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How to use test_3 method in avocado

Best Python code snippet using avocado_python

test_log_parser.py

Source:test_log_parser.py

1import os2import numpy as np3import pytest4import copy5from alchemistry_toolkit.parsers.log_parser import *6current_path = os.path.dirname(os.path.abspath(__file__))7data_path = os.path.join(current_path, 'sample_inputs')8file_lambda_MetaD = data_path + '/lambda_MetaD.log'9file_EXE_updating = data_path + '/EXE_updating.log'10file_EXE_equilibrated = data_path + '/EXE_equilibrated.log'11file_EXE_fixed = data_path + '/EXE_fixed.log'12# Test_1: lambda-MetaD simulaiton13test_1 = EXE_LogInfo(file_lambda_MetaD)14# Test 2: EXE with weights being updated by the WL algorithm (not yet equilibrated)15test_2 = EXE_LogInfo(file_EXE_updating)16# Test 3: EXE with weights being updated by the WL algorithm (equilibrated)17test_3 = EXE_LogInfo(file_EXE_equilibrated)18# Test_4: EXE with fixed weights19test_4 = EXE_LogInfo(file_EXE_fixed)20class Test_EXE_LogInfo:21         22    def test_init(self):23        expected_1 = {'init_w': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],24                      'input': file_lambda_MetaD, 'dt': 0.002, 25                      'nstlog': 1000, 'N_states': 9, 'fixed': False, 'cutoff': 0.001, 26                      'wl_scale': 0.8, 'wl_ratio': 0.8, 'init_wl': 0.5, 'temp': 298.0, 27                      'plumed_ver': '2.7.0-dev', 'type': 'lambda-MetaD', 'start': 506}28        # the metadata of EXE_upating.log and EXE_equilibrated.log are the same29        expected_2 = {'init_w': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 30                      'input': file_EXE_updating, 'dt': 0.002, 31                      'nstlog': 1000, 'N_states': 9, 'fixed': False, 'cutoff': 0.001, 32                      'wl_scale': 0.8, 'wl_ratio': 0.8, 'init_wl': 0.5, 'temp': 298.0, 33                      'start': 455, 'type': 'expanded_ensemble'}   34        35        expected_3 = copy.deepcopy(expected_2)36        expected_3['input'] = file_EXE_equilibrated37        expected_4 = {'init_w': [0.0, 7.67256, 13.8818, 16.9028, 18.8082, 20.5498, 21.2318, 17.6905, 14.8862], 38                      'input': file_EXE_fixed, 'dt': 0.002, 39                      'nstlog': 1000, 'N_states': 9, 'fixed': True, 'wl_scale': 0.8, 40                      'wl_ratio': 0.8, 'init_wl': 1, 'temp': 298.0, 'start': 454, 41                      'type': 'expanded_ensemble'}42        assert vars(test_1) == expected_143        assert vars(test_2) == expected_244        assert vars(test_3) == expected_345        assert vars(test_4) == expected_446    def test_get_final_data(self):47        c1 = np.array([31385, 29431, 27909, 27179, 26740, 26431, 26432, 26926, 27567])48        w1 = np.array([0, 7.74194, 14.13403, 17.32355, 19.28278, 20.68103, 20.67645, 18.44879, 15.61786])49        c2 = np.array([435, 370, 218, 222, 272, 313, 343, 197, 135])50        w2 = np.array([0, 7.82554, 14.33914, 17.30932, 18.97291, 20.22864, 20.66241, 18.78573, 16.29028])51        c3 = np.array([13776, 11901, 11877, 13516, 12248, 14788, 15091, 6630, 6415])52        w3 = np.array([0, 7.67256, 13.88177, 16.90285, 18.80824, 20.54981, 21.23185, 17.69051, 14.88619])53        c4 = np.array([35254, 29835, 28314, 31311, 28662, 34624, 37771, 13790, 10439])54        w4 = copy.deepcopy(w3)55        np.testing.assert_array_almost_equal(c1, test_1.get_final_data()[0], 10)56        np.testing.assert_array_almost_equal(w1, test_1.get_final_data()[1], 10)57        np.testing.assert_array_almost_equal(c2, test_2.get_final_data()[0], 10)58        np.testing.assert_array_almost_equal(w2, test_2.get_final_data()[1], 10)59        np.testing.assert_array_almost_equal(c3, test_3.get_final_data()[0], 10)60        np.testing.assert_array_almost_equal(w3, test_3.get_final_data()[1], 10)61        np.testing.assert_array_almost_equal(c4, test_4.get_final_data()[0], 10)62        np.testing.assert_array_almost_equal(w4, test_4.get_final_data()[1], 10)63        assert test_1.final_t == 500064        assert test_2.final_t == 100065        assert test_3.final_t == 500066        assert test_4.final_t == 500067        assert test_1.err_kt_f == 0.1297168        assert test_1.err_kcal_f == 0.0768169        assert test_2.err_kt_f == 1.1700770        assert test_2.err_kcal_f == 0.692971        assert test_3.err_kt_f == 0.7642972        assert test_3.err_kcal_f == 0.452673        assert test_4.err_kt_f == 1.2170374        assert test_4.err_kcal_f == 0.7207175        assert test_2.EXE_status == 'updating'76    77    def test_get_WL_data(self):78        # Test 1: EXE_updating79        t1 = np.array([0, 0.01758, 0.02574, 0.04432, 0.062, 0.07464, 0.10316, 80                       0.12102, 0.1402, 0.1864, 0.24034, 0.26614, 0.31396, 81                       0.32938, 0.42008, 0.5014, 0.56918, 0.6576, 0.7434, 0.9499])82        w1 = np.array([0.5, 0.4, 0.32, 0.256, 0.2048, 0.16384, 0.131072 , 0.1048576, 83                       0.0838861, 0.0671089, 0.0536871, 0.0429497, 0.0343597, 84                       0.0274878, 0.0219902, 0.0175922, 0.0140737, 0.011259 ,85                       0.0090072, 0.0072058])86        np.testing.assert_array_almost_equal(t1, test_2.get_WL_data()[0], 10)87        np.testing.assert_array_almost_equal(w1, test_2.get_WL_data()[1], 10)88        assert test_2.EXE_status == 'updating'89        # Test 2: EXE_equilibrated90        t2 = np.array([0, 0.01498, 0.02138, 0.0353 , 0.0536 , 0.07438, 0.09064,91                       0.11154, 0.13876, 0.18484, 0.21016, 0.26386, 0.31684, 0.33002,92                       0.3672, 0.43502, 0.50318, 0.58672, 0.67496, 0.7618 , 0.89586,93                       0.9931, 1.175, 1.27582, 1.57056, 1.66066, 1.90268, 2.19516,94                       2.87514])95        w2 = np.array([0.5, 0.4, 0.32, 0.256, 0.2048, 0.16384, 0.131072 , 0.1048576, 96                       0.0838861, 0.0671089, 0.0536871, 0.0429497, 0.0343597, 0.0274878, 97                       0.0219902, 0.0175922, 0.0140737, 0.011259, 0.0090072, 0.0072058, 98                       0.0057646, 0.0046117, 0.0036893, 0.0029515, 0.0023612, 0.0018889, 99                       0.0015112, 0.0012089, 0.0009671])100        equil_c = np.array([3158.0, 3449.0, 3599.0, 3654.0, 3723.0, 3576.0, 3408.0, 4653.0, 4722.0])101        equil_w = np.array([0.0, 7.67256, 13.88177, 16.90285, 18.80824, 20.54981, 21.23185, 17.69051, 14.88619])102        np.testing.assert_array_almost_equal(t2, test_3.get_WL_data()[0], 10)103        np.testing.assert_array_almost_equal(w2, test_3.get_WL_data()[1], 10)104        np.testing.assert_array_almost_equal(equil_c, test_3.equil_c, 10)105        np.testing.assert_array_almost_equal(equil_w, test_3.equil_w, 10)106        assert test_3.EXE_status == 'equilibrated'107        assert test_3.equil_t == 2.87516108        assert test_3.max_Nratio == 1.25208109        assert test_3.min_Nratio == 0.83737110        assert test_3.err_kt_eq == 0.40229111        assert test_3.err_kcal_eq == 0.23823112    def test_log_avg_weights(self):113        warning_msg_1 = 'Warning: The starting point of the weights average calculation is less than 0!'114        warning_msg_2 = 'Warning: The method does not apply to the simulation being analyzed!'115        warning_msg_3 = 'Warning: Invalid parameter specified!'116        117        # Test 1: lambda_MetaD118        # 3 cases with the final method: 119        # (1) 0 avg_len (last time frame), (2) avg_len = 0.01 ns and (3) avg_len that makes avg_start < 0120        expected_1 = np.array([ 0, 7.64644, 13.94576, 17.01108, 18.99344, 20.40077,121                               20.41216, 18.10093, 15.26629])122        f1 = np.array([14.72797, 14.8941, 15.22571, 15.53723, 15.59485, 15.61786])123        np.testing.assert_array_almost_equal(test_1.get_avg_weights(0)[0], test_1.get_final_data()[1], 10)124        np.testing.assert_array_almost_equal(test_1.get_avg_weights(0)[1], test_1.get_final_data()[1][-1], 10)125        126        np.testing.assert_array_almost_equal(test_1.get_avg_weights(0.01)[0], expected_1)127        np.testing.assert_array_almost_equal(test_1.get_avg_weights(0.01)[1], f1)128        129        with pytest.raises(ParameterError) as excinfo:130            test_1.get_avg_weights(10)131        assert warning_msg_1 in str(excinfo.value)132        # equilibrated method with lambda-MetaD133        with pytest.raises(SimulationTypeError) as excinfo:134            test_1.get_avg_weights(0.5, method='equilibrated')135        assert warning_msg_2 in str(excinfo.value)136        # invalid parameter137        with pytest.raises(ParameterError) as excinfo:138            test_1.get_avg_weights(0.5, method='test')139        assert warning_msg_3 in str(excinfo.value)140        # Test 2: EXE_equilibrated with the final method (same 3 cases)141        expected_2 = np.array([0, 7.67256, 13.88177, 16.90285, 18.80824, 20.54981,142                               21.23185, 17.69051, 14.88619])143        f2 = np.array([14.88619, 14.88619, 14.88619, 14.88619, 14.88619, 14.88619])144        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0)[0], test_3.get_final_data()[1], 10)145        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0)[1], test_3.get_final_data()[1][-1], 10)146        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0.01)[0], expected_2)147        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0.01)[1], f2)148        with pytest.raises(ParameterError) as excinfo:149            test_3.get_avg_weights(10)150        assert warning_msg_1 in str(excinfo.value)151        # Test 3: EXE_equilibrated with the equilibrated method152        expected_3 = np.array([0, 7.55507, 13.70642, 16.71106, 18.61573, 20.35754,153                               21.03571, 17.49389, 14.68957])154        f3 = np.array([14.62137, 14.62379, 14.6516 , 14.73141, 14.81968])155        test_3.get_WL_data()156        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0, method='equilibrated')[0], test_3.equil_w, 10)    157        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0, method='equilibrated')[1], test_3.equil_w[-1], 10)158        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0.01, method='equilibrated')[0], expected_3)159        np.testing.assert_array_almost_equal(test_3.get_avg_weights(0.01, method='equilibrated')[1], f3)160        with pytest.raises(ParameterError) as excinfo:161            test_3.get_avg_weights(5, method='equilibrated')...

test_transactions_autocommit_2.py

Source:test_transactions_autocommit_2.py

1#coding:utf-82"""3ID:          gtcs.transactions-autocommit-024TITLE:       Changes within AUTO COMMIT must be cancelled when exception raises in some TRIGGER5DESCRIPTION:6  Original test see in:7  https://github.com/FirebirdSQL/fbtcs/blob/master/GTCS/tests/AUTO_COMMIT.2.ESQL.script8  Test creates three tables (test_1, test_2 and test_3) and AI-trigger for one of them (test_1).9  This trigger does INSERTs into test_2 and test_3.10  For test_3 we create UNIQUE index that will prevent from insertion of duplicates.11  Then we add one record into test_3 with value = 1000.12  Finally, we try to add record into test_1 and after this INSERT its trigger attempts to add records,13  into test_2 and test_3. The latter will fail because of UK violation (we try to insert apropriate value14  into test-1 in order this exception be raised).15  Expected result: NONE of just performed INSERTS must be saved in DB. The only existing record must be16  in the table test_3 that we added there on initial phase.17  NB: we use custom TPB with fdb.isc_tpb_autocommit in order to start DML transactions in AUTOCOMMIT=1 mode.18FBTEST:      functional.gtcs.transactions_autocommit_219"""20import pytest21from firebird.qa import *22db = db_factory()23act = python_act('db', substitutions=[('[ \t]+', ' ')])24expected_stdout = """25    mon$auto_commit: 126    exception occured, gdscode: 33554434927    test_3 100028"""29@pytest.mark.skip('FIXME: Not IMPLEMENTED')30@pytest.mark.version('>=3')31def test_1(act: Action):32    pytest.fail("Not IMPLEMENTED")33# test_script_134#---35#36#  import os37#  import sys38#  import subprocess39#  import inspect40#  import time41#42#  os.environ["ISC_USER"] = user_name43#  os.environ["ISC_PASSWORD"] = user_password44#  db_conn.close()45#46#  #--------------------------------------------47#48#  def flush_and_close( file_handle ):49#      # https://docs.python.org/2/library/os.html#os.fsync50#      # If you're starting with a Python file object f,51#      # first do f.flush(), and52#      # then do os.fsync(f.fileno()), to ensure that all internal buffers associated with f are written to disk.53#      global os54#55#      file_handle.flush()56#      if file_handle.mode not in ('r', 'rb') and file_handle.name != os.devnull:57#          # otherwise: "OSError: [Errno 9] Bad file descriptor"!58#          os.fsync(file_handle.fileno())59#      file_handle.close()60#61#  #--------------------------------------------62#63#  def cleanup( f_names_list ):64#      global os65#      for f in f_names_list:66#         if type(f) == file:67#            del_name = f.name68#         elif type(f) == str:69#            del_name = f70#         else:71#            print('Unrecognized type of element:', f, ' - can not be treated as file.')72#            del_name = None73#74#         if del_name and os.path.isfile( del_name ):75#             os.remove( del_name )76#77#  #--------------------------------------------78#79#  sql_init='''80#      set bail on;81#      recreate table test_1 (x integer);82#      recreate table test_2 (x integer);83#      recreate table test_3 (x integer);84#      create unique index test_3_x_uniq on test_3 (x);85#      commit;86#      set term ^;87#      create or alter trigger trg_test1_ai for test_1 active after insert position 0 as88#      begin89#          insert into test_2 values (new.x * 10);90#          insert into test_3 values (new.x * 100);91#      end ^92#      set term ;^93#94#      insert into test_3 values (1000);95#      commit;96#  '''97#98#  f_init_sql = open( os.path.join(context['temp_directory'],'tmp_gtcs_tx_ac2.sql'), 'w', buffering = 0)99#  f_init_sql.write( sql_init )100#  flush_and_close( f_init_sql )101#102#  f_init_log = open( '.'.join( (os.path.splitext( f_init_sql.name )[0], 'log') ), 'w', buffering = 0)103#  f_init_err = open( '.'.join( (os.path.splitext( f_init_sql.name )[0], 'err') ), 'w', buffering = 0)104#105#  # This can take about 25-30 seconds:106#  ####################################107#  subprocess.call( [ context['isql_path'], dsn, '-q', '-i', f_init_sql.name ], stdout = f_init_log, stderr = f_init_err)108#109#  flush_and_close( f_init_log )110#  flush_and_close( f_init_err )111#112#  #CUSTOM_TX_PARAMS = ( [ fdb.isc_tpb_read_committed, fdb.isc_tpb_no_rec_version, fdb.isc_tpb_nowait, fdb.isc_tpb_autocommit ] )113#  CUSTOM_TX_PARAMS = ( [ fdb.isc_tpb_nowait, fdb.isc_tpb_autocommit ] )114#115#  con = fdb.connect( dsn = dsn )116#  tx = con.trans( default_tpb = CUSTOM_TX_PARAMS )117#118#  tx.begin()119#  cx=tx.cursor()120#121#  cx.execute('select mon$auto_commit from mon$transactions where mon$transaction_id = current_transaction')122#  for r in cx:123#      print( 'mon$auto_commit:', r[0] )124#125#  try:126#      cx.execute( 'insert into test_1 values(?)', (10,) ) # this leads to PK/UK violation in the table 'test_3'127#  except Exception as e:128#      #print('exception in ', inspect.stack()[0][3], ': ', sys.exc_info()[0])129#      print('exception occured, gdscode:', e[2])130#131#  tx.commit()132#133#  cx.execute("select 'test_1' tab_name, x from test_1 union all select 'test_2', x from test_2 union all select 'test_3', x from test_3")134#  for r in cx:135#      print( r[0], r[1] )136#137#  cx.close()138#  tx.close()139#  con.close()140#141#  # cleanup142#  #########143#  time.sleep(1)144#  cleanup( ( f_init_sql, f_init_log, f_init_err) )145#...

gen_plist.py

Source:gen_plist.py

1# -*- coding: utf-8 -*-2from __future__ import unicode_literals3import collections4import datetime5import os6import dictdumper7ROOT = os.path.dirname(os.path.realpath(__file__))8dumper_0 = dictdumper.PLIST(os.path.join(ROOT, '..', 'plist', 'test_0.py2.plist'))9test_1 = collections.OrderedDict()10test_1['foo'] = -111test_1['bar'] = u'Hello, world!'12test_1['boo'] = collections.OrderedDict()13test_1['boo']['foo_again'] = True14test_1['boo']['bar_again'] = memoryview(b'bytes')15test_1['boo']['boo_again'] = None16dumper_1 = dictdumper.PLIST(os.path.join(ROOT, 'plist', 'test_1.py2.plist'))17dumper_1(test_1, 'test_1')18test_2 = collections.OrderedDict()19test_2['foo'] = [1, 2.0, 3]20test_2['bar'] = (1.0, bytearray(b'a long long bytes'), 3.0)21test_2['boo'] = collections.OrderedDict()22test_2['boo']['foo_again'] = b'bytestring'23test_2['boo']['bar_again'] = datetime.datetime(2020, 1, 31, 20, 15, 10, 163010)24test_2['boo']['boo_again'] = float('-inf')25dumper_2 = dictdumper.PLIST(os.path.join(ROOT, 'plist', 'test_2.py2.plist'))26dumper_2(test_1, 'test_1')27dumper_2(test_2, 'test_2')28test_3 = collections.OrderedDict()29test_3['foo'] = u"stringstringstringstringstringstringstringstringstringstring"30test_3['bar'] = [31    u"s1", False, u"s3",32]33test_3['boo'] = [34    u"s4", collections.OrderedDict(), u"s6"35]36test_3['boo'][1]['s'] = u"5"37test_3['boo'][1]['j'] = u"5"38test_3['far'] = collections.OrderedDict()39test_3['far']['far_foo'] = [u"s1", u"s2", u"s3"]40test_3['far']['far_var'] = u"s4"41test_3['biu'] = float('nan')42dumper_3 = dictdumper.PLIST(os.path.join(ROOT, 'plist', 'test_3.py2.plist'))43dumper_3(test_1, 'test_1')44dumper_3(test_2, 'test_2')...

gen_json.py

Source:gen_json.py

1# -*- coding: utf-8 -*-2from __future__ import unicode_literals3import collections4import datetime5import os6import dictdumper7ROOT = os.path.dirname(os.path.realpath(__file__))8dumper_0 = dictdumper.JSON(os.path.join(ROOT, '..', 'json', 'test_0.py2.json'))9test_1 = collections.OrderedDict()10test_1['foo'] = -111test_1['bar'] = u'Hello, world!'12test_1['boo'] = collections.OrderedDict()13test_1['boo']['foo_again'] = True14test_1['boo']['bar_again'] = memoryview(b'bytes')15test_1['boo']['boo_again'] = None16dumper_1 = dictdumper.JSON(os.path.join(ROOT, 'json', 'test_1.py2.json'))17dumper_1(test_1, 'test_1')18test_2 = collections.OrderedDict()19test_2['foo'] = [1, 2.0, 3]20test_2['bar'] = (1.0, bytearray(b'a long long bytes'), 3.0)21test_2['boo'] = collections.OrderedDict()22test_2['boo']['foo_again'] = b'bytestring'23test_2['boo']['bar_again'] = datetime.datetime(2020, 1, 31, 20, 15, 10, 163010)24test_2['boo']['boo_again'] = float('-inf')25dumper_2 = dictdumper.JSON(os.path.join(ROOT, 'json', 'test_2.py2.json'))26dumper_2(test_1, 'test_1')27dumper_2(test_2, 'test_2')28test_3 = collections.OrderedDict()29test_3['foo'] = u"stringstringstringstringstringstringstringstringstringstring"30test_3['bar'] = [31    u"s1", False, u"s3",32]33test_3['boo'] = [34    u"s4", collections.OrderedDict(), u"s6"35]36test_3['boo'][1]['s'] = u"5"37test_3['boo'][1]['j'] = u"5"38test_3['far'] = collections.OrderedDict()39test_3['far']['far_foo'] = [u"s1", u"s2", u"s3"]40test_3['far']['far_var'] = u"s4"41test_3['biu'] = float('nan')42dumper_3 = dictdumper.JSON(os.path.join(ROOT, 'json', 'test_3.py2.json'))43dumper_3(test_1, 'test_1')44dumper_3(test_2, 'test_2')...

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