def test_read_epochs(cur_system, version, use_info, monkeypatch):
"""Test comparing reading an Epochs object and the FieldTrip version."""
pandas = _check_pandas_installed(strict=False)
has_pandas = pandas is not False
test_data_folder_ft = get_data_paths(cur_system)
mne_epoched = get_epochs(cur_system)
if use_info:
info = get_raw_info(cur_system)
pytestwarning = {'expected_warning': None}
else:
info = None
pytestwarning = no_info_warning
cur_fname = os.path.join(test_data_folder_ft,
'epoched_%s.mat' % (version, ))
if has_pandas:
if version == 'v73' and not _has_h5py():
with pytest.raises(ImportError):
mne.io.read_epochs_fieldtrip(cur_fname, info)
return
with pytest.warns(**pytestwarning):
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert isinstance(epoched_ft.metadata, pandas.DataFrame)
else:
with pytest.warns(None) as warn_record:
if version == 'v73' and not _has_h5py():
with pytest.raises(ImportError):
mne.io.read_epochs_fieldtrip(cur_fname, info)
return
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert epoched_ft.metadata is None
assert_warning_in_record(pandas_not_found_warning_msg, warn_record)
if pytestwarning['expected_warning'] is not None:
assert_warning_in_record(pytestwarning['match'], warn_record)
mne_data = mne_epoched.get_data()[:, :, :-1]
ft_data = epoched_ft.get_data()
check_data(mne_data, ft_data, cur_system)
check_info_fields(mne_epoched, epoched_ft, use_info)
# weird sfreq
from mne.externals.pymatreader import read_mat
def modify_mat(fname, variable_names=None, ignore_fields=None):
out = read_mat(fname, variable_names, ignore_fields)
if 'fsample' in out['data']:
out['data']['fsample'] = np.repeat(out['data']['fsample'], 2)
return out
monkeypatch.setattr(mne.externals.pymatreader, 'read_mat', modify_mat)
with pytest.warns(RuntimeWarning, match='multiple'):
mne.io.read_epochs_fieldtrip(cur_fname, info)
def test_read_epochs(cur_system, version, use_info, monkeypatch):
"""Test comparing reading an Epochs object and the FieldTrip version."""
pandas = _check_pandas_installed(strict=False)
has_pandas = pandas is not False
test_data_folder_ft = get_data_paths(cur_system)
mne_epoched = get_epochs(cur_system)
if use_info:
info = get_raw_info(cur_system)
ctx = nullcontext()
else:
info = None
ctx = pytest.warns(**no_info_warning)
cur_fname = os.path.join(test_data_folder_ft,
'epoched_%s.mat' % (version, ))
if has_pandas:
with ctx:
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert isinstance(epoched_ft.metadata, pandas.DataFrame)
else:
with _record_warnings() as warn_record:
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert epoched_ft.metadata is None
assert_warning_in_record(pandas_not_found_warning_msg, warn_record)
if info is None:
assert_warning_in_record(NOINFO_WARNING, warn_record)
mne_data = mne_epoched.get_data()[:, :, :-1]
ft_data = epoched_ft.get_data()
check_data(mne_data, ft_data, cur_system)
check_info_fields(mne_epoched, epoched_ft, use_info)
read_mat = pymatreader.read_mat
# weird sfreq
def modify_mat(fname, variable_names=None, ignore_fields=None):
out = read_mat(fname, variable_names, ignore_fields)
if 'fsample' in out['data']:
out['data']['fsample'] = np.repeat(out['data']['fsample'], 2)
return out
monkeypatch.setattr(pymatreader, 'read_mat', modify_mat)
with pytest.warns(RuntimeWarning, match='multiple'):
mne.io.read_epochs_fieldtrip(cur_fname, info)
def test_read_epochs(cur_system, version, use_info):
"""Test comparing reading an Epochs object and the FieldTrip version."""
pandas = _check_pandas_installed(strict=False)
has_pandas = pandas is not False
test_data_folder_ft = get_data_paths(cur_system)
mne_epoched = get_epochs(cur_system)
if use_info:
info = get_raw_info(cur_system)
pytestwarning = {'expected_warning': None}
else:
info = None
pytestwarning = no_info_warning
cur_fname = os.path.join(test_data_folder_ft,
'epoched_%s.mat' % (version, ))
if has_pandas:
if version == 'v73' and not _has_h5py():
with pytest.raises(ImportError):
mne.io.read_epochs_fieldtrip(cur_fname, info)
return
with pytest.warns(**pytestwarning):
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert isinstance(epoched_ft.metadata, pandas.DataFrame)
else:
with pytest.warns(None) as warn_record:
if version == 'v73' and not _has_h5py():
with pytest.raises(ImportError):
mne.io.read_epochs_fieldtrip(cur_fname, info)
return
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert epoched_ft.metadata is None
assert_warning_in_record(pandas_not_found_warning_msg, warn_record)
if pytestwarning['expected_warning'] is not None:
assert_warning_in_record(pytestwarning['match'], warn_record)
mne_data = mne_epoched.get_data()[:, :, :-1]
ft_data = epoched_ft.get_data()
check_data(mne_data, ft_data, cur_system)
check_info_fields(mne_epoched, epoched_ft, use_info)
def test_read_epochs(cur_system, version, use_info):
"""Test comparing reading an Epochs object and the FieldTrip version."""
pandas = _check_pandas_installed(strict=False)
has_pandas = pandas is not False
test_data_folder_ft = get_data_paths(cur_system)
mne_epoched = get_epochs(cur_system)
if use_info:
info = get_raw_info(cur_system)
pytestwarning = {'expected_warning': None}
else:
info = None
pytestwarning = no_info_warning
cur_fname = os.path.join(test_data_folder_ft,
'epoched_%s.mat' % (version,))
if has_pandas:
if version == 'v73' and not _has_h5py():
with pytest.raises(ImportError):
mne.io.read_epochs_fieldtrip(cur_fname, info)
return
with pytest.warns(**pytestwarning):
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert isinstance(epoched_ft.metadata, pandas.DataFrame)
else:
with pytest.warns(None) as warn_record:
if version == 'v73' and not _has_h5py():
with pytest.raises(ImportError):
mne.io.read_epochs_fieldtrip(cur_fname, info)
return
epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info)
assert epoched_ft.metadata is None
assert_warning_in_record(pandas_not_found_warning_msg, warn_record)
if pytestwarning['expected_warning'] is not None:
assert_warning_in_record(pytestwarning['match'], warn_record)
mne_data = mne_epoched.get_data()[:, :, :-1]
ft_data = epoched_ft.get_data()
check_data(mne_data, ft_data, cur_system)
check_info_fields(mne_epoched, epoched_ft, use_info)
def plot_design_matrix(inst,
predictors,
scale=True,
contrast='effect',
cmap='binary_r'):
"""
Parameters
----------
inst : instance of Epochs
The data that contains infromation about the design.
predictors : array-like
Names of the regressors.
contrast : str
Type of contrast to use. Can be 'effect' (1 vs -1) or
'dummy' (1 vs. 0). Defaults to effect.
scale : bool
Whether to scale predictors.
cmap : str
colormap to use. Default to binary_r.
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure object containing the plot.
Notes
-----
"""
import matplotlib.pyplot as plt
pd = _check_pandas_installed()
# possible formula
# '~face + phase_coherence' # could be parsed with split.
epochs = inst.copy()
design = epochs.metadata.copy()
samples = list(range(0, design.shape[0]))
names = predictors.copy()
data = design[names].copy()
# scale = True
cbar_kw = {}
# main = ['Face_Condition', 'Noise']
# preset
data = data.assign(intercept=1) # add intercept
data['face a - face b'] = np.where(design['face'] == 'A', 1, -1)
# dynamic
# predictors_recoded = dict(predictors=[])
# for predictor in predictors:
# if data[predictor].dtype == 'float64':
# continue
# else:
# predictors_recoded[] = {'%_recoded': bool(data[predictor])} % predictor
predictors = ['intercept', 'face a - face b', 'phase-coherence']
# scale
if scale:
for predictor in predictors:
# data['Intercept'] = design.Intercept.copy()
data[predictor] = np.interp(
data[predictor],
(data[predictor].min(), data[predictor].max()), (0, +1))
cbarlabel = "Weight [scaled 0 - 1]"
else:
cbarlabel = "Weight [uncaled]"
# interactions
# data['Face_x_Noise'] = np.zeros(data.shape[0], int)
# data['Face_x_Noise'] = data['Face_Condition'] * data['Noise']
data_to_show = np.array(data[predictors])
vmax = data_to_show.max()
vmin = data_to_show.min()
fig, ax = plt.subplots(figsize=(5, 5))
im = ax.imshow(data_to_show,
aspect='auto',
cmap=cmap,
vmax=vmax,
vmin=vmin)
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# create color bar
# cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
# cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# We want to show all ticks...
ax.set_xticks(np.arange(len(predictors)))
# ax.set_yticks(np.arange(len(samples)))
# ... and label them with the respective list entries
ax.set_xticklabels(predictors)
# ax.set_yticklabels(np.arange(len(samples)))
ax.set_xlabel('Predictors', fontsize=12)
ax.xaxis.set_label_coords(0.5, -0.20)
ax.set_ylabel('Epochs', fontsize=12)
ax.tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)
rotate = [name for name in predictors if len(name) > 10]
if rotate:
# rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(),
rotation=30,
ha="right",
rotation_mode="anchor")
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(data_to_show.shape[1] + 1) - .5, minor=True)
ax.set_yticks(np.array([0, data_to_show.shape[0]]), minor=True)
ax.grid(which="minor", color="black", linestyle='-', linewidth=1.5)
ax.tick_params(which="minor", bottom=False, left=False)
ax.set_title("Design Matrix")
fig.tight_layout()
plt.show()
return fig
