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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_trajectory_reconstruction_trajectory_reconstruction_region.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_trajectory_reconstruction_trajectory_reconstruction_region.py:


Region Level Trajectory reconstruction
======================================

This example shows how to calculate a IMU/foot trajectory_full over an entire gait sequence using
:class:`~gaitmap.trajectory_reconstruction.RegionLevelTrajectory`.
If you need an introduction to trajectory reconstruction in general, have a look
at:ref`this example <trajectory_stride>`.

.. GENERATED FROM PYTHON SOURCE LINES 12-22

Getting input data
------------------

For this example we need raw IMU data and a ROI list.
We use the available gaitmap example data, which is already in the correct gaitmap coordinate system.
Note, that the data starts and ends in a resting period, which is important for most integration methods to work
properly.

Because we only have a single gait sequence in the data, we will create a fake gait sequence list, that goes from
the start to the end of the dataset.

.. GENERATED FROM PYTHON SOURCE LINES 22-35

.. code-block:: default

    import matplotlib.pyplot as plt
    import pandas as pd

    from gaitmap.example_data import get_healthy_example_imu_data, get_healthy_example_stride_events
    from gaitmap.trajectory_reconstruction import RegionLevelTrajectory, RtsKalman
    from gaitmap.utils.datatype_helper import get_multi_sensor_names

    imu_data = get_healthy_example_imu_data()
    dummy_regions_list = pd.DataFrame([[0, len(imu_data["left_sensor"])]], columns=["start", "end"]).rename_axis("gs_id")
    dummy_regions_list = {k: dummy_regions_list for k in get_multi_sensor_names(imu_data)}
    dummy_regions_list["left_sensor"]







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
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        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>start</th>
          <th>end</th>
        </tr>
        <tr>
          <th>gs_id</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>7928</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 36-47

Selecting and Configuring Algorithms
------------------------------------

Like for the stride level method we need to choose an orientation and a position algorithm.
However, as we want to perform integration over a long time period, methods that can take advatage over the multiple
regions of zero velocity (ZUPTs) to perform corrections are preferable.
Therefore, the best choice for such region is a Kalman Filter.
As this takes care of both position and orientation estimation in one go, we can pass it as a `trajectory_method`.

The initial orientation will automatically be estimated by the `RegionLevelTrajectory` class using the first
n-samples.

.. GENERATED FROM PYTHON SOURCE LINES 47-53

.. code-block:: default

    trajectory_method = RtsKalman()
    # We set the ori and pos methods explicitly to `None` here to silence a warning indicating potential user error.
    # In general, when `trajectory_method` is provided `ori_method` and `pos_method` are ignored.
    trajectory_full = RegionLevelTrajectory(trajectory_method=trajectory_method, ori_method=None, pos_method=None)









.. GENERATED FROM PYTHON SOURCE LINES 54-56

Calculate and inspect results
-----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 56-77

.. code-block:: default

    sampling_frequency_hz = 204.8
    trajectory_full.estimate(data=imu_data, regions_of_interest=dummy_regions_list, sampling_rate_hz=sampling_frequency_hz)

    # select the position of the first (and only) gait sequence
    first_region_position = trajectory_full.position_["left_sensor"].loc[0]

    first_region_position.plot()
    plt.title("Left Foot Trajectory per axis")
    plt.xlabel("sample")
    plt.ylabel("position [m]")
    plt.show()

    # select the orientation of the first (and only) gait sequence
    first_region_orientation = trajectory_full.orientation_["left_sensor"].loc[0]

    first_region_orientation.plot()
    plt.title("Left Foot Orientation per axis")
    plt.xlabel("sample")
    plt.ylabel("orientation [a.u.]")
    plt.show()




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_001.png
         :alt: Left Foot Trajectory per axis
         :srcset: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_002.png
         :alt: Left Foot Orientation per axis
         :srcset: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gaitmap/checkouts/v2.4.0/gaitmap/trajectory_reconstruction/_trajectory_wrapper.py:213: UserWarning: Could not use complete window length for initializing orientation.
      warnings.warn("Could not use complete window length for initializing orientation.")
    /home/docs/checkouts/readthedocs.org/user_builds/gaitmap/checkouts/v2.4.0/gaitmap/trajectory_reconstruction/_trajectory_wrapper.py:213: UserWarning: Could not use complete window length for initializing orientation.
      warnings.warn("Could not use complete window length for initializing orientation.")




.. GENERATED FROM PYTHON SOURCE LINES 78-88

Calculate results per stride
----------------------------
However, usually we are not interested in the full trajectory of a region, but the we want to know the trajectory of
the strides within it.
We can do that by first calculating the trajectory_full for the entire region and then cutting out the parts of each
stride.
This can be done in a single step using `estimate_intersect`, which takes an additional stride list as input.
After using this method, the result contains the stride level trajectories.
Note, that compared to the results `StrideLevelTrajectory`, the initial position and orientation of each stride is
different and simply taken from the calculated trajectory_full.

.. GENERATED FROM PYTHON SOURCE LINES 88-115

.. code-block:: default

    stride_list = get_healthy_example_stride_events()

    trajectory_per_stride = trajectory_full.clone()
    trajectory_per_stride.estimate_intersect(
        data=imu_data,
        regions_of_interest=dummy_regions_list,
        stride_event_list=stride_list,
        sampling_rate_hz=sampling_frequency_hz,
    )

    # select the position of the first stride
    first_region_position = trajectory_per_stride.position_["left_sensor"].loc[0]

    first_region_position.plot()
    plt.title("Left Foot Trajectory per axis")
    plt.xlabel("sample")
    plt.ylabel("position [m]")
    plt.show()

    # select the orientation of the first stride
    first_region_orientation = trajectory_per_stride.orientation_["left_sensor"].loc[0]

    first_region_orientation.plot()
    plt.title("Left Foot Orientation per axis")
    plt.xlabel("sample")
    plt.ylabel("orientation [a.u.]")
    plt.show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_003.png
         :alt: Left Foot Trajectory per axis
         :srcset: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_004.png
         :alt: Left Foot Orientation per axis
         :srcset: /auto_examples/trajectory_reconstruction/images/sphx_glr_trajectory_reconstruction_region_004.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gaitmap/checkouts/v2.4.0/gaitmap/trajectory_reconstruction/_trajectory_wrapper.py:213: UserWarning: Could not use complete window length for initializing orientation.
      warnings.warn("Could not use complete window length for initializing orientation.")
    /home/docs/checkouts/readthedocs.org/user_builds/gaitmap/checkouts/v2.4.0/gaitmap/trajectory_reconstruction/_trajectory_wrapper.py:213: UserWarning: Could not use complete window length for initializing orientation.
      warnings.warn("Could not use complete window length for initializing orientation.")





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

   **Total running time of the script:** ( 0 minutes  22.257 seconds)

**Estimated memory usage:**  106 MB


.. _sphx_glr_download_auto_examples_trajectory_reconstruction_trajectory_reconstruction_region.py:

.. only:: html

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


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

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

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

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


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