matplotlib.scale
¶
Scales define the distribution of data values on an axis, e.g. a log scaling.
They are attached to an Axis
and hold a Transform
, which is
responsible for the actual data transformation.
See also axes.Axes.set_xscale
and the scales examples in the documentation.

class
matplotlib.scale.
FuncScale
(axis, functions)[source]¶ Bases:
matplotlib.scale.ScaleBase
Provide an arbitrary scale with usersupplied function for the axis.
Parameters:  axis
Axis
The axis for the scale.
 functions(callable, callable)
twotuple of the forward and inverse functions for the scale. The forward function must be monotonic.
Both functions must have the signature:
def forward(values: arraylike) > arraylike

get_transform
()[source]¶ Return the
FuncTransform
associated with this scale.

name
= 'function'¶
 axis

class
matplotlib.scale.
FuncScaleLog
(axis, functions, base=10)[source]¶ Bases:
matplotlib.scale.LogScale
Provide an arbitrary scale with usersupplied function for the axis and then put on a logarithmic axes.
Parameters:  axis
matplotlib.axis.Axis
The axis for the scale.
 functions(callable, callable)
twotuple of the forward and inverse functions for the scale. The forward function must be monotonic.
Both functions must have the signature:
def forward(values: arraylike) > arraylike
 basefloat, default: 10
Logarithmic base of the scale.

property
base
¶

name
= 'functionlog'¶
 axis

class
matplotlib.scale.
FuncTransform
(forward, inverse)[source]¶ Bases:
matplotlib.transforms.Transform
A simple transform that takes and arbitrary function for the forward and inverse transform.
Parameters:  forwardcallable
The forward function for the transform. This function must have an inverse and, for best behavior, be monotonic. It must have the signature:
def forward(values: arraylike) > arraylike
 inversecallable
The inverse of the forward function. Signature as
forward
.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

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 nonaffine part of this transformation.
transform(values)
is always equivalent totransform_affine(transform_non_affine(values))
.In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is always a noop.Parameters:  valuesarray
The input values as NumPy array of length
input_dims
or shape (N xinput_dims
).
Returns:  array
The output values as NumPy array of length
input_dims
or shape (N xoutput_dims
), depending on the input.

class
matplotlib.scale.
InvertedLogTransform
(base)[source]¶ Bases:
matplotlib.transforms.Transform
Parameters:  shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of
str(transform)
when DEBUG=True.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

output_dims
= 1¶ The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

transform_non_affine
(a)[source]¶ Apply only the nonaffine part of this transformation.
transform(values)
is always equivalent totransform_affine(transform_non_affine(values))
.In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is always a noop.Parameters:  valuesarray
The input values as NumPy array of length
input_dims
or shape (N xinput_dims
).
Returns:  array
The output values as NumPy array of length
input_dims
or shape (N xoutput_dims
), depending on the input.

class
matplotlib.scale.
InvertedSymmetricalLogTransform
(base, linthresh, linscale)[source]¶ Bases:
matplotlib.transforms.Transform
Parameters:  shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of
str(transform)
when DEBUG=True.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

output_dims
= 1¶ The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

transform_non_affine
(a)[source]¶ Apply only the nonaffine part of this transformation.
transform(values)
is always equivalent totransform_affine(transform_non_affine(values))
.In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is always a noop.Parameters:  valuesarray
The input values as NumPy array of length
input_dims
or shape (N xinput_dims
).
Returns:  array
The output values as NumPy array of length
input_dims
or shape (N xoutput_dims
), depending on the input.

class
matplotlib.scale.
LinearScale
(axis)[source]¶ Bases:
matplotlib.scale.ScaleBase
The default linear scale.

get_transform
()[source]¶ Return the transform for linear scaling, which is just the
IdentityTransform
.

name
= 'linear'¶


class
matplotlib.scale.
LogScale
(axis, **kwargs)[source]¶ Bases:
matplotlib.scale.ScaleBase
A standard logarithmic scale. Care is taken to only plot positive values.
Parameters:  axis
Axis
The axis for the scale.
 basefloat, default: 10
The base of the logarithm.
 nonpositive{'clip', 'mask'}, default: 'clip'
Determines the behavior for nonpositive values. They can either be 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 8 logarithmically spaced minor ticks between each major tick.

property
InvertedLogTransform
¶

property
LogTransform
¶

property
base
¶

get_transform
()[source]¶ Return the
LogTransform
associated with this scale.

name
= 'log'¶
 axis

class
matplotlib.scale.
LogTransform
(base, nonpositive='clip')[source]¶ Bases:
matplotlib.transforms.Transform
Parameters:  shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of
str(transform)
when DEBUG=True.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

output_dims
= 1¶ The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

transform_non_affine
(a)[source]¶ Apply only the nonaffine part of this transformation.
transform(values)
is always equivalent totransform_affine(transform_non_affine(values))
.In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is always a noop.Parameters:  valuesarray
The input values as NumPy array of length
input_dims
or shape (N xinput_dims
).
Returns:  array
The output values as NumPy array of length
input_dims
or shape (N xoutput_dims
), depending on the input.

class
matplotlib.scale.
LogisticTransform
(nonpositive='mask')[source]¶ Bases:
matplotlib.transforms.Transform
Parameters:  shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of
str(transform)
when DEBUG=True.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

output_dims
= 1¶ The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

class
matplotlib.scale.
LogitScale
(axis, nonpositive='mask', *, one_half='\x0crac{1}{2}', use_overline=False)[source]¶ Bases:
matplotlib.scale.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 almost linear around 0.5. It maps the interval ]0, 1[ onto ]infty, +infty[.
Parameters:  axis
matplotlib.axis.Axis
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 very close to 0 or 1.
 use_overlinebool, default: False
Indicate the usage of survival notation (overline{x}) in place of standard notation (1x) for probability close to one.
 one_halfstr, default: r"frac{1}{2}"
The string used for ticks formatter to represent 1/2.

get_transform
()[source]¶ Return the
LogitTransform
associated with this scale.

limit_range_for_scale
(vmin, vmax, minpos)[source]¶ Limit the domain to values between 0 and 1 (excluded).

name
= 'logit'¶
 axis

class
matplotlib.scale.
LogitTransform
(nonpositive='mask')[source]¶ Bases:
matplotlib.transforms.Transform
Parameters:  shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of
str(transform)
when DEBUG=True.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

output_dims
= 1¶ The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

class
matplotlib.scale.
ScaleBase
(axis)[source]¶ Bases:
object
The base class for all scales.
Scales are separable transformations, working on a single dimension.
Any subclasses will want to override:
And optionally:
Construct a new scale.
Notes
The following note is for scale implementors.
For backcompatibility reasons, scales take an
Axis
object as first argument. However, this argument should not be used: a single scale object should be usable by multipleAxis
es at the same time.

class
matplotlib.scale.
SymmetricalLogScale
(axis, **kwargs)[source]¶ Bases:
matplotlib.scale.ScaleBase
The symmetrical logarithmic scale is logarithmic in both the positive and negative directions from the origin.
Since the values close to zero tend toward infinity, there is a need to have a range around zero that is linear. The parameter linthresh allows the user to specify the size of this range (linthresh, linthresh).
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 place 8 logarithmically spaced minor ticks between each major tick. linscalefloat, optional
This allows the linear range
(linthresh, linthresh)
to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when linscale == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range.
Construct a new scale.
Notes
The following note is for scale implementors.
For backcompatibility reasons, scales take an
Axis
object as first argument. However, this argument should not be used: a single scale object should be usable by multipleAxis
es at the same time.
property
InvertedSymmetricalLogTransform
¶

property
SymmetricalLogTransform
¶

property
base
¶

get_transform
()[source]¶ Return the
SymmetricalLogTransform
associated with this scale.

property
linscale
¶

property
linthresh
¶

name
= 'symlog'¶

class
matplotlib.scale.
SymmetricalLogTransform
(base, linthresh, linscale)[source]¶ Bases:
matplotlib.transforms.Transform
Parameters:  shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of
str(transform)
when DEBUG=True.

has_inverse
= True¶ True if this transform has a corresponding inverse transform.

input_dims
= 1¶ The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

inverted
()[source]¶ Return the corresponding inverse transformation.
It holds
x == self.inverted().transform(self.transform(x))
.The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

is_separable
= True¶ True if this transform is separable in the x and y dimensions.

output_dims
= 1¶ The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

transform_non_affine
(a)[source]¶ Apply only the nonaffine part of this transformation.
transform(values)
is always equivalent totransform_affine(transform_non_affine(values))
.In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is always a noop.Parameters:  valuesarray
The input values as NumPy array of length
input_dims
or shape (N xinput_dims
).
Returns:  array
The output values as NumPy array of length
input_dims
or shape (N xoutput_dims
), depending on the input.

matplotlib.scale.
register_scale
(scale_class)[source]¶ Register a new kind of scale.
Parameters:  scale_classsubclass of
ScaleBase
The scale to register.
 scale_classsubclass of

matplotlib.scale.
scale_factory
(scale, axis, **kwargs)[source]¶ Return a scale class by name.
Parameters:  scale{'function', 'functionlog', 'linear', 'log', 'logit', 'symlog'}
 axis
matplotlib.axis.Axis