Raise warnings and resmpale on large data to _image.resample

dstansby · dstansby · commit 40b5664129e1 · 2021-09-07T19:13:13.000+01:00
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
@@ -7,6 +7,7 @@
 import os
 import logging
 from pathlib import Path
+import warnings
 
 import numpy as np
 import PIL.PngImagePlugin
@@ -166,7 +167,22 @@ def _resample(
     allocating the output array and fetching the relevant properties from the
     Image object *image_obj*.
     """
-
+    # AGG can only handle coordinates larger than 24-bit signed integers,
+    # so raise errors if the input data is larger than _image.resample can
+    # handle.
+    msg = ('Data with more than {n} cannot be accurately displayed. '
+           'Downsampling to less than {n} before displaying. '
+           'To remove this warning, manually downsample your data.')
+    if data.shape[1] > 2**23:
+        warnings.warn(msg.format(n='2**23 rows'))
+        step = int(np.ceil(data.shape[1] / 2**23))
+        data = data[:, ::step]
+        transform += Affine2D().scale(step, 1)
+    if data.shape[0] > 2**24:
+        warnings.warn(msg.format(n='2**24 columns'))
+        step = int(np.ceil(data.shape[0] / 2**24))
+        data = data[::step, :]
+        transform += Affine2D().scale(1, step)
     # decide if we need to apply anti-aliasing if the data is upsampled:
     # compare the number of displayed pixels to the number of
     # the data pixels.
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -1383,3 +1383,22 @@ def test_rgba_antialias():
     # alternating red and blue stripes become purple
     axs[3].imshow(aa, interpolation='antialiased', interpolation_stage='rgba',
                   cmap=cmap, vmin=-1.2, vmax=1.2)
+
+
+@pytest.mark.filterwarnings(r'ignore:Data with more than 2\*\*23 rows cannot be accurately displayed')
+@check_figures_equal()
+def test_large_image(fig_test, fig_ref):
+    ax_test = fig_test.subplots()
+    ax_ref = fig_ref.subplots()
+
+    array = np.zeros((1, 2**23 + 2))
+    array[:, array.size // 2:] = 1
+    im = ax_test.imshow(array, vmin=0, vmax=1,
+                        aspect='auto', extent=(0, 1, 0, 1),
+                        interpolation='none')
+
+    array = np.zeros((1, 2))
+    array[:, 1] = 1
+    im = ax_ref.imshow(array, vmin=0, vmax=1, aspect='auto',
+                       extent=(0, 1, 0, 1),
+                       interpolation='none')
