.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/run_compute_parcel_mean.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_run_compute_parcel_mean.py: Compute Parcel Aggregation. =========================== This example uses the ``ParcelAggregation`` marker to compute the mean of each parcel using the Tian subcortical parcellation (scale I, 16 ROIs) for a 3D NIfTI and the Schaefer parcellation (100 rois, 7 Yeo networks) for a 4D NIfTI. Authors: Federico Raimondo, Synchon Mandal License: BSD 3 clause .. GENERATED FROM PYTHON SOURCE LINES 13-25 .. code-block:: Python from junifer.testing.datagrabbers import ( ADHDTestingDataGrabber, UCLACNPVBMTestingDataGrabber, ) from junifer.datagrabber import DataType from junifer.datareader import DefaultDataReader from junifer.markers import ParcelAggregation from junifer.stats import AggFunc from junifer.utils import configure_logging .. GENERATED FROM PYTHON SOURCE LINES 26-27 Set the logging level to info to see extra information .. GENERATED FROM PYTHON SOURCE LINES 27-29 .. code-block:: Python configure_logging(level="INFO") .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-10-19 21:14:34 [info ] ===== Lib Versions ===== [junifer] pkg=utils 2026-10-19 21:14:34 [info ] click: 8.5.0 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] numpy: 2.4.6 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] scipy: 1.17.1 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] datalad: 1.7.1 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] pandas: 3.0.6 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] nibabel: 5.4.2 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] nilearn: 0.14.1 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] sqlalchemy: 2.1.5 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] ruamel.yaml: 0.19.1 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] h5py: 3.16.0 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] tqdm: 4.70.1 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] templateflow: 25.1.2 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] lapy: 1.7.0 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] junifer_data: 1.6.1 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] junifer: 0.0.8 [junifer] pkg=utils 2026-10-19 21:14:34 [info ] ======================== [junifer] pkg=utils .. GENERATED FROM PYTHON SOURCE LINES 30-31 Perform parcel aggregation on VBM GM data (3D) from the UCLA CNP dataset .. GENERATED FROM PYTHON SOURCE LINES 31-47 .. code-block:: Python with UCLACNPVBMTestingDataGrabber() as dg: # Get the first element element = dg.get_elements()[0] # Read the element element_data = DefaultDataReader().fit_transform(dg[element]) # Initialize marker marker = ParcelAggregation( parcellation="TianxS1x3TxMNInonlinear2009cAsym", method=AggFunc.Mean, ) # Compute feature feature = marker.fit_transform(element_data) # Print the output print(feature.keys()) print(feature["VBM_GM"]["aggregation"]["data"].shape) # Shape is (1 x parcels) .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-10-19 21:14:34 [info ] Parameters: {'types': ['VBM_GM'], 'datadir': '/tmp/tmpmc101b16', 'resolution': 2.0} [junifer] component=UCLACNPVBMTestingDataGrabber pkg=datagrabber step=datagrabber 2026-10-19 21:14:34 [info ] Getting element ('sub-10206',) [junifer] pkg=datagrabber step=datagrabber 2026-10-19 21:14:34 [info ] Reading VBM_GM from /home/docker/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmriprep/sub-10206/anat/sub-10206_T1w_space-MNI152NLin2009cAsym_class-GM_probtissue_res-2mm.nii [junifer] pkg=datareader step=datareader 2026-10-19 21:14:34 [info ] VBM_GM is of type NIFTI [junifer] pkg=datareader step=datareader 2026-10-19 21:14:34 [info ] Parameters: {'on': ['T1w', 'T2w', 'BOLD', 'VBM_GM', 'VBM_WM', 'VBM_CSF', 'fALFF', 'GCOR', 'LCOR'], 'name': 'ParcelAggregation', 'parcellation': ['TianxS1x3TxMNInonlinear2009cAsym'], 'method': 'mean', 'method_params': None, 'time_method': None, 'time_method_params': None, 'masks': None} [junifer] component=ParcelAggregation pkg=markers step=marker 2026-10-19 21:14:34 [info ] Computing DataType.VBM_GM [junifer] pkg=markers step=marker 2026-10-19 21:14:34 [info ] Parcellation parameters: [junifer] pkg=data 2026-10-19 21:14:34 [info ] resolution: 2.0 [junifer] pkg=data 2026-10-19 21:14:34 [info ] scale: 1 [junifer] pkg=data 2026-10-19 21:14:34 [info ] space: MNI152NLin2009cAsym [junifer] pkg=data 2026-10-19 21:14:34 [info ] magneticfield: 3T [junifer] pkg=data 2026-10-19 21:14:34 [info ] Loading parcellation: /home/docker/junifer_data/v8/parcellations/Melbourne/v1.4/3T/Subcortex-Only/Tian_Subcortex_S1_3T_2009cAsym.nii.gz [junifer] pkg=data 2026-10-19 21:14:35 [info ] No storage specified, returning dictionary [junifer] pkg=markers step=marker dict_keys([]) (1, 16) .. GENERATED FROM PYTHON SOURCE LINES 48-49 Perform parcel aggregation on BOLD data (4D) from ADHD dataset .. GENERATED FROM PYTHON SOURCE LINES 49-65 .. code-block:: Python with ADHDTestingDataGrabber() as dg: # Get the first element element = dg.get_elements()[0] # Read the element element_data = DefaultDataReader().fit_transform(dg[element]) # Initialize marker marker = ParcelAggregation( parcellation="Schaefer100x7", method=AggFunc.Mean, on=[DataType.BOLD], ) # Compute feature feature = marker.fit_transform(element_data) # Print the output print(feature.keys()) print(feature["BOLD"]["aggregation"]["data"].shape) # Shape is (timepoints x parcels) .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-10-19 21:14:35 [info ] Parameters: {'types': ['BOLD'], 'datadir': '/tmp/tmp5u84on0q', 'resolution': 3.0, 'n_timepoints': 50} [junifer] component=ADHDTestingDataGrabber pkg=datagrabber step=datagrabber [fetch_adhd] Dataset directory found: /home/docker/nilearn_data/adhd 2026-10-19 21:14:35 [info ] Getting element ('sub-01',) [junifer] pkg=datagrabber step=datagrabber 2026-10-19 21:14:35 [info ] Reading BOLD from /home/docker/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni_res-3mm_tp-50.nii [junifer] pkg=datareader step=datareader 2026-10-19 21:14:35 [info ] BOLD is of type NIFTI [junifer] pkg=datareader step=datareader 2026-10-19 21:14:35 [info ] Parameters: {'on': ['BOLD'], 'name': 'ParcelAggregation', 'parcellation': ['Schaefer100x7'], 'method': 'mean', 'method_params': None, 'time_method': None, 'time_method_params': None, 'masks': None} [junifer] component=ParcelAggregation pkg=markers step=marker 2026-10-19 21:14:35 [info ] Computing DataType.BOLD [junifer] pkg=markers step=marker 2026-10-19 21:14:35 [info ] Parcellation parameters: [junifer] pkg=data 2026-10-19 21:14:35 [info ] resolution: 3.0 [junifer] pkg=data 2026-10-19 21:14:35 [info ] n_rois: 100 [junifer] pkg=data 2026-10-19 21:14:35 [info ] yeo_networks: 7 [junifer] pkg=data 2026-10-19 21:14:35 [info ] Loading parcellation: /home/docker/junifer_data/v8/parcellations/Schaefer2018/Yeo2011/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz [junifer] pkg=data 2026-10-19 21:14:35 [info ] No storage specified, returning dictionary [junifer] pkg=markers step=marker dict_keys([]) (50, 100) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.169 seconds) .. _sphx_glr_download_auto_examples_run_compute_parcel_mean.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: run_compute_parcel_mean.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: run_compute_parcel_mean.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: run_compute_parcel_mean.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_