Source code for holoviews.core.overlay

Supplies Layer and related classes that allow overlaying of Views,
including Overlay. A Layer is the final extension of View base class
that allows Views to be overlayed on top of each other.

Also supplies ViewMap which is the primary multi-dimensional Map type
for indexing, slicing and animating collections of Views.

from functools import reduce
import numpy as np

import param
from .dimension import Dimension, Dimensioned, ViewableElement, ViewableTree
from .ndmapping import UniformNdMapping
from .layout import Composable, Layout, AdjointLayout
from .util import sanitize_identifier, unique_array, dimensioned_streams

[docs]class Overlayable(object): """ Overlayable provides a mix-in class to support the mul operation for overlaying multiple elements. """ def __mul__(self, other): "Overlay object with other object." if type(other).__name__ == 'DynamicMap': from .spaces import Callable def dynamic_mul(*args, **kwargs): element = other[args] return self * element callback = Callable(dynamic_mul, inputs=[self, other]) callback._is_overlay = True return other.clone(shared_data=False, callback=callback, streams=dimensioned_streams(other)) if isinstance(other, UniformNdMapping) and not isinstance(other, CompositeOverlay): items = [(k, self * v) for (k, v) in other.items()] return other.clone(items) elif isinstance(other, (AdjointLayout, ViewableTree)) and not isinstance(other, Overlay): return NotImplemented return Overlay([self, other])
[docs]class CompositeOverlay(ViewableElement, Composable): """ CompositeOverlay provides a common baseclass for Overlay classes. """ _deep_indexable = True
[docs] def hist(self, dimension=None, num_bins=20, bin_range=None, adjoin=True, index=0, **kwargs): """Computes and adjoins histogram along specified dimension(s). Defaults to first value dimension if present otherwise falls back to first key dimension. Args: dimension: Dimension(s) to compute histogram on num_bins (int, optional): Number of bins bin_range (tuple optional): Lower and upper bounds of bins adjoin (bool, optional): Whether to adjoin histogram index (int, optional): Index of layer to apply hist to Returns: AdjointLayout of element and histogram or just the histogram """ valid_ind = isinstance(index, int) and (0 <= index < len(self)) valid_label = index in [el.label for el in self] if not any([valid_ind, valid_label]): raise TypeError("Please supply a suitable index or label for the histogram data") hists = self.get(index).hist( adjoin=False, dimension=dimension, bin_range=bin_range, num_bins=num_bins, **kwargs) if not isinstance(hists, Layout): hists = [hists] if not isinstance(dimension, list): dimension = ['Default'] if adjoin: layout = self for hist in hists: layout = layout << hist layout.main_layer = index elif len(dimension) > 1: layout = hists else: layout = hists[0] return layout
[docs] def dimension_values(self, dimension, expanded=True, flat=True): """Return the values along the requested dimension. Args: dimension: The dimension to return values for expanded (bool, 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 flat (bool, optional): Whether to flatten array Returns: NumPy array of values along the requested dimension """ values = [] found = False for el in self: if dimension in el.dimensions(label=True): values.append(el.dimension_values(dimension)) found = True if not found: return super(CompositeOverlay, self).dimension_values(dimension, expanded, flat) values = [v for v in values if v is not None and len(v)] if not values: return np.array() vals = np.concatenate(values) return vals if expanded else unique_array(vals)
[docs]class Overlay(ViewableTree, CompositeOverlay): """ An Overlay consists of multiple Elements (potentially of heterogeneous type) presented one on top each other with a particular z-ordering. Overlays along with elements constitute the only valid leaf types of a Layout and in fact extend the Layout structure. Overlays are constructed using the * operator (building an identical structure to the + operator). """ def __init__(self, items=None, group=None, label=None, **params): self.__dict__['_fixed'] = False self.__dict__['_group'] = group self.__dict__['_label'] = label super(Overlay, self).__init__(items, **params) def __getitem__(self, key): """ Allows transparently slicing the Elements in the Overlay to select specific layers in an Overlay use the .get method. """ return Overlay([(k, v[key]) for k, v in self.items()])
[docs] def get(self, identifier, default=None): """Get a layer in the Overlay. Get a particular layer in the Overlay using its path string or an integer index. Args: identifier: Index or path string of the item to return default: Value to return if no item is found Returns: The indexed layer of the Overlay """ if isinstance(identifier, int): values = list( if 0 <= identifier < len(values): return values[identifier] else: return default return super(Overlay, self).get(identifier, default)
def __add__(self, other): "Composes Overlay with other object into a Layout" return Layout([self, other]) def __mul__(self, other): "Adds layer(s) from other object to Overlay" if type(other).__name__ == 'DynamicMap': from .spaces import Callable def dynamic_mul(*args, **kwargs): element = other[args] return self * element callback = Callable(dynamic_mul, inputs=[self, other]) callback._is_overlay = True return other.clone(shared_data=False, callback=callback, streams=dimensioned_streams(other)) elif not isinstance(other, ViewableElement): return NotImplemented return Overlay([self, other])
[docs] def collate(self): """ Collates any objects in the Overlay resolving any issues the recommended nesting structure. """ return reduce(lambda x,y: x*y, self.values())
@property def group(self): if self._group: return self._group elements = [el for el in self if not el._auxiliary_component] values = { for el in elements} types = {type(el) for el in elements} if values: group = list(values)[0] vtype = list(types)[0].__name__ else: group, vtype = [], '' if len(values) == 1 and group != vtype: return group else: return type(self).__name__ @group.setter def group(self, group): if not sanitize_identifier.allowable(group): raise ValueError("Supplied group %s contains invalid characters." % group) else: self._group = group @property def label(self): if self._label: return self._label labels = {el.label for el in self if not el._auxiliary_component} if len(labels) == 1: return list(labels)[0] else: return '' @label.setter def label(self, label): if not sanitize_identifier.allowable(label): raise ValueError("Supplied group %s contains invalid characters." % label) self._label = label @property def ddims(self): dimensions = [] dimension_names = [] for el in self: for dim in el.dimensions(): if not in dimension_names: dimensions.append(dim) dimension_names.append( return dimensions @property def shape(self): raise NotImplementedError # Deprecated methods
[docs] def collapse(self, function): "Deprecated method to collapse layers in the Overlay." self.param.warning('Overlay.collapse is deprecated, to' 'collapse multiple elements use a HoloMap.') elements = list(self) types = [type(el) for el in elements] values = [ for el in elements] if not len(set(types)) == 1 and len(set(values)) == 1: raise Exception("Overlay is not homogeneous in type or group " "and cannot be collapsed.") else: return elements[0].clone(types[0].collapse_data([ for el in elements], function, self.kdims))
[docs]class NdOverlay(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. """ kdims = param.List(default=[Dimension('Element')], constant=True, doc=""" List of dimensions the NdOverlay can be indexed by.""") _deep_indexable = True def __init__(self, overlays=None, kdims=None, **params): super(NdOverlay, self).__init__(overlays, kdims=kdims, **params)
__all__ = list(set([_k for _k, _v in locals().items() if isinstance(_v, type) and issubclass(_v, Dimensioned)])) + ['Overlayable']