Merge branch 'main' into output-base-name

Author: asmeurer

.github/workflows/codeql-analysis.yml

@@ -32,7 +32,7 @@ jobs:
           persist-credentials: false
 
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@ff0a06e83cb2de871e5a09832bc6a81e7276941f  # v3.28.18
+        uses: github/codeql-action/init@fca7ace96b7d713c7035871441bd52efbe39e27e  # v3.28.19
         with:
           languages: ${{ matrix.language }}
 
@@ -43,4 +43,4 @@ jobs:
           pip install --user -v .
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@ff0a06e83cb2de871e5a09832bc6a81e7276941f  # v3.28.18
+        uses: github/codeql-action/analyze@fca7ace96b7d713c7035871441bd52efbe39e27e  # v3.28.19

galleries/examples/color/colorbar_histogram.py

@@ -0,0 +1,48 @@
+"""
+=====================
+Histogram as colorbar
+=====================
+
+This example demonstrates how to use a colored histogram instead of a colorbar
+to not only show the color-to-value mapping, but also visualize the
+distribution of values.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import matplotlib.colors as mcolors
+
+# surface data
+delta = 0.025
+x = y = np.arange(-2.0, 2.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = np.exp(-(((X + 1) * 1.3) ** 2) - ((Y + 1) * 1.3) ** 2)
+Z2 = 2.5 * np.exp(-((X - 1) ** 2) - (Y - 1) ** 2)
+Z = Z1**0.25 - Z2**0.5
+
+# colormap & normalization
+bins = 30
+cmap = plt.get_cmap("RdYlBu_r")
+bin_edges = np.linspace(Z.min(), Z.max(), bins + 1)
+norm = mcolors.BoundaryNorm(bin_edges, cmap.N)
+
+# main plot
+fig, ax = plt.subplots(layout="constrained")
+im = ax.imshow(Z, cmap=cmap, origin="lower", extent=[-3, 3, -3, 3], norm=norm)
+
+# inset histogram
+cax = ax.inset_axes([1.18, 0.02, 0.25, 0.95])  # left, bottom, width, height
+
+# plot histogram
+counts, _ = np.histogram(Z, bins=bin_edges)
+midpoints = (bin_edges[:-1] + bin_edges[1:]) / 2
+distance = midpoints[1] - midpoints[0]
+cax.barh(midpoints, counts, height=0.8 * distance, color=cmap(norm(midpoints)))
+
+# styling
+cax.spines[:].set_visible(False)
+cax.set_yticks(bin_edges)
+cax.tick_params(axis="both", which="both", length=0)
+
+plt.show()

galleries/users_explain/text/fonts.py

@@ -27,30 +27,35 @@
 Matplotlib supports three font specifications (in addition to pdf 'core fonts',
 which are explained later in the guide):
 
-.. list-table:: Type of Fonts
-   :header-rows: 1
-
-   * - Type 1 (PDF)
-     - Type 3 (PDF/PS)
-     - TrueType (PDF)
-   * - One of the oldest types, introduced by Adobe
-     - Similar to Type 1 in terms of introduction
-     - Newer than previous types, used commonly today, introduced by Apple
-   * - Restricted subset of PostScript, charstrings are in bytecode
-     - Full PostScript language, allows embedding arbitrary code
-       (in theory, even render fractals when rasterizing!)
-     - Include a virtual machine that can execute code!
-   * - These fonts support font hinting
-     - Do not support font hinting
-     - Hinting supported (virtual machine processes the "hints")
-   * - Non-subsetted through Matplotlib
-     - Subsetted via external module ttconv
-     - Subsetted via external module
-       `fontTools <https://github.com/fonttools/fonttools>`__
+.. table:: Type of Fonts
+
+  +--------------------------+----------------------------+----------------------------+
+  | Type 1 (PDF with usetex) | Type 3 (PDF/PS)            | TrueType (PDF)             |
+  +==========================+============================+============================+
+  | One of the oldest types, | Similar to Type 1 in       | Newer than previous types, |
+  | introduced by Adobe      | terms of introduction      | used commonly today,       |
+  |                          |                            | introduced by Apple        |
+  +--------------------------+----------------------------+----------------------------+
+  | Restricted subset of     | Full PostScript language,  | Includes a virtual machine |
+  | PostScript, charstrings  | allows embedding arbitrary | that can execute code!     |
+  | are in bytecode          | code (in theory, even      |                            |
+  |                          | render fractals when       |                            |
+  |                          | rasterizing!)              |                            |
+  +--------------------------+----------------------------+----------------------------+
+  | Supports font            | Does not support font      | Supports font hinting      |
+  | hinting                  | hinting                    | (virtual machine processes |
+  |                          |                            | the "hints")               |
+  +--------------------------+----------------------------+----------------------------+
+  | Subsetted by code in     | Subsetted via external module                           |
+  | `matplotlib._type1font`  | `fontTools <https://github.com/fonttools/fonttools>`__  |
+  +--------------------------+----------------------------+----------------------------+
 
 .. note::
 
    Adobe disabled__ support for authoring with Type 1 fonts in January 2023.
+   Matplotlib uses Type 1 fonts for compatibility with TeX; when the usetex
+   feature is used with the PDF backend, Matplotlib reads the fonts used by
+   the TeX engine, which are usually Type 1.
 
    __ https://helpx.adobe.com/fonts/kb/postscript-type-1-fonts-end-of-support.html
 
@@ -83,14 +88,12 @@
 files, particularly with fonts with many glyphs such as those that support CJK
 (Chinese/Japanese/Korean).
 
-The solution to this problem is to subset the fonts used in the document and
-only embed the glyphs actually used.  This gets both vector text and small
-files sizes.  Computing the subset of the font required and writing the new
-(reduced) font are both complex problem and thus Matplotlib relies on
-`fontTools <https://fonttools.readthedocs.io/en/latest/>`__ and a vendored fork
-of ttconv.
-
-Currently Type 3, Type 42, and TrueType fonts are subsetted.  Type 1 fonts are not.
+To keep the output size reasonable while using vector fonts,
+Matplotlib embeds only the glyphs that are actually used in the document.
+This is known as font subsetting.
+Computing the font subset and writing the reduced font are both complex problems,
+which Matplotlib solves in most cases by using the
+`fontTools <https://fonttools.readthedocs.io/en/latest/>`__ library.
 
 Core Fonts
 ^^^^^^^^^^

lib/matplotlib/backends/backend_ps.py

@@ -406,7 +406,7 @@ class RendererPS(_backend_pdf_ps.RendererPDFPSBase):
     def __init__(self, width, height, pswriter, imagedpi=72):
         # Although postscript itself is dpi independent, we need to inform the
         # image code about a requested dpi to generate high resolution images
-        # and them scale them before embedding them.
+        # and then scale them before embedding them.
         super().__init__(width, height)
         self._pswriter = pswriter
         if mpl.rcParams['text.usetex']:

lib/matplotlib/cbook.py

@@ -2228,6 +2228,9 @@ def _g_sig_digits(value, delta):
     Return the number of significant digits to %g-format *value*, assuming that
     it is known with an error of *delta*.
     """
+    # For inf or nan, the precision doesn't matter.
+    if not math.isfinite(value):
+        return 0
     if delta == 0:
         if value == 0:
             # if both value and delta are 0, np.spacing below returns 5e-324
@@ -2241,11 +2244,10 @@ def _g_sig_digits(value, delta):
     # digits before the decimal point (floor(log10(45.67)) + 1 = 2): the total
     # is 4 significant digits.  A value of 0 contributes 1 "digit" before the
     # decimal point.
-    # For inf or nan, the precision doesn't matter.
     return max(
         0,
         (math.floor(math.log10(abs(value))) + 1 if value else 1)
-        - math.floor(math.log10(delta))) if math.isfinite(value) else 0
+        - math.floor(math.log10(delta)))
 
 
 def _unikey_or_keysym_to_mplkey(unikey, keysym):

lib/matplotlib/colors.py

@@ -949,7 +949,7 @@ def with_alpha(self, alpha):
         if not isinstance(alpha, Real):
             raise TypeError(f"'alpha' must be numeric or None, not {type(alpha)}")
         if not 0 <= alpha <= 1:
-            ValueError("'alpha' must be between 0 and 1, inclusive")
+            raise ValueError("'alpha' must be between 0 and 1, inclusive")
         new_cm = self.copy()
         if not new_cm._isinit:
             new_cm._init()