matplotlib.scale

class matplotlib.scale.FuncScale(axis, functions) [source]

Bases: matplotlib.scale.ScaleBase

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

Parameters:	axis: 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: def forward(values: array-like) -> array-like

get_transform(self) [source]

The transform for arbitrary scaling

name = 'function'

set_default_locators_and_formatters(self, axis) [source]

Set the locators and formatters to the same defaults as the linear scale.

class matplotlib.scale.FuncScaleLog(axis, functions, base=10) [source]

Bases: matplotlib.scale.LogScale

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

Parameters:	axis: 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: def forward(values: array-like) -> array-like base : float logarithmic base of the scale (default = 10)

base

get_transform(self) [source]

The transform for arbitrary scaling

name = 'functionlog'

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:	forward : callable 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: array-like) -> array-like inverse : callable The inverse of the forward function. Signature as forward.

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, values) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class matplotlib.scale.InvertedLog10Transform(**kwargs) [source]

Bases: matplotlib.scale.InvertedLogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 10.0

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

class matplotlib.scale.InvertedLog2Transform(**kwargs) [source]

Bases: matplotlib.scale.InvertedLogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.0

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

class matplotlib.scale.InvertedLogTransform(base) [source]

Bases: matplotlib.scale.InvertedLogTransformBase

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class matplotlib.scale.InvertedLogTransformBase(**kwargs) [source]

Bases: matplotlib.transforms.Transform

[Deprecated]

Notes

Deprecated since version 3.1:

has_inverse = True

input_dims = 1

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class matplotlib.scale.InvertedNaturalLogTransform(**kwargs) [source]

Bases: matplotlib.scale.InvertedLogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.718281828459045

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

class matplotlib.scale.InvertedSymmetricalLogTransform(base, linthresh, linscale) [source]

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class matplotlib.scale.LinearScale(axis, **kwargs) [source]

Bases: matplotlib.scale.ScaleBase

The default linear scale.

get_transform(self) [source]

The transform for linear scaling is just the IdentityTransform.

name = 'linear'

set_default_locators_and_formatters(self, axis) [source]

Set the locators and formatters to reasonable defaults for linear scaling.

class matplotlib.scale.Log10Transform(**kwargs) [source]

Bases: matplotlib.scale.LogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 10.0

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

class matplotlib.scale.Log2Transform(**kwargs) [source]

Bases: matplotlib.scale.LogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.0

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

class matplotlib.scale.LogScale(axis, **kwargs) [source]

Bases: matplotlib.scale.ScaleBase

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

basex/basey:

The base of the logarithm

nonposx/nonposy: {'mask', 'clip'}

non-positive values in x or y can be masked as invalid, or clipped to a very small positive number

subsx/subsy:

Where to place the subticks between each major tick. Should be a sequence of integers. 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.

class InvertedLog10Transform(**kwargs)

Bases: matplotlib.scale.InvertedLogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 10.0

inverted(self)

Return the corresponding inverse transformation.

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.

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

class InvertedLog2Transform(**kwargs)

Bases: matplotlib.scale.InvertedLogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.0

inverted(self)

Return the corresponding inverse transformation.

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.

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

class InvertedLogTransform(base)

Bases: matplotlib.scale.InvertedLogTransformBase

has_inverse = True

input_dims = 1

inverted(self)

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a)

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class InvertedNaturalLogTransform(**kwargs)

Bases: matplotlib.scale.InvertedLogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.718281828459045

inverted(self)

Return the corresponding inverse transformation.

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.

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

class Log10Transform(**kwargs)

Bases: matplotlib.scale.LogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 10.0

inverted(self)

Return the corresponding inverse transformation.

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.

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

class Log2Transform(**kwargs)

Bases: matplotlib.scale.LogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.0

inverted(self)

Return the corresponding inverse transformation.

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.

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

class LogTransform(base, nonpos='clip')

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self)

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a)

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class LogTransformBase(**kwargs)

Bases: matplotlib.transforms.Transform

[Deprecated]

Notes

Deprecated since version 3.1:

has_inverse = True

input_dims = 1

is_separable = True

output_dims = 1

transform_non_affine(self, a)

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class NaturalLogTransform(**kwargs)

Bases: matplotlib.scale.LogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.718281828459045

inverted(self)

Return the corresponding inverse transformation.

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.

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

base

get_transform(self) [source]

Return a Transform instance appropriate for the given logarithm base.

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

Limit the domain to positive values.

name = 'log'

set_default_locators_and_formatters(self, axis) [source]

Set the locators and formatters to specialized versions for log scaling.

class matplotlib.scale.LogTransform(base, nonpos='clip') [source]

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class matplotlib.scale.LogTransformBase(**kwargs) [source]

Bases: matplotlib.transforms.Transform

[Deprecated]

Notes

Deprecated since version 3.1:

has_inverse = True

input_dims = 1

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class matplotlib.scale.LogisticTransform(nonpos='mask') [source]

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

logistic transform (base 10)

class matplotlib.scale.LogitScale(axis, nonpos='mask') [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[.

nonpos: {'mask', 'clip'}

values beyond ]0, 1[ can be masked as invalid, or clipped to a number very close to 0 or 1

get_transform(self) [source]

Return a LogitTransform instance.

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

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

name = 'logit'

set_default_locators_and_formatters(self, axis) [source]

Set the Locator and Formatter objects on the given axis to match this scale.

class matplotlib.scale.LogitTransform(nonpos='mask') [source]

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

logit transform (base 10), masked or clipped

class matplotlib.scale.NaturalLogTransform(**kwargs) [source]

Bases: matplotlib.scale.LogTransformBase

[Deprecated]

Notes

Deprecated since version 3.1:

base = 2.718281828459045

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

class matplotlib.scale.ScaleBase(axis, **kwargs) [source]

Bases: object

The base class for all scales.

Scales are separable transformations, working on a single dimension.

Any subclasses will want to override:

• name
• get_transform()
• set_default_locators_and_formatters()

And optionally:

• limit_range_for_scale()

Construct a new scale.

Notes

The following note is for scale implementors.

For back-compatibility reasons, scales take an Axis object as first argument. However, this argument should not be used: a single scale object should be usable by multiple Axises at the same time.

get_transform(self) [source]

Return the Transform object associated with this scale.

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

Returns the range vmin, vmax, possibly limited to the domain supported by this scale.

minpos should be the minimum positive value in the data.

This is used by log scales to determine a minimum value.

set_default_locators_and_formatters(self, axis) [source]

Set the Locator and Formatter objects on the given axis to match this scale.

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:

basex, basey : float The base of the logarithm. Defaults to 10. linthreshx, linthreshy : float Defines the range (-x, x), within which the plot is linear. This avoids having the plot go to infinity around zero. Defaults to 2. subsx, subsy : sequence 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. linscalex, linscaley : float, 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.

class InvertedSymmetricalLogTransform(base, linthresh, linscale)

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self)

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a)

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

class SymmetricalLogTransform(base, linthresh, linscale)

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self)

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a)

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

get_transform(self) [source]

Return a SymmetricalLogTransform instance.

name = 'symlog'

set_default_locators_and_formatters(self, axis) [source]

Set the locators and formatters to specialized versions for symmetrical log scaling.

class matplotlib.scale.SymmetricalLogTransform(base, linthresh, linscale) [source]

Bases: matplotlib.transforms.Transform

has_inverse = True

input_dims = 1

inverted(self) [source]

Return the corresponding inverse transformation.

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.

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

is_separable = True

output_dims = 1

transform_non_affine(self, a) [source]

Performs only the non-affine part of the transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Accepts a numpy array of shape (N x input_dims) and returns a numpy array of shape (N x output_dims).

Alternatively, accepts a numpy array of length input_dims and returns a numpy array of length output_dims.

matplotlib.scale.get_scale_docs() [source]

[Deprecated] Helper function for generating docstrings related to scales.

Notes

Deprecated since version 3.1: get_scale_docs() is considered private API since 3.1 and will be removed from the public API in 3.3.

matplotlib.scale.get_scale_names() [source]

matplotlib.scale.register_scale(scale_class) [source]

Register a new kind of scale.

scale_class must be a subclass of ScaleBase.

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

Return a scale class by name.

Parameters:	scale : {function, functionlog, linear, log, logit, symlog} axis : Axis