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Merge pull request #12818 from jklymak/enh-arbitrary-scale
Enh arbitrary scale
2 parents a9b4fd5 + 9c74f77 commit c22847b

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‎.flake8‎

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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

‎doc/users/next_whats_new/2018-11-25-JMK.rst‎

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:orphan:
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New `~.scale.FuncScale` added for arbitrary axes scales
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````````````````````````````````````````````````````````
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A new `~.scale.FuncScale` class was added (and `~.scale.FuncTransform`)
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to allow the user to have arbitrary scale transformations without having to
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write a new subclass of `~.scale.ScaleBase`. This can be accessed by
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``ax.set_yscale('function', functions=(forward, inverse))``, where
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``forward`` and ``inverse`` are callables that return the scale transform and
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its inverse. See the last example in :doc:`/gallery/scales/scales`.

‎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.FuncScale` and the ``'function'`` 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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import numpy as np

‎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 ``'function'`` scale by
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supplying forward and inverse functions for the scale transformation.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.ticker import NullFormatter
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from matplotlib.ticker import NullFormatter, 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]
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ax.yaxis.set_minor_formatter(NullFormatter())
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# Function x**(1/2)
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def forward(x):
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return x**(1/2)
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def inverse(x):
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return x**2
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ax = axs[2, 0]
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ax.plot(x, y)
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ax.set_yscale('function', functions=(forward, inverse))
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ax.set_title('function: $x^{1/2}$')
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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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# Function Mercator transform
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def forward(a):
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a = np.deg2rad(a)
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return np.rad2deg(np.log(np.abs(np.tan(a) + 1.0 / np.cos(a))))
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def inverse(a):
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a = np.deg2rad(a)
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return np.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('function', functions=(forward, inverse))
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ax.set_title('function: 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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import matplotlib
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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.FuncScale

‎lib/matplotlib/scale.py‎

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return IdentityTransform()
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class FuncTransform(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 : callable
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The forward function for the transform. This function must have
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an inverse and, for best behavior, be monotonic.
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It must have the signature::
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def forward(values: array-like) -> array-like
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inverse : callable
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The inverse of the forward function. Signature as ``forward``.
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"""
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super().__init__()
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if callable(forward) and callable(inverse):
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self._forward = forward
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self._inverse = inverse
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else:
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raise ValueError('arguments to FuncTransform must '
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'be functions')
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def transform_non_affine(self, values):
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return self._forward(values)
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def inverted(self):
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return FuncTransform(self._inverse, self._forward)
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class FuncScale(ScaleBase):
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"""
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Provide an arbitrary scale with user-supplied function for the axis.
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"""
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name = 'function'
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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 : (callable, callable)
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two-tuple of the forward and inverse functions for the scale.
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The forward function must have an inverse and, for best behavior,
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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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"""
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forward, inverse = functions
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transform = FuncTransform(forward, inverse)
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self._transform = transform
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def get_transform(self):
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"""
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The transform for arbitrary scaling
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"""
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return self._transform
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def set_default_locators_and_formatters(self, axis):
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"""
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Set the locators and formatters to the same defaults as the
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linear scale.
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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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if (axis.axis_name == 'x' and rcParams['xtick.minor.visible']
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or axis.axis_name == 'y' and rcParams['ytick.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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class LogTransformBase(Transform):
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input_dims = 1
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output_dims = 1
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'log': LogScale,
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'symlog': SymmetricalLogScale,
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'logit': LogitScale,
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'function': FuncScale,
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}
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‎lib/matplotlib/tests/baseline_images/test_scale/function_scales.png‎

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‎lib/matplotlib/tests/test_scale.py‎

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from matplotlib.testing.decorators import image_comparison
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import matplotlib.pyplot as plt
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from matplotlib.scale import Log10Transform, InvertedLog10Transform
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import numpy as np
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import io
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import platform
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with pytest.warns(UserWarning):
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ax.set_ylim(top=-1)
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assert ax.get_ylim() == original_ylim
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@image_comparison(baseline_images=['function_scales'], remove_text=True,
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extensions=['png'], style='mpl20')
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def test_function_scale():
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def inverse(x):
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return x**2
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def forward(x):
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return x**(1/2)
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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('function', functions=(forward, inverse))
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ax.set_xlim(1, 1000)

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