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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Script that perform the first-level analysis of a dataset of the FIAC
Last updated by B.Thirion
Author : Lise Favre, Bertrand Thirion, 2008-2009
"""
import os
from numpy import arange
from nipy.externals.configobj import ConfigObj
from nipy.neurospin.utils.mask import compute_mask_files
from nipy.neurospin.glm_files_layout import glm_tools, contrast_tools
# -----------------------------------------------------------
# --------- Set the paths -----------------------------------
#-----------------------------------------------------------
DBPath = "/volatile/thirion/db/word_reading"
Subjects = ["sbt0801651598-0002-00001-000160-01"]#,
Acquisitions = ["default_acquisition"]
Sessions = ["session_01","session_02","session_03","session_04","session_05"]
model_id = "stimed"
fmri_wc = "asession_*_*.nii"
# ---------------------------------------------------------
# -------- General Information ----------------------------
# ---------------------------------------------------------
tr = 2.4
nb_frames = 154
frametimes = arange(nb_frames) * tr
Conditions = [ 'trial_%04d' %i for i in range(230) ]
# ---------------------------------------------------------
# ------ First level analysis parameters ---------------------
# ---------------------------------------------------------
#---------- Masking parameters
infTh = 0.75
supTh = 0.95
#---------- Design Matrix
hrf_model = "Canonical"
drift_model = "Cosine"
hfcut = 128
#-------------- GLM options
# Possible choices : "Kalman_AR1", "Kalman", "Ordinary Least Squares"
fit_algo = "Ordinary Least Squares"#"Kalman_AR1"
#-------------- Contrast Options
# Possible choices : "Contrast Name" or "Contrast Number"
save_mode = "Contrast Name"
# ------------------------------------------------------------------
# Launching Pipeline on all subjects, all acquisitions, all sessions
# -------------------------------------------------------------------
# fixme : normally all that part should be data-independent,
# i.e. a standard user should not have to look at it
# which means that the paths are completely set at that point
# Treat sequentially all subjects & acquisitions
for s in Subjects:
print "Subject : %s" % s
for a in Acquisitions:
# step 1. set all the paths
basePath = os.sep.join((DBPath, s, "fMRI", a))
paths = glm_tools.generate_all_brainvisa_paths( basePath, Sessions,
fmri_wc, model_id)
misc = ConfigObj(paths['misc'])
misc["sessions"] = Sessions
misc["tasks"] = Conditions
misc["mask_url"] = paths['mask']
misc.write()
# step 2. Create one design matrix for each session
design_matrices = {}
for sess in Sessions:
design_matrices[sess] = glm_tools.design_matrix(
paths['misc'], paths['dmtx'][sess], sess, paths['paradigm'],
frametimes, hrf_model=hrf_model, drift_model=drift_model,
hfcut=hfcut, model=model_id)
# step 3. Compute the Mask
# fixme : it should be possible to provide a pre-computed mask
print "Computing the Mask"
mask_array = compute_mask_files( paths['fmri'].values()[0][0],
paths['mask'], True, infTh, supTh)
# step 4. Creating functional contrasts
print "Creating Contrasts"
clist = contrast_tools.ContrastList(misc=ConfigObj(paths['misc']),
model=model_id)
contrast = clist.save_dic(paths['contrast_file'])
CompletePaths = glm_tools.generate_brainvisa_ouput_paths(
paths["contrasts"], contrast)
# step 5. Fit the glm for each session
glms = {}
for sess in Sessions:
print "Fitting GLM for session : %s" % sess
glms[sess] = glm_tools.glm_fit(
paths['fmri'][sess], design_matrices[sess],
paths['glm_dump'][sess], paths['glm_config'][sess],
fit_algo, paths['mask'])
#step 6. Compute Contrasts
print "Computing contrasts"
# this one breaks the memory
#contrast.pop('effect_of_interest')
contrast["contrast"].remove('effect_of_interest')
glm_tools.compute_contrasts(
contrast, misc, CompletePaths, glms, model=model_id)
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