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DOC: Simpify markevery demo
- Reword description - Remove non-distinguishing examples: We don't need three float examples, (0.0, 0.1) is the same as 0.1. - This makes for 9 cases, which can be laid out easily in a 3x3 grid, so we don't need axes calculation and removal logic.
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‎examples/lines_bars_and_markers/markevery_demo.py

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Markevery Demo
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==============
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This example demonstrates the various options for showing a marker at a
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subset of data points using the ``markevery`` property of a Line2D object.
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Integer arguments are fairly intuitive. e.g. ``markevery=5`` will plot every
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5th marker starting from the first data point.
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Float arguments allow markers to be spaced at approximately equal distances
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along the line. The theoretical distance along the line between markers is
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determined by multiplying the display-coordinate distance of the axes
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bounding-box diagonal by the value of ``markevery``. The data points closest
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to the theoretical distances will be shown.
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A slice or list/array can also be used with ``markevery`` to specify the
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markers to show.
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The ``markevery`` property of `.Line2D` allows drawing markers only at a
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subset of data points.
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The exact list of possible parameters is specified at `.Line2D.set_markevery`.
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In short:
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- A single integer N draws every N-th marker.
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- A tuple of integers (start, N) draws every N-th marker, starting at index
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*start*.
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- A list of integers draws the markers at the specified indices.
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- A slice draws the markers at the sliced indices.
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- A float results in approximately even spacing along the curve. The distance
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between markers is length of the Axes diagonal in data coordinates
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multiplied by the given float.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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# define a list of markevery cases to plot
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cases = [None,
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8,
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(30, 8),
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[16, 24, 30], [0, -1],
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slice(100, 200, 3),
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0.1, 0.3, 1.5,
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(0.0, 0.1), (0.45, 0.1)]
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# define the figure size and grid layout properties
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figsize = (10, 8)
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cols = 3
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rows = len(cases) // cols + 1
< 10000 /code>
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# define the data for cartesian plots
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cases = [
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None,
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8,
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(30, 8),
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[16, 24, 32],
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[0, -1],
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slice(100, 200, 3),
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0.1,
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0.4,
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(0.2, 0.4)
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]
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# data points
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delta = 0.11
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x = np.linspace(0, 10 - 2 * delta, 200) + delta
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y = np.sin(x) + 1.0 + delta
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def trim_axs(axs, N):
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"""
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Reduce *axs* to *N* Axes. All further Axes are removed from the figure.
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"""
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axs = axs.flat
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for ax in axs[N:]:
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ax.remove()
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return axs[:N]
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###############################################################################
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# Plot each markevery case for linear x and y scales
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# markevery with linear sacles
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# ----------------------------
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axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(rows, cols)
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axs = trim_axs(axs, len(cases))
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for ax, case in zip(axs, cases):
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ax.set_title('markevery=%s' % str(case))
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ax.plot(x, y, 'o', ls='-', ms=4, markevery=case)
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fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
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for ax, markevery in zip(axs.flat, cases):
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ax.set_title(f'markevery={markevery}')
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ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
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###############################################################################
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# Plot each markevery case for log x and y scales
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axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(rows, cols)
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axs = trim_axs(axs, len(cases))
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for ax, case in zip(axs, cases):
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ax.set_title('markevery=%s' % str(case))
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# markevery with log scales
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# -------------------------
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#
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# Note that the log scale causes a visual asymmetry in the marker distance for
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# the integer-based subsampling. In contrast, the float-based subsampling
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# creates even distributions, because it's based on fractions of the Axes
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# diagonal, not on data coordinates or data indices.
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fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
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for ax, markevery in zip(axs.flat, cases):
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ax.set_title(f'markevery={markevery}')
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.plot(x, y, 'o', ls='-', ms=4, markevery=case)
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ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
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###############################################################################
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# Plot each markevery case for linear x and y scales but zoomed in
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# note the behaviour when zoomed in. When a start marker offset is specified
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# it is always interpreted with respect to the first data point which might be
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# different to the first visible data point.
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axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(rows, cols)
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axs = trim_axs(axs, len(cases))
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for ax, case in zip(axs, cases):
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ax.set_title('markevery=%s' % str(case))
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ax.plot(x, y, 'o', ls='-', ms=4, markevery=case)
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# markevery on zoomed plots
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# -------------------------
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#
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# When a start marker offset is specified it is always interpreted with
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# respect to the first data point which might be different to the first
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# visible data point.
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fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
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for ax, markevery in zip(axs.flat, cases):
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ax.set_title(f'markevery={markevery}')
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ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
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ax.set_xlim((6, 6.7))
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ax.set_ylim((1.1, 1.7))
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# define data for polar plots
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###############################################################################
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# markevery on polar plots
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# ------------------------
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r = np.linspace(0, 3.0, 200)
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theta = 2 * np.pi * r
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###############################################################################
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# Plot each markevery case for polar plots
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axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(
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rows, cols, subplot_kw={'projection': 'polar'})
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axs = trim_axs(axs, len(cases))
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for ax, case in zip(axs, cases):
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ax.set_title('markevery=%s' % str(case))
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ax.plot(theta, r, 'o', ls='-', ms=4, markevery=case)
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fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True,
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subplot_kw={'projection': 'polar'})
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for ax, markevery in zip(axs.flat, cases):
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ax.set_title(f'markevery={markevery}')
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ax.plot(theta, r, 'o', ls='-', ms=4, markevery=markevery)
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plt.show()

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