def test_hdf(self, tmpdir):
from cmlreaders.readers.readers import EventReader
from unittest.mock import patch
efile = osp.join(osp.dirname(__file__), "data",
"R1111M_FR1_0_events.json")
filename = str(tmpdir.join("test.h5"))
events = EventReader.fromfile(efile,
subject="R1111M",
experiment="FR1")
ev = events[events.eegoffset > 0].sample(n=5)
rel_start, rel_stop = 0, 10
get_eeg = partial(self.make_eeg, ev, rel_start, rel_stop)
reader = self.reader
with patch.object(reader, "load_eeg", return_value=get_eeg()):
eeg = reader.load_eeg(events=ev, rel_start=0, rel_stop=10)
ts = eeg.to_ptsa()
ts.to_hdf(filename)
ts2 = TimeSeries.from_hdf(filename)
assert_timeseries_equal(ts, ts2)
def test_eeg_absolute(self, subject, events_filename, expected_basenames):
path = resource_filename("cmlreaders.test.data", events_filename)
events = EventReader.fromfile(path)
reader = EEGReader("eeg", subject)
new_events = reader._eegfile_absolute(events)
for eegfile in new_events[
new_events["eegfile"].notnull()]["eegfile"].unique():
assert eegfile in expected_basenames
def test_load_matlab(self, kind):
if kind in ["all_events", "task_events"]:
filename = "TJ001_events.mat"
else:
filename = "TJ001_math.mat"
path = datafile(filename)
df = EventReader.fromfile(path)
assert df.columns[0] == "eegoffset"
assert "experiment" in df.columns
assert len(df)
def load(self, **kwargs):
"""Overrides the generic load method so as to accept keyword arguments
to pass along to :meth:`as_timeseries`.
"""
if "events" in kwargs:
events = kwargs["events"]
else:
if self.session is None:
raise ValueError(
"A session must be specified to load an entire session of "
"EEG data!")
finder = PathFinder(subject=self.subject,
experiment=self.experiment,
session=self.session,
rootdir=self.rootdir)
events_file = finder.find("task_events")
all_events = EventReader.fromfile(events_file, self.subject,
self.experiment, self.session)
# Select only a single event with a valid eegfile just to get the
# filename
valid = all_events[(all_events["eegfile"].notnull())
& (all_events["eegfile"].str.len() > 0)]
events = pd.DataFrame(valid.iloc[0]).T.reset_index(drop=True)
# Set relative start and stop times if necessary. If they were
# already specified, these will allow us to subset the session.
if "rel_start" not in kwargs:
kwargs["rel_start"] = 0
if "rel_stop" not in kwargs:
kwargs["rel_stop"] = -1
if not len(events):
raise ValueError("No events found! Hint: did filtering events "
"result in at least one?")
elif len(events["subject"].unique()) > 1:
raise ValueError("Loading multiple sessions of EEG data requires "
"using events from only a single subject.")
if "rel_start" not in kwargs or "rel_stop" not in kwargs:
raise exc.IncompatibleParametersError(
"rel_start and rel_stop must be given with events")
# info = EEGMetaReader.fromfile(path, subject=self.subject)
# sample_rate = info["sample_rate"]
# dtype = info["data_format"]
self.scheme = kwargs.get("scheme", None)
events = self._eegfile_absolute(events.copy())
return self.as_timeseries(events, kwargs["rel_start"],
kwargs["rel_stop"])
def events():
path = resource_filename('cmlreaders.test.data', 'all_events.json')
return EventReader.fromfile(path, subject="R1389J")