from datetime import datetime
from logging import getLogger
from numpy import around, empty
from scipy.io import loadmat, savemat
from .utils import read_hdf5_chan_name
from ..utils import MissingDependency
try:
from h5py import File
except ImportError as err:
File = MissingDependency(err)
lg = getLogger(__name__)
VAR = 'data'
[docs]class FieldTrip:
"""Basic class to read the data.
Parameters
----------
filename : path to file
the name of the filename or directory
"""
def __init__(self, filename):
self.filename = filename
[docs] def return_hdr(self):
"""Return the header for further use.
Returns
-------
subj_id : str
subject identification code
start_time : datetime
start time of the dataset
s_freq : float
sampling frequency
chan_name : list of str
list of all the channels
n_samples : int
number of samples in the dataset
orig : dict
additional information taken directly from the header
Notes
-----
It only reads hdf5 matlab files and the VARiable needs to be called
'data'
h5py is necessary for this function
"""
# fieldtrip does not have this information
orig = dict()
subj_id = str()
start_time = datetime.fromordinal(1) # fake
try:
ft_data = loadmat(self.filename, struct_as_record=True,
squeeze_me=True)
if VAR not in ft_data:
raise KeyError('Save the FieldTrip variable as ''{}'''
''.format(VAR))
ft_data = ft_data[VAR]
s_freq = ft_data['fsample'].astype('float64').item()
n_samples = ft_data['trial'].item().shape[1]
chan_name = list(ft_data['label'].item())
except NotImplementedError:
with File(self.filename) as f:
if VAR not in f.keys():
raise KeyError('Save the FieldTrip variable as ''{}'''
''.format(VAR))
s_freq = int(f[VAR]['fsample'].value.squeeze())
chan_name = read_hdf5_chan_name(f, f[VAR]['label'])
n_samples = int(around(f[f[VAR]['trial'][0].item()].shape[0]))
return subj_id, start_time, s_freq, chan_name, n_samples, orig
[docs] def return_dat(self, chan, begsam, endsam):
"""Return the data as 2D numpy.ndarray.
Parameters
----------
chan : int or list
index (indices) of the channels to read
begsam : int
index of the first sample
endsam : int
index of the last sample
Returns
-------
numpy.ndarray
A 2d matrix, with dimension chan X samples
"""
TRL = 0
try:
ft_data = loadmat(self.filename, struct_as_record=True,
squeeze_me=True)
ft_data = ft_data[VAR]
data = ft_data['trial'].item(TRL)
except NotImplementedError:
from h5py import File
with File(self.filename) as f:
data = f[f[VAR]['trial'][TRL].item()].value.T
return data[chan, begsam:endsam]
[docs] def return_markers(self):
"""Return all the markers (also called triggers or events).
Returns
-------
list of dict
where each dict contains 'name' as str, 'start' and 'end' as float
in seconds from the start of the recordings, and 'chan' as list of
str with the channels involved (if not of relevance, it's None).
TODO
----
How to read markers / events from fieldtrip file
"""
return []
[docs]def write_fieldtrip(data, filename):
"""Export data to FieldTrip.
Parameters
----------
data : instance of ChanTime
data with only one trial
filename : path to file
file to export to (include '.mat')
Notes
-----
It saves mat file using Version 6 ('-v7') because it relies on scipy.io
functions. Therefore it cannot store data larger than 2 GB.
"""
n_trl = data.number_of('trial')
trial = empty(n_trl, dtype='O')
time = empty(n_trl, dtype='O')
for trl in range(n_trl):
trial[trl] = data.data[trl]
time[trl] = data.axis['time'][trl]
ft_data = {'fsample': float(data.s_freq),
'label': data.axis['chan'][0].astype('O'),
'trial': trial,
'time': time,
'cfg': 'Converted from wonambi on ' + str(datetime.now()),
}
savemat(filename, {VAR: ft_data})