def make_html(stat_map=None,
mesh=None,
threshold=None,
cmap=plotting.cm.cold_hot):
if stat_map is None:
stat_map = datasets.fetch_localizer_button_task()['tmaps'][0]
info, colors = full_brain_info(stat_map=stat_map,
mesh=mesh,
threshold=threshold,
cmap=cmap)
as_json = json.dumps(info)
as_html = HTML_TEMPLATE.replace('INSERT_STAT_MAP_JSON_HERE', as_json)
as_html = as_html.replace('INSERT_COLORSCALE_HERE', colors)
return as_html
def anat_preproc(filename):
from nilearn.datasets import fetch_localizer_button_task
from nilearn.datasets import load_mni152_template
from nilearn import plotting
from nilearn.image import resample_to_img
from nilearn.image import load_img
template = load_mni152_template()
localizer_dataset = fetch_localizer_button_task(get_anats=True)
localizer_tmap_filename = localizer_dataset.tmaps[0]
localizer_anat_filename = localizer_dataset.anats[0]
resampled_localizer_tmap = resample_to_img(filename, template)
tmap_img = load_img(filename)
original_shape = tmap_img.shape
original_affine = tmap_img.affine
resampled_shape = resampled_localizer_tmap.shape
resampled_affine = resampled_localizer_tmap.affine
template_img = load_img(template)
template_shape = template_img.shape
template_affine = template_img.affine
print("""Shape comparison:
-Original t-map image shape: {0}
-Resampled t-map image shape: {1}
-Template image shape: {2}
""".format(original_shape, resampled_shape, template_shape))
print("""Affine comparison:
-Original t-map image affine:\n{0}
-Resampled t-map image:\n{0}
-Template image affine:\n{2}
""".format(original_affine, resampled_affine, template_affine))
plotting.plot_stat_map(localizer_tmap_filename,
bg_img=localizer_anat_filename,
cut_coords=(36, -27, 66),
threshold=3,
title="t-map on original anat")
plotting.plot_stat_map(resampled_localizer_tmap,
bg_img=template,
cut_coords=(36, -27, 66),
threshold=3,
title="Resampled t-map on MNI template anat")
plotting.show()
""" Glass brain plotting in nilearn (all options) ============================================= This example goes through different options of the :func:`nilearn.plotting.plot_glass_brain` function (including plotting negative values). See :ref:`plotting` for more plotting functionalities. """ ############################################################################### # Retrieve the data from nilearn import datasets localizer_dataset = datasets.fetch_localizer_button_task() localizer_tmap_filename = localizer_dataset.tmaps[0] ############################################################################### # Demo glass brain plotting. from nilearn import plotting # Whole brain sagittal cuts plotting.plot_glass_brain(localizer_tmap_filename, threshold=3) ############################################################################### # With a colorbar plotting.plot_glass_brain(localizer_tmap_filename, threshold=3, colorbar=True)
See :ref:`plotting` for more details. """ ############################################################################### # First, we retrieve data from nilearn provided (general-purpose) datasets from nilearn import datasets # haxby dataset to have anatomical image, EPI images and masks haxby_dataset = datasets.fetch_haxby() haxby_anat_filename = haxby_dataset.anat[0] haxby_mask_filename = haxby_dataset.mask_vt[0] haxby_func_filename = haxby_dataset.func[0] # localizer dataset to have contrast maps localizer_dataset = datasets.fetch_localizer_button_task(get_anats=True) localizer_anat_filename = localizer_dataset.anats[0] localizer_tmap_filename = localizer_dataset.tmaps[0] ######################################## # Now, we show from here how to visualize the retrieved datasets using plotting # tools from nilearn. from nilearn import plotting ######################################## # Visualizing contrast map in three different orthogonal views - 'sagittal', # 'coronal' and 'axial' with coordinate positions in each view are given # of interest manually also with colorbar on the right side of the plots. # The first argument is a path to the filename of a constrast map,
"""
######################################################################
import warnings
warnings.simplefilter('ignore')
######################################################################
# Statistical maps
# ================
######################################################################
# Download and plot an individual-level statistical map and plot it on the
# subject's T1 image:
from nilearn import datasets, plotting
localizer = datasets.fetch_localizer_button_task()
plotting.view_img(localizer['tmap'], bg_img=localizer['anat'], threshold='97%')
######################################################################
# Download and plot a group-level statistical map:
img = datasets.fetch_neurovault_motor_task()['images'][0]
plotting.view_img(img, threshold='95%')
######################################################################
# More about dataset downloaders: https://nilearn.github.io/modules/reference.html#module-nilearn.datasets
#
# More about plotting: https://nilearn.github.io/plotting/index.html
def load_fsaverage():
return {
'pial_left': '/home/jerome/workspace/scratch/fsaverage/pial_left.gii',
'infl_left':
'/home/jerome/workspace/scratch/fsaverage/inflated_left.gii',
'pial_right':
'/home/jerome/workspace/scratch/fsaverage/pial_right.gii',
'infl_right':
'/home/jerome/workspace/scratch/fsaverage/inflated_right.gii'
}
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--out_file',
type=str,
default='surface_plot_standalone.html')
args = parser.parse_args()
# fsaverage = datasets.fetch_surf_fsaverage5()
fsaverage = load_fsaverage()
stat_map = datasets.fetch_localizer_button_task()['tmaps'][0]
as_json = to_three(fsaverage['pial_right'], stat_map,
fsaverage['pial_right'])
as_html = HTML_TEMPLATE
for k, v in as_json.items():
as_html = as_html.replace(k, v)
with open(args.out_file, 'w') as f:
f.write(as_html)
###############################################################################
# Retrieve the data
# ------------------
#
# Nilearn comes with set of functions that download public data from Internet
#
# Let us first see where the data will be downloded and stored on our disk:
#
from nilearn import datasets
print('Datasets shipped with nilearn are stored in: %r' %
datasets.get_data_dirs())
###############################################################################
# Let us now retrieve a motor task contrast maps corresponding to second subject
# from a localizer experiment
tmap_filenames = datasets.fetch_localizer_button_task()['tmaps']
print(tmap_filenames)
###############################################################################
# tmap_filenames is returned as a list. We need to take first one
tmap_filename = tmap_filenames[0]
###############################################################################
# Demo glass brain plotting
# --------------------------
from nilearn import plotting
# Whole brain sagittal cuts and map is thresholded at 3
plotting.plot_glass_brain(tmap_filename, threshold=3)
###############################################################################
Here we discover how to work with 3D and 4D niimgs.
"""
###############################################################################
# Downloading tutorial datasets from Internet
# --------------------------------------------
#
# Nilearn comes with functions that download public data from Internet
#
# Let's first check where the data is downloaded on our disk:
from nilearn import datasets
print('Datasets are stored in: %r' % datasets.get_data_dirs())
###############################################################################
# Let's now retrieve a motor contrast from a localizer experiment
tmap_filenames = datasets.fetch_localizer_button_task()['tmaps']
print(tmap_filenames)
###############################################################################
# tmap_filenames is a list of filenames. We need to take the first one
tmap_filename = tmap_filenames[0]
###############################################################################
# Visualizing a 3D file
# ----------------------
#
# The file contains a 3D volume, we can easily visualize it as a
# statistical map:
from nilearn import plotting
plotting.plot_stat_map(tmap_filename)
*dim* modifies the contrast of this image: dim=0 leaves the image
unchanged, negative values of *dim* enhance it, and positive values
decrease it (dim the background).
This *dim* argument may also be useful for the plot_roi function used to
display ROIs on top of a background image.
"""
#########################################################################
# Retrieve the data: the localizer dataset with contrast maps
# ------------------------------------------------------------
from nilearn import datasets
localizer_dataset = datasets.fetch_localizer_button_task(legacy_format=False)
# Contrast map of motor task
localizer_tmap_filename = localizer_dataset.tmap
# Subject specific anatomical image
localizer_anat_filename = localizer_dataset.anat
###########################################################################
# Plotting with enhancement of background image with dim=-.5
# --------------------------------------------------------------------------
from nilearn import plotting
plotting.plot_stat_map(localizer_tmap_filename,
bg_img=localizer_anat_filename,
cut_coords=(36, -27, 66),
threshold=3, title="dim=-.5",
dim=-.5)
See :ref:`plotting` for more details. """ ############################################################################### # First, we retrieve data from nilearn provided (general-purpose) datasets from nilearn import datasets # haxby dataset to have anatomical image, EPI images and masks haxby_dataset = datasets.fetch_haxby(n_subjects=1) haxby_anat_filename = haxby_dataset.anat[0] haxby_mask_filename = haxby_dataset.mask_vt[0] haxby_func_filename = haxby_dataset.func[0] # localizer dataset to have contrast maps localizer_dataset = datasets.fetch_localizer_button_task(get_anats=True) localizer_anat_filename = localizer_dataset.anats[0] localizer_tmap_filename = localizer_dataset.tmaps[0] ######################################## # Now, we show from here how to visualize the retrieved datasets using plotting # tools from nilearn. from nilearn import plotting ######################################## # Visualizing contrast map in three different orthogonal views - 'sagittal', # 'coronal' and 'axial' with coordinate positions in each view are given # of interest manually also with colorbar on the right side of the plots. # The first argument is a path to the filename of a constrast map,
# :func:`nilearn.plotting.view_img_on_surf` that give more interactive
# visualizations in a web browser. See :ref:`interactive-surface-plotting` for
# more details.
view = plotting.view_surf(fsaverage.infl_right,
texture,
threshold='90%',
bg_map=fsaverage.sulc_right)
# uncomment this to open the plot in a web browser:
view.open_in_browser()
##############################################################################
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
#view
##############################################################################
# We don't need to do the projection ourselves, we can use view_img_on_surf:
view = plotting.view_img_on_surf(stat_img,
threshold='90%',
surf_mesh='fsaverage')
view.open_in_browser()
#view
from nilearn import plotting, datasets
img = datasets.fetch_localizer_button_task()['tmap']
view = plotting.view_img_on_surf(img, threshold='90%', surf_mesh='fsaverage')
view.open_in_browser()
