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Commit1e2d23e

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ENH: add arbitrary-scale functionality
API: add ability to set formatter and locators from scale init
1 parentac0525e commit1e2d23e

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6 files changed

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-3
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6 files changed

+186
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‎.flake8

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@@ -197,6 +197,7 @@ per-file-ignores =
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examples/pyplots/whats_new_99_spines.py: E231, E402
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examples/recipes/placing_text_boxes.py: E501
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examples/scales/power_norm.py: E402
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examples/scales/scales.py: E402
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examples/shapes_and_collections/artist_reference.py: E402
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examples/shapes_and_collections/collections.py: E402
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examples/shapes_and_collections/compound_path.py: E402

‎examples/scales/custom_scale.py

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Create a custom scale, by implementing the scaling use for latitude data in a
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Mercator Projection.
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Unless you are making special use of the `~.Transform` class, you probably
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don't need to use this verbose method, and instead can use
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`~.matplotlib.scale.ArbitraryScale` and the ``'arbitrary'`` option of
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`~.matplotlib.axes.Axes.set_xscale` and `~.matplotlib.axes.Axes.set_yscale`.
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See the last example in :doc:`/gallery/scales/scales`.
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"""
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importnumpyasnp

‎examples/scales/scales.py

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======
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Illustrate the scale transformations applied to axes, e.g. log, symlog, logit.
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The last two examples are examples of using the ``'arbitrary'`` scale by
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supplying forward and inverse functions for the scale transformation.
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"""
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importnumpyasnp
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importmatplotlib.pyplotasplt
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frommatplotlib.tickerimportNullFormatter
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frommatplotlib.tickerimportNullFormatter,FixedLocator
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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x=np.arange(len(y))
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# plot with various axes scales
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fig,axs=plt.subplots(2,2,sharex=True)
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fig.subplots_adjust(left=0.08,right=0.98,wspace=0.3)
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fig,axs=plt.subplots(3,2,figsize=(6,8),
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constrained_layout=True)
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# linear
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ax=axs[0,0]
@@ -54,4 +57,73 @@
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ax.yaxis.set_minor_formatter(NullFormatter())
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# Arbitrary x**(1/2)
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defforward(x):
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returnx**(1/2)
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definverse(x):
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returnx**2
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ax=axs[2,0]
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ax.plot(x,y)
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ax.set_yscale('arbitrary',functions=(forward,inverse))
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ax.set_title('arbitrary: $x^{1/2}$')
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ax.grid(True)
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ax.yaxis.set_major_locator(FixedLocator(np.arange(0,1,0.2)**2))
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ax.yaxis.set_major_locator(FixedLocator(np.arange(0,1,0.2)))
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# Arbitrary Mercator transform
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defforward(a):
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a=np.deg2rad(a)
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# mask values too close to +/- 90, which are ill-defined.
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masked=np.ma.masked_where((a<=-np.pi/2+0.02)|
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(a>=np.pi/2-0.02),a)
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ifmasked.mask.any():
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returnnp.rad2deg(
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np.ma.log(np.abs(np.ma.tan(masked)+1.0/np.ma.cos(masked))))
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else:
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returnnp.rad2deg(np.log(np.abs(np.tan(a)+1.0/np.cos(a))))
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definverse(a):
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a=np.deg2rad(a)
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returnnp.rad2deg(np.arctan(np.sinh(a)))
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ax=axs[2,1]
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t=np.arange(-170.0,170.0,0.1)
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s=t/2.
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ax.plot(t,s,'-',lw=2)
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ax.set_yscale('arbitrary',functions=(forward,inverse))
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ax.set_title('arbitrary: Mercator')
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ax.grid(True)
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ax.set_xlim([-180,180])
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ax.yaxis.set_minor_formatter(NullFormatter())
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ax.yaxis.set_major_locator(FixedLocator(np.arange(-90,90,30)))
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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importmatplotlib
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matplotlib.axes.Axes.set_yscale
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matplotlib.axes.Axes.set_xscale
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matplotlib.axis.Axis.set_major_locator
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matplotlib.scale.LogitScale
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matplotlib.scale.LogScale
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matplotlib.scale.LinearScale
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matplotlib.scale.SymmetricalLogScale
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matplotlib.scale.ArbitraryScale

‎lib/matplotlib/scale.py

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@@ -82,6 +82,84 @@ def get_transform(self):
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returnIdentityTransform()
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classArbitraryTransform(Transform):
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"""
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A simple transform that takes and arbitrary function for the
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forward and inverse transform.
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"""
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input_dims=1
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output_dims=1
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is_separable=True
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has_inverse=True
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def__init__(self,forward,inverse):
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"""
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Parameters
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----------
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forward: The forward function for the transform
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inverse: The inverse of the forward function.
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"""
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super().__init__()
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ifcallable(forward)andcallable(inverse):
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self._forward=forward
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self._inverse=inverse
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else:
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raiseValueError('arguments to ArbitraryTransform must '
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'be functions')
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deftransform_non_affine(self,values):
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returnself._forward(values)
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definverted(self):
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returnArbitraryTransform(self._inverse,self._forward)
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classArbitraryScale(ScaleBase):
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"""
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Provide an arbitrary scale for the axis.
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"""
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name='arbitrary'
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def__init__(self,axis,functions):
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"""
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Parameters
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----------
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axis: the axis for the scale
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functions: (forward, inverse)
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two-tuple of the forward and inverse functions for the scale.
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"""
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forward,inverse=functions
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transform=ArbitraryTransform(forward,inverse)
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self._transform=transform
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defget_transform(self):
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"""
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The transform for linear scaling is just the
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:class:`~matplotlib.transforms.IdentityTransform`.
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"""
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returnself._transform
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defset_default_locators_and_formatters(self,axis):
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"""
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Set the locators and formatters to reasonable defaults for
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linear scaling.
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"""
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axis.set_major_locator(AutoLocator())
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axis.set_major_formatter(ScalarFormatter())
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axis.set_minor_formatter(NullFormatter())
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# update the minor locator for x and y axis based on rcParams
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ifrcParams['xtick.minor.visible']:
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axis.set_minor_locator(AutoMinorLocator())
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else:
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axis.set_minor_locator(NullLocator())
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classLogTransformBase(Transform):
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input_dims=1
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output_dims=1
@@ -545,6 +623,7 @@ def limit_range_for_scale(self, vmin, vmax, minpos):
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'log':LogScale,
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'symlog':SymmetricalLogScale,
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'logit':LogitScale,
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'arbitrary':ArbitraryScale,
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}
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‎lib/matplotlib/tests/test_scale.py

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@@ -1,6 +1,8 @@
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frommatplotlib.testing.decoratorsimportimage_comparison
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importmatplotlib.pyplotasplt
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frommatplotlib.scaleimportLog10Transform,InvertedLog10Transform
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importmatplotlib.tickerasmticker
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importnumpyasnp
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importio
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importplatform
@@ -148,3 +150,26 @@ def test_invalid_log_lims():
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withpytest.warns(UserWarning):
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ax.set_ylim(top=-1)
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assertax.get_ylim()==original_ylim
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definverse(x):
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returnx**2
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defforward(x):
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good=x>=0
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y=np.full_like(x,np.NaN)
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y[good]=x[good]**(1/2)
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returny
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@image_comparison(baseline_images=['arbitrary_scales'],remove_text=True,
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extensions=['png'],style='mpl20')
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deftest_arbitrary_scale():
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fig,ax=plt.subplots()
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x=np.arange(1,1000)
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ax.plot(x,x)
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ax.set_xscale('arbitrary',functions=(forward,inverse))
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ax.set_xlim(1,1000)

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