holoviews.core.element module#

class holoviews.core.element.Collator(data=None, **params)[source]#

Bases: NdMapping

Collator is an NdMapping type which can merge any number of HoloViews components with whatever level of nesting by inserting the Collators key dimensions on the HoloMaps. If the items in the Collator do not contain HoloMaps they will be created. Collator also supports filtering of Tree structures and dropping of constant dimensions.

Attributes:
static_dimensions

Return all constant dimensions.

Parameter Definitions

Parameters inherited from:

group = String(default='Collator', label='Group')

A string describing the data wrapped by the object.

vdims = List(bounds=(0, 0), default=[], label='Vdims')

Collator operates on HoloViews objects, if vdims are specified a value_transform function must also be supplied.

drop = List(bounds=(0, None), default=[], label='Drop')

List of dimensions to drop when collating data, specified as strings.

drop_constant = Boolean(default=False, label='Drop constant')

Whether to demote any non-varying key dimensions to constant dimensions.

filters = List(bounds=(0, None), default=[], label='Filters')

List of paths to drop when collating data, specified as strings or tuples.

progress_bar = Parameter(allow_None=True, label='Progress bar')

The progress bar instance used to report progress. Set to None to disable progress bars.

merge_type = ClassSelector(class_=<class 'holoviews.core.ndmapping.NdMapping'>, default=<class 'holoviews.core.spaces.HoloMap'>, label='Merge type')

value_transform = Callable(allow_None=True, label='Value transform')

If supplied the function will be applied on each Collator value during collation. This may be used to apply an operation to the data or load references from disk before they are collated into a displayable HoloViews object.

merge_type[source]#

alias of HoloMap

property static_dimensions#

Return all constant dimensions.

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

Bases: ViewableElement, Composable

CompositeOverlay provides a common baseclass for Overlay classes.

Methods

dimension_values(dimension[, expanded, flat])

Return the values along the requested dimension.

hist([dimension, num_bins, bin_range, ...])

Computes and adjoins histogram along specified dimension(s).

Parameter Definitions

Parameters inherited from:

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.

hist(dimension=None, num_bins=20, bin_range=None, adjoin=True, index=None, show_legend=False, **kwargs)[source]#

Computes and adjoins histogram along specified dimension(s).

Defaults to first value dimension if present otherwise falls back to first key dimension.

Parameters:
dimension

Dimension(s) to compute histogram on, Falls back the plot dimensions by default.

num_binsint, optional

Number of bins

bin_rangetuple, optional

Lower and upper bounds of bins

adjoinbool, optional

Whether to adjoin histogram

indexint, optional

Index of layer to apply hist to

show_legendbool, optional

Show legend in histogram (don’t show legend by default).

Returns:
AdjointLayout of element and histogram or just the
histogram
class holoviews.core.element.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:

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.element.Element(data, kdims=None, vdims=None, **params)[source]#

Bases: ViewableElement, Composable, Overlayable

Element is the atomic datastructure used to wrap some data with an associated visual representation, e.g. an element may represent a set of points, an image or a curve. Elements provide a common API for interacting with data of different types and define how the data map to a set of dimensions and how those map to the visual representation.

Methods

array([dimensions])

Convert dimension values to columnar array.

closest(coords, **kwargs)

Snap list or dict of coordinates to closest position.

dframe([dimensions, multi_index])

Convert dimension values to DataFrame.

hist([dimension, num_bins, bin_range, adjoin])

Computes and adjoins histogram along specified dimension(s).

reduce([dimensions, function, spreadfn])

Applies reduction along the specified dimension(s).

sample([samples, bounds, closest])

Samples values at supplied coordinates.

Parameter Definitions

Parameters inherited from:

group = String(constant=True, default='Element', label='Group')

A string describing the data wrapped by the object.

array(dimensions=None)[source]#

Convert dimension values to columnar array.

Parameters:
dimensions

List of dimensions to return

Returns:
Array of columns corresponding to each dimension
closest(coords, **kwargs)[source]#

Snap list or dict of coordinates to closest position.

Parameters:
coords

List of 1D or 2D coordinates

**kwargs

Coordinates specified as keyword pairs

Returns:
List of tuples of the snapped coordinates
Raises:
NotImplementedError

Raised if snapping is not supported

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
hist(dimension=None, num_bins=20, bin_range=None, adjoin=True, **kwargs)[source]#

Computes and adjoins histogram along specified dimension(s).

Defaults to first value dimension if present otherwise falls back to first key dimension.

Parameters:
dimension

Dimension(s) to compute histogram on

num_binsint, optional

Number of bins

bin_rangetuple, optional

Lower and upper bounds of bins

adjoinbool, optional

Whether to adjoin histogram

Returns:
AdjointLayout of element and histogram or just the
histogram
reduce(dimensions=None, function=None, spreadfn=None, **reduction)[source]#

Applies reduction along the specified dimension(s).

Allows reducing the values along one or more key dimension with the supplied function. Supports two signatures:

Reducing with a list of dimensions, e.g.:

ds.reduce([‘x’], np.mean)

Defining a reduction using keywords, e.g.:

ds.reduce(x=np.mean)

Parameters:
dimensions

Dimension(s) to apply reduction on Defaults to all key dimensions

function

Reduction operation to apply, e.g. numpy.mean

spreadfn

Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation.

**reductions

Keyword argument defining reduction Allows reduction to be defined as keyword pair of dimension and function

Returns:
The element after reductions have been applied.
sample(samples=None, bounds=None, closest=False, **sample_values)[source]#

Samples values at supplied coordinates.

Allows sampling of element with a list of coordinates matching the key dimensions, returning a new object containing just the selected samples. Supports multiple signatures:

Sampling with a list of coordinates, e.g.:

ds.sample([(0, 0), (0.1, 0.2), …])

Sampling a range or grid of coordinates, e.g.:

1D : ds.sample(3) 2D : ds.sample((3, 3))

Sampling by keyword, e.g.:

ds.sample(x=0)

Parameters:
samples

List of nd-coordinates to sample

bounds

Bounds of the region to sample Defined as two-tuple for 1D sampling and four-tuple for 2D sampling.

closest

Whether to snap to closest coordinates

**kwargs

Coordinates specified as keyword pairs Keywords of dimensions and scalar coordinates

Returns:
Element containing the sampled coordinates
class holoviews.core.element.Element2D(data, kdims=None, vdims=None, **params)[source]#

Bases: Element

Parameter Definitions

Parameters inherited from:

extents = Tuple(default=(None, None, None, None), label='Extents', length=4)

Allows overriding the extents of the Element in 2D space defined as four-tuple defining the (left, bottom, right and top) edges.

class holoviews.core.element.Element3D(data, kdims=None, vdims=None, **params)[source]#

Bases: Element2D

Parameter Definitions

Parameters inherited from:

extents = Tuple(default=(None, None, None, None, None, None), label='Extents', length=6)

Allows overriding the extents of the Element in 3D space defined as (xmin, ymin, zmin, xmax, ymax, zmax).

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

Bases: Layoutable, UniformNdMapping

Grids are distinct from Layouts as they ensure all contained elements to be of the same type. Unlike Layouts, which have integer keys, Grids usually have floating point keys, which correspond to a grid sampling in some two-dimensional space. This two-dimensional space may have to arbitrary dimensions, e.g. for 2D parameter spaces.

Attributes:
last

The last of a GridSpace is another GridSpace constituted of the last of the individual elements.

shape

Returns the 2D shape of the GridSpace as (rows, cols).

Methods

decollate()

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

keys([full_grid])

Returns the keys of the GridSpace

Parameter Definitions

Parameters inherited from:

kdims = List(bounds=(1, 2), default=[Dimension('X'), Dimension('Y')], 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.

decollate()[source]#

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

Decollation allows packing a GridSpace of DynamicMaps into a single DynamicMap that returns a GridSpace 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 GridSpace
keys(full_grid=False)[source]#

Returns the keys of the GridSpace

Parameters:
full_gridbool, optional

Return full cross-product of keys

Returns:
List of keys
property last#

The last of a GridSpace is another GridSpace constituted of the last of the individual elements. To access the elements by their X,Y position, either index the position directly or use the items() method.

property shape#

Returns the 2D shape of the GridSpace as (rows, cols).

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

Bases: Layoutable, UniformNdMapping, Overlayable

A HoloMap is an n-dimensional mapping of viewable elements or overlays. Each item in a HoloMap has an tuple key defining the values along each of the declared key dimensions, defining the discretely sampled space of values.

The visual representation of a HoloMap consists of the viewable objects inside the HoloMap which can be explored by varying one or more widgets mapping onto the key dimensions of the HoloMap.

Attributes:
opts

Methods

collate([merge_type, drop, drop_constant])

Collate allows reordering nested containers

decollate()

Packs HoloMap of DynamicMaps into a single DynamicMap that returns an HoloMap

grid([dimensions])

Group by supplied dimension(s) and lay out groups in grid

hist([dimension, num_bins, bin_range, ...])

Computes and adjoins histogram along specified dimension(s).

layout([dimensions])

Group by supplied dimension(s) and lay out groups

options(*args, **kwargs)

Applies simplified option definition returning a new object

overlay([dimensions])

Group by supplied dimension(s) and overlay each group

relabel([label, group, depth])

Clone object and apply new group and/or label.

Parameter Definitions

Parameters inherited from:

collate(merge_type=None, drop=None, drop_constant=False)[source]#

Collate allows reordering nested containers

Collation allows collapsing nested mapping types by merging their dimensions. In simple terms in merges nested containers into a single merged type.

In the simple case a HoloMap containing other HoloMaps can easily be joined in this way. However collation is particularly useful when the objects being joined are deeply nested, e.g. you want to join multiple Layouts recorded at different times, collation will return one Layout containing HoloMaps indexed by Time. Changing the merge_type will allow merging the outer Dimension into any other UniformNdMapping type.

Parameters:
merge_type

Type of the object to merge with

drop

List of dimensions to drop

drop_constant

Drop constant dimensions automatically

Returns:
Collated Layout or HoloMap
decollate()[source]#

Packs HoloMap of DynamicMaps into a single DynamicMap that returns an HoloMap

Decollation allows packing a HoloMap of DynamicMaps into a single DynamicMap that returns an HoloMap 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 an HoloMap
grid(dimensions=None, **kwargs)[source]#

Group by supplied dimension(s) and lay out groups in grid

Groups data by supplied dimension(s) laying the groups along the dimension(s) out in a GridSpace.

Parameters:
dimensionsDimension/str or list

Dimension or list of dimensions to group by

Returns:
GridSpace with supplied dimensions
hist(dimension=None, num_bins=20, bin_range=None, adjoin=True, individually=True, **kwargs)[source]#

Computes and adjoins histogram along specified dimension(s).

Defaults to first value dimension if present otherwise falls back to first key dimension.

Parameters:
dimension

Dimension(s) to compute histogram on

num_binsint, optional

Number of bins

bin_rangetuple, optional

Lower and upper bounds of bins

adjoinbool, optional

Whether to adjoin histogram

Returns:
AdjointLayout of HoloMap and histograms or just the
histograms
layout(dimensions=None, **kwargs)[source]#

Group by supplied dimension(s) and lay out groups

Groups data by supplied dimension(s) laying the groups along the dimension(s) out in a NdLayout.

Parameters:
dimensions

Dimension(s) to group by

Returns:
NdLayout with supplied dimensions
options(*args, **kwargs)[source]#

Applies simplified option definition returning a new object

Applies options defined in a flat format to the objects returned by the DynamicMap. If the options are to be set directly on the objects in the HoloMap 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)})

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
overlay(dimensions=None, **kwargs)[source]#

Group by supplied dimension(s) and overlay each group

Groups data by supplied dimension(s) overlaying the groups along the dimension(s).

Parameters:
dimensions

Dimension(s) of dimensions to group by

Returns:
NdOverlay object(s) with supplied dimensions
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
class holoviews.core.element.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:

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.element.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:

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.element.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:

group = String(constant=True, default='NdMapping', label='Group')

A string describing the data wrapped by the object.

class holoviews.core.element.NdOverlay(overlays=None, kdims=None, **params)[source]#

Bases: Overlayable, UniformNdMapping, CompositeOverlay

An NdOverlay allows a group of NdOverlay to be overlaid together. NdOverlay can be indexed out of an overlay and an overlay is an iterable that iterates over the contained layers.

Methods

decollate()

Packs NdOverlay of DynamicMaps into a single DynamicMap that returns an NdOverlay

Parameter Definitions

Parameters inherited from:

kdims = List(bounds=(0, None), constant=True, default=[Dimension('Element')], label='Kdims')

List of dimensions the NdOverlay can be indexed by.

decollate()[source]#

Packs NdOverlay of DynamicMaps into a single DynamicMap that returns an NdOverlay

Decollation allows packing a NdOverlay of DynamicMaps into a single DynamicMap that returns an NdOverlay 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 an NdOverlay
class holoviews.core.element.Tabular(data, kdims=None, vdims=None, **params)[source]#

Bases: Element

Baseclass to give an elements providing an API to generate a tabular representation of the object.

Attributes:
cols

Number of columns in table

rows

Number of rows in table (including header)

Methods

cell_type(row, col)

Type of the table cell, either 'data' or 'heading'

pprint_cell(row, col)

Formatted contents of table cell.

Parameter Definitions

Parameters inherited from:

cell_type(row, col)[source]#

Type of the table cell, either ‘data’ or ‘heading’

Parameters:
rowint

Integer index of table row

colint

Integer index of table column

Returns:
Type of the table cell, either ‘data’ or ‘heading’
property cols#

Number of columns in table

pprint_cell(row, col)[source]#

Formatted contents of table cell.

Parameters:
rowint

Integer index of table row

colint

Integer index of table column

Returns:
Formatted table cell contents
property rows#

Number of rows in table (including header)

class holoviews.core.element.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:

group = String(constant=True, default='ViewableElement', label='Group')

A string describing the data wrapped by the object.