holoviews.core package#

Subpackages#

Submodules#

Module contents#

class holoviews.core.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 | | | |___________|___|

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.

Applies to the main object in the AdjointLayout.

Parameters#

dimension

The dimension to return values for

expandedbool, optional

Whether to expand values 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#

NumPy 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 string 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.AttrTree(items=None, identifier=None, parent=None, dir_mode='default')[source]#

Bases: object

An AttrTree offers convenient, multi-level attribute access for collections of objects. AttrTree objects may also be combined together using the update method or merge classmethod. Here is an example of adding a ViewableElement to an AttrTree and accessing it:

>>> t = AttrTree()
>>> t.Example.Path = 1
>>> t.Example.Path
1

filter(path_filters)[source]#

Filters the loaded AttrTree using the supplied path_filters.

property fixed#

If fixed, no new paths can be created via attribute access

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

Get a node of the AttrTree using its path string.

Parameters#

identifier

Path string of the node to return

default

Value to return if no node is found

Returns#

The indexed node of the AttrTree

items()[source]#

Keys and nodes of the AttrTree

keys()[source]#

Keys of nodes in the AttrTree

classmethod merge(trees)[source]#

Merge a collection of AttrTree objects.

property path#

Returns the path up to the root for the current node.

pop(identifier, default=None)[source]#

Pop a node of the AttrTree using its path string.

Parameters#

identifier

Path string of the node to return

default

Value to return if no node is found

Returns#

The node that was removed from the AttrTree

set_path(path, val)[source]#

Set the given value at the supplied path where path is either a tuple of strings or a string in A.B.C format.

update(other)[source]#

Updated the contents of the current AttrTree with the contents of a second AttrTree.

values()[source]#

Nodes of the AttrTree

class holoviews.core.BoundingBox(**args)[source]#

Bases: BoundingRegion

A rectangular bounding box defined either by two points forming an axis-aligned rectangle (or simply a radius for a square).

contains(x, y)[source]#

Returns true if the given point is contained within the bounding box, where all boundaries of the box are considered to be inclusive.

contains_exclusive(x, y)[source]#

Return True if the given point is contained within the bounding box, where the bottom and right boundaries are considered exclusive.

containsbb_exclusive(x)[source]#

Returns true if the given BoundingBox x is contained within the bounding box, where at least one of the boundaries of the box has to be exclusive.

containsbb_inclusive(x)[source]#

Returns true if the given BoundingBox x is contained within the bounding box, including cases of exact match.

lbrt()[source]#

Return left,bottom,right,top values for the BoundingBox.

upperexclusive_contains(x, y)[source]#

Returns true if the given point is contained within the bounding box, where the right and upper boundaries are exclusive, and the left and lower boundaries are inclusive. Useful for tiling a plane into non-overlapping regions.

class holoviews.core.BoundingEllipse(**args)[source]#

Bases: BoundingBox

Similar to BoundingBox, but the region is the ellipse inscribed within the rectangle.

contains(x, y)[source]#

Returns true if the given point is contained within the bounding box, where all boundaries of the box are considered to be inclusive.

class holoviews.core.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.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: kdims, sort

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

Bases: ViewableElement, Composable

CompositeOverlay provides a common baseclass for Overlay classes.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

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

holoviews.core.dimension.ViewableElement: group

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

Return the values along the requested dimension.

Parameters#

dimension

The dimension to return values for

expandedbool, optional

Whether to expand values 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#

NumPy 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.Dataset(data=None, kdims=None, vdims=None, **kwargs)[source]#

Bases: Element

Dataset provides a general baseclass for Element types that contain structured data and supports a range of data formats.

The Dataset class supports various methods offering a consistent way of working with the stored data regardless of the storage format used. These operations include indexing, selection and various ways of aggregating or collapsing the data with a supplied function.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

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

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

A string describing the data wrapped by the object.

datatype = List(bounds=(0, None), default=['dataframe', 'dictionary', 'grid', 'xarray', 'multitabular', 'spatialpandas', 'dask_spatialpandas', 'dask', 'cuDF', 'array', 'ibis'], label='Datatype')

A priority list of the data types to be used for storage on the .data attribute. If the input supplied to the element constructor cannot be put into the requested format, the next format listed will be used until a suitable format is found (or the data fails to be understood).

add_dimension(dimension, dim_pos, dim_val, vdim=False, **kwargs)[source]#

Adds a dimension and its values to the Dataset

Requires the dimension name or object, the desired position in the key dimensions and a key value scalar or array of values, matching the length or shape of the Dataset.

Parameters#

dimension

Dimension or dimension spec to add

dim_posint

Integer index to insert dimension at

dim_valscalar or ndarray

Dimension value(s) to add

vdim

Disabled, this type does not have value dimensions

**kwargs

Keyword arguments passed to the cloned element

Returns#

Cloned object containing the new dimension

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

Aggregates data on the supplied dimensions.

Aggregates over the supplied key dimensions with the defined function or dim_transform specified as a tuple of the transformed dimension name and dim transform.

Parameters#

dimensions

Dimension(s) to aggregate on Default to all key dimensions

function

Aggregation function or transform to apply Supports both simple functions and dimension transforms

spreadfn

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

**kwargs

Keyword arguments either passed to the aggregation function or to create new names for the transformed variables

Returns#

Returns the aggregated Dataset

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

closest(coords=None, **kwargs)[source]#

Snaps coordinate(s) to closest coordinate in Dataset

Parameters#

coords

List of coordinates expressed as tuples

**kwargs

Coordinates defined as keyword pairs

Returns#

List of tuples of the snapped coordinates

Raises#

NotImplementedError

Raised if snapping is not supported

columns(dimensions=None)[source]#

Convert dimension values to a dictionary.

Returns a dictionary of column arrays along each dimension of the element.

Parameters#

dimensions

Dimensions to return as columns

Returns#

Dictionary of arrays for each dimension

compute()[source]#

Computes the data to a data format that stores the daata in memory, e.g. a Dask dataframe or array is converted to a Pandas DataFrame or NumPy array.

Returns#

Dataset with the data stored in in-memory format

property dataset#

The Dataset that this object was created from

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

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

Return the values along the requested dimension.

Parameters#

dimension

The dimension to return values for

expandedbool, optional

Whether to expand values 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#

NumPy array of values along 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

groupby(dimensions=None, container_type=<class 'holoviews.core.spaces.HoloMap'>, group_type=None, dynamic=False, **kwargs)[source]#

Groups object by one or more dimensions

Applies groupby operation over the specified dimensions returning an object of type container_type (expected to be dictionary-like) containing the groups.

Parameters#

dimensions

Dimension(s) to group by

container_type

Type to cast group container to

group_type

Type to cast each group to

dynamic

Whether to return a DynamicMap

**kwargs

Keyword arguments to pass to each group

Returns#

Returns object of supplied container_type containing the groups. If dynamic=True returns a DynamicMap instead.

property iloc#

Returns iloc indexer with support for columnar indexing.

Returns an iloc object providing a convenient interface to slice and index into the Dataset using row and column indices. Allow selection by integer index, slice and list of integer indices and boolean arrays.

Examples :

• Index the first row and column:

  dataset.iloc[0, 0]

• Select rows 1 and 2 with a slice:

  dataset.iloc[1:3, :]

• Select with a list of integer coordinates:

  dataset.iloc[[0, 2, 3]]

map(map_fn, specs=None, clone=True)[source]#

Map a function to all objects matching the specs

Recursively replaces elements using a map function when the specs apply, by default applies to all objects, e.g. to apply the function to all contained Curve objects:

dmap.map(fn, hv.Curve)

Parameters#

map_fn : Function to apply to each object specs : List of specs to match

List of types, functions or type[.group][.label] specs to select objects to return, by default applies to all objects.

clone

Whether to clone the object or transform inplace

Returns#

Returns the object after the map_fn has been applied

property ndloc#

Returns ndloc indexer with support for gridded indexing.

Returns an ndloc object providing nd-array like indexing for gridded datasets. Follows NumPy array indexing conventions, allowing for indexing, slicing and selecting a list of indices on multi-dimensional arrays using integer indices. The order of array indices is inverted relative to the Dataset key dimensions, e.g. an Image with key dimensions ‘x’ and ‘y’ can be indexed with image.ndloc[iy, ix], where iy and ix are integer indices along the y and x dimensions.

Examples :

• Index value in 2D array:

  dataset.ndloc[3, 1]

• Slice along y-axis of 2D array:

  dataset.ndloc[2:5, :]

• Vectorized (non-orthogonal) indexing along x- and y-axes:

  dataset.ndloc[[1, 2, 3], [0, 2, 3]]

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

persist()[source]#

Persists the results of a lazy data interface to memory to speed up data manipulation and visualization. If the particular data backend already holds the data in memory this is a no-op. Unlike the compute method this maintains the same data type.

Returns#

Dataset with the data persisted to memory

property pipeline#

Chain operation that evaluates the sequence of operations that was used to create this object, starting with the Dataset stored in dataset property

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

Return the lower and upper bounds of values along dimension.

Parameters#

dim

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

reduce(dimensions=None, function=None, spreadfn=None, **reductions)[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 Dataset after reductions have been applied.

reindex(kdims=None, vdims=None)[source]#

Reindexes Dataset dropping static or supplied kdims

Creates a new object with a reordered or reduced set of key dimensions. By default drops all non-varying key dimensions.x

Parameters#

kdimsoptional

New list of key dimensionsx

vdimsoptional

New list of value dimensions

Returns#

Reindexed object

relabel(label=None, group=None, depth=0)[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

sample(samples=None, bounds=None, closest=True, **kwargs)[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

select(selection_expr=None, selection_specs=None, **selection)[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])

• predicate expression: A holoviews.dim expression, e.g.:

  from holoviews import dim ds.select(selection_expr=dim(‘x’) % 2 == 0)

Parameters#

selection_exprholoviews.dim predicate expression

specifying selection.

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

property shape#

Returns the shape of the data.

sort(by=None, reverse=False)[source]#

Sorts the data by the values along the supplied dimensions.

Parameters#

by

Dimension(s) to sort by

reversebool, optional

Reverse sort order

Returns#

Sorted Dataset

property to#

Returns the conversion interface with methods to convert Dataset

transform(*args, **kwargs)[source]#

Transforms the Dataset according to a dimension transform.

Transforms may be supplied as tuples consisting of the dimension(s) and the dim transform to apply or keyword arguments mapping from dimension(s) to dim transforms. If the arg or kwarg declares multiple dimensions the dim transform should return a tuple of values for each.

A transform may override an existing dimension or add a new one in which case it will be added as an additional value dimension.

Parameters#

args

Specify the output arguments and transforms as a tuple of dimension specs and dim transforms

dropbool

Whether to drop all variables not part of the transform

keep_indexbool

Whether to keep indexes

Whether to apply transform on datastructure with index, e.g. pandas.Series or xarray.DataArray, (important for dask datastructures where index may be required to align datasets).

kwargs

Specify new dimensions in the form new_dim=dim_transform

Returns#

Transformed dataset with new dimensions

class holoviews.core.Dimension(spec, **params)[source]#

Bases: Parameterized

Dimension objects are used to specify some important general features that may be associated with a collection of values.

For instance, a Dimension may specify that a set of numeric values actually correspond to ‘Height’ (dimension name), in units of meters, with a descriptive label ‘Height of adult males’.

All dimensions object have a name that identifies them and a label containing a suitable description. If the label is not explicitly specified it matches the name.

These two parameters define the core identity of the dimension object and must match if two dimension objects are to be considered equivalent. All other parameters are considered optional metadata and are not used when testing for equality.

Unlike all the other parameters, these core parameters can be used to construct a Dimension object from a tuple. This format is sufficient to define an identical Dimension:

Dimension(‘a’, label=’Dimension A’) == Dimension((‘a’, ‘Dimension A’))

Everything else about a dimension is considered to reflect non-semantic preferences. Examples include the default value (which may be used in a visualization to set an initial slider position), how the value is to rendered as text (which may be used to specify the printed floating point precision) or a suitable range of values to consider for a particular analysis.

Units#

Full unit support with automated conversions are on the HoloViews roadmap. Once rich unit objects are supported, the unit (or more specifically the type of unit) will be part of the core dimension specification used to establish equality.

Until this feature is implemented, there are two auxiliary parameters that hold some partial information about the unit: the name of the unit and whether or not it is cyclic. The name of the unit is used as part of the pretty-printed representation and knowing whether it is cyclic is important for certain operations.

Parameter Definitions

---

label = String(allow_None=True, label='Label')

Unrestricted label used to describe the dimension. A label should succinctly describe the dimension and may contain any characters, including Unicode and LaTeX expression.

cyclic = Boolean(default=False, label='Cyclic')

Whether the range of this feature is cyclic such that the maximum allowed value (defined by the range parameter) is continuous with the minimum allowed value.

default = Parameter(allow_None=True, label='Default')

Default value of the Dimension which may be useful for widget or other situations that require an initial or default value.

nodata = Integer(allow_None=True, inclusive_bounds=(True, True), label='Nodata')

Optional missing-data value for integer data. If non-None, data with this value will be replaced with NaN.

range = Tuple(default=(None, None), label='Range', length=2)

Specifies the minimum and maximum allowed values for a Dimension. None is used to represent an unlimited bound.

soft_range = Tuple(default=(None, None), label='Soft range', length=2)

Specifies a minimum and maximum reference value, which may be overridden by the data.

step = Number(allow_None=True, inclusive_bounds=(True, True), label='Step')

Optional floating point step specifying how frequently the underlying space should be sampled. May be used to define a discrete sampling over the range.

type = Parameter(allow_None=True, label='Type')

Optional type associated with the Dimension values. The type may be an inbuilt constructor (such as int, str, float) or a custom class object.

unit = String(allow_None=True, label='Unit')

Optional unit string associated with the Dimension. For instance, the string ‘m’ may be used represent units of meters and ‘s’ to represent units of seconds.

value_format = Callable(allow_None=True, label='Value format')

Formatting function applied to each value before display.

values = List(bounds=(0, None), default=[], label='Values')

Optional specification of the allowed value set for the dimension that may also be used to retain a categorical ordering.

clone(spec=None, **overrides)[source]#

Clones the Dimension with new parameters

Derive a new Dimension that inherits existing parameters except for the supplied, explicit overrides

Parameters#

spectuple, optional

Dimension tuple specification

**overrides: Dimension parameter overrides

Returns#

Cloned Dimension object

property pprint_label#

The pretty-printed label string for the Dimension

pprint_value(value, print_unit=False)[source]#

Applies the applicable formatter to the value.

Parameters#

value

Dimension value to format

Returns#

Formatted dimension value

pprint_value_string(value)[source]#

Pretty print the dimension value and unit with title_format

Parameters#

value

Dimension value to format

Returns#

Formatted dimension value string with unit

property spec#

“Returns the Dimensions tuple specification

Returns#

tuple : Dimension tuple specification

class holoviews.core.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.

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#

dimension

The dimension to return values for

expandedbool, optional

Whether to expand values 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#

NumPy 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 default : optional

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.DynamicMap(callback, initial_items=None, streams=None, **params)[source]#

Bases: HoloMap

A DynamicMap is a type of HoloMap where the elements are dynamically generated by a callable. The callable is invoked with values associated with the key dimensions or with values supplied by stream parameters.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: vdims, sort

holoviews.core.ndmapping.NdMapping: group

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

The key dimensions of a DynamicMap map to the arguments of the callback. This mapping can be by position or by name.

callback = ClassSelector(allow_None=True, class_=<class 'holoviews.core.spaces.Callable'>, constant=True, label='Callback')

The callable used to generate the elements. The arguments to the callable includes any number of declared key dimensions as well as any number of stream parameters defined on the input streams. If the callable is an instance of Callable it will be used directly, otherwise it will be automatically wrapped in one.

streams = List(bounds=(0, None), constant=True, default=[], label='Streams')

List of Stream instances to associate with the DynamicMap. The set of parameter values across these streams will be supplied as keyword arguments to the callback when the events are received, updating the streams. Can also be supplied as a dictionary that maps parameters or panel widgets to callback argument names that will then be automatically converted to the equivalent list format.

cache_size = Integer(bounds=(1, None), default=500, inclusive_bounds=(True, True), label='Cache size')

The number of entries to cache for fast access. This is an LRU cache where the least recently used item is overwritten once the cache is full.

positional_stream_args = Boolean(constant=True, default=False, label='Positional stream args')

If False, stream parameters are passed to the callback as keyword arguments. If True, stream parameters are passed to callback as positional arguments. Each positional argument is a dict containing the contents of a stream. The positional stream arguments follow the positional arguments for each kdim, and they are ordered to match the order of the DynamicMap’s streams list.

add_dimension(dimension, dim_pos, dim_val, vdim=False, **kwargs)[source]#

Adds a dimension and its values to the object

Requires the dimension name or object, the desired position in the key dimensions and a key value scalar or sequence of the same length as the existing keys.

Parameters#

dimension

Dimension or dimension spec to add

dim_posint

Integer index to insert dimension at

dim_valscalar or ndarray

Dimension value(s) to add

vdim

Disabled, this type does not have value dimensions

**kwargs

Keyword arguments passed to the cloned element

Returns#

Cloned object containing the new dimension

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

collate()[source]#

Unpacks DynamicMap into container of DynamicMaps

Collation allows unpacking DynamicMaps which return Layout, NdLayout or GridSpace objects into a single such object containing DynamicMaps. Assumes that the items in the layout or grid that is returned do not change.

Returns#

Collated container containing DynamicMaps

property current_key#

Returns the current key value.

decollate()[source]#

Packs DynamicMap of nested DynamicMaps into a single DynamicMap that returns a non-dynamic element

Decollation allows packing a DynamicMap of nested DynamicMaps into a single DynamicMap that returns a simple (non-dynamic) element. 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 non-dynamic element

drop_dimension(dimensions)[source]#

Drops dimension(s) from keys

Parameters#

dimensions

Dimension(s) to drop

Returns#

Clone of object with with dropped dimension(s)

event(**kwargs)[source]#

Updates attached streams and triggers events

Automatically find streams matching the supplied kwargs to update and trigger events on them.

Parameters#

**kwargs

Events to update streams with

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

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#

gridGridSpace

GridSpace with supplied dimensions

groupby(dimensions=None, container_type=None, group_type=None, **kwargs)[source]#

Groups DynamicMap by one or more dimensions

Applies groupby operation over the specified dimensions returning an object of type container_type (expected to be dictionary-like) containing the groups.

Parameters#

dimensions : Dimension(s) to group by container_type : Type to cast group container to group_type : Type to cast each group to dynamic : Whether to return a DynamicMap **kwargs: Keyword arguments to pass to each group

Returns#

Returns object of supplied container_type containing the groups. If dynamic=True returns a DynamicMap instead.

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_bins : int, optional

Number of bins

bin_rangetuple, optional

Lower and upper bounds of bins

adjoinbool, optional

Whether to adjoin histogram

Returns#

AdjointLayout of DynamicMap and adjoined histogram if adjoin=True, otherwise just the histogram

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

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

Parameters#

dimensionsDimension/str or list

Dimension or list of dimensions to group by

Returns#

layoutNdLayout

NdLayout with supplied dimensions

map(map_fn, specs=None, clone=True, link_inputs=True)[source]#

Map a function to all objects matching the specs

Recursively replaces elements using a map function when the specs apply, by default applies to all objects, e.g. to apply the function to all contained Curve objects:

dmap.map(fn, hv.Curve)

Parameters#

map_fn : Function to apply to each object specs : List of specs to match

List of types, functions or type[.group][.label] specs to select objects to return, by default applies to all objects.

clone : Whether to clone the object or transform inplace

Returns#

Returns the object after the map_fn has been applied

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 returned by the DynamicMap 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

reindex(kdims=None, force=False)[source]#

Reorders key dimensions on DynamicMap

Create a new object with a reordered set of key dimensions. Dropping dimensions is not allowed on a DynamicMap.

Parameters#

kdims : List of dimensions to reindex the mapping with force : Not applicable to a DynamicMap

Returns#

Reindexed DynamicMap

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

reset()[source]#

Clear the DynamicMap cache

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

property unbounded#

Returns a list of key dimensions that are unbounded, excluding stream parameters. If any of these key dimensions are unbounded, the DynamicMap as a whole is also unbounded.

class holoviews.core.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.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

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

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.Element2D(data, kdims=None, vdims=None, **params)[source]#

Bases: Element

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

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

holoviews.core.element.Element: group

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

Bases: Element2D

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

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

holoviews.core.element.Element: group

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.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.GridMatrix(initial_items=None, kdims=None, **params)[source]#

Bases: GridSpace

GridMatrix is container type for heterogeneous Element types laid out in a grid. Unlike a GridSpace the axes of the Grid must not represent an actual coordinate space, but may be used to plot various dimensions against each other. The GridMatrix is usually constructed using the gridmatrix operation, which will generate a GridMatrix plotting each dimension in an Element against each other.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: vdims, sort

holoviews.core.ndmapping.NdMapping: group

holoviews.core.spaces.GridSpace: kdims

class holoviews.core.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.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.dimension.LabelledData: label

holoviews.core.dimension.Dimensioned: cdims

holoviews.core.ndmapping.MultiDimensionalMapping: vdims, sort

holoviews.core.ndmapping.NdMapping: group

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.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.

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

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.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.

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.