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How to use test_4 method in Lemoncheesecake
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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(°C):", width = 20)15lbl_cap = tk.Label(dist, text = "Capacity(kW):", width = 20)16lbl_liq_t = tk. Label(dist, text = "Liquid Temperature(°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 = ['-20°C','-15°C','-10°C','-5°C','0°C','5°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 = "!@#$%^&*()_+=<>,?/':;{}[]\|`~-"46 for i in np.arange(0, len(txtbx_evap_t.get()), 1):47 for j in np.arange(0, len(test_4), 1):48 if txtbx_evap_t.get()[i] == test_4[j]:49 count = count + 150 if count > 0:51 messagebox.showerror("Error","Be sure to enter the values in correctly.")52 else:53 noz_cap = pd.read_excel('Distributor table.xlsx', sheet_name = 'Distributor Nozzle Capacities')54 test_1 = txtbx_evap_t.get()55 if any(c.isalpha() for c in test_1):56 messagebox.showerror("Error","Enter in a valid value for Evap Temperature.")57 else:58 evap_t = float(txtbx_evap_t.get())59 if evap_t >= -25:60 return np.array(noz_cap[['Nozzle number:','-20°C']])61 elif evap_t < -25 and evap_t >= -35:62 return np.array(noz_cap[['Nozzle number:','-30°C']])63 elif evap_t < -35:64 return np.array(noz_cap[['Nozzle number:','-40°C']])65 66def dist_tube_caps():67 count = 068 test_4 = "!@#$%^&*()_+=<>,?/':;{}[]\|`~"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:','-20°C']])84 elif evap_t < -25 and evap_t >= -35:85 return np.array(tube_cap[['Tube Diameter:','-30°C']])86 elif evap_t < -35:87 return np.array(tube_cap[['Tube Diameter:','-40°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 # 建表æå®idå为å¯ä¸çº¦æä¸nameå为æ°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 # 常è§insertæå
¥ä¸æ¡æ°æ®35 # 使ç¨insert..update..EXCLUDEDè¯å¥ï¼åæ°æ®(array['c','d','a'],3,'tianjin1')æ´æ¹ä¸º(array['c','d','a'],3,YUNNAN)并æ°å¢ä¸æ¡æ°æ®(array['c','d'],4,'dalian1')36 # 使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¸
ç表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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