.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/06_manipulating_images/plot_nifti_simple.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_06_manipulating_images_plot_nifti_simple.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_06_manipulating_images_plot_nifti_simple.py:


Simple example of NiftiMasker use
=================================

Here is a simple example of automatic mask computation using the nifti masker.
The mask is computed and visualized.

.. include:: ../../../examples/masker_note.rst

.. GENERATED FROM PYTHON SOURCE LINES 13-14

Retrieve the brain development functional dataset

.. GENERATED FROM PYTHON SOURCE LINES 14-23

.. code-block:: Python


    from nilearn import datasets

    dataset = datasets.fetch_development_fmri(n_subjects=1)
    func_filename = dataset.func[0]

    # print basic information on the dataset
    print(f"First functional nifti image (4D) is at: {func_filename}")





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

 .. code-block:: none

    First functional nifti image (4D) is at: /home/himanshu/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz




.. GENERATED FROM PYTHON SOURCE LINES 24-25

Compute the mask

.. GENERATED FROM PYTHON SOURCE LINES 25-40

.. code-block:: Python

    from nilearn.maskers import NiftiMasker

    # As this is raw movie watching based EPI, the background is noisy and we
    # cannot rely on the 'background' masking strategy. We need to use the 'epi'
    # one
    nifti_masker = NiftiMasker(
        standardize="zscore_sample",
        mask_strategy="epi",
        memory="nilearn_cache",
        memory_level=2,
        smoothing_fwhm=8,
    )
    nifti_masker.fit(func_filename)
    mask_img = nifti_masker.mask_img_








.. GENERATED FROM PYTHON SOURCE LINES 41-42

Visualize the mask using the plot_roi method

.. GENERATED FROM PYTHON SOURCE LINES 42-50

.. code-block:: Python

    from nilearn.image.image import mean_img
    from nilearn.plotting import plot_roi

    # calculate mean image for the background
    mean_func_img = mean_img(func_filename)

    plot_roi(mask_img, mean_func_img, display_mode="y", cut_coords=4, title="Mask")




.. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_001.png
   :alt: plot nifti simple
   :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <nilearn.plotting.displays._slicers.YSlicer object at 0x7214f24beae0>



.. GENERATED FROM PYTHON SOURCE LINES 51-55

Visualize the mask using the 'generate_report' method
This report can be displayed in a Jupyter Notebook,
opened in-browser using the .open_in_browser() method,
or saved to a file using the .save_as_html(output_filepath) method.

.. GENERATED FROM PYTHON SOURCE LINES 55-58

.. code-block:: Python

    report = nifti_masker.generate_report()
    report






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <!-- CSS for the report -->
    <link
      rel="stylesheet"
      href="https://unpkg.com/purecss@1.0.0/build/pure-min.css"
      integrity="sha384-nn4HPE8lTHyVtfCBi5yW9d20FjT8BJwUXyWZT9InLYax14RDjBj46LmSztkmNP9w"
      crossorigin="anonymous"
    />
    <link
      rel="stylesheet"
      href="https://unpkg.com/purecss@1.0.0/build/grids-responsive-min.css"
    />
    <style type="text/css">
      /* Add a gutter to Pure's Columns */
      .pure-g > div {
        -webkit-box-sizing: border-box;
        -moz-box-sizing: border-box;
        box-sizing: border-box;
      }

      .pure-g > div {
        padding: 0 0.5em;
      }

      /*!
    Pure-button class copied from pure-min.css,
    in case reports are displayed offline.

    Pure v1.0.0
    Copyright 2013 Yahoo!
    Licensed under the BSD License.
    https://github.com/yahoo/pure/blob/master/LICENSE.md
    */

      .pure-button {
        /* Structure */
        display: inline-block;
        zoom: 1;
        line-height: normal;
        white-space: nowrap;
        vertical-align: middle;
        text-align: center;
        cursor: pointer;
        -webkit-user-drag: none;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        box-sizing: border-box;
      }

      .pure-button {
        /* Button styling */
        font-family: inherit;
        font-size: 100%;
        padding: 0.5em 1em;
        color: #444; /* rgba not supported (IE 8) */
        color: rgba(0, 0, 0, 0.8); /* rgba supported */
        border: 1px solid #999; /*IE 6/7/8*/
        border: none rgba(0, 0, 0, 0); /*IE9 + everything else*/
        background-color: #e6e6e6;
        text-decoration: none;
        border-radius: 2px;
      }

      /*!
    End pure-button class definition from pure-min.css
    */

      div.nilearn_report {
        padding-top: 0px;
        border: solid rgb(150, 150, 150);
        border-width: 1pt;
        border-radius: 6pt;
        overflow: hidden; /* Needed to avoid scrolling in jupyter :( */
        color: black;
        background-color: white;
      }

      .terminal div.nilearn_report {
        max-width: min(50ex, 100vw); /* Needed in vscode */
      }

      /* Isolate us a bit from the styles specified by other stylesheets */

      div.nilearn_report h1 {
        text-align: left;
        margin-left: 0pt;
        margin-right: 0pt;
        padding-top: 5pt;
        font-size: 1.8em;
        padding-left: 5pt;
      }

      div.nilearn_report h1:first-child {
        margin-top: 0pt;
        background-color: #f5f5f5;
        border-radius: 6pt 6pt 0pt 0pt;
        padding-left: 5pt;
      }

      div.nilearn_report summary::-webkit-details-marker {
        margin-right: 2px;
      }

      div.nilearn_report summary:focus {
        outline-style: none;
      }

      div.nilearn_report table {
        margin: 50px auto;
      }

      div.nilearn_report thead th {
        text-align: center;
      }

      div.nilearn_report tbody tr:nth-child(even) {
        background-color: #ddd;
      }

      div.nilearn_report div.raise {
        z-index: 1;
      }

      /* Tooltip container */
      div.nilearn_report .withtooltip {
        position: relative;
        border-bottom: 1px dotted black; /* If you want dots under the hoverable text */
      }

      /* Tooltip text */
      div.nilearn_report .withtooltip .tooltiptext {
        visibility: hidden;
        max-width: 35em;
        width: 90%;
        background-color: rgba(24, 24, 24, 0.9);
        color: #fff;
        text-align: left;
        font-size: 12pt;
        font-weight: 200;
        border-radius: 6px;
        padding: 5px;

        margin-left: 3pt;

        /* Position the tooltip text - see examples below! */
        position: absolute;
        z-index: 2;

        width: 99%;
        left: 2pt;
        top: 133%;
      }

      /* Show the tooltip text when you mouse over the tooltip container */
      div.nilearn_report .withtooltip:hover .tooltiptext {
        visibility: visible;
      }

      div.nilearn_report .image {
        position: relative;
        height: auto;
        width: 100%;
      }

      div.nilearn_report .image .overlay {
        position: absolute;
        top: 0;
        left: 0;
        opacity: 0;
        transition: 0.5s ease;
        height: auto;
        width: 100%;
        -webkit-transition: 0.5s ease;
      }

      div.nilearn_report .image:hover .overlay {
        opacity: 1;
      }

      .elem-warn {
        color: #ff0000;
        font-weight: bold;
      }

      .scrollit {
        overflow-x: scroll;
      }
    </style>

    <div class="nilearn_report">
      <h1 class="withtooltip">
        NiftiMasker
        <span class="tooltiptext">Applying a mask to extract time-series from Niimg-like objects.

        NiftiMasker is useful when preprocessing (detrending, standardization,
        resampling, etc.) of in-mask :term:`voxels&lt;voxel&gt;` is necessary.

        Use case:
        working with time series of :term:`resting-state` or task maps.

        </span>
      </h1>
      <div class="pure-g">
        <div class="pure-u-1 pure-u-md-2-3">
          <div class="image">
            <img
              class="pure-img"
              width="100%"
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   <cc:Work>
    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
    <dc:date>2024-04-09T16:32:42.866500</dc:date>
    <dc:format>image/svg+xml</dc:format>
    <dc:creator>
     <cc:Agent>
      <dc:title>Matplotlib v3.8.3, https://matplotlib.org/</dc:title>
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        <div class="pure-u-1 pure-u-md-1-3 raise">
 
          <p class="elem-desc">This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image. </p>
        </div>
      </div>
      <div class="pure-g">
        <div class="pure-u-1 pure-u-md-3-3 scrollit">
          <details>
            <summary class="pure-button">Parameters</summary>
            <table class="pure-table">
              <thead>
                <tr>
                  <th>Parameter</th>
                  <th>Value</th>
                </tr>
              </thead>
              <tbody>
             
                <tr>
                  <td data-column="Parameter">detrend</td>
                  <td data-column="Value">False</td>
                </tr>

                <tr>
                  <td data-column="Parameter">dtype</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">high_pass</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">high_variance_confounds</td>
                  <td data-column="Value">False</td>
                </tr>

                <tr>
                  <td data-column="Parameter">low_pass</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">mask_args</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">mask_img</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">mask_strategy</td>
                  <td data-column="Value">epi</td>
                </tr>

                <tr>
                  <td data-column="Parameter">memory</td>
                  <td data-column="Value">Memory(location=nilearn_cache/joblib)</td>
                </tr>

                <tr>
                  <td data-column="Parameter">memory_level</td>
                  <td data-column="Value">2</td>
                </tr>

                <tr>
                  <td data-column="Parameter">reports</td>
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                <tr>
                  <td data-column="Parameter">runs</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">smoothing_fwhm</td>
                  <td data-column="Value">8</td>
                </tr>

                <tr>
                  <td data-column="Parameter">standardize</td>
                  <td data-column="Value">zscore_sample</td>
                </tr>

                <tr>
                  <td data-column="Parameter">standardize_confounds</td>
                  <td data-column="Value">True</td>
                </tr>

                <tr>
                  <td data-column="Parameter">t_r</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">target_affine</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">target_shape</td>
                  <td data-column="Value">None</td>
                </tr>

                <tr>
                  <td data-column="Parameter">verbose</td>
                  <td data-column="Value">0</td>
                </tr>
              </tbody>
            </table>
          </details>
        </div>
      </div>
    </div>

    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-60

Preprocess data with the NiftiMasker

.. GENERATED FROM PYTHON SOURCE LINES 60-64

.. code-block:: Python

    nifti_masker.fit(func_filename)
    fmri_masked = nifti_masker.transform(func_filename)
    # fmri_masked is now a 2D matrix, (n_voxels x n_time_points)





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

 .. code-block:: none

    /home/himanshu/Desktop/nilearn_work/nilearn/nilearn/maskers/base_masker.py:123: UserWarning: Persisting input arguments took 0.59s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.
      region_signals, aux = cache(




.. GENERATED FROM PYTHON SOURCE LINES 65-66

Run an algorithm

.. GENERATED FROM PYTHON SOURCE LINES 66-72

.. code-block:: Python

    from sklearn.decomposition import FastICA

    n_components = 10
    ica = FastICA(n_components=n_components, random_state=42)
    components_masked = ica.fit_transform(fmri_masked.T).T





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

 .. code-block:: none

    /home/himanshu/.local/miniconda3/envs/nilearnpy/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:128: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
      warnings.warn(




.. GENERATED FROM PYTHON SOURCE LINES 73-74

Reverse masking, and display the corresponding map

.. GENERATED FROM PYTHON SOURCE LINES 74-94

.. code-block:: Python

    components = nifti_masker.inverse_transform(components_masked)

    from nilearn.image import index_img
    from nilearn.image.image import mean_img

    # Visualize results
    from nilearn.plotting import plot_stat_map, show

    # calculate mean image for the background
    mean_func_img = mean_img(func_filename)

    plot_stat_map(
        index_img(components, 0),
        mean_func_img,
        display_mode="y",
        cut_coords=4,
        title="Component 0",
    )

    show()



.. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_002.png
   :alt: plot nifti simple
   :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 17.455 seconds)

**Estimated memory usage:**  898 MB


.. _sphx_glr_download_auto_examples_06_manipulating_images_plot_nifti_simple.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.10.4?urlpath=lab/tree/notebooks/auto_examples/06_manipulating_images/plot_nifti_simple.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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