def get_template(space='mni152_1mm', mask=None):
if space == 'mni152_1mm':
if mask is None:
img = nib.load(datasets.fetch_icbm152_2009()['t1'])
elif mask == 'brain':
img = nib.load(datasets.fetch_icbm152_2009()['mask'])
elif mask == 'gm':
img = datasets.fetch_icbm152_brain_gm_mask(threshold=0.2)
else:
raise ValueError('Mask {0} not supported'.format(mask))
elif space == 'mni152_2mm':
if mask is None:
img = datasets.load_mni152_template()
elif mask == 'brain':
img = datasets.load_mni152_brain_mask()
elif mask == 'gm':
# this approach seems to approximate the 0.2 thresholded
# GM mask pretty well
temp_img = datasets.load_mni152_template()
data = temp_img.get_data()
data = data * -1
data[data != 0] += np.abs(np.min(data))
data = (data > 1200).astype(int)
img = nib.Nifti1Image(data, temp_img.affine)
else:
raise ValueError('Mask {0} not supported'.format(mask))
else:
raise ValueError('Space {0} not supported'.format(space))
return img
def get_template(space='mni152_1mm', mask=None):
"""
Load template file.
Parameters
----------
space : {'mni152_1mm', 'mni152_2mm', 'ale_2mm'}, optional
Template to load. Default is 'mni152_1mm'.
mask : {None, 'brain', 'gm'}, optional
Whether to return the raw template (None), a brain mask ('brain'), or
a gray-matter mask ('gm'). Default is None.
Returns
-------
img : :obj:`nibabel.nifti1.Nifti1Image`
Template image object.
"""
if space == 'mni152_1mm':
if mask is None:
img = nib.load(datasets.fetch_icbm152_2009()['t1'])
elif mask == 'brain':
img = nib.load(datasets.fetch_icbm152_2009()['mask'])
elif mask == 'gm':
img = datasets.fetch_icbm152_brain_gm_mask(threshold=0.2)
else:
raise ValueError('Mask {0} not supported'.format(mask))
elif space == 'mni152_2mm':
if mask is None:
img = datasets.load_mni152_template()
elif mask == 'brain':
img = datasets.load_mni152_brain_mask()
elif mask == 'gm':
# this approach seems to approximate the 0.2 thresholded
# GM mask pretty well
temp_img = datasets.load_mni152_template()
data = temp_img.get_data()
data = data * -1
data[data != 0] += np.abs(np.min(data))
data = (data > 1200).astype(int)
img = nib.Nifti1Image(data, temp_img.affine)
else:
raise ValueError('Mask {0} not supported'.format(mask))
elif space == 'ale_2mm':
if mask is None:
img = datasets.load_mni152_template()
else:
# Not the same as the nilearn brain mask, but should correspond to
# the default "more conservative" MNI152 mask in GingerALE.
img = nib.load(
op.join(get_resource_path(),
'templates/MNI152_2x2x2_brainmask.nii.gz'))
else:
raise ValueError('Space {0} not supported'.format(space))
return img
def test_fetch_icbm152_2009():
dataset = datasets.fetch_icbm152_2009(data_dir=tmpdir, verbose=0)
assert_true(isinstance(dataset.csf, _basestring))
assert_true(isinstance(dataset.eye_mask, _basestring))
assert_true(isinstance(dataset.face_mask, _basestring))
assert_true(isinstance(dataset.gm, _basestring))
assert_true(isinstance(dataset.mask, _basestring))
assert_true(isinstance(dataset.pd, _basestring))
assert_true(isinstance(dataset.t1, _basestring))
assert_true(isinstance(dataset.t2, _basestring))
assert_true(isinstance(dataset.t2_relax, _basestring))
assert_true(isinstance(dataset.wm, _basestring))
assert_equal(len(url_request.urls), 1)
def test_fetch_icbm152_2009():
dataset = datasets.fetch_icbm152_2009(data_dir=tmpdir, verbose=0)
assert_true(isinstance(dataset.csf, _basestring))
assert_true(isinstance(dataset.eye_mask, _basestring))
assert_true(isinstance(dataset.face_mask, _basestring))
assert_true(isinstance(dataset.gm, _basestring))
assert_true(isinstance(dataset.mask, _basestring))
assert_true(isinstance(dataset.pd, _basestring))
assert_true(isinstance(dataset.t1, _basestring))
assert_true(isinstance(dataset.t2, _basestring))
assert_true(isinstance(dataset.t2_relax, _basestring))
assert_true(isinstance(dataset.wm, _basestring))
assert_equal(len(url_request.urls), 1)
plotting.plot_stat_map(
z_map, threshold=threshold, colorbar=True,
title='sex effect on grey matter density (FDR = .05)')
###########################################################################
# Note that there does not seem to be any significant effect of sex on
# grey matter density on that dataset.
###########################################################################
# Generating a report
# -------------------
# It can be useful to quickly generate a
# portable, ready-to-view report with most of the pertinent information.
# This is easy to do if you have a fitted model and the list of contrasts,
# which we do here.
from nilearn.reporting import make_glm_report
icbm152_2009 = datasets.fetch_icbm152_2009()
report = make_glm_report(model=second_level_model,
contrasts=['age', 'sex'],
bg_img=icbm152_2009['t1'],
)
#########################################################################
# We have several ways to access the report:
# report # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
from scipy import stats
import matplotlib
matplotlib.use('Agg')
# pylint: disable= wrong-import-position, too-many-lines
import matplotlib.pyplot as plt # noqa
import matplotlib.cm as cm # noqa
os.environ['FSLOUTPUTTYPE'] = 'NIFTI_GZ'
LOGGER = logging.getLogger("NeuroReg")
TEMP_FOLDER_PATH = ""
DATA_FOLDER = ""
DB_PATH = ""
TEMPLATE_VOLUME = datasets.fetch_icbm152_2009(data_dir="./").get("t1")
TEMPLATE_MASK = datasets.fetch_icbm152_2009(data_dir="./").get("mask")
TEMPLATE_MASKED_VOLUME = ""
def setup(temp_path, data="glioma"):
"""setup for current computer """
# pylint: disable= global-statement
LOGGER.setLevel(logging.INFO)
file_handler = logging.handlers.RotatingFileHandler('log.txt',
maxBytes=5 * 10000000,
backupCount=5)
file_handler.set_name('log.txt')
file_handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
"""
# Load 4D probabilistic atlases
from nilearn import datasets
# Harvard Oxford Atlas
harvard_oxford = datasets.fetch_atlas_harvard_oxford('cort-prob-2mm')
harvard_oxford_sub = datasets.fetch_atlas_harvard_oxford('sub-prob-2mm')
# Multi Subject Dictionary Learning Atlas
msdl = datasets.fetch_atlas_msdl()
# Smith ICA Atlas and Brain Maps 2009
smith = datasets.fetch_atlas_smith_2009()
# ICBM tissue probability
icbm = datasets.fetch_icbm152_2009()
# Visualization
import matplotlib.pyplot as plt
from nilearn import plotting
atlas_types = {'Harvard_Oxford': harvard_oxford.maps,
'Harvard_Oxford sub': harvard_oxford_sub.maps,
'MSDL': msdl.maps, 'Smith 2009 10 RSNs': smith.rsn10,
'Smith2009 20 RSNs': smith.rsn20,
'Smith2009 70 RSNs': smith.rsn70,
'Smith2009 10 Brainmap': smith.bm10,
'Smith2009 20 Brainmap': smith.bm20,
'Smith2009 70 Brainmap': smith.bm70,
'ICBM tissues': (icbm['wm'], icbm['gm'], icbm['csf'])}
import nibabel as nib
import numpy as np
import pandas as pd
from nilearn import datasets, image, plotting
from neurolang import ExplicitVBROverlay, NeurolangPDL
###############################################################################
# Data preparation
# ----------------
###############################################################################
# Load the MNI atlas and resample it to 4mm voxels
mni_t1 = nib.load(datasets.fetch_icbm152_2009()["t1"])
mni_t1_4mm = image.resample_img(mni_t1, np.eye(3) * 4)
###############################################################################
# Load the NeuroSynth database
ns_database_fn, ns_features_fn = datasets.utils._fetch_files(
"neurolang",
[
(
"database.txt",
"https://github.com/neurosynth/neurosynth-data/raw/master/current_data.tar.gz",
{"uncompress": True},
),
(
"features.txt",
else:
os.nice(17)
path = "MM_TEMP_" + "{:%m_%d_%Y}_BE2".format(
datetime.datetime.now()) + "/"
util.setup(path, "MolekylareMarkorer")
#
# util.prepare_template(datasets.fetch_icbm152_2009(data_dir="./").get("t2"), util.TEMPLATE_MASK, True)
# moving_datasets_ids = find_images_from_pid([164])
# print(moving_datasets_ids, len(moving_datasets_ids))
# data_transforms = image_registration.get_transforms(moving_datasets_ids,
# image_registration.AFFINE,
# save_to_db=True)
util.prepare_template(
datasets.fetch_icbm152_2009(data_dir="./").get("t2"),
util.TEMPLATE_MASK, True)
moving_datasets_ids = find_images_from_pid([125, 2101])
print(moving_datasets_ids, len(moving_datasets_ids))
data_transforms = image_registration.get_transforms(
moving_datasets_ids, image_registration.AFFINE, save_to_db=True)
# moving_datasets_ids = find_images_from_pid([172])
# print(moving_datasets_ids, len(moving_datasets_ids))
# data_transforms = image_registration.get_transforms(moving_datasets_ids,
# image_registration.RIGID,
# save_to_db=True)
# print(moving_datasets_ids_affine, len(moving_datasets_ids_affine))
# data_transforms = image_registration.get_transforms(moving_datasets_ids_affine,
# image_registration.RIGID,
import numpy as np
import pandas as pd
logger = logging.getLogger('neurolang.probabilistic')
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler(sys.stderr))
warnings.filterwarnings("ignore")
###############################################################################
# Data preparation
# ----------------
###############################################################################
# Load the MNI template and resample it to 4mm voxels
mni_t1 = nib.load(datasets.fetch_icbm152_2009()['t1'])
mni_t1_4mm = image.resample_img(mni_t1, np.eye(3) * 4)
###############################################################################
# Load Destrieux's atlas
destrieux_dataset = datasets.fetch_atlas_destrieux_2009()
destrieux = nib.load(destrieux_dataset['maps'])
destrieux_resampled = image.resample_img(destrieux,
mni_t1_4mm.affine,
interpolation='nearest')
destrieux_resampled_data = np.asanyarray(destrieux_resampled.dataobj,
dtype=np.int32)
destrieux_voxels_ijk = destrieux_resampled_data.nonzero()
destrieux_voxels_value = destrieux_resampled_data[destrieux_voxels_ijk]
destrieux_table = pd.DataFrame(np.transpose(destrieux_voxels_ijk),
columns=['i', 'j', 'k'])
import nipype.interfaces.ants as ants
import nibabel as nib
import numpy as np
import matplotlib
matplotlib.use('Agg')
# pylint: disable= wrong-import-position
import matplotlib.pyplot as plt # noqa
import matplotlib.cm as cm # noqa
os.environ['FSLOUTPUTTYPE'] = 'NIFTI'
TEMP_FOLDER_PATH = ""
DATA_FOLDER = ""
DB_PATH = ""
TEMPLATE_VOLUME = datasets.fetch_icbm152_2009(data_dir="./").get("t1")
TEMPLATE_MASK = datasets.fetch_icbm152_2009(data_dir="./").get("mask")
TEMPLATE_MASKED_VOLUME = ""
def setup(temp_path):
"""setup for current computer """
# pylint: disable= global-statement
global TEMP_FOLDER_PATH
TEMP_FOLDER_PATH = temp_path
mkdir_p(TEMP_FOLDER_PATH)
setup_paths()
prepare_template(TEMPLATE_VOLUME, TEMPLATE_MASK)
def setup_paths():
######################################################################
# Grid search 'C' for prediction
# ---------------------------------
X_sc = StandardScaler()
estimator = SVC(kernel='linear')
param_grid = {'C': np.logspace(-3., 3., 10)}
# Grid search 'C' in linear SVC
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,
def fit(self, imgs, confounds=None):
"""
Compute the mask and the dynamic parcels across datasets.
Parameters
----------
imgs: list of Niimg-like objects
See http://nilearn.github.io/manipulating_images/input_output.html
Data on which the mask is calculated. If this is a list,
the affine is considered the same for all.
confounds: list of CSV file paths or 2D matrices
This parameter is passed to nilearn.signal.clean. Please see the
related documentation for details. Should match with the list
of imgs given.
Returns
-------
self: object
Returns the instance itself. Contains attributes listed
at the object level.
"""
self.masker_ = check_embedded_nifti_masker(self)
imgs, confounds = self._sanitize_imgs(imgs, confounds)
# Avoid warning with imgs != None
# if masker_ has been provided a mask_img
if self.masker_.mask_img is None:
self.masker_.fit(imgs)
else:
self.masker_.fit()
self.mask_img_ = self.masker_.mask_img_
# Load grey_matter segmentation
if self.grey_matter == "MNI":
mni = datasets.fetch_icbm152_2009()
self.grey_matter = mni.gm
if self.grey_matter is not None:
masker_anat = NiftiMasker(
mask_img=self.mask_img_, smoothing_fwhm=self.smoothing_fwhm
)
masker_anat.fit(self.grey_matter)
grey_matter = masker_anat.transform(self.grey_matter)
self.weights_grey_matter_ = (
1 - grey_matter
) + self.std_grey_matter * grey_matter
else:
self.weights_grey_matter_ = None
# Control random number generation
self.random_state = check_random_state(self.random_state)
# Check that number of batches is reasonable
if self.n_batch > len(imgs):
warnings.warn(
"{0} batches were requested, but only {1} datasets available. Using one dataset per batch instead.".format(
self.n_batch, len(imgs)
)
)
self.n_batch = len(imgs)
# mask_and_reduce step
if self.n_batch > 1:
stab_maps, dwell_time = self._mask_and_reduce_batch(imgs, confounds)
else:
stab_maps, dwell_time = self._mask_and_reduce(imgs, confounds)
# Return components
self.components_ = stab_maps
self.dwell_time_ = dwell_time
# Create embedding
self.embedding = Embedding(stab_maps.todense())
return self
import seaborn as sns
import os
from pathlib import Path
from scipy import io as sio
from pygsp import graphs
import shutil
# In[2]:
from nilearn.input_data import NiftiLabelsMasker
from nilearn.datasets import fetch_icbm152_2009
mnitemp = fetch_icbm152_2009()
glassermasker = NiftiLabelsMasker(labels_img='Glasser_masker.nii.gz',
mask_img=mnitemp['mask'])
glassermasker.fit()
# In[3]:
import numpy as np
coords = np.load('coords_with_order.npz')['coordinates']
ind_reord = np.load('coords_with_order.npz')['order']
# In[4]:
from nilearn.datasets import fetch_abide_pcp
