doc_content
stringlengths 1
386k
| doc_id
stringlengths 5
188
|
|---|---|
classmatplotlib.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 non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform
|
has_inverse=True
True if this transform has a corresponding inverse transform.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.has_inverse
|
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.input_dims
|
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.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.inverted
|
is_separable=True
True if this transform is separable in the x- and y- dimensions.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.is_separable
|
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.output_dims
|
transform_non_affine(a)[source]
Apply only the non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.transform_non_affine
|
classmatplotlib.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 non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform
|
has_inverse=True
True if this transform has a corresponding inverse transform.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.has_inverse
|
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.input_dims
|
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.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.inverted
|
is_separable=True
True if this transform is separable in the x- and y- dimensions.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.is_separable
|
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.output_dims
|
transform_non_affine(a)[source]
Apply only the non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.transform_non_affine
|
classmatplotlib.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'
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.LinearScale
|
get_transform()[source]
Return the transform for linear scaling, which is just the IdentityTransform.
|
matplotlib.scale_api#matplotlib.scale.LinearScale.get_transform
|
name='linear'
|
matplotlib.scale_api#matplotlib.scale.LinearScale.name
|
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.LinearScale.set_default_locators_and_formatters
|
classmatplotlib.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.
transform_non_affine(a)[source]
logistic transform (base 10)
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform
|
has_inverse=True
True if this transform has a corresponding inverse transform.
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform.has_inverse
|
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform.input_dims
|
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.
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform.inverted
|
is_separable=True
True if this transform is separable in the x- and y- dimensions.
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform.is_separable
|
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform.output_dims
|
transform_non_affine(a)[source]
logistic transform (base 10)
|
matplotlib.scale_api#matplotlib.scale.LogisticTransform.transform_non_affine
|
classmatplotlib.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
axismatplotlib.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 (1-x) 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'
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.LogitScale
|
get_transform()[source]
Return the LogitTransform associated with this scale.
|
matplotlib.scale_api#matplotlib.scale.LogitScale.get_transform
|
limit_range_for_scale(vmin, vmax, minpos)[source]
Limit the domain to values between 0 and 1 (excluded).
|
matplotlib.scale_api#matplotlib.scale.LogitScale.limit_range_for_scale
|
name='logit'
|
matplotlib.scale_api#matplotlib.scale.LogitScale.name
|
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.LogitScale.set_default_locators_and_formatters
|
classmatplotlib.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.
transform_non_affine(a)[source]
logit transform (base 10), masked or clipped
|
matplotlib.scale_api#matplotlib.scale.LogitTransform
|
has_inverse=True
True if this transform has a corresponding inverse transform.
|
matplotlib.scale_api#matplotlib.scale.LogitTransform.has_inverse
|
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.LogitTransform.input_dims
|
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.
|
matplotlib.scale_api#matplotlib.scale.LogitTransform.inverted
|
is_separable=True
True if this transform is separable in the x- and y- dimensions.
|
matplotlib.scale_api#matplotlib.scale.LogitTransform.is_separable
|
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.LogitTransform.output_dims
|
transform_non_affine(a)[source]
logit transform (base 10), masked or clipped
|
matplotlib.scale_api#matplotlib.scale.LogitTransform.transform_non_affine
|
classmatplotlib.scale.LogScale(axis, *, base=10, subs=None, nonpositive='clip')[source]
Bases: matplotlib.scale.ScaleBase A standard logarithmic scale. Care is taken to only plot positive values. Parameters
axisAxis
The axis for the scale.
basefloat, default: 10
The base of the logarithm.
nonpositive{'clip', 'mask'}, default: 'clip'
Determines the behavior for non-positive 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. propertybase
get_transform()[source]
Return the LogTransform associated with this scale.
limit_range_for_scale(vmin, vmax, minpos)[source]
Limit the domain to positive values.
name='log'
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.LogScale
|
get_transform()[source]
Return the LogTransform associated with this scale.
|
matplotlib.scale_api#matplotlib.scale.LogScale.get_transform
|
limit_range_for_scale(vmin, vmax, minpos)[source]
Limit the domain to positive values.
|
matplotlib.scale_api#matplotlib.scale.LogScale.limit_range_for_scale
|
name='log'
|
matplotlib.scale_api#matplotlib.scale.LogScale.name
|
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.LogScale.set_default_locators_and_formatters
|
classmatplotlib.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 non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.LogTransform
|
has_inverse=True
True if this transform has a corresponding inverse transform.
|
matplotlib.scale_api#matplotlib.scale.LogTransform.has_inverse
|
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.LogTransform.input_dims
|
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.
|
matplotlib.scale_api#matplotlib.scale.LogTransform.inverted
|
is_separable=True
True if this transform is separable in the x- and y- dimensions.
|
matplotlib.scale_api#matplotlib.scale.LogTransform.is_separable
|
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.LogTransform.output_dims
|
transform_non_affine(a)[source]
Apply only the non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.LogTransform.transform_non_affine
|
matplotlib.scale.register_scale(scale_class)[source]
Register a new kind of scale. Parameters
scale_classsubclass of ScaleBase
The scale to register.
|
matplotlib.scale_api#matplotlib.scale.register_scale
|
matplotlib.scale.scale_factory(scale, axis, **kwargs)[source]
Return a scale class by name. Parameters
scale{'function', 'functionlog', 'linear', 'log', 'logit', 'symlog'}
axismatplotlib.axis.Axis
|
matplotlib.scale_api#matplotlib.scale.scale_factory
|
classmatplotlib.scale.ScaleBase(axis)[source]
Bases: object The base class for all scales. Scales are separable transformations, working on a single dimension. Subclasses should override name
The scale's name. get_transform()
A method returning a Transform, which converts data coordinates to scaled coordinates. This transform should be invertible, so that e.g. mouse positions can be converted back to data coordinates. set_default_locators_and_formatters()
A method that sets default locators and formatters for an Axis that uses this scale. limit_range_for_scale()
An optional method that "fixes" the axis range to acceptable values, e.g. restricting log-scaled axes to positive values. 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()[source]
Return the Transform object associated with this scale.
limit_range_for_scale(vmin, vmax, minpos)[source]
Return the range vmin, vmax, restricted to the domain supported by this scale (if any). 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(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.ScaleBase
|
get_transform()[source]
Return the Transform object associated with this scale.
|
matplotlib.scale_api#matplotlib.scale.ScaleBase.get_transform
|
limit_range_for_scale(vmin, vmax, minpos)[source]
Return the range vmin, vmax, restricted to the domain supported by this scale (if any). minpos should be the minimum positive value in the data. This is used by log scales to determine a minimum value.
|
matplotlib.scale_api#matplotlib.scale.ScaleBase.limit_range_for_scale
|
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.ScaleBase.set_default_locators_and_formatters
|
classmatplotlib.scale.SymmetricalLogScale(axis, *, base=10, linthresh=2, subs=None, linscale=1)[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 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. propertybase
get_transform()[source]
Return the SymmetricalLogTransform associated with this scale.
propertylinscale
propertylinthresh
name='symlog'
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale
|
get_transform()[source]
Return the SymmetricalLogTransform associated with this scale.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale.get_transform
|
name='symlog'
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale.name
|
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale.set_default_locators_and_formatters
|
classmatplotlib.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 non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform
|
has_inverse=True
True if this transform has a corresponding inverse transform.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.has_inverse
|
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.input_dims
|
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.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.inverted
|
is_separable=True
True if this transform is separable in the x- and y- dimensions.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.is_separable
|
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.output_dims
|
transform_non_affine(a)[source]
Apply only the non-affine part of this 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. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
|
matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.transform_non_affine
|
matplotlib.set_loglevel(level)[source]
Set Matplotlib's root logger and root logger handler level, creating the handler if it does not exist yet. Typically, one should call set_loglevel("info") or set_loglevel("debug") to get additional debugging information. Parameters
level{"notset", "debug", "info", "warning", "error", "critical"}
The log level of the handler. Notes The first time this function is called, an additional handler is attached to Matplotlib's root handler; this handler is reused every time and this function simply manipulates the logger and handler's level.
|
matplotlib_configuration_api#matplotlib.set_loglevel
|
matplotlib.sphinxext.mathmpl A role and directive to display mathtext in Sphinx Warning In most cases, you will likely want to use one of Sphinx's builtin Math extensions instead of this one. Mathtext may be included in two ways:
Inline, using the role: This text uses inline math: :mathmpl:`\alpha > \beta`.
which produces: This text uses inline math: .
Standalone, using the directive: Here is some standalone math:
.. mathmpl::
\alpha > \beta
which produces: Here is some standalone math: Options The mathmpl role and directive both support the following options: fontsetstr, default: 'cm'
The font set to use when displaying math. See rcParams["mathtext.fontset"] (default: 'dejavusans'). fontsizefloat
The font size, in points. Defaults to the value from the extension configuration option defined below. Configuration options The mathtext extension has the following configuration options: mathmpl_fontsizefloat, default: 10.0
Default font size, in points. mathmpl_srcsetlist of str, default: []
Additional image sizes to generate when embedding in HTML, to support responsive resolution images. The list should contain additional x-descriptors ('1.5x', '2x', etc.) to generate (1x is the default and always included.) classmatplotlib.sphinxext.mathmpl.MathDirective(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine)[source]
The .. mathmpl:: directive, as documented in the module's docstring.
|
matplotlib.sphinxext_mathmpl_api
|
classmatplotlib.sphinxext.mathmpl.MathDirective(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine)[source]
The .. mathmpl:: directive, as documented in the module's docstring.
|
matplotlib.sphinxext_mathmpl_api#matplotlib.sphinxext.mathmpl.MathDirective
|
matplotlib.sphinxext.plot_directive A directive for including a Matplotlib plot in a Sphinx document By default, in HTML output, plot will include a .png file with a link to a high-res .png and .pdf. In LaTeX output, it will include a .pdf. The source code for the plot may be included in one of three ways:
A path to a source file as the argument to the directive: .. plot:: path/to/plot.py
When a path to a source file is given, the content of the directive may optionally contain a caption for the plot: .. plot:: path/to/plot.py
The plot caption.
Additionally, one may specify the name of a function to call (with no arguments) immediately after importing the module: .. plot:: path/to/plot.py plot_function1
Included as inline content to the directive: .. plot::
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
img = mpimg.imread('_static/stinkbug.png')
imgplot = plt.imshow(img)
Using doctest syntax: .. plot::
A plotting example:
>>> import matplotlib.pyplot as plt
>>> plt.plot([1, 2, 3], [4, 5, 6])
Options The plot directive supports the following options: format{'python', 'doctest'}
The format of the input. If unset, the format is auto-detected. include-sourcebool
Whether to display the source code. The default can be changed using the plot_include_source variable in conf.py (which itself defaults to False). encodingstr
If this source file is in a non-UTF8 or non-ASCII encoding, the encoding must be specified using the :encoding: option. The encoding will not be inferred using the -*- coding -*- metacomment. contextbool or str
If provided, the code will be run in the context of all previous plot directives for which the :context: option was specified. This only applies to inline code plot directives, not those run from files. If the :context: reset option is specified, the context is reset for this and future plots, and previous figures are closed prior to running the code. :context: close-figs keeps the context but closes previous figures before running the code. nofigsbool
If specified, the code block will be run, but no figures will be inserted. This is usually useful with the :context: option. captionstr
If specified, the option's argument will be used as a caption for the figure. This overwrites the caption given in the content, when the plot is generated from a file. Additionally, this directive supports all of the options of the image directive, except for target (since plot will add its own target). These include alt, height, width, scale, align and class. Configuration options The plot directive has the following configuration options: plot_include_source
Default value for the include-source option (default: False). plot_html_show_source_link
Whether to show a link to the source in HTML (default: True). plot_pre_code
Code that should be executed before each plot. If None (the default), it will default to a string containing: import numpy as np
from matplotlib import pyplot as plt
plot_basedir
Base directory, to which plot:: file names are relative to. If None or empty (the default), file names are relative to the directory where the file containing the directive is. plot_formats
File formats to generate (default: ['png', 'hires.png', 'pdf']). List of tuples or strings: [(suffix, dpi), suffix, ...]
that determine the file format and the DPI. For entries whose DPI was omitted, sensible defaults are chosen. When passing from the command line through sphinx_build the list should be passed as suffix:dpi,suffix:dpi, ... plot_html_show_formats
Whether to show links to the files in HTML (default: True). plot_rcparams
A dictionary containing any non-standard rcParams that should be applied before each plot (default: {}). plot_apply_rcparams
By default, rcParams are applied when :context: option is not used in a plot directive. If set, this configuration option overrides this behavior and applies rcParams before each plot. plot_working_directory
By default, the working directory will be changed to the directory of the example, so the code can get at its data files, if any. Also its path will be added to sys.path so it can import any helper modules sitting beside it. This configuration option can be used to specify a central directory (also added to sys.path) where data files and helper modules for all code are located. plot_template
Provide a customized template for preparing restructured text. classmatplotlib.sphinxext.plot_directive.PlotDirective(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine)[source]
The .. plot:: directive, as documented in the module's docstring. run()[source]
Run the plot directive.
exceptionmatplotlib.sphinxext.plot_directive.PlotError[source]
matplotlib.sphinxext.plot_directive.mark_plot_labels(app, document)[source]
To make plots referenceable, we need to move the reference from the "htmlonly" (or "latexonly") node to the actual figure node itself.
matplotlib.sphinxext.plot_directive.out_of_date(original, derived, includes=None)[source]
Return whether derived is out-of-date relative to original or any of the RST files included in it using the RST include directive (includes). derived and original are full paths, and includes is optionally a list of full paths which may have been included in the original.
matplotlib.sphinxext.plot_directive.render_figures(code, code_path, output_dir, output_base, context, function_name, config, context_reset=False, close_figs=False, code_includes=None)[source]
Run a pyplot script and save the images in output_dir. Save the images under output_dir with file names derived from output_base
matplotlib.sphinxext.plot_directive.run_code(code, code_path, ns=None, function_name=None)[source]
[Deprecated] Import a Python module from a path, and run the function given by name, if function_name is not None. Notes Deprecated since version 3.5.
matplotlib.sphinxext.plot_directive.split_code_at_show(text)[source]
[Deprecated] Split code at plt.show(). Notes Deprecated since version 3.5.
matplotlib.sphinxext.plot_directive.unescape_doctest(text)[source]
[Deprecated] Extract code from a piece of text, which contains either Python code or doctests. Notes Deprecated since version 3.5.
|
matplotlib.sphinxext_plot_directive_api
|
matplotlib.sphinxext.plot_directive.mark_plot_labels(app, document)[source]
To make plots referenceable, we need to move the reference from the "htmlonly" (or "latexonly") node to the actual figure node itself.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.mark_plot_labels
|
matplotlib.sphinxext.plot_directive.out_of_date(original, derived, includes=None)[source]
Return whether derived is out-of-date relative to original or any of the RST files included in it using the RST include directive (includes). derived and original are full paths, and includes is optionally a list of full paths which may have been included in the original.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.out_of_date
|
classmatplotlib.sphinxext.plot_directive.PlotDirective(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine)[source]
The .. plot:: directive, as documented in the module's docstring. run()[source]
Run the plot directive.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.PlotDirective
|
run()[source]
Run the plot directive.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.PlotDirective.run
|
exceptionmatplotlib.sphinxext.plot_directive.PlotError[source]
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.PlotError
|
matplotlib.sphinxext.plot_directive.render_figures(code, code_path, output_dir, output_base, context, function_name, config, context_reset=False, close_figs=False, code_includes=None)[source]
Run a pyplot script and save the images in output_dir. Save the images under output_dir with file names derived from output_base
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.render_figures
|
matplotlib.sphinxext.plot_directive.run_code(code, code_path, ns=None, function_name=None)[source]
[Deprecated] Import a Python module from a path, and run the function given by name, if function_name is not None. Notes Deprecated since version 3.5.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.run_code
|
matplotlib.sphinxext.plot_directive.split_code_at_show(text)[source]
[Deprecated] Split code at plt.show(). Notes Deprecated since version 3.5.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.split_code_at_show
|
matplotlib.sphinxext.plot_directive.unescape_doctest(text)[source]
[Deprecated] Extract code from a piece of text, which contains either Python code or doctests. Notes Deprecated since version 3.5.
|
matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.unescape_doctest
|
matplotlib.spines classmatplotlib.spines.Spine(axes, spine_type, path, **kwargs)[source]
Bases: matplotlib.patches.Patch An axis spine -- the line noting the data area boundaries. Spines are the lines connecting the axis tick marks and noting the boundaries of the data area. They can be placed at arbitrary positions. See set_position for more information. The default position is ('outward', 0). Spines are subclasses of Patch, and inherit much of their behavior. Spines draw a line, a circle, or an arc depending if set_patch_line, set_patch_circle, or set_patch_arc has been called. Line-like is the default. Parameters
axesAxes
The Axes instance containing the spine.
spine_typestr
The spine type.
pathPath
The Path instance used to draw the spine. Other Parameters
**kwargs
Valid keyword arguments are:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha unknown
animated bool
antialiased or aa bool or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float classmethodarc_spine(axes, spine_type, center, radius, theta1, theta2, **kwargs)[source]
Create and return an arc Spine.
classmethodcircular_spine(axes, center, radius, **kwargs)[source]
Create and return a circular Spine.
cla()[source]
[Deprecated] Notes Deprecated since version 3.4:
clear()[source]
Clear the current spine.
draw(renderer)[source]
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses.
get_bounds()[source]
Get the bounds of the spine.
get_patch_transform()[source]
Return the Transform instance mapping patch coordinates to data coordinates. For example, one may define a patch of a circle which represents a radius of 5 by providing coordinates for a unit circle, and a transform which scales the coordinates (the patch coordinate) by 5.
get_path()[source]
Return the path of this patch.
get_position()[source]
Return the spine position.
get_spine_transform()[source]
Return the spine transform.
get_window_extent(renderer=None)[source]
Return the window extent of the spines in display space, including padding for ticks (but not their labels) See also matplotlib.axes.Axes.get_tightbbox
matplotlib.axes.Axes.get_window_extent
classmethodlinear_spine(axes, spine_type, **kwargs)[source]
Create and return a linear Spine.
register_axis(axis)[source]
Register an axis. An axis should be registered with its corresponding spine from the Axes instance. This allows the spine to clear any axis properties when needed.
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, bounds=<UNSET>, capstyle=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, fill=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, patch_arc=<UNSET>, patch_circle=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, position=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
antialiased or aa bool or None
bounds (low: float, high: float)
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
patch_arc unknown
patch_circle unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position unknown
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float
set_bounds(low=None, high=None)[source]
Set the spine bounds. Parameters
lowfloat or None, optional
The lower spine bound. Passing None leaves the limit unchanged. The bounds may also be passed as the tuple (low, high) as the first positional argument.
highfloat or None, optional
The higher spine bound. Passing None leaves the limit unchanged.
set_color(c)[source]
Set the edgecolor. Parameters
ccolor
Notes This method does not modify the facecolor (which defaults to "none"), unlike the Patch.set_color method defined in the parent class. Use Patch.set_facecolor to set the facecolor.
set_patch_arc(center, radius, theta1, theta2)[source]
Set the spine to be arc-like.
set_patch_circle(center, radius)[source]
Set the spine to be circular.
set_patch_line()[source]
Set the spine to be linear.
set_position(position)[source]
Set the position of the spine. Spine position is specified by a 2 tuple of (position type, amount). The position types are: 'outward': place the spine out from the data area by the specified number of points. (Negative values place the spine inwards.) 'axes': place the spine at the specified Axes coordinate (0 to 1). 'data': place the spine at the specified data coordinate. Additionally, shorthand notations define a special positions: 'center' -> ('axes', 0.5) 'zero' -> ('data', 0.0)
classmatplotlib.spines.Spines(**kwargs)[source]
Bases: collections.abc.MutableMapping The container of all Spines in an Axes. The interface is dict-like mapping names (e.g. 'left') to Spine objects. Additionally it implements some pandas.Series-like features like accessing elements by attribute: spines['top'].set_visible(False)
spines.top.set_visible(False)
Multiple spines can be addressed simultaneously by passing a list: spines[['top', 'right']].set_visible(False)
Use an open slice to address all spines: spines[:].set_visible(False)
The latter two indexing methods will return a SpinesProxy that broadcasts all set_* calls to its members, but cannot be used for any other operation. classmethodfrom_dict(d)[source]
classmatplotlib.spines.SpinesProxy(spine_dict)[source]
Bases: object A proxy to broadcast set_* method calls to all contained Spines. The proxy cannot be used for any other operations on its members. The supported methods are determined dynamically based on the contained spines. If not all spines support a given method, it's executed only on the subset of spines that support it.
|
matplotlib.spines_api
|
classmatplotlib.spines.Spine(axes, spine_type, path, **kwargs)[source]
Bases: matplotlib.patches.Patch An axis spine -- the line noting the data area boundaries. Spines are the lines connecting the axis tick marks and noting the boundaries of the data area. They can be placed at arbitrary positions. See set_position for more information. The default position is ('outward', 0). Spines are subclasses of Patch, and inherit much of their behavior. Spines draw a line, a circle, or an arc depending if set_patch_line, set_patch_circle, or set_patch_arc has been called. Line-like is the default. Parameters
axesAxes
The Axes instance containing the spine.
spine_typestr
The spine type.
pathPath
The Path instance used to draw the spine. Other Parameters
**kwargs
Valid keyword arguments are:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha unknown
animated bool
antialiased or aa bool or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float classmethodarc_spine(axes, spine_type, center, radius, theta1, theta2, **kwargs)[source]
Create and return an arc Spine.
classmethodcircular_spine(axes, center, radius, **kwargs)[source]
Create and return a circular Spine.
cla()[source]
[Deprecated] Notes Deprecated since version 3.4:
clear()[source]
Clear the current spine.
draw(renderer)[source]
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses.
get_bounds()[source]
Get the bounds of the spine.
get_patch_transform()[source]
Return the Transform instance mapping patch coordinates to data coordinates. For example, one may define a patch of a circle which represents a radius of 5 by providing coordinates for a unit circle, and a transform which scales the coordinates (the patch coordinate) by 5.
get_path()[source]
Return the path of this patch.
get_position()[source]
Return the spine position.
get_spine_transform()[source]
Return the spine transform.
get_window_extent(renderer=None)[source]
Return the window extent of the spines in display space, including padding for ticks (but not their labels) See also matplotlib.axes.Axes.get_tightbbox
matplotlib.axes.Axes.get_window_extent
classmethodlinear_spine(axes, spine_type, **kwargs)[source]
Create and return a linear Spine.
register_axis(axis)[source]
Register an axis. An axis should be registered with its corresponding spine from the Axes instance. This allows the spine to clear any axis properties when needed.
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, bounds=<UNSET>, capstyle=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, fill=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, patch_arc=<UNSET>, patch_circle=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, position=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
antialiased or aa bool or None
bounds (low: float, high: float)
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
patch_arc unknown
patch_circle unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position unknown
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float
set_bounds(low=None, high=None)[source]
Set the spine bounds. Parameters
lowfloat or None, optional
The lower spine bound. Passing None leaves the limit unchanged. The bounds may also be passed as the tuple (low, high) as the first positional argument.
highfloat or None, optional
The higher spine bound. Passing None leaves the limit unchanged.
set_color(c)[source]
Set the edgecolor. Parameters
ccolor
Notes This method does not modify the facecolor (which defaults to "none"), unlike the Patch.set_color method defined in the parent class. Use Patch.set_facecolor to set the facecolor.
set_patch_arc(center, radius, theta1, theta2)[source]
Set the spine to be arc-like.
set_patch_circle(center, radius)[source]
Set the spine to be circular.
set_patch_line()[source]
Set the spine to be linear.
set_position(position)[source]
Set the position of the spine. Spine position is specified by a 2 tuple of (position type, amount). The position types are: 'outward': place the spine out from the data area by the specified number of points. (Negative values place the spine inwards.) 'axes': place the spine at the specified Axes coordinate (0 to 1). 'data': place the spine at the specified data coordinate. Additionally, shorthand notations define a special positions: 'center' -> ('axes', 0.5) 'zero' -> ('data', 0.0)
|
matplotlib.spines_api#matplotlib.spines.Spine
|
classmethodarc_spine(axes, spine_type, center, radius, theta1, theta2, **kwargs)[source]
Create and return an arc Spine.
|
matplotlib.spines_api#matplotlib.spines.Spine.arc_spine
|
classmethodcircular_spine(axes, center, radius, **kwargs)[source]
Create and return a circular Spine.
|
matplotlib.spines_api#matplotlib.spines.Spine.circular_spine
|
cla()[source]
[Deprecated] Notes Deprecated since version 3.4:
|
matplotlib.spines_api#matplotlib.spines.Spine.cla
|
clear()[source]
Clear the current spine.
|
matplotlib.spines_api#matplotlib.spines.Spine.clear
|
draw(renderer)[source]
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses.
|
matplotlib.spines_api#matplotlib.spines.Spine.draw
|
get_bounds()[source]
Get the bounds of the spine.
|
matplotlib.spines_api#matplotlib.spines.Spine.get_bounds
|
get_patch_transform()[source]
Return the Transform instance mapping patch coordinates to data coordinates. For example, one may define a patch of a circle which represents a radius of 5 by providing coordinates for a unit circle, and a transform which scales the coordinates (the patch coordinate) by 5.
|
matplotlib.spines_api#matplotlib.spines.Spine.get_patch_transform
|
get_path()[source]
Return the path of this patch.
|
matplotlib.spines_api#matplotlib.spines.Spine.get_path
|
get_position()[source]
Return the spine position.
|
matplotlib.spines_api#matplotlib.spines.Spine.get_position
|
get_spine_transform()[source]
Return the spine transform.
|
matplotlib.spines_api#matplotlib.spines.Spine.get_spine_transform
|
get_window_extent(renderer=None)[source]
Return the window extent of the spines in display space, including padding for ticks (but not their labels) See also matplotlib.axes.Axes.get_tightbbox
matplotlib.axes.Axes.get_window_extent
|
matplotlib.spines_api#matplotlib.spines.Spine.get_window_extent
|
classmethodlinear_spine(axes, spine_type, **kwargs)[source]
Create and return a linear Spine.
|
matplotlib.spines_api#matplotlib.spines.Spine.linear_spine
|
register_axis(axis)[source]
Register an axis. An axis should be registered with its corresponding spine from the Axes instance. This allows the spine to clear any axis properties when needed.
|
matplotlib.spines_api#matplotlib.spines.Spine.register_axis
|
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, bounds=<UNSET>, capstyle=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, fill=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, patch_arc=<UNSET>, patch_circle=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, position=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
antialiased or aa bool or None
bounds (low: float, high: float)
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
patch_arc unknown
patch_circle unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position unknown
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float
|
matplotlib.spines_api#matplotlib.spines.Spine.set
|
set_bounds(low=None, high=None)[source]
Set the spine bounds. Parameters
lowfloat or None, optional
The lower spine bound. Passing None leaves the limit unchanged. The bounds may also be passed as the tuple (low, high) as the first positional argument.
highfloat or None, optional
The higher spine bound. Passing None leaves the limit unchanged.
|
matplotlib.spines_api#matplotlib.spines.Spine.set_bounds
|
set_color(c)[source]
Set the edgecolor. Parameters
ccolor
Notes This method does not modify the facecolor (which defaults to "none"), unlike the Patch.set_color method defined in the parent class. Use Patch.set_facecolor to set the facecolor.
|
matplotlib.spines_api#matplotlib.spines.Spine.set_color
|
set_patch_arc(center, radius, theta1, theta2)[source]
Set the spine to be arc-like.
|
matplotlib.spines_api#matplotlib.spines.Spine.set_patch_arc
|
set_patch_circle(center, radius)[source]
Set the spine to be circular.
|
matplotlib.spines_api#matplotlib.spines.Spine.set_patch_circle
|
set_patch_line()[source]
Set the spine to be linear.
|
matplotlib.spines_api#matplotlib.spines.Spine.set_patch_line
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.