""" Provides Dimension objects for tracking the properties of a value, axis or map dimension. Also supplies the Dimensioned abstract baseclass for classes that accept Dimension values. """ import builtins import datetime as dt import re import weakref from collections import Counter, defaultdict from collections.abc import Iterable from functools import partial from itertools import chain from operator import itemgetter import numpy as np import param from . import util from .accessors import Apply, Opts, Redim from .options import Options, Store, cleanup_custom_options from .pprint import PrettyPrinter from .tree import AttrTree from .util import bytes_to_unicode # Alias parameter support for pickle loading ALIASES = {'key_dimensions': 'kdims', 'value_dimensions': 'vdims', 'constant_dimensions': 'cdims'} title_format = "{name}: {val}{unit}" redim = Redim # pickle compatibility - remove in 2.0 def param_aliases(d): """ Called from __setstate__ in LabelledData in order to load old pickles with outdated parameter names. Warning: We want to keep pickle hacking to a minimum! """ for old, new in ALIASES.items(): old_param = f'_{old}_param_value' new_param = f'_{new}_param_value' if old_param in d: d[new_param] = d.pop(old_param) return d def asdim(dimension): """Convert the input to a Dimension. Args: dimension: tuple, dict or string type to convert to Dimension Returns: A Dimension object constructed from the dimension spec. No copy is performed if the input is already a Dimension. """ return dimension if isinstance(dimension, Dimension) else Dimension(dimension) def dimension_name(dimension): """Return the Dimension.name for a dimension-like object. Args: dimension: Dimension or dimension string, tuple or dict Returns: The name of the Dimension or what would be the name if the input as converted to a Dimension. """ if isinstance(dimension, Dimension): return dimension.name elif isinstance(dimension, str): return dimension elif isinstance(dimension, tuple): return dimension[0] elif isinstance(dimension, dict): return dimension['name'] elif dimension is None: return None else: raise ValueError('%s type could not be interpreted as Dimension. ' 'Dimensions must be declared as a string, tuple, ' 'dictionary or Dimension type.' % type(dimension).__name__) def process_dimensions(kdims, vdims): """Converts kdims and vdims to Dimension objects. Args: kdims: List or single key dimension(s) specified as strings, tuples dicts or Dimension objects. vdims: List or single value dimension(s) specified as strings, tuples dicts or Dimension objects. Returns: Dictionary containing kdims and vdims converted to Dimension objects: {'kdims': [Dimension('x')], 'vdims': [Dimension('y')] """ dimensions = {} for group, dims in [('kdims', kdims), ('vdims', vdims)]: if dims is None: continue elif isinstance(dims, (tuple, str, Dimension, dict)): dims = [dims] elif not isinstance(dims, list): raise ValueError("{} argument expects a Dimension or list of dimensions, " "specified as tuples, strings, dictionaries or Dimension " "instances, not a {} type. Ensure you passed the data as the " "first argument.".format(group, type(dims).__name__)) dimensions[group] = [asdim(d) for d in dims] return dimensions class Dimension(param.Parameterized): """ Dimension objects are used to specify some important general features that may be associated with a collection of values. For instance, a Dimension may specify that a set of numeric values actually correspond to 'Height' (dimension name), in units of meters, with a descriptive label 'Height of adult males'. All dimensions object have a name that identifies them and a label containing a suitable description. If the label is not explicitly specified it matches the name. These two parameters define the core identity of the dimension object and must match if two dimension objects are to be considered equivalent. All other parameters are considered optional metadata and are not used when testing for equality. Unlike all the other parameters, these core parameters can be used to construct a Dimension object from a tuple. This format is sufficient to define an identical Dimension: Dimension('a', label='Dimension A') == Dimension(('a', 'Dimension A')) Everything else about a dimension is considered to reflect non-semantic preferences. Examples include the default value (which may be used in a visualization to set an initial slider position), how the value is to rendered as text (which may be used to specify the printed floating point precision) or a suitable range of values to consider for a particular analysis. Units ----- Full unit support with automated conversions are on the HoloViews roadmap. Once rich unit objects are supported, the unit (or more specifically the type of unit) will be part of the core dimension specification used to establish equality. Until this feature is implemented, there are two auxiliary parameters that hold some partial information about the unit: the name of the unit and whether or not it is cyclic. The name of the unit is used as part of the pretty-printed representation and knowing whether it is cyclic is important for certain operations. """ name = param.String(doc=""" Short name associated with the Dimension, such as 'height' or 'weight'. Valid Python identifiers make good names, because they can be used conveniently as a keyword in many contexts.""") label = param.String(default=None, doc=""" Unrestricted label used to describe the dimension. A label should succinctly describe the dimension and may contain any characters, including Unicode and LaTeX expression.""") cyclic = param.Boolean(default=False, doc=""" Whether the range of this feature is cyclic such that the maximum allowed value (defined by the range parameter) is continuous with the minimum allowed value.""") default = param.Parameter(default=None, doc=""" Default value of the Dimension which may be useful for widget or other situations that require an initial or default value.""") nodata = param.Integer(default=None, doc=""" Optional missing-data value for integer data. If non-None, data with this value will be replaced with NaN.""") range = param.Tuple(default=(None, None), doc=""" Specifies the minimum and maximum allowed values for a Dimension. None is used to represent an unlimited bound.""") soft_range = param.Tuple(default=(None, None), doc=""" Specifies a minimum and maximum reference value, which may be overridden by the data.""") step = param.Number(default=None, doc=""" Optional floating point step specifying how frequently the underlying space should be sampled. May be used to define a discrete sampling over the range.""") type = param.Parameter(default=None, doc=""" Optional type associated with the Dimension values. The type may be an inbuilt constructor (such as int, str, float) or a custom class object.""") unit = param.String(default=None, allow_None=True, doc=""" Optional unit string associated with the Dimension. For instance, the string 'm' may be used represent units of meters and 's' to represent units of seconds.""") value_format = param.Callable(default=None, doc=""" Formatting function applied to each value before display.""") values = param.List(default=[], doc=""" Optional specification of the allowed value set for the dimension that may also be used to retain a categorical ordering.""") # Defines default formatting by type type_formatters = {} unit_format = ' ({unit})' presets = {} # A dictionary-like mapping name, (name,) or # (name, unit) to a preset Dimension object def __init__(self, spec, **params): """ Initializes the Dimension object with the given name. """ if 'name' in params: raise KeyError('Dimension name must only be passed as the positional argument') all_params = {} if isinstance(spec, Dimension): all_params.update(spec.param.values()) elif isinstance(spec, str): if (spec, params.get('unit', None)) in self.presets.keys(): preset = self.presets[(str(spec), str(params['unit']))] all_params.update(preset.param.values()) elif spec in self.presets: all_params.update(self.presets[spec].param.values()) elif (spec,) in self.presets: all_params.update(self.presets[(spec,)].param.values()) all_params['name'] = spec all_params['label'] = spec elif isinstance(spec, tuple): try: all_params['name'], all_params['label'] = spec except ValueError as exc: raise ValueError( "Dimensions specified as a tuple must be a tuple " "consisting of the name and label not: %s" % str(spec) ) from exc if 'label' in params and params['label'] != all_params['label']: self.param.warning( f'Using label as supplied by keyword ({params["label"]!r}), ' f'ignoring tuple value {all_params["label"]!r}') elif isinstance(spec, dict): all_params.update(spec) try: all_params.setdefault('label', spec['name']) except KeyError as exc: raise ValueError( 'Dimension specified as a dict must contain a "name" key' ) from exc else: raise ValueError( '%s type could not be interpreted as Dimension. Dimensions must be ' 'declared as a string, tuple, dictionary or Dimension type.' % type(spec).__name__ ) all_params.update(params) if not all_params['name']: raise ValueError('Dimension name cannot be empty') if not all_params['label']: raise ValueError('Dimension label cannot be empty') values = params.get('values', []) if isinstance(values, str) and values == 'initial': self.param.warning("The 'initial' string for dimension values " "is no longer supported.") values = [] all_params['values'] = list(util.unique_array(values)) super().__init__(**all_params) if self.default is not None: if self.values and self.default not in values: raise ValueError(f'{self!r} default {self.default} not found in declared values: {self.values}') elif (self.range != (None, None) and ((self.range[0] is not None and self.default < self.range[0]) or (self.range[0] is not None and self.default > self.range[1]))): raise ValueError(f'{self!r} default {self.default} not in declared range: {self.range}') @property def spec(self): """"Returns the Dimensions tuple specification Returns: tuple: Dimension tuple specification """ return (self.name, self.label) def clone(self, spec=None, **overrides): """Clones the Dimension with new parameters Derive a new Dimension that inherits existing parameters except for the supplied, explicit overrides Args: spec (tuple, optional): Dimension tuple specification **overrides: Dimension parameter overrides Returns: Cloned Dimension object """ settings = dict(self.param.values(), **overrides) if spec is None: spec = (self.name, overrides.get('label', self.label)) if 'label' in overrides and isinstance(spec, str) : spec = (spec, overrides['label']) elif 'label' in overrides and isinstance(spec, tuple) : if overrides['label'] != spec[1]: self.param.warning( f'Using label as supplied by keyword ({overrides["label"]!r}), ' f'ignoring tuple value {spec[1]!r}') spec = (spec[0], overrides['label']) return self.__class__(spec, **{k:v for k,v in settings.items() if k not in ['name', 'label']}) def __hash__(self): """Hashes object on Dimension spec, i.e. (name, label). """ return hash(self.spec) def __setstate__(self, d): """ Compatibility for pickles before alias attribute was introduced. """ super().__setstate__(d) if '_label_param_value' not in d: self.label = self.name def __eq__(self, other): "Implements equals operator including sanitized comparison." if isinstance(other, Dimension): return self.spec == other.spec # For comparison to strings. Name may be sanitized. return other in [self.name, self.label, util.dimension_sanitizer(self.name)] def __ne__(self, other): "Implements not equal operator including sanitized comparison." return not self.__eq__(other) def __lt__(self, other): "Dimensions are sorted alphanumerically by name" return self.name < other.name if isinstance(other, Dimension) else self.name < other def __str__(self): return self.name def __repr__(self): return self.pprint() @property def pprint_label(self): "The pretty-printed label string for the Dimension" unit = ('' if self.unit is None else type(self.unit)(self.unit_format).format(unit=self.unit)) return bytes_to_unicode(self.label) + bytes_to_unicode(unit) def pprint(self): changed = self.param.values(onlychanged=True) if len({changed.get(k, k) for k in ['name','label']}) == 1: return f'Dimension({self.name!r})' params = self.param.objects('existing') ordering = sorted( sorted(changed.keys()), key=lambda k: ( -float('inf') if params[k].precedence is None else params[k].precedence)) kws = ", ".join(f'{k}={changed[k]!r}' for k in ordering if k != 'name') return f'Dimension({self.name!r}, {kws})' def pprint_value(self, value, print_unit=False): """Applies the applicable formatter to the value. Args: value: Dimension value to format Returns: Formatted dimension value """ own_type = type(value) if self.type is None else self.type formatter = (self.value_format if self.value_format else self.type_formatters.get(own_type)) if formatter: if callable(formatter): formatted_value = formatter(value) elif isinstance(formatter, str): if isinstance(value, (dt.datetime, dt.date)): formatted_value = value.strftime(formatter) elif isinstance(value, np.datetime64): formatted_value = util.dt64_to_dt(value).strftime(formatter) elif re.findall(r"\{(\w+)\}", formatter): formatted_value = formatter.format(value) else: formatted_value = formatter % value else: formatted_value = str(bytes_to_unicode(value)) if print_unit and self.unit is not None: formatted_value = formatted_value + ' ' + bytes_to_unicode(self.unit) return formatted_value def pprint_value_string(self, value): """Pretty print the dimension value and unit with title_format Args: value: Dimension value to format Returns: Formatted dimension value string with unit """ unit = '' if self.unit is None else ' ' + bytes_to_unicode(self.unit) value = self.pprint_value(value) return title_format.format(name=bytes_to_unicode(self.label), val=value, unit=unit) class LabelledData(param.Parameterized): """ LabelledData is a mix-in class designed to introduce the group and label parameters (and corresponding methods) to any class containing data. This class assumes that the core data contents will be held in the attribute called 'data'. Used together, group and label are designed to allow a simple and flexible means of addressing data. For instance, if you are collecting the heights of people in different demographics, you could specify the values of your objects as 'Height' and then use the label to specify the (sub)population. In this scheme, one object may have the parameters set to [group='Height', label='Children'] and another may use [group='Height', label='Adults']. Note: Another level of specification is implicit in the type (i.e class) of the LabelledData object. A full specification of a LabelledData object is therefore given by the tuple (, , label>). This additional level of specification is used in the traverse method. Any strings can be used for the group and label, but it can be convenient to use a capitalized string of alphanumeric characters, in which case the keys used for matching in the matches and traverse method will correspond exactly to {type}.{group}.{label}. Otherwise the strings provided will be sanitized to be valid capitalized Python identifiers, which works fine but can sometimes be confusing. """ group = param.String(default='LabelledData', constant=True, doc=""" A string describing the type of data contained by the object. By default this will typically mirror the class name.""") label = param.String(default='', constant=True, doc=""" Optional label describing the data, typically reflecting where or how it was measured. The label should allow a specific measurement or dataset to be referenced for a given group.""") _deep_indexable = False def __init__(self, data, id=None, plot_id=None, **params): """ All LabelledData subclasses must supply data to the constructor, which will be held on the .data attribute. This class also has an id instance attribute, which may be set to associate some custom options with the object. """ self.data = data self._id = None self.id = id self._plot_id = plot_id or builtins.id(self) if isinstance(params.get('label',None), tuple): (alias, long_name) = params['label'] util.label_sanitizer.add_aliases(**{alias:long_name}) params['label'] = long_name if isinstance(params.get('group',None), tuple): (alias, long_name) = params['group'] util.group_sanitizer.add_aliases(**{alias:long_name}) params['group'] = long_name super().__init__(**params) if not util.group_sanitizer.allowable(self.group): raise ValueError("Supplied group %r contains invalid characters." % self.group) elif not util.label_sanitizer.allowable(self.label): raise ValueError("Supplied label %r contains invalid characters." % self.label) @property def id(self): return self._id @id.setter def id(self, opts_id): """Handles tracking and cleanup of custom ids.""" old_id = self._id self._id = opts_id if old_id is not None: cleanup_custom_options(old_id) if opts_id is not None and opts_id != old_id: if opts_id not in Store._weakrefs: Store._weakrefs[opts_id] = [] ref = weakref.ref(self, partial(cleanup_custom_options, opts_id)) Store._weakrefs[opts_id].append(ref) 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 """ params = self.param.values() if new_type is None: clone_type = self.__class__ else: clone_type = new_type new_params = new_type.param.objects('existing') params = {k: v for k, v in params.items() if k in new_params} if params.get('group') == self.param.objects('existing')['group'].default: params.pop('group') settings = dict(params, **overrides) if 'id' not in settings: settings['id'] = self.id if data is None and shared_data: data = self.data if link: settings['plot_id'] = self._plot_id # Apply name mangling for __ attribute pos_args = getattr(self, '_' + type(self).__name__ + '__pos_params', []) return clone_type(data, *args, **{k:v for k,v in settings.items() if k not in pos_args}) def relabel(self, label=None, group=None, depth=0): """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 """ new_data = self.data if (depth > 0) and getattr(self, '_deep_indexable', False): new_data = [] for k, v in self.data.items(): relabelled = v.relabel(group=group, label=label, depth=depth-1) new_data.append((k, relabelled)) keywords = [('label', label), ('group', group)] kwargs = {k: v for k, v in keywords if v is not None} return self.clone(new_data, **kwargs) def matches(self, spec): """Whether the spec applies to this object. Args: spec: A function, spec or type to check for a match * A 'type[[.group].label]' string which is compared against the type, group and label of this object * A function which is given the object and returns a boolean. * An object type matched using isinstance. Returns: bool: Whether the spec matched this object. """ if callable(spec) and not isinstance(spec, type): return spec(self) elif isinstance(spec, type): return isinstance(self, spec) specification = (self.__class__.__name__, self.group, self.label) split_spec = tuple(spec.split('.')) if not isinstance(spec, tuple) else spec split_spec, nocompare = zip(*((None, True) if s == '*' or s is None else (s, False) for s in split_spec)) if all(nocompare): return True match_fn = itemgetter(*(idx for idx, nc in enumerate(nocompare) if not nc)) self_spec = match_fn(split_spec) unescaped_match = match_fn(specification[:len(split_spec)]) == self_spec if unescaped_match: return True sanitizers = [util.sanitize_identifier, util.group_sanitizer, util.label_sanitizer] identifier_specification = tuple(fn(ident, escape=False) for ident, fn in zip(specification, sanitizers)) identifier_match = match_fn(identifier_specification[:len(split_spec)]) == self_spec return identifier_match def traverse(self, fn=None, specs=None, full_breadth=True): """Traverses object returning matching items Traverses the set of children of the object, collecting the all objects matching the defined specs. Each object can be processed with the supplied function. Args: fn (function, optional): Function applied to matched objects specs: List of specs to match Specs must be types, functions or type[.group][.label] specs to select objects to return, by default applies to all objects. full_breadth: Whether to traverse all objects Whether to traverse the full set of objects on each container or only the first. Returns: list: List of objects that matched """ if fn is None: fn = lambda x: x if specs is not None and not isinstance(specs, (list, set, tuple)): specs = [specs] accumulator = [] matches = specs is None if not matches: for spec in specs: matches = self.matches(spec) if matches: break if matches: accumulator.append(fn(self)) # Assumes composite objects are iterables if self._deep_indexable: for el in self: if el is None: continue accumulator += el.traverse(fn, specs, full_breadth) if not full_breadth: break return accumulator def map(self, map_fn, specs=None, clone=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 """ if specs is not None and not isinstance(specs, (list, set, tuple)): specs = [specs] applies = specs is None or any(self.matches(spec) for spec in specs) if self._deep_indexable: deep_mapped = self.clone(shared_data=False) if clone else self for k, v in self.items(): new_val = v.map(map_fn, specs, clone) if new_val is not None: deep_mapped[k] = new_val if applies: deep_mapped = map_fn(deep_mapped) return deep_mapped else: return map_fn(self) if applies else self def __getstate__(self): "Ensures pickles save options applied to this objects." obj_dict = self.__dict__.copy() try: if Store.save_option_state and (obj_dict.get('_id', None) is not None): custom_key = '_custom_option_%d' % obj_dict['_id'] if custom_key not in obj_dict: obj_dict[custom_key] = {backend:s[obj_dict['_id']] for backend,s in Store._custom_options.items() if obj_dict['_id'] in s} else: obj_dict['_id'] = None except Exception: self.param.warning("Could not pickle custom style information.") return obj_dict def __setstate__(self, d): "Restores options applied to this object." d = param_aliases(d) # Backwards compatibility for objects before id was made a property opts_id = d['_id'] if '_id' in d else d.pop('id', None) try: load_options = Store.load_counter_offset is not None if load_options: matches = [k for k in d if k.startswith('_custom_option')] for match in matches: custom_id = int(match.split('_')[-1])+Store.load_counter_offset if not isinstance(d[match], dict): # Backward compatibility before multiple backends backend_info = {'matplotlib':d[match]} else: backend_info = d[match] for backend, info in backend_info.items(): if backend not in Store._custom_options: Store._custom_options[backend] = {} Store._custom_options[backend][custom_id] = info if backend_info: if custom_id not in Store._weakrefs: Store._weakrefs[custom_id] = [] ref = weakref.ref(self, partial(cleanup_custom_options, custom_id)) Store._weakrefs[opts_id].append(ref) d.pop(match) if opts_id is not None: opts_id += Store.load_counter_offset except Exception: self.param.warning("Could not unpickle custom style information.") d['_id'] = opts_id self.__dict__.update(d) super().__setstate__({}) class Dimensioned(LabelledData): """ Dimensioned is a base class that allows the data contents of a class to be associated with dimensions. The contents associated with dimensions may be partitioned into one of three types * key dimensions: These are the dimensions that can be indexed via the __getitem__ method. Dimension objects supporting key dimensions must support indexing over these dimensions and may also support slicing. This list ordering of dimensions describes the positional components of each multi-dimensional indexing operation. For instance, if the key dimension names are 'weight' followed by 'height' for Dimensioned object 'obj', then obj[80,175] indexes a weight of 80 and height of 175. Accessed using either kdims. * value dimensions: These dimensions correspond to any data held on the Dimensioned object not in the key dimensions. Indexing by value dimension is supported by dimension name (when there are multiple possible value dimensions); no slicing semantics is supported and all the data associated with that dimension will be returned at once. Note that it is not possible to mix value dimensions and deep dimensions. Accessed using either vdims. * deep dimensions: These are dynamically computed dimensions that belong to other Dimensioned objects that are nested in the data. Objects that support this should enable the _deep_indexable flag. Note that it is not possible to mix value dimensions and deep dimensions. Accessed using either ddims. Dimensioned class support generalized methods for finding the range and type of values along a particular Dimension. The range method relies on the appropriate implementation of the dimension_values methods on subclasses. The index of an arbitrary dimension is its positional index in the list of all dimensions, starting with the key dimensions, followed by the value dimensions and ending with the deep dimensions. """ cdims = param.Dict(default={}, doc=""" The constant dimensions defined as a dictionary of Dimension:value pairs providing additional dimension information about the object. Aliased with constant_dimensions.""") kdims = param.List(bounds=(0, None), constant=True, doc=""" The key dimensions defined as list of dimensions that may be used in indexing (and potential slicing) semantics. The order of the dimensions listed here determines the semantics of each component of a multi-dimensional indexing operation. Aliased with key_dimensions.""") vdims = param.List(bounds=(0, None), constant=True, doc=""" The value dimensions defined as the list of dimensions used to describe the components of the data. If multiple value dimensions are supplied, a particular value dimension may be indexed by name after the key dimensions. Aliased with value_dimensions.""") group = param.String(default='Dimensioned', constant=True, doc=""" A string describing the data wrapped by the object.""") __abstract = True _dim_groups = ['kdims', 'vdims', 'cdims', 'ddims'] _dim_aliases = dict(key_dimensions='kdims', value_dimensions='vdims', constant_dimensions='cdims', deep_dimensions='ddims') def __init__(self, data, kdims=None, vdims=None, **params): params.update(process_dimensions(kdims, vdims)) if 'cdims' in params: params['cdims'] = {d if isinstance(d, Dimension) else Dimension(d): val for d, val in params['cdims'].items()} super().__init__(data, **params) self.ndims = len(self.kdims) cdims = [(d.name, val) for d, val in self.cdims.items()] self._cached_constants = dict(cdims) self._settings = None # Instantiate accessors @property def apply(self): return Apply(self) @property def opts(self): return Opts(self) @property def redim(self): return Redim(self) def _valid_dimensions(self, dimensions): """Validates key dimension input Returns kdims if no dimensions are specified""" if dimensions is None: dimensions = self.kdims elif not isinstance(dimensions, list): dimensions = [dimensions] valid_dimensions = [] for dim in dimensions: if isinstance(dim, Dimension): dim = dim.name if dim not in self.kdims: raise Exception(f"Supplied dimensions {dim} not found.") valid_dimensions.append(dim) return valid_dimensions @property def ddims(self): "The list of deep dimensions" if self._deep_indexable and self: return self.values()[0].dimensions() else: return [] def dimensions(self, selection='all', label=False): """Lists the available dimensions on the object Provides convenient access to Dimensions on nested Dimensioned objects. Dimensions can be selected by their type, i.e. 'key' or 'value' dimensions. By default 'all' dimensions are returned. Args: selection: Type of dimensions to return The type of dimension, i.e. one of 'key', 'value', 'constant' or 'all'. label: Whether to return the name, label or Dimension Whether to return the Dimension objects (False), the Dimension names (True/'name') or labels ('label'). Returns: List of Dimension objects or their names or labels """ if label in ['name', True]: label = 'short' elif label == 'label': label = 'long' elif label: raise ValueError("label needs to be one of True, False, 'name' or 'label'") lambdas = {'k': (lambda x: x.kdims, {'full_breadth': False}), 'v': (lambda x: x.vdims, {}), 'c': (lambda x: x.cdims, {})} aliases = {'key': 'k', 'value': 'v', 'constant': 'c'} if selection in ['all', 'ranges']: groups = [d for d in self._dim_groups if d != 'cdims'] dims = [dim for group in groups for dim in getattr(self, group)] elif isinstance(selection, list): dims = [dim for group in selection for dim in getattr(self, f'{aliases.get(group)}dims')] elif aliases.get(selection) in lambdas: selection = aliases.get(selection, selection) lmbd, kwargs = lambdas[selection] key_traversal = self.traverse(lmbd, **kwargs) dims = [dim for keydims in key_traversal for dim in keydims] else: raise KeyError("Invalid selection %r, valid selections include" "'all', 'value' and 'key' dimensions" % repr(selection)) return [(dim.label if label == 'long' else dim.name) if label else dim for dim in dims] def get_dimension(self, dimension, default=None, strict=False): """Get a Dimension object by name or index. Args: dimension: Dimension to look up by name or integer index default (optional): Value returned if Dimension not found strict (bool, optional): Raise a KeyError if not found Returns: Dimension object for the requested dimension or default """ if dimension is not None and not isinstance(dimension, (int, str, Dimension)): raise TypeError('Dimension lookup supports int, string, ' 'and Dimension instances, cannot lookup ' 'Dimensions using %s type.' % type(dimension).__name__) all_dims = self.dimensions() if isinstance(dimension, int): if 0 <= dimension < len(all_dims): return all_dims[dimension] elif strict: raise KeyError(f"Dimension {dimension!r} not found") else: return default if isinstance(dimension, Dimension): dims = [d for d in all_dims if dimension == d] if strict and not dims: raise KeyError(f"{dimension!r} not found.") elif dims: return dims[0] else: return None else: dimension = dimension_name(dimension) name_map = {dim.spec: dim for dim in all_dims} name_map.update({dim.name: dim for dim in all_dims}) name_map.update({dim.label: dim for dim in all_dims}) name_map.update({util.dimension_sanitizer(dim.name): dim for dim in all_dims}) if strict and dimension not in name_map: raise KeyError(f"Dimension {dimension!r} not found.") else: return name_map.get(dimension, default) def get_dimension_index(self, dimension): """Get the index of the requested dimension. Args: dimension: Dimension to look up by name or by index Returns: Integer index of the requested dimension """ if isinstance(dimension, int): if (dimension < (self.ndims + len(self.vdims)) or dimension < len(self.dimensions())): return dimension else: return IndexError('Dimension index out of bounds') dim = dimension_name(dimension) try: dimensions = self.kdims+self.vdims return next(i for i, d in enumerate(dimensions) if d == dim) except StopIteration: raise Exception(f"Dimension {dim} not found in {self.__class__.__name__}.") from None def get_dimension_type(self, dim): """Get the type of the requested dimension. Type is determined by Dimension.type attribute or common type of the dimension values, otherwise None. Args: dimension: Dimension to look up by name or by index Returns: Declared type of values along the dimension """ dim_obj = self.get_dimension(dim) if dim_obj and dim_obj.type is not None: return dim_obj.type dim_vals = [type(v) for v in self.dimension_values(dim)] if len(set(dim_vals)) == 1: return dim_vals[0] else: return None def __getitem__(self, key): """ Multi-dimensional indexing semantics is determined by the list of key dimensions. For instance, the first indexing component will index the first key dimension. After the key dimensions are given, *either* a value dimension name may follow (if there are multiple value dimensions) *or* deep dimensions may then be listed (for applicable deep dimensions). """ return self 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] # Apply all indexes applying on this object vdims = self.vdims+['value'] if self.vdims else [] kdims = self.kdims local_kwargs = {k: v for k, v in kwargs.items() if k in kdims+vdims} # Check selection_spec applies if selection_specs is not None: if not isinstance(selection_specs, (list, tuple)): selection_specs = [selection_specs] matches = any(self.matches(spec) for spec in selection_specs) else: matches = True # Apply selection to self if local_kwargs and matches: ndims = self.ndims if any(d in self.vdims for d in kwargs): ndims = len(self.kdims+self.vdims) select = [slice(None) for _ in range(ndims)] for dim, val in local_kwargs.items(): if dim == 'value': select += [val] else: if isinstance(val, tuple): val = slice(*val) select[self.get_dimension_index(dim)] = val if self._deep_indexable: selection = self.get(tuple(select), None) if selection is None: selection = self.clone(shared_data=False) else: selection = self[tuple(select)] else: selection = self if not isinstance(selection, Dimensioned): return selection elif type(selection) is not type(self) and isinstance(selection, Dimensioned): # Apply the selection on the selected object of a different type dimensions = selection.dimensions() + ['value'] if any(kw in dimensions for kw in kwargs): selection = selection.select(selection_specs=selection_specs, **kwargs) elif isinstance(selection, Dimensioned) and selection._deep_indexable: # Apply the deep selection on each item in local selection items = [] for k, v in selection.items(): dimensions = v.dimensions() + ['value'] if any(kw in dimensions for kw in kwargs): items.append((k, v.select(selection_specs=selection_specs, **kwargs))) else: items.append((k, v)) selection = selection.clone(items) return selection def dimension_values(self, dimension, expanded=True, flat=True): """Return the values along the requested dimension. Args: dimension: The dimension to return values for expanded (bool, optional): Whether to expand values Whether to return the expanded values, behavior depends on the type of data: * Columnar: If false returns unique values * Geometry: If false returns scalar values per geometry * Gridded: If false returns 1D coordinates flat (bool, optional): Whether to flatten array Returns: NumPy array of values along the requested dimension """ val = self._cached_constants.get(dimension, None) if val: return np.array([val]) else: raise Exception(f"Dimension {dimension} not found in {self.__class__.__name__}.") def range(self, dimension, data_range=True, dimension_range=True): """Return the lower and upper bounds of values along dimension. Args: dimension: The dimension to compute the range on. data_range (bool): Compute range from data values dimension_range (bool): Include Dimension ranges Whether to include Dimension range and soft_range in range calculation Returns: Tuple containing the lower and upper bound """ dimension = self.get_dimension(dimension) if dimension is None or (not data_range and not dimension_range): return (None, None) elif all(util.isfinite(v) for v in dimension.range) and dimension_range: return dimension.range elif data_range: if dimension in self.kdims+self.vdims: dim_vals = self.dimension_values(dimension.name) lower, upper = util.find_range(dim_vals) else: dname = dimension.name match_fn = lambda x: dname in x.kdims + x.vdims range_fn = lambda x: x.range(dname) ranges = self.traverse(range_fn, [match_fn]) lower, upper = util.max_range(ranges) else: lower, upper = (np.nan, np.nan) if not dimension_range: return lower, upper return util.dimension_range(lower, upper, dimension.range, dimension.soft_range) def __repr__(self): return PrettyPrinter.pprint(self) def __str__(self): return repr(self) def options(self, *args, clone=True, **kwargs): """Applies simplified option definition returning a new object. Applies options on an object or nested group of objects in a flat format returning a new object with the options applied. If the options are to be set directly on the object 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)}) Identical to the .opts method but returns a clone of the object by default. 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 """ backend = kwargs.get('backend', None) if not (args or kwargs): options = None elif args and isinstance(args[0], str): options = {args[0]: kwargs} elif args and isinstance(args[0], list): if kwargs: raise ValueError('Please specify a list of option objects, or kwargs, but not both') options = args[0] elif args and [k for k in kwargs.keys() if k != 'backend']: raise ValueError("Options must be defined in one of two formats. " "Either supply keywords defining the options for " "the current object, e.g. obj.options(cmap='viridis'), " "or explicitly define the type, e.g. " "obj.options({'Image': {'cmap': 'viridis'}}). " "Supplying both formats is not supported.") elif args and all(isinstance(el, dict) for el in args): if len(args) > 1: self.param.warning('Only a single dictionary can be passed ' 'as a positional argument. Only processing ' 'the first dictionary') options = [Options(spec, **kws) for spec,kws in args[0].items()] elif args: options = list(args) elif kwargs: options = {type(self).__name__: kwargs} from ..util import opts if options is None: expanded_backends = [(backend, {})] elif isinstance(options, list): # assuming a flat list of Options objects expanded_backends = opts._expand_by_backend(options, backend) else: expanded_backends = [(backend, opts._expand_options(options, backend))] obj = self for backend, expanded in expanded_backends: obj = obj.opts._dispatch_opts(expanded, backend=backend, clone=clone) return obj def _repr_mimebundle_(self, include=None, exclude=None): """ Resolves the class hierarchy for the class rendering the object using any display hooks registered on Store.display hooks. The output of all registered display_hooks is then combined and returned. """ return Store.render(self) class ViewableElement(Dimensioned): """ A ViewableElement is a dimensioned datastructure that may be associated with a corresponding atomic visualization. An atomic visualization will display the data on a single set of axes (i.e. excludes multiple subplots that are displayed at once). The only new parameter introduced by ViewableElement is the title associated with the object for display. """ __abstract = True _auxiliary_component = False group = param.String(default='ViewableElement', constant=True) class ViewableTree(AttrTree, Dimensioned): """ A ViewableTree is an AttrTree with Viewable objects as its leaf nodes. It combines the tree like data structure of a tree while extending it with the deep indexable properties of Dimensioned and LabelledData objects. """ group = param.String(default='ViewableTree', constant=True) _deep_indexable = True def __init__(self, items=None, identifier=None, parent=None, **kwargs): if items and all(isinstance(item, Dimensioned) for item in items): items = self._process_items(items) params = {p: kwargs.pop(p) for p in list(self.param)+['id', 'plot_id'] if p in kwargs} AttrTree.__init__(self, items, identifier, parent, **kwargs) Dimensioned.__init__(self, self.data, **params) @classmethod def _process_items(cls, vals): "Processes list of items assigning unique paths to each." from .layout import AdjointLayout if type(vals) is cls: return vals.data elif isinstance(vals, (AdjointLayout, str)): # `string` vals isn't supported but checked anyway # for better exception message. vals = [vals] elif isinstance(vals, Iterable): vals = list(vals) items = [] counts = defaultdict(lambda: 1) cls._unpack_paths(vals, items, counts) items = cls._deduplicate_items(items) return items def __setstate__(self, d): """ Ensure that object does not try to reference its parent during unpickling. """ parent = d.pop('parent', None) d['parent'] = None super(AttrTree, self).__setstate__(d) self.__dict__['parent'] = parent @classmethod def _deduplicate_items(cls, items): "Deduplicates assigned paths by incrementing numbering" counter = Counter([path[:i] for path, _ in items for i in range(1, len(path)+1)]) if sum(counter.values()) == len(counter): return items new_items = [] counts = defaultdict(lambda: 0) for path, item in items: if counter[path] > 1: path = path + (util.int_to_roman(counts[path]+1),) else: inc = 1 while counts[path]: path = path[:-1] + (util.int_to_roman(counts[path]+inc),) inc += 1 new_items.append((path, item)) counts[path] += 1 return new_items @classmethod def _unpack_paths(cls, objs, items, counts): """ Recursively unpacks lists and ViewableTree-like objects, accumulating into the supplied list of items. """ if type(objs) is cls: objs = objs.items() for item in objs: path, obj = item if isinstance(item, tuple) else (None, item) if type(obj) is cls: cls._unpack_paths(obj, items, counts) continue new = path is None or len(path) == 1 path = util.get_path(item) if new else path new_path = util.make_path_unique(path, counts, new) items.append((new_path, obj)) @property def uniform(self): "Whether items in tree have uniform dimensions" from .traversal import uniform return uniform(self) def dimension_values(self, dimension, expanded=True, flat=True): """Return the values along the requested dimension. Concatenates values on all nodes with requested dimension. Args: dimension: The dimension to return values for expanded (bool, optional): Whether to expand values Whether to return the expanded values, behavior depends on the type of data: * Columnar: If false returns unique values * Geometry: If false returns scalar values per geometry * Gridded: If false returns 1D coordinates flat (bool, optional): Whether to flatten array Returns: NumPy array of values along the requested dimension """ dimension = self.get_dimension(dimension, strict=True).name all_dims = self.traverse(lambda x: [d.name for d in x.dimensions()]) if dimension in chain.from_iterable(all_dims): values = [el.dimension_values(dimension) for el in self if dimension in el.dimensions(label=True)] vals = np.concatenate(values) return vals if expanded else util.unique_array(vals) else: return super().dimension_values( dimension, expanded, flat) def __len__(self): return len(self.data)