.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/initial_contacts/_01_icd_ionescu.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_initial_contacts__01_icd_ionescu.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_initial_contacts__01_icd_ionescu.py:


.. _icd_ionescu:

ICD Ionescu
===========

This example shows how to use the initial_contacts Ionescu algorithm and how its results compare to the original
matlab implementation.

.. GENERATED FROM PYTHON SOURCE LINES 13-14

Import useful modules and packages

.. GENERATED FROM PYTHON SOURCE LINES 14-19

.. code-block:: default

    import matplotlib.pyplot as plt
    import pandas as pd
    from mobgap.data import LabExampleDataset
    from mobgap.initial_contacts import IcdIonescu








.. GENERATED FROM PYTHON SOURCE LINES 20-26

Loading some example data
-------------------------
.. note :: More infos about data loading can be found in the :ref:`data loading example <data_loading_example>`.

We load example data from the lab dataset together with the INDIP reference system.
We will use the INDIP "InitialContact_Event" output as ground truth.

.. GENERATED FROM PYTHON SOURCE LINES 26-31

.. code-block:: default


    example_data = LabExampleDataset(
        reference_system="INDIP", reference_para_level="wb"
    )








.. GENERATED FROM PYTHON SOURCE LINES 32-38

Performance on a single lab trial
---------------------------------
Below we apply the algorithm to a lab trail, where we only expect a single gait sequence.

Like most algorithms, the algorithm requires the data to be in body frame coordinates.
As we know the sensor was well aligned, we can just use ``to_body_frame`` to transform the data.

.. GENERATED FROM PYTHON SOURCE LINES 38-51

.. code-block:: default

    from mobgap.utils.conversions import to_body_frame

    single_test = example_data.get_subset(
        cohort="HA", participant_id="001", test="Test11", trial="Trial1"
    )
    imu_data = to_body_frame(single_test.data_ss)
    reference_wbs = single_test.reference_parameters_.wb_list

    sampling_rate_hz = single_test.sampling_rate_hz
    ref_ics = single_test.reference_parameters_.ic_list

    reference_wbs






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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          <td>1.021695</td>
          <td>Pause</td>
        </tr>
        <tr>
          <th>5</th>
          <td>11989</td>
          <td>12517</td>
          <td>6</td>
          <td>5.27</td>
          <td>3.514735</td>
          <td>0.880632</td>
          <td>95.832693</td>
          <td>1.021576</td>
          <td>Pause</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 52-56

Applying the algorithm
----------------------
Below we apply the shin algorithm to a lab trial.
We will use the `GsIterator` to iterate over the gait sequences and apply the algorithm to each wb.

.. GENERATED FROM PYTHON SOURCE LINES 56-68

.. code-block:: default

    from mobgap.pipeline import GsIterator

    iterator = GsIterator()

    for (gs, data), result in iterator.iterate(imu_data, reference_wbs):
        result.ic_list = (
            IcdIonescu().detect(data, sampling_rate_hz=sampling_rate_hz).ic_list_
        )

    detected_ics = iterator.results_.ic_list
    detected_ics






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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    </div>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 69-72

Matlab Outputs
--------------
To check if the algorithm was implemented correctly, we compare the results to the matlab implementation.

.. GENERATED FROM PYTHON SOURCE LINES 72-102

.. code-block:: default

    import json

    from mobgap import PACKAGE_ROOT


    def load_matlab_output(datapoint):
        p = datapoint.group_label
        with (
            PACKAGE_ROOT.parent
            / f"example_data/original_results/icd_ionescu/lab/{p.cohort}/{p.participant_id}/SD_Output.json"
        ).open() as f:
            original_results = json.load(f)["SD_Output"][p.time_measure][p.test][
                p.trial
            ]["SU"]["LowerBack"]["SD"]

        if not isinstance(original_results, list):
            original_results = [original_results]

        ics = {}
        for i, gs in enumerate(original_results, start=1):
            ics[i] = pd.DataFrame({"ic": gs["IC"]}).rename_axis(index="step_id")

        return (
            pd.concat(ics, names=["wb_id", ics[1].index.name])
            * datapoint.sampling_rate_hz
        ).astype("int64")


    detected_ics_matlab = load_matlab_output(single_test)
    detected_ics_matlab





.. 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;
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          <th>5</th>
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      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 103-116

Plotting the results
--------------------
With that we can compare the python, matlab and ground truth results.
We zoom in into one of the gait sequences to better see the output.
We can make a couple of main observations:

1. The python version finds the same ICs as the matlab version, but wil a small shift to the left (around 2-5
   samples/20-50 ms).
   This is likely due to some differences in the downsampling process.
2. Compared to the ground truth reference, both versions detect the IC too early most of the time.
3. Both algorithms can not detect the first IC of the gait sequence.
   However, this is expected, as per definition, this first IC marks the start of the WB in the reference system.
   Hence, there are no samples before that point the algorithm can use to detect the IC.

.. GENERATED FROM PYTHON SOURCE LINES 116-137

.. code-block:: default

    imu_data.reset_index(drop=True).plot(y="acc_is")

    plt.plot(
        ref_ics["ic"], imu_data["acc_is"].iloc[ref_ics["ic"]], "o", label="ref"
    )
    plt.plot(
        detected_ics["ic"],
        imu_data["acc_is"].iloc[detected_ics["ic"]],
        "x",
        label="icd_ionescu_py",
    )
    plt.plot(
        detected_ics_matlab["ic"],
        imu_data["acc_is"].iloc[detected_ics_matlab["ic"]],
        "+",
        label="icd_ionescu_matlab",
    )
    plt.xlim(reference_wbs.iloc[2]["start"] - 50, reference_wbs.iloc[2]["end"] + 50)
    plt.legend()
    plt.show()




.. image-sg:: /auto_examples/initial_contacts/images/sphx_glr__01_icd_ionescu_001.png
   :alt:  01 icd ionescu
   :srcset: /auto_examples/initial_contacts/images/sphx_glr__01_icd_ionescu_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 138-142

Evaluation of the algorithm against a reference
--------------------------------------------------
To quantify how the Python output compares to the reference labels, we are providing a range of evaluation functions.
See the :ref:`example on ICD evaluation <icd_evaluation>` for more details.


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

   **Total running time of the script:** (0 minutes 1.324 seconds)

**Estimated memory usage:**  9 MB


.. _sphx_glr_download_auto_examples_initial_contacts__01_icd_ionescu.py:

.. only:: html

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




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

      :download:`Download Python source code: _01_icd_ionescu.py <_01_icd_ionescu.py>`

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

      :download:`Download Jupyter notebook: _01_icd_ionescu.ipynb <_01_icd_ionescu.ipynb>`


.. only:: html

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

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