8000 Merge pull request #20503 from meeseeksmachine/auto-backport-of-pr-20… · matplotlib/matplotlib@156a0f6 · GitHub
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timhoffmtimhoffm
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Merge pull request #20503 from meeseeksmachine/auto-backport-of-pr-20488-on-v3.4.x
Backport PR #20488 on branch v3.4.x (FIX: Include 0 when checking lognorm vmin)
2 parents df9fa06 + dcb3708 commit 156a0f6

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‎lib/matplotlib/image.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -534,9 +534,9 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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# we have re-set the vmin/vmax to account for small errors
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# that may have moved input values in/out of range
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s_vmin, s_vmax = vrange
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if isinstance(self.norm, mcolors.LogNorm):
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if s_vmin < 0:
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s_vmin = max(s_vmin, np.finfo(scaled_dtype).eps)
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if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0:
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# Don't give 0 or negative values to LogNorm
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s_vmin = np.finfo(scaled_dtype).eps
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with cbook._setattr_cm(self.norm,
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vmin=s_vmin,
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vmax=s_vmax,

‎lib/matplotlib/tests/test_image.py

Lines changed: 10 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -1213,23 +1213,24 @@ def test_imshow_quantitynd():
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fig.canvas.draw()
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@pytest.mark.parametrize('x', [-1, 1])
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@check_figures_equal(extensions=['png'])
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def test_huge_range_log(fig_test, fig_ref):
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data = np.full((5, 5), -1, dtype=np.float64)
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def test_huge_range_log(fig_test, fig_ref, x):
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# parametrize over bad lognorm -1 values and large range 1 -> 1e20
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data = np.full((5, 5), x, dtype=np.float64)
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data[0:2, :] = 1E20
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ax = fig_test.subplots()
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im = ax.imshow(data, norm=colors.LogNorm(vmin=100, vmax=data.max()),
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interpolation='nearest', cmap='viridis')
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ax.imshow(data, norm=colors.LogNorm(vmin=1, vmax=data.max()),
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interpolation='nearest', cmap='viridis')
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data = np.full((5, 5), -1, dtype=np.float64)
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data = np.full((5, 5), x, dtype=np.float64)
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data[0:2, :] = 1000
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cmap = copy(plt.get_cmap('viridis'))
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cmap.set_under('w')
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ax = fig_ref.subplots()
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im = ax.imshow(data, norm=colors.Normalize(vmin=100, vmax=data.max()),
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interpolation='nearest', cmap=cmap)
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cmap = plt.get_cmap('viridis').with_extremes(under='w')
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ax.imshow(data, norm=colors.Normalize(vmin=1, vmax=data.max()),
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interpolation='nearest', cmap=cmap)
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@check_figures_equal()

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