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Fix Normalize(<signed integer array>). #6837

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Merged
merged 2 commits into from
Aug 1, 2016
Merged

Fix Normalize(<signed integer array>). #6837

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1a2c4b0
Fix Normalize(<signed integer array>).
anntzer Jul 26, 2016
ac0c85a
Fix float normalization too (float32->float32).
anntzer Jul 26, 2016
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12 changes: 8 additions & 4 deletions lib/matplotlib/colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -884,8 +884,9 @@ def process_value(value):
if is_scalar:
value = [value]
dtype = np.min_scalar_type(value)
dtype = (np.float32 if dtype.itemsize <= 2
else np.promote_types(dtype, float))
if np.issubdtype(dtype, np.integer) or dtype.type is np.bool_:
# bool_/int8/int16 -> float32; int32/int64 -> float64
dtype = np.promote_types(dtype, np.float32)
result = np.ma.array(value, dtype=dtype, copy=True)
return result, is_scalar

Expand All @@ -903,7 +904,9 @@ def __call__(self, value, clip=None):
result, is_scalar = self.process_value(value)

self.autoscale_None(result)
vmin, vmax = self.vmin, self.vmax
# Convert at least to float, without losing precision.
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)
if vmin == vmax:
result.fill(0) # Or should it be all masked? Or 0.5?
elif vmin > vmax:
Expand All @@ -927,7 +930,8 @@ def __call__(self, value, clip=None):
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)

if cbook.iterable(value):
val = np.ma.asarray(value)
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6 changes: 6 additions & 0 deletions lib/matplotlib/tests/test_colors.py
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Expand Up @@ -194,6 +194,12 @@ def test_Normalize():
_scalar_tester(norm, vals)
_mask_tester(norm, vals)

# Handle integer input correctly (don't overflow when computing max-min,
# i.e. 127-(-128) here).
vals = np.array([-128, 127], dtype=np.int8)
norm = mcolors.Normalize(vals.min(), vals.max())
assert_array_equal(np.asarray(norm(vals)), [0, 1])

# Don't lose precision on longdoubles (float128 on Linux):
# for array inputs...
vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
Expand Down