Source code for holoviews.core.spaces

import itertools
import types
import inspect

from numbers import Number
from itertools import groupby
from functools import partial
from collections import defaultdict
from contextlib import contextmanager

import numpy as np
import param

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



[docs]class HoloMap(UniformNdMapping, Overlayable): """ A HoloMap is an n-dimensional mapping of viewable elements or overlays. Each item in a HoloMap has an tuple key defining the values along each of the declared key dimensions, defining the discretely sampled space of values. The visual representation of a HoloMap consists of the viewable objects inside the HoloMap which can be explored by varying one or more widgets mapping onto the key dimensions of the HoloMap. """ data_type = (ViewableElement, NdMapping, Layout) def __init__(self, initial_items=None, kdims=None, group=None, label=None, **params): super(HoloMap, self).__init__(initial_items, kdims, group, label, **params) self.opts = Opts(self, mode='holomap')
[docs] def overlay(self, dimensions=None, **kwargs): """Group by supplied dimension(s) and overlay each group Groups data by supplied dimension(s) overlaying the groups along the dimension(s). Args: dimensions: Dimension(s) of dimensions to group by Returns: NdOverlay object(s) with supplied dimensions """ 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): """Group by supplied dimension(s) and lay out groups in grid Groups data by supplied dimension(s) laying the groups along the dimension(s) out in a GridSpace. Args: dimensions: Dimension/str or list Dimension or list of dimensions to group by Returns: GridSpace with supplied dimensions """ 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): """Group by supplied dimension(s) and lay out groups Groups data by supplied dimension(s) laying the groups along the dimension(s) out in a NdLayout. Args: dimensions: Dimension(s) to group by Returns: NdLayout with supplied dimensions """ 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 options(self, *args, **kwargs): """Applies simplified option definition returning a new object Applies options defined in a flat format to the objects returned by the DynamicMap. If the options are to be set directly on the objects in the HoloMap a simple format may be used, e.g.: obj.options(cmap='viridis', show_title=False) If the object is nested the options must be qualified using a type[.group][.label] specification, e.g.: obj.options('Image', cmap='viridis', show_title=False) or using: obj.options({'Image': dict(cmap='viridis', show_title=False)}) Args: *args: Sets of options to apply to object Supports a number of formats including lists of Options objects, a type[.group][.label] followed by a set of keyword options to apply and a dictionary indexed by type[.group][.label] specs. backend (optional): Backend to apply options to Defaults to current selected backend clone (bool, optional): Whether to clone object Options can be applied inplace with clone=False **kwargs: Keywords of options Set of options to apply to the object Returns: Returns the cloned object with the options applied """ data = OrderedDict([(k, v.options(*args, **kwargs)) for k, v in self.data.items()]) return self.clone(data)
[docs] def split_overlays(self): "Deprecated method to split overlays inside the HoloMap." if util.config.future_deprecations: self.warning("split_overlays is deprecated and is now " "a private method.") return self._split_overlays()
def _split_overlays(self): "Splits overlays inside the HoloMap into list of HoloMaps" if not issubclass(self.type, CompositeOverlay): return None, self.clone() item_maps = OrderedDict() for k, overlay in self.data.items(): 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([d.name 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[d.name]) 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 = {d.name: k for d, k in zip(dimensions, key)} layers = [] try: self_el = self.select(HoloMap, **key_map) if self.kdims else self[()] layers.append(self_el) except KeyError: pass try: other_el = other.select(HoloMap, **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, reverse=False): """Overlays items in the object with another object The mul (*) operator implements overlaying of different objects. This method tries to intelligently overlay mappings 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 = {d.name for d in self.kdims} other_set = {d.name 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): if reverse: value = other[other_key] * self[self_key] else: value = self[self_key] * other[other_key] items.append((new_key, value)) 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) and not isinstance(other, Layout): if isinstance(self, DynamicMap): def dynamic_mul(*args, **kwargs): element = self[args] if reverse: return other * element else: 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, other * v) if reverse else (k, v * other) for (k, v) in self.data.items()] return self.clone(items, label=self._label, group=self._group) else: return NotImplemented def __add__(self, obj): "Composes HoloMap with other object into a Layout" return Layout([self, obj]) def __lshift__(self, other): "Adjoin another object to this one returning an AdjointLayout" if isinstance(other, (ViewableElement, UniformNdMapping, Empty)): return AdjointLayout([self, other]) elif isinstance(other, AdjointLayout): return AdjointLayout(other.data+[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): """Collate allows reordering nested containers Collation allows collapsing nested mapping types by merging their dimensions. In simple terms in merges nested containers into a single merged type. In the simple case a HoloMap containing other HoloMaps can easily be joined in this way. However collation is particularly useful when the objects being joined are deeply nested, e.g. you want to join multiple Layouts recorded at different times, collation will return one Layout containing HoloMaps indexed by Time. Changing the merge_type will allow merging the outer Dimension into any other UniformNdMapping type. Args: merge_type: Type of the object to merge with drop: List of dimensions to drop drop_constant: Drop constant dimensions automatically Returns: Collated Layout or HoloMap """ 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): """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 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 hasattr(group.last, 'interface'): group_data = concat(group) if function: agg = group_data.aggregate(group.last.kdims, function, spreadfn, **kwargs) group_data = group.type(agg) else: group_data = [el.data for el in group] args = (group_data, function, group.last.kdims) data = group.type.collapse_data(*args, **kwargs) group_data = group.last.clone(data) collapsed[key] = group_data return collapsed if self.ndims-len(dimensions) else collapsed.last
[docs] def sample(self, samples=[], bounds=None, **sample_values): """Samples element values at supplied coordinates. Allows sampling of element with a list of coordinates matching the key dimensions, returning a new object containing just the selected samples. Supports multiple signatures: Sampling with a list of coordinates, e.g.: ds.sample([(0, 0), (0.1, 0.2), ...]) Sampling a range or grid of coordinates, e.g.: 1D: ds.sample(3) 2D: ds.sample((3, 3)) Sampling by keyword, e.g.: ds.sample(x=0) Args: samples: List of nd-coordinates to sample bounds: Bounds of the region to sample Defined as two-tuple for 1D sampling and four-tuple for 2D sampling. closest: Whether to snap to closest coordinates **kwargs: Coordinates specified as keyword pairs Keywords of dimensions and scalar coordinates Returns: A Table containing the sampled coordinates """ 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 elements with more than two dimensions.") samples = list(util.unique_iterator(self.last.closest(linsamples))) sampled = self.clone([(k, view.sample(samples, closest=False, **sample_values)) for k, view in self.data.items()]) from ..element import Table return Table(sampled.collapse())
[docs] def reduce(self, dimensions=None, function=None, spread_fn=None, **reduce_map): """Applies reduction to elements along the specified dimension(s). Allows reducing the values along one or more key dimension with the supplied function. Supports two signatures: Reducing with a list of dimensions, e.g.: ds.reduce(['x'], np.mean) Defining a reduction using keywords, e.g.: ds.reduce(x=np.mean) Args: dimensions: Dimension(s) to apply reduction on Defaults to all key dimensions function: Reduction operation to apply, e.g. numpy.mean spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation. **reductions: Keyword argument defining reduction Allows reduction to be defined as keyword pair of dimension and function Returns: The Dataset after reductions have been applied. """ from ..element import Table reduced_items = [(k, v.reduce(dimensions, function, spread_fn, **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 Table(self.clone(reduced_items).collapse())
[docs] def relabel(self, label=None, group=None, depth=1): """Clone object and apply new group and/or label. Applies relabeling to children up to the supplied depth. Args: label (str, optional): New label to apply to returned object group (str, optional): New group to apply to returned object depth (int, optional): Depth to which relabel will be applied If applied to container allows applying relabeling to contained objects up to the specified depth Returns: Returns relabelled object """ return super(HoloMap, self).relabel(label=label, group=group, depth=depth)
[docs] def hist(self, dimension=None, num_bins=20, bin_range=None, adjoin=True, individually=True, **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 Returns: AdjointLayout of HoloMap and histograms or just the histograms """ if dimension is not None and not isinstance(dimension, list): dimension = [dimension] histmaps = [self.clone(shared_data=False) for _ in (dimension or [None])] if individually: map_range = None else: if dimension is None: 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[<' + self.name + '>]_' if issubclass(self.type, (NdOverlay, Overlay)) and 'index' not in kwargs: kwargs['index'] = 0 for k, v in self.data.items(): hists = v.hist(adjoin=False, dimension=dimension, bin_range=bin_range, 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(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 holoviews.org. """ 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 self.args = None self.kwargs = None self._stream_memoization = self.memoize @property def argspec(self): return util.argspec(self.callable) @property def noargs(self): "Returns True if the callable takes no arguments" noargs = inspect.ArgSpec(args=[], varargs=None, keywords=None, defaults=None) return self.argspec == noargs
[docs] def clone(self, callable=None, **overrides): """Clones the Callable optionally with new settings Args: callable: New callable function to wrap **overrides: Parameter overrides to apply Returns: Cloned Callable object """ 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): """Calls the callable function with supplied args and kwargs. If enabled uses memoization to avoid calling function unneccessarily. Args: *args: Arguments passed to the callable function **kwargs: Keyword arguments passed to the callable function Returns: Return value of the wrapped callable function """ # Nothing to do for callbacks that accept no arguments kwarg_hash = kwargs.pop('_memoization_hash_', ()) (self.args, self.kwargs) = (args, kwargs) if not args and not kwargs and not any(kwarg_hash): 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._stream_memoization and not any(s.transient and s._triggering for s in streams) values = tuple(tuple(sorted(s.hashkey.items())) for s in streams) key = args + kwarg_hash + values hashed_key = util.deephash(key) if self.memoize else None if hashed_key is not None and 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 KeyError: # KeyError is caught separately because it is used to signal # invalid keys on DynamicMap and should not warn raise except Exception as e: posstr = ', '.join(['%r' % el for el in self.args]) if self.args else '' kwstr = ', '.join('%s=%r' % (k,v) for k,v in self.kwargs.items()) argstr = ', '.join([el for el in [posstr, kwstr] if el]) message = ("Callable raised \"{e}\".\n" "Invoked as {name}({argstr})") self.warning(message.format(name=self.name, argstr=argstr, e=repr(e))) 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 inspect.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(name=self.name)) raise
[docs]def get_nested_dmaps(dmap): """Recurses DynamicMap to find DynamicMaps inputs Args: dmap: DynamicMap to recurse to look for DynamicMap inputs Returns: List of DynamicMap instances that were found """ if not isinstance(dmap, DynamicMap): return [] dmaps = [dmap] for o in dmap.callback.inputs: dmaps.extend(get_nested_dmaps(o)) return list(set(dmaps))
[docs]def get_nested_streams(dmap): """Recurses supplied DynamicMap to find all streams Args: dmap: DynamicMap to recurse to look for streams Returns: List of streams that were found """ return list({s for dmap in get_nested_dmaps(dmap) for s in dmap.streams})
[docs]@contextmanager 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._stream_memoization) callable_obj._stream_memoization &= not any(s.transient and s._triggering for s in streams) try: yield except: raise finally: callable_obj._stream_memoization = memoization_state
[docs]class periodic(object): """ Implements the utility of the same name on DynamicMap. Used to defined periodic event updates that can be started and stopped. """ _periodic_util = util.periodic def __init__(self, dmap): self.dmap = dmap self.instance = None def __call__(self, period, count=None, param_fn=None, timeout=None, block=True): """Periodically trigger the streams on the DynamicMap. Run a non-blocking loop that updates the stream parameters using the event method. Runs count times with the specified period. If count is None, runs indefinitely. Args: period: Timeout between events in seconds count: Number of events to trigger param_fn: Function returning stream updates given count Stream parameter values should be returned as dictionary timeout: Overall timeout in seconds block: Whether the periodic callbacks should be blocking """ if self.instance is not None and not self.instance.completed: raise RuntimeError('Periodic process already running. ' 'Wait until it completes or call ' 'stop() before running a new periodic process') def inner(i): kwargs = {} if param_fn is None else param_fn(i) if kwargs: self.dmap.event(**kwargs) else: Stream.trigger(self.dmap.streams) instance = self._periodic_util(period, count, inner, timeout=timeout, block=block) instance.start() self.instance = instance
[docs] def stop(self): "Stop the periodic process." self.instance.stop()
def __str__(self): return "<holoviews.core.spaces.periodic method>"
[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, streams=None, **params): streams = (streams or []) # If callback is a parameterized method and watch is disabled add as stream param_watch_support = util.param_version >= '1.8.0' if util.is_param_method(callback) and params.get('watch', param_watch_support): streams.append(callback) 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 needs to be specified.') del params['sampled'] valid, invalid = Stream._process_streams(streams) 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))) super(DynamicMap, self).__init__(initial_items, callback=callback, streams=valid, **params) self.opts = Opts(self, mode='dynamicmap') 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, 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') self.periodic = periodic(self) @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.default: key.append(kdim.default) elif kdim.values: if all(util.isnumeric(v) for v in kdim.values): key.append(sorted(kdim.values)[0]) else: 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 util.is_number(low) and util.isfinite(low): if val < low: raise KeyError("Key value %s below lower bound %s" % (val, low)) if util.is_number(high) and util.isfinite(high): if val > high: raise KeyError("Key value %s above upper bound %s" % (val, high))
[docs] def event(self, **kwargs): """Updates attached streams and triggers events Automatically find streams matching the supplied kwargs to update and trigger events on them. Args: **kwargs: Events to update streams with """ 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))) streams = [] for stream in self.streams: applicable_kws = {k:v for k,v in kwargs.items() if k in set(stream.contents.keys())} if not applicable_kws: continue streams.append(stream) rkwargs = util.rename_stream_kwargs(stream, applicable_kws, reverse=True) stream.update(**rkwargs) Stream.trigger(streams)
def _style(self, retval): "Applies custom option tree to values return by the callback." if self.id not in Store.custom_options(): return retval spec = StoreOptions.tree_to_dict(Store.custom_options()[self.id]) return retval.opts(spec) def _execute_callback(self, *args): "Executes the callback with the appropriate args and kwargs" self._validate_key(args) # Validate input key # Additional validation needed to ensure kwargs don't clash kdims = [kdim.name for kdim in self.kdims] kwarg_items = [s.contents.items() for s in self.streams] hash_items = tuple(tuple(sorted(s.hashkey.items())) for s in self.streams)+args 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) if not isinstance(self.callback, Generator): kwargs['_memoization_hash_'] = hash_items with dynamicmap_memoization(self.callback, self.streams): retval = self.callback(*args, **kwargs) return self._style(retval)
[docs] def options(self, *args, **kwargs): """Applies simplified option definition returning a new object. Applies options defined in a flat format to the objects returned by the DynamicMap. If the options are to be set directly on the objects returned by the DynamicMap a simple format may be used, e.g.: obj.options(cmap='viridis', show_title=False) If the object is nested the options must be qualified using a type[.group][.label] specification, e.g.: obj.options('Image', cmap='viridis', show_title=False) or using: obj.options({'Image': dict(cmap='viridis', show_title=False)}) Args: *args: Sets of options to apply to object Supports a number of formats including lists of Options objects, a type[.group][.label] followed by a set of keyword options to apply and a dictionary indexed by type[.group][.label] specs. backend (optional): Backend to apply options to Defaults to current selected backend clone (bool, optional): Whether to clone object Options can be applied inplace with clone=False **kwargs: Keywords of options Set of options to apply to the object Returns: Returns the cloned object with the options applied """ if 'clone' not in kwargs: kwargs['clone'] = True return self.opts(*args, **kwargs)
[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 """ if 'link_inputs' in overrides and util.config.future_deprecations: self.warning('link_inputs argument to the clone method is ' 'deprecated, use the more general link ' 'argument instead.') link = link and overrides.pop('link_inputs', True) callback = overrides.pop('callback', self.callback) if data is None and shared_data: data = self.data if link and callback is self.callback: overrides['plot_id'] = self._plot_id clone = super(UniformNdMapping, self).clone( callback, shared_data, new_type, link, *(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=link) return clone
[docs] def reset(self): "Clear the DynamicMap cache" self.data = 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 (self.data) 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 dmap = Dynamic(self, operation=lambda obj, **dynkwargs: obj[data_slice], streams=self.streams) dmap.data = product.data return dmap 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)) dmap = Dynamic(self, operation=lambda obj, **dynkwargs: obj[data_slice], streams=self.streams) dmap.data = sliced.data return dmap return sliced def __getitem__(self, key): """Evaluates DynamicMap with specified key. Indexing into a DynamicMap evaluates the dynamic function with the specified key unless the key and corresponding value are already in the cache. This may also be used to evaluate multiple keys or even a cross-product of keys if a list of values per Dimension are defined. Once values are in the cache the DynamicMap can be cast to a HoloMap. Args: key: n-dimensional key corresponding to the key dimensions Scalar values will be evaluated as normal while lists of values will be combined to form the cross-product, making it possible to evaluate many keys at once. Returns: Returns evaluated callback return value for scalar key otherwise returns cloned DynamicMap containing the cross- product of evaluated items. """ # 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, cache.data 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): """Applies selection by dimension name Applies a selection along the dimensions of the object using keyword arguments. The selection may be narrowed to certain objects using selection_specs. For container objects the selection will be applied to all children as well. Selections may select a specific value, slice or set of values: * value: Scalar values will select rows along with an exact match, e.g.: ds.select(x=3) * slice: Slices may be declared as tuples of the upper and lower bound, e.g.: ds.select(x=(0, 3)) * values: A list of values may be selected using a list or set, e.g.: ds.select(x=[0, 1, 2]) Args: selection_specs: List of specs to match on A list of types, functions, or type[.group][.label] strings specifying which objects to apply the selection on. **selection: Dictionary declaring selections by dimension Selections can be scalar values, tuple ranges, lists of discrete values and boolean arrays Returns: Returns an Dimensioned object containing the selected data or a scalar if a single value was selected """ 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 obj.select(**kwargs) return obj if not isinstance(selection, DynamicMap): return dynamic_select(selection) else: from ..util import Dynamic dmap = Dynamic(self, operation=dynamic_select, streams=self.streams) dmap.data = selection.data return dmap
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 self.data) self.data.pop(first_key) self[key] = val
[docs] def map(self, map_fn, specs=None, clone=True, link_inputs=True): """Map a function to all objects matching the specs Recursively replaces elements using a map function when the specs apply, by default applies to all objects, e.g. to apply the function to all contained Curve objects: dmap.map(fn, hv.Curve) Args: map_fn: Function to apply to each object specs: List of specs to match List of types, functions or type[.group][.label] specs to select objects to return, by default applies to all objects. clone: Whether to clone the object or transform inplace Returns: Returns the object after the map_fn has been applied """ 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 obj.map(map_fn, specs, clone) dmap = Dynamic(self, operation=apply_map, streams=self.streams, link_inputs=link_inputs) dmap.data = deep_mapped.data return dmap return deep_mapped
[docs] def relabel(self, label=None, group=None, depth=1): """Clone object and apply new group and/or label. Applies relabeling to children up to the supplied depth. Args: label (str, optional): New label to apply to returned object group (str, optional): New group to apply to returned object depth (int, optional): Depth to which relabel will be applied If applied to container allows applying relabeling to contained objects up to the specified depth Returns: Returns relabelled object """ 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) dmap = Dynamic(self, streams=self.streams, operation=dynamic_relabel) dmap.data = relabelled.data with util.disable_constant(dmap): dmap.group = relabelled.group dmap.label = relabelled.label return dmap return relabelled
[docs] def split_overlays(self): "Deprecated method to split overlays inside the DynamicMap." if util.config.future_deprecations: self.warning("split_overlays is deprecated and is now " "a private method.") return self._split_overlays()
def _split_overlays(self): """ Splits a DynamicMap into its components. Only well defined for DynamicMap with consistent number and order of layers. """ if not len(self): raise ValueError('Cannot split DynamicMap before it has been initialized') elif not issubclass(self.type, CompositeOverlay): return None, self from ..util import Dynamic keys = list(self.last.data.keys()) dmaps = [] for key in keys: el = self.last.data[key] def split_overlay_callback(obj, overlay_key=key, overlay_el=el, **kwargs): spec = util.get_overlay_spec(obj, overlay_key, overlay_el) items = list(obj.data.items()) specs = [(i, util.get_overlay_spec(obj, k, v)) for i, (k, v) in enumerate(items)] match = util.closest_match(spec, specs) if match is None: raise KeyError('{spec} spec not found in {otype}. The split_overlays method ' 'only works consistently for a DynamicMap where the ' 'layers of the {otype} do not change.'.format( spec=spec, otype=type(obj).__name__)) return items[match][1] dmap = Dynamic(self, streams=self.streams, operation=split_overlay_callback) dmap.data = OrderedDict([(list(self.data.keys())[-1], self.last.data[key])]) dmaps.append(dmap) return keys, dmaps
[docs] def collate(self): """Unpacks DynamicMap into container of DynamicMaps Collation allows unpacking DynamicMaps which return Layout, NdLayout or GridSpace objects into a single such object containing DynamicMaps. Assumes that the items in the layout or grid that is returned do not change. Returns: Collated container containing DynamicMaps """ # 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) type_counter = defaultdict(int) # Get stream mapping from callback remapped_streams = [] streams = self.callback.stream_mapping for i, (k, v) in enumerate(initialized.last.data.items()): 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): layout = self[args] layout_type = type(layout).__name__ if len(container.keys()) != len(layout.keys()): raise ValueError('Collated DynamicMaps must return ' '%s with consistent number of items.' % layout_type) key = kwargs['selection_key'] index = kwargs['selection_index'] obj_type = kwargs['selection_type'] dyn_type_map = defaultdict(list) for k, v in layout.data.items(): if k == key: return layout[k] dyn_type_map[type(v)].append(v) dyn_type_counter = {t: len(vals) for t, vals in dyn_type_map.items()} if dyn_type_counter != type_counter: raise ValueError('The objects in a %s returned by a ' 'DynamicMap must consistently return ' 'the same number of items of the ' 'same type.' % layout_type) return dyn_type_map[obj_type][index] callback = Callable(partial(collation_cb, selection_key=k, selection_index=type_counter[type(v)], selection_type=type(v)), inputs=[self]) vdmap = self.clone(callback=callback, shared_data=False, streams=vstreams) type_counter[type(v)] += 1 # 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): """Groups DynamicMap by one or more dimensions Applies groupby operation over the specified dimensions returning an object of type container_type (expected to be dictionary-like) containing the groups. 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 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 = [(k.name, v) for k, v in inner_vals] outer_sel = [(k.name, v) for k, v in outer_vals] return self.select(**dict(inner_sel+outer_sel)) return self.clone([], callback=inner_fn, kdims=inner_kdims) else: dim_vals = [(d.name, d.values) for d in inner_kdims] dim_vals += [(d.name, [v]) for d, v in zip(outer_kdims, util.wrap_tuple(outer_key))] with item_check(False): selected = HoloMap(self.select(**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 = [(d.name, [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 = [(d.name, k) for d, k in inner_dims] return self.select(**dict(outer_vals+inner_vals)).last if inner_kdims or self.streams: 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 = [(d.name, self.get_dimension(d).values) for d in inner_kdims] with item_check(False): selected = HoloMap(self.select(**dict(outer_vals+inner_vals))) group = group_type(selected.reindex(inner_kdims)) groups.append((outer, group)) return container_type(groups, kdims=outer_kdims)
[docs] def grid(self, dimensions=None, **kwargs): """ Groups data by supplied dimension(s) laying the groups along the dimension(s) out in a GridSpace. Args: dimensions: Dimension/str or list Dimension or list of dimensions to group by Returns: grid: GridSpace GridSpace with supplied dimensions """ return self.groupby(dimensions, container_type=GridSpace, **kwargs)
[docs] def layout(self, dimensions=None, **kwargs): """ Groups data by supplied dimension(s) laying the groups along the dimension(s) out in a NdLayout. Args: dimensions: Dimension/str or list Dimension or list of dimensions to group by Returns: layout: NdLayout NdLayout with supplied dimensions """ return self.groupby(dimensions, container_type=NdLayout, **kwargs)
[docs] def overlay(self, dimensions=None, **kwargs): """Group by supplied dimension(s) and overlay each group Groups data by supplied dimension(s) overlaying the groups along the dimension(s). Args: dimensions: Dimension(s) of dimensions to group by Returns: NdOverlay object(s) with supplied dimensions """ 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, dimension=None, num_bins=20, bin_range=None, adjoin=True, **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 Returns: AdjointLayout of DynamicMap and adjoined histogram if adjoin=True, otherwise just the histogram """ 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): """Reorders key dimensions on DynamicMap Create a new object with a reordered set of key dimensions. Dropping dimensions is not allowed on a DynamicMap. Args: kdims: List of dimensions to reindex the mapping with force: Not applicable to a DynamicMap Returns: Reindexed DynamicMap """ if not isinstance(kdims, list): kdims = [kdims] 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, kdims=None, **params): super(GridSpace, self).__init__(initial_items, kdims=kdims, **params) if self.ndims > 2: raise Exception('Grids can have no more than two dimensions.') def __lshift__(self, other): "Adjoins another object to the GridSpace" if isinstance(other, (ViewableElement, UniformNdMapping)): return AdjointLayout([self, other]) elif isinstance(other, AdjointLayout): return AdjointLayout(other.data+[self]) else: raise TypeError('Cannot append {0} to a AdjointLayout'.format(type(other).__name__)) def _transform_indices(self, key): """Snaps indices into the GridSpace to the closest coordinate. Args: key: Tuple index into the GridSpace Returns: Transformed key snapped to closest numeric coordinates """ 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): keys = self.keys() for i, k in enumerate(key): if isinstance(k, slice): continue dim_keys = np.array([ke[i] for ke in keys]) if dim_keys.dtype.kind in 'OSU': continue snapped_val = dim_keys[np.argmin(np.abs(dim_keys-k))] key = list(key) key[i] = snapped_val key = tuple(key) return key
[docs] def keys(self, full_grid=False): """Returns the keys of the GridSpace Args: full_grid (bool, optional): Return full cross-product of keys Returns: List of keys """ 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 self.data.items()] else: last_items = self.data 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): "Composes the GridSpace with another object into a Layout." return Layout([self, obj]) @property def shape(self): "Returns the 2D shape of the GridSpace as (rows, cols)." 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)