def get_data(name):
"""Return the named Wheelerdata object"""
if name == 'fh':
data = FH()
elif name == 'butterfly':
data = Butterfly()
elif name == 'clock':
data = Clock()
elif name == 'polygon':
data = Polygon()
elif name == 'redgreen':
data = Redgreen()
elif name == "biasbox":
data = Biasbox()
else:
raise ValueError('Data not understood. Try fh, butterfly, clock,'
' polygon, or redgreen')
return data
"After"
"EVA wrong number of features")
assert eva_names.shape[0] == Xeva.shape[1], ("eva_names and Xeva"
"don't match")
return Xeva, eva_names
if __name__ == '__main__':
from wheelerdata.load.fh import FH
from fmrilearn.preprocess.labels import csv_to_targets
from fmrilearn.load import load_meta
from fmrilearn.load import load_nii
from fmrilearn.preprocess.labels import filter_targets
data = FH()
metas = data.get_metapaths_containing('rt')
targets = csv_to_targets(metas[0])
paths = data.get_roi_data_paths('Insula')
X = load_nii(paths[0], clean=True, sparse=False, smooth=False)
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X.astype(np.float))
X = X[targets['TR'], :]
X = X.mean(1)[:, np.newaxis]
y = targets['rt']
tc = targets['trialcount']
Xfir, flfir = fir(X, y, tc, 20, 1.5)
#Xeva, fleva = eva(X, y, tc, 11, 1.5)
assert Xeva.shape[1] == len(unique_fn) * X.shape[1], ("After"
"EVA wrong number of features")
assert eva_names.shape[0] == Xeva.shape[1], ("eva_names and Xeva"
"don't match")
return Xeva, eva_names
if __name__ == '__main__':
from wheelerdata.load.fh import FH
from fmrilearn.preprocess.labels import csv_to_targets
from fmrilearn.load import load_meta
from fmrilearn.load import load_nii
from fmrilearn.preprocess.labels import filter_targets
data = FH()
metas = data.get_metapaths_containing('rt')
targets = csv_to_targets(metas[0])
paths = data.get_roi_data_paths('Insula')
X = load_nii(paths[0], clean=True, sparse=False, smooth=False)
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X.astype(np.float))
X = X[targets['TR'],:]
X = X.mean(1)[:,np.newaxis]
y = targets['rt']
tc = targets['trialcount']
Xfir, flfir = fir(X, y, tc, 20, 1.5)
#Xeva, fleva = eva(X, y, tc, 11, 1.5)
def create(maskname, newname, expname, overwrite=False):
"""Save all subjects nifti1 datasets to ./roinii for the given mask/ROI name.
Parameters
---------
maskname : str
A valid ROI name (see my `roi` package at `https://github.com/andsoandso/roi`
newname : str
A new name for the roi
expname : str
A valid `wheelerdata.load.*` instance name
"""
# Experimental config
if expname == "fh":
data = FH()
elif expname == "butterfly":
data = Butterfly()
elif expname == "clock":
data = Clock()
elif expname == "polygon":
data = Polygon()
elif expname == "redgreen":
data = Redgreen()
elif expname == "biasbox":
data = Biasbox()
else:
raise ValueError("expname ({0}) not valid".format(expname))
basepath = data.datapath
paths = data.get_subject_paths()
scodes = data.scodes
datas = [
os.path.join(path, "war{0}.nii".format(expname)) for path in paths
]
# then create the roi data for each S.
for s, data in zip(scodes, datas):
saveas = os.path.join(basepath, 'roinii',
"{0}_{1}.nii.gz".format(newname, s))
if os.path.exists(saveas) and not overwrite:
print("{0} exists, moving on.".format(saveas))
continue
mask = roi.atlas.get_roi('HarvardOxford', maskname)
nii = roi.io.read_nifti(data)
maskednii = roi.pre.mask(nii, mask, standard=True)
# (Potentially expensive) sanity checks
assert sum([nz.size for nz in mask.get_data().nonzero()
]) != 0, ("Mask"
" {0} was empty".format(maskname))
assert sum([nz.size for nz in nii.get_data().nonzero()
]) != 0, ("Nifiti1"
" {0} was empty".format(maskname))
# It's possiblethat the ROI covers an area outside the
# functional acquisition window. Proceed but warn.
if sum([nz.size for nz in maskednii.get_data().nonzero()]) == 0:
print(
"{0} maskednii was empty. Nothing to write.".format(maskname))
continue
print("Writing {0}".format(saveas))
roi.io.write_nifti(maskednii, saveas)
# Log success
f = open("{0}_roinii.log".format(expname), "a")
f.write("{0}:{1}\n".format(maskname, newname))
f.close()
def create(maskname, newname, expname, overwrite=False):
"""Save all subjects nifti1 datasets to ./roinii for the given mask/ROI name.
Parameters
---------
maskname : str
A valid ROI name (see my `roi` package at `https://github.com/andsoandso/roi`
newname : str
A new name for the roi
expname : str
A valid `wheelerdata.load.*` instance name
"""
# Experimental config
if expname == "fh":
data = FH()
elif expname == "butterfly":
data = Butterfly()
elif expname == "clock":
data = Clock()
elif expname == "polygon":
data = Polygon()
elif expname == "redgreen":
data = Redgreen()
elif expname == "biasbox":
data = Biasbox()
else:
raise ValueError("expname ({0}) not valid".format(expname))
basepath = data.datapath
paths = data.get_subject_paths()
scodes = data.scodes
datas = [os.path.join(path,"war{0}.nii".format(expname)) for path in paths]
# then create the roi data for each S.
for s, data in zip(scodes, datas):
saveas = os.path.join(
basepath, 'roinii', "{0}_{1}.nii.gz".format(newname, s))
if os.path.exists(saveas) and not overwrite:
print("{0} exists, moving on.".format(saveas))
continue
mask = roi.atlas.get_roi('HarvardOxford', maskname)
nii = roi.io.read_nifti(data)
maskednii = roi.pre.mask(nii, mask, standard=True)
# (Potentially expensive) sanity checks
assert sum([nz.size for nz in mask.get_data().nonzero()]) != 0, ("Mask"
" {0} was empty".format(maskname))
assert sum([nz.size for nz in nii.get_data().nonzero()]) != 0, ("Nifiti1"
" {0} was empty".format(maskname))
# It's possiblethat the ROI covers an area outside the
# functional acquisition window. Proceed but warn.
if sum([nz.size for nz in maskednii.get_data().nonzero()]) == 0:
print("{0} maskednii was empty. Nothing to write.".format(maskname))
continue
print("Writing {0}".format(saveas))
roi.io.write_nifti(maskednii, saveas)
# Log success
f = open("{0}_roinii.log".format(expname), "a")
f.write("{0}:{1}\n".format(maskname, newname))
f.close()