8000 Add PiecewiseLinearNorm by dopplershift · Pull Request #4666 · matplotlib/matplotlib · GitHub
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Add PiecewiseLinearNorm #4666

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Add PiecewiseLinearNorm #4666

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7e93605
ENH: Add OffsetNorm and tests
phobson Nov 27, 2014
5e7c187
fix OffsetNorm docstring to numpydoc spec
phobson Mar 30, 2015
9a0b603
OffsetNorm blurb in whats_new
phobson Mar 30, 2015
fd9e624
removed OffsetNorm.inverse method
phobson Jun 6, 2015
633505d
attempting to create a baseline image
phobson Jun 6, 2015
ab11380
fix bad indexing when returning a scalar
phobson Jun 8, 2015
fa57cdd
the real test image
phobson Jun 8, 2015
9a01b9f
refactor tests a bit to handle non-invertable Norms
phobson Jun 8, 2015
1d8eb25
OffsetNorm -> PiecewiseLinearNorm
dopplershift Jul 11, 2015
57ca4fa
Merge branch 'add-asym-norm'
dopplershift Jul 11, 2015
2919b53
Propagate mask from input data
jkseppan Sep 3, 2015
bfc96a6
Remove unused clip parameter
jkseppan Sep 3, 2015
58c505c
Use process_value in PiecewiseLinearNorm
jkseppan Sep 3, 2015
b069a94
Return a scalar when passed in a scalar
jkseppan Sep 3, 2015
fc8ce8c
Return 0.5 for vcenter == vmax
jkseppan Sep 3, 2015
426353c
pep8 fixes
jkseppan Sep 3, 2015
ebd3753
8000 Merge pull request #2 from jkseppan/add-asym-norm
dopplershift Sep 3, 2015
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removed OffsetNorm.inverse method 
This will allow the ticks of colors to be spaced as desired.
Also simplified the math per the brilliant @joferkington
http://stackoverflow.com/a/20146989/1552748
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@phobson
phobson committed Jun 6, 2015
commit fd9e62480a52354bdfe20cc4ac2931ae963960cd
36 changes: 2 additions & 34 deletions lib/matplotlib/colors.py
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Expand Up @@ -1033,46 +1033,14 @@ def __call__(self, value, clip=None):
result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
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If clip is true, result is assigned here, but it's assigned again below.

mask=mask)

# ma division is very slow; we can take a shortcut
resdat = result.data

#First scale to -1 to 1 range, than to from 0 to 1.
resdat -= vcenter
resdat[resdat > 0] /= abs(vmax - vcenter)
resdat[resdat < 0] /= abs(vmin - vcenter)

resdat /= 2.
resdat += 0.5
result = np.ma.array(resdat, mask=result.mask, copy=False)
x, y = [vmin, vcenter, vmax], [0, 0.5, 1]
result = np.ma.masked_array(np.interp(value, x, y))

if is_scalar:
result = result[0]

return result

def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")

vmin, vcenter, vmax = self.vmin, self.vcenter, self.vmax
vmin = float(self.vmin)
vcenter = float(self.vcenter)
vmax = float(self.vmax)

if cbook.iterable(value):
val = ma.asarray(value)
val = 2 * (val - 0.5)
val[val > 0] *= abs(vmax - vcenter)
val[val < 0] *= abs(vmin - vcenter)
val += vcenter
return val
else:
val = 2 * (val - 0.5)
if val < 0:
return val * abs(vmin - vcenter) + vcenter
else:
return val * abs(vmax - vcenter) + vcenter

def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is None and np.size(A) > 0:
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