Source code for holoviews.core.spaces

import itertools
import types
from numbers import Number
from itertools import groupby
from functools import partial
from contextlib import contextmanager
from inspect import ArgSpec

import numpy as np
import param

from . import traversal, util
from .dimension import OrderedDict, Dimension, ViewableElement, redim
from .layout import Layout, AdjointLayout, NdLayout
from .ndmapping import UniformNdMapping, NdMapping, item_check
from .overlay import Overlay, CompositeOverlay, NdOverlay, Overlayable
from .options import Store, StoreOptions
from ..streams import Stream

[docs]class HoloMap(UniformNdMapping, Overlayable): """ A HoloMap can hold any number of DataLayers indexed by a list of dimension values. It also has a number of properties, which can find the x- and y-dimension limits and labels. """ data_type = (ViewableElement, NdMapping, Layout)
[docs] def overlay(self, dimensions=None, **kwargs): """ Splits the UniformNdMapping along a specified number of dimensions and overlays items in the split out Maps. Shows all HoloMap data When no dimensions are specified. """ dimensions = self._valid_dimensions(dimensions) if len(dimensions) == self.ndims: with item_check(False): return NdOverlay(self, **kwargs).reindex(dimensions) else: dims = [d for d in self.kdims if d not in dimensions] return self.groupby(dims, group_type=NdOverlay, **kwargs)
[docs] def grid(self, dimensions=None, **kwargs): """ GridSpace takes a list of one or two dimensions, and lays out the containing Views along these axes in a GridSpace. Shows all HoloMap data When no dimensions are specified. """ dimensions = self._valid_dimensions(dimensions) if len(dimensions) == self.ndims: with item_check(False): return GridSpace(self, **kwargs).reindex(dimensions) return self.groupby(dimensions, container_type=GridSpace, **kwargs)
[docs] def layout(self, dimensions=None, **kwargs): """ GridSpace takes a list of one or two dimensions, and lays out the containing Views along these axes in a GridSpace. Shows all HoloMap data When no dimensions are specified. """ dimensions = self._valid_dimensions(dimensions) if len(dimensions) == self.ndims: with item_check(False): return NdLayout(self, **kwargs).reindex(dimensions) return self.groupby(dimensions, container_type=NdLayout, **kwargs)
[docs] def split_overlays(self): """ Given a UniformNdMapping of Overlays of N layers, split out the layers into N separate Maps. """ if not issubclass(self.type, CompositeOverlay): return None, self.clone() item_maps = OrderedDict() for k, overlay in for key, el in overlay.items(): if key not in item_maps: item_maps[key] = [(k, el)] else: item_maps[key].append((k, el)) maps, keys = [], [] for k, layermap in item_maps.items(): maps.append(self.clone(layermap)) keys.append(k) return keys, maps
def _dimension_keys(self): """ Helper for __mul__ that returns the list of keys together with the dimension labels. """ return [tuple(zip([ for d in self.kdims], [k] if self.ndims == 1 else k)) for k in self.keys()] def _dynamic_mul(self, dimensions, other, keys): """ Implements dynamic version of overlaying operation overlaying DynamicMaps and HoloMaps where the key dimensions of one is a strict superset of the other. """ # If either is a HoloMap compute Dimension values if not isinstance(self, DynamicMap) or not isinstance(other, DynamicMap): keys = sorted((d, v) for k in keys for d, v in k) grouped = dict([(g, [v for _, v in group]) for g, group in groupby(keys, lambda x: x[0])]) dimensions = [d(values=grouped[]) for d in dimensions] map_obj = None # Combine streams map_obj = self if isinstance(self, DynamicMap) else other if isinstance(self, DynamicMap) and isinstance(other, DynamicMap): self_streams = util.dimensioned_streams(self) other_streams = util.dimensioned_streams(other) streams = list(util.unique_iterator(self_streams+other_streams)) else: streams = map_obj.streams def dynamic_mul(*key, **kwargs): key_map = { k for d, k in zip(dimensions, key)} layers = [] try: self_el =, **key_map) if self.kdims else self[()] layers.append(self_el) except KeyError: pass try: other_el =, **key_map) if other.kdims else other[()] layers.append(other_el) except KeyError: pass return Overlay(layers) callback = Callable(dynamic_mul, inputs=[self, other]) callback._is_overlay = True if map_obj: return map_obj.clone(callback=callback, shared_data=False, kdims=dimensions, streams=streams) else: return DynamicMap(callback=callback, kdims=dimensions, streams=streams) def __mul__(self, other): """ The mul (*) operator implements overlaying of different Views. This method tries to intelligently overlay Maps with differing keys. If the UniformNdMapping is mulled with a simple ViewableElement each element in the UniformNdMapping is overlaid with the ViewableElement. If the element the UniformNdMapping is mulled with is another UniformNdMapping it will try to match up the dimensions, making sure that items with completely different dimensions aren't overlaid. """ if isinstance(other, HoloMap): self_set = { for d in self.kdims} other_set = { for d in other.kdims} # Determine which is the subset, to generate list of keys and # dimension labels for the new view self_in_other = self_set.issubset(other_set) other_in_self = other_set.issubset(self_set) dims = [other.kdims, self.kdims] if self_in_other else [self.kdims, other.kdims] dimensions = util.merge_dimensions(dims) if self_in_other and other_in_self: # superset of each other keys = self._dimension_keys() + other._dimension_keys() super_keys = util.unique_iterator(keys) elif self_in_other: # self is superset dimensions = other.kdims super_keys = other._dimension_keys() elif other_in_self: # self is superset super_keys = self._dimension_keys() else: # neither is superset raise Exception('One set of keys needs to be a strict subset of the other.') if isinstance(self, DynamicMap) or isinstance(other, DynamicMap): return self._dynamic_mul(dimensions, other, super_keys) items = [] for dim_keys in super_keys: # Generate keys for both subset and superset and sort them by the dimension index. self_key = tuple(k for p, k in sorted( [(self.get_dimension_index(dim), v) for dim, v in dim_keys if dim in self.kdims])) other_key = tuple(k for p, k in sorted( [(other.get_dimension_index(dim), v) for dim, v in dim_keys if dim in other.kdims])) new_key = self_key if other_in_self else other_key # Append SheetOverlay of combined items if (self_key in self) and (other_key in other): items.append((new_key, self[self_key] * other[other_key])) elif self_key in self: items.append((new_key, Overlay([self[self_key]]))) else: items.append((new_key, Overlay([other[other_key]]))) return self.clone(items, kdims=dimensions, label=self._label, group=self._group) elif isinstance(other, self.data_type): if isinstance(self, DynamicMap): def dynamic_mul(*args, **kwargs): element = self[args] return element * other callback = Callable(dynamic_mul, inputs=[self, other]) callback._is_overlay = True return self.clone(shared_data=False, callback=callback, streams=[]) items = [(k, v * other) for (k, v) in] return self.clone(items, label=self._label, group=self._group) else: return NotImplemented def __add__(self, obj): return Layout.from_values([self, obj]) def __lshift__(self, other): if isinstance(other, (ViewableElement, UniformNdMapping)): return AdjointLayout([self, other]) elif isinstance(other, AdjointLayout): return AdjointLayout([self]) else: raise TypeError('Cannot append {0} to a AdjointLayout'.format(type(other).__name__))
[docs] def collate(self, merge_type=None, drop=[], drop_constant=False): """ Collation allows collapsing nested HoloMaps by merging their dimensions. 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. Specific dimensions may be dropped if they are redundant by supplying them in a list. Enabling drop_constant allows ignoring any non-varying dimensions during collation. """ from .element import Collator merge_type=merge_type if merge_type else self.__class__ return Collator(self, merge_type=merge_type, drop=drop, drop_constant=drop_constant)()
[docs] def collapse(self, dimensions=None, function=None, spreadfn=None, **kwargs): """ Allows collapsing one of any number of key dimensions on the HoloMap. Homogenous Elements may be collapsed by supplying a function, inhomogenous elements are merged. """ from .operation import MapOperation 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 = HoloMap([(0, self)]) else: raise KeyError("Supplied dimensions not found.") collapsed = groups.clone(shared_data=False) for key, group in groups.items(): if isinstance(function, MapOperation): collapsed[key] = function(group, **kwargs) else: group_data = [ for el in group] args = (group_data, function, group.last.kdims) if hasattr(group.last, 'interface'): col_data = group.type(group.table().aggregate(group.last.kdims, function, spreadfn, **kwargs)) else: data = group.type.collapse_data(*args, **kwargs) col_data = group.last.clone(data) collapsed[key] = col_data return collapsed if self.ndims > 1 else collapsed.last
[docs] def sample(self, samples=[], bounds=None, **sample_values): """ Sample each Element in the UniformNdMapping by passing either a list of samples or a tuple specifying the number of regularly spaced samples per dimension. Alternatively, a single sample may be requested using dimension-value pairs. Optionally, the bounds argument can be used to specify the bounding extent from which the coordinates are to regularly sampled. Regular sampling assumes homogenous and regularly sampled data. For 1D sampling, the shape is simply as the desired number of samples (and not a tuple). The bounds format for 1D sampling is the tuple (lower, upper) and the tuple (left, bottom, right, top) for 2D sampling. """ dims = self.last.ndims if isinstance(samples, tuple) or np.isscalar(samples): if dims == 1: xlim = self.last.range(0) lower, upper = (xlim[0], xlim[1]) if bounds is None else bounds edges = np.linspace(lower, upper, samples+1) linsamples = [(l+u)/2.0 for l,u in zip(edges[:-1], edges[1:])] elif dims == 2: (rows, cols) = samples if bounds: (l,b,r,t) = bounds else: l, r = self.last.range(0) b, t = self.last.range(1) xedges = np.linspace(l, r, cols+1) yedges = np.linspace(b, t, rows+1) xsamples = [(lx+ux)/2.0 for lx,ux in zip(xedges[:-1], xedges[1:])] ysamples = [(ly+uy)/2.0 for ly,uy in zip(yedges[:-1], yedges[1:])] Y,X = np.meshgrid(ysamples, xsamples) linsamples = list(zip(X.flat, Y.flat)) else: raise NotImplementedError("Regular sampling not implemented " "for high-dimensional Views.") samples = list(util.unique_iterator(self.last.closest(linsamples))) sampled = self.clone([(k, view.sample(samples, closest=False, **sample_values)) for k, view in]) return sampled.table()
[docs] def reduce(self, dimensions=None, function=None, **reduce_map): """ Reduce each Element in the HoloMap using a function supplied via the kwargs, where the keyword has to match a particular dimension in the Elements. """ from ..element import Table reduced_items = [(k, v.reduce(dimensions, function, **reduce_map)) for k, v in self.items()] if not isinstance(reduced_items[0][1], Table): params = dict(util.get_param_values(self.last), kdims=self.kdims, vdims=self.last.vdims) return Table(reduced_items, **params) return self.clone(reduced_items).table()
def relabel(self, label=None, group=None, depth=1): # Identical to standard relabel method except for default depth of 1 return super(HoloMap, self).relabel(label=label, group=group, depth=depth) def hist(self, num_bins=20, bin_range=None, adjoin=True, individually=True, **kwargs): histmaps = [self.clone(shared_data=False) for _ in kwargs.get('dimension', range(1))] if individually: map_range = None else: if 'dimension' not in kwargs: raise Exception("Please supply the dimension to compute a histogram for.") map_range = self.range(kwargs['dimension']) bin_range = map_range if bin_range is None else bin_range style_prefix = 'Custom[<' + + '>]_' if issubclass(self.type, (NdOverlay, Overlay)) and 'index' not in kwargs: kwargs['index'] = 0 for k, v in hists = v.hist(adjoin=False, bin_range=bin_range, individually=individually, num_bins=num_bins, style_prefix=style_prefix, **kwargs) if isinstance(hists, Layout): for i, hist in enumerate(hists): histmaps[i][k] = hist else: histmaps[0][k] = hists if adjoin: layout = self for hist in histmaps: layout = (layout << hist) if issubclass(self.type, (NdOverlay, Overlay)): layout.main_layer = kwargs['index'] return layout else: if len(histmaps) > 1: return Layout.from_values(histmaps) else: return histmaps[0]
[docs]class Callable(param.Parameterized): """ Callable allows wrapping callbacks on one or more DynamicMaps allowing their inputs (and in future outputs) to be defined. This makes it possible to wrap DynamicMaps with streams and makes it possible to traverse the graph of operations applied to a DynamicMap. Additionally, if the memoize attribute is True, a Callable will memoize the last returned value based on the arguments to the function and the state of all streams on its inputs, to avoid calling the function unnecessarily. Note that because memoization includes the streams found on the inputs it may be disabled if the stream requires it and is triggering. A Callable may also specify a stream_mapping which specifies the objects that are associated with interactive (i.e linked) streams when composite objects such as Layouts are returned from the callback. This is required for building interactive, linked visualizations (for the backends that support them) when returning Layouts, NdLayouts or GridSpace objects. When chaining multiple DynamicMaps into a pipeline, the link_inputs parameter declares whether the visualization generated using this Callable will inherit the linked streams. This parameter is used as a hint by the applicable backend. The mapping should map from an appropriate key to a list of streams associated with the selected object. The appropriate key may be a type[.group][.label] specification for Layouts, an integer index or a suitable NdLayout/GridSpace key. For more information see the DynamicMap tutorial at """ callable = param.Callable(default=None, constant=True, doc=""" The callable function being wrapped.""") inputs = param.List(default=[], constant=True, doc=""" The list of inputs the callable function is wrapping. Used to allow deep access to streams in chained Callables.""") link_inputs = param.Boolean(default=True, doc=""" If the Callable wraps around other DynamicMaps in its inputs, determines whether linked streams attached to the inputs are transferred to the objects returned by the Callable. For example the Callable wraps a DynamicMap with an RangeXY stream, this switch determines whether the corresponding visualization should update this stream with range changes originating from the newly generated axes.""") memoize = param.Boolean(default=True, doc=""" Whether the return value of the callable should be memoized based on the call arguments and any streams attached to the inputs.""") stream_mapping = param.Dict(default={}, constant=True, doc=""" Defines how streams should be mapped to objects returned by the Callable, e.g. when it returns a Layout.""") def __init__(self, callable, **params): super(Callable, self).__init__(callable=callable, **dict(params, name=util.callable_name(callable))) self._memoized = {} self._is_overlay = False @property def argspec(self): return util.argspec(self.callable) @property def noargs(self): "Returns True if the callable takes no arguments" noargs = ArgSpec(args=[], varargs=None, keywords=None, defaults=None) return self.argspec == noargs
[docs] def clone(self, callable=None, **overrides): """ Allows making a copy of the Callable optionally overriding the callable and other parameters. """ old = {k: v for k, v in self.get_param_values() if k not in ['callable', 'name']} params = dict(old, **overrides) callable = self.callable if callable is None else callable return self.__class__(callable, **params)
def __call__(self, *args, **kwargs): # Nothing to do for callbacks that accept no arguments (inargs, inkwargs) = (args, kwargs) if not args and not kwargs: return self.callable() inputs = [i for i in self.inputs if isinstance(i, DynamicMap)] streams = [] for stream in [s for i in inputs for s in get_nested_streams(i)]: if stream not in streams: streams.append(stream) memoize = self.memoize and not any(s.transient and s._triggering for s in streams) values = tuple(tuple(sorted(s.contents.items())) for s in streams) key = args + tuple(sorted(kwargs.items())) + values hashed_key = util.deephash(key) if memoize and hashed_key in self._memoized: return self._memoized[hashed_key] if self.argspec.varargs is not None: # Missing information on positional argument names, cannot promote to keywords pass elif len(args) != 0: # Turn positional arguments into keyword arguments pos_kwargs = {k:v for k,v in zip(self.argspec.args, args)} ignored = range(len(self.argspec.args),len(args)) if len(ignored): self.warning('Ignoring extra positional argument %s' % ', '.join('%s' % i for i in ignored)) clashes = set(pos_kwargs.keys()) & set(kwargs.keys()) if clashes: self.warning('Positional arguments %r overriden by keywords' % list(clashes)) args, kwargs = (), dict(pos_kwargs, **kwargs) try: ret = self.callable(*args, **kwargs) except: posstr = ', '.join(['%r' % el for el in inargs]) if inargs else '' kwstr = ', '.join('%s=%r' % (k,v) for k,v in inkwargs.items()) argstr = ', '.join([el for el in [posstr, kwstr] if el]) message = ("Exception raised in callable '{name}' of type '{ctype}'.\n" "Invoked as {name}({argstr})") self.warning(message.format(, ctype = type(self.callable).__name__, argstr=argstr)) raise if hashed_key is not None: self._memoized = {hashed_key : ret} return ret
[docs]class Generator(Callable): """ Generators are considered a special case of Callable that accept no arguments and never memoize. """ callable = param.ClassSelector(default=None, class_ = types.GeneratorType, constant=True, doc=""" The generator that is wrapped by this Generator.""") @property def argspec(self): return ArgSpec(args=[], varargs=None, keywords=None, defaults=None) def __call__(self): try: return next(self.callable) except StopIteration: raise except Exception: msg = 'Generator {name} raised the following exception:' self.warning(msg.format( raise
[docs]def get_nested_streams(dmap): """ Get all (potentially nested) streams from DynamicMap with Callable callback. """ layer_streams = list(dmap.streams) if not isinstance(dmap.callback, Callable): return list(set(layer_streams)) for o in dmap.callback.inputs: if isinstance(o, DynamicMap): layer_streams += get_nested_streams(o) return list(set(layer_streams))
[docs]def dynamicmap_memoization(callable_obj, streams): """ Determine whether the Callable should have memoization enabled based on the supplied streams (typically by a DynamicMap). Memoization is disabled if any of the streams require it it and are currently in a triggered state. """ memoization_state = bool(callable_obj.memoize) callable_obj.memoize &= not any(s.transient and s._triggering for s in streams) try: yield except: raise finally: callable_obj.memoize = memoization_state
[docs]class DynamicMap(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. """ # Declare that callback is a positional parameter (used in clone) __pos_params = ['callback'] kdims = param.List(default=[], constant=True, doc=""" The key dimensions of a DynamicMap map to the arguments of the callback. This mapping can be by position or by name.""") callback = param.ClassSelector(class_=Callable, constant=True, doc=""" 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 = param.List(default=[], constant=True, doc=""" 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.""" ) cache_size = param.Integer(default=500, doc=""" 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.""") def __init__(self, callback, initial_items=None, **params): if isinstance(callback, types.GeneratorType): callback = Generator(callback) elif not isinstance(callback, Callable): callback = Callable(callback) if 'sampled' in params: self.warning('DynamicMap sampled parameter is deprecated ' 'and no longer neededs to be specified.') del params['sampled'] super(DynamicMap, self).__init__(initial_items, callback=callback, **params) invalid = [s for s in self.streams if not isinstance(s, Stream)] if invalid: msg = ('The supplied streams list contains objects that ' 'are not Stream instances: {objs}') raise TypeError(msg.format(objs = ', '.join('%r' % el for el in invalid))) if self.callback.noargs: prefix = 'DynamicMaps using generators (or callables without arguments)' if self.kdims: raise Exception(prefix + ' must be declared without key dimensions') if len(self.streams)> 1: raise Exception(prefix + ' must have either streams=[] or a single, ' + 'stream instance without any stream parameters') if util.stream_parameters(self.streams) != []: raise Exception(prefix + ' cannot accept any stream parameters') self._posarg_keys = util.validate_dynamic_argspec(self.callback.argspec, self.kdims, self.streams) # Set source to self if not already specified for stream in self.streams: if stream.source is None: stream.source = self self.redim = redim(self, mode='dynamic') @property def unbounded(self): """ Returns a list of key dimensions that are unbounded, excluding stream parameters. If any of theses key dimensions are unbounded, the DynamicMap as a whole is also unbounded. """ unbounded_dims = [] # Dimensioned streams do not need to be bounded stream_params = set(util.stream_parameters(self.streams)) for kdim in self.kdims: if str(kdim) in stream_params: continue if kdim.values: continue if None in kdim.range: unbounded_dims.append(str(kdim)) return unbounded_dims def _initial_key(self): """ Construct an initial key for based on the lower range bounds or values on the key dimensions. """ key = [] undefined = [] stream_params = set(util.stream_parameters(self.streams)) for kdim in self.kdims: if str(kdim) in stream_params: key.append(None) elif kdim.values: key.append(kdim.values[0]) elif kdim.range[0] is not None: key.append(kdim.range[0]) else: undefined.append(kdim) if undefined: msg = ('Dimension(s) {undefined_dims} do not specify range or values needed ' 'to generate initial key') undefined_dims = ', '.join(['%r' % str(dim) for dim in undefined]) raise KeyError(msg.format(undefined_dims=undefined_dims)) return tuple(key) def _validate_key(self, key): """ Make sure the supplied key values are within the bounds specified by the corresponding dimension range and soft_range. """ if key == () and len(self.kdims) == 0: return () key = util.wrap_tuple(key) assert len(key) == len(self.kdims) for ind, val in enumerate(key): kdim = self.kdims[ind] low, high = util.max_range([kdim.range, kdim.soft_range]) if low is not np.NaN: if val < low: raise StopIteration("Key value %s below lower bound %s" % (val, low)) if high is not np.NaN: if val > high: raise StopIteration("Key value %s above upper bound %s" % (val, high))
[docs] def event(self, **kwargs): """ This method allows any of the available stream parameters (renamed as appropriate) to be updated in an event. """ if self.callback.noargs and self.streams == []: self.warning('No streams declared. To update a DynamicMaps using ' 'generators (or callables without arguments) use streams=[Next()]') return if self.streams == []: self.warning('No streams on DynamicMap, calling event will have no effect') return stream_params = set(util.stream_parameters(self.streams)) invalid = [k for k in kwargs.keys() if k not in stream_params] if invalid: msg = 'Key(s) {invalid} do not correspond to stream parameters' raise KeyError(msg.format(invalid = ', '.join('%r' % i for i in invalid))) for stream in self.streams: applicable_kws = {k:v for k,v in kwargs.items() if k in set(stream.contents.keys())} rkwargs = util.rename_stream_kwargs(stream, applicable_kws, reverse=True) stream.update(**rkwargs) Stream.trigger(self.streams)
def _style(self, retval): """ Use any applicable OptionTree of the DynamicMap to apply options to the return values of the callback. """ if not in Store.custom_options(): return retval spec = StoreOptions.tree_to_dict(Store.custom_options()[]) return retval(spec) def _execute_callback(self, *args): """ Execute the callback, validating both the input key and output key where applicable. """ self._validate_key(args) # Validate input key # Additional validation needed to ensure kwargs don't clash kdims = [ for kdim in self.kdims] kwarg_items = [s.contents.items() for s in self.streams] flattened = [(k,v) for kws in kwarg_items for (k,v) in kws if k not in kdims] if self._posarg_keys: kwargs = dict(flattened, **dict(zip(self._posarg_keys, args))) args = () else: kwargs = dict(flattened) with dynamicmap_memoization(self.callback, self.streams): retval = self.callback(*args, **kwargs) return self._style(retval)
[docs] def clone(self, data=None, shared_data=True, new_type=None, *args, **overrides): """ Clone method to adapt the slightly different signature of DynamicMap that also overrides Dimensioned clone to avoid checking items if data is unchanged. """ if data is None and shared_data: data = clone = super(UniformNdMapping, self).clone(overrides.pop('callback', self.callback), shared_data, new_type, *(data,) + args, **overrides) # Ensure the clone references this object to ensure # stream sources are inherited if clone.callback is self.callback: with util.disable_constant(clone): clone.callback = clone.callback.clone(inputs=[self], link_inputs=True) return clone
[docs] def reset(self): """ Return a cleared dynamic map with a cleared cached """ = OrderedDict() return self
def _cross_product(self, tuple_key, cache, data_slice): """ Returns a new DynamicMap if the key (tuple form) expresses a cross product, otherwise returns None. The cache argument is a dictionary (key:element pairs) of all the data found in the cache for this key. Each key inside the cross product is looked up in the cache ( to check if the appropriate element is available. Otherwise the element is computed accordingly. The data_slice may specify slices into each value in the the cross-product. """ if not any(isinstance(el, (list, set)) for el in tuple_key): return None if len(tuple_key)==1: product = tuple_key[0] else: args = [set(el) if isinstance(el, (list,set)) else set([el]) for el in tuple_key] product = itertools.product(*args) data = [] for inner_key in product: key = util.wrap_tuple(inner_key) if key in cache: val = cache[key] else: val = self._execute_callback(*key) if data_slice: val = self._dataslice(val, data_slice) data.append((key, val)) product = self.clone(data) if data_slice: from ..util import Dynamic return Dynamic(product, operation=lambda obj, **dynkwargs: obj[data_slice], streams=self.streams, shared_data=True) return product def _slice_bounded(self, tuple_key, data_slice): """ Slices bounded DynamicMaps by setting the soft_ranges on key dimensions and applies data slice to cached and dynamic values. """ slices = [el for el in tuple_key if isinstance(el, slice)] if any(el.step for el in slices): raise Exception("DynamicMap slices cannot have a step argument") elif len(slices) not in [0, len(tuple_key)]: raise Exception("Slices must be used exclusively or not at all") elif not slices: return None sliced = self.clone(self) for i, slc in enumerate(tuple_key): (start, stop) = slc.start, slc.stop if start is not None and start < sliced.kdims[i].range[0]: raise Exception("Requested slice below defined dimension range.") if stop is not None and stop > sliced.kdims[i].range[1]: raise Exception("Requested slice above defined dimension range.") sliced.kdims[i].soft_range = (start, stop) if data_slice: if not isinstance(sliced, DynamicMap): return self._dataslice(sliced, data_slice) else: from ..util import Dynamic if len(self): slices = [slice(None) for _ in range(self.ndims)] + list(data_slice) sliced = super(DynamicMap, sliced).__getitem__(tuple(slices)) return Dynamic(sliced, operation=lambda obj, **dynkwargs: obj[data_slice], streams=self.streams, shared_data=True) return sliced def __getitem__(self, key): """ Return an element for any key chosen key. Also allows for usual deep slicing semantics by slicing values in the cache and applying the deep slice to newly generated values. """ # Split key dimensions and data slices sample = False if key is Ellipsis: return self elif isinstance(key, (list, set)) and all(isinstance(v, tuple) for v in key): map_slice, data_slice = key, () sample = True else: map_slice, data_slice = self._split_index(key) tuple_key = util.wrap_tuple_streams(map_slice, self.kdims, self.streams) # Validation if not sample: sliced = self._slice_bounded(tuple_key, data_slice) if sliced is not None: return sliced # Cache lookup try: dimensionless = util.dimensionless_contents(get_nested_streams(self), self.kdims, no_duplicates=False) empty = util.stream_parameters(self.streams) == [] and self.kdims==[] if dimensionless or empty: raise KeyError('Using dimensionless streams disables DynamicMap cache') cache = super(DynamicMap,self).__getitem__(key) except KeyError: cache = None # If the key expresses a cross product, compute the elements and return product = self._cross_product(tuple_key, if cache else {}, data_slice) if product is not None: return product # Not a cross product and nothing cached so compute element. if cache is not None: return cache val = self._execute_callback(*tuple_key) if data_slice: val = self._dataslice(val, data_slice) self._cache(tuple_key, val) return val
[docs] def select(self, selection_specs=None, **kwargs): """ Allows slicing or indexing into the DynamicMap objects by supplying the dimension and index/slice as key value pairs. Select descends recursively through the data structure applying the key dimension selection and applies to dynamically generated items by wrapping the callback. The selection may also be selectively applied to specific objects by supplying the selection_specs as an iterable of specs, types or functions. """ if selection_specs is not None and not isinstance(selection_specs, (list, tuple)): selection_specs = [selection_specs] selection = super(DynamicMap, self).select(selection_specs, **kwargs) def dynamic_select(obj, **dynkwargs): if selection_specs is not None: matches = any(obj.matches(spec) for spec in selection_specs) else: matches = True if matches: return**kwargs) return obj if not isinstance(selection, DynamicMap): return dynamic_select(selection) else: from ..util import Dynamic return Dynamic(selection, operation=dynamic_select, streams=self.streams, shared_data=True)
def _cache(self, key, val): """ Request that a key/value pair be considered for caching. """ cache_size = (1 if util.dimensionless_contents(self.streams, self.kdims) else self.cache_size) if len(self) >= cache_size: first_key = next(k for k in[key] = val
[docs] def map(self, map_fn, specs=None, clone=True): """ Recursively replaces elements using a map function when the specification applies. Extends regular map with functionality to dynamically apply functions. """ deep_mapped = super(DynamicMap, self).map(map_fn, specs, clone) if isinstance(deep_mapped, type(self)): from ..util import Dynamic def apply_map(obj, **dynkwargs): return, specs, clone) dmap = Dynamic(deep_mapped, operation=apply_map, streams=self.streams, shared_data=True) return dmap return deep_mapped
[docs] def relabel(self, label=None, group=None, depth=1): """ Assign a new label and/or group to an existing LabelledData object, creating a clone of the object with the new settings. """ relabelled = super(DynamicMap, self).relabel(label, group, depth) if depth > 0: from ..util import Dynamic def dynamic_relabel(obj, **dynkwargs): return obj.relabel(group=group, label=label, depth=depth-1) return Dynamic(relabelled, streams=self.streams, shared_data=True, operation=dynamic_relabel) return relabelled
[docs] def collate(self): """ Collation allows reorganizing DynamicMaps with invalid nesting hierarchies. This is particularly useful when defining DynamicMaps returning an (Nd)Layout or GridSpace types. Collating will split the DynamicMap into individual DynamicMaps for each item in the container. Note that the composite object has to be of consistent length and types for this to work correctly. """ # Initialize if self.last is not None: initialized = self else: initialized = self.clone() initialized[initialized._initial_key()] if not isinstance(initialized.last, (Layout, NdLayout, GridSpace)): return self container = initialized.last.clone(shared_data=False) # Get stream mapping from callback remapped_streams = [] streams = self.callback.stream_mapping for i, (k, v) in enumerate( vstreams = streams.get(i, []) if not vstreams: if isinstance(initialized.last, Layout): for l in range(len(k)): path = '.'.join(k[:l]) if path in streams: vstreams = streams[path] break else: vstreams = streams.get(k, []) if any(s in remapped_streams for s in vstreams): raise ValueError( "The stream_mapping supplied on the Callable " "is ambiguous please supply more specific Layout " "path specs.") remapped_streams += vstreams # Define collation callback def collation_cb(*args, **kwargs): return self[args][kwargs['selection_key']] callback = Callable(partial(collation_cb, selection_key=k), inputs=[self]) vdmap = self.clone(callback=callback, shared_data=False, streams=vstreams) # Remap source of streams for stream in vstreams: if stream.source is self: stream.source = vdmap container[k] = vdmap unmapped_streams = [repr(stream) for stream in self.streams if (stream.source is self) and (stream not in remapped_streams) and stream.linked] if unmapped_streams: raise ValueError( 'The following streams are set to be automatically ' 'linked to a plot, but no stream_mapping specifying ' 'which item in the (Nd)Layout to link it to was found:\n%s' % ', '.join(unmapped_streams) ) return container
[docs] def groupby(self, dimensions=None, container_type=None, group_type=None, **kwargs): """ Implements a dynamic version of a groupby, which will intelligently expand either the inner or outer dimensions depending on whether the container_type or group_type is dynamic. To apply a groupby to a DynamicMap the dimensions, which are expanded into a non-dynamic type must define a fixed sampling via the values attribute. Using the dynamic groupby makes it incredibly easy to generate dynamic views into a high-dimensional space while taking advantage of the capabilities of NdOverlay, GridSpace and NdLayout types to visualize more than one Element at a time. """ if dimensions is None: dimensions = self.kdims if not isinstance(dimensions, (list, tuple)): dimensions = [dimensions] container_type = container_type if container_type else type(self) group_type = group_type if group_type else type(self) outer_kdims = [self.get_dimension(d) for d in dimensions] inner_kdims = [d for d in self.kdims if not d in outer_kdims] outer_dynamic = issubclass(container_type, DynamicMap) inner_dynamic = issubclass(group_type, DynamicMap) if ((not outer_dynamic and any(not d.values for d in outer_kdims)) or (not inner_dynamic and any(not d.values for d in inner_kdims))): raise Exception('Dimensions must specify sampling via ' 'values to apply a groupby') if outer_dynamic: def outer_fn(*outer_key, **dynkwargs): if inner_dynamic: def inner_fn(*inner_key, **dynkwargs): outer_vals = zip(outer_kdims, util.wrap_tuple(outer_key)) inner_vals = zip(inner_kdims, util.wrap_tuple(inner_key)) inner_sel = [(, v) for k, v in inner_vals] outer_sel = [(, v) for k, v in outer_vals] return**dict(inner_sel+outer_sel)) return self.clone([], callback=inner_fn, kdims=inner_kdims) else: dim_vals = [(, d.values) for d in inner_kdims] dim_vals += [(, [v]) for d, v in zip(outer_kdims, util.wrap_tuple(outer_key))] with item_check(False): selected = HoloMap(**dict(dim_vals))) return group_type(selected.reindex(inner_kdims)) if outer_kdims: return self.clone([], callback=outer_fn, kdims=outer_kdims) else: return outer_fn(()) else: outer_product = itertools.product(*[self.get_dimension(d).values for d in dimensions]) groups = [] for outer in outer_product: outer_vals = [(, [o]) for d, o in zip(outer_kdims, outer)] if inner_dynamic or not inner_kdims: def inner_fn(outer_vals, *key, **dynkwargs): inner_dims = zip(inner_kdims, util.wrap_tuple(key)) inner_vals = [(, k) for d, k in inner_dims] return**dict(outer_vals+inner_vals)).last if inner_kdims: group = self.clone(callback=partial(inner_fn, outer_vals), kdims=inner_kdims) else: group = inner_fn(outer_vals, ()) groups.append((outer, group)) else: inner_vals = [(, self.get_dimension(d).values) for d in inner_kdims] with item_check(False): selected = HoloMap(**dict(outer_vals+inner_vals))) group = group_type(selected.reindex(inner_kdims)) groups.append((outer, group)) return container_type(groups, kdims=outer_kdims)
def grid(self, dimensions=None, **kwargs): return self.groupby(dimensions, container_type=GridSpace, **kwargs) def layout(self, dimensions=None, **kwargs): return self.groupby(dimensions, container_type=NdLayout, **kwargs) def overlay(self, dimensions=None, **kwargs): if dimensions is None: dimensions = self.kdims else: if not isinstance(dimensions, (list, tuple)): dimensions = [dimensions] dimensions = [self.get_dimension(d, strict=True) for d in dimensions] dims = [d for d in self.kdims if d not in dimensions] return self.groupby(dims, group_type=NdOverlay)
[docs] def hist(self, num_bins=20, bin_range=None, adjoin=True, individually=True, **kwargs): """ Computes a histogram from the object and adjoins it by default. By default the histogram is computed for the bottom layer, which can be overriden by supplying an ``index`` and for the first value dimension, which may be overridden by supplying an explicit ``dimension``. """ def dynamic_hist(obj, **dynkwargs): if isinstance(obj, (NdOverlay, Overlay)): index = kwargs.get('index', 0) obj = obj.get(index) return obj.hist(num_bins=num_bins, bin_range=bin_range, adjoin=False, **kwargs) from ..util import Dynamic hist = Dynamic(self, streams=self.streams, link_inputs=False, operation=dynamic_hist) if adjoin: return self << hist else: return hist
[docs] def reindex(self, kdims=[], force=False): """ Reindexing a DynamicMap allows reordering the dimensions but not dropping an individual dimension. The force argument which usually allows dropping non-constant dimensions is therefore ignored and only for API consistency. """ kdims = [self.get_dimension(kd, strict=True) for kd in kdims] dropped = [kd for kd in self.kdims if kd not in kdims] if dropped: raise ValueError("DynamicMap does not allow dropping dimensions, " "reindex may only be used to reorder dimensions.") return super(DynamicMap, self).reindex(kdims, force)
def drop_dimension(self, dimensions): raise NotImplementedError('Cannot drop dimensions from a DynamicMap, ' 'cast to a HoloMap first.') def add_dimension(self, dimension, dim_pos, dim_val, vdim=False, **kwargs): raise NotImplementedError('Cannot add dimensions to a DynamicMap, ' 'cast to a HoloMap first.') def next(self): if self.callback.noargs: return self[()] else: raise Exception('The next method can only be used for DynamicMaps using' 'generators (or callables without arguments)') # For Python 2 and 3 compatibility __next__ = next
[docs]class GridSpace(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. """ kdims = param.List(default=[Dimension("X"), Dimension("Y")], bounds=(1,2)) def __init__(self, initial_items=None, **params): super(GridSpace, self).__init__(initial_items, **params) if self.ndims > 2: raise Exception('Grids can have no more than two dimensions.') def __mul__(self, other): if isinstance(other, GridSpace): if set(self.keys()) != set(other.keys()): raise KeyError("Can only overlay two ParameterGrids if their keys match") zipped = zip(self.keys(), self.values(), other.values()) overlayed_items = [(k, el1 * el2) for (k, el1, el2) in zipped] return self.clone(overlayed_items) elif isinstance(other, UniformNdMapping) and len(other) == 1: view = other.last elif isinstance(other, UniformNdMapping) and len(other) != 1: raise Exception("Can only overlay with HoloMap of length 1") else: view = other overlayed_items = [(k, el * view) for k, el in self.items()] return self.clone(overlayed_items) def __lshift__(self, other): if isinstance(other, (ViewableElement, UniformNdMapping)): return AdjointLayout([self, other]) elif isinstance(other, AdjointLayout): return AdjointLayout([self]) else: raise TypeError('Cannot append {0} to a AdjointLayout'.format(type(other).__name__)) def _transform_indices(self, key): """ Transforms indices by snapping to the closest value if values are numeric, otherwise applies no transformation. """ ndims = self.ndims if all(not (isinstance(el, slice) or callable(el)) for el in key): dim_inds = [] for dim in self.kdims: dim_type = self.get_dimension_type(dim) if isinstance(dim_type, type) and issubclass(dim_type, Number): dim_inds.append(self.get_dimension_index(dim)) str_keys = iter(key[i] for i in range(self.ndims) if i not in dim_inds) num_keys = [] if len(dim_inds): keys = list({tuple(k[i] if ndims > 1 else k for i in dim_inds) for k in self.keys()}) q = np.array([tuple(key[i] if ndims > 1 else key for i in dim_inds)]) idx = np.argmin([np.inner(q - np.array(x), q - np.array(x)) if len(dim_inds) == 2 else np.abs(q-x) for x in keys]) num_keys = iter(keys[idx]) key = tuple(next(num_keys) if i in dim_inds else next(str_keys) for i in range(self.ndims)) elif any(not (isinstance(el, slice) or callable(el)) for el in key): index_inds = [idx for idx, el in enumerate(key) if not isinstance(el, (slice, str))] if len(index_inds): index_ind = index_inds[0] dim_keys = np.array([k[index_ind] for k in self.keys()]) snapped_val = dim_keys[np.argmin(np.abs(dim_keys-key[index_ind]))] key = list(key) key[index_ind] = snapped_val key = tuple(key) return key
[docs] def keys(self, full_grid=False): """ Returns a complete set of keys on a GridSpace, even when GridSpace isn't fully populated. This makes it easier to identify missing elements in the GridSpace. """ keys = super(GridSpace, self).keys() if self.ndims == 1 or not full_grid: return keys dim1_keys = sorted(set(k[0] for k in keys)) dim2_keys = sorted(set(k[1] for k in keys)) return [(d1, d2) for d1 in dim1_keys for d2 in dim2_keys]
@property def last(self): """ 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. """ if self.type == HoloMap: last_items = [(k, v.last if isinstance(v, HoloMap) else v) for (k, v) in] else: last_items = return self.clone(last_items) def __len__(self): """ The maximum depth of all the elements. Matches the semantics of __len__ used by Maps. For the total number of elements, count the full set of keys. """ return max([(len(v) if hasattr(v, '__len__') else 1) for v in self.values()] + [0]) def __add__(self, obj): return Layout.from_values([self, obj]) @property def shape(self): keys = self.keys() if self.ndims == 1: return (len(keys), 1) return len(set(k[0] for k in keys)), len(set(k[1] for k in keys))
[docs]class GridMatrix(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. """ def _item_check(self, dim_vals, data): if not traversal.uniform(NdMapping([(0, self), (1, data)])): raise ValueError("HoloMaps dimensions must be consistent in %s." % type(self).__name__) NdMapping._item_check(self, dim_vals, data)