def __init__(self, times=None, durations=None, labels=None, units=None, name=None,
description=None, file_origin=None, array_annotations=None, **annotations):
'''
Initialize a new :class:`Epoch` instance.
'''
DataObject.__init__(self, name=name, file_origin=file_origin, description=description,
array_annotations=array_annotations, **annotations)
def __init__(self,
signal,
units=None,
dtype=None,
copy=True,
t_start=0 * pq.s,
sampling_rate=None,
sampling_period=None,
name=None,
file_origin=None,
description=None,
array_annotations=None,
**annotations):
'''
Initializes a newly constructed :class:`AnalogSignal` instance.
'''
# This method is only called when constructing a new AnalogSignal,
# not when slicing or viewing. We use the same call signature
# as __new__ for documentation purposes. Anything not in the call
# signature is stored in annotations.
# Calls parent __init__, which grabs universally recommended
# attributes and sets up self.annotations
DataObject.__init__(self,
name=name,
file_origin=file_origin,
description=description,
array_annotations=array_annotations,
**annotations)
def __init__(self, times=None, labels=None, units=None, name=None, description=None,
file_origin=None, array_annotations=None, **annotations):
'''
Initialize a new :class:`Event` instance.
'''
DataObject.__init__(self, name=name, file_origin=file_origin, description=description,
array_annotations=array_annotations, **annotations)
def __init__(self, image_data, units=None, dtype=None, copy=True, spatial_scale=None, sampling_period=None,
sampling_rate=None, name=None, description=None, file_origin=None,
**annotations):
'''
Initializes a newly constructed :class:`ImageSequence` instance.
'''
DataObject.__init__(self, name=name, file_origin=file_origin, description=description,
**annotations)
def __init__(self, times, signal, units=None, time_units=None, dtype=None, copy=True,
name=None, file_origin=None, description=None, array_annotations=None,
**annotations):
'''
Initializes a newly constructed :class:`IrregularlySampledSignal`
instance.
'''
DataObject.__init__(self, name=name, file_origin=file_origin, description=description,
array_annotations=array_annotations, **annotations)
def __init__(self, times, signal, units=None, time_units=None, dtype=None, copy=True,
name=None, file_origin=None, description=None, array_annotations=None,
**annotations):
'''
Initializes a newly constructed :class:`IrregularlySampledSignal`
instance.
'''
DataObject.__init__(self, name=name, file_origin=file_origin, description=description,
array_annotations=array_annotations, **annotations)
def test_arr_anns_at_index(self):
# Get them, test for desired type and size, content
datobj = DataObject([1, 2, 3, 4])
arr_ann = {
'anno1': [3, 4, 5, 6],
'anno2': ['ABC', 'DEF', 'GHI', 'JKL']
}
datobj.array_annotate(**arr_ann)
# Integer as index
ann_int = datobj.array_annotations_at_index(1)
self.assertEqual(ann_int, {'anno1': 4, 'anno2': 'DEF'})
# Negative integer as index
ann_int_back = datobj.array_annotations_at_index(-2)
self.assertEqual(ann_int_back, {'anno1': 5, 'anno2': 'GHI'})
# Slice as index
ann_slice = datobj.array_annotations_at_index(slice(1, 3))
self.assertTrue((ann_slice['anno1'] == np.array([4, 5])).all())
self.assertTrue((ann_slice['anno2'] == np.array(['DEF', 'GHI'])).all())
# Slice from beginning to end
ann_slice_all = datobj.array_annotations_at_index(slice(0, None))
self.assertTrue((ann_slice_all['anno1'] == np.array([3, 4, 5,
6])).all())
self.assertTrue(
(ann_slice_all['anno2'] == np.array(['ABC', 'DEF', 'GHI',
'JKL'])).all())
# Make sure that original object is edited when editing extracted array_annotations
ann_slice_all['anno1'][2] = 10
self.assertEqual(datobj.array_annotations_at_index(2)['anno1'], 10)
def test_array_annotate(self):
# Calls _check_array_annotations, so no need to test for these Errors here
datobj = DataObject([2, 3, 4])
arr_ann = {'anno1': [3, 4, 5], 'anno2': ['ABC', 'DEF', 'GHI']}
# Pass annotations
datobj.array_annotate(**arr_ann)
# Make sure they are correct
self.assertTrue((datobj.array_annotations['anno1'] == np.array([3, 4, 5])).all())
self.assertTrue(
(datobj.array_annotations['anno2'] == np.array(['ABC', 'DEF', 'GHI'])).all())
self.assertIsInstance(datobj.array_annotations, ArrayDict)
def test_arr_anns_at_index(self):
# Get them, test for desired type and size, content
datobj = DataObject([1, 2, 3, 4])
arr_ann = {'anno1': [3, 4, 5, 6], 'anno2': ['ABC', 'DEF', 'GHI', 'JKL']}
datobj.array_annotate(**arr_ann)
# Integer as index
ann_int = datobj.array_annotations_at_index(1)
self.assertEqual(ann_int, {'anno1': 4, 'anno2': 'DEF'})
# Negative integer as index
ann_int_back = datobj.array_annotations_at_index(-2)
self.assertEqual(ann_int_back, {'anno1': 5, 'anno2': 'GHI'})
# Slice as index
ann_slice = datobj.array_annotations_at_index(slice(1, 3))
self.assert_((ann_slice['anno1'] == np.array([4, 5])).all())
self.assert_((ann_slice['anno2'] == np.array(['DEF', 'GHI'])).all())
# Slice from beginning to end
ann_slice_all = datobj.array_annotations_at_index(slice(0, None))
self.assert_((ann_slice_all['anno1'] == np.array([3, 4, 5, 6])).all())
self.assert_((ann_slice_all['anno2'] == np.array(['ABC', 'DEF', 'GHI', 'JKL'])).all())
# Make sure that original object is edited when editing extracted array_annotations
ann_slice_all['anno1'][2] = 10
self.assertEqual(datobj.array_annotations_at_index(2)['anno1'], 10)
def __init__(self, times, t_stop, units=None, dtype=np.float, copy=True,
sampling_rate=1.0 * pq.Hz, t_start=0.0 * pq.s, waveforms=None, left_sweep=None,
name=None, file_origin=None, description=None, array_annotations=None,
**annotations):
'''
Initializes a newly constructed :class:`SpikeTrain` instance.
'''
# This method is only called when constructing a new SpikeTrain,
# not when slicing or viewing. We use the same call signature
# as __new__ for documentation purposes. Anything not in the call
# signature is stored in annotations.
# Calls parent __init__, which grabs universally recommended
# attributes and sets up self.annotations
DataObject.__init__(self, name=name, file_origin=file_origin, description=description,
array_annotations=array_annotations, **annotations)
def test_implicit_dict_check(self):
# DataObject instance that handles checking
datobj = DataObject(
[1, 2]) # Inherits from Quantity, so some data is required
# Correct annotations
arr1 = np.asarray(["ABC", "DEF"])
arr2 = np.asarray([3, 6])
corr_ann = {'anno1': arr1, 'anno2': arr2}
corr_ann_copy = copy.deepcopy(corr_ann)
# Implicit checks when setting item in dict directly
# Checking correct annotations should work fine
datobj.array_annotations['anno1'] = arr1
datobj.array_annotations.update({'anno2': arr2})
# Make sure the annotations have not been altered
self.assertTrue((
datobj.array_annotations['anno1'] == corr_ann_copy['anno1']).all())
self.assertTrue((
datobj.array_annotations['anno2'] == corr_ann_copy['anno2']).all())
# Now creating incorrect inputs:
# Nested dict
nested_ann = {'anno1': {'val1': arr1}, 'anno2': {'val2': arr2}}
with self.assertRaises(ValueError):
datobj.array_annotations['anno1'] = {'val1': arr1}
# Containing None
none_ann = corr_ann_copy
# noinspection PyTypeChecker
none_ann['anno2'] = None
with self.assertRaises(ValueError):
datobj.array_annotations['anno1'] = None
# Multi-dimensional arrays in annotations
multi_dim_ann = copy.deepcopy(corr_ann)
multi_dim_ann['anno2'] = multi_dim_ann['anno2'].reshape(1, 2)
with self.assertRaises(ValueError):
datobj.array_annotations.update(multi_dim_ann)
# Wrong length of annotations
len_ann = corr_ann
len_ann['anno1'] = np.asarray(['ABC', 'DEF', 'GHI'])
with self.assertRaises(ValueError):
datobj.array_annotations.update(len_ann)
# Scalar as array annotation raises Error if len(datobj)!=1
scalar_ann = copy.deepcopy(corr_ann)
# noinspection PyTypeChecker
scalar_ann['anno2'] = 3
with self.assertRaises(ValueError):
datobj.array_annotations.update(scalar_ann)
# But not if len(datobj) == 1, then it's wrapped into an array
# noinspection PyTypeChecker
scalar_ann['anno1'] = 'ABC'
datobj2 = DataObject([1])
datobj2.array_annotations.update(scalar_ann)
self.assertIsInstance(datobj2.array_annotations['anno1'], np.ndarray)
self.assertIsInstance(datobj2.array_annotations['anno2'], np.ndarray)
# Lists are also made to np.ndarrays
list_ann = {'anno1': [3, 6], 'anno2': ['ABC', 'DEF']}
datobj.array_annotations.update(list_ann)
self.assertIsInstance(datobj.array_annotations['anno1'], np.ndarray)
self.assertIsInstance(datobj.array_annotations['anno2'], np.ndarray)
def test_check_arr_ann(self):
# DataObject instance that handles checking
datobj = DataObject(
[1, 2]) # Inherits from Quantity, so some data is required
# Correct annotations
arr1 = np.asarray(["ABC", "DEF"])
arr2 = np.asarray([3, 6])
corr_ann = {'anno1': arr1, 'anno2': arr2}
corr_ann_copy = copy.deepcopy(corr_ann)
# Checking correct annotations should work fine
corr_ann = _normalize_array_annotations(corr_ann,
datobj._get_arr_ann_length())
# Make sure the annotations have not been altered
self.assertSequenceEqual(corr_ann.keys(), corr_ann_copy.keys())
self.assertTrue((corr_ann['anno1'] == corr_ann_copy['anno1']).all())
self.assertTrue((corr_ann['anno2'] == corr_ann_copy['anno2']).all())
# Now creating incorrect inputs:
# Nested dict
nested_ann = {'anno1': {'val1': arr1}, 'anno2': {'val2': arr2}}
with self.assertRaises(ValueError):
nested_ann = _normalize_array_annotations(
nested_ann, datobj._get_arr_ann_length())
# Containing None
none_ann = corr_ann_copy
# noinspection PyTypeChecker
none_ann['anno2'] = None
with self.assertRaises(ValueError):
none_ann = _normalize_array_annotations(
none_ann, datobj._get_arr_ann_length())
# Multi-dimensional arrays in annotations
multi_dim_ann = copy.deepcopy(corr_ann)
multi_dim_ann['anno2'] = multi_dim_ann['anno2'].reshape(1, 2)
with self.assertRaises(ValueError):
multi_dim_ann = _normalize_array_annotations(
multi_dim_ann, datobj._get_arr_ann_length())
# Wrong length of annotations
len_ann = corr_ann
len_ann['anno1'] = np.asarray(['ABC', 'DEF', 'GHI'])
with self.assertRaises(ValueError):
len_ann = _normalize_array_annotations(
len_ann, datobj._get_arr_ann_length())
# Scalar as array annotation raises Error if len(datobj)!=1
scalar_ann = copy.deepcopy(corr_ann)
# noinspection PyTypeChecker
scalar_ann['anno2'] = 3
with self.assertRaises(ValueError):
scalar_ann = _normalize_array_annotations(
scalar_ann, datobj._get_arr_ann_length())
# But not if len(datobj) == 1, then it's wrapped into an array
# noinspection PyTypeChecker
scalar_ann['anno1'] = 'ABC'
datobj2 = DataObject([1])
scalar_ann = _normalize_array_annotations(
scalar_ann, datobj2._get_arr_ann_length())
self.assertIsInstance(scalar_ann['anno1'], np.ndarray)
self.assertIsInstance(scalar_ann['anno2'], np.ndarray)
# Lists are also made to np.ndarrays
list_ann = {'anno1': [3, 6], 'anno2': ['ABC', 'DEF']}
list_ann = _normalize_array_annotations(list_ann,
datobj._get_arr_ann_length())
self.assertIsInstance(list_ann['anno1'], np.ndarray)
self.assertIsInstance(list_ann['anno2'], np.ndarray)
def test_implicit_dict_check(self):
# DataObject instance that handles checking
datobj = DataObject([1, 2]) # Inherits from Quantity, so some data is required
# Correct annotations
arr1 = np.asarray(["ABC", "DEF"])
arr2 = np.asarray([3, 6])
corr_ann = {'anno1': arr1, 'anno2': arr2}
corr_ann_copy = copy.deepcopy(corr_ann)
# Implicit checks when setting item in dict directly
# Checking correct annotations should work fine
datobj.array_annotations['anno1'] = arr1
datobj.array_annotations.update({'anno2': arr2})
# Make sure the annotations have not been altered
self.assertTrue((datobj.array_annotations['anno1'] == corr_ann_copy['anno1']).all())
self.assertTrue((datobj.array_annotations['anno2'] == corr_ann_copy['anno2']).all())
# Now creating incorrect inputs:
# Nested dict
nested_ann = {'anno1': {'val1': arr1}, 'anno2': {'val2': arr2}}
with self.assertRaises(ValueError):
datobj.array_annotations['anno1'] = {'val1': arr1}
# Containing None
none_ann = corr_ann_copy
# noinspection PyTypeChecker
none_ann['anno2'] = None
with self.assertRaises(ValueError):
datobj.array_annotations['anno1'] = None
# Multi-dimensional arrays in annotations
multi_dim_ann = copy.deepcopy(corr_ann)
multi_dim_ann['anno2'] = multi_dim_ann['anno2'].reshape(1, 2)
with self.assertRaises(ValueError):
datobj.array_annotations.update(multi_dim_ann)
# Wrong length of annotations
len_ann = corr_ann
len_ann['anno1'] = np.asarray(['ABC', 'DEF', 'GHI'])
with self.assertRaises(ValueError):
datobj.array_annotations.update(len_ann)
# Scalar as array annotation raises Error if len(datobj)!=1
scalar_ann = copy.deepcopy(corr_ann)
# noinspection PyTypeChecker
scalar_ann['anno2'] = 3
with self.assertRaises(ValueError):
datobj.array_annotations.update(scalar_ann)
# But not if len(datobj) == 1, then it's wrapped into an array
# noinspection PyTypeChecker
scalar_ann['anno1'] = 'ABC'
datobj2 = DataObject([1])
datobj2.array_annotations.update(scalar_ann)
self.assertIsInstance(datobj2.array_annotations['anno1'], np.ndarray)
self.assertIsInstance(datobj2.array_annotations['anno2'], np.ndarray)
# Lists are also made to np.ndarrays
list_ann = {'anno1': [3, 6], 'anno2': ['ABC', 'DEF']}
datobj.array_annotations.update(list_ann)
self.assertIsInstance(datobj.array_annotations['anno1'], np.ndarray)
self.assertIsInstance(datobj.array_annotations['anno2'], np.ndarray)
def test_check_arr_ann(self):
# DataObject instance that handles checking
datobj = DataObject([1, 2]) # Inherits from Quantity, so some data is required
# Correct annotations
arr1 = np.asarray(["ABC", "DEF"])
arr2 = np.asarray([3, 6])
corr_ann = {'anno1': arr1, 'anno2': arr2}
corr_ann_copy = copy.deepcopy(corr_ann)
# Checking correct annotations should work fine
corr_ann = _normalize_array_annotations(corr_ann, datobj._get_arr_ann_length())
# Make sure the annotations have not been altered
self.assertSequenceEqual(corr_ann.keys(), corr_ann_copy.keys())
self.assertTrue((corr_ann['anno1'] == corr_ann_copy['anno1']).all())
self.assertTrue((corr_ann['anno2'] == corr_ann_copy['anno2']).all())
# Now creating incorrect inputs:
# Nested dict
nested_ann = {'anno1': {'val1': arr1}, 'anno2': {'val2': arr2}}
with self.assertRaises(ValueError):
nested_ann = _normalize_array_annotations(nested_ann, datobj._get_arr_ann_length())
# Containing None
none_ann = corr_ann_copy
# noinspection PyTypeChecker
none_ann['anno2'] = None
with self.assertRaises(ValueError):
none_ann = _normalize_array_annotations(none_ann, datobj._get_arr_ann_length())
# Multi-dimensional arrays in annotations
multi_dim_ann = copy.deepcopy(corr_ann)
multi_dim_ann['anno2'] = multi_dim_ann['anno2'].reshape(1, 2)
with self.assertRaises(ValueError):
multi_dim_ann = _normalize_array_annotations(multi_dim_ann,
datobj._get_arr_ann_length())
# Wrong length of annotations
len_ann = corr_ann
len_ann['anno1'] = np.asarray(['ABC', 'DEF', 'GHI'])
with self.assertRaises(ValueError):
len_ann = _normalize_array_annotations(len_ann, datobj._get_arr_ann_length())
# Scalar as array annotation raises Error if len(datobj)!=1
scalar_ann = copy.deepcopy(corr_ann)
# noinspection PyTypeChecker
scalar_ann['anno2'] = 3
with self.assertRaises(ValueError):
scalar_ann = _normalize_array_annotations(scalar_ann, datobj._get_arr_ann_length())
# But not if len(datobj) == 1, then it's wrapped into an array
# noinspection PyTypeChecker
scalar_ann['anno1'] = 'ABC'
datobj2 = DataObject([1])
scalar_ann = _normalize_array_annotations(scalar_ann, datobj2._get_arr_ann_length())
self.assertIsInstance(scalar_ann['anno1'], np.ndarray)
self.assertIsInstance(scalar_ann['anno2'], np.ndarray)
# Lists are also made to np.ndarrays
list_ann = {'anno1': [3, 6], 'anno2': ['ABC', 'DEF']}
list_ann = _normalize_array_annotations(list_ann, datobj._get_arr_ann_length())
self.assertIsInstance(list_ann['anno1'], np.ndarray)
self.assertIsInstance(list_ann['anno2'], np.ndarray)
