Source code for holoviews.core.ndmapping

Supplies MultiDimensionalMapping and NdMapping which are multi-dimensional
map types. The former class only allows indexing whereas the latter
also enables slicing over multiple dimension ranges.

from itertools import cycle
from operator import itemgetter

import numpy as np
import pandas as pd
import param

from . import util
from .dimension import Dimension, Dimensioned, ViewableElement, asdim
from .util import (

[docs]class item_check: """ Context manager to allow creating NdMapping types without performing the usual item_checks, providing significant speedups when there are a lot of items. Should only be used when both keys and values are guaranteed to be the right type, as is the case for many internal operations. """ def __init__(self, enabled): self.enabled = enabled def __enter__(self): self._enabled = MultiDimensionalMapping._check_items MultiDimensionalMapping._check_items = self.enabled def __exit__(self, exc_type, exc_val, exc_tb): MultiDimensionalMapping._check_items = self._enabled
[docs]class sorted_context: """ Context manager to temporarily disable sorting on NdMapping types. Retains the current sort order, which can be useful as an optimization on NdMapping instances where sort=True but the items are already known to have been sorted. """ def __init__(self, enabled): self.enabled = enabled def __enter__(self): self._enabled = MultiDimensionalMapping.sort MultiDimensionalMapping.sort = self.enabled def __exit__(self, exc_type, exc_val, exc_tb): MultiDimensionalMapping.sort = self._enabled
[docs]class MultiDimensionalMapping(Dimensioned): """ An MultiDimensionalMapping is a Dimensioned mapping (like a dictionary or array) that uses fixed-length multidimensional keys. This behaves like a sparse N-dimensional array that does not require a dense sampling over the multidimensional space. If the underlying value for each (key, value) pair also supports indexing (such as a dictionary, array, or list), fully qualified (deep) indexing may be used from the top level, with the first N dimensions of the index selecting a particular Dimensioned object and the remaining dimensions indexing into that object. For instance, for a MultiDimensionalMapping with dimensions "Year" and "Month" and underlying values that are 2D floating-point arrays indexed by (r,c), a 2D array may be indexed with x[2000,3] and a single floating-point number may be indexed as x[2000,3,1,9]. In practice, this class is typically only used as an abstract base class, because the NdMapping subclass extends it with a range of useful slicing methods for selecting subsets of the data. Even so, keeping the slicing support separate from the indexing and data storage methods helps make both classes easier to understand. """ group = param.String(default='MultiDimensionalMapping', constant=True) kdims = param.List(default=[Dimension("Default")], constant=True) vdims = param.List(default=[], bounds=(0, 0), constant=True) sort = param.Boolean(default=True, doc=""" Whether the items should be sorted in the constructor.""") data_type = None # Optional type checking of elements _deep_indexable = False _check_items = True def __init__(self, initial_items=None, kdims=None, **params): if isinstance(initial_items, MultiDimensionalMapping): params = dict(util.get_param_values(initial_items), **dict(params)) if kdims is not None: params['kdims'] = kdims super().__init__({}, **dict(params)) self._next_ind = 0 self._check_key_type = True if initial_items is None: initial_items = [] if isinstance(initial_items, tuple): self._add_item(initial_items[0], initial_items[1]) elif not self._check_items: if isinstance(initial_items, dict): initial_items = initial_items.items() elif isinstance(initial_items, MultiDimensionalMapping): initial_items = = dict((k if isinstance(k, tuple) else (k,), v) for k, v in initial_items) if self.sort: self._resort() elif initial_items is not None: self.update(dict(initial_items)) def _item_check(self, dim_vals, data): """ Applies optional checks to individual data elements before they are inserted ensuring that they are of a certain type. Subclassed may implement further element restrictions. """ if not self._check_items: return elif self.data_type is not None and not isinstance(data, self.data_type): if isinstance(self.data_type, tuple): data_type = tuple(dt.__name__ for dt in self.data_type) else: data_type = self.data_type.__name__ slf = type(self).__name__ data = type(data).__name__ raise TypeError(f'{slf} does not accept {data} type, data elements have ' f'to be a {data_type}.') elif not len(dim_vals) == self.ndims: raise KeyError('The data contains keys of length %d, but the kdims ' 'only declare %d dimensions. Ensure that the number ' 'of kdims match the length of the keys in your data.' % (len(dim_vals), self.ndims)) def _add_item(self, dim_vals, data, sort=True, update=True): """ Adds item to the data, applying dimension types and ensuring key conforms to Dimension type and values. """ sort = sort and self.sort if not isinstance(dim_vals, tuple): dim_vals = (dim_vals,) self._item_check(dim_vals, data) # Apply dimension types dim_types = zip([kd.type for kd in self.kdims], dim_vals) dim_vals = tuple(v if None in [t, v] else t(v) for t, v in dim_types) valid_vals = zip(self.kdims, dim_vals) for dim, val in valid_vals: if dim.values and val is not None and val not in dim.values: raise KeyError(f'{dim} dimension value {val!r} not in' ' specified dimension values.') # Updates nested data structures rather than simply overriding them. if (update and (dim_vals in and isinstance([dim_vals], (MultiDimensionalMapping, dict))):[dim_vals].update(data) else:[dim_vals] = data if sort: self._resort() def _apply_key_type(self, keys): """ If a type is specified by the corresponding key dimension, this method applies the type to the supplied key. """ typed_key = () for dim, key in zip(self.kdims, keys): key_type = dim.type if key_type is None: typed_key += (key,) elif isinstance(key, slice): sl_vals = [key.start, key.stop, key.step] typed_key += (slice(*[key_type(el) if el is not None else None for el in sl_vals]),) elif key is Ellipsis: typed_key += (key,) elif isinstance(key, list): typed_key += ([key_type(k) for k in key],) else: typed_key += (key_type(key),) return typed_key def _split_index(self, key): """ Partitions key into key and deep dimension groups. If only key indices are supplied, the data is indexed with an empty tuple. Keys with indices than there are dimensions will be padded. """ if not isinstance(key, tuple): key = (key,) elif key == (): return (), () if key[0] is Ellipsis: num_pad = self.ndims - len(key) + 1 key = (slice(None),) * num_pad + key[1:] elif len(key) < self.ndims: num_pad = self.ndims - len(key) key = key + (slice(None),) * num_pad map_slice = key[:self.ndims] if self._check_key_type: map_slice = self._apply_key_type(map_slice) if len(key) == self.ndims: return map_slice, () else: return map_slice, key[self.ndims:] def _dataslice(self, data, indices): """ Returns slice of data element if the item is deep indexable. Warns if attempting to slice an object that has not been declared deep indexable. """ if self._deep_indexable and isinstance(data, Dimensioned) and indices: return data[indices] elif len(indices) > 0: self.param.warning('Cannot index into data element, extra data' ' indices ignored.') return data def _resort(self): = dict(dimension_sort(, self.kdims, self.vdims, range(self.ndims)))
[docs] def clone(self, data=None, shared_data=True, *args, **overrides): """Clones the object, overriding data and parameters. Args: data: New data replacing the existing data shared_data (bool, optional): Whether to use existing data new_type (optional): Type to cast object to link (bool, 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 """ with item_check(not shared_data and self._check_items): return super().clone(data, shared_data, *args, **overrides)
[docs] def groupby(self, dimensions, container_type=None, group_type=None, **kwargs): """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. Args: 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. """ if self.ndims == 1: self.param.warning('Cannot split Map with only one dimension.') return self elif not isinstance(dimensions, list): dimensions = [dimensions] container_type = container_type if container_type else type(self) group_type = group_type if group_type else type(self) dimensions = [self.get_dimension(d, strict=True) for d in dimensions] with item_check(False): sort = kwargs.pop('sort', self.sort) return util.ndmapping_groupby(self, dimensions, container_type, group_type, sort=sort, **kwargs)
[docs] def add_dimension(self, dimension, dim_pos, dim_val, vdim=False, **kwargs): """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. Args: dimension: Dimension or dimension spec to add dim_pos (int) Integer index to insert dimension at dim_val (scalar 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 """ dimension = asdim(dimension) if dimension in self.dimensions(): raise Exception(f'{} dimension already defined') if vdim and self._deep_indexable: raise Exception('Cannot add value dimension to object that is deep indexable') if vdim: dims = self.vdims[:] dims.insert(dim_pos, dimension) dimensions = dict(vdims=dims) dim_pos += self.ndims else: dims = self.kdims[:] dims.insert(dim_pos, dimension) dimensions = dict(kdims=dims) if isinstance(dim_val, str) or not hasattr(dim_val, '__iter__'): dim_val = cycle([dim_val]) elif not len(dim_val) == len(self): raise ValueError("Added dimension values must be same length" "as existing keys.") items = {} for dval, (key, val) in zip(dim_val, if vdim: new_val = list(val) new_val.insert(dim_pos, dval) items[key] = tuple(new_val) else: new_key = list(key) new_key.insert(dim_pos, dval) items[tuple(new_key)] = val return self.clone(items, **dict(dimensions, **kwargs))
[docs] def drop_dimension(self, dimensions): """Drops dimension(s) from keys Args: dimensions: Dimension(s) to drop Returns: Clone of object with with dropped dimension(s) """ dimensions = [dimensions] if np.isscalar(dimensions) else dimensions dims = [d for d in self.kdims if d not in dimensions] dim_inds = [self.get_dimension_index(d) for d in dims] key_getter = itemgetter(*dim_inds) return self.clone([(key_getter(k), v) for k, v in], kdims=dims)
[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 """ dimension = self.get_dimension(dimension, strict=True) if dimension in self.kdims: return np.array([k[self.get_dimension_index(dimension)] for k in]) if dimension in self.dimensions(): values = [el.dimension_values(dimension, expanded, flat) for el in self if dimension in el.dimensions()] vals = np.concatenate(values) return vals if expanded else util.unique_array(vals) else: return super().dimension_values(dimension, expanded, flat)
[docs] def reindex(self, kdims=None, force=False): """Reindexes object 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. Reducing the number of key dimensions will discard information from the keys. All data values are accessible in the newly created object as the new labels must be sufficient to address each value uniquely. Args: kdims (optional): New list of key dimensions after reindexing force (bool, optional): Whether to drop non-unique items Returns: Reindexed object """ if kdims is None: kdims = [] old_kdims = [ for d in self.kdims] if not isinstance(kdims, list): kdims = [kdims] elif not len(kdims): kdims = [d for d in old_kdims if not len(set(self.dimension_values(d))) == 1] indices = [self.get_dimension_index(el) for el in kdims] keys = [tuple(k[i] for i in indices) for k in] reindexed_items = dict( (k, v) for (k, v) in zip(keys, reduced_dims = { for d in self.kdims}.difference(kdims) dimensions = [self.get_dimension(d) for d in kdims if d not in reduced_dims] if len(set(keys)) != len(keys) and not force: raise Exception("Given dimension labels not sufficient" "to address all values uniquely") if len(keys): cdims = {self.get_dimension(d): self.dimension_values(d)[0] for d in reduced_dims} else: cdims = {} with item_check(indices == sorted(indices)): return self.clone(reindexed_items, kdims=dimensions, cdims=cdims)
@property def last(self): "Returns the item highest data item along the map dimensions." return list([-1] if len(self) else None @property def last_key(self): "Returns the last key value." return list(self.keys())[-1] if len(self) else None @property def info(self): """ Prints information about the Dimensioned object, including the number and type of objects contained within it and information about its dimensions. """ if (len(self.values()) > 0): info_str = self.__class__.__name__ +\ " containing %d items of type %s\n" % (len(self.keys()), type(self.values()[0]).__name__) else: info_str = self.__class__.__name__ + " containing no items\n" info_str += ('-' * (len(info_str)-1)) + "\n\n" aliases = {v: k for k, v in self._dim_aliases.items()} for group in self._dim_groups: dimensions = getattr(self, group) if dimensions: group = aliases[group].split('_')[0] info_str += f'{group.capitalize()} Dimensions: \n' for d in dimensions: dmin, dmax = self.range( if d.value_format: dmin, dmax = d.value_format(dmin), d.value_format(dmax) info_str += f'\t {d.pprint_label}: {dmin}...{dmax} \n' return info_str
[docs] def update(self, other): """Merges other item with this object Args: other: Object containing items to merge into this object Must be a dictionary or NdMapping type """ if isinstance(other, NdMapping): dims = [d for d in other.kdims if d not in self.kdims] if len(dims) == other.ndims: raise KeyError("Cannot update with NdMapping that has" " a different set of key dimensions.") elif dims: other = other.drop_dimension(dims) other = for key, data in other.items(): self._add_item(key, data, sort=False) if self.sort: self._resort()
[docs] def keys(self): " Returns the keys of all the elements." if self.ndims == 1: return [k[0] for k in] else: return list(
[docs] def values(self): "Returns the values of all the elements." return list(
[docs] def items(self): "Returns all elements as a list in (key,value) format." return list(zip(list(self.keys()), list(self.values())))
[docs] def get(self, key, default=None): "Standard get semantics for all mapping types" try: if key is None: return None return self[key] except KeyError: return default
[docs] def pop(self, key, default=None): "Standard pop semantics for all mapping types" if not isinstance(key, tuple): key = (key,) return, default)
def __getitem__(self, key): """ Allows multi-dimensional indexing in the order of the specified key dimensions, passing any additional indices to the data elements. """ if key in [Ellipsis, ()]: return self map_slice, data_slice = self._split_index(key) return self._dataslice([map_slice], data_slice) def __setitem__(self, key, value): "Adds item to mapping" self._add_item(key, value, update=False) def __str__(self): return repr(self) def __iter__(self): "Iterates over mapping values" return iter(self.values()) def __contains__(self, key): if self.ndims == 1: return key in else: return key in self.keys() def __len__(self): return len(
[docs]class NdMapping(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. """ group = param.String(default='NdMapping', constant=True) def __getitem__(self, indexslice): """ Allows slicing operations along the key and data dimensions. If no data slice is supplied it will return all data elements, otherwise it will return the requested slice of the data. """ if isinstance(indexslice, np.ndarray) and indexslice.dtype.kind == 'b': if not len(indexslice) == len(self): raise IndexError("Boolean index must match length of sliced object") selection = zip(indexslice, return self.clone([item for c, item in selection if c]) elif isinstance(indexslice, tuple) and indexslice == () and not self.kdims: return[()] elif (isinstance(indexslice, tuple) and indexslice == ()) or indexslice is Ellipsis: return self elif any(Ellipsis is sl for sl in wrap_tuple(indexslice)): indexslice = process_ellipses(self, indexslice) map_slice, data_slice = self._split_index(indexslice) map_slice = self._transform_indices(map_slice) map_slice = self._expand_slice(map_slice) if all(not (isinstance(el, (slice, set, list, tuple)) or callable(el)) for el in map_slice): return self._dataslice([map_slice], data_slice) else: conditions = self._generate_conditions(map_slice) items = for cidx, (condition, dim) in enumerate(zip(conditions, self.kdims)): values = dim.values items = [(k, v) for k, v in items if condition(values.index(k[cidx]) if values else k[cidx])] sliced_items = [] for k, v in items: val_slice = self._dataslice(v, data_slice) if val_slice or isinstance(val_slice, tuple): sliced_items.append((k, val_slice)) if len(sliced_items) == 0: raise KeyError('No items within specified slice.') with item_check(False): return self.clone(sliced_items) def _expand_slice(self, indices): """ Expands slices containing steps into a list. """ keys = list( expanded = [] for idx, ind in enumerate(indices): if isinstance(ind, slice) and ind.step is not None: dim_ind = slice(ind.start, ind.stop) if dim_ind == slice(None): condition = self._all_condition() elif dim_ind.start is None: condition = self._upto_condition(dim_ind) elif dim_ind.stop is None: condition = self._from_condition(dim_ind) else: condition = self._range_condition(dim_ind) dim_vals = unique_iterator(k[idx] for k in keys) expanded.append(set([k for k in dim_vals if condition(k)][::int(ind.step)])) else: expanded.append(ind) return tuple(expanded) def _transform_indices(self, indices): """ Identity function here but subclasses can implement transforms of the dimension indices from one coordinate system to another. """ return indices def _generate_conditions(self, map_slice): """ Generates filter conditions used for slicing the data structure. """ conditions = [] for dim, dim_slice in zip(self.kdims, map_slice): if isinstance(dim_slice, slice): start, stop = dim_slice.start, dim_slice.stop if dim.values: values = dim.values dim_slice = slice(None if start is None else values.index(start), None if stop is None else values.index(stop)) if dim_slice == slice(None): conditions.append(self._all_condition()) elif start is None: conditions.append(self._upto_condition(dim_slice)) elif stop is None: conditions.append(self._from_condition(dim_slice)) else: conditions.append(self._range_condition(dim_slice)) elif isinstance(dim_slice, (set, list)): if dim.values: dim_slice = [dim.values.index(dim_val) for dim_val in dim_slice] conditions.append(self._values_condition(dim_slice)) elif dim_slice is Ellipsis: conditions.append(self._all_condition()) elif callable(dim_slice): conditions.append(dim_slice) elif isinstance(dim_slice, (tuple)): raise IndexError("Keys may only be selected with sets or lists, not tuples.") else: if dim.values: dim_slice = dim.values.index(dim_slice) conditions.append(self._value_condition(dim_slice)) return conditions def _value_condition(self, value): return lambda x: x == value def _values_condition(self, values): return lambda x: x in values def _range_condition(self, slice): if slice.step is None: lmbd = lambda x: slice.start <= x < slice.stop else: lmbd = lambda x: slice.start <= x < slice.stop and not ( (x-slice.start) % slice.step) return lmbd def _upto_condition(self, slice): if slice.step is None: lmbd = lambda x: x < slice.stop else: lmbd = lambda x: x < slice.stop and not (x % slice.step) return lmbd def _from_condition(self, slice): if slice.step is None: lmbd = lambda x: x >= slice.start else: lmbd = lambda x: x >= slice.start and ((x-slice.start) % slice.step) return lmbd def _all_condition(self): return lambda x: True
[docs]class UniformNdMapping(NdMapping): """ A UniformNdMapping is a map of Dimensioned objects and is itself indexed over a number of specified dimensions. The dimension may be a spatial dimension (i.e., a ZStack), time (specifying a frame sequence) or any other combination of Dimensions. UniformNdMapping objects can be sliced, sampled, reduced, overlaid and split along its and its containing Element's dimensions. Subclasses should implement the appropriate slicing, sampling and reduction methods for their Dimensioned type. """ data_type = (ViewableElement, NdMapping) __abstract = True _deep_indexable = True _auxiliary_component = False def __init__(self, initial_items=None, kdims=None, group=None, label=None, **params): self._type = None self._group_check, = None, group self._label_check, self.label = None, label super().__init__(initial_items, kdims=kdims, **params)
[docs] def clone(self, data=None, shared_data=True, new_type=None, link=True, *args, **overrides): """Clones the object, overriding data and parameters. Args: data: New data replacing the existing data shared_data (bool, optional): Whether to use existing data new_type (optional): Type to cast object to link (bool, 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 """ settings = self.param.values() if settings.get('group', None) != self._group: settings.pop('group') if settings.get('label', None) != self._label: settings.pop('label') if new_type is None: clone_type = self.__class__ else: clone_type = new_type new_params = new_type.param.objects() settings = {k: v for k, v in settings.items() if k in new_params} settings = dict(settings, **overrides) if 'id' not in settings and new_type in [type(self), None]: settings['id'] = if data is None and shared_data: data = if link: settings['plot_id'] = self._plot_id # Apply name mangling for __ attribute pos_args = getattr(self, '_' + type(self).__name__ + '__pos_params', []) with item_check(not shared_data and self._check_items): return clone_type(data, *args, **{k:v for k,v in settings.items() if k not in pos_args})
[docs] def collapse(self, dimensions=None, function=None, spreadfn=None, **kwargs): """Concatenates and aggregates along supplied dimensions Useful to collapse stacks of objects into a single object, e.g. to average a stack of Images or Curves. Args: dimensions: Dimension(s) to collapse Defaults to all key dimensions function: Aggregation function to apply, e.g. numpy.mean spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation. **kwargs: Keyword arguments passed to the aggregation function Returns: Returns the collapsed element or HoloMap of collapsed elements """ from .data import concat from .overlay import CompositeOverlay if not dimensions: dimensions = self.kdims if not isinstance(dimensions, list): dimensions = [dimensions] if self.ndims > 1 and len(dimensions) != self.ndims: groups = self.groupby([dim for dim in self.kdims if dim not in dimensions]) elif all(d in self.kdims for d in dimensions): groups = UniformNdMapping([(0, self)], ['tmp']) else: raise KeyError("Supplied dimensions not found.") collapsed = groups.clone(shared_data=False) for key, group in groups.items(): last = group.values()[-1] if isinstance(last, UniformNdMapping): group_data = dict([ (k, v.collapse()) for k, v in group.items() ]) group = group.clone(group_data) if hasattr(group.values()[-1], 'interface'): group_data = concat(group) if function: agg = group_data.aggregate(group.last.kdims, function, spreadfn, **kwargs) group_data = group.type(agg) elif issubclass(group.type, CompositeOverlay) and hasattr(self, '_split_overlays'): keys, maps = self._split_overlays() group_data = group.type(dict([ (key, ndmap.collapse(function=function, spreadfn=spreadfn, **kwargs)) for key, ndmap in zip(keys, maps) ])) else: raise ValueError( "Could not determine correct collapse operation " "for items of type: {group.type!r}." ) collapsed[key] = group_data return collapsed if self.ndims-len(dimensions) else collapsed.last
[docs] def dframe(self, dimensions=None, multi_index=False): """Convert dimension values to DataFrame. Returns a pandas dataframe of columns along each dimension, either completely flat or indexed by key dimensions. Args: dimensions: Dimensions to return as columns multi_index: Convert key dimensions to (multi-)index Returns: DataFrame of columns corresponding to each dimension """ if dimensions is None: outer_dimensions = self.kdims inner_dimensions = None else: outer_dimensions = [self.get_dimension(d) for d in dimensions if d in self.kdims] inner_dimensions = [d for d in dimensions if d not in outer_dimensions] inds = [(d, self.get_dimension_index(d)) for d in outer_dimensions] dframes = [] for key, element in df = element.dframe(inner_dimensions, multi_index) names = [ for d in outer_dimensions] key_dims = [(, key[i]) for d, i in inds] if multi_index: length = len(df) indexes = [[v]*length for _, v in key_dims] if df.index.names != [None]: indexes += [df.index] names += list(df.index.names) df = df.set_index(indexes) df.index.names = names else: for dim, val in key_dims: dimn = 1 while dim in df: dim = dim+'_%d' % dimn if dim in df: dimn += 1 df.insert(0, dim, val) dframes.append(df) return pd.concat(dframes)
@property def group(self): "Group inherited from items" if self._group: return self._group group = get_ndmapping_label(self, 'group') if len(self) else None if group is None: return type(self).__name__ return group @group.setter def group(self, group): if group is not None and not sanitize_identifier.allowable(group): raise ValueError("Supplied group %s contains invalid " "characters." % self._group = group @property def label(self): "Label inherited from items" if self._label: return self._label elif len(self): label = get_ndmapping_label(self, 'label') return '' if label is None else label else: return '' @label.setter def label(self, label): if label is not None and not sanitize_identifier.allowable(label): raise ValueError("Supplied group %s contains invalid " "characters." % self._label = label @property def type(self): "The type of elements stored in the mapping." if self._type is None and len(self): self._type = self.values()[0].__class__ return self._type @property def empty_element(self): return self.type(None) def _item_check(self, dim_vals, data): if not self._check_items: return elif self.type is not None and (type(data) != self.type): raise AssertionError(f"{self.__class__.__name__} must only contain one type of object, not both {type(data).__name__} and {self.type.__name__}.") super()._item_check(dim_vals, data) def __mul__(self, other, reverse=False): from .overlay import Overlay if isinstance(other, type(self)): if self.kdims != other.kdims: raise KeyError("Can only overlay two %ss with " "non-matching key dimensions." % type(self).__name__) items = [] self_keys = list( other_keys = list( for key in util.unique_iterator(self_keys+other_keys): self_el = other_el = if self_el is None: item = [other_el] elif other_el is None: item = [self_el] elif reverse: item = [other_el, self_el] else: item = [self_el, other_el] items.append((key, Overlay(item))) return self.clone(items) overlayed_items = [(k, other * el if reverse else el * other) for k, el in self.items()] try: return self.clone(overlayed_items) except NotImplementedError: return NotImplemented def __rmul__(self, other): return self.__mul__(other, reverse=True)