

.. _sphx_glr_gallery_animation_unchained.py:


========================
MATPLOTLIB **UNCHAINED**
========================

Comparative path demonstration of frequency from a fake signal of a pulsar.
(mostly known because of the cover for Joy Division's Unknown Pleasures)

Author: Nicolas P. Rougier




.. image:: /gallery/animation/images/sphx_glr_unchained_001.png
    :align: center





.. code-block:: python

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

    # Fixing random state for reproducibility
    np.random.seed(19680801)


    # Create new Figure with black background
    fig = plt.figure(figsize=(8, 8), facecolor='black')

    # Add a subplot with no frame
    ax = plt.subplot(111, frameon=False)

    # Generate random data
    data = np.random.uniform(0, 1, (64, 75))
    X = np.linspace(-1, 1, data.shape[-1])
    G = 1.5 * np.exp(-4 * X * X)

    # Generate line plots
    lines = []
    for i in range(len(data)):
        # Small reduction of the X extents to get a cheap perspective effect
        xscale = 1 - i / 200.
        # Same for linewidth (thicker strokes on bottom)
        lw = 1.5 - i / 100.0
        line, = ax.plot(xscale * X, i + G * data[i], color="w", lw=lw)
        lines.append(line)

    # Set y limit (or first line is cropped because of thickness)
    ax.set_ylim(-1, 70)

    # No ticks
    ax.set_xticks([])
    ax.set_yticks([])

    # 2 part titles to get different font weights
    ax.text(0.5, 1.0, "MATPLOTLIB ", transform=ax.transAxes,
            ha="right", va="bottom", color="w",
            family="sans-serif", fontweight="light", fontsize=16)
    ax.text(0.5, 1.0, "UNCHAINED", transform=ax.transAxes,
            ha="left", va="bottom", color="w",
            family="sans-serif", fontweight="bold", fontsize=16)


    def update(*args):
        # Shift all data to the right
        data[:, 1:] = data[:, :-1]

        # Fill-in new values
        data[:, 0] = np.random.uniform(0, 1, len(data))

        # Update data
        for i in range(len(data)):
            lines[i].set_ydata(i + G * data[i])

        # Return modified artists
        return lines

    # Construct the animation, using the update function as the animation
    # director.
    anim = animation.FuncAnimation(fig, update, interval=10)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.099 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
