Python workflow.WorkflowManager类代码示例

 def test_settings_2(self, mock_ip):
     # - if index is None, field and value are valid, it'll set for all IPs
     test_wm = WorkflowManager([u'a', u'b', u'c'])
     self.assertEqual(3, mock_ip.call_count)  # to make sure we're using the mock, not real IP
     test_wm.settings(None, u'filter repeats', True)
     for i in xrange(3):
         self.assertEqual(True, test_wm._settings[i][u'filter repeats'])

    def test_intervs_1(self, mock_guc, mock_rfa, mock_rep):
        """Ensure _intervs() calls everything in the right order, with the right args & settings.
           This test uses all the default settings."""
        test_settings = {'simple or compound': 'compound', 'quality': False}
        test_pieces = [MagicMock(spec_set=IndexedPiece) for _ in range(3)]
        returns = ['get_data() {}'.format(i) for i in range(len(test_pieces))]
        for piece in test_pieces:
            piece.get_data.side_effect = lambda *x: returns.pop(0)
        expected = ['get_data() {}'.format(i) for i in range(len(test_pieces))]
        exp_analyzers = [noterest.NoteRestIndexer, interval.IntervalIndexer]

        test_wc = WorkflowManager(test_pieces)
        test_wc.settings(None, 'include rests', True)
        actual = test_wc._intervs()  # pylint: disable=protected-access

        self.assertEqual(0, mock_guc.call_count)
        self.assertEqual(len(test_pieces), len(expected), len(actual))
        self.assertEqual(0, mock_rep.call_count)
        mock_rfa.assert_called_once_with('interval.IntervalIndexer')
        for piece in test_pieces:
            piece.get_data.assert_called_once_with(exp_analyzers, test_settings)
        for i in range(len(actual)):
            # NB: in real use, _run_freq_agg() would aggregate a piece's voice pairs and save it in
            #     self._result... but since that method's mocked out, we have to check here the
            #     return of each piece's get_data() call
            self.assertSequenceEqual(expected[i], actual[i])

 def test_interval_ngrams_2(self, mock_two, mock_all, mock_var, mock_rfa):
     # --> same as test_interval_ngrams_1(), but with "count frequency" set to False
     # 1.) prepare mocks
     ind_pieces = [MagicMock(spec=IndexedPiece) for _ in xrange(3)]
     mock_rfa.return_value = u'mock_rfa() return value'
     mock_two.return_value = [u'mock_two() return value']
     mock_all.return_value = [u'mock_all() return value']
     mock_var.return_value = [u'mock_var() return value']
     expected = [mock_all.return_value, mock_two.return_value, mock_var.return_value]
     # 2.) run the test
     test_wm = WorkflowManager(ind_pieces)
     test_wm.settings(0, u'voice combinations', u'all')
     test_wm.settings(1, u'voice combinations', u'all pairs')
     test_wm.settings(2, u'voice combinations', u'[[0, 1]]')
     test_wm.settings(None, 'count frequency', False)
     actual = test_wm._interval_ngrams()
     # 3.) verify the mocks
     # NB: in actual use, _run_freq_agg() would have the final say on the value of
     #     test_wm._result... but it's mocked out, which means we can test whether
     #     _interval_ngrams() puts the right stuff there
     self.assertEqual(3, len(test_wm._result))
     for ret_val in expected:
         self.assertTrue(ret_val in test_wm._result)
     mock_two.assert_called_once_with(1)
     mock_all.assert_called_once_with(0)
     mock_var.assert_called_once_with(2)
     self.assertEqual(0, mock_rfa.call_count)
     self.assertEqual(expected, actual)
     self.assertSequenceEqual(expected, test_wm._result)

 def test_intervs_3(self, mock_int, mock_nri, mock_rep, mock_rfa, mock_ror):
     # --> test whether _intervs() when we specify an impossible voice pair ('[[0, 1, 2]]')
     # 1.) prepare the test and mocks
     test_settings = {u'simple or compound': u'compound', u'quality': False}
     test_pieces = [MagicMock(IndexedPiece, name=x) for x in [u'test1', u'test2', u'test3']]
     the_dicts = [MagicMock(dict, name=u'get_data() piece' + str(i), return_value=[4]) \
                  for i in xrange(3)]
     returns = the_dicts
     def side_effect(*args):
         # NB: we need to accept "args" as a mock framework formality
         # pylint: disable=W0613
         return returns.pop(0)
     for piece in test_pieces:
         piece.get_data.side_effect = side_effect
     mock_ror.return_value = [pandas.Series(['Rest', 'P5', 'm3']) for _ in xrange(len(test_pieces))]
     expected = [pandas.Series(['Rest', 'P5', 'm3']) for _ in xrange(len(test_pieces))]
     expected_pairs = [[0, 1, 2]]
     expected_error_message = WorkflowManager._REQUIRE_PAIRS_ERROR % len(expected_pairs[0])
     # 2.) run the test
     test_wc = WorkflowManager(test_pieces)
     # (have to set the voice-pair settings)
     for i in xrange(len(test_pieces)):
         test_wc._settings[i][u'voice combinations'] = unicode(expected_pairs)
     # 3.) verify
     self.assertRaises(RuntimeError, test_wc._intervs)
     try:
         test_wc._intervs()
     except RuntimeError as runerr:
         self.assertEqual(expected_error_message, runerr.message)

 def test_intervs_1(self, mock_int, mock_nri, mock_rfa, mock_ror):
     # --> test whether _intervs() calls all those things in the right order, with the right
     #     args, using all the default settings
     # 1.) prepare the test and mocks
     test_settings = {u'simple or compound': u'compound', u'quality': False}
     test_pieces = [MagicMock(IndexedPiece, name=x) for x in [u'test1', u'test2', u'test3']]
     returns = [MagicMock(dict, name=u'get_data() piece' + str(i), return_value=[4]) \
                for i in xrange(3)]
     def side_effect(*args):
         # NB: we need to accept "args" as a mock framework formality
         # pylint: disable=W0613
         return returns.pop(0)
     for piece in test_pieces:
         piece.get_data.side_effect = side_effect
     mock_ror.return_value = [pandas.Series(['Rest', 'P5', 'm3']) for _ in xrange(len(test_pieces))]
     expected = [pandas.Series(['P5', 'm3'], index=[1, 2]) for _ in xrange(len(test_pieces))]
     # 2.) run the test
     test_wc = WorkflowManager(test_pieces)
     actual = test_wc._intervs()
     # 3.) confirm everything was called in the right order
     for piece in test_pieces:
         self.assertEqual(1, piece.get_data.call_count)
         piece.get_data.assert_called_once_with([mock_nri, mock_int], test_settings)
     self.assertEqual(len(test_pieces), mock_ror.call_count)
     mock_rfa.assert_called_once_with()
     self.assertEqual(len(test_pieces), len(expected), len(actual))
     for i in xrange(len(actual)):
         # NB: in real use, _run_freq_agg() would aggregate a piece's voice pairs, so we
         #     wouldn't need to ask for the [0] index here... but also, this experiment shouldn't
         #     call _run_freq_agg() anyway
         self.assertSequenceEqual(list(expected[i]), list(actual[i][0]))
         self.assertSequenceEqual(list(expected[i].index), list(actual[i][0].index))

    def test_intervs_3(self, mock_guc, mock_rfa, mock_rep):
        """Ensure _intervs() calls everything in the right order, with the right args & settings.
           This uses the default *except* requires removing rests, so it's more complex."""
        test_settings = {'simple or compound': 'compound', 'quality': False}
        test_pieces = [MagicMock(spec_set=IndexedPiece) for _ in range(3)]
        returns = [pandas.DataFrame([pandas.Series(['3', 'Rest', '5'])],
                                    index=[['interval.IntervalIndexer'], ['0,1']]).T
                   for i in range(len(test_pieces))]
        for piece in test_pieces:
            piece.get_data.side_effect = lambda *x: returns.pop(0)
        exp_series_ind = ('interval.IntervalIndexer', '0,1')
        expected_df = pandas.DataFrame({exp_series_ind: pandas.Series(['3', '5'], index=[0, 2])})
        exp_analyzers = [noterest.NoteRestIndexer, interval.IntervalIndexer]

        test_wc = WorkflowManager(test_pieces)
        test_wc.settings(None, 'count frequency', False)
        actual = test_wc._intervs()  # pylint: disable=protected-access

        self.assertEqual(0, mock_guc.call_count)
        self.assertEqual(len(test_pieces), len(actual))
        self.assertEqual(0, mock_rfa.call_count)
        self.assertEqual(0, mock_rep.call_count)
        for piece in test_pieces:
            piece.get_data.assert_called_once_with(exp_analyzers, test_settings)
        for i in range(len(actual)):
            self.assertSequenceEqual(list(expected_df.columns), list(actual[i].columns))
            self.assertSequenceEqual(list(expected_df[exp_series_ind].index),
                                     list(actual[i][exp_series_ind].index))
            self.assertSequenceEqual(list(expected_df[exp_series_ind].values),
                                     list(actual[i][exp_series_ind].values))

 def test_load_1(self):
     # that "get_data" is called correctly on each thing
     test_wc = WorkflowManager([])
     test_wc._data = [mock.MagicMock(spec=IndexedPiece) for _ in xrange(5)]
     test_wc.load(u'pieces')
     for mock_piece in test_wc._data:
         mock_piece.get_data.assert_called_once_with([noterest.NoteRestIndexer])
     self.assertTrue(test_wc._loaded)

 def test_output_4(self):
     """ensure RuntimeError if self._result is None"""
     test_wc = WorkflowManager([])
     test_wc._result = None  # just in case
     self.assertRaises(RuntimeError, test_wc.output, 'R histogram')
     try:
         test_wc.output('R histogram')
     except RuntimeError as run_err:
         self.assertEqual(WorkflowManager._NO_RESULTS_ERROR, run_err.args[0])

 def test_lilypond_1a(self):
     """error conditions: if 'count frequency' is True (but the lengths are okay)"""
     test_wm = WorkflowManager(['fake piece'])
     test_wm._data = ['fake IndexedPiece']
     test_wm._result = ['fake results']
     # test twice like this to make sure (1) the try/except will definitely catch something, and
     # (2) we're not getting hit by another RuntimeError, of which there could be many
     with self.assertRaises(RuntimeError) as run_err:
         test_wm._make_lilypond(['paths'])
     self.assertEqual(WorkflowManager._COUNT_FREQUENCY_MESSAGE, run_err.exception.args[0])

 def test_get_dataframe_2(self):
     # test with name='asdf', top_x=3, threshold=auto
     test_wc = WorkflowManager([])
     test_wc._result = pandas.Series([i for i in xrange(10, 0, -1)])
     expected = pandas.DataFrame({'asdf': pandas.Series([10, 9, 8])})
     actual = test_wc._get_dataframe('asdf', 3)
     self.assertEqual(len(expected.columns), len(actual.columns))
     for i in expected.columns:
         self.assertSequenceEqual(list(expected.loc[:,i].index), list(actual.loc[:,i].index))
         self.assertSequenceEqual(list(expected.loc[:,i].values), list(actual.loc[:,i].values))

 def test_get_dataframe_4(self):
     # test with name=auto, top_x=5, threshold=7 (so threshold leaves fewer than 3 results)
     test_wc = WorkflowManager([])
     test_wc._result = pandas.Series([i for i in xrange(10, 0, -1)])
     expected = pandas.DataFrame({'data': pandas.Series([10, 9, 8])})
     actual = test_wc._get_dataframe(top_x=5, threshold=7)
     self.assertEqual(len(expected.columns), len(actual.columns))
     for i in expected.columns:
         self.assertSequenceEqual(list(expected.loc[:,i].index), list(actual.loc[:,i].index))
         self.assertSequenceEqual(list(expected.loc[:,i].values), list(actual.loc[:,i].values))

 def test_filter_dataframe_3(self):
     """test with top_x=3, threshold=5 (so the top_x still removes after threshold), name=auto"""
     test_wc = WorkflowManager([])
     test_wc._result = pandas.DataFrame({'data': pandas.Series([i for i in range(10, 0, -1)])})
     expected = pandas.DataFrame({'data': pandas.Series([10, 9, 8])})
     actual = test_wc._filter_dataframe(top_x=3, threshold=5)
     self.assertEqual(len(expected.columns), len(actual.columns))
     for i in expected.columns:
         self.assertSequenceEqual(list(expected[i].index), list(actual[i].index))
         self.assertSequenceEqual(list(expected[i].values), list(actual[i].values))

 def test_settings_9(self, mock_ip):
     # - if trying to set 'offset interval' to 0, it should actually be set to None
     test_wm = WorkflowManager([u'a', u'b', u'c'])
     self.assertEqual(3, mock_ip.call_count)  # to make sure we're using the mock, not real IP
     # "None" is default value, so first set to non-zero
     test_wm.settings(1, u'offset interval', 4.0)
     self.assertEqual(4.0, test_wm._settings[1][u'offset interval'])
     # now run our test
     test_wm.settings(1, u'offset interval', 0)
     self.assertEqual(None, test_wm._settings[1][u'offset interval'])

 def test_output_3(self):
     """ensure RuntimeError if there's an invalid instruction"""
     test_wc = WorkflowManager([])
     test_wc._result = [5]  # make sure that's not what causes it
     bad_instruction = 'eat dirt'
     self.assertRaises(RuntimeError, test_wc.output, bad_instruction)
     try:
         test_wc.output(bad_instruction)
     except RuntimeError as run_err:
         self.assertEqual(WorkflowManager._UNRECOGNIZED_INSTRUCTION.format(bad_instruction),
                          run_err.args[0])

 def test_filter_dataframe_5(self):
     """test with top_x=3, threshold=auto, name='asdf'; many input columns"""
     test_wc = WorkflowManager([])
     test_wc._result = pandas.DataFrame({('1', 'b'): pandas.Series([i for i in range(10, 0, -1)]),
                                         ('1', 'z'): pandas.Series([i for i in range(10, 20)]),
                                         ('2', 'e'): pandas.Series([i for i in range(40, 900)])})
     expected = pandas.DataFrame({'asdf': pandas.Series([10, 9, 8])})
     actual = test_wc._filter_dataframe(top_x=3, name='asdf')
     self.assertEqual(len(expected.columns), len(actual.columns))
     for i in expected.columns:
         self.assertSequenceEqual(list(expected[i].index), list(actual[i].index))
         self.assertSequenceEqual(list(expected[i].values), list(actual[i].values))