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 __init__(self):
try: Exp.__init__(self)
except AttributeError: pass
durations = get_durations()
tmp_trials = get_trials()
self.TR = 1.5
self.ISI = 1.5
self.trials = roi.timing.dtime(tmp_trials, durations, None, 0)
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 sim():
""" Test by simulation. """
# --
# Get that Ss data and trial information.
trials = get_trials()
durations = get_durations()
sdata = None
sim = Simtest().run('1')
roiglm = roi.base.Roi(1.5, '', trials, durations, sdata)
# Create data for the model
roiglm.bold = sim['model_01']['bold']
roiglm.create_hrf('mean_fir', {'window_size':30})
# roiglm.create_hrf('double_gamma')
roiglm.create_dm()
# Get, reformat (extract), and store the results.
return roiglm.run('simulate')
def main(num, roi_names):
""" The main worker for run4.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()
durations = get_durations()
sdata = get_behave_data(num)
sdata.update(get_similarity_data(num))
sdata.update(recode_rl_data(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
roiglm = Rewardrecode(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
