# matplotlib.ticker#

## 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:

 AutoLocator MaxNLocator with simple defaults. This is the default tick locator for most plotting. MaxNLocator Finds up to a max number of intervals with ticks at nice locations. LinearLocator Space ticks evenly from min to max. LogLocator Space ticks logarithmically from min to max. MultipleLocator Ticks and range are a multiple of base; either integer or float. FixedLocator Tick locations are fixed. IndexLocator Locator for index plots (e.g., where x = range(len(y))). NullLocator No ticks. SymmetricalLogLocator Locator for use with the symlog norm; works like LogLocator for the part outside of the threshold and adds 0 if inside the limits. AsinhLocator Locator for use with the asinh norm, attempting to space ticks approximately uniformly. LogitLocator Locator for logit scaling. 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.

Note

Locator instances should not be used with more than one Axis or Axes. So instead of:

locator = MultipleLocator(5)
ax.xaxis.set_major_locator(locator)
ax2.xaxis.set_major_locator(locator)


ax.xaxis.set_major_locator(MultipleLocator(5))
ax2.xaxis.set_major_locator(MultipleLocator(5))


### 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.

 NullFormatter No labels on the ticks. FixedFormatter Set the strings manually for the labels. FuncFormatter User defined function sets the labels. StrMethodFormatter Use string format method. FormatStrFormatter Use an old-style sprintf format string. ScalarFormatter Default formatter for scalars: autopick the format string. LogFormatter Formatter for log axes. LogFormatterExponent Format values for log axis using exponent = log_base(value). LogFormatterMathtext Format values for log axis using exponent = log_base(value) using Math text. LogFormatterSciNotation Format values for log axis using scientific notation. LogitFormatter Probability formatter. EngFormatter Format labels in engineering notation. 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, set_major_formatter and 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 Major and minor ticks for an example of setting major and minor ticks. See the matplotlib.dates module for more information and examples of using date locators and formatters.

class matplotlib.ticker.AsinhLocator(linear_width, numticks=11, symthresh=0.2, base=10, subs=None)[source]#

An axis tick locator specialized for the inverse-sinh scale

This is very unlikely to have any use beyond the AsinhScale class.

Note

This API is provisional and may be revised in the future based on early user feedback.

Parameters
linear_widthfloat

The scale parameter defining the extent of the quasi-linear region.

numticksint, default: 11

The approximate number of major ticks that will fit along the entire axis

symthreshfloat, default: 0.2

The fractional threshold beneath which data which covers a range that is approximately symmetric about zero will have ticks that are exactly symmetric.

baseint, default: 10

The number base used for rounding tick locations on a logarithmic scale. If this is less than one, then rounding is to the nearest integer multiple of powers of ten.

substuple, default: None

Multiples of the number base, typically used for the minor ticks, e.g. (2, 5) when base=10.

set_params(numticks=None, symthresh=None, base=None, subs=None)[source]#

Set parameters within this locator.

tick_values(vmin, vmax)[source]#

Return the values of the located ticks given vmin and vmax.

Note

To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance:

>>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]

class matplotlib.ticker.AutoLocator[source]#

Dynamically find major tick positions. This is actually a subclass of MaxNLocator, with parameters nbins = 'auto' and steps = [1, 2, 2.5, 5, 10].

To know the values of the non-public parameters, please have a look to the defaults of MaxNLocator.

class matplotlib.ticker.AutoMinorLocator(n=None)[source]#

Dynamically find minor tick positions based on the positions of major ticks. The scale must be linear with major ticks evenly spaced.

n is the number of subdivisions of the interval between major ticks; e.g., n=2 will place a single minor tick midway between major ticks.

If n is omitted or None, it will be set to 5 or 4.

tick_values(vmin, vmax)[source]#

Return the values of the located ticks given vmin and vmax.

Note

To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance:

>>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]

class matplotlib.ticker.EngFormatter(unit='', places=None, sep=' ', *, usetex=None, useMathText=None)[source]#

Format axis values using engineering prefixes to represent powers of 1000, plus a specified unit, e.g., 10 MHz instead of 1e7.

Parameters
unitstr, default: ""

Unit symbol to use, suitable for use with single-letter representations of powers of 1000. For example, 'Hz' or 'm'.

placesint, default: None

Precision with which to display the number, specified in digits after the decimal point (there will be between one and three digits before the decimal point). If it is None, the formatting falls back to the floating point format '%g', which displays up to 6 significant digits, i.e. the equivalent value for places varies between 0 and 5 (inclusive).

sepstr, default: " "

Separator used between the value and the prefix/unit. For example, one get '3.14 mV' if sep is " " (default) and '3.14mV' if sep is "". Besides the default behavior, some other useful options may be:

• sep="" to append directly the prefix/unit to the value;

• sep="\N{THIN SPACE}" (U+2009);

• sep="\N{NARROW NO-BREAK SPACE}" (U+202F);

• sep="\N{NO-BREAK SPACE}" (U+00A0).

usetexbool, default: rcParams["text.usetex"] (default: False)

To enable/disable the use of TeX's math mode for rendering the numbers in the formatter.

useMathTextbool, default: rcParams["axes.formatter.use_mathtext"] (default: False)

To enable/disable the use mathtext for rendering the numbers in the formatter.

ENG_PREFIXES = {-24: 'y', -21: 'z', -18: 'a', -15: 'f', -12: 'p', -9: 'n', -6: 'µ', -3: 'm', 0: '', 3: 'k', 6: 'M', 9: 'G', 12: 'T', 15: 'P', 18: 'E', 21: 'Z', 24: 'Y'}#
format_eng(num)[source]#

Format a number in engineering notation, appending a letter representing the power of 1000 of the original number. Some examples:

>>> format_eng(0)        # for self.places = 0
'0'

>>> format_eng(1000000)  # for self.places = 1
'1.0 M'

>>> format_eng("-1e-6")  # for self.places = 2
'-1.00 µ'

get_useMathText()[source]#
get_usetex()[source]#
set_useMathText(val)[source]#
set_usetex(val)[source]#
property useMathText#
property usetex#
class matplotlib.ticker.FixedFormatter(seq)[source]#

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.

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

get_offset()[source]#
set_offset_string(ofs)[source]#
class matplotlib.ticker.FixedLocator(locs, nbins=None)[source]#

Tick locations are fixed. If nbins is not None, the array of possible positions will be subsampled to keep the number of ticks <= nbins +1. The subsampling will be done so as to include the smallest absolute value; for example, if zero is included in the array of possibilities, then it is guaranteed to be one of the chosen ticks.

set_params(nbins=None)[source]#

Set parameters within this locator.

tick_values(vmin, vmax)[source]#

Return the locations of the ticks.

Note

Because the values are fixed, vmin and vmax are not used in this method.

class matplotlib.ticker.FormatStrFormatter(fmt)[source]#

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.

Negative numeric values will use a dash, not a Unicode minus; use mathtext to get a Unicode minus by wrapping the format specifier with $(e.g. "$%g$"). class matplotlib.ticker.Formatter[source]# Create a string based on a tick value and location. static fix_minus(s)[source]# 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 rcParams["axes.unicode_minus"] (default: True). format_data(value)[source]# Return the full string representation of the value with the position unspecified. format_data_short(value)[source]# Return a short string version of the tick value. Defaults to the position-independent long value. format_ticks(values)[source]# Return the tick labels for all the ticks at once. get_offset()[source]# locs = []# set_locs(locs)[source]# 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. class matplotlib.ticker.FuncFormatter(func)[source]# 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. get_offset()[source]# set_offset_string(ofs)[source]# class matplotlib.ticker.IndexLocator(base, offset)[source]# Place a tick on every multiple of some base number of points plotted, e.g., on every 5th point. It is assumed that you are doing index plotting; i.e., the axis is 0, len(data). This is mainly useful for x ticks. Place ticks every base data point, starting at offset. set_params(base=None, offset=None)[source]# Set parameters within this locator tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  class matplotlib.ticker.LinearLocator(numticks=None, presets=None)[source]# Determine the tick locations The first time this function is called it will try to set the number of ticks to make a nice tick partitioning. Thereafter the number of ticks will be fixed so that interactive navigation will be nice Use presets to set locs based on lom. A dict mapping vmin, vmax->locs property numticks# set_params(numticks=None, presets=None)[source]# Set parameters within this locator. tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  view_limits(vmin, vmax)[source]# Try to choose the view limits intelligently. class matplotlib.ticker.Locator[source]# Determine the tick locations; Note that the same locator should not be used across multiple Axis because the locator stores references to the Axis data and view limits. MAXTICKS = 1000# nonsingular(v0, v1)[source]# Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. • If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. • If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. • Otherwise, (v0, v1) is returned without modification. raise_if_exceeds(locs)[source]# Log at WARNING level if locs is longer than Locator.MAXTICKS. This is intended to be called immediately before returning locs from __call__ to inform users in case their Locator returns a huge number of ticks, causing Matplotlib to run out of memory. The "strange" name of this method dates back to when it would raise an exception instead of emitting a log. set_params(**kwargs)[source]# Do nothing, and raise a warning. Any locator class not supporting the set_params() function will call this. tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  view_limits(vmin, vmax)[source]# Select a scale for the range from vmin to vmax. Subclasses should override this method to change locator behaviour. class matplotlib.ticker.LogFormatter(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]# Base class for formatting ticks on a log or symlog scale. It may be instantiated directly, or subclassed. Parameters basefloat, default: 10. Base of the logarithm used in all calculations. labelOnlyBasebool, 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), 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. linthreshNone or float, 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 decades, use minor_thresholds=(1.5, 1.5). base(base)[source]# Change the base for labeling. Warning Should always match the base used for LogLocator format_data(value)[source]# Return the full string representation of the value with the position unspecified. format_data_short(value)[source]# Return a short string version of the tick value. Defaults to the position-independent long value. label_minor(labelOnlyBase)[source]# Switch minor tick labeling on or off. Parameters labelOnlyBasebool If True, label ticks only at integer powers of base. set_locs(locs=None)[source]# Use axis view limits to control which ticks are labeled. The locs parameter is ignored in the present algorithm. class matplotlib.ticker.LogFormatterExponent(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]# Format values for log axis using exponent = log_base(value). class matplotlib.ticker.LogFormatterMathtext(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]# Format values for log axis using exponent = log_base(value). class matplotlib.ticker.LogFormatterSciNotation(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]# Format values following scientific notation in a logarithmic axis. class matplotlib.ticker.LogLocator(base=10.0, subs=(1.0,), numdecs=4, numticks=None)[source]# Determine the tick locations for log axes Place ticks on the locations : subs[j] * base**i Parameters basefloat, default: 10.0 The base of the log used, so ticks are placed at base**n. subsNone or str or sequence of float, default: (1.0,) Gives the multiples of integer powers of the base at which to place ticks. The default places ticks only at integer powers of the base. The permitted string values are 'auto' and 'all', both of which use an algorithm based on the axis view limits to determine whether and how to put ticks between integer powers of the base. With 'auto', ticks are placed only between integer powers; with 'all', the integer powers are included. A value of None is equivalent to 'auto'. numticksNone or int, default: None The maximum number of ticks to allow on a given axis. The default of None will try to choose intelligently as long as this Locator has already been assigned to an axis using get_tick_space, but otherwise falls back to 9. base(base)[source]# Set the log base (major tick every base**i, i integer). nonsingular(vmin, vmax)[source]# Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. • If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. • If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. • Otherwise, (v0, v1) is returned without modification. set_params(base=None, subs=None, numdecs=None, numticks=None)[source]# Set parameters within this locator. subs(subs)[source]# Set the minor ticks for the log scaling every base**i*subs[j]. tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  view_limits(vmin, vmax)[source]# Try to choose the view limits intelligently. class matplotlib.ticker.LogitFormatter(*, use_overline=False, one_half='\\frac{1}{2}', minor=False, minor_threshold=25, minor_number=6)[source]# Probability formatter (using Math text). Parameters use_overlinebool, default: False If x > 1/2, with x = 1-v, indicate if x should be displayed as$overline{v}$. The default is to display$1-v$. one_halfstr, default: r"frac{1}{2}" The string used to represent 1/2. minorbool, default: False Indicate if the formatter is formatting minor ticks or not. Basically minor ticks are not labelled, except when only few ticks are provided, ticks with most space with neighbor ticks are labelled. See other parameters to change the default behavior. minor_thresholdint, default: 25 Maximum number of locs for labelling some minor ticks. This parameter have no effect if minor is False. minor_numberint, default: 6 Number of ticks which are labelled when the number of ticks is below the threshold. format_data_short(value)[source]# Return a short string version of the tick value. Defaults to the position-independent long value. set_locs(locs)[source]# 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. set_minor_number(minor_number)[source]# Set the number of minor ticks to label when some minor ticks are labelled. Parameters minor_numberint Number of ticks which are labelled when the number of ticks is below the threshold. set_minor_threshold(minor_threshold)[source]# Set the threshold for labelling minors ticks. Parameters minor_thresholdint Maximum number of locations for labelling some minor ticks. This parameter have no effect if minor is False. set_one_half(one_half)[source]# Set the way one half is displayed. one_halfstr, default: r"frac{1}{2}" The string used to represent 1/2. use_overline(use_overline)[source]# Switch display mode with overline for labelling p>1/2. Parameters use_overlinebool, default: False If x > 1/2, with x = 1-v, indicate if x should be displayed as$overline{v}$. The default is to display$1-v$. class matplotlib.ticker.LogitLocator(minor=False, *, nbins='auto')[source]# Determine the tick locations for logit axes Place ticks on the logit locations Parameters nbinsint or 'auto', optional Number of ticks. Only used if minor is False. minorbool, default: False Indicate if this locator is for minor ticks or not. property minor# nonsingular(vmin, vmax)[source]# Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. • If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. • If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. • Otherwise, (v0, v1) is returned without modification. set_params(minor=None, **kwargs)[source]# Set parameters within this locator. tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  class matplotlib.ticker.MaxNLocator(nbins=None, **kwargs)[source]# Find nice tick locations with no more than N being within the view limits. Locations beyond the limits are added to support autoscaling. Parameters nbinsint or 'auto', default: 10 Maximum number of intervals; one less than max number of ticks. If the string 'auto', the number of bins will be automatically determined based on the length of the axis. stepsarray-like, optional Sequence of nice numbers starting with 1 and ending with 10; e.g., [1, 2, 4, 5, 10], where the values are acceptable tick multiples. i.e. for the example, 20, 40, 60 would be an acceptable set of ticks, as would 0.4, 0.6, 0.8, because they are multiples of 2. However, 30, 60, 90 would not be allowed because 3 does not appear in the list of steps. integerbool, default: False If True, ticks will take only integer values, provided at least min_n_ticks integers are found within the view limits. symmetricbool, default: False If True, autoscaling will result in a range symmetric about zero. prune{'lower', 'upper', 'both', None}, default: None Remove edge ticks -- useful for stacked or ganged plots where the upper tick of one axes overlaps with the lower tick of the axes above it, primarily when rcParams["axes.autolimit_mode"] (default: 'data') is 'round_numbers'. If prune=='lower', the smallest tick will be removed. If prune == 'upper', the largest tick will be removed. If prune == 'both', the largest and smallest ticks will be removed. If prune is None, no ticks will be removed. min_n_ticksint, default: 2 Relax nbins and integer constraints if necessary to obtain this minimum number of ticks. default_params = {'integer': False, 'min_n_ticks': 2, 'nbins': 10, 'prune': None, 'steps': None, 'symmetric': False}# set_params(**kwargs)[source]# Set parameters for this locator. Parameters nbinsint or 'auto', optional stepsarray-like, optional integerbool, optional symmetricbool, optional prune{'lower', 'upper', 'both', None}, optional min_n_ticksint, optional tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  view_limits(dmin, dmax)[source]# Select a scale for the range from vmin to vmax. Subclasses should override this method to change locator behaviour. class matplotlib.ticker.MultipleLocator(base=1.0)[source]# Set a tick on each integer multiple of a base within the view interval. set_params(base)[source]# Set parameters within this locator. tick_values(vmin, vmax)[source]# Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc)) <type 'Locator'> >>> print(loc()) [1, 2, 3, 4]  view_limits(dmin, dmax)[source]# Set the view limits to the nearest multiples of base that contain the data. class matplotlib.ticker.NullFormatter[source]# Always return the empty string. class matplotlib.ticker.NullLocator[source]# No ticks tick_values(vmin, vmax)[source]# Return the locations of the ticks. Note Because the values are Null, vmin and vmax are not used in this method. class matplotlib.ticker.PercentFormatter(xmax=100, decimals=None, symbol='%', is_latex=False)[source]# Format numbers as a percentage. Parameters xmaxfloat Determines how the number is converted into a percentage. xmax is the data value that corresponds to 100%. Percentages are computed as x / xmax * 100. So if the data is already scaled to be percentages, xmax will be 100. Another common situation is where xmax is 1.0. decimalsNone or int The number of decimal places to place after the point. If None (the default), the number will be computed automatically. symbolstr or None A string that will be appended to the label. It may be None or empty to indicate that no symbol should be used. LaTeX special characters are escaped in symbol whenever latex mode is enabled, unless is_latex is True. is_latexbool If False, reserved LaTeX characters in symbol will be escaped. convert_to_pct(x)[source]# format_pct(x, display_range)[source]# Format the number as a percentage number with the correct number of decimals and adds the percent symbol, if any. If self.decimals is None, the number of digits after the decimal point is set based on the display_range of the axis as follows:  display_range decimals sample >50 0 x = 34.5 => 35% >5 1 x = 34.5 => 34.5% >0.5 2 x = 34.5 => 34.50% ... ... ... This method will not be very good for tiny axis ranges or extremely large ones. It assumes that the values on the chart are percentages displayed on a reasonable scale. property symbol# The configured percent symbol as a string. If LaTeX is enabled via rcParams["text.usetex"] (default: False), the special characters {'#', '$', '%', '&', '~', '_', '^', '\', '{', '}'} are automatically escaped in the string.

class matplotlib.ticker.ScalarFormatter(useOffset=None, useMathText=None, useLocale=None)[source]#

Format tick values as a number.

Parameters
useOffsetbool or float, default: rcParams["axes.formatter.useoffset"] (default: True)

Whether to use offset notation. See set_useOffset.

useMathTextbool, default: rcParams["axes.formatter.use_mathtext"] (default: False)

Whether to use fancy math formatting. See set_useMathText.

useLocalebool, default: rcParams["axes.formatter.use_locale"] (default: False).

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.

format_data(value)[source]#

Return the full string representation of the value with the position unspecified.

format_data_short(value)[source]#

Return a short string version of the tick value.

Defaults to the position-independent long value.

get_offset()[source]#

Return scientific notation, plus offset.

get_useLocale()[source]#

Return whether locale settings are used for formatting.

get_useMathText()[source]#

Return whether to use fancy math formatting.

get_useOffset()[source]#

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).

set_locs(locs)[source]#

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.

set_powerlimits(lims)[source]#

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 $$a \times 10^\mathrm{exp}$$ with $$1 <= |a| < 10$$, scientific notation will be used if exp <= min_exp or exp >= max_exp.

The default limits are controlled by rcParams["axes.formatter.limits"] (default: [-5, 6]).

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: $$1 \times 10^{-3}, 9.9 \times 10^{-3}, 0.01,$$ $$9999, 1 \times 10^4$$.

set_scientific(b)[source]#

Turn scientific notation on or off.

set_useLocale(val)[source]#

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

Parameters
valbool or None

None resets to rcParams["axes.formatter.use_locale"] (default: False).

set_useMathText(val)[source]#

Set whether to use fancy math formatting.

If active, scientific notation is formatted as $$1.2 \times 10^3$$.

Parameters
valbool or None

None resets to rcParams["axes.formatter.use_mathtext"] (default: False).

set_useOffset(val)[source]#

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
valbool 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 rcParams["axes.formatter.offset_threshold"] (default: 4).

• 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.

property useLocale#

Return whether locale settings are used for formatting.

property useMathText#

Return whether to use fancy math formatting.

property useOffset#

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).

class matplotlib.ticker.StrMethodFormatter(fmt)[source]#

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.

class matplotlib.ticker.SymmetricalLogLocator(transform=None, subs=None, linthresh=None, base=None)[source]#

Determine the tick locations for symmetric log axes.

Parameters
transformSymmetricalLogTransform, optional

If set, defines the base and linthresh of the symlog transform.

base, linthreshfloat, optional

The base and linthresh of the symlog transform, as documented for SymmetricalLogScale. These parameters are only used if transform is not set.

subssequence of float, default: [1]

The multiples of integer powers of the base where ticks are placed, i.e., ticks are placed at [sub * base**i for i in ... for sub in subs].

Notes

Either transform, or both base and linthresh, must be given.

set_params(subs=None, numticks=None)[source]#

Set parameters within this locator.

tick_values(vmin, vmax)[source]#

Return the values of the located ticks given vmin and vmax.

Note

To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance:

>>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]

view_limits(vmin, vmax)[source]#

Try to choose the view limits intelligently.

class matplotlib.ticker.TickHelper[source]#

Bases: object

axis = None#
create_dummy_axis(**kwargs)[source]#
set_axis(axis)[source]#
set_bounds(vmin, vmax)[source]#

[Deprecated]

Notes

Deprecated since version 3.5: Use Axis.set_view_interval and Axis.set_data_interval instead.

set_data_interval(vmin, vmax)[source]#

[Deprecated]

Notes

Deprecated since version 3.5: Use Axis.set_data_interval instead.

set_view_interval(vmin, vmax)[source]#

[Deprecated]

Notes

Deprecated since version 3.5: Use Axis.set_view_interval instead.