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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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Four 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 yeild 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.
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‎doc/users/whats_new/percent_formatter.rst

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This file was deleted.

‎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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fig.subplots_adjust(hspace=0.3)
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ax1 = fig.add_subplot(211)
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ax2 = fig.add_subplot(212, sharex=ax1, sharey=ax1)
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ax1.plot(temp_C, counts, drawstyle='steps-mid')
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ax2.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_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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ax2.xaxis.set_major_locator(ax1.xaxis.get_major_locator())
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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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fig.suptitle('Temperature vs Counts')
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ax1.set_xlabel('Temp (\u00B0C)')
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ax1.set_ylabel('Samples (Hex)')
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ax2.set_xlabel('Temp (\u00B0R)')
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ax2.set_ylabel('Samples (Hex)')
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plt.show()
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‎lib/matplotlib/tests/test_ticker.py

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assert '002-01' == tmp_form(2, 1)
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percentformatter_test_cases = (
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# Check explicitly set decimals over different intervals and values
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(100, 0, '%', 120, 100, '120%'),
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(100, 0, '%', 100, 90, '100%'),
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(100, 0, '%', 90, 50, '90%'),
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(100, 0, '%', 1.7, 40, '2%'),
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(100, 1, '%', 90.0, 100, '90.0%'),
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(100, 1, '%', 80.1, 90, '80.1%'),
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(100, 1, '%', 70.23, 50, '70.2%'),
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# 60.554 instead of 60.55: see https://bugs.python.org/issue5118
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(100, 1, '%', 60.554, 40, '60.6%'),
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# Check auto decimals over different intervals and values
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(100, None, '%', 95, 1, '95.00%'),
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(1.0, None, '%', 3, 6, '300%'),
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(17.0, None, '%', 1, 8.5, '6%'),
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(17.0, None, '%', 1, 8.4, '5.9%'),
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(5, None, '%', -100, 0.000001, '-2000.00000%'),
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# Check percent symbol
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(1.0, 2, None, 1.2, 100, '120.00'),
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(75, 3, '', 50, 100, '66.667'),
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(42, None, '^^Foobar$$', 21, 12, '50.0^^Foobar$$'),
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)
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@pytest.mark.parametrize('xmax, decimals, symbol, x, display_range, expected',
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percentformatter_test_cases)
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[
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# Check explicitly set decimals over different intervals and values
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(100, 0, '%', 120, 100, '120%'),
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(100, 0, '%', 100, 90, '100%'),
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(100, 0, '%', 90, 50, '90%'),
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(100, 0, '%', 1.7, 40, '2%'),
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(100, 1, '%', 90.0, 100, '90.0%'),
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(100, 1, '%', 80.1, 90, '80.1%'),
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(100, 1, '%', 70.23, 50, '70.2%'),
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# 60.554 instead of 60.55: see https://bugs.python.org/issue5118
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(100, 1, '%', 60.554, 40, '60.6%'),
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# Check auto decimals over different intervals and values
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(100, None, '%', 95, 1, '95.00%'),
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(1.0, None, '%', 3, 6, '300%'),
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(17.0, None, '%', 1, 8.5, '6%'),
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(17.0, None, '%', 1, 8.4, '5.9%'),
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(5, None, '%', -100, 0.000001, '-2000.00000%'),
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# Check percent symbol
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(1.0, 2, None, 1.2, 100, '120.00'),
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(75, 3, '', 50, 100, '66.667'),
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(42, None, '^^Foobar$$', 21, 12, '50.0^^Foobar$$'),
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])
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def test_percentformatter(xmax, decimals, symbol, x, display_range, expected):
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formatter = mticker.PercentFormatter(xmax, decimals, symbol)
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assert formatter.format_pct(x, display_range) == expected
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def test_TransformFormatter():
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"""
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Verifies that the linear transformations are being done correctly.
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"""
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def transform(x):
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return -x
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# Make a formatter using the default underlying formatter,
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# which is a Formatter instance, not just a generic callable
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fmt = mticker.TransformFormatter(transform)
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# Public (non-method) attributes
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assert fmt.transform is transform
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assert isinstance(fmt.formatter, mticker.ScalarFormatter)
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# .create_dummy_axis
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assert fmt.axis is None
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fmt.create_dummy_axis()
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assert fmt.axis is not None
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assert fmt.axis is fmt.formatter.axis
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# .set_axis
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prev_axis = fmt.axis
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fmt.set_axis(mticker._DummyAxis())
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assert fmt.axis is fmt.formatter.axis
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assert fmt.axis is not prev_axis
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# .set_view_interval
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fmt.set_view_interval(100, 200)
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assert np.array_equal(fmt.axis.get_view_interval(), [100, 200])
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# .set_data_interval
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fmt.set_data_interval(50, 60)
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assert np.array_equal(fmt.axis.get_data_interval(), [50, 60])
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# .set_bounds
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bounds = [-7, 7]
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fmt.set_bounds(*bounds)
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assert np.array_equal(fmt.axis.get_view_interval(), bounds)
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assert np.array_equal(fmt.axis.get_data_interval(), bounds)
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# .format_data, .format_data_short
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assert fmt.format_data(100.0) == '\u22121e2'
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assert fmt.format_data_short(-200.0) == '{:<12g}'.format(200)
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# .get_offset
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assert fmt.get_offset() == fmt.formatter.get_offset()
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# .set_locs
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locs = [1.0, 2.0, 3.0]
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transformed_locs = [-1.0, -2.0, -3.0]
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fmt.set_locs(locs)
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assert fmt.locs is locs
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assert fmt.formatter.locs == transformed_locs
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# .fix_minus
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val = '-19.0'
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assert fmt.fix_minus(val) == '\u221219.0'
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assert fmt.fix_minus(val) == fmt.formatter.fix_minus(val)
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# .__call__ needs to be tested after `set_locs` has been called at
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# least once.
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assert fmt(5.0) == '\u22125'
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# .set_formatter
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prev_axis = fmt.axis
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fmt.set_formatter(mticker.PercentFormatter())
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assert isinstance(fmt.formatter, mticker.PercentFormatter)
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assert fmt.axis is prev_axis
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assert fmt.formatter.axis is fmt.axis
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assert fmt.locs is locs
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assert fmt.formatter.locs == transformed_locs
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def test_EngFormatter_formatting():
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"""
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Create two instances of EngFormatter with default parameters, with and

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