.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_watchdata.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_watchdata.py:


=============================
Working with Time Series Data
=============================

This example shows how to load the included smartwatch inertial sensor dataset, and create time series data objects compatible with the `seglearn` pipeline.




.. image:: /auto_examples/images/sphx_glr_plot_watchdata_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    DATA STATS - AGGREGATED
    {'n_series': 140, 'n_classes': 7, 'n_TS_vars': 6, 'n_context_vars': 2, 'Total_Time': 4882.04, 'Series_Time_Mean': 34.87171428571428, 'Series_Time_Std': 8.757850351285423, 'Series_Time_Range': (18.94, 52.36)}

    DATA STATS - BY CLASS
      Class_labels  n_series  Total_Time  Series_Time_Mean  Series_Time_Std  Series_Time_Min  Series_Time_Max
    0          PEN        20      532.44            26.622         2.720845            21.28            31.10
    1          ABD        20      798.10            39.905         8.055554            22.48            49.10
    2          FEL        20      809.96            40.498         8.772099            22.30            50.84
    3           IR        20      747.90            37.395         9.085212            19.40            52.36
    4           ER        20      752.08            37.604         7.983878            21.30            49.24
    5         TRAP        20      611.56            30.578         6.099252            19.26            45.12
    6          ROW        20      630.00            31.500         5.957100            18.94            38.66
    /home/david/Code/seglearn/examples/plot_watchdata.py:67: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      plt.show()






|


.. code-block:: default

    # Author: David Burns
    # License: BSD

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    from seglearn.base import TS_Data
    from seglearn.datasets import load_watch
    from seglearn.util import check_ts_data, ts_stats

    data = load_watch()

    y = data['y']
    Xt = data['X']
    fs = 50  # sampling frequency

    # create time series data object with no contextual variables

    check_ts_data(Xt)

    # create time series data object with 2 contextual variables
    Xs = np.column_stack([data['side'], data['subject']])
    X = TS_Data(Xt, Xs)
    check_ts_data(X)

    # recover time series and contextual variables
    Xt = X.ts_data
    Xs = X.context_data

    # generate some statistics from the time series data
    results = ts_stats(X, y, fs=fs, class_labels=data['y_labels'])
    print("DATA STATS - AGGREGATED")
    print(results['total'])
    print("")
    print("DATA STATS - BY CLASS")
    print(pd.DataFrame(results['by_class']))

    # plot an instance from the data set
    # this plot shows 6-axis inertial sensor data recorded by someone doing shoulder pendulum exercise
    Xt0 = Xt[0]
    f, axes = plt.subplots(nrows=1, ncols=2)
    t = np.arange(len(Xt0)) / fs
    axes[0].plot(t, Xt0[:, 0], 'r-')
    axes[0].plot(t, Xt0[:, 1], 'g-')
    axes[0].plot(t, Xt0[:, 2], 'b-')
    axes[0].set_xlabel('time [s]')
    axes[0].set_ylabel('Acceleration [G]')
    axes[0].legend(data['X_labels'][0:3])

    axes[1].plot(t, Xt0[:, 3], 'r-')
    axes[1].plot(t, Xt0[:, 4], 'g-')
    axes[1].plot(t, Xt0[:, 5], 'b-')
    axes[1].set_xlabel('time [s]')
    axes[1].set_ylabel('Rotational Velocity [rad/s]')
    axes[1].legend(data['X_labels'][3:6])

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.209 seconds)


.. _sphx_glr_download_auto_examples_plot_watchdata.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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