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- # -*- coding: utf-8 -*-
- """
- Unit tests for the pandas bridge module.
- :copyright: Copyright 2014-2016 by the Elephant team, see AUTHORS.txt.
- :license: Modified BSD, see LICENSE.txt for details.
- """
- from __future__ import division, print_function
- import unittest
- from itertools import chain
- from neo.test.generate_datasets import fake_neo
- import numpy as np
- from numpy.testing import assert_array_equal
- import quantities as pq
- try:
- import pandas as pd
- from pandas.util.testing import assert_frame_equal, assert_index_equal
- except ImportError:
- HAVE_PANDAS = False
- else:
- import elephant.pandas_bridge as ep
- HAVE_PANDAS = True
- if HAVE_PANDAS:
- # Currying, otherwise the unittest will break with pandas>=0.16.0
- # parameter check_names is introduced in a newer versions than 0.14.0
- # this test is written for pandas 0.14.0
- def assert_index_equal(left, right):
- try:
- # pandas>=0.16.0
- return pd.util.testing.assert_index_equal(left, right,
- check_names=False)
- except TypeError:
- # pandas older version
- return pd.util.testing.assert_index_equal(left, right)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class MultiindexFromDictTestCase(unittest.TestCase):
- def test__multiindex_from_dict(self):
- inds = {'test1': 6.5,
- 'test2': 5,
- 'test3': 'test'}
- targ = pd.MultiIndex(levels=[[6.5], [5], ['test']],
- labels=[[0], [0], [0]],
- names=['test1', 'test2', 'test3'])
- res0 = ep._multiindex_from_dict(inds)
- self.assertEqual(targ.levels, res0.levels)
- self.assertEqual(targ.names, res0.names)
- self.assertEqual(targ.labels, res0.labels)
- def _convert_levels(levels):
- """Convert a list of levels to the format pandas returns for a MultiIndex.
- Parameters
- ----------
- levels : list
- The list of levels to convert.
- Returns
- -------
- list
- The the level in `list` converted to values like what pandas will give.
- """
- levels = list(levels)
- for i, level in enumerate(levels):
- if hasattr(level, 'lower'):
- try:
- level = unicode(level)
- except NameError:
- pass
- elif hasattr(level, 'date'):
- levels[i] = pd.DatetimeIndex(data=[level])
- continue
- elif level is None:
- levels[i] = pd.Index([])
- continue
- # pd.Index around pd.Index to convert to Index structure if MultiIndex
- levels[i] = pd.Index(pd.Index([level]))
- return levels
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class ConvertValueSafeTestCase(unittest.TestCase):
- def test__convert_value_safe__float(self):
- targ = 5.5
- value = targ
- res = ep._convert_value_safe(value)
- self.assertIs(res, targ)
- def test__convert_value_safe__str(self):
- targ = 'test'
- value = targ
- res = ep._convert_value_safe(value)
- self.assertIs(res, targ)
- def test__convert_value_safe__bytes(self):
- targ = 'test'
- value = b'test'
- res = ep._convert_value_safe(value)
- self.assertEqual(res, targ)
- def test__convert_value_safe__numpy_int_scalar(self):
- targ = 5
- value = np.array(5)
- res = ep._convert_value_safe(value)
- self.assertEqual(res, targ)
- self.assertFalse(hasattr(res, 'dtype'))
- def test__convert_value_safe__numpy_float_scalar(self):
- targ = 5.
- value = np.array(5.)
- res = ep._convert_value_safe(value)
- self.assertEqual(res, targ)
- self.assertFalse(hasattr(res, 'dtype'))
- def test__convert_value_safe__numpy_unicode_scalar(self):
- targ = u'test'
- value = np.array('test', dtype='U')
- res = ep._convert_value_safe(value)
- self.assertEqual(res, targ)
- self.assertFalse(hasattr(res, 'dtype'))
- def test__convert_value_safe__numpy_str_scalar(self):
- targ = u'test'
- value = np.array('test', dtype='S')
- res = ep._convert_value_safe(value)
- self.assertEqual(res, targ)
- self.assertFalse(hasattr(res, 'dtype'))
- def test__convert_value_safe__quantity_scalar(self):
- targ = (10., 'ms')
- value = 10. * pq.ms
- res = ep._convert_value_safe(value)
- self.assertEqual(res, targ)
- self.assertFalse(hasattr(res[0], 'dtype'))
- self.assertFalse(hasattr(res[0], 'units'))
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class SpiketrainToDataframeTestCase(unittest.TestCase):
- def test__spiketrain_to_dataframe__parents_empty(self):
- obj = fake_neo('SpikeTrain', seed=0)
- res0 = ep.spiketrain_to_dataframe(obj)
- res1 = ep.spiketrain_to_dataframe(obj, child_first=True)
- res2 = ep.spiketrain_to_dataframe(obj, child_first=False)
- res3 = ep.spiketrain_to_dataframe(obj, parents=True)
- res4 = ep.spiketrain_to_dataframe(obj, parents=True,
- child_first=True)
- res5 = ep.spiketrain_to_dataframe(obj, parents=True,
- child_first=False)
- res6 = ep.spiketrain_to_dataframe(obj, parents=False)
- res7 = ep.spiketrain_to_dataframe(obj, parents=False, child_first=True)
- res8 = ep.spiketrain_to_dataframe(obj, parents=False,
- child_first=False)
- targvalues = pq.Quantity(obj.magnitude, units=obj.units)
- targvalues = targvalues.rescale('s').magnitude[np.newaxis].T
- targindex = np.arange(len(targvalues))
- attrs = ep._extract_neo_attrs_safe(obj, parents=True, child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(1, len(res3.columns))
- self.assertEqual(1, len(res4.columns))
- self.assertEqual(1, len(res5.columns))
- self.assertEqual(1, len(res6.columns))
- self.assertEqual(1, len(res7.columns))
- self.assertEqual(1, len(res8.columns))
- self.assertEqual(len(obj), len(res0.index))
- self.assertEqual(len(obj), len(res1.index))
- self.assertEqual(len(obj), len(res2.index))
- self.assertEqual(len(obj), len(res3.index))
- self.assertEqual(len(obj), len(res4.index))
- self.assertEqual(len(obj), len(res5.index))
- self.assertEqual(len(obj), len(res6.index))
- self.assertEqual(len(obj), len(res7.index))
- self.assertEqual(len(obj), len(res8.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targvalues, res3.values)
- assert_array_equal(targvalues, res4.values)
- assert_array_equal(targvalues, res5.values)
- assert_array_equal(targvalues, res6.values)
- assert_array_equal(targvalues, res7.values)
- assert_array_equal(targvalues, res8.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- assert_array_equal(targindex, res2.index)
- assert_array_equal(targindex, res3.index)
- assert_array_equal(targindex, res4.index)
- assert_array_equal(targindex, res5.index)
- assert_array_equal(targindex, res6.index)
- assert_array_equal(targindex, res7.index)
- assert_array_equal(targindex, res8.index)
- self.assertEqual(['spike_number'], res0.index.names)
- self.assertEqual(['spike_number'], res1.index.names)
- self.assertEqual(['spike_number'], res2.index.names)
- self.assertEqual(['spike_number'], res3.index.names)
- self.assertEqual(['spike_number'], res4.index.names)
- self.assertEqual(['spike_number'], res5.index.names)
- self.assertEqual(['spike_number'], res6.index.names)
- self.assertEqual(['spike_number'], res7.index.names)
- self.assertEqual(['spike_number'], res8.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual(keys, res3.columns.names)
- self.assertEqual(keys, res4.columns.names)
- self.assertEqual(keys, res5.columns.names)
- self.assertEqual(keys, res6.columns.names)
- self.assertEqual(keys, res7.columns.names)
- self.assertEqual(keys, res8.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res3.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res4.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res5.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res6.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res7.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res8.columns.levels):
- assert_index_equal(value, level)
- def test__spiketrain_to_dataframe__noparents(self):
- blk = fake_neo('Block', seed=0)
- obj = blk.list_children_by_class('SpikeTrain')[0]
- res0 = ep.spiketrain_to_dataframe(obj, parents=False)
- res1 = ep.spiketrain_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.spiketrain_to_dataframe(obj, parents=False,
- child_first=False)
- targvalues = pq.Quantity(obj.magnitude, units=obj.units)
- targvalues = targvalues.rescale('s').magnitude[np.newaxis].T
- targindex = np.arange(len(targvalues))
- attrs = ep._extract_neo_attrs_safe(obj, parents=False,
- child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(len(obj), len(res0.index))
- self.assertEqual(len(obj), len(res1.index))
- self.assertEqual(len(obj), len(res2.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- assert_array_equal(targindex, res2.index)
- self.assertEqual(['spike_number'], res0.index.names)
- self.assertEqual(['spike_number'], res1.index.names)
- self.assertEqual(['spike_number'], res2.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- def test__spiketrain_to_dataframe__parents_childfirst(self):
- blk = fake_neo('Block', seed=0)
- obj = blk.list_children_by_class('SpikeTrain')[0]
- res0 = ep.spiketrain_to_dataframe(obj)
- res1 = ep.spiketrain_to_dataframe(obj, child_first=True)
- res2 = ep.spiketrain_to_dataframe(obj, parents=True)
- res3 = ep.spiketrain_to_dataframe(obj, parents=True, child_first=True)
- targvalues = pq.Quantity(obj.magnitude, units=obj.units)
- targvalues = targvalues.rescale('s').magnitude[np.newaxis].T
- targindex = np.arange(len(targvalues))
- attrs = ep._extract_neo_attrs_safe(obj, parents=True, child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(1, len(res3.columns))
- self.assertEqual(len(obj), len(res0.index))
- self.assertEqual(len(obj), len(res1.index))
- self.assertEqual(len(obj), len(res2.index))
- self.assertEqual(len(obj), len(res3.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targvalues, res3.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- assert_array_equal(targindex, res2.index)
- assert_array_equal(targindex, res3.index)
- self.assertEqual(['spike_number'], res0.index.names)
- self.assertEqual(['spike_number'], res1.index.names)
- self.assertEqual(['spike_number'], res2.index.names)
- self.assertEqual(['spike_number'], res3.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual(keys, res3.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res3.columns.levels):
- assert_index_equal(value, level)
- def test__spiketrain_to_dataframe__parents_parentfirst(self):
- blk = fake_neo('Block', seed=0)
- obj = blk.list_children_by_class('SpikeTrain')[0]
- res0 = ep.spiketrain_to_dataframe(obj, child_first=False)
- res1 = ep.spiketrain_to_dataframe(obj, parents=True, child_first=False)
- targvalues = pq.Quantity(obj.magnitude, units=obj.units)
- targvalues = targvalues.rescale('s').magnitude[np.newaxis].T
- targindex = np.arange(len(targvalues))
- attrs = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=False)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(len(obj), len(res0.index))
- self.assertEqual(len(obj), len(res1.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- self.assertEqual(['spike_number'], res0.index.names)
- self.assertEqual(['spike_number'], res1.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class EventToDataframeTestCase(unittest.TestCase):
- def test__event_to_dataframe__parents_empty(self):
- obj = fake_neo('Event', seed=42)
- res0 = ep.event_to_dataframe(obj)
- res1 = ep.event_to_dataframe(obj, child_first=True)
- res2 = ep.event_to_dataframe(obj, child_first=False)
- res3 = ep.event_to_dataframe(obj, parents=True)
- res4 = ep.event_to_dataframe(obj, parents=True, child_first=True)
- res5 = ep.event_to_dataframe(obj, parents=True, child_first=False)
- res6 = ep.event_to_dataframe(obj, parents=False)
- res7 = ep.event_to_dataframe(obj, parents=False, child_first=True)
- res8 = ep.event_to_dataframe(obj, parents=False, child_first=False)
- targvalues = obj.labels[:len(obj.times)][np.newaxis].T.astype('U')
- targindex = obj.times[:len(obj.labels)].rescale('s').magnitude
- attrs = ep._extract_neo_attrs_safe(obj, parents=True, child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(1, len(res3.columns))
- self.assertEqual(1, len(res4.columns))
- self.assertEqual(1, len(res5.columns))
- self.assertEqual(1, len(res6.columns))
- self.assertEqual(1, len(res7.columns))
- self.assertEqual(1, len(res8.columns))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res1.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res2.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res3.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res4.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res5.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res6.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res7.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res8.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targvalues, res3.values)
- assert_array_equal(targvalues, res4.values)
- assert_array_equal(targvalues, res5.values)
- assert_array_equal(targvalues, res6.values)
- assert_array_equal(targvalues, res7.values)
- assert_array_equal(targvalues, res8.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- assert_array_equal(targindex, res2.index)
- assert_array_equal(targindex, res3.index)
- assert_array_equal(targindex, res4.index)
- assert_array_equal(targindex, res5.index)
- assert_array_equal(targindex, res6.index)
- assert_array_equal(targindex, res7.index)
- assert_array_equal(targindex, res8.index)
- self.assertEqual(['times'], res0.index.names)
- self.assertEqual(['times'], res1.index.names)
- self.assertEqual(['times'], res2.index.names)
- self.assertEqual(['times'], res3.index.names)
- self.assertEqual(['times'], res4.index.names)
- self.assertEqual(['times'], res5.index.names)
- self.assertEqual(['times'], res6.index.names)
- self.assertEqual(['times'], res7.index.names)
- self.assertEqual(['times'], res8.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual(keys, res3.columns.names)
- self.assertEqual(keys, res4.columns.names)
- self.assertEqual(keys, res5.columns.names)
- self.assertEqual(keys, res6.columns.names)
- self.assertEqual(keys, res7.columns.names)
- self.assertEqual(keys, res8.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res3.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res4.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res5.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res6.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res7.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res8.columns.levels):
- assert_index_equal(value, level)
- def test__event_to_dataframe__noparents(self):
- blk = fake_neo('Block', seed=42)
- obj = blk.list_children_by_class('Event')[0]
- res0 = ep.event_to_dataframe(obj, parents=False)
- res1 = ep.event_to_dataframe(obj, parents=False, child_first=False)
- res2 = ep.event_to_dataframe(obj, parents=False, child_first=True)
- targvalues = obj.labels[:len(obj.times)][np.newaxis].T.astype('U')
- targindex = obj.times[:len(obj.labels)].rescale('s').magnitude
- attrs = ep._extract_neo_attrs_safe(obj, parents=False,
- child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res1.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res2.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- assert_array_equal(targindex, res2.index)
- self.assertEqual(['times'], res0.index.names)
- self.assertEqual(['times'], res1.index.names)
- self.assertEqual(['times'], res2.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- def test__event_to_dataframe__parents_childfirst(self):
- blk = fake_neo('Block', seed=42)
- obj = blk.list_children_by_class('Event')[0]
- res0 = ep.event_to_dataframe(obj)
- res1 = ep.event_to_dataframe(obj, child_first=True)
- res2 = ep.event_to_dataframe(obj, parents=True)
- res3 = ep.event_to_dataframe(obj, parents=True, child_first=True)
- targvalues = obj.labels[:len(obj.times)][np.newaxis].T.astype('U')
- targindex = obj.times[:len(obj.labels)].rescale('s').magnitude
- attrs = ep._extract_neo_attrs_safe(obj, parents=True, child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(1, len(res3.columns))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res1.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res2.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res3.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targvalues, res3.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- assert_array_equal(targindex, res2.index)
- assert_array_equal(targindex, res3.index)
- self.assertEqual(['times'], res0.index.names)
- self.assertEqual(['times'], res1.index.names)
- self.assertEqual(['times'], res2.index.names)
- self.assertEqual(['times'], res3.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual(keys, res3.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res3.columns.levels):
- assert_index_equal(value, level)
- def test__event_to_dataframe__parents_parentfirst(self):
- blk = fake_neo('Block', seed=42)
- obj = blk.list_children_by_class('Event')[0]
- res0 = ep.event_to_dataframe(obj, child_first=False)
- res1 = ep.event_to_dataframe(obj, parents=True, child_first=False)
- targvalues = obj.labels[:len(obj.times)][np.newaxis].T.astype('U')
- targindex = obj.times[:len(obj.labels)].rescale('s').magnitude
- attrs = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=False)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.labels)),
- len(res1.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targindex, res0.index)
- assert_array_equal(targindex, res1.index)
- self.assertEqual(['times'], res0.index.names)
- self.assertEqual(['times'], res1.index.names)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class EpochToDataframeTestCase(unittest.TestCase):
- def test__epoch_to_dataframe__parents_empty(self):
- obj = fake_neo('Epoch', seed=42)
- res0 = ep.epoch_to_dataframe(obj)
- res1 = ep.epoch_to_dataframe(obj, child_first=True)
- res2 = ep.epoch_to_dataframe(obj, child_first=False)
- res3 = ep.epoch_to_dataframe(obj, parents=True)
- res4 = ep.epoch_to_dataframe(obj, parents=True, child_first=True)
- res5 = ep.epoch_to_dataframe(obj, parents=True, child_first=False)
- res6 = ep.epoch_to_dataframe(obj, parents=False)
- res7 = ep.epoch_to_dataframe(obj, parents=False, child_first=True)
- res8 = ep.epoch_to_dataframe(obj, parents=False, child_first=False)
- minlen = min([len(obj.times), len(obj.durations), len(obj.labels)])
- targvalues = obj.labels[:minlen][np.newaxis].T.astype('U')
- targindex = np.vstack([obj.durations[:minlen].rescale('s').magnitude,
- obj.times[:minlen].rescale('s').magnitude])
- targvalues = targvalues[targindex.argsort()[0], :]
- targindex.sort()
- attrs = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(1, len(res3.columns))
- self.assertEqual(1, len(res4.columns))
- self.assertEqual(1, len(res5.columns))
- self.assertEqual(1, len(res6.columns))
- self.assertEqual(1, len(res7.columns))
- self.assertEqual(1, len(res8.columns))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res1.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res2.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res3.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res4.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res5.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res6.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res7.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res8.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targvalues, res3.values)
- assert_array_equal(targvalues, res4.values)
- assert_array_equal(targvalues, res5.values)
- assert_array_equal(targvalues, res6.values)
- assert_array_equal(targvalues, res7.values)
- assert_array_equal(targvalues, res8.values)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual(keys, res3.columns.names)
- self.assertEqual(keys, res4.columns.names)
- self.assertEqual(keys, res5.columns.names)
- self.assertEqual(keys, res6.columns.names)
- self.assertEqual(keys, res7.columns.names)
- self.assertEqual(keys, res8.columns.names)
- self.assertEqual([u'durations', u'times'], res0.index.names)
- self.assertEqual([u'durations', u'times'], res1.index.names)
- self.assertEqual([u'durations', u'times'], res2.index.names)
- self.assertEqual([u'durations', u'times'], res3.index.names)
- self.assertEqual([u'durations', u'times'], res4.index.names)
- self.assertEqual([u'durations', u'times'], res5.index.names)
- self.assertEqual([u'durations', u'times'], res6.index.names)
- self.assertEqual([u'durations', u'times'], res7.index.names)
- self.assertEqual([u'durations', u'times'], res8.index.names)
- self.assertEqual(2, len(res0.index.levels))
- self.assertEqual(2, len(res1.index.levels))
- self.assertEqual(2, len(res2.index.levels))
- self.assertEqual(2, len(res3.index.levels))
- self.assertEqual(2, len(res4.index.levels))
- self.assertEqual(2, len(res5.index.levels))
- self.assertEqual(2, len(res6.index.levels))
- self.assertEqual(2, len(res7.index.levels))
- self.assertEqual(2, len(res8.index.levels))
- assert_array_equal(targindex, res0.index.levels)
- assert_array_equal(targindex, res1.index.levels)
- assert_array_equal(targindex, res2.index.levels)
- assert_array_equal(targindex, res3.index.levels)
- assert_array_equal(targindex, res4.index.levels)
- assert_array_equal(targindex, res5.index.levels)
- assert_array_equal(targindex, res6.index.levels)
- assert_array_equal(targindex, res7.index.levels)
- assert_array_equal(targindex, res8.index.levels)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res3.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res4.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res5.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res6.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res7.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res8.columns.levels):
- assert_index_equal(value, level)
- def test__epoch_to_dataframe__noparents(self):
- blk = fake_neo('Block', seed=42)
- obj = blk.list_children_by_class('Epoch')[0]
- res0 = ep.epoch_to_dataframe(obj, parents=False)
- res1 = ep.epoch_to_dataframe(obj, parents=False, child_first=True)
- res2 = ep.epoch_to_dataframe(obj, parents=False, child_first=False)
- minlen = min([len(obj.times), len(obj.durations), len(obj.labels)])
- targvalues = obj.labels[:minlen][np.newaxis].T.astype('U')
- targindex = np.vstack([obj.durations[:minlen].rescale('s').magnitude,
- obj.times[:minlen].rescale('s').magnitude])
- targvalues = targvalues[targindex.argsort()[0], :]
- targindex.sort()
- attrs = ep._extract_neo_attrs_safe(obj, parents=False,
- child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res1.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res2.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual([u'durations', u'times'], res0.index.names)
- self.assertEqual([u'durations', u'times'], res1.index.names)
- self.assertEqual([u'durations', u'times'], res2.index.names)
- self.assertEqual(2, len(res0.index.levels))
- self.assertEqual(2, len(res1.index.levels))
- self.assertEqual(2, len(res2.index.levels))
- assert_array_equal(targindex, res0.index.levels)
- assert_array_equal(targindex, res1.index.levels)
- assert_array_equal(targindex, res2.index.levels)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- def test__epoch_to_dataframe__parents_childfirst(self):
- blk = fake_neo('Block', seed=42)
- obj = blk.list_children_by_class('Epoch')[0]
- res0 = ep.epoch_to_dataframe(obj)
- res1 = ep.epoch_to_dataframe(obj, child_first=True)
- res2 = ep.epoch_to_dataframe(obj, parents=True)
- res3 = ep.epoch_to_dataframe(obj, parents=True, child_first=True)
- minlen = min([len(obj.times), len(obj.durations), len(obj.labels)])
- targvalues = obj.labels[:minlen][np.newaxis].T.astype('U')
- targindex = np.vstack([obj.durations[:minlen].rescale('s').magnitude,
- obj.times[:minlen].rescale('s').magnitude])
- targvalues = targvalues[targindex.argsort()[0], :]
- targindex.sort()
- attrs = ep._extract_neo_attrs_safe(obj, parents=True, child_first=True)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(1, len(res2.columns))
- self.assertEqual(1, len(res3.columns))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res1.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res2.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res3.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- assert_array_equal(targvalues, res2.values)
- assert_array_equal(targvalues, res3.values)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual(keys, res2.columns.names)
- self.assertEqual(keys, res3.columns.names)
- self.assertEqual([u'durations', u'times'], res0.index.names)
- self.assertEqual([u'durations', u'times'], res1.index.names)
- self.assertEqual([u'durations', u'times'], res2.index.names)
- self.assertEqual([u'durations', u'times'], res3.index.names)
- self.assertEqual(2, len(res0.index.levels))
- self.assertEqual(2, len(res1.index.levels))
- self.assertEqual(2, len(res2.index.levels))
- self.assertEqual(2, len(res3.index.levels))
- assert_array_equal(targindex, res0.index.levels)
- assert_array_equal(targindex, res1.index.levels)
- assert_array_equal(targindex, res2.index.levels)
- assert_array_equal(targindex, res3.index.levels)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res2.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res3.columns.levels):
- assert_index_equal(value, level)
- def test__epoch_to_dataframe__parents_parentfirst(self):
- blk = fake_neo('Block', seed=42)
- obj = blk.list_children_by_class('Epoch')[0]
- res0 = ep.epoch_to_dataframe(obj, child_first=False)
- res1 = ep.epoch_to_dataframe(obj, parents=True, child_first=False)
- minlen = min([len(obj.times), len(obj.durations), len(obj.labels)])
- targvalues = obj.labels[:minlen][np.newaxis].T.astype('U')
- targindex = np.vstack([obj.durations[:minlen].rescale('s').magnitude,
- obj.times[:minlen].rescale('s').magnitude])
- targvalues = targvalues[targindex.argsort()[0], :]
- targindex.sort()
- attrs = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=False)
- keys, values = zip(*sorted(attrs.items()))
- values = _convert_levels(values)
- self.assertEqual(1, len(res0.columns))
- self.assertEqual(1, len(res1.columns))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res0.index))
- self.assertEqual(min(len(obj.times), len(obj.durations),
- len(obj.labels)),
- len(res1.index))
- assert_array_equal(targvalues, res0.values)
- assert_array_equal(targvalues, res1.values)
- self.assertEqual(keys, res0.columns.names)
- self.assertEqual(keys, res1.columns.names)
- self.assertEqual([u'durations', u'times'], res0.index.names)
- self.assertEqual([u'durations', u'times'], res1.index.names)
- self.assertEqual(2, len(res0.index.levels))
- self.assertEqual(2, len(res1.index.levels))
- assert_array_equal(targindex, res0.index.levels)
- assert_array_equal(targindex, res1.index.levels)
- for value, level in zip(values, res0.columns.levels):
- assert_index_equal(value, level)
- for value, level in zip(values, res1.columns.levels):
- assert_index_equal(value, level)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class MultiSpiketrainsToDataframeTestCase(unittest.TestCase):
- def setUp(self):
- if hasattr(self, 'assertItemsEqual'):
- self.assertCountEqual = self.assertItemsEqual
- def test__multi_spiketrains_to_dataframe__single(self):
- obj = fake_neo('SpikeTrain', seed=0, n=5)
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=False)
- res2 = ep.multi_spiketrains_to_dataframe(obj, parents=True)
- res3 = ep.multi_spiketrains_to_dataframe(obj, child_first=True)
- res4 = ep.multi_spiketrains_to_dataframe(obj, parents=False,
- child_first=True)
- res5 = ep.multi_spiketrains_to_dataframe(obj, parents=True,
- child_first=True)
- res6 = ep.multi_spiketrains_to_dataframe(obj, child_first=False)
- res7 = ep.multi_spiketrains_to_dataframe(obj, parents=False,
- child_first=False)
- res8 = ep.multi_spiketrains_to_dataframe(obj, parents=True,
- child_first=False)
- targ = ep.spiketrain_to_dataframe(obj)
- keys = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = 1
- targlen = len(obj)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targwidth, len(res4.columns))
- self.assertEqual(targwidth, len(res5.columns))
- self.assertEqual(targwidth, len(res6.columns))
- self.assertEqual(targwidth, len(res7.columns))
- self.assertEqual(targwidth, len(res8.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertEqual(targlen, len(res4.index))
- self.assertEqual(targlen, len(res5.index))
- self.assertEqual(targlen, len(res6.index))
- self.assertEqual(targlen, len(res7.index))
- self.assertEqual(targlen, len(res8.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- self.assertCountEqual(keys, res4.columns.names)
- self.assertCountEqual(keys, res5.columns.names)
- self.assertCountEqual(keys, res6.columns.names)
- self.assertCountEqual(keys, res7.columns.names)
- self.assertCountEqual(keys, res8.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_array_equal(targ.values, res3.values)
- assert_array_equal(targ.values, res4.values)
- assert_array_equal(targ.values, res5.values)
- assert_array_equal(targ.values, res6.values)
- assert_array_equal(targ.values, res7.values)
- assert_array_equal(targ.values, res8.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- assert_frame_equal(targ, res4)
- assert_frame_equal(targ, res5)
- assert_frame_equal(targ, res6)
- assert_frame_equal(targ, res7)
- assert_frame_equal(targ, res8)
- def test__multi_spiketrains_to_dataframe__unit_default(self):
- obj = fake_neo('Unit', seed=0, n=5)
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- objs = obj.spiketrains
- targ = [ep.spiketrain_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_frame_equal(targ, res0)
- def test__multi_spiketrains_to_dataframe__segment_default(self):
- obj = fake_neo('Segment', seed=0, n=5)
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- objs = obj.spiketrains
- targ = [ep.spiketrain_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_frame_equal(targ, res0)
- def test__multi_spiketrains_to_dataframe__block_noparents(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_spiketrains_to_dataframe(obj, parents=False)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.multi_spiketrains_to_dataframe(obj, parents=False,
- child_first=False)
- objs = obj.list_children_by_class('SpikeTrain')
- targ = [ep.spiketrain_to_dataframe(iobj,
- parents=False, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=False,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- def test__multi_spiketrains_to_dataframe__block_parents_childfirst(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=True)
- res2 = ep.multi_spiketrains_to_dataframe(obj, child_first=True)
- res3 = ep.multi_spiketrains_to_dataframe(obj, parents=True,
- child_first=True)
- objs = obj.list_children_by_class('SpikeTrain')
- targ = [ep.spiketrain_to_dataframe(iobj,
- parents=True, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_array_equal(targ.values, res3.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- def test__multi_spiketrains_to_dataframe__block_parents_parentfirst(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_spiketrains_to_dataframe(obj, child_first=False)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=True,
- child_first=False)
- objs = obj.list_children_by_class('SpikeTrain')
- targ = [ep.spiketrain_to_dataframe(iobj,
- parents=True, child_first=False)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=False).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- def test__multi_spiketrains_to_dataframe__list_noparents(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_spiketrains_to_dataframe(obj, parents=False)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.multi_spiketrains_to_dataframe(obj, parents=False,
- child_first=False)
- objs = (iobj.list_children_by_class('SpikeTrain') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.spiketrain_to_dataframe(iobj,
- parents=False, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=False,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- def test__multi_spiketrains_to_dataframe__list_parents_childfirst(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=True)
- res2 = ep.multi_spiketrains_to_dataframe(obj, child_first=True)
- res3 = ep.multi_spiketrains_to_dataframe(obj, parents=True,
- child_first=True)
- objs = (iobj.list_children_by_class('SpikeTrain') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.spiketrain_to_dataframe(iobj,
- parents=True, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_array_equal(targ.values, res3.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- def test__multi_spiketrains_to_dataframe__list_parents_parentfirst(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_spiketrains_to_dataframe(obj, child_first=False)
- res1 = ep.multi_spiketrains_to_dataframe(obj, parents=True,
- child_first=False)
- objs = (iobj.list_children_by_class('SpikeTrain') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.spiketrain_to_dataframe(iobj,
- parents=True, child_first=False)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=False).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- def test__multi_spiketrains_to_dataframe__tuple_default(self):
- obj = tuple(fake_neo('Block', seed=i, n=3) for i in range(3))
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- objs = (iobj.list_children_by_class('SpikeTrain') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.spiketrain_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_frame_equal(targ, res0)
- def test__multi_spiketrains_to_dataframe__iter_default(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_spiketrains_to_dataframe(iter(obj))
- objs = (iobj.list_children_by_class('SpikeTrain') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.spiketrain_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_frame_equal(targ, res0)
- def test__multi_spiketrains_to_dataframe__dict_default(self):
- obj = dict((i, fake_neo('Block', seed=i, n=3)) for i in range(3))
- res0 = ep.multi_spiketrains_to_dataframe(obj)
- objs = (iobj.list_children_by_class('SpikeTrain') for iobj in
- obj.values())
- objs = list(chain.from_iterable(objs))
- targ = [ep.spiketrain_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = max(len(iobj) for iobj in objs)
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_frame_equal(targ, res0)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class MultiEventsToDataframeTestCase(unittest.TestCase):
- def setUp(self):
- if hasattr(self, 'assertItemsEqual'):
- self.assertCountEqual = self.assertItemsEqual
- def test__multi_events_to_dataframe__single(self):
- obj = fake_neo('Event', seed=0, n=5)
- res0 = ep.multi_events_to_dataframe(obj)
- res1 = ep.multi_events_to_dataframe(obj, parents=False)
- res2 = ep.multi_events_to_dataframe(obj, parents=True)
- res3 = ep.multi_events_to_dataframe(obj, child_first=True)
- res4 = ep.multi_events_to_dataframe(obj, parents=False,
- child_first=True)
- res5 = ep.multi_events_to_dataframe(obj, parents=True,
- child_first=True)
- res6 = ep.multi_events_to_dataframe(obj, child_first=False)
- res7 = ep.multi_events_to_dataframe(obj, parents=False,
- child_first=False)
- res8 = ep.multi_events_to_dataframe(obj, parents=True,
- child_first=False)
- targ = ep.event_to_dataframe(obj)
- keys = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = 1
- targlen = min(len(obj.times), len(obj.labels))
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targwidth, len(res4.columns))
- self.assertEqual(targwidth, len(res5.columns))
- self.assertEqual(targwidth, len(res6.columns))
- self.assertEqual(targwidth, len(res7.columns))
- self.assertEqual(targwidth, len(res8.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertEqual(targlen, len(res4.index))
- self.assertEqual(targlen, len(res5.index))
- self.assertEqual(targlen, len(res6.index))
- self.assertEqual(targlen, len(res7.index))
- self.assertEqual(targlen, len(res8.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- self.assertCountEqual(keys, res4.columns.names)
- self.assertCountEqual(keys, res5.columns.names)
- self.assertCountEqual(keys, res6.columns.names)
- self.assertCountEqual(keys, res7.columns.names)
- self.assertCountEqual(keys, res8.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_array_equal(targ.values, res3.values)
- assert_array_equal(targ.values, res4.values)
- assert_array_equal(targ.values, res5.values)
- assert_array_equal(targ.values, res6.values)
- assert_array_equal(targ.values, res7.values)
- assert_array_equal(targ.values, res8.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- assert_frame_equal(targ, res4)
- assert_frame_equal(targ, res5)
- assert_frame_equal(targ, res6)
- assert_frame_equal(targ, res7)
- assert_frame_equal(targ, res8)
- def test__multi_events_to_dataframe__segment_default(self):
- obj = fake_neo('Segment', seed=0, n=5)
- res0 = ep.multi_events_to_dataframe(obj)
- objs = obj.events
- targ = [ep.event_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- def test__multi_events_to_dataframe__block_noparents(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_events_to_dataframe(obj, parents=False)
- res1 = ep.multi_events_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.multi_events_to_dataframe(obj, parents=False,
- child_first=False)
- objs = obj.list_children_by_class('Event')
- targ = [ep.event_to_dataframe(iobj, parents=False, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=False,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- def test__multi_events_to_dataframe__block_parents_childfirst(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_events_to_dataframe(obj)
- res1 = ep.multi_events_to_dataframe(obj, parents=True)
- res2 = ep.multi_events_to_dataframe(obj, child_first=True)
- res3 = ep.multi_events_to_dataframe(obj, parents=True,
- child_first=True)
- objs = obj.list_children_by_class('Event')
- targ = [ep.event_to_dataframe(iobj, parents=True, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res3.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- def test__multi_events_to_dataframe__block_parents_parentfirst(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_events_to_dataframe(obj, child_first=False)
- res1 = ep.multi_events_to_dataframe(obj, parents=True,
- child_first=False)
- objs = obj.list_children_by_class('Event')
- targ = [ep.event_to_dataframe(iobj, parents=True, child_first=False)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=False).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- def test__multi_events_to_dataframe__list_noparents(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_events_to_dataframe(obj, parents=False)
- res1 = ep.multi_events_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.multi_events_to_dataframe(obj, parents=False,
- child_first=False)
- objs = (iobj.list_children_by_class('Event') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.event_to_dataframe(iobj, parents=False, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=False,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- def test__multi_events_to_dataframe__list_parents_childfirst(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_events_to_dataframe(obj)
- res1 = ep.multi_events_to_dataframe(obj, parents=True)
- res2 = ep.multi_events_to_dataframe(obj, child_first=True)
- res3 = ep.multi_events_to_dataframe(obj, parents=True,
- child_first=True)
- objs = (iobj.list_children_by_class('Event') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.event_to_dataframe(iobj, parents=True, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res3.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- def test__multi_events_to_dataframe__list_parents_parentfirst(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_events_to_dataframe(obj, child_first=False)
- res1 = ep.multi_events_to_dataframe(obj, parents=True,
- child_first=False)
- objs = (iobj.list_children_by_class('Event') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.event_to_dataframe(iobj, parents=True, child_first=False)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=False).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- def test__multi_events_to_dataframe__tuple_default(self):
- obj = tuple(fake_neo('Block', seed=i, n=3) for i in range(3))
- res0 = ep.multi_events_to_dataframe(obj)
- objs = (iobj.list_children_by_class('Event') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.event_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- def test__multi_events_to_dataframe__iter_default(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_events_to_dataframe(iter(obj))
- objs = (iobj.list_children_by_class('Event') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.event_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- def test__multi_events_to_dataframe__dict_default(self):
- obj = dict((i, fake_neo('Block', seed=i, n=3)) for i in range(3))
- res0 = ep.multi_events_to_dataframe(obj)
- objs = (iobj.list_children_by_class('Event') for iobj in
- obj.values())
- objs = list(chain.from_iterable(objs))
- targ = [ep.event_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.labels))]
- for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class MultiEpochsToDataframeTestCase(unittest.TestCase):
- def setUp(self):
- if hasattr(self, 'assertItemsEqual'):
- self.assertCountEqual = self.assertItemsEqual
- def test__multi_epochs_to_dataframe__single(self):
- obj = fake_neo('Epoch', seed=0, n=5)
- res0 = ep.multi_epochs_to_dataframe(obj)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=False)
- res2 = ep.multi_epochs_to_dataframe(obj, parents=True)
- res3 = ep.multi_epochs_to_dataframe(obj, child_first=True)
- res4 = ep.multi_epochs_to_dataframe(obj, parents=False,
- child_first=True)
- res5 = ep.multi_epochs_to_dataframe(obj, parents=True,
- child_first=True)
- res6 = ep.multi_epochs_to_dataframe(obj, child_first=False)
- res7 = ep.multi_epochs_to_dataframe(obj, parents=False,
- child_first=False)
- res8 = ep.multi_epochs_to_dataframe(obj, parents=True,
- child_first=False)
- targ = ep.epoch_to_dataframe(obj)
- keys = ep._extract_neo_attrs_safe(obj, parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = 1
- targlen = min(len(obj.times), len(obj.durations), len(obj.labels))
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targwidth, len(res4.columns))
- self.assertEqual(targwidth, len(res5.columns))
- self.assertEqual(targwidth, len(res6.columns))
- self.assertEqual(targwidth, len(res7.columns))
- self.assertEqual(targwidth, len(res8.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertEqual(targlen, len(res4.index))
- self.assertEqual(targlen, len(res5.index))
- self.assertEqual(targlen, len(res6.index))
- self.assertEqual(targlen, len(res7.index))
- self.assertEqual(targlen, len(res8.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- self.assertCountEqual(keys, res4.columns.names)
- self.assertCountEqual(keys, res5.columns.names)
- self.assertCountEqual(keys, res6.columns.names)
- self.assertCountEqual(keys, res7.columns.names)
- self.assertCountEqual(keys, res8.columns.names)
- assert_array_equal(targ.values, res0.values)
- assert_array_equal(targ.values, res1.values)
- assert_array_equal(targ.values, res2.values)
- assert_array_equal(targ.values, res3.values)
- assert_array_equal(targ.values, res4.values)
- assert_array_equal(targ.values, res5.values)
- assert_array_equal(targ.values, res6.values)
- assert_array_equal(targ.values, res7.values)
- assert_array_equal(targ.values, res8.values)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- assert_frame_equal(targ, res4)
- assert_frame_equal(targ, res5)
- assert_frame_equal(targ, res6)
- assert_frame_equal(targ, res7)
- assert_frame_equal(targ, res8)
- def test__multi_epochs_to_dataframe__segment_default(self):
- obj = fake_neo('Segment', seed=0, n=5)
- res0 = ep.multi_epochs_to_dataframe(obj)
- objs = obj.epochs
- targ = [ep.epoch_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- def test__multi_epochs_to_dataframe__block_noparents(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_epochs_to_dataframe(obj, parents=False)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.multi_epochs_to_dataframe(obj, parents=False,
- child_first=False)
- objs = obj.list_children_by_class('Epoch')
- targ = [ep.epoch_to_dataframe(iobj, parents=False, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=False,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- def test__multi_epochs_to_dataframe__block_parents_childfirst(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_epochs_to_dataframe(obj)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=True)
- res2 = ep.multi_epochs_to_dataframe(obj, child_first=True)
- res3 = ep.multi_epochs_to_dataframe(obj, parents=True,
- child_first=True)
- objs = obj.list_children_by_class('Epoch')
- targ = [ep.epoch_to_dataframe(iobj, parents=True, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res3.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- def test__multi_epochs_to_dataframe__block_parents_parentfirst(self):
- obj = fake_neo('Block', seed=0, n=3)
- res0 = ep.multi_epochs_to_dataframe(obj, child_first=False)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=True,
- child_first=False)
- objs = obj.list_children_by_class('Epoch')
- targ = [ep.epoch_to_dataframe(iobj, parents=True, child_first=False)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=False).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- def test__multi_epochs_to_dataframe__list_noparents(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_epochs_to_dataframe(obj, parents=False)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=False,
- child_first=True)
- res2 = ep.multi_epochs_to_dataframe(obj, parents=False,
- child_first=False)
- objs = (iobj.list_children_by_class('Epoch') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.epoch_to_dataframe(iobj, parents=False, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=False,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- def test__multi_epochs_to_dataframe__list_parents_childfirst(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_epochs_to_dataframe(obj)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=True)
- res2 = ep.multi_epochs_to_dataframe(obj, child_first=True)
- res3 = ep.multi_epochs_to_dataframe(obj, parents=True,
- child_first=True)
- objs = (iobj.list_children_by_class('Epoch') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.epoch_to_dataframe(iobj, parents=True, child_first=True)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targwidth, len(res2.columns))
- self.assertEqual(targwidth, len(res3.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertEqual(targlen, len(res2.index))
- self.assertEqual(targlen, len(res3.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- self.assertCountEqual(keys, res2.columns.names)
- self.assertCountEqual(keys, res3.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res2.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res3.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- def test__multi_epochs_to_dataframe__list_parents_parentfirst(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_epochs_to_dataframe(obj, child_first=False)
- res1 = ep.multi_epochs_to_dataframe(obj, parents=True,
- child_first=False)
- objs = (iobj.list_children_by_class('Epoch') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.epoch_to_dataframe(iobj, parents=True, child_first=False)
- for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=False).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targwidth, len(res1.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertEqual(targlen, len(res1.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- self.assertCountEqual(keys, res1.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res1.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- def test__multi_epochs_to_dataframe__tuple_default(self):
- obj = tuple(fake_neo('Block', seed=i, n=3) for i in range(3))
- res0 = ep.multi_epochs_to_dataframe(obj)
- objs = (iobj.list_children_by_class('Epoch') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.epoch_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- def test__multi_epochs_to_dataframe__iter_default(self):
- obj = [fake_neo('Block', seed=i, n=3) for i in range(3)]
- res0 = ep.multi_epochs_to_dataframe(iter(obj))
- objs = (iobj.list_children_by_class('Epoch') for iobj in obj)
- objs = list(chain.from_iterable(objs))
- targ = [ep.epoch_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- def test__multi_epochs_to_dataframe__dict_default(self):
- obj = dict((i, fake_neo('Block', seed=i, n=3)) for i in range(3))
- res0 = ep.multi_epochs_to_dataframe(obj)
- objs = (iobj.list_children_by_class('Epoch') for iobj in
- obj.values())
- objs = list(chain.from_iterable(objs))
- targ = [ep.epoch_to_dataframe(iobj) for iobj in objs]
- targ = ep._sort_inds(pd.concat(targ, axis=1), axis=1)
- keys = ep._extract_neo_attrs_safe(objs[0], parents=True,
- child_first=True).keys()
- keys = list(keys)
- targwidth = len(objs)
- targlen = [iobj.times[:min(len(iobj.times), len(iobj.durations),
- len(iobj.labels))] for iobj in objs]
- targlen = len(np.unique(np.hstack(targlen)))
- self.assertGreater(len(objs), 0)
- self.assertEqual(targwidth, len(targ.columns))
- self.assertEqual(targwidth, len(res0.columns))
- self.assertEqual(targlen, len(targ.index))
- self.assertEqual(targlen, len(res0.index))
- self.assertCountEqual(keys, targ.columns.names)
- self.assertCountEqual(keys, res0.columns.names)
- assert_array_equal(
- np.array(targ.values, dtype=np.float),
- np.array(res0.values, dtype=np.float))
- assert_frame_equal(targ, res0)
- @unittest.skipUnless(HAVE_PANDAS, 'requires pandas')
- class SliceSpiketrainTestCase(unittest.TestCase):
- def setUp(self):
- obj = [fake_neo('SpikeTrain', seed=i, n=3) for i in range(10)]
- self.obj = ep.multi_spiketrains_to_dataframe(obj)
- def test_single_none(self):
- targ_start = self.obj.columns.get_level_values('t_start').values
- targ_stop = self.obj.columns.get_level_values('t_stop').values
- res0 = ep.slice_spiketrain(self.obj)
- res1 = ep.slice_spiketrain(self.obj, t_start=None)
- res2 = ep.slice_spiketrain(self.obj, t_stop=None)
- res3 = ep.slice_spiketrain(self.obj, t_start=None, t_stop=None)
- res0_start = res0.columns.get_level_values('t_start').values
- res1_start = res1.columns.get_level_values('t_start').values
- res2_start = res2.columns.get_level_values('t_start').values
- res3_start = res3.columns.get_level_values('t_start').values
- res0_stop = res0.columns.get_level_values('t_stop').values
- res1_stop = res1.columns.get_level_values('t_stop').values
- res2_stop = res2.columns.get_level_values('t_stop').values
- res3_stop = res3.columns.get_level_values('t_stop').values
- targ = self.obj
- self.assertFalse(res0 is targ)
- self.assertFalse(res1 is targ)
- self.assertFalse(res2 is targ)
- self.assertFalse(res3 is targ)
- assert_frame_equal(targ, res0)
- assert_frame_equal(targ, res1)
- assert_frame_equal(targ, res2)
- assert_frame_equal(targ, res3)
- assert_array_equal(targ_start, res0_start)
- assert_array_equal(targ_start, res1_start)
- assert_array_equal(targ_start, res2_start)
- assert_array_equal(targ_start, res3_start)
- assert_array_equal(targ_stop, res0_stop)
- assert_array_equal(targ_stop, res1_stop)
- assert_array_equal(targ_stop, res2_stop)
- assert_array_equal(targ_stop, res3_stop)
- def test_single_t_start(self):
- targ_start = .0001
- targ_stop = self.obj.columns.get_level_values('t_stop').values
- res0 = ep.slice_spiketrain(self.obj, t_start=targ_start)
- res1 = ep.slice_spiketrain(self.obj, t_start=targ_start, t_stop=None)
- res0_start = res0.columns.get_level_values('t_start').unique().tolist()
- res1_start = res1.columns.get_level_values('t_start').unique().tolist()
- res0_stop = res0.columns.get_level_values('t_stop').values
- res1_stop = res1.columns.get_level_values('t_stop').values
- targ = self.obj.values
- targ[targ < targ_start] = np.nan
- self.assertFalse(res0 is targ)
- self.assertFalse(res1 is targ)
- assert_array_equal(targ, res0.values)
- assert_array_equal(targ, res1.values)
- self.assertEqual([targ_start], res0_start)
- self.assertEqual([targ_start], res1_start)
- assert_array_equal(targ_stop, res0_stop)
- assert_array_equal(targ_stop, res1_stop)
- def test_single_t_stop(self):
- targ_start = self.obj.columns.get_level_values('t_start').values
- targ_stop = .0009
- res0 = ep.slice_spiketrain(self.obj, t_stop=targ_stop)
- res1 = ep.slice_spiketrain(self.obj, t_stop=targ_stop, t_start=None)
- res0_start = res0.columns.get_level_values('t_start').values
- res1_start = res1.columns.get_level_values('t_start').values
- res0_stop = res0.columns.get_level_values('t_stop').unique().tolist()
- res1_stop = res1.columns.get_level_values('t_stop').unique().tolist()
- targ = self.obj.values
- targ[targ > targ_stop] = np.nan
- self.assertFalse(res0 is targ)
- self.assertFalse(res1 is targ)
- assert_array_equal(targ, res0.values)
- assert_array_equal(targ, res1.values)
- assert_array_equal(targ_start, res0_start)
- assert_array_equal(targ_start, res1_start)
- self.assertEqual([targ_stop], res0_stop)
- self.assertEqual([targ_stop], res1_stop)
- def test_single_both(self):
- targ_start = .0001
- targ_stop = .0009
- res0 = ep.slice_spiketrain(self.obj,
- t_stop=targ_stop, t_start=targ_start)
- res0_start = res0.columns.get_level_values('t_start').unique().tolist()
- res0_stop = res0.columns.get_level_values('t_stop').unique().tolist()
- targ = self.obj.values
- targ[targ < targ_start] = np.nan
- targ[targ > targ_stop] = np.nan
- self.assertFalse(res0 is targ)
- assert_array_equal(targ, res0.values)
- self.assertEqual([targ_start], res0_start)
- self.assertEqual([targ_stop], res0_stop)
- if __name__ == '__main__':
- unittest.main()
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