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# Markevery Demo

This example demonstrates the various options for showing a marker at a subset of data points using the `markevery` property of a Line2D object.

Integer arguments are fairly intuitive. e.g. `markevery=5` will plot every 5th marker starting from the first data point.

Float arguments allow markers to be spaced at approximately equal distances along the line. The theoretical distance along the line between markers is determined by multiplying the display-coordinate distance of the axes bounding-box diagonal by the value of `markevery`. The data points closest to the theoretical distances will be shown.

A slice or list/array can also be used with `markevery` to specify the markers to show.

    from __future__ import division
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec

    # define a list of markevery cases to plot
    cases = [None,
             8,
             (30, 8),
             [16, 24, 30], [0, -1],
             slice(100, 200, 3),
             0.1, 0.3, 1.5,
             (0.0, 0.1), (0.45, 0.1)]

    # define the figure size and grid layout properties
    figsize = (10, 8)
    cols = 3
    gs = gridspec.GridSpec(len(cases) // cols + 1, cols)

    # define the data for cartesian plots
    delta = 0.11
    x = np.linspace(0, 10 - 2 * delta, 200) + delta
    y = np.sin(x) + 1.0 + delta

    # plot each markevery case for linear x and y scales
    fig1 = plt.figure(num=1, figsize=figsize)
    ax = []
    for i, case in enumerate(cases):
        row = (i // cols)
        col = i % cols
        ax.append(fig1.add_subplot(gs[row, col]))
        ax[-1].set_title('markevery=%s' % str(case))
        ax[-1].plot(x, y, 'o', ls='-', ms=4, markevery=case)
    #fig1.tight_layout()

    # plot each markevery case for log x and y scales
    fig2 = plt.figure(num=2, figsize=figsize)
    axlog = []
    for i, case in enumerate(cases):
        row = (i // cols)
        col = i % cols
        axlog.append(fig2.add_subplot(gs[row, col]))
        axlog[-1].set_title('markevery=%s' % str(case))
        axlog[-1].set_xscale('log')
        axlog[-1].set_yscale('log')
        axlog[-1].plot(x, y, 'o', ls='-', ms=4, markevery=case)
    fig2.tight_layout()

    # plot each markevery case for linear x and y scales but zoomed in
    # note the behaviour when zoomed in.  When a start marker offset is specified
    # it is always interpreted with respect to the first data point which might be
    # different to the first visible data point.
    fig3 = plt.figure(num=3, figsize=figsize)
    axzoom = []
    for i, case in enumerate(cases):
        row = (i // cols)
        col = i % cols
        axzoom.append(fig3.add_subplot(gs[row, col]))
        axzoom[-1].set_title('markevery=%s' % str(case))
        axzoom[-1].plot(x, y, 'o', ls='-', ms=4, markevery=case)
        axzoom[-1].set_xlim((6, 6.7))
        axzoom[-1].set_ylim((1.1, 1.7))
    fig3.tight_layout()

    # define data for polar plots
    r = np.linspace(0, 3.0, 200)
    theta = 2 * np.pi * r

    # plot each markevery case for polar plots
    fig4 = plt.figure(num=4, figsize=figsize)
    axpolar = []
    for i, case in enumerate(cases):
        row = (i // cols)
        col = i % cols
        axpolar.append(fig4.add_subplot(gs[row, col], projection='polar'))
        axpolar[-1].set_title('markevery=%s' % str(case))
        axpolar[-1].plot(theta, r, 'o', ls='-', ms=4, markevery=case)
    fig4.tight_layout()

    plt.show()

Total running time of the script: ( 0 minutes 2.199 seconds)

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