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)
import matplotlib.pyplot as plt
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)
import matplotlib.pyplot as plt
