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Backport PR #20488 on branch v3.4.x (FIX: Include 0 when checking lognorm vmin) #20503

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Merged
merged 1 commit into from
Jun 24, 2021
Merged

Backport PR #20488 on branch v3.4.x (FIX: Include 0 when checking lognorm vmin) #20503

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6 changes: 3 additions & 3 deletions 6 lib/matplotlib/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -534,9 +534,9 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
# 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)
if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0:
# Don't give 0 or negative values to LogNorm
s_vmin = np.finfo(scaled_dtype).eps
with cbook._setattr_cm(self.norm,
vmin=s_vmin,
vmax=s_vmax,
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19 changes: 10 additions & 9 deletions 19 lib/matplotlib/tests/test_image.py
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Expand Up @@ -1213,23 +1213,24 @@ def test_imshow_quantitynd():
fig.canvas.draw()


@pytest.mark.parametrize('x', [-1, 1])
@check_figures_equal(extensions=['png'])
def test_huge_range_log(fig_test, fig_ref):
data = np.full((5, 5), -1, dtype=np.float64)
def test_huge_range_log(fig_test, fig_ref, x):
# parametrize over bad lognorm -1 values and large range 1 -> 1e20
data = np.full((5, 5), x, 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')
ax.imshow(data, norm=colors.LogNorm(vmin=1, vmax=data.max()),
interpolation='nearest', cmap='viridis')

data = np.full((5, 5), -1, dtype=np.float64)
data = np.full((5, 5), x, dtype=np.float64)
data[0:2, :] = 1000

cmap = copy(plt.get_cmap('viridis'))
cmap.set_under('w')
ax = fig_ref.subplots()
im = ax.imshow(data, norm=colors.Normalize(vmin=100, vmax=data.max()),
interpolation='nearest', cmap=cmap)
cmap = plt.get_cmap('viridis').with_extremes(under='w')
ax.imshow(data, norm=colors.Normalize(vmin=1, vmax=data.max()),
interpolation='nearest', cmap=cmap)


@check_figures_equal()
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