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Commitb2783ed

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Merge pull request#14512 from jb-leger/logit_scale_stuff
ENH: Logit scale, changes in LogitLocator and LogitFormatter
2 parents667a100 +e113aff commitb2783ed

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Improvements in Logit scale ticker and formatter
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------------------------------------------------
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Introduced in version 1.5, the logit scale didn't have an appropriate ticker and
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formatter. Previously, the location of ticks was not zoom dependent, too many labels
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were displayed causing overlapping which broke readability, and label formatting
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did not adapt to precision.
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Starting from this version, the logit locator has nearly the same behavior as the
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locator for the log scale or the linear
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scale, depending on used zoom. The number of ticks is controlled. Some minor
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labels are displayed adaptively as sublabels in log scale. Formatting is adapted
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for probabilities and the precision is adapts to the scale.

‎examples/scales/logit_demo.py

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"""
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================
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Logit Demo
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================
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Examples of plots with logit axes.
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"""
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importnumpyasnp
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importmatplotlib.pyplotasplt
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xmax=10
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x=np.linspace(-xmax,xmax,10000)
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cdf_norm=np.array([np.math.erf(w/np.sqrt(2))/2+1/2forwinx])
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cdf_laplacian=np.array(
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[1/2*np.exp(w)ifw<0else1-1/2*np.exp(-w)forwinx]
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)
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cdf_cauchy=1/np.pi*np.arctan(x)+1/2
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fig,axs=plt.subplots(nrows=3,ncols=2,figsize=(6.4,8.5))
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# Common part, for the example, we will do the same plots on all graphs
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foriinrange(3):
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forjinrange(2):
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axs[i,j].plot(x,cdf_norm,label=r"$\mathcal{N}$")
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axs[i,j].plot(x,cdf_laplacian,label=r"$\mathcal{L}$")
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axs[i,j].plot(x,cdf_cauchy,label="Cauchy")
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axs[i,j].legend()
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axs[i,j].grid()
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# First line, logitscale, with standard notation
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axs[0,0].set(title="logit scale")
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axs[0,0].set_yscale("logit")
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axs[0,0].set_ylim(1e-5,1-1e-5)
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axs[0,1].set(title="logit scale")
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axs[0,1].set_yscale("logit")
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axs[0,1].set_xlim(0,xmax)
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axs[0,1].set_ylim(0.8,1-5e-3)
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# Second line, logitscale, with survival notation (with `use_overline`), and
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# other format display 1/2
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axs[1,0].set(title="logit scale")
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axs[1,0].set_yscale("logit",one_half="1/2",use_overline=True)
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axs[1,0].set_ylim(1e-5,1-1e-5)
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axs[1,1].set(title="logit scale")
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axs[1,1].set_yscale("logit",one_half="1/2",use_overline=True)
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axs[1,1].set_xlim(0,xmax)
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axs[1,1].set_ylim(0.8,1-5e-3)
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# Third line, linear scale
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axs[2,0].set(title="linear scale")
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axs[2,0].set_ylim(0,1)
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axs[2,1].set(title="linear scale")
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axs[2,1].set_xlim(0,xmax)
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axs[2,1].set_ylim(0.8,1)
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fig.tight_layout()
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plt.show()

‎examples/scales/scales.py

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ax.set_yscale('logit')
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ax.set_title('logit')
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ax.grid(True)
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# Format the minor tick labels of the y-axis into empty strings with
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# `NullFormatter`, to avoid cumbering the axis with too many labels.
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ax.yaxis.set_minor_formatter(NullFormatter())
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# Function x**(1/2)

‎lib/matplotlib/scale.py

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@@ -654,8 +654,15 @@ class LogitScale(ScaleBase):
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"""
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name='logit'
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def__init__(self,axis,nonpos='mask'):
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"""
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def__init__(
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self,
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axis,
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nonpos='mask',
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*,
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one_half=r"\frac{1}{2}",
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use_overline=False,
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):
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r"""
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Parameters
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----------
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axis : `matplotlib.axis.Axis`
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Determines the behavior for values beyond the open interval ]0, 1[.
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They can either be masked as invalid, or clipped to a number very
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close to 0 or 1.
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use_overline : bool, default: False
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Indicate the usage of survival notation (\overline{x}) in place of
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standard notation (1-x) for probability close to one.
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one_half : str, default: r"\frac{1}{2}"
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The string used for ticks formatter to represent 1/2.
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"""
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self._transform=LogitTransform(nonpos)
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self._use_overline=use_overline
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self._one_half=one_half
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defget_transform(self):
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"""Return the `.LogitTransform` associated with this scale."""
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# docstring inherited
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# ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...
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axis.set_major_locator(LogitLocator())
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axis.set_major_formatter(LogitFormatter())
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axis.set_major_formatter(
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LogitFormatter(
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one_half=self._one_half,
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use_overline=self._use_overline
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)
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)
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axis.set_minor_locator(LogitLocator(minor=True))
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axis.set_minor_formatter(LogitFormatter())
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axis.set_minor_formatter(
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LogitFormatter(
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minor=True,
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one_half=self._one_half,
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use_overline=self._use_overline
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)
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)
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deflimit_range_for_scale(self,vmin,vmax,minpos):
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"""

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