""" 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 overlaid 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 .layout import AdjointLayout, Composable, Layout, Layoutable from .ndmapping import UniformNdMapping from .util import dimensioned_streams, sanitize_identifier, unique_array class Overlayable: """ Overlayable provides a mix-in class to support the mul operation for overlaying multiple elements. """ def __mul__(self, other): "Overlay object with other object." # Local import to break the import cyclic dependency from .spaces import DynamicMap if isinstance(other, 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)) else: if isinstance(self, Overlay): if not isinstance(other, ViewableElement): return NotImplemented elif 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 try: return Overlay([self, other]) except NotImplementedError: return NotImplemented class CompositeOverlay(ViewableElement, Composable): """ CompositeOverlay provides a common baseclass for Overlay classes. """ _deep_indexable = True def hist(self, dimension=None, num_bins=20, bin_range=None, adjoin=True, index=None, show_legend=False, **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, Falls back the plot dimensions by default. 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 show_legend (bool, optional): Show legend in histogram (don't show legend by default). Returns: AdjointLayout of element and histogram or just the histogram """ # Get main layer to get plot dimensions main_layer_int_index = getattr(self, "main_layer", None) or 0 # Validate index, and extract as integer if not None if index is not None: 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") if valid_ind: main_layer_int_index = index if valid_label: main_layer_int_index = self.keys().index(index) if dimension is None: # Fallback to default dimensions of main element dimension = [dim.name for dim in self.values()[main_layer_int_index].kdims] # Compute histogram for each dimension and each element in OverLay hists_per_dim = { dim: dict([ # All histograms for a given dimension ( elem_key, elem.hist( adjoin=False, dimension=dim, bin_range=bin_range, num_bins=num_bins, **kwargs ) ) for i, (elem_key, elem) in enumerate(self.items()) if (index is None) or (getattr(elem, "label", None) == index) or (index == i) ]) for dim in dimension } # Create new Overlays of histograms hists_overlay_per_dim = { dim: self.clone(hists).opts(show_legend=show_legend) for dim, hists in hists_per_dim.items() } if adjoin: layout = self for dim in reversed(self.values()[main_layer_int_index].kdims): if dim.name in hists_overlay_per_dim: layout = layout << hists_overlay_per_dim[dim.name] layout.main_layer = main_layer_int_index elif len(dimension) > 1: layout = Layout(list(hists_overlay_per_dim.values())) else: layout = hists_overlay_per_dim[0] return layout 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().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) class Overlay(ViewableTree, CompositeOverlay, Layoutable, Overlayable): """ 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().__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()]) 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(self.data.values()) if 0 <= identifier < len(values): return values[identifier] else: return default return super().get(identifier, default) 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()) def decollate(self): """Packs Overlay of DynamicMaps into a single DynamicMap that returns an Overlay Decollation allows packing an Overlay of DynamicMaps into a single DynamicMap that returns an Overlay 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 Overlay """ from .decollate import decollate return decollate(self) @property def group(self): if self._group: return self._group elements = [el for el in self if not el._auxiliary_component] values = {el.group for el in elements} types = {type(el) for el in elements} if values: group = next(iter(values)) vtype = next(iter(types)).__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(f"Supplied group {group} contains invalid characters.") 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 next(iter(labels)) else: return '' @label.setter def label(self, label): if not sanitize_identifier.allowable(label): raise ValueError(f"Supplied group {label} contains invalid characters.") self._label = label @property def ddims(self): dimensions = [] dimension_names = [] for el in self: for dim in el.dimensions(): if dim.name not in dimension_names: dimensions.append(dim) dimension_names.append(dim.name) return dimensions @property def shape(self): raise NotImplementedError def clone(self, data=None, shared_data=True, new_type=None, link=True, **overrides): if data is None and link: overrides['plot_id'] = self._plot_id return super().clone(data, shared_data=shared_data, new_type=new_type, link=link, **overrides) 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().__init__(overlays, kdims=kdims, **params) def decollate(self): """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 """ from .decollate import decollate return decollate(self) __all__ = list({_k for _k, _v in locals().items() if isinstance(_v, type) and issubclass(_v, Dimensioned)}) + ['Overlayable']