"""
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.
"""
from __future__ import unicode_literals
import re
import datetime as dt
import weakref
from operator import itemgetter
from collections import defaultdict, Counter
from itertools import chain
from functools import partial
import param
import numpy as np
from . import util
from .accessors import Opts, Apply, Redim
from .options import Store, Options, cleanup_custom_options
from .pprint import PrettyPrinter
from .tree import AttrTree
from .util import basestring, OrderedDict, bytes_to_unicode, 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
[docs]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old pickles with outdated parameter names.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 = '_%s_param_value' % old
new_param = '_%s_param_value' % new
if old_param in d:
d[new_param] = d.pop(old_param)
return d
[docs]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.
"""
if isinstance(dimension, Dimension):
return dimension
elif isinstance(dimension, (tuple, dict, basestring)):
return Dimension(dimension)
else:
raise ValueError('%s type could not be interpreted as Dimension. '
'Dimensions must be declared as a string, tuple, '
'dictionary or Dimension type.')
[docs]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, basestring):
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__)
[docs]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, basestring, Dimension, dict)):
dims = [dims]
elif not isinstance(dims, list):
raise ValueError("%s argument expects a Dimension or list of dimensions, "
"specified as tuples, strings, dictionaries or Dimension "
"instances, not a %s type. Ensure you passed the data as the "
"first argument." % (group, type(dims).__name__))
for dim in dims:
if not isinstance(dim, (tuple, basestring, Dimension, dict)):
raise ValueError('Dimensions must be defined as a tuple, '
'string, dictionary or Dimension instance, '
'found a %s type.' % type(dim).__name__)
dimensions[group] = [asdim(d) for d in dims]
return dimensions
[docs]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.""")
value_format = param.Callable(default=None, doc="""
Formatting function applied to each value before display.""")
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.""")
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.""")
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.""")
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.""")
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.""")
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')
if isinstance(spec, Dimension):
existing_params = dict(spec.param.get_param_values())
elif (spec, params.get('unit', None)) in self.presets.keys():
preset = self.presets[(str(spec), str(params['unit']))]
existing_params = dict(preset.param.get_param_values())
elif isinstance(spec, dict):
existing_params = spec
elif spec in self.presets:
existing_params = dict(self.presets[spec].param.get_param_values())
elif (spec,) in self.presets:
existing_params = dict(self.presets[(spec,)].param.get_param_values())
else:
existing_params = {}
all_params = dict(existing_params, **params)
if isinstance(spec, tuple):
if not all(isinstance(s, basestring) for s in spec) or len(spec) != 2:
raise ValueError("Dimensions specified as a tuple must be a tuple "
"consisting of the name and label not: %s" % str(spec))
name, label = spec
all_params['name'] = name
all_params['label'] = label
if 'label' in params and (label != params['label']):
if params['label'] != label:
self.param.warning(
'Using label as supplied by keyword ({!r}), ignoring '
'tuple value {!r}'.format(params['label'], label))
all_params['label'] = params['label']
elif isinstance(spec, basestring):
all_params['name'] = spec
all_params['label'] = params.get('label', spec)
if all_params['name'] == '':
raise ValueError('Dimension name cannot be the empty string')
if all_params['label'] in ['', None]:
raise ValueError('Dimension label cannot be None or the empty string')
values = params.get('values', [])
if isinstance(values, basestring) 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(Dimension, self).__init__(**all_params)
if self.default is not None:
if self.values and self.default not in values:
raise ValueError('%r default %s not found in declared values: %s' %
(self, self.default, 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('%r default %s not in declared range: %s' %
(self, self.default, self.range))
@property
def spec(self):
""""Returns the Dimensions tuple specification
Returns:
tuple: Dimension tuple specification
"""
return (self.name, self.label)
def __call__(self, spec=None, **overrides):
self.param.warning('Dimension.__call__ method has been deprecated, '
'use the clone method instead.')
return self.clone(spec=spec, **overrides)
[docs] 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.get_param_values(), **overrides)
if spec is None:
spec = (self.name, overrides.get('label', self.label))
if 'label' in overrides and isinstance(spec, basestring) :
spec = (spec, overrides['label'])
elif 'label' in overrides and isinstance(spec, tuple) :
if overrides['label'] != spec[1]:
self.param.warning(
'Using label as supplied by keyword ({!r}), ignoring '
'tuple value {!r}'.format(overrides['label'], spec[1]))
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(Dimension, self).__setstate__(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)
[docs] def pprint(self):
changed = dict(self.param.get_param_values(onlychanged=True))
if len(set([changed.get(k, k) for k in ['name','label']])) == 1:
return 'Dimension({spec})'.format(spec=repr(self.name))
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('%s=%r' % (k, changed[k]) for k in ordering if k != 'name')
return 'Dimension({spec}, {kws})'.format(spec=repr(self.name), kws=kws)
[docs] 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, basestring):
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 = unicode(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
[docs] 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)
[docs]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
(<type>, <group>, 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 util.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(LabelledData, self).__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)
[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
"""
params = dict(self.param.get_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})
[docs] 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)
[docs] 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
[docs] 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
[docs] 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:
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:
self.param.warning("Could not unpickle custom style information.")
d['_id'] = opts_id
self.__dict__.update(d)
super(LabelledData, self).__setstate__({})
[docs]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=OrderedDict(), 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(Dimensioned, self).__init__(data, **params)
self.ndims = len(self.kdims)
cdims = [(d.name, val) for d, val in self.cdims.items()]
self._cached_constants = OrderedDict(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("Supplied dimensions %s not found." % dim)
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 []
[docs] 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, '%sdims' % aliases.get(group))]
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]
[docs] 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, basestring, 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("Dimension %r not found" % dimension)
else:
return default
if isinstance(dimension, Dimension):
dims = [d for d in all_dims if dimension == d]
if strict and not dims:
raise KeyError("%r not found." % dimension)
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("Dimension %r not found." % dimension)
else:
return name_map.get(dimension, default)
[docs] 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 [i for i, d in enumerate(dimensions) if d == dim][0]
except IndexError:
raise Exception("Dimension %s not found in %s." %
(dim, self.__class__.__name__))
[docs] 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
[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]
# 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
[docs] 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("Dimension %s not found in %s." %
(dimension, self.__class__.__name__))
[docs] 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 __unicode__(self):
return unicode(PrettyPrinter.pprint(self))
def __call__(self, options=None, **kwargs):
self.param.warning(
'Use of __call__ to set options will be deprecated '
'in the next major release (1.14.0). Use the equivalent .opts '
'method instead.')
if not kwargs and options is None:
return self.opts.clear()
return self.opts(options, **kwargs)
[docs] def options(self, *args, **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)
clone = kwargs.pop('clone', True)
if len(args) == 0 and len(kwargs)==0:
options = None
elif args and isinstance(args[0], basestring):
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)
[docs]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)
[docs]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)
[docs] @classmethod
def from_values(cls, vals):
"Deprecated method to construct tree from list of objects"
name = cls.__name__
param.main.param.warning("%s.from_values is deprecated, the %s "
"constructor may now be used directly."
% (name, name))
return cls(items=cls._process_items(vals))
@classmethod
def _process_items(cls, vals):
"Processes list of items assigning unique paths to each."
if type(vals) is cls:
return vals.data
elif not isinstance(vals, (list, tuple)):
vals = [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 i, (path, item) in enumerate(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)
[docs] 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(ViewableTree, self).dimension_values(
dimension, expanded, flat)
def __len__(self):
return len(self.data)
