scale
matplotlib.scale
-
class matplotlib.scale.InvertedLog10Transform(shorthand_name=None)
-
Bases:
matplotlib.transforms.Transform
Creates a new
TransformNode
.- shorthand_name - a string representing the ?name? of this
- transform. The name carries no significance other than to improve the readability of
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
.- shorthand_name - a string representing the ?name? of this
- transform. The name carries no significance other than to improve the readability of
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
.- shorthand_name - a string representing the ?name? of this
- transform. The name carries no significance other than to improve the readability of
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:
- base 10 (
Log10Transform
) - base 2 (
Log2Transform
) - base e (
NaturalLogTransform
) - arbitrary base (
LogTransform
)
- 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(shorthand_name=None)
-
Bases:
matplotlib.transforms.Transform
Creates a new
TransformNode
.- shorthand_name - a string representing the ?name? of this
- transform. The name carries no significance other than to improve the readability of
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
.- shorthand_name - a string representing the ?name? of this
- transform. The name carries no significance other than to improve the readability of
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
.- shorthand_name - a string representing the ?name? of this
- transform. The name carries no significance other than to improve the readability of
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.
- base 10 (
-
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[.
- 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()
-
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:
- And optionally:
-
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.
- minpos should be the minimum positive value in the data.
- This is used by log scales to determine a minimum value.
-
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).
- basex/basey:
- The base of the logarithm
- linthreshx/linthreshy:
- The range (-x, x) within which the plot is linear (to avoid having the plot go to infinity around zero).
- 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.
- linscalex/linscaley:
- This allows the linear range (-linthresh to 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()
-
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 ]
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