Merge pull request #21987 from meeseeksmachine/auto-backport-of-pr-21862-on-v3.5.x

QuLogic · web-flow · commit d912ccc9a555 · 2021-12-16T20:22:38.000-05:00
Backport PR #21862 on branch v3.5.x (DOC: Simplify markevery demo)
diff --git a/examples/lines_bars_and_markers/markevery_demo.py b/examples/lines_bars_and_markers/markevery_demo.py
@@ -3,99 +3,96 @@
 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.
+The ``markevery`` property of `.Line2D` allows drawing markers at a subset of
+data points.
+
+The list of possible parameters is specified at `.Line2D.set_markevery`.
+In short:
+
+- A single integer N draws every N-th marker.
+- A tuple of integers (start, N) draws every N-th marker, starting at data
+  index *start*.
+- A list of integers draws the markers at the specified indices.
+- A slice draws the markers at the sliced indices.
+- A float specifies the distance between markers as a fraction of the Axes
+  diagonal in screen space. This will lead to a visually uniform distribution
+  of the points along the line, irrespective of scales and zooming.
 """
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 # 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
-rows = len(cases) // cols + 1
-# define the data for cartesian plots
+cases = [
+    None,
+    8,
+    (30, 8),
+    [16, 24, 32],
+    [0, -1],
+    slice(100, 200, 3),
+    0.1,
+    0.4,
+    (0.2, 0.4)
+]
+
+# data points
 delta = 0.11
 x = np.linspace(0, 10 - 2 * delta, 200) + delta
 y = np.sin(x) + 1.0 + delta
 
-
-def trim_axs(axs, N):
-    """
-    Reduce *axs* to *N* Axes. All further Axes are removed from the figure.
-    """
-    axs = axs.flat
-    for ax in axs[N:]:
-        ax.remove()
-    return axs[:N]
-
 ###############################################################################
-# Plot each markevery case for linear x and y scales
+# markevery with linear scales
+# ----------------------------
 
-axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(rows, cols)
-axs = trim_axs(axs, len(cases))
-for ax, case in zip(axs, cases):
-    ax.set_title('markevery=%s' % str(case))
-    ax.plot(x, y, 'o', ls='-', ms=4, markevery=case)
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
+for ax, markevery in zip(axs.flat, cases):
+    ax.set_title(f'markevery={markevery}')
+    ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
 
 ###############################################################################
-# Plot each markevery case for log x and y scales
-
-axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(rows, cols)
-axs = trim_axs(axs, len(cases))
-for ax, case in zip(axs, cases):
-    ax.set_title('markevery=%s' % str(case))
+# markevery with log scales
+# -------------------------
+#
+# Note that the log scale causes a visual asymmetry in the marker distance for
+# when subsampling the data using an integer. In contrast, subsampling on
+# fraction of figure size creates even distributions, because it's based on
+# fractions of the Axes diagonal, not on data coordinates or data indices.
+
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
+for ax, markevery in zip(axs.flat, cases):
+    ax.set_title(f'markevery={markevery}')
     ax.set_xscale('log')
     ax.set_yscale('log')
-    ax.plot(x, y, 'o', ls='-', ms=4, markevery=case)
+    ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
 
 ###############################################################################
-# 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.
-
-axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(rows, cols)
-axs = trim_axs(axs, len(cases))
-for ax, case in zip(axs, cases):
-    ax.set_title('markevery=%s' % str(case))
-    ax.plot(x, y, 'o', ls='-', ms=4, markevery=case)
+# markevery on zoomed plots
+# -------------------------
+#
+# Integer-based *markevery* specifications select points from the underlying
+# data and are independent on the view. In contrast, float-based specifications
+# are related to the Axes diagonal. While zooming does not change the Axes
+# diagonal, it changes the displayed data range, and more points will be
+# displayed when zooming.
+
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
+for ax, markevery in zip(axs.flat, cases):
+    ax.set_title(f'markevery={markevery}')
+    ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
     ax.set_xlim((6, 6.7))
     ax.set_ylim((1.1, 1.7))
 
-# define data for polar plots
+###############################################################################
+# markevery on polar plots
+# ------------------------
+
 r = np.linspace(0, 3.0, 200)
 theta = 2 * np.pi * r
 
-###############################################################################
-# Plot each markevery case for polar plots
-
-axs = plt.figure(figsize=figsize, constrained_layout=True).subplots(
-    rows, cols, subplot_kw={'projection': 'polar'})
-axs = trim_axs(axs, len(cases))
-for ax, case in zip(axs, cases):
-    ax.set_title('markevery=%s' % str(case))
-    ax.plot(theta, r, 'o', ls='-', ms=4, markevery=case)
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True,
+                        subplot_kw={'projection': 'polar'})
+for ax, markevery in zip(axs.flat, cases):
+    ax.set_title(f'markevery={markevery}')
+    ax.plot(theta, r, 'o', ls='-', ms=4, markevery=markevery)
 
 plt.show()
