matplotlib.scale
¶matplotlib.scale.
InvertedLog10Transform
(shorthand_name=None)¶Bases: matplotlib.scale.InvertedLogTransformBase
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
base
= 10.0¶inverted
()¶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))
matplotlib.scale.
InvertedLog2Transform
(shorthand_name=None)¶Bases: matplotlib.scale.InvertedLogTransformBase
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
base
= 2.0¶inverted
()¶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))
matplotlib.scale.
InvertedLogTransform
(base)¶Bases: matplotlib.scale.InvertedLogTransformBase
inverted
()¶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))
matplotlib.scale.
InvertedLogTransformBase
(shorthand_name=None)¶Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
has_inverse
= True¶input_dims
= 1¶is_separable
= True¶output_dims
= 1¶transform_non_affine
(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
.
matplotlib.scale.
InvertedNaturalLogTransform
(shorthand_name=None)¶Bases: matplotlib.scale.InvertedLogTransformBase
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
base
= 2.718281828459045¶inverted
()¶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))
matplotlib.scale.
InvertedSymmetricalLogTransform
(base, linthresh, linscale)¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶inverted
()¶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
(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
.
matplotlib.scale.
LinearScale
(axis, **kwargs)¶Bases: matplotlib.scale.ScaleBase
The default linear scale.
get_transform
()¶The transform for linear scaling is just the
IdentityTransform
.
name
= 'linear'¶set_default_locators_and_formatters
(axis)¶Set the locators and formatters to reasonable defaults for linear scaling.
matplotlib.scale.
Log10Transform
(nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
base
= 10.0¶inverted
()¶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))
matplotlib.scale.
Log2Transform
(nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
base
= 2.0¶inverted
()¶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))
matplotlib.scale.
LogScale
(axis, **kwargs)¶Bases: matplotlib.scale.ScaleBase
A standard logarithmic scale. Care is taken so non-positive values are not plotted.
For computational efficiency (to push as much as possible to Numpy C code in the common cases), this scale provides different transforms depending on the base of the logarithm:
- base 10 (
Log10Transform
)- base 2 (
Log2Transform
)- base e (
NaturalLogTransform
)- arbitrary base (
LogTransform
)
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.
InvertedLog10Transform
(shorthand_name=None)¶Bases: matplotlib.scale.InvertedLogTransformBase
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
base
= 10.0¶inverted
()¶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))
InvertedLog2Transform
(shorthand_name=None)¶Bases: matplotlib.scale.InvertedLogTransformBase
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
base
= 2.0¶inverted
()¶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))
InvertedLogTransform
(base)¶Bases: matplotlib.scale.InvertedLogTransformBase
inverted
()¶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))
InvertedNaturalLogTransform
(shorthand_name=None)¶Bases: matplotlib.scale.InvertedLogTransformBase
Creates a new TransformNode
.
Parameters: | shorthand_name : str
|
---|
base
= 2.718281828459045¶inverted
()¶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))
Log10Transform
(nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
base
= 10.0¶inverted
()¶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))
Log2Transform
(nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
base
= 2.0¶inverted
()¶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))
LogTransform
(base, nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
inverted
()¶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))
LogTransformBase
(nonpos='clip')¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶is_separable
= True¶output_dims
= 1¶transform_non_affine
(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
.
NaturalLogTransform
(nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
base
= 2.718281828459045¶inverted
()¶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))
limit_range_for_scale
(vmin, vmax, minpos)¶Limit the domain to positive values.
name
= 'log'¶set_default_locators_and_formatters
(axis)¶Set the locators and formatters to specialized versions for log scaling.
matplotlib.scale.
LogTransform
(base, nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
inverted
()¶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))
matplotlib.scale.
LogTransformBase
(nonpos='clip')¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶is_separable
= True¶output_dims
= 1¶transform_non_affine
(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
.
matplotlib.scale.
LogisticTransform
(nonpos='mask')¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶inverted
()¶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
(a)¶logistic transform (base 10)
matplotlib.scale.
LogitScale
(axis, nonpos='mask')¶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[.
get_transform
()¶Return a LogitTransform
instance.
limit_range_for_scale
(vmin, vmax, minpos)¶Limit the domain to values between 0 and 1 (excluded).
name
= 'logit'¶matplotlib.scale.
LogitTransform
(nonpos='mask')¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶inverted
()¶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
(a)¶logit transform (base 10), masked or clipped
matplotlib.scale.
NaturalLogTransform
(nonpos='clip')¶Bases: matplotlib.scale.LogTransformBase
base
= 2.718281828459045¶inverted
()¶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))
matplotlib.scale.
ScaleBase
¶Bases: object
The base class for all scales.
Scales are separable transformations, working on a single dimension.
Any subclasses will want to override:
limit_range_for_scale
(vmin, vmax, minpos)¶Returns the range vmin, vmax, possibly limited to the domain supported by this scale.
matplotlib.scale.
SymmetricalLogScale
(axis, **kwargs)¶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).
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.
InvertedSymmetricalLogTransform
(base, linthresh, linscale)¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶inverted
()¶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
(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
.
SymmetricalLogTransform
(base, linthresh, linscale)¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶inverted
()¶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
(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
()¶Return a SymmetricalLogTransform
instance.
name
= 'symlog'¶set_default_locators_and_formatters
(axis)¶Set the locators and formatters to specialized versions for symmetrical log scaling.
matplotlib.scale.
SymmetricalLogTransform
(base, linthresh, linscale)¶Bases: matplotlib.transforms.Transform
has_inverse
= True¶input_dims
= 1¶inverted
()¶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
(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
.
matplotlib.scale.
get_scale_docs
()¶Helper function for generating docstrings related to scales.
matplotlib.scale.
get_scale_names
()¶matplotlib.scale.
register_scale
(scale_class)¶Register a new kind of scale.
scale_class must be a subclass of ScaleBase
.
matplotlib.scale.
scale_factory
(scale, axis, **kwargs)¶Return a scale class by name.
ACCEPTS: [ linear | log | logit | symlog ]