holoviews.core.layout module#

Supplies Pane, Layout, NdLayout and AdjointLayout. Pane extends View to allow multiple Views to be presented side-by-side in a NdLayout. An AdjointLayout allows one or two Views to be adjoined to a primary View to act as supplementary elements.

class holoviews.core.layout.AdjointLayout(data, **params)[source]#

Bases: Layoutable, Dimensioned

An AdjointLayout provides a convenient container to lay out some marginal plots next to a primary plot. This is often useful to display the marginal distributions of a plot next to the primary plot. AdjointLayout accepts a list of up to three elements, which are laid out as follows with the names ‘main’, ‘top’ and ‘right’:

________________
|     3     |   |
|___________|___|
|           |   |  1:  main
|           |   |  2:  right
|     1     | 2 |  3:  top
|           |   |
|___________|___|

Attributes:

ddims: The list of deep dimensions
last
main: Returns the main element in the AdjointLayout
right: Returns the right marginal element in the AdjointLayout
top: Returns the top marginal element in the AdjointLayout

Methods

`dimension_values`(dimension[, expanded, flat])	Return the values along the requested dimension.
`get`(key[, default])	Returns the viewable corresponding to the supplied string or integer based key.
`relabel`([label, group, depth])	Clone object and apply new group and/or label.

items
keys

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: group, cdims, vdims

kdims = List(bounds=(0, None), constant=True, default=[Dimension('AdjointLayout')], label='Kdims'): The key dimensions defined as list of dimensions that may be used in indexing (and potential slicing) semantics. The order of the dimensions listed here determines the semantics of each component of a multi-dimensional indexing operation. Aliased with key_dimensions.

property ddims#: The list of deep dimensions

dimension_values(dimension, expanded=True, flat=True)[source]#

Return the values along the requested dimension.

Parameters:

dimensionstr

The dimension to return values for.

expandedbool, optional

Whether to return the expanded values. Behavior depends on the type of data:

Columnar: If false, returns unique values
Geometry: If false, returns scalar values per geometry
Gridded: If false, returns 1D coordinates

flatbool, optional

Whether to flatten array.

Returns:

np.ndarray: Array of values along the requested dimension.

get(key, default=None)[source]#

Returns the viewable corresponding to the supplied string or integer based key.

Parameters:

keyNumeric or str index

0: ‘main’
1: ‘right’
2: ‘top’

default

Value returned if key not found

Returns:

Indexed value or supplied default

property group#: Group inherited from main element

property label#: Label inherited from main element

property main#: Returns the main element in the AdjointLayout

relabel(label=None, group=None, depth=1)[source]#

Clone object and apply new group and/or label.

Applies relabeling to child up to the supplied depth.

Parameters:

labelstr, optional: New label to apply to returned object
groupstr, optional: New group to apply to returned object
depthint, optional: Depth to which relabel will be applied If applied to container allows applying relabeling to contained objects up to the specified depth

Returns:

Returns relabelled object

property right#: Returns the right marginal element in the AdjointLayout

property top#: Returns the top marginal element in the AdjointLayout

class holoviews.core.layout.Dimensioned(data, kdims=None, vdims=None, **params)[source]#

Bases: LabelledData

Dimensioned is a base class that allows the data contents of a class to be associated with dimensions. The contents associated with dimensions may be partitioned into one of three types:

key dimensions
These are the dimensions that can be indexed via the __getitem__ method. Dimension objects supporting key dimensions must support indexing over these dimensions and may also support slicing. This list ordering of dimensions describes the positional components of each multi-dimensional indexing operation.

For instance, if the key dimension names are ‘weight’ followed by ‘height’ for Dimensioned object ‘obj’, then obj[80,175] indexes a weight of 80 and height of 175.

Accessed using either kdims.
value dimensions
These dimensions correspond to any data held on the Dimensioned object not in the key dimensions. Indexing by value dimension is supported by dimension name (when there are multiple possible value dimensions); no slicing semantics is supported and all the data associated with that dimension will be returned at once. Note that it is not possible to mix value dimensions and deep dimensions.

Accessed using either vdims.
deep dimensions
These are dynamically computed dimensions that belong to other Dimensioned objects that are nested in the data. Objects that support this should enable the _deep_indexable flag. Note that it is not possible to mix value dimensions and deep dimensions.

Accessed using either ddims.

Dimensioned class support generalized methods for finding the range and type of values along a particular Dimension. The range method relies on the appropriate implementation of the dimension_values methods on subclasses.

The index of an arbitrary dimension is its positional index in the list of all dimensions, starting with the key dimensions, followed by the value dimensions and ending with the deep dimensions.

Attributes:

apply
ddims: The list of deep dimensions
opts
redim

Methods

`dimension_values`(dimension[, expanded, flat])	Return the values along the requested dimension.
`dimensions`([selection, label])	Lists the available dimensions on the object
`get_dimension`(dimension[, default, strict])	Get a Dimension object by name or index.
`get_dimension_index`(dimension)	Get the index of the requested dimension.
`get_dimension_type`(dim)	Get the type of the requested dimension.
`options`(*args[, clone])	Applies simplified option definition returning a new object.
`range`(dimension[, data_range, dimension_range])	Return the lower and upper bounds of values along dimension.
`select`([selection_specs])	Applies selection by dimension name

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

group = String(constant=True, default='Dimensioned', label='Group'): A string describing the data wrapped by the object.
cdims = Dict(class_=<class 'dict'>, default={}, label='Cdims'): The constant dimensions defined as a dictionary of Dimension:value pairs providing additional dimension information about the object. Aliased with constant_dimensions.
kdims = List(bounds=(0, None), constant=True, default=[], label='Kdims'): The key dimensions defined as list of dimensions that may be used in indexing (and potential slicing) semantics. The order of the dimensions listed here determines the semantics of each component of a multi-dimensional indexing operation. Aliased with key_dimensions.
vdims = List(bounds=(0, None), constant=True, default=[], label='Vdims'): The value dimensions defined as the list of dimensions used to describe the components of the data. If multiple value dimensions are supplied, a particular value dimension may be indexed by name after the key dimensions. Aliased with value_dimensions.

property ddims#: The list of deep dimensions

dimension_values(dimension, expanded=True, flat=True)[source]#

Return the values along the requested dimension.

Parameters:

dimensionstr

The dimension to return values for.

expandedbool, optional

Whether to return the expanded values. Behavior depends on the type of data:

Columnar: If false, returns unique values
Geometry: If false, returns scalar values per geometry
Gridded: If false, returns 1D coordinates

flatbool, optional

Whether to flatten array.

Returns:

np.ndarray: Array of values along the requested dimension.

dimensions(selection='all', label=False)[source]#

Lists the available dimensions on the object

Provides convenient access to Dimensions on nested Dimensioned objects. Dimensions can be selected by their type, i.e. ‘key’ or ‘value’ dimensions. By default ‘all’ dimensions are returned.

Parameters:

selectionType of dimensions to return: The type of dimension, i.e. one of ‘key’, ‘value’, ‘constant’ or ‘all’.
labelWhether to return the name, label or Dimension: Whether to return the Dimension objects (False), the Dimension names (True/’name’) or labels (‘label’).

Returns:

List of Dimension objects or their names or labels

get_dimension(dimension, default=None, strict=False) → Dimension | None[source]#

Get a Dimension object by name or index.

Parameters:

dimension: Dimension to look up by name or integer index
defaultoptional: Value returned if Dimension not found
strictbool, optional: Raise a KeyError if not found

Returns:

Dimension object for the requested dimension or default

get_dimension_index(dimension)[source]#

Get the index of the requested dimension.

Parameters:

dimension: Dimension to look up by name or by index

Returns:

Integer index of the requested dimension

get_dimension_type(dim)[source]#

Get the type of the requested dimension.

Type is determined by Dimension.type attribute or common type of the dimension values, otherwise None.

Parameters:

dimension: Dimension to look up by name or by index

Returns:

Declared type of values along the dimension

options(*args, clone=True, **kwargs)[source]#

Applies simplified option definition returning a new object.

Applies options on an object or nested group of objects in a flat format returning a new object with the options applied. If the options are to be set directly on the object a simple format may be used, e.g.:

obj.options(cmap=’viridis’, show_title=False)

If the object is nested the options must be qualified using a type[.group][.label] specification, e.g.:

obj.options(‘Image’, cmap=’viridis’, show_title=False)

or using:

obj.options({‘Image’: dict(cmap=’viridis’, show_title=False)})

Identical to the .opts method but returns a clone of the object by default.

Parameters:

*args: Sets of options to apply to object: Supports a number of formats including lists of Options objects, a type[.group][.label] followed by a set of keyword options to apply and a dictionary indexed by type[.group][.label] specs.
backendoptional: Backend to apply options to Defaults to current selected backend
clonebool, optional: Whether to clone object Options can be applied inplace with clone=False
**kwargs: Keywords of options: Set of options to apply to the object

Returns:

Returns the cloned object with the options applied

range(dimension, data_range=True, dimension_range=True)[source]#

Return the lower and upper bounds of values along dimension.

Parameters:

dimension: The dimension to compute the range on.
data_rangebool: Compute range from data values
dimension_rangebool: Include Dimension ranges Whether to include Dimension range and soft_range in range calculation

Returns:

Tuple containing the lower and upper bound

select(selection_specs=None, **kwargs)[source]#

Applies selection by dimension name

Applies a selection along the dimensions of the object using keyword arguments. The selection may be narrowed to certain objects using selection_specs. For container objects the selection will be applied to all children as well.

Selections may select a specific value, slice or set of values:

value
Scalar values will select rows along with an exact match, e.g.:

ds.select(x=3)
slice
Slices may be declared as tuples of the upper and lower bound, e.g.:

ds.select(x=(0, 3))
values
A list of values may be selected using a list or set, e.g.:

ds.select(x=[0, 1, 2])

Parameters:

selection_specsList of specs to match on: A list of types, functions, or type[.group][.label] strings specifying which objects to apply the selection on.
**selection: Dictionary declaring selections by dimension: Selections can be scalar values, tuple ranges, lists of discrete values and boolean arrays

Returns:

Returns an Dimensioned object containing the selected data
or a scalar if a single value was selected

class holoviews.core.layout.Empty(*, cdims, kdims, vdims, group, label, name)[source]#

Bases: Dimensioned, Composable

Empty may be used to define an empty placeholder in a Layout. It can be placed in a Layout just like any regular Element and container type via the + operator or by passing it to the Layout constructor as a part of a list.

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims, kdims, vdims

group = String(default='Empty', label='Group'): A string describing the data wrapped by the object.

class holoviews.core.layout.Layout(items=None, identifier=None, parent=None, **kwargs)[source]#

Bases: Layoutable, ViewableTree

A Layout is an ViewableTree with ViewableElement objects as leaf values.

Unlike ViewableTree, a Layout supports a rich display, displaying leaf items in a grid style layout. In addition to the usual ViewableTree indexing, Layout supports indexing of items by their row and column index in the layout.

The maximum number of columns in such a layout may be controlled with the cols method.

Attributes:

shape: Tuple indicating the number of rows and columns in the Layout.

Methods

`clone`(args, *overrides)	Clones the Layout, overriding data and parameters.
`cols`(ncols)	Sets the maximum number of columns in the NdLayout.
`decollate`()	Packs Layout of DynamicMaps into a single DynamicMap that returns a Layout
`relabel`([label, group, depth])	Clone object and apply new group and/or label.

grid_items

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims, kdims, vdims

group = String(constant=True, default='Layout', label='Group'): A string describing the data wrapped by the object.

clone(*args, **overrides)[source]#

Clones the Layout, overriding data and parameters.

Parameters:

data: New data replacing the existing data
shared_databool, optional: Whether to use existing data
new_typeoptional: Type to cast object to
*args: Additional arguments to pass to constructor
**overrides: New keyword arguments to pass to constructor

Returns:

Cloned Layout object

cols(ncols)[source]#

Sets the maximum number of columns in the NdLayout.

Any items beyond the set number of cols will flow onto a new row. The number of columns control the indexing and display semantics of the NdLayout.

Parameters:

ncolsint: Number of columns to set on the NdLayout

decollate()[source]#

Packs Layout of DynamicMaps into a single DynamicMap that returns a Layout

Decollation allows packing a Layout of DynamicMaps into a single DynamicMap that returns a Layout of simple (non-dynamic) elements. All nested streams are lifted to the resulting DynamicMap, and are available in the streams property. The callback property of the resulting DynamicMap is a pure, stateless function of the stream values. To avoid stream parameter name conflicts, the resulting DynamicMap is configured with positional_stream_args=True, and the callback function accepts stream values as positional dict arguments.

Returns:

DynamicMap that returns a Layout

relabel(label=None, group=None, depth=1)[source]#

Clone object and apply new group and/or label.

Applies relabeling to children up to the supplied depth.

Parameters:

labelstr, optional: New label to apply to returned object
groupstr, optional: New group to apply to returned object
depthint, optional: Depth to which relabel will be applied If applied to container allows applying relabeling to contained objects up to the specified depth

Returns:

Returns relabelled object

property shape#: Tuple indicating the number of rows and columns in the Layout.

class holoviews.core.layout.NdLayout(initial_items=None, kdims=None, **params)[source]#

Bases: Layoutable, UniformNdMapping

NdLayout is a UniformNdMapping providing an n-dimensional data structure to display the contained Elements and containers in a layout. Using the cols method the NdLayout can be rearranged with the desired number of columns.

Attributes:

last: Returns another NdLayout constituted of the last views of the individual elements (if they are maps).
shape: Tuple indicating the number of rows and columns in the NdLayout.
uniform

Methods

`clone`(args, *overrides)	Clones the NdLayout, overriding data and parameters.
`cols`(ncols)	Sets the maximum number of columns in the NdLayout.
`grid_items`()	Compute a dict of {(row,column): (key, value)} elements from the current set of items and specified number of columns.

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: kdims, vdims, sort

holoviews.core.ndmapping.NdMapping: group

clone(*args, **overrides)[source]#

Clones the NdLayout, overriding data and parameters.

Parameters:

data: New data replacing the existing data
shared_databool, optional: Whether to use existing data
new_typeoptional: Type to cast object to
*args: Additional arguments to pass to constructor
**overrides: New keyword arguments to pass to constructor

Returns:

Cloned NdLayout object

cols(ncols)[source]#

Sets the maximum number of columns in the NdLayout.

Any items beyond the set number of cols will flow onto a new row. The number of columns control the indexing and display semantics of the NdLayout.

Parameters:

ncolsint: Number of columns to set on the NdLayout

grid_items()[source]#: Compute a dict of {(row,column): (key, value)} elements from the current set of items and specified number of columns.

property last#: Returns another NdLayout constituted of the last views of the individual elements (if they are maps).

property shape#: Tuple indicating the number of rows and columns in the NdLayout.

class holoviews.core.layout.NdMapping(initial_items=None, kdims=None, **params)[source]#

Bases: MultiDimensionalMapping

NdMapping supports the same indexing semantics as MultiDimensionalMapping but also supports slicing semantics.

Slicing semantics on an NdMapping is dependent on the ordering semantics of the keys. As MultiDimensionalMapping sort the keys, a slice on an NdMapping is effectively a way of filtering out the keys that are outside the slice range.

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: kdims, vdims, sort

group = String(constant=True, default='NdMapping', label='Group'): A string describing the data wrapped by the object.

class holoviews.core.layout.UniformNdMapping(initial_items=None, kdims=None, group=None, label=None, **params)[source]#

Bases: NdMapping

A UniformNdMapping is a map of Dimensioned objects and is itself indexed over a number of specified dimensions. The dimension may be a spatial dimension (i.e., a ZStack), time (specifying a frame sequence) or any other combination of Dimensions.

UniformNdMapping objects can be sliced, sampled, reduced, overlaid and split along its and its containing Element’s dimensions. Subclasses should implement the appropriate slicing, sampling and reduction methods for their Dimensioned type.

Attributes:

empty_element
type: The type of elements stored in the mapping.

Methods

`clone`([data, shared_data, new_type, link])	Clones the object, overriding data and parameters.
`collapse`([dimensions, function, spreadfn])	Concatenates and aggregates along supplied dimensions
`dframe`([dimensions, multi_index])	Convert dimension values to DataFrame.

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: kdims, vdims, sort

holoviews.core.ndmapping.NdMapping: group

clone(data=None, shared_data=True, new_type=None, link=True, *args, **overrides)[source]#

Clones the object, overriding data and parameters.

Parameters:

data: New data replacing the existing data
shared_databool, optional: Whether to use existing data
new_typeoptional: Type to cast object to
linkbool, optional: Whether clone should be linked Determines whether Streams and Links attached to original object will be inherited.
*args: Additional arguments to pass to constructor
**overrides: New keyword arguments to pass to constructor

Returns:

Cloned object

collapse(dimensions=None, function=None, spreadfn=None, **kwargs)[source]#

Concatenates and aggregates along supplied dimensions

Useful to collapse stacks of objects into a single object, e.g. to average a stack of Images or Curves.

Parameters:

dimensions: Dimension(s) to collapse Defaults to all key dimensions
function: Aggregation function to apply, e.g. numpy.mean
spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation.
**kwargs: Keyword arguments passed to the aggregation function

Returns:

Returns the collapsed element or HoloMap of collapsed
elements

dframe(dimensions=None, multi_index=False)[source]#

Convert dimension values to DataFrame.

Returns a pandas dataframe of columns along each dimension, either completely flat or indexed by key dimensions.

Parameters:

dimensions: Dimensions to return as columns
multi_index: Convert key dimensions to (multi-)index

Returns:

DataFrame of columns corresponding to each dimension

property group#: Group inherited from items

property label#: Label inherited from items

property type#: The type of elements stored in the mapping.

class holoviews.core.layout.ViewableElement(data, kdims=None, vdims=None, **params)[source]#

Bases: Dimensioned

A ViewableElement is a dimensioned datastructure that may be associated with a corresponding atomic visualization. An atomic visualization will display the data on a single set of axes (i.e. excludes multiple subplots that are displayed at once). The only new parameter introduced by ViewableElement is the title associated with the object for display.

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims, kdims, vdims

group = String(constant=True, default='ViewableElement', label='Group'): A string describing the data wrapped by the object.

class holoviews.core.layout.ViewableTree(items=None, identifier=None, parent=None, **kwargs)[source]#

Bases: AttrTree, Dimensioned

A ViewableTree is an AttrTree with Viewable objects as its leaf nodes. It combines the tree like data structure of a tree while extending it with the deep indexable properties of Dimensioned and LabelledData objects.

Attributes:

uniform: Whether items in tree have uniform dimensions

Methods

dimension_values(dimension[, expanded, flat])

Return the values along the requested dimension.

Parameter Definitions

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims, kdims, vdims

group = String(constant=True, default='ViewableTree', label='Group'): A string describing the data wrapped by the object.

dimension_values(dimension, expanded=True, flat=True)[source]#

Return the values along the requested dimension.

Parameters:

dimensionstr

The dimension to return values for.

expandedbool, optional

Whether to return the expanded values. Behavior depends on the type of data:

Columnar: If false, returns unique values
Geometry: If false, returns scalar values per geometry
Gridded: If false, returns 1D coordinates

flatbool, optional

Whether to flatten array.

Returns:

np.ndarray: Array of values along the requested dimension.

property uniform#: Whether items in tree have uniform dimensions