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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_load_and_explore_poses.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_examples_load_and_explore_poses.py:

Load and explore pose tracks
===============================

Load and explore an example dataset of pose tracks.

.. GENERATED FROM PYTHON SOURCE LINES 8-10

Imports
-------

.. GENERATED FROM PYTHON SOURCE LINES 10-15

.. code-block:: Python

    from matplotlib import pyplot as plt

    from movement import sample_data
    from movement.io import load_poses








.. GENERATED FROM PYTHON SOURCE LINES 16-21

Define the file path
--------------------
This should be a file output by one of our supported pose estimation
frameworks (e.g., DeepLabCut, SLEAP), containing predicted pose tracks.
For example, the path could be something like:

.. GENERATED FROM PYTHON SOURCE LINES 21-25

.. code-block:: Python


    # uncomment and edit the following line to point to your own local file
    # file_path = "/path/to/my/data.h5"








.. GENERATED FROM PYTHON SOURCE LINES 26-28

For the sake of this example, we will use the path to one of
the sample datasets provided with ``movement``.

.. GENERATED FROM PYTHON SOURCE LINES 28-34

.. code-block:: Python


    file_path = sample_data.fetch_dataset_paths(
        "SLEAP_three-mice_Aeon_proofread.analysis.h5"
    )["poses"]
    print(file_path)





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

 .. code-block:: none

    /home/runner/.movement/data/poses/SLEAP_three-mice_Aeon_proofread.analysis.h5




.. GENERATED FROM PYTHON SOURCE LINES 35-37

Load the data into movement
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 37-41

.. code-block:: Python


    ds = load_poses.from_sleap_file(file_path, fps=50)
    print(ds)





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

 .. code-block:: none

    <xarray.Dataset> Size: 27kB
    Dimensions:      (time: 601, individuals: 3, keypoints: 1, space: 2)
    Coordinates:
      * time         (time) float64 5kB 0.0 0.02 0.04 0.06 ... 11.96 11.98 12.0
      * individuals  (individuals) <U10 120B 'AEON3B_NTP' 'AEON3B_TP1' 'AEON3B_TP2'
      * keypoints    (keypoints) <U8 32B 'centroid'
      * space        (space) <U1 8B 'x' 'y'
    Data variables:
        position     (time, individuals, keypoints, space) float32 14kB 770.3 ......
        confidence   (time, individuals, keypoints) float32 7kB nan nan ... nan nan
    Attributes:
        fps:              50.0
        time_unit:        seconds
        source_software:  SLEAP
        source_file:      /home/runner/.movement/data/poses/SLEAP_three-mice_Aeon...
        ds_type:          poses




.. GENERATED FROM PYTHON SOURCE LINES 42-45

The loaded dataset contains two data variables:
``position`` and ``confidence``.
To get the position data:

.. GENERATED FROM PYTHON SOURCE LINES 45-47

.. code-block:: Python

    position = ds.position








.. GENERATED FROM PYTHON SOURCE LINES 48-53

Select and plot data with xarray
--------------------------------
You can use the ``sel`` method to index into ``xarray`` objects.
For example, we can get a ``DataArray`` containing only data
for a single keypoint of the first individual:

.. GENERATED FROM PYTHON SOURCE LINES 53-57

.. code-block:: Python


    da = position.sel(individuals="AEON3B_NTP", keypoints="centroid")
    print(da)





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

 .. code-block:: none

    <xarray.DataArray 'position' (time: 601, space: 2)> Size: 5kB
    770.3 1.062e+03 773.3 1.062e+03 773.3 ... 618.6 194.6 618.4 194.6 616.4
    Coordinates:
      * time         (time) float64 5kB 0.0 0.02 0.04 0.06 ... 11.96 11.98 12.0
        individuals  <U10 40B 'AEON3B_NTP'
        keypoints    <U8 32B 'centroid'
      * space        (space) <U1 8B 'x' 'y'




.. GENERATED FROM PYTHON SOURCE LINES 58-60

We could plot the x, y coordinates of this keypoint over time,
using ``xarray``'s built-in plotting methods:

.. GENERATED FROM PYTHON SOURCE LINES 60-62

.. code-block:: Python

    da.plot.line(x="time", row="space", aspect=2, size=2.5)




.. image-sg:: /examples/images/sphx_glr_load_and_explore_poses_001.png
   :alt: space = x, space = y
   :srcset: /examples/images/sphx_glr_load_and_explore_poses_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <xarray.plot.facetgrid.FacetGrid object at 0x7fbbfd7e4790>



.. GENERATED FROM PYTHON SOURCE LINES 63-65

Similarly we could plot the same keypoint's x, y coordinates
for all individuals:

.. GENERATED FROM PYTHON SOURCE LINES 65-69

.. code-block:: Python


    da = position.sel(keypoints="centroid")
    da.plot.line(x="time", row="individuals", aspect=2, size=2.5)




.. image-sg:: /examples/images/sphx_glr_load_and_explore_poses_002.png
   :alt: individuals = AEON3B_NTP, individuals = AEON3B_TP1, individuals = AEON3B_TP2
   :srcset: /examples/images/sphx_glr_load_and_explore_poses_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <xarray.plot.facetgrid.FacetGrid object at 0x7fbbfd7a13d0>



.. GENERATED FROM PYTHON SOURCE LINES 70-75

Trajectory plots
----------------
We are not limited to ``xarray``'s built-in plots.
For example, we can use ``matplotlib`` to plot trajectories
(using scatter plots):

.. GENERATED FROM PYTHON SOURCE LINES 75-89

.. code-block:: Python


    mouse_name = "AEON3B_TP1"

    plt.scatter(
        da.sel(individuals=mouse_name, space="x"),
        da.sel(individuals=mouse_name, space="y"),
        s=2,
        c=da.time,
        cmap="viridis",
    )
    plt.title(f"Trajectory of {mouse_name}")
    plt.xlabel("x")
    plt.ylabel("y")
    plt.colorbar(label="time (sec)")



.. image-sg:: /examples/images/sphx_glr_load_and_explore_poses_003.png
   :alt: Trajectory of AEON3B_TP1
   :srcset: /examples/images/sphx_glr_load_and_explore_poses_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <matplotlib.colorbar.Colorbar object at 0x7fbc0556f290>




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

   **Total running time of the script:** (0 minutes 0.713 seconds)


.. _sphx_glr_download_examples_load_and_explore_poses.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/neuroinformatics-unit/movement/gh-pages?filepath=notebooks/examples/load_and_explore_poses.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. only:: html

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

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