matplotlib.scale#

Scales define the distribution of data values on an axis, e.g. a log scaling.

The mapping is implemented throughTransform subclasses.

The following scales are built-in:

A user will often only use the scale name, e.g. when setting the scale throughset_xscale:ax.set_xscale("log").

See also thescales examples in the documentation.

Custom scaling can be achieved throughFuncScale, or by creating your ownScaleBase subclass and corresponding transforms (seeCustom scale).Third parties can register their scales by name throughregister_scale.

classmatplotlib.scale.ScaleBase(axis)[source]#

Bases:object

The base class for all scales.

Scales are separable transformations, working on a single dimension.

Subclasses should override

name

The scale's name.

get_transform()

A method returning aTransform, which converts data coordinates toscaled coordinates. This transform should be invertible, so that e.g.mouse positions can be converted back to data coordinates.

set_default_locators_and_formatters()

A method that sets default locators and formatters for anAxisthat uses this scale.

limit_range_for_scale()

An optional method that "fixes" the axis range to acceptable values,e.g. restricting log-scaled axes to positive values.

Construct a new scale.

Notes

The following note is for scale implementers.

For back-compatibility reasons, scales take anAxisobject as first argument. However, this argument should notbe used: a single scale object should be usable by multipleAxises at the same time.

get_transform()[source]#

Return theTransform object associated with this scale.

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

limit_range_for_scale(vmin,vmax,minpos)[source]#

Return the rangevmin,vmax, restricted to thedomain supported by this scale (if any).

minpos should be the minimum positive value in the data.This is used by log scales to determine a minimum value.

classmatplotlib.scale.LinearScale(axis)[source]#

Bases:ScaleBase

The default linear scale.

name='linear'#

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

get_transform()[source]#

Return the transform for linear scaling, which is just theIdentityTransform.

classmatplotlib.scale.FuncTransform(forward,inverse)[source]#

Bases:Transform

A simple transform that takes and arbitrary function for theforward and inverse transform.

Parameters:

forwardcallable

The forward function for the transform. This function must havean inverse and, for best behavior, be monotonic.It must have the signature:

defforward(values:array-like)->array-like

inversecallable

The inverse of the forward function. Signature asforward.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.FuncScale(axis,functions)[source]#

Bases:ScaleBase

Provide an arbitrary scale with user-supplied function for the axis.

Parameters:

axisAxis

The axis for the scale.

functions(callable, callable)

two-tuple of the forward and inverse functions for the scale.The forward function must be monotonic.

Both functions must have the signature:

defforward(values:array-like)->array-like

name='function'#

get_transform()[source]#

Return theFuncTransform associated with this scale.

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

classmatplotlib.scale.LogTransform(base,nonpositive='clip')[source]#

Bases:Transform

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.InvertedLogTransform(base)[source]#

Bases:Transform

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.LogScale(axis,*,base=10,subs=None,nonpositive='clip')[source]#

Bases:ScaleBase

A standard logarithmic scale. Care is taken to only plot positive values.

Parameters:

axisAxis

The axis for the scale.

basefloat, default: 10

The base of the logarithm.

nonpositive{'clip', 'mask'}, default: 'clip'

Determines the behavior for non-positive values. They can eitherbe masked as invalid, or clipped to a very small positive number.

subssequence of int, default: None

Where to place the subticks between each major tick. For example,in a log10 scale,[2,3,4,5,6,7,8,9] will place 8logarithmically spaced minor ticks between each major tick.

name='log'#

propertybase#

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

get_transform()[source]#

Return theLogTransform associated with this scale.

limit_range_for_scale(vmin,vmax,minpos)[source]#

Limit the domain to positive values.

classmatplotlib.scale.FuncScaleLog(axis,functions,base=10)[source]#

Bases:LogScale

Provide an arbitrary scale with user-supplied function for the axis andthen put on a logarithmic axes.

Parameters:

axisAxis

The axis for the scale.

functions(callable, callable)

two-tuple of the forward and inverse functions for the scale.The forward function must be monotonic.

Both functions must have the signature:

defforward(values:array-like)->array-like

basefloat, default: 10

Logarithmic base of the scale.

name='functionlog'#

propertybase#

get_transform()[source]#

Return theTransform associated with this scale.

classmatplotlib.scale.SymmetricalLogTransform(base,linthresh,linscale)[source]#

Bases:Transform

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.InvertedSymmetricalLogTransform(base,linthresh,linscale)[source]#

Bases:Transform

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.SymmetricalLogScale(axis,*,base=10,linthresh=2,subs=None,linscale=1)[source]#

Bases:ScaleBase

The symmetrical logarithmic scale is logarithmic in both thepositive and negative directions from the origin.

Since the values close to zero tend toward infinity, there is aneed to have a range around zero that is linear. The parameterlinthresh allows the user to specify the size of this range(-linthresh,linthresh).

SeeSymlog scale for a detailed description.

Parameters:

basefloat, default: 10

The base of the logarithm.

linthreshfloat, default: 2

Defines the range(-x,x), within which the plot is linear.This avoids having the plot go to infinity around zero.

subssequence of int

Where to place the subticks between each major tick.For example, in a log10 scale:[2,3,4,5,6,7,8,9] will place8 logarithmically spaced minor ticks between each major tick.

linscalefloat, optional

This allows the linear range(-linthresh,linthresh) to bestretched relative to the logarithmic range. Its value is the number ofdecades to use for each half of the linear range. For example, whenlinscale == 1.0 (the default), the space used for the positive andnegative halves of the linear range will be equal to one decade inthe logarithmic range.

Construct a new scale.

Notes

The following note is for scale implementers.

For back-compatibility reasons, scales take anAxisobject as first argument. However, this argument should notbe used: a single scale object should be usable by multipleAxises at the same time.

name='symlog'#

propertybase#

propertylinthresh#

propertylinscale#

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

get_transform()[source]#

Return theSymmetricalLogTransform associated with this scale.

classmatplotlib.scale.AsinhTransform(linear_width)[source]#

Bases:Transform

Inverse hyperbolic-sine transformation used byAsinhScale

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.InvertedAsinhTransform(linear_width)[source]#

Bases:Transform

Hyperbolic sine transformation used byAsinhScale

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

Apply only the non-affine part of this transformation.

transform(values) is always equivalent totransform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent totransform(values). In affine transformations, this isalways a no-op.

Parameters:

valuesarray

The input values as an array of lengthinput_dims orshape (N,input_dims).

Returns:

array

The output values as an array of lengthoutput_dims or shape(N,output_dims),depending on the input.

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.AsinhScale(axis,*,linear_width=1.0,base=10,subs='auto',**kwargs)[source]#

Bases:ScaleBase

A quasi-logarithmic scale based on the inverse hyperbolic sine (asinh)

For values close to zero, this is essentially a linear scale,but for large magnitude values (either positive or negative)it is asymptotically logarithmic. The transition between theselinear and logarithmic regimes is smooth, and has no discontinuitiesin the function gradient in contrast totheSymmetricalLogScale ("symlog") scale.

Specifically, the transformation of an axis coordinate\(a\) is\(a \rightarrow a_0 \sinh^{-1} (a / a_0)\) where\(a_0\)is the effective width of the linear region of the transformation.In that region, the transformation is\(a \rightarrow a + \mathcal{O}(a^3)\).For large values of\(a\) the transformation behaves as\(a \rightarrow a_0 \, \mathrm{sgn}(a) \ln |a| + \mathcal{O}(1)\).

Note

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

Parameters:

linear_widthfloat, default: 1

The scale parameter (elsewhere referred to as\(a_0\))defining the extent of the quasi-linear region,and the coordinate values beyond which the transformationbecomes asymptotically logarithmic.

baseint, default: 10

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

subssequence of int

Multiples of the number base used for minor ticks.If set to 'auto', this will use built-in defaults,e.g. (2, 5) for base=10.

name='asinh'#

auto_tick_multipliers={3:(2,),4:(2,),5:(2,),8:(2,4),10:(2,5),16:(2,4,8),64:(4,16),1024:(256,512)}#

propertylinear_width#

get_transform()[source]#

Return theTransform object associated with this scale.

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

classmatplotlib.scale.LogitTransform(nonpositive='mask')[source]#

Bases:Transform

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

logit transform (base 10), masked or clipped

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.LogisticTransform(nonpositive='mask')[source]#

Bases:Transform

Parameters:

shorthand_namestr

A string representing the "name" of the transform. The name carriesno significance other than to improve the readability ofstr(transform) when DEBUG=True.

input_dims=1#

The number of input dimensions of this transform.Must be overridden (with integers) in the subclass.

output_dims=1#

The number of output dimensions of this transform.Must be overridden (with integers) in the subclass.

transform_non_affine(values)[source]#

logistic transform (base 10)

inverted()[source]#

Return the corresponding inverse transformation.

It holdsx==self.inverted().transform(self.transform(x)).

The return value of this method should be treated astemporary. An update toself does not cause a correspondingupdate to its inverted copy.

has_inverse=True#

True if this transform has a corresponding inverse transform.

is_separable=True#

True if this transform is separable in the x- and y- dimensions.

classmatplotlib.scale.LogitScale(axis,nonpositive='mask',*,one_half='\\frac{1}{2}',use_overline=False)[source]#

Bases:ScaleBase

Logit scale for data between zero and one, both excluded.

This scale is similar to a log scale close to zero and to one, and almostlinear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[.

Parameters:

axisAxis

Currently unused.

nonpositive{'mask', 'clip'}

Determines the behavior for values beyond the open interval ]0, 1[.They can either be masked as invalid, or clipped to a number veryclose to 0 or 1.

use_overlinebool, default: False

Indicate the usage of survival notation (overline{x}) in place ofstandard notation (1-x) for probability close to one.

one_halfstr, default: r"frac{1}{2}"

The string used for ticks formatter to represent 1/2.

name='logit'#

get_transform()[source]#

Return theLogitTransform associated with this scale.

set_default_locators_and_formatters(axis)[source]#

Set the locators and formatters ofaxis to instances suitable forthis scale.

limit_range_for_scale(vmin,vmax,minpos)[source]#

Limit the domain to values between 0 and 1 (excluded).

matplotlib.scale.get_scale_names()[source]#

Return the names of the available scales.

matplotlib.scale.scale_factory(scale,axis,**kwargs)[source]#

Return a scale class by name.

Parameters:

scale{'asinh', 'function', 'functionlog', 'linear', 'log', 'logit', 'symlog'}

axisAxis

matplotlib.scale.register_scale(scale_class)[source]#

Register a new kind of scale.

Parameters:

scale_classsubclass ofScaleBase

The scale to register.