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PiecewiseLinearNorm#5061

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05c1b76
ENH: Add OffsetNorm and tests
phobsonNov 27, 2014
8ad54f3
fix OffsetNorm docstring to numpydoc spec
phobsonMar 30, 2015
03e506c
OffsetNorm blurb in whats_new
phobsonMar 30, 2015
04ea107
removed OffsetNorm.inverse method
phobsonJun 6, 2015
62c9efc
attempting to create a baseline image
phobsonJun 6, 2015
d95db3c
fix bad indexing when returning a scalar
phobsonJun 8, 2015
3cc84a6
the real test image
phobsonJun 8, 2015
47c447a
refactor tests a bit to handle non-invertable Norms
phobsonJun 8, 2015
36042b6
OffsetNorm -> PiecewiseLinearNorm
dopplershiftJul 11, 2015
56cbae2
Propagate mask from input data
jkseppanSep 3, 2015
1d1ff98
Remove unused clip parameter
jkseppanSep 3, 2015
14b6837
Use process_value in PiecewiseLinearNorm
jkseppanSep 3, 2015
a8d4956
Return a scalar when passed in a scalar
jkseppanSep 3, 2015
31bfd2a
Return 0.5 for vcenter == vmax
jkseppanSep 3, 2015
a68fff3
pep8 fixes
jkseppanSep 3, 2015
3293971
PiecewiseLinearNorm
OceanWolfSep 14, 2015
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OffsetNorm -> PiecewiseLinearNorm
  • Loading branch information
@dopplershift@OceanWolf
dopplershift authored andOceanWolf committedSep 13, 2015
commit36042b6c800afab3f419b9b513069e4a1107f76e
2 changes: 1 addition & 1 deletiondoc/users/whats_new.rst
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Expand Up@@ -62,7 +62,7 @@ determines the placement of the arrow head along the quiver line.

Offset Normalizers for Colormaps
````````````````````````````````
Paul Hobson/Geosyntec Consultants added a new :class:`matplotlib.colors.OffsetNorm`
Paul Hobson/Geosyntec Consultants added a new :class:`matplotlib.colors.PiecewiseLinearNorm`
class with the help of Till Stensitzki. This is particularly useful when using a
diverging colormap on data that are asymetrically centered around a logical value
(e.g., 0 when data range from -2 to 4).
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4 changes: 2 additions & 2 deletionslib/matplotlib/colors.py
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Expand Up@@ -964,7 +964,7 @@ def scaled(self):
return (self.vmin is not None and self.vmax is not None)


classOffsetNorm(Normalize):
classPiecewiseLinearNorm(Normalize):
"""
A subclass of matplotlib.colors.Normalize.

Expand DownExpand Up@@ -998,7 +998,7 @@ def __init__(self, vmin=None, vcenter=None, vmax=None, clip=False):
Examples
--------
>>> import matplotlib.colors as mcolors
>>> offset = mcolors.OffsetNorm(vmin=-2., vcenter=0., vmax=4.)
>>> offset = mcolors.PiecewiseLinearNorm(vmin=-2., vcenter=0., vmax=4.)
>>> data = [-2., -1., 0., 1., 2., 3., 4.]
>>> offset(data)
array([0., 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])
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46 changes: 23 additions & 23 deletionslib/matplotlib/tests/test_colors.py
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Expand Up@@ -227,44 +227,44 @@ def test_process_value_array(self):
assert_array_equal(res, np.array([5., 10.]))


classBaseOffsetNorm(BaseNormMixin):
normclass = mcolors.OffsetNorm
classBasePiecewiseLinearNorm(BaseNormMixin):
normclass = mcolors.PiecewiseLinearNorm
test_inverse = False

classtest_OffsetNorm_Even(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_Even(BasePiecewiseLinearNorm):
def setup(self):
self.norm = self.normclass(vmin=-1, vcenter=0, vmax=4)
self.vals = np.array([-1.0, -0.5, 0.0, 1.0, 2.0, 3.0, 4.0])
self.expected = np.array([0.0, 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])


classtest_OffsetNorm_Odd(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_Odd(BasePiecewiseLinearNorm):
def setup(self):
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=-2, vcenter=0, vmax=5)
self.vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
self.expected = np.array([0.0, 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])


classtest_OffsetNorm_AllNegative(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_AllNegative(BasePiecewiseLinearNorm):
def setup(self):
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=-10, vcenter=-8, vmax=-2)
self.vals = np.array([-10., -9., -8., -6., -4., -2.])
self.expected = np.array([0.0, 0.25, 0.5, 0.666667, 0.833333, 1.0])


classtest_OffsetNorm_AllPositive(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_AllPositive(BasePiecewiseLinearNorm):
def setup(self):
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=0, vcenter=3, vmax=9)
self.vals = np.array([0., 1.5, 3., 4.5, 6.0, 7.5, 9.])
self.expected = np.array([0.0, 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])


classtest_OffsetNorm_NoVs(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_NoVs(BasePiecewiseLinearNorm):
def setup(self):
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=None, vcenter=None, vmax=None)
self.vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
self.expected = np.array([0., 0.16666667, 0.33333333,
Expand All@@ -289,26 +289,26 @@ def test_vmax(self):
assert_equal(self.norm.vmax, self.expected_vmax)


classtest_OffsetNorm_VminEqualsVcenter(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_VminEqualsVcenter(BasePiecewiseLinearNorm):
def setup(self):
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=-2, vcenter=-2, vmax=2)
self.vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
self.expected = np.array([0.5, 0.625, 0.75, 0.875, 1.0])


classtest_OffsetNorm_VmaxEqualsVcenter(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_VmaxEqualsVcenter(BasePiecewiseLinearNorm):
def setup(self):
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=-2, vcenter=2, vmax=2)
self.vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
self.expected = np.array([0.0, 0.125, 0.25, 0.375, 0.5])


classtest_OffsetNorm_VsAllEqual(BaseOffsetNorm):
classtest_PiecewiseLinearNorm_VsAllEqual(BasePiecewiseLinearNorm):
def setup(self):
self.v = 10
self.normclass = mcolors.OffsetNorm
self.normclass = mcolors.PiecewiseLinearNorm
self.norm = self.normclass(vmin=self.v, vcenter=self.v, vmax=self.v)
self.vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
self.expected = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
Expand All@@ -321,28 +321,28 @@ def test_inverse(self):
)


classtest_OffsetNorm_Errors(object):
classtest_PiecewiseLinearNorm_Errors(object):
def setup(self):
self.vals = np.arange(50)

@raises(ValueError)
def test_VminGTVcenter(self):
norm = mcolors.OffsetNorm(vmin=10, vcenter=0, vmax=20)
norm = mcolors.PiecewiseLinearNorm(vmin=10, vcenter=0, vmax=20)
norm(self.vals)

@raises(ValueError)
def test_VminGTVmax(self):
norm = mcolors.OffsetNorm(vmin=10, vcenter=0, vmax=5)
norm = mcolors.PiecewiseLinearNorm(vmin=10, vcenter=0, vmax=5)
norm(self.vals)

@raises(ValueError)
def test_VcenterGTVmax(self):
norm = mcolors.OffsetNorm(vmin=10, vcenter=25, vmax=20)
norm = mcolors.PiecewiseLinearNorm(vmin=10, vcenter=25, vmax=20)
norm(self.vals)

@raises(ValueError)
def test_premature_scaling(self):
norm = mcolors.OffsetNorm()
norm = mcolors.PiecewiseLinearNorm()
norm.inverse(np.array([0.1, 0.5, 0.9]))


Expand All@@ -355,7 +355,7 @@ def test_offset_norm_img():

fig, (ax1, ax2) = plt.subplots(ncols=2)
cmap = plt.cm.coolwarm
norm = mcolors.OffsetNorm(vmin=-2, vcenter=0, vmax=7)
norm = mcolors.PiecewiseLinearNorm(vmin=-2, vcenter=0, vmax=7)

img1 = ax1.imshow(Z, cmap=cmap, norm=None)
cbar1 = fig.colorbar(img1, ax=ax1)
Expand Down
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