Matplotlib cheatsheets
+Cheatsheets
`,
+ e.g.
+
+ .. code-block:: bash
+
+ uv add matplotlib pyside6
+
+ .. _this issue: https://github.com/astral-sh/uv/issues/6893#issuecomment-2565965851
+
.. tab-item:: other
.. rst-class:: section-toc
diff --git a/doc/install/index.rst b/doc/install/index.rst
index 99ccc163a82e..2d9e724e6a73 100644
--- a/doc/install/index.rst
+++ b/doc/install/index.rst
@@ -28,7 +28,8 @@ precompiled wheel for your OS and Python.
The following backends work out of the box: Agg, ps, pdf, svg
Python is typically shipped with tk bindings which are used by
- TkAgg.
+ TkAgg. Notably, python-build-standalone – used by ``uv`` – does
+ not include tk bindings that are usable by Matplotlib.
For support of other GUI frameworks, LaTeX rendering, saving
animations and a larger selection of file formats, you can
diff --git a/doc/project/citing.rst b/doc/project/citing.rst
index 2cd317906bb5..d04ebec0d3c9 100644
--- a/doc/project/citing.rst
+++ b/doc/project/citing.rst
@@ -32,6 +32,9 @@ By version
.. START OF AUTOGENERATED
+v3.10.1
+ .. image:: ../_static/zenodo_cache/14940554.svg
+ :target: https://doi.org/10.5281/zenodo.14940554
v3.10.0
.. image:: ../_static/zenodo_cache/14464227.svg
:target: https://doi.org/10.5281/zenodo.14464227
diff --git a/environment.yml b/environment.yml
index 8c4221d0a0fa..d95cab0509ff 100644
--- a/environment.yml
+++ b/environment.yml
@@ -53,7 +53,7 @@ dependencies:
# testing
- black<24
- coverage
- - flake8>=3.8
+ - flake8>=3.8,<7.2
- flake8-docstrings>=1.4.0
- gtk4
- ipykernel
diff --git a/galleries/examples/scales/logit_demo.py b/galleries/examples/scales/logit_demo.py
index 22a56433ccd7..e8d42fc35711 100644
--- a/galleries/examples/scales/logit_demo.py
+++ b/galleries/examples/scales/logit_demo.py
@@ -4,6 +4,20 @@
===========
Examples of plots with logit axes.
+
+This example visualises how ``set_yscale("logit")`` works on probability plots
+by generating three distributions: normal, laplacian, and cauchy in one plot.
+
+The advantage of logit scale is that it effectively spreads out values close to 0 and 1.
+
+In a linear scale plot, probability values near 0 and 1 appear compressed,
+making it difficult to see differences in those regions.
+
+In a logit scale plot, the transformation expands these regions,
+making the graph cleaner and easier to compare across different probability values.
+
+This makes the logit scale especially useful when visalising probabilities in logistic
+regression, classification models, and cumulative distribution functions.
"""
import math
diff --git a/galleries/examples/shapes_and_collections/hatch_style_reference.py b/galleries/examples/shapes_and_collections/hatch_style_reference.py
index 724abde051b4..e0409d989e6b 100644
--- a/galleries/examples/shapes_and_collections/hatch_style_reference.py
+++ b/galleries/examples/shapes_and_collections/hatch_style_reference.py
@@ -62,3 +62,7 @@ def hatches_plot(ax, h):
# - `matplotlib.patches.Rectangle`
# - `matplotlib.axes.Axes.add_patch`
# - `matplotlib.axes.Axes.text`
+#
+# .. tags::
+#
+# purpose: reference
diff --git a/galleries/examples/text_labels_and_annotations/fancyarrow_demo.py b/galleries/examples/text_labels_and_annotations/fancyarrow_demo.py
index 8d0027831e4a..6a2700d20515 100644
--- a/galleries/examples/text_labels_and_annotations/fancyarrow_demo.py
+++ b/galleries/examples/text_labels_and_annotations/fancyarrow_demo.py
@@ -3,7 +3,11 @@
Annotation arrow style reference
================================
-Overview of the arrow styles available in `~.Axes.annotate`.
+Overview of the available `.ArrowStyle` settings. These are used for the *arrowstyle*
+parameter of `~.Axes.annotate` and `.FancyArrowPatch`.
+
+Each style can be configured with a set of parameters, which are stated along with
+their default values.
"""
import inspect
@@ -12,42 +16,38 @@
import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
+from matplotlib.patches import ArrowStyle
-styles = mpatches.ArrowStyle.get_styles()
+styles = ArrowStyle.get_styles()
ncol = 2
nrow = (len(styles) + 1) // ncol
-axs = (plt.figure(figsize=(4 * ncol, 1 + nrow))
- .add_gridspec(1 + nrow, ncol,
- wspace=.7, left=.1, right=.9, bottom=0, top=1).subplots())
+gridspec_kw = dict(wspace=0, hspace=0.05, left=0, right=1, bottom=0, top=1)
+fig, axs = plt.subplots(1 + nrow, ncol,
+ figsize=(4 * ncol, 1 + nrow), gridspec_kw=gridspec_kw)
for ax in axs.flat:
+ ax.set_xlim(-0.1, 4)
ax.set_axis_off()
for ax in axs[0, :]:
- ax.text(0, .5, "arrowstyle",
- transform=ax.transAxes, size="large", color="tab:blue",
- horizontalalignment="center", verticalalignment="center")
- ax.text(.35, .5, "default parameters",
- transform=ax.transAxes,
- horizontalalignment="left", verticalalignment="center")
+ ax.text(0, 0.5, "arrowstyle", size="large", color="tab:blue")
+ ax.text(1.4, .5, "default parameters", size="large")
for ax, (stylename, stylecls) in zip(axs[1:, :].T.flat, styles.items()):
- l, = ax.plot(.25, .5, "ok", transform=ax.transAxes)
- ax.annotate(stylename, (.25, .5), (-0.1, .5),
- xycoords="axes fraction", textcoords="axes fraction",
- size="large", color="tab:blue",
- horizontalalignment="center", verticalalignment="center",
+ # draw dot and annotation with arrowstyle
+ l, = ax.plot(1.25, 0, "o", color="darkgrey")
+ ax.annotate(stylename, (1.25, 0), (0, 0),
+ size="large", color="tab:blue", va="center", family="monospace",
arrowprops=dict(
- arrowstyle=stylename, connectionstyle="arc3,rad=-0.05",
- color="k", shrinkA=5, shrinkB=5, patchB=l,
+ arrowstyle=stylename, connectionstyle="arc3,rad=0",
+ color="black", shrinkA=5, shrinkB=5, patchB=l,
),
- bbox=dict(boxstyle="square", fc="w"))
+ bbox=dict(boxstyle="square", fc="w", ec="darkgrey"))
+ # draw default parameters
# wrap at every nth comma (n = 1 or 2, depending on text length)
s = str(inspect.signature(stylecls))[1:-1]
n = 2 if s.count(',') > 3 else 1
- ax.text(.35, .5,
+ ax.text(1.4, 0,
re.sub(', ', lambda m, c=itertools.count(1): m.group()
if next(c) % n else '\n', s),
- transform=ax.transAxes,
- horizontalalignment="left", verticalalignment="center")
+ verticalalignment="center", color="0.3")
plt.show()
diff --git a/galleries/examples/units/basic_units.py b/galleries/examples/units/basic_units.py
index 486918a8eafc..f7bdcc18b0dc 100644
--- a/galleries/examples/units/basic_units.py
+++ b/galleries/examples/units/basic_units.py
@@ -5,6 +5,14 @@
Basic units
===========
+
+This file implements a units library that supports registering arbitrary units,
+conversions between units, and math with unitized data. This library also implements a
+Matplotlib unit converter and registers its units with Matplotlib. This library is used
+in the examples to demonstrate Matplotlib's unit support. It is only maintained for the
+purposes of building documentation and should never be used outside of the Matplotlib
+documentation.
+
"""
import itertools
diff --git a/galleries/users_explain/figure/interactive_guide.rst b/galleries/users_explain/figure/interactive_guide.rst
index 5cc55edc0955..21658bb5849b 100644
--- a/galleries/users_explain/figure/interactive_guide.rst
+++ b/galleries/users_explain/figure/interactive_guide.rst
@@ -11,7 +11,7 @@ Interactive figures and asynchronous programming
Matplotlib supports rich interactive figures by embedding figures into
a GUI window. The basic interactions of panning and zooming in an
-Axes to inspect your data is "baked in" to Matplotlib. This is
+Axes to inspect your data is available out-of-the-box. This is
supported by a full mouse and keyboard event handling system that
you can use to build sophisticated interactive graphs.
@@ -23,6 +23,21 @@ handling system