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Backport PR #10133 on branch v2.1.x #10255

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Merged
merged 1 commit into from
Jan 16, 2018
Merged

Backport PR #10133 on branch v2.1.x #10255

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27 changes: 19 additions & 8 deletions lib/matplotlib/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -364,17 +364,25 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,

# TODO slice input array first
inp_dtype = A.dtype
if inp_dtype.kind == 'f':
scaled_dtype = A.dtype
else:
scaled_dtype = np.float32

a_min = A.min()
a_max = A.max()
# figure out the type we should scale to. For floats,
# leave as is. For integers cast to an appropriate-sized
# float. Small integers get smaller floats in an attempt
# to keep the memory footprint reasonable.
if a_min is np.ma.masked:
a_min, a_max = 0, 1 # all masked, so values don't matter
# all masked, so values don't matter
a_min, a_max = np.int32(0), np.int32(1)
if inp_dtype.kind == 'f':
scaled_dtype = A.dtype
else:
a_min = a_min.astype(scaled_dtype)
a_max = A.max().astype(scaled_dtype)
# probably an integer of some type.
da = a_max.astype(np.float64) - a_min.astype(np.float64)
if da > 1e8:
# give more breathing room if a big dynamic range
scaled_dtype = np.float64
else:
scaled_dtype = np.float32

# scale the input data to [.1, .9]. The Agg
# interpolators clip to [0, 1] internally, use a
Expand All @@ -386,6 +394,9 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
A_scaled = np.empty(A.shape, dtype=scaled_dtype)
A_scaled[:] = A
A_scaled -= a_min
a_min = a_min.astype(scaled_dtype)
a_max = a_max.astype(scaled_dtype)

if a_min != a_max:
A_scaled /= ((a_max - a_min) / 0.8)
A_scaled += 0.1
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Binary file added lib/matplotlib/tests/baseline_images/test_image/imshow_bignumbers.png
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13 changes: 13 additions & 0 deletions lib/matplotlib/tests/test_image.py
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Expand Up @@ -806,6 +806,19 @@ def test_imshow_flatfield():
im.set_clim(.5, 1.5)


@image_comparison(baseline_images=['imshow_bignumbers'],
remove_text=True, style='mpl20',
extensions=['png'])
def test_imshow_bignumbers():
# putting a big number in an array of integers shouldn't
# ruin the dynamic range of the resolved bits.
fig, ax = plt.subplots()
img = np.array([[1, 2, 1e12],[3, 1, 4]], dtype=np.uint64)
pc = ax.imshow(img)
pc.set_clim(0, 5)
plt.show()


@pytest.mark.parametrize(
"make_norm",
[colors.Normalize,
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