Source code for holoviews.core.accessors

Module for accessor objects for viewable HoloViews objects.
import copy
import sys

from collections import OrderedDict
from functools import wraps
from types import FunctionType

import param

from param.parameterized import add_metaclass

from . import util
from .pprint import PrettyPrinter

class AccessorPipelineMeta(type):
    def __new__(mcs, classname, bases, classdict):
        if '__call__' in classdict:
            classdict['__call__'] = mcs.pipelined(classdict['__call__'])

        inst = type.__new__(mcs, classname, bases, classdict)
        return inst

    def pipelined(mcs, __call__):
        def pipelined_call(*args, **kwargs):
            from ..operation.element import method as method_op, factory
            from .data import Dataset, MultiDimensionalMapping
            inst = args[0]

            if not hasattr(inst._obj, '_pipeline'):
                # Wrapped object doesn't support the pipeline property
                return __call__(*args, **kwargs)

            inst_pipeline = copy.copy(inst._obj. _pipeline)
            in_method = inst._obj._in_method
            if not in_method:
                inst._obj._in_method = True

                result = __call__(*args, **kwargs)

                if not in_method:
                    init_op = factory.instance(
                        kwargs={'mode': getattr(inst, 'mode', None)},
                    call_op = method_op.instance(

                    if isinstance(result, Dataset):
                        result._pipeline = inst_pipeline.instance(
                            operations=inst_pipeline.operations + [
                                init_op, call_op
                    elif isinstance(result, MultiDimensionalMapping):
                        for key, element in result.items():
                            getitem_op = method_op.instance(
                            element._pipeline = inst_pipeline.instance(
                                operations=inst_pipeline.operations + [
                                    init_op, call_op, getitem_op
                if not in_method:
                    inst._obj._in_method = False

            return result

        return pipelined_call

[docs]@add_metaclass(AccessorPipelineMeta) class Apply(object): """ Utility to apply a function or operation to all viewable elements inside the object. """ def __init__(self, obj, mode=None): self._obj = obj def __call__(self, apply_function, streams=[], link_inputs=True, link_dataset=True, dynamic=None, per_element=False, **kwargs): """Applies a function to all (Nd)Overlay or Element objects. Any keyword arguments are passed through to the function. If keyword arguments are instance parameters, or streams are supplied the returned object will dynamically update in response to changes in those objects. Args: apply_function: A callable function The function will be passed the return value of the DynamicMap as the first argument and any supplied stream values or keywords as additional keyword arguments. streams (list, optional): A list of Stream objects The Stream objects can dynamically supply values which will be passed to the function as keywords. link_inputs (bool, optional): Whether to link the inputs Determines whether Streams and Links attached to original object will be inherited. link_dataset (bool, optional): Whether to link the dataset Determines whether the dataset will be inherited. dynamic (bool, optional): Whether to make object dynamic By default object is made dynamic if streams are supplied, an instance parameter is supplied as a keyword argument, or the supplied function is a parameterized method. per_element (bool, optional): Whether to apply per element By default apply works on the leaf nodes, which includes both elements and overlays. If set it will apply directly to elements. kwargs (dict, optional): Additional keyword arguments Keyword arguments which will be supplied to the function. Returns: A new object where the function was applied to all contained (Nd)Overlay or Element objects. """ from .data import Dataset from .dimension import ViewableElement from .element import Element from .spaces import HoloMap, DynamicMap from ..util import Dynamic if isinstance(self._obj, DynamicMap) and dynamic == False: samples = tuple(d.values for d in self._obj.kdims) if not all(samples): raise ValueError('Applying a function to a DynamicMap ' 'and setting dynamic=False is only ' 'possible if key dimensions define ' 'a discrete parameter space.') if not len(samples): return self._obj[samples] return HoloMap(self._obj[samples]).apply( apply_function, streams, link_inputs, link_dataset, dynamic, per_element, **kwargs ) if isinstance(apply_function, str): args = kwargs.pop('_method_args', ()) method_name = apply_function def apply_function(object, **kwargs): method = getattr(object, method_name, None) if method is None: raise AttributeError('Applied method %s does not exist.' 'When declaring a method to apply ' 'as a string ensure a corresponding ' 'method exists on the object.' % method_name) return method(*args, **kwargs) if 'panel' in sys.modules: from panel.widgets.base import Widget kwargs = {k: v.param.value if isinstance(v, Widget) else v for k, v in kwargs.items()} spec = Element if per_element else ViewableElement applies = isinstance(self._obj, spec) params = {p: val for p, val in kwargs.items() if isinstance(val, param.Parameter) and isinstance(val.owner, param.Parameterized)} dependent_kws = any( (isinstance(val, FunctionType) and hasattr(val, '_dinfo')) or util.is_param_method(val, has_deps=True) for val in kwargs.values() ) if dynamic is None: is_dynamic = (bool(streams) or isinstance(self._obj, DynamicMap) or util.is_param_method(apply_function, has_deps=True) or params or dependent_kws) else: is_dynamic = dynamic if (applies or isinstance(self._obj, HoloMap)) and is_dynamic: return Dynamic(self._obj, operation=apply_function, streams=streams, kwargs=kwargs, link_inputs=link_inputs, link_dataset=link_dataset) elif applies: inner_kwargs = util.resolve_dependent_kwargs(kwargs) if hasattr(apply_function, 'dynamic'): inner_kwargs['dynamic'] = False new_obj = apply_function(self._obj, **inner_kwargs) if (link_dataset and isinstance(self._obj, Dataset) and isinstance(new_obj, Dataset) and new_obj._dataset is None): new_obj._dataset = self._obj.dataset return new_obj elif self._obj._deep_indexable: mapped = [] for k, v in new_val = v.apply(apply_function, dynamic=dynamic, streams=streams, link_inputs=link_inputs, link_dataset=link_dataset, **kwargs) if new_val is not None: mapped.append((k, new_val)) return self._obj.clone(mapped, link=link_inputs)
[docs] def aggregate(self, dimensions=None, function=None, spreadfn=None, **kwargs): """Applies a aggregate function to all ViewableElements. See :py:meth:`Dimensioned.aggregate` and :py:meth:`Apply.__call__` for more information. """ kwargs['_method_args'] = (dimensions, function, spreadfn) kwargs['per_element'] = True return self.__call__('aggregate', **kwargs)
[docs] def opts(self, *args, **kwargs): """Applies options to all ViewableElement objects. See :py:meth:`Dimensioned.opts` and :py:meth:`Apply.__call__` for more information. """ from ..util.transform import dim from ..streams import Params params = {} for arg in kwargs.values(): if isinstance(arg, dim): params.update(arg.params) streams = Params.from_params(params, watch_only=True) kwargs['streams'] = kwargs.get('streams', []) + streams kwargs['_method_args'] = args return self.__call__('opts', **kwargs)
[docs] def reduce(self, dimensions=[], function=None, spreadfn=None, **kwargs): """Applies a reduce function to all ViewableElement objects. See :py:meth:`Dimensioned.opts` and :py:meth:`Apply.__call__` for more information. """ kwargs['_method_args'] = (dimensions, function, spreadfn) kwargs['per_element'] = True return self.__call__('reduce', **kwargs)
[docs] def sample(self, samples=[], bounds=None, **kwargs): """Samples element values at supplied coordinates. See :py:meth:`Dataset.sample` and :py:meth:`Apply.__call__` for more information. """ kwargs['_method_args'] = (samples, bounds) kwargs['per_element'] = True return self.__call__('sample', **kwargs)
[docs] def select(self, **kwargs): """Applies a selection to all ViewableElement objects. See :py:meth:`Dimensioned.opts` and :py:meth:`Apply.__call__` for more information. """ return self.__call__('select', **kwargs)
[docs] def transform(self, *args, **kwargs): """Applies transforms to all Datasets. See :py:meth:`Dataset.transform` and :py:meth:`Apply.__call__` for more information. """ from ..util.transform import dim from ..streams import Params params = {} for _, arg in list(args)+list(kwargs.items()): if isinstance(arg, dim): params.update(arg.params) streams = Params.from_params(params, watch_only=True) kwargs['streams'] = kwargs.get('streams', []) + streams kwargs['_method_args'] = args kwargs['per_element'] = True return self.__call__('transform', **kwargs)
[docs]@add_metaclass(AccessorPipelineMeta) class Redim(object): """ Utility that supports re-dimensioning any HoloViews object via the redim method. """ def __init__(self, obj, mode=None): self._obj = obj # Can be 'dataset', 'dynamic' or None self.mode = mode def __str__(self): return "<holoviews.core.dimension.redim method>"
[docs] @classmethod def replace_dimensions(cls, dimensions, overrides): """Replaces dimensions in list with dictionary of overrides. Args: dimensions: List of dimensions overrides: Dictionary of dimension specs indexed by name Returns: list: List of dimensions with replacements applied """ from .dimension import Dimension replaced = [] for d in dimensions: if in overrides: override = overrides[] elif d.label in overrides: override = overrides[d.label] else: override = None if override is None: replaced.append(d) elif isinstance(override, (str, tuple)): replaced.append(d.clone(override)) elif isinstance(override, Dimension): replaced.append(override) elif isinstance(override, dict): replaced.append(d.clone(override.get('name',None), **{k:v for k,v in override.items() if k != 'name'})) else: raise ValueError('Dimension can only be overridden ' 'with another dimension or a dictionary ' 'of attributes') return replaced
def _filter_cache(self, dmap, kdims): """ Returns a filtered version of the DynamicMap cache leaving only keys consistently with the newly specified values """ filtered = [] for key, value in if not any(kd.values and v not in kd.values for kd, v in zip(kdims, key)): filtered.append((key, value)) return filtered def _transform_dimension(self, kdims, vdims, dimension): if dimension in kdims: idx = kdims.index(dimension) dimension = self._obj.kdims[idx] elif dimension in vdims: idx = vdims.index(dimension) dimension = self._obj.vdims[idx] return dimension def _create_expression_transform(self, kdims, vdims, exclude=[]): from .dimension import dimension_name from ..util.transform import dim def _transform_expression(expression): if dimension_name(expression.dimension) in exclude: dimension = expression.dimension else: dimension = self._transform_dimension( kdims, vdims, expression.dimension ) expression = expression.clone(dimension) ops = [] for op in expression.ops: new_op = dict(op) new_args = [] for arg in op['args']: if isinstance(arg, dim): arg = _transform_expression(arg) new_args.append(arg) new_op['args'] = tuple(new_args) new_kwargs = {} for kw, kwarg in op['kwargs'].items(): if isinstance(kwarg, dim): kwarg = _transform_expression(kwarg) new_kwargs[kw] = kwarg new_op['kwargs'] = new_kwargs ops.append(new_op) expression.ops = ops return expression return _transform_expression def __call__(self, specs=None, **dimensions): """ Replace dimensions on the dataset and allows renaming dimensions in the dataset. Dimension mapping should map between the old dimension name and a dictionary of the new attributes, a completely new dimension or a new string name. """ obj = self._obj redimmed = obj if obj._deep_indexable and self.mode != 'dataset': deep_mapped = [(k, v.redim(specs, **dimensions)) for k, v in obj.items()] redimmed = obj.clone(deep_mapped) if specs is not None: if not isinstance(specs, list): specs = [specs] matches = any(obj.matches(spec) for spec in specs) if self.mode != 'dynamic' and not matches: return redimmed kdims = self.replace_dimensions(obj.kdims, dimensions) vdims = self.replace_dimensions(obj.vdims, dimensions) zipped_dims = zip(obj.kdims+obj.vdims, kdims+vdims) renames = { nk for pk, nk in zipped_dims if !=} if self.mode == 'dataset': data = if renames: data = obj.interface.redim(obj, renames) transform = self._create_expression_transform(kdims, vdims, list(renames.values())) transforms = obj._transforms + [transform] clone = obj.clone(data, kdims=kdims, vdims=vdims, transforms=transforms) if self._obj.dimensions(label='name') == clone.dimensions(label='name'): # Ensure that plot_id is inherited as long as dimension # name does not change clone._plot_id = self._obj._plot_id return clone if self.mode != 'dynamic': return redimmed.clone(kdims=kdims, vdims=vdims) from ..util import Dynamic def dynamic_redim(obj, **dynkwargs): return obj.redim(specs, **dimensions) dmap = Dynamic(obj, streams=obj.streams, operation=dynamic_redim) = OrderedDict(self._filter_cache(redimmed, kdims)) with util.disable_constant(dmap): dmap.kdims = kdims dmap.vdims = vdims return dmap def _redim(self, name, specs, **dims): dimensions = {k:{name:v} for k,v in dims.items()} return self(specs, **dimensions) def cyclic(self, specs=None, **values): return self._redim('cyclic', specs, **values) def value_format(self, specs=None, **values): return self._redim('value_format', specs, **values) def range(self, specs=None, **values): return self._redim('range', specs, **values) def label(self, specs=None, **values): for k, v in values.items(): dim = self._obj.get_dimension(k) if dim and != dim.label and dim.label != v: raise ValueError('Cannot override an existing Dimension label') return self._redim('label', specs, **values) def soft_range(self, specs=None, **values): return self._redim('soft_range', specs, **values) def type(self, specs=None, **values): return self._redim('type', specs, **values) def nodata(self, specs=None, **values): return self._redim('nodata', specs, **values) def step(self, specs=None, **values): return self._redim('step', specs, **values) def default(self, specs=None, **values): return self._redim('default', specs, **values) def unit(self, specs=None, **values): return self._redim('unit', specs, **values) def values(self, specs=None, **ranges): return self._redim('values', specs, **ranges)
@add_metaclass(AccessorPipelineMeta) class Opts(object): def __init__(self, obj, mode=None): self._mode = mode self._obj = obj def get(self, group=None, backend=None, defaults=True): """Returns the corresponding Options object. Args: group: The options group. Flattens across groups if None. backend: Current backend if None otherwise chosen backend. defaults: Whether to include default option values Returns: Options object associated with the object containing the applied option keywords. """ from .options import Store, Options keywords = {} groups = Options._option_groups if group is None else [group] backend = backend if backend else Store.current_backend for group in groups: optsobj = Store.lookup_options(backend, self._obj, group, defaults=defaults) keywords = dict(keywords, **optsobj.kwargs) return Options(**keywords) def __call__(self, *args, **kwargs): """Applies nested options definition. Applies options on an object or nested group of objects in a flat format. Unlike the .options method, .opts modifies the options in place by default. If the options are to be set directly on the object a simple format may be used, e.g.: obj.opts(cmap='viridis', show_title=False) If the object is nested the options must be qualified using a type[.group][.label] specification, e.g.: obj.opts('Image', cmap='viridis', show_title=False) or using: obj.opts({'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 in place with clone=False **kwargs: Keywords of options Set of options to apply to the object For backwards compatibility, this method also supports the option group semantics now offered by the hv.opts.apply_groups utility. This usage will be deprecated and for more information see the apply_options_type docstring. Returns: Returns the object or a clone with the options applied """ if not(args) and not(kwargs): return self._obj if self._mode is None: apply_groups, _, _ = util.deprecated_opts_signature(args, kwargs) if apply_groups: msg = ("Calling the .opts method with options broken down by options " "group (i.e. separate plot, style and norm groups) is deprecated. " "Use the .options method converting to the simplified format " "instead or use hv.opts.apply_groups for backward compatibility.") param.main.param.warning(msg) return self._dispatch_opts( *args, **kwargs) def _dispatch_opts(self, *args, **kwargs): if self._mode is None: return self._base_opts(*args, **kwargs) elif self._mode == 'holomap': return self._holomap_opts(*args, **kwargs) elif self._mode == 'dynamicmap': return self._dynamicmap_opts(*args, **kwargs) def clear(self, clone=False): """Clears any options applied to the object. Args: clone: Whether to return a cleared clone or clear inplace Returns: The object cleared of any options applied to it """ return self._obj.opts(clone=clone) def info(self, show_defaults=False): """Prints a repr of the object including any applied options. Args: show_defaults: Whether to include default options """ pprinter = PrettyPrinter(show_options=True, show_defaults=show_defaults) print(pprinter.pprint(self._obj)) def _holomap_opts(self, *args, **kwargs): clone = kwargs.pop('clone', None) apply_groups, _, _ = util.deprecated_opts_signature(args, kwargs) data = OrderedDict([(k, v.opts(*args, **kwargs)) for k, v in]) # By default do not clone in .opts method if (apply_groups if clone is None else clone): return self._obj.clone(data) else: = data return self._obj def _dynamicmap_opts(self, *args, **kwargs): from ..util import Dynamic clone = kwargs.get('clone', None) apply_groups, _, _ = util.deprecated_opts_signature(args, kwargs) # By default do not clone in .opts method clone = (apply_groups if clone is None else clone) obj = self._obj if clone else self._obj.clone() dmap = Dynamic(obj, operation=lambda obj, **dynkwargs: obj.opts(*args, **kwargs), streams=self._obj.streams, link_inputs=True) if not clone: with util.disable_constant(self._obj): obj.callback = self._obj.callback self._obj.callback = dmap.callback dmap = self._obj = OrderedDict([(k, v.opts(*args, **kwargs)) for k, v in]) return dmap def _base_opts(self, *args, **kwargs): from .options import Options new_args = [] for arg in args: if isinstance(arg, Options) and arg.key is None: arg = arg(key=type(self._obj).__name__) new_args.append(arg) apply_groups, options, new_kwargs = util.deprecated_opts_signature(new_args, kwargs) # By default do not clone in .opts method clone = kwargs.get('clone', None) if apply_groups: from ..util import opts if options is not None: kwargs['options'] = options return opts.apply_groups(self._obj, **dict(kwargs, **new_kwargs)) kwargs['clone'] = False if clone is None else clone return self._obj.options(*new_args, **kwargs)