.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/run_ets_rss_marker.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_run_ets_rss_marker.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_run_ets_rss_marker.py:


Extracting root sum of squares from edge-wise timeseries.
=========================================================
This example uses a ``RSSETSMarker`` to compute root sum of squares
of the edge-wise timeseries using the Schaefer parcellation
(100 rois and 200 rois, 17 Yeo networks) for a 4D nifti BOLD file.

Authors: Leonard Sasse, Sami Hamdan, Nicolas Nieto, Synchon Mandal

License: BSD 3 clause

.. GENERATED FROM PYTHON SOURCE LINES 14-23

.. code-block:: Python


    import tempfile

    import junifer.testing.registry  # noqa: F401
    from junifer.api import collect, run
    from junifer.storage import SQLiteFeatureStorage
    from junifer.utils import configure_logging









.. GENERATED FROM PYTHON SOURCE LINES 24-25

Set the logging level to info to see extra information:

.. GENERATED FROM PYTHON SOURCE LINES 25-27

.. code-block:: Python

    configure_logging(level="INFO")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /__w/junifer/junifer/junifer/utils/logging.py:155: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead.
      vstring = str(getattr(module, "__version__", None))
    2025-05-09 12:40:01,213 - JUNIFER - INFO - ===== Lib Versions =====
    2025-05-09 12:40:01,213 - JUNIFER - INFO - click: 8.1.8
    2025-05-09 12:40:01,213 - JUNIFER - INFO - numpy: 1.26.4
    2025-05-09 12:40:01,213 - JUNIFER - INFO - scipy: 1.15.0
    2025-05-09 12:40:01,214 - JUNIFER - INFO - datalad: 1.1.5
    2025-05-09 12:40:01,214 - JUNIFER - INFO - pandas: 2.1.4
    2025-05-09 12:40:01,214 - JUNIFER - INFO - nibabel: 5.3.2
    2025-05-09 12:40:01,214 - JUNIFER - INFO - nilearn: 0.10.4
    2025-05-09 12:40:01,214 - JUNIFER - INFO - sqlalchemy: 2.0.40
    2025-05-09 12:40:01,214 - JUNIFER - INFO - ruamel.yaml: 0.18.10
    2025-05-09 12:40:01,214 - JUNIFER - INFO - tqdm: 4.66.6
    2025-05-09 12:40:01,214 - JUNIFER - INFO - templateflow: 24.2.2
    2025-05-09 12:40:01,214 - JUNIFER - INFO - junifer_data: None
    2025-05-09 12:40:01,214 - JUNIFER - INFO - junifer: 0.0.7.dev83
    2025-05-09 12:40:01,214 - JUNIFER - INFO - ========================




.. GENERATED FROM PYTHON SOURCE LINES 28-29

Define the DataGrabber interface

.. GENERATED FROM PYTHON SOURCE LINES 29-33

.. code-block:: Python

    datagrabber = {
        "kind": "SPMAuditoryTestingDataGrabber",
    }








.. GENERATED FROM PYTHON SOURCE LINES 34-35

Define the markers interface

.. GENERATED FROM PYTHON SOURCE LINES 35-48

.. code-block:: Python

    markers = [
        {
            "name": "Schaefer100x17_RSSETS",
            "kind": "RSSETSMarker",
            "parcellation": "Schaefer100x17",
        },
        {
            "name": "Schaefer200x17_RSSETS",
            "kind": "RSSETSMarker",
            "parcellation": "Schaefer200x17",
        },
    ]








.. GENERATED FROM PYTHON SOURCE LINES 49-51

Create a temporary directory for junifer feature extraction:
At the end you can read the extracted data into a ``pandas.DataFrame``.

.. GENERATED FROM PYTHON SOURCE LINES 51-75

.. code-block:: Python

    with tempfile.TemporaryDirectory() as tmpdir:
        # Define the storage interface
        storage = {
            "kind": "SQLiteFeatureStorage",
            "uri": f"{tmpdir}/test.sqlite",
        }
        # Run the defined junifer feature extraction pipeline
        run(
            workdir=tmpdir,
            datagrabber=datagrabber,
            markers=markers,
            storage=storage,
            elements=["sub001"],  # we calculate for one subject only
        )
        # Collect extracted features data
        collect(storage=storage)
        # Create storage object to read in extracted features
        db = SQLiteFeatureStorage(uri=storage["uri"])

        # List all the features
        print(db.list_features())
        # Read extracted features
        df_rssets = db.read_df(feature_name="BOLD_Schaefer200x17_RSSETS_rss_ets")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    2025-05-09 12:40:01,216 - JUNIFER - INFO - Registering RSSETSMarker in marker
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Validating Marker Collection
    2025-05-09 12:40:01,216 - JUNIFER - INFO - DataGrabber output type: ['BOLD', 'T1w']
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Validating Data Reader:
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Data Reader output type: ['BOLD', 'T1w']
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Validating Marker: Schaefer100x17_RSSETS
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Marker output type: ['timeseries']
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Validating storage for Schaefer100x17_RSSETS
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Validating Marker: Schaefer200x17_RSSETS
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Marker output type: ['timeseries']
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Validating storage for Schaefer200x17_RSSETS
    2025-05-09 12:40:01,216 - JUNIFER - INFO - Getting element sub001
    2025-05-09 12:40:03,274 - JUNIFER - INFO - Fitting pipeline
    2025-05-09 12:40:03,274 - JUNIFER - INFO - Reading BOLD from /tmp/tmpy0x_j90c/sub001_bold.nii.gz
    2025-05-09 12:40:03,274 - JUNIFER - INFO - BOLD is of type NIFTI
    2025-05-09 12:40:03,275 - JUNIFER - INFO - Reading T1w from /tmp/tmpy0x_j90c/sub001_T1w.nii.gz
    2025-05-09 12:40:03,275 - JUNIFER - INFO - T1w is of type NIFTI
    2025-05-09 12:40:03,276 - JUNIFER - INFO - Fitting marker Schaefer100x17_RSSETS
    2025-05-09 12:40:03,276 - JUNIFER - INFO - Computing BOLD
    2025-05-09 12:40:03,276 - JUNIFER - INFO - Parcellation will be warped from MNI152NLin6Asym to MNI152Lin using highest resolution
    2025-05-09 12:40:03,276 - JUNIFER - INFO - Parcellation parameters:
    2025-05-09 12:40:03,276 - JUNIFER - INFO -      resolution: None
    2025-05-09 12:40:03,276 - JUNIFER - INFO -      n_rois: 100
    2025-05-09 12:40:03,276 - JUNIFER - INFO -      yeo_networks: 17
    2025-05-09 12:40:03,276 - JUNIFER - INFO - Resolution set to None, using highest resolution.
    2025-05-09 12:40:03,351 - JUNIFER - INFO - Loading parcellation: /github/home/junifer_data/v2/parcellations/Schaefer2018/Yeo2011/Schaefer2018_100Parcels_17Networks_order_FSLMNI152_1mm.nii.gz
    2025-05-09 12:40:05,628 - JUNIFER - INFO - Downloading template MNI152Lin (T1w in resolution 1)
    2025-05-09 12:40:06,132 - JUNIFER - INFO - antsApplyTransforms command to be executed:
    antsApplyTransforms -d 3 -e 3 -n 'GenericLabel[NearestNeighbor]' -i /tmp/junifer/tmpxe1wwcwb/ants_parcellation_warper_Schaefer100x17_from_MNI152NLin6Asym_to_MNI152Lin_baba12fa-2cd2-11f0-b407-763dc8eb2af0vpp_pgo2/prewarp_parcellation.nii.gz -r /tmp/junifer/tmpxe1wwcwb/ants_parcellation_warper_Schaefer100x17_from_MNI152NLin6Asym_to_MNI152Lin_baba12fa-2cd2-11f0-b407-763dc8eb2af0vpp_pgo2/MNI152Lin_T1w.nii.gz -t /github/home/junifer_data/v2/.git/annex/objects/JQ/Pp/SHA256E-s145422752--5091350b36f951d455dabd429ebe86c493c75a0217dca311ae355f1d62e080b0.h5/SHA256E-s145422752--5091350b36f951d455dabd429ebe86c493c75a0217dca311ae355f1d62e080b0.h5 -o /tmp/junifer/tmpxe1wwcwb/ants_parcellation_warper_Schaefer100x17_from_MNI152NLin6Asym_to_MNI152Lin_baba12fa-2cd2-11f0-b407-763dc8eb2af0vpp_pgo2/parcellation_warped.nii.gz
    2025-05-09 12:40:12,388 - JUNIFER - INFO - antsApplyTransforms command succeeded with the following output:

    2025-05-09 12:40:15,278 - JUNIFER - INFO - Storing in <SQLiteFeatureStorage @ /tmp/tmphu25fj3x/test.sqlite (multiple output)>
    2025-05-09 12:40:15,299 - JUNIFER - INFO - Fitting marker Schaefer200x17_RSSETS
    2025-05-09 12:40:15,299 - JUNIFER - INFO - Computing BOLD
    2025-05-09 12:40:15,299 - JUNIFER - INFO - Parcellation will be warped from MNI152NLin6Asym to MNI152Lin using highest resolution
    2025-05-09 12:40:15,299 - JUNIFER - INFO - Parcellation parameters:
    2025-05-09 12:40:15,299 - JUNIFER - INFO -      resolution: None
    2025-05-09 12:40:15,299 - JUNIFER - INFO -      n_rois: 200
    2025-05-09 12:40:15,299 - JUNIFER - INFO -      yeo_networks: 17
    2025-05-09 12:40:15,299 - JUNIFER - INFO - Resolution set to None, using highest resolution.
    2025-05-09 12:40:15,373 - JUNIFER - INFO - Loading parcellation: /github/home/junifer_data/v2/parcellations/Schaefer2018/Yeo2011/Schaefer2018_200Parcels_17Networks_order_FSLMNI152_1mm.nii.gz
    2025-05-09 12:40:17,604 - JUNIFER - INFO - Downloading template MNI152Lin (T1w in resolution 1)
    2025-05-09 12:40:18,106 - JUNIFER - INFO - antsApplyTransforms command to be executed:
    antsApplyTransforms -d 3 -e 3 -n 'GenericLabel[NearestNeighbor]' -i /tmp/junifer/tmpxe1wwcwb/ants_parcellation_warper_Schaefer200x17_from_MNI152NLin6Asym_to_MNI152Lin_c1e45de2-2cd2-11f0-b407-763dc8eb2af0mom1oafu/prewarp_parcellation.nii.gz -r /tmp/junifer/tmpxe1wwcwb/ants_parcellation_warper_Schaefer200x17_from_MNI152NLin6Asym_to_MNI152Lin_c1e45de2-2cd2-11f0-b407-763dc8eb2af0mom1oafu/MNI152Lin_T1w.nii.gz -t /github/home/junifer_data/v2/.git/annex/objects/JQ/Pp/SHA256E-s145422752--5091350b36f951d455dabd429ebe86c493c75a0217dca311ae355f1d62e080b0.h5/SHA256E-s145422752--5091350b36f951d455dabd429ebe86c493c75a0217dca311ae355f1d62e080b0.h5 -o /tmp/junifer/tmpxe1wwcwb/ants_parcellation_warper_Schaefer200x17_from_MNI152NLin6Asym_to_MNI152Lin_c1e45de2-2cd2-11f0-b407-763dc8eb2af0mom1oafu/parcellation_warped.nii.gz
    2025-05-09 12:40:29,393 - JUNIFER - INFO - antsApplyTransforms command succeeded with the following output:

    2025-05-09 12:40:31,819 - JUNIFER - INFO - Storing in <SQLiteFeatureStorage @ /tmp/tmphu25fj3x/test.sqlite (multiple output)>
    2025-05-09 12:40:31,841 - JUNIFER - INFO - Marker collection fitting done
    2025-05-09 12:40:31,852 - JUNIFER - INFO - Collecting data using SQLiteFeatureStorage
    2025-05-09 12:40:31,852 - JUNIFER - INFO - Collecting data from /tmp/tmphu25fj3x/*test.sqlite

    file: 0it [00:00, ?it/s]

    feature:   0%|          | 0/2 [00:00<?, ?it/s]
    feature: 100%|██████████| 2/2 [00:00<00:00, 70.97it/s]

    file: 1it [00:00, 24.60it/s]
    2025-05-09 12:40:31,893 - JUNIFER - INFO - Collect done
    {'de47d0513cf22df792d264ecc363085a': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber', 'types': ['BOLD', 'T1w']}, 'dependencies': {'nilearn': '0.10.4'}, 'datareader': {'class': 'DefaultDataReader'}, 'type': 'BOLD', 'marker': {'agg_method': 'mean', 'agg_method_params': None, 'class': 'RSSETSMarker', 'masks': None, 'name': 'Schaefer100x17_RSSETS_rss_ets', 'parcellation': 'Schaefer100x17'}, '_element_keys': ['subject'], 'name': 'BOLD_Schaefer100x17_RSSETS_rss_ets'}, '0ff7a9e8f65fc25885bd0cd4936f87a4': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber', 'types': ['BOLD', 'T1w']}, 'dependencies': {'nilearn': '0.10.4'}, 'datareader': {'class': 'DefaultDataReader'}, 'type': 'BOLD', 'marker': {'agg_method': 'mean', 'agg_method_params': None, 'class': 'RSSETSMarker', 'masks': None, 'name': 'Schaefer200x17_RSSETS_rss_ets', 'parcellation': 'Schaefer200x17'}, '_element_keys': ['subject'], 'name': 'BOLD_Schaefer200x17_RSSETS_rss_ets'}}




.. GENERATED FROM PYTHON SOURCE LINES 76-77

Now we take a look at the dataframe

.. GENERATED FROM PYTHON SOURCE LINES 77-78

.. code-block:: Python

    df_rssets.head()





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th>root_sum_of_squares_ets</th>
        </tr>
        <tr>
          <th>subject</th>
          <th>timepoint</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">sub001</th>
          <th>0</th>
          <td>2502.888057</td>
        </tr>
        <tr>
          <th>1</th>
          <td>168.174038</td>
        </tr>
        <tr>
          <th>2</th>
          <td>102.849715</td>
        </tr>
        <tr>
          <th>3</th>
          <td>132.639660</td>
        </tr>
        <tr>
          <th>4</th>
          <td>109.615993</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 30.702 seconds)


.. _sphx_glr_download_auto_examples_run_ets_rss_marker.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: run_ets_rss_marker.ipynb <run_ets_rss_marker.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: run_ets_rss_marker.py <run_ets_rss_marker.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: run_ets_rss_marker.zip <run_ets_rss_marker.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_