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_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_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_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_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_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_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_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_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_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_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_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_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_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))
def test_ngrams_6(self):
# test madrigal51 with all-voice 2-grams and rests
test_wm = WorkflowManager(['vis/tests/corpus/madrigal51.mxl'])
test_wm.settings(0, 'voice combinations', 'all')
test_wm.settings(None, 'include rests', True)
test_wm.load('pieces')
test_wm.settings(0, 'continuer', '_')
actual = test_wm.run('interval n-grams')
exp_ind = list(NGramsTests.EXPECTED_6.index)
act_ind = list(actual.index)
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
for ind_item in exp_ind:
self.assertEqual(NGramsTests.EXPECTED_6[ind_item], actual[ind_item])
def test_ngrams_2(self):
# test all two-part combinations of bwv77; 5-grams
test_wm = WorkflowManager(['vis/tests/corpus/bwv77.mxl'])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', 'all pairs')
test_wm.settings(0, 'n', 5)
test_wm.settings(0, 'continuer', '_')
actual = test_wm.run('interval n-grams')[:10]
exp_ind = list(NGramsTests.EXPECTED_2.index)
act_ind = list(actual.index)
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
for ind_item in exp_ind:
self.assertEqual(NGramsTests.EXPECTED_2[ind_item], actual[ind_item])
def test_interval_ngrams_1(self, mock_two, mock_all, mock_var, mock_rfa):
# --> test with three pieces, each of which requires a different helper
# 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]]')
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
mock_two.assert_called_once_with(1)
mock_all.assert_called_once_with(0)
mock_var.assert_called_once_with(2)
mock_rfa.assert_called_once_with()
self.assertSequenceEqual(expected, actual)
self.assertSequenceEqual(expected, test_wm._result)
def test_ngrams_4(self):
# test all voices of bwv2; 3-grams; simple intervals
test_wm = WorkflowManager(['vis/tests/corpus/bwv2.xml'])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', 'all')
test_wm.settings(0, 'n', 2)
test_wm.settings(None, 'simple intervals', True)
actual = test_wm.run('interval n-grams')[:10]
exp_ind = list(NGramsTests.EXPECTED_4.index)
act_ind = list(actual.index)
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
for ind_item in exp_ind:
self.assertEqual(NGramsTests.EXPECTED_4[ind_item], actual[ind_item])
def test_ngrams_1(self):
# test the two highest voices of bwv77; 2-grams
test_wm = WorkflowManager(['vis/tests/corpus/bwv77.mxl'])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', '[[0, 1]]')
test_wm.settings(0, 'n', 2)
test_wm.settings(0, 'continuer', '_')
actual = test_wm.run('interval n-grams')
exp_ind = list(NGramsTests.EXPECTED_1.index)
act_ind = list(actual.index)
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
for ind_item in exp_ind:
self.assertEqual(NGramsTests.EXPECTED_1[ind_item], actual[ind_item])
def test_histogram_1(self, mock_call, mock_gdf, mock_join):
# with specified pathname; last experiment was intervals with 20 pieces; self._result is DF
test_wc = WorkflowManager([])
test_wc._previous_exp = u'intervals'
test_wc._data = [1 for _ in xrange(20)]
test_wc._result = MagicMock(spec=pandas.DataFrame)
path = u'pathname!'
actual = test_wc._make_histogram(path)
self.assertEqual(0, mock_gdf.call_count)
expected_args = [u'Rscript', u'--vanilla', mock_join.return_value,
path + u'.dta', path + u'.png', u'int', u'20']
mock_call.assert_called_once_with(expected_args)
self.assertEqual(path + u'.png', actual)
self.assertEqual(1, mock_join.call_count)
def test_ngrams_9c(self):
# same as 9b but 'interval quality' is set to False (by default).
test_wm = WorkflowManager(['vis/tests/corpus/Kyrie_short.krn'])
test_wm.load()
test_wm.settings(0, 'voice combinations', '[[1,3]]')
test_wm.settings(0, 'n', 2)
test_wm.settings(0, 'offset interval', 2.0)
actual = test_wm.run('interval n-grams')
exp_ind = list(NGramsTests.EXPECTED_9c.index)
act_ind = list(actual.index)
self.assertEqual(len(exp_ind), len(act_ind))
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
self.assertEqual(NGramsTests.EXPECTED_9c[ind_item], actual[ind_item])
def test_load_3(self):
# NB: this is more of an integration test
test_wc = WorkflowManager([u'vis/tests/corpus/try_opus.krn'])
test_wc.load('pieces')
self.assertEqual(3, len(test_wc))
# NOTE: we have to do this by digging until music21 imports metadata from **kern files, at
# which point we'll be able to use our very own metadata() method
exp_names = [u'Alex', u'Sarah', u'Emerald']
for i in xrange(3):
# first Score gets some extra metadata
which_el = 5 if i == 0 else 3
piece = test_wc._data[i]._import_score()
self.assertTrue(isinstance(piece[which_el], GlobalReference))
self.assertEqual(u'COM', piece[which_el].code)
self.assertEqual(exp_names[i], piece[which_el].value)
def test_run_off_rep_4(self, mock_off, mock_rep):
# run offset and repeat indexer
# setup
workm = WorkflowManager(['', '', ''])
workm._data = [None, MagicMock(spec=IndexedPiece), None]
workm._data[1].get_data.return_value = 24
workm.settings(1, 'offset interval', 0.5)
workm.settings(1, 'filter repeats', True)
in_val = 42
# run
actual = workm._run_off_rep(1, in_val)
# test
self.assertEqual(workm._data[1].get_data.return_value, actual)
self.assertEqual(2, workm._data[1].get_data.call_count)
workm._data[1].get_data.assert_any_call([mock_off], {'quarterLength': 0.5}, in_val)
workm._data[1].get_data.assert_any_call([mock_rep], {}, workm._data[1].get_data.return_value)
def test_extra_pairs_5(self):
# --> when there are lots of pairs, only some of which are desired, and there are invalid
vert_ints = {'4,20': 0, '0,1': 1, '11,12': 4, '0,2': 2, '1,2': 3, '256,128': 12}
combos = [[0, 1], [1, 2, 3, 4, 5], [0, 2], [1, 2], [9, 11, 43], [4]]
expected = {'0,1': 1, '0,2': 2, '1,2': 3}
actual = WorkflowManager._remove_extra_pairs(vert_ints, combos)
self.assertSequenceEqual(expected, actual)
def test_output_2(self, mock_lily):
# ensure output() calls _make_lilypond() as required
# 1: prepare
lily_path = u'the_path'
mock_lily.return_value = lily_path
test_wc = WorkflowManager([])
test_wc._previous_exp = u'intervals'
test_wc._data = [1 for _ in xrange(20)]
test_wc._result = MagicMock(spec=pandas.DataFrame)
path = u'pathname!'
expected_args = [path]
# 2: run
actual = test_wc.output('LilyPond', path)
# 3: check
self.assertEqual(lily_path, actual)
mock_lily.assert_called_once_with(*expected_args)
def test_intervals_7(self): # TODO: add a frequency-counted test
"""same as test_6 *but* no quality"""
# NB: the "expected" was hand-counted
expected = pandas.read_csv(os.path.join(VIS_PATH, 'tests', 'expecteds', 'bwv77', 'SA_intervals_nq.csv'),
comment='#', index_col=0, header=[0, 1], quotechar="'", dtype='object')
test_wm = WorkflowManager([os.path.join(VIS_PATH, 'tests', 'corpus', 'bwv77.mxl')])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', '[[0, 1]]')
test_wm.settings(None, 'count frequency', False)
test_wm.settings(None, 'interval quality', False)
actual = test_wm.run('intervals')
self.assertEqual(1, len(actual))
actual = actual[0].dropna()
self.assertDataFramesEqual(expected, actual)
def test_output_5(self, mock_table):
"""ensure output() calls export() as required"""
# 1: prepare
export_path = 'the_path'
mock_table.return_value = export_path
test_wc = WorkflowManager([])
test_wc._previous_exp = 'intervals'
test_wc._data = [1 for _ in range(20)]
test_wc._result = MagicMock(spec=pandas.DataFrame)
path = 'pathname!'
expected_args = ['Excel', path, None, None]
# 2: run
actual = test_wc.output('Excel', path)
# 3: check
self.assertEqual(export_path, actual)
mock_table.assert_called_once_with(*expected_args)
def test_extra_pairs_2(self):
# testing WorkflowManager._remove_extra_pairs()
# --> when no pairs are desired
vert_ints = {'0,1': 1, '0,2': 2, '1,2': 3}
combos = []
expected = {}
actual = WorkflowManager._remove_extra_pairs(vert_ints, combos)
self.assertSequenceEqual(expected, actual)
def test_extra_pairs_3(self):
# testing WorkflowManager._remove_extra_pairs()
# --> when there are desired pairs, but they are not present
vert_ints = {'0,1': 1, '0,2': 2, '1,2': 3}
combos = [[4, 20], [11, 12]]
expected = {}
actual = WorkflowManager._remove_extra_pairs(vert_ints, combos)
self.assertSequenceEqual(expected, actual)
def test_intervals_4(self):
"""test all combinations of madrigal51 with rests"""
test_wm = WorkflowManager(
[os.path.join(VIS_PATH, 'tests', 'corpus', 'madrigal51.mxl')])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', 'all pairs')
test_wm.settings(None, 'include rests', True)
actual = test_wm.run('intervals')
self.assertEqual(1, len(actual.columns))
actual = actual['aggregator.ColumnAggregator']
exp_ind = list(IntervalsTests.EXPECTED_4.index)
act_ind = list(actual.index)
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
for ind_item in exp_ind:
self.assertEqual(IntervalsTests.EXPECTED_4[ind_item],
actual[ind_item])
def test_intervs_2(self, mock_guc, mock_rfa, mock_rep):
"""Ensure _intervs() calls everything in the right order, with the right args & settings.
Same as test_intervs_1() but:
- calls _remove_extra_pairs(), and
- doesn't call _run_freq_agg()."""
mock_guc.return_value = [[0, 1]]
voice_combos = str(mock_guc.return_value)
exp_voice_combos = ['0,1']
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)
exp_into_mock_rep = [
'get_data() {}'.format(i) for i in range(len(test_pieces))
]
mock_rep_returns = [
'IndP-{} no pairs'.format(i) for i in range(len(test_pieces))
]
mock_rep.side_effect = lambda *x: mock_rep_returns.pop(0)
expected = [
'IndP-{} no pairs'.format(i) for i in range(len(test_pieces))
]
exp_analyzers = [noterest.NoteRestIndexer, interval.IntervalIndexer]
exp_mock_guc = [mock.call(i) for i in range(len(test_pieces))]
test_wc = WorkflowManager(test_pieces)
test_wc.settings(None, 'include rests', True)
test_wc.settings(None, 'count frequency', False)
test_wc.settings(None, 'voice combinations', voice_combos)
actual = test_wc._intervs() # pylint: disable=protected-access
self.assertSequenceEqual(exp_mock_guc, mock_guc.call_args_list)
self.assertEqual(len(test_pieces), len(expected), len(actual))
self.assertEqual(0, mock_rfa.call_count)
self.assertEqual(len(test_pieces), mock_rep.call_count)
for i in range(len(test_pieces)):
mock_rep.assert_any_call(exp_into_mock_rep[i], exp_voice_combos)
for piece in test_pieces:
piece.get_data.assert_called_once_with(exp_analyzers,
test_settings)
for i in range(len(actual)):
self.assertSequenceEqual(expected[i], actual[i])
def test_intervals_5(self):
"""test all combinations of vis_Test_Piece"""
test_wm = WorkflowManager(
[os.path.join(VIS_PATH, 'tests', 'corpus', 'vis_Test_Piece.xml')])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', 'all pairs')
test_wm.settings(None, 'include rests', True)
expected = pandas.read_csv(os.path.join(VIS_PATH, 'tests', 'corpus',
'test_intervals_5.csv'),
index_col=0)
actual = test_wm.run('intervals')
self.assertEqual(1, len(actual.columns))
self.assertSequenceEqual(list(expected.columns), list(actual.columns))
for col_name in expected.columns:
self.assertEqual(len(expected[col_name]), len(actual[col_name]))
for each_interval in expected[col_name].index:
# NOTE: for whatever reason, the "expected" file always imports with an Int64Index,
# so .loc() won't find things properly unless we give the int64 index to the
# expected and the str index to the actual
self.assertEqual(expected[col_name].loc[each_interval],
actual[col_name].loc[str(each_interval)])
def test_intervals_6(self): # TODO: add a frequency-counted test
"""test Soprano and Alto of bwv77; with quality; not counting frequency"""
# NB: the "expected" was hand-counted
expected = pandas.read_csv(os.path.join(VIS_PATH, 'tests', 'expecteds',
'bwv77', 'SA_intervals.csv'),
comment='#',
index_col=0,
header=[0, 1],
quotechar="'")
test_wm = WorkflowManager(
[os.path.join(VIS_PATH, 'tests', 'corpus', 'bwv77.mxl')])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', '[[0, 1]]')
test_wm.settings(None, 'count frequency', False)
test_wm.settings(None, 'interval quality', True)
actual = test_wm.run('intervals')
self.assertEqual(1, len(actual))
actual = actual[0].dropna()
self.assertDataFramesEqual(expected, actual)
def test_intervs_4(self):
"""Ensure _intervs() fails when given an impossible 'voice pair'."""
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)
exp_err_msg = WorkflowManager._REQUIRE_PAIRS_ERROR.format(3) # pylint: disable=protected-access
test_wc = WorkflowManager(test_pieces)
test_wc.settings(None, 'include rests', True)
test_wc.settings(None, 'voice combinations',
'[[0, 1, 2]]') # that's not a pair!
self.assertRaises(RuntimeError, test_wc._intervs) # pylint: disable=protected-access
try:
test_wc._intervs() # pylint: disable=protected-access
except RuntimeError as run_err:
self.assertEqual(exp_err_msg, run_err.args[0])
def test_intervals_1(self):
"""test the two highest voices of bwv77"""
test_wm = WorkflowManager(
[os.path.join(VIS_PATH, 'tests', 'corpus', 'bwv77.mxl')])
test_wm.load('pieces')
test_wm.settings(0, 'voice combinations', '[[0, 1]]')
actual = test_wm.run('intervals')
self.assertEqual(1, len(actual.columns))
actual = actual['aggregator.ColumnAggregator']
exp_ind = list(IntervalsTests.EXPECTED_1.index)
act_ind = list(actual.index)
for ind_item in exp_ind:
self.assertTrue(ind_item in act_ind)
for ind_item in exp_ind:
self.assertEqual(IntervalsTests.EXPECTED_1[ind_item],
actual[ind_item])
def import_files(request):
"""
Called with the /api/import URL to load files into the WorkflowManager and return the metadata
it discovers.
"""
filepaths = [
piece.file.path
for piece in Piece.objects.filter(user_id=request.session.session_key)
]
workm = WorkflowManager(filepaths)
workm.load('pieces')
request.session['workm'] = workm
return [{
"Filename": os.path.basename(workm.metadata(i, 'pathname')),
"Title": workm.metadata(i, 'title'),
"Part Names": workm.metadata(i, 'parts'),
"Offset": None,
"Part Combinations": None,
"Repeat Identical": False
} for i in xrange(len(workm))], 200
class WorkflowWrapper(QAbstractTableModel):
"""
This is a wrapper class for the :class:`vis.workflow.WorkflowManager` that allows its use as a
:class:`QAbstractTableModel` for PyQt4. This class only wraps :meth:`metadata` and
:meth:`setting`, which are those methods needed by PyQt. For all other :class:`Workflowmanager`
methods, access the internally-stored instance with :meth:`get_workflow_manager`.
** How to Use the WorkflowWrapper: **
The :class:`WorkflowWrapper` always returns valid values, and will not raise exceptions of its
own. However, "valid" values are not always "correct" or "expected." We recommend you use the
:class:`WorkflowWrapper` like this:
#. Instantiate the object.
#. Set the object as the model for its view.
#. Call :meth:`insertRows` with the total number of pieces to be added.
#. Call :meth:`setData` once per piece to set the pathname.
#. Once each row has a pathname, the object will instantiate its internal
:class:`WorkflowManager` instance and call its :meth:`load` method to import the score,
run the :class:`NoteRestIndexer`, and save its metadata.
#. Subsequent calls to :meth:`data` will return the most correct information available.
** How not to Use the WorkflowWrapper: **
We recommend you do not use the :class:`WorkflowWrapper` like this:
* Do not add pieces by calling :meth:`insertRows` then :meth:`setData` with the pathname, then
:meth:`insertRows` then :meth:`setData` with the pathname, and so on. In this case, the
:class:`WorkflowWrapper` would create a new :class:`WorkflowManager` after each call to
:meth:`setData`, which would import each piece many times.
* Do not add pieces after you modify a metadata or setting field. When you add a piece, a new
:class:`WorkflowManager` instance is created. The new instance replaces any customized
settings and metadata with the default values.
* Do not call :meth:`data` before you add the pathnames of all pieces. Real metadata is only
available after the :class:`WorkflowManager` is created, which happens after all the pieces
have a pathname. If you call :meth:`data` when there is no :class:`WorkflowManager`, the
return value will always be ``None``.
** Columns in the Data Model: **
The :class:`WorkflowWrapper` creates a two-dimensional data model, where each row represents an
:class:`IndexedPiece` stored in the :class:`WorkflowManager` and each column represents either
a setting or a metadatum field. The following six fields are different for each piece, and
should be displayed in the :class:`QTableView` widget:
* filename
* title
* parts_list
* offset_interval
* parts_combinations
* repeat_identical
The :class:`WorkflowManager` additionally wraps these data fields, which are shared by all
pieces, and will therefore not apear in the :class:`QTableView` widget:
* quality
* simple_ints
Use class properties with those names to specify columns to :meth:`data` and :meth:`getData`.
For example:
>>> workm.data((0, WorkflowWrapper.title), Qt.DisplayRole)
u'02_eleva'
>>> workm.setData((0, WorkflowWrapper.title), u'Elevator Love Letter', Qt.EditRole)
>>> workm.data((0, WorkflowWrapper.parts_list), Qt.DisplayRole)
[u'Amy Milan', u'Torquil Campbell', u'Evan Cranley', u'Chris Seligman', u'Pat McGee']
"""
# Public class variables to track which column has which data
# NOTE: Update _num_cols whenever you change the number of columns,
# since this variable is used by columnCount().
# NOTE: Update _header_names whenever you change the number or definition of
# columns, since this variale is used by headerData().
_num_cols = 6
_header_names = [
'Path', 'Title', 'List of Part Names', 'Offset Interval',
'Part Combinations', 'Repeat Identical'
]
# displayed fields
filename = 0
title = 1
parts_list = 2
offset_interval = 3
parts_combinations = 4
repeat_identical = 5
# non-displayed fields
quality = 100
simple_ints = 101
# instead of DisplayRole; used to tell data() to return the list of part names as a list
ListRole = 4000
# when a value hasn't been set, return a QVariant with this in it
default_value = u'(unset)'
def __init__(self, parent=QModelIndex()):
"""
Create a new :class:`WorkflowWrapper` instance.
"""
super(WorkflowWrapper, self).__init__()
self._pathnames = [
] # hold a list of pathnames for before the WorkflowManager
self._workm = None # hold the WorkflowManager
self._settings_changed = False # whether setData() was called (for settings_changed())
self._when_done_import = None # method to call when we're finished importing
def rowCount(self, parent=QModelIndex()):
"""
Return the number of pieces in this list. If the internal :class:`WorkflowManager` exists,
this is the number of piece stored there; otherwise it is the number of places for
pathnames.
:returns: The number of pieces in this list.
:rtype: ``int``
"""
if self._workm is None:
return len(self._pathnames)
else:
return len(self._workm)
def columnCount(self, parent=QModelIndex()):
"Return the number of columns in this WorkflowWrapper."
return WorkflowWrapper._num_cols
def data(self, index, role):
"""
Get the data for the piece and metadatum or setting specified. Only the "parts_list" column
responds to the WorkflowWrapper.ListRole, in which case a list of strings is returned
instead of a comma-separated list of part names.
:param index: The row-and-column index you wish to access. Either you can use a
:class:`QModelIndex` or a 2-tuple where the first element is an ``int`` representing the
index of the piece in the models, and the second element is one of the class properties
described above in "Columns in the Data Model."
:type index: :class:`QModelIndex` or 2-tuple of ``int``
:param role: Either Qt.DisplayRole or WorkflowWrapper.ListRole
:type role: ``int``
:returns: The requested data or, if ``index`` or ``role`` is invalid, ``None``.
:rtype: :class:`QVariant`
.. note:: The method always returns a :class:`QVariant`. Access the Python object with the
:meth:`toPyObject` method.
.. note:: If the internal :class:`WorkflowManager` has not been instantiated, the return
value is always ``None``.
.. note:: This method never actually returns ``None``, but rather an empty :class:`QVariant`
that will be ``None`` when you call :meth:`toPyObject` on it.
"""
if self._workm is None or (Qt.DisplayRole != role
and WorkflowWrapper.ListRole != role):
return QVariant()
# Set the row and column
row = None
column = None
if isinstance(index, QModelIndex):
# if the QModelIndex is invalid, we won't bother with it
if not index.isValid():
return QVariant()
# otherwise, get the row and column from the QModelIndex
row = index.row()
column = index.column()
else:
row = index[0]
column = index[1]
# Verify the row and column
if row >= self.rowCount() or column >= self._num_cols and \
(column != WorkflowWrapper.quality and column != WorkflowWrapper.simple_ints):
return QVariant()
post = None
if Qt.DisplayRole == role:
# displayed fields
if WorkflowWrapper.filename == column:
post = self._workm.metadata(row, u'pathname')
elif WorkflowWrapper.title == column:
post = self._workm.metadata(row, u'title')
elif WorkflowWrapper.parts_list == column:
post = u', '.join(self._workm.metadata(row, u'parts'))
elif WorkflowWrapper.offset_interval == column:
post = self._workm.settings(row, u'offset interval')
elif WorkflowWrapper.parts_combinations == column:
post = self._workm.settings(row, u'voice combinations')
elif WorkflowWrapper.repeat_identical == column:
# the wording in the GUI and WorkflowManager has opposite meanings
post = not self._workm.settings(row, u'filter repeats')
# non-displayed fields
elif WorkflowWrapper.quality == column:
post = self._workm.settings(None, u'interval quality')
elif WorkflowWrapper.simple_ints == column:
post = self._workm.settings(None, u'simple intervals')
else:
post = QVariant()
elif WorkflowWrapper.ListRole == role:
if WorkflowWrapper.parts_list == column:
post = self._workm.metadata(row, u'parts')
else:
post = QVariant()
else:
post = QVariant()
if not isinstance(post, QVariant):
if post is None:
post = WorkflowWrapper.default_value
post = QVariant(post)
return post
def headerData(self, section, orientation, role):
"""
Return the column names for a WorkflowWrapper instance.
Arguments:
- section: the index of the column you want the name of
- orientation: should be Qt.Horizontal; Qt.Vertical is ignored
- role: should be Qt.DisplayRole; others are ignored
If the section index is out of range, or the orientation or role is
different than expected, an empty QVariant is returned.
"""
# All of the column titles are stored as class variables. I decided to
# use the class name here, rather than "self," just in case they were
# accidentally changed in this instance. We do not want to allow that.
if Qt.Horizontal == orientation and Qt.DisplayRole == role and \
0 <= section < WorkflowWrapper._num_cols:
return WorkflowWrapper._header_names[section]
else:
return QVariant()
def setData(self, index, value, role):
"""
Set the data for the piece and metadatum or setting specified. If the internal
:class:`WorkflowManager` has not yet been created and this is the last pathname added,
instantiate the :class:`WorkflowManager` and call :meth:`load`.
:param index: The row-and-column index you wish to access. Either you can use a
:class:`QModelIndex` or a 2-tuple where the first element is an ``int`` representing the
index of the piece in the models, and the second element is one of the class properties
described above in "Columns in the Data Model."
:type index: :class:`QModelIndex` or 2-tuple of ``int``
:param value: The desired value of the setting or metadatum. If you submit a
:class:`QVariant`, we will call :meth:`toPyObject` before sending to the
:class:`WorkflowManager`.
:type value: :class:`QVariant` or any
:param role: This should be Qt.EditRole.
:type role: :class:`EditRole`
:returns: Whether the data was successfully set.
:rtype: ``True`` or ``False``
.. note:: If the internal :class:`WorkflowManager` has not been instantiated, you can only
set the ``pathname`` field. All other calls to :meth:`setData` will fail.
"""
if Qt.EditRole != role:
return False
# Set the row and column
row = None
column = None
if isinstance(index, QModelIndex):
# if the QModelIndex is invalid, we won't bother with it
if not index.isValid():
return False
# otherwise, get the row and column from the QModelIndex
row = index.row()
column = index.column()
else:
row = index[0]
column = index[1]
index = self.createIndex(row, column)
# Verify the row and column
if row >= self.rowCount() or column >= self._num_cols and \
(column != WorkflowWrapper.quality and column != WorkflowWrapper.simple_ints):
return False
set_val = value.toPyObject() if isinstance(value, QVariant) else value
# ensure we're trying to set a valid thing
if self._workm is None:
if WorkflowWrapper.filename != column:
return False
else:
self._pathnames[row] = set_val
ch_ind_1, ch_ind_2 = None, None
if all(self._pathnames):
self._workm = WorkflowManager(self._pathnames)
self._workm.load(u'pieces')
# now that we imported, all the data's changed
ch_ind_1 = self.createIndex(0, 0)
ch_ind_2 = self.createIndex(len(self), self._num_cols - 1)
# let the GUI know
self._when_done_import()
else:
# only one cell has changed
ch_ind_1 = ch_ind_2 = self.createIndex(row, column)
self.dataChanged.emit(ch_ind_1, ch_ind_2)
return True
# displayed fields
if WorkflowWrapper.filename == column:
self._workm.metadata(row, u'pathname', set_val)
elif WorkflowWrapper.title == column:
self._workm.metadata(row, u'title', set_val)
elif WorkflowWrapper.parts_list == column:
self._workm.metadata(row, u'parts', set_val)
elif WorkflowWrapper.offset_interval == column:
self._workm.settings(row, u'offset interval', set_val)
elif WorkflowWrapper.parts_combinations == column:
self._workm.settings(row, u'voice combinations', set_val)
elif WorkflowWrapper.repeat_identical == column:
# the wording in the GUI and WorkflowManager has opposite meanings
self._workm.settings(row, u'filter repeats', not set_val)
# non-displayed fields
elif WorkflowWrapper.quality == column:
self._workm.settings(None, u'interval quality', set_val)
elif WorkflowWrapper.simple_ints == column:
self._workm.settings(None, u'simple intervals', set_val)
else:
return False
self._settings_changed = True
self.dataChanged.emit(index, index)
return True
def insertRows(self, row, count, parent=QModelIndex()):
"""
Append new rows to the data model. Yes---append, not insert.
:param row: An argument that will be ignored.
:type row: any
:param count: The number of rows you want to append.
:type count: ``int``
.. note:: If the internal :class:`WorkflowManager` already exists, it is destroyed and all
metadata and settings are lost.
.. note:: We recommend you add all the rows you will need before you call :meth:`setData`
to set the pathnames of the newly-added rows.
"""
if self._workm is not None:
self._workm = None
self.beginInsertRows(parent, len(self._pathnames),
len(self._pathnames) + count - 1)
self._pathnames.extend([None for _ in xrange(count)])
self.endInsertRows()
def removeRows(self, row, count, parent=QModelIndex()):
"""
This is the opposite of insertRows(), and the arguments work in the same
way.
"""
pass
def __len__(self):
"Alias for rowCount()."
return self.rowCount()
def __getitem__(self, index):
"It's __getitem__(), what do you want?!"
return self._workm[index]
def connect_workflow_signals(self, finished_import):
"""
Connect the WorkflowManager's PyQt signals to the methods or functions given here.
"finished_import" is for the WorkM's "finished_import" signal.
"""
print('--> type: ' + str(finished_import)) # DEBUG
self._when_done_import = finished_import
#self._workm.finished_import.connect(finished_import)
#pass
def get_workflow_manager(self):
"""
Get the internal :class:`WorkflowManager` instance.
"""
return self._workm
def settings_changed(self):
"""
Know whether there has been a call to :meth:`setData` since the last time this method was
called.
"""
ret = self._settings_changed
self._settings_changed = False
return ret
def setData(self, index, value, role):
"""
Set the data for the piece and metadatum or setting specified. If the internal
:class:`WorkflowManager` has not yet been created and this is the last pathname added,
instantiate the :class:`WorkflowManager` and call :meth:`load`.
:param index: The row-and-column index you wish to access. Either you can use a
:class:`QModelIndex` or a 2-tuple where the first element is an ``int`` representing the
index of the piece in the models, and the second element is one of the class properties
described above in "Columns in the Data Model."
:type index: :class:`QModelIndex` or 2-tuple of ``int``
:param value: The desired value of the setting or metadatum. If you submit a
:class:`QVariant`, we will call :meth:`toPyObject` before sending to the
:class:`WorkflowManager`.
:type value: :class:`QVariant` or any
:param role: This should be Qt.EditRole.
:type role: :class:`EditRole`
:returns: Whether the data was successfully set.
:rtype: ``True`` or ``False``
.. note:: If the internal :class:`WorkflowManager` has not been instantiated, you can only
set the ``pathname`` field. All other calls to :meth:`setData` will fail.
"""
if Qt.EditRole != role:
return False
# Set the row and column
row = None
column = None
if isinstance(index, QModelIndex):
# if the QModelIndex is invalid, we won't bother with it
if not index.isValid():
return False
# otherwise, get the row and column from the QModelIndex
row = index.row()
column = index.column()
else:
row = index[0]
column = index[1]
index = self.createIndex(row, column)
# Verify the row and column
if row >= self.rowCount() or column >= self._num_cols and \
(column != WorkflowWrapper.quality and column != WorkflowWrapper.simple_ints):
return False
set_val = value.toPyObject() if isinstance(value, QVariant) else value
# ensure we're trying to set a valid thing
if self._workm is None:
if WorkflowWrapper.filename != column:
return False
else:
self._pathnames[row] = set_val
ch_ind_1, ch_ind_2 = None, None
if all(self._pathnames):
self._workm = WorkflowManager(self._pathnames)
self._workm.load(u'pieces')
# now that we imported, all the data's changed
ch_ind_1 = self.createIndex(0, 0)
ch_ind_2 = self.createIndex(len(self), self._num_cols - 1)
# let the GUI know
self._when_done_import()
else:
# only one cell has changed
ch_ind_1 = ch_ind_2 = self.createIndex(row, column)
self.dataChanged.emit(ch_ind_1, ch_ind_2)
return True
# displayed fields
if WorkflowWrapper.filename == column:
self._workm.metadata(row, u'pathname', set_val)
elif WorkflowWrapper.title == column:
self._workm.metadata(row, u'title', set_val)
elif WorkflowWrapper.parts_list == column:
self._workm.metadata(row, u'parts', set_val)
elif WorkflowWrapper.offset_interval == column:
self._workm.settings(row, u'offset interval', set_val)
elif WorkflowWrapper.parts_combinations == column:
self._workm.settings(row, u'voice combinations', set_val)
elif WorkflowWrapper.repeat_identical == column:
# the wording in the GUI and WorkflowManager has opposite meanings
self._workm.settings(row, u'filter repeats', not set_val)
# non-displayed fields
elif WorkflowWrapper.quality == column:
self._workm.settings(None, u'interval quality', set_val)
elif WorkflowWrapper.simple_ints == column:
self._workm.settings(None, u'simple intervals', set_val)
else:
return False
self._settings_changed = True
self.dataChanged.emit(index, index)
return True
