.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_revalidation/gait_sequences/_02_gsd_misorientation_analysis.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_revalidation_gait_sequences__02_gsd_misorientation_analysis.py>`
        to download the full example code.

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

.. _sphx_glr_auto_revalidation_gait_sequences__02_gsd_misorientation_analysis.py:


.. _gsd_misorientation_val_results:

GSD performance under simulated mounting errors
================================================

This analysis compares free-living gait sequence detection performance across
simulated lower-back sensor mounting orientations. It uses the result files
generated by :ref:`gsd_misorientation_val_gen`.

This analysis is more of a confirmation which algorithms work even when the
sensor is mounted incorrectly and which mounting orientations are more
problematic for the algorithms.

The plot shows the per-recording F1 score for each algorithm and orientation.
The table reports the overall number of detected gait sequences. Each simulated
orientation is produced by converting the TVS recording to body frame and
applying the rough mounting rotations used by the reorientation validation.

.. note::
    The standard GSD validation remains the reference for performance under the
    default Mobilise-D mounting assumption. This page isolates orientation
    robustness and therefore only compares Python algorithms that can be rerun
    on the simulated recordings.

.. GENERATED FROM PYTHON SOURCE LINES 27-38

.. code-block:: Python


    from pathlib import Path

    import matplotlib.pyplot as plt
    import pandas as pd
    import seaborn as sns
    from mobgap.data.validation_results import ValidationResultLoader
    from mobgap.plotting import move_legend_outside
    from mobgap.re_orientation.pipeline import REORIENTATION_LABELS
    from mobgap.utils.misc import get_env_var








.. GENERATED FROM PYTHON SOURCE LINES 39-41

Algorithms
----------

.. GENERATED FROM PYTHON SOURCE LINES 41-47

.. code-block:: Python

    algorithms = {
        "GsdIonescu": ("GsdIonescu", "MobGap"),
        "GsdIluz": ("GsdIluz", "MobGap"),
        "GsdIluz_orig_peak": ("GsdIluz", "MobGap (original peak)"),
    }








.. GENERATED FROM PYTHON SOURCE LINES 48-53

Loading Results
---------------
The result files add ``orientation`` to the usual TVS index. The standard
validation loader keeps unknown CSV columns as data columns, so we can still
use it for local and remote loading.

.. GENERATED FROM PYTHON SOURCE LINES 53-89

.. code-block:: Python

    local_data_path = (
        Path(get_env_var("MOBGAP_VALIDATION_DATA_PATH")) / "results"
        if int(get_env_var("MOBGAP_VALIDATION_USE_LOCAL_DATA", 0))
        else None
    )
    __RESULT_VERSION = "main"
    loader = ValidationResultLoader(
        "gsd_misorientation",
        result_path=local_data_path,
        version=__RESULT_VERSION,
    )

    orientation_order = list(REORIENTATION_LABELS)
    algorithm_order = [
        f"{algo} ({version})" for algo, version in algorithms.values()
    ]


    def load_free_living_results() -> pd.DataFrame:
        results = []
        for folder_name, (algo, version) in algorithms.items():
            result = (
                loader.load_single_results(folder_name, "free_living")
                .reset_index()
                .assign(
                    algo=algo,
                    version=version,
                    algo_with_version=f"{algo} ({version})",
                )
            )
            results.append(result)
        return pd.concat(results, ignore_index=True)


    free_living_results = load_free_living_results()





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

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 90-92

Plot Helpers
------------

.. GENERATED FROM PYTHON SOURCE LINES 92-132

.. code-block:: Python

    sns.set_context("talk")


    def plot_f1_by_orientation(data: pd.DataFrame, *, title: str) -> None:
        fig, ax = plt.subplots(figsize=(18, 7), constrained_layout=True)
        sns.boxplot(
            data=data,
            x="algo_with_version",
            y="f1_score",
            hue="orientation",
            order=algorithm_order,
            hue_order=orientation_order,
            showmeans=True,
            ax=ax,
        )
        ax.set_title(title)
        ax.set_xlabel("Algorithm")
        ax.set_ylabel("F1 score")
        ax.set_ylim(-0.02, 1.02)
        ax.tick_params(axis="x", rotation=20)
        ax.grid(True, axis="y", alpha=0.3)
        move_legend_outside(fig, ax, ncol=4)
        plt.show()


    def detected_gs_count_table(data: pd.DataFrame) -> pd.DataFrame:
        return (
            data.pivot_table(
                index="algo_with_version",
                columns="orientation",
                values="detected_num_gs",
                aggfunc="sum",
                sort=False,
            )
            .reindex(index=algorithm_order, columns=orientation_order)
            .rename_axis(index="Algorithm", columns="Orientation")
            .astype("Int64")
        )









.. GENERATED FROM PYTHON SOURCE LINES 133-137

Free-Living Comparison
----------------------
Every datapoint below is one free-living recording under one simulated
orientation.

.. GENERATED FROM PYTHON SOURCE LINES 137-142

.. code-block:: Python

    plot_f1_by_orientation(
        free_living_results,
        title="Free-living GSD F1 score by simulated orientation",
    )




.. image-sg:: /auto_revalidation/gait_sequences/images/sphx_glr__02_gsd_misorientation_analysis_001.png
   :alt: Free-living GSD F1 score by simulated orientation
   :srcset: /auto_revalidation/gait_sequences/images/sphx_glr__02_gsd_misorientation_analysis_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 143-145

Detected GS Counts
------------------

.. GENERATED FROM PYTHON SOURCE LINES 145-147

.. code-block:: Python

    detected_gs_count_table(free_living_results)






.. 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>Orientation</th>
          <th>identity</th>
          <th>pa_normal__rot_pa_pos90</th>
          <th>pa_normal__rot_pa_180</th>
          <th>pa_normal__rot_pa_neg90</th>
          <th>pa_flipped__rot_pa_0</th>
          <th>pa_flipped__rot_pa_pos90</th>
          <th>pa_flipped__rot_pa_180</th>
          <th>pa_flipped__rot_pa_neg90</th>
        </tr>
        <tr>
          <th>Algorithm</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>GsdIonescu (MobGap)</th>
          <td>5538</td>
          <td>5538</td>
          <td>5538</td>
          <td>5538</td>
          <td>5538</td>
          <td>5538</td>
          <td>5538</td>
          <td>5538</td>
        </tr>
        <tr>
          <th>GsdIluz (MobGap)</th>
          <td>7260</td>
          <td>11</td>
          <td>1</td>
          <td>1</td>
          <td>7057</td>
          <td>2</td>
          <td>1</td>
          <td>12</td>
        </tr>
        <tr>
          <th>GsdIluz (MobGap (original peak))</th>
          <td>4381</td>
          <td>6</td>
          <td>0</td>
          <td>0</td>
          <td>4662</td>
          <td>0</td>
          <td>0</td>
          <td>7</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 148-159

Conclusion
----------
As expected GsdIonescu works identically across all orientations, as it relies
on the norm of the accelerometer signal.
GsdIluz is heavily dependent on the correct orientation.
If the gravity direction is on the wrong axis, the algorithm fails to find any
gait sequence (with very few exceptions).
Flipping the sensor front-back (pa_flipped__rot_pa_0) is less problematic and
seems to result in only a small drop in performance for GsdIluz.
But as expected, it makes no sense to use GsdIluz if the sensor is expected to
be mounted in a different orientation than the default Mobilise-D one.


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

   **Total running time of the script:** (0 minutes 2.557 seconds)

**Estimated memory usage:**  82 MB


.. _sphx_glr_download_auto_revalidation_gait_sequences__02_gsd_misorientation_analysis.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: _02_gsd_misorientation_analysis.ipynb <_02_gsd_misorientation_analysis.ipynb>`

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

      :download:`Download Python source code: _02_gsd_misorientation_analysis.py <_02_gsd_misorientation_analysis.py>`

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

      :download:`Download zipped: _02_gsd_misorientation_analysis.zip <_02_gsd_misorientation_analysis.zip>`


.. only:: html

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