Python fetch_ibc_subjects_contrasts 예제들

예제 #1

파일: pairwise_alignment.py 프로젝트: bthirion/fmralign

"""

# Retrieving the data
# -------------------
# In this example we use the IBC dataset, which include a large number of
# different contrasts maps for 12 subjects. We download the images for
# subjects 1 and 2 (or retrieve them if they were already downloaded)
# Files is the list of paths for each subjects.
# df is a dataframe with metadata about each of them.
# mask is an appropriate nifti image to select the data.
#

from fmralign.fetch_example_data import fetch_ibc_subjects_contrasts

files, df, mask = fetch_ibc_subjects_contrasts(['sub-01', 'sub-02'])

###############################################################################
# Defining a masker
# -----------------
# Using the mask provided, define a nilearn masker that will be used
# to handle relevant data. For more information, visit :
# http://nilearn.github.io/manipulating_images/masker_objects.html
#

from nilearn.input_data import NiftiMasker

masker = NiftiMasker(mask_img=mask)
mask
masker.fit()

예제 #2

파일: plot_template_alignment.py 프로젝트: thomasbazeille/fmralign

"""

###############################################################################
# Retrieve the data
# -----------------
# In this example we use the IBC dataset, which includes a large number of
# different contrasts maps for 12 subjects.
# We download the images for subjects sub-01, sub-02, sub-04, sub-05, sub-06
# and sub-07 (or retrieve them if they were already downloaded).
# imgs is the list of paths to available statistical images for each subjects.
# df is a dataframe with metadata about each of them.
# mask is a binary image used to extract grey matter regions.
#

from fmralign.fetch_example_data import fetch_ibc_subjects_contrasts
imgs, df, mask_img = fetch_ibc_subjects_contrasts(
    ['sub-01', 'sub-02', 'sub-04', 'sub-05', 'sub-06', 'sub-07'])

###############################################################################
# Definine a masker
# -----------------
# We define a nilearn masker that will be used to handle relevant data.
#   For more information, visit :
#   'http://nilearn.github.io/manipulating_images/masker_objects.html'
#

from nilearn.input_data import NiftiMasker
masker = NiftiMasker(mask_img=mask_img).fit()

###############################################################################
# Prepare the data
# ----------------