FIX: special-case LogNorm when resealing

tacaswell · tacaswell · commit b23708a5af1a · 2020-09-11T17:15:30.000-04:00
closes #18415 We need to special case when rescaling the user input for interpolation with LogNorm. The issue is that due to the inherent precision limits of floating point math we can end up with errors on the order if 1e-18 of the overall data range when rescaling. If the bottom vlim is close to 0 and the top is order 10e20 than this error can result in the rescaled vmin being negative which then fails when we (temporarily) change the vmin/vmax on the norm. We started adjusting the vmin/vmax to work around the same issue with float precision in #17636 / 3dea5c7 to make sure that values exactly equal to the limits did not move across the boundary and get erroneously mapped is over/under. Long term we may want to add the ability for the norms to suggest to their clients what the constraints on the vmin/vmax are, but hard-coding a special case for LogNorm is the pragmatic solution.
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
@@ -534,9 +534,13 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                 resampled_masked = np.ma.masked_array(A_resampled, out_mask)
                 # we have re-set the vmin/vmax to account for small errors
                 # that may have moved input values in/out of range
+                s_vmin, s_vmax = vrange
+                if isinstance(self.norm, mcolors.LogNorm):
+                    if s_vmin < 0:
+                        s_vmin = max(s_vmin, np.finfo(scaled_dtype).eps)
                 with cbook._setattr_cm(self.norm,
-                                       vmin=vrange[0],
-                                       vmax=vrange[1],
+                                       vmin=s_vmin,
+                                       vmax=s_vmax,
                                        ):
                     output = self.norm(resampled_masked)
             else:
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -1204,3 +1204,22 @@ def test_imshow_quantitynd():
     ax.imshow(arr)
     # executing the draw should not raise an exception
     fig.canvas.draw()
+
+
+@check_figures_equal(extensions=['png'])
+def test_huge_range_log(fig_test, fig_ref):
+    data = np.full((5, 5), -1, dtype=np.float64)
+    data[0:2, :] = 1E20
+
+    ax = fig_test.subplots()
+    im = ax.imshow(data, norm=colors.LogNorm(vmin=100, vmax=data.max()),
+                   interpolation='nearest', cmap='viridis')
+
+    data = np.full((5, 5), -1, dtype=np.float64)
+    data[0:2, :] = 1000
+
+    cm = copy(plt.get_cmap('viridis'))
+    cm.set_under('w')
+    ax = fig_ref.subplots()
+    im = ax.imshow(data, norm=colors.Normalize(vmin=100, vmax=data.max()),
+                   interpolation='nearest', cmap=cm)
