def events(vhdr_path=None):
if vhdr_path is None:
vhdr_path = ui.ask_file("Pick a Brain Vision EEG Header File",
"Pick a Brain Vision EEG Header File",
ext=[('vhdr', 'Brain Vision Header File')])
if not vhdr_path:
return
hdr = vhdr(vhdr_path)
if hdr.markerfile is None:
raise IOError("No marker file referenced in %r" % vhdr_path)
elif hdr.DataType == 'FREQUENCYDOMAIN':
raise NotImplementedError
txt = open(hdr.markerfile).read()
m = marker_re.findall(txt)
m = np.array(m)
name, _ = os.path.split(os.path.basename(hdr.path))
ds = dataset(name=name)
ds['Mk'] = var(np.array(m[:,0], dtype=int))
ds['event_type'] = factor(m[:,1])
ds['event_ID'] = var(np.array(m[:,3], dtype=int))
ds['i_start'] = var(np.array(m[:,4], dtype=int))
ds['points'] = var(np.array(m[:,5], dtype=int))
ds['channel'] = var(np.array(m[:,6], dtype=int))
ds.info['hdr'] = hdr
ds.info['samplingrate'] = hdr.samplingrate
return ds
def export_durs(self):
ds = dataset()
idx = self.words == 'sp'
words = self.words[~idx]
durs = self.word_durs[~idx]
ds['words'] = factor([first.lower() + second.lower() for first, second in
zip(words[::2], words[1::2])])
ds['c1_dur'] = var(durs[::2])
ds['c2_dur'] = var(durs[1::2])
return ds
def _resample(Y, unit=None, replacement=True, samples=1000):
"""
Generator function to resample a dependent variable (Y) multiple times
unit: factor specdifying unit of measurement (e.g. subject). If unit is
specified, resampling proceeds by first resampling the categories of
unit (with or without replacement) and then shuffling the values
within unites (no replacement).
replacement: whether random samples should be drawn with replacement or
without
samples: number of samples to yield
"""
if isvar(Y):
Yout = Y.copy('_resampled')
Y
else:
Y = var(Y)
Yout = var(Y.copy(), name="Y resampled")
if unit:
ct = celltable(Y, unit)
unit_data = ct.get_data(out=list)
unit_indexes = ct.data_indexes.values()
x_out = Yout.x
if replacement:
n = len(ct.indexes)
for sample in xrange(samples):
source_ids = np.random.randint(n, size=n)
for index, source_index in zip(unit_indexes, source_ids):
data = unit_data[source_index]
np.random.shuffle(data)
x_out[index] = data
yield Yout
else:
for sample in xrange(samples):
random.shuffle(unit_data)
for index, data in zip(unit_indexes, unit_data):
np.random.shuffle(data)
x_out[index] = data
yield Yout
else:
if replacement:
N = Y.N
for i in xrange(samples):
index = np.random.randint(N)
Yout.x = Y.x[index]
yield Yout
else:
for i in xrange(samples):
np.random.shuffle(Yout.x)
yield Yout
def export_durs(self):
ds = dataset()
idx = self.words == 'sp'
words = self.words[~idx]
durs = self.word_durs[~idx]
ds['words'] = factor([
first.lower() + second.lower()
for first, second in zip(words[::2], words[1::2])
])
ds['c1_dur'] = var(durs[::2])
ds['c2_dur'] = var(durs[1::2])
return ds
def add_T_to(self, ds, Id='eventID', t_edf='t_edf'):
"""
Add edf trigger times as a variable to dataset ds.
These can then be used for Edf.add_by_T(ds) after ds hads been
decimated.
Parameters
----------
ds : dataset
The dataset to which the variable is added
Id : str | var | None
variable (or its name in the dataset) containing event IDs. Values
in this variable are checked against the events in the EDF file,
and an error is raised if there is a mismatch. This test can be
skipped by setting Id=None.
t_edf : str
Name for the target variable holding the edf trigger times.
"""
if Id:
self.assert_Id_match(ds=ds, Id=Id)
if isinstance(Id, str):
Id = ds[Id]
ds[t_edf] = var(self.triggers['T'])
def fiff_mne(ds, fwd='{fif}*fwd.fif', cov='{fif}*cov.fif', label=None, name=None,
tstart= -0.1, tstop=0.6, baseline=(None, 0)):
"""
adds data from one label as
"""
if name is None:
if label:
_, lbl = os.path.split(label)
lbl, _ = os.path.splitext(lbl)
name = lbl.replace('-', '_')
else:
name = 'stc'
info = ds.info['info']
raw = ds.info['raw']
fif_name = raw.info['filename']
fif_name, _ = os.path.splitext(fif_name)
if fif_name.endswith('raw'):
fif_name = fif_name[:-3]
fwd = fwd.format(fif=fif_name)
if '*' in fwd:
d, n = os.path.split(fwd)
names = fnmatch.filter(os.listdir(d), n)
if len(names) == 1:
fwd = os.path.join(d, names[0])
else:
raise IOError("No unique fwd file matching %r" % fwd)
cov = cov.format(fif=fif_name)
if '*' in cov:
d, n = os.path.split(cov)
names = fnmatch.filter(os.listdir(d), n)
if len(names) == 1:
cov = os.path.join(d, names[0])
else:
raise IOError("No unique cov file matching %r" % cov)
fwd = mne.read_forward_solution(fwd, force_fixed=False, surf_ori=True)
cov = mne.Covariance(cov)
inv = _mn.make_inverse_operator(info, fwd, cov, loose=0.2, depth=0.8)
epochs = mne_Epochs(ds, tstart=tstart, tstop=tstop, baseline=baseline)
# mne example:
snr = 3.0
lambda2 = 1.0 / snr ** 2
if label is not None:
label = mne.read_label(label)
stcs = _mn.apply_inverse_epochs(epochs, inv, lambda2, dSPM=False, label=label)
x = np.vstack(s.data.mean(0) for s in stcs)
s = stcs[0]
dims = ('case', var(s.times, 'time'),)
ds[name] = ndvar(x, dims, properties=None, info='')
return stcs
def _get_vessel_for_Y(self, Ydata):
Ydata = np.array([self._cfunc(data, axis=0) for data in Ydata])
T = np.arange(self._tw.tstart, self._tw.tend, 1 / self._samplingrate)
time = _vsl.var(T, name='time')
dims = ('case', time)
properties = {'samplingrate': self._samplingrate}
Y = _vsl.ndvar(Ydata, dims, properties=properties, name=self._name)
return Y
def get_triggers(self, Id='Id', T='t_edf'):
"""
Returns a dataset with trigger Ids and corresponding Edf time values
"""
ds = dataset()
ds[Id] = var(self.triggers['Id'])
ds[T] = self.get_T(name=T)
return ds
def evoked_ndvar(evoked, name='MEG'):
"Convert an mne Evoked object of a list thereof to an ndvar"
if isinstance(evoked, mne.fiff.Evoked):
x = evoked.data
dims = ()
else:
x = np.array([e.data for e in evoked])
dims = ('case',)
evoked = evoked[0]
sensor = sensor_net(evoked)
time = var(evoked.times, name='time')
properties = {'colorspace': _cs.get_MEG(2e-13)}
return ndvar(x, dims + (sensor, time), properties=properties, name=name)
def mark(self, ds, tstart= -0.1, tstop=0.6, good=None, bad=False,
use=['ESACC', 'EBLINK'], T='t_edf', target='accept'):
"""
Mark events in ds as acceptable or not. ds needs to contain edf trigger
times in a variable whose name is specified by the ``T`` argument.
Parameters
----------
ds : dataset
dataset that contains the data to work with.
tstart : scalar
start of the time window relevant for rejection.
tstop : scalar
stop of the time window relevant for rejection.
good : bool | None
vale assigned to epochs that should be retained based on
the eye-tracker data.
bad : bool | None
value that is assigned to epochs that should be rejected
based on the eye-tracker data.
use : list of str
Artifact categories to include
T : var
variable providing the trigger time values
target : var
variable to which the good/bad values are assigned (if it does not
exist, a new variable will be created with all values True
initially)
"""
if isinstance(target, str):
if target in ds:
target = ds[target]
else:
ds[target] = target = var(np.ones(ds.n_cases, dtype=bool))
if isinstance(T, str):
T = ds[T]
accept = self.get_accept(T, tstart=tstart, tstop=tstop, use=use)
if good is not None:
target[accept] = good
if bad is not None:
target[np.invert(accept)] = bad
def stc_ndvar(stc, subject='fsaverage', name=None, check=True):
"""
create an ndvar object from an mne SourceEstimate object
stc : SourceEstimate | list of SourceEstimates
The source estimate object(s).
subject : str
MRI subject (used for loading MRI in PySurfer plotting)
name : str | None
Ndvar name.
check : bool
If multiple stcs are provided, check if all stcs have the same times
and vertices.
"""
if isinstance(stc, mne.SourceEstimate):
case = False
x = stc.data
else:
case = True
stcs = stc
stc = stcs[0]
if check:
vert_lh, vert_rh = stc.vertno
times = stc.times
for stc_ in stcs[1:]:
assert np.all(times == stc_.times)
lh, rh = stc_.vertno
assert np.all(vert_lh == lh)
assert np.all(vert_rh == rh)
x = np.array([s.data for s in stcs])
time = var(stc.times, name='time')
ss = source_space(stc.vertno, subject=subject)
if case:
dims = ('case', ss, time)
else:
dims = (ss, time)
return ndvar(x, dims, name=name)
def get_dataset(self):
"get a dataframe containing Y and covariates"
Y, covs = self._collect_data()
indexes = Y.keys()
Ydata = [Y[index] for index in indexes]
Y = self._get_vessel_for_Y(Ydata)
ds = _vsl.dataset(Y)
# create _data objects
for var, valdict in covs.iteritems():
X = [valdict[index] for index in indexes]
if var.dict_enabled:
Y = _vsl.factor(X, name=var.name, random=var.random,
labels=var.dictionary, colors=var._color_dict)
else:
Y = _vsl.var(X, name=var.name)
ds.add(Y)
return ds
def var(path=None, name=None):
"""
Loads a ``var`` object from a text file by splitting at white-spaces.
path : str(path) | None
Source file. If None, a system file dialog is opened.
name : str | None
Name for the var.
"""
if path is None:
path = ui.ask_file("Select var File", "()")
FILE = open(path)
lines = FILE.read().split()
FILE.close()
is_bool = all(line in ['True', 'False'] for line in lines)
if is_bool:
x = np.genfromtxt(path, dtype=bool)
else:
x = np.loadtxt(path)
return _data.var(x, name=None)
def __init__(self, dataset, data='MEG', target='accept',
nplots=(6, 6), plotsize=(3, 1.5),
mean=True, topo=True, ylim=None, fill=True, aa=False, dpi=50):
"""
Plots all cases in the collection segment and allows visual selection
of cases. The selection can be retrieved through the get_selection
Method.
Arguments
----------
dataset : dataset
dataset on which to perform the selection.
nplots : 1 | 2 | (i, j)
Number of plots (including topo plots and mean).
mean : bool
Plot the page mean on each page.
topo : bool
Show a dynamically updated topographic plot at the bottom right.
ylim : scalar
y-limit of the butterfly plots.
fill : bool
Only show extrema in the butterfly plots, instead of all traces.
This is faster for data with many channels.
aa : bool
Antialiasing (matplolibt parameter).
Example::
>>> select_cases_butterfly(my_dataset)
[... visual selection of cases ...]
>>> cases = my_dataset['reject'] == False
>>> pruned_dataset = my_dataset[cases]
"""
# interpret plotting args
# variable keeping track of selection
if isinstance(data, basestring):
data = dataset[data]
self._data = data
if np.prod(nplots) == 1:
nplots = (1, 1)
mean = False
topo = False
elif np.prod(nplots) == 2:
mean = False
if nplots == 2:
nplots = (1, 2)
if isinstance(target, basestring):
try:
target = dataset[target]
except KeyError:
x = np.ones(dataset.N, dtype=bool)
target = _data.var(x, name=target)
dataset.add(target)
self._target = target
self._plot_mean = mean
self._plot_topo = topo
# prepare segments
self._nplots = nplots
n_per_page = self._n_per_page = np.prod(nplots) - bool(topo) - bool(mean)
n_pages = dataset.N // n_per_page + bool(dataset.N % n_per_page)
self._n_pages = n_pages
# get a list of IDS for each page
self._segs_by_page = []
for i in xrange(n_pages):
start = i * n_per_page
stop = min((i + 1) * n_per_page, dataset.N)
self._segs_by_page.append(range(start, stop))
# init wx frame
title = "select_cases_butterfly -> %r" % target.name
figsize = (plotsize[0] * nplots[0], plotsize[1] * nplots[1])
super(self.__class__, self).__init__(title=title, figsize=figsize, dpi=dpi)
# setup figure
self.figure.subplots_adjust(left=.01, right=.99, bottom=.05, top=.95,
hspace=.5)
# connect canvas
self.canvas.mpl_connect('button_press_event', self._on_click)
if self._is_wx:
self.canvas.mpl_connect('axes_leave_event', self._on_leave_axes)
# compile plot kwargs:
self._bfly_kwargs = {'extrema': fill}
if ylim is None:
ylim = data.properties.get('ylim', None)
if ylim:
self._bfly_kwargs['ylim'] = ylim
# finalize
self._dataset = dataset
self.show_page(0)
self._frame.store_canvas()
self._show()
def get_T(self, name='t_edf'):
"returns all trigger times in the dataset"
return var(self.triggers['T'], name=name)
def _get_vessel_for_Y(self, Ydata):
Ydata = [self._cfunc(data) for data in Ydata]
Y = _vsl.var(Ydata, name=self._name)
return Y
def epochs_ndvar(epochs, name='MEG', meg=True, eeg=False, exclude='bads',
mult=1, unit='T', properties=None, sensors=None):
"""
Convert an mne.Epochs object to an ndvar.
Parameters
----------
epoch : mne.Epochs
The epochs object
name : None | str
Name for the ndvar.
meg : bool or string
MEG channels to include (:func:`mne.fiff.pick_types` kwarg).
If True include all MEG channels. If False include None
If string it can be 'mag' or 'grad' to select only gradiometers
or magnetometers. It can also be 'ref_meg' to get CTF
reference channels.
eeg : bool
If True include EEG channels (:func:`mne.fiff.pick_types` kwarg).
exclude : list of string | str
Channels to exclude (:func:`mne.fiff.pick_types` kwarg).
If 'bads' (default), exclude channels in info['bads'].
If empty do not exclude any.
mult : scalar
multiply all data by a constant. If used, the ``unit`` kwarg should
specify the target unit, not the source unit.
unit : str
Unit of the data (default is 'T').
target : str
name for the new ndvar containing the epoch data
reject : None | scalar
Threshold for rejecting epochs (peak to peak). Requires a for of
mne-python which implements the Epochs.model['index'] variable.
raw : None | mne.fiff.Raw
Raw file providing the data; if ``None``, ``ds.info['raw']`` is used.
sensors : None | eelbrain.vessels.sensors.sensor_net
The default (``None``) reads the sensor locations from the fiff file.
If the fiff file contains incorrect sensor locations, a different
sensor_net can be supplied through this kwarg.
"""
props = {'proj': 'z root',
'unit': unit,
'ylim': 2e-12 * mult,
'summary_ylim': 3.5e-13 * mult,
'colorspace': _cs.get_MEG(2e-12 * mult),
'summary_colorspace': _cs.get_MEG(2e-13 * mult),
'samplingrate': epochs.info['sfreq'],
}
if properties:
props.update(properties)
picks = mne.fiff.pick_types(epochs.info, meg=meg, eeg=eeg, stim=False,
eog=False, include=[], exclude=exclude)
x = epochs.get_data()[:, picks]
if mult != 1:
x *= mult
if sensors is None:
sensor = sensor_net(epochs, picks=picks)
else:
sensor = sensors
time = var(epochs.times, name='time')
return ndvar(x, ('case', sensor, time), properties=props, name=name)
def events(raw=None, merge= -1, proj=False, name=None,
stim_channel='STI 014',
stim_channel_bl=0, verbose=False):
"""
Read events from a raw fiff file.
Use :func:`fiff_epochs` to load MEG data corresponding to those events.
Parameters
----------
raw : str(path) | None | mne.fiff.Raw
The raw fiff file from which to extract events (if ``None``, a file
dialog will be displayed).
merge : int
use to merge events lying in neighboring samples. The integer value
indicates over how many samples events should be merged, and the sign
indicates in which direction they should be merged (negative means
towards the earlier event, positive towards the later event).
proj : bool | str
Path to the projections file that will be loaded with the raw file.
``'{raw}'`` will be expanded to the raw file's path minus extension.
With ``proj=True``, ``'{raw}_*proj.fif'`` will be used,
looking for any projection file starting with the raw file's name.
If multiple files match the pattern, a ValueError will be raised.
name : str | None
A name for the dataset. If ``None``, the raw filename will be used.
stim_channel : str
Name of the trigger channel.
stim_channel_bl : int
For corrupted event channels:
After kit2fiff conversion of sqd files with unused trigger channels,
the resulting fiff file's event channel can contain a baseline other
than 0. This interferes with normal event extraction. If the baseline
value is provided as parameter, the events can still be extracted.
Returns
-------
events : dataset
A dataset with the following variables:
- *i_start*: the index of the event in the raw file.
- *eventID*: the event value.
The dataset's info dictionary contains the following values:
- *raw*: the mne Raw object.
- *samplingrate*: the samplingrate of the raw file.
"""
if raw is None or isinstance(raw, basestring):
raw = Raw(raw, proj=proj, verbose=verbose)
if name is None:
raw_file = raw.info['filename']
name = os.path.basename(raw_file)
if stim_channel_bl:
pick = mne.event.pick_channels(raw.info['ch_names'], include=stim_channel)
data, _ = raw[pick, :]
idx = np.where(np.abs(np.diff(data[0])) > 0)[0]
# find baseline NULL-events
values = data[0, idx + 1]
valid_events = np.where(values != stim_channel_bl)[0]
idx = idx[valid_events]
values = values[valid_events]
N = len(values)
events = np.empty((N, 3), dtype=np.int32)
events[:, 0] = idx
events[:, 1] = np.zeros_like(idx)
events[:, 2] = values
else:
events = mne.find_events(raw, verbose=verbose, stim_channel=stim_channel)
if len(events) == 0:
raise ValueError("No events found!")
if merge:
index = np.ones(len(events), dtype=bool)
diff = np.diff(events[:, 0])
where = np.where(diff <= abs(merge))[0]
if merge > 0:
# drop the earlier event
index[where] = False
else:
# drop the later event
index[where + 1] = False
# move the trigger value to the earlier event
for w in reversed(where):
i1 = w
i2 = w + 1
events[i1, 2] = events[i2, 2]
events = events[index]
istart = var(events[:, 0], name='i_start')
event = var(events[:, 2], name='eventID')
info = {'raw': raw,
'samplingrate': raw.info['sfreq'],
'info': raw.info}
return dataset(event, istart, name=name, info=info)
def tsv(path=None, names=True, types='auto', empty='nan', delimiter=None,
skiprows=0):
"""
returns a ``dataset`` with data from a tab-separated values file.
Parameters
----------
names : list of str | bool
* ``['name1', ...]`` use these names
* ``True``: look for names on the first line of the file
* ``False``: use "v1", "v2", ...
types : 'auto' | list of int
* ``'auto'`` -> import as var if all values can be converted float,
otherwise as factor
* list of 0=auto, 1=factor, 2=var. e.g. ``[0,1,1,0]``
empty :
value to substitute for empty cells
delimiter : str
value delimiting cells in the input file (None = any whitespace;
e.g., ``'\\t'``)
skiprows : int
Skip so many rows at the beginning of the file (for tsv files with
headers). Column names (if names==True) are expected to come after
the skipped rows.
"""
if path is None:
path = ui.ask_file("Select file to import as dataframe",
"Select file to import as dataframe")
if not path:
return
with open(path) as f:
for i in xrange(skiprows):
f.readline()
# read / create names
if names == True:
names = f.readline().split(delimiter)
names = [n.strip().strip('"') for n in names]
lines = []
for line in f:
values = []
for v in line.split(delimiter):
v = v.strip()
if not v:
v = empty
values.append(v)
lines.append(values)
n_vars = len(lines[0])
if not names:
names = ['v%i' % i for i in xrange(n_vars)]
n = len(names)
# decide whether to drop first column
if n_vars == n:
start = 0
elif n_vars == n + 1:
start = 1
else:
raise ValueError("number of header different from number of data")
if types in ['auto', None, False, True]:
types = [0] * n
else:
assert len(types) == n
# prepare for reading data
data = []
for _ in xrange(n):
data.append([])
# read rest of the data
for line in lines:
for i, v in enumerate(line[start:]):
for str_del in ["'", '"']:
if v[0] == str_del:
v = v.strip(str_del)
types[i] = 1
data[i].append(v)
ds = _data.dataset(name=os.path.basename(path))
for name, values, force_type in zip(names, data, types):
v = np.array(values)
if force_type in [0, 2]:
try:
v = v.astype(float)
f = _data.var(v, name=name)
except:
f = _data.factor(v, name=name)
else:
f = _data.factor(v, name=name)
ds.add(f)
return ds
def _ax_im_array(ax, layers, x='time', # vmax=None,
xlabel=True, ylabel=True, title=None, tick_spacing=.3):
"""
plots segment data as im
define a colorspace by supplying one of those kwargs: ``colorspace`` OR
``p`` OR ``vmax``
"""
handles = []
epoch = layers[0]
xdim = epoch.get_dim(x)
if epoch.ndim == 2:
xdim_i = epoch.dimnames.index(x)
ydim_i = {1:0, 0:1}[xdim_i]
y = epoch.dimnames[ydim_i]
else:
err = ("Need 2 dimensions, got %i" % epoch.ndim)
raise ValueError(err)
ydim = epoch.get_dim(y)
if y == 'sensor':
ydim = _dta.var(np.arange(len(ydim)), y)
map_kwargs = {'extent': [xdim[0], xdim[-1], ydim[0], ydim[-1]],
'aspect': 'auto'}
# plot
for l in layers:
h = _plt_im_array(ax, l, dims=(y, x), **map_kwargs)
handles.append(h)
if xlabel:
if xlabel is True:
xlabel = xdim.name
ax.set_xlabel(xlabel)
if ylabel:
if ylabel is True:
ylabel = ydim.name
ax.set_ylabel(ylabel)
# x-ticks
tickstart = math.ceil(xdim[0] / tick_spacing) * tick_spacing
tickend = xdim[-1] + tick_spacing / 1e4
ticklabels = np.arange(tickstart, tickend, tick_spacing)
ax.xaxis.set_ticks(ticklabels)
ax.x_fmt = "t = %.3f s"
# y-ticks
if y == 'sensor': # make sure y-ticklabels are all integers
locs = ax.yaxis.get_ticklocs()
if any(locs != locs.round()):
idx = np.where(locs == locs.round())[0]
locs = locs[idx]
labels = map(lambda x: str(int(x)), locs)
ax.yaxis.set_ticks(locs)
ax.yaxis.set_ticklabels(labels)
# title
if title is None:
if plt.rcParams['text.usetex']:
title = fmtxt.texify(epoch.name)
else:
title = epoch.name
ax.set_title(title)
return handles