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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 off set. 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:


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. You initialize this with a base, e.g., 10, and it picks axis limits and ticks that are multiples of your base.

The Locator subclasses defined here are

No ticks
Tick locations are fixed
locator for index plots (e.g., where x = range(len(y)))
evenly spaced ticks from min to max
logarithmically ticks from min to max
locator for use with with the symlog norm, works like the LogLocator for the part outside of the threshold and add 0 if inside the limits
ticks and range are a multiple of base;
either integer or float
choose a MultipleLocator and dyamically reassign it for intelligent ticking during navigation
finds up to a max number of ticks at nice locations
MaxNLocator with simple defaults. This is the default tick locator for most plotting.
locator for minor ticks when the axis is linear and the major ticks are uniformly spaced. It subdivides the major tick interval into a specified number of minor intervals, defaulting to 4 or 5 depending on the major interval.
Locator for logit scaling.

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( xmajorLocator )
ax.xaxis.set_minor_locator( xminorLocator )
ax.yaxis.set_major_locator( ymajorLocator )
ax.yaxis.set_minor_locator( yminorLocator )

The default minor locator is the NullLocator, e.g., 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.

No labels on the ticks
Set the strings from a list of labels
Set the strings manually for the labels
User defined function sets the labels
Use string format method
Use an old-style sprintf format string
Default formatter for scalars: autopick the format string
Formatter for log axes
Format values for log axis using exponent = log_base(value).
Format values for log axis using exponent = log_base(value) using Math text.
Format values for log axis using scientific notation.
Probability formatter.

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( xmajorFormatter )
ax.xaxis.set_minor_formatter( xminorFormatter )
ax.yaxis.set_major_formatter( ymajorFormatter )
ax.yaxis.set_minor_formatter( yminorFormatter )

See pylab_examples example code: 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.TickHelper

Bases: object

axis = None
set_bounds(vmin, vmax)
set_data_interval(vmin, vmax)
set_view_interval(vmin, vmax)
class matplotlib.ticker.Formatter

Bases: matplotlib.ticker.TickHelper

Create a string based on a tick value and location.


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 format_data() or call, you probably don’t want to use it for 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 fix_minus() should have an explicit format_data_short() method


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


Return a short string version of the tick value.

Defaults to the position-independent long value.

locs = []
class matplotlib.ticker.FixedFormatter(seq)

Bases: matplotlib.ticker.Formatter

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

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

class matplotlib.ticker.NullFormatter

Bases: matplotlib.ticker.Formatter

Always return the empty string.

class matplotlib.ticker.FuncFormatter(func)

Bases: matplotlib.ticker.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.

class matplotlib.ticker.FormatStrFormatter(fmt)

Bases: matplotlib.ticker.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.

class matplotlib.ticker.StrMethodFormatter(fmt)

Bases: matplotlib.ticker.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.

class matplotlib.ticker.ScalarFormatter(useOffset=None, useMathText=None, useLocale=None)

Bases: matplotlib.ticker.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.


Replace hyphens with a unicode minus.


Return a formatted string representation of a number.


Return a short formatted string representation of a number.


Return scientific notation, plus offset.


Set the locations of the ticks.


Sets size thresholds for scientific notation.

lims is a two-element sequence containing the powers of 10 that determine the switchover threshold. Numbers below 10**lims[0] and above 10**lims[1] 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

Method set_scientific()


Turn scientific notation on or off.

See also

Method set_powerlimits()

class matplotlib.ticker.LogFormatter(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)

Bases: matplotlib.ticker.Formatter

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

It may be instantiated directly, or subclassed.


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.


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.


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).


change the base for labeling.


Should always match the base used for LogLocator


Return a short formatted string representation of a number.


Switch minor tick labeling on or off.


labelOnlyBase : bool

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

pprint_val(x, d)

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)

Bases: matplotlib.ticker.LogFormatter

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

class matplotlib.ticker.LogFormatterMathtext(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)

Bases: matplotlib.ticker.LogFormatter

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

class matplotlib.ticker.Locator

Bases: matplotlib.ticker.TickHelper

Determine the tick locations;

Note, you should not use the same locator between different Axis because the locator stores references to the Axis data and view limits


autoscale the view limits


Pan numticks (can be positive or negative)


raise a RuntimeError if Locator attempts to create more than MAXTICKS locs


refresh internal information based on current lim


Do nothing, and rase a warning. Any locator class not supporting the set_params() function will call this.

tick_values(vmin, vmax)

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


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)

select a scale for the range from vmin to vmax

Normally this method is overridden by subclasses to change locator behaviour.


Zoom in/out on axis; if direction is >0 zoom in, else zoom out

class matplotlib.ticker.IndexLocator(base, offset)

Bases: matplotlib.ticker.Locator

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 on the i-th data points where (i-offset)%base==0

set_params(base=None, offset=None)

Set parameters within this locator

tick_values(vmin, vmax)
class matplotlib.ticker.FixedLocator(locs, nbins=None)

Bases: matplotlib.ticker.Locator

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 parameters within this locator.

tick_values(vmin, vmax)

” Return the locations of the ticks.


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

class matplotlib.ticker.NullLocator

Bases: matplotlib.ticker.Locator

No ticks

tick_values(vmin, vmax)

” Return the locations of the ticks.


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

class matplotlib.ticker.LinearLocator(numticks=None, presets=None)

Bases: matplotlib.ticker.Locator

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

set_params(numticks=None, presets=None)

Set parameters within this locator.

tick_values(vmin, vmax)
view_limits(vmin, vmax)

Try to choose the view limits intelligently

class matplotlib.ticker.LogLocator(base=10.0, subs=(1.0, ), numdecs=4, numticks=None)

Bases: matplotlib.ticker.Locator

Determine the tick locations for log axes

Place ticks on the locations : subs[j] * base**i


subs : None, string, or sequence of float, optional, 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'.


set the base of the log scaling (major tick every base**i, i integer)

nonsingular(vmin, vmax)
set_params(base=None, subs=None, numdecs=None, numticks=None)

Set parameters within this locator.


set the minor ticks for the log scaling every base**i*subs[j]

tick_values(vmin, vmax)
view_limits(vmin, vmax)

Try to choose the view limits intelligently

class matplotlib.ticker.AutoLocator

Bases: matplotlib.ticker.MaxNLocator

class matplotlib.ticker.MultipleLocator(base=1.0)

Bases: matplotlib.ticker.Locator

Set a tick on every integer that is multiple of base in the view interval


Set parameters within this locator.

tick_values(vmin, vmax)
view_limits(dmin, dmax)

Set the view limits to the nearest multiples of base that contain the data

class matplotlib.ticker.MaxNLocator(*args, **kwargs)

Bases: matplotlib.ticker.Locator

Select no more than N intervals at nice locations.

Keyword args:

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.
Sequence of nice numbers starting with 1 and ending with 10; e.g., [1, 2, 4, 5, 10]
If True, ticks will take only integer values, provided at least min_n_ticks integers are found within the view limits.
If True, autoscaling will result in a range symmetric about zero.
[‘lower’ | ‘upper’ | ‘both’ | 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'] 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==None, no ticks will be removed.
Relax nbins and integer constraints if necessary to obtain this minimum number of ticks.
bin_boundaries(vmin, vmax)

Deprecated since version 2.0: The bin_boundaries function was deprecated in version 2.0.

default_params = {'nbins': 10, 'steps': None, 'integer': False, 'symmetric': False, 'prune': None, 'min_n_ticks': 2}

Set parameters within this locator.

tick_values(vmin, vmax)
view_limits(dmin, dmax)
class matplotlib.ticker.AutoMinorLocator(n=None)

Bases: matplotlib.ticker.Locator

Dynamically find minor tick positions based on the positions of major ticks. Assumes the scale is linear and major ticks are 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)
class matplotlib.ticker.SymmetricalLogLocator(transform=None, subs=None, linthresh=None, base=None)

Bases: matplotlib.ticker.Locator

Determine the tick locations for symmetric log axes

place ticks on the location= base**i*subs[j]

set_params(subs=None, numticks=None)

Set parameters within this locator.

tick_values(vmin, vmax)
view_limits(vmin, vmax)

Try to choose the view limits intelligently