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Commit989c966

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ENH: Add func norm
1 parent1b9de36 commit989c966

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5 files changed

+85
-2
lines changed

5 files changed

+85
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lines changed

‎doc/api/colors_api.rst

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@@ -30,6 +30,7 @@ Classes
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PowerNorm
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SymLogNorm
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TwoSlopeNorm
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FuncNorm
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Functions
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---------

‎examples/userdemo/colormap_normalizations.py

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@@ -77,7 +77,6 @@
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shading='nearest')
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fig.colorbar(pcm,ax=ax[1],extend='both')
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80-
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###############################################################################
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# Custom Norm: An example with a customized normalization. This one
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# uses the example above, and normalizes the negative data differently

‎lib/matplotlib/colors.py

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@@ -1464,6 +1464,38 @@ def inverse(self, value):
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*bound_init_signature.parameters.values()])
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returnNorm
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@_make_norm_from_scale(
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scale.FuncScale,
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init=lambdafunctions,vmin=None,vmax=None,clip=False:None)
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classFuncNorm(Normalize):
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"""
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Arbitrary normalization using functions for the forward and inverse.
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Parameters
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----------
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functions : (callable, callable)
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two-tuple of the forward and inverse functions for the normalization.
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The forward function must be monotonic.
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Both functions must have the signature ::
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def forward(values: array-like) -> array-like
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vmin, vmax : float or None
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If *vmin* and/or *vmax* is not given, they are initialized from the
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minimum and maximum value, respectively, of the first input
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processed; i.e., ``__call__(A)`` calls ``autoscale_None(A)``.
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clip : bool, default: False
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If ``True`` values falling outside the range ``[vmin, vmax]``,
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are mapped to 0 or 1, whichever is closer, and masked values are
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set to 1. If ``False`` masked values remain masked.
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Clipping silently defeats the purpose of setting the over, under,
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and masked colors in a colormap, so it is likely to lead to
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surprises; therefore the default is ``clip=False``.
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"""
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14671499

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@_make_norm_from_scale(functools.partial(scale.LogScale,nonpositive="mask"))
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classLogNorm(Normalize):

‎lib/matplotlib/tests/test_colors.py

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@@ -526,6 +526,25 @@ def test_Normalize():
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assert0<norm(1+50*eps)<1
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deftest_FuncNorm():
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def_forward(x):
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return (x**2)
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def_inverse(x):
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returnnp.sqrt(x)
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norm=mcolors.FuncNorm((_forward,_inverse),vmin=0,vmax=10)
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expected=np.array([0,0.25,1])
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assert_array_almost_equal(norm([0,5,10]),expected)
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def_forward(x):
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returnnp.log10(x)
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def_inverse(x):
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return10**x
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norm=mcolors.FuncNorm((_forward,_inverse),vmin=0.1,vmax=10)
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lognorm=mcolors.LogNorm(vmin=0.1,vmax=10)
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assert_array_almost_equal(norm([0.2,5,10]),lognorm([0.2,5,10]))
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deftest_TwoSlopeNorm_autoscale():
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norm=mcolors.TwoSlopeNorm(vcenter=20)
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norm.autoscale([10,20,30,40])

‎tutorials/colors/colormapnorms.py

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@@ -169,14 +169,16 @@
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X,Y=np.mgrid[0:3:complex(0,N),0:2:complex(0,N)]
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Z1= (1+np.sin(Y*10.))*X**2
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fig,ax=plt.subplots(2,1)
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fig,ax=plt.subplots(2,1,constrained_layout=True)
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pcm=ax[0].pcolormesh(X,Y,Z1,norm=colors.PowerNorm(gamma=0.5),
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cmap='PuBu_r',shading='auto')
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fig.colorbar(pcm,ax=ax[0],extend='max')
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ax[0].set_title('PowerNorm()')
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pcm=ax[1].pcolormesh(X,Y,Z1,cmap='PuBu_r',shading='auto')
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fig.colorbar(pcm,ax=ax[1],extend='max')
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ax[1].set_title('Normalize()')
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plt.show()
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###############################################################################
@@ -274,17 +276,46 @@
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# Simple geographic plot, set aspect ratio beecause distance between lines of
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# longitude depends on latitude.
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ax.set_aspect(1/np.cos(np.deg2rad(49)))
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ax.set_title('TwoSlopeNorm(x)')
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fig.colorbar(pcm,shrink=0.6)
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plt.show()
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###############################################################################
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# FuncNorm: Arbitrary function normalization
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# ------------------------------------------
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#
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# If the above norms do not provide the normalization you want, and you
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# can express your normalization in terms of function, you can use
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# `~.colors.FuncNorm`. Note that this example is the same as
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# `PowerNorm` with a power of 0.5:
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def_forward(x):
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returnnp.sqrt(x)
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def_inverse(x):
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returnx**2
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N=100
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X,Y=np.mgrid[0:3:complex(0,N),0:2:complex(0,N)]
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Z1= (1+np.sin(Y*10.))*X**2
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fig,ax=plt.subplots()
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norm=colors.FuncNorm((_forward,_inverse),vmin=0,vmax=20)
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pcm=ax.pcolormesh(X,Y,Z1,norm=norm,cmap='PuBu_r',shading='auto')
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ax.set_title('FuncNorm(x)')
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fig.colorbar(pcm,shrink=0.6)
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plt.show()
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###############################################################################
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# Custom normalization: Manually implement two linear ranges
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# ----------------------------------------------------------
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#
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# The `.TwoSlopeNorm` described above makes a useful example for
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# defining your own norm.
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classMidpointNormalize(colors.Normalize):
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def__init__(self,vmin=None,vmax=None,vcenter=None,clip=False):
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self.vcenter=vcenter
@@ -303,5 +334,6 @@ def __call__(self, value, clip=None):
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pcm=ax.pcolormesh(longitude,latitude,topo,rasterized=True,norm=midnorm,
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cmap=terrain_map,shading='auto')
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ax.set_aspect(1/np.cos(np.deg2rad(49)))
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ax.set_title('Custom norm')
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fig.colorbar(pcm,shrink=0.6,extend='both')
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plt.show()

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