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Don't convert vmin, vmax to floats. #6700

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merged 2 commits into from
Jul 12, 2016
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Don't convert vmin, vmax to floats. #6700

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16f9778
Don't convert vmin, vmax to floats.
anntzer Jul 6, 2016
ff78a06
Proper norm'ing of float128 scalars too.
anntzer Jul 8, 2016
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28 changes: 8 additions & 20 deletions lib/matplotlib/colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -880,22 +880,13 @@ def process_value(value):
Experimental; we may want to add an option to force the
use of float32.
"""
if cbook.iterable(value):
is_scalar = False
result = np.ma.asarray(value)
if result.dtype.kind == 'f':
# this is overkill for lists of floats, but required
# to support pd.Series as input until we can reliable
# determine if result and value share memory in all cases
# (list, tuple, deque, ndarray, Series, ...)
result = result.copy()
elif result.dtype.itemsize > 2:
result = result.astype(float)
else:
result = result.astype(np.float32)
else:
is_scalar = True
result = np.ma.array([value]).astype(float)
is_scalar = not cbook.iterable(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))
result = np.ma.array(value, dtype=dtype, copy=True)
return result, is_scalar

def __call__(self, value, clip=None):
Expand All @@ -918,8 +909,6 @@ def __call__(self, value, clip=None):
elif vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
else:
vmin = float(vmin)
vmax = float(vmax)
if clip:
mask = np.ma.getmask(result)
result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
Expand All @@ -938,8 +927,7 @@ def __call__(self, value, clip=None):
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin = float(self.vmin)
vmax = float(self.vmax)
vmin, vmax = self.vmin, self.vmax

if cbook.iterable(value):
val = np.ma.asarray(value)
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10 changes: 10 additions & 0 deletions lib/matplotlib/tests/test_colors.py
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Expand Up @@ -194,6 +194,16 @@ def test_Normalize():
_scalar_tester(norm, vals)
_mask_tester(norm, vals)

# Don't lose precision on longdoubles (float128 on Linux):
# for array inputs...
vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
norm = mcolors.Normalize(vals.min(), vals.max())
assert_array_equal(np.asarray(norm(vals)), [0, 1])
# and for scalar ones.
eps = np.finfo(np.longdouble).resolution
norm = plt.Normalize(1, 1 + 100 * eps)
assert_equal(norm(1 + 50 * eps), .5)


def test_SymLogNorm():
"""
Expand Down