def get_localizations (X, y, cv, maskf, presels, sv):
mask, hdr, aff = au.get_nii_data(maskf)
maskidx = np.array(np.where(mask > 0))
hdr.set_data_dtype(np.dtype(np.float))
my_presels = np.zeros_like(presels[0])
my_svs = np.zeros_like(mask)
my_svs_done = False
#unmasking method found in:
#http://nisl.github.io/auto_examples/plot_ica_resting_state.html
from nisl.io import NiftiMasker
k = 0
cv.n = X.shape[0]
for train,test in cv:
X_train, y_train = X[train,:], y[train]
preselsvol = np.zeros_like (mask, dtype=np.float)
preselsvol[tuple(maskidx)] = presels[k] > 0
preselsnii = au.save_nibabel ('', preselsvol, aff, hdr)
my_presels += presels[k] > 0
if len(sv) > 0:
try:
nifti_masker = NiftiMasker(mask=preselsnii)
nifti_masker.fit (X_train[:,presels[k]>0], y_train)
niimg = nifti_masker.inverse_transform(sv[k][0])
#X_masked = nifti_masker.fit_transform (X_train[:,presels[k]>0], y_train) #,y_train, target_affine=aff, target_shape=hdr.get_data_shape()
#niimg = nifti_masker.inverse_transform(sv[0][0])
#act = np.ma.masked_array(niimg.get_data(), niimg.get_data() == 0)
my_svs += niimg.get_data()
my_svs_done = True
except:
pass
k += 1
my_presels /= cv.n_folds
my_svs /= cv.n_folds
prelocs = np.zeros_like (mask, dtype=np.float)
prelocs[tuple(maskidx)] = my_presels
return prelocs, my_svs, my_svs_done
def get_nii_data_from_folder(folder, use_verbs=False):
# inside the folder, search only for betasXX.nii files
beta_map_regex = r'betas(\d{2}).nii$'
beta_map_filter = re_filter(beta_map_regex)
beta_files = filter(beta_map_filter,
glob.glob(
os.path.join(folder, "*")))
beta_files = sorted(beta_files)
# get mask file. It must be in same folder
mask_file = os.path.join(folder, "mask.nii")
#new_folder = '/volatile/thirion/mygit/worddecoding/brain_reading/fmri/vl100318/fmri/' ## ugly
#mask_file = os.path.join(new_folder, "HO_epi.nii")
masker = NiftiMasker(mask_file, smooth=3.)
masker.fit(beta_files[0])
masked_nii_data = [masker.transform(beta_file)
for beta_file in beta_files]
# this returns a 3D array with dimensions
# (sessions, trials, voxels)
masked_nii_data = np.array(masked_nii_data)
# return only the useful values: The last 6 are
# drift regressors and are thrown away
masked_nii_data = masked_nii_data[:, :-6, :]
if use_verbs:
return masked_nii_data
else:
# In case we do not want the verbs (default case)
# we need to remove the lines corresponding to verbs
masked_nii_data = masked_nii_data.reshape(-1,
masked_nii_data.shape[-1])
masked_nii_data = masked_nii_data[:, masked_nii_data.std(0) > 0]
_, verbs, _, _, _ = parse_stimuli()
return masked_nii_data[verbs == False]
def get_nii_data_from_folder(folder, use_verbs=False):
# inside the folder, search only for betasXX.nii files
beta_map_regex = r'betas(\d{2}).nii$'
beta_map_filter = re_filter(beta_map_regex)
beta_files = filter(beta_map_filter, glob.glob(os.path.join(folder, "*")))
beta_files = sorted(beta_files)
# get mask file. It must be in same folder
mask_file = os.path.join(folder, "mask.nii")
#new_folder = '/volatile/thirion/mygit/worddecoding/brain_reading/fmri/vl100318/fmri/' ## ugly
#mask_file = os.path.join(new_folder, "HO_epi.nii")
masker = NiftiMasker(mask_file, smooth=3.)
masker.fit(beta_files[0])
masked_nii_data = [masker.transform(beta_file) for beta_file in beta_files]
# this returns a 3D array with dimensions
# (sessions, trials, voxels)
masked_nii_data = np.array(masked_nii_data)
# return only the useful values: The last 6 are
# drift regressors and are thrown away
masked_nii_data = masked_nii_data[:, :-6, :]
if use_verbs:
return masked_nii_data
else:
# In case we do not want the verbs (default case)
# we need to remove the lines corresponding to verbs
masked_nii_data = masked_nii_data.reshape(-1,
masked_nii_data.shape[-1])
masked_nii_data = masked_nii_data[:, masked_nii_data.std(0) > 0]
_, verbs, _, _, _ = parse_stimuli()
return masked_nii_data[verbs == False]
==================================
Here is a simple example of automatic mask computation using the nifti masker.
The mask is computed and visualized.
"""
### Load nyu_rest dataset #####################################################
from nisl import datasets
from nisl.io import NiftiMasker
dataset = datasets.fetch_nyu_rest(n_subjects=1)
### Compute the mask ##########################################################
nifti_masker = NiftiMasker(memory="nisl_cache", memory_level=2)
nifti_masker.fit(dataset.func[0])
mask = nifti_masker.mask_img_.get_data()
### Visualize the mask ########################################################
import pylab as pl
import numpy as np
import nibabel
pl.figure()
pl.axis('off')
pl.imshow(np.rot90(nibabel.load(dataset.func[0]).get_data()[..., 20, 0]),
interpolation='nearest', cmap=pl.cm.gray)
ma = np.ma.masked_equal(mask, False)
pl.imshow(np.rot90(ma[..., 20]), interpolation='nearest', cmap=pl.cm.autumn,
alpha=0.5)
pl.title("Mask")
### Restrict to faces and houses ##############################################
condition_mask = np.logical_or(conditions == 'face', conditions == 'house')
X = fmri_data[..., condition_mask]
y = y[condition_mask]
session = session[condition_mask]
conditions = conditions[condition_mask]
### Loading step ##############################################################
from nisl.io import NiftiMasker
from nibabel import Nifti1Image
nifti_masker = NiftiMasker(mask=mask, sessions=session,
memory='nisl_cache', memory_level=1)
niimg = Nifti1Image(X, affine)
X_masked = nifti_masker.fit(niimg).transform(niimg)
X_preprocessed = nifti_masker.inverse_transform(X_masked).get_data()
X_preprocessed = np.rollaxis(X_preprocessed, axis=-1)
mask = nifti_masker.mask_img_.get_data().astype(np.bool)
### Prepare the masks #########################################################
# Here we will use several masks :
# * mask is the originalmask
# * process_mask is a subset of mask, it contains voxels that should be
# processed (we only keep the slice z = 26 and the back of the brain to speed
# up computation)
process_mask = mask.copy()
process_mask[..., 38:] = False
process_mask[..., :36] = False
process_mask[:, 30:] = False
condition_mask = np.logical_or(conditions == 'face', conditions == 'house')
X = fmri_data[..., condition_mask]
y = y[condition_mask]
session = session[condition_mask]
conditions = conditions[condition_mask]
### Loading step ##############################################################
from nisl.io import NiftiMasker
from nibabel import Nifti1Image
# Detrending is disabled as we are not yet able to do it by session
nifti_masker = NiftiMasker(mask=mask,
detrend=True,
copy=False,
sessions=session)
niimg = Nifti1Image(X, affine)
X_masked = nifti_masker.fit(niimg).transform(niimg)
X_detrended = nifti_masker.inverse_transform(X_masked).get_data()
### Prepare the masks #########################################################
# Here we will use several masks :
# * mask is the originalmask
# * process_mask is a subset of mask, it contains voxels that should be
# processed (we only keep the slice z = 26 and the back of the brain to speed
# up computation)
process_mask = mask.copy()
process_mask[..., 38:] = False
process_mask[..., :36] = False
process_mask[:, 30:] = False
### Searchlight ###############################################################