.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/initial_contacts/_02_shin_algo.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__02_shin_algo.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_initial_contacts__02_shin_algo.py:


Shin Algo
=========

This example shows how to use the improved Shin algorithm and some examples on how the results compare to the original
matlab implementation.

.. GENERATED FROM PYTHON SOURCE LINES 9-15

.. code-block:: Python


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








.. GENERATED FROM PYTHON SOURCE LINES 16-23

Loading 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 output for initial contacts ("ic") as ground truth.


.. GENERATED FROM PYTHON SOURCE LINES 23-39

.. code-block:: Python

    from mobgap.utils.conversions import to_body_frame

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

    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





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        </tr>
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          <th>5</th>
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        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 40-44

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 44-57

.. code-block:: Python

    from mobgap.pipeline import GsIterator

    iterator = GsIterator()

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

    detected_ics = iterator.results_.ic_list
    detected_ics





.. raw:: html

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        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 58-61

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

.. GENERATED FROM PYTHON SOURCE LINES 61-91

.. code-block:: Python

    import json

    from mobgap import PROJECT_ROOT


    def load_matlab_output(datapoint):
        p = datapoint.group_label
        with (
            PROJECT_ROOT
            / f"example_data/original_results/icd_shin_improved/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">
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          <th>6</th>
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      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 92-106

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 5-10
   samples/50-100 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 106-128

.. code-block:: Python


    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="shin_algo_py",
    )
    plt.plot(
        detected_ics_matlab["ic"],
        imu_data["acc_is"].iloc[detected_ics_matlab["ic"]],
        "+",
        label="shin_algo_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__02_shin_algo_001.png
   :alt:  02 shin algo
   :srcset: /auto_examples/initial_contacts/images/sphx_glr__02_shin_algo_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 129-133

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 1.571 seconds)

**Estimated memory usage:**  80 MB


.. _sphx_glr_download_auto_examples_initial_contacts__02_shin_algo.py:

.. only:: html

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

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

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

      :download:`Download Python source code: _02_shin_algo.py <_02_shin_algo.py>`

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

      :download:`Download zipped: _02_shin_algo.zip <_02_shin_algo.zip>`


.. only:: html

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

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