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Commit36042b6

Browse files
dopplershiftOceanWolf
authored andcommitted
OffsetNorm -> PiecewiseLinearNorm
1 parent47c447a commit36042b6

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3 files changed

+26
-26
lines changed

3 files changed

+26
-26
lines changed

‎doc/users/whats_new.rst

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -62,7 +62,7 @@ determines the placement of the arrow head along the quiver line.
6262

6363
Offset Normalizers for Colormaps
6464
````````````````````````````````
65-
Paul Hobson/Geosyntec Consultants added a new:class:`matplotlib.colors.OffsetNorm`
65+
Paul Hobson/Geosyntec Consultants added a new:class:`matplotlib.colors.PiecewiseLinearNorm`
6666
class with the help of Till Stensitzki. This is particularly useful when using a
6767
diverging colormap on data that are asymetrically centered around a logical value
6868
(e.g., 0 when data range from -2 to 4).

‎lib/matplotlib/colors.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -964,7 +964,7 @@ def scaled(self):
964964
return (self.vminisnotNoneandself.vmaxisnotNone)
965965

966966

967-
classOffsetNorm(Normalize):
967+
classPiecewiseLinearNorm(Normalize):
968968
"""
969969
A subclass of matplotlib.colors.Normalize.
970970
@@ -998,7 +998,7 @@ def __init__(self, vmin=None, vcenter=None, vmax=None, clip=False):
998998
Examples
999999
--------
10001000
>>> import matplotlib.colors as mcolors
1001-
>>> offset = mcolors.OffsetNorm(vmin=-2., vcenter=0., vmax=4.)
1001+
>>> offset = mcolors.PiecewiseLinearNorm(vmin=-2., vcenter=0., vmax=4.)
10021002
>>> data = [-2., -1., 0., 1., 2., 3., 4.]
10031003
>>> offset(data)
10041004
array([0., 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])

‎lib/matplotlib/tests/test_colors.py

Lines changed: 23 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -227,44 +227,44 @@ def test_process_value_array(self):
227227
assert_array_equal(res,np.array([5.,10.]))
228228

229229

230-
classBaseOffsetNorm(BaseNormMixin):
231-
normclass=mcolors.OffsetNorm
230+
classBasePiecewiseLinearNorm(BaseNormMixin):
231+
normclass=mcolors.PiecewiseLinearNorm
232232
test_inverse=False
233233

234-
classtest_OffsetNorm_Even(BaseOffsetNorm):
234+
classtest_PiecewiseLinearNorm_Even(BasePiecewiseLinearNorm):
235235
defsetup(self):
236236
self.norm=self.normclass(vmin=-1,vcenter=0,vmax=4)
237237
self.vals=np.array([-1.0,-0.5,0.0,1.0,2.0,3.0,4.0])
238238
self.expected=np.array([0.0,0.25,0.5,0.625,0.75,0.875,1.0])
239239

240240

241-
classtest_OffsetNorm_Odd(BaseOffsetNorm):
241+
classtest_PiecewiseLinearNorm_Odd(BasePiecewiseLinearNorm):
242242
defsetup(self):
243-
self.normclass=mcolors.OffsetNorm
243+
self.normclass=mcolors.PiecewiseLinearNorm
244244
self.norm=self.normclass(vmin=-2,vcenter=0,vmax=5)
245245
self.vals=np.array([-2.0,-1.0,0.0,1.0,2.0,3.0,4.0,5.0])
246246
self.expected=np.array([0.0,0.25,0.5,0.6,0.7,0.8,0.9,1.0])
247247

248248

249-
classtest_OffsetNorm_AllNegative(BaseOffsetNorm):
249+
classtest_PiecewiseLinearNorm_AllNegative(BasePiecewiseLinearNorm):
250250
defsetup(self):
251-
self.normclass=mcolors.OffsetNorm
251+
self.normclass=mcolors.PiecewiseLinearNorm
252252
self.norm=self.normclass(vmin=-10,vcenter=-8,vmax=-2)
253253
self.vals=np.array([-10.,-9.,-8.,-6.,-4.,-2.])
254254
self.expected=np.array([0.0,0.25,0.5,0.666667,0.833333,1.0])
255255

256256

257-
classtest_OffsetNorm_AllPositive(BaseOffsetNorm):
257+
classtest_PiecewiseLinearNorm_AllPositive(BasePiecewiseLinearNorm):
258258
defsetup(self):
259-
self.normclass=mcolors.OffsetNorm
259+
self.normclass=mcolors.PiecewiseLinearNorm
260260
self.norm=self.normclass(vmin=0,vcenter=3,vmax=9)
261261
self.vals=np.array([0.,1.5,3.,4.5,6.0,7.5,9.])
262262
self.expected=np.array([0.0,0.25,0.5,0.625,0.75,0.875,1.0])
263263

264264

265-
classtest_OffsetNorm_NoVs(BaseOffsetNorm):
265+
classtest_PiecewiseLinearNorm_NoVs(BasePiecewiseLinearNorm):
266266
defsetup(self):
267-
self.normclass=mcolors.OffsetNorm
267+
self.normclass=mcolors.PiecewiseLinearNorm
268268
self.norm=self.normclass(vmin=None,vcenter=None,vmax=None)
269269
self.vals=np.array([-2.0,-1.0,0.0,1.0,2.0,3.0,4.0])
270270
self.expected=np.array([0.,0.16666667,0.33333333,
@@ -289,26 +289,26 @@ def test_vmax(self):
289289
assert_equal(self.norm.vmax,self.expected_vmax)
290290

291291

292-
classtest_OffsetNorm_VminEqualsVcenter(BaseOffsetNorm):
292+
classtest_PiecewiseLinearNorm_VminEqualsVcenter(BasePiecewiseLinearNorm):
293293
defsetup(self):
294-
self.normclass=mcolors.OffsetNorm
294+
self.normclass=mcolors.PiecewiseLinearNorm
295295
self.norm=self.normclass(vmin=-2,vcenter=-2,vmax=2)
296296
self.vals=np.array([-2.0,-1.0,0.0,1.0,2.0])
297297
self.expected=np.array([0.5,0.625,0.75,0.875,1.0])
298298

299299

300-
classtest_OffsetNorm_VmaxEqualsVcenter(BaseOffsetNorm):
300+
classtest_PiecewiseLinearNorm_VmaxEqualsVcenter(BasePiecewiseLinearNorm):
301301
defsetup(self):
302-
self.normclass=mcolors.OffsetNorm
302+
self.normclass=mcolors.PiecewiseLinearNorm
303303
self.norm=self.normclass(vmin=-2,vcenter=2,vmax=2)
304304
self.vals=np.array([-2.0,-1.0,0.0,1.0,2.0])
305305
self.expected=np.array([0.0,0.125,0.25,0.375,0.5])
306306

307307

308-
classtest_OffsetNorm_VsAllEqual(BaseOffsetNorm):
308+
classtest_PiecewiseLinearNorm_VsAllEqual(BasePiecewiseLinearNorm):
309309
defsetup(self):
310310
self.v=10
311-
self.normclass=mcolors.OffsetNorm
311+
self.normclass=mcolors.PiecewiseLinearNorm
312312
self.norm=self.normclass(vmin=self.v,vcenter=self.v,vmax=self.v)
313313
self.vals=np.array([-2.0,-1.0,0.0,1.0,2.0])
314314
self.expected=np.array([0.0,0.0,0.0,0.0,0.0])
@@ -321,28 +321,28 @@ def test_inverse(self):
321321
)
322322

323323

324-
classtest_OffsetNorm_Errors(object):
324+
classtest_PiecewiseLinearNorm_Errors(object):
325325
defsetup(self):
326326
self.vals=np.arange(50)
327327

328328
@raises(ValueError)
329329
deftest_VminGTVcenter(self):
330-
norm=mcolors.OffsetNorm(vmin=10,vcenter=0,vmax=20)
330+
norm=mcolors.PiecewiseLinearNorm(vmin=10,vcenter=0,vmax=20)
331331
norm(self.vals)
332332

333333
@raises(ValueError)
334334
deftest_VminGTVmax(self):
335-
norm=mcolors.OffsetNorm(vmin=10,vcenter=0,vmax=5)
335+
norm=mcolors.PiecewiseLinearNorm(vmin=10,vcenter=0,vmax=5)
336336
norm(self.vals)
337337

338338
@raises(ValueError)
339339
deftest_VcenterGTVmax(self):
340-
norm=mcolors.OffsetNorm(vmin=10,vcenter=25,vmax=20)
340+
norm=mcolors.PiecewiseLinearNorm(vmin=10,vcenter=25,vmax=20)
341341
norm(self.vals)
342342

343343
@raises(ValueError)
344344
deftest_premature_scaling(self):
345-
norm=mcolors.OffsetNorm()
345+
norm=mcolors.PiecewiseLinearNorm()
346346
norm.inverse(np.array([0.1,0.5,0.9]))
347347

348348

@@ -355,7 +355,7 @@ def test_offset_norm_img():
355355

356356
fig, (ax1,ax2)=plt.subplots(ncols=2)
357357
cmap=plt.cm.coolwarm
358-
norm=mcolors.OffsetNorm(vmin=-2,vcenter=0,vmax=7)
358+
norm=mcolors.PiecewiseLinearNorm(vmin=-2,vcenter=0,vmax=7)
359359

360360
img1=ax1.imshow(Z,cmap=cmap,norm=None)
361361
cbar1=fig.colorbar(img1,ax=ax1)

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