def _uniform_masking(fmri_list, tr, high_pass=0.01, smoothing=5):
""" Mask all the sessions uniformly, doing standardization, linear
detrending, DCT high_pas filtering and gaussian smoothing.
Parameters
----------
fmri_list: array-like
array containing multiple BOLD data from different sessions
high_pass: float
frequency at which to apply the high pass filter, defaults to 0.01
smoothing: float
spatial scale of the gaussian smoothing filter in mm, defaults to 5
Returns
-------
fmri_list_masked: array-like
array containing the masked data
"""
masker = MultiNiftiMasker(mask_strategy='epi',
standardize=True,
detrend=True,
high_pass=0.01,
t_r=tr,
smoothing_fwhm=smoothing)
fmri_list_masked = masker.fit_transform(fmri_list)
return fmri_list_masked
def split_fmri_data(subj, n_splits=10):
subj_preprocessed_path = spenc_dir+'synthesis/clean_data/sub{0:03d}/'.format(subj)
run_fn = [subj_preprocessed_path+'run{0:03d}.nii.gz'.format(i) for i in xrange(8)]
masker = MultiNiftiMasker()
data = np.concatenate(masker.fit_transform(run_fn), axis=0).astype('float32')
duration = np.array([902,882,876,976,924,878,1084,676])
# i did not kick out the first/last 4 samples per run yet
slice_nr_per_run = [dur/2 for dur in duration]
# use broadcasting to get indices to delete around the borders
idx_borders = np.cumsum(slice_nr_per_run[:-1])[:,np.newaxis] + \
np.arange(-4,4)[np.newaxis,:]
data = np.delete(data, idx_borders, axis=0)
# and we're going to remove the last fmri slice
# since it does not correspond to a movie part anymore
data = data[:-1, :]
# shape of TR samples
data = data[3:]
voxel_kfold = KFold(data.shape[1], n_folds=n_splits)
return [data[:, split] for _, split in voxel_kfold]
def _uniform_masking(fmri_list, tr, high_pass=0.01, smoothing=5):
""" Mask all the sessions uniformly, doing standardization, linear
detrending, DCT high_pas filtering and gaussian smoothing.
Parameters
----------
fmri_list: array-like
array containing multiple BOLD data from different sessions
high_pass: float
frequency at which to apply the high pass filter, defaults to 0.01
smoothing: float
spatial scale of the gaussian smoothing filter in mm, defaults to 5
Returns
-------
fmri_list_masked: array-like
array containing the masked data
"""
masker = MultiNiftiMasker(mask_strategy='epi', standardize=True,
detrend=True, high_pass=0.01,
t_r=tr, smoothing_fwhm=smoothing)
fmri_list_masked = masker.fit_transform(fmri_list)
return fmri_list_masked
def _check_input_data(data, mask_img=None, return_first_element=False):
if not isinstance(data, list):
data = [data]
if all(isinstance(x, nib.spatialimages.SpatialImage) for x in data):
masker = MultiNiftiMasker(mask_img)
data = masker.fit_transform(data)
elif all(isinstance(x, np.ndarray) for x in data):
pass
else:
raise ValueError('input_data must be an instance of numpy.ndarray or '
'nibabel.spatialimages.SpatialImage')
# when being used for Decode -- the actual image/array is needed
if return_first_element:
return data[0]
else:
return data
# We first create a masker, giving it the options that we care # about. Here we use standardizing of the data, as it is often important # for decoding mask_filename = os.getcwd() + "/dataset/train/Patient_01/GT.nii.gz" scan_filename = os.getcwd() + "/dataset/train/Patient_01/Patient_01.nii.gz" masker = MultiNiftiMasker(mask_img=mask_filename, standardize=True) print(masker) # We give the masker a filename and retrieve a 2D array ready # for machine learning with scikit-learn masker.fit(scan_filename) #masker.transform(scan_filename) scan_masked = masker.fit_transform(scan_filename) # calculate mean image for the background mean_func_img = mean_img(scan_filename) ''' plot_roi(masker.mask_img_, mean_func_img, display_mode='y', cut_coords=4, title="Mask") show() ''' # maxes = np.max(labelArray, axis=0) # calculate mean image for the background # mean_func_img = mean_img(filename) # plot_roi(mask_img, mean_func_img, display_mode='y', cut_coords=4, title="Mask") # https://nilearn.github.io/auto_examples/02_decoding/plot_simulated_data.html # Baysian ridge
CACHE_DIR = os.path.join('/', 'disk4t', 'mehdi',
'data', 'tmp')
dataset = datasets.fetch_adni_rs_fmri()
func_files = dataset['func']
dx_group = np.array(dataset['dx_group'])
###############################################################################
# 1- Masking
###############################################################################
masker = MultiNiftiMasker(mask_strategy='epi',
mask_args=dict(opening=1),
memory_level=2,
memory=CACHE_DIR,
n_jobs=8)
func_masked = masker.fit_transform(func_files)
###############################################################################
#2- Testing
###############################################################################
idx = {}
for g in ['AD', 'LMCI', 'EMCI', 'Normal']:
idx[g] = np.where(dx_group == g)
groups = [['AD', 'Normal'], ['AD', 'EMCI'], ['AD', 'LMCI'],
['EMCI', 'LMCI'], ['EMCI', 'Normal'], ['LMCI', 'Normal']]
for gr in groups:
test_var = np.ones((len(func_files), 1), dtype=float) # intercept
cv = GroupShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
######################################################################
# Predictive model - without reduction
# ------------------------------------
# Standard mask
icbm = fetch_icbm152_2009()
mask = icbm.mask
masker = MultiNiftiMasker(mask_img=mask,
target_affine=ref_img.affine,
target_shape=ref_img.shape,
n_jobs=5,
verbose=1)
X = masker.fit_transform(input_images)
X = np.vstack(X)
X = X_sc.fit_transform(X)
grid = GridSearchCV(estimator,
param_grid=param_grid,
cv=cv.split(X, y, groups),
verbose=1,
n_jobs=5)
# Prediction without reduction
grid.fit(X, y)
C = grid.best_params_['C']
svc = SVC(kernel='linear', C=C)
model_cv = GroupShuffleSplit(n_splits=20, test_size=0.3, random_state=0)
non_reduced_scores = cross_val_score(estimator=svc,
"""A simple example showing how to get X from 3D structural nifti images """ from os.path import join import glob import numpy as np from nilearn.input_data import MultiNiftiMasker # pre-computed mask. see script compute_mask.py for details mask = 'mask.nii.gz' # structural images data_dir = '/storage/store/data/pac2018_data/pac2018.zip.001_FILES' data_paths = sorted(glob.glob(join(data_dir, '*.nii'))) # Initialize MultiNiftiMasker for list of multiple subjects multi_masker = MultiNiftiMasker(mask_img=mask, mask_strategy='background') # Either use fit first and then transform to get X. fit method fits given mask # image to input images. X = multi_masker.fit_transform(data_paths) # outputs list of masked subjects X = np.concatenate(X) # (number of samples, number of features)
import pandas as pd
from dyneusr import DyNeuGraph
from nilearn.datasets import fetch_miyawaki2008
from nilearn.input_data import MultiNiftiMasker
from kmapper import KeplerMapper, Cover
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN
# Fetch dataset, extract time-series from ventral temporal (VT) mask
dataset = fetch_miyawaki2008()
masker = MultiNiftiMasker(mask_img=dataset.mask,
detrend=True,
standardize=False)
X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
#df = pd.read_csv(dataset.session_target[0], sep=" ")
#target, labels = pd.factorize(df.labels.values)
#y = pd.DataFrame({l:(target==i).astype(int) for i,l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2))
graph = mapper.map(lens, X, cover=Cover(20, 0.5), clusterer=DBSCAN(eps=20.))
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph)
dG.visualize('output.html')
import joblib
pca = PCA(n_components=1)
bart_masked = []
bart_masked_pca = []
bart_masked_id = []
for i in df['bart_file']:
for j in df['bart_mask']:
for k in df['subject_id']:
if (k in j) and (k in i):
bart_masker = MultiNiftiMasker(mask_img=j,
smoothing_fwhm=4,
standardize=True,
detrend=True,
memory='D:/cache',
memory_level=1)
x = bart_masker.fit_transform(index_img(i, slice(0, 100)))
bart_masked_pca.append(pca.fit_transform(x))
bart_masked_id.append(k)
#Load bart_masked_pca onto dataframe
bart_masked_pca_1 = pd.Series(bart_masked_pca, name='bart_masked_pca')
bart_masked_id_1 = pd.Series(bart_masked_id, name='subject_id')
data_masked_pca = {
'bart_masked_pca': bart_masked_pca_1,
'subject_id': bart_masked_id_1
}
df_masked_pca = pd.concat(data_masked_pca, axis=1)
df = df.merge(df_masked_pca, how='outer')
# %%
#Save bart numpy array
bart_masked_pca = np.asarray(bart_masked_pca)
