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

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

.. _sphx_glr_auto_examples_plot_stacks.py:


Stacked plots
=============
An example of a stacked plot.

.. GENERATED FROM PYTHON SOURCE LINES 6-56



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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_stacks_001.svg
         :alt: plot stacks
         :srcset: /auto_examples/images/sphx_glr_plot_stacks_001.svg
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_stacks_002.svg
         :alt: plot stacks
         :srcset: /auto_examples/images/sphx_glr_plot_stacks_002.svg
         :class: sphx-glr-multi-img





.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt
    import niceplots

    plt.style.use(niceplots.get_style())

    # Set the random seed to get consistent results.
    np.random.seed(314)

    # Create some fake data to plot.
    # Time will be the x-axis, position and velocity of a damped oscilator will be two plots stacked on top of each other
    # for the y-axes.
    nn = 30
    n_lines = 4
    phases = np.random.random_sample(n_lines) * 2.0 * np.pi
    damping = -np.random.random_sample(n_lines) * 2.0 * 250.0
    time = np.linspace(0, 250.0, nn)
    data = []

    for phase, damp in zip(phases, damping):
        Position = 1.0 + np.sin(2 * np.pi * time / 100 + phase) * np.exp(time / damp)
        Velocity = 2 * np.pi / 100 * np.cos(2 * np.pi * time / 100 + phase) * np.exp(time / damp)

        data.append({})
        data[-1]["Position (m)"] = Position
        data[-1]["Velocity (m/s)"] = Velocity

    # Call the stacked_plots function with the data we created.
    # Give the x-axis label and data first, then the dictionary containing all
    # y-axes information.
    # You can set some plot options here as well, like the pad distance for the
    # y-labels from the y-axes if you have long labels.
    # This function will save a pdf with the filename given.
    f, axarr = niceplots.stacked_plots("Time (s)", time, data[0], figsize=(10, 6), filename="opt_stacks.png")
    f.savefig("opt_stacks.svg")

    # stacked_plots can also accept a list of dicts to plot multiple strains
    # of data on the same set of plots.
    # We grabbed the fig and axarr handles here so that we can modify the plot.
    # Also, since there are many lines, we use the line_scaler option to reduce their thickness and keep the plot clean
    f, axarr = niceplots.stacked_plots(
        "Time (s)",
        time,
        data,
        figsize=(10, 6),
        line_scaler=0.5,
        filename="opt_stacks_more_data.png",
    )
    f.savefig("opt_stacks_more_data.svg")


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

   **Total running time of the script:** (0 minutes 1.645 seconds)


.. _sphx_glr_download_auto_examples_plot_stacks.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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