prepare_rand_mock() - Code Metrics - Inspection of "Feature/linear rvt (#45)" - qbahn/grortir - Measure and Improve Code Quality continuously with Scrutinizer

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by Wojtek

created 2016-10-12 21:43 UTC

prepare_rand_mock() B

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Complexity

Conditions

Size

Total Lines

658

Duplication

Lines	0
Ratio	0 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
cc	2
c	1
b	0
f	0
dl	0
loc	658
rs	8.2857

How to fix Long Method

1			"""File which contains data for testing regression with matlab script."""
2			from unittest import TestCase
3			from unittest.mock import patch
4
5			import networkx as nx
			0 ignored issues – show Configuration introduced 2016-10-12 21:41 UTC by Report Bug Copy Issue Report The import `networkx` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
6			import numpy as np
			0 ignored issues – show Configuration introduced 2016-10-12 21:41 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
7
8			from grortir.main.model.processes.factories.calls_process_factory import \
9			CallsProcessFactory
10			from grortir.main.optimizers.grouped_optimizer import GroupedOptimizer
11			from grortir.main.optimizers.grouping_strategy import GroupingStrategy
12			from grortir.main.pso.calls_optimization_strategy import \
13			CallsOptimizationStrategy
14			from grortir.main.pso.pso_algorithm import PsoAlgorithm
15
16
17			class TestLinearRVT(TestCase):
18			"""Test class for regression."""
19
20			def _regression_mocked(self, rand_mock, max_calls):
21			"""Test use some mocked values of random function from one known example
22			from matlab. Because we set the same values in rand function, we should
23			have equal values as in matlab. This is working example for 3 particles,
24			5 stages, 2 dimensions when all stages are in one group.
25			"""
26			prepare_rand_mock(rand_mock)
27			how_many_particles = 3
28			max_calls = max_calls
29			calls_factory = CallsProcessFactory("linear", 5,
30			max_calls,
31			[0, 0])
32			process = calls_factory.construct_process()
33			ordered_stages = nx.topological_sort(process)
34
35			grouping_strategy = GroupingStrategy(ordered_stages)
36			grouping_strategy.define_group(ordered_stages)
37			optimization_startegy = CallsOptimizationStrategy()
38			pso_algortihm = PsoAlgorithm(process, grouping_strategy,
39			optimization_startegy,
40			how_many_particles)
41			optimizer = GroupedOptimizer(process, grouping_strategy, pso_algortihm)
42
43			optimizer.optimize_process()
44
45			for index, stage in enumerate(ordered_stages):
46			self.assertAlmostEqual(stage.final_quality,
47			EXPECTED_RESULTS_X2_MATLAB[max_calls][index],
48			7)
49
50			@patch('grortir.main.pso.velocity_calculator.np.random.rand')
51			def test_matlab_regression_x2_300(self, rand_mock):
52			"""Test case for max_calls = 300."""
53			self._regression_mocked(rand_mock, 300)
54
55			@patch('grortir.main.pso.velocity_calculator.np.random.rand')
56			def test_matlab_regression_x2_102(self, rand_mock):
57			"""Test case for max_calls = 102."""
58			self._regression_mocked(rand_mock, 102)
59
60
61			EXPECTED_RESULTS_X2_MATLAB = {102: [
62			0.196381073803784,
63			0.182060227702233,
64			0.077236432031289,
65			0.044953250296990,
66			0.121949294446318
67			], 300: [
68			0.175906072353589,
69			0.174734347239612,
70			0.042669220710921,
71			0.077825241269615,
72			0.175914451614580
73			]}
74
75
76			def prepare_rand_mock(rand_mock):
77			"""Rand mock for:
78			3 particles
79			5 stages
80			2 dimensions
81			"""
82			returned_values = [ # rand_vec - positions particle 1
83			[0.076308289, 0.909593528],
84			[0.723465178, 0.93120602],
85			[0.501120464, 0.9501295],
86			[0.499882501, 0.909128375],
87			[0.380941133, 0.750409859],
88
89			# rand_vec - initial_velocities particle 1
90			[0.20484909, 0.719324123],
91			[0.477401155, 0.180451619],
92			[0.768647507, 0.426453573],
93			[0.276049048, 0.414885978],
94			[0.657399463, 0.709394394],
95
96			# rand_vec - positions particle 2
97			[0.779918792, 0.213385354],
98			[0.977989512, 0.024899228],
99			[0.072051133, 0.230302879],
100			[0.679229996, 0.133169446],
101			[0.065936347, 0.669013241],
102
103			# rand_vec - initial_velocities particle 2
104			[0.490765889, 0.412991829],
105			[0.365890386, 0.741118873],
106			[0.313994677, 0.634379869],
107			[0.452842933, 0.001426881],
108			[0.370351083, 0.524345597],
109
110			# rand_vec - positions particle 3
111			[0.438409231, 0.452123962],
112			[0.53849587, 0.600548917],
113			[0.26843898, 0.548489919],
114			[0.803739036, 0.523412581],
115			[0.288145599, 0.46775286],
116
117			# rand_vec - initial_velocities particle 3
118			[0.372384689, 0.906423269],
119			[0.837917994, 0.422374044],
120			[0.572625333, 0.522906201],
121			[0.352978366, 0.092262346],
122			[0.459092978, 0.696160464],
123
124			# rand_1
125			0.955468323002926,
126			# rand_2
127			0.682913854375418,
128			# etc.
129			0.053128690672958,
130			0.308852684863797,
131			0.592594687323373,
132			0.235120407257464,
133			0.964970999536127,
134			0.945048223792794,
135			0.848400880837689,
136			0.472323996288402,
137			0.841476714898397,
138			0.131110642347914,
139			0.308733657297835,
140			0.462996394154417,
141			0.741847200683467,
142			0.485825228708897,
143			0.136876118797451,
144			0.343536529704358,
145			0.324426169672443,
146			0.300418904318040,
147			0.165501400465787,
148			0.414901772573252,
149			0.448120657502723,
150			0.774900375814017,
151			0.796390700782548,
152			0.522390128001612,
153			0.460630296163277,
154			0.778213601543897,
155			0.887288951852735,
156			0.674918769866788,
157			0.800479048998464,
158			0.939111353732185,
159			0.040655809436686,
160			0.875671724870083,
161			0.276563072451134,
162			0.475764499424244,
163			0.796760956931902,
164			0.717242232275230,
165			0.147147571941693,
166			0.658748258954145,
167			0.069252070009835,
168			0.357070628352879,
169			0.812829550163706,
170			0.427704833027551,
171			0.599854437543446,
172			0.728161283147134,
173			0.821227608306559,
174			0.760515119980283,
175			0.007143275278998,
176			0.420256833672535,
177			0.463136218038147,
178			0.055499503112723,
179			0.541442132095220,
180			0.607770751443867,
181			0.828453194996125,
182			0.941809268191449,
183			0.128147854002733,
184			0.230430674410219,
185			0.659158397070561,
186			0.132473988816749,
187			0.224078636946319,
188			0.574862587589149,
189			0.169523723566209,
190			0.782230151323778,
191			0.856975626111760,
192			0.033674204502848,
193			0.532644804943674,
194			0.796951357443564,
195			0.975139681157116,
196			0.274258593194421,
197			0.169101063365383,
198			0.876700930181635,
199			0.909182463760849,
200			0.197532889467157,
201			0.441529742702640,
202			0.719232141692337,
203			0.845345158763973,
204			0.168275305367244,
205			0.664968962904761,
206			0.807835459296069,
207			0.549714120800727,
208			0.164716658779013,
209			0.035528804830363,
210			0.281533817848494,
211			0.807870846453552,
212			0.044766260845139,
213			0.008216505039312,
214			0.361616647712057,
215			0.063622285698552,
216			0.149486303324230,
217			0.023190368099362,
218			0.524719836768632,
219			0.696695902189413,
220			0.427053493013728,
221			0.134570458644688,
222			0.331357210962406,
223			0.590345854306335,
224			0.940661393396388,
225			0.992557719172640,
226			0.241602916653850,
227			0.010579906938346,
228			0.830640325993805,
229			0.926612937770057,
230			0.458603479767969,
231			0.771442344563682,
232			0.866199034084391,
233			0.609614798334559,
234			0.872627183144187,
235			0.023903026142331,
236			0.271595220259564,
237			0.277219582694295,
238			0.120632428306158,
239			0.910713454288238,
240			0.030439266665623,
241			0.672561029522113,
242			0.071339687257207,
243			0.360780493785221,
244			0.418099542677566,
245			0.181404289057367,
246			0.521014103282176,
247			0.534991760927916,
248			0.317043881936196,
249			0.737088059552291,
250			0.160202410619170,
251			0.192508218959855,
252			0.354511018045606,
253			0.378376133431419,
254			0.206285856500654,
255			0.918709205142268,
256			0.828093569546948,
257			0.106876744256641,
258			0.369485915660384,
259			0.232671095631974,
260			0.451078602950542,
261			0.276317186210793,
262			0.501806891118423,
263			0.922603153077957,
264			0.382511138929834,
265			0.650128328628633,
266			0.595621113020374,
267			0.751953561142208,
268			0.061665330169869,
269			0.744823673681810,
270			0.946275927172428,
271			0.603559551695486,
272			0.287579942119900,
273			0.672369218429286,
274			0.712048786781395,
275			0.656450286538602,
276			0.146930315693544,
277			0.973475568269701,
278			0.955383451820879,
279			0.424625532795641,
280			0.593637329408872,
281			0.039628573866894,
282			0.988634334561939,
283			0.818744992623299,
284			0.636502342429667,
285			0.761084744221686,
286			0.188029928582586,
287			0.307654590092852,
288			0.246393644285721,
289			0.596049120125150,
290			0.091904852163772,
291			0.895609985214942,
292			0.462275942130728,
293			0.444812364954379,
294			0.104711303573244,
295			0.684904037060114,
296			0.816893994342448,
297			0.629550171462332,
298			0.242022607464133,
299			0.785420721585510,
300			0.145679433383434,
301			0.827276328979597,
302			0.580705370611886,
303			0.289372811578436,
304			0.513243399843739,
305			0.628851403064647,
306			0.258589498592646,
307			0.846906560459974,
308			0.421253708599273,
309			0.892337457255232,
310			0.835468561147535,
311			0.099347514159631,
312			0.646254109448792,
313			0.310494566495742,
314			0.754053196983175,
315			0.542631657447731,
316			0.457598874651717,
317			0.895366784537062,
318			0.057253559800168,
319			0.557317625867979,
320			0.327911099296676,
321			0.035273336313463,
322			0.753518518702918,
323			0.561740656068876,
324			0.894145336956216,
325			0.598261227571831,
326			0.337034279430268,
327			0.985219655379547,
328			0.115714050609305,
329			0.052601742578789,
330			0.732621909452081,
331			0.370874899289484,
332			0.361465078628537,
333			0.876532156489572,
334			0.327293646515387,
335			0.888988596884109,
336			0.643987979231622,
337			0.329049543886762,
338			0.059532654412467,
339			0.245109352640558,
340			0.968413201216849,
341			0.405267640233955,
342			0.160033461445328,
343			0.298013249200853,
344			0.899575778861325,
345			0.164976715508502,
346			0.777860066567281,
347			0.134864631396897,
348			0.961393713437924,
349			0.530205386091562,
350			0.043169935597521,
351			0.930954677740200,
352			0.358225544459122,
353			0.731421278900107,
354			0.523708014799748,
355			0.092581803144957,
356			0.106059218020559,
357			0.149224950691087,
358			0.161200009216847,
359			0.052755793124503,
360			0.047013165032900,
361			0.948428296058719,
362			0.091291789989614,
363			0.508351675888390,
364			0.118629820373576,
365			0.214787851208711,
366			0.763072072781084,
367			0.938134821028349,
368			0.469712694723217,
369			0.005641953918998,
370			0.999208301397325,
371			0.047773212685936,
372			0.389447904030537,
373			0.539056700681636,
374			0.891733437554219,
375			0.823112111660542,
376			0.609386641044624,
377			0.398602290975866,
378			0.834478719162267,
379			0.869058386305670,
380			0.871904907345985,
381			0.717636435413349,
382			0.097787343977917,
383			0.298579428134399,
384			0.485922055987473,
385			0.505053367201341,
386			0.832873412737514,
387			0.237836773702923,
388			0.664590755548858,
389			0.374199897461594,
390			0.775482553226357,
391			0.195774329322454,
392			0.476627293393027,
393			0.103558580363407,
394			0.211990174132978,
395			0.935174686344270,
396			0.316047110507828,
397			0.892977004089998,
398			0.524303087485621,
399			0.039033151475225,
400			0.792981682333857,
401			0.051956895132490,
402			0.826943528363990,
403			0.008799110700279,
404			0.674111327346674,
405			0.165713924746117,
406			0.342875238126080,
407			0.952751394795348,
408			0.486233473130853,
409			0.657426385161858,
410			0.740735096261484,
411			0.109927976537212,
412			0.838795758110978,
413			0.913919938722552,
414			0.155420652349409,
415			0.546238428894773,
416			0.283505123187259,
417			0.740646730859789,
418			0.028270392857499,
419			0.512547003441387,
420			0.792814855356639,
421			0.734736907792527,
422			0.108183831433641,
423			0.832940108733776,
424			0.237840730117073,
425			0.807787631216831,
426			0.497128831862446,
427			0.160825765137294,
428			0.734040677567008,
429			0.807912952139197,
430			0.698901149985949,
431			0.964846339026962,
432			0.256144451205951,
433			0.235762326997982,
434			0.147230091811600,
435			0.539406209950657,
436			0.399448822385716,
437			0.356662048714573,
438			0.460304600931861,
439			0.274542227929074,
440			0.004260377665060,
441			0.471750688851412,
442			0.275884516508146,
443			0.449783608929888,
444			0.928875114011594,
445			0.179005406834722,
446			0.637483578006810,
447			0.644414896262441,
448			0.281293183556632,
449			0.470887996729068,
450			0.969611641631324,
451			0.342460125401140,
452			0.712345182452183,
453			0.840219155681310,
454			0.360691107716966,
455			0.989914134053230,
456			0.625869240477631,
457			0.500985270593922,
458			0.714907517506250,
459			0.423829933866559,
460			0.712326722843593,
461			0.274802383334603,
462			0.927208314345688,
463			0.541962837933759,
464			0.241649493587256,
465			0.437196668431502,
466			0.895096540620895,
467			0.807055319072113,
468			0.484468952038663,
469			0.351701676740330,
470			0.376247414102291,
471			0.981195357595743,
472			0.112212785131700,
473			0.889023895569209,
474			0.857608873426699,
475			0.224388405937294,
476			0.349358734167979,
477			0.569559886725433,
478			0.802392703740247,
479			0.950445405338136,
480			0.991808703583101,
481			0.281231706882225,
482			0.192363574340851,
483			0.487725152667488,
484			0.557563900934068,
485			0.656704680393275,
486			0.107591132314336,
487			0.646356019355153,
488			0.921379080678338,
489			0.915201067829541,
490			0.943773999300075,
491			0.922339597531884,
492			0.960777827482642,
493			0.048692537116349,
494			0.916018190702266,
495			0.271212807892103,
496			0.320665735466156,
497			0.458614240947963,
498			0.955280395007635,
499			0.605088902655730,
500			0.352693216652037,
501			0.374157647552767,
502			0.904928488823167,
503			0.822347568235971,
504			0.137537720235976,
505			0.248681866157169,
506			0.371945201069240,
507			0.216741990290173,
508			0.563481752713618,
509			0.223638759945981,
510			0.574924496446997,
511			0.936024621406188,
512			0.848675975533586,
513			0.099263463082568,
514			0.206904798370428,
515			0.412749242263999,
516			0.646127576659247,
517			0.386566362834885,
518			0.251612368340209,
519			0.344659037128738,
520			0.204013586529791,
521			0.861866719522683,
522			0.697251712343329,
523			0.411029102405943,
524			0.700431428209971,
525			0.519729317478726,
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720			0.992393866454483,
721			0.565933533985480,
722			0.182020076790290,
723			0.800192805760516,
724			0.076195188027430,
725			0.410000079369867,
726			0.573132263268180
727			]
728
729			returned_values_arrays = []
730			for row in returned_values:
731			returned_values_arrays.append(np.array(row))
732
733			rand_mock.side_effect = returned_values_arrays
734

Push — master ( 40c094...b6a1c4 )

prepare_rand_mock() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

qbahn / grortir

Push — master ( 40c094...b6a1c4 )

prepare_rand_mock() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

Duplication Side-by-Side

Filter issues like

2. Missing init.py files

2. Missing init.py files