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# Scales

Illustrate the scale transformations applied to axes, e.g. log, symlog, logit.

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.ticker import NullFormatter

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

    # make up some data in the interval ]0, 1[
    y = np.random.normal(loc=0.5, scale=0.4, size=1000)
    y = y[(y > 0) & (y < 1)]
    y.sort()
    x = np.arange(len(y))

    # plot with various axes scales
    fig, axs = plt.subplots(2, 2, sharex=True)
    fig.subplots_adjust(left=0.08, right=0.98, wspace=0.3)

    # linear
    ax = axs[0, 0]
    ax.plot(x, y)
    ax.set_yscale('linear')
    ax.set_title('linear')
    ax.grid(True)

    # log
    ax = axs[0, 1]
    ax.plot(x, y)
    ax.set_yscale('log')
    ax.set_title('log')
    ax.grid(True)

    # symmetric log
    ax = axs[1, 1]
    ax.plot(x, y - y.mean())
    ax.set_yscale('symlog', linthreshy=0.02)
    ax.set_title('symlog')
    ax.grid(True)

    # logit
    ax = axs[1, 0]
    ax.plot(x, y)
    ax.set_yscale('logit')
    ax.set_title('logit')
    ax.grid(True)
    ax.yaxis.set_minor_formatter(NullFormatter())

    plt.show()

Total running time of the script: ( 0 minutes 0.104 seconds)

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