def setUp(self):
# create a random noise signal with 50 epochs, 100 samples, and
# 2 sources
# even epochs and source 0: *= 5
# odd epochs and source 1: *= 5
self.s = np.random.randn(self.EPOCHS, self.SAMPLES, self.SOURCES)
self.s[::2, :, 0] *= 5
self.s[1::2, :, 1] *= 5
# the mixmatrix which converts our sources to channels
# X = As + noise
self.A = np.random.randn(self.CHANNELS, self.SOURCES)
# our 'signal' which 50 epochs, 100 samples and 10 channels
self.X = np.empty((self.EPOCHS, self.SAMPLES, self.CHANNELS))
for i in range(self.EPOCHS):
self.X[i] = np.dot(self.A, self.s[i].T).T
noise = np.random.randn(self.EPOCHS, self.SAMPLES,
self.CHANNELS) * 0.01
self.X += noise
a = np.array([1 for i in range(self.X.shape[0])])
a[0::2] = 0
axes = [a, np.arange(self.X.shape[1]), np.arange(self.X.shape[2])]
self.epo = Data(self.X,
axes=axes,
names=['class', 'time', 'channel'],
units=['#', 'ms', '#'])
self.epo.class_names = ['foo', 'bar']
self.filter = np.random.random((self.CHANNELS, self.CHANNELS))
def setUp(self):
# generate sources with independent variance modulations, the
# first source will be the target source
z = np.abs(np.random.randn(self.EPOCHS, self.SOURCES))
for i in range(self.SOURCES):
z[:, i] /= z[:, i].std()
self.s = np.random.randn(self.EPOCHS, self.SAMPLES, self.SOURCES)
for i in range(self.SOURCES):
for j in range(self.EPOCHS):
self.s[j, :, i] *= z[j, i]
# the mixmatrix which converts our sources to channels
# X = As + noise
self.A = np.random.randn(self.CHANNELS, self.SOURCES)
# our 'signal' which 50 epochs, 100 samples and 10 channels
self.X = np.empty((self.EPOCHS, self.SAMPLES, self.CHANNELS))
for i in range(self.EPOCHS):
self.X[i] = np.dot(self.A, self.s[i].T).T
noise = np.random.randn(self.EPOCHS, self.SAMPLES,
self.CHANNELS) * 0.01
self.X += noise
# convert to epo
axes = [
z[:, 0],
np.arange(self.X.shape[1]),
np.arange(self.X.shape[2])
]
self.epo = Data(self.X,
axes=axes,
names=['target_variable', 'time', 'channel'],
units=['#', 'ms', '#'])
def setUp(self):
data = np.random.randn(SAMPLES, CHANS)
data[:, 1] += 0.5 * data[:, 0]
data[:, 2] -= 0.5 * data[:, 0]
t = np.arange(SAMPLES)
chans = ['chan{i}'.format(i=i) for i in range(CHANS)]
self.cnt = Data(data, [t, chans], ['time', 'channels'], ['ms', '#'])
def test_eq_and_ne(self):
"""Check if __ne__ is properly implemented."""
d1 = Data(self.data, self.axes, self.names, self.units)
d2 = d1.copy()
# if __eq__ is implemented and __ne__ is not, this evaluates to
# True!
self.assertFalse(d1 == d2 and d1 != d2)
def test_eq_and_ne(self):
"""Check if __ne__ is properly implemented."""
d1 = Data(self.data, self.axes, self.names, self.units)
d2 = d1.copy()
# if __eq__ is implemented and __ne__ is not, this evaluates to
# True!
self.assertFalse(d1 == d2 and d1 != d2)
def test_correct_classlabels(self):
"""lda_train must throw an error if the class labels are not exactly [0, 1]."""
data = np.random.random((50, 100))
labels = np.zeros(50)
# only 0s -> fail
fv = Data(data=data,
axes=[labels, np.arange(100)],
units=['x', 'y'],
names=['foo', 'bar'])
with self.assertRaises(ValueError):
lda_train(fv)
# 0s and 1s -> ok
labels[1] = 1
fv = Data(data=data,
axes=[labels, np.arange(100)],
units=['x', 'y'],
names=['foo', 'bar'])
try:
lda_train(fv)
except ValueError:
self.fail()
# 0s, 1s, and 2s -> fail
labels[2] = 2
fv = Data(data=data,
axes=[labels, np.arange(100)],
units=['x', 'y'],
names=['foo', 'bar'])
with self.assertRaises(ValueError):
lda_train(fv)
def convert_mushu_data(data, markers, fs, channels):
"""Convert mushu data into wyrm's ``Data`` format.
This convenience method creates a continuous ``Data`` object from
the parameters given. The timeaxis always starts from zero and its
values are calculated from the sampling frequency ``fs`` and the
length of ``data``. The ``names`` and ``units`` attributes are
filled with default vaules.
Parameters
----------
data : 2d array
an 2 dimensional numpy array with the axes: (time, channel)
markers : list of tuples: (float, str)
a list of markers. Each element is a tuple of timestamp and
string. The timestamp is the time in ms relative to the onset of
the block of data. Note that negative values are *allowed* as
well as values bigger than the length of the block of data
returned. That is to be interpreted as a marker from the last
block and a marker for a future block respectively.
fs : float
the sampling frequency, this number is used to calculate the
timeaxis for the data
channels : list or 1d array of strings
the channel names
Returns
-------
cnt : continuous ``Data`` object
Examples
--------
Assuming that ``amp`` is an Amplifier instance from ``libmushu``,
already configured but not started yet:
>>> amp_fs = amp.get_sampling_frequency()
>>> amp_channels = amp.get_channels()
>>> amp.start()
>>> while True:
... data, markers = amp.get_data()
... cnt = convert_mushu_data(data, markers, amp_fs, amp_channels)
... # some more code
>>> amp.stop()
References
----------
https://github.com/bbci/mushu
"""
time_axis = np.linspace(0, 1000 * data.shape[0] / fs, data.shape[0], endpoint=False)
chan_axis = channels[:]
axes = [time_axis, chan_axis]
names = ['time', 'channel']
units = ['uV', '#']
cnt = Data(data=data.copy(), axes=axes, names=names, units=units)
cnt.markers = markers[:]
cnt.fs = fs
return cnt
def convert_mushu_data(data, markers, fs, channels):
"""Convert mushu data into wyrm's ``Data`` format.
This convenience method creates a continuous ``Data`` object from
the parameters given. The timeaxis always starts from zero and its
values are calculated from the sampling frequency ``fs`` and the
length of ``data``. The ``names`` and ``units`` attributes are
filled with default vaules.
Parameters
----------
data : 2d array
an 2 dimensional numpy array with the axes: (time, channel)
markers : list of tuples: (float, str)
a list of markers. Each element is a tuple of timestamp and
string. The timestamp is the time in ms relative to the onset of
the block of data. Note that negative values are *allowed* as
well as values bigger than the length of the block of data
returned. That is to be interpreted as a marker from the last
block and a marker for a future block respectively.
fs : float
the sampling frequency, this number is used to calculate the
timeaxis for the data
channels : list or 1d array of strings
the channel names
Returns
-------
cnt : continuous ``Data`` object
Examples
--------
Assuming that ``amp`` is an Amplifier instance from ``libmushu``,
already configured but not started yet:
>>> amp_fs = amp.get_sampling_frequency()
>>> amp_channels = amp.get_channels()
>>> amp.start()
>>> while True:
... data, markers = amp.get_data()
... cnt = convert_mushu_data(data, markers, amp_fs, amp_channels)
... # some more code
>>> amp.stop()
References
----------
https://github.com/bbci/mushu
"""
time_axis = np.linspace(0, 1000 * data.shape[0] / fs, data.shape[0], endpoint=False)
chan_axis = channels[:]
axes = [time_axis, chan_axis]
names = ['time', 'channel']
units = ['uV', '#']
cnt = Data(data=data.copy(), axes=axes, names=names, units=units)
cnt.markers = markers[:]
cnt.fs = fs
return cnt
class TestClearMarkers(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones * 2, axis=0)
cnt = np.append(cnt, ones * 3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 2000, 30, endpoint=False)
self.good_markers = [[-1000, 'a'], [-999, 'b'], [0, 'c'],
[1999.9999999, 'd']]
bad_markers = [[-1001, 'x'], [2000, 'x']]
markers = self.good_markers[:]
markers.extend(bad_markers)
classes = [0, 1, 2, 1]
# four cnts: 1s, -1s, and 0s
data = np.array([cnt * 0, cnt * 1, cnt * 2, cnt * 0])
self.dat = Data(data, [classes, time, channels],
['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.markers = markers
self.dat.fs = 10
def test_clear_markers(self):
"""Clear markers."""
dat = clear_markers(self.dat)
self.assertEqual(dat.markers, self.good_markers)
def test_clear_emtpy_markers(self):
"""Clearing emtpy markers has no effect."""
dat = self.dat.copy()
dat.markers = []
dat2 = clear_markers(dat)
self.assertEqual(dat, dat2)
def test_clear_nonexisting_markers(self):
"""Clearing emtpy markers has no effect."""
dat = self.dat.copy()
del dat.markers
dat2 = clear_markers(dat)
self.assertEqual(dat, dat2)
def test_clear_markers_w_empty_data(self):
"""Clearing emtpy dat should remove all markers."""
dat = self.dat.copy()
dat.data = np.array([])
dat2 = clear_markers(dat)
self.assertEqual(dat2.markers, [])
def test_clear_markes_swapaxes(self):
"""clear_markers must work with nonstandard timeaxis."""
dat = clear_markers(swapaxes(self.dat, 1, 2), timeaxis=2)
dat = swapaxes(dat, 1, 2)
dat2 = clear_markers(self.dat)
self.assertEqual(dat, dat2)
def test_clear_markers_copy(self):
"""clear_markers must not modify argument."""
cpy = self.dat.copy()
clear_markers(self.dat)
self.assertEqual(self.dat, cpy)
class TestClearMarkers(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones*2, axis=0)
cnt = np.append(cnt, ones*3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 2000, 30, endpoint=False)
self.good_markers = [[-1000, 'a'], [-999, 'b'], [0, 'c'], [1999.9999999, 'd']]
bad_markers = [[-1001, 'x'], [2000, 'x']]
markers = self.good_markers[:]
markers.extend(bad_markers)
classes = [0, 1, 2, 1]
# four cnts: 1s, -1s, and 0s
data = np.array([cnt * 0, cnt * 1, cnt * 2, cnt * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.markers = markers
self.dat.fs = 10
def test_clear_markers(self):
"""Clear markers."""
dat = clear_markers(self.dat)
self.assertEqual(dat.markers, self.good_markers)
def test_clear_emtpy_markers(self):
"""Clearing emtpy markers has no effect."""
dat = self.dat.copy()
dat.markers = []
dat2 = clear_markers(dat)
self.assertEqual(dat, dat2)
def test_clear_nonexisting_markers(self):
"""Clearing emtpy markers has no effect."""
dat = self.dat.copy()
del dat.markers
dat2 = clear_markers(dat)
self.assertEqual(dat, dat2)
def test_clear_markers_w_empty_data(self):
"""Clearing emtpy dat should remove all markers."""
dat = self.dat.copy()
dat.data = np.array([])
dat2 = clear_markers(dat)
self.assertEqual(dat2.markers, [])
def test_clear_markes_swapaxes(self):
"""clear_markers must work with nonstandard timeaxis."""
dat = clear_markers(swapaxes(self.dat, 1, 2), timeaxis=2)
dat = swapaxes(dat, 1, 2)
dat2 = clear_markers(self.dat)
self.assertEqual(dat, dat2)
def test_clear_markers_copy(self):
"""clear_markers must not modify argument."""
cpy = self.dat.copy()
clear_markers(self.dat)
self.assertEqual(self.dat, cpy)
def setUp(self):
ones = np.ones((10, 5))
# epo with 0, 1, 2
data = np.array([0 * ones, ones, 2 * ones])
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2]
self.dat = Data(data, [classes, time, channels],
['class', 'time', 'channel'], ['#', 'ms', '#'])
def setUp(self):
self.empty_dat = Data(np.array([]), [], [], [])
self.dat_1 = Data(
np.array([0, 0])[np.newaxis, :],
[np.array([0]), np.array(['ch1', 'ch2'])], ['time', 'channel'],
['ms', '#'])
self.dat_1.fs = 1000
self.dat_1.markers = [[0, 'x']]
self.dat_5 = reduce(append_cnt, [self.dat_1 for i in range(5)])
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 0, 10, endpoint=False)
classes = [0, 0, 0]
# three cnts: 1s, -1s, and 0s
data = np.array([ones, ones * -1, ones * 0])
self.dat = Data(data, [classes, time, channels],
['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.fs = 10
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2, 1]
class_names = ['zeros', 'ones', 'twoes']
# four cnts: 0s, 1s, -1s, and 0s
data = np.array([ones * 0, ones * 1, ones * 2, ones * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = class_names
def setUp(self):
ones = np.ones((10, 5))
# three blocks: 1s, -1s, and 0s
cnt_data = np.concatenate([ones, -ones, 0 * ones], axis=0)
classes = [0, 0, 0]
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 2000, 30, endpoint=False)
epo_data = np.array([cnt_data, cnt_data, cnt_data])
self.dat = Data(epo_data, [classes, time, channels],
['class', 'time', 'channels'], ['#', 'ms', '#'])
def setUp(self):
raw = np.arange(2000).reshape(-1, 5)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 4000, 400, endpoint=False)
fs = 100
marker = [[100, 'foo'], [200, 'bar']]
self.dat = Data(raw, [time, channels], ['time', 'channels'],
['ms', '#'])
self.dat.fs = fs
self.dat.markers = marker
def test_copy(self):
"""Copy must work."""
d1 = Data(self.data, self.axes, self.names, self.units)
d2 = d1.copy()
self.assertEqual(d1, d2)
# we can't really check of all references to be different in
# depth recursively, so we only check on the first level
for k in d1.__dict__:
self.assertNotEqual(id(getattr(d1, k)), id(getattr(d2, k)))
d2 = d1.copy(foo='bar')
self.assertEqual(d2.foo, 'bar')
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones * 2, axis=0)
cnt = np.append(cnt, ones * 3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 3000, 30, endpoint=False)
self.dat = Data(cnt, [time, channels], ['time', 'channel'],
['ms', '#'])
self.dat.markers = [[0, 'a'], [1, 'b']]
self.dat.fs = 10
def setUp(self):
# create epoched data with only 0s in class0, 1s in class1 and
# 2s in class2
cnt = np.ones((10, 3))
epo = np.array([0 * cnt, 1 * cnt, 2 * cnt])
time = np.arange(10)
channels = np.array(['ch1', 'ch2', 'ch3'])
classes = np.arange(3)
axes = ['class', 'time', 'channel']
units = ['#', 'ms', '#']
self.dat = Data(epo, [classes, time, channels], axes, units)
def test_copy(self):
"""Copy must work."""
d1 = Data(self.data, self.axes, self.names, self.units)
d2 = d1.copy()
self.assertEqual(d1, d2)
# we can't really check of all references to be different in
# depth recursively, so we only check on the first level
for k in d1.__dict__:
self.assertNotEqual(id(getattr(d1, k)), id(getattr(d2, k)))
d2 = d1.copy(foo='bar')
self.assertEqual(d2.foo, 'bar')
def setUp(self):
# create 100 samples and tree channels data
ones = np.ones((100, 3))
data = np.array([ones, ones * 2, ones * 3]).reshape(-1, 3)
time = np.linspace(0, 3000, 300, endpoint=False)
channels = ['a', 'b', 'c']
markers = [[500, 'M1'], [1500, 'M2'], [2500, 'M3']]
self.dat = Data(data, [time, channels], ['time', 'channels'],
['ms', '#'])
self.dat.markers = markers
self.dat.fs = 100
self.mrk_def = {'class 1': ['M1'], 'class 2': ['M2', 'M3']}
def setUp(self):
ones = np.ones((10, 2))
twoes = ones * 2
# 7 epochs
data = np.array([ones, ones, twoes, twoes, ones, twoes, twoes])
channels = ['c1', 'c2']
time = np.linspace(0, 1000, 10)
classes = [0, 0, 1, 1, 0, 1, 1]
class_names = ['ones', 'twoes']
self.dat = Data(data, [classes, time, channels],
['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = class_names
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones*2, axis=0)
cnt = np.append(cnt, ones*3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 3000, 30, endpoint=False)
classes = [0, 1, 2, 1]
# four cnts: 1s, -1s, and 0s
data = np.array([cnt * 0, cnt * 1, cnt * 2, cnt * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = ['zero', 'one', 'two']
class TestAppendEpo(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones*2, axis=0)
cnt = np.append(cnt, ones*3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 3000, 30, endpoint=False)
classes = [0, 1, 2, 1]
# four cnts: 1s, -1s, and 0s
data = np.array([cnt * 0, cnt * 1, cnt * 2, cnt * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = ['zero', 'one', 'two']
def test_append_epo(self):
"""append_epo."""
dat = append_epo(self.dat, self.dat)
self.assertEqual(dat.data.shape[0], 2*self.dat.data.shape[0])
self.assertEqual(len(dat.axes[0]), 2*len(self.dat.axes[0]))
np.testing.assert_array_equal(dat.data, np.concatenate([self.dat.data, self.dat.data], axis=0))
np.testing.assert_array_equal(dat.axes[0], np.concatenate([self.dat.axes[0], self.dat.axes[0]]))
self.assertEqual(dat.class_names, self.dat.class_names)
def test_append_epo_with_extra(self):
"""append_epo with extra must work with list and ndarrays."""
self.dat.a = list(range(10))
self.dat.b = np.arange(10)
dat = append_epo(self.dat, self.dat, extra=['a', 'b'])
self.assertEqual(dat.a, list(range(10)) + list(range(10)))
np.testing.assert_array_equal(dat.b, np.concatenate([np.arange(10), np.arange(10)]))
def test_append_epo_with_different_class_names(self):
"""test_append must raise a ValueError if class_names are different."""
a = self.dat.copy()
a.class_names = a.class_names[:-1]
with self.assertRaises(ValueError):
append_epo(a, self.dat)
append_epo(self.dat, a)
def test_append_epo_swapaxes(self):
"""append_epo must work with nonstandard timeaxis."""
dat = append_epo(swapaxes(self.dat, 0, 2), swapaxes(self.dat, 0, 2), classaxis=2)
dat = swapaxes(dat, 0, 2)
dat2 = append_epo(self.dat, self.dat)
self.assertEqual(dat, dat2)
def test_append_epo_copy(self):
"""append_epo means must not modify argument."""
cpy = self.dat.copy()
append_epo(self.dat, self.dat)
self.assertEqual(self.dat, cpy)
def load_mushu_data(meta):
"""Load saved EEG data in Mushu's format.
This method loads saved data in Mushu's format and returns a
continuous ``Data`` object.
Parameters
----------
meta : str
Path to `.meta` file. A Mushu recording consists of three
different files: `.eeg`, `.marker`, and `.meta`.
Returns
-------
dat : Data
Continuous Data object
Examples
--------
>>> dat = load_mushu_data('testrecording.meta')
"""
# reverse and replace and reverse again to replace only the last
# (occurrence of .meta)
datafile = meta[::-1].replace('atem.', 'gee.', 1)[::-1]
markerfile = meta[::-1].replace('atem.', 'rekram.', 1)[::-1]
assert path.exists(meta) and path.exists(datafile) and path.exists(
markerfile)
# load meta data
with open(meta) as fh:
metadata = json.load(fh)
fs = metadata['Sampling Frequency']
channels = np.array(metadata['Channels'])
# load eeg data
data = np.fromfile(datafile, np.float32)
data = data.reshape((-1, len(channels)))
# load markers
markers = []
with open(markerfile) as fh:
for line in fh:
ts, m = line.split(' ', 1)
markers.append([float(ts), str(m).strip()])
# construct Data
duration = len(data) * 1000 / fs
axes = [np.linspace(0, duration, len(data), endpoint=False), channels]
names = ['time', 'channels']
units = ['ms', '#']
dat = Data(data=data, axes=axes, names=names, units=units)
dat.fs = fs
dat.markers = markers
return dat
def setUp(self):
# create some data
fs = 100
dt = 5
self.freqs = [2, 7, 15]
amps = [30, 10, 2]
t = np.linspace(0, dt, fs*dt)
data = np.sum([a * np.sin(2*np.pi*t*f) for a, f in zip(amps, self.freqs)], axis=0)
data = data[:, np.newaxis]
data = np.concatenate([data, data], axis=1)
channel = np.array(['ch1', 'ch2'])
self.dat = Data(data, [t, channel], ['time', 'channel'], ['s', '#'])
self.dat.fs = fs
def setUp(self):
self.sorted_channels = np.array([name for name, pos in CHANNEL_10_20])
channels = self.sorted_channels.copy()
random.shuffle(channels)
raw = np.random.random((5, 10, len(channels)))
time = np.linspace(0, 1000, 10, endpoint=False)
epochs = np.array([0, 1, 0, 1, 0])
fs = 100
marker = [[100, 'foo'], [200, 'bar']]
self.dat = Data(raw, [epochs, time, channels],
['class', 'time', 'channels'], ['#', 'ms', '#'])
self.dat.fs = fs
self.dat.markers = marker
def setUp(self):
dat = np.zeros((SAMPLES, CHANS))
# [-10, -9, ... 20)
dat[:, 0] = np.arange(SAMPLES) - SAMPLES / 2
channels = ['chan{i}'.format(i=i) for i in range(CHANS)]
time = np.arange(SAMPLES)
self.cnt = Data(dat, [time, channels], ['time', 'channels'],
['ms', '#'])
# construct epo
epo_dat = np.array([dat + i for i in range(EPOS)])
classes = ['class{i}'.format(i=i) for i in range(EPOS)]
self.epo = Data(epo_dat, [classes, time, channels],
['class', 'time', 'channels'], ['#', 'ms', '#'])
class TestCorrectForBaseline(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
classes = [0, 0, 0]
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 0, 10, endpoint=False)
# three cnts: 1s, -1s, and 0s
data = np.array([ones, ones * -1, ones * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channels'], ['#', 'ms', '#'])
def test_correct_for_baseline_epo(self):
"""Test baselineing w/ epo like."""
# normal case
dat = correct_for_baseline(self.dat, [-500, 0])
np.testing.assert_array_equal(np.zeros((3, 10, 5)), dat.data)
# the full dat interval
dat = correct_for_baseline(self.dat, [dat.axes[-2][0], dat.axes[-2][-1]])
np.testing.assert_array_equal(np.zeros((3, 10, 5)), dat.data)
def test_correct_for_baseline_cnt(self):
"""Test baselineing w/ cnt like."""
data = self.dat.data.reshape(30, 5)
axes = [np.linspace(-1000, 2000, 30, endpoint=False), self.dat.axes[-1]]
units = self.dat.units[1:]
names = self.dat.names[1:]
dat = self.dat.copy(data=data, axes=axes, names=names, units=units)
dat2 = correct_for_baseline(dat, [-1000, 0])
np.testing.assert_array_equal(dat2.data, dat.data - 1)
def test_ival_checks(self):
"""Test for malformed ival parameter."""
with self.assertRaises(AssertionError):
correct_for_baseline(self.dat, [0, -1])
with self.assertRaises(AssertionError):
correct_for_baseline(self.dat, [self.dat.axes[-2][0]-1, 0])
with self.assertRaises(AssertionError):
correct_for_baseline(self.dat, [0, self.dat.axes[-2][1]+1])
def test_correct_for_baseline_copy(self):
"""Correct for baseline must not modify dat argument."""
cpy = self.dat.copy()
correct_for_baseline(self.dat, [-1000, 0])
self.assertEqual(cpy, self.dat)
def test_correct_for_baseline_swapaxes(self):
"""Correct for baseline must work with nonstandard timeaxis."""
dat = correct_for_baseline(swapaxes(self.dat, 0, 1), [-1000, 0], timeaxis=0)
dat = swapaxes(dat, 0, 1)
dat2 = correct_for_baseline(self.dat, [-1000, 0])
self.assertEqual(dat, dat2)
def load_mushu_data(meta):
"""Load saved EEG data in Mushu's format.
This method loads saved data in Mushu's format and returns a
continuous ``Data`` object.
Parameters
----------
meta : str
Path to `.meta` file. A Mushu recording consists of three
different files: `.eeg`, `.marker`, and `.meta`.
Returns
-------
dat : Data
Continuous Data object
Examples
--------
>>> dat = load_mushu_data('testrecording.meta')
"""
# reverse and replace and reverse again to replace only the last
# (occurrence of .meta)
datafile = meta[::-1].replace('atem.', 'gee.', 1)[::-1]
markerfile = meta[::-1].replace('atem.', 'rekram.', 1)[::-1]
assert path.exists(meta) and path.exists(datafile) and path.exists(markerfile)
# load meta data
with open(meta, 'r') as fh:
metadata = json.load(fh)
fs = metadata['Sampling Frequency']
channels = np.array(metadata['Channels'])
# load eeg data
data = np.fromfile(datafile, np.float32)
data = data.reshape((-1, len(channels)))
# load markers
markers = []
with open(markerfile, 'r') as fh:
for line in fh:
ts, m = line.split(' ', 1)
markers.append([float(ts), str(m).strip()])
# construct Data
duration = len(data) * 1000 / fs
axes = [np.linspace(0, duration, len(data), endpoint=False), channels]
names = ['time', 'channels']
units = ['ms', '#']
dat = Data(data=data, axes=axes, names=names, units=units)
dat.fs = fs
dat.markers = markers
return dat
def test_segment_dat_with_restriction_to_new_data_ival_pos_pos(self):
"""Online Segmentation with ival +something..+something must work correctly."""
data = np.ones((9, 3))
time = np.linspace(0, 900, 9, endpoint=False)
channels = 'a', 'b', 'c'
markers = [[100, 'x'], [200, 'x'], [300, 'x']]
dat = Data(data, [time, channels], ['time', 'channels'], ['ms', '#'])
dat.fs = 10
dat.markers = markers
mrk_def = {'class 1': ['x']}
# each tuple has (number of new samples, expected epocs)
samples_epos = [(0, 0), (1, 0), (2, 1), (3, 2), (4, 3), (5, 3)]
for s, e in samples_epos:
epo = segment_dat(dat, mrk_def, [100, 500], newsamples=s)
self.assertEqual(epo.data.shape[0], e)
def setUp(self):
data = np.random.randn(SAMPLES, CHANS)
data[:, 1] += 0.5 * data[:, 0]
data[:, 2] -= 0.5 * data[:, 0]
t = np.arange(SAMPLES)
chans = ['chan{i}'.format(i=i) for i in range(CHANS)]
self.cnt = Data(data, [t, chans], ['time', 'channels'], ['ms', '#'])
# construct epo
epo_dat = np.array([data for i in range(EPOS)])
classes = ['class{i}'.format(i=i) for i in range(EPOS)]
self.epo = Data(epo_dat, [classes, t, chans],
['class', 'time', 'channels'], ['#', 'ms', '#'])
# my little spatial filter
self.w = np.array([[0, 0.5, 1], [-1, 0.5, 0], [1, 0.5, 0]])
class TestSwapaxes(unittest.TestCase):
def setUp(self):
raw = np.arange(2000).reshape(-1, 5)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 4000, 400, endpoint=False)
fs = 100
marker = [[100, 'foo'], [200, 'bar']]
self.dat = Data(raw, [time, channels], ['time', 'channels'], ['ms', '#'])
self.dat.fs = fs
self.dat.markers = marker
def test_swapaxes(self):
"""Swapping axes."""
new = swapaxes(self.dat, 0, 1)
self.assertTrue((new.axes[0] == self.dat.axes[1]).all())
self.assertTrue((new.axes[1] == self.dat.axes[0]).all())
self.assertEqual(new.names[0], self.dat.names[1])
self.assertEqual(new.names[1], self.dat.names[0])
self.assertEqual(new.units[0], self.dat.units[1])
self.assertEqual(new.units[1], self.dat.units[0])
self.assertEqual(new.data.shape[::-1], self.dat.data.shape)
np.testing.assert_array_equal(new.data.swapaxes(0, 1), self.dat.data)
def test_swapaxes_copy(self):
"""Swapaxes must not modify argument."""
cpy = self.dat.copy()
swapaxes(self.dat, 0, 1)
self.assertEqual(cpy, self.dat)
def test_swapaxes_twice(self):
"""Swapping the same axes twice must result in original."""
dat = swapaxes(self.dat, 0, 1)
dat = swapaxes(dat, 0, 1)
self.assertEqual(dat, self.dat)
def setUp(self):
# X is a random mixture matrix of random variables
Sx = randn(self.SAMPLES, self.CHANNELS_X)
Ax = randn(self.CHANNELS_X, self.CHANNELS_X)
self.X = np.dot(Sx, Ax)
# Y is a random mixture matrix of random variables except the
# first component
Sy = randn(self.SAMPLES, self.CHANNELS_Y)
Sy[:, 0] = Sx[:, 0] + self.NOISE_LEVEL * randn(self.SAMPLES)
Ay = randn(self.CHANNELS_Y, self.CHANNELS_Y)
self.Y = np.dot(Sy, Ay)
# generate Data object
axes_x = [np.arange(self.X.shape[0]), np.arange(self.X.shape[1])]
axes_y = [np.arange(self.Y.shape[0]), np.arange(self.Y.shape[1])]
self.dat_x = Data(self.X, axes=axes_x, names=['time', 'channel'], units=['ms', '#'])
self.dat_y = Data(self.Y, axes=axes_y, names=['time', 'channel'], units=['ms', '#'])
class TestSelectChannels(unittest.TestCase):
def setUp(self):
raw = np.arange(20).reshape(4, 5)
channels = ["ca1", "ca2", "cb1", "cb2", "cc1"]
time = np.arange(4)
self.dat = Data(raw, [time, channels], ["time", "channels"], ["ms", "#"])
def test_select_channels(self):
"""Selecting channels with an array of regexes."""
channels = self.dat.data.copy()
self.dat = select_channels(self.dat, ["ca.*", "cc1"])
np.testing.assert_array_equal(self.dat.axes[-1], np.array(["ca1", "ca2", "cc1"]))
np.testing.assert_array_equal(self.dat.data, channels[:, np.array([0, 1, -1])])
def test_select_channels_inverse(self):
"""Removing channels with an array of regexes."""
channels = self.dat.data.copy()
self.dat = select_channels(self.dat, ["ca.*", "cc1"], invert=True)
np.testing.assert_array_equal(self.dat.axes[-1], np.array(["cb1", "cb2"]))
np.testing.assert_array_equal(self.dat.data, channels[:, np.array([2, 3])])
def test_select_channels_copy(self):
"""Select channels must not change the original parameter."""
cpy = self.dat.copy()
select_channels(self.dat, ["ca.*"])
self.assertEqual(cpy, self.dat)
def test_select_channels_swapaxis(self):
"""Select channels works with non default chanaxis."""
dat1 = select_channels(swapaxes(self.dat, 0, 1), ["ca.*"], chanaxis=0)
dat1 = swapaxes(dat1, 0, 1)
dat2 = select_channels(self.dat, ["ca.*"])
self.assertEqual(dat1, dat2)
def setUp(self):
# generate sources with independent variance modulations, the
# first source will be the target source
z = np.abs(np.random.randn(self.EPOCHS, self.SOURCES))
for i in range(self.SOURCES):
z[:, i] /= z[:, i].std()
self.s = np.random.randn(self.EPOCHS, self.SAMPLES, self.SOURCES)
for i in range(self.SOURCES):
for j in range(self.EPOCHS):
self.s[j, :, i] *= z[j, i]
# the mixmatrix which converts our sources to channels
# X = As + noise
self.A = np.random.randn(self.CHANNELS, self.SOURCES)
# our 'signal' which 50 epochs, 100 samples and 10 channels
self.X = np.empty((self.EPOCHS, self.SAMPLES, self.CHANNELS))
for i in range(self.EPOCHS):
self.X[i] = np.dot(self.A, self.s[i].T).T
noise = np.random.randn(self.EPOCHS, self.SAMPLES, self.CHANNELS) * 0.01
self.X += noise
# convert to epo
axes = [z[:, 0], np.arange(self.X.shape[1]), np.arange(self.X.shape[2])]
self.epo = Data(self.X,
axes=axes,
names=['target_variable', 'time', 'channel'],
units=['#', 'ms', '#'])
def setUp(self):
data = np.random.randn(SAMPLES, CHANS)
data[:, 1] += 0.5 * data[:, 0]
data[:, 2] -= 0.5 * data[:, 0]
t = np.arange(SAMPLES)
chans = ['chan{i}'.format(i=i) for i in range(CHANS)]
self.cnt = Data(data, [t, chans], ['time', 'channels'], ['ms', '#'])
class TestSwapaxes(unittest.TestCase):
def setUp(self):
raw = np.arange(2000).reshape(-1, 5)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 4000, 400, endpoint=False)
fs = 100
marker = [[100, 'foo'], [200, 'bar']]
self.dat = Data(raw, [time, channels], ['time', 'channels'],
['ms', '#'])
self.dat.fs = fs
self.dat.markers = marker
def test_swapaxes(self):
"""Swapping axes."""
new = swapaxes(self.dat, 0, 1)
self.assertTrue((new.axes[0] == self.dat.axes[1]).all())
self.assertTrue((new.axes[1] == self.dat.axes[0]).all())
self.assertEqual(new.names[0], self.dat.names[1])
self.assertEqual(new.names[1], self.dat.names[0])
self.assertEqual(new.units[0], self.dat.units[1])
self.assertEqual(new.units[1], self.dat.units[0])
self.assertEqual(new.data.shape[::-1], self.dat.data.shape)
np.testing.assert_array_equal(new.data.swapaxes(0, 1), self.dat.data)
def test_swapaxes_copy(self):
"""Swapaxes must not modify argument."""
cpy = self.dat.copy()
swapaxes(self.dat, 0, 1)
self.assertEqual(cpy, self.dat)
def test_swapaxes_twice(self):
"""Swapping the same axes twice must result in original."""
dat = swapaxes(self.dat, 0, 1)
dat = swapaxes(dat, 0, 1)
self.assertEqual(dat, self.dat)
def setUp(self):
# X is a random mixture matrix of random variables
Sx = randn(self.SAMPLES, self.CHANNELS_X)
Ax = randn(self.CHANNELS_X, self.CHANNELS_X)
X = np.dot(Sx, Ax)
# Y is a random mixture matrix of random variables except the
# first component
Sy = randn(self.SAMPLES, self.CHANNELS_Y)
Sy[:, 0] = Sx[:, 0] + self.NOISE_LEVEL * randn(self.SAMPLES)
Ay = randn(self.CHANNELS_Y, self.CHANNELS_Y)
Y = np.dot(Sy, Ay)
# generate Data object
axes_x = [np.arange(X.shape[0]), np.arange(X.shape[1])]
axes_y = [np.arange(Y.shape[0]), np.arange(Y.shape[1])]
self.dat_x = Data(X, axes=axes_x, names=['time', 'channel'], units=['ms', '#'])
self.dat_y = Data(Y, axes=axes_y, names=['time', 'channel'], units=['ms', '#'])
def setUp(self):
# create a random noise signal with 50 epochs, 100 samples, and
# 2 sources
# even epochs and source 0: *= 5
# odd epochs and source 1: *= 5
self.s = np.random.randn(self.EPOCHS, self.SAMPLES, self.SOURCES)
self.s[ ::2, :, 0] *= 5
self.s[1::2, :, 1] *= 5
# the mixmatrix which converts our sources to channels
# X = As + noise
self.A = np.random.randn(self.CHANNELS, self.SOURCES)
# our 'signal' which 50 epochs, 100 samples and 10 channels
self.X = np.empty((self.EPOCHS, self.SAMPLES, self.CHANNELS))
for i in range(self.EPOCHS):
self.X[i] = np.dot(self.A, self.s[i].T).T
noise = np.random.randn(self.EPOCHS, self.SAMPLES, self.CHANNELS) * 0.01
self.X += noise
a = np.array([1 for i in range(self.X.shape[0])])
a[0::2] = 0
axes = [a, np.arange(self.X.shape[1]), np.arange(self.X.shape[2])]
self.epo = Data(self.X, axes=axes, names=['class', 'time', 'channel'], units=['#', 'ms', '#'])
self.epo.class_names = ['foo', 'bar']
self.filter = np.random.random((self.CHANNELS, self.CHANNELS))
def setUp(self):
data = np.arange(10).reshape(5, 2)
axes = [np.arange(5), np.array(['ch1', 'ch2'])]
names = ['time', 'channel']
units = ['ms', '#']
fs = 1000
self.dat = Data(data, axes, names, units)
self.dat.fs = fs
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 0, 10, endpoint=False)
classes = [0, 0, 0]
# three cnts: 1s, -1s, and 0s
data = np.array([ones, ones * -1, ones * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
def test_init_with_inconsistent_values(self):
"""Test init with inconsistent values."""
data = self.data[np.newaxis, :]
with self.assertRaises(AssertionError):
Data(data, self.axes, self.names, self.units)
axes = self.axes[:]
axes[0] = np.arange(100)
with self.assertRaises(AssertionError):
Data(self.data, axes, self.names, self.units)
names = self.names[:]
names.append('baz')
with self.assertRaises(AssertionError):
Data(self.data, self.axes, names, self.units)
units = self.units[:]
units.append('u3')
with self.assertRaises(AssertionError):
Data(self.data, self.axes, self.names, units)
def test_raise_error_with_non_continuous_data(self):
"""Raise error if ``dat_x`` is not continuous Data object."""
dat = Data(randn(2, self.SAMPLES, self.CHANNELS_X),
axes=[[0, 1], self.dat_x.axes[0], self.dat_x.axes[1]],
names=['class', 'time', 'channel'],
units=['#', 'ms', '#'])
with self.assertRaises(AssertionError):
calculate_cca(dat, self.dat_x)
def setUp(self):
ones = np.ones((10, 5))
# epo with 0, 1, 2
data = np.array([0*ones, ones, 2*ones])
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2]
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
class TestSelectEpochs(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2, 1]
class_names = ['zeros', 'ones', 'twoes']
# four cnts: 0s, 1s, -1s, and 0s
data = np.array([ones * 0, ones * 1, ones * 2, ones * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = class_names
def test_select_epochs(self):
"""Selecting Epochs."""
# normal case
dat = select_epochs(self.dat, [0])
self.assertEqual(dat.data.shape[0], 1)
np.testing.assert_array_equal(dat.data, self.dat.data[[0]])
np.testing.assert_array_equal(dat.axes[0], self.dat.axes[0][0])
# normal every second
dat = select_epochs(self.dat, [0, 2])
self.assertEqual(dat.data.shape[0], 2)
np.testing.assert_array_equal(dat.data, self.dat.data[::2])
np.testing.assert_array_equal(dat.axes[0], self.dat.axes[0][::2])
# the full epo
dat = select_epochs(self.dat, list(range(self.dat.data.shape[0])))
np.testing.assert_array_equal(dat.data, self.dat.data)
np.testing.assert_array_equal(dat.axes[0], self.dat.axes[0])
# remove one
dat = select_epochs(self.dat, [0], invert=True)
self.assertEqual(dat.data.shape[0], 3)
np.testing.assert_array_equal(dat.data, self.dat.data[1:])
np.testing.assert_array_equal(dat.axes[0], self.dat.axes[0][1:])
# remove every second
dat = select_epochs(self.dat, [0, 2], invert=True)
self.assertEqual(dat.data.shape[0], 2)
np.testing.assert_array_equal(dat.data, self.dat.data[1::2])
np.testing.assert_array_equal(dat.axes[0], self.dat.axes[0][1::2])
def test_select_epochs_with_cnt(self):
"""Select epochs must raise an exception if called with cnt argument."""
del(self.dat.class_names)
with self.assertRaises(AssertionError):
select_epochs(self.dat, [0, 1])
def test_select_epochs_swapaxes(self):
"""Select epochs must work with nonstandard classaxis."""
dat = select_epochs(swapaxes(self.dat, 0, 2), [0, 1], classaxis=2)
dat = swapaxes(dat, 0, 2)
dat2 = select_epochs(self.dat, [0, 1])
self.assertEqual(dat, dat2)
def test_select_epochs_copy(self):
"""Select Epochs must not modify argument."""
cpy = self.dat.copy()
select_epochs(self.dat, [0, 1])
self.assertEqual(self.dat, cpy)
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones*2, axis=0)
cnt = np.append(cnt, ones*3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 3000, 30, endpoint=False)
self.dat = Data(cnt, [time, channels], ['time', 'channel'], ['ms', '#'])
self.dat.markers = [[0, 'a'], [1, 'b']]
def data_factory(data, axes=None, names=None, units=None, markers=None):
"""Helper method to create Data objects."""
if len(data) == 0:
axes = names = units = []
else:
if axes is None:
axes = []
for i in range(data.ndim):
a = [i * 10 for i in range(data.shape[i])]
axes.append(a)
if names is None:
names = ['name %i' % i for i in range(data.ndim)]
if units is None:
units = ['unit %i' % i for i in range(data.ndim)]
d = Data(data=data, axes=axes, names=names, units=units)
d.markers = markers if markers is not None else []
d.fs = 100
return d
class TestRereference(unittest.TestCase):
def setUp(self):
dat = np.zeros((SAMPLES, CHANS))
# [-10, -9, ... 20)
dat[:, 0] = np.arange(SAMPLES) - SAMPLES/2
channels = ['chan{i}'.format(i=i) for i in range(CHANS)]
time = np.arange(SAMPLES)
self.cnt = Data(dat, [time, channels], ['time', 'channels'], ['ms', '#'])
# construct epo
epo_dat = np.array([dat + i for i in range(EPOS)])
classes = ['class{i}'.format(i=i) for i in range(EPOS)]
self.epo = Data(epo_dat, [classes, time, channels], ['class', 'time', 'channels'], ['#', 'ms', '#'])
def test_rereference_cnt(self):
"""Rereference channels (cnt)."""
cnt_r = rereference(self.cnt, 'chan0')
dat_r = np.linspace(SAMPLES/2, -SAMPLES/2, SAMPLES, endpoint=False)
dat_r = [dat_r for i in range(CHANS)]
dat_r = np.array(dat_r).T
dat_r[:, 0] = 0
np.testing.assert_array_equal(cnt_r.data, dat_r)
def test_rereference_epo(self):
"""Rereference channels (epo)."""
epo_r = rereference(self.epo, 'chan0')
dat_r = np.linspace(SAMPLES/2, -SAMPLES/2, SAMPLES, endpoint=False)
dat_r = [dat_r for i in range(CHANS)]
dat_r = np.array(dat_r).T
dat_r[:, 0] = 0
dat_r = np.array([dat_r for i in range(EPOS)])
np.testing.assert_array_equal(epo_r.data, dat_r)
def test_raise_value_error(self):
"""Raise ValueError if channel not found."""
with self.assertRaises(ValueError):
rereference(self.cnt, 'foo')
def test_case_insensitivity(self):
"""rereference should not care about case."""
try:
rereference(self.cnt, 'ChAN0')
except ValueError:
self.fail()
def test_rereference_copy(self):
"""rereference must not modify arguments."""
cpy = self.cnt.copy()
rereference(self.cnt, 'chan0')
self.assertEqual(self.cnt, cpy)
def test_rereference_swapaxes(self):
"""rereference must work with nonstandard chanaxis."""
dat = rereference(swapaxes(self.epo, 1, 2), 'chan0', chanaxis=1)
dat = swapaxes(dat, 1, 2)
dat2 = rereference(self.epo, 'chan0')
self.assertEqual(dat, dat2)
def setUp(self):
raw = np.arange(2000).reshape(-1, 5)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 4000, 400, endpoint=False)
fs = 100
marker = [[100, 'foo'], [200, 'bar']]
self.dat = Data(raw, [time, channels], ['time', 'channels'], ['ms', '#'])
self.dat.fs = fs
self.dat.markers = marker
class TestVariance(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
# epo with 0, 1, 2
data = np.array([0*ones, ones, 2*ones])
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2]
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
def test_variance(self):
"""Variance."""
dat = variance(self.dat)
# test the resulting dat has one axis less (the middle one)
self.assertEqual(dat.data.shape, self.dat.data.shape[::2])
# each epoch should have a variance of zero, test if the var of
# all epochs is 0
self.assertEqual(dat.data.var(), 0)
self.assertEqual(len(dat.axes), len(self.dat.axes)-1)
def test_variance_with_cnt(self):
"""variance must work with cnt argument."""
data = self.dat.data[1]
axes = self.dat.axes[1:]
names = self.dat.names[1:]
units = self.dat.units[1:]
dat = self.dat.copy(data=data, axes=axes, names=names, units=units)
dat = variance(dat)
self.assertEqual(dat.data.var(), 0)
self.assertEqual(len(dat.axes), len(self.dat.axes)-2)
def test_variance_swapaxes(self):
"""variance must work with nonstandard timeaxis."""
dat = variance(swapaxes(self.dat, 1, 2), timeaxis=2)
# we don't swap back here as variance removes the timeaxis
dat2 = variance(self.dat)
self.assertEqual(dat, dat2)
def test_variance_copy(self):
"""variance must not modify argument."""
cpy = self.dat.copy()
variance(self.dat)
self.assertEqual(self.dat, cpy)
class TestRectifytChannels(unittest.TestCase):
def setUp(self):
raw = np.arange(20).reshape(4, 5)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.arange(4)
self.dat = Data(raw, [time, channels], ['time', 'channels'], ['ms', '#'])
def test_rectify_channels(self):
"""Rectify channels of positive and negative data must be equal."""
dat = rectify_channels(self.dat.copy(data=-self.dat.data))
dat2 = rectify_channels(self.dat)
self.assertEqual(dat, dat2)
def test_rectify_channels_copy(self):
"""Rectify channels must not change the original parameter."""
cpy = self.dat.copy()
rectify_channels(self.dat)
self.assertEqual(cpy, self.dat)
def setUp(self):
ones = np.ones((10, 5))
channels = ["ca1", "ca2", "cb1", "cb2", "cc1"]
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2, 1]
class_names = ["zeros", "ones", "twoes"]
# four cnts: 0s, 1s, -1s, and 0s
data = np.array([ones * 0, ones * 1, ones * 2, ones * 0])
self.dat = Data(data, [classes, time, channels], ["class", "time", "channel"], ["#", "ms", "#"])
self.dat.class_names = class_names
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 1000, 10, endpoint=False)
classes = [0, 1, 2, 1]
class_names = ['zeros', 'ones', 'twoes']
# four cnts: 0s, 1s, -1s, and 0s
data = np.array([ones * 0, ones * 1, ones * 2, ones * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = class_names
class TestSelectIval(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(-1000, 0, 10, endpoint=False)
classes = [0, 0, 0]
# three cnts: 1s, -1s, and 0s
data = np.array([ones, ones * -1, ones * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.fs = 10
def test_select_ival(self):
"""Selecting Intervals."""
# normal case
dat = select_ival(self.dat, [-500, 0])
self.assertEqual(dat.axes[1][0], -500)
self.assertEqual(dat.axes[1][-1],-100)
# the full dat interval
dat = select_ival(self.dat, [self.dat.axes[1][0], self.dat.axes[1][-1] + 1])
self.assertEqual(dat.axes[1][0], self.dat.axes[1][0])
self.assertEqual(dat.axes[1][-1], self.dat.axes[1][-1])
np.testing.assert_array_equal(dat.data, self.dat.data)
def test_select_ival_with_markers(self):
"""Selecting Intervals with markers."""
# normal case
good_markers = [[-499,99, 'x'], [-500, 'x'], [-0.0001, 'x']]
bad_markers = [[501, 'y'], [0, 'y'], [1, 'y']]
self.dat.markers = good_markers[:]
self.dat.markers.extend(bad_markers)
dat = select_ival(self.dat, [-500, 0])
self.assertEqual(dat.markers, good_markers)
def test_ival_checks(self):
"""Test for malformed ival parameter."""
with self.assertRaises(AssertionError):
select_ival(self.dat, [0, -1])
with self.assertRaises(AssertionError):
select_ival(self.dat, [self.dat.axes[1][0]-1, 0])
with self.assertRaises(AssertionError):
select_ival(self.dat, [0, self.dat.axes[1][-1]+1])
def test_select_ival_copy(self):
"""Select_ival must not modify the argument."""
cpy = self.dat.copy()
select_ival(cpy, [-500, 0])
self.assertEqual(cpy, self.dat)
def test_select_ival_swapaxes(self):
"""select_ival must work with nonstandard timeaxis."""
dat = select_ival(swapaxes(self.dat, 0, 1), [-500, 0], timeaxis=0)
dat = swapaxes(dat, 0, 1)
dat2 = select_ival(self.dat, [-500, 0])
self.assertEqual(dat, dat2)
def setUp(self):
ones = np.ones((10, 2))
twoes = ones * 2
# 7 epochs
data = np.array([ones, ones, twoes, twoes, ones, twoes, twoes])
channels = ['c1', 'c2']
time = np.linspace(0, 1000, 10)
classes = [0, 0, 1, 1, 0, 1, 1]
class_names = ['ones', 'twoes']
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
self.dat.class_names = class_names
def setUp(self):
ones = np.ones((10, 5))
# cnt with 1, 2, 3
cnt = np.append(ones, ones*2, axis=0)
cnt = np.append(cnt, ones*3, axis=0)
channels = ['ca1', 'ca2', 'cb1', 'cb2', 'cc1']
time = np.linspace(0, 3000, 30, endpoint=False)
classes = [0, 1, 2, 1]
# four cnts: 1s, -1s, and 0s
data = np.array([cnt * 0, cnt * 1, cnt * 2, cnt * 0])
self.dat = Data(data, [classes, time, channels], ['class', 'time', 'channel'], ['#', 'ms', '#'])
def setUp(self):
# create 100 samples and tree channels data
ones = np.ones((100, 3))
data = np.array([ones, ones * 2, ones * 3]).reshape(-1, 3)
time = np.linspace(0, 3000, 300, endpoint=False)
channels = ["a", "b", "c"]
markers = [[500, "M1"], [1500, "M2"], [2500, "M3"]]
self.dat = Data(data, [time, channels], ["time", "channels"], ["ms", "#"])
self.dat.markers = markers
self.dat.fs = 100
self.mrk_def = {"class 1": ["M1"], "class 2": ["M2", "M3"]}
def setUp(self):
dat = np.zeros((SAMPLES, CHANS))
# [-10, -9, ... 20)
dat[:, 0] = np.arange(SAMPLES) - SAMPLES/2
channels = ['chan{i}'.format(i=i) for i in range(CHANS)]
time = np.arange(SAMPLES)
self.cnt = Data(dat, [time, channels], ['time', 'channels'], ['ms', '#'])
# construct epo
epo_dat = np.array([dat + i for i in range(EPOS)])
classes = ['class{i}'.format(i=i) for i in range(EPOS)]
self.epo = Data(epo_dat, [classes, time, channels], ['class', 'time', 'channels'], ['#', 'ms', '#'])