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openfmri.py
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openfmri.py
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import os
import re
import csv
import glob
from StringIO import StringIO
from functools import partial
from os.path import exists
import numpy as np
import pandas as pd
import nibabel as nb
from joblib import Memory, Parallel, delayed
from nipy.modalities.fmri.design_matrix import make_dmtx
from nipy.modalities.fmri.experimental_paradigm import EventRelatedParadigm
from nipy.modalities.fmri.experimental_paradigm import BlockParadigm
def _check_csv_file(f, delimiter):
possible_delimiters = ['\t', ' ']
content = f.read()
new = []
for line in content.split('\n'):
for sep in possible_delimiters:
line = re.sub('%s+' % sep, delimiter, line).strip()
new.append(line)
return StringIO('\n'.join(new))
def _csv_to_dict(path, key_end_pos=1, join_values=False, delimiter=' ', quotechar='"'):
if join_values and isinstance(join_values, bool):
join_values = '_'
data = []
with open(path, 'rb') as f:
# look for stupid separators
f = _check_csv_file(f, delimiter=delimiter)
reader = csv.reader(f, delimiter=delimiter, quotechar=quotechar)
for row in reader:
if len(row) == 2:
data.append(row)
else:
key = '_'.join(row[:key_end_pos])
value = row[key_end_pos:]
if len(value) == 1:
value = value[0]
if join_values:
value = join_values.join(value)
data.append((key, value))
return dict(data)
def _collect_openfmri_dataset(study_dir, img_ext='nii.gz'):
# parse study-level metadata files
dataset = {}
join_study = partial(os.path.join, study_dir)
study_id = os.path.basename(study_dir.strip())
dataset['study_id'] = study_id
if exists(join_study('scan_key.txt')):
dataset.update(_csv_to_dict(join_study('scan_key.txt')))
if exists(join_study('study_key.txt')):
with open(join_study('study_key.txt'), 'rb') as f:
dataset['name'] = f.read().strip()
if exists(join_study('task_key.txt')):
dataset.update(_csv_to_dict(join_study('task_key.txt'), key_end_pos=1, join_values=' '))
# parse model-level metadata files
model = {}
for model_dir in sorted(glob.glob(join_study('models', '*'))):
join_model = partial(os.path.join, model_dir)
model_id = os.path.basename(model_dir.strip())
model[model_id] = {}
if exists(join_model('condition_key.txt')):
model[model_id]['conditions'] = _csv_to_dict(
join_model('condition_key.txt'), key_end_pos=2)
if exists(join_model('task_contrasts.txt')):
model[model_id]['contrasts'] = _csv_to_dict(
join_model('task_contrasts.txt'), key_end_pos=2)
model[model_id]['orthogonalize'] = None
if exists(join_model('orthogonalize.txt')):
model[model_id]['orthogonalize'] = pd.DataFrame.from_csv(
join_model('orthogonalize.txt'), header=-1, sep=' ')
# parse subject-level files and align it with study and model
structural = []
functional = []
conditions = {}
contrasts = {}
for subject_dir in sorted(glob.glob(join_study('sub???'))):
join_subject = partial(os.path.join, subject_dir)
subject_id = os.path.basename(subject_dir.strip())
# Anatomy data
preproc_anat = {}
anat_files = join_subject('model', '*', 'anatomy', 'highres001.%s' % img_ext)
for anat_file in sorted(glob.glob(anat_files)):
model_id, _, _ = anat_file.split(os.path.sep)[-3:]
preproc_anat.setdefault('study', []).append(study_id)
preproc_anat.setdefault('subject', []).append(subject_id)
preproc_anat.setdefault('model', []).append(model_id)
preproc_anat.setdefault('preproc_anatomy', []).append(anat_file)
preproc_anat = pd.DataFrame(preproc_anat)
raw_anat = {}
anat_file = join_subject('anatomy', 'highres001.%s' % img_ext)
anat_file = anat_file if os.path.exists(anat_file) else np.nan
raw_anat.setdefault('study', []).append(study_id)
raw_anat.setdefault('subject', []).append(subject_id)
raw_anat.setdefault('raw_anatomy', []).append(anat_file)
raw_anat = pd.DataFrame(raw_anat)
if raw_anat.shape[0] == 0:
anat = preproc_anat
elif preproc_anat.shape[0] == 0:
anat = raw_anat
else:
anat = preproc_anat.merge(raw_anat)
structural.append(anat)
# BOLD data
preproc_func = {}
bold_files = join_subject('model', '*', 'BOLD', '*', 'bold.%s' % img_ext)
for bold_file in sorted(glob.glob(bold_files)):
model_id, _, session_id, _ = bold_file.split(os.path.sep)[-4:]
task_id, run_id = session_id.split('_')
movement_file = os.path.join(os.path.split(bold_file)[0], 'motion.txt')
movement_file = movement_file if exists(movement_file) else np.nan
preproc_func.setdefault('study', []).append(study_id)
preproc_func.setdefault('subject', []).append(subject_id)
preproc_func.setdefault('model', []).append(model_id)
preproc_func.setdefault('task', []).append(task_id)
preproc_func.setdefault('task_name', []).append(dataset.get(task_id, task_id))
preproc_func.setdefault('run', []).append(run_id)
preproc_func.setdefault('preproc_bold', []).append(bold_file)
preproc_func.setdefault('movement', []).append(movement_file)
preproc_func.setdefault('TR', []).append(dataset.get('TR', np.nan))
preproc_func = pd.DataFrame(preproc_func)
raw_func = {}
bold_files = join_subject('BOLD', '*', 'bold.%s' % img_ext)
for bold_file in sorted(glob.glob(bold_files)):
session_id, _ = bold_file.split(os.path.sep)[-2:]
task_id, run_id = session_id.split('_')
raw_func.setdefault('study', []).append(study_id)
raw_func.setdefault('subject', []).append(subject_id)
raw_func.setdefault('task', []).append(task_id)
raw_func.setdefault('task_name', []).append(dataset.get(task_id, task_id))
raw_func.setdefault('run', []).append(run_id)
raw_func.setdefault('raw_bold', []).append(bold_file)
raw_func.setdefault('TR', []).append(dataset.get('TR', np.nan))
raw_func = pd.DataFrame(raw_func)
if raw_func.shape[0] == 0:
func = preproc_func
elif preproc_func.shape[0] == 0:
func = raw_func
else:
func = preproc_func.merge(raw_func)
functional.append(func)
# condition files
cond_files = join_subject('model', '*', 'onsets', '*', '*.txt')
for cond_file in sorted(glob.glob(cond_files)):
model_id, _, session_id, cond_id = cond_file.split(os.path.sep)[-4:]
task_id, run_id = session_id.split('_')
cond_id = cond_id[:-4]
cond_name = model.get(model_id, model['model001'])['conditions'].get('%s_%s' % (task_id, cond_id), cond_id)
conditions.setdefault('study', []).append(study_id)
conditions.setdefault('subject', []).append(subject_id)
conditions.setdefault('model', []).append(model_id)
conditions.setdefault('task', []).append(task_id)
conditions.setdefault('task_name', []).append(dataset.get(task_id, task_id))
conditions.setdefault('run', []).append(run_id)
conditions.setdefault('condition', []).append(cond_id)
conditions.setdefault('condition_name', []).append(cond_name)
conditions.setdefault('condition_file', []).append(cond_file)
# contrast files
contrast_files = join_subject('model', '*', '*_maps', '*.%s' % img_ext)
for contrast_file in sorted(glob.glob(contrast_files)):
model_id, dtype, contrast_id = contrast_file.split(os.path.sep)[-3:]
contrast_id = contrast_id.split('.%s' % img_ext)[0]
contrasts.setdefault('study', []).append(study_id)
contrasts.setdefault('subject', []).append(subject_id)
contrasts.setdefault('model', []).append(model_id)
contrasts.setdefault('dtype', []).append(dtype)
contrasts.setdefault('contrast', []).append(contrast_id)
contrasts.setdefault('contrast_file', []).append(contrast_file)
return (dataset, model,
pd.concat(structural, ignore_index=True),
pd.concat(functional, ignore_index=True),
pd.DataFrame(conditions), pd.DataFrame(contrasts))
def collect_openfmri(study_dirs, img_ext='nii.gz', memory=Memory(None), n_jobs=1):
"""Collect paths and identifiers of OpenfMRI datasets.
Parameters
----------
study_dirs: list
The list of the datasets paths.
n_jobs: int
Number of jobs.
Returns
-------
structual: DataFrame with anat images.
functional: DataFrame with func images.
conditions: DataFrame with conditions files.
contrasts: DataFrame with subject-level contrasts
Warning
-------
All the files from the openfmri structure are not yet collected.
Among those: motion.txt, orthogonalize.txt
"""
if isinstance(study_dirs, basestring):
study_dirs = glob.glob(study_dirs)
results = Parallel(n_jobs=n_jobs, pre_dispatch='n_jobs')(
delayed(memory.cache(_collect_openfmri_dataset))(study_dir, img_ext=img_ext)
for study_dir in study_dirs)
datasets = [r[0] for r in results]
models = [r[1] for r in results]
structural = pd.concat([r[2] for r in results], ignore_index=True)
functional = pd.concat([r[3] for r in results], ignore_index=True)
conditions = pd.concat([r[4] for r in results], ignore_index=True)
contrasts = pd.concat([r[5] for r in results], ignore_index=True)
# merge datasets and models
datasets_ = {}
for dataset, model in zip(datasets, models):
dataset['models'] = model
datasets_[dataset['study_id']] = dataset
return datasets_, structural, functional, conditions, contrasts
def load_glm_inputs(study_dirs, hrf_model='canonical', drift_model='cosine',
img_ext='nii.gz', memory=Memory(None), n_jobs=1):
"""Returns data (almost) ready to be used for a GLM.
"""
datasets, structural, functional, conditions, contrasts = \
collect_openfmri(study_dirs, img_ext=img_ext, memory=memory, n_jobs=n_jobs)
main = functional.merge(conditions)
# computing design matrices
print 'Computing models...'
results = Parallel(n_jobs=n_jobs, pre_dispatch='n_jobs')(
delayed(memory.cache(_make_design_matrix))(
run_df, hrf_model, drift_model, orthogonalize=datasets[group_id[0]]['models'][group_id[2]]['orthogonalize'])
for group_id, group_df in main.groupby(['study', 'subject', 'model'])
for run_id, run_df in group_df.groupby(['task', 'run'])
)
# collect results
print 'Collecting...'
glm_inputs = {}
for group_id, group_df in main.groupby(['study', 'subject', 'model']):
study_id, subject_id, model_id = group_id
for session_id, run_df in group_df.groupby(['task', 'run']):
task_id, run_id = session_id
bold_file, dm = results.pop(0)
glm_inputs.setdefault(group_id, {}).setdefault('bold', []).append(bold_file)
glm_inputs.setdefault(group_id, {}).setdefault('design', []).append(dm)
glm_inputs.setdefault(group_id, {}).setdefault(
model_id, _make_contrasts(datasets, study_id, model_id, hrf_model, group_df))
glm_inputs.setdefault(group_id, {}).setdefault(
'%s_per_run' % model_id, _make_contrasts(
datasets, study_id, model_id, hrf_model, group_df, per_run=True))
return glm_inputs
def _make_contrasts(datasets, study_id, model_id, hrf_model, group_df, per_run=False):
contrasts = {}
model_contrasts = datasets[study_id]['models'][model_id]['contrasts']
if per_run:
model_contrasts_ = {}
for session_id, run_df in group_df.groupby(['task', 'run']):
task_id, run_id = session_id
for con_id in model_contrasts:
new_con_id = '%s_%s_%s' % (
con_id.split('_', 1)[0], run_id, con_id.split('_', 1)[1])
if new_con_id.startswith(task_id):
model_contrasts_[new_con_id] = model_contrasts[con_id]
model_contrasts = model_contrasts_
for session_id, run_df in group_df.groupby(['task', 'run']):
task_id, run_id = session_id
for con_id in model_contrasts:
con_val = model_contrasts[con_id]
con_val = np.array(con_val).astype(np.float)
if 'derivative' in hrf_model:
con_val = np.insert(con_val, np.arange(con_val.size) + 1, 0).tolist()
if (not con_id.startswith(task_id) and not per_run) or (
(not con_id.startswith(task_id) or not run_id in con_id) and per_run):
con_val = None
contrasts.setdefault(con_id, []).append(con_val)
return contrasts
def load_classification_inputs(study_dirs, img_ext='nii.gz', memory=Memory(None), n_jobs=1):
"""Returns data (almost) ready to be used for a
classification of the timeseries.
"""
# datasets, structural, functional, conditions, contrasts = \
# collect_openfmri(study_dirs, img_ext=img_ext, memory=memory, n_jobs=n_jobs)
# functional['movement'] = np.nan
glm_inputs = load_glm_inputs(study_dirs, hrf_model='canonical', drift_model='blank',
img_ext=img_ext, memory=memory, n_jobs=n_jobs)
classif_inputs = {}
for key in glm_inputs:
classif_inputs[key] = {}
classif_inputs[key]['bold'] = glm_inputs[key]['bold']
classif_inputs[key]['target'] = []
for dm in glm_inputs[key]['design']:
cols = dm.columns.values
cols = [col for col in cols if 'movement' not in col and 'constant' not in col]
dm = dm[cols].values
p = np.percentile(dm, 85)
target = pd.DataFrame(dict(zip(cols, (dm > p).T.astype('int'))))
classif_inputs[key]['target'].append(target)
return classif_inputs
def _make_design_matrix(run_frame, hrf_model='canonical', drift_model='cosine', orthogonalize=None):
# print ' %s' % ' '.join(run_frame[['study', 'subject', 'model', 'task', 'run']].values[0])
bold_file = run_frame.preproc_bold.unique()[0]
n_scans = nb.load(bold_file).shape[-1]
tr = float(run_frame.TR.unique()[0])
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
movement_regressors = run_frame.movement.unique()[0]
movement_regressors = np.recfromtxt(movement_regressors) \
if isinstance(movement_regressors, basestring) else None
names = []
times = []
durations = []
amplitudes = []
for condition_id, condition_name, condition_file in run_frame[['condition', 'condition_name', 'condition_file']].values:
conditions = _csv_to_dict(condition_file)
if condition_id == 'empty_evs':
conditions = sorted(conditions.keys())
for c in conditions:
names.append(['cond%03i' % int(c)])
times.append([0])
durations.append([0])
amplitudes.append([0])
else:
keys = sorted(conditions.keys())
times.append(np.array(keys).astype('float'))
names.append(['%s_%s' % (condition_id, condition_name)] * len(keys))
durations.append([float(conditions[k][0]) for k in keys])
amplitudes.append([float(conditions[k][1]) for k in keys])
times = np.concatenate(times).ravel()
order = np.argsort(times)
times = times[order]
names = np.concatenate(names)[order]
durations = np.concatenate(durations)[order]
amplitudes = np.concatenate(amplitudes)[order]
if durations.sum() == 0:
paradigm = EventRelatedParadigm(names, times, amplitudes)
else:
paradigm = BlockParadigm(names, times, durations, amplitudes)
if movement_regressors is None:
design_matrix = make_dmtx(
frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model)
else:
mov_reg_names = ['movement_%i' % r
for r in range(movement_regressors.shape[1])]
design_matrix = make_dmtx(
frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model,
add_regs=movement_regressors, add_reg_names=mov_reg_names)
# orthogonalize regressors
if orthogonalize is not None and not 'derivative' in hrf_model:
task_id = run_frame.task.unique()[0]
for x, y in orthogonalize[orthogonalize.index == task_id].values:
x_ = design_matrix.matrix[:, x]
y_ = design_matrix.matrix[:, y]
z = _orthogonalize_vectors(x_, y_)
design_matrix.matrix[:, x] = z
return bold_file, pd.DataFrame(dict(zip(design_matrix.names, design_matrix.matrix.T)))
def _orthogonalize_vectors(x, y):
x = np.array(x)
y = np.array(y)
s = np.dot(x, y) / np.sum(y ** 2)
return (x - y * s)
if __name__ == '__main__':
memory = Memory('/storage/workspace/yschwart/cache')
base_dir = '/storage/workspace/yschwart/new_brainpedia'
# glob intra_stats folders to get contrasts
study_dirs = sorted(glob.glob(os.path.join(base_dir, 'intra_stats', 'ds001')))
_, _, _, _, contrasts = collect_openfmri(study_dirs, memory=memory, n_jobs=-1)
# glob preproc folders
study_dirs = sorted(glob.glob(os.path.join(base_dir, 'preproc', 'ds001')))
datasets, structural, functional, conditions, _ = collect_openfmri(study_dirs, memory=memory, n_jobs=-1)
# we can merge dataframes!
df = functional.merge(conditions)
# we can filter the dataframes!
functional = functional[functional.study == 'ds001']
# computes design matrices for the given dataframes
glm_inputs = load_glm_inputs(study_dirs, memory=memory, n_jobs=1)
glm_inputs = load_glm_inputs(study_dirs, hrf_model='canonical with derivative', memory=memory, n_jobs=-1)
# computes classification targets for the given dataframes
classif_inputs = load_classification_inputs(study_dirs, memory=memory, n_jobs=-1)