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Diverging norm#5054

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jkseppan wants to merge16 commits intomatplotlib:masterfromjkseppan:diverging-norm
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92bc85b
ENH: Add OffsetNorm and tests
phobsonNov 27, 2014
17d12ca
fix OffsetNorm docstring to numpydoc spec
phobsonMar 30, 2015
a6b5d77
OffsetNorm blurb in whats_new
phobsonMar 30, 2015
8c5e14e
removed OffsetNorm.inverse method
phobsonJun 6, 2015
5a84ad2
attempting to create a baseline image
phobsonJun 6, 2015
68e67db
fix bad indexing when returning a scalar
phobsonJun 8, 2015
1f59b20
the real test image
phobsonJun 8, 2015
67e745b
refactor tests a bit to handle non-invertable Norms
phobsonJun 8, 2015
23f197a
OffsetNorm -> PiecewiseLinearNorm
dopplershiftJul 11, 2015
bb56923
Propagate mask from input data
jkseppanSep 3, 2015
35dec37
Remove unused clip parameter
jkseppanSep 3, 2015
5fc932f
Use process_value in PiecewiseLinearNorm
jkseppanSep 3, 2015
2cdf0a5
Return a scalar when passed in a scalar
jkseppanSep 3, 2015
53ed8ae
Return 0.5 for vcenter == vmax
jkseppanSep 3, 2015
b7f42f4
pep8 fixes
jkseppanSep 3, 2015
9d48773
Rename to DivergingNorm
jkseppanSep 13, 2015
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Return a scalar when passed in a scalar
Since possibly some earlier versions of numpy returned a scalar,wrap the value in atleast_1d before indexing.
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@jkseppan
jkseppan committedSep 13, 2015
commit2cdf0a5fd9c3879bf01a28830fb208fa3766a8ff
2 changes: 2 additions & 0 deletionslib/matplotlib/colors.py
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Expand Up@@ -1026,6 +1026,8 @@ def __call__(self, value, clip=None):
result = ma.masked_array(np.interp(result, x, y),
mask=ma.getmask(result))

if is_scalar:
result = np.atleast_1d(result)[0]
return result

def autoscale_None(self, A):
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