Add tutorial about autoscaling (#18840)

* Add tutorial about autoscaling

* Condensed  arguments formatting

* Language corrections.

* Apply suggestions from code review

Many thanks to @jklymak. I accept the suggestions as they are,
but I sense that there will be need for some code fixups.

Co-authored-by: Jody Klymak <jklymak@gmail.com>

* Fix code after applying suggestions.

* Apply all suggestions from code review

Will be revised.

Co-authored-by: Jody Klymak <jklymak@gmail.com>

* Fix flake8 formatting.

* Apply suggestions from code review

Co-authored-by: Tim Hoffmann <2836374+timhoffm@users.noreply.github.com>

* Remove fig.show()

* Don't use "unchanged" use "default"

Co-authored-by: Jody Klymak <jklymak@gmail.com>
Co-authored-by: Tim Hoffmann <2836374+timhoffm@users.noreply.github.com>

tutorials/intermediate/autoscale.py

@@ -0,0 +1,172 @@
+"""
+Autoscaling
+===========
+
+The limits on an axis can be set manually (e.g. ``ax.set_xlim(xmin, xmax)``)
+or Matplotlib can set them automatically based on the data already on the axes.
+There are a number of options to this autoscaling behaviour, discussed below.
+"""
+
+###############################################################################
+# We will start with a simple line plot showing that autoscaling
+# extends the axis limits 5% beyond the data limits (-2π, 2π).
+
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
+y = np.sinc(x)
+
+fig, ax = plt.subplots()
+ax.plot(x, y)
+
+###############################################################################
+# Margins
+# -------
+# The default margin around the data limits is 5%:
+
+ax.margins()
+
+###############################################################################
+# The margins can be made larger using `~matplotlib.axes.Axes.margins`:
+
+fig, ax = plt.subplots()
+ax.plot(x, y)
+ax.margins(0.2, 0.2)
+
+###############################################################################
+# In general, margins can be in the range (-0.5, ∞), where negative margins set
+# the axes limits to a subrange of the data range, i.e. they clip data.
+# Using a single number for margins affects both axes, a single margin can be
+# customized using keyword arguments ``x`` or ``y``, but positional and keyword
+# interface cannot be combined.
+
+fig, ax = plt.subplots()
+ax.plot(x, y)
+ax.margins(y=-0.2)
+
+###############################################################################
+# Sticky edges
+# ------------
+# There are plot elements (`.Artist`\s) that are usually used without margins.
+# For example false-color images (e.g. created with `.Axes.imshow`) are not
+# considered in the margins calculation.
+#
+
+xx, yy = np.meshgrid(x, x)
+zz = np.sinc(np.sqrt((xx - 1)**2 + (yy - 1)**2))
+
+fig, ax = plt.subplots(ncols=2, figsize=(12, 8))
+ax[0].imshow(zz)
+ax[0].set_title("default margins")
+ax[1].imshow(zz)
+ax[1].margins(0.2)
+ax[1].set_title("margins(0.2)")
+
+###############################################################################
+# This override of margins is determined by "sticky edges", a
+# property of `.Artist` class that can suppress adding margins to axis
+# limits. The effect of sticky edges can be disabled on an Axes by changing
+# `~matplotlib.axes.Axes.use_sticky_edges`.
+# Artists have a property `.Artist.sticky_edges`, and the values of
+# sticky edges can be changed by writing to ``Artist.sticky_edges.x`` or
+# ``.Artist.sticky_edges.y``.
+#
+# The following example shows how overriding works and when it is needed.
+
+fig, ax = plt.subplots(ncols=3, figsize=(16, 10))
+ax[0].imshow(zz)
+ax[0].margins(0.2)
+ax[0].set_title("default use_sticky_edges\nmargins(0.2)")
+ax[1].imshow(zz)
+ax[1].margins(0.2)
+ax[1].use_sticky_edges = False
+ax[1].set_title("use_sticky_edges=False\nmargins(0.2)")
+ax[2].imshow(zz)
+ax[2].margins(-0.2)
+ax[2].set_title("default use_sticky_edges\nmargins(-0.2)")
+
+###############################################################################
+# We can see that setting ``use_sticky_edges`` to *False* renders the image
+# with requested margins.
+#
+# While sticky edges don't increase the axis limits through extra margins,
+# negative margins are still taken into accout. This can be seen in
+# the reduced limits of the third image.
+#
+# Controlling autoscale
+# ---------------------
+#
+# By default, the limits are
+# recalculated every time you add a new curve to the plot:
+
+fig, ax = plt.subplots(ncols=2, figsize=(12, 8))
+ax[0].plot(x, y)
+ax[0].set_title("Single curve")
+ax[1].plot(x, y)
+ax[1].plot(x * 2.0, y)
+ax[1].set_title("Two curves")
+
+###############################################################################
+# However, there are cases when you don't want to automatically adjust the
+# viewport to new data.
+#
+# One way to disable autoscaling is to manually set the
+# axis limit. Let's say that we want to see only a part of the data in
+# greater detail. Setting the ``xlim`` persists even if we add more curves to
+# the data. To recalculate the new limits  calling `.Axes.autoscale` will
+# toggle the functionality manually.
+
+fig, ax = plt.subplots(ncols=2, figsize=(12, 8))
+ax[0].plot(x, y)
+ax[0].set_xlim(left=-1, right=1)
+ax[0].plot(x + np.pi * 0.5, y)
+ax[0].set_title("set_xlim(left=-1, right=1)\n")
+ax[1].plot(x, y)
+ax[1].set_xlim(left=-1, right=1)
+ax[1].plot(x + np.pi * 0.5, y)
+ax[1].autoscale()
+ax[1].set_title("set_xlim(left=-1, right=1)\nautoscale()")
+
+###############################################################################
+# We can check that the first plot has autoscale disabled and that the second
+# plot has it enabled again by using `.Axes.get_autoscale_on()`:
+
+print(ax[0].get_autoscale_on())  # False means disabled
+print(ax[1].get_autoscale_on())  # True means enabled -> recalculated
+
+###############################################################################
+# Arguments of the autoscale function give us precise control over the process
+# of autoscaling. A combination of arguments ``enable``, and ``axis`` sets the
+# autoscaling feature for the selected axis (or both). The argument ``tight``
+# sets the margin of the selected axis to zero. To preserve settings of either
+# ``enable`` or ``tight`` you can set the opposite one to *None*, that way
+# it should not be modified. However, setting ``enable`` to *None* and tight
+# to *True* affects both axes regardless of the ``axis`` argument.
+
+fig, ax = plt.subplots()
+ax.plot(x, y)
+ax.margins(0.2, 0.2)
+ax.autoscale(enable=None, axis="x", tight=True)
+
+print(ax.margins())
+
+###############################################################################
+# Working with collections
+# ------------------------
+#
+# Autoscale works out of the box for all lines, patches, and images added to
+# the axes. One of the artists that it won't work with is a `.Collection`.
+# After adding a collection to the axes, one has to manually trigger the
+# `~matplotlib.axes.Axes.autoscale_view()` to recalculate
+# axes limits.
+
+fig, ax = plt.subplots()
+collection = mpl.collections.StarPolygonCollection(
+    5, 0, [250, ],  # five point star, zero angle, size 250px
+    offsets=np.column_stack([x, y]),  # Set the positions
+    transOffset=ax.transData,  # Propagate transformations of the Axes
+)
+ax.add_collection(collection)
+ax.autoscale_view()