624, 1.1198995113372803, 1.508939266204834] ost = dict() ost['label'] = 'OST' ost['running_time'] = [0.9020464420318604, 1.149639368057251, 2.6382675170898438] otl = dict() otl['label'] = 'OTL' otl['running_time'] = [0.09352946281433105, 0.12285518646240234, 0.1560497283935547] apo = dict() apo['label'] = 'APO' apo['running_time'] = [42.97541284561157, 45.240522384643555, 69.9119827747345] algorithms = list() algorithms.append(po) algorithms.append(ost) algorithms.append(otl) algorithms.append(apo) xtick = ['M=10', 'M=11', 'M=12'] fig, ax = plt.subplots() color_list = {'PO with w=0.5':'blue', 'OST':'red', 'OTL':'green', 'APO':'olive'} index = np.arange(len(xtick)) bar_width = 0.2 opacity = 0.8 i = 0 for algo in algorithms: ax.bar(index + i * bar_width, algo['running_time'], bar_width, alpha=opacity, color=color_list[algo['label']], label=algo['label']) i = i + 1 ax.set_xlabel('Number of cloud centers', fontsize='x-large') ax.set_ylabel('Running time (s)', fontsize='x-large') ax.set_yscale('log') ax.set_xticks(index + bar_width) ax.set_xticklabels(xtick) ax.legend(fontsize='large', loc="upper left") fig.tight_layout() plt.show() def draw_performance_results_handover(): handover_list = [0.5] + [5*x for x in range(20) if x != 0] OST = dict() OTL = dict() PO = dict() APO = dict() OST['state'] = [] OTL['state'] = [] PO['state'] = [] OST['load'] = [] OTL['load'] = [] PO['load'] = [] OST['num_func'] = [] OTL['num_func'] = [] PO['num_func'] = [] for h in handover_list: filename = "data/pareto_" + 'h' + "_" + str(h) + ".js" f = open(filename) pareto_optimal_points = ujson.load(f) pareto_optimal_points.sort(key=lambda x: x[0]) OST['state'].append(pareto_optimal_points[0][0]) OST['load'].append(pareto_optimal_points[0][1]) OST['num_func'].append(pareto_optimal_points[0][2]) OTL['state'].append(pareto_optimal_points[len(pareto_optimal_points)-1][0]) OTL['load'].append(pareto_optimal_points[len(pareto_optimal_points)-1][1]) OTL['num_func'].append(pareto_optimal_points[len(pareto_optimal_points)-1][2]) filename = "data/pareto_weight_" + 'h' + "_" + str(h) + ".js" f = open(filename) pareto_optimal_weight_map = ujson.load(f) for point in pareto_optimal_weight_map.keys(): if 0.5 in pareto_optimal_weight_map[point]: p_ = literal_eval(point) PO['state'].append(p_[0]) PO['load'].append(p_[1]) PO['num_func'].append(p_[2]) break print('--------------------------', h, '-----------') print('best state', pareto_optimal_points[0][0]) print('worst state', pareto_optimal_points[len(pareto_optimal_points)-1][0]) print('best load', pareto_optimal_points[len(pareto_optimal_points)-1][1]) print('worst load', pareto_optimal_points[0][1]) print('PO state', p_[0]) print('PO load', p_[1]) APO['state'] = [32.0, 320.0, 640.0, 960.0, 1280.0, 1600.0, 1920.0, 2240.0, 2560.0, 2880.0, 3200.0, 3520.0, 3840.0, 4160.0, 4480.0, 4800.0, 5120.0, 5440.0, 5760.0, 6080.0] APO['load'] = [132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 132000.0, 165000.0] APO['num_func'] = [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0] figures = ['state', 'load', 'num_func'] ylabel_map = {'state': 'State Transfer Cost', 'load':'Traffic Load', 'num_func':'Number of StateMF sets'} for fig in figures: plt.plot(handover_list, OST[fig], color='red', marker='*', linestyle='solid', label='OST') plt.plot(handover_list, OTL[fig], color='green', marker='o', linestyle='solid', label='OTL') plt.plot(handover_list, PO[fig], color='blue', marker='^', linestyle='solid', label='PO with w=0.5') plt.plot(handover_list, APO[fig], color='olive', marker='x', linestyle='solid', label='APO') plt.xlabel('Handover frequency', fontsize='x-large') plt.ylabel(ylabel_map[fig], fontsize='x-large') plt.legend() plt.show() def draw_performance_results_request(): request_list = [x for x in range(10)] OST = dict() OTL = dict() PO = dict() APO = dict() OST['state'] = [] OTL['state'] = [] PO['state'] = [] OST['load'] = [] OTL['load'] = [] PO['load'] = [] OST['num_func'] = [] OTL['num_func'] = [] PO['num_func'] = [] for u in request_list: filename = "data/pareto_" + 'u' + "_" + str(u) + ".js" f = open(filename) pareto_optimal_points = ujson.load(f) pareto_optimal_points.sort(key=lambda x: x[0]) OST['state'].append(pareto_optimal_points[0][0]) OST['load'].append(pareto_optimal_points[0][1]) OST['num_func'].append(pareto_optimal_points[0][2]) OTL['state'].append(pareto_optimal_points[len(pareto_optimal_points)-1][0]) OTL['load'].append(pareto_optimal_points[len(pareto_optimal_points)-1][1]) OTL['num_func'].append(pareto_optimal_points[len(pareto_optimal_points)-1][2]) filename = "data/pareto_weight_" + 'u' + "_" + str(u) + ".js" f = open(filename) pareto_optimal_weight_map = ujson.load(f) for point in pareto_optimal_weight_map.keys(): if 0.5 in pareto_optimal_weight_map[point]: p_ = literal_eval(point) PO['state'].append(p_[0]) PO['load'].append(p_[1]) PO['num_func'].append(p_[2]) break print('--------------------------', u, '-----------') print('best state', pareto_optimal_points[0][0]) print('worst state', pareto_optimal_points[len(pareto_optimal_points)-1][0]) print('best load', pareto_optimal_points[len(pareto_optimal_points)-1][1]) print('worst load', pareto_optimal_points[0][1]) print('PO state', p_[0]) print('PO load', p_[1]) APO['state'] = [2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 3200.0, 3600.0, 3600.0, 3600.0, 3600.0] APO['load'] = [27500.0, 55000.0, 82500.0,110000.0, 137500.0, 132000.0, 115500.0, 132000.0, 148500, 165000.0] APO['num_func'] = [2.0, 2.0, 2.0, 2.0, 2.0,3.0, 4.0, 4.0, 4.0, 4.0] figures = ['state', 'load', 'num_func'] ylabel_map = {'state': 'State Transfer Cost', 'load': 'Traffic Load', 'num_func': 'Number of StateMF sets'} for fig in figures: plt.plot(request_list, OST[fig], color='red', marker='*', linestyle='solid', label='OST') plt.plot(request_list, OTL[fig], color='green', marker='o', linestyle='solid', label='OTL') plt.plot(request_list, PO[fig], color='blue', marker='^', linestyle='solid', label='PO with w=0.5') plt.plot(request_list, APO[fig], color='olive', marker='x', linestyle='solid', label='APO') plt.xlabel('Number of session requests', fontsize='x-large') plt.ylabel(ylabel_map[fig], fontsize='x-large') plt.legend() plt.show() def draw_performance_results_ue(): ue_num_list = [1] + [50*x for x in range(20) if x != 0] OST = dict() OTL = dict() PO = dict() APO = dict() OST['state'] = [] OTL['state'] = [] PO['state'] = [] OST['load'] = [] OTL['load'] = [] PO['load'] = [] OST['num_func'] = [] OTL['num_func'] = [] PO['num_func'] = [] for n in ue_num_list: filename = "data/pareto_" + 'n' + "_" + str(n) + ".js" f = open(filename) pareto_optimal_points = ujson.load(f) pareto_optimal_points.sort(key=lambda x: x[0]) OST['state'].append(pareto_optimal_points[0][0]) OST['load'].append(pareto_optimal_points[0][1]) OST['num_func'].append(pareto_optimal_points[0][2]) OTL['state'].append(pareto_optimal_points[len(pareto_optimal_points)-1][0]) OTL['load'].append(pareto_optimal_points[len(pareto_optimal_points)-1][1]) OTL['num_func'].append(pareto_optimal_points[len(pareto_optimal_points)-1][2]) filename = "data/pareto_weight_" + 'n' + "_" + str(n) + ".js" f = open(filename) pareto_optimal_weight_map = ujson.load(f) for point in pareto_optimal_weight_map.keys(): if 0.5 in pareto_optimal_weight_map[point]: p_ = literal_eval(point) PO['state'].append(p_[0]) PO['load'].append(p_[1]) PO['num_func'].append(p_[2]) break print('--------------------------', n, '-----------') print('best state', pareto_optimal_points[0][0]) print('worst state', pareto_optimal_points[len(pareto_optimal_points)-1][0]) print('best load', pareto_optimal_points[len(pareto_optimal_points)-1][1]) print('worst load', pareto_optimal_points[0][1]) print('PO state', p_[0]) print('PO load', p_[1]) APO['state'] = [2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 3200.0, 3200.0, 3600.0, 3200.0, 3600.0, 3600.0, 3600.0, 3600.0, 3600.0, 3600.0] APO['load'] = [300.0, 15000.0, 30000.0, 45000.0, 60000.0, 75000.0, 90000.0, 105000.0, 120000.0, 135000.0, 120000.0, 132000.0, 108000, 156000, 126000.0, 135000.0, 144000.0, 153000.0, 162000.0, 171000.0] APO['num_func'] = [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 4.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0] figures = ['state', 'load', 'num_func'] ylabel_map = {'state': 'State Transfer Cost', 'load': 'Traffic Load', 'num_func': 'Number of StateMF sets'} for fig in figures: plt.plot(ue_num_list, OST[fig], color='red', marker='*', linestyle='solid', label='OST') plt.plot(ue_num_list, OTL[fig], color='green', marker='o', linestyle='solid', label='OTL') plt.plot(ue_num_list, PO[fig], color='blue', marker='^', linestyle='solid', label='PO with w=0.5') plt.plot(ue_num_list, APO[fig], color='olive', marker='x', linestyle='solid', label='APO') plt.xlabel('Number of UEs', fontsize='x-large') plt.ylabel(ylabel_map[fig], fontsize='x-large') plt.legend() plt.show() draw_running_time()

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10c8928d6be910cf9d19c0702382215a71adadb8

154

py

Python

codesignal/array/alternatingSums.py

peterlamar/python-cp-cheatsheet

f9f854064a3c657c04fab27d0a496401bfa97da1

[ "Apache-2.0" ]

140

2020-10-21T13:23:52.000Z

2022-03-31T15:09:45.000Z

codesignal/array/alternatingSums.py

ajibolashodipo/python-cp-cheatsheet

f9f854064a3c657c04fab27d0a496401bfa97da1

[ "Apache-2.0" ]

1

2021-07-22T14:01:25.000Z

codesignal/array/alternatingSums.py

ajibolashodipo/python-cp-cheatsheet

f9f854064a3c657c04fab27d0a496401bfa97da1

[ "Apache-2.0" ]

33

2020-10-21T14:17:02.000Z

2022-03-25T11:25:03.000Z

""" For a = [50, 60, 60, 45, 70], the output should be alternatingSums(a) = [180, 105]. """ def alternatingSums(a): return [sum(a[::2]), sum(a[1::2])]

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130

py

Python

projects/code_combat/4_Backwoods_Forest/089-Defense_of_Plainswood/defense_of_plainswood.py

only-romano/junkyard

b60a25b2643f429cdafee438d20f9966178d6f36

[ "MIT" ]

null

projects/code_combat/4_Backwoods_Forest/089-Defense_of_Plainswood/defense_of_plainswood.py

only-romano/junkyard

b60a25b2643f429cdafee438d20f9966178d6f36

[ "MIT" ]

null

projects/code_combat/4_Backwoods_Forest/089-Defense_of_Plainswood/defense_of_plainswood.py

only-romano/junkyard

b60a25b2643f429cdafee438d20f9966178d6f36

[ "MIT" ]

null

hero.cast("haste", hero) hero.moveXY(40, 25) hero.buildXY("fence", 40, 20) hero.moveXY(40, 35) hero.buildXY("fence", 40, 52)

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6

80470827baa26156485c11fd4f3192a646c04553

181

py

Python

pyticker/view/pyticker_styles.py

priyanshus/pyticker

4f84b6c907cc3e405f5b3b98eb5c6f11913d7aff

[ "MIT" ]

null

pyticker/view/pyticker_styles.py

priyanshus/pyticker

4f84b6c907cc3e405f5b3b98eb5c6f11913d7aff

[ "MIT" ]

8

2021-03-19T06:24:02.000Z

2021-03-21T07:25:44.000Z

pyticker/view/pyticker_styles.py

priyanshus/pyticker

4f84b6c907cc3e405f5b3b98eb5c6f11913d7aff

[ "MIT" ]

null

class PyTickerStyles(object): GREY_BACKGROUND_BLACK_TEXT = "bg:#e5e7e9 #000000" DARK_GREY_BACKGROUND_BLACK_TEXT = "bg:#b2babb #000000" INPUT_FIELD = "class:input-field"

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8048484e8098083a8c6cdf13da247ddde1d14335

9,897

py

Python

beaconsite/tests/test_views.py

brand-fabian/varfish-server

6a084d891d676ff29355e72a29d4f7b207220283

[ "MIT" ]

14

2019-09-30T12:44:17.000Z

2022-02-04T14:45:16.000Z

beaconsite/tests/test_views.py

brand-fabian/varfish-server

6a084d891d676ff29355e72a29d4f7b207220283

[ "MIT" ]

244

2021-03-26T15:13:15.000Z

2022-03-31T15:48:04.000Z

beaconsite/tests/test_views.py

brand-fabian/varfish-server

6a084d891d676ff29355e72a29d4f7b207220283

[ "MIT" ]

8

2020-05-19T21:55:13.000Z

2022-03-31T07:02:58.000Z

"""Tests for UI views in the beaconsite app""" from Crypto.PublicKey import RSA from django.urls import reverse from test_plus.test import TestCase from beaconsite.models import Consortium, Site from beaconsite.tests.factories import ConsortiumFactory, SiteFactory from variants.tests.factories import ProjectFactory class TestViewsBase(TestCase): def setUp(self): self.superuser = self.make_user("superuser") self.superuser.is_superuser = True self.superuser.is_staff = True self.superuser.save() self.project = ProjectFactory() self.consortium = ConsortiumFactory() self.site = SiteFactory(role=Site.LOCAL, state=Site.ENABLED) class TestIndexView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get(reverse("beaconsite:index")) self.assertEqual(response.status_code, 200) self.assertIsNotNone(response.context["consortium_list"]) self.assertIsNotNone(response.context["site_list"]) self.assertEqual(response.context["consortium_list"][0].pk, self.consortium.pk) self.assertEqual(response.context["site_list"][0].pk, self.site.pk) class TestConsortiumListView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get(reverse("beaconsite:consortium-list")) self.assertEqual(response.status_code, 200) self.assertIsNotNone(response.context["object_list"]) self.assertEqual(response.context["object_list"][0].pk, self.consortium.pk) class TestConsortiumCreateView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get(reverse("beaconsite:consortium-create")) self.assertEqual(response.status_code, 200) def test_create(self): self.assertEqual(Consortium.objects.count(), 1) post_data = { "title": "XXX", "identifier": "xxx", "description": "ddd", "state": Consortium.ENABLED, "sites": [self.site.pk], "projects": [self.project.pk], } with self.login(self.superuser): response = self.client.post(reverse("beaconsite:consortium-create"), post_data) self.assertEqual(response.status_code, 302) latest_consortium = Consortium.objects.order_by("-date_created")[0] self.assertEqual( response.url, reverse( "beaconsite:consortium-detail", kwargs={"consortium": latest_consortium.sodar_uuid} ), ) self.assertEqual(Consortium.objects.count(), 2) class TestConsortiumUpdateView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get( reverse( "beaconsite:consortium-update", kwargs={"consortium": self.consortium.sodar_uuid}, ) ) self.assertEqual(response.status_code, 200) self.assertIsNotNone(response.context["object"]) def test_update(self): self.assertEqual(Consortium.objects.count(), 1) post_data = { "title": "XXX", "identifier": "xxx", "description": "ddd", "state": Consortium.ENABLED, "sites": [self.site.pk], "projects": [self.project.pk], } with self.login(self.superuser): response = self.client.post( reverse( "beaconsite:consortium-update", kwargs={"consortium": self.consortium.sodar_uuid}, ), post_data, ) self.assertEqual(response.status_code, 302) latest_consortium = Consortium.objects.order_by("-date_created")[0] self.assertEqual( response.url, reverse( "beaconsite:consortium-detail", kwargs={"consortium": latest_consortium.sodar_uuid} ), ) self.assertEqual(Consortium.objects.count(), 1) self.consortium.refresh_from_db() for key in ("title", "identifier", "description", "state"): self.assertEqual(getattr(self.consortium, key), post_data[key]) self.assertEqual([self.site.pk], [s.pk for s in self.consortium.sites.all()]) self.assertEqual([self.project.pk], [p.pk for p in self.consortium.projects.all()]) class TestConsortiumDeleteView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get( reverse( "beaconsite:consortium-delete", kwargs={"consortium": self.consortium.sodar_uuid}, ) ) self.assertEqual(response.status_code, 200) def test_delete(self): # Assert precondition self.assertEqual(Consortium.objects.all().count(), 1) with self.login(self.superuser): response = self.client.post( reverse( "beaconsite:consortium-delete", kwargs={"consortium": self.consortium.sodar_uuid}, ) ) self.assertEqual(response.status_code, 302) self.assertEqual(response.url, reverse("beaconsite:consortium-list")) # Assert postconditions self.assertEqual(Consortium.objects.all().count(), 0) class TestSiteListView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get(reverse("beaconsite:site-list")) self.assertEqual(response.status_code, 200) self.assertIsNotNone(response.context["object_list"]) self.assertEqual(response.context["object_list"][0].pk, self.site.pk) class TestSiteCreateView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get(reverse("beaconsite:site-create")) self.assertEqual(response.status_code, 200) def test_create(self): self.assertEqual(Site.objects.count(), 1) rsa_key = RSA.generate(2048) public_key = rsa_key.public_key().export_key("PEM").decode("ascii") post_data = { "title": "XXX", "identifier": "xxx", "description": "ddd", "state": Site.ENABLED, "role": Site.REMOTE, "entrypoint_url": "http://site.example.com", "key_algo": Site.RSA_SHA256, "public_key": public_key, "consortia": [self.consortium.pk], } with self.login(self.superuser): response = self.client.post(reverse("beaconsite:site-create"), post_data) self.assertEqual(response.status_code, 302) latest_site = Site.objects.order_by("-date_created")[0] self.assertEqual( response.url, reverse("beaconsite:site-detail", kwargs={"site": latest_site.sodar_uuid}), ) self.assertEqual(Site.objects.count(), 2) class TestSiteUpdateView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get( reverse("beaconsite:site-update", kwargs={"site": self.site.sodar_uuid},) ) self.assertEqual(response.status_code, 200) self.assertIsNotNone(response.context["object"]) def test_update(self): self.assertEqual(Site.objects.count(), 1) rsa_key = RSA.generate(2048) public_key = rsa_key.public_key().export_key("PEM").decode("ascii") post_data = { "title": "XXX", "identifier": "xxx", "description": "ddd", "state": Site.ENABLED, "role": Site.REMOTE, "entrypoint_url": "http://site.example.com", "key_algo": Site.RSA_SHA256, "public_key": public_key, "consortia": [self.consortium.pk], } with self.login(self.superuser): response = self.client.post( reverse("beaconsite:site-update", kwargs={"site": self.site.sodar_uuid},), post_data, ) self.assertEqual(response.status_code, 302) latest_site = Site.objects.order_by("-date_created")[0] self.assertEqual( response.url, reverse("beaconsite:site-detail", kwargs={"site": latest_site.sodar_uuid}), ) self.assertEqual(Site.objects.count(), 1) self.site.refresh_from_db() keys = ( "title", "identifier", "description", "state", "role", "entrypoint_url", "key_algo", "public_key", ) for key in keys: self.assertEqual(getattr(self.site, key), post_data[key]) self.assertEqual([self.consortium.pk], [s.pk for s in self.site.consortia.all()]) class TestSiteDeleteView(TestViewsBase): def test_render(self): with self.login(self.superuser): response = self.client.get( reverse("beaconsite:site-delete", kwargs={"site": self.site.sodar_uuid},) ) self.assertEqual(response.status_code, 200) def test_delete(self): # Assert precondition self.assertEqual(Site.objects.all().count(), 1) with self.login(self.superuser): response = self.client.post( reverse("beaconsite:site-delete", kwargs={"site": self.site.sodar_uuid},) ) self.assertEqual(response.status_code, 302) self.assertEqual(response.url, reverse("beaconsite:site-list")) # Assert postconditions self.assertEqual(Site.objects.all().count(), 0)

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null

0

1

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null

0

6

33cf49c5d0c7460dee1333354694d5b741f29a21

12,413

py

Python

tests/thriftclient.py

aiden0z/flask-thriftclient

34f14f75f5dd9163ecff6d6279ed82d6f53540e0

[ "BSD-2-Clause" ]

null

tests/thriftclient.py

aiden0z/flask-thriftclient

34f14f75f5dd9163ecff6d6279ed82d6f53540e0

[ "BSD-2-Clause" ]

null

tests/thriftclient.py

aiden0z/flask-thriftclient

34f14f75f5dd9163ecff6d6279ed82d6f53540e0

[ "BSD-2-Clause" ]

null

# -*- coding:utf-8 -*- from thrift.transport import * from thrift.transport import TSSLSocket from thrift.protocol import * from thrift.protocol import TCompactProtocol import unittest from flask import Flask from flask_thriftclient import ThriftClient class StubClient: def __init__(self, protocol): pass class TestSequenceFunctions(unittest.TestCase): def setUp(self): self.app = Flask(__name__) def test_default_values(self): client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSocket.TSocket)) self.assertTrue(isinstance(client.protocol, TBinaryProtocol.TBinaryProtocol)) self.assertEquals(client.transport.port, 9090) self.assertEquals(client.transport.host, "localhost") def test_transport_bad_scheme(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "bad://whatever" with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_transport_none_transport(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = None with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_transport_empty_url(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "" with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_transport_no_scheme(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "/tmp/somefile" with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_transport_tcp_noport(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcp://192.168.0.42" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSocket.TSocket)) self.assertEquals(client.transport.port, 9090) self.assertEquals(client.transport.host, "192.168.0.42") def test_transport_tcp_longurl(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "tcp://mydomain.foo.com:5921/whatever?its=21;not=zrzer#used" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSocket.TSocket)) self.assertEquals(client.transport.port, 5921) self.assertEquals(client.transport.host, "mydomain.foo.com") def test_transport_unix_1(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "unix:///tmp/testunixsocket" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSocket.TSocket)) self.assertEquals(client.transport._unix_socket, "/tmp/testunixsocket") def test_transport_unix_2(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "unix:/tmp/testunixsocket" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSocket.TSocket)) self.assertEquals(client.transport._unix_socket, "/tmp/testunixsocket") def test_transport_unix_bad(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "unix://tmp/testunixsocket" with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_transport_http(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "http://foo.bar.com:8080/end/point" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, THttpClient.THttpClient)) self.assertEquals(client.transport.scheme, "http") self.assertEquals(client.transport.host, "foo.bar.com") self.assertEquals(client.transport.port, 8080) self.assertEquals(client.transport.path, "/end/point") def test_transport_https(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "https://foo.bar.com:8080/end/point" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, THttpClient.THttpClient)) self.assertEquals(client.transport.scheme, "https") self.assertEquals(client.transport.host, "foo.bar.com") self.assertEquals(client.transport.port, 8080) self.assertEquals(client.transport.path, "/end/point") def test_transport_http_defaultport(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "http://foo.bar.com/end/point" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, THttpClient.THttpClient)) self.assertEquals(client.transport.scheme, "http") self.assertEquals(client.transport.port, 80) def test_transport_defaultport(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "https://foo.bar.com/end/point" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, THttpClient.THttpClient)) self.assertEquals(client.transport.scheme, "https") self.assertEquals(client.transport.port, 443) def test_transport_tcps_no_cert(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcps://192.168.0.42" self.app.config["THRIFTCLIENT_SSL_VALIDATE"] = False client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSSLSocket.TSSLSocket)) self.assertEquals(client.transport.port, 9090) self.assertEquals(client.transport.host, "192.168.0.42") def test_transport_tcps_with_cert(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcps://192.168.0.42" self.app.config["THRIFTCLIENT_SSL_CA_CERTS"] = "tests/cacert.pem" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSSLSocket.TSSLSocket)) self.assertEquals(client.transport.port, 9090) self.assertEquals(client.transport.host, "192.168.0.42") def test_transport_tcps_forgot_cert(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcps://192.168.0.42" self.app.config["THRIFTCLIENT_SSL_VALIDATE"] = True self.app.config["THRIFTCLIENT_SSL_CA_CERTS"] = None with self.assertRaises(IOError): client = ThriftClient(StubClient, self.app) def test_transport_tcps_unreadable_cert(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcps://192.168.0.42" self.app.config["THRIFTCLIENT_SSL_VALIDATE"] = True self.app.config["THRIFTCLIENT_SSL_CA_CERTS"] = "missingcert" with self.assertRaises(IOError): client = ThriftClient(StubClient, self.app) def test_transport_unixs_no_cert(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "unixs:/tmp/thriftsocketfile" self.app.config["THRIFTCLIENT_SSL_VALIDATE"] = False client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSSLSocket.TSSLSocket)) self.assertEquals(client.transport._unix_socket, "/tmp/thriftsocketfile") def test_transport_unixs_no_cert_2(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "unixs:///tmp/thriftsocketfile" self.app.config["THRIFTCLIENT_SSL_VALIDATE"] = False client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSSLSocket.TSSLSocket)) self.assertEquals(client.transport._unix_socket, "/tmp/thriftsocketfile") def test_transport_unixs_bad_hostname(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "unixs://tmp/thriftsocketfile" self.app.config["THRIFTCLIENT_SSL_VALIDATE"] = False with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_transport_unixs_with_cert(self): self.app.config[ "THRIFTCLIENT_TRANSPORT"] = "unixs:/tmp/thriftsocketfile" self.app.config["THRIFTCLIENT_SSL_CA_CERTS"] = "tests/cacert.pem" client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.transport, TSSLSocket.TSSLSocket)) self.assertEquals(client.transport._unix_socket, "/tmp/thriftsocketfile") def test_protocol_bad(self): self.app.config["THRIFTCLIENT_PROTOCOL"] = "BAD" with self.assertRaises(RuntimeError): client = ThriftClient(StubClient, self.app) def test_protocol_binary(self): self.app.config["THRIFTCLIENT_PROTOCOL"] = ThriftClient.BINARY client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.protocol, TBinaryProtocol.TBinaryProtocol)) def test_protocol_compact(self): self.app.config["THRIFTCLIENT_PROTOCOL"] = ThriftClient.COMPACT client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.protocol, TCompactProtocol.TCompactProtocol)) def test_protocol_json(self): self.app.config["THRIFTCLIENT_PROTOCOL"] = ThriftClient.JSON client = ThriftClient(StubClient, self.app) self.assertTrue(isinstance(client.protocol, TJSONProtocol.TJSONProtocol)) def test_connection(self): """ http connections aren't really opened, so we can tests them without a server """ self.app.config["THRIFTCLIENT_TRANSPORT"] = "http://localhost:8735" client = ThriftClient(StubClient, self.app) @self.app.route("/testme") def testme(): return "OK" if client.transport.isOpen() else "KO" testclient = self.app.test_client() ret = testclient.get("/testme") self.assertEquals(ret.data, "OK") self.assertFalse(client.transport.isOpen()) def test_connection_no_server(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcp://localhost:8735" client = ThriftClient(StubClient, self.app) @self.app.route("/testme") def testme(): return "KO" testclient = self.app.test_client() ret = testclient.get("/testme") self.assertEquals(ret.status_code, 500) self.assertFalse(client.transport.isOpen()) def test_no_alwaysconnect(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "tcp://localhost:8735" self.app.config["THRIFTCLIENT_ALWAYS_CONNECT"] = False client = ThriftClient(StubClient, self.app) @self.app.route("/testme") def testme(): return "KO" if client.transport.isOpen() else "OK" testclient = self.app.test_client() ret = testclient.get("/testme") self.assertEquals(ret.data, "OK") self.assertFalse(client.transport.isOpen()) def test_connect_ctx(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "http://localhost:8735" self.app.config["THRIFTCLIENT_ALWAYS_CONNECT"] = False client = ThriftClient(StubClient, self.app) with client.connect(): self.assertTrue(client.transport.isOpen()) self.assertFalse(client.transport.isOpen()) def test_autoconnect(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "http://localhost:8735" self.app.config["THRIFTCLIENT_ALWAYS_CONNECT"] = False client = ThriftClient(StubClient, self.app) @self.app.route("/testme") @client.autoconnect def testme(): return "OK" if client.transport.isOpen() else "KO" testclient = self.app.test_client() ret = testclient.get("/testme") self.assertEquals(ret.data, "OK") self.assertFalse(client.transport.isOpen()) def test_autoconnect_with_alwaysconnect(self): self.app.config["THRIFTCLIENT_TRANSPORT"] = "http://localhost:8735" self.app.config["THRIFTCLIENT_ALWAYS_CONNECT"] = False client = ThriftClient(StubClient, self.app) @self.app.route("/testme") @client.autoconnect def testme(): return "OK" if client.transport.isOpen() else "KO" testclient = self.app.test_client() ret = testclient.get("/testme") self.assertEquals(ret.data, "OK") self.assertFalse(client.transport.isOpen()) if __name__ == "__main__": unittest.main()

41.794613

100

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0.880716

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0.85681

0.795023

0.780924

0.762413

0

0.015301

0.210263

12,413

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0.164536

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0.004255

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null

0

1

0

null

0

6

1d3fd13de0cb755a844c63e46caedb1822b96d88

197

py

Python

demo/admin.py

dzhuang/django-galleryfield

12bdbd2ee4d036e92e4dd15130b98fbcd25c5991

[ "MIT" ]

9

2021-08-23T15:49:59.000Z

2022-03-30T10:18:39.000Z

demo/admin.py

dzhuang/django-galleryfield

12bdbd2ee4d036e92e4dd15130b98fbcd25c5991

[ "MIT" ]

1

2021-08-10T19:53:33.000Z

demo/admin.py

dzhuang/django-gallery-widget

12bdbd2ee4d036e92e4dd15130b98fbcd25c5991

[ "MIT" ]

2

2021-08-22T11:30:46.000Z

2021-12-01T00:22:21.000Z

from django.contrib import admin from demo.models import DemoGallery from galleryfield.models import BuiltInGalleryImage admin.site.register(DemoGallery) admin.site.register(BuiltInGalleryImage)

24.625

51

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23

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null

0

1

0

1

0

1

0

6

1d49c0e7821913091c0a013ec7c7937c7dafdae1

2,408

py

Python

tests/test_views_functional.py

olivier-2018/SE_project

3c555e4289ee5faf4a1a71220baa47045ade1f51

[ "MIT" ]

null

tests/test_views_functional.py

olivier-2018/SE_project

3c555e4289ee5faf4a1a71220baa47045ade1f51

[ "MIT" ]

19

2021-12-26T23:03:09.000Z

2022-01-14T15:42:16.000Z

tests/test_views_functional.py

olivier-2018/SoftwareEngg_project

3c555e4289ee5faf4a1a71220baa47045ade1f51

[ "MIT" ]

null

import pytest import os from src_api import create_app from src_api.views import load_audio_sequence, make_prediction, load_ML_model import tensorflow as tf flask_app = create_app() @pytest.mark.xfail @pytest.mark.skipif(tf.__version__ != "2.7.0", reason="Tensorflow v2.7.0 only available with pip (not conda)") @pytest.mark.parametrize( "filename, testfile_path, model_prediction", [ ("one_16000.wav", "testfiles", 1), ("two_22050.wav", "testfiles", 2), ( "three_8000.wav", "testfiles", 3, ), ( "three_96000.wav", "testfiles", 3, ), ], ) def test_load_ML_model_1(filename: str, testfile_path: str, model_prediction: int) -> bool: """ Test if model prediction is correct """ with flask_app.app_context(): model_path = "/ML_model/audio_MNIST_v3-TF_v2.7.0.tf" model = load_ML_model(model_path) testfile_path_full = os.path.join(flask_app.config["APP_FOLDER"], "tests", "testfiles") audio_sequence = load_audio_sequence(filename, testfile_path_full, sampling_rate=8000, max_seq_length=8000) prediction = make_prediction(model, audio_sequence, model_input_dim=8000) assert prediction == model_prediction @pytest.mark.xfail @pytest.mark.skipif(tf.__version__ != "2.3.0", reason="Tensorflow v2.3.0 only available with conda") @pytest.mark.parametrize( "filename, testfile_path, model_prediction", [ ("one_16000.wav", "testfiles", 1), ("two_22050.wav", "testfiles", 2), ( "three_8000.wav", "testfiles", 3, ), ( "three_96000.wav", "testfiles", 3, ), ], ) def test_load_ML_model_2(filename: str, testfile_path: str, model_prediction: int) -> bool: """ Test if model prediction is correct """ with flask_app.app_context(): model_path = "/ML_model/audio_MNIST_v3-TF_v2.3.0.tf" model = load_ML_model(model_path) testfile_path_full = os.path.join(flask_app.config["APP_FOLDER"], "tests", "testfiles") audio_sequence = load_audio_sequence(filename, testfile_path_full, sampling_rate=8000, max_seq_length=8000) prediction = make_prediction(model, audio_sequence, model_input_dim=8000) assert prediction == model_prediction

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null

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1d9121294ba8b637d742180b1ec61545aa1af87b

25

py

Python

tool/bug_finder/__init__.py

MageWeiG/karonte

2ffe649557adccdd3c2c77c0ae0a5f27a385fdcb

[ "BSD-2-Clause" ]

1

2021-04-15T12:00:56.000Z

tool/bug_finder/__init__.py

cascades-sjtu/karonte

2ffe649557adccdd3c2c77c0ae0a5f27a385fdcb

[ "BSD-2-Clause" ]

null

tool/bug_finder/__init__.py

cascades-sjtu/karonte

2ffe649557adccdd3c2c77c0ae0a5f27a385fdcb

[ "BSD-2-Clause" ]

1

2021-06-09T07:37:59.000Z

from bug_finder import *

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d517967d295da64892131bf297fffbd3743641f9

45

py

Python

code_sandbox/tasks_queue_app/src/__init__.py

gretkierewicz/gret_code_examples

623dce8fd3319091e3bdea946af7093442abb1bf

[ "MIT" ]

null

code_sandbox/tasks_queue_app/src/__init__.py

gretkierewicz/gret_code_examples

623dce8fd3319091e3bdea946af7093442abb1bf

[ "MIT" ]

null

code_sandbox/tasks_queue_app/src/__init__.py

gretkierewicz/gret_code_examples

623dce8fd3319091e3bdea946af7093442abb1bf

[ "MIT" ]

null

from .entities import * from .tasks import *

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23

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6

d54b1872645cf1b9e28eeef8b7e5bd1c49bd4048

30

py

Python

py2app_tests/plugin_with_scripts/helper2.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

193

2020-01-15T09:34:20.000Z

2022-03-18T19:14:16.000Z

py2app_tests/plugin_with_scripts/helper2.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

185

2020-01-15T08:38:27.000Z

2022-03-27T17:29:29.000Z

py2app_tests/plugin_with_scripts/helper2.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

23

2020-01-24T14:47:18.000Z

2022-02-22T17:19:47.000Z

import code print("Helper 2")

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17

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6

d58f04d88181667fe907f3a4f41c52fb122cd0c4

134

py

Python

mongodb/factory/results/statistics.py

RaenonX/Jelly-Bot-API

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

5

2020-08-26T20:12:00.000Z

2020-12-11T16:39:22.000Z

mongodb/factory/results/statistics.py

RaenonX/Jelly-Bot

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

234

2019-12-14T03:45:19.000Z

2020-08-26T18:55:19.000Z

mongodb/factory/results/statistics.py

RaenonX/Jelly-Bot-API

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

2

2019-10-23T15:21:15.000Z

2020-05-22T09:35:55.000Z

from dataclasses import dataclass from ._base import ModelResult @dataclass class RecordAPIStatisticsResult(ModelResult): pass

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6

891e0c99f3529840e2b5ebe4cfe50190d05cb58b

132

py

Python

glooey/drawing/__init__.py

Rahuum/glooey

932edca1c8fdd710f1941038e47ac8d25a31a1a8

[ "MIT" ]

86

2016-11-28T12:34:28.000Z

2022-03-17T13:49:49.000Z

glooey/drawing/__init__.py

Rahuum/glooey

932edca1c8fdd710f1941038e47ac8d25a31a1a8

[ "MIT" ]

57

2017-03-07T10:11:52.000Z

2022-01-16T19:35:33.000Z

glooey/drawing/__init__.py

Rahuum/glooey

932edca1c8fdd710f1941038e47ac8d25a31a1a8

[ "MIT" ]

9

2017-03-15T18:55:50.000Z

2022-02-17T14:52:49.000Z

from .color import * from .text import * from .artists import * from .stencil import * from .alignment import * from .grid import *

18.857143

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132

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893632a341aacd1f6d992275676642f8327138c9

4,282

py

Python

src/abaqus/Section/GasketSection.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

7

2022-01-21T09:15:45.000Z

2022-02-15T09:31:58.000Z

src/abaqus/Section/GasketSection.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

src/abaqus/Section/GasketSection.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

import typing from abaqusConstants import * from .Section import Section class GasketSection(Section): """The GasketSection object defines the properties of a gasket section. The GasketSection object is derived from the Section object. Notes ----- This object can be accessed by: .. code-block:: python import section mdb.models[name].sections[name] import odbSection session.odbs[name].sections[name] The corresponding analysis keywords are: - GASKET SECTION """ def __init__(self, name: str, material: str, crossSection: float = 1, initialGap: float = 0, initialThickness: typing.Union[SymbolicConstant, float] = DEFAULT, initialVoid: float = 0, stabilizationStiffness: typing.Union[SymbolicConstant, float] = DEFAULT): """This method creates a GasketSection object. Notes ----- This function can be accessed by: .. code-block:: python mdb.models[name].GasketSection session.odbs[name].GasketSection Parameters ---------- name A String specifying the repository key. material A String specifying the name of the material of which the gasket is made or material that defines gasket behavior. crossSection A Float specifying the cross-sectional area, width, or out-of-plane thickness, if applicable, depending on the gasket element type. The default value is 1.0. initialGap A Float specifying the initial gap. The default value is 0.0. initialThickness The SymbolicConstant DEFAULT or a Float specifying the initial gasket thickness. If DEFAULT is specified, the initial thickness is determined using nodal coordinates. The default value is DEFAULT. initialVoid A Float specifying the initial void. The default value is 0.0. stabilizationStiffness The SymbolicConstant DEFAULT or a Float specifying the default stabilization stiffness used in all but link elements to stabilize gasket elements that are not supported at all nodes, such as those that extend outside neighboring components. If DEFAULT is specified, a value is used equal to 10–9 times the initial compressive stiffness in the thickness direction. The default value is DEFAULT. Returns ------- A GasketSection object. and ValueError. """ super().__init__() pass def setValues(self, crossSection: float = 1, initialGap: float = 0, initialThickness: typing.Union[SymbolicConstant, float] = DEFAULT, initialVoid: float = 0, stabilizationStiffness: typing.Union[SymbolicConstant, float] = DEFAULT): """This method modifies the GasketSection object. Parameters ---------- crossSection A Float specifying the cross-sectional area, width, or out-of-plane thickness, if applicable, depending on the gasket element type. The default value is 1.0. initialGap A Float specifying the initial gap. The default value is 0.0. initialThickness The SymbolicConstant DEFAULT or a Float specifying the initial gasket thickness. If DEFAULT is specified, the initial thickness is determined using nodal coordinates. The default value is DEFAULT. initialVoid A Float specifying the initial void. The default value is 0.0. stabilizationStiffness The SymbolicConstant DEFAULT or a Float specifying the default stabilization stiffness used in all but link elements to stabilize gasket elements that are not supported at all nodes, such as those that extend outside neighboring components. If DEFAULT is specified, a value is used equal to 10–9 times the initial compressive stiffness in the thickness direction. The default value is DEFAULT. Raises ------ ValueError. """ pass

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8943603c3622f6ade7ee60bd1a21351a25ed5b31

10,249

py

Python

pfrl/q_functions/state_action_q_functions.py

g-votte/pfrl

4c30c1d73f0941a2b649b62937eec346bb55a95e

[ "MIT" ]

3

2020-12-18T03:45:45.000Z

2021-10-15T03:38:05.000Z

pfrl/q_functions/state_action_q_functions.py

g-votte/pfrl

4c30c1d73f0941a2b649b62937eec346bb55a95e

[ "MIT" ]

10

2020-08-23T17:30:47.000Z

2020-11-07T16:52:41.000Z

pfrl/q_functions/state_action_q_functions.py

g-votte/pfrl

4c30c1d73f0941a2b649b62937eec346bb55a95e

[ "MIT" ]

1

2020-11-08T17:42:04.000Z

from pfrl.nn.mlp import MLP from pfrl.nn.mlp_bn import MLPBN from pfrl.q_function import StateActionQFunction import torch import torch.nn as nn import torch.nn.functional as F from pfrl.initializers import init_lecun_normal class SingleModelStateActionQFunction(nn.Module, StateActionQFunction): """Q-function with discrete actions. Args: model (nn.Module): Module that is callable and outputs action values. """ def __init__(self, model): super().__init__(model=model) def forward(self, x, a): h = self.model(x, a) return h class FCSAQFunction(MLP, StateActionQFunction): """Fully-connected (s,a)-input Q-function. Args: n_dim_obs (int): Number of dimensions of observation space. n_dim_action (int): Number of dimensions of action space. n_hidden_channels (int): Number of hidden channels. n_hidden_layers (int): Number of hidden layers. nonlinearity (callable): Nonlinearity between layers. It must accept a Variable as an argument and return a Variable with the same shape. Nonlinearities with learnable parameters such as PReLU are not supported. It is not used if n_hidden_layers is zero. last_wscale (float): Scale of weight initialization of the last layer. """ def __init__( self, n_dim_obs, n_dim_action, n_hidden_channels, n_hidden_layers, nonlinearity=F.relu, last_wscale=1.0, ): self.n_input_channels = n_dim_obs + n_dim_action self.n_hidden_layers = n_hidden_layers self.n_hidden_channels = n_hidden_channels self.nonlinearity = nonlinearity super().__init__( in_size=self.n_input_channels, out_size=1, hidden_sizes=[self.n_hidden_channels] * self.n_hidden_layers, nonlinearity=nonlinearity, last_wscale=last_wscale, ) def forward(self, state, action): h = torch.cat((state, action), dim=1) return super().forward(h) class FCLSTMSAQFunction(nn.Module, StateActionQFunction): """Fully-connected + LSTM (s,a)-input Q-function. Args: n_dim_obs (int): Number of dimensions of observation space. n_dim_action (int): Number of dimensions of action space. n_hidden_channels (int): Number of hidden channels. n_hidden_layers (int): Number of hidden layers. nonlinearity (callable): Nonlinearity between layers. It must accept a Variable as an argument and return a Variable with the same shape. Nonlinearities with learnable parameters such as PReLU are not supported. last_wscale (float): Scale of weight initialization of the last layer. """ def __init__( self, n_dim_obs, n_dim_action, n_hidden_channels, n_hidden_layers, nonlinearity=F.relu, last_wscale=1.0, ): raise NotImplementedError() self.n_input_channels = n_dim_obs + n_dim_action self.n_hidden_layers = n_hidden_layers self.n_hidden_channels = n_hidden_channels self.nonlinearity = nonlinearity super().__init__() self.fc = MLP( self.n_input_channels, n_hidden_channels, [self.n_hidden_channels] * self.n_hidden_layers, nonlinearity=nonlinearity, ) self.lstm = nn.LSTM( num_layers=1, input_size=n_hidden_channels, hidden_size=n_hidden_channels ) self.out = nn.Linear(n_hidden_channels, 1) for (n, p) in self.lstm.named_parameters(): if "weight" in n: init_lecun_normal(p) else: nn.init.zeros_(p) init_lecun_normal(self.out.weight, scale=last_wscale) nn.init.zeros_(self.out.bias) def forward(self, x, a): h = torch.cat((x, a), dim=1) h = self.nonlinearity(self.fc(h)) h = self.lstm(h) return self.out(h) class FCBNSAQFunction(MLPBN, StateActionQFunction): """Fully-connected + BN (s,a)-input Q-function. Args: n_dim_obs (int): Number of dimensions of observation space. n_dim_action (int): Number of dimensions of action space. n_hidden_channels (int): Number of hidden channels. n_hidden_layers (int): Number of hidden layers. normalize_input (bool): If set to True, Batch Normalization is applied to both observations and actions. nonlinearity (callable): Nonlinearity between layers. It must accept a Variable as an argument and return a Variable with the same shape. Nonlinearities with learnable parameters such as PReLU are not supported. It is not used if n_hidden_layers is zero. last_wscale (float): Scale of weight initialization of the last layer. """ def __init__( self, n_dim_obs, n_dim_action, n_hidden_channels, n_hidden_layers, normalize_input=True, nonlinearity=F.relu, last_wscale=1.0, ): self.n_input_channels = n_dim_obs + n_dim_action self.n_hidden_layers = n_hidden_layers self.n_hidden_channels = n_hidden_channels self.normalize_input = normalize_input self.nonlinearity = nonlinearity super().__init__( in_size=self.n_input_channels, out_size=1, hidden_sizes=[self.n_hidden_channels] * self.n_hidden_layers, normalize_input=self.normalize_input, nonlinearity=nonlinearity, last_wscale=last_wscale, ) def forward(self, state, action): h = torch.cat((state, action), dim=1) return super().forward(h) class FCBNLateActionSAQFunction(nn.Module, StateActionQFunction): """Fully-connected + BN (s,a)-input Q-function with late action input. Actions are not included until the second hidden layer and not normalized. This architecture is used in the DDPG paper: http://arxiv.org/abs/1509.02971 Args: n_dim_obs (int): Number of dimensions of observation space. n_dim_action (int): Number of dimensions of action space. n_hidden_channels (int): Number of hidden channels. n_hidden_layers (int): Number of hidden layers. It must be greater than or equal to 1. normalize_input (bool): If set to True, Batch Normalization is applied nonlinearity (callable): Nonlinearity between layers. It must accept a Variable as an argument and return a Variable with the same shape. Nonlinearities with learnable parameters such as PReLU are not supported. last_wscale (float): Scale of weight initialization of the last layer. """ def __init__( self, n_dim_obs, n_dim_action, n_hidden_channels, n_hidden_layers, normalize_input=True, nonlinearity=F.relu, last_wscale=1.0, ): assert n_hidden_layers >= 1 self.n_input_channels = n_dim_obs + n_dim_action self.n_hidden_layers = n_hidden_layers self.n_hidden_channels = n_hidden_channels self.normalize_input = normalize_input self.nonlinearity = nonlinearity super().__init__() # No need to pass nonlinearity to obs_mlp because it has no # hidden layers self.obs_mlp = MLPBN( in_size=n_dim_obs, out_size=n_hidden_channels, hidden_sizes=[], normalize_input=normalize_input, normalize_output=True, ) self.mlp = MLP( in_size=n_hidden_channels + n_dim_action, out_size=1, hidden_sizes=([self.n_hidden_channels] * (self.n_hidden_layers - 1)), nonlinearity=nonlinearity, last_wscale=last_wscale, ) self.output = self.mlp.output def forward(self, state, action): h = self.nonlinearity(self.obs_mlp(state)) h = torch.cat((h, action), dim=1) return self.mlp(h) class FCLateActionSAQFunction(nn.Module, StateActionQFunction): """Fully-connected (s,a)-input Q-function with late action input. Actions are not included until the second hidden layer and not normalized. This architecture is used in the DDPG paper: http://arxiv.org/abs/1509.02971 Args: n_dim_obs (int): Number of dimensions of observation space. n_dim_action (int): Number of dimensions of action space. n_hidden_channels (int): Number of hidden channels. n_hidden_layers (int): Number of hidden layers. It must be greater than or equal to 1. nonlinearity (callable): Nonlinearity between layers. It must accept a Variable as an argument and return a Variable with the same shape. Nonlinearities with learnable parameters such as PReLU are not supported. last_wscale (float): Scale of weight initialization of the last layer. """ def __init__( self, n_dim_obs, n_dim_action, n_hidden_channels, n_hidden_layers, nonlinearity=F.relu, last_wscale=1.0, ): assert n_hidden_layers >= 1 self.n_input_channels = n_dim_obs + n_dim_action self.n_hidden_layers = n_hidden_layers self.n_hidden_channels = n_hidden_channels self.nonlinearity = nonlinearity super().__init__() # No need to pass nonlinearity to obs_mlp because it has no # hidden layers self.obs_mlp = MLP( in_size=n_dim_obs, out_size=n_hidden_channels, hidden_sizes=[] ) self.mlp = MLP( in_size=n_hidden_channels + n_dim_action, out_size=1, hidden_sizes=([self.n_hidden_channels] * (self.n_hidden_layers - 1)), nonlinearity=nonlinearity, last_wscale=last_wscale, ) self.output = self.mlp.output def forward(self, state, action): h = self.nonlinearity(self.obs_mlp(state)) h = torch.cat((h, action), dim=1) return self.mlp(h)

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0

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0

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0

null

0

6

89781308092b8deb6241b78c4b6aa97eef23ff25

4,521

py

Python

tests/integration_tests/test_undo_redo.py

drahoja9/BI-OOP-CAD

afec7d44b1c5502a6bf94f78759c46337f750ea3

[ "MIT" ]

null

tests/integration_tests/test_undo_redo.py

drahoja9/BI-OOP-CAD

afec7d44b1c5502a6bf94f78759c46337f750ea3

[ "MIT" ]

null

tests/integration_tests/test_undo_redo.py

drahoja9/BI-OOP-CAD

afec7d44b1c5502a6bf94f78759c46337f750ea3

[ "MIT" ]

null

import io from typing import Dict from app.controller import Controller from app.shapes import Shape def test_undo_redo(controller: Controller, shape_commands, stream: io.StringIO, shapes: Dict[str, Shape]): for command in shape_commands: controller.execute_command(command) assert controller._command_engine._redos == [] assert controller._gui._ui.actionUndo.isEnabled() is True assert controller._gui._ui.actionRedo.isEnabled() is False for i in range(len(shape_commands)): controller.undo() assert controller._command_engine._undos == [] assert controller._command_engine._redos == [ shape_commands[4], shape_commands[3], shape_commands[2], shape_commands[1], shape_commands[0], ] assert controller._shapes._shapes == [] assert controller._gui._ui.actionUndo.isEnabled() is False assert controller._gui._ui.actionRedo.isEnabled() is True assert controller._gui._ui.history.toPlainText() == '' res = ( f'{shapes["dot"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n{shapes["circle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n' ) assert stream.getvalue() == res controller.redo() controller.redo() assert controller._command_engine._undos == [ shape_commands[0], shape_commands[1] ] assert controller._command_engine._redos == [ shape_commands[4], shape_commands[3], shape_commands[2] ] assert controller._shapes._shapes == [ shapes['dot'], shapes['line'] ] assert controller._gui._ui.actionUndo.isEnabled() is True assert controller._gui._ui.actionRedo.isEnabled() is True assert controller._gui._ui.history.toPlainText() == ( f' > {shape_commands[0]}\n{shapes["dot"]}\n' f' > {shape_commands[1]}\n{shapes["line"]}' ) res = ( f'{shapes["dot"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n{shapes["circle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n' f'{shapes["dot"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' ) assert stream.getvalue() == res controller.execute_command(shape_commands[0]) assert controller._command_engine._undos == [ shape_commands[0], shape_commands[1], shape_commands[0] ] assert controller._command_engine._redos == [] assert controller._shapes._shapes == [ shapes['dot'], shapes['line'], shapes['dot'] ] assert controller._gui._ui.actionUndo.isEnabled() is True assert controller._gui._ui.actionRedo.isEnabled() is False assert controller._gui._ui.history.toPlainText() == ( f' > {shape_commands[0]}\n{shapes["dot"]}\n' f' > {shape_commands[1]}\n{shapes["line"]}\n' f' > {shape_commands[0]}\n{shapes["dot"]}' ) res = ( f'{shapes["dot"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n{shapes["circle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n{shapes["rectangle"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["polyline"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n' f'{shapes["dot"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n' f'{shapes["dot"]}\n{shapes["line"]}\n{shapes["dot"]}\n' ) assert stream.getvalue() == res

40.366071

111

0.594116

574

4,521

4.54878

0.090592

0.152815

0.134048

0.134814

0.878591

0.865186

0.848717

0.776714

0.712371

0

0.005177

0.188233

4,521

111

112

40.72973

0.706267

0

0.657143

0

0.085714

0.400354

0.363194

0

0.228571

1

0.009524

false

0

0.038095

0

0.047619

0

null

0

1

0

null

0

6

897ef9a689d50c202a1c3cf0b195e3d66aa3c455

213

py

Python

classifier/decision_tree/__init__.py

ecohealthalliance/eha_grit

cb95b759222ca7a416dd7d439571e7b610dd5e23

[ "Apache-2.0" ]

null

classifier/decision_tree/__init__.py

ecohealthalliance/eha_grit

cb95b759222ca7a416dd7d439571e7b610dd5e23

[ "Apache-2.0" ]

null

classifier/decision_tree/__init__.py

ecohealthalliance/eha_grit

cb95b759222ca7a416dd7d439571e7b610dd5e23

[ "Apache-2.0" ]

null

from classifier import sklearn_classifier from sklearn.tree import DecisionTreeClassifier def classify(train, test): tree = DecisionTreeClassifier() return sklearn_classifier.classify(train, test, tree)

26.625

57

0.807512

23

213

7.391304

0.478261

0.2

0.247059

0

0.131455

213

7

58

30.428571

0.918919

0

1

0.2

false

0

0.4

0

0.8

0

1

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null

0

1

0

1

0

null

0

1

0

1

0

6

89b34e054bb6f75590d6b29e2a97de1ce1095c83

3,720

py

Python

tracking_wo_bnw/src/tracktor/datasets/mot_wrapper.py

OpenSuze/mot_neural_solver

44a5c8270b238535fc0ca83cb5758d43757e2637

[ "MIT" ]

null

tracking_wo_bnw/src/tracktor/datasets/mot_wrapper.py

OpenSuze/mot_neural_solver

44a5c8270b238535fc0ca83cb5758d43757e2637

[ "MIT" ]

null

tracking_wo_bnw/src/tracktor/datasets/mot_wrapper.py

OpenSuze/mot_neural_solver

44a5c8270b238535fc0ca83cb5758d43757e2637

[ "MIT" ]

null

import torch from torch.utils.data import Dataset from .mot_sequence import MOT17Sequence, MOT20Sequence, MOTSynthSequence class MOTSynthWrapper(Dataset): """A Wrapper for the MOT_Sequence class to return multiple sequences.""" def __init__(self, split, dets, dataloader): """Initliazes all subset of the dataset. Keyword arguments: split -- the split of the dataset to use dataloader -- args for the MOT_Sequence dataloader """ train_sequences = ['0000', '0001', '0002', '0003', '0004', '0005', '0006', '0007'] test_sequences = ['0008', '0009'] if "train" == split: sequences = train_sequences elif "test" == split: sequences = test_sequences elif "all" == split: sequences = train_sequences + test_sequences elif split in train_sequences + test_sequences: sequences = [split] else: raise NotImplementedError("MOT split not available.") self._data = [] for s in sequences: if dets == '17': self._data.append(MOTSynthSequence(seq_name=s, dets='DPM17', **dataloader)) self._data.append(MOTSynthSequence(seq_name=s, dets='FRCNN17', **dataloader)) self._data.append(MOTSynthSequence(seq_name=s, dets='SDP17', **dataloader)) else: self._data.append(MOTSynthSequence(seq_name=s, dets=dets, **dataloader)) def __len__(self): return len(self._data) def __getitem__(self, idx): return self._data[idx] class MOT17Wrapper(Dataset): """A Wrapper for the MOT_Sequence class to return multiple sequences.""" def __init__(self, split, dets, dataloader): """Initliazes all subset of the dataset. Keyword arguments: split -- the split of the dataset to use dataloader -- args for the MOT_Sequence dataloader """ train_sequences = ['MOT17-02', 'MOT17-04', 'MOT17-05', 'MOT17-09', 'MOT17-10', 'MOT17-11', 'MOT17-13'] test_sequences = ['MOT17-01', 'MOT17-03', 'MOT17-06', 'MOT17-07', 'MOT17-08', 'MOT17-12', 'MOT17-14'] if "train" == split: sequences = train_sequences elif "test" == split: sequences = test_sequences elif "all" == split: sequences = train_sequences + test_sequences elif f"MOT17-{split}" in train_sequences + test_sequences: sequences = [f"MOT17-{split}"] else: raise NotImplementedError("MOT split not available.") self._data = [] for s in sequences: if dets == '17': self._data.append(MOT17Sequence(seq_name=s, dets='DPM17', **dataloader)) self._data.append(MOT17Sequence(seq_name=s, dets='FRCNN17', **dataloader)) self._data.append(MOT17Sequence(seq_name=s, dets='SDP17', **dataloader)) else: self._data.append(MOT17Sequence(seq_name=s, dets=dets, **dataloader)) def __len__(self): return len(self._data) def __getitem__(self, idx): return self._data[idx] class MOT20Wrapper(MOT17Wrapper): """A Wrapper for the MOT_Sequence class to return multiple sequences.""" def __init__(self, split, dataloader): """Initliazes all subset of the dataset. Keyword arguments: split -- the split of the dataset to use dataloader -- args for the MOT_Sequence dataloader """ train_sequences = ['MOT20-01', 'MOT20-02', 'MOT20-03', 'MOT20-05'] test_sequences = ['MOT20-04', 'MOT20-06', 'MOT20-07', 'MOT20-08'] if "train" == split: sequences = train_sequences elif "test" == split: sequences = test_sequences elif "all" == split: sequences = train_sequences + test_sequences elif f"MOT20-{split}" in train_sequences + test_sequences: sequences = [f"MOT20-{split}"] else: raise NotImplementedError("MOT20 split not available.") self._data = [] for s in sequences: self._data.append(MOT20Sequence(seq_name=s, **dataloader)) def __len__(self): return len(self._data) def __getitem__(self, idx): return self._data[idx]

31.794872

104

0.700538

484

3,720

5.190083

0.17562

0.057325

0.078822

0.038217

0.806131

0.789013

0.730892

0.625

0

0.055805

0.161828

3,720

116

105

32.068966

0.74984

0.174462

0

0.618421

0

0.138068

0

1

0.118421

false

0

0.039474

0.078947

0.276316

0

null

0

1

0

null

0

6

982426e26649c0aaa7a195196291c360553a7340

37

py

Python

nataliestar/__init__.py

Orkestrate/Orkestrate-SC2AI

2d81ce204ff4b333389dbad52f5f36ca6cb7ac44

[ "MIT" ]

null

nataliestar/__init__.py

Orkestrate/Orkestrate-SC2AI

2d81ce204ff4b333389dbad52f5f36ca6cb7ac44

[ "MIT" ]

null

nataliestar/__init__.py

Orkestrate/Orkestrate-SC2AI

2d81ce204ff4b333389dbad52f5f36ca6cb7ac44

[ "MIT" ]

null

from nataliestar import agent_helper

18.5

36

0.891892

5

37

6.4

1

0

0.108108

37

1

37

0.969697

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7f8728b913de67f5ae89d59233503d41a5805ca1

3,415

py

Python

MLlib/loss_func.py

Player0109/ML-DL-implementation

338a89229da541e4285a3ad22508bfef5effc5e5

[ "BSD-3-Clause" ]

null

MLlib/loss_func.py

Player0109/ML-DL-implementation

338a89229da541e4285a3ad22508bfef5effc5e5

[ "BSD-3-Clause" ]

null

MLlib/loss_func.py

Player0109/ML-DL-implementation

338a89229da541e4285a3ad22508bfef5effc5e5

[ "BSD-3-Clause" ]

2

2020-10-02T07:13:20.000Z

2021-04-13T14:04:35.000Z

import numpy as np from MLlib.activations import sigmoid class MeanSquaredError(): """ Calculate Mean Squared Error. """ @staticmethod def loss(X, Y, W): """ Calculate loss by mean square method. PARAMETERS ========== X:ndarray(dtype=float,ndim=1) input vector Y:ndarray(dtype=float) output vector W:ndarray(dtype=float) Weights RETURNS ======= array of mean squared losses """ M = X.shape[0] return np.sum((np.dot(X, W).T - Y) ** 2) / (2 * M) @staticmethod def derivative(X, Y, W): """ Calculate derivative for mean square method. PARAMETERS ========== X:ndarray(dtype=float,ndim=1) input vector Y:ndarray(dtype=float) output vector W:ndarray(dtype=float) Weights RETURNS ======= array of derivates """ M = X.shape[0] return np.dot((np.dot(X, W).T - Y), X).T / M class LogarithmicError(): """ Calculate Logarithmic Error. """ @staticmethod def loss(X, Y, W): """ Calculate loss by logarithmic error method. PARAMETERS ========== X:ndarray(dtype=float,ndim=1) input vector Y:ndarray(dtype=float) output vector W:ndarray(dtype=float) Weights RETURNS ======= array of logarithmic losses """ M = X.shape[0] H = sigmoid(np.dot(X, W).T) return (1/M)*(np.sum((-Y)*np.log(H)-(1-Y)*np.log(1-H))) @staticmethod def derivative(X, Y, W): """ Calculate derivative for logarithmic error method. PARAMETERS ========== X:ndarray(dtype=float,ndim=1) input vector Y:ndarray(dtype=float) output vector W:ndarray(dtype=float) Weights RETURNS ======= array of derivates """ M = X.shape[0] H = sigmoid(np.dot(X, W).T) return (1/M)*(np.dot(X.T, (H-Y).T)) class AbsoluteError(): """ Calculate Absolute Error. """ @staticmethod def loss(X, Y, W): """ Calculate loss by absolute error method. PARAMETERS ========== X:ndarray(dtype=float,ndim=1) input vector Y:ndarray(dtype=float) output vector W:ndarray(dtype=float) Weights RETURNS ======= array of absolute losses """ M = X.shape[0] return np.sum(np.absolute(np.dot(X, W).T - Y)) / M @staticmethod def derivative(X, Y, W): """ Calculate derivative for absolute error method. PARAMETERS ========== X:ndarray(dtype=float,ndim=1) input vector Y:ndarray(dtype=float) output vector W:ndarray(dtype=float) Weights RETURNS ======= array of derivates """ M = X.shape[0] AbsError = (np.dot(X, W).T-Y) return np.dot( np.divide( AbsError, np.absolute(AbsError), out=np.zeros_like(AbsError), where=(np.absolute(AbsError)) != 0), X ).T/M

20.207101

63

0.475549

373

3,415

4.351206

0.160858

0.133087

0.18854

0.044362

0.790511

0.786815

0.760937

0.696858

0

0.009139

0.391215

3,415

168

64

20.327381

0.771525

0.428111

0

0.512821

0

1

0.153846

false

0

0.051282

0

0.435897

0

null

0

1

0

1

0

null

0

6

7f8bfbb21b7c36d33d3655bac044e98ab70c8823

100

py

Python

src/dataset/__init__.py

morgen-stern/squeezeDet

e7758bf61f9814a23701746f27728044f250a8a0

[ "BSD-2-Clause" ]

109

2019-04-29T03:30:42.000Z

2022-03-31T03:06:26.000Z

src/dataset/__init__.py

morgen-stern/squeezeDet

e7758bf61f9814a23701746f27728044f250a8a0

[ "BSD-2-Clause" ]

25

2019-03-25T00:27:39.000Z

2022-03-27T20:29:23.000Z

src/dataset/__init__.py

morgen-stern/squeezeDet

e7758bf61f9814a23701746f27728044f250a8a0

[ "BSD-2-Clause" ]

35

2019-02-12T20:50:32.000Z

2022-01-05T11:25:06.000Z

from __future__ import absolute_import from .kitti import kitti from .pascal_voc import pascal_voc

20

38

0.85

15

100

5.2

0.466667

0.230769

0

0.13

100

4

39

25

0.896552

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f6cef7840d070c274e14d606818da3b3a46a02df

39

py

Python

hello_world.py

Michikouwu/primerrepo3c

22f8fcc5c4927657e7fbfca52e06af3d9680eeaf

[ "MIT" ]

null

hello_world.py

Michikouwu/primerrepo3c

22f8fcc5c4927657e7fbfca52e06af3d9680eeaf

[ "MIT" ]

null

hello_world.py

Michikouwu/primerrepo3c

22f8fcc5c4927657e7fbfca52e06af3d9680eeaf

[ "MIT" ]

null

print('Hola mundo, soy del tercero C')

19.5

38

0.717949

7

39

4

1

0

0.153846

39

1

39

0.848485

0

0.74359

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

f6d8c8a99244db8a20ea3506a33e3aae74b74812

81

py

Python

smart-contracts/wake_up_neo.py

deanpress/neo-local

d7a1217402f06c597862735de103f53c2840cf09

[ "MIT" ]

1

2018-05-19T18:12:07.000Z

smart-contracts/wake_up_neo.py

deanpress/neo-local

d7a1217402f06c597862735de103f53c2840cf09

[ "MIT" ]

null

smart-contracts/wake_up_neo.py

deanpress/neo-local

d7a1217402f06c597862735de103f53c2840cf09

[ "MIT" ]

null

from boa.blockchain.vm.Neo.Runtime import Log def Main(): Log("Wake up, NEO!")

20.25

45

0.703704

14

81

4.071429

0.857143

0

0.135802

81

4

46

20.25

0.814286

0

0.158537

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

63e42cf5d754746a43128ce4d9be23160a9a0cc4

4,107

py

Python

app/migrations/0001_initial.py

vsanasc/Demo-Social-Media-API

5c39746a5f270864c7d9edb05f435857d87fcb3a

[ "MIT" ]

null

app/migrations/0001_initial.py

vsanasc/Demo-Social-Media-API

5c39746a5f270864c7d9edb05f435857d87fcb3a

[ "MIT" ]

null

app/migrations/0001_initial.py

vsanasc/Demo-Social-Media-API

5c39746a5f270864c7d9edb05f435857d87fcb3a

[ "MIT" ]

null

# Generated by Django 3.2.8 on 2021-10-10 20:32 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Profile', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('birthdate', models.DateField(blank=True, null=True)), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Post', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now=True)), ('modified_at', models.DateField(auto_now_add=True)), ('status', models.SmallIntegerField(choices=[(1, 'Active'), (0, 'Inactive'), (-1, 'Deleted')], default=1)), ('text', models.TextField()), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Friendship', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now=True)), ('modified_at', models.DateField(auto_now_add=True)), ('status', models.SmallIntegerField(choices=[(1, 'Active'), (0, 'Inactive'), (-1, 'Deleted')], default=1)), ('state', models.PositiveSmallIntegerField(choices=[(1, 'Pending'), (2, 'Accepted'), (3, 'Rejected')], default=1)), ('receiver', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='friendship_received', to=settings.AUTH_USER_MODEL)), ('requester', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='friendship_requests', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Comment', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now=True)), ('modified_at', models.DateField(auto_now_add=True)), ('status', models.SmallIntegerField(choices=[(1, 'Active'), (0, 'Inactive'), (-1, 'Deleted')], default=1)), ('text', models.TextField()), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.post')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Attachment', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now=True)), ('modified_at', models.DateField(auto_now_add=True)), ('status', models.SmallIntegerField(choices=[(1, 'Active'), (0, 'Inactive'), (-1, 'Deleted')], default=1)), ('file', models.FileField(upload_to='files')), ('is_image', models.BooleanField()), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.post')), ], options={ 'abstract': False, }, ), ]

48.317647

159

0.572924

402

4,107

5.701493

0.228856

0.031414

0.048866

0.076789

0.747382

0.737347

0

0.011729

0.273436

4,107

84

160

48.892857

0.756367

0.010957

0

0.662338

1

0

0.107143

0

1

0

false

0

0.038961

0

0.090909

0

null

0

1

0

1

0

null

0

6

120f9da585eb6f79a6d1db4f86387d48d2ad34cb

152

py

Python

forum/attachments/admin.py

successIA/Forum

08de91a033da2c3779acbf95dfe0210eb1276a26

[ "MIT" ]

null

forum/attachments/admin.py

successIA/Forum

08de91a033da2c3779acbf95dfe0210eb1276a26

[ "MIT" ]

6

2020-08-13T18:54:33.000Z

2021-06-10T20:20:16.000Z

forum/attachments/admin.py

successIA/ClassicForum

08de91a033da2c3779acbf95dfe0210eb1276a26

[ "MIT" ]

null

from django import forms from django.contrib import admin from django.db import models from .models import Attachment admin.site.register(Attachment)

19

32

0.828947

22

152

5.727273

0.5

0.238095

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0.125

152

7

33

21.714286

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null

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0

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6

124ddc6702f3ad4ff884472481cbc2927ff3ae55

141

py

Python

aula1406/pessoa.py

fillipesouza/aulasdelogicaprogramacao

409a9b82433eea9bcef2203c7c48ac0ab698f5db

[ "MIT" ]

1

2021-06-30T11:53:21.000Z

aula1406/pessoa.py

fillipesouza/aulasdelogicaprogramacao

409a9b82433eea9bcef2203c7c48ac0ab698f5db

[ "MIT" ]

null

aula1406/pessoa.py

fillipesouza/aulasdelogicaprogramacao

409a9b82433eea9bcef2203c7c48ac0ab698f5db

[ "MIT" ]

25

2021-04-17T00:36:10.000Z

2021-06-01T17:28:16.000Z

class Pessoa: def __init__(self, idade: int): self._idade = idade def __str__(self): return f'idade {self._idade}'

20.142857

38

0.602837

18

141

4.166667

0.555556

0.36

0

0.283688

141

6

39

23.5

0.742574

0

0.141844

0

1

0.4

false

0

0.2

0.8

0

1

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null

1

0

1

0

null

0

1

0

1

0

6

d6085927ffc2c75fd4069228b9adc8caee5a3b39

263

py

Python

soda/core/soda/execution/derived_formula.py

duyet/soda-core

92a52e0d7c1e88624d0637123cfcb2610af6d112

[ "Apache-2.0" ]

null

soda/core/soda/execution/derived_formula.py

duyet/soda-core

92a52e0d7c1e88624d0637123cfcb2610af6d112

[ "Apache-2.0" ]

null

soda/core/soda/execution/derived_formula.py

duyet/soda-core

92a52e0d7c1e88624d0637123cfcb2610af6d112

[ "Apache-2.0" ]

null

from typing import Callable, Dict class DerivedFormula: def __init__(self, function: Callable, metric_dependencies: Dict[str, "Metric"]): self.function: Callable = function self.metric_dependencies: Dict[str, "Metric"] = metric_dependencies

32.875

85

0.73384

29

263

6.413793

0.482759

0.290323

0.215054

0.268817

0.333333

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7

86

37.571429

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1

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null

1

0

1

0

null

0

1

0

6

d6180545867885bc64b5bfe188290c16301df8b8

313

py

Python

orange3/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

130

2015-07-28T03:41:21.000Z

2022-03-16T03:07:41.000Z

orange3/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

119

2015-08-01T00:54:06.000Z

2021-01-05T13:00:46.000Z

orange3/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

72

2015-07-29T02:35:56.000Z

2022-02-26T14:31:15.000Z

import Orange.classification._simple_tree import Orange.classification._tree_scorers import Orange.data._contingency import Orange.data._io import Orange.data._valuecount import Orange.data._variable import Orange.preprocess._discretize import Orange.preprocess._relieff import Orange.widgets.utils._grid_density

31.3

42

0.881789

40

313

6.6

0.45

0.409091

0.242424

0

0.057508

313

9

43

34.777778

0.894915

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d61f3a564be3dc69c28d17fe1c2f313fcfaffb89

45

py

Python

python/main.py

vwallajabad/VariableX_discord.bot

280ffeaea305f09af35d60b06e06c2f82dd80bcd

[ "MIT" ]

null

python/main.py

vwallajabad/VariableX_discord.bot

280ffeaea305f09af35d60b06e06c2f82dd80bcd

[ "MIT" ]

null

python/main.py

vwallajabad/VariableX_discord.bot

280ffeaea305f09af35d60b06e06c2f82dd80bcd

[ "MIT" ]

null

from bot import discord_code discord_code()

11.25

28

0.822222

7

45

5

0.714286

0.628571

0

0.133333

45

3

29

15

0.897436

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d624dee20476ec576ec34a67545ccd5ebb1ddd4e

35

py

Python

EvoCluster/__init__.py

soumitri2001/EvoCluster

2f8e3f21c7045478394e7e02a22835f7c184c0c7

[ "Apache-2.0" ]

12

2019-12-21T16:29:15.000Z

2022-01-03T01:24:14.000Z

EvoCluster/__init__.py

soumitri2001/EvoCluster

2f8e3f21c7045478394e7e02a22835f7c184c0c7

[ "Apache-2.0" ]

3

2020-06-07T07:52:40.000Z

2021-10-08T16:13:49.000Z

EvoCluster/__init__.py

RaneemQaddoura/EvoCluster

001dfb4c1f00db84ad1c2f2228eed6112d7e65b1

[ "Apache-2.0" ]

14

2019-12-28T19:55:48.000Z

2021-12-31T14:46:03.000Z

from ._evocluster import EvoCluster

35

0.885714

4

35

7.5

0.75

0

0.085714

35

1

35

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c393c3937c55da45a48e1e80ff79cc4b205719cf

23

py

Python

accounts/signals/__init__.py

tavoxr/django-crm

d6ce34b8e8e93c3ae9853df34641868d4c891125

[ "MIT" ]

null

accounts/signals/__init__.py

tavoxr/django-crm

d6ce34b8e8e93c3ae9853df34641868d4c891125

[ "MIT" ]

null

accounts/signals/__init__.py

tavoxr/django-crm

d6ce34b8e8e93c3ae9853df34641868d4c891125

[ "MIT" ]

null

from .register import *

23

0.782609

3

23

6

1

0

0.130435

23

1

23

0.9

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c39c5e4046f28fcc4e91ec74339bc60025fd922f

151

py

Python

Bronze/Bronze_V/24860.py

masterTyper/baekjoon_solved_ac

b9ce14d9bdaa5b5b06735ad075fb827de9f44b9c

[ "MIT" ]

null

Bronze/Bronze_V/24860.py

masterTyper/baekjoon_solved_ac

b9ce14d9bdaa5b5b06735ad075fb827de9f44b9c

[ "MIT" ]

null

Bronze/Bronze_V/24860.py

masterTyper/baekjoon_solved_ac

b9ce14d9bdaa5b5b06735ad075fb827de9f44b9c

[ "MIT" ]

null

Vk, Jk = map(int, input().split()) Vl, Jl = map(int, input().split()) Vh, Dh, Jh = map(int, input().split()) print((Vk * Jk + Vl * Jl) * Vh * Dh * Jh)

30.2

41

0.536424

27

151

3

0.444444

0.222222

0.407407

0.592593

0

0.192053

151

5

41

30.2

0.663934

0

1

0

true

0

0.25

1

0

null

1

0

1

0

null

0

1

0

6

c3c34d9e8a13a9e577ba42cddde505295537bb04

39

py

Python

overload/__init__.py

Corwind/python-overload

87d8a13273316a1bd02e0be7ff884ab77dc71dd2

[ "Beerware" ]

1

2015-12-22T15:40:23.000Z

overload/__init__.py

Corwind/python-overload

87d8a13273316a1bd02e0be7ff884ab77dc71dd2

[ "Beerware" ]

null

overload/__init__.py

Corwind/python-overload

87d8a13273316a1bd02e0be7ff884ab77dc71dd2

[ "Beerware" ]

null

from overload.overload import overload

19.5

38

0.871795

5

39

6.8

0.6

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39

1

39

0.971429

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true

0

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null

0

1

0

1

0

1

0

6

7f0f15d90612f19b4953ba9fc5bc5676e8d6ff0c

10,610

py

Python

amiet_tools/functions/flat_plate_response.py

Toktom/amiet_tools

e4104db9a0c3784159378f680ebb89caa5ada053

[ "BSD-3-Clause" ]

null

amiet_tools/functions/flat_plate_response.py

Toktom/amiet_tools

e4104db9a0c3784159378f680ebb89caa5ada053

[ "BSD-3-Clause" ]

null

amiet_tools/functions/flat_plate_response.py

Toktom/amiet_tools

e4104db9a0c3784159378f680ebb89caa5ada053

[ "BSD-3-Clause" ]

null

"""Author: Fabio Casagrande Hirono""" import numpy as np from .fresnel import fr_int, fr_int_cc import scipy.special as ss def delta_p(rho0, b, w0, Kx, ky, xy, Mach): """ Calculates the pressure jump response 'delta_p' for a single turbulent gust. Parameters ---------- rho0 : float Density of air. b : float Airfoil semichord. w0 : float Gust amplitude. Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. xy : ({2, 3}, Ny, Nx) array_like 2D array containing (x, y) coordinates of airfoil surface mesh. Mach : float Mean flow Mach number. Returns ------- delta_p : (Ny, Nx) array_like Surface pressure jump over airfoil surface mesh in response to a single turbulent gust with wavenumbers (Kx, ky) and amplitude 'w0'. """ # pressure difference over the whole airfoil surface delta_p = np.zeros(xy[0].shape, 'complex') if xy.ndim == 3: # unsteady lift over the chord line (mid-span) g_x = np.zeros(xy[0][0].shape, 'complex') # calculates the unsteady lift over the chord g_x = g_LE(xy[0][0], Kx, ky, Mach, b) # broadcasts a copy of 'g_x' to 'delta_p' delta_p = g_x[np.newaxis, :] elif xy.ndim == 2: # unsteady lift over the chord line (mid-span) g_x = np.zeros(xy[0].shape, 'complex') # calculates the unsteady lift over the chord delta_p = g_LE(xy[0], Kx, ky, Mach, b) # adds the constants and the 'k_y' oscillating component delta_p = 2*np.pi*rho0*w0*delta_p*np.exp(-1j*ky*xy[1]) return delta_p def g_LE(xs, Kx, ky, Mach, b): """ Airfoil non-dimensional chordwise pressure jump in response to a single gust. Parameters ---------- xs : (Ny, Nx) or (Nx,) array_like Airfoil surface mesh chordwise coordinates. Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. Mach : float Mean flow Mach number. b : float Airfoil semichord. Returns ------- g_LE : (Ny, Nx) array_like Non-dimensional chordwise surface pressure jump over airfoil surface mesh in response to a single turbulent gust with wavenumbers (Kx, ky) and amplitude 'w0'. Notes ----- This function provides the airfoil responses for either subcritical or supercritical gusts. For critical gusts, the airfoil response is interpolated from slightly sub- and slightly supercritical responses. """ beta = np.sqrt(1-Mach**2) ky_critical = Kx*Mach/beta # p_diff < 0: supercritical # p_diff > 0: subcritical p_diff = (np.abs(ky) - ky_critical)/ky_critical # supercritical gusts if p_diff < -1e-3: return g_LE_super(xs, Kx, ky, Mach, b) elif p_diff > 1e-3: return g_LE_sub(xs, Kx, ky, Mach, b) else: # get gusts 1% above and below critical ky ky_sp = ky*0.99 ky_sb = ky*1.01 g_sp = g_LE_super(xs, Kx, ky_sp, Mach, b) g_sb = g_LE_sub(xs, Kx, ky_sb, Mach, b) return (g_sp + g_sb)/2. def g_LE_super(xs, Kx, ky, Mach, b): """ Returns airfoil non-dimensional pressure jump for supercritical gusts. Parameters ---------- xs : (Ny, Nx) or (Nx,) array_like Airfoil surface mesh chordwise coordinates. Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. Mach : float Mean flow Mach number. b : float Airfoil semichord. Returns ------- g_LE_super : (Ny, Nx) array_like Non-dimensional chordwise surface pressure jump over airfoil surface mesh in response to a single supercritical turbulent gust with wavenumbers (Kx, ky) Notes ----- This function includes two terms of the Schwarzchild technique; the first term contains the solution for a infinite-chord airfoil with a leading edge but no trailing edge, while the second term contains a correction factor for a infinite-chord airfoil with a trailing edge but no leading edge. """ beta = np.sqrt(1-Mach**2) mu_h = Kx*b/(beta**2) mu_a = mu_h*Mach kappa = np.sqrt(mu_a**2 - (ky*b/beta)**2) g1_sp = (np.exp(-1j*((kappa - mu_a*Mach)*((xs/b) + 1) + np.pi/4)) / (np.pi*np.sqrt(np.pi*((xs/b) + 1)*(Kx*b + (beta**2)*kappa)))) g2_sp = -(np.exp(-1j*((kappa - mu_a*Mach)*((xs/b) + 1) + np.pi/4)) * (1-(1+1j)*fr_int_cc(2*kappa*(1-xs/b))) / (np.pi*np.sqrt(2*np.pi*(Kx*b + (beta**2)*kappa)))) return g1_sp + g2_sp def g_LE_sub(xs, Kx, ky, Mach, b): """ Returns airfoil non-dimensional pressure jump for subcritical gusts. Parameters ---------- xs : (Ny, Nx) or (Nx,) array_like Airfoil surface mesh chordwise coordinates. Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. Mach : float Mean flow Mach number. b : float Airfoil semichord. Returns ------- g_LE_sub : (Ny, Nx) array_like Non-dimensional chordwise surface pressure jump over airfoil surface mesh in response to a single subcritical turbulent gust with wavenumbers (Kx, ky) Notes ----- This function includes two terms of the Schwarzchild technique; the first term contains the solution for a infinite-chord airfoil with a leading edge but no trailing edge, while the second term contains a correction factor for a infinite-chord airfoil with a trailing edge but no leading edge. """ beta = np.sqrt(1-Mach**2) mu_h = Kx*b/(beta**2) mu_a = mu_h*Mach kappa1 = np.sqrt(((ky*b/beta)**2) - mu_a**2) g1_sb = (np.exp((-kappa1 + 1j*mu_a*Mach)*((xs/b) + 1))*np.exp(-1j*np.pi/4) / (np.pi*np.sqrt(np.pi*((xs/b) + 1)*(Kx*b - 1j*(beta**2)*kappa1)))) g2_sb = -(np.exp((-kappa1 + 1j*mu_a*Mach)*((xs/b) + 1)) * np.exp(-1j*np.pi/4)*(1 - ss.erf(2*kappa1*(1-xs/b))) / (np.pi*np.sqrt(2*np.pi*(Kx*b - 1j*(beta**2)*kappa1)))) return g1_sb + g2_sb def L_LE(x, sigma, Kx, ky, Mach, b): """ Returns the effective lift functions - i.e. chordwise integrated surface pressures Parameters ---------- x : (M,) array_like 1D array of observer locations 'x'-coordinates sigma : (M,) array_like 1D array of observer locations flow-corrected distances Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. Mach : float Mean flow Mach number. b : float Airfoil semichord. Returns ------- L_LE : (M,) array_like Effective lift function for all observer locations. Notes ----- These functions are the chordwise integrated surface pressures, and are parts of the far-field-approximated model for airfoil-turbulente noise. """ beta = np.sqrt(1-Mach**2) ky_critical = Kx*Mach/beta # percentage difference in ky # p_diff < 0: supercritical / p_diff > 0: subcritical p_diff = (np.abs(ky) - ky_critical)/ky_critical # supercritical gusts if p_diff < -1e-3: return L_LE_super(x, sigma, Kx, ky, Mach, b) elif p_diff > 1e-3: return L_LE_sub(x, sigma, Kx, ky, Mach, b) else: # get gusts 1% above and below critical ky ky_sp = ky*0.99 ky_sb = ky*1.01 L_sp = L_LE_super(x, sigma, Kx, ky_sp, Mach, b) L_sb = L_LE_sub(x, sigma, Kx, ky_sb, Mach, b) return (L_sp + L_sb)/2. def L_LE_super(x, sigma, Kx, Ky, Mach, b): """ Returns the effective lift functions for supercritical gusts Parameters ---------- x : (M,) array_like 1D array of observer locations 'x'-coordinates sigma : (M,) array_like 1D array of observer locations flow-corrected distances Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. Mach : float Mean flow Mach number. b : float Airfoil semichord. Returns ------- Notes ----- These functions are the chordwise integrated surface pressures, and are parts of the far-field-approximated model for airfoil-turbulente noise. """ beta = np.sqrt(1-Mach**2) mu_h = Kx*b/(beta**2) mu_a = mu_h*Mach kappa = np.sqrt(mu_a**2 - (Ky*b/beta)**2) H1 = kappa - mu_a*x/sigma H2 = mu_a*(Mach - x*sigma) - np.pi/4 L1 = ((1/np.pi)*np.sqrt(2/((Kx*b + (beta**2)*kappa)*H1)) * fr_int_cc(2*H1)*np.exp(1j*H2)) L2 = ((np.exp(1j*H2) / (np.pi*H1*np.sqrt(2*np.pi*(Kx*b + (beta**2)*kappa)))) * (1j*(1 - np.exp(-2j*H1)) + (1 - 1j)*(fr_int_cc(4*kappa) - np.sqrt(2*kappa/(kappa + mu_a*x/sigma)) * np.exp(-2j*H1) * fr_int_cc(2*(kappa + mu_a*x/sigma))))) return L1+L2 def L_LE_sub(x, sigma, Kx, Ky, Mach, b): """ Returns the effective lift functions for subcritical gusts Parameters ---------- x : (M,) array_like 1D array of observer locations 'x'-coordinates sigma : (M,) array_like 1D array of observer locations flow-corrected distances Kx : float Chordwise turbulent gust wavenumber. ky : float Spanwise turbulent gust wavenumber. Mach : float Mean flow Mach number. b : float Airfoil semichord. Returns ------- Notes ----- These functions are the chordwise integrated surface pressures, and are parts of the far-field-approximated model for airfoil-turbulente noise. """ beta = np.sqrt(1-Mach**2) mu_h = Kx*b/(beta**2) mu_a = mu_h*Mach kappa1 = np.sqrt((Ky*b/beta)**2 - (mu_a**2)) H2 = mu_a*(Mach - x*sigma) - np.pi/4 H3 = kappa1 - 1j*mu_a*x/sigma L1 = ((1/np.pi)*np.sqrt(2/((Kx*b - 1j*(beta**2)*kappa1) * (1j*kappa1 - mu_a*x/sigma))) * fr_int(2*(1j*kappa1 - mu_a*x/sigma))*np.exp(1j*H2)) L2 = ((1j*np.exp(1j*H2) / (np.pi*H3*np.sqrt(2*np.pi*(Kx*b - 1j*(beta**2)*kappa1)))) * (1 - np.exp(-2*H3) - ss.erf(np.sqrt(4*kappa1)) + 2*np.exp(-2*H3)*np.sqrt(kappa1/(1j*kappa1 + mu_a*x/sigma)) * fr_int(2*(1j*kappa1 - mu_a*x/sigma)))) return L1+L2

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null

0

1

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null

0

6

613c6cff5b0fac686b1c4e8b70b149f5f25a2861

199

py

Python

tomo_encoders/tasks/__init__.py

arshadzahangirchowdhury/TomoEncoders

9c2b15fd515d864079f198546821faee5d78df17

[ "BSD-3-Clause" ]

null

tomo_encoders/tasks/__init__.py

arshadzahangirchowdhury/TomoEncoders

9c2b15fd515d864079f198546821faee5d78df17

[ "BSD-3-Clause" ]

null

tomo_encoders/tasks/__init__.py

arshadzahangirchowdhury/TomoEncoders

9c2b15fd515d864079f198546821faee5d78df17

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ """ from tomo_encoders.tasks import * from tomo_encoders.tasks.void_metrology import VoidMetrology from tomo_encoders.tasks import void_mapper

15.307692

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0.743719

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5.296296

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1

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1

0

6

613f826bfdedb2b95034a8f4027632b2d39db56d

434

py

Python

hknweb/views/indrel.py

Boomaa23/hknweb

2c2ce38b5f1c0c6e04ba46282141557357bd5326

[ "MIT" ]

20

2018-01-07T02:15:43.000Z

2021-09-15T04:25:50.000Z

hknweb/views/indrel.py

Boomaa23/hknweb

2c2ce38b5f1c0c6e04ba46282141557357bd5326

[ "MIT" ]

292

2018-02-01T18:31:18.000Z

2022-03-30T22:15:08.000Z

hknweb/views/indrel.py

Boomaa23/hknweb

2c2ce38b5f1c0c6e04ba46282141557357bd5326

[ "MIT" ]

85

2017-11-13T06:33:13.000Z

2022-03-30T20:32:55.000Z

from django.shortcuts import render def index(request): return render(request, "indrel/index.html") def resume_book(request): return render(request, "indrel/resume_book.html") def infosessions(request): return render(request, "indrel/infosessions.html") def career_fair(request): return render(request, "indrel/career_fair.html") def contact_us(request): return render(request, "indrel/contact_us.html")

19.727273

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61820a8103b4521fcbc8ab56387714b7d7e7c231

287

py

Python

test/test_utils.py

droope/netlib

a3107474f9f336f28dc195f1406a4e035aa51c84

[ "MIT" ]

null

test/test_utils.py

droope/netlib

a3107474f9f336f28dc195f1406a4e035aa51c84

[ "MIT" ]

null

test/test_utils.py

droope/netlib

a3107474f9f336f28dc195f1406a4e035aa51c84

[ "MIT" ]

null

from netlib import utils def test_hexdump(): assert utils.hexdump("one\0"*10) def test_cleanBin(): assert utils.cleanBin("one") == "one" assert utils.cleanBin("\00ne") == ".ne" assert utils.cleanBin("\nne") == "\nne" assert utils.cleanBin("\nne", True) == ".ne"

20.5

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6

f66e7dea2ca1fcc00ca4a17791b761b8b516fc06

9,081

py

Python

FEM/EulerBernoulliBeam.py

ZibraMax/FEM

b868c60408a4f83dec4bb424d66be0b20e2ac71b

[ "MIT" ]

10

2021-03-21T18:38:40.000Z

2022-02-22T01:32:06.000Z

FEM/EulerBernoulliBeam.py

ZibraMax/FEM

b868c60408a4f83dec4bb424d66be0b20e2ac71b

[ "MIT" ]

null

FEM/EulerBernoulliBeam.py

ZibraMax/FEM

b868c60408a4f83dec4bb424d66be0b20e2ac71b

[ "MIT" ]

1

2022-02-08T04:40:59.000Z

"""Euler Bernoulli Beam implementation [WIP] """ from .Solvers import NoLineal from .Elements.E1D.EulerBernoulliElement import EulerBernoulliElement from .Core import Core, Geometry from tqdm import tqdm import numpy as np import matplotlib.pyplot as plt import logging class EulerBernoulliBeam(Core): """Creates a Euler Bernoulli beam problem Args: geometry (Geometry): 1D 2 variables per node problem geometry. Geometry must have Euler Bernoulli elements. EI (float): Young's moduli multiplied by second moment of area (inertia). cf (float, optional): Soil coeficient. Defaults to 0. """ def __init__(self, geometry: Geometry, EI: float, cf: float = 0, f: float = 0) -> None: """Creates a Euler Bernoulli beam problem Args: geometry (Geometry): 1D 2 variables per node problem geometry. Geometry must have Lineal elements. EI (float): Young's moduli multiplied by second moment of area (inertia). cf (float, optional): Soil coeficient. Defaults to 0. """ self.a = EI self.f = f self.cf = cf if isinstance(EI, float): self.a = lambda x: EI if isinstance(f, float): self.f = lambda x: f if isinstance(f, float): self.cf = lambda x: cf if geometry.nvn == 1: logging.warning( 'Border conditions lost, please usea a geometry with 2 variables per node (nvn=2)') Core.__init__(self, geometry) for i in range(len(self.elements)): self.elements[i] = EulerBernoulliElement( self.elements[i].coords, self.elements[i].gdl) def elementMatrices(self) -> None: """Calculate the element matrices usign Guass Legendre quadrature. """ for e in tqdm(self.elements, unit='Element'): _x, _p = e.T(e.Z.T) _h = e.hermit(e.Z.T) jac, dpz = e.J(e.Z.T) detjac = np.linalg.det(jac) # _j = np.linalg.inv(jac) # dpx = _j @ dpz _dh = e.dhermit(e.Z.T) for i in range(e.n): for j in range(e.n): for k in range(len(e.Z)): # + self.c(_x[k])*_p[k][i]*_p[k][j] e.Ke[i, j] += (self.a(_x[k])*_dh[1][i][k] * _dh[1][j][k]+self.cf(_x[k, 0])*_h[k][i]*_h[k][j])*detjac[k]*e.W[k] for k in range(len(e.Z)): e.Fe[i][0] += self.f(_x[k])*_h[k][i]*detjac[k]*e.W[k] def postProcess(self, plot=True) -> None: """Post process the solution. Shows graphs of displacement, rotation, shear and moment. """ X = [] U1 = [] U2 = [] U3 = [] U4 = [] for e in self.elements: _x, _u, du = e.giveSolution(True) X += _x.T[0].tolist() U1 += _u.tolist() U2 += (du[:, 0]).tolist() U3 += (du[:, 1]*self.a(_x.T[0])).tolist() U4 += (du[:, 2]*self.a(_x.T[0])).tolist() if plot: fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) ax4 = fig.add_subplot(2, 2, 4) ax1.plot(X, U1) ax1.grid() ax2.plot(X, U2) ax2.grid() ax3.plot(X, U3) ax3.grid() ax4.plot(X, U4) ax4.grid() ax1.set_title(r'$U(x)$') ax2.set_title(r'$\frac{dU}{dx}$') ax3.set_title(r'$\frac{d^2U}{dx^2}$') ax4.set_title(r'$\frac{d^3U}{dx^3}$') return X, U1, U2, U3, U4 class EulerBernoulliBeamNonLineal(Core): """Creates a Euler Bernoulli beam problem Args: geometry (Geometry): 1D 2 variables per node problem geometry. Geometry must have Euler Bernoulli elements. EI (float): Young's moduli multiplied by second moment of area (inertia). cf (float, optional): Soil coeficient. Defaults to 0. """ def __init__(self, geometry: Geometry, EI: float, EA: float, fx: float = 0, fy: float = 0) -> None: """Creates a Euler Bernoulli beam problem Args: geometry (Geometry): 1D 2 variables per node problem geometry. Geometry must have Lineal elements. EI (float): Young's moduli multiplied by second moment of area (inertia). cf (float, optional): Soil coeficient. Defaults to 0. """ self.Axx = EI self.Dxx = EA self.fx0 = fx self.fy0 = fy if isinstance(EI, float): self.Axx = lambda x: EA if isinstance(EI, float): self.Dxx = lambda x: EI if isinstance(fx, float): self.fx0 = lambda x: fx if isinstance(fy, float): self.fy0 = lambda x: fy if geometry.nvn == 1: logging.warning( 'Border conditions lost, please usea a geometry with 2 variables per node (nvn=2)') Core.__init__(self, geometry, solver=NoLineal.LoadControl) for i in range(len(self.elements)): self.elements[i] = EulerBernoulliElement( self.elements[i].coords, self.elements[i].gdl, nvn=3) def elementMatrices(self) -> None: """Calculate the element matrices usign Guass Legendre quadrature. """ for e in tqdm(self.elements, unit='Element'): en2 = int(e.n/2) k11 = np.zeros([2, 2]) k12 = np.zeros([2, 4]) k22 = np.zeros([4, 4]) f1 = np.zeros([2, 1]) f2 = np.zeros([4, 1]) # Integración completa _x, _p = e.T(e.Z.T) _h = e.hermit(e.Z.T) jac, dpz = e.J(e.Z.T) detjac = np.linalg.det(jac) _j = np.linalg.inv(jac) dpx = _j @ dpz _dh = e.dhermit(e.Z.T) for i in range(4): for j in range(4): for k in range(len(e.Z)): k22[i, j] += (self.Dxx(_x[k])*_dh[1][i][k] * _dh[1][j][k])*detjac[k]*e.W[k] if i < 2 and j < 2: k11[i, j] += (self.Axx(_x[k])*dpx[k][0][i] * dpx[k][0][j])*detjac[k]*e.W[k] for k in range(len(e.Z)): if i < 2: f1[i][0] += self.fx(_x[k])*_p[k][i]*detjac[k]*e.W[k] f2[i][0] += self.fy(_x[k])*_h[k][i]*detjac[k]*e.W[k] # Integración reducida _x, _p = e.T(e.Zr.T) _h = e.hermit(e.Zr.T) jac, dpz = e.J(e.Zr.T) detjac = np.linalg.det(jac) _j = np.linalg.inv(jac) dpx = _j @ dpz _dh = e.dhermit(e.Zr.T) for i in range(4): for j in range(4): for k in range(len(e.Zr)): ue = e.Ue.flatten()[[1, 2, 4, 5]] dw = ue @ _dh[0, :, k].T # + self.c(_x[k])*_p[k][i]*_p[k][j] if i < 2: k12[i, j] += 1.0/2.0*(self.Axx(_x[k])*dw*dpx[k][0][i] * _dh[0][j][k])*detjac[k]*e.Wr[k] k22[i, j] += 1.0/2.0*(self.Axx(_x[k])*dw**2*_dh[0][i][k] * _dh[0][j][k])*detjac[k]*e.Wr[k] e.Ke[np.ix_([0, 3], [0, 3])] = k11 e.Ke[np.ix_([1, 2, 4, 5], [1, 2, 4, 5])] = k22 e.Ke[np.ix_([0, 3], [1, 2, 4, 5])] = k12 e.Ke[np.ix_([1, 2, 4, 5], [0, 3])] = 2*k12.T e.Fe[[0, 3]] = f1 e.Fe[[1, 2, 4, 5]] = f2 def postProcess(self, plot=True) -> None: """Post process the solution. Shows graphs of displacement, rotation, shear and moment. """ X = [] U1 = [] U2 = [] U3 = [] U4 = [] for e in self.elements: ueflex = e.Ue.flatten()[[1, 2, 4, 5]] ueax = e.Ue.flatten()[[0, 3]] e.Ue = ueflex _x, _u, du = e.giveSolution(True) X += _x.T[0].tolist() U1 += _u.tolist() U2 += (du[:, 0]).tolist() U3 += (du[:, 1]).tolist() U4 += (du[:, 2]).tolist() if plot: fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) ax4 = fig.add_subplot(2, 2, 4) ax1.plot(X, U1) ax1.grid() ax2.plot(X, U2) ax2.grid() ax3.plot(X, U3) ax3.grid() ax4.plot(X, U4) ax4.grid() ax1.set_title(r'$U(x)$') ax2.set_title(r'$\frac{dU}{dx}$') ax3.set_title(r'$\frac{d^2U}{dx^2}$') ax4.set_title(r'$\frac{d^3U}{dx^3}$') return X, U1, U2, U3, U4

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null

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6

9ca02f28fab1aafa754bf16cfe23932280756aab

131

py

Python

backend/site/MWS_Backend/apiserver/models/__init__.py

singapore19/team-3

f021dc98f809faa62932be09c0ed00bec2aa5af3

[ "Net-SNMP", "Xnet", "RSA-MD" ]

null

backend/site/MWS_Backend/apiserver/models/__init__.py

singapore19/team-3

f021dc98f809faa62932be09c0ed00bec2aa5af3

[ "Net-SNMP", "Xnet", "RSA-MD" ]

null

backend/site/MWS_Backend/apiserver/models/__init__.py

singapore19/team-3

f021dc98f809faa62932be09c0ed00bec2aa5af3

[ "Net-SNMP", "Xnet", "RSA-MD" ]

null

from .job import * from .leave import * from .trip import * from .tripjob import * # from .driver import * # from .staff import *

16.375

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0

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0

6

9cf03cad5066a7818f34cc7c2acaf0bc3c4efc29

71

py

Python

autox/autox_ts/models/__init__.py

OneToolsCollection/4paradigm-AutoX

f8e838021354de17f5bb9bc44e9d68d12dda6427

[ "Apache-2.0" ]

null

autox/autox_ts/models/__init__.py

OneToolsCollection/4paradigm-AutoX

f8e838021354de17f5bb9bc44e9d68d12dda6427

[ "Apache-2.0" ]

null

autox/autox_ts/models/__init__.py

OneToolsCollection/4paradigm-AutoX

f8e838021354de17f5bb9bc44e9d68d12dda6427

[ "Apache-2.0" ]

null

from .ts_lgb_model import ts_lgb_model from .cnn_model import cnn_model

35.5

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71

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1

0

1

0

1

0

6

9cf1cae3e6c00047801eefca96572219ad14c597

151

py

Python

movement_assistant/bots/telebot/deletecall.py

davidwickerhf/movement-assistant

570380adf440faa36993ab8f52e386584a90fec8

[ "MIT" ]

3

2020-06-11T13:06:21.000Z

2020-06-11T21:35:41.000Z

movement_assistant/bots/telebot/deletecall.py

davidwickerhf/movement-assistant

570380adf440faa36993ab8f52e386584a90fec8

[ "MIT" ]

25

2020-04-29T16:44:05.000Z

2020-06-11T08:18:47.000Z

movement_assistant/bots/telebot/deletecall.py

davidwickerhf/fff-transparency-wg

570380adf440faa36993ab8f52e386584a90fec8

[ "MIT" ]

1

2020-12-23T09:33:05.000Z

from movement_assistant.bots.telebot import * def delete_call(update, context): botupdate = interface.authenticate(update, context, 0, True)

25.166667

64

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6.222222

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0

1

0

6

146501d0f9514561351c68fc963a2fdaf36f177b

8,685

py

Python

tests/queryset/test_queryset_aggregation.py

shellcodesniper/mongoengine

d76cb345be98045cde0fa078569cc8021c0d0162

[ "MIT" ]

3

2019-06-18T07:54:38.000Z

2022-01-22T23:27:41.000Z

tests/queryset/test_queryset_aggregation.py

shellcodesniper/mongoengine

d76cb345be98045cde0fa078569cc8021c0d0162

[ "MIT" ]

1

2022-01-22T23:27:23.000Z

tests/queryset/test_queryset_aggregation.py

shellcodesniper/mongoengine

d76cb345be98045cde0fa078569cc8021c0d0162

[ "MIT" ]

null

import unittest import warnings from pymongo.read_preferences import ReadPreference from mongoengine import * from tests.utils import MongoDBTestCase class TestQuerysetAggregate(MongoDBTestCase): def test_read_preference_aggregation_framework(self): class Bar(Document): txt = StringField() meta = {"indexes": ["txt"]} # Aggregates with read_preference pipeline = [] bars = Bar.objects.read_preference( ReadPreference.SECONDARY_PREFERRED ).aggregate(pipeline) assert ( bars._CommandCursor__collection.read_preference == ReadPreference.SECONDARY_PREFERRED ) def test_queryset_aggregation_framework(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects(age__lte=22).aggregate(pipeline) assert list(data) == [ {"_id": p1.pk, "name": "ISABELLA LUANNA"}, {"_id": p2.pk, "name": "WILSON JUNIOR"}, ] pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects(age__lte=22).order_by("-name").aggregate(pipeline) assert list(data) == [ {"_id": p2.pk, "name": "WILSON JUNIOR"}, {"_id": p1.pk, "name": "ISABELLA LUANNA"}, ] pipeline = [ {"$group": {"_id": None, "total": {"$sum": 1}, "avg": {"$avg": "$age"}}} ] data = ( Person.objects(age__gte=17, age__lte=40) .order_by("-age") .aggregate(pipeline) ) assert list(data) == [{"_id": None, "avg": 29, "total": 2}] pipeline = [{"$match": {"name": "Isabella Luanna"}}] data = Person.objects().aggregate(pipeline) assert list(data) == [{u"_id": p1.pk, u"age": 16, u"name": u"Isabella Luanna"}] def test_queryset_aggregation_with_skip(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects.skip(1).aggregate(pipeline) assert list(data) == [ {"_id": p2.pk, "name": "WILSON JUNIOR"}, {"_id": p3.pk, "name": "SANDRA MARA"}, ] def test_queryset_aggregation_with_limit(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects.limit(1).aggregate(pipeline) assert list(data) == [{"_id": p1.pk, "name": "ISABELLA LUANNA"}] def test_queryset_aggregation_with_sort(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects.order_by("name").aggregate(pipeline) assert list(data) == [ {"_id": p1.pk, "name": "ISABELLA LUANNA"}, {"_id": p3.pk, "name": "SANDRA MARA"}, {"_id": p2.pk, "name": "WILSON JUNIOR"}, ] def test_queryset_aggregation_with_skip_with_limit(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = list(Person.objects.skip(1).limit(1).aggregate(pipeline)) assert list(data) == [{"_id": p2.pk, "name": "WILSON JUNIOR"}] # Make sure limit/skip chaining order has no impact data2 = Person.objects.limit(1).skip(1).aggregate(pipeline) assert data == list(data2) def test_queryset_aggregation_with_sort_with_limit(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects.order_by("name").limit(2).aggregate(pipeline) assert list(data) == [ {"_id": p1.pk, "name": "ISABELLA LUANNA"}, {"_id": p3.pk, "name": "SANDRA MARA"}, ] # Verify adding limit/skip steps works as expected pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}, {"$limit": 1}] data = Person.objects.order_by("name").limit(2).aggregate(pipeline) assert list(data) == [{"_id": p1.pk, "name": "ISABELLA LUANNA"}] pipeline = [ {"$project": {"name": {"$toUpper": "$name"}}}, {"$skip": 1}, {"$limit": 1}, ] data = Person.objects.order_by("name").limit(2).aggregate(pipeline) assert list(data) == [{"_id": p3.pk, "name": "SANDRA MARA"}] def test_queryset_aggregation_with_sort_with_skip(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects.order_by("name").skip(2).aggregate(pipeline) assert list(data) == [{"_id": p2.pk, "name": "WILSON JUNIOR"}] def test_queryset_aggregation_with_sort_with_skip_with_limit(self): class Person(Document): name = StringField() age = IntField() Person.drop_collection() p1 = Person(name="Isabella Luanna", age=16) p2 = Person(name="Wilson Junior", age=21) p3 = Person(name="Sandra Mara", age=37) Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] data = Person.objects.order_by("name").skip(1).limit(1).aggregate(pipeline) assert list(data) == [{"_id": p3.pk, "name": "SANDRA MARA"}] def test_queryset_aggregation_deprecated_interface(self): class Person(Document): name = StringField() Person.drop_collection() p1 = Person(name="Isabella Luanna") p2 = Person(name="Wilson Junior") p3 = Person(name="Sandra Mara") Person.objects.insert([p1, p2, p3]) pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}] # Make sure a warning is emitted with warnings.catch_warnings(): warnings.simplefilter("error", DeprecationWarning) with self.assertRaises(DeprecationWarning): Person.objects.order_by("name").limit(2).aggregate(*pipeline) # Make sure old interface works as expected with a 1-step pipeline data = Person.objects.order_by("name").limit(2).aggregate(*pipeline) assert list(data) == [ {"_id": p1.pk, "name": "ISABELLA LUANNA"}, {"_id": p3.pk, "name": "SANDRA MARA"}, ] # Make sure old interface works as expected with a 2-steps pipeline pipeline = [{"$project": {"name": {"$toUpper": "$name"}}}, {"$limit": 1}] data = Person.objects.order_by("name").limit(2).aggregate(*pipeline) assert list(data) == [{"_id": p1.pk, "name": "ISABELLA LUANNA"}] if __name__ == "__main__": unittest.main()

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0.067797

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0.268854

8,685

253

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false

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0

6

1adf3c39deafb89f07781fdc98dc7da7d60bebef

223,391

py

Python

Tutorials/Tutorial 02 Uncertainty Estimation/toolbox_AEM.py

MaxRamgraber/Simple-AEM-Toolbox

27751103f5e504dd675ba6225f2aee9f85d7c85d

[ "MIT" ]

3

2021-06-16T12:27:22.000Z

2022-01-04T11:21:35.000Z

toolbox_AEM.py

MaxRamgraber/Simple-AEM-Toolbox

27751103f5e504dd675ba6225f2aee9f85d7c85d

[ "MIT" ]

null

toolbox_AEM.py

MaxRamgraber/Simple-AEM-Toolbox

27751103f5e504dd675ba6225f2aee9f85d7c85d

[ "MIT" ]

3

2021-06-17T11:20:20.000Z

2022-01-12T09:56:56.000Z

#import numpy as np #from mpmath import mpc,mpmathify,ellipfun,acos,ellipf #import matplotlib.pyplot as plt #plt.close('all') class Model: def __init__(self,head_offset=0,aquifer_type='unconfined',domain_center=0+0j, domain_radius=1,H = None,variables=[],priors=[],observations=[], likelihood_dictionary=None): """ This creates a model base object, to which we can add other elements. Parameters: head_offset - [scalar] : aquifer base elevation in [length units] aquifer_type - [string] : specifies the aquifer type; either 'confined', 'unconfined', or 'convertible' domain_center - [complex] : x + iy coordinate of center of the circular, physical domain in [length units]; can also be specified as a vector of length 2 domain_radius - [scalar] : radius of the circular domain in [length units] H - [scalar] : aquifer top elevation in [length units]; only used if the aquifer is 'confined' or 'convertible' If MCMC use is intended, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['head_offset','H']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys observations - [list] : list of dictionaries, one for each hydraulic head observations; each dictionary must contain a 'location' and a 'head key', with a complex and real number, respectively likelihood_dictionary - [dict] : dictionary with keys 'distribution' and any keywords required to specify the probability distribution; only required if this toolbox's logposterior function is used """ import numpy as np # Set potential scaling variables self.head_offset = head_offset self.aquifer_type = aquifer_type self.H = H # Set domain scaling variables self.domain_center = domain_center self.domain_radius = domain_radius if not np.isscalar(self.domain_center): self.domain_center = self.domain_center[0] + 1j*self.domain_center[1] # Check input for validity self.check_input() # Define a list for Analytic Elements self.elementlist = [] self.variables = variables self.priors = priors self.observations = observations self.likelihood_dictionary = likelihood_dictionary # This function scrapes the model and its elements for unknown variables, # then gives this instance three new variables: # self.num_params Number of unknown variables # self.params List of unknown variables # self.param_names List of names of unknown variables # self.priors List of prior dictionaries for unknow variables self.take_parameter_inventory() self.linear_solver = False # Pre-allocate the function matrix and parameter vector for the linear solver self.matrix_solver = [] self.params_vector = [] def update(self): import copy # self.take_parameter_inventory() # Unpack the parameter vector to their respective instances ----------- # Count through the parameters counter = -1 # Go through all unknown variables in the model class, if any for var in self.variables: # Replace the old variable counter += 1 exec("self.%s = copy.copy(self.params[counter])" % var) # Count through the parameters counter = -1 # Go through all elements for e in self.elementlist: # Go through all the element's unknown variables for var in e.variables: # Replace the old variable counter += 1 exec("e.%s = copy.copy(self.params[counter])" % var) # Update all other elements e.update() def check_input(self): # Check if aquifer type is valid if self.aquifer_type != 'confined' and \ self.aquifer_type != 'unconfined' and \ self.aquifer_type != 'convertible': raise Exception("aquifer_type must be either 'confined', 'unconfined', or 'convertible'.") if (self.aquifer_type == 'confined' or self.aquifer_type == 'convertible') and \ self.H is None: raise Exception("depth of confined layer 'H' must be specified if aquifer is confined or convertible.") def evaluate(self,z,mode='potential',derivatives='all',return_error_flag=False, suppress_warnings = False): import numpy as np import copy # Ensure that the evaluation points are complex z = self.complexify(z) if return_error_flag: error_flag = False self.update() # Inverse maps from disk to square, # Not inverse maps from square to disk # If there is at least one prescribed head element, prepare the linear solver if self.linear_solver: matrix,solution_vector = self.set_up_linear_system() # Find all elements which require the solver part_of_solver = [(isinstance(e, ElementHeadBoundary) or isinstance(e, ElementNoFlowBoundary) or isinstance(e, ElementInhomogeneity)) for e in self.elementlist] part_of_solver = [idx for idx,val in enumerate(part_of_solver) if val] not_part_of_solver = [i for i in np.arange(len(self.elementlist)) if i not in part_of_solver] # Solve the system of linear equations param_vec = np.linalg.solve(matrix,solution_vector) # Assign those parameters to each element counter = 0 for idx in part_of_solver: # Extract the current element... e = self.elementlist[idx] # ...and assign the correct strength e.strength = copy.copy(param_vec[counter:counter+e.segments]) # Then update the entry counter counter += e.segments # ===================================================================== # Now that the coefficients are set, evaluate the results # ===================================================================== if mode == 'potential': # Coordinates in canonical space are the start values z_canonical = copy.copy(z) z = np.zeros(z.shape,dtype=np.complex) # z *= 0 #-marked- for e in self.elementlist: z += e.evaluate(z_canonical) elif mode == 'gradient': # Coordinates in canonical space are the start values z_canonical = copy.copy(z) z = np.zeros(z.shape,dtype=np.complex) # z *= 0 #-marked- for e in self.elementlist: z += e.evaluate_gradient(z_canonical,derivatives=derivatives) elif mode == 'head': # Coordinates in canonical space are the start values z_canonical = copy.copy(z) z = np.zeros(z.shape,dtype=np.complex) # z *= 0 #-marked- for e in self.elementlist: z += e.evaluate(z_canonical) # First, get the base conductivity for e in self.elementlist: if isinstance(e, ElementMoebiusBase) or isinstance(e, ElementUniformBase): temp_k = np.ones(z_canonical.shape)*e.k for e in self.elementlist: if isinstance(e, ElementInhomogeneity): inside = e.are_points_inside_polygon(z_canonical) temp_k[inside] = e.k # The hydraulic potential can never be negative; set it to zero # (drying of an area) for any regions where it is negative, then # issue a warning if len(np.where(np.real(z) <= 0)[0]) > 0: if not suppress_warnings: print('WARNING: negative or zero potential detected at some evaluation points. Consider lowering head_offset or prescribing prior limits.') z[np.where(np.real(z) <= 0)] = 1j*np.imag(z[np.where(np.real(z) <= 0)]) if return_error_flag: error_flag = True if self.aquifer_type == 'confined': # Strack 1989, Eq. 8.12 z = (np.real(z) + 0.5*temp_k*self.H**2)/(temp_k*self.H) + \ 1j*np.imag(z) elif self.aquifer_type == 'unconfined': # Strack 1989, Eq. 8.13 z = np.sqrt(2*(np.real(z))/temp_k) + 1j*np.imag(z) elif self.aquifer_type == 'convertible': # Decide which equation to use for what points # confined: Strack 1989, Eq. 8.12 # unconfined: Strack 1989, Eq. 8.13 limit = 0.5*temp_k/self.H**2 index_conf = np.where(np.real(z) >= limit)[0] index_unconf = np.where(np.real(z) < limit)[0] # Handle the confined part z[index_conf] = \ (np.real(z[index_conf]) + 0.5*temp_k[index_conf]*self.H**2)/(temp_k[index_conf]*self.H) + \ 1j*np.imag(z[index_conf]) # Handle the unconfined part z[index_unconf] = \ np.sqrt(2*(np.real(z[index_unconf]))/temp_k[index_unconf]) + 1j*np.imag(z[index_unconf]) # Offset the head z += self.head_offset else: raise Exception("Mode must be either 'potential', 'gradient', or 'head'.") if return_error_flag: return z,error_flag else: return z def set_up_linear_system(self): """ This function sets up the system of linear equations required to solve for the unknown coefficients of prescribed head boundaries, no-flow boundaries, and polygonal inhomogeneities. """ import numpy as np import copy # Find all elements which require the solver # First, find all elements which are either Line Sinks, Doublets, or Inhomogeneities part_of_solver = [(isinstance(e, ElementHeadBoundary) or isinstance(e, ElementNoFlowBoundary) or isinstance(e, ElementInhomogeneity)) for e in self.elementlist] # Only keep the elements which must be part of the linear system... part_of_solver = [idx for idx,val in enumerate(part_of_solver) if val] # ...and prepare a second set of indices for its complement not_part_of_solver = [i for i in np.arange(len(self.elementlist)) if i not in part_of_solver] # These elements invariably consist of segments - find out how many there are in total num_segments = np.sum([self.elementlist[idx].segments for idx in part_of_solver]) # ===================================================================== # Now create the matrix # ===================================================================== # Pre-allocate arrays for the linear solver matrix = np.zeros((num_segments,num_segments)) # The counter will keep track at what row we are row = 0 # Go through all elements for i in part_of_solver: # Find the corresponding element e = self.elementlist[i] # We need a second counter for the columns col = 0 # e is the element we are currently looking at - the row -, now we # must go through all other elements which are part of the solver # and check what they contribute to the control points of this element for i2 in part_of_solver: # Find the corresponding element e2 = self.elementlist[i2] # If the row element is a HeadLineSink, we must extract potentials if isinstance(e, ElementHeadBoundary): # Evaluate the contributions of this element to the control points if e != e2: block = e2.evaluate( z = e.zc, detailed = True, override_parameters = True).T else: block = e2.evaluate( z = e.zc, detailed = True, override_parameters = True, evaluate_self = True).T elif isinstance(e, ElementNoFlowBoundary): # Evaluate the contributions of this element to the control points block = e2.evaluate_gradient( z = e.zc, detailed = True, derivatives = 'phi', override_parameters = True).T # Project the partial derivatives onto the normal vector # The projection is a->b = <a,b>/||b||^2*b # Let's try it with the inner product instead # The normal vector is already normalized # We should have as many normal vectors as we have control points # Go through them all, and project each gradient onto the normal vector for idx,nv in enumerate(e.segment_nvec): # Calculate the inner product between the returned partial # derivatives and the segment's normal vector block[idx,:] = np.inner( np.column_stack(( np.real(block[idx,:]), np.imag(block[idx,:]) )), np.asarray([np.real(nv),np.imag(nv)]).T )[:,0] elif isinstance(e, ElementInhomogeneity): # If this inhomogeneity evaluates itself if i == i2: # Retrieve own matrix contribution block = copy.copy(e2.block) # This contribution is incomplete, subtract A_star from # its diagonal # Prepare a vector of outside conductivities; all are # the background conductivity by default for e3 in self.elementlist: if isinstance(e3, ElementMoebiusBase) or isinstance(e3, ElementUniformBase): A_star = np.ones(e2.zc.shape)*e3.k/(e2.k - e3.k) # Get add matrix addmat = np.identity(block.shape[0]) np.fill_diagonal(addmat,A_star) # Subtract it from the retrieved block block -= addmat else: # Evaluate the contributions of this element to the control points block = e2.evaluate( z = e.zc, detailed = True, override_parameters = True).T # Write this block into the matrix matrix[row:row+e.segments,col:col+e2.segments] = copy.copy(np.real(block)) # Update the column counter col += e2.segments # Update the row counter row += e.segments # ===================================================================== # Now create the solution_vector # ===================================================================== # Pre-allocate spac efor the solution vector solution_vector = np.zeros(num_segments) # The counter will keep track at what row we are counter = 0 # Go through all elements for i in part_of_solver: # Find the corresponding element e = self.elementlist[i] # If the element is a HeadLineSink, we must assign the difference # between the head target and the background contributions if isinstance(e, ElementHeadBoundary): # Step 1: Assign the head targets solution_vector[counter:counter+e.segments] = \ copy.copy(e.phi_target) # solution_vector[counter:counter+e.segments] = \ # copy.copy(e.head_target) # # Step 2: Background potential -------------------------------- # solution_vector[counter:counter+e.segments] -= \ # np.real(self.evaluate(e.zc)) # Step 3: All elements ---------------------------------------- for idx in not_part_of_solver: solution_vector[counter:counter+e.segments] -= \ np.real(self.elementlist[idx].evaluate(e.zc)) # If the element is a no-flow boundary, we must assign the difference # between the head target and the background contributions if isinstance(e, ElementNoFlowBoundary): # # Step 1: Background gradient --------------------------------- # temp = self.evaluate_gradient(e.zc,derivatives='phi') # Step 2: Gradients from all elements ------------------------- temp = np.zeros(e.zc.shape,dtype=np.complex) for idx in not_part_of_solver: temp += \ self.elementlist[idx].evaluate_gradient(e.zc,derivatives='phi') # Step 3: Project gradients onto normal vector ---------------- for ix,nv in enumerate(e.segment_nvec): solution_vector[counter+ix] = \ -np.inner( np.asarray([np.real(nv),np.imag(nv)])[:,0], np.asarray([np.real(temp[ix]),np.imag(temp[ix])]) ) # If the element is an Inhomogeneity, we must simply assign the potentials # induced by other elements if isinstance(e, ElementInhomogeneity): # # Step 1: Background potential -------------------------------- # solution_vector[counter:counter+e.segments] -= \ # np.real(self.evaluate(e.zc)) # Step 2: All elements ---------------------------------------- for idx in not_part_of_solver: solution_vector[counter:counter+e.segments] -= \ np.real(self.elementlist[idx].evaluate(e.zc)) # Update the counter counter += e.segments self.matrix = matrix self.solvec = solution_vector return matrix, solution_vector def gradients(self,z): import numpy as np # Extract the gradients and return them grad = np.zeros(z.shape,dtype=np.complex) for e in self.elementlist: grad += e.evaluate_gradient(z) return grad def take_parameter_inventory(self): # Find the number of unknown variables self.num_params = 0 self.params = [] self.param_names = [] # First see if they are any unknown variables in the main model if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.num_params += 1 exec("self.params += [self.%s]" % var) if 'name' in list(self.priors[idx].keys()): self.param_names += [self.priors[idx]['name']] else: self.param_names += ['unknown'] # Check if the prior matches the number of parameters if len(self.priors) != self.num_params: raise Exception('Number of priors must match number of parameters. Number of parameters:'+str(self.num_params)+' / Number of priors:'+str(len(self.priors))) def logprior(self,params,priors,verbose=False): import numpy as np import scipy.stats import copy import math # from toolbox_AEM import ElementMoebiusBase,ElementMoebiusOverlay def check_limits(params,var_dict): reject = None import numpy as np # Check if any limits are prescribed if 'limits' in list(var_dict.keys()): if var_dict['limits'][0] is not None and type(var_dict['limits'][0]) != str: if np.isscalar(params): if params <= var_dict['limits'][0]: reject = True else: for entry in params: if entry <= var_dict['limits'][0]: reject = True if var_dict['limits'][1] is not None and type(var_dict['limits'][1]) != str: if np.isscalar(params): if params >= var_dict['limits'][1]: reject = True else: for entry in params: if entry >= var_dict['limits'][1]: reject = True var_dict.pop('limits') return reject,var_dict # Find the base element MoebiusBase_index = None for idx,e in enumerate(self.elementlist): if isinstance(e, ElementMoebiusBase) and 'r' in e.variables: MoebiusBase_index = idx # Find any Möbius overlay element MoebiusOverlay_index = None for idx,e in enumerate(self.elementlist): if isinstance(e, ElementMoebiusOverlay) and 'r' in e.variables: if MoebiusOverlay_index is None: MoebiusOverlay_index = [idx] else: MoebiusOverlay_index += [idx] logprior = [] reject = False if MoebiusBase_index is not None: if self.elementlist[MoebiusBase_index].are_points_clockwise(): # print('base clockwise') reject = True # Check if the control points fulfill the minimum angular spacing r = np.degrees(self.elementlist[MoebiusBase_index].r) angular_limit = np.degrees(self.elementlist[MoebiusBase_index].angular_limit) if np.abs((r[0]-r[1] + 180) % 360 - 180) < angular_limit or \ np.abs((r[1]-r[2] + 180) % 360 - 180) < angular_limit or \ np.abs((r[2]-r[0] + 180) % 360 - 180) < angular_limit: # print('base angular limit violation') reject = True if MoebiusOverlay_index is not None: for idx in MoebiusOverlay_index: if self.elementlist[idx].are_points_clockwise(): reject = True # Check if the control points fulfill the minimum angular spacing r = np.degrees(self.elementlist[idx].r) angular_limit = np.degrees(self.elementlist[idx].angular_limit) if np.abs((r[0]-r[1] + 180) % 360 - 180) < angular_limit or \ np.abs((r[1]-r[2] + 180) % 360 - 180) < angular_limit or \ np.abs((r[2]-r[0] + 180) % 360 - 180) < angular_limit: reject = True # if not reject: # print('WOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOP') for var in range(len(priors)): # If the logprior is to be rejected due to a violation of limits, # break the loop if reject: break # Create a copy of this variable's prior dictionary var_dict = copy.copy(priors[var]) # Check if the user specified any converter converter_used = False if 'converter' in list(var_dict.keys()) and 'deconverter' in list(var_dict.keys()): # Activate the logarithmic boolean and save the base converter_used = True # Extract the converter and the deconverter converter = var_dict['converter'] deconverter = var_dict['deconverter'] # Remove the keys from the dictionary var_dict.pop('converter') var_dict.pop('deconverter') # And convert the variable params[var] = converter(params[var]) elif 'converter' in list(var_dict.keys()) or 'deconverter' in list(var_dict.keys()): raise Exception('Both a converter and a deconverter must be specified if either are used for a variable.') # Remove size if it was specified if 'size' in list(var_dict.keys()): del var_dict['size'] # This prior is a univariate normal distribution if var_dict['distribution'] == 'norm' or var_dict['distribution'] == 'normal': # Remove the variable name from the dictionary var_dict.pop('distribution') if 'name' in list(var_dict.keys()): var_dict.pop('name') temp, var_dict = check_limits(params = params[var], var_dict = var_dict) if temp is not None: reject = True # Check if any limits are prescribed # if 'limits' in list(var_dict.keys()): # if var_dict['limits'][0] is not None and type(var_dict['limits'][0]) != str: # if params[var] < var_dict['limits'][0]: # reject = True # if var_dict['limits'][1] is not None and type(var_dict['limits'][1]) != str: # if params[var] > var_dict['limits'][1]: # reject = True # var_dict.pop('limits') # Add to the logprior logprior += [np.sum(scipy.stats.norm.logpdf(x=params[var],**var_dict))] # This prior is a multivariate normal distribution elif var_dict['distribution'] == 'multivariate_normal' or var_dict['distribution'] == 'multivariate normal': # Remove the variable name from the dictionary var_dict.pop('distribution') if 'name' in list(var_dict.keys()): var_dict.pop('name') temp, var_dict = check_limits(params = params[var], var_dict = var_dict) if temp is not None: reject = True # # Check if any limits are prescribed # if 'limits' in list(var_dict.keys()): # if var_dict['limits'][0] is not None and type(var_dict['limits'][0]) != str: # if any(params[var] < var_dict['limits'][0]): # reject = True # if var_dict['limits'][1] is not None and type(var_dict['limits'][1]) != str: # if any(params[var] > var_dict['limits'][1]): # reject = True # var_dict.pop('limits') # Add to the logprior logprior += [np.sum(scipy.stats.multivariate_normal.logpdf(x=params[var],**var_dict))] # This prior is a beta distribution elif var_dict['distribution'] == 'beta': # Remove the variable name from the dictionary var_dict.pop('distribution') if 'name' in list(var_dict.keys()): var_dict.pop('name') # A beta distribution has natural limits; if none are prescribed, add them if 'limits' not in list(var_dict.keys()): var_dict['limits'] = [0,1] temp, var_dict = check_limits(params = params[var], var_dict = var_dict) if temp is not None: reject = True # # Check if any limits are prescribed # if 'limits' in list(var_dict.keys()): # if var_dict['limits'][0] is not None and type(var_dict['limits'][0]) != str: # if params[var] < var_dict['limits'][0]: # reject = True # if var_dict['limits'][1] is not None and type(var_dict['limits'][1]) != str: # if params[var] > var_dict['limits'][1]: # reject = True # var_dict.pop('limits') # Add to the logprior logprior += [np.sum(scipy.stats.beta.logpdf(x=params[var],**var_dict))] # This prior is an exponential distribution elif var_dict['distribution'] == 'expon' or var_dict['distribution'] == 'exponential': # SPECIAL EXCEPTION ------------------------------------------- # The argument of the exponential distribution is considered as # its absolute value to permit evaluation of negative values. # Checking for limits happens before this, so negative limits # can be applied. # ------------------------------------------------------------- # Remove the variable name from the dictionary var_dict.pop('distribution') if 'name' in list(var_dict.keys()): var_dict.pop('name') # An exponential distribution has natural limits; if none are prescribed, add them if 'limits' not in list(var_dict.keys()): var_dict['limits'] = [0,None] temp, var_dict = check_limits(params = params[var], var_dict = var_dict) if temp is not None: reject = True # # Check if any limits are prescribed # if 'limits' in list(var_dict.keys()): # if var_dict['limits'][0] is not None and type(var_dict['limits'][0]) != str: # if params[var] < var_dict['limits'][0]: # reject = True # if var_dict['limits'][1] is not None and type(var_dict['limits'][1]) != str: # if params[var] > var_dict['limits'][1]: # reject = True # var_dict.pop('limits') # Add to the logprior logprior += [np.sum(scipy.stats.expon.logpdf(x=np.abs(params[var]),**var_dict))] # This prior is a von Mises distribution elif var_dict['distribution'] == 'unif' or var_dict['distribution'] == 'uniform': # Remove the variable name from the dictionary var_dict.pop('distribution') if 'name' in list(var_dict.keys()): var_dict.pop('name') temp, var_dict = check_limits(params = params[var], var_dict = var_dict) if temp is not None: reject = True # Add to the logprior logprior += [np.sum(scipy.stats.uniform.logpdf(x=params[var],**var_dict))] # This prior is a von Mises distribution elif var_dict['distribution'] == 'vonmises' or var_dict['distribution'] == 'von mises' or var_dict['distribution'] == 'von Mises': # Remove the variable name from the dictionary var_dict.pop('distribution') if 'name' in list(var_dict.keys()): var_dict.pop('name') temp, var_dict = check_limits(params = params[var], var_dict = var_dict) if temp is not None: reject = True # # Check if any limits are prescribed # if 'limits' in list(var_dict.keys()): # if var_dict['limits'][0] is not None and type(var_dict['limits'][0]) != str: # if params[var] < var_dict['limits'][0]: # reject = True # if var_dict['limits'][1] is not None and type(var_dict['limits'][1]) != str: # if params[var] > var_dict['limits'][1]: # reject = True # var_dict.pop('limits') # Add to the logprior logprior += [np.sum(scipy.stats.vonmises.logpdf(x=params[var],**var_dict))] else: raise Exception("Specified distribution name '" + str(var_dict['distribution']) + \ "' not understood. Valid distribution names are: 'norm', " + \ "'multivariate_normal', 'beta', 'expon', or 'vonmises'") # If a converter are used, deconvert the variables if converter_used: params[var] = deconverter(params[var]) # Check if variables have been prescribed as limits counter = -1 for idx,e in enumerate(self.elementlist): # If the logprior is to be rejected due to a violation of limits, # break the loop if reject: break # At what value was the counter at the start of this element counter_elementstart = counter # Check the priors list for prior in e.priors: # Increment the counter variable counter += 1 # Check if this prior entry has prescribed limits if 'limits' in prior: # Check if the lower limit is a string (a variable) if type(prior['limits'][0]) == str: # Check where this variable is, store it limit = None for idx,var in enumerate(e.variables): if prior['limits'][0] == var: limit = params[counter_elementstart+1+idx] if limit is None: raise Exception("variable '"+prior['limits'][0]+"' marked as limit not part of the variables of element "+str(e)) if params[counter] < limit: reject = True # Check if the upper limit is a string (a variable) if type(prior['limits'][1]) == str: # Check where this variable is, store it limit = None for idx,var in enumerate(e.variables): if prior['limits'][1] == var: limit = params[counter_elementstart+1+idx] if limit is None: raise Exception("variable '"+prior['limits'][1]+"' marked as limit not part of the variables of element "+str(e)) if params[counter] > limit: reject = True # Return the logprior only if the sample isn't rejected if not reject: res = logprior else: res = None if verbose: print('Logprior calculation rejected because at least one variable violated prescribed limits.') return res def loglikelihood(self,observations,likelihood_dictionary,predictions = None): import numpy as np import scipy.stats import copy loglikelihood = None obs_dict = copy.deepcopy(likelihood_dictionary) # If no predictions have been provided, map forward if predictions is None: # Get the well positions z = [] for entry in observations: z += [copy.copy(entry['location'])] z = np.asarray(z) predictions,error_flag = copy.copy(np.real(self.evaluate( z, mode='head', return_error_flag=True, suppress_warnings=True))) # If any of the predictions is NaN, raise an error flag if not error_flag and np.isnan(predictions).any(): error_flag = True else: error_flag = False predictions = np.asarray(predictions) # Create a vector of observations obs_vec = [] for entry in observations: obs_vec += [copy.copy(entry['head'])] obs_vec = np.asarray(obs_vec) # Get the prediction residuals residuals = obs_vec - predictions # This prior is a von Mises distribution if obs_dict['distribution'] == 'norm' or obs_dict['distribution'] == 'normal': # Remove superfluous keys obs_dict.pop('distribution') if 'name' in list(obs_dict.keys()): obs_dict.pop('name') if 'loc' in list(obs_dict.keys()): print("Warning: 'loc' specified for the loglikeligood pdf. This value is overwritten by the predictions.") obs_dict.pop('loc') # Add to the logprior loglikelihood = scipy.stats.norm.logpdf(x=obs_vec,loc=predictions,**obs_dict) # This prior is a von Mises distribution elif obs_dict['distribution'] == 'multivariate_normal' or obs_dict['distribution'] == 'multivariate normal': # Remove superfluous keys obs_dict.pop('distribution') if 'name' in list(obs_dict.keys()): obs_dict.pop('name') if 'mean' in list(obs_dict.keys()): print("Warning: 'mean' specified for the loglikeligood pdf. This value is overwritten by the predictions.") obs_dict.pop('mean') # Add to the logprior loglikelihood = np.sum(scipy.stats.multivariate_normal.logpdf(x=obs_vec,mean=predictions,**obs_dict)) if error_flag: loglikelihood = None return loglikelihood, residuals def logposterior(self,params,likelihood_dictionary=None,priors=None, observations=None,verbose=False,predictions=None,return_residuals=False): """ This function returns the unnormalized logposterior of the model, intended for use in any external inference routines. If the proposal violates any parameter limits, or the model detects instabilities, the logposterior will return None instead of a numerical value. Parameters: params - [list] : list of parameters to be evaluated, corresponding to the list entries in 'variables' likelihood_dictionary - [dict] : dictionary with keys 'distribution' and any keywords required to specify the probability distribution; only required if this toolbox's logposterior function is used priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys observations - [list] : list of dictionaries, one for each hydraulic head observations; each dictionary must contain a 'location' and a 'head key', with a complex and real number, respectively verbose - [boolean] : flag passed on to the logprior function, controlling whether additional information is printed or not predictions - [list] : optional list of predictions at the observation locations; predictions are simulated for the defined 'params' if not specified return_residuals- [boolean] : flag for whether only the logposterior is returned (False), or whether both the logposterior and the observation residuals (True) is returned """ # Fetch any unspecified variables if likelihood_dictionary is None: likelihood_dictionary = self.likelihood_dictionary if self.likelihood_dictionary is None: raise Exception('Likelihood_dictionary must be specified to evaluate the logposterior density.') if priors is None: priors = self.priors if self.priors == []: raise Exception('No priors are specified. Priors are required to evaluate the logposterior density.') if observations is None: observations = self.observations if self.observations == []: raise Exception('No observations are specified. Observations are required to evaluate the likelihood for the logposterior density.') # Evaluate the logprior logpri = self.logprior( params = params, priors = priors, verbose = False) # If the logprior was evaluated successfully, evaluate the loglikelihood if logpri is not None: loglik, residuals = self.loglikelihood( observations = observations, likelihood_dictionary = likelihood_dictionary, predictions = predictions) else: loglik = None residuals = None # If both the logprior and loglikelihood were valid, calculate the # unnormalized logposterior if logpri is not None and loglik is not None: logpost = logpri + loglik else: logpost = None # Also return the loglikelihood's residuals, if requested if return_residuals: return logpost, residuals else: return logpost def complexify(self,z): """ This function takes the provided set of points and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,color='xkcd:grey'): """ This function plots all elements in the elementlist """ import matplotlib.pyplot as plt import numpy as np # Plot the model domain plt.plot( np.cos(np.linspace(0,2*np.pi,361))*self.domain_radius + np.real(self.domain_center), np.sin(np.linspace(0,2*np.pi,361))*self.domain_radius + np.imag(self.domain_center), color=color) for e in self.elementlist: e.plot() def trace_gradient(self,p,direction='upgradient',stepsize=1,well_snap_distance = 1): """ This function takes a regular grid defined by X and Y meshgrid arrays and follows the gradient of a corresponding Z array starting from a point p. p : starting point for gradient tracing XY : array of points between which the gradient tracing is interpolated Z : hydraulic head at XY direction : whether we trace 'upgradient' or 'downgradient' stepsize : size of successive steps for gradient tracing visualize : plots the results, if desired Del : Delaunay triangulation, calculated if missing triang : triangulation, calculated if missing thresh : side length threshold above which vertices are rejected """ if not direction == 'upgradient' and not direction == 'downgradient': raise Exception("direction must be either 'upgradient' or 'downgradient'.") import scipy.spatial import numpy as np import matplotlib.pyplot as plt import shapely.geometry ring = np.column_stack(( np.cos(np.linspace(0,2*np.pi,361)), np.sin(np.linspace(0,2*np.pi,361)) )) ring *= self.domain_radius ring += np.asarray([np.real(self.domain_center),np.imag(self.domain_center)]) # First, find all elements which could be stoppers stoppers = [] stoppers.append(shapely.geometry.LineString(ring)) for e in self.elementlist: if isinstance(e, ElementHeadBoundary): # Head Boundaries are valid end points stoppers.append(shapely.geometry.LineString(e.line[:,:2])) if isinstance(e, ElementWell): # Wells are valid end points stoppers.append(shapely.geometry.Point(np.asarray([np.real(e.zc),np.imag(e.zc)]))) if isinstance(e, ElementLineSink): # Line Sinks are valid end points stoppers.append(shapely.geometry.LineString(e.line[:,:2])) if isinstance(e, ElementNoFlowBoundary): # No-flow Boundaries are valid end points stoppers.append(shapely.geometry.LineString(e.line[:,:2])) def gradient(p1,p2,p3,z1,z2,z3): area = abs((p1[0]*(p2[1]-p3[1])+p2[0]*(p3[1]-p1[1])+p3[0]*(p1[1]-p2[1]))/2) M = np.asarray( [[p2[1]-p3[1], p3[1]-p1[1], p1[1]-p2[1]], [p3[0]-p2[0], p1[0]-p3[0], p2[0]-p1[0]]]) U = np.asarray([z1,z2,z3]).reshape((3,1)) # Solution based on http://pers.ge.imati.cnr.it/livesu/papers/MLP18/MLP18.pdf Equation 1 return np.dot(M,U)[:,0]/(2*area) # Check if the start point is complex, if yes, turn it into a real vector if np.iscomplex(p).any(): p = np.asarray([np.real(p),np.imag(p)]) # Depending on the direction, add a gradient if direction == 'upgradient': stepsize = stepsize else: stepsize = -stepsize # Set the repeater boolean to True repeater = True # Re-arrange the starting point into an array points = np.asarray(p).copy().reshape((1,2)) # """ # Get three points testpoints = np.asarray([ points[-1,0] + 1j*points[-1,1], points[-1,0] + stepsize/100 + 1j*points[-1,1], points[-1,0] + 1j*points[-1,1] + 1j*stepsize/100]) testpoints = np.real(self.evaluate(testpoints,mode='head')) grad = np.asarray([ testpoints[1]-testpoints[0], testpoints[2]-testpoints[0]])/stepsize*100 grad = grad/np.linalg.norm(grad) # """ # grad = self.evaluate( # z = points, # mode = 'gradient', # derivatives = 'phi') # # grad = np.asarray([np.real(grad), np.imag(grad)]) # grad = grad/np.linalg.norm(grad) # And save the result to the points array points = np.row_stack(( points.copy(), points + grad*stepsize)) # Now start the while loop, trace until the end while repeater: # The last point in the array is the starting point p = points[-1,:] # """ testpoints = np.asarray([ points[-1,0] + 1j*points[-1,1], points[-1,0] + stepsize/100 + 1j*points[-1,1], points[-1,0] + 1j*points[-1,1] + 1j*stepsize/100]) testpoints = np.real(self.evaluate(testpoints,mode='head')) grad = np.asarray([ testpoints[1]-testpoints[0], testpoints[2]-testpoints[0]])/stepsize*100 grad = grad/np.linalg.norm(grad) # """ # grad = self.evaluate( # z = points[-1,:], # mode = 'gradient', # derivatives = 'phi') # # grad = np.asarray([np.real(grad), np.imag(grad)]) # grad = grad/np.linalg.norm(grad) # And append the next step to the list points = np.row_stack(( points, points[-1,:] + grad*stepsize)) line = shapely.geometry.LineString(points[-2:,:]) # Check for stopping elements for stop in stoppers: # If this stopper is a well, check for distance if stop.type == 'Point': point = shapely.geometry.Point(points[-1,:]) if point.distance(stop) <= well_snap_distance: points[-1,:] = np.asarray(point.xy)[:,0] repeater = False # Else, we can check for intersection else: if line.intersects(stop): if line.intersection(stop).type == 'Point': points[-1,:] = np.asarray(line.intersection(stop).xy)[:,0] repeater = False else: print(type(line.intersection(stop))) print((type(line.intersection(stop)) == 'Point')) points[-1,:] = np.asarray(line.intersection(stop)[0].xy)[:,0] repeater = False # # Check for oscillation # p2p = points[-3,:]-points[-2,:] # p1p = points[-2,:]-points[-1,:] # if np.inner(p1p,p2p) < 0: # # The trace direction has change by more than 90 degrees, i.e. # # turned back; stop iterating # points = points[:-1,:] # repeater = False return points #%% class ElementMoebiusBase: def __init__(self,model,r=None,a=None,b=None,c=None,d=None,head_min=0,head_max=1, k=1,variables=[],priors=[],proposals=[],angular_limit=1,use_SC=True): """ This implements the Möbius base flow element, which can induce curving, converging, or diverging regional flow. Parameters: model - [object] : the model object to which this element is added r - [vector] : rotations of the three Moebius control points in counter-clockwise radians from East a - [scalar] : coefficient of the Moebius transformation; calculated from r if not specified b - [scalar] : coefficient of the Moebius transformation; calculated from r if not specified c - [scalar] : coefficient of the Moebius transformation; calculated from r if not specified d - [scalar] : coefficient of the Moebius transformation; calculated from r if not specified head_min - [scalar] : minimum hydraulic head (mapped to -1 in the unit square) head_max - [scalar] : maximum hydraulic head (mapped to +1 in the unit square) k - [scalar] : background hydraulic conductivity in canonical units (e.g., 1E-4 [length]/[time]) use_SC - [boolean] : a flag to denote whether the Möbius base uses the Schwarz-Christoffel transformation from the unit square to the unit disk; changes to this flag affect the flow field, particularly near the edges If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['r','phi_min']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys angular_limit - [scalar] : a limit which prevents control points (A,B,C, or D) getting closer to each other than the specified value in radians; this acts as protection against improbable or unrealistic flow fields induced by the Möbius transformation """ import numpy as np # Append the base to the elementlist self.model = model model.elementlist.append(self) # Define an angular limit. This is designed to keep the Möbius control # points from getting arbitrarily close to each other; defined in radians self.angular_limit = angular_limit # Get the Schwarz-Christoffel flag self.use_SC = use_SC # Set Moebius values self.r = r self.a = a self.b = b self.c = c self.d = d # Set potential scaling variables self.head_min = head_min self.head_max = head_max # Assign the hydraulic conductivity of the base model self.k = k # The model requires the base flow in terms of hydraulic potential (phi) # The function head_to_potential extracts the following variables: # phi_min hydraulic potential corresponding to head_min # phi_max hydraulic potential corresponding to head_max self.head_to_potential() # Check input for validity self.check_input() # Define the original control points in the Moebius base disk self.z0 = np.asarray( [np.complex(np.cos(-0.25*np.pi),np.sin(-0.25*np.pi)), np.complex(np.cos(0.25*np.pi),np.sin(0.25*np.pi)), np.complex(np.cos(0.75*np.pi),np.sin(0.75*np.pi))]) # If only rotation is specified, get the Moebius coefficients if self.r is not None and (self.a is None and self.b is None and \ self.c is None and self.d is None): # Find Moebius coefficients self.find_moebius_coefficients() self.variables = variables self.priors = priors self.proposals = proposals self.Ke = 1.854 if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.variables += [var] self.model.priors += [self.priors[idx]] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): # If this model is updated, make sure to repeat any initialization # Find Moebius coefficients self.find_moebius_coefficients() # Flip h_min and h_max, if necessary if self.head_min > self.head_max: temp = self.head_min self.head_min = self.head_max self.head_min = temp # Update potential self.head_to_potential() def check_input(self): import numpy as np # See if either control point rotations or a full set of Moebius # coefficients are specified if self.r is None and (self.a is None or self.b is None or \ self.c is None or self.d is None): raise Exception('Either control point rotations r or Moebius coefficients a, b, c, and d must be specified.') # Check if phi_min is smaller than phi_offset, switch if necessary if self.head_min > self.head_max: raise Exception('Minimum potential phi_min is larger than maximum potential phi_max.') # Check if the control points fulfill the minimum angular spacing r = np.degrees(self.r) if np.abs((r[0]-r[1] + 180) % 360 - 180) < self.angular_limit or \ np.abs((r[1]-r[2] + 180) % 360 - 180) < self.angular_limit or \ np.abs((r[2]-r[0] + 180) % 360 - 180) < self.angular_limit: raise Exception('Control points '+str(self.r)+' are too close to each other. Define different control points or adjust the angular limit: '+str(self.angular_limit)) def evaluate(self,z): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Coordinates in canonical space are the start values z_canonical = copy.copy(z) # Scale the canonical disk to unity canonical disk z = (z - self.model.domain_center)/self.model.domain_radius # Map from canonical disk to Möbius base z = self.moebius(z,inverse=True) # Map from Möbius base to unit square if self.use_SC: z = self.disk_to_square(z) # Rescale the complex potential z = (z+1)/2 * (self.phi_max-self.phi_min) + self.phi_min return z def evaluate_gradient(self,z,derivatives = 'all'): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Map from the canonical disk to Möbius base z_mb = (copy.copy(z) - self.model.domain_center)/self.model.domain_radius # dz_mb / dz_c grad_4 = 1/self.model.domain_radius # Map from Möbius base to unit disk z_ud = self.moebius(copy.copy(z_mb),inverse=True) # dz_ud / dz_mb grad_3 = (self.a*self.d-self.b*self.c)/(self.c*z_mb-self.a)**2 if self.use_SC: grad_2 = 2/(self.Ke*np.sqrt(z_ud**4+1)) grad_1 = (self.phi_max-self.phi_min)/2 if self.use_SC: grad = grad_1*grad_2*grad_3*grad_4 else: grad = grad_1*grad_3*grad_4 if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def complex_integral(self,func,a,b): """ This implements the Gauss-Kronrod integration for complex-valued functions. We use this to evaluate the Legendre incomplete elliptic integral of the first kind, since it is about ten times as fast as using mpmath's ellipf function. Since this integration is a major computational bottleneck of this function, we stick with this approach. The equations below are adapted from: https://stackoverflow.com/questions/5965583/use-scipy-integrate-quad-to-integrate-complex-numbers """ import scipy from scipy import array def quad_routine(func, a, b, x_list, w_list): c_1 = (b-a)/2.0 c_2 = (b+a)/2.0 eval_points = map(lambda x: c_1*x+c_2, x_list) func_evals = list(map(func, eval_points)) # Python 3: make a list here return c_1 * sum(array(func_evals) * array(w_list)) def quad_gauss_7(func, a, b): x_gauss = [-0.949107912342759, -0.741531185599394, -0.405845151377397, 0, 0.405845151377397, 0.741531185599394, 0.949107912342759] w_gauss = array([0.129484966168870, 0.279705391489277, 0.381830050505119, 0.417959183673469, 0.381830050505119, 0.279705391489277,0.129484966168870]) return quad_routine(func,a,b,x_gauss, w_gauss) def quad_kronrod_15(func, a, b): x_kr = [-0.991455371120813,-0.949107912342759, -0.864864423359769, -0.741531185599394, -0.586087235467691,-0.405845151377397, -0.207784955007898, 0.0, 0.207784955007898,0.405845151377397, 0.586087235467691, 0.741531185599394, 0.864864423359769, 0.949107912342759, 0.991455371120813] w_kr = [0.022935322010529, 0.063092092629979, 0.104790010322250, 0.140653259715525, 0.169004726639267, 0.190350578064785, 0.204432940075298, 0.209482141084728, 0.204432940075298, 0.190350578064785, 0.169004726639267, 0.140653259715525, 0.104790010322250, 0.063092092629979, 0.022935322010529] return quad_routine(func,a,b,x_kr, w_kr) class Memorize: # Python 3: no need to inherit from object def __init__(self, func): self.func = func self.eval_points = {} def __call__(self, *args): if args not in self.eval_points: self.eval_points[args] = self.func(*args) return self.eval_points[args] def quad(func,a,b): ''' Output is the 15 point estimate; and the estimated error ''' func = Memorize(func) # Memorize function to skip repeated function calls. g7 = quad_gauss_7(func,a,b) k15 = quad_kronrod_15(func,a,b) # I don't have much faith in this error estimate taken from wikipedia # without incorporating how it should scale with changing limits return [k15, (200*scipy.absolute(g7-k15))**1.5] return quad(func,a,b) def angle_to_unit_circle(self): import numpy as np # Angle must be provided in radians, counter-clockwise from 3 o'clock return np.cos(self.r)+1j*np.sin(self.r) def find_moebius_coefficients(self): import numpy as np # Find the images of the z0 control points w0 = self.angle_to_unit_circle() # Then calculate the four parameters for the corresponding Möbius map self.a = np.linalg.det(np.asarray( [[self.z0[0]*w0[0], w0[0], 1], [self.z0[1]*w0[1], w0[1], 1], [self.z0[2]*w0[2], w0[2], 1]])) self.b = np.linalg.det(np.asarray( [[self.z0[0]*w0[0], self.z0[0], w0[0]], [self.z0[1]*w0[1], self.z0[1], w0[1]], [self.z0[2]*w0[2], self.z0[2], w0[2]]])) self.c = np.linalg.det(np.asarray( [[self.z0[0], w0[0], 1], [self.z0[1], w0[1], 1], [self.z0[2], w0[2], 1]])) self.d = np.linalg.det(np.asarray( [[self.z0[0]*w0[0], self.z0[0], 1], [self.z0[1]*w0[1], self.z0[1], 1], [self.z0[2]*w0[2], self.z0[2], 1]])) return def moebius(self,z,inverse=False): if not inverse: z = (self.a*z+self.b)/(self.c*z+self.d) else: z = (-self.d*z+self.b)/(self.c*z-self.a) return z def square_to_disk(self,z,k='default'): import numpy as np from mpmath import mpc,mpmathify,ellipfun if k == 'default': k = 1/mpmathify(np.sqrt(2)) Ke = 1.854 cn = ellipfun('cn') if type(z) is complex: z = np.asarray([z]) zf = np.ndarray.flatten(z) w = np.zeros(zf.shape)*1j pre_factor = mpc(1,-1)/mpmathify(np.sqrt(2)) mid_factor = Ke*(mpc(1,1)/2) for idx,entry in enumerate(zf): # Go through all complex numbers # Calculate result temp = pre_factor*cn( u = mid_factor*entry-Ke, k = k) # Then place it into the array w[idx] = np.complex(temp.real,temp.imag) # Now reshape the array back to its original shape z = w.reshape(z.shape).copy() return z def disk_to_square(self,z,k='default'): import numpy as np Ke = 1.854 if type(z) is complex: z = np.asarray([z]) zf = np.ndarray.flatten(z) w = np.zeros(zf.shape)*1j # Using the Gauss-Kronrod integration is about 10 times faster than # using the mpmath.ellipf function if k == 'default': k = 1/np.sqrt(2) m = k**2 pre_factor = (1-1j)/(-Ke) mid_factor = (1+1j)/np.sqrt(2) temp = [pre_factor*self.complex_integral( func = lambda t: (1-m*np.sin(t)**2)**(-0.5), a = 0, b = i)[0] + 1 - 1j for i in np.arccos(zf*mid_factor)] w = np.asarray(temp) # Now reshape the array back to its original shape z = w.reshape(z.shape).copy() return z def are_points_clockwise(self): import numpy as np verts = np.zeros((3,2)) verts[0,:] = np.asarray([np.cos(self.r[0]),np.sin(self.r[0])]) verts[1,:] = np.asarray([np.cos(self.r[1]),np.sin(self.r[1])]) verts[2,:] = np.asarray([np.cos(self.r[2]),np.sin(self.r[2])]) signed_area = 0 for vtx in range(verts.shape[0]): x1 = verts[vtx,0] y1 = verts[vtx,1] if vtx == verts.shape[0]-1: # Last vertex x2 = verts[0,0] y2 = verts[0,1] else: x2 = verts[vtx+1,0] y2 = verts[vtx+1,1] signed_area += (x1 * y2 - x2 * y1)/2 return (signed_area < 0) def head_to_potential(self): for idx,h in enumerate([self.head_min-self.model.head_offset,self.head_max-self.model.head_offset]): if self.model.aquifer_type == 'confined' or (self.model.aquifer_type == 'convertible' and h >= self.model.H): # Strack 1989, Eq. 8.6 pot = self.k*self.model.H*h - 0.5*self.k*self.model.H**2 elif self.model.aquifer_type == 'unconfined' or (self.model.aquifer_type == 'convertible' and h < self.model.H): # Strack 1989, Eq. 8.7 pot = 0.5*self.k*h**2 if idx == 0: self.phi_min = pot elif idx == 1: self.phi_max = pot def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,label_offset = 1.1,fontsize=12,fontcolor='xkcd:dark grey', pointcolor='xkcd:dark grey',pointsize=50,horizontalalignment='center', verticalalignment='center',color_low = 'xkcd:cerulean', color_high = 'xkcd:orangish red',**kwargs): """ This function plots the Möbius control/reference points on the unit disk. """ import numpy as np import matplotlib.pyplot as plt import math # Get the coordinates of the control points z_A = (1-1j)/np.abs(1-1j) z_A = self.moebius(z_A,inverse=False)*self.model.domain_radius + self.model.domain_center z_A = np.asarray([np.real(z_A),np.imag(z_A)]) z_B = (1+1j)/np.abs(1+1j) z_B = self.moebius(z_B,inverse=False)*self.model.domain_radius + self.model.domain_center z_B = np.asarray([np.real(z_B),np.imag(z_B)]) z_C = (-1+1j)/np.abs(-1+1j) z_C = self.moebius(z_C,inverse=False)*self.model.domain_radius + self.model.domain_center z_C = np.asarray([np.real(z_C),np.imag(z_C)]) z_D = (-1-1j)/np.abs(-1-1j) z_D = self.moebius(z_D,inverse=False)*self.model.domain_radius + self.model.domain_center z_D = np.asarray([np.real(z_D),np.imag(z_D)]) dc = self.model.domain_center if np.isscalar(dc): dc = np.asarray([np.real(dc),np.imag(dc)]) a_low = np.linspace( math.atan2( z_C[1]-dc[1], z_C[0]-dc[0]), math.atan2( z_D[1]-dc[1], z_D[0]-dc[0]), 360) if abs(a_low[0]-a_low[-1]) > np.pi: a_low = np.concatenate(( np.linspace(np.min(a_low),-np.pi,360), np.linspace(np.pi,np.max(a_low),360) )) a_high = np.linspace( math.atan2( z_A[1]-dc[1], z_A[0]-dc[0]), math.atan2( z_B[1]-dc[1], z_B[0]-dc[0]), 360) if abs(a_high[0]-a_high[-1]) > np.pi: a_high = np.concatenate(( np.linspace(np.min(a_high),-np.pi,360), np.linspace(np.pi,np.max(a_high),360) )) plt.plot(np.cos(a_low)*self.model.domain_radius + dc[0], np.sin(a_low)*self.model.domain_radius + dc[1], color = color_low,linewidth=2) plt.plot(np.cos(a_high)*self.model.domain_radius + dc[0], np.sin(a_high)*self.model.domain_radius + dc[1], color = color_high,linewidth=2) plt.scatter(z_A[0],z_A[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.scatter(z_B[0],z_B[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.scatter(z_C[0],z_C[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.scatter(z_D[0],z_D[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.text(z_A[0]*label_offset,z_A[1]*label_offset,'A',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) plt.text(z_B[0]*label_offset,z_B[1]*label_offset,'B',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) plt.text(z_C[0]*label_offset,z_C[1]*label_offset,'C',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) plt.text(z_D[0]*label_offset,z_D[1]*label_offset,'D',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) #%% class ElementUniformBase: def __init__(self,model,alpha=0,head_min=0,head_max=1,k=1, variables=[],priors=[]): """ This implements the uniform base flow element. Parameters: model - [object] : the model object to which this element is added alpha - [scalar] : direction of the uniform flow in counter-clockwise radians from East head_min - [scalar] : minimum hydraulic head (mapped to -1 in the unit square) head_max - [scalar] : maximum hydraulic head (mapped to +1 in the unit square) k - [scalar] : background hydraulic conductivity in canonical units (e.g., 1E-4 [length]/[time]) If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['r','phi_min']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys observations - [list] : list of dictionaries, one for each hydraulic head observations; each dictionary must contain a 'location' and a 'head key', with a complex and real number, respectively """ import numpy as np # Append the base to the elementlist self.model = model model.elementlist.append(self) # Set orientation value self.alpha = alpha # Set potential scaling variables self.head_min = head_min self.head_max = head_max # Assign the hydraulic conductivity of the base model self.k = k # The model requires the base flow in terms of hydraulic potential (phi) # The function head_to_potential extracts the following variables: # phi_min hydraulic potential corresponding to head_min # phi_max hydraulic potential corresponding to head_max self.head_to_potential() # Check input for validity self.check_input() self.variables = variables self.priors = priors if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.variables += [var] self.model.priors += [self.priors[idx]] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): # Update potential self.head_to_potential() def check_input(self): # Check if phi_min is smaller than phi_offset, switch if necessary if self.head_min > self.head_max: raise Exception('Minimum potential phi_min is larger than maximum potential phi_max.') def evaluate(self,z): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Coordinates in canonical space are the start values z_canonical = copy.copy(z) # head_min and head_max lie on opposite points of the circular model domain Q = (self.phi_max-self.phi_min)/(self.model.domain_radius*2) # Rotate the flow field z = Q*z_canonical*np.exp(-1j*self.alpha) # And offset it to phi_min z = z + (self.phi_max-self.phi_min)/2 + self.phi_min return z def evaluate_gradient(self,z,derivatives = 'all'): import numpy as np # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # head_min and head_max lie on opposite points of the circular model domain Q = (self.phi_max-self.phi_min)/(self.model.domain_radius*2) # Extract the derivative grad = Q*np.exp(-1j*self.alpha) if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def head_to_potential(self): for idx,h in enumerate([self.head_min-self.model.head_offset,self.head_max-self.model.head_offset]): if self.model.aquifer_type == 'confined' or (self.model.aquifer_type == 'convertible' and h >= self.model.H): # Strack 1989, Eq. 8.6 pot = self.k*self.model.H*h - 0.5*self.k*self.model.H**2 elif self.model.aquifer_type == 'unconfined' or (self.model.aquifer_type == 'convertible' and h < self.model.H): # Strack 1989, Eq. 8.7 pot = 0.5*self.k*h**2 if idx == 0: self.phi_min = pot elif idx == 1: self.phi_max = pot def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,color_low = 'xkcd:cerulean',color_high='xkcd:orangish red', s = 50, **kwargs): import numpy as np import matplotlib.pyplot as plt high = np.asarray([ np.cos(self.alpha)*self.model.domain_radius + np.real(self.model.domain_center), np.sin(self.alpha)*self.model.domain_radius + np.imag(self.model.domain_center)]) low = -high plt.scatter(low[0],low[1],s=50,color=color_low,zorder=11,**kwargs) plt.scatter(high[0],high[1],s=50,color=color_high,zorder=11,**kwargs) plt.arrow(low[0]*0.9,low[1]*0.9,low[0]*0.15,low[1]*0.15,color=color_low, zorder=11,head_width = 50,width = 20) plt.arrow(high[0]*1.1,high[1]*1.1,-high[0]*0.15,-high[1]*0.15,color=color_high, zorder=11,head_width = 50,width = 20) #%% class ElementHeadBoundary: def __init__(self, model, line, line_ht, segments = None, influence = None, connectivity = 1, connectivity_normdist = None, variables = [], priors=[]): """ This implements a prescribed head boundary. Parameters: model - [object] : the model object to which this element is added line - [array] : either a real N-by-2 matrix or complex vector of length N specifying the vertices of a line string tracing the element's path line_ht - [vector] : a real vector of length N specifying the corresponding prescribed hydraulic heads at the line string's vertices segments - [scalar] : this element has a subdivision function; if a finer resolution than the number of segments in 'line' is desired, specify a larger number here; the function will then subdivide 'line' and 'line_ht' so as to create segments of as equal length as possible influence - [scalar] : radius of zero influence of each line segment; set to twice the model domain_radius if unspecified connectivity - [scalar] : either a real scalar or vector of length M, specifying if the aquifer is fully connected (1) or unconnected (0) to the HeadBoundary connectivity_normdist - [vector] : if connectivity is a vector, this specifies the normalized distances 0,...,d,...,1 along which the M connectivity nodes are placed; connectivity values are then linearly interpolated for each segment If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['line_ht']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys """ import numpy as np from scipy.interpolate import interp1d import copy # Connect this element to the solver self.model = model model.elementlist.append(self) model.linear_solver = True # Prepare the stochastic variables self.variables = variables self.priors = priors # Initialize the head target and connectivity variables self.line_ht = line_ht self.connectivity = connectivity if np.isscalar(self.connectivity): # Connectivity provided is uniform self.connectivity_uniform = True else: # Connectivity provided self.connectivity_uniform = False # Check if normalized distances were provided if connectivity_normdist is None: raise Exception('If connectivity is not uniform, a vector of equal length containing normalized distances (e.g., [0., 0.25, 0.6, 1.]) must be specified.') # Check if connectivity_normdist is valid if np.min(connectivity_normdist) < 0 or np.max(connectivity_normdist) > 1: raise Exception('connectivity_normdist values must be between 0 and 1. Current values: '+str(connectivity_normdist)) # Check if connectivity_normdist is sorted if not (connectivity_normdist == np.sort(connectivity_normdist)).all(): raise Exception('connectivity_normdist values must be provided in ascending order. Current values: '+str(connectivity_normdist)) self.connectivity_normdist = connectivity_normdist # --------------------------------------------------------------------- # Subdivide the provided no flow boundary into #segments pieces # Complexify the line, if it wasn't already complex line = self.complexify(line) # The subdivision algorith requires the line coordinates as a real N-by-2 matrix line = np.column_stack(( np.real(line)[:,np.newaxis], np.imag(line)[:,np.newaxis])) # Make a copy of the line self.line_raw = copy.copy(line) # Check if a subdivision has been specified if segments is None: # No subdivision required self.segments = line.shape[0]-1 else: # Otherwise, set target self.segments = segments # A number of consistency checks if self.segments < self.line_raw.shape[0]-1: raise Exception('Number of segments '+str(self.segments)+" mustn't be smaller than number of line points "+str(line.shape[0])+'.') if len(line_ht) != line.shape[0]: raise Exception('Number of head prescriptions must equal number of vertices: '+str(len(line_ht))+' =/= '+str(line.shape[0])) if self.segments > self.line_raw.shape[0]: # Subdivide the line self.line = self.subdivide_line(np.column_stack((line,self.line_ht)),self.segments) self.line_c = copy.copy(self.line[:,0] + 1j*self.line[:,1]) self.line_ht = copy.copy(self.line[:,2]) else: # Otherwise, reconstruct the line format self.line = self.line_raw.copy() self.line_c = self.line[:,0] + 1j*self.line[:,1] self.line_ht = line_ht # --------------------------------------------------------------------- # Assign the initial strength variables for each segment self.strength = np.ones(self.segments) # Prepare the influence range for this line sink if influence is None: # If no influence range is specified, set it to twice the domain radius # to ensure that no point in the model domain will lie outside this range self.influence = self.model.domain_radius*2 else: self.influence = influence # Prepare a few variables for this element self.L = [] # Length of each line segment self.zc = [] # Center of each line segment self.head_target = [] # Head target at each line segment for seg in range(self.segments): self.L += [np.abs(self.line_c[seg+1] - self.line_c[seg])] self.zc += [(self.line_c[seg]+self.line_c[seg+1])/2] self.head_target += [(self.line_ht[seg]+self.line_ht[seg+1])/2] # Convert list of segment centers to array self.zc = np.asarray(self.zc) self.head_target = np.asarray(self.head_target) # Now form a vector of cumulative distances self.cumdist = [] for seg in range(self.segments): if seg == 0: self.cumdist.append(np.abs(self.zc[0]-self.line_c[0])) else: self.cumdist.append(np.abs(self.zc[seg]-self.zc[seg-1])) self.cumdist = np.cumsum(np.asarray(self.cumdist)) self.cumdist /= (self.cumdist[-1] + np.abs(self.zc[-1]-self.line_c[-1])) if not self.connectivity_uniform: # Interpolate the connectivity from scipy.interpolate import interp1d itp = interp1d(self.connectivity_normdist,self.connectivity) self.connectivity_interpolated = itp(self.cumdist) # Convert the head targets to potential targets self.set_potential_target() # Check if the prior matches the number of parameters if len(self.priors) != len(self.variables): raise Exception('Number of priors must match number of unknown variables. Number of priors: '+str(self.priors)+' / Number of unknown variables: '+str(len(self.variables))) # Go through all elements if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.priors += [self.priors[idx]] self.model.variables += [var] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): import numpy as np # Update the potential targets self.set_potential_target() if not self.connectivity_uniform: # Interpolate the connectivity from scipy.interpolate import interp1d itp = interp1d(self.connectivity_normdist,self.connectivity) self.connectivity_interpolated = itp(self.cumdist) # self.zc = self.xc + 1j*self.yc # self.L = np.abs(self.z2 - self.z1) # influence_pt = (self.z2-self.z1)*self.influence/self.L + self.z1 # Z = (2*influence_pt-(self.z1+self.z2))/(self.z2-self.z1) # part1 = np.nan_to_num((Z+1)*np.log(Z+1)) # part2 = np.nan_to_num((Z-1)*np.log(Z-1)) # self.offset_outside = self.L / (4*np.pi) * (part1 - part2) def evaluate_gradient(self,z,detailed = False, derivatives = 'all', override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # 'detailed' returns the results as a matrix instead of a summed vector if detailed: grad = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: grad = np.zeros(z.shape, dtype = np.complex) for seg in range(self.segments): Z = (2*z-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) Z[np.where(np.abs(np.imag(Z)) < 1E-10)] = np.real(Z[np.where(np.abs(np.imag(Z)) < 1E-10)]) # Now get the gradient d omega(z)/dZ if self.connectivity_uniform: # If the connectivity is uniform, i.e. does not vary along the boundary if not override_parameters: temp = self.strength[seg]*self.connectivity*self.L[seg]/4/np.pi*(np.log(Z+1) - np.log(Z-1)) else: temp = self.L[seg]*self.connectivity/4/np.pi*(np.log(Z+1) - np.log(Z-1)) else: # If the connectivity is not uniform, i.e. does vary along the boundary if not override_parameters: temp = self.strength[seg]*self.connectivity_interpolated[seg]*self.L[seg]/4/np.pi*(np.log(Z+1) - np.log(Z-1)) else: temp = self.L[seg]*self.connectivity_interpolated[seg]/4/np.pi*(np.log(Z+1) - np.log(Z-1)) # To get d omega(z)/dz we can use the product rule # d omega(z)/dz = d omega(z)/dZ * dZ/dz # hence: temp = temp*2/(self.line_c[seg+1]-self.line_c[seg]) if detailed: grad[seg,:] = copy.copy(temp) else: grad += temp if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def evaluate(self,z,detailed = False, override_parameters = False, evaluate_self = False): import copy import numpy as np # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) if detailed: res = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: res = np.zeros(z.shape, dtype = np.complex) for seg in range(self.segments): # Convert to local coordinates Z = (2*z-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) Z[np.where(np.abs(np.imag(Z)) < 1E-10)] = np.real(Z[np.where(np.abs(np.imag(Z)) < 1E-10)]) if self.connectivity_uniform: # If the connectivity is uniform, i.e. does not vary along the boundary # Evaluate the complex potential offset by a distance in the if not override_parameters: temp = self.strength[seg]*self.connectivity*self.L[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) elif override_parameters and evaluate_self: # This is a unique addition. In order to make the connectivity # work as an element in conjunction with all others, we must # 'trick' it into thinking its connectivity is 1. This is only # ever activated to evaluate the effects of a prescribed head # boundary on itself (a diagonal block in setting up the matrix # for the linear system of equations). temp = self.L[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) else: temp = self.L[seg]*self.connectivity/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) else: # If the connectivity is not uniform, i.e. does vary along the boundary # Evaluate the complex potential offset by a distance in the if not override_parameters: temp = self.strength[seg]*self.connectivity_interpolated[seg]*self.L[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) elif override_parameters and evaluate_self: # This is a unique addition. In order to make the connectivity # work as an element in conjunction with all others, we must # 'trick' it into thinking its connectivity is 1. This is only # ever activated to evaluate the effects of a prescribed head # boundary on itself (a diagonal block in setting up the matrix # for the linear system of equations). temp = self.L[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) else: temp = self.L[seg]*self.connectivity_interpolated[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) # If evaluated directly at the endpoints, the result would be NaN # They should be zero, see Bakker 2009 temp = np.nan_to_num(temp) if detailed: res[seg,:] = copy.copy(temp) else: res += temp return res def subdivide_line(self,line,segments): import numpy as np import copy # If array is one-dimensional, reshape it appropriately if len(line.shape) == 1: line = line.reshape((line.shape[0],1)) D = line.shape[1] # Calculate the lengths of original segments length = [np.linalg.norm(line[seg,:]-line[seg+1,:]) for seg in range(line.shape[0]-1)] # Normalize the length of the original segments length /= np.sum(length) # Calculate the number of new segments we must create, the line already has # (#vertices-1) segments. We only require the difference new_segments = segments - line.shape[0] + 1 # Calculate where those segments should go bins = np.concatenate(( [0] , np.cumsum(length) )) # Add Extend the bin length a bit to prevent errors from arithmetic under- or overflow bins[0] -= 1E-10 bins[-1] += 1E-10 # Distribute vertices along the segments x = np.linspace(0,1,new_segments+1) num_vertices = [] for seg in range(line.shape[0]-1): if seg == 0: num_vertices += [len(np.where(x <= bins[1])[0])] else: num_vertices += [len(np.where(np.logical_and( x > bins[seg], x <= bins[seg+1]))[0])] # Subidivide the original segments new_vertices = [] for seg in range(line.shape[0]-1): temp = None for d in range(D): if temp is None: temp = copy.copy(line[seg,d] + (line[seg+1,d]-line[seg,d]) * np.linspace(0,1,num_vertices[seg]+2)[1:-1]) else: temp = np.column_stack(( temp, copy.copy(line[seg,d] + (line[seg+1,d]-line[seg,d]) * np.linspace(0,1,num_vertices[seg]+2)[1:-1]))) new_vertices += [copy.copy(temp)] # Create the seed for the new line new_line = copy.copy(line[0,:].reshape((1,D)) ) # Assemble the new line for seg in range(line.shape[0]-1): # Add the new segments, then the next original vertex new_line = np.row_stack(( new_line, new_vertices[seg], line[seg+1,:])) return new_line def set_potential_target(self): import copy import numpy as np # Get the hydraulic conductivities at the segment control points for e in self.model.elementlist: if isinstance(e, ElementMoebiusBase) or isinstance(e, ElementUniformBase): temp_k = np.ones(self.zc.shape)*e.k for e in self.model.elementlist: if isinstance(e, ElementInhomogeneity): inside = e.are_points_inside_polygon(self.zc) temp_k[inside] = e.k # Create a list of hydraulic potential targets self.phi_target = copy.copy(self.head_target - self.model.head_offset) if self.model.aquifer_type == 'confined': # Strack 1989, Eq. 8.6 self.phi_target = temp_k*self.model.H*self.phi_target - \ 0.5*temp_k*self.model.H**2 elif self.model.aquifer_type == 'unconfined': # Strack 1989, Eq. 8.7 self.phi_target = 0.5*temp_k*self.phi_target**2 elif self.model.aquifer_type == 'convertible': # Find out which points are confined and which are unconfined index_conf = np.where(self.phi_target >= self.model.H)[0] index_unconf = np.where(self.phi_target < self.model.H)[0] # Account for the confined points # confined: Strack 1989, Eq. 8.6 self.phi_target[index_conf] = \ temp_k[index_conf]*self.model.H*self.phi_target[index_conf] - \ 0.5*temp_k[index_conf]*self.model.H**2 # unconfined: Strack 1989, Eq. 8.7 self.phi_target[index_unconf] = \ 0.5*temp_k[index_unconf]*self.phi_target[index_unconf]**2 def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,col='xkcd:kermit green',zorder=12,linewidth=5,**kwargs): import matplotlib.pyplot as plt import numpy as np plt.plot(np.real(self.line_c),np.imag(self.line_c),color=col, zorder=zorder,linewidth=linewidth,**kwargs) #%% class ElementNoFlowBoundary: def __init__(self, model, line, segments = None, variables = [], priors=[]): """ This implements a no-flow boundary. Parameters: model - [object] : the model object to which this element is added line - [array] : either a real N-by-2 matrix or complex vector of length N specifying the vertices of a line string tracing the element's path segments - [scalar] : this element has a subdivision function; if a finer resolution than the number of segments in 'line' is desired, specify a larger number here; the function will then subdivide 'line' and 'line_ht' so as to create segments of as equal length as possible If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['line_ht']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys """ import numpy as np from scipy.interpolate import interp1d import copy # Append this element to the specified model self.model = model model.elementlist.append(self) model.linear_solver = True # --------------------------------------------------------------------- # Subdivide the provided no flow boundary into segments pieces # Complexify the line, if it wasn't already complex line = self.complexify(line) # The subdivision algorith requires the line coordinates as a real N-by-2 matrix line = np.column_stack(( np.real(line)[:,np.newaxis], np.imag(line)[:,np.newaxis])) self.line_raw = copy.copy(line) if segments is None: self.segments = line.shape[0]-1 else: self.segments = segments if self.segments < self.line_raw.shape[0]-1: raise Exception('Prescribed number of line segments '+str(self.segments)+" mustn't be smaller than base number of segments "+str(line.shape[0]-1)+'.') if self.segments > self.line_raw.shape[0]-1: # Subdivide the line self.line = self.subdivide_line(line,self.segments) self.line_c = self.line[:,0] + 1j*self.line[:,1] else: self.line = self.line_raw.copy() self.line_c = self.line[:,0] + 1j*self.line[:,1] # Also get the normal vector components to each segment self.line_nvec = self.line[:,1] - 1j*self.line[:,0] self.line_nvec = self.line_nvec/np.abs(self.line_nvec) # --------------------------------------------------------------------- # Get strength parameters for each vertex self.strength = np.ones(self.segments) self.zc = [] self.segment_nvec = [] self.L = [] for seg in range(self.segments): self.zc += [(self.line_c[seg]+self.line_c[seg+1])/2] # Calculate the normal vector to this segment self.segment_nvec += [(self.line_c[seg]-self.line_c[seg+1])] self.segment_nvec[-1]= [np.imag(self.segment_nvec[-1])-1j*np.real(self.segment_nvec[-1])] self.L += [np.abs(self.line_c[seg+1] - self.line_c[seg])] self.zc = np.asarray(self.zc) # Extract target variables self.variables = variables self.priors = priors self.L = np.asarray(self.L) # Check if the prior matches the number of parameters if len(self.priors) != len(self.variables): raise Exception('Number of priors must match number of unknown variables. Number of priors: '+str(self.priors)+' / Number of unknown variables: '+str(len(self.variables))) # Go through all elements if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.priors += [self.priors[idx]] self.model.variables += [var] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): import numpy as np def evaluate_gradient(self,z,detailed = False,derivatives = 'all',override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # 'detailed' returns the results as a matrix instead of a summed vector if detailed: grad = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: grad = np.zeros(z.shape, dtype = np.complex) # Go through all line segments for seg in range(self.segments): # Convert z to the local variable Z Z = (2*copy.copy(z)-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) #-marked- last influence # Add to the result file d Omega(Z) / dZ if not override_parameters: temp = 1j*self.strength[seg]/(np.pi-np.pi*Z**2) else: temp = 1j/(np.pi-np.pi*Z**2) # Multiply the result with dZ/dz to obtain dOmega(Z)/dz temp = temp*2/(self.line_c[seg+1]-self.line_c[seg]) if detailed: grad[seg,:] = copy.copy(temp) else: grad += copy.copy(temp) if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def evaluate(self,z,detailed = False,override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # 'detailed' returns the results as a matrix instead of a summed vector if detailed: res = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: res = np.zeros(z.shape, dtype = np.complex) # Go through all line segments for seg in range(self.segments): # Convert z to the local variable Z Z = (2*copy.copy(z)-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) #-marked- last influence # # If any Z values are at zero, offset them by a bit # indices = np.where(np.abs(Z) < 1E-10) # Z[indices] = 1E-10 indices = np.where(np.logical_and( np.abs(Z-1) > 1E-10, np.abs(Z+1) > 1E-10) )[0] indices = np.ones(Z.shape,dtype=bool) # indices_minus_1 = np.where(np.abs(Z+1) > 1E-10)[0] # Term 1 term_1 = np.zeros(Z.shape,dtype=np.complex) term_1[indices] = (Z[indices]+1)*np.log((Z[indices]-1)/(Z[indices]+1)) term_2 = np.zeros(Z.shape,dtype=np.complex) term_2[indices] = (Z[indices]-1)*np.log((Z[indices]-1)/(Z[indices]+1)) term_1 = np.nan_to_num(term_1) term_2 = np.nan_to_num(term_2) # indices_plus_1 = np.where(np.abs(Z-1) > 1E-10)[0] # indices_minus_1 = np.where(np.abs(Z+1) > 1E-10)[0] # # Term 1 # term_1 = np.zeros(Z.shape,dtype=np.complex) # term_1[indices_plus_1] = (Z[indices_plus_1]+1)*np.log((Z[indices_plus_1]-1)/(Z[indices_plus_1]+1)) # term_2 = np.zeros(Z.shape,dtype=np.complex) # term_2[indices_minus_1] = (Z[indices_minus_1]-1)*np.log((Z[indices_minus_1]-1)/(Z[indices_minus_1]+1)) # Blablablub if not override_parameters: temp = self.strength[seg]/(4*np.pi*1j) * \ (term_1 - term_2) else: temp = 1/(4*np.pi*1j) * \ (term_1 - term_2) # # Blablablub # if not override_parameters: # temp = self.strength[seg]/(4*np.pi*1j) * \ # ((Z+1)*np.log((Z-1)/(Z+1)) - (Z-1)*np.log((Z-1)/(Z+1))) # else: # temp = 1/(4*np.pi*1j) * \ # ((Z+1)*np.log((Z-1)/(Z+1)) - (Z-1)*np.log((Z-1)/(Z+1))) if detailed: res[seg,:] = copy.copy(temp) else: res += copy.copy(temp) return res def subdivide_line(self,line,segments): import numpy as np import copy # If array is one-dimensional, reshape it appropriately if len(line.shape) == 1: line = line.reshape((line.shape[0],1)) D = line.shape[1] # Calculate the lengths of original segments length = [np.linalg.norm(line[seg,:]-line[seg+1,:]) for seg in range(line.shape[0]-1)] # Normalize the length of the original segments length /= np.sum(length) # Calculate the number of new segments we must create, the line already has # (#vertices-1) segments. We only require the difference new_segments = segments - line.shape[0] + 1 # Calculate where those segments should go bins = np.concatenate(( [0] , np.cumsum(length) )) # Add Extend the bin length a bit to prevent errors from arithmetic under- or overflow bins[0] -= 1E-10 bins[-1] += 1E-10 # Distribute vertices along the segments x = np.linspace(0,1,new_segments) num_vertices = [] for seg in range(line.shape[0]-1): if seg == 0: num_vertices += [len(np.where(x <= bins[1])[0])] else: num_vertices += [len(np.where(np.logical_and( x > bins[seg], x <= bins[seg+1]))[0])] # Subidivide the original segments new_vertices = [] for seg in range(line.shape[0]-1): temp = None for d in range(D): if temp is None: temp = copy.copy(line[seg,d] + (line[seg+1,d]-line[seg,d]) * np.linspace(0,1,num_vertices[seg]+2)[1:-1]) else: temp = np.column_stack(( temp, copy.copy(line[seg,d] + (line[seg+1,d]-line[seg,d]) * np.linspace(0,1,num_vertices[seg]+2)[1:-1]))) new_vertices += [copy.copy(temp)] # Create the seed for the new line new_line = copy.copy(line[0,:].reshape((1,D)) ) # Assemble the new line for seg in range(line.shape[0]-1): # Add the new segments, then the next original vertex new_line = np.row_stack(( new_line, new_vertices[seg], line[seg+1,:])) return new_line def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,color='xkcd:dark grey',linewidth=5,zorder=10,**kwargs): import matplotlib.pyplot as plt import numpy as np plt.plot(np.real(self.line_c),np.imag(self.line_c),color=color, linewidth = linewidth, zorder = zorder, **kwargs) #%% class ElementInhomogeneity: def __init__(self, model, polygon, segments = None, k = 0.1, variables = [], priors=[], snap_distance = 1E-10, zero_cutoff = 1E-10, snap = True): """ This implements a zonal hydraulic conductivity inhomogeneity. Parameters: model - [object] : the model object to which this element is added polygon - [array] : either a real N-by-2 matrix or complex vector of length N specifying the vertices of a polygon tracing the element's shape segments - [scalar] : this element has a subdivision function; if a finer resolution than the number of segments in 'polygon' is desired, specify a larger number here; the function will then subdivide 'line' and 'line_ht' so as to create segments of as equal length as possible k - [scalar] : hydraulic conductivity inside the inhomogeneity in canonical units (e.g., 1E-5 [length units]/[time units]) If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['line_ht']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys """ import numpy as np import copy import matplotlib.path # Append this element to the specified model self.model = model model.elementlist.append(self) model.linear_solver = True # Prepare the polygon variable self.polygon = polygon self.polygon = self.complexify(self.polygon) self.snap_distance = snap_distance self.zero_cutoff = zero_cutoff # Is the polygon closed? If not, close it temporarily if np.abs(self.polygon[0]-self.polygon[-1]) > self.snap_distance: self.polygon = np.asarray(list(self.polygon)+[self.polygon[0]]) # Also create an array with real coordinates self.polygon_XY = np.column_stack(( np.real(copy.copy(self.polygon))[:,np.newaxis], np.imag(copy.copy(self.polygon))[:,np.newaxis] )) # Is the polygon counter-clockwise? If not, correct it if self.are_vertices_clockwise(self.polygon_XY): self.polygon = np.flip(self.polygon) self.polygon_XY = np.flipud(self.polygon_XY) # Do we wish to subdivide the polygon? # First, check if the user specified a desired segment count if segments is None: self.segments = self.polygon.shape[0]-1 else: self.segments = segments if self.segments < self.polygon.shape[0]-1: raise Exception('Prescribed number of line segments '+str(self.segments)+" mustn't be smaller than the number of vertices "+str(polygon.shape[0]-1)+'.') # Subdivide the polygon, if desired if self.segments > self.polygon.shape[0]-1: self.polygon_XY = self.subdivide_line(self.polygon_XY,self.segments) self.polygon = self.polygon_XY[:,0] + 1j*self.polygon_XY[:,1] # Un-close the polygon again self.polygon_XY = self.polygon_XY[:-1,:] self.polygon = self.polygon[:-1] # If vertex snapping is enabled, snap all outside vertices onto the domain edge if snap: self.snap_to_domain() # This is a hack: We shrink the polygon by a small amount. This ensures # that no issues arise from evaluating points directly on the boundary, # and allows us to consider inhomogeneities directly bounding each other; # there might be other ways to solve this issue alternatively self.polygon_XY = self.shrink_polygon( polygon = self.polygon_XY, offset = 1E-10) self.polygon = self.polygon_XY[:,0] + 1j*self.polygon_XY[:,1] # The control points of the inhomogeneity are simply its vertices # This is required for the linear solver self.zc = self.polygon # Raise an exception if this inhomogeneity intersects any of the previous # inhomogeneities for e in self.model.elementlist[:-1]: if isinstance(e, ElementInhomogeneity): if any(e.are_points_inside_polygon(self.zc)): raise Exception('Inhomogeneities may not intersect each other.') # Create a path with the edges of the polygon # We can use this path to find out if evaluation points are inside or # or outside the inhomogeneity. self.linepath = matplotlib.path.Path(self.polygon_XY) # Get strength parameters for each vertex self.strength = np.ones(self.segments) # Assign the hydraulic conductivity of the inhomogeneity self.k = k # Extract target variables self.variables = variables self.priors = priors # Prepare the matrix block containing the effect of this element onto # itself for future use in solving the linear system. The matrix requires # subtraction of the A_star variable from its diagonal entries for completion self.block = self.matrix_contribution() # Check if the prior matches the number of parameters if len(self.priors) != len(self.variables): raise Exception('Number of priors must match number of unknown variables. Number of priors: '+str(self.priors)+' / Number of unknown variables: '+str(len(self.variables))) if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.priors += [self.priors[idx]] self.model.variables += [var] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): import numpy as np def evaluate_gradient(self,z,detailed = False,derivatives = 'all',override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # 'detailed' returns the results as a matrix instead of a summed vector if detailed: grad = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: grad = np.zeros(z.shape, dtype = np.complex) # Go through all line segments for seg in range(self.segments): # Find the next (seg_plus) and previous (seg_minus) vertex if seg == self.segments-1: seg_plus = 0 seg_minus = seg-1 else: seg_plus = seg+1 seg_minus = seg-1 if override_parameters: # Calculate the gradient temp = 1j/(2*np.pi)* (\ np.log((self.polygon[seg] - z)/(self.polygon[seg_minus]-z)) / \ (self.polygon[seg_minus]-self.polygon[seg]) - \ np.log((self.polygon[seg_plus] - z)/(self.polygon[seg]-z)) / \ (self.polygon[seg]-self.polygon[seg_plus]) ) else: # Calculate the gradient temp = self.strength[seg]*1j/(2*np.pi)* (\ np.log((self.polygon[seg] - z)/(self.polygon[seg_minus]-z)) / \ (self.polygon[seg_minus]-self.polygon[seg]) - \ np.log((self.polygon[seg_plus] - z)/(self.polygon[seg]-z)) / \ (self.polygon[seg]-self.polygon[seg_plus]) ) if detailed: grad[seg,:] = copy.copy(temp) else: grad += copy.copy(temp) if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def evaluate(self,z,detailed = False,override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Define the segment influence functions def F(Z): return np.nan_to_num(-0.5*(Z-1)*np.log((Z-1)/(Z+1))) - 1 def G(Z): return np.nan_to_num(+0.5*(Z+1)*np.log((Z-1)/(Z+1))) + 1 # 'detailed' returns the results as a matrix instead of a summed vector if detailed: res = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: res = np.zeros(z.shape, dtype = np.complex) # Go through all line segments for seg in range(self.segments): temp = np.zeros(z.shape, dtype=np.complex) # Find the next (seg_plus) and previous (seg_minus) vertex if seg == self.segments-1: seg_plus = 0 seg_minus = seg-1 else: seg_plus = seg+1 seg_minus = seg-1 # Get a vector of distances dist = np.abs(z - self.polygon[seg]) if override_parameters: # First, use the standard solution for points which aren't snapped to vertices Z_before = \ (2*z - (self.polygon[seg_minus] + self.polygon[seg]))/(self.polygon[seg] - self.polygon[seg_minus]) Z_after = \ (2*z - (self.polygon[seg] + self.polygon[seg_plus]))/(self.polygon[seg_plus] - self.polygon[seg]) # # Prevent errors from underflow # Z_before[np.where(np.abs(np.imag(Z_before)) < 1E-10)] = np.real(Z_before[np.where(np.abs(np.imag(Z_before)) < 1E-10)]) # Z_after[np.where(np.abs(np.imag(Z_after)) < 1E-10)] = np.real(Z_after[np.where(np.abs(np.imag(Z_after)) < 1E-10)]) # Z_before[np.where(np.abs(np.real(Z_before)) < 1E-10)] = 1j*np.imag(Z_before[np.where(np.abs(np.real(Z_before)) < 1E-10)]) # Z_after[np.where(np.abs(np.real(Z_after)) < 1E-10)] = 1j*np.imag(Z_after[np.where(np.abs(np.real(Z_after)) < 1E-10)]) temp = 1/(2*np.pi*1j)*(G(Z_before)+F(Z_after)) else: # First, use the standard solution for points which aren't snapped to vertices Z_before = \ (2*z - (self.polygon[seg_minus] + self.polygon[seg]))/(self.polygon[seg] - self.polygon[seg_minus]) Z_after = \ (2*z - (self.polygon[seg] + self.polygon[seg_plus]))/(self.polygon[seg_plus] - self.polygon[seg]) # # Prevent errors from underflow # Z_before[np.where(np.abs(np.imag(Z_before)) < 1E-10)] = np.real(Z_before[np.where(np.abs(np.imag(Z_before)) < 1E-10)]) # Z_after[np.where(np.abs(np.imag(Z_after)) < 1E-10)] = np.real(Z_after[np.where(np.abs(np.imag(Z_after)) < 1E-10)]) # Z_before[np.where(np.abs(np.real(Z_before)) < 1E-10)] = 1j*np.imag(Z_before[np.where(np.abs(np.real(Z_before)) < 1E-10)]) # Z_after[np.where(np.abs(np.real(Z_after)) < 1E-10)] = 1j*np.imag(Z_after[np.where(np.abs(np.real(Z_after)) < 1E-10)]) temp = self.strength[seg]/(2*np.pi*1j)*(G(Z_before)+F(Z_after)) if detailed: res[seg,:] = copy.copy(temp) else: res += copy.copy(temp) self.res = res return res def matrix_contribution(self): """ This function writes a block into the matrix for the solution of the system of linear equations. It only evaluates the influence of the element itself onto itself. For its influence on other inhomogeneities, use the evaluate function with detailed = True. """ import numpy as np import copy # The functions F and G sometimes return NaN, errors we catch through # np.nan_to_num. Suppress these error messages. import warnings warnings.filterwarnings('ignore') # Define the segment influence functions def F(Z): return np.nan_to_num(-0.5*(Z-1)*np.log((Z-1)/(Z+1))) - 1 def G(Z): return np.nan_to_num(+0.5*(Z+1)*np.log((Z-1)/(Z+1))) + 1 # We evaluate this block at its own vertices z = self.polygon # Pre-allocate an empty matrix for the block block = np.zeros((self.segments,self.segments)) self.angles = [] self.temp = [] # Go through all vertices in the polygon for seg in range(self.segments): # Set the previous, current, and next vertex of the polygon if seg == self.segments-1: seg_minus = seg-1 seg_center = seg seg_plus = 0 else: seg_minus = seg-1 seg_center = seg seg_plus = seg+1 self.temp.append([self.polygon[seg_plus]-self.polygon[seg_center], self.polygon[seg_center]-self.polygon[seg_minus]]) # To evaluate the effect of a vertex on itself, it is computationally # cleanest to evaluate it in terms of angles; these angles are # calculated according to Strack 1989, Eq. 35.29 and 35.30 newtemp = np.angle(self.polygon[seg_plus]-self.polygon[seg_center]) - \ np.angle(self.polygon[seg_center]-self.polygon[seg_minus]) if newtemp < -np.pi: newtemp += 2*np.pi if newtemp > +np.pi: newtemp -= 2*np.pi # self.angles.append(newtemp) # Sometimes, numerical imprecision causes the angle to fall outside # the range 0 and 2 pi; in that case, flip it back inside # if newtemp < 0: newtemp += 2*np.pi # if newtemp > 2*np.pi: newtemp -= 2*np.pi newtemp -= np.pi self.angles.append(newtemp) # Write the diagonal entries of the matrix block[seg,seg] = 1/(2*np.pi)*newtemp # Here we would normally add the factor for the conductivity difference # to the diagonal entries; since we only prepare the matrix here, # we skip it # # Determine the A_star variable (Strack 1989 35.4, 35.38) # A_star = self.model.k/(self.k - self.model.k) # block[seg,seg] -= A_star # Then handle all off-diagonal contributions for seg2 in range(self.segments): # Skip the diagonal if seg2 != seg: # Get the indices of the past, current, and next vertex if seg2 == self.segments-1: seg_minus = seg2-1 seg_center = seg2 seg_plus = 0 else: seg_minus = seg2-1 seg_center = seg2 seg_plus = seg2+1 # Calculate the local coordinates Z_before = \ (2*z[seg] - (self.polygon[seg_minus] + self.polygon[seg_center]))/(self.polygon[seg_center] - self.polygon[seg_minus]) Z_after = \ (2*z[seg] - (self.polygon[seg_center] + self.polygon[seg_plus]))/(self.polygon[seg_plus] - self.polygon[seg_center]) # And write the result into the correct matrix entries block[seg,seg2] = copy.copy(np.real(1/(2*np.pi*1j)*(G(Z_before)+F(Z_after)))) return block def are_vertices_clockwise(self,line): """ This function takes an string of 2-D vertices of a polygon, provided as a N x 2 numpy array, and returns a boolean specifying whether the vertices are provided in clock-wise or counter-clock-wise order. Parameters: line - Required : numpy array of polygon vertices (N by 2) """ import numpy as np signed_area = 0 for idx in range(line.shape[0]): x1 = line[idx,0] y1 = line[idx,1] if idx == line.shape[0]-1: x2 = line[0,0] y2 = line[0,1] else: x2 = line[idx+1,0] y2 = line[idx+1,1] signed_area += (x1 * y2 - x2 * y1) return (np.sign(signed_area) == -1.) def are_points_inside_polygon(self,z): import matplotlib.path import numpy as np indices = self.linepath.contains_points( np.column_stack(( np.real(z), np.imag(z)))) return indices def are_points_on_polygon(self,points,line = None,snap_distance = 1E-10): import numpy as np points = np.column_stack(( np.real(points)[:,np.newaxis], np.imag(points)[:,np.newaxis])) if line is None: # Get the polygon and close it line = np.row_stack((self.polygon_XY,self.polygon_XY[0,:])) # Pre-allocate space for the indices indices = np.zeros(points.shape[0],dtype=np.bool) # Go through all line segments for n in range(line.shape[0]-1): a = line[n,:].reshape((1,2)) b = line[n+1,:].reshape((1,2)) # Normalized tangent vectors d_ba = b - a d = np.divide(d_ba, (np.hypot(d_ba[:, 0], d_ba[:, 1]).reshape(-1, 1))) # Signed parallel distance components # Row-wise dot products of 2D vectors s = np.multiply(a - points, d).sum(axis=1) t = np.multiply(points - b, d).sum(axis=1) # Clamped parallel distance h = np.maximum.reduce([s, t, np.zeros(len(s))]) # Perpendicular distance component # Row-wise cross products of 2D vectors d_pa = points - a c = d_pa[:, 0] * d[:, 1] - d_pa[:, 1] * d[:, 0] # Calculate the distance d = np.hypot(h, c) indices[np.where(d <= snap_distance)] = True return indices def subdivide_line(self,line,segments): import numpy as np import copy # If array is one-dimensional, reshape it appropriately if len(line.shape) == 1: line = line.reshape((line.shape[0],1)) D = line.shape[1] # Calculate the lengths of original segments length = [np.linalg.norm(line[seg,:]-line[seg+1,:]) for seg in range(line.shape[0]-1)] # Normalize the length of the original segments length /= np.sum(length) # Calculate the number of new segments we must create, the line already has # (#vertices-1) segments. We only require the difference new_segments = segments - line.shape[0] + 1 # Calculate where those segments should go bins = np.concatenate(( [0] , np.cumsum(length) )) # Add Extend the bin length a bit to prevent errors from arithmetic under- or overflow bins[0] -= 1E-10 bins[-1] += 1E-10 # Distribute vertices along the segments x = np.linspace(0,1,new_segments) num_vertices = [] for seg in range(line.shape[0]-1): if seg == 0: num_vertices += [len(np.where(x <= bins[1])[0])] else: num_vertices += [len(np.where(np.logical_and( x > bins[seg], x <= bins[seg+1]))[0])] # Subidivide the original segments new_vertices = [] for seg in range(line.shape[0]-1): temp = None for d in range(D): if temp is None: temp = copy.copy(np.linspace(line[seg,d],line[seg+1,d],num_vertices[seg]+2)[1:-1]) else: temp = np.column_stack(( temp, copy.copy(np.linspace(line[seg,d],line[seg+1,d],num_vertices[seg]+2)[1:-1]) )) new_vertices += [copy.copy(temp)] # Create the seed for the new line new_line = copy.copy(line[0,:].reshape((1,D)) ) # Assemble the new line for seg in range(line.shape[0]-1): # Add the new segments, then the next original vertex new_line = np.row_stack(( new_line, new_vertices[seg], line[seg+1,:])) return new_line def shrink_polygon(self,polygon, offset = 1): """ This function shrinks a user-provided polygon. Parameters: polygon - Required : a 2-D array of polygon vertices offset - Required : a scalar defining the distance by which we wish to shrink the polygon (default = 1) """ import numpy as np import copy import math def angle(x1, y1, x2, y2): numer = (x1*x2 + y1*y2) denom = np.sqrt((x1**2 + y1**2) * (x2**2 + y2**2)) print(numer) print(denom) print( math.acos(numer/denom) ) return math.acos(numer/denom) def cross_sign(x1, y1, x2, y2): return x1*y2 > x2*y1 # If the polygon is closed, un-close it closed = False if np.linalg.norm(polygon[0,:]-polygon[-1,:]) < 1E-10: polygon = polygon[:-1,:] closed = True # Make sure polygon is counter-clockwise if self.are_vertices_clockwise(np.row_stack((polygon,polygon[0,:]))): polygon = np.flipud(polygon) polygon_shrinked = copy.copy(polygon) for idx in range(polygon.shape[0]): if idx == polygon.shape[0]-1: vtx_before = idx-1 vtx_center = idx vtx_after = 0 else: vtx_before = idx-1 vtx_center = idx vtx_after = idx+1 side_before = polygon[vtx_center,:] - polygon[vtx_before,:] side_after = polygon[vtx_after,:] - polygon[vtx_center,:] side_before /= np.linalg.norm(side_before) side_after /= np.linalg.norm(side_after) nvec_before = np.asarray([-side_before[1], side_before[0]]) nvec_after = np.asarray([-side_after[1], side_after[0]]) vtx1_before = polygon[vtx_before,:] + nvec_before*offset vtx2_before = polygon[vtx_center,:] + nvec_before*offset vtx1_after = polygon[vtx_center,:] + nvec_after*offset vtx2_after = polygon[vtx_after,:] + nvec_after*offset p = vtx1_before r = (vtx2_before-vtx1_before) q = vtx1_after s = (vtx2_after-vtx1_after) if np.cross(r,s) == 0: # Lines are collinear polygon_shrinked[idx,:] = vtx2_before else: # Lines are not collinear t = np.cross(q - p,s)/(np.cross(r,s)) # This is the intersection point polygon_shrinked[idx,:] = p + t*r if closed: polygon_shrinked = np.row_stack(( polygon_shrinked, polygon_shrinked[0,:])) return polygon_shrinked def snap_to_domain(self): """ This function takes the user-specified polygon and snaps any vertices outside the model domain onto the domain's edge. """ import numpy as np # Calculate the distances of all edge vertices from the center dist = np.abs(self.polygon-self.model.domain_center) # Any vertex with a distance larger than the domain radius lies outside indices = np.where(dist > self.model.domain_radius)[0] # Go through all outside vertices for idx in indices: #Center the vertex to the model temp = self.polygon[idx]-self.model.domain_center # And collapse its norm to unity temp /= dist[idx] # Then scale it to the boundary temp *= self.model.domain_radius # And translate it back to global variables temp += self.model.domain_center # Then save it to the polygon variables self.polygon[idx] = temp # It is possible that some vertices have folded onto themselves repeat = 0 while repeat != 2: # Calculate the interior angles self.angles = np.zeros(self.polygon.shape[0]) for idx in range(self.polygon.shape[0]): # Set the previous, current, and next vertex of the polygon if idx == self.polygon.shape[0]-1: seg_minus = idx-1 seg_center = idx seg_plus = 0 else: seg_minus = idx-1 seg_center = idx seg_plus = idx+1 # This is the routine to calculate the interior angle of a vertex newtemp = np.angle(self.polygon[seg_plus]-self.polygon[seg_center]) - \ np.angle(self.polygon[seg_center]-self.polygon[seg_minus]) newtemp -= np.pi # Restrict it to the range between -pi and +pi while newtemp < -np.pi: newtemp += 2*np.pi while newtemp > +np.pi: newtemp -= 2*np.pi # Save the angles to the list self.angles[idx] = newtemp # All vertices whose interior angles are less than five degrees are # considered degenerate and are removed indices = np.ones(self.polygon.shape[0],dtype=bool) indices[np.where(np.abs(self.angles) < np.radians(5))[0]] = False # Remove the degenerate vertices self.polygon = self.polygon[indices] if np.sum(indices) == len(indices): repeat += 1 # Update the dependent variables self.polygon_XY = np.column_stack(( np.real(self.polygon)[:,np.newaxis], np.imag(self.polygon)[:,np.newaxis] )) self.segments = self.polygon.shape[0] def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,facecolor='xkcd:silver',edgecolor='xkcd:dark grey',zorder=1, alpha=0.5,linewidth=2,**kwargs): import matplotlib.pyplot as plt import numpy as np plt.fill(np.real(self.polygon),np.imag(self.polygon),edgecolor=edgecolor, facecolor=facecolor,alpha=alpha,zorder=zorder,linewidth=linewidth,**kwargs) #%% class ElementAreaSink: def __init__(self, model, polygon, segments = None, strength = 1, variables = [], priors=[], snap_distance = 1E-10, snap = False, influence = None): """ This implements an area sink. Parameters: model - [object] : the model object to which this element is added polygon - [array] : either a real N-by-2 matrix or complex vector of length N specifying the vertices of a polygon tracing the element's path segments - [scalar] : this element has a subdivision function; if a finer resolution than the number of segments in 'line' is desired, specify a larger number here; the function will then subdivide 'line' and 'line_ht' so as to create segments of as equal length as possible strength - [scalar] : injection or extraction rate of this element in [length unit]^3/[length unit]^2/[time unit] If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['line_ht']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys """ import numpy as np import copy import matplotlib.path import math # Append this element to the specified model self.model = model model.elementlist.append(self) # This element adds water, so it also requires an influence range if influence is None: self.influence = self.model.domain_radius*2 else: self.influence = influence # Complexify the polygon, if it isn't already complex polygon = self.complexify(polygon) # Prepare the polygon variable self.polygon = polygon # Is the polygon closed? If not, close it temporarily self.snap_distance = snap_distance if np.abs(self.polygon[0]-self.polygon[-1]) > self.snap_distance: self.polygon = np.asarray(list(self.polygon)+[self.polygon[0]]) # Also create an array with real coordinates self.polygon_XY = np.column_stack(( np.real(copy.copy(self.polygon))[:,np.newaxis], np.imag(copy.copy(self.polygon))[:,np.newaxis] )) # Is the polygon counter-clockwise? If not, correct it if self.are_vertices_clockwise(self.polygon_XY): self.polygon = np.flip(self.polygon) self.polygon_XY = np.flipud(self.polygon_XY) # Do we wish to subdivide the polygon? # First, check if the user specified a desired segment count if segments is None: self.segments = self.polygon.shape[0]-1 else: self.segments = segments if self.segments < self.polygon.shape[0]-1: raise Exception('Prescribed number of line segments '+str(self.segments)+" mustn't be smaller than the number of vertices "+str(polygon.shape[0]-1)+'.') # Subdivide the polygon, if desired if self.segments > self.polygon.shape[0]-1: self.polygon_XY = self.subdivide_line(self.polygon_XY,self.segments) self.polygon = self.polygon_XY[:,0] + 1j*self.polygon_XY[:,1] # This is a hack: We shrink the polygon by a small amount. This should ensure # that no issues arise from evaluating points directly on the boundary; # there might be other ways to solve this issue alternatively self.polygon_XY = self.shrink_polygon( polygon = self.polygon_XY, offset = 1E-10) self.polygon = self.polygon_XY[:,0] + 1j*self.polygon_XY[:,1] # Un-close the polygon again self.polygon_XY = self.polygon_XY[:-1,:] self.polygon = self.polygon[:-1] # If vertex snapping is enabled, snap all outside vertices onto the domain edge if snap: self.snap_to_domain() # ===================================================================== # Now some area-sink-specific work # ===================================================================== # Get the angles of all segments to the x axis # required for the local coordinates, Strack 1989, 37.19 self.alpha = np.zeros(self.segments) for seg in range(self.segments): if seg == self.segments-1: nextseg = 0 else: nextseg = seg+1 # Get the side vector, then normalize it temp = self.polygon[nextseg]-self.polygon[seg] temp /= np.abs(temp) self.alpha[seg] = math.asin(np.imag(temp)) # Get the central point of the polygon self.zc = np.mean(self.polygon) # Calculate the area of the polygon with the shoelace formula: self.A = self.get_polygon_area() # Calculate the coefficients c0, c1, c2 for all segments self.L = np.zeros(self.segments) for seg in range(self.segments): if seg == self.segments-1: nextseg = 0 else: nextseg = seg+1 # Save the length of the segment self.L[seg] = np.abs(self.polygon[nextseg]-self.polygon[seg]) # Get strength parameters for each vertex self.strength = strength # Extract target variables self.variables = variables self.priors = priors # # Prepare the matrix block containing the effect of this element onto # # itself for future use in solving the linear system. The matrix requires # # subtraction of the A_star variable from its diagonal entries for completion # self.block = self.matrix_contribution() # Check if the prior matches the number of parameters if len(self.priors) != len(self.variables): raise Exception('Number of priors must match number of unknown variables. Number of priors: '+str(self.priors)+' / Number of unknown variables: '+str(len(self.variables))) if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.priors += [self.priors[idx]] self.model.variables += [var] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): import numpy as np def get_polygon_area(self): import numpy as np return 0.5*np.abs(np.dot(np.real(self.polygon),np.roll(np.imag(self.polygon),1))-np.dot(np.imag(self.polygon),np.roll(np.real(self.polygon),1))) def evaluate_gradient(self,z,detailed = False,derivatives = 'all',override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # # 'detailed' returns the results as a matrix instead of a summed vector # if detailed: # grad = np.zeros((self.segments,z.shape[0]), dtype = np.complex) # else: # grad = np.zeros(z.shape, dtype = np.complex) grad = np.zeros(z.shape, dtype = np.complex) # Assemble vectors of Z_plus_1 and Z_minus_1 Z_plus_1 = [] Z_minus_1 = [] log_ratio = [] for seg in range(self.segments): if seg == self.segments-1: nextseg = 0 else: nextseg = seg+1 # Get the subtraction and addition Z_minus_1.append(2*(copy.copy(z)-self.polygon[nextseg]) /(self.polygon[nextseg]-self.polygon[seg])) Z_plus_1.append(2*(copy.copy(z)-self.polygon[seg]) /(self.polygon[nextseg]-self.polygon[seg])) log_ratio.append(np.log(Z_minus_1[seg]/Z_plus_1[seg])) for seg in range(self.segments): if seg == self.segments-1: nextseg = 0 else: nextseg = seg+1 dZdz = 2/(self.polygon[nextseg]-self.polygon[seg]) dHdZ = log_ratio[seg] + (self.polygon[nextseg]-self.polygon[seg])/(z - self.polygon[0]) # Calculate local variables Z = (2*copy.copy(z) - self.polygon[seg] - self.polygon[nextseg])/(self.polygon[nextseg]-self.polygon[seg]) # Get the H function # indices = np.where(np.abs(Z_plus_1[seg]) < 1E-10)[0] H = Z_plus_1[seg]*log_ratio[seg] + 2 # H[indices] = 2 for seg2 in np.arange(seg+1,self.segments,1): H += 2*log_ratio[seg2] grad += self.L[seg]**2*(H + (Z-np.conj(Z))*dHdZ)*dZdz # print(grad) # Add the pre-factor grad *= self.strength/(32*np.pi*1j) # Add the second term # This equation (8.599) should be divided be 2, not 4, I believe # It is derived from equation 8.598, where the factor us 2 grad -= self.strength*self.A/(2*np.pi)/(z - self.polygon[0]) # grad += self.strength*self.A/(4*np.pi)/(z - np.mean(self.polygon)) # print(grad) if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def evaluate(self,z,detailed = False,override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # This follows the approach outlined in Strack 2017, 8.26 z = np.asarray(list(z)+[np.mean(self.polygon)+self.influence]) res = np.zeros(z.shape,dtype=np.complex) # Assemble vectors of Z_plus_1 and Z_minus_1 Z_plus_1 = [] log_ratio = [] for seg in range(self.segments): if seg == self.segments-1: nextseg = 0 else: nextseg = seg+1 # Get the subtraction and addition Z_minus_1 = 2*(z-self.polygon[nextseg]) /(self.polygon[nextseg]-self.polygon[seg]) Z_plus_1.append(2*(z-self.polygon[seg]) /(self.polygon[nextseg]-self.polygon[seg])) log_ratio.append(np.log(Z_minus_1/Z_plus_1[seg])) for seg in range(self.segments): if seg == self.segments-1: nextseg = 0 else: nextseg = seg+1 # Calculate local variables # Z = (z - self.zc[seg])/self.v[seg] Z = (2*z - self.polygon[seg] - self.polygon[nextseg])/(self.polygon[nextseg]-self.polygon[seg]) # Get the H function # indices = np.where(np.abs(Z_plus_1[seg]) < 1E-10)[0] H = Z_plus_1[seg]*log_ratio[seg] + 2 # H[indices] = 2 for seg2 in np.arange(seg+1,self.segments,1): H += 2*log_ratio[seg2] # Combine that shit res += self.L[seg]**2*(Z - np.conj(Z))*H # Pre-factor res *= self.strength/(32*np.pi*1j) # And the second term res -= self.strength*self.A/(2*np.pi)*np.log(z-self.polygon[0]) # Correct the offset for the correction factor res -= np.real(res[-1]) # Remove the correction factor res = res[:-1] return res def are_vertices_clockwise(self,line): """ This function takes an string of 2-D vertices of a polygon, provided as a N x 2 numpy array, and returns a boolean specifying whether the vertices are provided in clock-wise or counter-clock-wise order. Parameters: line - Required : numpy array of polygon vertices (N by 2) """ import numpy as np signed_area = 0 for idx in range(line.shape[0]): x1 = line[idx,0] y1 = line[idx,1] if idx == line.shape[0]-1: x2 = line[0,0] y2 = line[0,1] else: x2 = line[idx+1,0] y2 = line[idx+1,1] signed_area += (x1 * y2 - x2 * y1) return (np.sign(signed_area) == -1.) def subdivide_line(self,line,segments): import numpy as np import copy # If array is one-dimensional, reshape it appropriately if len(line.shape) == 1: line = line.reshape((line.shape[0],1)) D = line.shape[1] # Calculate the lengths of original segments length = [np.linalg.norm(line[seg,:]-line[seg+1,:]) for seg in range(line.shape[0]-1)] # Normalize the length of the original segments length /= np.sum(length) # Calculate the number of new segments we must create, the line already has # (#vertices-1) segments. We only require the difference new_segments = segments - line.shape[0] + 1 # Calculate where those segments should go bins = np.concatenate(( [0] , np.cumsum(length) )) # Add Extend the bin length a bit to prevent errors from arithmetic under- or overflow bins[0] -= 1E-10 bins[-1] += 1E-10 # Distribute vertices along the segments x = np.linspace(0,1,new_segments) num_vertices = [] for seg in range(line.shape[0]-1): if seg == 0: num_vertices += [len(np.where(x <= bins[1])[0])] else: num_vertices += [len(np.where(np.logical_and( x > bins[seg], x <= bins[seg+1]))[0])] # Subidivide the original segments new_vertices = [] for seg in range(line.shape[0]-1): temp = None for d in range(D): if temp is None: temp = copy.copy(np.linspace(line[seg,d],line[seg+1,d],num_vertices[seg]+2)[1:-1]) else: temp = np.column_stack(( temp, copy.copy(np.linspace(line[seg,d],line[seg+1,d],num_vertices[seg]+2)[1:-1]) )) new_vertices += [copy.copy(temp)] # Create the seed for the new line new_line = copy.copy(line[0,:].reshape((1,D)) ) # Assemble the new line for seg in range(line.shape[0]-1): # Add the new segments, then the next original vertex new_line = np.row_stack(( new_line, new_vertices[seg], line[seg+1,:])) return new_line def snap_to_domain(self): """ This function takes the user-specified polygon and snaps any vertices outside the model domain onto the domain's edge. """ import numpy as np # Calculate the distances of all edge vertices from the center dist = np.abs(self.polygon-self.model.domain_center) # Any vertex with a distance larger than the domain radius lies outside indices = np.where(dist > self.model.domain_radius)[0] # Go through all outside vertices for idx in indices: #Center the vertex to the model temp = self.polygon[idx]-self.model.domain_center # And collapse its norm to unity temp /= dist[idx] # Then scale it to the boundary temp *= self.model.domain_radius # And translate it back to global variables temp += self.model.domain_center # Then save it to the polygon variables self.polygon[idx] = temp # It is possible that some vertices have folded onto themselves repeat = 0 while repeat != 2: # Calculate the interior angles self.angles = np.zeros(self.polygon.shape[0]) for idx in range(self.polygon.shape[0]): # Set the previous, current, and next vertex of the polygon if idx == self.polygon.shape[0]-1: seg_minus = idx-1 seg_center = idx seg_plus = 0 else: seg_minus = idx-1 seg_center = idx seg_plus = idx+1 # This is the routine to calculate the interior angle of a vertex newtemp = np.angle(self.polygon[seg_plus]-self.polygon[seg_center]) - \ np.angle(self.polygon[seg_center]-self.polygon[seg_minus]) newtemp -= np.pi # Restrict it to the range between -pi and +pi while newtemp < -np.pi: newtemp += 2*np.pi while newtemp > +np.pi: newtemp -= 2*np.pi # Save the angles to the list self.angles[idx] = newtemp # All vertices whose interior angles are less than five degrees are # considered degenerate and are removed indices = np.ones(self.polygon.shape[0],dtype=bool) indices[np.where(np.abs(self.angles) < np.radians(5))[0]] = False # Remove the degenerate vertices self.polygon = self.polygon[indices] if np.sum(indices) == len(indices): repeat += 1 # Update the dependent variables self.polygon_XY = np.column_stack(( np.real(self.polygon)[:,np.newaxis], np.imag(self.polygon)[:,np.newaxis] )) self.segments = self.polygon.shape[0] def shrink_polygon(self,polygon, offset = 1): """ This function shrinks a user-provided polygon. Parameters: polygon - Required : a 2-D array of polygon vertices offset - Required : a scalar defining the distance by which we wish to shrink the polygon (default = 1) """ import numpy as np import copy import math def angle(x1, y1, x2, y2): numer = (x1*x2 + y1*y2) denom = np.sqrt((x1**2 + y1**2) * (x2**2 + y2**2)) print(numer) print(denom) print( math.acos(numer/denom) ) return math.acos(numer/denom) def cross_sign(x1, y1, x2, y2): return x1*y2 > x2*y1 # If the polygon is closed, un-close it closed = False if np.linalg.norm(polygon[0,:]-polygon[-1,:]) < 1E-10: polygon = polygon[:-1,:] closed = True # Make sure polygon is counter-clockwise if self.are_vertices_clockwise(np.row_stack((polygon,polygon[0,:]))): polygon = np.flipud(polygon) polygon_shrinked = copy.copy(polygon) for idx in range(polygon.shape[0]): if idx == polygon.shape[0]-1: vtx_before = idx-1 vtx_center = idx vtx_after = 0 else: vtx_before = idx-1 vtx_center = idx vtx_after = idx+1 side_before = polygon[vtx_center,:] - polygon[vtx_before,:] side_after = polygon[vtx_after,:] - polygon[vtx_center,:] side_before /= np.linalg.norm(side_before) side_after /= np.linalg.norm(side_after) nvec_before = np.asarray([-side_before[1], side_before[0]]) nvec_after = np.asarray([-side_after[1], side_after[0]]) vtx1_before = polygon[vtx_before,:] + nvec_before*offset vtx2_before = polygon[vtx_center,:] + nvec_before*offset vtx1_after = polygon[vtx_center,:] + nvec_after*offset vtx2_after = polygon[vtx_after,:] + nvec_after*offset p = vtx1_before r = (vtx2_before-vtx1_before) q = vtx1_after s = (vtx2_after-vtx1_after) if np.cross(r,s) == 0: # Lines are collinear polygon_shrinked[idx,:] = vtx2_before else: # Lines are not collinear t = np.cross(q - p,s)/(np.cross(r,s)) # This is the intersection point polygon_shrinked[idx,:] = p + t*r if closed: polygon_shrinked = np.row_stack(( polygon_shrinked, polygon_shrinked[0,:])) return polygon_shrinked def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,facecolor_extract='xkcd:orangish red', edgecolor_extract='xkcd:crimson',facecolor_inject='xkcd:cerulean', edgecolor_inject='xkcd:cobalt',zorder=12,alpha=0.5,linewidth=5,**kwargs): import matplotlib.pyplot as plt import numpy as np if self.strength < 0: col_face = facecolor_extract col_edge = edgecolor_extract else: col_face = facecolor_inject col_edge = edgecolor_inject plt.fill(np.real(self.polygon),np.imag(self.polygon),edgecolor=col_edge, facecolor=col_face,alpha=alpha,zorder=zorder,linewidth=linewidth, **kwargs) #%% class ElementLineSink: def __init__(self, model, line, segments = None, influence = None, strength = 1, variables = [], priors=[]): """ This implements a line sink. Parameters: model - [object] : the model object to which this element is added line - [array] : either a real N-by-2 matrix or complex vector of length N specifying the vertices of a line string tracing the element's path segments - [scalar] : this element has a subdivision function; if a finer resolution than the number of segments in 'line' is desired, specify a larger number here; the function will then subdivide 'line' and 'line_ht' so as to create segments of as equal length as possible influence - [scalar] : radius of zero influence of each line segment; set to twice the model domain_radius if unspecified strength - [scalar] : this specifies the injection or extraction rate of the element If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['line_ht']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys """ import numpy as np from scipy.interpolate import interp1d import copy self.model = model model.elementlist.append(self) self.variables = variables self.priors = priors # --------------------------------------------------------------------- # Subdivide the provided no flow boundary into #segments pieces self.line_raw = copy.copy(line) if segments is None: self.segments = line.shape[0]-1 else: self.segments = segments if self.segments < self.line_raw.shape[0]-1: raise Exception('Number of segments '+str(self.segments)+" mustn't be smaller than number of line points "+str(line.shape[0])+'.') if self.segments > self.line_raw.shape[0]: # Subdivide the line self.line = self.subdivide_line(line,self.segments) self.line_c = copy.copy(self.line[:,0] + 1j*self.line[:,1]) else: self.line = self.line_raw.copy() self.line_c = self.line[:,0] + 1j*self.line[:,1] # Also get the normal vector components to each segment self.line_nvec = self.line[:,1] - 1j*self.line[:,0] self.line_nvec = self.line_nvec/np.abs(self.line_nvec) # --------------------------------------------------------------------- self.strength = np.ones(self.segments)*strength if influence is None: self.influence = self.model.domain_radius*2 else: self.influence = influence self.Zi = [] self.offset_outside = [] self.L = [] self.zc = [] self.segment_nvec = [] self.head_target = [] for seg in range(self.segments): self.L += [np.abs(self.line_c[seg+1] - self.line_c[seg])] influence_pt = (self.line_c[seg+1]-self.line_c[seg])*self.influence/self.L[seg] + self.line_c[seg] Z = (2*influence_pt-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) self.Zi += [copy.copy(Z)] self.zc += [(self.line_c[seg]+self.line_c[seg+1])/2] # Calculate the normal vector to this segment self.segment_nvec += [(self.line_c[seg]-self.line_c[seg+1])] self.segment_nvec[-1]= [np.imag(self.segment_nvec[-1])-1j*np.real(self.segment_nvec[-1])] part1 = np.nan_to_num((Z+1)*np.log(Z+1)) part2 = np.nan_to_num((Z-1)*np.log(Z-1)) self.offset_outside += [self.L[seg] / (4*np.pi) * (part1 - part2)] # Convert list of segment centers to array self.zc = np.asarray(self.zc) # Check if the prior matches the number of parameters if len(self.priors) != len(self.variables): raise Exception('Number of priors must match number of unknown variables. Number of priors: '+str(self.priors)+' / Number of unknown variables: '+str(len(self.variables))) # Go through all elements if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.priors += [self.priors[idx]] self.model.variables += [var] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): import numpy as np # self.zc = self.xc + 1j*self.yc # self.L = np.abs(self.z2 - self.z1) # influence_pt = (self.z2-self.z1)*self.influence/self.L + self.z1 # Z = (2*influence_pt-(self.z1+self.z2))/(self.z2-self.z1) # part1 = np.nan_to_num((Z+1)*np.log(Z+1)) # part2 = np.nan_to_num((Z-1)*np.log(Z-1)) # self.offset_outside = self.L / (4*np.pi) * (part1 - part2) def evaluate_gradient(self,z,detailed = False, derivatives = 'all', override_parameters = False): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # 'detailed' returns the results as a matrix instead of a summed vector if detailed: grad = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: grad = np.zeros(z.shape, dtype = np.complex) for seg in range(self.segments): Z = (2*z-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) # Now get the gradient d omega(z)/dZ if not override_parameters: temp = self.strength[seg]*self.L[seg]/4/np.pi*(np.log(Z+1) - np.log(Z-1)) else: temp = self.L[seg]/4/np.pi*(np.log(Z+1) - np.log(Z-1)) # To get d omega(z)/dz we can use the product rule # d omega(z)/dz = d omega(z)/dZ * dZ/dz # hence: temp = temp*2/(self.line_c[seg+1]-self.line_c[seg]) if detailed: grad[seg,:] = copy.copy(temp) else: grad += temp if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def evaluate(self,z,detailed = False, override_parameters = False): import copy import numpy as np # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) if detailed: res = np.zeros((self.segments,z.shape[0]), dtype = np.complex) else: res = np.zeros(z.shape, dtype = np.complex) for seg in range(self.segments): # Convert to local coordinates Z = (2*z-(self.line_c[seg]+self.line_c[seg+1]))/(self.line_c[seg+1]-self.line_c[seg]) # Evaluate the complex potential offset by a distance in the if not override_parameters: temp = self.strength[seg]*self.L[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) else: temp = self.L[seg]/4/np.pi * (\ (Z+1)*np.log(Z+1) - \ (Z-1)*np.log(Z-1) - \ (2/self.L[seg]*self.influence+2)*np.log(2/self.L[seg]*self.influence+2) + \ (2/self.L[seg]*self.influence)*np.log(2/self.L[seg]*self.influence)) # If evaluated directly at the endpoints, the result would be NaN # They should be zero, see Bakker 2009 temp = np.nan_to_num(temp) if detailed: res[seg,:] = copy.copy(temp) else: res += temp return res def subdivide_line(self,line,segments): import numpy as np import copy # If array is one-dimensional, reshape it appropriately if len(line.shape) == 1: line = line.reshape((line.shape[0],1)) D = line.shape[1] # Calculate the lengths of original segments length = [np.linalg.norm(line[seg,:]-line[seg+1,:]) for seg in range(line.shape[0]-1)] # Normalize the length of the original segments length /= np.sum(length) # Calculate the number of new segments we must create, the line already has # (#vertices-1) segments. We only require the difference new_segments = segments - line.shape[0] + 1 # Calculate where those segments should go bins = np.concatenate(( [0] , np.cumsum(length) )) # Add Extend the bin length a bit to prevent errors from arithmetic under- or overflow bins[0] -= 1E-10 bins[-1] += 1E-10 # Distribute vertices along the segments x = np.linspace(0,1,new_segments) num_vertices = [] for seg in range(line.shape[0]-1): if seg == 0: num_vertices += [len(np.where(x <= bins[1])[0])] else: num_vertices += [len(np.where(np.logical_and( x > bins[seg], x <= bins[seg+1]))[0])] # Subidivide the original segments new_vertices = [] for seg in range(line.shape[0]-1): temp = None for d in range(D): if temp is None: temp = copy.copy(line[seg,d] + (line[seg+1,d]-line[seg,d]) * np.linspace(0,1,num_vertices[seg]+2)[1:-1]) else: temp = np.column_stack(( temp, copy.copy(line[seg,d] + (line[seg+1,d]-line[seg,d]) * np.linspace(0,1,num_vertices[seg]+2)[1:-1]))) new_vertices += [copy.copy(temp)] # Create the seed for the new line new_line = copy.copy(line[0,:].reshape((1,D)) ) # Assemble the new line for seg in range(line.shape[0]-1): # Add the new segments, then the next original vertex new_line = np.row_stack(( new_line, new_vertices[seg], line[seg+1,:])) return new_line def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,color_extract='xkcd:orangish red',color_inject='xkcd:cerulean', zorder=12,linewidth=5,**kwargs): import matplotlib.pyplot as plt import numpy as np if self.strength < 0: col = color_extract else: col = color_inject plt.plot(np.real(self.line_c),np.imag(self.line_c),color=col, zorder=zorder,linewidth=linewidth,**kwargs) #%% class ElementWell: def __init__(self, model, zc, rw, influence = None, head_change = -1, strength = 1, drawdown_specified = False, variables = [], priors = []): """ This implements an injection or extraction well. Parameters: model - [object] : the model object to which this element is added zw - [vector] : either a complex scalar or a real vector of length 2 specifying the xy coordinates of the well rw - [scalar] : a real scalar specifying the screen radius of the well in [length units] strength - [scalar] : extraction or injection rate at this well in [length units]^3/[time units] head_change - [scalar] : alternative to strength, induces the prescribed drawdown at the well; only used if drawdown_specified is True drawdown_specified - [boolean] : flag for whether the well's strength is determined through a prescribed head_change; defaults to False If MCMC is used, we further require: variable - [list] : list of variables which are inferred by MCMC, example: ['line_ht']; leave empty if unused priors - [list] : list of dictionaries, one for each unknown 'variable'; each dictionary must contain the name of distribution (in scipy.stats) and the relevant parameters as keys """ import numpy as np self.model = model model.elementlist.append(self) self.variables = variables self.priors = priors if influence is None: # If no influence radius is specified, set it to twice the model radius self.influence = 2*self.model.domain_radius else: # Otherwise, set it to the user-defined value self.influence = influence # The well's strength defines its effect on the flow field; this is # overwritten later on to achieve the desired head_change which depends # on the aquifer parameters self.strength = strength # This is the well's position in terms of complex coordinates self.zc = zc if not np.isscalar(self.zc): self.zc = self.zc[0] + 1j*self.zc[1] # The well radius is specified in canonical units self.rw = rw # Check if drawdown specified self.drawdown_specified = drawdown_specified if self.drawdown_specified: # Get the head change variable self.head_change = head_change # Adjust the strength so that the desired drawdown is achieved self.set_potential_target() # Check if the prior matches the number of parameters if len(self.priors) != len(self.variables): raise Exception('Number of priors must match number of unknown variables. Number of priors: '+str(self.priors)+' / Number of unknown variables: '+str(len(self.variables))) # Go through all elements if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.priors += [self.priors[idx]] self.model.variables += [var] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): if self.drawdown_specified: self.set_potential_target() def evaluate_gradient(self,z,derivatives = 'all'): import numpy as np # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Find the indices of wells dist = np.abs(z-self.zc) idx_inside = np.where(dist < self.rw)[0] idx_outside = np.where(dist > self.influence)[0] idx_valid = [i for i in np.arange(len(z),dtype=int) if (i not in idx_inside and i not in idx_outside)] # Correct the coordinates --------------------------------------------- # Set the well center to the origin of the complex plane zs = z.copy()-self.zc # Pre-allocate an array for the gradient grad = np.zeros(zs.shape, dtype=np.complex) # Calculate the gradient # grad[idx_valid] = self.strength/(zs[idx_valid]*np.log(self.rw/self.influence)) grad[idx_valid] = -self.strength/(2*np.pi)/zs[idx_valid] # If partial derivatives are demanded, calculate them if derivatives == 'phi': # phi corresponds to the hydraulic potential dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': # psi corresponds to the stream function dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': # all returns the complex derivative raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def evaluate(self,z): import numpy as np # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Effect at influence range temp = -self.strength/(2*np.pi)*np.log(self.influence) # Find the indices of wells dist = np.abs(z-self.zc) idx_inside = np.where(dist < self.rw)[0] # Correct the coordinates --------------------------------------------- # Center the evaluation points on the well zs = z.copy()-self.zc # Snap points inside the well to the well edge zs[idx_inside] = self.rw + 0j # Calculate the complex potential res = -self.strength/(2*np.pi)*np.log(zs) - temp return res def set_potential_target(self): """ We define the drawdown in terms of head, but for the calculations we require it in terms of potential. """ import copy import numpy as np # Get the hydraulic conductivity for e in self.model.elementlist: if isinstance(e, ElementMoebiusBase) or isinstance(e, ElementUniformBase): temp_k = e.k for e in self.model.elementlist: if isinstance(e, ElementInhomogeneity): if e.are_points_inside_polygon(self.zc): temp_k = e.k # Create a list of hydraulic potential targets self.strength = copy.copy(self.head_change) if self.model.aquifer_type == 'confined': # Strack 1989, Eq. 8.6 self.strength = temp_k*self.model.H*self.strength - \ 0.5*temp_k*self.model.H**2 elif self.model.aquifer_type == 'unconfined': # Strack 1989, Eq. 8.7 self.strength = 0.5*temp_k*self.strength**2 elif self.model.aquifer_type == 'convertible': # Find out which points are confined and which are unconfined index_conf = np.where(self.strength >= self.model.H)[0] index_unconf = np.where(self.strength < self.model.H)[0] # Account for the confined points # confined: Strack 1989, Eq. 8.6 self.strength[index_conf] = \ temp_k[index_conf]*self.model.H*self.strength[index_conf] - \ 0.5*temp_k[index_conf]*self.model.H**2 # unconfined: Strack 1989, Eq. 8.7 self.strength[index_unconf] = \ 0.5*temp_k[index_unconf]*self.strength[index_unconf]**2 def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,color_extract='xkcd:orangish red',color_inject='xkcd:cerulean', zorder=15,s=100,edgecolor='xkcd:dark grey',linewidth=2,**kwargs): import matplotlib.pyplot as plt import numpy as np if self.strength < 0: col = color_extract marker = '^' else: col = color_inject marker = 'v' plt.scatter(np.real(self.zc),np.imag(self.zc),s=s,color=col, marker=marker,zorder=zorder,edgecolor=edgecolor, linewidth=linewidth,**kwargs) #%% class ElementMoebiusOverlay: def __init__(self,model,r=None,a=None,b=None,c=None,d=None,head_min=0,head_max=1, variables=[],priors=[],angular_limit=1): """ Similar to the Möbius base, but an additive overlay. Unfinished. """ import numpy as np # Append the base to the elementlist self.model = model model.elementlist.append(self) # Define an angular limit. This is designed to keep the Möbius control # points from getting arbitrarily close to each other; defined in radians self.angular_limit = angular_limit # Set Moebius values self.r = r self.a = a self.b = b self.c = c self.d = d # Set potential scaling variables self.head_min = head_min self.head_max = head_max # The model requires the base flow in terms of hydraulic potential (phi) # The function head_to_potential extracts the following variables: # phi_min hydraulic potential corresponding to head_min # phi_max hydraulic potential corresponding to head_max self.head_to_potential() # Check input for validity self.check_input() # Define the original control points in the Moebius base disk self.z0 = np.asarray( [np.complex(np.cos(-0.25*np.pi),np.sin(-0.25*np.pi)), np.complex(np.cos(0.25*np.pi),np.sin(0.25*np.pi)), np.complex(np.cos(0.75*np.pi),np.sin(0.75*np.pi))]) # If only rotation is specified, get the Moebius coefficients if self.r is not None and (self.a is None and self.b is None and \ self.c is None and self.d is None): # Find Moebius coefficients self.find_moebius_coefficients() self.variables = variables self.priors = priors self.Ke = 1.854 if len(self.variables) > 0: # There are some model variables specified for idx,var in enumerate(self.variables): self.model.num_params += 1 exec("self.model.params += [self.%s]" % var) self.model.variables += [var] self.model.priors += [self.priors[idx]] if 'name' in list(self.priors[idx].keys()): self.model.param_names += [self.priors[idx]['name']] else: self.model.param_names += ['unknown'] def update(self): # If this model is updated, make sure to repeat any initialization # Find Moebius coefficients self.find_moebius_coefficients() def check_input(self): import numpy as np # See if either control point rotations or a full set of Moebius # coefficients are specified if self.r is None and (self.a is None or self.b is None or \ self.c is None or self.d is None): raise Exception('Either control point rotations r or Moebius coefficients a, b, c, and d must be specified.') # Check if phi_min is smaller than phi_offset, switch if necessary if self.phi_min > self.phi_max: raise Exception('Minimum potential phi_min is larger than maximum potential phi_max.') # Check if the control points fulfill the minimum angular spacing r = np.degrees(self.r) if np.abs((r[0]-r[1] + 180) % 360 - 180) < self.angular_limit or \ np.abs((r[1]-r[2] + 180) % 360 - 180) < self.angular_limit or \ np.abs((r[2]-r[0] + 180) % 360 - 180) < self.angular_limit: raise Exception('Control points '+str(self.r)+' are too close to each other. Define different control points or adjust the angular limit: '+str(self.angular_limit)) def evaluate(self,z): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Coordinates in canonical space are the start values z_canonical = copy.copy(z) # Scale the canonical disk to unity canonical disk z = (z - self.model.domain_center)/self.model.domain_radius # Map from canonical disk to Möbius base z = self.moebius(z,inverse=True) # Map from Möbius base to unit square z = self.disk_to_square(z) # Rescale the complex potential z = (z+1)/2 * (self.phi_max-self.phi_min) + self.phi_min return z def evaluate_gradient(self,z,derivatives = 'all'): import numpy as np import copy # Complexify the evaluation points, if they aren't already complex z = self.complexify(z) # Map from the canonical disk to Möbius base z_mb = (copy.copy(z) - self.model.domain_center)/self.model.domain_radius # dz_mb / dz_c grad_4 = 1/self.model.domain_radius # Map from Möbius base to unit disk z_ud = self.moebius(copy.copy(z_mb),inverse=True) # dz_ud / dz_mb grad_3 = (self.a*self.d-self.b*self.c)/(self.c*z_mb-self.a)**2 grad_2 = 2/(self.Ke*np.sqrt(z_ud**4+1)) grad_1 = (self.phi_max-self.phi_min)/2 grad = grad_1*grad_2*grad_3*grad_4 if derivatives == 'phi': dudx = np.real(grad) dudy = -np.imag(grad) grad = dudx + 1j*dudy elif derivatives == 'psi': dvdx = -np.imag(np.conjugate(grad)) dvdy = np.real(np.conjugate(grad)) grad = dvdx + 1j*dvdy elif derivatives != 'all': raise Exception("'derivatives' has to be either 'all' (complex derivative), " + \ "'phi' (hydraulic potential partial derivatives), or 'psi' " + \ "(flow line partial derivatives)") return grad def complex_integral(self,func,a,b): """ This implements the Gauss-Kronrod integration for complex-valued functions. We use this to evaluate the Legendre incomplete elliptic integral of the first kind, since it is about ten times as fast as using mpmath's ellipf function. Since this integration is a major computational bottleneck of this function, we stick with this approach. The equations below are adapted from: https://stackoverflow.com/questions/5965583/use-scipy-integrate-quad-to-integrate-complex-numbers """ import scipy from scipy import array def quad_routine(func, a, b, x_list, w_list): c_1 = (b-a)/2.0 c_2 = (b+a)/2.0 eval_points = map(lambda x: c_1*x+c_2, x_list) func_evals = list(map(func, eval_points)) # Python 3: make a list here return c_1 * sum(array(func_evals) * array(w_list)) def quad_gauss_7(func, a, b): x_gauss = [-0.949107912342759, -0.741531185599394, -0.405845151377397, 0, 0.405845151377397, 0.741531185599394, 0.949107912342759] w_gauss = array([0.129484966168870, 0.279705391489277, 0.381830050505119, 0.417959183673469, 0.381830050505119, 0.279705391489277,0.129484966168870]) return quad_routine(func,a,b,x_gauss, w_gauss) def quad_kronrod_15(func, a, b): x_kr = [-0.991455371120813,-0.949107912342759, -0.864864423359769, -0.741531185599394, -0.586087235467691,-0.405845151377397, -0.207784955007898, 0.0, 0.207784955007898,0.405845151377397, 0.586087235467691, 0.741531185599394, 0.864864423359769, 0.949107912342759, 0.991455371120813] w_kr = [0.022935322010529, 0.063092092629979, 0.104790010322250, 0.140653259715525, 0.169004726639267, 0.190350578064785, 0.204432940075298, 0.209482141084728, 0.204432940075298, 0.190350578064785, 0.169004726639267, 0.140653259715525, 0.104790010322250, 0.063092092629979, 0.022935322010529] return quad_routine(func,a,b,x_kr, w_kr) class Memorize: # Python 3: no need to inherit from object def __init__(self, func): self.func = func self.eval_points = {} def __call__(self, *args): if args not in self.eval_points: self.eval_points[args] = self.func(*args) return self.eval_points[args] def quad(func,a,b): ''' Output is the 15 point estimate; and the estimated error ''' func = Memorize(func) # Memorize function to skip repeated function calls. g7 = quad_gauss_7(func,a,b) k15 = quad_kronrod_15(func,a,b) # I don't have much faith in this error estimate taken from wikipedia # without incorporating how it should scale with changing limits return [k15, (200*scipy.absolute(g7-k15))**1.5] return quad(func,a,b) def angle_to_unit_circle(self): import numpy as np # Angle must be provided in radians, counter-clockwise from 3 o'clock return np.cos(self.r)+1j*np.sin(self.r) def find_moebius_coefficients(self): import numpy as np # Find the images of the z0 control points w0 = self.angle_to_unit_circle() # Then calculate the four parameters for the corresponding Möbius map self.a = np.linalg.det(np.asarray( [[self.z0[0]*w0[0], w0[0], 1], [self.z0[1]*w0[1], w0[1], 1], [self.z0[2]*w0[2], w0[2], 1]])) self.b = np.linalg.det(np.asarray( [[self.z0[0]*w0[0], self.z0[0], w0[0]], [self.z0[1]*w0[1], self.z0[1], w0[1]], [self.z0[2]*w0[2], self.z0[2], w0[2]]])) self.c = np.linalg.det(np.asarray( [[self.z0[0], w0[0], 1], [self.z0[1], w0[1], 1], [self.z0[2], w0[2], 1]])) self.d = np.linalg.det(np.asarray( [[self.z0[0]*w0[0], self.z0[0], 1], [self.z0[1]*w0[1], self.z0[1], 1], [self.z0[2]*w0[2], self.z0[2], 1]])) return def moebius(self,z,inverse=False): if not inverse: z = (self.a*z+self.b)/(self.c*z+self.d) else: z = (-self.d*z+self.b)/(self.c*z-self.a) return z def square_to_disk(self,z,k='default'): import numpy as np from mpmath import mpc,mpmathify,ellipfun if k == 'default': k = 1/mpmathify(np.sqrt(2)) Ke = 1.854 cn = ellipfun('cn') if type(z) is complex: z = np.asarray([z]) zf = np.ndarray.flatten(z) w = np.zeros(zf.shape)*1j pre_factor = mpc(1,-1)/mpmathify(np.sqrt(2)) mid_factor = Ke*(mpc(1,1)/2) for idx,entry in enumerate(zf): # Go through all complex numbers # Calculate result temp = pre_factor*cn( u = mid_factor*entry-Ke, k = k) # Then place it into the array w[idx] = np.complex(temp.real,temp.imag) # Now reshape the array back to its original shape z = w.reshape(z.shape).copy() return z def disk_to_square(self,z,k='default'): import numpy as np Ke = 1.854 if type(z) is complex: z = np.asarray([z]) zf = np.ndarray.flatten(z) w = np.zeros(zf.shape)*1j # Using the Gauss-Kronrod integration is about 10 times faster than # using the mpmath.ellipf function if k == 'default': k = 1/np.sqrt(2) m = k**2 pre_factor = (1-1j)/(-Ke) mid_factor = (1+1j)/np.sqrt(2) temp = [pre_factor*self.complex_integral( func = lambda t: (1-m*np.sin(t)**2)**(-0.5), a = 0, b = i)[0] + 1 - 1j for i in np.arccos(zf*mid_factor)] w = np.asarray(temp) # Now reshape the array back to its original shape z = w.reshape(z.shape).copy() return z def are_points_clockwise(self): import numpy as np verts = np.zeros((3,2)) verts[0,:] = np.asarray([np.cos(self.r[0]),np.sin(self.r[0])]) verts[1,:] = np.asarray([np.cos(self.r[1]),np.sin(self.r[1])]) verts[2,:] = np.asarray([np.cos(self.r[2]),np.sin(self.r[2])]) signed_area = 0 for vtx in range(verts.shape[0]): x1 = verts[vtx,0] y1 = verts[vtx,1] if vtx == verts.shape[0]-1: # Last vertex x2 = verts[0,0] y2 = verts[0,1] else: x2 = verts[vtx+1,0] y2 = verts[vtx+1,1] signed_area += (x1 * y2 - x2 * y1)/2 return (signed_area < 0) def head_to_potential(self): # Extract the hydraulic conductivity from the base element k = self.model.elementlist[0].k for idx,h in enumerate([self.head_min,self.head_max]): if self.model.aquifer_type == 'confined' or (self.model.aquifer_type == 'convertible' and h >= self.model.H): # Strack 1989, Eq. 8.6 pot = k*self.model.H*h - 0.5*k*self.model.H**2 elif self.model.aquifer_type == 'unconfined' or (self.model.aquifer_type == 'convertible' and h < self.model.H): # Strack 1989, Eq. 8.7 pot = 0.5*k*h**2 if idx == 0: self.phi_min = pot elif idx == 1: self.phi_max = pot def complexify(self,z): """ This function takes the provided line or polygon and converts it into a complex-valued vector, if it isn't already provided as one.' """ import numpy as np if not np.iscomplex(z).any(): if len(z.shape) != 2 or z.shape[1] != 2: raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.') else: z = z[:,0] + 1j*z[:,1] return z def plot(self,label_offset = 1.1,fontsize=12,fontcolor='xkcd:dark grey', pointcolor='xkcd:dark grey',pointsize=50,horizontalalignment='center', verticalalignment='center',color_low = 'xkcd:cerulean', color_high = 'xkcd:orangish red',**kwargs): """ This function plots the Möbius control/reference points on the unit disk. """ import numpy as np import matplotlib.pyplot as plt import math # Get the coordinates of the control points z_A = (1-1j)/np.abs(1-1j) z_A = self.moebius(z_A,inverse=False)*self.model.domain_radius + self.model.domain_center z_A = np.asarray([np.real(z_A),np.imag(z_A)]) z_B = (1+1j)/np.abs(1+1j) z_B = self.moebius(z_B,inverse=False)*self.model.domain_radius + self.model.domain_center z_B = np.asarray([np.real(z_B),np.imag(z_B)]) z_C = (-1+1j)/np.abs(-1+1j) z_C = self.moebius(z_C,inverse=False)*self.model.domain_radius + self.model.domain_center z_C = np.asarray([np.real(z_C),np.imag(z_C)]) z_D = (-1-1j)/np.abs(-1-1j) z_D = self.moebius(z_D,inverse=False)*self.model.domain_radius + self.model.domain_center z_D = np.asarray([np.real(z_D),np.imag(z_D)]) a_low = np.linspace(math.atan2(z_C[1],z_C[0]),math.atan2(z_D[1],z_D[0]),360) if abs(a_low[0]-a_low[-1]) > np.pi: a_low = np.concatenate(( np.linspace(np.min(a_low),-np.pi,360), np.linspace(np.pi,np.max(a_low),360) )) a_high = np.linspace(math.atan2(z_A[1],z_A[0]),math.atan2(z_B[1],z_B[0]),360) if abs(a_high[0]-a_high[-1]) > np.pi: a_high = np.concatenate(( np.linspace(np.min(a_high),-np.pi,360), np.linspace(np.pi,np.max(a_high),360) )) plt.plot(np.cos(a_low)*self.model.domain_radius + self.model.domain_center, np.sin(a_low)*self.model.domain_radius + self.model.domain_center, color = color_low,linewidth=2) plt.plot(np.cos(a_high)*self.model.domain_radius + self.model.domain_center, np.sin(a_high)*self.model.domain_radius + self.model.domain_center, color = color_high,linewidth=2) plt.scatter(z_A[0],z_A[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.scatter(z_B[0],z_B[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.scatter(z_C[0],z_C[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.scatter(z_D[0],z_D[1],s=pointsize,color=pointcolor,zorder=11,**kwargs) plt.text(z_A[0]*label_offset,z_A[1]*label_offset,'A',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) plt.text(z_B[0]*label_offset,z_B[1]*label_offset,'B',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) plt.text(z_C[0]*label_offset,z_C[1]*label_offset,'C',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) plt.text(z_D[0]*label_offset,z_D[1]*label_offset,'D',fontsize=fontsize, horizontalalignment=horizontalalignment,verticalalignment=verticalalignment, color=fontcolor,**kwargs) #%% def equidistant_points_in_circle(rings = 3, radius = 1, offset = 0+0j): """ This function creates equidistant points on a number of specified rings inside a unit disk. Parameters: rings - [scalar] : number of rings on which equidistant points are placed; the more rings, the more points radius - [scalar] : radius by which the unit disk is scaled """ import numpy as np import math # If the offset is complex, convert it to a real vector of length 2 if np.iscomplex(offset).any(): if not np.isscalar(offset): raise Exception('Shape format not understood. Provide the offset either as a complex scalar, or as a real numpy array of shape (2,).') else: offset = np.asarray([np.real(offset),np.imag(offset)]) if np.isscalar(offset): offset = np.zeros(2) # Pre-allocate lists for the X and Y coordinates x = [] y = [] # Then go through each ring for k in range(rings): if k > 0: pts = round(np.pi/math.asin(1/(2*k))) else: pts = 1 theta = np.linspace(0, 2*np.pi, pts) rad = k/(rings-1) x += list(np.sin(theta)*rad) y += list(np.cos(theta)*rad) # Combine both lists to a common array XY = np.column_stack(( np.asarray(x), np.asarray(y))) # And scale it, if desired XY *= radius # Apply the offset XY[:,0] += offset[0] XY[:,1] += offset[1] return XY

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1aef31c40ca2800068409f3a8847859625ad49b7

225

py

Python

office365/sharepoint/audit/audit.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/audit/audit.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/audit/audit.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.sharepoint.base_entity import BaseEntity class Audit(BaseEntity): """ Enables auditing of how site collections, sites, lists, folders, and list items are accessed, changed, and used. """ pass

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210679c4352581f818f84b0e6bf6289ce9deb608

46

py

Python

binary_clock/__init__.py

zeycus/binary_clock

4d7bf6a4d432fdb0f9dccde9d94173ac4935b05f

[ "MIT" ]

null

binary_clock/__init__.py

zeycus/binary_clock

4d7bf6a4d432fdb0f9dccde9d94173ac4935b05f

[ "MIT" ]

null

binary_clock/__init__.py

zeycus/binary_clock

4d7bf6a4d432fdb0f9dccde9d94173ac4935b05f

[ "MIT" ]

2

2018-02-16T21:16:41.000Z

2022-03-18T05:15:25.000Z

from .binclockWrapper import binclock_wrapper

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2144704faae460c6a062d40147ec2d52a11302fe

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py

Python

server/editors/admin.py

nickdotreid/opioid-mat-decision-aid

bbc2a0d8931d59cd6ab64b0b845e88c8dc1af5d1

[ "MIT" ]

null

server/editors/admin.py

nickdotreid/opioid-mat-decision-aid

bbc2a0d8931d59cd6ab64b0b845e88c8dc1af5d1

[ "MIT" ]

27

2018-09-30T07:59:21.000Z

2020-11-05T19:25:41.000Z

server/editors/admin.py

nickdotreid/opioid-mat-decision-aid

bbc2a0d8931d59cd6ab64b0b845e88c8dc1af5d1

[ "MIT" ]

null

from django.contrib import admin from .models import Editor @admin.register(Editor) class PageAdmin(admin.ModelAdmin): pass

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dcff7d128e3c6aff411835ed9b14181710e71bb1

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py

Python

python_codes/test.py

penginsl/electrophysiology_analysis

a38c4b1e1cb018fd670edb3157ddfba6cc2a3285

[ "MIT" ]

2

2020-12-27T01:25:46.000Z

2021-02-21T07:45:08.000Z

python_codes/test.py

li-shen-amy/electrophysiology_analysis

a38c4b1e1cb018fd670edb3157ddfba6cc2a3285

[ "MIT" ]

null

python_codes/test.py

li-shen-amy/electrophysiology_analysis

a38c4b1e1cb018fd670edb3157ddfba6cc2a3285

[ "MIT" ]

null

from read_lvb import read_single_lvb read_single_lvb('8_20_2019_pen1_0_1x10s.lvb', rec_len=1, trial_num=10)

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b4a758c488ad96a9e6cf231d9114525ad34184ce

177

py

Python

httpsig_cffi/__init__.py

hawkowl/httpsig

af5c8bcce7c93f5734b1a48722580d02c5d25bc7

[ "MIT" ]

1

2016-10-03T17:38:07.000Z

httpsig_cffi/__init__.py

hawkowl/httpsig

af5c8bcce7c93f5734b1a48722580d02c5d25bc7

[ "MIT" ]

1

2016-09-27T11:03:42.000Z

2016-10-03T17:42:11.000Z

httpsig_cffi/__init__.py

hawkowl/httpsig

af5c8bcce7c93f5734b1a48722580d02c5d25bc7

[ "MIT" ]

4

2016-07-27T06:05:10.000Z

2019-06-26T22:04:09.000Z

from .sign import Signer, HeaderSigner from .verify import Verifier, HeaderVerifier from ._version import get_versions __version__ = get_versions()['version'] del get_versions

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2ef761cd8d1016273df97d4fa8d6cd67e2a5984b

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py

Python

Inheritance/class_Inheritance/project_players_and_monsters/muse_elf.py

vasetousa/OOP

e4fedc497dd149c9800613ea11846e0e770d122c

[ "MIT" ]

null

Inheritance/class_Inheritance/project_players_and_monsters/muse_elf.py

vasetousa/OOP

e4fedc497dd149c9800613ea11846e0e770d122c

[ "MIT" ]

null

Inheritance/class_Inheritance/project_players_and_monsters/muse_elf.py

vasetousa/OOP

e4fedc497dd149c9800613ea11846e0e770d122c

[ "MIT" ]

null

from Encapsulation.project_wild_cat_zoo import Elf class MuseElf(Elf): pass

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2c06123c16b710a8e76d42eceec9f3bb74023ba8

48

py

Python

meta_policy_search/envs/__init__.py

behzadhaghgoo/cml

e659c7ae10a52bbe1cbabf9d359aea43af19eb12

[ "MIT" ]

210

2018-10-17T01:04:48.000Z

2022-03-09T16:17:06.000Z

meta_policy_search/envs/__init__.py

behzadhaghgoo/cml

e659c7ae10a52bbe1cbabf9d359aea43af19eb12

[ "MIT" ]

13

2018-10-25T20:01:09.000Z

2022-01-24T13:11:24.000Z

meta_policy_search/envs/__init__.py

behzadhaghgoo/cml

e659c7ae10a52bbe1cbabf9d359aea43af19eb12

[ "MIT" ]

55

2018-10-18T22:00:51.000Z

2021-11-24T00:06:31.000Z

from meta_policy_search.envs.base import MetaEnv

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0

6

25a704bb8c7432afced1746b5db5782eeb6b9260

108

py

Python

dop_python_pip_package/main_utils/math.py

RyanAngJY/dop_python_pip_package

483cddc94e555080f11131667cf4ddad5ee4002a

[ "MIT" ]

null

dop_python_pip_package/main_utils/math.py

RyanAngJY/dop_python_pip_package

483cddc94e555080f11131667cf4ddad5ee4002a

[ "MIT" ]

null

dop_python_pip_package/main_utils/math.py

RyanAngJY/dop_python_pip_package

483cddc94e555080f11131667cf4ddad5ee4002a

[ "MIT" ]

null

import numpy as np def add(num1, num2): return num1 + num2 def random(): return np.random.rand(2)

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6

25b23f92a690b5dc2591fb3a015a2920be3c2714

375

py

Python

pattern4/hamburg_store_v5/src/WhiteFeatherChicken.py

icexmoon/design-pattern-with-python

bb897e886fe52bb620db0edc6ad9d2e5ecb067af

[ "MIT" ]

null

pattern4/hamburg_store_v5/src/WhiteFeatherChicken.py

icexmoon/design-pattern-with-python

bb897e886fe52bb620db0edc6ad9d2e5ecb067af

[ "MIT" ]

null

pattern4/hamburg_store_v5/src/WhiteFeatherChicken.py

icexmoon/design-pattern-with-python

bb897e886fe52bb620db0edc6ad9d2e5ecb067af

[ "MIT" ]

null

####################################################### # # WhiteFeatherChicken.py # Python implementation of the Class WhiteFeatherChicken # Generated by Enterprise Architect # Created on: 19-6��-2021 21:34:31 # Original author: 70748 # ####################################################### from .Chicken import Chicken class WhiteFeatherChicken(Chicken): pass

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null

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6

25b461901a114bfef4e2befb760d938c643080a9

1,798

py

Python

entity/block.py

chenwenhang/StockVisualAnalysis

8f186224f0001a5c1d7fe86f2540bb9221c81707

[ "MIT" ]

1

2021-06-23T11:43:26.000Z

entity/block.py

chenwenhang/StockVisualAnalysis

8f186224f0001a5c1d7fe86f2540bb9221c81707

[ "MIT" ]

null

entity/block.py

chenwenhang/StockVisualAnalysis

8f186224f0001a5c1d7fe86f2540bb9221c81707

[ "MIT" ]

1

2021-11-17T12:13:45.000Z

from entity.item_entity import ItemEntity class BlockEntity(ItemEntity): def __init__(self, block): ItemEntity.__init__(self) self.field_set = block def get_code(self): return self.field_set["code"] def get_name(self): return self.field_set["name"] def get_indicator(self): return self.field_set["indicator"] def get_RPS250(self): return self.field_set["RPS250"] def get_RPS120(self): return self.field_set["RPS120"] def get_RPS60(self): return self.field_set["RPS60"] def get_RPS30(self): return self.field_set["RPS30"] def get_RPS10(self): return self.field_set["RPS10"] def get_date(self): return self.field_set["date"] def get_grade(self): return self.field_set["grade"] def set_code(self, code): self.field_set["code"] = code def set_name(self, name): self.field_set["name"] = name def set_indicator(self, indicator): self.field_set["indicator"] = indicator def set_RPS250(self, RPS250): self.field_set["RPS250"] = RPS250 def set_RPS120(self, RPS120): self.field_set["RPS120"] = RPS120 def set_RPS60(self, RPS60): self.field_set["RPS60"] = RPS60 def set_RPS30(self, RPS30): self.field_set["RPS30"] = RPS30 def set_RPS10(self, RPS10): self.field_set["RPS10"] = RPS10 def set_date(self, date): self.field_set["date"] = date def set_grade(self, grade): self.field_set["grade"] = self.field_set["RPS10"] * 0.4 + \ self.field_set["RPS30"] * 0.3 + \ self.field_set["RPS60"] * 0.2 + \ self.field_set["RPS120"] * 0.1

25.323944

67

0.587875

233

1,798

4.304721

0.128755

0.224327

0.299103

0.189432

0.219342

0

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null

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null

0

1

0

1

0

6

25b6fe6e7e963061d5676e07d2b8b093d960a651

95

py

Python

simpleimage/__init__.py

adilu/simpleimage

5e38340fb619d4350a3de7aa9a3a8b0145a6b568

[ "MIT" ]

1

2021-05-07T13:53:42.000Z

simpleimage/__init__.py

adilu/simpleimage

5e38340fb619d4350a3de7aa9a3a8b0145a6b568

[ "MIT" ]

null

simpleimage/__init__.py

adilu/simpleimage

5e38340fb619d4350a3de7aa9a3a8b0145a6b568

[ "MIT" ]

null

# Inside of __init__.py from simpleimage.Image import Image from simpleimage.Pixel import Pixel

31.666667

35

0.842105

14

95

5.428571

0.642857

0.394737

0

0.115789

95

3

36

31.666667

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true

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null

0

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1

0

1

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6

d320c91ea6b461e0c7b131bd8b8d8de493e7eeac

20,058

py

Python

models/bioproc/proc_models.py

LukeItUp/BioProc

dc7c38dd7d3b0ed01419892d81eb7fbe39a6b584

[ "CC-BY-4.0" ]

null

models/bioproc/proc_models.py

LukeItUp/BioProc

dc7c38dd7d3b0ed01419892d81eb7fbe39a6b584

[ "CC-BY-4.0" ]

null

models/bioproc/proc_models.py

LukeItUp/BioProc

dc7c38dd7d3b0ed01419892d81eb7fbe39a6b584

[ "CC-BY-4.0" ]

null

import numpy as np from bioproc.hill_functions import * """ FLIP-FLOP MODELS """ # MASTER-SLAVE D FLIP-FLOP QSSA MODEL def ff_stochastic_model(Y, T, params, omega): p = np.zeros(12) a, not_a, q, not_q, d, clk = Y alpha1, alpha2, alpha3, alpha4, delta1, delta2, Kd, n = params p[0] = alpha1*(pow(d/(Kd*omega), n)/(1 + pow(d/(Kd*omega), n) + pow(clk/(Kd*omega), n) + pow(d/(Kd*omega), n)*pow(clk/(Kd*omega), n)))*omega p[1] = alpha2*(1/(1 + pow(not_a/(Kd*omega), n)))*omega p[2] = delta1*a p[3] = alpha1*(1/(1 + pow(d/(Kd*omega), n) + pow(clk/(Kd*omega), n) + pow(d/(Kd*omega), n)*pow(clk/(Kd*omega), n)))*omega p[4] = alpha2*(1/(1 + pow(a/(Kd*omega), n)))*omega p[5] = delta1*not_a p[6] = alpha3*((pow(a/(Kd*omega), n)*pow(clk/(Kd*omega), n))/(1 + pow(a/(Kd*omega), n) + pow(clk/(Kd*omega), n) + pow(a/(Kd*omega), n)*pow(clk/(Kd*omega), n)))*omega p[7] = alpha4*(1/(1 + pow(not_q/(Kd*omega), n)))*omega p[8] = delta2*q p[9] = alpha3*((pow(not_a/(Kd*omega), n)*pow(clk/(Kd*omega), n))/(1 + pow(not_a/(Kd*omega), n) + pow(clk/(Kd*omega), n) + pow(not_a/(Kd*omega), n)*pow(clk/(Kd*omega), n)))*omega p[10] = alpha4*(1/(1 + pow(q/(Kd*omega), n)))*omega p[11] = delta2*not_q #propensities return p # MASTER-SLAVE D FLIP-FLOP MODEL def ff_ode_model(Y, T, params): a, not_a, q, not_q, d, clk = Y alpha1, alpha2, alpha3, alpha4, delta1, delta2, Kd, n = params da_dt = alpha1*(pow(d/Kd, n)/(1 + pow(d/Kd, n) + pow(clk/Kd, n) + pow(d/Kd, n)*pow(clk/Kd, n))) + alpha2*(1/(1 + pow(not_a/Kd, n))) - delta1 *a dnot_a_dt = alpha1*(1/(1 + pow(d/Kd, n) + pow(clk/Kd, n) + pow(d/Kd, n)*pow(clk/Kd, n))) + alpha2*(1/(1 + pow(a/Kd, n))) - delta1*not_a dq_dt = alpha3*((pow(a/Kd, n)*pow(clk/Kd, n))/(1 + pow(a/Kd, n) + pow(clk/Kd, n) + pow(a/Kd, n)*pow(clk/Kd, n))) + alpha4*(1/(1 + pow(not_q/Kd, n))) - delta2*q dnot_q_dt = alpha3*((pow(not_a/Kd, n)*pow(clk/Kd, n))/(1 + pow(not_a/Kd, n) + pow(clk/Kd, n) + pow(not_a/Kd, n)*pow(clk/Kd, n))) + alpha4*(1/(1 + pow(q/Kd, n))) - delta2*not_q return np.array([da_dt, dnot_a_dt, dq_dt, dnot_q_dt]) # FF MODEL WITH ASYNCHRONOUS RESET AND SET # dodana parametra deltaE, KM # dodani vhodni spremenljivki RESET in SET # dodano 23. 1. 2020 def ff_ode_model_RS(Y, T, params): a, not_a, q, not_q, d, clk, RESET, SET = Y repress_both = True if repress_both: sum_one = a + q sum_zero = not_a + not_q alpha1, alpha2, alpha3, alpha4, delta1, delta2, Kd, n, deltaE, KM = params da_dt = alpha1*(pow(d/Kd, n)/(1 + pow(d/Kd, n) + pow(clk/Kd, n) + pow(d/Kd, n)*pow(clk/Kd, n))) + alpha2*(1/(1 + pow(not_a/Kd, n))) - delta1 *a #deltaE = delta1 if repress_both: da_dt += -a*(deltaE*RESET/(KM+sum_one)) else: da_dt += -a*(deltaE*RESET/(KM+a)) dnot_a_dt = alpha1*(1/(1 + pow(d/Kd, n) + pow(clk/Kd, n) + pow(d/Kd, n)*pow(clk/Kd, n))) + alpha2*(1/(1 + pow(a/Kd, n))) - delta1*not_a if repress_both: dnot_a_dt += -not_a*(deltaE*SET/(KM+sum_zero)) else: dnot_a_dt += -not_a*(deltaE*SET/(KM+not_a)) #deltaE = delta2 dq_dt = alpha3*((pow(a/Kd, n)*pow(clk/Kd, n))/(1 + pow(a/Kd, n) + pow(clk/Kd, n) + pow(a/Kd, n)*pow(clk/Kd, n))) + alpha4*(1/(1 + pow(not_q/Kd, n))) - delta2*q if repress_both: dq_dt += -q*(deltaE*RESET/(KM+sum_one)) dnot_q_dt = alpha3*((pow(not_a/Kd, n)*pow(clk/Kd, n))/(1 + pow(not_a/Kd, n) + pow(clk/Kd, n) + pow(not_a/Kd, n)*pow(clk/Kd, n))) + alpha4*(1/(1 + pow(q/Kd, n))) - delta2*not_q if repress_both: dnot_q_dt += -not_q*(deltaE*SET/(KM+sum_zero)) return np.array([da_dt, dnot_a_dt, dq_dt, dnot_q_dt]) """ ADRESSING MODELS """ # ADDRESSING 1-BIT QSSA MODEL def addressing_stochastic_one_bit_model(Y, T, params, omega): alpha, delta, Kd, n = params _,_, q1, not_q1, i1, i2 = Y p = np.zeros(4) p[0] = alpha*activate_1(not_q1, Kd*omega, n)*omega p[1] = delta*i1 p[2] = alpha*activate_1(q1, Kd*omega, n)*omega p[3] = delta*i2 #propensities return p # ADDRESSING 2-BIT QSSA MODEL def addressing_stochastic_two_bit_model(Y, T, params, omega): alpha, delta, Kd, n = params _, _, q1, not_q1, _, _, q2, not_q2, i1, i2, i3, i4 = Y p = np.zeros(8) p[0] = alpha * activate_2(not_q1, not_q2, Kd*omega, n)*omega p[1] = delta * i1 p[2] = alpha * activate_2(q1, not_q2, Kd*omega, n)*omega p[3] = delta * i2 p[4] = alpha * activate_2(q1, q2, Kd*omega, n)*omega p[5] = delta * i3 p[6] = alpha * activate_2(not_q1, q2, Kd*omega, n)*omega p[7] = delta * i4 #propensities return p # ADDRESSING 3-BIT QSSA MODEL def addressing_stochastic_three_bit_model(Y, T, params, omega): alpha, delta, Kd, n = params _, _, q1, not_q1, _, _, q2, not_q2, _, _, q3, not_q3, i1, i2, i3, i4, i5, i6 = Y p = np.zeros(12) p[0] = alpha * activate_2(not_q1, not_q3, Kd*omega, n)*omega p[1] = delta * i1 p[2] = alpha * activate_2(q1, not_q2, Kd*omega, n)*omega p[3] = delta * i2 p[4] = alpha * activate_2(q2, not_q3, Kd*omega, n)*omega p[5] = delta * i3 p[6] = alpha * activate_2(q1, q3, Kd*omega, n)*omega p[7] = delta * i4 p[8] = alpha * activate_2(not_q1, q2, Kd*omega, n)*omega p[9] = delta * i5 p[10] = alpha * activate_2(not_q2, q3, Kd*omega, n)*omega p[11] = delta * i6 #propensities return p # ONE BIT ADDRESSING MODEL SIMPLE def one_bit_simple_addressing_ode_model(Y, T, params): alpha, delta, Kd, n = params q1, not_q1, i1, i2 = Y di1_dt = alpha * activate_1(not_q1, Kd, n) - delta * i1 di2_dt = alpha * activate_1(q1, Kd, n) - delta * i2 return np.array([di1_dt, di2_dt]) # TWO BIT ADDRESSING MODEL SIMPLE def two_bit_simple_addressing_ode_model(Y, T, params): alpha, delta, Kd, n = params q1, not_q1, q2, not_q2, i1, i2, i3, i4 = Y di1_dt = alpha * activate_2(not_q1, not_q2, Kd, n) - delta * i1 di2_dt = alpha * activate_2(q1, not_q2, Kd, n) - delta * i2 di3_dt = alpha * activate_2(q1, q2, Kd, n) - delta * i3 di4_dt = alpha * activate_2(not_q1, q2, Kd, n) - delta * i4 return np.array([di1_dt, di2_dt, di3_dt, di4_dt]) # THREE BIT ADDRESSING MODEL SIMPLE def three_bit_simple_addressing_ode_model(Y, T, params): alpha, delta, Kd, n = params q1, not_q1, q2, not_q2, q3, not_q3, i1, i2, i3, i4, i5, i6 = Y di1_dt = alpha * activate_2(not_q1, not_q3, Kd, n) - delta * i1 di2_dt = alpha * activate_2(q1, not_q2, Kd, n) - delta * i2 di3_dt = alpha * activate_2(q2, not_q3, Kd, n) - delta * i3 di4_dt = alpha * activate_2(q1, q3, Kd, n) - delta * i4 di5_dt = alpha * activate_2(not_q1, q2, Kd, n) - delta * i5 di6_dt = alpha * activate_2(not_q2, q3, Kd, n) - delta * i6 return np.array([di1_dt, di2_dt, di3_dt, di4_dt, di5_dt, di6_dt]) # FOUR BIT ADDRESSING MODEL SIMPLE def four_bit_simple_addressing_ode_model(Y, T, params): alpha, delta, Kd, n = params q1, not_q1, q2, not_q2, q3, not_q3, q4, not_q4, i1, i2, i3, i4, i5, i6, i7, i8 = Y di1_dt = alpha * activate_2(not_q1, not_q4, Kd, n) - delta * i1 di2_dt = alpha * activate_2(q1, not_q2, Kd, n) - delta * i2 di3_dt = alpha * activate_2(q2, not_q3, Kd, n) - delta * i3 di4_dt = alpha * activate_2(q3, not_q4, Kd, n) - delta * i4 di5_dt = alpha * activate_2(q1, q4, Kd, n) - delta * i5 di6_dt = alpha * activate_2(not_q1, q2, Kd, n) - delta * i6 di7_dt = alpha * activate_2(not_q2, q3, Kd, n) - delta * i7 di8_dt = alpha * activate_2(not_q3, q4, Kd, n) - delta * i8 return np.array([di1_dt, di2_dt, di3_dt, di4_dt, di5_dt, di6_dt, di7_dt, di8_dt]) # FIVE BIT ADDRESSING MODEL SIMPLE def five_bit_simple_addressing_ode_model(Y, T, params): alpha, delta, Kd, n = params q1, not_q1, q2, not_q2, q3, not_q3, q4, not_q4, q5, not_q5, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10 = Y di1_dt = alpha * activate_2(not_q1, not_q5, Kd, n) - delta * i1 di2_dt = alpha * activate_2(q1, not_q2, Kd, n) - delta * i2 di3_dt = alpha * activate_2(q2, not_q3, Kd, n) - delta * i3 di4_dt = alpha * activate_2(q3, not_q4, Kd, n) - delta * i4 di5_dt = alpha * activate_2(q4, not_q5, Kd, n) - delta * i5 di6_dt = alpha * activate_2(q1, q5, Kd, n) - delta * i6 di7_dt = alpha * activate_2(not_q1, q2, Kd, n) - delta * i7 di8_dt = alpha * activate_2(not_q2, q3, Kd, n) - delta * i8 di9_dt = alpha * activate_2(not_q3, q4, Kd, n) - delta * i9 di10_dt = alpha * activate_2(not_q4, q5, Kd, n) - delta * i10 return np.array([di1_dt, di2_dt, di3_dt, di4_dt, di5_dt, di6_dt, di7_dt, di8_dt, di9_dt, di10_dt]) """ JOHSON COUNTER MODELS """ # TOP MODEL (JOHNSON): ONE BIT MODEL WITH EXTERNAL CLOCK def one_bit_model(Y, T, params): a, not_a, q, not_q= Y clk = get_clock(T) d = not_q Y_FF1 = [a, not_a, q, not_q, d, clk] dY = ff_ode_model(Y_FF1, T, params) return dY # TOP MODEL (JOHNSON): TWO BIT MODEL WITH EXTERNAL CLOCK def two_bit_model(Y, T, params): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2 = Y clk = get_clock(T) d1 = not_q2 d2 = q1 Y_FF1 = [a1, not_a1, q1, not_q1, d1, clk] Y_FF2 = [a2, not_a2, q2, not_q2, d2, clk] dY1 = ff_ode_model(Y_FF1, T, params) dY2 = ff_ode_model(Y_FF2, T, params) dY = np.append(dY1, dY2) return dY # TOP MODEL (JOHNSON): THREE BIT MODEL WITH EXTERNAL CLOCK def three_bit_model(Y, T, params): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3 = Y clk = get_clock(T) d1 = not_q3 d2 = q1 d3 = q2 Y_FF1 = [a1, not_a1, q1, not_q1, d1, clk] Y_FF2 = [a2, not_a2, q2, not_q2, d2, clk] Y_FF3 = [a3, not_a3, q3, not_q3, d3, clk] dY1 = ff_ode_model(Y_FF1, T, params) dY2 = ff_ode_model(Y_FF2, T, params) dY3 = ff_ode_model(Y_FF3, T, params) dY = np.append(np.append(dY1, dY2), dY3) return dY # TOP MODEL (JOHNSON): FOUR BIT MODEL WITH EXTERNAL CLOCK def four_bit_model(Y, T, params): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, a4, not_a4, q4, not_q4 = Y clk = get_clock(T) d1 = not_q4 d2 = q1 d3 = q2 d4 = q3 Y_FF1 = [a1, not_a1, q1, not_q1, d1, clk] Y_FF2 = [a2, not_a2, q2, not_q2, d2, clk] Y_FF3 = [a3, not_a3, q3, not_q3, d3, clk] Y_FF4 = [a4, not_a4, q4, not_q4, d4, clk] dY1 = ff_ode_model(Y_FF1, T, params) dY2 = ff_ode_model(Y_FF2, T, params) dY3 = ff_ode_model(Y_FF3, T, params) dY4 = ff_ode_model(Y_FF4, T, params) dY = np.append(np.append(np.append(dY1, dY2), dY3), dY4) return dY # TOP MODEL (JOHNSON): FIVE BIT MODEL WITH EXTERNAL CLOCK def five_bit_model(Y, T, params): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, a4, not_a4, q4, not_q4, a5, not_a5, q5, not_q5 = Y clk = get_clock(T) d1 = not_q5 d2 = q1 d3 = q2 d4 = q3 d5 = q4 Y_FF1 = [a1, not_a1, q1, not_q1, d1, clk] Y_FF2 = [a2, not_a2, q2, not_q2, d2, clk] Y_FF3 = [a3, not_a3, q3, not_q3, d3, clk] Y_FF4 = [a4, not_a4, q4, not_q4, d4, clk] Y_FF5 = [a5, not_a5, q5, not_q5, d5, clk] dY1 = ff_ode_model(Y_FF1, T, params) dY2 = ff_ode_model(Y_FF2, T, params) dY3 = ff_ode_model(Y_FF3, T, params) dY4 = ff_ode_model(Y_FF4, T, params) dY5 = ff_ode_model(Y_FF5, T, params) dY = np.append(np.append(np.append(np.append(dY1, dY2), dY3), dY4), dY5) return dY """ JOHSON COUNTER MODELS THAT USE FLIP-FLOPS WITH ASYNCRHONOUS SET/RESET dodano 23. 1. 2020 """ # TOP MODEL (JOHNSON): ONE BIT MODEL WITH EXTERNAL CLOCK AND FLIP-FLOPS WITH ASYNCRHONOUS SET/RESET def one_bit_model_RS(Y, T, params): a, not_a, q, not_q, R, S = Y clk = get_clock(T) d = not_q Y_FF1 = [a, not_a, q, not_q, d, clk, R, S] dY = ff_ode_model_RS(Y_FF1, T, params) return dY # TOP MODEL (JOHNSON): TWO BIT MODEL WITH EXTERNAL CLOCK AND FLIP-FLOPS WITH ASYNCRHONOUS SET/RESET def two_bit_model_RS(Y, T, params): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, R1, S1, R2, S2 = Y clk = get_clock(T) d1 = not_q2 d2 = q1 Y_FF1 = [a1, not_a1, q1, not_q1, d1, clk, R1, S1] Y_FF2 = [a2, not_a2, q2, not_q2, d2, clk, R2, S2] dY1 = ff_ode_model_RS(Y_FF1, T, params) dY2 = ff_ode_model_RS(Y_FF2, T, params) dY = np.append(dY1, dY2) return dY # TOP MODEL (JOHNSON): THREE BIT MODEL WITH EXTERNAL CLOCK AND FLIP-FLOPS WITH ASYNCRHONOUS SET/RESET def three_bit_model_RS(Y, T, params): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, R1, S1, R2, S2, R3, S3 = Y clk = get_clock(T) d1 = not_q3 d2 = q1 d3 = q2 Y_FF1 = [a1, not_a1, q1, not_q1, d1, clk, R1, S1] Y_FF2 = [a2, not_a2, q2, not_q2, d2, clk, R2, S2] Y_FF3 = [a3, not_a3, q3, not_q3, d3, clk, R3, S3] dY1 = ff_ode_model_RS(Y_FF1, T, params) dY2 = ff_ode_model_RS(Y_FF2, T, params) dY3 = ff_ode_model_RS(Y_FF3, T, params) dY = np.append(np.append(dY1, dY2), dY3) return dY """ PROCESSOR MODEL !!!OPTIMIZACIJA NAD TEMI MODELI!!! """ # TOP MODEL OF PROCESSOR WITH ONE BIT ADDRESSING def one_bit_processor_ext(Y, T, params_johnson, params_addr): a1, not_a1, q1, not_q1, i1, i2 = Y Y_johnson = [a1, not_a1, q1, not_q1] Y_address = [q1, not_q1, i1, i2] dY_johnson = one_bit_model(Y_johnson, T, params_johnson) dY_addr = one_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY # TOP MODEL OF PROCESSOR WITH TWO BIT ADDRESSING def two_bit_processor_ext(Y, T, params_johnson, params_addr): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, i1, i2, i3, i4 = Y Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2] Y_address = [q1, not_q1, q2, not_q2, i1, i2, i3, i4] dY_johnson = two_bit_model(Y_johnson, T, params_johnson) dY_addr = two_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY # TOP MODEL OF PROCESSOR WITH THREE BIT ADDRESSING def three_bit_processor_ext(Y, T, params_johnson, params_addr): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, i1, i2, i3, i4, i5, i6 = Y Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3] Y_address = [q1, not_q1, q2, not_q2, q3, not_q3, i1, i2, i3, i4, i5, i6] dY_johnson = three_bit_model(Y_johnson, T, params_johnson) dY_addr = three_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY # TOP MODEL OF PROCESSOR WITH FOUR BIT ADDRESSING def four_bit_processor_ext(Y, T, params_johnson, params_addr): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, a4, not_a4, q4, not_q4, i1, i2, i3, i4, i5, i6, i7, i8 = Y Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, a4, not_a4, q4, not_q4] Y_address = [q1, not_q1, q2, not_q2, q3, not_q3, q4, not_q4, i1, i2, i3, i4, i5, i6, i7, i8] dY_johnson = four_bit_model(Y_johnson, T, params_johnson) dY_addr = four_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY # TOP MODEL OF PROCESSOR WITH FIVE BIT ADDRESSING def five_bit_processor_ext(Y, T, params_johnson, params_addr): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, a4, not_a4, q4, not_q4, a5, not_a5, q5, not_q5, i1, i2, i3, i4, i5, i6, i7, i8 ,i9, i10 = Y Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, a4, not_a4, q4, not_q4, a5, not_a5, q5, not_q5] Y_address = [q1, not_q1, q2, not_q2, q3, not_q3, q4, not_q4, q5, not_q5, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10] dY_johnson = five_bit_model(Y_johnson, T, params_johnson) dY_addr = five_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY """ PROCESSOR MODEL WITH EXTERNAL CLOCK AND RS inputs external clock is required, more robust jumps allowed dodano 23. 1. 2020 """ # TOP MODEL OF PROCESSOR WITH ONE BIT ADDRESSING AND FLIP-FLOP WITH RS ASYNCHRONOUS INPUTS def one_bit_processor_ext_RS(Y, T, params_johnson_RS, params_addr): a1, not_a1, q1, not_q1, i1, i2 = Y R1 = 0 S1 = 0 Y_johnson = [a1, not_a1, q1, not_q1, R1, S1] Y_address = [q1, not_q1, i1, i2] dY_johnson = one_bit_model_RS(Y_johnson, T, params_johnson_RS) dY_addr = one_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY # TOP MODEL OF PROCESSOR WITH TWO BIT ADDRESSING AND FLIP-FLOPS WITH RS ASYNCHRONOUS INPUTS def two_bit_processor_ext_RS(Y, T, params_johnson_RS, params_addr): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, i1, i2, i3, i4 = Y R1 = 0 S1 = 0 R2 = 0 S2 = 0 Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, R1, S1, R2, S2] Y_address = [q1, not_q1, q2, not_q2, i1, i2, i3, i4] dY_johnson = two_bit_model_RS(Y_johnson, T, params_johnson_RS) dY_addr = two_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY # TOP MODEL OF PROCESSOR WITH THREE BIT ADDRESSING AND FLIP-FLOPS WITH RS ASYNCHRONOUS INPUTS def three_bit_processor_ext_RS(Y, T, params_johnson_RS, params_addr, jump_src, jump_dst, i_src, i_dst): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, i1, i2, i3, i4, i5, i6 = Y i_src = eval(i_src) R = [0,0,0] S = [0,0,0] for i in range(len(jump_src)): if jump_src[i] > jump_dst[i]: R[i] = i_src elif jump_src[i] < jump_dst[i]: S[i] = i_src R1, R2, R3 = R if T > 1 else [100,100,100] S1, S2, S3 = S Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, R1, S1, R2, S2, R3, S3] Y_address = [q1, not_q1, q2, not_q2, q3, not_q3, i1, i2, i3, i4, i5, i6] dY_johnson = three_bit_model_RS(Y_johnson, T, params_johnson_RS) dY_addr = three_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY """ PROCESSOR MODEL WITH EXTERNAL CLOCK AND RS inputs AND JUMP CONDITIONS dodano 24. 1. 2020 """ def get_condition(x0, delta, t): return x0 * np.e**(-delta*t) # TOP MODEL OF PROCESSOR WITH THREE BIT ADDRESSING AND CONDITIONAL JUMPS def three_bit_processor_ext_RS_cond(Y, T, params_johnson_RS, params_addr, jump_src, jump_dst, i_src, i_dst, condition): a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, i1, i2, i3, i4, i5, i6 = Y x0_cond, delta_cond, KD_cond, condition_type = condition cond = get_condition(x0_cond, delta_cond, T) i_src = eval(i_src) R = np.array([0,0,0]) S = np.array([0,0,0]) for i in range(len(jump_src)): if jump_src[i] > jump_dst[i]: R[i] = i_src elif jump_src[i] < jump_dst[i]: S[i] = i_src if condition_type == "induction": R = induction(R, cond, KD_cond) S = induction(S, cond, KD_cond) else: R = inhibition(R, cond, KD_cond) S = inhibition(S, cond, KD_cond) R1, R2, R3 = R S1, S2, S3 = S Y_johnson = [a1, not_a1, q1, not_q1, a2, not_a2, q2, not_q2, a3, not_a3, q3, not_q3, R1, S1, R2, S2, R3, S3] Y_address = [q1, not_q1, q2, not_q2, q3, not_q3, i1, i2, i3, i4, i5, i6] dY_johnson = three_bit_model_RS(Y_johnson, T, params_johnson_RS) dY_addr = three_bit_simple_addressing_ode_model(Y_address, T, params_addr) dY = np.append(dY_johnson, dY_addr) return dY

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fastiqa/vqa.py

baidut/PatchVQ

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2020-12-05T09:11:20.000Z

2022-03-28T07:49:13.000Z

fastiqa/vqa.py

utlive/PatchVQ

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fastiqa/vqa.py

utlive/PatchVQ

040486b6342dfd36695f1daea0b5c4d77d728a23

[ "Unlicense" ]

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2020-12-29T21:52:07.000Z

2022-03-18T15:12:50.000Z

from fastai.vision.all import * from fastai.distributed import * from fastiqa.bunches.vqa.vid2mos import * from fastiqa.bunches.vqa.vid_sp2mos import * from fastiqa.bunches.vqa.single_vid2mos import * from fastiqa.bunches.feat2mos import * from fastiqa.bunches.vqa.test_videos import * from fastiqa.models._body_head import * from fastiqa.models.resnet_3d import * from fastiqa.models.inception_head import * from fastiqa.models._roi_pool import * from fastiqa.learn import * from fastiqa.iqa_exp import *

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{{cookiecutter.repo_name}}/tests/test_{{cookiecutter.repo_name}}_package.py

tjelvar-olsson/cookiecutter-pypackage

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{{cookiecutter.repo_name}}/tests/test_{{cookiecutter.repo_name}}_package.py

tjelvar-olsson/cookiecutter-pypackage

58c90986572b54063edc5809700140bd650f8bf4

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2019-08-05T00:38:50.000Z

"""Test the {{ cookiecutter.repo_name }} package.""" def test_version_is_string(): import {{ cookiecutter.repo_name }} assert isinstance({{ cookiecutter.repo_name }}.__version__, str)

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Python

content/downloads/code/hello_world.py

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2017-03-23T02:03:19.000Z

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content/downloads/code/hello_world.py

Jerska/jakevdp.github.io-source

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2017-10-11T15:11:49.000Z

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content/downloads/code/hello_world.py

Jerska/jakevdp.github.io-source

ee516b93a8d83457b1f11f21dba9016f685e887d

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2017-03-08T18:41:07.000Z

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import sys import os print("hello_world")

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py

Python

sources/viewgui/app_main_window/__init__.py

Groomsha/lan-map

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null

sources/viewgui/app_main_window/__init__.py

Groomsha/lan-map

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sources/viewgui/app_main_window/__init__.py

Groomsha/lan-map

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from .ui_app_main_window import *

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6cf5868cddef0acac801849800692c7ecb79b242

62

py

Python

src/packs/__init__.py

Flaiers/flatype

ebb951fb1dee2075779a19fd090166bb1347658f

[ "MIT" ]

2

2021-07-31T20:01:36.000Z

2021-09-07T13:37:42.000Z

src/packs/__init__.py

Flaiers/flatype

ebb951fb1dee2075779a19fd090166bb1347658f

[ "MIT" ]

null

src/packs/__init__.py

Flaiers/flatype

ebb951fb1dee2075779a19fd090166bb1347658f

[ "MIT" ]

2

2021-09-07T13:37:43.000Z

2021-10-31T20:19:29.000Z

from .db import * from .hashing import * from .types import *

15.5

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0.193548

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6

6cfcc432d9799eeca3082db6ec3f5c398e9ea2d0

19,094

py

Python

pybind/slxos/v16r_1_00b/rmon/event_entry/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/rmon/event_entry/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/rmon/event_entry/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class event_entry(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-rmon - based on the path /rmon/event-entry. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__event_index','__event_description','__log','__event_community','__event_owner',) _yang_name = 'event-entry' _rest_name = 'event' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__event_community = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1 .. 127']}), default=unicode("__default_community"), is_leaf=True, yang_name="event-community", rest_name="trap", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Send trap for the event', u'alt-name': u'trap'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='string', is_config=True) self.__event_index = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), restriction_dict={'range': [u'1 .. 65535']}), is_leaf=True, yang_name="event-index", rest_name="event-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-index-type', is_config=True) self.__event_owner = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[a-zA-Z]{1}([-a-zA-Z0-9\\.\\\\\\\\@#\\+\\*\$\$=\\{~\\}%<>=$_\\[\\]\\|]{0,14})', 'length': [u'1 .. 15']}), is_leaf=True, yang_name="event-owner", rest_name="owner", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Owner identity', u'alt-name': u'owner'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='owner-string', is_config=True) self.__event_description = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'min .. 127']}), default=unicode("__default_description"), is_leaf=True, yang_name="event-description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Event description', u'alt-name': u'description'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-description-type', is_config=True) self.__log = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="log", rest_name="log", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Log the event'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='empty', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'rmon', u'event-entry'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'rmon', u'event'] def _get_event_index(self): """ Getter method for event_index, mapped from YANG variable /rmon/event_entry/event_index (event-index-type) """ return self.__event_index def _set_event_index(self, v, load=False): """ Setter method for event_index, mapped from YANG variable /rmon/event_entry/event_index (event-index-type) If this variable is read-only (config: false) in the source YANG file, then _set_event_index is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_event_index() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), restriction_dict={'range': [u'1 .. 65535']}), is_leaf=True, yang_name="event-index", rest_name="event-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-index-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """event_index must be of a type compatible with event-index-type""", 'defined-type': "brocade-rmon:event-index-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), restriction_dict={'range': [u'1 .. 65535']}), is_leaf=True, yang_name="event-index", rest_name="event-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-index-type', is_config=True)""", }) self.__event_index = t if hasattr(self, '_set'): self._set() def _unset_event_index(self): self.__event_index = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), restriction_dict={'range': [u'1 .. 65535']}), is_leaf=True, yang_name="event-index", rest_name="event-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-index-type', is_config=True) def _get_event_description(self): """ Getter method for event_description, mapped from YANG variable /rmon/event_entry/event_description (event-description-type) """ return self.__event_description def _set_event_description(self, v, load=False): """ Setter method for event_description, mapped from YANG variable /rmon/event_entry/event_description (event-description-type) If this variable is read-only (config: false) in the source YANG file, then _set_event_description is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_event_description() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'min .. 127']}), default=unicode("__default_description"), is_leaf=True, yang_name="event-description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Event description', u'alt-name': u'description'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-description-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """event_description must be of a type compatible with event-description-type""", 'defined-type': "brocade-rmon:event-description-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'min .. 127']}), default=unicode("__default_description"), is_leaf=True, yang_name="event-description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Event description', u'alt-name': u'description'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-description-type', is_config=True)""", }) self.__event_description = t if hasattr(self, '_set'): self._set() def _unset_event_description(self): self.__event_description = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'min .. 127']}), default=unicode("__default_description"), is_leaf=True, yang_name="event-description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Event description', u'alt-name': u'description'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='event-description-type', is_config=True) def _get_log(self): """ Getter method for log, mapped from YANG variable /rmon/event_entry/log (empty) """ return self.__log def _set_log(self, v, load=False): """ Setter method for log, mapped from YANG variable /rmon/event_entry/log (empty) If this variable is read-only (config: false) in the source YANG file, then _set_log is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_log() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="log", rest_name="log", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Log the event'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """log must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="log", rest_name="log", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Log the event'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='empty', is_config=True)""", }) self.__log = t if hasattr(self, '_set'): self._set() def _unset_log(self): self.__log = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="log", rest_name="log", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Log the event'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='empty', is_config=True) def _get_event_community(self): """ Getter method for event_community, mapped from YANG variable /rmon/event_entry/event_community (string) """ return self.__event_community def _set_event_community(self, v, load=False): """ Setter method for event_community, mapped from YANG variable /rmon/event_entry/event_community (string) If this variable is read-only (config: false) in the source YANG file, then _set_event_community is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_event_community() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1 .. 127']}), default=unicode("__default_community"), is_leaf=True, yang_name="event-community", rest_name="trap", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Send trap for the event', u'alt-name': u'trap'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='string', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """event_community must be of a type compatible with string""", 'defined-type': "string", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1 .. 127']}), default=unicode("__default_community"), is_leaf=True, yang_name="event-community", rest_name="trap", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Send trap for the event', u'alt-name': u'trap'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='string', is_config=True)""", }) self.__event_community = t if hasattr(self, '_set'): self._set() def _unset_event_community(self): self.__event_community = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1 .. 127']}), default=unicode("__default_community"), is_leaf=True, yang_name="event-community", rest_name="trap", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Send trap for the event', u'alt-name': u'trap'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='string', is_config=True) def _get_event_owner(self): """ Getter method for event_owner, mapped from YANG variable /rmon/event_entry/event_owner (owner-string) """ return self.__event_owner def _set_event_owner(self, v, load=False): """ Setter method for event_owner, mapped from YANG variable /rmon/event_entry/event_owner (owner-string) If this variable is read-only (config: false) in the source YANG file, then _set_event_owner is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_event_owner() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[a-zA-Z]{1}([-a-zA-Z0-9\\.\\\\\\\\@#\\+\\*\$\$=\\{~\\}%<>=$_\\[\\]\\|]{0,14})', 'length': [u'1 .. 15']}), is_leaf=True, yang_name="event-owner", rest_name="owner", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Owner identity', u'alt-name': u'owner'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='owner-string', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """event_owner must be of a type compatible with owner-string""", 'defined-type': "brocade-rmon:owner-string", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[a-zA-Z]{1}([-a-zA-Z0-9\\.\\\\\\\\@#\\+\\*\$\$=\\{~\\}%<>=$_\\[\\]\\|]{0,14})', 'length': [u'1 .. 15']}), is_leaf=True, yang_name="event-owner", rest_name="owner", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Owner identity', u'alt-name': u'owner'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='owner-string', is_config=True)""", }) self.__event_owner = t if hasattr(self, '_set'): self._set() def _unset_event_owner(self): self.__event_owner = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[a-zA-Z]{1}([-a-zA-Z0-9\\.\\\\\\\\@#\\+\\*\$\$=\\{~\\}%<>=$_\\[\\]\\|]{0,14})', 'length': [u'1 .. 15']}), is_leaf=True, yang_name="event-owner", rest_name="owner", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Owner identity', u'alt-name': u'owner'}}, namespace='urn:brocade.com:mgmt:brocade-rmon', defining_module='brocade-rmon', yang_type='owner-string', is_config=True) event_index = __builtin__.property(_get_event_index, _set_event_index) event_description = __builtin__.property(_get_event_description, _set_event_description) log = __builtin__.property(_get_log, _set_log) event_community = __builtin__.property(_get_event_community, _set_event_community) event_owner = __builtin__.property(_get_event_owner, _set_event_owner) _pyangbind_elements = {'event_index': event_index, 'event_description': event_description, 'log': log, 'event_community': event_community, 'event_owner': event_owner, }

71.246269

595

0.719231

2,586

19,094

5.062258

0.076953

0.04125

0.047055

0.021389

0.806126

0.783745

0.762585

0.75487

0.742266

0.739821

0

0.010954

0.129831

19,094

267

596

71.513109

0.776949

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0.406977

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0.166128

0

1

0.104651

false

0

0.046512

0

0.273256

0

null

0

1

0

null

0

6

9f22ecadee15b29febc27386c5d75768609cf0a2

150

py

Python

answers/pandas_answer8.py

monocilindro/foss4g-geopandas

12afbc787c1f65cc046234b41166bd62bbb6ac29

[ "Apache-2.0" ]

null

answers/pandas_answer8.py

monocilindro/foss4g-geopandas

12afbc787c1f65cc046234b41166bd62bbb6ac29

[ "Apache-2.0" ]

null

answers/pandas_answer8.py

monocilindro/foss4g-geopandas

12afbc787c1f65cc046234b41166bd62bbb6ac29

[ "Apache-2.0" ]

null

sns.catplot(x="Youth_Unemployment_(claimant)_rate_18-24_(Dec-15)", y="Largest_migrant_population_arrived_during_2015/16", kind="box", data=boroughs);

75

149

0.813333

23

150

4.869565

1

0

0.082192

0.026667

150

1

150

0.684932

0

0.673333

0.653333

0

1

0

true

0

1

0

null

0

1

0

1

0

1

null

0

1

0

6

9f51da4a2c4d547f1a99a64f4fd42f332e3077f5

16,308

py

Python

src/networks/_original/ConvLSTM_and_UNet.py

claudius-kienle/self-supervised-depth-denoising

4dffb30e8ef5022ef665825d26f45f67bf712cfd

[ "MIT" ]

2

2021-12-02T15:06:28.000Z

2021-12-03T09:48:32.000Z

src/networks/_original/ConvLSTM_and_UNet.py

claudius-kienle/self-supervised-depth-denoising

4dffb30e8ef5022ef665825d26f45f67bf712cfd

[ "MIT" ]

23

2022-02-24T09:17:03.000Z

2022-03-21T16:57:58.000Z

src/networks/_original/ConvLSTM_and_UNet.py

alr-internship/self-supervised-depth-denoising

4dffb30e8ef5022ef665825d26f45f67bf712cfd

[ "MIT" ]

null

import torch.nn as nn import torch import torch.nn as nn import torch.nn.functional as F from common import conv, norm, ListModule, BloorPool, ConvLSTMCell from unet_parts import unetDown class UNet(nn.Module): ''' upsample_mode in ['deconv', 'nearest', 'bilinear'] pad in ['zero', 'replication', 'none'] ''' def __init__( self, num_input_channels=3, num_output_channels=3, feature_scale=4, more_layers=0, concat_x=False, upsample_mode='deconv', pad='zero', norm_layer='in', last_act='sigmoid', need_bias=True, downsample_mode='max' ): super(UNet, self).__init__() self.feature_scale = feature_scale self.more_layers = more_layers self.concat_x = concat_x filters = [64, 128, 256, 512, 1024] # WHATS FEATURE SCALE???? filters = [x // self.feature_scale for x in filters] # unetConv2 == DoubleConv self.start = unetConv2( num_input_channels, filters[0] if not concat_x else filters[0] - num_input_channels, norm_layer, need_bias, pad ) # unetDown == Down self.down1 = unetDown( filters[0], filters[1] if not concat_x else filters[1] - num_input_channels, norm_layer, need_bias, pad, downsample_mode ) self.down2 = unetDown( filters[1], filters[2] if not concat_x else filters[2] - num_input_channels, norm_layer, need_bias, pad, downsample_mode ) self.down3 = unetDown( filters[2], filters[3] if not concat_x else filters[3] - num_input_channels, norm_layer, need_bias, pad, downsample_mode ) self.down4 = unetDown( filters[3], filters[4] if not concat_x else filters[4] - num_input_channels, norm_layer, need_bias, pad, downsample_mode ) '''not in original original unet implementation as mentioned to be in paper''' # more downsampling layers if self.more_layers > 0: self.more_downs = [ unetDown(filters[4], filters[4] if not concat_x else filters[4] - num_input_channels, norm_layer, need_bias, pad) for i in range(self.more_layers)] self.more_ups = [unetUp(filters[4], upsample_mode, need_bias, pad, same_num_filt=True) for i in range(self.more_layers)] self.more_downs = ListModule(*self.more_downs) self.more_ups = ListModule(*self.more_ups) # Up self.up4 = unetUp(filters[3], upsample_mode, need_bias, pad) self.up3 = unetUp(filters[2], upsample_mode, need_bias, pad) self.up2 = unetUp(filters[1], upsample_mode, need_bias, pad) self.up1 = unetUp(filters[0], upsample_mode, need_bias, pad) # OutConv self.final = conv(filters[0], num_output_channels, 1, bias=need_bias, pad=pad) if last_act == 'sigmoid': self.final = nn.Sequential(self.final, nn.Sigmoid()) elif last_act == 'tanh': self.final = nn.Sequential(self.final, nn.Tanh()) def forward(self, inputs): # Downsample downs = [inputs] down = nn.AvgPool2d(2, 2) for i in range(4 + self.more_layers): downs.append(down(downs[-1])) in64 = self.start(inputs) if self.concat_x: in64 = torch.cat([in64, downs[0]], 1) down1 = self.down1(in64) if self.concat_x: down1 = torch.cat([down1, downs[1]], 1) down2 = self.down2(down1) if self.concat_x: down2 = torch.cat([down2, downs[2]], 1) down3 = self.down3(down2) if self.concat_x: down3 = torch.cat([down3, downs[3]], 1) down4 = self.down4(down3) if self.concat_x: down4 = torch.cat([down4, downs[4]], 1) if self.more_layers > 0: prevs = [down4] for kk, d in enumerate(self.more_downs): # print(prevs[-1].size()) out = d(prevs[-1]) if self.concat_x: out = torch.cat([out, downs[kk + 5]], 1) prevs.append(out) up_ = self.more_ups[-1](prevs[-1], prevs[-2]) for idx in range(self.more_layers - 1): l = self.more_ups[self.more - idx - 2] up_ = l(up_, prevs[self.more - idx - 2]) else: up_ = down4 up4 = self.up4(up_, down3) up3 = self.up3(up4, down2) up2 = self.up2(up3, down1) up1 = self.up1(up2, in64) return self.final(up1) class unetConv2(nn.Module): def __init__(self, in_size, out_size, norm_layer, need_bias, pad, kernel_size=3): super(unetConv2, self).__init__() # print(pad) if norm_layer is not None: self.conv1 = nn.Sequential(conv(in_size, out_size, kernel_size, bias=need_bias, pad=pad), norm(out_size, norm_layer), nn.ReLU(),) self.conv2 = nn.Sequential(conv(out_size, out_size, kernel_size, bias=need_bias, pad=pad), norm(out_size, norm_layer), nn.ReLU(),) else: self.conv1 = nn.Sequential(conv(in_size, out_size, kernel_size, bias=need_bias, pad=pad), nn.ReLU(),) self.conv2 = nn.Sequential(conv(out_size, out_size, kernel_size, bias=need_bias, pad=pad), nn.ReLU(),) def forward(self, inputs): outputs = self.conv1(inputs) outputs = self.conv2(outputs) return outputs class unetUp(nn.Module): def __init__(self, out_size, upsample_mode, need_bias, pad, same_num_filt=False, kernel_size=3): super(unetUp, self).__init__() num_filt = out_size if same_num_filt else out_size * 2 if upsample_mode == 'deconv': self.up = nn.ConvTranspose2d( num_filt, out_size, 4, stride=2, padding=1) self.conv = unetConv2(out_size * 2, out_size, None, need_bias, pad) elif upsample_mode == 'bilinear' or upsample_mode == 'nearest': self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode=upsample_mode), conv(num_filt, out_size, kernel_size, bias=need_bias, pad=pad)) self.conv = unetConv2(out_size * 2, out_size, None, need_bias, pad) else: assert False def forward(self, inputs1, inputs2): in1_up = self.up(inputs1) if (inputs2.size(2) != in1_up.size(2)) or (inputs2.size(3) != in1_up.size(3)): diff2 = (inputs2.size(2) - in1_up.size(2)) // 2 diff3 = (inputs2.size(3) - in1_up.size(3)) // 2 inputs2_ = inputs2[:, :, diff2: diff2 + in1_up.size(2), diff3: diff3 + in1_up.size(3)] else: inputs2_ = inputs2 output = self.conv(torch.cat([in1_up, inputs2_], 1)) return output class LSTMUNet(nn.Module): ''' upsample_mode in ['deconv', 'nearest', 'bilinear'] pad in ['zero', 'replication', 'none'] ''' def __init__(self, num_input_channels=3, num_output_channels=3, feature_scale=4, more_layers=0, concat_x=False, upsample_mode='deconv', pad='zero', norm_layer='in', last_act='sigmoid', need_bias=True, downsample_mode='max'): super(LSTMUNet, self).__init__() self.feature_scale = feature_scale self.more_layers = more_layers self.concat_x = concat_x filters = [64, 128, 256, 512, 1024] filters = [x // self.feature_scale for x in filters] self.start = unetConv2( num_input_channels, filters[0] if not concat_x else filters[0] - num_input_channels, norm_layer, need_bias, pad) self.down1 = unetDown(filters[0], filters[1] if not concat_x else filters[1] - num_input_channels, norm_layer, need_bias, pad, downsample_mode) self.down2 = unetDown(filters[1], filters[2] if not concat_x else filters[2] - num_input_channels, norm_layer, need_bias, pad, downsample_mode) self.down3 = unetDown(filters[2], filters[3] if not concat_x else filters[3] - num_input_channels, norm_layer, need_bias, pad, downsample_mode) self.down4 = unetDown(filters[3], filters[4] if not concat_x else filters[4] - num_input_channels, norm_layer, need_bias, pad, downsample_mode) # more downsampling layers if self.more_layers > 0: self.more_downs = [ unetDown(filters[4], filters[4] if not concat_x else filters[4] - num_input_channels, norm_layer, need_bias, pad) for i in range(self.more_layers)] self.more_ups = [unetUp(filters[4], upsample_mode, need_bias, pad, same_num_filt=True) for i in range(self.more_layers)] self.more_downs = ListModule(*self.more_downs) self.more_ups = ListModule(*self.more_ups) self.up4 = unetUp(filters[3], upsample_mode, need_bias, pad) self.up3 = NewunetUp(filters[2], upsample_mode, need_bias, pad) self.up2 = NewunetUp(filters[1], upsample_mode, need_bias, pad) self.up1 = unetUp(filters[0], upsample_mode, need_bias, pad) self.final = conv(filters[0], num_output_channels, 1, bias=need_bias, pad=pad) if last_act == 'sigmoid': self.final = nn.Sequential(self.final, nn.Sigmoid()) elif last_act == 'tanh': self.final = nn.Sequential(self.final, nn.Tanh()) self.first_conv_lstm_cell = ConvLSTMCell(filters[1], filters[1], 5) self.second_conv_lstm_cell = ConvLSTMCell(filters[2], filters[2], 5) self.third_conv_lstm_cell = ConvLSTMCell(filters[3], filters[3], 5) self.fourth_conv_lstm_cell = ConvLSTMCell(filters[2]*2, filters[2], 5) self.fifth_conv_lstm_cell = ConvLSTMCell(filters[1]*2, filters[1], 5) def forward(self, all_inputs): seq_len = all_inputs.shape[1] res = [] for seq_idx in range(seq_len): inputs = all_inputs[:, seq_idx, :, :, :] # Downsample downs = [inputs] down = nn.AvgPool2d(2, 2) for i in range(4 + self.more_layers): downs.append(down(downs[-1])) in64 = self.start(inputs) if self.concat_x: in64 = torch.cat([in64, downs[0]], 1) down1 = self.down1(in64) if self.concat_x: down1 = torch.cat([down1, downs[1]], 1) if seq_idx == 0: c_1 = self.first_conv_lstm_cell.init_hidden( batch_size=inputs.shape[0], shape=(down1.shape[-2], down1.shape[-1])) down1, c_1 = self.first_conv_lstm_cell(down1, c_1[0], c_1[1]) c_1 = [down1, c_1] down2 = self.down2(down1) if self.concat_x: down2 = torch.cat([down2, downs[2]], 1) if seq_idx == 0: c_2 = self.second_conv_lstm_cell.init_hidden( batch_size=inputs.shape[0], shape=(down2.shape[-2], down2.shape[-1])) down2, c_2 = self.second_conv_lstm_cell(down2, c_2[0], c_2[1]) c_2 = [down2, c_2] down3 = self.down3(down2) if self.concat_x: down3 = torch.cat([down3, downs[3]], 1) if seq_idx == 0: c_3 = self.third_conv_lstm_cell.init_hidden( batch_size=inputs.shape[0], shape=(down3.shape[-2], down3.shape[-1])) down3, c_3 = self.third_conv_lstm_cell(down3, c_3[0], c_3[1]) c_3 = [down3, c_3] down4 = self.down4(down3) if self.concat_x: down4 = torch.cat([down4, downs[4]], 1) if self.more_layers > 0: prevs = [down4] for kk, d in enumerate(self.more_downs): # print(prevs[-1].size()) out = d(prevs[-1]) if self.concat_x: out = torch.cat([out, downs[kk + 5]], 1) prevs.append(out) up_ = self.more_ups[-1](prevs[-1], prevs[-2]) for idx in range(self.more_layers - 1): l = self.more_ups[self.more - idx - 2] up_ = l(up_, prevs[self.more - idx - 2]) else: up_ = down4 up4 = self.up4(up_, down3) up3 = self.up3(up4, down2) if seq_idx == 0: c_4 = self.fourth_conv_lstm_cell.init_hidden( batch_size=inputs.shape[0], shape=(up3.shape[-2], up3.shape[-1])) up3, c_4 = self.fourth_conv_lstm_cell(up3, c_4[0], c_4[1]) c_4 = [up3, c_4] up2 = self.up2(up3, down1) if seq_idx == 0: c_5 = self.fifth_conv_lstm_cell.init_hidden( batch_size=inputs.shape[0], shape=(up2.shape[-2], up2.shape[-1])) up2, c_5 = self.fifth_conv_lstm_cell(up2, c_5[0], c_5[1]) c_5 = [up2, c_5] up1 = self.up1(up2, in64) res.append(self.final(up1)[:, None]) return torch.cat(res, 1) class NewunetUp(nn.Module): def __init__(self, out_size, upsample_mode, need_bias, pad, same_num_filt=False, kernel_size=3): super(NewunetUp, self).__init__() num_filt = out_size if same_num_filt else out_size * 2 if upsample_mode == 'deconv': self.up = nn.ConvTranspose2d( num_filt, out_size, 4, stride=2, padding=1) self.conv = unetConv2(out_size * 2, out_size, None, need_bias, pad) elif upsample_mode == 'bilinear' or upsample_mode == 'nearest': self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode=upsample_mode), conv(num_filt, out_size, kernel_size, bias=need_bias, pad=pad)) # self.conv= unetConv2(out_size * 2, out_size, None, need_bias, pad) else: assert False def forward(self, inputs1, inputs2): in1_up = self.up(inputs1) if (inputs2.size(2) != in1_up.size(2)) or (inputs2.size(3) != in1_up.size(3)): diff2 = (inputs2.size(2) - in1_up.size(2)) // 2 diff3 = (inputs2.size(3) - in1_up.size(3)) // 2 inputs2_ = inputs2[:, :, diff2: diff2 + in1_up.size(2), diff3: diff3 + in1_up.size(3)] else: inputs2_ = inputs2 # output= self.conv(torch.cat([in1_up, inputs2_], 1)) output = torch.cat([in1_up, inputs2_], 1) return output class DumpyModel(nn.Module): ''' upsample_mode in ['deconv', 'nearest', 'bilinear'] pad in ['zero', 'replication', 'none'] ''' def __init__(self, num_input_channels=3, num_output_channels=3, feature_scale=4, more_layers=0, concat_x=False, upsample_mode='deconv', pad='zero', norm_layer='in', last_act='sigmoid', need_bias=True, downsample_mode='max'): super(DumpyModel, self).__init__() filters = [64, 128, 256, 512, 1024] filters = [x // feature_scale for x in filters] self.start = unetConv2(num_input_channels, 1, norm_layer, need_bias, pad) def forward(self, all_inputs): # seq_len = all_inputs.shape[1] # res = [] # for seq_idx in range(seq_len): # res.append(self.start(all_inputs[:,seq_idx])[:,None]) # res = torch.cat(res, dim=1) res = self.start(all_inputs) # print("Model Inp shape:", all_inputs.shape) # print("Model Out shape:", res.shape) return res

38.28169

163

0.552183

2,112

16,308

4.037879

0.07339

0.038462

0.049015

0.027908

0.881918

0.854479

0.835131

0.808161

0.804644

0.783537

0

0.044761

0.328734

16,308

425

164

38.371765

0.734265

0.051079

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0.006431

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0.038585

false

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0.019293

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0

null

0

1

0

null

0

6

9f799bda407a12c5a02b0d0658b7ebc06757c547

5,411

py

Python

tests/dredd-hooks/relation_hook.py

marianoleonardo/auth

2e2e50e1cd6f224c9551ca57c25beda6b956cb4e

[ "Apache-2.0" ]

2

2017-10-16T12:03:32.000Z

2020-10-28T02:51:09.000Z

tests/dredd-hooks/relation_hook.py

marianoleonardo/auth

2e2e50e1cd6f224c9551ca57c25beda6b956cb4e

[ "Apache-2.0" ]

33

2017-10-05T19:43:17.000Z

2022-02-25T13:22:05.000Z

tests/dredd-hooks/relation_hook.py

marianoleonardo/auth

2e2e50e1cd6f224c9551ca57c25beda6b956cb4e

[ "Apache-2.0" ]

22

2017-08-23T13:35:42.000Z

2021-11-25T16:32:14.000Z

import dredd_hooks as hooks import controller.CRUDController as crud import controller.RelationshipController as rship import crud_api_hook as crud import auth_hook as auth from database.flaskAlchemyInit import db, HTTPRequestError from dojot.module import Log LOGGER = Log().color_log() USER_GROUP = [] USER_PERMS = [] GROUP_PERMS = [] REQUESTER = { "userid": 0, "username": "dredd" } @hooks.before("Relationship management " "> Manage relationships between users and groups " "> Add user to group") def create_sample_group_user(transaction): global USER_GROUP user_id, group_id = auth.create_sample_users(transaction) transaction['fullPath'] = transaction['fullPath'].replace("/1/", f"/{user_id[0]}/") transaction['fullPath'] = transaction['fullPath'].replace("/101", f"/{group_id[2]}") USER_GROUP.append((user_id[0], group_id[2])) @hooks.before("Relationship management " "> Manage relationships between users and groups " "> Remove a user from group") def create_sample_associated_group_user(transaction): global USER_GROUP, REQUESTER user_id, group_id = auth.create_sample_users(transaction) transaction['fullPath'] = transaction['fullPath'].replace("/1/", f"/{user_id[0]}/") transaction['fullPath'] = transaction['fullPath'].replace("/101", f"/{group_id[2]}") rship.add_user_group(db.session, user_id[0], group_id[2], REQUESTER) USER_GROUP.append((user_id[0], group_id[1])) @hooks.before("Relationship management " "> Manage relationships between users and permissions " "> Give a permission to a user") def create_sample_user_perm(transaction): global USER_PERMS user_id, group_id = auth.create_sample_users(transaction) perm_id = crud.create_sample_perms(transaction) transaction['fullPath'] = transaction['fullPath'].replace("/1/", f"/{user_id[0]}/") transaction['fullPath'] = transaction['fullPath'].replace("/201", f"/{perm_id}") USER_PERMS.append((user_id[0], perm_id)) @hooks.before("Relationship management " "> Manage relationships between users and permissions " "> Revoke a user permission") def create_sample_associated_user_perm(transaction): global USER_PERMS, REQUESTER user_id, group_id = auth.create_sample_users(transaction) perm_id = crud.create_sample_perms(transaction) transaction['fullPath'] = transaction['fullPath'].replace("/1/", f"/{user_id[0]}/") transaction['fullPath'] = transaction['fullPath'].replace("/201", f"/{perm_id}") rship.add_user_permission(db.session, user_id[0], perm_id, REQUESTER) USER_PERMS.append((user_id[0], perm_id)) @hooks.before("Relationship management " "> Manage relationships between group and permissions " "> Give a permission to a group") def create_sample_group_perm(transaction): global GROUP_PERMS perm_id = crud.create_sample_perms(transaction) group_id = crud.create_sample_groups(transaction) transaction['fullPath'] = transaction['fullPath'].replace("/101/", f"/{group_id[0]}/") transaction['fullPath'] = transaction['fullPath'].replace("/201", f"/{perm_id}") GROUP_PERMS.append((group_id[0], perm_id)) @hooks.before("Relationship management " "> Manage relationships between group and permissions " "> Revoke a group permission") def create_sample_associated_group_perm(transaction): global GROUP_PERMS perm_id = crud.create_sample_perms(transaction) group_id = crud.create_sample_groups(transaction) transaction['fullPath'] = transaction['fullPath'].replace("/101/", f"/{group_id[0]}/") transaction['fullPath'] = transaction['fullPath'].replace("/201", f"/{perm_id}") rship.add_group_permission(db.session, group_id[0], perm_id, REQUESTER) GROUP_PERMS.append((group_id[0], perm_id)) @hooks.after("Relationship management " "> Manage relationships between users and groups " "> Add user to group") @hooks.after("Relationship management " "> Manage relationships between users and groups " "> Remove a user from group") @hooks.after("Relationship management " "> Manage relationships between users and permissions " "> Give a permission to a user") @hooks.after("Relationship management " "> Manage relationships between users and permissions " "> Revoke a user permission") @hooks.after("Relationship management " "> Manage relationships between group and permissions " "> Give a permission to a group") @hooks.after("Relationship management " "> Manage relationships between group and permissions " "> Revoke a group permission") def clean_associations(transaction): for user_id, group_id in USER_GROUP: try: rship.remove_user_group(db.session, user_id, group_id, REQUESTER) except HTTPRequestError as e: pass for group_id, perm_id in GROUP_PERMS: try: rship.remove_group_permission(db.session, group_id, perm_id, REQUESTER) except HTTPRequestError as e: pass for user_id, perm_id in USER_PERMS: try: rship.remove_user_permission(db.session, user_id, perm_id, REQUESTER) except HTTPRequestError as e: pass

41.623077

90

0.686195

649

5,411

5.516179

0.11094

0.127374

0.093855

0.13743

0.865922

0.828492

0.741341

0.712291

0.672346

0

0.011264

0.196082

5,411

129

91

41.945736

0.811724

0

0.663636

0

0.301053

0

1

0.063636

false

0.027273

0.063636

0

0.127273

0

null

0

1

0

null

0

6

4c93dd269715b78fb5c43bd3c25c60b9d7921a5d

1,528

py

Python

paz/backend/image/__init__.py

niqbal996/paz

f27205907367415d5b21f90e1a1d1d1ce598e889

[ "MIT" ]

1

2021-04-12T22:09:22.000Z

paz/backend/image/__init__.py

albertofernandezvillan/paz

9fbd50b993f37e1e807297a29c6044c09967c9cc

[ "MIT" ]

null

paz/backend/image/__init__.py

albertofernandezvillan/paz

9fbd50b993f37e1e807297a29c6044c09967c9cc

[ "MIT" ]

null

from .opencv_image import RGB2BGR from .opencv_image import BGR2RGB from .opencv_image import RGB2HSV from .opencv_image import HSV2RGB from .opencv_image import RGB2GRAY from .opencv_image import cast_image from .opencv_image import resize_image from .opencv_image import convert_color_space from .opencv_image import load_image from .opencv_image import random_saturation from .opencv_image import random_brightness from .opencv_image import random_contrast from .opencv_image import random_hue from .opencv_image import random_flip_left_right from .opencv_image import show_image from .opencv_image import warp_affine from .opencv_image import write_image from .opencv_image import random_shape_crop from .opencv_image import make_random_plain_image from .opencv_image import blend_alpha_channel from .opencv_image import concatenate_alpha_mask from .opencv_image import split_and_normalize_alpha_channel from .opencv_image import gaussian_image_blur from .opencv_image import median_image_blur from .opencv_image import random_image_blur from .opencv_image import translate_image from .opencv_image import sample_scaled_translation from .opencv_image import get_rotation_matrix from .draw import draw_random_polygon from .draw import draw_circle from .draw import put_text from .draw import draw_line from .draw import draw_rectangle from .draw import draw_dot from .draw import draw_cube from .draw import draw_random_polygon from .draw import draw_filled_polygon from .draw import lincolor from .draw import make_mosaic

36.380952

59

0.871073

236

1,528

5.300847

0.258475

0.223821

0.335731

0.470024

0.449241

0.254197

0.078337

0

0.00365

0.103403

1,528

41

60

37.268293

0.909489

0

0.051282

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

4ccdd61f532045ca6cdb651d6aab227c9281a5ca

121

py

Python

example.py

ZhukovAlexander/lambdify

e291c15bacffc871cd1c10aefe9f132420259dfd

[ "Apache-2.0" ]

51

2016-04-07T12:50:08.000Z

2020-05-19T14:56:47.000Z

example.py

ZhukovAlexander/easy-lambda

e291c15bacffc871cd1c10aefe9f132420259dfd

[ "Apache-2.0" ]

null

example.py

ZhukovAlexander/easy-lambda

e291c15bacffc871cd1c10aefe9f132420259dfd

[ "Apache-2.0" ]

8

2016-04-08T10:05:30.000Z

2020-01-20T14:01:05.000Z

from lambdify import Lambda, UPDATE_EXPLICIT @Lambda.f(name='echo') def echo(*args, **kwargs): return args, kwargs

17.285714

44

0.719008

17

121

5.058824

0.764706

0.232558

0

0.14876

121

6

45

20.166667

0.834951

0

0.033058

0

1

0.25

true

0

0.25

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

9802f22bc89782e9a0842bff485870f55548fb60

165

py

Python

test/unit/test_command_line.py

buddly27/jound

0f3c1fed055a5f28231daafc762ea7d934639d9e

[ "MIT" ]

null

test/unit/test_command_line.py

buddly27/jound

0f3c1fed055a5f28231daafc762ea7d934639d9e

[ "MIT" ]

null

test/unit/test_command_line.py

buddly27/jound

0f3c1fed055a5f28231daafc762ea7d934639d9e

[ "MIT" ]

null

# :coding: utf-8 from jound.command_line import main def test_with_defaults(): """Command executes successfully with defaults.""" assert main([]) is None

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6

9819e8801dd5cbb40d09ba326e69390a5964bbf8

272

py

Python

async_rx/subject/__init__.py

geronimo-iia/async-rx

366a2fc5e4e717a0441f1ee8522ef6d5e857566c

[ "MIT" ]

4

2020-05-02T00:14:29.000Z

2022-02-12T14:17:21.000Z

async_rx/subject/__init__.py

geronimo-iia/async-rx

366a2fc5e4e717a0441f1ee8522ef6d5e857566c

[ "MIT" ]

4

2020-05-05T16:21:00.000Z

2021-08-05T23:31:30.000Z

async_rx/subject/__init__.py

geronimo-iia/async-rx

366a2fc5e4e717a0441f1ee8522ef6d5e857566c

[ "MIT" ]

null

from .rx_subject import rx_subject from .rx_subject_from import rx_subject_from from .rx_subject_replay import rx_subject_replay from .rx_subject_behavior import rx_subject_behavior __all__ = ["rx_subject", "rx_subject_from", "rx_subject_replay", "rx_subject_behavior"]

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null

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1

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6

e25572df44485e98b412a610b63df30dd0ebd83d

16,315

py

Python

TestMotorMode.py

sohamranade/RoboticsStudio

ed6794d6264a238646768726254a87c49926a96c

[ "MIT" ]

null

TestMotorMode.py

sohamranade/RoboticsStudio

ed6794d6264a238646768726254a87c49926a96c

[ "MIT" ]

null

TestMotorMode.py

sohamranade/RoboticsStudio

ed6794d6264a238646768726254a87c49926a96c

[ "MIT" ]

null

from lx16a import * from math import sin, cos, pi import time import xlwt import pandas as pd import numpy as np from xlwt import Workbook class RecordMotorData(): def __init__ (self, servo): self.servo = servo; self.id = []; self.angleOffset = []; self.physicalPos = []; self.virtualPos = []; self.temp = []; self.voltage = []; def record(self): self.id.append(int(self.servo.IDRead())) self.angleOffset.append(int(self.servo.angleOffsetRead())) self.physicalPos.append(int(self.servo.getPhysicalPos())) self.virtualPos.append(int(self.servo.getVirtualPos())) self.temp.append(int(self.servo.tempRead())) self.voltage.append(int(self.servo.vInRead())) def save2CSV(recordMotorDataList): id = []; angleOffset = []; physicalPos = []; virtualPos = []; temp = []; voltage = []; for recordMotorData in recordMotorDataList: id.extend(recordMotorData.id); angleOffset.extend(recordMotorData.angleOffset); physicalPos.extend(recordMotorData.physicalPos); virtualPos.extend(recordMotorData.virtualPos); temp.extend(recordMotorData.temp); voltage.extend(recordMotorData.voltage); df = pd.DataFrame(list(zip(id, angleOffset, physicalPos, virtualPos, temp, voltage)), columns = ["Id", "Angle offset", "Physical pos", "Virtual pos", "Temp", "Voltage"]) df.to_csv(r'MotorData.csv') def initializeMotor(servo1): targetPos = 120; initialPos = servo1.getPhysicalPos(); error = targetPos-initialPos print(servo1.IDRead()) print(initialPos) print(error) t=0; while (abs(sin(t*2*pi/360)*error) < abs(error)): # print("Motor id is ", servo1.IDRead()) # print("Physical pos is ", servo1.getPhysicalPos()) # print("Virtual pos is ", servo1.getVirtualPos()) servo1.moveTimeWrite(sin(t*2*pi/360)*error+initialPos) time.sleep(.01) t+=3 def resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E): initializeMotor(servoL1); initializeMotor(servoL2); initializeMotor(servoA1S); initializeMotor(servoA1E); initializeMotor(servoA2S); initializeMotor(servoA2E); def danceLegs1(servo1, servo2,cycles, relativeAngleStep, r1, r2): print("Starting dance 1") direction = 1; for i in range(0,cycles): t=0; initialPos1 = servo1.getPhysicalPos(); initialPos2 = servo2.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*relativeAngleStep) # print("Motor id is ", servo1.IDRead()) # print("Physical pos is ", servo1.getPhysicalPos()) # print("Virtual pos is ", servo1.getVirtualPos()) # print("Motor id is ", servo2.IDRead()) # print("Physical pos is ", servo2.getPhysicalPos()) # print("Virtual pos is ", servo2.getVirtualPos()) servo1.moveTimeWrite(direction*sin(t*3*pi/360)*relativeAngleStep+initialPos1); servo2.moveTimeWrite(direction*sin(t*3*pi/360)*relativeAngleStep+initialPos2); time.sleep(.01) t+=5; direction= -direction; def turnL1A2(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); #servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); #servoArm1S.moveTimeWrite(direction*sin(4*t*2*pi/360)*angleStepShoulder+initialPosA1S); #servoArm1E.moveTimeWrite(direction*sin(4*t*2*pi/360)*angleStepElbow+initialPosA1E); servoArm2S.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA2S); servoArm2E.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def turnL1A1(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); #servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); servoArm1S.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA1S); servoArm1E.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA1E); # servoArm2S.moveTimeWrite(-direction*sin(4*t*2*pi/360)*angleStepShoulder+initialPosA2S); # servoArm2E.moveTimeWrite(-direction*sin(4*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def turnL2A1(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) #servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); servoArm1S.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA1S); servoArm1E.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA1E); #servoArm2S.moveTimeWrite(direction*sin(4*t*2*pi/360)*angleStepShoulder+initialPosA2S); #servoArm2E.moveTimeWrite(direction*sin(4*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def turnL2A2(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) #servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); # servoArm1S.moveTimeWrite(-direction*sin(4*t*2*pi/360)*angleStepShoulder+initialPosA1S); # servoArm1E.moveTimeWrite(-direction*sin(4*t*2*pi/360)*angleStepElbow+initialPosA1E); servoArm2S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA2S); servoArm2E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def danceArmsAlternate(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) # servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); # servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); servoArm1S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA1S); servoArm1E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA1E); time.sleep(.01) servoArm2S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA2S); servoArm2E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def danceArmsSynchronized(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) # servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); # servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); servoArm1S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA1S); servoArm1E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA1E); time.sleep(.01) servoArm2S.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA2S); servoArm2E.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def danceLegsAndArms1(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); servoArm1S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA1S); servoArm1E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA1E); servoArm2S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA2S); servoArm2E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) def danceLegsAndArms2(servoLeg1, servoLeg2, servoArm1S, servoArm1E, servoArm2S, servoArm2E, cycles, angleStepLeg, angleStepShoulder, angleStepElbow, r1, r2): print("Starting dance legs with arms") direction = 1; for i in range(0,cycles): t=0; initialPosL1 = servoLeg1.getPhysicalPos(); initialPosL2 = servoLeg2.getPhysicalPos(); initialPosA1S = servoArm1S.getPhysicalPos(); initialPosA1E = servoArm1E.getPhysicalPos(); initialPosA2S = servoArm2S.getPhysicalPos(); initialPosA2E = servoArm2E.getPhysicalPos(); while (t<180): r1.record(); r2.record(); # print(sin(t*2*pi/360)*angleStepLeg) # print("Motor id is ", servoLeg1.IDRead()) # print("Physical pos is ", servoLeg1.getPhysicalPos()) # print("Virtual pos is ", servoLeg1.getVirtualPos()) # print("Motor id is ", servoLeg2.IDRead()) # print("Physical pos is ", servoLeg2.getPhysicalPos()) # print("Virtual pos is ", servoLeg2.getVirtualPos()) servoLeg1.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL1); servoLeg2.moveTimeWrite(direction*sin(t*2*pi/360)*angleStepLeg+initialPosL2); servoArm1S.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA1S); servoArm1E.moveTimeWrite(direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA1E); servoArm2S.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepShoulder+initialPosA2S); servoArm2E.moveTimeWrite(-direction*sin(2*t*2*pi/360)*angleStepElbow+initialPosA2E); time.sleep(.01) t+=5; direction= -direction; resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) # This is the port that the controller board is connected to # This will be different for different computers # On Windows, try the ports COM1, COM2, COM3, etc... # On Raspbian, try each port in /dev/ try: LX16A.initialize("COM9") servoL1 = LX16A(7) servoL2 = LX16A(8) servoA1S = LX16A(2) servoA1E = LX16A(1) servoA2S = LX16A(4) servoA2E = LX16A(3) servoL1.motorMode(800) servoL2.motorMode(-800) time.sleep(3) servoL1.servoMode() servoL2.servoMode() print("Resetting to home position") resetAllMotors(servoL1, servoL2, servoA1S, servoA1E, servoA2S, servoA2E) print("Finished resetting") except KeyboardInterrupt: quit()

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6

e2844540c03734c3d5aede77a6cb320fc331436e

40

py

Python

src/quickmenus/scripts/quickmenus/qmenus/__init__.py

bohdon/maya-quickmenus

3a49d4dab534fd32c649878efba3a6d6e63ac677

[ "MIT" ]

6

2017-12-15T08:49:22.000Z

2020-09-04T10:03:46.000Z

src/quickmenus/scripts/quickmenus/fmenus/__init__.py

bohdon/maya-quickmenus

3a49d4dab534fd32c649878efba3a6d6e63ac677

[ "MIT" ]

null

src/quickmenus/scripts/quickmenus/fmenus/__init__.py

bohdon/maya-quickmenus

3a49d4dab534fd32c649878efba3a6d6e63ac677

[ "MIT" ]

1

2018-08-23T03:49:15.000Z

from core import * from menus import *

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6

e299629d4d5eec6869ab2bb35035220746d7fe90

1,298

py

Python

Lib_dati.py

rsd-dev/Soccer_Odds

47dccbfd2c702e8b5e6cd42814c3f77dbfee7ca9

[ "MIT" ]

null

Lib_dati.py

rsd-dev/Soccer_Odds

47dccbfd2c702e8b5e6cd42814c3f77dbfee7ca9

[ "MIT" ]

null

Lib_dati.py

rsd-dev/Soccer_Odds

47dccbfd2c702e8b5e6cd42814c3f77dbfee7ca9

[ "MIT" ]

null

csv_campionati = ["premier", "championship", "conference", "serieA", "serieB", "ligaA", "ligaB", "ligueA", "ligueB", "bundesA", "bundesB", "eredivisie", "portugalA", "turkeyA"] csv_urls=["https://www.football-data.co.uk/mmz4281/2021/E0.csv", "https://www.football-data.co.uk/mmz4281/2021/E1.csv", "https://www.football-data.co.uk/mmz4281/2021/EC.csv", "https://www.football-data.co.uk/mmz4281/2021/I1.csv", "https://www.football-data.co.uk/mmz4281/2021/I2.csv", "https://www.football-data.co.uk/mmz4281/2021/SP1.csv", "https://www.football-data.co.uk/mmz4281/2021/SP2.csv", "https://www.football-data.co.uk/mmz4281/2021/F1.csv", "https://www.football-data.co.uk/mmz4281/2021/F2.csv", "https://www.football-data.co.uk/mmz4281/2021/D1.csv", "https://www.football-data.co.uk/mmz4281/2021/D2.csv", "https://www.football-data.co.uk/mmz4281/2021/N1.csv", "https://www.football-data.co.uk/mmz4281/2021/P1.csv", "https://www.football-data.co.uk/mmz4281/2021/T1.csv",] csv_nsquadre = [20, 24, 24, 20, 20, 20, 22, 20, 20, 18, 18, 18, 18, 21]

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6

2c33dce3a5f24783c00fceee5840dd4c015f1b0f

49

py

Python

tests/unit/__init__.py

tcc-td-puc-minas-indtexbr/standard-manager-api

23b7db7e32f4a1409bb0558bd2edabfd50e3596d

[ "MIT" ]

3

2020-03-07T19:21:47.000Z

2021-10-02T15:27:20.000Z

tests/unit/__init__.py

tcc-td-puc-minas-indtexbr/standard-manager-api

23b7db7e32f4a1409bb0558bd2edabfd50e3596d

[ "MIT" ]

null

tests/unit/__init__.py

tcc-td-puc-minas-indtexbr/standard-manager-api

23b7db7e32f4a1409bb0558bd2edabfd50e3596d

[ "MIT" ]

null

from tests import register_paths register_paths()

24.5

32

0.877551

7

49

5.857143

0.714286

0.634146

0

0.081633

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2

33

24.5

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0

1

0

null

0

1

0

1

0

6

2c88ce7f940e555c46d8b7ea44d1bd4667b5fcbf

47

py

Python

schwa/repository/__init__.py

SBST-DPG/schwa

d09660e4b5bb665114c35ebe291e5620e59f4c4c

[ "MIT" ]

9

2015-05-21T10:13:27.000Z

2020-11-06T22:21:03.000Z

schwa/repository/__init__.py

XiaoxueRenS/schwa

d09660e4b5bb665114c35ebe291e5620e59f4c4c

[ "MIT" ]

5

2021-01-12T09:57:36.000Z

2021-07-20T08:29:16.000Z

schwa/repository/__init__.py

XiaoxueRenS/schwa

d09660e4b5bb665114c35ebe291e5620e59f4c4c

[ "MIT" ]

9

2015-05-14T09:31:15.000Z

2021-02-07T02:53:17.000Z

from .file import * from .repository import *

11.75

25

0.723404

6

47

5.666667

0.666667

0

0.191489

47

3

26

15.666667

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true

0

1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

2cb2b7a11fed7f4c536fdcd72955015d016184e1

74

py

Python

pyner/named_entity/__init__.py

chantera/pyner

6de19713871e923c997495c07e2ec249bded8671

[ "MIT" ]

1

2019-06-16T00:52:26.000Z

pyner/named_entity/__init__.py

chantera/pyner

6de19713871e923c997495c07e2ec249bded8671

[ "MIT" ]

null

pyner/named_entity/__init__.py

chantera/pyner

6de19713871e923c997495c07e2ec249bded8671

[ "MIT" ]

null

from .dataset import * from .evaluator import * from .recognizer import *

18.5

25

0.756757

9

74

6.222222

0.555556

0.357143

0

0.162162

74

3

26

24.666667

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

3920a62485976ef0d1531a38d622ba6a81681584

127

py

Python

devodsconnector/__init__.py

DevoInc/python-ds-connector

64e5de6bc85536309455713120c551202b99bd39

[ "MIT" ]

4

2020-04-24T00:18:32.000Z

2022-03-24T19:19:29.000Z

devodsconnector/__init__.py

DevoInc/python-ds-connector

64e5de6bc85536309455713120c551202b99bd39

[ "MIT" ]

3

2020-04-27T22:10:30.000Z

2021-02-11T18:51:50.000Z

devodsconnector/__init__.py

DevoInc/python-ds-connector

64e5de6bc85536309455713120c551202b99bd39

[ "MIT" ]

3

2019-08-01T19:03:25.000Z

2020-04-27T21:40:07.000Z

from .reader import Reader from .writer import Writer from .json_writer import JSONWriter from .__version__ import __version__

25.4

36

0.84252

17

127

5.764706

0.411765

0.244898

0

0.125984

127

4

37

31.75

0.882883

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

392b0e0e6077a0976f1fdaabeb8be1d924457a97

101

py

Python

zsec_aws_tools_extensions/__init__.py

zuoralabs/zsec-aws-tools-extensions

63d1aa1a5b3f79ea31cd0c5c44006b41033f96a6

[ "BSD-2-Clause" ]

null

zsec_aws_tools_extensions/__init__.py

zuoralabs/zsec-aws-tools-extensions

63d1aa1a5b3f79ea31cd0c5c44006b41033f96a6

[ "BSD-2-Clause" ]

5

2020-05-20T04:53:05.000Z

2020-07-31T00:33:39.000Z

zsec_aws_tools_extensions/__init__.py

zuoralabs/zsec-aws-tools-extensions

63d1aa1a5b3f79ea31cd0c5c44006b41033f96a6

[ "BSD-2-Clause" ]

1

2020-10-03T12:20:34.000Z

from .deployment import zip_string, PartialAWSResourceCollection, PartialResource, partial_resources

50.5

100

0.891089

9

101

9.777778

1

0

0.069307

101

1

101

0.93617

0

1

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true

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1

0

1

0

null

0

1

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1

0

null

0

1

0

1

0

1

0

6

3938a7af27a276afcd6271710e95ba5db738e215

17,068

py

Python

tests/endpoints/test_project_endpoints.py

eshults5/imbi

2ebf32264fe35a886b8f04086c28b9a05be1194a

[ "BSD-3-Clause" ]

null

tests/endpoints/test_project_endpoints.py

eshults5/imbi

2ebf32264fe35a886b8f04086c28b9a05be1194a

[ "BSD-3-Clause" ]

null

tests/endpoints/test_project_endpoints.py

eshults5/imbi

2ebf32264fe35a886b8f04086c28b9a05be1194a

[ "BSD-3-Clause" ]

null

import json import uuid import jsonpatch from imbi.endpoints.project import link, project from tests import base class AsyncHTTPTestCase(base.TestCaseWithReset): ADMIN_ACCESS = True TRUNCATE_TABLES = [ 'v1.configuration_systems', 'v1.data_centers', 'v1.deployment_types', 'v1.environments', 'v1.orchestration_systems', 'v1.project_link_types', 'v1.project_types', 'v1.namespaces' ] def setUp(self): super().setUp() self._configuration_system = self.create_configuration_system() self._data_center = self.create_data_center() self._deployment_type = self.create_deployment_type() self._environments = self.create_environments() self._namespace = self.create_namespace() self._orchestration_system = self.create_orchestration_system() self._project_link_type = self.create_project_link_type() self._project_type = self.create_project_type() def create_configuration_system(self): record = { 'name': str(uuid.uuid4()), 'description': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/configuration_system', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['name'] def create_data_center(self): record = { 'name': str(uuid.uuid4()), 'description': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/data_center', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['name'] def create_deployment_type(self): record = { 'name': str(uuid.uuid4()), 'description': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/deployment_type', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['name'] def create_environments(self): environments = [] for iteration in range(0, 2): record = { 'name': str(uuid.uuid4()), 'description': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/environment', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) environments.append(record['name']) return environments def create_orchestration_system(self): record = { 'name': str(uuid.uuid4()), 'description': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/orchestration_system', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['name'] def create_project_link_type(self): record = { 'link_type': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/project_link_type', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['link_type'] def create_project_type(self): record = { 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4()), 'description': str(uuid.uuid4()), 'icon_class': 'fas fa-blind' } result = self.fetch('/admin/project_type', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['name'] def create_namespace(self): record = { 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4().hex), 'icon_class': 'fas fa-blind', 'maintained_by': [] } result = self.fetch('/admin/namespace', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) return record['name'] def test_project_lifecycle(self): record = { 'namespace': self._namespace, 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4().hex), 'description': str(uuid.uuid4()), 'data_center': self._data_center, 'project_type': self._project_type, 'configuration_system': self._configuration_system, 'deployment_type': self._deployment_type, 'orchestration_system': self._orchestration_system, 'environments': self._environments } url = self.get_url('/projects/{}/{}'.format( self._namespace, record['name'])) # Create result = self.fetch('/projects', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) self.assertIsNotNone(result.headers['Date']) self.assertIsNone(result.headers.get('Last-Modified', None)) self.assert_link_header_equals(result, url) self.assertEqual( result.headers['Cache-Control'], 'public, max-age={}'.format( project.RecordRequestHandler.TTL)) new_value = json.loads(result.body.decode('utf-8')) self.assertEqual( new_value['created_by'], self.USERNAME[self.ADMIN_ACCESS]) for field in ['created_by', 'last_modified_by']: del new_value[field] self.assertDictEqual(new_value, record) # PATCH updated = dict(record) updated['description'] = str(uuid.uuid4()) patch = jsonpatch.make_patch(record, updated) patch_value = patch.to_string().encode('utf-8') result = self.fetch( url, method='PATCH', body=patch_value, headers=self.headers) self.assertEqual(result.code, 200) new_value = json.loads(result.body.decode('utf-8')) for field in ['created_by', 'last_modified_by']: self.assertEqual( new_value[field], self.USERNAME[self.ADMIN_ACCESS]) del new_value[field] self.assertDictEqual(new_value, updated) # Patch no change result = self.fetch( url, method='PATCH', body=patch_value, headers=self.headers) self.assertEqual(result.code, 304) # GET result = self.fetch(url, headers=self.headers) self.assertEqual(result.code, 200) self.assertIsNotNone(result.headers['Date']) self.assertIsNotNone(result.headers['Last-Modified']) self.assert_link_header_equals(result, url) self.assertEqual( result.headers['Cache-Control'], 'public, max-age={}'.format( project.RecordRequestHandler.TTL)) new_value = json.loads(result.body.decode('utf-8')) for field in ['created_by', 'last_modified_by']: self.assertEqual( new_value[field], self.USERNAME[self.ADMIN_ACCESS]) del new_value[field] self.assertDictEqual(new_value, updated) # DELETE result = self.fetch(url, method='DELETE', headers=self.headers) self.assertEqual(result.code, 204) # GET record should not exist result = self.fetch(url, headers=self.headers) self.assertEqual(result.code, 404) # DELETE should fail as record should not exist result = self.fetch(url, method='DELETE', headers=self.headers) self.assertEqual(result.code, 404) def test_create_with_missing_fields(self): record = { 'namespace': self._namespace, 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4().hex), 'data_center': self._data_center, 'project_type': self._project_type, 'configuration_system': self._configuration_system, 'deployment_type': self._deployment_type, 'orchestration_system': self._orchestration_system, 'environments': self._environments } url = self.get_url('/projects/{}/{}'.format( self._namespace, record['name'])) # Create result = self.fetch('/projects', method='POST', body=json.dumps(record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) self.assertIsNone(result.headers.get('Last-Modified', None)) self.assert_link_header_equals(result, url) self.assertEqual( result.headers['Cache-Control'], 'public, max-age={}'.format( project.RecordRequestHandler.TTL)) new_value = json.loads(result.body.decode('utf-8')) self.assertEqual( new_value['created_by'], self.USERNAME[self.ADMIN_ACCESS]) for field in ['created_by', 'last_modified_by']: del new_value[field] record['description'] = None self.assertDictEqual(new_value, record) def test_dependencies(self): project_a = { 'namespace': self._namespace, 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4().hex), 'data_center': self._data_center, 'project_type': self._project_type, 'configuration_system': self._configuration_system, 'deployment_type': self._deployment_type, 'orchestration_system': self._orchestration_system, 'environments': self._environments } result = self.fetch( '/projects', method='POST', headers=self.headers, body=json.dumps(project_a).encode('utf-8')) self.assertEqual(result.code, 200) project_b = { 'namespace': self._namespace, 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4().hex), 'data_center': self._data_center, 'project_type': self._project_type, 'configuration_system': self._configuration_system, 'deployment_type': self._deployment_type, 'orchestration_system': self._orchestration_system, 'environments': self._environments } result = self.fetch( '/projects', method='POST', headers=self.headers, body=json.dumps(project_b).encode('utf-8')) self.assertEqual(result.code, 200) # Create the dependency result = self.fetch( '/projects/{}/{}/dependencies'.format( self._namespace, project_b['name']), method='POST', headers=self.headers, body=json.dumps({ 'dependency_namespace': self._namespace, 'dependency_name': project_a['name'] }).encode('utf-8')) self.assertEqual(result.code, 200) result = self.fetch( '/projects/{}/{}/dependencies'.format( self._namespace, project_b['name']), method='GET', headers=self.headers) self.assertEqual(result.code, 200) self.assertListEqual( json.loads(result.body.decode('utf-8')), [{ 'dependency_namespace': self._namespace, 'dependency_name': project_a['name'] }]) result = self.fetch( '/projects/{}/{}/dependencies/{}/{}'.format( self._namespace, project_b['name'], self._namespace, project_a['name']), method='GET', headers=self.headers) self.assertEqual(result.code, 200) self.assertDictEqual( json.loads(result.body.decode('utf-8')), { 'created_by': self.USERNAME[self.ADMIN_ACCESS], 'namespace': self._namespace, 'name': project_b['name'], 'dependency_namespace': self._namespace, 'dependency_name': project_a['name'] }) result = self.fetch( '/projects/{}/{}/dependencies/{}/{}'.format( self._namespace, project_b['name'], self._namespace, project_a['name']), method='DELETE', headers=self.headers) self.assertEqual(result.code, 204) result = self.fetch( '/projects/{}/{}/dependencies/{}/{}'.format( self._namespace, project_b['name'], self._namespace, project_a['name']), method='GET', headers=self.headers) self.assertEqual(result.code, 404) def test_links(self): project_record = { 'namespace': self._namespace, 'name': str(uuid.uuid4()), 'slug': str(uuid.uuid4().hex), 'data_center': self._data_center, 'project_type': self._project_type, 'configuration_system': self._configuration_system, 'deployment_type': self._deployment_type, 'orchestration_system': self._orchestration_system, 'environments': self._environments } result = self.fetch('/projects', method='POST', body=json.dumps(project_record).encode('utf-8'), headers=self.headers) self.assertEqual(result.code, 200) record = { 'namespace': self._namespace, 'name': project_record['name'], 'link_type': self._project_link_type, 'url': 'https://github.com/AWeber/Imbi' } url = self.get_url('/projects/{}/{}/links/{}'.format( self._namespace, project_record['name'], self._project_link_type)) # Create result = self.fetch( '/projects/{}/{}/links'.format( self._namespace, project_record['name']), headers=self.headers, method='POST', body=json.dumps(record).encode('utf-8')) self.assertEqual(result.code, 200) link_record = json.loads(result.body.decode('utf-8')) self.assert_link_header_equals(result, url) self.assertEqual( result.headers['Cache-Control'], 'public, max-age={}'.format( link.RecordRequestHandler.TTL)) self.assertEqual( link_record['created_by'], self.USERNAME[self.ADMIN_ACCESS]) self.assertEqual(link_record['url'], record['url']) # Get links result = self.fetch('/projects/{}/{}/links'.format( self._namespace, project_record['name']), headers=self.headers) self.assertEqual(result.code, 200) self.assert_link_header_equals( result, self.get_url('/projects/{}/{}/links'.format( self._namespace, project_record['name']))) records = [] for row in json.loads(result.body.decode('utf-8')): for field in {'created_at', 'last_modified_at'}: del row[field] records.append(row) self.assertListEqual(records, [link_record]) # PATCH updated = dict(record) updated['url'] = 'https://gitlab.com/AWeber/Imbi' patch = jsonpatch.make_patch(record, updated) patch_value = patch.to_string().encode('utf-8') result = self.fetch( url, method='PATCH', body=patch_value, headers=self.headers) self.assertEqual(result.code, 200) self.assert_link_header_equals(result, url) record = json.loads(result.body.decode('utf-8')) for field in {'created_by', 'last_modified_by'}: del record[field] self.assertDictEqual(record, updated) # Patch no change result = self.fetch( url, method='PATCH', body=patch_value, headers=self.headers) self.assertEqual(result.code, 304) self.assert_link_header_equals(result, url) # Get result = self.fetch(url, headers=self.headers) self.assertEqual(result.code, 200) record = json.loads(result.body.decode('utf-8')) for field in {'created_by', 'last_modified_by'}: del record[field] self.assertDictEqual(record, updated) # Delete result = self.fetch(url, method='DELETE', headers=self.headers) self.assertEqual(result.code, 204) # Get 404 result = self.fetch(url, headers=self.headers) self.assertEqual(result.code, 404)

39.236782

79

0.570073

1,743

17,068

5.40677

0.079748

0.067699

0.077992

0.082237

0.838497

0.820352

0.812924

0.794249

0.764643

0.752016

0

0.013414

0.296813

17,068

434

80

39.327189

0.771788

0.011718

0

0.688347

0

0.146698

0.024568

0

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1

0.03523

false

0

0.01355

0

0.078591

0

null

0

1

0

null

0

6

3942bd983903dfa848b8fa3815a701af06331a17

1,462

py

Python

src/skallel_tensor/__init__.py

alimanfoo/skallel-tensor

0019440b24de24141b046739a02ad587dc621748

[ "MIT" ]

2

2019-08-22T21:48:58.000Z

2020-02-17T15:44:23.000Z

src/skallel_tensor/__init__.py

alimanfoo/skallel-tensor

0019440b24de24141b046739a02ad587dc621748

[ "MIT" ]

9

2019-07-04T00:42:22.000Z

2019-10-01T18:41:13.000Z

src/skallel_tensor/__init__.py

alimanfoo/skallel-tensor

0019440b24de24141b046739a02ad587dc621748

[ "MIT" ]

1

2019-06-25T07:36:51.000Z

# flake8: noqa from .version import version as __version__ # Import the public API. from .api import ( genotypes_locate_hom, genotypes_locate_het, genotypes_locate_call, genotypes_count_alleles, genotypes_to_called_allele_counts, genotypes_to_missing_allele_counts, genotypes_to_allele_counts, genotypes_to_allele_counts_melt, genotypes_to_major_allele_counts, genotypes_to_haplotypes, allele_counts_to_frequencies, allele_counts_allelism, allele_counts_max_allele, variants_to_dataframe, select_slice, select_indices, select_mask, select_range, select_values, concatenate, ) # Import these modules to ensure that their implementation functions are # registered with the API for dispatching. from . import numpy_backend from . import dask_backend from . import cuda_backend __all__ = [ 'genotypes_locate_hom', 'genotypes_locate_het', 'genotypes_locate_call', 'genotypes_count_alleles', 'genotypes_to_called_allele_counts', 'genotypes_to_missing_allele_counts', 'genotypes_to_allele_counts', 'genotypes_to_allele_counts_melt', 'genotypes_to_major_allele_counts', 'genotypes_to_haplotypes', 'allele_counts_to_frequencies', 'allele_counts_allelism', 'allele_counts_max_allele', 'variants_to_dataframe', 'select_slice', 'select_indices', 'select_mask', 'select_range', 'select_values', 'concatenate', ]

26.107143

72

0.75855

172

1,462

5.872093

0.30814

0.190099

0.166337

0.182178

0.766337

0

0.000829

0.175103

1,462

55

73

26.581818

0.83665

0.100547

0

0.329008

0.242748

0

1

0

false

0

0.104167

0

0.104167

0

null

0

1

0

1

0

null

0

6

394c298d9897a1286f5f636ceac311e3c416584c

38

py

Python

smsframework_yunpian/__init__.py

vihtinsky/py-smsframework-yunpian

d965f16a5202ea1096d28fb8fa4e1dd1c3599b09

[ "BSD-2-Clause" ]

null

smsframework_yunpian/__init__.py

vihtinsky/py-smsframework-yunpian

d965f16a5202ea1096d28fb8fa4e1dd1c3599b09

[ "BSD-2-Clause" ]

null

smsframework_yunpian/__init__.py

vihtinsky/py-smsframework-yunpian

d965f16a5202ea1096d28fb8fa4e1dd1c3599b09

[ "BSD-2-Clause" ]

null

from .provider import YunpianProvider

19

37

0.868421

4

38

8.25

1

0

0.105263

38

1

38

0.970588

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

39530839430cc10d20b763e8c0f82b34e0cc026d

31

py

Python

plugins/holland.backup.mysql_lvm/holland/backup/mysql_lvm/plugin/__init__.py

jkoelker/holland

b53497002b090db24fbbf0545c0683b4b727ab34

[ "BSD-3-Clause" ]

1

2019-06-06T01:07:34.000Z

plugins/holland.backup.mysql_lvm/holland/backup/mysql_lvm/plugin/__init__.py

jkoelker/holland

b53497002b090db24fbbf0545c0683b4b727ab34

[ "BSD-3-Clause" ]

null

plugins/holland.backup.mysql_lvm/holland/backup/mysql_lvm/plugin/__init__.py

jkoelker/holland

b53497002b090db24fbbf0545c0683b4b727ab34

[ "BSD-3-Clause" ]

2

2015-12-04T12:17:59.000Z

2022-03-23T07:22:02.000Z

from raw import MysqlLVMBackup

15.5

30

0.870968

4

31

6.75

1

0

0.129032

31

1

31

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1a3fe6405fe577d0765e5e8ddad6772cb99b75fc

25

py

Python

wisdem/pymap/__init__.py

ptrbortolotti/WISDEM

2b7e44716d022e2f62140073dd078c5deeb8bf0a

[ "Apache-2.0" ]

3

2018-10-07T06:05:37.000Z

2021-04-27T18:21:59.000Z

wisdem/pymap/__init__.py

ptrbortolotti/WISDEM

2b7e44716d022e2f62140073dd078c5deeb8bf0a

[ "Apache-2.0" ]

17

2019-09-13T22:21:15.000Z

2019-10-25T20:04:26.000Z

wisdem/pymap/__init__.py

ptrbortolotti/WISDEM

2b7e44716d022e2f62140073dd078c5deeb8bf0a

[ "Apache-2.0" ]

7

2018-09-08T06:02:04.000Z

2021-06-04T07:51:23.000Z

from .pymap import pyMAP

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2018746e3fa7691c247f5e48020d95bb3b54a33f

154

py

Python

ltrc/ltrc/classes/manage_choices.py

iscenigmax/ltrc-registry

86c4e52e0d76e686c39a357957af35846a8fc391

[ "Unlicense" ]

null

ltrc/ltrc/classes/manage_choices.py

iscenigmax/ltrc-registry

86c4e52e0d76e686c39a357957af35846a8fc391

[ "Unlicense" ]

null

ltrc/ltrc/classes/manage_choices.py

iscenigmax/ltrc-registry

86c4e52e0d76e686c39a357957af35846a8fc391

[ "Unlicense" ]

null

#!/usr/bin/python # -*- encoding: utf-8 -*- import datetime def year_choices(): return [(r, r) for r in range(2015, datetime.date.today().year+1)]

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649eeacad78faa5ef2ee061aef5acf00d095b8ae

7,353

py

Python

Train_Eval/Evaluator.py

lucasliu0928/KGDAL

a7e33515d6383763e10508db498adec36a97c97d

[ "MIT" ]

5

2021-06-15T16:51:06.000Z

2022-03-15T12:36:54.000Z

Train_Eval/Evaluator.py

lucasliu0928/KGDAL

a7e33515d6383763e10508db498adec36a97c97d

[ "MIT" ]

null

Train_Eval/Evaluator.py

lucasliu0928/KGDAL

a7e33515d6383763e10508db498adec36a97c97d

[ "MIT" ]

1

2022-03-15T12:36:53.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Mar 10 21:37:06 2021 @author: lucasliu """ import tensorflow as tf import pandas as pd import sys import os CURR_DIR = os.path.dirname(os.path.abspath("./")) #Set system path to KGDAL sys.path.append(CURR_DIR) from Model.LSTM_ATTonTime import MyLSTM,MyLSTM_4grps from Model.LSTM_ATTonTimeAndFeature_WithThresFeatures import AttnOnFeatures_ScaleAtt_4grps_withThresFeature from Model.LSTM_Vanila import VanillaLSTM from Ultility import Evaluation_funcs #tf.keras.backend.set_floatx('float32') def external_eval_1grp(ckpt_idx_to_restore,X_Validation,y_Validation,timesteps,n_feature,latent_dim,outdir): #restore model reconstructed_model = VanillaLSTM(timesteps,n_feature,latent_dim) ckpt = tf.train.Checkpoint(step=tf.Variable(1), mod = reconstructed_model) #manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) #ckpt.restore(manager.latest_checkpoint).expect_partial() ckpt.restore(outdir + '/tf_ckpts/ckpt-' + str(ckpt_idx_to_restore)).expect_partial() #use expect_partial to only restore vars used for validation, removed warnings pred_prob = reconstructed_model.predict(X_Validation, verbose=0) pred_classes = Evaluation_funcs.compute_performance2(y_Validation,pred_prob,False,0.5) #return performance at roc cutoff point accuracy,precision1,recall1,f11,precision0,recall0,f10 = Evaluation_funcs.compute_performance1(pred_classes,y_Validation) #return performance at threhold 0.5 roc_auc = Evaluation_funcs.roc(y_Validation,pred_prob,False) pr_auc = Evaluation_funcs.PR_AUC(y_Validation, pred_prob) F1_Class0,F3_Class1 = Evaluation_funcs.F_beta(y_Validation,pred_classes) perf_tb = pd.DataFrame([[accuracy,roc_auc,pr_auc,precision1,recall1,f11,precision0,recall0,f10,F1_Class0,F3_Class1]],columns=['ACC','ROC_AUC',"PR_AUC",'PREC1','RECALL1','F1_1','PREC0','RECALL0','F1_0','F1_Class0','F3_Class1']) perf_tb.to_csv(outdir + '/perf0314.csv') #Two grps def external_eval(ckpt_idx_to_restore,X_Validation_A,X_Validation_B,y_Validation,timesteps,n_featureA,n_featureB,outdir): #restore model reconstructed_model = MyLSTM(timesteps,n_featureA,n_featureB,8) ckpt = tf.train.Checkpoint(step=tf.Variable(1), mod = reconstructed_model) #manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) #ckpt.restore(manager.latest_checkpoint).expect_partial() ckpt.restore(outdir + '/tf_ckpts/ckpt-' + str(ckpt_idx_to_restore)).expect_partial() #use expect_partial to only restore vars used for validation, removed warnings pred_prob = reconstructed_model(X_Validation_A,X_Validation_B, training=False) loss_object = tf.keras.losses.BinaryCrossentropy() t_loss2 = loss_object(y_Validation, pred_prob) pred_classes = Evaluation_funcs.compute_performance2(y_Validation,pred_prob,False,0.5) #return performance at roc cutoff point accuracy,precision1,recall1,f11,precision0,recall0,f10 = Evaluation_funcs.compute_performance1(pred_classes,y_Validation) #return performance at threhold 0.5 roc_auc = Evaluation_funcs.roc(y_Validation,pred_prob,False) pr_auc = Evaluation_funcs.PR_AUC(y_Validation, pred_prob) F1_Class0,F3_Class1 = Evaluation_funcs.F_beta(y_Validation,pred_classes) perf_tb = pd.DataFrame([[accuracy,roc_auc,pr_auc,precision1,recall1,f11,precision0,recall0,f10,F1_Class0,F3_Class1]],columns=['ACC','ROC_AUC',"PR_AUC",'PREC1','RECALL1','F1_1','PREC0','RECALL0','F1_0','F1_Class0','F3_Class1']) perf_tb.to_csv(outdir + '/perf0311.csv') return t_loss2 def external_eval_4grps(ckpt_idx_to_restore,X_Validation_A,X_Validation_B,X_Validation_C,X_Validation_D,y_Validation,timesteps,n_featureA,n_featureB,n_featureC,n_featureD,outdir): #restore model reconstructed_model = MyLSTM_4grps(timesteps,n_featureA,n_featureB,n_featureC,n_featureD,8) ckpt = tf.train.Checkpoint(step=tf.Variable(1), mod = reconstructed_model) #manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) #ckpt.restore(manager.latest_checkpoint).expect_partial() ckpt.restore(outdir + '/tf_ckpts/ckpt-' + str(ckpt_idx_to_restore)).expect_partial() #use expect_partial to only restore vars used for validation, removed warnings pred_prob = reconstructed_model(X_Validation_A,X_Validation_B,X_Validation_C,X_Validation_D, training=False) loss_object = tf.keras.losses.BinaryCrossentropy() t_loss2 = loss_object(y_Validation, pred_prob) pred_classes = Evaluation_funcs.compute_performance2(y_Validation,pred_prob,False,0.5) #return performance at roc cutoff point accuracy,precision1,recall1,f11,precision0,recall0,f10 = Evaluation_funcs.compute_performance1(pred_classes,y_Validation) #return performance at threhold 0.5 roc_auc = Evaluation_funcs.roc(y_Validation,pred_prob,False) pr_auc = Evaluation_funcs.PR_AUC(y_Validation, pred_prob) F1_Class0,F3_Class1 = Evaluation_funcs.F_beta(y_Validation,pred_classes) perf_tb = pd.DataFrame([[accuracy,roc_auc,pr_auc,precision1,recall1,f11,precision0,recall0,f10,F1_Class0,F3_Class1]],columns=['ACC','ROC_AUC',"PR_AUC",'PREC1','RECALL1','F1_1','PREC0','RECALL0','F1_0','F1_Class0','F3_Class1']) perf_tb.to_csv(outdir + '/perf0311.csv') return t_loss2 def external_eval_4grps_withThFeatures(ckpt_idx_to_restore,X_Validation_A,X_Validation_B,X_Validation_C,X_Validation_D,X_Validation_A_th,X_Validation_B_th,X_Validation_C_th,X_Validation_D_th,y_Validation,timesteps,n_featureA,n_featureB,n_featureC,n_featureD,n_features_A_th,n_features_B_th,n_features_C_th,n_features_D_th,outdir): #restore model reconstructed_model = AttnOnFeatures_ScaleAtt_4grps_withThresFeature(timesteps,n_featureA,n_featureB,n_featureC,n_featureD,n_features_A_th,n_features_B_th,n_features_C_th,n_features_D_th,8) ckpt = tf.train.Checkpoint(step=tf.Variable(1), mod = reconstructed_model) #manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) #ckpt.restore(manager.latest_checkpoint).expect_partial() ckpt.restore(outdir + '/tf_ckpts/ckpt-' + str(ckpt_idx_to_restore)).expect_partial() #use expect_partial to only restore vars used for validation, removed warnings pred_prob = reconstructed_model(X_Validation_A,X_Validation_B,X_Validation_C,X_Validation_D,X_Validation_A_th,X_Validation_B_th,X_Validation_C_th,X_Validation_D_th, training=False) loss_object = tf.keras.losses.BinaryCrossentropy() t_loss2 = loss_object(y_Validation, pred_prob) pred_classes = Evaluation_funcs.compute_performance2(y_Validation,pred_prob,False,0.5) #return performance at roc cutoff point accuracy,precision1,recall1,f11,precision0,recall0,f10 = Evaluation_funcs.compute_performance1(pred_classes,y_Validation) #return performance at threhold 0.5 roc_auc = Evaluation_funcs.roc(y_Validation,pred_prob,False) pr_auc = Evaluation_funcs.PR_AUC(y_Validation, pred_prob) F1_Class0,F3_Class1 = Evaluation_funcs.F_beta(y_Validation,pred_classes) perf_tb = pd.DataFrame([[accuracy,roc_auc,pr_auc,precision1,recall1,f11,precision0,recall0,f10,F1_Class0,F3_Class1]],columns=['ACC','ROC_AUC',"PR_AUC",'PREC1','RECALL1','F1_1','PREC0','RECALL0','F1_0','F1_Class0','F3_Class1']) perf_tb.to_csv(outdir + '/perf0315.csv') return t_loss2

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1,096

7,353

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0.060506

0.052255

0.886322

0.858636

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0.831316

0.823066

0

0.03388

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7,353

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0.787381

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0.063074

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false

0

0.119403

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0

null

0

1

0

null

0

6

64d1f7bde6417d1c3348d8f1e650930fc64a7b6c

89

py

Python

src/schedulers.py

pomelyu/DNNProject

08fa94788a18c28f867c4ea46e9e18b8f65ee334

[ "MIT" ]

null

src/schedulers.py

pomelyu/DNNProject

08fa94788a18c28f867c4ea46e9e18b8f65ee334

[ "MIT" ]

null

src/schedulers.py

pomelyu/DNNProject

08fa94788a18c28f867c4ea46e9e18b8f65ee334

[ "MIT" ]

null

from .utils.mlconfig_torch import register_torch_schedulers register_torch_schedulers()

22.25

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0.88764

11

89

6.727273

0.636364

0.351351

0.621622

0

0.067416

89

3

60

29.666667

0.891566

0

1

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true

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0.5

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null

1

0

1

0

null

0

1

0

1

0

6

b3b75bbd90993bda8366c31f96dd021d424cee24

159

py

Python

29.operacoes_com_lista/11.reverse.py

robinson-1985/python-zero-dnc

df510d67e453611fcd320df1397cdb9ca47fecb8

[ "MIT" ]

null

29.operacoes_com_lista/11.reverse.py

robinson-1985/python-zero-dnc

df510d67e453611fcd320df1397cdb9ca47fecb8

[ "MIT" ]

null

29.operacoes_com_lista/11.reverse.py

robinson-1985/python-zero-dnc

df510d67e453611fcd320df1397cdb9ca47fecb8

[ "MIT" ]

null

# Reverse() -> Inverte a ordem da lista. lista_4 = [10,9,8,7,5,6,4,2,3,1,2,3] print(lista_4) lista_4.reverse() print(lista_4) lista_4.reverse() print(lista_4)

19.875

40

0.698113

35

159

3

0.485714

0.342857

0.314286

0.304762

0.561905

0

0.132867

0.100629

159

8

41

19.875

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null

0

1

0

6

b3d7ed335364b1e88ed408644ba93e7ccb559b0e

32

py

Python

web/routes/views/__init__.py

coinForRich/coin-for-rich

ed7d3b0101ede3340d919d0d28c52ba6e797943e

[ "MIT" ]

55

2021-09-15T04:34:13.000Z

2022-03-20T18:11:01.000Z

web/routes/views/__init__.py

coinForRich/coin-for-rich

ed7d3b0101ede3340d919d0d28c52ba6e797943e

[ "MIT" ]

20

2021-08-25T14:52:33.000Z

2022-03-05T23:46:43.000Z

web/routes/views/__init__.py

coinForRich/coin-for-rich

ed7d3b0101ede3340d919d0d28c52ba6e797943e

[ "MIT" ]

26

2021-09-15T04:51:21.000Z

2022-02-01T04:45:08.000Z

from .views import views_router

16

31

0.84375

5

32

5.2

0.8

0

0.125

32

1

32

0.928571

0

1

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1

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1

0

6

3741124b4074960d4caba5f632908d6e3697f3ce

180

py

Python

CodeChef/LONG/FEB15/XRMTRX/a.py

VastoLorde95/Competitive-Programming

6c990656178fb0cd33354cbe5508164207012f24

[ "MIT" ]

170

2017-07-25T14:47:29.000Z

2022-01-26T19:16:31.000Z

CodeChef/LONG/FEB15/XRMTRX/a.py

navodit15/Competitive-Programming

6c990656178fb0cd33354cbe5508164207012f24

[ "MIT" ]

null

CodeChef/LONG/FEB15/XRMTRX/a.py

navodit15/Competitive-Programming

6c990656178fb0cd33354cbe5508164207012f24

[ "MIT" ]

55

2017-07-28T06:17:33.000Z

2021-10-31T03:06:22.000Z

for _ in xrange(input()): l, r = [int(x) for x in raw_input().split()] for i in xrange (l,r+1): print 'row',i,'\t', for j in xrange(l,r+1): print i^j,'\t', print print

20

45

0.561111

38

180

2.605263

0.421053

0.242424

0.181818

0.20202

0.323232

0

0.014184

0.216667

180

8

46

22.5

0.687943

0

0.25

0

0.038889

0

null

0

null

0.5

0

null

1

0

1

0

null

0

1

0

1

0

6

377f369bdf1d503bfb993bedf4a04ce8b6d08efa

238

py

Python

FreeCodeCamp/Inheritance/Chef.py

NikiReis/Python_OOP

8071d641f4895b28584317c0896834c354107df2

[ "MIT" ]

null

FreeCodeCamp/Inheritance/Chef.py

NikiReis/Python_OOP

8071d641f4895b28584317c0896834c354107df2

[ "MIT" ]

null

FreeCodeCamp/Inheritance/Chef.py

NikiReis/Python_OOP

8071d641f4895b28584317c0896834c354107df2

[ "MIT" ]

null

class Chef: def make_chicken(self): print("The chef make a chicken") def make_salad(self): print("The chef makes salad") def make_special_dish(self): print("The chef makes a special dish")

26.444444

46

0.605042

33

238

4.242424

0.393939

0.15

0.257143

0.342857

0.3

0

0.302521

238

9

46

26.444444

0.843373

0

0.311688

0

1

0.428571

false

0

0.571429

0.428571

0

null

0

1

0

1

0

null

0

1

0

1

0

6

8065311e12bb2b723b59d14d1896ade469f39d48

17,635

py

Python

services/api/tests/integration/proxy_tests.py

ohsu-computational-biology/dms-aa

4aabae8b5ada539fa010a79970093c93fbbddb01

[ "MIT" ]

null

services/api/tests/integration/proxy_tests.py

ohsu-computational-biology/dms-aa

4aabae8b5ada539fa010a79970093c93fbbddb01

[ "MIT" ]

10

2016-12-07T01:37:41.000Z

2017-01-20T22:20:52.000Z

services/api/tests/integration/proxy_tests.py

ohsu-computational-biology/euler

4aabae8b5ada539fa010a79970093c93fbbddb01

[ "MIT" ]

null

#!/usr/bin/env python """ Test proxy """ import urllib import json # assumes OS_USERNAME has access to only one project MY_PROJECT = 'BRCA-UK' MY_GENESET = 'GS1' MY_GENE = 'ENSG00000141510' def test_should_logout_ok(client, app): """ should respond with ok /api/v1/auth/logout """ headers = {'Authorization': _login_bearer_token(client, app), 'Content-Type': 'application/json'} r = client.post('/api/v1/auth/logout', headers=headers) assert r.status_code == 200 def test_should_create_external_entityset_ok(client, app): """ should respond with ok /api/v1/entityset/external """ headers = {'Authorization': _login_bearer_token(client, app), 'Content-Type': 'application/json'} data = {"filters": {}, "size": 173535, "type": "DONOR", "name": "Input donor set", "description": "", "isTransient": True, "sortBy": "fileName", "sortOrder": "DESCENDING"} r = client.post('/api/v1/entityset/external', headers=headers, data=json.dumps(data)) assert r.status_code == 201 assert r.json['id'] def test_should_validate_token_ok(client, app): """ should respond with ok and response for token """ headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/auth/verify', headers=headers) assert r.status_code == 200 def test_should_reject_missing_token(client, app): """ should respond with ok and response for token """ r = client.get('/api/v1/auth/verify') assert r.status_code == 401 def test_redact_download_info(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/download/info/current/Projects', headers=headers) assert r.status_code == 200 assert len(r.json) > 0 for dir in r.json: assert MY_PROJECT in dir['name'] or 'README' in dir['name'] def test_post_analysis_enrichment(client, app): """ should respond with ok and response from dcc """ headers = {'Authorization': _login_bearer_token(client, app), 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'} form = dict([('sort', u'affectedDonorCountFiltered'), ('params', u'{"maxGeneSetCount":50,"fdr":0.05,"universe":"REACTOME_PATHWAYS","maxGeneCount":50}'), # NOQA ('order', u'DESC'), ('filters', u'{"gene":{"id":{"is":["ES:2d097244-2aac-4ae5-a428-7bff28adad46"]}}}')]) # NOQA r = client.post('/api/v1/analysis/enrichment', headers=headers, data=form) assert r.status_code == 202 def test_post_analysis_enrichment_no_data(client, app): """ should respond with ok and response from dcc """ headers = {'Authorization': _login_bearer_token(client, app), 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'} r = client.post('/api/v1/analysis/enrichment', headers=headers) assert r.status_code == 400 def test_post_analysis_enrichment_no_header(client, app): """ should respond with ok and response from dcc """ headers = {'Authorization': _login_bearer_token(client, app)} r = client.post('/api/v1/analysis/enrichment', headers=headers) assert r.status_code == 400 def test_donors_returns_ok(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} params = {'from': 1, 'include': 'facets', 'size': 25} r = client.get('/api/v1/donors', query_string=params, headers=headers) assert r.status_code == 200 assert r.json.keys() == [u'pagination', u'hits', u'facets'] for hit in r.json['hits']: assert hit['projectId'] == MY_PROJECT if 'projectId' in r.json['facets']: for term in r.json['facets']['projectId']['terms']: assert term['term'] == MY_PROJECT def test_genes_returns_ok(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} params = {'from': 1, 'include': 'facets', 'size': 25} filters = {"donor": {"projectId": {"is": ["BRCA-UK"]}}} filters = urllib.quote_plus(json.dumps(filters)) params_filtered = {'from': 1, 'include': 'facets', 'size': 25, 'filters': filters} r = client.get('/api/v1/genes', query_string=params, headers=headers) assert r.status_code == 200 assert r.json.keys() == [u'pagination', u'hits', u'facets'] r_filtered = client.get('/api/v1/genes', query_string=params_filtered, headers=headers) assert r_filtered.status_code == 200 for i in range(len(r.json['hits'])): assert r.json['hits'][i] == r_filtered.json['hits'][i] def test_genes_count_returns_ok(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {"gene": {"hasPathway": 'true'}} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters} filters = {"gene": {"hasPathway": 'true'}, "donor": {"projectId": {"is": [MY_PROJECT]}}} filters = urllib.quote_plus(json.dumps(filters)) params_filtered = {'filters': filters} r = client.get('/api/v1/genes/count', query_string=params, headers=headers) assert r.status_code == 200 r = int(r.json) r_filtered = client.get('/api/v1/genes/count', query_string=params_filtered, headers=headers) assert r_filtered.status_code == 200 r_filtered = int(r_filtered.json) assert r == r_filtered def test_genes_mutations_counts(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters} filters = {"donor": {"projectId": {"is": [MY_PROJECT]}}} filters = urllib.quote_plus(json.dumps(filters)) params_filtered = {'filters': filters} r = client.get('/api/v1/genes/{}/mutations/counts'.format(MY_GENE), query_string=params, headers=headers) assert r.status_code == 200 r_json = r.json r_filtered = client.get('/api/v1/genes/{}/mutations/counts'.format(MY_GENE), # NOQA query_string=params_filtered, headers=headers) assert r_filtered.status_code == 200 assert r_json == r_filtered.json def test_genesets_genes_counts(client, app): headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/genesets/{}/genes/counts'.format(MY_GENESET), headers=headers) # NOQA assert r.status_code == 200 assert r.json[MY_GENESET] != 0 def test_bad_genesets_genes_counts(client, app): headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/genesets/{}/genes/counts'.format('NOT_A_GENESET'), headers=headers) # NOQA assert r.status_code == 200 assert r.json['NOT_A_GENESET'] == 0 def test_mutations_returns_ok(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} params = {'from': 1, 'include': 'facets', 'size': 25} filters = {"donor": {"projectId": {"is": ["BRCA-UK"]}}} filters = urllib.quote_plus(json.dumps(filters)) params_filtered = {'from': 1, 'include': 'facets', 'size': 25, 'filters': filters} r = client.get('/api/v1/mutations', query_string=params, headers=headers) assert r.status_code == 200 assert r.json.keys() == [u'pagination', u'hits', u'facets'] r_filtered = client.get('/api/v1/mutations', query_string=params_filtered, headers=headers) assert r_filtered.status_code == 200 for i in range(len(r.json['hits'])): assert r.json['hits'][i] == r_filtered.json['hits'][i] def test_occurrences(client, app): """ should respond with ok and response from dcc, with MY_PROJECT in results """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters} filters = {"donor": {"projectId": {"is": [MY_PROJECT]}}} filters = urllib.quote_plus(json.dumps(filters)) params_filtered = {'filters': filters} r = client.get('/api/v1/occurrences', query_string=params, headers=headers) assert r.status_code == 200 r_json = r.json r_filtered = client.get('/api/v1/occurrences', query_string=params_filtered, headers=headers) assert r_filtered.status_code == 200 assert r_json == r_filtered.json def test_donors_facets_only_ok(client, app): """ should respond with ok and response from dcc, when facetsOnly=true and filter parameter created by browser is bad """ headers = {'Authorization': _login_bearer_token(client, app)} params = {'facetsOnly': True, 'include': 'facets', 'size': 10, 'from': 1, 'filters': '{"donor":{"id":{"is":["ES:undefined"]}}}'} r = client.get('/api/v1/donors', query_string=params, headers=headers) assert r.status_code == 400 def test_status_returns_ok(client): """ should respond with ok and response from dcc """ r = client.get('/api/version') assert r.status_code == 200 assert r.json.keys() == ['indexCommit', 'indexName', 'api', 'portal', 'portalCommit'] def test_files_summary(client, app): """ should respond with ok and response from dcc """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {"file": {"projectCode": {"is": ["BRCA-UK"]}}} filters = urllib.quote_plus(json.dumps(filters)) params = {'from': 1, 'include': 'facets', 'size': 25} params_filtered = {'filters': filters, 'from': 1, 'include': 'facets', 'size': 25} r = client.get('/api/v1/repository/files/summary', query_string=params, headers=headers) assert r.status_code == 200 assert r.json.keys() == [u'projectCount', u'totalFileSize', u'donorCount', u'primarySiteCount', u'fileCount'] r_filtered = client.get('/api/v1/repository/files/summary', query_string=params_filtered, headers=headers) assert r_filtered.status_code == 200 assert r_filtered.json.keys() == [u'projectCount', u'totalFileSize', u'donorCount', u'primarySiteCount', u'fileCount'] for key in r.json.keys(): assert r.json[key] == r_filtered.json[key] def test_files_returns_ok(client, app): """ should respond with ok and response from dcc """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {"file": {"repoName": {"is": ["Collaboratory - Toronto"]}}} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters, 'from': 1, 'include': 'facets', 'size': 25} r = client.get('/api/v1/repository/files', query_string=params, headers=headers) assert r.status_code == 200 assert r.json.keys() == [u'hits', u'termFacets', u'pagination'] for hit in r.json['hits']: for donor in hit['donors']: assert donor['projectCode'] == MY_PROJECT def test_files_returns_unauthorized_for_no_token(client, app): """ should respond with ok and response from dcc """ filters = {"file": {"repoName": {"is": ["Collaboratory - Toronto"]}}} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters, 'from': 1, 'include': 'facets', 'size': 25} r = client.get('/api/v1/repository/files', query_string=params) assert r.status_code == 401 def test_files_returns_unauthorized_for_bad_projects(client, app): """ should respond with 401 if project codes don't match """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {"file": {"repoName": {"is": ["Collaboratory - Toronto"]}, "projectCode": {"is": ["X", "Y"]}}} # NOQA filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters, 'from': 1, 'include': 'facets', 'size': 25} r = client.get('/api/v1/repository/files', query_string=params, headers=headers) assert r.status_code == 401 def test_projects_returns_empty_list_if_unauthenticated(client, app): """ /api/v1/projects """ r = client.get('/api/v1/projects') assert r.status_code == 200 assert len(r.json['hits']) == 0 def test_projects_returns_list_if_authenticated(client, app): """ /api/v1/projects """ headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/projects', headers=headers) assert r.status_code == 200 assert len(r.json['hits']) == 1 def test_projects_returns_list_if_project_specified(client, app): """ /api/v1/projects """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {"project": {"id": {"is": [MY_PROJECT]}}} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters} r = client.get('/api/v1/projects', headers=headers, query_string=params) assert r.status_code == 200 assert len(r.json['hits']) == 1 def test_projects_returns_list_if_not_project_specified(client, app): """ /api/v1/projects """ headers = {'Authorization': _login_bearer_token(client, app)} filters = {"project": {"id": {"not": []}}} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters} r = client.get('/api/v1/projects', headers=headers, query_string=params) assert r.status_code == 200 assert len(r.json['hits']) == 1 def test_gene_project_donor_counts(client, app): headers = {'Authorization': _login_bearer_token(client, app)} filters = {"mutation": {"functionalImpact": {"is": "High"}}} filters = urllib.quote_plus(json.dumps(filters)) params = {'filters': filters} r = client.get('/api/v1/ui/search/gene-project-donor-counts/ENSG00000005339??', # NOQA query_string=params, headers=headers) # NOQA assert r.status_code == 200 assert r.json['ENSG00000005339'] assert r.json['ENSG00000005339']['terms'][0]['term'] == MY_PROJECT def test_donor_mutation_counts(client, app): headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/ui/search/projects/donor-mutation-counts', headers=headers) # NOQA assert r.status_code == 200 assert r.json[MY_PROJECT] assert len(r.json.keys()) == 1 def test_projects_history(client, app): headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/projects/history', headers=headers) assert r.status_code == 200 for group in r.json: assert group['group'] == MY_PROJECT def test_projects_genes(client, app): headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/projects/{}/genes'.format(MY_PROJECT), headers=headers) # NOQA assert r.status_code == 200 def test_projects_genes_bad_project(client, app): headers = {'Authorization': _login_bearer_token(client, app)} r = client.get('/api/v1/projects/{}/genes'.format('NOT_A_PROJECT'), headers=headers) # NOQA assert r.status_code == 401 def test_get_manifests(client, app): headers = {'Authorization': _login_bearer_token(client, app)} url = '/api/v1/manifests?repos=collaboratory&format=tarball&filters={"file":{"id":{"is":"FI661960"}}}' # NOQA r = client.get(url, headers=headers) assert r.status_code == 200 def test_get_manifests_exacloud(client, app): headers = {'Authorization': _login_bearer_token(client, app)} # this file is actually in the BRCA repo, # (since the test user has access to that dir) # we've overridden the repo to force an exacloud response url = '/api/v1/manifests?repos=exacloud&format=tarball&filters={"file":{"id":{"is":"FI661960"}}}' # NOQA r = client.get(url, headers=headers) assert r.status_code == 200 # should only have one file assert r.data.count('scp $SCP_OPTS') == 1 def test_get_manifests_exacloud_nofind(client, app): headers = {'Authorization': _login_bearer_token(client, app)} # this file is actually in the BRCA repo, # (since the test user has access to that dir) # we've overridden the repo to force an exacloud response url = '/api/v1/manifests?repos=exacloud&format=tarball&filters={"file":{"id":{"is":"DUMMYFILEID"}}}' # NOQA r = client.get(url, headers=headers) assert r.status_code == 400 def test_get_manifests_noauth(client, app): headers = {} url = '/api/v1/manifests?repos=collaboratory&format=tarball&filters={"file":{"id":{"is":"FI661960"}}}' # NOQA r = client.get(url, headers=headers) assert r.status_code == 401 def _login_bearer_token(client, app): global global_id_token return 'Bearer {}'.format(global_id_token)

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0

null

0

1

0

null

0

6

0382267c2695edb914ba348e76d1cb8e5c2c2550

2,394

py

Python

hintcast/hintcast.py

davocarli/hintcast

86eaba2d094ac2089f960d82e60a36a698f45f52

[ "MIT" ]

null

hintcast/hintcast.py

davocarli/hintcast

86eaba2d094ac2089f960d82e60a36a698f45f52

[ "MIT" ]

null

hintcast/hintcast.py

davocarli/hintcast

86eaba2d094ac2089f960d82e60a36a698f45f52

[ "MIT" ]

null

from inspect import getfullargspec from types import FunctionType import logging def cast_hints(*args, **kwargs): cast_none = True strict = False if 'cast_none' in kwargs: cast_none = kwargs['cast_none'] if 'strict' in kwargs: strict = kwargs['strict'] def outer(func): spec = getfullargspec(func) def wrapper(*args, **kwargs): new_args = [] new_kwargs = {} for i in range(len(args)): arg_name = spec.args[i] passed_value = args[i] if (cast_none or passed_value is not None) and \ arg_name in spec.annotations and not \ isinstance(passed_value, spec.annotations[arg_name]): try: passed_value = spec.annotations[arg_name](passed_value) except Exception as e: if strict: raise TypeError(f'Could not convert {arg_name} to {spec.annotations[arg_name]}. {e}') logging.warning(f'Could not convert {arg_name} to {spec.annotations[arg_name]}. {e}') new_args.append(passed_value) for arg_name in kwargs: passed_value = kwargs[arg_name] if arg_name in kwargs else None if (cast_none or passed_value is not None) and \ arg_name in spec.annotations and not \ isinstance(passed_value, spec.annotations[arg_name]): try: passed_value = spec.annotations[arg_name](passed_value) except Exception as e: if strict: raise TypeError(f'Could not convert {arg_name} to {spec.annotations[arg_name]}. {e}') logging.warning(f'Could not convert {arg_name} to {spec.annotations[arg_name]}. {e}') new_kwargs[arg_name] = passed_value return func(*new_args, **new_kwargs) return wrapper if isinstance(args[0] if len(args) > 0 else None, FunctionType): return outer(args[0]) return outer def strict_hints(func): spec = getfullargspec(func) def wrapper(*args, **kwargs): for i in range(len(args)): arg_name = spec.args[i] passed_value = args[i] if arg_name in spec.annotations and not \ isinstance(passed_value, spec.annotations[arg_name]): raise TypeError(f'{arg_name} is not of type {spec.annotations[arg_name]}') for arg_name in kwargs: passed_value = kwargs[arg_name] if arg_name in kwargs else None if arg_name in spec.annotations and not \ isinstance(passed_value, spec.annotations[arg_name]): raise TypeError(f'{arg_name} is not of type {spec.annotations[arg_name]}') return func(*args, **kwargs) return wrapper

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1

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1

0

null

0

1

0

6

03c497ca8af4bfcccb12c10d117a633216b9595b

69

py

Python

scripts_nbs/foobar_script.py

hainesm6-learning/Jupyter_Notebook_learning

62ae95fe124f98f249fa8749f852b04f5aeedf1b

[ "MIT" ]

null

scripts_nbs/foobar_script.py

hainesm6-learning/Jupyter_Notebook_learning

62ae95fe124f98f249fa8749f852b04f5aeedf1b

[ "MIT" ]

null

scripts_nbs/foobar_script.py

hainesm6-learning/Jupyter_Notebook_learning

62ae95fe124f98f249fa8749f852b04f5aeedf1b

[ "MIT" ]

null

import sys print(f"Hello {sys.argv[1]}. This is a python module ;)")

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0.923077

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0.016949

0.144928

69

3

57

23

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0.671429

0

1

0

true

0

0.5

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0.5

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0

null

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1

0

1

0

null

0

1

0

1

0

1

0

6

2094dc95de99edd89ce6800e205134c52ff969b2

38

py

Python

ancilla/ancilla/foundation/node/plugins/__init__.py

frenzylabs/ancilla

3469272f17e1a5092d033cdc099f86f3052e744f

[ "Apache-2.0" ]

7

2020-03-31T19:52:59.000Z

2021-05-21T08:38:47.000Z

ancilla/ancilla/foundation/node/plugins/__init__.py

frenzylabs/ancilla

3469272f17e1a5092d033cdc099f86f3052e744f

[ "Apache-2.0" ]

15

2020-04-01T13:52:07.000Z

2020-04-01T13:52:11.000Z

ancilla/ancilla/foundation/node/plugins/__init__.py

frenzylabs/ancilla

3469272f17e1a5092d033cdc099f86f3052e744f

[ "Apache-2.0" ]

null

from .layerkeep import LayerkeepPlugin

38

0.894737

4

38

8.5

1

0

0.078947

38

1

38

0.971429

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1

0

true

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1

0

null

0

1

0

1

0

1

0

6

45cef31f6a84e8196c546026701213aa11096bf9

28,958

py

Python

tests/test_api.py

rdas1/TravelKit-Backend

131921258188defeb6edf970c71e807525de9dd2

[ "MIT" ]

null

tests/test_api.py

rdas1/TravelKit-Backend

131921258188defeb6edf970c71e807525de9dd2

[ "MIT" ]

null

tests/test_api.py

rdas1/TravelKit-Backend

131921258188defeb6edf970c71e807525de9dd2

[ "MIT" ]

null

# -*- coding: utf-8 -*- import json import unittest from flask import url_for from flask_testing import TestCase from flask_login import login_user from app import create_app, db from app.models import User, Todo, TodoList class TodolistAPITestCase(TestCase): def create_app(self): return create_app('testing') def setUp(self): db.create_all() self.username_alice = 'alice' def tearDown(self): db.session.remove() db.drop_all() def assert404Response(self, response): self.assert_404(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['error'], 'Not found') def assert400Response(self, response): self.assert_400(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['error'], 'Bad Request') @staticmethod def setup_new_user(username): user_data = { 'username': username, 'email': username + '@example.com', 'password': 'example_password' } return user_data @staticmethod def get_headers(): return { 'Accept': 'application/json', 'Content-Type': 'application/json' } def add_user(self, username): user_data = self.setup_new_user(username) User.from_dict(user_data) return User.query.filter_by(username=username).first() @staticmethod def add_todolist(title, username=None): todolist = TodoList(title=title, creator=username).save() return TodoList.query.filter_by(id=todolist.id).first() def add_todo(self, description, todolist_id, username=None): todolist = TodoList.query.filter_by(id=todolist_id).first() todo = Todo(description=description, todolist_id=todolist.id, creator=username).save() return Todo.query.filter_by(id=todo.id).first() def add_user_through_json_post(self, username): user_data = self.setup_new_user(username) return self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) def create_admin(self): new_user = self.setup_new_user('admin') new_user['is_admin'] = True return User.from_dict(new_user) def test_main_route(self): response = self.client.get(url_for('api.get_routes')) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertTrue('users' in json_response) self.assertTrue('todolists' in json_response) def test_not_found(self): response = self.client.get('/api/not/found') self.assert404Response(response) # test api post calls def test_add_user(self): post_response = self.add_user_through_json_post(self.username_alice) self.assertEqual(post_response.headers['Content-Type'], 'application/json') self.assert_status(post_response, 201) response = self.client.get(url_for('api.get_users')) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) users = json_response['users'] self.assertEqual(users[0]['username'], self.username_alice) def test_add_user_only_using_the_username(self): user_data = {'username': self.username_alice} response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_only_using_the_username_and_email(self): user_data = { 'username': self.username_alice, 'email': self.username_alice + '@example.com', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_with_to_long_username(self): user_data = { 'username': 65 * 'a', 'email': self.username_alice + '@example.com', 'password': 'correcthorsebatterystaple', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_with_invalid_username(self): user_data = { 'username': 'not a valid username', 'email': self.username_alice + '@example.com', 'password': 'correcthorsebatterystaple', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_without_username(self): user_data = { 'username': '', 'email': self.username_alice + '@example.com', 'password': 'correcthorsebatterystaple', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_with_invalid_email(self): user_data = { 'username': self.username_alice, 'email': self.username_alice + 'example.com', 'password': 'correcthorsebatterystaple', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_withoout_email(self): user_data = { 'username': self.username_alice, 'email': '', 'password': 'correcthorsebatterystaple', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_with_too_long_email(self): user_data = { 'username': self.username_alice, 'email': 53 * 'a' + '@example.com', 'password': 'correcthorsebatterystaple', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_without_password(self): user_data = { 'username': self.username_alice, 'email': self.username_alice + '@example.com', 'password': '', } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_user_with_extra_fields(self): user_data = { 'username': self.username_alice, 'email': self.username_alice + '@example.com', 'password': 'correcthorsebatterystaple', 'extra-field': 'will be ignored' } post_response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assertEqual(post_response.headers['Content-Type'], 'application/json') self.assert_status(post_response, 201) response = self.client.get(url_for('api.get_users')) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual( json_response['users'][0]['username'], self.username_alice ) def test_add_user_only_using_the_username_and_password(self): user_data = { 'username': self.username_alice, 'password': 'correcthorsebatterystaple' } response = self.client.post(url_for('api.add_user'), headers=self.get_headers(), data=json.dumps(user_data)) self.assert400Response(response) def test_add_todolist(self): post_response = self.client.post( url_for('api.add_todolist'), headers=self.get_headers(), data=json.dumps({'title': 'todolist'}) ) self.assert_status(post_response, 201) # the expected id of the todolist is 1, as it is the first to be added response = self.client.get(url_for('api.get_todolist', todolist_id=1)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['title'], 'todolist') def test_add_todolist_without_title(self): response = self.client.post( url_for('api.add_todolist'), headers=self.get_headers() ) # opposed to the form, the title is a required argument self.assert400Response(response) def test_add_todolist_with_too_long_title(self): response = self.client.post( url_for('api.add_todolist'), headers=self.get_headers(), data=json.dumps({'title': 129 * 't'}) ) self.assert400Response(response) def test_add_user_todolist(self): self.add_user(self.username_alice) post_response = self.client.post( url_for('api.add_user_todolist', username=self.username_alice), headers=self.get_headers(), data=json.dumps({'title': 'todolist'}) ) self.assert_status(post_response, 201) response = self.client.get( url_for('api.get_user_todolists', username=self.username_alice)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) # check title, creator are set correctly and a total of one todolist todolists = json_response['todolists'] self.assertEqual(todolists[0]['title'], 'todolist') self.assertEqual(todolists[0]['creator'], self.username_alice) self.assertEqual(len(todolists), 1) def test_add_user_todolist_when_user_does_not_exist(self): post_response = self.client.post( url_for('api.add_user_todolist', username=self.username_alice), headers=self.get_headers(), data=json.dumps({'title': 'todolist'}) ) self.assert404Response(post_response) def test_add_user_todolist_todo(self): todolist_title = 'new todolist' self.add_user(self.username_alice) new_todolist = self.add_todolist(todolist_title, self.username_alice) post_response = self.client.post( url_for('api.add_user_todolist_todo', username=self.username_alice, todolist_id=new_todolist.id), headers=self.get_headers(), data=json.dumps({ 'description': 'new todo', 'creator': self.username_alice, 'todolist_id': new_todolist.id }) ) self.assert_status(post_response, 201) response = self.client.get(url_for('api.get_user_todolist_todos', username=self.username_alice, todolist_id=new_todolist.id)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) # check title, creator are set correctly and a total of one todo todos = json_response['todos'] self.assertEqual(todos[0]['description'], 'new todo') self.assertEqual(todos[0]['creator'], self.username_alice) self.assertEqual(len(todos), 1) def test_add_user_todolist_todo_when_todolist_does_not_exist(self): self.add_user(self.username_alice) post_response = self.client.post( url_for('api.add_user_todolist_todo', username=self.username_alice, todolist_id=1), headers=self.get_headers(), data=json.dumps({ 'description': 'new todo', 'creator': self.username_alice, 'todolist_id': 1 }) ) self.assert404Response(post_response) def test_add_user_todolist_todo_without_todo_data(self): todolist_title = 'new todolist' self.add_user(self.username_alice) new_todolist = self.add_todolist(todolist_title, self.username_alice) post_response = self.client.post( url_for('api.add_user_todolist_todo', username=self.username_alice, todolist_id=new_todolist.id), headers=self.get_headers() ) self.assert400Response(post_response) def test_add_todolist_todo(self): new_todolist = TodoList().save() # todolist with default title post_response = self.client.post( url_for('api.add_todolist_todo', todolist_id=new_todolist.id), headers=self.get_headers(), data=json.dumps({ 'description': 'new todo', 'creator': 'null', 'todolist_id': new_todolist.id }) ) self.assert_status(post_response, 201) response = self.client.get(url_for('api.get_todolist_todos', todolist_id=new_todolist.id)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) # check title, creator are set correctly and a total of one todo todos = json_response['todos'] self.assertEqual(todos[0]['description'], 'new todo') self.assertEqual(todos[0]['creator'], None) self.assertEqual(len(todos), 1) def test_add_todolist_todo_when_todolist_does_not_exist(self): post_response = self.client.post( url_for('api.add_todolist_todo', todolist_id=1), headers=self.get_headers(), data=json.dumps({ 'description': 'new todo', 'creator': 'null', 'todolist_id': 1 }) ) self.assert404Response(post_response) def test_add_todolist_todo_without_todo_data(self): new_todolist = TodoList().save() post_response = self.client.post( url_for('api.add_todolist_todo', todolist_id=new_todolist.id), headers=self.get_headers() ) self.assert400Response(post_response) # test api get calls def test_get_users(self): self.add_user(self.username_alice) response = self.client.get(url_for('api.get_users')) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['users'][0]['username'], self.username_alice) def test_get_users_when_no_users_exist(self): response = self.client.get(url_for('api.get_users')) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['users'], []) def test_get_user(self): self.add_user(self.username_alice) response = self.client.get( url_for('api.get_user', username=self.username_alice)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['username'], self.username_alice) def test_get_user_when_user_does_not_exist(self): response = self.client.get( url_for('api.get_user', username=self.username_alice)) self.assert404Response(response) def test_get_todolists(self): todolist_title = 'new todolist ' self.add_user(self.username_alice) self.add_todolist(todolist_title + '1', self.username_alice) self.add_todolist(todolist_title + '2', self.username_alice) response = self.client.get(url_for('api.get_todolists')) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) todolists = json_response['todolists'] self.assertEqual(todolists[0]['title'], 'new todolist 1') self.assertEqual(todolists[0]['creator'], self.username_alice) self.assertEqual(todolists[1]['title'], 'new todolist 2') self.assertEqual(todolists[1]['creator'], self.username_alice) self.assertEqual(len(todolists), 2) def test_get_todolists_when_no_todolists_exist(self): response = self.client.get(url_for('api.get_todolists')) self.assert_200(response) todolists = json.loads(response.data.decode('utf-8'))['todolists'] self.assertEqual(todolists, []) def test_get_user_todolists(self): todolist_title = 'new todolist ' self.add_user(self.username_alice) self.add_todolist(todolist_title + '1', self.username_alice) self.add_todolist(todolist_title + '2', self.username_alice) response = self.client.get(url_for('api.get_user_todolists', username=self.username_alice)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) todolists = json_response['todolists'] self.assertEqual(todolists[0]['title'], 'new todolist 1') self.assertEqual(todolists[0]['creator'], self.username_alice) self.assertEqual(todolists[1]['title'], 'new todolist 2') self.assertEqual(todolists[1]['creator'], self.username_alice) self.assertEqual(len(todolists), 2) def test_get_user_todolists_when_user_does_not_exist(self): response = self.client.get(url_for('api.get_user_todolists', username=self.username_alice)) self.assert404Response(response) def test_get_user_todolists_when_user_has_no_todolists(self): self.add_user(self.username_alice) response = self.client.get(url_for('api.get_user_todolists', username=self.username_alice)) self.assert_200(response) todolists = json.loads(response.data.decode('utf-8'))['todolists'] self.assertEqual(todolists, []) def test_get_todolist_todos(self): todolist_title = 'new todolist' new_todolist = self.add_todolist(todolist_title) self.add_todo('first', new_todolist.id) self.add_todo('second', new_todolist.id) response = self.client.get(url_for('api.get_todolist_todos', todolist_id=new_todolist.id)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) todos = json_response['todos'] self.assertEqual(todos[0]['description'], 'first') self.assertEqual(todos[0]['creator'], None) self.assertEqual(todos[1]['description'], 'second') self.assertEqual(todos[1]['creator'], None) self.assertEqual(len(todos), 2) def test_get_todolist_todos_when_todolist_does_not_exist(self): response = self.client.get(url_for('api.get_todolist_todos', todolist_id=1)) self.assert404Response(response) def test_get_todolist_todos_when_todolist_has_no_todos(self): todolist_title = 'new todolist' new_todolist = self.add_todolist(todolist_title) response = self.client.get(url_for('api.get_todolist_todos', todolist_id=new_todolist.id)) self.assert_200(response) todos = json.loads(response.data.decode('utf-8'))['todos'] self.assertEqual(todos, []) def test_get_user_todolist_todos(self): todolist_title = 'new todolist' self.add_user(self.username_alice) new_todolist = self.add_todolist(todolist_title, self.username_alice) self.add_todo('first', new_todolist.id, self.username_alice) self.add_todo('second', new_todolist.id, self.username_alice) response = self.client.get(url_for('api.get_user_todolist_todos', username=self.username_alice, todolist_id=new_todolist.id)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) todos = json_response['todos'] self.assertEqual(todos[0]['description'], 'first') self.assertEqual(todos[0]['creator'], self.username_alice) self.assertEqual(todos[1]['description'], 'second') self.assertEqual(todos[1]['creator'], self.username_alice) self.assertEqual(len(todos), 2) def test_get_user_todolist_todos_when_user_does_not_exist(self): response = self.client.get( url_for('api.get_user_todolist_todos', username=self.username_alice, todolist_id=1)) self.assert404Response(response) def test_get_user_todolist_todos_when_todolist_does_not_exist(self): self.add_user(self.username_alice) response = self.client.get( url_for('api.get_user_todolist_todos', username=self.username_alice, todolist_id=1)) self.assert404Response(response) def test_get_user_todolist_todos_when_todolist_has_no_todos(self): todolist_title = 'new todolist' self.add_user(self.username_alice) new_todolist = self.add_todolist(todolist_title, self.username_alice) response = self.client.get(url_for('api.get_user_todolist_todos', username=self.username_alice, todolist_id=new_todolist.id)) self.assert_200(response) todos = json.loads(response.data.decode('utf-8'))['todos'] self.assertEqual(todos, []) def test_get_different_user_todolist_todos(self): first_username = self.username_alice second_username = 'bob' todolist_title = 'new todolist' first_user = self.add_user(first_username) self.add_user(second_username) new_todolist = self.add_todolist(todolist_title, second_username) self.add_todo('first', new_todolist.id, second_username) self.add_todo('second', new_todolist.id, second_username) response = self.client.get(url_for('api.get_user_todolist_todos', username=first_user, todolist_id=new_todolist.id)) self.assert404Response(response) def test_get_user_todolist(self): todolist_title = 'new todolist' self.add_user(self.username_alice) new_todolist = self.add_todolist(todolist_title, self.username_alice) response = self.client.get(url_for('api.get_user_todolist', username=self.username_alice, todolist_id=new_todolist.id)) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['title'], todolist_title) self.assertEqual(json_response['creator'], self.username_alice) def test_get_user_todolist_when_user_does_not_exist(self): response = self.client.get(url_for('api.get_user_todolist', username=self.username_alice, todolist_id=1)) self.assert404Response(response) def test_get_user_todolist_when_todolist_does_not_exist(self): self.add_user(self.username_alice) response = self.client.get(url_for('api.get_user_todolist', username=self.username_alice, todolist_id=1)) self.assert404Response(response) # test api put call def test_update_todo_status_to_finished(self): todolist = self.add_todolist('new todolist') todo = self.add_todo('first', todolist.id) self.assertFalse(todo.is_finished) self.client.put( url_for('api.update_todo_status', todo_id=todo.id), headers=self.get_headers(), data=json.dumps({'is_finished': True}) ) todo = Todo.query.get(todo.id) self.assertTrue(todo.is_finished) def test_update_todo_status_to_open(self): todolist = self.add_todolist('new todolist') todo = self.add_todo('first', todolist.id) todo.finished() self.assertTrue(todo.is_finished) self.client.put( url_for('api.update_todo_status', todo_id=todo.id), headers=self.get_headers(), data=json.dumps({'is_finished': False}) ) todo = Todo.query.get(todo.id) self.assertFalse(todo.is_finished) self.assertTrue(todo.finished_at is None) def test_change_todolist_title(self): todolist = self.add_todolist('new todolist') response = self.client.put( url_for('api.change_todolist_title', todolist_id=todolist.id), headers=self.get_headers(), data=json.dumps({'title': 'changed title'}) ) self.assert_200(response) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['title'], 'changed title') def test_change_todolist_title_too_long_title(self): todolist = self.add_todolist('new todolist') response = self.client.put( url_for('api.change_todolist_title', todolist_id=todolist.id), headers=self.get_headers(), data=json.dumps({'title': 129 * 't'}) ) self.assert_400(response) def test_change_todolist_title_empty_title(self): todolist = self.add_todolist('new todolist') response = self.client.put( url_for('api.change_todolist_title', todolist_id=todolist.id), headers=self.get_headers(), data=json.dumps({'title': ''}) ) self.assert_400(response) def test_change_todolist_title_without_title(self): todolist = self.add_todolist('new todolist') response = self.client.put( url_for('api.change_todolist_title', todolist_id=todolist.id), headers=self.get_headers() ) self.assert_400(response) # test api delete calls @unittest.skip('because acquiring admin rights is currently an issue') def test_delete_user(self): admin = self.create_admin() login_user(admin) user = self.add_user(self.username_alice) user_id = user.id response = self.client.delete( url_for('api.delete_user', user_id=user_id), headers=self.get_headers(), data=json.dumps({'user_id': user_id}) ) self.assert_200(response) response = self.client.get(url_for('api.get_user', user_id=user_id)) self.assert_404(response) @unittest.skip('because acquiring admin rights is currently an issue') def test_delete_todolist(self): admin = self.create_admin() login_user(admin) todolist = self.add_todolist('new todolist') todolist_id = todolist.id response = self.client.delete( url_for('api.delete_todolist', todolist_id=todolist_id), headers=self.get_headers(), data=json.dumps({'todolist_id': todolist_id}) ) self.assert_200(response) response = self.client.get( url_for('api.get_todolist', todolist_id=todolist_id) ) self.assert_404(response) @unittest.skip('because acquiring admin rights is currently an issue') def test_delete_todo(self): admin = self.create_admin() login_user(admin) todolist = self.add_todolist('new todolist') todo = self.add_todo('new todo', todolist.id) todo_id = todo.id response = self.client.delete( url_for('api.delete_todo', todo_id=todo_id), headers=self.get_headers(), data=json.dumps({'todo_id': todo_id}) ) self.assert_200(response) response = self.client.get( url_for('api.get_todo', todo_id=todo_id) ) self.assert_404(response)

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45e6bee10dae333a0858479bb54c712eb59e3832

6,189

py

Python

tests/unit/test_exceptions.py

t20100/pip

9cf35b25e25a47b41480d5b2dc82b8ebd1eeb6a0

[ "MIT" ]

1

2021-12-08T19:50:41.000Z

tests/unit/test_exceptions.py

t20100/pip

9cf35b25e25a47b41480d5b2dc82b8ebd1eeb6a0

[ "MIT" ]

null

tests/unit/test_exceptions.py

t20100/pip

9cf35b25e25a47b41480d5b2dc82b8ebd1eeb6a0

[ "MIT" ]

null

"""Tests the presentation style of exceptions.""" import textwrap import pytest from pip._internal.exceptions import DiagnosticPipError class TestDiagnosticPipErrorCreation: def test_fails_without_reference(self) -> None: class DerivedError(DiagnosticPipError): pass with pytest.raises(AssertionError) as exc_info: DerivedError(message="", context=None, hint_stmt=None) assert str(exc_info.value) == "error reference not provided!" def test_can_fetch_reference_from_subclass(self) -> None: class DerivedError(DiagnosticPipError): reference = "subclass-reference" obj = DerivedError(message="", context=None, hint_stmt=None) assert obj.reference == "subclass-reference" def test_can_fetch_reference_from_arguments(self) -> None: class DerivedError(DiagnosticPipError): pass obj = DerivedError( message="", context=None, hint_stmt=None, reference="subclass-reference" ) assert obj.reference == "subclass-reference" @pytest.mark.parametrize( "name", [ "BADNAME", "BadName", "bad_name", "BAD_NAME", "_bad", "bad-name-", "bad--name", "-bad-name", "bad-name-due-to-1-number", ], ) def test_rejects_non_kebab_case_names(self, name: str) -> None: class DerivedError(DiagnosticPipError): reference = name with pytest.raises(AssertionError) as exc_info: DerivedError(message="", context=None, hint_stmt=None) assert str(exc_info.value) == "error reference must be kebab-case!" class TestDiagnosticPipErrorPresentation_ASCII: def test_complete(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context="Something went wrong\nvery wrong.", attention_stmt="You did something wrong, which is what caused this error.", hint_stmt="Do it better next time, by trying harder.", ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Something went wrong very wrong. Note: You did something wrong, which is what caused this error. Hint: Do it better next time, by trying harder. """ ) def test_no_context(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context=None, attention_stmt="You did something wrong, which is what caused this error.", hint_stmt="Do it better next time, by trying harder.", ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Note: You did something wrong, which is what caused this error. Hint: Do it better next time, by trying harder. """ ) def test_no_note(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context="Something went wrong\nvery wrong.", attention_stmt=None, hint_stmt="Do it better next time, by trying harder.", ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Something went wrong very wrong. Hint: Do it better next time, by trying harder. """ ) def test_no_hint(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context="Something went wrong\nvery wrong.", attention_stmt="You did something wrong, which is what caused this error.", hint_stmt=None, ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Something went wrong very wrong. Note: You did something wrong, which is what caused this error. """ ) def test_no_context_no_hint(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context=None, attention_stmt="You did something wrong, which is what caused this error.", hint_stmt=None, ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Note: You did something wrong, which is what caused this error. """ ) def test_no_context_no_note(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context=None, attention_stmt=None, hint_stmt="Do it better next time, by trying harder.", ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Hint: Do it better next time, by trying harder. """ ) def test_no_hint_no_note(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context="Something went wrong\nvery wrong.", attention_stmt=None, hint_stmt=None, ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( Something went wrong very wrong. """ ) def test_no_hint_no_note_no_context(self) -> None: err = DiagnosticPipError( reference="test-diagnostic", message="Oh no!\nIt broke. :(", context=None, hint_stmt=None, attention_stmt=None, ) assert str(err) == textwrap.dedent( """\ Oh no! It broke. :( """ )

28.920561

87

0.536597

629

6,189

5.165342

0.147854

0.019698

0.029548

0.071407

0.868883

0.80948

0.748846

0.719298

0

0.000254

0.36274

6,189

213

88

29.056338

0.823529

0.006948

0

0.552846

0

0.21234

0.004953

0

0.113821

1

0.097561

false

0.01626

0.02439

0

0.170732

0

null

0

1

0

null

0

6

b30bb2218c19e7f10854fdf02cb93b4dcb245063

529

py

Python

the_platform/views.py

Thames1990/BadBatBets

8dffb69561668b8991bf4103919e4b254d4ca56a

[ "MIT" ]

null

the_platform/views.py

Thames1990/BadBatBets

8dffb69561668b8991bf4103919e4b254d4ca56a

[ "MIT" ]

null

the_platform/views.py

Thames1990/BadBatBets

8dffb69561668b8991bf4103919e4b254d4ca56a

[ "MIT" ]

null

from django.shortcuts import render from profiles.util import user_authenticated def error403(request): return render(request, 'the_platform/403.html', { 'user_authenticated': user_authenticated(request.user) }) def error404(request): return render(request, 'the_platform/404.html', { 'user_authenticated': user_authenticated(request.user) }) def error500(request): return render(request, 'the_platform/500.html', { 'user_authenticated': user_authenticated(request.user) })

24.045455

62

0.718336

59

529

6.271186

0.355932

0.321622

0.154054

0.210811

0.713514

0.413514

0.281081

0

0.041002

0.170132

529

21

63

25.190476

0.801822

0

0.428571

0

0.221172

0.119093

0

1

0.214286

false

0

0.142857

0.214286

0.571429

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

b35a597c9bba7640cb0c97538ba3f521dec7cf4c

39

py

Python

pyemma/_base/progress/__init__.py

trendelkampschroer/PyEMMA

ee5784d5c1c5bc070fe2e9e6ad4f24b36185dc20

[ "BSD-2-Clause" ]

1

2020-01-21T16:55:38.000Z

pyemma/_base/progress/__init__.py

trendelkampschroer/PyEMMA

ee5784d5c1c5bc070fe2e9e6ad4f24b36185dc20

[ "BSD-2-Clause" ]

1

2022-01-10T18:09:25.000Z

pyemma/_base/progress/__init__.py

clonker/PyEMMA

a36534ce2ec6a799428dfbdef0465c979e6c68aa

[ "BSD-2-Clause" ]

null

from .reporter import ProgressReporter

19.5

38

0.871795

4

39

8.5

1

0

0.102564

39

1

39

0.971429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b364b5d3e8ca67d92e0f8b39dc17fdb127aadfa5

116

py

Python

arc085_a.py

hythof/atc

12cb94ebe693e1f469ce0d982bc2924b586552cd

[ "CC0-1.0" ]

null

arc085_a.py

hythof/atc

12cb94ebe693e1f469ce0d982bc2924b586552cd

[ "CC0-1.0" ]

null

arc085_a.py

hythof/atc

12cb94ebe693e1f469ce0d982bc2924b586552cd

[ "CC0-1.0" ]

null

n,m = [int(x) for x in open(0).read().split()] # (1900M+100(N-M))/(1/2^M) print(int(((1900*m+100*(n-m))/(1/2**m))))

29

46

0.517241

28

116

2.142857

0.571429

0.1

0.166667

0.2

0.266667

0

0.179245

0.086207

116

3

47

38.666667

0.386792

0.206897

0

1

0

true

0

0.5

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6