y_random = dataset.label[12:]
y_shape = (10, 10)
### Preprocess data ###########################################################
from utils import masking, signal, cm
sys.stderr.write("Preprocessing data...")
t0 = time.time()
# Load and mask fMRI data
X_train = []
for x_random in X_random:
# Mask data
x_img = nibabel.load(x_random)
x = masking.apply_mask(x_img, dataset.mask)
x = signal.clean(x, standardize=True, detrend=True)
X_train.append(x[offset:])
# Load target data and reshape it in 2D
y_train = []
for y in y_random:
y_train.append(np.reshape(np.loadtxt(y, dtype=np.int, delimiter=','),
(-1,) + y_shape, order='F')[:-offset].astype(float))
X_train = np.vstack(X_train)
y_train = np.vstack(y_train)
# Flatten the stimuli
y_train = np.reshape(y_train, (-1, y_shape[0] * y_shape[1]))
X_random = dataset.func[12:]
y_random = dataset.label[12:]
y_shape = (10, 10)
### Preprocess data ###########################################################
from utils import masking, signal, cm
sys.stderr.write("Preprocessing data...")
t0 = time.time()
# Load and mask fMRI data
X_train = []
for x_random in X_random:
# Mask data
x_img = nibabel.load(x_random)
x = masking.apply_mask(x_img, dataset.mask)
x = signal.clean(x, standardize=True, detrend=True)
X_train.append(x[offset:])
# Load target data and reshape it in 2D
y_train = []
for y in y_random:
y_train.append(
np.reshape(np.loadtxt(y, dtype=np.int, delimiter=','),
(-1, ) + y_shape,
order='F')[:-offset].astype(float))
X_train = np.vstack(X_train)
y_train = np.vstack(y_train)
# Flatten the stimuli
def plot_ica_map(map_3d, vmax):
# Mask the background
map_3d = np.ma.masked_array(map_3d,
np.logical_not(mask_img.get_data().astype(bool)))
# Normalize the map
section = map_3d[:, :, z]
pl.figure(figsize=(3.8, 4.5))
pl.axes([0, 0, 1, 1])
pl.imshow(np.rot90(section), interpolation='nearest',
vmax=vmax, vmin=-vmax, cmap=cmap)
pl.axis('off')
# Mask data
X_ = []
for x in dataset.func:
X_.append(masking.apply_mask(x, mask_img, smoothing_fwhm=6.))
X = X_
# Clean signals
X_ = []
for x in X:
X_.append(signal.clean(x, standardize=True, detrend=False))
X = X_
### CanICA ####################################################################
if not exists(join(path, 'canica.nii.gz')):
try:
from nilearn.decomposition.canica import CanICA
t0 = time.time()
canica = CanICA(n_components=n_components, mask=mask_img,
brain states <http://hal.inria.fr/inria-00589201>`_, Michel et al,
Pattern Recognition 2011.
"""
### Load nyu_rest dataset #####################################################
from os.path import join
import numpy as np
import pylab as pl
from utils import datasets, masking, signal
import nibabel
adhd_mask = join('utils', 'adhd_mask.nii.gz')
dataset = datasets.fetch_adhd(n_subjects=1)
X = masking.apply_mask(dataset.func[0], adhd_mask)
X = signal.clean(X, standardize=True, detrend=False)
X_smoothed = masking.apply_mask(dataset.func[0], adhd_mask, smoothing_fwhm=6.)
X_smoothed = signal.clean(X_smoothed, standardize=True, detrend=False)
mask = nibabel.load(adhd_mask).get_data().astype(np.bool)
z = 42
def plot_labels(labels, seed):
labels = labels.astype(int)
n_colors = np.max(labels)
cut = labels[:, :, z]
np.random.seed(seed)
colors = np.random.random(size=(n_colors + 1, 3))
Pattern Recognition 2011.
"""
### Load nyu_rest dataset #####################################################
from os.path import join
import numpy as np
import pylab as pl
from utils import datasets, masking, signal
import nibabel
adhd_mask = join('utils', 'adhd_mask.nii.gz')
dataset = datasets.fetch_adhd(n_subjects=1)
X = masking.apply_mask(dataset.func[0], adhd_mask)
X = signal.clean(X, standardize=True, detrend=False)
X_smoothed = masking.apply_mask(dataset.func[0], adhd_mask,
smoothing_fwhm=6.)
X_smoothed = signal.clean(X_smoothed, standardize=True, detrend=False)
mask = nibabel.load(adhd_mask).get_data().astype(np.bool)
z = 42
def plot_labels(labels, seed):
labels = labels.astype(int)
n_colors = np.max(labels)
cut = labels[:, :, z]
np.random.seed(seed)
colors = np.random.random(size=(n_colors + 1, 3))
# Normalize the map
section = map_3d[:, :, z]
pl.figure(figsize=(3.8, 4.5))
pl.axes([0, 0, 1, 1])
pl.imshow(np.rot90(section),
interpolation='nearest',
vmax=vmax,
vmin=-vmax,
cmap=cmap)
pl.axis('off')
# Mask data
X_ = []
for x in dataset.func:
X_.append(masking.apply_mask(x, mask_img, smoothing_fwhm=6.))
X = X_
# Clean signals
X_ = []
for x in X:
X_.append(signal.clean(x, standardize=True, detrend=False))
X = X_
### CanICA ####################################################################
if not exists(join(path, 'canica.nii.gz')):
try:
from nilearn.decomposition.canica import CanICA
t0 = time.time()
canica = CanICA(n_components=n_components,
mean_img = fmri_data.mean(axis=-1) ### 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] ### Masking step ############################################################## from utils import masking, signal from nibabel import Nifti1Image # Mask data X_img = Nifti1Image(X, affine) X = masking.apply_mask(X_img, mask, smoothing_fwhm=4) X = signal.clean(X, standardize=True, detrend=False) ############################################################################### # # # F-score # # # ############################################################################### from sklearn.feature_selection import f_classif f_values, p_values = f_classif(X, y) p_values = -np.log10(p_values) p_values[np.isnan(p_values)] = 0 p_values[p_values > 10] = 10 p_unmasked = masking.unmask(p_values, mask)
