.. _pylab_examples-markevery_demo: pylab_examples example code: markevery_demo.py ============================================== .. plot:: /home/tcaswell/other_source/matplotlib/doc/mpl_examples/pylab_examples/markevery_demo.py :: """ 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], polar = True)) axpolar[-1].set_title('markevery=%s' % str(case)) axpolar[-1].plot(theta, r, 'o', ls='-', ms=4, markevery=case) fig4.tight_layout() plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)