xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

52af16f4cd532ef2b8c44114828d251a9e48640b

77

py

Python

django_walletpass/tests/__init__.py

Develatio/django-passbook

38de7dd55709c8c090511cb9f7a2e59417ab2fb9

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

7

2020-03-31T00:02:00.000Z

2021-07-11T19:10:49.000Z

django_walletpass/tests/__init__.py

Develatio/django-passbook

38de7dd55709c8c090511cb9f7a2e59417ab2fb9

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

4

2019-06-08T09:43:17.000Z

2020-11-16T23:06:43.000Z

django_walletpass/tests/__init__.py

Develatio/django-passbook

38de7dd55709c8c090511cb9f7a2e59417ab2fb9

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

6

2020-01-21T12:08:07.000Z

2022-03-31T17:40:31.000Z

# pylint: disable=wildcard-import from django_walletpass.tests.main import *

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52ca53402550c3d329f3fd614d012905378e77b1

88

py

Python

unicodedata_reader/__init__.py

kojiishi/unicode-data-parser

5938f0c763d31cca06e10426958097ea7bcf7f66

[ "Apache-2.0" ]

2

2021-08-30T12:12:33.000Z

2021-11-25T14:17:38.000Z

unicodedata_reader/__init__.py

kojiishi/unicode-data-parser

5938f0c763d31cca06e10426958097ea7bcf7f66

[ "Apache-2.0" ]

4

2021-11-03T22:20:40.000Z

2021-12-15T22:25:31.000Z

unicodedata_reader/__init__.py

kojiishi/unicodedata-reader

0ff8360da75a51e5a348ec2bfe9922e9a48353e1

[ "Apache-2.0" ]

null

from .entry import * from .reader import * from .compressor import * from .cli import *

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eae5c2cc557d96ef2d2c7992f664a231d08a0e21

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py

Python

insights/parsers/ceph_log.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

121

2017-05-30T20:23:25.000Z

2022-03-23T12:52:15.000Z

insights/parsers/ceph_log.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

1,977

2017-05-26T14:36:03.000Z

2022-03-31T10:38:53.000Z

insights/parsers/ceph_log.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

244

2017-05-30T20:22:57.000Z

2022-03-26T10:09:39.000Z

""" CephLog - file ``/var/log/ceph/ceph.log`` ========================================= """ from insights import LogFileOutput, parser from insights.specs import Specs @parser(Specs.ceph_log) class CephLog(LogFileOutput): """ Parse the ``/var/log/ceph/ceph.log`` file. Provide access to ceph logs using the LogFileOutput parser class. Sample log lines:: 2017-05-31 13:01:44.034376 mon.0 192.xx.xx.xx:6789/0 742585 : cluster [INF] pgmap v5133969: 320 pgs: 3 active+clean+scrubbing+deep, 317 active+clean; 898 GB data, 1828 GB used, 48447 GB / 50275 GB avail; 2027 kB/s rd, 20215 kB/s wr, 711 op/s 2017-05-31 13:01:45.041760 mon.0 192.xx.xx.xx:6789/0 742586 : cluster [INF] pgmap v5133970: 320 pgs: 3 active+clean+scrubbing+deep, 317 active+clean; 898 GB data, 1828 GB used, 48447 GB / 50275 GB avail; 1606 kB/s rd, 17354 kB/s wr, 718 op/s 2017-05-31 13:01:46.933829 osd.22 192.xx.xx.xx:6814/42154 172581 : cluster [WRN] 44 slow requests, 2 included below; oldest blocked for > 49.982746 secs 2017-05-31 13:01:46.933946 osd.22 192.xx.xx.xx:6814/42154 172582 : cluster [WRN] slow request 30.602517 seconds old, received at 2017-05-31 13:01:06.330484: osd_op(client.3395798.0:2855671 1.54392173 gnocchi_06c8214c-afae-4e64-8a4a-a466c4f257dc_1244160000.0_median_86400.0_v3 [write 26253~9] snapc 0=[] ondisk+write+known_if_redirected e487) currently waiting for subops from 23 2017-05-31 13:01:46.933955 osd.22 192.xx.xx.xx:6814/42154 172583 : cluster [WRN] slow request 30.530961 seconds old, received at 2017-05-31 13:01:06.402041: osd_op(client.324182.0:46141816 1.e637a4b3 measure [omap-rm-keys 0~107] snapc 0=[] ondisk+write+skiprwlocks+known_if_redirected e487) currently waiting for subops from 23 2017-05-31 13:01:47.050539 mon.0 192.xx.xx.xx:6789/0 742589 : cluster [INF] pgmap v5133971: 320 pgs: 3 active+clean+scrubbing+deep, 317 active+clean; 898 GB data, 1828 GB used, 48447 GB / 50275 GB avail; 1597 kB/s rd, 7259 kB/s wr, 398 op/s 2017-05-31 13:01:48.057187 mon.0 192.xx.xx.xx:6789/0 742590 : cluster [INF] pgmap v5133972: 320 pgs: 3 active+clean+scrubbing+deep, 317 active+clean; 898 GB data, 1828 GB used, 48447 GB / 50275 GB avail; 2373 kB/s rd, 5138 kB/s wr, 354 op/s 2017-05-31 13:01:49.064950 mon.0 192.xx.xx.xx:6789/0 742598 : cluster [INF] pgmap v5133973: 320 pgs: 3 active+clean+scrubbing+deep, 317 active+clean; 898 GB data, 1828 GB used, 48447 GB / 50275 GB avail; 4187 kB/s rd, 10266 kB/s wr, 714 op/s 2017-05-31 13:01:50.069437 mon.0 192.xx.xx.xx:6789/0 742599 : cluster [INF] pgmap v5133974: 320 pgs: 3 active+clean+scrubbing+deep, 317 active+clean; 898 GB data, 1828 GB used, 48447 GB / 50275 GB avail; 470 MB/s rd, 11461 kB/s wr, 786 op/s Examples: >>> len(ceph_log.get("[WRN] slow request")) == 2 True >>> from datetime import datetime >>> len(list(ceph_log.get_after(datetime(2017, 5, 31, 13, 1, 46)))) 7 """ pass

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eafddb3a312a88defe824ac9ec3553df794b98a2

323

py

Python

back/service/handler.py

PHIKN1GHT/secp_2020

d77e91251d7237f0a47a362e480e536a17993726

[ "MIT" ]

null

back/service/handler.py

PHIKN1GHT/secp_2020

d77e91251d7237f0a47a362e480e536a17993726

[ "MIT" ]

null

back/service/handler.py

PHIKN1GHT/secp_2020

d77e91251d7237f0a47a362e480e536a17993726

[ "MIT" ]

null

from server import app from flask import render_template ''' @app.errorhandler(404) def internal_error(error): #return render_template('404.html'), 404 return render_template('404.html'), 404 @app.errorhandler(500) def internal_error(error): db.session.rollback() return render_template('500.html'), 500'''

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6

dc1d6de27387b94aa9208635cc90be409d328c19

208

py

Python

mpas_analysis/shared/regions/__init__.py

ytakano3/MPAS-Analysis

fbb1f5189782b9abe9ff368f7c96484fd4832937

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

43

2016-08-31T22:59:54.000Z

2022-01-13T06:46:04.000Z

mpas_analysis/shared/regions/__init__.py

ytakano3/MPAS-Analysis

fbb1f5189782b9abe9ff368f7c96484fd4832937

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

764

2016-07-01T20:15:22.000Z

2022-03-14T19:03:17.000Z

mpas_analysis/shared/regions/__init__.py

ytakano3/MPAS-Analysis

fbb1f5189782b9abe9ff368f7c96484fd4832937

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

35

2016-06-22T20:36:18.000Z

2021-12-29T15:25:15.000Z

from mpas_analysis.shared.regions.compute_region_masks_subtask \ import ComputeRegionMasksSubtask, get_feature_list from mpas_analysis.shared.regions.compute_region_masks \ import ComputeRegionMasks

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6

f4ad4f2b4778ef94968305db873d8508f0890835

53

py

Python

examples/example_get_exe.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

31

2021-05-12T16:54:34.000Z

2022-02-17T12:36:52.000Z

examples/example_get_exe.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

1

2021-07-23T02:58:07.000Z

2021-09-03T21:53:29.000Z

examples/example_get_exe.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

1

2021-05-12T22:12:20.000Z

from thatlib import get_exe print(get_exe("python"))

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6

f4d670cb18640b0cdc3fd7542edb248bd386cfe0

2,530

py

Python

pirates/leveleditor/worldData/pirateerMap.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

81

2018-04-08T18:14:24.000Z

2022-01-11T07:22:15.000Z

pirates/leveleditor/worldData/pirateerMap.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

4

2018-09-13T20:41:22.000Z

2022-01-08T06:57:00.000Z

pirates/leveleditor/worldData/pirateerMap.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

26

2018-05-26T12:49:27.000Z

2021-09-11T09:11:59.000Z

from pandac.PandaModules import Point3, VBase3 objectStruct = {'Objects': {'1151689157.98hreister': {'Type': 'Region','Name': 'default','Objects': {'1151689233.71hreister': {'Type': 'Island','File': 'pirateerCove1','Hpr': VBase3(0.0, 0.0, 0.0),'Pos': Point3(1779.297, -699.482, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/islands/pvpcove_zero'}},'1151689243.57hreister': {'Type': 'Island','File': 'pirateerCove0','Hpr': VBase3(0.0, 0.0, 0.0),'Pos': Point3(-1834.8, -673.045, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/islands/pvpcove_zero'}},'1151689665.9hreister': {'Type': 'Ship Spawn Node','Flagship': False,'Hpr': VBase3(-131.111, 0.0, 0.0),'Level': '2','Pos': Point3(2026.39, -743.076, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Merchant','Team': '0'},'1151689729.21hreister': {'Type': 'Ship Spawn Node','Flagship': False,'Hpr': VBase3(-129.146, 0.0, 0.0),'Level': '2','Pos': Point3(-1606.538, -707.017, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Merchant','Team': '0'},'1154027786.73hreister': {'Type': 'Ship Spawn Node','Flagship': False,'Hpr': Point3(90.0, 0.0, 0.0),'Level': '1','Pos': Point3(-1635.0, -108.0, -10.0),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Interceptor','Team': '0','Visual': {}},'1154027853.04hreister': {'Type': 'Ship Spawn Node','Flagship': False,'Hpr': VBase3(98.233, 0.0, 0.0),'Level': '1','Pos': Point3(2058.161, -1137.117, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Interceptor','Team': '0'},'1161214367.89jubutler': {'Type': 'Ship Spawn Node','Flagship': False,'Hpr': VBase3(-84.652, 0.0, 0.0),'Level': '1','Pos': Point3(-1584.656, -1113.676, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Interceptor','Team': '0'}},'Visual': {}}},'Layers': {},'ObjectIds': {'1151689157.98hreister': '["Objects"]["1151689157.98hreister"]','1151689233.71hreister': '["Objects"]["1151689157.98hreister"]["Objects"]["1151689233.71hreister"]','1151689243.57hreister': '["Objects"]["1151689157.98hreister"]["Objects"]["1151689243.57hreister"]','1151689665.9hreister': '["Objects"]["1151689157.98hreister"]["Objects"]["1151689665.9hreister"]','1151689729.21hreister': '["Objects"]["1151689157.98hreister"]["Objects"]["1151689729.21hreister"]','1154027786.73hreister': '["Objects"]["1151689157.98hreister"]["Objects"]["1154027786.73hreister"]','1154027853.04hreister': '["Objects"]["1151689157.98hreister"]["Objects"]["1154027853.04hreister"]','1161214367.89jubutler': '["Objects"]["1151689157.98hreister"]["Objects"]["1161214367.89jubutler"]'}}

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760849408b06ca45bc43661640857a70332eefdc

194

py

Python

pyguymer3/hostname.py

Guymer/PyGuymer3

c2e2788a8b65854fa1e84d6ba5017fb2544fc195

[ "Apache-2.0" ]

9

2019-05-14T08:45:53.000Z

2021-11-23T09:38:56.000Z

pyguymer3/hostname.py

Guymer/PyGuymer3

c2e2788a8b65854fa1e84d6ba5017fb2544fc195

[ "Apache-2.0" ]

2

2019-11-19T17:23:11.000Z

2020-10-11T12:43:35.000Z

pyguymer3/hostname.py

Guymer/PyGuymer3

c2e2788a8b65854fa1e84d6ba5017fb2544fc195

[ "Apache-2.0" ]

2

2019-11-17T10:13:51.000Z

2020-05-26T19:35:33.000Z

def hostname(): # Import standard modules ... import socket # Get (potentially fully-qualified) hostname and return the first part ... return socket.gethostname().split(".")[0]

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52482e1438132e3c12be4e394e24558d35ecf833

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py

Python

mealpy/evolutionary_based/DE.py

rishavpramanik/mealpy

d4a4d5810f15837764e4ee61517350fef3dc92b3

[ "MIT" ]

null

mealpy/evolutionary_based/DE.py

rishavpramanik/mealpy

d4a4d5810f15837764e4ee61517350fef3dc92b3

[ "MIT" ]

null

mealpy/evolutionary_based/DE.py

rishavpramanik/mealpy

d4a4d5810f15837764e4ee61517350fef3dc92b3

[ "MIT" ]

null

# !/usr/bin/env python # Created by "Thieu" at 09:48, 16/03/2020 ----------% # Email: nguyenthieu2102@gmail.com % # Github: https://github.com/thieu1995 % # --------------------------------------------------% import numpy as np from mealpy.optimizer import Optimizer from scipy.stats import cauchy from copy import deepcopy class BaseDE(Optimizer): """ The original version of: Differential Evolution (DE) Links: 1. https://doi.org/10.1016/j.swevo.2018.10.006 Hyper-parameters should fine tuned in approximate range to get faster convergence toward the global optimum: + wf (float): [0.5, 0.95], weighting factor, default = 0.8 + cr (float): [0.5, 0.95], crossover rate, default = 0.9 + strategy (int): [0, 5], there are lots of variant version of DE algorithm, + 0: DE/current-to-rand/1/bin + 1: DE/best/1/bin + 2: DE/best/2/bin + 3: DE/rand/2/bin + 4: DE/current-to-best/1/bin + 5: DE/current-to-rand/1/bin Examples ~~~~~~~~ >>> import numpy as np >>> from mealpy.evolutionary_based.DE import BaseDE >>> >>> def fitness_function(solution): >>> return np.sum(solution**2) >>> >>> problem_dict1 = { >>> "fit_func": fitness_function, >>> "lb": [-10, -15, -4, -2, -8], >>> "ub": [10, 15, 12, 8, 20], >>> "minmax": "min", >>> } >>> >>> epoch = 1000 >>> pop_size = 50 >>> wf = 0.7 >>> cr = 0.9 >>> strategy = 0 >>> model = BaseDE(problem_dict1, epoch, pop_size, wf, cr, strategy) >>> best_position, best_fitness = model.solve() >>> print(f"Solution: {best_position}, Fitness: {best_fitness}") References ~~~~~~~~~~ [1] Mohamed, A.W., Hadi, A.A. and Jambi, K.M., 2019. Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm and Evolutionary Computation, 50, p.100455. """ def __init__(self, problem, epoch=10000, pop_size=100, wf=0.8, cr=0.9, strategy=0, **kwargs): """ Args: problem (dict): The problem dictionary epoch (int): maximum number of iterations, default = 10000 pop_size (int): number of population size, default = 100 wf (float): weighting factor, default = 0.8 cr (float): crossover rate, default = 0.9 strategy (int): Different variants of DE, default = 0 """ super().__init__(problem, kwargs) self.epoch = self.validator.check_int("epoch", epoch, [1, 100000]) self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000]) self.wf = self.validator.check_float("wf", wf, (0, 1.0)) self.cr = self.validator.check_float("cr", cr, (0, 1.0)) self.strategy = self.validator.check_int("strategy", strategy, [0, 5]) self.nfe_per_epoch = self.pop_size self.sort_flag = False def _mutation__(self, current_pos, new_pos): pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < self.cr, current_pos, new_pos) return self.amend_position(pos_new, self.problem.lb, self.problem.ub) def evolve(self, epoch): """ The main operations (equations) of algorithm. Inherit from Optimizer class Args: epoch (int): The current iteration """ pop = [] if self.strategy == 0: # Choose 3 random element and different to i for idx in range(0, self.pop_size): idx_list = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 3, replace=False) pos_new = self.pop[idx_list[0]][self.ID_POS] + self.wf * \ (self.pop[idx_list[1]][self.ID_POS] - self.pop[idx_list[2]][self.ID_POS]) pos_new = self._mutation__(self.pop[idx][self.ID_POS], pos_new) pop.append([pos_new, None]) elif self.strategy == 1: for idx in range(0, self.pop_size): idx_list = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 2, replace=False) pos_new = self.g_best[self.ID_POS] + self.wf * (self.pop[idx_list[0]][self.ID_POS] - self.pop[idx_list[1]][self.ID_POS]) pos_new = self._mutation__(self.pop[idx][self.ID_POS], pos_new) pop.append([pos_new, None]) elif self.strategy == 2: for idx in range(0, self.pop_size): idx_list = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 4, replace=False) pos_new = self.g_best[self.ID_POS] + self.wf * (self.pop[idx_list[0]][self.ID_POS] - self.pop[idx_list[1]][self.ID_POS]) + \ self.wf * (self.pop[idx_list[2]][self.ID_POS] - self.pop[idx_list[3]][self.ID_POS]) pos_new = self._mutation__(self.pop[idx][self.ID_POS], pos_new) pop.append([pos_new, None]) elif self.strategy == 3: for idx in range(0, self.pop_size): idx_list = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 5, replace=False) pos_new = self.pop[idx_list[0]][self.ID_POS] + self.wf * \ (self.pop[idx_list[1]][self.ID_POS] - self.pop[idx_list[2]][self.ID_POS]) + \ self.wf * (self.pop[idx_list[3]][self.ID_POS] - self.pop[idx_list[4]][self.ID_POS]) pos_new = self._mutation__(self.pop[idx][self.ID_POS], pos_new) pop.append([pos_new, None]) elif self.strategy == 4: for idx in range(0, self.pop_size): idx_list = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 2, replace=False) pos_new = self.pop[idx][self.ID_POS] + self.wf * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + \ self.wf * (self.pop[idx_list[0]][self.ID_POS] - self.pop[idx_list[1]][self.ID_POS]) pos_new = self._mutation__(self.pop[idx][self.ID_POS], pos_new) pop.append([pos_new, None]) else: for idx in range(0, self.pop_size): idx_list = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 3, replace=False) pos_new = self.pop[idx][self.ID_POS] + self.wf * (self.pop[idx_list[0]][self.ID_POS] - self.pop[idx][self.ID_POS]) + \ self.wf * (self.pop[idx_list[1]][self.ID_POS] - self.pop[idx_list[2]][self.ID_POS]) pos_new = self._mutation__(self.pop[idx][self.ID_POS], pos_new) pop.append([pos_new, None]) pop = self.update_target_wrapper_population(pop) # create new pop by comparing fitness of corresponding each member in pop and children self.pop = self.greedy_selection_population(self.pop, pop) class JADE(Optimizer): """ The variant version of: Differential Evolution (JADE) Links: 1. https://doi.org/10.1109/TEVC.2009.2014613 Hyper-parameters should fine tuned in approximate range to get faster convergence toward the global optimum: + miu_f (float): [0.4, 0.6], initial adaptive f, default = 0.5 + miu_cr (float): [0.4, 0.6], initial adaptive cr, default = 0.5 + pt (float): [0.05, 0.2], The percent of top best agents (p in the paper), default = 0.1 + ap (float): [0.05, 0.2], The Adaptation Parameter control value of f and cr (c in the paper), default=0.1 Examples ~~~~~~~~ >>> import numpy as np >>> from mealpy.evolutionary_based.DE import JADE >>> >>> def fitness_function(solution): >>> return np.sum(solution**2) >>> >>> problem_dict1 = { >>> "fit_func": fitness_function, >>> "lb": [-10, -15, -4, -2, -8], >>> "ub": [10, 15, 12, 8, 20], >>> "minmax": "min", >>> } >>> >>> epoch = 1000 >>> pop_size = 50 >>> miu_f = 0.5 >>> miu_cr = 0.5 >>> pt = 0.1 >>> ap = 0.1 >>> model = JADE(problem_dict1, epoch, pop_size, miu_f, miu_cr, pt, ap) >>> best_position, best_fitness = model.solve() >>> print(f"Solution: {best_position}, Fitness: {best_fitness}") References ~~~~~~~~~~ [1] Zhang, J. and Sanderson, A.C., 2009. JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), pp.945-958. """ def __init__(self, problem, epoch=10000, pop_size=100, miu_f=0.5, miu_cr=0.5, pt=0.1, ap=0.1, **kwargs): """ Args: problem (dict): The problem dictionary epoch (int): maximum number of iterations, default = 10000 pop_size (int): number of population size, default = 100 miu_f (float): initial adaptive f, default = 0.5 miu_cr (float): initial adaptive cr, default = 0.5 pt (float): The percent of top best agents (p in the paper), default = 0.1 ap (float): The Adaptation Parameter control value of f and cr (c in the paper), default=0.1 """ super().__init__(problem, kwargs) self.epoch = self.validator.check_int("epoch", epoch, [1, 100000]) self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000]) # the initial f, location is changed then that f is good self.miu_f = self.validator.check_float("miu_f", miu_f, (0, 1.0)) # the initial cr, self.miu_cr = self.validator.check_float("miu_cr", miu_cr, (0, 1.0)) # np.random.uniform(0.05, 0.2) # the x_best is select from the top 100p % solutions self.pt = self.validator.check_float("pt", pt, (0, 1.0)) # np.random.uniform(1/20, 1/5) # the adaptation parameter control value of f and cr self.ap = self.validator.check_float("ap", ap, (0, 1.0)) self.nfe_per_epoch = self.pop_size self.sort_flag = False ## Dynamic variable, changing in run time self.dyn_miu_cr = self.miu_cr self.dyn_miu_f = self.miu_f self.dyn_pop_archive = list() ### Survivor Selection def lehmer_mean(self, list_objects): temp = sum(list_objects) return 0 if temp == 0 else sum(list_objects ** 2) / temp def evolve(self, epoch): """ The main operations (equations) of algorithm. Inherit from Optimizer class Args: epoch (int): The current iteration """ list_f = list() list_cr = list() temp_f = list() temp_cr = list() pop_sorted = self.get_sorted_strim_population(self.pop) pop = [] for idx in range(0, self.pop_size): ## Calculate adaptive parameter cr and f cr = np.random.normal(self.dyn_miu_cr, 0.1) cr = np.clip(cr, 0, 1) while True: f = cauchy.rvs(self.dyn_miu_f, 0.1) if f < 0: continue elif f > 1: f = 1 break temp_f.append(f) temp_cr.append(cr) top = int(self.pop_size * self.pt) x_best = pop_sorted[np.random.randint(0, top)] x_r1 = self.pop[np.random.choice(list(set(range(0, self.pop_size)) - {idx}))] new_pop = self.pop + self.dyn_pop_archive while True: x_r2 = new_pop[np.random.randint(0, len(new_pop))] if np.any(x_r2[self.ID_POS] - x_r1[self.ID_POS]) and np.any(x_r2[self.ID_POS] - self.pop[idx][self.ID_POS]): break x_new = self.pop[idx][self.ID_POS] + f * (x_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + f * (x_r1[self.ID_POS] - x_r2[self.ID_POS]) pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < cr, x_new, self.pop[idx][self.ID_POS]) j_rand = np.random.randint(0, self.problem.n_dims) pos_new[j_rand] = x_new[j_rand] pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) pop.append([pos_new, None]) pop = self.update_target_wrapper_population(pop) for idx in range(0, self.pop_size): if self.compare_agent(pop[idx], self.pop[idx]): self.dyn_pop_archive.append(deepcopy(self.pop[idx])) list_cr.append(temp_cr[idx]) list_f.append(temp_f[idx]) self.pop[idx] = deepcopy(pop[idx]) # Randomly remove solution temp = len(self.dyn_pop_archive) - self.pop_size if temp > 0: idx_list = np.random.choice(range(0, len(self.dyn_pop_archive)), temp, replace=False) archive_pop_new = [] for idx, solution in enumerate(self.dyn_pop_archive): if idx not in idx_list: archive_pop_new.append(solution) self.dyn_pop_archive = deepcopy(archive_pop_new) # Update miu_cr and miu_f if len(list_cr) == 0: self.dyn_miu_cr = (1 - self.ap) * self.dyn_miu_cr + self.ap * 0.5 else: self.dyn_miu_cr = (1 - self.ap) * self.dyn_miu_cr + self.ap * np.mean(np.array(list_cr)) if len(list_f) == 0: self.dyn_miu_f = (1 - self.ap) * self.dyn_miu_f + self.ap * 0.5 else: self.dyn_miu_f = (1 - self.ap) * self.dyn_miu_f + self.ap * self.lehmer_mean(np.array(list_f)) return pop class SADE(Optimizer): """ The original version of: Self-Adaptive Differential Evolution (SADE) Links: 1. https://doi.org/10.1109/CEC.2005.1554904 Examples ~~~~~~~~ >>> import numpy as np >>> from mealpy.evolutionary_based.DE import SADE >>> >>> def fitness_function(solution): >>> return np.sum(solution**2) >>> >>> problem_dict1 = { >>> "fit_func": fitness_function, >>> "lb": [-10, -15, -4, -2, -8], >>> "ub": [10, 15, 12, 8, 20], >>> "minmax": "min", >>> } >>> >>> epoch = 1000 >>> pop_size = 50 >>> model = SADE(problem_dict1, epoch, pop_size) >>> best_position, best_fitness = model.solve() >>> print(f"Solution: {best_position}, Fitness: {best_fitness}") References ~~~~~~~~~~ [1] Qin, A.K. and Suganthan, P.N., 2005, September. Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE. """ def __init__(self, problem, epoch=10000, pop_size=100, **kwargs): """ Args: problem (dict): The problem dictionary epoch (int): maximum number of iterations, default = 10000 pop_size (int): number of population size, default = 100 """ super().__init__(problem, kwargs) self.epoch = self.validator.check_int("epoch", epoch, [1, 100000]) self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000]) self.nfe_per_epoch = self.pop_size self.sort_flag = False self.loop_probability = 50 self.loop_cr = 5 self.ns1 = self.ns2 = self.nf1 = self.nf2 = 0 self.crm = 0.5 self.p1 = 0.5 # Dynamic variable self.dyn_list_cr = list() def evolve(self, epoch): """ The main operations (equations) of algorithm. Inherit from Optimizer class Args: epoch (int): The current iteration """ pop = [] list_probability = [] list_cr = [] for idx in range(0, self.pop_size): ## Calculate adaptive parameter cr and f cr = np.random.normal(self.crm, 0.1) cr = np.clip(cr, 0, 1) list_cr.append(cr) while True: f = np.random.normal(0.5, 0.3) if f < 0: continue elif f > 1: f = 1 break id1, id2, id3 = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 3, replace=False) if np.random.rand() < self.p1: x_new = self.pop[id1][self.ID_POS] + f * (self.pop[id2][self.ID_POS] - self.pop[id3][self.ID_POS]) pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < cr, x_new, self.pop[idx][self.ID_POS]) j_rand = np.random.randint(0, self.problem.n_dims) pos_new[j_rand] = x_new[j_rand] pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) pop.append([pos_new, None]) list_probability.append(True) else: x_new = self.pop[idx][self.ID_POS] + f * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + \ f * (self.pop[id1][self.ID_POS] - self.pop[id2][self.ID_POS]) pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < cr, x_new, self.pop[idx][self.ID_POS]) j_rand = np.random.randint(0, self.problem.n_dims) pos_new[j_rand] = x_new[j_rand] pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) pop.append([pos_new, None]) list_probability.append(False) pop = self.update_target_wrapper_population(pop) for idx in range(0, self.pop_size): if list_probability[idx]: if self.compare_agent(pop[idx], self.pop[idx]): self.ns1 += 1 self.pop[idx] = deepcopy(pop[idx]) else: self.nf1 += 1 else: if self.compare_agent(pop[idx], self.pop[idx]): self.ns2 += 1 self.dyn_list_cr.append(list_cr[idx]) self.pop[idx] = deepcopy(pop[idx]) else: self.nf2 += 1 # Update cr and p1 if (epoch + 1) / self.loop_cr == 0: self.crm = np.mean(self.dyn_list_cr) self.dyn_list_cr = list() if (epoch + 1) / self.loop_probability == 0: self.p1 = self.ns1 * (self.ns2 + self.nf2) / (self.ns2 * (self.ns1 + self.nf1) + self.ns1 * (self.ns2 + self.nf2)) self.ns1 = self.ns2 = self.nf1 = self.nf2 = 0 class SHADE(Optimizer): """ The variant version of: Success-History Adaptation Differential Evolution (SHADE) Links: 1. https://doi.org/10.1109/CEC.2013.6557555 Hyper-parameters should fine tuned in approximate range to get faster convergence toward the global optimum: + miu_f (float): [0.4, 0.6], initial weighting factor, default = 0.5 + miu_cr (float): [0.4, 0.6], initial cross-over probability, default = 0.5 Examples ~~~~~~~~ >>> import numpy as np >>> from mealpy.evolutionary_based.DE import SHADE >>> >>> def fitness_function(solution): >>> return np.sum(solution**2) >>> >>> problem_dict1 = { >>> "fit_func": fitness_function, >>> "lb": [-10, -15, -4, -2, -8], >>> "ub": [10, 15, 12, 8, 20], >>> "minmax": "min", >>> } >>> >>> epoch = 1000 >>> pop_size = 50 >>> miu_f = 0.5 >>> miu_cr = 0.5 >>> model = SHADE(problem_dict1, epoch, pop_size, miu_f, miu_cr) >>> best_position, best_fitness = model.solve() >>> print(f"Solution: {best_position}, Fitness: {best_fitness}") References ~~~~~~~~~~ [1] Tanabe, R. and Fukunaga, A., 2013, June. Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE. """ def __init__(self, problem, epoch=750, pop_size=100, miu_f=0.5, miu_cr=0.5, **kwargs): """ Args: problem (dict): The problem dictionary epoch (int): maximum number of iterations, default = 10000 pop_size (int): number of population size, default = 100 miu_f (float): initial weighting factor, default = 0.5 miu_cr (float): initial cross-over probability, default = 0.5 """ super().__init__(problem, kwargs) self.epoch = self.validator.check_int("epoch", epoch, [1, 100000]) self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000]) # the initial f, location is changed then that f is good self.miu_f = self.validator.check_float("miu_f", miu_f, (0, 1.0)) # the initial cr, self.miu_cr = self.validator.check_float("miu_cr", miu_cr, (0, 1.0)) self.nfe_per_epoch = self.pop_size self.sort_flag = False # Dynamic variable self.dyn_miu_f = miu_f * np.ones(self.pop_size) # list the initial f, self.dyn_miu_cr = miu_cr * np.ones(self.pop_size) # list the initial cr, self.dyn_pop_archive = list() self.k_counter = 0 ### Survivor Selection def weighted_lehmer_mean(self, list_objects, list_weights): up = list_weights * list_objects ** 2 down = list_weights * list_objects return sum(up) / sum(down) def evolve(self, epoch): """ The main operations (equations) of algorithm. Inherit from Optimizer class Args: epoch (int): The current iteration """ list_f = list() list_cr = list() list_f_index = list() list_cr_index = list() list_f_new = np.ones(self.pop_size) list_cr_new = np.ones(self.pop_size) pop_old = deepcopy(self.pop) pop_sorted = self.get_sorted_strim_population(self.pop) pop = [] for idx in range(0, self.pop_size): ## Calculate adaptive parameter cr and f idx_rand = np.random.randint(0, self.pop_size) cr = np.random.normal(self.dyn_miu_cr[idx_rand], 0.1) cr = np.clip(cr, 0, 1) while True: f = cauchy.rvs(self.dyn_miu_f[idx_rand], 0.1) if f < 0: continue elif f > 1: f = 1 break list_cr_new[idx] = cr list_f_new[idx] = f p = np.random.uniform(2 / self.pop_size, 0.2) top = int(self.pop_size * p) x_best = pop_sorted[np.random.randint(0, top)] x_r1 = self.pop[np.random.choice(list(set(range(0, self.pop_size)) - {idx}))] new_pop = self.pop + self.dyn_pop_archive while True: x_r2 = new_pop[np.random.randint(0, len(new_pop))] if np.any(x_r2[self.ID_POS] - x_r1[self.ID_POS]) and np.any(x_r2[self.ID_POS] - self.pop[idx][self.ID_POS]): break x_new = self.pop[idx][self.ID_POS] + f * (x_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + f * (x_r1[self.ID_POS] - x_r2[self.ID_POS]) pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < cr, x_new, self.pop[idx][self.ID_POS]) j_rand = np.random.randint(0, self.problem.n_dims) pos_new[j_rand] = x_new[j_rand] pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) pop.append([pos_new, None]) pop = self.update_target_wrapper_population(pop) for i in range(0, self.pop_size): if self.compare_agent(pop[i], self.pop[i]): list_cr.append(list_cr_new[i]) list_f.append(list_f_new[i]) list_f_index.append(i) list_cr_index.append(i) self.pop[i] = deepcopy(pop[i]) self.dyn_pop_archive.append(deepcopy(pop[i])) # Randomly remove solution temp = len(self.dyn_pop_archive) - self.pop_size if temp > 0: idx_list = np.random.choice(range(0, len(self.dyn_pop_archive)), temp, replace=False) archive_pop_new = [] for idx, solution in enumerate(self.dyn_pop_archive): if idx not in idx_list: archive_pop_new.append(solution) self.dyn_pop_archive = deepcopy(archive_pop_new) # Update miu_cr and miu_f if len(list_f) != 0 and len(list_cr) != 0: # Eq.13, 14, 10 list_fit_old = np.ones(len(list_cr_index)) list_fit_new = np.ones(len(list_cr_index)) idx_increase = 0 for i in range(0, self.pop_size): if i in list_cr_index: list_fit_old[idx_increase] = pop_old[i][self.ID_TAR][self.ID_FIT] list_fit_new[idx_increase] = self.pop[i][self.ID_TAR][self.ID_FIT] idx_increase += 1 temp = sum(abs(list_fit_new - list_fit_old)) if temp == 0: list_weights = 1.0 / len(list_fit_new) * np.ones(len(list_fit_new)) else: list_weights = abs(list_fit_new - list_fit_old) / temp self.dyn_miu_cr[self.k_counter] = sum(list_weights * np.array(list_cr)) self.dyn_miu_f[self.k_counter] = self.weighted_lehmer_mean(np.array(list_f), list_weights) self.k_counter += 1 if self.k_counter >= self.pop_size: self.k_counter = 0 class L_SHADE(Optimizer): """ The original version of: Linear Population Size Reduction Success-History Adaptation Differential Evolution (LSHADE) Links: 1. https://metahack.org/CEC2014-Tanabe-Fukunaga.pdf Hyper-parameters should fine tuned in approximate range to get faster convergence toward the global optimum: + miu_f (float): [0.4, 0.6], initial weighting factor, default = 0.5 + miu_cr (float): [0.4, 0.6], initial cross-over probability, default = 0.5 Examples ~~~~~~~~ >>> import numpy as np >>> from mealpy.evolutionary_based.DE import L_SHADE >>> >>> def fitness_function(solution): >>> return np.sum(solution**2) >>> >>> problem_dict1 = { >>> "fit_func": fitness_function, >>> "lb": [-10, -15, -4, -2, -8], >>> "ub": [10, 15, 12, 8, 20], >>> "minmax": "min", >>> } >>> >>> epoch = 1000 >>> pop_size = 50 >>> miu_f = 0.5 >>> miu_cr = 0.5 >>> model = L_SHADE(problem_dict1, epoch, pop_size, miu_f, miu_cr) >>> best_position, best_fitness = model.solve() >>> print(f"Solution: {best_position}, Fitness: {best_fitness}") References ~~~~~~~~~~ [1] Tanabe, R. and Fukunaga, A.S., 2014, July. Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE. """ def __init__(self, problem, epoch=750, pop_size=100, miu_f=0.5, miu_cr=0.5, **kwargs): """ Args: problem (dict): The problem dictionary epoch (int): maximum number of iterations, default = 10000 pop_size (int): number of population size, default = 100 miu_f (float): initial weighting factor, default = 0.5 miu_cr (float): initial cross-over probability, default = 0.5 """ super().__init__(problem, kwargs) self.epoch = self.validator.check_int("epoch", epoch, [1, 100000]) self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000]) # the initial f, location is changed then that f is good self.miu_f = self.validator.check_float("miu_f", miu_f, (0, 1.0)) # the initial cr, self.miu_cr = self.validator.check_float("miu_cr", miu_cr, (0, 1.0)) self.nfe_per_epoch = self.pop_size self.sort_flag = False # Dynamic variable self.dyn_miu_f = self.miu_f * np.ones(self.pop_size) # list the initial f, self.dyn_miu_cr = self.miu_cr * np.ones(self.pop_size) # list the initial cr, self.dyn_pop_archive = list() self.dyn_pop_size = self.pop_size self.k_counter = 0 self.n_min = int(self.pop_size / 5) ### Survivor Selection def weighted_lehmer_mean(self, list_objects, list_weights): up = sum(list_weights * list_objects ** 2) down = sum(list_weights * list_objects) return up / down if down != 0 else 0.5 def evolve(self, epoch): """ The main operations (equations) of algorithm. Inherit from Optimizer class Args: epoch (int): The current iteration """ list_f = list() list_cr = list() list_f_index = list() list_cr_index = list() list_f_new = np.ones(self.pop_size) list_cr_new = np.ones(self.pop_size) pop_old = deepcopy(self.pop) pop_sorted = self.get_sorted_strim_population(self.pop) pop = [] for idx in range(0, self.pop_size): ## Calculate adaptive parameter cr and f idx_rand = np.random.randint(0, self.pop_size) cr = np.random.normal(self.dyn_miu_cr[idx_rand], 0.1) cr = np.clip(cr, 0, 1) while True: f = cauchy.rvs(self.dyn_miu_f[idx_rand], 0.1) if f < 0: continue elif f > 1: f = 1 break list_cr_new[idx] = cr list_f_new[idx] = f p = np.random.uniform(0.15, 0.2) top = int(self.dyn_pop_size * p) x_best = pop_sorted[np.random.randint(0, top)] x_r1 = self.pop[np.random.choice(list(set(range(0, self.dyn_pop_size)) - {idx}))] new_pop = self.pop + self.dyn_pop_archive while True: x_r2 = new_pop[np.random.randint(0, len(new_pop))] if np.any(x_r2[self.ID_POS] - x_r1[self.ID_POS]) and np.any(x_r2[self.ID_POS] - self.pop[idx][self.ID_POS]): break x_new = self.pop[idx][self.ID_POS] + f * (x_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + f * (x_r1[self.ID_POS] - x_r2[self.ID_POS]) pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < cr, x_new, self.pop[idx][self.ID_POS]) j_rand = np.random.randint(0, self.problem.n_dims) pos_new[j_rand] = x_new[j_rand] pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) pop.append([pos_new, None]) pop = self.update_target_wrapper_population(pop) for i in range(0, self.pop_size): if self.compare_agent(pop[i], self.pop[i]): list_cr.append(list_cr_new[i]) list_f.append(list_f_new[i]) list_f_index.append(i) list_cr_index.append(i) self.pop[i] = deepcopy(pop[i]) self.dyn_pop_archive.append(deepcopy(self.pop[i])) # Randomly remove solution temp = len(self.dyn_pop_archive) - self.pop_size if temp > 0: idx_list = np.random.choice(range(0, len(self.dyn_pop_archive)), temp, replace=False) archive_pop_new = [] for idx, solution in enumerate(self.dyn_pop_archive): if idx not in idx_list: archive_pop_new.append(solution) self.dyn_pop_archive = deepcopy(archive_pop_new) # Update miu_cr and miu_f if len(list_f) != 0 and len(list_cr) != 0: # Eq.13, 14, 10 list_fit_old = np.ones(len(list_cr_index)) list_fit_new = np.ones(len(list_cr_index)) idx_increase = 0 for i in range(0, self.dyn_pop_size): if i in list_cr_index: list_fit_old[idx_increase] = pop_old[i][self.ID_TAR][self.ID_FIT] list_fit_new[idx_increase] = self.pop[i][self.ID_TAR][self.ID_FIT] idx_increase += 1 total_fit = sum(np.abs(list_fit_new - list_fit_old)) list_weights = 0 if total_fit == 0 else np.abs(list_fit_new - list_fit_old) / total_fit self.dyn_miu_cr[self.k_counter] = sum(list_weights * np.array(list_cr)) self.dyn_miu_f[self.k_counter] = self.weighted_lehmer_mean(np.array(list_f), list_weights) self.k_counter += 1 if self.k_counter >= self.dyn_pop_size: self.k_counter = 0 # Linear Population Size Reduction self.dyn_pop_size = round(self.pop_size + epoch * ((self.n_min - self.pop_size) / self.epoch)) class SAP_DE(Optimizer): """ The original version of: Differential Evolution with Self-Adaptive Populations (SAP_DE) Links: 1. https://doi.org/10.1007/s00500-005-0537-1 Hyper-parameters should fine tuned in approximate range to get faster convergence toward the global optimum: + branch (str): ["ABS" or "REL"], gaussian (absolute) or uniform (relative) method Examples ~~~~~~~~ >>> import numpy as np >>> from mealpy.evolutionary_based.DE import SAP_DE >>> >>> def fitness_function(solution): >>> return np.sum(solution**2) >>> >>> problem_dict1 = { >>> "fit_func": fitness_function, >>> "lb": [-10, -15, -4, -2, -8], >>> "ub": [10, 15, 12, 8, 20], >>> "minmax": "min", >>> } >>> >>> epoch = 1000 >>> pop_size = 50 >>> branch = "ABS" >>> model = SAP_DE(problem_dict1, epoch, pop_size, branch) >>> best_position, best_fitness = model.solve() >>> print(f"Solution: {best_position}, Fitness: {best_fitness}") References ~~~~~~~~~~ [1] Teo, J., 2006. Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10(8), pp.673-686. """ ID_CR = 2 ID_MR = 3 ID_PS = 4 def __init__(self, problem, epoch=750, pop_size=100, branch="ABS", **kwargs): """ Args: problem (dict): The problem dictionary epoch (int): maximum number of iterations, default = 10000 pop_size (int): number of population size, default = 100 branch (str): gaussian (absolute) or uniform (relative) method """ super().__init__(problem, kwargs) self.epoch = self.validator.check_int("epoch", epoch, [1, 100000]) self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000]) self.branch = self.validator.check_str("branch", branch, ["ABS", "REL"]) self.fixed_pop_size = self.pop_size self.nfe_per_epoch = self.pop_size self.sort_flag = False def create_solution(self, lb=None, ub=None): """ To get the position, fitness wrapper, target and obj list + A[self.ID_POS] --> Return: position + A[self.ID_TAR] --> Return: [target, [obj1, obj2, ...]] + A[self.ID_TAR][self.ID_FIT] --> Return: target + A[self.ID_TAR][self.ID_OBJ] --> Return: [obj1, obj2, ...] Returns: list: solution with format [position, target, crossover_rate, mutation_rate, pop_size] """ position = self.generate_position(lb, ub) position = self.amend_position(position, lb, ub) target = self.get_target_wrapper(position) crossover_rate = np.random.uniform(0, 1) mutation_rate = np.random.uniform(0, 1) if self.branch == "ABS": pop_size = int(10 * self.problem.n_dims + np.random.normal(0, 1)) else: # elif self.branch == "REL": pop_size = int(10 * self.problem.n_dims + np.random.uniform(-0.5, 0.5)) return [position, target, crossover_rate, mutation_rate, pop_size] def edit_to_range(self, var=None, lower=0, upper=1, func_value=None): while var <= lower or var >= upper: if var <= lower: var += func_value() if var >= upper: var -= func_value() return var def evolve(self, epoch): """ The main operations (equations) of algorithm. Inherit from Optimizer class Args: epoch (int): The current iteration """ pop = [] for idx in range(0, self.pop_size): # Choose 3 random element and different to idx idxs = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 3, replace=False) j = np.random.randint(0, self.pop_size) self.F = np.random.uniform(0, 1) ## Crossover if np.random.uniform(0, 1) < self.pop[idx][self.ID_CR] or idx == j: pos_new = self.pop[idxs[0]][self.ID_POS] + self.F * (self.pop[idxs[1]][self.ID_POS] - self.pop[idxs[2]][self.ID_POS]) cr_new = self.pop[idxs[0]][self.ID_CR] + self.F * (self.pop[idxs[1]][self.ID_CR] - self.pop[idxs[2]][self.ID_CR]) mr_new = self.pop[idxs[0]][self.ID_MR] + self.F * (self.pop[idxs[1]][self.ID_MR] - self.pop[idxs[2]][self.ID_MR]) if self.branch == "ABS": ps_new = self.pop[idxs[0]][self.ID_PS] + int(self.F * (self.pop[idxs[1]][self.ID_PS] - self.pop[idxs[2]][self.ID_PS])) else: # elif self.branch == "REL": ps_new = self.pop[idxs[0]][self.ID_PS] + self.F * (self.pop[idxs[1]][self.ID_PS] - self.pop[idxs[2]][self.ID_PS]) pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) cr_new = self.edit_to_range(cr_new, 0, 1, np.random.random) mr_new = self.edit_to_range(mr_new, 0, 1, np.random.random) pop.append([pos_new, None, cr_new, mr_new, ps_new]) else: pop.append(deepcopy(self.pop[idx])) ## Mutation if np.random.uniform(0, 1) < self.pop[idxs[0]][self.ID_MR]: pos_new = self.pop[idx][self.ID_POS] + np.random.normal(0, self.pop[idxs[0]][self.ID_MR]) cr_new = np.random.normal(0, 1) mr_new = np.random.normal(0, 1) if self.branch == "ABS": ps_new = self.pop[idx][self.ID_PS] + int(np.random.normal(0.5, 1)) else: # elif self.branch == "REL": ps_new = self.pop[idx][self.ID_PS] + np.random.normal(0, self.pop[idxs[0]][self.ID_MR]) pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub) pop.append([pos_new, None, cr_new, mr_new, ps_new]) pop = self.update_target_wrapper_population(pop) # Calculate new population size total = sum([pop[i][self.ID_PS] for i in range(0, self.pop_size)]) if self.branch == "ABS": m_new = int(total / self.pop_size) else: # elif self.branch == "REL": m_new = int(self.pop_size + total) if m_new <= 4: m_new = self.fixed_pop_size + int(np.random.uniform(0, 4)) elif m_new > 4 * self.fixed_pop_size: m_new = self.fixed_pop_size - int(np.random.uniform(0, 4)) ## Change population by population size if m_new <= self.pop_size: self.pop = pop[:m_new] else: pop_sorted = self.get_sorted_strim_population(pop) self.pop = pop + pop_sorted[:m_new - self.pop_size] self.pop_size = len(self.pop)

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5250a8f188395b5f8c73fdfddf229b45c7a3d3f6

174

py

Python

aluno/admin.py

latreta/dvestagio

d18f7c7184748c7b88e335ae9ffd2bdcc197d14f

[ "MIT" ]

null

aluno/admin.py

latreta/dvestagio

d18f7c7184748c7b88e335ae9ffd2bdcc197d14f

[ "MIT" ]

null

aluno/admin.py

latreta/dvestagio

d18f7c7184748c7b88e335ae9ffd2bdcc197d14f

[ "MIT" ]

null

from django.contrib import admin from django.contrib.auth.admin import UserAdmin from .models import Aluno # Register your models here. admin.site.register(Aluno, UserAdmin)

29

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6

5262e576a63823ae200e3245888f42016621ac5d

487

py

Python

redbrick/common/constants.py

dereklukacs/redbrick-sdk

4cf93444c1d808694c1601334f9e039e616dfd3d

[ "MIT" ]

1

2020-11-26T04:25:15.000Z

redbrick/common/constants.py

redbrick-ai/redbrick-sdk

4cf93444c1d808694c1601334f9e039e616dfd3d

[ "MIT" ]

33

2021-02-04T17:51:53.000Z

2022-03-17T07:28:36.000Z

redbrick/common/constants.py

dereklukacs/redbrick-sdk

4cf93444c1d808694c1601334f9e039e616dfd3d

[ "MIT" ]

1

2021-06-09T10:06:35.000Z

"""Constants.""" MAX_CONCURRENCY = 30 MAX_RETRY_ATTEMPTS = 5 DEFAULT_URL = "https://api.redbrickai.com" ORG_API_HAS_CHANGED = ( "this api has changed recently, try running help(redbrick.get_org)" + " or visiting https://redbrick-sdk.readthedocs.io/en/stable/#redbrick.get_org" ) PROJECT_API_HAS_CHANGED = ( "this api has changed recently, try running help(redbrick.get_project)" + " or visiting https://redbrick-sdk.readthedocs.io/en/stable/#redbrick.get_project" )

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6

527f2c2e7e0c7dddde7b68daa613de9b412ddef9

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py

Python

azure-iot-device/tests/iothub/shared_client_tests.py

askpatrickw/azure-iot-sdk-python

3c5bc5bd857697b95fc3a57c07d2b9e7af2563a6

[ "MIT" ]

null

azure-iot-device/tests/iothub/shared_client_tests.py

askpatrickw/azure-iot-sdk-python

3c5bc5bd857697b95fc3a57c07d2b9e7af2563a6

[ "MIT" ]

null

azure-iot-device/tests/iothub/shared_client_tests.py

askpatrickw/azure-iot-sdk-python

3c5bc5bd857697b95fc3a57c07d2b9e7af2563a6

[ "MIT" ]

null

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- """This module contains tests that are shared between sync/async clients i.e. tests for things defined in abstract clients""" import pytest import logging import os import io import six import socks from azure.iot.device.common import auth from azure.iot.device.common.auth import sastoken as st from azure.iot.device.common.auth import connection_string as cs from azure.iot.device.iothub.pipeline import IoTHubPipelineConfig from azure.iot.device.common.pipeline.config import DEFAULT_KEEPALIVE from azure.iot.device.iothub.abstract_clients import ( RECEIVE_TYPE_NONE_SET, RECEIVE_TYPE_HANDLER, RECEIVE_TYPE_API, ) from azure.iot.device.iothub import edge_hsm from azure.iot.device import ProxyOptions from azure.iot.device import exceptions as client_exceptions logging.basicConfig(level=logging.DEBUG) ################################ # SHARED DEVICE + MODULE TESTS # ################################ class SharedIoTHubClientInstantiationTests(object): @pytest.mark.it( "Stores the MQTTPipeline from the 'mqtt_pipeline' parameter in the '_mqtt_pipeline' attribute" ) def test_mqtt_pipeline_attribute(self, client_class, mqtt_pipeline, http_pipeline): client = client_class(mqtt_pipeline, http_pipeline) assert client._mqtt_pipeline is mqtt_pipeline @pytest.mark.it( "Stores the HTTPPipeline from the 'http_pipeline' parameter in the '_http_pipeline' attribute" ) def test_sets_http_pipeline_attribute(self, client_class, mqtt_pipeline, http_pipeline): client = client_class(mqtt_pipeline, http_pipeline) assert client._http_pipeline is http_pipeline @pytest.mark.it("Sets on_connected handler in the MQTTPipeline") def test_sets_on_connected_handler_in_pipeline( self, client_class, mqtt_pipeline, http_pipeline ): client = client_class(mqtt_pipeline, http_pipeline) assert client._mqtt_pipeline.on_connected is not None assert client._mqtt_pipeline.on_connected == client._on_connected @pytest.mark.it("Sets on_disconnected handler in the MQTTPipeline") def test_sets_on_disconnected_handler_in_pipeline( self, client_class, mqtt_pipeline, http_pipeline ): client = client_class(mqtt_pipeline, http_pipeline) assert client._mqtt_pipeline.on_disconnected is not None assert client._mqtt_pipeline.on_disconnected == client._on_disconnected @pytest.mark.it("Sets on_method_request_received handler in the MQTTPipeline") def test_sets_on_method_request_received_handler_in_pipleline( self, client_class, mqtt_pipeline, http_pipeline ): client = client_class(mqtt_pipeline, http_pipeline) assert client._mqtt_pipeline.on_method_request_received is not None assert ( client._mqtt_pipeline.on_method_request_received == client._inbox_manager.route_method_request ) @pytest.mark.it("Sets the Receive Mode/Type for the client as yet-unchosen") def test_initial_receive_mode(self, client_class, mqtt_pipeline, http_pipeline): client = client_class(mqtt_pipeline, http_pipeline) assert client._receive_type == RECEIVE_TYPE_NONE_SET @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubClientCreateMethodUserOptionTests(object): @pytest.fixture def option_test_required_patching(self, mocker): """Override this fixture in a subclass if unique patching is required""" pass @pytest.mark.it( "Sets the 'product_info' user option parameter on the PipelineConfig, if provided" ) def test_product_info_option( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): product_info = "MyProductInfo" client_create_method(*create_method_args, product_info=product_info) # Get configuration object, and ensure it was used for both protocol pipelines assert mock_mqtt_pipeline_init.call_count == 1 config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) assert config == mock_http_pipeline_init.call_args[0][0] assert config.product_info == product_info @pytest.mark.it( "Sets the 'websockets' user option parameter on the PipelineConfig, if provided" ) def test_websockets_option( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): client_create_method(*create_method_args, websockets=True) # Get configuration object, and ensure it was used for both protocol pipelines assert mock_mqtt_pipeline_init.call_count == 1 config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) assert config == mock_http_pipeline_init.call_args[0][0] assert config.websockets # TODO: Show that input in the wrong format is formatted to the correct one. This test exists # in the IoTHubPipelineConfig object already, but we do not currently show that this is felt # from the API level. @pytest.mark.it("Sets the 'cipher' user option parameter on the PipelineConfig, if provided") def test_cipher_option( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): cipher = "DHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA:ECDHE-ECDSA-AES128-GCM-SHA256" client_create_method(*create_method_args, cipher=cipher) # Get configuration object, and ensure it was used for both protocol pipelines assert mock_mqtt_pipeline_init.call_count == 1 config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) assert config == mock_http_pipeline_init.call_args[0][0] assert config.cipher == cipher @pytest.mark.it( "Sets the 'server_verification_cert' user option parameter on the PipelineConfig, if provided" ) def test_server_verification_cert_option( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): server_verification_cert = "fake_server_verification_cert" client_create_method(*create_method_args, server_verification_cert=server_verification_cert) # Get configuration object, and ensure it was used for both protocol pipelines assert mock_mqtt_pipeline_init.call_count == 1 config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) assert config == mock_http_pipeline_init.call_args[0][0] assert config.server_verification_cert == server_verification_cert @pytest.mark.it( "Sets the 'proxy_options' user option parameter on the PipelineConfig, if provided" ) def test_proxy_options( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): proxy_options = ProxyOptions(proxy_type=socks.HTTP, proxy_addr="127.0.0.1", proxy_port=8888) client_create_method(*create_method_args, proxy_options=proxy_options) # Get configuration object, and ensure it was used for both protocol pipelines assert mock_mqtt_pipeline_init.call_count == 1 config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) assert config == mock_http_pipeline_init.call_args[0][0] assert config.proxy_options is proxy_options @pytest.mark.it( "Sets the 'keep_alive' user option parameter on the PipelineConfig, if provided" ) def test_keep_alive_options( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): keepalive_value = 60 client_create_method(*create_method_args, keep_alive=keepalive_value) # Get configuration object, and ensure it was used for both protocol pipelines assert mock_mqtt_pipeline_init.call_count == 1 config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) assert config == mock_http_pipeline_init.call_args[0][0] assert config.keep_alive == keepalive_value @pytest.mark.it("Raises a TypeError if an invalid user option parameter is provided") def test_invalid_option( self, option_test_required_patching, client_create_method, create_method_args ): with pytest.raises(TypeError): client_create_method(*create_method_args, invalid_option="some_value") @pytest.mark.it("Sets default user options if none are provided") def test_default_options( self, mocker, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): client_create_method(*create_method_args) # Both pipelines use the same IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] is mock_http_pipeline_init.call_args[0][0] config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) # Pipeline Config has default options set that were not user-specified assert config.product_info == "" assert config.websockets is False assert config.cipher == "" assert config.proxy_options is None assert config.server_verification_cert is None assert config.keep_alive == DEFAULT_KEEPALIVE # TODO: consider splitting this test class up into device/module specific test classes to avoid # the conditional logic in some tests @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubClientCreateFromConnectionStringTests( SharedIoTHubClientCreateMethodUserOptionTests ): @pytest.fixture def client_create_method(self, client_class): """Provides the specific create method for use in universal tests""" return client_class.create_from_connection_string @pytest.fixture def create_method_args(self, connection_string): """Provides the specific create method args for use in universal tests""" return [connection_string] @pytest.mark.it( "Creates a SasToken that uses a SymmetricKeySigningMechanism, from the values in the provided connection string" ) def test_sastoken(self, mocker, client_class, connection_string): sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") sastoken_mock = mocker.patch.object(st, "SasToken") cs_obj = cs.ConnectionString(connection_string) custom_ttl = 1000 client_class.create_from_connection_string(connection_string, sastoken_ttl=custom_ttl) # Determine expected URI based on class under test if client_class.__name__ == "IoTHubDeviceClient": expected_uri = "{hostname}/devices/{device_id}".format( hostname=cs_obj[cs.HOST_NAME], device_id=cs_obj[cs.DEVICE_ID] ) else: expected_uri = "{hostname}/devices/{device_id}/modules/{module_id}".format( hostname=cs_obj[cs.HOST_NAME], device_id=cs_obj[cs.DEVICE_ID], module_id=cs_obj[cs.MODULE_ID], ) # SymmetricKeySigningMechanism created using the connection string's SharedAccessKey assert sksm_mock.call_count == 1 assert sksm_mock.call_args == mocker.call(key=cs_obj[cs.SHARED_ACCESS_KEY]) # Token was created with a SymmetricKeySigningMechanism, the expected URI, and custom ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, sksm_mock.return_value, ttl=custom_ttl ) @pytest.mark.it( "Uses 3600 seconds (1 hour) as the default SasToken TTL if no custom TTL is provided" ) def test_sastoken_default(self, mocker, client_class, connection_string): sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") sastoken_mock = mocker.patch.object(st, "SasToken") cs_obj = cs.ConnectionString(connection_string) client_class.create_from_connection_string(connection_string) # Determine expected URI based on class under test if client_class.__name__ == "IoTHubDeviceClient": expected_uri = "{hostname}/devices/{device_id}".format( hostname=cs_obj[cs.HOST_NAME], device_id=cs_obj[cs.DEVICE_ID] ) else: expected_uri = "{hostname}/devices/{device_id}/modules/{module_id}".format( hostname=cs_obj[cs.HOST_NAME], device_id=cs_obj[cs.DEVICE_ID], module_id=cs_obj[cs.MODULE_ID], ) # SymmetricKeySigningMechanism created using the connection string's SharedAccessKey assert sksm_mock.call_count == 1 assert sksm_mock.call_args == mocker.call(key=cs_obj[cs.SHARED_ACCESS_KEY]) # Token was created with a SymmetricKeySigningMechanism, the expected URI, and default ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, sksm_mock.return_value, ttl=3600 ) @pytest.mark.it( "Creates MQTT and HTTP Pipelines with an IoTHubPipelineConfig object containing the SasToken and values from the connection string" ) def test_pipeline_config( self, mocker, client_class, connection_string, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): sastoken_mock = mocker.patch.object(st, "SasToken") cs_obj = cs.ConnectionString(connection_string) client_class.create_from_connection_string(connection_string) # Verify pipelines created with an IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] is mock_http_pipeline_init.call_args[0][0] assert isinstance(mock_mqtt_pipeline_init.call_args[0][0], IoTHubPipelineConfig) # Verify the IoTHubPipelineConfig is constructed as expected config = mock_mqtt_pipeline_init.call_args[0][0] assert config.device_id == cs_obj[cs.DEVICE_ID] assert config.hostname == cs_obj[cs.HOST_NAME] assert config.sastoken is sastoken_mock.return_value if client_class.__name__ == "IoTHubModuleClient": assert config.module_id == cs_obj[cs.MODULE_ID] assert config.blob_upload is False assert config.method_invoke is False else: assert config.module_id is None assert config.blob_upload is True assert config.method_invoke is False if cs_obj.get(cs.GATEWAY_HOST_NAME): assert config.gateway_hostname == cs_obj[cs.GATEWAY_HOST_NAME] else: assert config.gateway_hostname is None @pytest.mark.it( "Returns an instance of an IoTHub client using the created MQTT and HTTP pipelines" ) def test_client_returned( self, mocker, client_class, connection_string, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): client = client_class.create_from_connection_string(connection_string) assert isinstance(client, client_class) assert client._mqtt_pipeline is mock_mqtt_pipeline_init.return_value assert client._http_pipeline is mock_http_pipeline_init.return_value @pytest.mark.it("Raises ValueError when given an invalid connection string") @pytest.mark.parametrize( "bad_cs", [ pytest.param("not-a-connection-string", id="Garbage string"), pytest.param( "HostName=value.domain.net;DeviceId=my_device;SharedAccessKey=Invalid", id="Shared Access Key invalid", ), pytest.param( "HostName=value.domain.net;WrongValue=Invalid;SharedAccessKey=Zm9vYmFy", id="Contains extraneous data", ), pytest.param("HostName=value.domain.net;DeviceId=my_device", id="Incomplete"), ], ) def test_raises_value_error_on_bad_connection_string(self, client_class, bad_cs): with pytest.raises(ValueError): client_class.create_from_connection_string(bad_cs) @pytest.mark.it("Raises ValueError if a SasToken creation results in failure") def test_raises_value_error_on_sastoken_failure(self, mocker, client_class, connection_string): sastoken_mock = mocker.patch.object(st, "SasToken") token_err = st.SasTokenError("Some SasToken failure") sastoken_mock.side_effect = token_err with pytest.raises(ValueError) as e_info: client_class.create_from_connection_string(connection_string) assert e_info.value.__cause__ is token_err class SharedIoTHubClientPROPERTYHandlerTests(object): @pytest.fixture(autouse=True) def teardown(self, client, handler_name): """In an async context this makes sure tasks are finished""" yield try: setattr(client, handler_name, None) except client_exceptions.ClientError: # In some tests involving locked client receive modes, trying to set # back to None will raise errors (because handlers are disallowed). # Just catch the error and keep it moving - if the client mode was locked # then there's no need for the cleanup anyway pass @pytest.mark.it("Can have its value set and retrieved") def test_read_write(self, client, handler, handler_name): assert getattr(client, handler_name) is None setattr(client, handler_name, handler) assert getattr(client, handler_name) is handler @pytest.mark.it("Reflects the value of the handler manager property of the same name") def test_set_on_handler_manager(self, client, handler, handler_name): assert getattr(client, handler_name) is None assert getattr(client, handler_name) is getattr(client._handler_manager, handler_name) setattr(client, handler_name, handler) assert getattr(client, handler_name) is handler assert getattr(client, handler_name) is getattr(client._handler_manager, handler_name) @pytest.mark.it( "Implicitly enables the corresponding feature if not already enabled, when a handler value is set" ) def test_enables_feature_only_if_not_already_enabled( self, mocker, client, handler, handler_name, feature_name, mqtt_pipeline ): # Feature will appear disabled mqtt_pipeline.feature_enabled.__getitem__.return_value = False # Set handler setattr(client, handler_name, handler) # Feature was enabled assert mqtt_pipeline.enable_feature.call_count == 1 assert mqtt_pipeline.enable_feature.call_args[0][0] == feature_name mqtt_pipeline.enable_feature.reset_mock() # Feature will appear already enabled mqtt_pipeline.feature_enabled.__getitem__.return_value = True # Set handler setattr(client, handler_name, handler) # Feature was not enabled again assert mqtt_pipeline.enable_feature.call_count == 0 @pytest.mark.it( "Implicitly disables the corresponding feature if not already disabled, when handler value is set back to None" ) def test_disables_feature_only_if_not_already_disabled( self, mocker, client, handler_name, feature_name, mqtt_pipeline ): # Feature will appear enabled mqtt_pipeline.feature_enabled.__getitem__.return_value = True # Set handler to None setattr(client, handler_name, None) # Feature was disabled assert mqtt_pipeline.disable_feature.call_count == 1 assert mqtt_pipeline.disable_feature.call_args[0][0] == feature_name mqtt_pipeline.disable_feature.reset_mock() # Feature will appear already disabled mqtt_pipeline.feature_enabled.__getitem__.return_value = False # Set handler to None setattr(client, handler_name, None) # Feature was not disabled again assert mqtt_pipeline.disable_feature.call_count == 0 @pytest.mark.it( "Locks the client to Handler Receive Mode if the receive mode has not yet been set" ) def test_receive_mode_not_set(self, client, handler, handler_name): assert client._receive_type is RECEIVE_TYPE_NONE_SET setattr(client, handler_name, handler) assert client._receive_type is RECEIVE_TYPE_HANDLER @pytest.mark.it( "Does not modify the client receive mode if it has already been set to Handler Receive Mode" ) def test_receive_mode_set_handler(self, client, handler, handler_name): client._receive_type = RECEIVE_TYPE_HANDLER setattr(client, handler_name, handler) assert client._receive_type is RECEIVE_TYPE_HANDLER @pytest.mark.it( "Raises a ClientError and does nothing else if the client receive mode has already been set to API Receive Mode" ) def test_receive_mode_set_api(self, client, handler, handler_name, mqtt_pipeline): client._receive_type = RECEIVE_TYPE_API # Error was raised with pytest.raises(client_exceptions.ClientError): setattr(client, handler_name, handler) # Feature was not enabled assert mqtt_pipeline.enable_feature.call_count == 0 # NOTE: If more properties are added, this class should become a general purpose properties testclass class SharedIoTHubClientPROPERTYConnectedTests(object): @pytest.mark.it("Cannot be changed") def test_read_only(self, client): with pytest.raises(AttributeError): client.connected = not client.connected @pytest.mark.it("Reflects the value of the root stage property of the same name") def test_reflects_pipeline_property(self, client, mqtt_pipeline): mqtt_pipeline.connected = True assert client.connected mqtt_pipeline.connected = False assert not client.connected ############################## # SHARED DEVICE CLIENT TESTS # ############################## @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubDeviceClientCreateFromSymmetricKeyTests( SharedIoTHubClientCreateMethodUserOptionTests ): hostname = "durmstranginstitute.farend" device_id = "MySnitch" symmetric_key = "Zm9vYmFy" @pytest.fixture def client_create_method(self, client_class): """Provides the specific create method for use in universal tests""" return client_class.create_from_symmetric_key @pytest.fixture def create_method_args(self): """Provides the specific create method args for use in universal tests""" return [self.symmetric_key, self.hostname, self.device_id] @pytest.mark.it( "Creates a SasToken that uses a SymmetricKeySigningMechanism, from the values provided in parameters" ) def test_sastoken(self, mocker, client_class): sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") sastoken_mock = mocker.patch.object(st, "SasToken") expected_uri = "{hostname}/devices/{device_id}".format( hostname=self.hostname, device_id=self.device_id ) custom_ttl = 1000 client_class.create_from_symmetric_key( symmetric_key=self.symmetric_key, hostname=self.hostname, device_id=self.device_id, sastoken_ttl=custom_ttl, ) # SymmetricKeySigningMechanism created using the provided symmetric key assert sksm_mock.call_count == 1 assert sksm_mock.call_args == mocker.call(key=self.symmetric_key) # SasToken created with the SymmetricKeySigningMechanism, the expected URI, and the custom ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, sksm_mock.return_value, ttl=custom_ttl ) @pytest.mark.it( "Uses 3600 seconds (1 hour) as the default SasToken TTL if no custom TTL is provided" ) def test_sastoken_default(self, mocker, client_class): sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") sastoken_mock = mocker.patch.object(st, "SasToken") expected_uri = "{hostname}/devices/{device_id}".format( hostname=self.hostname, device_id=self.device_id ) client_class.create_from_symmetric_key( symmetric_key=self.symmetric_key, hostname=self.hostname, device_id=self.device_id ) # SymmetricKeySigningMechanism created using the provided symmetric key assert sksm_mock.call_count == 1 assert sksm_mock.call_args == mocker.call(key=self.symmetric_key) # SasToken created with the SymmetricKeySigningMechanism, the expected URI, and the default ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, sksm_mock.return_value, ttl=3600 ) @pytest.mark.it( "Creates MQTT and HTTP pipelines with an IoTHubPipelineConfig object containing the SasToken and values provided in parameters" ) def test_pipeline_config( self, mocker, client_class, mock_mqtt_pipeline_init, mock_http_pipeline_init ): sastoken_mock = mocker.patch.object(st, "SasToken") client_class.create_from_symmetric_key( symmetric_key=self.symmetric_key, hostname=self.hostname, device_id=self.device_id ) # Verify pipelines created with an IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] is mock_http_pipeline_init.call_args[0][0] assert isinstance(mock_mqtt_pipeline_init.call_args[0][0], IoTHubPipelineConfig) # Verify the IoTHubPipelineConfig is constructed as expected config = mock_mqtt_pipeline_init.call_args[0][0] assert config.device_id == self.device_id assert config.hostname == self.hostname assert config.gateway_hostname is None assert config.sastoken is sastoken_mock.return_value assert config.blob_upload is True assert config.method_invoke is False @pytest.mark.it( "Returns an instance of an IoTHubDeviceClient using the created MQTT and HTTP pipelines" ) def test_client_returned( self, mocker, client_class, mock_mqtt_pipeline_init, mock_http_pipeline_init ): client = client_class.create_from_symmetric_key( symmetric_key=self.symmetric_key, hostname=self.hostname, device_id=self.device_id ) assert isinstance(client, client_class) assert client._mqtt_pipeline is mock_mqtt_pipeline_init.return_value assert client._http_pipeline is mock_http_pipeline_init.return_value @pytest.mark.it("Raises ValueError if a SasToken creation results in failure") def test_raises_value_error_on_sastoken_failure(self, mocker, client_class): sastoken_mock = mocker.patch.object(st, "SasToken") token_err = st.SasTokenError("Some SasToken failure") sastoken_mock.side_effect = token_err with pytest.raises(ValueError) as e_info: client_class.create_from_symmetric_key( symmetric_key=self.symmetric_key, hostname=self.hostname, device_id=self.device_id ) assert e_info.value.__cause__ is token_err @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubDeviceClientCreateFromX509CertificateTests( SharedIoTHubClientCreateMethodUserOptionTests ): hostname = "durmstranginstitute.farend" device_id = "MySnitch" @pytest.fixture def client_create_method(self, client_class): """Provides the specific create method for use in universal tests""" return client_class.create_from_x509_certificate @pytest.fixture def create_method_args(self, x509): """Provides the specific create method args for use in universal tests""" return [x509, self.hostname, self.device_id] @pytest.mark.it( "Creates MQTT and HTTP pipelines with an IoTHubPipelineConfig object containing the X509 and other values provided in parameters" ) def test_pipeline_config( self, mocker, client_class, x509, mock_mqtt_pipeline_init, mock_http_pipeline_init ): client_class.create_from_x509_certificate( x509=x509, hostname=self.hostname, device_id=self.device_id ) # Verify pipelines created with an IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] == mock_http_pipeline_init.call_args[0][0] assert isinstance(mock_mqtt_pipeline_init.call_args[0][0], IoTHubPipelineConfig) # Verify the IoTHubPipelineConfig is constructed as expected config = mock_mqtt_pipeline_init.call_args[0][0] assert config.device_id == self.device_id assert config.hostname == self.hostname assert config.gateway_hostname is None assert config.x509 is x509 assert config.blob_upload is True assert config.method_invoke is False @pytest.mark.it( "Returns an instance of an IoTHubDeviceclient using the created MQTT and HTTP pipelines" ) def test_client_returned( self, mocker, client_class, x509, mock_mqtt_pipeline_init, mock_http_pipeline_init ): client = client_class.create_from_x509_certificate( x509=x509, hostname=self.hostname, device_id=self.device_id ) assert isinstance(client, client_class) assert client._mqtt_pipeline is mock_mqtt_pipeline_init.return_value assert client._http_pipeline is mock_http_pipeline_init.return_value @pytest.mark.it("Raises a TypeError if the 'sastoken_ttl' kwarg is supplied by the user") def test_sastoken_ttl(self, client_class, x509): with pytest.raises(TypeError): client_class.create_from_x509_certificate( x509=x509, hostname=self.hostname, device_id=self.device_id, sastoken_ttl=1000 ) ############################## # SHARED MODULE CLIENT TESTS # ############################## @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubModuleClientCreateFromX509CertificateTests( SharedIoTHubClientCreateMethodUserOptionTests ): hostname = "durmstranginstitute.farend" device_id = "MySnitch" module_id = "Charms" @pytest.fixture def client_create_method(self, client_class): """Provides the specific create method for use in universal tests""" return client_class.create_from_x509_certificate @pytest.fixture def create_method_args(self, x509): """Provides the specific create method args for use in universal tests""" return [x509, self.hostname, self.device_id, self.module_id] @pytest.mark.it( "Creates MQTT and HTTP pipelines with an IoTHubPipelineConfig object containing the X509 and other values provided in parameters" ) def test_pipeline_config( self, mocker, client_class, x509, mock_mqtt_pipeline_init, mock_http_pipeline_init ): client_class.create_from_x509_certificate( x509=x509, hostname=self.hostname, device_id=self.device_id, module_id=self.module_id ) # Verify pipelines created with an IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] == mock_http_pipeline_init.call_args[0][0] assert isinstance(mock_mqtt_pipeline_init.call_args[0][0], IoTHubPipelineConfig) # Verify the IoTHubPipelineConfig is constructed as expected config = mock_mqtt_pipeline_init.call_args[0][0] assert config.device_id == self.device_id assert config.hostname == self.hostname assert config.gateway_hostname is None assert config.x509 is x509 assert config.blob_upload is False assert config.method_invoke is False @pytest.mark.it( "Returns an instance of an IoTHubDeviceclient using the created MQTT and HTTP pipelines" ) def test_client_returned( self, mocker, client_class, x509, mock_mqtt_pipeline_init, mock_http_pipeline_init ): client = client_class.create_from_x509_certificate( x509=x509, hostname=self.hostname, device_id=self.device_id, module_id=self.module_id ) assert isinstance(client, client_class) assert client._mqtt_pipeline is mock_mqtt_pipeline_init.return_value assert client._http_pipeline is mock_http_pipeline_init.return_value @pytest.mark.it("Raises a TypeError if the 'sastoken_ttl' kwarg is supplied by the user") def test_sastoken_ttl(self, client_class, x509): with pytest.raises(TypeError): client_class.create_from_x509_certificate( x509=x509, hostname=self.hostname, device_id=self.device_id, sastoken_ttl=1000 ) @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubModuleClientClientCreateFromEdgeEnvironmentUserOptionTests( SharedIoTHubClientCreateMethodUserOptionTests ): """This class inherites the user option tests shared by all create method APIs, and overrides tests in order to accomodate unique requirements for the .create_from_edge_enviornment() method. Because .create_from_edge_environment() tests are spread accross multiple test units (i.e. test classes), these overrides are done in this class, which is then inherited by all .create_from_edge_environment() test units below. """ @pytest.fixture def client_create_method(self, client_class): """Provides the specific create method for use in universal tests""" return client_class.create_from_edge_environment @pytest.fixture def create_method_args(self): """Provides the specific create method args for use in universal tests""" return [] @pytest.mark.it( "Raises a TypeError if the 'server_verification_cert' user option parameter is provided" ) def test_server_verification_cert_option( self, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): """THIS TEST OVERRIDES AN INHERITED TEST""" # Override to test that server_verification_cert CANNOT be provided in Edge scenarios with pytest.raises(TypeError): client_create_method( *create_method_args, server_verification_cert="fake_server_verification_cert" ) @pytest.mark.it("Sets default user options if none are provided") def test_default_options( self, mocker, option_test_required_patching, client_create_method, create_method_args, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): """THIS TEST OVERRIDES AN INHERITED TEST""" # Override so that can avoid the check on server_verification_cert being None # as in Edge scenarios, it is not None client_create_method(*create_method_args) # Both pipelines use the same IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] is mock_http_pipeline_init.call_args[0][0] config = mock_mqtt_pipeline_init.call_args[0][0] assert isinstance(config, IoTHubPipelineConfig) # Pipeline Config has default options that were not specified assert config.product_info == "" assert config.websockets is False assert config.cipher == "" assert config.proxy_options is None assert config.keep_alive == DEFAULT_KEEPALIVE @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubModuleClientCreateFromEdgeEnvironmentWithContainerEnvTests( SharedIoTHubModuleClientClientCreateFromEdgeEnvironmentUserOptionTests ): @pytest.fixture def option_test_required_patching(self, mocker, mock_edge_hsm, edge_container_environment): """THIS FIXTURE OVERRIDES AN INHERITED FIXTURE""" mocker.patch.dict(os.environ, edge_container_environment, clear=True) @pytest.mark.it( "Creates a SasToken that uses an IoTEdgeHsm, from the values extracted from the Edge environment and the user-provided TTL" ) def test_sastoken(self, mocker, client_class, mock_edge_hsm, edge_container_environment): mocker.patch.dict(os.environ, edge_container_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") expected_uri = "{hostname}/devices/{device_id}/modules/{module_id}".format( hostname=edge_container_environment["IOTEDGE_IOTHUBHOSTNAME"], device_id=edge_container_environment["IOTEDGE_DEVICEID"], module_id=edge_container_environment["IOTEDGE_MODULEID"], ) custom_ttl = 1000 client_class.create_from_edge_environment(sastoken_ttl=custom_ttl) # IoTEdgeHsm created using the extracted values assert mock_edge_hsm.call_count == 1 assert mock_edge_hsm.call_args == mocker.call( module_id=edge_container_environment["IOTEDGE_MODULEID"], generation_id=edge_container_environment["IOTEDGE_MODULEGENERATIONID"], workload_uri=edge_container_environment["IOTEDGE_WORKLOADURI"], api_version=edge_container_environment["IOTEDGE_APIVERSION"], ) # SasToken created with the IoTEdgeHsm, the expected URI and the custom ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, mock_edge_hsm.return_value, ttl=custom_ttl ) @pytest.mark.it( "Uses 3600 seconds (1 hour) as the default SasToken TTL if no custom TTL is provided" ) def test_sastoken_default( self, mocker, client_class, mock_edge_hsm, edge_container_environment ): mocker.patch.dict(os.environ, edge_container_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") expected_uri = "{hostname}/devices/{device_id}/modules/{module_id}".format( hostname=edge_container_environment["IOTEDGE_IOTHUBHOSTNAME"], device_id=edge_container_environment["IOTEDGE_DEVICEID"], module_id=edge_container_environment["IOTEDGE_MODULEID"], ) client_class.create_from_edge_environment() # IoTEdgeHsm created using the extracted values assert mock_edge_hsm.call_count == 1 assert mock_edge_hsm.call_args == mocker.call( module_id=edge_container_environment["IOTEDGE_MODULEID"], generation_id=edge_container_environment["IOTEDGE_MODULEGENERATIONID"], workload_uri=edge_container_environment["IOTEDGE_WORKLOADURI"], api_version=edge_container_environment["IOTEDGE_APIVERSION"], ) # SasToken created with the IoTEdgeHsm, the expected URI, and the default ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, mock_edge_hsm.return_value, ttl=3600 ) @pytest.mark.it( "Uses an IoTEdgeHsm as the SasToken signing mechanism even if any Edge local debug environment variables may also be present" ) def test_hybrid_env( self, mocker, client_class, mock_edge_hsm, edge_container_environment, edge_local_debug_environment, ): hybrid_environment = merge_dicts(edge_container_environment, edge_local_debug_environment) mocker.patch.dict(os.environ, hybrid_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") mock_sksm = mocker.patch.object(auth, "SymmetricKeySigningMechanism") client_class.create_from_edge_environment() assert mock_sksm.call_count == 0 # we did NOT use SK signing mechanism assert mock_edge_hsm.call_count == 1 # instead, we still used edge hsm assert mock_edge_hsm.call_args == mocker.call( module_id=edge_container_environment["IOTEDGE_MODULEID"], generation_id=edge_container_environment["IOTEDGE_MODULEGENERATIONID"], workload_uri=edge_container_environment["IOTEDGE_WORKLOADURI"], api_version=edge_container_environment["IOTEDGE_APIVERSION"], ) assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( mocker.ANY, mock_edge_hsm.return_value, ttl=3600 ) @pytest.mark.it( "Creates MQTT and HTTP pipelines with an IoTHubPipelineConfig object containing the SasToken and values extracted from the Edge environment" ) def test_pipeline_config( self, mocker, client_class, mock_edge_hsm, edge_container_environment, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): mocker.patch.dict(os.environ, edge_container_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") client_class.create_from_edge_environment() # Verify pipelines created with an IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] is mock_http_pipeline_init.call_args[0][0] assert isinstance(mock_mqtt_pipeline_init.call_args[0][0], IoTHubPipelineConfig) # Verify the IoTHubPipelineConfig is constructed as expected config = mock_mqtt_pipeline_init.call_args[0][0] assert config.device_id == edge_container_environment["IOTEDGE_DEVICEID"] assert config.module_id == edge_container_environment["IOTEDGE_MODULEID"] assert config.hostname == edge_container_environment["IOTEDGE_IOTHUBHOSTNAME"] assert config.gateway_hostname == edge_container_environment["IOTEDGE_GATEWAYHOSTNAME"] assert config.sastoken is sastoken_mock.return_value assert ( config.server_verification_cert == mock_edge_hsm.return_value.get_certificate.return_value ) assert config.method_invoke is True assert config.blob_upload is False @pytest.mark.it( "Returns an instance of an IoTHubModuleClient using the created MQTT and HTTP pipelines" ) def test_client_returns( self, mocker, client_class, mock_edge_hsm, edge_container_environment, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): mocker.patch.dict(os.environ, edge_container_environment, clear=True) client = client_class.create_from_edge_environment() assert isinstance(client, client_class) assert client._mqtt_pipeline is mock_mqtt_pipeline_init.return_value assert client._http_pipeline is mock_http_pipeline_init.return_value @pytest.mark.it("Raises OSError if the environment is missing required variables") @pytest.mark.parametrize( "missing_env_var", [ "IOTEDGE_MODULEID", "IOTEDGE_DEVICEID", "IOTEDGE_IOTHUBHOSTNAME", "IOTEDGE_GATEWAYHOSTNAME", "IOTEDGE_APIVERSION", "IOTEDGE_MODULEGENERATIONID", "IOTEDGE_WORKLOADURI", ], ) def test_bad_environment( self, mocker, client_class, edge_container_environment, missing_env_var ): # Remove a variable from the fixture del edge_container_environment[missing_env_var] mocker.patch.dict(os.environ, edge_container_environment, clear=True) with pytest.raises(OSError): client_class.create_from_edge_environment() @pytest.mark.it( "Raises OSError if there is an error retrieving the server verification certificate from Edge with the IoTEdgeHsm" ) def test_bad_edge_auth(self, mocker, client_class, edge_container_environment, mock_edge_hsm): mocker.patch.dict(os.environ, edge_container_environment, clear=True) my_edge_error = edge_hsm.IoTEdgeError() mock_edge_hsm.return_value.get_certificate.side_effect = my_edge_error with pytest.raises(OSError) as e_info: client_class.create_from_edge_environment() assert e_info.value.__cause__ is my_edge_error @pytest.mark.it("Raises ValueError if a SasToken creation results in failure") def test_raises_value_error_on_sastoken_failure( self, mocker, client_class, edge_container_environment, mock_edge_hsm ): mocker.patch.dict(os.environ, edge_container_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") token_err = st.SasTokenError("Some SasToken failure") sastoken_mock.side_effect = token_err with pytest.raises(ValueError) as e_info: client_class.create_from_edge_environment() assert e_info.value.__cause__ is token_err @pytest.mark.usefixtures("mock_mqtt_pipeline_init", "mock_http_pipeline_init") class SharedIoTHubModuleClientCreateFromEdgeEnvironmentWithDebugEnvTests( SharedIoTHubModuleClientClientCreateFromEdgeEnvironmentUserOptionTests ): @pytest.fixture def option_test_required_patching(self, mocker, mock_open, edge_local_debug_environment): """THIS FIXTURE OVERRIDES AN INHERITED FIXTURE""" mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) @pytest.fixture def mock_open(self, mocker): return mocker.patch.object(io, "open") @pytest.mark.it( "Creates a SasToken that uses a SymmetricKeySigningMechanism, from the values in the connection string extracted from the Edge local debug environment, as well as the user-provided TTL" ) def test_sastoken(self, mocker, client_class, mock_open, edge_local_debug_environment): mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") sastoken_mock = mocker.patch.object(st, "SasToken") cs_obj = cs.ConnectionString(edge_local_debug_environment["EdgeHubConnectionString"]) expected_uri = "{hostname}/devices/{device_id}/modules/{module_id}".format( hostname=cs_obj[cs.HOST_NAME], device_id=cs_obj[cs.DEVICE_ID], module_id=cs_obj[cs.MODULE_ID], ) custom_ttl = 1000 client_class.create_from_edge_environment(sastoken_ttl=custom_ttl) # SymmetricKeySigningMechanism created using the connection string's Shared Access Key assert sksm_mock.call_count == 1 assert sksm_mock.call_args == mocker.call(key=cs_obj[cs.SHARED_ACCESS_KEY]) # SasToken created with the SymmetricKeySigningMechanism, the expected URI, and the custom ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, sksm_mock.return_value, ttl=custom_ttl ) @pytest.mark.it( "Uses 3600 seconds (1 hour) as the default SasToken TTL if no custom TTL is provided" ) def test_sastoken_default(self, mocker, client_class, mock_open, edge_local_debug_environment): mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") sastoken_mock = mocker.patch.object(st, "SasToken") cs_obj = cs.ConnectionString(edge_local_debug_environment["EdgeHubConnectionString"]) expected_uri = "{hostname}/devices/{device_id}/modules/{module_id}".format( hostname=cs_obj[cs.HOST_NAME], device_id=cs_obj[cs.DEVICE_ID], module_id=cs_obj[cs.MODULE_ID], ) client_class.create_from_edge_environment() # SymmetricKeySigningMechanism created using the connection string's Shared Access Key assert sksm_mock.call_count == 1 assert sksm_mock.call_args == mocker.call(key=cs_obj[cs.SHARED_ACCESS_KEY]) # SasToken created with the SymmetricKeySigningMechanism, the expected URI and default ttl assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( expected_uri, sksm_mock.return_value, ttl=3600 ) @pytest.mark.it( "Only uses Edge local debug variables if no Edge container variables are present in the environment" ) def test_auth_provider_and_pipeline_hybrid_env( self, mocker, client_class, edge_container_environment, edge_local_debug_environment, mock_open, mock_edge_hsm, ): # This test verifies that the presence of edge container environment variables means the # code will follow the edge container environment creation path (using the IoTEdgeHsm) # even if edge local debug variables are present. hybrid_environment = merge_dicts(edge_container_environment, edge_local_debug_environment) mocker.patch.dict(os.environ, hybrid_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") sksm_mock = mocker.patch.object(auth, "SymmetricKeySigningMechanism") client_class.create_from_edge_environment() assert sksm_mock.call_count == 0 # we did NOT use SK signing mechanism assert mock_edge_hsm.call_count == 1 # instead, we still used edge HSM assert mock_edge_hsm.call_args == mocker.call( module_id=edge_container_environment["IOTEDGE_MODULEID"], generation_id=edge_container_environment["IOTEDGE_MODULEGENERATIONID"], workload_uri=edge_container_environment["IOTEDGE_WORKLOADURI"], api_version=edge_container_environment["IOTEDGE_APIVERSION"], ) assert sastoken_mock.call_count == 1 assert sastoken_mock.call_args == mocker.call( mocker.ANY, mock_edge_hsm.return_value, ttl=3600 ) @pytest.mark.it( "Extracts the server verification certificate from the file indicated by the filepath extracted from the Edge local debug environment" ) def test_open_ca_cert(self, mocker, client_class, edge_local_debug_environment, mock_open): mock_file_handle = mock_open.return_value.__enter__.return_value mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) client_class.create_from_edge_environment() assert mock_open.call_count == 1 assert mock_open.call_args == mocker.call( edge_local_debug_environment["EdgeModuleCACertificateFile"], mode="r" ) assert mock_file_handle.read.call_count == 1 assert mock_file_handle.read.call_args == mocker.call() @pytest.mark.it( "Creates MQTT and HTTP pipelines with an IoTHubPipelineConfig object containing the SasToken and values extracted from the Edge local debug environment" ) def test_pipeline_config( self, mocker, client_class, mock_open, edge_local_debug_environment, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") mock_file_handle = mock_open.return_value.__enter__.return_value ca_cert_file_contents = "some cert" mock_file_handle.read.return_value = ca_cert_file_contents cs_obj = cs.ConnectionString(edge_local_debug_environment["EdgeHubConnectionString"]) client_class.create_from_edge_environment() # Verify pipelines created with an IoTHubPipelineConfig assert mock_mqtt_pipeline_init.call_count == 1 assert mock_http_pipeline_init.call_count == 1 assert mock_mqtt_pipeline_init.call_args[0][0] is mock_http_pipeline_init.call_args[0][0] assert isinstance(mock_mqtt_pipeline_init.call_args[0][0], IoTHubPipelineConfig) # Verify the IoTHubPipelingConfig is constructed as expected config = mock_mqtt_pipeline_init.call_args[0][0] assert config.device_id == cs_obj[cs.DEVICE_ID] assert config.module_id == cs_obj[cs.MODULE_ID] assert config.hostname == cs_obj[cs.HOST_NAME] assert config.gateway_hostname == cs_obj[cs.GATEWAY_HOST_NAME] assert config.sastoken is sastoken_mock.return_value assert config.server_verification_cert == ca_cert_file_contents assert config.method_invoke is True assert config.blob_upload is False @pytest.mark.it( "Returns an instance of an IoTHub client using the created MQTT and HTTP pipelines" ) def test_client_returned( self, mocker, client_class, mock_open, edge_local_debug_environment, mock_mqtt_pipeline_init, mock_http_pipeline_init, ): mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) client = client_class.create_from_edge_environment() assert isinstance(client, client_class) assert client._mqtt_pipeline is mock_mqtt_pipeline_init.return_value assert client._http_pipeline is mock_http_pipeline_init.return_value @pytest.mark.it("Raises OSError if the environment is missing required variables") @pytest.mark.parametrize( "missing_env_var", ["EdgeHubConnectionString", "EdgeModuleCACertificateFile"] ) def test_bad_environment( self, mocker, client_class, edge_local_debug_environment, missing_env_var, mock_open ): # Remove a variable from the fixture del edge_local_debug_environment[missing_env_var] mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) with pytest.raises(OSError): client_class.create_from_edge_environment() @pytest.mark.it( "Raises ValueError if the connection string in the EdgeHubConnectionString environment variable is invalid" ) @pytest.mark.parametrize( "bad_cs", [ pytest.param("not-a-connection-string", id="Garbage string"), pytest.param( "HostName=value.domain.net;DeviceId=my_device;ModuleId=my_module;SharedAccessKey=Invalid", id="Shared Access Key invalid", ), pytest.param( "HostName=value.domain.net;WrongValue=Invalid;SharedAccessKey=Zm9vYmFy", id="Contains extraneous data", ), pytest.param("HostName=value.domain.net;DeviceId=my_device", id="Incomplete"), ], ) def test_bad_connection_string( self, mocker, client_class, edge_local_debug_environment, bad_cs, mock_open ): edge_local_debug_environment["EdgeHubConnectionString"] = bad_cs mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) with pytest.raises(ValueError): client_class.create_from_edge_environment() @pytest.mark.it( "Raises ValueError if the filepath in the EdgeModuleCACertificateFile environment variable is invalid" ) def test_bad_filepath(self, mocker, client_class, edge_local_debug_environment, mock_open): # To make tests compatible with Python 2 & 3, redfine errors try: FileNotFoundError # noqa: F823 except NameError: FileNotFoundError = IOError mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) my_fnf_error = FileNotFoundError() mock_open.side_effect = my_fnf_error with pytest.raises(ValueError) as e_info: client_class.create_from_edge_environment() assert e_info.value.__cause__ is my_fnf_error @pytest.mark.it( "Raises ValueError if the file referenced by the filepath in the EdgeModuleCACertificateFile environment variable cannot be opened" ) def test_bad_file_io(self, mocker, client_class, edge_local_debug_environment, mock_open): # Raise a different error in Python 2 vs 3 if six.PY2: error = IOError() else: error = OSError() mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) mock_open.side_effect = error with pytest.raises(ValueError) as e_info: client_class.create_from_edge_environment() assert e_info.value.__cause__ is error @pytest.mark.it("Raises ValueError if a SasToken creation results in failure") def test_raises_value_error_on_sastoken_failure( self, mocker, client_class, edge_local_debug_environment, mock_open ): mocker.patch.dict(os.environ, edge_local_debug_environment, clear=True) sastoken_mock = mocker.patch.object(st, "SasToken") token_err = st.SasTokenError("Some SasToken failure") sastoken_mock.side_effect = token_err with pytest.raises(ValueError) as e_info: client_class.create_from_edge_environment() assert e_info.value.__cause__ is token_err #################### # HELPER FUNCTIONS # #################### def merge_dicts(d1, d2): d3 = d1.copy() d3.update(d2) return d3

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Python

test/test_scrapbook_book.py

clach04/PyWebScrapBook

310e8f20cc5337336875679246b9269265b4476a

[ "MIT" ]

39

2019-04-10T18:07:40.000Z

2022-02-07T07:11:30.000Z

test/test_scrapbook_book.py

clach04/PyWebScrapBook

310e8f20cc5337336875679246b9269265b4476a

[ "MIT" ]

56

2019-05-07T23:29:14.000Z

2022-02-24T10:33:43.000Z

test/test_scrapbook_book.py

clach04/PyWebScrapBook

310e8f20cc5337336875679246b9269265b4476a

[ "MIT" ]

15

2019-06-12T05:16:43.000Z

2022-01-16T13:24:11.000Z

from unittest import mock import unittest import os import shutil import io import re import zipfile import time import functools from webscrapbook import WSB_DIR, Config from webscrapbook import util from webscrapbook.scrapbook.host import Host from webscrapbook.scrapbook import book as wsb_book from webscrapbook.scrapbook.book import Book root_dir = os.path.abspath(os.path.dirname(__file__)) test_root = os.path.join(root_dir, 'test_scrapbook_book') def setUpModule(): # mock out user config global mockings mockings = [ mock.patch('webscrapbook.scrapbook.host.WSB_USER_DIR', os.path.join(test_root, 'wsb')), mock.patch('webscrapbook.WSB_USER_DIR', os.path.join(test_root, 'wsb')), mock.patch('webscrapbook.WSB_USER_CONFIG', test_root), ] for mocking in mockings: mocking.start() def tearDownModule(): # stop mock for mocking in mockings: mocking.stop() class TestBook(unittest.TestCase): @classmethod def setUpClass(cls): cls.maxDiff = 8192 cls.test_root = os.path.join(test_root, 'general') cls.test_wsbdir = os.path.join(cls.test_root, WSB_DIR) cls.test_config = os.path.join(cls.test_root, WSB_DIR, 'config.ini') def setUp(self): """Set up a general temp test folder """ try: shutil.rmtree(self.test_root) except NotADirectoryError: os.remove(self.test_root) except FileNotFoundError: pass os.makedirs(self.test_wsbdir) def tearDown(self): """Remove general temp test folder """ try: shutil.rmtree(self.test_root) except NotADirectoryError: os.remove(self.test_root) except FileNotFoundError: pass def create_general_config(self): with open(self.test_config, 'w', encoding='UTF-8') as f: f.write("""[book ""] name = scrapbook top_dir = data_dir = data tree_dir = tree index = tree/map.html no_tree = false """) def test_init01(self): """Check basic""" with open(self.test_config, 'w', encoding='UTF-8') as f: f.write("""[book ""] name = scrapbook top_dir = data_dir = data tree_dir = tree index = tree/map.html no_tree = false """) host = Host(self.test_root) book = Book(host) self.assertEqual(book.host, host) self.assertEqual(book.id, '') self.assertEqual(book.name, 'scrapbook') self.assertEqual(book.root, self.test_root) self.assertEqual(book.top_dir, self.test_root) self.assertEqual(book.data_dir, os.path.join(self.test_root, 'data')) self.assertEqual(book.tree_dir, os.path.join(self.test_root, 'tree')) self.assertFalse(book.no_tree) def test_init02(self): """Check book_id param""" with open(self.test_config, 'w', encoding='UTF-8') as f: f.write("""[book "book1"] name = scrapbook1 top_dir = data_dir = tree_dir = .wsb/tree index = .wsb/tree/map.html no_tree = false """) host = Host(self.test_root) book = Book(host, 'book1') self.assertEqual(book.host, host) self.assertEqual(book.id, 'book1') self.assertEqual(book.name, 'scrapbook1') self.assertEqual(book.root, self.test_root) self.assertEqual(book.top_dir, self.test_root) self.assertEqual(book.data_dir, self.test_root) self.assertEqual(book.tree_dir, os.path.join(self.test_root, '.wsb', 'tree')) self.assertFalse(book.no_tree) def test_init03(self): """Check modified path""" with open(self.test_config, 'w', encoding='UTF-8') as f: f.write("""[app] root = public [book ""] name = scrapbook top_dir = sb data_dir = data tree_dir = tree index = tree/map.html no_tree = false """) host = Host(self.test_root) book = Book(host) self.assertEqual(book.host, host) self.assertEqual(book.id, '') self.assertEqual(book.name, 'scrapbook') self.assertEqual(book.root, self.test_root) self.assertEqual(book.top_dir, os.path.join(self.test_root, 'public', 'sb')) self.assertEqual(book.data_dir, os.path.join(self.test_root, 'public', 'sb', 'data')) self.assertEqual(book.tree_dir, os.path.join(self.test_root, 'public', 'sb', 'tree')) self.assertFalse(book.no_tree) def test_get_subpath(self): self.create_general_config() book = Book(Host(self.test_root)) self.assertEqual(book.get_subpath(os.path.join(self.test_root, 'tree', 'meta.js')), 'tree/meta.js') def test_get_tree_file(self): self.create_general_config() book = Book(Host(self.test_root)) self.assertEqual(book.get_tree_file('meta'), os.path.join(self.test_root, 'tree', 'meta.js')) self.assertEqual(book.get_tree_file('toc', 1), os.path.join(self.test_root, 'tree', 'toc1.js')) def test_iter_tree_files01(self): self.create_general_config() os.makedirs(os.path.join(self.test_root, 'tree')) with open(os.path.join(self.test_root, 'tree', 'meta.js'), 'w', encoding='UTF-8') as f: pass with open(os.path.join(self.test_root, 'tree', 'meta1.js'), 'w', encoding='UTF-8') as f: pass with open(os.path.join(self.test_root, 'tree', 'meta2.js'), 'w', encoding='UTF-8') as f: pass book = Book(Host(self.test_root)) self.assertEqual(list(book.iter_tree_files('meta')), [ os.path.join(self.test_root, 'tree', 'meta.js'), os.path.join(self.test_root, 'tree', 'meta1.js'), os.path.join(self.test_root, 'tree', 'meta2.js'), ]) def test_iter_tree_files02(self): """Break since nonexisting index""" self.create_general_config() os.makedirs(os.path.join(self.test_root, 'tree')) with open(os.path.join(self.test_root, 'tree', 'meta.js'), 'w', encoding='UTF-8') as f: pass with open(os.path.join(self.test_root, 'tree', 'meta1.js'), 'w', encoding='UTF-8') as f: pass with open(os.path.join(self.test_root, 'tree', 'meta3.js'), 'w', encoding='UTF-8') as f: pass book = Book(Host(self.test_root)) self.assertEqual(list(book.iter_tree_files('meta')), [ os.path.join(self.test_root, 'tree', 'meta.js'), os.path.join(self.test_root, 'tree', 'meta1.js'), ]) def test_iter_tree_files03(self): """Works when directory not exist""" book = Book(Host(self.test_root)) self.assertEqual(list(book.iter_tree_files('meta')), []) @mock.patch('webscrapbook.scrapbook.book.Book.iter_tree_files') def test_iter_meta_files(self, mock_func): book = Book(Host(self.test_root)) for i in book.iter_meta_files(): pass mock_func.assert_called_once_with('meta') @mock.patch('webscrapbook.scrapbook.book.Book.iter_tree_files') def test_iter_toc_files(self, mock_func): book = Book(Host(self.test_root)) for i in book.iter_toc_files(): pass mock_func.assert_called_once_with('toc') @mock.patch('webscrapbook.scrapbook.book.Book.iter_tree_files') def test_iter_fulltext_files(self, mock_func): book = Book(Host(self.test_root)) for i in book.iter_fulltext_files(): pass mock_func.assert_called_once_with('fulltext') def test_load_tree_file01(self): """Test normal loading""" self.create_general_config() with open(os.path.join(self.test_root, 'meta.js'), 'w', encoding='UTF-8') as f: f.write("""/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.meta({ "20200101000000000": { "index": "20200101000000000/index.html", "title": "Dummy", "type": "", "create": "20200101000000000", "modify": "20200101000000000" } })""") book = Book(Host(self.test_root)) self.assertEqual( book.load_tree_file(os.path.join(self.test_root, 'meta.js')), { '20200101000000000': { 'index': '20200101000000000/index.html', 'title': 'Dummy', 'type': '', 'create': '20200101000000000', 'modify': '20200101000000000', }, }) def test_load_tree_file02(self): """Test malformed wrapping""" self.create_general_config() with open(os.path.join(self.test_root, 'meta.js'), 'w', encoding='UTF-8') as f: f.write(""" scrapbook.meta({ "20200101000000000": { "index": "20200101000000000/index.html", "title": "Dummy", "type": "", "create": "20200101000000000", "modify": "20200101000000000" } }""") book = Book(Host(self.test_root)) with self.assertRaises(wsb_book.TreeFileMalformedWrappingError): book.load_tree_file(os.path.join(self.test_root, 'meta.js')) def test_load_tree_file03(self): """Test malformed wrapping""" self.create_general_config() with open(os.path.join(self.test_root, 'meta.js'), 'w', encoding='UTF-8') as f: f.write(""" scrapbook.meta{ "20200101000000000": { "index": "20200101000000000/index.html", "title": "Dummy", "type": "", "create": "20200101000000000", "modify": "20200101000000000" } })""") book = Book(Host(self.test_root)) with self.assertRaises(wsb_book.TreeFileMalformedWrappingError): book.load_tree_file(os.path.join(self.test_root, 'meta.js')) def test_load_tree_file04(self): """Test malformed wrapping""" self.create_general_config() with open(os.path.join(self.test_root, 'meta.js'), 'w', encoding='UTF-8') as f: f.write("""({ "20200101000000000": { "index": "20200101000000000/index.html", "title": "Dummy", "type": "", "create": "20200101000000000", "modify": "20200101000000000" } })""") book = Book(Host(self.test_root)) with self.assertRaises(wsb_book.TreeFileMalformedWrappingError): book.load_tree_file(os.path.join(self.test_root, 'meta.js')) def test_load_tree_file05(self): """Test malformed JSON""" self.create_general_config() with open(os.path.join(self.test_root, 'meta.js'), 'w', encoding='UTF-8') as f: f.write(""" scrapbook.meta({ '20200101000000000': { index: '20200101000000000/index.html', title: 'Dummy', type: '', create: '20200101000000000', modify: '20200101000000000' } })""") book = Book(Host(self.test_root)) with self.assertRaises(wsb_book.TreeFileMalformedJsonError): book.load_tree_file(os.path.join(self.test_root, 'meta.js')) def test_load_tree_file06(self): """Test empty file should not error out.""" self.create_general_config() with open(os.path.join(self.test_root, 'meta.js'), 'w', encoding='UTF-8') as f: f.write('') book = Book(Host(self.test_root)) self.assertEqual(book.load_tree_file(os.path.join(self.test_root, 'meta.js')), {}) def test_load_tree_files01(self): """Test normal loading - Item of same ID from the latter overwrites the formatter. - Item with None value should be removed. """ self.create_general_config() os.makedirs(os.path.join(self.test_root, 'tree')) with open(os.path.join(self.test_root, 'tree', 'meta.js'), 'w', encoding='UTF-8') as f: f.write("""/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.meta({ "20200101000000000": { "index": "20200101000000000/index.html", "title": "Dummy", "type": "", "create": "20200101000000000", "modify": "20200101000000000", "comment": "comment" }, "20200101000000001": { "index": "20200101000000001/index.html", "title": "Dummy1", "type": "", "create": "20200101000000001", "modify": "20200101000000001", "comment": "comment1" }, "20200101000000002": { "index": "20200101000000002/index.html", "title": "Dummy2", "type": "", "create": "20200101000000002", "modify": "20200101000000002", "comment": "comment2" } })""") with open(os.path.join(self.test_root, 'tree', 'meta1.js'), 'w', encoding='UTF-8') as f: f.write("""/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.meta({ "20200101000000001": { "index": "20200101000000001/index.html", "title": "Dummy1rev", "type": "", "create": "20200101000000001", "modify": "20200101000000011" }, "20200101000000002": null, "20200101000000003": { "index": "20200101000000003/index.html", "title": "Dummy3", "type": "", "create": "20200101000000003", "modify": "20200101000000003", "comment": "comment3" } })""") book = Book(Host(self.test_root)) self.assertEqual(book.load_tree_files('meta'), { '20200101000000000': { 'index': '20200101000000000/index.html', 'title': 'Dummy', 'type': '', 'create': '20200101000000000', 'modify': '20200101000000000', 'comment': 'comment', }, '20200101000000001': { 'index': '20200101000000001/index.html', 'title': 'Dummy1rev', 'type': '', 'create': '20200101000000001', 'modify': '20200101000000011', }, '20200101000000003': { 'index': '20200101000000003/index.html', 'title': 'Dummy3', 'type': '', 'create': '20200101000000003', 'modify': '20200101000000003', 'comment': 'comment3', }, }) def test_load_tree_files02(self): """Works when directory not exist""" book = Book(Host(self.test_root)) self.assertEqual(book.load_tree_files('meta'), {}) @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_meta_files01(self, mock_func): book = Book(Host(self.test_root)) book.load_meta_files() mock_func.assert_called_once_with('meta') @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_meta_files02(self, mock_func): book = Book(Host(self.test_root)) book.meta = {} book.load_meta_files() mock_func.assert_not_called() @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_meta_files03(self, mock_func): book = Book(Host(self.test_root)) book.meta = {} book.load_meta_files(refresh=True) mock_func.assert_called_once_with('meta') @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_toc_files01(self, mock_func): book = Book(Host(self.test_root)) book.load_toc_files() mock_func.assert_called_once_with('toc') @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_toc_files02(self, mock_func): book = Book(Host(self.test_root)) book.toc = {} book.load_toc_files() mock_func.assert_not_called() @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_toc_files03(self, mock_func): book = Book(Host(self.test_root)) book.toc = {} book.load_toc_files(refresh=True) mock_func.assert_called_once_with('toc') @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_fulltext_files01(self, mock_func): book = Book(Host(self.test_root)) book.load_fulltext_files() mock_func.assert_called_once_with('fulltext') @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_fulltext_files02(self, mock_func): book = Book(Host(self.test_root)) book.fulltext = {} book.load_fulltext_files() mock_func.assert_not_called() @mock.patch('webscrapbook.scrapbook.book.Book.load_tree_files') def test_load_fulltext_files03(self, mock_func): book = Book(Host(self.test_root)) book.fulltext = {} book.load_fulltext_files(refresh=True) mock_func.assert_called_once_with('fulltext') def test_save_meta_files01(self): self.create_general_config() book = Book(Host(self.test_root)) book.meta = { '20200101000000000': {'title': 'Dummy 1 中文'}, '20200101000000001': {'title': 'Dummy 2 中文'}, } book.save_meta_files() with open(os.path.join(self.test_root, 'tree', 'meta.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.meta({ "20200101000000000": { "title": "Dummy 1 中文" }, "20200101000000001": { "title": "Dummy 2 中文" } })""") @mock.patch('webscrapbook.scrapbook.book.Book.SAVE_META_THRESHOLD', 3) def test_save_meta_files02(self): self.create_general_config() book = Book(Host(self.test_root)) book.meta = { '20200101000000000': {'title': 'Dummy 1 中文'}, '20200101000000001': {'title': 'Dummy 2 中文'}, '20200101000000002': {'title': 'Dummy 3 中文'}, '20200101000000003': {'title': 'Dummy 4 中文'}, } book.save_meta_files() with open(os.path.join(self.test_root, 'tree', 'meta.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.meta({ "20200101000000000": { "title": "Dummy 1 中文" }, "20200101000000001": { "title": "Dummy 2 中文" } })""") with open(os.path.join(self.test_root, 'tree', 'meta1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.meta({ "20200101000000002": { "title": "Dummy 3 中文" }, "20200101000000003": { "title": "Dummy 4 中文" } })""") def test_save_meta_files03(self): self.create_general_config() os.makedirs(os.path.join(self.test_root, 'tree')) with open(os.path.join(self.test_root, 'tree', 'meta.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy') with open(os.path.join(self.test_root, 'tree', 'meta1.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy1') with open(os.path.join(self.test_root, 'tree', 'meta2.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy2') with open(os.path.join(self.test_root, 'tree', 'meta3.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy3') book = Book(Host(self.test_root)) book.meta = { '20200101000000000': {'title': 'Dummy 1 中文'}, '20200101000000001': {'title': 'Dummy 2 中文'}, } book.save_meta_files() with open(os.path.join(self.test_root, 'tree', 'meta.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.meta({ "20200101000000000": { "title": "Dummy 1 中文" }, "20200101000000001": { "title": "Dummy 2 中文" } })""") self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'meta1.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'meta2.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'meta3.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'meta4.js'))) def test_save_meta_files04(self): """Check if U+2028 and U+2029 are escaped in the embedded JSON.""" self.create_general_config() book = Book(Host(self.test_root)) book.meta = { '20200101\u2028000000000': {'title\u20281': 'Dummy 1\u2028中文'}, '20200101\u2029000000001': {'title\u20292': 'Dummy 2\u2029中文'}, } book.save_meta_files() with open(os.path.join(self.test_root, 'tree', 'meta.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), r"""/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.meta({ "20200101\u2028000000000": { "title\u20281": "Dummy 1\u2028中文" }, "20200101\u2029000000001": { "title\u20292": "Dummy 2\u2029中文" } })""") def test_save_toc_files01(self): self.create_general_config() book = Book(Host(self.test_root)) book.toc = { 'root': [ '20200101000000000', '20200101000000001', '20200101000000002', ], '20200101000000000': [ '20200101000000003' ] } book.save_toc_files() with open(os.path.join(self.test_root, 'tree', 'toc.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.toc({ "root": [ "20200101000000000", "20200101000000001", "20200101000000002" ], "20200101000000000": [ "20200101000000003" ] })""") @mock.patch('webscrapbook.scrapbook.book.Book.SAVE_TOC_THRESHOLD', 3) def test_save_toc_files02(self): self.create_general_config() book = Book(Host(self.test_root)) book.toc = { 'root': [ '20200101000000000', '20200101000000001', '20200101000000002', '20200101000000003', '20200101000000004', ], '20200101000000001': [ '20200101000000011' ], '20200101000000002': [ '20200101000000021' ], '20200101000000003': [ '20200101000000031', '20200101000000032' ], } book.save_toc_files() with open(os.path.join(self.test_root, 'tree', 'toc.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.toc({ "root": [ "20200101000000000", "20200101000000001", "20200101000000002", "20200101000000003", "20200101000000004" ] })""") with open(os.path.join(self.test_root, 'tree', 'toc1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.toc({ "20200101000000001": [ "20200101000000011" ], "20200101000000002": [ "20200101000000021" ] })""") with open(os.path.join(self.test_root, 'tree', 'toc2.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.toc({ "20200101000000003": [ "20200101000000031", "20200101000000032" ] })""") def test_save_toc_files03(self): self.create_general_config() os.makedirs(os.path.join(self.test_root, 'tree')) with open(os.path.join(self.test_root, 'tree', 'toc.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy') with open(os.path.join(self.test_root, 'tree', 'toc1.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy1') with open(os.path.join(self.test_root, 'tree', 'toc2.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy2') with open(os.path.join(self.test_root, 'tree', 'toc4.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy4') book = Book(Host(self.test_root)) book.toc = { 'root': [ '20200101000000000', '20200101000000001', '20200101000000002', ], '20200101000000000': [ '20200101000000003' ] } book.save_toc_files() with open(os.path.join(self.test_root, 'tree', 'toc.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.toc({ "root": [ "20200101000000000", "20200101000000001", "20200101000000002" ], "20200101000000000": [ "20200101000000003" ] })""") self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'toc1.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'toc2.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'toc3.js'))) self.assertTrue(os.path.exists(os.path.join(self.test_root, 'tree', 'toc4.js'))) def test_save_toc_files04(self): """Check if U+2028 and U+2029 are escaped in the embedded JSON.""" self.create_general_config() book = Book(Host(self.test_root)) book.toc = { 'root': [ '20200101\u2028000000000', '20200101\u2029000000001', ], '20200101\u2028000000000': [ '20200101\u2029000000003' ] } book.save_toc_files() with open(os.path.join(self.test_root, 'tree', 'toc.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), r"""/** * Feel free to edit this file, but keep data code valid JSON format. */ scrapbook.toc({ "root": [ "20200101\u2028000000000", "20200101\u2029000000001" ], "20200101\u2028000000000": [ "20200101\u2029000000003" ] })""") def test_save_fulltext_files01(self): self.create_general_config() book = Book(Host(self.test_root)) book.fulltext = { "20200101000000000": { 'index.html': { 'content': 'dummy text 1 中文', } }, "20200101000000001": { 'index.html': { 'content': 'dummy text 2 中文', } }, } book.save_fulltext_files() with open(os.path.join(self.test_root, 'tree', 'fulltext.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101000000000": { "index.html": { "content": "dummy text 1 中文" } }, "20200101000000001": { "index.html": { "content": "dummy text 2 中文" } } })""") @mock.patch('webscrapbook.scrapbook.book.Book.SAVE_FULLTEXT_THRESHOLD', 10) def test_save_fulltext_files02(self): self.create_general_config() book = Book(Host(self.test_root)) book.fulltext = { "20200101000000000": { 'index.html': { 'content': 'dummy text 1 中文', }, 'frame.html': { 'content': 'frame page content', }, }, "20200101000000001": { 'index.html': { 'content': 'dummy text 2 中文', }, }, "20200101000000002": { 'index.html': { 'content': 'dummy text 3 中文', }, }, } book.save_fulltext_files() with open(os.path.join(self.test_root, 'tree', 'fulltext.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101000000000": { "index.html": { "content": "dummy text 1 中文" }, "frame.html": { "content": "frame page content" } } })""") with open(os.path.join(self.test_root, 'tree', 'fulltext1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101000000001": { "index.html": { "content": "dummy text 2 中文" } } })""") with open(os.path.join(self.test_root, 'tree', 'fulltext2.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101000000002": { "index.html": { "content": "dummy text 3 中文" } } })""") @mock.patch('webscrapbook.scrapbook.book.Book.SAVE_FULLTEXT_THRESHOLD', 10) def test_save_fulltext_files03(self): self.create_general_config() os.makedirs(os.path.join(self.test_root, 'tree')) with open(os.path.join(self.test_root, 'tree', 'fulltext.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy') with open(os.path.join(self.test_root, 'tree', 'fulltext1.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy1') with open(os.path.join(self.test_root, 'tree', 'fulltext2.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy2') with open(os.path.join(self.test_root, 'tree', 'fulltext3.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy3') with open(os.path.join(self.test_root, 'tree', 'fulltext4.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy4') with open(os.path.join(self.test_root, 'tree', 'fulltext6.js'), 'w', encoding='UTF-8') as fh: fh.write('dummy6') book = Book(Host(self.test_root)) book.fulltext = { "20200101000000000": { 'index.html': { 'content': 'dummy text 1 中文', }, 'frame.html': { 'content': 'frame page content', }, }, "20200101000000001": { 'index.html': { 'content': 'dummy text 2 中文', }, }, } book.save_fulltext_files() with open(os.path.join(self.test_root, 'tree', 'fulltext.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101000000000": { "index.html": { "content": "dummy text 1 中文" }, "frame.html": { "content": "frame page content" } } })""") with open(os.path.join(self.test_root, 'tree', 'fulltext1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), """/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101000000001": { "index.html": { "content": "dummy text 2 中文" } } })""") self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'fulltext2.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'fulltext3.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'fulltext4.js'))) self.assertFalse(os.path.exists(os.path.join(self.test_root, 'tree', 'fulltext5.js'))) self.assertTrue(os.path.exists(os.path.join(self.test_root, 'tree', 'fulltext6.js'))) def test_save_fulltext_files04(self): """Check if U+2028 and U+2029 are escaped in the embedded JSON.""" self.create_general_config() book = Book(Host(self.test_root)) book.fulltext = { "20200101\u2028000000000": { 'index.html': { 'content': 'dummy text 1 中文', } }, "20200101\u2029000000001": { 'index.html': { 'content': 'dummy text 2 中文', } }, } book.save_fulltext_files() with open(os.path.join(self.test_root, 'tree', 'fulltext.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), r"""/** * This file is generated by WebScrapBook and is not intended to be edited. */ scrapbook.fulltext({ "20200101\u2028000000000": { "index.html": { "content": "dummy text 1 中文" } }, "20200101\u2029000000001": { "index.html": { "content": "dummy text 2 中文" } } })""") @mock.patch('webscrapbook.scrapbook.host.Host.auto_backup') def test_backup(self, mock_func): test_file = os.path.join(self.test_root, 'tree', 'meta.js') host = Host(self.test_root) book = Book(host) book.backup(test_file) mock_func.assert_called_with(test_file) book.backup(test_file, base=self.test_wsbdir, move=False) mock_func.assert_called_with(test_file, base=self.test_wsbdir, move=False) @mock.patch('webscrapbook.scrapbook.host.FileLock') def test_get_lock01(self, mock_filelock): self.create_general_config() host = Host(self.test_root) book = Book(host) book.get_lock('test') mock_filelock.assert_called_once_with(host, 'book--test') @mock.patch('webscrapbook.scrapbook.host.FileLock') def test_get_lock02(self, mock_filelock): """With parameters""" self.create_general_config() host = Host(self.test_root) book = Book(host) book.get_lock('test', timeout=10, stale=120, poll_interval=0.3, assume_acquired=True) mock_filelock.assert_called_once_with(host, 'book--test', timeout=10, stale=120, poll_interval=0.3, assume_acquired=True) @mock.patch('webscrapbook.scrapbook.book.Book.get_lock') def test_get_tree_lock01(self, mock_get_lock): self.create_general_config() host = Host(self.test_root) book = Book(host) book.get_tree_lock() mock_get_lock.assert_called_once_with('tree') @mock.patch('webscrapbook.scrapbook.book.Book.get_lock') def test_get_tree_lock02(self, mock_get_lock): """With parameters""" self.create_general_config() host = Host(self.test_root) book = Book(host) book.get_tree_lock(timeout=10, stale=120, poll_interval=0.3, assume_acquired=True) mock_get_lock.assert_called_once_with('tree', timeout=10, stale=120, poll_interval=0.3, assume_acquired=True) def test_get_index_paths01(self): self.create_general_config() book = Book(Host(self.test_root)) self.assertEqual(book.get_index_paths('20200101000000000/index.html'), ['index.html']) self.assertEqual(book.get_index_paths('20200101000000000.html'), ['20200101000000000.html']) self.assertEqual(book.get_index_paths('20200101000000000.htz'), ['index.html']) def test_get_index_paths02(self): """MAFF with single page""" self.create_general_config() os.makedirs(os.path.join(self.test_root, 'data')) archive_file = os.path.join(self.test_root, 'data', '20200101000000000.maff') with zipfile.ZipFile(archive_file, 'w') as zh: zh.writestr('20200101000000000/index.html', """dummy""") book = Book(Host(self.test_root)) self.assertEqual(book.get_index_paths('20200101000000000.maff'), ['20200101000000000/index.html']) def test_get_index_paths03(self): """MAFF with multiple pages""" self.create_general_config() os.makedirs(os.path.join(self.test_root, 'data')) archive_file = os.path.join(self.test_root, 'data', '20200101000000000.maff') with zipfile.ZipFile(archive_file, 'w') as zh: zh.writestr('20200101000000000/index.html', """dummy""") zh.writestr('20200101000000001/index.html', """dummy""") book = Book(Host(self.test_root)) self.assertEqual(book.get_index_paths('20200101000000000.maff'), ['20200101000000000/index.html', '20200101000000001/index.html']) def test_get_index_paths04(self): """MAFF with no page""" self.create_general_config() os.makedirs(os.path.join(self.test_root, 'data')) archive_file = os.path.join(self.test_root, 'data', '20200101000000000.maff') with zipfile.ZipFile(archive_file, 'w') as zh: pass book = Book(Host(self.test_root)) self.assertEqual(book.get_index_paths('20200101000000000.maff'), []) def test_get_icon_file01(self): """Pass if file not exist.""" book = Book(Host(self.test_root)) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': 'http://example.com', })) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': 'data:image/bmp;base64,Qk08AAAAAAAAADYAAAAoAAAAAQAAAAEAAAABACAAAAAAAAYAAAASCwAAEgsAAAAAAAAAAAAAAP8AAAAA', })) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': '//example.com', })) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': '/favicon.ico', })) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': '', })) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': '?id=123', })) self.assertIsNone(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': '#test', })) self.assertEqual(book.get_icon_file({ 'icon': 'favicon.ico?id=123#test', }), os.path.join(book.data_dir, 'favicon.ico'), ) self.assertEqual(book.get_icon_file({ 'icon': '%E4%B8%AD%E6%96%87%231.ico?id=123#test', }), os.path.join(book.data_dir, '中文#1.ico'), ) self.assertEqual(book.get_icon_file({ 'index': '20200101000000000/index.html', 'icon': 'favicon.ico?id=123#test', }), os.path.join(book.data_dir, '20200101000000000', 'favicon.ico'), ) self.assertEqual(book.get_icon_file({ 'index': '20200101000000000.html', 'icon': 'favicon.ico?id=123#test', }), os.path.join(book.data_dir, 'favicon.ico'), ) self.assertEqual(book.get_icon_file({ 'index': '20200101000000000.maff', 'icon': 'favicon.ico?id=123#test', }), os.path.join(book.data_dir, 'favicon.ico'), ) self.assertEqual(book.get_icon_file({ 'index': '20200101000000000.maff', 'icon': '.wsb/tree/favicon/dbc82be549e49d6db9a5719086722a4f1c5079cd.bmp?id=123#test', }), os.path.join(book.tree_dir, 'favicon', 'dbc82be549e49d6db9a5719086722a4f1c5079cd.bmp'), ) def test_load_note_file01(self): """Test for common note file wrapper.""" test_file = os.path.join(self.test_root, 'index.html') with open(test_file, 'w', encoding='UTF-8') as f: f.write("""\ <!DOCTYPE html><html><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width"><style>pre{white-space: pre-wrap; overflow-wrap: break-word;}</style></head><body><pre> Note content 2nd line 3rd line </pre></body></html>""") book = Book(Host(self.test_root)) content = book.load_note_file(test_file) self.assertEqual(content, """\ Note content 2nd line 3rd line""") def test_load_note_file02(self): """Test for common legacy note file wrapper.""" test_file = os.path.join(self.test_root, 'index.html') with open(test_file, 'w', encoding='UTF-8') as f: f.write("""\ <html><head><meta http-equiv="Content-Type" content="text/html;Charset=UTF-8"></head><body><pre> Note content 2nd line 3rd line </pre></body></html>""") book = Book(Host(self.test_root)) content = book.load_note_file(test_file) self.assertEqual(content, """\ Note content 2nd line 3rd line""") def test_load_note_file03(self): """Return original text if malformatted.""" test_file = os.path.join(self.test_root, 'index.html') html = """\ <html><head><meta http-equiv="Content-Type" content="text/html;Charset=UTF-8"></head><body> Note content 2nd line 3rd line </body></html>""" with open(test_file, 'w', encoding='UTF-8') as f: f.write(html) book = Book(Host(self.test_root)) content = book.load_note_file(test_file) self.assertEqual(content, html) def test_save_note_file01(self): """Test saving. Enforce LF linefeeds.""" test_file = os.path.join(self.test_root, 'index.html') book = Book(Host(self.test_root)) book.save_note_file(test_file, """\ Note content 2nd line 3rd line""") with open(test_file, encoding='UTF-8', newline='') as fh: self.assertEqual(fh.read(), """\ <!DOCTYPE html><html><head>\ <meta charset="UTF-8">\ <meta name="viewport" content="width=device-width">\ <style>pre { white-space: pre-wrap; overflow-wrap: break-word; }</style>\ </head><body><pre> Note content 2nd line 3rd line </pre></body></html>""") def test_auto_backup(self): """Auto backup tree files if backup_dir is set.""" test_dir = os.path.join(self.test_root, WSB_DIR, 'tree') os.makedirs(test_dir) meta0 = """ scrapbook.meta({ "20200101000000000": { "index": "20200101000000000/index.html", "title": "Dummy", "type": "", "create": "20200101000000000", "modify": "20200101000000000" } })""" meta1 = """ scrapbook.meta({ "20200101000000001": { "index": "20200101000000001/index.html", "title": "Dummy", "type": "", "create": "20200101000000001", "modify": "20200101000000001" } })""" toc0 = """ scrapbook.toc({ "root": [ "20200101000000000", "20200101000000001", "20200101000000002" ], "20200101000000000": [ "20200101000000003" ] })""" toc1 = """ scrapbook.toc({ "20200101000000001": [ "20200101000000004" ] })""" fulltext0 = """ scrapbook.fulltext({ "20200101000000000": { "index.html": { "content": "dummy text 1 中文" } }, "20200101000000001": { "index.html": { "content": "dummy text 2 中文" } } })""" fulltext1 = """ scrapbook.fulltext({ "20200101000000002": { "index.html": { "content": "dummy text 2 中文" } } })""" with open(os.path.join(test_dir, 'meta.js'), 'w', encoding='UTF-8') as fh: fh.write(meta0) with open(os.path.join(test_dir, 'meta1.js'), 'w', encoding='UTF-8') as fh: fh.write(meta1) with open(os.path.join(test_dir, 'toc.js'), 'w', encoding='UTF-8') as fh: fh.write(toc0) with open(os.path.join(test_dir, 'toc1.js'), 'w', encoding='UTF-8') as fh: fh.write(toc1) with open(os.path.join(test_dir, 'fulltext.js'), 'w', encoding='UTF-8') as fh: fh.write(fulltext0) with open(os.path.join(test_dir, 'fulltext1.js'), 'w', encoding='UTF-8') as fh: fh.write(fulltext1) host = Host(self.test_root) book = Book(host) host.init_backup() book.load_meta_files() book.load_toc_files() book.load_fulltext_files() book.save_meta_files() book.save_toc_files() book.save_fulltext_files() with open(os.path.join(host._backup_dir, WSB_DIR, 'tree', 'meta.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), meta0) with open(os.path.join(host._backup_dir, WSB_DIR, 'tree', 'meta1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), meta1) with open(os.path.join(host._backup_dir, WSB_DIR, 'tree', 'toc.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), toc0) with open(os.path.join(host._backup_dir, WSB_DIR, 'tree', 'toc1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), toc1) with open(os.path.join(host._backup_dir, WSB_DIR, 'tree', 'fulltext.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), fulltext0) with open(os.path.join(host._backup_dir, WSB_DIR, 'tree', 'fulltext1.js'), encoding='UTF-8') as fh: self.assertEqual(fh.read(), fulltext1) if __name__ == '__main__': unittest.main()

33.893585

187

0.592242

5,337

44,909

4.8368

0.061458

0.055474

0.075308

0.052607

0.884481

0.87131

0.861548

0.843844

0.815991

0.779383

0

0.11438

0.252243

44,909

1,324

188

33.919184

0.654328

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0.632264

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0.100607

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0.052905

false

0.010408

0.012142

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0.065915

0

null

0

1

0

null

0

6

bfeb485a221c30bddf15245bcce0968a19e46f2c

2,076

py

Python

data/price_history_data/convert_TREB_data_format.py

Johnny-Fung/toronto-housing-market-map-visualization

06d2e0b2ef521c883546c364c4b14236e01c5d51

[ "MIT" ]

1

2020-12-24T00:16:13.000Z

data/price_history_data/convert_TREB_data_format.py

Johnny-Fung/toronto-housing-market-map-visualization

06d2e0b2ef521c883546c364c4b14236e01c5d51

[ "MIT" ]

null

data/price_history_data/convert_TREB_data_format.py

Johnny-Fung/toronto-housing-market-map-visualization

06d2e0b2ef521c883546c364c4b14236e01c5d51

[ "MIT" ]

null

price_history_2015="$413,268 $371,473 $336,311 $239,010 $227,809 $398,996 $403,729 $353,405 $220,980 $203,186 $459,297 $461,085 $1,078,036 $551,382 $503,344 $364,826 $399,342 $430,636 $644,972 $554,170 $275,601 $849,539 $396,181 $410,221 $381,013 $272,015 $457,796 $472,744 $229,586 $217,508 $287,573 $284,288 $266,932 $223,534 $273,988 $195,500 $211,738" price_history_2016="$439,648 $438,538 $391,078 $308,862 $261,944 $438,473 $456,983 $384,248 $303,172 $233,132 $487,963 $501,458 $955,023 $609,719 $533,115 $378,942 $439,497 $483,833 $667,246 $533,638 $314,875 $856,814 $372,426 $435,610 $393,582 $307,949 $536,336 $521,210 $266,057 $246,844 $341,592 $294,003 $302,657 $275,024 $302,408 $183,929 $231,798" price_history_2017="$523,992 $539,020 $466,527 $365,449 $282,638 $516,201 $535,188 $443,103 $326,041 $305,194 $603,524 $614,867 $1,004,927 $665,622 $709,867 $438,652 $568,002 $597,910 $977,375 $638,565 $403,536 $1,382,429 $429,959 $533,156 $475,790 $392,071 $628,122 $609,705 $326,371 $331,098 $416,961 $468,983 $381,355 $324,884 $387,423 $225,500 $317,545" price_history_2018="$583,007 $643,642 $486,300 $413,764 $342,793 $595,349 $519,710 $501,311 $392,586 $331,714 $667,328 $684,002 $1,146,225 $760,806 $762,995 $470,152 $558,430 $673,248 $1,265,141 $704,492 $442,576 $975,553 $522,466 $567,718 $528,943 $413,818 $688,117 $556,879 $374,662 $358,357 $434,249 $500,357 $400,839 $373,187 $411,051 $274,929 $351,553" price_history_2019="$693,186 $668,225 $522,132 $456,438 $407,395 $615,858 $782,350 $545,753 $429,451 $387,794 $699,362 $713,678 $1,193,506 $961,755 $749,073 $540,111 $617,363 $712,111 $1,107,117 $706,420 $456,621 $910,883 $576,679 $627,852 $548,236 $459,159 $780,758 $660,538 $450,524 $404,360 $460,422 $577,957 $429,925 $415,568 $448,522 $325,223 $372,163" print("2015") print(price_history_2015.replace(" ","\n")) print("2016") print(price_history_2016.replace(" ","\n")) print("2017") print(price_history_2017.replace(" ","\n")) print("2018") print(price_history_2018.replace(" ","\n")) print("2019") print(price_history_2019.replace(" ","\n"))

115.333333

357

0.677746

432

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3.210648

0.726852

0.086518

0.061283

0

0.628068

0.097303

2,076

17

358

122.117647

0.11206

0

0.333333

0.823218

0

1

0

false

0

0.666667

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null

0

1

0

1

0

1

0

null

0

1

0

6

8729664ecd521faffb856bfed61361048469383f

66

py

Python

boards/microbit/main.py

nagexiucai/howtoiot

f97e13d7e1bcc41eee68ae3e1357acaa344091d1

[ "Apache-2.0" ]

null

boards/microbit/main.py

nagexiucai/howtoiot

f97e13d7e1bcc41eee68ae3e1357acaa344091d1

[ "Apache-2.0" ]

null

boards/microbit/main.py

nagexiucai/howtoiot

f97e13d7e1bcc41eee68ae3e1357acaa344091d1

[ "Apache-2.0" ]

null

import microbit print(dir(microbit)) # Write your code here :-)

11

26

0.712121

9

66

5.222222

0.888889

0

0.166667

66

5

27

13.2

0.854545

0.363636

0

0.2

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

1

0

6

87596da149f998da9290b500c634052db63b5f53

78

py

Python

novelsave/utils/adapters/__init__.py

mHaisham/novelsave

011b6c5d705591783aee64662bc88b207bdc7205

[ "Apache-2.0" ]

15

2020-11-05T10:05:01.000Z

2021-06-28T14:43:56.000Z

novelsave/utils/adapters/__init__.py

mHaisham/novelsave

011b6c5d705591783aee64662bc88b207bdc7205

[ "Apache-2.0" ]

21

2020-11-01T04:36:56.000Z

2021-08-16T09:36:48.000Z

novelsave/utils/adapters/__init__.py

mHaisham/novelsave

011b6c5d705591783aee64662bc88b207bdc7205

[ "Apache-2.0" ]

null

from .source_adapter import SourceAdapter from .dto_adapter import DTOAdapter

26

41

0.871795

10

78

6.6

0.7

0.393939

0

0.102564

78

2

42

39

0.942857

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

5e40d145c5ecf9aaf3f31b95dead440fa8e1c2a2

37,101

py

Python

sympy/parsing/latex/_antlr/latexlexer.py

qcgm1978/sympy

cc46047f4449b525b7b0edd4c634bf93d6e7c83d

[ "BSD-3-Clause" ]

2

2021-01-09T23:11:25.000Z

2021-01-11T15:04:22.000Z

sympy/parsing/latex/_antlr/latexlexer.py

qcgm1978/sympy

cc46047f4449b525b7b0edd4c634bf93d6e7c83d

[ "BSD-3-Clause" ]

3

2021-02-28T03:58:40.000Z

2021-03-07T06:12:47.000Z

sympy/parsing/latex/_antlr/latexlexer.py

qcgm1978/sympy

cc46047f4449b525b7b0edd4c634bf93d6e7c83d

[ "BSD-3-Clause" ]

2

2021-01-08T23:03:23.000Z

2021-01-13T18:57:02.000Z

# encoding: utf-8 # *** GENERATED BY `setup.py antlr`, DO NOT EDIT BY HAND *** # # Generated from ../LaTeX.g4, derived from latex2sympy # latex2sympy is licensed under the MIT license # https://github.com/augustt198/latex2sympy/blob/master/LICENSE.txt # # Generated with antlr4 # antlr4 is licensed under the BSD-3-Clause License # https://github.com/antlr/antlr4/blob/master/LICENSE.txt from __future__ import print_function from antlr4 import * from io import StringIO import sys def serializedATN(): with StringIO() as buf: buf.write(u"\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2") buf.write(u"Y\u0384\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4") buf.write(u"\7\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r") buf.write(u"\t\r\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22") buf.write(u"\4\23\t\23\4\24\t\24\4\25\t\25\4\26\t\26\4\27\t\27\4") buf.write(u"\30\t\30\4\31\t\31\4\32\t\32\4\33\t\33\4\34\t\34\4\35") buf.write(u"\t\35\4\36\t\36\4\37\t\37\4 \t \4!\t!\4\"\t\"\4#\t#\4") 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buf.write(u"\b\t\2\2\u0111\22\3\2\2\2\u0112\u0113\7^\2\2\u0113\u0114") buf.write(u"\7p\2\2\u0114\u0115\7g\2\2\u0115\u0116\7i\2\2\u0116\u0117") buf.write(u"\7o\2\2\u0117\u0118\7g\2\2\u0118\u0119\7f\2\2\u0119\u011a") buf.write(u"\7u\2\2\u011a\u011b\7r\2\2\u011b\u011c\7c\2\2\u011c\u011d") buf.write(u"\7e\2\2\u011d\u011e\7g\2\2\u011e\u011f\3\2\2\2\u011f") buf.write(u"\u0120\b\n\2\2\u0120\24\3\2\2\2\u0121\u0122\7^\2\2\u0122") buf.write(u"\u0123\7p\2\2\u0123\u0124\7g\2\2\u0124\u0125\7i\2\2\u0125") buf.write(u"\u0126\7v\2\2\u0126\u0127\7j\2\2\u0127\u0128\7k\2\2\u0128") buf.write(u"\u0129\7e\2\2\u0129\u012a\7m\2\2\u012a\u012b\7u\2\2\u012b") buf.write(u"\u012c\7r\2\2\u012c\u012d\7c\2\2\u012d\u012e\7e\2\2\u012e") buf.write(u"\u012f\7g\2\2\u012f\u0130\3\2\2\2\u0130\u0131\b\13\2") buf.write(u"\2\u0131\26\3\2\2\2\u0132\u0133\7^\2\2\u0133\u0134\7") buf.write(u"n\2\2\u0134\u0135\7g\2\2\u0135\u0136\7h\2\2\u0136\u0137") buf.write(u"\7v\2\2\u0137\u0138\3\2\2\2\u0138\u0139\b\f\2\2\u0139") buf.write(u"\30\3\2\2\2\u013a\u013b\7^\2\2\u013b\u013c\7t\2\2\u013c") buf.write(u"\u013d\7k\2\2\u013d\u013e\7i\2\2\u013e\u013f\7j\2\2\u013f") buf.write(u"\u0140\7v\2\2\u0140\u0141\3\2\2\2\u0141\u0142\b\r\2\2") buf.write(u"\u0142\32\3\2\2\2\u0143\u0144\7^\2\2\u0144\u0145\7x\2") buf.write(u"\2\u0145\u0146\7t\2\2\u0146\u0147\7w\2\2\u0147\u0148") buf.write(u"\7n\2\2\u0148\u0177\7g\2\2\u0149\u014a\7^\2\2\u014a\u014b") buf.write(u"\7x\2\2\u014b\u014c\7e\2\2\u014c\u014d\7g\2\2\u014d\u014e") buf.write(u"\7p\2\2\u014e\u014f\7v\2\2\u014f\u0150\7g\2\2\u0150\u0177") buf.write(u"\7t\2\2\u0151\u0152\7^\2\2\u0152\u0153\7x\2\2\u0153\u0154") buf.write(u"\7d\2\2\u0154\u0155\7q\2\2\u0155\u0177\7z\2\2\u0156\u0157") buf.write(u"\7^\2\2\u0157\u0158\7x\2\2\u0158\u0159\7u\2\2\u0159\u015a") buf.write(u"\7m\2\2\u015a\u015b\7k\2\2\u015b\u0177\7r\2\2\u015c\u015d") buf.write(u"\7^\2\2\u015d\u015e\7x\2\2\u015e\u015f\7u\2\2\u015f\u0160") buf.write(u"\7r\2\2\u0160\u0161\7c\2\2\u0161\u0162\7e\2\2\u0162\u0177") 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buf.write(u"\7v\2\2\u01f7D\3\2\2\2\u01f8\u01f9\7^\2\2\u01f9\u01fa") buf.write(u"\7u\2\2\u01fa\u01fb\7w\2\2\u01fb\u01fc\7o\2\2\u01fcF") buf.write(u"\3\2\2\2\u01fd\u01fe\7^\2\2\u01fe\u01ff\7r\2\2\u01ff") buf.write(u"\u0200\7t\2\2\u0200\u0201\7q\2\2\u0201\u0202\7f\2\2\u0202") buf.write(u"H\3\2\2\2\u0203\u0204\7^\2\2\u0204\u0205\7g\2\2\u0205") buf.write(u"\u0206\7z\2\2\u0206\u0207\7r\2\2\u0207J\3\2\2\2\u0208") buf.write(u"\u0209\7^\2\2\u0209\u020a\7n\2\2\u020a\u020b\7q\2\2\u020b") buf.write(u"\u020c\7i\2\2\u020cL\3\2\2\2\u020d\u020e\7^\2\2\u020e") buf.write(u"\u020f\7n\2\2\u020f\u0210\7p\2\2\u0210N\3\2\2\2\u0211") buf.write(u"\u0212\7^\2\2\u0212\u0213\7u\2\2\u0213\u0214\7k\2\2\u0214") buf.write(u"\u0215\7p\2\2\u0215P\3\2\2\2\u0216\u0217\7^\2\2\u0217") buf.write(u"\u0218\7e\2\2\u0218\u0219\7q\2\2\u0219\u021a\7u\2\2\u021a") buf.write(u"R\3\2\2\2\u021b\u021c\7^\2\2\u021c\u021d\7v\2\2\u021d") buf.write(u"\u021e\7c\2\2\u021e\u021f\7p\2\2\u021fT\3\2\2\2\u0220") buf.write(u"\u0221\7^\2\2\u0221\u0222\7e\2\2\u0222\u0223\7u\2\2\u0223") buf.write(u"\u0224\7e\2\2\u0224V\3\2\2\2\u0225\u0226\7^\2\2\u0226") buf.write(u"\u0227\7u\2\2\u0227\u0228\7g\2\2\u0228\u0229\7e\2\2\u0229") buf.write(u"X\3\2\2\2\u022a\u022b\7^\2\2\u022b\u022c\7e\2\2\u022c") buf.write(u"\u022d\7q\2\2\u022d\u022e\7v\2\2\u022eZ\3\2\2\2\u022f") buf.write(u"\u0230\7^\2\2\u0230\u0231\7c\2\2\u0231\u0232\7t\2\2\u0232") buf.write(u"\u0233\7e\2\2\u0233\u0234\7u\2\2\u0234\u0235\7k\2\2\u0235") buf.write(u"\u0236\7p\2\2\u0236\\\3\2\2\2\u0237\u0238\7^\2\2\u0238") buf.write(u"\u0239\7c\2\2\u0239\u023a\7t\2\2\u023a\u023b\7e\2\2\u023b") buf.write(u"\u023c\7e\2\2\u023c\u023d\7q\2\2\u023d\u023e\7u\2\2\u023e") buf.write(u"^\3\2\2\2\u023f\u0240\7^\2\2\u0240\u0241\7c\2\2\u0241") buf.write(u"\u0242\7t\2\2\u0242\u0243\7e\2\2\u0243\u0244\7v\2\2\u0244") buf.write(u"\u0245\7c\2\2\u0245\u0246\7p\2\2\u0246`\3\2\2\2\u0247") buf.write(u"\u0248\7^\2\2\u0248\u0249\7c\2\2\u0249\u024a\7t\2\2\u024a") buf.write(u"\u024b\7e\2\2\u024b\u024c\7e\2\2\u024c\u024d\7u\2\2\u024d") buf.write(u"\u024e\7e\2\2\u024eb\3\2\2\2\u024f\u0250\7^\2\2\u0250") buf.write(u"\u0251\7c\2\2\u0251\u0252\7t\2\2\u0252\u0253\7e\2\2\u0253") buf.write(u"\u0254\7u\2\2\u0254\u0255\7g\2\2\u0255\u0256\7e\2\2\u0256") buf.write(u"d\3\2\2\2\u0257\u0258\7^\2\2\u0258\u0259\7c\2\2\u0259") buf.write(u"\u025a\7t\2\2\u025a\u025b\7e\2\2\u025b\u025c\7e\2\2\u025c") buf.write(u"\u025d\7q\2\2\u025d\u025e\7v\2\2\u025ef\3\2\2\2\u025f") buf.write(u"\u0260\7^\2\2\u0260\u0261\7u\2\2\u0261\u0262\7k\2\2\u0262") buf.write(u"\u0263\7p\2\2\u0263\u0264\7j\2\2\u0264h\3\2\2\2\u0265") buf.write(u"\u0266\7^\2\2\u0266\u0267\7e\2\2\u0267\u0268\7q\2\2\u0268") buf.write(u"\u0269\7u\2\2\u0269\u026a\7j\2\2\u026aj\3\2\2\2\u026b") buf.write(u"\u026c\7^\2\2\u026c\u026d\7v\2\2\u026d\u026e\7c\2\2\u026e") buf.write(u"\u026f\7p\2\2\u026f\u0270\7j\2\2\u0270l\3\2\2\2\u0271") buf.write(u"\u0272\7^\2\2\u0272\u0273\7c\2\2\u0273\u0274\7t\2\2\u0274") buf.write(u"\u0275\7u\2\2\u0275\u0276\7k\2\2\u0276\u0277\7p\2\2\u0277") buf.write(u"\u0278\7j\2\2\u0278n\3\2\2\2\u0279\u027a\7^\2\2\u027a") buf.write(u"\u027b\7c\2\2\u027b\u027c\7t\2\2\u027c\u027d\7e\2\2\u027d") buf.write(u"\u027e\7q\2\2\u027e\u027f\7u\2\2\u027f\u0280\7j\2\2\u0280") buf.write(u"p\3\2\2\2\u0281\u0282\7^\2\2\u0282\u0283\7c\2\2\u0283") buf.write(u"\u0284\7t\2\2\u0284\u0285\7v\2\2\u0285\u0286\7c\2\2\u0286") buf.write(u"\u0287\7p\2\2\u0287\u0288\7j\2\2\u0288r\3\2\2\2\u0289") buf.write(u"\u028a\7^\2\2\u028a\u028b\7n\2\2\u028b\u028c\7h\2\2\u028c") buf.write(u"\u028d\7n\2\2\u028d\u028e\7q\2\2\u028e\u028f\7q\2\2\u028f") buf.write(u"\u0290\7t\2\2\u0290t\3\2\2\2\u0291\u0292\7^\2\2\u0292") buf.write(u"\u0293\7t\2\2\u0293\u0294\7h\2\2\u0294\u0295\7n\2\2\u0295") buf.write(u"\u0296\7q\2\2\u0296\u0297\7q\2\2\u0297\u0298\7t\2\2\u0298") buf.write(u"v\3\2\2\2\u0299\u029a\7^\2\2\u029a\u029b\7n\2\2\u029b") buf.write(u"\u029c\7e\2\2\u029c\u029d\7g\2\2\u029d\u029e\7k\2\2\u029e") buf.write(u"\u029f\7n\2\2\u029fx\3\2\2\2\u02a0\u02a1\7^\2\2\u02a1") buf.write(u"\u02a2\7t\2\2\u02a2\u02a3\7e\2\2\u02a3\u02a4\7g\2\2\u02a4") buf.write(u"\u02a5\7k\2\2\u02a5\u02a6\7n\2\2\u02a6z\3\2\2\2\u02a7") buf.write(u"\u02a8\7^\2\2\u02a8\u02a9\7u\2\2\u02a9\u02aa\7s\2\2\u02aa") buf.write(u"\u02ab\7t\2\2\u02ab\u02ac\7v\2\2\u02ac|\3\2\2\2\u02ad") buf.write(u"\u02ae\7^\2\2\u02ae\u02af\7v\2\2\u02af\u02b0\7k\2\2\u02b0") buf.write(u"\u02b1\7o\2\2\u02b1\u02b2\7g\2\2\u02b2\u02b3\7u\2\2\u02b3") buf.write(u"~\3\2\2\2\u02b4\u02b5\7^\2\2\u02b5\u02b6\7e\2\2\u02b6") buf.write(u"\u02b7\7f\2\2\u02b7\u02b8\7q\2\2\u02b8\u02b9\7v\2\2\u02b9") buf.write(u"\u0080\3\2\2\2\u02ba\u02bb\7^\2\2\u02bb\u02bc\7f\2\2") buf.write(u"\u02bc\u02bd\7k\2\2\u02bd\u02be\7x\2\2\u02be\u0082\3") buf.write(u"\2\2\2\u02bf\u02c0\7^\2\2\u02c0\u02c1\7h\2\2\u02c1\u02c2") buf.write(u"\7t\2\2\u02c2\u02c3\7c\2\2\u02c3\u02c4\7e\2\2\u02c4\u0084") buf.write(u"\3\2\2\2\u02c5\u02c6\7^\2\2\u02c6\u02c7\7d\2\2\u02c7") buf.write(u"\u02c8\7k\2\2\u02c8\u02c9\7p\2\2\u02c9\u02ca\7q\2\2\u02ca") buf.write(u"\u02cb\7o\2\2\u02cb\u0086\3\2\2\2\u02cc\u02cd\7^\2\2") buf.write(u"\u02cd\u02ce\7f\2\2\u02ce\u02cf\7d\2\2\u02cf\u02d0\7") buf.write(u"k\2\2\u02d0\u02d1\7p\2\2\u02d1\u02d2\7q\2\2\u02d2\u02d3") buf.write(u"\7o\2\2\u02d3\u0088\3\2\2\2\u02d4\u02d5\7^\2\2\u02d5") buf.write(u"\u02d6\7v\2\2\u02d6\u02d7\7d\2\2\u02d7\u02d8\7k\2\2\u02d8") buf.write(u"\u02d9\7p\2\2\u02d9\u02da\7q\2\2\u02da\u02db\7o\2\2\u02db") buf.write(u"\u008a\3\2\2\2\u02dc\u02dd\7^\2\2\u02dd\u02de\7o\2\2") buf.write(u"\u02de\u02df\7c\2\2\u02df\u02e0\7v\2\2\u02e0\u02e1\7") buf.write(u"j\2\2\u02e1\u02e2\7k\2\2\u02e2\u02e3\7v\2\2\u02e3\u008c") buf.write(u"\3\2\2\2\u02e4\u02e5\7a\2\2\u02e5\u008e\3\2\2\2\u02e6") buf.write(u"\u02e7\7`\2\2\u02e7\u0090\3\2\2\2\u02e8\u02e9\7<\2\2") buf.write(u"\u02e9\u0092\3\2\2\2\u02ea\u02eb\t\2\2\2\u02eb\u0094") buf.write(u"\3\2\2\2\u02ec\u02f0\7f\2\2\u02ed\u02ef\5\u0093J\2\u02ee") buf.write(u"\u02ed\3\2\2\2\u02ef\u02f2\3\2\2\2\u02f0\u02f1\3\2\2") buf.write(u"\2\u02f0\u02ee\3\2\2\2\u02f1\u02fa\3\2\2\2\u02f2\u02f0") buf.write(u"\3\2\2\2\u02f3\u02fb\t\3\2\2\u02f4\u02f6\7^\2\2\u02f5") buf.write(u"\u02f7\t\3\2\2\u02f6\u02f5\3\2\2\2\u02f7\u02f8\3\2\2") buf.write(u"\2\u02f8\u02f6\3\2\2\2\u02f8\u02f9\3\2\2\2\u02f9\u02fb") buf.write(u"\3\2\2\2\u02fa\u02f3\3\2\2\2\u02fa\u02f4\3\2\2\2\u02fb") buf.write(u"\u0096\3\2\2\2\u02fc\u02fd\t\3\2\2\u02fd\u0098\3\2\2") buf.write(u"\2\u02fe\u02ff\t\4\2\2\u02ff\u009a\3\2\2\2\u0300\u0302") buf.write(u"\5\u0099M\2\u0301\u0300\3\2\2\2\u0302\u0303\3\2\2\2\u0303") buf.write(u"\u0301\3\2\2\2\u0303\u0304\3\2\2\2\u0304\u030c\3\2\2") buf.write(u"\2\u0305\u0306\7.\2\2\u0306\u0307\5\u0099M\2\u0307\u0308") buf.write(u"\5\u0099M\2\u0308\u0309\5\u0099M\2\u0309\u030b\3\2\2") buf.write(u"\2\u030a\u0305\3\2\2\2\u030b\u030e\3\2\2\2\u030c\u030a") buf.write(u"\3\2\2\2\u030c\u030d\3\2\2\2\u030d\u0326\3\2\2\2\u030e") buf.write(u"\u030c\3\2\2\2\u030f\u0311\5\u0099M\2\u0310\u030f\3\2") buf.write(u"\2\2\u0311\u0314\3\2\2\2\u0312\u0310\3\2\2\2\u0312\u0313") buf.write(u"\3\2\2\2\u0313\u031c\3\2\2\2\u0314\u0312\3\2\2\2\u0315") buf.write(u"\u0316\7.\2\2\u0316\u0317\5\u0099M\2\u0317\u0318\5\u0099") buf.write(u"M\2\u0318\u0319\5\u0099M\2\u0319\u031b\3\2\2\2\u031a") buf.write(u"\u0315\3\2\2\2\u031b\u031e\3\2\2\2\u031c\u031a\3\2\2") buf.write(u"\2\u031c\u031d\3\2\2\2\u031d\u031f\3\2\2\2\u031e\u031c") buf.write(u"\3\2\2\2\u031f\u0321\7\60\2\2\u0320\u0322\5\u0099M\2") buf.write(u"\u0321\u0320\3\2\2\2\u0322\u0323\3\2\2\2\u0323\u0321") buf.write(u"\3\2\2\2\u0323\u0324\3\2\2\2\u0324\u0326\3\2\2\2\u0325") buf.write(u"\u0301\3\2\2\2\u0325\u0312\3\2\2\2\u0326\u009c\3\2\2") buf.write(u"\2\u0327\u032b\7(\2\2\u0328\u032a\5\u0093J\2\u0329\u0328") buf.write(u"\3\2\2\2\u032a\u032d\3\2\2\2\u032b\u032c\3\2\2\2\u032b") buf.write(u"\u0329\3\2\2\2\u032c\u032f\3\2\2\2\u032d\u032b\3\2\2") buf.write(u"\2\u032e\u0327\3\2\2\2\u032e\u032f\3\2\2\2\u032f\u0330") buf.write(u"\3\2\2\2\u0330\u033c\7?\2\2\u0331\u0339\7?\2\2\u0332") buf.write(u"\u0334\5\u0093J\2\u0333\u0332\3\2\2\2\u0334\u0337\3\2") buf.write(u"\2\2\u0335\u0336\3\2\2\2\u0335\u0333\3\2\2\2\u0336\u0338") buf.write(u"\3\2\2\2\u0337\u0335\3\2\2\2\u0338\u033a\7(\2\2\u0339") buf.write(u"\u0335\3\2\2\2\u0339\u033a\3\2\2\2\u033a\u033c\3\2\2") buf.write(u"\2\u033b\u032e\3\2\2\2\u033b\u0331\3\2\2\2\u033c\u009e") buf.write(u"\3\2\2\2\u033d\u033e\7^\2\2\u033e\u033f\7p\2\2\u033f") buf.write(u"\u0340\7g\2\2\u0340\u0341\7s\2\2\u0341\u00a0\3\2\2\2") buf.write(u"\u0342\u0343\7>\2\2\u0343\u00a2\3\2\2\2\u0344\u0345\7") buf.write(u"^\2\2\u0345\u0346\7n\2\2\u0346\u0347\7g\2\2\u0347\u034e") buf.write(u"\7s\2\2\u0348\u0349\7^\2\2\u0349\u034a\7n\2\2\u034a\u034e") buf.write(u"\7g\2\2\u034b\u034e\5\u00a5S\2\u034c\u034e\5\u00a7T\2") buf.write(u"\u034d\u0344\3\2\2\2\u034d\u0348\3\2\2\2\u034d\u034b") buf.write(u"\3\2\2\2\u034d\u034c\3\2\2\2\u034e\u00a4\3\2\2\2\u034f") buf.write(u"\u0350\7^\2\2\u0350\u0351\7n\2\2\u0351\u0352\7g\2\2\u0352") buf.write(u"\u0353\7s\2\2\u0353\u0354\7s\2\2\u0354\u00a6\3\2\2\2") buf.write(u"\u0355\u0356\7^\2\2\u0356\u0357\7n\2\2\u0357\u0358\7") buf.write(u"g\2\2\u0358\u0359\7s\2\2\u0359\u035a\7u\2\2\u035a\u035b") buf.write(u"\7n\2\2\u035b\u035c\7c\2\2\u035c\u035d\7p\2\2\u035d\u035e") buf.write(u"\7v\2\2\u035e\u00a8\3\2\2\2\u035f\u0360\7@\2\2\u0360") buf.write(u"\u00aa\3\2\2\2\u0361\u0362\7^\2\2\u0362\u0363\7i\2\2") buf.write(u"\u0363\u0364\7g\2\2\u0364\u036b\7s\2\2\u0365\u0366\7") buf.write(u"^\2\2\u0366\u0367\7i\2\2\u0367\u036b\7g\2\2\u0368\u036b") buf.write(u"\5\u00adW\2\u0369\u036b\5\u00afX\2\u036a\u0361\3\2\2") buf.write(u"\2\u036a\u0365\3\2\2\2\u036a\u0368\3\2\2\2\u036a\u0369") buf.write(u"\3\2\2\2\u036b\u00ac\3\2\2\2\u036c\u036d\7^\2\2\u036d") buf.write(u"\u036e\7i\2\2\u036e\u036f\7g\2\2\u036f\u0370\7s\2\2\u0370") buf.write(u"\u0371\7s\2\2\u0371\u00ae\3\2\2\2\u0372\u0373\7^\2\2") buf.write(u"\u0373\u0374\7i\2\2\u0374\u0375\7g\2\2\u0375\u0376\7") buf.write(u"s\2\2\u0376\u0377\7u\2\2\u0377\u0378\7n\2\2\u0378\u0379") buf.write(u"\7c\2\2\u0379\u037a\7p\2\2\u037a\u037b\7v\2\2\u037b\u00b0") buf.write(u"\3\2\2\2\u037c\u037d\7#\2\2\u037d\u00b2\3\2\2\2\u037e") buf.write(u"\u0380\7^\2\2\u037f\u0381\t\3\2\2\u0380\u037f\3\2\2\2") buf.write(u"\u0381\u0382\3\2\2\2\u0382\u0380\3\2\2\2\u0382\u0383") buf.write(u"\3\2\2\2\u0383\u00b4\3\2\2\2\33\2\u00ba\u00ca\u00d9\u00ea") buf.write(u"\u010e\u0176\u01f1\u02f0\u02f8\u02fa\u0303\u030c\u0312") buf.write(u"\u031c\u0323\u0325\u032b\u032e\u0335\u0339\u033b\u034d") buf.write(u"\u036a\u0382\3\b\2\2") return buf.getvalue() class LaTeXLexer(Lexer): atn = ATNDeserializer().deserialize(serializedATN()) decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ] T__0 = 1 WS = 2 THINSPACE = 3 MEDSPACE = 4 THICKSPACE = 5 QUAD = 6 QQUAD = 7 NEGTHINSPACE = 8 NEGMEDSPACE = 9 NEGTHICKSPACE = 10 CMD_LEFT = 11 CMD_RIGHT = 12 IGNORE = 13 ADD = 14 SUB = 15 MUL = 16 DIV = 17 L_PAREN = 18 R_PAREN = 19 L_BRACE = 20 R_BRACE = 21 L_BRACE_LITERAL = 22 R_BRACE_LITERAL = 23 L_BRACKET = 24 R_BRACKET = 25 BAR = 26 R_BAR = 27 L_BAR = 28 L_ANGLE = 29 R_ANGLE = 30 FUNC_LIM = 31 LIM_APPROACH_SYM = 32 FUNC_INT = 33 FUNC_SUM = 34 FUNC_PROD = 35 FUNC_EXP = 36 FUNC_LOG = 37 FUNC_LN = 38 FUNC_SIN = 39 FUNC_COS = 40 FUNC_TAN = 41 FUNC_CSC = 42 FUNC_SEC = 43 FUNC_COT = 44 FUNC_ARCSIN = 45 FUNC_ARCCOS = 46 FUNC_ARCTAN = 47 FUNC_ARCCSC = 48 FUNC_ARCSEC = 49 FUNC_ARCCOT = 50 FUNC_SINH = 51 FUNC_COSH = 52 FUNC_TANH = 53 FUNC_ARSINH = 54 FUNC_ARCOSH = 55 FUNC_ARTANH = 56 L_FLOOR = 57 R_FLOOR = 58 L_CEIL = 59 R_CEIL = 60 FUNC_SQRT = 61 CMD_TIMES = 62 CMD_CDOT = 63 CMD_DIV = 64 CMD_FRAC = 65 CMD_BINOM = 66 CMD_DBINOM = 67 CMD_TBINOM = 68 CMD_MATHIT = 69 UNDERSCORE = 70 CARET = 71 COLON = 72 DIFFERENTIAL = 73 LETTER = 74 NUMBER = 75 EQUAL = 76 NEQ = 77 LT = 78 LTE = 79 LTE_Q = 80 LTE_S = 81 GT = 82 GTE = 83 GTE_Q = 84 GTE_S = 85 BANG = 86 SYMBOL = 87 channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ] modeNames = [ u"DEFAULT_MODE" ] literalNames = [ u"<INVALID>", u"','", u"'\\quad'", u"'\\qquad'", u"'\\negmedspace'", u"'\\negthickspace'", u"'\\left'", u"'\\right'", u"'+'", u"'-'", u"'*'", u"'/'", u"'('", u"')'", u"'{'", u"'}'", u"'\\{'", u"'\\}'", u"'['", u"']'", u"'|'", u"'\\right|'", u"'\\left|'", u"'\\langle'", u"'\\rangle'", u"'\\lim'", u"'\\int'", u"'\\sum'", u"'\\prod'", u"'\\exp'", u"'\\log'", u"'\\ln'", u"'\\sin'", u"'\\cos'", u"'\\tan'", u"'\\csc'", u"'\\sec'", u"'\\cot'", u"'\\arcsin'", u"'\\arccos'", u"'\\arctan'", u"'\\arccsc'", u"'\\arcsec'", u"'\\arccot'", u"'\\sinh'", u"'\\cosh'", u"'\\tanh'", u"'\\arsinh'", u"'\\arcosh'", u"'\\artanh'", u"'\\lfloor'", u"'\\rfloor'", u"'\\lceil'", u"'\\rceil'", u"'\\sqrt'", u"'\\times'", u"'\\cdot'", u"'\\div'", u"'\\frac'", u"'\\binom'", u"'\\dbinom'", u"'\\tbinom'", u"'\\mathit'", u"'_'", u"'^'", u"':'", u"'\\neq'", u"'<'", u"'\\leqq'", u"'\\leqslant'", u"'>'", u"'\\geqq'", u"'\\geqslant'", u"'!'" ] symbolicNames = [ u"<INVALID>", u"WS", u"THINSPACE", u"MEDSPACE", u"THICKSPACE", u"QUAD", u"QQUAD", u"NEGTHINSPACE", u"NEGMEDSPACE", u"NEGTHICKSPACE", u"CMD_LEFT", u"CMD_RIGHT", u"IGNORE", u"ADD", u"SUB", u"MUL", u"DIV", u"L_PAREN", u"R_PAREN", u"L_BRACE", u"R_BRACE", u"L_BRACE_LITERAL", u"R_BRACE_LITERAL", u"L_BRACKET", u"R_BRACKET", u"BAR", u"R_BAR", u"L_BAR", u"L_ANGLE", u"R_ANGLE", u"FUNC_LIM", u"LIM_APPROACH_SYM", u"FUNC_INT", u"FUNC_SUM", u"FUNC_PROD", u"FUNC_EXP", u"FUNC_LOG", u"FUNC_LN", u"FUNC_SIN", u"FUNC_COS", u"FUNC_TAN", u"FUNC_CSC", u"FUNC_SEC", u"FUNC_COT", u"FUNC_ARCSIN", u"FUNC_ARCCOS", u"FUNC_ARCTAN", u"FUNC_ARCCSC", u"FUNC_ARCSEC", u"FUNC_ARCCOT", u"FUNC_SINH", u"FUNC_COSH", u"FUNC_TANH", u"FUNC_ARSINH", u"FUNC_ARCOSH", u"FUNC_ARTANH", u"L_FLOOR", u"R_FLOOR", u"L_CEIL", u"R_CEIL", u"FUNC_SQRT", u"CMD_TIMES", u"CMD_CDOT", u"CMD_DIV", u"CMD_FRAC", u"CMD_BINOM", u"CMD_DBINOM", u"CMD_TBINOM", u"CMD_MATHIT", u"UNDERSCORE", u"CARET", u"COLON", u"DIFFERENTIAL", u"LETTER", u"NUMBER", u"EQUAL", u"NEQ", u"LT", u"LTE", u"LTE_Q", u"LTE_S", u"GT", u"GTE", u"GTE_Q", u"GTE_S", u"BANG", u"SYMBOL" ] ruleNames = [ u"T__0", u"WS", u"THINSPACE", u"MEDSPACE", u"THICKSPACE", u"QUAD", u"QQUAD", u"NEGTHINSPACE", u"NEGMEDSPACE", u"NEGTHICKSPACE", u"CMD_LEFT", u"CMD_RIGHT", u"IGNORE", u"ADD", u"SUB", u"MUL", u"DIV", u"L_PAREN", u"R_PAREN", u"L_BRACE", u"R_BRACE", u"L_BRACE_LITERAL", u"R_BRACE_LITERAL", u"L_BRACKET", u"R_BRACKET", u"BAR", u"R_BAR", u"L_BAR", u"L_ANGLE", u"R_ANGLE", u"FUNC_LIM", u"LIM_APPROACH_SYM", u"FUNC_INT", u"FUNC_SUM", u"FUNC_PROD", u"FUNC_EXP", u"FUNC_LOG", u"FUNC_LN", u"FUNC_SIN", u"FUNC_COS", u"FUNC_TAN", u"FUNC_CSC", u"FUNC_SEC", u"FUNC_COT", u"FUNC_ARCSIN", u"FUNC_ARCCOS", u"FUNC_ARCTAN", u"FUNC_ARCCSC", u"FUNC_ARCSEC", u"FUNC_ARCCOT", u"FUNC_SINH", u"FUNC_COSH", u"FUNC_TANH", u"FUNC_ARSINH", u"FUNC_ARCOSH", u"FUNC_ARTANH", u"L_FLOOR", u"R_FLOOR", u"L_CEIL", u"R_CEIL", u"FUNC_SQRT", u"CMD_TIMES", u"CMD_CDOT", u"CMD_DIV", u"CMD_FRAC", u"CMD_BINOM", u"CMD_DBINOM", u"CMD_TBINOM", u"CMD_MATHIT", u"UNDERSCORE", u"CARET", u"COLON", u"WS_CHAR", u"DIFFERENTIAL", u"LETTER", u"DIGIT", u"NUMBER", u"EQUAL", u"NEQ", u"LT", u"LTE", u"LTE_Q", u"LTE_S", u"GT", u"GTE", u"GTE_Q", u"GTE_S", u"BANG", u"SYMBOL" ] grammarFileName = u"LaTeX.g4" def __init__(self, input=None, output=sys.stdout): super(LaTeXLexer, self).__init__(input, output=output) self.checkVersion("4.7.2") self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache()) self._actions = None self._predicates = None

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1

null

0

6

5e8bf8eca1d600a3b3ab4d5acda13b22ed5f6235

23

py

Python

src/tcanvas/__init__.py

ast0815/tcanvas

d33fdc3ed9d965e06bcff1eaf059a5b665278883

[ "MIT" ]

null

src/tcanvas/__init__.py

ast0815/tcanvas

d33fdc3ed9d965e06bcff1eaf059a5b665278883

[ "MIT" ]

null

src/tcanvas/__init__.py

ast0815/tcanvas

d33fdc3ed9d965e06bcff1eaf059a5b665278883

[ "MIT" ]

null

from .tcanvas import *

11.5

22

0.73913

3

23

5.666667

1

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0.173913

23

1

23

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0

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1

0

null

0

1

0

1

0

1

0

6

5e933cd008d5e3ec8047e077356a4ab3268eaa70

139

py

Python

skel_io/__path.py

isosc/skel-io

0abc23a10ce40d6f9bce9d3b9fb65018746ddef7

[ "Apache-2.0" ]

null

skel_io/__path.py

isosc/skel-io

0abc23a10ce40d6f9bce9d3b9fb65018746ddef7

[ "Apache-2.0" ]

null

skel_io/__path.py

isosc/skel-io

0abc23a10ce40d6f9bce9d3b9fb65018746ddef7

[ "Apache-2.0" ]

null

from pathlib import Path def get_project_root() -> Path: r"""Returns project root folder""" return Path(__file__).parent.parent

17.375

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0.179856

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true

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1

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null

0

1

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6

5ebe63e19b602c3ff17506c27866ed441a831ba0

25

py

Python

emout/plot/__init__.py

Nkzono99/emout

77eb2b90ce5408055715f1aeb0a4479ccb97668d

[ "MIT" ]

null

emout/plot/__init__.py

Nkzono99/emout

77eb2b90ce5408055715f1aeb0a4479ccb97668d

[ "MIT" ]

null

emout/plot/__init__.py

Nkzono99/emout

77eb2b90ce5408055715f1aeb0a4479ccb97668d

[ "MIT" ]

null

from .basic_plot import *

25

0.8

4

25

4.75

1

0

0.12

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1

25

0.863636

0

1

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true

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null

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1

0

null

0

1

0

1

0

1

0

6

5ecd959890e34189f06092f9294af4c7f447dcb5

400

py

Python

titan/django_pkg/djangoapp/resources.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

titan/django_pkg/djangoapp/resources.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

titan/django_pkg/djangoapp/resources.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

from dataclasses import dataclass, field from moonleap import Resource from titan.project_pkg.service import Tool @dataclass class DjangoApp(Tool): pass @dataclass class DjangoConfig(Resource): settings: dict = field(default_factory=dict) urls: list = field(default_factory=list) urls_imports: list = field(default_factory=list) cors_urls: list = field(default_factory=list)

22.222222

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true

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null

0

1

0

1

0

6

0d96f2fffd95a0bb5a4be7eef2f674942331dd6f

154

py

Python

mud/handlers/__init__.py

erwanaubry/alamud_IUT_Escape

cc9e77203245a9b933300edc2efb9bd5fcd8abc3

[ "Unlicense" ]

null

mud/handlers/__init__.py

erwanaubry/alamud_IUT_Escape

cc9e77203245a9b933300edc2efb9bd5fcd8abc3

[ "Unlicense" ]

null

mud/handlers/__init__.py

erwanaubry/alamud_IUT_Escape

cc9e77203245a9b933300edc2efb9bd5fcd8abc3

[ "Unlicense" ]

null

38.5

79

0.305195

11

154

4.272727

1

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0.090909

154

3

80

51.333333

0.3

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true

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1

0

1

0

null

1

0

1

0

6

0dc6faefe15bf562ec2e3eff7f3b9a1224ab152a

9,168

py

Python

tests/test_auditor/test_auditor_job.py

gzcf/polyaxon

77ac8838c6444a36541e6c28aba7ae42de392fee

[ "MIT" ]

null

tests/test_auditor/test_auditor_job.py

gzcf/polyaxon

77ac8838c6444a36541e6c28aba7ae42de392fee

[ "MIT" ]

null

tests/test_auditor/test_auditor_job.py

gzcf/polyaxon

77ac8838c6444a36541e6c28aba7ae42de392fee

[ "MIT" ]

null

# pylint:disable=ungrouped-imports from unittest.mock import patch import pytest import activitylogs import auditor import tracker from event_manager.events import job as job_events from factories.factory_plugins import NotebookJobFactory from factories.factory_projects import ProjectFactory from tests.utils import BaseTest @pytest.mark.auditor_mark class AuditorJobTest(BaseTest): """Testing subscribed events""" def setUp(self): self.job = NotebookJobFactory(project=ProjectFactory()) auditor.validate() auditor.setup() tracker.validate() tracker.setup() activitylogs.validate() activitylogs.setup() super().setUp() @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_created(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_CREATED, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_updated(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_UPDATED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_started(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_STARTED, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_started_triggered(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_STARTED_TRIGGERED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_deleted(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_DELETED, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_triggered_deleted(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_DELETED_TRIGGERED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_stopped(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_STOPPED, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_stopped_triggered(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_STOPPED_TRIGGERED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_viewed(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_VIEWED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_bookmarked(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_BOOKMARKED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_unbookmarked(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_UNBOOKMARKED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_new_status(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_NEW_STATUS, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_failed(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_FAILED, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_succeeded(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_SUCCEEDED, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_done(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_DONE, instance=self.job) assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 0 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_logs_viewed_triggered(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_LOGS_VIEWED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_restarted_triggered(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_RESTARTED_TRIGGERED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_statuses_viewed_triggered(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_STATUSES_VIEWED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1 @patch('tracker.service.TrackerService.record_event') @patch('activitylogs.service.ActivityLogService.record_event') def test_job_outputs_downloaded(self, activitylogs_record, tracker_record): auditor.record(event_type=job_events.JOB_OUTPUTS_DOWNLOADED, instance=self.job, actor_id=1, actor_name='foo') assert tracker_record.call_count == 1 assert activitylogs_record.call_count == 1

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0.231239

9,168

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0.171429

0

null

0

1

0

null

0

6

0dcaace2ddce852fce495001ba0f40a33ca831eb

138

py

Python

ambra_sdk/service/entrypoints/keyimage.py

dyens/sdk-python

24bf05268af2832c70120b84fd53bf44862cffec

[ "Apache-2.0" ]

null

ambra_sdk/service/entrypoints/keyimage.py

dyens/sdk-python

24bf05268af2832c70120b84fd53bf44862cffec

[ "Apache-2.0" ]

null

ambra_sdk/service/entrypoints/keyimage.py

dyens/sdk-python

24bf05268af2832c70120b84fd53bf44862cffec

[ "Apache-2.0" ]

null

from ambra_sdk.service.entrypoints.generated.keyimage import \ Keyimage as GKeyimage class Keyimage(GKeyimage): """Keyimage."""

19.714286

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138

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true

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0

null

0

1

0

1

0

1

0

6

0de7a71926ac77799c245ad12e0b561d4273a1ea

113

py

Python

graphflow/simvoltage/__init__.py

vishalbelsare/GraphFlow-1

6a1a1c441521bf7642d21de297277bedf98f6d31

[ "MIT" ]

7

2018-08-19T10:15:02.000Z

2019-02-25T07:48:42.000Z

graphflow/simvoltage/__init__.py

vishalbelsare/GraphFlow-1

6a1a1c441521bf7642d21de297277bedf98f6d31

[ "MIT" ]

3

2019-06-26T16:30:54.000Z

2021-04-11T03:23:04.000Z

graphflow/simvoltage/__init__.py

vishalbelsare/GraphFlow-1

6a1a1c441521bf7642d21de297277bedf98f6d31

[ "MIT" ]

3

2018-11-30T02:49:04.000Z

2019-12-31T20:51:43.000Z

from .resistancedist import GraphResistanceDistance from .SocialNetworkSimVoltage import SocialNetworkSimVoltage

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0df08d8b97a462eb62a07f07ef461e8479bb18f4

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py

Python

config/elements.py

sindavid/get_free_amazon_ebooks

ba861da0e3500ca9db24f438d5afea5b3f7d5014

[ "MIT" ]

null

config/elements.py

sindavid/get_free_amazon_ebooks

ba861da0e3500ca9db24f438d5afea5b3f7d5014

[ "MIT" ]

null

config/elements.py

sindavid/get_free_amazon_ebooks

ba861da0e3500ca9db24f438d5afea5b3f7d5014

[ "MIT" ]

null

elements = { 'login_page': { 'input_email': '//*[@id="ap_email"]', 'btn_continue': '//*[@id="continue"]', 'input_password': '//*[@id="ap_password"]', 'btn_login': '//*[@id="signInSubmit"]' }, 'header_confirm': '/html/body/div[1]/header/div/div[1]/div[3]/div/a[1]/div/span', 'bnt_buy_one_click': '/html/body/div[1]/div[2]/div[1]/div[2]/div/span[4]/div[1]/div[{0}]/div/span/div/div/div[2]/div[2]/div/div[2]/div[1]/div/div[2]/div/span/span/a', 'title_ebook': '/html/body/div[1]/div[2]/div[1]/div[2]/div/span[3]/div[2]/div[{0}]/div/span/div/div/div[2]/div[2]/div/div[1]/div/div/div[1]/h2/a/span', 'img_ebook': '/html/body/div[1]/div[2]/div[1]/div[2]/div/span[4]/div[1]/div[{0}]/div/span/div/div/div[2]/div[1]/div/div/span/a/div', 'btn_next_page': 'a-last', 'if_bought': '/html/body/div[1]/div[3]/div/div/div/p[3]', 'btn_buy': 'one-click-button', 'name_ebook': '/html/body/div[1]/div[2]/div[2]/div/div/div[1]/div/div[1]/div[1]/div[1]/div/div/div[1]/span[1]', 'confirm_message': '/html/body/div[1]/div[2]/div[2]/div/div/div[1]/div/div[1]/div[1]/div[1]/div/div/div[1]/span[2]', 'confirm_bought': '/html/body/div[2]/div[2]/div[3]/div[2]/div/div/div/div/div/div/span' }

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1,241

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null

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6

0df9668fec096ba8f70bccbe8bf81c7ea03e328c

500

py

Python

001 - 050/ex011.py

SocrammBR/Desafios-Python-CursoEmVideo

bd2454a24134500343ece91b936c169d3a66f89e

[ "MIT" ]

null

001 - 050/ex011.py

SocrammBR/Desafios-Python-CursoEmVideo

bd2454a24134500343ece91b936c169d3a66f89e

[ "MIT" ]

null

001 - 050/ex011.py

SocrammBR/Desafios-Python-CursoEmVideo

bd2454a24134500343ece91b936c169d3a66f89e

[ "MIT" ]

null

# Código Original # altura = float(input('Digite a altura da parede em metros: ')) # largura = float(input('Digite a largura da parede em metros: ')) # metro_quadrado = altura*largura # print(f'Você precisará de {metro_quadrado/2}l de tinta') # Desafio da aula 11 altura = float(input('\033[33mDigite a altura da parede em metros: ')) largura = float(input('\033[33mDigite a largura da parede em metros: ')) metro_quadrado = altura*largura print (f'Você precisará de {metro_quadrado/2}l de tinta')

38.461538

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0.734

78

500

4.653846

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0.110193

0.176309

0.826446

0.732782

0.512397

0

0.032787

0.146

500

13

73

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false

0

0.25

0

null

0

1

0

null

0

6

2176cc3ca46341b832c20e95ccafa89608cddf3e

302

py

Python

desafios/desafio010.py

genisyskernel/cursoemvideo-python

dec301e33933388c886fe78010f38adfb24dae82

[ "MIT" ]

1

2020-10-26T04:33:14.000Z

desafios/desafio010.py

genisyskernel/cursoemvideo-python

dec301e33933388c886fe78010f38adfb24dae82

[ "MIT" ]

null

desafios/desafio010.py

genisyskernel/cursoemvideo-python

dec301e33933388c886fe78010f38adfb24dae82

[ "MIT" ]

null

valor_real = float(input("\033[1;30mDigite o valor na carteira?\033[m \033[1;32mR$\033[m")) dolares = valor_real / 3.27 print("\033[1;37mO valor\033[m \033[1;32mR$: {0:.2f} reais\033[m \033[1;37mpode comprar\033[m \033[1;32mUSD$: {1:.2f} dolares\033[m \033[1;37m!\033[m".format(valor_real, dolares))

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1

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null

0

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21870086d5a697beca5ff647e63360582def8db9

122

py

Python

src/View/__init__.py

wklock/nasal-modeling

35623d1c6f62a2ee07a99d01a475ea1ca735f0ff

[ "BSD-3-Clause" ]

null

src/View/__init__.py

wklock/nasal-modeling

35623d1c6f62a2ee07a99d01a475ea1ca735f0ff

[ "BSD-3-Clause" ]

4

2021-06-08T19:00:37.000Z

2022-03-11T23:15:09.000Z

src/View/__init__.py

wklock/nasal-modeling

35623d1c6f62a2ee07a99d01a475ea1ca735f0ff

[ "BSD-3-Clause" ]

1

2019-05-16T08:11:25.000Z

from src.View.ContouringWorkspace import ContouringWorkspace from src.View.ResizingImageCanvas import ResizingImageCanvas

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218fba0a2a129255d0cbe59253b6f9fa68e5b789

45

py

Python

test/children/tesla.py

Hira63S/KnightRyder

d4b7238d8fc8dfcdfbbb9fd5d232f6273c76840e

[ "MIT" ]

1

2020-12-19T15:44:25.000Z

test/children/tesla.py

Hira63S/PythonPractice

5eadc04f2fb056b04db59a658d5914ea847be7d2

[ "MIT" ]

null

test/children/tesla.py

Hira63S/PythonPractice

5eadc04f2fb056b04db59a658d5914ea847be7d2

[ "MIT" ]

null

def Tesla(): print('Hello! I am tesla')

11.25

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3.714286

0.857143

0

0.244444

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0

1

0

1

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6

21ac6db36f5c2c7be0e3bcf9280e6ee809dd5b37

146

py

Python

libary/developer.py

NekoFanatic/kaiji

7ae8e12d4e821e7d28d78034e1ec044ed75f9536

[ "MIT" ]

null

libary/developer.py

NekoFanatic/kaiji

7ae8e12d4e821e7d28d78034e1ec044ed75f9536

[ "MIT" ]

null

libary/developer.py

NekoFanatic/kaiji

7ae8e12d4e821e7d28d78034e1ec044ed75f9536

[ "MIT" ]

null

class Developer: DUNDY = 321730481903370240 ADM = 600443374587346989 TEMPLAR = 108281077319077888 GHOSTRIDER = 846009958062358548

24.333333

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146

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null

0

1

0

6

21af1029ca8c8361da1627389c73fb11f7ff6195

5,298

py

Python

backend/tests/test_apps_admin_workspaces.py

fief-dev/fief

cbfeec11da7a03aa345cb7ceb088b5d8ec9d6ab1

[ "MIT" ]

1

2022-02-13T17:39:42.000Z

backend/tests/test_apps_admin_workspaces.py

fief-dev/fief

cbfeec11da7a03aa345cb7ceb088b5d8ec9d6ab1

[ "MIT" ]

1

2022-02-13T14:46:24.000Z

backend/tests/test_apps_admin_workspaces.py

fief-dev/fief

cbfeec11da7a03aa345cb7ceb088b5d8ec9d6ab1

[ "MIT" ]

null

from unittest.mock import AsyncMock, MagicMock import httpx import pytest from fastapi import status from fief.models import Workspace from fief.schemas.user import UserDB from fief.services.workspace_db import WorkspaceDatabaseConnectionError @pytest.mark.asyncio @pytest.mark.workspace_host class TestListWorkspaces: async def test_unauthorized(self, test_client_admin: httpx.AsyncClient): response = await test_client_admin.get("/workspaces/") assert response.status_code == status.HTTP_401_UNAUTHORIZED @pytest.mark.authenticated_admin(mode="api_key") async def test_unauthorized_with_api_key( self, test_client_admin: httpx.AsyncClient ): response = await test_client_admin.get("/workspaces/") assert response.status_code == status.HTTP_401_UNAUTHORIZED @pytest.mark.authenticated_admin(mode="session") async def test_valid( self, test_client_admin: httpx.AsyncClient, workspace: Workspace ): response = await test_client_admin.get("/workspaces/") assert response.status_code == status.HTTP_200_OK json = response.json() assert json["count"] == 1 assert json["results"][0]["id"] == str(workspace.id) @pytest.mark.asyncio class TestCheckConnection: async def test_unauthorized(self, test_client_admin: httpx.AsyncClient): response = await test_client_admin.post("/workspaces/check-connection") assert response.status_code == status.HTTP_401_UNAUTHORIZED @pytest.mark.authenticated_admin(mode="api_key") async def test_unauthorized_with_api_key( self, test_client_admin: httpx.AsyncClient ): response = await test_client_admin.post("/workspaces/check-connection") assert response.status_code == status.HTTP_401_UNAUTHORIZED @pytest.mark.authenticated_admin(mode="session") async def test_db_connection_error( self, test_client_admin: httpx.AsyncClient, workspace_db_mock: MagicMock ): workspace_db_mock.check_connection.return_value = (False, "An error occured") response = await test_client_admin.post( "/workspaces/check-connection", json={ "database_type": "POSTGRESQL", "database_host": "db.bretagne.duchy", "database_port": 5432, "database_username": "anne", "database_password": "hermine", "database_name": "fief", }, ) assert response.status_code == status.HTTP_400_BAD_REQUEST json = response.json() assert json["detail"] == "An error occured" @pytest.mark.authenticated_admin(mode="session") async def test_success( self, test_client_admin: httpx.AsyncClient, workspace_db_mock: MagicMock ): workspace_db_mock.check_connection.return_value = (True, None) response = await test_client_admin.post( "/workspaces/check-connection", json={ "database_type": "POSTGRESQL", "database_host": "db.bretagne.duchy", "database_port": 5432, "database_username": "anne", "database_password": "hermine", "database_name": "fief", }, ) assert response.status_code == status.HTTP_200_OK @pytest.mark.asyncio class TestCreateWorkspace: async def test_unauthorized(self, test_client_admin: httpx.AsyncClient): response = await test_client_admin.post("/workspaces/") assert response.status_code == status.HTTP_401_UNAUTHORIZED @pytest.mark.authenticated_admin(mode="api_key") async def test_unauthorized_with_api_key( self, test_client_admin: httpx.AsyncClient ): response = await test_client_admin.post("/workspaces/") assert response.status_code == status.HTTP_401_UNAUTHORIZED @pytest.mark.authenticated_admin(mode="session") async def test_db_connection_error( self, test_client_admin: httpx.AsyncClient, workspace_creation_mock: MagicMock ): workspace_creation_mock.create.side_effect = WorkspaceDatabaseConnectionError( "An error occured" ) response = await test_client_admin.post( "/workspaces/", json={"name": "Burgundy"}, ) assert response.status_code == status.HTTP_400_BAD_REQUEST json = response.json() assert json["detail"] == "An error occured" @pytest.mark.authenticated_admin(mode="session") async def test_success( self, test_client_admin: httpx.AsyncClient, workspace_creation_mock: MagicMock, workspace: Workspace, workspace_admin_user: UserDB, ): workspace_creation_mock.create.side_effect = AsyncMock(return_value=workspace) response = await test_client_admin.post( "/workspaces/", json={"name": "Burgundy"}, ) assert response.status_code == status.HTTP_201_CREATED json = response.json() assert "id" in json workspace_creation_mock.create.assert_called_once() create_call_args = workspace_creation_mock.create.call_args create_call_args[1]["user_id"] == workspace_admin_user.id

33.961538

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21c33c12b98e1797e63078f852ddb83f17d2d35e

96

py

Python

venv/lib/python3.8/site-packages/debugpy/adapter/servers.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/debugpy/adapter/servers.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/debugpy/adapter/servers.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/5e/d2/7f/3671e1de0b98c55df45f65a912c9a07f22b6e999663400107ad9d0f39d

96

0.895833

9

96

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0

1

0

null

1

0

1

0

6

21f1385f418f3304c5cf5888d996420edd75cbe4

96

py

Python

venv/lib/python3.8/site-packages/pastel/pastel.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/pastel/pastel.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/pastel/pastel.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/bc/ad/6d/2e2e65f608298ed8db5b28d66d52bd45884851828699bbbdee6a3f0579

96

0.895833

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1

0

1

0

null

1

0

1

0

6

df54184b0330fdc35609de2aebe16cb6b8e42bd3

43

py

Python

vnaddress/combined.py

datkt1998/vnaddress_standardize

22c67d934a1a16add1ce1b6011ce759de8bb8440

[ "MIT" ]

3

2020-10-27T03:36:31.000Z

2021-07-31T16:39:00.000Z

vnaddress/combined.py

datkt1998/vnaddress_standardize

22c67d934a1a16add1ce1b6011ce759de8bb8440

[ "MIT" ]

4

2020-10-13T07:16:30.000Z

2022-01-24T02:23:38.000Z

vnaddress/combined.py

datkt1998/vnaddress_standardize

22c67d934a1a16add1ce1b6011ce759de8bb8440

[ "MIT" ]

7

2020-06-01T16:04:25.000Z

2021-12-04T14:06:53.000Z

def test(): print("QQQQQQQQQQQQQQQQQQ")

21.5

31

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6

10f776c812b8efd243579d95fd2ab5c78d845fcd

33

py

Python

src/example_package2/example2.py

martinwimpff/python-package-template

a1d181f9bae0157b19e8177d8dea8ce2a76e9f9c

[ "MIT" ]

2

2022-03-24T18:53:48.000Z

2022-03-24T18:53:53.000Z

src/example_package2/example2.py

martinwimpff/python-package-template

a1d181f9bae0157b19e8177d8dea8ce2a76e9f9c

[ "MIT" ]

null

src/example_package2/example2.py

martinwimpff/python-package-template

a1d181f9bae0157b19e8177d8dea8ce2a76e9f9c

[ "MIT" ]

null

def add_two(n): return n + 2

11

16

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33

2.571429

0.857143

0

0.043478

0.30303

33

2

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null

0

1

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1

0

6

3395f8bae5bdd8b60524a935578b35ae82d00945

85

py

Python

GPTime/__init__.py

statisticalbiotechnology/GPTime

a073d1ef2eff0bd92c4b070c721d869cf1b7843a

[ "Apache-2.0" ]

8

2016-07-19T18:37:38.000Z

2021-05-12T02:52:23.000Z

GPTime/__init__.py

statisticalbiotechnology/GPTime

a073d1ef2eff0bd92c4b070c721d869cf1b7843a

[ "Apache-2.0" ]

1

2018-11-13T16:29:36.000Z

GPTime/__init__.py

statisticalbiotechnology/GPTime

a073d1ef2eff0bd92c4b070c721d869cf1b7843a

[ "Apache-2.0" ]

3

2016-11-17T02:20:40.000Z

2020-09-24T09:17:59.000Z

from . import peptides from . import features from . import model from . import data

17

22

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85

5.416667

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0.188235

85

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null

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1

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1

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6

339a64778ea8f4bc86e2f2a767778b6b4ed8d4f9

19

py

Python

optimization/tools/dali_preprocess.py

AICryptoGroup/TorchSlim

5e1a5eb994b7b22e226cce9ee3849a623ddaacb7

[ "Apache-2.0" ]

5

2022-03-11T09:35:33.000Z

2022-03-26T14:47:03.000Z

optimization/tools/dali_preprocess.py

AICryptoGroup/TorchSlim

5e1a5eb994b7b22e226cce9ee3849a623ddaacb7

[ "Apache-2.0" ]

null

optimization/tools/dali_preprocess.py

AICryptoGroup/TorchSlim

5e1a5eb994b7b22e226cce9ee3849a623ddaacb7

[ "Apache-2.0" ]

1

2022-03-11T09:47:28.000Z

#TODO comming soon

9.5

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1

0

6

339ba392dea8ccf75ced044cf80ded310e99c9d4

54

py

Python

login.py

13754499307/python2

afa3857a69cc9488c6e764245b629523412267a2

[ "MIT" ]

null

login.py

13754499307/python2

afa3857a69cc9488c6e764245b629523412267a2

[ "MIT" ]

null

login.py

13754499307/python2

afa3857a69cc9488c6e764245b629523412267a2

[ "MIT" ]

null

a = 1 b = 2 d = 4 c = 3 e = 6 e = 5 f = 7 g = 8

3.6

5

0.296296

16

54

1

0.9375

0

0.333333

0.555556

54

14

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3.857143

0.333333

0

1

0

false

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1

0

1

null

0

1

0

1

0

null

0

6

33e552b3b5a2d2bf6fec356300f4936e94878aad

21

py

Python

collagen/parallel/__init__.py

MIPT-Oulu/Collagen

0cbc4285d60e5c9fcc89f629fcf4321e80b7452c

[ "MIT" ]

4

2019-05-14T14:44:51.000Z

2020-03-13T08:37:48.000Z

collagen/parallel/__init__.py

MIPT-Oulu/Collagen

0cbc4285d60e5c9fcc89f629fcf4321e80b7452c

[ "MIT" ]

26

2019-04-21T20:35:22.000Z

2022-03-12T00:32:57.000Z

collagen/parallel/__init__.py

MIPT-Oulu/Collagen

0cbc4285d60e5c9fcc89f629fcf4321e80b7452c

[ "MIT" ]

1

2019-05-14T14:53:28.000Z

from ._apex import *

10.5

20

0.714286

3

21

4.666667

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0.190476

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21

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1

0

null

0

1

0

1

0

1

0

6

d5101b79866a0aacfdf5353158f0299658890e26

168

py

Python

apps/homes/__init__.py

tuanquanghpvn/flask-intro

4dbc6bfbbdee13bc601b7ba8f10ede3635a2cfaf

[ "MIT" ]

null

apps/homes/__init__.py

tuanquanghpvn/flask-intro

4dbc6bfbbdee13bc601b7ba8f10ede3635a2cfaf

[ "MIT" ]

null

apps/homes/__init__.py

tuanquanghpvn/flask-intro

4dbc6bfbbdee13bc601b7ba8f10ede3635a2cfaf

[ "MIT" ]

null

from flask import Blueprint homes_bluesrprints = Blueprint('homes', __name__, template_folder='templates', static_folder='static', url_prefix='/') from . import views

33.6

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168

6.2

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0

0.095238

168

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0

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null

0

1

0

1

0

6

1d32cce521d16c306a9a4e12bb758c94f5318543

69

py

Python

peakina/readers/__init__.py

ToucanToco/peakina

7fed8de8f04d263cc7f704d5d16533cc8676a350

[ "BSD-3-Clause" ]

10

2019-01-24T10:32:20.000Z

2022-03-04T18:24:59.000Z

peakina/readers/__init__.py

ToucanToco/peakina

7fed8de8f04d263cc7f704d5d16533cc8676a350

[ "BSD-3-Clause" ]

94

2018-12-26T11:00:40.000Z

2022-03-31T14:10:57.000Z

peakina/readers/__init__.py

ToucanToco/peakina

7fed8de8f04d263cc7f704d5d16533cc8676a350

[ "BSD-3-Clause" ]

4

2018-11-25T21:39:03.000Z

2022-02-21T21:38:55.000Z

# flake8: noqa from .json import read_json from .xml import read_xml

17.25

27

0.782609

12

69

4.333333

0.583333

0.384615

0

0.017241

0.15942

69

3

28

23

0.87931

0.173913

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

1d39c496ea8e89e6b8e095aa4b8233b127120058

15,489

py

Python

sdk/python/pulumi_azure/compute/configuration_policy_assignment.py

aangelisc/pulumi-azure

71dd9c75403146e16f7480e5a60b08bc0329660e

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

null

sdk/python/pulumi_azure/compute/configuration_policy_assignment.py

aangelisc/pulumi-azure

71dd9c75403146e16f7480e5a60b08bc0329660e

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

null

sdk/python/pulumi_azure/compute/configuration_policy_assignment.py

aangelisc/pulumi-azure

71dd9c75403146e16f7480e5a60b08bc0329660e

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['ConfigurationPolicyAssignmentArgs', 'ConfigurationPolicyAssignment'] @pulumi.input_type class ConfigurationPolicyAssignmentArgs: def __init__(__self__, *, configuration: pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs'], virtual_machine_id: pulumi.Input[str], location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a ConfigurationPolicyAssignment resource. :param pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs'] configuration: A `configuration` block as defined below. :param pulumi.Input[str] virtual_machine_id: The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. :param pulumi.Input[str] location: The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. :param pulumi.Input[str] name: The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. """ pulumi.set(__self__, "configuration", configuration) pulumi.set(__self__, "virtual_machine_id", virtual_machine_id) if location is not None: pulumi.set(__self__, "location", location) if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter def configuration(self) -> pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs']: """ A `configuration` block as defined below. """ return pulumi.get(self, "configuration") @configuration.setter def configuration(self, value: pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs']): pulumi.set(self, "configuration", value) @property @pulumi.getter(name="virtualMachineId") def virtual_machine_id(self) -> pulumi.Input[str]: """ The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. """ return pulumi.get(self, "virtual_machine_id") @virtual_machine_id.setter def virtual_machine_id(self, value: pulumi.Input[str]): pulumi.set(self, "virtual_machine_id", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: """ The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. """ return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @pulumi.input_type class _ConfigurationPolicyAssignmentState: def __init__(__self__, *, configuration: Optional[pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs']] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, virtual_machine_id: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering ConfigurationPolicyAssignment resources. :param pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs'] configuration: A `configuration` block as defined below. :param pulumi.Input[str] location: The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. :param pulumi.Input[str] name: The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. :param pulumi.Input[str] virtual_machine_id: The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. """ if configuration is not None: pulumi.set(__self__, "configuration", configuration) if location is not None: pulumi.set(__self__, "location", location) if name is not None: pulumi.set(__self__, "name", name) if virtual_machine_id is not None: pulumi.set(__self__, "virtual_machine_id", virtual_machine_id) @property @pulumi.getter def configuration(self) -> Optional[pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs']]: """ A `configuration` block as defined below. """ return pulumi.get(self, "configuration") @configuration.setter def configuration(self, value: Optional[pulumi.Input['ConfigurationPolicyAssignmentConfigurationArgs']]): pulumi.set(self, "configuration", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: """ The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. """ return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="virtualMachineId") def virtual_machine_id(self) -> Optional[pulumi.Input[str]]: """ The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. """ return pulumi.get(self, "virtual_machine_id") @virtual_machine_id.setter def virtual_machine_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "virtual_machine_id", value) class ConfigurationPolicyAssignment(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, configuration: Optional[pulumi.Input[pulumi.InputType['ConfigurationPolicyAssignmentConfigurationArgs']]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, virtual_machine_id: Optional[pulumi.Input[str]] = None, __props__=None): """ Applies a Configuration Policy to a Virtual Machine. ## Import Virtual Machine Configuration Policy Assignments can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:compute/configurationPolicyAssignment:ConfigurationPolicyAssignment example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Compute/virtualMachines/vm1/providers/Microsoft.GuestConfiguration/guestConfigurationAssignments/assignment1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['ConfigurationPolicyAssignmentConfigurationArgs']] configuration: A `configuration` block as defined below. :param pulumi.Input[str] location: The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. :param pulumi.Input[str] name: The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. :param pulumi.Input[str] virtual_machine_id: The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. """ ... @overload def __init__(__self__, resource_name: str, args: ConfigurationPolicyAssignmentArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Applies a Configuration Policy to a Virtual Machine. ## Import Virtual Machine Configuration Policy Assignments can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:compute/configurationPolicyAssignment:ConfigurationPolicyAssignment example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Compute/virtualMachines/vm1/providers/Microsoft.GuestConfiguration/guestConfigurationAssignments/assignment1 ``` :param str resource_name: The name of the resource. :param ConfigurationPolicyAssignmentArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ConfigurationPolicyAssignmentArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, configuration: Optional[pulumi.Input[pulumi.InputType['ConfigurationPolicyAssignmentConfigurationArgs']]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, virtual_machine_id: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ConfigurationPolicyAssignmentArgs.__new__(ConfigurationPolicyAssignmentArgs) if configuration is None and not opts.urn: raise TypeError("Missing required property 'configuration'") __props__.__dict__["configuration"] = configuration __props__.__dict__["location"] = location __props__.__dict__["name"] = name if virtual_machine_id is None and not opts.urn: raise TypeError("Missing required property 'virtual_machine_id'") __props__.__dict__["virtual_machine_id"] = virtual_machine_id super(ConfigurationPolicyAssignment, __self__).__init__( 'azure:compute/configurationPolicyAssignment:ConfigurationPolicyAssignment', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, configuration: Optional[pulumi.Input[pulumi.InputType['ConfigurationPolicyAssignmentConfigurationArgs']]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, virtual_machine_id: Optional[pulumi.Input[str]] = None) -> 'ConfigurationPolicyAssignment': """ Get an existing ConfigurationPolicyAssignment resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['ConfigurationPolicyAssignmentConfigurationArgs']] configuration: A `configuration` block as defined below. :param pulumi.Input[str] location: The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. :param pulumi.Input[str] name: The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. :param pulumi.Input[str] virtual_machine_id: The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _ConfigurationPolicyAssignmentState.__new__(_ConfigurationPolicyAssignmentState) __props__.__dict__["configuration"] = configuration __props__.__dict__["location"] = location __props__.__dict__["name"] = name __props__.__dict__["virtual_machine_id"] = virtual_machine_id return ConfigurationPolicyAssignment(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def configuration(self) -> pulumi.Output['outputs.ConfigurationPolicyAssignmentConfiguration']: """ A `configuration` block as defined below. """ return pulumi.get(self, "configuration") @property @pulumi.getter def location(self) -> pulumi.Output[str]: """ The Azure location where the Virtual Machine Configuration Policy Assignment should exist. Changing this forces a new resource to be created. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the Virtual Machine Configuration Policy Assignment. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @property @pulumi.getter(name="virtualMachineId") def virtual_machine_id(self) -> pulumi.Output[str]: """ The resource ID of the Virtual Machine which this Guest Configuration Assignment should apply to. Changing this forces a new resource to be created. """ return pulumi.get(self, "virtual_machine_id")

50.126214

309

0.692491

1,680

15,489

6.202381

0.101786

0.076583

0.055086

0.050672

0.784645

0.764012

0.750288

0.730806

0.718042

0.708637

0

0.005907

0.22403

15,489

308

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50.288961

0.861053

0.384402

0

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1

0

0.147517

0.07088

0

1

0.152542

false

0.00565

0.039548

0

0.282486

0

null

0

1

0

1

0

null

0

6

1d527b4b6d499267eb2b0179e407f601a8ba73f6

42

py

Python

maps/flooded_valley/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

maps/flooded_valley/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

maps/flooded_valley/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

from .flooded_valley import FloodedValley

21

41

0.880952

5

42

7.2

1

0

0.095238

42

1

42

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1d6cd3aa80424f9d33665475f8ccb776d8e52200

8,561

py

Python

automatic_replenishment_system/retail_core/migrations/0001_initial.py

udwivedi394/automatic_replenishment

c2fde9a94329147ee33b3d7f4f8826378d278f51

[ "MIT" ]

null

automatic_replenishment_system/retail_core/migrations/0001_initial.py

udwivedi394/automatic_replenishment

c2fde9a94329147ee33b3d7f4f8826378d278f51

[ "MIT" ]

null

automatic_replenishment_system/retail_core/migrations/0001_initial.py

udwivedi394/automatic_replenishment

c2fde9a94329147ee33b3d7f4f8826378d278f51

[ "MIT" ]

null

# Generated by Django 2.0.13 on 2019-04-21 04:13 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='BrandModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('name', models.CharField(db_index=True, max_length=256)), ('ranking_model', models.CharField(choices=[('static', 'Static'), ('dynamic', 'Dynamic')], max_length=128)), ], options={ 'verbose_name': 'Brand', 'verbose_name_plural': 'Brands', }, ), migrations.CreateModel( name='BSQ', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('bsq', models.IntegerField()), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ], options={ 'verbose_name': 'BSQ', 'verbose_name_plural': 'BSQ', }, ), migrations.CreateModel( name='ProductModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('product_code', models.CharField(db_index=True, max_length=256)), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ], options={ 'verbose_name': 'Product', 'verbose_name_plural': 'Products', }, ), migrations.CreateModel( name='SalesTransaction', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('date', models.DateTimeField()), ('quantity', models.IntegerField()), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.ProductModel')), ], options={ 'verbose_name': 'Sale Transaction', 'verbose_name_plural': 'Sales Transactions', }, ), migrations.CreateModel( name='StaticPriorityModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('static_priority_rank', models.IntegerField()), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ], options={ 'verbose_name': 'Static Priority', 'verbose_name_plural': 'Static Priorities', }, ), migrations.CreateModel( name='StoreInventoryModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('date', models.DateTimeField()), ('closing_inventory', models.IntegerField()), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.ProductModel')), ], options={ 'verbose_name': 'Store Inventory', 'verbose_name_plural': 'Store Inventory', }, ), migrations.CreateModel( name='StoreModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('store_code', models.CharField(db_index=True, max_length=256)), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ], options={ 'verbose_name': 'Store', 'verbose_name_plural': 'Stores', }, ), migrations.CreateModel( name='WarehouseInventoryModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('date', models.DateTimeField()), ('closing_inventory', models.IntegerField()), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.ProductModel')), ], options={ 'verbose_name': 'Warehouse Inventory', 'verbose_name_plural': 'Warehouse Inventory', }, ), migrations.CreateModel( name='WarehouseModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True, db_index=True)), ('updated_at', models.DateTimeField(auto_now=True, db_index=True)), ('warehouse_code', models.CharField(db_index=True, max_length=256)), ('brand_model', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.BrandModel')), ('store', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.StoreModel')), ], options={ 'verbose_name': 'Warehouse', 'verbose_name_plural': 'Warehouses', }, ), migrations.AddField( model_name='storeinventorymodel', name='store', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.StoreModel'), ), migrations.AddField( model_name='staticprioritymodel', name='store', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.StoreModel'), ), migrations.AddField( model_name='salestransaction', name='store', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.StoreModel'), ), migrations.AddField( model_name='bsq', name='product', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.ProductModel'), ), migrations.AddField( model_name='bsq', name='store', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='retail_core.StoreModel'), ), ]

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null

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1

0

1

0

null

0

6

1d987e5f9e342128c669cf8fb3f8e192fe20c65f

63

py

Python

forge/blade/item/tool.py

jarbus/neural-mmo

7ad02fab50f2781c0a71f7d2afd10c1503110736

[ "MIT" ]

1,450

2019-03-04T15:47:38.000Z

2022-03-30T03:33:35.000Z

forge/blade/item/tool.py

jarbus/neural-mmo

7ad02fab50f2781c0a71f7d2afd10c1503110736

[ "MIT" ]

34

2019-03-05T09:50:38.000Z

2021-08-31T15:20:27.000Z

forge/blade/item/tool.py

LaudateCorpus1/neural-mmo

a9a7c34a1fb24fbf252e2958bdb869c213e580a3

[ "MIT" ]

164

2019-03-04T16:09:19.000Z

2022-02-26T15:43:40.000Z

from forge.blade.item import Item class Tool(Item.Item): pass

15.75

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63

4.454545

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0.126984

63

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null

0

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1

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6

d53b4eb04616817318c490e935ebd11d92435c98

181

py

Python

rest_in_python/utils.py

Varun2604/rest_in_python

cd8e0c7b2862c43176d249a9de26e0133ae7a7af

[ "MIT" ]

null

rest_in_python/utils.py

Varun2604/rest_in_python

cd8e0c7b2862c43176d249a9de26e0133ae7a7af

[ "MIT" ]

null

rest_in_python/utils.py

Varun2604/rest_in_python

cd8e0c7b2862c43176d249a9de26e0133ae7a7af

[ "MIT" ]

null

from flask import Response def get_as_response(data, success=True): return Response(response=data, headers={'content-type': 'application/json'}, status=200 if success else 400)

45.25

112

0.773481

26

181

5.307692

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0.037267

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null

0

1

0

null

0

1

0

1

0

6

d546bdc043277b331a975c7f9688f555fd7b5ef0

81

py

Python

pureskillgg_makenew_pyskill/notebook/__init__.py

pureskillgg/makenew-pyskill

3045f0639506fcaefd3191dada76277598bbb1eb

[ "MIT" ]

null

pureskillgg_makenew_pyskill/notebook/__init__.py

pureskillgg/makenew-pyskill

3045f0639506fcaefd3191dada76277598bbb1eb

[ "MIT" ]

null

pureskillgg_makenew_pyskill/notebook/__init__.py

pureskillgg/makenew-pyskill

3045f0639506fcaefd3191dada76277598bbb1eb

[ "MIT" ]

null

""" Setup local Jupyter notebooks """ from .setup_notebook import setup_notebook

16.2

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10

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6.2

0.7

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0.123457

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4

43

20.25

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null

0

1

0

1

0

1

0

6

d55fa92057887aea1488a52c2c40af41a5643927

136

py

Python

tools/pubschemata.py

surchs/dandischema

4fd78fe55e2c8efeb0ce27fba5652ab87a10958c

[ "Apache-2.0" ]

1

2021-09-19T10:56:25.000Z

tools/pubschemata.py

surchs/dandischema

4fd78fe55e2c8efeb0ce27fba5652ab87a10958c

[ "Apache-2.0" ]

121

2021-05-20T18:35:32.000Z

2022-03-31T13:13:52.000Z

tools/pubschemata.py

surchs/dandischema

4fd78fe55e2c8efeb0ce27fba5652ab87a10958c

[ "Apache-2.0" ]

4

2021-05-20T22:03:21.000Z

2021-09-22T23:31:00.000Z

import sys from dandischema.metadata import publish_model_schemata if __name__ == "__main__": publish_model_schemata(sys.argv[1])

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136

5.333333

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6

6345bd50a7fa096d40a919e74d955c806e77f8d5

8,957

py

Python

SimModel_Python_API/simmodel_swig/Release/SimMaterialLayerSet_OpaqueLayerSet_Door.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

3

2016-05-30T15:12:16.000Z

2022-03-22T08:11:13.000Z

SimModel_Python_API/simmodel_swig/Release/SimMaterialLayerSet_OpaqueLayerSet_Door.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

21

2016-06-13T11:33:45.000Z

2017-05-23T09:46:52.000Z

SimModel_Python_API/simmodel_swig/Release/SimMaterialLayerSet_OpaqueLayerSet_Door.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

null

# This file was automatically generated by SWIG (http://www.swig.org). # Version 3.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2, 6, 0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_SimMaterialLayerSet_OpaqueLayerSet_Door', [dirname(__file__)]) except ImportError: import _SimMaterialLayerSet_OpaqueLayerSet_Door return _SimMaterialLayerSet_OpaqueLayerSet_Door if fp is not None: try: _mod = imp.load_module('_SimMaterialLayerSet_OpaqueLayerSet_Door', fp, pathname, description) finally: fp.close() return _mod _SimMaterialLayerSet_OpaqueLayerSet_Door = swig_import_helper() del swig_import_helper else: import _SimMaterialLayerSet_OpaqueLayerSet_Door del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static): if _newclass: object.__setattr__(self, name, value) else: self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr_nondynamic(self, class_type, name, static=1): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) if (not static): return object.__getattr__(self, name) else: raise AttributeError(name) def _swig_getattr(self, class_type, name): return _swig_getattr_nondynamic(self, class_type, name, 0) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object: pass _newclass = 0 try: import weakref weakref_proxy = weakref.proxy except: weakref_proxy = lambda x: x import base import SimMaterialLayerSet_OpaqueLayerSet_Ceiling import SimMaterialLayerSet_Default_Default class SimMaterialLayerSet_OpaqueLayerSet_Door(SimMaterialLayerSet_OpaqueLayerSet_Ceiling.SimMaterialLayerSet_OpaqueLayerSet): __swig_setmethods__ = {} for _s in [SimMaterialLayerSet_OpaqueLayerSet_Ceiling.SimMaterialLayerSet_OpaqueLayerSet]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterialLayerSet_OpaqueLayerSet_Door, name, value) __swig_getmethods__ = {} for _s in [SimMaterialLayerSet_OpaqueLayerSet_Ceiling.SimMaterialLayerSet_OpaqueLayerSet]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterialLayerSet_OpaqueLayerSet_Door, name) __repr__ = _swig_repr def SimMatLayerSet_OutsideLayer(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_SimMatLayerSet_OutsideLayer(self, *args) def SimMatLayerSet_Layer_2_10(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_SimMatLayerSet_Layer_2_10(self, *args) def T24DoorCertMethod(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_T24DoorCertMethod(self, *args) def T24DoorOpenType(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_T24DoorOpenType(self, *args) def T24DoorType(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_T24DoorType(self, *args) def __init__(self, *args): this = _SimMaterialLayerSet_OpaqueLayerSet_Door.new_SimMaterialLayerSet_OpaqueLayerSet_Door(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door__clone(self, f, c) __swig_destroy__ = _SimMaterialLayerSet_OpaqueLayerSet_Door.delete_SimMaterialLayerSet_OpaqueLayerSet_Door __del__ = lambda self: None SimMaterialLayerSet_OpaqueLayerSet_Door_swigregister = _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_swigregister SimMaterialLayerSet_OpaqueLayerSet_Door_swigregister(SimMaterialLayerSet_OpaqueLayerSet_Door) class SimMaterialLayerSet_OpaqueLayerSet_Door_sequence(base.sequence_common): __swig_setmethods__ = {} for _s in [base.sequence_common]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterialLayerSet_OpaqueLayerSet_Door_sequence, name, value) __swig_getmethods__ = {} for _s in [base.sequence_common]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterialLayerSet_OpaqueLayerSet_Door_sequence, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimMaterialLayerSet_OpaqueLayerSet_Door.new_SimMaterialLayerSet_OpaqueLayerSet_Door_sequence(*args) try: self.this.append(this) except: self.this = this def assign(self, n, x): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_assign(self, n, x) def begin(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_begin(self, *args) def end(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_end(self, *args) def rbegin(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_rbegin(self, *args) def rend(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_rend(self, *args) def at(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_at(self, *args) def front(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_front(self, *args) def back(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_back(self, *args) def push_back(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_push_back(self, *args) def pop_back(self): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_pop_back(self) def detach_back(self, pop=True): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_detach_back(self, pop) def insert(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_insert(self, *args) def erase(self, *args): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_erase(self, *args) def detach(self, position, r, erase=True): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_detach(self, position, r, erase) def swap(self, x): return _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_swap(self, x) __swig_destroy__ = _SimMaterialLayerSet_OpaqueLayerSet_Door.delete_SimMaterialLayerSet_OpaqueLayerSet_Door_sequence __del__ = lambda self: None SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_swigregister = _SimMaterialLayerSet_OpaqueLayerSet_Door.SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_swigregister SimMaterialLayerSet_OpaqueLayerSet_Door_sequence_swigregister(SimMaterialLayerSet_OpaqueLayerSet_Door_sequence) # This file is compatible with both classic and new-style classes.

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1

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null

0

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6

6388eb204db85deae502956d96ba700b272b91a7

75

py

Python

doctr/utils/__init__.py

mzeidhassan/doctr

14b376e07d31b09b6bd31bceebf6ffb477c30f08

[ "Apache-2.0" ]

1

2021-09-26T06:03:10.000Z

doctr/utils/__init__.py

mzeidhassan/doctr

14b376e07d31b09b6bd31bceebf6ffb477c30f08

[ "Apache-2.0" ]

null

doctr/utils/__init__.py

mzeidhassan/doctr

14b376e07d31b09b6bd31bceebf6ffb477c30f08

[ "Apache-2.0" ]

null

from .geometry import * from .common_types import * from .metrics import *

18.75

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0

1

0

1

0

1

0

6

892131c74e300a31db22c61e8166de2d25b19033

39

py

Python

__main__.py

35359595/pyfs

23688b59a1f86c1c9ee76051849703f9e22da3f4

[ "Apache-2.0" ]

null

__main__.py

35359595/pyfs

23688b59a1f86c1c9ee76051849703f9e22da3f4

[ "Apache-2.0" ]

null

__main__.py

35359595/pyfs

23688b59a1f86c1c9ee76051849703f9e22da3f4

[ "Apache-2.0" ]

null

#!/bin/bash/env python3 import tvcheck

13

23

0.769231

6

39

5

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2

24

19.5

0.828571

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true

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1

0

null

0

1

0

1

0

1

0

6

89238e21b0ef9afa2c7139ef63e4b7d39ea97684

47

py

Python

geco/mips/graph_coloring/__init__.py

FreestyleBuild/GeCO

6db1a549b3145b3bc5d3025a9bccc03be6575564

[ "MIT" ]

8

2020-12-16T09:59:05.000Z

2022-03-18T09:48:43.000Z

geco/mips/graph_coloring/__init__.py

FreestyleBuild/GeCO

6db1a549b3145b3bc5d3025a9bccc03be6575564

[ "MIT" ]

101

2020-11-09T10:20:03.000Z

2022-03-24T13:50:06.000Z

geco/mips/graph_coloring/__init__.py

FreestyleBuild/GeCO

6db1a549b3145b3bc5d3025a9bccc03be6575564

[ "MIT" ]

3

2021-04-06T13:26:03.000Z

2022-03-22T13:22:16.000Z

from geco.mips.graph_coloring.generic import *

23.5

46

0.829787

7

47

5.428571

1

0

0.085106

47

1

47

0.883721

0

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true

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0

1

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1

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null

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1

0

1

0

1

0

6

8931bf1a634ff1fdeb557edf81ff16bd5adbb4e3

102

py

Python

packages/watchmen-pipeline-surface/src/watchmen_pipeline_surface/__init__.py

Indexical-Metrics-Measure-Advisory/watchmen

c54ec54d9f91034a38e51fd339ba66453d2c7a6d

[ "MIT" ]

null

packages/watchmen-pipeline-surface/src/watchmen_pipeline_surface/__init__.py

Indexical-Metrics-Measure-Advisory/watchmen

c54ec54d9f91034a38e51fd339ba66453d2c7a6d

[ "MIT" ]

null

packages/watchmen-pipeline-surface/src/watchmen_pipeline_surface/__init__.py

Indexical-Metrics-Measure-Advisory/watchmen

c54ec54d9f91034a38e51fd339ba66453d2c7a6d

[ "MIT" ]

null

from .main import get_pipeline_surface_routers from .surface import pipeline_surface, PipelineSurface

34

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13

102

6.615385

0.615385

0.348837

0

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0

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true

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0

null

0

1

0

1

0

1

0

6

895e267c47ed1c0208b6314ea03002f639590590

45

py

Python

materialize_nav/__init__.py

justengel-django/django_materialize_nav

35349041e41defe599f959047d9e3f6e4bed38c2

[ "MIT" ]

1

2020-11-02T11:20:59.000Z

materialize_nav/__init__.py

justengel-django/django_materialize_nav

35349041e41defe599f959047d9e3f6e4bed38c2

[ "MIT" ]

1

2021-04-25T07:10:37.000Z

2021-04-26T16:34:19.000Z

materialize_nav/__init__.py

justengel-django/django_materialize_nav

35349041e41defe599f959047d9e3f6e4bed38c2

[ "MIT" ]

1

2021-01-30T14:36:12.000Z

from .__meta__ import version as __version__

22.5

44

0.844444

6

45

5

0.833333

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0.133333

45

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45

0.769231

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null

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null

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titan/api_pkg/pipeline/resources.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

titan/api_pkg/pipeline/resources.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

titan/api_pkg/pipeline/resources.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

from dataclasses import dataclass from moonleap import Resource @dataclass class Pipeline(Resource): pass

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asystem-anode/src/main/python/anode/plugin/darksky/__init__.py

ggear/asystem_archive

b97f67218e8aa60991fba386c9e73d27d20d6c47

[ "Apache-2.0" ]

4

2019-03-26T13:57:54.000Z

2021-11-04T04:55:49.000Z

asystem-anode/src/main/python/anode/plugin/darksky/__init__.py

ggear/asystem_archive

b97f67218e8aa60991fba386c9e73d27d20d6c47

[ "Apache-2.0" ]

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2021-03-25T21:27:09.000Z

2022-02-11T03:38:48.000Z

asystem-anode/src/main/python/anode/plugin/darksky/__init__.py

ggear/asystem_archive

b97f67218e8aa60991fba386c9e73d27d20d6c47

[ "Apache-2.0" ]

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2019-04-02T19:20:34.000Z

2019-08-13T16:39:52.000Z

from darksky import *

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Python

venv/lib/python3.8/site-packages/numpy/core/tests/test_conversion_utils.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

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2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/core/tests/test_conversion_utils.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

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2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/core/tests/test_conversion_utils.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/e9/d6/fb/d7a91f6bc17824d7499aa0fa4959a960ca85b59a43d568fd6b25738d66

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Python

Source/boost_1_33_1/libs/python/example/test_std_pair.py

spxuw/RFIM

32b78fbb90c7008b1106b0cff4f8023ae83c9b6d

[ "MIT" ]

null

Source/boost_1_33_1/libs/python/example/test_std_pair.py

spxuw/RFIM

32b78fbb90c7008b1106b0cff4f8023ae83c9b6d

[ "MIT" ]

null

Source/boost_1_33_1/libs/python/example/test_std_pair.py

spxuw/RFIM

32b78fbb90c7008b1106b0cff4f8023ae83c9b6d

[ "MIT" ]

null

import std_pair_ext assert std_pair_ext.foo() == (3, 5) print "OK"

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py

Python

src/TAME_simax.py

charlescolley/TKP_simax_plots

9c6b65c1800a8d12614cc62dae34670d36a575dd

[ "MIT" ]

null

src/TAME_simax.py

charlescolley/TKP_simax_plots

9c6b65c1800a8d12614cc62dae34670d36a575dd

[ "MIT" ]

null

src/TAME_simax.py

charlescolley/TKP_simax_plots

9c6b65c1800a8d12614cc62dae34670d36a575dd

[ "MIT" ]

null

#from sqlalchemy import true from sqlalchemy import false import CTAMELogParser as CT_LP from plotting_style import * def call_all_plots(): # # -- Low Rank Structure -- # # max_rank_experiments() TAME_vs_LRTAME_clique_scaling_summarized() # Appendix plots TAME_vs_LRTAME_rank_1_case_singular_values() # Supplementary File TAME_vs_LRTAME_clique_scaling_detailed() # # -- Random Graph Experiments -- # # RandomGeometricRG_PostProcessing_is_needed() LambdaTAME_increasing_clique_size_v2() #Supplementary Plots RandomGeometricDupNoise_allModes() RandomGeometricERNoise_allModes() # NOTE: default params are n = 250, p = .05 # # -- LVGNA Experiments -- # # LVGNA_end_to_end_relative_to_TAME_table_with_microplots() make_LVGNA_TTVMatchingRatio_runtime_plots() # Supplementary figures LVGNA_pre_and_post_processed() # # -- Dominant Eigenpair Experiments -- # # tensor_kronecker_product_eigenspaces_as_row() def render_and_save_all_plots(output_path=None): if output_path is None: output_path = "../rendered_figures/" assert output_path[-1] == "/" make_path = lambda filename: output_path + filename # # -- Low Rank Structure -- # # max_rank_experiments(save_path=make_path("MaxRankExperiments.pdf")) print(f"saved fig {make_path('MaxRankExperiments.pdf')}") TAME_vs_LRTAME_clique_scaling_summarized(save_path=make_path("TAME_LRTAME_clique_scaling_summarized.pdf")) print(f"saved fig {make_path('TAME_LRTAME_clique_scaling_summarized.pdf')}") # Appendix plots TAME_vs_LRTAME_rank_1_case_singular_values(save_path=make_path("LRTAME_vs_TAME_rank_1_case_secondSingularValues.pdf")) print(f"saved fig {make_path('LRTAME_vs_TAME_rank_1_case_secondSingularValues')}") # Supplementary File TAME_vs_LRTAME_clique_scaling_detailed(save_path=make_path("TAME_LRTAME_clique_scaling.pdf")) #TODO: need to update with 8 + 9 Clique results # violin plots style need to be updated too. print(f"saved fig {make_path('TAME_LRTAME_clique_scaling.pdf')}") # # -- Random Graph Experiments -- # # RandomGeometricRG_PostProcessing_is_needed(save_path=make_path('SynthAlignments_SizeExps_theNeedForPostProcessing.pdf')) print(f"saved fig {make_path('SynthAlignments_SizeExps_theNeedForPostProcessing.pdf')}") LambdaTAME_increasing_clique_size_v2(save_path=make_path('PostProcessing_KNearest_IncreasingMotifs.pdf')) print(f"saved fig {make_path('PostProcessing_KNearest_IncreasingMotifs.pdf')}") #Supplementary Plots RandomGeometricDupNoise_allModes(save_path=make_path('DupNoiseRG_exps.pdf')) print(f"saved fig {make_path('DupNoiseRG_exps.pdf')}") RandomGeometricERNoise_allModes(save_path=make_path('ERNoiseRG_exps.pdf')) print(f"saved fig {make_path('ERNoiseRG_exps.pdf')}") # TODO: add in LREA post processing results # update plot style # NOTE: default params are n = 250, p = .05 # # -- LVGNA Experiments -- # # LVGNA_end_to_end_relative_to_TAME_table_with_microplots(save_path=make_path('LVGNA_end_to_end_vs_TAME.pdf')) print(f"saved fig {make_path('LVGNA_end_to_end_vs_TAME.pdf')}") make_LVGNA_TTVMatchingRatio_runtime_plots(save_path=make_path('LVGNA_TTVMatchingRatio_noLowRank.pdf')) print(f"saved fig {make_path('LVGNA_TTVMatchingRatio_noLowRank.pdf')}") # Supplementary figures LVGNA_pre_and_post_processed(save_path=make_path('TAME_LVGNA_PreAndPostProcessing.pdf')) print(f"saved fig {make_path('TAME_LVGNA_PreAndPostProcessing.pdf')}") # # -- Dominant Eigenpair Experiments -- # # tensor_kronecker_product_eigenspaces_as_row(save_path=make_path('TKPDominantEigenpairs.pdf')) print(f"saved fig {make_path('TKPDominantEigenpairs.pdf')}") # # Matlab Experiment Summary stats # def verify_ARST_tensors_are_generated_tensors(): """ Copies of the tensors are saved within the AReigST files too. This function verifies all the tensors are the same to ensure correct spectra are used across all algorithms.""" tensor_path = TKP_RESULTS + "tensor_problems/" tensors = {} for file in os.listdir(tensor_path): tenParamStr = file.split(".mat")[0].split("weighted_TKP_")[-1] mat_file_obj = scipy.io.loadmat(tensor_path+file) A = mat_file_obj["A"][0][0][0] B = mat_file_obj["B"][0][0][0] A_kron_B = mat_file_obj["A_kron_B"][0] tensors[tenParamStr] = [A,B,A_kron_B] ARST_result_path = TKP_RESULTS + "AREigST/" for file in os.listdir(ARST_result_path): tenParamStr = file.split("_results.mat")[0].split("weighted_kronzeig_exp_")[-1] mat_file_obj = scipy.io.loadmat(ARST_result_path+file) A = mat_file_obj["A"][0][0][0] B = mat_file_obj["B"][0][0][0] A_kron_B = mat_file_obj["A_kron_B"][0] A_prob, B_prob, A_kron_B_prob = tensors[tenParamStr] print(f"file:{file}\n |A-Ap|={np.sum(A - A_prob)} |B -Bp|={np.sum(B - B_prob)} |A_kron_B -A_kron_Bp|={np.sum(A_kron_B - A_kron_B_prob)}") def tensor_kronecker_product_eigenspaces(save_path=None): # # Data loading drivers # def process_AReigSTdata(): result_path = TKP_RESULTS + "AReigST/" # # Load data from files # success_codes = [] eigenvectors_found = {} for file in os.listdir(result_path): tensor_param_str = file.split("_results.mat")[0].split("exp_")[-1] mat_file = scipy.io.loadmat(result_path + file) codes = [] for key in ["info_B","info_A","info_A_kron_B"]: codes.append(mat_file[key][0]["success"][0][0][0]) A_eigvals = mat_file["lmd_A"].reshape(-1) B_eigvals = mat_file["lmd_B"].reshape(-1) A_kron_B_eigvals = mat_file["lmd_A_kron_B"].reshape(-1) A_eigvecs = mat_file["eigvec_A"] B_eigvecs = mat_file["eigvec_B"] A_kron_B_eigvecs = mat_file["eigvec_A_kron_B"] eigenvectors_found[tensor_param_str] = [A_eigvals, A_eigvecs, B_eigvals, B_eigvecs, A_kron_B_eigvals, A_kron_B_eigvecs] success_codes.append(codes) return eigenvectors_found,success_codes def process_NCMdata(subdirectory="NCM_sampling/"): results_path = TKP_RESULTS +subdirectory eigenvectors_found = {} for file in os.listdir(results_path): tensor_param_str = file.split("_delta:")[0].split("NCM_")[-1] matfile_object = scipy.io.loadmat(results_path+file) A_eigvals, A_eigvecs, _,_ = matfile_object["A_output"][0][0] B_eigvals, B_eigvecs, _,_= matfile_object["B_output"][0][0] A_kron_B_eigvals, A_kron_B_eigvecs, _,_ = matfile_object["A_kron_B_output"][0][0] A_eigvecs = np.transpose(A_eigvecs) B_eigvecs = np.transpose(B_eigvecs) A_kron_B_eigvecs = np.transpose(A_kron_B_eigvecs) A_eigvals = A_eigvals.reshape(-1) B_eigvals = B_eigvals.reshape(-1) A_kron_B_eigvals = A_kron_B_eigvals.reshape(-1) eigenvectors_found[tensor_param_str] = [A_eigvals, A_eigvecs, B_eigvals, B_eigvecs, A_kron_B_eigvals, A_kron_B_eigvecs] return eigenvectors_found AReigST_codes = process_AReigSTdata()[1] eigenpairs_found = [ process_AReigSTdata()[0], process_NCMdata(subdirectory="NCM_sampling/"), process_NCMdata(subdirectory="ONCM_sampling/"), ] #return eigenpairs_found # # Consolidate the eigevectors found for each tensor # def update_max(eigvals,eigvec,curr_eigpair): # curr_eigpair::(eigval,eigvec) vals = abs(eigvals) idx = np.argmax(vals) #print(vals) lmd = vals[idx] if lmd > curr_eigpair[0]: return (lmd,eigvec[idx,:]), True else: return curr_eigpair, False dominant_eigenpairs = [] algorithm_with_best_results = [] for tenParamStr in eigenpairs_found[0].keys(): max_A_eigpair = (-np.Inf,[]) max_B_eigpair = (-np.Inf,[]) max_A_kron_B_eigpair = (-np.Inf,[]) algorithm_chosen = [-1,-1,-1] # 3 entries for A,B,A_kron_B for alg_idx,exp in enumerate(eigenpairs_found): (A_eigvals, A_eigvecs, B_eigvals, B_eigvecs, A_kron_B_eigvals, A_kron_B_eigvecs) = exp[tenParamStr] max_A_eigpair, was_updated = update_max(A_eigvals, A_eigvecs,max_A_eigpair) if was_updated: algorithm_chosen[0] = alg_idx max_B_eigpair, was_updated = update_max(B_eigvals, B_eigvecs,max_B_eigpair) if was_updated: algorithm_chosen[1] = alg_idx max_A_kron_B_eigpair, was_updated = update_max(A_kron_B_eigvals, A_kron_B_eigvecs, max_A_kron_B_eigpair) if was_updated: algorithm_chosen[2] = alg_idx algorithm_with_best_results.append(algorithm_chosen) dominant_eigenpairs.append([max_A_eigpair,max_B_eigpair,max_A_kron_B_eigpair]) lmd_diff = [] subspace_angle = [] for i,((A_eigpair,B_eigpair,A_kron_B_eigpair),file) in enumerate(zip(dominant_eigenpairs,eigenpairs_found[0].keys())): (A_val,A_vec) = A_eigpair (B_val,B_vec) = B_eigpair (A_kron_B_val,A_kron_B_vec) = A_kron_B_eigpair lmd_diff.append((A_val*B_val-A_kron_B_val)/A_kron_B_val) subspace_angle.append(1-abs(np.dot(np.kron(B_vec,A_vec),A_kron_B_vec))) print(f"{file}: diff:{lmd_diff[-1]} angle:{subspace_angle[-1]} ARST_codes:{AReigST_codes[i]}") # # Plotting Subroutines # def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.8,format="default",xlim=None,xscale="linear",column_type=None): if xscale=="linear": v = ax.violinplot(data,[.5], points=100, showmeans=False,widths=.15, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) elif xscale=="log": v = ax.violinplot(np.log10(data),[.5], points=100, showmeans=False,widths=.15,showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1),(x0,y1+.7)]] v["cmedians"].set_segments(newMedianLines) # -- place extremal markers underneath extremal_tick_ypos = .25 # -- write data values as text bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.7,pad=.01) if column_type is None: if format == "default": ax.annotate(f"{np.median(data):.{precision}f}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8).set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8).set_bbox(bbox) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8).set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8).set_bbox(bbox) else: print(f"expecting format to be either 'default' or 'scientific', got:{format}") elif column_type == "merged_axis": pass else: raise ValueError("column_type expecting 'merged_axis' or None, but got {column_type}\n") if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor("None") b.set_edgecolor(c) b.set_alpha(v_alpha) # -- only plot the top half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 1]) b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 data = [np.random.normal() for i in range(50)] v = ax.violinplot(data, points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() ax.set_ylim(.5,1.0) ax.patch.set_alpha(0.0) # turn off background # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) ax.annotate("median",xy=(.5,.325),xycoords="axes fraction",ha="center",va="center",fontsize=10) ax.annotate(f"min",xy=(.025,-.075),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) ax.annotate(f"max",xy=(.975,-.075),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor(c) b.set_alpha(.3) b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) fig = plt.figure(figsize=(2.1,2)) n = 1 m = 2 # 4 gs = fig.add_gridspec(m, 1, left=0.025, right=0.975,top=.975,bottom=.2, wspace=.05,hspace=.1) all_axes = np.empty(m,object) for j in range(m): all_axes[j] = fig.add_subplot(gs[j]) replace_zeros_with_machine_epsilon = lambda vals: [2e-16 if x == 0 else abs(x) for x in vals] make_violin_plot(all_axes[0],replace_zeros_with_machine_epsilon(lmd_diff),xscale="log",c="k",format="scientific",precision=3) make_violin_plot(all_axes[1],replace_zeros_with_machine_epsilon(subspace_angle),xscale="log",c="k",format="scientific",precision=3) # # Touch up Axes # legend_axis = all_axes[1].inset_axes([.2,-.5,.6,.5]) make_violin_plot_legend(legend_axis) for ax in chain(all_axes.reshape(-1),[legend_axis]): ax.set_xticklabels([]) ax.set_yticklabels([]) ax.tick_params(axis="both",which='major', length=0,pad=6) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) #for ax in all_axes: #ax.set_ylim(.3,.8) n = len(all_axes.reshape(-1)) for (i,ax) in zip(range(n-1,0,-1),all_axes.reshape(-1)): ax.set_zorder(i) all_axes[0].annotate(r"$||\lambda_B||\lambda_A| - |\lambda_{B \otimes A}||/|\lambda_{B \otimes A}|$",xy=(0.5 , 0.2), xycoords='axes fraction',ha="center",va="top",fontsize=12) all_axes[1].annotate(r"$1-|\langle {\bf v_{B}} \otimes {\bf v_{A}},{\bf v_{B \otimes A}}\rangle|$",xy=(0.5 , 0.2), xycoords='axes fraction',ha="center",va="top",fontsize=12) if save_path is None: plt.show() else: plt.savefig(save_path) def tensor_kronecker_product_eigenspaces_as_row(save_path=None): # # Data loading drivers # def process_AReigSTdata(): result_path = TKP_RESULTS + "AReigST/" # # Load data from files # success_codes = [] eigenvectors_found = {} for file in os.listdir(result_path): tensor_param_str = file.split("_results.mat")[0].split("exp_")[-1] mat_file = scipy.io.loadmat(result_path + file) codes = [] for key in ["info_B","info_A","info_A_kron_B"]: codes.append(mat_file[key][0]["success"][0][0][0]) A_eigvals = mat_file["lmd_A"].reshape(-1) B_eigvals = mat_file["lmd_B"].reshape(-1) A_kron_B_eigvals = mat_file["lmd_A_kron_B"].reshape(-1) A_eigvecs = mat_file["eigvec_A"] B_eigvecs = mat_file["eigvec_B"] A_kron_B_eigvecs = mat_file["eigvec_A_kron_B"] eigenvectors_found[tensor_param_str] = [A_eigvals, A_eigvecs, B_eigvals, B_eigvecs, A_kron_B_eigvals, A_kron_B_eigvecs] success_codes.append(codes) return eigenvectors_found,success_codes def process_NCMdata(subdirectory="NCM_sampling/"): results_path = TKP_RESULTS +subdirectory eigenvectors_found = {} for file in os.listdir(results_path): tensor_param_str = file.split("_delta:")[0].split("NCM_")[-1] matfile_object = scipy.io.loadmat(results_path+file) A_eigvals, A_eigvecs, _,_ = matfile_object["A_output"][0][0] B_eigvals, B_eigvecs, _,_= matfile_object["B_output"][0][0] A_kron_B_eigvals, A_kron_B_eigvecs, _,_ = matfile_object["A_kron_B_output"][0][0] A_eigvecs = np.transpose(A_eigvecs) B_eigvecs = np.transpose(B_eigvecs) A_kron_B_eigvecs = np.transpose(A_kron_B_eigvecs) A_eigvals = A_eigvals.reshape(-1) B_eigvals = B_eigvals.reshape(-1) A_kron_B_eigvals = A_kron_B_eigvals.reshape(-1) eigenvectors_found[tensor_param_str] = [A_eigvals, A_eigvecs, B_eigvals, B_eigvecs, A_kron_B_eigvals, A_kron_B_eigvecs] return eigenvectors_found AReigST_codes = process_AReigSTdata()[1] eigenpairs_found = [ process_AReigSTdata()[0], process_NCMdata(subdirectory="NCM_sampling/"), process_NCMdata(subdirectory="ONCM_sampling/"), ] #return eigenpairs_found # # Consolidate the eigevectors found for each tensor # def update_max(eigvals,eigvec,curr_eigpair): # curr_eigpair::(eigval,eigvec) vals = abs(eigvals) idx = np.argmax(vals) #print(vals) lmd = vals[idx] if lmd > curr_eigpair[0]: return (lmd,eigvec[idx,:]), True else: return curr_eigpair, False dominant_eigenpairs = [] algorithm_with_best_results = [] for tenParamStr in eigenpairs_found[0].keys(): max_A_eigpair = (-np.Inf,[]) max_B_eigpair = (-np.Inf,[]) max_A_kron_B_eigpair = (-np.Inf,[]) algorithm_chosen = [-1,-1,-1] # 3 entries for A,B,A_kron_B for alg_idx,exp in enumerate(eigenpairs_found): (A_eigvals, A_eigvecs, B_eigvals, B_eigvecs, A_kron_B_eigvals, A_kron_B_eigvecs) = exp[tenParamStr] max_A_eigpair, was_updated = update_max(A_eigvals, A_eigvecs,max_A_eigpair) if was_updated: algorithm_chosen[0] = alg_idx max_B_eigpair, was_updated = update_max(B_eigvals, B_eigvecs,max_B_eigpair) if was_updated: algorithm_chosen[1] = alg_idx max_A_kron_B_eigpair, was_updated = update_max(A_kron_B_eigvals, A_kron_B_eigvecs, max_A_kron_B_eigpair) if was_updated: algorithm_chosen[2] = alg_idx algorithm_with_best_results.append(algorithm_chosen) dominant_eigenpairs.append([max_A_eigpair,max_B_eigpair,max_A_kron_B_eigpair]) lmd_diff = [] subspace_angle = [] for i,((A_eigpair,B_eigpair,A_kron_B_eigpair),file) in enumerate(zip(dominant_eigenpairs,eigenpairs_found[0].keys())): (A_val,A_vec) = A_eigpair (B_val,B_vec) = B_eigpair (A_kron_B_val,A_kron_B_vec) = A_kron_B_eigpair lmd_diff.append((A_val*B_val-A_kron_B_val)/A_kron_B_val) subspace_angle.append(1-abs(np.dot(np.kron(B_vec,A_vec),A_kron_B_vec))) print(f"{file}: diff:{lmd_diff[-1]} angle:{subspace_angle[-1]} ARST_codes:{AReigST_codes[i]}") # # Plotting Subroutines # def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.3,format="default",xlim=None,xscale="linear",column_type=None): if xscale=="linear": v = ax.violinplot(data,[.5], points=100, showmeans=False,widths=.15, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) elif xscale=="log": v = ax.violinplot(np.log10(data),[.5], points=100, showmeans=False,widths=.15,showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1-.01),(x0,y1+.7)]] v["cmedians"].set_segments(newMedianLines) # -- place extremal markers underneath extremal_tick_ypos = .25 # -- write data values as text bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.7,pad=.01) if column_type is None: if format == "default": ax.annotate(f"{np.median(data):.{precision}f}",xy=(.5,.375),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8).set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}f}",xy=(.975,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8).set_bbox(bbox) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.375),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8).set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.975,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8).set_bbox(bbox) else: print(f"expecting format to be either 'default' or 'scientific', got:{format}") elif column_type == "merged_axis": pass else: raise ValueError("column_type expecting 'merged_axis' or None, but got {column_type}\n") if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(.3) # -- only plot the top half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 1]) b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 data = [np.random.normal() for i in range(50)] v = ax.violinplot(data, points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() ax.set_ylim(.5,1.0) ax.patch.set_alpha(0.0) # turn off background # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) ax.annotate("median",xy=(.5,.325),xycoords="axes fraction",ha="center",va="center",fontsize=10) ax.annotate(f"min",xy=(.025,-.075),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) ax.annotate(f"max",xy=(.975,-.075),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(.3) #b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) fig = plt.figure(figsize=(5.75,1)) n = 1 m = 2 # 4 gs = fig.add_gridspec(1, m, left=0.2, right=0.975,top=.975,bottom=.05, wspace=.025,hspace=.1) all_axes = np.empty(m,object) for j in range(m): all_axes[j] = fig.add_subplot(gs[j]) replace_zeros_with_machine_epsilon = lambda vals: [2e-16 if x == 0 else abs(x) for x in vals] make_violin_plot(all_axes[0],replace_zeros_with_machine_epsilon(lmd_diff),xscale="log",c="k",v_alpha=.3,format="scientific",precision=3) make_violin_plot(all_axes[1],replace_zeros_with_machine_epsilon(subspace_angle),xscale="log",c="k",v_alpha=.3,format="scientific",precision=3) # # Touch up Axes # legend_axis = all_axes[0].inset_axes([-0.45,.3,.4,.5]) make_violin_plot_legend(legend_axis) for ax in chain(all_axes.reshape(-1),[legend_axis]): ax.set_xticklabels([]) ax.set_yticklabels([]) ax.tick_params(axis="both",which='major', length=0,pad=6) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) #for ax in all_axes: #ax.set_ylim(.3,.8) n = len(all_axes.reshape(-1)) for (i,ax) in zip(range(n-1,0,-1),all_axes.reshape(-1)): ax.set_zorder(i) all_axes[0].annotate(r"$||\lambda_B||\lambda_A| - |\lambda_{B \otimes A}||/|\lambda_{B \otimes A}|$",xy=(0.5 , 0.2), xycoords='axes fraction',ha="center",va="top",fontsize=12) all_axes[1].annotate(r"$1-|\langle {\bf v_{B}} \otimes {\bf v_{A}},{\bf v_{B \otimes A}}\rangle|$",xy=(0.5 , 0.2), xycoords='axes fraction',ha="center",va="top",fontsize=12) if save_path is None: plt.show() else: plt.savefig(save_path) # # TAME Low Rank Structure + Scaling # def TAME_vs_LRTAME_rank_1_case_singular_values(save_path=None): f = plt.figure(figsize=(7,3)) gs = f.add_gridspec(nrows=1, ncols=3, left=0.05, right=0.975,wspace=0.3,hspace=0.1,top=.975,bottom=.175) all_ax = np.empty(3,object) for i in range(3): all_ax[i] = f.add_subplot(gs[i]) TAME_LVGNA_rank_1_case_singular_values(all_ax[0]) TAME_RandomGeometric_rank_1_singular_values_v2(all_ax[1],noiseModel="dupNoise") TAME_RandomGeometric_rank_1_singular_values_v2(all_ax[2],noiseModel="ERNoise") # # Tweak Axis Details # all_ax[0].set_ylabel(r"$\sigma_2$",fontsize=14,labelpad=-5) for ax in all_ax[:2]: ax.annotate(r"$\epsilon$", xy=(1.125, .05), xycoords='axes fraction', c=purple_c,fontsize=12) for ax in all_ax: ax.set_ylim(5e-17,1e-7) ax.set_yticks([1e-16,1e-14,1e-12,1e-10,1e-08]) ax.tick_params(axis="both",which='major', length=0,pad=6) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) all_ax[0].set_yticklabels([]) all_ax[0].set_xlim(2e5,4e11) for ax in all_ax[1:]: ax.set_xlim(3e5,3e11) ax.set_yticklabels(["",1e-14,1e-12,1e-10,1e-8]) ax.set_xticks([1e6,1e7,1e8,1e9,1e10,1e11]) title_size = 12 x_loc = .125 all_ax[0].annotate("LVGNA",xy=(x_loc,.875), xycoords='axes fraction',ha="left",va="top",fontsize=title_size) all_ax[1].annotate("Duplication\nNoise",xy=(x_loc,.875), xycoords='axes fraction',ha="left",va="top",fontsize=title_size) all_ax[2].annotate(u"Erdős Rényi"+"\nNoise",xy=(x_loc,.875), xycoords='axes fraction',ha="left",va="top",fontsize=title_size) if save_path is None: plt.show() else: plt.savefig(save_path) # - LRTAME has more accurate singular values for Triangle Adjancency tensors - # def TAME_LVGNA_rank_1_case_singular_values(ax=None): datapath = TAME_RESULTS + "rank1_singular_values/" with open(datapath + "TAME_LVGNA_iter_30_no_match_tol_1e-12.json","r") as f: TAME_data = json.load(f) TAME_data= TAME_data[-1] with open(datapath + "LowRankTAME_LVGNA_iter_30_no_match_tol_1e-12.json","r") as f: LowRankTAME_data = json.load(f) LowRankTAME_data = LowRankTAME_data[-1] showFig = False if ax is None: f = plt.figure(figsize=(3,3)) ax = plt.gca() showFig = True def process_data(data): nonzero_second_largest_sing_vals = [] zero_second_largest_sing_vals = [] nonzero_vertex_products = [] zero_vertex_products = [] nonzero_triangle_products = [] zero_triangle_products = [] for file_A,file_B,_,_,profile in data: graph_A = " ".join(file_A.split(".ssten")[0].split("_")) graph_B = " ".join(file_B.split(".ssten")[0].split("_")) profile_dict = profile[0][-1] #normalize the singular values for s in profile_dict["sing_vals"]: total = sum(s) s[:] = [s_i/total for s_i in s] #max_rank = int(max(profile_dict["ranks"])) #sing_vals = [s[1] if len(s) > 1 else 2e-16 for s in profile_dict["sing_vals"] sing_vals = [(i,s[1]) if len(s) > 1 else (i,2e-16) for (i,s) in enumerate(profile_dict["sing_vals"])] #find max sing val, and check iterates rank i,sing2_val = max(sing_vals,key=lambda x:x[1]) rank = profile_dict["ranks"][i] if rank > 1: #if max_rank > 1.0: nonzero_second_largest_sing_vals.append(sing2_val) nonzero_vertex_products.append(vertex_counts[graph_A]*vertex_counts[graph_B]) nonzero_triangle_products.append(triangle_counts[graph_A]*triangle_counts[graph_B]) else: zero_second_largest_sing_vals.append(sing2_val) zero_vertex_products.append(vertex_counts[graph_A]*vertex_counts[graph_B]) zero_triangle_products.append(triangle_counts[graph_A]*triangle_counts[graph_B]) return nonzero_second_largest_sing_vals, nonzero_vertex_products, nonzero_triangle_products, zero_second_largest_sing_vals, zero_vertex_products, zero_triangle_products TAME_nonzero_second_largest_sing_vals, TAME_nonzero_vertex_products, \ TAME_nonzero_triangle_products, TAME_zero_second_largest_sing_vals,\ TAME_zero_vertex_products, TAME_zero_triangle_products = process_data(TAME_data) LowRankTAME_nonzero_second_largest_sing_vals, LowRankTAME_nonzero_vertex_products, \ LowRankTAME_nonzero_triangle_products, LowRankTAME_zero_second_largest_sing_vals,\ LowRankTAME_zero_vertex_products, LowRankTAME_zero_triangle_products = process_data(LowRankTAME_data) ax.set_yscale("log") ax.grid(which="major", axis="both") if showFig: ax.annotate(r"$\epsilon$", xy=(1.06, .02), xycoords='axes fraction', c=purple_c) ax.set_ylabel(r"$\sigma_2$") ax.set_ylim(1e-16,1e-7) ax.annotate("TAME", xy=(.6,.55),xycoords='axes fraction',fontsize=12,c=T_color) ax.annotate("LowRankTAME", xy=(.2,.1),xycoords='axes fraction',fontsize=12,c=LRT_color) ax.set_xlabel(r"|$T_A$||$T_B$|") ax.set_yscale("log") ax.set_xscale("log") #scatter plot formatting marker_size = 25 marker_alpha = .5 ax.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o',c=T_color,s=marker_size,alpha=marker_alpha) ax.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=T_color,s=marker_size) #plot machine epsilon ax.plot([1e5,1e13],[2e-16]*2,c=purple_c,zorder=1) ax.scatter(LowRankTAME_nonzero_triangle_products,LowRankTAME_nonzero_second_largest_sing_vals,marker='o', c=LRT_color,s=marker_size,alpha=marker_alpha) scatter = ax.scatter(LowRankTAME_zero_triangle_products,LowRankTAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=LRT_color,s=marker_size,zorder=2) ax.set_xlim(7e4,1e12) ax.set_xticks([1e6,1e7,1e8,1e9,1e10,1e11]) if showFig: plt.show() #Shows the noise introduced by the TAME routine by considering second largest # singular values in the rank 1 case (alpha=1.0, beta =0.0), plots againts both # |V_A||V_B| and |T_A||T_B| for comparison. Data plotted for RandomGeometric Graphs # degreedist = LogNormal(5,1). def TAME_RandomGeometric_rank_1_case_singular_values(axes=None): """ Note: This an old figure """ if axes is None: f,axes = plt.subplots(1,1,dpi=60) f.set_size_inches(3, 3) with open(TAME_RESULTS + "Rank1SingularValues/LowRankTAME_RandomGeometric_log5_iter_30_n_100_20K_no_match_tol_1e-12.json","r") as f: LowRankTAME_data = json.load(f) with open(TAME_RESULTS + "Rank1SingularValues/TAME_RandomGeometric_degreedist:log5_alphas:[1.0]_betas:[0.0]_iter:30_trials:10_n:[1e2,5e2,1e3,2e3,5e3,1e4,2e4]_no_match_tol:1e-12.json","r") as f: TAME_data = json.load(f) def process_RandomGeometricResults(data): nonzero_second_largest_sing_vals = [] zero_second_largest_sing_vals = [] nonzero_vertex_products = [] zero_vertex_products = [] nonzero_triangle_products = [] zero_triangle_products = [] n_values = set() for p,seed,p_remove,n,_,max_tris,profiles in data: n_values.add(n) params, profile_dict = profiles[0] #should only be alpha = 1.0, beta = 0.0 #normalize the singular values for s in profile_dict["sing_vals"]: s = [0.0 if x is None else x for x in s] #saving to json seems to introduce Nones when reading from saved Julia files total = sum(s) s[:] = [s_i/total for s_i in s] #max_rank = int(max(profile_dict["ranks"])) sing_vals = [(i,s[1]) if len(s) > 1 else (i,2e-16) for (i,s) in enumerate(profile_dict["sing_vals"])] #find max sing val, and check iterates rank i,sing2_val = max(sing_vals,key=lambda x:x[1]) rank = profile_dict["ranks"][i] if rank > 1: #print([sum(s) for s in profile_dict["sing_vals"]]) nonzero_second_largest_sing_vals.append(sing2_val) nonzero_vertex_products.append(n**2) nonzero_triangle_products.append(max_tris**2) #TODO: need to use seed to compute actual triangle counts else: zero_second_largest_sing_vals.append(sing2_val) zero_vertex_products.append(n**2) zero_triangle_products.append(max_tris**2) return n_values, nonzero_second_largest_sing_vals, nonzero_vertex_products, nonzero_triangle_products, zero_second_largest_sing_vals, zero_vertex_products, zero_triangle_products n_values, LowRankTAME_nonzero_second_largest_sing_vals, LowRankTAME_nonzero_vertex_products,\ LowRankTAME_nonzero_triangle_products, LowRankTAME_zero_second_largest_sing_vals,\ LowRankTAME_zero_vertex_products, LowRankTAME_zero_triangle_products =\ process_RandomGeometricResults(LowRankTAME_data) _, TAME_nonzero_second_largest_sing_vals, TAME_nonzero_vertex_products,\ TAME_nonzero_triangle_products, TAME_zero_second_largest_sing_vals,\ TAME_zero_vertex_products, TAME_zero_triangle_products =\ process_RandomGeometricResults(TAME_data) # # Make Triangle_Triangle plots # ax = axes #ax = plt.subplot(122) #format the axis ax.set_yscale("log") ax.grid(which="major", axis="y") #ax.set_ylabel(r"max $\sigma_2") #ax.set_ylabel(r"max [$\sum_{i=2}^k\sigma_i]") #ax.yaxis.set_ticks_position('right') #ax.tick_params(labeltop=False, labelright=True) ax.set_xlabel(r"|$T_A$||$T_B$|") ax.set_yscale("log") ax.set_xscale("log") ax.set_xlim(3e5,7e11) ax.set_xticks([1e7,1e8,1e11]) #ax.set_ylim(1e-16,1e-7) ax.annotate(r"$\epsilon$", xy=(1.06, .02), xycoords='axes fraction', c=purple_c) ax.set_ylabel(r"$\sigma_2$") #scatter plot formatting marker_size = 20 marker_alpha = 1.0 #plot the TAME Data """ plt.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o',c=darker_t4_color,s=marker_size) plt.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=darker_t4_color,s=marker_size) """ #plot machine epsilon plt.plot([3e5,7e11],[2e-16]*2,c=purple_c,zorder=1) #plot LowRankTAME Data plt.scatter(LowRankTAME_nonzero_triangle_products,LowRankTAME_nonzero_second_largest_sing_vals,marker='o', c=LRT_color,s=marker_size,zorder=2) plt.scatter(LowRankTAME_zero_triangle_products,LowRankTAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=LRT_color,s=marker_size,zorder=2) #plot TAME Data plt.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o', c=T_color,s=marker_size,zorder=3) plt.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=T_color,s=marker_size,zorder=3) axins = ax.inset_axes([.6,.15,.25,.25]) # zoom = 6 axins.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o', c=T_color,s=marker_size) axins.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=T_color,s=marker_size) # sub region of the original image #axins.set_xlim(9e9, 3e11) axins.set_xlim(4e9, 1e10) axins.set_ylim(5e-13, 1.5e-12) axins.set_xscale("log") axins.set_yscale("log") axins.set_xticks([]) axins.minorticks_off() axins.set_yticks([]) mark_inset(ax, axins, loc1=3, loc2=1, fc="none", ec="0.5",alpha=.5,zorder=1) #axins.tick_params(labelleft=False, labelbottom=False) #axins.set_yticks([]) """ axins.set_xticklabels([]) axins.set_yticklabels([]) """ #TODO: combining plots, potentially remove later plt.tight_layout() plt.show() def TAME_data_extraction(): results_path = TAME_RESULTS + "MaxRankExperiments/" T_file = "TAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.5]_noiseModel:Duplication_sp:[0.25]_trials:50_MaxRankResults.json" new_data = [] new_file = "TAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[1.0]_beta:[0.0]_n:[100,500,1000,2000,5000,10000]_p:[0.5]_noiseModel:Duplication_sp:[0.25]_trials:50_MaxRankResults.json" with open(results_path+T_file,"r") as f: TAME_data = json.load(f) for (seed,p,n,sp,acc,dw_acc,tri_match,A_tri,B_tri,max_tris,profiles) in TAME_data: new_profiles = [] for (param, profiling) in profiles: if param == "α:1.0_β:0.0": new_profiles.append([param, profiling]) new_data.append((seed,p,n,sp,acc,dw_acc,tri_match,A_tri,B_tri,max_tris,new_profiles)) with open(results_path+new_file,"w") as f: json.dump(new_data,f) return new_data def TAME_RandomGeometric_rank_1_singular_values_v2(ax=None,noiseModel="dupNoise"): showFig=False if ax is None: f = plt.figure(dpi=60) ax = plt.gca() f.set_size_inches(3, 4) showFig=True # # Load the Data # results_path = TAME_RESULTS + "MaxRankExperiments/" results_path = TAME_RESULTS + "TAME_iterate_max_rank/" def process_data(data): nonzero_second_largest_sing_vals = [] zero_second_largest_sing_vals = [] nonzero_vertex_products = [] zero_vertex_products = [] nonzero_triangle_products = [] zero_triangle_products = [] n_values = set() for datum in data: if noiseModel == "dupNoise": (seed,p,n,sp,acc,dw_acc,tri_match,A_tri,B_tri,max_tris,profiles) = datum elif noiseModel == "ERNoise": (seed,p,n,acc,dw_acc,tri_match,A_tri,B_tri,max_tris,profiles) = datum for (params, profile_dict) in profiles: if params == "α:1.0_β:0.0": #normalize the singular values for s in profile_dict["sing_vals"]: s = [0.0 if x is None else x for x in s] #saving to json seems to introduce Nones when reading from saved Julia files total = sum(s) s[:] = [s_i/total for s_i in s] sing_vals = [(i,s[1]) if len(s) > 1 else (i,2e-16) for (i,s) in enumerate(profile_dict["sing_vals"])] #find max sing val, and check iterates rank i,sing2_val = max(sing_vals,key=lambda x:x[1]) rank = profile_dict["ranks"][i] if rank > 1: #print([sum(s) for s in profile_dict["sing_vals"]]) nonzero_second_largest_sing_vals.append(sing2_val) nonzero_vertex_products.append(n**2) nonzero_triangle_products.append(max_tris**2) #TODO: need to use seed to compute actual triangle counts else: zero_second_largest_sing_vals.append(sing2_val) zero_vertex_products.append(n**2) zero_triangle_products.append(max_tris**2) return nonzero_second_largest_sing_vals, zero_second_largest_sing_vals,\ nonzero_vertex_products, zero_vertex_products,\ nonzero_triangle_products, zero_triangle_products,\ n_values if noiseModel == "dupNoise": LRT_file = "LRTAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.5]_noiseModel:Duplication_sp:[0.25]_trials:50_MaxRankResults.json" elif noiseModel == "ERNoise": LRT_file = "LRTAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.05]_noiseModel:ER_trials:50_MaxRankResults.json" with open(results_path + LRT_file,"r") as f: LowRankTAME_nonzero_second_largest_sing_vals, LowRankTAME_zero_second_largest_sing_vals,\ LowRankTAME_nonzero_vertex_products, LowRankTAME_zero_vertex_products,\ LowRankTAME_nonzero_triangle_products, LowRankTAME_zero_triangle_products,\ n_values = process_data(json.load(f)) #TODO: extra out the \alpha:1.0, \beta = 0.0 case into a seperate file #T_file = "TAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.5]_noiseModel:Duplication_sp:[0.25]_trials:50_MaxRankResults.json" if noiseModel == "dupNoise": T_file = "TAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[1.0]_beta:[0.0]_n:[100,500,1000,2000,5000,10000]_p:[0.5]_noiseModel:Duplication_sp:[0.25]_trials:50_MaxRankResults.json" elif noiseModel == "ERNoise": T_file = "TAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[1.0]_beta:[0.0]_n:[100,500,1000,2000,5000,10000]_p:[0.05]_noiseModel:ER_trials:50_MaxRankResults.json" with open(results_path+T_file,"r") as f: TAME_nonzero_second_largest_sing_vals, TAME_zero_second_largest_sing_vals,\ TAME_nonzero_vertex_products, TAME_zero_vertex_products,\ TAME_nonzero_triangle_products, TAME_zero_triangle_products,\ n_values = process_data(json.load(f)) # # Plot the Data # ax.set_yscale("log") ax.grid(which="major", axis="both") #ax.set_ylabel(r"max $\sigma_2") #ax.set_ylabel(r"max [$\sum_{i=2}^k\sigma_i]") #ax.yaxis.set_ticks_position('right') #ax.tick_params(labeltop=False, labelright=True) ax.set_xlabel(r"|$T_A$||$T_B$|") ax.set_yscale("log") ax.set_xscale("log") ax.set_xlim(3e5,3e11) ax.set_xticks([1e7,1e8,1e11]) #ax.set_ylim(1e-16,1e-7) if showFig: ax.annotate(r"$\epsilon$", xy=(1.06, .02), xycoords='axes fraction', c=purple_c) ax.set_ylabel(r"$\sigma_2$") #scatter plot formatting marker_size = 25 marker_alpha = .2 #plot the TAME Data """ plt.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o',c=darker_t4_color,s=marker_size) plt.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=darker_t4_color,s=marker_size) """ #plot machine epsilon ax.plot([3e5,7e11],[2e-16]*2,c=purple_c,zorder=1) #plot LowRankTAME Data ax.scatter(LowRankTAME_nonzero_triangle_products,LowRankTAME_nonzero_second_largest_sing_vals,marker='o', c=LRT_color,s=marker_size,zorder=2,alpha=marker_alpha) ax.scatter(LowRankTAME_zero_triangle_products,LowRankTAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=LRT_color,s=marker_size,zorder=2) #plot TAME Data ax.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker="o", c=T_color,s=marker_size,zorder=3,alpha=marker_alpha) ax.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,marker="o",facecolors='none',edgecolors=T_color,s=marker_size,zorder=3) # # Add in inset axes # """ axin_axes = [] if noiseModel == "dupNoise": box_parameters = [ ([.4,.125,.125,.2],(9e7, 4.5e8),(1e-13, 8e-13)), ([.525,.125,.125,.2],(7e8, 2e9),(3e-13, 1e-12)), ([.65,.125,.125,.2],(4e9, 8.5e9),(5e-13, 1.5e-12)), ([.775,.125,.125,.2],(2e10, 4e10),(6e-13, 3e-12)), ] else: box_parameters = [ ([.4,.125,.125,.2],(2e8, 2e9),(5e-13, 1e-12)), ([.525,.125,.125,.2],(8e8, 2e9),(3e-13, 1e-12)), ([.65,.125,.125,.2],(6e9, 1e10),(8e-13, 2e-12)), ([.775,.125,.125,.2],(2e10, 5e10),(9.5e-13, 3.5e-12)), ] for (axin_loc,xlims,ylims) in box_parameters: axins = ax.inset_axes(axin_loc) # zoom = 6 axin_axes.append(axins) axins.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o', c=T_color,s=marker_size,alpha=marker_alpha) axins.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=T_color,s=marker_size) # sub region of the original image #axins.set_xlim(9e9, 3e11) axins.set_xlim(*xlims) axins.set_ylim(*ylims) axins.set_xscale("log") axins.set_yscale("log") axins.set_xticks([]) axins.minorticks_off() axins.set_yticks([]) mark_inset(ax, axins, loc1=3, loc2=1, fc="none", ec="0.5",alpha=.5,zorder=1) """ """ if noiseModel == "dupNoise": axins = ax.inset_axes([.3,.1,.4,.2]) else: axins = ax.inset_axes([.6,.15,.25,.25]) axins.scatter(TAME_nonzero_triangle_products,TAME_nonzero_second_largest_sing_vals,marker='o', c=T_color,s=marker_size) axins.scatter(TAME_zero_triangle_products,TAME_zero_second_largest_sing_vals,facecolors='none',edgecolors=T_color,s=marker_size) # sub region of the original image if noiseModel == "dupNoise": #axins.set_xlim(9e9, 3e11) axins.set_xlim(5e7, 1e10) axins.set_ylim(1e-13, 1e-12) else: axins.set_xlim(4e9, 1e10) axins.set_ylim(5e-13, 1.5e-12) axins.set_xscale("log") axins.set_yscale("log") axins.set_xticks([]) axins.minorticks_off() axins.set_yticks([]) mark_inset(ax, axins, loc1=3, loc2=1, fc="none", ec="0.5",alpha=.5,zorder=1) """ if showFig: plt.tight_layout() plt.show() # - TAME iterates are low rank - # def max_rank_experiments(save_path=None): #f = plt.figure(dpi=60) #f.set_size_inches(10, 4) f = plt.figure(figsize=(5.2,5)) n = 3 m = 2 gs = f.add_gridspec(nrows=n, ncols=m, left=0.05, right=0.975,wspace=0.225,hspace=0.125,top=.975,bottom=.1) all_ax = np.empty((n,m),object) for i in range(n): for j in range(m): if j == 0: all_ax[i,j] = f.add_subplot(gs[i,j]) else: all_ax[i,j] = f.add_subplot(gs[i,j],sharex=all_ax[i,0]) DupNoise_data = "LRTAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.5]_noiseModel:Duplication_sp:[0.25]_trials:50_MaxRankResults.json" ERNoise_data = "LRTAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.05]_noiseModel:ER_trials:50_MaxRankResults.json" max_rank_synthetic_data(all_ax[0,:],ERNoise_data,"ER") max_rank_synthetic_data(all_ax[1,:],DupNoise_data,"Duplication") max_rank_LVGNA_data(all_ax[2,:]) checkboard_color = [.925]*3 for ax in all_ax.reshape(-1): ax.grid(which="major", axis="both") ax.set_xscale("log") ax.set_xlim(1e5,1e12) # # -- handle tick marks -- # # for i,ax in enumerate(all_ax[:,1]): ax.set_ylabel("max rank\n min{n,m}",ha="center") #ax.set_ylabel(r"max rank/$\min{\{n,m\}}$") ax.annotate('', xy=(-.1, .25), xycoords='axes fraction', xytext=(-.1, 0.75), arrowprops=dict(arrowstyle="-", color='k')) if i != 2: tick_ha = "right" else: tick_ha = "left" for tick in ax.yaxis.get_majorticklabels(): tick.set_horizontalalignment(tick_ha) ax.tick_params(axis="y",which="both",direction="in",pad=-25) ax.set_yscale("log") if i != 2: ax.set_ylim(5e-3,1.5) else: ax.set_ylim(5e-4,2e-1) # -- add in Plot labels x_loc = .85 title_size = 12 if i == 0: ax.annotate(u"Erdős Rényi"+"\nNoise",xy=(.9,.675), xycoords='axes fraction',ha="right",fontsize=title_size) elif i == 1: ax.annotate("Duplication\n Noise",xy=(.9,.675), xycoords='axes fraction',ha="right",fontsize=title_size) elif i == 2: x_loc = .85 ax.annotate("LVGNA",xy=(x_loc,.7), xycoords='axes fraction',ha="right",fontsize=title_size) #ax.yaxis.set_ticks_position('right') if i != 2: ax.tick_params(axis="x",direction="out",which='both', length=0) #ax.set_ylabel("maximum rank") all_ax[2,1].tick_params(labeltop=False, labelright=True) # -- set labels -- # for i,ax in enumerate(all_ax[:,0]): ax.set_ylabel("max rank") ax.set_xlim(5e4,5e11) ax.set_xticks([1e5,1e6,1e7,1e8,1e9,1e10,1e11]) ax.tick_params(axis="y",which="both",direction="in",pad=-25) if i != 2: ax.set_ylim(0,320) ax.tick_params(axis="x",direction="out",which='both', length=0) ax.set_yticks([50,100,150,200,250,300]) ax.set_xticklabels([]) #for tl in ax.get_yticklabels(): # tl.set_bbox(bbox) else: ax.set_ylim(0,150) ax.set_yticks([50,100,125]) ax.set_xticklabels([1e5,1e6,1e7,1e8,1e9,1e10,1e11]) # -- add in annotations all_ax[2,0].xaxis.set_major_formatter(mpl.ticker.LogFormatterMathtext()) for j in range(m): #parity = 1 for (i,ax) in enumerate(all_ax[:,j]): parity = 1 for pos in ['left','bottom','top','right']: ax.spines[pos].set_visible(False) if j == 1: ax.tick_params(axis="y",which='minor', length=0) if i == 2: ax.yaxis.set_ticks_position('right') """ if i == 0: ax.spines['bottom'].set_visible(False) elif i == 2: ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('right') else: ax.spines['bottom'].set_visible(False) ax.spines['top'].set_visible(False) """ if parity == -1: all_ax[i,j].patch.set_facecolor(checkboard_color) if parity == 1: bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1.0,pad=.1) else: bbox = dict(boxstyle="round", ec=checkboard_color, fc=checkboard_color, alpha=1.0,pad=.1) for tl in ax.get_yticklabels(): tl.set_bbox(bbox) parity *= -1 parity *= -1 for ax in all_ax[2,:]: ax.set_xlabel(r"$|T_A||T_B|$") ax.tick_params(axis="x",direction="out",which='both', length=0) if save_path is None: plt.show() else: plt.savefig(save_path) def max_rank_LVGNA_data(axes,includeSampledEigShift=False): data_path = TAME_RESULTS + "TAME_iterate_max_rank/" with open(data_path + "LowRankTAME_LVGNA.json", 'r') as f: MM_results = json.load(f) MM_exp_data, param_indices,MM_file_names = process_TAME_output2(MM_results[1]) if includeSampledEigShift: beta_100_loc = (.475, .75) else: beta_100_loc = (.05, .10) label_meta = [ ((.05, .35),"o",red_c,"solid"), ((.05, .575),"v",green_shift_c,"dotted"), ((.075, .75),"*",purple_c,"dashed"), (beta_100_loc,"s",blue_c,"dashdot") ] for (param, j),(loc,marker,c,linestyle) in zip(param_indices.items(),label_meta): n_points = [] max_ranks = [] normalized_max_ranks = [] param_label = f"β:{int(float(param.split('β:')[-1]))}" axes[0].annotate(param_label, xy=loc, xycoords='axes fraction',c=c) for i in range(MM_exp_data.shape[0]): graph_names = [str.join(" ", x.split(".ssten")[0].split("_")) for x in MM_file_names[i]] min_n = np.min([vertex_counts[f] for f in graph_names]) tri_counts = triangle_counts[graph_names[0]]*triangle_counts[graph_names[1]] # tri_counts = sum([triangle_counts[graph_names[0]] for f in graph_names]) n_points.append(tri_counts) max_ranks.append(np.max(MM_exp_data[i,:,j,:])) normalized_max_ranks.append(np.max(MM_exp_data[i,:,j,:])/min_n) plot_1d_loess_smoothing(n_points,max_ranks,.3,axes[0],c=c)#,linestyle=linestyle plot_1d_loess_smoothing(n_points,normalized_max_ranks,.3,axes[1],c=c,logFilter=True) #linestyle=linestyle, if includeSampledEigShift: (filename,color,label,loc,linestyle) = ( "LVGNAMaxEigShiftRanks_alphas:[0.5,1.0]_iter:30_SSHOPMSamples:1000_tol:1.0e-16_results.json", [.25]*3, "β:"+r"$\lambda_A\lambda_B$", (-.05,.02), (0,(3,1,1,1,1,1))) with open(data_path + filename,"r") as f: data = json.load(f) tri_products = [] max_ranks = [] normalized_max_ranks = [] tri_count =lambda f: triangle_counts[str.join(" ", f.split(".ssten")[0].split("_"))] for (graphA, graphB,shiftA,shiftB,profiles) in data: graph_names = [str.join(" ", x.split(".ssten")[0].split("_")) for x in [graphA, graphB]] min_n = np.min([vertex_counts[f] for f in graph_names]) tri_products.append(tri_count(graphA)*tri_count(graphB)) max_ranks.append(max([max(profile["ranks"]) for (p,profile) in profiles])) normalized_max_ranks.append(max([max(profile["ranks"]) for (p,profile) in profiles])/min_n) plot_1d_loess_smoothing(tri_products,max_ranks,.3,axes[0],c=color,logFilter=True) #,linestyle=linestyle plot_1d_loess_smoothing(tri_products,normalized_max_ranks,.3,axes[1],c=color,logFilter=True)#,linestyle=linestyle axes[0].annotate(label, xy=loc, xycoords='axes fraction',c=color) def max_rank_synthetic_data(axes,filename,noise_model="ER"): #with open(TAME_RESULTS + "MaxRankExperiments/","r") as f: with open(TAME_RESULTS + "TAME_iterate_max_rank/" + filename,"r") as f: #with open(TAME_RESULTS + "MaxRankExperiments/LowRankTAME_RandomGeometric_degreedist_log5_iter:15_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_n:[100,500,1K,2K,5K,10K,20K]_noMatching_pRemove:[.01,.05]_tol:1e-12_trialcount:50.json","r") as f: synth_results = json.load(f) MM_exp_data, n_vals, p_vals, param_vals, tri_counts = process_synthetic_TAME_output2(synth_results,noise_model) print(np.mean(MM_exp_data)) #ax = axes[0] #ax.set_xscale("log") #ax.set_ylim(00,315) if filename == "LRTAME_noMatch_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0,10.0,100.0]_n:[100,500,1000,2000,5000,10000]_p:[0.05]_noiseModel:ER_trials:50_MaxRankResults.json": label_meta = [ ((.825, .09),"o",red_c,"solid"), ((.825, .26),"v",green_shift_c,"dotted"), ((.825, .6), "*",purple_c,"dashed"), ((.825, .8), "s",blue_c,"dashdot")] else: label_meta = [ ((.85, .11), "o",red_c,"solid"), ((.85, .25),"v",green_shift_c,"dotted"), ((.85, .55), "*",purple_c,"dashed"), ((.85, .825), "s",blue_c,"dashdot")]# no_ylim_points #label_meta = [((.92, .25),t1_color),((.8, .51),t2_color),((.625, .8),purple_c),((.25, .88),t4_color)] #beta=100 annotations beta_100_meta = [(.15,.4),(.42,1.01),(.51,1.01),(.6,1.01),(.725,1.01),(.8,1.01),(.9,1.01)] #beta=10 annotations beta_10_meta = [(.15,.32),(.42,.515),(.51,.6),(.6,.65),(.725,.75),(.8,.95),(.9,.95)] for ((params,k),(loc,marker,c,linestyle)) in zip(param_vals.items(),label_meta): max_vals = [] normalized_max_vals = [] mean_tris = [] for n,j in n_vals.items(): max_vals.append(np.max(MM_exp_data[:,j,:,k,:])) normalized_max_vals.append(np.max(MM_exp_data[:,j,:,k,:])/n) mean_tris.append(np.mean(tri_counts[:,j,:,k])) axes[0].plot(mean_tris,max_vals,c=c)#,linestyle=linestyle axes[1].plot(mean_tris,normalized_max_vals,c=c)#,linestyle=linestyle param_label = f"β:{int(float(params.split('β:')[-1]))}" axes[0].annotate(param_label, xy=loc, xycoords='axes fraction',c=c) # - TAME & LRTAM don't scale for larger motifs - # def TAME_vs_LRTAME_clique_scaling_detailed(save_path=None,global_ax= None): plt.rc('text.latex', preamble=r'\usepackage{/Users/charlie/Documents/Code/TKPExperimentPlots/latex/dgleich-math}\usepackage{amsmath}') # # subplot routines # def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.3,format="default",xlim=None,xscale="linear",column_type=None): if xscale=="linear": v = ax.violinplot(data,[.5], points=100, showmeans=False,widths=.15, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) elif xscale=="log": v = ax.violinplot(np.log10(data), points=100, showmeans=False,widths=.15,showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1),(x0,y1+.7)]] v["cmedians"].set_segments(newMedianLines) # -- write data values as text bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.05) extremal_tick_ypos = .1 if column_type is None: if format == "default": ax.annotate(f"{np.median(data):.{precision}f}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10)#.set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8).set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8).set_bbox(bbox) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8).set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8).set_bbox(bbox) else: print(f"expecting format to be either 'default' or 'scientific', got:{format}") elif column_type == "merged_axis": pass else: raise ValueError("column_type expecting 'merged_axis' or None, but got {column_type}\n") if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(v_alpha) # -- only plot the top half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 1]) b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 v = ax.violinplot([np.random.normal() for i in range(50)], points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) ax.set_ylim(.5,1.0) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) ax.annotate("median",xy=(.5,.35),xycoords="axes fraction",ha="center",va="center",fontsize=10) ax.annotate(f"min",xy=(.025,-.125),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) ax.annotate(f"max",xy=(.975,-.125),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor(c) b.set_alpha(.3) b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) # # Parse Data # results_path = TAME_RESULTS + "RG_DupNoise/" elemwise_list_sum =lambda l1,l2: [a + b for (a,b) in zip(l1,l2)] filename = "LowRankTAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[3,4,5,6,7,8]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json" with open(results_path + filename,"r") as file: data = json.load(file) LR_results = {} debug_results = {} LR_seeds = {} for (order, LRTAME_output) in data: trials = len(LRTAME_output) LR_results[order] = { "full runtime":{}, "contraction runtime":{}, "A motifs":[], "ranks":{}, } debug_results[order] = { "runtimes":[], "ranks":[] } LR_seeds[order] = [] for trial_idx,(seed,p,n,sp,accuracy,dup_tol_accuracy,motifs_matched,A_motifs,B_motifs,A_motif_dist,B_motif_dist,profiling) in enumerate(LRTAME_output): LR_results[order]["A motifs"].append(A_motifs) LR_seeds[order].append(seed) for (params,profile) in profiling: rt = reduce(elemwise_list_sum,[ profile["low_rank_factoring_timings"], profile["contraction_timings"], profile["matching_timings"], profile["scoring_timings"], ]) if params in LR_results[order]["full runtime"]: LR_results[order]["full runtime"][params].append(rt) else: LR_results[order]["full runtime"][params] = [rt] contract_rt = reduce(elemwise_list_sum,[ profile["low_rank_factoring_timings"], profile["contraction_timings"], ]) if params in LR_results[order]["contraction runtime"]: LR_results[order]["contraction runtime"][params].append(contract_rt) else: LR_results[order]["contraction runtime"][params] = [contract_rt] if params == 'α:0.5_β:1.0': debug_results[order]["ranks"].append(profile["ranks"]) debug_results[order]["runtimes"].append(contract_rt) if params in LR_results[order]["ranks"]: LR_results[order]["ranks"][params].append(profile["ranks"]) else: LR_results[order]["ranks"][params] = [profile["ranks"]] for key in ["contraction runtime","full runtime"]: for (param,rts) in LR_results[order][key].items(): LR_results[order][key][param] = np.median(np.array(rts),axis=0) def get_TAME_data(): file = "TAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[25]_orders:[3,4,5,6,7]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json" files = [ "TAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[3,4,5,6,7]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json", "TAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[8]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json", ] data = [] for file in files: with open(results_path + file,"r") as f: data.extend(json.load(f)) T_results = {} for (order, TAME_output) in data: trials = len(TAME_output) T_results[order] = { "contraction runtime":{}, "full runtime":{}, } for trial_idx,(seed,p,n,sp,accuracy,dup_tol_accuracy,motifs_matched,A_motifs,B_motifs,A_motif_dist,B_motif_dist,profiling) in enumerate(TAME_output): for (params,profile) in profiling: rt = reduce(elemwise_list_sum,[ profile["contraction_timings"], profile["matching_timings"], profile["scoring_timings"], ]) if params in T_results[order]: T_results[order]["full runtime"][params].append(rt) else: T_results[order]["full runtime"][params] = [rt] if params in T_results[order]: T_results[order]["contraction runtime"][params].append(profile["contraction_timings"]) else: T_results[order]["contraction runtime"][params] = [profile["contraction_timings"]] for key in ["contraction runtime","full runtime"]: for (param,rts) in T_results[order][key].items(): T_results[order][key][param] = np.median(np.array(rts),axis=0) """ for params in T_results[order]["contraction runtime"].keys(): T_results[order]["contraction runtime"][params] = [rt/trials for rt in T_results[order]["contraction runtime"][params]] for params in T_results[order]["full runtime"].keys(): T_results[order]["full runtime"][params] = [rt/trials for rt in T_results[order]["full runtime"][params]] """ return T_results T_results = get_TAME_data() file = "LambdaTAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[3,4,5,6,7]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json" with open(results_path + file,"r") as file: data = json.load(file) LT_results = {} all_shifts = [ 'α:0.5_β:0.0', 'α:0.5_β:1.0', 'α:1.0_β:0.0', 'α:1.0_β:1.0', ] for (order, TAME_output) in data: trials = len(TAME_output) LT_results[order] = { "contraction runtime":{}, "full runtime":{}, } for trial_idx,(seed,p,n,sp,accuracy,dup_tol_accuracy,motifs_matched,A_motifs,B_motifs,A_motif_dist,B_motif_dist,profile) in enumerate(TAME_output): # aggregate the runtimes full_rt = reduce(elemwise_list_sum,[ profile["Matching Timings"], profile["TAME_timings"], profile["Scoring Timings"], ]) for (i,rt) in enumerate(full_rt): params = all_shifts[i] if params in LT_results[order]["full runtime"]: LT_results[order]["full runtime"][params].append(rt) else: LT_results[order]["full runtime"][params] = [rt] for (i,rt) in enumerate(profile["TAME_timings"]): params = all_shifts[i] if params in LT_results[order]["contraction runtime"]: LT_results[order]["contraction runtime"][params].append(rt) else: LT_results[order]["contraction runtime"][params] = [rt] for key in ["contraction runtime","full runtime"]: for (param,rts) in LT_results[order][key].items(): LT_results[order][key][param] = np.median(np.array(rts)) #return LR_results, T_results # # Create Plots # if global_ax is None: fig = plt.figure(figsize=(5.5,3)) global_ax = plt.gca() global_ax.set_axis_off() parity = 1 #used for checkerboard effect in plots. linestyles= { 'α:0.5_β:0.0':"dotted", 'α:0.5_β:1.0':"solid", 'α:1.0_β:0.0':(0,(3,1,1,1)), 'α:1.0_β:1.0':(0,(5,1)) } motif_label = 0 rt_data = 1 rank_data = 2 A_motif_data = 3 n = 4 m = len(LT_results.keys()) widths = [.5,3,2,1] col_width_ratios = [1]*m col_width_ratios.append(.8) # +1 for vioin legend gs = fig.add_gridspec(nrows=n,ncols=m + 1,hspace=0.1,wspace=0.1, height_ratios=widths,width_ratios=col_width_ratios, left=.18,right=.99,top=.925,bottom=.05) gs_ax = np.empty((n,m),object) iterate_tick_idx = 2 # -- plot runtime data -- # for idx,((LRT_order,LRT_data),(T_order,T_data),(LT_order,LT_data)) in enumerate(zip(LR_results.items(),T_results.items(),LT_results.items())): assert LRT_order == T_order assert LRT_order == LT_order order = LRT_order for row in range(n): ax = fig.add_subplot(gs[row,idx]) gs_ax[row,idx] = ax if row == motif_label: ax.annotate(f"{order}",xy=(.5, .5), xycoords='axes fraction', c="k",size=10,ha="center",va="center") elif row == rt_data: for (exp_data,c) in zip([LRT_data,T_data],[LRT_color,T_color]): for (param,runtime) in exp_data["contraction runtime"].items(): if param == 'α:0.5_β:0.0' or param == 'α:1.0_β:1.0': continue #ignore partial shifts gs_ax[row,idx].plot(runtime,c=c,linestyle=linestyles[param]) iterations = len(runtime) for (param,runtime) in LT_data["contraction runtime"].items(): if param == 'α:0.5_β:0.0' or param == 'α:1.0_β:1.0': continue gs_ax[row,idx].axhline(runtime/iterations,c=LT_color,linestyle=linestyles[param]) elif row == rank_data: for (param,ranks) in LRT_data["ranks"].items(): if param == 'α:0.5_β:0.0' or param == 'α:1.0_β:1.0': continue if idx == iterate_tick_idx: ax.set_zorder(3.5) ax.plot(np.median(np.array(ranks),axis=0),c=[.1]*3,linestyle=linestyles[param]) #if T_order == 7: # for (motifs,rank) in sorted(zip(LRT_data["A motifs"],ranks),key=lambda x:x[0]): # print(f"motifs:{motifs} ranks:{rank}") elif row == A_motif_data: make_violin_plot(ax,LRT_data["A motifs"],precision=0,c="k") # -- plot motif data -- # # # Adjust Axes # gs_ax[motif_label,0].set_ylabel(" Clique Size",rotation=0,labelpad=32.5,ha="center",va="center") subylabel_xpos = -.7 gs_ax[rt_data,0].set_ylabel("Contraction\nRuntime (s)",rotation=0,labelpad=32.5,ha="center",va="center") gs_ax[rt_data,0].annotate("25 unique\ntrials",xy=(subylabel_xpos, .15), xycoords='axes fraction',ha="center",fontsize=7,style='italic') for (idx,ax) in enumerate(gs_ax[rt_data,:]): ax.set_ylim(5e-5,5e3) ax.set_yscale("log") ax.yaxis.set_ticks_position('right') ax.set_yticks([1e-4,1e-3,1e-2,1e-1,1e0,1e1,1e2,1e3]) if idx == m-1: ax.set_yticklabels([r"$10^{-4}$",r"$10^{-3}$",None,r"$10^{-1}$",None,r"$10^1$",None,r"$10^3$"]) else: ax.set_yticklabels([]) ax.set_xticks([0,6,14]) ax.grid(True) ax.set_xlim(-1,15) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1.0,pad=.01) gs_ax[rt_data,3].annotate("LR-TAME",c=LRT_color,xy=(.225, .375), xycoords='axes fraction').set_bbox(bbox) gs_ax[rt_data,3].annotate("TAME",c=T_color,xy=(.2, .85), xycoords='axes fraction').set_bbox(bbox) gs_ax[rt_data,3].annotate(r"$\Lambda$"+"-TAME",c=LT_color,xy=(.225, .14), xycoords='axes fraction').set_bbox(bbox) gs_ax[rank_data,0].set_ylabel("Iterate\nRank",rotation=0,labelpad=32.5,ha="center",va="center") gs_ax[rank_data,0].annotate("max rank=100",xy=(subylabel_xpos, .15), xycoords='axes fraction',ha="center",fontsize=7,style='italic') for (idx,ax) in enumerate(gs_ax[rank_data,:].reshape(-1)): ax.set_ylim(-1,32.5) ax.yaxis.set_ticks_position('right') ax.set_yticks([0,1,5,10,15,20,25,30]) if idx == m-1: ax.set_yticklabels([None,None,5,None,15,None,25,None]) else: ax.set_yticklabels([]) ax.set_xticks([0,6,14]) ax.grid(True) ax.set_xlim(-1,15) # -- add in shift annotations -- # gs_ax[rank_data,1].annotate('α=.5 β=1\n(both shifts)',xy=(.375, .65), xycoords='axes fraction',fontsize=6,ha="left",zorder=5) x_loc = .18 gs_ax[rank_data,1].annotate('β=0 (no shifts)',xy=(x_loc, .1),xycoords='axes fraction',fontsize=6, ha="left",va="bottom") gs_ax[rank_data,1].annotate('α=1',xy=(x_loc - .01, .225), xycoords='axes fraction',fontsize=6,ha="left") legend_ax = fig.add_subplot(gs[A_motif_data,-1]) make_violin_plot_legend(legend_ax) gs_ax[A_motif_data,0].set_ylabel("A motifs",rotation=0,labelpad=32.5,ha="center",va="center") gs_ax[A_motif_data,0].annotate("samples="+r"$10^4$",xy=(subylabel_xpos, .01), xycoords='axes fraction',ha="center",fontsize=7,style='italic') for ax in chain(gs_ax[[A_motif_data,motif_label],:].reshape(-1),[legend_ax]): ax.set_yticklabels([]) for ax in chain(gs_ax.reshape(-1),[legend_ax]): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_xticklabels([]) #add back in right most column tick marks gs_ax[rt_data,m-1].tick_params(axis="y",direction="out",which='both', length=2) gs_ax[rank_data,m-1].tick_params(axis="y",direction="out",which='both', length=2) gs_ax[rank_data,iterate_tick_idx].xaxis.set_label_position("top") gs_ax[rank_data,iterate_tick_idx].xaxis.set_ticks_position('top') gs_ax[rank_data,iterate_tick_idx].tick_params(axis="x",direction="out", pad=-17.5,length=5) gs_ax[rank_data,iterate_tick_idx].set_xticklabels([1,5,15]) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1.0,pad=-.01) gs_ax[rank_data,iterate_tick_idx].annotate("iteration "+r"$(\ell)$",xy=(.5,1.25),ha="center",xycoords='axes fraction',fontsize=10)#.set_bbox(bbox) if save_path is None: plt.show() else: plt.savefig(save_path) def TAME_vs_LRTAME_clique_scaling_summarized(save_path=None): """This version stacks the results of LR-TAME and TAME on top of one another.""" plt.rc('text.latex', preamble=r'\usepackage{/Users/charlie/Documents/Code/TKPExperimentPlots/latex/dgleich-math}\usepackage{amsmath}') # # subplot routines # extremal_tick_ypos = .2 # subroutine globals def underline_text(ax,text,c,linestyle): tb = text.get_tightbbox(fig.canvas.get_renderer()).transformed(fig.transFigure.inverted()) ax.annotate('', xy=(tb.xmin,tb.y0), xytext=(tb.xmax,tb.y0), xycoords="figure fraction",arrowprops=dict(arrowstyle="-", color=c,linestyle=linestyle,linewidth=1.5,alpha=.8)) def mark_as_algorithm(ax,text,c,linestyle,algorithm="LRTAME"): tb = text.get_tightbbox(fig.canvas.get_renderer()).transformed(fig.transFigure.inverted()) # calculate asymmetry of x and y axes: x0, y0 = fig.transFigure.transform((0, 0)) # lower left in pixels x1, y1 = fig.transFigure.transform((1, 1)) # upper right in pixes dx = x1 - x0 dy = y1 - y0 maxd = max(dx, dy) if algorithm == "LRTAME": radius=.02 height = radius * maxd / dy width = radius * maxd / dx p=ax.add_patch(patches.Ellipse((tb.xmin-.015,tb.y0+(5/8)*tb.height),width, height,color=LRT_color,transform=fig.transFigure,clip_on=False)) elif algorithm == "TAME": side_length=.015 height = side_length * maxd / dy width = side_length * maxd / dx p=ax.add_patch(patches.Rectangle((tb.xmax+.01,tb.y0+.5*(tb.height - side_length)), width, height,color=T_color, transform=fig.transFigure,clip_on=False)) else: raise ValueError(f"algorithm must be either 'TAME' or 'LRTAME', got {algorithm}.\n") def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.3,format="default",xlim=None,xscale="linear",column_type=None): bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.01) if xscale=="linear": v = ax.violinplot(data,[.5], points=100, showmeans=False,widths=.15, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) elif xscale=="log": v = ax.violinplot(np.log10(data), points=100, showmeans=False,widths=.15,showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1),(x0,y1+.7)]] v["cmedians"].set_segments(newMedianLines) # -- write data values as text if column_type is None: if format == "default": ax.annotate(f"{np.median(data):.{precision}f}",xy=(.475,.4),xycoords="axes fraction",ha="center",fontsize=10)#.set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}f}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data):.{precision}f}",xy=(.975,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) else: print(f"expecting format to be either 'default' or 'scientific', got:{format}") elif column_type == "merged_axis": pass else: raise ValueError("column_type expecting 'merged_axis' or None, but got {column_type}\n") if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(v_alpha) #b.set_color(c) # -- only plot the top half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 1]) b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) #new_max_y += .04 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def make_violin_plot_merged_axis(ax,data1,data2,c1,c2,marker1,marker2,format=None,**kwargs): make_violin_plot(ax,data1,**dict(kwargs,c=c1,column_type="merged_axis")) make_violin_plot(ax,data2,**dict(kwargs,format=format,c=c2,column_type="merged_axis")) # add markers to the median lines marker_size = 12.5 marker_y_loc = ax.get_ylim()[-1] ax.scatter(np.log10(np.median(data1)),marker_y_loc,marker=T_marker,color=c1,s=marker_size) ax.scatter(np.log10(np.median(data2)),marker_y_loc,marker=LRT_marker,color=c2,s=marker_size) ax.set_ylim(0.9175, 1.1) # #ax.scatter(np.median(data1),.65,marker=marker1,s=5) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.01) min1 = np.min(data1) min2 = np.min(data2) if min1 < min2: #text = f"{min1:.{kwargs['precision']}f}" #underlined_annotation(fig,ax,(.075,extremal_tick_ypos),text,linestyle=LRT_linestyle,ha="left",fontsize=8,alpha=.8) text = ax.annotate(f"{min1:.{kwargs['precision']}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) #mark_as_algorithm(ax,text,T_color,T_linestyle,algorithm="TAME") #underline_text(ax,text,T_color,T_linestyle) """ tb = text.get_tightbbox(fig.canvas.get_renderer()).transformed(fig.transFigure.inverted()) ax.annotate('', xy=(tb.xmin,tb.y0), xytext=(tb.xmax,tb.y0), xycoords="figure fraction",arrowprops=dict(arrowstyle="-", color='k',linestyle=T_linestyle)) """ else: text = ax.annotate(f"{min2:.{kwargs['precision']}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) #mark_as_algorithm(ax,text,LRT_color,LRT_linestyle,algorithm="LRTAME") #underline_text(ax,text,LRT_color,LRT_linestyle) #minimum_val = min([np.min(data1),np.min(data2)]) maximum_val = min([np.max(data1),np.max(data2)]) max1 = np.max(data1) max2 = np.max(data2) if max1 > max2: text = f"{maximum_val:.{kwargs['precision']}f}" text = ax.annotate(f"{max1:.{kwargs['precision']}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) #mark_as_algorithm(ax,text,T_color,T_linestyle,algorithm="TAME") #underline_text(ax,text,T_color,T_linestyle) #underlined_annotation(fig,ax,(.925,extremal_tick_ypos),text,linestyle=LRT_linestyle,ha="right",fontsize=8,alpha=.8) else: text = ax.annotate(f"{maximum_val:.{kwargs['precision']}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) #mark_as_algorithm(ax,text,LRT_color,LRT_linestyle,algorithm="LRTAME") #underline_text(ax,text,LRT_color,LRT_linestyle) ax.annotate(f"{np.median(data1):.{kwargs['precision']}f}",xy=(.7,.5),xycoords="axes fraction",ha="center",fontsize=10)#.set_bbox(bbox) ax.annotate(f"{np.median(data2):.{kwargs['precision']}f}",xy=(.3,.5),xycoords="axes fraction",ha="center",fontsize=10)#.set_bbox(bbox) #for x in sorted(dir(text)): # print(x) """ if format is None: ax.annotate(f"{np.median(data1):.{kwargs[:precision]}f}",xy=(.5,.2),xycoords="axes fraction",ha="center",fontsize=10) ax.annotate(f"{np.min(data1):.{precision}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data1):.{precision}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.2),xycoords="axes fraction",ha="center",fontsize=10) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) else: print(f"expecting format to be 'scientific' or None, got:{format}") """ def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 v = ax.violinplot([np.random.normal() for i in range(50)], points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() ax.set_ylim(.5,1.0) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] #newMedianLines = [[(x0,y0-.125),(x0,y0 + (y1-y0)/2 -.1)]]#,[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.3,pad=.01) ax.annotate("median",xy=(.5,.375),xycoords="axes fraction",ha="center",va="center",fontsize=10)#.set_bbox(bbox) ax.annotate(f"min",xy=(.025,-.125),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) ax.annotate(f"max",xy=(.975,-.125),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(.3) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def make_merged_violin_plot_legend(ax): np.random.seed(12)#Ok looking:12 data1 = [np.random.normal(-.25,.25) for i in range(50)] v1= ax.violinplot(data1, points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) data2 = [np.random.normal(.5,.25) for i in range(50)] v2 = ax.violinplot(data2, points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False) ax.set_ylim(.5,1.0) marker_size = 12.5 marker_y_loc = ax.get_ylim()[-1] - .05 ax.scatter(-0.282,marker_y_loc,marker=LRT_marker,color=LRT_color,s=marker_size) ax.scatter(np.log10(np.median(data2))+.795,marker_y_loc,marker=T_marker,color=T_color,s=marker_size) bbox = dict(boxstyle="sawtooth", ec="w", fc="w", alpha=.3,pad=-.15) for (c,v) in [(LRT_color,v1),(T_color,v2)]: # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] #newMedianLines = [[(x0,y0-.125),(x0,y0 + (y1-y0)/2 -.1)]]#,[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) med_label1 = ax.annotate("LR-TAME med.",xy=(.275,.35),xycoords="axes fraction",ha="center",va="center",fontsize=9)#.set_bbox(bbox) #ax.annotate("median",xy=(.25,.35),xycoords="axes fraction",ha="center",va="top",fontsize=9)#.set_bbox(bbox) #mark_as_algorithm(ax,med_label1,LRT_color,LRT_linestyle,algorithm="LRTAME") med_label2 = ax.annotate("TAME med.",xy=(.725,.35),xycoords="axes fraction",ha="center",va="center",fontsize=9)#.set_bbox(bbox) #ax.annotate("median",xy=(.7,.35),xycoords="axes fraction",ha="center",va="top",fontsize=9)#.set_bbox(bbox) #mark_as_algorithm(ax,med_label2,T_color,T_linestyle,algorithm="TAME") min_label = ax.annotate(f"min",xy=(.075,-.125),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) #mark_as_algorithm(ax,min_label,LRT_color,LRT_linestyle,algorithm="LRTAME") max_label = ax.annotate(f"max",xy=(.925,-.125),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) #mark_as_algorithm(ax,max_label,T_color,T_linestyle,algorithm="TAME") v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(.3) #b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) # # Parse Data # results_path = TAME_RESULTS + "RG_DupNoise/" elemwise_list_sum =lambda l1,l2: [a + b for (a,b) in zip(l1,l2)] filename = "LowRankTAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[3,4,5,6,7,8]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json" with open(results_path + filename,"r") as file: data = json.load(file) LR_results = {} debug_results = {} LR_seeds = {} for (order, LRTAME_output) in data: trials = len(LRTAME_output) LR_results[order] = { "full runtime":{}, "contraction runtime":{}, "A motifs":[], "ranks":{}, } debug_results[order] = { "runtimes":[], "ranks":[] } LR_seeds[order] = [] for trial_idx,(seed,p,n,sp,accuracy,dup_tol_accuracy,motifs_matched,A_motifs,B_motifs,A_motif_dist,B_motif_dist,profiling) in enumerate(LRTAME_output): LR_results[order]["A motifs"].append(A_motifs) LR_seeds[order].append(seed) for (params,profile) in profiling: contract_rt = reduce(elemwise_list_sum,[ profile["low_rank_factoring_timings"], profile["contraction_timings"], ]) if params in LR_results[order]["contraction runtime"]: LR_results[order]["contraction runtime"][params].append(np.max(contract_rt)) else: LR_results[order]["contraction runtime"][params] = [np.max(contract_rt)] if params == 'α:0.5_β:1.0': debug_results[order]["ranks"].append(profile["ranks"]) debug_results[order]["runtimes"].append(contract_rt) ranks = profile["ranks"] if len(ranks) < 15: # if algorithm terminated from tol bounds, extend last rank to fill the rest ranks.extend([ranks[-1]]*(15-len(ranks))) if params in LR_results[order]["ranks"]: LR_results[order]["ranks"][params].append(profile["ranks"]) else: LR_results[order]["ranks"][params] = [profile["ranks"]] #return debug_results file = "TAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[3,4,5,6,7]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json" with open(results_path + file,"r") as file: data = json.load(file) T_results = {} for (order, TAME_output) in data: trials = len(TAME_output) T_results[order] = { "contraction runtime":{}, "full runtime":{}, } for trial_idx,(seed,p,n,sp,accuracy,dup_tol_accuracy,motifs_matched,A_motifs,B_motifs,A_motif_dist,B_motif_dist,profiling) in enumerate(TAME_output): for (params,profile) in profiling: if params in T_results[order]["contraction runtime"]: T_results[order]["contraction runtime"][params].append(np.max(profile["contraction_timings"])) else: T_results[order]["contraction runtime"][params] = [np.max(profile["contraction_timings"])] file = "LambdaTAME_RandomGeometric_degreedist:LogNormal_alpha:[0.5,1.0]_beta:[0.0,1.0]_n:[100]_orders:[3,4,5,6,7]_p:0.5_sample:10000_sp:[0.2]_trials:25_results.json" with open(results_path + file,"r") as file: data = json.load(file) LT_results = {} all_shifts = [ 'α:0.5_β:0.0', 'α:0.5_β:1.0', 'α:1.0_β:0.0', 'α:1.0_β:1.0', ] for (order, TAME_output) in data: trials = len(TAME_output) LT_results[order] = { "contraction runtime":{}, "full runtime":{}, } for trial_idx,(seed,p,n,sp,accuracy,dup_tol_accuracy,motifs_matched,A_motifs,B_motifs,A_motif_dist,B_motif_dist,profile) in enumerate(TAME_output): # aggregate the runtimes full_rt = reduce(elemwise_list_sum,[ profile["Matching Timings"], profile["TAME_timings"], profile["Scoring Timings"], ]) for (i,rt) in enumerate(full_rt): params = all_shifts[i] if params in LT_results[order]["full runtime"]: LT_results[order]["full runtime"][params].append(rt) else: LT_results[order]["full runtime"][params] = [rt] for (i,rt) in enumerate(profile["TAME_timings"]): params = all_shifts[i] if params in LT_results[order]["contraction runtime"]: LT_results[order]["contraction runtime"][params].append(rt) else: LT_results[order]["contraction runtime"][params] = [rt] # # Create Plots # fig = plt.figure(figsize=(5,3)) global_ax = plt.gca() global_ax.set_axis_off() parity = 1 #used for checkerboard effect in plots. linestyles= { 'α:0.5_β:0.0':"dotted", 'α:0.5_β:1.0':"solid", 'α:1.0_β:0.0':(0,(3,1,1,1)), 'α:1.0_β:1.0':(0,(5,1)) } # column assignment motif_label = 0 A_motif_data = 1 rank_data = 2 TAME_rt = 3 LRTAME_rt = 3 LTAME_rt = 5 #rt_data = 1 main_gs = fig.add_gridspec(2, 1,hspace=0.0,wspace=0.0, height_ratios = [1,.15], left=0.05,right=.975,top=.85,bottom=0.025) n = len(T_results.keys()) m = 4 # - 1 for no LT # - 1 for merging TAME w/ LRT heights = [.1,.3,.3,.7] row_height_ratios = [1]*n #col_width_ratios.append(.8) gs = main_gs[0].subgridspec(nrows=n,ncols=m,hspace=0.0,wspace=0.1, width_ratios=heights,height_ratios=row_height_ratios) legend_gs = main_gs[1].subgridspec(nrows=1,ncols=20) gs_ax = np.empty((n,m),object) iterate_tick_idx = 2 # -- plot runtime data -- # LambdaTAME_runtime_data = [] motif_label_xloc = .4 for idx,((LRT_order,LRT_data),(T_order,T_data),(LT_order,LT_data)) in enumerate(zip(LR_results.items(),T_results.items(),LT_results.items())): assert LRT_order == T_order assert LRT_order == LT_order LambdaTAME_runtime_data.append((T_order,np.max(LT_data["contraction runtime"]['α:0.5_β:1.0']))) order = LRT_order for col in range(m): if idx == 0: ax = fig.add_subplot(gs[idx,col]) else: ax = fig.add_subplot(gs[idx,col],sharex=gs_ax[0,col]) gs_ax[idx,col] = ax if col == motif_label: ax.annotate(f"{order}",xy=(motif_label_xloc, .5), xycoords='axes fraction', c="k",size=10,ha="center",va="center")#,weight="bold") elif col == TAME_rt: #ax.set_xscale("log") make_violin_plot_merged_axis(ax,T_data["contraction runtime"]['α:0.5_β:1.0'], LRT_data["contraction runtime"]['α:0.5_β:1.0'], T_color,LRT_color,T_marker,LRT_marker, precision=2,v_alpha=.3,xscale="log") elif col == LRTAME_rt: #ax.set_xscale("log") pass elif col == LTAME_rt: make_violin_plot(ax,LT_data["contraction runtime"]['α:0.5_β:1.0'], precision=1,c=LT_color,v_alpha=.3,format = "scientific",xscale="log") elif col == A_motif_data: make_violin_plot(ax,LRT_data["A motifs"],precision=0,c="k",v_alpha=.3) elif col == rank_data: make_violin_plot(ax,np.array(LRT_data["ranks"]['α:0.5_β:1.0']).max(axis=1),precision=0,c="k",v_alpha=.3) #ax.set_xlim(13,61) print(f"(order,LambdTAME maximum contraction runtimes):\n{LambdaTAME_runtime_data}") vp_legend_ax = fig.add_subplot(legend_gs[3:8]) make_violin_plot_legend(vp_legend_ax) merged_vp_legend_ax = fig.add_subplot(legend_gs[11:20]) make_merged_violin_plot_legend(merged_vp_legend_ax) #marker_legend_ax = fig.add_subplot(legend_gs[6:10]) label_font = 11 #LRT_label = marker_legend_ax.annotate("LR-TAME",rotation=0,ha="right",va="top",xy=(.85, 1.0), xycoords='axes fraction',c=LRT_color,fontsize=label_font) #mark_as_algorithm(marker_legend_ax,LRT_label,LRT_color,LRT_linestyle,"LRTAME") #TAME_label = marker_legend_ax.annotate("TAME",rotation=0,ha="right",va="bottom",xy=(.85, 0.0), xycoords='axes fraction',c=T_color,fontsize=label_font) #mark_as_algorithm(marker_legend_ax ,TAME_label,T_color,T_linestyle,"TAME") #make_violin_plot_legend(merged_vp_legend_ax) # -- create legends -- # # # Adjust Axes # # -- Set the column titles -- # title_ypos = 1.1 annotation_ypos = .6 gs_ax[0,motif_label].annotate("Clique\nSize",ha="center",va="bottom",xy=(motif_label_xloc, title_ypos), xycoords='axes fraction') #bbox = dict(boxstyle="round", ec=checkboard_color, fc=checkboard_color, alpha=1.0,pad=-.1) #gs_ax[0,LRTAME_rt].set_ylabel("Max Iterate\nTTV Time (s)",rotation=0,labelpad=30,ha="center",va="center")#,c=LT_color,xy=(.3, .125), xycoords='axes fraction').set_bbox(bbox) gs_ax[0,LRTAME_rt].annotate("Longest\nContraction (s)",rotation=0,ha="center",va="bottom",xy=(.5, title_ypos), xycoords='axes fraction')#,c=LT_color,xy=(.3, .125), xycoords='axes fraction').set_bbox(bbox) gs_ax[0,rank_data].annotate("Max (LR-)TAME\nIterate Rank",ha="center",va="bottom",xy=(.5, title_ypos), xycoords='axes fraction') gs_ax[0,A_motif_data].annotate("A motifs",xy=(.5, title_ypos), xycoords='axes fraction',ha="center",va="bottom") #y_pos = -1.25 #gs_ax[-1,-1].annotate("LRT - LowRankTAME",xy=(.625, y_pos), xycoords='axes fraction',ha="right",va="center",fontsize=11,c=LRT_color) #gs_ax[-1,-1].annotate("T - TAME",xy=(1.0, y_pos), xycoords='axes fraction',ha="right",va="center",fontsize=11,c=T_color)# # -- make a label for shared x-axis super_title_ypos = .875 additional_ax = [ global_ax,vp_legend_ax,merged_vp_legend_ax,#marker_legend_ax #legend_ax # annotation_ax, ] for ax in chain(gs_ax.reshape(-1),additional_ax): ax.set_yticklabels([]) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_xticklabels([]) if save_path is None: plt.show() else: plt.savefig(save_path) # # LVGNA Alignments # def LVGNA_end_to_end_relative_to_TAME_table_with_microplots(save_path=None): fig = plt.figure(figsize=(5,4.2)) # # Subroutines # def make_microplot_legend(ax): ax.grid(True) ax.set_ylim(-.1,1.1) ax.set_xlim(-.1,1.1) ax.set_xticks(np.linspace(0,1,5)) ax.set_yticks(np.linspace(0,1,5)) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_xlabel(r"$|T_A||T_B|$",labelpad=-2) ax.set_ylabel(r"Data",labelpad=-2) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1,pad=0.0) ax.annotate("log",xy=(.4,.1),xycoords="axes fraction",va="center",fontsize=6).set_bbox(bbox) ax.annotate("log",xy=(.1,.4),xycoords="axes fraction",ha="center",rotation=90,fontsize=6).set_bbox(bbox) def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 v = ax.violinplot([np.random.normal() for i in range(50)], points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() ax.set_ylim(.5,1.0) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] #newMedianLines = [[(x0,y0-.125),(x0,y0 + (y1-y0)/2 -.1)]]#,[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) ax.annotate("median",xy=(.5,.35),xycoords="axes fraction",ha="center",va="center",fontsize=10) ax.annotate(f"min",xy=(.025,0),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) ax.annotate(f"max",xy=(.975,0),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(.3) #b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def underline_text(ax,renderer,text,c,linestyle): tb = text.get_tightbbox(renderer).transformed(fig.transFigure.inverted()) ax.annotate('', xy=(tb.xmin,tb.y0), xytext=(tb.xmax,tb.y0), xycoords="figure fraction",arrowprops=dict(arrowstyle="-", color=c,linestyle=linestyle,linewidth=1.5,alpha=.8)) def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.8,format="default",xlim=None,xscale="linear",column_type=None): #background_v = ax.violinplot(data, points=100, positions=[0.5], showmeans=False, # showextrema=False, showmedians=False,widths=.5,vert=False)#,quantiles=[[.2,.8]]*len(data2)) # #positions=[0.5], ,widths=.5 if xscale=="linear": v = ax.violinplot(data,[.5], points=100, showmeans=False,widths=.15, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) elif xscale=="log": v = ax.violinplot(np.log10(data), points=100, showmeans=False,widths=.15,showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") #ax.set_ylim(0.95, 1.3) #ax.set_xlim(np.min(data),np.max(data)) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1),(x0,y1+.7)]] v["cmedians"].set_segments(newMedianLines) # -- place extremal markers underneath """ v['cbars'].set_segments([]) # turns off x-axis spine for segment in [v["cmaxes"],v["cmins"]]: ((x,y0),(_,y1)) = segment.get_segments()[0] segment.set_segments([[(x,0.45),(x,.525)]]) segment.set_color(c) """ # -- write data values as text bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.05) extremal_tick_ypos = .25 if column_type is None: if format == "default": ax.annotate(f"{np.median(data):.{precision}f}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10)#.set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}f}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8)#.set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}f}",xy=(.975,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8)#.set_bbox(bbox) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10)#.set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8)#.set_bbox(bbox) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.975,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8)#.set_bbox(bbox) else: print(f"expecting format to be either 'default' or 'scientific', got:{format}") elif column_type == "merged_axis": pass else: raise ValueError("column_type expecting 'merged_axis' or None, but got {column_type}\n") if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(v_alpha) #b.set_color(c) # -- only plot the top half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 1]) b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) #new_max_y += .04 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) #clip_to_top_of_violin(v["cmaxes"]) #clip_to_top_of_violin(v["cmins"]) # # Load and Parse Pre + Post Processing information. # TAME_tri_results,TAME_edge_match,\ TAME_pp_edge_match,TAME_pp_tri_match,\ TAME_runtimes, TAME_impTTV_runtimes, TAME_BM_runtimes,\ TAME_pp_runtime, exp_idx = CT_LP.parse_results() full_TAME_runtimes = TAME_runtimes + TAME_pp_runtime graph_names = [" ".join(file.split(".smat")[0].split("_")) for (file,idx) in sorted(exp_idx.items(),key=lambda x:x[1])] gn_dict = {graph:i for (i,graph) in enumerate(graph_names)} LGRAAL_graphs, LGRAAL_tri_results, LGRAAL_runtimes, _,_, _,_ = get_results() LGRAAL_perm = [gn_dict[graph] for graph in LGRAAL_graphs] LGRAAL_tri_results = LGRAAL_tri_results[np.ix_(LGRAAL_perm, LGRAAL_perm)] LGRAAL_runtimes = LGRAAL_runtimes[np.ix_(LGRAAL_perm, LGRAAL_perm)] #data_path = TAME_RESULTS + "LVGNA_Experiments/klauPostProcessing/" data_path = TAME_RESULTS + "LVGNA_Alignments/" file = "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_samples:3000000_data.json" file = "LVGNA_pairwiseAlignment_LambdaTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:GramMatching_postProcessing:KlauAlgo_profile:true_tol:1e-6_results.json" with open(data_path + file,"r") as f: data = json.load(f) vertex_products = [] edge_products = [] motif_products = [] LT_klau_tri_match_ratios = [] LT_klau_edge_match_ratios = [] LT_klau_runtimes = [] #return data #for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LT_profile,LT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity) in data: for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LT_profile,LT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] pre_processed_time = sum([sum(v) for (k,v) in LT_profile.items() if "Timings" in k]) LT_klau_runtimes.append(full_TAME_runtimes[i,j]/(pre_processed_time+klau_setup_rt+klau_rt)) vertex_A = vertex_counts[" ".join(file_i.split(".smat")[0].split("_"))] vertex_B = vertex_counts[" ".join(file_j.split(".smat")[0].split("_"))] vertex_products.append(vertex_A*vertex_B) edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] edge_products.append(edges_A*edges_B) #motif_products.append(A_Motifs[0]*B_Motifs[0]) motif_products.append(A_Motifs*B_Motifs) LT_klau_edge_match_ratios.append((klau_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) #LT_klau_tri_match_ratios.append((klau_tris_matched/min(A_Motifs[0],B_Motifs[0]))/TAME_pp_tri_match[i,j]) LT_klau_tri_match_ratios.append((klau_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) """ #with open(data_path + "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_postProcessing:SuccessiveKlau_samples:3000000_data.json","r") as f: with open(data_path + "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_postProcessing:SuccessiveKlau-const-iter:5-maxIter:500_samples:10000000_data.json","r") as f: data = json.load(f) successive_klau_tri_match_ratios = [] successive_klau_edge_match_ratios = [] successive_klau_runtimes = [] for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LT_profile,LT_edges_matched,sklau_edges_matched,sklau_tris_matched,_,successive_klau_profiling) in data: edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] successive_klau_edge_match_ratios.append(sklau_edges_matched/min(edges_A,edges_B)) successive_klau_tri_match_ratios.append(sklau_tris_matched/min(A_Motifs[0],B_Motifs[0])) successive_klau_runtimes.append(sum(successive_klau_profiling["runtime"])) """ file = "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_postProcessing:TabuSearch_samples:30000000_data.json" file = "LVGNA_pairwiseAlignment_LambdaTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:GramMatching_postProcessing:LocalSearch_profile:true_tol:1e-6_results.json" with open(data_path + file,"r") as f: data = json.load(f) LT_tabu_tri_match_ratios = [] LT_tabu_edge_match_ratios = [] LT_tabu_runtimes = [] #return data #for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LT_profile,LT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LT_profile,LT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LT_tabu_edge_match_ratios.append((tabu_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) #LT_tabu_tri_match_ratios.append((tabu_tris_matched/min(A_Motifs[0],B_Motifs[0]))/TAME_pp_tri_match[i,j]) LT_tabu_tri_match_ratios.append((tabu_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) pre_processed_time = sum([sum(v) for (k,v) in LT_profile.items() if "Timings" in k]) LT_tabu_runtimes.append(full_TAME_runtimes[i,j]/(pre_processed_time + tabu_full_rt)) file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_order:3_postProcessing:KlauAlgo_profile:true_samples:3000000_tol:1e-6_results.json" file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_postProcessing:KlauAlgo_profile:true_tol:1e-6_results_colMajor.json" with open(data_path + file,"r") as f: data = json.load(f) #add in missing \beta = 1.0 case LRT_klau_tri_match_ratios = [] LRT_klau_edge_match_ratios = [] LRT_klau_runtimes = [] #for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LRT_profile,LRT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,LRT_profile,LRT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LRT_klau_edge_match_ratios.append((klau_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) LRT_klau_tri_match_ratios.append((klau_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) pre_processed_time = 0.0 for (param, profile) in LRT_profile: pre_processed_time += sum([sum(v) for (k,v) in profile.items() if "timings" in k]) post_processed_time = klau_setup_rt + klau_rt LRT_klau_runtimes.append(full_TAME_runtimes[i,j]/(pre_processed_time + post_processed_time)) file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_iter:15_order:3_postProcessing:TabuSearch_profile:true_tol:1e-6_results.json" file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_postProcessing:LocalSearch_profile:true_tol:1e-6_results_colMajor.json" with open(data_path +file ,"r") as f: data = json.load(f) LRT_tabu_tri_match_ratios = [] LRT_tabu_edge_match_ratios = [] LRT_tabu_runtimes = [] #for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LRT_profile,LRT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,LRT_profile,LRT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] #print(f"{LRT_matchedMotifs}/min({A_Motifs},{B_Motifs})") LRT_tabu_edge_match_ratios.append((tabu_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) LRT_tabu_tri_match_ratios.append((tabu_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) pre_processed_time = 0.0 for (param, profile) in LRT_profile: pre_processed_time += sum([sum(v) for (k,v) in profile.items() if "timings" in k]) LRT_tabu_runtimes.append(full_TAME_runtimes[i,j]/(pre_processed_time + tabu_full_rt)) """ file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_postProcessing:LocalSearch_profile:true_tol:1e-6_results_colMajor.json" with open(data_path +file ,"r") as f: data = json.load(f) for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,LRT_profile,LRT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: #print(f"{LRT_matchedMotifs}/min({A_Motifs},{B_Motifs})") #print(LRT_profile[0]["ranks"]) """ with open(data_path + "LVGNA_pairwiseAlignment_LowRankTAME_lrm_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_order:3_postProcessing:KlauAlgo_profile:true_samples:3000000_tol:1e-6_results.json",'r') as f: data = json.load(f) LRT_lrm_klau_tri_match_ratios = [] LRT_lrm_klau_edge_match_ratios = [] LRT_lrm_klau_runtimes = [] for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LRT_profile,LRT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LRT_lrm_klau_edge_match_ratios.append((klau_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) LRT_lrm_klau_tri_match_ratios.append((klau_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) pre_processed_time = 0.0 for (param, profile) in LRT_profile: pre_processed_time += sum([sum(v) for (k,v) in profile.items() if "timings" in k]) post_processed_time = klau_setup_rt + klau_rt LRT_lrm_klau_runtimes.append(full_TAME_runtimes[i,j]/(pre_processed_time + post_processed_time)) with open(data_path + "LVGNA_pairwiseAlignment_LowRankTAME_lrm_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_order:3_postProcessing:TabuSearch_profile:true_samples:3000000_tol:1e-6_results.json",'r') as f: data = json.load(f) #add in missing \beta = 1.0 case LRT_lrm_tabu_tri_match_ratios = [] LRT_lrm_tabu_edge_match_ratios = [] LRT_lrm_tabu_runtimes = [] for (file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LRT_profile,LRT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LRT_lrm_tabu_edge_match_ratios.append((tabu_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) LRT_lrm_tabu_tri_match_ratios.append((tabu_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) pre_processed_time = 0.0 for (param, profile) in LRT_profile: pre_processed_time += sum([sum(v) for (k,v) in profile.items() if "timings" in k]) LRT_lrm_tabu_runtimes.append(full_TAME_runtimes[i,j]/(pre_processed_time + tabu_full_rt)) with open(data_path + "LVGNA_pairwiseAlignment_LowRankEigenAlign_postProcessing:TabuSearch_profile:true_results.json") as f: data = json.load(f) #for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LREA_profile,LREA_edges_matched,LREA_klau_edges_matched,LREA_klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: LREA_tabu_tri_match_ratios = [] LREA_tabu_edge_match_ratios = [] LREA_tabu_runtimes = [] for (file_i,file_j,LREA_matchedMotifs,A_Motifs,B_Motifs,LREA_runtime,LREA_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LREA_tabu_edge_match_ratios.append((tabu_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) LREA_tabu_tri_match_ratios.append((tabu_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) LREA_tabu_runtimes.append(full_TAME_runtimes[i,j]/(LREA_runtime+tabu_full_rt)) with open(data_path + "LVGNA_pairwiseAlignment_LowRankEigenAlign_postProcessing:KlauAlgo_profile:true_results.json") as f: data = json.load(f) LREA_klau_tri_match_ratios = [] LREA_klau_edge_match_ratios = [] LREA_klau_runtimes = [] for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LREA_runtime,LREA_edges_matched,LREA_klau_edges_matched,LREA_klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LREA_klau_edge_match_ratios.append((LREA_klau_edges_matched/min(edges_A,edges_B))/TAME_pp_edge_match[i,j]) LREA_klau_tri_match_ratios.append((LREA_klau_tris_matched/min(A_Motifs,B_Motifs))/TAME_pp_tri_match[i,j]) LREA_klau_runtimes.append(full_TAME_runtimes[i,j]/(LREA_runtime+klau_setup_rt+klau_rt)) # # Plot the Data # # -- Table Data -- # tri_match_idx = 0 edge_match_idx = 1 runtime_idx = 2 results_to_plot = [ (LT_tabu_tri_match_ratios,LT_tabu_edge_match_ratios,LT_tabu_runtimes,LT_Tabu_color,LT_Tabu_linestyle,r"$\Lambda$"+"-TAME\nLocal Search",(-.45,-.01)), (LT_klau_tri_match_ratios,LT_klau_edge_match_ratios,LT_klau_runtimes,LT_Klau_color,LT_Klau_linestyle,r"$\Lambda$"+"-TAME\nKlau",(-.425,-.01)), (LRT_tabu_tri_match_ratios,LRT_tabu_edge_match_ratios,LRT_tabu_runtimes,LRT_Tabu_color,LRT_Tabu_linestyle,"LR-TAME\nLocal Search",(-.475,-.01)), #(LRT_lrm_tabu_tri_match_ratios,LRT_lrm_tabu_edge_match_ratios,LRT_lrm_tabu_runtimes,LRT_Tabu_color,LRT_Tabu_linestyle,"LRT-lrm-Tabu",(-.675,-.01)), (LRT_klau_tri_match_ratios,LRT_klau_edge_match_ratios,LRT_klau_runtimes,LRT_Klau_color,LRT_Klau_linestyle,"LR-TAME\nKlau",(-.475,-.01)), #(LRT_lrm_klau_tri_match_ratios,LRT_lrm_klau_edge_match_ratios,LRT_lrm_klau_runtimes,LRT_Klau_color,LRT_Klau_linestyle,"LRT-lrm-Klau",(-.675,-.01)), (LREA_tabu_tri_match_ratios,LREA_tabu_edge_match_ratios,LREA_tabu_runtimes,LREA_Tabu_color,LREA_Tabu_linestyle,"LR-EigenAlign\nLocal Search",(-.55,-.01)), (LREA_klau_tri_match_ratios,LREA_klau_edge_match_ratios,LREA_klau_runtimes,LREA_Klau_color,LREA_Klau_linestyle,"LR-EigenAlign\nKlau",(-.55,-.01)) ] main_gs = fig.add_gridspec(2, 1, hspace=0.0,wspace=0.2, height_ratios = [1,.25], left=0.2, right=0.975, top=.85, bottom=.05) #legend_gs = main_gs[1].subgridspec(nrows=1,ncols=20) table_gs = main_gs[0].subgridspec(nrows=len(results_to_plot),ncols=3,wspace=0.05,hspace=0.0) microplot_gs = main_gs[1].subgridspec(nrows=1,ncols=3,wspace=0.2,hspace=0.0) all_axes = np.empty((len(results_to_plot)+1,3),object) # +1 for microplots loess_frac = .3 annotations = [] for i,(tri_match_data,edge_match_data,runtime_data,color,linestyle,label,underline_xs) in enumerate(results_to_plot): if i == 0: for j in [tri_match_idx,runtime_idx,edge_match_idx]: all_axes[i,j] = fig.add_subplot(table_gs[i,j]) all_axes[-1,j] = fig.add_subplot(microplot_gs[-1,j]) else: for j in [tri_match_idx,runtime_idx,edge_match_idx]: all_axes[i,j] = fig.add_subplot(table_gs[i,j],sharex=all_axes[0,j]) #all_axes[i,tri_match_idx] = fig.add_subplot(table_gs[i,tri_match_idx],sharex=all_axes[0,tri_match_idx]) #all_axes[i,runtime_idx] = fig.add_subplot(table_gs[i,runtime_idx],sharex=all_axes[0,runtime_idx]) #all_axes[i,edge_match_idx] = fig.add_subplot(table_gs[i,edge_match_idx],sharex=all_axes[0,edge_match_idx]) make_violin_plot(all_axes[i,tri_match_idx],tri_match_data,precision=2,c=color,v_alpha=.3,format="default",xlim=None,xscale="linear",column_type=None) make_violin_plot(all_axes[i,edge_match_idx],edge_match_data,precision=2,c=color,v_alpha=.3,format="default",xlim=None,xscale="log",column_type=None) make_violin_plot(all_axes[i,runtime_idx],runtime_data,precision=1,c=color,v_alpha=.3,format="default",xlim=None,xscale="log",column_type=None) """ (line_x_start,line_x_end) = underline_xs y = .4 line_x_start = -.7 line_x_end = -.01 all_axes[i,0].annotate('', xy=(line_x_start, y), xycoords='axes fraction', xytext=(line_x_end,y), arrowprops=dict(arrowstyle="-", color=color,linestyle=linestyle,linewidth=3)) """ annotations.append(all_axes[i,0].annotate(label,xy=(-.4,.5), xycoords='axes fraction',ha="center",va="center",c=color,fontsize=9)) plot_1d_loess_smoothing(motif_products,tri_match_data,loess_frac,all_axes[-1,tri_match_idx],c=color,logFilter=True,logFilterAx="x")#,linestyle=linestyle plot_1d_loess_smoothing(motif_products,edge_match_data,loess_frac,all_axes[-1,edge_match_idx],c=color,logFilter=True,logFilterAx="x")#,linestyle=linestyle plot_1d_loess_smoothing(motif_products,runtime_data,loess_frac,all_axes[-1,runtime_idx],c=color,logFilter=True,logFilterAx="x")#,linestyle=linestyle # # Format the Axes # microplot_legend = all_axes[-1,0].inset_axes([-.6,.2,.45,.65]) violinplot_legend = all_axes[0,0].inset_axes([-.65,1.2,.6,1.0]) for ax in chain(all_axes.reshape(-1),[microplot_legend,violinplot_legend]): #ax.set_xscale("log") #ax.grid(True) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_xticks([]) ax.set_yticks([]) make_microplot_legend(microplot_legend) make_violin_plot_legend(violinplot_legend) for (j,ax) in enumerate(all_axes[-1,:]): ax.grid(True) ax.set_xscale("log") if j != 0: ax.set_yscale("log") ax.yaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_minor_formatter(NullFormatter()) ax.set_xticks([1e6,1e7,1e8,1e9,1e10,1e11]) ax.set_xticklabels([]) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1,pad=0.01) all_axes[-1,0].annotate("linear",xy=(.125,.825),xycoords="axes fraction",ha="center",va="center",rotation=90,fontsize=6).set_bbox(bbox) #denotes microplot in column 0 is linear #all_axes[-1,-1].set_yscale("log") bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1,pad=0.0) #ax.annotate("log",xy=(.1,.4),xycoords="axes fraction",ha="center",rotation=90,fontsize=6).set_bbox(bbox) #all_axes[-1,edge_match_idx].set_yscale("log") #all_axes[-1,runtime_idx].set_yscale("log") all_axes[-1,tri_match_idx].set_yticks(np.linspace(.2,1.8,5)) all_axes[-1,tri_match_idx].set_ylim(0,2) all_axes[-1,edge_match_idx].set_yticks(np.linspace(1.0,7,5)) all_axes[-1,edge_match_idx].set_ylim(.8,7.5) all_axes[-1,runtime_idx].set_yticks([1e0,1e1,1e2,1e3]) all_axes[-1,runtime_idx].set_ylim(.5,1.5e3) # - row/col labels - # title_ypos = 1.2 line_offset = .3 line_x_start = .15 line_x_end = .85 all_axes[0,tri_match_idx].annotate("Algo. Tri Match\n TAME Tri Match",xy=(.5, title_ypos), xycoords='axes fraction',ha="center") all_axes[0,tri_match_idx].annotate('', xy=(.075, title_ypos+line_offset), xycoords='axes fraction', xytext=(.925, title_ypos+line_offset), arrowprops=dict(arrowstyle="-", color='k')) all_axes[0,edge_match_idx].annotate("Algo. Edge Match\n TAME Edge Match",xy=(.5, title_ypos), xycoords='axes fraction',ha="center") all_axes[0,edge_match_idx].annotate('', xy=(.0, title_ypos+line_offset), xycoords='axes fraction', xytext=(1.0, title_ypos+line_offset), arrowprops=dict(arrowstyle="-", color='k')) all_axes[0,runtime_idx].annotate("TAME Runtime\n Algo. Runtime",xy=(.5, title_ypos), xycoords='axes fraction',ha="center") all_axes[0,runtime_idx].annotate('', xy=(.1, title_ypos+line_offset), xycoords='axes fraction', xytext=(.925, title_ypos+line_offset), arrowprops=dict(arrowstyle="-", color='k')) """ renderer = fig.canvas.get_renderer() for i,row_label in enumerate(annotations): print(i) underline_text(all_axes[i,0],renderer,row_label,color,linestyle) """ if save_path is None: plt.show() else: plt.savefig(save_path) def make_LVGNA_TTVMatchingRatio_runtime_plots(ax=None,useLowRank=False,save_path=None): """ new version of MatchingRatio plot in the new simax-v2 style """ Opt_tri_ratio,Opt_edge_match,\ Opt_pp_edge_match,Opt_pp_tri_match,\ Runtimes, TAME_impTTV_runtimes, TAME_BM_runtimes,\ post_processing_runtime, exp_idx = CT_LP.parse_results() graph_names = [" ".join(file.split(".smat")[0].split("_")) for (file,idx) in sorted(exp_idx.items(),key=lambda x:x[1])] TAME_BM_ratio = np.divide(TAME_BM_runtimes,TAME_impTTV_runtimes) datapath = TAME_RESULTS + "LVGNA_alignments/" def process_LowRankTAME_data(f): _, results = json.load(f) # # Parse Input files # #Format the data to #exp_idx = {name:i for i,name in enumerate(graph_names)} matchingRuntimes = np.zeros((len(exp_idx),len(exp_idx))) contractionRuntimes = np.zeros((len(exp_idx),len(exp_idx))) for (file_A,file_B,matched_tris,max_tris,param_profiles) in results: graph_A = file_A.split(".ssten")[0] + ".smat" graph_B = file_B.split(".ssten")[0] + ".smat" i = exp_idx[graph_A] j = exp_idx[graph_B] #sum over all params totalContractionRuntime = 0.0 contraction_times = ['qr_timings', 'contraction_timings','svd_timings'] totalMatchingRuntime = 0.0 for params, profile in param_profiles: contraction_timings = [v for k,v in profile.items() if k in contraction_times] totalContractionRuntime += sum(reduce(lambda l1,l2: [x + y for x,y in zip(l1,l2)],contraction_timings)) totalMatchingRuntime += sum(profile['matching_timings']) contractionRuntimes[i,j] = totalContractionRuntime contractionRuntimes[j,i] = totalContractionRuntime matchingRuntimes[i,j] = totalMatchingRuntime matchingRuntimes[j,i] = totalMatchingRuntime return matchingRuntimes, contractionRuntimes with open(datapath+"LowRankTAME_LVGNA_alpha_[.5,1.0]_beta_[0,1e0,1e1,1e2]_iter_15.json", "r") as f: matchingRuntimes, contractionRuntimes = process_LowRankTAME_data(f) LowRankTAME_ratio = np.divide(matchingRuntimes,contractionRuntimes) with open(datapath+"LowRankTAME_LVGNA_alpha_[.5,1.0]_beta_[0,1,1e1,1e2]_iter_15_low_rank_matching.json", "r") as f: matchingRuntimes,contractionRuntimes = process_LowRankTAME_data(f) LowRankTAME_LRM_ratio = np.divide(matchingRuntimes,contractionRuntimes) def parseLambdaTAMEData(exp_results): #exp_idx = {name:i for i,name in enumerate(graph_names)} ratio_data = np.zeros((len(exp_idx),len(exp_idx))) for (file_A,file_B,matched_tris,max_tris,_,runtime) in exp_results: graph_A = file_A.split(".ssten")[0] + ".smat" graph_B = file_B.split(".ssten")[0] + ".smat" i = exp_idx[graph_A] j = exp_idx[graph_B] contraction_rt = sum(runtime['TAME_timings']) matching_rt = sum(runtime['Matching Timings']) ratio_data[j,i] = matching_rt/contraction_rt ratio_data[i,j] = matching_rt/contraction_rt return ratio_data #with open(TAME_RESULTS + "LVGNA_Experiments/LambdaTAME_LVGNA_results_alphas:[.5,1.0]_betas:[0,1e0,1e1,1e2]_iter:15.json","r") as f: with open(datapath+"LVGNA_pairwairAlignemnt_LambdaTAME_alphas:[.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:rankOneMatching_profile:true_tol:1e-6_results.json","r") as f: LambdaTAME_ratio = parseLambdaTAMEData(json.load(f)) #with open(TAME_RESULTS + "LVGNA_Experiments/LVGNA_pairwairAlignemnt_LambdaTAME_alphas:[.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:GramMatching_profile:true_tol:1e-6_results.json","r") as f: with open(datapath+"LVGNA_pairwiseAlignment_LambdaTAME_alphas:[.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:GramMatching_MatchingTol:1e-8_profile:true_tol:1e-6_results.json","r") as f: LambdaTAMEGramMatching_ratio = parseLambdaTAMEData(json.load(f)) n = len(graph_names) problem_sizes = [] TAME_exp_ratios = [] LambdaTAME_exp_ratios = [] LambdaTAMEGramMatching_exp_ratios = [] LowRankTAME_exp_ratios = [] LowRankTAME_LRM_exp_ratios = [] Is,Js = np.triu_indices(n,k=1) for i,j in zip(Is,Js): TAME_exp_ratios.append(TAME_BM_ratio[i,j]) LambdaTAME_exp_ratios.append(LambdaTAME_ratio[i,j]) LambdaTAMEGramMatching_exp_ratios.append(LambdaTAMEGramMatching_ratio[i,j]) LowRankTAME_exp_ratios.append(LowRankTAME_ratio[i, j]) LowRankTAME_LRM_exp_ratios.append(LowRankTAME_LRM_ratio[i, j]) problem_sizes.append(triangle_counts[graph_names[i]]*triangle_counts[graph_names[j]]) # # Plot results # if ax is None: fig =plt.figure(figsize=(4.25,3.5)) spec = fig.add_gridspec(nrows=1, ncols=1,left=.175,right=.9,top=.95,bottom=.125) ax = fig.add_subplot(spec[0,0]) show_plot = True else: show_plot = False #ax = [ax] #jerry rigged if useLowRank: ax.set_ylim(1e-3,1e5) fontsize=10 else: ax.set_ylim(1e-2,1e5) fontsize=14 ax.set_xlim(2e5,5e11) label_font_size = None #if show_plot: ax.set_ylabel("matching runtime\ncontraction runtime",fontsize=label_font_size) x_loc = -.19 ax.annotate('', xy=(x_loc, .29), xycoords='axes fraction', xytext=(x_loc, 0.71), arrowprops=dict(arrowstyle="-", color='k')) #else: # ax.set_ylabel("matching runtime / contraction runtime") ax.set_xlabel(r"|$T_A$||$T_B$|") #left axis labels ax.set_xscale("log") ax.set_yscale("log") loess_smoothing_frac = .3 ax.grid(which="major",zorder=-2) ax.axhspan(1e-5,1,alpha=.1,color="k") scatter_alpha = 0.5 #TODO: update marker types to use global vars ax.scatter(problem_sizes,TAME_exp_ratios,label="TAME", c=T_color,marker=T_marker,alpha=scatter_alpha) plot_1d_loess_smoothing(problem_sizes,TAME_exp_ratios,loess_smoothing_frac,ax,c=T_color,logFilter=True)#linestyle=T_linestyle, #ax[0].plot(range(len(old_TAME_performance)), old_TAME_performance, label="TAME", c=t4_color) if useLowRank: T_annotationloc = (.1, .55) else: T_annotationloc = (.5, .05) ax.annotate("TAME (C++)",xy=T_annotationloc, xycoords='axes fraction', c=T_color,fontsize=fontsize) ax.scatter(problem_sizes,LowRankTAME_exp_ratios,label="LowRankTAME", c=LRT_color,alpha=scatter_alpha,marker=LRT_marker) plot_1d_loess_smoothing(problem_sizes,LowRankTAME_exp_ratios,loess_smoothing_frac,ax,c=LRT_color,logFilter=True)#linestyle=LRT_linestyle, if useLowRank: LRT_annotationloc = (.01, .38) else: LRT_annotationloc = (.8, .36) ax.annotate("LowRank\nTAME",xy=LRT_annotationloc, xycoords='axes fraction', c=LRT_color,ha="left",fontsize=fontsize) ax.scatter(problem_sizes,LambdaTAMEGramMatching_exp_ratios,label="$\Lambda$-TAME", c=LT_color ,marker=LT_marker,alpha=scatter_alpha) plot_1d_loess_smoothing(problem_sizes,LambdaTAMEGramMatching_exp_ratios,loess_smoothing_frac,ax,c=LT_color)#,linestyle=LT_linestyle #ax[0].plot(range(len(new_TAME_performance)), new_TAME_performance, label="$\Lambda$-TAME", c=t2_color) if useLowRank: LT_annotationloc = (.1, .825) else: LT_annotationloc = (.1, .86) ax.annotate("$\Lambda$-TAME",xy=LT_annotationloc, xycoords='axes fraction', c=LT_color,fontsize=fontsize) if useLowRank: ax.scatter(problem_sizes,LowRankTAME_LRM_exp_ratios,facecolors='none',edgecolors=LRT_lrm_color,label="LowRankTAME-(lrm)",marker=LRT_lrm_marker,alpha=scatter_alpha) plot_1d_loess_smoothing(problem_sizes,LowRankTAME_LRM_exp_ratios,loess_smoothing_frac,ax,c=LRT_lrm_color,logFilter=True)#,linestyle=LRT_lrm_linestyle ax.annotate("LowRankTAME-(lrm)",xy=(.075, .05), xycoords='axes fraction', c=LRT_lrm_color,fontsize=fontsize) #print(new_TAME_exp_runtimes) ax.scatter(problem_sizes,LambdaTAME_exp_ratios,label="$\Lambda$-TAME-(rom)",facecolors='none', edgecolors=LT_rom_color,marker=LT_rom_marker,alpha=scatter_alpha) plot_1d_loess_smoothing(problem_sizes,LambdaTAME_exp_ratios,loess_smoothing_frac,ax,c=LT_rom_color,logFilter=True)#,linestyle=LT_rom_linestyle #ax[0].plot(range(len(new_TAME_performance)), new_TAME_performance, label="$\Lambda$-TAME", c=t2_color) ax.annotate("$\Lambda$-TAME\n(rom)",xy=(.25, .225), xycoords='axes fraction', ha="center",c=LT_rom_color,fontsize=fontsize) #ax.text(.95, .6,"contraction\ndominates")#, ha="center",c="k",rotation=90) #ax.text(.95, .1,"contraction\ndominates")#, ha="center",c="k",rotation=90) if useLowRank: ax.annotate("matching\ndominates",xy=(1.05, .6), xycoords='axes fraction', ha="center",c="k",rotation=90,fontsize=label_font_size) ax.annotate("contraction\ndominates",xy=(1.05, 0.05), xycoords='axes fraction', ha="center",c="k",rotation=90,fontsize=label_font_size) else: ax.annotate("matching\ndominates",xy=(1.05, .55), xycoords='axes fraction', ha="center",c="k",rotation=90,fontsize=label_font_size) ax.annotate("contraction\ndominates",xy=(1.05, 0.025), xycoords='axes fraction', ha="center",c="k",rotation=90,fontsize=label_font_size) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) if show_plot and save_path is None: plt.show() if not (save_path is None): plt.savefig(save_path) # Supplementary File def LVGNA_pre_and_post_processed(save_path=None): # # Load in Data # data_path = TAME_RESULTS + "LVGNA_Alignments/" file = "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_samples:3000000_data.json" file = "LVGNA_pairwiseAlignment_LambdaTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:GramMatching_postProcessing:KlauAlgo_profile:true_tol:1e-6_results.json" with open(data_path +file,"r") as f: data = json.load(f) vertex_products = [] edge_products = [] motif_products = [] LT_klau_tri_match = [] LT_klau_edge_match = [] LT_klau_runtimes = [] filenames = [] #for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LT_profile,LT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity) in data: for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LT_profile,LT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: filenames.append((file_i,file_j)) vertex_A = vertex_counts[" ".join(file_i.split(".smat")[0].split("_"))] vertex_B = vertex_counts[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LT_klau_runtimes.append(klau_setup_rt+klau_rt) vertex_products.append(vertex_A*vertex_B) edge_products.append(edges_A*edges_B) #motif_products.append(A_Motifs[0]*B_Motifs[0]) motif_products.append(A_Motifs*B_Motifs) LT_klau_edge_match.append(klau_edges_matched/min(edges_A,edges_B)) #LT_klau_tri_match.append(klau_tris_matched/min(A_Motifs[0],B_Motifs[0])) LT_klau_tri_match.append(klau_tris_matched/min(A_Motifs,B_Motifs)) """ with open(data_path + "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_postProcessing:SuccessiveKlau-const-iter:5-maxIter:500_samples:10000000_data.json","r") as f: data = json.load(f) successive_klau_tri_match_ratios = [] successive_klau_edge_match_ratios = [] successive_klau_runtimes = [] for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LT_profile,LT_edges_matched,sklau_edges_matched,sklau_tris_matched,_,successive_klau_profiling) in data: edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] successive_klau_edge_match_ratios.append(sklau_edges_matched/min(edges_A,edges_B)) successive_klau_tri_match_ratios.append(sklau_tris_matched/min(A_Motifs[0],B_Motifs[0])) successive_klau_runtimes.append(sum(successive_klau_profiling["runtime"])) """ #LT data comes from here file = "LVGNA_pairwiseAlignment_LambdaTAME_MultiMotif_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_order:3_postProcessing:TabuSearch_samples:30000000_data.json" file = "LVGNA_pairwiseAlignment_LambdaTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_matchingMethod:GramMatching_postProcessing:LocalSearch_profile:true_tol:1e-6_results.json" with open(data_path + file,"r") as f: data = json.load(f) LT_tabu_tri_match = [] LT_tabu_edge_match = [] LT_tabu_runtimes = [] LT_tri_match = [] LT_runtimes = [] #for i,(file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LT_profile,LT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,full_rt) in enumerate(data): for i,(file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LT_profile,LT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,full_rt) in enumerate(data): assert (file_i,file_j) == filenames[i] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] #LT_tri_match.append(LT_matchedMotifs[0]/min(A_Motifs[0],B_Motifs[0])) LT_tri_match.append(LT_matchedMotifs/min(A_Motifs,B_Motifs)) LT_runtimes.append(sum([sum(profile) for profile in LT_profile.values()])) LT_tabu_edge_match.append(tabu_edges_matched/min(edges_A,edges_B)) #LT_tabu_tri_match.append(tabu_tris_matched/min(A_Motifs[0],B_Motifs[0])) LT_tabu_tri_match.append(tabu_tris_matched/min(A_Motifs,B_Motifs)) LT_tabu_runtimes.append(full_rt) def parse_Cpp_data(files): # given the file pairs to make the data consistent with preP_tri_match_ratio = [] preP_edge_match_ratio = [] preP_runtime = [] postP_tri_match_ratio = [] postP_edge_match_ratio = [] postP_runtime = [] Opt_tri_ratio,Opt_edge_match,\ Opt_pp_edge_match,Opt_pp_tri_match,\ Runtimes, impTTV_runtimes, matching_runtimes,\ post_processing_runtime, indexing = CT_LP.parse_results() for file_idx,(file_i,file_j) in enumerate(files): i = indexing[file_i] j = indexing[file_j] preP_tri_match_ratio.append(Opt_tri_ratio[i,j]) preP_edge_match_ratio.append(Opt_edge_match[i,j]) preP_runtime.append(Runtimes[i,j]) postP_tri_match_ratio.append(Opt_pp_tri_match[i,j]) postP_edge_match_ratio.append(Opt_pp_edge_match[i,j]) postP_runtime.append(post_processing_runtime[i,j]) return preP_tri_match_ratio, preP_edge_match_ratio, preP_runtime, postP_tri_match_ratio, postP_edge_match_ratio, postP_runtime, indexing # LREA data comes from here with open(data_path + "LVGNA_pairwiseAlignment_LowRankEigenAlign_postProcessing:TabuSearch_profile:true_results.json") as f: data = json.load(f) #for (file_i,file_j,LT_matchedMotifs,A_Motifs,B_Motifs,LREA_profile,LREA_edges_matched,LREA_klau_edges_matched,LREA_klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in data: LREA_tri_match = [] LREA_runtimes = [] LREA_tabu_tri_match = [] LREA_tabu_edge_match = [] LREA_tabu_runtimes = [] for i,(file_i,file_j,LREA_matchedMotifs,A_Motifs,B_Motifs,LREA_profile,LREA_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,full_rt) in enumerate(data): assert filenames[i] == (file_i,file_j) edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LREA_tri_match.append(LREA_matchedMotifs/min(A_Motifs,B_Motifs)) LREA_runtimes.append(LREA_profile) LREA_tabu_edge_match.append(tabu_edges_matched/min(edges_A,edges_B)) LREA_tabu_tri_match.append(tabu_tris_matched/min(A_Motifs,B_Motifs)) LREA_tabu_runtimes.append(full_rt) with open(data_path + "LVGNA_pairwiseAlignment_LowRankEigenAlign_postProcessing:KlauAlgo_profile:true_results.json") as f: data = json.load(f) LREA_klau_tri_match = [] LREA_klau_edge_match = [] LREA_klau_runtimes = [] for i,(file_i,file_j,_,A_Motifs,B_Motifs,LREA_profile,LREA_edges_matched,LREA_klau_edges_matched,LREA_klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in enumerate(data): assert filenames[i] == (file_i,file_j) edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LREA_klau_edge_match.append(LREA_klau_edges_matched/min(edges_A,edges_B)) LREA_klau_tri_match.append(LREA_klau_tris_matched/min(A_Motifs,B_Motifs)) LREA_klau_runtimes.append(klau_setup_rt+klau_rt) file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_order:3_postProcessing:KlauAlgo_profile:true_samples:3000000_tol:1e-6_results.json" file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_postProcessing:KlauAlgo_profile:true_tol:1e-6_results_colMajor.json" with open(data_path + file,"r") as f: data = json.load(f) #add in missing \beta = 1.0 case LRT_klau_tri_match = [] LRT_klau_edge_match = [] LRT_klau_runtimes = [] #for i,(file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LRT_profile,LRT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in enumerate(data): for i,(file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,LRT_profile,LRT_edges_matched,klau_edges_matched,klau_tris_matched,_,klau_setup_rt,klau_rt,L_sparsity,f_status) in enumerate(data): assert filenames[i] == (file_i,file_j) #i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] #j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LRT_klau_edge_match.append(klau_edges_matched/min(edges_A,edges_B)) LRT_klau_tri_match.append(klau_tris_matched/min(A_Motifs,B_Motifs)) LRT_klau_runtimes.append(klau_setup_rt + klau_rt) # LRT data comes from here file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[1.0,0.5]_betas:[0.0,1.0,10.0,100.0]_iter:15_order:3_postProcessing:TabuSearch_profile:true_tol:1e-6_results.json" file = "LVGNA_pairwiseAlignment_LowRankTAME_alphas:[0.5,1.0]_betas:[0.0,1.0,10.0,100.0]_iter:15_postProcessing:LocalSearch_profile:true_tol:1e-6_results_colMajor.json" with open(data_path + file,"r") as f: data = json.load(f) LRT_tri_match = [] LRT_runtimes = [] LRT_tabu_tri_match = [] LRT_tabu_edge_match = [] LRT_tabu_runtimes = [] LRT_runtimes = [] #for i,(file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,A_motifDistribution,B_motifDistribution,LRT_profile,LRT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in enumerate(data): for i,(file_i,file_j,LRT_matchedMotifs,A_Motifs,B_Motifs,LRT_profile,LRT_edges_matched,tabu_edges_matched,tabu_tris_matched,matching,tabu_profile,tabu_full_rt) in enumerate(data): assert filenames[i] == (file_i,file_j) #i = gn_dict[" ".join(file_i.split(".smat")[0].split("_"))] #j = gn_dict[" ".join(file_j.split(".smat")[0].split("_"))] edges_A = edge_counts[file_i.split(".smat")[0]] edges_B = edge_counts[file_j.split(".smat")[0]] LRT_tabu_edge_match.append(tabu_edges_matched/min(edges_A,edges_B)) LRT_tabu_tri_match.append(tabu_tris_matched/min(A_Motifs,B_Motifs)) LRT_tabu_runtimes.append(tabu_full_rt) pre_processed_time = 0.0 for (param, profile) in LRT_profile: pre_processed_time += sum([sum(v) for (k,v) in profile.items() if "timings" in k]) LRT_tri_match.append(LRT_matchedMotifs/min(A_Motifs,B_Motifs)) LRT_runtimes.append(pre_processed_time) TAME_preP_tri_match_ratio, TAME_preP_edge_match_ratio, TAME_preP_runtime,\ TAME_postP_tri_match_ratio, TAME_postP_edge_match_ratio, TAME_postP_runtime, \ exp_indexing = parse_Cpp_data(filenames) graph_names = [" ".join(file.split(".smat")[0].split("_")) for (file,idx) in sorted(exp_indexing.items(),key=lambda x:x[1])] gn_dict = {graph:i for (i,graph) in enumerate(graph_names)} LGRAAL_graphs, LGRAAL_tri_results, LGRAAL_runtimes, _,_, _,_ = get_results() LGRAAL_perm = [gn_dict[graph] for graph in LGRAAL_graphs] LGRAAL_tri_results = LGRAAL_tri_results[np.ix_(LGRAAL_perm, LGRAAL_perm)] LGRAAL_runtimes = LGRAAL_runtimes[np.ix_(LGRAAL_perm, LGRAAL_perm)] LGRAAL_tri_match = [] LGRAAL_runtime = [] for (file_i,file_j) in filenames: i = exp_indexing[file_i] j = exp_indexing[file_j] LGRAAL_tri_match.append(LGRAAL_tri_results[i,j]) LGRAAL_runtime.append(LGRAAL_runtimes[i,j]) # # Plot the Data # fig = plt.figure(figsize=(9,7)) gs = fig.add_gridspec(nrows=2, ncols=1, left=0.05, right=0.975,top=.95,bottom=.1, wspace=0.1,hspace=0.1 ) height_ratios = [.5,1.0,1.0] tri_match_gs = gs[0].subgridspec(nrows=3,ncols=5,hspace=0.4,wspace=0.2,height_ratios=height_ratios) tri_match_axes = np.empty((3,5),object) runtime_gs = gs[1].subgridspec(nrows=1,ncols=2, #left=0.125, right=0.9,top=.95,bottom=.075, wspace=0.1,hspace=0.1) runtime_axes = np.empty(2,object) # -- Plot Runtime Data -- # for i in range(2): runtime_axes[i] = fig.add_subplot(runtime_gs[i]) pre_index = 0 post_index = 1 loess_frac = .3 annotation_fontsize = 10 preP_data_to_plot = [ (LRT_runtimes,LRT_color,LRT_linestyle,"LowRankTAME",(.665,.61),12.6,"both"),#-.05,.43) (LREA_runtimes,LREigenAlign_color,LREigenAlign_linestyle,"LowRankEigenAlign",(.5,.075),7.5,"both"), (LT_runtimes,LT_color,LT_linestyle,r"$\Lambda-$TAME",(.35,.3),0,"both"), (TAME_preP_runtime,T_color,T_linestyle,"TAME (C++)",(.6,.7),20,"both"), (LGRAAL_runtime,LGRAAL_color,LGRAAL_linestyle,"LGRAAL",(.3,.6),0,"both"), ] for (tri_match_data,color,linestyle,annotation_text,annotation_loc,annotation_angle,logFilterAx) in preP_data_to_plot: #runtime_axes[pre_index].scatter(motif_products,tri_match_data) plot_1d_loess_smoothing(motif_products,tri_match_data,loess_frac,runtime_axes[pre_index], c=color,logFilter=True,logFilterAx=logFilterAx)#linestyle=linestyle, runtime_axes[pre_index].annotate(annotation_text,xy=annotation_loc,c=color, xycoords='axes fraction',ha="left",rotation=annotation_angle, fontsize=annotation_fontsize) postP_data_to_plot = [ (LRT_klau_runtimes,LRT_Klau_color,LRT_Klau_linestyle,"LRT-Klau",(.1,.45),0), (LRT_tabu_runtimes,LRT_Tabu_color,LRT_Tabu_linestyle,"LRT-LS",(.1,.175),-5), (LREA_klau_runtimes,LREA_Klau_color,LRT_Klau_linestyle,"LREA-Klau",(.35,.41),12.5), (LREA_tabu_runtimes,LREA_Tabu_color,LREA_Tabu_linestyle,"LREA-LS",(.9,.6),12.5), (LT_klau_runtimes,LT_Klau_color,LT_Klau_linestyle,r"$\Lambda$T-Klau",(.9,.45),10), (LT_tabu_runtimes,LT_Tabu_color,LT_Tabu_linestyle,r"$\Lambda$T-LS",(.525,.225),7.5), (TAME_postP_runtime,T_color,T_linestyle,"TAME (C++)\nLocal Search",(.525,.75),0), ] for (tri_match_data,color,linestyle,annotation_text,annotation_loc,annotation_angle) in postP_data_to_plot: plot_1d_loess_smoothing(motif_products,tri_match_data,loess_frac,runtime_axes[post_index], c=color,logFilter=True,logFilterAx="both")#linestyle=linestyle, runtime_axes[post_index].annotate(annotation_text,xy=annotation_loc,c=color, xycoords='axes fraction',ha="center",rotation=annotation_angle, fontsize=annotation_fontsize) annotation_fontsize = 12 # -- Plot Tri Match Data -- # for i in range(3): for j in range(5): tri_match_axes[i,j] = fig.add_subplot(tri_match_gs[i,j]) preP_triMatch_data_to_plot = [ (LGRAAL_tri_match,LGRAAL_color,LGRAAL_linestyle), (LT_tri_match,LT_color,LT_linestyle), (LRT_tri_match,LRT_color,LRT_linestyle), (LREA_tri_match,LREigenAlign_color,LREigenAlign_linestyle), (TAME_preP_tri_match_ratio,T_color,T_linestyle), ] for j,(exp_tri_match,color,linestyle) in enumerate(preP_triMatch_data_to_plot): tri_match_axes[0,j].scatter(motif_products,exp_tri_match,s=5,c=color,zorder=3,alpha=.5) plot_1d_loess_smoothing(motif_products,exp_tri_match,loess_frac,tri_match_axes[0,j], c=color,logFilter=True,logFilterAx="x")#,linestyle=linestyle postP_triMatch_data_to_plot = [ [(None,LGRAAL_color,LGRAAL_linestyle)], [(LT_tabu_tri_match,LT_Tabu_color,LT_Tabu_linestyle),(LT_klau_tri_match,LT_Klau_color,LT_Klau_linestyle)], [(LRT_tabu_tri_match,LRT_Tabu_color,LRT_Tabu_linestyle),(LRT_klau_tri_match,LRT_Klau_color,LRT_Klau_linestyle)], [(LREA_tabu_tri_match,LREA_Tabu_color,LREA_Tabu_linestyle),(LREA_klau_tri_match,LREA_Klau_color,LREA_Klau_linestyle)], [(TAME_postP_tri_match_ratio,T_color,T_linestyle)], ] for j,post_processed_exps in enumerate(postP_triMatch_data_to_plot): for (exp_tri_match,color,linestyle) in post_processed_exps: if exp_tri_match is not None: tri_match_axes[1,j].scatter(motif_products,exp_tri_match,s=5,c=color,zorder=3,alpha=.25) plot_1d_loess_smoothing(motif_products,exp_tri_match,loess_frac,tri_match_axes[1,j], c=color,logFilter=True,logFilterAx="both")#linestyle=linestyle, postP_edgeMatch_data_to_plot = [ [(None,LGRAAL_color,LGRAAL_linestyle)], [(LT_tabu_edge_match,LT_Tabu_color,LT_Tabu_linestyle),(LT_klau_edge_match,LT_Klau_color,LT_Klau_linestyle)], [(LRT_tabu_edge_match,LRT_Tabu_color,LRT_Tabu_linestyle),(LRT_klau_edge_match,LRT_Klau_color,LRT_Klau_linestyle)], [(LREA_tabu_edge_match,LREA_Tabu_color,LREA_Tabu_linestyle),(LREA_klau_edge_match,LREA_Klau_color,LREA_Klau_linestyle)], [(TAME_postP_edge_match_ratio,T_color,T_linestyle)], ] for j,post_processed_exps in enumerate(postP_edgeMatch_data_to_plot): for (exp_tri_match,color,linestyle) in post_processed_exps: if exp_tri_match is not None: tri_match_axes[2,j].scatter(motif_products,exp_tri_match,s=5,c=color,zorder=3,alpha=.25) plot_1d_loess_smoothing(motif_products,exp_tri_match,loess_frac,tri_match_axes[2,j], c=color,logFilter=True,logFilterAx="both")#linestyle=linestyle, # # Touch up the Axes # for ax in chain(runtime_axes.reshape(-1),tri_match_axes.reshape(-1)): ax.set_xscale("log") ax.grid(True) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_xticks([1e6,1e7,1e8,1e9,1e10,1e11]) for ax in runtime_axes: ax.set_xlabel(r"$|T_A||T_B|$") ax.set_yscale("log") ax.set_ylim(5e0,3e6) ax.set_yticks([1e1,1e2,1e3,1e4,1e5,1e6]) ax.tick_params(axis="y",which="both",pad=6) runtime_axes[0].set_yticklabels([]) for j,ax in enumerate(tri_match_axes[0,:]): ax.set_ylim(0,.5) ax.set_yticks([0.0,.25,.5]) if j == 0: ax.set_yticklabels([]) else: ax.set_yticklabels(["0",".25",".5"],ha='center') for j,ax in enumerate(tri_match_axes[1,:]): ax.set_ylim(0,1) ax.set_yticks([0.0,.25,.5,.75,1.0]) if j == 1: ax.set_yticklabels([]) else: ax.set_yticklabels(["0",".25",".5",".75","1"],ha='center') for j,ax in enumerate(tri_match_axes[2,:]): ax.set_ylim(0,.75) ax.set_yticks([0.0,.25,.5,.75]) if j == 1: ax.set_yticklabels([]) else: ax.set_yticklabels(["0",".25",".5",".75"],ha='center') for ax in tri_match_axes.reshape(-1): ax.set_xticklabels([]) ax.tick_params(axis="y",which="both",pad=12) tri_match_axes[1,0].axes.set_axis_off() tri_match_axes[2,0].axes.set_axis_off() #LGRAAL doesn't have post processing preP_tri_match_annotations = [ ("LGRAAL",LGRAAL_color,(.5,1.3)), (r"$\Lambda-$TAME",LT_color,(.5,1.3)), ("LowRankTAME",LRT_color,(.5,1.3)), ("LowRank\nEigenAlign",LREigenAlign_color,(.5,1.3)), ("TAME",T_color,(.5,1.3)), ] for j,(label,color,xy) in enumerate(preP_tri_match_annotations): tri_match_axes[0,j].annotate(label,xy=xy,c=color,xycoords='axes fraction',ha="center",va="top", fontsize=annotation_fontsize) postP_tri_match_annotations = [ [(None,LGRAAL_color,(.5,.5),1)], [(r"$\Lambda$T-Klau",LT_Klau_color,(.8,.01),1),(r"$\Lambda$T-LS",LT_Tabu_color,(.7,.7),1)], [("LRT-Klau",LRT_Klau_color,(.75,-.1),1),("LRT-LS",LRT_Tabu_color,(.5,.65),1)], [("LREA-Klau",LREA_Klau_color,(.7,-.15),1),("LREA-LS",LREA_Tabu_color,(.65,.65),1)], [("TAME (C++)\nLocal Search",T_color,(.4,.375),2)], ] for j,postP_annotations in enumerate(postP_tri_match_annotations): for (label,color,xy,row_idx) in postP_annotations: if label is not None: tri_match_axes[row_idx,j].annotate(label,xy=xy,c=color,xycoords='axes fraction',ha="center", fontsize=annotation_fontsize) tri_match_axes[0,0].set_ylabel("matched tris\n"+r"$\min{\{|T_A|,|T_B|\}}$",labelpad=-7.5) xloc = -.18 tri_match_axes[0,0].annotate('', xy=(xloc, -.4), xycoords='axes fraction', xytext=(xloc, 1.4), arrowprops=dict(arrowstyle="-", color='k')) tri_match_axes[0,0].set_xlabel(r"$|T_A||T_B|$") tri_match_axes[1,1].set_ylabel("(refined)\nmatched tris\n"+r"$\min{\{|T_A|,|T_B|\}}$",labelpad=-7.5) xloc = -.18 tri_match_axes[1,1].annotate('', xy=(xloc, .025), xycoords='axes fraction', xytext=(xloc, .975), arrowprops=dict(arrowstyle="-", color='k')) tri_match_axes[2,1].set_ylabel("(refined)\nmatched edges\n"+r"$\min{\{|E_A|,|E_B|\}}$",labelpad=-7.5) xloc = -.18 tri_match_axes[2,1].annotate('', xy=(xloc, -.05), xycoords='axes fraction', xytext=(xloc, 1.05), arrowprops=dict(arrowstyle="-", color='k')) runtime_axes[0].set_ylabel("runtime (s)") title_size = 18 bbox = dict(boxstyle="round", ec="w", fc="w", alpha=1.0,pad=.05) x_loc = 0.18 y_loc = .975 y_gap = .1 runtime_axes[0].annotate("Pre",xy=(x_loc,y_loc), xycoords='axes fraction',ha="center",va="top",fontsize=title_size).set_bbox(bbox) runtime_axes[0].annotate("Processed",xy=(x_loc,y_loc-y_gap), xycoords='axes fraction',ha="center",va="top",fontsize=title_size//2).set_bbox(bbox) x_loc = 0.2025 runtime_axes[1].annotate("Post",xy=(x_loc,y_loc), xycoords='axes fraction',ha="center",va="top",fontsize=title_size).set_bbox(bbox) runtime_axes[1].annotate("Processed",xy=(x_loc,y_loc-y_gap), xycoords='axes fraction',ha="center",va="top",fontsize=title_size//2).set_bbox(bbox) # -- # if save_path is None: plt.show() else: plt.savefig(save_path) # # Random Graph Experiment Plots # def RandomGeometricRG_PostProcessing_is_needed(save_path=None): data_location = TAME_RESULTS + "klauExps/" fig = plt.figure(figsize=(4.5,4.5)) #fig, axes = plt.subplots(2,2,figsize=(4.25,5)) n = 2 m = 2 spec = fig.add_gridspec(nrows=n, ncols=m,hspace=0.1,wspace=0.15,left=.15,right=.975,top=.95,bottom=.15) all_ax = [] axes = np.empty((n,m),object) for i in range(n): for j in range(m): ax = fig.add_subplot(spec[i,j]) axes[i,j] = ax def process_ER_Noise_data(data,version="klau_old"): p_idx = {p:i for (i,p) in enumerate(sorted(set([datum[1] for datum in data])))} n_idx = {p:i for (i,p) in enumerate(sorted(set([datum[2] for datum in data])))} trials = int(len(data)/(len(p_idx)*len(n_idx))) accuracy = np.zeros((len(p_idx),len(n_idx),trials)) LT_klauAccuracy = np.zeros((len(p_idx),len(n_idx),trials)) triMatch = np.zeros((len(p_idx),len(n_idx),trials)) LT_klauTriMatch = np.zeros((len(p_idx),len(n_idx),trials)) trial_idx = np.zeros((len(p_idx),len(n_idx)),int) for datum in data: if version == "klau_new": (seed,p,n,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt,L_sparsity,fbounds) = datum elif version == "klau_old": (seed,p,n,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) = datum elif version == "tabu": (seed,p,n,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) = datum else: raise ValueError("only supports ") i = p_idx[p] j = n_idx[n] accuracy[i,j,trial_idx[i,j]] = acc LT_klauAccuracy[i,j,trial_idx[i,j]] = klau_acc triMatch[i,j,trial_idx[i,j]] = matched_tris/min(tri_A,tri_B) LT_klauTriMatch[i,j,trial_idx[i,j]] = klau_tri_match/min(tri_A,tri_B) trial_idx[i,j] += 1 return accuracy, LT_klauAccuracy, triMatch, LT_klauTriMatch, p_idx, n_idx def process_Dup_Noise_data(data,version="old"): p_idx = {p:i for (i,p) in enumerate(sorted(set([datum[1] for datum in data])))} n_idx = {n:i for (i,n) in enumerate(sorted(set([datum[2] for datum in data])))} sp_idx = {sp:i for (i,sp) in enumerate(sorted(set([datum[3] for datum in data])))} trials = int(len(data)/(len(p_idx)*len(n_idx)*len(sp_idx))) accuracy = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) klauAccuracy = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) triMatch = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) LT_klauTriMatch = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) trial_idx = np.zeros((len(p_idx),len(n_idx),len(sp_idx)),int) for datum in data: if version == "old": (seed,p,n,sp,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) = datum elif version == "new klau": (seed,p,n,sp,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt,L_sparsity,fstatus) = datum else: print(f"only supports 'old' and 'new klau', got {version}") i = p_idx[p] j = n_idx[n] k = sp_idx[sp] accuracy[i,j,k,trial_idx[i,j,k]] = acc klauAccuracy[i,j,k,trial_idx[i,j,k]] = klau_acc triMatch[i,j,k,trial_idx[i,j,k]] = matched_tris/min(tri_A,tri_B) LT_klauTriMatch[i,j,k,trial_idx[i,j,k]] = klau_tri_match/min(tri_A,tri_B) trial_idx[i,j,k] += 1 return accuracy, klauAccuracy, triMatch,LT_klauTriMatch, p_idx, n_idx, sp_idx def make_percentile_plot(plot_ax, x_domain,data,color,hatch=None,**kwargs): #TODO: check on scope priorities of ax for closures lines = [(lambda col: np.percentile(col,50),1.0,color) ] ribbons = [ (20,80,.05,color) ] #plot_percentiles(plot_ax, data.T, x_domain, lines, ribbons,**kwargs) n,m = data.shape percentile_linewidth=.01 for (lower_percentile,upper_percentile,alpha,color) in ribbons: #plot_ax.plot(np.percentile(data.T, lower_percentile, axis=0),c=color,linewidth=percentile_linewidth) #plot_ax.plot(np.percentile(data.T, upper_percentile, axis=0),c=color,linewidth=percentile_linewidth) plot_ax.fill_between(x_domain, np.percentile(data.T, lower_percentile, axis=0), np.percentile(data.T, upper_percentile, axis=0), facecolor=color,alpha=.1,edgecolor=color) for (col_func,alpha,color) in lines: line_data = [] for i in range(n): line_data.append(col_func(data[i,:])) plot_ax.plot(x_domain,line_data,alpha=alpha,c=color,**kwargs) # # Erdos Reyni Noise # data_location = TAME_RESULTS + "RG_ERNoise/" file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json' with open(data_location + file,'r') as f: accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx = process_ER_Noise_data(json.load(f),"klau_new") file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:TabuSearch_trialcount:20.json' with open(data_location + file,'r') as f: _, LT_TabuAccuracy, _, LT_TabuTriMatch, p_idx, n_idx = process_ER_Noise_data(json.load(f),"tabu") file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: LRT_accuracy, LRT_KlauAccuracy, LRT_triMatch, LRT_KlauTriMatch, p_idx, n_idx = process_ER_Noise_data(json.load(f),"klau_new") file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_TabuAccuracy,_,LRT_TabuTriMatch, p_idx, n_idx = process_ER_Noise_data(json.load(f),"tabu") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_lrm_KlauAccuracy, _, LRT_lrm_KlauTriMatch, p_idx, n_idx = process_ER_Noise_data(json.load(f),"klau_new") file = "LowRankEigenAlign_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: LREA_Accuracy, LREA_KlauAccuracy, LREA_TriMatch, LREA_KlauTriMatch, p_idx, n_idx = process_ER_Noise_data(json.load(f),"klau_new") file = "LowRankEigenAlign_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: _, LREA_TabuAccuracy, _, LREA_TabuTriMatch, _, _ = process_ER_Noise_data(json.load(f),"tabu") sub_ax = axes[:,0] spectral_embedding_exps = [ (accuracy,triMatch,LT_color,LT_linestyle), (LRT_accuracy,LRT_triMatch,LRT_color,LRT_linestyle), (LREA_Accuracy, LREA_TriMatch,LREigenAlign_color,LREigenAlign_linestyle), ] post_processing_exps = [ (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,LT_Tabu_linestyle), #(LRT_lrm_KlauAccuracy,LRT_lrm_KlauTriMatch,LRM_lrm_Klau_color), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,LRT_Klau_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,LRT_Tabu_linestyle), (LREA_TabuAccuracy, LREA_TabuTriMatch,LREA_Tabu_color,LREA_Tabu_linestyle), (LREA_KlauAccuracy, LREA_KlauTriMatch,LREA_Klau_color,LREA_Klau_linestyle), ] for (acc,tri,c,linestyle) in chain(spectral_embedding_exps,post_processing_exps): make_percentile_plot(axes[0,0],n_idx.keys(),acc[0,:,:],c,linestyle=linestyle) make_percentile_plot(axes[1,0],n_idx.keys(),tri[0,:,:],c,linestyle=linestyle) # # Duplcation Noise # default_p = .5 default_sp = .25 sub_ax = axes[:,1] data_location = TAME_RESULTS + "RG_DupNoise/" file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx, sp_idx = process_Dup_Noise_data(json.load(f)) file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json' with open(data_location + file,'r') as f: _, LT_TabuAccuracy,_,LT_TabuTriMatch, p_idx, n_idx, sp_idx = process_Dup_Noise_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: LRT_accuracy, LRT_TabuAccuracy,LRT_triMatch,LRT_TabuTriMatch, p_idx, n_idx, sp_idx = process_Dup_Noise_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_KlauAccuracy,_,LRT_KlauTriMatch, p_idx, n_idx, sp_idx = process_Dup_Noise_data(json.load(f),"new klau") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_lrm_KlauAccuracy,_,LRT_lrm_KlauTriMatch, p_idx, n_idx, sp_idx = process_Dup_Noise_data(json.load(f),"new klau") file ="LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: LREA_Accuracy, LREA_KlauAccuracy,LREA_TriMatch,LREA_KlauTriMatch, _, _, _ = process_Dup_Noise_data(json.load(f),"new klau") file ="LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: _, LREA_TabuAccuracy,_,LREA_TabuTriMatch, _, _, _ = process_Dup_Noise_data(json.load(f)) n_exps = [ (accuracy,triMatch,LT_color,None,LT_linestyle), (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,None,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,None,LT_Tabu_linestyle), (LRT_accuracy,LRT_triMatch,LRT_color,None,LRT_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,None,LRT_Tabu_linestyle), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,None,LRT_Klau_linestyle), (LREA_Accuracy, LREA_TriMatch,LREigenAlign_color,None,LREigenAlign_linestyle), (LREA_KlauAccuracy, LREA_KlauTriMatch,LREA_Klau_color,None,LREA_Klau_linestyle), (LREA_TabuAccuracy,LREA_TabuTriMatch,LREA_Tabu_color,None,LREA_Tabu_linestyle), #(LRT_lrm_KlauAccuracy,LRT_lrm_KlauTriMatch,LRT_lrm_Klau_color), ] for (acc, tri, c,hatch,linestyle) in n_exps: make_percentile_plot(sub_ax[0],n_idx.keys(),acc[p_idx[default_p],:,sp_idx[default_sp],:],c,linestyle=linestyle) make_percentile_plot(sub_ax[1],n_idx.keys(),tri[p_idx[default_p],:,sp_idx[default_sp],:],c,linestyle=linestyle) # # Final touches on axis # title_size = 12 bbox = dict(boxstyle="round", fc="w",ec="w",alpha=1.0,pad=.05) axes[0,0].annotate(u"Erdős Rényi",xy=(.975,.975), xycoords='axes fraction',ha="right",va="top",fontsize=title_size).set_bbox(bbox) axes[0,1].annotate("Duplication",xy=(.975,.975), xycoords='axes fraction',ha="right",va="top",fontsize=title_size).set_bbox(bbox) axes[1,1].annotate(r"$\Lambda$T",xy=(.4,.25),color=LT_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-15) axes[1,1].annotate("LRT",xy=(.4,.1),color=LRT_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-7.5) axes[1,1].annotate("LREA",xy=(.01,.005),color=LREigenAlign_color,xycoords="axes fraction",ha="left",va="bottom",fontsize=10,rotation=0) axes[1,0].annotate("LRT-Klau",xy=(.575,.325),color=LRT_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-5) axes[1,0].annotate(r"$\Lambda$T-"+"Klau",xy=(.6,.62),color=LT_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-26) axes[1,0].annotate("LREA-Klau",xy=(.275,.55),color=LREA_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-35) axes[0,1].annotate(r"$\Lambda$T-"+"LS",xy=(.775,.525),color=LT_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-2.5) axes[0,1].annotate("LRT-LS",xy=(.775,.74),color=LRT_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=0) axes[0,1].annotate("LREA-LS",xy=(.375,.575),color=LREA_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-40) for ax in axes[0,:]: ax.set_xticklabels([]) for ax in axes[1,:]: ax.tick_params(axis="x",direction="out",pad=1) ax.set_xticklabels(["100","250","500","1000","1250","1500"],rotation=60) ax.set_xlabel(r"$|V_A|$") for ax in axes.reshape(-1): ax.set_ylim(0.0,1.00) ax.grid(True) axes[0,0].set_ylabel("accuracy") axes[1,0].set_ylabel("matched tris\n"+r"$\min{\{|T_A|,|T_B|\}}$",labelpad=0) xloc = -.26 axes[1,0].annotate('', xy=(xloc, .2), xycoords='axes fraction', xytext=(xloc, 0.8), arrowprops=dict(arrowstyle="-", color='k')) #axes[0,0].set_yticklabels([]) for ax in axes[:,1]: ax.set_yticklabels([]) #axes[1,1].yaxis.set_label_position("right") for ax in axes[:,1]: ax.yaxis.tick_right() for ax in axes.reshape(-1): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_ylim(-.00,1.00) ax.grid(True) ax.set_xlim(min(n_idx.keys()),max(n_idx.keys())) ax.set_xticks([100, 250, 500, 1000, 1250, 1500]) ax.set_xlim(100,1500) if save_path is None: plt.show() else: plt.savefig(save_path) # Supplemental File def RandomGeometricDupNoise_allModes(save_path=None): data_location = TAME_RESULTS + "RG_DupNoise/" fig= plt.figure(figsize=(5.75,4)) n = 2 m = 3 spec = fig.add_gridspec(nrows=n, ncols=m,hspace=0.1,wspace=0.15,left=.125,right=.975,top=.975,bottom=.2) all_ax = [] all_ax_gs = np.empty((n,m),object) def process_data(data,version="old"): p_idx = {p:i for (i,p) in enumerate(sorted(set([datum[1] for datum in data])))} n_idx = {n:i for (i,n) in enumerate(sorted(set([datum[2] for datum in data])))} sp_idx = {sp:i for (i,sp) in enumerate(sorted(set([datum[3] for datum in data])))} trials = int(len(data)/(len(p_idx)*len(n_idx)*len(sp_idx))) accuracy = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) klauAccuracy = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) triMatch = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) LT_klauTriMatch = np.zeros((len(p_idx),len(n_idx),len(sp_idx),trials)) trial_idx = np.zeros((len(p_idx),len(n_idx),len(sp_idx)),int) for datum in data: if version == "old": (seed,p,n,sp,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) = datum elif version == "new klau": (seed,p,n,sp,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt,L_sparsity,fstatus) = datum else: print(f"only supports 'old' and 'new klau', got {version}") i = p_idx[p] j = n_idx[n] k = sp_idx[sp] accuracy[i,j,k,trial_idx[i,j,k]] = acc klauAccuracy[i,j,k,trial_idx[i,j,k]] = klau_acc triMatch[i,j,k,trial_idx[i,j,k]] = matched_tris/min(tri_A,tri_B) LT_klauTriMatch[i,j,k,trial_idx[i,j,k]] = klau_tri_match/min(tri_A,tri_B) trial_idx[i,j,k] += 1 return accuracy, klauAccuracy, triMatch,LT_klauTriMatch, p_idx, n_idx, sp_idx def make_percentile_plot(plot_ax, x_domain,data,color,hatch=None,**kwargs): lines = [(lambda col: np.percentile(col,50),1.0,color) ] ribbons = [ (20,80,.05,color) ] n,m = data.shape percentile_linewidth=.01 for (lower_percentile,upper_percentile,alpha,color) in ribbons: plot_ax.fill_between(x_domain, np.percentile(data.T, lower_percentile, axis=0), np.percentile(data.T, upper_percentile, axis=0), facecolor=color,alpha=.1,edgecolor=color) for (col_func,alpha,color) in lines: line_data = [] for i in range(n): line_data.append(col_func(data[i,:])) plot_ax.plot(x_domain,line_data,alpha=alpha,c=color,**kwargs) #hatches LRT_Klau_hatch = None#"+" LT_Klau_hatch = None#"x" LRT_Tabu_hatch = None#"+" LT_Tabu_hatch = None#"+" default_p = .5 default_n = 250 default_sp = .25 # # p_edge experiments # sub_ax = [fig.add_subplot(spec[i,0]) for i in [0,1]] all_ax_gs[:,0] = sub_ax all_ax.append(sub_ax) file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) LT_file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + LT_file,'r') as f: _, LT_TabuAccuracy,_,LT_TabuTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) LRT_file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + LRT_file,'r') as f: LRT_accuracy, LRT_TabuAccuracy,LRT_triMatch,LRT_TabuTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_KlauAccuracy,_,LRT_KlauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f),"new klau") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_lrm_KlauAccuracy,_,LRT_lrm_KlauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f),"new klau") file = "LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: _, LREA_TabuAccuracy, _ ,LREA_TabuTriMatch, _,_,_ = process_data(json.load(f)) file = "LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: LREA_Accuracy, LREA_KlauAccuracy, LREA_TriMatch ,LREA_KlauTriMatch, _,_,_ = process_data(json.load(f),"new klau") p_exps = [ (accuracy,triMatch,LT_color,None,LT_linestyle), (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,LT_Klau_hatch,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,LT_Tabu_hatch,LT_Tabu_linestyle), (LRT_accuracy,LRT_triMatch,LRT_color,None,LRT_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,LRT_Tabu_hatch,LRT_Tabu_linestyle), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,LRT_Klau_hatch,LRT_Klau_linestyle), (LREA_Accuracy, LREA_TriMatch, LREigenAlign_color,None,LREigenAlign_linestyle), (LREA_TabuAccuracy, LREA_TabuTriMatch, LREA_Tabu_color,None,LREA_Tabu_linestyle), (LREA_KlauAccuracy, LREA_KlauTriMatch, LREA_Klau_color,None,LREA_Klau_linestyle), #(LRT_lrm_KlauAccuracy,LRT_lrm_KlauTriMatch,LREA_Klau_color,None,LREA_Klau_linestyle), ] for (acc, tri, c,hatch,linestyle) in p_exps: make_percentile_plot(sub_ax[0],p_idx.keys(),acc[:,n_idx[default_n],sp_idx[default_sp],:],c,hatch,linestyle=linestyle) make_percentile_plot(sub_ax[1],p_idx.keys(),tri[:,n_idx[default_n],sp_idx[default_sp],:],c,hatch,linestyle=linestyle) for ax in sub_ax: ax.set_xticks([0.0,.25,.5,.75,1.0]) ax.set_xlim(min(p_idx.keys()),max(p_idx.keys())) sub_ax[1].set_xticklabels(["0.0",".25",r"${\bf .5}$",".75","1.0"],rotation=60) sub_ax[1].set_xlabel(r"$p_{edge}$") # # n size experiments # sub_ax = [fig.add_subplot(spec[i,1]) for i in [0,1]] all_ax.append(sub_ax) all_ax_gs[:,1] = sub_ax file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.75]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json' with open(data_location + file,'r') as f: _, LT_TabuAccuracy,_,LT_TabuTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: LRT_accuracy, LRT_TabuAccuracy,LRT_triMatch,LRT_TabuTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_KlauAccuracy,_,LRT_KlauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f),"new klau") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_lrm_KlauAccuracy,_,LRT_lrm_KlauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f),"new klau") file ="LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: LREA_Accuracy, LREA_KlauAccuracy,LREA_TriMatch,LREA_KlauTriMatch, _, _, _ = process_data(json.load(f),"new klau") file ="LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:Duplication_p:[0.5]_sp:[0.25]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: _, LREA_TabuAccuracy,_,LREA_TabuTriMatch, _, _, _ = process_data(json.load(f)) n_exps = [ (accuracy,triMatch,LT_color,None,LT_linestyle), (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,LT_Klau_hatch,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,LT_Tabu_hatch,LT_Tabu_linestyle), (LRT_accuracy,LRT_triMatch,LRT_color,None,LRT_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,LRT_Tabu_hatch,LRT_Tabu_linestyle), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,LRT_Klau_hatch,LRT_Klau_linestyle), (LREA_Accuracy, LREA_TriMatch,LREigenAlign_color,None,LREigenAlign_linestyle), (LREA_KlauAccuracy, LREA_KlauTriMatch,LREA_Klau_color,None,LREA_Klau_linestyle), (LREA_TabuAccuracy,LREA_TabuTriMatch,LREA_Tabu_color,None,LREA_Tabu_linestyle), #(LRT_lrm_KlauAccuracy,LRT_lrm_KlauTriMatch,LRT_lrm_Klau_color,None,"solid"), ] for (acc, tri, c,hatch,linestyle) in n_exps: make_percentile_plot(sub_ax[0],n_idx.keys(),acc[p_idx[default_p],:,sp_idx[default_sp],:],c,hatch,linestyle=linestyle) make_percentile_plot(sub_ax[1],n_idx.keys(),tri[p_idx[default_p],:,sp_idx[default_sp],:],c,hatch,linestyle=linestyle) for ax in sub_ax: ax.set_xticks([100, 250, 500, 1000, 1250, 1500]) ax.set_xlim(100,1500) sub_ax[1].set_xticklabels(["100",r"${\bf 250}$","500","1000","1250","1500"],rotation=60) sub_ax[1].set_xlabel(r"$|V_A|$") # # step percentage experiments # sub_ax = [fig.add_subplot(spec[i,2]) for i in [0,1]] all_ax.append(sub_ax) all_ax_gs[:,2] = sub_ax file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:TabuSearch_trialcount:20.json' with open(data_location + file,'r') as f: _, LT_TabuAccuracy, _ ,LT_TabuTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: LRT_accuracy, LRT_TabuAccuracy, LRT_triMatch ,LRT_TabuTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f)) file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LRT_KlauAccuracy, _ ,LRT_KlauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f),"new klau") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LRT_lrm_KlauAccuracy, _ ,LRT_lrm_KlauTriMatch, p_idx, n_idx, sp_idx = process_data(json.load(f),version="new klau") file = "LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: LREA_Accuracy, LREA_TabuAccuracy, LREA_TriMatch ,LREA_TabuTriMatch,_,_,_ = process_data(json.load(f)) file = "LowRankEigenAlign_graphType:RG_degdist:LogNormal-log5_n:[250]_noiseModel:Duplication_p:[0.5]_sp:[0.05,0.1,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: _, LREA_KlauAccuracy, _ ,LREA_KlauTriMatch,p_idx2, n_idx2, sp_idx2 = process_data(json.load(f),"new klau") sp_exps = [ (accuracy,triMatch,LT_color,None,LT_linestyle), (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,LT_Klau_hatch,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,LT_Tabu_hatch,LT_Tabu_linestyle), (LRT_accuracy,LRT_triMatch,LRT_color,None,LRT_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,LRT_Tabu_hatch,LRT_Tabu_linestyle), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,LRT_Klau_hatch,LRT_Klau_linestyle), (LREA_Accuracy, LREA_TriMatch,LREigenAlign_color,None,LREigenAlign_linestyle), (LREA_TabuAccuracy, LREA_TabuTriMatch,LREA_Tabu_color,None,LREA_Tabu_linestyle), (LREA_KlauAccuracy, LREA_KlauTriMatch,LREA_Klau_color,None,LREA_Klau_linestyle), #(LRT_lrm_KlauAccuracy, LRT_lrm_KlauTriMatch,LRT_lrm_Klau_color,None,"solid") ] for (acc, tri, c,hatch,linestyle) in sp_exps: make_percentile_plot(sub_ax[0],sp_idx.keys(),acc[p_idx[default_p],n_idx[default_n],:,:],c,hatch,linestyle=linestyle) make_percentile_plot(sub_ax[1],sp_idx.keys(),tri[p_idx[default_p],n_idx[default_n],:,:],c,hatch,linestyle=linestyle) for ax in sub_ax: ax.set_xticks([.05,.1,.25,.5]) ax.set_xlim(0.05,.5) sub_ax[1].set_xticklabels(["5%","10%",r"${\bf 25\%}$","50%"],rotation=60)#52.5 shift = -.25 sub_ax[1].annotate(r"$|V_B|-|V_A|$",xy=(.5,-.09+shift),xycoords='axes fraction',ha="center") sub_ax[1].annotate('', xy=(.23, -.125+shift), xycoords='axes fraction', xytext=(.77, -.125+shift), arrowprops=dict(arrowstyle="-", color='k',linewidth=.5)) sub_ax[1].annotate(r"$|V_A|$",xy=(.5,-.21+shift),xycoords='axes fraction',ha="center") sub_ax[1].annotate(r"(%)",xy=(.78, -.125+shift),xycoords='axes fraction',ha="left",va="center") # # Final Axes touch up # all_ax_gs[0,0].set_ylabel("accuracy") all_ax_gs[1,0].set_ylabel("matched tris\n"+r"$\min{\{|T_A|,|T_B|\}}$",labelpad=1) xloc = -.31 all_ax_gs[1,0].annotate('', xy=(xloc , .18), xycoords='axes fraction', xytext=(xloc, 0.82), arrowprops=dict(arrowstyle="-", color='k')) for ax in all_ax_gs[0,:]: ax.set_xticklabels([]) for ax in all_ax_gs[1,:]: ax.tick_params(axis="x",direction="out",pad=1) for ax in all_ax_gs[:,1:].reshape(-1): ax.set_yticklabels([]) #all_ax_gs[0,0].set_yticklabels([]) #all_ax_gs[1,0].set_yticklabels([]) #for ax in all_ax_gs[:,2]: # ax.yaxis.tick_right() for ax in all_ax_gs.reshape(-1): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_ylim(0.0,1.0) ax.grid(True) all_ax_gs[1,1].annotate(r"$\Lambda$-TAME",xy=(.4,.26),color=LT_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-15) all_ax_gs[0,2].annotate(r"$\Lambda$T" +"-Klau",xy=(.65,.875),color=LT_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-17.5) all_ax_gs[1,2].annotate(r"$\Lambda$T" +"-LS",xy=(.825,.9),color=LT_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-7) all_ax_gs[1,2].annotate("LRT-TAME",xy=(.75,.15),color=LRT_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-7.5) all_ax_gs[1,1].annotate(r"LRT-LS",xy=(.8,.85),color=LRT_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10) all_ax_gs[1,2].annotate(r"LRT-Klau",xy=(.3,.675),color=LRT_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-35) all_ax_gs[1,0].annotate("LREA",xy=(.325,.075),color=LREigenAlign_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-10) all_ax_gs[1,1].annotate(r"LREA-LS",xy=(.5,.65),color=LREA_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-25) all_ax_gs[1,1].annotate(r"LREA-Klau",xy=(.7,.3),color=LREA_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-30) if save_path is None: plt.show() else: plt.savefig(save_path) def RandomGeometricERNoise_allModes(save_path=None): data_location = TAME_RESULTS + "RG_ERNoise/" fig = plt.figure(figsize=(4.25,4.25)) n = 2 m = 2 spec = fig.add_gridspec(nrows=n, ncols=m,hspace=0.1,wspace=0.15,left=.15,right=.97,top=.975,bottom=.2) all_ax = [] axes = np.empty((n,m),object) for i in range(n): for j in range(m): ax = fig.add_subplot(spec[i,j]) axes[i,j] = ax def process_data(data,version="klau_old"): p_idx = {p:i for (i,p) in enumerate(sorted(set([datum[1] for datum in data])))} n_idx = {p:i for (i,p) in enumerate(sorted(set([datum[2] for datum in data])))} trials = int(len(data)/(len(p_idx)*len(n_idx))) accuracy = np.zeros((len(p_idx),len(n_idx),trials)) LT_klauAccuracy = np.zeros((len(p_idx),len(n_idx),trials)) triMatch = np.zeros((len(p_idx),len(n_idx),trials)) LT_klauTriMatch = np.zeros((len(p_idx),len(n_idx),trials)) trial_idx = np.zeros((len(p_idx),len(n_idx)),int) for datum in data: if version == "klau_new": (seed,p,n,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt,L_sparsity,fbounds) = datum elif version == "klau_old": (seed,p,n,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) = datum elif version == "tabu": (seed,p,n,acc,dup_tol_acc,matched_tris,tri_A,tri_B,_,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) = datum else: raise ValueError("only supports ") i = p_idx[p] j = n_idx[n] accuracy[i,j,trial_idx[i,j]] = acc LT_klauAccuracy[i,j,trial_idx[i,j]] = klau_acc triMatch[i,j,trial_idx[i,j]] = matched_tris/min(tri_A,tri_B) LT_klauTriMatch[i,j,trial_idx[i,j]] = klau_tri_match/min(tri_A,tri_B) trial_idx[i,j] += 1 return accuracy, LT_klauAccuracy, triMatch, LT_klauTriMatch, p_idx, n_idx def make_percentile_plot(plot_ax, x_domain,data,color,hatch=None,**kwargs): #TODO: check on scope priorities of ax for closures lines = [(lambda col: np.percentile(col,50),1.0,color) ] ribbons = [ (20,80,.05,color) ] #plot_percentiles(plot_ax, data.T, x_domain, lines, ribbons,**kwargs) n,m = data.shape percentile_linewidth=.01 for (lower_percentile,upper_percentile,alpha,color) in ribbons: #plot_ax.plot(np.percentile(data.T, lower_percentile, axis=0),c=color,linewidth=percentile_linewidth) #plot_ax.plot(np.percentile(data.T, upper_percentile, axis=0),c=color,linewidth=percentile_linewidth) plot_ax.fill_between(x_domain, np.percentile(data.T, lower_percentile, axis=0), np.percentile(data.T, upper_percentile, axis=0), facecolor=color,alpha=.1,edgecolor=color) """ plot_ax.fill_between(x_domain, np.percentile(data.T, lower_percentile, axis=0), np.percentile(data.T, upper_percentile, axis=0), facecolor="None",hatch=hatch,edgecolor=color,alpha=.4) """ for (col_func,alpha,color) in lines: line_data = [] for i in range(n): line_data.append(col_func(data[i,:])) plot_ax.plot(x_domain,line_data,alpha=alpha,c=color,**kwargs) # # p_remove experiments # sub_ax = axes[:,0] #file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.0,0.005,0.01,0.05,0.1,0.2]_postProcess:KlauAlgo_trialcount:20.json" file = "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx = process_data(json.load(f),"klau_new") files=[ # "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.0,0.005,0.01,0.05,0.1,0.2]_postProcess:TabuSearch_trialcount:20.json", "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.05,0.15,0.25,0.3,0.4,0.5]_postProcess:TabuSearch_trialcount:20.json", "LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.1,0.2]_postProcess:TabuSearch_trialcount:20.json" ] res = [] for file in files: with open(data_location + file,'r') as f: res.extend(json.load(f)) _, LT_TabuAccuracy, _, LT_TabuTriMatch, p_idx, n_idx = process_data(res,"tabu") file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) LRT_accuracy, LRT_KlauAccuracy, LRT_triMatch, LRT_KlauTriMatch, p_idx, n_idx = process_data(json.load(f),"klau_new") file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.4,0.5]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LRT_TabuAccuracy,_,LRT_TabuTriMatch, p_idx, n_idx = process_data(json.load(f),"tabu") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LRT_lrm_klauAccuracy,_,LRT_lrm_klauTriMatch, p_idx, n_idx = process_data(json.load(f),"klau_new") file = "LowRankEigenAlign_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.4,0.5]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) LREA_accuracy, LREA_klauAccuracy,LREA_triMatch,LREA_klauTriMatch, _ ,_ = process_data(json.load(f),"klau_new") file = "LowRankEigenAlign_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[250]_noiseModel:ErdosReyni_p:[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.4,0.5]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LREA_tabuAccuracy,_,LREA_tabuTriMatch, _,_ = process_data(json.load(f),"tabu") #make_percentile_plot(sub_ax[0],p_idx.keys(),LT_TabuAccuracy[:,0,:],LT_Tabu_color) #make_percentile_plot(sub_ax[1],p_idx.keys(),LT_TabuTriMatch[:,0,:],LT_Tabu_color) p_exps = [ (accuracy,triMatch,LT_color,LT_linestyle), (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,LT_Tabu_linestyle), #(LRT_lrm_klauAccuracy,LRT_lrm_klauTriMatch,LRM_lrm_Klau_color), (LRT_accuracy,LRT_triMatch,LRT_color,LRT_linestyle), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,LRT_Klau_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,LRT_Tabu_linestyle), (LREA_accuracy,LREA_triMatch,LREigenAlign_color,LREigenAlign_linestyle), (LREA_tabuAccuracy,LREA_tabuTriMatch,LREA_Tabu_color,LREA_Tabu_linestyle), (LREA_klauAccuracy,LREA_klauTriMatch,LREA_Klau_color,LREA_Klau_linestyle), ] for (acc,tri,c,linestyle) in p_exps: make_percentile_plot(sub_ax[0],p_idx.keys(),acc[:,0,:],c,linestyle=linestyle) make_percentile_plot(sub_ax[1],p_idx.keys(),tri[:,0,:],c,linestyle=linestyle) for ax in sub_ax: ax.set_xticks([0.01, 0.05, 0.1, 0.2,.3,.4]) ax.set_xlim(min(p_idx.keys()),.4) sub_ax[1].set_xticklabels([1.e-02, r"${\bf 0.05}$", .1, .2,.3,.4],rotation=60) sub_ax[1].set_xlabel(r"$p_{remove} \equiv p$"+'\n'+r"$p_{add}=\frac{p\rho}{1-\rho}$",ha="center",labelpad=-5) # # n size experiments # sub_ax = axes[:,1] file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json' with open(data_location + file,'r') as f: accuracy, LT_klauAccuracy,triMatch,LT_klauTriMatch, p_idx, n_idx = process_data(json.load(f),"klau_new") #file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.01]_postProcess:TabuSearch_trialcount:20.json' file = 'LambdaTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:TabuSearch_trialcount:20.json' with open(data_location + file,'r') as f: #return json.load(f) _, LT_TabuAccuracy, _, LT_TabuTriMatch, p_idx, n_idx = process_data(json.load(f),"tabu") file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) LRT_accuracy, LRT_KlauAccuracy, LRT_triMatch, LRT_KlauTriMatch, p_idx, n_idx = process_data(json.load(f),"klau_new") file = "LowRankTAME_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LRT_TabuAccuracy,_,LRT_TabuTriMatch, p_idx, n_idx = process_data(json.load(f),"tabu") file = "LowRankTAME-lrm_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LRT_lrm_KlauAccuracy, _, LRT_lrm_KlauTriMatch, p_idx, n_idx = process_data(json.load(f),"klau_new") file = "LowRankEigenAlign_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:KlauAlgo_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) LREA_accuracy, LREA_klauAccuracy,LREA_triMatch,LREA_klauTriMatch, _ ,_ = process_data(json.load(f),"klau_new") file = "LowRankEigenAlign_graphType:RG_alphas:[.5,1.0]_betas:[0.0,1e0,1e1,1e2]_degdist:LogNormal-log5_n:[100,250,500,1000,1250,1500]_noiseModel:ErdosReyni_p:[0.05]_postProcess:TabuSearch_trialcount:20.json" with open(data_location + file,'r') as f: #return json.load(f) _, LREA_tabuAccuracy,_,LREA_tabuTriMatch, _,_ = process_data(json.load(f),"tabu") n_exps = [ (accuracy,triMatch,LT_color,LT_linestyle), (LT_klauAccuracy,LT_klauTriMatch,LT_Klau_color,LT_Klau_linestyle), (LT_TabuAccuracy,LT_TabuTriMatch,LT_Tabu_color,LT_Tabu_linestyle), #(LRT_lrm_KlauAccuracy,LRT_lrm_KlauTriMatch,LRM_lrm_Klau_color), (LRT_accuracy,LRT_triMatch,LRT_color,LRT_linestyle), (LRT_KlauAccuracy,LRT_KlauTriMatch,LRT_Klau_color,LRT_Klau_linestyle), (LRT_TabuAccuracy,LRT_TabuTriMatch,LRT_Tabu_color,LRT_Tabu_linestyle), (LREA_accuracy,LREA_triMatch,LREigenAlign_color,LREigenAlign_linestyle), (LREA_tabuAccuracy,LREA_tabuTriMatch,LREA_Tabu_color,LREA_Tabu_linestyle), (LREA_klauAccuracy,LREA_klauTriMatch,LREA_Klau_color,LREA_Klau_linestyle), ] for (acc,tri,c,linestyle) in n_exps: make_percentile_plot(sub_ax[0],n_idx.keys(),acc[0,:,:],c,linestyle=linestyle) make_percentile_plot(sub_ax[1],n_idx.keys(),tri[0,:,:],c,linestyle=linestyle) for ax in sub_ax: ax.set_xlim(min(n_idx.keys()),max(n_idx.keys())) ax.set_xticks([100, 250, 500, 1000, 1250, 1500]) sub_ax[1].set_xticklabels(["100",r"${\bf 250}$","500","1000","1250","1500"],rotation=60) sub_ax[1].set_xlabel(r"$|V_A|$") axes[1,1].annotate(r"$\Lambda$-TAME",xy=(.175,.35),color=LT_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-40) axes[1,0].annotate(r"$\Lambda$T-"+"Klau",xy=(.45,.35),color=LT_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-45) axes[0,0].annotate(r"$\Lambda$T-"+"LS",xy=(.475,.425),color=LT_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-50) axes[0,1].annotate("LR-TAME",xy=(.825,.17),color=LRT_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-2.5) axes[0,1].annotate("LRT-Klau",xy=(.325,.475),color=LRT_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=0) axes[0,1].annotate("LRT-LS",xy=(.85,.73),color=LRT_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-10) axes[1,1].annotate("LREA",xy=(.125,.075),color=LREigenAlign_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-20) axes[1,1].annotate("LREA-Klau",xy=(.6,.525),color=LREA_Klau_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-25) axes[1,0].annotate("LREA-LS",xy=(.8,.4),color=LREA_Tabu_color,xycoords="axes fraction",ha="center",va="center",fontsize=10,rotation=-10) axes[0,0].set_ylabel("accuracy") axes[1,0].set_ylabel("matched tris\n"+r"$\min{\{|T_A|,|T_B|\}}$",labelpad=2.5) xloc = -.3 axes[1,0].annotate('', xy=(xloc, .2), xycoords='axes fraction', xytext=(xloc, 0.8), arrowprops=dict(arrowstyle="-", color='k')) for ax in axes[0,:]: ax.set_xticklabels([]) for ax in axes[:,1]: ax.set_yticklabels([]) for ax in axes[1,:]: ax.tick_params(axis="x",direction="out",pad=1) for ax in axes[:,1]: ax.yaxis.tick_right() for ax in axes.reshape(-1): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.tick_params(axis="both",direction="out",which='both', length=0) ax.set_ylim(0.0,1.0) ax.grid(True) if save_path is None: plt.show() else: plt.savefig(save_path) # # K nearest Neighbors Post Processing Experiments # def LambdaTAME_increasing_clique_size(save_path=None): plt.rc('text.latex', preamble=r'\usepackage{/Users/charlie/Documents/Code/TKPExperimentPlots/latex/dgleich-math}') fig = plt.figure(figsize=(6,3.5)) global_ax = plt.gca() data_location = TAME_RESULTS + "klauExps/" data_location = TAME_RESULTS + "RG_DupNoise/" def process_klau_data(data,version="LT"): order_idx = {order:i for (i,order) in enumerate(sorted(set([datum[0] for datum in data])))} k_idx = {k:i for (i,k) in enumerate(sorted(set([datum[1] for datum in data])))} trials =len(data[0][-1]) runtime = np.zeros((len(order_idx),len(k_idx),trials)) accuracy = np.zeros((len(order_idx),len(k_idx),trials)) postProcessingAccuracy = np.zeros((len(order_idx),len(k_idx),trials)) triMatch = np.zeros((len(order_idx),len(k_idx),trials)) postProcessingRuntime = np.zeros((len(order_idx),len(k_idx),trials)) #sparsity = np.zeros((len(order_idx),len(k_idx),trials)) trial_idx = np.zeros((len(order_idx),len(k_idx)),int) vertex_coverage = {} A_motifs_counts = {} for (order,k,results) in data: # expecting results: # (seed,p, n, sp, acc, dup_tol_acc, matched_matching_score, A_motifs, B_motifs, A_motifDistribution, B_motifsDistribution,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) i = order_idx[order] j = k_idx[k] accuracy[i,j,:] = [x[4] for x in results] if version=="LT": runtime[i,j,:] = [sum([sum(val) for val in result[11].values()]) for result in results] postProcessingAccuracy[i,j,:] = [x[15] for x in results] postProcessingRuntime[i,j,:] = [x[17] + x[18] for x in results] #sparsity[i,j,:] = [x[19] for x in results] if k == min(k_idx.keys()): #only do this once over k #determine the size of A vs. B motif data if len(results[0][9]) == results[0][2]: vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[9][0])))/x[2] for x in results] A_motifs_counts[order]= [x[7][0] for x in results] else: vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[10][0])))/x[2] for x in results] A_motifs_counts[order]= [x[8][0] for x in results] elif version=="LRT": postProcessingAccuracy[i,j,:] = [x[14] for x in results] postProcessingRuntime[i,j,:] = [x[16] + x[17] for x in results] if version=="LT": return accuracy,runtime, postProcessingAccuracy, postProcessingRuntime,vertex_coverage,A_motifs_counts, order_idx, k_idx #triMatch,LT_klauTriMatch, p_idx, n_idx else: return accuracy, postProcessingAccuracy, postProcessingRuntime,order_idx, k_idx #triMatch,LT_klauTriMatch, p_idx, n_idx def process_tabu_data(data,version="LT"): order_idx = {order:i for (i,order) in enumerate(sorted(set([datum[0] for datum in data])))} k_idx = {k:i for (i,k) in enumerate(sorted(set([datum[1] for datum in data])))} trials =len(data[0][-1]) accuracy = np.zeros((len(order_idx),len(k_idx),trials)) tabuAccuracy = np.zeros((len(order_idx),len(k_idx),trials)) triMatch = np.zeros((len(order_idx),len(k_idx),trials)) tabuRuntime = np.zeros((len(order_idx),len(k_idx),trials)) #sparsity = np.zeros((len(order_idx),len(k_idx),trials)) trial_idx = np.zeros((len(order_idx),len(k_idx)),int) vertex_coverage = {} for (order,k,results) in data: # expecting results: # (seed,p, n, sp, acc, dup_tol_acc, matched_matching_score, A_motifs, B_motifs, A_motifDistribution, B_motifsDistribution,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) i = order_idx[order] j = k_idx[k] accuracy[i,j,:] = [x[4] for x in results] if version == "LT": tabuAccuracy[i,j,:] = [x[15] for x in results] tabuRuntime[i,j,:] = [x[18] for x in results] #sparsity[i,j,:] = [x[19] for x in results] if k == min(k_idx.keys()): #only do this once over k vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[9][0])))/x[2] for x in results] elif version == "LRT": print(i," ",j) tabuAccuracy[i,j,:] = [x[14] for x in results] tabuRuntime[i,j,:] = [x[17] for x in results] if version == "LT": return accuracy, tabuAccuracy,tabuRuntime,vertex_coverage, order_idx, k_idx else: return accuracy, tabuAccuracy,tabuRuntime,order_idx, k_idx filename = "RandomGeometric_degreedist:LogNormal_KlauAlgokvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_KAmiter:1000_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" with open(data_location+filename,'r') as f: accuracy,LT_runtime, LT_klauAccuracy,LT_klauRuntime,vertex_coverage,A_motifs_counts, order_idx, k_idx = process_klau_data(json.load(f)) filename = "RandomGeometric_degreedist:LogNormal_TabuSearchkvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" with open(data_location+filename,'r') as f: _, LT_TabuAccuracy, LT_TabuRuntime, _, order_idx, k_idx = process_tabu_data(json.load(f)) def make_percentile_plot(plot_ax, x_domain,data,color,**kwargs): lines = [(lambda col: np.percentile(col,50),1.0,color) ] ribbons = [ (20,80,.2,color) ] plot_percentiles(plot_ax, data.T, x_domain, lines, ribbons,**kwargs) def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.8): background_v = ax.violinplot(data, points=100, positions=[0.5], showmeans=False, showextrema=False, showmedians=False,widths=.5,vert=False)#,quantiles=[[.2,.8]]*len(data2)) v = ax.violinplot(data, points=100, positions=[.5], showmeans=False, showextrema=False, showmedians=True,widths=.5,vert=False)#,quantiles=[[.2,.8]]*len(data2)) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y0-.1),(x0,y0 + (y1-y0)/2 -.1)],[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] v["cmedians"].set_segments(newMedianLines) ax.annotate(f"{np.median(data):.{precision}f}",xy=(.5,.38),xycoords="axes fraction",ha="center",fontsize=10) ax.annotate(f"{np.min(data):.{precision}f}",xy=(0,.8),xycoords="axes fraction",ha="left",fontsize=6,alpha=.8) ax.annotate(f"{np.max(data):.{precision}f}",xy=(1,.1),xycoords="axes fraction",ha="right",fontsize=6,alpha=.8) if c is not None: v["cmedians"].set_color("k") v["cmedians"].set_alpha(.3) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor(c) b.set_alpha(v_alpha) #b.set_alpha(1) b.set_color(c) for b in background_v["bodies"]: b.set_facecolor("w") b.set_edgecolor("w") b.set_color("w") def make_violin_plot_legend(ax,c="k"): np.random.seed(12) v = ax.violinplot([np.random.normal() for i in range(50)], points=100, positions=[.5], showmeans=False, showextrema=False, showmedians=True,widths=.5,vert=False)#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y0-.1),(x0,y0 + (y1-y0)/2 -.1)],[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] v["cmedians"].set_segments(newMedianLines) ax.annotate("med.",xy=(.5,.5),xycoords="axes fraction",ha="center",va="center",fontsize=10) ax.annotate(f"min",xy=(0,.8),xycoords="axes fraction",ha="left",fontsize=6,alpha=.8) ax.annotate(f"max",xy=(1,.1),xycoords="axes fraction",ha="right",fontsize=6,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor(c) b.set_alpha(.2) b.set_color(c) # -- add in order labels overhead -- # global_ax.set_yticklabels([]) global_ax.set_yticklabels([]) widths = [.5, 3, 2, .8,.8] spec = fig.add_gridspec(nrows=5,ncols=1+len(order_idx),hspace=0.0,wspace=0.0,height_ratios=widths,left=.15,right=.95) allCAx = [] allAccAx = [] allRtAx = [] allVCAx = [] allMotifCountAx = [] allSparsityAx = [] first = True annotation_idx = 4 if filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": k_tick_idx = 0 else: k_tick_idx = 4 parity = 0 for idx,(order,i) in enumerate(order_idx.items()): # # Clique size # ax = fig.add_subplot(spec[0,i]) allCAx.append(ax) if idx % 2 == parity: ax.patch.set_facecolor('k') ax.patch.set_alpha(0.1) ax.annotate(f"{order}",xy=(.5, .5), xycoords='axes fraction', c="k",size=10,ha="center",va="center") ax.set_xticklabels([]) ax.set_yticklabels([]) # # Accuracy Plots # if idx == annotation_idx: ax = fig.add_subplot(spec[1,i],zorder=5)#,sharey=allAccAx[0]) else: ax = fig.add_subplot(spec[1,i])#,sharey=allAccAx[0]) allAccAx.append(ax) if idx % 2 != parity: ax.patch.set_facecolor('k') ax.patch.set_alpha(0.1) ax.set_yticks([0.0,0.25,.5,.75,1.0]) ax.set_ylim(0,1.0) plt.axhline(y=np.median(accuracy[i,:,:]), color=LT_color,linestyle=LT_linestyle) plt.axhline(y=np.max(accuracy[i,:,:]), color=LT_color,linestyle="dotted") make_percentile_plot(ax,k_idx.keys(),LT_klauAccuracy[i,:,:],LT_Klau_color)#,linestyle=LT_Klau_linestyle make_percentile_plot(ax,k_idx.keys(),LT_TabuAccuracy[i,:,:],LT_Tabu_color)#,linestyle=LT_Tabu_linestyle ax.set_xticklabels([]) ax.set_yticklabels([]) # # Runtime Plots # if idx == k_tick_idx: ax = fig.add_subplot(spec[2,i],zorder=3) else: ax = fig.add_subplot(spec[2,i]) allRtAx.append(ax) if idx % 2 == parity: ax.patch.set_facecolor('k') ax.patch.set_alpha(0.1) if filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": ax.set_ylim(np.min(LT_klauRuntime[:,:,:]),600) elif filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": ax.set_ylim(np.min(LT_klauRuntime[:,:,:]),1200) else: ax.set_ylim(np.min(LT_klauRuntime[:,:,:]),2100) #ax.set_yscale("log") if idx != 0: ax.set_yticklabels([]) ax.set_xticks([15,45,90]) ax.set_xticklabels([]) make_percentile_plot(ax,k_idx.keys(),LT_klauRuntime[i,:,:],LT_Klau_color,linestyle=LT_Klau_linestyle) make_percentile_plot(ax,k_idx.keys(),LT_TabuRuntime[i,:,:],LT_Tabu_color,linestyle=LT_Tabu_linestyle) plt.axhline(y=np.median(LT_runtime[i,:,:]), color=LT_color,linestyle=LT_linestyle) # # Vertex Coverage # ax = fig.add_subplot(spec[3,i]) allVCAx.append(ax) if idx % 2 != parity: make_violin_plot(ax,vertex_coverage[order],c="w") ax.patch.set_facecolor('k') ax.patch.set_alpha(0.1) else: make_violin_plot(ax,vertex_coverage[order],c="k",v_alpha=.1) # # Motif Counts # ax = fig.add_subplot(spec[4,i]) allMotifCountAx.append(ax) if idx % 2 == parity: make_violin_plot(ax,A_motifs_counts[order],c="w",precision=0) ax.patch.set_facecolor('k') ax.patch.set_alpha(0.1) else: make_violin_plot(ax,A_motifs_counts[order],c="k",v_alpha=.1,precision=0) violinLegendAx = fig.add_subplot(spec[3,-1]) for ax in chain(allAccAx,allRtAx,allCAx,allVCAx,allMotifCountAx,allSparsityAx,[global_ax],[violinLegendAx]): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.yaxis.set_ticks_position('none') ax.xaxis.set_ticks_position('none') ax.set_xticklabels([]) ax.set_yticklabels([]) for ax in chain(allAccAx,allRtAx,allSparsityAx): ax.grid(True) ax.set_xticks([15,45,90]) ax.set_xlim(min(k_idx.keys()),max(k_idx.keys())) # - make violin plot legend - # make_violin_plot_legend(violinLegendAx) # -- Add in x-domain annotations mid plot -- # allRtAx[k_tick_idx].xaxis.set_label_position("top") allRtAx[k_tick_idx].xaxis.set_ticks_position('top') allRtAx[k_tick_idx].tick_params(axis="x",direction="out", pad=-15) allAccAx[k_tick_idx].annotate("nearest neighbors ("+r"$K$"+')',xy=(.5,.05),ha="center",xycoords='axes fraction') allRtAx[k_tick_idx].set_xticklabels([15,45,90],zorder=5) # -- Alternate tick labels to opposite axes -- # allCAx[0].annotate("Clique Size",xy=(-.1,.5),ha="right",va="center",xycoords='axes fraction') allAccAx[-1].yaxis.set_ticks_position('right') allAccAx[-1].set_yticklabels([0,.25,.5,.75,1.0]) allAccAx[-1].tick_params(axis="both",direction="out",which='both', length=7.5) allAccAx[0].set_ylabel("Accuracy",rotation=0,labelpad=0,ha="right") allRtAx[-1].yaxis.set_label_position("right") allRtAx[0].annotate("Runtime (s)",xy=(-.1,.5),ha="right",va="center",xycoords='axes fraction') allRtAx[-1].yaxis.set_ticks_position('right') if filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": allRtAx[0].set_yticklabels(["10 s","2 min","5 min","10 min"]) for ax in allRtAx: ax.set_yticks([1e0,1e1,120,300,600]) elif filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": allRtAx[0].set_yticklabels(["10 s","5 min","10 min","15 min","20 min"]) for ax in allRtAx: ax.set_yticks([1e1,300,600,900,1200]) else: for i,ax in enumerate(allRtAx): ax.set_yscale("log") ax.set_yticks([1e0,1e1,1e2,1e3,1e4]) ax.set_ylim(1e-1,2e4) if i == len(allRtAx)-1: ax.set_yticklabels([r"$10^0$",r"$10^1$",r"$10^2$",r"$10^3$",None]) else: ax.set_yticklabels([]) allVCAx[0].annotate("Vertex\nCoverage",xy=(-.1,.5),ha="right",va="center",xycoords='axes fraction') # -- Annotate accuracy plots -- # allAccAx[5].annotate(r"$\Lambda$T"+"-Klau", xy=(.5, .675), xycoords='axes fraction', c=LT_Klau_color,size=10,ha="center",rotation=20) allAccAx[5].annotate(r"$\Lambda$T"+"-LS", xy=(.575, .5), xycoords='axes fraction', c=LT_Tabu_color,size=10,ha="center",zorder=4) allAccAx[annotation_idx].annotate("maximum "+r"$\Lambda$"+"T", xy=(.975, .45), xycoords='axes fraction', c=LT_color,size=10,ha="right") allAccAx[annotation_idx].annotate("median "+r"$\Lambda$"+"T", xy=(.975, .275), xycoords='axes fraction', c=LT_color,size=10,ha="right") allMotifCountAx[0].annotate("A Motifs",xy=(-.1,.5),ha="right",va="center",xycoords='axes fraction') plt.tight_layout() if save_path is None: plt.show() else: plt.savefig(save_path) def LambdaTAME_increasing_clique_size_v2(save_path=None): plt.rc('text.latex', preamble=r'\usepackage{/Users/charlie/Documents/Code/TKPExperimentPlots/latex/dgleich-math}') fig = plt.figure(figsize=(6,3.5)) global_ax = plt.gca() data_location = TAME_RESULTS + "klauExps/" data_location = TAME_RESULTS + "RG_DupNoise/" def process_klau_data(data,version="LT"): order_idx = {order:i for (i,order) in enumerate(sorted(set([datum[0] for datum in data])))} k_idx = {k:i for (i,k) in enumerate(sorted(set([datum[1] for datum in data])))} trials =len(data[0][-1]) runtime = np.zeros((len(order_idx),len(k_idx),trials)) accuracy = np.zeros((len(order_idx),len(k_idx),trials)) postProcessingAccuracy = np.zeros((len(order_idx),len(k_idx),trials)) triMatch = np.zeros((len(order_idx),len(k_idx),trials)) postProcessingRuntime = np.zeros((len(order_idx),len(k_idx),trials)) #sparsity = np.zeros((len(order_idx),len(k_idx),trials)) trial_idx = np.zeros((len(order_idx),len(k_idx)),int) vertex_coverage = {} A_motifs_counts = {} for (order,k,results) in data: # expecting results: # (seed,p, n, sp, acc, dup_tol_acc, matched_matching_score, A_motifs, B_motifs, A_motifDistribution, B_motifsDistribution,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) i = order_idx[order] j = k_idx[k] accuracy[i,j,:] = [x[4] for x in results] if version=="LT": runtime[i,j,:] = [sum([sum(val) for val in result[11].values()]) for result in results] postProcessingAccuracy[i,j,:] = [x[15] for x in results] postProcessingRuntime[i,j,:] = [x[17] + x[18] for x in results] #sparsity[i,j,:] = [x[19] for x in results] if k == min(k_idx.keys()): #only do this once over k #determine the size of A vs. B motif data if len(results[0][9]) == results[0][2]: vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[9][0])))/x[2] for x in results] A_motifs_counts[order]= [x[7][0] for x in results] else: vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[10][0])))/x[2] for x in results] A_motifs_counts[order]= [x[8][0] for x in results] elif version=="LRT": postProcessingAccuracy[i,j,:] = [x[14] for x in results] postProcessingRuntime[i,j,:] = [x[16] + x[17] for x in results] if version=="LT": return accuracy,runtime, postProcessingAccuracy, postProcessingRuntime,vertex_coverage,A_motifs_counts, order_idx, k_idx #triMatch,LT_klauTriMatch, p_idx, n_idx else: return accuracy, postProcessingAccuracy, postProcessingRuntime,order_idx, k_idx #triMatch,LT_klauTriMatch, p_idx, n_idx def process_tabu_data(data,version="LT"): order_idx = {order:i for (i,order) in enumerate(sorted(set([datum[0] for datum in data])))} k_idx = {k:i for (i,k) in enumerate(sorted(set([datum[1] for datum in data])))} trials =len(data[0][-1]) accuracy = np.zeros((len(order_idx),len(k_idx),trials)) tabuAccuracy = np.zeros((len(order_idx),len(k_idx),trials)) triMatch = np.zeros((len(order_idx),len(k_idx),trials)) tabuRuntime = np.zeros((len(order_idx),len(k_idx),trials)) #sparsity = np.zeros((len(order_idx),len(k_idx),trials)) trial_idx = np.zeros((len(order_idx),len(k_idx)),int) vertex_coverage = {} for (order,k,results) in data: # expecting results: # (seed,p, n, sp, acc, dup_tol_acc, matched_matching_score, A_motifs, B_motifs, A_motifDistribution, B_motifsDistribution,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) i = order_idx[order] j = k_idx[k] accuracy[i,j,:] = [x[4] for x in results] if version == "LT": tabuAccuracy[i,j,:] = [x[15] for x in results] tabuRuntime[i,j,:] = [x[18] for x in results] #sparsity[i,j,:] = [x[19] for x in results] if k == min(k_idx.keys()): #only do this once over k vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[9][0])))/x[2] for x in results] elif version == "LRT": print(i," ",j) tabuAccuracy[i,j,:] = [x[14] for x in results] tabuRuntime[i,j,:] = [x[17] for x in results] if version == "LT": return accuracy, tabuAccuracy,tabuRuntime,vertex_coverage, order_idx, k_idx else: return accuracy, tabuAccuracy,tabuRuntime,order_idx, k_idx filename = "RandomGeometric_degreedist:LogNormal_KlauAlgokvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_KAmiter:1000_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" with open(data_location+filename,'r') as f: accuracy,LT_runtime, LT_klauAccuracy,LT_klauRuntime,vertex_coverage,A_motifs_counts, order_idx, k_idx = process_klau_data(json.load(f)) filename = "RandomGeometric_degreedist:LogNormal_TabuSearchkvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" with open(data_location+filename,'r') as f: _, LT_TabuAccuracy, LT_TabuRuntime, _, order_idx, k_idx = process_tabu_data(json.load(f)) def make_percentile_plot(plot_ax, x_domain,data,color,**kwargs): lines = [(lambda col: np.percentile(col,50),1.0,color) ] ribbons = [ (20,80,.2,color) ] plot_percentiles(plot_ax, data.T, x_domain, lines, ribbons,**kwargs) def make_violin_plot_v2(ax,data,precision=2,c=None,v_alpha=.8,format="default",xlim=None,xscale="linear"): med_bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.05) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.01) max_elem = np.max(data) center = np.min(data) scaled_data = [(d-center)/max_elem for d in data] if xscale=="linear": v = ax.violinplot(data,[.455], points=100, showmeans=False,widths=.08, showextrema=False, showmedians=True,vert=True)#,quantiles=[[.2,.8]]*len(data2)) ax.set_xlim(0.4, 0.54) elif xscale=="log": v = ax.violinplot(np.log10(data),[.455], points=100, showmeans=False,widths=.005, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") # -- update median lines to have a gap -- # ((x0,y0),(x1,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(0.415,y1),(.43,y1)]] v["cmedians"].set_segments(newMedianLines) # -- write data values as text tick_ypos = .1 def format_string(val): if np.abs(val-1.0) < 1e-2: return f"{val:.{1}f}" elif val >= 1000: return f"{round(val/1000,1)}k" else: return f"{val:.{precision}f}".strip("0") #format_string = lambda val: f"{val:.{1}f}" if np.abs(val-1.0) <1e-2 else f"{val:.{precision}f}".strip("0") xloc=.4 ax.annotate(format_string(np.median(data)),xy=(xloc,.4),xycoords="axes fraction",ha="left",va="center",fontsize=10)#.set_bbox(med_bbox) #ax.annotate(format_string(np.min(data)),xy=(.025,.1),xycoords="axes fraction",ha="left",fontsize=6,alpha=.8)#.set_bbox(bbox) #ax.annotate(format_string(np.max(data)),xy=(.925,.8),xycoords="axes fraction",ha="right",fontsize=6,alpha=.8)#.set_bbox(bbox) ax.annotate(format_string(np.max(data)),xy=(xloc,.8),xycoords="axes fraction",ha="left",va="center",fontsize=8,alpha=.8)#.set_bbox(bbox) ax.annotate(format_string(np.min(data)),xy=(xloc,.0),xycoords="axes fraction",ha="left",va="center",fontsize=8,alpha=.8)#.set_bbox(bbox) if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor(c) #b.set_facecolor("None") b.set_edgecolor("None") #b.set_edgecolor(c) b.set_alpha(v_alpha) #b.set_color(c) # -- only plot the left half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 0]) b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], -np.inf,m) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) #new_max_y += .04 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) #clip_to_top_of_violin(v["cmedians"]) def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 v = ax.violinplot([np.random.normal() for i in range(50)], points=100, positions=[.1], showmeans=False, showextrema=False, showmedians=True,vert=True,widths=[.5])#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() #ax.set_ylim(.5,1.0) # -- update median lines to have a gap -- # ((x0,y0),(x1,y1)) = v["cmedians"].get_segments()[0] #newMedianLines = [[(x0,y0-.125),(x0,y0 + (y1-y0)/2 -.1)]]#,[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] newMedianLines = [[(x1-2.5,y1),(x1-.3,y1)]] v["cmedians"].set_segments(newMedianLines) xloc = .6 ax.annotate(f"min ",xy=(xloc,0.2),xycoords="axes fraction",ha="left",va="center",fontsize=11,alpha=.8) ax.annotate("med.",xy=(xloc,.475),xycoords="axes fraction",ha="left",va="center",fontsize=14) ax.annotate(f"max ",xy=(xloc,.75),xycoords="axes fraction",ha="left",va="center",fontsize=11,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor("None") b.set_alpha(.3) m = np.mean(b.get_paths()[0].vertices[:, 0]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0],-np.inf,m) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) #clip_to_top_of_violin(v["cmedians"]) # -- add in order labels overhead -- # global_ax.set_yticklabels([]) global_ax.set_yticklabels([]) widths = [.7,.3] #spec = fig.add_gridspec(nrows=5,,hspace=0.2,wspace=0.1,height_ratios=widths,left=.2,right=1.075,top=.95,bottom=.05) global_spec = fig.add_gridspec(nrows=2, ncols=1, left=.2,right=1.075,top=.95,bottom=.05, wspace=0.0,hspace=0.1, height_ratios=widths ) widths = [.5, 3, 2] top_gs = global_spec[0].subgridspec(nrows=3,ncols=1+len(order_idx), hspace=0.2,wspace=0.1, height_ratios=widths) bottom_gs = global_spec[1].subgridspec(nrows=2,ncols=1+len(order_idx), #left=0.125, right=0.9,top=.95,bottom=.075, wspace=0.1,hspace=0.4) allCAx = [] allAccAx = [] allRtAx = [] allVCAx = [] allMotifCountAx = [] allSparsityAx = [] all_axes = np.empty((5,len(order_idx)),object) first = True annotation_idx = 1 if filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": k_tick_idx = 0 else: k_tick_idx = 0 parity = 0 for idx,(order,i) in enumerate(order_idx.items()): # # Clique size # ax = fig.add_subplot(top_gs[0,i]) all_axes[0,i] = ax allCAx.append(ax) ax.annotate(f"{order}",xy=(.5, .85), xycoords='axes fraction', c="k",size=10,ha="center",va="center",weight="bold") ax.set_xticklabels([]) ax.set_yticklabels([]) # # Accuracy Plots # if idx == annotation_idx: ax = fig.add_subplot(top_gs[1,i],zorder=5)#,sharey=allAccAx[0]) elif idx == k_tick_idx: ax = fig.add_subplot(top_gs[1,i],zorder=5) else: ax = fig.add_subplot(top_gs[1,i])#,sharey=allAccAx[0]) all_axes[1,i] = ax allAccAx.append(ax) #if idx % 2 != parity: # ax.patch.set_facecolor('k') # ax.patch.set_alpha(0.1) ax.set_yticks([0.0,0.25,.5,.75,1.0]) ax.set_ylim(0,1.0) plt.axhline(y=np.median(accuracy[i,:,:]), color=LT_color,linestyle=LT_linestyle) plt.axhline(y=np.max(accuracy[i,:,:]), color=LT_color,linestyle="dotted") make_percentile_plot(ax,k_idx.keys(),LT_klauAccuracy[i,:,:],LT_Klau_color)#,linestyle=LT_Klau_linestyle) make_percentile_plot(ax,k_idx.keys(),LT_TabuAccuracy[i,:,:],LT_Tabu_color)#,linestyle=LT_Tabu_linestyle) ax.set_xticklabels([]) ax.set_yticklabels([]) # # Runtime Plots # """ if idx == k_tick_idx: ax = fig.add_subplot(spec[2,i],zorder=3) else: """ ax = fig.add_subplot(top_gs[2,i]) all_axes[2,i] = ax allRtAx.append(ax) if filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": ax.set_ylim(np.min(LT_klauRuntime[:,:,:]),600) elif filename == "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json": ax.set_ylim(np.min(LT_klauRuntime[:,:,:]),1200) else: ax.set_ylim(np.min(LT_klauRuntime[:,:,:]),2100) #ax.set_yscale("log") if idx != 0: ax.set_yticklabels([]) ax.set_xticks([15,45,90]) ax.set_xticklabels([]) make_percentile_plot(ax,k_idx.keys(),LT_klauRuntime[i,:,:],LT_Klau_color)#,linestyle=LT_Klau_linestyle) make_percentile_plot(ax,k_idx.keys(),LT_TabuRuntime[i,:,:],LT_Tabu_color)#,linestyle=LT_Tabu_linestyle) plt.axhline(y=np.median(LT_runtime[i,:,:]), color=LT_color,linestyle=LT_linestyle) # # Vertex Coverage # if i == 0: ax = fig.add_subplot(bottom_gs[0,i]) else: ax = fig.add_subplot(bottom_gs[0,i],sharey=all_axes[3,0]) #ax = fig.add_subplot(spec[3,i]) all_axes[3,i] = ax allVCAx.append(ax) make_violin_plot_v2(ax,vertex_coverage[order],c="k",v_alpha=.2) # # Motif Counts # ''' if i == 0: ax = fig.add_subplot(spec[4,i]) else: ax = fig.add_subplot(spec[4,i],sharey=allMotifCountAx[0]) ''' if i == 0: ax = fig.add_subplot(bottom_gs[1,i]) else: ax = fig.add_subplot(bottom_gs[1,i],sharey=all_axes[4,0]) all_axes[4,i] = ax allMotifCountAx.append(ax) make_violin_plot_v2(ax,A_motifs_counts[order],c="k",v_alpha=.2, precision=0)#,xscale="log") violinLegendAx = allVCAx[0].inset_axes([-2.1,-1.75,.5,1.5]) #for ax in chain(allAccAx,allRtAx,allCAx,allVCAx,allMotifCountAx,allSparsityAx,[global_ax],[violinLegendAx]): for ax in chain(all_axes.reshape(-1),[global_ax],[violinLegendAx]): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.yaxis.set_ticks_position('none') ax.xaxis.set_ticks_position('none') ax.set_xticklabels([]) ax.set_yticklabels([]) for ax in all_axes[1:3,:].reshape(-1): ax.xaxis.set_ticks_position('top') ax.grid(True,axis='y',linewidth=.5) ax.tick_params(axis="x",direction="in", pad=-15) ax.set_xticks([15,45,90]) ax.set_xlim(min(k_idx.keys()),max(k_idx.keys())) for ax in all_axes[3:,:].reshape(-1): ax.patch.set_facecolor('none') for i,ax in enumerate(all_axes[2,:]): ax.set_yscale("log") ax.set_yticks([1e-1,1e0,1e1,1e2,1e3,1e4]) ax.set_ylim(1e-1,1e4) if i == 0: ax.set_yticklabels([r"$10^{-1}$",r"$10^0$",r"$10^1$",r"$10^2$",r"$10^3$",None],ha="left") ax.tick_params(axis="y",direction="out",which='both', length=7.5,pad=20) else: ax.set_yticklabels([]) y_gridlines = ax.yaxis.get_gridlines() for i in [0,-1]: if i == 0: y_gridlines[i].set_linewidth(1.5*y_gridlines[i].get_linewidth()) if i == -1: y_gridlines[i].set_color("k") for ax in allAccAx: #ax.xaxis.set_ticks_position('top') y_gridlines = ax.yaxis.get_gridlines() for i in [0,-1]: y_gridlines[i].set_linewidth(1.5*y_gridlines[i].get_linewidth()) if i == -1: y_gridlines[i].set_color("k") # - make violin plot legend - # make_violin_plot_legend(violinLegendAx) # -- Alternate tick labels to opposite axes -- # label_xpos = -.1 allCAx[0].annotate("Clique Size",xy=(label_xpos,.85),ha="right",va="center",xycoords='axes fraction') #allAccAx[-1].yaxis.set_ticks_position('right') allAccAx[0].set_yticklabels([0,.25,.5,.75,1.0]) allAccAx[0].tick_params(axis="y",direction="out",which='both', length=7.5) #allAccAx[0].set_ylabel("Accuracy",rotation=90,labelpad=0,ha="right") allAccAx[0].annotate("Accuracy",xy=(-1.5,.5),rotation=0,ha="center",va="center",xycoords='axes fraction') #allRtAx[-1].yaxis.set_label_position("right") allRtAx[0].annotate("Runtime\n(seconds)",xy=(-1.5,.5),ha="center",va="center",xycoords='axes fraction',rotation=0) allVCAx[0].annotate("Vertex\nCoverage",xy=(label_xpos,.5),ha="right",va="center",xycoords='axes fraction') # -- Annotate accuracy plots -- # allAccAx[0].annotate(r"$\Lambda$T"+"-Klau", xy=(.5, .675), xycoords='axes fraction', c=LT_Klau_color,size=10,ha="center",rotation=20) allAccAx[0].annotate(r"$\Lambda$T"+"-LS", xy=(.575, .3), xycoords='axes fraction', c=LT_Tabu_color,size=10,ha="center",zorder=4) allAccAx[annotation_idx].annotate("maximum "+r"$\Lambda$"+"T", xy=(.025, .375), xycoords='axes fraction', c=LT_color,size=10,ha="left") allAccAx[annotation_idx].annotate("median "+r"$\Lambda$"+"T", xy=(.025, .1), xycoords='axes fraction', c=LT_color,size=10,ha="left") allMotifCountAx[0].annotate("Motifs\nin A",xy=(label_xpos,.5),ha="right",va="center",xycoords='axes fraction') # -- Add in x-domain annotations mid plot -- # allAccAx[k_tick_idx].xaxis.set_label_position("top") allAccAx[k_tick_idx].xaxis.set_ticks_position('top') allAccAx[k_tick_idx].tick_params(axis="x",direction="in", pad=.1) allAccAx[k_tick_idx].annotate("nearest\nneighbors ("+r"$K$"+')',xy=(0.0,-.125),ha="left",va="bottom",xycoords='axes fraction',zorder=10,fontsize=8) allAccAx[k_tick_idx].set_xticklabels([15,45,90],zorder=3) #allAccAx[k_tick_idx].grid(zorder=0) #plt.tight_layout() if save_path is None: plt.show() else: plt.savefig(save_path) def LambdaTAME_increasing_clique_size_summarized(save_path=None): plt.rc('text.latex', preamble=r'\usepackage{/Users/charlie/Documents/Code/TKPExperimentPlots/latex/dgleich-math}') 5,3 fig = plt.figure(figsize=(6,5)) global_ax = plt.gca() # # Subroutines # def underline_text(ax,text,c,linestyle): tb = text.get_tightbbox(fig.canvas.get_renderer()).transformed(fig.transFigure.inverted()) ax.annotate('', xy=(tb.xmin,tb.y0), xytext=(tb.xmax,tb.y0), xycoords="figure fraction",arrowprops=dict(arrowstyle="-", color=c,linestyle=linestyle,linewidth=1.5,alpha=.8)) def mark_as_algorithm(ax,text,c,linestyle,algorithm="Klau"): tb = text.get_tightbbox(fig.canvas.get_renderer()).transformed(fig.transFigure.inverted()) # calculate asymmetry of x and y axes: x0, y0 = fig.transFigure.transform((0, 0)) # lower left in pixels x1, y1 = fig.transFigure.transform((1, 1)) # upper right in pixes dx = x1 - x0 dy = y1 - y0 maxd = max(dx, dy) #ax.annotate('', xy=(tb.xmin,tb.y0), xytext=(tb.xmax,tb.y0), xycoords="figure fraction",arrowprops=dict(arrowstyle="-", color=LRT_color,linestyle=LRT_linestyle,linewidth=1.5,alpha=.8)) """ def algorithm_patches(algo): if algo == "LRTAME": radius=.02 height = radius * maxd / dy width = radius * maxd / dx return patches.Ellipse((tb.xmin-.015,tb.y0+(5/8)*tb.height),width, height,color=LRT_color,transform=fig.transFigure) elif algo == "TAME": side_length=.015 height = side_length * maxd / dy width = side_length * maxd / dx return patches.Rectangle((tb.xmax+.01,tb.y0+.5*(tb.height - side_length)), width, height,color=T_color, transform=fig.transFigure) else: raise ValueError(f"algorithm must be either 'TAME' or 'LRTAME', got {algo}.\n") """ if algorithm == "Klau": radius=.01 height = radius * maxd / dy width = radius * maxd / dx p=ax.add_patch(patches.Ellipse((tb.xmin-.015,tb.y0+(5/8)*tb.height),width, height,color=LT_Klau_color,transform=fig.transFigure,clip_on=False)) elif algorithm == "Tabu": side_length=.0075 height = side_length * maxd / dy width = side_length * maxd / dx p=ax.add_patch(patches.Rectangle((tb.xmax+.01,tb.y0+.5*(tb.height - side_length)), width, height,color=LT_Tabu_color, transform=fig.transFigure,clip_on=False)) else: raise ValueError(f"algorithm must be either 'Klau' or 'Tabu', got {algorithm}.\n") #p=ax.add_patch(algorithm_patches(algorithm)) #ax.add_patch(patches.Ellipse((tb.xmin-.015,tb.y0+tb.height/2),width, height,color=LRT_color,transform=fig.transFigure)) #ax.add_patch(patches.Ellipse((tb.xmax,tb.y0),width, height,color=LRT_color,#transform=fig.transFigure),) """ #width = .05 #height = .01 xshift = tb.width*.2 height = .03 width = .0075 ax.add_patch(patches.Rectangle((tb.xmin-xshift,tb.y0 + (3/4)*tb.height),width, height,color=LRT_color,transform=fig.transFigure)) height = .015 #* maxd / dy width = .02 #* maxd / dx xshift = tb.width*.1 ax.add_patch(patches.Rectangle((tb.xmin-xshift,tb.y1), width,height,color=LRT_color,transform=fig.transFigure)) """ extremal_tick_ypos = .1 def make_violin_plot(ax,data,precision=2,c=None,v_alpha=.8,format="default",xlim=None,xscale="linear",column_type=None): #background_v = ax.violinplot(data, points=100, positions=[0.5], showmeans=False, # showextrema=False, showmedians=False,widths=.5,vert=False)#,quantiles=[[.2,.8]]*len(data2)) # #positions=[0.5], ,widths=.5 if xscale=="linear": v = ax.violinplot(data,[.5], points=100, showmeans=False,widths=.15, showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) elif xscale=="log": v = ax.violinplot(np.log10(data), points=100, showmeans=False,widths=.15,showextrema=False, showmedians=True,vert=False)#,quantiles=[[.2,.8]]*len(data2)) else: raise ValueError(f"supports xscale values: 'linear' & 'log'; Got {xscale}.") #ax.set_ylim(0.95, 1.3) #ax.set_xlim(np.min(data),np.max(data)) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] newMedianLines = [[(x0,y1),(x0,y1+.7)]] v["cmedians"].set_segments(newMedianLines) # -- place extremal markers underneath """ v['cbars'].set_segments([]) # turns off x-axis spine for segment in [v["cmaxes"],v["cmins"]]: ((x,y0),(_,y1)) = segment.get_segments()[0] segment.set_segments([[(x,0.45),(x,.525)]]) segment.set_color(c) """ # -- write data values as text bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.05) extremal_tick_ypos = .1 if column_type is None: if format == "default": ax.annotate(f"{np.median(data):.{precision}f}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data):.{precision}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) else: print(f"expecting format to be either 'default' or 'scientific', got:{format}") elif column_type == "merged_axis": pass else: raise ValueError("column_type expecting 'merged_axis' or None, but got {column_type}\n") if c is not None: v["cmedians"].set_color(c) v["cmedians"].set_alpha(.75) for b in v['bodies']: # set colors b.set_facecolor("None") b.set_edgecolor(c) b.set_alpha(v_alpha) #b.set_color(c) # -- only plot the top half of violin plot -- # m = np.mean(b.get_paths()[0].vertices[:, 1]) b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) # -- clip the top of the med-line to the top of the violin plot -- # def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) #new_max_y += .04 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) #clip_to_top_of_violin(v["cmaxes"]) #clip_to_top_of_violin(v["cmins"]) clip_to_top_of_violin(v["cmedians"]) def make_violin_plot_merged_axis(ax,data1,data2,c1,c2,marker1,marker2,format=None,**kwargs): make_violin_plot(ax,data1,**dict(kwargs,c=c1,column_type="merged_axis")) make_violin_plot(ax,data2,**dict(kwargs,format=format,c=c2,column_type="merged_axis")) #ax.scatter(np.median(data1),.65,marker=marker1,s=5) marker_size = 12.5 print(ax.get_ylim()) x,marker_y_loc = ax.get_ylim() ax.set_ylim(x,marker_y_loc + .05) ax.scatter(np.median(data1),marker_y_loc,marker=LT_Klau_marker,color=LT_Klau_color,s=marker_size) ax.scatter(np.median(data2),marker_y_loc,marker=LT_Tabu_marker,color=LT_Tabu_color,s=marker_size) min1 = np.min(data1) min2 = np.min(data2) if min1 < min2: #text = f"{min1:.{kwargs['precision']}f}" #underlined_annotation(fig,ax,(.075,extremal_tick_ypos),text,linestyle=LRT_linestyle,ha="left",fontsize=8,alpha=.8) text = ax.annotate(f"{min1:.{kwargs['precision']}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) mark_as_algorithm(ax,text,T_color,T_linestyle,algorithm="Klau") #underline_text(ax,text,T_color,T_linestyle) """ tb = text.get_tightbbox(fig.canvas.get_renderer()).transformed(fig.transFigure.inverted()) ax.annotate('', xy=(tb.xmin,tb.y0), xytext=(tb.xmax,tb.y0), xycoords="figure fraction",arrowprops=dict(arrowstyle="-", color='k',linestyle=T_linestyle)) """ else: text = ax.annotate(f"{min2:.{kwargs['precision']}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) mark_as_algorithm(ax,text,LRT_color,LRT_linestyle,algorithm="Tabu") #underline_text(ax,text,LRT_color,LRT_linestyle) #minimum_val = min([np.min(data1),np.min(data2)]) maximum_val = min([np.max(data1),np.max(data2)]) max1 = np.max(data1) max2 = np.max(data2) if max1 > max2: text = f"{maximum_val:.{kwargs['precision']}f}" text = ax.annotate(f"{max1:.{kwargs['precision']}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) mark_as_algorithm(ax,text,T_color,T_linestyle,algorithm="Klau") #underline_text(ax,text,T_color,T_linestyle) #underlined_annotation(fig,ax,(.925,extremal_tick_ypos),text,linestyle=LRT_linestyle,ha="right",fontsize=8,alpha=.8) else: text = ax.annotate(f"{maximum_val:.{kwargs['precision']}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) mark_as_algorithm(ax,text,LRT_color,LRT_linestyle,algorithm="Tabu") #underline_text(ax,text,LRT_color,LRT_linestyle) bbox = dict(boxstyle="round", ec="w", fc="w", alpha=.5,pad=.025) ax.annotate(f"{np.median(data1):.{kwargs['precision']}f}",xy=(.7,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) ax.annotate(f"{np.median(data2):.{kwargs['precision']}f}",xy=(.3,.4),xycoords="axes fraction",ha="center",fontsize=10).set_bbox(bbox) #for x in sorted(dir(text)): # print(x) """ if format is None: ax.annotate(f"{np.median(data1):.{kwargs[:precision]}f}",xy=(.5,.2),xycoords="axes fraction",ha="center",fontsize=10) ax.annotate(f"{np.min(data1):.{precision}f}",xy=(.075,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data1):.{precision}f}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) elif format == "scientific": ax.annotate(f"{np.median(data):.{precision}e}",xy=(.5,.2),xycoords="axes fraction",ha="center",fontsize=10) ax.annotate(f"{np.min(data):.{precision}e}",xy=(.025,extremal_tick_ypos),xycoords="axes fraction",ha="left",fontsize=8,alpha=.8) ax.annotate(f"{np.max(data):.{precision}e}",xy=(.925,extremal_tick_ypos),xycoords="axes fraction",ha="right",fontsize=8,alpha=.8) else: print(f"expecting format to be 'scientific' or None, got:{format}") """ def make_violin_plot_legend(ax,c="k"): np.random.seed(12)#Ok looking:12 v = ax.violinplot([np.random.normal() for i in range(50)], points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) #ax.axes.set_axis_off() ax.set_ylim(.5,1.0) # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] #newMedianLines = [[(x0,y0-.125),(x0,y0 + (y1-y0)/2 -.1)]]#,[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) ax.annotate("median",xy=(.5,.4),xycoords="axes fraction",ha="center",va="center",fontsize=10) ax.annotate(f"min",xy=(.025,-.125),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) ax.annotate(f"max",xy=(.975,-.125),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) if c is not None: v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor(c) b.set_alpha(.3) b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) def make_merged_violin_plot_legend(ax): np.random.seed(12)#Ok looking:12 v1= ax.violinplot([np.random.normal(-.25,.25) for i in range(50)], points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False)#,quantiles=[[.2,.8]]*len(data2)) v2 = ax.violinplot([np.random.normal(.5,.25) for i in range(50)], points=100, positions=[.6], showmeans=False, showextrema=False, showmedians=True,widths=.6,vert=False) ax.set_ylim(.5,1.0) for (c,v) in [(LRT_color,v1),(T_color,v2)]: # -- update median lines to have a gap -- # ((x0,y0),(_,y1)) = v["cmedians"].get_segments()[0] #newMedianLines = [[(x0,y0-.125),(x0,y0 + (y1-y0)/2 -.1)]]#,[(x0,y0 + (y1-y0)/2 +.1),(x0,y1+.1)]] newMedianLines = [[(x0,y1 +.05),(x0,1.5)]] v["cmedians"].set_segments(newMedianLines) med_label1 = ax.annotate(r"$\Lambda$T"+"Klau med.",xy=(.25,.35),xycoords="axes fraction",ha="center",va="center",fontsize=10) #mark_as_algorithm(ax,med_label1,LRT_color,LRT_linestyle,algorithm="LRTAME") med_label2 = ax.annotate(r"$\Lambda$T"+"Tabu med.",xy=(.7,.35),xycoords="axes fraction",ha="center",va="center",fontsize=10) #mark_as_algorithm(ax,med_label2,T_color,T_linestyle,algorithm="TAME") min_label = ax.annotate(f"min",xy=(.075,-.125),xycoords="axes fraction",ha="left",fontsize=9,alpha=.8) mark_as_algorithm(ax,min_label,LRT_color,LRT_linestyle,algorithm="Klau") max_label = ax.annotate(f"max",xy=(.925,-.125),xycoords="axes fraction",ha="right",fontsize=9,alpha=.8) mark_as_algorithm(ax,max_label,T_color,T_linestyle,algorithm="Tabu") v["cmedians"].set_color(c) for b in v['bodies']: b.set_facecolor(c) b.set_edgecolor(c) b.set_alpha(.3) b.set_color(c) m = np.mean(b.get_paths()[0].vertices[:, 1]) # modify the paths to not go further right than the center b.get_paths()[0].vertices[:, 1] = np.clip(b.get_paths()[0].vertices[:, 1],m, np.inf) def clip_to_top_of_violin(segment): med_line_x = segment.get_paths()[0].vertices[0, 0] # find the y-vals of the violin plots near the med-lines distances = [abs(p[0]- med_line_x) for p in b.get_paths()[0].vertices] k = 5 closest_x_points = np.argpartition(distances,k)[:k] new_max_y = np.max([b.get_paths()[0].vertices[idx,1] for idx in closest_x_points] ) new_max_y += .02 #clip the lines segment.get_paths()[0].vertices[:, 1] = np.clip(segment.get_paths()[0].vertices[:, 1],-np.inf,new_max_y) clip_to_top_of_violin(v["cmedians"]) data_location = TAME_RESULTS + "RG_DupNoise/" def process_klau_data(data,version="LT"): order_idx = {order:i for (i,order) in enumerate(sorted(set([datum[0] for datum in data])))} k_idx = {k:i for (i,k) in enumerate(sorted(set([datum[1] for datum in data])))} trials =len(data[0][-1]) runtime = np.zeros((len(order_idx),len(k_idx),trials)) accuracy = np.zeros((len(order_idx),len(k_idx),trials)) postProcessingAccuracy = np.zeros((len(order_idx),len(k_idx),trials)) triMatch = np.zeros((len(order_idx),len(k_idx),trials)) postProcessingRuntime = np.zeros((len(order_idx),len(k_idx),trials)) #sparsity = np.zeros((len(order_idx),len(k_idx),trials)) trial_idx = np.zeros((len(order_idx),len(k_idx)),int) vertex_coverage = {} A_motifs_counts = {} for (order,k,results) in data: # expecting results: # (seed,p, n, sp, acc, dup_tol_acc, matched_matching_score, A_motifs, B_motifs, A_motifDistribution, B_motifsDistribution,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) i = order_idx[order] j = k_idx[k] print(f"p:{results[0][1]} n:{results[0][2]} sp:{results[0][3]}") accuracy[i,j,:] = [x[4] for x in results] if version=="LT": runtime[i,j,:] = [sum([sum(val) for val in result[11].values()]) for result in results] postProcessingAccuracy[i,j,:] = [x[15] for x in results] postProcessingRuntime[i,j,:] = [x[17] + x[18] for x in results] #sparsity[i,j,:] = [x[19] for x in results] if k == min(k_idx.keys()): #only do this once over k #determine the size of A vs. B motif data if len(results[0][9]) == results[0][2]: vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[9][0])))/x[2] for x in results] A_motifs_counts[order]= [x[7][0] for x in results] else: vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[10][0])))/x[2] for x in results] A_motifs_counts[order]= [x[8][0] for x in results] elif version=="LRT": postProcessingAccuracy[i,j,:] = [x[14] for x in results] postProcessingRuntime[i,j,:] = [x[16] + x[17] for x in results] if version=="LT": return accuracy,runtime, postProcessingAccuracy, postProcessingRuntime,vertex_coverage,A_motifs_counts, order_idx, k_idx #triMatch,LT_klauTriMatch, p_idx, n_idx else: return accuracy, postProcessingAccuracy, postProcessingRuntime,order_idx, k_idx #triMatch,LT_klauTriMatch, p_idx, n_idx def process_tabu_data(data,version="LT"): order_idx = {order:i for (i,order) in enumerate(sorted(set([datum[0] for datum in data])))} k_idx = {k:i for (i,k) in enumerate(sorted(set([datum[1] for datum in data])))} trials =len(data[0][-1]) accuracy = np.zeros((len(order_idx),len(k_idx),trials)) tabuAccuracy = np.zeros((len(order_idx),len(k_idx),trials)) triMatch = np.zeros((len(order_idx),len(k_idx),trials)) tabuRuntime = np.zeros((len(order_idx),len(k_idx),trials)) #sparsity = np.zeros((len(order_idx),len(k_idx),trials)) trial_idx = np.zeros((len(order_idx),len(k_idx)),int) vertex_coverage = {} for (order,k,results) in data: # expecting results: # (seed,p, n, sp, acc, dup_tol_acc, matched_matching_score, A_motifs, B_motifs, A_motifDistribution, B_motifsDistribution,profiling,edges,klau_edges,klau_tri_match,klau_acc,_,klau_setup,klau_rt) i = order_idx[order] j = k_idx[k] accuracy[i,j,:] = [x[4] for x in results] if version == "LT": tabuAccuracy[i,j,:] = [x[15] for x in results] tabuRuntime[i,j,:] = [x[18] for x in results] #sparsity[i,j,:] = [x[19] for x in results] if k == min(k_idx.keys()): #only do this once over k vertex_coverage[order] = [len(list(filter(lambda y: y!= 0.0,x[9][0])))/x[2] for x in results] elif version == "LRT": print(i," ",j) tabuAccuracy[i,j,:] = [x[14] for x in results] tabuRuntime[i,j,:] = [x[17] for x in results] if version == "LT": return accuracy, tabuAccuracy,tabuRuntime,vertex_coverage, order_idx, k_idx else: return accuracy, tabuAccuracy,tabuRuntime,order_idx, k_idx # # Parse the Data # #filename = "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json" filename = "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_KAmiter:1000_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json" # exp uses sp:10 filename = "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_KAmiter:100_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" filename = "RandomGeometric_degreedist:LogNormal_KlauAlgokvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_KAmiter:1000_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" #filename = "RandomGeometric_degreedist:LogNormal_kvals:[15,30,45,60,75,90]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json" print("loading Klau Data") with open(data_location+filename,'r') as f: # with open(data_location+filename,"r") as f: accuracy,LT_runtime, LT_klauAccuracy,LT_klauRuntime,vertex_coverage,A_motifs_counts, order_idx, k_idx = process_klau_data(json.load(f)) filename = "RandomGeometric_degreedist:LogNormal_TabuSearchkvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json" filename = "RandomGeometric_degreedist:LogNormal_TabuSearchkvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_p:[0.5]_samples:1000000_sp:[0.25]_trials:25_data.json" print("loading Local Search Data") with open(data_location+filename,'r') as f: _, LT_TabuAccuracy, LT_TabuRuntime, _, order_idx, k_idx = process_tabu_data(json.load(f)) """ filename = "RandomGeometric_LRTAME_degreedist:LogNormal_KlauAlgokvals:[15,30,45,60,75,90]_n:[500]_noiseModel:Duplication_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json" with open(data_location+filename,'r') as f: _, LRT_klauAccuracy, LRT_klauRuntime, order_idx, k_idx = process_klau_data(json.load(f),"LRT") filename = "RandomGeometric_LRTAME_degreedist:LogNormal_TabuSearchkvals:[15,30,45,60,75,90]_noiseModel:Duplication_n:[500]_orders:[2,3,4,5,6,7,8,9]_samples:1000000_trials:25_data.json" with open(data_location+filename,'r') as f: _, LRT_TabuAccuracy, LRT_TabuRuntime, order_idx, k_idx = process_tabu_data(json.load(f),"LRT") """ """ Old Plotting code def make_percentile_plot(plot_ax, x_domain,data,color,**kwargs): #TODO: check on scope priorities of ax for closures lines = [(lambda col: np.percentile(col,50),1.0,color) ] ribbons = [ (20,80,.2,color) ] plot_percentiles(plot_ax, data.T, x_domain, lines, ribbons,**kwargs) """ # -- add in order labels overhead -- # global_ax.set_yticklabels([]) global_ax.set_yticklabels([]) order_label_idx = 0 gap1 = 1 accuracy_idx = 2 vertex_coverage_idx = 3 motif_count_idx = 4 gap2 = 5 runtime_idx = 6 widths = [1, .25,4,2,2,.25,4] # = [1]*len(order_idx) spec = fig.add_gridspec(nrows=len(order_idx)+1,ncols=5+2, hspace=0.05,wspace=0.025, left=.025,right=.975,top=.85,bottom=.025, width_ratios=widths) allCAx = [] allAccAx = [] allRtAx = [] allVCAx = [] allMotifCountAx = [] allSparsityAx = [] first = True annotation_idx = 4 k_tick_idx = 4 parity = 0 for idx,(order,i) in enumerate(order_idx.items()): # # Clique size # ax = fig.add_subplot(spec[i,order_label_idx]) allCAx.append(ax) ax.annotate(f"{order}",xy=(.5, .5), xycoords='axes fraction', c="k",size=10,ha="center",va="center") ax.set_xticklabels([]) ax.set_yticklabels([]) ax = fig.add_subplot(spec[i,accuracy_idx])#,sharey=allAccAx[0]) allAccAx.append(ax) #if idx % 2 != parity: # ax.patch.set_facecolor('k') # ax.patch.set_alpha(0.1) # -- Accuracy Plots -- # #ax.set_yticks([0.0,0.25,.5,.75,1.0]) #ax.set_ylim(0,1.0) #ax.set_xticks([]) #plt.axhline(y=np.median(accuracy[i,:,:]), color=LT_color,linestyle=LT_linestyle) #plt.axhline(y=np.max(accuracy[i,:,:]), color=LT_color,linestyle="dotted") default_k = 30 make_violin_plot_merged_axis(ax,LT_klauAccuracy[i,k_idx[default_k],:],LT_TabuAccuracy[i,k_idx[default_k],:],LT_Klau_color,LT_Tabu_color,None,None,precision=2) #make_violin_plot(ax,LT_klauAccuracy[i,k_idx[default_k],:],precision=2,c=LT_Klau_color) #make_violin_plot(ax,LT_TabuAccuracy[i,k_idx[default_k],:],precision=2,c=LT_Tabu_color) # # Runtime Plots # ax = fig.add_subplot(spec[i,runtime_idx]) allRtAx.append(ax) make_violin_plot_merged_axis(ax,LT_klauRuntime[i,k_idx[default_k],:],LT_TabuRuntime[i,k_idx[default_k],:],LT_Klau_color,LT_Tabu_color,None,None,precision=1) #make_violin_plot(ax,LT_klauRuntime[i,k_idx[default_k],:],precision=2,c=LT_Klau_color) #make_violin_plot(ax,LT_TabuRuntime[i,k_idx[default_k],:],precision=2,c=LT_Tabu_color) # # Vertex Coverage # ax = fig.add_subplot(spec[i,vertex_coverage_idx]) allVCAx.append(ax) make_violin_plot(ax,vertex_coverage[order],c="k",v_alpha=.1) # # Motif Counts # ax = fig.add_subplot(spec[i,motif_count_idx]) allMotifCountAx.append(ax) make_violin_plot(ax,A_motifs_counts[order],c="k",v_alpha=.1,precision=0) violinLegendAx = fig.add_subplot(spec[-1,1:3]) violinMergedAxisLegendAx = fig.add_subplot(spec[-1,3:6]) for ax in chain(allAccAx,allRtAx,allCAx,allVCAx,allMotifCountAx,allSparsityAx,[global_ax,violinLegendAx,violinMergedAxisLegendAx]):#,[violinLegendAx]): ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.yaxis.set_ticks_position('none') ax.xaxis.set_ticks_position('none') ax.set_xticklabels([]) ax.set_yticklabels([]) """ for ax in chain(allAccAx,allRtAx,allSparsityAx): ax.set_xticks([15,45,90]) ax.set_xlim(min(k_idx.keys()),max(k_idx.keys())) """ # - make violin plot legend - # make_violin_plot_legend(violinLegendAx) make_merged_violin_plot_legend(violinMergedAxisLegendAx) # -- Add in x-domain annotations mid plot -- # #allRtAx[k_tick_idx].xaxis.set_label_position("top") #allRtAx[k_tick_idx].xaxis.set_ticks_position('top') #allRtAx[k_tick_idx].tick_params(axis="x",direction="out", pad=-15) #allAccAx[k_tick_idx].annotate("nearest neighbors",xy=(.5,.05),ha="center",xycoords='axes fraction') #allRtAx[k_tick_idx].set_xticklabels([15,45,90],zorder=5) #allRtAx[k_tick_idx].set_axisbelow(True) #allRtAx[0].set_xlabel("nearest\nneighbors") # -- Alternate tick labels to opposite axes -- # title_yloc = 1.25 allCAx[0].annotate("Clique\nSize",xy=(.5,title_yloc ),ha="center",va="center",xycoords='axes fraction') allAccAx[0].annotate("Accuracy",xy=(.5,title_yloc ),ha="center",va="center",xycoords='axes fraction') allRtAx[0].annotate("Runtime (s)",xy=(.5,title_yloc ),ha="center",va="center",xycoords='axes fraction') allVCAx[0].annotate("Vertex\nCoverage",xy=(.5,title_yloc),ha="center",va="center",xycoords='axes fraction') # -- Annotate accuracy plots -- # allMotifCountAx[0].annotate("A Motifs",xy=(.5,title_yloc ),ha="center",va="center",xycoords='axes fraction') plt.tight_layout() if save_path is None: plt.show() else: plt.savefig(save_path)

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98873f33583fca23ec3ac952660f87031e424681

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py

Python

Python/libraries/recognizers-sequence/recognizers_sequence/resources/__init__.py

ParadoxARG/Recognizers-Text

70c2a368e48fb0694f8a185574d6dd6076b29362

[ "MIT" ]

null

Python/libraries/recognizers-sequence/recognizers_sequence/resources/__init__.py

ParadoxARG/Recognizers-Text

70c2a368e48fb0694f8a185574d6dd6076b29362

[ "MIT" ]

null

Python/libraries/recognizers-sequence/recognizers_sequence/resources/__init__.py

ParadoxARG/Recognizers-Text

70c2a368e48fb0694f8a185574d6dd6076b29362

[ "MIT" ]

null

from .base_phone_numbers import BasePhoneNumbers

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py

Python

tankobon/__init__.py

ongyx/tankobon

d74894c12381a570938fe50a326449a8df2dc65b

[ "MIT" ]

1

2021-07-01T15:17:24.000Z

tankobon/__init__.py

ongyx/tankobon

d74894c12381a570938fe50a326449a8df2dc65b

[ "MIT" ]

1

2021-06-21T04:26:41.000Z

2021-06-22T04:30:18.000Z

tankobon/__init__.py

ongyx/tankobon

d74894c12381a570938fe50a326449a8df2dc65b

[ "MIT" ]

null

# coding: utf8 """Yet another manga scraper and downloader""" from .__version__ import __version__ from .core import Cache, Downloader from .sources.base import Parser

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7f9425bcfb4f1cf30dd8023def05540ac1d866c2

133

py

Python

app/services/v2/healthcheck.py

rvmoura96/flask-template

d1383be7e17bff580e3ddf61ae580271c30201c4

[ "MIT" ]

2

2019-09-25T19:19:11.000Z

2019-10-08T01:05:35.000Z

app/services/v2/healthcheck.py

rvmoura96/flask-template

d1383be7e17bff580e3ddf61ae580271c30201c4

[ "MIT" ]

10

2019-09-13T23:41:42.000Z

2020-05-10T21:12:32.000Z

app/services/v2/healthcheck.py

rvmoura96/flask-template

d1383be7e17bff580e3ddf61ae580271c30201c4

[ "MIT" ]

9

2019-09-30T15:26:23.000Z

2020-09-28T23:36:25.000Z

from flask_restful import Resource import app from app.services.healthcheck import HealthApi class HealthApiV2(HealthApi): pass

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f6d4c21895b6767fbe4da94408261ec0a9fce043

377

py

Python

openmdao.lib/src/openmdao/lib/doegenerators/api.py

OzanCKN/OpenMDAO-Framework

05e9d4b9bc41d0ec00a7073545146c925cd33b0b

[ "Apache-2.0" ]

1

2015-11-05T11:14:45.000Z

openmdao.lib/src/openmdao/lib/doegenerators/api.py

janus/OpenMDAO-Framework

05e9d4b9bc41d0ec00a7073545146c925cd33b0b

[ "Apache-2.0" ]

null

openmdao.lib/src/openmdao/lib/doegenerators/api.py

janus/OpenMDAO-Framework

05e9d4b9bc41d0ec00a7073545146c925cd33b0b

[ "Apache-2.0" ]

1

2020-07-15T02:45:54.000Z

"""Pseudo package providing a central place to access all of the OpenMDAO doegenerators in the standard library.""" from openmdao.lib.doegenerators.full_factorial import FullFactorial from openmdao.lib.doegenerators.optlh import OptLatinHypercube from openmdao.lib.doegenerators.uniform import Uniform from openmdao.lib.doegenerators.central_composite import CentralComposite

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6

63dfc85c5f1a168f3d17412579ff15b24c5e852d

36

py

Python

tests/fixtures/serve/app.py

checkr/fdep

36fba98e6b3af35adf238c61f700a8138c090a5e

[ "MIT" ]

9

2017-01-21T00:08:17.000Z

2019-10-20T09:26:25.000Z

tests/fixtures/serve/app.py

checkr/fdep

36fba98e6b3af35adf238c61f700a8138c090a5e

[ "MIT" ]

2

2017-01-28T02:17:20.000Z

2018-06-18T20:27:47.000Z

tests/fixtures/serve/app.py

checkr/fdep

36fba98e6b3af35adf238c61f700a8138c090a5e

[ "MIT" ]

null

def classify(text): return True

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null

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6

63e4e02f88d18272c3b9b59cca1f59a376103b37

401

py

Python

gym_cap/heuristic/__init__.py

raide-project/ctf_public

fdee45c77e3dd8e7f52bb28117ff111682cb88bf

[ "NCSA" ]

6

2019-04-20T23:11:46.000Z

2021-12-16T20:47:17.000Z

gym_cap/heuristic/__init__.py

raide-project/ctf_public

fdee45c77e3dd8e7f52bb28117ff111682cb88bf

[ "NCSA" ]

59

2019-04-19T17:55:29.000Z

2020-06-10T07:02:14.000Z

gym_cap/heuristic/__init__.py

raide-project/ctf_public

fdee45c77e3dd8e7f52bb28117ff111682cb88bf

[ "NCSA" ]

5

2019-04-19T20:13:03.000Z

2020-05-30T18:33:20.000Z

from gym_cap.heuristic.policy import Policy from gym_cap.heuristic.patrol import Patrol from gym_cap.heuristic.roomba import Roomba from gym_cap.heuristic.zeros import Zeros from gym_cap.heuristic.random import Random from gym_cap.heuristic.defense import Defense from gym_cap.heuristic.astar_flag import AStar from gym_cap.heuristic.spiral import Spiral from gym_cap.heuristic.fighter import Fighter

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6

124ed99657012f492d4906c0edc7e79c0541fdba

60

py

Python

src/main/__init__.py

zrev2220/open-contest

5dbb383a19b8ff4dfe5bdcf18379cb8aa13b47b9

[ "Apache-2.0" ]

1

2019-04-02T14:44:46.000Z

src/main/__init__.py

zrev2220/open-contest

5dbb383a19b8ff4dfe5bdcf18379cb8aa13b47b9

[ "Apache-2.0" ]

19

2019-04-02T16:52:43.000Z

2019-04-11T21:12:25.000Z

src/main/__init__.py

zrev2220/open-contest

5dbb383a19b8ff4dfe5bdcf18379cb8aa13b47b9

[ "Apache-2.0" ]

3

2019-03-29T18:09:23.000Z

2019-03-29T18:09:42.000Z

from .util import * from .web import * from .setup import *

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89cab447521d80678027e80bc45c6e1f5f1dc455

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py

Python

src/queryStrategy.py

davidtw999/BEMPS

446fe813e1534352a564ae8e3e45712e04904b36

[ "MIT" ]

2

2022-02-08T23:29:30.000Z

2022-03-11T04:40:46.000Z

src/queryStrategy.py

davidtw999/BEMPS

446fe813e1534352a564ae8e3e45712e04904b36

[ "MIT" ]

null

src/queryStrategy.py

davidtw999/BEMPS

446fe813e1534352a564ae8e3e45712e04904b36

[ "MIT" ]

null

import random from sklearn.manifold import TSNE import numpy as np from scipy.spatial.distance import cdist import torch from sklearn.cluster import KMeans from torch.nn.functional import normalize ## Random generator for X prime def random_generator_for_x_prime(x_dim, size): sample_indices = random.sample(range(0, x_dim), round(x_dim * size)) return sorted(sample_indices) ## CoreLog def bemps_corelog(probs_B_K_C, X): ## Pr(y|theta,x) pr_YThetaX_X_E_Y = probs_B_K_C pr_ThetaL = 1 / pr_YThetaX_X_E_Y.shape[1] ## Generate random number of x' xp_indices = random_generator_for_x_prime(pr_YThetaX_X_E_Y.shape[0], X) pr_YhThetaXp_Xp_E_Yh = pr_YThetaX_X_E_Y[xp_indices, :, :] ## Transpose dimension of Pr(y|theta,x), and calculate pr(theta|L,(x,y)) pr_YThetaX_X_E_Y = pr_ThetaL * pr_YThetaX_X_E_Y pr_YThetaX_X_Y_E = torch.transpose(pr_YThetaX_X_E_Y, 1, 2) ## transpose by dimension E and Y sum_pr_YThetaX_X_Y_1 = torch.sum(pr_YThetaX_X_Y_E, dim=-1).unsqueeze(dim=-1) pr_ThetaLXY_X_Y_E = pr_YThetaX_X_Y_E / sum_pr_YThetaX_X_Y_1 ## Calculate pr(y_hat) pr_ThetaLXY_X_1_Y_E = pr_ThetaLXY_X_Y_E.unsqueeze(dim=1) pr_Yhat_X_Xp_Y_Yh = torch.matmul(pr_ThetaLXY_X_1_Y_E, pr_YhThetaXp_Xp_E_Yh) ## Calculate core MSE by using unsqueeze into same dimension for pr(y_hat) and pr(y_hat|theta,x) pr_YhThetaXp_1_1_Xp_E_Yh = pr_YhThetaXp_Xp_E_Yh.unsqueeze(dim = 0).unsqueeze(dim = 0) pr_YhThetaXp_X_Y_Xp_E_Yh = pr_YhThetaXp_1_1_Xp_E_Yh.repeat(pr_Yhat_X_Xp_Y_Yh.shape[0], pr_Yhat_X_Xp_Y_Yh.shape[2], 1, 1, 1) pr_Yhat_1_X_Xp_Y_Yh = pr_Yhat_X_Xp_Y_Yh.unsqueeze(dim = 0) pr_Yhat_E_X_Xp_Y_Yh = pr_Yhat_1_X_Xp_Y_Yh.repeat(pr_YhThetaXp_Xp_E_Yh.shape[1],1,1,1,1) pr_Yhat_X_Y_Xp_E_Yh = pr_Yhat_E_X_Xp_Y_Yh.transpose(0,3).transpose(0,1) core_mse = torch.mul(pr_YhThetaXp_X_Y_Xp_E_Yh, torch.div(pr_YhThetaXp_X_Y_Xp_E_Yh, pr_Yhat_X_Y_Xp_E_Yh)) core_mse_X_Y = torch.sum(torch.sum(core_mse.sum(dim=-1), dim=-1),dim=-1) ## Calculate RR pr_YLX_X_Y = torch.sum(pr_YThetaX_X_Y_E, dim=-1) rr = torch.sum(torch.mul(pr_YLX_X_Y, core_mse_X_Y), dim=-1) / pr_YhThetaXp_Xp_E_Yh.shape[0] return rr ## CoreMSE def bemps_coremse(probs_B_K_C, X): ## Pr(y|theta,x) pr_YThetaX_X_E_Y = probs_B_K_C pr_ThetaL = 1 / pr_YThetaX_X_E_Y.shape[1] ## Generate random number of x' xp_indices = random_generator_for_x_prime(pr_YThetaX_X_E_Y.shape[0], X) pr_YhThetaXp_Xp_E_Yh = pr_YThetaX_X_E_Y[xp_indices, :, :] ## Transpose dimension of Pr(y|theta,x), and calculate pr(theta|L,(x,y)) pr_YThetaX_X_E_Y = pr_ThetaL * pr_YThetaX_X_E_Y pr_YThetaX_X_Y_E = torch.transpose(pr_YThetaX_X_E_Y, 1, 2) ## transpose by dimension E and Y sum_pr_YThetaX_X_Y_1 = torch.sum(pr_YThetaX_X_Y_E, dim=-1).unsqueeze(dim=-1) pr_ThetaLXY_X_Y_E = pr_YThetaX_X_Y_E / sum_pr_YThetaX_X_Y_1 ## Calculate pr(y_hat) pr_ThetaLXY_X_1_Y_E = pr_ThetaLXY_X_Y_E.unsqueeze(dim=1) pr_Yhat_X_Xp_Y_Yh = torch.matmul(pr_ThetaLXY_X_1_Y_E, pr_YhThetaXp_Xp_E_Yh) ## Calculate core MSE by using unsqueeze into same dimension for pr(y_hat) and pr(y_hat|theta,x) pr_YhThetaXp_1_1_Xp_E_Yh = pr_YhThetaXp_Xp_E_Yh.unsqueeze(dim = 0).unsqueeze(dim = 0) pr_YhThetaXp_X_Y_Xp_E_Yh = pr_YhThetaXp_1_1_Xp_E_Yh.repeat(pr_Yhat_X_Xp_Y_Yh.shape[0], pr_Yhat_X_Xp_Y_Yh.shape[2], 1, 1, 1) pr_Yhat_1_X_Xp_Y_Yh = pr_Yhat_X_Xp_Y_Yh.unsqueeze(dim = 0) pr_Yhat_E_X_Xp_Y_Yh = pr_Yhat_1_X_Xp_Y_Yh.repeat(pr_YhThetaXp_Xp_E_Yh.shape[1],1,1,1,1) pr_Yhat_X_Y_Xp_E_Yh = pr_Yhat_E_X_Xp_Y_Yh.transpose(0,3).transpose(0,1) core_mse = (pr_YhThetaXp_X_Y_Xp_E_Yh - pr_Yhat_X_Y_Xp_E_Yh).pow(2) core_mse_X_Y = torch.sum(torch.sum(core_mse.sum(dim=-1), dim=-1),dim=-1) ## Calculate RR pr_YLX_X_Y = torch.sum(pr_YThetaX_X_Y_E, dim=-1) rr = torch.sum(torch.mul(pr_YLX_X_Y, core_mse_X_Y), dim=-1)/pr_YhThetaXp_Xp_E_Yh.shape[0] return rr ## CoreMSE batch mode def bemps_coremse_batch(probs_B_K_C, batch_size, X, T): ## Pr(y|theta,x) pr_YThetaX_X_E_Y = probs_B_K_C pr_ThetaL = 1 / pr_YThetaX_X_E_Y.shape[1] ## Generate random number of x' xp_indices = random_generator_for_x_prime(pr_YThetaX_X_E_Y.shape[0], X) pr_YhThetaXp_Xp_E_Yh = pr_YThetaX_X_E_Y[xp_indices, :, :] ## Transpose dimension of Pr(y|theta,x), and calculate pr(theta|L,(x,y)) pr_YThetaX_X_E_Y = pr_ThetaL * pr_YThetaX_X_E_Y pr_YThetaX_X_Y_E = torch.transpose(pr_YThetaX_X_E_Y, 1, 2) ## transpose by dimension E and Y sum_pr_YThetaX_X_Y_1 = torch.sum(pr_YThetaX_X_Y_E, dim=-1).unsqueeze(dim=-1) pr_ThetaLXY_X_Y_E = pr_YThetaX_X_Y_E / sum_pr_YThetaX_X_Y_1 ## Calculate pr(y_hat) pr_ThetaLXY_X_1_Y_E = pr_ThetaLXY_X_Y_E.unsqueeze(dim=1) pr_Yhat_X_Xp_Y_Yh = torch.matmul(pr_ThetaLXY_X_1_Y_E, pr_YhThetaXp_Xp_E_Yh) ## Calculate core MSE by using unsqueeze into same dimension for pr(y_hat) and pr(y_hat|theta,x) pr_YhThetaXp_1_1_Xp_E_Yh = pr_YhThetaXp_Xp_E_Yh.unsqueeze(dim = 0).unsqueeze(dim = 0) pr_YhThetaXp_X_Y_Xp_E_Yh = pr_YhThetaXp_1_1_Xp_E_Yh.repeat(pr_Yhat_X_Xp_Y_Yh.shape[0], pr_Yhat_X_Xp_Y_Yh.shape[2], 1, 1, 1) pr_Yhat_1_X_Xp_Y_Yh = pr_Yhat_X_Xp_Y_Yh.unsqueeze(dim = 0) pr_Yhat_E_X_Xp_Y_Yh = pr_Yhat_1_X_Xp_Y_Yh.repeat(pr_YhThetaXp_Xp_E_Yh.shape[1],1,1,1,1) pr_Yhat_X_Y_Xp_E_Yh = pr_Yhat_E_X_Xp_Y_Yh.transpose(0,3).transpose(0,1) core_mse = (pr_YhThetaXp_X_Y_Xp_E_Yh - pr_Yhat_X_Y_Xp_E_Yh).pow(2) core_mse_X_Y_Xp = torch.sum(core_mse.sum(dim=-1), dim=-1) core_mse_X_Xp_Y = torch.transpose(core_mse_X_Y_Xp, 1, 2) core_mse_Xp_X_Y = torch.transpose(core_mse_X_Xp_Y, 0, 1) ## Calculate RR pr_YLX_X_Y = torch.sum(pr_YThetaX_X_Y_E, dim=-1) rr_Xp_X_Y = pr_YLX_X_Y.unsqueeze(0) * core_mse_Xp_X_Y rr_Xp_X = torch.sum(rr_Xp_X_Y, dim=-1) rr_X_Xp = torch.transpose(rr_Xp_X, 0, 1) rr = clustering(rr_X_Xp, probs_B_K_C, T, batch_size) return rr ## CoreMSE top rank mode def bemps_coremse_batch_topk(probs_B_K_C, batch_size, X): ## Pr(y|theta,x) pr_YThetaX_X_E_Y = probs_B_K_C pr_ThetaL = 1 / pr_YThetaX_X_E_Y.shape[1] ## Generate random number of x' xp_indices = random_generator_for_x_prime(pr_YThetaX_X_E_Y.shape[0], X) pr_YhThetaXp_Xp_E_Yh = pr_YThetaX_X_E_Y[xp_indices, :, :] ## Transpose dimension of Pr(y|theta,x), and calculate pr(theta|L,(x,y)) pr_YThetaX_X_E_Y = pr_ThetaL * pr_YThetaX_X_E_Y pr_YThetaX_X_Y_E = torch.transpose(pr_YThetaX_X_E_Y, 1, 2) ## transpose by dimension E and Y sum_pr_YThetaX_X_Y_1 = torch.sum(pr_YThetaX_X_Y_E, dim=-1).unsqueeze(dim=-1) pr_ThetaLXY_X_Y_E = pr_YThetaX_X_Y_E / sum_pr_YThetaX_X_Y_1 ## Calculate pr(y_hat) pr_ThetaLXY_X_1_Y_E = pr_ThetaLXY_X_Y_E.unsqueeze(dim=1) pr_Yhat_X_Xp_Y_Yh = torch.matmul(pr_ThetaLXY_X_1_Y_E, pr_YhThetaXp_Xp_E_Yh) ## Calculate core MSE by using unsqueeze into same dimension for pr(y_hat) and pr(y_hat|theta,x) pr_YhThetaXp_1_1_Xp_E_Yh = pr_YhThetaXp_Xp_E_Yh.unsqueeze(dim=0).unsqueeze(dim=0) pr_YhThetaXp_X_Y_Xp_E_Yh = pr_YhThetaXp_1_1_Xp_E_Yh.repeat(pr_Yhat_X_Xp_Y_Yh.shape[0], pr_Yhat_X_Xp_Y_Yh.shape[2], 1, 1, 1) pr_Yhat_1_X_Xp_Y_Yh = pr_Yhat_X_Xp_Y_Yh.unsqueeze(dim=0) pr_Yhat_E_X_Xp_Y_Yh = pr_Yhat_1_X_Xp_Y_Yh.repeat(pr_YhThetaXp_Xp_E_Yh.shape[1], 1, 1, 1, 1) pr_Yhat_X_Y_Xp_E_Yh = pr_Yhat_E_X_Xp_Y_Yh.transpose(0, 3).transpose(0, 1) core_mse = (pr_YhThetaXp_X_Y_Xp_E_Yh - pr_Yhat_X_Y_Xp_E_Yh).pow(2) core_mse_X_Y = torch.sum(torch.sum(core_mse.sum(dim=-1), dim=-1), dim=-1) ## Calculate RR pr_YLX_X_Y = torch.sum(pr_YThetaX_X_Y_E, dim=-1) rr = torch.sum(torch.mul(pr_YLX_X_Y, core_mse_X_Y), dim=-1) / pr_YhThetaXp_Xp_E_Yh.shape[0] return rr.topk(batch_size).indices.numpy() ## CoreLog top rank mode def bemps_corelog_batch_topk(probs_B_K_C, batch_size, X): ## Pr(y|theta,x) pr_YThetaX_X_E_Y = probs_B_K_C pr_ThetaL = 1 / pr_YThetaX_X_E_Y.shape[1] ## Generate random number of x' xp_indices = random_generator_for_x_prime(pr_YThetaX_X_E_Y.shape[0], X) pr_YhThetaXp_Xp_E_Yh = pr_YThetaX_X_E_Y[xp_indices, :, :] ## Transpose dimension of Pr(y|theta,x), and calculate pr(theta|L,(x,y)) pr_YThetaX_X_E_Y = pr_ThetaL * pr_YThetaX_X_E_Y pr_YThetaX_X_Y_E = torch.transpose(pr_YThetaX_X_E_Y, 1, 2) ## transpose by dimension E and Y sum_pr_YThetaX_X_Y_1 = torch.sum(pr_YThetaX_X_Y_E, dim=-1).unsqueeze(dim=-1) pr_ThetaLXY_X_Y_E = pr_YThetaX_X_Y_E / sum_pr_YThetaX_X_Y_1 ## Calculate pr(y_hat) pr_ThetaLXY_X_1_Y_E = pr_ThetaLXY_X_Y_E.unsqueeze(dim=1) pr_Yhat_X_Xp_Y_Yh = torch.matmul(pr_ThetaLXY_X_1_Y_E, pr_YhThetaXp_Xp_E_Yh) ## Calculate core MSE by using unsqueeze into same dimension for pr(y_hat) and pr(y_hat|theta,x) pr_YhThetaXp_1_1_Xp_E_Yh = pr_YhThetaXp_Xp_E_Yh.unsqueeze(dim = 0).unsqueeze(dim = 0) pr_YhThetaXp_X_Y_Xp_E_Yh = pr_YhThetaXp_1_1_Xp_E_Yh.repeat(pr_Yhat_X_Xp_Y_Yh.shape[0], pr_Yhat_X_Xp_Y_Yh.shape[2], 1, 1, 1) pr_Yhat_1_X_Xp_Y_Yh = pr_Yhat_X_Xp_Y_Yh.unsqueeze(dim = 0) pr_Yhat_E_X_Xp_Y_Yh = pr_Yhat_1_X_Xp_Y_Yh.repeat(pr_YhThetaXp_Xp_E_Yh.shape[1],1,1,1,1) pr_Yhat_X_Y_Xp_E_Yh = pr_Yhat_E_X_Xp_Y_Yh.transpose(0,3).transpose(0,1) core_mse = torch.mul(pr_YhThetaXp_X_Y_Xp_E_Yh, torch.div(pr_YhThetaXp_X_Y_Xp_E_Yh, pr_Yhat_X_Y_Xp_E_Yh)) core_mse_X_Y = torch.sum(torch.sum(core_mse.sum(dim=-1), dim=-1),dim=-1) ## Calculate RR pr_YLX_X_Y = torch.sum(pr_YThetaX_X_Y_E, dim=-1) rr = torch.sum(torch.mul(pr_YLX_X_Y, core_mse_X_Y), dim=-1) / pr_YhThetaXp_Xp_E_Yh.shape[0] return rr.topk(batch_size).indices.numpy() ## CoreLog batch mode def bemps_corelog_batch(probs_B_K_C, batch_size, X, T): ## Pr(y|theta,x) pr_YThetaX_X_E_Y = probs_B_K_C pr_ThetaL = 1 / pr_YThetaX_X_E_Y.shape[1] ## Generate random number of x' xp_indices = random_generator_for_x_prime(pr_YThetaX_X_E_Y.shape[0], X) pr_YhThetaXp_Xp_E_Yh = pr_YThetaX_X_E_Y[xp_indices, :, :] ## Transpose dimension of Pr(y|theta,x), and calculate pr(theta|L,(x,y)) pr_YThetaX_X_E_Y = pr_ThetaL * pr_YThetaX_X_E_Y pr_YThetaX_X_Y_E = torch.transpose(pr_YThetaX_X_E_Y, 1, 2) ## transpose by dimension E and Y sum_pr_YThetaX_X_Y_1 = torch.sum(pr_YThetaX_X_Y_E, dim=-1).unsqueeze(dim=-1) pr_ThetaLXY_X_Y_E = pr_YThetaX_X_Y_E / sum_pr_YThetaX_X_Y_1 ## Calculate pr(y_hat) pr_ThetaLXY_X_1_Y_E = pr_ThetaLXY_X_Y_E.unsqueeze(dim=1) pr_Yhat_X_Xp_Y_Yh = torch.matmul(pr_ThetaLXY_X_1_Y_E, pr_YhThetaXp_Xp_E_Yh) ## Calculate core MSE by using unsqueeze into same dimension for pr(y_hat) and pr(y_hat|theta,x) pr_YhThetaXp_1_1_Xp_E_Yh = pr_YhThetaXp_Xp_E_Yh.unsqueeze(dim = 0).unsqueeze(dim = 0) pr_YhThetaXp_X_Y_Xp_E_Yh = pr_YhThetaXp_1_1_Xp_E_Yh.repeat(pr_Yhat_X_Xp_Y_Yh.shape[0], pr_Yhat_X_Xp_Y_Yh.shape[2], 1, 1, 1) pr_Yhat_1_X_Xp_Y_Yh = pr_Yhat_X_Xp_Y_Yh.unsqueeze(dim = 0) pr_Yhat_E_X_Xp_Y_Yh = pr_Yhat_1_X_Xp_Y_Yh.repeat(pr_YhThetaXp_Xp_E_Yh.shape[1],1,1,1,1) pr_Yhat_X_Y_Xp_E_Yh = pr_Yhat_E_X_Xp_Y_Yh.transpose(0,3).transpose(0,1) core_mse = torch.mul(pr_YhThetaXp_X_Y_Xp_E_Yh, torch.div(pr_YhThetaXp_X_Y_Xp_E_Yh, pr_Yhat_X_Y_Xp_E_Yh)) core_mse_X_Y_Xp = torch.sum(core_mse.sum(dim=-1), dim=-1) core_mse_X_Xp_Y = torch.transpose(core_mse_X_Y_Xp, 1, 2) core_mse_Xp_X_Y = torch.transpose(core_mse_X_Xp_Y, 0, 1) ## Calculate RR pr_YLX_X_Y = torch.sum(pr_YThetaX_X_Y_E, dim=-1) rr_Xp_X_Y = pr_YLX_X_Y.unsqueeze(0) * core_mse_Xp_X_Y rr_Xp_X = torch.sum(rr_Xp_X_Y, dim=-1) rr_X_Xp = torch.transpose(rr_Xp_X, 0, 1) rr = clustering(rr_X_Xp, probs_B_K_C, T, batch_size) return rr ## cluster methods def clustering(rr_X_Xp, probs_B_K_C, T, batch_size): rr_X = torch.sum(rr_X_Xp, dim=-1) rr_topk_X = torch.topk(rr_X, round(probs_B_K_C.shape[0] * T)) rr_topk_X_indices = rr_topk_X.indices.cpu().detach().numpy() rr_X_Xp = rr_X_Xp[rr_topk_X_indices] rr_X_Xp = normalize(rr_X_Xp) # rr_X_Xp = convert_embedding_by_tsne(rr_X_Xp) rr = kmeans(rr_X_Xp, batch_size) rr = [rr_topk_X_indices[x] for x in rr] return rr ## sub fuction for kmeans ++ def closest_center_dist(rr, centers): dist = torch.cdist(rr, rr[centers]) cd = dist.min(axis=1).values return cd ## kmeans def kmeans(rr, k): kmeans = KMeans(n_clusters=k, n_jobs=-1).fit(rr) centers = kmeans.cluster_centers_ # find the nearest point to centers centroids = cdist(centers, rr).argmin(axis=1) centroids_set = np.unique(centroids) m = k - len(centroids_set) if m > 0: pool = np.delete(np.arange(len(rr)), centroids_set) p = np.random.choice(len(pool), m) centroids = np.concatenate((centroids_set, pool[p]), axis = None) return centroids ## create tsne feature space def convert_embedding_by_tsne(X): tsne = TSNE(n_components=3, random_state=100) X = tsne.fit_transform(X) return X ## random a single index def random_queries(len_samples): rand_index = random.sample(range(len_samples), 1) return rand_index[0] ## random a set of indices def random_queries_batch(len_samples, batch_size): rand_index = random.sample(range(len_samples), batch_size) return rand_index ## mean of the probability def prob_mean(probs_B_K_C, dim: int, keepdim: bool = False): return torch.mean(probs_B_K_C, dim=dim, keepdim=keepdim) ## entropy def entropy(probs_B_K_C, dim: int, keepdim: bool = False): return -torch.sum((torch.log(probs_B_K_C) * probs_B_K_C).double(), dim=dim, keepdim=keepdim) ## max entropy def max_entropy_acquisition_function(probs_B_K_C): return entropy(prob_mean(probs_B_K_C, dim=1, keepdim=False), dim=-1) ## test function def test_function(): ## generate data model classes torch.manual_seed(1) probs_matrix = torch.rand((100, 5, 2)) probs_matrix = torch.softmax(probs_matrix, dim=2) probs_matrix = torch.FloatTensor(probs_matrix) def main(): test_function() if __name__ == "__main__": main()

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6

d6047e863cea06769d5681ac0b2ac8c4c92f6227

69

py

Python

mindhome_alpha/erpnext/regional/germany/setup.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:55:29.000Z

mindhome_alpha/erpnext/regional/germany/setup.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

null

mindhome_alpha/erpnext/regional/germany/setup.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:39:01.000Z

import os import frappe def setup(company=None, patch=True): pass

9.857143

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0.753623

11

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0

0.15942

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6

d64314046e4362ece51dddc0d86c474119ab81dc

31

py

Python

Slasher/__init__.py

tagptroll1/Slasher

88a40d5e21746cb0a97a5b3dd1885a54be5b76cf

[ "MIT" ]

null

Slasher/__init__.py

tagptroll1/Slasher

88a40d5e21746cb0a97a5b3dd1885a54be5b76cf

[ "MIT" ]

null

Slasher/__init__.py

tagptroll1/Slasher

88a40d5e21746cb0a97a5b3dd1885a54be5b76cf

[ "MIT" ]

null

print("This does nothing atm.")

31

0.741935

5

31

4.6

1

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0.096774

31

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31

0.821429

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0.6875

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null

0

1

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1

0

6

c38b86cb97281597610aeeee867f50e912d5350e

169

py

Python

chunky/logger.py

ymkjp/chunky

9bd83f26a9cbc05cbd5225931a75e83c6ac3e7fb

[ "MIT" ]

null

chunky/logger.py

ymkjp/chunky

9bd83f26a9cbc05cbd5225931a75e83c6ac3e7fb

[ "MIT" ]

null

chunky/logger.py

ymkjp/chunky

9bd83f26a9cbc05cbd5225931a75e83c6ac3e7fb

[ "MIT" ]

null

import logging import logging.config class Logger: def __init__(self): pass def setup(self): return logging.config.fileConfig('logging.conf')

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0.674556

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169

5.5

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null

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0

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6

c3d415ea3ff93a28de8969170e7b072060647855

3,658

py

Python

checkarg/text.py

felipebrumpereira/checkarg

1dad052af183def92a7213add68dc91fe7f4462c

[ "MIT" ]

null

checkarg/text.py

felipebrumpereira/checkarg

1dad052af183def92a7213add68dc91fe7f4462c

[ "MIT" ]

null

checkarg/text.py

felipebrumpereira/checkarg

1dad052af183def92a7213add68dc91fe7f4462c

[ "MIT" ]

null

from typing import List, Union import checkarg.none_type as NoneType from checkarg.exceptions import ArgumentException def is_not_whitespace( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if len(value.strip()) <= 0: raise ArgumentException(argument_name) if exception is None else exception def is_not_empty( value: Union[str, List], argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if isinstance(value, str): value = value.strip() if len(value) <= 0: raise ArgumentException(argument_name) if exception is None else exception def is_alphanumeric( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if not value.isalnum(): raise ArgumentException(argument_name) if exception is None else exception def is_alphabetic( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if not value.isalpha(): raise ArgumentException(argument_name) if exception is None else exception def is_number( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) try: float(value) except Exception: raise ArgumentException(argument_name) if exception is None else exception def is_integer( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) try: int(value) except Exception: raise ArgumentException(argument_name) if exception is None else exception def is_lowercase( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if not value.islower(): raise ArgumentException(argument_name) if exception is None else exception def is_uppercase( value: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if not value.isupper(): raise ArgumentException(argument_name) if exception is None else exception def has_length( value: str, length: int, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if len(value) != length: raise ArgumentException(argument_name) if exception is None else exception def has_length_between( value: str, min_lenght: int, max_lenght: int, argument_name: str = None, exception: Exception = None, ) -> None: NoneType.is_not_none(value, argument_name, exception) value_length = len(value) if value_length < min_lenght or value_length > max_lenght: raise ArgumentException(argument_name) if exception is None else exception def is_equal_to( value: str, expected: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if value != expected: raise ArgumentException(argument_name) if exception is None else exception def is_not_equal_to( value: str, expected: str, argument_name: str = None, exception: Exception = None ) -> None: NoneType.is_not_none(value, argument_name, exception) if value == expected: raise ArgumentException(argument_name) if exception is None else exception

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null

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1

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null

0

6

c3d4ad7df87df2a71a1b0c71824d47846650c6aa

77

py

Python

pythonrails_templates/tags/tag_body.py

PythonRails/Templates

39871d33a613b33de8f7c90073acd0b9590c4084

[ "MIT" ]

null

pythonrails_templates/tags/tag_body.py

PythonRails/Templates

39871d33a613b33de8f7c90073acd0b9590c4084

[ "MIT" ]

null

pythonrails_templates/tags/tag_body.py

PythonRails/Templates

39871d33a613b33de8f7c90073acd0b9590c4084

[ "MIT" ]

null

from pythonrails_templates import BaseTag class TagBody(BaseTag): pass

12.833333

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null

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1

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1

0

6

c3eb765fc7b88c7d4f6f921d31d8cb9962525e09

65

py

Python

eod/plugins/yolox/utils/__init__.py

Helicopt/EOD

b5db36f4ce267bf64d093b8174bde2c4097b4718

[ "Apache-2.0" ]

1

2022-01-12T01:51:39.000Z

eod/tasks/det/plugins/yolox/utils/__init__.py

YZW-explorer/EOD

f10e64de86c0f356ebf5c7e923f4042eec4207b1

[ "Apache-2.0" ]

null

eod/tasks/det/plugins/yolox/utils/__init__.py

YZW-explorer/EOD

f10e64de86c0f356ebf5c7e923f4042eec4207b1

[ "Apache-2.0" ]

null

from .hook_helper import * # noqa from .lr_helper import * # noqa

32.5

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6

c3f10d62324b7dc35de1e7672b5965c1bd0ba2c6

139

py

Python

src/tsgettoolbox/ulmo/cdec/historical/__init__.py

timcera/tsgettoolbox

828306aefaa097a74abd8e71605bd19eeda29058

[ "BSD-3-Clause" ]

4

2017-11-21T20:22:47.000Z

2021-09-27T13:27:05.000Z

src/tsgettoolbox/ulmo/cdec/historical/__init__.py

timcera/tsgettoolbox

828306aefaa097a74abd8e71605bd19eeda29058

[ "BSD-3-Clause" ]

21

2016-04-28T16:52:18.000Z

2021-12-16T17:00:27.000Z

src/tsgettoolbox/ulmo/cdec/historical/__init__.py

timcera/tsgettoolbox

828306aefaa097a74abd8e71605bd19eeda29058

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from .core import get_data, get_sensors, get_station_sensors, get_stations

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null

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1

0

1

0

1

0

6

61816c150d0a4f5571f8ba24be390c3324c88890

12,065

py

Python

gehm/utils/np_distances.py

IngoMarquart/gehm

389f3cd786c8899e453584de204cac0719c37400

[ "MIT" ]

null

gehm/utils/np_distances.py

IngoMarquart/gehm

389f3cd786c8899e453584de204cac0719c37400

[ "MIT" ]

null

gehm/utils/np_distances.py

IngoMarquart/gehm

389f3cd786c8899e453584de204cac0719c37400

[ "MIT" ]

null

""" Distances that make use of numpy algorithms and should be used outside of the PyTorch model""" import networkx as nx from typing import Union from torch import Tensor from numpy import ndarray import numpy as np import networkx as nx from scipy.spatial.distance import pdist, squareform import torch from torch.nn.functional import cosine_similarity from torch import cdist from gehm.utils.funcs import row_norm def second_order_proximity( adjacency_matrix: Union[Tensor, ndarray], indecies: Union[Tensor, ndarray, list] = None, whole_graph_proximity: bool = True, to_batch: bool = False, distance_metric: str = "cosine", norm_rows_in_sample: bool = False, norm_rows: bool = True, ) -> Tensor: """ Takes an adjacency matrix and generates second-order node proximities, also known as structural equivalence relations. Nodes are similar, if they share similar ties to alters. Diagonal elements are set to zero. Note that this includes non-PyTorch operations! Parameters ---------- adjacency_matrix: Union[Tensor, ndarray] Input adjacency_matrix indecies : Union[Tensor,ndarray,list] List of node indecies to consider in the matrix whole_graph_proximity : bool, optional If True, similarities between nodes in indecies is computed based on all alters in the matrix (including those not in indecies) If False, similarities are only calculated based on nodes contained in indecies. to_batch : bool, optional If true, will remove the row entries of nodes not in indecies If norm_rows is True, will also re-norm the rows, by default True distance_metric : str, optional Any distance metric from scipy.spatial.distance that works without parameter, by default 'cosine' norm_rows_in_sample : bool, optional If True, distances are scaled such that the highest distance is 1. This implies that distances depend on the sample provided, by default False norm_rows: bool, optional If True, distances are scaled for each node, such that sum(a_ij)=1 This does not take into account the similarity to itself, a_ii, which is always 0. Returns ------- ndarray Similarity matrix of dimension len(node_ids)^2 """ if indecies is None: indecies = np.arange(0, adjacency_matrix.shape[0]) else: if isinstance(indecies, list): indecies = np.array(indecies) if isinstance(indecies, Tensor): indecies = indecies.numpy() if isinstance(adjacency_matrix, Tensor): adjacency_matrix = adjacency_matrix.numpy() if not whole_graph_proximity: adjacency_matrix = adjacency_matrix[indecies, :] adjacency_matrix = adjacency_matrix[:, indecies] similarity_matrix = pdist(adjacency_matrix, metric=distance_metric) similarity_matrix = 1 - squareform(similarity_matrix) similarity_matrix = similarity_matrix - np.eye( similarity_matrix.shape[0], similarity_matrix.shape[1] ) if norm_rows_in_sample: similarity_matrix = similarity_matrix / np.max( similarity_matrix ) # Norm max similarity within the sample to 1 if norm_rows and not to_batch: similarity_matrix = row_norm(similarity_matrix) similarity_matrix = np.nan_to_num(similarity_matrix, copy=False) if whole_graph_proximity: similarity_matrix = similarity_matrix[indecies, :] if to_batch: similarity_matrix = whole_graph_rows_to_batch( similarity_matrix, indecies, norm_rows=norm_rows ) return torch.as_tensor(similarity_matrix) def nx_first_order_proximity( G: Union[nx.Graph, nx.DiGraph], node_ids: Union[Tensor, ndarray, list], whole_graph_proximity: bool = True, to_batch: bool = False, norm_rows_in_sample: bool = False, norm_rows: bool = True, ) -> Tensor: """ Takes a networkx graph G and generates first-order node proximities. Diagonal elements are set to zero. Note that this includes non-PyTorch operations! Parameters ---------- G : Union[nx.Graph,nx.DiGraph] Input graph node_ids : Union[Tensor,ndarray,list] List of nodes. Must exist in G. whole_graph_proximity : bool, optional If True, similarities between nodes in node_ids is computed based on all alters in the graph (including those not in node_ids) If False, similarities are only calculated based on nodes contained in node_ids. ATTN: Note that if True, ordering of rows reflects G.nodes if False, ordering reflects node_ids supplied (subnetwork) by default True to_batch : bool, optional If true, will remove the row entries of nodes not in node_list If norm_rows is True, will also re-norm the rows, by default True norm_rows_in_sample : bool, optional If True, distances are scaled such that the highest distance is 1. This implies that distances depend on the sample provided, by default False norm_rows: bool, optional If True, distances are scaled for each node, such that sum(a_ij)=1 This does not take into account the similarity to itself, a_ii, which is always 0. Returns ------- ndarray Similarity matrix of dimension len(node_ids)^2 """ if isinstance(node_ids, list): node_ids = np.array(node_ids) if isinstance(node_ids, Tensor): node_ids = node_ids.numpy() if whole_graph_proximity: adjacency_matrix = np.zeros([len(G.nodes), len(G.nodes)]) else: adjacency_matrix = np.zeros([len(node_ids), len(node_ids)]) if whole_graph_proximity: adjacency_matrix = np.array(nx.adjacency_matrix(G, weight="weight").todense()) else: G_sub = G.subgraph(node_ids) for i, node in enumerate(node_ids): for j, (alter, datadict) in enumerate(G_sub[node].items()): if hasattr(datadict, "weight"): weight = datadict["weight"] else: weight = 1 adjacency_matrix[i, j] = weight if norm_rows_in_sample: adjacency_matrix = adjacency_matrix / np.max( adjacency_matrix ) # Norm max similarity within the sample to 1 if norm_rows and not to_batch: adjacency_matrix = row_norm(adjacency_matrix) adjacency_matrix = np.nan_to_num(adjacency_matrix, copy=False) if whole_graph_proximity: selection = np.searchsorted(np.array(G.nodes), node_ids) assert ( np.array(G.nodes)[selection] == node_ids ).all(), "Internal error, subsetting nodes" adjacency_matrix = adjacency_matrix[selection, :] if to_batch: adjacency_matrix = whole_graph_rows_to_batch( adjacency_matrix, selection, norm_rows=norm_rows ) return torch.as_tensor(adjacency_matrix) def nx_second_order_proximity( G: Union[nx.Graph, nx.DiGraph], node_ids: Union[Tensor, ndarray, list], whole_graph_proximity: bool = True, to_batch: bool = False, distance_metric: str = "cosine", norm_rows_in_sample: bool = False, norm_rows: bool = True, ) -> Tensor: """ Takes a networkx graph G and generates second-order node proximities, also known as structural equivalence relations. Nodes are similar, if they share similar ties to alters. Diagonal elements are set to zero. Note that this includes non-PyTorch operations! Parameters ---------- G : Union[nx.Graph,nx.DiGraph] Input graph node_ids : Union[Tensor,ndarray,list] List of nodes. Must exist in G. whole_graph_proximity : bool, optional If True, similarities between nodes in node_ids is computed based on all alters in the graph (including those not in node_ids) If False, similarities are only calculated based on nodes contained in node_ids. ATTN: Note that if True, ordering of rows reflects G.nodes if False, ordering reflects node_ids supplied (subnetwork) by default True to_batch : bool, optional If true, will remove the row entries of nodes not in node_list If norm_rows is True, will also re-norm the rows, by default True distance_metric : str, optional Any distance metric from scipy.spatial.distance that works without parameter, by default 'cosine' norm_rows_in_sample : bool, optional If True, distances are scaled such that the highest distance is 1. This implies that distances depend on the sample provided, by default False norm_rows: bool, optional If True, distances are scaled for each node, such that sum(a_ij)=1 This does not take into account the similarity to itself, a_ii, which is always 0. Returns ------- ndarray Similarity matrix of dimension len(node_ids)^2 """ if isinstance(node_ids, list): node_ids = np.array(node_ids) if isinstance(node_ids, Tensor): node_ids = node_ids.numpy() if whole_graph_proximity: adjacency_matrix = np.zeros([len(G.nodes), len(G.nodes)]) similarity_matrix = np.zeros([len(node_ids), len(G.nodes)]) else: adjacency_matrix = np.zeros([len(node_ids), len(node_ids)]) similarity_matrix = np.zeros([len(node_ids), len(node_ids)]) if whole_graph_proximity: adjacency_matrix = nx.adjacency_matrix(G, weight="weight").todense() else: G_sub = G.subgraph(node_ids) for i, node in enumerate(node_ids): for j, (alter, datadict) in enumerate(G_sub[node].items()): if hasattr(datadict, "weight"): weight = datadict["weight"] else: weight = 1 adjacency_matrix[i, j] = weight similarity_matrix = pdist(adjacency_matrix, metric=distance_metric) similarity_matrix = 1 - squareform(similarity_matrix) similarity_matrix = similarity_matrix - np.eye( similarity_matrix.shape[0], similarity_matrix.shape[1] ) if norm_rows_in_sample: similarity_matrix = similarity_matrix / np.max( similarity_matrix ) # Norm max similarity within the sample to 1 if norm_rows and not to_batch: similarity_matrix = row_norm(similarity_matrix) similarity_matrix = np.nan_to_num(similarity_matrix, copy=False) if whole_graph_proximity: selection = np.searchsorted(np.array(G.nodes), node_ids) assert ( np.array(G.nodes)[selection] == node_ids ).all(), "Internal error, subsetting nodes" similarity_matrix = similarity_matrix[selection, :] if to_batch: similarity_matrix = whole_graph_rows_to_batch( similarity_matrix, selection, norm_rows=norm_rows ) return torch.as_tensor(similarity_matrix) def whole_graph_rows_to_batch( similarity_matrix: Union[Tensor, ndarray], indecies: Union[Tensor, ndarray, list], norm_rows: bool = True, ) -> Tensor: """ Sorts matrix according to indecies and row-normalizes if desired Parameters ---------- similarity_matrix : Union[Tensor,ndarray] input indecies : Union[Tensor, ndarray, list] indecies with order norm_rows : bool, optional whether to row norm, by default True Returns ------- Tensor similarity_matrix """ similarity_matrix = similarity_matrix[:, indecies] if norm_rows: similarity_matrix = row_norm(similarity_matrix) return torch.as_tensor(similarity_matrix)

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0.780985

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0.2707

12,065

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0.867371

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false

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null

0

6

61bd86ebf343376a705c9c9720b7ade08e7a31b1

58

py

Python

code/models/__init__.py

michael-1003/EfficientNet-1D

ec7ff79fbde647b97d5c60359eba3c68beae934b

[ "MIT" ]

4

2021-06-23T02:58:42.000Z

2022-01-19T11:09:38.000Z

code/models/__init__.py

michael-1003/EfficientNet-1D

ec7ff79fbde647b97d5c60359eba3c68beae934b

[ "MIT" ]

1

2021-06-23T03:19:45.000Z

code/models/__init__.py

michael-1003/EfficientNet-1D

ec7ff79fbde647b97d5c60359eba3c68beae934b

[ "MIT" ]

3

2021-05-22T09:24:50.000Z

2021-06-23T03:13:08.000Z

from .cnn1d_adaptive import * from .mlp_adaptive import *

29

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0.793103

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6

4ee5c30fdd5948324b448dd199c921e30bccd8e3

81

py

Python

segmate/util/__init__.py

justacid/segmate

7b66b207ca353805f7ad9c7e003645cd2cbc227a

[ "MIT" ]

null

segmate/util/__init__.py

justacid/segmate

7b66b207ca353805f7ad9c7e003645cd2cbc227a

[ "MIT" ]

null

segmate/util/__init__.py

justacid/segmate

7b66b207ca353805f7ad9c7e003645cd2cbc227a

[ "MIT" ]

null

from . import draw from . import mask from .qimage import to_qimage, from_qimage

20.25

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0.790123

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1

0

1

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6

f618c02f65290fbc84359a3cdcb941a996dbb49a

5,508

py

Python

test.py

tagias/tagias-python

60b8c2161c9f262958a42d977199a7e0249432ec

[ "MIT" ]

null

test.py

tagias/tagias-python

60b8c2161c9f262958a42d977199a7e0249432ec

[ "MIT" ]

null

test.py

tagias/tagias-python

60b8c2161c9f262958a42d977199a7e0249432ec

[ "MIT" ]

null

# import the tagias api helper classes from tagias.tagias import TagiasHelper, TagiasHelper2, TagiasError, TagiasTypes, TagiasStatuses, TagiasOperation # Replace the test API key with your own private API key apiKey = 'test' # Testing the TAGIAS external API methods using TagiasHelper class def test(apiKey): try: print('Test Start') # create tagias helper object helper = TagiasHelper(apiKey) # create a new package newPackage = helper.create_package('Test package', TagiasTypes.Keypoints, 'Put one point only in the center of the image', None, None, 'https://p.tagias.com/samples/', ['dog.8001.jpg', 'dog.8002.jpg', 'dog.8003.jpg']) print('Package {} was created with {} image(s)'.format(newPackage['id'], newPackage['pictures_num'])) try: # modify the package's status helper.set_package_status(newPackage['id'], TagiasStatuses.STOPPED) except TagiasError as e: # handle a TagiasError exception print('{} package\'s status was NOT modified: {}'.format(newPackage['id'], e.message)) # get the package's properties package = helper.get_package(newPackage['id']) print('New package properties:') for prop in package: print(' * {}: {}'.format(prop, package[prop])) # get the list of all your packages packages = helper.get_packages() print('Packages:') for package in packages: print(' * {} {} {} {}'.format(package['id'], package['name'], package['status'], package['created'])) if package['status'] == TagiasStatuses.FINISHED: # get the package's result if it's already finished result = helper.get_result(package['id']) print(' Result: {}'.format(result)) try: # request the package's result to be send to the callback endpoint helper.request_result(package['id']) except TagiasError as e: # handle a TagiasError exception print('{} package\'s result was NOT requested: {}'.format(package['id'], e.message)) # get current balance and financial operations balance = helper.get_balance() print('Current balance: {} USD'.format(balance['balance'])) print('Operations:') for op in balance['operations']: print(' * {}: {} USD, {}'.format(op['date'], op['amount'], op['note'])) print('Test End') except TagiasError as e: # handle a TagiasError exception print('TagiasError: {} ({})'.format(e.message, e.code)) # Testing the TAGIAS external API methods using TagiasHelper2 class def test2(apiKey): try: print('Test2 Start') # create tagias helper object helper = TagiasHelper2(apiKey) # create a new package newPackage = helper.create_package('Test package', TagiasTypes.Keypoints, 'Put one point only in the center of the image', None, None, 'https://p.tagias.com/samples/', ['dog.8001.jpg', 'dog.8002.jpg', 'dog.8003.jpg']) print('Package {} was created with {} image(s)'.format(newPackage.id, newPackage.pictures_num)) try: # modify the package's status helper.set_package_status(newPackage.id, TagiasStatuses.STOPPED) except TagiasError as e: # handle a TagiasError exception print('{} package\'s status was NOT modified: {}'.format(newPackage.id, e.message)) # get the package's properties package = helper.get_package(newPackage.id) print('New package properties:') for prop in dir(package): if not prop.startswith('_'): print(' * {}: {}'.format(prop, getattr(package, prop))) # get the list of all your packages packages = helper.get_packages() print('Packages:') for package in packages: print(' * {} {} {} {}'.format(package.id, package.name, package.status, package.created)) if package.status == TagiasStatuses.FINISHED: # get the package's result if it's already finished result = helper.get_result(package.id) print(result) try: # request the package's result to be send to the callback endpoint helper.request_result(package.id) except TagiasError as e: # handle a TagiasError exception print('{} package\'s result was NOT requested: {}'.format(package.id, e.message)) # get current balance and financial operations balance = helper.get_balance() print('Current balance: {} USD'.format(balance.balance)) print('Operations:') for op in balance.operations: print(' * {}: {} USD, {}'.format(op.date, op.amount, op.note)) print('Test2 End') except TagiasError as e: # handle a TagiasError exception print('TagiasError: {} ({})'.format(e.message, e.code)) # test with TagiasHelper class # test(apiKey) # test with TagiasHelper2 class test2(apiKey)

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0

null

0

1

0

null

0

6

f62123edf19806dede42e78c0071fe85ac60aa62

48

py

Python

insight_tools/machine_learning/__init__.py

Insight-by-Team/insight-tools

c854970769d4149306b876d4e480e68a1485e7b6

[ "MIT" ]

null

insight_tools/machine_learning/__init__.py

Insight-by-Team/insight-tools

c854970769d4149306b876d4e480e68a1485e7b6

[ "MIT" ]

null

insight_tools/machine_learning/__init__.py

Insight-by-Team/insight-tools

c854970769d4149306b876d4e480e68a1485e7b6

[ "MIT" ]

null

from .hparams_assistant import HParamsAssistant

24

47

0.895833

5

48

8.4

1

0

0.083333

48

1

48

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f670376159fbdba595a1429d75e6e134bdc2d388

258,350

py

Python

instances/passenger_demand/pas-20210422-1717-int1/5.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/5.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/5.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 19157 passenger_arriving = ( (14, 8, 6, 8, 8, 0, 0, 1, 4, 0, 0, 2, 0, 7, 3, 3, 4, 3, 2, 0, 1, 0, 2, 2, 0, 0), # 0 (5, 5, 6, 5, 3, 2, 5, 2, 3, 5, 1, 1, 0, 7, 6, 5, 3, 8, 5, 1, 2, 2, 1, 0, 0, 0), # 1 (5, 9, 4, 2, 7, 4, 2, 1, 2, 0, 0, 0, 0, 10, 3, 5, 1, 6, 3, 3, 2, 3, 1, 0, 0, 0), # 2 (9, 3, 5, 2, 7, 1, 6, 2, 2, 4, 0, 1, 0, 12, 6, 2, 8, 8, 10, 1, 2, 0, 2, 2, 0, 0), # 3 (8, 7, 7, 10, 5, 0, 2, 1, 3, 1, 0, 0, 0, 8, 5, 4, 1, 3, 2, 3, 0, 0, 1, 1, 0, 0), # 4 (5, 4, 5, 3, 4, 3, 3, 5, 2, 0, 3, 0, 0, 6, 5, 7, 3, 7, 1, 2, 3, 3, 1, 3, 2, 0), # 5 (7, 4, 9, 9, 3, 4, 6, 1, 2, 3, 1, 0, 0, 6, 6, 6, 7, 2, 2, 4, 1, 0, 3, 1, 1, 0), # 6 (7, 5, 9, 3, 7, 4, 1, 5, 1, 1, 2, 1, 0, 4, 3, 8, 2, 8, 1, 5, 2, 2, 1, 0, 0, 0), # 7 (14, 6, 4, 9, 9, 3, 2, 0, 1, 2, 0, 0, 0, 5, 6, 5, 5, 7, 3, 1, 5, 4, 1, 1, 0, 0), # 8 (9, 7, 4, 10, 6, 3, 3, 0, 1, 2, 0, 0, 0, 4, 5, 6, 2, 7, 5, 1, 0, 4, 1, 2, 1, 0), # 9 (8, 5, 4, 9, 9, 5, 5, 6, 3, 0, 0, 1, 0, 6, 9, 7, 5, 6, 6, 1, 4, 4, 3, 1, 0, 0), # 10 (11, 6, 9, 10, 5, 3, 6, 1, 1, 1, 0, 0, 0, 11, 6, 3, 2, 11, 5, 2, 1, 4, 3, 1, 0, 0), # 11 (9, 4, 7, 5, 9, 4, 3, 2, 3, 1, 2, 1, 0, 5, 10, 9, 5, 12, 4, 5, 6, 4, 6, 4, 0, 0), # 12 (10, 7, 9, 8, 8, 2, 3, 3, 3, 1, 2, 1, 0, 7, 10, 4, 4, 5, 6, 2, 2, 5, 2, 2, 1, 0), # 13 (9, 13, 10, 12, 6, 1, 5, 7, 5, 5, 1, 0, 0, 7, 6, 4, 11, 9, 7, 5, 0, 3, 3, 2, 0, 0), # 14 (14, 10, 14, 7, 10, 6, 2, 5, 5, 0, 1, 1, 0, 4, 6, 7, 7, 5, 3, 8, 5, 6, 6, 1, 0, 0), # 15 (10, 9, 11, 8, 5, 3, 5, 5, 3, 1, 0, 2, 0, 8, 9, 8, 7, 12, 3, 2, 2, 7, 3, 1, 0, 0), # 16 (13, 8, 13, 8, 7, 3, 3, 2, 5, 1, 0, 0, 0, 3, 8, 5, 5, 5, 2, 3, 4, 5, 3, 1, 2, 0), # 17 (8, 10, 7, 7, 3, 5, 6, 8, 4, 3, 3, 1, 0, 11, 8, 6, 13, 13, 3, 8, 0, 6, 1, 1, 1, 0), # 18 (5, 13, 12, 11, 6, 5, 4, 2, 1, 2, 3, 1, 0, 8, 5, 8, 8, 7, 9, 4, 2, 0, 4, 3, 0, 0), # 19 (15, 13, 5, 4, 7, 2, 6, 4, 4, 4, 2, 3, 0, 18, 7, 8, 4, 6, 3, 3, 3, 4, 2, 1, 1, 0), # 20 (9, 15, 5, 9, 9, 3, 7, 2, 6, 0, 1, 2, 0, 10, 9, 10, 11, 8, 5, 2, 5, 3, 1, 1, 0, 0), # 21 (10, 9, 12, 5, 11, 3, 4, 5, 6, 1, 0, 1, 0, 9, 9, 3, 4, 15, 8, 5, 2, 3, 2, 3, 3, 0), # 22 (8, 7, 3, 6, 2, 3, 2, 4, 5, 2, 2, 2, 0, 6, 5, 5, 1, 12, 6, 2, 3, 3, 4, 1, 4, 0), # 23 (6, 7, 9, 9, 8, 6, 3, 6, 6, 2, 0, 1, 0, 12, 11, 8, 9, 9, 10, 2, 2, 6, 0, 2, 4, 0), # 24 (15, 13, 8, 9, 9, 3, 5, 5, 5, 0, 2, 1, 0, 8, 9, 11, 9, 8, 7, 2, 1, 2, 5, 2, 0, 0), # 25 (10, 6, 9, 4, 7, 3, 4, 6, 3, 1, 0, 1, 0, 8, 9, 5, 7, 9, 5, 7, 3, 1, 3, 0, 1, 0), # 26 (8, 10, 7, 6, 9, 7, 2, 2, 7, 1, 0, 2, 0, 7, 8, 6, 6, 4, 5, 4, 2, 4, 5, 2, 1, 0), # 27 (11, 9, 6, 10, 6, 2, 3, 5, 7, 3, 1, 1, 0, 6, 8, 4, 6, 4, 3, 2, 2, 2, 1, 0, 2, 0), # 28 (11, 9, 7, 12, 8, 4, 0, 7, 1, 1, 0, 0, 0, 8, 7, 9, 9, 4, 3, 7, 3, 4, 0, 1, 2, 0), # 29 (9, 10, 12, 13, 8, 6, 2, 2, 2, 2, 1, 1, 0, 10, 10, 4, 6, 7, 7, 6, 2, 4, 2, 1, 2, 0), # 30 (11, 9, 10, 4, 5, 0, 6, 6, 1, 1, 1, 1, 0, 12, 9, 6, 5, 3, 11, 4, 2, 7, 6, 4, 0, 0), # 31 (18, 9, 9, 6, 9, 0, 1, 1, 7, 3, 2, 0, 0, 9, 5, 6, 3, 8, 3, 1, 2, 2, 3, 3, 2, 0), # 32 (10, 10, 10, 6, 2, 12, 7, 0, 7, 2, 3, 0, 0, 5, 12, 8, 1, 14, 6, 4, 2, 6, 3, 1, 0, 0), # 33 (15, 9, 11, 11, 10, 4, 5, 1, 9, 3, 0, 1, 0, 9, 7, 7, 5, 6, 7, 2, 1, 3, 4, 0, 2, 0), # 34 (14, 13, 9, 18, 7, 6, 2, 4, 4, 1, 3, 1, 0, 9, 10, 7, 8, 4, 4, 5, 4, 2, 1, 3, 3, 0), # 35 (8, 5, 6, 4, 11, 3, 6, 5, 6, 2, 0, 2, 0, 15, 6, 6, 6, 5, 6, 3, 4, 3, 3, 1, 2, 0), # 36 (6, 8, 15, 4, 6, 3, 3, 4, 1, 4, 0, 0, 0, 15, 8, 11, 5, 12, 1, 6, 1, 5, 6, 1, 0, 0), # 37 (15, 17, 9, 7, 8, 4, 5, 2, 2, 0, 0, 1, 0, 10, 9, 3, 6, 8, 5, 2, 4, 5, 4, 1, 1, 0), # 38 (7, 11, 6, 11, 12, 3, 4, 4, 2, 1, 3, 1, 0, 9, 7, 10, 2, 8, 4, 7, 2, 7, 0, 1, 2, 0), # 39 (9, 5, 9, 12, 5, 1, 3, 4, 2, 1, 0, 1, 0, 11, 7, 5, 6, 15, 4, 4, 1, 4, 3, 1, 1, 0), # 40 (8, 5, 7, 10, 11, 5, 7, 3, 6, 1, 2, 1, 0, 18, 9, 6, 8, 8, 5, 6, 2, 3, 2, 1, 2, 0), # 41 (10, 12, 7, 10, 9, 2, 3, 4, 3, 1, 0, 0, 0, 7, 11, 3, 6, 8, 4, 4, 5, 6, 3, 0, 0, 0), # 42 (11, 10, 12, 7, 10, 8, 4, 1, 2, 3, 1, 1, 0, 16, 7, 14, 5, 12, 9, 1, 1, 5, 2, 3, 2, 0), # 43 (8, 14, 12, 8, 6, 4, 3, 2, 4, 1, 1, 0, 0, 14, 10, 11, 5, 9, 4, 2, 2, 5, 4, 2, 0, 0), # 44 (8, 14, 9, 3, 6, 3, 4, 6, 2, 0, 1, 2, 0, 7, 11, 7, 5, 15, 1, 3, 4, 2, 3, 0, 0, 0), # 45 (9, 11, 8, 9, 5, 1, 2, 3, 4, 5, 1, 0, 0, 11, 12, 10, 6, 8, 6, 4, 3, 11, 7, 2, 2, 0), # 46 (11, 10, 13, 12, 7, 5, 8, 4, 2, 2, 0, 0, 0, 11, 6, 10, 9, 3, 6, 5, 2, 3, 5, 5, 0, 0), # 47 (8, 7, 6, 7, 6, 4, 6, 1, 3, 2, 1, 1, 0, 12, 11, 7, 3, 14, 7, 0, 1, 3, 4, 3, 1, 0), # 48 (8, 8, 9, 10, 7, 3, 1, 3, 3, 4, 1, 0, 0, 12, 11, 7, 2, 6, 5, 1, 1, 2, 1, 3, 1, 0), # 49 (6, 11, 8, 5, 13, 2, 2, 4, 4, 1, 2, 1, 0, 10, 12, 6, 4, 4, 5, 4, 2, 2, 4, 2, 0, 0), # 50 (10, 11, 8, 14, 7, 4, 3, 6, 5, 0, 1, 0, 0, 6, 14, 2, 3, 6, 5, 4, 3, 4, 3, 1, 2, 0), # 51 (13, 6, 12, 10, 5, 2, 4, 2, 2, 2, 1, 0, 0, 12, 8, 11, 5, 11, 1, 7, 3, 7, 1, 1, 1, 0), # 52 (12, 14, 10, 6, 10, 3, 4, 6, 6, 3, 1, 0, 0, 10, 13, 5, 3, 11, 6, 4, 1, 5, 3, 2, 0, 0), # 53 (13, 10, 6, 11, 3, 5, 5, 7, 9, 0, 3, 0, 0, 6, 8, 7, 4, 4, 3, 4, 2, 4, 3, 2, 0, 0), # 54 (11, 8, 10, 13, 8, 6, 3, 4, 2, 2, 1, 2, 0, 5, 7, 6, 4, 10, 8, 4, 1, 4, 2, 0, 1, 0), # 55 (10, 13, 9, 6, 5, 4, 1, 4, 4, 5, 0, 2, 0, 8, 6, 6, 9, 7, 3, 6, 5, 4, 6, 0, 0, 0), # 56 (12, 5, 4, 10, 10, 3, 4, 5, 4, 1, 1, 0, 0, 12, 4, 5, 10, 7, 6, 1, 1, 5, 4, 1, 0, 0), # 57 (12, 14, 7, 12, 9, 0, 5, 3, 4, 1, 2, 0, 0, 10, 9, 4, 2, 6, 5, 6, 1, 2, 1, 1, 3, 0), # 58 (7, 9, 4, 7, 6, 1, 0, 2, 3, 2, 0, 0, 0, 10, 11, 9, 5, 7, 4, 2, 3, 4, 4, 2, 0, 0), # 59 (12, 6, 8, 12, 5, 4, 3, 5, 3, 2, 1, 0, 0, 18, 7, 9, 8, 5, 3, 9, 1, 6, 5, 4, 1, 0), # 60 (13, 10, 9, 11, 9, 4, 3, 3, 5, 1, 2, 0, 0, 10, 9, 8, 2, 8, 0, 8, 1, 3, 3, 1, 2, 0), # 61 (10, 12, 7, 10, 4, 5, 4, 3, 3, 3, 1, 0, 0, 6, 6, 6, 5, 11, 4, 3, 6, 3, 1, 2, 0, 0), # 62 (12, 12, 13, 5, 7, 11, 3, 4, 6, 2, 1, 1, 0, 17, 9, 11, 5, 6, 2, 4, 4, 2, 2, 0, 0, 0), # 63 (10, 11, 11, 6, 10, 2, 2, 2, 3, 4, 5, 0, 0, 9, 7, 6, 7, 4, 8, 10, 4, 4, 2, 0, 1, 0), # 64 (9, 6, 7, 14, 9, 6, 7, 5, 4, 1, 2, 0, 0, 12, 7, 4, 7, 12, 1, 2, 6, 5, 4, 0, 0, 0), # 65 (11, 7, 10, 13, 8, 2, 7, 2, 5, 0, 1, 0, 0, 14, 12, 7, 3, 9, 5, 5, 2, 1, 2, 1, 1, 0), # 66 (12, 7, 9, 9, 13, 3, 6, 3, 6, 1, 0, 0, 0, 14, 7, 7, 6, 6, 2, 5, 1, 2, 6, 2, 1, 0), # 67 (5, 9, 13, 13, 5, 4, 2, 4, 2, 1, 2, 1, 0, 8, 18, 11, 4, 13, 5, 6, 2, 5, 2, 0, 2, 0), # 68 (13, 3, 7, 10, 6, 7, 4, 1, 5, 2, 2, 0, 0, 6, 12, 3, 4, 3, 2, 2, 1, 3, 2, 2, 0, 0), # 69 (4, 9, 8, 7, 7, 4, 1, 4, 2, 1, 2, 0, 0, 8, 6, 8, 9, 9, 4, 6, 2, 2, 3, 2, 0, 0), # 70 (14, 9, 8, 9, 5, 8, 2, 1, 1, 1, 1, 2, 0, 17, 9, 15, 5, 8, 2, 4, 3, 2, 2, 1, 3, 0), # 71 (13, 6, 7, 12, 8, 4, 4, 3, 1, 1, 1, 1, 0, 14, 6, 6, 6, 12, 4, 4, 3, 4, 4, 2, 1, 0), # 72 (9, 7, 6, 12, 6, 1, 3, 2, 6, 2, 1, 1, 0, 9, 7, 8, 3, 10, 9, 6, 4, 2, 3, 2, 1, 0), # 73 (11, 11, 9, 8, 6, 7, 4, 1, 3, 3, 1, 1, 0, 5, 10, 4, 4, 7, 3, 4, 2, 6, 0, 0, 0, 0), # 74 (14, 8, 10, 8, 9, 3, 3, 3, 5, 3, 2, 2, 0, 12, 12, 5, 6, 4, 4, 3, 3, 0, 1, 2, 1, 0), # 75 (9, 13, 9, 9, 7, 3, 3, 3, 5, 3, 1, 1, 0, 13, 8, 4, 6, 3, 7, 1, 1, 5, 1, 2, 0, 0), # 76 (13, 8, 8, 9, 7, 5, 5, 2, 3, 0, 0, 0, 0, 13, 5, 5, 5, 13, 2, 7, 0, 7, 0, 1, 1, 0), # 77 (9, 7, 4, 14, 9, 2, 4, 4, 5, 2, 1, 1, 0, 9, 13, 8, 2, 2, 4, 3, 1, 3, 6, 0, 0, 0), # 78 (6, 12, 11, 13, 8, 3, 2, 2, 4, 0, 2, 0, 0, 10, 8, 5, 3, 8, 6, 3, 10, 1, 3, 4, 0, 0), # 79 (14, 15, 1, 10, 6, 2, 2, 3, 4, 0, 2, 1, 0, 6, 11, 11, 3, 9, 4, 3, 2, 4, 3, 1, 2, 0), # 80 (15, 5, 3, 9, 8, 1, 8, 2, 4, 1, 1, 2, 0, 14, 9, 8, 4, 10, 1, 3, 3, 9, 3, 3, 1, 0), # 81 (12, 10, 8, 16, 7, 5, 6, 1, 3, 1, 2, 1, 0, 11, 12, 5, 5, 10, 3, 6, 2, 3, 3, 2, 0, 0), # 82 (9, 13, 13, 3, 5, 3, 3, 4, 3, 1, 2, 1, 0, 11, 11, 13, 4, 4, 4, 4, 1, 7, 2, 3, 1, 0), # 83 (9, 4, 7, 5, 6, 2, 5, 5, 5, 1, 1, 0, 0, 11, 9, 10, 3, 3, 5, 0, 2, 3, 1, 1, 1, 0), # 84 (9, 12, 12, 11, 4, 3, 5, 4, 2, 1, 2, 0, 0, 10, 7, 6, 5, 9, 2, 4, 6, 4, 0, 0, 0, 0), # 85 (12, 6, 8, 6, 14, 4, 3, 3, 2, 1, 0, 1, 0, 7, 10, 4, 4, 6, 3, 4, 3, 4, 5, 1, 1, 0), # 86 (13, 10, 8, 3, 3, 5, 3, 4, 3, 1, 1, 0, 0, 11, 9, 9, 5, 9, 6, 3, 4, 5, 1, 2, 1, 0), # 87 (7, 6, 8, 9, 5, 2, 3, 2, 3, 0, 0, 0, 0, 8, 9, 2, 5, 4, 2, 1, 0, 3, 3, 2, 0, 0), # 88 (10, 10, 8, 6, 9, 4, 2, 4, 2, 1, 1, 2, 0, 7, 11, 8, 7, 13, 1, 5, 2, 1, 5, 1, 1, 0), # 89 (1, 6, 7, 8, 8, 9, 2, 1, 5, 2, 0, 1, 0, 12, 13, 10, 4, 12, 3, 2, 1, 1, 2, 3, 0, 0), # 90 (10, 12, 3, 11, 7, 4, 2, 3, 2, 5, 2, 2, 0, 12, 6, 7, 8, 5, 2, 7, 2, 3, 3, 5, 2, 0), # 91 (10, 10, 7, 5, 7, 4, 6, 3, 6, 1, 0, 0, 0, 16, 6, 8, 9, 8, 2, 0, 4, 4, 4, 1, 0, 0), # 92 (10, 8, 9, 6, 6, 0, 5, 3, 7, 0, 2, 0, 0, 6, 11, 5, 4, 9, 8, 0, 6, 0, 5, 3, 0, 0), # 93 (5, 10, 7, 11, 4, 3, 2, 2, 5, 0, 0, 1, 0, 10, 7, 8, 5, 7, 1, 1, 2, 2, 2, 0, 0, 0), # 94 (7, 3, 13, 8, 8, 4, 2, 5, 6, 3, 1, 0, 0, 7, 3, 6, 3, 4, 2, 4, 5, 5, 3, 4, 1, 0), # 95 (6, 8, 5, 5, 10, 3, 1, 2, 1, 2, 1, 2, 0, 8, 8, 7, 2, 12, 1, 4, 0, 3, 2, 2, 0, 0), # 96 (15, 11, 12, 7, 6, 2, 3, 5, 5, 1, 1, 0, 0, 12, 8, 6, 4, 2, 3, 0, 1, 9, 6, 3, 0, 0), # 97 (11, 8, 2, 5, 5, 4, 4, 2, 7, 5, 4, 1, 0, 6, 6, 9, 10, 8, 5, 8, 2, 2, 2, 0, 0, 0), # 98 (9, 8, 6, 8, 10, 1, 4, 3, 6, 5, 0, 0, 0, 20, 8, 7, 4, 10, 3, 5, 0, 3, 9, 1, 1, 0), # 99 (20, 6, 10, 4, 8, 5, 2, 4, 1, 3, 1, 0, 0, 11, 2, 5, 4, 10, 4, 5, 4, 2, 1, 3, 0, 0), # 100 (9, 5, 10, 8, 7, 7, 2, 3, 4, 1, 1, 1, 0, 7, 8, 6, 6, 7, 2, 4, 2, 3, 2, 5, 1, 0), # 101 (12, 8, 9, 7, 4, 2, 0, 3, 2, 4, 1, 1, 0, 11, 11, 8, 5, 4, 6, 9, 0, 4, 4, 0, 0, 0), # 102 (13, 4, 9, 1, 9, 2, 3, 5, 1, 1, 0, 0, 0, 6, 6, 6, 7, 4, 4, 3, 4, 1, 4, 1, 0, 0), # 103 (11, 7, 8, 4, 7, 6, 2, 3, 5, 0, 1, 0, 0, 4, 5, 4, 1, 9, 5, 0, 5, 2, 4, 3, 2, 0), # 104 (14, 10, 12, 4, 8, 3, 5, 2, 3, 0, 3, 1, 0, 9, 2, 2, 6, 7, 6, 1, 4, 6, 2, 2, 1, 0), # 105 (9, 7, 7, 11, 15, 3, 3, 7, 6, 1, 1, 1, 0, 13, 10, 10, 7, 6, 3, 3, 5, 7, 0, 1, 2, 0), # 106 (9, 8, 12, 4, 8, 4, 3, 1, 2, 0, 0, 2, 0, 9, 12, 4, 2, 12, 3, 6, 3, 5, 3, 3, 0, 0), # 107 (14, 5, 10, 6, 7, 2, 2, 1, 5, 1, 3, 0, 0, 10, 3, 10, 7, 4, 3, 1, 8, 4, 3, 1, 1, 0), # 108 (9, 9, 3, 7, 7, 2, 2, 4, 5, 2, 3, 1, 0, 14, 6, 2, 5, 4, 1, 4, 1, 3, 2, 0, 0, 0), # 109 (10, 8, 11, 9, 10, 4, 1, 1, 4, 2, 4, 0, 0, 9, 8, 7, 8, 6, 3, 3, 6, 3, 5, 3, 0, 0), # 110 (10, 9, 7, 5, 7, 3, 0, 4, 4, 0, 0, 0, 0, 15, 7, 6, 5, 3, 6, 3, 3, 6, 5, 3, 0, 0), # 111 (11, 8, 9, 11, 6, 3, 3, 3, 5, 1, 1, 0, 0, 9, 8, 7, 4, 4, 4, 1, 1, 4, 1, 0, 2, 0), # 112 (5, 8, 8, 10, 7, 5, 4, 0, 5, 1, 0, 1, 0, 11, 5, 3, 1, 11, 4, 2, 2, 3, 3, 1, 1, 0), # 113 (6, 8, 9, 6, 10, 5, 1, 1, 7, 1, 2, 1, 0, 8, 10, 3, 2, 11, 3, 1, 2, 2, 2, 4, 1, 0), # 114 (9, 8, 6, 6, 7, 3, 4, 2, 3, 2, 1, 0, 0, 10, 2, 9, 6, 9, 1, 4, 1, 2, 3, 2, 1, 0), # 115 (10, 7, 5, 8, 11, 2, 4, 4, 5, 1, 1, 0, 0, 10, 11, 4, 6, 9, 8, 6, 2, 3, 3, 4, 0, 0), # 116 (14, 12, 8, 6, 8, 3, 5, 4, 1, 0, 1, 0, 0, 6, 6, 5, 2, 3, 6, 4, 2, 4, 2, 2, 1, 0), # 117 (4, 11, 5, 12, 3, 3, 5, 5, 3, 2, 0, 0, 0, 6, 9, 4, 7, 5, 1, 4, 3, 5, 2, 0, 0, 0), # 118 (8, 11, 4, 6, 3, 2, 2, 3, 2, 2, 0, 1, 0, 7, 6, 13, 10, 8, 2, 2, 3, 0, 2, 2, 1, 0), # 119 (6, 6, 10, 6, 8, 3, 3, 3, 3, 2, 4, 0, 0, 7, 5, 4, 4, 6, 2, 1, 2, 5, 4, 5, 0, 0), # 120 (8, 7, 7, 5, 10, 2, 6, 8, 6, 4, 1, 0, 0, 8, 4, 8, 7, 11, 2, 3, 3, 6, 0, 3, 0, 0), # 121 (13, 6, 9, 8, 2, 2, 2, 1, 6, 1, 1, 0, 0, 9, 9, 3, 4, 10, 3, 3, 4, 2, 1, 4, 0, 0), # 122 (9, 7, 9, 9, 8, 1, 7, 5, 5, 2, 0, 0, 0, 7, 4, 5, 5, 3, 6, 1, 2, 8, 5, 2, 0, 0), # 123 (7, 7, 5, 10, 3, 3, 3, 3, 3, 2, 0, 0, 0, 9, 3, 3, 6, 5, 3, 3, 2, 6, 3, 2, 3, 0), # 124 (14, 5, 6, 4, 13, 5, 1, 4, 3, 1, 0, 2, 0, 10, 6, 5, 7, 6, 4, 3, 2, 2, 1, 0, 1, 0), # 125 (8, 5, 6, 13, 10, 1, 7, 1, 2, 0, 1, 0, 0, 7, 6, 7, 5, 8, 6, 4, 4, 0, 1, 2, 1, 0), # 126 (9, 3, 10, 11, 2, 1, 5, 1, 0, 0, 2, 1, 0, 10, 6, 7, 3, 6, 2, 5, 5, 5, 1, 0, 0, 0), # 127 (14, 7, 9, 6, 7, 2, 2, 2, 4, 3, 1, 0, 0, 11, 4, 2, 6, 6, 6, 0, 1, 4, 4, 1, 0, 0), # 128 (8, 4, 7, 15, 6, 1, 0, 1, 2, 2, 0, 0, 0, 14, 8, 5, 1, 3, 3, 5, 1, 2, 7, 0, 0, 0), # 129 (6, 3, 6, 4, 10, 2, 6, 2, 3, 2, 0, 0, 0, 14, 8, 5, 0, 7, 2, 2, 1, 4, 2, 0, 0, 0), # 130 (11, 8, 9, 8, 6, 1, 1, 1, 6, 2, 0, 0, 0, 8, 3, 6, 7, 7, 2, 2, 2, 1, 3, 0, 1, 0), # 131 (8, 4, 10, 5, 7, 2, 1, 5, 1, 1, 0, 0, 0, 9, 14, 7, 5, 5, 2, 3, 2, 2, 0, 0, 0, 0), # 132 (8, 6, 8, 15, 10, 4, 1, 2, 4, 4, 2, 1, 0, 6, 11, 5, 5, 5, 4, 3, 1, 1, 0, 1, 0, 0), # 133 (8, 4, 8, 14, 7, 1, 2, 3, 1, 1, 1, 0, 0, 7, 11, 5, 2, 6, 4, 3, 4, 3, 0, 2, 0, 0), # 134 (5, 11, 4, 13, 10, 2, 3, 2, 2, 1, 1, 1, 0, 7, 8, 2, 2, 5, 4, 3, 5, 2, 2, 1, 2, 0), # 135 (9, 9, 4, 12, 2, 1, 3, 1, 7, 0, 3, 0, 0, 15, 9, 2, 4, 9, 1, 2, 1, 1, 3, 4, 0, 0), # 136 (12, 2, 7, 6, 6, 3, 5, 2, 3, 0, 0, 0, 0, 9, 4, 7, 5, 11, 1, 3, 4, 9, 1, 1, 1, 0), # 137 (9, 5, 4, 10, 6, 3, 2, 3, 2, 3, 1, 0, 0, 11, 5, 8, 2, 7, 5, 4, 2, 6, 4, 3, 0, 0), # 138 (9, 5, 6, 4, 7, 5, 3, 3, 2, 2, 0, 1, 0, 12, 6, 5, 6, 5, 2, 3, 0, 1, 4, 0, 0, 0), # 139 (11, 13, 4, 6, 5, 6, 3, 2, 7, 0, 3, 1, 0, 3, 4, 5, 4, 6, 7, 6, 4, 3, 2, 0, 1, 0), # 140 (9, 7, 7, 8, 9, 2, 1, 4, 5, 1, 0, 0, 0, 9, 5, 13, 5, 10, 2, 5, 2, 2, 3, 2, 1, 0), # 141 (8, 5, 6, 8, 7, 1, 5, 1, 6, 2, 0, 0, 0, 2, 0, 8, 2, 5, 1, 1, 4, 2, 4, 3, 0, 0), # 142 (9, 7, 6, 9, 11, 2, 1, 1, 2, 2, 1, 4, 0, 13, 3, 3, 2, 3, 4, 3, 5, 6, 2, 0, 3, 0), # 143 (15, 11, 7, 9, 8, 4, 3, 6, 2, 1, 0, 0, 0, 7, 4, 4, 1, 6, 2, 2, 3, 1, 5, 0, 1, 0), # 144 (14, 4, 5, 5, 8, 3, 3, 2, 3, 2, 0, 0, 0, 10, 10, 7, 4, 5, 4, 0, 1, 6, 6, 0, 0, 0), # 145 (10, 7, 7, 5, 6, 1, 2, 5, 4, 7, 0, 1, 0, 14, 10, 5, 3, 9, 4, 4, 4, 2, 3, 1, 1, 0), # 146 (6, 4, 4, 7, 6, 1, 0, 2, 2, 1, 3, 2, 0, 8, 6, 7, 3, 6, 2, 3, 3, 2, 3, 1, 0, 0), # 147 (11, 3, 8, 5, 4, 3, 4, 1, 3, 4, 4, 0, 0, 11, 6, 2, 5, 9, 2, 1, 1, 6, 3, 2, 0, 0), # 148 (12, 7, 10, 8, 4, 2, 4, 2, 6, 1, 0, 0, 0, 8, 10, 3, 2, 5, 1, 2, 2, 1, 1, 1, 1, 0), # 149 (4, 2, 8, 11, 6, 3, 1, 6, 0, 2, 3, 3, 0, 8, 8, 5, 2, 9, 3, 3, 6, 4, 1, 1, 0, 0), # 150 (5, 4, 1, 4, 4, 2, 1, 3, 3, 1, 0, 2, 0, 10, 5, 5, 4, 5, 6, 2, 4, 3, 3, 2, 0, 0), # 151 (4, 5, 5, 7, 7, 2, 1, 2, 5, 0, 0, 1, 0, 14, 12, 3, 4, 5, 3, 1, 1, 1, 2, 1, 1, 0), # 152 (6, 1, 9, 9, 5, 1, 2, 1, 2, 0, 0, 0, 0, 12, 8, 8, 1, 7, 7, 3, 0, 3, 1, 2, 0, 0), # 153 (8, 4, 11, 8, 3, 4, 0, 4, 3, 3, 0, 1, 0, 7, 3, 6, 5, 6, 6, 4, 1, 5, 2, 1, 0, 0), # 154 (8, 7, 10, 3, 6, 6, 2, 1, 1, 2, 3, 0, 0, 11, 14, 8, 6, 5, 2, 2, 2, 2, 3, 0, 0, 0), # 155 (5, 10, 5, 8, 3, 2, 2, 4, 2, 0, 1, 1, 0, 10, 7, 4, 3, 3, 4, 3, 1, 5, 3, 1, 0, 0), # 156 (8, 10, 7, 7, 9, 0, 3, 1, 3, 2, 0, 2, 0, 8, 8, 7, 4, 5, 0, 1, 3, 3, 3, 0, 0, 0), # 157 (8, 2, 5, 11, 5, 1, 1, 1, 3, 1, 1, 0, 0, 4, 3, 11, 6, 4, 3, 4, 3, 5, 3, 1, 0, 0), # 158 (10, 5, 5, 4, 6, 6, 1, 0, 3, 2, 1, 1, 0, 7, 5, 5, 4, 7, 5, 3, 2, 1, 3, 2, 0, 0), # 159 (7, 4, 8, 5, 5, 3, 1, 3, 4, 0, 1, 1, 0, 12, 7, 3, 6, 11, 2, 1, 3, 5, 1, 3, 0, 0), # 160 (4, 4, 4, 3, 8, 3, 1, 4, 7, 1, 1, 2, 0, 4, 8, 5, 5, 8, 5, 2, 1, 2, 4, 0, 1, 0), # 161 (6, 3, 6, 10, 7, 4, 2, 1, 4, 2, 1, 0, 0, 4, 6, 6, 1, 12, 3, 3, 1, 2, 1, 0, 0, 0), # 162 (10, 10, 4, 3, 3, 6, 3, 4, 2, 0, 0, 0, 0, 5, 8, 4, 4, 8, 3, 1, 4, 2, 2, 0, 0, 0), # 163 (11, 2, 8, 9, 6, 1, 5, 1, 3, 0, 3, 0, 0, 1, 5, 3, 6, 4, 2, 1, 2, 4, 4, 0, 1, 0), # 164 (5, 6, 9, 7, 3, 1, 3, 2, 2, 1, 1, 0, 0, 10, 6, 3, 3, 9, 2, 1, 2, 4, 3, 0, 0, 0), # 165 (6, 4, 4, 5, 6, 4, 1, 1, 4, 1, 0, 3, 0, 3, 5, 4, 0, 10, 5, 0, 3, 4, 0, 1, 1, 0), # 166 (6, 1, 6, 7, 4, 1, 1, 0, 2, 2, 0, 1, 0, 4, 6, 3, 5, 7, 5, 1, 2, 4, 3, 0, 0, 0), # 167 (1, 5, 3, 5, 4, 3, 2, 3, 0, 1, 0, 0, 0, 8, 5, 6, 5, 4, 2, 0, 0, 1, 5, 1, 0, 0), # 168 (9, 3, 10, 5, 6, 0, 5, 3, 3, 1, 0, 0, 0, 8, 6, 2, 4, 9, 0, 2, 2, 4, 2, 0, 0, 0), # 169 (10, 3, 8, 7, 5, 4, 3, 3, 1, 0, 0, 0, 0, 1, 3, 3, 2, 5, 3, 3, 1, 0, 3, 0, 2, 0), # 170 (4, 2, 6, 3, 1, 2, 2, 0, 4, 0, 1, 0, 0, 8, 5, 4, 4, 3, 0, 2, 1, 5, 3, 0, 0, 0), # 171 (3, 5, 4, 1, 0, 2, 3, 0, 4, 2, 1, 0, 0, 5, 6, 2, 1, 5, 3, 3, 4, 2, 2, 0, 0, 0), # 172 (7, 2, 3, 4, 5, 2, 1, 2, 3, 3, 0, 0, 0, 7, 3, 2, 5, 4, 0, 1, 1, 1, 2, 0, 1, 0), # 173 (5, 2, 5, 6, 1, 4, 3, 0, 4, 0, 1, 0, 0, 7, 6, 1, 7, 4, 1, 2, 1, 2, 1, 1, 0, 0), # 174 (4, 3, 7, 2, 5, 3, 1, 3, 3, 0, 1, 0, 0, 3, 5, 7, 2, 6, 1, 0, 0, 1, 1, 1, 0, 0), # 175 (2, 2, 5, 2, 7, 1, 3, 1, 3, 2, 0, 0, 0, 11, 5, 1, 1, 5, 2, 1, 1, 1, 4, 0, 0, 0), # 176 (1, 0, 7, 3, 5, 3, 1, 0, 2, 1, 0, 1, 0, 6, 1, 1, 4, 4, 2, 2, 2, 2, 4, 0, 0, 0), # 177 (1, 4, 5, 3, 3, 0, 0, 2, 1, 1, 0, 0, 0, 5, 3, 3, 1, 6, 2, 1, 0, 2, 1, 1, 0, 0), # 178 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179 ) station_arriving_intensity = ( (5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0 (5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1 (5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2 (6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3 (6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4 (6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5 (6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6 (7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7 (7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8 (7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9 (8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10 (8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 11 (8.667088817726812, 9.489881405573698, 8.95070006742254, 10.675827168075612, 9.563219293573377, 5.391850545151869, 7.1179591795908115, 7.982199221043521, 10.460503079426179, 6.794507560025572, 7.224861604080934, 8.411664284420068, 8.734563961465534), # 12 (8.902268288217876, 9.74214343986562, 9.188628184802662, 10.959673758460044, 9.819877431709601, 5.5352062818482235, 7.307062923246056, 8.193991500089481, 10.738561310012932, 6.974998797102904, 7.416990890908869, 8.63523254363492, 8.966837737406735), # 13 (9.120837768766716, 9.975659960507588, 9.408875319349146, 11.222426674868792, 10.05790139104599, 5.667908086666534, 7.482114821904661, 8.390042668435246, 10.995954642409421, 7.142078656252334, 7.594842732261284, 8.84218680647856, 9.181849875652563), # 14 (9.321501903268855, 10.188989479504217, 9.610082028117542, 11.462464196805985, 10.275849331148308, 5.789137058744912, 7.642034201749626, 8.569143135599756, 11.23109473373482, 7.29471557214749, 7.757319337619419, 9.031249705859171, 9.37827342756938), # 15 (9.5029653356198, 10.380690508860132, 9.790888868163425, 11.678164603775716, 10.472279411582333, 5.898074297221459, 7.785740388963976, 8.73008331110196, 11.442393241108286, 7.431877979461996, 7.9033229164645125, 9.20114387468494, 9.554781444523545), # 16 (9.663932709715075, 10.549321560579946, 9.949936396542352, 11.867906175282112, 10.645749791913838, 5.993900901234285, 7.9121527097307105, 8.871653604460818, 11.628261821648984, 7.552534312869467, 8.031755678277799, 9.350591945864055, 9.710046977881415), # 17 (9.803108669450204, 10.693441146668274, 10.08586517030988, 12.030067190829278, 10.794818631708589, 6.075797969921503, 8.020190490232851, 8.99264442519526, 11.787112132476096, 7.6556530070435365, 8.141519832540508, 9.478316552304715, 9.842743079009345), # 18 (9.919197858720699, 10.811607779129744, 10.197315746521578, 12.163025929921314, 10.918044090532366, 6.142946602421208, 8.108773056653394, 9.091846182824245, 11.917355830708779, 7.740202496657828, 8.231517588733878, 9.583040326915096, 9.951542799273696), # 19 (10.010904921422082, 10.902379969968962, 10.282928682233003, 12.265160672062354, 11.013984327950944, 6.194527897871518, 8.176819735175362, 9.168049286866717, 12.017404573466198, 7.805151216385958, 8.30065115633915, 9.66348590260339, 10.035119190040824), # 20 (10.076934501449866, 10.964316231190558, 10.341344534499719, 12.334849696756486, 11.081197503530088, 6.229722955410535, 8.223249851981759, 9.220044146841623, 12.085670017867521, 7.849467600901555, 8.34782274483756, 9.718375912277793, 10.092145302677078), # 21 (10.115991242699579, 10.995975074799144, 10.371203860377285, 12.370471283507836, 11.118241776835575, 6.247712874176367, 8.2469827332556, 9.246621172267915, 12.120563821031915, 7.872120084878242, 8.37193456371034, 9.74643298884649, 10.121294188548827), # 22 (10.13039336334264, 10.999723593964335, 10.374923182441702, 12.374930812757203, 11.127732056032597, 6.25, 8.249804002259339, 9.249493827160494, 12.124926234567901, 7.874792272519433, 8.37495803716174, 9.749897576588934, 10.125), # 23 (10.141012413034153, 10.997537037037038, 10.374314814814815, 12.374381944444446, 11.133107613614852, 6.25, 8.248253812636166, 9.2455, 12.124341666666666, 7.87315061728395, 8.37462457912458, 9.749086419753086, 10.125), # 24 (10.15140723021158, 10.993227023319616, 10.373113854595337, 12.373296039094651, 11.138364945594503, 6.25, 8.24519890260631, 9.237654320987655, 12.123186728395062, 7.869918838591678, 8.373963399426362, 9.747485139460448, 10.125), # 25 (10.161577019048034, 10.986859396433472, 10.371336762688616, 12.37168544238683, 11.143503868421105, 6.25, 8.240686718308721, 9.226104938271606, 12.1214762345679, 7.865150708733425, 8.372980483850855, 9.745115683584821, 10.125), # 26 (10.171520983716636, 10.978499999999999, 10.369, 12.369562499999999, 11.148524198544214, 6.25, 8.234764705882354, 9.211, 12.119225, 7.858899999999999, 8.371681818181818, 9.742, 10.125), # 27 (10.181238328390501, 10.968214677640603, 10.366120027434842, 12.366939557613168, 11.153425752413401, 6.25, 8.22748031146615, 9.192487654320988, 12.116447839506172, 7.851220484682213, 8.370073388203018, 9.73816003657979, 10.125), # 28 (10.19072825724275, 10.95606927297668, 10.362713305898492, 12.36382896090535, 11.15820834647822, 6.25, 8.218880981199066, 9.170716049382715, 12.113159567901235, 7.842165935070874, 8.368161179698216, 9.733617741197987, 10.125), # 29 (10.199989974446497, 10.94212962962963, 10.358796296296296, 12.360243055555555, 11.162871797188236, 6.25, 8.209014161220043, 9.145833333333332, 12.109375, 7.83179012345679, 8.365951178451178, 9.728395061728394, 10.125), # 30 (10.209022684174858, 10.926461591220852, 10.354385459533608, 12.356194187242798, 11.167415920993008, 6.25, 8.19792729766804, 9.117987654320988, 12.105108950617284, 7.820146822130773, 8.363449370245666, 9.722513946044812, 10.125), # 31 (10.217825590600954, 10.909131001371742, 10.349497256515773, 12.35169470164609, 11.171840534342095, 6.25, 8.185667836681999, 9.087327160493828, 12.100376234567902, 7.807289803383631, 8.360661740865444, 9.715996342021034, 10.125), # 32 (10.226397897897897, 10.890203703703703, 10.344148148148149, 12.346756944444444, 11.176145453685063, 6.25, 8.172283224400871, 9.054, 12.095191666666667, 7.793272839506173, 8.357594276094275, 9.708864197530863, 10.125), # 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139 (8.444997677025897, 6.801298785613425, 8.647308796115487, 10.012835745039444, 9.92156467458478, 5.445445909628379, 5.400580673463397, 5.795978747590996, 10.388476938898332, 5.420555393811186, 6.358719326686294, 7.562716378308592, 8.849082182878314), # 140 (8.40124789791083, 6.758192090297021, 8.61893536244316, 9.973361639464553, 9.885718431458253, 5.431951548851015, 5.370940751340795, 5.78451993660327, 10.36727094769768, 5.396366207481251, 6.331940092293238, 7.532266740021525, 8.816572659637913), # 141 (8.356443573718156, 6.714206505295466, 8.58972123390407, 9.93279651265672, 9.848855495829087, 5.418015949208927, 5.340622296506126, 5.772639944200211, 10.345333550752942, 5.371546573421828, 6.304441160030697, 7.500982076994594, 8.783144913695466), # 142 (8.310548338670674, 6.669281647641981, 8.559626999172925, 9.891087380676975, 9.810945743940529, 5.403611506868106, 5.3095798374875685, 5.760292330179432, 10.322615447166147, 5.3460546332438525, 6.276174032075593, 7.4688164188730894, 8.748768651462617), # 143 (8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144 (8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145 (8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146 (8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 147 (8.063435698409021, 6.428454865758288, 8.394574836251083, 9.663527205335797, 9.604640409120561, 5.323590520492767, 5.1419159781226265, 5.689914528726257, 10.195588798172029, 5.207045296958447, 6.1216180331039824, 7.29316423943207, 8.561599320349941), # 148 (8.010231316911412, 6.37662632989083, 8.358372326536443, 9.613841379606303, 9.55981716078387, 5.3057933414453995, 5.105574593092441, 5.673785940749067, 10.167151135875338, 5.176640493898813, 6.08772327670891, 7.254747233294191, 8.520895795019237), # 149 (7.955681464118564, 6.323435840603979, 8.321013831352694, 9.562640661132138, 9.513736229890526, 5.287334092861249, 5.0681909035756005, 5.656864649737456, 10.137587660650752, 5.1452703759971765, 6.0527208398597425, 7.215127439578763, 8.479031698279647), # 150 (7.899749774253275, 6.268823014930954, 8.282459939374542, 9.50987206597433, 9.466367492683776, 5.268185170906305, 5.029719438100283, 5.639104215489043, 10.106849071600289, 5.112893084864478, 6.016562224733405, 7.174258887931072, 8.435976736542818), # 151 (7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152 (7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153 (7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154 (7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155 (7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156 (7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157 (7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158 (7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159 (7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160 (7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161 (7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162 (6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163 (6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164 (6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165 (6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166 (6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167 (6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168 (5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169 (5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170 (5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171 (5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172 (5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173 (4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174 (4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175 (4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176 (4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177 (3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_arriving_acc = ( (14, 8, 6, 8, 8, 0, 0, 1, 4, 0, 0, 2, 0, 7, 3, 3, 4, 3, 2, 0, 1, 0, 2, 2, 0, 0), # 0 (19, 13, 12, 13, 11, 2, 5, 3, 7, 5, 1, 3, 0, 14, 9, 8, 7, 11, 7, 1, 3, 2, 3, 2, 0, 0), # 1 (24, 22, 16, 15, 18, 6, 7, 4, 9, 5, 1, 3, 0, 24, 12, 13, 8, 17, 10, 4, 5, 5, 4, 2, 0, 0), # 2 (33, 25, 21, 17, 25, 7, 13, 6, 11, 9, 1, 4, 0, 36, 18, 15, 16, 25, 20, 5, 7, 5, 6, 4, 0, 0), # 3 (41, 32, 28, 27, 30, 7, 15, 7, 14, 10, 1, 4, 0, 44, 23, 19, 17, 28, 22, 8, 7, 5, 7, 5, 0, 0), # 4 (46, 36, 33, 30, 34, 10, 18, 12, 16, 10, 4, 4, 0, 50, 28, 26, 20, 35, 23, 10, 10, 8, 8, 8, 2, 0), # 5 (53, 40, 42, 39, 37, 14, 24, 13, 18, 13, 5, 4, 0, 56, 34, 32, 27, 37, 25, 14, 11, 8, 11, 9, 3, 0), # 6 (60, 45, 51, 42, 44, 18, 25, 18, 19, 14, 7, 5, 0, 60, 37, 40, 29, 45, 26, 19, 13, 10, 12, 9, 3, 0), # 7 (74, 51, 55, 51, 53, 21, 27, 18, 20, 16, 7, 5, 0, 65, 43, 45, 34, 52, 29, 20, 18, 14, 13, 10, 3, 0), # 8 (83, 58, 59, 61, 59, 24, 30, 18, 21, 18, 7, 5, 0, 69, 48, 51, 36, 59, 34, 21, 18, 18, 14, 12, 4, 0), # 9 (91, 63, 63, 70, 68, 29, 35, 24, 24, 18, 7, 6, 0, 75, 57, 58, 41, 65, 40, 22, 22, 22, 17, 13, 4, 0), # 10 (102, 69, 72, 80, 73, 32, 41, 25, 25, 19, 7, 6, 0, 86, 63, 61, 43, 76, 45, 24, 23, 26, 20, 14, 4, 0), # 11 (111, 73, 79, 85, 82, 36, 44, 27, 28, 20, 9, 7, 0, 91, 73, 70, 48, 88, 49, 29, 29, 30, 26, 18, 4, 0), # 12 (121, 80, 88, 93, 90, 38, 47, 30, 31, 21, 11, 8, 0, 98, 83, 74, 52, 93, 55, 31, 31, 35, 28, 20, 5, 0), # 13 (130, 93, 98, 105, 96, 39, 52, 37, 36, 26, 12, 8, 0, 105, 89, 78, 63, 102, 62, 36, 31, 38, 31, 22, 5, 0), # 14 (144, 103, 112, 112, 106, 45, 54, 42, 41, 26, 13, 9, 0, 109, 95, 85, 70, 107, 65, 44, 36, 44, 37, 23, 5, 0), # 15 (154, 112, 123, 120, 111, 48, 59, 47, 44, 27, 13, 11, 0, 117, 104, 93, 77, 119, 68, 46, 38, 51, 40, 24, 5, 0), # 16 (167, 120, 136, 128, 118, 51, 62, 49, 49, 28, 13, 11, 0, 120, 112, 98, 82, 124, 70, 49, 42, 56, 43, 25, 7, 0), # 17 (175, 130, 143, 135, 121, 56, 68, 57, 53, 31, 16, 12, 0, 131, 120, 104, 95, 137, 73, 57, 42, 62, 44, 26, 8, 0), # 18 (180, 143, 155, 146, 127, 61, 72, 59, 54, 33, 19, 13, 0, 139, 125, 112, 103, 144, 82, 61, 44, 62, 48, 29, 8, 0), # 19 (195, 156, 160, 150, 134, 63, 78, 63, 58, 37, 21, 16, 0, 157, 132, 120, 107, 150, 85, 64, 47, 66, 50, 30, 9, 0), # 20 (204, 171, 165, 159, 143, 66, 85, 65, 64, 37, 22, 18, 0, 167, 141, 130, 118, 158, 90, 66, 52, 69, 51, 31, 9, 0), # 21 (214, 180, 177, 164, 154, 69, 89, 70, 70, 38, 22, 19, 0, 176, 150, 133, 122, 173, 98, 71, 54, 72, 53, 34, 12, 0), # 22 (222, 187, 180, 170, 156, 72, 91, 74, 75, 40, 24, 21, 0, 182, 155, 138, 123, 185, 104, 73, 57, 75, 57, 35, 16, 0), # 23 (228, 194, 189, 179, 164, 78, 94, 80, 81, 42, 24, 22, 0, 194, 166, 146, 132, 194, 114, 75, 59, 81, 57, 37, 20, 0), # 24 (243, 207, 197, 188, 173, 81, 99, 85, 86, 42, 26, 23, 0, 202, 175, 157, 141, 202, 121, 77, 60, 83, 62, 39, 20, 0), # 25 (253, 213, 206, 192, 180, 84, 103, 91, 89, 43, 26, 24, 0, 210, 184, 162, 148, 211, 126, 84, 63, 84, 65, 39, 21, 0), # 26 (261, 223, 213, 198, 189, 91, 105, 93, 96, 44, 26, 26, 0, 217, 192, 168, 154, 215, 131, 88, 65, 88, 70, 41, 22, 0), # 27 (272, 232, 219, 208, 195, 93, 108, 98, 103, 47, 27, 27, 0, 223, 200, 172, 160, 219, 134, 90, 67, 90, 71, 41, 24, 0), # 28 (283, 241, 226, 220, 203, 97, 108, 105, 104, 48, 27, 27, 0, 231, 207, 181, 169, 223, 137, 97, 70, 94, 71, 42, 26, 0), # 29 (292, 251, 238, 233, 211, 103, 110, 107, 106, 50, 28, 28, 0, 241, 217, 185, 175, 230, 144, 103, 72, 98, 73, 43, 28, 0), # 30 (303, 260, 248, 237, 216, 103, 116, 113, 107, 51, 29, 29, 0, 253, 226, 191, 180, 233, 155, 107, 74, 105, 79, 47, 28, 0), # 31 (321, 269, 257, 243, 225, 103, 117, 114, 114, 54, 31, 29, 0, 262, 231, 197, 183, 241, 158, 108, 76, 107, 82, 50, 30, 0), # 32 (331, 279, 267, 249, 227, 115, 124, 114, 121, 56, 34, 29, 0, 267, 243, 205, 184, 255, 164, 112, 78, 113, 85, 51, 30, 0), # 33 (346, 288, 278, 260, 237, 119, 129, 115, 130, 59, 34, 30, 0, 276, 250, 212, 189, 261, 171, 114, 79, 116, 89, 51, 32, 0), # 34 (360, 301, 287, 278, 244, 125, 131, 119, 134, 60, 37, 31, 0, 285, 260, 219, 197, 265, 175, 119, 83, 118, 90, 54, 35, 0), # 35 (368, 306, 293, 282, 255, 128, 137, 124, 140, 62, 37, 33, 0, 300, 266, 225, 203, 270, 181, 122, 87, 121, 93, 55, 37, 0), # 36 (374, 314, 308, 286, 261, 131, 140, 128, 141, 66, 37, 33, 0, 315, 274, 236, 208, 282, 182, 128, 88, 126, 99, 56, 37, 0), # 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164 (1597, 1307, 1289, 1334, 1161, 541, 535, 503, 602, 265, 181, 110, 0, 1558, 1271, 1041, 800, 1215, 650, 552, 417, 585, 454, 251, 117, 0), # 165 (1603, 1311, 1293, 1339, 1167, 545, 536, 504, 606, 266, 181, 113, 0, 1561, 1276, 1045, 800, 1225, 655, 552, 420, 589, 454, 252, 118, 0), # 166 (1609, 1312, 1299, 1346, 1171, 546, 537, 504, 608, 268, 181, 114, 0, 1565, 1282, 1048, 805, 1232, 660, 553, 422, 593, 457, 252, 118, 0), # 167 (1610, 1317, 1302, 1351, 1175, 549, 539, 507, 608, 269, 181, 114, 0, 1573, 1287, 1054, 810, 1236, 662, 553, 422, 594, 462, 253, 118, 0), # 168 (1619, 1320, 1312, 1356, 1181, 549, 544, 510, 611, 270, 181, 114, 0, 1581, 1293, 1056, 814, 1245, 662, 555, 424, 598, 464, 253, 118, 0), # 169 (1629, 1323, 1320, 1363, 1186, 553, 547, 513, 612, 270, 181, 114, 0, 1582, 1296, 1059, 816, 1250, 665, 558, 425, 598, 467, 253, 120, 0), # 170 (1633, 1325, 1326, 1366, 1187, 555, 549, 513, 616, 270, 182, 114, 0, 1590, 1301, 1063, 820, 1253, 665, 560, 426, 603, 470, 253, 120, 0), # 171 (1636, 1330, 1330, 1367, 1187, 557, 552, 513, 620, 272, 183, 114, 0, 1595, 1307, 1065, 821, 1258, 668, 563, 430, 605, 472, 253, 120, 0), # 172 (1643, 1332, 1333, 1371, 1192, 559, 553, 515, 623, 275, 183, 114, 0, 1602, 1310, 1067, 826, 1262, 668, 564, 431, 606, 474, 253, 121, 0), # 173 (1648, 1334, 1338, 1377, 1193, 563, 556, 515, 627, 275, 184, 114, 0, 1609, 1316, 1068, 833, 1266, 669, 566, 432, 608, 475, 254, 121, 0), # 174 (1652, 1337, 1345, 1379, 1198, 566, 557, 518, 630, 275, 185, 114, 0, 1612, 1321, 1075, 835, 1272, 670, 566, 432, 609, 476, 255, 121, 0), # 175 (1654, 1339, 1350, 1381, 1205, 567, 560, 519, 633, 277, 185, 114, 0, 1623, 1326, 1076, 836, 1277, 672, 567, 433, 610, 480, 255, 121, 0), # 176 (1655, 1339, 1357, 1384, 1210, 570, 561, 519, 635, 278, 185, 115, 0, 1629, 1327, 1077, 840, 1281, 674, 569, 435, 612, 484, 255, 121, 0), # 177 (1656, 1343, 1362, 1387, 1213, 570, 561, 521, 636, 279, 185, 115, 0, 1634, 1330, 1080, 841, 1287, 676, 570, 435, 614, 485, 256, 121, 0), # 178 (1656, 1343, 1362, 1387, 1213, 570, 561, 521, 636, 279, 185, 115, 0, 1634, 1330, 1080, 841, 1287, 676, 570, 435, 614, 485, 256, 121, 0), # 179 ) passenger_arriving_rate = ( (5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 0 (5.354327152019974, 5.399222302966028, 4.629455492775127, 4.968858189957462, 3.948326891649491, 1.9518237573581576, 2.209734470631847, 2.066464051210712, 2.164081775444303, 1.0547451730692876, 0.7471826893260219, 0.4351013884011963, 0.0, 5.419791647439855, 4.786115272413158, 3.73591344663011, 3.164235519207862, 4.328163550888606, 2.8930496716949965, 2.209734470631847, 1.3941598266843982, 1.9741634458247455, 1.6562860633191545, 0.9258910985550255, 0.49083839117872996, 0.0), # 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28 (10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29 (10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30 (10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 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34 (10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35 (10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36 (10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37 (10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38 (10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 39 (10.279880423902163, 9.82532435985368, 8.579318129858253, 9.225718557098766, 7.468608692451679, 3.6458333333333335, 4.025536088921165, 3.649369855967079, 4.015830761316872, 1.9167981252857802, 1.3881315729309558, 0.8036313062033228, 0.0, 10.125, 8.83994436823655, 6.940657864654778, 5.750394375857339, 8.031661522633744, 5.1091177983539104, 4.025536088921165, 2.604166666666667, 3.7343043462258394, 3.0752395190329227, 1.7158636259716507, 0.8932113054412438, 0.0), # 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115 (9.273179873237634, 7.0850892578507265, 7.648776824986561, 8.077999612699802, 7.036792350922519, 3.330080178417474, 3.0133024087639466, 2.5272417970412473, 3.6020604464092765, 1.480198339612387, 1.1519343218785802, 0.6802102664572789, 0.0, 9.43260725975589, 7.482312931030067, 5.7596716093929015, 4.44059501883716, 7.204120892818553, 3.5381385158577463, 3.0133024087639466, 2.3786286988696244, 3.5183961754612594, 2.6926665375666015, 1.5297553649973124, 0.6440990234409752, 0.0), # 116 (9.243829442823772, 7.04830504435266, 7.632093362938321, 8.056111029444182, 7.02313718419674, 3.323555141118853, 2.9998041194738763, 2.5208740813181603, 3.5959129388307343, 1.4747943502270324, 1.1479250164705472, 0.6781935872119792, 0.0, 9.413047004858225, 7.46012945933177, 5.739625082352736, 4.424383050681096, 7.1918258776614685, 3.5292237138454245, 2.9998041194738763, 2.3739679579420376, 3.51156859209837, 2.6853703431480613, 1.5264186725876645, 0.6407550040320601, 0.0), # 117 (9.214297245985211, 7.0120436072457135, 7.615497548340306, 8.03435789116525, 7.009286202577227, 3.317159168158581, 2.9865122214896576, 2.51479527536254, 3.5899160365213114, 1.46948389924369, 1.143989752709904, 0.6761992632811126, 0.0, 9.393295573379024, 7.438191896092237, 5.71994876354952, 4.40845169773107, 7.179832073042623, 3.5207133855075567, 2.9865122214896576, 2.369399405827558, 3.5046431012886137, 2.678119297055084, 1.5230995096680613, 0.6374585097496104, 0.0), # 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157 (7.464680946405239, 5.353748694041236, 6.644659961585297, 6.817327186238432, 6.062454070580665, 2.9814309445183143, 2.3625533604639286, 2.285748730145572, 3.2838873638663655, 1.213341479072786, 0.9542659587564906, 0.570633297016195, 0.0, 8.096485859415345, 6.276966267178143, 4.771329793782452, 3.640024437218358, 6.567774727732731, 3.200048222203801, 2.3625533604639286, 2.129593531798796, 3.0312270352903323, 2.2724423954128112, 1.3289319923170593, 0.48670442673102154, 0.0), # 158 (7.382286766978402, 5.282809876299521, 6.58894818200249, 6.7529828690913405, 6.010127539854418, 2.95965229467081, 2.334106381692858, 2.2696723053184926, 3.2621424204073812, 1.2005702485246865, 0.9445694892698324, 0.5651135436402591, 0.0, 8.025427646920194, 6.216248980042849, 4.722847446349162, 3.601710745574059, 6.5242848408147625, 3.17754122744589, 2.334106381692858, 2.114037353336293, 3.005063769927209, 2.250994289697114, 1.3177896364004982, 0.4802554432999565, 0.0), # 159 (7.284872094904309, 5.202172001162321, 6.51826746496324, 6.673933132806645, 5.94428008756453, 2.9308657560278157, 2.301121874191892, 2.248166328969728, 3.2324750757428835, 1.1853014129657236, 0.9328765847682567, 0.5583751624073207, 0.0, 7.93642060889358, 6.142126786480525, 4.664382923841283, 3.55590423889717, 6.464950151485767, 3.147432860557619, 2.301121874191892, 2.0934755400198686, 2.972140043782265, 2.2246443776022153, 1.3036534929926482, 0.47292472737839286, 0.0), # 160 (7.17322205458596, 5.11236079574043, 6.4333724765919245, 6.5809293778175455, 5.865595416188075, 2.895420057582683, 2.263840723003438, 2.2215002221290754, 3.1952765889996724, 1.1676645482927346, 0.9192902757666179, 0.5504806224089643, 0.0, 7.830374044819097, 6.055286846498606, 4.596451378833089, 3.5029936448782033, 6.390553177999345, 3.1101003109807053, 2.263840723003438, 2.0681571839876307, 2.9327977080940375, 2.1936431259391824, 1.2866744953183848, 0.46476007234003913, 0.0), # 161 (7.048121770426357, 5.013901987144635, 6.335017883012913, 6.474723004557244, 5.7747572282021356, 2.853663928328766, 2.2225038131699044, 2.1899434058263343, 3.150938219304545, 1.147789230402558, 0.9039135927797701, 0.5414923927367745, 0.0, 7.708197254180333, 5.956416320104519, 4.519567963898851, 3.4433676912076736, 6.30187643860909, 3.065920768156868, 2.2225038131699044, 2.03833137737769, 2.8873786141010678, 2.158241001519082, 1.2670035766025827, 0.4558092715586033, 0.0), # 162 (6.9103563668284975, 4.90732130248573, 6.223958350350585, 6.35606541345895, 5.672449226083792, 2.8059460972594175, 2.1773520297337003, 2.153765301091302, 3.0998512257843016, 1.1258050351920315, 0.8868495663225682, 0.5314729424823361, 0.0, 7.570799536460879, 5.846202367305696, 4.43424783161284, 3.3774151055760937, 6.199702451568603, 3.015271421527823, 2.1773520297337003, 2.0042472123281554, 2.836224613041896, 2.118688471152984, 1.2447916700701172, 0.4461201184077937, 0.0), # 163 (6.760710968195384, 4.793144468874502, 6.100948544729314, 6.225708004955863, 5.559355112310126, 2.752615293367992, 2.128626257737233, 2.113235328953779, 3.0424068675657407, 1.1018415385579923, 0.8682012269098661, 0.5204847407372336, 0.0, 7.419090191144328, 5.725332148109569, 4.34100613454933, 3.305524615673976, 6.0848137351314815, 2.9585294605352903, 2.128626257737233, 1.9661537809771372, 2.779677556155063, 2.075236001651955, 1.2201897089458629, 0.43574040626131844, 0.0), # 164 (6.599970698930017, 4.671897213421746, 5.966743132273474, 6.084402179481189, 5.436158589358215, 2.694020245647842, 2.076567382222911, 2.068622910443561, 2.9789964037756596, 1.0760283163972786, 0.8480716050565187, 0.5085902565930517, 0.0, 7.25397851771427, 5.594492822523568, 4.2403580252825925, 3.2280849491918353, 5.957992807551319, 2.8960720746209856, 2.076567382222911, 1.9243001754627442, 2.7180792946791077, 2.0281340598270634, 1.1933486264546949, 0.42471792849288603, 0.0), # 165 (6.428920683435397, 4.54410526323825, 5.82209677910744, 5.932899337468126, 5.3035433597051425, 2.630509683092322, 2.021416288233143, 2.020197466590449, 2.9100110935408576, 1.0484949446067282, 0.8265637312773799, 0.49585195914137514, 0.0, 7.0763738156542955, 5.454371550555126, 4.1328186563869, 3.145484833820184, 5.820022187081715, 2.8282764532266285, 2.021416288233143, 1.8789354879230868, 2.6517716798525712, 1.9776331124893758, 1.1644193558214881, 0.41310047847620457, 0.0), # 166 (6.248346046114523, 4.410294345434805, 5.667764151355587, 5.771950879349882, 5.1621931258279865, 2.562432334694784, 1.9634138608103373, 1.9682284184242402, 2.835842195988133, 1.0193709990831787, 0.8037806360873045, 0.48233231747378824, 0.0, 6.887185384447996, 5.30565549221167, 4.0189031804365225, 3.058112997249536, 5.671684391976266, 2.755519785793936, 1.9634138608103373, 1.8303088104962744, 2.5810965629139933, 1.9239836264499612, 1.1335528302711175, 0.4009358495849823, 0.0), # 167 (6.059031911370395, 4.270990187122201, 5.50449991514229, 5.60230820555966, 5.012791590203827, 2.490136929448583, 1.902800984996902, 1.9129851869747332, 2.7568809702442847, 0.9887860557234682, 0.7798253500011468, 0.468093800681876, 0.0, 6.6873225235789615, 5.149031807500635, 3.8991267500057343, 2.9663581671704042, 5.513761940488569, 2.6781792617646265, 1.902800984996902, 1.7786692353204163, 2.5063957951019136, 1.867436068519887, 1.100899983028458, 0.3882718351929274, 0.0), # 168 (5.861763403606015, 4.1267185154112305, 5.333058736591924, 5.4247227165306615, 4.856022455309747, 2.413972196347072, 1.8398185458352458, 1.8547371932717271, 2.6735186754361124, 0.9568696904244344, 0.7548009035337614, 0.45319887785722274, 0.0, 6.477694532530785, 4.985187656429449, 3.774004517668807, 2.8706090712733023, 5.347037350872225, 2.596632070580418, 1.8398185458352458, 1.724265854533623, 2.4280112276548733, 1.808240905510221, 1.066611747318385, 0.3751562286737483, 0.0), # 169 (5.657325647224384, 3.978005057412684, 5.154195281828863, 5.23994581269609, 4.692569423622822, 2.334286864383604, 1.7747074283677764, 1.7937538583450197, 2.5861465706904125, 0.9237514790829147, 0.7288103272000027, 0.4377100180914133, 0.0, 6.259210710787055, 4.814810199005545, 3.6440516360000137, 2.7712544372487433, 5.172293141380825, 2.5112554016830275, 1.7747074283677764, 1.6673477602740028, 2.346284711811411, 1.7466486042320304, 1.0308390563657726, 0.36163682340115316, 0.0), # 170 (5.4465037666285, 3.82537554023735, 4.968664216977482, 5.048728894489152, 4.523116197620137, 2.2514296625515327, 1.7077085176369027, 1.7303046032244096, 2.495155915133985, 0.8895609975957474, 0.7019566515147247, 0.4216896904760322, 0.0, 6.032780357831365, 4.638586595236354, 3.509783257573624, 2.6686829927872413, 4.99031183026797, 2.4224264445141737, 1.7077085176369027, 1.6081640446796661, 2.2615580988100685, 1.6829096314963843, 0.9937328433954964, 0.3477614127488501, 0.0), # 171 (5.230082886221365, 3.6693556909960217, 4.777220208162156, 4.851823362343048, 4.348346479778769, 2.1657493198442115, 1.6390626986850327, 1.664658848939696, 2.4009379678936282, 0.8544278218597702, 0.6743429069927823, 0.4052003641026643, 0.0, 5.799312773147303, 4.457204005129307, 3.3717145349639117, 2.56328346557931, 4.8018759357872565, 2.3305223885155746, 1.6390626986850327, 1.5469637998887225, 2.1741732398893845, 1.6172744541143496, 0.9554440416324312, 0.3335777900905475, 0.0), # 172 (5.00884813040598, 3.510471236799489, 4.58061792150726, 4.649980616690982, 4.168943972575801, 2.077594565254994, 1.5690108565545748, 1.5970860165206766, 2.303883988096141, 0.8184815277718206, 0.6460721241490297, 0.3883045080628938, 0.0, 5.5597172562184625, 4.271349588691831, 3.2303606207451483, 2.4554445833154612, 4.607767976192282, 2.235920423128947, 1.5690108565545748, 1.483996118039281, 2.0844719862879004, 1.5499935388969943, 0.916123584301452, 0.31913374879995354, 0.0), # 173 (4.783584623585344, 3.349247904758541, 4.3796120231371685, 4.443952057966156, 3.9855923784883105, 1.987314127777233, 1.4977938762879377, 1.5278555269971503, 2.204385234868321, 0.7818516912287369, 0.6172473334983214, 0.37106459144830567, 0.0, 5.314903106528433, 4.081710505931362, 3.0862366674916064, 2.34555507368621, 4.408770469736642, 2.1389977377960103, 1.4977938762879377, 1.4195100912694523, 1.9927961892441552, 1.4813173526553853, 0.8759224046274336, 0.3044770822507765, 0.0), # 174 (4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175 (4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176 (4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177 (3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 0.0), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_allighting_rate = ( (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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4 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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91 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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166 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 8991598675325360468762009371570610170 #index for seed sequence child child_seed_index = ( 1, # 0 4, # 1 )

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0

6

9cd2819a2d5876b9825a6bc3bf2faaf0e39d122a

39

py

Python

web/views/__init__.py

zhengbigbig/python_demo

29b62bea9e5abaa02e51744a926f722d1b99ec8d

[ "MIT" ]

null

web/views/__init__.py

zhengbigbig/python_demo

29b62bea9e5abaa02e51744a926f722d1b99ec8d

[ "MIT" ]

null

web/views/__init__.py

zhengbigbig/python_demo

29b62bea9e5abaa02e51744a926f722d1b99ec8d

[ "MIT" ]

null

from myproject.web.views.index import *

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0.820513

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1

0

6

9cdc483f749eb3e693ef71ab058dc7b97c0d4d13

144

py

Python

ipylabel/__init__.py

crabtr26/ipylabel

9a7062b0438fcddc3253553ecbde3dc38826b68c

[ "MIT" ]

null

ipylabel/__init__.py

crabtr26/ipylabel

9a7062b0438fcddc3253553ecbde3dc38826b68c

[ "MIT" ]

null

ipylabel/__init__.py

crabtr26/ipylabel

9a7062b0438fcddc3253553ecbde3dc38826b68c

[ "MIT" ]

null

from ._version import get_versions __version__ = get_versions()['version'] del get_versions from .dashboards import * from .templates import *

20.571429

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0.791667

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6

9cf66bbc0e66a28b8f4bbc3615d42c92e08b9447

45

py

Python

cuisine/measurements/__init__.py

j3kstrum/recipe-tweaker

941d2fed87fc2c568b29d3b3baee3f8ebfe4daca

[ "MIT" ]

null

cuisine/measurements/__init__.py

j3kstrum/recipe-tweaker

941d2fed87fc2c568b29d3b3baee3f8ebfe4daca

[ "MIT" ]

null

cuisine/measurements/__init__.py

j3kstrum/recipe-tweaker

941d2fed87fc2c568b29d3b3baee3f8ebfe4daca

[ "MIT" ]

null

from cuisine.measurements.grams import Grams

22.5

44

0.866667

6

45

6.5

0.833333

0

0.088889

45

1

45

0.95122

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true

0

1

0

1

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6

149e8613b21e72b7b2a5636cf491440044f41f7b

105

py

Python

app/main/__init__.py

Anitha987/Ani-Newlight-pro

c5f481678daa403c304f0908bb759f5f3c583f11

[ "Unlicense" ]

null

app/main/__init__.py

Anitha987/Ani-Newlight-pro

c5f481678daa403c304f0908bb759f5f3c583f11

[ "Unlicense" ]

null

app/main/__init__.py

Anitha987/Ani-Newlight-pro

c5f481678daa403c304f0908bb759f5f3c583f11

[ "Unlicense" ]

null

from flask import Blueprint main = Blueprint('main', __name__) from . import errors from . import views

26.25

34

0.761905

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105

5.428571

0.571429

0.342105

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26.25

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1aeeb5b00fb20bb4f6db0a7ae809aa8ca3804439

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py

Python

command_test/git_test.py

WommyInStandingPosition/YtbDataApiRelated

4856ad2ee5be49bb74c79c3d6649f9d1fdbdc85d

[ "MIT" ]

null

command_test/git_test.py

WommyInStandingPosition/YtbDataApiRelated

4856ad2ee5be49bb74c79c3d6649f9d1fdbdc85d

[ "MIT" ]

null

command_test/git_test.py

WommyInStandingPosition/YtbDataApiRelated

4856ad2ee5be49bb74c79c3d6649f9d1fdbdc85d

[ "MIT" ]

null

#commit from ubuntu #commit from windows

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2109467cb9b5633152168295ce8f8cb0a9678618

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py

Python

algomorphism/figures/__init__.py

efth-mcl/algomorphism

5b69d19701e450020a539196215575706e7ff675

[ "MIT" ]

null

algomorphism/figures/__init__.py

efth-mcl/algomorphism

5b69d19701e450020a539196215575706e7ff675

[ "MIT" ]

null

algomorphism/figures/__init__.py

efth-mcl/algomorphism

5b69d19701e450020a539196215575706e7ff675

[ "MIT" ]

null

from . import nn from . import graphs

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dcecdffebfb8d38f44a2af140ef98c13c581c8dc

18,285

py

Python

tests/iqfeed/test_iqfeed_postgres_cache.py

alexanu/atpy

3f4b5cfe7de7633ef053d2feaddae421806a9799

[ "MIT" ]

24

2018-03-22T06:22:11.000Z

2022-03-14T09:04:44.000Z

tests/iqfeed/test_iqfeed_postgres_cache.py

alexanu/atpy

3f4b5cfe7de7633ef053d2feaddae421806a9799

[ "MIT" ]

null

tests/iqfeed/test_iqfeed_postgres_cache.py

alexanu/atpy

3f4b5cfe7de7633ef053d2feaddae421806a9799

[ "MIT" ]

9

2018-03-22T06:22:11.000Z

2020-09-19T16:47:13.000Z

import shutil import tempfile import unittest from pandas.util.testing import assert_frame_equal from sqlalchemy import create_engine import atpy.data.cache.lmdb_cache as lmdb_cache import atpy.data.iqfeed.iqfeed_history_provider as iq_history from atpy.data.cache.postgres_cache import * from atpy.data.iqfeed.iqfeed_level_1_provider import get_fundamentals, get_splits_dividends from atpy.data.iqfeed.iqfeed_postgres_cache import * class TestPostgresCache(unittest.TestCase): """ Test InfluxDBCache """ def test_update_to_latest_intraday(self): with IQFeedHistoryProvider(num_connections=2) as history: table_name = 'bars_test' try: url = 'postgresql://postgres:postgres@localhost:5432/test' engine = create_engine(url) con = psycopg2.connect(url) con.autocommit = True cur = con.cursor() cur.execute(create_bars.format(table_name)) cur.execute(bars_indices.format(table_name)) bgn_prd = datetime.datetime(2017, 3, 1).astimezone(tz.gettz('US/Eastern')) end_prd = datetime.datetime(2017, 3, 2) filters = (BarsInPeriodFilter(ticker="IBM", bgn_prd=bgn_prd, end_prd=end_prd, interval_len=3600, ascend=True, interval_type='s'), BarsInPeriodFilter(ticker="AAPL", bgn_prd=bgn_prd, end_prd=end_prd, interval_len=3600, ascend=True, interval_type='s')) data = [history.request_data(f, sync_timestamps=False) for f in filters] filters_no_limit = (BarsInPeriodFilter(ticker="IBM", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s'), BarsInPeriodFilter(ticker="AAPL", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s'), BarsInPeriodFilter(ticker="AMZN", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s')) for datum, f in zip(data, filters): del datum['timestamp'] del datum['total_volume'] del datum['number_of_trades'] datum['symbol'] = f.ticker datum['interval'] = '3600_s' datum = datum.tz_localize(None) datum.to_sql(table_name, con=engine, if_exists='append') latest_old = pd.read_sql("select symbol, max(timestamp) as timestamp from {0} group by symbol".format(table_name), con=con, index_col=['symbol'])['timestamp'] update_to_latest(url=url, bars_table=table_name, symbols={('AAPL', 3600, 's'), ('AMZN', 3600, 's')}, noncache_provider=noncache_provider(history), time_delta_back=relativedelta(years=10)) data_no_limit = [history.request_data(f, sync_timestamps=False) for f in filters_no_limit] latest_current = pd.read_sql("select symbol, max(timestamp) as timestamp from {0} group by symbol".format(table_name), con=con, index_col=['symbol'])['timestamp'] self.assertEqual(len(latest_current), len(latest_old) + 1) self.assertEqual(len([k for k in latest_current.keys() & latest_old.keys()]) + 1, len(latest_current)) for k in latest_current.keys() & latest_old.keys(): self.assertGreater(latest_current[k], latest_old[k]) cache_data_no_limit = [request_bars(conn=engine, bars_table=table_name, interval_len=3600, interval_type='s', symbol=f.ticker, bgn_prd=f.bgn_prd.astimezone(tz.tzutc()) + relativedelta(microseconds=1)) for f in filters_no_limit] for df1, df2 in zip(data_no_limit, cache_data_no_limit): del df1['timestamp'] del df1['total_volume'] del df1['number_of_trades'] del df1['symbol'] del df1['volume'] del df2['volume'] assert_frame_equal(df1, df2, check_exact=False, check_less_precise=True) finally: con.cursor().execute("DROP TABLE IF EXISTS {0};".format(table_name)) def test_update_to_latest_daily(self): url = 'postgresql://postgres:postgres@localhost:5432/test' con = psycopg2.connect(url) con.autocommit = True with IQFeedHistoryProvider(num_connections=2) as history: table_name = 'bars_test' try: engine = create_engine(url) cur = con.cursor() cur.execute(create_bars.format(table_name)) cur.execute(bars_indices.format(table_name)) bgn_prd = datetime.datetime(2017, 3, 1).date() end_prd = datetime.datetime(2017, 3, 2).date() filters = (BarsDailyForDatesFilter(ticker="IBM", bgn_dt=bgn_prd, end_dt=end_prd, ascend=True), BarsDailyForDatesFilter(ticker="AAPL", bgn_dt=bgn_prd, end_dt=end_prd, ascend=True)) filters_no_limit = (BarsDailyForDatesFilter(ticker="IBM", bgn_dt=bgn_prd, end_dt=None, ascend=True), BarsDailyForDatesFilter(ticker="AAPL", bgn_dt=bgn_prd, end_dt=None, ascend=True), BarsDailyForDatesFilter(ticker="AMZN", bgn_dt=bgn_prd, end_dt=None, ascend=True)) data = [history.request_data(f, sync_timestamps=False) for f in filters] for datum, f in zip(data, filters): del datum['timestamp'] del datum['open_interest'] datum['symbol'] = f.ticker datum['interval'] = '1_d' datum = datum.tz_localize(None) datum.to_sql(table_name, con=engine, if_exists='append') latest_old = pd.read_sql("select symbol, max(timestamp) as timestamp from {0} group by symbol".format(table_name), con=con, index_col=['symbol'])['timestamp'] update_to_latest(url=url, bars_table=table_name, symbols={('AAPL', 1, 'd'), ('AMZN', 1, 'd')}, noncache_provider=noncache_provider(history), time_delta_back=relativedelta(years=10)) latest_current = pd.read_sql("select symbol, max(timestamp) as timestamp from {0} group by symbol".format(table_name), con=con, index_col=['symbol'])['timestamp'] self.assertEqual(len(latest_current), len(latest_old) + 1) self.assertEqual(len([k for k in latest_current.keys() & latest_old.keys()]) + 1, len(latest_current)) for k in latest_current.keys() & latest_old.keys(): self.assertGreater(latest_current[k], latest_old[k]) data_no_limit = [history.request_data(f, sync_timestamps=False) for f in filters_no_limit] cache_data_no_limit = [request_bars(conn=engine, bars_table=table_name, interval_len=1, interval_type='d', symbol=f.ticker, bgn_prd=datetime.datetime.combine(f.bgn_dt, datetime.datetime.min.time()).astimezone(tz.tzutc()) + relativedelta(microseconds=1)) for f in filters_no_limit] for df1, df2 in zip(data_no_limit, cache_data_no_limit): del df1['timestamp'] del df1['open_interest'] del df1['symbol'] assert_frame_equal(df1, df2) finally: con.cursor().execute("DROP TABLE IF EXISTS {0};".format(table_name)) def test_bars_in_period(self): with IQFeedHistoryProvider(num_connections=2) as history: tmpdir = tempfile.mkdtemp() table_name = 'bars_test' url = 'postgresql://postgres:postgres@localhost:5432/test' engine = create_engine(url) con = psycopg2.connect(url) con.autocommit = True try: cur = con.cursor() cur.execute(create_bars.format(table_name)) cur.execute(bars_indices.format(table_name)) now = datetime.datetime.now() bgn_prd = datetime.datetime(now.year - 1, 3, 1).astimezone(tz.gettz('US/Eastern')) filters = (BarsInPeriodFilter(ticker="IBM", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s'), BarsInPeriodFilter(ticker="AAPL", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s')) data = [history.request_data(f, sync_timestamps=False) for f in filters] for datum, f in zip(data, filters): del datum['timestamp'] del datum['total_volume'] del datum['number_of_trades'] datum['symbol'] = f.ticker datum['interval'] = '3600_s' datum = datum.tz_localize(None) datum.to_sql(table_name, con=engine, if_exists='append') now = datetime.datetime.now() # test all symbols no cache bgn_prd = datetime.datetime(now.year - 1, 3, 1, tzinfo=tz.gettz('UTC')) bars_in_period = BarsInPeriodProvider(conn=con, interval_len=3600, interval_type='s', bars_table=table_name, bgn_prd=bgn_prd, delta=relativedelta(days=30), overlap=relativedelta(microseconds=-1)) for i, df in enumerate(bars_in_period): self.assertFalse(df.empty) lmdb_cache.write(bars_in_period.current_cache_key(), df, tmpdir) start, end = bars_in_period._periods[bars_in_period._deltas] self.assertGreaterEqual(df.index[0][0], start) self.assertGreater(end, df.index[-1][0]) self.assertGreater(end, df.index[0][0]) self.assertEqual(i, len(bars_in_period._periods) - 1) self.assertGreater(i, 0) # test all symbols cache bgn_prd = datetime.datetime(now.year - 1, 3, 1, tzinfo=tz.gettz('UTC')) bars_in_period = BarsInPeriodProvider(conn=con, interval_len=3600, interval_type='s', bars_table=table_name, bgn_prd=bgn_prd, delta=relativedelta(days=30), overlap=relativedelta(microseconds=-1), cache=functools.partial(lmdb_cache.read_pickle, lmdb_path=tmpdir)) for i, df in enumerate(bars_in_period): self.assertFalse(df.empty) start, end = bars_in_period._periods[bars_in_period._deltas] self.assertGreaterEqual(df.index[0][0], start) self.assertGreater(end, df.index[-1][0]) self.assertGreater(end, df.index[0][0]) self.assertEqual(i, len(bars_in_period._periods) - 1) self.assertGreater(i, 0) # test symbols group bgn_prd = datetime.datetime(now.year - 1, 3, 1, tzinfo=tz.gettz('UTC')) bars_in_period = BarsInPeriodProvider(conn=con, interval_len=3600, interval_type='s', bars_table=table_name, bgn_prd=bgn_prd, symbol=['AAPL', 'IBM'], delta=relativedelta(days=30), overlap=relativedelta(microseconds=-1)) for i, df in enumerate(bars_in_period): self.assertFalse(df.empty) start, end = bars_in_period._periods[bars_in_period._deltas] self.assertGreaterEqual(df.index[0][0], start) self.assertGreater(end, df.index[-1][0]) self.assertGreater(end, df.index[0][0]) self.assertEqual(i, len(bars_in_period._periods) - 1) self.assertGreater(i, 0) finally: shutil.rmtree(tmpdir) con.cursor().execute("DROP TABLE IF EXISTS bars_test;") def test_bars_by_symbol(self): with IQFeedHistoryProvider(num_connections=2) as history: tmpdir = tempfile.mkdtemp() table_name = 'bars_test' url = 'postgresql://postgres:postgres@localhost:5432/test' engine = create_engine(url) con = psycopg2.connect(url) con.autocommit = True try: cur = con.cursor() cur.execute(create_bars.format(table_name)) cur.execute(bars_indices.format(table_name)) iq_history.BarsFilter(ticker="IBM", interval_len=3600, interval_type='s', max_bars=1000) filters = (iq_history.BarsFilter(ticker="IBM", interval_len=3600, interval_type='s', max_bars=1000), iq_history.BarsFilter(ticker="AAPL", interval_len=3600, interval_type='s', max_bars=1000)) data = [history.request_data(f, sync_timestamps=False) for f in filters] for datum, f in zip(data, filters): del datum['timestamp'] del datum['total_volume'] del datum['number_of_trades'] datum['symbol'] = f.ticker datum['interval'] = '3600_s' datum = datum.tz_localize(None) datum.to_sql(table_name, con=engine, if_exists='append') bars_per_symbol = BarsBySymbolProvider(conn=con, records_per_query=1000, interval_len=3600, interval_type='s', table_name=table_name) for i, df in enumerate(bars_per_symbol): self.assertEqual(len(df), 1000) self.assertEqual(i, 1) bars_per_symbol = BarsBySymbolProvider(conn=con, records_per_query=100, interval_len=3600, interval_type='s', table_name=table_name) for i, df in enumerate(bars_per_symbol): self.assertEqual(len(df), 1000) self.assertEqual(i, 1) bars_per_symbol = BarsBySymbolProvider(conn=con, records_per_query=2000, interval_len=3600, interval_type='s', table_name=table_name) for i, df in enumerate(bars_per_symbol): self.assertEqual(len(df), 2000) self.assertTrue(isinstance(df.index, pd.MultiIndex)) self.assertEqual(i, 0) finally: shutil.rmtree(tmpdir) con.cursor().execute("DROP TABLE IF EXISTS bars_test;") def test_symbol_counts(self): with IQFeedHistoryProvider(num_connections=2) as history: table_name = 'bars_test' url = 'postgresql://postgres:postgres@localhost:5432/test' engine = create_engine(url) con = psycopg2.connect(url) con.autocommit = True try: cur = con.cursor() cur.execute(create_bars.format(table_name)) cur.execute(bars_indices.format(table_name)) now = datetime.datetime.now() bgn_prd = datetime.datetime(now.year - 1, 3, 1).astimezone(tz.gettz('US/Eastern')) filters = (BarsInPeriodFilter(ticker="IBM", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s'), BarsInPeriodFilter(ticker="AAPL", bgn_prd=bgn_prd, end_prd=None, interval_len=3600, ascend=True, interval_type='s')) data = [history.request_data(f, sync_timestamps=False) for f in filters] for datum, f in zip(data, filters): del datum['timestamp'] del datum['total_volume'] del datum['number_of_trades'] datum['symbol'] = f.ticker datum['interval'] = '3600_s' datum = datum.tz_localize(None) datum.to_sql(table_name, con=engine, if_exists='append') counts = request_symbol_counts(conn=con, interval_len=3600, interval_type='s', symbol=["IBM", "AAPL"], bars_table=table_name) self.assertEqual(counts.size, 2) self.assertGreater(counts.min(), 0) finally: con.cursor().execute("DROP TABLE IF EXISTS bars_test;") def test_update_adjustments(self): table_name = 'adjustments_test' url = 'postgresql://postgres:postgres@localhost:5432/test' con = psycopg2.connect(url) con.autocommit = True try: adjustments = get_splits_dividends({'IBM', 'AAPL', 'GOOG', 'MSFT'}) cur = con.cursor() cur.execute(create_json_data.format(table_name)) insert_df_json(con, table_name, adjustments) now = datetime.datetime.now() df = request_adjustments(con, table_name, symbol=['IBM', 'AAPL', 'MSFT', 'GOOG'], bgn_prd=datetime.datetime(year=now.year - 30, month=now.month, day=now.day), end_prd=datetime.datetime(year=now.year + 2, month=now.month, day=now.day), provider='iqfeed') self.assertFalse(df.empty) assert_frame_equal(adjustments, df) finally: con.cursor().execute("DROP TABLE IF EXISTS {0};".format(table_name)) def test_update_fundamentals(self): table_name = 'iqfeed_fundamentals' url = 'postgresql://postgres:postgres@localhost:5432/test' con = psycopg2.connect(url) con.autocommit = True try: cur = con.cursor() cur.execute(create_json_data.format(table_name)) fundamentals = get_fundamentals({'IBM', 'AAPL', 'GOOG', 'MSFT'}) update_fundamentals(conn=con, fundamentals=fundamentals, table_name=table_name) fund = request_fundamentals(con, symbol=['IBM', 'AAPL', 'GOOG'], table_name=table_name) self.assertTrue(isinstance(fund, pd.DataFrame)) self.assertEqual(len(fund), 3) finally: con.cursor().execute("DROP TABLE IF EXISTS {0};".format(table_name)) if __name__ == '__main__': unittest.main()

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0.067164

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null

0

1

0

null

0

6

dcfda73a1a60904d3c75248e8a385f90860d61d2

307

py

Python

EgammaAnalysis/Configuration/python/EgammaAnalysis_SkimPaths_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-08-09T08:42:11.000Z

EgammaAnalysis/Configuration/python/EgammaAnalysis_SkimPaths_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

null

EgammaAnalysis/Configuration/python/EgammaAnalysis_SkimPaths_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-04-03T19:23:27.000Z

import FWCore.ParameterSet.Config as cms # # # Collection of all Skim paths for Egamma # # #Egamma skims from EgammaAnalysis.CSA07Skims.EgammaVeryHighEtPath_cff import * from EgammaAnalysis.CSA07Skims.EgammaZPlusEMOrJetPath_cff import * from EgammaAnalysis.CSA07Skims.EgammaWPlusEMOrJetPath_cff import *

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1

0

1

0

6

0d397c4113d5797102f2c5179fffb46a6a272d2d

31

py

Python

xignitegh/__init__.py

xignite-python/xignitegh

d7f53bfaeb8ee35bdc89f61e64408622426cd452

[ "MIT" ]

null

xignitegh/__init__.py

xignite-python/xignitegh

d7f53bfaeb8ee35bdc89f61e64408622426cd452

[ "MIT" ]

null

xignitegh/__init__.py

xignite-python/xignitegh

d7f53bfaeb8ee35bdc89f61e64408622426cd452

[ "MIT" ]

null

from .xignitegh import Xignite

15.5

30

0.83871

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null

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1

0

1

0

1

0

6

b4c0895b2cce68b6203afd5937fba2abec2ba78e

5,080

py

Python

content_interactions_stats/utils.py

aaboffill/django-content-interactions

8ea881e46cc6d5c375542939bb69d2980efdec23

[ "BSD-3-Clause" ]

null

content_interactions_stats/utils.py

aaboffill/django-content-interactions

8ea881e46cc6d5c375542939bb69d2980efdec23

[ "BSD-3-Clause" ]

null

content_interactions_stats/utils.py

aaboffill/django-content-interactions

8ea881e46cc6d5c375542939bb69d2980efdec23

[ "BSD-3-Clause" ]

null

# coding=utf-8 from django.db.models import F def item_like_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.likes = F('likes')+1 stats_obj.save() def item_dislike_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) if not created: stats_obj.likes = F('likes')-1 stats_obj.save() def item_new_rating_process(item_id, item_content_type, rating): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.ratings = F('ratings')+1 if rating == 5: stats_obj.rating_5_count = F('rating_5_count')+1 elif rating == 4: stats_obj.rating_4_count = F('rating_4_count')+1 elif rating == 3: stats_obj.rating_3_count = F('rating_3_count')+1 elif rating == 2: stats_obj.rating_2_count = F('rating_2_count')+1 elif rating == 1: stats_obj.rating_1_count = F('rating_1_count')+1 stats_obj.save() def item_updated_rating_process(item_id, item_content_type, old_rating, rating): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) if not created and (old_rating != rating): # remove ald rating if old_rating == 5: stats_obj.rating_5_count = F('rating_5_count')-1 elif old_rating == 4: stats_obj.rating_4_count = F('rating_4_count')-1 elif old_rating == 3: stats_obj.rating_3_count = F('rating_3_count')-1 elif old_rating == 2: stats_obj.rating_2_count = F('rating_2_count')-1 elif old_rating == 1: stats_obj.rating_1_count = F('rating_1_count')-1 # add new rating if rating == 5: stats_obj.rating_5_count = F('rating_5_count')+1 elif rating == 4: stats_obj.rating_4_count = F('rating_4_count')+1 elif rating == 3: stats_obj.rating_3_count = F('rating_3_count')+1 elif rating == 2: stats_obj.rating_2_count = F('rating_2_count')+1 elif rating == 1: stats_obj.rating_1_count = F('rating_1_count')+1 elif created: stats_obj.ratings = F('ratings')+1 # add new rating if rating == 5: stats_obj.rating_5_count = F('rating_5_count')+1 elif rating == 4: stats_obj.rating_4_count = F('rating_4_count')+1 elif rating == 3: stats_obj.rating_3_count = F('rating_3_count')+1 elif rating == 2: stats_obj.rating_2_count = F('rating_2_count')+1 elif rating == 1: stats_obj.rating_1_count = F('rating_1_count')+1 stats_obj.save() def item_marked_favorite_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.favorite_marks = F('favorite_marks')+1 stats_obj.save() def item_unmarked_favorite_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) if not created: stats_obj.favorite_marks = F('favorite_marks')-1 stats_obj.save() def item_shared_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.shares = F('shares')+1 stats_obj.save() def item_visited_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.visits = F('visits')+1 stats_obj.save() def item_denounced_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.denounces = F('denounces')+1 stats_obj.save() def item_denounce_removed_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.denounces = F('denounces')-1 stats_obj.save() def item_got_comment_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) stats_obj.comments = F('comments')+1 stats_obj.save() def item_comment_deleted_process(item_id, item_content_type): from models import Stats stats_obj, created = Stats.objects.get_or_create(object_pk=item_id, content_type=item_content_type) if not created: stats_obj.comments = F('comments')-1 stats_obj.save()

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784

5,080

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0.079082

0.137129

0.110193

0.062443

0.932048

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0

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5,080

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0

null

0

1

0

null

0

6

b4d42c2b729596de6b3a27fa1ca7c1ab4e0dbfff

2,600

py

Python

django-proj/gencert/models.py

Nurul-GC/GCertificate

c58d5c6bb4500c11934da091a8f1fbee11da99e0

[ "BSD-3-Clause" ]

1

2021-09-04T13:03:30.000Z

django-proj/gencert/models.py

Nurul-GC/GCertificate

c58d5c6bb4500c11934da091a8f1fbee11da99e0

[ "BSD-3-Clause" ]

null

django-proj/gencert/models.py

Nurul-GC/GCertificate

c58d5c6bb4500c11934da091a8f1fbee11da99e0

[ "BSD-3-Clause" ]

1

2022-03-03T07:48:13.000Z

# ****************************************************************************** # Copyright (c) 2021 Nurul-GC. * # ****************************************************************************** from django.db import models # Create your models here. class CP(models.Model): """certificate of perticipation model""" LANG = ( ('English', 'English'), ('Português', 'Português') ) TYPES = ( ('Certificado de Participação', 'Certificado de Participação'), ('Certificate of Participation', 'Certificate of Participation') ) logo = models.FileField(upload_to=f'media/logos/') language = models.CharField(choices=LANG, max_length=10, help_text='Escolha o idioma do certificado!') document_title = models.CharField(max_length=50, help_text='Digite um titulo para o documento!') document_subject = models.CharField(choices=TYPES, max_length=30, help_text='Escolha o tema do certificado!') company_name = models.CharField(max_length=50, help_text='Digite o nome da instituição emissora!') student_name = models.CharField(max_length=50, help_text='Digite o nome do estudante!') description = models.TextField(max_length=1000, help_text='Digite o conteudo descritivo e motivo desta certificação!') def __str__(self): return f"{self.student_name.name} certified by {self.company_name.name}" class TC(models.Model): """training certificate model""" LANG = ( ('English', 'English'), ('Português', 'Português') ) TYPES = ( ('Training Certificate', 'Training Certificate'), ('Certificado de Formação', 'Certificado de Formação'), ) logo = models.FileField(upload_to=f'media/logos/') language = models.CharField(choices=LANG, max_length=10, help_text='Escolha o idioma do certificado!') document_title = models.CharField(max_length=50, help_text='Digite um titulo para o documento!') document_subject = models.CharField(choices=TYPES, max_length=30, help_text='Escolha o tema do certificado!') company_name = models.CharField(max_length=50, help_text='Digite o nome da instituição emissora!') student_name = models.CharField(max_length=50, help_text='Digite o nome do estudante!') course_name = models.CharField(max_length=50, help_text='Digite o nome do curso!') description = models.TextField(max_length=1000, help_text='Digite o conteudo descritivo e motivo desta certificação!') def __str__(self): return f"{self.student_name.name} certified by {self.company_name.name}"

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0

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0

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false

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0

null

0

1

0

null

0

1

0

6

b4e119937a26d4fce5685276bfb1c8666535b542

177

py

Python

simulariumio/springsalad/__init__.py

allen-cell-animated/simularium-conversion

47ba9a5a8105cf5cd36592d859252df642b1f1f9

[ "Apache-2.0" ]

null

simulariumio/springsalad/__init__.py

allen-cell-animated/simularium-conversion

47ba9a5a8105cf5cd36592d859252df642b1f1f9

[ "Apache-2.0" ]

null

simulariumio/springsalad/__init__.py

allen-cell-animated/simularium-conversion

47ba9a5a8105cf5cd36592d859252df642b1f1f9

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from .springsalad_converter import SpringsaladConverter # noqa: F401 from .springsalad_data import SpringsaladData # noqa: F401

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6

b4ea53d596fe2d3b961668e3a3637776c22ba150

10,031

py

Python

tests/networkapi_test.py

globocom/globomap-loader-napi

a3621e1396c14730d131315ae50ce2d7c1228765

[ "Apache-2.0" ]

3

2017-08-31T13:35:49.000Z

2019-07-11T11:37:21.000Z

tests/networkapi_test.py

globocom/globomap-loader-napi

a3621e1396c14730d131315ae50ce2d7c1228765

[ "Apache-2.0" ]

4

2017-09-06T22:34:49.000Z

2019-07-11T12:33:12.000Z

tests/networkapi_test.py

globocom/globomap-loader-napi

a3621e1396c14730d131315ae50ce2d7c1228765

[ "Apache-2.0" ]

2

2017-09-06T20:46:33.000Z

2019-07-11T12:12:38.000Z

""" Copyright 2018 Globo.com Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest2 from mock import patch from globomap_driver_napi.networkapi import NetworkAPI from globomap_driver_napi.networkapi import NetworkAPIClientError class TestNetworkAPI(unittest2.TestCase): """Test using client networkapi""" def test_get_vip(self): """Test assert called get vip""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiVipRequest.get').start() requests_mock.return_value = {'vips': [{'id': 1}]} napi = NetworkAPI() data = napi.get_vip(1) requests_mock.assert_called_once_with(ids=[1], kind='details') self.assertDictEqual(data, {'id': 1}) def test_get_pool_by_member_id(self): """Test assert called get pool by member""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiPool.search').start() requests_mock.return_value = {'server_pools': [{'id': 1}]} napi = NetworkAPI() data = napi.get_pool_by_member_id(1) requests_mock.assert_called_once_with( search={'extends_search': [{'serverpoolmember': 1}]}, kind='details') self.assertDictEqual(data, {'id': 1}) def test_get_pool(self): """Test assert called get pool""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiPool.get').start() requests_mock.return_value = {'server_pools': [{'id': 1}]} napi = NetworkAPI() data = napi.get_pool(1) requests_mock.assert_called_once_with(ids=[1], kind='details') self.assertDictEqual(data, {'id': 1}) def test_get_vip_by_portpool_id(self): """Test assert called get vip by port""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiVipRequest.search').start() requests_mock.return_value = {'vips': [{'id': 1}]} napi = NetworkAPI() data = napi.get_vip_by_portpool_id(1) requests_mock.assert_called_once_with( search={'extends_search': [ {'viprequestport__viprequestportpool': 1}]}, kind='details') self.assertDictEqual(data, {'id': 1}) def test_get_network_ipv4_id(self): """Test assert called get network v4""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiNetworkIPv4.get').start() requests_mock.return_value = {'networks': [{'id': 1}]} napi = NetworkAPI() data = napi.get_network_ipv4_id(1) requests_mock.assert_called_once_with( ids=[1], fields=['id', 'network_type__details', 'active', 'networkv4', 'vlan__basic'] ) self.assertDictEqual(data, {'id': 1}) def test_get_network_ipv6_id(self): """Test assert called get network v6""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiNetworkIPv6.get').start() requests_mock.return_value = {'networks': [{'id': 1}]} napi = NetworkAPI() data = napi.get_network_ipv6_id(1) requests_mock.assert_called_once_with( ids=[1], fields=['id', 'network_type__details', 'active', 'networkv6', 'vlan__basic'] ) self.assertDictEqual(data, {'id': 1}) def test_get_environment(self): """Test assert called get environment""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiEnvironment.get').start() requests_mock.return_value = {'environments': [{'id': 1}]} napi = NetworkAPI() data = napi.get_environment(1) requests_mock.assert_called_once_with( ids=[1], include=['default_vrf__details', 'father_environment__basic']) self.assertDictEqual(data, {'id': 1}) def test_get_vlan(self): """Test assert called get vlan""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiVlan.get').start() requests_mock.return_value = {'vlans': [{'id': 1}]} napi = NetworkAPI() data = napi.get_vlan(1) requests_mock.assert_called_once_with( ids=[1], include=['environment__basic'] ) self.assertDictEqual(data, {'id': 1}) def test_get_ipv4_by_ip_equipment_id(self): """Test assert called get ipv4 by ip eqpt""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiIPv4.search').start() requests_mock.return_value = {'ips': [{'id': 1}]} napi = NetworkAPI() data = napi.get_ipv4_by_ip_equipment_id(1) requests_mock.assert_called_once_with( search={'extends_search': [{'ipequipamento': 1}]}, fields=['networkipv4', 'ip_formated'] ) self.assertDictEqual(data, {'id': 1}) def test_get_ipv6_by_ip_equipment_id(self): """Test assert called get ipv6 by ip eqpt""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiIPv6.search').start() requests_mock.return_value = {'ips': [{'id': 1}]} napi = NetworkAPI() data = napi.get_ipv6_by_ip_equipment_id(1) requests_mock.assert_called_once_with( search={'extends_search': [{'ipv6equipament': 1}]}, fields=['networkipv6', 'ip_formated'] ) self.assertDictEqual(data, {'id': 1}) def test_get_equipment(self): """Test assert called get equipment""" requests_mock = patch( 'networkapiclient.ClientFactory.ApiEquipment.get').start() requests_mock.return_value = {'equipments': [{'id': 1}]} napi = NetworkAPI() data = napi.get_equipment(1) requests_mock.assert_called_once_with( ids=[1], include=['equipment_type__details', 'ipv4__basic__networkipv4', 'ipv6__basic__networkipv6'] ) self.assertDictEqual(data, {'id': 1}) def test_exception_get_pool(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiPool.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_pool(3) self.assertEqual(data, []) def test_exception_get_pool_by_member_id(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiPool.search').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_pool_by_member_id(3) self.assertEqual(data, []) def test_exception_get_vip(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiVipRequest.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_vip(3) self.assertEqual(data, []) def test_exception_get_vip_by_portpool_id(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiVipRequest.search').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_vip_by_portpool_id(3) self.assertEqual(data, []) def test_exception_get_equipment(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiEquipment.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_equipment(3) self.assertEqual(data, []) def test_exception_get_network_ipv4_id(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiNetworkIPv4.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_network_ipv4_id(3) self.assertEqual(data, []) def test_exception_get_network_ipv6_id(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiNetworkIPv6.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_network_ipv6_id(3) self.assertEqual(data, []) def test_exception_get_ipv4_by_ip_equipment_id(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiIPv4.search').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_ipv4_by_ip_equipment_id(3) self.assertEqual(data, []) def test_exception_get_ipv6_by_ip_equipment_id(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiIPv6.search').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_ipv6_by_ip_equipment_id(3) self.assertEqual(data, []) def test_exception_get_vlan(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiVlan.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_vlan(3) self.assertEqual(data, []) def test_exception_get_environment(self): requests_mock = patch( 'networkapiclient.ClientFactory.ApiEnvironment.get').start() requests_mock.side_effect = NetworkAPIClientError('') napi = NetworkAPI() data = napi.get_environment(3) self.assertEqual(data, [])

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0.634334

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10,031

5.586175

0.141935

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0.061706

0.119782

0.846065

0.829401

0.776275

0.738492

0.720343

0.656327

0

0.01356

0.250125

10,031

301

82

33.325581

0.79221

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0

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0

0.176516

0.133111

0

0.167513

1

0.111675

false

0

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0.137056

0

null

0

1

0

null

0

6

370245eb46b3d78a6658c94fe9720f31d963d0c3

3,129

py

Python

stocksdata/indices.py

LatticeData/StockDataPythonClient

cb6a6fe348af7efc9f396a8b8a36e3452eb3630a

[ "Apache-2.0" ]

4

2021-08-09T03:37:41.000Z

2021-08-17T21:42:54.000Z

stocksdata/indices.py

LatticeData/StockDataPythonClient

cb6a6fe348af7efc9f396a8b8a36e3452eb3630a

[ "Apache-2.0" ]

null

stocksdata/indices.py

LatticeData/StockDataPythonClient

cb6a6fe348af7efc9f396a8b8a36e3452eb3630a

[ "Apache-2.0" ]

1

2021-08-24T22:50:42.000Z

from stocksdata.util.ttlcache import daily_cache from stocksdata.util.get import * ## indices #################################################################################################################################### @daily_cache def s_and_p_composition(): return get_json("/market/index/s-and-p-five-hundred").get("stocks") @daily_cache def nasdaq_composition(): return get_json("/market/index/nasdaq-composite").get("stocks") @daily_cache def russel_one_thousand_composition(): return get_json("/market/index/russel-one-thousand").get("stocks") @daily_cache def amex_oil_composition(): return get_json("/market/index/amex-oil-index").get("stocks") @daily_cache def djia_composition(): return get_json("/market/index/djia").get("stocks") @daily_cache def bbc_global_composition(): return get_json("/market/index/bbc-global-thirty").get("stocks") @daily_cache def ibovespa_composition(): return get_json("/market/index/ibovespa").get("stocks") @daily_cache def ftse100_composition(): return get_json("/market/index/ftse-one-hundred").get("stocks") @daily_cache def ftse250_composition(): return get_json("/market/index/ftse-two-fifty").get("stocks") @daily_cache def nifty_fifty_composition(): return get_json("/market/index/nifty-fifty").get("stocks") @daily_cache def djgt_fifty_composition(): return get_json("/market/index/dow-jones-global-titans-fifty").get("stocks") @daily_cache def dax_thirty_composition(): return get_json("/market/index/dax-thirty").get("stocks") @daily_cache def euro100_composition(): return get_json("/market/index/euro-next-one-hundred").get("stocks") @daily_cache def djta_composition(): return get_json("/market/index/down-jones-transportation-average").get("stocks") @daily_cache def djua_composition(): return get_json("/market/index/down-jones-utility-average").get("stocks") @daily_cache def nasdaq100_composition(): return get_json("/market/index/nasdaq-one-hundred").get("stocks") @daily_cache def phlx_semi_composition(): return get_json("/market/index/phlx-semiconductor").get("stocks") @daily_cache def phlx_gold_composition(): return get_json("/market/index/phlx-gold-and-silver").get("stocks") @daily_cache def nikkei225_composition(): return get_json("/market/index/nikkei-two-twenty-five").get("stocks") @daily_cache def omx_nordic_composition(): return get_json("/market/index/omx-nordic-forty").get("stocks") @daily_cache def nyse_arca_composition(): return get_json("/market/index/nyse-arca-major-market-index").get("stocks") @daily_cache def s_and_p_400(): return get_json("/market/index/s-and-p-four-hundred").get("stocks") @daily_cache def s_and_p_100(): return get_json("/market/index/s-and-p-one-hundred").get("stocks") @daily_cache def s_and_p_global_100(): return get_json("/market/index/s-and-p-global-one-hundred").get("stocks") @daily_cache def russel_2000_composition(): return get_json("/market/index/russel-two-thousand").get("stocks") @daily_cache def niftybank(): return get_json("/market/index/niftybank").get("stocks")

28.972222

143

0.715564

431

3,129

4.965197

0.169374

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0.157944

0.230841

0.791589

0.728972

0.390654

0.142523

0.061682

0

0.009811

0.087888

3,129

107

144

29.242991

0.740014

0.002237

0

0.325

0

0.332552

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0

1

0.325

true

0

0.025

0.325

0.675

0

null

0

1

0

1

0

null

0

1

0

1

0

6

37125fc3aab221406d9eb5643d8daff7c6fd5e54

41

py

Python

cnn_example/config/__init__.py

4620511/cnn-example

b89b235d68569fac4769f50ee2f49ee16c750f08

[ "MIT" ]

1

2019-02-15T18:06:34.000Z

cnn_example/config/__init__.py

4620511/cnn-example

b89b235d68569fac4769f50ee2f49ee16c750f08

[ "MIT" ]

null

cnn_example/config/__init__.py

4620511/cnn-example

b89b235d68569fac4769f50ee2f49ee16c750f08

[ "MIT" ]

null

from .config import Config # noqa: F401

20.5

40

0.731707

6

41

5

0.833333

0

0.090909

0.195122

41

1

41

0.818182

0.243902

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3716214a35a72bf207b32c97da51cac5b38213be

207

py

Python

nose.py

RoadRunner11/roadrunner

d0f3cda83aa17974cb3d68f3f96adcfb7f05caf9

[ "Apache-2.0" ]

null

nose.py

RoadRunner11/roadrunner

d0f3cda83aa17974cb3d68f3f96adcfb7f05caf9

[ "Apache-2.0" ]

null

nose.py

RoadRunner11/roadrunner

d0f3cda83aa17974cb3d68f3f96adcfb7f05caf9

[ "Apache-2.0" ]

null

from nose.tools import assert_equal def get_string(x, y): return str(x)+str(y) print("passed") assert_equal(get_string(4, 5), '45') assert_equal(get_string('hello', 'world'), 'helloworld')

18.818182

57

0.671498

32

207

4.15625

0.65625

0.24812

0.210526

0.300752

0

0.023121

0.164251

207

10

58

20.7

0.745665

0

0.142132

0

0.5

1

0.166667

false

0.166667

0.5

0.166667

0

null

1

0

null

0

1

0

1

0

1

0

6

371e7c650fa37e71272786050053b3df7fa1b51b

38

py

Python

cfnet/__init__.py

bchidest/CFNet

d8e73bd33cfd62edf76abed99efb9e4bdd7dbc48

[ "Apache-2.0" ]

26

2019-06-02T06:18:21.000Z

2022-02-03T07:32:49.000Z

cfnet/__init__.py

JaspreetSinghMaan/CFNet

d8e73bd33cfd62edf76abed99efb9e4bdd7dbc48

[ "Apache-2.0" ]

null

cfnet/__init__.py

JaspreetSinghMaan/CFNet

d8e73bd33cfd62edf76abed99efb9e4bdd7dbc48

[ "Apache-2.0" ]

5

2019-03-11T08:22:47.000Z

2021-07-10T15:59:56.000Z

import network import run import data

9.5

14

0.842105

6

38

5.333333

0.666667

0

0.157895

38

3

15

12.666667

1

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true

0

1

0

1

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1

0

null

0

1

0

1

0

1

0

6

372aef0e4ac8e9e811756461cded440a3ad502de

44

py

Python

surveys/forms/__init__.py

GeorgeVelikov/Surffee

1d89f423d9275aa34c5b51ebbf5457078cdc4d71

[ "MIT" ]

null

surveys/forms/__init__.py

GeorgeVelikov/Surffee

1d89f423d9275aa34c5b51ebbf5457078cdc4d71

[ "MIT" ]

null

surveys/forms/__init__.py

GeorgeVelikov/Surffee

1d89f423d9275aa34c5b51ebbf5457078cdc4d71

[ "MIT" ]

null

from .surveys import * from .users import *

14.666667

22

0.727273

6

44

5.333333

0.666667

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0.181818

44

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22

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true

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1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2e9705c55a72c8748549f02754d42cb0208cc237

4,959

py

Python

tests/test_cli_sugar.py

chrisgilmerproj/brewing

fd8251e5bf34c20342034187fb30d9fffc723aa8

[ "MIT" ]

21

2016-08-16T17:34:17.000Z

2021-05-09T13:44:48.000Z

tests/test_cli_sugar.py

chrisgilmerproj/brewing

fd8251e5bf34c20342034187fb30d9fffc723aa8

[ "MIT" ]

16

2016-11-16T17:07:12.000Z

2020-12-16T18:11:49.000Z

tests/test_cli_sugar.py

chrisgilmerproj/brewing

fd8251e5bf34c20342034187fb30d9fffc723aa8

[ "MIT" ]

4

2016-08-16T16:40:49.000Z

2019-10-31T01:53:00.000Z

# -*- coding: utf-8 -*- import unittest from brew.cli.sugar import get_parser from brew.cli.sugar import get_sugar_conversion from brew.cli.sugar import main class TestCliSugar(unittest.TestCase): def setUp(self): self.brix = 22.0 self.plato = 22.0 self.sg = 1.092 def test_get_sugar_conversion_brix_to_brix(self): out = get_sugar_conversion(self.brix, None, None, u"b") self.assertEquals(round(out, 1), self.brix) def test_get_sugar_conversion_brix_to_plato(self): out = get_sugar_conversion(self.brix, None, None, u"p") self.assertEquals(round(out, 1), self.plato) def test_get_sugar_conversion_brix_to_sg(self): out = get_sugar_conversion(self.brix, None, None, u"s") self.assertEquals(round(out, 3), self.sg) def test_get_sugar_conversion_plato_to_brix(self): out = get_sugar_conversion(None, self.plato, None, u"b") self.assertEquals(round(out, 1), self.brix) def test_get_sugar_conversion_plato_to_plato(self): out = get_sugar_conversion(None, self.plato, None, u"p") self.assertEquals(round(out, 1), self.plato) def test_get_sugar_conversion_plato_to_sg(self): out = get_sugar_conversion(None, self.plato, None, u"s") self.assertEquals(round(out, 3), self.sg) def test_get_sugar_conversion_sg_to_brix(self): out = get_sugar_conversion(None, None, self.sg, u"b") self.assertEquals(round(out, 1), self.brix) def test_get_sugar_conversion_sg_to_plato(self): out = get_sugar_conversion(None, None, self.sg, u"p") self.assertEquals(round(out, 1), self.plato) def test_get_sugar_conversion_sg_to_sg(self): out = get_sugar_conversion(None, None, self.sg, u"s") self.assertEquals(round(out, 3), self.sg) def test_get_sugar_conversion_all_brix(self): out = get_sugar_conversion(self.brix, None, None, None) expected = u"SG\tPlato\tBrix\n1.092\t22.0\t22.0" self.assertEquals(out, expected) def test_get_sugar_conversion_all_plato(self): out = get_sugar_conversion(None, self.plato, None, None) expected = u"SG\tPlato\tBrix\n1.092\t22.0\t22.0" self.assertEquals(out, expected) def test_get_sugar_conversion_all_sg(self): out = get_sugar_conversion(None, None, self.sg, None) expected = u"SG\tPlato\tBrix\n1.092\t22.0\t22.0" self.assertEquals(out, expected) class TestCliArgparserSugar(unittest.TestCase): def setUp(self): self.parser = get_parser() def test_get_parser_brix_in_none_out(self): args = [u"-b", u"22.0"] out = self.parser.parse_args(args) expected = {u"brix": 22.0, u"plato": None, u"sg": None, u"out": None} self.assertEquals(out.__dict__, expected) def test_get_parser_plato_in_none_out(self): args = [u"-p", u"22.0"] out = self.parser.parse_args(args) expected = {u"brix": None, u"plato": 22.0, u"sg": None, u"out": None} self.assertEquals(out.__dict__, expected) def test_get_parser_sg_in_none_out(self): args = [u"-s", u"1.060"] out = self.parser.parse_args(args) expected = {u"brix": None, u"plato": None, u"sg": 1.060, u"out": None} self.assertEquals(out.__dict__, expected) def test_get_parser_sg_in_brix_out(self): args = [u"-s", u"1.060", u"-o", u"b"] out = self.parser.parse_args(args) expected = {u"brix": None, u"plato": None, u"sg": 1.060, u"out": u"b"} self.assertEquals(out.__dict__, expected) class TestCliMainSugar(unittest.TestCase): def setUp(self): class Parser(object): def __init__(self, output): self.output = output def parse_args(self): class Args(object): pass args = Args() if self.output: for k, v in self.output.items(): setattr(args, k, v) return args def g_parser(output=None): return Parser(output) self.parser_fn = g_parser self.main = main def test_main_no_args(self): args = {u"output": {u"brix": None, u"plato": None, u"sg": None, u"out": None}} with self.assertRaises(SystemExit): self.main(parser_fn=self.parser_fn, parser_kwargs=args) def test_main_no_kwargs(self): with self.assertRaises(AttributeError): self.main(parser_fn=self.parser_fn) def test_main_two_args(self): args = {u"output": {u"brix": 22.0, u"plato": 22.0, u"sg": None, u"out": None}} with self.assertRaises(SystemExit): self.main(parser_fn=self.parser_fn, parser_kwargs=args) def test_main_one_arg(self): args = {u"output": {u"brix": 22.0, u"plato": None, u"sg": None, u"out": None}} self.main(parser_fn=self.parser_fn, parser_kwargs=args)

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