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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.ticker — Matplotlib 3.3.1 documentation</title>

title="Search within Matplotlib 3.3.1 documentation"

href="../../_static/opensearch.xml"/>

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<h1>Source code for matplotlib.ticker</h1><div class="highlight"><pre>

"""

Tick locating and formatting

============================

This module contains classes for configuring tick locating and formatting.

Generic tick locators and formatters are provided, as well as domain specific

custom ones.

Although the locators know nothing about major or minor ticks, they are used

by the Axis class to support major and minor tick locating and formatting.

Tick locating

-------------

The Locator class is the base class for all tick locators. The locators

handle autoscaling of the view limits based on the data limits, and the

choosing of tick locations. A useful semi-automatic tick locator is

`MultipleLocator`. It is initialized with a base, e.g., 10, and it picks

axis limits and ticks that are multiples of that base.

The Locator subclasses defined here are

:class:`AutoLocator`

`MaxNLocator` with simple defaults. This is the default tick locator for

most plotting.

:class:`MaxNLocator`

Finds up to a max number of intervals with ticks at nice locations.

:class:`LinearLocator`

Space ticks evenly from min to max.

:class:`LogLocator`

Space ticks logarithmically from min to max.

:class:`MultipleLocator`

Ticks and range are a multiple of base; either integer or float.

:class:`FixedLocator`

Tick locations are fixed.

:class:`IndexLocator`

Locator for index plots (e.g., where ``x = range(len(y))``).

:class:`NullLocator`

No ticks.

:class:`SymmetricalLogLocator`

Locator for use with with the symlog norm; works like `LogLocator` for the

part outside of the threshold and adds 0 if inside the limits.

:class:`LogitLocator`

Locator for logit scaling.

:class:`OldAutoLocator`

Choose a `MultipleLocator` and dynamically reassign it for intelligent

ticking during navigation.

:class:`AutoMinorLocator`

Locator for minor ticks when the axis is linear and the

major ticks are uniformly spaced. Subdivides the major

tick interval into a specified number of minor intervals,

defaulting to 4 or 5 depending on the major interval.

There are a number of locators specialized for date locations - see

the :mod:`.dates` module.

You can define your own locator by deriving from Locator. You must

override the ``__call__`` method, which returns a sequence of locations,

and you will probably want to override the autoscale method to set the

view limits from the data limits.

If you want to override the default locator, use one of the above or a custom

locator and pass it to the x or y axis instance. The relevant methods are::

ax.xaxis.set_major_locator(xmajor_locator)

ax.xaxis.set_minor_locator(xminor_locator)

ax.yaxis.set_major_locator(ymajor_locator)

ax.yaxis.set_minor_locator(yminor_locator)

The default minor locator is `NullLocator`, i.e., no minor ticks on by default.

Tick formatting

---------------

Tick formatting is controlled by classes derived from Formatter. The formatter

operates on a single tick value and returns a string to the axis.

:class:`NullFormatter`

No labels on the ticks.

:class:`IndexFormatter`

Set the strings from a list of labels.

:class:`FixedFormatter`

Set the strings manually for the labels.

:class:`FuncFormatter`

User defined function sets the labels.

:class:`StrMethodFormatter`

Use string `format` method.

:class:`FormatStrFormatter`

Use an old-style sprintf format string.

:class:`ScalarFormatter`

Default formatter for scalars: autopick the format string.

:class:`LogFormatter`

Formatter for log axes.

:class:`LogFormatterExponent`

Format values for log axis using ``exponent = log_base(value)``.

:class:`LogFormatterMathtext`

Format values for log axis using ``exponent = log_base(value)``

using Math text.

:class:`LogFormatterSciNotation`

Format values for log axis using scientific notation.

:class:`LogitFormatter`

Probability formatter.

:class:`EngFormatter`

Format labels in engineering notation.

:class:`PercentFormatter`

Format labels as a percentage.

You can derive your own formatter from the Formatter base class by

simply overriding the ``__call__`` method. The formatter class has

access to the axis view and data limits.

To control the major and minor tick label formats, use one of the

following methods::

ax.xaxis.set_major_formatter(xmajor_formatter)

ax.xaxis.set_minor_formatter(xminor_formatter)

ax.yaxis.set_major_formatter(ymajor_formatter)

ax.yaxis.set_minor_formatter(yminor_formatter)

In addition to a `.Formatter` instance, `~.Axis.set_major_formatter` and

`~.Axis.set_minor_formatter` also accept a ``str`` or function. ``str`` input

will be internally replaced with an autogenerated `.StrMethodFormatter` with

the input ``str``. For function input, a `.FuncFormatter` with the input

function will be generated and used.

See :doc:`/gallery/ticks_and_spines/major_minor_demo` for an

example of setting major and minor ticks. See the :mod:`matplotlib.dates`

module for more information and examples of using date locators and formatters.

"""

import itertools

import logging

import locale

import math

from numbers import Integral

import numpy as np

import matplotlib as mpl

from matplotlib import cbook

from matplotlib import transforms as mtransforms

_log = logging.getLogger(__name__)

__all__ = ('TickHelper', 'Formatter', 'FixedFormatter',

'NullFormatter', 'FuncFormatter', 'FormatStrFormatter',

'StrMethodFormatter', 'ScalarFormatter', 'LogFormatter',

'LogFormatterExponent', 'LogFormatterMathtext',

'IndexFormatter', 'LogFormatterSciNotation',

'LogitFormatter', 'EngFormatter', 'PercentFormatter',

'OldScalarFormatter',

'Locator', 'IndexLocator', 'FixedLocator', 'NullLocator',

'LinearLocator', 'LogLocator', 'AutoLocator',

'MultipleLocator', 'MaxNLocator', 'AutoMinorLocator',

'SymmetricalLogLocator', 'LogitLocator', 'OldAutoLocator')

class _DummyAxis:

__name__ = "dummy"

def __init__(self, minpos=0):

self.dataLim = mtransforms.Bbox.unit()

self.viewLim = mtransforms.Bbox.unit()

self._minpos = minpos

def get_view_interval(self):

return self.viewLim.intervalx

def set_view_interval(self, vmin, vmax):

self.viewLim.intervalx = vmin, vmax

def get_minpos(self):

return self._minpos

def get_data_interval(self):

return self.dataLim.intervalx

def set_data_interval(self, vmin, vmax):

self.dataLim.intervalx = vmin, vmax

def get_tick_space(self):

# Just use the long-standing default of nbins==9

return 9

<div class="viewcode-block" id="TickHelper"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.TickHelper">[docs]</a>class TickHelper:

axis = None

<div class="viewcode-block" id="TickHelper.set_axis"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.TickHelper.set_axis">[docs]</a> def set_axis(self, axis):

self.axis = axis</div>

<div class="viewcode-block" id="TickHelper.create_dummy_axis"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.TickHelper.create_dummy_axis">[docs]</a> def create_dummy_axis(self, **kwargs):

if self.axis is None:

self.axis = _DummyAxis(**kwargs)</div>

<div class="viewcode-block" id="TickHelper.set_view_interval"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.TickHelper.set_view_interval">[docs]</a> def set_view_interval(self, vmin, vmax):

self.axis.set_view_interval(vmin, vmax)</div>

<div class="viewcode-block" id="TickHelper.set_data_interval"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.TickHelper.set_data_interval">[docs]</a> def set_data_interval(self, vmin, vmax):

self.axis.set_data_interval(vmin, vmax)</div>

<div class="viewcode-block" id="TickHelper.set_bounds"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.TickHelper.set_bounds">[docs]</a> def set_bounds(self, vmin, vmax):

self.set_view_interval(vmin, vmax)

self.set_data_interval(vmin, vmax)</div></div>

<div class="viewcode-block" id="Formatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter">[docs]</a>class Formatter(TickHelper):

"""

Create a string based on a tick value and location.

"""

# some classes want to see all the locs to help format

# individual ones

locs = []

def __call__(self, x, pos=None):

"""

Return the format for tick value *x* at position pos.

``pos=None`` indicates an unspecified location.

"""

raise NotImplementedError('Derived must override')

<div class="viewcode-block" id="Formatter.format_ticks"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter.format_ticks">[docs]</a> def format_ticks(self, values):

"""Return the tick labels for all the ticks at once."""

self.set_locs(values)

return [self(value, i) for i, value in enumerate(values)]</div>

<div class="viewcode-block" id="Formatter.format_data"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter.format_data">[docs]</a> def format_data(self, value):

"""

Return the full string representation of the value with the

position unspecified.

"""

return self.__call__(value)</div>

<div class="viewcode-block" id="Formatter.format_data_short"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter.format_data_short">[docs]</a> def format_data_short(self, value):

"""

Return a short string version of the tick value.

Defaults to the position-independent long value.

"""

return self.format_data(value)</div>

<div class="viewcode-block" id="Formatter.get_offset"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter.get_offset">[docs]</a> def get_offset(self):

return ''</div>

<div class="viewcode-block" id="Formatter.set_locs"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter.set_locs">[docs]</a> def set_locs(self, locs):

"""

Set the locations of the ticks.

This method is called before computing the tick labels because some

formatters need to know all tick locations to do so.

"""

self.locs = locs</div>

<div class="viewcode-block" id="Formatter.fix_minus"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.Formatter.fix_minus">[docs]</a> @staticmethod

def fix_minus(s):

"""

Some classes may want to replace a hyphen for minus with the proper

unicode symbol (U+2212) for typographical correctness. This is a

helper method to perform such a replacement when it is enabled via

:rc:`axes.unicode_minus`.

"""

return (s.replace('-', '\N{MINUS SIGN}')

if mpl.rcParams['axes.unicode_minus']

else s)</div>

def _set_locator(self, locator):

"""Subclasses may want to override this to set a locator."""

pass</div>

<div class="viewcode-block" id="IndexFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.IndexFormatter">[docs]</a>@cbook.deprecated("3.3")

class IndexFormatter(Formatter):

"""

Format the position x to the nearest i-th label where ``i = int(x + 0.5)``.

Positions where ``i < 0`` or ``i > len(list)`` have no tick labels.

Parameters

----------

labels : list

List of labels.

"""

def __init__(self, labels):

self.labels = labels

self.n = len(labels)

"""

Return the format for tick value *x* at position pos.

The position is ignored and the value is rounded to the nearest

integer, which is used to look up the label.

"""

i = int(x + 0.5)

if i < 0 or i >= self.n:

return ''

else:

return self.labels[i]</div>

<div class="viewcode-block" id="NullFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.NullFormatter">[docs]</a>class NullFormatter(Formatter):

"""Always return the empty string."""

# docstring inherited

return ''</div>

<div class="viewcode-block" id="FixedFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter">[docs]</a>class FixedFormatter(Formatter):

"""

Return fixed strings for tick labels based only on position, not value.

.. note::

`.FixedFormatter` should only be used together with `.FixedLocator`.

Otherwise, the labels may end up in unexpected positions.

"""

def __init__(self, seq):

"""Set the sequence *seq* of strings that will be used for labels."""

self.seq = seq

self.offset_string = ''

"""

Return the label that matches the position, regardless of the value.

For positions ``pos < len(seq)``, return ``seq[i]`` regardless of

*x*. Otherwise return empty string. ``seq`` is the sequence of

strings that this object was initialized with.

"""

if pos is None or pos >= len(self.seq):

return ''

else:

return self.seq[pos]

<div class="viewcode-block" id="FixedFormatter.get_offset"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter.get_offset">[docs]</a> def get_offset(self):

return self.offset_string</div>

<div class="viewcode-block" id="FixedFormatter.set_offset_string"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter.set_offset_string">[docs]</a> def set_offset_string(self, ofs):

self.offset_string = ofs</div></div>

<div class="viewcode-block" id="FuncFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FuncFormatter">[docs]</a>class FuncFormatter(Formatter):

"""

Use a user-defined function for formatting.

The function should take in two inputs (a tick value ``x`` and a

position ``pos``), and return a string containing the corresponding

tick label.

"""

def __init__(self, func):

self.func = func

self.offset_string = ""

"""

Return the value of the user defined function.

*x* and *pos* are passed through as-is.

"""

return self.func(x, pos)

<div class="viewcode-block" id="FuncFormatter.get_offset"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FuncFormatter.get_offset">[docs]</a> def get_offset(self):

return self.offset_string</div>

<div class="viewcode-block" id="FuncFormatter.set_offset_string"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FuncFormatter.set_offset_string">[docs]</a> def set_offset_string(self, ofs):

self.offset_string = ofs</div></div>

<div class="viewcode-block" id="FormatStrFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.FormatStrFormatter">[docs]</a>class FormatStrFormatter(Formatter):

"""

Use an old-style ('%' operator) format string to format the tick.

The format string should have a single variable format (%) in it.

It will be applied to the value (not the position) of the tick.

"""

def __init__(self, fmt):

self.fmt = fmt

"""

Return the formatted label string.

Only the value *x* is formatted. The position is ignored.

"""

return self.fmt % x</div>

<div class="viewcode-block" id="StrMethodFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.StrMethodFormatter">[docs]</a>class StrMethodFormatter(Formatter):

"""

Use a new-style format string (as used by `str.format`) to format the tick.

The field used for the tick value must be labeled *x* and the field used

for the tick position must be labeled *pos*.

"""

def __init__(self, fmt):

self.fmt = fmt

"""

Return the formatted label string.

*x* and *pos* are passed to `str.format` as keyword arguments

with those exact names.

"""

return self.fmt.format(x=x, pos=pos)</div>

<div class="viewcode-block" id="OldScalarFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.OldScalarFormatter">[docs]</a>@cbook.deprecated("3.3")

class OldScalarFormatter(Formatter):

"""

Tick location is a plain old number.

"""

"""

Return the format for tick val *x* based on the width of the axis.

The position *pos* is ignored.

"""

xmin, xmax = self.axis.get_view_interval()

# If the number is not too big and it's an int, format it as an int.

if abs(x) < 1e4 and x == int(x):

return '%d' % x

d = abs(xmax - xmin)

fmt = ('%1.3e' if d < 1e-2 else

'%1.3f' if d <= 1 else

'%1.2f' if d <= 10 else

'%1.1f' if d <= 1e5 else

'%1.1e')

s = fmt % x

tup = s.split('e')

if len(tup) == 2:

mantissa = tup[0].rstrip('0').rstrip('.')

sign = tup[1][0].replace('+', '')

exponent = tup[1][1:].lstrip('0')

s = '%se%s%s' % (mantissa, sign, exponent)

else:

s = s.rstrip('0').rstrip('.')

return s</div>

<div class="viewcode-block" id="ScalarFormatter"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter">[docs]</a>class ScalarFormatter(Formatter):

"""

Format tick values as a number.

Parameters

----------

useOffset : bool or float, default: :rc:`axes.formatter.useoffset`

Whether to use offset notation. See `.set_useOffset`.

useMathText : bool, default: :rc:`axes.formatter.use_mathtext`

Whether to use fancy math formatting. See `.set_useMathText`.

useLocale : bool, default: :rc:`axes.formatter.use_locale`.

Whether to use locale settings for decimal sign and positive sign.

See `.set_useLocale`.

Notes

-----

In addition to the parameters above, the formatting of scientific vs.

floating point representation can be configured via `.set_scientific`

and `.set_powerlimits`).

**Offset notation and scientific notation**

Offset notation and scientific notation look quite similar at first sight.

Both split some information from the formatted tick values and display it

at the end of the axis.

- The scientific notation splits up the order of magnitude, i.e. a

multiplicative scaling factor, e.g. ``1e6``.

- The offset notation separates an additive constant, e.g. ``+1e6``. The

offset notation label is always prefixed with a ``+`` or ``-`` sign

and is thus distinguishable from the order of magnitude label.

The following plot with x limits ``1_000_000`` to ``1_000_010`` illustrates

the different formatting. Note the labels at the right edge of the x axis.

.. plot::

lim = (1_000_000, 1_000_010)

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, gridspec_kw={'hspace': 2})

ax1.set(title='offset_notation', xlim=lim)

ax2.set(title='scientific notation', xlim=lim)

ax2.xaxis.get_major_formatter().set_useOffset(False)

ax3.set(title='floating point notation', xlim=lim)

ax3.xaxis.get_major_formatter().set_useOffset(False)

ax3.xaxis.get_major_formatter().set_scientific(False)

"""

def __init__(self, useOffset=None, useMathText=None, useLocale=None):

if useOffset is None:

useOffset = mpl.rcParams['axes.formatter.useoffset']

self._offset_threshold = \

mpl.rcParams['axes.formatter.offset_threshold']

self.set_useOffset(useOffset)

self._usetex = mpl.rcParams['text.usetex']

if useMathText is None:

useMathText = mpl.rcParams['axes.formatter.use_mathtext']

self.set_useMathText(useMathText)

self.orderOfMagnitude = 0

self.format = ''

self._scientific = True

self._powerlimits = mpl.rcParams['axes.formatter.limits']

if useLocale is None:

useLocale = mpl.rcParams['axes.formatter.use_locale']

self._useLocale = useLocale

<div class="viewcode-block" id="ScalarFormatter.get_useOffset"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.get_useOffset">[docs]</a> def get_useOffset(self):

"""

Return whether automatic mode for offset notation is active.

This returns True if ``set_useOffset(True)``; it returns False if an

explicit offset was set, e.g. ``set_useOffset(1000)``.

See Also

--------

ScalarFormatter.set_useOffset

"""

return self._useOffset</div>

<div class="viewcode-block" id="ScalarFormatter.set_useOffset"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.set_useOffset">[docs]</a> def set_useOffset(self, val):

"""

Set whether to use offset notation.

When formatting a set numbers whose value is large compared to their

range, the formatter can separate an additive constant. This can

shorten the formatted numbers so that they are less likely to overlap

when drawn on an axis.

Parameters

----------

val : bool or float

- If False, do not use offset notation.

- If True (=automatic mode), use offset notation if it can make

the residual numbers significantly shorter. The exact behavior

is controlled by :rc:`axes.formatter.offset_threshold`.

- If a number, force an offset of the given value.

Examples

--------

With active offset notation, the values

``100_000, 100_002, 100_004, 100_006, 100_008``

will be formatted as ``0, 2, 4, 6, 8`` plus an offset ``+1e5``, which

is written to the edge of the axis.

"""

if val in [True, False]:

self.offset = 0

self._useOffset = val

else:

self._useOffset = False

self.offset = val</div>

useOffset = property(fget=get_useOffset, fset=set_useOffset)

<div class="viewcode-block" id="ScalarFormatter.get_useLocale"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.get_useLocale">[docs]</a> def get_useLocale(self):

"""

Return whether locale settings are used for formatting.

See Also

--------

ScalarFormatter.set_useLocale

"""

return self._useLocale</div>

<div class="viewcode-block" id="ScalarFormatter.set_useLocale"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.set_useLocale">[docs]</a> def set_useLocale(self, val):

"""

Set whether to use locale settings for decimal sign and positive sign.

Parameters

----------

val : bool or None

*None* resets to :rc:`axes.formatter.use_locale`.

"""

if val is None:

self._useLocale = mpl.rcParams['axes.formatter.use_locale']

else:

self._useLocale = val</div>

useLocale = property(fget=get_useLocale, fset=set_useLocale)

<div class="viewcode-block" id="ScalarFormatter.get_useMathText"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.get_useMathText">[docs]</a> def get_useMathText(self):

"""

Return whether to use fancy math formatting.

See Also

--------

ScalarFormatter.set_useMathText

"""

return self._useMathText</div>

<div class="viewcode-block" id="ScalarFormatter.set_useMathText"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.set_useMathText">[docs]</a> def set_useMathText(self, val):

r"""

Set whether to use fancy math formatting.

If active, scientific notation is formatted as :math:`1.2 \times 10^3`.

Parameters

----------

val : bool or None

*None* resets to :rc:`axes.formatter.use_mathtext`.

"""

if val is None:

self._useMathText = mpl.rcParams['axes.formatter.use_mathtext']

else:

self._useMathText = val</div>

useMathText = property(fget=get_useMathText, fset=set_useMathText)

"""

Return the format for tick value *x* at position *pos*.

"""

if len(self.locs) == 0:

return ''

else:

xp = (x - self.offset) / (10. ** self.orderOfMagnitude)

if abs(xp) < 1e-8:

xp = 0

if self._useLocale:

s = locale.format_string(self.format, (xp,))

else:

s = self.format % xp

return self.fix_minus(s)

<div class="viewcode-block" id="ScalarFormatter.set_scientific"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.set_scientific">[docs]</a> def set_scientific(self, b):

"""

Turn scientific notation on or off.

See Also

--------

ScalarFormatter.set_powerlimits

"""

self._scientific = bool(b)</div>

<div class="viewcode-block" id="ScalarFormatter.set_powerlimits"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.set_powerlimits">[docs]</a> def set_powerlimits(self, lims):

r"""

Set size thresholds for scientific notation.

Parameters

----------

lims : (int, int)

A tuple *(min_exp, max_exp)* containing the powers of 10 that

determine the switchover threshold. For a number representable as

:math:`a \times 10^\mathrm{exp}`` with :math:`1 <= |a| < 10`,

scientific notation will be used if ``exp <= min_exp`` or

``exp >= max_exp``.

The default limits are controlled by :rc:`axes.formatter.limits`.

In particular numbers with *exp* equal to the thresholds are

written in scientific notation.

Typically, *min_exp* will be negative and *max_exp* will be

positive.

For example, ``formatter.set_powerlimits((-3, 4))`` will provide

the following formatting:

:math:`1 \times 10^{-3}, 9.9 \times 10^{-3}, 0.01,`

:math:`9999, 1 \times 10^4`.

See Also

--------

ScalarFormatter.set_scientific

"""

if len(lims) != 2:

raise ValueError("'lims' must be a sequence of length 2")

self._powerlimits = lims</div>

<div class="viewcode-block" id="ScalarFormatter.format_data_short"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.format_data_short">[docs]</a> def format_data_short(self, value):

# docstring inherited

if isinstance(value, np.ma.MaskedArray) and value.mask:

return ""

if isinstance(value, Integral):

fmt = "%d"

else:

if getattr(self.axis, "__name__", "") in ["xaxis", "yaxis"]:

if self.axis.__name__ == "xaxis":

axis_trf = self.axis.axes.get_xaxis_transform()

axis_inv_trf = axis_trf.inverted()

screen_xy = axis_trf.transform((value, 0))

neighbor_values = axis_inv_trf.transform(

screen_xy + [[-1, 0], [+1, 0]])[:, 0]

else: # yaxis:

axis_inv_trf = axis_trf.inverted()

screen_xy = axis_trf.transform((0, value))

neighbor_values = axis_inv_trf.transform(

screen_xy + [[0, -1], [0, +1]])[:, 1]

delta = abs(neighbor_values - value).max()

else:

# Rough approximation: no more than 1e4 divisions.

delta = np.diff(self.axis.get_view_interval()) / 1e4

# If e.g. value = 45.67 and delta = 0.02, then we want to round to

# 2 digits after the decimal point (floor(log10(0.02)) = -2);

# 45.67 contributes 2 digits before the decimal point

# (floor(log10(45.67)) + 1 = 2): the total is 4 significant digits.

# A value of 0 contributes 1 "digit" before the decimal point.

sig_digits = max(

0,

(math.floor(math.log10(abs(value))) + 1 if value else 1)

- math.floor(math.log10(delta)))

fmt = f"%-#.{sig_digits}g"

return (

self.fix_minus(

locale.format_string(fmt, (value,)) if self._useLocale else

fmt % value))</div>

<div class="viewcode-block" id="ScalarFormatter.format_data"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.format_data">[docs]</a> def format_data(self, value):

# docstring inherited

if self._useLocale:

s = locale.format_string('%1.10e', (value,))

else:

s = '%1.10e' % value

s = self._formatSciNotation(s)

return self.fix_minus(s)</div>

<div class="viewcode-block" id="ScalarFormatter.get_offset"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.get_offset">[docs]</a> def get_offset(self):

"""

Return scientific notation, plus offset.

"""

return ''

s = ''

if self.orderOfMagnitude or self.offset:

offsetStr = ''

sciNotStr = ''

if self.offset:

offsetStr = self.format_data(self.offset)

if self.offset > 0:

offsetStr = '+' + offsetStr

if self.orderOfMagnitude:

if self._usetex or self._useMathText:

sciNotStr = self.format_data(10 ** self.orderOfMagnitude)

else:

sciNotStr = '1e%d' % self.orderOfMagnitude

if self._useMathText or self._usetex:

if sciNotStr != '':

sciNotStr = r'\times\mathdefault{%s}' % sciNotStr

s = r'$%s\mathdefault{%s}$' % (sciNotStr, offsetStr)

else:

s = ''.join((sciNotStr, offsetStr))

return self.fix_minus(s)</div>

<div class="viewcode-block" id="ScalarFormatter.set_locs"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.set_locs">[docs]</a> def set_locs(self, locs):

# docstring inherited

self.locs = locs

if len(self.locs) > 0:

if self._useOffset:

self._compute_offset()

self._set_order_of_magnitude()

self._set_format()</div>

def _compute_offset(self):

locs = self.locs

# Restrict to visible ticks.

vmin, vmax = sorted(self.axis.get_view_interval())

locs = np.asarray(locs)

locs = locs[(vmin <= locs) & (locs <= vmax)]

if not len(locs):

self.offset = 0

return

lmin, lmax = locs.min(), locs.max()

# Only use offset if there are at least two ticks and every tick has

# the same sign.

if lmin == lmax or lmin <= 0 <= lmax:

self.offset = 0

return

# min, max comparing absolute values (we want division to round towards

# zero so we work on absolute values).

abs_min, abs_max = sorted([abs(float(lmin)), abs(float(lmax))])

sign = math.copysign(1, lmin)

# What is the smallest power of ten such that abs_min and abs_max are

# equal up to that precision?

# Note: Internally using oom instead of 10 ** oom avoids some numerical

# accuracy issues.

oom_max = np.ceil(math.log10(abs_max))

oom = 1 + next(oom for oom in itertools.count(oom_max, -1)

if abs_min // 10 ** oom != abs_max // 10 ** oom)

if (abs_max - abs_min) / 10 ** oom <= 1e-2:

# Handle the case of straddling a multiple of a large power of ten

# (relative to the span).

# What is the smallest power of ten such that abs_min and abs_max

# are no more than 1 apart at that precision?

if abs_max // 10 ** oom - abs_min // 10 ** oom > 1)

# Only use offset if it saves at least _offset_threshold digits.

n = self._offset_threshold - 1

self.offset = (sign * (abs_max // 10 ** oom) * 10 ** oom

else 0)

def _set_order_of_magnitude(self):

# if scientific notation is to be used, find the appropriate exponent

# if using an numerical offset, find the exponent after applying the

# offset. When lower power limit = upper <> 0, use provided exponent.

if not self._scientific:

self.orderOfMagnitude = 0

return

if self._powerlimits[0] == self._powerlimits[1] != 0:

# fixed scaling when lower power limit = upper <> 0.

self.orderOfMagnitude = self._powerlimits[0]

return

# restrict to visible ticks

locs = np.abs(locs)

if not len(locs):

self.orderOfMagnitude = 0

return

if self.offset:

oom = math.floor(math.log10(vmax - vmin))

else:

if locs[0] > locs[-1]:

val = locs[0]

else:

val = locs[-1]

if val == 0:

oom = 0

else:

if oom <= self._powerlimits[0]:

self.orderOfMagnitude = oom

elif oom >= self._powerlimits[1]:

self.orderOfMagnitude = oom

else:

self.orderOfMagnitude = 0

def _set_format(self):

# set the format string to format all the ticklabels

if len(self.locs) < 2:

# Temporarily augment the locations with the axis end points.

_locs = [*self.locs, *self.axis.get_view_interval()]

else:

_locs = self.locs

locs = (np.asarray(_locs) - self.offset) / 10. ** self.orderOfMagnitude

loc_range = np.ptp(locs)

# Curvilinear coordinates can yield two identical points.

if loc_range == 0:

loc_range = np.max(np.abs(locs))

# Both points might be zero.

if loc_range == 0:

loc_range = 1

if len(self.locs) < 2:

# We needed the end points only for the loc_range calculation.

locs = locs[:-2]

loc_range_oom = int(math.floor(math.log10(loc_range)))

# first estimate:

sigfigs = max(0, 3 - loc_range_oom)

# refined estimate:

thresh = 1e-3 * 10 ** loc_range_oom

while sigfigs >= 0:

if np.abs(locs - np.round(locs, decimals=sigfigs)).max() < thresh:

sigfigs -= 1

else:

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