from itertools import groupby
import numpy as np
import param
from .dimension import Dimensioned, ViewableElement, asdim
from .layout import Composable, Layout, NdLayout
from .ndmapping import OrderedDict, NdMapping
from .overlay import Overlayable, NdOverlay, CompositeOverlay
from .spaces import HoloMap, GridSpace
from .tree import AttrTree
from .util import get_param_values
[docs]class Element(ViewableElement, Composable, Overlayable):
"""
Element is the atomic datastructure used to wrap some data with
an associated visual representation, e.g. an element may
represent a set of points, an image or a curve. Elements provide
a common API for interacting with data of different types and
define how the data map to a set of dimensions and how those
map to the visual representation.
"""
group = param.String(default='Element', constant=True)
__abstract = True
_selection_streams = ()
def _get_selection_expr_for_stream_value(self, **kwargs):
return None, None, None
[docs] def hist(self, dimension=None, num_bins=20, bin_range=None,
adjoin=True, **kwargs):
"""Computes and adjoins histogram along specified dimension(s).
Defaults to first value dimension if present otherwise falls
back to first key dimension.
Args:
dimension: Dimension(s) to compute histogram on
num_bins (int, optional): Number of bins
bin_range (tuple optional): Lower and upper bounds of bins
adjoin (bool, optional): Whether to adjoin histogram
Returns:
AdjointLayout of element and histogram or just the
histogram
"""
from ..operation import histogram
if not isinstance(dimension, list): dimension = [dimension]
hists = []
for d in dimension[::-1]:
hist = histogram(self, num_bins=num_bins, bin_range=bin_range,
dimension=d, **kwargs)
hists.append(hist)
if adjoin:
layout = self
for didx in range(len(dimension)):
layout = layout << hists[didx]
elif len(dimension) > 1:
layout = Layout(hists)
else:
layout = hists[0]
return layout
#======================#
# Subclassable methods #
#======================#
def __getitem__(self, key):
if key is ():
return self
else:
raise NotImplementedError("%s currently does not support getitem" %
type(self).__name__)
def __nonzero__(self):
"""Indicates whether the element is empty.
Subclasses may override this to signal that the Element
contains no data and can safely be dropped during indexing.
"""
return True
def __contains__(self, dimension):
"Whether element contains the Dimension"
return dimension in self.dimensions()
def __iter__(self):
"Disable iterator interface."
raise NotImplementedError('Iteration on Elements is not supported.')
__bool__ = __nonzero__
[docs] @classmethod
def collapse_data(cls, data, function=None, kdims=None, **kwargs):
"""
Deprecated method to perform collapse operations, which may
now be performed through concatenation and aggregation.
"""
raise NotImplementedError("Collapsing not implemented for %s." % cls.__name__)
[docs] def closest(self, coords, **kwargs):
"""Snap list or dict of coordinates to closest position.
Args:
coords: List of 1D or 2D coordinates
**kwargs: Coordinates specified as keyword pairs
Returns:
List of tuples of the snapped coordinates
Raises:
NotImplementedError: Raised if snapping is not supported
"""
raise NotImplementedError
[docs] def sample(self, samples=[], bounds=None, closest=False, **sample_values):
"""Samples values at supplied coordinates.
Allows sampling of element with a list of coordinates matching
the key dimensions, returning a new object containing just the
selected samples. Supports multiple signatures:
Sampling with a list of coordinates, e.g.:
ds.sample([(0, 0), (0.1, 0.2), ...])
Sampling a range or grid of coordinates, e.g.:
1D: ds.sample(3)
2D: ds.sample((3, 3))
Sampling by keyword, e.g.:
ds.sample(x=0)
Args:
samples: List of nd-coordinates to sample
bounds: Bounds of the region to sample
Defined as two-tuple for 1D sampling and four-tuple
for 2D sampling.
closest: Whether to snap to closest coordinates
**kwargs: Coordinates specified as keyword pairs
Keywords of dimensions and scalar coordinates
Returns:
Element containing the sampled coordinates
"""
raise NotImplementedError
[docs] def reduce(self, dimensions=[], function=None, spreadfn=None, **reduction):
"""Applies reduction along the specified dimension(s).
Allows reducing the values along one or more key dimension
with the supplied function. Supports two signatures:
Reducing with a list of dimensions, e.g.:
ds.reduce(['x'], np.mean)
Defining a reduction using keywords, e.g.:
ds.reduce(x=np.mean)
Args:
dimensions: Dimension(s) to apply reduction on
Defaults to all key dimensions
function: Reduction operation to apply, e.g. numpy.mean
spreadfn: Secondary reduction to compute value spread
Useful for computing a confidence interval, spread, or
standard deviation.
**reductions: Keyword argument defining reduction
Allows reduction to be defined as keyword pair of
dimension and function
Returns:
The element after reductions have been applied.
"""
raise NotImplementedError
def _reduce_map(self, dimensions, function, reduce_map):
if dimensions and reduce_map:
raise Exception("Pass reduced dimensions either as an argument "
"or as part of the kwargs not both.")
if len(set(reduce_map.values())) > 1:
raise Exception("Cannot define reduce operations with more than "
"one function at a time.")
if reduce_map:
reduce_map = reduce_map.items()
if dimensions:
reduce_map = [(d, function) for d in dimensions]
elif not reduce_map:
reduce_map = [(d, function) for d in self.kdims]
reduced = [(self.get_dimension(d, strict=True).name, fn)
for d, fn in reduce_map]
grouped = [(fn, [dim for dim, _ in grp]) for fn, grp in groupby(reduced, lambda x: x[1])]
return grouped[0]
[docs] def dframe(self, dimensions=None, multi_index=False):
"""Convert dimension values to DataFrame.
Returns a pandas dataframe of columns along each dimension,
either completely flat or indexed by key dimensions.
Args:
dimensions: Dimensions to return as columns
multi_index: Convert key dimensions to (multi-)index
Returns:
DataFrame of columns corresponding to each dimension
"""
import pandas as pd
if dimensions is None:
dimensions = [d.name for d in self.dimensions()]
else:
dimensions = [self.get_dimension(d, strict=True).name for d in dimensions]
column_names = dimensions
dim_vals = OrderedDict([(dim, self.dimension_values(dim)) for dim in column_names])
df = pd.DataFrame(dim_vals)
if multi_index:
df = df.set_index([d for d in dimensions if d in self.kdims])
return df
[docs] def array(self, dimensions=None):
"""Convert dimension values to columnar array.
Args:
dimensions: List of dimensions to return
Returns:
Array of columns corresponding to each dimension
"""
if dimensions is None:
dims = [d for d in self.kdims + self.vdims]
else:
dims = [self.get_dimension(d, strict=True) for d in dimensions]
columns, types = [], []
for dim in dims:
column = self.dimension_values(dim)
columns.append(column)
types.append(column.dtype.kind)
if len(set(types)) > 1:
columns = [c.astype('object') for c in columns]
return np.column_stack(columns)
######################
# Deprecations #
######################
[docs] def table(self, datatype=None):
"Deprecated method to convert any Element to a Table."
self.param.warning(
"The table method is deprecated and should no "
"longer be used. Instead cast the %s to a "
"a Table directly." % type(self).__name__)
if datatype and not isinstance(datatype, list):
datatype = [datatype]
from ..element import Table
return Table(self, **(dict(datatype=datatype) if datatype else {}))
[docs] def mapping(self, kdims=None, vdims=None, **kwargs):
"Deprecated method to convert data to dictionary"
self.param.warning(
"The mapping method is deprecated and should no "
"longer be used. Use another one of the common "
"formats instead, e.g. .dframe, .array or .columns.")
length = len(self)
if not kdims: kdims = self.kdims
if kdims:
keys = zip(*[self.dimension_values(dim.name)
for dim in self.kdims])
else:
keys = [()]*length
if not vdims: vdims = self.vdims
if vdims:
values = zip(*[self.dimension_values(dim.name)
for dim in vdims])
else:
values = [()]*length
return OrderedDict(zip(keys, values))
[docs]class Tabular(Element):
"""
Baseclass to give an elements providing an API to generate a
tabular representation of the object.
"""
__abstract = True
@property
def rows(self):
"Number of rows in table (including header)"
return len(self) + 1
@property
def cols(self):
"Number of columns in table"
return len(self.dimensions())
[docs] def pprint_cell(self, row, col):
"""Formatted contents of table cell.
Args:
row (int): Integer index of table row
col (int): Integer index of table column
Returns:
Formatted table cell contents
"""
ndims = self.ndims
if col >= self.cols:
raise Exception("Maximum column index is %d" % self.cols-1)
elif row >= self.rows:
raise Exception("Maximum row index is %d" % self.rows-1)
elif row == 0:
if col >= ndims:
if self.vdims:
return self.vdims[col - ndims].pprint_label
else:
return ''
return self.kdims[col].pprint_label
else:
dim = self.get_dimension(col)
return dim.pprint_value(self.iloc[row-1, col])
[docs] def cell_type(self, row, col):
"""Type of the table cell, either 'data' or 'heading'
Args:
row (int): Integer index of table row
col (int): Integer index of table column
Returns:
Type of the table cell, either 'data' or 'heading'
"""
return 'heading' if row == 0 else 'data'
[docs]class Element2D(Element):
extents = param.Tuple(default=(None, None, None, None),doc="""
Allows overriding the extents of the Element in 2D space defined
as four-tuple defining the (left, bottom, right and top) edges.""")
__abstract = True
[docs]class Element3D(Element2D):
extents = param.Tuple(default=(None, None, None, None, None, None), doc="""
Allows overriding the extents of the Element in 3D space
defined as (xmin, ymin, zmin, xmax, ymax, zmax).""")
__abstract = True
_selection_streams = ()
[docs]class Collator(NdMapping):
"""
Collator is an NdMapping type which can merge any number
of HoloViews components with whatever level of nesting
by inserting the Collators key dimensions on the HoloMaps.
If the items in the Collator do not contain HoloMaps
they will be created. Collator also supports filtering
of Tree structures and dropping of constant dimensions.
"""
drop = param.List(default=[], doc="""
List of dimensions to drop when collating data, specified
as strings.""")
drop_constant = param.Boolean(default=False, doc="""
Whether to demote any non-varying key dimensions to
constant dimensions.""")
filters = param.List(default=[], doc="""
List of paths to drop when collating data, specified
as strings or tuples.""")
group = param.String(default='Collator')
progress_bar = param.Parameter(default=None, doc="""
The progress bar instance used to report progress. Set to
None to disable progress bars.""")
merge_type = param.ClassSelector(class_=NdMapping, default=HoloMap,
is_instance=False,instantiate=False)
value_transform = param.Callable(default=None, doc="""
If supplied the function will be applied on each Collator
value during collation. This may be used to apply an operation
to the data or load references from disk before they are collated
into a displayable HoloViews object.""")
vdims = param.List(default=[], doc="""
Collator operates on HoloViews objects, if vdims are specified
a value_transform function must also be supplied.""")
_deep_indexable = False
_auxiliary_component = False
_nest_order = {HoloMap: ViewableElement,
GridSpace: (HoloMap, CompositeOverlay, ViewableElement),
NdLayout: (GridSpace, HoloMap, ViewableElement),
NdOverlay: Element}
def __init__(self, data=None, **params):
if isinstance(data, Element):
params = dict(get_param_values(data), **params)
if 'kdims' not in params:
params['kdims'] = data.kdims
if 'vdims' not in params:
params['vdims'] = data.vdims
data = data.mapping()
super(Collator, self).__init__(data, **params)
def __call__(self):
"""
Filter each Layout in the Collator with the supplied
path_filters. If merge is set to True all Layouts are
merged, otherwise an NdMapping containing all the
Layouts is returned. Optionally a list of dimensions
to be ignored can be supplied.
"""
constant_dims = self.static_dimensions
ndmapping = NdMapping(kdims=self.kdims)
num_elements = len(self)
for idx, (key, data) in enumerate(self.data.items()):
if isinstance(data, AttrTree):
data = data.filter(self.filters)
if len(self.vdims) and self.value_transform:
vargs = dict(zip(self.dimensions('value', label=True), data))
data = self.value_transform(vargs)
if not isinstance(data, Dimensioned):
raise ValueError("Collator values must be Dimensioned objects "
"before collation.")
dim_keys = zip(self.kdims, key)
varying_keys = [(d, k) for d, k in dim_keys if not self.drop_constant or
(d not in constant_dims and d not in self.drop)]
constant_keys = [(d, k) for d, k in dim_keys if d in constant_dims
and d not in self.drop and self.drop_constant]
if varying_keys or constant_keys:
data = self._add_dimensions(data, varying_keys,
dict(constant_keys))
ndmapping[key] = data
if self.progress_bar is not None:
self.progress_bar(float(idx+1)/num_elements*100)
components = ndmapping.values()
accumulator = ndmapping.last.clone(components[0].data)
for component in components:
accumulator.update(component)
return accumulator
@property
def static_dimensions(self):
"""
Return all constant dimensions.
"""
dimensions = []
for dim in self.kdims:
if len(set(self.dimension_values(dim.name))) == 1:
dimensions.append(dim)
return dimensions
def _add_dimensions(self, item, dims, constant_keys):
"""
Recursively descend through an Layout and NdMapping objects
in order to add the supplied dimension values to all contained
HoloMaps.
"""
if isinstance(item, Layout):
item.fixed = False
dim_vals = [(dim, val) for dim, val in dims[::-1]
if dim not in self.drop]
if isinstance(item, self.merge_type):
new_item = item.clone(cdims=constant_keys)
for dim, val in dim_vals:
dim = asdim(dim)
if dim not in new_item.kdims:
new_item = new_item.add_dimension(dim, 0, val)
elif isinstance(item, self._nest_order[self.merge_type]):
if len(dim_vals):
dimensions, key = zip(*dim_vals)
new_item = self.merge_type({key: item}, kdims=list(dimensions),
cdims=constant_keys)
else:
new_item = item
else:
new_item = item.clone(shared_data=False, cdims=constant_keys)
for k, v in item.items():
new_item[k] = self._add_dimensions(v, dims[::-1], constant_keys)
if isinstance(new_item, Layout):
new_item.fixed = True
return new_item
__all__ = list(set([_k for _k, _v in locals().items()
if isinstance(_v, type) and issubclass(_v, Dimensioned)]))
