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"""
==============
Markevery Demo
==============
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 matplotlib.pyplot as plt
import numpy as np
# define a list of markevery cases to plot
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
# %%
# markevery with linear scales
# ----------------------------
fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
for ax, markevery in zip(axs.flat, cases):
ax.set_title(f'markevery={markevery}')
ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
# %%
# 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), layout='constrained')
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=markevery)
# %%
# 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), layout='constrained')
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))
# %%
# markevery on polar plots
# ------------------------
r = np.linspace(0, 3.0, 200)
theta = 2 * np.pi * r
fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained',
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()
# %%
# .. tags::
#
# component: marker
# plot-type: line
# level: beginner
|
