.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/lrd/_01_mccamley.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_lrd__01_mccamley.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_lrd__01_mccamley.py:


McCamley L/R detector
=====================

The McCamley L/R detector is a simple algorithm to detect the laterality of initial contacts based on the sign
of the angular velocity signal.
We use a modified version of the original McCamley algorithm, which includes a smoothing filter to reduce the
influence of noise on the detection.

This example shows how to use the algorithm and compares the output to the reference labels on some example data.

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. code-block:: default


    import pandas as pd

    from mobgap.data import LabExampleDataset








.. GENERATED FROM PYTHON SOURCE LINES 18-26

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.

We only use the data from the "simulated daily living" activity test from a single particomand.

.. GENERATED FROM PYTHON SOURCE LINES 26-38

.. code-block:: default


    example_data = LabExampleDataset(reference_system="INDIP", reference_para_level="wb")
    single_test = example_data.get_subset(cohort="MS", participant_id="001", test="Test11", trial="Trial1")

    imu_data = single_test.data["LowerBack"]
    reference_wbs = single_test.reference_parameters_.wb_list

    sampling_rate_hz = single_test.sampling_rate_hz
    ref_ics = single_test.reference_parameters_.ic_list
    ref_ics_rel_to_gs = single_test.reference_parameters_relative_to_wb_.ic_list

    reference_wbs





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          <td>Pause</td>
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          <th>5</th>
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          <th>6</th>
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          <td>0.599360</td>
          <td>Pause</td>
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    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 39-49

Applying the algorithm using reference ICs
------------------------------------------
We use the McCamley algorithm to detect the laterality of the initial contacts.
For this we need the IMU data and the indices of the initial contacts per GS.
To focus this example on the L/R detection, we use the reference ICs from the INDIP system as input.
In a real application, we would use the output of the IC-detectors as input.

We will use the `GsIterator` to iterate over the gait sequences and apply the algorithm to each wb.
Note, that we use the ``ic_list`` result key, as the output of all L/R detectors is identical to the output of the
IC-detectors, but with an additional ``lr_label`` column.

.. GENERATED FROM PYTHON SOURCE LINES 49-62

.. code-block:: default

    from mobgap.lrd import LrdMcCamley
    from mobgap.pipeline import GsIterator

    iterator = GsIterator()

    for (gs, data), result in iterator.iterate(imu_data, reference_wbs):
        result.ic_list = (
            LrdMcCamley().detect(data, ic_list=ref_ics_rel_to_gs.loc[gs.id], sampling_rate_hz=sampling_rate_hz).ic_lr_list_
        )

    detected_ics = iterator.results_.ic_list
    detected_ics






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    <p>91 rows × 2 columns</p>
    </div>
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.. GENERATED FROM PYTHON SOURCE LINES 63-67

Compare the results to the reference
------------------------------------
We compare the detected initial contacts to the reference labels.
One easy way to compare the results is to visualize them as colorful bars.

.. GENERATED FROM PYTHON SOURCE LINES 67-88

.. code-block:: default


    import matplotlib.pyplot as plt


    def plot_lr(ref, detected):
        fig, ax = plt.subplots(figsize=(15, 5))
        # We plot one box either (red or blue depending on the laterality) for each detected IC ignoring the actual time
        for (_, row), (_, ref_row) in zip(detected.iterrows(), ref.iterrows()):
            ax.plot([row["ic"], row["ic"]], [0, 0.98], color="r" if row["lr_label"] == "left" else "b", linewidth=5)
            ax.plot(
                [ref_row["ic"], ref_row["ic"]], [1.02, 2], color="r" if ref_row["lr_label"] == "left" else "b", linewidth=5
            )

        ax.set_yticks([0.5, 1.5])
        ax.set_yticklabels(["Detected", "Reference"])
        return fig, ax


    fig, _ = plot_lr(ref_ics, detected_ics)
    fig.show()




.. image-sg:: /auto_examples/lrd/images/sphx_glr__01_mccamley_001.png
   :alt:  01 mccamley
   :srcset: /auto_examples/lrd/images/sphx_glr__01_mccamley_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 89-91

If we zoom in on a longer WB, we can see that for some ICs the L/R label does not match.
But, in particular for regular gait in the center of the WB, the labels match quite well.

.. GENERATED FROM PYTHON SOURCE LINES 91-96

.. code-block:: default


    fig, ax = plot_lr(ref_ics, detected_ics)
    ax.set_xlim(12000, 15000)
    fig.show()




.. image-sg:: /auto_examples/lrd/images/sphx_glr__01_mccamley_002.png
   :alt:  01 mccamley
   :srcset: /auto_examples/lrd/images/sphx_glr__01_mccamley_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 97-98

We can also quantify the agreement between the detected and the reference labels using typical classification metrics.

.. GENERATED FROM PYTHON SOURCE LINES 98-103

.. code-block:: default

    from sklearn.metrics import classification_report

    pd.DataFrame(
        classification_report(ref_ics.lr_label, detected_ics.lr_label, target_names=["left", "right"], output_dict=True)
    ).T





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**Estimated memory usage:**  15 MB


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