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ENH: Added TransformFormatter to matplotlib.ticker
Tests included. Example code provided with some recipes from previous attempt.
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‎doc/users/whats_new/new_formatters.rst

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Two new Formatters added to `matplotlib.ticker`
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-----------------------------------------------
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Two new formatters have been added for displaying some specialized
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tick labels:
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- :class:`matplotlib.ticker.PercentFormatter`
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- :class:`matplotlib.ticker.TransformFormatter`
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:class:`matplotlib.ticker.PercentFormatter`
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```````````````````````````````````````````
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This new formatter has some nice features like being able to convert
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from arbitrary data scales to percents, a customizable percent symbol
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and either automatic or manual control over the decimal points.
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:class:`matplotlib.ticker.TransformFormatter`
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```````````````````````````````````````````````
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A more generic version of :class:`matplotlib.ticker.FuncFormatter` that
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allows the tick values to be transformed before being passed to an
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underlying formatter. The transformation can yield results of arbitrary
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type, so for example, using `int` as the transformation will allow
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:class:`matplotlib.ticker.StrMethodFormatter` to use integer format
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strings. If the underlying formatter is an instance of
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:class:`matplotlib.ticker.Formatter`, it will be configured correctly
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through this class.

‎doc/users/whats_new/percent_formatter.rst

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This file was deleted.

‎examples/ticks_and_spines/tick-formatters.py

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@@ -19,25 +19,23 @@ def setup(ax):
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ax.set_xlim(0, 5)
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ax.set_ylim(0, 1)
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ax.patch.set_alpha(0.0)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
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plt.figure(figsize=(8, 5))
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n = 6
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n = 7
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# Null formatter
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ax = plt.subplot(n, 1, 1)
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setup(ax)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
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ax.xaxis.set_major_formatter(ticker.NullFormatter())
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ax.xaxis.set_minor_formatter(ticker.NullFormatter())
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ax.text(0.0, 0.1, "NullFormatter()", fontsize=16, transform=ax.transAxes)
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# Fixed formatter
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ax = plt.subplot(n, 1, 2)
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setup(ax)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
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majors = ["", "0", "1", "2", "3", "4", "5"]
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ax.xaxis.set_major_formatter(ticker.FixedFormatter(majors))
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minors = [""] + ["%.2f" % (x-int(x)) if (x-int(x))
@@ -54,8 +52,6 @@ def major_formatter(x, pos):
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ax = plt.subplot(n, 1, 3)
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setup(ax)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
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ax.xaxis.set_major_formatter(ticker.FuncFormatter(major_formatter))
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ax.text(0.0, 0.1, 'FuncFormatter(lambda x, pos: "[%.2f]" % x)',
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fontsize=15, transform=ax.transAxes)
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# FormatStr formatter
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ax = plt.subplot(n, 1, 4)
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setup(ax)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
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ax.xaxis.set_major_formatter(ticker.FormatStrFormatter(">%d<"))
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ax.text(0.0, 0.1, "FormatStrFormatter('>%d<')",
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fontsize=15, transform=ax.transAxes)
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# Scalar formatter
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ax = plt.subplot(n, 1, 5)
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setup(ax)
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ax.xaxis.set_major_locator(ticker.AutoLocator())
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ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
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ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
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ax.text(0.0, 0.1, "ScalarFormatter()", fontsize=15, transform=ax.transAxes)
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# StrMethod formatter
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ax = plt.subplot(n, 1, 6)
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setup(ax)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
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ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
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ax.xaxis.set_major_formatter(ticker.StrMethodFormatter("{x}"))
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ax.text(0.0, 0.1, "StrMethodFormatter('{x}')",
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fontsize=15, transform=ax.transAxes)
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# TransformFormatter
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ax = plt.subplot(n, 1, 7)
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setup(ax)
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ax.xaxis.set_major_formatter(ticker.TransformFormatter(lambda x: 7 - 2 * x))
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ax.text(0.0, 0.1, "TransformFormatter(lambda x: 7 - 2 * x)",
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fontsize=15, transform=ax.transAxes)
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# Push the top of the top axes outside the figure because we only show the
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# bottom spine.
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plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=1.05)

‎examples/ticks_and_spines/tick_transform_formatter.py

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"""
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Demo of the `matplotlib.ticker.TransformFormatter` class.
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This code demonstrates two features:
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1. A linear transformation of the input values. A callable class for
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doing the transformation is presented as a recipe here. The data
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type of the inputs does not change.
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2. A transformation of the input type. The example here allows
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`matplotlib.ticker.StrMethodFormatter` to handle integer formats
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('b', 'o', 'd', 'n', 'x', 'X'), which will normally raise an error
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if used directly. This transformation is associated with a
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`matplotlib.ticker.MaxNLocator` which has `integer` set to True to
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ensure that the inputs are indeed integers.
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The same histogram is plotted in two sub-plots with a shared x-axis.
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Each axis shows a different temperature scale: one in degrees Celsius,
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one in degrees Rankine (the Fahrenheit analogue of Kelvins). This is one
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of the few examples of recognized scientific units that have both a
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scale and an offset relative to each other.
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"""
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import numpy as np
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from matplotlib import pyplot as plt
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from matplotlib.axis import Ticker
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from matplotlib.ticker import (
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TransformFormatter, StrMethodFormatter, MaxNLocator
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)
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class LinearTransform:
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"""
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A callable class that transforms input values to output according to
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a linear transformation.
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"""
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def __init__(self, in_start=None, in_end=None, out_start=None, out_end=None):
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"""
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Sets up the transformation such that `in_start` gets mapped to
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`out_start` and `in_end` gets mapped to `out_end`. The following
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shortcuts apply when only some of the inputs are specified:
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- none: no-op
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- in_start: translation to zero
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- out_start: translation from zero
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- in_end: scaling to one (divide input by in_end)
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- out_end: scaling from one (multiply input by in_end)
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- in_start, out_start: translation
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- in_end, out_end: scaling (in_start and out_start zero)
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- in_start, out_end: in_end=out_end, out_start=0
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- in_end, out_start: in_start=0, out_end=in_end
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Based on the following rules:
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- start missing: set start to zero
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- both ends are missing: set ranges to 1.0
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- one end is missing: set it to the other end
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"""
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self._in_offset = 0.0 if in_start is None else in_start
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self._out_offset = 0.0 if out_start is None else out_start
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if in_end is None:
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if out_end is None:
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self._in_scale = 1.0
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else:
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self._in_scale = out_end - self._in_offset
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else:
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self._in_scale = in_end - self._in_offset
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if out_end is None:
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if in_end is None:
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self._out_scale = 1.0
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else:
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self._out_scale = in_end - self._out_offset
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else:
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self._out_scale = out_end - self._out_offset
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def __call__(self, x):
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"""
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Transforms the input value `x` according to the rule set up in
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`__init__`.
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"""
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return ((x - self._in_offset) * self._out_scale / self._in_scale +
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self._out_offset)
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# X-data
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temp_C = np.arange(-5.0, 5.1, 0.25)
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# Y-data
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counts = 15.0 * np.exp(-temp_C**2 / 25)
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# Add some noise
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counts += np.random.normal(scale=4.0, size=counts.shape)
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if counts.min() < 0:
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counts += counts.min()
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fig = plt.figure()
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ax1 = fig.add_subplot(111)
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ax2 = fig.add_subplot(111, sharex=ax1, sharey=ax1, frameon=False)
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ax1.plot(temp_C, counts, drawstyle='steps-mid')
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ax1.xaxis.set_major_formatter(StrMethodFormatter('{x:0.2f}'))
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# This step is necessary to allow the shared x-axes to have different
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# Formatter and Locator objects.
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ax2.xaxis.major = Ticker()
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# 0C -> 491.67R (definition), -273.15C (0K)->0R (-491.67F)(definition)
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ax2.xaxis.set_major_locator(ax1.xaxis.get_major_locator())
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ax2.xaxis.set_major_formatter(
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TransformFormatter(LinearTransform(in_start=-273.15, in_end=0,
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out_end=491.67),
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StrMethodFormatter('{x:0.2f}')))
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# The y-axes share their locators and formatters, so only one needs to
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# be set
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ax1.yaxis.set_major_locator(MaxNLocator(integer=True))
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# Setting the transfrom to `int` will only alter the type, not the
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# actual value of the ticks
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ax1.yaxis.set_major_formatter(
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TransformFormatter(int, StrMethodFormatter('{x:02X}')))
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ax1.set_xlabel('Temperature (\u00B0C)')
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ax1.set_ylabel('Samples (Hex)')
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ax2.set_xlabel('Temperature (\u00B0R)')
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ax1.xaxis.tick_top()
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ax1.xaxis.set_label_position('top')
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plt.show()

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