How to use test_4 method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

test_transformations.py

Source:test_transformations.py

1import unittest23import numpy as np4import pandas as pd5from pandas.testing import assert_frame_equal67from pandas_extras import (8    concatenate_columns, expand_list, expand_lists,9    extract_dict_key, extract_dictionary, merge_columns,10)111213class TransformationsTestCase(unittest.TestCase):14    def test_expand_list_pos_01(self):15        df = pd.DataFrame(16            {17                'test_index': [1, 2, 3, 4, 5, 6],18                'trial_num': [1, 2, 3, 1, 2, 3],19                'subject': [1, 1, 1, 2, 2, 2],20                'samples': [21                    [1, 2, 3, 4],22                    [1, 2, 3],23                    [1, 2],24                    [1],25                    [],26                    None,27                ]28            }29        ).set_index('test_index')30        expected = pd.DataFrame(31            {32                'newcol': [1, 2, 3, 4, 1, 2, 3, 1, 2, 1, None, None],33                'subject': [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],34                'trial_num': [1, 1, 1, 1, 2, 2, 2, 3, 3, 1, 2, 3],35                'test_index': [1, 1, 1, 1, 2, 2, 2, 3, 3, 4, 5, 6]36            }37        )38        assert_frame_equal(expand_list(df, 'samples', 'newcol').reset_index(),39                           expected, check_like=True, check_dtype=False)404142    def test_expand_list_pos_02(self):43        df = pd.DataFrame(44            {45                'trial_num': [1, 2, 3, 1, 2, 3],46                'subject': [1, 1, 1, 2, 2, 2],47                'samples': [48                    [1, 2, 3, 4],49                    [1, 2, 3],50                    [1, 2],51                    [1],52                    [],53                    None,54                ]55            }56        ).set_index(['trial_num', 'subject'])57        expected = pd.DataFrame(58            {59                'samples': [1, 2, 3, 4, 1, 1, 2, 3, None, 1, 2, None],60                'subject': [1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2],61                'trial_num': [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3]62            }63        )64        assert_frame_equal(expand_list(df, 'samples').reset_index(), expected, check_like=True)6566    def test_expand_list_pos_03(self):67        df = pd.DataFrame(68            {69                'trial_num': [1, 2, 3, 1, 2, 3],70                'subject': [1, 1, 1, 2, 2, 2],71                'samples': [72                    [1, 2, 3, 4],73                    [1, 2, 3],74                    [1, 2],75                    [1],76                    [],77                    np.NaN,78                ]79            }80        ).set_index(['trial_num', 'subject'])81        expected = pd.DataFrame(82            {83                'samples': [1, 2, 3, 4, 1, 1, 2, 3, None, 1, 2, None],84                'subject': [1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2],85                'trial_num': [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3]86            }87        )88        assert_frame_equal(expand_list(df, 'samples').reset_index(), expected, check_like=True)8990    def test_expand_lists_pos_01(self):91        df = pd.DataFrame(92            {93                'trial_num': [1, 2, 3, 1, 2, 3],94                'subject': [1, 1, 1, 2, 2, 2],95                'samples': [96                    [1, 2, 3, 4],97                    [1, 2, 3],98                    [1, 2],99                    [1],100                    [],101                    None,102                ],103                'samples2': [104                    [1, 2, 3, 4],105                    [1, 2, 3],106                    [1, 2],107                    [1],108                    [],109                    None,110                ]111            }112        )113        expected = pd.DataFrame(114            {115                'newcol': [1, 2, 3, 4, 1, 2, 3, 1, 2, 1, None, None],116                'newcol2': [1, 2, 3, 4, 1, 2, 3, 1, 2, 1, None, None],117                'subject': [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],118                'trial_num': [1, 1, 1, 1, 2, 2, 2, 3, 3, 1, 2, 3]119            }120        )121        assert_frame_equal(122            expand_lists(df, ['samples', 'samples2'], ['newcol', 'newcol2']).reset_index().drop('index', axis=1),123            expected,124            check_like=True125        )126127    def test_expand_lists_pos_02(self):128        df = pd.DataFrame(129            {130                'trial_num': [1, 2, 3, 1, 2, 3],131                'subject': [1, 1, 1, 2, 2, 2],132                'samples': [133                    [1, 2, 3, 4],134                    [1, 2, 3],135                    [1],136                    [1],137                    [],138                    None,139                ],140                'samples2': [141                    [1, 2],142                    [3],143                    [1, 2],144                    [1],145                    [],146                    None,147                ]148            }149        ).set_index(['trial_num', 'subject'])150        expected = pd.DataFrame(151            {152                'samples': [1, 2, 3, 4, 1, 1, 2, 3, None, 1, None, None],153                'samples2': [1, 2, None, None, 1, 3, None, None, None, 1, 2, None],154                'subject': [1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2],155                'trial_num': [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3]156            }157        )158        assert_frame_equal(expand_lists(df, ['samples', 'samples2']).reset_index(), expected, check_like=True)159160    def test_expand_lists_pos_03(self):161        df = pd.DataFrame(162            {163                'trial_num': [1, 2, 3, 1, 2, 3],164                'subject': [1, 1, 1, 2, 2, 2],165                'samples': [166                    [{'testkey': 1}, {'testkey': 2}, {'testkey': 3}, {'testkey': 4}],167                    [{'testkey': 1}, {'testkey': 2}, {'testkey': 3}],168                    [{'testkey': 1}, {'testkey': 2}],169                    [{'testkey': 1}],170                    [],171                    None,172                ],173                'other_samples': [174                    [1, 2, 3, 4],175                    [1, 2, 3],176                    [1, 2],177                    [1],178                    [],179                    None,180                ]181            }182        )183        expected = pd.DataFrame(184            {185                'newcol': [{'testkey': 1.0}, {'testkey': 2.0}, {'testkey': 3.0}, {'testkey': 4.0},186                           {'testkey': 1.0}, {'testkey': 2.0}, {'testkey': 3.0}, {'testkey': 1.0},187                           {'testkey': 2.0}, {'testkey': 1.0}, None, None],188                'newcol2': [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 1.0, 2.0, 1.0, None, None],189                'subject': [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],190                'trial_num': [1, 1, 1, 1, 2, 2, 2, 3, 3, 1, 2, 3]191            }192        )193        assert_frame_equal(194            expand_lists(df, ['samples', 'other_samples'], ['newcol', 'newcol2']).reset_index(drop=True),195            expected,196            check_like=True197        )198199    def test_expand_lists_pos_04(self):200        df = pd.DataFrame(201            {202                'trial_num': [1, 2, 3, 1, 2, 3],203                'subject': [1, 1, 1, 2, 2, 2],204                'samples': [205                    [{'testkey': 1}, {'testkey': 2}, {'testkey': 3}, {'testkey': 4}],206                    [{'testkey': 1}, {'testkey': 2}, {'testkey': 3}],207                    [{'testkey': 1}, {'testkey': 2}],208                    [{'testkey': 1}],209                    [],210                    ['this will be NaN, as None is not iterable'],211                ],212                'other_samples': [213                    [1, 2, 3, 4],214                    [1, 2, 3],215                    [1, 2],216                    [],217                    [1],218                    None,219                ]220            }221        )222        expected = pd.DataFrame(223            {224                'newcol': [{'testkey': 1.0}, {'testkey': 2.0}, {'testkey': 3.0}, {'testkey': 4.0},225                           {'testkey': 1.0}, {'testkey': 2.0}, {'testkey': 3.0}, {'testkey': 1.0},226                           {'testkey': 2.0}, {'testkey': 1.0}, None, None],227                'newcol2': [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 1.0, 2.0, None, 1.0, None],228                'subject': [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],229                'trial_num': [1, 1, 1, 1, 2, 2, 2, 3, 3, 1, 2, 3]230            }231        )232        assert_frame_equal(233            expand_lists(df, ['samples', 'other_samples'], ['newcol', 'newcol2']).reset_index(drop=True),234            expected,235            check_like=True236        )237238    def test_extract_dict_key_pos_01(self):239        df = pd.DataFrame(240            {241                'trial_num': [1, 2, 1, 2],242                'subject': [1, 1, 2, 2],243                'samples': [244                    {'A': 1, 'B': 2, 'C': None},245                    {'A': 3, 'B': 4, 'C': 5},246                    {'A': 6, 'B': 7, 'C': None},247                    None,248                ]249            }250        )251        expected = pd.DataFrame(252            {253                'trial_num': [1, 2, 1, 2],254                'subject': [1, 1, 2, 2],255                'samples': [256                    {'A': 1, 'B': 2, 'C': None},257                    {'A': 3, 'B': 4, 'C': 5},258                    {'A': 6, 'B': 7, 'C': None},259                    None,260                ],261                'samples.A': [1, 3, 6, None]262            }263        )264        assert_frame_equal(extract_dict_key(df, 'samples', 'A').reset_index(drop=True), expected, check_like=True)265266    def test_extract_dict_key_pos_02(self):267        df = pd.DataFrame(268            {269                'trial_num': [1, 2, 1, 2],270                'subject': [1, 1, 2, 2],271                'samples': [272                    {'A': 1, 'B': 2, 'C': None},273                    {'A': 3, 'B': 4, 'C': 5},274                    {'A': 6, 'B': 7, 'C': None},275                    {'B': 8, 'C': None},276                ]277            }278        )279        expected = pd.DataFrame(280            {281                'trial_num': [1, 2, 1, 2],282                'subject': [1, 1, 2, 2],283                'samples': [284                    {'A': 1, 'B': 2, 'C': None},285                    {'A': 3, 'B': 4, 'C': 5},286                    {'A': 6, 'B': 7, 'C': None},287                    {'B': 8, 'C': None},288                ],289                'newcol': [1, 3, 6, None]290            }291        )292        assert_frame_equal(293            extract_dict_key(df, 'samples', 'A', 'newcol').reset_index(drop=True),294            expected, check_like=True295        )296297    def test_extract_dict_key_pos_03(self):298        df = pd.DataFrame(299            {300                'trial_num': [1, 2, 1, 2],301                'subject': [1, 1, 2, 2],302                'samples': [np.NaN, np.NaN, np.NaN, np.NaN]303            }304        )305        expected = pd.DataFrame(306            {307                'trial_num': [1, 2, 1, 2],308                'subject': [1, 1, 2, 2],309                'samples': [np.NaN, np.NaN, np.NaN, np.NaN],310                'newcol': [np.NaN, np.NaN, np.NaN, np.NaN]311            }312        )313        assert_frame_equal(314            extract_dict_key(df, 'samples', 'A', 'newcol').reset_index(drop=True),315            expected, check_like=True316        )317318    def test_extract_dict_key_pos_04(self):319        df = pd.DataFrame(320            {321                'trial_num': [1, 2, 1, 2],322                'subject': [1, 1, 2, 2],323            }324        )325        with self.assertRaises(KeyError):326            extract_dict_key(df, 'samples', 'A', 'newcol')327328    def test_extract_dict_key_pos_05(self):329        df = pd.DataFrame(330            columns=('trial_num', 'subject', 'samples')331        )332        self.assertIn('newcol', extract_dict_key(df, 'samples', 'A', 'newcol').columns.to_list())333334    def test_extract_dictionary_pos_01(self):335        df = pd.DataFrame(336            {337                'trial_num': [1, 2, 1, 2],338                'subject': [1, 1, 2, 2],339                'samples': [340                    {'A': 1, 'B': 2, 'C': None},341                    {'A': 3, 'B': 4, 'C': 5},342                    {'A': 6, 'B': 7, 'C': None},343                    None,344                ]345            }346        )347        expected = pd.DataFrame(348            {349                'trial_num': [1, 2, 1, 2],350                'subject': [1, 1, 2, 2],351                'samples.A': [1, 3, 6, None],352                'samples.B': [2, 4, 7, None],353            }354        )355        assert_frame_equal(356            extract_dictionary(df, 'samples', ['A', 'B']).reset_index(drop=True),357            expected, check_like=True358        )359360    def test_extract_dictionary_pos_02(self):361        df = pd.DataFrame(362            {363                'trial_num': [1, 2, 1, 2],364                'subject': [1, 1, 2, 2],365                'samples': [366                    {'A': 1, 'B': 2, 'C': None},367                    {'A': 3, 'B': 4, 'C': 5},368                    {'A': 6, 'B': 7, 'C': None},369                    None,370                ]371            }372        )373        expected = pd.DataFrame(374            {375                'trial_num': [1, 2, 1, 2],376                'subject': [1, 1, 2, 2],377                'newcol.A': [1, 3, 6, None],378                'newcol.B': [2, 4, 7, None],379            }380        )381        assert_frame_equal(382            extract_dictionary(df, 'samples', ['A', 'B'], 'newcol').reset_index(drop=True),383            expected, check_like=True384        )385386    def test_extract_dictionary_pos_03(self):387        df = pd.DataFrame(388            {389                'trial_num': [1, 2, 1, 2],390                'subject': [1, 1, 2, 2],391                'samples': [392                    {'A': 1, 'B': 2, 'C': None},393                    {'A': 3, 'B': 4, 'C': 5},394                    {'A': 6, 'B': 7, 'C': None},395                    None,396                ]397            }398        )399        expected = pd.DataFrame(400            {401                'trial_num': [1, 2, 1, 2],402                'subject': [1, 1, 2, 2],403                'samples.A': [1, 3, 6, None],404                'samples.B': [2, 4, 7, None],405                'samples.C': [None, 5, None, None]406            }407        )408        assert_frame_equal(extract_dictionary(df, 'samples').reset_index(drop=True), expected, check_like=True)409410    def test_extract_dictionary_pos_04(self):411        df = pd.DataFrame(412            {413                'trial_num': [1, 2, 1, 2],414                'subject': [1, 1, 2, 2],415                'samples': [416                    {'A': 1, 'B': 2, 'C': None},417                    {'A': 3, 'B': 4, 'C': 5},418                    {'A': 6, 'B': 7, 'C': None},419                    None,420                ]421            }422        )423        expected = pd.DataFrame(424            {425                'trial_num': [1, 2, 1, 2],426                'subject': [1, 1, 2, 2],427                'A': [1, 3, 6, None],428                'B': [2, 4, 7, None],429                'C': [None, 5, None, None]430            }431        )432        assert_frame_equal(433            extract_dictionary(df, 'samples', prefix='').reset_index(drop=True),434            expected, check_like=True435        )436437    def test_extract_dictionary_pos_05(self):438        df = pd.DataFrame(439            {440                'trial_num': [1, 2, 1, 2],441                'subject': [1, 1, 2, 2],442                'samples': [None, None, None, None]443            }444        )445        expected = pd.DataFrame(446            {447                'trial_num': [1, 2, 1, 2],448                'subject': [1, 1, 2, 2]449            }450        )451        assert_frame_equal(452            extract_dictionary(df, 'samples', prefix='').reset_index(drop=True),453            expected, check_like=True454        )455456    def test_extract_dictionary_pos_06(self):457        df = pd.DataFrame({458            'trial_num': [1, 2, 1, 2],459            'subject': [1, 1, 2, 2],460            'samples': [461                None,462                {'A': 1, 'B': 2, 'C': None},463                {'A': 3, 'B': 4, 'C': 5},464                {'A': 6, 'B': 7, 'C': None},465            ]466        })467        expected = pd.DataFrame({468            'trial_num': [1, 2, 1, 2],469            'subject': [1, 1, 2, 2],470            'A': [None, 1, 3, 6],471            'B': [None, 2, 4, 7],472            'C': [None, None, 5, None]473        })474        assert_frame_equal(475            extract_dictionary(df, 'samples', prefix='').reset_index(drop=True),476            expected, check_like=True477        )478479    def test_merge_columns(self):480        dataframe = pd.DataFrame([481            {482                'test_1': pd.NaT,483                'test_2': [],484                'test_3': 'TEST',485                'test_4': 'TEST2'486            },487            {488                'test_1': 'TEST3',489                'test_2': ['TEST'],490                'test_3': 'TEST',491                'test_4': 'TEST2'492            },493            {494                'test_1': np.NaN,495                'test_2': None,496                'test_3': 'TEST5',497                'test_4': 'TEST6'498            }499        ])500        expected_result_first = pd.DataFrame([501            {502                'test_1': None,503                'test_2': [],504                'test_3': 'TEST',505                'test_4': 'TEST2',506                'new_col_name': 'TEST'507            },508            {509                'test_1': 'TEST3',510                'test_2': ['TEST'],511                'test_3': 'TEST',512                'test_4': 'TEST2',513                'new_col_name': 'TEST3'514            },515            {516                'test_1': None,517                'test_2': None,518                'test_3': 'TEST5',519                'test_4': 'TEST6',520                'new_col_name': 'TEST5'521            }522        ])523        expected_result_last = pd.DataFrame([524            {525                'test_1': None,526                'test_2': [],527                'test_3': 'TEST',528                'test_4': 'TEST2',529                'new_col_name': 'TEST2'530            },531            {532                'test_1': 'TEST3',533                'test_2': ['TEST'],534                'test_3': 'TEST',535                'test_4': 'TEST2',536                'new_col_name': 'TEST2'537            },538            {539                'test_1': None,540                'test_2': None,541                'test_3': 'TEST5',542                'test_4': 'TEST6',543                'new_col_name': 'TEST6'544            }545        ])546        merge_columns(dataframe, ['test_1', 'test_3', 'test_4'], 'new_col_name', keep='first')547        assert_frame_equal(dataframe, expected_result_first, check_like=True)548        merge_columns(dataframe, ['test_1', 'test_3', 'test_4'], 'new_col_name', keep='last')549        assert_frame_equal(dataframe, expected_result_last, check_like=True)550        with self.assertRaises(ValueError):551            merge_columns(dataframe, ['test_1', 'test_3', 'test_4'], 'new_col_name', keep='something_wrong')552        with self.assertRaises(ValueError):553            merge_columns(dataframe, ['test_1', 'test_3', 'test_4'], 'new_col_name', aggr=sum, keep='first')554555    def test_merge_columns_aggr(self):556        dataframe = pd.DataFrame([557            {558                'test_1': 1,559                'test_2': [],560                'test_3': 5,561                'test_4': 9562            },563            {564                'test_1': 0,565                'test_2': ['TEST'],566                'test_3': 9,567                'test_4': 7568            },569            {570                'test_1': 1,571                'test_2': None,572                'test_3': 8,573                'test_4': 1574            }575        ])576        expected_result = pd.DataFrame([577            {578                'test_1': 1,579                'test_2': [],580                'test_3': 5,581                'test_4': 9,582                'new_col_name': 15583            },584            {585                'test_1': 0,586                'test_2': ['TEST'],587                'test_3': 9,588                'test_4': 7,589                'new_col_name': 16590            },591            {592                'test_1': 1,593                'test_2': None,594                'test_3': 8,595                'test_4': 1,596                'new_col_name': 10597            }598        ])599        merge_columns(dataframe, ['test_1', 'test_3', 'test_4'], 'new_col_name', aggr=sum)600        assert_frame_equal(dataframe, expected_result, check_like=True, check_dtype=False)601        with self.assertRaises(ValueError):602            merge_columns(dataframe, ['test_1', 'test_3', 'test_4'], 'new_col_name', aggr='sum')603604    def test_concatenate_columns_pos_01(self):605        dataframe = pd.DataFrame([606            {'key': 'TICKET-1', 'assignee': 'Bob', 'reporter': 'Alice'},607            {'key': 'TICKET-2', 'assignee': 'Bob', 'reporter': 'Alice'},608            {'key': 'TICKET-3', 'assignee': 'Bob', 'reporter': 'Alice'},609        ]).set_index('key')610        expected = pd.DataFrame([611            {'key': 'TICKET-1', 'user': 'Bob'},612            {'key': 'TICKET-1', 'user': 'Alice'},613            {'key': 'TICKET-2', 'user': 'Bob'},614            {'key': 'TICKET-2', 'user': 'Alice'},615            {'key': 'TICKET-3', 'user': 'Bob'},616            {'key': 'TICKET-3', 'user': 'Alice'},617        ]).set_index('key')618        assert_frame_equal(concatenate_columns(dataframe, ['assignee', 'reporter'], 'user'), expected)619620    def test_concatenate_columns_pos_02(self):621        dataframe = pd.DataFrame([622            {'key': 'TICKET-1', 'assignee': 'Bob', 'reporter': 'Alice'},623            {'key': 'TICKET-2', 'assignee': 'Bob', 'reporter': 'Alice'},624            {'key': 'TICKET-3', 'assignee': 'Bob', 'reporter': 'Alice'},625        ]).set_index('key')626        expected = pd.DataFrame([627            {'key': 'TICKET-1', 'user': 'Bob', 'role': 'assignee'},628            {'key': 'TICKET-1', 'user': 'Alice', 'role': 'reporter'},629            {'key': 'TICKET-2', 'user': 'Bob', 'role': 'assignee'},630            {'key': 'TICKET-2', 'user': 'Alice', 'role': 'reporter'},631            {'key': 'TICKET-3', 'user': 'Bob', 'role': 'assignee'},632            {'key': 'TICKET-3', 'user': 'Alice', 'role': 'reporter'},633        ]).set_index('key')[['user', 'role']]634        assert_frame_equal(635            concatenate_columns(dataframe, ['assignee', 'reporter'], 'user', descriptor='role'),636            expected637        )638639    def test_concatenate_columns_pos_03(self):640        dataframe = pd.DataFrame([641            {'key': 'TICKET-1', 'assignee': 'Bob', 'reporter': 'Alice'},642            {'key': 'TICKET-2', 'assignee': 'Bob', 'reporter': 'Alice'},643            {'key': 'TICKET-3', 'assignee': 'Bob', 'reporter': 'Alice'},644        ]).set_index('key')645        expected = pd.DataFrame([646            {'key': 'TICKET-1', 'user': 'Bob', 'role': 'a'},647            {'key': 'TICKET-1', 'user': 'Alice', 'role': 'r'},648            {'key': 'TICKET-2', 'user': 'Bob', 'role': 'a'},649            {'key': 'TICKET-2', 'user': 'Alice', 'role': 'r'},650            {'key': 'TICKET-3', 'user': 'Bob', 'role': 'a'},651            {'key': 'TICKET-3', 'user': 'Alice', 'role': 'r'},652        ]).set_index('key')[['user', 'role']]653        mapper = {'assignee': 'a', 'reporter': 'r'}654        assert_frame_equal(655            concatenate_columns(dataframe, ['assignee', 'reporter'], 'user', descriptor='role', mapper=mapper),656            expected657        )658659    def test_concatenate_columns_non_existent_col(self):660        dataframe = pd.DataFrame([661            {'key': 'TICKET-1', 'assignee': 'Bob', 'reporter': 'Alice'},662            {'key': 'TICKET-2', 'assignee': 'Bob', 'reporter': 'Alice'},663            {'key': 'TICKET-3', 'assignee': 'Bob', 'reporter': 'Alice'},664        ]).set_index('key')665        expected = pd.DataFrame([666            {'key': 'TICKET-1', 'user': 'Bob', 'role': 'a'},667            {'key': 'TICKET-1', 'user': 'Alice', 'role': 'r'},668            {'key': 'TICKET-2', 'user': 'Bob', 'role': 'a'},669            {'key': 'TICKET-2', 'user': 'Alice', 'role': 'r'},670            {'key': 'TICKET-3', 'user': 'Bob', 'role': 'a'},671            {'key': 'TICKET-3', 'user': 'Alice', 'role': 'r'},672        ]).set_index('key')[['user', 'role']]673        mapper = {'assignee': 'a', 'reporter': 'r'}674        assert_frame_equal(675            concatenate_columns(dataframe, ['assignee', 'reporter', 'creator'], 'user', descriptor='role', mapper=mapper),676            expected677        )678679680if __name__ == '__main__':
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HCHEdistmods.py

Source:HCHEdistmods.py

1# -*- coding: utf-8 -*-2"""3Created on Mon Dec  4 08:47:49 201745@author: MatthewLazenby6"""7import tkinter as tk8import numpy as np9import pandas as pd10from tkinter import messagebox11dist = tk.Tk()12dist.title("R744 Distributors")1314lbl_evap_t = tk.Label(dist, text = "Evap Temperature(Â°C):", width = 20)15lbl_cap = tk.Label(dist, text = "Capacity(kW):", width = 20)16lbl_liq_t = tk. Label(dist, text = "Liquid Temperature(Â°C):", width = 20)17lbl_circ = tk.Label(dist, text = "Number of Circuits:", width = 20)18lbl_tail_len = tk.Label(dist, text = "Tail Length(mm):", width = 20)19txtbx_cap = tk.Entry(dist, width = 20)20txtbx_evap_t = tk.Entry(dist, width = 20)21Liqs = ['-20Â°C','-15Â°C','-10Â°C','-5Â°C','0Â°C','5Â°C']22liq_temps = tk.StringVar(dist)23liq_temps.set(Liqs[5])24txtbx_liq_t = tk.OptionMenu(dist, liq_temps, *Liqs)25Tubes = [300, 450, 600, 760, 900, 1050, 1200, 1350, 1500, 1650, 1800]26tube_lens = tk.StringVar(dist)27tube_lens.set(Tubes[0])28txtbx_circ = tk.Entry(dist, width = 20)29txtbx_tail_len = tk.OptionMenu(dist, tube_lens,*Tubes)3031lbl_evap_t.grid(row = 0, column = 0, sticky = tk.E)32txtbx_evap_t.grid(row = 0, column = 1)33lbl_cap.grid(row = 1, column = 0, sticky = tk.E)34txtbx_cap.grid(row = 1, column = 1)35lbl_liq_t.grid(row = 2, column = 0, sticky = tk.E)36txtbx_liq_t.grid(row = 2, column = 1)37lbl_circ.grid(row = 3, column = 0, sticky = tk.E)38txtbx_circ.grid(row = 3, column = 1)39lbl_tail_len.grid(row = 4, column = 0, sticky = tk.E)40txtbx_tail_len.grid(row = 4, column = 1)414243def dist_nozz_caps():44    count = 045    test_4 = "!@#$%^&*()_+=<>,?/':;{}[]\|`~-"46    for i in np.arange(0, len(txtbx_evap_t.get()), 1):47        for j in np.arange(0, len(test_4), 1):48            if txtbx_evap_t.get()[i] == test_4[j]:49                count = count + 150    if count > 0:51        messagebox.showerror("Error","Be sure to enter the values in correctly.")52    else:53        noz_cap = pd.read_excel('Distributor table.xlsx', sheet_name = 'Distributor Nozzle Capacities')54        test_1 = txtbx_evap_t.get()55        if any(c.isalpha() for c in test_1):56            messagebox.showerror("Error","Enter in a valid value for Evap Temperature.")57        else:58            evap_t = float(txtbx_evap_t.get())59            if evap_t >= -25:60                return np.array(noz_cap[['Nozzle number:','-20Â°C']])61            elif evap_t < -25 and evap_t >= -35:62                return np.array(noz_cap[['Nozzle number:','-30Â°C']])63            elif evap_t < -35:64                return np.array(noz_cap[['Nozzle number:','-40Â°C']])65            66def dist_tube_caps():67    count = 068    test_4 = "!@#$%^&*()_+=<>,?/':;{}[]\|`~"69    for i in np.arange(0, len(txtbx_evap_t.get()), 1):70        for j in np.arange(0, len(test_4), 1):71            if txtbx_evap_t.get()[i] == test_4[j]:72                count = count + 173    if count > 0:74        messagebox.showerror("Error","Be sure to enter the values in correctly.")75    else:76        test_1 = txtbx_evap_t.get()77        if any(c.isalpha() for c in test_1):78            messagebox.showerror("Error","Enter in a valid value for Evap Temperature.")79        else:80            evap_t = float(txtbx_evap_t.get())81            tube_cap = pd.read_excel('Distributor table.xlsx', sheet_name = 'Distributor Capacity per tube')82            if evap_t >= -25:83                return np.array(tube_cap[['Tube Diameter:','-20Â°C']])84            elif evap_t < -25 and evap_t >= -35:85                return np.array(tube_cap[['Tube Diameter:','-30Â°C']])86            elif evap_t < -35:87                return np.array(tube_cap[['Tube Diameter:','-40Â°C']])88            89            90def calc_dist():91    test_1 = txtbx_evap_t.get()92    test_2 = txtbx_cap.get()93    test_3 = txtbx_circ.get()94    test_4 = "!@#$%^&*()_+=<>,?/':;{}[]\|`~"95    count = 096    for i in np.arange(0, len(txtbx_evap_t.get()), 1):97        for j in np.arange(0, len(test_4), 1):98            if txtbx_evap_t.get()[i] == test_4[j]:99                count = count + 1100    for i in np.arange(0, len(txtbx_cap.get()), 1):101        for j in np.arange(0, len(test_4), 1):102            if txtbx_cap.get()[i] == test_4[j]:103                count = count + 1104    for i in np.arange(0, len(txtbx_circ.get()), 1):105        for j in np.arange(0, len(test_4), 1):106            if txtbx_circ.get()[i] == test_4[j]:107                count = count + 1108    if any(c.isalpha() for c in test_1) or any(c.isalpha() for c in test_2) or any(c.isalpha() for c in test_3) or count>0 or txtbx_evap_t.get() == "" or txtbx_circ.get() == "" or txtbx_cap.get() == "":109        messagebox.showerror("Error","Be sure to enter the values in correctly.")110    else:111        capacity = float(txtbx_cap.get())112        liq_t = str(liq_temps.get())113        circ = int(txtbx_circ.get())114        tail_len = float(tube_lens.get())115116        liq_temp = pd.read_excel('Distributor table.xlsx', sheet_name = 'Liquid Temperature')117        liq_cf = np.array(liq_temp[liq_t])[0]118        nozzy = capacity/liq_cf119        for i in np.arange(0,len(dist_nozz_caps())-1,1):120            compare = dist_nozz_caps()[i]121            compare_1 = dist_nozz_caps()[i+1]122            if compare[1] < nozzy and compare_1[1] > nozzy:123                noz_capacity = compare_1[0]124            elif compare[1] == nozzy:125                noz_capacity = compare[0]126            elif compare_1[1] == nozzy:127                noz_capacity = compare_1[0]128129        if nozzy >= float(dist_nozz_caps()[0][1]) and nozzy <= float(dist_nozz_caps()[len(dist_nozz_caps())-1][1]):       130            tail_table = pd.read_excel('Distributor table.xlsx', sheet_name = 'Tubelength') 131            tail_cf = np.array(tail_table[tail_len])[0]132            tube_cap = ((capacity/circ)/tail_cf)/liq_cf133            if tube_cap >= float(dist_tube_caps()[0][1]) and tube_cap <= float(dist_tube_caps()[len(dist_tube_caps())-1][1]):134                for k in np.arange(0,len(dist_tube_caps())-1,1):135                    tube = dist_tube_caps()[k]136                    tube_1 = dist_tube_caps()[k+1]137                    if tube[1] < tube_cap and tube_1[1] > tube_cap:138                        tube_di = tube_1[0]139                    elif tube[1] == tube_cap:140                        tube_di = tube_1[0]141                    elif tube_1[1] == tube_cap:142                        tube_di = tube_1[0]143            elif tube_cap < float(dist_tube_caps()[0][1]):144                tube_di = dist_tube_caps()[0][0]145            elif tube_cap > float(dist_tube_caps()[len(dist_tube_caps())-1][1]):146                messagebox.showerror("Error", "The tube diameter could not be calculated based on the values in the Sporlan tables.")147148            hche = pd.read_excel('Distributor table.xlsx', sheet_name = 'HCHE') 149            if tube_di == "3/16":150                mod = np.array(hche[['Model:','3/16','Nozzle size:','Conn. Size OD:','Body size ']])151                dist_mod = []152            elif tube_di == "1/4":153                mod = np.array(hche[['Model:','1/4','Nozzle size:','Conn. Size OD:','Body size ']])154                dist_mod = []155            elif tube_di == "5/16":156                mod = np.array(hche[['Model:','5/16','Nozzle size:','Conn. Size OD:','Body size ']])157                dist_mod = []158            elif tube_di == "3/8":159                mod = np.array(hche[['Model:','3/8','Nozzle size:','Conn. Size OD:','Body size ']])160                dist_mod = []161162            for i in np.arange(0,(len(mod)),1):163                chk = mod[i]164                chk_1 = chk[1]165                if str(chk_1) != 'nan':166                    chk_1 = chk[1]167                    for k in np.arange(0,len(chk_1),1):168                        if chk_1[k] == "-":169                            last = chk_1[k+1:len(chk_1)]170                            first = chk_1[0:k]171                            if int(first) <= int(circ) and int(last) >= int(circ):172                                dist_mod.append(chk[0])173            if dist_mod == []:174                messagebox.showerror("Error","No distributor was calculted.")175            else:176                display = dist_mod, circ, tube_di, noz_capacity177                msg = "Possible distributor models:\n%a -%a -%a -%a"%display178                messagebox.showinfo("Suggested Distributors", msg)179            180                return dist_mod,noz_capacity,tube_di,int(circ)181        else:182            messagebox.showerror("Error", "The nozzle size could not be calculated based on the values in the Sporlan tables.")183184button_dist = tk.Button(dist, text = "Calculate Distributor",command = calc_dist)185button_dist.grid(row = 5, column = 0, columnspan = 6)186button_dist.focus_set()
...

config.py

Source:config.py

1################################################################################2##                                                                            ##3## This file is a part of TADEK.                                              ##4##                                                                            ##5## TADEK - Test Automation in a Distributed Environment                       ##6## (http://tadek.comarch.com)                                                 ##7##                                                                            ##8## Copyright (C) 2011 Comarch S.A.                                            ##9## All rights reserved.                                                       ##10##                                                                            ##11## TADEK is free software for non-commercial purposes. For commercial ones    ##12## we offer a commercial license. Please check http://tadek.comarch.com for   ##13## details or write to tadek-licenses@comarch.com                             ##14##                                                                            ##15## You can redistribute it and/or modify it under the terms of the            ##16## GNU General Public License as published by the Free Software Foundation,   ##17## either version 3 of the License, or (at your option) any later version.    ##18##                                                                            ##19## TADEK is distributed in the hope that it will be useful,                   ##20## but WITHOUT ANY WARRANTY; without even the implied warranty of             ##21## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the              ##22## GNU General Public License for more details.                               ##23##                                                                            ##24## You should have received a copy of the GNU General Public License          ##25## along with TADEK bundled with this file in the file LICENSE.               ##26## If not, see http://www.gnu.org/licenses/.                                  ##27##                                                                            ##28## Please notice that Contributor Agreement applies to any contribution       ##29## you make to TADEK. The Agreement must be completed, signed and sent        ##30## to Comarch before any contribution is made. You should have received       ##31## a copy of Contribution Agreement along with TADEK bundled with this file   ##32## in the file CONTRIBUTION_AGREEMENT.pdf or see http://tadek.comarch.com     ##33## or write to tadek-licenses@comarch.com                                     ##34##                                                                            ##35################################################################################36import unittest37import ConfigParser38import os39from tadek.core import config40__all__ = ["ConfigTest"]41_PROGRAM_NAME = 'unittest'42class ConfigTest(unittest.TestCase):43    _filenames = ('__testCaseConfig0__', '__testCaseConfig1__')44    _configCheck = ConfigParser.ConfigParser()45    _populated = False46    def _parserReload(self):47        self._configCheck = ConfigParser.ConfigParser()48        return self._configCheck.read((self._files), )49    def _setUpTestFiles(self):50        try:51            os.mkdir(os.path.dirname(self._files[1]))52        except OSError:53            pass54        test_file = open(self._files[1], 'w')55        configWriter = ConfigParser.ConfigParser()56        configWriter.add_section('Test_2')57        configWriter.set('Test_2', 'test2_1', '2')58        configWriter.set('Test_2', 'test2_2', '22')59        configWriter.set('Test_2', 'test2_3', 'True')60        configWriter.add_section('Test_3')61        configWriter.set('Test_3', 'test3_1', '3')62        configWriter.set('Test_3', 'test3_2', '33')63        configWriter.add_section('Test_4')64        configWriter.set('Test_4', 'test4_1', '4')65        configWriter.set('Test_4', 'test4_2', '44')66        configWriter.set('Test_4', 'test4_3', 'True')67        configWriter.set('Test_4', 'test4_4', 'Test')68        configWriter.write(test_file)69        test_file.close()70        test_file = open(self._files[1],'r')71        config.update(self._filenames[1], test_file)72        test_file.close()73    def setUp(self):74        files = []75        for file in self._filenames:76            files.append(os.path.join(config._USER_CONF_DIR, _PROGRAM_NAME,77                                      ''.join((file, config._CONF_FILE_EXT))))78        self._files = tuple(files)79        for file in files:80            try:81                os.remove(file)82            except OSError:83                pass84    def tearDown(self):85        for file_ in self._files:86            try:87                os.remove(file_)88            except OSError:89                pass90    def testGetProgramName(self):91        self.assertEqual(_PROGRAM_NAME, config.getProgramName())92    def testFileCreation(self):93        config.set(self._filenames[0])94        files = self._parserReload()95        self.assertTrue(self._files[0] in files)96    def testValueAddToNewFile(self):97        config.set(self._filenames[0], 'Test_0')98        config.set(self._filenames[0], 'Test_1', 'test1_1', 'True')99        config.set(self._filenames[0], 'Test_1', 'test1_2')100        self._parserReload()101        self.assertTrue(self._configCheck.has_section('Test_0'))102        self.assertTrue(self._configCheck.has_section('Test_1'))103        self.assertTrue(self._configCheck.get('Test_1', 'test1_1'))104        self.assertEqual('', self._configCheck.get('Test_1', 'test1_2'))105    def testValueAddExistingSection(self):106        self._setUpTestFiles()107        config.set(self._filenames[1], 'Test_5', 'test5_1', '5')108        self._parserReload()109        self.assertEqual('5', self._configCheck.get('Test_5', 'test5_1'))110    def testValueGet(self):111        self._setUpTestFiles()112        self.assertTrue(self._filenames[1] in config.get())113        self.assertTrue('Test_4' in config.get(self._filenames[1]))114        self.assertEqual(['test4_1', 'test4_2', 'test4_3', 'test4_4'],115                           sorted(config.get(self._filenames[1], 'Test_4')))116        self.assertEqual('44', config.get(self._filenames[1], 'Test_4',117                                                                    'test4_2'))118    def testValueRemove(self):119        self._setUpTestFiles()120        self._parserReload()121        self.assertEqual('4', self._configCheck.get('Test_4', 'test4_1'))122        config.remove(self._filenames[1], 'Test_4', 'test4_1')123        self._parserReload()124        self.assertRaises(ConfigParser.NoOptionError,125                            self._configCheck.get,'Test_4', 'test4_1')126        self.assertTrue('Test_4' in self._configCheck.sections())127        config.remove(self._filenames[1], 'Test_4')128        self.assertTrue(self._files[1] in self._parserReload())129        self.assertFalse('Test_4' in self._configCheck.sections())130        config.remove(self._filenames[1])131        self.assertFalse(self._files[1] in self._parserReload())132    def testGetBool(self):133        self._setUpTestFiles()134        self.assertEqual(True, config.getBool(self._filenames[1],135                                                         'Test_4', 'test4_3'))136        self.assertEqual(None, config.getBool(self._filenames[1],137                                                         'Test_4', 'test4_4'))138    def testListValue(self):139        self._setUpTestFiles()140        value = ["item1", "item2", "item3"]141        config.set(self._filenames[1], "Test_5", "test5_1", value)142        self.failUnlessEqual(value, config.getList(self._filenames[1],143                                                  "Test_5", "test5_1"))144    def testTupleValue(self):145        self._setUpTestFiles()146        value = (1, "item2", False)147        config.set(self._filenames[1], "Test_5", "test5_2", value)148        self.failUnlessEqual(["1", "item2", "False"],149                     config.getList(self._filenames[1], "Test_5", "test5_2"))150if __name__ == "__main__":...

Opengauss_Function_DML_Upsert_Case0118.py

Source:Opengauss_Function_DML_Upsert_Case0118.py

...27        logger.info('------------------------Opengauss_Function_DML_Upsert_Case0118å¼å§æ§è¡-----------------------------')28    def test_sysadmin_user_permission(self):29        # å»ºè¡¨æå®idåä¸ºå¯ä¸çº¦æä¸nameåä¸ºæ°ç»ç±»å30        sql_cmd1 = commonsh.execut_db_sql(''' drop table if exists test_4;31                                     create table test_4(name char[]  ,id int unique ,address nvarchar2(50)) ;''')32        logger.info(sql_cmd1)33        self.assertIn(constant.CREATE_TABLE_SUCCESS, sql_cmd1)34        # å¸¸è§insertæå¥ä¸æ¡æ°æ®35        # ä½¿ç¨insert..update..EXCLUDEDè¯å¥ï¼åæ°æ®(array['c','d','a'],3,'tianjin1')æ´æ¹ä¸º(array['c','d','a'],3,YUNNAN)å¹¶æ°å¢ä¸æ¡æ°æ®(array['c','d'],4,'dalian1')36        # ä½¿ç¨insert..update..EXCLUDEDè¯å¥ï¼ä¸¤æ¡æ°æ®ä¸»é®åéå¤ï¼æ´æ¹åçæ°æ®ä¸º(array['c','d','e'],3,'YUNNAN1')å(array['c','d','g'],4,'DAQING')37        sql_cmd2 = commonsh.execut_db_sql('''insert into test_4 values(array['c','d','a'],3,'tianjin1');38        explain analyse insert into test_4 values(array['c','d','e'],3,'yunnan'),(array['c','d'],4,'dalian1') ON duplicate key update address=upper(EXCLUDED.address);39        explain analyze insert into test_4 values(array['c','d','e'],3,'yunnan1'),(array['c','d','g'],4,'daqing') ON duplicate key update address=upper(EXCLUDED.address),name=EXCLUDED.name;''')40        logger.info(sql_cmd2)41        self.assertIn(constant.INSERT_SUCCESS_MSG, sql_cmd2)42       # æ¸çè¡¨æ°æ®43        sql_cmd3 = commonsh.execut_db_sql('''truncate test_4;''')44        logger.info(sql_cmd3)45        self.assertIn(constant.TRUNCATE_SUCCESS_MSG, sql_cmd3)...

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