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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 3.2.0 documentation</title>

title="Search within Matplotlib 3.2.0 documentation"

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

</head><body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.2.0). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.2.0</div>

<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>

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

"""

Tick locating and formatting

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

This module contains classes to support completely configurable tick

locating and formatting. 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. Generic tick locators and

formatters are provided, as well as domain specific custom ones.

Default Formatter

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

The default formatter identifies when the x-data being plotted is a

small range on top of a large offset. To reduce the chances that the

ticklabels overlap, the ticks are labeled as deltas from a fixed offset.

For example::

ax.plot(np.arange(2000, 2010), range(10))

will have tick of 0-9 with an offset of +2e3. If this is not desired

turn off the use of the offset on the default formatter::

ax.get_xaxis().get_major_formatter().set_useOffset(False)

set the rcParam ``axes.formatter.useoffset=False`` to turn it off

globally, or set a different formatter.

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 `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)

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

import numpy as np

from matplotlib import rcParams

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')

def _mathdefault(s):

return '\\mathdefault{%s}' % s

class _DummyAxis:

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):

"""

Returns 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):

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> def fix_minus(self, s):

"""

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

proper unicode symbol (U+2212) for typographical correctness.

The default is to not replace it.

Note, if you use this method, e.g., in :meth:`format_data` or

call, you probably don't want to use it for

:meth:`format_data_short` since the toolbar uses this for

interactive coord reporting and I doubt we can expect GUIs

across platforms will handle the unicode correctly. So for

now the classes that override :meth:`fix_minus` should have an

explicit :meth:`format_data_short` method

"""

return 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>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.

"""

"""

Returns an empty string for all inputs.

"""

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.

"""

def __init__(self, seq):

"""

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

"""

self.seq = seq

self.offset_string = ''

"""

Returns 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

"""

Return the value of the user defined function.

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

"""

return self.func(x, pos)</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 value must be labeled *x* and the field used

for the 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>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 class="viewcode-block" id="OldScalarFormatter.pprint_val"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.OldScalarFormatter.pprint_val">[docs]</a> @cbook.deprecated("3.1")

def pprint_val(self, x, d):

"""

Formats the value *x* based on the size of the axis range *d*.

"""

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

return '%d' % x

if d < 1e-2:

fmt = '%1.3e'

elif d < 1e-1:

fmt = '%1.3f'

elif d > 1e5:

fmt = '%1.1e'

elif d > 10:

fmt = '%1.1f'

elif d > 1:

fmt = '%1.2f'

else:

fmt = '%1.3f'

s = fmt % x

else:

return s</div></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.

Tick value is interpreted as a plain old number. If

``useOffset==True`` and the data range is much smaller than the data

average, then an offset will be determined such that the tick labels

are meaningful. Scientific notation is used for ``data < 10^-n`` or

``data >= 10^m``, where ``n`` and ``m`` are the power limits set

using ``set_powerlimits((n, m))``. The defaults for these are

controlled by the ``axes.formatter.limits`` rc parameter.

"""

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

# useOffset allows plotting small data ranges with large offsets: for

# example: [1+1e-9, 1+2e-9, 1+3e-9] useMathText will render the offset

# and scientific notation in mathtext

if useOffset is None:

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

self._offset_threshold = rcParams['axes.formatter.offset_threshold']

self.set_useOffset(useOffset)

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

if useMathText is None:

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

self.set_useMathText(useMathText)

self.orderOfMagnitude = 0

self.format = ''

self._scientific = True

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

if useLocale is None:

useLocale = 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 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):

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 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):

if val is None:

self._useLocale = 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 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):

if val is None:

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

else:

self._useMathText = val</div>

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

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

"""

Replace hyphens with a unicode minus.

"""

if rcParams['text.usetex'] or not rcParams['axes.unicode_minus']:

return s

else:

return s.replace('-', '\N{MINUS SIGN}')</div>

"""

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 np.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):

"""

Sets size thresholds for scientific notation.

Parameters

----------

lims : (min_exp, max_exp)

A tuple containing the powers of 10 that determine the switchover

threshold. Numbers below ``10**min_exp`` and above ``10**max_exp``

will be displayed in scientific notation.

For example, ``formatter.set_powerlimits((-3, 4))`` sets the

pre-2007 default in which scientific notation is used for

numbers less than 1e-3 or greater than 1e4.

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):

"""

Return a short formatted string representation of a number.

"""

if self._useLocale:

return locale.format_string('%-12g', (value,))

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

return ''

else:

return '%-12g' % 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):

"""

Return a formatted string representation of a number.

"""

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:

if sciNotStr != '':

sciNotStr = r'\times%s' % _mathdefault(sciNotStr)

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

elif self._usetex:

if sciNotStr != '':

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

else:

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):

"""

Set the locations of the ticks.

"""

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:

break

sigfigs += 1

self.format = '%1.' + str(sigfigs) + 'f'

if self._usetex:

self.format = '$%s$' % self.format

elif self._useMathText:

self.format = '$%s$' % _mathdefault(self.format)

<div class="viewcode-block" id="ScalarFormatter.pprint_val"><a class="viewcode-back" href="../../api/ticker_api.html#matplotlib.ticker.ScalarFormatter.pprint_val">[docs]</a> @cbook.deprecated("3.1")

def pprint_val(self, x):

xp = 0

if self._useLocale:

return locale.format_string(self.format, (xp,))

else:

return self.format % xp</div>

def _formatSciNotation(self, s):

# transform 1e+004 into 1e4, for example

if self._useLocale:

decimal_point = locale.localeconv()['decimal_point']

positive_sign = locale.localeconv()['positive_sign']

else:

decimal_point = '.'

positive_sign = '+'

try:

significand = tup[0].rstrip('0').rstrip(decimal_point)

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

if self._useMathText or self._usetex:

if significand == '1' and exponent != '':

# reformat 1x10^y as 10^y

significand = ''

if exponent:

exponent = '10^{%s%s}' % (sign, exponent)

if significand and exponent:

return r'%s{\times}%s' % (significand, exponent)

else:

return r'%s%s' % (significand, exponent)

else:

s = ('%se%s%s' % (significand, sign, exponent)).rstrip('e')

return s

except IndexError:

return s</div>

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

"""

Base class for formatting ticks on a log or symlog scale.

It may be instantiated directly, or subclassed.

Parameters

----------

base : float, optional, default: 10.

Base of the logarithm used in all calculations.

labelOnlyBase : bool, optional, default: False

If True, label ticks only at integer powers of base.

This is normally True for major ticks and False for

minor ticks.

minor_thresholds : (subset, all), optional, default: (1, 0.4)

If labelOnlyBase is False, these two numbers control

the labeling of ticks that are not at integer powers of

base; normally these are the minor ticks. The controlling

parameter is the log of the axis data range. In the typical

case where base is 10 it is the number of decades spanned

by the axis, so we can call it 'numdec'. If ``numdec <= all``,

all minor ticks will be labeled. If ``all < numdec <= subset``,

then only a subset of minor ticks will be labeled, so as to

avoid crowding. If ``numdec > subset`` then no minor ticks will

be labeled.

linthresh : None or float, optional, default: None

If a symmetric log scale is in use, its ``linthresh``

parameter must be supplied here.

Notes

-----

The `set_locs` method must be called to enable the subsetting

logic controlled by the ``minor_thresholds`` parameter.

In some cases such as the colorbar, there is no distinction between

major and minor ticks; the tick locations might be set manually,

or by a locator that puts ticks at integer powers of base and

at intermediate locations. For this situation, disable the

minor_thresholds logic by using ``minor_thresholds=(np.inf, np.inf)``,

so that all ticks will be labeled.

To disable labeling of minor ticks when 'labelOnlyBase' is False,

use ``minor_thresholds=(0, 0)``. This is the default for the

"classic" style.

Examples

--------

To label a subset of minor ticks when the view limits span up

to 2 decades, and all of the ticks when zoomed in to 0.5 decades

or less, use ``minor_thresholds=(2, 0.5)``.

To label all minor ticks when the view limits span up to 1.5

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