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from __future__ import division |
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import logging |
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from datetime import datetime |
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from functools import partial |
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from niprov.basefile import BaseFile |
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from niprov.libraries import Libraries |
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class FifFile(BaseFile): |
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def __init__(self, location, **kwargs): |
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super(FifFile, self).__init__(location, **kwargs) |
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self.libs = self.dependencies.getLibraries() |
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def inspect(self): |
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provenance = super(FifFile, self).inspect() |
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""" try: |
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img = self.libs.mne.io.Raw(self.path, allow_maxshield=True) |
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except ValueError: |
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pass |
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else: |
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inspect file |
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Return |
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""" |
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ftypes = [ |
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('cov', self.libs.mne.read_cov), |
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('epo', self.libs.mne.read_epochs), |
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('ave', self.libs.mne.read_evokeds), |
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('fwd', self.libs.mne.read_forward_solution), |
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('trans', self.libs.mne.read_trans), |
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('raw', partial(self.libs.mne.io.read_raw_fif, |
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allow_maxshield=True)), |
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('proj', self.libs.mne.read_proj), |
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] |
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oldLevel = logging.getLogger('mne').getEffectiveLevel() |
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logging.getLogger('mne').setLevel(logging.ERROR) |
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for ftype, readfif in ftypes: |
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try: |
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img = readfif(self.path) |
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if img == []: |
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continue |
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break |
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except (ValueError, IOError): |
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continue |
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else: |
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ftype = 'other' |
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logging.getLogger('mne').setLevel(oldLevel) |
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if ftype == 'raw': |
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sub = img.info['subject_info'] |
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if sub is not None: |
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provenance['subject'] = sub['first_name']+' '+sub['last_name'] |
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provenance['project'] = img.info['proj_name'] |
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acqTS = img.info['meas_date'][0] |
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provenance['acquired'] = datetime.fromtimestamp(acqTS) |
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T = img.last_samp - img.first_samp + 1 |
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provenance['dimensions'] = [img.info['nchan'], T] |
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provenance['sampling-frequency'] = img.info['sfreq'] |
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provenance['duration'] = T/img.info['sfreq'] |
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if ftype == 'epo': |
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provenance['lowpass'] = img.info['lowpass'] |
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provenance['highpass'] = img.info['highpass'] |
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provenance['bad-channels'] = img.info['bads'] |
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provenance['dimensions'] = [img.events.shape[0], img.times.shape[0]] |
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if ftype == 'ave': |
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nEvokeds = len(img) |
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provenance['dimensions'] = [nEvokeds] + list(img[0].data.shape) |
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if ftype == 'cov': |
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provenance['dimensions'] = list(img.data.shape) |
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if ftype == 'proj': |
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provenance['projection-description'] = img[0]['desc'] |
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provenance['fif-type'] = ftype |
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provenance['modality'] = 'MEG' |
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return provenance |
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81
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def attach(self, form='json'): |
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""" |
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Attach the current provenance to the file by appending it as a |
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json-encoded string to the 'description' header field. |
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This is only attempted if the file has been inspect()-ed and |
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has been determined to be a raw fif file. |
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Args: |
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form (str): Data format in which to serialize provenance. Defaults |
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to 'json'. |
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""" |
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if 'fif-type' in self.provenance: |
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if self.provenance['fif-type'] == 'raw': |
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info = self.libs.mne.io.read_info(self.path) |
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provstr = self.getProvenance(form) |
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info['description'] = info['description']+' NIPROV:'+provstr |
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self.libs.mne.io.write_info(self.path, info) |
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100
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ilogue /
niprov
