doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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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.transformations#matplotlib.transforms.Transform.inverted |
is_separable=False
True if this transform is separable in the x- and y- dimensions. | matplotlib.transformations#matplotlib.transforms.Transform.is_separable |
output_dims=None
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.transformations#matplotlib.transforms.Transform.output_dims |
transform(values)[source]
Apply this transformation on the given array of values. 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.transformations#matplotlib.transforms.Transform.transform |
transform_affine(values)[source]
Apply only the affine part of this transformation on the given array of values. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally a no-op. In affine transformations, this is equivalent to transfor... | matplotlib.transformations#matplotlib.transforms.Transform.transform_affine |
transform_angles(angles, pts, radians=False, pushoff=1e-05)[source]
Transform a set of angles anchored at specific locations. Parameters
angles(N,) array-like
The angles to transform.
pts(N, 2) array-like
The points where the angles are anchored.
radiansbool, default: False
Whether angles are radians or... | matplotlib.transformations#matplotlib.transforms.Transform.transform_angles |
transform_bbox(bbox)[source]
Transform the given bounding box. For smarter transforms including caching (a common requirement in Matplotlib), see TransformedBbox. | matplotlib.transformations#matplotlib.transforms.Transform.transform_bbox |
transform_non_affine(values)[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. Pa... | matplotlib.transformations#matplotlib.transforms.Transform.transform_non_affine |
transform_path(path)[source]
Apply the transform to Path path, returning a new Path. In some cases, this transform may insert curves into the path that began as line segments. | matplotlib.transformations#matplotlib.transforms.Transform.transform_path |
transform_path_affine(path)[source]
Apply the affine part of this transform to Path path, returning a new Path. transform_path(path) is equivalent to transform_path_affine(transform_path_non_affine(values)). | matplotlib.transformations#matplotlib.transforms.Transform.transform_path_affine |
transform_path_non_affine(path)[source]
Apply the non-affine part of this transform to Path path, returning a new Path. transform_path(path) is equivalent to transform_path_affine(transform_path_non_affine(values)). | matplotlib.transformations#matplotlib.transforms.Transform.transform_path_non_affine |
transform_point(point)[source]
Return a transformed point. This function is only kept for backcompatibility; the more general transform method is capable of transforming both a list of points and a single point. The point is given as a sequence of length input_dims. The transformed point is returned as a sequence of ... | matplotlib.transformations#matplotlib.transforms.Transform.transform_point |
classmatplotlib.transforms.TransformedBbox(bbox, transform, **kwargs)[source]
Bases: matplotlib.transforms.BboxBase A Bbox that is automatically transformed by a given transform. When either the child bounding box or transform changes, the bounds of this bbox will update accordingly. Parameters
bboxBbox
transfo... | matplotlib.transformations#matplotlib.transforms.TransformedBbox |
__init__(bbox, transform, **kwargs)[source]
Parameters
bboxBbox
transformTransform | matplotlib.transformations#matplotlib.transforms.TransformedBbox.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.TransformedBbox.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.TransformedBbox.__str__ |
get_points()[source] | matplotlib.transformations#matplotlib.transforms.TransformedBbox.get_points |
classmatplotlib.transforms.TransformedPatchPath(patch)[source]
Bases: matplotlib.transforms.TransformedPath A TransformedPatchPath caches a non-affine transformed copy of the Patch. This cached copy is automatically updated when the non-affine part of the transform or the patch changes. Parameters
patchPatch
... | matplotlib.transformations#matplotlib.transforms.TransformedPatchPath |
__init__(patch)[source]
Parameters
patchPatch | matplotlib.transformations#matplotlib.transforms.TransformedPatchPath.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.TransformedPatchPath.__module__ |
classmatplotlib.transforms.TransformedPath(path, transform)[source]
Bases: matplotlib.transforms.TransformNode A TransformedPath caches a non-affine transformed copy of the Path. This cached copy is automatically updated when the non-affine part of the transform changes. Note Paths are considered immutable by this c... | matplotlib.transformations#matplotlib.transforms.TransformedPath |
__init__(path, transform)[source]
Parameters
pathPath
transformTransform | matplotlib.transformations#matplotlib.transforms.TransformedPath.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.TransformedPath.__module__ |
get_affine()[source] | matplotlib.transformations#matplotlib.transforms.TransformedPath.get_affine |
get_fully_transformed_path()[source]
Return a fully-transformed copy of the child path. | matplotlib.transformations#matplotlib.transforms.TransformedPath.get_fully_transformed_path |
get_transformed_path_and_affine()[source]
Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation. | matplotlib.transformations#matplotlib.transforms.TransformedPath.get_transformed_path_and_affine |
get_transformed_points_and_affine()[source]
Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation. Unlike get_transformed_path_and_affine(), no interpolation will be performed. | matplotlib.transformations#matplotlib.transforms.TransformedPath.get_transformed_points_and_affine |
classmatplotlib.transforms.TransformNode(shorthand_name=None)[source]
Bases: object The base class for anything that participates in the transform tree and needs to invalidate its parents or be invalidated. This includes classes that are not really transforms, such as bounding boxes, since some transforms depend on b... | matplotlib.transformations#matplotlib.transforms.TransformNode |
__copy__()[source] | matplotlib.transformations#matplotlib.transforms.TransformNode.__copy__ |
__deepcopy__(memo)[source] | matplotlib.transformations#matplotlib.transforms.TransformNode.__deepcopy__ |
__dict__=mappingproxy({'__module__': 'matplotlib.transforms', '__doc__': '\n The base class for anything that participates in the transform tree\n and needs to invalidate its parents or be invalidated. This includes\n classes that are not really transforms, such as bounding boxes, since some\n transforms depend on boun... | matplotlib.transformations#matplotlib.transforms.TransformNode.__dict__ |
__getstate__()[source] | matplotlib.transformations#matplotlib.transforms.TransformNode.__getstate__ |
__init__(shorthand_name=None)[source]
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. | matplotlib.transformations#matplotlib.transforms.TransformNode.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.TransformNode.__module__ |
__setstate__(data_dict)[source] | matplotlib.transformations#matplotlib.transforms.TransformNode.__setstate__ |
__weakref__
list of weak references to the object (if defined) | matplotlib.transformations#matplotlib.transforms.TransformNode.__weakref__ |
frozen()[source]
Return a frozen copy of this transform node. The frozen copy will not be updated when its children change. Useful for storing a previously known state of a transform where copy.deepcopy() might normally be used. | matplotlib.transformations#matplotlib.transforms.TransformNode.frozen |
INVALID=3 | matplotlib.transformations#matplotlib.transforms.TransformNode.INVALID |
INVALID_AFFINE=2 | matplotlib.transformations#matplotlib.transforms.TransformNode.INVALID_AFFINE |
INVALID_NON_AFFINE=1 | matplotlib.transformations#matplotlib.transforms.TransformNode.INVALID_NON_AFFINE |
invalidate()[source]
Invalidate this TransformNode and triggers an invalidation of its ancestors. Should be called any time the transform changes. | matplotlib.transformations#matplotlib.transforms.TransformNode.invalidate |
is_affine=False | matplotlib.transformations#matplotlib.transforms.TransformNode.is_affine |
is_bbox=False | matplotlib.transformations#matplotlib.transforms.TransformNode.is_bbox |
pass_through=False
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid. | matplotlib.transformations#matplotlib.transforms.TransformNode.pass_through |
set_children(*children)[source]
Set the children of the transform, to let the invalidation system know which transforms can invalidate this transform. Should be called from the constructor of any transforms that depend on other transforms. | matplotlib.transformations#matplotlib.transforms.TransformNode.set_children |
classmatplotlib.transforms.TransformWrapper(child)[source]
Bases: matplotlib.transforms.Transform A helper class that holds a single child transform and acts equivalently to it. This is useful if a node of the transform tree must be replaced at run time with a transform of a different type. This class allows that rep... | matplotlib.transformations#matplotlib.transforms.TransformWrapper |
__eq__(other)[source]
Return self==value. | matplotlib.transformations#matplotlib.transforms.TransformWrapper.__eq__ |
__hash__=None | matplotlib.transformations#matplotlib.transforms.TransformWrapper.__hash__ |
__init__(child)[source]
child: A Transform instance. This child may later be replaced with set(). | matplotlib.transformations#matplotlib.transforms.TransformWrapper.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.TransformWrapper.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.TransformWrapper.__str__ |
frozen()[source]
Return a frozen copy of this transform node. The frozen copy will not be updated when its children change. Useful for storing a previously known state of a transform where copy.deepcopy() might normally be used. | matplotlib.transformations#matplotlib.transforms.TransformWrapper.frozen |
pass_through=True
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid. | matplotlib.transformations#matplotlib.transforms.TransformWrapper.pass_through |
set(child)[source]
Replace the current child of this transform with another one. The new child must have the same number of input and output dimensions as the current child. | matplotlib.transformations#matplotlib.transforms.TransformWrapper.set |
matplotlib.tri Unstructured triangular grid functions. classmatplotlib.tri.Triangulation(x, y, triangles=None, mask=None)[source]
An unstructured triangular grid consisting of npoints points and ntri triangles. The triangles can either be specified by the user or automatically generated using a Delaunay triangulati... | matplotlib.tri_api |
classmatplotlib.tri.CubicTriInterpolator(triangulation, z, kind='min_E', trifinder=None, dz=None)[source]
Bases: matplotlib.tri.triinterpolate.TriInterpolator Cubic interpolator on a triangular grid. In one-dimension - on a segment - a cubic interpolating function is defined by the values of the function and its deri... | matplotlib.tri_api#matplotlib.tri.CubicTriInterpolator |
gradient(x, y)[source]
Returns a list of 2 masked arrays containing interpolated derivatives at the specified (x, y) points. Parameters
x, yarray-like
x and y coordinates of the same shape and any number of dimensions. Returns
dzdx, dzdynp.ma.array
2 masked arrays of the same shape as x and y; values co... | matplotlib.tri_api#matplotlib.tri.CubicTriInterpolator.gradient |
classmatplotlib.tri.LinearTriInterpolator(triangulation, z, trifinder=None)[source]
Bases: matplotlib.tri.triinterpolate.TriInterpolator Linear interpolator on a triangular grid. Each triangle is represented by a plane so that an interpolated value at point (x, y) lies on the plane of the triangle containing (x, y). ... | matplotlib.tri_api#matplotlib.tri.LinearTriInterpolator |
gradient(x, y)[source]
Returns a list of 2 masked arrays containing interpolated derivatives at the specified (x, y) points. Parameters
x, yarray-like
x and y coordinates of the same shape and any number of dimensions. Returns
dzdx, dzdynp.ma.array
2 masked arrays of the same shape as x and y; values co... | matplotlib.tri_api#matplotlib.tri.LinearTriInterpolator.gradient |
classmatplotlib.tri.TrapezoidMapTriFinder(triangulation)[source]
Bases: matplotlib.tri.trifinder.TriFinder TriFinder class implemented using the trapezoid map algorithm from the book "Computational Geometry, Algorithms and Applications", second edition, by M. de Berg, M. van Kreveld, M. Overmars and O. Schwarzkopf. T... | matplotlib.tri_api#matplotlib.tri.TrapezoidMapTriFinder |
classmatplotlib.tri.TriAnalyzer(triangulation)[source]
Define basic tools for triangular mesh analysis and improvement. A TriAnalyzer encapsulates a Triangulation object and provides basic tools for mesh analysis and mesh improvement. Parameters
triangulationTriangulation
The encapsulated triangulation to analy... | matplotlib.tri_api#matplotlib.tri.TriAnalyzer |
circle_ratios(rescale=True)[source]
Return a measure of the triangulation triangles flatness. The ratio of the incircle radius over the circumcircle radius is a widely used indicator of a triangle flatness. It is always <= 0.5 and == 0.5 only for equilateral triangles. Circle ratios below 0.01 denote very flat triang... | matplotlib.tri_api#matplotlib.tri.TriAnalyzer.circle_ratios |
get_flat_tri_mask(min_circle_ratio=0.01, rescale=True)[source]
Eliminate excessively flat border triangles from the triangulation. Returns a mask new_mask which allows to clean the encapsulated triangulation from its border-located flat triangles (according to their circle_ratios()). This mask is meant to be subseque... | matplotlib.tri_api#matplotlib.tri.TriAnalyzer.get_flat_tri_mask |
classmatplotlib.tri.Triangulation(x, y, triangles=None, mask=None)[source]
An unstructured triangular grid consisting of npoints points and ntri triangles. The triangles can either be specified by the user or automatically generated using a Delaunay triangulation. Parameters
x, y(npoints,) array-like
Coordinate... | matplotlib.tri_api#matplotlib.tri.Triangulation |
calculate_plane_coefficients(z)[source]
Calculate plane equation coefficients for all unmasked triangles from the point (x, y) coordinates and specified z-array of shape (npoints). The returned array has shape (npoints, 3) and allows z-value at (x, y) position in triangle tri to be calculated using z = array[tri, 0] ... | matplotlib.tri_api#matplotlib.tri.Triangulation.calculate_plane_coefficients |
get_cpp_triangulation()[source]
Return the underlying C++ Triangulation object, creating it if necessary. | matplotlib.tri_api#matplotlib.tri.Triangulation.get_cpp_triangulation |
staticget_from_args_and_kwargs(*args, **kwargs)[source]
Return a Triangulation object from the args and kwargs, and the remaining args and kwargs with the consumed values removed. There are two alternatives: either the first argument is a Triangulation object, in which case it is returned, or the args and kwargs are ... | matplotlib.tri_api#matplotlib.tri.Triangulation.get_from_args_and_kwargs |
get_masked_triangles()[source]
Return an array of triangles that are not masked. | matplotlib.tri_api#matplotlib.tri.Triangulation.get_masked_triangles |
get_trifinder()[source]
Return the default matplotlib.tri.TriFinder of this triangulation, creating it if necessary. This allows the same TriFinder object to be easily shared. | matplotlib.tri_api#matplotlib.tri.Triangulation.get_trifinder |
set_mask(mask)[source]
Set or clear the mask array. Parameters
maskNone or bool array of length ntri | matplotlib.tri_api#matplotlib.tri.Triangulation.set_mask |
classmatplotlib.tri.TriContourSet(ax, *args, **kwargs)[source]
Bases: matplotlib.contour.ContourSet Create and store a set of contour lines or filled regions for a triangular grid. This class is typically not instantiated directly by the user but by tricontour and tricontourf. Attributes
axAxes
The Axes object ... | matplotlib.tri_api#matplotlib.tri.TriContourSet |
classmatplotlib.tri.TriFinder(triangulation)[source]
Abstract base class for classes used to find the triangles of a Triangulation in which (x, y) points lie. Rather than instantiate an object of a class derived from TriFinder, it is usually better to use the function Triangulation.get_trifinder. Derived classes impl... | matplotlib.tri_api#matplotlib.tri.TriFinder |
classmatplotlib.tri.TriInterpolator(triangulation, z, trifinder=None)[source]
Abstract base class for classes used to interpolate on a triangular grid. Derived classes implement the following methods:
__call__(x, y), where x, y are array-like point coordinates of the same shape, and that returns a masked array of t... | matplotlib.tri_api#matplotlib.tri.TriInterpolator |
classmatplotlib.tri.TriRefiner(triangulation)[source]
Abstract base class for classes implementing mesh refinement. A TriRefiner encapsulates a Triangulation object and provides tools for mesh refinement and interpolation. Derived classes must implement:
refine_triangulation(return_tri_index=False, **kwargs) , wher... | matplotlib.tri_api#matplotlib.tri.TriRefiner |
classmatplotlib.tri.UniformTriRefiner(triangulation)[source]
Bases: matplotlib.tri.trirefine.TriRefiner Uniform mesh refinement by recursive subdivisions. Parameters
triangulationTriangulation
The encapsulated triangulation (to be refined) refine_field(z, triinterpolator=None, subdiv=3)[source]
Refine a... | matplotlib.tri_api#matplotlib.tri.UniformTriRefiner |
refine_field(z, triinterpolator=None, subdiv=3)[source]
Refine a field defined on the encapsulated triangulation. Parameters
z(npoints,) array-like
Values of the field to refine, defined at the nodes of the encapsulated triangulation. (n_points is the number of points in the initial triangulation)
triinterpol... | matplotlib.tri_api#matplotlib.tri.UniformTriRefiner.refine_field |
refine_triangulation(return_tri_index=False, subdiv=3)[source]
Compute an uniformly refined triangulation refi_triangulation of the encapsulated triangulation. This function refines the encapsulated triangulation by splitting each father triangle into 4 child sub-triangles built on the edges midside nodes, recursing ... | matplotlib.tri_api#matplotlib.tri.UniformTriRefiner.refine_triangulation |
matplotlib.type1font A class representing a Type 1 font. This version reads pfa and pfb files and splits them for embedding in pdf files. It also supports SlantFont and ExtendFont transformations, similarly to pdfTeX and friends. There is no support yet for subsetting. Usage: >>> font = Type1Font(filename)
>>> clear_pa... | matplotlib.type1font |
classmatplotlib.type1font.Type1Font(input)[source]
Bases: object A class representing a Type-1 font, for use by backends. Attributes
partstuple
A 3-tuple of the cleartext part, the encrypted part, and the finale of zeros.
decryptedbytes
The decrypted form of parts[1].
propdict[str, Any]
A dictionary of ... | matplotlib.type1font#matplotlib.type1font.Type1Font |
decrypted | matplotlib.type1font#matplotlib.type1font.Type1Font.decrypted |
parts | matplotlib.type1font#matplotlib.type1font.Type1Font.parts |
prop | matplotlib.type1font#matplotlib.type1font.Type1Font.prop |
transform(effects)[source]
Return a new font that is slanted and/or extended. Parameters
effectsdict
A dict with optional entries:
'slant'float, default: 0
Tangent of the angle that the font is to be slanted to the right. Negative values slant to the left.
'extend'float, default: 1
Scaling factor fo... | matplotlib.type1font#matplotlib.type1font.Type1Font.transform |
matplotlib.units The classes here provide support for using custom classes with Matplotlib, e.g., those that do not expose the array interface but know how to convert themselves to arrays. It also supports classes with units and units conversion. Use cases include converters for custom objects, e.g., a list of datetime... | matplotlib.units_api |
classmatplotlib.units.AxisInfo(majloc=None, minloc=None, majfmt=None, minfmt=None, label=None, default_limits=None)[source]
Bases: object Information to support default axis labeling, tick labeling, and limits. An instance of this class must be returned by ConversionInterface.axisinfo. Parameters
majloc, minlocLo... | matplotlib.units_api#matplotlib.units.AxisInfo |
exceptionmatplotlib.units.ConversionError[source]
Bases: TypeError | matplotlib.units_api#matplotlib.units.ConversionError |
classmatplotlib.units.ConversionInterface[source]
Bases: object The minimal interface for a converter to take custom data types (or sequences) and convert them to values Matplotlib can use. staticaxisinfo(unit, axis)[source]
Return an AxisInfo for the axis with the specified units.
staticconvert(obj, unit, ax... | matplotlib.units_api#matplotlib.units.ConversionInterface |
staticaxisinfo(unit, axis)[source]
Return an AxisInfo for the axis with the specified units. | matplotlib.units_api#matplotlib.units.ConversionInterface.axisinfo |
staticconvert(obj, unit, axis)[source]
Convert obj using unit for the specified axis. If obj is a sequence, return the converted sequence. The output must be a sequence of scalars that can be used by the numpy array layer. | matplotlib.units_api#matplotlib.units.ConversionInterface.convert |
staticdefault_units(x, axis)[source]
Return the default unit for x or None for the given axis. | matplotlib.units_api#matplotlib.units.ConversionInterface.default_units |
staticis_numlike(x)[source]
[Deprecated] The Matplotlib datalim, autoscaling, locators etc work with scalars which are the units converted to floats given the current unit. The converter may be passed these floats, or arrays of them, even when units are set. Notes Deprecated since version 3.5. | matplotlib.units_api#matplotlib.units.ConversionInterface.is_numlike |
classmatplotlib.units.DecimalConverter[source]
Bases: matplotlib.units.ConversionInterface Converter for decimal.Decimal data to float. staticconvert(value, unit, axis)[source]
Convert Decimals to floats. The unit and axis arguments are not used. Parameters
valuedecimal.Decimal or iterable
Decimal or list o... | matplotlib.units_api#matplotlib.units.DecimalConverter |
staticconvert(value, unit, axis)[source]
Convert Decimals to floats. The unit and axis arguments are not used. Parameters
valuedecimal.Decimal or iterable
Decimal or list of Decimal need to be converted | matplotlib.units_api#matplotlib.units.DecimalConverter.convert |
classmatplotlib.units.Registry[source]
Bases: dict Register types with conversion interface. get_converter(x)[source]
Get the converter interface instance for x, or None. | matplotlib.units_api#matplotlib.units.Registry |
get_converter(x)[source]
Get the converter interface instance for x, or None. | matplotlib.units_api#matplotlib.units.Registry.get_converter |
matplotlib.use(backend, *, force=True)[source]
Select the backend used for rendering and GUI integration. Parameters
backendstr
The backend to switch to. This can either be one of the standard backend names, which are case-insensitive: interactive backends: GTK3Agg, GTK3Cairo, GTK4Agg, GTK4Cairo, MacOSX, nbAgg... | matplotlib_configuration_api#matplotlib.use |
matplotlib.widgets GUI neutral widgets Widgets that are designed to work for any of the GUI backends. All of these widgets require you to predefine a matplotlib.axes.Axes instance and pass that as the first parameter. Matplotlib doesn't try to be too smart with respect to layout -- you will have... | matplotlib.widgets_api |
classmatplotlib.widgets.AxesWidget(ax)[source]
Bases: matplotlib.widgets.Widget Widget connected to a single Axes. To guarantee that the widget remains responsive and not garbage-collected, a reference to the object should be maintained by the user. This is necessary because the callback registry maintains only weak-... | matplotlib.widgets_api#matplotlib.widgets.AxesWidget |
connect_event(event, callback)[source]
Connect a callback function with an event. This should be used in lieu of figure.canvas.mpl_connect since this function stores callback ids for later clean up. | matplotlib.widgets_api#matplotlib.widgets.AxesWidget.connect_event |
disconnect_events()[source]
Disconnect all events created by this widget. | matplotlib.widgets_api#matplotlib.widgets.AxesWidget.disconnect_events |
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