# alphabetic order
publications = np.union1d(publications[1], publications[0]).tolist()
html = html % (len(publications), gen_date)
# sort by year of publication
years = list()
for pub in publications:
m = re.search('\d{4} -', pub)
if m is None:
years.append(-1)
else:
years.append(int(m.group(0)[:-2]))
order = np.argsort(years)[::-1]
publications = [publications[idx] for idx in order]
# filter out publications not containing (http://, https://, ftp://)
publications = [
p for p in publications
if any(sub in p for sub in ('http://', 'https://', 'ftp://'))
]
# create rst & cleanup
this_html = cite_template.substitute(publications=publications)
this_html = this_html.replace('…', '...')
html += this_html
# output an rst file
with open(op.join('..', 'cited.rst'), 'w') as f:
f.write(html.encode('utf8'))
# get a union of the citations for the two papers, sorted in
# alphabetic order
publications = np.union1d(publications[1], publications[0]).tolist()
html = html % (len(publications), gen_date)
# sort by year of publication
years = list()
for pub in publications:
m = re.search(r'\d{4} -', pub)
if m is None:
years.append(-1)
else:
years.append(int(m.group(0)[:-2]))
order = np.argsort(years)[::-1]
publications = [publications[idx] for idx in order]
# filter out publications not containing (http://, https://, ftp://)
publications = [p for p in publications if
any(sub in p for sub in ('http://', 'https://', 'ftp://'))]
# create rst & cleanup
this_html = cite_template.substitute(publications=publications)
this_html = this_html.replace('…', '...')
html += this_html
# output an rst file
with open(op.join('..', 'cited.rst'), 'w') as f:
f.write(html.encode('utf8'))
def make_report(root, session=None, verbose=True):
"""Create a methods paragraph string from BIDS dataset.
Summarizes the REQUIRED components in the BIDS specification
and also some RECOMMENDED components. Currently, the methods
paragraph summarize the:
- dataset_description.json file
- (optional) participants.tsv file
- (optional) datatype-agnostic files for (M/I)EEG data,
which reads files from the ``*_scans.tsv`` file.
Parameters
----------
root : str | pathlib.Path
The path of the root of the BIDS compatible folder.
session : str | None
The (optional) session for a item. Corresponds to "ses".
verbose : bool | None
Set verbose output to true or false.
Returns
-------
paragraph : str
The paragraph wrapped with 80 characters per line
describing the summary of the subjects.
"""
# high level summary
subjects = get_entity_vals(root, entity_key='subject')
sessions = get_entity_vals(root, entity_key='session')
modalities = get_datatypes(root)
# only summarize allowed modalities (MEG/EEG/iEEG) data
# map them to a pretty looking string
datatype_map = {
'meg': 'MEG',
'eeg': 'EEG',
'ieeg': 'iEEG',
}
modalities = [
datatype_map[datatype] for datatype in modalities
if datatype in datatype_map.keys()
]
# REQUIRED: dataset_description.json summary
dataset_summary = _summarize_dataset(root)
# RECOMMENDED: participants summary
participant_summary = _summarize_participants_tsv(root)
# RECOMMENDED: scans summary
scans_summary = _summarize_scans(root, session=session, verbose=verbose)
# turn off 'recommended' report summary
# if files are not available to summarize
if not participant_summary:
participant_template = ''
else:
content = f'{FUNCTION_TEMPLATE}{PARTICIPANTS_TEMPLATE}'
participant_template = Template(content=content)
participant_template = participant_template.substitute(
**participant_summary)
if verbose:
print(f'The participant template found: {participant_template}')
dataset_summary['PARTICIPANTS_TEMPLATE'] = str(participant_template)
if not scans_summary:
datatype_agnostic_template = ''
else:
datatype_agnostic_template = DATATYPE_AGNOSTIC_TEMPLATE
dataset_summary.update({
'system': modalities,
'n_subjects': len(subjects),
'n_sessions': len(sessions),
'sessions': sessions,
})
# XXX: add channel summary for modalities (ieeg, meg, eeg)
# create the content and mne Template
# lower-case templates are "Recommended",
# while upper-case templates are "Required".
content = f'{FUNCTION_TEMPLATE}{BIDS_DATASET_TEMPLATE}' \
f'{datatype_agnostic_template}'
paragraph = Template(content=content)
paragraph = paragraph.substitute(**dataset_summary, **scans_summary)
# Clean paragraph
paragraph = paragraph.replace('\n', ' ')
paragraph = paragraph.replace(' ', ' ')
return '\n'.join(textwrap.wrap(paragraph, width=80))
"""
===========================
Plotting the analysis chain
===========================
Run the analysis.
"""
import codecs
import os.path as op
from mne.externals.tempita import Template
with codecs.open(op.join(op.dirname(__file__), 'template_analysis.py'), 'r',
'utf-8') as f:
lines = f.readlines()
template = Template(u''.join(lines))
for subject in range(1, 20):
py_str = template.substitute(subject_id='%02d' % subject)
out_fname = op.join(op.dirname(__file__), '..', '..', 'results',
'single_subject_analysis',
'plot_analysis_%02d.py' % subject)
with codecs.open(out_fname, 'w', 'utf-8') as f:
f.write(py_str)
