def main(num, ana_roi_names):
""" For each entry in <ana_roi_names> extract subject <num>'s data. """
# Remember where we started then
# Get dir for num, and change to dir
start_dir = os.getcwd()
new_dir = subdir(num)
print("Changing to {0}".format(new_dir))
os.chdir(new_dir)
# Join the A/B data
print("Joining task A and B.")
func1 = roi.io.read_nifti("wartaskA.nii")
func2 = roi.io.read_nifti("wartaskB.nii")
func = roi.pre.join_time(func1, func2)
# Loop over ana_roi_names
for ana in ana_roi_names:
print(ana)
# Get the roi mask
# and apply it to func
print("Masking.")
anaroi = roi.atlas.get_roi("HarvardOxford", ana)
masked_func = roi.pre.mask(func, anaroi)
# Store the result
print("Writing.")
roi.io.write_nifti(masked_func, 'wartaskAB_' + ana + '.gz')
os.chdir(start_dir)
def data(num):
""" Run a single subjects data. """
sdir = subdir(num)
roi_names = [
"wartaskAB_Right Accumbens_str2.nii.gz",
"wartaskAB_Right Accumbens.nii.gz",
"wartaskAB_Right Caudate.nii.gz"]
# --
# Get that Ss data and trial information.
sdata = get_behave_data(num)
sdata.update(get_similarity_data(num))
sdata.update(get_rl_data(num))
trials = get_trials_combined()
durations = get_durations()
# --
# Go!
roi_results = []
for name in roi_names:
print(name)
spath = os.path.join(sdir, name)
# Init this roi's models
roiglm = fmri.catreward.roi.base.Catreward(
1.5, spath, trials, durations, sdata)
# Get, reformat (extract), and store the results.
roi_results.append(roiglm.run(name))
return roi_results
def meandata(num):
""" Run a single subjects data, drawing the BOLD data
from the /bold dir text files. """
sdir = subdir(num)
roi_names = [
"wartaskAB_Right Accumbens_str2_bold.txt",
"wartaskAB_Right Accumbens_bold.txt",
"wartaskAB_Right Caudate_bold.txt"]
# --
# Get that Ss data and trial information.
sdata = get_behave_data(num)
sdata.update(get_similarity_data(num))
sdata.update(get_rl_data(num))
trials = get_trials()
durations = get_durations()
# --
# Go!
roi_results = []
for name in roi_names:
print(name)
spath = os.path.join(sdir, 'bold', name)
# Init this roi's models
roiglm = fmri.catreward.roi.exps.base.CatMean(
1.5, spath, trials, durations, sdata)
# Get, reformat (extract), and store the results.
roi_results.append(roiglm.run(name))
return roi_results
def run(num, roi_class_name, trials_name):
""" For subject <num>, run all models for all ROIs.
Note: Must be run from the parent folder of all the fMRI datasets."""
sdir = subdir(num)
roi_class_path = roi_class_name.split('.')
txt_classes = ['CatMean', 'Nobox', 'CatMeanFir', 'Subtime']
if roi_class_name == 'Catreward':
roi_names = get_roi_names(kind='nii')
elif roi_class_name in txt_classes:
roi_names = get_roi_names(kind='txt')
sdir = os.path.join(sdir, 'bold')
## Swich to the bold dir too
else:
raise ValueError("Could not find suitable ROI names.")
# --
# Get that Ss data and trial information.
sdata = get_behave_data(num)
sdata.update(get_similarity_data(num))
sdata.update(get_rl_data(num))
durations = get_durations()
trials = None
if trials_name == 'get_trials':
trials = get_trials()
elif trials_name == 'get_trials_combined':
trials = get_trials_combined()
else:
raise ValueError("trials_name was unkown.")
# --
# Go!
results = []
for name in roi_names:
print(name)
spath = os.path.join(sdir, name)
# Init this roi's models
Roiclass = getattr(bclasses, roi_class_name)
roiglm = Roiclass(1.5, spath, trials, durations, sdata)
# Get, reformat (extract), and store the results.
# And del it to keep memory reasonable
results.append(roiglm.run(name))
return results
def main(num, roi_names, roi_class_name):
""" The main worker for run3.py.
<num> is a valid subject number code (101-118)
<roi_names> is the names of the rois you wish to analyze
<roi_class_name> is the name of the fmri.catreward.roi.exps.base
class you want to use for the experiment.
This script saves each Ss data to seperate files, returning nothing. """
# --
# Get that Ss data and trial information.
trials = get_trials_combined()
durations = get_durations()
sdata = get_behave_data(num)
sdata.update(get_similarity_data(num))
sdata.update(get_rl_data(num))
sdata.update(get_cumrewards(num))
## Add all data to sdata, a dict
# --
# The fMRI data is located at:
sdir = subdir(num)
# --
results = []
for name in roi_names:
print(name)
spath = os.path.join(sdir, 'bold', name)
# Init this roi's models
Roiclass = getattr(bclasses, roi_class_name)
roiglm = Roiclass(1.5, spath, trials, durations, sdata)
# Get, reformat (extract), and store the results.
# And del it to keep memory reasonable
results.append(roiglm.run(name))
return results
