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
from types import FunctionType
import numpy as np
import param
from . import traversal, util
from .accessors import Opts, Redim
from .dimension import OrderedDict, Dimension, ViewableElement
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
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)
@property
def opts(self):
return 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."
self.param.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.clone(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=util.dimensioned_streams(self))
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 decollate(self):
"""Packs HoloMap of DynamicMaps into a single DynamicMap that returns an
HoloMap
Decollation allows packing a HoloMap of DynamicMaps into a single DynamicMap
that returns an HoloMap of simple (non-dynamic) elements. All nested streams
are lifted to the resulting DynamicMap, and are available in the `streams`
property. The `callback` property of the resulting DynamicMap is a pure,
stateless function of the stream values. To avoid stream parameter name
conflicts, the resulting DynamicMap is configured with
positional_stream_args=True, and the callback function accepts stream values
as positional dict arguments.
Returns:
DynamicMap that returns an HoloMap
"""
from .decollate import decollate
return decollate(self)
[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
"""
self.param.warning('The HoloMap.sample method is deprecated, '
'for equivalent functionality use '
'HoloMap.apply.sample().collapse().')
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.
"""
self.param.warning('The HoloMap.reduce method is deprecated, '
'for equivalent functionality use '
'HoloMap.apply.reduce().collapse().')
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.""")
operation_kwargs = param.Dict(default={}, constant=True, doc="""
Potential dynamic keyword arguments associated with the
operation.""")
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.""")
operation = param.Callable(default=None, doc="""
The function being applied by the Callable. May be used
to record the transform(s) being applied inside the
callback function.""")
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.param.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.param.warning('Ignoring extra positional argument %s'
% ', '.join('%s' % i for i in ignored))
clashes = set(pos_kwargs.keys()) & set(kwargs.keys())
if clashes:
self.param.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.param.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.param.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.""")
positional_stream_args = param.Boolean(default=False, constant=True, doc="""
If False, stream parameters are passed to the callback as keyword arguments.
If True, stream parameters are passed to callback as positional arguments.
Each positional argument is a dict containing the contents of a stream.
The positional stream arguments follow the positional arguments for each kdim,
and they are ordered to match the order of the DynamicMap's streams list.
""")
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
if (params.get('watch', True) and (util.is_param_method(callback, has_deps=True) or
(isinstance(callback, FunctionType) and hasattr(callback, '_dinfo')))):
streams.append(callback)
if isinstance(callback, types.GeneratorType):
callback = Generator(callback)
elif not isinstance(callback, Callable):
callback = Callable(callback)
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)
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 self._stream_parameters() != []:
raise Exception(prefix + ' cannot accept any stream parameters')
if self.positional_stream_args:
self._posarg_keys = None
else:
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.periodic = periodic(self)
@property
def opts(self):
return Opts(self, mode='dynamicmap')
@property
def redim(self):
return 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(self._stream_parameters())
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 _stream_parameters(self):
return util.stream_parameters(
self.streams, no_duplicates=not self.positional_stream_args
)
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(self._stream_parameters())
for kdim in self.kdims:
if str(kdim) in stream_params:
key.append(None)
elif kdim.default is not None:
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.param.warning(
'No streams declared. To update a DynamicMaps using '
'generators (or callables without arguments) use streams=[Next()]')
return
if self.streams == []:
self.param.warning('No streams on DynamicMap, calling event '
'will have no effect')
return
stream_params = set(self._stream_parameters())
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:
contents = stream.contents
applicable_kws = {k:v for k,v in kwargs.items()
if k in set(contents.keys())}
if not applicable_kws and contents:
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.positional_stream_args:
kwargs = {}
args = args + tuple([s.contents for s in self.streams])
elif 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
"""
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:
from ..operation import function
with util.disable_constant(clone):
op = function.instance(fn=lambda x, **kwargs: x)
clone.callback = clone.callback.clone(
inputs=[self], link_inputs=link, operation=op,
operation_kwargs={}
)
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
elif self.positional_stream_args:
# First positional args are dynamic map kdim indices, remaining args
# are stream values, not data_slice values
map_slice, _ = self._split_index(key)
data_slice = ()
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 = self._stream_parameters() == [] 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=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, no_duplicates=not self.positional_stream_args)
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."
self.param.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 decollate(self):
"""Packs DynamicMap of nested DynamicMaps into a single DynamicMap that
returns a non-dynamic element
Decollation allows packing a DynamicMap of nested DynamicMaps into a single
DynamicMap that returns a simple (non-dynamic) element. All nested streams are
lifted to the resulting DynamicMap, and are available in the `streams`
property. The `callback` property of the resulting DynamicMap is a pure,
stateless function of the stream values. To avoid stream parameter name
conflicts, the resulting DynamicMap is configured with
positional_stream_args=True, and the callback function accepts stream values
as positional dict arguments.
Returns:
DynamicMap that returns a non-dynamic element
"""
from .decollate import decollate
return decollate(self)
[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 = []
self_dstreams = util.dimensioned_streams(self)
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])
vstreams = list(util.unique_iterator(self_dstreams + vstreams))
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:
callback = Callable(partial(inner_fn, outer_vals),
inputs=[self])
group = self.clone(
callback=callback, 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)
[docs] def drop_dimension(self, dimensions):
raise NotImplementedError('Cannot drop dimensions from a DynamicMap, '
'cast to a HoloMap first.')
[docs] 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 = list(OrderedDict.fromkeys(k[0] for k in keys))
dim2_keys = list(OrderedDict.fromkeys(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] def decollate(self):
"""Packs GridSpace of DynamicMaps into a single DynamicMap that returns a
GridSpace
Decollation allows packing a GridSpace of DynamicMaps into a single DynamicMap
that returns a GridSpace of simple (non-dynamic) elements. All nested streams
are lifted to the resulting DynamicMap, and are available in the `streams`
property. The `callback` property of the resulting DynamicMap is a pure,
stateless function of the stream values. To avoid stream parameter name
conflicts, the resulting DynamicMap is configured with
positional_stream_args=True, and the callback function accepts stream values
as positional dict arguments.
Returns:
DynamicMap that returns a GridSpace
"""
from .decollate import decollate
return decollate(self)
[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)
