def get_behav(r, s, txt=False):
"""Load behavioral data file for subject `s` on run `r`
Remove bad activations based on `respcat`
Parameters
----------
s : int
Subject number
r : int
Run number
Returns
-------
behav : pd.DataFrame
Examples
--------
>>> get_behav(1, 1)[:5]
onset duration gain loss PTval respnum respcat RT gain_ind
0 4 3 18 12 6.12 2 1 1.793 1
1 8 3 10 15 -4.85 3 0 1.637 0
2 18 3 34 16 18.16 1 1 1.316 1
3 24 3 18 5 13.05 1 1 1.670 1
4 28 3 26 13 13.13 2 1 1.232 1
"""
s, r = n_convert(s, r)
f_behav = '../data/ds005/sub'+s+\
'/behav/task001_run'+r+\
'/behavdata.txt'
behav = pd.read_csv(f_behav, sep='\t')
behav.insert(1, 'duration', 3)
behav = behav[behav.respcat>=0]
behav['gain_ind'] = (behav.gain >= behav.loss) * 1
behav.reset_index(drop=True, inplace=True)
if txt == True:
return f_behav
else:
return behav
def get_cond(r, s, c, txt=False):
"""Load condition data file `c` for subject `s` on run `r`
Parameters
----------
r : int
Run number
s : int
Subject number
c : int
Condition file
Returns
-------
cond : pd.DataFrame
Examples
--------
>>> get_cond(1, 1, 1)[:5]
onset duration amplitude
0 0 3 1
1 4 3 1
2 8 3 1
3 18 3 1
4 24 3 1
"""
s, r, c = n_convert(s, r, c)
f_cond = '../data/ds005/sub'+s+\
'/model/model001/onsets/task001_run'+r+\
'/cond'+c+'.txt'
cond = pd.DataFrame(np.loadtxt(f_cond),
columns=['onset', 'duration', 'amplitude'])
if txt == True:
return f_cond
else:
return cond
def get_image(r, s):
"""Load .nii file for subject `s` on run `r`
Parameters
----------
r : int
Run number
s : int
Subject number
Returns
-------
Nifti1Image object
Example
-------
>>> get_image(1, 1).shape
(64, 64, 34, 240)
"""
s, r = n_convert(s, r)
f_img = '../data/ds005/sub'+s+\
'/BOLD/task001_run'+r+\
'/bold.nii.gz'
return nib.load(f_img)