class matplotlib.scale.InvertedLog10Transform(shorthand_name=None)
Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
str(transform)
when DEBUG=True.base = 10.0
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class matplotlib.scale.InvertedLog2Transform(shorthand_name=None)
Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
str(transform)
when DEBUG=True.base = 2.0
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class matplotlib.scale.InvertedLogTransform(base)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class matplotlib.scale.InvertedNaturalLogTransform(shorthand_name=None)
Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
str(transform)
when DEBUG=True.base = 2.718281828459045
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class matplotlib.scale.InvertedSymmetricalLogTransform(base, linthresh, linscale)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class 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.
class matplotlib.scale.Log10Transform(nonpos)
Bases: matplotlib.scale.LogTransformBase
base = 10.0
inverted()
transform_non_affine(a)
class matplotlib.scale.Log2Transform(nonpos)
Bases: matplotlib.scale.LogTransformBase
base = 2.0
inverted()
transform_non_affine(a)
class 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:
Log10Transform
)Log2Transform
)NaturalLogTransform
)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.
class InvertedLog10Transform(shorthand_name=None)
Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
str(transform)
when DEBUG=True.base = 10.0
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class LogScale.InvertedLog2Transform(shorthand_name=None)
Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
str(transform)
when DEBUG=True.base = 2.0
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class LogScale.InvertedLogTransform(base)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class LogScale.InvertedNaturalLogTransform(shorthand_name=None)
Bases: matplotlib.transforms.Transform
Creates a new TransformNode
.
str(transform)
when DEBUG=True.base = 2.718281828459045
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class LogScale.Log10Transform(nonpos)
Bases: matplotlib.scale.LogTransformBase
base = 10.0
inverted()
transform_non_affine(a)
class LogScale.Log2Transform(nonpos)
Bases: matplotlib.scale.LogTransformBase
base = 2.0
inverted()
transform_non_affine(a)
class LogScale.LogTransform(base, nonpos)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class LogScale.LogTransformBase(nonpos)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
is_separable = True
output_dims = 1
class LogScale.NaturalLogTransform(nonpos)
Bases: matplotlib.scale.LogTransformBase
base = 2.718281828459045
inverted()
transform_non_affine(a)
LogScale.get_transform()
Return a Transform
instance appropriate for the given logarithm base.
LogScale.limit_range_for_scale(vmin, vmax, minpos)
Limit the domain to positive values.
LogScale.name = 'log'
LogScale.set_default_locators_and_formatters(axis)
Set the locators and formatters to specialized versions for log scaling.
class matplotlib.scale.LogTransform(base, nonpos)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class matplotlib.scale.LogTransformBase(nonpos)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
is_separable = True
output_dims = 1
class matplotlib.scale.LogisticTransform(nonpos='mask')
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
logistic transform (base 10)
class 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'
set_default_locators_and_formatters(axis)
class matplotlib.scale.LogitTransform(nonpos)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
logit transform (base 10), masked or clipped
class matplotlib.scale.NaturalLogTransform(nonpos)
Bases: matplotlib.scale.LogTransformBase
base = 2.718281828459045
inverted()
transform_non_affine(a)
class 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:
get_transform()
Return the Transform
object associated with this scale.
limit_range_for_scale(vmin, vmax, minpos)
Returns the range vmin, vmax, possibly limited to the domain supported by this scale.
class 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.
class InvertedSymmetricalLogTransform(base, linthresh, linscale)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
class SymmetricalLogScale.SymmetricalLogTransform(base, linthresh, linscale)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
SymmetricalLogScale.get_transform()
Return a SymmetricalLogTransform
instance.
SymmetricalLogScale.name = 'symlog'
SymmetricalLogScale.set_default_locators_and_formatters(axis)
Set the locators and formatters to specialized versions for symmetrical log scaling.
class matplotlib.scale.SymmetricalLogTransform(base, linthresh, linscale)
Bases: matplotlib.transforms.Transform
has_inverse = True
input_dims = 1
inverted()
is_separable = True
output_dims = 1
transform_non_affine(a)
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 ]
© 2012–2016 Matplotlib Development Team. All rights reserved.
Licensed under the Matplotlib License Agreement.
http://matplotlib.org/1.5.3/api/scale_api.html