def test_fex():
with pytest.raises(Exception):
fex = Fex().read_feat()
with pytest.raises(Exception):
fex = Fex().read_facet()
with pytest.raises(Exception):
fex = Fex().read_openface()
with pytest.raises(Exception):
fex = Fex().read_affectiva()
# For iMotions-FACET data files
# test reading iMotions file < version 6
filename = os.path.join(get_test_data_path(), "iMotions_Test_v5.txt")
dat = Fex(read_facet(filename), sampling_freq=30)
# test reading iMotions file > version 6
filename = os.path.join(get_test_data_path(), "iMotions_Test_v6.txt")
df = read_facet(filename)
# Test slicing functions.
assert df.aus().shape == (519, 20)
assert df.emotions().shape == (519, 12)
assert df.facebox().shape == (519, 4)
assert df.time().shape[-1] == 4
assert df.design().shape[-1] == 4
# Test metadata propagation to sliced series
assert df.iloc[0].aus().shape == (20, )
assert df.iloc[0].emotions().shape == (12, )
assert df.iloc[0].facebox().shape == (4, )
assert df.iloc[0].time().shape == (4, )
assert df.iloc[0].design().shape == (4, )
sessions = np.array([[x] * 10
for x in range(1 + int(len(df) / 10))]).flatten()[:-1]
dat = Fex(df, sampling_freq=30, sessions=sessions)
dat = dat[[
"Joy",
"Anger",
"Surprise",
"Fear",
"Contempt",
"Disgust",
"Sadness",
"Confusion",
"Frustration",
"Neutral",
"Positive",
"Negative",
]]
# Test Session ValueError
with pytest.raises(ValueError):
Fex(df, sampling_freq=30, sessions=sessions[:10])
# Test KeyError
with pytest.raises(KeyError):
Fex(read_facet(filename, features=["NotHere"]), sampling_freq=30)
# Test length
assert len(dat) == 519
# Test sessions generator
assert len(np.unique(dat.sessions)) == len([x for x in dat.itersessions()])
# Test metadata propagation
assert dat[["Joy"]].sampling_freq == dat.sampling_freq
assert dat.iloc[:, 0].sampling_freq == dat.sampling_freq
assert dat.iloc[0, :].sampling_freq == dat.sampling_freq
assert dat.loc[[0], :].sampling_freq == dat.sampling_freq
assert dat.loc[:, ["Joy"]].sampling_freq == dat.sampling_freq
# assert dat.loc[0].sampling_freq == dat.sampling_freq # DOES NOT WORK YET
# Test Downsample
assert len(dat.downsample(target=10)) == 52
# Test upsample
assert len(dat.upsample(target=60, target_type="hz")) == (len(dat) - 1) * 2
# Test interpolation
assert (dat.interpolate(method="linear").isnull().sum()["Positive"] <
dat.isnull().sum()["Positive"])
dat = dat.interpolate(method="linear")
# Test distance
d = dat[["Positive"]].distance()
assert isinstance(d, Adjacency)
assert d.square_shape()[0] == len(dat)
# Test Copy
assert isinstance(dat.copy(), Fex)
assert dat.copy().sampling_freq == dat.sampling_freq
# Test rectification
rectified = df.rectification()
assert (df[df.au_columns].isna().sum()[0] <
rectified[rectified.au_columns].isna().sum()[0])
# Test pspi
assert len(df.calc_pspi()) == len(df)
# Test baseline
assert isinstance(dat.baseline(baseline="median"), Fex)
assert isinstance(dat.baseline(baseline="mean"), Fex)
assert isinstance(dat.baseline(baseline="begin"), Fex)
assert isinstance(dat.baseline(baseline=dat.mean()), Fex)
assert isinstance(dat.baseline(baseline="median", ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline="mean", ignore_sessions=True), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline="median", normalize="pct"), Fex)
assert isinstance(dat.baseline(baseline="mean", normalize="pct"), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), normalize="pct"), Fex)
assert isinstance(
dat.baseline(baseline="median", ignore_sessions=True, normalize="pct"),
Fex)
assert isinstance(
dat.baseline(baseline="mean", ignore_sessions=True, normalize="pct"),
Fex)
assert isinstance(
dat.baseline(baseline=dat.mean(),
ignore_sessions=True,
normalize="pct"), Fex)
# Test ValueError
with pytest.raises(ValueError):
dat.baseline(baseline="BadValue")
# Test summary
dat2 = dat.loc[:, ["Positive", "Negative"]].interpolate()
out = dat2.extract_summary(min=True, max=True, mean=True)
assert len(out) == len(np.unique(dat2.sessions))
assert np.array_equal(out.sessions, np.unique(dat2.sessions))
assert out.sampling_freq == dat2.sampling_freq
assert dat2.shape[1] * 3 == out.shape[1]
out = dat2.extract_summary(min=True,
max=True,
mean=True,
ignore_sessions=True)
assert len(out) == 1
assert dat2.shape[1] * 3 == out.shape[1]
# Check if file is missing columns
data_bad = dat.iloc[:, 0:10]
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Check if file has too many columns
data_bad = dat.copy()
data_bad["Test"] = 0
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Test clean
assert isinstance(dat.clean(), Fex)
assert dat.clean().columns is dat.columns
assert dat.clean().sampling_freq == dat.sampling_freq
# Test Decompose
n_components = 3
stats = dat.decompose(algorithm="pca", axis=1, n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
stats = dat.decompose(algorithm="ica", axis=1, n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm="nnmf",
axis=1,
n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
stats = dat.decompose(algorithm="fa", axis=1, n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
stats = dat.decompose(algorithm="pca", axis=0, n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
stats = dat.decompose(algorithm="ica", axis=0, n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm="nnmf",
axis=0,
n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
stats = dat.decompose(algorithm="fa", axis=0, n_components=n_components)
assert n_components == stats["components"].shape[1]
assert n_components == stats["weights"].shape[1]
def test_fex():
# For iMotions-FACET data files
# test reading iMotions file < version 6
dat = Fex(read_facet(join(get_test_data_path(), 'iMotions_Test_v2.txt')),
sampling_freq=30)
# test reading iMotions file > version 6
filename = join(get_test_data_path(), 'iMotions_Test.txt')
df = read_facet(filename)
sessions = np.array([[x] * 10
for x in range(1 + int(len(df) / 10))]).flatten()[:-1]
dat = Fex(df, sampling_freq=30, sessions=sessions)
# Test Session ValueError
with pytest.raises(ValueError):
Fex(df, sampling_freq=30, sessions=sessions[:10])
# Test KeyError
with pytest.raises(KeyError):
Fex(read_facet(filename, features=['NotHere']), sampling_freq=30)
# Test length
assert len(dat) == 519
# Test Info
assert isinstance(dat.info(), str)
# Test sessions generator
assert len(np.unique(dat.sessions)) == len([x for x in dat.itersessions()])
# Test metadata propagation
assert dat['Joy'].sampling_freq == dat.sampling_freq
assert dat.iloc[:, 0].sampling_freq == dat.sampling_freq
# Test Downsample
assert len(dat.downsample(target=10)) == 52
# Test upsample
assert len(dat.upsample(target=60, target_type='hz')) == (len(dat) - 1) * 2
# Test interpolation
assert np.sum(dat.interpolate(method='linear').isnull().sum() == 0) == len(
dat.columns)
dat = dat.interpolate(method='linear')
# Test distance
d = dat.distance()
assert isinstance(d, Adjacency)
assert d.square_shape()[0] == len(dat)
# Test Copy
assert isinstance(dat.copy(), Fex)
assert dat.copy().sampling_freq == dat.sampling_freq
# Test baseline
assert isinstance(dat.baseline(baseline='median'), Fex)
assert isinstance(dat.baseline(baseline='mean'), Fex)
assert isinstance(dat.baseline(baseline='begin'), Fex)
assert isinstance(dat.baseline(baseline=dat.mean()), Fex)
assert isinstance(dat.baseline(baseline='median', ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline='mean', ignore_sessions=True), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline='median', normalize='pct'), Fex)
assert isinstance(dat.baseline(baseline='mean', normalize='pct'), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), normalize='pct'), Fex)
assert isinstance(
dat.baseline(baseline='median', ignore_sessions=True, normalize='pct'),
Fex)
assert isinstance(
dat.baseline(baseline='mean', ignore_sessions=True, normalize='pct'),
Fex)
assert isinstance(
dat.baseline(baseline=dat.mean(),
ignore_sessions=True,
normalize='pct'), Fex)
# Test ValueError
with pytest.raises(ValueError):
dat.baseline(baseline='BadValue')
# Test summary
dat2 = dat.loc[:, ['Positive', 'Negative']].interpolate()
out = dat2.extract_summary(min=True, max=True, mean=True)
assert len(out) == len(np.unique(dat2.sessions))
assert np.array_equal(out.sessions, np.unique(dat2.sessions))
assert out.sampling_freq == dat2.sampling_freq
assert dat2.shape[1] * 3 == out.shape[1]
out = dat2.extract_summary(min=True,
max=True,
mean=True,
ignore_sessions=True)
assert len(out) == 1
assert dat2.shape[1] * 3 == out.shape[1]
# Check if file is missing columns
data_bad = dat.iloc[:, 0:10]
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Check if file has too many columns
data_bad = dat.copy()
data_bad['Test'] = 0
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Test clean
assert isinstance(dat.clean(), Fex)
assert dat.clean().columns is dat.columns
assert dat.clean().sampling_freq == dat.sampling_freq
# Test Decompose
n_components = 3
stats = dat.decompose(algorithm='pca', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='ica', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm='nnmf',
axis=1,
n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='fa', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='pca', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='ica', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm='nnmf',
axis=0,
n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='fa', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
def test_fex(tmpdir):
# For iMotions-FACET data files
# test reading iMotions file < version 6
dat = Fex(read_facet(join(get_test_data_path(), 'iMotions_Test_v2.txt')),
sampling_freq=30)
# test reading iMotions file > version 6
filename = join(get_test_data_path(), 'iMotions_Test.txt')
df = read_facet(filename)
sessions = np.array([[x] * 10
for x in range(1 + int(len(df) / 10))]).flatten()[:-1]
dat = Fex(df, sampling_freq=30, sessions=sessions)
# Test KeyError
class MyTestCase(unittest.TestCase):
def test1(self):
with self.assertRaises(KeyError):
Fex(read_facet(filename, features=['NotHere']),
sampling_freq=30)
# Test length
assert len(dat) == 519
# Test Info
assert isinstance(dat.info(), str)
# Test sessions generator
assert len(np.unique(dat.sessions)) == len([x for x in dat.itersessions()])
# Test metadata propagation
assert dat['Joy'].sampling_freq == dat.sampling_freq
assert dat.iloc[:, 0].sampling_freq == dat.sampling_freq
# Test Downsample
assert len(dat.downsample(target=10)) == 52
# Test upsample
assert len(dat.upsample(target=60, target_type='hz')) == (len(dat) - 1) * 2
# Test interpolation
assert np.sum(dat.interpolate(method='linear').isnull().sum() == 0) == len(
dat.columns)
dat = dat.interpolate(method='linear')
# Test distance
d = dat.distance()
assert isinstance(d, Adjacency)
assert d.square_shape()[0] == len(dat)
# Test Copy
assert isinstance(dat.copy(), Fex)
assert dat.copy().sampling_freq == dat.sampling_freq
# Test baseline
assert isinstance(dat.baseline(baseline='median'), Fex)
assert isinstance(dat.baseline(baseline='mean'), Fex)
assert isinstance(dat.baseline(baseline=dat.mean()), Fex)
assert isinstance(dat.baseline(baseline='median', ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline='mean', ignore_sessions=True), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline='median', normalize='pct'), Fex)
assert isinstance(dat.baseline(baseline='mean', normalize='pct'), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), normalize='pct'), Fex)
assert isinstance(
dat.baseline(baseline='median', ignore_sessions=True, normalize='pct'),
Fex)
assert isinstance(
dat.baseline(baseline='mean', ignore_sessions=True, normalize='pct'),
Fex)
assert isinstance(
dat.baseline(baseline=dat.mean(),
ignore_sessions=True,
normalize='pct'), Fex)
# Test facet subclass
facet = Facet(filename=filename, sampling_freq=30)
facet.read_file()
assert len(facet) == 519
# Test PSPI calculation
assert len(facet.calc_pspi()) == len(facet)
# Test Fextractor class
extractor = Fextractor()
dat = dat.interpolate() # interpolate data to get rid of NAs
f = .5
num_cyc = 3 # for wavelet extraction
# Test each extraction method
extractor.mean(fex_object=dat)
extractor.max(fex_object=dat)
extractor.min(fex_object=dat)
#extractor.boft(fex_object=dat, min_freq=.01, max_freq=.20, bank=1)
extractor.multi_wavelet(fex_object=dat)
extractor.wavelet(fex_object=dat, freq=f, num_cyc=num_cyc)
# Test Fextracor merge method
newdat = extractor.merge(out_format='long')
assert newdat['sessions'].nunique() == 52
assert isinstance(newdat, DataFrame)
assert len(extractor.merge(out_format='long')) == 24960
assert len(extractor.merge(out_format='wide')) == 52
# Test wavelet extraction
extractor = Fextractor()
extractor.wavelet(fex_object=dat,
freq=f,
num_cyc=num_cyc,
ignore_sessions=False)
extractor.wavelet(fex_object=dat,
freq=f,
num_cyc=num_cyc,
ignore_sessions=True)
wavelet = extractor.extracted_features[0] # ignore_sessions = False
assert wavelet.sampling_freq == dat.sampling_freq
assert len(wavelet) == len(dat)
wavelet = extractor.extracted_features[1] # ignore_sessions = True
assert wavelet.sampling_freq == dat.sampling_freq
assert len(wavelet) == len(dat)
assert np.array_equal(wavelet.sessions, dat.sessions)
for i in ['filtered', 'phase', 'magnitude', 'power']:
extractor = Fextractor()
extractor.wavelet(fex_object=dat,
freq=f,
num_cyc=num_cyc,
ignore_sessions=True,
mode=i)
wavelet = extractor.extracted_features[0]
assert wavelet.sampling_freq == dat.sampling_freq
assert len(wavelet) == len(dat)
# Test multi wavelet
dat2 = dat.loc[:, ['Positive', 'Negative']].interpolate()
n_bank = 4
extractor = Fextractor()
extractor.multi_wavelet(fex_object=dat2,
min_freq=.1,
max_freq=2,
bank=n_bank,
mode='power',
ignore_sessions=False)
out = extractor.extracted_features[0]
assert n_bank * dat2.shape[1] == out.shape[1]
assert len(out) == len(dat2)
assert np.array_equal(out.sessions, dat2.sessions)
assert out.sampling_freq == dat2.sampling_freq
# Test Bag Of Temporal Features Extraction
facet_filled = facet.fillna(0)
assert isinstance(facet_filled, Facet)
extractor = Fextractor()
extractor.boft(facet_filled)
assert isinstance(extractor.extracted_features[0], DataFrame)
filters, histograms = 8, 12
assert extractor.extracted_features[0].shape[
1] == facet.columns.shape[0] * filters * histograms
# Test mean, min, and max Features Extraction
# assert isinstance(facet_filled.extract_mean(), Facet)
# assert isinstance(facet_filled.extract_min(), Facet)
# assert isinstance(facet_filled.extract_max(), Facet)
# Test if a method returns subclass.
facet = facet.downsample(target=10, target_type='hz')
assert isinstance(facet, Facet)
### Test Openface importer and subclass ###
# For OpenFace data file
filename = join(get_test_data_path(), 'OpenFace_Test.csv')
openface = Fex(read_openface(filename), sampling_freq=30)
# Test KeyError
class MyTestCase(unittest.TestCase):
def test1(self):
with self.assertRaises(KeyError):
Fex(read_openface(filename, features=['NotHere']),
sampling_freq=30)
# Test length
assert len(openface) == 100
# Test loading from filename
openface = Openface(filename=filename, sampling_freq=30)
openface.read_file()
# Test length?
assert len(openface) == 100
# Test PSPI calculation b/c diff from facet
assert len(openface.calc_pspi()) == len(openface)
# Test if a method returns subclass.
openface = openface.downsample(target=10, target_type='hz')
assert isinstance(openface, Openface)
# Check if file is missing columns
data_bad = dat.iloc[:, 0:10]
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Check if file has too many columns
data_bad = dat.copy()
data_bad['Test'] = 0
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Test clean
assert isinstance(dat.clean(), Fex)
assert dat.clean().columns is dat.columns
assert dat.clean().sampling_freq == dat.sampling_freq
# Test Decompose
n_components = 3
stats = dat.decompose(algorithm='pca', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='ica', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm='nnmf',
axis=1,
n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='fa', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='pca', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='ica', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm='nnmf',
axis=0,
n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='fa', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
def test_fex():
with pytest.raises(Exception):
fex = Fex().read_feat()
with pytest.raises(Exception):
fex = Fex().read_facet()
with pytest.raises(Exception):
fex = Fex().read_openface()
with pytest.raises(Exception):
fex = Fex().read_affectiva()
# For iMotions-FACET data files
# test reading iMotions file < version 6
filename = os.path.join(get_test_data_path(), 'iMotions_Test_v5.txt')
dat = Fex(read_facet(filename), sampling_freq=30)
# test reading iMotions file > version 6
filename = os.path.join(get_test_data_path(), 'iMotions_Test_v6.txt')
df = read_facet(filename)
# Test slicing functions.
assert df.aus().shape == (519, 20)
assert df.emotions().shape == (519, 12)
assert df.facebox().shape == (519, 4)
assert df.time().shape[-1] == 4
assert df.design().shape[-1] == 4
sessions = np.array([[x] * 10
for x in range(1 + int(len(df) / 10))]).flatten()[:-1]
dat = Fex(df, sampling_freq=30, sessions=sessions)
dat = dat[[
'Joy', 'Anger', 'Surprise', 'Fear', 'Contempt', 'Disgust', 'Sadness',
'Confusion', 'Frustration', 'Neutral', 'Positive', 'Negative'
]]
# Test Session ValueError
with pytest.raises(ValueError):
Fex(df, sampling_freq=30, sessions=sessions[:10])
# Test KeyError
with pytest.raises(KeyError):
Fex(read_facet(filename, features=['NotHere']), sampling_freq=30)
# Test length
assert len(dat) == 519
# Test sessions generator
assert len(np.unique(dat.sessions)) == len([x for x in dat.itersessions()])
# Test metadata propagation
assert dat[['Joy']].sampling_freq == dat.sampling_freq
assert dat.iloc[:, 0].sampling_freq == dat.sampling_freq
# Test Downsample
assert len(dat.downsample(target=10)) == 52
# Test upsample
assert len(dat.upsample(target=60, target_type='hz')) == (len(dat) - 1) * 2
# Test interpolation
assert dat.interpolate(method='linear').isnull().sum(
)['Positive'] < dat.isnull().sum()['Positive']
dat = dat.interpolate(method='linear')
# Test distance
d = dat[['Positive']].distance()
assert isinstance(d, Adjacency)
assert d.square_shape()[0] == len(dat)
# Test Copy
assert isinstance(dat.copy(), Fex)
assert dat.copy().sampling_freq == dat.sampling_freq
# Test rectification
rectified = df.rectification()
assert df[df.au_columns].isna().sum()[0] < rectified[
rectified.au_columns].isna().sum()[0]
# Test pspi
assert len(df.calc_pspi()) == len(df)
# Test baseline
assert isinstance(dat.baseline(baseline='median'), Fex)
assert isinstance(dat.baseline(baseline='mean'), Fex)
assert isinstance(dat.baseline(baseline='begin'), Fex)
assert isinstance(dat.baseline(baseline=dat.mean()), Fex)
assert isinstance(dat.baseline(baseline='median', ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline='mean', ignore_sessions=True), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), ignore_sessions=True),
Fex)
assert isinstance(dat.baseline(baseline='median', normalize='pct'), Fex)
assert isinstance(dat.baseline(baseline='mean', normalize='pct'), Fex)
assert isinstance(dat.baseline(baseline=dat.mean(), normalize='pct'), Fex)
assert isinstance(
dat.baseline(baseline='median', ignore_sessions=True, normalize='pct'),
Fex)
assert isinstance(
dat.baseline(baseline='mean', ignore_sessions=True, normalize='pct'),
Fex)
assert isinstance(
dat.baseline(baseline=dat.mean(),
ignore_sessions=True,
normalize='pct'), Fex)
# Test ValueError
with pytest.raises(ValueError):
dat.baseline(baseline='BadValue')
# Test summary
dat2 = dat.loc[:, ['Positive', 'Negative']].interpolate()
out = dat2.extract_summary(min=True, max=True, mean=True)
assert len(out) == len(np.unique(dat2.sessions))
assert np.array_equal(out.sessions, np.unique(dat2.sessions))
assert out.sampling_freq == dat2.sampling_freq
assert dat2.shape[1] * 3 == out.shape[1]
out = dat2.extract_summary(min=True,
max=True,
mean=True,
ignore_sessions=True)
assert len(out) == 1
assert dat2.shape[1] * 3 == out.shape[1]
# Check if file is missing columns
data_bad = dat.iloc[:, 0:10]
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Check if file has too many columns
data_bad = dat.copy()
data_bad['Test'] = 0
with pytest.raises(Exception):
_check_if_fex(data_bad, imotions_columns)
# Test clean
assert isinstance(dat.clean(), Fex)
assert dat.clean().columns is dat.columns
assert dat.clean().sampling_freq == dat.sampling_freq
# Test Decompose
n_components = 3
stats = dat.decompose(algorithm='pca', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='ica', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm='nnmf',
axis=1,
n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='fa', axis=1, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='pca', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='ica', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
new_dat = dat + 100
stats = new_dat.decompose(algorithm='nnmf',
axis=0,
n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
stats = dat.decompose(algorithm='fa', axis=0, n_components=n_components)
assert n_components == stats['components'].shape[1]
assert n_components == stats['weights'].shape[1]
