From c98c4b84f3517ffc94a53899f9005df56e5dbb3e Mon Sep 17 00:00:00 2001
From: Antony Lee <anntzer.lee@gmail.com>
Date: Tue, 20 Apr 2021 23:34:32 +0200
Subject: [PATCH] Shorten the repr of scaling transforms.

Scaling transforms (with just nonzero diagonal terms) are quite common,
and special-casing them makes many transform reprs shorter, helping with
debugging.  See e.g. changes in the test_transforms reference output.
---
 lib/matplotlib/tests/test_transforms.py | 25 +++++--------------------
 lib/matplotlib/transforms.py            |  9 ++++++++-
 2 files changed, 13 insertions(+), 21 deletions(-)

diff --git a/lib/matplotlib/tests/test_transforms.py b/lib/matplotlib/tests/test_transforms.py
index d073e8c13dea..a31e8de9951a 100644
--- a/lib/matplotlib/tests/test_transforms.py
+++ b/lib/matplotlib/tests/test_transforms.py
@@ -508,14 +508,8 @@ def test_str_transform():
                     IdentityTransform(),
                     IdentityTransform())),
             CompositeAffine2D(
-                Affine2D(
-                    [[1. 0. 0.]
-                     [0. 1. 0.]
-                     [0. 0. 1.]]),
-                Affine2D(
-                    [[1. 0. 0.]
-                     [0. 1. 0.]
-                     [0. 0. 1.]]))),
+                Affine2D().scale(1.0),
+                Affine2D().scale(1.0))),
         PolarTransform(
             PolarAxesSubplot(0.125,0.1;0.775x0.8),
             use_rmin=True,
@@ -537,14 +531,8 @@ def test_str_transform():
                     TransformedBbox(
                         Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
                         CompositeAffine2D(
-                            Affine2D(
-                                [[1. 0. 0.]
-                                 [0. 1. 0.]
-                                 [0. 0. 1.]]),
-                            Affine2D(
-                                [[1. 0. 0.]
-                                 [0. 1. 0.]
-                                 [0. 0. 1.]]))),
+                            Affine2D().scale(1.0),
+                            Affine2D().scale(1.0))),
                     LockableBbox(
                         Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
                         [[-- --]
@@ -555,10 +543,7 @@ def test_str_transform():
                 BboxTransformTo(
                     TransformedBbox(
                         Bbox(x0=0.0, y0=0.0, x1=8.0, y1=6.0),
-                        Affine2D(
-                            [[80.  0.  0.]
-                             [ 0. 80.  0.]
-                             [ 0.  0.  1.]])))))))"""
+                        Affine2D().scale(80.0)))))))"""
 
 
 def test_transform_single_point():
diff --git a/lib/matplotlib/transforms.py b/lib/matplotlib/transforms.py
index e2051a41346b..7866cf46fb56 100644
--- a/lib/matplotlib/transforms.py
+++ b/lib/matplotlib/transforms.py
@@ -1887,7 +1887,14 @@ def __init__(self, matrix=None, **kwargs):
         self._mtx = matrix.copy()
         self._invalid = 0
 
-    __str__ = _make_str_method("_mtx")
+    _base_str = _make_str_method("_mtx")
+
+    def __str__(self):
+        return (self._base_str()
+                if (self._mtx != np.diag(np.diag(self._mtx))).any()
+                else f"Affine2D().scale({self._mtx[0, 0]}, {self._mtx[1, 1]})"
+                if self._mtx[0, 0] != self._mtx[1, 1]
+                else f"Affine2D().scale({self._mtx[0, 0]})")
 
     @staticmethod
     def from_values(a, b, c, d, e, f):