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group_multregress_openfmri.py
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group_multregress_openfmri.py
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"""
ROUGH DRAFT
for help:
contact: annepark@mit.edu
"""
import os
from nipype import config
#config.enable_provenance()
from nipype import Workflow, Node, MapNode, Function
from nipype import DataGrabber, DataSink
from nipype.interfaces.fsl import (Merge, FLAMEO, ContrastMgr,
SmoothEstimate, Cluster, ImageMaths, MultipleRegressDesign)
import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as util
from nipype.interfaces.fsl.maths import BinaryMaths
get_len = lambda x: len(x)
def l1_contrasts_num(model_id, task_id, dataset_dir):
import numpy as np
import os
contrast_def = []
contrasts = 0
contrast_file = os.path.join(dataset_dir, 'models', 'model%03d' % model_id,
'task_contrasts.txt')
if os.path.exists(contrast_file):
with open(contrast_file, 'rt') as fp:
contrast_def.extend([np.array(row.split()) for row in fp.readlines() if row.strip()])
for row in contrast_def:
if row[0] != 'task%03d' % task_id:
continue
contrasts = contrasts + 1
cope_id = range(1, contrasts + 1)
return cope_id
def get_sub_vars(dataset_dir, task_id, model_id):
import numpy as np
import os
import pandas as pd
sub_list_file = os.path.join(dataset_dir, 'groups', 'participant_key.txt')
behav_file = os.path.join(dataset_dir, 'groups', 'behav.txt')
group_contrast_file = os.path.join(dataset_dir, 'groups', 'contrasts.txt')
subs_list = pd.read_table(sub_list_file, index_col=0)['task%03d' % int(task_id)]
subs_needed = subs_list.index[np.nonzero(subs_list)[0]]
behav_info = pd.read_table(behav_file, index_col=0)
missing_subjects = np.setdiff1d(subs_needed, behav_info.index.tolist())
if len(missing_subjects) > 0:
raise ValueError('Subjects %s are missing from participant key' % ' '.join(missing_subjects))
contrast_defs=[]
with open(group_contrast_file, 'rt') as fp:
contrast_defs = fp.readlines()
contrasts = []
for row in contrast_defs:
if 'task%03d' % int(task_id) not in row:
continue
regressor_names = eval('[' + row.split(' [')[1].split(']')[0] + ']')
for val in regressor_names:
if val not in behav_info.keys():
raise ValueError('Regressor %s not in behav.txt file' % val)
contrast_name = row.split()[1]
contrast_vector = np.array(row.split('] ')[1].rstrip().split()).astype(float).tolist()
con = [tuple([contrast_name, 'T', regressor_names, contrast_vector])]
contrasts.append(con)
regressors_needed = []
for idx, con in enumerate(contrasts):
model_regressor = {}
for cond in con[0][2]:
values = behav_info.ix[subs_needed, cond].values
if tuple(np.unique(values).tolist()) not in [(1,), (0, 1)]:
values = values - values.mean()
model_regressor[cond] = values.tolist()
regressors_needed.append(model_regressor)
groups = [1 for val in subs_needed]
return regressors_needed, contrasts, groups, subs_needed.values.tolist()
def run_palm(cope_file, design_file, contrast_file, group_file, mask_file,
cluster_threshold=3.09):
import os
from glob import glob
from nipype.interfaces.base import CommandLine
#cmd = ("palm -i {cope_file} -m {mask_file} -d {design_file} -t {contrast_file} -eb {group_file} -T "
# "-C {cluster_threshold} -Cstat extent -fdr -noniiclass -twotail -logp -zstat")
#cl = CommandLine(cmd.format(cope_file=cope_file, mask_file=mask_file, design_file=design_file,
# contrast_file=contrast_file,
# group_file=group_file, cluster_threshold=cluster_threshold))
# XXX: ideally we should make it more fancy, but since we're only doing
# 1-sample t-tests we need to omit the design, contrast, and group files
# as for PALM's FAQs
cmd = ("palm -i {cope_file} -m {mask_file} -T "
"-C {cluster_threshold} -Cstat extent -fdr -noniiclass -twotail -logp -zstat")
cl = CommandLine(cmd.format(cope_file=cope_file, mask_file=mask_file,
cluster_threshold=cluster_threshold))
results = cl.run(terminal_output='file')
return [os.path.join(os.getcwd(), val) for val in sorted(glob('palm*'))]
def group_multregress_openfmri(dataset_dir, model_id=None, task_id=None, l1output_dir=None, out_dir=None,
no_reversal=False, plugin=None, plugin_args=None, flamemodel='flame1',
nonparametric=False, use_spm=False):
meta_workflow = Workflow(name='mult_regress')
meta_workflow.base_dir = work_dir
for task in task_id:
cope_ids = l1_contrasts_num(model_id, task, dataset_dir)
regressors_needed, contrasts, groups, subj_list = get_sub_vars(dataset_dir, task, model_id)
for idx, contrast in enumerate(contrasts):
wk = Workflow(name='model_%03d_task_%03d_contrast_%s' % (model_id, task, contrast[0][0]))
info = Node(util.IdentityInterface(fields=['model_id', 'task_id', 'dataset_dir', 'subj_list']),
name='infosource')
info.inputs.model_id = model_id
info.inputs.task_id = task
info.inputs.dataset_dir = dataset_dir
dg = Node(DataGrabber(infields=['model_id', 'task_id', 'cope_id'],
outfields=['copes', 'varcopes']), name='grabber')
dg.inputs.template = os.path.join(l1output_dir,
'model%03d/task%03d/%s/%scopes/%smni/%scope%02d.nii%s')
if use_spm:
dg.inputs.template_args['copes'] = [['model_id', 'task_id', subj_list, '', 'spm/',
'', 'cope_id', '']]
dg.inputs.template_args['varcopes'] = [['model_id', 'task_id', subj_list, 'var', 'spm/',
'var', 'cope_id', '.gz']]
else:
dg.inputs.template_args['copes'] = [['model_id', 'task_id', subj_list, '', '', '',
'cope_id', '.gz']]
dg.inputs.template_args['varcopes'] = [['model_id', 'task_id', subj_list, 'var', '',
'var', 'cope_id', '.gz']]
dg.iterables=('cope_id', cope_ids)
dg.inputs.sort_filelist = False
wk.connect(info, 'model_id', dg, 'model_id')
wk.connect(info, 'task_id', dg, 'task_id')
model = Node(MultipleRegressDesign(), name='l2model')
model.inputs.groups = groups
model.inputs.contrasts = contrasts[idx]
model.inputs.regressors = regressors_needed[idx]
mergecopes = Node(Merge(dimension='t'), name='merge_copes')
wk.connect(dg, 'copes', mergecopes, 'in_files')
mergevarcopes = Node(Merge(dimension='t'), name='merge_varcopes')
wk.connect(dg, 'varcopes', mergevarcopes, 'in_files')
mask_file = fsl.Info.standard_image('MNI152_T1_2mm_brain_mask.nii.gz')
flame = Node(FLAMEO(), name='flameo')
flame.inputs.mask_file = mask_file
flame.inputs.run_mode = flamemodel
#flame.inputs.infer_outliers = True
wk.connect(model, 'design_mat', flame, 'design_file')
wk.connect(model, 'design_con', flame, 't_con_file')
wk.connect(mergecopes, 'merged_file', flame, 'cope_file')
wk.connect(mergevarcopes, 'merged_file', flame, 'var_cope_file')
wk.connect(model, 'design_grp', flame, 'cov_split_file')
if nonparametric:
palm = Node(Function(input_names=['cope_file', 'design_file', 'contrast_file',
'group_file', 'mask_file', 'cluster_threshold'],
output_names=['palm_outputs'],
function=run_palm),
name='palm')
palm.inputs.cluster_threshold = 3.09
palm.inputs.mask_file = mask_file
palm.plugin_args = {'sbatch_args': '-p om_all_nodes -N1 -c2 --mem=10G', 'overwrite': True}
wk.connect(model, 'design_mat', palm, 'design_file')
wk.connect(model, 'design_con', palm, 'contrast_file')
wk.connect(mergecopes, 'merged_file', palm, 'cope_file')
wk.connect(model, 'design_grp', palm, 'group_file')
smoothest = Node(SmoothEstimate(), name='smooth_estimate')
wk.connect(flame, 'zstats', smoothest, 'zstat_file')
smoothest.inputs.mask_file = mask_file
cluster = Node(Cluster(), name='cluster')
wk.connect(smoothest,'dlh', cluster, 'dlh')
wk.connect(smoothest, 'volume', cluster, 'volume')
cluster.inputs.connectivity = 26
cluster.inputs.threshold = 2.3
cluster.inputs.pthreshold = 0.05
cluster.inputs.out_threshold_file = True
cluster.inputs.out_index_file = True
cluster.inputs.out_localmax_txt_file = True
wk.connect(flame, 'zstats', cluster, 'in_file')
ztopval = Node(ImageMaths(op_string='-ztop', suffix='_pval'),
name='z2pval')
wk.connect(flame, 'zstats', ztopval,'in_file')
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = os.path.join(out_dir, 'task%03d' % task, contrast[0][0])
sinker.inputs.substitutions = [('_cope_id', 'contrast'),
('_maths_', '_reversed_')]
wk.connect(flame, 'zstats', sinker, 'stats')
wk.connect(cluster, 'threshold_file', sinker, 'stats.@thr')
wk.connect(cluster, 'index_file', sinker, 'stats.@index')
wk.connect(cluster, 'localmax_txt_file', sinker, 'stats.@localmax')
if nonparametric:
wk.connect(palm, 'palm_outputs', sinker, 'stats.palm')
if not no_reversal:
zstats_reverse = Node( BinaryMaths() , name='zstats_reverse')
zstats_reverse.inputs.operation = 'mul'
zstats_reverse.inputs.operand_value = -1
wk.connect(flame, 'zstats', zstats_reverse, 'in_file')
cluster2=cluster.clone(name='cluster2')
wk.connect(smoothest, 'dlh', cluster2, 'dlh')
wk.connect(smoothest, 'volume', cluster2, 'volume')
wk.connect(zstats_reverse, 'out_file', cluster2, 'in_file')
ztopval2 = ztopval.clone(name='ztopval2')
wk.connect(zstats_reverse, 'out_file', ztopval2, 'in_file')
wk.connect(zstats_reverse, 'out_file', sinker, 'stats.@neg')
wk.connect(cluster2, 'threshold_file', sinker, 'stats.@neg_thr')
wk.connect(cluster2, 'index_file',sinker, 'stats.@neg_index')
wk.connect(cluster2, 'localmax_txt_file', sinker, 'stats.@neg_localmax')
meta_workflow.add_nodes([wk])
return meta_workflow
if __name__ == '__main__':
import argparse
defstr = ' (default %(default)s)'
parser = argparse.ArgumentParser(prog='group_multregress_openfmri.py',
description=__doc__)
parser.add_argument('-m', '--model', default=1, type=int,
help="Model index" + defstr)
parser.add_argument('-t', '--task', default=[1], nargs='+',
type=int, help="Task index" + defstr)
parser.add_argument("-o", "--output_dir", dest="outdir",
help="Output directory base")
parser.add_argument('-d', '--datasetdir', required=True)
parser.add_argument("-l1", "--l1_output_dir", dest="l1out_dir",
help="l1_output directory ")
parser.add_argument("-w", "--work_dir", dest="work_dir",
help="Output directory base")
parser.add_argument("-p", "--plugin", dest="plugin",
default='Linear',
help="Plugin to use" + defstr)
parser.add_argument("--plugin_args", dest="plugin_args",
help="Plugin arguments")
parser.add_argument("--norev",action='store_true',
help="do not generate reverse contrasts")
parser.add_argument("--use_spm",action='store_true', default=False,
help="use spm estimation results from 1st level")
parser.add_argument("--nonparametric", action='store_true', default=False,
help="Run non-parametric estimation using palm" + defstr)
parser.add_argument('-f','--flame', dest='flamemodel', default='flame1',
choices=('ols', 'flame1', 'flame12'),
help='tool to use for dicom conversion' + defstr)
parser.add_argument("--sleep", dest="sleep", default=60., type=float,
help="Time to sleep between polls" + defstr)
parser.add_argument("--write-graph", default="",
help="Do not run, just write the graph to specified file")
args = parser.parse_args()
outdir = args.outdir
work_dir = os.getcwd()
if args.work_dir:
work_dir = os.path.abspath(args.work_dir)
if args.outdir:
outdir = os.path.abspath(outdir)
if args.l1out_dir:
l1_outdir=os.path.abspath(args.l1out_dir)
else:
l1_outdir=os.path.join(args.datasetdir, 'l1output')
outdir = os.path.join(outdir, 'model%03d' % args.model)
wf = group_multregress_openfmri(model_id=args.model,
task_id=args.task,
l1output_dir=l1_outdir,
out_dir=outdir,
dataset_dir=os.path.abspath(args.datasetdir),
no_reversal=args.norev,
flamemodel=args.flamemodel,
nonparametric=args.nonparametric,
use_spm=args.use_spm)
wf.config['execution']['poll_sleep_duration'] = args.sleep
if args.write_graph:
wf.write_graph(args.write_graph, graph2use='orig')
elif args.plugin_args:
wf.run(args.plugin, plugin_args=eval(args.plugin_args))
else:
wf.run(args.plugin)